{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98b9b886-806a-4849-a111-93b239e9d048",
   "metadata": {},
   "source": [
    "# Demo FAC on-demand quicklook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a806781-2caf-45b1-afb9-2a703d10644a",
   "metadata": {},
   "source": [
    "Using SwarmPAL implementation in development. See <https://swarmpal.readthedocs.io/en/latest/guides/fac/intro_fac.html>\n",
    "\n",
    "This example uses HAPI instead of the VirES API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f33ecc4-ed2c-4716-9c75-8f0363dd0a6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:12:57.537306Z",
     "iopub.status.busy": "2024-08-29T08:12:57.533714Z",
     "iopub.status.idle": "2024-08-29T08:13:10.880611Z",
     "shell.execute_reply": "2024-08-29T08:13:10.872749Z"
    }
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "from hapiclient import hapi\n",
    "import numpy as np\n",
    "from swarmpal.io import create_paldata, PalDataItem\n",
    "from swarmpal.toolboxes import fac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7389ea1e-99b6-4cf0-a1a6-924fec7c13b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:13:10.970756Z",
     "iopub.status.busy": "2024-08-29T08:13:10.963606Z",
     "iopub.status.idle": "2024-08-29T08:13:11.000468Z",
     "shell.execute_reply": "2024-08-29T08:13:10.994661Z"
    }
   },
   "outputs": [],
   "source": [
    "SERVER = \"https://vires.services/hapi\"\n",
    "DATASET = \"SW_OPER_MAGA_LR_1B\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62e9eda-970d-4914-95e5-ab077cf8ac6f",
   "metadata": {},
   "source": [
    "## Find latest available time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0b941f4-be41-4512-a258-0324c81a33a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:13:11.025821Z",
     "iopub.status.busy": "2024-08-29T08:13:11.022345Z",
     "iopub.status.idle": "2024-08-29T08:13:11.773674Z",
     "shell.execute_reply": "2024-08-29T08:13:11.767884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2024, 8, 25, 23, 59, 59, tzinfo=datetime.timezone.utc)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = hapi(SERVER, DATASET)\n",
    "latest_available = dt.datetime.fromisoformat(meta[\"stopDate\"])\n",
    "latest_available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d3c6fc-81a0-471c-847c-3e7c7a9f409f",
   "metadata": {},
   "source": [
    "## Fetch latest available day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ee356f0-eb73-477a-8dac-bccd917645e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:13:11.798456Z",
     "iopub.status.busy": "2024-08-29T08:13:11.794996Z",
     "iopub.status.idle": "2024-08-29T08:13:23.259884Z",
     "shell.execute_reply": "2024-08-29T08:13:23.253546Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/swarmpal-runner/lib/python3.11/site-packages/hapiclient/hapitime.py:287: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  Time = pandas.to_datetime(Time, infer_datetime_format=True).tz_convert(tzinfo).to_pydatetime()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataTree('paldata', parent=None)\n",
      "└── DataTree('SW_OPER_MAGA_LR_1B')\n",
      "        Dimensions:      (Timestamp: 86400, NEC: 3)\n",
      "        Coordinates:\n",
      "          * Timestamp    (Timestamp) datetime64[ns] 2024-08-24T23:59:59 ... 2024-08-2...\n",
      "        Dimensions without coordinates: NEC\n",
      "        Data variables:\n",
      "            Latitude     (Timestamp) float64 -3.933 -3.87 -3.806 ... 65.26 65.2 65.13\n",
      "            Longitude    (Timestamp) float64 -35.61 -35.61 -35.61 ... 137.5 137.5 137.5\n",
      "            Radius       (Timestamp) float64 6.847e+06 6.847e+06 ... 6.834e+06 6.834e+06\n",
      "            B_NEC        (Timestamp, NEC) float64 1.865e+04 -6.146e+03 ... 4.655e+04\n",
      "            B_NEC_Model  (Timestamp, NEC) float64 1.865e+04 -6.144e+03 ... 4.655e+04\n",
      "        Attributes:\n",
      "            PAL_meta:  {\"analysis_window\": [\"2024-08-24T23:59:59\", \"2024-08-25T23:59:...\n"
     ]
    }
   ],
   "source": [
    "start = latest_available - dt.timedelta(days=1)\n",
    "stop = latest_available\n",
    "data = create_paldata(\n",
    "    PalDataItem.from_hapi(\n",
    "        server=SERVER,\n",
    "        dataset=DATASET,\n",
    "        parameters=\"Latitude,Longitude,Radius,B_NEC,B_NEC_Model\",\n",
    "        start=start.strftime(\"%Y-%m-%dT%H:%M:%S\"),\n",
    "        stop=stop.strftime(\"%Y-%m-%dT%H:%M:%S\"),\n",
    "    )\n",
    ")\n",
    "# Temporary fix to data labelling\n",
    "data = data.rename_dims(\n",
    "    {\"B_NEC_dim1\": \"NEC\", \"B_NEC_Model_dim1\": \"NEC\"}\n",
    ")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005a67e2-b336-4ba7-809e-1d52726875d4",
   "metadata": {},
   "source": [
    "## Apply FAC algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0c61a18-217d-4095-857b-778a7f2c2203",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:13:23.284084Z",
     "iopub.status.busy": "2024-08-29T08:13:23.281990Z",
     "iopub.status.idle": "2024-08-29T08:13:24.671890Z",
     "shell.execute_reply": "2024-08-29T08:13:24.665681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataTree('paldata', parent=None)\n",
      "│   Dimensions:  ()\n",
      "│   Data variables:\n",
      "│       *empty*\n",
      "│   Attributes:\n",
      "│       PAL_meta:  {\"FAC_singlesat\": {\"dataset\": \"SW_OPER_MAGA_LR_1B\", \"model_var...\n",
      "└── DataTree('SW_OPER_MAGA_LR_1B')\n",
      "    │   Dimensions:      (Timestamp: 86400, NEC: 3)\n",
      "    │   Coordinates:\n",
      "    │     * Timestamp    (Timestamp) datetime64[ns] 2024-08-24T23:59:59 ... 2024-08-2...\n",
      "    │   Dimensions without coordinates: NEC\n",
      "    │   Data variables:\n",
      "    │       Latitude     (Timestamp) float64 -3.933 -3.87 -3.806 ... 65.26 65.2 65.13\n",
      "    │       Longitude    (Timestamp) float64 -35.61 -35.61 -35.61 ... 137.5 137.5 137.5\n",
      "    │       Radius       (Timestamp) float64 6.847e+06 6.847e+06 ... 6.834e+06 6.834e+06\n",
      "    │       B_NEC        (Timestamp, NEC) float64 1.865e+04 -6.146e+03 ... 4.655e+04\n",
      "    │       B_NEC_Model  (Timestamp, NEC) float64 1.865e+04 -6.144e+03 ... 4.655e+04\n",
      "    │   Attributes:\n",
      "    │       PAL_meta:  {\"analysis_window\": [\"2024-08-24T23:59:59\", \"2024-08-25T23:59:...\n",
      "    └── DataTree('output')\n",
      "            Dimensions:    (Timestamp: 86399)\n",
      "            Coordinates:\n",
      "              * Timestamp  (Timestamp) datetime64[ns] 2024-08-24T23:59:59.500000 ... 2024...\n",
      "            Data variables:\n",
      "                FAC        (Timestamp) float64 -0.09739 0.001669 ... 0.03192 0.002472\n"
     ]
    }
   ],
   "source": [
    "process = fac.processes.FAC_singlesat(\n",
    "    config={\n",
    "        \"dataset\": DATASET,\n",
    "        \"model_varname\": \"B_NEC_Model\",\n",
    "        \"measurement_varname\": \"B_NEC\",\n",
    "    },\n",
    ")\n",
    "data.swarmpal.apply(process)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "490f093c-3129-497a-bc6a-d50893b9c43e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-29T08:13:24.696581Z",
     "iopub.status.busy": "2024-08-29T08:13:24.692658Z",
     "iopub.status.idle": "2024-08-29T08:13:53.380694Z",
     "shell.execute_reply": "2024-08-29T08:13:53.373918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 2 Axes>,\n",
       " array([<Axes: ylabel='d/dt($\\\\Delta$B)'>,\n",
       "        <Axes: xlabel='Timestamp', ylabel='FAC [uA/m2]'>], dtype=object))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/swarmpal-runner/lib/python3.11/site-packages/IPython/core/events.py:93: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/swarmpal-runner/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.swarmpal_fac.quicklook()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:swarmpal-runner]",
   "language": "python",
   "name": "conda-env-swarmpal-runner-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}