def main(example_file: str,
array_format: str = FORMAT_NUMPY,
verbose: bool = False):
"""
Main entry point for execution engine.
Args:
example_file: The MDF file to execute.
array_format: The format of arrays to use. Allowed values: 'numpy' or 'tensorflow'.
verbose: Whether to print output to standard out during execution.
"""
from modeci_mdf.utils import load_mdf, print_summary
mod_graph = load_mdf(example_file).graphs[0]
if verbose:
print("Loaded Graph:")
print_summary(mod_graph)
print("------------------")
eg = EvaluableGraph(mod_graph, verbose)
eg.evaluate(array_format=array_format)
return eg
def main():
mod = Model(id="abc_conditions")
mod_graph = Graph(id="abc_conditions_example")
mod.graphs.append(mod_graph)
input_node = Node(id="input0")
input_node.parameters.append(Parameter(id="input_level", value=0.0))
input_node.parameters.append(Parameter(id="count_0", value="count_0 + 1"))
op1 = OutputPort(id="out_port")
op1.value = "input_level"
input_node.output_ports.append(op1)
mod_graph.nodes.append(input_node)
def create_simple_node(graph, id_, sender=None):
n = Node(id=id_)
graph.nodes.append(n)
ip1 = InputPort(id="input_port1", shape="(1,)")
n.input_ports.append(ip1)
n.output_ports.append(OutputPort(id="output_1", value=ip1.id))
if sender is not None:
simple_connect(sender, n, graph)
return n
a = create_simple_node(mod_graph, "A", input_node)
a.parameters.append(Parameter(id="count_A", value="count_A + 1"))
b = create_simple_node(mod_graph, "B", a)
b.parameters.append(Parameter(id="count_B", value="count_B + 1"))
c = create_simple_node(mod_graph, "C", a)
c.parameters.append(Parameter(id="count_C", value="count_C+ 1"))
cond_i = Condition(type="BeforeNCalls", dependencies=input_node.id, n=1)
cond_a = Condition(type="Always")
cond_b = Condition(type="EveryNCalls", dependencies=a.id, n=2)
cond_c = Condition(type="EveryNCalls", dependencies=a.id, n=3)
cond_term = Condition(
type="And",
dependencies=[
Condition(type="AfterNCalls", dependencies=c.id, n=2),
Condition(type="JustRan", dependencies=a.id),
],
)
mod_graph.conditions = ConditionSet(
node_specific={
input_node.id: cond_i,
a.id: cond_a,
b.id: cond_b,
c.id: cond_c
},
termination={"environment_state_update": cond_term},
)
mod.to_json_file(
os.path.join(os.path.dirname(__file__), "%s.json" % mod.id))
mod.to_yaml_file(
os.path.join(os.path.dirname(__file__), "%s.yaml" % mod.id))
print_summary(mod_graph)
import sys
if "-run" in sys.argv:
verbose = True
# verbose = False
from modeci_mdf.execution_engine import EvaluableGraph
from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW
format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY
eg = EvaluableGraph(mod_graph, verbose=verbose)
eg.evaluate(array_format=format)
if "-graph" in sys.argv:
mod.to_graph_image(
engine="dot",
output_format="png",
view_on_render=False,
level=3,
filename_root="abc_conditions",
only_warn_on_fail=
True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
)
from modeci_mdf.utils import load_mdf, print_summary
verbose = True
if len(sys.argv) < 3:
print(
"Usage:\n\n python graphviz.py MDF_JSON_FILE level [%s]\n\n" %
NO_VIEW +
"where level = 1, 2 or 3. Include %s to supress viewing generated graph on render\n"
% NO_VIEW)
exit()
example = sys.argv[1]
view = NO_VIEW not in sys.argv
model = load_mdf(example)
mod_graph = model.graphs[0]
print("------------------")
print("Loaded Graph:")
print_summary(mod_graph)
print("------------------")
# nmllite_file = example.replace('.json','.nmllite.json')
mdf_to_graphviz(mod_graph,
engine=engines["d"],
view_on_render=view,
level=int(sys.argv[2]))
def main():
mod = Model(id="ABCD")
mod_graph = Graph(id="abcd_example")
mod.graphs.append(mod_graph)
input_node = Node(id="input0", metadata={"color": ".8 .8 .8"})
input_node.parameters.append(Parameter(id="input_level", value=0.0))
op1 = OutputPort(id="out_port")
op1.value = "input_level"
input_node.output_ports.append(op1)
mod_graph.nodes.append(input_node)
print(input_node)
print(input_node.output_ports)
# a = create_example_node('A', mod_graph)
a = Node(id="A", metadata={"color": ".8 0 0"})
mod_graph.nodes.append(a)
ip1 = InputPort(id="input_port1")
a.input_ports.append(ip1)
a.parameters.append(Parameter(id="slope", value=abcd.A_slope))
a.parameters.append(Parameter(id="intercept", value=abcd.A_intercept))
f1 = Parameter(
id="linear_func",
function="linear",
args={
"variable0": ip1.id,
"slope": "slope",
"intercept": "intercept"
},
)
a.parameters.append(f1)
a.output_ports.append(OutputPort(id="output_1", value="linear_func"))
e1 = simple_connect(input_node, a, mod_graph)
b = Node(id="B", metadata={"color": "0 .8 0"})
mod_graph.nodes.append(b)
ip1 = InputPort(id="input_port1")
b.input_ports.append(ip1)
b.parameters.append(Parameter(id="gain", value=abcd.B_gain))
b.parameters.append(Parameter(id="bias", value=abcd.B_bias))
b.parameters.append(Parameter(id="offset", value=abcd.B_offset))
f1 = Parameter(
id="logistic_func",
function="logistic",
args={
"variable0": ip1.id,
"gain": "gain",
"bias": "bias",
"offset": "offset"
},
)
b.parameters.append(f1)
b.output_ports.append(OutputPort(id="output_1", value="logistic_func"))
simple_connect(a, b, mod_graph)
c = Node(id="C", metadata={"color": "0 0 .8"})
mod_graph.nodes.append(c)
ip1 = InputPort(id="input_port1", shape="(1,)")
c.input_ports.append(ip1)
c.parameters.append(Parameter(id="scale", value=abcd.C_scale))
c.parameters.append(Parameter(id="rate", value=abcd.C_rate))
c.parameters.append(Parameter(id="bias", value=abcd.C_bias))
c.parameters.append(Parameter(id="offset", value=abcd.C_offset))
f1 = Parameter(
id="exponential_func",
function="exponential",
args={
"variable0": ip1.id,
"scale": "scale",
"rate": "rate",
"bias": "bias",
"offset": "offset",
},
)
c.parameters.append(f1)
c.output_ports.append(OutputPort(id="output_1", value="exponential_func"))
simple_connect(b, c, mod_graph)
d = Node(id="D", metadata={"color": ".8 0 .8"})
mod_graph.nodes.append(d)
ip1 = InputPort(id="input_port1", shape="(1,)")
d.input_ports.append(ip1)
d.parameters.append(Parameter(id="scale", value=abcd.D_scale))
f1 = Parameter(id="sin_func",
function="sin",
args={
"variable0": ip1.id,
"scale": "scale"
})
d.parameters.append(f1)
d.output_ports.append(OutputPort(id="output_1", value="sin_func"))
simple_connect(c, d, mod_graph)
print(mod)
print("------------------")
# print(mod.to_json())
new_file = mod.to_json_file("%s.json" % mod.id)
new_file = mod.to_yaml_file("%s.yaml" % mod.id)
print_summary(mod_graph)
import sys
if "-run" in sys.argv:
verbose = True
# verbose = False
from modeci_mdf.execution_engine import EvaluableGraph
from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW
format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY
eg = EvaluableGraph(mod_graph, verbose=verbose)
eg.evaluate(array_format=format)
if "-graph" in sys.argv:
mod.to_graph_image(
engine="dot",
output_format="png",
view_on_render=False,
level=1,
filename_root="abcd",
only_warn_on_fail=
True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
)
mod.to_graph_image(
engine="dot",
output_format="png",
view_on_render=False,
level=3,
filename_root="abcd_3",
only_warn_on_fail=
True, # Makes sure test of this doesn't fail on Windows on GitHub Actions
)