{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matplotlib: Beyond the basics\n", "\n", "## Status and plan for today\n", "\n", "By now you know the basics of:\n", "\n", "* Numpy array creation and manipulation.\n", "* Display of data in numpy arrays, sufficient for interactive exploratory work.\n", "\n", "Hopefully after this notebook you will:\n", "\n", "* Know how to polish those figures to the point where they can go to a journal.\n", "* Understand matplotlib's internal model enough to:\n", " - know where to look for knobs to fine-tune\n", " - better understand the help and examples online\n", " - use it as a development platform for complex visualization\n", " \n", "## Resources\n", "\n", "* The [official matplotlib documentation](https://matplotlib.org/contents.html) is detailed and comprehensive. Particularly useful sections are:\n", " - The [gallery](https://matplotlib.org/gallery/index.html).\n", " - The [high level overview of its dual APIs, `pyplot` and object-oriented](https://matplotlib.org/api/pyplot_summary.html).\n", " - The [topical tutorials](https://matplotlib.org/tutorials/index.html).\n", "\n", "\n", "\n", "* A detailed [tutorial](https://www.labri.fr/perso/nrougier/teaching/matplotlib) by Nicolas Rougier, similar in style to the ones we saw for Numpy.\n", "* The fantastic [Python Graph Gallery](https://python-graph-gallery.com), which provides a large collection of plots with emphasis on statistical visualizations. It uses [Seaborn](https://seaborn.pydata.org) extensively.\n", "* In this tutorial we'll focus on \"raw\" matplotlib, but for a wide variety of statistical visualization tasks, using Seaborn makes life much easier. We'll dive into its [tutorial](https://seaborn.pydata.org/tutorial.html) later on.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib's main APIs: ``pyplot`` and object-oriented\n", "\n", "Matplotlib is a library that can be thought of as having two main ways of being\n", "used:\n", "\n", "- via ``pyplot`` calls, as a high-level, matlab-like library that automatically\n", " manages details like figure creation.\n", "\n", "- via its internal object-oriented structure, that offers full control over all\n", " aspects of the figure, at the cost of slightly more verbose calls for the\n", " common case.\n", "\n", "The pyplot api:\n", "\n", "- Easiest to use.\n", "- Sufficient for simple and moderately complex plots.\n", "- Does not offer complete control over all details.\n", "\n", "Before we look at our first simple example, we must activate matplotlib support in the notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# a few widely used tools from numpy\n", "from numpy import sin, cos, exp, sqrt, pi, linspace, arange" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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KIhpaa9YZTqOzoNQB0ot9nWE7di53AjOLfR0gIokiskJErjzXRSIyynZeYlZW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\nXmZmVggXFDMzK4QLSuU2pg4wRmXPD+VvQ9nzQ/n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gqBPDrrIGkmMjKAiSaZAv54PxDJoY3OVCfSFIxy8MVZSbzIGKJq0z+IGgTgw7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\nSmTtvEyum5HO4txkiyNV/mZSUjTfuGk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(0, 2 * pi)\n", "y = sin(x)\n", "plt.plot(x, y, label='sin(x)')\n", "plt.legend()\n", "plt.title('Harmonic')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "\n", "# Add one line to that plot\n", "z = cos(x)\n", "plt.plot(x, z, label='cos(x)')\n", "\n", "# Make a second figure with a simple plot\n", "plt.figure()\n", "plt.plot(x, sin(2*x), label='sin(2x)')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how to create the same two plots, using explicit management of the figure and axis objects:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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KIhpaa9YZTqOzoNQB0ot9nWE7di53AjOLfR0gIokiskJErjzXRSIyynZeYlZW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\nXmZmVggXFDMzK4QLSuU2pg4wRmXPD+VvQ9nzQ/n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gqBPDrrIGkmMjKAiSaZAv54PxDJoY3OVCfSFIxy8MVZSbzIGKJq0z+IGgTgw7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\nSmTtvEyum5HO4txkiyNV/mZSUjTfuGk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots() # we manually make a figure and axis\n", "ax.plot(x,y, label='sin(x)') # it's the axis who plots\n", "ax.legend()\n", "ax.set_title('Harmonic') # we set the title on the axis\n", "ax.set_xlabel('x') # same with labels\n", "ax.set_ylabel('y')\n", "\n", "# Make a second figure with a simple plot. We can name the figure with a\n", "# different variable name as well as its axes, and then control each\n", "f1, ax1 = plt.subplots()\n", "ax1.plot(x, sin(2*x), label='sin(2x)')\n", "ax1.legend()\n", "\n", "# Since we now have variables for each axis, we can add back to the first\n", "# figure even after making the second\n", "ax.plot(x, z, label='cos(x)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It’s important to understand the existence of these objects, even if you use mostly the top-level pyplot calls most of the time. Many things can be accomplished in MPL with mostly pyplot and a little bit of tweaking of the underlying objects. We’ll revisit the object-oriented API later.\n", "\n", "Important commands to know about, and which matplotlib uses internally a lot:\n", "\n", " gcf() # get current figure\n", " gca() # get current axis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making subplots\n", "\n", "\n", "The simplest command is:\n", "\n", " f, ax = plt.subplots()\n", "\n", "which is equivalent to:\n", "\n", " f = plt.figure()\n", " ax = f.add_subplot(111)\n", "\n", "By passing arguments to `subplots`, you can easily create a regular plot grid:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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kHGf8fDn17yg4yBkDEd1ERC8R0VEi+lCbn7+HiFaI6Cn1z/uafnYHER1R/9wh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\nN7VTUTR61zx75mRqD7Sy59DRoHtxDGREhUHSLZJqJHVLqh7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LRSupNAwS/Hi/bxjOOzWdYThpdQN7jxVnGE5oPAbTSuM4pB6DU7Uinzvu4EIu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\nDJbLakIUw3zjlM/vBMO7HLYbOoFr3eC2m1vKa0duKRYVTou2tdCnZrk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(0, 2*pi, 400)\n", "y = sin(x**2)\n", "\n", "# Just a figure and one subplot\n", "f, ax = plt.subplots()\n", "ax.plot(x, y)\n", "ax.set_title('Simple plot')\n", "\n", "# Two subplots, unpack the output array immediately\n", "f, (ax1, ax2) = plt.subplots(1, 2)\n", "ax1.plot(x, y)\n", "ax2.scatter(x, y)\n", "\n", "# Put a figure-level title\n", "f.suptitle('Two plots');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, an arbitrarily complex grid can be made with ``subplot2grid``:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f = plt.figure()\n", "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n", "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n", "ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)\n", "ax4 = plt.subplot2grid((3,3), (2, 0))\n", "ax5 = plt.subplot2grid((3,3), (2, 1))\n", "\n", "# Let's turn off visibility of all tick labels here\n", "for ax in f.axes:\n", " for t in ax.get_xticklabels()+ax.get_yticklabels():\n", " t.set_visible(False)\n", "\n", "# And add a figure-level title at the top\n", "f.suptitle('Subplot2grid');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manipulating properties across matplotlib\n", "\n", "In matplotlib, most properties for lines, colors, etc, can be set directly in\n", "the call:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,2,3], linestyle='--', color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But for finer control you can get a hold of the returned line object (more on\n", "these objects later):\n", "\n", " In [1]: line, = plot([1,2,3])\n", "\n", "These line objects have a lot of properties you can control, a full list is\n", "seen here by tab-completing in IPython:\n", "\n", " In [2]: line.set\n", " line.set line.set_drawstyle line.set_mec\n", " line.set_aa line.set_figure line.set_mew\n", " line.set_agg_filter line.set_fillstyle line.set_mfc\n", " line.set_alpha line.set_gid line.set_mfcalt\n", " line.set_animated line.set_label line.set_ms\n", " line.set_antialiased line.set_linestyle line.set_picker\n", " line.set_axes line.set_linewidth line.set_pickradius\n", " line.set_c line.set_lod line.set_rasterized\n", " line.set_clip_box line.set_ls line.set_snap\n", " line.set_clip_on line.set_lw line.set_solid_capstyle\n", " line.set_clip_path line.set_marker line.set_solid_joinstyle\n", " line.set_color line.set_markeredgecolor line.set_transform\n", " line.set_contains line.set_markeredgewidth line.set_url\n", " line.set_dash_capstyle line.set_markerfacecolor line.set_visible\n", " line.set_dashes line.set_markerfacecoloralt line.set_xdata\n", " line.set_dash_joinstyle line.set_markersize line.set_ydata\n", " line.set_data line.set_markevery line.set_zorder\n", " \n", "\n", "But the `setp` call (short for set property) can be very useful, especially\n", "while working interactively because it contains introspection support, so you\n", "can learn about the valid calls as you work:\n", "\n", " In [7]: line, = plot([1,2,3])\n", "\n", " In [8]: setp(line, 'linestyle')\n", " linestyle: [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''`` ] and any drawstyle in combination with a linestyle, e.g. ``'steps--'``. \n", "\n", " In [9]: setp(line)\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", " ...\n", " ... much more output elided\n", " ...\n", "\n", "In the first form, it shows you the valid values for the 'linestyle' property,\n", "and in the second it shows you all the acceptable properties you can set on the\n", "line object. This makes it very easy to discover how to customize your figures\n", "to get the visual results you need.\n", "\n", "Furthermore, setp can manipulate multiple objects at a time:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[None, None, None, None]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UGL6FBg2i27dgRf0MuVOtGvDgg7JthC4HSmam+eARy4oB0HafHiMGHofzwGpG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\nMbPZDR+DPwwzUocOF+Y2OIQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(0, 2*pi)\n", "y1 = sin(x)\n", "y2 = sin(2*x)\n", "lines = plt.plot(x, y1, x, y2)\n", "\n", "# We will set the width and color of all lines in the figure at once:\n", "plt.setp(lines, linewidth=2, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, if you know what properties you want to set on a specific object, a\n", "plain ``set`` call is typically the simplest form:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[None, None, None]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "line, = plt.plot([1,2,3])\n", "line.set(lw=2, c='red',ls='--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding what matplotlib returns: lines, axes and figures\n", "\n", "\n", "### Lines\n", "\n", "\n", "In a simple plot:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,2,3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The return value of the plot call is a list of lines, which can be manipulated\n", "further. If you capture the line object (in this case it's a single line so we\n", "use a one-element tuple):" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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66DAnv3PnuKMSEamRlmzYFe5hbZ1jjoHnn4fbbgszeJT0RSQLaMmGulq9OqyR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\ntgN2S3n3hwgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "line, = plt.plot([1,2,3])\n", "line.set_color('r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One line property that is particularly useful to be aware of is ``set_data``:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a plot and hold the line object\n", "line, = plt.plot([1,2,3], label='my data')\n", "plt.grid()\n", "plt.title('My title')\n", "\n", "# ... later, we may want to modify the x/y data but keeping the rest of the\n", "# figure intact, with our new data:\n", "x = linspace(0, 1)\n", "y = x**2\n", "\n", "# This can be done by operating on the data object itself\n", "line.set_data(x, y)\n", "\n", "# Now we must set the axis limits manually. Note that we can also use xlim\n", "# and ylim to set the x/y limits separately.\n", "plt.axis([0,1,0,1])\n", "\n", "# Note, alternatively this can be done with:\n", "ax = plt.gca() # get currently active axis object\n", "ax.relim()\n", "ax.autoscale_view()\n", "\n", "# as well as requesting matplotlib to draw\n", "plt.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The next important component, axes\n", "\n", " \n", "The ``axis`` call above was used to set the x/y limits of the axis. And in\n", "previous examples we called ``.plot`` directly on axis objects. Axes are the\n", "main object that contains a lot of the user-facing functionality of matplotlib:\n", "\n", " In [15]: f = plt.figure()\n", "\n", " In [16]: ax = f.add_subplot(111)\n", "\n", " In [17]: ax.\n", " Display all 299 possibilities? (y or n)\n", " ax.acorr ax.hitlist\n", " ax.add_artist ax.hlines\n", " ax.add_callback ax.hold\n", " ax.add_collection ax.ignore_existing_data_limits\n", " ax.add_line ax.images\n", " ax.add_patch ax.imshow\n", " \n", " ... etc.\n", "\n", "Many of the commands in ``plt.`` are nothing but wrappers around axis\n", "calls, with machinery to automatically create a figure and add an axis to it if\n", "there wasn't one to begin with. The output of most axis actions that draw\n", "something is a collection of lines (or other more complex geometric objects)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Enclosing it all, the figure\n", "\n", "\n", "The enclosing object is the ``figure``, that holds all axes:\n", "\n", " In [17]: f = plt.figure()\n", "\n", " In [18]: f.add_subplot(211)\n", " Out[18]: \n", "\n", " In [19]: f.axes\n", " Out[19]: []\n", "\n", " In [20]: f.add_subplot(212)\n", " Out[20]: \n", "\n", " In [21]: f.axes\n", " Out[21]: \n", " [,\n", " ]\n", "\n", "The basic view of matplotlib is: a figure contains one or more axes, axes draw\n", "and return collections of one or more geometric objects (lines, patches, etc).\n", "\n", "For all the gory details on this topic, see the matplotlib [artist tutorial](http://matplotlib.sourceforge.net/users/artists.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Anatomy of a common plot\n", "\n", "\n", "Let's make a simple plot that contains a few commonly used decorations" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'p(x)')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nLT2qXnM4+2MvMmFROBExRjHHIa2CmNi/KX4lVMwxX6d3wIL7jIrBVZrAiEVQ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\nWe6Yv4WPdp7E11vxh5HtWfvcQEkmQlTBpVpZZ86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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots()\n", "\n", "# Three simple polyniomials\n", "x = linspace(-1, 1)\n", "y1,y2,y3 = [x**i for i in [1,2,3]]\n", "\n", "# Plot each with a label (for a legend)\n", "ax.plot(x, y1, label='linear')\n", "ax.plot(x, y2, label='cuadratic')\n", "ax.plot(x, y3, label='cubic')\n", "# Make all lines drawn so far thicker\n", "plt.setp(ax.lines, linewidth=2)\n", "\n", "# Add a grid and a legend that doesn't overlap the lines\n", "ax.grid(True)\n", "ax.legend(loc='lower right')\n", "\n", "# Add black horizontal and vertical lines through the origin\n", "ax.axhline(0, color='black')\n", "ax.axvline(0, color='black')\n", "\n", "# Set main text elements of the plot\n", "ax.set_title('Some polynomials')\n", "ax.set_xlabel('x')\n", "ax.set_ylabel('p(x)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Common plot types\n", "\n", "\n", "### Error plots\n", "\n", "\n", "First a very simple error plot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Simplest errorbars, 0.2 in x, 0.4 in y')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# example data\n", "x = arange(0.1, 4, 0.5)\n", "y = exp(-x)\n", "\n", "# example variable error bar values\n", "yerr = 0.1 + 0.2*sqrt(x)\n", "xerr = 0.1 + yerr\n", "\n", "# First illustrate basic pyplot interface, using defaults where possible.\n", "plt.figure()\n", "plt.errorbar(x, y, xerr=0.2, yerr=0.4)\n", "plt.title(\"Simplest errorbars, 0.2 in x, 0.4 in y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now a more elaborate one, using the OO interface to exercise more features." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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mOwtNZ0mZmb/aeQHLgTeATbHXVuDhDlzjBmB6eD+EKPMdmue5PYkyTY/weQVw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\nr3P1yFf3LZ9iVPG1AEfEPvc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# same data/errors as before\n", "x = arange(0.1, 4, 0.5)\n", "y = exp(-x)\n", "yerr = 0.1 + 0.2*sqrt(x)\n", "xerr = 0.1 + yerr\n", "\n", "fig, axs = plt.subplots(nrows=2, ncols=2)\n", "ax = axs[0,0]\n", "ax.errorbar(x, y, yerr=yerr, fmt='o')\n", "ax.set_title('Vert. symmetric')\n", "\n", "# With 4 subplots, reduce the number of axis ticks to avoid crowding.\n", "ax.locator_params(nbins=4)\n", "\n", "ax = axs[0,1]\n", "ax.errorbar(x, y, xerr=xerr, fmt='o')\n", "ax.set_title('Hor. symmetric')\n", "\n", "ax = axs[1,0]\n", "ax.errorbar(x, y, yerr=[yerr, 2*yerr], xerr=[xerr, 2*xerr], fmt='--o', label='foo')\n", "ax.legend()\n", "ax.set_title('H, V asymmetric')\n", "\n", "ax = axs[1,1]\n", "ax.set_yscale('log')\n", "# Here we have to be careful to keep all y values positive:\n", "ylower = np.maximum(1e-2, y - yerr)\n", "yerr_lower = y - ylower\n", "\n", "ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr,\n", " fmt='o', ecolor='g')\n", "ax.set_title('Mixed sym., log y')\n", "\n", "# Fix layout to minimize overlap between titles and marks \n", "# https://matplotlib.org/users/tight_layout_guide.html\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logarithmic plots\n", "\n", "A simple log plot" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lBtuzi4kJDeDhK3txa1K8jkHkZLQAKJs5WVbNSysP8s7GIyBwx+huzL20BzFh\n+kSpJzLGsOZAAc9/d4C0I4UkRgby+DV9mTi4i7YROAktAKrdyqrreGPNIV5ffYjK2npuGRnPo1f1\npmt4B6ujKSdgjOG7PXk8t2If+06UMrBrKE9M6MelvaO0DchiWgDUBautb+CdjUd44fsMTpXXcN2g\nGB6/pg+9OoVYHU05ofoGw/LtOfz56/1kF1YyunsEv5zYnyFx4ef+YWUXWgDUBVm1P5/ffZbOwfxy\nxvWK5Ilr+/3HxCVKtaamroH3Nh/lhe8PUFBWwy0j4/ifCX118DkLOF0BEJH+wKM0zh3wnTHmlXP9\njBYAxzmUX8YzX+zhu715JEYG8utJA7iiXye9lFfnrbSqlhdXZrBwbSb+Pt48dEUvZo9L1B5DDmTT\nAiAiC4FJQN7pmb+a3p8APE/jxC9vGGP+0IZ1eQGvG2PuPteyWgDsr6Sqlhe/z+DNdY1/rA9f0Yu7\n9I9V2UBmQTnPfLGbb/fk0S0ykF9NHMBV/fWkwhFsXQAuBcqAxc2mfvSmcerHq2mcBzgFmE5jMXi2\nxSrmGGPyRGQy8CTwojFmybm2qwXAfowxLN+ey9Of7+FkeTW3jIzjp9fq5bqyvVX783n6891k5JUx\nvk80/3fDIO06bGc2vwXUyty/Y4HfGmOubXr9cwBjTMuDf2vr+sIYM/Fcy2kBsI+jJyv41T92sXp/\nPkPjwnj6hkHaYKfsqra+gcUbjvCXr/dRbwyPXdWHuy/urs8P2IkjxgKKBbKavc4GRp8l0GXAjYA/\n8OVZlpsLzAVISEhoRzzVUm19A6+vOcTz3x7A19uL/508kDvGdMNb+24rO/P19uLui7tz/eAYnvpH\nOn/4ai+fbs3h9zcOZkRCR6vjeaz2FIDWjhpnvJwwxvwA/HCulRpj5gPzofEK4AKzqRbSjhTyi2U7\n2XeilAkDY3hq8gC6hGl/fuVYXcI6MH9WEivSj/Pb5enc9Mp67hjdjf+Z0FcnBbJAewpANhDf7HUc\nkNu+OMrWKmvq+eOKvSxaf5guoQG8PiuJqwd0tjqW8nDXDoxhXK8o/vz1Pt5af5ivdx/nDzcN4fK+\nnayO5lHacwMuBegtIt1FxA+YBiy3TSxlC2lHTnH939fw5rrDzBrTjW/+e7we/JXTCPb34amfDOTT\nB8cR1sGX2W+m8LOlOyitqrU6msdoUwEQkfeADUBfEckWkbuNMXXAQ8AKYA/woTEm3X5RVVtV1dbz\n7Jd7uOXVDdTUNbDk3tH875RBBOlk68oJDYkL57OHL+b+y3ryUVoWE/62hnUZBVbH8gj6JLCb2Z5V\nxOMfbScjr4zpyQn8cmJ/gvV5JTS/AAAQWElEQVTAr1zElqOF/PSj7RzKL+eOMQn8/Lr+euJyAXRG\nMA9TV9/AC99n8OLKDKKD/XlrTjLj+0RbHUup8zIioSNfPnIJz63Yx8J1mazeX8Dfpg3TnkJ2op1w\n3UB2YQXT5m/k+e8OMHloV1b816V68FcuK8DXm19PGsAHc8fSYAy3vLqBl1ZmUN/gvHcrXJUWABf3\nxY5jXPf8GvYeL+X5acP4623DCOug3emU60vuHsGXj17C9YO78NyKfdz+xkaOF1dZHcutaAFwURU1\ndfxs6Q4eXLKFHtHBfPnIJUwZFmt1LKVsKjTAl79PG8ZzNw9hR3YxE55fzdfpx62O5Ta0ALig9Nxi\nJr2wlg/Tsnjgsp4snTeWhEgdW0W5JxHhlqR4Pn/4YuI6dmDu22n86tOdVNXWWx3N5WkBcDEfpBxl\n6svrKa+u4927R/PEhH46noryCD2ig1l2/zjmXtqDdzYe5aZX1nP0ZIXVsVyaHjlcRFVtPU8s3c7P\nPt5JcmIEXz5yCRf1irI6llIO5efjxS+u78/Cu5LIOlXBpBfW8O3uE1bHcllaAFzA0ZMV3Pjyej5M\nzebhK3rx1pxkIoP9rY6llGWu6NeZLx65hITIQO5ZnMpzK/ZqL6ELoAXAyX2z+wQTX1hDTlElC+9K\n4vFr+uronUoB8RGBLJ13EdOT43lp5UFmLthEQVm11bFcihYAJ1XfYPjjP/dy7+JUukUG8vnDF3NF\nPx3HR6nmAny9efbGIfzx5iGkHSlk4t/XkHak0OpYLkMLgBMqqarlnrdSePmHg0wbFc/SeRfpDEpK\nncWtSfEse+Ai/H28mT5/Ix+lZp37h5QWAGeTWVDO1JfWseZAAU/fMIg/3DSEAF+dn1epcxnYNYzl\nD41jVPeO/M/SHTz9+W7q6husjuXUtAA4kTUH8pny4lpOldfw9t2jmTmmm9WRlHIp4YF+vDU7mbsu\nSmTB2kxmL0qhuEKHlz4TLQBOwBjDgrWZ3LlwM13COrD8oYsZ2zPS6lhKuSQfby9+O3kgf7hxMBsP\nneSGl9eRkVdqdSynpAXAYjV1DTzRdLl6Vf/OfPyA3u9XyhamJSew5N4xlFTWMvWl9azcl2d1JKfj\nsAIgIl4i8oyIvCAidzpqu86suKKWWQs38VFaY//+V+8YqWP3K2VDoxIjWP7wxcRHBHL3ohTe3njE\n6khOpa0zgi0UkTwR2dXi/Qkisk9EMkTkyXOsZgoQC9TSOJ+wR8s6VcFNr64n7Ughf71tKI9f0xcv\n7d+vlM3Fhnfgo3ljuaxvJ3796S5+/+UeGvShMaDtVwCLgAnN3xARb+Al4DpgADBdRAaIyGAR+bzF\nRyegL7DBGPPfwP222wXXsz2riKkvryOvpIrFc0YzdXic1ZGUcmtB/j7MnzmSO8YkMH/1IR56b4sO\nJkcbZwQzxqwWkcQWbycDGcaYQwAi8j4wxRjzLDCp5TpEJBuoaXrpsf/y3+w+wSPvbSUy2I/3546h\nV6cQqyMp5RF8vL14esogukUE8cyXezhRsonXZyUREeRndTTLtKcNIBZo/rRFdtN7Z7IMuFZEXgBW\nn2khEZkrIqkikpqfn9+OeM5n0bpM5r6dSp/OwXzywDg9+CvlYCLCvZf24OXbR7Arp5gbX15HZkG5\n1bEs054C0NoN6zPeWDPGVBhj7jbGPGyMeeksy803xiQZY5Kio91jWkNjDM9+uYffftbY0+f9uWOJ\nDtHB3JSyyvWDuzT2EKqq48aX17Etq8jqSJZoTwHIBuKbvY4DctsXx/3U1TfwP0t38NrqQ8wc041X\n7xhJBz99slcpq43s1pFl919ESIAvM17fyJoD7nXHoS3aUwBSgN4i0l1E/IBpwHLbxHIPVbX1zHsn\njaVp2Tx2VW9+N2WgjuSplBNJjApqnFEvIpA5i1L4fIdnncO2tRvoe8AGoK+IZIvI3caYOuAhYAWw\nB/jQGJNuv6iupbiyllkLNvPd3jyenjKQx67qg4ge/JVyNp1CA/jgvrEMj+/Iw+9t5e0Nh62O5DBi\njPP2h01KSjKpqalWxzhveSVVzFq4mYP5Zfzl1mH8ZGhXqyMppc6hqraeh5Zs4ds9eTx6ZW8eu6q3\ny560iUiaMSbpXMvpUBA2duRkOTe/uoGjpypYeNcoPfgr5SICfL159Y6R3Dwyjue/O8Bv/pHu9g+M\n6bgDNpSRV8qM1zdRW9/AknvHMCw+3OpISqnz4OPtxXM3DyEiyI/5qw9RXl3HH28ego+3e54rawGw\nkd25JcxcsAkR4YP7xtKns/bxV8oViQi/uL4/If4+/Pmb/VTXNfC3acPwdcMioAXABnZkFzFzwWYC\n/bx5957R9IgOtjqSUqqdHr6yNwG+3jzz5R5q6ht4ccZw/H3cqwu3+5U0B0s7corbX99ESIAPH943\nVg/+SrmRey/twe+mDOSb3Se4d3EalTXuNYqNFoB22HDwJDMXbCY6xJ+P5o3VcfyVckOzxiby/24a\nzJoD+cxetJny6jqrI9mMFoALtGp/Pne9uZm4jh14/74xdAnrYHUkpZSd3DYqgb/eOoyUw4XMWriZ\nkir3mGZSC8AFWLU/n3sXp9IzOpj3546lU0iA1ZGUUnZ2w/BYXpw+nO1ZRdy5cDOlblAEtACcp7UH\nCpi7OJVe0cEsuXe0Rw8lq5SnuW5wF16cMYKd2cXc9WYKZS5+O0gLwHlYn1HA3W+l0D0qiHfvGU14\noB78lfI0EwbF8ML04WzLKmL2m67dJqAFoI02HDzJnLdSSIxsPPh31DN/pTzWdYO78Py0YWw5WsTs\nRSlU1LhmEdAC0AabDp1kzqIU4jsG8u69o4kM1rH8lfJ0k4Z05S+3DiX18CnuXpTqkl1EtQCcQ8rh\nU8xelELX8ACW3DuGKD34K6WaTBkWy59vHcrGzJPcszjF5eYZ1gJwFtuyirhr4WZiQgN4794xOouX\nUuo/TB0ex59uHsr6gyeZ+3Ya1XWuUwS0AJzBvuOl3LlwMxHBfiy5dwydQrWrp1KqdTeNjOPZqYNZ\nvT+fx97fRl19g9WR2sRhBUBEBojIhyLyiojc7KjtXojDBeXcsWAT/j5evHv3GGLC9OCvlDq7ackJ\n/Gpif77adZwnl+10iaGk2zoj2EIRyRORXS3enyAi+0QkQ0SePMdqrgNeMMbcD8y6wLx2d6y4ktvf\n2ERdfQPv3jOahEgd3kEp1Tb3XNKDx67qzdK0bH73+W6cecItaPtooIuAF4HFp98QEW/gJeBqGieI\nTxGR5YA38GyLn58DvA08JSKTgcj2xbaPgrJqbn9jEyWVtSy5dwy9dUhnpdR5evTK3pRW1bFgbSYh\nAT48fk1fqyOdUZsKgDFmtYgktng7GcgwxhwCEJH3gSnGmGeBSWdY1YNNhWPZhcW1n9Nz+OYWVbJ4\nzmgGx4VZHUkp5YJEhF9N7E95dR0vfJ9BSIAPcy/taXWsVrVnPoBYIKvZ62xg9JkWbiogvwCCgOfO\nstxcYC5AQkJCO+K1XUVNHXMWpXAgr5Q37hxFcvcIh2xXKeWeRIRnpg6mrLqO33+5l2B/X2aMdszx\n7Hy0pwC0NlvyGW94GWMO03RgPxtjzHxgPjROCn+h4dqqtr6BB9/dwtajhbw0YwTj+0Tbe5NKKQ/g\n7SX85dZhVNTU88tPdxIR5MuEQV2sjvUj7ekFlA3EN3sdB+S2L45jGWP4xbKdrNyXz//dMJjrBjvX\nf45SyrX5+Xjx0owRDI8P55H3t7Hp0EmrI/1IewpACtBbRLqLiB8wDVhum1iO8dyKfXyUls1jV/V2\nysszpZTr6+DnzYI7RxHfsQP3LE5l7/ESqyP9S1u7gb4HbAD6iki2iNxtjKkDHgJWAHuAD40x6faL\naluL1mXy8g8HmZ6cwKNX9rY6jlLKjXUM8mPx3aMJ9PPmzoWbyS6ssDoSAOLM/VSTkpJMamqqzdf7\n+Y5cHn5vK1f378wrd4zE26u15gyllLKtvcdLuOXVDUSH+PPxvIvsNqqwiKQZY5LOtZzHDQWxPqOA\n//5gO0ndOvL36cP14K+Ucph+MaG8MSuJ7MJK5rxl/TDSHlUA0nOLmft2GolRgbwxaxQBvt5WR1JK\neZjRPSL5+7RhbM8q4qElWy0dN8hjCsCx4krmLEohJMCHt+YkExboa3UkpZSHmjCoC/87ZRDf783j\nqeXplg0Z0Z7nAFxGaVUts99Moby6no/mjaVLWAerIymlPNzMMd3IPlXBa6sP0S0y0JKnhd2+ANTV\nN/DQkq0cyCtj4V2j6N8l1OpISikFwM8m9COrsILff7mX+I6BDn8Wya1vARlj+M3ydFbtz+fpKYP0\nKV+llFPxanpaeHhCOI99sI0tRwsdu32Hbs3B5q8+xJJNR5k3vqc+6KWUckoBvt68MSuJzqEB3PtW\nKkdPOu4ZAbctAF/sOMazX+1l4pAuPHGt8w7HqpRSkcH+vDl7FHUNhrsWbaaoosYh23XLApB2pJD/\n+nAbIxLC+fMtQ/HSvv5KKSfXMzqY12aOJOtUBfc5aG5htywAC9dmEhMawOuzkrSvv1LKZYzpEckf\nbx5C6pFCUjLt3x7glr2A/nrbMArKqokM9rc6ilJKnZepw+MYkdCRbpFBdt+WW14B+Pl40TVc+/or\npVyTIw7+4KYFQCml1LlpAVBKKQ/l1MNBi0g+cMTqHBcgCiiwOoQDedr+gu6zp3DVfe5mjDnnk69O\nXQBclYiktmUsbnfhafsLus+ewt33WW8BKaWUh9ICoJRSHkoLgH3MtzqAg3na/oLus6dw633WNgCl\nlPJQegWglFIeSguAHYnIT0XEiEiU1VnsTUSeE5G9IrJDRD4RkXCrM9mLiEwQkX0ikiEiT1qdx95E\nJF5EVorIHhFJF5FHrc7kCCLiLSJbReRzq7PYixYAOxGReOBq4KjVWRzkG2CQMWYIsB/4ucV57EJE\nvIGXgOuAAcB0ERlgbSq7qwMeN8b0B8YAD3rAPgM8CuyxOoQ9aQGwn78CTwAe0chijPnaGFPX9HIj\nEGdlHjtKBjKMMYeMMTXA+8AUizPZlTHmmDFmS9PXpTQeFGOtTWVfIhIHTATesDqLPWkBsAMRmQzk\nGGO2W53FInOAr6wOYSexQFaz19m4+cGwORFJBIYDm6xNYnd/o/EErsHqIPbklsNBO4KIfAvEtPKt\nXwK/AK5xbCL7O9s+G2P+0bTML2m8ZfCuI7M5UGuzC3nEVZ6IBAMfA48ZY0qszmMvIjIJyDPGpInI\nZVbnsSctABfIGHNVa++LyGCgO7BdRKDxVsgWEUk2xhx3YESbO9M+nyYidwKTgCuN+/Yvzgbim72O\nA3ItyuIwIuJL48H/XWPMMqvz2Nk4YLKIXA8EAKEi8o4x5g6Lc9mcPgdgZyJyGEgyxrjigFJtJiIT\ngL8A440x+VbnsRcR8aGxkftKIAdIAWYYY9ItDWZH0ngm8xZwyhjzmNV5HKnpCuCnxphJVmexB20D\nULbyIhACfCMi20TkVasD2UNTQ/dDwAoaG0M/dOeDf5NxwEzgiqb/221NZ8fKxekVgFJKeSi9AlBK\nKQ+lBUAppTyUFgCllPJQWgCUUspDaQFQSikPpQVAKaU8lBYApZTyUFoAlFLKQ/1/0AkuIdkMKX8A\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(-5, 5)\n", "y = exp(-x**2)\n", "\n", "f, (ax1, ax2) = plt.subplots(2, 1)\n", "ax1.plot(x, y)\n", "ax2.semilogy(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more elaborate log plot using 'symlog', that treats a specified range as\n", "linear (thus handling values near zero) and symmetrizes negative values:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'symlog both')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NAehkilo68gfD3LflIOfOK+CsufmpLo6mJUy8AeRlEbkispRt3JRSl42hTHq4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\nu5fqzv392w+1GIN+Nr32FkfzpmYN5Ea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kU7tw+wraBZCKurYyAiVlvC0/vcvlBqPbA4iI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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(-50, 50, 100)\n", "y = linspace(0, 100, 100)\n", "\n", "# Create the figure and axes\n", "f, (ax1, ax2, ax3) = plt.subplots(3, 1)\n", "\n", "# Symlog on the x axis\n", "ax1.plot(x, y)\n", "ax1.set_xscale('symlog')\n", "ax1.set_ylabel('symlogx')\n", "# Grid for both axes\n", "ax1.grid(True)\n", "# Minor grid on too for x\n", "ax1.xaxis.grid(True, which='minor')\n", "\n", "# Symlog on the y axis\n", "ax2.plot(y, x)\n", "ax2.set_yscale('symlog')\n", "ax2.set_ylabel('symlogy')\n", "\n", "# Symlog on both\n", "ax3.plot(x, sin(x / 3.0))\n", "ax3.set_xscale('symlog')\n", "ax3.set_yscale('symlog')\n", "ax3.grid(True)\n", "ax3.set_ylabel('symlog both')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar plots" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T8oytUpoeg4h4FDhM0iHALZJ+GxGb840yW6XHAHgQuCoiNiatJ8XQ5BgcAwyJ\niHWS3g38StJhEfFyHrG5RtDxlgDvahyJiHOB44Hq3CKqrFb3X9Ig4A7gPyPimVwizF5Zn4GIaCBp\nGtkZ+4taOwZHAN+StBQ4H/hCepHpzqbFYxARr0bEunT6AuAZ4OBcosSJIAsPkLR9n1My7W15BZOD\nFvdf0u7A3cDnI+KRPIKrkNaOwTBJPdLX+wHDgaUVjzB7LR6DiHhPRAyNiKHANOD/RcTOeBZda5+D\n6vTZLEjaHziIpM8kF76yOAPp2SBXkfzyWUPyq+/6iJiZ/graDdgFWA9MiIin8oo1Cy3tP8mH/fPA\nX0oWnxARL1Q8yIy1cgx2AS4BXgfeAL4SEb/KK84stfZ/ULLM5cDGiLgylyAz1srnYAvwlfTvVuCy\niPh1bnE6EZiZFZubhszMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCu7/A+iQNxKJ\nyhuRAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# a bar plot with errorbars\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "N = 5\n", "menMeans = (20, 35, 30, 31, 27)\n", "menStd = (2, 3, 4, 1, 2)\n", "\n", "ind = arange(N) # the x locations for the groups\n", "width = 0.35 # the width of the bars\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)\n", "\n", "womenMeans = (25, 32, 34, 21, 29)\n", "womenStd = (3, 5, 2, 3, 3)\n", "rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)\n", "\n", "# add some\n", "ax.set_ylabel('Scores')\n", "ax.set_title('Scores by group and gender')\n", "ax.set_xticks(ind+width)\n", "ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n", "\n", "ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatter plots\n", "\n", "The ``scatter`` command produces scatter plots with arbitrary markers." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FycjjmOHXwy3b4LZd0DmNh4xopi64EcACeqRbH/lmMqEQZZMvoer++wi++Fen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\nBb2mTiV3xAh2/fnPIELvG24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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import cm\n", "\n", "t = linspace(0.0, 6*pi, 100)\n", "y = exp(-0.1*t)*cos(t)\n", "phase = t % 2*pi\n", "f = plt.figure()\n", "ax = f.add_subplot(111)\n", "ax.scatter(t, y, s=100*abs(y), c=phase, cmap=cm.jet)\n", "ax.set_ylim(-1,1)\n", "ax.grid()\n", "ax.axhline(0, color='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Consider you have the following data in a text file (The file `data/stations.txt` contains the full dataset):\n", "\n", " # Station Lat Long Elev \n", " BIRA 26.4840 87.2670 0.0120\n", " BUNG 27.8771 85.8909 1.1910\n", " GAIG 26.8380 86.6318 0.1660\n", " HILE 27.0482 87.3242 2.0880\n", " ... etc.\n", " \n", " \n", "These are the names of seismographic stations in the Himalaya, with their coordinates and elevations in Kilometers.\n", "\n", "1. Make a scatter plot of all of these, using both the size and the color to (redundantly) encode elevation. Label each station by its 4-letter code, and add a colorbar on the right that shows the color-elevation map.\n", " \n", "2. If you have the basemap toolkit installed, repeat the same exercise but draw a grid with parallels and meridians, add rivers in cyan and country boundaries in yellow. Also, draw the background using the NASA BlueMarble image of Earth. You can install it with `conda install basemap`.\n", "\n", " \n", "**Tips**\n", " \n", "* You can check whether you have Basemap installed with:\n", " \n", " from mpl_toolkits.basemap import Basemap\n", " \n", "* For the basemap part, choose a text label color that provides adequate reading contrast over the image background.\n", " \n", "* Create your Basemap with 'i' resolution, otherwise it will take forever to draw." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms\n", "\n", "Matplotlib has a built-in command for histograms." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu, sigma = 100, 15\n", "x = mu + sigma * np.random.randn(10000)\n", "\n", "# the histogram of the data\n", "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n", "\n", "plt.xlabel('Smarts')\n", "plt.ylabel('Probability')\n", "plt.title('Histogram of IQ')\n", "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n", "plt.axis([40, 160, 0, 0.03])\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aribitrary text and LaTeX support\n", "\n", "In matplotlib, text can be added either relative to an individual axis object\n", "or to the whole figure.\n", "\n", "These commands add text to the Axes:\n", "\n", "- title() - add a title\n", "- xlabel() - add an axis label to the x-axis\n", "- ylabel() - add an axis label to the y-axis\n", "- text() - add text at an arbitrary location\n", "- annotate() - add an annotation, with optional arrow\n", "\n", "And these act on the whole figure:\n", "\n", "- figtext() - add text at an arbitrary location\n", "- suptitle() - add a title\n", "\n", "And any text field can contain LaTeX expressions for mathematics, as long as\n", "they are enclosed in ``$`` signs.\n", "\n", "This example illustrates all of them:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 10, 0, 10]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U3Xffffz999+53i5P6ytSpAhPPvmkVZaUlATAwIED3Y5MkpKSKFasGA6Hg5Ej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\nfovbencf3027H9oR9k4Yoe+Ect/k+9h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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n", "\n", "ax = fig.add_subplot(111)\n", "fig.subplots_adjust(top=0.85)\n", "ax.set_title('axes title')\n", "\n", "ax.set_xlabel('xlabel')\n", "ax.set_ylabel('ylabel')\n", "\n", "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n", " bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})\n", "\n", "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n", "\n", "ax.text(3, 2, 'unicode: Institut für Festkörperphysik')\n", "\n", "ax.text(0.95, 0.01, 'colored text in axes coords',\n", " verticalalignment='bottom', horizontalalignment='right',\n", " transform=ax.transAxes,\n", " color='green', fontsize=15)\n", "\n", "\n", "ax.plot([2], [1], 'o')\n", "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n", " arrowprops=dict(facecolor='black', shrink=0.05))\n", "\n", "ax.axis([0, 10, 0, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Image display\n", "\n", "The ``imshow`` command can display single or multi-channel images. A simple\n", "array of random numbers, plotted in grayscale:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FCy+8IHmN765atart2bNH/WwWn64BzWfOnGlmCbLpli1b1M9sAJMmTdL90u/nz59XusV1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\nVaukq+nRo4enaPTMM8/+vCUZpOA4jg0ZMkTkIEQJEHz06NHy1GxXlT5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17g9CcMSIEUoh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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import cm\n", "plt.imshow(np.random.rand(128, 128), cmap=cm.gray, interpolation='nearest')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A real photograph is a multichannel image, `imshow` interprets it correctly:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UzmGulL1m21L+n6KMYbYSlGy/bFAP0js3bQxXaGsXirwcNqmPWVw/L6MfEUmxueB/8K2U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\naPgQeUoJ8Xgc27arY4UKMIWx9RvS5fwuNG+aw3sHTMb85gMW5iqMEZcSt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = plt.imread('data/stained_glass_barcelona.png')\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Write a notebook where you can load an image and then perform the following operations on it:\n", " \n", "1. Create a figure with four plots that show both the full-color image and color channel of the image with the right colormap for that color. Ensure that the axes are linked so zooming in one image zooms the same region in the others.\n", " \n", "2. Compute a luminosity and per-channel histogram and display all four histograms in one figure, giving each a separate plot (hint: a 4x1 plot works best for this). Link the appropriate axes together.\n", " \n", "3. Create a black-and-white (or more precisely, grayscale) version of the image. Compare the results from a naive average of all three channels with that of a model that uses 30% red, 59% green and 11% blue, by displaying all three (full color and both grayscales) side by side with linked axes for zooming.\n", " \n", "Hint: look for the matplotlib image tutorial." ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }