plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to plotting with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a demonstration of how to use the library matplotlib to plot data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first example, we will plot a series of points.  \n",
    "\n",
    "First, we import the plotting library.  To do this, we issue the command \"import matplot.pyplot as plt\".  Now we can call any function from that library by prefacing its name with the string \"plt.\"\n",
    "\n",
    "Next, we create two lists: x_data, which contains the values of the $x$-coordinates of the points, and y_data, which containts the values of the $y$-coordinates of the points.\n",
    "\n",
    "Then, \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_data = [0.0, 1.0, 2.0, 3.0]\n",
    "y_data = [0.5, 7.3, 5.4, 2.7]\n",
    "\n",
    "plt.plot(x_data, y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the formatting of the plot by issuing additional optional arguments to the function plot.  In this case, we plot the points as small circles and omit the line connencting them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AJEO4ASCZ/wM00qq+uLhopAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_data = [0.0, 1.0, 2.0, 3.0]\n",
    "y_data = [0.5, 7.3, 5.4, 2.7]\n",
    "\n",
    "plt.plot(x_data, y_data, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see a list of additional optional arguments that can be used, just issue the help function on plot.  Note that help function should work on any Python function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\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",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the  appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "            linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using the\n",
      "    'axes.prop_cycle' rcParam.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional. If not given, they default to\n",
      "        ``[0, ..., N-1]``.\n",
      "    \n",
      "        Commonly, these parameters are arrays of length N. However,\n",
      "        scalars are supported as well (equivalent to an array with\n",
      "        constant value).\n",
      "    \n",
      "        The parameters can also be 2-dimensional. Then, the columns\n",
      "        represent separate data sets.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
      "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            `plot('n', 'o', '', data=obj)`.\n",
      "    \n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, optional, default: True\n",
      "        These parameters determined if the view limits are adapted to\n",
      "        the data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\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",
      "    \n",
      "        If you make multiple lines with one plot command, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "          agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n",
      "      alpha: float (0.0 transparent through 1.0 opaque) \n",
      "      animated: bool \n",
      "      antialiased or aa: bool \n",
      "      clip_box: a `.Bbox` instance \n",
      "      clip_on: bool \n",
      "      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.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 `.Figure` instance \n",
      "      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n",
      "      gid: an id string \n",
      "      label: object \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: `.AbstractPathEffect` \n",
      "      picker: float distance in points or callable pick function ``fn(artist, event)`` \n",
      "      pickradius: float distance in points\n",
      "      rasterized: bool or None \n",
      "      sketch_params: (scale: float, length: float, randomness: float) \n",
      "      snap: bool or None \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: bool \n",
      "      xdata: 1D array \n",
      "      ydata: 1D array \n",
      "      zorder: float \n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    lines\n",
      "        A list of `.Line2D` objects representing the plotted data.\n",
      "    \n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of variing size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[color][marker][line]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    **Colors**\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",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\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",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'ro'   # red circles\n",
      "        'g-'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        'k^:'  # black triangle_up markers connected by a dotted line\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": "markdown",
   "metadata": {},
   "source": [
    " In this particular example, we plot the function $e^{-x}$.  \n",
    "\n",
    "First we import the numpy library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, World\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x_data = np.arange(0.0, 5.0, 0.01)\n",
    "y_data = [np.exp(-x) for x in x_data]\n",
    "plt.plot(x_data, y_data)\n",
    "\n",
    "\n",
    "print('Hello, World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "time.sleep(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, Leo!'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def say_hello(recipient):\n",
    "    return f'Hello, {recipient}!'\n",
    "say_hello('Leo')\n"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}