Source code for africanus.calibration.utils.residual_vis

# -*- coding: utf-8 -*-

import numpy as np
from functools import wraps
from africanus.util.docs import DocstringTemplate
from africanus.util.numba import generated_jit, njit
from africanus.calibration.utils import check_type
from africanus.calibration.utils.utils import DIAG_DIAG, DIAG, FULL


def subtract_model_factory(mode):
    if mode == DIAG_DIAG:
        def subtract_model(a1j, blj, a2j, model, out):
            n_dir = np.shape(model)[0]
            out[...] = blj
            for s in range(n_dir):
                out -= a1j[s]*model[s]*np.conj(a2j[s])
    elif mode == DIAG:
        def subtract_model(a1j, blj, a2j, model, out):
            n_dir = np.shape(model)[0]
            out[...] = blj
            for s in range(n_dir):
                out[0, 0] -= a1j[s, 0]*model[s, 0, 0] * np.conj(a2j[s, 0])
                out[0, 1] -= a1j[s, 0]*model[s, 0, 1] * np.conj(a2j[s, 1])
                out[1, 0] -= a1j[s, 1]*model[s, 1, 0] * np.conj(a2j[s, 0])
                out[1, 1] -= a1j[s, 1]*model[s, 1, 1] * np.conj(a2j[s, 1])
    elif mode == FULL:
        def subtract_model(a1j, blj, a2j, model, out):
            n_dir = np.shape(model)[0]
            out[...] = blj
            for s in range(n_dir):
                # precompute resuable terms
                t1 = a1j[s, 0, 0]*model[s, 0, 0]
                t2 = a1j[s, 0, 1]*model[s, 1, 0]
                t3 = a1j[s, 0, 0]*model[s, 0, 1]
                t4 = a1j[s, 0, 1]*model[s, 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]*model[s, 0, 0]
                t2 = a1j[s, 1, 1]*model[s, 1, 0]
                t3 = a1j[s, 1, 0]*model[s, 0, 1]
                t4 = a1j[s, 1, 1]*model[s, 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')(subtract_model)


[docs]@generated_jit(nopython=True, nogil=True, cache=True) def residual_vis(time_bin_indices, time_bin_counts, antenna1, antenna2, jones, vis, flag, model): mode = check_type(jones, vis) subtract_model = subtract_model_factory(mode) @wraps(residual_vis) def _residual_vis_fn(time_bin_indices, time_bin_counts, antenna1, antenna2, jones, vis, flag, model): # 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] vis_shape = np.shape(vis) n_chan = vis_shape[1] residual = np.zeros(vis_shape, dtype=vis.dtype) 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): if not np.any(flag[row, nu]): subtract_model( gp[nu], vis[row, nu], gq[nu], model[row, nu], residual[row, nu]) return residual return _residual_vis_fn
RESIDUAL_VIS_DOCS = DocstringTemplate(""" Computes residual visibilities given model visibilities and gains solutions. 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) Gain solutions of shape :code:`(time, ant, chan, dir, corr)` or :code:`(time, ant, chan, dir, corr, corr)`. vis : $(array_type) Data values of shape :code:`(row, chan, corr)`. or :code:`(row, chan, corr, corr)`. flag : $(array_type) Flag data of shape :code:`(row, chan, corr)` or :code:`(row, chan, corr, corr)` model : $(array_type) Model data values of shape :code:`(row, chan, dir, corr)` or :code:`(row, chan, dir, corr, corr)`. Returns ------- residual : $(array_type) Residual visibilities of shape :code:`(time, ant, chan, dir, corr)` or :code:`(time, ant, chan, dir, corr, corr)`. """) try: residual_vis.__doc__ = RESIDUAL_VIS_DOCS.substitute( array_type=":class:`numpy.ndarray`") except AttributeError: pass