def test_sample_constant_distribution(self):
"""Returns a constant sample distribution."""
constant_dist = Distribution(dist_type="constant", constant=1)
sample = constant_dist.sample(5)
expected_sample = constant_dist.constant*ones(5)
assert all(sample == expected_sample)
def test_sample_positive_constant_distribution(self):
"""Returns a constant positive sample distribution."""
constant_dist = Distribution(dist_type="constant", constant=1)
sample = constant_dist.sample_positive()
expected = constant_dist.constant*ones(1)
assert sample == expected
def test_sample_None_distribution(self):
"""Returns a None sample distribution."""
None_dist = Distribution(dist_type=None)
sample = None_dist.sample(size=10)
expected_sample = full(10, None)
assert all(sample == expected_sample)
def fixture_update_velocities(self) -> None:
pytest.angle_variance = 0.1
pytest.angle_variance_2 = 0.5
pytest.angle_variance_field = 0.1
large_sample = 500
pytest.data_large_sample = {
"vx": -20 * random.random(large_sample) + 10,
"vy": -20 * random.random(large_sample) + 10
}
pytest.distrib = Distribution(dist_type="numpy",
dist_name="normal",
loc=10.0,
scale=1.0)
pytest.delta_angles = Distribution(
dist_type="numpy",
dist_name="normal",
loc=0.0,
scale=pytest.angle_variance).sample(size=large_sample)
pytest.data_field = {
"field": array(["field_2", "field_1"]),
"vx": array([1.0, 2.0]),
"vy": array([0, 2.0]),
}
pytest.delta_angles_2 = Distribution(
dist_type="numpy",
dist_name="normal",
loc=0.0,
scale=pytest.angle_variance_2).sample(large_sample)
def fixture_initialize_velocities(self):
distribution = Distribution(dist_type="numpy",
dist_name="gamma",
shape=2)
angle_dist = Distribution(dist_type="constant", constant=pi / 4)
distribution_2 = Distribution(dist_type="constant", constant=1)
angle_dist_2 = Distribution(dist_type="constant", constant=0)
indexes = [0, 1, 2, 3, 4]
return distribution, angle_dist, distribution_2, angle_dist_2, indexes
def test_sample_numpy_distribution(self):
"""Returns a numpy sample distribution."""
numpy_dist = Distribution(
dist_type="numpy",
dist_name="beta",
a=1, b=1
)
sample = numpy_dist.sample(5)
expected = random.default_rng(seed=numpy_dist.seed).beta(1, 1, 5)
assert all(sample == expected)
def test_empirical_distribution_SystemError_filename_does_not_exist(self):
"""
Raises a SystemError creating an empirical type distribution with a
wrong filename.
"""
with pytest.raises(SystemError, match=pytest.error_message):
Distribution(dist_type="empirical", filename=pytest.wrong_filename)
def test_empirical_distribution_using_file(
self,
fixture_empirical_distribution
):
"""
Creates an empirical type distribution using the filename parameter and
assigns the attributes: dist_type, seed and kd_estimator.
"""
empirical_dist = Distribution(
dist_type="empirical",
data=None,
filename=pytest.filename,
kernel="gaussian",
bandwidth=0.1
)
expected_kd_stimator = KernelDensity(
kernel="gaussian",
bandwidth=0.1
).fit(pytest.data_file.reshape(-1, 1)).sample(
n_samples=10,
random_state=0
)
kd_estimator = empirical_dist.kd_estimator.sample(
n_samples=10,
random_state=0
)
assert empirical_dist.dist_type == "empirical"
assert empirical_dist.seed == int(time())
assert empirical_dist.filename == pytest.filename
assert all(kd_estimator == expected_kd_stimator)
def test_empirical_distribution_using_data(
self,
fixture_empirical_distribution
):
"""
Creates an empirical type distribution using the data parameter and
assigns the attributes: dist_type, seed and kd_estimator.
"""
empirical_dist = Distribution(
dist_type="empirical",
data=pytest.data_1D,
filename=None,
kernel="gaussian",
bandwidth=0.1
)
expected_kd_stimator = str(
KernelDensity(
kernel="gaussian",
bandwidth=0.1
).fit(pytest.data_1D.reshape(-1, 1))
)
kd_estimator = str(empirical_dist.kd_estimator)
assert empirical_dist.dist_type == "empirical"
assert empirical_dist.seed == int(time())
assert kd_estimator == expected_kd_stimator
def test_wrong_numpy_name(self, fixture_numpy_distribution):
"""
Raises a SystemError creating a numpy distribution with a wrong
dist_name.
"""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(dist_type="numpy", dist_name=pytest.wrong_name)
def fixture_set_velocities(self):
distribution = Distribution(dist_type="constant", constant=5)
delta_angles = Distribution(dist_type="numpy",
dist_name="normal",
loc=0.0,
scale=0.1).sample(5)
angles = [0, pi / 4, pi / 2, pi, 3 * pi / 4]
pytest.vx = [
5 * cos(angle + delta_angle)
for (angle, delta_angle) in zip(angles, delta_angles)
]
pytest.vy = [
5 * sin(angle + delta_angle)
for (angle, delta_angle) in zip(angles, delta_angles)
]
return distribution, delta_angles
def test_sample_positivie_numpy(self):
"""Returns a numpy positive sample distribution."""
numpy_dist = Distribution(
dist_type="numpy",
dist_name="normal",
loc=-10, scale=0.2
)
sample = numpy_dist.sample_positive()
expected = abs(
random.default_rng(
seed=numpy_dist.seed
).normal(-10, 0.2)
)
assert sample == expected
def test_sample_positive_weights_distribution_data(
self,
fixture_weights_distribution
):
"""Returns a weights sample distribution from a data array."""
weights_dist = Distribution(
dist_type="weights",
data=pytest.data_2D
)
sample = weights_dist.sample_positive(5)
expected_sample = abs(
random.default_rng(int(time())).choice(
a=pytest.data_2D[:, 0], p=pytest.data_2D[:, 1], size=5
)
)
assert all(sample == expected_sample)
def test_None_distribution(self):
"""
Creates a None type distribution and assigns the attributes: constant,
dist_type and seed
"""
None_dist = Distribution(dist_type=None)
assert None_dist.constant == None
assert None_dist.dist_type == None
assert None_dist.seed == int(time())
def test_weights_distribution_SystemError_data_ndim_is_incorrect(
self,
fixture_weights_distribution
):
"""
Raises a SystemError creating a weights type distribution with a
wrong filename.
"""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(dist_type="weights", data=pytest.data_3D)
def test_constant_distribution_ValueError_no_constant(
self,
fixture_constant_distribution
):
"""
Raises a SystemError creating a constant type distribution when no
constant is passing to the constructor method.
"""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(dist_type="constant")
def test_empirical_distribution_SystemError_KernelDensity_estimator(
self,
fixture_empirical_distribution
):
"""Raises an error when a wrong KernelDensity estimator is passed."""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(
dist_type="empirical",
data=pytest.data_1D,
kernel="Gaussian",
bandwidth=0.1
)
def test_constant_distribution(self, fixture_constant_distribution):
"""
Creates a constant distribution and assigns the attributes: constant,
dist_type and seed.
"""
constant_dist = Distribution(
dist_type="constant",
constant=pytest.constant
)
assert constant_dist.constant == pytest.constant
assert constant_dist.dist_type == "constant"
assert constant_dist.seed == int(time())
def test_weights_distribution_SystemError_filename_does_not_exist(
self,
fixture_weights_distribution
):
"""
Raises a SystemError creating a weights type distribution with a
wrong filename.
"""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(
dist_type="weights",
filename=pytest.wrong_filename
)
def test_sample_empirical_distribution_file(
self,
fixture_empirical_distribution
):
"""Returns an empirical sample distribution from a file."""
empirical_dist = Distribution(
dist_type="empirical",
data=pytest.data_1D,
kernel="gaussian",
bandwidth=0.1
)
sample = empirical_dist.sample(5)
expected_sample = KernelDensity(
kernel="gaussian",
bandwidth=0.1
).fit(pytest.data_1D.reshape(-1, 1)).sample(
n_samples=5, random_state=int(time())
).flatten()
assert all(sample == expected_sample)
def test_weights_distribution_filename(self, fixture_weights_distribution):
"""
Creates a weights type distribution using the filename parameter
and assigns the attributes: dist_type, seed, xi and pi.
"""
weights_dist = Distribution(
dist_type="weights",
filename=pytest.filename
)
assert weights_dist.dist_type == "weights"
assert weights_dist.seed == int(time())
assert all(weights_dist.xi == pytest.data_file[:, 0])
assert all(weights_dist.pi == pytest.data_file[:, 1])
def test_weights_distribution_ValueError_None_data_neither_file(
self,
fixture_weights_distribution
):
"""Raises an error when None data neither file is passed."""
with pytest.raises(
SystemError,
match=pytest.error_message_None_file_neither_data
):
assert Distribution(
dist_type="weights",
data=None,
filename=None,
)
def test_empirical_distribution_SystemError_data_ndim_is_incorrect(
self,
fixture_empirical_distribution
):
"""
Raises a SystemError creating an empirical type distribution when
data is not a 1-D array.
"""
with pytest.raises(SystemError, match=pytest.error_message):
assert Distribution(
dist_type="empirical",
data=pytest.data_3D,
filename=None,
kernel="gaussian",
bandwidth=0.1
)
def test_numpy_distribution(self, fixture_numpy_distribution):
"""
Creates a numpy distribution and assigns the attribute
numpy_distribution.
"""
numpy_dist = Distribution(
dist_type="numpy",
dist_name=pytest.dist_name
)
name = numpy_dist.numpy_distribution.__name__
gen = numpy_dist.random_number_generator
expected_gen = random.default_rng(seed=int(time()))
assert numpy_dist.dist_type == "numpy"
assert numpy_dist.seed == int(time())
assert all(gen.random(size=3) == expected_gen.random(size=3))
assert name == pytest.dist_name
def test_wrong_type(self):
"""Raises a SystemErrorError when wrong type distribution is passed."""
with pytest.raises(SystemError, ):
assert Distribution(dist_type="NumPyy")
