> 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:6c91efd8 tags:
%% Cell type:markdown id:d2fb44b4 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)
...
...
@@ -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:994a4e47 tags:
%% Cell type:code id:43229971 tags:
```
fig = plt.figure()
ax = fig.add_subplot()
ax.plot([1, 2, 3], [1.3, 4.2, 3.1])
```
%% Cell type:markdown id:0c244a1a tags:
%% Cell type:markdown id:8d4bee33 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:87b60efe tags:
%% Cell type:code id:2cc5123a tags:
```
plt.getp(ax)
```
%% Cell type:markdown id:9e8785b0 tags:
%% Cell type:markdown id:37251f4a 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:1e9372b7 tags:
%% Cell type:code id:db290a0a tags:
```
plt.getp(ax.lines[0])
```
%% Cell type:markdown id:afd6a54e tags:
%% Cell type:markdown id:ae053e0c 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:5bff872f tags:
%% Cell type:code id:8bd710d5 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:510185fb tags:
%% Cell type:markdown id:28b82718 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:52ffc81d tags:
%% Cell type:code id:89a20086 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:48464ab0 tags:
%% Cell type:markdown id:852c2d46 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:d047679b tags:
%% Cell type:code id:5c14ec50 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:c8fe247b tags:
%% Cell type:markdown id:338c7239 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:09b2b281 tags:
%% Cell type:markdown id:ff58c930 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:b4f4a54b tags:
%% Cell type:code id:c1651d0c 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:37bc60de tags:
%% Cell type:markdown id:5676c42d 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:c4b8f402 tags:
%% Cell type:code id:b6841514 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:7c6e0fc1 tags:
%% Cell type:markdown id:c27500eb tags:
You can also set any of these properties by calling `Axes.set` directly:
%% Cell type:code id:ff0502fc tags:
%% Cell type:code id:4aa8461b 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:0db1eb83 tags:
%% Cell type:markdown id:e69e0f4b 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:20ca99eb tags:
%% Cell type:code id:db2b0e6e tags:
```
plt.getp(axes.get_xaxis())
```
%% Cell type:markdown id:35fe5da3 tags:
%% Cell type:markdown id:48b79b04 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:1620a4da tags:
%% Cell type:code id:e36ee821 tags:
```
%matplotlib nbagg
```
%% Cell type:markdown id:b0024423 tags:
%% Cell type:markdown id:68b0aac8 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:b67ee344 tags:
%% Cell type:code id:b81eb924 tags:
```
import matplotlib
matplotlib.use("MacOSX")
```
%% Cell type:markdown id:36ca2dfc tags:
%% Cell type:markdown id:14663014 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.
