l and m components in the last dimension.
frequency : :class:`numpy.ndarray`
frequencies of shape :code:`(chan,)`
dtype : np.dtype, optional
Datatype of result. Should be either np.complex64 or np.complex128.
If ``None``, :func:`numpy.result_type` is used to infer the data type
from the inputs.
""",
returns="""
Returns
-------
visibilties : :class:`numpy.ndarray`
complex of shape :code:`(row, chan, corr)`
""")
im_to_vis.__doc__ = doc_tuple_to_str(im_to_vis_docs)
vis_to_im_docs = _DFT_DOCSTRING(
preamble="""
Computes visibility to image mapping
of an ideal interferometer:
.. math::
{\\Large \\sum_k e^{ 2 \\pi i (u_k l + v_k m + w_k (n - 1))} \\cdot V_k}
""", # noqa
parameters="""
Parameters
----------
""" Dask wrapper for vis_to_im function """
if vis.chunks[0] != uvw.chunks[0]:
raise ValueError("Vis chunks and uvw chunks must "
"match on first axis")
if vis.chunks[1] != frequency.chunks[0]:
raise ValueError("Vis chunks must match frequency "
"chunks on second axis")
if vis.chunks != flags.chunks:
raise ValueError("Vis chunks must match flags " "chunks on all axes")
ims = da.core.blockwise(_vis_to_im_wrapper,
("row", "source", "chan", "corr"),
vis, ("row", "chan", "corr"),
uvw, ("row", "(u,v,w)"),
lm, ("source", "(l,m)"),
frequency, ("chan", ),
flags, ("row", "chan", "corr"),
adjust_chunks={"row": 1},
dtype=dtype,
dtype_=dtype)
return ims.sum(axis=0)
im_to_vis.__doc__ = doc_tuple_to_str(
im_to_vis_docs, [(":class:`numpy.ndarray`", ":class:`dask.array.Array`")])
vis_to_im.__doc__ = doc_tuple_to_str(
vis_to_im_docs, [(":class:`numpy.ndarray`", ":class:`dask.array.Array`")])