{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate model-level fields to a 3D-field, save it to netCDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:07.711491Z",
     "iopub.status.busy": "2025-01-13T11:32:07.711171Z",
     "iopub.status.idle": "2025-01-13T11:32:08.235406Z",
     "shell.execute_reply": "2025-01-13T11:32:08.234928Z"
    }
   },
   "outputs": [],
   "source": [
    "# for figures in notebook\n",
    "%matplotlib inline\n",
    "# import & initialize epygram\n",
    "import epygram\n",
    "epygram.init_env()\n",
    "# path to file\n",
    "import os\n",
    "INPUTS_DIR = os.path.join('..', 'inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:08.237598Z",
     "iopub.status.busy": "2025-01-13T11:32:08.237370Z",
     "iopub.status.idle": "2025-01-13T11:32:08.378807Z",
     "shell.execute_reply": "2025-01-13T11:32:08.378300Z"
    }
   },
   "outputs": [],
   "source": [
    "# open resource as a meta-CL-resource, CL = Combine Levels\n",
    "r = epygram.resources.meta_resource(os.path.join(INPUTS_DIR, 'ICMSHAROM+0022'), 'r', 'CL')\n",
    "# in this case, field need to be defined with GRIB2 nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:08.380946Z",
     "iopub.status.busy": "2025-01-13T11:32:08.380728Z",
     "iopub.status.idle": "2025-01-13T11:32:10.833468Z",
     "shell.execute_reply": "2025-01-13T11:32:10.832957Z"
    }
   },
   "outputs": [],
   "source": [
    "f = r.readfield({'discipline':0, 'parameterCategory':0, 'parameterNumber':0, 'typeOfFirstFixedSurface':119})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:10.835620Z",
     "iopub.status.busy": "2025-01-13T11:32:10.835405Z",
     "iopub.status.idle": "2025-01-13T11:32:10.953519Z",
     "shell.execute_reply": "2025-01-13T11:32:10.953044Z"
    }
   },
   "outputs": [],
   "source": [
    "print(f.spectral)\n",
    "f.sp2gp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:10.955444Z",
     "iopub.status.busy": "2025-01-13T11:32:10.955239Z",
     "iopub.status.idle": "2025-01-13T11:32:10.957882Z",
     "shell.execute_reply": "2025-01-13T11:32:10.957493Z"
    }
   },
   "outputs": [],
   "source": [
    "print(f.getdata(d4=True).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:32:10.959519Z",
     "iopub.status.busy": "2025-01-13T11:32:10.959327Z",
     "iopub.status.idle": "2025-01-13T11:32:11.982326Z",
     "shell.execute_reply": "2025-01-13T11:32:11.981804Z"
    }
   },
   "outputs": [],
   "source": [
    "f.dump_to_nc('/tmp/temp.nc', variablename='temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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