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Closes idaholab#1119
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@PaulTalbot-INL
PaulTalbot-INL committed Feb 25, 2021
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33 changes: 33 additions & 0 deletions tests/framework/ensembleModelTests/NDEnsemble2Outs/NDModel.py
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# Copyright 2017 Battelle Energy Alliance, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np

def run(raven, info):
"""
A dummy model used to check the connections for the EnsembleModel
@ In, raven, object, object to store members on
@ In, info, dict, dictionary containing inputs from RAVEN
@ Out, None
"""
x = np.linspace(0,1,11)
y = np.linspace(0,1,11)
c = np.zeros((len(x), len(y)))
c += x * raven.a
c = c.T
c += y * raven.b
raven.c = c
raven.x = x
raven.y = y
raven._indexMap = np.atleast_1d({'c': ['x', 'y']})

23 changes: 23 additions & 0 deletions tests/framework/ensembleModelTests/NDEnsemble2Outs/Summary.py
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# Copyright 2017 Battelle Energy Alliance, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np

def run(raven, info):
"""
A dummy model used to check the connections for the EnsembleModel
@ In, raven, object, object to store members on
@ In, info, dict, dictionary containing inputs from RAVEN
@ Out, None
"""
raven.d = raven.c.sum()
243 changes: 243 additions & 0 deletions tests/framework/ensembleModelTests/gold/NDEnsemble2Outs/nd1.csv
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x,y,a,b,c
0.0,0.0,2,3,0.0
0.0,0.1,2,3,0.3
0.0,0.2,2,3,0.6
0.0,0.30000000000000004,2,3,0.9
0.0,0.4,2,3,1.2
0.0,0.5,2,3,1.5
0.0,0.6000000000000001,2,3,1.8
0.0,0.7000000000000001,2,3,2.1
0.0,0.8,2,3,2.4
0.0,0.9,2,3,2.7
0.0,1.0,2,3,3.0
0.1,0.0,2,3,0.2
0.1,0.1,2,3,0.5
0.1,0.2,2,3,0.8
0.1,0.30000000000000004,2,3,1.1
0.1,0.4,2,3,1.4
0.1,0.5,2,3,1.7
0.1,0.6000000000000001,2,3,2.0
0.1,0.7000000000000001,2,3,2.3
0.1,0.8,2,3,2.6
0.1,0.9,2,3,2.9
0.1,1.0,2,3,3.2
0.2,0.0,2,3,0.4
0.2,0.1,2,3,0.7
0.2,0.2,2,3,1.0
0.2,0.30000000000000004,2,3,1.3
0.2,0.4,2,3,1.6
0.2,0.5,2,3,1.9
0.2,0.6000000000000001,2,3,2.2
0.2,0.7000000000000001,2,3,2.5
0.2,0.8,2,3,2.8
0.2,0.9,2,3,3.1
0.2,1.0,2,3,3.4
0.30000000000000004,0.0,2,3,0.6
0.30000000000000004,0.1,2,3,0.9
0.30000000000000004,0.2,2,3,1.2
0.30000000000000004,0.30000000000000004,2,3,1.5
0.30000000000000004,0.4,2,3,1.8
0.30000000000000004,0.5,2,3,2.1
0.30000000000000004,0.6000000000000001,2,3,2.4
0.30000000000000004,0.7000000000000001,2,3,2.7
0.30000000000000004,0.8,2,3,3.0
0.30000000000000004,0.9,2,3,3.3
0.30000000000000004,1.0,2,3,3.6
0.4,0.0,2,3,0.8
0.4,0.1,2,3,1.1
0.4,0.2,2,3,1.4
0.4,0.30000000000000004,2,3,1.7
0.4,0.4,2,3,2.0
0.4,0.5,2,3,2.3
0.4,0.6000000000000001,2,3,2.6
0.4,0.7000000000000001,2,3,2.9
0.4,0.8,2,3,3.2
0.4,0.9,2,3,3.5
0.4,1.0,2,3,3.8
0.5,0.0,2,3,1.0
0.5,0.1,2,3,1.3
0.5,0.2,2,3,1.6
0.5,0.30000000000000004,2,3,1.9
0.5,0.4,2,3,2.2
0.5,0.5,2,3,2.5
0.5,0.6000000000000001,2,3,2.8
0.5,0.7000000000000001,2,3,3.1
0.5,0.8,2,3,3.4
0.5,0.9,2,3,3.7
0.5,1.0,2,3,4.0
0.6000000000000001,0.0,2,3,1.2
0.6000000000000001,0.1,2,3,1.5
0.6000000000000001,0.2,2,3,1.8
0.6000000000000001,0.30000000000000004,2,3,2.1
0.6000000000000001,0.4,2,3,2.4
0.6000000000000001,0.5,2,3,2.7
0.6000000000000001,0.6000000000000001,2,3,3.0
0.6000000000000001,0.7000000000000001,2,3,3.3
0.6000000000000001,0.8,2,3,3.6
0.6000000000000001,0.9,2,3,3.9
0.6000000000000001,1.0,2,3,4.2
0.7000000000000001,0.0,2,3,1.4
0.7000000000000001,0.1,2,3,1.7
0.7000000000000001,0.2,2,3,2.0
0.7000000000000001,0.30000000000000004,2,3,2.3
0.7000000000000001,0.4,2,3,2.6
0.7000000000000001,0.5,2,3,2.9
0.7000000000000001,0.6000000000000001,2,3,3.2
0.7000000000000001,0.7000000000000001,2,3,3.5
0.7000000000000001,0.8,2,3,3.8
0.7000000000000001,0.9,2,3,4.1
0.7000000000000001,1.0,2,3,4.4
0.8,0.0,2,3,1.6
0.8,0.1,2,3,1.9
0.8,0.2,2,3,2.2
0.8,0.30000000000000004,2,3,2.5
0.8,0.4,2,3,2.8
0.8,0.5,2,3,3.1
0.8,0.6000000000000001,2,3,3.4
0.8,0.7000000000000001,2,3,3.7
0.8,0.8,2,3,4.0
0.8,0.9,2,3,4.3
0.8,1.0,2,3,4.6
0.9,0.0,2,3,1.8
0.9,0.1,2,3,2.1
0.9,0.2,2,3,2.4
0.9,0.30000000000000004,2,3,2.7
0.9,0.4,2,3,3.0
0.9,0.5,2,3,3.3
0.9,0.6000000000000001,2,3,3.6
0.9,0.7000000000000001,2,3,3.9
0.9,0.8,2,3,4.2
0.9,0.9,2,3,4.5
0.9,1.0,2,3,4.8
1.0,0.0,2,3,2.0
1.0,0.1,2,3,2.3
1.0,0.2,2,3,2.6
1.0,0.30000000000000004,2,3,2.9
1.0,0.4,2,3,3.2
1.0,0.5,2,3,3.5
1.0,0.6000000000000001,2,3,3.8
1.0,0.7000000000000001,2,3,4.1
1.0,0.8,2,3,4.4
1.0,0.9,2,3,4.7
1.0,1.0,2,3,5.0
0.0,0.0,2,3,0.0
0.0,0.1,2,3,0.3
0.0,0.2,2,3,0.6
0.0,0.30000000000000004,2,3,0.9
0.0,0.4,2,3,1.2
0.0,0.5,2,3,1.5
0.0,0.6000000000000001,2,3,1.8
0.0,0.7000000000000001,2,3,2.1
0.0,0.8,2,3,2.4
0.0,0.9,2,3,2.7
0.0,1.0,2,3,3.0
0.1,0.0,2,3,0.2
0.1,0.1,2,3,0.5
0.1,0.2,2,3,0.8
0.1,0.30000000000000004,2,3,1.1
0.1,0.4,2,3,1.4
0.1,0.5,2,3,1.7
0.1,0.6000000000000001,2,3,2.0
0.1,0.7000000000000001,2,3,2.3
0.1,0.8,2,3,2.6
0.1,0.9,2,3,2.9
0.1,1.0,2,3,3.2
0.2,0.0,2,3,0.4
0.2,0.1,2,3,0.7
0.2,0.2,2,3,1.0
0.2,0.30000000000000004,2,3,1.3
0.2,0.4,2,3,1.6
0.2,0.5,2,3,1.9
0.2,0.6000000000000001,2,3,2.2
0.2,0.7000000000000001,2,3,2.5
0.2,0.8,2,3,2.8
0.2,0.9,2,3,3.1
0.2,1.0,2,3,3.4
0.30000000000000004,0.0,2,3,0.6
0.30000000000000004,0.1,2,3,0.9
0.30000000000000004,0.2,2,3,1.2
0.30000000000000004,0.30000000000000004,2,3,1.5
0.30000000000000004,0.4,2,3,1.8
0.30000000000000004,0.5,2,3,2.1
0.30000000000000004,0.6000000000000001,2,3,2.4
0.30000000000000004,0.7000000000000001,2,3,2.7
0.30000000000000004,0.8,2,3,3.0
0.30000000000000004,0.9,2,3,3.3
0.30000000000000004,1.0,2,3,3.6
0.4,0.0,2,3,0.8
0.4,0.1,2,3,1.1
0.4,0.2,2,3,1.4
0.4,0.30000000000000004,2,3,1.7
0.4,0.4,2,3,2.0
0.4,0.5,2,3,2.3
0.4,0.6000000000000001,2,3,2.6
0.4,0.7000000000000001,2,3,2.9
0.4,0.8,2,3,3.2
0.4,0.9,2,3,3.5
0.4,1.0,2,3,3.8
0.5,0.0,2,3,1.0
0.5,0.1,2,3,1.3
0.5,0.2,2,3,1.6
0.5,0.30000000000000004,2,3,1.9
0.5,0.4,2,3,2.2
0.5,0.5,2,3,2.5
0.5,0.6000000000000001,2,3,2.8
0.5,0.7000000000000001,2,3,3.1
0.5,0.8,2,3,3.4
0.5,0.9,2,3,3.7
0.5,1.0,2,3,4.0
0.6000000000000001,0.0,2,3,1.2
0.6000000000000001,0.1,2,3,1.5
0.6000000000000001,0.2,2,3,1.8
0.6000000000000001,0.30000000000000004,2,3,2.1
0.6000000000000001,0.4,2,3,2.4
0.6000000000000001,0.5,2,3,2.7
0.6000000000000001,0.6000000000000001,2,3,3.0
0.6000000000000001,0.7000000000000001,2,3,3.3
0.6000000000000001,0.8,2,3,3.6
0.6000000000000001,0.9,2,3,3.9
0.6000000000000001,1.0,2,3,4.2
0.7000000000000001,0.0,2,3,1.4
0.7000000000000001,0.1,2,3,1.7
0.7000000000000001,0.2,2,3,2.0
0.7000000000000001,0.30000000000000004,2,3,2.3
0.7000000000000001,0.4,2,3,2.6
0.7000000000000001,0.5,2,3,2.9
0.7000000000000001,0.6000000000000001,2,3,3.2
0.7000000000000001,0.7000000000000001,2,3,3.5
0.7000000000000001,0.8,2,3,3.8
0.7000000000000001,0.9,2,3,4.1
0.7000000000000001,1.0,2,3,4.4
0.8,0.0,2,3,1.6
0.8,0.1,2,3,1.9
0.8,0.2,2,3,2.2
0.8,0.30000000000000004,2,3,2.5
0.8,0.4,2,3,2.8
0.8,0.5,2,3,3.1
0.8,0.6000000000000001,2,3,3.4
0.8,0.7000000000000001,2,3,3.7
0.8,0.8,2,3,4.0
0.8,0.9,2,3,4.3
0.8,1.0,2,3,4.6
0.9,0.0,2,3,1.8
0.9,0.1,2,3,2.1
0.9,0.2,2,3,2.4
0.9,0.30000000000000004,2,3,2.7
0.9,0.4,2,3,3.0
0.9,0.5,2,3,3.3
0.9,0.6000000000000001,2,3,3.6
0.9,0.7000000000000001,2,3,3.9
0.9,0.8,2,3,4.2
0.9,0.9,2,3,4.5
0.9,1.0,2,3,4.8
1.0,0.0,2,3,2.0
1.0,0.1,2,3,2.3
1.0,0.2,2,3,2.6
1.0,0.30000000000000004,2,3,2.9
1.0,0.4,2,3,3.2
1.0,0.5,2,3,3.5
1.0,0.6000000000000001,2,3,3.8
1.0,0.7000000000000001,2,3,4.1
1.0,0.8,2,3,4.4
1.0,0.9,2,3,4.7
1.0,1.0,2,3,5.0
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