Visualization with Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0,10,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IQgzvOH12ks3LObyTVUhNQyOjUgJUGykEuMKEL18whH7+3hLYz0zZbTzjDEHV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\ntGASg2EYhtGCSQyGYRhGC/8fUbcJSQosyyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144d843ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x),'--')\n",
    "plt.plot(x,np.cos(x),'*')\n",
    "plt.savefig('a.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, **kwargs)\n",
      "    Plot lines and/or markers to the\n",
      "    :class:`~matplotlib.axes.Axes`.  *args* is a variable length\n",
      "    argument, allowing for multiple *x*, *y* pairs with an\n",
      "    optional format string.  For example, each of the following is\n",
      "    legal::\n",
      "    \n",
      "        plot(x, y)        # plot x and y using default line style and color\n",
      "        plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "        plot(y)           # plot y using x as index array 0..N-1\n",
      "        plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    If *x* and/or *y* is 2-dimensional, then the corresponding columns\n",
      "    will be plotted.\n",
      "    \n",
      "    If used with labeled data, make sure that the color spec is not\n",
      "    included as an element in data, as otherwise the last case\n",
      "    ``plot(\"v\",\"r\", data={\"v\":..., \"r\":...)``\n",
      "    can be interpreted as the first case which would do ``plot(v, r)``\n",
      "    using the default line style and color.\n",
      "    \n",
      "    If not used with labeled data (i.e., without a data argument),\n",
      "    an arbitrary number of *x*, *y*, *fmt* groups can be specified, as in::\n",
      "    \n",
      "        a.plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "    Return value is a list of lines that were added.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'.  To change this behavior, you can edit the\n",
      "    axes.prop_cycle rcParam.\n",
      "    \n",
      "    The following format string characters are accepted to control\n",
      "    the line style or marker:\n",
      "    \n",
      "    ================    ===============================\n",
      "    character           description\n",
      "    ================    ===============================\n",
      "    ``'-'``             solid line style\n",
      "    ``'--'``            dashed line style\n",
      "    ``'-.'``            dash-dot line style\n",
      "    ``':'``             dotted line style\n",
      "    ``'.'``             point marker\n",
      "    ``','``             pixel marker\n",
      "    ``'o'``             circle marker\n",
      "    ``'v'``             triangle_down marker\n",
      "    ``'^'``             triangle_up marker\n",
      "    ``'<'``             triangle_left marker\n",
      "    ``'>'``             triangle_right marker\n",
      "    ``'1'``             tri_down marker\n",
      "    ``'2'``             tri_up marker\n",
      "    ``'3'``             tri_left marker\n",
      "    ``'4'``             tri_right marker\n",
      "    ``'s'``             square marker\n",
      "    ``'p'``             pentagon marker\n",
      "    ``'*'``             star marker\n",
      "    ``'h'``             hexagon1 marker\n",
      "    ``'H'``             hexagon2 marker\n",
      "    ``'+'``             plus marker\n",
      "    ``'x'``             x marker\n",
      "    ``'D'``             diamond marker\n",
      "    ``'d'``             thin_diamond marker\n",
      "    ``'|'``             vline marker\n",
      "    ``'_'``             hline marker\n",
      "    ================    ===============================\n",
      "    \n",
      "    \n",
      "    The following color abbreviations are supported:\n",
      "    \n",
      "    ==========  ========\n",
      "    character   color\n",
      "    ==========  ========\n",
      "    'b'         blue\n",
      "    'g'         green\n",
      "    'r'         red\n",
      "    'c'         cyan\n",
      "    'm'         magenta\n",
      "    'y'         yellow\n",
      "    'k'         black\n",
      "    'w'         white\n",
      "    ==========  ========\n",
      "    \n",
      "    In addition, you can specify colors in many weird and\n",
      "    wonderful ways, including full names (``'green'``), hex\n",
      "    strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n",
      "    grayscale intensities as a string (``'0.8'``).  Of these, the\n",
      "    string specifications can be used in place of a ``fmt`` group,\n",
      "    but the tuple forms can be used only as ``kwargs``.\n",
      "    \n",
      "    Line styles and colors are combined in a single format string, as in\n",
      "    ``'bo'`` for blue circles.\n",
      "    \n",
      "    The *kwargs* can be used to set line properties (any property that has\n",
      "    a ``set_*`` method).  You can use this to set a line label (for auto\n",
      "    legends), linewidth, anitialising, marker face color, etc.  Here is an\n",
      "    example::\n",
      "    \n",
      "        plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
      "        plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
      "        axis([0, 4, 0, 10])\n",
      "        legend()\n",
      "    \n",
      "    If you make multiple lines with one plot command, the kwargs\n",
      "    apply to all those lines, e.g.::\n",
      "    \n",
      "        plot(x1, y1, x2, y2, antialiased=False)\n",
      "    \n",
      "    Neither line will be antialiased.\n",
      "    \n",
      "    You do not need to use format strings, which are just\n",
      "    abbreviations.  All of the line properties can be controlled\n",
      "    by keyword arguments.  For example, you can set the color,\n",
      "    marker, linestyle, and markercolor with::\n",
      "    \n",
      "        plot(x, y, color='green', linestyle='dashed', marker='o',\n",
      "             markerfacecolor='blue', markersize=12).\n",
      "    \n",
      "    See :class:`~matplotlib.lines.Line2D` for details.\n",
      "    \n",
      "    The kwargs are :class:`~matplotlib.lines.Line2D` properties:\n",
      "    \n",
      "      agg_filter: unknown\n",
      "      alpha: float (0.0 transparent through 1.0 opaque) \n",
      "      animated: [True | False] \n",
      "      antialiased or aa: [True | False] \n",
      "      axes: an :class:`~matplotlib.axes.Axes` instance \n",
      "      clip_box: a :class:`matplotlib.transforms.Bbox` instance \n",
      "      clip_on: [True | False] \n",
      "      clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] \n",
      "      color or c: any matplotlib color \n",
      "      contains: a callable function \n",
      "      dash_capstyle: ['butt' | 'round' | 'projecting'] \n",
      "      dash_joinstyle: ['miter' | 'round' | 'bevel'] \n",
      "      dashes: sequence of on/off ink in points \n",
      "      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n",
      "      figure: a :class:`matplotlib.figure.Figure` instance \n",
      "      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n",
      "      gid: an id string \n",
      "      label: string or anything printable with '%s' conversion. \n",
      "      linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n",
      "      linewidth or lw: float value in points \n",
      "      marker: :mod:`A valid marker style <matplotlib.markers>`\n",
      "      markeredgecolor or mec: any matplotlib color \n",
      "      markeredgewidth or mew: float value in points \n",
      "      markerfacecolor or mfc: any matplotlib color \n",
      "      markerfacecoloralt or mfcalt: any matplotlib color \n",
      "      markersize or ms: float \n",
      "      markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n",
      "      path_effects: unknown\n",
      "      picker: float distance in points or callable pick function ``fn(artist, event)`` \n",
      "      pickradius: float distance in points \n",
      "      rasterized: [True | False | None] \n",
      "      sketch_params: unknown\n",
      "      snap: unknown\n",
      "      solid_capstyle: ['butt' | 'round' |  'projecting'] \n",
      "      solid_joinstyle: ['miter' | 'round' | 'bevel'] \n",
      "      transform: a :class:`matplotlib.transforms.Transform` instance \n",
      "      url: a url string \n",
      "      visible: [True | False] \n",
      "      xdata: 1D array \n",
      "      ydata: 1D array \n",
      "      zorder: any number \n",
      "    \n",
      "    kwargs *scalex* and *scaley*, if defined, are passed on to\n",
      "    :meth:`~matplotlib.axes.Axes.autoscale_view` to determine\n",
      "    whether the *x* and *y* axes are autoscaled; the default is\n",
      "    *True*.\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All arguments with the following names: 'x', 'y'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x144d8510390>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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DQ6D2jcVYu6aEZDLJo48+yvHjx2lpaeHWW2/lwIEDbN++PfOyGDb0aza4wpQ0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\nSSp8Awest7h5ULj9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144d70b6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,3,1) # rows, col, panel-number\n",
    "plt.plot(x,np.sin(x))\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(x,np.cos(x))\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(x,np.tan(x))"
   ]
  },
  {
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}