5 - Sentinel-1 SLC Batch processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font face=\"Calibri\" size=\"2\"> <i>Open SAR Toolkit - Tutorial 5, version 1.0, September 2019. Andreas Vollrath, ESA/ESRIN phi-lab</i>\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](auxiliary/header_image.PNG)\n",
    "\n",
    "--------\n",
    "\n",
    "# OST Tutorial V\n",
    "## How to do a batch processing of Sentinel-1 SLC data based on bursts\n",
    "\n",
    "--------\n",
    "\n",
    "**Short description**\n",
    "\n",
    "This notebook introduces you to the general workflow of OST for the batch processing of SLC data using the *Sentunel1_SCLBatch* class. This is a subclass of the *Sentinel1* class, and thus inherits all the functionality of the *Generic* and the *Sentinel1* classes. In addition, calibrated backscatter will be jointly produced together with the interferometric coherence and the dual-pol H/A/alpha decomposition.\n",
    "\n",
    "--------\n",
    "\n",
    "**Requirements**\n",
    "\n",
    "- a PC/Mac with at least 16GB of RAM\n",
    "- about 50GB of free disk space\n",
    "- a Copernicus Open Data Hub user account, valid for at least 7 days (https://scihub.copernicus.eu)\n",
    "--------\n",
    "\n",
    "**NOTE:** all cells that have an * after its number can be executed without changing any code. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1* - Import of the class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "from pprint import pprint\n",
    "\n",
    "from ost import Sentinel1Batch as Sen1Batch\n",
    "from ost.helpers import vector, raster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Define your project parameters and intialize the class\n",
    "\n",
    "\n",
    "As a subclass of the *Sentinel-1* class, the *Sentinel1_SLCBatch* class inherit all attributes nd methods from the *Generic* and *Sentinel1* class. \n",
    "In addition it provides attributes and methods for the ARD definition, the inventory of bursts, as well as the processing of the bursts to ARD, and the subsequent creation of timeseries, timescans and large-scale mosaics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Project directory already exists.No data has been deleted at this point butmake sure you really want to use this folder.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " Your Copernicus Scihub Username: suprd\n",
      " Your Copernicus Scihub Password: ·········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'relativeorbitnumber' to 'relativeor'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'producttype' to 'producttyp'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'relativeorbit' to 'relativeor'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'product_type' to 'product_ty'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'download_path' to 'download_p'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'relativeorbit' to 'relativeor'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'product_type' to 'product_ty'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'download_path' to 'download_p'\n"
     ]
    }
   ],
   "source": [
    "# define a project directory\n",
    "home = os.getenv('HOME')\n",
    "# create a processing directory\n",
    "project_dir = join(home, 'Tutorial_5')\n",
    "\n",
    "# Vienna lat lon\n",
    "lat = 48.20849\n",
    "lon= 16.37208\n",
    "\n",
    "# define aoi with a shapefile\n",
    "aoi = vector.latlon_to_wkt(lat, lon, buffer_meter=10000, envelope=True)\n",
    "\n",
    "#define the start and end date\n",
    "start = '2020-02-01'\n",
    "end = '2020-02-15'\n",
    "\n",
    "# initialize the class to s1_grd instance\n",
    "s1_slc = Sen1Batch(\n",
    "    project_dir=project_dir,\n",
    "    aoi = aoi,\n",
    "    start = start,\n",
    "    end = end,\n",
    "    product_type='SLC',\n",
    "    polarisation='VV,VH',\n",
    "    ard_type='OST',\n",
    "    processing_dir=join(project_dir, 'processing'),\n",
    "    download_dir=join(project_dir, 'download'),\n",
    "    metadata_concurency=6\n",
    "    \n",
    ")\n",
    "\n",
    "# trigger the search\n",
    "s1_slc.search()\n",
    "\n",
    "# optional: once you did the search the first time, you can load \n",
    "# the full inventory uncommenting the follwoing 2 lines\n",
    "# and commenting the search command\n",
    "#s1_grd.inventory_file = join(s1_grd.inventory_dir, 'full.inventory.shp')\n",
    "#s1_grd.read_inventory()\n",
    "\n",
    "# do the refinement\n",
    "s1_slc.refine()\n",
    "\n",
    "# TODO debug missing products download\n",
    "# s1_slc.download()\n",
    "\n",
    "s1_slc.set_ard_parameters('OST')\n",
    "s1_slc.ard_parameters['resolution'] = 50\n",
    "\n",
    "# s1_slc._to_ard(    \n",
    "#     overwrite=False, \n",
    "#     subset=s1_grd.aoi\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3* - Data inventory\n",
    "\n",
    "We take the known functionality from the *Sentinel1* class to search for available acquisitions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1368x1368 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---------------------------------------------------\n",
    "# for plotting purposes we use this iPython magic\n",
    "%matplotlib inline\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (19, 19)\n",
    "#---------------------------------------------------\n",
    "\n",
    "# we plot the full Inventory on a map\n",
    "s1_slc.plot_inventory(transperancy=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4* - Select a mosaic key\n",
    "\n",
    "We select one of our mosaic key, seen in the summarising information of the previous refine command, and check the coverage by plotting the footprints on a map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (13, 13)\n",
    "\n",
    "key = 'DESCENDING_VVVH'\n",
    "s1_slc.refined_inventory_dict[key]\n",
    "s1_slc.plot_inventory(s1_slc.refined_inventory_dict[key], 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (5*) - Download of scenes\n",
    "\n",
    "The download routine, already known from the *Sentinel1* class, is used to download the scenes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ost.project.Sentinel1Batch at 0x7fd0c7101c50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1_slc.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - Creating a burst inventory\n",
    "\n",
    "Sentinel-1 does not come with predefined footprints. This is a problem when working on larger areas and coherence should be processed, which results from two images of the same orbit taken at different times. However, there is a smaller part of the imagery referred to as bursts, which is uniquelly defined on its position by the AnxTime attribute in the Sentinel-1 annotation files. By creating a database, that holds a burst id, consisting of orbit, subswath and AnxTime, and the repsective scene and its burst number, we can subsequently process burst by burst. \n",
    "\n",
    "The advantages are:\n",
    "- less memory needs\n",
    "- can be more easily scaled for parallel processing on clusters\n",
    "- processing is reduced by considering only the bursts actually overlapping with the AOI\n",
    "- by using SLC data, polarimetric decompositions as well as interferometric coherence can be generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'index_right' to 'index_righ'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ost.project.Sentinel1Batch at 0x7fd0c7101c50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key = 'DESCENDING_VVVH'\n",
    "s1_slc.create_burst_inventory(key=key, refine=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (13, 13)\n",
    "s1_slc.plot_inventory(s1_slc.burst_inventory, transperancy=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our burst inventory holds 11 bursts to process.\n",
      "------------------------------------------\n",
      "   AnxTime BurstNr      Date   Direction  \\\n",
      "12   21707       4  20200203  DESCENDING   \n",
      "13   21735       5  20200203  DESCENDING   \n",
      "14   21762       6  20200203  DESCENDING   \n",
      "37   21707       2  20200209  DESCENDING   \n",
      "38   21735       3  20200209  DESCENDING   \n",
      "39   21762       4  20200209  DESCENDING   \n",
      "46   21725       2  20200209  DESCENDING   \n",
      "47   21752       3  20200209  DESCENDING   \n",
      "75   21707       4  20200215  DESCENDING   \n",
      "76   21735       5  20200215  DESCENDING   \n",
      "77   21762       6  20200215  DESCENDING   \n",
      "\n",
      "                                              SceneID SwathID Track  \\\n",
      "12  S1B_IW_SLC__1SDV_20200203T050110_20200203T0501...     IW3   124   \n",
      "13  S1B_IW_SLC__1SDV_20200203T050110_20200203T0501...     IW3   124   \n",
      "14  S1B_IW_SLC__1SDV_20200203T050110_20200203T0501...     IW3   124   \n",
      "37  S1A_IW_SLC__1SDV_20200209T050158_20200209T0502...     IW3   124   \n",
      "38  S1A_IW_SLC__1SDV_20200209T050158_20200209T0502...     IW3   124   \n",
      "39  S1A_IW_SLC__1SDV_20200209T050158_20200209T0502...     IW3   124   \n",
      "46  S1A_IW_SLC__1SDV_20200209T050158_20200209T0502...     IW2   124   \n",
      "47  S1A_IW_SLC__1SDV_20200209T050158_20200209T0502...     IW2   124   \n",
      "75  S1B_IW_SLC__1SDV_20200215T050110_20200215T0501...     IW3   124   \n",
      "76  S1B_IW_SLC__1SDV_20200215T050110_20200215T0501...     IW3   124   \n",
      "77  S1B_IW_SLC__1SDV_20200215T050110_20200215T0501...     IW3   124   \n",
      "\n",
      "                                             geometry             bid  \n",
      "12  POLYGON ((16.68100 48.43400, 16.63900 48.26800...  D124_IW3_21707  \n",
      "13  POLYGON ((16.63900 48.26800, 16.59800 48.10300...  D124_IW3_21735  \n",
      "14  POLYGON ((16.59800 48.10300, 16.55600 47.93800...  D124_IW3_21762  \n",
      "37  POLYGON ((16.67800 48.43300, 16.63700 48.26800...  D124_IW3_21707  \n",
      "38  POLYGON ((16.63700 48.26800, 16.59600 48.10300...  D124_IW3_21735  \n",
      "39  POLYGON ((16.59600 48.10300, 16.55300 47.93800...  D124_IW3_21762  \n",
      "46  POLYGON ((17.83200 48.18800, 17.78600 48.02300...  D124_IW2_21725  \n",
      "47  POLYGON ((17.78600 48.02300, 17.74100 47.85700...  D124_IW2_21752  \n",
      "75  POLYGON ((16.68100 48.43300, 16.64000 48.26800...  D124_IW3_21707  \n",
      "76  POLYGON ((16.64000 48.26800, 16.59800 48.10300...  D124_IW3_21735  \n",
      "77  POLYGON ((16.59800 48.10300, 16.55600 47.93700...  D124_IW3_21762  \n"
     ]
    }
   ],
   "source": [
    "print('Our burst inventory holds {} bursts to process.'.format(len(s1_slc.burst_inventory)))\n",
    "print('------------------------------------------')\n",
    "pprint(s1_slc.burst_inventory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - Analysis Ready-Data format for various SLC products and the multi-temporal data products\n",
    "\n",
    "ARD formats for SLC data, apart from the backscatter are not well defined. Nonetheless, OST allows to produce the dual-pol H/A/Alpha decomposition as well as the interferometric coherence of consecutive acquisitions in an automated manner. \n",
    "\n",
    "In addition, the time-series of the resulting products can be produced, first burst-by-burst, and then followed by mosaicking. The same is true for the generation of timescan, where 5 different statistics can be computed.\n",
    "\n",
    "For the creation of time-series 2 additional parameters are added:\n",
    "- *to_db_mt*: output the time-series data in dB scale\n",
    "- *mt_speckle-filter*: apply the multi-temporal speckle filter (standard Snap parameters, i.e. Refined Lee)\n",
    "\n",
    "For the creation of the timescans 2 additional parameters are added:\n",
    "- *metrics*: The stastical measures applied over each timeseries. By default all measures, consisting of average (avg), maximum (max), minimum (min), standard deviation (std) and Coeffcient of Variance (cov)\n",
    "- *outlier_removal*: In some cases images are affected by artifacts with strong signals. For the calculation of the timescan layers, extreme values can be excluded setting this parameter to True.\n",
    "\n",
    "\n",
    "For this example exercise we customize some of the ARD parameters to create aminimal processing example and save on prcessing time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      " Original OST ARD parameters\n",
      "{'apply_ls_mask': False,\n",
      " 'border_noise': True,\n",
      " 'create_ls_mask': False,\n",
      " 'dem': 'SRTM 1Sec HGT',\n",
      " 'dtype_output': 'float32',\n",
      " 'ls_mask_create': False,\n",
      " 'metrics': ['avg', 'max', 'min', 'std', 'cov', 'count'],\n",
      " 'mt_speckle_filter': True,\n",
      " 'polarisation': 'VV,VH,HH,HV',\n",
      " 'product_type': 'GTCgamma',\n",
      " 'remove_outliers': True,\n",
      " 'resampling': 'CUBIC_CONVOLUTION',\n",
      " 'resolution': 50,\n",
      " 'speckle_filter': False,\n",
      " 'to_db': False,\n",
      " 'to_db_mt': True,\n",
      " 'type': 'OST'}\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('---------------------------------')\n",
    "print(' Original OST ARD parameters')\n",
    "pprint(s1_slc.ard_parameters)\n",
    "print('---------------------------------')\n",
    "\n",
    "# s1_batch.ard_parameters['single ARD']['resolution'] = 30\n",
    "# s1_batch.ard_parameters['single ARD']['product type'] = 'GTCgamma'\n",
    "# s1_batch.ard_parameters['single ARD']['create ls mask'] = False\n",
    "# s1_batch.ard_parameters['single ARD']['apply ls mask'] = False\n",
    "# s1_batch.ard_parameters['single ARD']['to_db'] = False\n",
    "# s1_batch.ard_parameters['single ARD']['remove speckle'] = False\n",
    "# s1_batch.ard_parameters['single ARD']['remove pol speckle'] = False\n",
    "# s1_batch.ard_parameters['time-series ARD']['remove mt speckle'] = False\n",
    "# s1_batch.ard_parameters['time-series ARD']['to db'] = True\n",
    "\n",
    "# print('---------------------------------')\n",
    "# print(' Customized ARD parameters')\n",
    "# pprint(s1_slc.ard_parameters)\n",
    "# print('---------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8* - Launch processing\n",
    "\n",
    "The *burst_to_ard* method allows to execute the full workflow consiting of:\n",
    "1 Burst to ARD: each burst undergoes the ARD generation routine.\n",
    "2 ARD to Timeseries: a timeseries is created for all overlapping bursts\n",
    "3 Timeseries to Timescan: the multi-temporal metrics are calculated for all burst timeseries\n",
    "4 Mossaicking: All bursts of the timeseries as well as the timescans are mosaicked to a single file\n",
    "\n",
    "**Note** The overwrite parameter allows to start from scratch, meaning that all previously processed data is deleted. Otherwise the routine will restart from where it stopped. At the moment this aplies only for the Burst to ARD routine, which is the most processing intense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:ost.helpers.bursts:INFO: Getting burst info from S1B_IW_SLC__1SDV_20200203T050110_20200203T050137_020100_0260B2_638C.\n",
      "2020-02-19 15:12:53,618 DEBUG ost.helpers.bursts INFO: Getting burst info from S1B_IW_SLC__1SDV_20200203T050110_20200203T050137_020100_0260B2_638C.\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      "DEBUG:ost.helpers.bursts:INFO: Getting burst info from S1A_IW_SLC__1SDV_20200209T050158_20200209T050225_031171_039576_DD91.\n",
      "2020-02-19 15:12:53,923 DEBUG ost.helpers.bursts INFO: Getting burst info from S1A_IW_SLC__1SDV_20200209T050158_20200209T050225_031171_039576_DD91.\n",
      "DEBUG:ost.helpers.bursts:INFO: Getting burst info from S1B_IW_SLC__1SDV_20200215T050110_20200215T050137_020275_026654_4B5A.\n",
      "2020-02-19 15:12:54,226 DEBUG ost.helpers.bursts INFO: Getting burst info from S1B_IW_SLC__1SDV_20200215T050110_20200215T050137_020275_026654_4B5A.\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'index_right' to 'index_righ'\n",
      "2020-02-19 15:12:54,903 WARNING fiona._env Normalized/laundered field name: 'index_right' to 'index_righ'\n",
      "DEBUG:ost.project:INFO: Starting OST ARD processing for SLC\n",
      "2020-02-19 15:12:54,933 DEBUG ost.project INFO: Starting OST ARD processing for SLC\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 4 at date 20200203.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 5 at date 20200203.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 6 at date 20200203.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 2 at date 20200209.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 4 at date 20200209.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 2 at date 20200209.\n",
      "2020-02-19 15:12:54,983 DEBUG ost.s1.batch_burst INFO: Entering burst 4 at date 20200203.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 3 at date 20200209.\n",
      "2020-02-19 15:12:54,984 DEBUG ost.s1.batch_burst INFO: Entering burst 6 at date 20200203.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 3 at date 20200209.\n",
      "2020-02-19 15:12:54,984 DEBUG ost.s1.batch_burst INFO: Entering burst 5 at date 20200203.\n",
      "2020-02-19 15:12:54,984 DEBUG ost.s1.batch_burst INFO: Entering burst 2 at date 20200209.\n",
      "2020-02-19 15:12:54,984 DEBUG ost.s1.batch_burst INFO: Entering burst 4 at date 20200209.\n",
      "2020-02-19 15:12:54,984 DEBUG ost.s1.batch_burst INFO: Entering burst 2 at date 20200209.\n",
      "2020-02-19 15:12:54,985 DEBUG ost.s1.batch_burst INFO: Entering burst 3 at date 20200209.\n",
      "2020-02-19 15:12:54,985 DEBUG ost.s1.batch_burst INFO: Entering burst 3 at date 20200209.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21762 from 20200203 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21707 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21735 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW2_21725 from 20200209 already processed\n",
      "2020-02-19 15:12:54,994 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21707 from 20200209 already processed\n",
      "2020-02-19 15:12:54,994 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21762 from 20200203 already processed\n",
      "2020-02-19 15:12:54,994 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21735 from 20200209 already processed\n",
      "2020-02-19 15:12:54,995 DEBUG ost.s1.batch_burst INFO: Burst D124_IW2_21752 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW2_21752 from 20200209 already processed\n",
      "2020-02-19 15:12:54,994 DEBUG ost.s1.batch_burst INFO: Burst D124_IW2_21725 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21762 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21735 from 20200203 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21707 from 20200203 already processed\n",
      "2020-02-19 15:12:54,996 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21762 from 20200209 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 6 at date 20200215.\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 5 at date 20200215.\n",
      "2020-02-19 15:12:54,996 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21735 from 20200203 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Entering burst 4 at date 20200215.\n",
      "2020-02-19 15:12:54,997 DEBUG ost.s1.batch_burst INFO: Entering burst 5 at date 20200215.\n",
      "2020-02-19 15:12:54,997 DEBUG ost.s1.batch_burst INFO: Entering burst 4 at date 20200215.\n",
      "2020-02-19 15:12:54,996 DEBUG ost.s1.batch_burst INFO: Entering burst 6 at date 20200215.\n",
      "2020-02-19 15:12:54,996 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21707 from 20200203 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21707 from 20200215 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21762 from 20200215 already processed\n",
      "DEBUG:ost.s1.batch_burst:INFO: Burst D124_IW3_21735 from 20200215 already processed\n",
      "2020-02-19 15:12:55,003 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21762 from 20200215 already processed\n",
      "2020-02-19 15:12:55,003 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21707 from 20200215 already processed\n",
      "2020-02-19 15:12:55,003 DEBUG ost.s1.batch_burst INFO: Burst D124_IW3_21735 from 20200215 already processed\n",
      "DEBUG:ost.project:INFO:OST ARD processing for SLC DONE!\n",
      "2020-02-19 15:12:55,013 DEBUG ost.project INFO:OST ARD processing for SLC DONE!\n"
     ]
    }
   ],
   "source": [
    "# Setup logging so we can see what could go wrong\n",
    "import logging\n",
    "from ost.log import set_log_level\n",
    "set_log_level(logging.DEBUG)\n",
    "\n",
    "key = 'DESCENDING_VVVH'\n",
    "s1_slc.create_burst_inventory(key=key, refine=True)\n",
    "s1_slc.to_ard(subset=s1_slc.aoi, overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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