def test_filter_image():
# data = np.random.random((30, 30), dtype=np.float32)
fname = 'tests/test_files/1904-66_SIN.fits'
outbase = 'dlme'
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
# hdu = fits.getheader(fname)
# shape = hdu[0]['NAXIS1'], hdu[0]['NAXIS2']
BANE.filter_image(fname, step_size=[10, 10], box_size=[100, 100], cores=1, out_base=outbase)
if not os.path.exists(rms):
raise AssertionError()
os.remove(rms)
if not os.path.exists(bkg):
raise AssertionError()
os.remove(bkg)
BANE.filter_image(fname, cores=2, out_base=outbase, twopass=True, compressed=True)
if not os.path.exists(rms):
raise AssertionError()
os.remove(rms)
if not os.path.exists(bkg):
raise AssertionError()
os.remove(bkg)
def test_quantitative():
"""Test that the images are equal to a pre-calculated version"""
fbase = 'tests/test_files/1904-66_SIN'
outbase = 'dlme'
BANE.filter_image(fbase+'.fits', out_base=outbase, cores=2, nslice=2)
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
ref_rms = fbase + '_rms.fits'
ref_bkg = fbase + '_bkg.fits'
r1 = fits.getdata(rms)
r2 = fits.getdata(ref_rms)
b1 = fits.getdata(bkg)
b2 = fits.getdata(ref_bkg)
os.remove(rms)
os.remove(bkg)
if not np.allclose(r1, r2, atol=0.01, equal_nan=True):
raise AssertionError("rms is wrong")
if not np.allclose(b1, b2, atol=0.003, equal_nan=True):
raise AssertionError("bkg is wrong")
return
def test_quantitative():
"""Test that the images are equal to a pre-calculated version"""
fbase = 'tests/test_files/1904-66_SIN'
outbase = 'dlme'
BANE.filter_image(fbase+'.fits', out_base=outbase, cores=2, nslice=2)
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
ref_rms = fbase + '_rms.fits'
ref_bkg = fbase + '_bkg.fits'
r1 = fits.getdata(rms)
r2 = fits.getdata(ref_rms)
b1 = fits.getdata(bkg)
b2 = fits.getdata(ref_bkg)
os.remove(rms)
os.remove(bkg)
if not np.allclose(r1, r2, atol=0.01, equal_nan=True):
raise AssertionError("rms is wrong")
if not np.allclose(b1, b2, atol=0.003, equal_nan=True):
raise AssertionError("bkg is wrong")
return
def test_filter_image():
"""Test filter image"""
# data = np.random.random((30, 30), dtype=np.float32)
fname = 'tests/test_files/1904-66_SIN.fits'
outbase = 'dlme'
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
# hdu = fits.getheader(fname)
# shape = hdu[0]['NAXIS1'], hdu[0]['NAXIS2']
BANE.filter_image(fname, step_size=(10, 10), box_size=(100, 100), cores=2, out_base=outbase)
if not os.path.exists(rms):
raise AssertionError()
os.remove(rms)
if not os.path.exists(bkg):
raise AssertionError()
os.remove(bkg)
BANE.filter_image(fname, cores=2, out_base=outbase, twopass=True, compressed=True)
if not os.path.exists(rms):
raise AssertionError()
os.remove(rms)
if not os.path.exists(bkg):
raise AssertionError()
os.remove(bkg)
def test_mask_data():
data = np.ones((10, 10), dtype=np.float32)
mask = data.copy()
mask[3:5, 0:2] = np.nan
BANE.mask_img(data, mask)
# check that the nan regions overlap
if not np.all(np.isnan(data) == np.isnan(mask)):
raise AssertionError()
def test_optimum_sections():
# typical case
if not BANE.optimum_sections(8, (64, 64)) == (2, 4):
raise AssertionError()
# redundant case
if not BANE.optimum_sections(1, (134, 1200)) == (1, 1):
raise AssertionError()
def get_rms_background(imagename, cores=16):
outbase = os.path.splitext(imagename)[0]
rmsimage = outbase + '_rms.fits'
bgimage = outbase + '_bkg.fits'
if not (os.path.exists(rmsimage) and os.path.exists(bgimage)):
BANE.filter_image(imagename, outbase, mask=False, cores=cores)
else:
logger.info('Using existing background and rms images %s, %s' %
(bgimage, rmsimage))
return rmsimage, bgimage
def get_rms_background(imagename, cores=16):
outbase=os.path.splitext(imagename)[0]
rmsimage=outbase + '_rms.fits'
bgimage=outbase + '_bkg.fits'
if not (os.path.exists(rmsimage) and os.path.exists(bgimage)):
BANE.filter_image(imagename,
outbase,
mask=False,
cores=cores)
else:
logger.info('Using existing background and rms images %s, %s' % (bgimage,rmsimage))
return rmsimage, bgimage
def test_sigmaclip():
# normal usage case
data = np.random.random(100)
data[13] = np.nan
if not len(BANE.sigmaclip(data, 3, 4, reps=4)) > 0:
raise AssertionError()
# test list where all elements get clipped
if not len(BANE.sigmaclip([-10, 10], 1, 2, reps=2)) == 0:
raise AssertionError()
# test empty list
if not len(BANE.sigmaclip([], 0, 3)) == 0:
raise AssertionError()
def test_sigmaclip():
"""Test the sigmaclipping"""
# normal usage case
data = np.ones(100)
if not BANE.sigmaclip(data, 3, 4, reps=4)[0] == 1. :
raise AssertionError()
data[13] = np.nan
if not BANE.sigmaclip(data, 3, 4, reps=4)[0] == 1.:
raise AssertionError()
# test empty list
if not np.isnan(BANE.sigmaclip([], 0, 3)[0]):
raise AssertionError()
def test_sigmaclip():
"""Test the sigmaclipping"""
# normal usage case
data = np.ones(100)
if not BANE.sigmaclip(data, 3, 4, reps=4)[0] == 1. :
raise AssertionError()
data[13] = np.nan
if not BANE.sigmaclip(data, 3, 4, reps=4)[0] == 1.:
raise AssertionError()
# test empty list
if not np.isnan(BANE.sigmaclip([], 0, 3)[0]):
raise AssertionError()
def test_ND_images():
"""Test that ND images are treated correctly"""
fbase = 'tests/test_files/small_{0}D.fits'
outbase = 'dlme'
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
# this should work just fine, but trigger different NAXIS checks
for fname in [fbase.format(n) for n in [3,4]]:
BANE.filter_image(fname, out_base=outbase)
os.remove(rms)
os.remove(bkg)
fname = fbase.format(5)
try:
BANE.filter_image(fname,out_base=outbase)
except Exception as e:
pass
else:
raise AssertionError()
def test_ND_images():
"""Test that ND images are treated correctly"""
fbase = 'tests/test_files/small_{0}D.fits'
outbase = 'dlme'
rms = outbase + '_rms.fits'
bkg = outbase + '_bkg.fits'
# this should work just fine, but trigger different NAXIS checks
for fname in [fbase.format(n) for n in [3,4]]:
BANE.filter_image(fname, out_base=outbase)
os.remove(rms)
os.remove(bkg)
fname = fbase.format(5)
try:
BANE.filter_image(fname,out_base=outbase)
except Exception as e:
pass
else:
raise AssertionError()
def test_BSCALE():
"""Test that BSCALE present and not 1.0 is handled properly"""
fbase = 'tests/test_files/1904-66_SIN'
outbase = 'dlme'
hdu = fits.open(fbase+'.fits')
hdu[0].header['BSCALE'] = 1.0
hdu.writeto('dlme.fits')
try:
BANE.filter_image(outbase+'.fits', out_base=outbase, cores=1, nslice=1)
except ValueError as e:
raise AssertionError("BSCALE=1.0 causes crash")
finally:
os.remove('dlme.fits')
hdu[0].header['BSCALE'] = 2.0
hdu.writeto('dlme.fits')
try:
BANE.filter_image(outbase+'.fits', out_base=outbase, cores=1, nslice=1)
except ValueError as e:
raise AssertionError("BSCALE=2.0 causes crash")
finally:
os.remove('dlme.fits')
return