def test_dataflow_evaluation_no_input_two_outputs():
df = DataFlow()
vid = df.add_vertex()
pid0 = df.add_out_port(vid, "out1")
df.set_actor(vid, FuncNode({}, {}, double_fixed_function))
dfs = DataflowState(df)
env = 0
algo = BruteEvaluation(df)
algo.eval(env, dfs, vid)
assert tuple(dfs.get_data(pid0)) == (5, 5)
algo.clear()
dfs.reinit()
pid1 = df.add_out_port(vid, "out2")
algo.eval(env, dfs, vid)
assert dfs.get_data(pid0) == 5
assert dfs.get_data(pid1) == 5
algo.clear()
dfs.reinit()
pid2 = df.add_out_port(vid, "out3")
assert_raises(UserWarning, lambda: algo.eval(env, dfs, vid))
def test_dataflow_state_reinit():
df = DataFlow()
vid1 = df.add_vertex()
pid10 = df.add_in_port(vid1, "in")
pid11 = df.add_out_port(vid1, "out")
vid2 = df.add_vertex()
pid21 = df.add_out_port(vid2, "out")
vid3 = df.add_vertex()
pid31 = df.add_in_port(vid3, "in1")
pid32 = df.add_in_port(vid3, "in2")
pid33 = df.add_out_port(vid3, "res")
vid4 = df.add_vertex()
pid41 = df.add_in_port(vid4, "in")
df.connect(pid11, pid31)
df.connect(pid21, pid32)
df.connect(pid33, pid41)
dfs = DataflowState(df)
dfs.set_data(pid10, 0)
for i, pid in enumerate([pid11, pid21, pid33]):
dfs.set_data(pid, i)
dfs.reinit()
assert dfs.is_ready_for_evaluation()
for pid in (pid11, pid21, pid33):
raises(KeyError, lambda: dfs.get_data(pid))
def test_dataflow_state_reinit():
df = DataFlow()
vid1 = df.add_vertex()
pid10 = df.add_in_port(vid1, "in")
pid11 = df.add_out_port(vid1, "out")
vid2 = df.add_vertex()
pid21 = df.add_out_port(vid2, "out")
vid3 = df.add_vertex()
pid31 = df.add_in_port(vid3, "in1")
pid32 = df.add_in_port(vid3, "in2")
pid33 = df.add_out_port(vid3, "res")
vid4 = df.add_vertex()
pid41 = df.add_in_port(vid4, "in")
df.connect(pid11, pid31)
df.connect(pid21, pid32)
df.connect(pid33, pid41)
dfs = DataflowState(df)
dfs.set_data(pid10, 0)
for i, pid in enumerate([pid11, pid21, pid33]):
dfs.set_data(pid, i)
dfs.reinit()
assert dfs.is_ready_for_evaluation()
for pid in (pid11, pid21, pid33):
assert_raises(KeyError, lambda: dfs.get_data(pid))
def test_dataflow_evaluation_no_input_two_outputs():
df = DataFlow()
vid = df.add_vertex()
pid0 = df.add_out_port(vid, "out1")
df.set_actor(vid, FuncNode({}, {}, double_fixed_function))
dfs = DataflowState(df)
env = 0
algo = BruteEvaluation(df)
algo.eval(env, dfs, vid)
assert tuple(dfs.get_data(pid0)) == (5, 5)
algo.clear()
dfs.reinit()
pid1 = df.add_out_port(vid, "out2")
algo.eval(env, dfs, vid)
assert dfs.get_data(pid0) == 5
assert dfs.get_data(pid1) == 5
algo.clear()
dfs.reinit()
pid2 = df.add_out_port(vid, "out3")
assert_raises(UserWarning, lambda: algo.eval(env, dfs, vid))
def test_dataflow_evaluation_single_input_no_output():
df = DataFlow()
vid = df.add_vertex()
pid0 = df.add_in_port(vid, "in")
df.set_actor(vid, FuncNode({}, {}, print))
dfs = DataflowState(df)
env = 0
algo = BruteEvaluation(df)
dfs.set_data(pid0, 1)
algo.eval(env, dfs, vid)
assert dfs.get_data(pid0) == 1
df.set_actor(vid, FuncNode({}, {}, int))
dfs.reinit()
algo.clear()
assert_raises(UserWarning, lambda: algo.eval(env, dfs, vid))
def test_dataflow_evaluation_single_input_no_output():
df = DataFlow()
vid = df.add_vertex()
pid0 = df.add_in_port(vid, "in")
df.set_actor(vid, FuncNode({}, {}, print_func))
dfs = DataflowState(df)
env = 0
algo = BruteEvaluation(df)
dfs.set_data(pid0, 1)
algo.eval(env, dfs, vid)
assert dfs.get_data(pid0) == 1
df.set_actor(vid, FuncNode({}, {}, int))
dfs.reinit()
algo.clear()
assert_raises(UserWarning, lambda: algo.eval(env, dfs, vid))
