{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run average by dim operation\n",
    "\n",
    "**Rooki** calls climate data operations on the **rook** processing service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['ROOK_URL'] = 'http://rook.dkrz.de/wps'\n",
    "\n",
    "from rooki import rooki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**parameters of average operation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rooki.average?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## average by dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = rooki.average(\n",
    "    collection='c3s-cmip6.ScenarioMIP.INM.INM-CM5-0.ssp245.r1i1p1f1.day.tas.gr1.v20190619',\n",
    "    dims=['latitude', 'longitude'],\n",
    ")\n",
    "resp.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = resp.datasets()[0]\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(resp.provenance_image())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## workflow: subset + average \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rooki import operators as ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas = ops.Input(\n",
    "    'tas', ['c3s-cmip6.ScenarioMIP.INM.INM-CM5-0.ssp245.r1i1p1f1.day.tas.gr1.v20190619']\n",
    ")\n",
    "\n",
    "sub = ops.Subset(tas, time=\"2016/2016\")\n",
    "\n",
    "#wf = ops.Average(sub, dims=[\"latitude\", \"longitude\"])\n",
    "wf = ops.Average(sub, dims=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = wf.orchestrate()\n",
    "resp.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = resp.datasets()[0]\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(resp.provenance_image())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
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}