def test_DEC(coords, coord_types, ra, dec, new_dec, out_dec):
my_wcs = makeWCS(coords=coords, coord_types=coord_types)
image = AstroImage(wcs=my_wcs)
assert image.ra == ra
assert image.dec == dec
image.dec = new_dec
assert image.dec == out_dec
def test_addHistory(history):
image = AstroImage()
for item in history:
image.addHistory(item)
for item in history:
assert item in image.history
for item in image.history:
assert item in history
def test_PA(pa_in, pa, scale, new_pa, pa_out):
image = AstroImage(pa=pa_in, scale=scale)
assert abs(image.pa - pa) < 1.e-4
assert abs(image.xscale - scale[0]) < 1.e-4
assert abs(image.yscale - scale[1]) < 1.e-4
image.pa = new_pa
assert abs(image.pa - pa_out) < 1.e-4
assert abs(image.xscale - scale[0]) < 1.e-4
assert abs(image.yscale - scale[1]) < 1.e-4
def test_crop(input, size, offset, result):
im1 = AstroImage(data=input)
im1.crop(size[0], size[1], offset[0], offset[1])
verifyData(im1.data, result)
def test_rescale(input, scale, result):
im1 = AstroImage(data=input)
im1.rescale(scale)
verifyData(im1.data, result)
im1.rescale([1., 1.])
verifyData(im1.data, input)
def test_addWithAlignment(in1, in2, inputs, result):
im1 = AstroImage(data=in1)
im2 = AstroImage(data=in2, **inputs)
im1.addWithAlignment(im2)
verifyData(im1.data, result)
def test_addWithOffset(in1, in2, offset, inputs, result):
im1 = AstroImage(data=in1, **inputs)
im2 = AstroImage(data=in2)
im1.addWithOffset(im2, offset[0], offset[1])
verifyData(im1.data, result)
def test_rotate(input, angle, output):
image = AstroImage(data=input)
image.rotate(angle)
verifyData(image.data, output)
def test_convolve(input, kernel, result):
input_image = AstroImage(data=input)
kernel_image = AstroImage(data=kernel)
input_image.convolve(kernel_image)
verifyData(input_image.data, result)
S3dir = os.path.join(PPOL_dir, 'S3_Astrometry')
# This is the location where all pyPol data will be saved
pyPol_data = 'C:\\Users\\Jordan\\FITS_data\\Mimir_data\\pyPol_data'
# This is the directory where the 2MASS tiles of the targets have been saved
# Go to "http://hachi.ipac.caltech.edu/" to download 2MASS tiles
TMASSdir = "C:\\Users\\Jordan\\Libraries\\python\\Mimir_pyPol\\2MASSimages"
# Setup new directory for background subtracted data
bkgSubDir = os.path.join(pyPol_data, 'bkgSubtracted')
if (not os.path.isdir(bkgSubDir)):
os.mkdir(bkgSubDir, 0o755)
# Read in Kokopelli mask generated in previous step
kokopelliMask = (AstroImage('kokopelliMask.fits').arr != 0)
# Read in the indexFile data and select the filenames
indexFile = os.path.join(pyPol_data, 'reducedFileIndex.csv')
fileIndex = Table.read(indexFile, format='csv')
# Grab the file basenames for later use
fileIndexFileNames = np.array(
[os.path.basename(file1) for file1 in fileIndex['Filename'].data])
# Modify the fileIndex to include rejections by residual value
if 'Background Cut' not in fileIndex.keys():
fileIndex.add_column(
Column(name='Background Cut', data=np.repeat(0, len(fileIndex))))
# Determine which parts of the fileIndex pertain to science images
def test_updateHeader(header_in, header_out):
image = AstroImage()
for k, v in header_in:
image.updateHeader(k, v)
for k, v in header_out:
assert image.header[k] == v
def test_creation(inputs, results):
image = AstroImage(**inputs)
data = numpy.zeros((image.ysize, image.xsize))
verifyParameters(image, results)
verifyData(image.data, data)
print('\tTarget : {0}'.format(thisTarget))
print('\tWaveband : {0}'.format(thisWaveband))
# Read in the polAng image
fileCheck = True
polAngImgs = []
polAngs = []
for polAng in range(0, 601, 200):
fileName = os.path.join(
polAngDir, '_'.join([thisTarget, thisWaveband,
str(polAng)]) + '.fits')
# If the file exists, then read it into a dictionary
if os.path.isfile(fileName):
polAngs.append(polAng)
polAngImgs.append(AstroImage(fileName))
else:
fileCheck = False
# Check that all the necessarry files are present
if not fileCheck:
print("\tSome required Polaroid Angle files are missing.")
continue
# Use the "align_stack" method to align the newly created image list
print('\nAligning images\n')
polAngImgs = image_tools.align_images(polAngImgs,
mode='cross_correlate',
subPixel=True,
padding=np.nan)
# Setup new directory for polarimetry data
polarimetryDir = os.path.join(pyPol_data, 'Polarimetry')
if (not os.path.isdir(polarimetryDir)):
os.mkdir(polarimetryDir, 0o755)
stokesDir = os.path.join(polarimetryDir, 'stokesImgs')
if (not os.path.isdir(stokesDir)):
os.mkdir(stokesDir, 0o755)
# Setup Mimir detector properties
read_noise = 19.0 # electrons
effective_gain = 8.21 # electrons/ADU
# Read in the Kokopelli mask
if os.path.isfile('kokopelliMask.fits'):
kokopelliMask = AstroImage('kokopelliMask.fits')
else:
print('Kokopelli Mask is not built... go back and build it with step 2.')
pdb.set_trace()
################################################################################
# READ IN CALIBRATION DATA
################################################################################
# Read in the instrumental U and Q images (and associated uncertainties)
# First do H-band
# Q images
H_calDir = os.path.join(PPOLcodeDir, 'H-Pol_Pinst_Default')
H_Q_instFile = os.path.join(H_calDir, 'q_inst.fits')
H_sQ_instFile = os.path.join(H_calDir, 'q_sig_inst.fits')
H_Q_instImg = AstroImage(H_Q_instFile)
H_sQ_instImg = AstroImage(H_sQ_instFile)
H_Q_instImg.sigma = H_sQ_instImg.arr.copy()
def test_bin(input, bin, result):
im1 = AstroImage(data=input)
im1.bin(bin[0], bin[1])
verifyData(im1.data, result)
def test_addCatalogue(inputs, catalogue, final):
image = AstroImage(**inputs)
out_cat = image.addCatalogue(catalogue)
if os.path.exists(out_cat):
os.remove(out_cat)
verifyData(image.data, final)
TMASSdir = "C:\\Users\\Jordan\\Libraries\\python\\Mimir_pyPol\\2MASSimages"
# This dictionary specifies the angle range (degrees East of North) to include
# in producing radial-brightness proflies. This is especially for NGC2023
angleDict = {'NGC2023': (0, 360)}
# This is the location of the supersky images
bkgImagesDir = os.path.join(pyPol_data, 'bkgImages')
# Setup new directory for background subtracted data
bkgSubDir = os.path.join(pyPol_data, 'bkgSubtracted')
if (not os.path.isdir(bkgSubDir)):
os.mkdir(bkgSubDir, 0o755)
# Read in Kokopelli mask generated in previous step
kokopelliMask = (AstroImage('kokopelliMask.fits').arr != 0)
# Read in the indexFile data and select the filenames
indexFile = os.path.join(pyPol_data, 'reducedFileIndex.csv')
fileIndex = Table.read(indexFile, format='csv')
# Check if the fileIndex already has a "Background" column.
# If not, then add one.
if 'Background' not in fileIndex.keys():
fileIndex.add_column(Column(name='Background',
data = np.repeat(-99.9, len(fileIndex))))
# Determine which parts of the fileIndex pertain to science images
useFiles = np.where(fileIndex['Use'] == 1)
badFiles = np.where(fileIndex['Use'] == 0)
# Begin by initalizing some arrays to store the image classifications
telRA = []
telDec = []
name = []
waveBand = []
HWPang = []
binType = []
expTime = []
night = []
fileCounter = 0
percentage = 0
#Loop through each file in the fileList variable
for file in fileList:
# Read in the image
tmpImg = AstroImage(file)
# Grab the RA and Dec from the header
# Parse the pointing for this file
tmpRA = coord.Angle(tmpImg.header['TELRA'], unit=u.hour)
tmpDec = coord.Angle(tmpImg.header['TELDEC'], unit=u.degree)
telRA.append(tmpRA.degree)
telDec.append(tmpDec.degree)
# Classify each file type and binning
tmpName = tmpImg.header['OBJECT']
if len(tmpName) < 1:
tmpName = 'blank'
name.append(tmpName)
# Parse the HWP number
