I am making some bar plots using a polar projection. The data are all large numbers far from the origin and thus I'm using the ax.set_rlim to make them easier to distinguish. However, when I set the rlim, some of the bars are cut off around the origin. This is not an issue when I do not set the rlim, but I can't present my data like this. Why is this happening and is there a way I can fix it?
Here is an example of the issue:
import matplotlib
import numpy as np
Sectors = np.arange(0,2*np.pi,np.pi/4)
Data = np.array([100,99,100,101,100.5,100.25,99.25,99.75])
fig, ax = plt.subplots(nrows = 1, ncols = 1, subplot_kw={'projection': 'polar'})
ax.bar(Sectors,Data)
ax.set_rlim(98,102)
plt.show()
Note, this does not happen if I don't apply the rlim. eg:
import matplotlib
import numpy as np
Sectors = np.arange(0,2*np.pi,np.pi/4)
Data = np.array([100,99,100,101,100.5,100.25,99.25,99.75])
fig, ax = plt.subplots(nrows = 1, ncols = 1, subplot_kw={'projection': 'polar'})
ax.bar(Sectors,Data)
#ax.set_rlim(98,102)
plt.show()
Any help is greatly appreciated!
This is a very strange effect indeed.
But there seems to be a workaround using the bottom keyword to bar. The trick is to set the bottom to the inner rlim (in this case 98) and specify the data relative to the bottom value.
import matplotlib.pyplot as plt
import numpy as np
Sectors = np.arange(0,2*np.pi,np.pi/4)
Data = np.array([100,99,100,101,100.5,100.25,99.25,99.75])
fig, ax = plt.subplots(nrows = 1, ncols = 1, subplot_kw={'projection': 'polar'})
ax.bar(Sectors,Data-98, bottom=98)
ax.set_rlim(98,102)
plt.show()
Looks like a silly round-off error in matplotlib. I bumped up your numbers by a factor 10 and all but one wedge showed correctly. Setting the rlim() to a larger range also shows some improvement. If you need to put this in a presentation, cover up the middle with a drawn in circle.
All bandaids I am afraid....
Related
I do have a question with matplotlib in python. I create different figures, where every figure should have the same height to print them in a publication/poster next to each other.
If the y-axis has a label on the very top, this shrinks the height of the box with the plot. So I use MaxNLocator to remove the upper and lower y-tick. In some plots, I want to have the 1.0 as a number on the y-axis, because I have normalized data. So I need a solution, which expands in these cases the y-axis and ensures 1.0 is a y-Tick, but does not corrupt the size of the figure using tight_layout().
Here is a minimal example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0,1,num=11)
y = np.linspace(1,.42,num=11)
fig,axs = plt.subplots(1,1)
axs.plot(x,y)
locator=MaxNLocator(prune='both',nbins=5)
axs.yaxis.set_major_locator(locator)
plt.tight_layout()
fig.show()
Here is a link to a example-pdf, which shows the problems with height of upper boxline.
I tried to work with adjust_subplots() but this is of no use for me, because I vary the size of the figures and want to have same the font size all the time, which changes the margins.
Question is:
How can I use MaxNLocator and specify a number which has to be in the y-axis?
Hopefully someone of you has some advice.
Greetings,
Laenan
Assuming that you know in advance how many plots there will be in 1 row on a page one way to solve this would be to put all those plots into one figure - matplotlib will make sure they are alinged on axes:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0, 1, num=11)
y = np.linspace(1, .42, num=11)
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,3), gridspec_kw={'wspace':.2})
ax1.plot(x,y)
ax2.plot(x,y)
locator=MaxNLocator(prune='both', nbins=5)
ax1.yaxis.set_major_locator(locator)
# You don't need to use tight_layout and using it might give an error
# plt.tight_layout()
fig.show()
I do have a question with matplotlib in python. I create different figures, where every figure should have the same height to print them in a publication/poster next to each other.
If the y-axis has a label on the very top, this shrinks the height of the box with the plot. So I use MaxNLocator to remove the upper and lower y-tick. In some plots, I want to have the 1.0 as a number on the y-axis, because I have normalized data. So I need a solution, which expands in these cases the y-axis and ensures 1.0 is a y-Tick, but does not corrupt the size of the figure using tight_layout().
Here is a minimal example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0,1,num=11)
y = np.linspace(1,.42,num=11)
fig,axs = plt.subplots(1,1)
axs.plot(x,y)
locator=MaxNLocator(prune='both',nbins=5)
axs.yaxis.set_major_locator(locator)
plt.tight_layout()
fig.show()
Here is a link to a example-pdf, which shows the problems with height of upper boxline.
I tried to work with adjust_subplots() but this is of no use for me, because I vary the size of the figures and want to have same the font size all the time, which changes the margins.
Question is:
How can I use MaxNLocator and specify a number which has to be in the y-axis?
Hopefully someone of you has some advice.
Greetings,
Laenan
Assuming that you know in advance how many plots there will be in 1 row on a page one way to solve this would be to put all those plots into one figure - matplotlib will make sure they are alinged on axes:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0, 1, num=11)
y = np.linspace(1, .42, num=11)
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,3), gridspec_kw={'wspace':.2})
ax1.plot(x,y)
ax2.plot(x,y)
locator=MaxNLocator(prune='both', nbins=5)
ax1.yaxis.set_major_locator(locator)
# You don't need to use tight_layout and using it might give an error
# plt.tight_layout()
fig.show()
Is there an easy way to draw a zigzag or wavy line in matplotlib?
I'm aware of the different line styles (http://matplotlib.org/examples/lines_bars_and_markers/line_styles_reference.html), and I'm of course aware that instead of plotting
plt.figure(); plt.plot(n.linspace(0.7,1.42,100),[0.7]*100)
I could plot
plt.figure(); plt.plot(n.linspace(0.7,1.42,100),[0.69,0.71]*50)
for a zigzag-line, but I was wondering whether there was a more straightforward way?
Yes there is, but it comes with a little bit of fallout. The easiest way is to use the xkcd mode in matplotlib.
import numpy as np
import matplotlib.pyplot as plt
plt.xkcd()
plt.figure()
plt.plot(np.linspace(0.7,1.42,100),[0.7]*100)
plt.show()
Which gives you the following:
If you take a look at the code used to achieve this you will find that the xkcd function makes some changes to the rcParams dictionary. Most notably the entry rcParams['path.sketch'] = (scale, length, randomness) which is a path effect that is able to simulate a hand drawn look. The default parameters used by xkcd style are:
# explanation from the docstring of the xkcd function
scale = 1 # amplitude of the wiggle
length = 100 # length of the wiggle along the line
randomness = 2 # scale factor for shrinking and expanding the length
You can change the entries in the rcParams dictionary if you import it from the matplotlib package. In the following example I increased the randomness value from 2 to 100:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['path.sketch'] = (1, 100, 100)
plt.plot(np.linspace(0.7,1.42,100),[0.7]*100)
plt.show()
Which will result in the following plot:
As you can see, more jiggling and the font used for the ticks is still 'normal'. However, the style is also used to draw the axes and so far I have not found a way around that.
Two workarounds could be:
Work without drawn borders/ spines.
Plot spines and line independently (hard and annoying to automize).
Dig through the documentation of matplotlib and path styles and find out if there is a way to set path styles only for a subset of drawn lines.
Option 1 can be achieved like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['path.sketch'] = (10, 10, 100)
fig = plt.plot(np.linspace(0.7,1.42,100),[0.7]*100)
for pos, spine in fig[0].axes.spines.items():
spine.set_visible(False)
plt.show()
Which, in my opinion look quite ok. borders around plots are highly overrated anyways.
Edit: Less Chaos
To get an evenly waved line, set the randomness parameter to 1 and pick small values for amplitude and length:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['path.sketch'] = (3, 10, 1)
fig = plt.plot(np.linspace(0.7,1.42,100),[0.7]*100)
for pos, spine in fig[0].axes.spines.items():
spine.set_visible(False)
plt.show()
Bonus image: More Chaos
rcParams['path.sketch'] = (100, 1, 100)
You can apply the change in rcParams['path.sketch'] dictionary only to selected curves using with.
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
# prepare some fancy data
x = np.linspace(0,5,200)
y_0 = 10*x**0.2-x**1.5
y_1 = 20*np.sin(x)
y_2 = x**2
# prepare figure and axis
fig, ax = plt.subplots(nrows=1, ncols = 1, figsize = (5,3), dpi = 128)
# plot with some normal style
ax.plot(x, y_0, color = 'gray', ls='-.', lw = 2, label = 'normal style')
# now plot the wavy-like style!!!!
with mpl.rc_context({'path.sketch': (5, 15, 1)}):
ax.plot(x, y_1, color = 'blue', label = 'wavy style!')
# again plot with some different normal style
ax.plot(x, y_2, color = 'orange', ls = '-', lw = 3, label = 'again normal style')
ax.legend(loc='best') # turn on legend with automatic best location
plt.show()
I am attempting to align two sets of separate y-axis using python and matplotlib, and am running into behavior I don't understand. Here is my code so far:
import matplotlib.pyplot as mplot
import numpy as np
fig = mplot.figure()
ax1 = fig.add_subplot(111)
t = np.arange(1, 4, 1)
s1 = np.exp(t)
ax2 = ax1.twinx()
ax1.semilogx(t, s1)
ax2.set_yticks(2*ax1.get_yticks())
mplot.show()
This produces the expected result (from http://postimg.org/image/qowrjnnr5/):
however, changing the definition of t to
t = np.arrange(1, 3, 1)
produces the result (http://postimg.org/image/swanojt0b):
where you can see that the y axis ticks on the right side are off-shifted.
What am I missing in order to prevent this issue?
Thanks!
The two y axes do not have the same limits: in one case you fluke the same lower value in the automatic range calculation while in the other you don't. If you define one yaxis range in terms of the other, I think you achieve what you want:
lim1 = ax1.get_ylim()
lim2 = (lim1[0]*2, lim1[1] *2)
ax2.set_ylim(lim2)
(and if you don't explicitly set the ax2 yticks then ticks will still get rendered if you move beyond the original range in interactive mode).
I'm using quadmesh to create a simple polar projection plot. Here's a minimal script which produces basically what I'm trying to do:
from __future__ import unicode_literals
import numpy as np
import matplotlib.pyplot as plt
def make_plot(data,fig,subplot):
nphi,nt = data.shape
phi_coords = np.linspace(0,np.pi*2,nphi+1) - np.pi/2.
theta_coords = np.linspace(0,np.radians(35),nt+1)
ax = fig.add_subplot(subplot,projection='polar')
ax.set_thetagrids((45,90,135,180,225,270,315,360),(9,12,15,18,21,24,3,6))
ax.set_rgrids(np.arange(10,35,10),fmt='%s\u00b0')
theta,phi = np.meshgrid(phi_coords,theta_coords)
quadmesh = ax.pcolormesh(theta,phi,data)
ax.grid(True)
fig.colorbar(quadmesh,ax=ax)
return fig,ax
a = np.zeros((360,71)) + np.arange(360)[:,None]
b = np.random.random((360,71))
fig = plt.figure()
t1 = make_plot(a,fig,121)
t2 = make_plot(b,fig,122)
fig.savefig('test.png')
The above script creates a plot which looks like this:
I would like the colorbars to:
Not overlap the 6 label.
be scaled such that they are approximately the same height as the plot.
Is there any trick to make this work properly? (Note that this layout isn't the only one I will be using -- e.g. I might use a 1x2 layout, or a 4x4 layout ... It seems like there should be some way to scale the colorbar to the same height as the associated plot...)
This combination (and values near to these) seems to "magically" work for me to keep the colorbar scaled to the plot, no matter what size the display.
plt.colorbar(im,fraction=0.046, pad=0.04)
You can do this with a combination of the pad, shrink, and aspect kwargs:
from __future__ import unicode_literals
import numpy as np
import matplotlib.pyplot as plt
def make_plot(data,fig,subplot):
nphi,nt = data.shape
phi_coords = np.linspace(0,np.pi*2,nphi+1) - np.pi/2.
theta_coords = np.linspace(0,np.radians(35),nt+1)
ax = fig.add_subplot(subplot,projection='polar')
ax.set_thetagrids((45,90,135,180,225,270,315,360),(9,12,15,18,21,24,3,6))
ax.set_rgrids(np.arange(10,35,10),fmt='%s\u00b0')
theta,phi = np.meshgrid(phi_coords,theta_coords)
quadmesh = ax.pcolormesh(theta,phi,data)
ax.grid(True)
cb = fig.colorbar(quadmesh,ax=ax, shrink=.5, pad=.2, aspect=10)
return fig,ax,cb
a = np.zeros((360,71)) + np.arange(360)[:,None]
b = np.random.random((360,71))
fig = plt.figure()
t1 = make_plot(a,fig,121)
t2 = make_plot(b,fig,122)
figure.colorbar doc
The best value for these parameters will depend on the aspect ratio of the axes.
The size of the axes seems to not get shrink-wrapped to the polar plot, thus in the 1x2 arrangement there is a lot of space above and below the plot that are part in the axes object, but empty. The size of the color bar is keyed off of the rectangular size, not the round size, hence why the default values are not working well. There is probably a way to do the shrink-wrapping, but I do not know how to do that.
An alternate method is to force your figure to be the right aspect ratio ex:
fig.set_size_inches(10, 4) # for 1x2
fig.set_size_inches(4, 10) # for 2x1
which makes the sub plots square, so the default values more-or-less work.