def test_getFeatureCollectionGeoType_all(self):
testpyGDP = pyGDP.pyGDPwebProcessing()
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = None
testFeatureCollection = testPyGDP._getFeatureCollectionGeoType(shapefile,attribute,value)
assert_equal(testFeatureCollection.query.filters, [])
def test_getFeatureCollectionGeoType_all(self):
testpyGDP = pyGDP.pyGDPwebProcessing()
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = None
testFeatureCollection = testPyGDP._getFeatureCollectionGeoType(
shapefile, attribute, value)
assert_equal(testFeatureCollection.query.filters, [])
def test_submit_FWGS_multi_stat_var_named(self):
pyGDP.WPS_URL = 'http://cida.usgs.gov/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
shapefileAttribute = 'STATE'
attributeValue = 'Wisconsin'
datasetURI = 'http://cida.usgs.gov/thredds/dodsC/prism' # Note that this test also tests the http to dods conversion for urls.
dataType = ['ppt', 'tmx']
Coverage = 'true'
Delim = 'COMMA'
stats = ['MEAN', 'STD_DEV']
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(
geoType=shapefile,
dataSetURI=datasetURI,
varID=dataType,
startTime=timeStart,
endTime=timeEnd,
attribute=shapefileAttribute,
value=attributeValue,
gmlIDs=None,
verbose=False,
coverage=Coverage,
delim=Delim,
stat=stats,
grpby='STATISTIC',
timeStep='false',
summAttr='false')
assert_equal(os.path.getsize(outputFile_handle), 375)
def test_get_dataset_parameters(self):
DATASET_URI = "dods://cida.usgs.gov/qa/thredds/dodsC/prism"
testPyGDP = pyGDP.pyGDPwebProcessing()
datatypes = testPyGDP.getDataType(DATASET_URI, True)
assert_equal(len(datatypes), 3)
assert "ppt" in datatypes
def test_submit_FWGS_multi_stat_var_named(self):
pyGDP.WPS_URL='http://cida.usgs.gov/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
shapefileAttribute = 'STATE'
attributeValue = 'Wisconsin'
datasetURI = 'http://cida.usgs.gov/thredds/dodsC/prism' # Note that this test also tests the http to dods conversion for urls.
dataType = ['ppt','tmx']
Coverage='true'
Delim='COMMA'
stats = ['MEAN','STD_DEV']
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(geoType=shapefile, dataSetURI=datasetURI, varID=dataType, startTime=timeStart, endTime=timeEnd, attribute=shapefileAttribute, value=attributeValue, gmlIDs=None, verbose=False, coverage=Coverage, delim=Delim, stat=stats, grpby='STATISTIC', timeStep='false', summAttr='false')
assert_equal(os.path.getsize(outputFile_handle), 375)
# def test_submit_FWGS_no_time(self):
# pyGDP.WPS_URL='http://cida.usgs.gov/gdp/process/WebProcessingService'
# pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
# testPyGDP = pyGDP.pyGDPwebProcessing()
#
# shapefile = 'sample:simplified_HUC8s'
# shapefileAttribute = 'HUC_8'
# attributeValue = '08010211'
# datasetURI = 'http://raster.nationalmap.gov/ArcGIS/services/TNM_LandCover/MapServer/WCSServer' # Note that this test also tests the http to dods conversion for urls.
# dataType = '6'
# Coverage='true'
# Delim='COMMA'
# stats = ['MEAN','STD_DEV']
# timeStart = None
# timeEnd = None
# outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(geoType=shapefile, dataSetURI=datasetURI, varID=dataType, startTime=timeStart, endTime=timeEnd, attribute=shapefileAttribute, value=attributeValue, gmlIDs=None, verbose=False, coverage=Coverage, delim=Delim, stat=stats, grpby='STATISTIC', timeStep='false', summAttr='false')
#
# assert_equal(os.path.getsize(outputFile_handle), 58)
#
# def test_submit_FWGS_arc(self):
# pyGDP.WPS_URL='http://cida.usgs.gov/gdp/process/WebProcessingService'
# pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/GeospatialFabric/MapServer/WFSServer'
# testPyGDP = pyGDP.pyGDPwebProcessing()
#
# shapefile = 'GeospatialFabric_mows_mapping:NHDPlus_Catchment'
# attribute = 'hru_id'
# value='99'
# datasetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
# dataType = 'ppt'
# timeStart = '1900-01-01T00:00:00.000Z'
# timeEnd = '1900-02-01T00:00:00.000Z'
#
# outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(shapefile, datasetURI, dataType, timeStart, timeEnd, attribute, value, coverage=False)
#
# assert_equal(os.path.getsize(outputFile_handle), 95)
def test_get_shapefile_attributes(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attributes = testPyGDP.getAttributes(shapefile)
assert_equal(len(attributes), 10)
assert('STATE' in attributes)
def test_get_shapefile_attributes_arc(self):
pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/GeospatialFabric/MapServer/WFSServer'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'GeospatialFabric_mows_mapping:NHDPlus_Catchment'
attributes = testPyGDP.getAttributes(shapefile)
assert('hru_id' in attributes)
def test_get_uri(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
dataseturis = testPyGDP.getDataSetURI(anyText='prism')
uris = []
for dat in dataseturis:
for uri in dat[2]:
uris.append(uri)
assert_equal(len(dataseturis), 3)
assert_equal('dods://cida.usgs.gov/thredds/dodsC/prism' in uris, True)
def test_get_uri(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
dataseturis=testPyGDP.getDataSetURI(anyText='prism')
uris=[]
for dat in dataseturis:
for uri in dat[2]:
uris.append(uri)
assert_equal(len(dataseturis), 3)
assert_equal('dods://cida.usgs.gov/thredds/dodsC/prism' in uris, True)
def test_get_shapefile_attributes(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attributes = testPyGDP.getAttributes(shapefile)
assert_equal(len(attributes), 10)
assert ('STATE' in attributes)
def test_get_shapefile_attributes_arc(self):
pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/GeospatialFabric/MapServer/WFSServer'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'GeospatialFabric_mows_mapping:NHDPlus_Catchment'
attributes = testPyGDP.getAttributes(shapefile)
assert ('hru_id' in attributes)
def test_get_time_range(self):
DATASET_URI = "dods://cida.usgs.gov/qa/thredds/dodsC/prism"
testPyGDP = pyGDP.pyGDPwebProcessing()
datatype = "ppt"
trange = testPyGDP.getTimeRange(DATASET_URI, datatype)
assert_equal(len(trange), 2)
assert_equal(trange[0], "1895-01-01T00:00:00Z")
def test_submit_WCSIntersection(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:simplified_HUC8s'
attribute = 'SUBBASIN'
value = 'Alafia'
dataSetURI = 'http://raster.nationalmap.gov/ArcGIS/services/TNM_LandCover/MapServer/WCSServer'
dataType = '6'
outputFile_handle = testPyGDP.submitFeatureCoverageWCSIntersection(shapefile, dataSetURI, dataType, attribute, value, verbose=True)
assert_equal(os.path.getsize(outputFile_handle), 1918261)
def test_get_dataset_parameters(self):
pyGDP.WPS_Service = 'http://cida.usgs.gov/gdp/utility/WebProcessingService'
DATASET_URI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
testPyGDP = pyGDP.pyGDPwebProcessing()
datatypes = testPyGDP.getDataType(DATASET_URI, True)
assert_equal(len(datatypes), 3)
assert ('ppt' in datatypes)
def test_get_shapefile_values(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_States'
attribute = 'STATE'
values = testPyGDP.getValues(shapefile,attribute)
assert_equal(len(values), 49)
assert('Wisconsin' in values)
def test_get_dataset_parameters(self):
pyGDP.WPS_Service= 'http://cida.usgs.gov/qa/climate/gdp/utility/WebProcessingService'
DATASET_URI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
testPyGDP = pyGDP.pyGDPwebProcessing()
datatypes = testPyGDP.getDataType(DATASET_URI, True)
assert_equal(len(datatypes), 3)
assert('ppt' in datatypes)
def test_submit_WCSIntersection(self):
pyGDP.WPS_URL='http://cida.usgs.gov/qa/climate/gdp/process/WebProcessingService'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:simplified_HUC8s'
attribute = 'HUC_8'
value = '08010211'
dataSetURI = 'http://raster.nationalmap.gov/ArcGIS/services/TNM_LandCover/MapServer/WCSServer'
dataType = '6'
outputFile_handle = testPyGDP.submitFeatureCoverageWCSIntersection(shapefile, dataSetURI, dataType, attribute, value)
assert_equal(os.path.getsize(outputFile_handle), 1574029)
def test_get_time_range(self):
pyGDP.WPS_Service= 'http://cida.usgs.gov/qa/climate/gdp/utility/WebProcessingService'
DATASET_URI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
testPyGDP = pyGDP.pyGDPwebProcessing()
datatype = 'ppt'
trange = testPyGDP.getTimeRange(DATASET_URI, datatype)
assert_equal(len(trange), 2)
assert_equal(trange[0], '1895-01-01T00:00:00Z')
def test_get_shapefile_values_arc(self):
pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/GeospatialFabric/MapServer/WFSServer'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'GeospatialFabric_mows_mapping:NHDPlus_Catchment'
attribute = 'hru_id'
values = testPyGDP.getValues(shapefile, attribute, limitFeatures=100)
assert_equal(len(values), 100)
assert ('100' in values)
def test_get_shapefile_values(self):
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
values = testPyGDP.getValues(shapefile, attribute)
assert_equal(len(values), 49)
assert ('Wisconsin' in values)
def test_getFeatureCollectionGeoType_single(self):
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Wisconsin'
testFeatureCollection = testPyGDP._getFeatureCollectionGeoType(shapefile,attribute,value)
assert_equal(len(testFeatureCollection.query.filters), 36)
def test_get_shapefile_values_arc(self):
pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/GeospatialFabric/MapServer/WFSServer'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile='GeospatialFabric_mows_mapping:NHDPlus_Catchment'
attribute='hru_id'
values = testPyGDP.getValues(shapefile,attribute,limitFeatures=100)
assert_equal(len(values), 100)
assert('100' in values)
def test_get_shapefile_values(self):
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
values = testPyGDP.getValues(shapefile,attribute)
assert_equal(len(values), 49)
assert('Wisconsin' in values)
def test_get_time_range(self):
pyGDP.WPS_Service = 'http://cida.usgs.gov/gdp/utility/WebProcessingService'
DATASET_URI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
testPyGDP = pyGDP.pyGDPwebProcessing()
datatype = 'ppt'
trange = testPyGDP.getTimeRange(DATASET_URI, datatype)
assert_equal(len(trange), 2)
assert_equal(trange[0], '1895-01-01T00:00:00Z')
def test_submit_FWGS(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_States'
attribute = 'STATE'
value = 'Wisconsin'
userPoly = [(-102.8184, 39.5273), (-102.8184, 37.418), (-101.2363, 37.418), (-101.2363,39.5273), (-102.8184, 39.5273)]
datasetURI = 'dods://cida.usgs.gov/qa/thredds/dodsC/prism'
dataType = 'ppt'
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1950-01-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(shapefile, datasetURI, dataType, timeStart, timeEnd, attribute, value)
assert_equal(os.path.getsize(outputFile_handle), 18416)
def test_getFeatureCollectionGeoType_single(self):
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Wisconsin'
testFeatureCollection = testPyGDP._getFeatureCollectionGeoType(
shapefile, attribute, value)
assert_equal(len(testFeatureCollection.query.filters), 36)
def test_submit_FWGS_arc(self):
pyGDP.WPS_URL='http://cida.usgs.gov/qa/climate/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://www.sciencebase.gov/arcgis/services/GeospatialFabric/mows_mapping/MapServer/WFSServer'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'GeospatialFabric_mows_mapping:NHDPlus_Catchment'
attribute = 'hru_id'
value='99'
datasetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
dataType = 'ppt'
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-02-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(shapefile, datasetURI, dataType, timeStart, timeEnd, attribute, value, coverage=False)
assert_equal(os.path.getsize(outputFile_handle), 95)
def test_submit_FCGC(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_States'
attribute = 'STATE'
value = 'Rhode Island'
dataSetURI = 'http://cida.usgs.gov/ArcGIS/services/statsgo_muid/MapServer/WCSServer'
dataType = '1'
outputFile_handle = testPyGDP.submitFeatureCategoricalGridCoverage(shapefile, dataSetURI, dataType, attribute, value, verbose=True)
# This test is not currently working because what comes from
# testPyGDP.submitFeatureCategoricalGridCoverage() is a NoneType
# even through I've verified that it consistently writes a file
# of the size below. I expect a string to come back from this
# function
assert_equal(os.path.getsize(outputFile_handle), 650)
def main():
gdp = pyGDP.pyGDPwebProcessing()
sfiles = gdp.getShapefiles()
for s in sfiles:
print s
shapefile = 'sample:CONUS_States'
print
print 'Get Attributes:'
attributes = gdp.getAttributes(shapefile)
for a in attributes:
print a
print
print 'Get values:'
values = gdp.getValues(shapefile, 'STATE')
for v in values:
print v
print
print 'Getting available Dataset URIS'
datasetURIs = gdp.getDataSetURI()
dataSetURI = getInput(datasetURIs)
print ''
print 'Getting available dataTypes'
dataTypes = gdp.getDataType(dataSetURI)
dataType = getInput(dataTypes)
print
print 'Getting time range from dataset'
timeRange = gdp.getTimeRange(dataSetURI, dataType)
for i in timeRange:
print i
print
print 'Submitting request'
output = gdp.submitFeatureWeightedGridStatistics(shapefile, dataSetURI,
dataType, timeRange[0],
timeRange[0], 'STATE',
'Wisconsin')
print output
def test_submit_FCOD(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_States'
attribute = 'STATE'
value = 'Alabama'
dataSetURI = 'dods://cida.usgs.gov/thredds/dodsC/gmo/GMO_w_meta.ncml'
dataType = 'Prcp'
timeStart = '1950-01-01T00:00:00.000Z'
timeEnd = '1951-01-31T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureCoverageOPenDAP(shapefile, dataSetURI, dataType, timeStart, timeEnd, attribute, value, verbose=True)
# This test is not currently working because what comes from
# testPyGDP.submitFeatureCoverageOPenDAP() is a NoneType
# even though I've verified that it constistently writes a
# file of the size below. I expect a string to come back from
# this function
assert_equal(os.path.getsize(outputFile_handle), 2067840)
def test_submit_FWGS_no_time(self):
pyGDP.WPS_URL='http://cida.usgs.gov/qa/climate/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:simplified_HUC8s'
shapefileAttribute = 'HUC_8'
attributeValue = '08010211'
datasetURI = 'http://raster.nationalmap.gov/ArcGIS/services/TNM_LandCover/MapServer/WCSServer' # Note that this test also tests the http to dods conversion for urls.
dataType = '6'
Coverage='true'
Delim='COMMA'
stats = ['MEAN','STD_DEV']
timeStart = None
timeEnd = None
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(geoType=shapefile, dataSetURI=datasetURI, varID=dataType, startTime=timeStart, endTime=timeEnd, attribute=shapefileAttribute, value=attributeValue, gmlIDs=None, verbose=False, coverage=Coverage, delim=Delim, stat=stats, grpby='STATISTIC', timeStep='false', summAttr='false')
assert_equal(os.path.getsize(outputFile_handle), 57)
def test_submit_FWGS_multi_stat_var_named(self):
pyGDP.WPS_URL='http://cida.usgs.gov/qa/climate/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_States'
shapefileAttribute = 'STATE'
attributeValue = 'Wisconsin'
datasetURI = 'http://cida.usgs.gov/thredds/dodsC/prism' # Note that this test also tests the http to dods conversion for urls.
dataType = ['ppt','tmx']
Coverage='true'
Delim='COMMA'
stats = ['MEAN','STD_DEV']
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(geoType=shapefile, dataSetURI=datasetURI, varID=dataType, startTime=timeStart, endTime=timeEnd, attribute=shapefileAttribute, value=attributeValue, gmlIDs=None, verbose=False, coverage=Coverage, delim=Delim, stat=stats, grpby='STATISTIC', timeStep='false', summAttr='false')
assert_equal(os.path.getsize(outputFile_handle), 375)
def test_submit_FCGC(self):
pyGDP.WPS_URL = 'http://cida.usgs.gov/gdp/process/WebProcessingService'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Rhode Island'
dataSetURI = 'http://cida.usgs.gov/ArcGIS/services/statsgo_muid/MapServer/WCSServer'
dataType = '1'
outputFile_handle = testPyGDP.submitFeatureCategoricalGridCoverage(
shapefile, dataSetURI, dataType, attribute, value, verbose=True)
# This test is not currently working because what comes from
# testPyGDP.submitFeatureCategoricalGridCoverage() is a NoneType
# even through I've verified that it consistently writes a file
# of the size below. I expect a string to come back from this
# function
assert_equal(os.path.getsize(outputFile_handle), 650)
def main():
gdp = pyGDP.pyGDPwebProcessing()
sfiles = gdp.getShapefiles()
for s in sfiles:
print s
shapefile = 'sample:CONUS_States'
print
print 'Get Attributes:'
attributes = gdp.getAttributes(shapefile)
for a in attributes:
print a
print
print 'Get values:'
values = gdp.getValues(shapefile, 'STATE')
for v in values:
print v
print
print 'Getting available Dataset URIS'
datasetURIs = gdp.getDataSetURI()
dataSetURI = getInput(datasetURIs)
print ''
print 'Getting available dataTypes'
dataTypes = gdp.getDataType(dataSetURI)
dataType = getInput(dataTypes)
print
print 'Getting time range from dataset'
timeRange = gdp.getTimeRange(dataSetURI, dataType)
for i in timeRange:
print i
print
print 'Submitting request'
output = gdp.submitFeatureWeightedGridStatistics(shapefile, dataSetURI, dataType, timeRange[0], timeRange[0], 'STATE', 'Wisconsin')
print output
def test_submit_FWGS(self):
pyGDP.WPS_URL = 'http://cida.usgs.gov/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Wisconsin'
userPoly = [(-102.8184, 39.5273), (-102.8184, 37.418),
(-101.2363, 37.418), (-101.2363, 39.5273),
(-102.8184, 39.5273)]
datasetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
dataType = 'ppt'
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(
shapefile, datasetURI, dataType, timeStart, timeEnd, attribute,
value)
assert_equal(os.path.getsize(outputFile_handle), 133)
def test_submit_FWGS_multi_stat_var(self):
pyGDP.WPS_URL='http://cida.usgs.gov/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Wisconsin'
datasetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
dataType = ['ppt','tmx']
gmlIDs=None
verbose=False
coverage='true'
delim='COMMA'
stats = ['MEAN','STD_DEV']
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(shapefile, datasetURI, dataType, timeStart, timeEnd, attribute, value, gmlIDs, verbose, coverage, delim, stats)
assert_equal(os.path.getsize(outputFile_handle), 375)
def test_submit_FCOD(self):
pyGDP.WPS_URL = 'http://cida.usgs.gov/gdp/process/WebProcessingService'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Alabama'
dataSetURI = 'dods://cida.usgs.gov/thredds/dodsC/gmo/GMO_w_meta.ncml'
dataType = 'Prcp'
timeStart = '1950-01-01T00:00:00.000Z'
timeEnd = '1950-01-02T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureCoverageOPenDAP(
shapefile, dataSetURI, dataType, timeStart, timeEnd, attribute,
value)
# This test is not currently working because what comes from
# testPyGDP.submitFeatureCoverageOPenDAP() is a NoneType
# even though I've verified that it constistently writes a
# file of the size below. I expect a string to come back from
# this function
assert_equal(os.path.getsize(outputFile_handle), 14312)
def main():
gdp = pyGDP.pyGDPwebProcessing()
sfiles = gdp.getShapefiles()
for s in sfiles:
print s
shapefile = getInput(sfiles)
print
print 'Get Attributes:'
attributes = gdp.getAttributes(shapefile)
attribute = getInput(attributes)
print
print 'Get values:'
values = gdp.getValues(shapefile, attribute)
value = getInput_2(values)
print
searchString = getInput_4()
datasetURIs = gdp.getDataSetURI(anyText=searchString)
dataSetURI = getInput_3(datasetURIs)
print ''
print 'Getting available dataTypes'
dataTypes = gdp.getDataType(dataSetURI)
dataType = getInput(dataTypes)
print
print 'Getting time range from dataset'
timeRange = gdp.getTimeRange(dataSetURI, dataType)
for i in timeRange:
print i
print
print 'Submitting request'
output = gdp.submitFeatureWeightedGridStatistics(shapefile, dataSetURI, dataType, timeRange[0], timeRange[0], attribute, value, verbose=True)
print output
def test_submit_FWGS_multi_stat_var(self):
pyGDP.WPS_URL = 'http://cida.usgs.gov/gdp/process/WebProcessingService'
pyGDP.WFS_URL = 'http://cida.usgs.gov/gdp/geoserver/wfs'
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Wisconsin'
datasetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'
dataType = ['ppt', 'tmx']
gmlIDs = None
verbose = False
coverage = 'true'
delim = 'COMMA'
stats = ['MEAN', 'STD_DEV']
timeStart = '1900-01-01T00:00:00.000Z'
timeEnd = '1900-03-01T00:00:00.000Z'
outputFile_handle = testPyGDP.submitFeatureWeightedGridStatistics(
shapefile, datasetURI, dataType, timeStart, timeEnd, attribute,
value, gmlIDs, verbose, coverage, delim, stats)
assert_equal(os.path.getsize(outputFile_handle), 375)
def main_func(curdir, set, region):
def Region_lookup(region):
region_properties = []
if region == 'nhru':
region_properties.append(371)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d35da1e4b0ca184833940c')
region_properties.append('false')
if region == 'R01':
region_properties.append(2462)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5244735ae4b05b217bada04e')
region_properties.append('false')
if region == 'R02':
region_properties.append(4827)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52696784e4b0584cbe9168ee')
region_properties.append('false')
if region == 'R03':
region_properties.append(9899)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5283bd23e4b047efbbb57922')
region_properties.append('false')
if region == 'R04':
region_properties.append(5936)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5284ff57e4b063f258e61b9d')
region_properties.append('false')
if region == 'R05':
region_properties.append(7182)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/528516bbe4b063f258e62161')
region_properties.append('false')
if region == 'R06':
region_properties.append(2303)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d75296e4b055e0afd5be2c')
region_properties.append('true')
if region == 'R07':
region_properties.append(8205)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52851cd5e4b063f258e643dd')
region_properties.append('true')
if region == 'R08':
region_properties.append(4449)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52854695e4b063f258e6513c')
region_properties.append('true')
if region == 'R10L':
region_properties.append(8603)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/520031dee4b0ad2d97189db2')
region_properties.append('true')
elif region =='R10U':
region_properties.append(10299)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5216849ee4b0b45d6ba61e2e')
region_properties.append('false')
elif region == 'R11':
region_properties.append(7373)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d1f9ebe4b08b18a62d586b')
region_properties.append('true')
elif region == 'R12':
region_properties.append(7815)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5201328ae4b009d47a4c247a')
region_properties.append('false')
elif region == 'R13':
region_properties.append(1958)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d752b9e4b055e0afd5be36')
region_properties.append('false')
elif region == 'R14':
region_properties.append(3879)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52029c68e4b0e21cafa4b40c')
region_properties.append('false')
elif region == 'R15':
region_properties.append(3441)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5285389ae4b063f258e64863')
region_properties.append('false')
elif region == 'R16':
region_properties.append(2664)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52853f97e4b063f258e64875')
region_properties.append('false')
return region_properties
def list_define(set):
if set == 'cccma_cgmc3_1':
return ['cccma_cgmc3'+str(scenario), 'http://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'cnrm_cm3':
return ['cnrm_cm3'+str(scenario), 'http://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'gfdl_cm2_1':
return ['gfdl_cm2_1'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'ipsl_cm4':
return ['ipsl_cm4'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'miroc3_2_medres':
return ['miroc3_2_medres'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'miub_echo_g':
return ['miub_echo_g'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'mri_cgcm2_3_2a':
return ['mri_cgcm2_3_2a'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
elif set == 'mpi_echam5':
return ['mpi_echam5'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
global csvfile, vt_dataset, nhru, length, vt_datatype, url
import pyGDP
Region_return=Region_lookup(region)
hrus = Region_return[0]
nhru = hrus
ScienceBase_URL= Region_return[1]
pyGDP.WFS_URL = ScienceBase_URL
url = pyGDP.WFS_URL
pyGDP = pyGDP.pyGDPwebProcessing()
# change working directory so the GDP output will be written there
# Datasets and their properties
# run 1, 2 , and 3
#**********************************
# run 1 only
#scenario = 'a2'
# other scenarios are 'a1b' and 'b1'
#scenarios = ['a2','a1b','b1']
scenarios = ['a2', 'a1b']
timestart = '2046-01-15T00:00:00.000Z'
timeend='2100-12-15T00:00:00.000Z'
#**********************************
# Datasets for each scenario - note that mpi_echam5 is not run for scenario a1b
#a1b
#data=[cccma_cgmc3_1,cnrm_cm3,gfdl_cm2_1,ipsl_cm4,miroc3_2_medres,miub_echo_g,mri_cgcm2_3_2a]
#a2
#data=[cccma_cgmc3_1,cnrm_cm3,gfdl_cm2_1,ipsl_cm4,miroc3_2_medres,miub_echo_g,mpi_echam5,mri_cgcm2_3_2a]
#b1
#data=[cccma_cgmc3_1,cnrm_cm3,gfdl_cm2_1,ipsl_cm4,miroc3_2_medres,miub_echo_g,mpi_echam5,mri_cgcm2_3_2a]
# get list of shapefiles uploaded to the GDP
shapefiles = pyGDP.getShapefiles()
for shp in shapefiles:
print shp
# feature loaded from sciencebase
#should shapefile be sb:SP_hru instead?
shapefile = 'sb:nhru'
user_attribute = 'hru_id_loc'
user_value = None
os.chdir(curdir)
dir = os.getcwd()
vt_data = list_define(set)
vt_datatype = vt_data[5:]
#for scenario in scenarios:
# cnrm_cm3 = ['cnrm_cm3'+str(scenario), 'http://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'cnrm_cm3-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# gfdl_cm2_1 = ['gfdl_cm2_1'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'gfdl_cm2_1-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# ipsl_cm4 = ['ipsl_cm4'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'ipsl_cm4-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# mpi_echam5 = ['mpi_echam5'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'mpi_echam5-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# miroc3_2_medres = ['miroc3_2_medres'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'miroc3_2_medres-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# cccma_cgmc3_1 = ['cccma_cgmc3'+str(scenario), 'http://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'cccma_cgcm3_1-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# miub_echo_g = ['miub_echo_g'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'miub_echo_g-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
#
# mri_cgcm2_3_2a = ['mri_cgcm2_3_2a'+str(scenario), 'dods://pcmdi8.llnl.gov/thredds/dodsC/bcca/BCCA_0.125deg-gregorian-sres-monthly', 'Monthly', timestart, timeend,\
# 'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-pr-BCCA_0-125deg-monthly',
# 'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-tasmax-BCCA_0-125deg-monthly',
# 'mri_cgcm2_3_2a-gregorian-sres'+str(scenario)+'-run1-tasmin-BCCA_0-125deg-monthly']
# if scenario == 'a1b':
# data=[cccma_cgmc3_1,cnrm_cm3,gfdl_cm2_1,ipsl_cm4,miroc3_2_medres,miub_echo_g,mri_cgcm2_3_2a]
# elif scenario == 'a2':
# data=[cccma_cgmc3_1,cnrm_cm3,gfdl_cm2_1,ipsl_cm4,miroc3_2_medres,miub_echo_g,mpi_echam5,mri_cgcm2_3_2a]
timestart = time.time()
#################
#scenarios = ['a2', 'a1b']
scenario = 'a2'
#for dataset in data:
#file_loc = dir+'\\'+scenario+'\\'+dataset[0]
file_loc = dir+'\\'+set
if not os.path.exists(file_loc):
os.mkdir(file_loc)
os.chdir(file_loc)
#print "The current scenario is: " +scenario + "dataset being worked on is: " + dataset[0]
print "The current scenario is: " +scenario + "dataset being worked on is: " + set
#The url of each dataset
dataSet = vt_data[1]
#dataSet = dataset[1]
#The precipitation and temperatures of each dataset
#Start at position(not index) 5 until the end of the
#dictionary's key(which is a list)
dataType=vt_data[5:]
#dataType = dataset[5:]
# daily or monthly for additional aggregation/formatting (not appended yet)
timeStep = vt_data[2]
length = timeStep
#timeStep = dataset[2]
#Start date
timeBegin = vt_data[3]
#timeBegin = dataset[3]
#End date
timeEnd = vt_data[4]
#timeEnd = dataset[4]
# data processing arguments
gmlIDs=None
verbose=True
#coverage = 'false' check if on US border/ocean
coverage = Region_return[2]
delim='COMMA'
stats='MEAN'
# run the pyGDP
start = time.time()
outputPath = pyGDP.submitFeatureWeightedGridStatistics(shapefile, dataSet, dataType, timeBegin, timeEnd, user_attribute, user_value, gmlIDs, verbose, coverage, delim, stats)
end = time.time()
print 'Start time is: ' + str(start)
print 'End time is: ' + str(end)
print 'Total time was: ' + str(end-start)
print outputPath
# copy the output and rename it
# ind = 5
# for var in range(5, len(dataset)):
# line = dataset[var].split('-')
# dataset[ind] = line[4]
# ind += 1
#
# dataType = dataset[5:]
#shutil.copy2(outputPath, region+'_'+dataset[0]+'.csv')
shutil.copy2(outputPath, region+'_'+vt_data[0]+'.csv')
# csvfile = os.getcwd()+region+'_'+dataset[0]+'.csv'
# #Parse the csv file
# index = 0
#
# csvread = csv.reader(open(region+'_'+dataset[0] + '.csv', "rb"))
#
# csvwrite = csv.writer(open(dataType[0] + '.csv', "wb"))
#
# temp = csvread
# var = temp.next()
# var[0] = '#'+dataType[0]
# #Gets gage ids
# gage = temp.next()
#
# #Writes current variable to csv file
# csvwrite.writerow(var)
# #Writes all gage ids to csv file
# csvwrite.writerow(gage)
#
# for variable in dataType:
#
# for row in csvread:
# #if on last variable
# if variable == dataType[len(dataType) - 1]:
# csvwrite.writerow(row)
# else:
# if (row[0] in '#'+dataType[index+1]) or (row[0] in '# '+dataType[index+1]):
# #Line 33 is used for titling the csv file the name of the variable (like tmin, ppt, or tmax)
# var = '#'+dataType[index+1]
# parsedFiles.append(os.getcwd()+'\\'+variable+'.csv')
# csvwrite = csv.writer(open(dataType[index+1] + '.csv', "wb"))
# row[1:] = ""
# row[0] = var
# csvwrite.writerow(row)
# csvwrite.writerow(gage)
# if len(dataType) == 2:
# csvread.next()
# else:
# csvread.next()
# csvwrite.writerow(csvread.next())
# csvwrite.writerow(csvread.next())
# break
# else:
# if dataType[index+1] not in row[0] and row[0] not in dataType[index+1]:
# csvwrite.writerow(row)
# print "Finished parsing " + variable + ".csv"
# parsedFiles.append(os.getcwd()+'\\'+variable+'.csv')
# # use index to keep track of next variable
# if (index + 1) < len(dataType):
# index += 1
timeend = time.time()
print 'Start time of pyGDP: ' + str(timestart)
print 'End time of pyGDP: ' + str(timeend)
print 'Total time of pyGDP: ' + str(timeend-timestart)
os.chdir(dir)
def create_web_processing_object():
new_web_processing = pyGDP.pyGDPwebProcessing()
return new_web_processing
def test_get_shapefile_list(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefiles = testPyGDP.getShapefiles()
assert_not_equal(len(shapefiles), 0)
def main():
# This instantiates a pyGDP web processing object. All other processes are done through the web processing
# object.
gdp = pyGDP.pyGDPwebProcessing()
# Returns a list of shapefiles that are currently sitting on the GDP server.
# It's possible to upload your own shapefile using the .uploadShapefile function.
sfiles = gdp.getShapefiles()
for s in sfiles:
print(s)
shapefile = get_input(sfiles)
print()
print('Get Attributes:')
# Gets shapefile dbf attributes of the file you chose from the previous selection process.
# A good example of levels of detail processed by on GDP.
attributes = gdp.getAttributes(shapefile)
attribute = get_input_1(attributes)
print()
print('Get values:')
# Yet another level of detail down on the shapefile. This time it is values of an attribute or shapefile.
# Does all the web processing necessary to show the user what they are working with.
values = gdp.getValues(shapefile, attribute)
value = get_input_2(values)
print()
# Allows the user to select a dods dataset for processing. The getDataSetURI function returns a lot of
# metadata making it helpful to narrow down the search with anyText.
search_string = getinput_4()
dataset_uris = gdp.getDataSetURI(anyText=search_string)
dataset_uri = get_input_3(dataset_uris)
print()
print('Getting available data_types... \n')
# Gives a list of the available data types within the dods dataset for processing.
data_types = gdp.getDataType(dataset_uri)
data_type = get_input_5(data_types)
print()
print('Getting time range from dataset...')
# This example only uses the first time range (see submitFeatureWeightedGridStatistics execution)
time_range = gdp.getTimeRange(dataset_uri, data_type)
for i in time_range:
print(i)
print()
print('Submitting request...')
# Time for some heavy web services processing. The submitFeatureWeightedGridStaistics is the end product of
# all the variable choosing we have done up until this point. It takes a lot of inputs and sends them all
# through a remote processing algorithm associated with GDP. The result is a file downloaded to the location
# of the executing script, also returned is the URL associated with the download (it usually gives a csv file).
output = gdp.submitFeatureWeightedGridStatistics(shapefile,
dataset_uri,
data_type,
time_range[0],
time_range[0],
attribute,
value,
verbose=True)
print()
print(output)
print()
print(
"The resulting ouput file (which should now exist in the folder where "
+
"this example was executed) holds the Feature Weighted Grid Statistics "
+
"(just the mean value if you did the default options) of the area chose "
+
"from the 'value' shapefile (from GDP). \nFor more details refer to comments "
+ "in the main method of this example script.")
from __future__ import print_function
import pyGDP
pyGDP = pyGDP.pyGDPwebProcessing()
"""
This example shows how to use multiple dataTypes and Statistics.
"""
shapefile = 'sample:simplified_huc8'
user_attribute = 'SUBBASIN'
user_value = 'Baraboo'
dataSet = 'dods://cida.usgs.gov/thredds/dodsC/gmo/GMO_w_meta.ncml'
dataType = ['Prcp', 'Tavg', 'Tmax', 'Tmin']
timeBegin = '1970-01-24T00:00:00.000Z'
timeEnd = '1970-01-25T00:00:00.000Z'
gmlIDs = None
verbose = True
coverage = 'true'
delim = 'COMMA'
stats = ['MEAN', 'STD_DEV']
print('Processing request.')
outputPath = pyGDP.submitFeatureWeightedGridStatistics(shapefile, dataSet, dataType, timeBegin, timeEnd,
user_attribute, user_value, gmlIDs, verbose,
coverage, delim, stats)
def arc_FWGS(step):
test_pyGDP = pyGDP.pyGDPwebProcessing(wfs_url=world.WFS_URL)
world.output_file = test_pyGDP.submitFeatureWeightedGridStatistics(world.shapefile, world.dataset_uri,
world.data_type, world.time_start,
world.time_end, world.attribute, world.value,
coverage=False, verbose=False)
def create_web_processing_object():
new_web_processing = pyGDP.pyGDPwebProcessing()
return new_web_processing
def main_func(region, currdir, timestart, timeend):#, scenarios):
def Region_lookup(region):
region_properties = []
if region == 'nhru':
region_properties.append(371)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d35da1e4b0ca184833940c')
region_properties.append('false')
elif region == 'R01':
region_properties.append(2462)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5244735ae4b05b217bada04e')
region_properties.append('false')
elif region == 'R02':
region_properties.append(4827)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52696784e4b0584cbe9168ee')
region_properties.append('false')
elif region == 'R03':
region_properties.append(9899)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5283bd23e4b047efbbb57922')
region_properties.append('false')
elif region == 'R04':
region_properties.append(5936)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5284ff57e4b063f258e61b9d')
region_properties.append('false')
elif region == 'R05':
region_properties.append(7182)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/528516bbe4b063f258e62161')
region_properties.append('false')
elif region == 'R06':
region_properties.append(2303)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d75296e4b055e0afd5be2c')
region_properties.append('true')
elif region == 'R07':
region_properties.append(8205)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52851cd5e4b063f258e643dd')
region_properties.append('true')
elif region == 'R08':
region_properties.append(4449)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52854695e4b063f258e6513c')
region_properties.append('true')
elif region == 'R10L':
region_properties.append(8603)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/520031dee4b0ad2d97189db2')
region_properties.append('true')
elif region =='R10U':
region_properties.append(10299)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5216849ee4b0b45d6ba61e2e')
region_properties.append('false')
elif region == 'R11':
region_properties.append(7373)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d1f9ebe4b08b18a62d586b')
region_properties.append('true')
elif region == 'R12':
region_properties.append(7815)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5201328ae4b009d47a4c247a')
region_properties.append('false')
elif region == 'R13':
region_properties.append(1958)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/51d752b9e4b055e0afd5be36')
region_properties.append('false')
elif region == 'R14':
region_properties.append(3879)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52029c68e4b0e21cafa4b40c')
region_properties.append('false')
elif region == 'R15':
region_properties.append(3441)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/5285389ae4b063f258e64863')
region_properties.append('false')
elif region == 'R16':
region_properties.append(2664)
region_properties.append('https://www.sciencebase.gov/catalogMaps/mapping/ows/52853f97e4b063f258e64875')
region_properties.append('false')
return region_properties
global parsedFiles, csvfile, vistrails_data_set, nhru, url
import pyGDP
#region = 'R13'
parsedFiles = []
vistrails_data_set = []
Region_return=Region_lookup(region)
hrus = Region_return[0]
nhru = hrus
ScienceBase_URL= Region_return[1]
pyGDP.WFS_URL = ScienceBase_URL
# call web processing module
pyGDP = pyGDP.pyGDPwebProcessing()
# change working directory so the GDP output will be written there
# Datasets and their properties
# run 1, 2 , and 3
#**********************************
# run 1 only
#scenario = 'a2'
# other scenarios are 'a1b' and 'b1'
scenarios = ['a2','a1b'] # not running b1 or a1fi
#ends 2012
timestart = '1950-01-15T00:00:00.000Z'
timeend='1960-12-15T00:00:00.000Z'
shapefiles = pyGDP.getShapefiles()
for shp in shapefiles:
print shp
shapefile = 'sb:nhru'
user_attribute = 'hru_id_loc'
user_value = None
#os.chdir('C:\\Users\\reimandy\\workspace\\userpackages\\WaterBalanceModel\\Step1_CLIMATE_DATA\\'+region+'\\MAURERBREKE')
dir = currdir
gcmRun = '1'
for scenario in scenarios:
cccma_cgcm3_1_1 = ['sres'+scenario+'_cccma-cgcm3-1_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_cccma-cgcm3-1_1_Prcp',
'sres'+scenario+'_cccma-cgcm3-1_1_Tavg']
gfdl_cm2_1_1_1 = ['sres'+scenario+'_gfdl-cm2-1_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_gfdl-cm2-1_1_Prcp',
'sres'+scenario+'_gfdl-cm2-1_1_Tavg']
miroc3_2_medres_1 = ['sres'+scenario+'_miroc3-2-medres_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_miroc3-2-medres_1_Prcp',
'sres'+scenario+'_miroc3-2-medres_1_Tavg']
miub_echo_g_1_1 = ['sres'+scenario+'_miub-echo-g_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_miub-echo-g_1_Prcp',
'sres'+scenario+'_miub-echo-g_1_Tavg']
mpi_echam5_1 = ['sres'+scenario+'_mpi-echam5_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_mpi-echam5_1_Prcp',
'sres'+scenario+'_mpi-echam5_1_Tavg']
mri_cgcm2_3_2a_1 = ['sres'+scenario+'_mri-cgcm2-3-2a_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_mri-cgcm2-3-2a_1_Prcp',
'sres'+scenario+'_mri-cgcm2-3-2a_1_Tavg']
# New MaurerBreke Statistically downscaled datasets (put with other MB datasets)
bccr_bcm2_0 = ['sres'+scenario+'_bccr-bcm2-0_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_bccr-bcm2-0_'+gcmRun+'_Prcp',
'sres'+scenario+'_bccr-bcm2-0_'+gcmRun+'_Tavg']
cnrm_cm3 = ['sres'+scenario+'_cnrm-cm3_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_cnrm-cm3_'+gcmRun+'_Prcp',
'sres'+scenario+'_cnrm-cm3_'+gcmRun+'_Tavg']
csiro_mk3_0 = ['sres'+scenario+'_csiro-mk3-0_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_csiro-mk3-0_'+gcmRun+'_Prcp',
'sres'+scenario+'_csiro-mk3-0_'+gcmRun+'_Tavg']
giss_model_e_r = ['sres'+scenario+'_giss-model-e-r_2','dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_giss-model-e-r_2_Prcp',
'sres'+scenario+'_giss-model-e-r_2_Tavg']
inmcm3_0 = ['sres'+scenario+'_inmcm3-0_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_inmcm3-0_'+gcmRun+'_Prcp',
'sres'+scenario+'_inmcm3-0_'+gcmRun+'_Tavg']
ipsl_cm4 = ['sres'+scenario+'_ipsl-cm4_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_ipsl-cm4_'+gcmRun+'_Prcp',
'sres'+scenario+'_ipsl-cm4_'+gcmRun+'_Tavg']
ncar_ccsm3_0 = ['sres'+scenario+'_ncar-ccsm3-0_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_ncar-ccsm3-0_'+gcmRun+'_Prcp',
'sres'+scenario+'_ncar-ccsm3-0_'+gcmRun+'_Tavg']
ncar_pcm1 = ['sres'+scenario+'_ncar-pcm1_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_ncar-pcm1_'+gcmRun+'_Prcp',
'sres'+scenario+'_ncar-pcm1_'+gcmRun+'_Tavg']
ukmo_hadcm3 = ['sres'+scenario+'_ukmo-hadcm3_'+gcmRun,'dods://cida.usgs.gov/thredds/dodsC/maurer/maurer_brekke_w_meta.ncml','Monthly',timestart,timeend,\
'sres'+scenario+'_ukmo-hadcm3_'+gcmRun+'_Prcp',
'sres'+scenario+'_ukmo-hadcm3_'+gcmRun+'_Tavg']
data = [cccma_cgcm3_1_1,gfdl_cm2_1_1_1,miroc3_2_medres_1,miub_echo_g_1_1,mpi_echam5_1,mri_cgcm2_3_2a_1]
for dataset in data:
if len(scenario) == 2:
name = dataset[0]
name = name[7:]
else:
name = dataset[0]
name = name[8:]
file_loc = str(dir.name)+'\\Step1_CLIMATE_DATA\\'+region+'\\'+scenario+'\\'+name
if not os.path.exists(file_loc):
os.mkdir(file_loc)
os.chdir(file_loc)
print "The current dataset being worked on is: " + name
dataSet = dataset[1]
dataType = dataset[5:]
timestep = dataset[2]
timeBegin = dataset[3]
timeEnd = dataset[4]
gmlIDs = None
verbose = True
coverage = Region_return[2]
delim = 'COMMA'
stats = 'MEAN'
start = time.time()
outputPath = pyGDP.submitFeatureWeightedGridStatistics(shapefile, dataSet, dataType, timeBegin, timeEnd, user_attribute, user_value, gmlIDs, verbose, coverage, delim, stats)
end = time.time()
print "Start time is: " + str(start)
print 'End time is: ' + str(end)
print 'Total time was: ' + str(end-start)
print outputPath
shutil.copy2(outputPath, region+'_'+name+'.csv')
csvfile = os.getcwd()+region+'_'+name+'.csv'
# dataType = ['Prcp', 'Tavg']
# vistrails_data_set = ['Prcp', 'Tavg']
#csvread = csv.reader(open(region+'_'+name+'.csv', 'rb'))
#csvwrite = csv.writer(open(dataType[0]+'.csv', 'wb'))
#parsedFiles.append(dataType[0]+'.csv')
#index = 0
#temp = csvread
#var = temp.next()
#var[0] = '#'+dataType[0]
#gage = temp.next()
#csvwrite.writerow(var)
#csvwrite.writerow(gage)
# for variable in dataType:
#
# for row in csvread:
# if variable == dataType[len(dataType) - 1]:
# csvwrite.writerow(row)
# else:
# if (row[0] in '#'+dataType[index+1]) or (row[0] in '# ' + dataType[index+1]):
# var = '#'+dataType[index+1]
# csvwrite = csv.writer(open(dataType[index+1] + '.csv', 'wb'))
# parsedFiles.append(dataType[index+1]+'.csv')
# row[1:] = ''
# row[0] = var
# csvwrite.writerow(row)
# csvwrite.writerow(gage)
#
# if len(dataType) == 2:
# csvwrite.writerow(csvread.next())
# else:
# csvread.next()
# csvwrite.writerow(csvread.next())
# csvwrite.writerow(csvread.next())
# break
# else:
# if dataType[index+1] not in row[0] and row[0] not in dataType[index+1]:
# csvwrite.writerow(row)
# print 'Finished parsing ' + variable + '.csv'
#
# if (index+1)<len(dataType):
# index += 1
#
# os.chdir(dir)
#main_func('nhru', os.getcwd(), '', '')
import pyGDP
pyGDP = pyGDP.pyGDPwebProcessing()
shapefile = 'sample:CONUS_states'
attribute = 'STATE'
value = 'Alabama'
dataSetURI = 'https://cida.usgs.gov/ArcGIS/services/SSURGO_Products/MapServer/WCSServer'
dataType = '1'
pyGDP.submitFeatureCoverageWCSIntersection(shapefile,
dataSetURI,
dataType,
attribute,
value,
verbose=True)
def test_get_shapefile_list(self):
testPyGDP = pyGDP.pyGDPwebProcessing()
shapefiles = testPyGDP.getShapefiles()
assert_not_equal(len(shapefiles), 0)
def point_to_bogus_geoserver(step):
import pyGDP
test_pyGDP = pyGDP.pyGDPwebProcessing(WFS_URL = 'https://bogus/bogy/geoserver/wfs')
world.bogus_wfs = test_pyGDP.wfsUrl
def point_to_bogus_geoserver(step):
import pyGDP
test_pyGDP = pyGDP.pyGDPwebProcessing(
wfs_url='https://bogus/bogy/geoserver/wfs')
world.bogus_wfs = test_pyGDP.wfsUrl
