def get_agent_statuses_data():
base_statuses = [[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]]
en_statuses = [
[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
# Uniform across ensemble
[[0, 1, 2], [0, 1, 2], [0, 1, 2]],
# Mixed, no majority
[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
# Mixed, majority
[[0, 1, 1], [1, 1, 2], [0, 2, 2]]
]
inclusion = [
None, AgentIncluder.BASE, AgentIncluder.MODE_EN, AgentIncluder.MODE_EN,
AgentIncluder.MODE_EN
]
expected = [[False, True, False], [False, True, False],
[False, True, False], [False, False, False],
[False, True, False]]
output = wrap_up([base_statuses, en_statuses, inclusion, expected])
return output
def get_make_noise_data():
shapes = (10, 20)
R_vectors = (np.ones(shapes[0]), np.full(shapes[1], 1.5))
output = wrap_up([shapes, R_vectors])
return output
def get_state_vector_statuses_data():
base_statuses = [[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]]
en_statuses = [[[0, 1, 2], [0, 1, 2], [0, 1, 2]],
[[0, 1, 2], [0, 1, 2], [0, 1, 2]],
[[1, 0, 2], [1, 0, 2], [1, 0, 2]],
[[1, 0, 2], [1, 0, 2], [1, 0, 2]]]
inclusion = [
AgentIncluder.BASE, AgentIncluder.BASE, AgentIncluder.MODE_EN,
AgentIncluder.MODE_EN
]
vector_mode = [
EnsembleKalmanFilterType.STATE, EnsembleKalmanFilterType.DUAL_EXIT,
EnsembleKalmanFilterType.STATE, EnsembleKalmanFilterType.DUAL_EXIT
]
expected = [[False, False, True, True, False, False],
[False, False, False, True, True, True, False, False, False],
[True, True, False, False, False, False],
[True, True, True, False, False, False, False, False, False]]
output = wrap_up(
[base_statuses, en_statuses, inclusion, vector_mode, expected])
return output
def get_np_cov_data():
arrays = [np.array([[0, 2], [1, 1], [2, 0]]).T]
expected = [np.array([[1., -1.], [-1., 1.]])]
outputs = wrap_up([arrays, expected])
return outputs
def get_population_mean_max_data():
diffs = ([0, 3, 1, 4, 2], [0, 0, 3, 3, 0], [1, 1, 1, 1, 1])
ensemble_actives = ([4, 4, 4, 4, 4], [1, 2, 3, 2, 3], [0, 0, 1, 0, 0])
expected = (2.5, 2, 5)
output = wrap_up([diffs, ensemble_actives, expected])
return output
def get_population_mean_base_data():
diffs = ([0, 1, 1, 1, 3], [0, 4, 0, 2, 3], [1, 0, 0, 1, 0])
active_pops = (3, 2, 2)
expected = (2, 4.5, 1)
output = wrap_up([diffs, active_pops, expected])
return output
def get_mean_data():
results = ([2, 5, 3, 6, 4], [3, 6, 9, 12, 15], [0, 0, 0, 0, 0])
truths = ([2, 2, 2, 2, 2], [3, 6, 6, 9, 15], [1, 1, 1, 1, 1])
expected = (2, 1.2, 1)
output = wrap_up([results, truths, expected])
return output
def get_distance_error_default_data():
x_errors = ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [1, 2, 3, 4, 5])
y_errors = ([1, 1, 1, 1, 1], [0, 0, 0, 0, 0], [2, 4, 6, 8, 10])
expected = (1, 1, 3 * sqrt(5))
output = wrap_up([x_errors, y_errors, expected])
return output
def get_destination_vector_data():
destinations = ([[0, 100], [100, 0], [50, 50], [300, 100],
[125, 225]], [[325, 725], [125, 0], [5, 100], [300, 3],
[17, 23]])
expected = (np.array([0, 100, 100, 0, 50, 50, 300, 100, 125, 225]),
np.array([325, 725, 125, 0, 5, 100, 300, 3, 17, 23]))
outputs = wrap_up([destinations, expected])
return outputs
def get_update_status_data():
m_statuses = [(1, 1, 1, 1, 1), (1, 1, 1, 1, 1), (0, 0, 0, 0, 0),
(0, 0, 0, 0, 0), (1, 0, 1, 0, 1), (1, 0, 1, 0, 1)]
filter_status = (True, False, True, False, True, False)
expected = (True, True, False, False, True, True)
output = wrap_up([m_statuses, filter_status, expected])
return output
def get_distance_error_base_data():
x_errors = ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [1, 2, 3, 4, 5])
y_errors = ([1, 1, 1, 1, 1], [0, 0, 0, 0, 0], [2, 4, 6, 8, 10])
n_active = (1, 2, 3)
expected = (5, 2.5, 5 * sqrt(5))
output = wrap_up([x_errors, y_errors, n_active, expected])
return output
def get_filter_vector_data():
vector = [np.arange(5) for _ in range(3)]
statuses = [[False, False, False, False, False],
[True, True, True, True, True],
[True, False, True, False, False]]
expected = [np.array([]), np.array([0, 1, 2, 3, 4]), np.array([0, 2])]
output = wrap_up([vector, statuses, expected])
return output
def get_update_state_mean_data():
state_ensembles = (np.array([[11, 10, 9, 9, 11], [5, 5, 5, 4, 6],
[21, 15, 18, 18, 18], [0, 0, 0, 0, 0]]),
np.array([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2],
[1, 2, 3, 4, 5]]))
expected = (np.array([10, 5, 18, 0]), np.array([1, 2, 3]))
outputs = wrap_up([state_ensembles, expected])
return outputs
def get_calculate_rmse_default_data():
x_truths = ([22, 53, 45], [123, 321, 222])
y_truths = ([18, 46, 80], [11, 32, 23])
x_results = ([26, 50, 40], [120, 336, 210])
y_results = ([15, 50, 80], [15, 40, 18])
expected = (5, 35 / 3)
output = wrap_up([x_truths, y_truths, x_results, y_results, expected])
return output
def get_origin_vector_data():
origins = ([[0, 0], [0, 0], [0, 0], [0, 0], [0,
0]], [[0, 100], [100, 0],
[300, 125], [125, 300],
[0, 0]], [[100, 125],
[0, 0],
[125, 225],
[0, 0],
[750, 125]])
statuses = ([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [1, 0, 1, 0, 1])
expected = (np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
np.array([0, 100, 100, 0, 300, 125, 125, 300, 0, 0]),
np.array([100, 125, 0, 0, 125, 225, 0, 0, 750, 125]))
outputs = wrap_up([origins, statuses, expected])
return outputs
def get_separate_coords_exits_data():
state_vectors = [
np.array([10, 20, 3]),
np.array([10, 12, 15, 20, 35, 25, 3, 4, 5]),
np.array([10, 12, 15, 9, 5, 20, 35, 25, 4, 13, 3, 4, 5, 7, 2])
]
pop_sizes = (1, 3, 5)
expected = [(np.array([10]), np.array([20]), np.array([3])),
(np.array([10, 12, 15]), np.array([20, 35,
25]), np.array([3, 4, 5])),
(np.array([10, 12, 15, 9,
5]), np.array([20, 35, 25, 4,
13]), np.array([3, 4, 5, 7, 2]))]
output = wrap_up([state_vectors, pop_sizes, expected])
return output
def get_make_gain_matrix_data():
x = np.array([[5, 6, 4], [10, 11, 9]])
y = np.array([[11, 10, 10, 9, 10], [5, 6, 4, 4, 6], [21, 15, 18, 18, 18],
[0, 0, 0, 0, 0]])
state_ensembles = (x, y)
data_covariances = (np.identity(2), np.identity(4))
Hs = (np.identity(2), np.identity(4))
ex_x = 1 / 3 * np.ones((2, 2))
ex_y = np.array([[0.2494382, 0.13932584, 0.12134831, 0.0],
[0.13932584, 0.44719101, -0.09438202, 0.0],
[0.12134831, -0.09438202, 0.78876404, 0.0],
[0.0, 0.0, 0.0, 0.0]])
expected = (ex_x, ex_y)
output = wrap_up([state_ensembles, data_covariances, Hs, expected])
return output
def get_forecast_error_data():
# Case 1: None inclusion
# Case 2: Base inclusion, ensemble var 1
# Case 3: Base inclusion, ensemble var 2
# Case 4: Mode inclusion, base var 1
# Case 5: Mode inclusion, base var 2
inclusion = [
None, AgentIncluder.BASE, AgentIncluder.BASE, AgentIncluder.MODE_EN,
AgentIncluder.MODE_EN
]
base_statuses = [[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 1, 1], [0, 0, 0]]
ensemble_statuses = [[[1, 1, 1], [1, 0, 1], [1, 0, 0]],
[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 0, 1], [1, 0, 0]],
[[1, 1, 1], [1, 0, 1], [1, 0, 1]],
[[1, 1, 1], [1, 0, 1], [1, 0, 1]]]
t = np.array([1, 1, 5, 10, 22, 76])
truth = [t for _ in range(5)]
state_mean = [
np.array([4, 5, 9, 7, 25, 80]),
np.array([6, 13, 9, 7, 25, 80]),
np.array([6, 13, 9, 7, 25, 80]),
np.array([6, 13, 9, 7, 25, 80]),
np.array([6, 13, 9, 7, 25, 80])
]
expected = [5, 9, 9, 9, 9]
output = wrap_up([
inclusion, base_statuses, ensemble_statuses, truth, state_mean,
expected
])
return output