{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95957d8e-e713-4661-88bc-956bfa1f0b6a",
   "metadata": {},
   "source": [
    "# Download daily ERA5 data from 1940 onwards (with using papermill) \n",
    "(script only downloads the not already downloaded data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c4b3ad0-54a1-4545-ad5f-e80f750571e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import papermill as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf087dc8-1fd2-4107-948a-db5f7cf2db67",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "year1 = 1940\n",
    "year2 = 2026 # 2026"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d433db37-aab4-416e-9afd-dfcadfc1ce19",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [f'{y:04d}' for y in range(year1, year2)]\n",
    "#months = [f'{m:02d}' for m in range(4, 13)] # 1\n",
    "months = [f'{m:02d}' for m in range(1, 13)] # 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b559087e-a4a0-4161-baef-1d40237f2fd5",
   "metadata": {},
   "source": [
    "**Check if data is already correctly available  ... otherwise download it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c40bcb4-d67d-4e92-9518-ef4ffb4e214f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "base_dir = '/home/www/oggm/climate/era5/daily/v1.2/unflattened/' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c50c63a8-6b3b-44ed-8ecc-c15708ed0f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_files = []\n",
    "for year in years:\n",
    "    for month in months:\n",
    "        filename_t2m = f\"ERA5_Daily_t2m_{year}_{month}.nc\"\n",
    "        filepath_t2m = os.path.join(base_dir, filename_t2m)\n",
    "        filename_tp = f\"ERA5_Daily_tp_{year}_{month}.nc\"\n",
    "        filepath_tp = os.path.join(base_dir, filename_tp)\n",
    "        \n",
    "        if not os.path.exists(filepath_tp) or not os.path.exists(filepath_t2m):\n",
    "            missing_files.append(f'{year}_{month}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc1aa34a-7b1d-4054-b820-2b9d20a055eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "272f7eb5-b997-4a97-b75f-ffe8cf0af91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/lschuster/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Executing: 100%|██████████████████████████████| 12/12 [02:21<00:00, 11.81s/cell]\n",
      "Executing: 100%|██████████████████████████████| 12/12 [00:55<00:00,  4.65s/cell]\n"
     ]
    }
   ],
   "source": [
    "for f in missing_files:\n",
    "    year, month = f.split('_')\n",
    "    pm.execute_notebook('download_papermill.ipynb',\n",
    "                        f'papermill_download_notebooks/'+f'download_{year}_{month}.ipynb',\n",
    "                        parameters=dict(year=year, month=month)\n",
    "                       )\n",
    "    time.sleep(5) # maybe that helps to have less throttling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a127def3-7f2d-401a-91e1-3196bf10528c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "pygments_lexer": "ipython3",
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