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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": "3.7.6-final"
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  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.6 64-bit (conda)",
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 },
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 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Download additional (large) test data\n",
    "\n",
    "Download additional large test data from public sources to '../data'.\n",
    "\n",
    "Data is used for performance testing and some of the notebooks.\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make data dir (already in .gitignore)\n",
    "import os\n",
    "data_dir = \"../data\"\n",
    "if not os.path.exists(data_dir):\n",
    "    os.makedirs(data_dir)"
   ]
  },
  {
   "source": [
    "File download from url to filename"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "import requests\n",
    "\n",
    "def file_download(url,filename):\n",
    "    print(f\"Download {url} \\nto {filename}\")\n",
    "    r = requests.get(url, stream=True)\n",
    "    size = int(r.headers.get('content-length', 0))\n",
    "    if os.path.exists(filename):\n",
    "        if size == os.path.getsize(filename):\n",
    "            print(\"Local file already exists, skip download.\")\n",
    "            return\n",
    "    pbar = tqdm(total=size, unit='iB', unit_scale=True)  \n",
    "    with open(filename, 'wb') as file:\n",
    "        for chunk in r.iter_content(1024*1024*2): # To-Do: best choice for chunk size?\n",
    "            pbar.update(len(chunk))\n",
    "            file.write(chunk)\n",
    "    pbar.close()"
   ]
  },
  {
   "source": [
    "## Altimetry test data from DHI blob storage"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Download https://automodelstorage.blob.core.windows.net/globalaltimetry/altimetry_3a_2018_filter1.dfs0 \n",
      "to ../data/altimetry_3a_2018_filter1.dfs0\n",
      "Local file already exists, skip download.\n",
      "Download https://automodelstorage.blob.core.windows.net/globalaltimetry/SW_gwm_3a_extracted_2018.dfs0 \n",
      "to ../data/SW_gwm_3a_extracted_2018.dfs0\n",
      "Local file already exists, skip download.\n",
      "Download https://automodelstorage.blob.core.windows.net/globalaltimetry/SW_gwm_domain_Hm0_201801.dfsu \n",
      "to ../data/SW_gwm_domain_Hm0_201801.dfsu\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "HBox(children=(FloatProgress(value=0.0, max=1580035484.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "413bb08e251640068b6e4a3f6a523d83"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filenames = [\"altimetry_3a_2018_filter1.dfs0\",\n",
    "             \"SW_gwm_3a_extracted_2018.dfs0\",\n",
    "             \"SW_gwm_domain_Hm0_201801.dfsu\"]\n",
    "for fn in filenames:\n",
    "    url = f\"https://automodelstorage.blob.core.windows.net/globalaltimetry/{fn}\"\n",
    "    fn_target = f\"{data_dir}/{fn}\"\n",
    "    file_download(url,fn_target)"
   ]
  },
  {
   "source": [
    "## NDBC buoy data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## CFSR ..."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}