def do_plot(self, line):
keys = matchItems(line, self.fetchSupportedKeys())
if len(keys) == 0:
self.lastCommandFailed("Must have at least one GenData key")
return False
case_list = self.shellContext()["plot_settings"].getCurrentPlotCases()
for key in keys:
key, report_step = key.split("@", 1)
report_step = int(report_step)
plot = ShellPlot("%s at report step: %d" %(key, report_step))
for case_name in case_list:
data = GenDataCollector.loadGenData(self.ert(), case_name, key, report_step)
if not data.empty:
plot.plotGenData(data, legend_label=case_name)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(self.ert(), key, report_step)
if obs_key is not None:
obs_data = GenDataObservationCollector.loadGenDataObservations(self.ert(), case_name, [obs_key])
if not obs_data.empty:
plot.plotObservations(obs_data, obs_key)
plot.showLegend()
def test_gen_data_collector(self):
config = self.createTestPath("local/snake_oil/snake_oil.ert")
with ErtTestContext("python/enkf/export/gen_data_collector", config) as context:
ert = context.getErt()
with self.assertRaises(KeyError):
data = GenDataCollector.loadGenData(ert, "default_0", "RFT_XX", 199)
with self.assertRaises(ValueError):
data = GenDataCollector.loadGenData(ert, "default_0", "SNAKE_OIL_OPR_DIFF", 198)
data1 = GenDataCollector.loadGenData(ert, "default_0", "SNAKE_OIL_OPR_DIFF", 199)
self.assertFloatEqual(data1[0][0], -0.008206)
self.assertFloatEqual(data1[24][1], -0.119255)
self.assertFloatEqual(data1[24][1000], -0.258516)
def gatherGenDataData(ert, case, key):
""" :rtype: pandas.DataFrame """
key, report_step = key.split("@", 1)
report_step = int(report_step)
try:
data = GenDataCollector.loadGenData(ert, case, key, report_step)
except ValueError:
data = DataFrame()
return data.dropna() # removes all rows that has a NaN
def test_gen_data_collector(self):
config = self.createTestPath("Statoil/config/with_GEN_DATA_RFT/config")
with ErtTestContext("python/enkf/export/gen_data_collector", config) as context:
ert = context.getErt()
with self.assertRaises(KeyError):
data = GenDataCollector.loadGenData(ert, "default" , "RFT_XX" , 5)
with self.assertRaises(ValueError):
data = GenDataCollector.loadGenData(ert, "default" , "RFT_WI_1" , 90)
data1 = GenDataCollector.loadGenData(ert, "default" , "RFT_WI_1" , 9)
data2 = GenDataCollector.loadGenData(ert, "default" , "RFT_WI_2" , 5)
self.assertFloatEqual( data1[0][0] , 346.088074)
self.assertFloatEqual( data1[24][1] , 364.461090)
self.assertFloatEqual( data2[0][1] , 263.419434)
self.assertTrue( math.isnan( data2[0][0] ) )
def test_gen_data_collector(self):
config = self.createTestPath("local/snake_oil/snake_oil.ert")
with ErtTestContext("python/enkf/export/gen_data_collector",
config) as context:
ert = context.getErt()
with self.assertRaises(KeyError):
data = GenDataCollector.loadGenData(ert, "default_0", "RFT_XX",
199)
with self.assertRaises(ValueError):
data = GenDataCollector.loadGenData(ert, "default_0",
"SNAKE_OIL_OPR_DIFF", 198)
data1 = GenDataCollector.loadGenData(ert, "default_0",
"SNAKE_OIL_OPR_DIFF", 199)
self.assertFloatEqual(data1[0][0], -0.008206)
self.assertFloatEqual(data1[24][1], -0.119255)
self.assertFloatEqual(data1[24][1000], -0.258516)
def test_gen_data_collector(self):
config = self.createTestPath("Statoil/config/with_GEN_DATA_RFT/config")
with ErtTestContext("python/enkf/export/gen_data_collector",
config) as context:
ert = context.getErt()
with self.assertRaises(KeyError):
data = GenDataCollector.loadGenData(ert, "default", "RFT_XX",
5)
with self.assertRaises(ValueError):
data = GenDataCollector.loadGenData(ert, "default", "RFT_WI_1",
90)
data1 = GenDataCollector.loadGenData(ert, "default", "RFT_WI_1", 9)
data2 = GenDataCollector.loadGenData(ert, "default", "RFT_WI_2", 5)
self.assertFloatEqual(data1[0][0], 346.088074)
self.assertFloatEqual(data1[24][1], 364.461090)
self.assertFloatEqual(data2[0][1], 263.419434)
self.assertTrue(math.isnan(data2[0][0]))
def do_print(self, line):
keys = matchItems(line, self.fetchSupportedKeys())
if len(keys) == 0:
self.lastCommandFailed("Must have at least one GenData key")
return False
case_name = self.ert().getEnkfFsManager().getCurrentFileSystem().getCaseName()
for key in keys:
key, report_step = key.split("@", 1)
report_step = int(report_step)
data = GenDataCollector.loadGenData(self.ert(), case_name, key, report_step)
print(data)
def run(self, output_file, trajectory_path , case_list=None, infer_iteration=True):
"""The run method will export the RFT's for all wells and all cases.
The successfull operation of this method hinges on two naming
conventions:
1. All the GEN_DATA RFT observations have key RFT_$WELL
2. The trajectory files are in $trajectory_path/$WELL.txt
"""
wells = set()
obs_pattern = "RFT_*"
enkf_obs = self.ert().getObservations()
obs_keys = enkf_obs.getMatchingKeys(obs_pattern , obs_type = EnkfObservationImplementationType.GEN_OBS)
cases = []
if case_list is not None:
cases = case_list.split(",")
if case_list is None or len(cases) == 0:
cases = [self.ert().getEnkfFsManager().getCurrentFileSystem().getCaseName()]
data_frame = pandas.DataFrame()
for index, case in enumerate(cases):
case = case.strip()
case_frame = pandas.DataFrame()
if not self.ert().getEnkfFsManager().caseExists(case):
raise UserWarning("The case '%s' does not exist!" % case)
if not self.ert().getEnkfFsManager().caseHasData(case):
raise UserWarning("The case '%s' does not have any data!" % case)
if infer_iteration:
iteration_number = self.inferIterationNumber(case)
else:
iteration_number = index
for obs_key in obs_keys:
well = obs_key.replace("RFT_","")
wells.add( well )
obs_vector = enkf_obs[obs_key]
data_key = obs_vector.getDataKey()
report_step = obs_vector.activeStep()
obs_node = obs_vector.getNode( report_step )
rft_data = GenDataCollector.loadGenData( self.ert() , case , data_key , report_step )
fs = self.ert().getEnkfFsManager().getFileSystem( case )
realizations = fs.realizationList( RealizationStateEnum.STATE_HAS_DATA )
# Trajectory
trajectory_file = os.path.join( trajectory_path , "%s.txt" % well)
trajectory = WellTrajectory( trajectory_file )
arg = ArgLoader.load( trajectory_file , column_names = ["utm_x" , "utm_y" , "md" , "tvd"])
tvd_arg = arg["tvd"]
data_size = len(tvd_arg)
# Observations
obs = numpy.empty(shape = (data_size , 2 ) , dtype=numpy.float64)
obs.fill( numpy.nan )
for (value,std,data_index) in obs_node:
obs[data_index,0] = value
obs[data_index,1] = std
real_data = pandas.DataFrame( index = ["Realization","Well"])
for iens in realizations:
realization_frame = pandas.DataFrame( data = {"TVD" : tvd_arg ,
"Pressure" : rft_data[iens],
"ObsValue" : obs[:,0],
"ObsStd" : obs[:,1]},
columns = ["TVD" , "Pressure" , "ObsValue" , "ObsStd"])
realization_frame["Realization"] = iens
realization_frame["Well"] = well
realization_frame["Case"] = case
realization_frame["Iteration"] = iteration_number
case_frame = pandas.concat( [case_frame , realization_frame] )
data_frame = pandas.concat([data_frame, case_frame])
data_frame.set_index(["Realization" , "Well" , "Case" , "Iteration"] , inplace = True)
data_frame.to_csv(output_file)
export_info = "Exported RFT information for wells: %s to: %s " % (", ".join(list(wells)) , output_file)
return export_info
