def smooth_lightcone(lightcone, z_array, box_size_mpc=False, max_baseline=2., ratio=1.):
"""
This smooths in both angular and frequency direction assuming both to be smoothed by same scale.
Parameters:
* lightcone (numpy array): The lightcone that is to be smoothed.
* z_array (float) : The lowest value of the redshift in the lightcone or the whole redshift array.
* box_size_mpc (float) : The box size in Mpc. Default value is determined from
the box size set for the simulation (set_sim_constants)
* max_baseline (float) : The maximun baseline of the telescope in km. Default value
is set as 2 km (SKA core).
* ratio (int) : It is the ratio of smoothing scale in frequency direction and
the angular direction (Default value: 1).
Returns:
* (Smoothed_lightcone, redshifts)
"""
if (not box_size_mpc): box_size_mpc=conv.LB
if(z_array.shape[0] == lightcone.shape[2]):
input_redshifts = z_array.copy()
else:
z_low = z_array
cell_size = 1.0*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)
output_dtheta = (1+input_redshifts)*21e-5/max_baseline
output_ang_res = output_dtheta*cm.z_to_cdist(input_redshifts)
output_dz = ratio*output_ang_res/const.c
for i in xrange(len(output_dz)):
output_dz[i] = output_dz[i] * hubble_parameter(input_redshifts[i])
output_lightcone = smooth_lightcone_tophat(lightcone, input_redshifts, output_dz)
output_lightcone = smooth_lightcone_gauss(output_lightcone, output_ang_res*lightcone.shape[0]/box_size_mpc)
return output_lightcone, input_redshifts
def calc_dt_lightcone(xfrac, dens, lowest_z, los_axis=2):
'''
Calculate the differential brightness temperature assuming T_s >> T_CMB
for lightcone data.
Parameters:
* xfrac (string or numpy array): the name of the ionization
fraction file (must be cbin), or the xfrac lightcone data
* dens (string or numpy array): the name of the density
file (must be cbin), or the density data
* lowest_z (float): the lowest redshift of the lightcone volume
* los_axis = 2 (int): the line-of-sight axis
Returns:
The differential brightness temperature as a numpy array with
the same dimensions as xfrac.
'''
try:
xfrac = read_cbin(xfrac)
except Exception:
pass
try:
dens = read_cbin(dens)
except:
pass
dens = dens.astype('float64')
cell_size = conv.LB / xfrac.shape[(los_axis + 1) % 3]
cdist_low = cosmology.z_to_cdist(lowest_z)
cdist = np.arange(xfrac.shape[los_axis]) * cell_size + cdist_low
z = cosmology.cdist_to_z(cdist)
return _dt(dens, xfrac, z)
def calc_dt_lightcone(xfrac, dens, lowest_z, los_axis=2):
"""
Calculate the differential brightness temperature assuming T_s >> T_CMB
for lightcone data.
Parameters:
* xfrac (string or numpy array): the name of the ionization
fraction file (must be cbin), or the xfrac lightcone data
* dens (string or numpy array): the name of the density
file (must be cbin), or the density data
* lowest_z (float): the lowest redshift of the lightcone volume
* los_axis = 2 (int): the line-of-sight axis
Returns:
The differential brightness temperature as a numpy array with
the same dimensions as xfrac.
"""
try:
xfrac = read_cbin(xfrac)
except Exception:
pass
try:
dens = read_cbin(dens)
except:
pass
dens = dens.astype("float64")
cell_size = conv.LB / xfrac.shape[(los_axis + 1) % 3]
cdist_low = cosmology.z_to_cdist(lowest_z)
cdist = np.arange(xfrac.shape[los_axis]) * cell_size + cdist_low
z = cosmology.cdist_to_z(cdist)
return _dt(dens, xfrac, z)
def smooth_coeval(cube,
z,
box_size_mpc=False,
max_baseline=2.,
ratio=1.,
nu_axis=2):
"""
This smooths the coeval cube by Gaussian in angular direction and by tophat along the third axis.
Parameters:
* coeval_cube (numpy array): The lightcone that is to be smoothed.
* z (float) : The redshift of the coeval cube.
* box_size_mpc (float) : The box size in Mpc. Default value is determined from
the box size set for the simulation (set_sim_constants)
* max_baseline (float) : The maximun baseline of the telescope in km. Default value
is set as 2 km (SKA core).
* ratio (int) : It is the ratio of smoothing scale in frequency direction and
the angular direction (Default value: 1).
Returns:
* Smoothed_coeval_cube
"""
if (~box_size_mpc): box_size_mpc = conv.LB
output_dtheta = (1 + z) * 21e-5 / max_baseline
output_ang_res = output_dtheta * cm.z_to_cdist(
z) * cube.shape[0] / box_size_mpc
output_cube = smooth_coeval_tophat(cube,
output_ang_res * ratio,
nu_axis=nu_axis)
output_cube = smooth_coeval_gauss(output_cube,
output_ang_res,
nu_axis=nu_axis)
return output_cube
def smooth_lightcone(lightcone,
z_array,
box_size_mpc=False,
max_baseline=2.,
ratio=1.):
"""
This smooths in both angular and frequency direction assuming both to be smoothed by same scale.
Parameters:
* lightcone (numpy array): The lightcone that is to be smoothed.
* z_array (float) : The lowest value of the redshift in the lightcone or the whole redshift array.
* box_size_mpc (float) : The box size in Mpc. Default value is determined from
the box size set for the simulation (set_sim_constants)
* max_baseline (float) : The maximun baseline of the telescope in km. Default value
is set as 2 km (SKA core).
* ratio (int) : It is the ratio of smoothing scale in frequency direction and
the angular direction (Default value: 1).
Returns:
* (Smoothed_lightcone, redshifts)
"""
if (not box_size_mpc): box_size_mpc = conv.LB
if (z_array.shape[0] == lightcone.shape[2]):
input_redshifts = z_array.copy()
else:
z_low = z_array
cell_size = 1.0 * 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)
output_dtheta = (1 + input_redshifts) * 21e-5 / max_baseline
output_ang_res = output_dtheta * cm.z_to_cdist(input_redshifts)
output_dz = ratio * output_ang_res / const.c
for i in xrange(len(output_dz)):
output_dz[i] = output_dz[i] * hubble_parameter(input_redshifts[i])
output_lightcone = smooth_lightcone_tophat(lightcone, input_redshifts,
output_dz)
output_lightcone = smooth_lightcone_gauss(
output_lightcone, output_ang_res * lightcone.shape[0] / box_size_mpc)
return output_lightcone, input_redshifts
def calc_dt_full_lightcone(xfrac,
temp,
dens,
lowest_z,
los_axis=2,
correct=True):
'''
Calculate the differential brightness temperature assuming only that Lyman alpha is fully coupled so T_s = T_k
(NOT T_s >> T_CMB) for lightcone data. UNTESTED
Parameters:
* xfrac (string or numpy array): the name of the ionization
fraction file (must be cbin), or the xfrac lightcone data
* temp (string or numpy array): the name of the temperature
file (must be cbin), or the temp lightcone data
* dens (string or numpy array): the name of the density
file (must be cbin), or the density data
* lowest_z (float): the lowest redshift of the lightcone volume
* los_axis = 2 (int): the line-of-sight axis
* correct = True (bool): if true include a correction for
partially ionized cells.
Returns:
The differential brightness temperature as a numpy array with
the same dimensions as xfrac.
'''
try:
xfrac = read_cbin(xfrac)
except Exception:
pass
try:
temp = read_cbin(temp)
except Exception:
pass
try:
dens = read_cbin(dens)
except:
pass
dens = dens.astype('float64')
cell_size = conv.LB / xfrac.shape[(los_axis + 1) % 3]
cdist_low = cosmology.z_to_cdist(lowest_z)
cdist = np.arange(xfrac.shape[los_axis]) * cell_size + cdist_low
z = cosmology.cdist_to_z(cdist)
print "Redshift: ", str(z)
return _dt_full(dens, xfrac, temp, z, correct)
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 = scipy.signal.fftconvolve(lightcone,
tophat3d,
mode='same')
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_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 calc_dt_full_lightcone(xfrac, temp, dens, lowest_z, los_axis = 2, correct=True):
'''
Calculate the differential brightness temperature assuming only that Lyman alpha is fully coupled so T_s = T_k
(NOT T_s >> T_CMB) for lightcone data. UNTESTED
Parameters:
* xfrac (string or numpy array): the name of the ionization
fraction file (must be cbin), or the xfrac lightcone data
* temp (string or numpy array): the name of the temperature
file (must be cbin), or the temp lightcone data
* dens (string or numpy array): the name of the density
file (must be cbin), or the density data
* lowest_z (float): the lowest redshift of the lightcone volume
* los_axis = 2 (int): the line-of-sight axis
* correct = True (bool): if true include a correction for
partially ionized cells.
Returns:
The differential brightness temperature as a numpy array with
the same dimensions as xfrac.
'''
try:
xfrac = read_cbin(xfrac)
except Exception:
pass
try:
temp = read_cbin(temp)
except Exception:
pass
try:
dens = read_cbin(dens)
except:
pass
dens = dens.astype('float64')
cell_size = conv.LB/xfrac.shape[(los_axis+1)%3]
cdist_low = cosmology.z_to_cdist(lowest_z)
cdist = np.arange(xfrac.shape[los_axis])*cell_size + cdist_low
z = cosmology.cdist_to_z(cdist)
print "Redshift: ", str(z)
return _dt_full(dens, xfrac,temp, z, correct)
def smooth_coeval(cube, z, box_size_mpc=False, max_baseline=2., ratio=1., nu_axis=2):
"""
This smooths the coeval cube by Gaussian in angular direction and by tophat along the third axis.
Parameters:
* coeval_cube (numpy array): The lightcone that is to be smoothed.
* z (float) : The redshift of the coeval cube.
* box_size_mpc (float) : The box size in Mpc. Default value is determined from
the box size set for the simulation (set_sim_constants)
* max_baseline (float) : The maximun baseline of the telescope in km. Default value
is set as 2 km (SKA core).
* ratio (int) : It is the ratio of smoothing scale in frequency direction and
the angular direction (Default value: 1).
Returns:
* Smoothed_coeval_cube
"""
if (~box_size_mpc): box_size_mpc=conv.LB
output_dtheta = (1+z)*21e-5/max_baseline
output_ang_res = output_dtheta*cm.z_to_cdist(z) * cube.shape[0]/box_size_mpc
output_cube = smooth_coeval_tophat(cube, output_ang_res*ratio, nu_axis=nu_axis)
output_cube = smooth_coeval_gauss(output_cube, output_ang_res, nu_axis=nu_axis)
return output_cube
