Open in Colab: https://colab.research.google.com/github/casangi/cngi_prototype/blob/master/docs/visibilities.ipynb


Visibilities

An overview of the visibility data structure and manipulation.

This walkthrough is designed to be run in a Jupyter notebook on Google Colaboratory. To open the notebook in colab, go here

[ ]:
# Installation
# for this demonstration we will use the data from the ALMA First Look at Imaging CASAguide
import os
print("installing cngi (takes a few minutes)...")
os.system("apt-get install libgfortran3")
os.system("pip install --index-url https://casa-pip.nrao.edu/repository/pypi-casa-release/simple casatools==6.0.0.27")
os.system("pip install cngi-prototype==0.0.57")

print("downloading MeasurementSet from CASAguide First Look at Imaging...")
os.system("wget https://bulk.cv.nrao.edu/almadata/public/working/sis14_twhya_calibrated_flagged.ms.tar")
os.system("tar -xvf sis14_twhya_calibrated_flagged.ms.tar")

print('complete')
installing cngi (takes a few minutes)...
downloading MeasurementSet from CASAguide First Look at Imaging...
complete

Initialize the Environment

InitializeFramework instantiates a client object (does not need to be returned and saved by caller). Once this object exists, all Dask objects automatically know to use it for parallel execution.

>>> from cngi.direct import InitializeFramework
>>> client = InitializeFramework(workers=4, memory='2GB')
>>> print(client)
<Client: 'tcp://127.0.0.1:33227' processes=4 threads=4, memory=8.00 GB>

Omitting this step will cause the subsequent Dask dataframe operations to use the default built-in scheduler for parallel execution (which can actually be faster on local machines anyway)

Google Colab doesn’t really support the dask.distributed environment particularly well, so we will let Dask use its default scheduler.

Convert an MS to Xarray/Zarr

CNGI uses an xarray data model and the zarr storage format to hold the contents of the MS.

The zarr format is a directory structure with partitions and chunks held under the top level folder. The MS data will be partitioned by the Data Description ID (DDI) column. A DDI is a unique SPW/Polarization pair so the data has a fixed shape within each DDI (but potentially different shapes across DDIs).

Various index columns are converted to xarray coordinates rather than data variables. Polarization and Spectral Window table data pertaining to each DDI is held in the coordinates and attributes section.

Remaining tables (other than the main visibilities, spw and polarization) are held in a global partition of all other meta data.

[ ]:
from cngi.conversion import convert_ms

convert_ms('sis14_twhya_calibrated_flagged.ms');
processing sis14_twhya_calibrated_flagged.ms
writing global partition
meta data processing time  0.8073570728302002
**********************************
Processing ddi 0
Selecting and sorting time  0.025266170501708984
n_time: 410   n_baseline: 210   n_chan: 384   n_pol: 2  chunking:  [100, 400, 20, 1]  batchsize:  100
idx_range, UVW 0 18414 (18414, 3)
idx_range, FLAG 0 18414 (18414, 384, 2)
idx_range, WEIGHT 0 18414 (18414, 2)
idx_range, SIGMA 0 18414 (18414, 2)
idx_range, ARRAY_ID 0 18414 (18414,)
col name ARRAY_ID
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, EXPOSURE 0 18414 (18414,)
col name EXPOSURE
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, FEED1 0 18414 (18414,)
col name FEED1
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, FEED2 0 18414 (18414,)
col name FEED2
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, FIELD_ID 0 18414 (18414,)
idx_range, FLAG_ROW 0 18414 (18414,)
col name FLAG_ROW
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, INTERVAL 0 18414 (18414,)
idx_range, OBSERVATION_ID 0 18414 (18414,)
idx_range, PROCESSOR_ID 0 18414 (18414,)
idx_range, SCAN_NUMBER 0 18414 (18414,)
idx_range, STATE_ID 0 18414 (18414,)
idx_range, TIME_CENTROID 0 18414 (18414,)
col name TIME_CENTROID
full datashape (100, 210)
data.shape (18414,)
full data subshape (18414,)
idx_range, DATA 0 18414 (18414, 384, 2)
idx_range, UVW 18414 20620 (20620, 3)
idx_range, FLAG 18414 20620 (20620, 384, 2)
idx_range, WEIGHT 18414 20620 (20620, 2)
idx_range, SIGMA 18414 20620 (20620, 2)
idx_range, ARRAY_ID 18414 20620 (20620,)
col name ARRAY_ID
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, EXPOSURE 18414 20620 (20620,)
col name EXPOSURE
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, FEED1 18414 20620 (20620,)
col name FEED1
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, FEED2 18414 20620 (20620,)
col name FEED2
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, FIELD_ID 18414 20620 (20620,)
idx_range, FLAG_ROW 18414 20620 (20620,)
col name FLAG_ROW
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, INTERVAL 18414 20620 (20620,)
idx_range, OBSERVATION_ID 18414 20620 (20620,)
idx_range, PROCESSOR_ID 18414 20620 (20620,)
idx_range, SCAN_NUMBER 18414 20620 (20620,)
idx_range, STATE_ID 18414 20620 (20620,)
idx_range, TIME_CENTROID 18414 20620 (20620,)
col name TIME_CENTROID
full datashape (100, 210)
data.shape (20620,)
full data subshape (20620,)
idx_range, DATA 18414 20620 (20620, 384, 2)
idx_range, UVW 39034 20485 (20485, 3)
idx_range, FLAG 39034 20485 (20485, 384, 2)
idx_range, WEIGHT 39034 20485 (20485, 2)
idx_range, SIGMA 39034 20485 (20485, 2)
idx_range, ARRAY_ID 39034 20485 (20485,)
col name ARRAY_ID
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, EXPOSURE 39034 20485 (20485,)
col name EXPOSURE
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, FEED1 39034 20485 (20485,)
col name FEED1
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, FEED2 39034 20485 (20485,)
col name FEED2
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, FIELD_ID 39034 20485 (20485,)
idx_range, FLAG_ROW 39034 20485 (20485,)
col name FLAG_ROW
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, INTERVAL 39034 20485 (20485,)
idx_range, OBSERVATION_ID 39034 20485 (20485,)
idx_range, PROCESSOR_ID 39034 20485 (20485,)
idx_range, SCAN_NUMBER 39034 20485 (20485,)
idx_range, STATE_ID 39034 20485 (20485,)
idx_range, TIME_CENTROID 39034 20485 (20485,)
col name TIME_CENTROID
full datashape (100, 210)
data.shape (20485,)
full data subshape (20485,)
idx_range, DATA 39034 20485 (20485, 384, 2)
idx_range, UVW 59519 18944 (18944, 3)
idx_range, FLAG 59519 18944 (18944, 384, 2)
idx_range, WEIGHT 59519 18944 (18944, 2)
idx_range, SIGMA 59519 18944 (18944, 2)
idx_range, ARRAY_ID 59519 18944 (18944,)
col name ARRAY_ID
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, EXPOSURE 59519 18944 (18944,)
col name EXPOSURE
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, FEED1 59519 18944 (18944,)
col name FEED1
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, FEED2 59519 18944 (18944,)
col name FEED2
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, FIELD_ID 59519 18944 (18944,)
idx_range, FLAG_ROW 59519 18944 (18944,)
col name FLAG_ROW
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, INTERVAL 59519 18944 (18944,)
idx_range, OBSERVATION_ID 59519 18944 (18944,)
idx_range, PROCESSOR_ID 59519 18944 (18944,)
idx_range, SCAN_NUMBER 59519 18944 (18944,)
idx_range, STATE_ID 59519 18944 (18944,)
idx_range, TIME_CENTROID 59519 18944 (18944,)
col name TIME_CENTROID
full datashape (100, 210)
data.shape (18944,)
full data subshape (18944,)
idx_range, DATA 59519 18944 (18944, 384, 2)
idx_range, UVW 78463 2100 (2100, 3)
idx_range, FLAG 78463 2100 (2100, 384, 2)
idx_range, WEIGHT 78463 2100 (2100, 2)
idx_range, SIGMA 78463 2100 (2100, 2)
idx_range, ARRAY_ID 78463 2100 (2100,)
col name ARRAY_ID
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, EXPOSURE 78463 2100 (2100,)
col name EXPOSURE
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, FEED1 78463 2100 (2100,)
col name FEED1
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, FEED2 78463 2100 (2100,)
col name FEED2
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, FIELD_ID 78463 2100 (2100,)
idx_range, FLAG_ROW 78463 2100 (2100,)
col name FLAG_ROW
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, INTERVAL 78463 2100 (2100,)
idx_range, OBSERVATION_ID 78463 2100 (2100,)
idx_range, PROCESSOR_ID 78463 2100 (2100,)
idx_range, SCAN_NUMBER 78463 2100 (2100,)
idx_range, STATE_ID 78463 2100 (2100,)
idx_range, TIME_CENTROID 78463 2100 (2100,)
col name TIME_CENTROID
full datashape (10, 210)
data.shape (2100,)
full data subshape (2100,)
idx_range, DATA 78463 2100 (2100, 384, 2)
Completed ddi 0  process time  18.539486408233643
**********************************

In our example, there is one DDI (0), so the resulting visibilities zarr directory has one partition plus the global data

[ ]:
!ls sis14_twhya_calibrated_flagged.vis.zarr/
0  global

Open and Inspect Visibility Data

Each SPW in the visiblity data must go in to its own xarray dataset.

Before trying to open something, we can use the describe_vis function to see what’s what in the zarr directory.

[ ]:
from cngi.dio import describe_vis

describe_vis('sis14_twhya_calibrated_flagged.vis.zarr')
spw_id size_GB channels times baselines fields
ddi
0 0 1.053953 384 410 210 410

If multiple DDI partitions exist, then each one would need to be read individually. In our example, there is only DDI 0, so we will read that one.

[ ]:
from cngi.dio import read_vis

# returns an xarray dataset representation of the specified zarr directory partition
vis_xds = read_vis('sis14_twhya_calibrated_flagged.vis.zarr', ddi=0)

Within the xarray dataset visibility structure, we find the old column names from the MS main table spread between several locations

Most of the main table columns are stored in the Data variables section, while the Coordinates section holds what used to be id and index columns. The Dimensions section holds the size of the visibility data axes as determined by the coordinates. Lastly, the Attributes section holds the remaining metadata from the polarization and spectral window tables associated with this DDI.

[ ]:
vis_xds
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • baseline: 210
    • chan: 384
    • pair: 2
    • pol: 2
    • receptor: 2
    • spw: 1
    • time: 410
    • uvw_index: 3
    • antennas
      (baseline, pair)
      int32
      dask.array<chunksize=(210, 2), meta=np.ndarray>
      Array Chunk
      Bytes 1.68 kB 1.68 kB
      Shape (210, 2) (210, 2)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      2 210
    • baseline
      (baseline)
      int64
      0 1 2 3 4 5 ... 205 206 207 208 209
      array([  0,   1,   2, ..., 207, 208, 209])
    • chan
      (chan)
      float64
      3.725e+11 3.725e+11 ... 3.728e+11
      array([3.725331e+11, 3.725337e+11, 3.725343e+11, ..., 3.727656e+11,
             3.727662e+11, 3.727669e+11])
    • chan_width
      (chan)
      float64
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 3.07 kB 160 B
      Shape (384,) (20,)
      Count 21 Tasks 20 Chunks
      Type float64 numpy.ndarray
      384 1
    • corr_product
      (receptor, pol)
      int32
      dask.array<chunksize=(2, 1), meta=np.ndarray>
      Array Chunk
      Bytes 16 B 8 B
      Shape (2, 2) (2, 1)
      Count 3 Tasks 2 Chunks
      Type int32 numpy.ndarray
      2 2
    • effective_bw
      (chan)
      float64
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 3.07 kB 160 B
      Shape (384,) (20,)
      Count 21 Tasks 20 Chunks
      Type float64 numpy.ndarray
      384 1
    • field
      (time)
      <U10
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 16.40 kB 4.00 kB
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type numpy.ndarray
      410 1
    • interval
      (time)
      float64
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 3.28 kB 800 B
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type float64 numpy.ndarray
      410 1
    • observation
      (time)
      <U22
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 36.08 kB 8.80 kB
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type numpy.ndarray
      410 1
    • pol
      (pol)
      int32
      9 12
      array([ 9, 12], dtype=int32)
    • processor
      (time)
      <U14
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 22.96 kB 5.60 kB
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type numpy.ndarray
      410 1
    • resolution
      (chan)
      float64
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 3.07 kB 160 B
      Shape (384,) (20,)
      Count 21 Tasks 20 Chunks
      Type float64 numpy.ndarray
      384 1
    • scan
      (time)
      int32
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 1.64 kB 400 B
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type int32 numpy.ndarray
      410 1
    • spw
      (spw)
      int32
      0
      array([0], dtype=int32)
    • state
      (time)
      <U82
      dask.array<chunksize=(100,), meta=np.ndarray>
      Array Chunk
      Bytes 134.48 kB 32.80 kB
      Shape (410,) (100,)
      Count 6 Tasks 5 Chunks
      Type numpy.ndarray
      410 1
    • time
      (time)
      datetime64[ns]
      2012-11-19T07:37:00 ... 2012-11-19T09:11:01.631999969
      array(['2012-11-19T07:37:00.000000000', '2012-11-19T07:37:06.048000336',
             '2012-11-19T07:37:12.095999718', ..., '2012-11-19T09:10:49.536000252',
             '2012-11-19T09:10:55.583999634', '2012-11-19T09:11:01.631999969'],
            dtype='datetime64[ns]')
    • uvw_index
      (uvw_index)
      <U2
      'uu' 'vv' 'ww'
      array(['uu', 'vv', 'ww'], dtype='<U2')
    • ARRAY_ID
      (time, baseline)
      int32
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 344.40 kB 84.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type int32 numpy.ndarray
      210 410
    • DATA
      (time, baseline, chan, pol)
      complex128
      dask.array<chunksize=(100, 210, 20, 1), meta=np.ndarray>
      Array Chunk
      Bytes 1.06 GB 6.72 MB
      Shape (410, 210, 384, 2) (100, 210, 20, 1)
      Count 201 Tasks 200 Chunks
      Type complex128 numpy.ndarray
      410 1 2 384 210
    • EXPOSURE
      (time, baseline)
      float64
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 688.80 kB 168.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type float64 numpy.ndarray
      210 410
    • FEED1
      (time, baseline)
      int32
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 344.40 kB 84.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type int32 numpy.ndarray
      210 410
    • FEED2
      (time, baseline)
      int32
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 344.40 kB 84.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type int32 numpy.ndarray
      210 410
    • FLAG
      (time, baseline, chan, pol)
      bool
      dask.array<chunksize=(100, 210, 20, 1), meta=np.ndarray>
      Array Chunk
      Bytes 66.12 MB 420.00 kB
      Shape (410, 210, 384, 2) (100, 210, 20, 1)
      Count 201 Tasks 200 Chunks
      Type bool numpy.ndarray
      410 1 2 384 210
    • FLAG_ROW
      (time, baseline)
      bool
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 86.10 kB 21.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type bool numpy.ndarray
      210 410
    • SIGMA
      (time, baseline, pol)
      float64
      dask.array<chunksize=(100, 210, 1), meta=np.ndarray>
      Array Chunk
      Bytes 1.38 MB 168.00 kB
      Shape (410, 210, 2) (100, 210, 1)
      Count 11 Tasks 10 Chunks
      Type float64 numpy.ndarray
      2 210 410
    • TIME_CENTROID
      (time, baseline)
      float64
      dask.array<chunksize=(100, 210), meta=np.ndarray>
      Array Chunk
      Bytes 688.80 kB 168.00 kB
      Shape (410, 210) (100, 210)
      Count 6 Tasks 5 Chunks
      Type float64 numpy.ndarray
      210 410
    • UVW
      (time, baseline, uvw_index)
      float64
      dask.array<chunksize=(100, 210, 3), meta=np.ndarray>
      Array Chunk
      Bytes 2.07 MB 504.00 kB
      Shape (410, 210, 3) (100, 210, 3)
      Count 6 Tasks 5 Chunks
      Type float64 numpy.ndarray
      3 210 410
    • WEIGHT
      (time, baseline, pol)
      float64
      dask.array<chunksize=(100, 210, 1), meta=np.ndarray>
      Array Chunk
      Bytes 1.38 MB 168.00 kB
      Shape (410, 210, 2) (100, 210, 1)
      Count 11 Tasks 10 Chunks
      Type float64 numpy.ndarray
      2 210 410
  • assoc_nature :
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
    assoc_spw_id :
    [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 20]
    auto_correlations :
    0
    bbc_no :
    2
    ddi :
    0
    freq_group :
    0
    freq_group_name :
    if_conv_chain :
    0
    meas_freq_ref :
    5
    name :
    ALMA_RB_07#BB_2#SW-01#FULL_RES
    net_sideband :
    2
    num_chan :
    384
    ref_frequency :
    372533086425.9812
    total_bandwidth :
    234375000.0

We can access the global meta data by reading the global partition. This is not associated with any single DDI and refers to the entire collection. Tables with contents that can be indexed cleanly are stored as data variables and coordinates, while remaining tables with more variable structure are stored as attributes.

Note that there are no visiblities or other main table columns in this partition.

[ ]:
global_xds = read_vis('sis14_twhya_calibrated_flagged.vis.zarr', ddi='global')

global_xds
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • antenna: 26
    • d1: 1
    • d2: 2
    • d3: 3
    • feed: 1
    • field: 7
    • observation: 1
    • processor: 3
    • receptors: 3
    • source: 5
    • spw: 1
    • state: 20
    • time_fcmd: 5067
    • time_hist: 1132
    • antenna
      (antenna)
      int64
      0 1 2 3 4 5 6 ... 20 21 22 23 24 25
      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23, 24, 25])
    • feed
      (feed)
      int64
      0
      array([0])
    • field
      (field)
      <U10
      'J0522-364' 'J0539+145' ... '3c279'
      array(['J0522-364', 'J0539+145', 'Ceres', 'J1037-295', 'TW Hya (4)',
             'TW Hya (5)', '3c279'], dtype='<U10')
    • observation
      (observation)
      <U22
      'uid://A002/X327408/X6f'
      array(['uid://A002/X327408/X6f'], dtype='<U22')
    • processor
      (processor)
      <U14
      'CORRELATOR (0)' ... 'CORRELATOR (2)'
      array(['CORRELATOR (0)', 'RADIOMETER', 'CORRELATOR (2)'], dtype='<U14')
    • receptors
      (receptors)
      int64
      0 1 2
      array([0, 1, 2])
    • source
      (source)
      int32
      0 1 2 3 4
      array([0, 1, 2, 3, 4], dtype=int32)
    • spw
      (spw)
      int64
      0
      array([0])
    • state
      (state)
      <U82
      'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (0)' ... 'OBSERVE_TARGET#ON_SOURCE (19)'
      array(['CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (0)',
             'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (1)',
             'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (2)',
             'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (3)',
             'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (4)',
             'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (5)',
             'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (6)',
             'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (7)',
             'CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (8)',
             'CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (9)',
             'OBSERVE_TARGET#ON_SOURCE (10)', 'OBSERVE_TARGET#ON_SOURCE (11)',
             'OBSERVE_TARGET#ON_SOURCE (12)', 'OBSERVE_TARGET#ON_SOURCE (13)',
             'OBSERVE_TARGET#ON_SOURCE (14)', 'OBSERVE_TARGET#ON_SOURCE (15)',
             'OBSERVE_TARGET#ON_SOURCE (16)', 'OBSERVE_TARGET#ON_SOURCE (17)',
             'OBSERVE_TARGET#ON_SOURCE (18)', 'OBSERVE_TARGET#ON_SOURCE (19)'],
            dtype='<U82')
    • time_fcmd
      (time_fcmd)
      datetime64[ns]
      2012-11-19T07:28:06.096000671 ... 2012-11-19T09:11:31.978034973
      array(['2012-11-19T07:28:06.096000671', '2012-11-19T07:28:29.918247223',
             '2012-11-19T07:28:30.013791084', ..., '2012-11-19T09:11:31.956748009',
             '2012-11-19T09:11:31.967335701', '2012-11-19T09:11:31.978034973'],
            dtype='datetime64[ns]')
    • time_hist
      (time_hist)
      datetime64[ns]
      2012-11-28T06:33:06.728000641 ... 2014-05-01T14:00:24.296000481
      array(['2012-11-28T06:33:06.728000641', '2012-11-28T06:33:06.833000183',
             '2012-11-28T06:33:06.876000404', ..., '2014-05-01T14:00:24.162000657',
             '2014-05-01T14:00:24.198999409', '2014-05-01T14:00:24.296000481'],
            dtype='datetime64[ns]')
    • ANT_DISH_DIAMETER
      (antenna)
      float64
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 208 B 208 B
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      26 1
    • ANT_FLAG_ROW
      (antenna)
      bool
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 26 B 26 B
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      26 1
    • ANT_MOUNT
      (antenna)
      <U16
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 1.66 kB 1.66 kB
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      26 1
    • ANT_NAME
      (antenna)
      <U16
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 1.66 kB 1.66 kB
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      26 1
    • ANT_OFFSET
      (antenna, d3)
      float64
      dask.array<chunksize=(26, 3), meta=np.ndarray>
      Array Chunk
      Bytes 624 B 624 B
      Shape (26, 3) (26, 3)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      3 26
    • ANT_POSITION
      (antenna, d3)
      float64
      dask.array<chunksize=(26, 3), meta=np.ndarray>
      Array Chunk
      Bytes 624 B 624 B
      Shape (26, 3) (26, 3)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      3 26
    • ANT_STATION
      (antenna)
      <U16
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 1.66 kB 1.66 kB
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      26 1
    • ANT_TYPE
      (antenna)
      <U16
      dask.array<chunksize=(26,), meta=np.ndarray>
      Array Chunk
      Bytes 1.66 kB 1.66 kB
      Shape (26,) (26,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      26 1
    • FCMD_APPLICATION
      (time_hist)
      <U16
      dask.array<chunksize=(1132,), meta=np.ndarray>
      Array Chunk
      Bytes 72.45 kB 72.45 kB
      Shape (1132,) (1132,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1132 1
    • FCMD_APPLIED
      (time_fcmd)
      bool
      dask.array<chunksize=(5067,), meta=np.ndarray>
      Array Chunk
      Bytes 5.07 kB 5.07 kB
      Shape (5067,) (5067,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      5067 1
    • FCMD_COMMAND
      (time_fcmd)
      <U75
      dask.array<chunksize=(1267,), meta=np.ndarray>
      Array Chunk
      Bytes 1.52 MB 380.10 kB
      Shape (5067,) (1267,)
      Count 5 Tasks 4 Chunks
      Type numpy.ndarray
      5067 1
    • FCMD_INTERVAL
      (time_fcmd)
      float64
      dask.array<chunksize=(5067,), meta=np.ndarray>
      Array Chunk
      Bytes 40.54 kB 40.54 kB
      Shape (5067,) (5067,)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      5067 1
    • FCMD_LEVEL
      (time_fcmd)
      int32
      dask.array<chunksize=(5067,), meta=np.ndarray>
      Array Chunk
      Bytes 20.27 kB 20.27 kB
      Shape (5067,) (5067,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      5067 1
    • FCMD_MESSAGE
      (time_hist)
      <U188
      dask.array<chunksize=(283,), meta=np.ndarray>
      Array Chunk
      Bytes 851.26 kB 212.82 kB
      Shape (1132,) (283,)
      Count 5 Tasks 4 Chunks
      Type numpy.ndarray
      1132 1
    • FCMD_OBJECT_ID
      (time_hist)
      int32
      dask.array<chunksize=(1132,), meta=np.ndarray>
      Array Chunk
      Bytes 4.53 kB 4.53 kB
      Shape (1132,) (1132,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      1132 1
    • FCMD_OBSERVATION_ID
      (time_hist)
      int32
      dask.array<chunksize=(1132,), meta=np.ndarray>
      Array Chunk
      Bytes 4.53 kB 4.53 kB
      Shape (1132,) (1132,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      1132 1
    • FCMD_ORIGIN
      (time_hist)
      <U24
      dask.array<chunksize=(1132,), meta=np.ndarray>
      Array Chunk
      Bytes 108.67 kB 108.67 kB
      Shape (1132,) (1132,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1132 1
    • FCMD_PRIORITY
      (time_hist)
      <U16
      dask.array<chunksize=(1132,), meta=np.ndarray>
      Array Chunk
      Bytes 72.45 kB 72.45 kB
      Shape (1132,) (1132,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1132 1
    • FCMD_REASON
      (time_fcmd)
      <U47
      dask.array<chunksize=(1267,), meta=np.ndarray>
      Array Chunk
      Bytes 952.60 kB 238.20 kB
      Shape (5067,) (1267,)
      Count 5 Tasks 4 Chunks
      Type numpy.ndarray
      5067 1
    • FCMD_SEVERITY
      (time_fcmd)
      int32
      dask.array<chunksize=(5067,), meta=np.ndarray>
      Array Chunk
      Bytes 20.27 kB 20.27 kB
      Shape (5067,) (5067,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      5067 1
    • FCMD_TYPE
      (time_fcmd)
      <U16
      dask.array<chunksize=(2534,), meta=np.ndarray>
      Array Chunk
      Bytes 324.29 kB 162.18 kB
      Shape (5067,) (2534,)
      Count 3 Tasks 2 Chunks
      Type numpy.ndarray
      5067 1
    • FEED_BEAM_ID
      (spw, antenna, feed)
      int32
      dask.array<chunksize=(1, 26, 1), meta=np.ndarray>
      Array Chunk
      Bytes 104 B 104 B
      Shape (1, 26, 1) (1, 26, 1)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      1 26 1
    • FEED_BEAM_OFFSET
      (spw, antenna, feed, d2, receptors)
      float64
      dask.array<chunksize=(1, 26, 1, 2, 3), meta=np.ndarray>
      Array Chunk
      Bytes 1.25 kB 1.25 kB
      Shape (1, 26, 1, 2, 3) (1, 26, 1, 2, 3)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      26 1 3 2 1
    • FEED_INTERVAL
      (spw, antenna, feed)
      float64
      dask.array<chunksize=(1, 26, 1), meta=np.ndarray>
      Array Chunk
      Bytes 208 B 208 B
      Shape (1, 26, 1) (1, 26, 1)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 26 1
    • FEED_NUM_RECEPTORS
      (spw, antenna, feed)
      int32
      dask.array<chunksize=(1, 26, 1), meta=np.ndarray>
      Array Chunk
      Bytes 104 B 104 B
      Shape (1, 26, 1) (1, 26, 1)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      1 26 1
    • FEED_POLARIZATION_TYPE
      (spw, antenna, feed, receptors)
      <U16
      dask.array<chunksize=(1, 26, 1, 3), meta=np.ndarray>
      Array Chunk
      Bytes 4.99 kB 4.99 kB
      Shape (1, 26, 1, 3) (1, 26, 1, 3)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1 1 3 1 26
    • FEED_POL_RESPONSE
      (spw, antenna, feed, receptors, receptors)
      complex128
      dask.array<chunksize=(1, 26, 1, 3, 3), meta=np.ndarray>
      Array Chunk
      Bytes 3.74 kB 3.74 kB
      Shape (1, 26, 1, 3, 3) (1, 26, 1, 3, 3)
      Count 2 Tasks 1 Chunks
      Type complex128 numpy.ndarray
      26 1 3 3 1
    • FEED_POSITION
      (spw, antenna, feed, d3)
      float64
      dask.array<chunksize=(1, 26, 1, 3), meta=np.ndarray>
      Array Chunk
      Bytes 624 B 624 B
      Shape (1, 26, 1, 3) (1, 26, 1, 3)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 1 3 1 26
    • FEED_RECEPTOR_ANGLE
      (spw, antenna, feed, receptors)
      float64
      dask.array<chunksize=(1, 26, 1, 3), meta=np.ndarray>
      Array Chunk
      Bytes 624 B 624 B
      Shape (1, 26, 1, 3) (1, 26, 1, 3)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 1 3 1 26
    • FEED_TIME
      (spw, antenna, feed)
      datetime64[ns]
      dask.array<chunksize=(1, 26, 1), meta=np.ndarray>
      Array Chunk
      Bytes 208 B 208 B
      Shape (1, 26, 1) (1, 26, 1)
      Count 2 Tasks 1 Chunks
      Type datetime64[ns] numpy.ndarray
      1 26 1
    • FIELD_CODE
      (field)
      <U16
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 448 B 448 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      7 1
    • FIELD_DELAY_DIR
      (field, d2, d1)
      float64
      dask.array<chunksize=(7, 2, 1), meta=np.ndarray>
      Array Chunk
      Bytes 112 B 112 B
      Shape (7, 2, 1) (7, 2, 1)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 2 7
    • FIELD_DelayDir_Ref
      (field)
      int32
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 28 B 28 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      7 1
    • FIELD_FLAG_ROW
      (field)
      bool
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 7 B 7 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      7 1
    • FIELD_NUM_POLY
      (field)
      int32
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 28 B 28 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      7 1
    • FIELD_PHASE_DIR
      (field, d2, d1)
      float64
      dask.array<chunksize=(7, 2, 1), meta=np.ndarray>
      Array Chunk
      Bytes 112 B 112 B
      Shape (7, 2, 1) (7, 2, 1)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 2 7
    • FIELD_PhaseDir_Ref
      (field)
      int32
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 28 B 28 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      7 1
    • FIELD_REFERENCE_DIR
      (field, d2, d1)
      float64
      dask.array<chunksize=(7, 2, 1), meta=np.ndarray>
      Array Chunk
      Bytes 112 B 112 B
      Shape (7, 2, 1) (7, 2, 1)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 2 7
    • FIELD_RefDir_Ref
      (field)
      int32
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 28 B 28 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      7 1
    • FIELD_SOURCE_ID
      (field)
      int32
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 28 B 28 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      7 1
    • FIELD_TIME
      (field)
      datetime64[ns]
      dask.array<chunksize=(7,), meta=np.ndarray>
      Array Chunk
      Bytes 56 B 56 B
      Shape (7,) (7,)
      Count 2 Tasks 1 Chunks
      Type datetime64[ns] numpy.ndarray
      7 1
    • OBS_FLAG_ROW
      (observation)
      bool
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 1 B 1 B
      Shape (1,) (1,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      1 1
    • OBS_OBSERVER
      (observation)
      <U16
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 64 B 64 B
      Shape (1,) (1,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1 1
    • OBS_RELEASE_DATE
      (observation)
      float64
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 8 B 8 B
      Shape (1,) (1,)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      1 1
    • OBS_SCHEDULE_TYPE
      (observation)
      <U16
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 64 B 64 B
      Shape (1,) (1,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1 1
    • OBS_TELESCOPE_NAME
      (observation)
      <U16
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 64 B 64 B
      Shape (1,) (1,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      1 1
    • OBS_TIME_RANGE
      (observation, d2)
      datetime64[ns]
      dask.array<chunksize=(1, 2), meta=np.ndarray>
      Array Chunk
      Bytes 16 B 16 B
      Shape (1, 2) (1, 2)
      Count 2 Tasks 1 Chunks
      Type datetime64[ns] numpy.ndarray
      2 1
    • PROC_FLAG_ROW
      (processor)
      bool
      dask.array<chunksize=(3,), meta=np.ndarray>
      Array Chunk
      Bytes 3 B 3 B
      Shape (3,) (3,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      3 1
    • PROC_MODE_ID
      (processor)
      int32
      dask.array<chunksize=(3,), meta=np.ndarray>
      Array Chunk
      Bytes 12 B 12 B
      Shape (3,) (3,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      3 1
    • PROC_SUB_TYPE
      (processor)
      <U21
      dask.array<chunksize=(3,), meta=np.ndarray>
      Array Chunk
      Bytes 252 B 252 B
      Shape (3,) (3,)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      3 1
    • PROC_TYPE_ID
      (processor)
      int32
      dask.array<chunksize=(3,), meta=np.ndarray>
      Array Chunk
      Bytes 12 B 12 B
      Shape (3,) (3,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      3 1
    • SRC_CALIBRATION_GROUP
      (spw, source)
      int32
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 20 B 20 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      5 1
    • SRC_CODE
      (spw, source)
      <U16
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 320 B 320 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      5 1
    • SRC_DIRECTION
      (spw, source, d2)
      float64
      dask.array<chunksize=(1, 5, 2), meta=np.ndarray>
      Array Chunk
      Bytes 80 B 80 B
      Shape (1, 5, 2) (1, 5, 2)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      2 5 1
    • SRC_INTERVAL
      (spw, source)
      float64
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 40 B 40 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      5 1
    • SRC_NAME
      (spw, source)
      <U16
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 320 B 320 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type numpy.ndarray
      5 1
    • SRC_NUM_LINES
      (spw, source)
      int32
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 20 B 20 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      5 1
    • SRC_PROPER_MOTION
      (spw, source, d2)
      float64
      dask.array<chunksize=(1, 5, 2), meta=np.ndarray>
      Array Chunk
      Bytes 80 B 80 B
      Shape (1, 5, 2) (1, 5, 2)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      2 5 1
    • SRC_TIME
      (spw, source)
      datetime64[ns]
      dask.array<chunksize=(1, 5), meta=np.ndarray>
      Array Chunk
      Bytes 40 B 40 B
      Shape (1, 5) (1, 5)
      Count 2 Tasks 1 Chunks
      Type datetime64[ns] numpy.ndarray
      5 1
    • STATE_CAL
      (state)
      float64
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 160 B 160 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      20 1
    • STATE_FLAG_ROW
      (state)
      bool
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 20 B 20 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      20 1
    • STATE_LOAD
      (state)
      float64
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 160 B 160 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type float64 numpy.ndarray
      20 1
    • STATE_REF
      (state)
      bool
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 20 B 20 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      20 1
    • STATE_SIG
      (state)
      bool
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 20 B 20 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type bool numpy.ndarray
      20 1
    • STATE_SUB_SCAN
      (state)
      int32
      dask.array<chunksize=(20,), meta=np.ndarray>
      Array Chunk
      Bytes 80 B 80 B
      Shape (20,) (20,)
      Count 2 Tasks 1 Chunks
      Type int32 numpy.ndarray
      20 1
  • asdm_antennaid :
    ['Antenna_0', 'Antenna_1', 'Antenna_2', 'Antenna_3', 'Antenna_4', 'Antenna_5', 'Antenna_6', 'Antenna_7', 'Antenna_8', 'Antenna_9', 'Antenna_10', 'Antenna_11', 'Antenna_12', 'Antenna_13', 'Antenna_14', 'Antenna_15', 'Antenna_16', 'Antenna_17', 'Antenna_18', 'Antenna_19', 'Antenna_20', 'Antenna_21', 'Antenna_22', 'Antenna_23', 'Antenna_24', 'Antenna_25']
    asdm_antennamake :
    ['AEM_12', 'AEM_12', 'AEM_12', 'AEM_12', 'AEM_12', 'AEM_12', 'AEM_12', 'AEM_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12', 'VERTEX_12']
    asdm_antennaname :
    ['DA41', 'DA42', 'DA44', 'DA45', 'DA46', 'DA48', 'DA49', 'DA50', 'DV01', 'DV02', 'DV04', 'DV05', 'DV06', 'DV07', 'DV08', 'DV10', 'DV13', 'DV15', 'DV16', 'DV17', 'DV18', 'DV19', 'DV20', 'DV21', 'DV22', 'DV23', 'DA41', 'DA42', 'DA44', 'DA45', 'DA46', 'DA48', 'DA49', 'DA50', 'DV01', 'DV02', 'DV04', 'DV05', 'DV06', 'DV07', 'DV08', 'DV10', 'DV13', 'DV15', 'DV16', 'DV17', 'DV18', 'DV19', 'DV20', 'DV21', 'DV22', 'DV23', 'DA41', 'DA42', 'DA44', 'DA45', 'DA46', 'DA48', 'DA49', 'DA50', 'DV01', 'DV02', 'DV04', 'DV05', 'DV06', 'DV07', 'DV08', 'DV10', 'DV13', 'DV15', 'DV16', 'DV17', 'DV18', 'DV19', 'DV20', 'DV21', 'DV22', 'DV23', 'DA41', 'DA42', 'DA44', 'DA45', 'DA46', 'DA48', 'DA49', 'DA50', 'DV01', 'DV02', 'DV04', 'DV05', 'DV06', 'DV07', 'DV08', 'DV10', 'DV13', 'DV15', 'DV16', 'DV17', 'DV18', 'DV19', 'DV20', 'DV21', 'DV22', 'DV23', 'DA41', 'DA42', 'DA44', 'DA45', 'DA46', 'DA48', 'DA49', 'DA50', 'DV01', 'DV02', 'DV04', 'DV05', 'DV06', 'DV07', 'DV08', 'DV10', 'DV13', 'DV15', 'DV16', 'DV17', 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    asdm_antennatype :
    ['GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED', 'GROUND_BASED']
    asdm_assocantennaid :
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    asdm_caldataid :
    ['CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_0', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_2', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_4', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_6', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_9', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_11', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_13', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_16', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_18', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_20', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_22', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_24', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_26', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_28', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_30', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_32', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_36', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_38', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_40', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_44', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_46', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_48', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_52', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_54', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_56', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_60', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62', 'CalData_62']
    asdm_calreductionid :
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    asdm_dishdiameter :
    [12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0]
    asdm_drypath :
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    asdm_freqlo :
    [[[97963275000.0]], [[97963275000.0], [9968650000.0], [4000000000.0]], [[97963275000.0], [9968650000.0], [4000000000.0]], [[97963275000.0], [8031150000.0], [4000000000.0]], [[97963275000.0], [8031150000.0], [4000000000.0]], [[97963275000.0], [8031350000.0], [4000000000.0]], [[97963275000.0], [8031350000.0], [4000000000.0]], [[97963275000.0], [10031350000.0], [4000000000.0]], [[97963275000.0], [10031350000.0], [4000000000.0]], [[364944056250.0]], [[364944056250.0], [9791500000.0], [4000000000.0]], [[364944056250.0], [9791500000.0], [4000000000.0]], [[364944056250.0], [10037943750.199976], [4000000000.0]], [[364944056250.0], [10037943750.199976], [4000000000.0]], [[364944056250.0], [8665298750.1], [4000000000.0]], [[364944056250.0], [8665298750.1], [4000000000.0]], [[364944056250.0], [9352646250.199976], [4000000000.0]], [[364944056250.0], [9352646250.199976], [4000000000.0]], [[364944056250.0], [9791500000.0], [4000000000.0]], [[364944056250.0], [9791500000.0], [4000000000.0]], [[364944056250.0], [10037943750.199976], [4000000000.0]], [[364944056250.0], [10037943750.199976], [4000000000.0]], [[364944056250.0], [8665298750.1], [4000000000.0]], [[364944056250.0], [8665298750.1], [4000000000.0]], [[364944056250.0], [9352646250.199976], [4000000000.0]], [[364944056250.0], [9352646250.199976], [4000000000.0]]]
    asdm_frequencyband :
    ['UNSPECIFIED', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'ALMA_RB_03', 'UNSPECIFIED', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07', 'ALMA_RB_07']
    asdm_inputantennanames :
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[['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']], [['DA41']], [['DA42']], [['DA44']], [['DA45']], [['DA46']], [['DA48']], [['DA49']], [['DA50']], [['DV01']], [['DV02']], [['DV04']], [['DV05']], [['DV06']], [['DV07']], [['DV08']], [['DV10']], [['DV13']], [['DV15']], [['DV16']], [['DV17']], [['DV18']], [['DV19']], [['DV20']], [['DV21']], [['DV22']], [['DV23']]]
    asdm_name :
    ['WVR', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'WVR', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED', 'UNDEFINED']
    asdm_numchan :
    [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
    asdm_numinputantennas :
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
    asdm_numlo :
    [1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
    asdm_numpoly :
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
    asdm_offset :
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    asdm_pathcoeff :
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    asdm_position :
    [[[9.9e-05], [-0.000115], [7.500033]], [[3.8e-05], [0.000292], [7.500074]], [[3.4e-05], [0.000626], [7.49732]], [[0.000333], [0.000643], [7.498208]], [[0.000289], [-0.001381], [7.499906]], [[-8.4e-05], [6.7e-05], [7.499763]], [[1e-05], [0.000534], [7.49897]], [[-0.000907], [-0.000308], [7.499382]], [[0.000473], [-0.00052], [6.999459]], [[-0.000637], [-0.000554], [7.001657]], [[-0.000803], [-0.000153], [6.999907]], [[-0.000309], [-0.001532], [6.998909]], [[-0.000114], [-0.000213], [6.999847]], [[0.01131], [0.017653], [6.970559]], [[-0.00012], [-0.001173], [6.998989]], [[0.001398], [-0.003363], [6.998192]], [[-0.000196], [-0.000406], [7.001167]], [[1.6e-05], [0.000255], [6.99965]], [[0.000129], [0.000137], [6.999042]], [[0.000393], [0.000168], [6.99868]], [[0.000618], [-0.001002], [6.999753]], [[0.000461], [-0.001071], [6.99965]], [[0.000975], [-0.000832], [7.000273]], [[-0.000795], [0.000289], [7.000906]], [[-0.000195], [7.4e-05], [6.999527]], [[0.000288], [4.7e-05], [6.999332]]]
    asdm_receiverid :
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    asdm_receiversideband :
    ['NOSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'NOSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB', 'TSB']
    asdm_reftemp :
    [[[[119.45744705200195], [48.738977725689224], [28.160551951481747], [22.21908532656156]]], [[[126.82360018216647], [40.38715582627516], [29.606380976163425], [24.355150662935696]]], [[[122.62845582228441], [52.45590107257549], [28.77484886462872], [23.059795893155613]]], [[[124.81207832923302], [52.87978641803448], [28.213841658372147], [22.855840389545147]]], [[[121.6668586730957], [49.8969720693735], [29.77100966526912], [24.09357004899245]]], [[[122.88836200420673], [52.99444653437688], [28.225390360905575], [22.67221186711238]]], [[[117.38252962552585], [49.310424217811], [27.15787806877723], [21.741148435152493]]], [[[124.71102435772235], [52.009916892418495], [28.87555716587947], [23.163367344782902]]], [[[125.58806786170372], [51.987132145808296], [27.31159672370324], [22.10403082920955]]], [[[125.60942165668195], [48.895204103910004], [24.07163304548997], [18.00851682516245]]], [[[128.6383508535532], [54.729009921734146], [28.021235319284294], [22.538579280559834]]], [[[125.32442973210262], [51.970823581402115], [27.807448313786434], [22.496627807617188]]], [[[120.37373410738431], [52.8651604285607], [27.40825990530161], [22.050706423245945]]], [[[120.45102662306566], [52.47819533714881], [28.809876295236442], [22.96160265115591]]], [[[120.01988572340746], [52.656881479116585], [27.891988314115086], [22.581699958214394]]], [[[119.8958000769982], [52.600863676804764], [27.162039536696213], [21.887827139634354]]], [[[122.98727475679837], [54.36367475069486], [28.61934463794415], [21.763064311100887]]], [[[123.43129524817833], [54.073181299062874], [28.61099888728215], [17.781177080594578]]], [[[121.9565899188702], [51.63589272132287], [28.183165623591496], [22.21050555889423]]], [[[119.86265241182767], [52.25161933898926], [28.036710005540115], [22.457052817711464]]], [[[120.46189586932843], [53.33759293189416], [28.274427560659554], [22.917400653545673]]], [[[125.32828903198242], [53.12569090036246], [29.103421211242676], [23.398020891042854]]], [[[120.08404130202074], [49.68480374262883], [28.626587060781624], [22.840154501108024]]], [[[120.1370752774752], [50.39895131037785], [28.37114898975079], [22.737161489633415]]], [[[124.0751598064716], [49.454077353844276], [28.555194267859825], [21.8967227935791]]], [[[122.43283345149113], [53.98532133836012], [28.338086201594425], [22.294737962576058]]], [[[119.69024203373836], [48.74777581141545], [28.235763513124905], [22.235023461855373]]], [[[127.0429533444918], [40.36383526141827], [29.645652367518498], [24.45144612972553]]], [[[122.63377424386832], [52.44198835813082], [28.770630286290096], [23.083857426276573]]], [[[125.34878936180702], [53.077156213613655], [28.354163573338436], [23.00345178750845]]], [[[121.98445892333984], [49.98618169931265], [29.823569407829872], [24.117591014275185]]], [[[123.18433629549466], [53.133706899789665], [28.357131554530216], [22.7672030375554]]], [[[117.5516793177678], [49.326990787799545], [27.23976623094999], [21.645824285653923]]], [[[124.83575850266676], [52.03812621189998], [28.916106920975906], [23.23396110534668]]], [[[125.88381107036884], [52.05661179469182], [27.295504826765793], [22.175971324627216]]], [[[125.76002370394193], [48.91630810957689], [24.143658014444206], [18.0540043757512]]], [[[128.74116134643555], [54.69064206343431], [27.982932200798622], [22.565950723794792]]], [[[125.43836608299843], [52.01047200423021], [27.69427600273719], [22.540639950678898]]], [[[120.69273523183969], [52.98671179551344], [27.486930773808407], [22.06152453789344]]], [[[120.83637868441068], [52.601083535414475], [28.870420676011307], [23.062129460848293]]], [[[120.12483640817496], [52.74493232140174], [27.97344288459191], [22.597495775956375]]], [[[120.16830855149489], [52.65827054243822], [27.15910236652081], [21.841927931858944]]], [[[123.39362848722018], [54.56437815152682], [28.8169144483713], [21.8760698391841]]], [[[124.19509535569411], [54.62109697782076], [28.791140666374794], [17.927385843717136]]], [[[121.87264647850624], [51.549146578862114], [28.152167870448185], [22.15942544203538]]], [[[120.54868111243614], [52.71970770909236], [28.298031403468205], [22.634133082169754]]], [[[120.76045036315918], [53.45276869260348], [28.268873544839714], [22.949705123901367]]], [[[125.43324279785156], [53.07920521956224], [29.111436403714695], [23.437802718235897]]], [[[120.28837732168344], [49.725373634925255], [28.704361622150127], [22.840566891890305]]], [[[120.15423495952899], [50.38036779256967], [28.309066002185528], [22.749476176041824]]], [[[124.53460165170523], [49.57667739574726], [28.706566150371845], [21.85314647967999]]], [[[122.70730942946214], [54.09474842364971], [28.41847669161283], [22.346705473386326]]], [[[119.28391647338867], [48.726768493652344], [28.223426818847656], [22.257286071777344]]], [[[126.82207870483398], [40.36891174316406], [29.639692306518555], [24.40064239501953]]], [[[122.33158874511719], [52.32742500305176], [28.759841918945312], [22.994117736816406]]], [[[125.1683464050293], [53.090402603149414], [28.371278762817383], [22.94567108154297]]], [[[121.94824600219727], [49.969825744628906], [29.86244487762451], [24.177586555480957]]], [[[122.83114624023438], [52.990684509277344], [28.311062812805176], [22.674697875976562]]], [[[117.20147705078125], [49.16707229614258], [27.098970413208008], [21.672017097473145]]], [[[124.31418991088867], [51.9091682434082], [28.8391695022583], [23.131935119628906]]], [[[125.6465950012207], [52.01581573486328], [27.361637115478516], [22.17392921447754]]], [[[125.35272979736328], [48.8057975769043], [24.23413848876953], [18.212862968444824]]], [[[128.3864517211914], [54.551292419433594], [27.977055549621582], [22.47166633605957]]], [[[125.41627502441406], [52.032249450683594], [27.686256408691406], [22.534225463867188]]], [[[120.22098922729492], [52.86605644226074], [27.41575336456299], [22.09412670135498]]], [[[120.43556594848633], [52.50573921203613], [28.952319145202637], [23.078259468078613]]], [[[119.7694320678711], [52.66358757019043], [27.952674865722656], [22.600895881652832]]], [[[119.62335968017578], [52.48293685913086], [27.09455394744873], [21.75443744659424]]], [[[123.2200813293457], [54.47625923156738], [28.638961791992188], [21.79822826385498]]], [[[124.40661239624023], [54.507402420043945], [29.131482124328613], [18.222856521606445]]], [[[121.59576034545898], [51.56206512451172], [28.13032054901123], [22.235458374023438]]], [[[120.34108352661133], [52.6981258392334], [28.249773025512695], [22.598024368286133]]], [[[120.56093215942383], [53.40971755981445], [28.312901496887207], [22.98938274383545]]], [[[125.0782699584961], [53.01543426513672], [29.13069725036621], [23.424148559570312]]], [[[119.77285766601562], [49.688968658447266], [28.65851879119873], [22.888948440551758]]], [[[119.82967758178711], [50.30939865112305], [28.33573341369629], [22.628433227539062]]], [[[124.22731399536133], [49.52158164978027], [28.600499153137207], [21.860705375671387]]], [[[122.27004623413086], [53.998722076416016], [28.36460304260254], [22.198488235473633]]], [[[121.16678547409346], [49.41399538292075], [28.565760216622984], [22.50680369251179]]], [[[128.68738879797593], [41.1232598862558], [30.007827866752194], [24.656595535998076]]], [[[124.36090656496444], [53.22964139254588], [29.172798786523206], [23.27671092411257]]], [[[126.96297951464383], [53.903529041218306], [28.695732890434986], [23.168979878695506]]], [[[123.46588861717368], [50.66445048350208], [30.132144712052256], [24.369843464977336]]], [[[124.80858899962227], [53.9212982969464], [28.668328285217285], [22.946808077254385]]], [[[119.10879048761332], [50.08431992440853], [27.50484767050113], [21.883286368172122]]], [[[126.50845905519881], [52.862967329205205], [29.24558499174298], [23.46083175011401]]], [[[127.55248605980063], [52.80213240857394], [27.703335977950186], [22.473201895659823]]], [[[127.41890183934626], [49.75557028572514], [24.462748707465405], [18.3657233760042]]], [[[130.37176542462043], [55.4943042251299], [28.32020718196653], [22.765498593168438]]], [[[127.32393351141012], [52.89943407166679], [28.161704333323353], [22.810654712173175]]], [[[122.2902875216502], [53.73387160391178], [27.887775367161012], [22.34023630394126]]], [[[122.30069898209481], [53.32468425102954], [29.185206377281332], [23.2718649090461]]], [[[121.66178116708431], [53.41897726958653], [28.296343101645416], [22.83221550707547]]], [[[121.78683594037902], [53.454573649280476], [27.51406923330055], [22.052775490958737]]], [[[124.91933131667803], [55.274312037342], [29.001004614919985], [22.085117879903542]]], [[[125.8273521279389], [55.29506269490944], [29.284758189939105], [18.39142630235204]]], [[[123.85991395194576], [52.50174878678232], [28.53067799334256], [22.42459871184151]]], [[[122.2624218059036], [53.545717887158666], [28.685594432758837], [22.852238061293114]]], [[[122.42551378933888], [54.237437734064066], [28.62101274166467], [23.183073943515993]]], [[[126.93722210290296], [53.747133866795956], [29.37819605053596], [23.566587268181568]]], [[[121.93709377072892], [50.481483423484946], [29.029946525141877], [23.13094740093879]]], [[[121.69554476468068], [51.051633331010926], [28.67023588576407], [22.97925299518513]]], [[[125.99941836663012], [50.209374013936745], [28.978595229814637], [22.159765207542563]]], [[[124.11648753903947], [54.760827550348246], [28.748869428094828], [22.515529074758852]]], [[[137.33166188207167], [58.021325078503835], [33.048957035459324], [25.88688929327603]]], [[[145.2346007248451], [50.524465232059875], [34.62690024540342], [28.096233104837353]]], [[[140.82878428492052], [62.46131857510271], [33.659853310420594], [26.625216845808357]]], [[[143.22215955010776], [63.0037349832469], [33.29935731558964], [26.58584029099037]]], [[[139.577039521316], [59.22479432204674], [34.75001026022023], [27.84874337295006]]], [[[141.33569546403555], [63.36780758561759], [33.50133303938241], [26.593074798583984]]], [[[135.3716162319841], [59.01664076180294], [32.19866706585062], [25.409404820409314]]], [[[142.9504331391433], [62.04222988260204], [34.03182180996599], [26.973102701121363]]], [[[143.78060860469424], [61.78075527322704], [32.056191740364866], [25.882771130265862]]], [[[143.70770105822334], [58.580056420687974], [28.776150736315497], [21.575112638802363]]], [[[146.71491688695448], [64.70259778252964], [32.66218159116548], [26.1571207375362]]], [[[143.02937211661504], [61.29644828007139], [32.35588205271754], [25.89120259778253]]], [[[138.351047910493], [62.737815593851025], [32.11854276985958], [25.594334503699994]]], [[[138.2556899498249], [61.9529818830819], [33.56817534874226], [26.532682484593884]]], [[[137.66430295746903], [62.17499081841831], [32.53866327219996], [26.0443220467403]]], [[[137.8026307204674], [62.24892964856378], [31.7501848812761], [25.27821297481142]]], [[[140.75801244275323], [64.16408420431203], [33.37675239299906], [25.207025330642175]]], [[[141.51958281418374], [63.842248324690196], [33.595740351183665], [21.93116234088766]]], [[[140.24807897107354], [61.45579818199421], [33.04065217642948], [25.731738715336242]]], [[[138.32942147090517], [62.46439427342908], [33.008807741362475], [26.14066722475249]]], [[[138.4145165805159], [63.0917529402108], [33.07037905989022], [26.511683760018183]]], [[[143.03367088580953], [62.58105652907799], [33.66539488167599], [26.790903683366448]]], [[[137.83691511483028], [58.80374211278455], [33.37687354252256], [26.424820275142274]]], [[[137.5126442744814], [59.301788330078125], [32.928343016525794], [26.212298886529332]]], [[[142.27656607792295], [58.60660631903286], [33.277407021358094], [25.30161509020575]]], [[[140.15921441439926], [63.77398221246128], [33.20147455149684], [25.90177134809823]]], [[[145.16962432861328], [62.728267669677734], [35.75091743469238], [27.95165252685547]]], [[[153.08985137939453], [55.3447322845459], [37.14159393310547], [30.08080291748047]]], [[[148.8927993774414], [67.50557327270508], [36.35787391662598], [28.649012565612793]]], [[[150.9681396484375], [67.5787124633789], [35.93475341796875], [28.73569965362549]]], [[[147.3592529296875], [63.575666427612305], [37.468780517578125], [29.902870178222656]]], [[[149.09839630126953], [67.90692520141602], [36.06000518798828], [28.611310958862305]]], [[[143.14337158203125], [63.55692481994629], [34.86435508728027], [27.478302001953125]]], [[[150.92286682128906], [66.76960754394531], [36.60577201843262], [29.016525268554688]]], [[[151.8416519165039], [66.37432861328125], [34.309322357177734], [27.45330810546875]]], [[[151.3822250366211], [62.986846923828125], [30.909154891967773], [23.149601936340332]]], [[[154.77723693847656], [69.52168273925781], [35.078834533691406], [27.78265380859375]]], [[[150.94428253173828], [65.89956665039062], [34.74209022521973], [27.636375427246094]]], [[[146.35736083984375], [67.61880493164062], [34.502084732055664], [27.51012134552002]]], 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    asdm_sidebandlo :
    [[['DSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['DSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['USB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']], [['LSB'], ['LSB'], ['LSB']]]
    asdm_spectralwindowid :
    ['SpectralWindow_0', 'SpectralWindow_27', 'SpectralWindow_28', 'SpectralWindow_29', 'SpectralWindow_30', 'SpectralWindow_31', 'SpectralWindow_32', 'SpectralWindow_33', 'SpectralWindow_34', 'SpectralWindow_0', 'SpectralWindow_35', 'SpectralWindow_36', 'SpectralWindow_37', 'SpectralWindow_38', 'SpectralWindow_39', 'SpectralWindow_40', 'SpectralWindow_41', 'SpectralWindow_42', 'SpectralWindow_43', 'SpectralWindow_44', 'SpectralWindow_45', 'SpectralWindow_46', 'SpectralWindow_47', 'SpectralWindow_48', 'SpectralWindow_49', 'SpectralWindow_50']
    asdm_startvalidtime :
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    asdm_stationid :
    ['Station_0', 'Station_1', 'Station_2', 'Station_3', 'Station_4', 'Station_5', 'Station_6', 'Station_7', 'Station_8', 'Station_9', 'Station_10', 'Station_11', 'Station_12', 'Station_13', 'Station_14', 'Station_15', 'Station_16', 'Station_17', 'Station_18', 'Station_19', 'Station_20', 'Station_21', 'Station_22', 'Station_23', 'Station_24', 'Station_25']
    asdm_time :
    [4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999, 4860026863.941999]
    asdm_timeinterval :
    [[[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]], [[4860026863.941999], [4363345172.912776]]]
    asdm_type :
    ['ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'ANTENNA_PAD', 'WEATHER_STATION', 'WEATHER_STATION']
    asdm_water :
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    asdm_wvrmethod :
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'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 'ATM_MODEL', 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    cald_antenna_id :
    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18]
    cald_cal_load_names :
    [[['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']], [['AMBIENT_LOAD'], ['HOT_LOAD']]]
    cald_feed_id :
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    cald_interval :
    [4363345170.433152, 4363345169.85944, 4363345169.284687, 4363345168.667396, 4363345168.097176, 4363345167.522891, 4363345166.920414, 4363345166.305278, 4363345165.721708, 4363345165.162808, 4363345164.562414, 4363345163.963759, 4363345163.41397, 4363345162.786249, 4363345162.21285, 4363345161.636672, 4363345161.063832, 4363345160.448878, 1614.69819, 4363345159.305263, 4363345158.734004, 4363345158.151685, 4363345157.597146, 4363345156.995468, 4363345156.420189, 4363345155.840908, 1.527555, 500.554658, 0.517737, 1.023741, 0.541935, 1.526568, 701.048636, 0.516942, 1.528021, 0.532313, 206.564523, 0.526649, 1.032871, 0.517957, 499.093801, 0.516933, 0.516782, 1.53887, 217.188377, 0.521819, 499.168836, 0.516913, 1.026706, 0.51385, 1496.212287, 1.547301, 288.405731, 1.587401, 4363339118.873723]
    cald_num_cal_load :
    [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
    cald_num_receptor :
    [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
    cald_spectral_window_id :
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    cald_temperature_load :
    [[[16.850000381469727], [83.25000762939453]], [[14.150003433227539], [84.25000762939453]], [[13.250009536743164], [83.94998931884766]], [[15.850000381469727], [84.25000762939453]], [[15.400003433227539], [83.89998626708984]], [[15.549997329711914], [84.39998626708984]], [[15.700006484985352], [83.44998931884766]], [[15.350000381469727], [83.9000015258789]], [[17.250009536743164], [84.20000457763672]], [[18.44999122619629], [82.6500015258789]], [[18.9999942779541], [84.3499984741211]], [[17.70000648498535], [83.9000015258789]], [[18.94999122619629], [83.54999542236328]], [[18.100000381469727], [83.1500015258789]], [[18.4999942779541], [83.04999542236328]], [[17.800012588500977], [84.1500015258789]], [[18.350000381469727], [83.54999542236328]], [[18.4999942779541], [84.3499984741211]], [[17.250009536743164], [83.49999237060547]], [[16.65000343322754], [82.79999542236328]], [[17.600000381469727], [83.70000457763672]], [[20.850000381469727], [83.99999237060547]], [[18.850000381469727], [83.54999542236328]], [[17.350000381469727], [84.1500015258789]], [[18.44999122619629], [83.95000457763672]], [[17.899988174438477], [83.25000762939453]], [[17.250009536743164], [464.0999755859375]], [[17.250009536743164], [83.49999237060547]], [[12.950006484985352], [83.49999237060547]], [[12.950006484985352], [464.0999755859375]], [[17.250009536743164], [464.0999755859375]], [[17.250009536743164], [1155.35009765625]], [[17.250009536743164], [83.49999237060547]], [[844.699951171875], [83.49999237060547]], [[844.699951171875], [844.699951171875]], [[430.9499816894531], [844.699951171875]], [[17.20000648498535], [83.49999237060547]], [[430.9499816894531], [83.49999237060547]], [[844.699951171875], [83.49999237060547]], [[430.9499816894531], [83.49999237060547]], [[17.20000648498535], [83.49999237060547]], [[430.9499816894531], [83.49999237060547]], [[844.699951171875], [464.0999755859375]], [[844.699951171875], [844.699951171875]], [[1544.550048828125], [1535.9500732421875]], [[844.699951171875], [83.49999237060547]], [[844.699951171875], [844.699951171875]], [[430.9499816894531], [83.49999237060547]], [[430.9499816894531], [1155.35009765625]], [[17.20000648498535], [1155.35009765625]], [[17.20000648498535], [83.49999237060547]], [[430.9499816894531], [83.54999542236328]], [[17.20000648498535], [83.5999984741211]], [[17.20000648498535], [844.699951171875]], [[17.20000648498535], [83.49999237060547]]]
    cald_time :
    [7041699451.638201, 7041699451.925056, 7041699452.212432, 7041699452.521078, 7041699452.806189, 7041699453.09333, 7041699453.394568, 7041699453.702137, 7041699453.993923, 7041699454.273371, 7041699454.573568, 7041699454.872896, 7041699455.147791, 7041699455.461652, 7041699455.74835, 7041699456.03644, 7041699456.322859, 7041699456.630337, 4860027684.316245, 7041699457.202145, 7041699457.487774, 7041699457.778933, 7041699458.056202, 7041699458.357041, 7041699458.644681, 7041699458.934321, 4860028492.429118, 4860028743.470224, 4860028994.006421, 4860028994.77716, 4860028995.559998, 4860028996.59425, 4860029347.881852, 4860029698.664639, 4860029699.687122, 4860029700.71729, 4860029804.265707, 4860029907.811293, 4860029908.591053, 4860029909.366467, 4860030159.172347, 4860030408.977714, 4860030409.494572, 4860030410.522397, 4860030519.886022, 4860030628.741118, 4860030878.586447, 4860031128.42932, 4860031129.20113, 4860031129.971408, 4860031878.334476, 4860032627.214271, 4860032772.190786, 4860032917.187352, 7041702477.417915]
    weat_antenna_id :
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    weat_dew_point :
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    weat_dew_point_flag :
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    weat_interval :
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    weat_ns_wx_station_id :
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    weat_ns_wx_station_position :
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    weat_rel_humidity_flag :
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    weat_temperature :
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    weat_temperature_flag :
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    weat_time :
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    weat_wind_direction :
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    weat_wind_speed_flag :
    [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]

Plotting

We can quickly spot check data fields. This is handy during subsequent analysis.

The visplot function supports both standard x-y plots and x-y-c color mesh visualization. The mode is determined by the number of dimensions in the data passed in

[ ]:
from cngi.vis import visplot

# 1-D plot - let's just see the fields
# this plots field values over the range of data
visplot(vis_xds.field)
_images/visibilities_16_0.png
[ ]:
# 2-D plot - fields versus scans
visplot(vis_xds.field, axis='scan')
_images/visibilities_17_0.png
[ ]:
# 2-D plot of 4-D data - DATA over time
# when we specify an x-axis (time), the other axes (baseline, chan, pol) are averaged together
visplot(vis_xds.DATA, axis='time')
_images/visibilities_18_0.png
[ ]:
# 3-D plot of 4-D data - DATA over time and channel
# we can give two axes to create a color mesh visualization
visplot(vis_xds.DATA, axis=['time','chan'])
_images/visibilities_19_0.png

Coordinate Strings

Whenever possible, integer index values from the old MS format have been replaced with the actual string name of the value being indexed.

This allows selection by either the index position or the actual string name (as shown in the following section).

[ ]:
import numpy as np

# field_id (int) -> field (string)
print(np.unique(vis_xds.field))
['3c279' 'Ceres' 'J0522-364' 'J1037-295' 'TW Hya (5)']
[ ]:
# processor_id -> processor
print(np.unique(vis_xds.processor))
['CORRELATOR (2)']
[ ]:
# observation_id -> observation
print(np.unique(vis_xds.observation))
['uid://A002/X327408/X6f']
[ ]:
# state_id -> state
print(np.unique(vis_xds.state))
['CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (4)'
 'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (5)'
 'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (6)'
 'CALIBRATE_AMPLI#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (7)'
 'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (0)'
 'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (1)'
 'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (2)'
 'CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (3)'
 'CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (8)'
 'CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE (9)'
 'OBSERVE_TARGET#ON_SOURCE (10)' 'OBSERVE_TARGET#ON_SOURCE (11)'
 'OBSERVE_TARGET#ON_SOURCE (12)' 'OBSERVE_TARGET#ON_SOURCE (13)'
 'OBSERVE_TARGET#ON_SOURCE (14)' 'OBSERVE_TARGET#ON_SOURCE (15)'
 'OBSERVE_TARGET#ON_SOURCE (16)' 'OBSERVE_TARGET#ON_SOURCE (17)'
 'OBSERVE_TARGET#ON_SOURCE (18)' 'OBSERVE_TARGET#ON_SOURCE (19)']

Selection and Splitting

The xarray Dataset format has extensive built-in functions for selecting data and splitting by different criteria.

Rather than cluttering each CNGI function with many parameters for data selection, instead the Dataset should be appropriately split before calling the relevant CNGI API function(s).

[ ]:
# select by dimension indices
new_xds = vis_xds.isel(time=[76,77,78], chan=[6,7,8,12])

visplot(new_xds.DATA, ['time', 'chan'])
_images/visibilities_26_0.png
[ ]:
# select by range of dimension values
new_xds = vis_xds.sel(chan=slice(372.59e9, 372.63e9))

visplot(new_xds.DATA, ['time', 'chan'])
_images/visibilities_27_0.png
[ ]:
# select by coordinate values
new_xds = vis_xds.where((vis_xds.field == 'Ceres') & (vis_xds.chan < 372.7e9), drop=True)

visplot(new_xds.DATA, ['time', 'chan'])
_images/visibilities_28_0.png
[ ]:
# select by data variable values - find all baselines with antenna 5
new_xds = vis_xds.where((vis_xds.antennas[:,0] == 5) | (vis_xds.antennas[:,1] == 5), drop=True)

visplot(new_xds.baseline)
_images/visibilities_29_0.png

Flagging

Any boolean data variable can be used to flag any other data variable of common dimension(s).

Flagged values are set to np.nan, and subsequent math/analysis should be of the type that ignores nan values.

This is a simple operation and does not necessarily require a special function, but one is provided for conveinience. Here we will show both ways.

[ ]:
# here is what the raw DATA column looks like
visplot(vis_xds.DATA, ['time', 'chan'])

# another way to look at the data, pick a random baseline (34)
visplot(vis_xds.DATA[:,34], 'chan')
_images/visibilities_31_0.png
_images/visibilities_31_1.png
[ ]:
# apply the FLAG and FLAG_ROW data variables
flagged_xds = vis_xds.where((vis_xds.FLAG == 0) & (vis_xds.FLAG_ROW == 0))

# the white line is a set of flagged values (np.nan's)
visplot(flagged_xds.DATA, ['time', 'chan'])
visplot(flagged_xds.DATA[:,34], 'chan')
_images/visibilities_32_0.png
_images/visibilities_32_1.png
[ ]:
# or with the cngi applyflags function
from cngi.vis import applyflags

flagged_xds = applyflags(vis_xds, flags=['FLAG', 'FLAG_ROW'])

visplot(flagged_xds.DATA[:,34], 'chan')
_images/visibilities_33_0.png
[ ]:
# one difference is that the applyflags function will remove the flag variables from the return dataset
print(flagged_xds.data_vars)
Data variables:
    ARRAY_ID       (time, baseline) float64 dask.array<chunksize=(100, 210), meta=np.ndarray>
    DATA           (time, baseline, chan, pol) complex128 dask.array<chunksize=(100, 210, 20, 1), meta=np.ndarray>
    EXPOSURE       (time, baseline) float64 dask.array<chunksize=(100, 210), meta=np.ndarray>
    FEED1          (time, baseline) float64 dask.array<chunksize=(100, 210), meta=np.ndarray>
    FEED2          (time, baseline) float64 dask.array<chunksize=(100, 210), meta=np.ndarray>
    SIGMA          (time, baseline, pol) float64 dask.array<chunksize=(100, 210, 1), meta=np.ndarray>
    TIME_CENTROID  (time, baseline) float64 dask.array<chunksize=(100, 210), meta=np.ndarray>
    UVW            (time, baseline, uvw_index) float64 dask.array<chunksize=(100, 210, 3), meta=np.ndarray>
    WEIGHT         (time, baseline, pol) float64 dask.array<chunksize=(100, 210, 1), meta=np.ndarray>

Averaging and Smoothing

Averaging functions will change the shape of the resulting xarray dataset along the dimension being averaged.

Smoothing functions always return an xarray dataset with the same dimensions as the original.

Channel Averaging

The channel averaging function looks for all data variables in the dataset with a channel dimension and averages by the specified bin width. The returned dataset will have a different channel dimension size.

[ ]:
from cngi.vis import chanaverage, visplot

# average 5 channels of original unflagged data
avg_xds = chanaverage(vis_xds, width=5)

# compare the original to the channel averaged
visplot(vis_xds.DATA[30,0,:,0], 'chan')
visplot(avg_xds.DATA[30,0,:,0], 'chan')
_images/visibilities_37_0.png
_images/visibilities_37_1.png
[ ]:
# confirm new channel dimension after averaging
print(dict(vis_xds.dims))
print(dict(avg_xds.dims))
{'baseline': 210, 'chan': 384, 'pair': 2, 'pol': 2, 'receptor': 2, 'spw': 1, 'time': 410, 'uvw_index': 3}
{'baseline': 210, 'chan': 76, 'pair': 2, 'pol': 2, 'receptor': 2, 'spw': 1, 'time': 410, 'uvw_index': 3}

Since every variable with a channel dimension in the dataset is averaged, this will also include the FLAG variable. FLAG is a boolean type with values of 0 or 1 that are averaged over the width, resulting in a decimal number. The result is then typcast back to boolean, which is the same as just rounding up.

Long story short, if any channel in the width is flagged, the resulting averaged channel will also be flagged

[ ]:
# compare the original flags to the channel averaged flags
visplot(vis_xds.FLAG[30,0,:,0], 'chan')
visplot(avg_xds.FLAG[30,0,:,0], 'chan')
_images/visibilities_40_0.png
_images/visibilities_40_1.png

One might want to apply flags before channel averaging

[ ]:
from cngi.vis import applyflags

flg_xds = applyflags(vis_xds, flags=['FLAG'])
avg_flg_xds = chanaverage(flg_xds, width=5)

visplot(flg_xds.DATA[30,0,:,0], 'chan')
visplot(avg_flg_xds.DATA[30,0,:,0], 'chan')
_images/visibilities_42_0.png
_images/visibilities_42_1.png

Or apply flags after channel averaging

[ ]:
flg_avg_xds = applyflags(avg_xds, flags=['FLAG'])

visplot(flg_avg_xds.DATA[30,0,:,0], 'chan')
_images/visibilities_44_0.png

There is a small difference of two points, made more clear when we overlay the plots

[ ]:
visplot(avg_flg_xds.DATA[30,0,:,0], 'chan', drawplot=False)
visplot(flg_avg_xds.DATA[30,0,:,0], 'chan', overplot=True)
_images/visibilities_46_0.png

Time Averaging

The time averaging function looks for all data variables in the dataset with a time dimension and averages by the specified bin width. Spanning across states within a scan, scans of a each state, or both yields different results. The returned dataset will have a different time dimension size.

First let’s see the relationship between scans, states, and time in this data

[ ]:
from cngi.vis import visplot

visplot(vis_xds.scan, 'time')
visplot(vis_xds.state, 'time')
visplot(vis_xds.state, 'scan')
_images/visibilities_48_0.png
_images/visibilities_48_1.png
_images/visibilities_48_2.png

Let’s start by not spanning anything. The comparison plots will look at all times across baseline 100, channel 100, and polarization 0 (random selection)

[ ]:
from cngi.vis import timeaverage

# average 7 time steps, don't span anything (separate by scan and state)
avg_xds = timeaverage(vis_xds, width=7, timespan='none')

# compare the original to the time averaged
visplot(vis_xds.DATA[:,100,100,0], 'time', drawplot=False)
visplot(avg_xds.DATA[:,100,100,0], 'time', overplot=True)
_images/visibilities_50_0.png
[ ]:
# confirm new channel dimension after averaging
print(dict(vis_xds.dims))
print(dict(avg_xds.dims))
{'baseline': 210, 'chan': 384, 'pair': 2, 'pol': 2, 'receptor': 2, 'spw': 1, 'time': 410, 'uvw_index': 3}
{'baseline': 210, 'chan': 384, 'pair': 2, 'pol': 2, 'receptor': 2, 'spw': 1, 'time': 82, 'uvw_index': 3}
[ ]:
# average 7 time steps spanning states within each scan
state_avg_xds = timeaverage(vis_xds, width=7, timespan='state')

visplot(state_avg_xds.DATA[:,100,100,0], 'time')
_images/visibilities_52_0.png
[ ]:
# average 7 time steps spanning all scans per state
scan_avg_xds = timeaverage(vis_xds, width=7, timespan='scan')

visplot(scan_avg_xds.DATA[:,100,100,0], 'time')
_images/visibilities_53_0.png
[ ]:
# average 7 time steps spanning all scans and states
both_avg_xds = timeaverage(vis_xds, width=7, timespan='both')

visplot(both_avg_xds.DATA[:,100,100,0], 'time')
_images/visibilities_54_0.png

We can inspect the change to scans over time as a result of the different spanning

[ ]:
visplot(avg_xds.scan, 'time', drawplot=False)
visplot(state_avg_xds.scan, 'time', overplot=True, drawplot=False)
visplot(scan_avg_xds.scan, 'time', overplot=True, drawplot=False)
visplot(both_avg_xds.scan, 'time', overplot=True)
_images/visibilities_56_0.png

Flagging works the same way as in channel averaging. Flags are averaged with all other data sharing the time axis. They may be applied before or after time averaging.

[ ]:
from cngi.vis import applyflags

avg_flg_xds = timeaverage(applyflags(vis_xds, flags=['FLAG']), width=5, timespan='both')
flg_avg_xds = applyflags(timeaverage(vis_xds, width=5, timespan='both'), flags=['FLAG'])

visplot(avg_flg_xds.DATA[:,100,100,0], 'time', drawplot=False)
visplot(flg_avg_xds.DATA[:,100,100,0], 'time', overplot=True)
_images/visibilities_58_0.png

Channel Smoothing

Channel smoothing allows a variety of window shapes to be convolved across the channel dimension to smooth over changes from bin to bin. The standard CASA hanning smooth is supported as the default option. The returned dataset will have the same dimensions as the original.

[ ]:
from cngi.vis import chansmooth

smooth_xds = chansmooth(vis_xds)

visplot(vis_xds.DATA[30,0,:,0], 'chan', drawplot=False)
visplot(smooth_xds.DATA[30,0,:,0], 'chan', overplot=True)
_images/visibilities_60_0.png

chansmooth uses the scipy.signal package to set the window shape. Therefore, all the window functions supported by scipy are also supported here. The size of the window width is configurable.

https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows

[ ]:
bohman_xds = chansmooth(vis_xds, type='bohman', size=7)

visplot(smooth_xds.DATA[30,0,:,0], 'chan', drawplot=False)
visplot(bohman_xds.DATA[30,0,:,0], 'chan', overplot=True)
_images/visibilities_62_0.png

non-unity gains are also supported to amplify or attenuate the output

[ ]:
hann_xds = chansmooth(vis_xds, type='hann', size=5, gain=1.0)
amp_hann_xds = chansmooth(vis_xds, type='hann', size=5, gain=3.0)

visplot(hann_xds.DATA[30,0,:,0], 'chan', drawplot=False)
visplot(amp_hann_xds.DATA[30,0,:,0], 'chan', overplot=True)
_images/visibilities_64_0.png

UV Fitting

Various modeling can be performed in the UV-domain through polynomial regression on the visibility data.

The uvcontfit function takes a source data variable in the xarray dataset and fits a polynomial of fitorder across the channel axis. The resulting model is placed in the target data variable and retains the dimensionality of the source. Specified channels may be excluded to focus on fitting only continuum visibilities and not line emissions.

In our example dataset we know that the Ceres has a line emission, so let’s focus on just that field.

[ ]:
from cngi.vis import uvcontfit, visplot

ceres_xds = vis_xds.where(vis_xds.field == 'Ceres', drop=True)

fit_xds = uvcontfit(ceres_xds, source='DATA', target='CONTFIT')

visplot(fit_xds.DATA, ['time', 'chan'])
visplot(fit_xds.CONTFIT, ['time', 'chan'])
_images/visibilities_66_0.png
_images/visibilities_66_1.png

What we are seeing is merged across baselines and polarizations. Let’s look at just one time/baseline/pol sample and examine the fit. The linear fit is skewed by the line emission towards one side of the channel axis.

[ ]:
visplot(fit_xds.DATA[10,10,:,0], ['chan'], drawplot=False)
visplot(fit_xds.CONTFIT[10,10,:,0], ['chan'], overplot=True)
_images/visibilities_68_0.png

Let’s omit the channels with the line emission and do the fit again. We should see less skewing in the fitted continuum.

The fits appear lower in value than the mean of the data when viewing the magnitude of complex visibilities. The real and imaginary parts are fitted individually. We examine just the real piece here to see a that the fit is indeed a decent approximation through the center of the data.

[ ]:
excludechans = np.where( (vis_xds.chan > 3.726e11) & (vis_xds.chan < 3.7262e11) )[0]

fit_xds2 = uvcontfit(ceres_xds, source='DATA', target='CONTFIT', fitorder=1, excludechans=excludechans)

includechans = np.setdiff1d( range(vis_xds.dims['chan']), excludechans)
visplot(fit_xds2.DATA[10,10,includechans,0].real, ['chan'], drawplot=False)
visplot(fit_xds.CONTFIT[10,10,:,0].real, ['chan'], drawplot=False, overplot=True)
visplot(fit_xds2.CONTFIT[10,10,:,0].real, ['chan'], overplot=True)
_images/visibilities_70_0.png

For even more fun, we can try a higher order fit. Let’s use all channels to allow the line emission to exaggerate the non-linearity, but only plot the continuum DATA channels so the y-scale stays smaller.

[ ]:
fit_xds3 = uvcontfit(ceres_xds, source='DATA', target='CONTFIT', fitorder=3, excludechans=[])

visplot(fit_xds3.DATA[10,10,includechans,0].real, ['chan'], drawplot=False)
visplot(fit_xds3.CONTFIT[10,10,:,0].real, ['chan'], overplot=True)
_images/visibilities_72_0.png

Several metrics related to the quality of the fit are stored in the xarray dataset attributes section as metadata. They are named with a prefix matching the target parameter in uvcontfit.

[ ]:
metrics = [kk for kk in fit_xds.attrs.keys() if kk.startswith('CONTFIT')]
print(metrics)

print(fit_xds.CONTFIT_rms_error)
['CONTFIT_rms_error', 'CONTFIT_min_max_error', 'CONTFIT_bw_frac', 'CONTFIT_freq_frac']
<xarray.DataArray ()>
dask.array<pow, shape=(), dtype=complex128, chunksize=(), chunktype=numpy.ndarray>

One thing to note is that these metrics are dask elements, and are not actually computed until explicity requested. Things like visplot explicity force the computation of the data needed for plotting, but nothing has forced these metrics to be computed yet. So we will call .compute() to see their values.

[ ]:
print('rms error with line included: ', fit_xds.CONTFIT_rms_error.compute().item())
print('rms error with line excluded: ', fit_xds2.CONTFIT_rms_error.compute().item())
rms error with line included:  (16.444908051664182-0.7566763926282183j)
rms error with line excluded:  (0.37306136672597817-0.007993456030923633j)