def average_main(main_ds, time_bin_secs, chan_bin_size, group_row_chunks,
respect_flag_row):
"""
Parameters
----------
main_ds : list of Datasets
Dataset containing Measurement Set columns.
Should have a DATA_DESC_ID attribute.
time_bin_secs : float
Number of time bins to average together
chan_bin_size : int
Number of channels to average together
group_row_chunks : int, optional
Number of row chunks to concatenate together
respect_flag_row : bool
Respect FLAG_ROW instead of using FLAG
for computing row flags.
Returns
-------
avg
tuple containing averaged data
"""
# Get the appropriate spectral window and polarisation Dataset
# Must have a single DDID table
output_ds = []
for ds in main_ds:
if respect_flag_row is False:
ds = ds.assign(FLAG_ROW=(("row", ), ds.FLAG.data.all(axis=(1, 2))))
dv = ds.data_vars
# Default kwargs
kwargs = {
'time_bin_secs': time_bin_secs,
'chan_bin_size': chan_bin_size,
'vis': dv['DATA'].data
}
# Other columns with directly transferable names
columns = [
'FLAG_ROW', 'TIME_CENTROID', 'EXPOSURE', 'WEIGHT', 'SIGMA', 'UVW',
'FLAG', 'WEIGHT_SPECTRUM', 'SIGMA_SPECTRUM'
]
for c in columns:
try:
kwargs[c.lower()] = dv[c].data
except KeyError:
pass
# Set up the average operation
avg = time_and_channel(dv['TIME'].data, dv['INTERVAL'].data,
dv['ANTENNA1'].data, dv['ANTENNA2'].data,
**kwargs)
output_ds.append(
output_dataset(avg, ds.FIELD_ID, ds.DATA_DESC_ID, ds.SCAN_NUMBER,
group_row_chunks))
return output_ds
def average_main(main_ds,
field_ds,
time_bin_secs,
chan_bin_size,
fields,
scan_numbers,
group_row_chunks,
respect_flag_row,
viscolumn="DATA"):
"""
Parameters
----------
main_ds : list of Datasets
Dataset containing Measurement Set columns.
Should have a DATA_DESC_ID attribute.
field_ds : list of Datasets
Each Dataset corresponds to a row of the FIELD table.
time_bin_secs : float
Number of time bins to average together
chan_bin_size : int
Number of channels to average together
fields : list
scan_numbers : list
group_row_chunks : int, optional
Number of row chunks to concatenate together
respect_flag_row : bool
Respect FLAG_ROW instead of using FLAG
for computing row flags.
viscolumn: string
name of column to average
Returns
-------
avg
tuple containing averaged data
"""
output_ds = []
for ds in main_ds:
if fields and ds.FIELD_ID not in fields:
continue
if scan_numbers and ds.SCAN_NUMBER not in scan_numbers:
continue
if respect_flag_row is False:
ds = ds.assign(FLAG_ROW=(("row", ), ds.FLAG.data.all(axis=(1, 2))))
dv = ds.data_vars
# Default kwargs.
kwargs = {
'time_bin_secs': time_bin_secs,
'chan_bin_size': chan_bin_size,
'vis': dv[viscolumn].data
}
# Other columns with directly transferable names
columns = [
'FLAG_ROW', 'TIME_CENTROID', 'EXPOSURE', 'WEIGHT', 'SIGMA', 'UVW',
'FLAG', 'WEIGHT_SPECTRUM', 'SIGMA_SPECTRUM'
]
for c in columns:
try:
kwargs[c.lower()] = dv[c].data
except KeyError:
pass
# Set up the average operation
avg = time_and_channel(dv['TIME'].data, dv['INTERVAL'].data,
dv['ANTENNA1'].data, dv['ANTENNA2'].data,
**kwargs)
output_ds.append(
output_dataset(avg, ds.FIELD_ID, ds.DATA_DESC_ID, ds.SCAN_NUMBER,
group_row_chunks))
return output_ds
def average_main(main_ds,
time_bin_secs,
chan_bin_size,
group_row_chunks,
respect_flag_row=True,
viscolumn="DATA",
sel_cols=None):
""" Perform averaging of the main table of the MS
At this point,the input :attr:`main_ds` is a list containing an MS
grouped by DDID, FIELD_ID & SCAN_NUMBER.
Parameters
----------
main_ds : list of Datasets
Dataset containing Measurement Set columns.
Should have a DATA_DESC_ID attribute.
time_bin_secs : float
Number of time bins to average together
chan_bin_size : int
Number of channels to average together
group_row_chunks : int, optional
Number of row chunks to concatenate together
respect_flag_row : bool
Respect FLAG_ROW instead of using FLAG
for computing row flags.
viscolumn: string
name of column to average
sel_cols: list
Columns that need to be present in the dataset
Returns
-------
avg
tuple containing averaged data
"""
output_ds = []
# for each group
for ds in main_ds:
if respect_flag_row is False:
if len(ds.FLAG.dims) > 2:
ds = ds.assign(FLAG_ROW=(("row", ),
ds.FLAG.data.all(axis=(1, 2))))
else:
ds = ds.assign(FLAG_ROW=(("row", ), ds.FLAG.data.all(axis=1)))
# store the subgroup's data variables
dv = ds.data_vars
# Default kwargs.
kwargs = {
"time_bin_secs": time_bin_secs,
"chan_bin_size": chan_bin_size,
"vis": dv[viscolumn].data
}
# Other columns with directly transferable names
# columns = ["FLAG_ROW", "TIME_CENTROID", "EXPOSURE", "WEIGHT",
# "SIGMA",
# "UVW", "FLAG", "WEIGHT_SPECTRUM", "SIGMA_SPECTRUM"]
columns = ["TIME_CENTROID"] + sel_cols
for c in columns:
if c != viscolumn:
try:
kwargs[c.lower()] = dv[c].data
except KeyError:
pass
# Set up the average operation
avg = time_and_channel(**kwargs)
output_ds.append(
output_dataset(avg,
ds.FIELD_ID,
ds.DATA_DESC_ID,
ds.SCAN_NUMBER,
group_row_chunks,
viscolumn=viscolumn))
return output_ds