attributes = pyGDP.getAttributes(shapefile)
for attr in attributes:
print(attr)
# Grab the values from the STATE attribute of sample:CONUS_States
usr_attribute = 'STATE'
values = pyGDP.getValues(shapefile, usr_attribute)
for v in values:
print(v)
# Choose Delaware
# Note that removing "value" in the main request below will run all values in the shapefile.
value = ['Delaware']
# Search for datasets
dataSetURIs = pyGDP.getDataSetURI(anyText='wicci')
for dataset in dataSetURIs:
print(dataset)
# Loop through datasets of interest, in this case the first three OPeNDAP urls.
for dataSetURI in dataSetURIs[1][2][0:3]:
# Get the available data types associated with the dataSetURI
dataTypes = pyGDP.getDataType(dataSetURI)
print(dataTypes)
# For this example just run the first dataType. This dataType list should be modified if multiple
# datatypes are required.
dataType = dataTypes[0]
print(dataType)
# Get available time range for the dataset.
print attr
print
# Grab the values from 'OBJECTID' and 'upload:OKCNTYD'
usr_attribute = 'OBJECTID'
values = pyGDP.getValues(OKshapefile, usr_attribute)
for v in values:
print v
print
#We set our value to 5
usr_value = 5
# our shapefile = 'upload:OKCNTYD', usr_attribute = 'OBJECTID', and usr_value = 13
# We get the dataset URI that we are interested in
dataSetURIs = pyGDP.getDataSetURI(anyText='prism')
pp = pprint.PrettyPrinter(indent=5, width=60)
pp.pprint(dataSetURIs)
print
# Set our datasetURI
dataSetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
# Get the available data types associated with the dataset
dataType = 'ppt'
# Get available time range on the dataset
timeRange = pyGDP.getTimeRange(dataSetURI, dataType)
for t in timeRange:
print t
timeBegin = '1900-01-01T00:00:00.000Z'
timeEnd = '1901-01-01T00:00:00.000Z'
print
shapefile = 'sample:CONUS_States'
attributes = pyGDP.getAttributes(shapefile)
for attr in attributes:
print attr
print
# Grab the values from 'OBJECTID' and 'upload:OKCNTYD'
usr_attribute = 'STATE'
values = pyGDP.getValues(shapefile, usr_attribute)
for v in values:
print v
print
# our shapefile = 'upload:OKCNTYD', usr_attribute = 'OBJECTID', and usr_value = 13
# We get the dataset URI that we are interested in
dataSetURIs = pyGDP.getDataSetURI()
for d in dataSetURIs:
print d
# Set our datasetURI
dataSetURI = 'dods://igsarm-cida-thredds1.er.usgs.gov:8080/thredds/dodsC/gmo/GMO_w_meta.ncml'
# Get the available data types associated with the dataset
dataType = 'Prcp'
# Get available time range on the dataset
timeRange = pyGDP.getTimeRange(dataSetURI, dataType)
for t in timeRange:
print t
"""
Instead of submitting in a value, we submit a list of gmlIDs associated
with either a small portion of that value, or multiple values.
attributes = pyGDP.getAttributes(shapefile)
for attr in attributes:
print attr
# Grab the values from the STATE attribute of sample:CONUS_States
usr_attribute = 'STATE'
values = pyGDP.getValues(shapefile, usr_attribute)
for v in values:
print v
# Choose Colorado
value = ['Colorado']
# Search for datasets
dataSetURIs = pyGDP.getDataSetURI(anyText='prism')
for dataset in dataSetURIs:
print dataset
# Set our datasetURI to the OPeNDAP/dods response for the prism dataset.
dataSetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
# Get the available data types associated with the dataset
dataTypes = pyGDP.getDataType(dataSetURI)
for dataType in dataTypes:
print dataType
dataType = 'ppt'
# Get available time range for the dataset.
timeRange = pyGDP.getTimeRange(dataSetURI, dataType)
print "\nuploaded {} to server ".format(shp)
except:
print "\nshapefile {} found on server".format(shp)
continue
shapefiles=['upload:' + os.path.split(f)[1][:-4] for f in zipped_shapefiles]
shapefile = shapefiles[0] # for now just using one shapefile
# in the this case, the station identifiers are just consecutive integers
values = pyGDP.getValues(shapefile, attribute)
#values = map(str,sorted(map(int, values))) # could enforce order, but doesn't seem to make a difference
# Search for datasets
print "\nGetting datasets and datatypes..."
dataSetURIs = pyGDP.getDataSetURI(anyText=URI_designator)
dataSetURIs = dataSetURIs[1][2] # this probably needs to be hard-coded based on results of line above
# get datasets that contain the specified URI names
dataSetURIs = [[d for d in dataSetURIs if os.path.split(d)[1] == n][0] for n in URI_names]
if len(dataSetURIs) > 0:
print '\nFound:'
for n in dataSetURIs:
print '{}'.format(n)
# in case of restart, trim already-processed entries from datasets
if restart:
if download_datasets == 'individually':
rec_URIs.pop() # if downloading together and last URI is in recfile, the dataset downloaded OK
dataSetURIs = [d for d in dataSetURIs if d not in rec_URIs]
print v
print
"""
Instead of specifically specifying a value, we get request to get
the gmlID of these values and append them to a gmlID to be used
as an input instead of value.
"""
wisGMLID = pyGDP.getGMLIDs(shapefile, usr_attribute, 'Wisconsin')
michGMLID = pyGDP.getGMLIDs(shapefile, usr_attribute, 'Michigan')
minnGDMLID = pyGDP.getGMLIDs(shapefile, usr_attribute, 'Minnesota')
gmlIDs = wisGMLID + michGMLID + minnGDMLID
# our shapefile = 'upload:OKCNTYD', usr_attribute = 'OBJECTID', and usr_value = 13
# We get the dataset URI that we are interested in
dataSetURIs = pyGDP.getDataSetURI()
for d in dataSetURIs:
print d
# Set our datasetURI
dataSetURI = 'dods://igsarm-cida-thredds1.er.usgs.gov:8080/thredds/dodsC/gmo/GMO_w_meta.ncml'
# Get the available data types associated with the dataset
dataType = 'Prcp'
# Get available time range on the dataset
timeRange = pyGDP.getTimeRange(dataSetURI, dataType)
for t in timeRange:
print t
"""
Instead of submitting in a value, we submit a list of gmlIDs associated
with either a small portion of that value, or multiple values.
for attr in attributes:
print attr
# Grab the values from the STATE attribute of sample:CONUS_States
usr_attribute = 'STATE'
values = pyGDP.getValues(shapefile, usr_attribute)
for v in values:
print v
# Choose Delaware
# Note that removing "value" in the main request below will run all values in the shapefile.
value = ['Delaware']
# Search for datasets
dataSetURIs = pyGDP.getDataSetURI(anyText='wicci')
for dataset in dataSetURIs:
print dataset
# Loop through datasets of interest, in this case the first three OPeNDAP urls.
for dataSetURI in dataSetURIs[1][2][0:3]:
# Get the available data types associated with the dataSetURI
dataTypes = pyGDP.getDataType(dataSetURI)
print dataTypes
# For this example just run the first dataType. This dataType list should be modified if multiple datatypes are required.
dataType = dataTypes[0]
print dataType
# Get available time range for the dataset.
timeRange = pyGDP.getTimeRange(dataSetURI, dataType)
print timeRange
# Execute a GeatureWeightedGridStatistics request and return the path to the output file.
print "\nuploaded {} to server ".format(shp)
except:
print "\nshapefile {} found on server".format(shp)
continue
shapefiles = ['upload:' + os.path.split(f)[1][:-4] for f in zipped_shapefiles]
shapefile = shapefiles[0] # for now just using one shapefile
# in the this case, the station identifiers are just consecutive integers
values = pyGDP.getValues(shapefile, attribute)
#values = map(str,sorted(map(int, values))) # could enforce order, but doesn't seem to make a difference
# Search for datasets
print "\nGetting datasets and datatypes..."
dataSetURIs = pyGDP.getDataSetURI(anyText=URI_designator)
dataSetURIs = dataSetURIs[1][
2] # this probably needs to be hard-coded based on results of line above
# get datasets that contain the specified URI names
dataSetURIs = [[d for d in dataSetURIs if os.path.split(d)[1] == n][0]
for n in URI_names]
if len(dataSetURIs) > 0:
print '\nFound:'
for n in dataSetURIs:
print '{}'.format(n)
# in case of restart, trim already-processed entries from datasets
if restart:
if download_datasets == 'individually':
rec_URIs.pop(
