{ "cells": [ { "cell_type": "markdown", "id": "f412df9c-35ac-418d-a4f6-b95edef8478e", "metadata": {}, "source": [ "# Ephemeris 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": [ "https://open-bitbucket.nrao.edu/projects/CASA/repos/casatestdata/browse/unittest/tclean/venus_ephem_test.ms" ] }, { "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 15:17:27,795\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 15:17:27,795\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/venus_ephem_test.ms \n" ] } ], "source": [ "import toolviper\n", "ms_file = \"venus_ephem_test.ms\"\n", "toolviper.utils.data.download(file=ms_file)" ] }, { "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 15:17:28,092\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', 'OBS_MODE', 'OBSERVATION_ID', 'EPHEMERIS_ID'] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:17:28,094\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Number of partitions: 1 \n", "[\u001b[38;2;128;05;128m2026-04-20 15:17:28,094\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 [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], FIELD [0, 1, 2, 3, 4, 5, 6], SCAN [7, 11], EPHEMERIS [0] \n" ] } ], "source": [ "from xradio.measurement_set import convert_msv2_to_processing_set\n", "ms_file = \"venus_ephem_test.ms\"\n", "\n", "main_chunksize = {\"frequency\": 1, \"time\": 20} # baseline, polarization\n", "outfile = \"venus_ephem_test.ps.zarr\"\n", "convert_msv2_to_processing_set(\n", " in_file=ms_file,\n", " out_file=outfile,\n", " parallel_mode=\"none\",\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
0venus_ephem_test_0[OBSERVE_TARGET#ON_SOURCE](140, 1128, 1, 2)uid://A002/Xd7be9d/X499e[XX, YY][11, 7]X1847499280#ALMA_RB_06#BB_1#SW-01#FULL_RES_0[UNSPECIFIED][Venus_0, Venus_1, Venus_2, Venus_3, Venus_4, ...[Venus_0][OSSO_Line_1(ID=0)]Ephemerisuid://A001/X133d/X169fuid://A001/X133d/X169auid://A001/X12ea/X7152.452498e+112.452498e+11
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" ], "text/plain": [ " name scan_intents shape \\\n", "0 venus_ephem_test_0 [OBSERVE_TARGET#ON_SOURCE] (140, 1128, 1, 2) \n", "\n", " execution_block_UID polarization scan_name \\\n", "0 uid://A002/Xd7be9d/X499e [XX, YY] [11, 7] \n", "\n", " spw_name spw_intents \\\n", "0 X1847499280#ALMA_RB_06#BB_1#SW-01#FULL_RES_0 [UNSPECIFIED] \n", "\n", " field_name source_name \\\n", "0 [Venus_0, Venus_1, Venus_2, Venus_3, Venus_4, ... [Venus_0] \n", "\n", " line_name field_coords session_reference_UID \\\n", "0 [OSSO_Line_1(ID=0)] Ephemeris uid://A001/X133d/X169f \n", "\n", " scheduling_block_UID project_UID start_frequency \\\n", "0 uid://A001/X133d/X169a uid://A001/X12ea/X715 2.452498e+11 \n", "\n", " end_frequency \n", "0 2.452498e+11 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps_name = outfile\n", "\n", "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 'venus_ephem_test_0'>\n",
       "Group: /venus_ephem_test_0\n",
       "│   Dimensions:                     (time: 140, baseline_id: 1128, frequency: 1,\n",
       "│                                    polarization: 2, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 1kB 1.547e+09 ... 1.547e+09\n",
       "│       field_name                  (time) <U27 15kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 12kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 9kB 0 1 2 ... 1125 1126 1127\n",
       "│       baseline_antenna1_name      (baseline_id) <U9 41kB dask.array<chunksize=(1128,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U9 41kB dask.array<chunksize=(1128,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 8B 2.452e+11\n",
       "│     * polarization                (polarization) <U2 16B 'XX' 'YY'\n",
       "│     * uvw_label                   (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "│   Data variables:\n",
       "│       EFFECTIVE_INTEGRATION_TIME  (time, baseline_id) float64 1MB dask.array<chunksize=(20, 1128), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 316kB dask.array<chunksize=(20, 1128, 1, 2), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 1MB dask.array<chunksize=(20, 1128), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 4MB dask.array<chunksize=(20, 1128, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 3MB dask.array<chunksize=(20, 1128, 1, 2), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 1MB dask.array<chunksize=(20, 1128, 1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:17:28.127657+00:00\n",
       "│       creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "│       data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "│       observation_info:  {'execution_block_UID': 'uid://A002/Xd7be9d/X499e', 'o...\n",
       "│       processor_info:    {'sub_type': 'ALMA_CORRELATOR_MODE', 'type': 'CORRELAT...\n",
       "│       schema_version:    4.0.0\n",
       "│       type:              visibility\n",
       "├── Group: /venus_ephem_test_0/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 47, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U9 2kB 'DA41_A058' ... 'DV25_A006'\n",
       "│           mount                   (antenna_name) <U6 1kB dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U4 752B dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U4 752B dask.array<chunksize=(47,), 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 376B dask.array<chunksize=(47, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 376B dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 1kB dask.array<chunksize=(47, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 752B dask.array<chunksize=(47, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  ALMA\n",
       "│           relocatable_antennas:    True\n",
       "│           type:                    antenna\n",
       "├── Group: /venus_ephem_test_0/field_and_source_base_xds\n",
       "│       Dimensions:                       (time: 140, sky_dir_label: 2,\n",
       "│                                          sky_dis_label: 1, line_label: 1,\n",
       "│                                          cartesian_pos_label: 3, time_ephemeris: 2,\n",
       "│                                          ellipsoid_dir_label: 2,\n",
       "│                                          ellipsoid_dis_label: 1)\n",
       "│       Coordinates:\n",
       "│           field_name                    (time) <U27 15kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "│           source_name                   (time) <U27 15kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "│         * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "│         * sky_dis_label                 (sky_dis_label) <U4 16B 'dist'\n",
       "│         * line_label                    (line_label) <U21 84B '0'\n",
       "│           line_name                     (time, line_label) <U17 10kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "│         * cartesian_pos_label           (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "│         * time_ephemeris                (time_ephemeris) float64 16B 1.547e+09 1.54...\n",
       "│         * ellipsoid_dir_label           (ellipsoid_dir_label) <U3 24B 'lon' 'lat'\n",
       "│         * ellipsoid_dis_label           (ellipsoid_dis_label) <U4 16B 'dist'\n",
       "│       Data variables:\n",
       "│           FIELD_PHASE_CENTER_DIRECTION  (time, sky_dir_label) float64 2kB dask.array<chunksize=(140, 2), meta=np.ndarray>\n",
       "│           FIELD_PHASE_CENTER_DISTANCE   (time, sky_dis_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "│           LINE_REST_FREQUENCY           (time, line_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "│           LINE_SYSTEMIC_VELOCITY        (time, line_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "│           OBSERVER_POSITION             (cartesian_pos_label) float64 24B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "│           SOURCE_DIRECTION              (time_ephemeris, sky_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "│           SOURCE_DISTANCE               (time_ephemeris, sky_dis_label) float64 16B dask.array<chunksize=(2, 1), meta=np.ndarray>\n",
       "│           SOURCE_RADIAL_VELOCITY        (time_ephemeris) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "│           SUB_OBSERVER_DIRECTION        (time_ephemeris, ellipsoid_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           type:     field_and_source_ephemeris\n",
       "└── Group: /venus_ephem_test_0/weather_xds\n",
       "        Dimensions:              (station_name: 5, time_weather: 147,\n",
       "                                  cartesian_pos_label: 3)\n",
       "        Coordinates:\n",
       "          * station_name         (station_name) <U14 280B 'Meteo129' ... 'MeteoTB2'\n",
       "          * time_weather         (time_weather) float64 1kB 1.547e+09 ... 1.547e+09\n",
       "          * cartesian_pos_label  (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "        Data variables:\n",
       "            DEW_POINT            (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "            PRESSURE             (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "            REL_HUMIDITY         (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "            STATION_POSITION     (station_name, cartesian_pos_label) float64 120B dask.array<chunksize=(5, 3), meta=np.ndarray>\n",
       "            TEMPERATURE          (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "            WIND_DIRECTION       (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "            WIND_SPEED           (station_name, time_weather) float64 6kB dask.array<chunksize=(5, 147), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            type:     weather
" ], "text/plain": [ "\n", "Group: /venus_ephem_test_0\n", "│ Dimensions: (time: 140, baseline_id: 1128, frequency: 1,\n", "│ polarization: 2, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 1kB 1.547e+09 ... 1.547e+09\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 9kB 0 1 2 ... 1125 1126 1127\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 8B 2.452e+11\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 316kB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 1MB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 4MB 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: 2026-04-20T21:17:28.127657+00:00\n", "│ creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", "│ data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", "│ observation_info: {'execution_block_UID': 'uid://A002/Xd7be9d/X499e', 'o...\n", "│ processor_info: {'sub_type': 'ALMA_CORRELATOR_MODE', 'type': 'CORRELAT...\n", "│ schema_version: 4.0.0\n", "│ type: visibility\n", "├── Group: /venus_ephem_test_0/antenna_xds\n", "│ Dimensions: (antenna_name: 47, 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 376B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 1kB dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 752B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: ALMA\n", "│ relocatable_antennas: True\n", "│ type: antenna\n", "├── Group: /venus_ephem_test_0/field_and_source_base_xds\n", "│ Dimensions: (time: 140, sky_dir_label: 2,\n", "│ sky_dis_label: 1, line_label: 1,\n", "│ cartesian_pos_label: 3, time_ephemeris: 2,\n", "│ ellipsoid_dir_label: 2,\n", "│ ellipsoid_dis_label: 1)\n", "│ Coordinates:\n", "│ field_name (time) \n", "│ source_name (time) \n", "│ * sky_dir_label (sky_dir_label) \n", "│ * cartesian_pos_label (cartesian_pos_label) \n", "│ FIELD_PHASE_CENTER_DISTANCE (time, sky_dis_label) float64 1kB dask.array\n", "│ LINE_REST_FREQUENCY (time, line_label) float64 1kB dask.array\n", "│ LINE_SYSTEMIC_VELOCITY (time, line_label) float64 1kB dask.array\n", "│ OBSERVER_POSITION (cartesian_pos_label) float64 24B dask.array\n", "│ SOURCE_DIRECTION (time_ephemeris, sky_dir_label) float64 32B dask.array\n", "│ SOURCE_DISTANCE (time_ephemeris, sky_dis_label) float64 16B dask.array\n", "│ SOURCE_RADIAL_VELOCITY (time_ephemeris) float64 16B dask.array\n", "│ SUB_OBSERVER_DIRECTION (time_ephemeris, ellipsoid_dir_label) float64 32B dask.array\n", "│ Attributes:\n", "│ type: field_and_source_ephemeris\n", "└── Group: /venus_ephem_test_0/weather_xds\n", " Dimensions: (station_name: 5, time_weather: 147,\n", " cartesian_pos_label: 3)\n", " Coordinates:\n", " * station_name (station_name) \n", " PRESSURE (station_name, time_weather) float64 6kB dask.array\n", " REL_HUMIDITY (station_name, time_weather) float64 6kB dask.array\n", " STATION_POSITION (station_name, cartesian_pos_label) float64 120B dask.array\n", " TEMPERATURE (station_name, time_weather) float64 6kB dask.array\n", " WIND_DIRECTION (station_name, time_weather) float64 6kB dask.array\n", " WIND_SPEED (station_name, time_weather) float64 6kB dask.array\n", " Attributes:\n", " type: weather" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "ps_xdt[\"venus_ephem_test_0\"]" ] }, { "cell_type": "code", "execution_count": 6, "id": "079098b0-f78e-4ace-907a-5c63f157b107", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'execution_block_UID': 'uid://A002/Xd7be9d/X499e',\n", " 'observer': ['sv2421'],\n", " 'observing_log': '',\n", " 'project_UID': 'uid://A001/X12ea/X715',\n", " 'release_date': '1858-11-17T00:00:00',\n", " 'scheduling_block_UID': 'uid://A001/X133d/X169a',\n", " 'session_reference_UID': 'uid://A001/X133d/X169f'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt = ps_xdt[\"venus_ephem_test_0\"]\n", "ms_xdt.observation_info" ] }, { "cell_type": "code", "execution_count": 7, "id": "ba761deb-b7a9-4b2b-8163-3050413fe59c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'antenna_xds'>\n",
       "Group: /venus_ephem_test_0/antenna_xds\n",
       "    Dimensions:                 (time: 140, baseline_id: 1128, frequency: 1,\n",
       "                                 polarization: 2, uvw_label: 3, antenna_name: 47,\n",
       "                                 cartesian_pos_label: 3, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name            (antenna_name) <U9 2kB 'DA41_A058' ... 'DV25_A006'\n",
       "        mount                   (antenna_name) <U6 1kB dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "        station_name            (antenna_name) <U4 752B dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "        telescope_name          (antenna_name) <U4 752B dask.array<chunksize=(47,), 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 376B dask.array<chunksize=(47, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id             (baseline_id) int64 9kB 0 1 2 3 ... 1125 1126 1127\n",
       "      * frequency               (frequency) float64 8B 2.452e+11\n",
       "      * polarization            (polarization) <U2 16B 'XX' 'YY'\n",
       "      * time                    (time) float64 1kB 1.547e+09 1.547e+09 ... 1.547e+09\n",
       "      * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        ANTENNA_DISH_DIAMETER   (antenna_name) float64 376B dask.array<chunksize=(47,), meta=np.ndarray>\n",
       "        ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 1kB dask.array<chunksize=(47, 3), meta=np.ndarray>\n",
       "        ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 752B dask.array<chunksize=(47, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        overall_telescope_name:  ALMA\n",
       "        relocatable_antennas:    True\n",
       "        type:                    antenna
" ], "text/plain": [ "\n", "Group: /venus_ephem_test_0/antenna_xds\n", " Dimensions: (time: 140, baseline_id: 1128, frequency: 1,\n", " polarization: 2, uvw_label: 3, antenna_name: 47,\n", " cartesian_pos_label: 3, 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", " Inherited coordinates:\n", " * baseline_id (baseline_id) int64 9kB 0 1 2 3 ... 1125 1126 1127\n", " * frequency (frequency) float64 8B 2.452e+11\n", " * polarization (polarization) \n", " ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 1kB dask.array\n", " ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 752B dask.array\n", " Attributes:\n", " overall_telescope_name: ALMA\n", " relocatable_antennas: True\n", " type: antenna" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt[\"antenna_xds\"] # or ms_xdt.antenna_xds" ] }, { "cell_type": "code", "execution_count": 8, "id": "24e79ab8", "metadata": {}, "outputs": [], "source": [ "field_and_source_xds = ms_xdt.xr_ms.get_field_and_source_xds()" ] }, { "cell_type": "code", "execution_count": 9, "id": "ae7861c7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DatasetView> Size: 56kB\n",
       "Dimensions:                       (time: 140, sky_dir_label: 2,\n",
       "                                   sky_dis_label: 1, line_label: 1,\n",
       "                                   cartesian_pos_label: 3, time_ephemeris: 2,\n",
       "                                   ellipsoid_dir_label: 2, baseline_id: 1128,\n",
       "                                   frequency: 1, polarization: 2, uvw_label: 3,\n",
       "                                   ellipsoid_dis_label: 1)\n",
       "Coordinates: (12/15)\n",
       "  * time                          (time) float64 1kB 1.547e+09 ... 1.547e+09\n",
       "    field_name                    (time) <U27 15kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "    source_name                   (time) <U27 15kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "  * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "  * sky_dis_label                 (sky_dis_label) <U4 16B 'dist'\n",
       "  * line_label                    (line_label) <U21 84B '0'\n",
       "    ...                            ...\n",
       "  * ellipsoid_dir_label           (ellipsoid_dir_label) <U3 24B 'lon' 'lat'\n",
       "  * baseline_id                   (baseline_id) int64 9kB 0 1 2 ... 1126 1127\n",
       "  * frequency                     (frequency) float64 8B 2.452e+11\n",
       "  * polarization                  (polarization) <U2 16B 'XX' 'YY'\n",
       "  * uvw_label                     (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "  * ellipsoid_dis_label           (ellipsoid_dis_label) <U4 16B 'dist'\n",
       "Data variables:\n",
       "    FIELD_PHASE_CENTER_DIRECTION  (time, sky_dir_label) float64 2kB dask.array<chunksize=(140, 2), meta=np.ndarray>\n",
       "    FIELD_PHASE_CENTER_DISTANCE   (time, sky_dis_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "    LINE_REST_FREQUENCY           (time, line_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "    LINE_SYSTEMIC_VELOCITY        (time, line_label) float64 1kB dask.array<chunksize=(140, 1), meta=np.ndarray>\n",
       "    OBSERVER_POSITION             (cartesian_pos_label) float64 24B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "    SOURCE_DIRECTION              (time_ephemeris, sky_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "    SOURCE_DISTANCE               (time_ephemeris, sky_dis_label) float64 16B dask.array<chunksize=(2, 1), meta=np.ndarray>\n",
       "    SOURCE_RADIAL_VELOCITY        (time_ephemeris) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "    SUB_OBSERVER_DIRECTION        (time_ephemeris, ellipsoid_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    type:     field_and_source_ephemeris
" ], "text/plain": [ " Size: 56kB\n", "Dimensions: (time: 140, sky_dir_label: 2,\n", " sky_dis_label: 1, line_label: 1,\n", " cartesian_pos_label: 3, time_ephemeris: 2,\n", " ellipsoid_dir_label: 2, baseline_id: 1128,\n", " frequency: 1, polarization: 2, uvw_label: 3,\n", " ellipsoid_dis_label: 1)\n", "Coordinates: (12/15)\n", " * time (time) float64 1kB 1.547e+09 ... 1.547e+09\n", " field_name (time) \n", " source_name (time) \n", " * sky_dir_label (sky_dir_label) \n", " FIELD_PHASE_CENTER_DISTANCE (time, sky_dis_label) float64 1kB dask.array\n", " LINE_REST_FREQUENCY (time, line_label) float64 1kB dask.array\n", " LINE_SYSTEMIC_VELOCITY (time, line_label) float64 1kB dask.array\n", " OBSERVER_POSITION (cartesian_pos_label) float64 24B dask.array\n", " SOURCE_DIRECTION (time_ephemeris, sky_dir_label) float64 32B dask.array\n", " SOURCE_DISTANCE (time_ephemeris, sky_dis_label) float64 16B dask.array\n", " SOURCE_RADIAL_VELOCITY (time_ephemeris) float64 16B dask.array\n", " SUB_OBSERVER_DIRECTION (time_ephemeris, ellipsoid_dir_label) float64 32B dask.array\n", "Attributes:\n", " type: field_and_source_ephemeris" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "field_and_source_xds" ] }, { "cell_type": "code", "execution_count": 10, "id": "6cd15d9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dims': ('sky_dir_label',),\n", " 'attrs': {'frame': 'icrs', 'type': 'sky_coord', 'units': 'rad'},\n", " 'data': [-2.1054387263055148, -0.2961237230862559],\n", " 'coords': {'sky_dir_label': {'dims': ('sky_dir_label',),\n", " 'attrs': {},\n", " 'data': ['ra', 'dec']}},\n", " 'name': 'FIELD_PHASE_CENTER_DIRECTION'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.xr_ms.get_field_and_source_xds().FIELD_PHASE_CENTER_DIRECTION.isel(time=0,drop=True).to_dict()" ] }, { "cell_type": "code", "execution_count": 11, "id": "23704913", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ], "text/plain": [ " Size: 24B\n", "array([0, 0, 0])\n", "Coordinates:\n", " * time (time) int64 24B 0 1 2\n", "Attributes:\n", " test: 0\n", " test2: 1" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xds = xds*10\n", "xds" ] }, { "cell_type": "code", "execution_count": 15, "id": "5820a73e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'attrs': {'coordinate_system': 'geocentric',\n", " 'frame': 'ITRF',\n", " 'origin_object_name': 'earth',\n", " 'type': 'location',\n", " 'units': 'rad'},\n", " 'data': array([-1.1825466 , -0.39941499]),\n", " 'dims': ('sky_dir_label',),\n", " 'coords': {'sky_dir_label': {'dims': ('sky_dir_label',),\n", " 'data': ['ra', 'dec']}}}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "dis = {\n", " \"attrs\": {\n", " \"coordinate_system\": \"geocentric\",\n", " \"frame\": \"ITRF\",\n", " \"origin_object_name\": \"earth\",\n", " \"type\": \"location\",\n", " \"units\": \"m\",\n", " },\n", " \"data\": [6379946.01326443],\n", " \"dims\": (\"sky_dis_label\",),\n", " \"coords\": {'sky_dis_label': {'dims': ('sky_dis_label',),\n", " 'data': ['m']}\n", " },\n", " }\n", "\n", "\n", "dir = {\n", " \"attrs\": {\n", " \"coordinate_system\": \"geocentric\",\n", " \"frame\": \"ITRF\",\n", " \"origin_object_name\": \"earth\",\n", " \"type\": \"location\",\n", " \"units\": \"rad\",\n", " },\n", " \"data\": np.array([-1.1825465955049892, -0.3994149869262738]),\n", " \"dims\" : (\"sky_dir_label\",),\n", " \"coords\": {'sky_dir_label': {'dims': ('sky_dir_label',),\n", " 'data': ['ra', 'dec']}\n", " },\n", " \n", " }\n", "\n", "dir" ] }, { "cell_type": "code", "execution_count": 16, "id": "e344b7e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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