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 get_spectrum(plt, ifu, x, y, directory_path=None):
"""
Extract a single spectrum from a MaNGA observation.
Args:
plt (:obj:`int`):
Plate number
ifu (:obj:`int`):
IFU identifier
x (:obj:`int`):
The spaxel coordinate along the RA axis.
y (:obj:`int`):
The spaxel coordinate along the DEC axis.
directory_path (:obj:`str`, optional):
Directory with the DRP LOGCUBE file. If None, uses the
default directory path based on the environmental
variables.
Returns:
:obj:`tuple`: Returns 4 numpy vectors: The wavelength, flux,
flux inverse variance, and spectral resolution extracted from
the datacube.
"""
cube = MaNGADataCube.from_plateifu(plt, ifu, directory_path=directory_path)
flat_indx = cube.spatial_shape[1] * x + y
# This function always returns as masked array
flux = cube.copy_to_masked_array(attr='flux', flag=cube.do_not_fit_flags())
ivar = cube.copy_to_masked_array(attr='ivar', flag=cube.do_not_fit_flags())
sres = cube.copy_to_array(attr='sres')
return cube.wave, flux[flat_indx, :], ivar[flat_indx, :], sres[
flat_indx, :]
def test_read_lin():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file(),
log=False)
assert not cube.log, 'Wavelength sampling should be linear'
assert numpy.isclose(numpy.std(numpy.diff(cube.wave)), 0.), \
'Wavelength sampling should be linear'
def get_spectra(plt, ifu, x, y, directory_path=None):
cube = MaNGADataCube.from_plateifu(plt, ifu, directory_path=directory_path)
flat_indx = cube.spatial_shape[1]*x+y
# This function always returns as masked array
flux = cube.copy_to_masked_array(attr='flux', flag=cube.do_not_fit_flags())
ivar = cube.copy_to_masked_array(attr='ivar', flag=cube.do_not_fit_flags())
sres = cube.copy_to_array(attr='sres')
return cube.wave, flux[flat_indx,:], ivar[flat_indx,:], sres[flat_indx,:]
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_rectification_shape():
# Load the datacube and the row-stacked spectra
cube = MaNGADataCube.from_plateifu(7815, 3702, directory_path=remote_data_file())
cube.load_rss()
# Get the recitification parameters
pixelscale, rlim, sigma, recenter, width_buffer \
= MaNGARSS._parse_rectification_parameters(None, None, None, None, None)
# Get the cube dimensions
cube.rss._cube_dimensions(pixelscale=pixelscale, recenter=recenter, width_buffer=width_buffer)
# Make sure they match what the DRP produced
assert cube.spatial_shape == (cube.rss.nx, cube.rss.ny), 'Mismatched cube spatial dimensions'
def test_read_correl():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file(),
covar_ext='GCORREL')
assert isinstance(cube.covar, Covariance), 'Incorrect type for covariance.'
assert cube.covar.shape == (cube.nspec,
cube.nspec), 'Covariance has incorrect shape.'
assert cube.covar.is_correlation, 'Covariance object should be in a correlation mode.'
# Check that the variances are all unity (or close to it when it's defined)
unique_var = numpy.unique(cube.covar.var)
assert numpy.allclose(unique_var[unique_var > 0],
1.), 'Bad variance values'
def test_match_resolution():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
tpl = TemplateLibrary('MILESHC',
cube=cube,
match_resolution=True,
velscale_ratio=4,
hardcopy=False)
# Resolution should be virtually identical in unmasked regions
indx = tpl['MASK'].data == 0
assert numpy.std(tpl.sres(tpl['WAVE'].data[indx[0]]) - tpl['SPECRES'].data[0,indx[0]]) < 0.1, \
'Spectral resolution difference is above tolerance.'
def test_wcs():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
x, y = cube.mean_sky_coordinates(offset=None)
assert x[0, 0] > x[-1, 0], 'RA should increase from large to small indices'
assert y[0, 0] < y[0,
-1], 'DEC should increase from small to small indices'
assert numpy.unravel_index(numpy.argmin( numpy.square(x - cube.prihdr['OBJRA'])
+ numpy.square(y - cube.prihdr['OBJDEC'])), x.shape) \
== (21,21), 'Object should be at cube center.'
x, y = cube.mean_sky_coordinates(center_coo=(x[0, 0], y[0, 0]))
assert numpy.isclose(x[0, 0], 0.0) and numpy.isclose(
y[0, 0], 0.0), 'Offset incorrect'
x, y = cube.mean_sky_coordinates()
assert abs(x[21, 21]) < 1e-2 and abs(y[21, 21]) < 1e-2, 'Offset incorrect'
def test_rectification_recovery():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file(),
covar_ext='GCORREL')
cube.load_rss()
hdu = fits.open(cube.file_path())
channel = hdu['GCORREL'].header['BBINDEX']
gcorrel = numpy.zeros(eval(hdu['GCORREL'].header['COVSHAPE']), dtype=float)
i = numpy.ravel_multi_index(
(hdu['GCORREL'].data['INDXI_C1'], hdu['GCORREL'].data['INDXI_C2']),
cube.spatial_shape)
j = numpy.ravel_multi_index(
(hdu['GCORREL'].data['INDXJ_C1'], hdu['GCORREL'].data['INDXJ_C2']),
cube.spatial_shape)
gcorrel[i, j] = hdu['GCORREL'].data['RHOIJ']
gcorrel[j, i] = hdu['GCORREL'].data['RHOIJ']
assert numpy.allclose(cube.covar.toarray(), gcorrel), 'Bad covariance read'
flux, C = cube.rss.rectify_wavelength_plane(channel, return_covar=True)
assert numpy.allclose(cube.flux[..., channel],
flux), 'Bad flux rectification'
ivar = numpy.ma.power(C.variance().reshape(cube.spatial_shape),
-1).filled(0.0)
assert numpy.allclose(cube.ivar[..., channel],
ivar), 'Bad inverse variance rectification'
C.to_correlation()
assert numpy.allclose(C.toarray(), gcorrel), 'Bad covariance calculation'
sres = numpy.ma.divide(cube.rss.wave[channel],
cube.rss.instrumental_dispersion_plane(channel).ravel()) \
/ DAPConstants.sig2fwhm
# WARNING: The computations done by the DRP and DAP are different
# in detail, but (at least for this test cube) the results are
# virtually identical except for notable outliers.
assert numpy.ma.median(cube.sres[...,channel].ravel() - sres) < 0.1, \
'Bad spectral resolution rectification'
def test_read():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
assert cube.log, 'Should read the log-binned version by default.'
assert cube.wcs is not None, 'WCS should be defined.'
assert cube.shape[:
2] == cube.spatial_shape, 'Spatial shape should be first two axes.'
assert cube.nspec == numpy.prod(
cube.spatial_shape), 'Definition of number of spectra changed.'
assert cube.sres is not None, 'Spectral resolution data was not constructed.'
assert cube.sres_ext == 'LSFPRE', 'Should default to LSFPRE extension.'
assert abs(cube.pixelscale -
cube._get_pixelscale()) < 1e-6, 'Bad match in pixel scale.'
# NOTE: This is worse than it should be because of how the WCS in MaNGA is defined.
assert numpy.all(numpy.absolute(cube.wave - cube._get_wavelength_vector()) < 2e-4), \
'Bad calculation of wavelength vector.'
assert cube.covar is None, 'Covariance should not have been read'
def test_covariance():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
with pytest.raises(ValueError):
# Have to load the RSS first
cube.covariance_matrix(1000)
# Load the RSS
cube.load_rss()
# Construct a covariance matrix
C = cube.covariance_matrix(1000)
assert C.shape == (1764, 1764), 'Bad covariance shape'
# Make it a correlation matrix and check it
C.to_correlation()
# Check that the variances are all unity (or close to it when it's defined)
unique_var = numpy.unique(numpy.diag(C.toarray()))
assert numpy.allclose(unique_var[unique_var > 0],
1.), 'Bad correlation diagonal'
# Try multiple channels
C = cube.covariance_cube(channels=[1000, 2000])
assert numpy.array_equal(C.input_indx, [1000, 2000]), 'Bad matrix indices'
assert C.shape == (1764, 1764, 2), 'Bad covariance shape'
# Try to convert multiple channels
C.to_correlation()
# And reverting it
C.revert_correlation()
# Try to generate an approximate correlation matrix, covariance
# matrix, and covariance cube
approxC = cube.approximate_correlation_matrix()
approxC = cube.approximate_covariance_matrix(1000)
approxC = cube.approximate_covariance_cube(channels=[1000, 2000])
# Variance should be the same for direct and approximate calculations
assert numpy.allclose(approxC.variance(),
C.variance()), 'Variances should be the same.'
def test_match_resolution():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
tpl = TemplateLibrary('MILESHC',
cube=cube,
match_resolution=True,
velscale_ratio=4,
hardcopy=False,
output_path=remote_data_file())
# Resolution should be virtually identical in unmasked regions
indx = tpl['MASK'].data == 0
assert numpy.std(tpl.sres(tpl['WAVE'].data[indx[0]]) - tpl['SPECRES'].data[0,indx[0]]) < 0.1, \
'Spectral resolution difference is above tolerance.'
# Check the file that would have been written has the expected path
assert cube.directory_path == tpl.directory_path, 'Cube and TPL paths should match.'
assert tpl.file_name().startswith(
cube.output_root), 'TPL file should start with the cube root'
def test_load_rss():
cube = MaNGADataCube.from_plateifu(7815,
3702,
directory_path=remote_data_file())
cube.load_rss()
def resample_test():
# Read the example datacube and get the expected redshift You can download
# these data using
# https://github.com/sdss/mangadap/blob/master/download_test_data.py
plt = 7815
ifu = 3702
drpver = 'v3_1_1'
directory_path = defaults.dap_source_dir() / 'data' / 'remote'
cube = MaNGADataCube.from_plateifu(plt, ifu, directory_path=directory_path)
drpall_file = directory_path / f'drpall-{drpver}.fits'
z = get_redshift(plt, ifu, drpall_file)
# Pull out two example spectra from the datacube
old_wave = cube.wave
old_flux = numpy.ma.MaskedArray(cube.flux[10, 10:12, :],
mask=cube.mask[10, 10:12, :] > 0)
old_flux[:, (old_wave > 5570) & (old_wave < 5586)] = numpy.ma.masked
old_ferr = numpy.ma.power(cube.ivar[10, 10:12, :], -0.5)
if spectres is not None:
# Use spectres to resample the spectrum, ignoring last pixel
indx = (old_wave > old_wave[0] / (1 + z)) & (old_wave < old_wave[-2] /
(1 + z))
t = time.perf_counter()
new_flux_spectres = numpy.empty((old_flux.shape[0], numpy.sum(indx)),
dtype=float)
new_ferr_spectres = numpy.empty((old_flux.shape[0], numpy.sum(indx)),
dtype=float)
for i in range(old_flux.shape[0]):
new_flux_spectres[i,:], new_ferr_spectres[i,:] \
= spectres.spectres(old_wave[indx], old_wave/(1+z), old_flux[i,:].filled(0.0),
spec_errs=old_ferr[i,:].filled(0.0))
print('SpectRes Time: ', time.perf_counter() - t)
# Use a brute-force integration of the spectra to resample the spectrum
t = time.perf_counter()
borders = grid_borders(numpy.array([old_wave[0], old_wave[-1]]),
old_wave.size,
log=True)[0]
_p = numpy.repeat(borders, 2)[1:-1].reshape(-1, 2)
new_flux_brute = numpy.array([
passband_integral(old_wave / (1 + z), f, passband=_p, log=True)
for f in old_flux.filled(0.0)
])
new_flux_brute /= (_p[:, 1] - _p[:, 0])[None, :]
print('Brute Force Time: ', time.perf_counter() - t)
# Use the Resample class to resample the spectrum
t = time.perf_counter()
r = Resample(old_flux,
e=old_ferr,
x=old_wave / (1 + z),
newRange=[old_wave[0], old_wave[-1]],
inLog=True,
newLog=True)
print('Resample Time: ', time.perf_counter() - t)
# Estimate the differences between the resampling methods (these should all
# be the same to nearly numerical accuracy)
print('Mean diff:')
if spectres is not None:
print(' spectres - brute = {0:.5e}'.format(
numpy.mean(
numpy.absolute(new_flux_spectres - new_flux_brute[:, indx]))))
print(' spectres - resample = {0:.5e}'.format(
numpy.mean(numpy.absolute(new_flux_spectres - r.outy[:, indx]))))
print(' brute - resample = {0:.5e}'.format(
numpy.mean(numpy.absolute(new_flux_brute - r.outy))))
# Plot the original and resampled versions for all spectra. The resampled
# versions should all be indistinguishable.
for i in range(old_flux.shape[0]):
pyplot.plot(old_wave / (1 + z), old_flux[i, :], label='Data')
if spectres is not None:
pyplot.plot(old_wave[indx],
new_flux_spectres[i, :],
label='spectres')
pyplot.plot(old_wave, new_flux_brute[i, :], label='brute')
pyplot.plot(r.outx, r.outy[i, :], label='Resample')
pyplot.plot(r.outx, r.outf[i, :], label='Good-pixel Mask')
pyplot.legend()
pyplot.xlabel('Wavelength')
pyplot.ylabel('Flux')
pyplot.show()
