xarray Datasets from CASA Tables.
Project description
Constructs xarray Datasets from CASA Tables via python-casacore.
The DataArrays contained in the Dataset are dask arrays backed by
deferred calls to pyrap.tables.table.getcol.
Supports writing DataArrays back to the respective column in the Table.
The intention behind this package is to support the Measurement Set as a data source and sink for the purposes of writing parallel, distributed Radio Astronomy algorithms.
import dask.array as da
from xarrayms import xds_from_table, xds_to_table
# Create xarray dataset from Measurement Set "WSRT.MS"
ds = xds_from_table("WSRT.MS")
# Set the flag DataArray to it's inverse
ds['flag'] = (ds.flag.dims, da.logical_not(ds.flag))
# Write the flag column back to the Measurement Set
xds_to_table(ds, "WSRT.MS", "FLAG").compute()
print ds
<xarray.Dataset>
Dimensions: ((u,v,w): 3, chan: 64, corr: 4, row: 6552, table_row: 6552)
Coordinates:
* row (row) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
* table_row (table_row) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
Dimensions without coordinates: (u,v,w), chan, corr
Data variables:
ANTENNA1 (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
ANTENNA2 (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
ARRAY_ID (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
CORRECTED_DATA (row, chan, corr) complex64 dask.array<shape=(6552, 64, 4), chunksize=(1000, 64, 4)>
DATA (row, chan, corr) complex64 dask.array<shape=(6552, 64, 4), chunksize=(1000, 64, 4)>
EXPOSURE (row) float64 dask.array<shape=(6552,), chunksize=(1000,)>
FEED1 (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
FEED2 (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
FLAG (row, chan, corr) bool dask.array<shape=(6552, 64, 4), chunksize=(1000, 64, 4)>
FLAG_ROW (row) bool dask.array<shape=(6552,), chunksize=(1000,)>
IMAGING_WEIGHT (row, chan) float32 dask.array<shape=(6552, 64), chunksize=(1000, 64)>
INTERVAL (row) float64 dask.array<shape=(6552,), chunksize=(1000,)>
MODEL_DATA (row, chan, corr) complex64 dask.array<shape=(6552, 64, 4), chunksize=(1000, 64, 4)>
OBSERVATION_ID (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
PROCESSOR_ID (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
SCAN_NUMBER (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
SIGMA (row, corr) float32 dask.array<shape=(6552, 4), chunksize=(1000, 4)>
STATE_ID (row) int32 dask.array<shape=(6552,), chunksize=(1000,)>
TIME (row) float64 dask.array<shape=(6552,), chunksize=(1000,)>
TIME_CENTROID (row) float64 dask.array<shape=(6552,), chunksize=(1000,)>
UVW (row, (u,v,w)) float64 dask.array<shape=(6552, 3), chunksize=(1000, 3)>
WEIGHT (row, corr) float32 dask.array<shape=(6552, 4), chunksize=(1000, 4)>
Attributes:
FIELD_ID: 0
DATA_DESC_ID: 0Documentation
Limitations
Many Measurement Sets columns are defined as variably shaped, but the actual data is fixed. xarray-ms will infer the shape of the data from the first row and must be consistent with that of other rows. For example, this may be issue where multiple Spectral Windows are present in the Measurement Set with differing channels per SPW.
xarray-ms works around this by partitioning the Measurement Set into multiple datasets. The first row’s shape is used to infer the shape of the partition. Thus, in the case of multiple Spectral Window’s, we can partition the Measurement Set by DATA_DESC_ID to create a dataset for each Spectral Window.
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