def test_resolve_call():
explorer = Explorer()
# Simple call
@explorer.add_function(provides='version')
def version():
return 12
ver = explorer.get_variable('version')
assert ver == 12
# Complex call
@explorer.add_function(provides=('surname','middlename'), requires='name')
def surname(name):
return {'John': ('Doe', None), 'Martin':('King', 'Luther')}[name]
@explorer.add_function(provides='age', requires=('name',))
def age(name):
return {'John': 12, 'Martin': 15}[name]
@explorer.add_function(provides=('person', ), requires=('name','age','surname'))
def make_person(name, surname, age):
return Person(name, surname, age)
person = explorer.get_variable('person', name='John')
print(person)
assert person.age == 12
assert person.surname == 'Doe'
person = explorer.get_variable('person', name='Martin')
print(person)
assert person.age == 15
middle = explorer.get_variable('middlename', name='Martin')
assert middle == 'Luther'
people = explorer.map_variable('person', name=['Martin', 'John'])
assert len(people) == 2
assert people[0].name == 'Martin'
assert people[0].age == 15
def test_add_with_provider():
explorer = Explorer()
@explorer.provider
def y(x, m):
return x*2 + m
calls_z = 0
@explorer.provider(cache=False)
def z(y):
nonlocal calls_z
calls_z += 1
return calls_z + y
zv = explorer.get_variable('z', x=1, m=5)
assert zv == 1*2+1+5
zs = explorer.map_variable('z', x=[1, 2, 3], m=[2])
print(zs)
assert all(zs == [6, 9, 12])
sim.simulate(circuit)
data = backend._profile_results
return tuple(tuple([tuple(x[0]), x[1]]) for x in data.values())
@ex.provider
def step_sim_time(sim_profile, tn):
ignored_vars = tn.bra_vars+tn.ket_vars
times = [x[1] for x in sim_profile]
return tuple(times[len(ignored_vars):])
# -
f = ex.draw_dependency_graph(figsize=(7,6), node_size=20)
estimators = ex.map_variable('step_flops', d=ds, edge_idx=edge_indices, n=[N], p=[p])
times = ex.map_variable('step_sim_time', d=ds, edge_idx=edge_indices, n=[N], p=[p])
est_flat = np.concatenate(estimators.flatten())
times_flat = np.concatenate(times.flatten())
filt = times_flat<1e-1
plt.scatter(est_flat[filt], times_flat[filt])
plt.grid()
plt.xlabel('estimated FLOP')
plt.ylabel('Runtime')
# ### Analyze simulator FLOP/s
# #### Plot time vs estimated FLOP
