PythonNotebooks/indexFileNavigation/index_file_navigation_boundingbox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> ABSTRACT </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All CMEMS in situ data products can be found and downloaded after [registration](http://marine.copernicus.eu/services-portfolio/register-now/) via [CMEMS catalogue](http://marine.copernicus.eu/services-portfolio/access-to-products/).\n",
    "\n",
    "Such channel is advisable just for sporadic netCDF donwloading because when operational, interaction with the web user interface is not practical. In this context though, the use of scripts for ftp file transference is is a much more advisable approach.\n",
    "\n",
    "As long as every line of such files contains information about the netCDFs contained within the different directories [see at tips why](https://github.com/CopernicusMarineInsitu/INSTACTraining/blob/master/tips/README.md), it is posible for users to loop over its lines to download only those that matches a number of specifications such as spatial coverage, time coverage, provider, data_mode, parameters or file_name related (region, data type, TS or PF, platform code, or/and platform category, timestamp)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>PREREQUISITES</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [credentias](http://marine.copernicus.eu/services-portfolio/register-now/)\n",
    "- aimed [in situ product name](http://cmems-resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013.pdf)\n",
    "- aimed [hosting distribution unit](https://github.com/CopernicusMarineInsitu/INSTACTraining/blob/master/tips/README.md)\n",
    "- aimed [index file](https://github.com/CopernicusMarineInsitu/INSTACTraining/blob/master/tips/README.md)\n",
    "\n",
    "i.e:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user =  #type CMEMS user name within colons\n",
    "password = #type CMEMS password within colons\n",
    "product_name = 'INSITU_BAL_NRT_OBSERVATIONS_013_032' #type aimed CMEMS in situ product \n",
    "distribution_unit = 'cmems.smhi.se' #type aimed hosting institution\n",
    "index_file = 'index_latest.txt' #type aimed index file name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>DOWNLOAD</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Index file download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ftplib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'221 Goodbye.'"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftp=ftplib.FTP(distribution_unit,user,password) \n",
    "ftp.cwd(\"Core\")\n",
    "ftp.cwd(product_name) \n",
    "remote_filename= index_file\n",
    "local_filename = remote_filename\n",
    "local_file = open(local_filename, 'wb')\n",
    "ftp.retrbinary('RETR ' + remote_filename, local_file.write)\n",
    "local_file.close()\n",
    "ftp.quit()\n",
    "#ready when 221 Goodbye.!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>QUICK VIEW</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from random import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "index = np.genfromtxt(index_file, skip_header=6, unpack=False, delimiter=',', dtype=None,\n",
    "           names=['catalog_id', 'file_name', 'geospatial_lat_min', 'geospatial_lat_max',\n",
    "                     'geospatial_lon_min', 'geospatial_lon_max',\n",
    "                     'time_coverage_start', 'time_coverage_end', \n",
    "                     'provider', 'date_update', 'data_mode', 'parameters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = randint(0,len(index)) #ramdom line of the index file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "                    <a href=ftp://cmems.smhi.se/Core/INSITU_BAL_NRT_OBSERVATIONS_013_032/latest/20170506/BO_LATEST_TS_MO_Stenungsund_20170506.nc>ftp://cmems.smhi.se/Core/INSITU_BAL_NRT_OBSERVATIONS_013_032/latest/20170506/BO_LATEST_TS_MO_Stenungsund_20170506.nc</a>\n",
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       "                    2017-05-06T00:00:00Z\n",
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       "                    2017-05-06T23:00:00Z\n",
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       "                    2017-05-12T13:01:38Z\n",
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       "                    parameters\n",
       "                \n",
       "                \n",
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       "                    SLEV DEPH\n",
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       "        "
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     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = [index[dataset]['catalog_id'], '<a href='+index[dataset]['file_name']+'>'+index[dataset]['file_name']+'</a>', index[dataset]['geospatial_lat_min'], index[dataset]['geospatial_lat_max'],\n",
    "                 index[dataset]['geospatial_lon_min'], index[dataset]['geospatial_lon_max'], index[dataset]['time_coverage_start'],\n",
    "                 index[dataset]['time_coverage_end'], index[dataset]['provider'], index[dataset]['date_update'], index[dataset]['data_mode'],\n",
    "                 index[dataset]['parameters']]\n",
    "headers = ['catalog_id', 'file_name', 'geospatial_lat_min', 'geospatial_lat_max',\n",
    "                     'geospatial_lon_min', 'geospatial_lon_max',\n",
    "                     'time_coverage_start', 'time_coverage_end', \n",
    "                     'provider', 'date_update', 'data_mode', 'parameters']\n",
    "df = pd.DataFrame(values, index=headers, columns=[dataset])\n",
    "df.style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>FILTERING CRITERIA</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import box, multipoint\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding the above glimpse, it is posible to filter by 12 criteria. As example we will setup next a filter to only download those files that contains data within a defined boundingbox."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Aimed boundingbox "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "targeted_geospatial_lat_min = 55.0   # enter min latitude of your bounding box\n",
    "targeted_geospatial_lat_max =  70.0   # enter max latitude of your bounding box\n",
    "targeted_geospatial_lon_min = 12.0  # enter min longitude of your bounding box\n",
    "targeted_geospatial_lon_max =  26.00  # enter max longitude of your bounding box   \n",
    "\n",
    "targeted_bounding_box = box(targeted_geospatial_lon_min, targeted_geospatial_lat_min, targeted_geospatial_lon_max, targeted_geospatial_lat_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. netCDF filtering/selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total: 2336\n"
     ]
    }
   ],
   "source": [
    "selected_netCDFs = [];\n",
    "for netCDF in index:    \n",
    "        file_name = netCDF['file_name']\n",
    "        \n",
    "        geospatial_lat_min = float(netCDF['geospatial_lat_min'])\n",
    "        geospatial_lat_max = float(netCDF['geospatial_lat_max'])\n",
    "        geospatial_lon_min = float(netCDF['geospatial_lon_min'])\n",
    "        geospatial_lon_max = float(netCDF['geospatial_lon_max'])\n",
    "        \n",
    "        bounding_box = shapely.geometry.box(geospatial_lon_min, geospatial_lat_min, geospatial_lon_max, geospatial_lat_max)\n",
    "        bounding_box_centroid = bounding_box.centroid \n",
    "        \n",
    "        if (targeted_bounding_box.contains(bounding_box_centroid)):\n",
    "            selected_netCDFs.append(file_name)\n",
    "            \n",
    "print(\"total: \" +str(len(selected_netCDFs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> SELECTION DOWNLOAD </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for nc in selected_netCDFs:\n",
    "\n",
    "    last_idx_slash = nc.rfind('/')\n",
    "    ncdf_file_name = nc[last_idx_slash+1:]\n",
    "    folders = nc.split('/')[3:len(nc.split('/'))-1]\n",
    "    host = nc.split('/')[2] #or distribution unit\n",
    "    \n",
    "    ftp=ftplib.FTP(host,user,password) \n",
    "    for folder in folders:\n",
    "        ftp.cwd(folder)\n",
    "                                   \n",
    "    local_file = open(ncdf_file_name, 'wb')\n",
    "    ftp.retrbinary('RETR '+ncdf_file_name, local_file.write)\n",
    "    local_file.close()                             \n",
    "\n",
    "    ftp.quit()"
   ]
  }
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