> 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) to see if they have what you want.
### Adding error bars
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`:
```
x = np.arange(5)
y1 = [0.3, 0.5, 0.7, 0.1, 0.3]
yerr = [0.12, 0.28, 0.1, 0.25, 0.6]
xerr = 0.3
plt.error(x, y1, yerr, xerr, marker='s')
```
### Shading regions
An area below a plot can be shaded using `plt.fill`
```
x = np.linspace(0, 2, 100)
plt.fill(x, np.sin(x * np.pi))
```
This can be nicely combined with a polar projection, to create nice polar plots:
```
plt.subplot(project='polar')
theta = np.linspace(0, 2 * np.pi, 100)
plt.fill(theta, np.exp(-2 * np.cos(theta) ** 2))
```
The area between two lines can be shaded using `fill_between`:
```
x = np.linspace(0, 10, 100)
y = 5 * np.sin(x) + x - 0.1 * x ** 2
yl = x - 0.1 * x ** 2 - 2.5
yu = yl + 5
plt.plot(x, y, 'r')
plt.fill_between(x, yl, yu)
```
### Displaying images
The main command for displaying images is `plt.imshow` (use `plt.pcolor` for cases where you do not have a regular grid)
```
import nibabel as nib
import os.path as op
nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)
imdat = nim.get_data().astype(float)
imslc = imdat[:,:,70]
plt.imshow(imslc, cmap=plt.cm.gray)
plt.colorbar()
plt.grid('off')
```
Note that matplotlib will use the **voxel data orientation**, and that
configuring the plot orientation is **your responsibility**. To rotate a
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:
```
plt.imshow(imslc.T, cmap=plt.cm.gray)
plt.xlim(reversed(plt.xlim()))
plt.ylim(reversed(plt.ylim()))
plt.colorbar()
plt.grid('off')
```
### Adding lines, arrows, and text
Adding horizontal/vertical lines, arrows, and text:
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:
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()`.
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.
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.
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.
For example, the equivalent of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])` is:
```
fig = plt.figure()
ax = fig.add_subplot()
ax.plot([1, 2, 3], [1.3, 4.2, 3.1])
```
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.
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.
## 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.
While we could do this in the procedural interface:
```
plt.subplot(221)
plt.title("Upper left")
plt.subplot(222)
plt.title("Upper right")
plt.subplot(223)
plt.title("Lower left")
plt.subplot(224)
plt.title("Lower right")
```
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.
The recommended way to this instead is:
```
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title("Upper left")
axes[0, 1].set_title("Upper right")
axes[1, 0].set_title("Lower left")
axes[1, 1].set_title("Lower right")
```
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.
> 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)
### Adjusting plot layout
The default layout of sub-plots is usually good enough, however sometimes you will need some extra space to the plot to accomodate your large axes labels and ticks or you want to get rid of some of the whitespace.
fig.set_suptitle("group of plots, where each row shares the x-axis and each column the y-axis")
fig.subplots_adjust(width=0, height=0, top=0.9)
```
### Advanced grid configurations (GridSpec)
You can create more advanced grid layouts using [GridSpec](https://matplotlib.org/stable/tutorials/intermediate/gridspec.html).
An example taken from that website is:
```
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(3, 3)
f3_ax1 = fig.add_subplot(gs[0, :])
f3_ax1.set_title('gs[0, :]')
f3_ax2 = fig.add_subplot(gs[1, :-1])
f3_ax2.set_title('gs[1, :-1]')
f3_ax3 = fig.add_subplot(gs[1:, -1])
f3_ax3.set_title('gs[1:, -1]')
f3_ax4 = fig.add_subplot(gs[-1, 0])
f3_ax4.set_title('gs[-1, 0]')
f3_ax5 = fig.add_subplot(gs[-1, -2])
f3_ax5.set_title('gs[-1, -2]')
```
## Styling your plot
### Setting title and labels
You can edit a large number of plot properties by using the `Axes.set_*` interface.
We have already seen several examples of this above, but here is one more:
```
fig, axes = plt.subplots()
axes.plot([1, 2, 3], [2.3, 4.1, 0.8])
axes.set_xlabel('xlabel')
axes.set_ylabel('ylabel')
axes.set_title('title')
```
You can also set any of these properties by calling `Axes.set` directly:
```
fig, axes = plt.subplots()
axes.plot([1, 2, 3], [2.3, 4.1, 0.8])
axes.set(
xlabel='xlabel',
ylabel='ylabel',
title='title',
)
```
> To match the matlab API and save some typing the equivalent commands in the procedural interface do not have the `set_` preset. So, they are `plt.xlabel`, `plt.ylabel`, `plt.title`. This is also true for many of the `set_` commands we will see below.
You can edit the font of the text either when setting the label or after the fact.
As an example, here are three ways to change the label colours:
```
fig, axes = plt.subplots()
axes.plot([1, 2, 3], [2.3, 4.1, 0.8])
axes.set_xlabel("xlabel", color='red') # set color when setting text label
label = axes.set_ylabel("ylabel") # keep track of the Text object returned by `set_?`
label.set_color('blue')
axes.set_title("title")
axes.get_title().set_color('green') # use `get_?` to get the Text object after the fact
```
### Editing the x- and y-axis
We can change many of the properties of the x- and y-axis by using `set_` commands.
- The range shown on an axis can be set using `ax.set_xlim` (or `plt.xlim`)
- You can switch to a logarithmic (or other) axis using `ax.set_xscale('log')`
- The location of the ticks can be set using `ax.set_xticks` (or `plt.xticks`)
- The text shown for the ticks can be set using `ax.set_xticklabels` (or as a second argument to `plt.xticks`)
- The style of the ticks can be adjusted by looping through the ticks (obtained through `ax.get_xticks` or calling `plt.xticks` without arguments).
Any figure you produce in the notebook will be shown by default once you
### I produced a plot in my python script, but it does not show up?
Add `plt.show()` to the end of your script (or save the figure to a file using `plt.savefig` or `fig.savefig`).
`plt.show` will show the image to you and will block the script to allow you to take in and adjust the figure before saving or discarding it.
### Changing where the image appaers: backends
Matplotlib works across a wide range of environments: Linux, Mac OS, Windows, in the browser, and more.
The exact detail of how to show you your plot will be different across all of these environments.
This procedure used to translate your `Figure`/`Axes` objects into an actual visualisation is called the backend.
In this notebook we were using the `inline` backend, which is the default when running in a notebook.
While very robust, this backend has the disadvantage that it only produces static plots.
We could have had interactive plots if only we had changed backends to `nbagg`.
You can change backends in the IPython terminal/notebook using:
```
%matplotlib nbagg
```
> If you are using Jupyterlab (new version of the jupyter notebook) the `nbagg` backend will not work. Instead you will have to install `ipympl` and then use the `widgets` backend to get an interactive backend (this also works in the old notebooks).
In python scripts, this will give you a syntax error and you should instead use:
```
import matplotlib
matplotlib.use("osx")
```
Usually, the default backend will be fine, so you will not have to set it.
Note that setting it explicitly will make your script less portable.
