def test_distribcomp_list_feature(self):
size = 15
model = om.Group()
model.add_subsystem("indep", om.IndepVarComp('x', np.zeros(size)))
model.add_subsystem("C2", DistribComp(size=size))
model.add_subsystem("C3", Summer(size=size))
model.connect('indep.x', 'C2.invec')
model.connect('C2.outvec', 'C3.invec', src_indices=om.slicer[:])
prob = om.Problem(model)
prob.setup()
# prior to model execution, the global shape of a distributed variable is not available
# and only the local portion of the value is available
model.C2.list_inputs(hierarchical=False, shape=True, global_shape=True, print_arrays=True)
model.C2.list_outputs(hierarchical=False, shape=True, global_shape=True, print_arrays=True)
prob['indep.x'] = np.ones(size)
prob.run_model()
# after model execution, the global shape of a distributed variable is available
# and the complete global value is available
model.C2.list_inputs(hierarchical=False, shape=True, global_shape=True, print_arrays=True)
model.C2.list_outputs(hierarchical=False, shape=True, global_shape=True, print_arrays=True)
# note that the shape of the input variable for the non-distributed Summer component
# is different on each processor, use the all_procs argument to display on all processors
model.C3.list_inputs(hierarchical=False, shape=True, global_shape=True, print_arrays=True, all_procs=True)
assert_near_equal(prob['C3.sum'], -5.)
def test_distribcomp_feature(self):
size = 15
model = om.Group()
# Distributed component "C2" requires an IndepVarComp to supply inputs.
model.add_subsystem("indep", om.IndepVarComp('x', np.zeros(size)))
model.add_subsystem("C2", DistribComp(size=size))
model.add_subsystem("C3", Summer(size=size))
model.connect('indep.x', 'C2.invec')
# to copy the full distributed output C2.outvec into C3.invec on all procs, we need
# to specify src_indices=om.slicer[:]
model.connect('C2.outvec', 'C3.invec', src_indices=om.slicer[:])
prob = om.Problem(model)
prob.setup()
prob.set_val('indep.x', np.ones(size))
prob.run_model()
assert_near_equal(prob.get_val('C2.invec'),
np.ones((8,)) if model.comm.rank == 0 else np.ones((7,)))
assert_near_equal(prob.get_val('C2.outvec'),
2*np.ones((8,)) if model.comm.rank == 0 else -3*np.ones((7,)))
assert_near_equal(prob.get_val('C3.sum'), -5.)
def test_distribcomp_feature(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.distributed_components import DistribComp, Summer
size = 15
model = om.Group()
model.add_subsystem("indep", om.IndepVarComp('x', np.zeros(size)))
model.add_subsystem("C2", DistribComp(size=size))
model.add_subsystem("C3", Summer(size=size))
model.connect('indep.x', 'C2.invec')
model.connect('C2.outvec', 'C3.invec')
prob = om.Problem(model)
prob.setup()
prob['indep.x'] = np.ones(size)
prob.run_model()
assert_near_equal(prob['C2.invec'],
np.ones((8,)) if model.comm.rank == 0 else np.ones((7,)))
assert_near_equal(prob['C2.outvec'],
2*np.ones((8,)) if model.comm.rank == 0 else -3*np.ones((7,)))
assert_near_equal(prob['C3.sum'], -5.)
def test_distribcomp_list_vars(self):
print_opts = {'linewidth': 1024}
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
print_opts['legacy'] = '1.13'
size = 15
model = om.Group()
model.add_subsystem("indep", om.IndepVarComp('x', np.zeros(size)))
model.add_subsystem("C2", DistribComp(size=size))
model.add_subsystem("C3", Summer(size=size))
model.connect('indep.x', 'C2.invec')
model.connect('C2.outvec', 'C3.invec', src_indices=om.slicer[:])
prob = om.Problem(model)
prob.setup()
# prior to model execution, the global shape of a distributed variable is not available
# and only the local portion of the value is available
stream = StringIO()
with printoptions(**print_opts):
model.C2.list_inputs(hierarchical=False, shape=True, global_shape=True,
print_arrays=True, out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Input(s) in 'C2'",
'',
'varname val shape global_shape',
'------- --------------- ----- ------------',
'invec |3.87298334621| (8,) (15,)',
' val:',
' array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
stream = StringIO()
with printoptions(**print_opts):
model.C2.list_outputs(hierarchical=False, shape=True, global_shape=True,
print_arrays=True, out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Explicit Output(s) in 'C2'",
'',
'varname val shape global_shape',
'------- --------------- ----- ------------',
'outvec |3.87298334621| (8,) (15,)',
' val:',
' array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
# run the model
prob['indep.x'] = np.ones(size)
prob.run_model()
# after model execution, the global shape of a distributed variable is available
# and the complete global value is available
stream = StringIO()
with printoptions(**print_opts):
model.C2.list_inputs(hierarchical=False, shape=True, global_shape=True,
print_arrays=True, out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Input(s) in 'C2'",
'',
'varname val shape global_shape',
'------- --------------- ----- ------------',
'invec |3.87298334621| (8,) (15,)',
' val:',
' array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
stream = StringIO()
with printoptions(**print_opts):
model.C2.list_outputs(hierarchical=False, shape=True, global_shape=True,
print_arrays=True, out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Explicit Output(s) in 'C2'",
'',
'varname val shape global_shape',
'------- -------------- ----- ------------',
'outvec |9.74679434481| (8,) (15,)',
' val:',
' array([ 2., 2., 2., 2., 2., 2., 2., 2., -3., -3., -3., -3., -3., -3., -3.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
stream = StringIO()
with printoptions(**print_opts):
model.C3.list_inputs(hierarchical=False, shape=True, global_shape=True, all_procs=True,
print_arrays=True, out_stream=stream)
text = stream.getvalue().split('\n')
print('\n'.join(text))
norm = '|9.74679434481|'
shape = (15,)
value = '[2., 2., 2., 2., 2., 2., 2., 2., -3., -3., -3., -3., -3., -3., -3.]'
expected = [
"1 Input(s) in 'C3'",
'',
'varname val shape global_shape',
'------- ------------------- ----- ------------',
'invec {} {} {} '.format(norm, shape, shape),
' val:',
' array({})'.format(value),
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
assert_near_equal(prob['C3.sum'], -5.)
def test_distribcomp_list_vars(self):
from openmdao.test_suite.components.distributed_components import DistribComp, Summer
print_opts = {'linewidth': 1024}
from distutils.version import LooseVersion
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
print_opts['legacy'] = '1.13'
size = 15
model = om.Group()
model.add_subsystem("indep", om.IndepVarComp('x', np.zeros(size)))
model.add_subsystem("C2", DistribComp(size=size))
model.add_subsystem("C3", Summer(size=size))
model.connect('indep.x', 'C2.invec')
model.connect('C2.outvec', 'C3.invec')
prob = om.Problem(model)
prob.setup()
# prior to model execution, the global shape of a distributed variable is not available
# and only the local portion of the value is available
stream = cStringIO()
with printoptions(**print_opts):
model.C2.list_inputs(hierarchical=False,
shape=True,
global_shape=True,
print_arrays=True,
out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Input(s) in 'C2'", '------------------', '',
'varname value shape global_shape',
'------- --------------- ----- ------------',
'invec |2.82842712475| (8,) Unavailable ',
' value:',
' array([1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(
remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
stream = cStringIO()
with printoptions(**print_opts):
model.C2.list_outputs(hierarchical=False,
shape=True,
global_shape=True,
print_arrays=True,
out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Explicit Output(s) in 'C2'", '----------------------------',
'', 'varname value shape global_shape',
'------- --------------- ----- ------------',
'outvec |2.82842712475| (8,) Unavailable ',
' value:',
' array([1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(
remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
# run the model
prob['indep.x'] = np.ones(size)
prob.run_model()
# after model execution, the global shape of a distributed variable is available
# and the complete global value is available
stream = cStringIO()
with printoptions(**print_opts):
model.C2.list_inputs(hierarchical=False,
shape=True,
global_shape=True,
print_arrays=True,
out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Input(s) in 'C2'", '------------------', '',
'varname value shape global_shape',
'-------- --------------- ----- ------------',
'C2.invec |3.87298334621| (8,) (15,) ',
' value:',
' array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(
remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
stream = cStringIO()
with printoptions(**print_opts):
model.C2.list_outputs(hierarchical=False,
shape=True,
global_shape=True,
print_arrays=True,
out_stream=stream)
if prob.comm.rank == 0:
text = stream.getvalue().split('\n')
expected = [
"1 Explicit Output(s) in 'C2'", '----------------------------',
'', 'varname value shape global_shape',
'--------- -------------- ----- ------------',
'C2.outvec |9.74679434481| (8,) (15,) ',
' value:',
' array([ 2., 2., 2., 2., 2., 2., 2., 2., -3., -3., -3., -3., -3., -3., -3.])'
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(
remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
# note that the shape of the input variable for the non-distributed Summer component
# is different on each processor, use the all_procs argument to display on all processors
stream = cStringIO()
with printoptions(**print_opts):
model.C3.list_inputs(hierarchical=False,
shape=True,
global_shape=True,
all_procs=True,
print_arrays=True,
out_stream=stream)
text = stream.getvalue().split('\n')
if prob.comm.rank == 0:
norm = '|5.65685424949|'
shape = (8, )
value = '[2., 2., 2., 2., 2., 2., 2., 2.]'
else:
norm = '|7.93725393319|'
shape = (7, )
value = '[-3., -3., -3., -3., -3., -3., -3.]'
expected = [
"1 Input(s) in 'C3'",
'------------------',
'',
'varname value shape global_shape',
'-------- ------------------- ----- ------------',
'C3.invec {} {} {} '.format(norm, shape, shape),
' value:',
' array({})'.format(value),
]
for i, line in enumerate(expected):
if line and not line.startswith('-'):
self.assertEqual(
remove_whitespace(text[i]), remove_whitespace(line),
'\nExpected: %s\nReceived: %s\n' % (line, text[i]))
assert_rel_error(self, prob['C3.out'], -5.)