{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting with python\n",
"\n",
"The main plotting module in python is `matplotlib`. There is a lot\n",
"that can be done with it - see the [webpage](https://matplotlib.org/gallery/index.html)\n",
"\n",
"\n",
"## Contents\n",
"\n",
"* [Running inside a notebook](#inside-notebook)\n",
" * [2D plots](#2D-plots)\n",
" * [Histograms and Bar Plots](#histograms)\n",
" * [Scatter plots](#scatter-plots)\n",
" * [Subplots](#subplots)\n",
" * [Displaying Images](#displaying-images)\n",
" * [3D plots](#3D-plots)\n",
"* [Running in a standalone script](#plotting-in-scripts)\n",
"* [Exercise](#exercise)\n",
"\n",
"---\n",
"\n",
"\n",
"<a class=\"anchor\" id=\"inside-notebook\"></a>\n",
"## Inside a notebook\n",
"\n",
"Inside a jupyter notebook you get access to this in a slightly\n",
"different way, compared to other modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This only needs to be done once in a notebook, like for standard imports.\n",
"\n",
"> There are also other alternatives, including interactive versions - see the practical on Jupyter notebooks for more information about this.\n",
"\n",
"\n",
"The library works very similarly to plotting in matlab. Let's start\n",
"with some simple examples.\n",
"\n",
"<a class=\"anchor\" id=\"2D-plots\"></a>\n",
"### 2D plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"plt.style.use('bmh')\n",
"\n",
"x = np.linspace(-np.pi, np.pi, 256)\n",
"cosx, sinx = np.cos(x), np.sin(x)\n",
"\n",
"plt.plot(x, cosx)\n",
"plt.plot(x, sinx, color='red', linewidth=4, linestyle='-.')\n",
"plt.plot(x, sinx**2)\n",
"plt.xlim(-np.pi, np.pi)\n",
"plt.title('Our first plots')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Note that the `plt.style.use('bmh')` command is not necessary, but it\n",
"> does make nicer looking plots in general. You can use `ggplot`\n",
"> instead of `bmh` if you want something resembling plots made by R.\n",
"> For a list of options run: `print(plt.style.available)`\n",
"\n",
"You can also save the objects and interrogate/set their properties, as\n",
"well as those for the general axes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hdl = plt.plot(x, cosx)\n",
"print(hdl[0].get_color())\n",
"hdl[0].set_color('#707010')\n",
"hdl[0].set_linewidth(0.5)\n",
"plt.grid('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `dir()` or `help()` or the online docs to get more info on what\n",
"you can do with these.\n",
"\n",
"<a class=\"anchor\" id=\"histograms\"></a>\n",
"### Histograms and bar charts\n",
"\n",
"For a simple histogram you can do this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r = np.random.rand(1000)\n",
"n,bins,_ = plt.hist((r-0.5)**2, bins=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where it also returns the number of elements in each bin, as `n`, and\n",
"the bin centres, as `bins`.\n",
"\n",
"> The `_` in the third part on the left\n",
"> hand side is a shorthand for just throwing away the corresponding part\n",
"> of the return structure.\n",
"\n",
"\n",
"There is also a call for doing bar plots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"samp1 = r[0:10]\n",
"samp2 = r[10:20]\n",
"bwidth = 0.3\n",
"xcoord = np.arange(10)\n",
"plt.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1')\n",
"plt.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2')\n",
"plt.legend(loc='upper left')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"scatter-plots\"></a>\n",
"### Scatter plots\n",
"\n",
"It would be possible to use `plot()` to create a scatter plot, but\n",
"there is also an alternative: `scatter()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"# setup some sizes for each point (arbitrarily example here)\n",
"ssize = 100*abs(samp1-samp2) + 10\n",
"ax.scatter(samp1, samp2, s=ssize, alpha=0.5)\n",
"# now add the y=x line\n",
"allsamps = np.hstack((samp1,samp2))\n",
"ax.plot([min(allsamps),max(allsamps)],[min(allsamps),max(allsamps)], color='red', linestyle='--')\n",
"plt.xlim(min(allsamps),max(allsamps))\n",
"plt.ylim(min(allsamps),max(allsamps))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Note that in this case we use the first line return to get a handle to\n",
"> the axis, `ax`, and the figure ,`fig`. The axis can be used instead of\n",
"> `plt` in most cases, although the `xlim()` and `ylim()` calls can only\n",
"> be done through `plt`.\n",
"\n",
"> In general, figures and subplots can be created in matplotlib in a\n",
"> similar fashion to matlab, but they do not have to be explicitly\n",
"> invoked as you can see from the earlier examples.\n",
"\n",
"<a class=\"anchor\" id=\"subplots\"></a>\n",
"### Subplots\n",
"\n",
"These are very similar to matlab:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.subplot(2, 1, 1)\n",
"plt.plot(x,cosx, '.-')\n",
"plt.xlim(-np.pi, np.pi)\n",
"plt.ylabel('Full sampling')\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(x[::30], cosx[::30], '.-')\n",
"plt.xlim(-np.pi, np.pi)\n",
"plt.ylabel('Subsampled')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"displaying-images\"></a>\n",
"### Displaying images\n",
"\n",
"The main command for displaying images is `imshow()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import nibabel as nib\n",
"import os.path as op\n",
"nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)\n",
"imdat = nim.get_data().astype(float)\n",
"imslc = imdat[:,:,70]\n",
"plt.imshow(imslc, cmap=plt.cm.gray)\n",
"plt.colorbar()\n",
"plt.grid('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that matplotlib will use the **voxel data orientation**, and that\n",
"configuring the plot orientation is **your responsibility**. To rotate a\n",
"slice, simply transpose the data (`.T`). To invert the data along along an\n",
"axis, you don't need to modify the data - simply swap the axis limits around:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(imslc.T, cmap=plt.cm.gray)\n",
"plt.xlim(reversed(plt.xlim()))\n",
"plt.ylim(reversed(plt.ylim()))\n",
"plt.colorbar()\n",
"plt.grid('off')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"3D-plots\"></a>\n",
"### 3D plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Taken from https://matplotlib.org/gallery/mplot3d/wire3d.html#sphx-glr-gallery-mplot3d-wire3d-py\n",
"\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"# Grab some test data.\n",
"X, Y, Z = axes3d.get_test_data(0.05)\n",
"\n",
"# Plot a basic wireframe.\n",
"ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Surface renderings are many other plots are possible - see 3D examples on\n",
"the [matplotlib webpage](https://matplotlib.org/gallery/index.html#mplot3d-examples-index)\n",
"\n",
"---\n",
"\n",
"<a class=\"anchor\" id=\"plotting-in-scripts\"></a>\n",
"## Plotting from standalone scripts\n",
"\n",
"When running from a standalone script, the same `matplotlib` import is required,\n",
"but the line `%matplotlib <backend>` should *not* be used.\n",
"\n",
"In a script it is also necessary to _finish_ with `plt.show()` as\n",
"otherwise nothing is actually displayed. For example, the above\n",
"examples would setup a plot but the actual graphic would only appear\n",
"after the `plt.show()` command was executed. Furthermore, control is\n",
"not returned to the script immediately as the plot is interactive by default.\n",
"\n",
"---\n",
"\n",
"<a class=\"anchor\" id=\"exercise\"></a>\n",
"## Exercise\n",
"\n",
"Find a different type of plot (e.g., boxplot, violin plot, quiver\n",
"plot, pie chart, etc.), look up\n",
"the documentation and then write _your own code that calls this_ to create a plot\n",
"from some data that you create yourself (i.e., don't just blindly copy\n",
"example code from the docs)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Make up some data and do the funky plot"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}