def beam_convolve(input_array, z, fov_mpc, beam_w = None, max_baseline = None, \
beamshape='gaussian'):
'''
Convolve input_array with a beam of the specified form.
The beam can be specified either by a width in arcminutes,
or as a maximum baseline. You must specify exctly one of these
parameters.
Parameters:
* input_array (numpy array): the array to be convolved
* z (float): the redshift of the map
* fov_mpc (float): the field of view in cMpc
* beam_w (float) = None: the width of the beam in arcminutes
* max_baseline (float): the maximum baseline in meters
(can be specified instead of beam_w)
* beamshape (string): The shape of the beam
(only 'gaussian' supported at this time)
Returns:
The convolved array (a numpy array with the same dimensions
as input_array).
'''
if (not beam_w) and (not max_baseline):
raise Exception(
'Please specify either a beam width or a maximum baseline')
elif not beam_w: #Calculate beam width from max baseline
beam_w = get_beam_w(max_baseline, z)
angle = angular_size(fov_mpc * 1000. / (1.0 + z), z) / 60.
mx = input_array.shape[0]
print_msg('Field of view is %.2f arcminutes' % (angle))
print_msg('Convolving with %s beam of size %.2f arcminutes...' % \
(beamshape, beam_w) )
#Convolve with beam
if beamshape == 'gaussian':
sigma0 = (beam_w) / angle / (2.0 * np.sqrt(2.0 * np.log(2.))) * mx
kernel = gauss_kernel(sigma=sigma0, size=mx)
else:
raise Exception('Unknown beamshape: %g' % beamshape)
#out = signal.fftconvolve(input_array, kernel)
out = fftconvolve(input_array, kernel)
#fftconvolve makes the output twice the size, so return only the central part
ox = out.shape[0]
return out[ox * 0.25:ox * 0.75, ox * 0.25:ox * 0.75]
def beam_convolve(input_array, z, fov_mpc, beam_w = None, max_baseline = None, \
beamshape='gaussian'):
'''
Convolve input_array with a beam of the specified form.
The beam can be specified either by a width in arcminutes,
or as a maximum baseline. You must specify exctly one of these
parameters.
Parameters:
* input_array (numpy array): the array to be convolved
* z (float): the redshift of the map
* fov_mpc (float): the field of view in cMpc
* beam_w (float) = None: the width of the beam in arcminutes
* max_baseline (float): the maximum baseline in meters
(can be specified instead of beam_w)
* beamshape (string): The shape of the beam
(only 'gaussian' supported at this time)
Returns:
The convolved array (a numpy array with the same dimensions
as input_array).
'''
if (not beam_w) and (not max_baseline):
raise Exception('Please specify either a beam width or a maximum baseline')
elif not beam_w: #Calculate beam width from max baseline
beam_w = get_beam_w(max_baseline, z)
angle = angular_size(fov_mpc*1000./(1.0 + z), z)/60.
mx = input_array.shape[0]
print_msg('Field of view is %.2f arcminutes' % (angle) )
print_msg('Convolving with %s beam of size %.2f arcminutes...' % \
(beamshape, beam_w) )
#Convolve with beam
if beamshape == 'gaussian':
sigma0 = (beam_w)/angle/(2.0 * np.sqrt(2.0*np.log(2.)))*mx
kernel = gauss_kernel(sigma=sigma0, size=mx)
else:
raise Exception('Unknown beamshape: %g' % beamshape)
#out = signal.fftconvolve(input_array, kernel)
out = fftconvolve(input_array, kernel)
#fftconvolve makes the output twice the size, so return only the central part
ox = out.shape[0]
return out[ox*0.25:ox*0.75, ox*0.25:ox*0.75]
def smooth_with_kernel(input_array, kernel):
'''
Smooth the input array with an arbitrary kernel.
Parameters:
* input_array (numpy array): the array to smooth
* kernel (numpy array): the smoothing kernel. Must
be the same size as the input array
Returns:
The smoothed array. A numpy array with the same
dimensions as the input.
'''
assert len(input_array.shape) == len(kernel.shape)
out = fftconvolve(input_array, kernel)
return out
def smooth_with_kernel(input_array, kernel): ''' Smooth the input array with an arbitrary kernel. Parameters: * input_array (numpy array): the array to smooth * kernel (numpy array): the smoothing kernel. Must be the same size as the input array Returns: The smoothed array. A numpy array with the same dimensions as the input. ''' assert len(input_array.shape) == len(kernel.shape) out = fftconvolve(input_array, kernel) return out
def bin_lightcone_in_frequency(lightcone, z_low, box_size_mpc, dnu):
'''
Bin a lightcone in frequency bins.
Parameters:
* lightcone (numpy array): the lightcone in length units
* z_low (float): the lowest redshift of the lightcone
* box_size_mpc (float): the side of the lightcone in Mpc
* dnu (float): the width of the frequency bins in MHz
Returns:
The lightcone, binned in frequencies with high frequencies first
The frequencies along the line of sight in MHz
'''
#Figure out dimensions and make output volume
cell_size = box_size_mpc/lightcone.shape[0]
distances = cm.z_to_cdist(z_low) + np.arange(lightcone.shape[2])*cell_size
input_redshifts = cm.cdist_to_z(distances)
input_frequencies = cm.z_to_nu(input_redshifts)
nu1 = input_frequencies[0]
nu2 = input_frequencies[-1]
output_frequencies = np.arange(nu1, nu2, -dnu)
output_lightcone = np.zeros((lightcone.shape[0], lightcone.shape[1], \
len(output_frequencies)))
#Bin in frequencies by smoothing and indexing
max_cell_size = cm.nu_to_cdist(output_frequencies[-1])-cm.nu_to_cdist(output_frequencies[-2])
smooth_scale = np.round(max_cell_size/cell_size)
if smooth_scale < 1:
smooth_scale = 1
hf.print_msg('Smooth along LoS with scale %f' % smooth_scale)
tophat3d = np.ones((1,1,smooth_scale))
tophat3d /= np.sum(tophat3d)
lightcone_smoothed = fftconvolve(lightcone, tophat3d)
for i in range(output_lightcone.shape[2]):
nu = output_frequencies[i]
idx = hf.find_idx(input_frequencies, nu)
output_lightcone[:,:,i] = lightcone_smoothed[:,:,idx]
return output_lightcone, output_frequencies
def bin_lightcone_in_mpc(lightcone, frequencies, cell_size_mpc):
'''
Bin a lightcone in Mpc slices along the LoS
'''
distances = cm.nu_to_cdist(frequencies)
n_output_cells = (distances[-1]-distances[0])/cell_size_mpc
output_distances = np.arange(distances[0], distances[-1], cell_size_mpc)
output_lightcone = np.zeros((lightcone.shape[0], lightcone.shape[1], n_output_cells))
#Bin in Mpc by smoothing and indexing
smooth_scale = np.round(len(frequencies)/n_output_cells)
tophat3d = np.ones((1,1,smooth_scale))
tophat3d /= np.sum(tophat3d)
lightcone_smoothed = fftconvolve(lightcone, tophat3d, mode='same')
for i in range(output_lightcone.shape[2]):
idx = hf.find_idx(distances, output_distances[i])
output_lightcone[:,:,i] = lightcone_smoothed[:,:,idx]
output_redshifts = cm.cdist_to_z(output_distances)
return output_lightcone, output_redshifts