{ "cells": [ { "cell_type": "markdown", "id": "09058b89-2898-4852-9f2a-84dcd27acaee", "metadata": {}, "source": [ "# VLBA conversion guide" ] }, { "cell_type": "code", "execution_count": 1, "id": "14a699d5-bf6e-4ebe-bfa5-dcccbec0ec2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XRADIO version 1.1.3 already installed.\n" ] } ], "source": [ "from importlib.metadata import version\n", "import os\n", "\n", "try:\n", " import xradio\n", "\n", " print(\"XRADIO version\", version(\"xradio\"), \"already installed.\")\n", "except ImportError as e:\n", " print(e)\n", " print(\"Installing XRADIO\")\n", "\n", " os.system(\"pip install xradio\")\n", "\n", " import xradio\n", "\n", " print(\"xradio version\", version(\"xradio\"), \" installed.\")" ] }, { "cell_type": "markdown", "id": "a939f97e-6c76-47b0-aef5-256df2e908f9", "metadata": {}, "source": [ "## Download dataset" ] }, { "cell_type": "markdown", "id": "805daf71", "metadata": {}, "source": [ "Original VLBA dataset gotten from https://casaguides.nrao.edu/index.php?title=VLBA_Basic_Phase-referencing_Calibration_and_Imaging.\n", "\n", "Reduced the data using:\n", "\n", "```Python\n", "mstransform(vis='VLBA_TL016B.ms/',outputvis='VLBA_TL016B_split_lsrk.ms',spw='0:0~5,1:0~5',field='0,1', timerange='2022/02/21/06:14:00~2022/02/21/07:55:45.00', regridms=True,outframe='lsrk',datacolumn='all')\n", "```\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "134a99e3-c5a2-443c-96d4-f44fae59555a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:20:58,776\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Initializing download... \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:58,777\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m File already exists: /Users/vdesouza/work/xradio/docs/source/measurement_set/guides/VLBA_TL016B_split.ms \n" ] } ], "source": [ "import toolviper\n", "\n", "toolviper.utils.data.download(file=\"VLBA_TL016B_split.ms\")" ] }, { "cell_type": "markdown", "id": "f8435c96-7010-4b79-8be6-51f7f6993b5f", "metadata": {}, "source": [ "## Convert to Processing Set" ] }, { "cell_type": "code", "execution_count": 3, "id": "ee52d124-2c17-450b-879f-1f86f0ae265c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,172\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Updated partition scheme used: ['DATA_DESC_ID', 'OBSERVATION_ID', 'FIELD_ID'] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,173\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Number of partitions: 4 \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,173\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [0], STATE [None], FIELD [0], SCAN [0], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,313\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,425\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [1], STATE [None], FIELD [0], SCAN [0], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,556\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,652\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [0], STATE [None], FIELD [1], SCAN [0], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,789\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n", "[\u001b[38;2;128;05;128m2026-04-20 15:20:59,959\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [1], STATE [None], FIELD [1], SCAN [0], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:00,095\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n" ] } ], "source": [ "from xradio.measurement_set import convert_msv2_to_processing_set\n", "\n", "ms_file = \"VLBA_TL016B_split.ms\"\n", "\n", "main_chunksize = {\"frequency\": 1, \"time\": 20} # baseline, polarization\n", "outfile = \"VLBA_TL016B_split_lsrk.ps.zarr\"\n", "convert_msv2_to_processing_set(\n", " in_file=ms_file,\n", " out_file=outfile,\n", " parallel_mode=\"none\",\n", " partition_scheme=[\"FIELD_ID\"],\n", " persistence_mode='w',\n", " main_chunksize=main_chunksize,\n", ")" ] }, { "cell_type": "markdown", "id": "fbd02679-0df8-4fa5-8036-6f22f534e386", "metadata": {}, "source": [ "## Processing Set" ] }, { "cell_type": "code", "execution_count": 4, "id": "dab986ca-55f8-4a4a-ba59-9c97fcc2ca84", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namescan_intentsshapeexecution_block_UIDpolarizationscan_namespw_namespw_intentsfield_namesource_nameline_namefield_coordssession_reference_UIDscheduling_block_UIDproject_UIDstart_frequencyend_frequency
0VLBA_TL016B_split_0[None](200, 55, 6, 2)---[RR, LL][0]spw_0[UNSPECIFIED][4C39.25_0][Unknown][][fk5, 9h27m03.01s, 39d02m20.85s]------TL016B5.004000e+095.006500e+09
1VLBA_TL016B_split_1[None](200, 55, 6, 2)---[RR, LL][0]spw_1[UNSPECIFIED][4C39.25_0][Unknown][][fk5, 9h27m03.01s, 39d02m20.85s]------TL016B5.068000e+095.070500e+09
2VLBA_TL016B_split_2[None](540, 55, 6, 2)---[RR, LL][0]spw_0[UNSPECIFIED][J1154+6022_1][Unknown][][fk5, 11h54m04.54s, 60d22m20.82s]------TL016B5.004000e+095.006500e+09
3VLBA_TL016B_split_3[None](540, 55, 6, 2)---[RR, LL][0]spw_1[UNSPECIFIED][J1154+6022_1][Unknown][][fk5, 11h54m04.54s, 60d22m20.82s]------TL016B5.068000e+095.070500e+09
\n", "
" ], "text/plain": [ " name scan_intents shape execution_block_UID \\\n", "0 VLBA_TL016B_split_0 [None] (200, 55, 6, 2) --- \n", "1 VLBA_TL016B_split_1 [None] (200, 55, 6, 2) --- \n", "2 VLBA_TL016B_split_2 [None] (540, 55, 6, 2) --- \n", "3 VLBA_TL016B_split_3 [None] (540, 55, 6, 2) --- \n", "\n", " polarization scan_name spw_name spw_intents field_name source_name \\\n", "0 [RR, LL] [0] spw_0 [UNSPECIFIED] [4C39.25_0] [Unknown] \n", "1 [RR, LL] [0] spw_1 [UNSPECIFIED] [4C39.25_0] [Unknown] \n", "2 [RR, LL] [0] spw_0 [UNSPECIFIED] [J1154+6022_1] [Unknown] \n", "3 [RR, LL] [0] spw_1 [UNSPECIFIED] [J1154+6022_1] [Unknown] \n", "\n", " line_name field_coords session_reference_UID \\\n", "0 [] [fk5, 9h27m03.01s, 39d02m20.85s] --- \n", "1 [] [fk5, 9h27m03.01s, 39d02m20.85s] --- \n", "2 [] [fk5, 11h54m04.54s, 60d22m20.82s] --- \n", "3 [] [fk5, 11h54m04.54s, 60d22m20.82s] --- \n", "\n", " scheduling_block_UID project_UID start_frequency end_frequency \n", "0 --- TL016B 5.004000e+09 5.006500e+09 \n", "1 --- TL016B 5.068000e+09 5.070500e+09 \n", "2 --- TL016B 5.004000e+09 5.006500e+09 \n", "3 --- TL016B 5.068000e+09 5.070500e+09 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from xradio.measurement_set import open_processing_set\n", "\n", "ps_xdt = open_processing_set(ps_store=outfile)\n", "ps_xdt.xr_ps.summary()" ] }, { "cell_type": "code", "execution_count": 5, "id": "259aa56b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'VLBA_TL016B_split_3'>\n",
       "Group: /VLBA_TL016B_split_3\n",
       "│   Dimensions:                     (time: 540, baseline_id: 55, frequency: 6,\n",
       "│                                    polarization: 2, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 4kB 1.645e+09 ... 1.645e+09\n",
       "│       field_name                  (time) <U32 69kB dask.array<chunksize=(540,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 45kB dask.array<chunksize=(540,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 440B 0 1 2 3 ... 51 52 53 54\n",
       "│       baseline_antenna1_name      (baseline_id) <U2 440B dask.array<chunksize=(55,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U2 440B dask.array<chunksize=(55,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "│     * polarization                (polarization) <U2 16B 'RR' 'LL'\n",
       "│     * uvw_label                   (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "│   Data variables:\n",
       "│       EFFECTIVE_INTEGRATION_TIME  (time, baseline_id) float64 238kB dask.array<chunksize=(20, 55), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 356kB dask.array<chunksize=(20, 55, 1, 2), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 238kB dask.array<chunksize=(20, 55), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 713kB dask.array<chunksize=(20, 55, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 3MB dask.array<chunksize=(20, 55, 1, 2), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 1MB dask.array<chunksize=(20, 55, 1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:20:59.970221+00:00\n",
       "│       creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "│       data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "│       observation_info:  {'observer': ['PLUTO'], 'observing_log': '[]', 'projec...\n",
       "│       processor_info:    {'sub_type': '', 'type': ''}\n",
       "│       schema_version:    4.0.0\n",
       "│       type:              visibility\n",
       "├── Group: /VLBA_TL016B_split_3/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 10, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "│           mount                   (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│         * cartesian_pos_label     (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "│         * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type       (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 240B dask.array<chunksize=(10, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 160B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  VLBA\n",
       "│           relocatable_antennas:    False\n",
       "│           type:                    antenna\n",
       "├── Group: /VLBA_TL016B_split_3/field_and_source_base_xds\n",
       "│       Dimensions:                       (field_name: 1, sky_dir_label: 2)\n",
       "│       Coordinates:\n",
       "│         * field_name                    (field_name) <U32 128B 'J1154+6022_1'\n",
       "│           source_name                   (field_name) <U7 28B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│         * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "│       Data variables:\n",
       "│           FIELD_PHASE_CENTER_DIRECTION  (field_name, sky_dir_label) float64 16B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           type:     field_and_source\n",
       "├── Group: /VLBA_TL016B_split_3/gain_curve_xds\n",
       "│       Dimensions:                 (antenna_name: 10, poly_term: 1, receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "│           antenna_id              (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           gain_curve_type         (antenna_name) <U9 360B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           mount                   (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│         * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type       (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│       Dimensions without coordinates: poly_term\n",
       "│       Data variables:\n",
       "│           GAIN_CURVE              (antenna_name, poly_term, receptor_label) float64 160B dask.array<chunksize=(10, 1, 2), meta=np.ndarray>\n",
       "│           GAIN_CURVE_INTERVAL     (antenna_name) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           GAIN_CURVE_SENSITIVITY  (antenna_name, receptor_label) float64 160B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           measured_date:  2022-02-21T09:50:25\n",
       "│           type:           gain_curve\n",
       "├── Group: /VLBA_TL016B_split_3/phase_calibration_xds\n",
       "│       Dimensions:                   (antenna_name: 10, time_phase_cal: 141,\n",
       "│                                      receptor_label: 2, tone_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name              (antenna_name) <U2 80B 'BR' 'FD' ... 'PT' 'SC'\n",
       "│           antenna_id                (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           mount                     (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           station_name              (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           telescope_name            (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│         * time_phase_cal            (time_phase_cal) float64 1kB 1.645e+09 ... 1.64...\n",
       "│         * receptor_label            (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type         (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│         * tone_label                (tone_label) <U6 48B 'freq_0' 'freq_1'\n",
       "│       Data variables:\n",
       "│           PHASE_CAL                 (antenna_name, time_phase_cal, receptor_label, tone_label) complex64 45kB dask.array<chunksize=(10, 141, 2, 2), meta=np.ndarray>\n",
       "│           PHASE_CAL_CABLE_CAL       (antenna_name, time_phase_cal) float64 11kB dask.array<chunksize=(10, 141), meta=np.ndarray>\n",
       "│           PHASE_CAL_INTERVAL        (antenna_name, time_phase_cal) float64 11kB dask.array<chunksize=(10, 141), meta=np.ndarray>\n",
       "│           PHASE_CAL_TONE_FREQUENCY  (antenna_name, time_phase_cal, receptor_label, tone_label) float64 45kB dask.array<chunksize=(10, 141, 2, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           type:     phase_calibration\n",
       "├── Group: /VLBA_TL016B_split_3/system_calibration_xds\n",
       "│       Dimensions:            (antenna_name: 10, time_system_cal: 1192,\n",
       "│                               receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name       (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'OV' 'PT' 'SC'\n",
       "│           antenna_id         (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           mount              (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           station_name       (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│           telescope_name     (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "│         * time_system_cal    (time_system_cal) float64 10kB 1.645e+09 ... 1.645e+09\n",
       "│         * receptor_label     (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type  (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           TSYS               (antenna_name, time_system_cal, receptor_label) float64 191kB dask.array<chunksize=(10, 1192, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           type:     system_calibration\n",
       "└── Group: /VLBA_TL016B_split_3/weather_xds\n",
       "        Dimensions:              (station_name: 10, time_weather: 856,\n",
       "                                  cartesian_pos_label: 3)\n",
       "        Coordinates:\n",
       "          * station_name         (station_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "          * time_weather         (time_weather) float64 7kB 1.645e+09 ... 1.645e+09\n",
       "          * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "        Data variables:\n",
       "            DEW_POINT            (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            H2O                  (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            IONOS_ELECTRON       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            PRESSURE             (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            STATION_POSITION     (station_name, cartesian_pos_label) float64 240B dask.array<chunksize=(10, 3), meta=np.ndarray>\n",
       "            TEMPERATURE          (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            WIND_DIRECTION       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "            WIND_SPEED           (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            type:     weather
" ], "text/plain": [ "\n", "Group: /VLBA_TL016B_split_3\n", "│ Dimensions: (time: 540, baseline_id: 55, frequency: 6,\n", "│ polarization: 2, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 4kB 1.645e+09 ... 1.645e+09\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 440B 0 1 2 3 ... 51 52 53 54\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 48B 5.068e+09 ... 5.07e+09\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 356kB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 238kB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 713kB dask.array\n", "│ VISIBILITY (time, baseline_id, frequency, polarization) complex64 3MB dask.array\n", "│ WEIGHT (time, baseline_id, frequency, polarization) float32 1MB dask.array\n", "│ Attributes:\n", "│ creation_date: 2026-04-20T21:20:59.970221+00:00\n", "│ creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", "│ data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", "│ observation_info: {'observer': ['PLUTO'], 'observing_log': '[]', 'projec...\n", "│ processor_info: {'sub_type': '', 'type': ''}\n", "│ schema_version: 4.0.0\n", "│ type: visibility\n", "├── Group: /VLBA_TL016B_split_3/antenna_xds\n", "│ Dimensions: (antenna_name: 10, cartesian_pos_label: 3,\n", "│ receptor_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * cartesian_pos_label (cartesian_pos_label) \n", "│ Data variables:\n", "│ ANTENNA_DISH_DIAMETER (antenna_name) float64 80B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 240B dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 160B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: VLBA\n", "│ relocatable_antennas: False\n", "│ type: antenna\n", "├── Group: /VLBA_TL016B_split_3/field_and_source_base_xds\n", "│ Dimensions: (field_name: 1, sky_dir_label: 2)\n", "│ Coordinates:\n", "│ * field_name (field_name) \n", "│ * sky_dir_label (sky_dir_label) \n", "│ Attributes:\n", "│ type: field_and_source\n", "├── Group: /VLBA_TL016B_split_3/gain_curve_xds\n", "│ Dimensions: (antenna_name: 10, poly_term: 1, receptor_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ gain_curve_type (antenna_name) \n", "│ mount (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * receptor_label (receptor_label) \n", "│ Dimensions without coordinates: poly_term\n", "│ Data variables:\n", "│ GAIN_CURVE (antenna_name, poly_term, receptor_label) float64 160B dask.array\n", "│ GAIN_CURVE_INTERVAL (antenna_name) float64 80B dask.array\n", "│ GAIN_CURVE_SENSITIVITY (antenna_name, receptor_label) float64 160B dask.array\n", "│ Attributes:\n", "│ measured_date: 2022-02-21T09:50:25\n", "│ type: gain_curve\n", "├── Group: /VLBA_TL016B_split_3/phase_calibration_xds\n", "│ Dimensions: (antenna_name: 10, time_phase_cal: 141,\n", "│ receptor_label: 2, tone_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ mount (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * time_phase_cal (time_phase_cal) float64 1kB 1.645e+09 ... 1.64...\n", "│ * receptor_label (receptor_label) \n", "│ * tone_label (tone_label) \n", "│ PHASE_CAL_CABLE_CAL (antenna_name, time_phase_cal) float64 11kB dask.array\n", "│ PHASE_CAL_INTERVAL (antenna_name, time_phase_cal) float64 11kB dask.array\n", "│ PHASE_CAL_TONE_FREQUENCY (antenna_name, time_phase_cal, receptor_label, tone_label) float64 45kB dask.array\n", "│ Attributes:\n", "│ type: phase_calibration\n", "├── Group: /VLBA_TL016B_split_3/system_calibration_xds\n", "│ Dimensions: (antenna_name: 10, time_system_cal: 1192,\n", "│ receptor_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ mount (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * time_system_cal (time_system_cal) float64 10kB 1.645e+09 ... 1.645e+09\n", "│ * receptor_label (receptor_label) \n", "│ Data variables:\n", "│ TSYS (antenna_name, time_system_cal, receptor_label) float64 191kB dask.array\n", "│ Attributes:\n", "│ type: system_calibration\n", "└── Group: /VLBA_TL016B_split_3/weather_xds\n", " Dimensions: (station_name: 10, time_weather: 856,\n", " cartesian_pos_label: 3)\n", " Coordinates:\n", " * station_name (station_name) \n", " H2O (station_name, time_weather) float64 68kB dask.array\n", " IONOS_ELECTRON (station_name, time_weather) float64 68kB dask.array\n", " PRESSURE (station_name, time_weather) float64 68kB dask.array\n", " STATION_POSITION (station_name, cartesian_pos_label) float64 240B dask.array\n", " TEMPERATURE (station_name, time_weather) float64 68kB dask.array\n", " WIND_DIRECTION (station_name, time_weather) float64 68kB dask.array\n", " WIND_SPEED (station_name, time_weather) float64 68kB dask.array\n", " Attributes:\n", " type: weather" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt = ps_xdt[\"VLBA_TL016B_split_3\"]\n", "ms_xdt" ] }, { "cell_type": "code", "execution_count": 6, "id": "ba761deb-b7a9-4b2b-8163-3050413fe59c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 6kB\n",
       "Dimensions:                 (antenna_name: 10, cartesian_pos_label: 3,\n",
       "                             receptor_label: 2, baseline_id: 55, frequency: 6,\n",
       "                             polarization: 2, time: 540, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * antenna_name            (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "    mount                   (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    station_name            (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    telescope_name          (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "  * cartesian_pos_label     (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "  * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "    polarization_type       (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "  * baseline_id             (baseline_id) int64 440B 0 1 2 3 4 ... 51 52 53 54\n",
       "  * frequency               (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "  * polarization            (polarization) <U2 16B 'RR' 'LL'\n",
       "  * time                    (time) float64 4kB 1.645e+09 1.645e+09 ... 1.645e+09\n",
       "  * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Data variables:\n",
       "    ANTENNA_DISH_DIAMETER   (antenna_name) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 240B dask.array<chunksize=(10, 3), meta=np.ndarray>\n",
       "    ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 160B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    overall_telescope_name:  VLBA\n",
       "    relocatable_antennas:    False\n",
       "    type:                    antenna
" ], "text/plain": [ " Size: 6kB\n", "Dimensions: (antenna_name: 10, cartesian_pos_label: 3,\n", " receptor_label: 2, baseline_id: 55, frequency: 6,\n", " polarization: 2, time: 540, uvw_label: 3)\n", "Coordinates:\n", " * antenna_name (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * cartesian_pos_label (cartesian_pos_label) \n", " * baseline_id (baseline_id) int64 440B 0 1 2 3 4 ... 51 52 53 54\n", " * frequency (frequency) float64 48B 5.068e+09 ... 5.07e+09\n", " * polarization (polarization) \n", " ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 240B dask.array\n", " ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 160B dask.array\n", "Attributes:\n", " overall_telescope_name: VLBA\n", " relocatable_antennas: False\n", " type: antenna" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ant_xds = ms_xdt.antenna_xds.ds\n", "ant_xds" ] }, { "cell_type": "code", "execution_count": 7, "id": "18332331-244b-46b9-9025-e57bcff54280", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'ANTENNA_POSITION' (antenna_name: 10, cartesian_pos_label: 3)> Size: 240B\n",
       "dask.array<open_dataset-ANTENNA_POSITION, shape=(10, 3), dtype=float64, chunksize=(10, 3), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * antenna_name         (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "    mount                (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    station_name         (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    telescope_name       (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "  * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "Attributes:\n",
       "    coordinate_system:   geocentric\n",
       "    frame:               ITRS\n",
       "    origin_object_name:  earth\n",
       "    type:                location\n",
       "    units:               m
" ], "text/plain": [ " Size: 240B\n", "dask.array\n", "Coordinates:\n", " * antenna_name (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * cartesian_pos_label (cartesian_pos_label) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'ANTENNA_POSITION' (antenna_name: 10, cartesian_pos_label: 3)> Size: 240B\n",
       "array([[-2112065.339753, -3705356.513964,  4726813.595927],\n",
       "       [-1324009.438905, -5332181.956657,  3231962.338382],\n",
       "       [ 1446374.723555, -4447939.69817 ,  4322306.215011],\n",
       "       [-1995678.95979 , -5037317.696765,  3357327.949827],\n",
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       "       [ -130872.637324, -4762317.087915,  4226850.972202],\n",
       "       [-2409150.566965, -4478573.065876,  3838617.29146 ],\n",
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       "       [ 2607848.714994, -5488069.460221,  1932739.843634]])\n",
       "Coordinates:\n",
       "  * antenna_name         (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "    mount                (antenna_name) <U6 240B 'ALT-AZ' 'ALT-AZ' ... 'ALT-AZ'\n",
       "    station_name         (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "    telescope_name       (antenna_name) <U4 160B 'VLBA' 'VLBA' ... 'VLBA' 'VLBA'\n",
       "  * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "Attributes:\n",
       "    coordinate_system:   geocentric\n",
       "    frame:               ITRS\n",
       "    origin_object_name:  earth\n",
       "    type:                location\n",
       "    units:               m
" ], "text/plain": [ " Size: 240B\n", "array([[-2112065.339753, -3705356.513964, 4726813.595927],\n", " [-1324009.438905, -5332181.956657, 3231962.338382],\n", " [ 1446374.723555, -4447939.69817 , 4322306.215011],\n", " [-1995678.95979 , -5037317.696765, 3357327.949827],\n", " [-1449752.711882, -4975298.576836, 3709123.785811],\n", " [-5464075.303632, -2495247.54864 , 2148297.629883],\n", " [ -130872.637324, -4762317.087915, 4226850.972202],\n", " [-2409150.566965, -4478573.065876, 3838617.29146 ],\n", " [-1640954.066439, -5014816.028293, 3575411.724719],\n", " [ 2607848.714994, -5488069.460221, 1932739.843634]])\n", "Coordinates:\n", " * antenna_name (antenna_name) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'ANTENNA_RECEPTOR_ANGLE' (antenna_name: 10, receptor_label: 2)> Size: 160B\n",
       "dask.array<open_dataset-ANTENNA_RECEPTOR_ANGLE, shape=(10, 2), dtype=float64, chunksize=(10, 2), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * antenna_name       (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'OV' 'PT' 'SC'\n",
       "    mount              (antenna_name) <U6 240B 'ALT-AZ' 'ALT-AZ' ... 'ALT-AZ'\n",
       "    station_name       (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'OV' 'PT' 'SC'\n",
       "    telescope_name     (antenna_name) <U4 160B 'VLBA' 'VLBA' ... 'VLBA' 'VLBA'\n",
       "  * receptor_label     (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "    polarization_type  (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    type:     quantity\n",
       "    units:    rad
" ], "text/plain": [ " Size: 160B\n", "dask.array\n", "Coordinates:\n", " * antenna_name (antenna_name) \n", "Attributes:\n", " type: quantity\n", " units: rad" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ant_xds.ANTENNA_RECEPTOR_ANGLE" ] }, { "cell_type": "code", "execution_count": 10, "id": "1154b59c-7f7d-4361-9c96-aab2ccab4377", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 5kB\n",
       "Dimensions:                       (field_name: 1, sky_dir_label: 2,\n",
       "                                   baseline_id: 55, frequency: 6,\n",
       "                                   polarization: 2, time: 540, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * field_name                    (field_name) <U32 128B 'J1154+6022_1'\n",
       "    source_name                   (field_name) <U7 28B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "  * baseline_id                   (baseline_id) int64 440B 0 1 2 3 ... 52 53 54\n",
       "  * frequency                     (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "  * polarization                  (polarization) <U2 16B 'RR' 'LL'\n",
       "  * time                          (time) float64 4kB 1.645e+09 ... 1.645e+09\n",
       "  * uvw_label                     (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Data variables:\n",
       "    FIELD_PHASE_CENTER_DIRECTION  (field_name, sky_dir_label) float64 16B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    type:     field_and_source
" ], "text/plain": [ " Size: 5kB\n", "Dimensions: (field_name: 1, sky_dir_label: 2,\n", " baseline_id: 55, frequency: 6,\n", " polarization: 2, time: 540, uvw_label: 3)\n", "Coordinates:\n", " * field_name (field_name) \n", " * sky_dir_label (sky_dir_label) \n", "Attributes:\n", " type: field_and_source" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.xr_ms.get_field_and_source_xds()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b16a8ffd-c363-44d8-89f7-cd1c7d6b0cda", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'weather_xds'>\n",
       "Group: /VLBA_TL016B_split_3/weather_xds\n",
       "    Dimensions:              (time: 540, baseline_id: 55, frequency: 6,\n",
       "                              polarization: 2, uvw_label: 3, station_name: 10,\n",
       "                              time_weather: 856, cartesian_pos_label: 3)\n",
       "    Coordinates:\n",
       "      * station_name         (station_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "      * time_weather         (time_weather) float64 7kB 1.645e+09 ... 1.645e+09\n",
       "      * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "    Inherited coordinates:\n",
       "      * baseline_id          (baseline_id) int64 440B 0 1 2 3 4 5 ... 50 51 52 53 54\n",
       "      * frequency            (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "      * polarization         (polarization) <U2 16B 'RR' 'LL'\n",
       "      * time                 (time) float64 4kB 1.645e+09 1.645e+09 ... 1.645e+09\n",
       "      * uvw_label            (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        DEW_POINT            (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        H2O                  (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        IONOS_ELECTRON       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        PRESSURE             (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        STATION_POSITION     (station_name, cartesian_pos_label) float64 240B dask.array<chunksize=(10, 3), meta=np.ndarray>\n",
       "        TEMPERATURE          (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        WIND_DIRECTION       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "        WIND_SPEED           (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        type:     weather
" ], "text/plain": [ "\n", "Group: /VLBA_TL016B_split_3/weather_xds\n", " Dimensions: (time: 540, baseline_id: 55, frequency: 6,\n", " polarization: 2, uvw_label: 3, station_name: 10,\n", " time_weather: 856, cartesian_pos_label: 3)\n", " Coordinates:\n", " * station_name (station_name) \n", " H2O (station_name, time_weather) float64 68kB dask.array\n", " IONOS_ELECTRON (station_name, time_weather) float64 68kB dask.array\n", " PRESSURE (station_name, time_weather) float64 68kB dask.array\n", " STATION_POSITION (station_name, cartesian_pos_label) float64 240B dask.array\n", " TEMPERATURE (station_name, time_weather) float64 68kB dask.array\n", " WIND_DIRECTION (station_name, time_weather) float64 68kB dask.array\n", " WIND_SPEED (station_name, time_weather) float64 68kB dask.array\n", " Attributes:\n", " type: weather" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.weather_xds" ] }, { "cell_type": "code", "execution_count": 12, "id": "9376f0e3-7ce1-47fd-b174-84c27a11b20d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 491kB\n",
       "Dimensions:              (station_name: 10, time_weather: 856,\n",
       "                          cartesian_pos_label: 3, baseline_id: 55,\n",
       "                          frequency: 6, polarization: 2, time: 540, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * station_name         (station_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "  * time_weather         (time_weather) float64 7kB 1.645e+09 ... 1.645e+09\n",
       "  * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "  * baseline_id          (baseline_id) int64 440B 0 1 2 3 4 5 ... 50 51 52 53 54\n",
       "  * frequency            (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "  * polarization         (polarization) <U2 16B 'RR' 'LL'\n",
       "  * time                 (time) float64 4kB 1.645e+09 1.645e+09 ... 1.645e+09\n",
       "  * uvw_label            (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Data variables:\n",
       "    DEW_POINT            (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    H2O                  (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    IONOS_ELECTRON       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    PRESSURE             (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    STATION_POSITION     (station_name, cartesian_pos_label) float64 240B dask.array<chunksize=(10, 3), meta=np.ndarray>\n",
       "    TEMPERATURE          (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    WIND_DIRECTION       (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "    WIND_SPEED           (station_name, time_weather) float64 68kB dask.array<chunksize=(10, 856), meta=np.ndarray>\n",
       "Attributes:\n",
       "    type:     weather
" ], "text/plain": [ " Size: 491kB\n", "Dimensions: (station_name: 10, time_weather: 856,\n", " cartesian_pos_label: 3, baseline_id: 55,\n", " frequency: 6, polarization: 2, time: 540, uvw_label: 3)\n", "Coordinates:\n", " * station_name (station_name) \n", " H2O (station_name, time_weather) float64 68kB dask.array\n", " IONOS_ELECTRON (station_name, time_weather) float64 68kB dask.array\n", " PRESSURE (station_name, time_weather) float64 68kB dask.array\n", " STATION_POSITION (station_name, cartesian_pos_label) float64 240B dask.array\n", " TEMPERATURE (station_name, time_weather) float64 68kB dask.array\n", " WIND_DIRECTION (station_name, time_weather) float64 68kB dask.array\n", " WIND_SPEED (station_name, time_weather) float64 68kB dask.array\n", "Attributes:\n", " type: weather" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_xds = ms_xdt.weather_xds.ds\n", "weather_xds" ] }, { "cell_type": "code", "execution_count": 13, "id": "98aa2430-bb99-4534-8f14-3161321e8e51", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 6kB\n",
       "Dimensions:                 (antenna_name: 10, poly_term: 1, receptor_label: 2,\n",
       "                             baseline_id: 55, frequency: 6, polarization: 2,\n",
       "                             time: 540, uvw_label: 3)\n",
       "Coordinates: (12/13)\n",
       "  * antenna_name            (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'PT' 'SC'\n",
       "    antenna_id              (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    gain_curve_type         (antenna_name) <U9 360B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    mount                   (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    station_name            (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    telescope_name          (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    ...                      ...\n",
       "    polarization_type       (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "  * baseline_id             (baseline_id) int64 440B 0 1 2 3 4 ... 51 52 53 54\n",
       "  * frequency               (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "  * polarization            (polarization) <U2 16B 'RR' 'LL'\n",
       "  * time                    (time) float64 4kB 1.645e+09 1.645e+09 ... 1.645e+09\n",
       "  * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Dimensions without coordinates: poly_term\n",
       "Data variables:\n",
       "    GAIN_CURVE              (antenna_name, poly_term, receptor_label) float64 160B dask.array<chunksize=(10, 1, 2), meta=np.ndarray>\n",
       "    GAIN_CURVE_INTERVAL     (antenna_name) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    GAIN_CURVE_SENSITIVITY  (antenna_name, receptor_label) float64 160B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    measured_date:  2022-02-21T09:50:25\n",
       "    type:           gain_curve
" ], "text/plain": [ " Size: 6kB\n", "Dimensions: (antenna_name: 10, poly_term: 1, receptor_label: 2,\n", " baseline_id: 55, frequency: 6, polarization: 2,\n", " time: 540, uvw_label: 3)\n", "Coordinates: (12/13)\n", " * antenna_name (antenna_name) \n", " gain_curve_type (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " ... ...\n", " polarization_type (antenna_name, receptor_label) \n", " * baseline_id (baseline_id) int64 440B 0 1 2 3 4 ... 51 52 53 54\n", " * frequency (frequency) float64 48B 5.068e+09 ... 5.07e+09\n", " * polarization (polarization) \n", " GAIN_CURVE_INTERVAL (antenna_name) float64 80B dask.array\n", " GAIN_CURVE_SENSITIVITY (antenna_name, receptor_label) float64 160B dask.array\n", "Attributes:\n", " measured_date: 2022-02-21T09:50:25\n", " type: gain_curve" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gain_curve_xdt = ms_xdt.gain_curve_xds\n", "gain_curve_xdt.ds" ] }, { "cell_type": "code", "execution_count": 14, "id": "fcf40843-f23a-4652-bfcc-32c7e9c53fab", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'phase_calibration_xds'>\n",
       "Group: /VLBA_TL016B_split_3/phase_calibration_xds\n",
       "    Dimensions:                   (time: 540, baseline_id: 55, frequency: 6,\n",
       "                                   polarization: 2, uvw_label: 3, antenna_name: 10,\n",
       "                                   time_phase_cal: 141, receptor_label: 2,\n",
       "                                   tone_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name              (antenna_name) <U2 80B 'BR' 'FD' ... 'PT' 'SC'\n",
       "        antenna_id                (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        mount                     (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        station_name              (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        telescope_name            (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "      * time_phase_cal            (time_phase_cal) float64 1kB 1.645e+09 ... 1.64...\n",
       "      * receptor_label            (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "        polarization_type         (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "      * tone_label                (tone_label) <U6 48B 'freq_0' 'freq_1'\n",
       "    Inherited coordinates:\n",
       "      * baseline_id               (baseline_id) int64 440B 0 1 2 3 4 ... 51 52 53 54\n",
       "      * frequency                 (frequency) float64 48B 5.068e+09 ... 5.07e+09\n",
       "      * polarization              (polarization) <U2 16B 'RR' 'LL'\n",
       "      * time                      (time) float64 4kB 1.645e+09 ... 1.645e+09\n",
       "      * uvw_label                 (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        PHASE_CAL                 (antenna_name, time_phase_cal, receptor_label, tone_label) complex64 45kB dask.array<chunksize=(10, 141, 2, 2), meta=np.ndarray>\n",
       "        PHASE_CAL_CABLE_CAL       (antenna_name, time_phase_cal) float64 11kB dask.array<chunksize=(10, 141), meta=np.ndarray>\n",
       "        PHASE_CAL_INTERVAL        (antenna_name, time_phase_cal) float64 11kB dask.array<chunksize=(10, 141), meta=np.ndarray>\n",
       "        PHASE_CAL_TONE_FREQUENCY  (antenna_name, time_phase_cal, receptor_label, tone_label) float64 45kB dask.array<chunksize=(10, 141, 2, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        type:     phase_calibration
" ], "text/plain": [ "\n", "Group: /VLBA_TL016B_split_3/phase_calibration_xds\n", " Dimensions: (time: 540, baseline_id: 55, frequency: 6,\n", " polarization: 2, uvw_label: 3, antenna_name: 10,\n", " time_phase_cal: 141, receptor_label: 2,\n", " tone_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * time_phase_cal (time_phase_cal) float64 1kB 1.645e+09 ... 1.64...\n", " * receptor_label (receptor_label) \n", " * tone_label (tone_label) \n", " PHASE_CAL_CABLE_CAL (antenna_name, time_phase_cal) float64 11kB dask.array\n", " PHASE_CAL_INTERVAL (antenna_name, time_phase_cal) float64 11kB dask.array\n", " PHASE_CAL_TONE_FREQUENCY (antenna_name, time_phase_cal, receptor_label, tone_label) float64 45kB dask.array\n", " Attributes:\n", " type: phase_calibration" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phase_calibration_xdt = ms_xdt.phase_calibration_xds\n", "phase_calibration_xdt" ] }, { "cell_type": "code", "execution_count": 15, "id": "90ac7854-58b4-408f-9adc-6ae8b6d17b19", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'system_calibration_xds'>\n",
       "Group: /VLBA_TL016B_split_3/system_calibration_xds\n",
       "    Dimensions:            (time: 540, baseline_id: 55, frequency: 6,\n",
       "                            polarization: 2, uvw_label: 3, antenna_name: 10,\n",
       "                            time_system_cal: 1192, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name       (antenna_name) <U2 80B 'BR' 'FD' 'HN' ... 'OV' 'PT' 'SC'\n",
       "        antenna_id         (antenna_name) int32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        mount              (antenna_name) <U6 240B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        station_name       (antenna_name) <U2 80B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "        telescope_name     (antenna_name) <U4 160B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "      * time_system_cal    (time_system_cal) float64 10kB 1.645e+09 ... 1.645e+09\n",
       "      * receptor_label     (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "        polarization_type  (antenna_name, receptor_label) <U1 80B dask.array<chunksize=(10, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id        (baseline_id) int64 440B 0 1 2 3 4 5 ... 50 51 52 53 54\n",
       "      * frequency          (frequency) float64 48B 5.068e+09 5.068e+09 ... 5.07e+09\n",
       "      * polarization       (polarization) <U2 16B 'RR' 'LL'\n",
       "      * time               (time) float64 4kB 1.645e+09 1.645e+09 ... 1.645e+09\n",
       "      * uvw_label          (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        TSYS               (antenna_name, time_system_cal, receptor_label) float64 191kB dask.array<chunksize=(10, 1192, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        type:     system_calibration
" ], "text/plain": [ "\n", "Group: /VLBA_TL016B_split_3/system_calibration_xds\n", " Dimensions: (time: 540, baseline_id: 55, frequency: 6,\n", " polarization: 2, uvw_label: 3, antenna_name: 10,\n", " time_system_cal: 1192, receptor_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * time_system_cal (time_system_cal) float64 10kB 1.645e+09 ... 1.645e+09\n", " * receptor_label (receptor_label) \n", " Inherited coordinates:\n", " * baseline_id (baseline_id) int64 440B 0 1 2 3 4 5 ... 50 51 52 53 54\n", " * frequency (frequency) float64 48B 5.068e+09 5.068e+09 ... 5.07e+09\n", " * polarization (polarization) \n", " Attributes:\n", " type: system_calibration" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "system_calibration_xds = ms_xdt.system_calibration_xds\n", "system_calibration_xds" ] }, { "cell_type": "code", "execution_count": 16, "id": "662f3899", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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