Once I have created a system of subplots in a figure with
fig, ((ax1, ax2)) = plt.subplots(1, 2)
can I play around with the position of ax2, for example, by shifting it a little bit to the right or the left?
In other words, can I customize the position of an axes object in a figure after it has been created as a subplot element?
If so, how could I code this?
Thanks for thinking along
You can use commands get_position and set_position like in this example:
import matplotlib.pyplot as plt
fig, ((ax1, ax2)) = plt.subplots(1, 2)
box = ax1.get_position()
box.x0 = box.x0 + 0.05
box.x1 = box.x1 + 0.05
ax1.set_position(box)
plt.show()
which results in this:
You'll notice I've used attributes x0 and x1 (first and last X coordinate of the box) to shift the plot in 0.05 in that axis. The logic applies to y also.
In fact should the shift be to big and the boxes will overlap (like in this image with a shift of 0.2).
Related
I'm trying to zoom in a 3D plot. I'm using the ax.set_box_aspect() fonction. When doing so, the axis are zoomed in, they appear bigger, but the area where the data can be seen stay at the same size as before (the plot are not using the total available space).
The aim in the end is to have two axis, the first one 3d, the other one 2d. I would have wanted the first plot to take all the space available at the top half of the figure.
Here is the code before the Zoom
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#---- generate data
nn = 100
X = np.random.randn(nn)*20 + 0
Y = np.random.randn(nn)*50 + 30
Z = np.random.randn(nn)*10 + -5
#---- check aspect ratio
asx, asy, asz = np.ptp(X), np.ptp(Y), np.ptp(Z)
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(211, projection='3d')
#---- set box aspect ratio
ax.set_box_aspect((asx,asy,asz))
scat = ax.scatter(X, Y, Z, c=X+Y+Z, s=500, alpha=0.8)
ax.set_xlabel('X-axis'); ax.set_ylabel('Y-axis'); ax.set_zlabel('Z-axis')
ax = fig.add_subplot(212)
plt.show()
Before using the zoom
And now when I zoom in, the scatter is limitted in a square frame :
ax.set_box_aspect((asx,asy,asz), zoom = 2 )
After using the zoom
(The data used for the plot doesn't matter here, it is just to showcase my issue.)
I tried changing the axis limit with set_xlim3d or set_xlim, but in either case, the result is the same.
It seems like the showing area (I can't find the right word for it) stays a square no matter what.
I didn't find any usefull information on that matter online, (maybe from the lack of vocabulary to describe my problem).
I would like to use the "west" anchor for my tick labels for a twinx (right-side) axis. Looking at the plot below, for example, I would like the left side of the tick labels to be aligned with the right axis.
I attempted a few things below, to no avail.
import matplotlib.pyplot as plt
X = [1,2,3]
fig, ax = plt.subplots()
ax.plot(X)
ax.set_ylim([1,3])
ax.set_yticks(X)
axR = ax.twinx()
axR.set_ylim(ax.get_ylim())
axR.set_yticks(ax.get_yticks())
axR.set_yticklabels(['-1.00', '2.00', '3.00'], ha='right')
# axR.set_yticklabels(['-1.00', '2.00', '3.00'], ha='right', bbox_to_anchor='W')
# axR.set_yticklabels(['-1.00', '2.00', '3.00'], ha='right', bbox=dict(bbox_to_anchor='W'))
# bbox can have args from: https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch
fig.show()
So I had the same problem and stumbled on this question. I tried quite a bit and I basically decided I would need to find the right side of the labels when they are left aligned on the right side and then right align them from this point.
I tried a few things but don't have a lot of experience, so it's not perfect, but it seems to work by finding the coordinates as a bbox. I converted that back and forth to get it as an array (probably a shorter way that I don't know). I then took the gap of the largest one and added that to the spacing.
A few notes: I'm doing this on a subplot, hence ax2. I've also already moved the axis tick labels to the right side with ax2.yaxis.tick_right()
r = plt.gcf().canvas.get_renderer()
coord = ax2.yaxis.get_tightbbox(r)
ytickcoord = [yticks.get_window_extent() for yticks in ax2.get_yticklabels()]
inv = ax2.transData.inverted()
ytickdata = [inv.transform(a) for a in ytickcoord]
ytickdatadisplay = [ax2.transData.transform(a) for a in ytickdata]
gap = [a[1][0]-a[0][0] for a in ytickdatadisplay]
for tick in ax2.yaxis.get_majorticklabels():
tick.set_horizontalalignment("right")
ax2.yaxis.set_tick_params(pad=max(gap)+1)}
Update: I have recently been sent the solution to a similar problem with left alignment on the left side. From this solution, I believe this can be simplified to:
import matplotlib as mpl
import matplotlib.pyplot as plt
fig = plt.figure(figsize =(5,3))
ax = fig.add_axes([0,0,1,1])
plt.plot([0,100,200])
ax.yaxis.tick_right()
# Draw plot to have current tick label positions
plt.draw()
# Read max width of tick labels
ytickcoord = max([yticks.get_window_extent(renderer = plt.gcf().canvas.get_renderer()).width for yticks in ax.get_yticklabels()])
# Change ticks to right aligned
ax.axes.set_yticklabels(ax.yaxis.get_majorticklabels(),ha = "right")
# Add max width of tick labels
ax.yaxis.set_tick_params(pad=ytickcoord+1)
plt.show()
plt.close("all")
I am trying to plot multiple time series in one plot. The scales are different, so they need separate y-axis, and I want a specific time series to have its y-axis on the right. I also want that time series to be behind the others. But I find that when I use secondary_y=True, this time series is always brought to the front, even if the code to plot it comes before the others. How can I control the order of the plots when using secondary_y=True (or is there an alternative)?
Furthermore, when I use secondary_y=True the y-axis on the left no longer adapts to appropriate values. Is there a fixed for this?
# imports
import numpy as np
import matplotlib.pyplot as plt
# dummy data
lenx = 1000
x = range(lenx)
np.random.seed(4)
y1 = np.random.randn(lenx)
y1 = pd.Series(y1, index=x)
y2 = 50.0 + y1.cumsum()
# plot time series.
# use ax to make Pandas plot them in the same plot.
ax = y2.plot.area(secondary_y=True)
y1.plot(ax=ax)
So what I would like is to have the blue area plot behind the green time series, and to have the left y-axis take appropriate values for the green time series:
https://i.stack.imgur.com/6QzPV.png
Perhaps something like the following using matplotlib.axes.Axes.twinx instead of using secondary_y, and then following the approach in this answer to move the twinned axis to the background:
# plot time series.
fig, ax = plt.subplots()
y1.plot(ax=ax, color='green')
ax.set_zorder(10)
ax.patch.set_visible(False)
ax1 = ax.twinx()
y2.plot.area(ax=ax1, color='blue')
I am trying to add an arbitrarily big white margin (or padding) to a figure with subplots because I would like the subtitle of the figure not to overlap with any of the subplots or titles of these subplots. I am using Matplotlib 3.1.2.
Currently, I have the following source code.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(2, 1, figsize=(15, 10))
n = 10
x = np.arange(0, n)
y = np.random.rand(n)
ax[0].plot(x, y)
ax[0].set_xlabel('x')
ax[0].set_ylabel('y')
y = np.random.rand(n)
ax[1].plot(x, y)
ax[1].set_xlabel('x')
ax[1].set_ylabel('y')
fig.suptitle("I want to have white space around me!!!")
# fig.tight_layout(rect=[0, 0.03, 1, 0.80])
plt.subplots_adjust(top=0.85)
plt.show()
If I try to use either tight_layout or subplots_adjust (as suggested in several answers to this question Matplotlib tight_layout() doesn't take into account figure suptitle), it doesn't seem to have any effect on the margins. Here's the result of the execution of the previous example.
Is there a way to add an arbitrarily big white margin to the left, right, bottom and (or) top of a figure (with subplots)? I would like to specify the figure size and arbitrarily increase or decrease the white space around an image. I also would like the solution to work in case I decide to add a title for each of the subplots. How can this be done?
fig, axs = plt.subplots(2,1, figsize=(5,5))
fig.patch.set_facecolor('grey')
fig.suptitle("Look at all that grey space around me!!!")
fig.subplots_adjust(top=0.6, bottom=0.4, left=0.4, right=0.6)
I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):
You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.
Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect
When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.
positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!
As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])