def test_get_product_odata_scihub_down():
api = SentinelAPI("mock_user", "mock_password")
request_url = "https://scihub.copernicus.eu/apihub/odata/v1/Products('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')?$format=json"
with requests_mock.mock() as rqst:
rqst.get(
request_url,
text="Mock SciHub is Down", status_code=503
)
with pytest.raises(SentinelAPIError) as excinfo:
api.get_product_odata('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')
assert excinfo.value.msg == "Mock SciHub is Down"
rqst.get(
request_url,
text='{"error":{"code":null,"message":{"lang":"en","value":'
'"No Products found with key \'8df46c9e-a20c-43db-a19a-4240c2ed3b8b\' "}}}',
status_code=500
)
with pytest.raises(SentinelAPIError) as excinfo:
api.get_product_odata('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')
assert excinfo.value.msg == "No Products found with key \'8df46c9e-a20c-43db-a19a-4240c2ed3b8b\' "
rqst.get(
request_url,
text="Mock SciHub is Down", status_code=200
)
with pytest.raises(SentinelAPIError) as excinfo:
api.get_product_odata('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')
assert excinfo.value.msg == "Mock SciHub is Down"
# Test with a real "server under maintenance" response
rqst.get(
request_url,
text=textwrap.dedent("""\
<!doctype html>
<title>The Sentinels Scientific Data Hub</title>
<link href='https://fonts.googleapis.com/css?family=Open+Sans' rel='stylesheet' type='text/css'>
<style>
body { text-align: center; padding: 125px; background: #fff;}
h1 { font-size: 50px; }
body { font: 20px 'Open Sans',Helvetica, sans-serif; color: #333; }
article { display: block; text-align: left; width: 820px; margin: 0 auto; }
a { color: #0062a4; text-decoration: none; font-size: 26px }
a:hover { color: #1b99da; text-decoration: none; }
</style>
<article>
<img alt="" src="/datahub.png" style="float: left;margin: 20px;">
<h1>The Sentinels Scientific Data Hub will be back soon!</h1>
<div style="margin-left: 145px;">
<p>
Sorry for the inconvenience,<br/>
we're performing some maintenance at the moment.<br/>
</p>
<!--<p><a href="https://scihub.copernicus.eu/news/News00098">https://scihub.copernicus.eu/news/News00098</a></p>-->
<p>
We'll be back online shortly!
</p>
</div>
</article>
"""),
status_code=502)
with pytest.raises(SentinelAPIError) as excinfo:
api.get_product_odata('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')
assert "The Sentinels Scientific Data Hub will be back soon!" in excinfo.value.msg
def test_invalid_query():
api = SentinelAPI(**_api_auth)
with pytest.raises(SentinelAPIError) as excinfo:
api.query(raw="xxx:yyy")
def test_get_product_odata_full():
api = SentinelAPI(**_api_auth)
expected_full = {
'8df46c9e-a20c-43db-a19a-4240c2ed3b8b': {
'id': '8df46c9e-a20c-43db-a19a-4240c2ed3b8b',
'title': 'S1A_EW_GRDM_1SDV_20151121T100356_20151121T100429_008701_00C622_A0EC',
'size': 143549851,
'md5': 'D5E4DF5C38C6E97BF7E7BD540AB21C05',
'date': datetime(2015, 11, 21, 10, 3, 56, 675000),
'footprint': 'POLYGON((-63.852531 -5.880887,-67.495872 -5.075419,-67.066071 -3.084356,-63.430576 -3.880541,-63.852531 -5.880887))',
'url': "https://scihub.copernicus.eu/apihub/odata/v1/Products('8df46c9e-a20c-43db-a19a-4240c2ed3b8b')/$value",
'Acquisition Type': 'NOMINAL',
'Carrier rocket': 'Soyuz',
'Cycle number': 64,
'Date': datetime(2015, 11, 21, 10, 3, 56, 675000),
'Filename': 'S1A_EW_GRDM_1SDV_20151121T100356_20151121T100429_008701_00C622_A0EC.SAFE',
'Footprint': '<gml:Polygon srsName="http://www.opengis.net/gml/srs/epsg.xml#4326" xmlns:gml="http://www.opengis.net/gml"> <gml:outerBoundaryIs> <gml:LinearRing> <gml:coordinates>-5.880887,-63.852531 -5.075419,-67.495872 -3.084356,-67.066071 -3.880541,-63.430576 -5.880887,-63.852531</gml:coordinates> </gml:LinearRing> </gml:outerBoundaryIs></gml:Polygon>',
'Format': 'SAFE',
'Identifier': 'S1A_EW_GRDM_1SDV_20151121T100356_20151121T100429_008701_00C622_A0EC',
'Ingestion Date': datetime(2015, 11, 21, 13, 22, 4, 992000),
'Instrument': 'SAR-C',
'Instrument abbreviation': 'SAR-C SAR',
'Instrument description': '<a target="_blank" href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel-1</a>',
'Instrument description text': 'The SAR Antenna Subsystem (SAS) is developed and build by AstriumGmbH. It is a large foldable planar phased array antenna, which isformed by a centre panel and two antenna side wings. In deployedconfiguration the antenna has an overall aperture of 12.3 x 0.84 m.The antenna provides a fast electronic scanning capability inazimuth and elevation and is based on low loss and highly stablewaveguide radiators build in carbon fibre technology, which arealready successfully used by the TerraSAR-X radar imaging mission.The SAR Electronic Subsystem (SES) is developed and build byAstrium Ltd. It provides all radar control, IF/ RF signalgeneration and receive data handling functions for the SARInstrument. The fully redundant SES is based on a channelisedarchitecture with one transmit and two receive chains, providing amodular approach to the generation and reception of wide-bandsignals and the handling of multi-polarisation modes. One keyfeature is the implementation of the Flexible Dynamic BlockAdaptive Quantisation (FD-BAQ) data compression concept, whichallows an efficient use of on-board storage resources and minimisesdownlink times.',
'Instrument mode': 'EW',
'Instrument name': 'Synthetic Aperture Radar (C-band)',
'Instrument swath': 'EW',
'JTS footprint': 'POLYGON ((-63.852531 -5.880887,-67.495872 -5.075419,-67.066071 -3.084356,-63.430576 -3.880541,-63.852531 -5.880887))',
'Launch date': 'April 3rd, 2014',
'Mission datatake id': 50722,
'Mission type': 'Earth observation',
'Mode': 'EW',
'NSSDC identifier': '0000-000A',
'Operator': 'European Space Agency',
'Orbit number (start)': 8701,
'Orbit number (stop)': 8701,
'Pass direction': 'DESCENDING',
'Phase identifier': 1,
'Polarisation': 'VV VH',
'Product class': 'S',
'Product class description': 'SAR Standard L1 Product',
'Product composition': 'Slice',
'Product level': 'L1',
'Product type': 'GRD',
'Relative orbit (start)': 54, 'Relative orbit (stop)': 54, 'Resolution': 'Medium',
'Satellite': 'Sentinel-1',
'Satellite description': '<a target="_blank" href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel-1</a>',
'Satellite name': 'Sentinel-1',
'Satellite number': 'A',
'Sensing start': datetime(2015, 11, 21, 10, 3, 56, 675000),
'Sensing stop': datetime(2015, 11, 21, 10, 4, 29, 714000),
'Size': '223.88 MB',
'Slice number': 1,
'Start relative orbit number': 54,
'Status': 'ARCHIVED',
'Stop relative orbit number': 54,
'Timeliness Category': 'Fast-24h'
},
'44517f66-9845-4792-a988-b5ae6e81fd3e': {
'id': '44517f66-9845-4792-a988-b5ae6e81fd3e',
'title': 'S2A_OPER_PRD_MSIL1C_PDMC_20151228T112523_R110_V20151227T142229_20151227T142229',
'size': 5854429622,
'md5': '48C5648C2644CE07207B3C943DEDEB44',
'date': datetime(2015, 12, 27, 14, 22, 29),
'footprint': 'POLYGON((-58.80274769505742 -4.565257232533263,-58.80535376268811 -5.513960396525286,-57.90315169909761 -5.515947033626909,-57.903151791669515 -5.516014389089381,-57.85874693129081 -5.516044812342758,-57.814323596961835 -5.516142631941845,-57.81432351345917 -5.516075248310466,-57.00018056571297 -5.516633044843839,-57.000180565731384 -5.516700066819259,-56.95603179187787 -5.51666329264377,-56.91188395837315 -5.516693539799448,-56.91188396736038 -5.51662651925904,-56.097209386295305 -5.515947927683427,-56.09720929423562 -5.516014937246069,-56.053056977999596 -5.5159111504805916,-56.00892491028779 -5.515874390220655,-56.00892501130261 -5.515807411549814,-55.10621586418906 -5.513685455771881,-55.108821882251775 -4.6092845892233,-54.20840287327946 -4.606372862374043,-54.21169990975238 -3.658594390979672,-54.214267703869346 -2.710949551849636,-55.15704255065496 -2.7127451087194463,-56.0563616875051 -2.71378646425769,-56.9561852630143 -2.7141556791285275,-57.8999998009875 -2.713837142510183,-57.90079161941062 -3.6180222056692726,-58.800616247288836 -3.616721351843382,-58.80274769505742 -4.565257232533263))',
'url': "https://scihub.copernicus.eu/apihub/odata/v1/Products('44517f66-9845-4792-a988-b5ae6e81fd3e')/$value",
'Cloud cover percentage': 18.153846153846153,
'Date': datetime(2015, 12, 27, 14, 22, 29),
'Degraded MSI data percentage': 0, 'Degraded ancillary data percentage': 0,
'Filename': 'S2A_OPER_PRD_MSIL1C_PDMC_20151228T112523_R110_V20151227T142229_20151227T142229.SAFE',
'Footprint': '<gml:Polygon srsName="http://www.opengis.net/gml/srs/epsg.xml#4326" xmlns:gml="http://www.opengis.net/gml"> <gml:outerBoundaryIs> <gml:LinearRing> <gml:coordinates>-4.565257232533263,-58.80274769505742 -5.513960396525286,-58.80535376268811 -5.515947033626909,-57.90315169909761 -5.516014389089381,-57.903151791669515 -5.516044812342758,-57.85874693129081 -5.516142631941845,-57.814323596961835 -5.516075248310466,-57.81432351345917 -5.516633044843839,-57.00018056571297 -5.516700066819259,-57.000180565731384 -5.51666329264377,-56.95603179187787 -5.516693539799448,-56.91188395837315 -5.51662651925904,-56.91188396736038 -5.515947927683427,-56.097209386295305 -5.516014937246069,-56.09720929423562 -5.5159111504805916,-56.053056977999596 -5.515874390220655,-56.00892491028779 -5.515807411549814,-56.00892501130261 -5.513685455771881,-55.10621586418906 -4.6092845892233,-55.108821882251775 -4.606372862374043,-54.20840287327946 -3.658594390979672,-54.21169990975238 -2.710949551849636,-54.214267703869346 -2.7127451087194463,-55.15704255065496 -2.71378646425769,-56.0563616875051 -2.7141556791285275,-56.9561852630143 -2.713837142510183,-57.8999998009875 -3.6180222056692726,-57.90079161941062 -3.616721351843382,-58.800616247288836 -4.565257232533263,-58.80274769505742</gml:coordinates> </gml:LinearRing> </gml:outerBoundaryIs></gml:Polygon>',
'Format': 'SAFE',
'Format correctness': 'PASSED',
'General quality': 'PASSED',
'Generation time': datetime(2015, 12, 28, 11, 25, 23, 357),
'Geometric quality': 'PASSED',
'Identifier': 'S2A_OPER_PRD_MSIL1C_PDMC_20151228T112523_R110_V20151227T142229_20151227T142229',
'Ingestion Date': datetime(2015, 12, 28, 10, 57, 13, 725000),
'Instrument': 'MSI',
'Instrument abbreviation': 'MSI',
'Instrument mode': 'INS-NOBS',
'Instrument name': 'Multi-Spectral Instrument',
'JTS footprint': 'POLYGON ((-58.80274769505742 -4.565257232533263,-58.80535376268811 -5.513960396525286,-57.90315169909761 -5.515947033626909,-57.903151791669515 -5.516014389089381,-57.85874693129081 -5.516044812342758,-57.814323596961835 -5.516142631941845,-57.81432351345917 -5.516075248310466,-57.00018056571297 -5.516633044843839,-57.000180565731384 -5.516700066819259,-56.95603179187787 -5.51666329264377,-56.91188395837315 -5.516693539799448,-56.91188396736038 -5.51662651925904,-56.097209386295305 -5.515947927683427,-56.09720929423562 -5.516014937246069,-56.053056977999596 -5.5159111504805916,-56.00892491028779 -5.515874390220655,-56.00892501130261 -5.515807411549814,-55.10621586418906 -5.513685455771881,-55.108821882251775 -4.6092845892233,-54.20840287327946 -4.606372862374043,-54.21169990975238 -3.658594390979672,-54.214267703869346 -2.710949551849636,-55.15704255065496 -2.7127451087194463,-56.0563616875051 -2.71378646425769,-56.9561852630143 -2.7141556791285275,-57.8999998009875 -2.713837142510183,-57.90079161941062 -3.6180222056692726,-58.800616247288836 -3.616721351843382,-58.80274769505742 -4.565257232533263))',
'Mission datatake id': 'GS2A_20151227T140932_002681_N02.01',
'NSSDC identifier': '2015-000A',
'Orbit number (start)': 2681,
'Pass direction': 'DESCENDING',
'Platform serial identifier': 'Sentinel-2A',
'Processing baseline': 2.01,
'Processing level': 'Level-1C',
'Product type': 'S2MSI1C',
'Radiometric quality': 'PASSED',
'Relative orbit (start)': 110,
'Satellite': 'Sentinel-2',
'Satellite name': 'Sentinel-2',
'Satellite number': 'A',
'Sensing start': datetime(2015, 12, 27, 14, 22, 29),
'Sensing stop': datetime(2015, 12, 27, 14, 22, 29),
'Sensor quality': 'PASSED',
'Size': '5.50 GB'
}
}
for id, expected in expected_full.items():
ret = api.get_product_odata(id, full=True)
assert set(ret) == set(expected)
for k in ret:
assert ret[k] == expected[k]
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
from datetime import date
from lib.python import es_logging as log
import json
logger = log.my_logger(__name__)
import re
# connect to the API
api = SentinelAPI('vijaycharan.v', 'creationvv1!',
'https://scihub.copernicus.eu/dhus')
geojson_roi = '/srv/www/eStation2/apps/tools/ex_geojson.geojson'
datetime_start = '20180119'
datetime_end = date(2018, 01, 20)
platformname = 'Sentinel-1'
# download single scene by known product id
#api.download(<product_id>)
# download all results from the search
#api.download_all(products)
# GeoJSON FeatureCollection containing footprints and metadata of the scenes
#api.to_geojson(products)
# GeoPandas GeoDataFrame with the metadata of the scenes and the footprints as geometries
# api.to_geodataframe(products)
#
# # Get basic information about the product: its title, file size, MD5 sum, date, footprint and
# # its download url
# api.get_product_odata(<product_id>)
#
def satdownload(product_id, geojson, download_path='./downloads/',
remove_trash=False, api=None, download_only=False):
"""
Downloads, extracts and crops products.
Args:
product_id: str
Example: "e3fea737-a83b-4fec-8a5a-68ed8d647c71"
geojson: str
Path to geojson file.
download_path: str, optional
location to download products.
remove_trash: bool, default Fasle
remove unnecessary files after downloading.
download_only: bool, default False
Download only (Do not extract).
api: SentinelAPI api object
"""
print('Satdownload for ' + product_id)
logging.debug('satdownload: ' + product_id)
# create downloads folder
if os.path.isdir(download_path) is False:
os.mkdir(download_path)
if api is None:
api = SentinelAPI(USERNAME, PASSWORD,
'https://scihub.copernicus.eu/dhus')
# query product information
product_info = api.get_product_odata(product_id, full=True)
sentinel = product_info['Satellite']
# directory for images only
target_directory = os.path.join(download_path, product_info['title'])
if os.path.isdir(target_directory):
print('Product is already processed, skipping product...')
return
# download
if os.path.isfile(os.path.join(
download_path, product_info['title'] + '.zip')) is True:
print(product_info['title'] + '.zip' + ' exist.')
else:
satdownload_zip(product_info['id'], download_path, api=api)
# skip extraction part
if download_only is True:
return
# extract zip file
zipfile_path = os.path.join(download_path, product_info['title'] + '.zip')
zip_ref = zipfile.ZipFile(zipfile_path, 'r')
zip_ref.extractall(download_path)
zip_ref.close()
if os.path.isdir(
os.path.join(download_path, product_info['Filename'])) is False:
raise Exception('Directory not found after unzipping.')
# clearing target directory
if os.path.isdir(target_directory) is True:
shutil.rmtree(target_directory)
os.mkdir(target_directory)
selection = transform_coordinates(coordinates_from_geojson(geojson))
if sentinel == 'Sentinel-2':
# product can contain many tails (located in ./GRANULE/)
granule = os.path.join(download_path, product_info['Filename'],
'GRANULE')
for i, tail_name in enumerate(os.listdir(granule)):
print('\ttail name: ' + tail_name)
tail_folder_name = 'tail.{}'.format(i)
os.mkdir(os.path.join(target_directory, tail_folder_name))
# image directories are different for different product types
image_dir = os.path.join(granule, tail_name, 'IMG_DATA')
if product_info['Product type'] == 'S2MSI2Ap':
image_dir = os.path.join(image_dir, 'R10m')
# move bands into target directory
for image in os.listdir(image_dir):
image_prime = image
if product_info['Product type'] == 'S2MSI2Ap':
image_prime = image_prime[4:-8] + '.jp2'
os.rename(os.path.join(image_dir, image),
os.path.join(target_directory,
tail_folder_name, image_prime))
elif sentinel == 'Sentinel-1':
# shift selection for sentinel-1 products
dx, dy = 130.54544882194287, 20.162166196209284
selection[:, 0] = selection[:, 0] + dx
selection[:, 1] = selection[:, 1] - dy
# create tail folder
tail_folder_name = 'tail.{}'.format(0)
os.mkdir(os.path.join(target_directory, tail_folder_name))
# image directories are different for different product types
image_dir = os.path.join(download_path, product_info['Filename'],
'measurement')
# move bands into target directory
for image in os.listdir(image_dir):
image_path = os.path.join(image_dir, image)
gdal.Warp(image_path, gdal.Open(image_path), dstSRS='EPSG:32638')
os.rename(image_path, os.path.join(target_directory,
tail_folder_name, image))
else:
print('Unknown satellite')
# save info file
product_info_series = pandas.Series(product_info)
with open(os.path.join(target_directory, 'info.txt'), 'w') as f:
f.write(product_info_series.to_string())
with open(os.path.join(target_directory, 'info.json'), 'w') as f:
product_info_series.to_json(f)
# remove unnecessary files
if remove_trash is True:
os.remove(zipfile_path)
shutil.rmtree(os.path.join(download_path, product_info['Filename']))
# cropping images
print(target_directory)
for tail_name in os.listdir(target_directory):
if os.path.isdir(os.path.join(target_directory, tail_name)) is False:
continue
print('\tprocessing ' + tail_name + ' ...')
process_tail(os.path.join(target_directory, tail_name), selection,
remove_trash=remove_trash)
print('\n\n')
end = datetime(2016, 12, 31)
it = end + timedelta(days=1)
os.chdir('D:\\AA-remotesensing-artificial-structures\\sensing_data\\raw\\timeseries\\lisboa-setubal\\s2')
while it.date() != start.date():
it -= timedelta(days=1)
completedir = glob.glob('*' + it.date().strftime("%Y%m%d") + '*')
completes = [x for x in completedir if x not in glob.glob('*' + it.date().strftime("%Y%m%d") + '*.incomplete')]
if(len(completes) > 0):
print("Dia: " + str(it.date()) + " já obtido previamente. Skipping.")
continue
successful = False
while not successful:
for tries in range(0, 5, 1):
try:
print("Dia: " + str(it.date()))
api = SentinelAPI('amneves', 'Amnandre12')
footprint = geojson_to_wkt(read_geojson('geo.geojson'))
products = api.query(footprint,
date=(it.date().strftime("%Y%m%d"), (it + timedelta(days=1)).date().strftime("%Y%m%d")),
platformname='Sentinel-2',
producttype='S2MSI1C',
area_relation='Contains',
cloudcoverpercentage=(0, 30))
dataframe = api.to_dataframe(products)
count = dataframe.shape[0]
print(str(count) + " produto(s) neste dia.")
#api.download_all(products)
#download(api, products)
if count == 1:
nome = dataframe.get_values()[0][0]
p = multiprocessing.Process(target=foo, name="Foo", args=(api,products))
file_name = None
product_type = None
platform_name = None
orbit_direction = None
polarisation_mode = None
cloud_cover_percentage = None
sensor_operational_mode = None
# post-search modes
printProducts = True
writeToDB = False
downloadProducts = False
getGeoJSON = False
# connect to the API
api = SentinelAPI(username, password, url)
# read geojson
geojson = os.path.join(geojson_dir, '%s.geojson' % areacode)
footprint = geojson_to_wkt(read_geojson(geojson))
raw_query = ''
if file_name is not None:
raw_query = raw_query + 'filename:%s AND ' % file_name
if product_type is not None:
raw_query = raw_query + 'producttype:%s AND ' % product_type
if platform_name is not None:
raw_query = raw_query + 'platformname:%s AND ' % platform_name
if orbit_direction is not None:
raw_query = raw_query + 'orbitdirection:%s AND ' % orbit_direction
if polarisation_mode is not None:
def __attrs_post_init__(self):
self.month_range = relativedelta(months=1)
self.api = SentinelAPI(self.user.name, self.user.password,
'https://scihub.copernicus.eu/dhus')
# -*- coding: utf-8 -*-
"""
Sentinel-5 P data pull
@author: Kalkberg
"""
from sentinelsat import SentinelAPI # install via pip
import os
import glob
from shapely import wkt
# Set up API
api = SentinelAPI(user='******', password='******', api_url='https://s5phub.copernicus.eu/dhus')
# Define area of interest in WKT format
# Go to https://arthur-e.github.io/Wicket/sandbox-gmaps3.html and draw one out
AOI = ''
# Date
startdate = '2020401'
enddate = '20200430'
frequency = 1 # every nth day in date range will be downloaded
# Download list of Sentinel S5-P NO2 products in region of interest
products = api.query(AOI,
date=(startdate,enddate),
platformname='Sentinel-5',
producttype='L2__NO2___', # useful data types 'L2__SO2___' and 'L2__NO2___'
processingmode='Offline', # 'Near real time' or 'Offline'
)
(59.51138530046753, 24.825137916849023),
(59.459087606762346, 24.907535377786523),
(59.4147455486766, 24.929508034036523),
(59.39832075950073, 24.844363991067773),
(59.37664183245853, 24.814151588724023),
(59.35249898189222, 24.75304013852871),
(59.32798867805195, 24.573825660989648)]
# Copernicus Hub likes coordinates in lng,lat format
return Polygon([(y, x) for x, y in tln_points])
username = "******"
password = "******"
hub = SentinelAPI(username, password, "https://scihub.copernicus.eu/dhus")
data_products = hub.query(
get_tallinn_polygon(), # which area interests you
date=("20200101", "20200420"),
cloudcoverpercentage=(0, 10), # we don't want clouds
platformname="Sentinel-2",
processinglevel="Level-2A" # more processed, ready to use data
)
data_products = hub.to_geodataframe(data_products)
# we want to avoid downloading overlapping images, so selecting by this keyword
data_products = data_products[data_products["title"].str.contains("T35VLF")]
print(data_products.shape)
select_date2 = select_date - step
print("Step 1: Download Sentinel SAR Product in " + config.name_of_area +
" area with select Dates between " + format_date(select_date2) +
" and " + format_date(select_date))
logger.info("Download Sentinel SAR Product in " + config.name_of_area +
" area with select Dates between " +
format_date(select_date2) + " and " + format_date(select_date))
end_date = format_date(select_date + timedelta(days=1))
start_date = format_date(select_date2)
url = config.url
username = config.username # ask ITC for the username and password
password = config.password
# # Get info product
api = SentinelAPI(username, password) # fill with SMARTSeeds user and password
footprint = geojson_to_wkt(input_geojson)
products = api.query(footprint,
producttype=type_sar,
orbitdirection=orbit,
date="[{0} TO {1}]".format(start_date, end_date))
dirpath = cwd + config.sentineldirpath
if not os.path.exists(dirpath):
os.makedirs(dirpath)
api.download_all(products, directory_path=dirpath, checksum=True)
zipfiles = glob("{}*.zip".format(dirpath))
polygons = []
def test_check_existing(tmpdir):
api = SentinelAPI(**_api_auth)
ids = [
"5618ce1b-923b-4df2-81d9-50b53e5aded9",
"d8340134-878f-4891-ba4f-4df54f1e3ab4",
"1f62a176-c980-41dc-b3a1-c735d660c910"
]
names = ["S1A_WV_OCN__2SSV_20150526T081641_20150526T082418_006090_007E3E_104C",
"S1A_WV_OCN__2SSV_20150526T211029_20150526T211737_006097_007E78_134A",
"S1A_WV_OCN__2SSH_20150603T092625_20150603T093332_006207_008194_521E"]
paths = [tmpdir.join(fn + ".zip") for fn in names]
path_strings = list(map(str, paths))
# Init files used for testing
api.download(ids[0], str(tmpdir))
# File #1: complete and correct
assert paths[0].check(exists=1, file=1)
# File #2: complete but incorrect
with paths[1].open("wb") as f:
size = 130102
f.seek(size - 1)
f.write(b'\0')
# File #3: incomplete
dummy_content = b'aaaaaaaaaaaaaaaaaaaaaaaaa'
with paths[2].open("wb") as f:
f.write(dummy_content)
assert paths[2].check(exists=1, file=1)
# Test
expected = {str(paths[1]), str(paths[2])}
result = api.check_files(ids=ids, directory=str(tmpdir))
assert set(result) == expected
assert result[paths[1]][0]['id'] == ids[1]
assert result[paths[2]][0]['id'] == ids[2]
assert paths[0].check(exists=1, file=1)
assert paths[1].check(exists=1, file=1)
assert paths[2].check(exists=1, file=1)
result = api.check_files(paths=path_strings)
assert set(result) == expected
assert result[paths[1]][0]['id'] == ids[1]
assert result[paths[2]][0]['id'] == ids[2]
assert paths[0].check(exists=1, file=1)
assert paths[1].check(exists=1, file=1)
assert paths[2].check(exists=1, file=1)
result = api.check_files(paths=path_strings, delete=True)
assert set(result) == expected
assert result[paths[1]][0]['id'] == ids[1]
assert result[paths[2]][0]['id'] == ids[2]
assert paths[0].check(exists=1, file=1)
assert not paths[1].check(exists=1, file=1)
assert not paths[2].check(exists=1, file=1)
missing_file = str(tmpdir.join(
"S1A_EW_GRDH_1SDH_20141003T003840_20141003T003920_002658_002F54_4DD1.zip"))
result = api.check_files(paths=[missing_file])
assert set(result) == {missing_file}
assert result[missing_file][0]['id']
with pytest.raises(ValueError):
api.check_files(ids=ids)
with pytest.raises(ValueError):
api.check_files()
tmpdir.remove()
def test_download_invalid_id():
api = SentinelAPI(**_api_auth)
uuid = "1f62a176-c980-41dc-xxxx-c735d660c910"
with pytest.raises(SentinelAPIError) as excinfo:
api.download(uuid)
assert 'Invalid key' in excinfo.value.msg
def test_download(tmpdir):
api = SentinelAPI(**_api_auth)
uuid = "1f62a176-c980-41dc-b3a1-c735d660c910"
filename = "S1A_WV_OCN__2SSH_20150603T092625_20150603T093332_006207_008194_521E"
expected_path = tmpdir.join(filename + ".zip")
tempfile_path = tmpdir.join(filename + ".zip.incomplete")
# Download normally
product_info = api.download(uuid, str(tmpdir), checksum=True)
assert expected_path.samefile(product_info["path"])
assert not tempfile_path.check(exists=1)
assert product_info["title"] == filename
assert product_info["size"] == expected_path.size()
assert product_info["downloaded_bytes"] == expected_path.size()
hash = expected_path.computehash("md5")
modification_time = expected_path.mtime()
expected_product_info = product_info
# File exists, expect nothing to happen
product_info = api.download(uuid, str(tmpdir))
assert not tempfile_path.check(exists=1)
assert expected_path.mtime() == modification_time
expected_product_info["downloaded_bytes"] = 0
assert product_info == expected_product_info
# Create invalid but full-sized tempfile, expect re-download
expected_path.move(tempfile_path)
with tempfile_path.open("wb") as f:
f.seek(expected_product_info["size"] - 1)
f.write(b'\0')
assert tempfile_path.computehash("md5") != hash
product_info = api.download(uuid, str(tmpdir))
assert expected_path.check(exists=1, file=1)
assert expected_path.computehash("md5") == hash
expected_product_info["downloaded_bytes"] = expected_product_info["size"]
assert product_info == expected_product_info
# Create invalid tempfile, without checksum check
# Expect continued download and no exception
dummy_content = b'aaaaaaaaaaaaaaaaaaaaaaaaa'
with tempfile_path.open("wb") as f:
f.write(dummy_content)
expected_path.remove()
product_info = api.download(uuid, str(tmpdir), checksum=False)
assert not tempfile_path.check(exists=1)
assert expected_path.check(exists=1, file=1)
assert expected_path.computehash("md5") != hash
expected_product_info["downloaded_bytes"] = expected_product_info["size"] - len(dummy_content)
assert product_info == expected_product_info
# Create invalid tempfile, with checksum check
# Expect continued download and exception raised
dummy_content = b'aaaaaaaaaaaaaaaaaaaaaaaaa'
with tempfile_path.open("wb") as f:
f.write(dummy_content)
expected_path.remove()
with pytest.raises(InvalidChecksumError):
api.download(uuid, str(tmpdir), checksum=True)
assert not tempfile_path.check(exists=1)
assert not expected_path.check(exists=1, file=1)
tmpdir.remove()
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt user = '******' password = '******' url = 'https://s5phub.copernicus.eu/dhus' search_polygon = './data/landkreis_osnabrueck.geojson' start_date = '20190910' end_date = '20190911' # query api for available products api = SentinelAPI(user, password, url) footprint = geojson_to_wkt(read_geojson(search_polygon)) products = api.query(area=footprint, date=(start_date, end_date)) # convert to panas data frame products_df = api.to_dataframe(products) # inspect data products_df.head() # view top of df products_df.columns # show column names # filter only one product of CO where = products_df.producttypedescription == 'Carbon Monoxide' one_id = products_df.uuid[where][0] # download one product api.download(one_id)
import os
import pandas as pd
import zipfile
import datetime
from sentinelsat import SentinelAPI
#from geopandas import GeoSeries
#import geopandas as gpd
#from shapely.geometry import Polygon
user = '******'
password = '******'
api = SentinelAPI(user, password, 'https://scihub.copernicus.eu/dhus')
class Sentinel_downloader:
def image_down(footprint, Date_Ini, Date_Fin, analysis_area, zipped_folder,
unzipped_folder, lotes_uni, user_analysis, municipio,
departamento):
#formato de fechas
Date_Ini_c = Date_Ini.replace('-', '')
Date_Fin_c = Date_Fin.replace('-', '')
#listar imagenes disponibles
products = api.query(footprint,
date=(Date_Ini_c, Date_Fin_c),
platformname='Sentinel-2',
processinglevel='Level-1C')
#Listado de imagenes satelitales que contienen el area de interes
products_gdf = api.to_geodataframe(products)
#organizado por fecha
products_gdf_sorted = products_gdf.sort_values(
['beginposition'],
ascending=[True
dictionar=intersecting_features[i]
else:
key=list(intersecting_features[i].keys())[0]
if key in list(dictionar.keys()):
dictionar[key]=dictionar[key]+intersecting_features[i][key]
else:
dictionar[key]=intersecting_features[i][key]
for key in dictionar.keys():
if dictionar[key]/np.sum(list(dictionar.values())) >= percentage:
return key
else:
return '0'
# %%
#instantiating SentinelAPI connection for imagery download
api = SentinelAPI('mitja', 'Copernicus12!','https://scihub.copernicus.eu/dhus')
# %%
wkt_point='POINT (23.6383667 40.5790194)'
time_period=(datetime.date(2020, 3, 1),datetime.date(2020,5,1))
cloudcover_range=(0, 1)
# %%
products = api.query(wkt_point, date=time_period, platformname='Sentinel-2', cloudcoverpercentage=cloudcover_range)
# %%
min_coverage = 1
for p in products:
if 'tileid' in list(products[p].keys()):
if products[p]['cloudcoverpercentage']<min_coverage:
min_coverage=products[p]['cloudcoverpercentage']
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)
### Basic parameters, working folders, etc.
working_folder = r"S:\users\...\godthaab_iceberg_detection"
platformname = "sentinel_1"
imagedir = os.path.join(working_folder, "images", platformname, "downloaded")
zipfile_dir = os.path.join(imagedir, "zip")
scriptpath = os.path.dirname(os.path.realpath(__file__))
today = datetime.now().strftime("%Y%m%d")
### List containing orbitnumber and slice of the relevant scenes
orbitnumber_slice = [[54, 1], [127, 5], [25, 5]]
### Create api connection to sentinelhub using sentinelsat
api = SentinelAPI('username', 'password', 'https://scihub.copernicus.eu/dhus')
### Search by polygon, time, and SciHub query keywords
products_df = pd.DataFrame()
for scene in orbitnumber_slice:
print(scene)
products = api.query(date=('NOW-7DAYS', 'NOW'),
platformname='Sentinel-1',
sensoroperationalmode='IW',
producttype='GRD',
polarisationmode='HH HV',
relativeorbitnumber=scene[0],
slicenumber=scene[1])
### Convert to Pandas DataFrame
import datetime
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
import psycopg2
api = SentinelAPI('MRDEVISH', 'Research1')
today_date = datetime.date.today()
footprint = geojson_to_wkt(read_geojson('search_polygon_10.geojson'))
platformname = 'Sentinel-3'
producttype='SL_2_LST___'
products = api.query(footprint,platformname=platformname,producttype=producttype, date = (today_date-1,today_date))
products_pandas = api.to_geodataframe(products)
Names = products_pandas['Product Name']
connection1 = psycopg2.connect(user="******",
password="******",
host="127.0.0.1",
port="5432",
database="db_products")
cursor1 = connection1.cursor()
query0 = """select "Product Name" from "Products"; """
cursor1.execute(query0)
records = cursor1.fetchone()
num=1
undownloaded = []
for i in Names:
if i not in records:
cursor1 = connection1.cursor()
id_ = len(records)+num
query = """INSERT INTO "Products" Values({},{},{},0);""".format(id_,i,today_date)
cursor1.execute(query0)
num=num+1
import pika
This function queries the S2 L2A archive via scihub and returns a list of tiles,
with metadata for a region and time period of interest, with an option to filter
by cloud cover. It uses the sentinelsat package, and a ROI is specified using a
geoJSON file.
Note that to use fmask to isolate cloud cover more accurately than the default
layer, it is necessary to download the corresponding L1C tile. It is recommended
that ultimately the AWS route is taken for downloading the datasets
"""
import datetime
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
username = '******'
pswd = '<password>'
download_api = SentinelAPI(username,
pswd,
api_url='https://scihub.copernicus.eu/dhus/')
download_dir = '/disk/scratch/local.2/dmilodow/Sentinel2/L2A/'
sites = [
'Ardfern1', 'Ardfern2', 'Arisaig', 'Auchteraw', 'GlenLoy', 'Mandally',
'Achdalieu'
]
start_date = datetime.datetime.strptime(
'2019-01-01', '%Y-%m-%d').date() # start date for time period of interest
end_date = datetime.datetime.strptime(
'2020-01-01', '%Y-%m-%d').date() # end date for time period of interest
max_cloud_cover = 67 # percent
s1list = []
import os
import json
import argparse
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
from datetime import date
api = SentinelAPI('artuntun', 'h48n4zqwe!',
'https://scihub.copernicus.eu/dhus')
# pass the input-file and date
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument("-i",
"--input",
help="Input coordinates",
default='./california.json')
parser.add_argument("-ds",
"--date-start",
help="dates to search thru",
default='20190719')
parser.add_argument("-de",
"--date-end",
help="dates to search thru",
default='20190729')
args = parser.parse_args()
# download single scene by known product id
# api.download('c23ccf2b-a133-48b4-8389-1778972893dc')
# search by polygon, time, and SciHub query keywords
footprint = geojson_to_wkt(read_geojson(args.input))
products = api.query(footprint,
def search_sentinels(platform_name, df, aoi, dt=2, user=None, pwd=None,
proj_string='+init=EPSG:3995', product_type=None,
min_cloud_cover=0, max_cloud_cover=100,
swath_type=None, f_out=None):
"""
Search Sentinel-1/2 images overlapping ICESat-2 data within +- dt
Parameters:
-----------
platform_name : str ['Sentinel-1 | Sentinel-2']
name of the platform for which images will be searched
df : panda dataframe
ICESat-2 data
aoi: str, list
area of interest as WKT string or bounding box[lllon, lllat, urlon, urlat]
dt: int, float
difference in hours between CS2 and S2
user : str
username to connect to the Copernicus Scientific Hub
pwd : str
password to connect to the Copernicus Scientific Hub
proj_string: str
projection string to be used with the pyproj module
product_type : str
name of the type of product to be searched (more info at https://scihub.copernicus.eu/userguide/)
swath_type : str
name of the type of swath to be searched (Sentinel-1 only, more info at https://scihub.copernicus.eu/userguide/)
min_cloud_cover: int, float
Minimum cloud coverage in percentage (Sentinel-2 only)
max_cloud_cover: int, float
Maximum cloud coverage in percentage (Sentinel-2 only)
f_out : str
path to file where to write results
Returns: (to be finished!)
--------
"""
#==========================================================================
# Pre-processing
#==========================================================================
### Imports
from sentinelsat import SentinelAPI
# import wkt
import pyproj
import numpy as np
import shapely.geometry as sg
from shapely.wkt import dumps, loads
from astropy.time import Time, TimeDelta
from tqdm import tqdm
### Convert aoi to shapely polygon in projected CRS
# define projection
print("Creating AOI polygon...")
proj = pyproj.Proj(proj_string)
# read aoi polygon
if type(aoi) == str:
aoi_temp = loads(aoi)
elif type(aoi) in (list, tuple):
aoi_temp = sg.box(aoi[0], aoi[1], aoi[2], aoi[3])
aoi = aoi_temp.wkt
else:
print("ERROR: 'aoi' should be provided as a WKT string or bounding box (list)")
sys.exit(1)
### Check input parameters
if product_type == None:
if platform_name == 'Sentinel-1':
product_type = 'GRD'
print("product_type set to: ", product_type)
if platform_name == 'Sentinel-2':
product_type = 'S2MSI1C'
print("product_type set to: ", product_type)
if swath_type == None and platform_name == 'Sentinel-1':
swath_type = 'EW'
print("swath_type set to: ", swath_type)
# project coordinates and convert to shapely polygon
x, y = proj(aoi_temp.exterior.xy[0], aoi_temp.exterior.xy[1])
aoi_poly = sg.Polygon(list(zip(x, y)))
### Convert dt to astropy time object
dtt = TimeDelta(3600 * dt, format='sec')
#==========================================================================
# Processing
#==========================================================================
### Project IS2 data to desired CRS
print("Selecting orbit data inside AOI...")
lon, lat = np.array(df['lons']), np.array(df['lats'])
x, y = proj(lon, lat)
### Extract IS2 orbit number
is2_orbits = np.unique(df['orbit_number'])
print("N. of orbits/points inside AOI: {}/{}".format(len(is2_orbits),
len(df)))
### Extract time period from IS2 data to query the server
t_is2 = Time(df['time'], scale='utc')
t_is2_start = min(t_is2) - dtt
t_is2_stop = max(t_is2) + dtt
### Read metadata
print("Query for metadata...")
api = SentinelAPI(user, pwd,'https://scihub.copernicus.eu/dhus',
timeout=600)
if platform_name == 'Sentinel-1':
md = api.query(area=aoi, date=(t_is2_start.datetime, t_is2_stop.datetime),
platformname='Sentinel-1', area_relation='Intersects',
producttype=product_type, sensoroperationalmode=swath_type)
elif platform_name == 'Sentinel-2':
md = api.query(area=aoi, date=(t_is2_start.datetime, t_is2_stop.datetime),
platformname='Sentinel-2', area_relation='Intersects',
cloudcoverpercentage=(min_cloud_cover, max_cloud_cover),
producttype=product_type)
print("N. of total images: {}".format(len(md)))
if len(md) == 0:
return [], [], [], [], [], []
### Convert Sentinel-2 time strings to astropy time objects
t_sen = {}
print("Converting time to astropy objects...")
for el in md:
t_sen[el] = Time(md[el]['beginposition'], format='datetime',
scale='utc')
### Loop over orbits to find images that satisfy time costraints
TimeDict = {}
t_is2 = []
print("Looping over orbits to find intersections within {}h...".format(dt))
for c, o in tqdm(enumerate(is2_orbits)):
### select CS2 data
d_is2 = df[df['orbit_number'] == o]
### compute CS2 track central time
t_temp = Time(d_is2['time'], scale='utc')
t_start_is2 = min(t_temp)
t_stop_is2 = max(t_temp)
t_is2_o = t_start_is2 + (t_stop_is2 - t_start_is2) / 2
t_is2.append(t_is2_o)
### save dict keys of images within +-dt from CS2
i_t = np.array(
[el for el in md if np.abs((t_sen[el] - t_is2_o).sec) <= dtt.sec])
TimeDict[o] = i_t
# get unique images within +-dt from all orbit data
i_sen_t_int = set(np.concatenate(list(TimeDict.values())).ravel())
print("N. of images within {}h: {}".format(dt, len(i_sen_t_int)))
if len(i_sen_t_int) == 0:
return [], [], [], [], [], []
### Project images corner coordinates and convert to shapely polygons
print("Creating images footprint polygons...")
# loop over them, project corner coords and create polygons
SenPolygonsDict = {}
for i in i_sen_t_int:
# load S2 footprint
aoi_sen = loads(md[i]['footprint'])
# check if multipolygon has more than 1 polygon defined
if len(aoi_sen) > 1:
print("WARNING: footprint for product {}".format(i),
"is defined by more than 1 polygon!!!")
aoi_sen = aoi_sen[0]
# project corner coords
x_sen, y_sen = proj(aoi_sen.exterior.xy[0], aoi_sen.exterior.xy[1])
# add polygon to dictionary
SenPolygonsDict[i] = sg.Polygon(list(zip(x_sen, y_sen)))
### Loop over orbits to find spatial intersections
print("Looping over orbits to find intersections...")
orbit_number = []
product_name = []
browse_url = []
download_url = []
t_diff = []
md_out = {}
for c, o in tqdm(enumerate(is2_orbits)):
### select CS2 data
i = df['orbit_number'] == o
# check if track has at least 2 points
if sum(i) < 2:
continue
d_is2 = df[i]
x_is2 = x[i]
y_is2 = y[i]
### create shapely line from CS track
is2_line = sg.LineString(list(zip(x_is2, y_is2)))
### collect LS8 polygon indices
i_sen = TimeDict[o]
### Loop over S2 polygons
for i_poly in i_sen:
ls_poly = SenPolygonsDict[i_poly]
if is2_line.intersects(ls_poly):
orbit_number.append(o)
t_diff.append((t_sen[i_poly] - t_is2[c]).sec / 3600)
product_name.append(md[i_poly]['filename'])
download_url.append(md[i_poly]['link'])
browse_url.append(md[i_poly]['link_icon'])
md_out[i_poly] = md[i_poly]
print("N. of total intersections: {}".format(len(orbit_number)))
### Print to file
if f_out != None:
print("Printing results to {}...".format(f_out))
with open(f_out, 'w') as fp:
fp.write("orbit_number,t_diff_(h),product_id,dowload_url,browse_url\n")
for i in range(len(orbit_number)):
fp.write("{},{:.2f},{},{},{}\n".format(
orbit_number[i], t_diff[i], product_name[i],
download_url[i], browse_url[i]))
return orbit_number, product_name, browse_url, download_url, t_diff, md_out
os.remove(zipfile_path)
shutil.rmtree(os.path.join(download_path, product_info['Filename']))
# cropping images
print(target_directory)
for tail_name in os.listdir(target_directory):
if os.path.isdir(os.path.join(target_directory, tail_name)) is False:
continue
print('\tprocessing ' + tail_name + ' ...')
process_tail(os.path.join(target_directory, tail_name), selection,
remove_trash=remove_trash)
print('\n\n')
if __name__ == '__main__':
api = SentinelAPI(USERNAME, PASSWORD, 'https://scihub.copernicus.eu/dhus')
args = parse_arguments()
ids = args.ids
download_path = os.path.abspath(os.path.join('./', args.directory))
geojson = args.geojson
if os.path.splitext(ids)[1] == '.csv':
with open(ids, 'r') as csvfile:
csvfile.readline()
for line in csv.reader(csvfile, delimiter=','):
product_id = line[0]
satdownload(product_id, geojson, download_path, api=api,
download_only=args.download)
elif os.path.splitext(ids)[1] == '.txt':
with open(ids, 'r') as txtfile:
# based on https://pypi.python.org/pypi/sentinelsat
# http://sentinelsat.readthedocs.io/en/stable/api.html
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
from shapely.geometry import mapping
from ocgis import GeomCabinetIterator
import json
from flyingpigeon import config
import getpass
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
password = getpass.getpass()
api = SentinelAPI('nilshempelmann', password)
geom = {
"type":
"Polygon",
"coordinates": [[[14.00, 8.00], [16.00, 8.00], [16.00, 10.00],
[14.00, 10.00], [14.00, 8.00]]]
}
# geom = {"type": "Polygon", "coordinates": [[[-69.87682044199994, 12.427394924000097], [-70.05809485599988, 12.537176825000088], [-70.04873613199993, 12.632147528000104], [-69.93639075399994, 12.53172435100005], [-69.87682044199994, 12.427394924000097]]]}
# footprint = geojson_to_wkt(read_geojson('search_polygon.geojson'))
footprint = geojson_to_wkt(geom)
from datetime import datetime as dt
from datetime import timedelta
def download_best(_box: box, download_path: str, user: str,
pw: str) -> tp.List[str]:
_api = SentinelAPI(user, pw, 'https://scihub.copernicus.eu/dhus')
file_path = os.path.join(download_path, "save.csv")
if not os.path.exists(file_path):
products = _api.query(
_box,
date=('NOW-1MONTH', 'NOW'),
platformname='Sentinel-2',
processinglevel='Level-1C',
cloudcoverpercentage=(0, 10),
)
products_df = _api.to_dataframe(products)
tile_ids = []
def _unknown_tile_id(x: str, t_ids: tp.List) -> bool:
ret_val = x in t_ids
if not ret_val:
t_ids.append(x)
return not ret_val
# sort products
products_df_sorted = products_df.sort_values(["cloudcoverpercentage"],
ascending=[True])
# sort out tiles double tiles with higher cloud coverage
first_tiles = [
_unknown_tile_id(x, tile_ids)
for x in list(products_df_sorted['tileid'].array)
]
# first_titles = np.vectorize(_unknown_tile_id(lambda x:x, tile_ids))(products_df_sorted['tileid'].array)
products_df_sorted_unique = products_df_sorted[first_tiles]
if not os.path.exists(download_path):
os.makedirs(download_path)
products_df_sorted_unique.to_csv(file_path)
else:
products_df_sorted_unique = pd.read_pickle(file_path)
products_df_sorted_unique['area'] = [
__estimate_area(loads(e))
for e in list(products_df_sorted_unique['footprint'].array)
]
# sort out areas smaller than three quarter of the full size of 100 km * 100 km
products_df_sorted_unique_larger = products_df_sorted_unique[
products_df_sorted_unique['area'] > 100000 * 100000 / 4 * 3]
_api.download_all(products_df_sorted_unique_larger.uuid, download_path)
# estimate area from footprint
return [
os.path.join(download_path, x) for x in products_df_sorted_unique.title
]
import numpy as np
ROOT_DIR = os.path.join(os.getcwd(), "..")
SENTINELPRODUCTS_DIR = os.path.join(ROOT_DIR, "sentineldata", "products")
GEOJSON_DIR = os.path.join(ROOT_DIR, "datasets")
REGION_DATA_FILE = os.path.join(GEOJSON_DIR, "regions.geojson")
NDVI_DIR = os.path.join(SENTINELPRODUCTS_DIR, "ndvi")
TRAIN_DIR = os.path.join(GEOJSON_DIR, "train")
VAL_DIR = os.path.join(GEOJSON_DIR, "val")
TEST_DIR = os.path.join(GEOJSON_DIR, "test")
url = "https://scihub.copernicus.eu/dhus"
user = sys.argv[1]
pw = sys.argv[2]
api = SentinelAPI(user, pw, url)
def download_sentinel_products_for_ROI(geojson_file):
print("Searching products for %s" % geojson_file)
feature = read_geojson(geojson_file)
footprint = geojson_to_wkt(feature.geometry)
date = feature.properties["date"]
incubation = feature.properties["incubation"]
# TODO adjustable coverage interval
# Config file?
products = api.query(footprint,
def test_count():
api = SentinelAPI(**_api_kwargs)
count = api.count(None, ("20150101", "20151231"))
assert count > 100000
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
import mysql.connector
import time
LOGIN = '******'
PASSWORD = '******'
URL = 'https://scihub.copernicus.eu/dhus'
api = SentinelAPI(LOGIN, PASSWORD, URL)
while True:
mydb = mysql.connector.connect(host="host",
user="******",
password="******",
database="db")
mycursor = mydb.cursor()
mycursor.execute("SELECT * FROM links")
myresult = mycursor.fetchall()
# products = api.query(date=(date(2020, 8, 8), date(2020, 8, 9)), platformname='Sentinel-2')
# products = api.query(date=('NOW-8HOURS', 'NOW'), producttype='SLC')
products = api.query(date=('NOW-8HOURS', 'NOW'), platformname='Sentinel-1')
links = []
for i, v in enumerate(products):
exist = False
for link in myresult:
if link[2] == products[v]['link']:
exist = True
def test_get_product_info_bad_key():
api = SentinelAPI(**_api_auth)
with pytest.raises(SentinelAPIError) as excinfo:
api.get_product_odata('invalid-xyz')
assert excinfo.value.msg == "InvalidKeyException : Invalid key (invalid-xyz) to access Products"
def test_dhus_version(dhus_url, version):
api = SentinelAPI("mock_user", "mock_password", api_url=dhus_url)
request_url = dhus_url + "/api/stub/version"
with requests_mock.mock() as rqst:
rqst.get(request_url, json={"value": version})
assert api.dhus_version == version