{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The right* way to plot: gridspec\n", "\\* It really is ...\n", "### PyCoffee @ Vitacura - 7 April 2016 - fvogt@eso.org\n", "\n", "**premise:** \n", "- matplotlib is the one-stop shop for all 2-D plotting in Python. Easy to make rapid plots & tons of examples. \n", "- **But at times frustrating** to get beautiful & complex plots: \n", " - subplots locations, sizes, ratios\n", " - colorbar location, size, **width**, alignment\n", " - labels outside plotting window \n", " - layout updates \n", " - ...\n", "\n", "**gridspec:** \n", "- a simple, elegant, quick and **essential** way to handle multiple plotting areas in Python/matplotlib \n", "- http://matplotlib.org/users/gridspec.html\n", "\n", "**utility**: \n", "- essential for complex subplots \n", "- extremely useful for plots with colorbars\n", "- great (and good practice) for ANY plot\n", "\n", "**cost**: \n", "- 2 lines + \n", "- 1 sub-module import\n", "\n", "**gain**:\n", "- total control over the plot\n", "- accuracy\n", "- elegance\n", "- infinite powers\n", "- ...\n", "\n", "** take-home message **: \n", "- $\\texttt{plt.subplot(2,1,1)}$ $\\leftarrow$ **NEVER EVER AGAIN !** \n", "- $\\texttt{plt.colorbar()}$ $\\leftarrow$ **NEVER EVER AGAIN !** \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A-1) The goal\n", "\n", "![demo-image](./gridspec_demo.png)\n", "\n", "#### A-2) The concept\n", "\n", "![demo-image](./gridspec_concept.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### B) Set the stage: import the required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: MacOSX\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import numpy as np # For creating some fake data\n", "\n", "# Use some notebook magic - forget the next line in normal scripted code\n", "%pylab \n", "\n", "import matplotlib.pyplot as plt # Gives access to basic plotting functions\n", "\n", "import matplotlib.gridspec as gridspec # GRIDSPEC !\n", "\n", "from matplotlib.colorbar import Colorbar # For dealing with Colorbars the proper way - TBD in a separate PyCoffee ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### C) Create some fake datasets to play with ..." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ok, let us start by creating some fake data\n", "x = np.random.randn(1000)\n", "y = np.random.randn(1000)\n", "z = np.sqrt(x**2+y**2)\n", "\n", "# `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n", "# with random floats sampled from a univariate \"normal\" (Gaussian)\n", "# distribution of mean 0 and variance 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### D-1) Get started with the plotting " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First, create the figure\n", "plt.close(1)\n", "fig = plt.figure(1, figsize=(15,8))\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Now, create the gridspec structure, as required\n", "gs = gridspec.GridSpec(3,4, height_ratios=[0.05,1,0.2], width_ratios=[1,0.2,0.2,1])\n", "\n", "# 3 rows, 4 columns, each with the required size ratios. \n", "# Also make sure the margins and spacing are apropriate\n", "\n", "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n", "\n", "# Note: I set the margins to make it look good on my screen ...\n", "# BUT: this is irrelevant for the saved image, if using bbox_inches='tight'in savefig !\n", "\n", "# Note: Here, I use a little trick. I only have three vertical layers of plots : \n", "# a scatter plot, a histogram, and a line plot. So, in principle, I could use a 3x3 structure. \n", "# However, I want to have the histogram 'closer' from the scatter plot than the line plot.\n", "# So, I insert a 4th layer between the histogram and line plot, \n", "# keep it empty, and use its thickness (the 0.2 above) to adjust the space as required.\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### D-2) The scatter plot & colorbar" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First, the scatter plot\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1 = plt.subplot(gs[1,0]) # place it where it should be.\n", "# --------------------------------------------------------\n", "\n", "# The plot itself\n", "plt1 = ax1.scatter(x, y, c = z, \n", " marker = 's', s=20, edgecolor = 'none',alpha =1,\n", " cmap = 'magma_r', vmin =0 , vmax = 4)\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1.grid(True)\n", "ax1.set_xlim([-4,4])\n", "ax1.set_ylim([-4,4])\n", "ax1.set_xlabel(r' ') # Force this empty !\n", "ax1.set_xticks(np.linspace(-4,4,9)) # Force this to what I want - for consistency with histogram below !\n", "ax1.set_xticklabels([]) # Force this empty !\n", "ax1.set_ylabel(r'My y label')\n", "\n", "# and let us not forget the colorbar above !\n", "# --------------------------------------------------------\n", "cbax = plt.subplot(gs[0,0]) # Place it where it should be.\n", "# --------------------------------------------------------\n", "\n", "cb = Colorbar(ax = cbax, mappable = plt1, orientation = 'horizontal', ticklocation = 'top')\n", "cb.set_label(r'Colorbar !', labelpad=10)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### D-3) The first histogram (vertical)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NOTE: I guess that a kernel density plot on top of the histogram would be better from a scientific standpoint. \n", "# But this is only meant as an illustration of a side-plot, so who cares ?\n", "\n", "# And now the histogram\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1v = plt.subplot(gs[1,1])\n", "# --------------------------------------------------------\n", "\n", "\n", "# Plot the data\n", "bins = np.arange(-4,4,0.1)\n", "ax1v.hist(y,bins=bins, orientation='horizontal', color='k', edgecolor='w')\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1v.set_yticks(np.linspace(-4,4,9)) # Ensures we have the same ticks as the scatter plot !\n", "ax1v.set_xticklabels([])\n", "ax1v.set_yticklabels([])\n", "ax1v.set_ylim([-4,4])\n", "ax1v.grid(True)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### D-4) The second histogram (horizontal)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# And now another histogram\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1h = plt.subplot(gs[2,0])\n", "# --------------------------------------------------------\n", "\n", "\n", "# Plot the data\n", "bins = np.arange(-4,4,0.1)\n", "ax1h.hist(x, bins=bins, orientation='vertical', color='k', edgecolor='w')\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1h.set_xticks(np.linspace(-4,4,9)) # Ensures we have the same ticks as the scatter plot !\n", "ax1h.set_yticklabels([])\n", "ax1h.set_xlim([-4,4])\n", "ax1h.set_xlabel(r'My x label')\n", "ax1h.grid(True)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### D-5) The line plot" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Finally, show some 'spectra' in the right panel\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax2 = plt.subplot(gs[0:2,3]) # Make it span the entire height of the figure (3 rows)\n", "# --------------------------------------------------------\n", "\n", "# Plot the data\n", "plt.plot(x[::20], ls = '-', color='darkviolet', lw=2)\n", "plt.plot(y[::20], ls = '-', color ='tomato', lw=2)\n", "\n", "# Define the limits, labels, ticks as required\n", "ax2.set_xlabel('My other x label')\n", "ax2.set_ylabel('My other y label')\n", "ax2.set_ylim([-4,4])\n", "ax2.grid(True)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### D-6) Plot and save it all" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Save it and display it\n", "fig.savefig('gridspec_demo.pdf', bbox_inches='tight') \n", "# bbox_inches -> crops the extra space if any !\n", "\n", "fig.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "#### E) Final comments\n", "\n", "gridspec does not do \"insets\" of plots. \n", "imshow always a bit tricky if axis ratio is set.\n", "\n", "#### F) rcparams anyone ?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### G) Python 2.7 *vs* Python 3.x. Thoughts ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### H) Gridspec & Jupyter notebooks (inline)\n", "\n", "gridspec also works fine inside a jupyter notebook. The examples before used an external plot window for clarity during the setp-by-step construction process of the final plot. Below, you can get the same figure inside the notebook. Note that this will require to restart the kernel before running it." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABAIAAAI1CAYAAABMj1iaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEgEAAAAAAHQQEgEAAAAAAHSQ/w9brqoAu9B/aQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np # For creating some fake data\n", "\n", "# Use some notebook magic - forget the next line in normal scripted code\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt # Gives access to basic plotting functions\n", "import matplotlib.gridspec as gridspec # GRIDSPEC !\n", "from matplotlib.colorbar import Colorbar # For dealing with Colorbars the proper way - TBD in a separate PyCoffee ?\n", "\n", "# Ok, let us start by creating some fake data\n", "x = np.random.randn(1000)\n", "y = np.random.randn(1000)\n", "z = np.sqrt(x**2+y**2)\n", "\n", "# `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n", "# with random floats sampled from a univariate \"normal\" (Gaussian)\n", "# distribution of mean 0 and variance 1.\n", "\n", "\n", "fig = plt.figure(1, figsize=(15,8))\n", "\n", "\n", "# Now, create the gridspec structure, as required\n", "gs = gridspec.GridSpec(3,4, height_ratios=[0.05,1,0.2], width_ratios=[1,0.2,0.2,1])\n", "\n", "# 3 rows, 4 columns, each with the required size ratios. \n", "# Also make sure the margins and spacing are apropriate\n", "\n", "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n", "\n", "# Note: I set the margins to make it look good on my screen ...\n", "# BUT: this is irrelevant for the saved image, if using bbox_inches='tight'in savefig !\n", "\n", "# Note: Here, I use a little trick. I only have three vertical layers of plots : \n", "# a scatter plot, a histogram, and a line plot. So, in principle, I could use a 3x3 structure. \n", "# However, I want to have the histogram 'closer' from the scatter plot than the line plot.\n", "# So, I insert a 4th layer between the histogram and line plot, \n", "# keep it empty, and use its thickness (the 0.2 above) to adjust the space as required.\n", "\n", "# First, the scatter plot\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1 = plt.subplot(gs[1,0]) # place it where it should be.\n", "# --------------------------------------------------------\n", "\n", "# The plot itself\n", "plt1 = ax1.scatter(x, y, c = z, \n", " marker = 's', s=20, edgecolor = 'none',alpha =1,\n", " cmap = 'magma_r', vmin =0 , vmax = 4)\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1.grid(True)\n", "ax1.set_xlim([-4,4])\n", "ax1.set_ylim([-4,4])\n", "ax1.set_xlabel(r' ') # Force this empty !\n", "ax1.set_xticks(np.linspace(-4,4,9)) # Force this to what I want - for consistency with histogram below !\n", "ax1.set_xticklabels([]) # Force this empty !\n", "ax1.set_ylabel(r'My y label')\n", "\n", "# and let us not forget the colorbar above !\n", "# --------------------------------------------------------\n", "cbax = plt.subplot(gs[0,0]) # Place it where it should be.\n", "# --------------------------------------------------------\n", "\n", "cb = Colorbar(ax = cbax, mappable = plt1, orientation = 'horizontal', ticklocation = 'top')\n", "cb.set_label(r'Colorbar !', labelpad=10)\n", "\n", "# NOTE: I guess that a kernel density plot on top of the histogram would be better from a scientific standpoint. \n", "# But this is only meant as an illustration of a side-plot, so who cares ?\n", "\n", "# And now the histogram\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1v = plt.subplot(gs[1,1])\n", "# --------------------------------------------------------\n", "\n", "\n", "# Plot the data\n", "bins = np.arange(-4,4,0.1)\n", "ax1v.hist(y,bins=bins, orientation='horizontal', color='k', edgecolor='w')\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1v.set_yticks(np.linspace(-4,4,9)) # Ensures we have the same ticks as the scatter plot !\n", "ax1v.set_xticklabels([])\n", "ax1v.set_yticklabels([])\n", "ax1v.set_ylim([-4,4])\n", "ax1v.grid(True)\n", "\n", "# And now another histogram\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax1h = plt.subplot(gs[2,0])\n", "# --------------------------------------------------------\n", "\n", "\n", "# Plot the data\n", "bins = np.arange(-4,4,0.1)\n", "ax1h.hist(x, bins=bins, orientation='vertical', color='k', edgecolor='w')\n", "\n", "# Define the limits, labels, ticks as required\n", "ax1h.set_xticks(np.linspace(-4,4,9)) # Ensures we have the same ticks as the scatter plot !\n", "ax1h.set_yticklabels([])\n", "ax1h.set_xlim([-4,4])\n", "ax1h.set_xlabel(r'My x label')\n", "ax1h.grid(True)\n", "\n", "# Finally, show some 'spectra' in the right panel\n", "# Use the gridspec magic to place it\n", "# --------------------------------------------------------\n", "ax2 = plt.subplot(gs[0:2,3]) # Make it span the entire height of the figure (3 rows)\n", "# --------------------------------------------------------\n", "\n", "# Plot the data\n", "plt.plot(x[::20], ls = '-', color='darkviolet', lw=2)\n", "plt.plot(y[::20], ls = '-', color ='tomato', lw=2)\n", "\n", "# Define the limits, labels, ticks as required\n", "ax2.set_xlabel('My other x label')\n", "ax2.set_ylabel('My other y label')\n", "ax2.set_ylim([-4,4])\n", "ax2.grid(True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }