{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b3aae73",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nrao/astrohack/blob/v1.0.2/docs/locit_tutorial.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9151b55a",
   "metadata": {},
   "source": [
    "![astrohack](astrohack_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6db5ff",
   "metadata": {},
   "source": [
    "# Antenna position correction tutorial\n",
    "\n",
    "`extract_locit` and `locit` are utilities designed to help determine antenna position shifts after antenna relocation.\n",
    "To do so they rely on a phase gain calibration table created by `CASA` from antenna pointing data.\n",
    "The process in `CASA` consists of:\n",
    "1. `split` out the actual pointing data from the original pointing measurement set (MS), it might contain data taken while the antennas are still slewing.\n",
    "2. `fringe_fit` the MS using a single source with no delay rates, this is done to flatten a spectral window.\n",
    "3. `apply_cal` the fringe_fit solution.\n",
    "4. Channel average the MS using `split`.\n",
    "5. Compute an average phase gain solution for each source using `gaincal`\n",
    "\n",
    "To simplify the process in `CASA` a script is distributed within `astrohack` that the user can simply fill in the parameters for the data reduction and then run it within `CASA`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4beb8248-5a07-4673-82fd-4a74b8f31c38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:08.713509785Z",
     "start_time": "2026-02-10T16:59:06.231728658Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:02.122919Z",
     "iopub.status.busy": "2026-06-08T22:30:02.122589Z",
     "iopub.status.idle": "2026-06-08T22:30:03.532304Z",
     "shell.execute_reply": "2026-06-08T22:30:03.531905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AstroHACK version 1.0.2 already installed.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "try:\n",
    "    import astrohack\n",
    "\n",
    "    print(\"AstroHACK version\", astrohack.__version__, \"already installed.\")\n",
    "except ImportError as e:\n",
    "    print(e)\n",
    "    print(\"Installing AstroHACK\")\n",
    "\n",
    "    os.system(\"pip install astrohack\")\n",
    "\n",
    "    import astrohack\n",
    "\n",
    "    print(\"astrohack version\", astrohack.__version__, \" installed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bf8960-b27e-48b0-bc54-6ae3bb237f44",
   "metadata": {},
   "source": [
    "## Download Tutorial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aec0ae71-efb0-4f1e-9fe2-29f0dab3b82a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:08.850156882Z",
     "start_time": "2026-02-10T16:59:08.717397817Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:03.533554Z",
     "iopub.status.busy": "2026-06-08T22:30:03.533476Z",
     "iopub.status.idle": "2026-06-08T22:30:03.694818Z",
     "shell.execute_reply": "2026-06-08T22:30:03.694006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:03,535\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m   toolviper: \u001b[0m Initializing download... \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60c25678cd9f427e9dafaeb1fcbe4fe1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The Cal table used here is a placeholder, there should be a better dataset to be used with the tutorial\n",
    "import toolviper\n",
    "\n",
    "toolviper.utils.data.download(file=\"locit-input-pha.cal\", folder=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf58722-847f-493c-8963-3985c69b738d",
   "metadata": {},
   "source": [
    "## Position and locit Data File API\n",
    "\n",
    "As part of the `astroHACK` API a set of functions to allow users to easily open on disk locit and position files has been provided. Each function takes an `astroHACK` locit or position file name as an argument and returns an object related to the given file type. Each object allows the user to access data via dictionary keys with values consisting of the relevant dataset. Each object also provides a `summary()` helper function to list available keys for each file. An example call for each file type is show below and the API documentation for all data-io functions can be found [here](https://astrohack.readthedocs.io/en/latest/_api/autoapi/astrohack/dio/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cccb50-52bf-4adb-af00-c54c6430c784",
   "metadata": {},
   "source": [
    "```python\n",
    "from astrohack import open_locit\n",
    "from astrohack import open_position\n",
    "\n",
    "locit_data = open_locit(file='./data/locit-input-pha.locit.zarr')\n",
    "position_data = open_position(file='./data/locit-input-pha.position.zarr')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4798fae4-a9c7-4b8f-b6df-f4fa35657e1c",
   "metadata": {},
   "source": [
    "## Setup Dask Local Cluster\n",
    "\n",
    "The local Dask client handles scheduling and worker managment for the parallelization. The user has the option of choosing the number of cores and memory allocations for each worker howerver, we recommend a minimum of 1Gb per core with standard settings.\n",
    "\n",
    "\n",
    "A significant amount of information related to the client and scheduling can be found using the [Dask Dashboard](https://docs.dask.org/en/stable/dashboard.html). This is a built-in dashboard native to Dask and allows the user to monitor the workers during processing. This is especially useful for profilling. For those that are interested in working soley within Jupyterlab a dashboard extension is available for [Jupyterlab](https://github.com/dask/dask-labextension#dask-jupyterlab-extension).\n",
    "\n",
    "![dashboard](../_media/dashboard.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9dbf9b71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:08.904536453Z",
     "start_time": "2026-02-10T16:59:08.855428454Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:03.699018Z",
     "iopub.status.busy": "2026-06-08T22:30:03.698915Z",
     "iopub.status.idle": "2026-06-08T22:30:04.544324Z",
     "shell.execute_reply": "2026-06-08T22:30:04.543845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:03,713\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m It is recommended that the local cache directory be set using the \u001b[38;2;50;50;205mdask_local_dir\u001b[0m parameter. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:04,542\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m Client <MenrvaClient: 'tcp://127.0.0.1:64279' processes=4 threads=4, memory=3.73 GiB> \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<MenrvaClient: 'tcp://127.0.0.1:64279' processes=4 threads=4, memory=3.73 GiB>\n"
     ]
    }
   ],
   "source": [
    "from toolviper.dask.client import local_client\n",
    "\n",
    "parallel = True\n",
    "\n",
    "if parallel:\n",
    "    client = local_client(cores=4, memory_limit=\"1GB\")\n",
    "    print(client)\n",
    "else:\n",
    "    client = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6a51bb-ce33-42cb-abc1-760a0dadc8a8",
   "metadata": {},
   "source": [
    "## Extract locit\n",
    "\n",
    "The first step in determining the antenna position corrections is to extract the data from the phase gains calibration table and store it in a convenient format for further processing.\n",
    "\n",
    "In the calibration table the data is organized by time, but we want organized by antenna → DDI → time for simplicity of processing in `locit`.\n",
    "\n",
    "Also, the data in the calibration table may contain more than one reference antenna, which would scramble the results obtained by `locit`, hence we throw away data that has a different reference antenna than the main reference antenna in `extract_locit`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d42b0f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:08.953339017Z",
     "start_time": "2026-02-10T16:59:08.906637380Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:04.545531Z",
     "iopub.status.busy": "2026-06-08T22:30:04.545453Z",
     "iopub.status.idle": "2026-06-08T22:30:04.547177Z",
     "shell.execute_reply": "2026-06-08T22:30:04.546809Z"
    }
   },
   "outputs": [],
   "source": [
    "cal_table = \"./data/locit-input-pha.cal\"\n",
    "locit_name = \"./data/locit-input-pha.locit.zarr\"\n",
    "position_name = \"./data/locit-input-pha.position.zarr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba508eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:11.368858913Z",
     "start_time": "2026-02-10T16:59:08.956368116Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:04.548093Z",
     "iopub.status.busy": "2026-06-08T22:30:04.548035Z",
     "iopub.status.idle": "2026-06-08T22:30:05.848603Z",
     "shell.execute_reply": "2026-06-08T22:30:05.848207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:04,551\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m Creating output file name: ./data/locit-input-pha.locit.zarr \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:04,801\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m Consolidating ./data/locit-input-pha.locit.zarr... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 691 ms, sys: 610 ms, total: 1.3 s\n",
      "Wall time: 1.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from astrohack import extract_locit\n",
    "\n",
    "locit_mds = extract_locit(\n",
    "    cal_table,  # The calibration table containing the phase gains\n",
    "    locit_name=locit_name,  # The name for the created locit file\n",
    "    ant=\"all\",  # Antenna selection, None means 'All'\n",
    "    ddi=\"all\",  # DDI selection, None means 'ALL'\n",
    "    overwrite=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46f8e09-64eb-4c62-a994-7aabf9f11ef3",
   "metadata": {},
   "source": [
    "`extract_locit` creates a file that is called a locit file. This file contains the phase gains for each antenna but also contains two important tables, the source and antenna tables.\n",
    "\n",
    "`extract_locit` also returns the opened locit file as a `locit_mds` object. The first step in interacting with the `locit_mds` object is calling its summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebfff08e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:11.447659738Z",
     "start_time": "2026-02-10T16:59:11.370136780Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.849850Z",
     "iopub.status.busy": "2026-06-08T22:30:05.849784Z",
     "iopub.status.idle": "2026-06-08T22:30:05.852487Z",
     "shell.execute_reply": "2026-06-08T22:30:05.852154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "###                               Summary for:                               ###\n",
      "###                    ./data/locit-input-pha.locit.zarr                     ###\n",
      "################################################################################\n",
      "\n",
      "Data origin:\n",
      "creation_time:    2026-06-08 16:30:04 MDT\n",
      "creator_function: extract_locit\n",
      "origin:           astrohack\n",
      "version:          1.0.2\n",
      "\n",
      "Input Parameters:\n",
      "+------------+-----------------------------------+\n",
      "| Parameter  | Value                             |\n",
      "+------------+-----------------------------------+\n",
      "| ant        | all                               |\n",
      "| cal_table  | ./data/locit-input-pha.cal        |\n",
      "| ddi        | all                               |\n",
      "| locit_name | ./data/locit-input-pha.locit.zarr |\n",
      "| overwrite  | True                              |\n",
      "+------------+-----------------------------------+\n",
      "\n",
      "Available methods:\n",
      "+------------------------------+-----------------------------------------------+\n",
      "| Methods                      | Description                                   |\n",
      "+------------------------------+-----------------------------------------------+\n",
      "| add_node                     | Add a node to the data tree file structure,   |\n",
      "|                              | however this node is not yet consolidated     |\n",
      "|                              | into the data tree         structure,         |\n",
      "|                              | consolidate must be called to integrate all   |\n",
      "|                              | nodes writen by add_node onto the tree        |\n",
      "|                              | structure.                                    |\n",
      "| consolidate                  | Traverse own file structure on disk           |\n",
      "|                              | consolidating metadata to create a unified    |\n",
      "|                              | data tree entity.                             |\n",
      "| create_from_input_parameters | Create an AstrohackBaseFile object from a     |\n",
      "|                              | filename and initializes xdtree root          |\n",
      "|                              | attributes.                                   |\n",
      "| is_close_to                  | Tests if self and other_mds are close to each |\n",
      "|                              | other.                                        |\n",
      "| items                        | Get children items                            |\n",
      "| keys                         | Get children keys                             |\n",
      "| open                         | Open Base file.                               |\n",
      "| plot_array_configuration     | Plot antenna positions.                       |\n",
      "| plot_source_positions        | Plot source positions in either FK5 or        |\n",
      "|                              | precessed right ascension and declination.    |\n",
      "| print_array_configuration    | Prints a table containing the array           |\n",
      "|                              | configuration                                 |\n",
      "| print_source_table           | Prints a table with the sources observed for  |\n",
      "|                              | antenna location determination                |\n",
      "| summary                      | Prints summary of this Astrohack File object, |\n",
      "|                              | with available data, attributes and methods   |\n",
      "| values                       | Get children values                           |\n",
      "| write                        | Write mds to disk by saving the data tree to  |\n",
      "|                              | a file                                        |\n",
      "+------------------------------+-----------------------------------------------+\n",
      "\n",
      "Data Contents:\n",
      "+----------+--------------------+\n",
      "| Antenna  | DDI                |\n",
      "+----------+--------------------+\n",
      "| ant_ea01 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea02 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea04 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea05 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea06 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea07 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea08 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea09 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea10 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea11 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea12 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea13 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea15 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea16 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea17 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea18 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea19 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea20 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea21 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea22 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea23 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea24 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea25 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea26 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea27 | ['ddi_0', 'ddi_1'] |\n",
      "| ant_ea28 | ['ddi_0', 'ddi_1'] |\n",
      "+----------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "locit_mds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63f8d83-f353-446c-bbac-3664cc1d5144",
   "metadata": {},
   "source": [
    "From the summary, we can see that the locit file contains 26 antennas and 2 DDIs per antenna, as well as 4 different methods related to the visualization of the source and antenna tables. To inspect the data contained in a DDI for an antenna, we simply access the dictionary keys as so,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "287fc029-e03f-4b05-abdd-ac56e6c5f40b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:11.503199686Z",
     "start_time": "2026-02-10T16:59:11.448744457Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.853472Z",
     "iopub.status.busy": "2026-06-08T22:30:05.853413Z",
     "iopub.status.idle": "2026-06-08T22:30:05.859063Z",
     "shell.execute_reply": "2026-06-08T22:30:05.858714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "  margin-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree &#x27;ddi_0&#x27;&gt;\n",
       "Group: /ant_ea06/ddi_0\n",
       "    Dimensions:         (p0_time: 33, p1_time: 34)\n",
       "    Coordinates:\n",
       "      * p0_time         (p0_time) float64 264B 6.018e+04 6.018e+04 ... 6.018e+04\n",
       "      * p1_time         (p1_time) float64 272B 6.018e+04 6.018e+04 ... 6.018e+04\n",
       "    Data variables:\n",
       "        P0_PHASE_GAINS  (p0_time) float32 132B 0.2488 0.6855 ... 0.9096 -0.7223\n",
       "        P0_FIELD_ID     (p0_time) int32 132B 0 1 2 3 4 5 6 ... 19 13 25 26 27 28 29\n",
       "        P1_PHASE_GAINS  (p1_time) float32 136B 0.2527 0.6873 ... 0.9876 -0.7128\n",
       "        P1_FIELD_ID     (p1_time) int32 136B 0 1 2 3 4 5 6 ... 19 13 25 26 27 28 29\n",
       "    Attributes:\n",
       "        frequency:            8223000000.0\n",
       "        bandwidth:            [128000000.0]\n",
       "        polarization_scheme:  [&#x27;R&#x27;, &#x27;L&#x27;]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div><div class='xr-obj-name'>&#x27;ddi_0&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b9c871e2-faeb-418a-926c-644a835a590f' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-b9c871e2-faeb-418a-926c-644a835a590f' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>p0_time</span>: 33</li><li><span class='xr-has-index'>p1_time</span>: 34</li></ul></div></li><li class='xr-section-item'><input id='section-6c175403-8807-4798-bbe3-4c75e10c1fc5' class='xr-section-summary-in' type='checkbox' checked /><label for='section-6c175403-8807-4798-bbe3-4c75e10c1fc5' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p0_time</span></div><div class='xr-var-dims'>(p0_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.018e+04 6.018e+04 ... 6.018e+04</div><input id='attrs-8c54c672-969c-4584-820f-0dee4c915b96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c54c672-969c-4584-820f-0dee4c915b96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dba6d15e-14f3-4acf-ac19-032d94f4f24b' class='xr-var-data-in' type='checkbox'><label for='data-dba6d15e-14f3-4acf-ac19-032d94f4f24b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60181.251088, 60181.253322, 60181.255799, 60181.257917, 60181.259734,\n",
       "       60181.261702, 60181.264468, 60181.269109, 60181.272963, 60181.274989,\n",
       "       60181.277257, 60181.278947, 60181.281308, 60181.28404 , 60181.286135,\n",
       "       60181.28838 , 60181.291297, 60181.295672, 60181.297813, 60181.300706,\n",
       "       60181.302188, 60181.304468, 60181.307199, 60181.30904 , 60181.311065,\n",
       "       60181.313264, 60181.316459, 60181.3211  , 60181.32338 , 60181.325776,\n",
       "       60181.327755, 60181.329468, 60181.331005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p1_time</span></div><div class='xr-var-dims'>(p1_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.018e+04 6.018e+04 ... 6.018e+04</div><input id='attrs-cf4b8b6b-a145-4e43-9d58-83f474350f1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf4b8b6b-a145-4e43-9d58-83f474350f1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b682b8b5-4eb2-42f1-ab6d-a9cef213889b' class='xr-var-data-in' type='checkbox'><label for='data-b682b8b5-4eb2-42f1-ab6d-a9cef213889b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60181.251088, 60181.253322, 60181.255799, 60181.257917, 60181.259734,\n",
       "       60181.261702, 60181.264468, 60181.269109, 60181.272963, 60181.274989,\n",
       "       60181.277257, 60181.278947, 60181.281308, 60181.28404 , 60181.286135,\n",
       "       60181.28838 , 60181.291297, 60181.293195, 60181.295672, 60181.297813,\n",
       "       60181.300706, 60181.302188, 60181.304468, 60181.307199, 60181.30904 ,\n",
       "       60181.311065, 60181.313264, 60181.316459, 60181.3211  , 60181.32338 ,\n",
       "       60181.325776, 60181.327755, 60181.329468, 60181.331005])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dca95cac-67a9-46a1-afa8-428913cba5d6' class='xr-section-summary-in' type='checkbox' checked /><label for='section-dca95cac-67a9-46a1-afa8-428913cba5d6' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>P0_PHASE_GAINS</span></div><div class='xr-var-dims'>(p0_time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2488 0.6855 ... 0.9096 -0.7223</div><input id='attrs-808258c4-2364-49f4-89d3-0971d0f8024c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-808258c4-2364-49f4-89d3-0971d0f8024c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0695ebfa-9e21-4d2e-aa3e-ce4523f5a5df' class='xr-var-data-in' type='checkbox'><label for='data-0695ebfa-9e21-4d2e-aa3e-ce4523f5a5df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.24883333,  0.6854823 ,  1.7767435 ,  1.9923453 ,  0.97429353,\n",
       "        0.6961403 , -0.13565262, -0.03879251,  0.5598337 ,  1.1808091 ,\n",
       "        0.6038034 ,  0.84350705, -0.05069962, -0.25024307, -0.23539317,\n",
       "       -0.36430448,  0.41267714,  1.0520335 ,  0.46626073, -0.00626519,\n",
       "       -0.0596388 ,  0.44207767,  1.9018288 ,  1.4943582 ,  1.3843187 ,\n",
       "        0.7374729 , -0.06916168, -0.05970273,  0.14967299,  0.91806144,\n",
       "        0.62769264,  0.90957785, -0.7222955 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P0_FIELD_ID</span></div><div class='xr-var-dims'>(p0_time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 13 25 26 27 28 29</div><input id='attrs-88d687ed-d40d-458d-8a6f-6650d37e95f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88d687ed-d40d-458d-8a6f-6650d37e95f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f130d6-9851-45bf-aa6d-3eb6cc049377' class='xr-var-data-in' type='checkbox'><label for='data-85f130d6-9851-45bf-aa6d-3eb6cc049377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13,  7, 14, 15,\n",
       "       17, 18, 19, 19, 20, 21, 22, 23, 24, 19, 13, 25, 26, 27, 28, 29],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P1_PHASE_GAINS</span></div><div class='xr-var-dims'>(p1_time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2527 0.6873 ... 0.9876 -0.7128</div><input id='attrs-8918388f-388a-46ba-9958-1c2ae35774f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8918388f-388a-46ba-9958-1c2ae35774f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ccd8747-0fde-46ca-86b6-d8ab7a906e73' class='xr-var-data-in' type='checkbox'><label for='data-9ccd8747-0fde-46ca-86b6-d8ab7a906e73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.25268933,  0.6872853 ,  1.7767537 ,  1.9923573 ,  0.98147464,\n",
       "        0.6991499 , -0.13607042, -0.03579992,  0.56215703,  1.1828684 ,\n",
       "        0.68960786,  0.9137128 , -0.05012422, -0.24743016, -0.23203918,\n",
       "       -0.36216304,  0.4124685 , -0.8885556 ,  1.0531927 ,  0.46335402,\n",
       "       -0.00477054, -0.05276185,  0.4452874 ,  1.8993199 ,  1.4975506 ,\n",
       "        1.3873667 ,  0.73859894, -0.06954874, -0.05909763,  0.14787272,\n",
       "        0.9153613 ,  0.67110896,  0.98757756, -0.71279883], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P1_FIELD_ID</span></div><div class='xr-var-dims'>(p1_time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 13 25 26 27 28 29</div><input id='attrs-28ebf4e1-a972-448d-ad95-7bb835fa75ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28ebf4e1-a972-448d-ad95-7bb835fa75ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51b5b5c-bbc7-41c9-b52b-a5fb66fd3f7c' class='xr-var-data-in' type='checkbox'><label for='data-f51b5b5c-bbc7-41c9-b52b-a5fb66fd3f7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13,  7, 14, 15,\n",
       "       16, 17, 18, 19, 19, 20, 21, 22, 23, 24, 19, 13, 25, 26, 27, 28, 29],\n",
       "      dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c8598b5-548e-4d6a-bbed-decdb1131653' class='xr-section-summary-in' type='checkbox' checked /><label for='section-7c8598b5-548e-4d6a-bbed-decdb1131653' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>frequency :</span></dt><dd>8223000000.0</dd><dt><span>bandwidth :</span></dt><dd>[128000000.0]</dd><dt><span>polarization_scheme :</span></dt><dd>[&#x27;R&#x27;, &#x27;L&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataTree 'ddi_0'>\n",
       "Group: /ant_ea06/ddi_0\n",
       "    Dimensions:         (p0_time: 33, p1_time: 34)\n",
       "    Coordinates:\n",
       "      * p0_time         (p0_time) float64 264B 6.018e+04 6.018e+04 ... 6.018e+04\n",
       "      * p1_time         (p1_time) float64 272B 6.018e+04 6.018e+04 ... 6.018e+04\n",
       "    Data variables:\n",
       "        P0_PHASE_GAINS  (p0_time) float32 132B 0.2488 0.6855 ... 0.9096 -0.7223\n",
       "        P0_FIELD_ID     (p0_time) int32 132B 0 1 2 3 4 5 6 ... 19 13 25 26 27 28 29\n",
       "        P1_PHASE_GAINS  (p1_time) float32 136B 0.2527 0.6873 ... 0.9876 -0.7128\n",
       "        P1_FIELD_ID     (p1_time) int32 136B 0 1 2 3 4 5 6 ... 19 13 25 26 27 28 29\n",
       "    Attributes:\n",
       "        frequency:            8223000000.0\n",
       "        bandwidth:            [128000000.0]\n",
       "        polarization_scheme:  ['R', 'L']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locit_mds[\"ant_ea06\"][\"ddi_0\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c28223c-0c66-46d0-a736-b39ee7f2834c",
   "metadata": {},
   "source": [
    "### Inspecting the Sources in the dataset\n",
    "\n",
    "When trying to determine the antenna position correction, we are always interested in knowing the distribution in the sky of the sources used in the pointing observation. The antenna position corrections in X and Y are affected by the hour-angle coverage of the observations, while the Z position correction is affected by the declination coverage of the observations.\n",
    "\n",
    "First we will print the source table, and second we will plot the sources on a simplified sky plot for easier visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17076778",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:11.584814672Z",
     "start_time": "2026-02-10T16:59:11.524302806Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.860061Z",
     "iopub.status.busy": "2026-06-08T22:30:05.860004Z",
     "iopub.status.idle": "2026-06-08T22:30:05.862604Z",
     "shell.execute_reply": "2026-06-08T22:30:05.862247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sources:\n",
      "+----+----------+---------------+----------------+---------------+----------------+\n",
      "| Id | Name     | RA FK5        | DEC FK5        | RA precessed  | DEC precessed  |\n",
      "+----+----------+---------------+----------------+---------------+----------------+\n",
      "| 0  | 2023+544 | 20h23m55.833s | +54°27m35.789s | 20h23m21.769s | +54°32m16.844s |\n",
      "| 1  | 2005+778 | 20h05m30.999s | +77°52m43.247s | 20h03m29.593s | +77°56m51.192s |\n",
      "| 10 | 1957-387 | 19h57m59.819s | -38°45m06.356s | 19h58m23.302s | -38°41m20.677s |\n",
      "| 11 | 2109-411 | 21h09m33.189s | -41°10m20.605s | 21h09m53.676s | -41°04m35.841s |\n",
      "| 12 | 2158-150 | 21h58m06.282s | -15°01m09.327s | 21h58m11.952s | -14°54m20.025s |\n",
      "| 13 | 2212+239 | 22h12m05.966s | +23°55m40.543s | 22h12m00.818s | +24°02m43.254s |\n",
      "| 14 | 0010+174 | 00h10m33.991s | +17°24m18.762s | 00h10m35.414s | +17°32m13.416s |\n",
      "| 15 | 0204-170 | 02h04m57.674s | -17°01m19.840s | 02h04m53.468s | -16°54m21.691s |\n",
      "| 16 | 0339-017 | 03h39m30.938s | -01°46m35.803s | 03h39m30.282s | -01°41m51.278s |\n",
      "| 17 | 0541+532 | 05h41m16.170s | +53°12m24.810s | 05h41m57.080s | +53°13m00.171s |\n",
      "| 18 | 0251+432 | 02h51m34.537s | +43°15m15.829s | 02h51m54.884s | +43°20m58.310s |\n",
      "| 19 | 2255+420 | 22h55m36.708s | +42°02m52.533s | 22h55m30.074s | +42°10m25.423s |\n",
      "| 2  | 0841+708 | 08h41m24.365s | +70°53m42.174s | 08h42m30.408s | +70°48m38.004s |\n",
      "| 20 | 2230+697 | 22h30m36.470s | +69°46m28.077s | 22h30m06.312s | +69°53m41.036s |\n",
      "| 21 | 1048+717 | 10h48m27.620s | +71°43m35.939s | 10h48m54.061s | +71°36m15.652s |\n",
      "| 22 | 1436+636 | 14h36m45.802s | +63°36m37.866s | 14h36m05.678s | +63°30m46.884s |\n",
      "| 23 | 1635+381 | 16h35m15.493s | +38°08m04.500s | 16h34m52.902s | +38°05m24.487s |\n",
      "| 24 | 1850+284 | 18h50m27.590s | +28°25m13.120s | 18h50m11.904s | +28°27m01.772s |\n",
      "| 25 | 2136+006 | 21h36m38.586s | +00°41m54.214s | 21h36m39.595s | +00°48m19.125s |\n",
      "| 26 | 2000-178 | 20h00m57.090s | -17°48m57.672s | 20h01m07.016s | -17°45m02.504s |\n",
      "| 27 | 2151-304 | 21h51m55.524s | -30°27m53.698s | 21h52m06.987s | -30°21m12.141s |\n",
      "| 28 | 2230-397 | 22h30m40.279s | -39°42m52.067s | 22h30m52.146s | -39°35m31.857s |\n",
      "| 29 | 0024-420 | 00h24m42.990s | -42°02m03.953s | 00h24m41.747s | -41°54m02.091s |\n",
      "| 3  | 1419+543 | 14h19m46.597s | +54°23m14.787s | 14h19m21.177s | +54°17m02.436s |\n",
      "| 4  | 1549+506 | 15h49m17.469s | +50°38m05.788s | 15h48m45.517s | +50°34m04.200s |\n",
      "| 5  | 1734+389 | 17h34m20.579s | +38°57m51.443s | 17h33m56.032s | +38°57m07.858s |\n",
      "| 6  | 2052+365 | 20h52m52.050s | +36°35m35.309s | 20h52m36.284s | +36°41m01.557s |\n",
      "| 7  | 2236+284 | 22h36m22.471s | +28°28m57.413s | 22h36m17.552s | +28°36m19.785s |\n",
      "| 8  | 1824+107 | 18h24m02.855s | +10°44m23.774s | 18h23m57.669s | +10°45m15.862s |\n",
      "| 9  | 1743-038 | 17h43m58.856s | -03°50m04.617s | 17h44m01.506s | -03°50m38.554s |\n",
      "+----+----------+---------------+----------------+---------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "locit_mds.print_source_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d493ac87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:12.295890969Z",
     "start_time": "2026-02-10T16:59:11.604949876Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.863519Z",
     "iopub.status.busy": "2026-06-08T22:30:05.863464Z",
     "iopub.status.idle": "2026-06-08T22:30:05.983146Z",
     "shell.execute_reply": "2026-06-08T22:30:05.982752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locit_plot_folder = \"locit_mds_plots\"\n",
    "\n",
    "locit_mds.plot_source_positions(\n",
    "    locit_plot_folder,  # destination for the plot\n",
    "    labels=True,  # Display source labels on plot\n",
    "    precessed=False,  # Plot FK5 (J2000) coordinates instead of precessed coordinates\n",
    "    display=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f17a734-16ec-4ce8-8a87-c025e5e3ef98",
   "metadata": {},
   "source": [
    "### Inspecting the array configuration in the dataset\n",
    "\n",
    "Another important piece of information when determining antenna position corrections is which antennas are present in the observations and where are they located in the array. We have introduced two methods to display this information, the first, `print_array_configuration`, displays all the antennas for the array, accompanied by their positions if they are present in the dataset. The second method, `plot_array_configuration`, plots the positions of the antennas in the dataset; antennas not present are simply skipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d5a3d53",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:12.375004666Z",
     "start_time": "2026-02-10T16:59:12.319607567Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.984193Z",
     "iopub.status.busy": "2026-06-08T22:30:05.984137Z",
     "iopub.status.idle": "2026-06-08T22:30:05.987344Z",
     "shell.execute_reply": "2026-06-08T22:30:05.987008Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "EVLA antennas, # of antennas 26:\n",
      "+------+---------+-----------------+----------------+--------------+\n",
      "| Name | Station |    Longitude    |    Latitude    |  Radius [m]  |\n",
      "+------+---------+-----------------+----------------+--------------+\n",
      "| ea01 |   W32   | -107°39m54.777s | +33°52m27.200s | 6373591.7591 |\n",
      "| ea02 |   N72   | -107°38m10.526s | +34°04m12.216s | 6373536.5113 |\n",
      "| ea03 |   N/A   |       N/A       |      N/A       |     N/A      |\n",
      "| ea04 |   E48   | -107°30m56.082s | +33°51m38.381s | 6373617.9185 |\n",
      "| ea05 |   W40   | -107°41m13.482s | +33°51m43.069s | 6373588.2127 |\n",
      "| ea06 |   MAS   | -107°37m41.283s | +33°53m41.999s | 6373580.9899 |\n",
      "| ea07 |   E16   | -107°36m09.822s | +33°53m40.005s | 6373579.2062 |\n",
      "| ea08 |   N56   | -107°37m47.893s | +34°00m38.380s | 6373545.7480 |\n",
      "| ea09 |   W24   | -107°38m49.036s | +33°53m04.046s | 6373590.5071 |\n",
      "| ea10 |   N40   | -107°37m29.504s | +33°57m44.409s | 6373559.2211 |\n",
      "| ea11 |   W56   | -107°44m26.689s | +33°49m54.626s | 6373595.3443 |\n",
      "| ea12 |   E08   | -107°36m48.898s | +33°53m55.133s | 6373576.7701 |\n",
      "| ea13 |   W16   | -107°37m57.387s | +33°53m32.978s | 6373581.2948 |\n",
      "| ea14 |   N/A   |       N/A       |      N/A       |     N/A      |\n",
      "| ea15 |   N16   | -107°37m10.878s | +33°54m47.970s | 6373570.5968 |\n",
      "| ea16 |   E24   | -107°35m13.358s | +33°53m18.138s | 6373593.7134 |\n",
      "| ea17 |   N64   | -107°37m58.700s | +34°02m20.511s | 6373539.3645 |\n",
      "| ea18 |   N32   | -107°37m22.024s | +33°56m33.579s | 6373563.0524 |\n",
      "| ea19 |   E32   | -107°34m01.480s | +33°52m50.288s | 6373605.2045 |\n",
      "| ea20 |   W64   | -107°46m20.056s | +33°48m50.918s | 6373597.0976 |\n",
      "| ea21 |   E72   | -107°24m42.347s | +33°49m18.007s | 6373584.7068 |\n",
      "| ea22 |   N24   | -107°37m16.123s | +33°55m37.653s | 6373567.7498 |\n",
      "| ea23 |   N08   | -107°37m07.487s | +33°54m15.819s | 6373574.9212 |\n",
      "| ea24 |   W72   | -107°48m23.996s | +33°47m41.208s | 6373594.9645 |\n",
      "| ea25 |   E56   | -107°29m04.138s | +33°50m54.915s | 6373622.6190 |\n",
      "| ea26 |   W48   | -107°42m44.329s | +33°50m52.098s | 6373595.5052 |\n",
      "| ea27 |   E40   | -107°32m35.422s | +33°52m16.922s | 6373618.9935 |\n",
      "| ea28 |   W08   | -107°37m21.648s | +33°53m52.993s | 6373578.4916 |\n",
      "+------+---------+-----------------+----------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "locit_mds.print_array_configuration(\n",
    "    relative=False\n",
    ")  # antenna positions printed are relative to the array center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7330e982",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:13.311075353Z",
     "start_time": "2026-02-10T16:59:12.398365807Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:05.988205Z",
     "iopub.status.busy": "2026-06-08T22:30:05.988151Z",
     "iopub.status.idle": "2026-06-08T22:30:06.143782Z",
     "shell.execute_reply": "2026-06-08T22:30:06.143420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locit_mds.plot_array_configuration(\n",
    "    locit_plot_folder,  # Folder in which to save the plot\n",
    "    stations=True,  # Toggle to display the station name alongside the antenna name\n",
    "    zoff=False,  # Toggle to display the antenna elevation offset by its name\n",
    "    unit=\"km\",  # Length unit for the plot\n",
    "    box_size=5,  # Size of the box for the inner array in the unit specified in unit\n",
    "    display=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b62e549",
   "metadata": {},
   "source": [
    "## Locit\n",
    "\n",
    "After we have inspected the `locit_mds` object we can now use `locit` to obtain antenna position corrections. In this dataset a single antenna, ea06 has been moved, and hence we could skip the other antennas and get position corrections for only it and the reference antenna, ea28. \n",
    "But here we will be getting corrections for all antennas as this can help point out systematic errors with the dataset, such as choosing a bad reference antenna.\n",
    "We include the reference antenna in the fit as a sanity check, the position corrections for the reference antenna, as well as the fixed delay and delay rate are by construction, 0, if they aren't there is something wrong with the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fc53d91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:25.734062085Z",
     "start_time": "2026-02-10T16:59:13.314120459Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:06.144867Z",
     "iopub.status.busy": "2026-06-08T22:30:06.144808Z",
     "iopub.status.idle": "2026-06-08T22:30:08.803675Z",
     "shell.execute_reply": "2026-06-08T22:30:08.803329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:06,148\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m Creating output file name: ./data/locit-input-pha.position.zarr \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[38;2;128;05;128m2026-06-08 16:30:08,748\u001b[0m] \u001b[38;2;50;50;205m    INFO\u001b[0m\u001b[38;2;112;128;144m      client: \u001b[0m Consolidating ./data/locit-input-pha.position.zarr... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 994 ms, sys: 86.2 ms, total: 1.08 s\n",
      "Wall time: 2.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from astrohack import locit\n",
    "\n",
    "position_mds = locit(\n",
    "    locit_name,\n",
    "    position_name=position_name,  # Name of the position file to be created by locit\n",
    "    elevation_limit=10.0,  # Elevation under which no sources are considered\n",
    "    polarization=\"both\",  # Combine both R and L polarization phase gains for increased SNR\n",
    "    fit_engine=\"scipy\",  # Fit data using scipy\n",
    "    fit_kterm=False,\n",
    "    fit_delay_rate=True,  # Fit delay rate\n",
    "    ant=\"all\",  # Select all antennas\n",
    "    ddi=\"all\",  # Select all DDIs\n",
    "    combine_ddis=\"simple\",  # Combine delays from all DDIs to obtain a single solution with increased SNR\n",
    "    parallel=parallel,  # Do fitting in parallel\n",
    "    overwrite=True,  # Overwrite previously created position file\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9489a77-7429-4e75-9b46-42c71f4a6747",
   "metadata": {},
   "source": [
    "`locit` creates a file that is called a position file. This file contains the delays, and the fitted delay model for each antenna\n",
    "\n",
    "`locit` also returns the opened position file as a `position_mds` object. The first step in interacting with the `position_mds` object is calling its summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15ef87d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:25.810880934Z",
     "start_time": "2026-02-10T16:59:25.735874960Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:08.804825Z",
     "iopub.status.busy": "2026-06-08T22:30:08.804765Z",
     "iopub.status.idle": "2026-06-08T22:30:08.807084Z",
     "shell.execute_reply": "2026-06-08T22:30:08.806764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "###                               Summary for:                               ###\n",
      "###                   ./data/locit-input-pha.position.zarr                   ###\n",
      "################################################################################\n",
      "\n",
      "Data origin:\n",
      "creation_time:    2026-06-08 16:30:06 MDT\n",
      "creator_function: locit\n",
      "origin:           astrohack\n",
      "version:          1.0.2\n",
      "\n",
      "Input Parameters:\n",
      "+-----------------+--------------------------------------+\n",
      "| Parameter       | Value                                |\n",
      "+-----------------+--------------------------------------+\n",
      "| ant             | all                                  |\n",
      "| combine_ddis    | simple                               |\n",
      "| ddi             | all                                  |\n",
      "| elevation_limit | 10.0                                 |\n",
      "| fit_delay_rate  | True                                 |\n",
      "| fit_engine      | scipy                                |\n",
      "| fit_kterm       | False                                |\n",
      "| locit_name      | ./data/locit-input-pha.locit.zarr    |\n",
      "| overwrite       | True                                 |\n",
      "| parallel        | True                                 |\n",
      "| polarization    | both                                 |\n",
      "| position_name   | ./data/locit-input-pha.position.zarr |\n",
      "+-----------------+--------------------------------------+\n",
      "\n",
      "Available methods:\n",
      "+------------------------------+-----------------------------------------------+\n",
      "| Methods                      | Description                                   |\n",
      "+------------------------------+-----------------------------------------------+\n",
      "| add_node                     | Add a node to the data tree file structure,   |\n",
      "|                              | however this node is not yet consolidated     |\n",
      "|                              | into the data tree         structure,         |\n",
      "|                              | consolidate must be called to integrate all   |\n",
      "|                              | nodes writen by add_node onto the tree        |\n",
      "|                              | structure.                                    |\n",
      "| consolidate                  | Traverse own file structure on disk           |\n",
      "|                              | consolidating metadata to create a unified    |\n",
      "|                              | data tree entity.                             |\n",
      "| create_from_input_parameters | Create an AstrohackBaseFile object from a     |\n",
      "|                              | filename and initializes xdtree root          |\n",
      "|                              | attributes.                                   |\n",
      "| export_locit_fit_results     | Export antenna position fit results to a text |\n",
      "|                              | file.                                         |\n",
      "| export_results_to_parminator | Export antenna position fit results to a VLA  |\n",
      "|                              | parminator file.                              |\n",
      "| is_close_to                  | Tests if self and other_mds are close to each |\n",
      "|                              | other.                                        |\n",
      "| items                        | Get children items                            |\n",
      "| keys                         | Get children keys                             |\n",
      "| open                         | Open Base file.                               |\n",
      "| plot_delays                  | Plot the delays used for antenna position     |\n",
      "|                              | fitting and optionally the resulting fit.     |\n",
      "| plot_position_corrections    | Plot Antenna position corrections on an array |\n",
      "|                              | configuration plot                            |\n",
      "| plot_sky_coverage            | Plot the sky coverage of the data used for    |\n",
      "|                              | antenna position fitting                      |\n",
      "| summary                      | Prints summary of this Astrohack File object, |\n",
      "|                              | with available data, attributes and methods   |\n",
      "| values                       | Get children values                           |\n",
      "| write                        | Write mds to disk by saving the data tree to  |\n",
      "|                              | a file                                        |\n",
      "+------------------------------+-----------------------------------------------+\n",
      "\n",
      "Data Contents:\n",
      "+----------+\n",
      "| Antenna  |\n",
      "+----------+\n",
      "| ant_ea01 |\n",
      "| ant_ea02 |\n",
      "| ant_ea04 |\n",
      "| ant_ea05 |\n",
      "| ant_ea06 |\n",
      "| ant_ea07 |\n",
      "| ant_ea08 |\n",
      "| ant_ea09 |\n",
      "| ant_ea10 |\n",
      "| ant_ea11 |\n",
      "| ant_ea12 |\n",
      "| ant_ea13 |\n",
      "| ant_ea15 |\n",
      "| ant_ea16 |\n",
      "| ant_ea17 |\n",
      "| ant_ea18 |\n",
      "| ant_ea19 |\n",
      "| ant_ea20 |\n",
      "| ant_ea21 |\n",
      "| ant_ea22 |\n",
      "| ant_ea23 |\n",
      "| ant_ea24 |\n",
      "| ant_ea25 |\n",
      "| ant_ea26 |\n",
      "| ant_ea27 |\n",
      "| ant_ea28 |\n",
      "+----------+\n"
     ]
    }
   ],
   "source": [
    "position_mds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef51b5c-251e-4e16-b777-c6a0e1f9cd70",
   "metadata": {},
   "source": [
    "From the summary we can see that the position file contains simply 2 antennas and no DDIs, as well as 4 different methods:\n",
    "- `export_fit_results` exports the antenna position corrections to an ascii file.\n",
    "- `plot_sky_coverage` plots the sky coverage for an antenna and DDI (if present).\n",
    "- `plot_delays` plots the measured delays as a function of time, hour angle, declination and elevation,\n",
    "- `plot_position_corrections` Plots the position corrections on an array plot, making it easier to identify systematics\n",
    "\n",
    "To inspect the data contained in the position file for an antenna we can then simply do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5535c581",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:25.882146037Z",
     "start_time": "2026-02-10T16:59:25.821412762Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:08.808055Z",
     "iopub.status.busy": "2026-06-08T22:30:08.807993Z",
     "iopub.status.idle": "2026-06-08T22:30:08.812379Z",
     "shell.execute_reply": "2026-06-08T22:30:08.812078Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "  margin-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree &#x27;ant_ea06&#x27;&gt;\n",
       "Group: /ant_ea06\n",
       "    Dimensions:      (time: 110)\n",
       "    Coordinates:\n",
       "      * time         (time) float64 880B 0.0 0.002234 0.004711 ... 0.07838 0.07992\n",
       "    Data variables:\n",
       "        DECLINATION  (time) float64 880B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "        DELAYS       (time) float32 440B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "        ELEVATION    (time) float64 880B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "        HOUR_ANGLE   (time) float64 880B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "        LST          (time) float64 880B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "        MODEL        (time) float64 880B dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;\n",
       "    Attributes:\n",
       "        antenna_info:       {&#x27;geocentric_position&#x27;: [-1602152.0314, -5042031.7101...\n",
       "        chi_squared:        3.4313971058949244e-23\n",
       "        elevation_limit:    0.17453292519943295\n",
       "        fixed_delay_error:  2.1332775692767194e-12\n",
       "        fixed_delay_fit:    2.5331244523801818e-11\n",
       "        frequency:          [8223000000.0, 8823000000.0]\n",
       "        polarization:       both\n",
       "        position_error:     [1.7528819693317142e-12, 1.409076306999354e-12, 1.552...\n",
       "        position_fit:       [-2.989452216420335e-11, -1.2258925490302729e-11, -5....\n",
       "        rate_error:         2.7610890405204374e-11\n",
       "        rate_fit:           8.518924151072614e-12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div><div class='xr-obj-name'>&#x27;ant_ea06&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8a1e0586-53de-47a0-a498-08fd6dfb997b' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-8a1e0586-53de-47a0-a498-08fd6dfb997b' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 110</li></ul></div></li><li class='xr-section-item'><input id='section-2eeaf39b-bf8d-4d5b-8869-bea78710a8ea' class='xr-section-summary-in' type='checkbox' checked /><label for='section-2eeaf39b-bf8d-4d5b-8869-bea78710a8ea' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.002234 ... 0.07838 0.07992</div><input id='attrs-f1864cd5-e558-4b16-b02a-fb1d84e57744' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1864cd5-e558-4b16-b02a-fb1d84e57744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed22757-13c5-4b95-b3d9-9ef5fd5b3c0d' class='xr-var-data-in' type='checkbox'><label for='data-aed22757-13c5-4b95-b3d9-9ef5fd5b3c0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.233796e-03, 4.710648e-03, 1.061343e-02, 1.337963e-02,\n",
       "       1.802083e-02, 2.187500e-02, 3.021956e-02, 3.295139e-02, 3.504630e-02,\n",
       "       3.729167e-02, 4.020833e-02, 4.458333e-02, 4.672454e-02, 4.961806e-02,\n",
       "       5.109954e-02, 5.337963e-02, 5.611111e-02, 5.795139e-02, 6.217593e-02,\n",
       "       6.537037e-02, 7.001157e-02, 7.229167e-02, 7.468750e-02, 7.666667e-02,\n",
       "       7.837963e-02, 7.991687e-02, 0.000000e+00, 2.233796e-03, 4.710648e-03,\n",
       "       1.061343e-02, 1.337963e-02, 1.802083e-02, 2.187500e-02, 3.021956e-02,\n",
       "       3.295139e-02, 3.504630e-02, 3.729167e-02, 4.020833e-02, 4.210648e-02,\n",
       "       4.458333e-02, 4.672454e-02, 4.961806e-02, 5.109954e-02, 5.337963e-02,\n",
       "       5.611111e-02, 5.795139e-02, 6.217593e-02, 6.537037e-02, 7.001157e-02,\n",
       "       7.229167e-02, 7.468750e-02, 7.666667e-02, 7.837963e-02, 7.991687e-02,\n",
       "       1.455192e-11, 2.233796e-03, 4.710648e-03, 1.061343e-02, 1.337963e-02,\n",
       "       1.802083e-02, 2.187500e-02, 3.021956e-02, 3.295139e-02, 3.504630e-02,\n",
       "       3.729167e-02, 4.020833e-02, 4.458333e-02, 4.672454e-02, 4.961806e-02,\n",
       "       5.109954e-02, 5.337963e-02, 5.611111e-02, 5.795139e-02, 6.217593e-02,\n",
       "       6.537037e-02, 7.001157e-02, 7.229167e-02, 7.468750e-02, 7.666667e-02,\n",
       "       7.837963e-02, 7.991687e-02, 1.455192e-11, 2.233796e-03, 4.710648e-03,\n",
       "       1.061343e-02, 1.337963e-02, 1.802083e-02, 2.187500e-02, 3.021956e-02,\n",
       "       3.295139e-02, 3.504630e-02, 3.729167e-02, 4.020833e-02, 4.210648e-02,\n",
       "       4.458333e-02, 4.672454e-02, 4.961806e-02, 5.109954e-02, 5.337963e-02,\n",
       "       5.611111e-02, 5.795139e-02, 6.217593e-02, 6.537037e-02, 7.001157e-02,\n",
       "       7.229167e-02, 7.468750e-02, 7.666667e-02, 7.837963e-02, 7.991687e-02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba66e42d-f404-476e-9a78-234171d7fa21' class='xr-section-summary-in' type='checkbox' checked /><label for='section-ba66e42d-f404-476e-9a78-234171d7fa21' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>DECLINATION</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-ae9f4c8f-9b38-4db0-a77f-1b9872f5fe6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae9f4c8f-9b38-4db0-a77f-1b9872f5fe6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f7d4e53-f3f9-4d49-946e-e27f2e3a2005' class='xr-var-data-in' type='checkbox'><label for='data-9f7d4e53-f3f9-4d49-946e-e27f2e3a2005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 880 B </td>\n",
       "                        <td> 880 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DELAYS</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-4797a89a-5ce2-4fc0-8f13-5d0a4614a996' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4797a89a-5ce2-4fc0-8f13-5d0a4614a996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-779452c8-1281-4418-afa7-5762bbda8aa5' class='xr-var-data-in' type='checkbox'><label for='data-779452c8-1281-4418-afa7-5762bbda8aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 440 B </td>\n",
       "                        <td> 440 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELEVATION</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-b5ee60d4-d5e6-45f3-ac3a-1a408f08cbde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5ee60d4-d5e6-45f3-ac3a-1a408f08cbde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f30ab87-39c6-488d-9df0-9ded5cfa9409' class='xr-var-data-in' type='checkbox'><label for='data-4f30ab87-39c6-488d-9df0-9ded5cfa9409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 880 B </td>\n",
       "                        <td> 880 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HOUR_ANGLE</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-bf0ddc6a-ea0f-4e39-9a6b-a27b4e4b1396' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf0ddc6a-ea0f-4e39-9a6b-a27b4e4b1396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94f0670d-9333-40bb-9fc5-e59aabeeb64b' class='xr-var-data-in' type='checkbox'><label for='data-94f0670d-9333-40bb-9fc5-e59aabeeb64b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 880 B </td>\n",
       "                        <td> 880 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LST</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-f1c86eb9-b6ff-46c0-a1f4-1f3390b849f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1c86eb9-b6ff-46c0-a1f4-1f3390b849f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d708eb8-fe7d-4fd7-9211-b633cfc08714' class='xr-var-data-in' type='checkbox'><label for='data-2d708eb8-fe7d-4fd7-9211-b633cfc08714' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 880 B </td>\n",
       "                        <td> 880 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODEL</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(110,), meta=np.ndarray&gt;</div><input id='attrs-48a28d46-603e-4c10-b48f-fc1bd605e82d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48a28d46-603e-4c10-b48f-fc1bd605e82d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb1bc928-fd68-4725-b196-13cd5402ebae' class='xr-var-data-in' type='checkbox'><label for='data-bb1bc928-fd68-4725-b196-13cd5402ebae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 880 B </td>\n",
       "                        <td> 880 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (110,) </td>\n",
       "                        <td> (110,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >110</text>\n",
       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-234f1098-53a9-44fe-b47c-bc7cae157370' class='xr-section-summary-in' type='checkbox' /><label for='section-234f1098-53a9-44fe-b47c-bc7cae157370' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>antenna_info :</span></dt><dd>{&#x27;geocentric_position&#x27;: [-1602152.0314, -5042031.7101, 3554371.9199], &#x27;id&#x27;: 4, &#x27;latitude&#x27;: 0.591579342845593, &#x27;longitude&#x27;: -1.8784653075569, &#x27;name&#x27;: &#x27;ea06&#x27;, &#x27;offset&#x27;: [0.0, 0.0007195018991999999, 0.0], &#x27;radius&#x27;: 6373580.989921693, &#x27;reference&#x27;: False, &#x27;station&#x27;: &#x27;MAS&#x27;}</dd><dt><span>chi_squared :</span></dt><dd>3.4313971058949244e-23</dd><dt><span>elevation_limit :</span></dt><dd>0.17453292519943295</dd><dt><span>fixed_delay_error :</span></dt><dd>2.1332775692767194e-12</dd><dt><span>fixed_delay_fit :</span></dt><dd>2.5331244523801818e-11</dd><dt><span>frequency :</span></dt><dd>[8223000000.0, 8823000000.0]</dd><dt><span>polarization :</span></dt><dd>both</dd><dt><span>position_error :</span></dt><dd>[1.7528819693317142e-12, 1.409076306999354e-12, 1.5522917320443764e-12]</dd><dt><span>position_fit :</span></dt><dd>[-2.989452216420335e-11, -1.2258925490302729e-11, -5.1622774747517774e-12]</dd><dt><span>rate_error :</span></dt><dd>2.7610890405204374e-11</dd><dt><span>rate_fit :</span></dt><dd>8.518924151072614e-12</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataTree 'ant_ea06'>\n",
       "Group: /ant_ea06\n",
       "    Dimensions:      (time: 110)\n",
       "    Coordinates:\n",
       "      * time         (time) float64 880B 0.0 0.002234 0.004711 ... 0.07838 0.07992\n",
       "    Data variables:\n",
       "        DECLINATION  (time) float64 880B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "        DELAYS       (time) float32 440B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "        ELEVATION    (time) float64 880B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "        HOUR_ANGLE   (time) float64 880B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "        LST          (time) float64 880B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "        MODEL        (time) float64 880B dask.array<chunksize=(110,), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        antenna_info:       {'geocentric_position': [-1602152.0314, -5042031.7101...\n",
       "        chi_squared:        3.4313971058949244e-23\n",
       "        elevation_limit:    0.17453292519943295\n",
       "        fixed_delay_error:  2.1332775692767194e-12\n",
       "        fixed_delay_fit:    2.5331244523801818e-11\n",
       "        frequency:          [8223000000.0, 8823000000.0]\n",
       "        polarization:       both\n",
       "        position_error:     [1.7528819693317142e-12, 1.409076306999354e-12, 1.552...\n",
       "        position_fit:       [-2.989452216420335e-11, -1.2258925490302729e-11, -5....\n",
       "        rate_error:         2.7610890405204374e-11\n",
       "        rate_fit:           8.518924151072614e-12"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_mds[\"ant_ea06\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c811a4d-2a40-446e-ab7d-28bf34f62f6e",
   "metadata": {},
   "source": [
    "The following plot of the sky coverage of the sources for antenna ea06, gives us an idea of how good our results can be. From it we see that basically all possible hour-angles and declinations are covered, which implies that the position correction determinations are as good as they can be given the observing conditions are good and stable enough.\n",
    "\n",
    "Weather may complicate this measurement by introducing anisotropic and time dependant delays, limiting the methods accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "29164f2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:26.966244216Z",
     "start_time": "2026-02-10T16:59:25.885745707Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:08.813320Z",
     "iopub.status.busy": "2026-06-08T22:30:08.813264Z",
     "iopub.status.idle": "2026-06-08T22:30:09.082079Z",
     "shell.execute_reply": "2026-06-08T22:30:09.081680Z"
    }
   },
   "outputs": [],
   "source": [
    "position_plot_folder = \"position_mds_exports\"\n",
    "\n",
    "position_mds.plot_sky_coverage(\n",
    "    position_plot_folder,  # Folder to contain plot\n",
    "    ant=\"ea06\",  # Plot only antenna ea06\n",
    "    ddi=\"all\",  # DDI selection irrelevant because we are combining DDIs\n",
    "    time_unit=\"hour\",  # Unit for observation duration\n",
    "    angle_unit=\"deg\",  # Unit for sky coordinates\n",
    "    display=True,\n",
    "    parallel=parallel\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fd3f8a-59ec-4744-abaf-3a56d1b9169e",
   "metadata": {},
   "source": [
    "Below we export the fit results to an ascii file and display it for analysis.\n",
    "In it we can see that the results for the reference antenna are all 0 and the delay RMS is very small, which is indeed what is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0485f8e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:27.050297575Z",
     "start_time": "2026-02-10T16:59:26.970142611Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:09.083360Z",
     "iopub.status.busy": "2026-06-08T22:30:09.083304Z",
     "iopub.status.idle": "2026-06-08T22:30:09.089441Z",
     "shell.execute_reply": "2026-06-08T22:30:09.089116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------+------------+-----------+-------------------+---------------+---------------+---------------+-------------------+\n",
      "|  Antenna   | Station | RMS [nsec] | RMS [deg] |  F. delay [nsec]  | X offset [mm] | Y offset [mm] | Z offset [mm] |  Rate [nsec/hour] |\n",
      "+------------+---------+------------+-----------+-------------------+---------------+---------------+---------------+-------------------+\n",
      "|    ea01    |   W32   |  1.57e-02  |    48.1   |  0.0153 ±  0.0056 |   0.6 ±  1.1  |   5.5 ±  1.4  |  -1.6 ±  1.2  | -0.0001 ±  0.0031 |\n",
      "|    ea02    |   N72   |  1.71e-02  |    52.5   |  0.0290 ±  0.0062 |   3.4 ±  1.2  |   7.6 ±  1.5  |  -7.0 ±  1.3  | -0.0020 ±  0.0034 |\n",
      "|    ea04    |   E48   |  2.04e-02  |    62.7   | -0.0346 ±  0.0074 |   3.6 ±  1.5  |  -3.9 ±  1.8  |   3.3 ±  1.6  |  0.0073 ±  0.0040 |\n",
      "|    ea05    |   W40   |  1.45e-02  |    44.6   |  0.0108 ±  0.0052 |   2.6 ±  1.1  |   3.4 ±  1.3  |  -1.1 ±  1.1  | -0.0005 ±  0.0028 |\n",
      "|    ea06    |   MAS   |  5.86e-03  |    18.0   |  0.0253 ±  0.0021 |  -0.8 ±  0.4  |   9.7 ±  0.5  |  -1.5 ±  0.5  |  0.0004 ±  0.0012 |\n",
      "|    ea07    |   E16   |  1.19e-02  |    36.6   | -0.0002 ±  0.0043 |   1.4 ±  0.9  |  -0.7 ±  1.0  |  -1.2 ±  0.9  |  0.0048 ±  0.0023 |\n",
      "|    ea08    |   N56   |  1.87e-02  |    57.5   |  0.0048 ±  0.0069 |  -6.2 ±  1.4  |   1.2 ±  1.7  |   1.8 ±  1.5  | -0.0046 ±  0.0037 |\n",
      "|    ea09    |   W24   |  1.29e-02  |    39.7   |  0.0170 ±  0.0047 |  -1.8 ±  0.9  |   5.0 ±  1.1  |  -3.1 ±  1.0  | -0.0014 ±  0.0025 |\n",
      "|    ea10    |   N40   |  1.34e-02  |    41.0   |  0.0026 ±  0.0048 |  -3.5 ±  1.0  |  -0.8 ±  1.2  |  -2.8 ±  1.0  |  0.0015 ±  0.0026 |\n",
      "|    ea11    |   W56   |  1.91e-02  |    58.5   |  0.0014 ±  0.0069 |   8.7 ±  1.4  |   4.4 ±  1.7  |  -0.4 ±  1.5  |  0.0069 ±  0.0037 |\n",
      "|    ea12    |   E08   |  9.89e-03  |    30.3   | -0.0079 ±  0.0036 |  -0.2 ±  0.7  |  -1.4 ±  0.9  |   0.5 ±  0.8  |  0.0007 ±  0.0019 |\n",
      "|    ea13    |   W16   |  9.31e-03  |    28.6   |  0.0114 ±  0.0034 |  -0.7 ±  0.7  |   3.3 ±  0.8  |  -1.2 ±  0.7  | -0.0008 ±  0.0018 |\n",
      "|    ea15    |   N16   |  1.03e-02  |    31.7   | -0.0044 ±  0.0037 |  -1.2 ±  0.7  |  -1.2 ±  0.9  |   1.3 ±  0.8  |  0.0036 ±  0.0020 |\n",
      "|    ea16    |   E24   |  1.01e-02  |    30.9   | -0.0232 ±  0.0037 |   0.6 ±  0.7  |  -0.5 ±  0.9  |   2.6 ±  0.8  |  0.0125 ±  0.0020 |\n",
      "|    ea17    |   N64   |  1.86e-02  |    57.1   |  0.0317 ±  0.0069 |   2.9 ±  1.4  |   7.2 ±  1.7  |  -2.4 ±  1.5  | -0.0094 ±  0.0037 |\n",
      "|    ea18    |   N32   |  9.77e-03  |    30.0   | -0.0150 ±  0.0035 |  -3.6 ±  0.7  |  -3.9 ±  0.9  |   0.4 ±  0.8  |  0.0028 ±  0.0019 |\n",
      "|    ea19    |   E32   |  9.90e-03  |    30.4   | -0.0251 ±  0.0037 |  -1.4 ±  0.7  |   0.5 ±  0.9  |   0.7 ±  0.8  |  0.0120 ±  0.0019 |\n",
      "|    ea20    |   W64   |  1.34e-02  |    41.1   | -0.0121 ±  0.0048 |   8.5 ±  1.0  |   4.8 ±  1.2  |   1.6 ±  1.0  |  0.0142 ±  0.0026 |\n",
      "|    ea21    |   E72   |  2.10e-02  |    64.3   | -0.0187 ±  0.0075 |   4.7 ±  1.5  |  -4.8 ±  1.8  |   1.9 ±  1.6  |  0.0058 ±  0.0041 |\n",
      "|    ea22    |   N24   |  9.14e-03  |    28.0   | -0.0144 ±  0.0033 |  -2.1 ±  0.7  |  -4.9 ±  0.8  |   0.6 ±  0.7  |  0.0042 ±  0.0018 |\n",
      "|    ea23    |   N08   |  9.80e-03  |    30.1   |  0.0063 ±  0.0035 |  -0.8 ±  0.7  |   1.6 ±  0.9  |  -1.3 ±  0.8  |  0.0014 ±  0.0019 |\n",
      "|    ea24    |   W72   |  2.81e-02  |    86.1   | -0.0055 ±  0.0101 |   4.8 ±  2.0  |   1.4 ±  2.5  |   5.6 ±  2.2  | -0.0060 ±  0.0055 |\n",
      "|    ea25    |   E56   |  1.71e-02  |    52.4   | -0.0258 ±  0.0061 |   3.2 ±  1.2  |  -2.9 ±  1.5  |   0.8 ±  1.3  |  0.0143 ±  0.0033 |\n",
      "|    ea26    |   W48   |  1.92e-02  |    58.8   | -0.0154 ±  0.0069 |   2.0 ±  1.4  |   2.1 ±  1.7  |   6.4 ±  1.5  |  0.0056 ±  0.0038 |\n",
      "|    ea27    |   E40   |  1.00e-02  |    30.8   | -0.0420 ±  0.0037 |  -1.3 ±  0.7  |  -5.3 ±  0.9  |   2.3 ±  0.8  |  0.0138 ±  0.0020 |\n",
      "| ea28 (ref) |   W08   |  5.94e-11  |     0.0   |  0.0000 ±  0.0000 |  -0.0 ±  0.0  |  -0.0 ±  0.0  |  -0.0 ±  0.0  | -0.0000 ±  0.0000 |\n",
      "+------------+---------+------------+-----------+-------------------+---------------+---------------+---------------+-------------------+\n"
     ]
    }
   ],
   "source": [
    "position_export_folder = \"position_mds_exports\"\n",
    "\n",
    "position_mds.export_locit_fit_results(\n",
    "    position_export_folder,  # Folder to contain antenna position corrections file\n",
    "    ant=\"all\",  # See results for all antennas\n",
    "    position_unit=\"mm\",  # Unit for the position corrections\n",
    "    delay_unit=\"nsec\",  # Unit for delays\n",
    "    time_unit=\"hour\",  # Unit for delay rate denominator\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75145f5a-48a4-4b21-aba8-ec8e3afb490a",
   "metadata": {},
   "source": [
    "Now we plot the delays and the delay model that was fitted with `locit`. From this plot we can see that model delays agree very well with the observed delays leading to a good confidence in the position corrections derived with `locit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "518380af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:28.583652236Z",
     "start_time": "2026-02-10T16:59:27.056871352Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:09.090690Z",
     "iopub.status.busy": "2026-06-08T22:30:09.090612Z",
     "iopub.status.idle": "2026-06-08T22:30:09.486443Z",
     "shell.execute_reply": "2026-06-08T22:30:09.486018Z"
    }
   },
   "outputs": [],
   "source": [
    "position_mds.plot_delays(\n",
    "    position_plot_folder,  # Folder to contain plot\n",
    "    ant=\"ea06\",  # Plot only antenna ea06\n",
    "    ddi=\"all\",  # DDI selection irrelevant because we are combining DDIs\n",
    "    time_unit=\"hour\",  # Unit for observation duration\n",
    "    angle_unit=\"deg\",  # Unit for sky coordinates\n",
    "    delay_unit=\"nsec\",  # Unit for delays\n",
    "    plot_model=True,  # Plot fitted delay model\n",
    "    display=True,\n",
    "    parallel=parallel\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cca14ad-775b-4692-9927-d3cd072c3b28",
   "metadata": {},
   "source": [
    "One extra way to check for systematic errors in antenna position determinations is to plot the corrections for the whole array.\n",
    "If all the corrections point the same way this might be an indication that the chosen reference_antenna has an error in its position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "712d9bb3-f828-4a4c-b166-38a1087c7e58",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:30.250336095Z",
     "start_time": "2026-02-10T16:59:28.602675682Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:09.487561Z",
     "iopub.status.busy": "2026-06-08T22:30:09.487500Z",
     "iopub.status.idle": "2026-06-08T22:30:09.768398Z",
     "shell.execute_reply": "2026-06-08T22:30:09.767950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "position_mds.plot_position_corrections(\n",
    "    position_plot_folder,  # Folder to contain plot\n",
    "    unit=\"km\",  # Unit for the x and Y axes\n",
    "    box_size=5,  # Size for the box containing the inner array\n",
    "    scaling=250,  # scaling to be applied to corrections\n",
    "    display=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41f7a882-beef-4bdf-a217-d36502d21f59",
   "metadata": {},
   "source": [
    "Finally, when we are certain that our results are good we can then export the fit results to an ASCII file that is formatted for input by the VLA parminator software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fae5b2fe-885d-4d3a-af3c-b24256d679e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:30.396968039Z",
     "start_time": "2026-02-10T16:59:30.252146014Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:09.769480Z",
     "iopub.status.busy": "2026-06-08T22:30:09.769401Z",
     "iopub.status.idle": "2026-06-08T22:30:09.772756Z",
     "shell.execute_reply": "2026-06-08T22:30:09.772422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parminator file contents:\n",
      "\n",
      "W32, ,Y,$ 0.0055\n",
      "W32, ,Z,$-0.0016\n",
      "W40, ,X,$ 0.0026\n",
      "W40, ,Y,$ 0.0034\n",
      "W40, ,Z,$-0.0011\n",
      "MAS, ,Y,$ 0.0097\n",
      "MAS, ,Z,$-0.0015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "position_mds.export_results_to_parminator(\n",
    "    \"ant_pos_cor_24-10-14.PAR\",  # name of the output parminator file\n",
    "    ant=[\"ea06\", \"ea01\", \"ea05\"],  # Selected moved antennas\n",
    "    correction_threshold=0.001,  # Threshold for valid corrections in meters (i.e. minimum value for correction to appear in parminator file)\n",
    ")\n",
    "\n",
    "print(\"Parminator file contents:\\n\")\n",
    "for line in open(\"ant_pos_cor_24-10-14.PAR\"):\n",
    "    print(line[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0f2139a695bf57d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-10T16:59:30.419486294Z",
     "start_time": "2026-02-10T16:59:30.409748612Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-08T22:30:09.773617Z",
     "iopub.status.busy": "2026-06-08T22:30:09.773562Z",
     "iopub.status.idle": "2026-06-08T22:30:09.775784Z",
     "shell.execute_reply": "2026-06-08T22:30:09.775500Z"
    }
   },
   "outputs": [],
   "source": [
    "if parallel:\n",
    "    client.close()"
   ]
  }
 ],
 "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": "3.13.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "60c25678cd9f427e9dafaeb1fcbe4fe1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_c654737545a74ed0870123352afd8d80",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">locit-input-pha.cal <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "locit-input-pha.cal \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "c654737545a74ed0870123352afd8d80": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}