# -*- coding: utf-8 -*-
import numpy as np
from africanus.util.docs import DocstringTemplate
from africanus.util.numba import generated_jit, njit
from africanus.calibration.utils import check_type
from africanus.constants import minus_two_pi_over_c as m2pioc
from africanus.calibration.utils.utils import DIAG_DIAG, DIAG, FULL
def jones_mul_factory(mode):
if mode == DIAG_DIAG:
def jones_mul(a1j, model, a2j, uvw, freq, lm, out):
n_dir = np.shape(model)[0]
u, v, w = uvw
for s in range(n_dir):
l, m = lm[s]
n = np.sqrt(1 - l**2 - m**2)
real_phase = m2pioc * freq * (u*l + v*m + w*(n-1))
source_vis = model[s] * np.exp(1.0j*real_phase)/n
for c in range(out.shape[-1]):
out[c] += a1j[s, c]*source_vis[c]*np.conj(a2j[s, c])
elif mode == DIAG:
def jones_mul(a1j, model, a2j, uvw, freq, lm, out):
n_dir = np.shape(model)[0]
u, v, w = uvw
for s in range(n_dir):
l, m = lm[s]
n = np.sqrt(1 - l**2 - m**2)
real_phase = m2pioc * freq * (u*l + v*m + w*(n-1))
source_vis = model[s] * np.exp(1.0j*real_phase)/n
out[0, 0] += a1j[s, 0]*source_vis[0, 0] * np.conj(a2j[s, 0])
out[0, 1] += a1j[s, 0]*source_vis[0, 1] * np.conj(a2j[s, 1])
out[1, 0] += a1j[s, 1]*source_vis[1, 0] * np.conj(a2j[s, 0])
out[1, 1] += a1j[s, 1]*source_vis[1, 1] * np.conj(a2j[s, 1])
elif mode == FULL:
def jones_mul(a1j, model, a2j, uvw, freq, lm, out):
n_dir = np.shape(model)[0]
u, v, w = uvw
for s in range(n_dir):
l, m = lm[s]
n = np.sqrt(1 - l**2 - m**2)
real_phase = m2pioc * freq * (u*l + v*m + w*(n-1))
source_vis = model[s] * np.exp(1.0j*real_phase)/n
# precompute resuable terms
t1 = a1j[s, 0, 0]*source_vis[0, 0]
t2 = a1j[s, 0, 1]*source_vis[1, 0]
t3 = a1j[s, 0, 0]*source_vis[0, 1]
t4 = a1j[s, 0, 1]*source_vis[1, 1]
tmp = np.conj(a2j[s].T)
# overwrite with result
out[0, 0] += t1*tmp[0, 0] +\
t2*tmp[0, 0] +\
t3*tmp[1, 0] +\
t4*tmp[1, 0]
out[0, 1] += t1*tmp[0, 1] +\
t2*tmp[0, 1] +\
t3*tmp[1, 1] +\
t4*tmp[1, 1]
t1 = a1j[s, 1, 0]*source_vis[0, 0]
t2 = a1j[s, 1, 1]*source_vis[1, 0]
t3 = a1j[s, 1, 0]*source_vis[0, 1]
t4 = a1j[s, 1, 1]*source_vis[1, 1]
out[1, 0] += t1*tmp[0, 0] +\
t2*tmp[0, 0] +\
t3*tmp[1, 0] +\
t4*tmp[1, 0]
out[1, 1] += t1*tmp[0, 1] +\
t2*tmp[0, 1] +\
t3*tmp[1, 1] +\
t4*tmp[1, 1]
return njit(nogil=True, inline='always')(jones_mul)
[docs]@generated_jit(nopython=True, nogil=True, cache=True)
def compute_and_corrupt_vis(time_bin_indices, time_bin_counts, antenna1,
antenna2, jones, model, uvw, freq, lm):
mode = check_type(jones, model, vis_type='model')
jones_mul = jones_mul_factory(mode)
def _compute_and_corrupt_vis_fn(time_bin_indices, time_bin_counts,
antenna1, antenna2, jones, model,
uvw, freq, lm):
# for dask arrays we need to adjust the chunks to
# start counting from zero
time_bin_indices -= time_bin_indices.min()
n_tim = np.shape(time_bin_indices)[0]
model_shape = np.shape(model)
vis_shape = (antenna1.shape[0],) + (freq.shape[0],) + model.shape[3:]
vis = np.zeros(vis_shape, dtype=jones.dtype)
n_chan = model_shape[1]
for t in range(n_tim):
for row in range(time_bin_indices[t],
time_bin_indices[t] + time_bin_counts[t]):
p = int(antenna1[row])
q = int(antenna2[row])
gp = jones[t, p]
gq = jones[t, q]
for nu in range(n_chan):
jones_mul(gp[nu], model[t, nu], gq[nu], uvw[row],
freq[nu], lm[t], vis[row, nu])
return vis
return _compute_and_corrupt_vis_fn
COMPUTE_AND_CORRUPT_VIS_DOCS = DocstringTemplate("""
Corrupts time variable component model with arbitrary
Jones terms. Currrently only time variable point source
models are supported.
Parameters
----------
time_bin_indices : $(array_type)
The start indices of the time bins
of shape :code:`(utime)`
time_bin_counts : $(array_type)
The counts of unique time in each
time bin of shape :code:`(utime)`
antenna1 : $(array_type)
First antenna indices of shape :code:`(row,)`.
antenna2 : $(array_type)
Second antenna indices of shape :code:`(row,)`
jones : $(array_type)
Gains of shape :code:`(utime, ant, chan, dir, corr)`
or :code:`(utime, ant, chan, dir, corr, corr)`.
model : $(array_type)
Model image as a function of time with shape
:code:`(utime, chan, dir, corr)` or
:code:`(utime, chan, dir, corr, corr)`.
uvw : $(array_type)
uvw coordinates of shape :code:`(row, 3)`
lm : $(array_type)
Source lm coordinates as a function of time
:code:`(utime, dir, 2)`
Returns
-------
vis : $(array_type)
visibilities of shape
:code:`(row, chan, corr)`
or :code:`(row, chan, corr, corr)`.
""")
try:
compute_and_corrupt_vis.__doc__ = COMPUTE_AND_CORRUPT_VIS_DOCS.substitute(
array_type=":class:`numpy.ndarray`")
except AttributeError:
pass