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We want to assign colors to layers. I had a few experiments. trying to get a range of colors from red to yellow-white yielded images too monotonous so I couldn't see difference between "red" galaxies and not so red galaxies. Also, I thought to assign fixed colors. but if we only have NIRCam images, the colors' maximum redness is 440/2100 from maximum. So I tried dividing the colormap col by the maximum filter.
I create the colormap like this: col = matplotlib.cm.jet(filter/np.max(filter))[:, :3] # colormap based on the filters
Then create RGB like this:
mean = np.zeros((layers.shape[0], layers.shape[1], 3))
for lay in range(layers.shape[2]):
for ic in range(3):
mean[:, :, ic] = np.nansum(np.array([mean[:, :, ic], layers[:, :, lay]*(1/layers.shape[2])*col[lay, ic]]), 0)
Here are two results, one for NIRCam only, nicely detailed but with little blue, and another using NIRCam + MIRI, more blue but some of the fine details are smoothed out.
For this example layers has the shape of (4633, 4127, 13)
We want to assign colors to layers. I had a few experiments. trying to get a range of colors from red to yellow-white yielded images too monotonous so I couldn't see difference between "red" galaxies and not so red galaxies. Also, I thought to assign fixed colors. but if we only have NIRCam images, the colors' maximum redness is 440/2100 from maximum. So I tried dividing the colormap
col
by the maximum filter.I create the colormap like this:
col = matplotlib.cm.jet(filter/np.max(filter))[:, :3] # colormap based on the filters
Then create RGB like this:
Here are two results, one for NIRCam only, nicely detailed but with little blue, and another using NIRCam + MIRI, more blue but some of the fine details are smoothed out.
For this example layers has the shape of (4633, 4127, 13)
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