# Third Party Modules
import WeoGeoAPI
# Establish connection to WeoGeo Market
weos = WeoGeoAPI.weoSession("market.weogeo.com", "username", "password")
weos.connect()
print weos
# Set initial job parameters. The 'note' variable is optional. Here we only want one layer, '10m High Res'.
# Spatial resolution is 1 (native). Use 2, 3 or 4 to deliver as 2x/3x/4x coarser.
# Reproject our order to NAD83-Geo(EPSG:4269).
newJob = WeoGeoAPI.weoJob(
datasetToken="5dbdb7db-1acf-4f19-b629-04b54f907552",
layers=["10m High Res"],
outputFormat="GeoTIFF",
coordinateSystem="EPSG:4269",
spatialResolution="1",
note="Extract of area around Oregon.",
acceptLicense=True,
)
# Set crop box around Oregon. EPSG must be GEO(EPSG:4326) or Spherical Mercator(EPSG:3857).
newJob.setBoxCropArea("EPSG:4326", 46.17, 42.13, -116.28, -124.33)
# Create job object to be used for ordering.
job_response, job_output = weos.createJob(newJob)
# Pricing and size information
response, price = weos.getPrice(newJob)
print "\n-Order Summary-"
print "Price: " + price["price"]
"""
In this example we will demonstrate how to order a standard job using information we
logged from the getdatasets_by_area.py example. A job on a Standard listing delivers all data
and therefore do not allow for customization. The dataset used in this example is "NASA: Earth at Night - 2000",
which can be found here: http://market.trimbledata.com/#/datasets/5416193f-ba3d-f45e-bd9c-6dbf8193bfad
"""
import WeoGeoAPI
# Establish connection to Trimble Data Marketplace
session = WeoGeoAPI.weoSession('market.trimbledata.com', 'username', 'password')
session.connect()
print session
# Create job object. Since there is no customization involved we only need to supply the token accept the license.
testJob = WeoGeoAPI.weoJob(dataset_token='5416193f-ba3d-f45e-bd9c-6dbf8193bfad',
content_license_acceptance=True)
# Create job object to be used for ordering.
response = session.createJob(testJob)
# Pricing and size information
price = session.getPrice(testJob)
print "\n-Order Summary-"
print "Price: " + price.content['job_price']['price']
print "Size: " + price.content['job_price']['human_estimated_data_size']
# Complete the order
if response.status != 201:
print response.content
exit(0)
else:
import WeoGeoAPI
# Establish connection to WeoGeo Market
weos = WeoGeoAPI.weoSession('market.weogeo.com', 'username', 'password')
weos.connect()
print weos
# Create text file to log tokens
outfile = open('job_tokens.txt', 'w')
# Create lists for job instances and another list for their tokens
jobs = []
jobtokens = []
# Create standard job object and append to jobs list
standardJob = WeoGeoAPI.weoJob( datasetToken = '9dc42e34-cbd0-6952-ad6c-fb39eb23fd0a',
acceptLicense = True)
jobs.append(standardJob)
# Create vector job object and append to jobs list
vectorJob = WeoGeoAPI.weoJob( datasetToken = 'bfc2b36e-3d0d-4a6d-935d-e9ab090aaa3c',
layers = ['Area Hydrography', 'Linear Hydrography'],
outputFormat = 'SHAPE',
note = 'Extract of area around Portland, OR.',
acceptLicense = True )
vectorJob.setClipAreaCoordinateSystem( 'EPSG:4326' )
vectorJob.addClipAreaPoints((-122.55,45.43), (-122.46,45.43), (-122.14,45.32), (-122.14,45.27), (-122.55,45.27))
jobs.append(vectorJob)
# Create raster job object and append to jobs list
rasterJob = WeoGeoAPI.weoJob( datasetToken = '5dbdb7db-1acf-4f19-b629-04b54f907552',
layers = ['10m High Res'],
information we logged from the getdatasets_by_area.py example. The dataset used in this
example is "TIGER/Line 2014", which can be found here:
http://market.trimbledata.com/#/datasets/tiger-line-2014
"""
import WeoGeoAPI
# Establish connection to Trimble Data Marketplace
session = WeoGeoAPI.weoSession('market.trimbledata.com', 'username', 'password')
session.connect()
print session
# Set initial job parameters. The 'note' variable is optional. Datasets with no layers do not need to be specified.
newJob = WeoGeoAPI.weoJob(dataset_token='3d52ffef-50cf-41e5-aadf-a5ec3dc5fc11',
layers=['All Roads', 'Census Tract'],
job_file_format='SHAPE',
note='Extract of area around Portland, OR.',
content_license_acceptance=True)
# Create a polygon selection using pairs of X,Y points that are in sequence.
newJob.setClipAreaCoordinateSystem('EPSG:4326')
newJob.addClipAreaPoints(((-122.55, 45.43), (-122.46, 45.43), (-122.14, 45.32), (-122.14, 45.27), (-122.55, 45.27), (-122.55, 45.43)))
# Create job object to be used for ordering.
response = session.createJob(newJob)
# Pricing and size information
price = session.getPrice(newJob)
print "\n-Order Summary-"
print "Price: " + price.content['job_price']['price']
print "Size: " + price.content['job_price']['human_estimated_data_size']
import WeoGeoAPI
# Establish connection to Trimble Data Marketplace
session = WeoGeoAPI.weoSession('market.trimbledata.com', 'username', 'password')
session.connect()
print session
# Set initial job parameters. The 'note' variable is optional.
# Here we only want one layer, '10m High Res'.
# Spatial resolution is 1 (native). Use 2, 3 or 4 to deliver as 2x/3x/4x coarser.
# Reproject our order to NAD83-Geo(EPSG:4269).
newJob = WeoGeoAPI.weoJob(dataset_token='5dbdb7db-1acf-4f19-b629-04b54f907552',
layers=['10m High Res'],
job_file_format='GeoTIFF',
job_datum_projection='EPSG:4269',
job_spatial_resolution='1',
note='Extract of area around Oregon.',
content_license_acceptance=True)
# Set crop box around Oregon. EPSG must be GEO(EPSG:4326) or Spherical Mercator(EPSG:3857).
newJob.setBoxCropArea('EPSG:4326', 46.17, 42.13, -116.28, -124.33)
# Create job object to be used for ordering.
response = session.createJob(newJob)
# Pricing and size information
price = session.getPrice(newJob)
print "\n-Order Summary-"
print "Price: " + price.content['job_price']['price']
print "Size: " + price.content['job_price']['human_estimated_data_size']
import WeoGeoAPI
# Establish connection to Trimble Data Marketplace
session = WeoGeoAPI.weoSession('market.trimbledata.com', 'username', 'password')
session.connect()
print session
# Create text file to log tokens
outfile = open('job_tokens.txt', 'w')
# Create lists for job instances and another list for their tokens
jobs = []
jobtokens = []
# Create standard job object and append to jobs list
standardJob = WeoGeoAPI.weoJob(dataset_token='5416193f-ba3d-f45e-bd9c-6dbf8193bfad',
content_license_acceptance=True)
jobs.append(standardJob)
# Create vector job object and append to jobs list
vectorJob = WeoGeoAPI.weoJob(dataset_token='3d52ffef-50cf-41e5-aadf-a5ec3dc5fc11',
layers=['All Roads', 'Census Tract'],
job_file_format='SHAPE',
note='Extract of area around Portland, OR.',
content_license_acceptance=True)
vectorJob.setClipAreaCoordinateSystem('EPSG:4326')
vectorJob.addClipAreaPoints(((-122.55, 45.43), (-122.46, 45.43), (-122.14, 45.32), (-122.14, 45.27), (-122.55, 45.27)))
jobs.append(vectorJob)
# Create raster job object and append to jobs list
rasterJob = WeoGeoAPI.weoJob(dataset_token='5dbdb7db-1acf-4f19-b629-04b54f907552',
layers=['10m High Res'],
