def test_lsq_with_covar():
# NOTE: This only fits the velocity field....
# Read the data to fit
data_root = remote_data_file()
kin = manga.MaNGAGasKinematics.from_plateifu(8138, 12704, cube_path=data_root,
maps_path=data_root, covar=True)
print('Forcing covariance to be positive definite.')
kin.vel_covar = util.impose_positive_definite(kin.vel_covar)
# Set the rotation curve
rc = HyperbolicTangent(lb=numpy.array([0., 1e-3]), ub=numpy.array([500., kin.max_radius()]))
# Set the disk velocity field
disk = AxisymmetricDisk(rc=rc) #, dc=dc)
# Fit it with a non-linear least-squares optimizer
# import time
# t = time.perf_counter()
disk.lsq_fit(kin, sb_wgt=True)
# print(f'First fit (no covar): {time.perf_counter()-t} s')
# Rejected based on error-weighted residuals, accounting for intrinsic scatter
resid = kin.vel - kin.bin(disk.model())
err = 1/numpy.sqrt(kin.vel_ivar)
scat = scatter.IntrinsicScatter(resid, err=err, gpm=disk.vel_gpm)
sig, rej, gpm = scat.iter_fit(fititer=5) #, verbose=2)
# Check
assert sig > 8., 'Different intrinsic scatter'
assert numpy.sum(rej) == 19, 'Different number of pixels were rejected'
# Refit with new mask, include scatter and covariance
kin.vel_mask = numpy.logical_not(gpm)
p0 = disk.par
# t = time.perf_counter()
disk.lsq_fit(kin, scatter=sig, sb_wgt=True, p0=p0, ignore_covar=False,
assume_posdef_covar=True) #, verbose=2)
# print(f'Second fit (w/ covar): {time.perf_counter()-t} s')
# Reject
resid = kin.vel - kin.bin(disk.model())
scat = scatter.IntrinsicScatter(resid, covar=kin.vel_covar, gpm=disk.vel_gpm,
assume_posdef_covar=True)
sig, rej, gpm = scat.iter_fit(fititer=5) #, verbose=2)
# Check
assert sig > 5., 'Different intrinsic scatter'
assert numpy.sum(rej) == 7, 'Different number of pixels were rejected'
# Model parameters
assert numpy.all(numpy.absolute(disk.par[:2]) < 0.1), 'Center changed'
assert 165. < disk.par[2] < 167., 'PA changed'
assert 56. < disk.par[3] < 58., 'Inclination changed'
assert 249. < disk.par[5] < 252., 'Projected rotation changed'
def test_lsq_psf():
# Read the data to fit
data_root = remote_data_file()
kin = manga.MaNGAGasKinematics.from_plateifu(8138, 12704, cube_path=data_root,
maps_path=data_root)
# Set the rotation curve
rc = HyperbolicTangent(lb=numpy.array([0., 1e-3]), ub=numpy.array([500., kin.max_radius()]))
# Set the disk velocity field
disk = AxisymmetricDisk(rc=rc)
# Fit it with a non-linear least-squares optimizer
disk.lsq_fit(kin) #, verbose=2)
assert numpy.all(numpy.absolute(disk.par[:2]) < 0.1), 'Center changed'
assert 165. < disk.par[2] < 167., 'PA changed'
assert 55. < disk.par[3] < 59., 'Inclination changed'
assert 252. < disk.par[5] < 255., 'Projected rotation changed'
def test_lsq_nopsf():
# Read the data to fit
data_root = remote_data_file()
kin = manga.MaNGAGasKinematics.from_plateifu(8078,
12703,
cube_path=data_root,
maps_path=data_root)
nrun = 100
for i in range(nrun):
print('{0}/{1}'.format(i + 1, nrun), end='\r')
# Set the rotation curve
rc = HyperbolicTangent()
# Set the disk velocity field
disk = AxisymmetricDisk(rc)
# Fit it with a non-linear least-squares optimizer
disk.lsq_fit(kin)
print('{0}/{0}'.format(nrun))