> 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:finished-canon tags:
%% Cell type:markdown id:manual-bacon 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)
...
...
@@ -277,12 +277,14 @@
<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.
To prettify this plots, we first need to know what all the features are called:
Based on this plot let's figure out 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:
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.
...
...
@@ -292,19 +294,19 @@
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:original-melissa tags:
%% Cell type:code id:earned-anaheim tags:
``` python
fig=plt.figure()
ax=fig.add_subplot()
ax.plot([1,2,3],[1.3,4.2,3.1])
```
%% Cell type:markdown id:handy-anniversary tags:
%% Cell type:markdown id:valued-hungary 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.
...
...
@@ -312,11 +314,11 @@
<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:
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:binding-tobacco tags:
%% Cell type:code id:joint-chick tags:
``` python
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:canadian-africa tags:
%% Cell type:markdown id:illegal-spanish 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:
You can also set any of these properties by calling `Axes.set` directly:
%% Cell type:code id:afraid-fields tags:
%% Cell type:code id:suspended-biography tags:
``` python
fig,axes=plt.subplots()
axes.plot([1,2,3],[2.3,4.1,0.8])
axes.set(
...
...
@@ -440,26 +442,26 @@
ylabel='ylabel',
title='title',
)
```
%% Cell type:markdown id:hired-journey tags:
%% Cell type:markdown id:annual-hundred 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:
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:directed-trouble tags:
%% Cell type:code id:fifty-relaxation tags:
``` python
%matplotlibnbagg
```
%% Cell type:markdown id:indian-integrity tags:
%% Cell type:markdown id:fifty-sunglasses 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:pregnant-raising tags:
%% Cell type:code id:underlying-dealer tags:
``` python
importmatplotlib
matplotlib.use("osx")
```
%% Cell type:markdown id:ceramic-liquid tags:
%% Cell type:markdown id:general-subscriber 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.
Based on this plot let's figure out 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:
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()`.
