def match_pinholes(translationPass,holeSpacingPass,barrelCenterPass,barrelAmountPass,rotationAnglePass,writeoutString='test',plotTitleString='test',plot=True):
# write the git hash
write_hash('git_hash_match_pinholes.txt')
# read in median image of pinholes
hdul = fits.open(calibrated_pinholes_data_stem)
imagePinholes = hdul[0].data.copy()
# make model pinhole locations: distorted and undistorted
coordsModel_d, coordsModel_not_d = find_pinhole_centroids.put_down_grid_guesses(
translationPass,
holeSpacingPass,
barrelCenterPass,
barrelAmountPass,
rotationAnglePass)
# find empirical pinhole locations
coordsEmpirical = find_pinhole_centroids.find_psf_centers(
imagePinholes,
20.,
30000.)
# option for adding in some additional points manually
##xCoordsMissed = [71.774,697.353,1460.66]
##yCoordsMissed = [1267.57,1404.06,737.932]
'''
xCoordsFound = np.concatenate((xCoordsFoundAutomated,
xCoordsMissed),
axis=0)
yCoordsFound = np.concatenate((yCoordsFoundAutomated,
yCoordsMissed),
axis=0)
'''
# match and sort the model coordinates (distorted and undistorted) with the empirical pinhole coordinates
coordsModel_d_matched, coordsModel_not_d_matched, coordsEmpirical_matched = find_pinhole_centroids.match_model_empirical(
coordsModel_d,
coordsModel_not_d,
coordsEmpirical,
imagePinholes,
plotTitleString,
plot=plot)
# pickle
picklename='pinhole_coordinates_'+writeoutString+'.pkl'
fo=open(picklename,'wb')
pickle.dump({'coordsModel_d_matched':coordsModel_d_matched,
'coordsModel_not_d_matched':coordsModel_not_d_matched,
'coordsEmpirical_matched':coordsEmpirical_matched},
fo)
fo.close()
print('------------------------------')
print('Saved pinhole grid info to:')
print(picklename)
print('------------------------------')
def find_stars(dateString, number_of_dithers):
star_coords_every_dither = {
} # initialize dictionary to hold the coords of found stars
# initialize dictionary to contain x,y of stars in pixel space
dictStars = {}
for ditherPos in range(0, number_of_dithers):
print("------------------------------")
print("Finding star positions in dither position " + str(ditherPos) +
" ...")
# read in image and header
imageMedian, header = fits.getdata(
calibrated_trapezium_data_stem + 'step04_ditherMedians/lm_' +
dateString + '_dither_' + '%02i' % ditherPos + '.fits',
0,
header=True)
# find star locations; input parameters may need some fine-tuning to get good results
coordsAsterism = find_pinhole_centroids.find_psf_centers(
imageMedian, 20., 800.)
coordsAsterismBright = find_pinhole_centroids.find_psf_centers(
imageMedian, 20.,
3000.) # this is just for guiding the eye in identifying the stars
if (len(coordsAsterism) >
1): # if there is >1 star, there are >=1 baselines
'''
import ipdb; ipdb.set_trace()
star_coords_every_dither[keyName] = np.transpose([coordsAsterism[:,0], coordsAsterism[:,1]])
'''
indicesArray = np.arange(np.size(coordsAsterism[:, 0]))
print("------------------------------")
print(
"Please check the plot and note the indices from among the printed star positions which are TRUE "
"stars and are also IDENTIFIABLE (i.e., their RA and DEC is known. Then close the plot to continue."
)
print("------------------------------")
print('Index, x, y:')
dataFrame = pd.DataFrame(coordsAsterism, columns=['[x]', '[y]'])
print(dataFrame)
#print(np.transpose([indicesArray,coordsAsterism[:,0],coordsAsterism[:,1]]))
# plots for cross-checking found centroids with true stars (save plots manually as desired)
plt.imshow(imageMedian, origin="lower", cmap="gray")
plt.scatter(coordsAsterism[:, 0], coordsAsterism[:, 1])
#import ipdb; ipdb.set_trace()
[
plt.annotate(str(dataFrame.index.values[i]),
(coordsAsterism[i, 0], coordsAsterism[i, 1]),
fontsize=20,
color="white")
for i in range(len(dataFrame.index.values))
] # put index numbers next to star
plt.scatter(coordsAsterismBright[:, 0],
coordsAsterismBright[:, 1],
s=60,
color="yellow")
plt.title(
"LMIRCam Trapezium observation, UT 2017 Nov 6\ndither position "
+ "%02i" % ditherPos)
plt.show()
# which coordinates should be pickled?
print("------------------------------")
print(
'Type the indices, separated by spaces, of the elements that are good'
)
goodPts = [int(x) for x in input().split()] # user inputs
print("------------------------------")
print(
"Type the star names (in the above order, separated by spaces) as per the naming convention in star_coords.csv"
)
namesGoodPts = [str(x) for x in input().split()] # user inputs
print("------------------------------")
# append the coordinate info
keyName = "dither_pos_" + "%02i" % ditherPos # name of dictionary key for this dither
dictStars[keyName] = [goodPts, namesGoodPts, dataFrame]
else:
print('Did not find >=1 stellar pairs!'
) # number of baselines is zero
print("------------------------------")
# pickle
pickle.dump(dictStars, open("identified_stars_" + dateString + ".p", "wb"))
print("Star dictionary pickled as " + "identified_stars_" + dateString +
".p")
print("------------------------------")
def find_stars(dateString,number_of_dithers):
star_coords_every_dither = {} # initialize dictionary to hold the coords of found stars
# initialize dictionary to contain x,y of stars in pixel space
dictStars = {}
# obtain the list of files which have been dewarped and de-rotated
# according to the PA in the FITS headers
asterism_frames_directory_retrieve = str(config["data_dirs"]["DIR_ASTERISM_DEROT"])
asterism_frames_post_derot_names = list(glob.glob(os.path.join(asterism_frames_directory_retrieve, "*.fits")))
for ditherPos in range(0,len(asterism_frames_post_derot_names)):
print("------------------------------")
print("Finding star positions in dither position "+str(ditherPos)+" ...")
# read in image and header
imageMedian, header = fits.getdata(asterism_frames_post_derot_names[ditherPos],
0, header=True)
# find star locations; input parameters may need some fine-tuning to get good results
coordsAsterism = find_pinhole_centroids.find_psf_centers(imageMedian,20.,800.)
coordsAsterismBright = find_pinhole_centroids.find_psf_centers(imageMedian,20.,3000.) # this is just for guiding the eye in identifying the stars
if (len(coordsAsterism)>1): # if there is >1 star, there are >=1 baselines
'''
import ipdb; ipdb.set_trace()
star_coords_every_dither[keyName] = np.transpose([coordsAsterism[:,0], coordsAsterism[:,1]])
'''
indicesArray = np.arange(np.size(coordsAsterism[:,0]))
print("------------------------------")
print("Please check the plot and note the indices from among the printed star positions which are TRUE "
"stars and are also IDENTIFIABLE (i.e., their RA and DEC is known. Then close the plot to continue.")
print("------------------------------")
print('Index, x, y:')
dataFrame = pd.DataFrame(coordsAsterism, columns=['[x]','[y]'])
print(dataFrame)
#print(np.transpose([indicesArray,coordsAsterism[:,0],coordsAsterism[:,1]]))
# plots for cross-checking found centroids with true stars (save plots manually as desired)
plt.imshow(imageMedian, origin="lower", cmap="gray", vmin=0, vmax=1500)
plt.scatter(coordsAsterism[:,0], coordsAsterism[:,1], color="yellow", label="Questionable detections")
#import ipdb; ipdb.set_trace()
# put index numbers next to star
[plt.annotate(str(dataFrame.index.values[i]), (coordsAsterism[i,0], coordsAsterism[i,1]), fontsize=20, color="white") for i in range(len(dataFrame.index.values))]
plt.scatter(coordsAsterismBright[:,0], coordsAsterismBright[:,1], s=60, color="orange", label="Strong detections")
plt.title("LMIRCam Trapezium observation, "+dateString+
"\n"+str(os.path.basename(asterism_frames_post_derot_names[ditherPos])))
plt.legend()
plt.show()
# which coordinates should be pickled?
print("------------------------------")
print('Type the indices, separated by spaces, of the elements that are good')
goodPts = [int(x) for x in input().split()] # user inputs
print("------------------------------")
print("Type the star names (in the above order, separated by spaces) as per the naming convention in star_coords.csv")
namesGoodPts = [str(x) for x in input().split()] # user inputs
print("------------------------------")
# append the coordinate info
keyName = "dither_pos_"+"%02i"%ditherPos # name of dictionary key for this dither
dictStars[keyName] = [goodPts, namesGoodPts, dataFrame]
else:
print('Did not find >=1 stellar pairs!') # number of baselines is zero
print("------------------------------")
# pickle
pickle.dump(dictStars, open("identified_stars_"+dateString+".p", "wb"))
print("Star dictionary pickled as "+"identified_stars_"+dateString+".p")
print("------------------------------")
from astrom_lmircam_soln import dewarp
from astrom_lmircam_soln import make_barb_plot
from astropy.io import fits
import matplotlib.pyplot as plt
import pickle
#####################################################################
# RETRIEVE MEDIAN PINHOLE GRID IMAGE, FIND PINHOLE CENTERS
# (note this section will require multiple run-throughs until optimal parameters are specified: the ideal grid coords, the missed pinhole coords, etc.)
hdul = fits.open(calibrated_pinholes_data_stem+'pinhole_image_median_vignettingBlocked.fits') # median image of pinholes, with vignetted regions masked
imagePinholes = hdul[0].data.copy()
xCoordsIdealFullGrid, yCoordsIdealFullGrid = find_pinhole_centroids.put_down_grid_guesses(48.0,0.65) # sets down an 'ideal' set of pinholes made to match the real pinholes as closely as possible
xCoordsFoundAutomated, yCoordsFoundAutomated = find_pinhole_centroids.find_psf_centers(imagePinholes,20.,50000.) # finds the actual st of pinholes
# manually-found locations of pinholes that the above routine missed
xCoordsMissed = [71.774,697.353,1460.66]
yCoordsMissed = [1267.57,1404.06,737.932]
# remove subset of pinhole guesses that are in the vignetted region
xCoordsIdeal = xCoordsIdealFullGrid[np.where(np.logical_or(xCoordsIdealFullGrid>36,
yCoordsIdealFullGrid>760))]
yCoordsIdeal = yCoordsIdealFullGrid[np.where(np.logical_or(xCoordsIdealFullGrid>36,
yCoordsIdealFullGrid>760))]
# concatenate arrays
xCoordsFound = np.concatenate((xCoordsFoundAutomated,
xCoordsMissed),
axis=0)
import matplotlib.pyplot as plt
import pickle
#####################################################################
# RETRIEVE MEDIAN PINHOLE GRID IMAGE, FIND PINHOLE CENTERS
# (note this section will require multiple run-throughs until optimal parameters are specified: the ideal grid coords, the missed pinhole coords, etc.)
hdul = fits.open(calibrated_pinholes_data_stem +
'pinhole_image_median_vignettingBlocked.fits'
) # median image of pinholes, with vignetted regions masked
imagePinholes = hdul[0].data.copy()
xCoordsIdealFullGrid, yCoordsIdealFullGrid = find_pinhole_centroids.put_down_grid_guesses(
48.0, 0.65
) # sets down an 'ideal' set of pinholes made to match the real pinholes as closely as possible
xCoordsFoundAutomated, yCoordsFoundAutomated = find_pinhole_centroids.find_psf_centers(
imagePinholes, 20., 50000.) # finds the actual st of pinholes
# manually-found locations of pinholes that the above routine missed
xCoordsMissed = [71.774, 697.353, 1460.66]
yCoordsMissed = [1267.57, 1404.06, 737.932]
# remove subset of pinhole guesses that are in the vignetted region
xCoordsIdeal = xCoordsIdealFullGrid[np.where(
np.logical_or(xCoordsIdealFullGrid > 36, yCoordsIdealFullGrid > 760))]
yCoordsIdeal = yCoordsIdealFullGrid[np.where(
np.logical_or(xCoordsIdealFullGrid > 36, yCoordsIdealFullGrid > 760))]
# concatenate arrays
xCoordsFound = np.concatenate((xCoordsFoundAutomated, xCoordsMissed), axis=0)
yCoordsFound = np.concatenate((yCoordsFoundAutomated, yCoordsMissed), axis=0)
import photutils
from photutils import DAOStarFinder
number_of_dithers = 18 # number of dither positions over the asterism
star_coords_every_dither = {} # initialize dictionary to hold the coords of found stars
for ditherPos in range(0,number_of_dithers):
print("Finding star positions in dither position "+str(ditherPos)+" ...")
# read in image and header
imageMedian, header = fits.getdata(calibrated_trapezium_data_stem+
'step04_ditherMedians/median_dither_'+'%02i'%ditherPos+'.fits',
0, header=True)
keyName = "dither_pos_"+str(ditherPos)
# find star locations; input parameters may need some find-tuning to get good results
xCoordsAsterism, yCoordsAsterism = find_pinhole_centroids.find_psf_centers(imageMedian,20.,15000.)
star_coords_every_dither[keyName] = np.transpose([xCoordsAsterism, yCoordsAsterism])
print(np.transpose([xCoordsAsterism,yCoordsAsterism]))
print("Please check the plot and note true positives among the printed star positions. Close the plot to continue.")
# plots for cross-checking found centroids with true stars (save plots manually as desired)
plt.imshow(imageMedian, origin="lower")
plt.scatter(xCoordsAsterism, yCoordsAsterism)
plt.title("LMIRCam Trapezium observation, UT 2016 Nov 12\ndither position "+"%02i"%ditherPos)
plt.show()
# optional: save centroids as a FYI pickle binary file (remember: some coordinates are false positives! need to manually check)
#pickle.dump(star_coords_every_dither, open("centroid_coords_trapezium_ut_2016_nov_12.p", "wb"))