def mu_binning(powerspectrum,
los_axis=0,
mubins=20,
kbins=10,
box_dims=None,
weights=None,
exclude_zero_modes=True,
binning='log'):
'''
This function is for internal use only.
'''
if weights != None:
powerspectrum *= weights
assert (len(powerspectrum.shape) == 3)
k_comp, k = _get_k(powerspectrum, box_dims)
mu = _get_mu(k_comp, k, los_axis)
#Calculate k values, and make k bins
kbins = _get_kbins(kbins, box_dims, k, binning=binning)
dk = (kbins[1:] - kbins[:-1]) / 2.
n_kbins = len(kbins) - 1
#Exclude k_perp = 0 modes
if exclude_zero_modes:
good_idx = _get_nonzero_idx(powerspectrum.shape, los_axis)
else:
good_idx = np.ones_like(powerspectrum)
#Make mu bins
if isinstance(mubins, int):
mubins = np.linspace(-1., 1., mubins + 1)
dmu = (mubins[1:] - mubins[:-1]) / 2.
n_mubins = len(mubins) - 1
#Remove the zero component from the power spectrum. mu is undefined here
powerspectrum[tuple(np.array(powerspectrum.shape) / 2)] = 0.
#Bin the data
print_msg('Binning data...')
outdata = np.zeros((n_mubins, n_kbins))
for ki in range(n_kbins):
print_msg('Bin %d of %d' % (ki, n_kbins))
kmin = kbins[ki]
kmax = kbins[ki + 1]
kidx = get_eval()('(k >= kmin) & (k < kmax)')
kidx *= good_idx
for i in range(n_mubins):
mu_min = mubins[i]
mu_max = mubins[i + 1]
idx = get_eval()('(mu >= mu_min) & (mu < mu_max) & kidx')
outdata[i, ki] = np.mean(powerspectrum[idx])
if weights != None:
outdata[i, ki] /= weights[idx].mean()
return outdata, mubins[:-1] + dmu, kbins[:-1] + dk
def mu_binning(powerspectrum, los_axis = 0, mubins=20, kbins=10, box_dims=None, weights=None,
exclude_zero_modes=True, binning='log'):
'''
This function is for internal use only.
'''
if weights != None:
powerspectrum *= weights
assert(len(powerspectrum.shape)==3)
k_comp, k = _get_k(powerspectrum, box_dims)
mu = _get_mu(k_comp, k, los_axis)
#Calculate k values, and make k bins
kbins = _get_kbins(kbins, box_dims, k, binning=binning)
dk = (kbins[1:]-kbins[:-1])/2.
n_kbins = len(kbins)-1
#Exclude k_perp = 0 modes
if exclude_zero_modes:
good_idx = _get_nonzero_idx(powerspectrum.shape, los_axis)
else:
good_idx = np.ones_like(powerspectrum)
#Make mu bins
if isinstance(mubins,int):
mubins = np.linspace(-1., 1., mubins+1)
dmu = (mubins[1:]-mubins[:-1])/2.
n_mubins = len(mubins)-1
#Remove the zero component from the power spectrum. mu is undefined here
powerspectrum[tuple(np.array(powerspectrum.shape)/2)] = 0.
#Bin the data
print_msg('Binning data...')
outdata = np.zeros((n_mubins,n_kbins))
for ki in range(n_kbins):
print_msg('Bin %d of %d' % (ki, n_kbins))
kmin = kbins[ki]
kmax = kbins[ki+1]
kidx = get_eval()('(k >= kmin) & (k < kmax)')
kidx *= good_idx
for i in range(n_mubins):
mu_min = mubins[i]
mu_max = mubins[i+1]
idx = get_eval()('(mu >= mu_min) & (mu < mu_max) & kidx')
outdata[i,ki] = np.mean(powerspectrum[idx])
if weights != None:
outdata[i,ki] /= weights[idx].mean()
return outdata, mubins[:-1]+dmu, kbins[:-1]+dk
def _get_k(input_array, box_dims):
'''
Get the k values for input array with given dimensions.
Return k components and magnitudes.
For internal use.
'''
dim = len(input_array.shape)
if dim == 1:
x = np.arange(len(input_array))
center = x.max()/2.
kx = 2.*np.pi*(x-center)/box_dims[0]
return [kx], kx
elif dim == 2:
x,y = np.indices(input_array.shape, dtype='int32')
center = np.array([(x.max()-x.min())/2, (y.max()-y.min())/2])
kx = 2.*np.pi * (x-center[0])/box_dims[0]
ky = 2.*np.pi * (y-center[1])/box_dims[1]
k = np.sqrt(kx**2 + ky**2)
return [kx, ky], k
elif dim == 3:
x,y,z = np.indices(input_array.shape, dtype='int32')
center = np.array([(x.max()-x.min())/2, (y.max()-y.min())/2, \
(z.max()-z.min())/2])
kx = 2.*np.pi * (x-center[0])/box_dims[0]
ky = 2.*np.pi * (y-center[1])/box_dims[1]
kz = 2.*np.pi * (z-center[2])/box_dims[2]
k = get_eval()('(kx**2 + ky**2 + kz**2 )**(1./2.)')
return [kx,ky,kz], k
def _get_k(input_array, box_dims):
'''
Get the k values for input array with given dimensions.
Return k components and magnitudes.
For internal use.
'''
dim = len(input_array.shape)
if dim == 1:
x = np.arange(len(input_array))
center = x.max() / 2.
kx = 2. * np.pi * (x - center) / box_dims[0]
return [kx], kx
elif dim == 2:
x, y = np.indices(input_array.shape, dtype='int32')
center = np.array([(x.max() - x.min()) / 2, (y.max() - y.min()) / 2])
kx = 2. * np.pi * (x - center[0]) / box_dims[0]
ky = 2. * np.pi * (y - center[1]) / box_dims[1]
k = np.sqrt(kx**2 + ky**2)
return [kx, ky], k
elif dim == 3:
x, y, z = np.indices(input_array.shape, dtype='int32')
center = np.array([(x.max()-x.min())/2, (y.max()-y.min())/2, \
(z.max()-z.min())/2])
kx = 2. * np.pi * (x - center[0]) / box_dims[0]
ky = 2. * np.pi * (y - center[1]) / box_dims[1]
kz = 2. * np.pi * (z - center[2]) / box_dims[2]
k = get_eval()('(kx**2 + ky**2 + kz**2 )**(1./2.)')
return [kx, ky, kz], k