Changeset 24313 for issm/trunk/test/NightlyRun/test244.py

Timestamp:

11/01/19 12:01:57 (5 years ago)

Author:

Mathieu Morlighem

Message:

merged trunk-jpl and trunk for revision 24310

Location:

issm/trunk

Files:

• . (modified) (1 prop)
• test (modified) (1 prop)
• test/NightlyRun/test244.py (modified) (4 diffs)

issm/trunk/test/NightlyRun/test244.py

@@ -19 +19 @@
 from dmeth_params_set import *
 
-md = triangle(model(),'../Exp/Square.exp',200000.)
-md = setmask(md,'all','')
-md = parameterize(md,'../Par/SquareShelf.py')
-md = setflowequation(md,'SSA','all')
+md = triangle(model(), '../Exp/Square.exp', 300000.)
+md = setmask(md, 'all', '')
+md = parameterize(md, '../Par/SquareShelf.py')
+md = setflowequation(md, 'SSA', 'all')
 md.materials.rho_ice = 910
-md.cluster = generic('name',gethostname(),'np',3)
+md.cluster = generic('name', gethostname(), 'np', 3)
 md.geometry.bed = md.geometry.base
 
 # Use of Gemb method for SMB computation
 md.smb = SMBgemb()
-md.smb.setdefaultparameters(md.mesh,md.geometry)
+md.smb.setdefaultparameters(md.mesh, md.geometry)
+md.smb.dsnowIdx = 0
 
 #load hourly surface forcing date from 1979 to 2009:
-inputs = spio.loadmat('../Data/gemb_input.mat',squeeze_me = True)
+inputs = spio.loadmat('../Data/gemb_input.mat', squeeze_me=True)
 
-#setup the inputs: 
-md.smb.Ta = np.append(np.tile(np.conjugate(inputs['Ta0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.V = np.append(np.tile(np.conjugate(inputs['V0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.dswrf = np.append(np.tile(np.conjugate(inputs['dsw0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.dlwrf = np.append(np.tile(np.conjugate(inputs['dlw0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.P = np.append(np.tile(np.conjugate(inputs['P0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.eAir = np.append(np.tile(np.conjugate(inputs['eAir0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.pAir = np.append(np.tile(np.conjugate(inputs['pAir0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.pAir = np.append(np.tile(np.conjugate(inputs['pAir0']),(md.mesh.numberofelements,1)),np.conjugate([inputs['dateN']]),axis=0)
-md.smb.Vz = np.tile(np.conjugate(inputs['LP']['Vz']),(md.mesh.numberofelements,1)).flatten()
-md.smb.Tz = np.tile(np.conjugate(inputs['LP']['Tz']),(md.mesh.numberofelements,1)).flatten()
-md.smb.Tmean = np.tile(np.conjugate(inputs['LP']['Tmean']),(md.mesh.numberofelements,1)).flatten()
-md.smb.C = np.tile(np.conjugate(inputs['LP']['C']),(md.mesh.numberofelements,1)).flatten()
+#setup the inputs:
+md.smb.Ta = np.append(np.tile(np.conjugate(inputs['Ta0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.V = np.append(np.tile(np.conjugate(inputs['V0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.dswrf = np.append(np.tile(np.conjugate(inputs['dsw0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.dlwrf = np.append(np.tile(np.conjugate(inputs['dlw0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.P = np.append(np.tile(np.conjugate(inputs['P0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.eAir = np.append(np.tile(np.conjugate(inputs['eAir0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.pAir = np.append(np.tile(np.conjugate(inputs['pAir0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.pAir = np.append(np.tile(np.conjugate(inputs['pAir0']), (md.mesh.numberofelements, 1)), np.conjugate([inputs['dateN']]), axis=0)
+md.smb.Vz = np.tile(np.conjugate(inputs['LP']['Vz']), (md.mesh.numberofelements, 1)).flatten()
+md.smb.Tz = np.tile(np.conjugate(inputs['LP']['Tz']), (md.mesh.numberofelements, 1)).flatten()
+md.smb.Tmean = np.tile(np.conjugate(inputs['LP']['Tmean']), (md.mesh.numberofelements, 1)).flatten()
+md.smb.C = np.tile(np.conjugate(inputs['LP']['C']), (md.mesh.numberofelements, 1)).flatten()
 
 #smb settings
-md.smb.requested_outputs = ['SmbDz','SmbT','SmbD','SmbRe','SmbGdn','SmbGsp','SmbEC','SmbA','SmbMassBalance']
+md.smb.requested_outputs = ['SmbDz', 'SmbT', 'SmbD', 'SmbRe', 'SmbGdn', 'SmbGsp', 'SmbEC', 'SmbA', 'SmbMassBalance']
 
-#only run smb core: 
+#only run smb core:
 md.transient.isstressbalance = 0
 md.transient.ismasstransport = 1
 md.transient.isthermal = 0
 
-#time stepping: 
+#time stepping:
 md.timestepping.start_time = 1965.
 md.timestepping.final_time = 1965.75
-md.timestepping.time_step = 1./365.0
+md.timestepping.time_step = 1. / 365.0
 md.timestepping.interp_forcings = 0.
 
@@ -68 +69 @@
 #partitioning
 md.qmu.numberofpartitions = md.mesh.numberofelements
-md = partitioner(md,'package','linear')
-md.qmu.partition = (md.qmu.partition - 1).reshape(-1,1).T
+md = partitioner(md, 'package', 'linear', 'type', 'element')
+md.qmu.epartition = (md.qmu.epartition - 1)
 
 #variables
-md.qmu.variables.surface_mass_balanceC = normal_uncertain.normal_uncertain('scaled_SmbC',1,0.5)
+md.qmu.variables.surface_mass_balanceC = normal_uncertain.normal_uncertain('scaled_SmbC', 1, 0.5)
 Tmin = 273.
-telms = np.atleast_2d(np.min(md.smb.Ta[0:-1,:],1))
+telms = np.atleast_2d(np.min(md.smb.Ta[0:-1, :], 1))
 mint_on_partition = telms.flatten()
 for pa in range(np.size(mint_on_partition)):
-        vi = np.where(md.qmu.partition == pa)
-        mint = telms[vi]*1.05
-        pos = np.where(mint < Tmin)
-        mint[pos] = Tmin
-        mint_on_partition[pa] = max(mint/telms[vi])
+    vi = np.where(md.qmu.epartition == pa)
+    mint = telms[0, vi] * 1.05
+    pos = np.where(mint < Tmin)
+    mint[pos] = Tmin
+    mint_on_partition[pa] = max(mint / telms[0, vi])
 
 mint_on_partition[np.where(np.isnan(mint_on_partition))] = 10**-10
-md.qmu.variables.surface_mass_balanceTa = uniform_uncertain.uniform_uncertain('scaled_SmbTa',1,0.05)
+md.qmu.variables.surface_mass_balanceTa = uniform_uncertain.uniform_uncertain('scaled_SmbTa', 1, 0.05)
 md.qmu.variables.surface_mass_balanceTa[0].lower = 0.95
-md.qmu.variables.surface_mass_balanceTa[0].upper = np.maximum(np.minimum(np.maximum(1.05,mint_on_partition),0.9999),0.0001)
+md.qmu.variables.surface_mass_balanceTa[0].upper = np.maximum(np.minimum(np.maximum(1.05, mint_on_partition), 0.9999), 0.0001)
 
 #responses
-md.qmu.responses.IceVolume = response_function.response_function('IceVolume',[],[0.0001,0.001,0.01,0.25,0.5,0.75,0.99,0.999,0.9999])
-md.qmu.responses.IceMass = response_function.response_function('IceMass',[],[0.0001,0.001,0.01,0.25,0.5,0.75,0.99,0.999,0.9999])
-md.qmu.responses.TotalSmb = response_function.response_function('TotalSmb',[],[0.0001,0.001,0.01,0.25,0.5,0.75,0.99,0.999,0.9999])
+md.qmu.responses.IceVolume = response_function.response_function('IceVolume', [], [0.0001, 0.001, 0.01, 0.25, 0.5, 0.75, 0.99, 0.999, 0.9999])
+md.qmu.responses.IceMass = response_function.response_function('IceMass', [], [0.0001, 0.001, 0.01, 0.25, 0.5, 0.75, 0.99, 0.999, 0.9999])
+md.qmu.responses.TotalSmb = response_function.response_function('TotalSmb', [], [0.0001, 0.001, 0.01, 0.25, 0.5, 0.75, 0.99, 0.999, 0.9999])
 
 #  nond_sampling study
 md.qmu.method = dakota_method.dakota_method('nond_samp')
-md.qmu.method = dmeth_params_set(md.qmu.method,'seed',1234,'samples',3,'sample_type','lhs')
+md.qmu.method = dmeth_params_set(md.qmu.method, 'seed', 1234, 'samples', 3, 'sample_type', 'lhs')
 dver = str(version)
-if ((int(dver[0]) == 4 and int(dver[2])>2) or int(dver[0])>4):
-        md.qmu.method = dmeth_params_set(md.qmu.method,'rng','rnum2')
+if ((int(dver[0]) == 4 and int(dver[2]) > 2) or int(dver[0]) > 4):
+    md.qmu.method = dmeth_params_set(md.qmu.method, 'rng', 'rnum2')
 
 #parameters
@@ -108 +109 @@
 
 if version >= 6:
-        md.qmu.params.analysis_driver = 'matlab'
-        md.qmu.params.evaluation_scheduling = 'master'
-        md.qmu.params.processors_per_evaluation = 2
+    md.qmu.params.analysis_driver = 'matlab'
+    md.qmu.params.evaluation_scheduling = 'master'
+    md.qmu.params.processors_per_evaluation = 2
 else:
-        md.qmu.params.analysis_driver = 'stressbalance'
-        md.qmu.params.evaluation_concurrency = 1
+    md.qmu.params.analysis_driver = 'stressbalance'
+    md.qmu.params.evaluation_concurrency = 1
 
 
-md.stressbalance.reltol = 10**-5 #tighten for qmu analyses
-md.transient.requested_outputs = ['IceVolume','TotalSmb','IceMass']
+md.stressbalance.reltol = 10**-5  #tighten for qmu analyses
+md.transient.requested_outputs = ['IceVolume', 'TotalSmb', 'IceMass']
 
 #solve
-md.verbose = verbose('000000000')       # this line is recommended
-md = solve(md,'Transient','overwrite','y')
+md.verbose = verbose('000000000')  # this line is recommended
+md = solve(md, 'Transient', 'overwrite', 'y')
 md.qmu.results = md.results.dakota
 
@@ -127 +128 @@
 md.results.dakota.moments = []
 for i in range(3):
-        md.results.dakota.moments.append(md.results.dakota.dresp_out[i].mean)
+    md.results.dakota.moments.append(md.results.dakota.dresp_out[i].mean)
 
 for i in range(3):
-        md.results.dakota.moments.append(md.results.dakota.dresp_out[i].stddev)
+    md.results.dakota.moments.append(md.results.dakota.dresp_out[i].stddev)
 
 field_names = ['moments']
 field_tolerances = [1e-11]
 field_values = [md.results.dakota.moments]
-