def test_wcs():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
# Unrestricted
x, y = rss.mean_sky_coordinates()
methods = available_reduction_assessments()
i = numpy.where([m['key'] == 'SNRG' for m in methods])[0]
assert len(i) == 1, 'Could not find correct reduction assessment definition.'
# Weighted by the g-band
_x, _y = rss.mean_sky_coordinates(response_func=methods[i[0]]['response_func'])
assert numpy.ma.amax(x-_x) - numpy.ma.amin(x-_x) > 0, 'Should be different'
assert numpy.ma.amax(y-_y) - numpy.ma.amin(y-_y) > 0, 'Should be different'
# Find a cluster of dithers
d = numpy.ma.sqrt(numpy.square(_x) + numpy.square(_y))
srt = numpy.argsort(d)
theta = numpy.arctan2(-_y, _x)
indx = theta[srt[:10]] < -2
assert numpy.sum(indx) == 5, 'Should find close fiber positions'
indx = srt[:10][indx]
bin_indx = numpy.full(rss.nspec, -1, dtype=int)
bin_indx[indx] = 0
bins, area = rss.binned_on_sky_area(bin_indx, response_func=methods[i[0]]['response_func'])
assert numpy.array_equal(bins, [0]), 'Should only be one bin'
try:
import shapely
except:
# Could not import shapely for proper calcultion, so test the stupid calculation
assert area[0] == numpy.sum(rss.area[bin_indx > -1]), \
'Stupid calculation is just the sum of the fiber area'
else:
# Check the proper calculation
assert area[0] < numpy.sum(rss.area[bin_indx > -1]), \
'Area should be substantially smaller than the stupid calculation yields.'
def test_stats():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
methods = available_reduction_assessments()
i = numpy.where([m['key'] == 'SNRG' for m in methods])[0]
assert len(i) == 1, 'Could not find correct reduction assessment definition.'
cenwave = rss.central_wavelength(response_func=methods[i[0]]['response_func'])
assert numpy.isclose(cenwave, 4686.2), 'Central wavelength calculation changed'
sig, var, snr = rss.flux_stats(response_func=methods[i[0]]['response_func'])
assert sig.shape == (rss.nspec,), 'Should be one measurement per spectrum.'
assert isinstance(sig, numpy.ma.MaskedArray), 'Expected masked arrays'
assert numpy.ma.amax(snr) > 15, 'S/N changed'
assert numpy.ma.median(snr) < 3, 'S/N changed'
# Try it with the linear rss
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file(), log=False)
_sig, _var, _snr = rss.flux_stats(response_func=methods[i[0]]['response_func'])
# TODO: Not sure why these are not closer.
assert numpy.absolute(numpy.ma.median((sig-_sig)/_sig)) < 0.01, \
'Signal should be the same to better than 1%.'
assert numpy.absolute(numpy.ma.median((var-_var)/_var)) < 0.03, \
'Variance should be the same to better than 3%.'
assert numpy.absolute(numpy.ma.median((snr-_snr)/_snr)) < 0.02, \
'S/N should be the same to better than 2%.'
def test_stats():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
# Create a fake bin map
bin_indx = numpy.arange(cube.nspec / 4,
dtype=int).reshape(cube.spatial_shape[0] // 2,
cube.spatial_shape[0] // 2)
bin_indx = numpy.repeat(bin_indx, 2, axis=0)
bin_indx = numpy.repeat(bin_indx, 2, axis=1)
# Get the bin area
bins, area = cube.binned_on_sky_area(bin_indx)
assert numpy.array_equal(bins,
numpy.arange(cube.nspec / 4)), 'Bad bin list'
assert numpy.allclose(area, 1.), 'Bad area calculation'
methods = available_reduction_assessments()
i = numpy.where([m['key'] == 'SNRG' for m in methods])[0]
assert len(
i) == 1, 'Could not find correct reduction assessment definition.'
cen_wave = cube.central_wavelength(
response_func=methods[i[0]]['response_func'],
flag=cube.do_not_use_flags())
assert numpy.isclose(cen_wave, 4638.0), 'Central wavelength changed.'
cen_wave = cube.central_wavelength(waverange=[4000, 8000],
flag=cube.do_not_use_flags(),
fluxwgt=True)
assert numpy.isclose(cen_wave, 5895.7), 'Central wavelength changed.'
cen_wave = cube.central_wavelength(waverange=[4000, 8000],
flag=cube.do_not_use_flags(),
per_pixel=False)
assert numpy.isclose(cen_wave, 6044.9), 'Central wavelength changed.'
sig, var, snr = cube.flux_stats(
response_func=methods[i[0]]['response_func'])
assert sig.shape == cube.spatial_shape, 'Should be shaped as a map.'
assert isinstance(sig, numpy.ma.MaskedArray), 'Expected masked arrays'
assert numpy.ma.amax(snr) > 60, 'S/N changed'
# Try it with the linear cube
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file(),
log=False)
_sig, _var, _snr = cube.flux_stats(
response_func=methods[i[0]]['response_func'])
# TODO: Not sure why these are not closer.
assert numpy.absolute(numpy.ma.median((sig-_sig)/_sig)) < 0.01, \
'Signal should be the same to better than 1%.'
assert numpy.absolute(numpy.ma.median((var-_var)/_var)) < 0.03, \
'Variance should be the same to better than 3%.'
assert numpy.absolute(numpy.ma.median((snr-_snr)/_snr)) < 0.02, \
'S/N should be the same to better than 2%.'
def test_copyto():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
flux = cube.copy_to_array()
assert not isinstance(flux,
numpy.ma.MaskedArray), 'Should output normal array'
assert flux.shape[0] == cube.nspec, 'Should be flattened into a 2D array.'
assert flux.shape[1] == cube.nwave, 'Should be flattened into a 2D array.'
# Apply a wavelength mask
waverange = [5000, 7000]
flux = cube.copy_to_array(waverange=waverange)
indx = (cube.wave > waverange[0]) & (cube.wave < waverange[1])
assert flux.shape[1] == numpy.sum(indx), 'Wavelength range masking failed'
# Find the spaxels with non-zero signal
methods = available_reduction_assessments()
i = numpy.where([m['key'] == 'SNRG' for m in methods])[0]
assert len(
i) == 1, 'Could not find correct reduction assessment definition.'
sig, var, snr = cube.flux_stats(
response_func=methods[i[0]]['response_func'])
indx = ((sig > 0) & numpy.invert(numpy.ma.getmaskarray(sig))).data.ravel()
ngood = numpy.sum(indx)
# Select the spaxels with non-zero signal
flux = cube.copy_to_array(waverange=waverange, select_bins=indx)
assert flux.shape[0] == ngood, 'Bin selection failed'
# Get the masked array
flux = cube.copy_to_masked_array()
assert isinstance(flux,
numpy.ma.MaskedArray), 'Should output a masked array'
assert flux.shape[0] == cube.nspec, 'Should be flattened into a 2D array.'
assert flux.shape[1] == cube.nwave, 'Should be flattened into a 2D array.'
# Select the spaxels with non-zero signal
flux = cube.copy_to_masked_array(select_bins=indx)
assert flux.shape[0] == ngood, 'Bin selection failed'
# Try to get the inverse variance
i = cube.nspec // 2 + cube.spatial_shape[1] // 2
ivar = cube.copy_to_masked_array(attr='ivar')
assert ivar.shape == (cube.nspec, cube.nwave), 'Bad ivar shape'
assert numpy.array_equal(
cube.ivar[numpy.unravel_index(i, cube.spatial_shape)],
ivar[i].data), 'Did not pull ivar data.'
# Try to get the spectral resolution
sres = cube.copy_to_masked_array(attr='sres')
assert sres.shape == (cube.nspec, cube.nwave), 'Bad sres shape'
assert numpy.array_equal(
cube.sres[numpy.unravel_index(i, cube.spatial_shape)],
sres[i].data), 'Did not pull sres data.'
def test_avail():
method_list = available_reduction_assessments()
assert len(method_list) > 0, 'No reduction assessment methods available'