{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# netCDF specificities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:23.836034Z",
     "iopub.status.busy": "2025-01-13T11:35:23.835400Z",
     "iopub.status.idle": "2025-01-13T11:35:24.364183Z",
     "shell.execute_reply": "2025-01-13T11:35:24.363791Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# for figures in notebook\n",
    "\n",
    "# import & initialize epygram\n",
    "import epygram\n",
    "epygram.init_env()\n",
    "\n",
    "import os\n",
    "INPUTS_DIR = os.path.join('..', 'inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.366204Z",
     "iopub.status.busy": "2025-01-13T11:35:24.366026Z",
     "iopub.status.idle": "2025-01-13T11:35:24.379475Z",
     "shell.execute_reply": "2025-01-13T11:35:24.379036Z"
    }
   },
   "outputs": [],
   "source": [
    "r = epygram.open(os.path.join(INPUTS_DIR, 'aladin.197901.nc'), 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.381043Z",
     "iopub.status.busy": "2025-01-13T11:35:24.380859Z",
     "iopub.status.idle": "2025-01-13T11:35:24.383952Z",
     "shell.execute_reply": "2025-01-13T11:35:24.383602Z"
    }
   },
   "outputs": [],
   "source": [
    "# Equivalent of `ncdump -h`\n",
    "r.what()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify non-trivial variables for grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genuinely, this line:\n",
    "\n",
    "`r.readfield('psl')`\n",
    "\n",
    "would result in the following error:\n",
    "\n",
    "`# [2021/01/19-18:59:55][epygram.formats.netCDF][find_grid_in_variables:0459][ERROR]: unable to find X_grid in variables. Please specify with readfield() argument adhoc_behaviour={'X_grid':'name_of_the_variable'} for instance or my_resource.behave(X_grid='name_of_the_variable')`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.385653Z",
     "iopub.status.busy": "2025-01-13T11:35:24.385475Z",
     "iopub.status.idle": "2025-01-13T11:35:24.394927Z",
     "shell.execute_reply": "2025-01-13T11:35:24.394551Z"
    }
   },
   "outputs": [],
   "source": [
    "# Hence we need to specify grids\n",
    "# just for this read\n",
    "f = r.readfield('psl', adhoc_behaviour={'X_grid':'nav_lon', 'Y_grid':'nav_lat'})\n",
    "# or for the whole runtime\n",
    "r.behave(X_grid='nav_lon', Y_grid='nav_lat')\n",
    "f = r.readfield('psl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.396581Z",
     "iopub.status.busy": "2025-01-13T11:35:24.396337Z",
     "iopub.status.idle": "2025-01-13T11:35:24.398910Z",
     "shell.execute_reply": "2025-01-13T11:35:24.398558Z"
    }
   },
   "outputs": [],
   "source": [
    "print(f.validity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read only a single index of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.400541Z",
     "iopub.status.busy": "2025-01-13T11:35:24.400313Z",
     "iopub.status.idle": "2025-01-13T11:35:24.405174Z",
     "shell.execute_reply": "2025-01-13T11:35:24.404880Z"
    }
   },
   "outputs": [],
   "source": [
    "f = r.readfield('psl', only={'time_counter':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:35:24.406720Z",
     "iopub.status.busy": "2025-01-13T11:35:24.406477Z",
     "iopub.status.idle": "2025-01-13T11:35:24.408963Z",
     "shell.execute_reply": "2025-01-13T11:35:24.408600Z"
    }
   },
   "outputs": [],
   "source": [
    "print(f.validity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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 "nbformat": 4,
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