def load_and_reduce(filename, add_noise=False, rms_noise=0.001,
nsig=3):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
from scipy.stats import norm
cube += norm.rvs(0.0, rms_noise, cube.shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
def load_and_reduce(filename,
add_noise=False,
rms_noise=0.001,
nsig=3,
slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
# Optionally scale noise by 1/10th of the 98th percentile in the cube
if rms_noise == 'scaled':
rms_noise = 0.1 * np.percentile(cube[np.isfinite(cube)], 98)
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise, slice_shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
def load_and_reduce(filename, add_noise=False, rms_noise=0.001,
nsig=3, slicewise_noise=True):
'''
Load the cube in and derive the property arrays.
'''
if add_noise:
if rms_noise is None:
raise TypeError("Must specify value of rms noise.")
cube, hdr = getdata(filename, header=True)
# Optionally scale noise by 1/10th of the 98th percentile in the cube
if rms_noise == 'scaled':
rms_noise = 0.1*np.percentile(cube[np.isfinite(cube)], 98)
from scipy.stats import norm
if not slicewise_noise:
cube += norm.rvs(0.0, rms_noise, cube.shape)
else:
spec_shape = cube.shape[0]
slice_shape = cube.shape[1:]
for i in range(spec_shape):
cube[i, :, :] += norm.rvs(0.0, rms_noise, slice_shape)
sc = SpectralCube(data=cube, wcs=WCS(hdr))
mask = LazyMask(np.isfinite, sc)
sc = sc.with_mask(mask)
else:
sc = filename
reduc = Mask_and_Moments(sc, scale=rms_noise)
reduc.make_mask(mask=reduc.cube > nsig * reduc.scale)
reduc.make_moments()
reduc.make_moment_errors()
return reduc.to_dict()
cube2 = SpectralCube.read(fits2)
# Shorten the name for the plots
fits1 = os.path.basename(fits1)
fits2 = os.path.basename(fits2)
# Naive error estimation. Useful for only testing out the methods.
scale1 = cube1.std().value
scale2 = cube2.std().value
set1 = Mask_and_Moments(cube1, scale=scale1)
# mask = cube1 > sigma * set1.scale
# set1.make_mask(mask=mask)
set1.make_moments()
set1.make_moment_errors()
dataset1 = set1.to_dict()
set2 = Mask_and_Moments(cube2, scale=scale2)
# mask = cube2 > sigma * set2.scale
# set2.make_mask(mask=mask)
set2.make_moments()
set2.make_moment_errors()
dataset2 = set2.to_dict()
# Wavelet Transform
from turbustat.statistics import Wavelet_Distance
wavelet_distance = Wavelet_Distance(
dataset1["moment0"], dataset2["moment0"]).distance_metric(verbose=True,
label1=fits1,
if add_noise:
data1 += np.random.normal(0.0, 0.788 / 10, data1.shape)
data2 += np.random.normal(0.0, 0.788 / 10, data2.shape)
cube1 = SpectralCube(data=data1, wcs=WCS(hdr1))
cube1 = cube1.with_mask(LazyMask(np.isfinite, cube1))
cube2 = SpectralCube(data=data2, wcs=WCS(hdr2))
cube2 = cube2.with_mask(LazyMask(np.isfinite, cube2))
set1 = Mask_and_Moments(cube1)
mask = cube1 > 3*set1.scale
set1.make_mask(mask=mask)
set1.make_moments()
set1.make_moment_errors()
dataset1 = set1.to_dict()
set2 = Mask_and_Moments(cube2)
mask = cube2 > 3*set2.scale
set2.make_mask(mask=mask)
set2.make_moments()
set2.make_moment_errors()
dataset2 = set2.to_dict()
# Wavelet Transform
from turbustat.statistics import Wavelet_Distance
wavelet_distance = Wavelet_Distance(dataset1["integrated_intensity"],
dataset2["integrated_intensity"]).distance_metric(verbose=True)
