Slicing Multidimensional Data

WCSAxes can ultimately only plot two-dimensional data. If we have an n-dimensional dataset, we have to select which dimensions to use for the x and y axis of the image. This example will show how to slice a FITS data cube and plot an image from it.

Slicing the WCS object

Like the example introduced in Getting started, we will read in the data using astropy.io.fits and parse the WCS information. The original FITS file can be downloaded from here.

from astropy.wcs import WCS
from wcsaxes import datasets
hdu = datasets.fetch_l1448_co_hdu()
wcs = WCS(hdu.header)
image_data = hdu.data

This is a three-dimensional dataset which you can check by looking at the header information by:

>>> hdu.header  
...
NAXIS = 3 /number of axes
CTYPE1  = 'RA---SFL'           /
CTYPE2  = 'DEC--SFL'           /
CTYPE3  = 'VELO-LSR'           /
...

The header keyword ‘NAXIS’ gives the number of dimensions of the dataset. The keywords ‘CTYPE1’, ‘CTYPE2’ and ‘CTYPE3’ give the data type of these dimensions to be right ascension, declination and velocity respectively.

We then instantiate the WCSAxes using the WCS object and select the slices we want to plot:

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=wcs, slices=(50, 'y', 'x'))

By setting slices=(50, 'y', 'x'), we have chosen to plot the second dimension on the y-axis and the third dimension on the x-axis. Even though we are not plotting the all the dimensions, we have to specify which slices to select for the dimensions that are not shown. In this example, we are not plotting the first dimension so we have selected the slice 50 to display. You can experiment with this by changing the selected slice and looking at how the plotted image changes.

Plotting the image

We then add the axes to the image and plot it using the method imshow().

ax.coords[2].set_ticks(exclude_overlapping=True)
ax.imshow(image_data[:, :, 50].transpose(), cmap=plt.cm.gist_heat)
_images/slicing_datacubes-3.png

Here, image_data is an ndarray object. In Numpy, the order of the axes is reversed so the first dimension in the FITS file appears last, the last dimension appears first and so on. Therefore the index passed to imshow() should be the same as passed to slices but in reversed order. We also need to transpose() image_data as we have reversed the dimensions plotted on the x and y axes in the slice.

If we don’t want to reverse the dimensions plotted, we can simply do:

fig = plt.figure(figsize=(6,3))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=wcs, slices=(50, 'x', 'y'))
ax.imshow(image_data[:, :, 50], cmap=plt.cm.gist_heat)
_images/slicing_datacubes-5.png