Commit 96f3b243 authored by Michiel Cottaar's avatar Michiel Cottaar Committed by Michiel Cottaar
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Explain plt.getp

parent e8296319
%% Cell type:markdown id:christian-smart tags: %% Cell type:markdown id:5567ba9e 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:quick-postage tags: %% Cell type:code id:3917392c tags:
``` python ```
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
``` ```
%% Cell type:markdown id:prescribed-writing tags: %% Cell type:markdown id:76688c00 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:turkish-marsh tags: %% Cell type:code id:00d5ff18 tags:
``` python ```
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:compact-modeling tags: %% Cell type:markdown id:3d0472b7 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:distinct-coordinate tags: %% Cell type:code id:38d3f3ab tags:
``` python ```
plt.plot? plt.plot?
``` ```
%% Cell type:markdown id:green-dutch tags: %% Cell type:markdown id:867ea1f5 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:adjacent-satellite tags: %% Cell type:code id:c705366a tags:
``` python ```
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:external-meaning tags: %% Cell type:markdown id:f7e493a7 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:simple-korean tags: %% Cell type:code id:c2b6c5c5 tags:
``` python ```
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:pediatric-sullivan tags: %% Cell type:markdown id:c7dd3aa9 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:bright-preparation tags: %% Cell type:code id:33b81ef4 tags:
``` python ```
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:asian-mailing tags: %% Cell type:markdown id:22de1aac 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:massive-relative tags: %% Cell type:code id:0c445269 tags:
``` python ```
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:positive-insight tags: %% Cell type:markdown id:41a54dd8 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:intensive-taste tags: %% Cell type:code id:9d7c817d tags:
``` python ```
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:boolean-metropolitan tags: %% Cell type:markdown id:eca1cea7 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:basic-cambridge tags: %% Cell type:code id:939fcf82 tags:
``` python ```
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:twelve-assist tags: %% Cell type:markdown id:cb1a8d17 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:stuck-teaching tags: %% Cell type:code id:f7221bc3 tags:
``` python ```
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:metric-chemical tags: %% Cell type:markdown id:86d77cf6 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:democratic-israel tags: %% Cell type:code id:b96cbc10 tags:
``` python ```
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:connected-consideration tags: %% Cell type:markdown id:5a0defe8 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:engaged-lottery tags: %% Cell type:code id:3f11d97b tags:
``` python ```
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:paperback-stylus tags: %% Cell type:markdown id:aa3fb87b 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:acoustic-kitchen tags: %% Cell type:code id:63f18c75 tags:
``` python ```
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:frank-master tags: %% Cell type:markdown id:be2facc0 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:initial-passing tags: %% Cell type:code id:1a960ddf tags:
``` python ```
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:specialized-maintenance tags: %% Cell type:markdown id:070f5772 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:weighted-publicity tags: %% Cell type:code id:c7f6c0f6 tags:
``` python ```
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:manual-bacon tags: %% Cell type:markdown id:de291180 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 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:earned-anaheim tags: %% Cell type:code id:3d3482ef tags:
``` python ```
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:valued-hungary tags: %% Cell type:markdown id:4923481d 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):
%% Cell type:code id:c0c64d42 tags:
```
plt.getp(ax)
```
%% Cell type:markdown id:76b789ab tags:
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
%% Cell type:code id:02f9cb81 tags:
```
plt.getp(ax.lines[0])
```
%% Cell type:markdown id:9a192752 tags:
This shows us all the properties stored about this line,
including its coordinates in many different formats
(`data`, `path`, `xdata`, `ydata`, or `xydata`),
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:intensive-bruce tags: %% Cell type:code id:3cafa27a tags:
``` python ```
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) plt.subplot(223)
plt.title("Lower left") plt.title("Lower left")
plt.subplot(224) plt.subplot(224)
plt.title("Lower right") plt.title("Lower right")
``` ```
%% Cell type:markdown id:upset-stanley tags: %% Cell type:markdown id:51141fee tags:
For such a simple example, this works fine. But for longer examples you would find yourself constantly looking back through the For such a simple example, this works fine. But for longer examples you would find yourself constantly looking back through the
code to figure out which of the subplots this specific `plt.title` command is affecting. code to figure out which of the subplots this specific `plt.title` command is affecting.
The recommended way to this instead is: The recommended way to this instead is: