{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SKA-Low conversion guide" ] }, { "cell_type": "code", "execution_count": 1, "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", "metadata": {}, "source": [ "## Download Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:19:58,240\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 15:19:58,241\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/ska_low_sim_18s.ms \n" ] } ], "source": [ "import toolviper\n", "\n", "toolviper.utils.data.download(file=\"ska_low_sim_18s.ms\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert to Processing Set" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:19:58,528\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 15:19:58,529\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 15:19:58,529\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 [0], FIELD [0], SCAN [0], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:58,580\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m SOURCE table empty for (unique) source_id [0] and spectral_window_id 0. \n" ] } ], "source": [ "from xradio.measurement_set import convert_msv2_to_processing_set\n", "\n", "ms_file = \"ska_low_sim_18s.ms\"\n", "outfile = \"ska_low_sim_18s.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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Processing Set" ] }, { "cell_type": "code", "execution_count": 4, "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
0ska_low_sim_18s_0[OBSERVE_TARGET#ON_SOURCE](67, 153, 27, 4)---[XX, XY, YX, YY][0]spw_0[UNSPECIFIED][_0][Unknown][][fk5, 11h20m00.00s, -10d00m00.00s]------1.013862e+081.736084e+08
\n", "
" ], "text/plain": [ " name scan_intents shape \\\n", "0 ska_low_sim_18s_0 [OBSERVE_TARGET#ON_SOURCE] (67, 153, 27, 4) \n", "\n", " execution_block_UID polarization scan_name spw_name spw_intents \\\n", "0 --- [XX, XY, YX, YY] [0] spw_0 [UNSPECIFIED] \n", "\n", " field_name source_name line_name field_coords \\\n", "0 [_0] [Unknown] [] [fk5, 11h20m00.00s, -10d00m00.00s] \n", "\n", " session_reference_UID scheduling_block_UID project_UID start_frequency \\\n", "0 --- --- 1.013862e+08 \n", "\n", " end_frequency \n", "0 1.736084e+08 " ] }, "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, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'ska_low_sim_18s_0'>\n",
       "Group: /ska_low_sim_18s_0\n",
       "│   Dimensions:                     (time: 67, baseline_id: 153, frequency: 27,\n",
       "│                                    polarization: 4, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 536B 9.47e+08 ... 9.47e+08\n",
       "│       field_name                  (time) <U23 6kB dask.array<chunksize=(67,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 6kB dask.array<chunksize=(67,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 1kB 0 1 2 3 ... 150 151 152\n",
       "│       baseline_antenna1_name      (baseline_id) <U24 15kB dask.array<chunksize=(153,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U24 15kB dask.array<chunksize=(153,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 216B 1.014e+08 ... 1.736e+08\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 82kB dask.array<chunksize=(67, 153), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 1MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 82kB dask.array<chunksize=(67, 153), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 246kB dask.array<chunksize=(67, 153, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 9MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 4MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:19:58.537824+00:00\n",
       "│       creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "│       data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "│       observation_info:  {'observer': ['fd382'], 'observing_log': "['']", 'proj...\n",
       "│       processor_info:    {'sub_type': '', 'type': ''}\n",
       "│       schema_version:    4.0.0\n",
       "│       type:              visibility\n",
       "├── Group: /ska_low_sim_18s_0/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 18, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U24 2kB 's0000 (station345_S8-1)'...\n",
       "│           mount                   (antenna_name) <U5 360B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U5 360B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U12 864B dask.array<chunksize=(18,), 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 144B dask.array<chunksize=(18, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 144B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 432B dask.array<chunksize=(18, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 288B dask.array<chunksize=(18, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  OSKAR 2.10.0\n",
       "│           relocatable_antennas:    False\n",
       "│           type:                    antenna\n",
       "├── Group: /ska_low_sim_18s_0/field_and_source_base_xds\n",
       "│       Dimensions:                       (field_name: 1, sky_dir_label: 2)\n",
       "│       Coordinates:\n",
       "│         * field_name                    (field_name) <U23 92B '_0'\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: /ska_low_sim_18s_0/phased_array_xds\n",
       "        Dimensions:                       (antenna_name: 18,\n",
       "                                           cartesian_pos_label_local: 3,\n",
       "                                           cartesian_pos_label: 3, receptor_label: 2,\n",
       "                                           element_id: 256)\n",
       "        Coordinates:\n",
       "          * antenna_name                  (antenna_name) <U24 2kB 's0000 (station345_...\n",
       "          * cartesian_pos_label_local     (cartesian_pos_label_local) <U1 12B 'p' ......\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 144B dask.array<chunksize=(18, 2), meta=np.ndarray>\n",
       "          * element_id                    (element_id) int64 2kB 0 1 2 3 ... 253 254 255\n",
       "        Data variables:\n",
       "            PHASED_ARRAY_COORDINATE_AXES  (antenna_name, cartesian_pos_label_local, cartesian_pos_label) float64 1kB dask.array<chunksize=(18, 3, 3), meta=np.ndarray>\n",
       "            PHASED_ARRAY_ELEMENT_FLAG     (antenna_name, receptor_label, element_id) bool 9kB dask.array<chunksize=(18, 2, 256), meta=np.ndarray>\n",
       "            PHASED_ARRAY_ELEMENT_OFFSET   (antenna_name, cartesian_pos_label_local, element_id) float64 111kB dask.array<chunksize=(18, 3, 256), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            type:     phased_array
" ], "text/plain": [ "\n", "Group: /ska_low_sim_18s_0\n", "│ Dimensions: (time: 67, baseline_id: 153, frequency: 27,\n", "│ polarization: 4, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 536B 9.47e+08 ... 9.47e+08\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 1kB 0 1 2 3 ... 150 151 152\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 216B 1.014e+08 ... 1.736e+08\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 1MB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 82kB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 246kB dask.array\n", "│ VISIBILITY (time, baseline_id, frequency, polarization) complex64 9MB dask.array\n", "│ WEIGHT (time, baseline_id, frequency, polarization) float32 4MB dask.array\n", "│ Attributes:\n", "│ creation_date: 2026-04-20T21:19:58.537824+00:00\n", "│ creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", "│ data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", "│ observation_info: {'observer': ['fd382'], 'observing_log': \"['']\", 'proj...\n", "│ processor_info: {'sub_type': '', 'type': ''}\n", "│ schema_version: 4.0.0\n", "│ type: visibility\n", "├── Group: /ska_low_sim_18s_0/antenna_xds\n", "│ Dimensions: (antenna_name: 18, 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 144B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 432B dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 288B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: OSKAR 2.10.0\n", "│ relocatable_antennas: False\n", "│ type: antenna\n", "├── Group: /ska_low_sim_18s_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", "│ Attributes:\n", "│ type: field_and_source\n", "└── Group: /ska_low_sim_18s_0/phased_array_xds\n", " Dimensions: (antenna_name: 18,\n", " cartesian_pos_label_local: 3,\n", " cartesian_pos_label: 3, receptor_label: 2,\n", " element_id: 256)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " * element_id (element_id) int64 2kB 0 1 2 3 ... 253 254 255\n", " Data variables:\n", " PHASED_ARRAY_COORDINATE_AXES (antenna_name, cartesian_pos_label_local, cartesian_pos_label) float64 1kB dask.array\n", " PHASED_ARRAY_ELEMENT_FLAG (antenna_name, receptor_label, element_id) bool 9kB dask.array\n", " PHASED_ARRAY_ELEMENT_OFFSET (antenna_name, cartesian_pos_label_local, element_id) float64 111kB dask.array\n", " Attributes:\n", " type: phased_array" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps_xdt[\"ska_low_sim_18s_0\"]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DatasetView> Size: 15MB\n",
       "Dimensions:                     (time: 67, baseline_id: 153, frequency: 27,\n",
       "                                 polarization: 4, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * time                        (time) float64 536B 9.47e+08 ... 9.47e+08\n",
       "    field_name                  (time) <U23 6kB dask.array<chunksize=(67,), meta=np.ndarray>\n",
       "    scan_name                   (time) <U21 6kB dask.array<chunksize=(67,), meta=np.ndarray>\n",
       "  * baseline_id                 (baseline_id) int64 1kB 0 1 2 3 ... 150 151 152\n",
       "    baseline_antenna1_name      (baseline_id) <U24 15kB dask.array<chunksize=(153,), meta=np.ndarray>\n",
       "    baseline_antenna2_name      (baseline_id) <U24 15kB dask.array<chunksize=(153,), meta=np.ndarray>\n",
       "  * frequency                   (frequency) float64 216B 1.014e+08 ... 1.736e+08\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 82kB dask.array<chunksize=(67, 153), meta=np.ndarray>\n",
       "    FLAG                        (time, baseline_id, frequency, polarization) bool 1MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "    TIME_CENTROID               (time, baseline_id) float64 82kB dask.array<chunksize=(67, 153), meta=np.ndarray>\n",
       "    UVW                         (time, baseline_id, uvw_label) float64 246kB dask.array<chunksize=(67, 153, 3), meta=np.ndarray>\n",
       "    VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 9MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "    WEIGHT                      (time, baseline_id, frequency, polarization) float32 4MB dask.array<chunksize=(67, 153, 27, 4), meta=np.ndarray>\n",
       "Attributes:\n",
       "    creation_date:     2026-04-20T21:19:58.537824+00:00\n",
       "    creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "    data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "    observation_info:  {'observer': ['fd382'], 'observing_log': "['']", 'proj...\n",
       "    processor_info:    {'sub_type': '', 'type': ''}\n",
       "    schema_version:    4.0.0\n",
       "    type:              visibility
" ], "text/plain": [ " Size: 15MB\n", "Dimensions: (time: 67, baseline_id: 153, frequency: 27,\n", " polarization: 4, uvw_label: 3)\n", "Coordinates:\n", " * time (time) float64 536B 9.47e+08 ... 9.47e+08\n", " field_name (time) \n", " scan_name (time) \n", " * baseline_id (baseline_id) int64 1kB 0 1 2 3 ... 150 151 152\n", " baseline_antenna1_name (baseline_id) \n", " baseline_antenna2_name (baseline_id) \n", " * frequency (frequency) float64 216B 1.014e+08 ... 1.736e+08\n", " * polarization (polarization) \n", " FLAG (time, baseline_id, frequency, polarization) bool 1MB dask.array\n", " TIME_CENTROID (time, baseline_id) float64 82kB dask.array\n", " UVW (time, baseline_id, uvw_label) float64 246kB dask.array\n", " VISIBILITY (time, baseline_id, frequency, polarization) complex64 9MB dask.array\n", " WEIGHT (time, baseline_id, frequency, polarization) float32 4MB dask.array\n", "Attributes:\n", " creation_date: 2026-04-20T21:19:58.537824+00:00\n", " creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", " data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", " observation_info: {'observer': ['fd382'], 'observing_log': \"['']\", 'proj...\n", " processor_info: {'sub_type': '', 'type': ''}\n", " schema_version: 4.0.0\n", " type: visibility" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "msv4_xdt = ps_xdt[\"ska_low_sim_18s_0\"]\n", "msv4_xdt.ds" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 6kB\n",
       "Dimensions:                 (antenna_name: 18, cartesian_pos_label: 3,\n",
       "                             receptor_label: 2, baseline_id: 153,\n",
       "                             frequency: 27, polarization: 4, time: 67,\n",
       "                             uvw_label: 3)\n",
       "Coordinates:\n",
       "  * antenna_name            (antenna_name) <U24 2kB 's0000 (station345_S8-1)'...\n",
       "    mount                   (antenna_name) <U5 360B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "    station_name            (antenna_name) <U5 360B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "    telescope_name          (antenna_name) <U12 864B dask.array<chunksize=(18,), 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 144B dask.array<chunksize=(18, 2), meta=np.ndarray>\n",
       "  * baseline_id             (baseline_id) int64 1kB 0 1 2 3 ... 149 150 151 152\n",
       "  * frequency               (frequency) float64 216B 1.014e+08 ... 1.736e+08\n",
       "  * polarization            (polarization) <U2 32B 'XX' 'XY' 'YX' 'YY'\n",
       "  * time                    (time) float64 536B 9.47e+08 9.47e+08 ... 9.47e+08\n",
       "  * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Data variables:\n",
       "    ANTENNA_DISH_DIAMETER   (antenna_name) float64 144B dask.array<chunksize=(18,), meta=np.ndarray>\n",
       "    ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 432B dask.array<chunksize=(18, 3), meta=np.ndarray>\n",
       "    ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 288B dask.array<chunksize=(18, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    overall_telescope_name:  OSKAR 2.10.0\n",
       "    relocatable_antennas:    False\n",
       "    type:                    antenna
" ], "text/plain": [ " Size: 6kB\n", "Dimensions: (antenna_name: 18, cartesian_pos_label: 3,\n", " receptor_label: 2, baseline_id: 153,\n", " frequency: 27, polarization: 4, time: 67,\n", " 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 1kB 0 1 2 3 ... 149 150 151 152\n", " * frequency (frequency) float64 216B 1.014e+08 ... 1.736e+08\n", " * polarization (polarization) \n", " ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 432B dask.array\n", " ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 288B dask.array\n", "Attributes:\n", " overall_telescope_name: OSKAR 2.10.0\n", " relocatable_antennas: False\n", " type: antenna" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ant_xds = ps_xdt.xr_ps.get_combined_antenna_xds()\n", "ant_xds" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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