{ "cells": [ { "cell_type": "markdown", "id": "d398b960-06bd-4e61-a3a8-2e4944b48170", "metadata": {}, "source": [ "# ngEHT 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": [ "fitsidi from https://almascience.eso.org/almadata/ec/eht/2016.1.01114.V/group.uid___A001_X87c_X245.ec_jlgomez.e17a10-7-hi-oj287-3C279-fits.tgz\n", "\n", "```python\n", "importfitsidi('E17A10.0.bin0000.source0000.FITS',vis='E17A10.0.bin0000.source0000.ms')\n", "mstransform(vis='E17A10.0.bin0000.source0000.ms',outputvis='ngEHT_E17A10.0.bin0000.source0000_split_lsrk.ms',spw='8:55~61,29:20~27', regridms=True,outframe='lsrk',datacolumn='all')\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 15:19:36,129\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 15:19:36,130\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/ngEHT_E17A10.0.bin0000.source0000_split.ms \n" ] } ], "source": [ "import toolviper\n", "\n", "toolviper.utils.data.download(file=\"ngEHT_E17A10.0.bin0000.source0000_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 15:19:36,642\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'] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:36,643\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Number of partitions: 2 \n", "[\u001b[38;2;128;05;128m2026-04-20 15:19:36,643\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 15:19:36,717\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 15:19:37,145\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 15:19:37,217\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 = \"ngEHT_E17A10.0.bin0000.source0000_split.ms\"\n", "\n", "main_chunksize = {\"frequency\": 1, \"time\": 20} # baseline, polarization\n", "outfile = \"ngEHT_E17A10.0.bin0000.source0000_split_lsrk.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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namescan_intentsshapeexecution_block_UIDpolarizationscan_namespw_namespw_intentsfield_namesource_nameline_namefield_coordssession_reference_UIDscheduling_block_UIDproject_UIDstart_frequencyend_frequency
0ngEHT_E17A10.0.bin0000.source0000_split_0[None](2250, 20, 7, 4)---[RR, RL, LR, LL][0]spw_0[UNSPECIFIED][3C279_0][Unknown][][fk5, 12h56m11.17s, -5d47m21.52s]------E17A102.286593e+112.286623e+11
1ngEHT_E17A10.0.bin0000.source0000_split_1[None](2250, 20, 8, 4)---[RR, RL, LR, LL][0]spw_1[UNSPECIFIED][3C279_0][Unknown][][fk5, 12h56m11.17s, -5d47m21.52s]------E17A102.298723e+112.298758e+11
\n", "
" ], "text/plain": [ " name scan_intents shape \\\n", "0 ngEHT_E17A10.0.bin0000.source0000_split_0 [None] (2250, 20, 7, 4) \n", "1 ngEHT_E17A10.0.bin0000.source0000_split_1 [None] (2250, 20, 8, 4) \n", "\n", " execution_block_UID polarization scan_name spw_name spw_intents \\\n", "0 --- [RR, RL, LR, LL] [0] spw_0 [UNSPECIFIED] \n", "1 --- [RR, RL, LR, LL] [0] spw_1 [UNSPECIFIED] \n", "\n", " field_name source_name line_name field_coords \\\n", "0 [3C279_0] [Unknown] [] [fk5, 12h56m11.17s, -5d47m21.52s] \n", "1 [3C279_0] [Unknown] [] [fk5, 12h56m11.17s, -5d47m21.52s] \n", "\n", " session_reference_UID scheduling_block_UID project_UID start_frequency \\\n", "0 --- --- E17A10 2.286593e+11 \n", "1 --- --- E17A10 2.298723e+11 \n", "\n", " end_frequency \n", "0 2.286623e+11 \n", "1 2.298758e+11 " ] }, "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": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataTree 'ngEHT_E17A10.0.bin0000.source0000_split_0'>\n",
       "Group: /ngEHT_E17A10.0.bin0000.source0000_split_0\n",
       "│   Dimensions:                     (time: 2250, baseline_id: 20, frequency: 7,\n",
       "│                                    polarization: 4, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 18kB 1.492e+09 ... 1.492e+09\n",
       "│       field_name                  (time) <U27 243kB dask.array<chunksize=(2250,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 189kB dask.array<chunksize=(2250,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 160B 0 1 2 3 ... 16 17 18 19\n",
       "│       baseline_antenna1_name      (baseline_id) <U2 160B dask.array<chunksize=(20,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U2 160B dask.array<chunksize=(20,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 56B 2.287e+11 ... 2.287e+11\n",
       "│     * polarization                (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "│     * uvw_label                   (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "│   Data variables:\n",
       "│       EFFECTIVE_INTEGRATION_TIME  (time, baseline_id) float64 360kB dask.array<chunksize=(20, 20), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 1MB dask.array<chunksize=(20, 20, 1, 4), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 360kB dask.array<chunksize=(20, 20), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 1MB dask.array<chunksize=(20, 20, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 10MB dask.array<chunksize=(20, 20, 1, 4), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 5MB dask.array<chunksize=(20, 20, 1, 4), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:19:36.659187+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: /ngEHT_E17A10.0.bin0000.source0000_split_0/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 6, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U2 48B 'AA' 'AP' 'AZ' 'LM' 'PV' 'SP'\n",
       "│           mount                   (antenna_name) <U16 384B dask.array<chunksize=(6,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U2 48B dask.array<chunksize=(6,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U4 96B dask.array<chunksize=(6,), 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 48B dask.array<chunksize=(6, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 48B dask.array<chunksize=(6,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 144B dask.array<chunksize=(6, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 96B dask.array<chunksize=(6, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  VLBA\n",
       "│           relocatable_antennas:    False\n",
       "│           type:                    antenna\n",
       "└── Group: /ngEHT_E17A10.0.bin0000.source0000_split_0/field_and_source_base_xds\n",
       "        Dimensions:                       (field_name: 1, sky_dir_label: 2)\n",
       "        Coordinates:\n",
       "          * field_name                    (field_name) <U27 108B '3C279_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
" ], "text/plain": [ "\n", "Group: /ngEHT_E17A10.0.bin0000.source0000_split_0\n", "│ Dimensions: (time: 2250, baseline_id: 20, frequency: 7,\n", "│ polarization: 4, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 18kB 1.492e+09 ... 1.492e+09\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 160B 0 1 2 3 ... 16 17 18 19\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 56B 2.287e+11 ... 2.287e+11\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 1MB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 360kB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 1MB dask.array\n", "│ VISIBILITY (time, baseline_id, frequency, polarization) complex64 10MB dask.array\n", "│ WEIGHT (time, baseline_id, frequency, polarization) float32 5MB dask.array\n", "│ Attributes:\n", "│ creation_date: 2026-04-20T21:19:36.659187+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: /ngEHT_E17A10.0.bin0000.source0000_split_0/antenna_xds\n", "│ Dimensions: (antenna_name: 6, 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 48B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 144B dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 96B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: VLBA\n", "│ relocatable_antennas: False\n", "│ type: antenna\n", "└── Group: /ngEHT_E17A10.0.bin0000.source0000_split_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" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt = ps_xdt[\"ngEHT_E17A10.0.bin0000.source0000_split_0\"]\n", "ms_xdt" ] }, { "cell_type": "code", "execution_count": 6, "id": "ba761deb-b7a9-4b2b-8163-3050413fe59c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataTree 'antenna_xds'>\n",
       "Group: /\n",
       "    Dimensions:                 (time: 2250, baseline_id: 20, frequency: 7,\n",
       "                                 polarization: 4, uvw_label: 3, antenna_name: 6,\n",
       "                                 cartesian_pos_label: 3, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * time                    (time) float64 18kB 1.492e+09 ... 1.492e+09\n",
       "      * baseline_id             (baseline_id) int64 160B 0 1 2 3 4 ... 16 17 18 19\n",
       "      * frequency               (frequency) float64 56B 2.287e+11 ... 2.287e+11\n",
       "      * polarization            (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "      * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "      * antenna_name            (antenna_name) <U2 48B 'AA' 'AP' 'AZ' 'LM' 'PV' 'SP'\n",
       "        mount                   (antenna_name) <U16 384B 'ALT-AZ' ... 'ALT-AZ'\n",
       "        station_name            (antenna_name) <U2 48B 'AA' 'AP' 'AZ' 'LM' 'PV' 'SP'\n",
       "        telescope_name          (antenna_name) <U4 96B 'VLBA' 'VLBA' ... 'VLBA'\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 48B 'R' ... 'L'\n",
       "    Data variables:\n",
       "        ANTENNA_DISH_DIAMETER   (antenna_name) float64 48B 12.0 12.0 ... 30.0 10.0\n",
       "        ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 144B ...\n",
       "        ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 96B 0.0 .....\n",
       "    Attributes:\n",
       "        overall_telescope_name:  VLBA\n",
       "        relocatable_antennas:    False\n",
       "        type:                    antenna
" ], "text/plain": [ "\n", "Group: /\n", " Dimensions: (time: 2250, baseline_id: 20, frequency: 7,\n", " polarization: 4, uvw_label: 3, antenna_name: 6,\n", " cartesian_pos_label: 3, receptor_label: 2)\n", " Coordinates:\n", " * time (time) float64 18kB 1.492e+09 ... 1.492e+09\n", " * baseline_id (baseline_id) int64 160B 0 1 2 3 4 ... 16 17 18 19\n", " * frequency (frequency) float64 56B 2.287e+11 ... 2.287e+11\n", " * polarization (polarization) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DatasetView> Size: 18kB\n",
       "Dimensions:                       (field_name: 1, sky_dir_label: 2,\n",
       "                                   baseline_id: 20, frequency: 7,\n",
       "                                   polarization: 4, time: 2250, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * field_name                    (field_name) <U27 108B '3C279_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",
       "  * baseline_id                   (baseline_id) int64 160B 0 1 2 3 ... 17 18 19\n",
       "  * frequency                     (frequency) float64 56B 2.287e+11 ... 2.287...\n",
       "  * polarization                  (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "  * time                          (time) float64 18kB 1.492e+09 ... 1.492e+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: 18kB\n", "Dimensions: (field_name: 1, sky_dir_label: 2,\n", " baseline_id: 20, frequency: 7,\n", " polarization: 4, time: 2250, 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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.xr_ms.get_field_and_source_xds()" ] }, { "cell_type": "code", "execution_count": 8, "id": "8c29a5dc", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps_xdt.xr_ps.plot_antenna_positions_2d()" ] }, { "cell_type": "code", "execution_count": 9, "id": "f74e0c80", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps_xdt.xr_ps.plot_phase_centers()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "17ee3f44606e4a64b146b46311098e19": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_bb5a5c3158474547aadc445a997eb526", "outputs": [ { "data": { "text/html": "
ngEHT_E17A10.0.bin0000.s ... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n
\n", "text/plain": "ngEHT_E17A10.0.bin0000.s ... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "bb5a5c3158474547aadc445a997eb526": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }