{ "cells": [ { "cell_type": "markdown", "id": "65d25e63-8899-4955-b8c5-2d70cac43556", "metadata": {}, "source": [ "# MeerKAT 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": "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 15:19:05,662\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 15:19:05,662\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/small_meerkat.ms \n" ] } ], "source": [ "import toolviper\n", "\n", "toolviper.utils.data.download(file=\"small_meerkat.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 15:19:06,067\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'] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:06,068\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Number of partitions: 3 \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:06,069\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 [1], FIELD [0], SCAN [1], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:06,236\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 [2], FIELD [1], SCAN [2, 4, 6], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:06,370\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 [3], FIELD [2], SCAN [3, 5], EPHEMERIS [None] \n" ] } ], "source": [ "from xradio.measurement_set import convert_msv2_to_processing_set\n", "\n", "ms_file = \"small_meerkat.ms\"\n", "main_chunksize = {\"frequency\": 1, \"time\": 20} # baseline, polarization\n", "outfile = \"small_meerkat.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
0small_meerkat_0[CALIBRATE_BANDPASS, CALIBRATE_FLUX](74, 6, 50, 4)---[XX, XY, YX, YY][1]spw_0[UNSPECIFIED][J1939-6342_0][J1939-6342_0][][fk5, 19h39m25.03s, -63d42m45.60s]------20231115-00223.265869e+093.276337e+09
1small_meerkat_1[CALIBRATE_PHASE, CALIBRATE_AMPLI](42, 6, 50, 4)---[XX, XY, YX, YY][2, 4, 6]spw_0[UNSPECIFIED][J1619-8418_1][J1619-8418_1][][fk5, 16h19m33.97s, -84d18m19.10s]------20231115-00223.265869e+093.276337e+09
2small_meerkat_2[TARGET](223, 6, 50, 4)---[XX, XY, YX, YY][3, 5]spw_0[UNSPECIFIED][J0358-8103_2][J0358-8103_2][][fk5, 3h58m31.50s, -81d03m45.70s]------20231115-00223.265869e+093.276337e+09
\n", "
" ], "text/plain": [ " name scan_intents shape \\\n", "0 small_meerkat_0 [CALIBRATE_BANDPASS, CALIBRATE_FLUX] (74, 6, 50, 4) \n", "1 small_meerkat_1 [CALIBRATE_PHASE, CALIBRATE_AMPLI] (42, 6, 50, 4) \n", "2 small_meerkat_2 [TARGET] (223, 6, 50, 4) \n", "\n", " execution_block_UID polarization scan_name spw_name spw_intents \\\n", "0 --- [XX, XY, YX, YY] [1] spw_0 [UNSPECIFIED] \n", "1 --- [XX, XY, YX, YY] [2, 4, 6] spw_0 [UNSPECIFIED] \n", "2 --- [XX, XY, YX, YY] [3, 5] spw_0 [UNSPECIFIED] \n", "\n", " field_name source_name line_name \\\n", "0 [J1939-6342_0] [J1939-6342_0] [] \n", "1 [J1619-8418_1] [J1619-8418_1] [] \n", "2 [J0358-8103_2] [J0358-8103_2] [] \n", "\n", " field_coords session_reference_UID \\\n", "0 [fk5, 19h39m25.03s, -63d42m45.60s] --- \n", "1 [fk5, 16h19m33.97s, -84d18m19.10s] --- \n", "2 [fk5, 3h58m31.50s, -81d03m45.70s] --- \n", "\n", " scheduling_block_UID project_UID start_frequency end_frequency \n", "0 --- 20231115-0022 3.265869e+09 3.276337e+09 \n", "1 --- 20231115-0022 3.265869e+09 3.276337e+09 \n", "2 --- 20231115-0022 3.265869e+09 3.276337e+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": "070399fc-9639-4481-bc27-3a56ffd8180d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'small_meerkat_0'>\n",
       "Group: /small_meerkat_0\n",
       "│   Dimensions:                     (time: 74, baseline_id: 6, frequency: 50,\n",
       "│                                    polarization: 4, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 592B 1.7e+09 1.7e+09 ... 1.7e+09\n",
       "│       field_name                  (time) <U32 9kB dask.array<chunksize=(74,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 6kB dask.array<chunksize=(74,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 48B 0 1 2 3 4 5\n",
       "│       baseline_antenna1_name      (baseline_id) <U4 96B dask.array<chunksize=(6,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U4 96B dask.array<chunksize=(6,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 400B 3.266e+09 ... 3.276e+09\n",
       "│     * polarization                (polarization) <U2 32B 'XX' 'XY' 'YX' 'YY'\n",
       "│     * uvw_label                   (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "│   Data variables:\n",
       "│       EFFECTIVE_INTEGRATION_TIME  (time, baseline_id) float64 4kB dask.array<chunksize=(20, 6), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 89kB dask.array<chunksize=(20, 6, 1, 4), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 4kB dask.array<chunksize=(20, 6), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 11kB dask.array<chunksize=(20, 6, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 710kB dask.array<chunksize=(20, 6, 1, 4), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 355kB dask.array<chunksize=(20, 6, 1, 4), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:19:06.085883+00:00\n",
       "│       creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "│       data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "│       observation_info:  {'observer': ['Kim McAlpine'], 'observing_log': "['una...\n",
       "│       processor_info:    {'sub_type': '', 'type': ''}\n",
       "│       schema_version:    4.0.0\n",
       "│       type:              visibility\n",
       "├── Group: /small_meerkat_0/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 3, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U4 48B 'm000' 'm002' 'm063'\n",
       "│           mount                   (antenna_name) <U6 72B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U4 48B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U7 84B dask.array<chunksize=(3,), 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 24B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 24B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 72B dask.array<chunksize=(3, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 48B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  MeerKAT\n",
       "│           relocatable_antennas:    False\n",
       "│           type:                    antenna\n",
       "└── Group: /small_meerkat_0/field_and_source_base_xds\n",
       "        Dimensions:                       (field_name: 1, sky_dir_label: 2)\n",
       "        Coordinates:\n",
       "          * field_name                    (field_name) <U32 128B 'J1939-6342_0'\n",
       "            source_name                   (field_name) <U32 128B 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",
       "            SOURCE_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": [ "\n", "Group: /small_meerkat_0\n", "│ Dimensions: (time: 74, baseline_id: 6, frequency: 50,\n", "│ polarization: 4, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 592B 1.7e+09 1.7e+09 ... 1.7e+09\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 48B 0 1 2 3 4 5\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 400B 3.266e+09 ... 3.276e+09\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 89kB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 4kB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 11kB dask.array\n", "│ VISIBILITY (time, baseline_id, frequency, polarization) complex64 710kB dask.array\n", "│ WEIGHT (time, baseline_id, frequency, polarization) float32 355kB dask.array\n", "│ Attributes:\n", "│ creation_date: 2026-04-20T21:19:06.085883+00:00\n", "│ creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", "│ data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", "│ observation_info: {'observer': ['Kim McAlpine'], 'observing_log': \"['una...\n", "│ processor_info: {'sub_type': '', 'type': ''}\n", "│ schema_version: 4.0.0\n", "│ type: visibility\n", "├── Group: /small_meerkat_0/antenna_xds\n", "│ Dimensions: (antenna_name: 3, 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 24B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 72B dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 48B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: MeerKAT\n", "│ relocatable_antennas: False\n", "│ type: antenna\n", "└── Group: /small_meerkat_0/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", " SOURCE_DIRECTION (field_name, sky_dir_label) float64 16B dask.array\n", " Attributes:\n", " type: field_and_source" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt = ps_xdt[\"small_meerkat_0\"]\n", "ms_xdt" ] }, { "cell_type": "code", "execution_count": 6, "id": "c1d82a1d-9061-40b2-9cdf-313c9f16d911", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 1kB\n",
       "Dimensions:                       (field_name: 1, sky_dir_label: 2,\n",
       "                                   baseline_id: 6, frequency: 50,\n",
       "                                   polarization: 4, time: 74, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * field_name                    (field_name) <U32 128B 'J1939-6342_0'\n",
       "    source_name                   (field_name) <U32 128B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "  * baseline_id                   (baseline_id) int64 48B 0 1 2 3 4 5\n",
       "  * frequency                     (frequency) float64 400B 3.266e+09 ... 3.27...\n",
       "  * polarization                  (polarization) <U2 32B 'XX' 'XY' 'YX' 'YY'\n",
       "  * time                          (time) float64 592B 1.7e+09 ... 1.7e+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",
       "    SOURCE_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: 1kB\n", "Dimensions: (field_name: 1, sky_dir_label: 2,\n", " baseline_id: 6, frequency: 50,\n", " polarization: 4, time: 74, uvw_label: 3)\n", "Coordinates:\n", " * field_name (field_name) \n", " * sky_dir_label (sky_dir_label) \n", " SOURCE_DIRECTION (field_name, sky_dir_label) float64 16B dask.array\n", "Attributes:\n", " type: field_and_source" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.xr_ms.get_field_and_source_xds()" ] }, { "cell_type": "code", "execution_count": 7, "id": "8872ab88-daf3-46bf-b23d-331ea5f31bde", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'antenna_xds'>\n",
       "Group: /small_meerkat_0/antenna_xds\n",
       "    Dimensions:                 (time: 74, baseline_id: 6, frequency: 50,\n",
       "                                 polarization: 4, uvw_label: 3, antenna_name: 3,\n",
       "                                 cartesian_pos_label: 3, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name            (antenna_name) <U4 48B 'm000' 'm002' 'm063'\n",
       "        mount                   (antenna_name) <U6 72B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "        station_name            (antenna_name) <U4 48B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "        telescope_name          (antenna_name) <U7 84B dask.array<chunksize=(3,), 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 24B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id             (baseline_id) int64 48B 0 1 2 3 4 5\n",
       "      * frequency               (frequency) float64 400B 3.266e+09 ... 3.276e+09\n",
       "      * polarization            (polarization) <U2 32B 'XX' 'XY' 'YX' 'YY'\n",
       "      * time                    (time) float64 592B 1.7e+09 1.7e+09 ... 1.7e+09\n",
       "      * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        ANTENNA_DISH_DIAMETER   (antenna_name) float64 24B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "        ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 72B dask.array<chunksize=(3, 3), meta=np.ndarray>\n",
       "        ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 48B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        overall_telescope_name:  MeerKAT\n",
       "        relocatable_antennas:    False\n",
       "        type:                    antenna
" ], "text/plain": [ "\n", "Group: /small_meerkat_0/antenna_xds\n", " Dimensions: (time: 74, baseline_id: 6, frequency: 50,\n", " polarization: 4, uvw_label: 3, antenna_name: 3,\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 48B 0 1 2 3 4 5\n", " * frequency (frequency) float64 400B 3.266e+09 ... 3.276e+09\n", " * polarization (polarization) \n", " ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 72B dask.array\n", " ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 48B dask.array\n", " Attributes:\n", " overall_telescope_name: MeerKAT\n", " relocatable_antennas: False\n", " type: antenna" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.antenna_xds" ] }, { "cell_type": "code", "execution_count": 8, "id": "5a96a0ed", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps_xdt.xr_ps.plot_phase_centers()" ] }, { "cell_type": "code", "execution_count": 9, "id": "c304cb4b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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