Develop Reference

Matplotlib: Is there a way to set default scatter styles with matplotlibrc?

I do a fair amount of plotting with pandas and matplotlib, and I've been looking for a way to help set the default scatter styles in my matplotlibrc file. I do not want to have to edit the matplotlib sourcecode to get default behaviors, because I need to be able to allow coworkers to reproduce work (and want to keep things consistent between machines/VEs, and update without worrying about busting things).
As an example, I want to be able to do:
plt.scatter(
df["dep_var"],
df["ind_var"],
alpha=0.5,
facecolors="none",
edgecolors="#444444",
linewidth=2,
)
With:
plt.scatter(
df["dep_var"],
df["ind_var"],
)
With overwritten alpha, face colors, edge colors, and linewidth in the matplotlibrc file (or some other non-source code location). But when I add
scatter.alpha : 0.5
to my matplotlibrc file, I get a Bad Key warning.
Is there a way to achieve this?

I think that a possible solution is use the RC parameter like:
plt.rcParams["scatter.edgecolors"] = "#444444"
plt.rcParams["lines.linewidth"]=2
You can check more about ket for Rc here
You only need to run this once! If you restart the kernel it will be reverted to the default.

how to make easy and efficient plots on Python

I use matplotlib for my plots, I find it great, but sometimes too much complicated. Here an example:
import matplotlib.pyplot as plt
import numpy as np
idx1 = -3
idx2 = 3
x = np.arange(-3, 3, 0.01)
y = np.sin(np.pi*x*7)/(np.pi*x*7)
major_ticks = np.arange(idx1, idx2, 1)
minor_ticks = np.arange(idx1, idx2, 0.1)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylim(-0.3, 1.2)
ax.set_xlim(idx1, idx2)
ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor = True)
ax.grid(True, which = 'both')
ax.tick_params(axis = 'x', labelsize = 18)
ax.tick_params(axis = 'y', labelsize = 18)
ax.plot(x, y)
plt.show()
Is there anything implemented on matplotlib and/or seaborn in which I can provide all these plot settings just as argument of a function only? It may considerably reduce the number of code lines and make the script easier both to write and understand.

Matplotlib provides an object oriented API. This means that all the elements of the figure are acutally objects for which one can get and set properties and which can be easily manipulated. This makes matplotlib really flexible such that it can produce almost any plot you'd imagine.
Since a plot may consist of a hundred or more elements, a function that would allow the same flexibility would need that amount of possible arguments. It is not necessarily easier to remember all possible arguments of a function than all possible attributes of a class.
Having a single function call that does all of this, does not necessarily mean that you have to type in less characters. The commands would just be ordered differently.
Furthermore the object oriented approach allows to keep things seperate. Some properties of the axes, like the grid or the axis labels are completely independend on what you plot to the axes. So you wouldn't want to set the xticks in the call to plot, because they are simply not related and it may be very confusing to set twice the same ticklabels when plotting two lines in the same axes.
On the other hand, matplotlib is really easy. In order to produce a plot you need two lines
import matplotlib.pyplot as plt
plt.plot([1,2,3],[2,1,3])
which sets most of the parameters exactly as they are needed. The more you want to customize this plot, the more settings you have to apply. Which is fine as it allows the user himself to determine how much in depth he wants to control the appearance of the plot.
Most matplotlib codes can be separated into three parts.
Setting the style
Creating the plot
Customizing the plot
Setting the style in the case of the code from the question involves e.g. the ticklabel size and the use of a grid. Those properties can set as it's done in the code but it may indeed be that one always wants to use the same properities here and finds it annoying to type the same parameters in every time one creates a plot. Therefore matplotlib provides general style settings, called rcParams. They can be set at the beginning of a script, e.g.
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['axes.grid '] = True
plt.rcParams['axes.labelsize'] = 18
and will be applied to all plots within the script. It is also possible to define a complete stylesheet using those parameters. For more information see the Customizing matplotlib article.
It is equally possible to use predefined stylesheets for certain applications.
Simply importing import seaborn is also a possible way to change the style.
Creating the plot can not be simplified much more. It's clear that one needs as many plotting commands as items to plot. Creating the figure and axes like
fig, ax = plt.subplots()
saves one line though.
Equally no simplification is possible if customizing ticks or tickmarks are required. One may however consider to use Tickers and Formatters for this purpose.
At the end one may of course consider to write a custom function which performs much of those tasks, but everyone can decide if that is useful for himself.

Browsing around I saw this wabe page.
This line of code can summarise many settings
import matplotlib as mpl
mpl.rc('lines', linewidth=2, color='r')

ax.set is very useful for this:
ax.set(xlim=(idx1, idx2), ylim=(-0.3, 1.2),
xticks=major_ticks, ...)
You can only set simple single-argument properties (e.g. those which don't need further keywords), but it's a nice timesaver.

draw order of grid lines and data in pyplot [duplicate]

This question already has answers here:
Matplotlib: draw grid lines behind other graph elements
(7 answers)
Closed 7 years ago.
I've got a library function which plots data into a pyplot figure containing several subplots.
I just added grid lines to all of the subplots but they overlay the actual data but I would prefer them to be in the background.
I've tried changing the order in which the plotting and ax.plot() ax.grid() commands are executed but that has no influence.
Is there a way to force the grid into the background?
Related bonus question: I'm also using axhline to designate the x=0 line but it always assumes the grid colour even though it is being specified in a different one ...
The way the code currently works:
def plot_the_things(fig=None):
# in case no figure is provided, make a new one,
# otherwise add to the existing one
plot_fig=fig if fig else plt.figure()
#[...some calculations of data ...]
plot_ax1 = plot_fig.add_subplot(3,3,1)
plot_ax1.axhline(y=0, ls='-', color='0.5')
plot_ax1.plot(self.diff_3[:,0],self.diff_3[:,1])
# [...setting labels, adapt axes limits in case the new data needs wider ones..]
plot_ax1.grid(b=True, which='major', axis='both', c='0.75', ls='-', linewidth=1)
# this is repeated in similar fashion for the other axes -- there are 9 of
# them, each plotting something different in a different axes
This function is called several times over. More precisely: It's actually part of a class. I have multiple instances of this class and call all of them, passing in the same figure object. Each instance then draws its own data, which works fine, and even the axhline() was shown properly (below the data!) but after I put in the command to add the grid, it always shows up on top of the data and covers the axhline, which is annoying.
... any way to fix this?
(I think I could and maybe should also move all the things that only need to run once to a place where they aren't repeatedly executed but time and mental resources are scant right now, so I went with the quickest way that worked... but I wouldn't expect this to change anything)

Use the zorder kwarg to your plot and axhline calls. The grid is plotted at zorder=2.5, so place the axhline and plot above this:
plot_ax1.axhline(y=0, ls='-', color='0.5', zorder=3)
plot_ax1.plot(self.diff_3[:,0],self.diff_3[:,1], zorder=4)
plot_ax1.grid(b=True, which='major', axis='both', c='0.75', ls='-', linewidth=1)
More info: here, and here.

How can I get matplotlib to show full subplots in an animation?

I'm trying to write a simple immune system simulator. I'm modeling infected tissue as a simple grid of cells and various intracellular signals, and I'd like to animate movement of cells in one plot and the intensity of viral presence in another as the infection progresses. I'm doing so with the matshow function provided by matplotlib. However, when I plot the two next to each other, the full grid gets clipped unless I stretch out the window myself. I can't address the problem at all when saving to an mp4.
Here's the default view, which is identical to what I observe when saving to mp4:
And here's what it looks like after stretching out the viewer window
I'm running Python 2.7.9 with matplotlib 1.4.2 on OS X 10.10.2, using ffmpeg 2.5.2 (installed via Homebrew). Below is the code I'm using to generate the animation. I tried using plt.tight_layout() but it didn't affect the problem. If anyone has any advice as to how to solve this, I'd really appreciate it! I'd especially like to be able to save it without viewing with plt.show(). Thanks!
def animate(self, fname=None, frames=100):
fig, (agent_ax, signal_ax) = plt.subplots(1, 2, sharey=True)
agent_ax.set_ylim(0, self.grid.shape[0])
agent_ax.set_xlim(0, self.grid.shape[1])
signal_ax.set_ylim(0, self.grid.shape[0])
signal_ax.set_xlim(0, self.grid.shape[1])
agent_mat = agent_ax.matshow(self.display_grid(),
vmin=0, vmax=10)
signal_mat = signal_ax.matshow(self.signal_display(virus),
vmin=0, vmax=20)
fig.colorbar(signal_mat)
def anim_update(tick):
self.update()
self.diffuse()
agent_mat.set_data(self.display_grid())
signal_mat.set_data(self.signal_display(virus))
return agent_mat, signal_mat
anim = animation.FuncAnimation(fig, anim_update, frames=frames,
interval=3000, blit=False)
if fname:
anim.save(fname, fps=5, extra_args=['-vcodec', 'libx264'])
else:
plt.show()

According to the matplotlib documentation
Because of how matshow() tries to set the figure aspect ratio to be the one of the array, if you provide the number of an already existing figure, strange things may happen.
I think you're better off using imshow instead (which I believe is what matshow calls. That has an aspect keyword argument which you could use if it doesn't work automatically.
Also according to the matplotlib documentation,
Sets origin to ‘upper’, ‘interpolation’ to ‘nearest’ and ‘aspect’ to equal.
I think you want to do the first two, but leave aspect as auto.

Well, one simple solution would be to just specify the width of the figure when creating it:
fig, (agent_ax, signal_ax) = plt.subplots(1, 2, sharey=True,
figsize=(16,6))

Using matplotlib, how do I whiten the background of the axis label?

Is there a way to whiten out the background of the axis label so that when it crosses the axis line itself, the latter does not run through it?
For example, this script (the best I managed so far)
#!/usr/bin/python
import matplotlib.pyplot as plt
xx=[1,2,3]
yy=[2,3,4]
dy=[0.1,0.2,0.05]
fig=plt.figure()
ax=fig.add_subplot(111)
ax.errorbar(xx,yy,dy,fmt='ro-',ms=6,elinewidth=4)
ax.set_xlim([0.,3.4])
ax.set_ylim([0.,4.4])
ax.set_xlabel(r'$T/t$',fontsize=16)
ax.set_ylabel(r'$S(\mathbf{Q})L^{1+\eta}$',fontsize=16)
# position the axis labels
ax.xaxis.set_label_coords(1,0)
ax.yaxis.set_label_coords(0.1,0.93)
ax.yaxis.get_label().set_rotation('horizontal')
ax.yaxis.get_label().set_backgroundcolor('w')
#ax.yaxis.get_label().set_zorder(222) #doesn't do the trick
plt.show()
produces almost what I'm looking for, but still the y-axis runs over the label: .

By default, the left spine has a zorder of 2.5. For some reason this seems to cause problems; maybe there's something in the code which only works if they're integral? Anyway, if you add
ax.spines['left'].set_zorder(2)
or more generally
ax.spines['left'].set_zorder(ax.yaxis.get_label().get_zorder()-1)
before the show, it should work. Also, set_ylabel returns the ylab object itself, so if you use "ylab = ax.set_ylabel(stuff)" you can avoid all the ax.yaxis.get_label() calls later.

Does this link help you?
http://matplotlib.sourceforge.net/faq/howto_faq.html#automatically-make-room-for-tick-labels
You can simply shift the y-axis to the right to allows some space for the $S(\mathbf{Q})L^{1+\eta}$ mark be fully placed before the axis line.