Welcome to the JWST/HST Alignment Tool (JHAT)
Installation
jhat works on Python 3+ and requires the following Python packages (these should install automatically when you install jhat):
Install using pip
Using pip:
pip install jhat
Install Development Version
Using git:
git clone https://github.com/arminrest/jhat.git
cd jhat
python setup.py install
Interpreting Plots
This is the initial plot to check if things are going the right direction. Here each point is a source detected in the image. Blue points have passed the initial cuts, gray points fail cuts are are not used. If there is a locus in both dx and dy, then the code will be able to find the correct alignment parameters. The second row just shows the same sources as a function of some photometric parameters.
This is an example of the same set of plots for a case where the alignment has failed. No locus is apparent in dx or dy. If there is no relation to be seen in this initial plot, then cuts and params need to be changed/loosened (see Useful Parameters), in this case d2d_max should be increased.
The second important set of plots comes from the selection of good matches between reference catalog and target image source catalog. First the best rotation and offset is chosen for dx, which is successful if there is a clear peak in the upper left panel (histogram peak value versus slope). In the second row, blue dots are sources that are kept after rotation, and red are the original unrotated dx residuals (lower left panel). Then a 3sigma cut is done on the sources in the dx parameter space, and red indicates the cut values in the far right center row. Finally, the the same is done for dy, but ONLY with sources that already passed the dx cut.
Finally, we can check what the alignment did by observing the pre and post wcs-correction plots.
Useful Parameters
Here we describe the most useful parameters and what changing them does inside of the code.
d2d_max
sharpness_lim
roundness1_lim
delta_mag_lim
objmag_lim
refmag_lim
slope_min
Nbright4match
Nbright
xshift/yshift
rough_cut_px_min/max
d_rotated_Nsigma
Command Line Commands
Here we have a directory called “miri_example” that has some MIRI cals. JHAT can also be run from the command line in the following way:
run_st_wcs_align.py miri_example/jw02666001001_02101_00001_mirimage_cal.fits --outrootdir '.'
--outsubdir aligned --overwr -v --refcat gaia --saveplots -tt -pp --histocut_order dxdy
Which produces the following plots:
Improving Alignment
Sometimes, in particular if there are not enough sources or the images are particularly poorly aligned, JHAT has a difficult time finding a good alignment (or it can be improved). Here are a few methods for improving the results. First we add cuts on sharpness, roundness, and brightness:
run_st_wcs_align.py miri_example/jw02666001001_02101_00001_mirimage_cal.fits --outrootdir '.'
--outsubdir aligned --overwr -v --refcat gaia --saveplots -tt -pp --histocut_order dxdy
--roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --sharpness_lim 0.6 0.85 --refmag_lim 16 25
Next we increase the allowed distance between matches between the reference and target catalogs (d2d_max),
and limit the difference between measured magnitudes between the catalogs.
run_st_wcs_align.py miri_example/jw02666001001_02101_00001_mirimage_cal.fits --outrootdir '.'
--outsubdir aligned --overwr -v --refcat gaia --saveplots -tt -pp --histocut_order dxdy
--roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --refmag_lim 16 25 --delta_mag_lim -2 2 --d2d_max 1.5
Finally, we might use the previous results to guess the rough needed offset, and apply it directly before the matching begins:
run_st_wcs_align.py miri_example/jw02666001001_02101_00001_mirimage_cal.fits --outrootdir '.'
--outsubdir aligned --overwr -v --refcat gaia --saveplots -tt -pp --histocut_order dxdy
--roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --refmag_lim 16 25 --delta_mag_lim -2 2
--d2d_max 1.5 --xshift 3 --yshift -4
Level 3 From Aligned Cals
We run F560W with the best options determined from the Improving Alignment example:
run_st_wcs_align_batch.py --input_dir '.' --input_files 'miri_example/*_cal.fits' --outrootdir aligned
--outsubdir F560W_level2_gaia --overwr -v --refcat gaia --saveplots -tt -pp --histocut_order dxdy
--roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --refmag_lim 16 25 --delta_mag_lim -2 2
--d2d_max 1.5 --xshift 3 --yshift -4 --filter F560W
Now we run the notebook that runs level3 and creates the mosaic and the corresponding catalog
(On GitHub here).
That notebook produces a catalog (F560W_snr3_npix10_cat.ecsv), which we choose
as our secondary astrometric catalog.
Now we run the rest of the filters. However, we remove –delta_mag_lim, since this cut depends on the filter!!! Note that we define the necessary column names from the catalog, which are different from the defaults.
run_st_wcs_align_batch.py --input_dir '.' --input_files 'miri_example/*_cal.fits' --outrootdir miri_example
--outsubdir ALL_level2_catF560W --overwr -v --refcat F560W_snr3_npix10_cat.ecsv --saveplots -tt -pp
--histocut_order dxdy --roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --refmag_lim 16 25 --d2d_max 1.5
--xshift 3 --yshift -4 --iterate_with_xyshifts --refcat_racol sky_centroid.ra
--refcat_deccol sky_centroid.dec --refcat_magcol aper50_abmag --refcat_magerrcol aper50_abmag_err
--filters F560W F1000W F1280W F1130W F1500W F1800W
We find that F1500W F1800W mostly work, but don’t have many stars. Therefore we run it with –d_rotated_Nsigma 0.0: Too few stars to do a 3-sigma cut.
run_st_wcs_align_batch.py --input_dir '.' --input_files 'miri_example/*_cal.fits' --outrootdir miri_example
--outsubdir ALLRED_level2_catF560W --overwr -vvv --refcat F560W_snr3_npix10_cat.ecsv --saveplots -tt -pp
--histocut_order dxdy --roundness1_lim -0.5 0.5 --objmag_lim 14 21.5 --refmag_lim 16 25 --d2d_max 1.5 --xshift 3
--yshift -4 --iterate_with_xyshifts --refcat_racol sky_centroid.ra --refcat_deccol sky_centroid.dec --refcat_magcol
aper50_abmag --refcat_magerrcol aper50_abmag_err --filters F1500W F1800W F2100W --d_rotated_Nsigma 0.0
Examples
Note
Click here to download the full example code
JWST MIRI
Aligning JWST/MIRI images with JHAT.
An example MIRI Dataset is downloaded, and then a series of alignment methods are used. For more information on the key parameters used for alignment see Useful Parameters.
import sys,os,glob
from astropy.io import fits
from astropy.table import Table
from astropy.nddata import extract_array
from astropy.coordinates import SkyCoord
from astropy import wcs
from astropy.wcs.utils import skycoord_to_pixel
from astropy import units as u
import numpy as np
import matplotlib.pyplot as plt
from astroquery.mast import Observations
from astropy.visualization import (simple_norm,LinearStretch)
import jhat
from jhat import jwst_photclass,st_wcs_align
Relative Alignment
Download some Data
For this example we download 2 MIRI cal images from MAST. They’re the same field and different filters. Note that the code will also work for level 3 images.
obs_table1 = Observations.query_criteria(obs_id='jw02107-o038_t019_miri_f770w')
data_products_by_obs = Observations.get_product_list(obs_table1)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==2]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='CAL'][0]
Observations.download_products(data_products_by_obs,extension='fits')
obs_table2 = Observations.query_criteria(obs_id='jw02107-c1018_t019_miri_f1130w')
data_products_by_obs = Observations.get_product_list(obs_table2)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==2]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='CAL'][0]
Observations.download_products(data_products_by_obs,extension='fits')
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02107038001_02101_00001_mirimage_cal.fits to ./mastDownload/JWST/jw02107038001_02101_00001_mirimage/jw02107038001_02101_00001_mirimage_cal.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02107038001_02105_00001_mirimage_cal.fits to ./mastDownload/JWST/jw02107038001_02105_00001_mirimage/jw02107038001_02105_00001_mirimage_cal.fits ... [Done]
Examine the Reference Image
files = glob.glob('mastDownload/JWST/*miri*/*cal.fits')
ref_image = files[0]
print(ref_image)
ref_fits = fits.open(ref_image)
ref_data = fits.open(ref_image)['SCI',1].data
norm1 = simple_norm(ref_data,stretch='log',min_cut=5,max_cut=25)
plt.imshow(ref_data, origin='lower',
norm=norm1,cmap='gray')
plt.gca().tick_params(labelcolor='none',axis='both',color='none')
plt.show()
mastDownload/JWST/jw02107038001_02101_00001_mirimage/jw02107038001_02101_00001_mirimage_cal.fits
Zoom in to see the offset
Here add an artificial offset to the wcs, and then we see the same star in both images at the same ra/dec location, demonstrating a large offset between the images.
star_location = SkyCoord('23:09:44.0809','-43:26:05.613',unit=(u.hourangle,u.deg))
align_image = files[1]
align_fits = fits.open(align_image)
align_fits['SCI',1].header['CRPIX1']+=2
align_fits['SCI',1].header['CRPIX2']+=2
align_fits.writeto(align_image,overwrite=True)
align_data = fits.open(align_image)['SCI',1].data
ref_y,ref_x = skycoord_to_pixel(star_location,wcs.WCS(ref_fits['SCI',1],ref_fits))
align_y,align_x = skycoord_to_pixel(star_location,wcs.WCS(align_fits['SCI',1],align_fits))
ref_cutout = extract_array(ref_data,(11,11),(ref_x,ref_y))
align_cutout = extract_array(align_data,(11,11),(align_x,align_y))
norm1 = simple_norm(ref_cutout,stretch='log',min_cut=-1,max_cut=200)
norm2 = simple_norm(align_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,2)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[0].tick_params(labelcolor='none',axis='both',color='none')
axes[1].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T17:29:42.548' from MJD-BEG.
Set DATE-AVG to '2022-07-06T17:29:53.648' from MJD-AVG.
Set DATE-END to '2022-07-06T17:30:04.748' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.176807 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353152 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740801417.596 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T17:47:53.158' from MJD-BEG.
Set DATE-AVG to '2022-07-06T17:48:32.008' from MJD-AVG.
Set DATE-END to '2022-07-06T17:49:10.859' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.174733 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353284 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740817774.322 from OBSGEO-[XYZ]'.
warnings.warn(
Create a Photometric Catalog for Relative Alignment
We choose one of the images to be the reference image, and then create a catalog that we will use to align the other image.
jwst_phot = jwst_photclass()
jwst_phot.run_phot(imagename=ref_image,photfilename='auto',overwrite=True)
ref_catname = ref_image.replace('.fits','.phot.txt') # the default
refcat = Table.read(ref_catname,format='ascii')
print(refcat)
0 mastDownload/JWST/jw02107038001_02101_00001_mirimage/jw02107038001_02101_00001_mirimage_cal.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T17:29:42.548' from MJD-BEG.
Set DATE-AVG to '2022-07-06T17:29:53.648' from MJD-AVG.
Set DATE-END to '2022-07-06T17:30:04.748' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.176807 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353152 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740801417.596 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/units/function/logarithmic.py:47: RuntimeWarning: invalid value encountered in log10
return dex.to(self._function_unit, np.log10(x))
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:897: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/flux))
aper_sum_4.1px annulus_median_4.1px aper_bkg_4.1px ... x_idl y_idl
-------------- -------------------- -------------- ... ---------- ----------
297.650667 -99.99 -5238.189153 ... 37.152004 -55.873059
378.648855 5.548146 290.651448 ... 30.043803 -54.923731
115.583682 -99.99 -5238.189153 ... -76.001952 -54.071845
294.794123 0.126702 6.63754 ... -76.013673 -51.700329
234.943054 0.218582 11.450907 ... -76.018672 -50.556787
467.419303 6.36806 333.604394 ... -3.833359 -48.74132
342.922546 0.173903 9.110269 ... -76.032743 -47.064001
371.015027 0.207516 10.871191 ... -76.037395 -45.257877
173.426586 0.099091 5.191109 ... -76.03994 -44.794514
838.584206 7.751832 406.096233 ... -9.072841 -43.561009
... ... ... ... ... ...
133.430719 0.1092 5.720696 ... -75.754248 30.346453
245.152683 0.405534 21.244786 ... -75.721452 34.663273
233.690538 0.556301 29.143019 ... -75.716036 35.792661
445.263408 0.72363 37.908921 ... -75.646588 46.09179
176.538 0.783822 41.062174 ... -75.634492 47.785745
617.329614 4.899353 256.663045 ... 36.555135 50.32043
622.234759 4.964599 260.081101 ... 36.55006 51.069329
330.330828 0.707696 37.074164 ... -75.607653 52.275188
378.108748 0.679931 35.619658 ... -75.600128 53.678531
547.845954 -99.99 -5238.189153 ... -75.589207 55.779377
459.438067 -99.99 -5238.189153 ... 36.520526 55.674128
Length = 211 rows
Align the second image
The plots outputted here show the various steps used by jhat to determine the true matching sources in the image, and the subsequent correction needed for optimal alignment.
wcs_align = st_wcs_align()
wcs_align.run_all(align_image,
telescope='jwst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=1,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
0 ./mastDownload/jw02107038001_02105_00001_mirimage.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T17:47:53.158' from MJD-BEG.
Set DATE-AVG to '2022-07-06T17:48:32.008' from MJD-AVG.
Set DATE-END to '2022-07-06T17:49:10.859' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.174733 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353284 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740817774.322 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/units/function/logarithmic.py:47: RuntimeWarning: invalid value encountered in log10
return dex.to(self._function_unit, np.log10(x))
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:897: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/flux))
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
0.000049 -0.025 54.211365 28 -0.241148 1.0 False
Keeping 75 out of 75, skippin 0 because of null values in columns d_rot_tmp
median: -0.161845
75.000000 percentile cut: max residual for cut: 0.212953
median: -0.162629
i:00 mean:-0.162629(0.014451) stdev:0.107171(0.010127) X2norm:0.99 Nchanged:0 Ngood:56 Nclip:19
mean: -0.144962
i:01 mean:-0.144962(0.017233) stdev:0.136786(0.012090) X2norm:1.00 Nchanged:8 Ngood:64 Nclip:11
mean: -0.145593
i:02 mean:-0.145593(0.019481) stdev:0.159460(0.013674) X2norm:1.00 Nchanged:4 Ngood:68 Nclip:7
mean: -0.158406
i:03 mean:-0.158406(0.020980) stdev:0.174271(0.014729) X2norm:1.00 Nchanged:2 Ngood:70 Nclip:5
mean: -0.158419
i:04 mean:-0.158419(0.022668) stdev:0.191002(0.015917) X2norm:1.00 Nchanged:2 Ngood:72 Nclip:3
mean: -0.158419
i:05 mean:-0.158419(0.022668) stdev:0.191002(0.015917) X2norm:1.00 Nchanged:0 Ngood:72 Nclip:3
slope intercept maxval index d_bestguess fwhm multimax
0.000049 -0.025195 41.262524 36 0.051438 1.0 False
Keeping 61 out of 61, skippin 0 because of null values in columns d_rot_tmp
median: 0.109562
75.000000 percentile cut: max residual for cut: 0.288823
median: 0.102123
i:00 mean:0.102123(0.021089) stdev:0.139885(0.014745) X2norm:0.99 Nchanged:0 Ngood:45 Nclip:16
mean: 0.114686
i:01 mean:0.114686(0.024837) stdev:0.177373(0.017393) X2norm:1.00 Nchanged:7 Ngood:52 Nclip:9
mean: 0.105753
i:02 mean:0.105753(0.028650) stdev:0.214397(0.020080) X2norm:1.00 Nchanged:5 Ngood:57 Nclip:4
mean: 0.126734
i:03 mean:0.126734(0.033214) stdev:0.257275(0.023293) X2norm:1.00 Nchanged:4 Ngood:61 Nclip:0
mean: 0.126734
i:04 mean:0.126734(0.033214) stdev:0.257275(0.023293) X2norm:1.00 Nchanged:0 Ngood:61 Nclip:0
*** Note: close plots to continue!
/Users/jpierel/CodeBase/tweakreg_hack/tweakreg_hack/tweakreg_step_hack.py:540: AstropyDeprecationWarning: The JWSTgWCS class is deprecated and may be removed in a future version.
Use JWSTWCSCorrector instead.
im = JWSTgWCS(
replacing SIP ./mastDownload/jw02107038001_02105_00001_mirimage_jhat.fits
./mastDownload/jw02107038001_02105_00001_mirimage_jhat.fits
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.174733 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353284 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740817774.322 from OBSGEO-[XYZ]'.
warnings.warn(
*** Note: close plots to continue!
0
Check the Output
The reference image has not changed, but let’s read in the newly aligned image and compare with the original. subsequent correction needed for optimal alignment.
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('cal.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,3)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[2].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[2].set_title('Aligned')
for i in range(3):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.174733 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353284 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740817774.322 from OBSGEO-[XYZ]'.
warnings.warn(
Total running time of the script: ( 0 minutes 32.073 seconds)
Note
Click here to download the full example code
JWST NIRCAM
Aligning JWST/NIRCAM images with JHAT.
An example NIRCam Dataset is downloaded, and then a series of alignment methods are used. For more information on the key parameters used for alignment see Useful Parameters.
import sys,os,glob
from astropy.io import fits
from astropy.table import Table
from astropy.nddata import extract_array
from astropy.coordinates import SkyCoord
from astropy import wcs
from astropy.wcs.utils import skycoord_to_pixel
from astropy import units as u
import numpy as np
import matplotlib.pyplot as plt
from astroquery.mast import Observations
from astropy.visualization import (simple_norm,LinearStretch)
import jhat
from jhat import jwst_photclass,st_wcs_align
Relative Alignment
Download some Data
For this example we download 2 JWST NIRCam images from MAST. They’re the same field but different filters. Note that the code will also work for level 3 data images.
obs_table1 = Observations.query_criteria(obs_id='jw02107-o041_t019_nircam_clear-f200w')
data_products_by_obs = Observations.get_product_list(obs_table1)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==2]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='CAL'][0]
Observations.download_products(data_products_by_obs,extension='fits')
obs_table2 = Observations.query_criteria(obs_id='jw02107-o041_t019_nircam_clear-f360m')
data_products_by_obs = Observations.get_product_list(obs_table2)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==2]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='CAL'][0]
Observations.download_products(data_products_by_obs,extension='fits')
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02107041001_02101_00001_nrcb1_cal.fits to ./mastDownload/JWST/jw02107041001_02101_00001_nrcb1/jw02107041001_02101_00001_nrcb1_cal.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02107041001_02101_00001_nrcblong_cal.fits to ./mastDownload/JWST/jw02107041001_02101_00001_nrcblong/jw02107041001_02101_00001_nrcblong_cal.fits ... [Done]
Examine the Reference Image
ref_image = glob.glob('mastDownload/JWST/*nrcb1*/*cal.fits')[0]
ref_fits = fits.open(ref_image)
ref_data = fits.open(ref_image)['SCI',1].data
norm1 = simple_norm(ref_data,stretch='linear',min_cut=-.5,max_cut=3)
plt.imshow(ref_data, origin='lower',
norm=norm1,cmap='gray')
plt.gca().tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Zoom in to see the offset
Here add an artificial offset to the wcs, and then we see the same star in both images at the same ra/dec location, demonstrating a large offset between the images.
star_location = SkyCoord('23:09:41.0532','-43:26:41.128',unit=(u.hourangle,u.deg))
align_image = glob.glob('mastDownload/JWST/*long*/*cal.fits')[0]
align_fits = fits.open(align_image)
align_fits['SCI',1].header['CRPIX1']+=1
align_fits['SCI',1].header['CRPIX2']+=1
align_fits.writeto(align_image,overwrite=True)
align_data = fits.open(align_image)['SCI',1].data
ref_y,ref_x = skycoord_to_pixel(star_location,wcs.WCS(ref_fits['SCI',1],ref_fits))
align_y,align_x = skycoord_to_pixel(star_location,wcs.WCS(align_fits['SCI',1],align_fits))
ref_cutout = extract_array(ref_data,(11,11),(ref_x,ref_y))
align_cutout = extract_array(align_data,(11,11),(align_x,align_y))
norm1 = simple_norm(ref_cutout,stretch='linear',min_cut=-.5,max_cut=3)
norm2 = simple_norm(align_cutout,stretch='linear',min_cut=-.5,max_cut=3)
fig,axes = plt.subplots(1,2)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[0].tick_params(labelcolor='none',axis='both',color='none')
axes[1].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T19:16:42.721' from MJD-BEG.
Set DATE-AVG to '2022-07-06T19:17:14.932' from MJD-AVG.
Set DATE-END to '2022-07-06T19:17:47.142' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T19:16:42.721' from MJD-BEG.
Set DATE-AVG to '2022-07-06T19:17:14.932' from MJD-AVG.
Set DATE-END to '2022-07-06T19:17:47.142' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
Create a Photometric Catalog for Relative Alignment
We choose one of the images to be the reference image, and then create a catalog that we will use to align the other image.
jwst_phot = jwst_photclass()
jwst_phot.run_phot(imagename=ref_image,photfilename='auto',overwrite=True,ee_radius=80)
ref_catname = ref_image.replace('.fits','.phot.txt') # the default
refcat = Table.read(ref_catname,format='ascii')
print(refcat)
0 mastDownload/JWST/jw02107041001_02101_00001_nrcb1/jw02107041001_02101_00001_nrcb1_cal.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T19:16:42.721' from MJD-BEG.
Set DATE-AVG to '2022-07-06T19:17:14.932' from MJD-AVG.
Set DATE-END to '2022-07-06T19:17:47.142' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/units/function/logarithmic.py:47: RuntimeWarning: invalid value encountered in log10
return dex.to(self._function_unit, np.log10(x))
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:897: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/flux))
aper_sum_5.7px annulus_median_5.7px aper_bkg_5.7px ... x_idl y_idl
-------------- -------------------- -------------- ... ---------- ----------
105.310801 0.910453 92.21433 ... -11.710449 -31.373861
121.56116 0.960956 97.329433 ... 1.390701 -31.374766
60.232352 0.498815 50.521961 ... 18.604013 -31.26976
126.737312 1.019729 103.282174 ... -31.055244 -31.32962
113.529229 0.802048 81.234601 ... -18.547597 -31.34987
105.370409 0.767367 77.721958 ... -13.204667 -31.345555
89.411974 0.648643 65.697099 ... 11.290307 -31.300338
110.788413 0.861123 87.217932 ... -0.18106 -31.287703
124.473601 0.862357 87.342937 ... 6.266814 -31.255725
108.728849 0.849587 86.04952 ... 10.634677 -31.259596
... ... ... ... ... ...
33.278942 0.200456 20.302942 ... -18.177622 29.366304
23.6942 0.04802 4.863676 ... -17.540134 29.519926
18.277825 0.013074 1.324192 ... -9.43527 29.52744
0.06818 -99.99 -10127.382506 ... -2.732965 29.758751
14.784146 -99.99 -10127.382506 ... 20.030247 29.877627
47.576291 0.323802 32.795919 ... 8.796703 31.023101
36.125728 0.251559 25.478925 ... 26.612871 31.127561
39.783969 0.184818 18.719089 ... -15.095758 31.1144
341.811179 0.27702 28.0577 ... -9.937289 31.082671
53.600708 0.336015 34.032935 ... 8.547109 31.138805
50.031236 0.275069 27.860125 ... 13.180869 31.166268
Length = 2756 rows
Align the second image
The plots outputted here show the various steps used by jhat to determine the true matching sources in the image, and the subsequent correction needed for optimal alignment.
wcs_align = st_wcs_align()
wcs_align.run_all(align_image,
telescope='jwst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=1,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
0 ./mastDownload/jw02107041001_02101_00001_nrcblong.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T19:16:42.721' from MJD-BEG.
Set DATE-AVG to '2022-07-06T19:17:14.932' from MJD-AVG.
Set DATE-END to '2022-07-06T19:17:47.142' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/units/function/logarithmic.py:47: RuntimeWarning: invalid value encountered in log10
return dex.to(self._function_unit, np.log10(x))
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:897: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/flux))
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
0.000146 -0.15 83.958993 76 -0.50978 0.8 False
Keeping 109 out of 109, skippin 0 because of null values in columns d_rot_tmp
median: -0.572386
75.000000 percentile cut: max residual for cut: 0.188131
median: -0.587588
i:00 mean:-0.587588(0.010388) stdev:0.092914(0.007300) X2norm:0.99 Nchanged:0 Ngood:81 Nclip:28
mean: -0.580012
i:01 mean:-0.580012(0.012575) stdev:0.121920(0.008845) X2norm:1.00 Nchanged:14 Ngood:95 Nclip:14
mean: -0.557890
i:02 mean:-0.557890(0.014267) stdev:0.143378(0.010038) X2norm:1.00 Nchanged:7 Ngood:102 Nclip:7
mean: -0.554064
i:03 mean:-0.554064(0.015446) stdev:0.157514(0.010870) X2norm:1.00 Nchanged:3 Ngood:105 Nclip:4
mean: -0.554064
i:04 mean:-0.554064(0.015446) stdev:0.157514(0.010870) X2norm:1.00 Nchanged:0 Ngood:105 Nclip:4
slope intercept maxval index d_bestguess fwhm multimax
-1.734723e-17 1.776357e-14 79.085569 6 -0.534655 0.8 False
Keeping 103 out of 103, skippin 0 because of null values in columns d_rot_tmp
median: -0.559999
75.000000 percentile cut: max residual for cut: 0.191320
median: -0.559999
i:00 mean:-0.559999(0.010883) stdev:0.094879(0.007646) X2norm:0.99 Nchanged:0 Ngood:77 Nclip:26
mean: -0.554858
i:01 mean:-0.554858(0.013144) stdev:0.124003(0.009243) X2norm:1.00 Nchanged:13 Ngood:90 Nclip:13
mean: -0.565181
i:02 mean:-0.565181(0.015176) stdev:0.148691(0.010675) X2norm:1.00 Nchanged:7 Ngood:97 Nclip:6
mean: -0.564816
i:03 mean:-0.564816(0.016002) stdev:0.158407(0.011258) X2norm:1.00 Nchanged:2 Ngood:99 Nclip:4
mean: -0.569514
i:04 mean:-0.569514(0.016529) stdev:0.164459(0.011629) X2norm:1.00 Nchanged:1 Ngood:100 Nclip:3
mean: -0.569514
i:05 mean:-0.569514(0.016529) stdev:0.164459(0.011629) X2norm:1.00 Nchanged:0 Ngood:100 Nclip:3
*** Note: close plots to continue!
/Users/jpierel/CodeBase/tweakreg_hack/tweakreg_hack/tweakreg_step_hack.py:540: AstropyDeprecationWarning: The JWSTgWCS class is deprecated and may be removed in a future version.
Use JWSTWCSCorrector instead.
im = JWSTgWCS(
replacing SIP ./mastDownload/jw02107041001_02101_00001_nrcblong_jhat.fits
./mastDownload/jw02107041001_02101_00001_nrcblong_jhat.fits
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
*** Note: close plots to continue!
0
Check the Output
The reference image has not changed, but let’s read in the newly aligned image and compare with the original. subsequent correction needed for optimal alignment.
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('cal.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='linear',min_cut=-.5,max_cut=3)
fig,axes = plt.subplots(1,3)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[2].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[2].set_title('Aligned')
for i in range(3):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
Align to Catalog
You can also align each image to the Gaia DR3 catalog, or you could replace the catalog created in step one with your own catalog of the field.
wcs_align.run_all(align_image,
telescope='jwst',
outsubdir='mastDownload',
overwrite=True,
d2d_max=.5,
showplots=0,
refcatname='Gaia',
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('cal.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='linear',min_cut=-.5,max_cut=3)
fig,axes = plt.subplots(1,2)
axes[0].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[1].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('To Align')
axes[1].set_title('Aligned')
for i in range(2):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
0 ./mastDownload/jw02107041001_02101_00001_nrcblong.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'datfix' made the change 'Set DATE-BEG to '2022-07-06T19:16:42.721' from MJD-BEG.
Set DATE-AVG to '2022-07-06T19:17:14.932' from MJD-AVG.
Set DATE-END to '2022-07-06T19:17:47.142' from MJD-END'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/units/function/logarithmic.py:47: RuntimeWarning: invalid value encountered in log10
return dex.to(self._function_unit, np.log10(x))
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:897: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/flux))
INFO: Query finished. [astroquery.utils.tap.core]
Number of stars: 21
### NO propoer motion correction!!!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in sqrt
result = getattr(ufunc, method)(*inputs, **kwargs)
Number of stars after removing nan's: 21
slope intercept maxval index d_bestguess fwhm multimax
0.001367 -1.4 3.661515 35 0.706414 0.8 False
Keeping 5 out of 5, skippin 0 because of null values in columns d_rot_tmp
median: 0.644163
75.000000 percentile cut: max residual for cut: 0.071771
median: 0.646769
i:00 mean:0.646769(0.001950) stdev:0.002758(0.001126) X2norm:0.79 Nchanged:0 Ngood:3 Nclip:2
mean: 0.646657
i:01 mean:0.646657(0.001725) stdev:0.002440(0.000996) X2norm:1.00 Nchanged:0 Ngood:3 Nclip:2
slope intercept maxval index d_bestguess fwhm multimax
0.000537 -0.55 3.0 4 -1.643322 0.8 False
Keeping 3 out of 3, skippin 0 because of null values in columns d_rot_tmp
median: -1.609357
i:00 mean:-1.609357(0.083353) stdev:0.117879(0.048124) X2norm:0.79 Nchanged:0 Ngood:3 Nclip:0
mean: -1.633248
i:01 mean:-1.633248(0.070913) stdev:0.100286(0.040942) X2norm:1.00 Nchanged:0 Ngood:3 Nclip:0
/Users/jpierel/CodeBase/tweakreg_hack/tweakreg_hack/tweakreg_step_hack.py:540: AstropyDeprecationWarning: The JWSTgWCS class is deprecated and may be removed in a future version.
Use JWSTWCSCorrector instead.
im = JWSTgWCS(
replacing SIP ./mastDownload/jw02107041001_02101_00001_nrcblong_jhat.fits
./mastDownload/jw02107041001_02101_00001_nrcblong_jhat.fits
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/wcs/wcs.py:725: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to -72.164999 from OBSGEO-[XYZ].
Set OBSGEO-B to -38.353872 from OBSGEO-[XYZ].
Set OBSGEO-H to 1740894174.999 from OBSGEO-[XYZ]'.
warnings.warn(
Total running time of the script: ( 1 minutes 42.234 seconds)
Note
Click here to download the full example code
Hubble
Aligning HST images with JHAT.
An example HST Dataset is downloaded, and then a series of alignment methods are used. For more information on the key parameters used for alignment see Useful Parameters.
import sys,os,glob
from astropy.io import fits
from astropy.table import Table
from astropy.nddata import extract_array
from astropy.coordinates import SkyCoord
from astropy import wcs
from astropy.wcs.utils import skycoord_to_pixel
from astropy import units as u
import numpy as np
import matplotlib.pyplot as plt
from astroquery.mast import Observations
from astropy.visualization import (simple_norm,LinearStretch)
import jhat
from jhat import hst_photclass,st_wcs_align
Relative Alignment
Download some Data
For this example we download 2 HST DRZ images from MAST. They’re the same filter and same field, just separated in time.
obs_table = Observations.query_criteria(obs_id='hst_16264_12_wfc3_ir_f110w_iebc12')
obs_table1 = obs_table[obs_table['filters']=='F110W']
obs_table = Observations.query_criteria(obs_id='hst_16264_15_wfc3_ir_f110w_iebc15')
obs_table2 = obs_table[obs_table['filters']=='F110W']
data_products_by_obs = Observations.get_product_list(obs_table1)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==3]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='DRZ'][0]
Observations.download_products(data_products_by_obs,extension='fits')
data_products_by_obs = Observations.get_product_list(obs_table2)
data_products_by_obs = data_products_by_obs[data_products_by_obs['calib_level']==3]
data_products_by_obs = data_products_by_obs[data_products_by_obs['productSubGroupDescription']=='DRZ'][0]
Observations.download_products(data_products_by_obs,extension='fits')
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits to ./mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/hst_16264_15_wfc3_ir_f110w_iebc15_drz.fits to ./mastDownload/HST/hst_16264_15_wfc3_ir_f110w_iebc15/hst_16264_15_wfc3_ir_f110w_iebc15_drz.fits ... [Done]
Examine the Reference Image
files = glob.glob('mastDownload/HST/*/*drz.fits')
ref_image = files[0]
ref_fits = fits.open(ref_image)
ref_data = fits.open(ref_image)['SCI',1].data
norm1 = simple_norm(ref_data,stretch='log',min_cut=-1,max_cut=15)
plt.imshow(ref_data, origin='lower',
norm=norm1,cmap='gray')
plt.gca().tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Zoom in to see the offset
Here add an artificial offset to the wcs, and then we see the same star in both images at the same ra/dec location, demonstrating a large offset between the images.
star_location = SkyCoord('21:29:40.5351','+0:04:42.697',unit=(u.hourangle,u.deg))
align_image = files[1]
align_fits = fits.open(align_image)
align_fits['SCI',1].header['CRPIX1']+=2
align_fits['SCI',1].header['CRPIX2']+=2
align_fits.writeto(align_image,overwrite=True)
align_data = fits.open(align_image)['SCI',1].data
ref_y,ref_x = skycoord_to_pixel(star_location,wcs.WCS(ref_fits['SCI',1],ref_fits))
align_y,align_x = skycoord_to_pixel(star_location,wcs.WCS(align_fits['SCI',1],align_fits))
ref_cutout = extract_array(ref_data,(11,11),(ref_x,ref_y))
align_cutout = extract_array(align_data,(11,11),(align_x,align_y))
norm1 = simple_norm(ref_cutout,stretch='log',min_cut=-1,max_cut=200)
norm2 = simple_norm(align_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,2)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[0].tick_params(labelcolor='none',axis='both',color='none')
axes[1].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Create a Photometric Catalog for Relative Alignment
We choose one of the images to be the reference image, and then create a catalog that we will use to align the other image.
hst_phot = hst_photclass(psf_fwhm=1.8,aperture_radius=5)
hst_phot.run_phot(imagename=ref_image,photfilename='auto',overwrite=True)
ref_catname = ref_image.replace('.fits','.phot.txt') # the default
refcat = Table.read(ref_catname,format='ascii')
print(refcat)
0 mastDownload/HST/hst_16264_15_wfc3_ir_f110w_iebc15/hst_16264_15_wfc3_ir_f110w_iebc15_drz.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
aper_sum_5.0px annulus_median_5.0px aper_bkg_5.0px ... x_idl y_idl
-------------- -------------------- -------------- ... --------- ----------
98.701908 1.222414 96.008154 ... 10.282901 -58.751291
112.858933 1.215701 95.480899 ... 0.099716 -56.988332
101.387021 1.221539 95.939432 ... 11.171638 -55.244084
103.395491 1.223192 96.06924 ... 12.050499 -54.702786
98.175518 1.216526 95.545731 ... 18.3728 -52.885932
105.185721 1.21896 95.736903 ... 20.125268 -52.409859
98.467777 1.222879 96.044673 ... 8.878883 -51.562419
101.178176 1.223382 96.084188 ... 9.447841 -51.376254
100.858931 1.222232 95.993843 ... 9.589212 -51.288441
97.386204 1.211678 95.164984 ... -1.323612 -51.067408
... ... ... ... ... ...
106.700574 1.228211 96.463442 ... 38.894969 98.502383
100.028718 1.227917 96.440353 ... 38.374139 99.52547
97.812416 1.220746 95.877198 ... 26.977886 99.641609
97.602401 1.223992 96.132097 ... 37.761718 100.233421
105.374585 1.219728 95.797236 ... 33.155942 100.494187
97.821326 1.222629 96.025086 ... 36.519167 101.346028
102.604081 1.224514 96.173087 ... 22.996227 101.696774
97.176703 1.223466 96.090817 ... 29.490227 101.683135
106.491719 1.221887 95.966789 ... 28.696314 105.056795
136.421682 1.214225 95.364994 ... 36.206768 105.813417
105.467064 1.229289 96.548109 ... 30.779631 106.393258
Length = 769 rows
Align the second image
The plots outputted here show the various steps used by jhat to determine the true matching sources in the image, and the subsequent correction needed for optimal alignment.
wcs_align = st_wcs_align()
wcs_align.run_all(align_image,
telescope='hst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=.5,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
Warning: Setting aperture radius to twice the psf_fwhm (4.000000)
0 ./mastDownload/hst_16264_12_wfc3_ir_f110w_iebc12.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
-0.000098 0.071191 183.673678 28 2.022576 0.8 False
Keeping 222 out of 222, skippin 0 because of null values in columns d_rot_tmp
median: 2.110833
75.000000 percentile cut: max residual for cut: 0.076543
median: 2.112779
i:00 mean:2.112779(0.002520) stdev:0.032375(0.001777) X2norm:1.00 Nchanged:0 Ngood:166 Nclip:56
mean: 2.114075
i:01 mean:2.114075(0.002887) stdev:0.038415(0.002036) X2norm:1.00 Nchanged:12 Ngood:178 Nclip:44
mean: 2.113588
i:02 mean:2.113588(0.003081) stdev:0.041561(0.002172) X2norm:1.00 Nchanged:5 Ngood:183 Nclip:39
mean: 2.113580
i:03 mean:2.113580(0.003189) stdev:0.043262(0.002249) X2norm:1.00 Nchanged:2 Ngood:185 Nclip:37
mean: 2.110885
i:04 mean:2.110885(0.003397) stdev:0.046577(0.002396) X2norm:1.00 Nchanged:4 Ngood:189 Nclip:33
mean: 2.111600
i:05 mean:2.111600(0.003454) stdev:0.047488(0.002436) X2norm:1.00 Nchanged:1 Ngood:190 Nclip:32
mean: 2.112334
i:06 mean:2.112334(0.003631) stdev:0.050314(0.002561) X2norm:1.00 Nchanged:3 Ngood:193 Nclip:29
mean: 2.110830
i:07 mean:2.110830(0.003748) stdev:0.052203(0.002643) X2norm:1.00 Nchanged:2 Ngood:195 Nclip:27
mean: 2.109305
i:08 mean:2.109305(0.003863) stdev:0.054084(0.002725) X2norm:1.00 Nchanged:2 Ngood:197 Nclip:25
mean: 2.108492
i:09 mean:2.108492(0.003929) stdev:0.055147(0.002771) X2norm:1.00 Nchanged:1 Ngood:198 Nclip:24
slope intercept maxval index d_bestguess fwhm multimax
0.000146 -0.10686 185.08144 5 1.964436 0.8 False
Keeping 198 out of 198, skippin 0 because of null values in columns d_rot_tmp
median: 1.946136
75.000000 percentile cut: max residual for cut: 0.064640
median: 1.947841
i:00 mean:1.947841(0.002509) stdev:0.030420(0.001768) X2norm:1.00 Nchanged:0 Ngood:148 Nclip:50
mean: 1.939984
i:01 mean:1.939984(0.002941) stdev:0.037665(0.002073) X2norm:1.00 Nchanged:17 Ngood:165 Nclip:33
mean: 1.938798
i:02 mean:1.938798(0.003106) stdev:0.040264(0.002190) X2norm:1.00 Nchanged:4 Ngood:169 Nclip:29
mean: 1.937518
i:03 mean:1.937518(0.003415) stdev:0.045050(0.002408) X2norm:1.00 Nchanged:6 Ngood:175 Nclip:23
mean: 1.938289
i:04 mean:1.938289(0.003692) stdev:0.049402(0.002604) X2norm:1.00 Nchanged:5 Ngood:180 Nclip:18
mean: 1.939838
i:05 mean:1.939838(0.004048) stdev:0.055060(0.002855) X2norm:1.00 Nchanged:6 Ngood:186 Nclip:12
mean: 1.939851
i:06 mean:1.939851(0.004173) stdev:0.057063(0.002943) X2norm:1.00 Nchanged:2 Ngood:188 Nclip:10
mean: 1.940734
i:07 mean:1.940734(0.004244) stdev:0.058191(0.002993) X2norm:1.00 Nchanged:1 Ngood:189 Nclip:9
mean: 1.940734
i:08 mean:1.940734(0.004244) stdev:0.058191(0.002993) X2norm:1.00 Nchanged:0 Ngood:189 Nclip:9
*** Note: close plots to continue!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/jwst/datamodels/util.py:234: NoTypeWarning: model_type not found. Opening mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits as a ImageModel
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
*** Note: close plots to continue!
0
Check the Output
The reference image has not changed, but let’s read in the newly aligned image and compare with the original. subsequent correction needed for optimal alignment.
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('drz.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,3)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[2].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[2].set_title('Aligned')
for i in range(3):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Align to Gaia
You can also align each image to the Gaia DR3 catalog, or you could replace the catalog created in step one with your own catalog of the field.
wcs_align.run_all(align_image,
telescope='hst',
outsubdir='mastDownload',
overwrite=True,
d2d_max=.5,
showplots=0,
refcatname='Gaia',
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('drz.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,2)
axes[0].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[1].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('To Align')
axes[1].set_title('Aligned')
for i in range(2):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Warning: Setting aperture radius to twice the psf_fwhm (4.000000)
0 ./mastDownload/hst_16264_12_wfc3_ir_f110w_iebc12.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
INFO: Query finished. [astroquery.utils.tap.core]
Number of stars: 81
### NO propoer motion correction!!!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in sqrt
result = getattr(ufunc, method)(*inputs, **kwargs)
Number of stars after removing nan's: 81
slope intercept maxval index d_bestguess fwhm multimax
0.000537 -0.391553 6.837554 6 1.974222 0.8 False
Keeping 9 out of 9, skippin 0 because of null values in columns d_rot_tmp
median: 1.981504
75.000000 percentile cut: max residual for cut: 0.180525
median: 2.017531
i:00 mean:2.017531(0.051121) stdev:0.114310(0.032998) X2norm:0.91 Nchanged:0 Ngood:6 Nclip:3
mean: 2.001886
i:01 mean:2.001886(0.055165) stdev:0.145953(0.036488) X2norm:1.00 Nchanged:2 Ngood:8 Nclip:1
mean: 2.001886
i:02 mean:2.001886(0.055165) stdev:0.145953(0.036488) X2norm:1.00 Nchanged:0 Ngood:8 Nclip:1
slope intercept maxval index d_bestguess fwhm multimax
0.000146 -0.10686 5.236 6 2.360891 1.0 False
Keeping 8 out of 8, skippin 0 because of null values in columns d_rot_tmp
median: 2.342233
75.000000 percentile cut: max residual for cut: 0.358199
median: 2.342233
i:00 mean:2.342233(0.100460) stdev:0.224635(0.064847) X2norm:0.91 Nchanged:0 Ngood:6 Nclip:2
mean: 2.331019
i:01 mean:2.331019(0.114384) stdev:0.302631(0.075658) X2norm:1.00 Nchanged:2 Ngood:8 Nclip:0
mean: 2.331019
i:02 mean:2.331019(0.114384) stdev:0.302631(0.075658) X2norm:1.00 Nchanged:0 Ngood:8 Nclip:0
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/jwst/datamodels/util.py:234: NoTypeWarning: model_type not found. Opening mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits as a ImageModel
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
Large Offsets
Sometimes the initial images are so poorly aligned, that the code fails. Here we read in the same image as in the first example, and add an additional 3 pixel offset in the wcs.
files = glob.glob('mastDownload/HST/*/*drz.fits')
align_image = files[1]
align_fits = fits.open(align_image)
align_fits['SCI',1].header['CRPIX1']+=3
align_fits['SCI',1].header['CRPIX2']+=3
align_fits.writeto(align_image,overwrite=True)
align_data = fits.open(align_image)['SCI',1].data
ref_y,ref_x = skycoord_to_pixel(star_location,wcs.WCS(ref_fits['SCI',1],ref_fits))
align_y,align_x = skycoord_to_pixel(star_location,wcs.WCS(align_fits['SCI',1],align_fits))
ref_cutout = extract_array(ref_data,(11,11),(ref_x,ref_y))
align_cutout = extract_array(align_data,(11,11),(align_x,align_y))
norm1 = simple_norm(ref_cutout,stretch='log',min_cut=-1,max_cut=200)
norm2 = simple_norm(align_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,2)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[0].tick_params(labelcolor='none',axis='both',color='none')
axes[1].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
wcs_align = st_wcs_align()
try:
wcs_align.run_all(align_image,
telescope='hst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=.5,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
except:
print('Failed for not enough matches!')
Warning: Setting aperture radius to twice the psf_fwhm (4.000000)
0 ./mastDownload/hst_16264_12_wfc3_ir_f110w_iebc12.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
0.002051 -1.49502 8.877268 34 1.149103 0.8 False
Keeping 13 out of 13, skippin 0 because of null values in columns d_rot_tmp
median: 1.142187
75.000000 percentile cut: max residual for cut: 0.473790
median: 1.142187
i:00 mean:1.142187(0.034755) stdev:0.098302(0.023170) X2norm:0.94 Nchanged:0 Ngood:9 Nclip:4
mean: 1.128891
i:01 mean:1.128891(0.033318) stdev:0.094236(0.022212) X2norm:1.00 Nchanged:0 Ngood:9 Nclip:4
slope intercept maxval index d_bestguess fwhm multimax
-0.001123 0.819263 3.661515 19 1.083046 0.8 False
Keeping 4 out of 4, skippin 0 because of null values in columns d_rot_tmp
median: 1.045332
75.000000 percentile cut: max residual for cut: 0.138967
median: 1.095192
i:00 mean:1.095192(0.064008) stdev:0.090521(0.036955) X2norm:0.79 Nchanged:0 Ngood:3 Nclip:1
mean: 1.079986
i:01 mean:1.079986(0.055176) stdev:0.078030(0.031856) X2norm:1.00 Nchanged:0 Ngood:3 Nclip:1
*** Note: close plots to continue!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/jwst/datamodels/util.py:234: NoTypeWarning: model_type not found. Opening mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits as a ImageModel
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
Failed for not enough matches!
This is what a failure looks like (compare to the plots above). There are now a couple of options here. You can increase the d2d_max parameter, which increases the allowed distance between sources being matched in the reference and target images:
wcs_align = st_wcs_align()
wcs_align.run_all(align_image,
telescope='hst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=1,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('drz.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,3)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[2].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[2].set_title('Aligned')
for i in range(3):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Warning: Setting aperture radius to twice the psf_fwhm (4.000000)
0 ./mastDownload/hst_16264_12_wfc3_ir_f110w_iebc12.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
-0.000293 0.213574 151.767497 58 5.058877 0.8 False
Keeping 177 out of 177, skippin 0 because of null values in columns d_rot_tmp
median: 5.100640
75.000000 percentile cut: max residual for cut: 0.091924
median: 5.091393
i:00 mean:5.091393(0.004565) stdev:0.052249(0.003216) X2norm:1.00 Nchanged:0 Ngood:132 Nclip:45
mean: 5.101872
i:01 mean:5.101872(0.005427) stdev:0.067565(0.003825) X2norm:1.00 Nchanged:24 Ngood:156 Nclip:21
mean: 5.103992
i:02 mean:5.103992(0.005907) stdev:0.074955(0.004164) X2norm:1.00 Nchanged:6 Ngood:162 Nclip:15
mean: 5.102721
i:03 mean:5.102721(0.006008) stdev:0.076467(0.004235) X2norm:1.00 Nchanged:1 Ngood:163 Nclip:14
mean: 5.102721
i:04 mean:5.102721(0.006008) stdev:0.076467(0.004235) X2norm:1.00 Nchanged:0 Ngood:163 Nclip:14
slope intercept maxval index d_bestguess fwhm multimax
0.000098 -0.07124 153.147275 54 4.948694 0.8 False
Keeping 162 out of 162, skippin 0 because of null values in columns d_rot_tmp
median: 4.942136
75.000000 percentile cut: max residual for cut: 0.062217
median: 4.946718
i:00 mean:4.946718(0.002966) stdev:0.032492(0.002089) X2norm:1.00 Nchanged:0 Ngood:121 Nclip:41
mean: 4.942254
i:01 mean:4.942254(0.003583) stdev:0.042396(0.002525) X2norm:1.00 Nchanged:20 Ngood:141 Nclip:21
mean: 4.941777
i:02 mean:4.941777(0.003869) stdev:0.046592(0.002727) X2norm:1.00 Nchanged:5 Ngood:146 Nclip:16
mean: 4.942611
i:03 mean:4.942611(0.004236) stdev:0.051878(0.002985) X2norm:1.00 Nchanged:5 Ngood:151 Nclip:11
mean: 4.943574
i:04 mean:4.943574(0.004317) stdev:0.053053(0.003043) X2norm:1.00 Nchanged:1 Ngood:152 Nclip:10
mean: 4.943574
i:05 mean:4.943574(0.004317) stdev:0.053053(0.003043) X2norm:1.00 Nchanged:0 Ngood:152 Nclip:10
*** Note: close plots to continue!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/jwst/datamodels/util.py:234: NoTypeWarning: model_type not found. Opening mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits as a ImageModel
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
*** Note: close plots to continue!
Or you can apply a rough guess for the offset, and then use a smaller d2d_max for matching:
wcs_align = st_wcs_align()
wcs_align.run_all(align_image,
telescope='hst',
outsubdir='mastDownload',
refcat_racol='ra',
refcat_deccol='dec',
refcat_magcol='mag',
refcat_magerrcol='dmag',
overwrite=True,
d2d_max=.25,
xshift=5,
yshift=5,
showplots=2,
refcatname=ref_catname,
histocut_order='dxdy',
sharpness_lim=(0.3,0.9),
roundness1_lim=(-0.7, 0.7),
SNR_min= 3,
dmag_max=1.0,
objmag_lim =(14,24))
aligned_image = os.path.join('mastDownload',os.path.basename(align_image).replace('drz.fits','jhat.fits'))
aligned_fits = fits.open(aligned_image)
aligned_data = fits.open(aligned_image)['SCI',1].data
aligned_y,aligned_x = skycoord_to_pixel(star_location,wcs.WCS(aligned_fits['SCI',1],aligned_fits))
aligned_cutout = extract_array(aligned_data,(11,11),(aligned_x,aligned_y))
norm3 = simple_norm(aligned_cutout,stretch='log',min_cut=-1,max_cut=200)
fig,axes = plt.subplots(1,3)
axes[0].imshow(ref_cutout, origin='lower',
norm=norm1,cmap='gray')
axes[1].imshow(align_cutout, origin='lower',
norm=norm2,cmap='gray')
axes[2].imshow(aligned_cutout, origin='lower',
norm=norm3,cmap='gray')
axes[0].set_title('Reference')
axes[1].set_title('To Align')
axes[2].set_title('Aligned')
for i in range(3):
axes[i].tick_params(labelcolor='none',axis='both',color='none')
plt.show()
Warning: Setting aperture radius to twice the psf_fwhm (4.000000)
0 ./mastDownload/hst_16264_12_wfc3_ir_f110w_iebc12.phot.txt
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py:411: AstropyUserWarning: Input data contains invalid values (NaNs or infs), which were automatically clipped.
warnings.warn('Input data contains invalid values (NaNs or '
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1942: RuntimeWarning: invalid value encountered in log10
phot['mag'] = -2.5*np.log10(phot['aper_sum_bkgsub'])+ee_corr+zp
/Users/jpierel/CodeBase/jhat/jhat/simple_jwst_phot.py:1945: RuntimeWarning: invalid value encountered in log10
phot['magerr'] = 2.5 * np.log10(1.0 + (fluxerr/phot['aper_sum_bkgsub']))
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
*** Note: close plot to continue!
slope intercept maxval index d_bestguess fwhm multimax
-0.000049 0.035596 210.533193 11 5.118499 0.8 False
Keeping 228 out of 228, skippin 0 because of null values in columns d_rot_tmp
median: 5.108273
75.000000 percentile cut: max residual for cut: 0.073336
median: 5.111246
i:00 mean:5.111246(0.002647) stdev:0.034510(0.001866) X2norm:1.00 Nchanged:0 Ngood:171 Nclip:57
mean: 5.113886
i:01 mean:5.113886(0.003071) stdev:0.041878(0.002165) X2norm:1.00 Nchanged:16 Ngood:187 Nclip:41
mean: 5.113807
i:02 mean:5.113807(0.003391) stdev:0.047237(0.002392) X2norm:1.00 Nchanged:8 Ngood:195 Nclip:33
mean: 5.113819
i:03 mean:5.113819(0.003501) stdev:0.049019(0.002470) X2norm:1.00 Nchanged:2 Ngood:197 Nclip:31
mean: 5.113098
i:04 mean:5.113098(0.003558) stdev:0.049936(0.002509) X2norm:1.00 Nchanged:1 Ngood:198 Nclip:30
mean: 5.111626
i:05 mean:5.111626(0.003673) stdev:0.051807(0.002590) X2norm:1.00 Nchanged:2 Ngood:200 Nclip:28
mean: 5.110125
i:06 mean:5.110125(0.003788) stdev:0.053702(0.002672) X2norm:1.00 Nchanged:2 Ngood:202 Nclip:26
mean: 5.109359
i:07 mean:5.109359(0.003846) stdev:0.054669(0.002713) X2norm:1.00 Nchanged:1 Ngood:203 Nclip:25
mean: 5.109359
i:08 mean:5.109359(0.003846) stdev:0.054669(0.002713) X2norm:1.00 Nchanged:0 Ngood:203 Nclip:25
slope intercept maxval index d_bestguess fwhm multimax
0.000146 -0.10686 187.297193 5 4.964436 0.8 False
Keeping 203 out of 203, skippin 0 because of null values in columns d_rot_tmp
median: 4.946334
75.000000 percentile cut: max residual for cut: 0.066828
median: 4.947336
i:00 mean:4.947336(0.002530) stdev:0.031086(0.001783) X2norm:1.00 Nchanged:0 Ngood:152 Nclip:51
mean: 4.940657
i:01 mean:4.940657(0.002958) stdev:0.038228(0.002086) X2norm:1.00 Nchanged:16 Ngood:168 Nclip:35
mean: 4.938915
i:02 mean:4.938915(0.003185) stdev:0.041767(0.002245) X2norm:1.00 Nchanged:5 Ngood:173 Nclip:30
mean: 4.937633
i:03 mean:4.937633(0.003487) stdev:0.046521(0.002459) X2norm:1.00 Nchanged:6 Ngood:179 Nclip:24
mean: 4.939127
i:04 mean:4.939127(0.003714) stdev:0.050106(0.002619) X2norm:1.00 Nchanged:4 Ngood:183 Nclip:20
mean: 4.939913
i:05 mean:4.939913(0.004011) stdev:0.054843(0.002828) X2norm:1.00 Nchanged:5 Ngood:188 Nclip:15
mean: 4.939926
i:06 mean:4.939926(0.004134) stdev:0.056835(0.002916) X2norm:1.00 Nchanged:2 Ngood:190 Nclip:13
mean: 4.940799
i:07 mean:4.940799(0.004205) stdev:0.057955(0.002965) X2norm:1.00 Nchanged:1 Ngood:191 Nclip:12
mean: 4.940799
i:08 mean:4.940799(0.004205) stdev:0.057955(0.002965) X2norm:1.00 Nchanged:0 Ngood:191 Nclip:12
*** Note: close plots to continue!
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.name the following error occurred:
'WFC3' is not one of ['NIRCAM', 'NIRSPEC', 'MIRI', 'TFI', 'FGS', 'NIRISS', 'ANY', 'N/A']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Instrument used to acquire the data'),
('type', 'string'),
('enum',
['NIRCAM',
'NIRSPEC',
'MIRI',
'TFI',
'FGS',
'NIRISS',
'ANY',
'N/A']),
('fits_keyword', 'INSTRUME'),
('blend_table', True)])
On instance:
'WFC3'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.instrument.detector the following error occurred:
'IR' is not one of ['NRCA1', 'NRCA2', 'NRCA3', 'NRCA4', 'NRCALONG', 'NRCB1', 'NRCB2', 'NRCB3', 'NRCB4', 'NRCBLONG', 'NRS1', 'NRS2', 'ANY', 'MIRIMAGE', 'MIRIFULONG', 'MIRIFUSHORT', 'NIS', 'GUIDER1', 'GUIDER2', 'N/A', 'MULTIPLE']
Failed validating 'enum' in schema:
OrderedDict([('title', 'Name of detector used to acquire the data'),
('type', 'string'),
('enum',
['NRCA1',
'NRCA2',
'NRCA3',
'NRCA4',
'NRCALONG',
'NRCB1',
'NRCB2',
'NRCB3',
'NRCB4',
'NRCBLONG',
'NRS1',
'NRS2',
'ANY',
'MIRIMAGE',
'MIRIFULONG',
'MIRIFUSHORT',
'NIS',
'GUIDER1',
'GUIDER2',
'N/A',
'MULTIPLE']),
('fits_keyword', 'DETECTOR'),
('blend_table', True),
('blend_rule', 'multi')])
On instance:
'IR'
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/stdatamodels/validate.py:38: ValidationWarning: While validating meta.subarray.name the following error occurred:
False is not of type 'string'
Failed validating 'type' in schema:
OrderedDict([('title', 'Subarray used'),
('type', 'string'),
('anyOf',
[{'enum': ['8X8',
'32X32',
'128X128',
'2048X64',
'SUB128CNTR',
'SUB128DIAG',
'SUB128LL',
'SUB32CNTR',
'SUB32DIAG',
'SUB32LL',
'SUB8CNTR',
'SUB8DIAG',
'SUB8LL',
'SUBIDSTRIPCENTER',
'SUBIDSTRIPLL',
'SUBTUNE32CENTERG1',
'SUBTUNE32CENTERG2',
'SUBTUNE32LLG1',
'SUBTUNE32LLG2']},
{'enum': ['BRIGHTSKY',
'MASK1065',
'MASK1140',
'MASK1550',
'MASKLYOT',
'SLITLESSPRISM',
'SUB128',
'SUB256',
'SUB64',
'SUBPRISM']},
{'enum': ['FULLP',
'MASK210R',
'MASK335R',
'MASK430R',
'MASKLWB',
'MASKSWB',
'SUB160',
'SUB160P',
'SUB320',
'SUB320A335R',
'SUB320A430R',
'SUB320ALWB',
'SUB320B335R',
'SUB320B430R',
...
warnings.warn(errmsg, ValidationWarning)
/Users/jpierel/miniconda3/envs/tweakreg/lib/python3.10/site-packages/jwst/datamodels/util.py:234: NoTypeWarning: model_type not found. Opening mastDownload/HST/hst_16264_12_wfc3_ir_f110w_iebc12/hst_16264_12_wfc3_ir_f110w_iebc12_drz.fits as a ImageModel
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
*** Note: close plots to continue!
Total running time of the script: ( 0 minutes 57.951 seconds)
Gallery generated by Sphinx-Gallery
API Documentation
jhat
st_wcs_align
Created on Thu Apr 21 14:32:42 2022
@author: arest, bhilbert, mcorrenti, acanipe, jpierel
- class jhat.st_wcs_align.st_wcs_align[source]
Main class for alignment.
- outrootdir
output root directory. The output directoy is the output root directory + the outsubdir if not None
- Type:
- telescope
If None, then telescope is determined automatically from the filename (“jw*” and “hst*” for JWST and HST, respectively)
- Type:
- skip_if_exists
Skip doing the analysis of a given input image if the cal file already exists, assuming the full analysis has been already done
- Type:
- refcat_magcol
mag column of reference catalog. If None and not one of the default refcats like gaia, then 3rd column is used
- Type:
- refcat_colorcol
color column of reference catalog. If None, then not used
- Type:
str:
- refcat_pmflag
Apply the proper motion correction (only for catalogs it is applicable, e.g., gaia
- Type:
- refcat_pmmedian
Apply the MEDIAN proper motion correction (only for catalogs it is applicable, e.g., gaia
- Type:
- photfilename
photometry output filename. if “auto”, the fits in the image filename is substituted with phot.txt
- Type:
- load_photcat_if_exists
If the photometric catalog file already exists, skip recreating it.
- Type:
- rematch_refcat
if –load_photcat_if_exists and the photcat already exists, load the photcat, but rematch with refcat
- Type:
- objmag_lim
limits on mag, the magnitude of the objects in the image (iterable of length 2)
- refmag_lim
limits on refcat_mainfilter, the magnitude of the reference catalog (iterable of length 2)
- slope_min
minimum slope for linear correction applied to dx/dy. This effectively accounts for rotation. slopes go from slopemin to -slopemin
- Type:
- Nbright
Use only Nbright brightest objects in image that are matched to refcat for alignment
- Type:
- histocut_order
histocut_order defines whether the histogram cut is first done for dx or first for dy (choices are ‘dxdy’ or ‘dydx’)
- Type:
- xshift
added to the x coordinate before calculating ra,dec (only impacts ra,dec, not x). This can be used to correct for large shifts before matching!
- Type:
- yshift
added to the y coordinate before calculating ra,dec (only impacts ra,dec, not y). This can be used to correct for large shifts before matching!
- Type:
- iterate_with_xyshifts
After the first histogram fit, redo the match with refcat with x/yshift=median(dx/dy) and redo histofit. Use this if the offsets are big, since the second iteration will give you better matching with the refcat
- Type:
- showplots
showplots=1: most important plots. showplots=2: all plots (debug/test/finetune)
- Type:
- saveplots
saveplots=1: most important plots. saveplots=2: all plots (debug/test/finetune)
- Type:
- rough_cut_px_min
first rough cut: best d_rotated+-rough_cut_pix. This is the lower limit for rough_cut
- Type:
- rough_cut_px_max
first rough cut: best d_rotated+-rough_cut_pix. This is the upper limit for rough_cut
- Type:
- d_rotated_Nsigma
Nsigma for sigma cut of d_rotated. If 0.0, then 3-sigma cut is skipped
- Type:
simple_jwst_phot
Created on Wed Apr 27 09:21:15 2022
@author: arest, jpierel, mcorrenti
This is class wrapper around doing simple photometry on a single JWST image
- class jhat.simple_jwst_phot.hst_photclass(psf_fwhm=2, aperture_radius=None, verbose=0)[source]
The photometry class for HST images.
- aperture_phot(filt=None, pupil=None, radii_Nfwhm=None, radius_Nfwhm_sky_in=None, radius_Nfwhm_sky_out=None, radius_Nfwhm_for_mag=None, primaryhdr=None, scihdr=None)[source]
Aperture photometry routine for HST.
- Returns:
table_aper
- Return type:
astropy.table.Table
- match_refcat(max_sep=1.0, borderpadding=40, refcatshort=None, ixs_obj=None, ixs_refcat=None)[source]
Matches the photometry catalog to the reference catalog.
- Parameters:
max_sep (float) – Maximum separation between sources in arcseconds
borderpadding (float) – Pixel separation required from border of image
refcatshort (string, optional) – Short name of reference catalog that is used as prefix for the column names. The default is None. If None, then refcatshort is set to self.refcat.short
indices (list) – The indices to access the photometry catalog, default None (use the full catalog)
- Return type:
None.
- class jhat.simple_jwst_phot.jwst_photclass(verbose=0)[source]
The photometry class for JWST images.
- aperture_phot(filt=None, pupil=None, radii_Nfwhm=None, radius_Nfwhm_sky_in=None, radius_Nfwhm_sky_out=None, radius_Nfwhm_for_mag=None, primaryhdr=None, scihdr=None)[source]
Aperture photometry routine for HST.
- Returns:
table_aper
- Return type:
astropy.table.Table
- match_refcat(max_sep=1.0, borderpadding=40, refcatshort=None, ixs_obj=None, ixs_refcat=None)[source]
Matches the photometry catalog to the reference catalog.
- Parameters:
max_sep (float) – Maximum separation between sources in arcseconds
borderpadding (float) – Pixel separation required from border of image
refcatshort (string, optional) – Short name of reference catalog that is used as prefix for the column names. The default is None. If None, then refcatshort is set to self.refcat.short
indices (list) – The indices to access the photometry catalog, default None (use the full catalog)
- Return type:
None.
st_wcs_align_batch
Created on Mon Apr 25 09:39:07 2022
@author: arest
Citing JHAT
Please use the following bibcode citation for JHAT:
@MISC{Rest2023_jhat,
author = {{Rest}, Armin and {Pierel}, Justin and {Correnti}, Matteo and {Hilbert}, Bryan and {Canipe}, Alicia and {Sunnquist}, Ben and {Fox}, Ori,
title = "{The JWST HST Alignment Tool (JHAT)}",
howpublished = {Zenodo},
year = 2023,
month = may,
eid = {10.5281/zenodo.7892935},
doi = {10.5281/zenodo.7892935},
version = {v2},
publisher = {Zenodo},
}
Primary Contributors
Matteo Correnti
Alicia Canipe
Bryan Hilbert
Ben Sunnquist