def test_sub_graph_with_arg_input():
graph0 = Graph()
graph0_0 = Graph()
with pytest.raises(PyungoError) as err:
graph0.add_node(graph0_0,
args=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
assert "Node with Graph can only accept kwargs input. However, get args = ['inp1_1', 'inp1_2']" in str(
err.value)
def test_simple_parallel():
""" TODO: We could mock and make sure things are called correctly """
graph = Graph(pool_size=2)
graph.add_node(par_f_my_function, inputs=['a', 'b'], outputs=['c'])
graph.add_node(par_f_my_function3, inputs=['d', 'a'], outputs=['e'])
graph.add_node(par_f_my_function2, inputs=['c'], outputs=['d'])
graph.add_node(par_f_my_function2, inputs=['c'], outputs=['f'])
graph.add_node(par_f_my_function2, inputs=['c'], outputs=['g'])
res = graph.calculate(data={'a': 2, 'b': 3})
assert res == -1.5
def test_simple_without_decorator():
graph = Graph()
def f_my_function(a, b):
return a + b
def f_my_function3(d, a):
return d - a
def f_my_function2(c):
return c / 10.
graph.add_node(f_my_function, inputs=['a', 'b'], outputs=['c'])
graph.add_node(f_my_function3, inputs=['d', 'a'], outputs=['e'])
graph.add_node(f_my_function2, inputs=['c'], outputs=['d'])
res = graph.calculate(data={'a': 2, 'b': 3})
assert res == -1.5
assert graph.data['e'] == -1.5
def unpack_dict(f): # (ordered)
def wrap(*args, **kwargs):
res = f(*args, **kwargs)
return [res[k] for k in res]
return wrap
# parallelism not needed for this example
graph = Graph(parallel=False)
graph.add_node(unpack_df(pvlib.solarposition.get_solarposition),
inputs=['index', 'latitude', 'longitude'],
outputs=[
'apparent_elevation', 'apparent_zenith', 'azimuth',
'elevation', 'equation_of_time', 'zenith'
])
graph.add_node(pvlib.irradiance.extraradiation,
inputs=['index'],
outputs=['dni_extra'])
graph.add_node(pvlib.atmosphere.relativeairmass,
inputs=['apparent_zenith'],
outputs=['airmass'])
graph.add_node(pvlib.irradiance.haydavies,
inputs=[
'surface_tilt', 'surface_azimuth', 'DHI', 'DNI',
'dni_extra', 'apparent_zenith', 'azimuth'
],
outputs=['poa_sky_diffuse'])
graph.add_node(pvlib.irradiance.grounddiffuse,
def test_deep_prefix_id_ordered_nodes():
def f_my_function1(inp1_1, inp1_2):
return inp1_1 + inp1_2
def f_my_function2(inp2_1):
return inp2_1 / 10.
def f_my_function3(inp3_1, inp3_2):
return inp3_1 - inp3_2
graph0 = Graph()
graph0_0 = Graph()
graph0_1 = Graph()
graph0_1_0 = Graph()
# inp2_1 = 1.5, inp3_2 = 0.15, out=0.1785
graph0.add_node(graph0_0, kwargs=['inp1_1', 'inp1_2'], outputs=['inp2_1'])
graph0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0.add_node(graph0_1,
kwargs=[('inp1', 'inp1_1'), ('inp2', 'inp3_2')],
outputs=['out'])
graph0_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# inp2_1 = 0.215, inp3_2 = 0.0215, out = 0.1785
graph0_1_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_1_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_1_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# out1 = 0.2, out2 = 0.015, out=0.1785
graph0_1.add_node(f_my_function2, inputs=['inp1'], outputs=['out1'])
graph0_1.add_node(f_my_function2, inputs=['inp2'], outputs=['out2'])
graph0_1.add_node(graph0_1_0,
kwargs=[('inp1_1', 'out1'), ('inp1_2', 'out2')],
outputs=['out3'])
prefix_ids, deep_ordered_nodes = zip(
*graph0.deep_prefix_id_ordered_nodes())
graph0_nodes = graph0.ordered_nodes
graph0_0_nodes = graph0_0.ordered_nodes
graph0_1_nodes = graph0_1.ordered_nodes
graph0_1_0_nodes = graph0_1_0.ordered_nodes
graph0_prefix_ids = [n.id for n in graph0.ordered_nodes]
graph0_0_prefix_ids = [
graph0_nodes[0].id + '.' + n.id for n in graph0_0.ordered_nodes
]
graph0_1_prefix_ids = [
graph0_nodes[2].id + '.' + n.id for n in graph0_1.ordered_nodes
]
graph0_1_0_prefix_ids = [
graph0_nodes[2].id + '.' + graph0_1_nodes[2].id + '.' + n.id
for n in graph0_1_0.ordered_nodes
]
assert deep_ordered_nodes[0] is graph0_nodes[0]
for n1, n2 in zip(deep_ordered_nodes[1:4], graph0_0_nodes):
assert n1 is n2
assert deep_ordered_nodes[4] is graph0_nodes[1]
assert deep_ordered_nodes[5] is graph0_nodes[2]
for n1, n2 in zip(deep_ordered_nodes[6:9], graph0_1_nodes):
assert n1 is n2
for n1, n2 in zip(deep_ordered_nodes[9:], graph0_1_0_nodes):
assert n1 is n2
assert prefix_ids[0] == graph0_prefix_ids[0]
for pi1, pi2 in zip(prefix_ids[1:4], graph0_0_prefix_ids):
assert pi1 == pi2
assert prefix_ids[4] == graph0_prefix_ids[1]
assert prefix_ids[5] == graph0_prefix_ids[2]
for pi1, pi2 in zip(prefix_ids[6:9], graph0_1_prefix_ids):
assert pi1 == pi2
for pi1, pi2 in zip(prefix_ids[9:], graph0_1_0_prefix_ids):
assert pi1 == pi2
def test_deep_ordered_nodes():
def f_my_function1(inp1_1, inp1_2):
return inp1_1 + inp1_2
def f_my_function2(inp2_1):
return inp2_1 / 10.
def f_my_function3(inp3_1, inp3_2):
return inp3_1 - inp3_2
graph0 = Graph()
graph0_0 = Graph()
graph0_1 = Graph()
graph0_1_0 = Graph()
# inp2_1 = 1.5, inp3_2 = 0.15, out=0.1785
graph0.add_node(graph0_0, kwargs=['inp1_1', 'inp1_2'], outputs=['inp2_1'])
graph0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0.add_node(graph0_1,
kwargs=[('inp1', 'inp1_1'), ('inp2', 'inp3_2')],
outputs=['out'])
graph0_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# inp2_1 = 0.215, inp3_2 = 0.0215, out = 0.1785
graph0_1_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_1_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_1_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# out1 = 0.2, out2 = 0.015, out=0.1785
graph0_1.add_node(f_my_function2, inputs=['inp1'], outputs=['out1'])
graph0_1.add_node(f_my_function2, inputs=['inp2'], outputs=['out2'])
graph0_1.add_node(graph0_1_0,
kwargs=[('inp1_1', 'out1'), ('inp1_2', 'out2')],
outputs=['out3'])
deep_ordered_nodes = graph0.deep_ordered_nodes
graph0_nodes = graph0.ordered_nodes
graph0_0_nodes = graph0_0.ordered_nodes
graph0_1_nodes = graph0_1.ordered_nodes
graph0_1_0_nodes = graph0_1_0.ordered_nodes
assert deep_ordered_nodes[0] is graph0_nodes[0]
for n1, n2 in zip(deep_ordered_nodes[1:4], graph0_0_nodes):
assert n1 is n2
assert deep_ordered_nodes[4] is graph0_nodes[1]
assert deep_ordered_nodes[5] is graph0_nodes[2]
for n1, n2 in zip(deep_ordered_nodes[6:9], graph0_1_nodes):
assert n1 is n2
for n1, n2 in zip(deep_ordered_nodes[9:], graph0_1_0_nodes):
assert n1 is n2
def test_sub_graph():
def f_my_function1(inp1_1, inp1_2):
return inp1_1 + inp1_2
def f_my_function2(inp2_1):
return inp2_1 / 10.
def f_my_function3(inp3_1, inp3_2):
return inp3_1 - inp3_2
graph0 = Graph()
graph0_0 = Graph()
graph0_1 = Graph()
graph0_1_0 = Graph()
# inp2_1 = 1.5, inp3_2 = 0.15, out=0.1785
graph0.add_node(graph0_0, kwargs=['inp1_1', 'inp1_2'], outputs=['inp2_1'])
graph0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0.add_node(graph0_1,
kwargs=[('inp1', 'inp1_1'), ('inp2', 'inp3_2')],
outputs=['out'])
graph0_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# inp2_1 = 0.215, inp3_2 = 0.0215, out = 0.1785
graph0_1_0.add_node(f_my_function1,
inputs=['inp1_1', 'inp1_2'],
outputs=['inp2_1'])
graph0_1_0.add_node(f_my_function2, inputs=['inp2_1'], outputs=['inp3_2'])
graph0_1_0.add_node(f_my_function3,
inputs=[('inp3_1', 'inp1_1'), 'inp3_2'],
outputs=['out'])
# out1 = 0.2, out2 = 0.015, out=0.1785
graph0_1.add_node(f_my_function2, inputs=['inp1'], outputs=['out1'])
graph0_1.add_node(f_my_function2, inputs=['inp2'], outputs=['out2'])
graph0_1.add_node(graph0_1_0,
kwargs=[('inp1_1', 'out1'), ('inp1_2', 'out2')],
outputs=['out3'])
for _ in range(2):
res = graph0(inp1_1=2, inp1_2=3)
assert 0.1785 == pytest.approx(res)
assert graph0.data['out'] == res
assert graph0_0.data['out'] == 1.5
assert graph0_1.data['out3'] == pytest.approx(0.1785)
assert graph0_1_0.data['out'] == pytest.approx(0.1785)