Commit 8ecc39a4 authored by Michiel Cottaar's avatar Michiel Cottaar Committed by Michiel Cottaar
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added link to artist tutorial

parent 96f3b243
%% Cell type:markdown id:5567ba9e tags: %% Cell type:markdown id:4ba14387 tags:
# Matplotlib tutorial # Matplotlib tutorial
The main plotting library in python is `matplotlib`. The main plotting library in python is `matplotlib`.
It provides a simple interface to just explore the data, It provides a simple interface to just explore the data,
while also having a lot of flexibility to create publication-worthy plots. while also having a lot of flexibility to create publication-worthy plots.
In fact, the vast majority of python-produced plots in papers will be either produced In fact, the vast majority of python-produced plots in papers will be either produced
directly using matplotlib or by one of the many plotting libraries built on top of directly using matplotlib or by one of the many plotting libraries built on top of
matplotlib (such as [seaborn](https://seaborn.pydata.org/) or [nilearn](https://nilearn.github.io/)). matplotlib (such as [seaborn](https://seaborn.pydata.org/) or [nilearn](https://nilearn.github.io/)).
Like everything in python, there is a lot of help available online (just google it or ask your local pythonista). Like everything in python, there is a lot of help available online (just google it or ask your local pythonista).
A particularly useful resource for matplotlib is the [gallery](https://matplotlib.org/gallery/index.html). A particularly useful resource for matplotlib is the [gallery](https://matplotlib.org/gallery/index.html).
Here you can find a wide range of plots. Here you can find a wide range of plots.
Just find one that looks like what you want to do and click on it to see (and copy) the code used to generate the plot. Just find one that looks like what you want to do and click on it to see (and copy) the code used to generate the plot.
## Contents ## Contents
- [Basic plotting commands](#basic-plotting-commands) - [Basic plotting commands](#basic-plotting-commands)
- [Line plots](#line) - [Line plots](#line)
- [Scatter plots](#scatter) - [Scatter plots](#scatter)
- [Histograms and bar plots](#histograms) - [Histograms and bar plots](#histograms)
- [Adding error bars](#error) - [Adding error bars](#error)
- [Shading regions](#shade) - [Shading regions](#shade)
- [Displaying images](#image) - [Displaying images](#image)
- [Adding lines, arrows, text](#annotations) - [Adding lines, arrows, text](#annotations)
- [Using the object-oriented interface](#OO) - [Using the object-oriented interface](#OO)
- [Multiple plots (i.e., subplots)](#subplots) - [Multiple plots (i.e., subplots)](#subplots)
- [Adjusting plot layouts](#layout) - [Adjusting plot layouts](#layout)
- [Advanced grid configurations (GridSpec)](#grid-spec) - [Advanced grid configurations (GridSpec)](#grid-spec)
- [Styling your plot](#styling) - [Styling your plot](#styling)
- [Setting title and labels](#labels) - [Setting title and labels](#labels)
- [Editing the x- and y-axis](#axis) - [Editing the x- and y-axis](#axis)
- [FAQ](#faq) - [FAQ](#faq)
- [Why am I getting two images?](#double-image) - [Why am I getting two images?](#double-image)
- [I produced a plot in my python script, but it does not show up](#show) - [I produced a plot in my python script, but it does not show up](#show)
- [Changing where the image appears: backends](#backends) - [Changing where the image appears: backends](#backends)
<a class="anchor" id="basic-plotting-commands"></a> <a class="anchor" id="basic-plotting-commands"></a>
## Basic plotting commands ## Basic plotting commands
Let's start with the basic imports: Let's start with the basic imports:
%% Cell type:code id:3917392c tags: %% Cell type:code id:3f5212f2 tags:
``` ```
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
``` ```
%% Cell type:markdown id:76688c00 tags: %% Cell type:markdown id:9b66b866 tags:
<a class="anchor" id="line"></a> <a class="anchor" id="line"></a>
### Line plots ### Line plots
A basic lineplot can be made just by calling `plt.plot`: A basic lineplot can be made just by calling `plt.plot`:
%% Cell type:code id:00d5ff18 tags: %% Cell type:code id:520e577b tags:
``` ```
plt.plot([1, 2, 3], [1.3, 4.2, 3.1]) plt.plot([1, 2, 3], [1.3, 4.2, 3.1])
``` ```
%% Cell type:markdown id:3d0472b7 tags: %% Cell type:markdown id:88a5db94 tags:
To adjust how the line is plotted, check the documentation: To adjust how the line is plotted, check the documentation:
%% Cell type:code id:38d3f3ab tags: %% Cell type:code id:ba7b1bf7 tags:
``` ```
plt.plot? plt.plot?
``` ```
%% Cell type:markdown id:867ea1f5 tags: %% Cell type:markdown id:1eb64212 tags:
As you can see there are a lot of options. As you can see there are a lot of options.
The ones you will probably use most often are: The ones you will probably use most often are:
- `linestyle`: how the line is plotted (set to '' to omit the line) - `linestyle`: how the line is plotted (set to '' to omit the line)
- `marker`: how the points are plotted (these are not plotted by default) - `marker`: how the points are plotted (these are not plotted by default)
- `color`: what color to use (defaults to cycling through a set of 7 colors) - `color`: what color to use (defaults to cycling through a set of 7 colors)
%% Cell type:code id:c705366a tags: %% Cell type:code id:b0571451 tags:
``` ```
theta = np.linspace(0, 2 * np.pi, 101) theta = np.linspace(0, 2 * np.pi, 101)
plt.plot(np.sin(theta), np.cos(theta)) plt.plot(np.sin(theta), np.cos(theta))
plt.plot([-0.3, 0.3], [0.3, 0.3], marker='o', linestyle='', markersize=20) plt.plot([-0.3, 0.3], [0.3, 0.3], marker='o', linestyle='', markersize=20)
plt.plot(0, -0.1, marker='s', color='black') plt.plot(0, -0.1, marker='s', color='black')
x = np.linspace(-0.5, 0.5, 5) x = np.linspace(-0.5, 0.5, 5)
plt.plot(x, x ** 2 - 0.5, linestyle='--', marker='+', color='red') plt.plot(x, x ** 2 - 0.5, linestyle='--', marker='+', color='red')
``` ```
%% Cell type:markdown id:f7e493a7 tags: %% Cell type:markdown id:85597103 tags:
Because these keywords are so common, you can actually set one or more of them by passing in a string as the third argument. Because these keywords are so common, you can actually set one or more of them by passing in a string as the third argument.
%% Cell type:code id:c2b6c5c5 tags: %% Cell type:code id:e8c50bcf tags:
``` ```
x = np.linspace(0, 1, 11) x = np.linspace(0, 1, 11)
plt.plot(x, x) plt.plot(x, x)
plt.plot(x, x ** 2, '--') # sets the linestyle to dashed plt.plot(x, x ** 2, '--') # sets the linestyle to dashed
plt.plot(x, x ** 3, 's') # sets the marker to square (and turns off the line) plt.plot(x, x ** 3, 's') # sets the marker to square (and turns off the line)
plt.plot(x, x ** 4, '^y:') # sets the marker to triangles (i.e., '^'), linestyle to dotted (i.e., ':'), and the color to yellow (i.e., 'y') plt.plot(x, x ** 4, '^y:') # sets the marker to triangles (i.e., '^'), linestyle to dotted (i.e., ':'), and the color to yellow (i.e., 'y')
``` ```
%% Cell type:markdown id:c7dd3aa9 tags: %% Cell type:markdown id:84e7d60e tags:
<a class="anchor" id="scatter"></a> <a class="anchor" id="scatter"></a>
### Scatter plots ### Scatter plots
The main extra feature of `plt.scatter` over `plt.plot` is that you can vary the color and size of the points based on some other variable array: The main extra feature of `plt.scatter` over `plt.plot` is that you can vary the color and size of the points based on some other variable array:
%% Cell type:code id:33b81ef4 tags: %% Cell type:code id:fff43424 tags:
``` ```
x = np.random.rand(30) x = np.random.rand(30)
y = np.random.rand(30) y = np.random.rand(30)
plt.scatter(x, y, x * 30, y) plt.scatter(x, y, x * 30, y)
plt.colorbar() # adds a colorbar plt.colorbar() # adds a colorbar
``` ```
%% Cell type:markdown id:22de1aac tags: %% Cell type:markdown id:ed3c393d tags:
The third argument is the variable determining the size, while the fourth argument is the variable setting the color. The third argument is the variable determining the size, while the fourth argument is the variable setting the color.
<a class="anchor" id="histograms"></a> <a class="anchor" id="histograms"></a>
### Histograms and bar plots ### Histograms and bar plots
For a simple histogram you can do this: For a simple histogram you can do this:
%% Cell type:code id:0c445269 tags: %% Cell type:code id:87e83be8 tags:
``` ```
r = np.random.rand(1000) r = np.random.rand(1000)
n,bins,_ = plt.hist((r-0.5)**2, bins=30) n,bins,_ = plt.hist((r-0.5)**2, bins=30)
``` ```
%% Cell type:markdown id:41a54dd8 tags: %% Cell type:markdown id:78abc3b7 tags:
where it also returns the number of elements in each bin, as `n`, and where it also returns the number of elements in each bin, as `n`, and
the bin centres, as `bins`. the bin centres, as `bins`.
> The `_` in the third part on the left > The `_` in the third part on the left
> hand side is a shorthand for just throwing away the corresponding part > hand side is a shorthand for just throwing away the corresponding part
> of the return structure. > of the return structure.
There is also a call for doing bar plots: There is also a call for doing bar plots:
%% Cell type:code id:9d7c817d tags: %% Cell type:code id:b2945a9f tags:
``` ```
samp1 = r[0:10] samp1 = r[0:10]
samp2 = r[10:20] samp2 = r[10:20]
bwidth = 0.3 bwidth = 0.3
xcoord = np.arange(10) xcoord = np.arange(10)
plt.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1') plt.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1')
plt.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2') plt.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2')
plt.legend(loc='upper left') plt.legend(loc='upper left')
``` ```
%% Cell type:markdown id:eca1cea7 tags: %% Cell type:markdown id:09f070f4 tags:
> If you want more advanced distribution plots beyond a simple histogram, have a look at the seaborn [gallery](https://seaborn.pydata.org/examples/index.html) for (too?) many options. > If you want more advanced distribution plots beyond a simple histogram, have a look at the seaborn [gallery](https://seaborn.pydata.org/examples/index.html) for (too?) many options.
<a class="anchor" id="error"></a> <a class="anchor" id="error"></a>
### Adding error bars ### Adding error bars
If your data is not completely perfect and has for some obscure reason some uncertainty associated with it, If your data is not completely perfect and has for some obscure reason some uncertainty associated with it,
you can plot these using `plt.error`: you can plot these using `plt.error`:
%% Cell type:code id:939fcf82 tags: %% Cell type:code id:79fb7453 tags:
``` ```
x = np.arange(5) x = np.arange(5)
y1 = [0.3, 0.5, 0.7, 0.1, 0.3] y1 = [0.3, 0.5, 0.7, 0.1, 0.3]
yerr = [0.12, 0.28, 0.1, 0.25, 0.6] yerr = [0.12, 0.28, 0.1, 0.25, 0.6]
xerr = 0.3 xerr = 0.3
plt.errorbar(x, y1, yerr, xerr, marker='s', linestyle='') plt.errorbar(x, y1, yerr, xerr, marker='s', linestyle='')
``` ```
%% Cell type:markdown id:cb1a8d17 tags: %% Cell type:markdown id:d54c0bbc tags:
<a class="anchor" id="shade"></a> <a class="anchor" id="shade"></a>
### Shading regions ### Shading regions
An area below a plot can be shaded using `plt.fill` An area below a plot can be shaded using `plt.fill`
%% Cell type:code id:f7221bc3 tags: %% Cell type:code id:599096f8 tags:
``` ```
x = np.linspace(0, 2, 100) x = np.linspace(0, 2, 100)
plt.fill(x, np.sin(x * np.pi)) plt.fill(x, np.sin(x * np.pi))
``` ```
%% Cell type:markdown id:86d77cf6 tags: %% Cell type:markdown id:369696fa tags:
This can be nicely combined with a polar projection, to create 2D orientation distribution functions: This can be nicely combined with a polar projection, to create 2D orientation distribution functions:
%% Cell type:code id:b96cbc10 tags: %% Cell type:code id:b97730b0 tags:
``` ```
plt.subplot(projection='polar') plt.subplot(projection='polar')
theta = np.linspace(0, 2 * np.pi, 100) theta = np.linspace(0, 2 * np.pi, 100)
plt.fill(theta, np.exp(-2 * np.cos(theta) ** 2)) plt.fill(theta, np.exp(-2 * np.cos(theta) ** 2))
``` ```
%% Cell type:markdown id:5a0defe8 tags: %% Cell type:markdown id:049fbd3b tags:
The area between two lines can be shaded using `fill_between`: The area between two lines can be shaded using `fill_between`:
%% Cell type:code id:3f11d97b tags: %% Cell type:code id:672b1757 tags:
``` ```
x = np.linspace(0, 10, 1000) x = np.linspace(0, 10, 1000)
y = 5 * np.sin(5 * x) + x - 0.1 * x ** 2 y = 5 * np.sin(5 * x) + x - 0.1 * x ** 2
yl = x - 0.1 * x ** 2 - 5 yl = x - 0.1 * x ** 2 - 5
yu = yl + 10 yu = yl + 10
plt.plot(x, y, 'r') plt.plot(x, y, 'r')
plt.fill_between(x, yl, yu) plt.fill_between(x, yl, yu)
``` ```
%% Cell type:markdown id:aa3fb87b tags: %% Cell type:markdown id:e866c409 tags:
<a class="anchor" id="image"></a> <a class="anchor" id="image"></a>
### Displaying images ### Displaying images
The main command for displaying images is `plt.imshow` (use `plt.pcolor` for cases where you do not have a regular grid) The main command for displaying images is `plt.imshow` (use `plt.pcolor` for cases where you do not have a regular grid)
%% Cell type:code id:63f18c75 tags: %% Cell type:code id:42fc8081 tags:
``` ```
import nibabel as nib import nibabel as nib
import os.path as op import os.path as op
nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False) nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)
imdat = nim.get_data().astype(float) imdat = nim.get_data().astype(float)
imslc = imdat[:,:,70] imslc = imdat[:,:,70]
plt.imshow(imslc, cmap=plt.cm.gray) plt.imshow(imslc, cmap=plt.cm.gray)
plt.colorbar() plt.colorbar()
plt.grid('off') plt.grid('off')
``` ```
%% Cell type:markdown id:be2facc0 tags: %% Cell type:markdown id:83fb2bb3 tags:
Note that matplotlib will use the **voxel data orientation**, and that Note that matplotlib will use the **voxel data orientation**, and that
configuring the plot orientation is **your responsibility**. To rotate a configuring the plot orientation is **your responsibility**. To rotate a
slice, simply transpose the data (`.T`). To invert the data along along an slice, simply transpose the data (`.T`). To invert the data along along an
axis, you don't need to modify the data - simply swap the axis limits around: axis, you don't need to modify the data - simply swap the axis limits around:
%% Cell type:code id:1a960ddf tags: %% Cell type:code id:8aa53d09 tags:
``` ```
plt.imshow(imslc.T, cmap=plt.cm.gray) plt.imshow(imslc.T, cmap=plt.cm.gray)
plt.xlim(reversed(plt.xlim())) plt.xlim(reversed(plt.xlim()))
plt.ylim(reversed(plt.ylim())) plt.ylim(reversed(plt.ylim()))
plt.colorbar() plt.colorbar()
plt.grid('off') plt.grid('off')
``` ```
%% Cell type:markdown id:070f5772 tags: %% Cell type:markdown id:43fce6ce tags:
> It is easier to produce informative brain images using nilearn or fsleyes > It is easier to produce informative brain images using nilearn or fsleyes
<a class="anchor" id="annotations"></a> <a class="anchor" id="annotations"></a>
### Adding lines, arrows, and text ### Adding lines, arrows, and text
Adding horizontal/vertical lines, arrows, and text: Adding horizontal/vertical lines, arrows, and text:
%% Cell type:code id:c7f6c0f6 tags: %% Cell type:code id:37c81436 tags:
``` ```
plt.axhline(-1) # horizontal line plt.axhline(-1) # horizontal line
plt.axvline(1) # vertical line plt.axvline(1) # vertical line
plt.arrow(0.2, -0.2, 0.2, -0.8, length_includes_head=True, width=0.01) plt.arrow(0.2, -0.2, 0.2, -0.8, length_includes_head=True, width=0.01)
plt.text(0.5, 0.5, 'middle of the plot', transform=plt.gca().transAxes, ha='center', va='center') plt.text(0.5, 0.5, 'middle of the plot', transform=plt.gca().transAxes, ha='center', va='center')
plt.annotate("line crossing", (1, -1), (0.8, -0.8), arrowprops={}) # adds both text and arrow; need to set the arrowprops keyword for the arrow to be plotted plt.annotate("line crossing", (1, -1), (0.8, -0.8), arrowprops={}) # adds both text and arrow; need to set the arrowprops keyword for the arrow to be plotted
``` ```
%% Cell type:markdown id:de291180 tags: %% Cell type:markdown id:40daff02 tags:
By default the locations of the arrows and text will be in data coordinates (i.e., whatever is on the axes), By default the locations of the arrows and text will be in data coordinates (i.e., whatever is on the axes),
however you can change that. For example to find the middle of the plot in the last example we use however you can change that. For example to find the middle of the plot in the last example we use
axes coordinates, which are always (0, 0) in the lower left and (1, 1) in the upper right. axes coordinates, which are always (0, 0) in the lower left and (1, 1) in the upper right.
See the matplotlib [transformations tutorial](https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html) See the matplotlib [transformations tutorial](https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html)
for more detail. for more detail.
<a class="anchor" id="OO"></a> <a class="anchor" id="OO"></a>
## Using the object-oriented interface ## Using the object-oriented interface
In the examples above we simply added multiple lines/points/bars/images In the examples above we simply added multiple lines/points/bars/images
(collectively called artists in matplotlib) to a single plot. (collectively called [artists](https://matplotlib.org/stable/tutorials/intermediate/artists.html) in matplotlib) to a single plot.
To prettify this plots, we first need to know what all the features are called: To prettify this plots, we first need to know what all the features are called:
![anatomy of a plot](https://matplotlib.org/stable/_images/anatomy.png) ![anatomy of a plot](https://matplotlib.org/stable/_images/anatomy.png)
Using the terms in this plot let's see what our first command of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])` Using the terms in this plot let's see what our first command of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])`
actually does: actually does:
1. First it creates a figure and makes this the active figure. Being the active figure means that any subsequent commands will affect figure. You can find the active figure at any point by calling `plt.gcf()`. 1. First it creates a figure and makes this the active figure. Being the active figure means that any subsequent commands will affect figure. You can find the active figure at any point by calling `plt.gcf()`.
2. Then it creates an Axes or Subplot in the figure and makes this the active axes. Any subsequent commands will reuse this active axes. You can find the active axes at any point by calling `plt.gca()`. 2. Then it creates an Axes or Subplot in the figure and makes this the active axes. Any subsequent commands will reuse this active axes. You can find the active axes at any point by calling `plt.gca()`.
3. Finally it creates a Line2D artist containing the x-coordinates `[1, 2, 3]` and `[1.3, 4.2, 3.1]` ands adds this to the active axes. 3. Finally it creates a Line2D artist containing the x-coordinates `[1, 2, 3]` and `[1.3, 4.2, 3.1]` ands adds this to the active axes.
4. At some later time, when actually creating the plot, matplotlib will also automatically determine for you a default range for the x-axis and y-axis and where the ticks should be. 4. At some later time, when actually creating the plot, matplotlib will also automatically determine for you a default range for the x-axis and y-axis and where the ticks should be.
This concept of an "active" figure and "active" axes can be very helpful with a single plot, it can quickly get very confusing when you have multiple sub-plots within a figure or even multiple figures. This concept of an "active" figure and "active" axes can be very helpful with a single plot, it can quickly get very confusing when you have multiple sub-plots within a figure or even multiple figures.
In that case we want to be more explicit about what sub-plot we want to add the artist to. In that case we want to be more explicit about what sub-plot we want to add the artist to.
We can do this by switching from the "procedural" interface used above to the "object-oriented" interface. We can do this by switching from the "procedural" interface used above to the "object-oriented" interface.
The commands are very similar, we just have to do a little more setup. The commands are very similar, we just have to do a little more setup.
For example, the equivalent of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])` is: For example, the equivalent of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])` is:
%% Cell type:code id:3d3482ef tags: %% Cell type:code id:660d1559 tags:
``` ```
fig = plt.figure() fig = plt.figure()
ax = fig.add_subplot() ax = fig.add_subplot()
ax.plot([1, 2, 3], [1.3, 4.2, 3.1]) ax.plot([1, 2, 3], [1.3, 4.2, 3.1])
``` ```
%% Cell type:markdown id:4923481d tags: %% Cell type:markdown id:2ad9cfbf tags:
Note that here we explicitly create the figure and add a single sub-plot to the figure. Note that here we explicitly create the figure and add a single sub-plot to the figure.
We then call the `plot` function explicitly on this figure. We then call the `plot` function explicitly on this figure.
The "Axes" object has all of the same plotting command as we used above, The "Axes" object has all of the same plotting command as we used above,
although the commands to adjust the properties of things like the title, x-axis, and y-axis are slighly different. although the commands to adjust the properties of things like the title, x-axis, and y-axis are slighly different.
`plt.getp` gives a helpful summary of the properties of a matplotlib object (and what you might change): `plt.getp` gives a helpful summary of the properties of a matplotlib object (and what you might change):
%% Cell type:code id:c0c64d42 tags: %% Cell type:code id:7be3b246 tags:
``` ```
plt.getp(ax) plt.getp(ax)
``` ```
%% Cell type:markdown id:76b789ab tags: %% Cell type:markdown id:7fbf6acd tags:
When going through this list carefully you might have spotted that the plotted line is stored in the `lines` property. When going through this list carefully you might have spotted that the plotted line is stored in the `lines` property.
Let's have a look at this line in more detail Let's have a look at this line in more detail
%% Cell type:code id:02f9cb81 tags: %% Cell type:code id:80481e71 tags:
``` ```
plt.getp(ax.lines[0]) plt.getp(ax.lines[0])
``` ```
%% Cell type:markdown id:9a192752 tags: %% Cell type:markdown id:fa8e6fee tags:
This shows us all the properties stored about this line, This shows us all the properties stored about this line,
including its coordinates in many different formats including its coordinates in many different formats
(`data`, `path`, `xdata`, `ydata`, or `xydata`), (`data`, `path`, `xdata`, `ydata`, or `xydata`),
the line style and width (`linestyle`, `linewidth`), `color`, etc. the line style and width (`linestyle`, `linewidth`), `color`, etc.
<a class="anchor" id="subplots"></a> <a class="anchor" id="subplots"></a>
## Multiple plots (i.e., subplots) ## Multiple plots (i.e., subplots)
As stated one of the strengths of the object-oriented interface is that it is easier to work with multiple plots. As stated one of the strengths of the object-oriented interface is that it is easier to work with multiple plots.
While we could do this in the procedural interface: While we could do this in the procedural interface:
%% Cell type:code id:3cafa27a tags: %% Cell type:code id:83b536d2 tags:
``` ```
plt.subplot(221) plt.subplot(221)
plt.title("Upper left") plt.title("Upper left")
plt.subplot(222) plt.subplot(222)
plt.title("Upper right") plt.title("Upper right")
plt.subplot(223)