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Fix bda overload to return an implementation #307

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May 23, 2024
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Fix bda overload to return an implementation #307

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d0ab3a6
Fix bda overload to return an implementation
sjperkins May 23, 2024
2b6b41f
[skip ci] Update HISTORY.rst
sjperkins May 23, 2024
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1 change: 1 addition & 0 deletions HISTORY.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ History

X.Y.Z (YYYY-MM-DD)
------------------
* Fix bda overload to return an implementation (:pr:`307`)
* Upgrade obsolete readthedocs configuration (:pr:`304`)

0.3.6 (2024-04-15)
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177 changes: 119 additions & 58 deletions africanus/averaging/bda_avg.py
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Expand Up @@ -3,6 +3,7 @@
from collections import namedtuple

import numpy as np
from numba import types

from africanus.averaging.bda_mapping import bda_mapper, RowMapOutput
from africanus.averaging.shared import chan_corrs, merge_flags, vis_output_arrays
Expand Down Expand Up @@ -757,72 +758,132 @@ def nb_bda_impl(
time_bin_secs=None,
min_nchan=1,
):
# Merge flag_row and flag arrays
flag_row = merge_flags(flag_row, flag)
if is_numba_type_none(chan_width):
return TypeError(f"chan_width must be provided")

meta = bda_mapper(
if is_numba_type_none(chan_freq):
return TypeError(f"chan_freq must be provided")

if is_numba_type_none(uvw):
raise TypeError(f"uvw must be provided")

valid_types = (
types.misc.NoneType,
types.misc.Omitted,
types.scalars.Float,
types.scalars.Integer,
)

if not isinstance(max_uvw_dist, valid_types):
raise TypeError(f"max_uvw_dist ({max_uvw_dist}) must be a scalar float")

if not isinstance(max_fov, valid_types):
raise TypeError(f"max_fov ({max_fov}) must be a scalar float")

if not isinstance(decorrelation, valid_types):
raise TypeError(f"decorrelation ({decorrelation}) must be a scalar float")

if not isinstance(time_bin_secs, valid_types):
raise TypeError(f"time_bin_secs ({time_bin_secs}) must be a scalar float")

valid_types = (types.misc.NoneType, types.misc.Omitted, types.scalars.Integer)

if not isinstance(min_nchan, valid_types):
raise TypeError(f"min_nchan ({min_nchan}) must be an integer")

def impl(
time,
interval,
antenna1,
antenna2,
uvw,
chan_width,
chan_freq,
max_uvw_dist,
flag_row=flag_row,
max_fov=max_fov,
decorrelation=decorrelation,
time_bin_secs=time_bin_secs,
min_nchan=min_nchan,
)
time_centroid=None,
exposure=None,
flag_row=None,
uvw=None,
weight=None,
sigma=None,
chan_freq=None,
chan_width=None,
effective_bw=None,
resolution=None,
visibilities=None,
flag=None,
weight_spectrum=None,
sigma_spectrum=None,
max_uvw_dist=None,
max_fov=3.0,
decorrelation=0.98,
time_bin_secs=None,
min_nchan=1,
):
# Merge flag_row and flag arrays
flag_row = merge_flags(flag_row, flag)

meta = bda_mapper(
time,
interval,
antenna1,
antenna2,
uvw,
chan_width,
chan_freq,
max_uvw_dist,
flag_row=flag_row,
max_fov=max_fov,
decorrelation=decorrelation,
time_bin_secs=time_bin_secs,
min_nchan=min_nchan,
)

row_avg = row_average(
meta,
antenna1,
antenna2,
flag_row, # noqa: F841
time_centroid,
exposure,
uvw,
weight=weight,
sigma=sigma,
)
row_avg = row_average(
meta,
antenna1,
antenna2,
flag_row, # noqa: F841
time_centroid,
exposure,
uvw,
weight=weight,
sigma=sigma,
)

row_chan_avg = row_chan_average(
meta, # noqa: F841
flag_row=flag_row,
visibilities=visibilities,
flag=flag,
weight_spectrum=weight_spectrum,
sigma_spectrum=sigma_spectrum,
)
row_chan_avg = row_chan_average(
meta, # noqa: F841
flag_row=flag_row,
visibilities=visibilities,
flag=flag,
weight_spectrum=weight_spectrum,
sigma_spectrum=sigma_spectrum,
)

# Have to explicitly write it out because numba tuples
# are highly constrained types
return AverageOutput(
meta.map,
meta.offsets,
meta.decorr_chan_width,
meta.time,
meta.interval,
meta.chan_width,
meta.flag_row,
row_avg.antenna1,
row_avg.antenna2,
row_avg.time_centroid,
row_avg.exposure,
row_avg.uvw,
row_avg.weight,
row_avg.sigma,
# None, # chan_data.chan_freq,
# None, # chan_data.chan_width,
# None, # chan_data.effective_bw,
# None, # chan_data.resolution,
row_chan_avg.visibilities,
row_chan_avg.flag,
row_chan_avg.weight_spectrum,
row_chan_avg.sigma_spectrum,
)
# Have to explicitly write it out because numba tuples
# are highly constrained types
return AverageOutput(
meta.map,
meta.offsets,
meta.decorr_chan_width,
meta.time,
meta.interval,
meta.chan_width,
meta.flag_row,
row_avg.antenna1,
row_avg.antenna2,
row_avg.time_centroid,
row_avg.exposure,
row_avg.uvw,
row_avg.weight,
row_avg.sigma,
# None, # chan_data.chan_freq,
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Is there a reason why chan_freq is not returned?

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@sjperkins sjperkins May 23, 2024 •
edited
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Good question. Part of the reason is that it technically transforms to (row, chan) dimensionality as the frequencies and channel widths can also vary per row. This sort of data is available on the meta object, higher up in this namedtuple construction. There's a chan_width member with (row, chan) dimensionality. However, there isn't a chan_freq member. Do you need it? I suppose it wouldn't be necessary if one were only doing time BDA.

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Indeed, I don't need it for this reason but just wanted to make sure it's not an oversight.

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Ah there's already an issue

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I should add that when doing time only BDA you shoudn't need uvw to be expanded as rowchan. This actually uses quite a bit of memory. It's not a train smash for now but it might be worth adding a time only BDA function in the future that doesn't do this

# None, # chan_data.chan_width,
# None, # chan_data.effective_bw,
# None, # chan_data.resolution,
row_chan_avg.visibilities,
row_chan_avg.flag,
row_chan_avg.weight_spectrum,
row_chan_avg.sigma_spectrum,
)

return impl


BDA_DOCS = DocstringTemplate(
Expand Down
16 changes: 14 additions & 2 deletions africanus/averaging/tests/test_bda_averaging.py
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Expand Up @@ -7,7 +7,7 @@
import pytest

from africanus.averaging.bda_mapping import RowMapOutput
from africanus.averaging.bda_avg import row_average, row_chan_average
from africanus.averaging.bda_avg import bda as bda_avg, row_average, row_chan_average
from africanus.averaging.dask import bda as dask_bda


Expand Down Expand Up @@ -94,7 +94,7 @@ def _calc_sigma(weight, sigma, rows):
return np.sqrt(numerator / denominator)


def test_bda_avg(bda_test_map, inv_bda_test_map, flags):
def test_bda_avg_in_parts(bda_test_map, inv_bda_test_map, flags):
rs = np.random.RandomState(42)

# Derive flag_row from flags
Expand All @@ -119,6 +119,7 @@ def test_bda_avg(bda_test_map, inv_bda_test_map, flags):
weight = rs.normal(size=(in_row, in_corr))
sigma = rs.normal(size=(in_row, in_corr))
chan_width = np.repeat(0.856e9 / out_chan, out_chan)
chan_freq = np.linspace(0.856e9, 2 * 0.856e9, chan_width.size)

# Aggregate time and interval, in_row => out_row
# first channel in the map. We're only averaging over
Expand Down Expand Up @@ -236,6 +237,17 @@ def test_bda_avg(bda_test_map, inv_bda_test_map, flags):
assert_array_almost_equal(row_chan_avg.weight_spectrum, out_ws)
assert_array_almost_equal(row_chan_avg.sigma_spectrum, out_ss)

result = bda_avg(
time=time,
interval=interval,
antenna1=ant1,
antenna2=ant2,
visibilities=vis,
uvw=uvw,
chan_width=chan_width,
chan_freq=chan_freq,
)


@pytest.mark.parametrize("vis_format", ["ragged", "flat"])
def test_dask_bda_avg(vis_format):
Expand Down