I'm trying to write a function to display astronomical images with a colorbar on the top (automaticly with the same length of the x-axis).
I'm having problem because when I try to put the tick on the top it doesn't do anything...it keeps the tick on the bottom of the colorbar (and also the tick on the y-axis of the colobar).
I think that could be a problem with the WCS coordinate of the x-axis, because when i try to do it without the projection it work well!
import numpy as np
import matplotlib.pyplot as plt
from astropy import wcs
from matplotlib.colors import PowerNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import cm
#WCS coordinate system
w = wcs.WCS(naxis=2)
w.wcs.crpix = [23.5, 23.5]
w.wcs.cdelt = np.array([-0.0035, 0.0035])
w.wcs.crval = [266.8451, -28.151658]
w.wcs.ctype = ["RA---TAN", "DEC--TAN"]
w.wcs.set_pv([(2, 1, 45.0)])
#generate an array as image test
data = (np.arange(10000).reshape((100,100)))
#display image
fig = plt.figure()
ax = plt.gca(projection=w)
graf = ax.imshow(data, origin='lower', cmap=cm.viridis, norm=PowerNorm(1))
#colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("top", size="5%")
cbar = fig.colorbar(graf, cax=cax, orientation='horizontal')
cax.xaxis.set_ticks_position('top')
fig.show()
Thanks!
You can fix this issue using matplotlib's axes class.
...
import matplotlib.axes as maxes
cax = divider.append_axes("top", size="5%", axes_class=maxes.Axes)
...
You need to use the internal machinery of the WCSAxes to handle the ticks in the WCS projection. It looks like WCSAxes handles the colorbar ticks through a coordinate map container (you can find it in cbar.ax.coords) instead of the xaxis/yaxis attributes (that don't seem to be used much).
So, after running your code, the following trick worked for me and the xticks moved up:
c_x = cbar.ax.coords['x']
c_x.set_ticklabel_position('t')
cbar.update_normal(cax)
To get something like this to work, I needed a few additional parameters:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cax.coords[0].grid(False)
cax.coords[1].grid(False)
cax.tick_params(direction='in')
cax.coords[0].set_ticks(alpha=0, color='w', size=0, values=[]*u.dimensionless_unscaled)
cax.coords[1].set_ticklabel_position('r')
cax.coords[1].set_axislabel_position('r')
because the default axis gad the grid on, the labels to the left, and x-axis labels enabled. I'm not sure why the original post didn't have issues with this.
Related
How to display an image with imshow on all the width of an axe (viewport) but keeping the ratio 'equal' ?
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(nrows=2, figsize=(8,8))
img = ax[0].imshow(np.random.random((10,10)), aspect='equal')
line = ax[1].plot([12,34],[45,78])
I would like that the image width be aligned on the line plot keeping an equal ratio even if that implies having blank around. My application has a zooming feature coded on the image in fact so I would to offer all the width possible for its display.
So in few words, I would like the same width as with aspect='auto' but with square pixels.
Is it possible ? Thanks.
Found with a reading of Axes class - set explicitly size (width/height) of axes in given units
and the use of
set_adjustable('datalim')
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(nrows=2, figsize=(8,8))
img = ax[0].imshow(np.random.random((10,10)), aspect='equal')
ax[0].set_adjustable('datalim')
line = ax[1].plot([12,34],[45,78])
I am trying to plot the surface temperature from a NetCDF file using Cartopy and contourf. The domain of my plot is 30S to 60N and 90.044495E to 89.95552E (so all the way around the Earth centered on 90W). Here is a section of my code:
import numpy as np
import wrf as wrf
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
cart_proj = wrf.get_cartopy(skintemp)
lats, lons = wrf.latlon_coords(skintemp)
ax = plt.axes(projection=cart_proj)
ax.coastlines('50m', linewidth=0.8)
clevels = np.linspace(230,300,8)
cmap = plt.cm.YlOrRd
contours_fill = plt.contourf(wrf.to_np(lons), wrf.to_np(lats), skintemp, cmap=cmap, levels = clevels, transform=ccrs.PlateCarree(),extend="both")
cbar = plt.colorbar(contours_fill, shrink = .65, orientation='horizontal', pad=.05)
plt.show()
skintemp, lats and lons are all 2D arrays with dimensions (454, 1483), ordered (lat,lon), and cart_proj = wrf.projection.MercatorWithLatTS.
When I show the plot, it's distorted and incorrect:
I have determined that the issue has to do with the non-zero central longitude. The problem appears to be when the longitude changes from 179.90082 to -179.85632. lons.values[0,370]=179.90082, so I changed contourf to the following:
contours_fill = plt.contourf(wrf.to_np(lons[:,0:371]), wrf.to_np(lats[:,0:371]), skintemp[:,0:371], cmap=cmap, levels = clevels, transform=ccrs.PlateCarree(),extend="both")
which produces the following correct figure:
And when I change contourf to:
contours_fill = plt.contourf(wrf.to_np(lons[:,371:-1]), wrf.to_np(lats[:,371:-1]), skintemp[:,371:-1], cmap=cmap, levels = clevels, transform=ccrs.PlateCarree(),extend="both")
I get the other part of the map:
I cannot seem to get both parts of the map to display correctly together. I tried using contourf twice in the same plot, one for each section of the map, but only the last contourf line plots. Any help would be much appreciated!
I am trying to shorten a colorbar by half. Does anyone know how to do this? I tried cax.get_position() and then cax.set_position(), but this method did not work.
Besides, it seems that axes created by axes_grid1 has the same bbox positions as the original axes. Is this a bug?
PS. I have to use axes_grid1 to create colorbar axes, because I need to use tight_layout() afterwards, and tight_layout() only applies to axes created by axes_grid1 but not ones created by add_axes().
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
plt.figure()
ax = plt.gca()
im = ax.imshow(np.arange(100).reshape((10,10)))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
bbox1 = ax.get_position()
print(bbox1)
bbox1 = cax.get_position()
print(bbox1)
plt.colorbar(im, cax=cax)
plt.show()
The whole point of the axes_divider is to divide the axes to make space for a new axes. This ensures that all axes have the same surrounding box. And that is the box you see being printed.
Some of the usual ways to create a colorbar, at a certain location in the figue are shown in this question. Here the problem seems to be to be able to call tight_layout. This is achievable with the following two options. (There might be others still.)
A. using gridspec
I'm not too sure about the exact requirements here, but it seems that using a normal grid layout would be more in the direction of what you need here.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 2, width_ratios=[95,5],)
ax = fig.add_subplot(gs[:, 0])
im = ax.imshow(np.arange(100).reshape((10,10)))
cax = fig.add_subplot(gs[1, 1])
fig.colorbar(im, cax=cax, ax=ax)
plt.tight_layout()
plt.show()
B. Using axes_grid1
If you really need to use axes_grid1, it might become a little bit more complicated.
import matplotlib.pyplot as plt
import matplotlib.axes
from mpl_toolkits.axes_grid1 import make_axes_locatable, Size
import numpy as np
fig, ax = plt.subplots()
im = ax.imshow(np.arange(100).reshape((10,10)))
divider = make_axes_locatable(ax)
pad = 0.03
pad_size = Size.Fraction(pad, Size.AxesY(ax))
xsize = Size.Fraction(0.05, Size.AxesX(ax))
ysize = Size.Fraction(0.5-pad/2., Size.AxesY(ax))
divider.set_horizontal([Size.AxesX(ax), pad_size, xsize])
divider.set_vertical([ysize, pad_size, ysize])
ax.set_axes_locator(divider.new_locator(0, 0, ny1=-1))
cax = matplotlib.axes.Axes(ax.get_figure(),
ax.get_position(original=True))
locator = divider.new_locator(nx=2, ny=0)
cax.set_axes_locator(locator)
fig.add_axes(cax)
fig.colorbar(im, cax=cax)
plt.tight_layout()
plt.show()
Whenever I use imshow() to plot an image, plotting 1D data over it in a twinned bottom x-axis changes the size and aspect ratio of the initial x-axis created with for imshow(). How do I avoid this behavior? Here is how to reproduce the issue:
import numpy as np
import matplotlib
matplotlib.use('macosx')
import matplotlib.pyplot as plt
im = np.random.rand(2856, 4290)
light_curve = im[1000, :]
fig = plt.figure(1, figsize=(10,10))
ax1 = plt.subplot(2,1,1)
ax1.imshow(im, cmap='gray', origin='lower')
ax2 = plt.subplot(2,1,2)
ax2.imshow(im, cmap='gray', origin='lower')
# Setting aspect ratio to equal does not help
ax2.set_aspect('equal')
ax21 = ax2.twinx()
ax21.plot(light_curve, alpha=0.7)
# Setting axis limits does not help
ax1.axis([0, im.shape[1], 0, im.shape[0]])
ax21.set_xlim([0, im.shape[1]])
And here is what it looks like with my graphical backend (macosx, if that is of any relevance)
Isn't it the purpose of twinx() used above to help with this in the first place?
So how may I keep the initial imshow() x-axis fixed and have the subsequent axis of the 1D plot simply fit, without resizing or messing with the aspect ratio, without going completely manual with building my axes?
It is indeed a bit unfortunate that the aspect does not propagate to the twin axes in the sense that it would have the same box around it.
I think the only way to overcome this is to calculate the aspect manually and set it for the twin axes as well.
import numpy as np
import matplotlib.pyplot as plt
im = np.random.rand(285, 429)
light_curve = im[100, :]
fig = plt.figure(1, figsize=(8,8))
ax1 = plt.subplot(2,1,1)
ax1.imshow(im, cmap='gray', origin='lower')
ax2 = plt.subplot(2,1,2)
ax2.imshow(im, cmap='gray', origin='lower')
ax2.set_aspect("equal", "box-forced")
ax21 = ax2.twinx()
ax21.plot(light_curve, alpha=0.7)
# Setting axis limits does not help
ax21.set_xlim(ax1.get_xlim())
a = np.diff(ax21.get_ylim())[0]/np.diff(ax1.get_xlim())*im.shape[1]/im.shape[0]
ax21.set_aspect(1./a, "box-forced")
plt.show()
I'd like to draw a (vertical) colorbar, which has two different scales (corresponding to two different units for the same quantity) on each side. Think Fahrenheit on one side and Celsius on the other side. Obviously, I'd need to specify the ticks for each side individually.
Any idea how I can do this?
That should get you started:
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10))
plt.pcolormesh(x)
# create the colorbar
# the aspect of the colorbar is set to 'equal', we have to set it to 'auto',
# otherwise twinx() will do weird stuff.
cbar = plt.colorbar()
pos = cbar.ax.get_position()
cbar.ax.set_aspect('auto')
# create a second axes instance and set the limits you need
ax2 = cbar.ax.twinx()
ax2.set_ylim([-2,1])
# resize the colorbar (otherwise it overlays the plot)
pos.x0 +=0.05
cbar.ax.set_position(pos)
ax2.set_position(pos)
plt.show()
If you create a subplot for the colorbar, you can create a twin axes for that subplot and manipulate it like a normal axes.
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1,2.7)
X,Y = np.meshgrid(x,x)
Z = np.exp(-X**2-Y**2)*.9+0.1
fig, (ax, cax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[15,1]})
im =ax.imshow(Z, vmin=0.1, vmax=1)
cbar = plt.colorbar(im, cax=cax)
cax2 = cax.twinx()
ticks=np.arange(0.1,1.1,0.1)
iticks=1./np.array([10,3,2,1.5,1])
cbar.set_ticks(ticks)
cbar.set_label("z")
cbar.ax.yaxis.set_label_position("left")
cax2.set_ylim(0.1,1)
cax2.set_yticks(iticks)
cax2.set_yticklabels(1./iticks)
cax2.set_ylabel("1/z")
plt.show()
Note that in newer version of matplotlib, the above answers no long work (as #Ryan Skene pointed out). I'm using v3.3.2. The secondary_yaxis function works for the colorbars in the same way as for regular plot axes and gives one colorbar with two scales: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.secondary_yaxis.html#matplotlib.axes.Axes.secondary_yaxis
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10)) #let's assume these are temperatures in Fahrenheit
im = plt.imshow(x)
# create the colorbar
cbar = plt.colorbar(im,pad=0.1) #you may need to adjust this padding for the secondary colorbar label[enter image description here][1]
cbar.set_label('Temperature ($^\circ$F)')
# define functions that relate the two colorbar scales
# e.g., Celcius to Fahrenheit and vice versa
def F_to_C(x):
return (x-32)*5/9
def C_to_F(x):
return (x*9/5)+32
# create a second axes
cbar2 = cbar.ax.secondary_yaxis('left',functions=(F_to_C,C_to_F))
cbar2.set_ylabel('Temperatrue ($\circ$C)')
plt.show()
I am using an inset axis for my colorbar and, for some reason, I found the above to answers no longer worked as of v3.4.2. The twinx took up the entire original subplot.
So I just replicated the inset axis (instead of using twinx) and increased the zorder on the original inset.
axkws = dict(zorder=2)
cax = inset_axes(
ax, width="100%", height="100%", bbox_to_anchor=bbox,
bbox_transform=ax.transAxes, axes_kwargs=axkws
)
cbar = self.fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap), cax=cax)
cbar.ax.yaxis.set_ticks_position('left')
caxx = inset_axes(
ax, width="100%", height="100%",
bbox_to_anchor=bbox, bbox_transform=ax.transAxes
)
caxx.yaxis.set_ticks_position('right')