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from __future__ import print_function, division, absolute_import

Solutions for image visualization

Some exercises make use of code in the notebook. Other exercises will require a GUI interaction. In those cases, one or more images of results are included.

1. matplotlib: Contours from another image, and secondary axes

Using matplotlib and astropy:

display the file .data/w5.fits as a bitmap with log stretch and min-max scaling
overlay the data in image extension 1 of ./data/0259p6031_1342192088_SpirePhoto_L20_PMP350_SPG14.0.fits.fits.gz as white contours with levels drawn at [0.7, 1.4, 3] image units (Jy/beam)
display coordinate axes and grid (green, alpha=1) in (RA, Dec)
overlay a coordinate grid (cyan, alpha=1) and axis labels in Galactic longitude and latitude



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import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.visualization import (MinMaxInterval,
                                  LogStretch,
                                  ImageNormalize)
%matplotlib inline



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hdu = fits.open('./data/w5.fits')[0]
wcs = WCS(hdu.header)

hdu2 = fits.open('./data/0259p6031_1342192088_SpirePhoto_L20_PMP350_SPG14.0.fits.gz')[1]

norm = ImageNormalize(hdu.data, interval=MinMaxInterval(),
                      stretch=LogStretch())



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fig = plt.figure(figsize=(8,8))
ax = plt.subplot(projection=wcs)

overlay = ax.get_coords_overlay('galactic')

plt.imshow(hdu.data, norm=norm, origin="lower", cmap='Greys_r')

ax.coords['ra'].set_ticks(color='green')
ax.coords['dec'].set_ticks(color='green')

ax.coords['ra'].set_axislabel('Right Ascension')
ax.coords['dec'].set_axislabel('Declination')

ax.coords.grid(color='green', linestyle='solid', alpha=1.0)

overlay['l'].set_ticks(color='cyan')
overlay['b'].set_ticks(color='cyan')

overlay['l'].set_axislabel('Galactic Longitude')
overlay['b'].set_axislabel('Galactic Latitude')

overlay.grid(color='cyan', linestyle='solid', alpha=1.0)


ax.contour(hdu2.data, transform=ax.get_transform(WCS(hdu2.header)),
           levels=[0.7,1.4,3], colors='white');

2. RGB-3-color images

Using astropy and reproject (installable with pip install reproject), follow these instructions in the Astropy documentation to make color RGB images. Compare the second one to Figure 1 of Lupton et al 2004.



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import numpy as np
from astropy.visualization import make_lupton_rgb
from astropy.io import fits
from reproject import reproject_interp



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# Read in the three images downloaded from here:
g = fits.open('http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-g-001737-5-0039.fits.bz2')[0]
r = fits.open('http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-r-001737-5-0039.fits.bz2')[0]
i = fits.open('http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-i-001737-5-0039.fits.bz2')[0]



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# remap r and i onto g
r_new, r_mask = reproject_interp(r, g.header)
i_new, i_mask = reproject_interp(i, g.header)

# zero out the unmapped values
i_new[np.logical_not(i_mask)] = 0
r_new[np.logical_not(r_mask)] = 0

# red=i, green=r, blue=g
# make a file with the default scaling
rgb_default = make_lupton_rgb(i_new, r_new, g.data, filename="ngc6976-default.jpeg")
# this scaling is very similar to the one used in Lupton et al. (2004)
rgb = make_lupton_rgb(i_new, r_new, g.data, Q=10, stretch=0.5, filename="ngc6976.jpeg")









    



WARNING: FITSFixedWarning: RADECSYS= 'ICRS ' / International Celestial Ref. System 
the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]
WARNING: FITSFixedWarning: RADECSYS= 'ICRS ' / International Celestial Ref. System 
the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]

3. RGB colors of Herschel-SPIRE images

Reproject the 250 um image of W5 in ./data/0259p6031_1342192088_SpirePhoto_L20_PMP250_SPG14.0.fits.gz (extension 1) and the 350 micron image in ./data/0259p6031_1342192088_SpirePhoto_L20_PMP350_SPG14.0.fits.gz onto the 500 micron image in that directory, and try the same 3-color procedures.



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w5_250 = fits.open('./data/0259p6031_1342192088_SpirePhoto_L20_PMP250_SPG14.0.fits.gz')[1]
w5_350 = fits.open('./data/0259p6031_1342192088_SpirePhoto_L20_PMP350_SPG14.0.fits.gz')[1]
w5_500 = fits.open('./data/0259p6031_1342192088_SpirePhoto_L20_PMP500_SPG14.0.fits.gz')[1]



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im250, msk250 = reproject_interp(w5_250, w5_500.header)
im350, msk350 = reproject_interp(w5_350, w5_500.header)



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# zero out the unmapped values
im250[np.logical_not(msk250)] = 0
im350[np.logical_not(msk350)] = 0



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rgb_w5_default = make_lupton_rgb(im250, im350, w5_500.data, filename="w5-default.jpeg")









    



/Users/shupe/anaconda/lib/python3.5/site-packages/astropy/visualization/lupton_rgb.py:158: RuntimeWarning: invalid value encountered in less
  c[c < 0] = 0                # individual bands can still be < 0, even if fac isn't



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rgb_w5 = make_lupton_rgb(im250, im350, w5_500.data, Q=10, stretch=0.5, filename="w5.jpeg")









    



/Users/shupe/anaconda/lib/python3.5/site-packages/astropy/visualization/lupton_rgb.py:158: RuntimeWarning: invalid value encountered in less
  c[c < 0] = 0                # individual bands can still be < 0, even if fac isn't

4. Ginga reference viewer

If you haven't already, install Ginga with pip install ginga.

Launch the Ginga reference viewer from the shell.
Read in the three Herschel-SPIRE images in ./data/0259*fits.gz into separate channels
- Use top-level menu "Channel -> Add Channel"
- Use top-level menu "File -> Load Image"
Start the WCS Match plugin and blink the images

5. Firefly with PTF Images

Point your browser to IRSA's PTF image service.
Search position 312.503802, -0.706603 in the Level 1 images.
Go to Catalogs, overlay Gaia catalog
Select different images; and select different Gaia sources in an image (two tabs in table).

6. Glue with a photometric redshift catalog

If you haven't already, install Glue with conda install -c conda-forge glueviz

Start glue from the command line
Import a photometric redshift catalog from ./data/mrrphotz_xmmbrite.dat
Import the image ./data/xmm-lss-cutout-350um
Link the ra and dec from the table to Right Ascenscion and Declination from the image
Drag the image and start an Image Viewer
Drag the catalog over the image

Table alz2 is $log_{10}(1+z_{phot})$ for free $A_V$ solution. Define a new component photz inside Glue as np.power( 10, mrrphotz_xmmbrite.dat:alz2 ) - 1.0 and add it to the table
Make a histogram of spectroscopic redshift, spectz0. Select the subset of spectz0 > 0 and call it non-zero specz
Make a scatter plot of photz vs spectz0 for this subset

Facet the non-zero specz into 5 subsets by nbopt (number of optical bands). Explore the spatial distribution of the data with the highest number of optical bands.

Advanced: Install Firefly Standalone

If you have Java or are comfortable installing it, you can install a local Firefly server.

Check that you have java 1.8 installed with java -version

Download Firefly Standalone from release page (https://github.com/Caltech-IPAC/firefly/releases

Start with java -jar firefly-exec.war

Firefly will be available on localhost port 80: http://localhost:8080/firefly/

Once it is running, if you have done pip install firefly_client:

upload the file .data/w5.fits and display it
overlay the w5_wise.tbl

The steps are in the ImageVizSlides notebook.



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