Add seeding to fixture args

tests/conftest.py

@@ -7,6 +7,8 @@
 from torch.distributions import Uniform
 
 
+# If a fixture is doing anything random,
+# it should take this function as an argument
 @pytest.fixture(autouse=True)
 def seed_everything():
     seed = 101589

tests/transforms/test_iirfilter.py

@@ -128,67 +128,67 @@ def test_filters_synthetic_signal(sample_rate, order):
 
 
 @pytest.fixture()
-def chirp_mass(request):
+def chirp_mass(seed_everything, request):
     dist = Uniform(5, 100)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def mass_ratio():
+def mass_ratio(seed_everything):
     dist = Uniform(0.125, 0.99)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def a_1(request):
+def a_1(seed_everything, request):
     dist = Uniform(0, 0.90)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def a_2(request):
+def a_2(seed_everything, request):
     dist = Uniform(0, 0.90)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def tilt_1(request):
+def tilt_1(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def tilt_2(request):
+def tilt_2(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def phi_12(request):
+def phi_12(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def phi_jl(request):
+def phi_jl(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def distance(request):
+def distance(seed_everything, request):
     dist = Uniform(100, 3000)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def theta_jn(request):
+def theta_jn(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 
 
 @pytest.fixture()
-def phase(request):
+def phase(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample(torch.Size((N_SAMPLES,)))
 

tests/waveforms/cbc/test_cbc_waveforms.py

@@ -23,91 +23,91 @@ def sample_rate(request):
 
 
 @pytest.fixture()
-def chirp_mass(request):
+def chirp_mass(seed_everything, request):
     dist = Uniform(5, 100)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def mass_ratio():
+def mass_ratio(seed_everything):
     dist = Uniform(0.125, 0.99)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def a_1(request):
+def a_1(seed_everything, request):
     dist = Uniform(0, 0.90)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def a_2(request):
+def a_2(seed_everything, request):
     dist = Uniform(0, 0.90)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def tilt_1(request):
+def tilt_1(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def tilt_2(request):
+def tilt_2(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def phi_12(request):
+def phi_12(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def phi_jl(request):
+def phi_jl(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def distance(request):
+def distance(seed_everything, request):
     dist = Uniform(100, 3000)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def distance_far(request):
+def distance_far(seed_everything, request):
     dist = Uniform(400, 3000)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def distance_close(request):
+def distance_close(seed_everything, request):
     dist = Uniform(100, 400)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def theta_jn(request):
+def theta_jn(seed_everything, request):
     dist = Uniform(0, torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def phase(request):
+def phase(seed_everything, request):
     dist = Uniform(0, 2 * torch.pi)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def chi_1(request):
+def chi_1(seed_everything, request):
     dist = Uniform(-0.999, 0.999)
     return dist.sample((N_SAMPLES,))
 
 
 @pytest.fixture()
-def chi_2(request):
+def chi_2(seed_everything, request):
     dist = Uniform(-0.999, 0.999)
     return dist.sample((N_SAMPLES,))