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%% Cell type:markdown id:551c06a5 tags: %% Cell type:markdown id:ignored-think tags:
# Plotting with python # 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)
- [Line plots](#line)
- [Scatter plots](#scatter)
- [Histograms and bar plots](#histograms)
- [Adding error bars](#error)
- [Shading regions](#shade)
- [Displaying images](#image)
- [Adding lines, arrows, text](#annotations)
- [Using the object-oriented interface](#OO)
- [Multiple plots (i.e., subplots)](#subplots)
- [Adjusting plot layouts](#layout)
- [Advanced grid configurations (GridSpec)](#grid-spec)
- [Styling your plot](#styling)
- [Setting title and labels](#labels)
- [Editing the x- and y-axis](#axis)
- [FAQ](#faq)
- [Why am I getting two images?](#double-image)
- [I produced a plot in my python script, but it does not show up](#show)
- [Changing where the image appears: backends](#backends)
<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:16caed03 tags: %% Cell type:code id:material-fundamentals tags:
``` ``` python
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
``` ```
%% Cell type:markdown id:de78e9ca tags: %% Cell type:markdown id:dying-savings tags:
<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:a6b829fa tags: %% Cell type:code id:determined-melissa 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:e17e9bab tags: %% Cell type:markdown id:optional-bloom 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:5d89403a tags: %% Cell type:code id:electric-purpose tags:
``` ``` python
plt.plot? plt.plot?
``` ```
%% Cell type:markdown id:c91a5bd4 tags: %% Cell type:markdown id:offshore-narrative 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:84b58452 tags: %% Cell type:code id:younger-recall 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:0d2e8301 tags: %% Cell type:markdown id:fewer-wednesday 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:10c2404c tags: %% Cell type:code id:romance-payment 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:1e340fc7 tags: %% Cell type:markdown id:democratic-setting tags:
<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:7f3852a6 tags: %% Cell type:code id:homeless-opening 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:dcb5d48c tags: %% Cell type:markdown id:sitting-scheme 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>
### 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:50770a55 tags: %% Cell type:code id:trained-mechanism 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:17bda7f4 tags: %% Cell type:markdown id:convinced-framework 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:85dbb204 tags: %% Cell type:code id:convinced-cricket 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:acbbe7b5 tags: %% Cell type:markdown id:historical-content 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>
### 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:e749b87e tags: %% Cell type:code id:broken-deviation 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:33914264 tags: %% Cell type:markdown id:shared-afghanistan tags:
<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:df50543b tags: %% Cell type:code id:changed-dressing 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:068a8056 tags: %% Cell type:markdown id:infrared-chinese 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:3b75271e tags: %% Cell type:code id:alternative-johnson 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:fff9ddbe tags: %% Cell type:markdown id:primary-momentum 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:2bf5186f tags: %% Cell type:code id:naughty-colors 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:de59cfd2 tags: %% Cell type:markdown id:acknowledged-illustration tags:
<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:c28e90a1 tags: %% Cell type:code id:protected-toolbox 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:58546cf4 tags: %% Cell type:markdown id:short-turkey 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:a7004276 tags: %% Cell type:code id:vulnerable-vegetation 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:1f36a7a9 tags: %% Cell type:markdown id:danish-sandwich 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>
### 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:f411a442 tags: %% Cell type:code id:checked-helping 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:62d70058 tags: %% Cell type:markdown id:finished-canon 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>
## 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:
[[https://matplotlib.org/stable/_images/anatomy.png]] [[https://matplotlib.org/stable/_images/anatomy.png]]
Based on this plot let's figure out what our first command of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])` Based on this plot let's figure out 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:19752271 tags: %% Cell type:code id:original-melissa 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:fe750f08 tags: %% Cell type:markdown id:handy-anniversary 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.
<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:7f35488a tags: %% Cell type:code id:occupied-enforcement 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:490dd65c tags: %% Cell type:markdown id:encouraging-poultry 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:
%% Cell type:code id:b779ce08 tags: %% Cell type:code id:elect-printer tags:
``` ``` python
fig, axes = plt.subplots(nrows=2, ncols=2) fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title("Upper left") axes[0, 0].set_title("Upper left")
axes[0, 1].set_title("Upper right") axes[0, 1].set_title("Upper right")
axes[1, 0].set_title("Lower left") axes[1, 0].set_title("Lower left")
axes[1, 1].set_title("Lower right") axes[1, 1].set_title("Lower right")
``` ```
%% Cell type:markdown id:15f4138d tags: %% Cell type:markdown id:brief-auction tags:
Here we use `plt.subplots`, which creates both a new figure for us and a grid of sub-plots. Here we use `plt.subplots`, which creates both a new figure for us and a grid of sub-plots.
The returned `axes` object is in this case a 2x2 array of `Axes` objects, to which we set the title using the normal numpy indexing. The returned `axes` object is in this case a 2x2 array of `Axes` objects, to which we set the title using the normal numpy indexing.
> Seaborn is great for creating grids of closely related plots. Before you spent a lot of time implementing your own have a look if seaborn already has what you want on their [gallery](https://seaborn.pydata.org/examples/index.html)