> 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.
<aclass="anchor"id="error"></a>
### Adding error bars
If your data is not completely perfect and has for some obscure reason some uncertainty associated with it,
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
```
%% 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),
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.
See the matplotlib [transformations tutorial](https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html)
for more detail.
<aclass="anchor"id="OO"></a>
## Using the object-oriented interface
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:
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])`
...
...
@@ -294,45 +294,45 @@
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:
%% Cell type:code id:3d3482ef tags:
%% Cell type:code id:660d1559 tags:
```
fig = plt.figure()
ax = fig.add_subplot()
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.
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.
`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)
```
%% 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.
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])
```
%% Cell type:markdown id:9a192752 tags:
%% Cell type:markdown id:fa8e6fee 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.
...
...
@@ -340,11 +340,11 @@
<aclass="anchor"id="subplots"></a>
## 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:
%% Cell type:code id:3cafa27a tags:
%% Cell type:code id:83b536d2 tags:
```
plt.subplot(221)
plt.title("Upper left")
plt.subplot(222)
...
...
@@ -353,55 +353,55 @@
plt.title("Lower left")
plt.subplot(224)
plt.title("Lower right")
```
%% Cell type:markdown id:51141fee tags:
%% Cell type:markdown id:3438c4f7 tags:
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:
%% Cell type:code id:a7a6b2c7 tags:
%% Cell type:code id:4731d436 tags:
```
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")
```
%% Cell type:markdown id:a1ec90e2 tags:
%% Cell type:markdown id:bca7abe6 tags:
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)
<aclass="anchor"id="layout"></a>
### Adjusting plot layout
The default layout of sub-plots often leads to overlap between the labels/titles of the various subplots (as above) or to excessive amounts of whitespace in between. We can often fix this by just adding `fig.tight_layout` (or `plt.tight_layout`) after making the plot:
%% Cell type:code id:0a5f85cc tags:
%% Cell type:code id:83fab131 tags:
```
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")
fig.tight_layout()
```
%% Cell type:markdown id:ab07e5ab tags:
%% Cell type:markdown id:609cdb3f tags:
Uncomment `fig.tight_layout` and see how it adjusts the spacings between the plots automatically to reduce the whitespace.
If you want more explicit control, you can use `fig.subplots_adjust` (or `plt.subplots_adjust` to do this for the active figure).
For example, we can remove any whitespace between the plots using:
fig.suptitle("group of plots, sharing x- and y-axes")
fig.subplots_adjust(wspace=0, hspace=0, top=0.9)
```
%% Cell type:markdown id:260dc7eb tags:
%% Cell type:markdown id:18d903e1 tags:
<aclass="anchor"id="grid-spec"></a>
### 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:
%% Cell type:code id:f58613d6 tags:
%% Cell type:code id:e98b00c8 tags:
```
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(3, 3)
f3_ax1 = fig.add_subplot(gs[0, :])
...
...
@@ -433,34 +433,34 @@
f3_ax4.set_title('gs[-1, 0]')
f3_ax5 = fig.add_subplot(gs[-1, -2])
f3_ax5.set_title('gs[-1, -2]')
```
%% Cell type:markdown id:cf0ef5c8 tags:
%% Cell type:markdown id:0d363e0e tags:
<aclass="anchor"id="styling"></a>
## Styling your plot
<aclass="anchor"id="labels"></a>
### 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:
%% Cell type:code id:6f49e003 tags:
%% Cell type:code id:9553b91a tags:
```
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')
```
%% Cell type:markdown id:d1c2e77c tags:
%% Cell type:markdown id:79b53065 tags:
You can also set any of these properties by calling `Axes.set` directly:
%% Cell type:code id:5d744cf1 tags:
%% Cell type:code id:97643220 tags:
```
fig, axes = plt.subplots()
axes.plot([1, 2, 3], [2.3, 4.1, 0.8])
axes.set(
...
...
@@ -468,27 +468,27 @@
ylabel='ylabel',
title='title',
)
```
%% Cell type:markdown id:9e621844 tags:
%% Cell type:markdown id:e2fbb884 tags:
> 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 when setting the label or after the fact using the object-oriented interface:
As illustrated earlier, we can get a more complete list of the things we could change about the x-axis by looking at its properties:
%% Cell type:code id:ca4c208f tags:
%% Cell type:code id:ab2633a6 tags:
```
plt.getp(axes.get_xlabel())
```
%% Cell type:markdown id:61b4f566 tags:
%% Cell type:markdown id:440d18ce tags:
<aclass="anchor"id="faq"></a>
## FAQ
<aclass="anchor"id="double-image"></a>
### Why am I getting two images?
...
...
@@ -553,28 +553,28 @@
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:
%% Cell type:code id:33900afe tags:
%% Cell type:code id:93dbeb6e tags:
```
%matplotlib nbagg
```
%% Cell type:markdown id:7273f530 tags:
%% Cell type:markdown id:d53eef2b tags:
> 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:
%% Cell type:code id:b50352a0 tags:
%% Cell type:code id:c16b4394 tags:
```
import matplotlib
matplotlib.use("osx")
```
%% Cell type:markdown id:2a436f07 tags:
%% Cell type:markdown id:96529a3c tags:
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.
