def stash_data_to_disk(step):
world.stashfile_pathname = os.path.abspath('scratch/bare.csv')
#remove any preexisting/leftover stash instance with this file pathname
if os.path.exists(world.stashfile_pathname):
os.remove(world.stashfile_pathname)
import pywqp_client
client_instance = pywqp_client.RESTClient()
client_instance.stash_response(world.response, world.stashfile_pathname)
assert os.path.exists(world.stashfile_pathname)
def stash_message_to_disk(step):
world.stashfile_pathname = os.path.abspath('scratch/bare.csv')
#remove any preexisting/leftover stash instance with this EXPECTED file pathname
expected_stashfile_pathname = world.stashfile_pathname + '.http'
if os.path.exists(expected_stashfile_pathname):
os.remove(expected_stashfile_pathname)
import pywqp_client
client_instance = pywqp_client.RESTClient()
client_instance.stash_response(world.response, world.stashfile_pathname, raw_http=True)
# did the method call do the expected thing?
assert os.path.exists(expected_stashfile_pathname)
def make_call(step, verb):
verb = verb.lower()
# use pywqp_client
setup_target_path()
import pywqp_client
client_instance = pywqp_client.RESTClient()
# params
params = {}
params['countrycode'] = world.countrycode
params['statecode'] = world.statecode
params['countycode'] = world.countycode
params['characteristicName'] = world.characteristic
# default
contenttype = 'text/csv'
if hasattr(world, 'contenttype'):
contenttype = world.contenttype
world.response = client_instance.request_wqp_data(verb, world.wqpserver, world.resourcetype, params, contenttype)
print('\n\n' + color_green + 'calling dataframe irow(' + str(row_to_display) + ')' + color_stop)
print(dataframe.irow(row_to_display))
print(color_green + 'done with irow(' + str(row_to_display) + ')' + color_stop)
# make pandas.DataFrame from the other data structure (rows-first)
if datadict2:
dataframe = pandas.DataFrame(data=datadict2, columns=wqx_mappings.tabular_defs[table_type])
print('\n\n' + color_yellow + 'calling dataframe irow(' + str(row_to_display) + ')' + color_stop)
print(dataframe.irow(row_to_display))
print(color_yellow + 'done with irow(' + str(row_to_display) + ')' + color_stop)
print('\n\n')
# use pywqp_client to fetch as dataframe
import pywqp_client
client_instance = pywqp_client.RESTClient()
# params
params = {}
params['countrycode'] = 'US'
params['statecode'] = 'US:55'
params['countycode'] = 'US:55:015'
params['characteristicName'] = 'pH'
response = client_instance.request_wqp_data('get', 'http://www.waterqualitydata.us', 'station', params, 'text/xml')
print('status code: ' + str(response.status_code))
for header in response.headers:
print(header + ': ' + response.headers[header])
print
than the date on which a particular measurement is actually obtained.)
startDateHi
sampleMedia
characteristicType
characteristicName
pCode (An NWIS-only classification scheme for sampling procedures. Non-NWIS
data sources will not provide any results to a query with a pCode
parameter.)
analyticalMethod (a classification of analytical protocols curated by NEMI
(see [http://www.nemi.gov] for more information.) The value of this
parameter is a published NEMI URI, _fully urlencoded_.)
'''
# paramfile is a listing of parameters, one per line, empty lines ignored.
# each parameter is of the form <name>=<value> (no angle brackets in real life.)
client = pywqp_client.RESTClient()
validator = pywqp_validator.WQPValidator()
def paramfile_args(filename):
paramfile = open(filename, 'r')
paramexprs = paramfile.readlines()
paramfile.close()
retval = {}
for paramexpr in paramexprs:
# process commandline arguments
#if sys.argv['paramfile']:
# do nothing so far
if sys.argv['wqpResourceType']:
for label in ('station', 'result', 'simplestation', 'bio'):
print('label: ' + label + '; response type path: ' +