I have an (as yet incomplete) image of the whole sky. In order to display it properly it needs to be projected onto an ellipse (specifically an Aitoff projection).
I have tried various versions of this:
plt.subplot(111, projection="aitoff")
plt.imshow(image, vmin=0.004, vmax=0.01, extent=[0,360,-90,90])
plt.show()
...and have tried changing the values in the extent kwarg to radians, as well as using pcolor or pcolormesh instead of imshow.
These have given me: an empty Aitoff plot, various Aitoff plots with all or part of my image sitting inside it, but not filling it, or an Aitoff plot with a small part of my image (one or two pixels by the looks of things) completely filling it.
My whole image sitting within a plot
The unprojected image
I also do not have access to things like Basemap or astroproj as I'm using a machine owned by my university.
Edit: As was pointed out by another user the above example is not Minimal, Complete, and Verifiable. Below is a version which should be:
A=np.random.rand(180,360)
plt.imshow(A)
plt.show()
plt.subplot(111, projection="aitoff")
plt.pcolormesh(A)
plt.show()
I want the entire image generated in the plt.imshow() command to be projected in the Aitoff figure. Instead only a few pixels are. Any ideas?
Thanks!
Using imshow in non-rectilinear projections will mostly fail. But instead pcolormesh may be used.
The aitoff projection ranges from -π to π in horizontal and from -π/2 to π/2 in vertical direction. This is the range of values to use when plotting the pcolormesh plot.
import numpy as np
import matplotlib.pyplot as plt
im = plt.imread("house.jpg")
x = np.linspace(-np.pi,np.pi,im.shape[1])
y = np.linspace(-np.pi/2,np.pi/2,im.shape[0])
X,Y = np.meshgrid(x,y)
plt.subplot(111, projection="aitoff")
plt.pcolormesh(X,Y[::-1],im[:,:,2])
plt.show()
Related
I am using jupyter-lab for plotting a dataframe.
fig = df.plot().get_figure()
fig.savefig("test.png")
Unfortunately, the surroundings of the plot (the space that is not between the x and y axis), where the coordinates are displayed are transparent, meaning a checkered grey-black pattern, which makes the coordinates practically unreadable. Is there any way of widening the non-transparent area so that the coordinates are included?
There are a couple of ways that you can achieve this:
Update the matplotlib rcParams:
import matplotlib as mpl
mpl.rcParams.update({"figure.facecolor": "white"})
this will affect all the plots after you set this parameter in this script.
Set the figure facecolor for a single figure:
fig = df.plot().get_figure()
fig.set_facecolor("white")
I am trying to plot a two dimensional numpy matrix (say, Kappa) using pcolor .
The skeleton of the code is this:
from pylab import pcolor, show, colorbar, xticks, yticks
import numpy as np
plt.figure(1)
pcolor(np.transpose(Kappa))
plt.colorbar()
plt.tight_layout()
plt.show()
At the top of the colorbar, I see a weird way in which the highest scale of the colorbar is written. I have attached a picture of the the said output (and I have highlighted the troublesome part by putting it inside a red box.)
Following suggestions, I have uploaded the matrix to my Google Drive. You can use this link to access the matrix.
I am wondering if anyone has faced similar issues with colorbars in pylab? I will appreciate any help.
Hi I'd like to recreate the following plot with matplotlib and pandas.
I started to use boxplot but i'm struggling to manipulate the kwargs.
Is there a simple way to use boxplot or do I need to recreate the chart enitrely.
One issue I had was also adding the current data?
Best regards
The boxplot from matplotlib has indeed some limitations. For you to have full control over how the plot looks I would advise using Patches to draw Rectangles for example (code from Rectangles link):
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.add_patch(
patches.Rectangle(
(0.1, 0.1), # (x,y)
0.5, # width
0.5, # height
)
)
fig1.savefig('rect1.png', dpi=90, bbox_inches='tight')
This is useful because you'll only need this and a normal plot command (for lines) in matplotlib to do a boxplot. This will give you immense control about color and shape and it's fairly easy to build. You also have text there you'll need for which you can use matplotlib text. The last thing are those markers which are very doable with a scatter.
A boxplot is a shape that tells you information such a minimum, maximum, and percentiles (25,50,75). You can calculate this very easily with numpy percentile.
The details of the plot (labels at the bottom, legend, title in box, and so on) can also be achieved but tinkering with labels, manually building a title box and so on.
It will give you some work but these are the commands you need.
Is it possible to plot contours over a polar stereographic map with the latest version of cartopy?
I'd like to see an example of how this is done as I'm struggling to work it out myself!
The stereographic projection is causing a couple of headaches and is probably the projection which has raised the most issues for cartopy's polygon transformations code.
The following example show how one should produce a polar stereographic plot with cartopy.
Please note: even with this code, it is possible to tweak the sample data resolution and find that the plot takes ~30 minutes to actually render (that is a bug which we will need to sort sooner rather than later).
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from cartopy.examples.waves import sample_data
ax = plt.axes(projection=ccrs.NorthPolarStereo())
x, y, z = sample_data((100, 200))
cs = ax.contourf(x, y, z, 50,
transform=ccrs.PlateCarree(),
cmap='gist_ncar')
ax.coastlines()
# without the set_global, currently, the plot is tiny because the limits
# are being erroneously being set (opened issue for that)
ax.set_global()
plt.show()
Hopefully that will show you how one should make a polar stereographic contour plot in cartopy. If you having problems with your data have a look at the open issues tagged "Geometry transforms" and see if you are getting something similar, if not, go ahead and open an issue and we can look into it.
Note: This answer is relating to cartopy v0.5.x (i.e. just before a v0.5 release), and many of the bugs mentioned here should hopefully be squashed in future releases.
Hope that helps,
I have to translate an image plotting script from matlab to matplotlib/pylab, and I'm trying to achieve the same effect as the matlab image below:
As you can see, the z order of the plots seem to be higher than the z order of the grid, so the markers are not hidden by the axes. However, I can't figure out a way to do the same with my matplotlib image:
I'm wondering if it is possible to get the same display without having to increase the limits of the y axis.
To get the marker to show beyond the axes you can turn the clipping off. This can be done using the keyword argument in the plot command clip_on=False.
For example:
import matplotlib.pyplot as plt
plt.plot(range(5), range(5), 'ro', markersize=20, clip_on=False, zorder=100)
plt.show()
This is a complete example of how to use the zorder kwarg: http://matplotlib.sourceforge.net/examples/pylab_examples/zorder_demo.html
Note that a higher z-order equates to a graph-element being more in the foreground.
For your second question, have a look at the figsize kwarg to instances of the Figure class: http://matplotlib.sourceforge.net/api/figure_api.html?highlight=figsize#matplotlib.figure.Figure
If you run into issues, please post some of your code and we'll be able to give more-detailed recommendations. Best of luck.
If you're plotting the lines one after the other, just change the order of the plotting calls and that would fix the z order.