def test_stats():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
method = ReductionAssessmentDef()
# 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=method.response)
assert numpy.isclose(cenwave,
4686.2), 'Central wavelength calculation changed'
sig, var, snr = rss.flux_stats(response_func=method.response)
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=method.response)
# 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_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 calculate_covariance_cube(plate, ifudesign, ofile, nc=1, wave=None, directory_path=None):
print(' PLATE: {0}'.format(plate))
print(' IFUDESIGN: {0}'.format(ifudesign))
# Access the DRP RSS file
print('Attempting to open RSS file:')
rss = MaNGARSS.from_plateifu(plate, ifudesign, directory_path=directory_path)
print(' FOUND: {0}'.format(rss.file_path()))
if wave is not None:
channel = numpy.argsort(numpy.absolute(rss.wave - wave))[0]
print('Nearest wavelength channel has wavelength {0:.1f} ang.'.format(rss.wave[channel]))
C = rss.covariance_matrix(channel)
else:
if nc >= rss.nwave or nc == 0:
print('Calculating full covariance cube ...')
C = rss.covariance_cube()
print('... done.')
elif nc == 1:
channel = rss.nwave//2
print('Calculating covariance matrix at central channel: '
'{0:.1f} ang.'.format(rss.wave[channel]))
C = rss.covariance_matrix(channel)
else:
print('Calculating covariance in {0} wavelength channels...'.format(nc))
channels = numpy.linspace(0, rss.nwave-1, num=nc, dtype=int)
C = rss.covariance_cube(channels=channels)
print('Writing data to {0}.'.format(ofile))
C.write(ofile, clobber=True) # Write the data
def test_read_lin():
rss = MaNGARSS.from_plateifu(7815,
3702,
directory_path=remote_data_file(),
log=False)
assert not rss.log, 'Wavelength sampling should be linear'
assert numpy.isclose(numpy.std(numpy.diff(rss.wave)), 0.), \
'Wavelength sampling should be linear'
def test_read():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
assert rss.log, 'Should read the log-binned version by default.'
assert len(rss.shape) == 2, 'Row-stacked spectra are 2D'
assert rss.shape == (rss.nspec, rss.nwave), 'Shape mismatch'
assert rss.sres is not None, 'Spectral resolution data was not constructed.'
assert rss.sres_ext == 'LSFPRE', 'Should default to LSFPRE extension.'
assert rss.xpos.shape == rss.shape, 'On-sky coordinates are wavelength-dependent'
assert numpy.all(rss.area == numpy.pi), 'Area is pi square arcsec'
assert rss.area.shape == (rss.nspec, ), 'Area is wavelength-independent'
assert numpy.all(numpy.absolute(numpy.asarray(rss.pointing_offset())) < 0.01), \
'Pointing offset for this observation should be small.'
def test_copyto():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
flux = rss.copy_to_array()
assert not isinstance(flux,
numpy.ma.MaskedArray), 'Should output normal array'
assert flux.shape == rss.shape, 'Both should be 2D arrays.'
# Apply a wavelength mask
waverange = [5000, 7000]
flux = rss.copy_to_array(waverange=waverange)
indx = (rss.wave > waverange[0]) & (rss.wave < waverange[1])
assert flux.shape[1] == numpy.sum(indx), 'Wavelength range masking failed'
# Find the spaxels with non-zero signal
method = ReductionAssessmentDef()
# 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 = rss.flux_stats(response_func=method.response)
indx = ((sig > 0) & numpy.invert(numpy.ma.getmaskarray(sig))).data.ravel()
ngood = numpy.sum(indx)
# Select the spaxels with non-zero signal
flux = rss.copy_to_array(waverange=waverange, select_bins=indx)
assert flux.shape[0] == ngood, 'Bin selection failed'
# Get the masked array
flux = rss.copy_to_masked_array()
assert isinstance(flux,
numpy.ma.MaskedArray), 'Should output a masked array'
assert flux.shape == rss.shape, 'Both should be 2D arrays.'
# Select the spaxels with non-zero signal
flux = rss.copy_to_masked_array(select_bins=indx)
assert flux.shape[0] == ngood, 'Bin selection failed'
# Try to get the inverse variance
i = rss.nspec // 2
ivar = rss.copy_to_masked_array(attr='ivar')
assert ivar.shape == rss.shape, 'Bad ivar shape'
assert numpy.array_equal(rss.ivar[i],
ivar[i].data), 'Did not pull ivar data.'
# Try to get the spectral resolution
sres = rss.copy_to_masked_array(attr='sres')
assert sres.shape == rss.shape, 'Bad sres shape'
assert numpy.array_equal(rss.sres[i],
sres[i].data), 'Did not pull sres data.'
def test_covariance():
rss = MaNGARSS.from_plateifu(7815, 3702, directory_path=remote_data_file())
# Construct a covariance matrix
C = rss.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 = rss.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()