class ObsCube(object):
"""
A wrapping class of SpectralCube which prepares observational data
cubes to be compared to any other data cube.
Parameters
----------
cube : str
Path to file.
mask : numpy.ndarray, any mask class from spectral_cube, optional
Mask to be applied to the cube.
algorithm : {NAME HERE}, optional
Name of the cleaning algorithm to use.
Example
-------
```
from turbustat.cube_tools import ObsCube
cube = ObsCube("data.fits")
cube.apply_cleaning(algorithm="SUPERAWESOMECLEANING")
```
"""
def __init__(self, cube, mask=None, algorithm=None, beam=None):
raise NotImplementedError("ObsCube is not yet implemented for "
"general use.")
super(ObsCube, self).__init__()
self.cube = SpectralCube.read(cube)
self.algorithm = algorithm
# Make sure mask is an accepted type
if mask is not None:
_check_mask(mask)
self.mask = mask
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam)
def clean_cube(self, algorithm=None):
raise NotImplementedError("")
def apply_mask(self, mask=None):
'''
Check if the given mask is acceptable abd apply to
SpectralCube.
'''
# Update mask
if mask is not None:
_check_mask(mask)
self.mask = mask
# Create the mask, auto masking nan values
default_mask = np.isfinite(self.cube.filled_data[:])
if self.mask is not None:
self.mask = CompositeMask(default_mask, self.mask)
else:
self.mask = default_mask
# Apply mask to spectral cube object
self.cube = self.cube.with_mask(mask)
return self
def _update(self, data=None, wcs=None, beam=None, method="MAD"):
'''
Helper function to update classes.
'''
# Check if we need a new SpectralCube
if data is None and wcs is None:
pass
else:
if data is None:
data = self.cube.unmasked_data[:]
if wcs is None:
wcs = self.cube.wcs
# Make new SpectralCube object
self.cube = SpectralCube(data=data, wcs=wcs)
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam, method=method)
def compute_properties(self):
'''
Use SpectralCube to compute the moments. Also compute the integrated
intensity based on the noise properties from Noise.
'''
self._moment0 = self.cube.moment0().value
self._moment1 = self.cube.moment1().value
self._moment2 = self.cube.moment2().value
_get_int_intensity(self)
return self
@property
def moment0(self):
return self._moment0
@property
def moment1(self):
return self._moment1
@property
def moment2(self):
return self._moment2
@property
def intint(self):
return self._intint
def prep(self, mask=None, algorithm=None):
'''
Prepares the cube to be compared to another cube.
'''
if not mask is None:
self.apply_mask()
self.clean_cube()
self.compute_properties()
return self
# Create a synthetic X-dimension in km/s
xarr = np.linspace(50, 70, 41) # km/s
# Define a line width, which will vary across our image
# It will increase from 1 km/s to 4 km/s over the X-direction (RA)
sigma = np.outer(np.linspace(1, 1.5, 2), np.ones(4)).T
# Define a line center, which will vary in the opposite direction,
# along increasing Y-direction (declination)
centroid = np.outer(np.ones(2), np.linspace(58, 62, 4)).T
data = np.exp(-(np.tile(xarr, (2, 4, 1)).T - centroid)**2 / (2. * sigma**2))
cube = SpectralCube(data=data, wcs=mywcs)
# Sanity checks: do the moments accurately recover the inputs?
assert (np.abs(cube.moment1().to(u.km / u.s).value - centroid).max()) < 1e-5
assert (np.abs(cube.moment2().to(u.km**2 / u.s**2).value -
sigma**2).max()) < 1e-5
# Create a pyspeckit cube
pcube = pyspeckit.Cube(cube=cube)
# For convenience, convert the X-axis to km/s
# (WCSLIB automatically converts to m/s even if you give it km/s)
pcube.xarr.convert_to_unit(u.km / u.s)
# Set up the fitter by doing a preliminary fit
pcube.specfit(fittype='gaussian', guesses='moments')
# Fit each spectrum with a gaussian
# First, assemble the guesses:
class SimCube(object):
'''
A wrapping class to prepare a simulated spectral data cube for
comparison with another cube.
'''
def __init__(self, cube, beam=None, mask=None, method="MAD", compute=True):
# Initialize cube object
self.cube = SpectralCube.read(cube)
if mask is not None:
_check_mask(mask)
self.mask = mask
if beam is not None:
_check_beam(mask)
# Initialize noise object
self.noise = Noise(self.cube, beam=beam, method=method)
def add_noise(self):
'''
Use Noise to add synthetic noise to the data. Then update
SpectralCube.
'''
# Create the noisy cube
self.noise.get_noise_cube()
noise_data = self.noise.noise_cube +\
self.cube.filled_data[:]
# Update SpectralCube object
self._update(data=noise_data)
return self
def clean_cube(self, algorithm=None):
raise NotImplementedError("")
def apply_mask(self, mask=None):
'''
Check if the given mask is acceptable abd apply to
SpectralCube.
'''
# Update mask
if mask is not None:
_check_mask(mask)
self.mask = mask
# Create the mask, auto masking nan values
default_mask = np.isfinite(self.cube.filled_data[:])
if self.mask is not None:
self.mask = CompositeMask(default_mask, self.mask)
else:
self.mask = default_mask
# Apply mask to spectral cube object
self.cube = self.cube.with_mask(mask)
return self
def _update(self, data=None, wcs=None, beam=None, method="MAD"):
'''
Helper function to update classes.
'''
# Check if we need a new SpectralCube
if data is None and wcs is None:
pass
else:
if data is None:
data = self.cube.unmasked_data[:]
if wcs is None:
wcs = self.cube.wcs
# Make new SpectralCube object
self.cube = SpectralCube(data=data, wcs=wcs)
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam, method=method)
def compute_properties(self):
'''
Use SpectralCube to compute the moments. Also compute the integrated
intensity based on the noise properties from Noise.
'''
self._moment0 = self.cube.moment0().value
self._moment1 = self.cube.moment1().value
self._moment2 = self.cube.moment2().value
_get_int_intensity(self)
return self
@property
def moment0(self):
return self._moment0
@property
def moment1(self):
return self._moment1
@property
def moment2(self):
return self._moment2
@property
def intint(self):
return self._intint
def sim_prep(self, mask=None):
'''
Prepares the cube when being compared to another simulation.
This entails:
* Optionally applying a mask to the data.
* Computing the cube's property arrays
'''
if not mask is None:
self.apply_mask()
self.compute_properties()
return self
def obs_prep(self, mask=None):
'''
Prepares the cube when being compared to observational data.
This entails:
* Optionally applying a mask to the data.
* Add synthetic noise based on the cube's properties.
* Computing the cube's property arrays
'''
if not mask is None:
self.apply_mask()
self.add_noise()
self.compute_properties()
return self
# Create a synthetic X-dimension in km/s xarr = np.linspace(50, 70, 41) # km/s # Define a line width, which will vary across our image # It will increase from 1 km/s to 4 km/s over the X-direction (RA) sigma = np.outer(np.linspace(1,1.5,2), np.ones(4)).T # Define a line center, which will vary in the opposite direction, # along increasing Y-direction (declination) centroid = np.outer(np.ones(2), np.linspace(58, 62, 4)).T data = np.exp(-(np.tile(xarr, (2, 4, 1)).T - centroid)**2 / (2.*sigma**2)) cube = SpectralCube(data=data, wcs=mywcs) # Sanity checks: do the moments accurately recover the inputs? assert (np.abs(cube.moment1().to(u.km/u.s).value - centroid).max()) < 1e-5 assert (np.abs(cube.moment2().to(u.km**2/u.s**2).value - sigma**2).max()) < 1e-5 # Create a pyspeckit cube pcube = pyspeckit.Cube(cube=cube) # For convenience, convert the X-axis to km/s # (WCSLIB automatically converts to m/s even if you give it km/s) pcube.xarr.convert_to_unit(u.km/u.s) # Set up the fitter by doing a preliminary fit pcube.specfit(fittype='gaussian', guesses='moments') # Fit each spectrum with a gaussian # First, assemble the guesses: guesses = np.array([cube.max(axis=0).value,
class SimCube(object):
'''
A wrapping class to prepare a simulated spectral data cube for
comparison with another cube.
'''
def __init__(self, cube, beam=None, mask=None, method="MAD", compute=True):
# Initialize cube object
self.cube = SpectralCube.read(cube)
if mask is not None:
_check_mask(mask)
self.mask = mask
if beam is not None:
_check_beam(mask)
# Initialize noise object
self.noise = Noise(self.cube, beam=beam, method=method)
def add_noise(self):
'''
Use Noise to add synthetic noise to the data. Then update
SpectralCube.
'''
# Create the noisy cube
self.noise.get_noise_cube()
noise_data = self.noise.noise_cube +\
self.cube.filled_data[:]
# Update SpectralCube object
self._update(data=noise_data)
return self
def clean_cube(self, algorithm=None):
raise NotImplementedError("")
def apply_mask(self, mask=None):
'''
Check if the given mask is acceptable abd apply to
SpectralCube.
'''
# Update mask
if mask is not None:
_check_mask(mask)
self.mask = mask
# Create the mask, auto masking nan values
default_mask = np.isfinite(self.cube.filled_data[:])
if self.mask is not None:
self.mask = CompositeMask(default_mask, self.mask)
else:
self.mask = default_mask
# Apply mask to spectral cube object
self.cube = self.cube.with_mask(mask)
return self
def _update(self, data=None, wcs=None, beam=None, method="MAD"):
'''
Helper function to update classes.
'''
# Check if we need a new SpectralCube
if data is None and wcs is None:
pass
else:
if data is None:
data = self.cube.unmasked_data[:]
if wcs is None:
wcs = self.cube.wcs
# Make new SpectralCube object
self.cube = SpectralCube(data=data, wcs=wcs)
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam, method=method)
def compute_properties(self):
'''
Use SpectralCube to compute the moments. Also compute the integrated
intensity based on the noise properties from Noise.
'''
self._moment0 = self.cube.moment0().value
self._moment1 = self.cube.moment1().value
self._moment2 = self.cube.moment2().value
_get_int_intensity(self)
return self
@property
def moment0(self):
return self._moment0
@property
def moment1(self):
return self._moment1
@property
def moment2(self):
return self._moment2
@property
def intint(self):
return self._intint
def sim_prep(self, mask=None):
'''
Prepares the cube when being compared to another simulation.
This entails:
* Optionally applying a mask to the data.
* Computing the cube's property arrays
'''
if not mask is None:
self.apply_mask()
self.compute_properties()
return self
def obs_prep(self, mask=None):
'''
Prepares the cube when being compared to observational data.
This entails:
* Optionally applying a mask to the data.
* Add synthetic noise based on the cube's properties.
* Computing the cube's property arrays
'''
if not mask is None:
self.apply_mask()
self.add_noise()
self.compute_properties()
return self
class ObsCube(object):
"""
A wrapping class of SpectralCube which prepares observational data cubes
to be compared to any other data cube.
Parameters
----------
cube : str
Path to file.
mask : numpy.ndarray, any mask class from spectral_cube, optional
Mask to be applied to the cube.
algorithm : {NAME HERE}, optional
Name of the cleaning algorithm to use.
Example
-------
```
from turbustat.cube_tools import ObsCube
cube = ObsCube("data.fits")
cube.apply_cleaning(algorithm="SUPERAWESOMECLEANING")
```
"""
def __init__(self, cube, mask=None, algorithm=None, beam=None):
super(ObsCube, self).__init__()
self.cube = sc.SpectralCube.read(cube)
self.algorithm = algorithm
# Make sure mask is an accepted type
if mask is not None:
_check_mask(mask)
self.mask = mask
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam)
def clean_cube(self, algorithm=None):
raise NotImplementedError("")
def apply_mask(self, mask=None):
'''
Check if the given mask is acceptable abd apply to
SpectralCube.
'''
# Update mask
if mask is not None:
_check_mask(mask)
self.mask = mask
# Create the mask, auto masking nan values
default_mask = np.isfinite(self.cube.filled_data[:])
if self.mask is not None:
self.mask = CompositeMask(default_mask, self.mask)
else:
self.mask = default_mask
# Apply mask to spectral cube object
self.cube = self.cube.with_mask(mask)
return self
def _update(self, data=None, wcs=None, beam=None, method="MAD"):
'''
Helper function to update classes.
'''
# Check if we need a new SpectralCube
if data is None and wcs is None:
pass
else:
if data is None:
data = self.cube.unmasked_data[:]
if wcs is None:
wcs = self.cube.wcs
# Make new SpectralCube object
self.cube = SpectralCube(data=data, wcs=wcs)
if beam is not None:
_check_beam(beam)
self.noise = Noise(self.cube, beam=beam, method=method)
def compute_properties(self):
'''
Use SpectralCube to compute the moments. Also compute the integrated
intensity based on the noise properties from Noise.
'''
self._moment0 = self.cube.moment0().value
self._moment1 = self.cube.moment1().value
self._moment2 = self.cube.moment2().value
_get_int_intensity(self)
return self
@property
def moment0(self):
return self._moment0
@property
def moment1(self):
return self._moment1
@property
def moment2(self):
return self._moment2
@property
def intint(self):
return self._intint
def prep(self, mask=None, algorithm=None):
'''
Prepares the cube to be compared to another cube.
'''
if not mask is None:
self.apply_mask()
self.clean_cube()
self.compute_properties()
return self
