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reformat with black
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@JoepVanlier
black authored and JoepVanlier committed Aug 8, 2023
1 parent b20816b commit fdcae72
Showing 1 changed file with 35 additions and 31 deletions.
66 changes: 35 additions & 31 deletions lumicks/pylake/population/tests/test_dwelltimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def test_likelihood(exponential_data):
(np.array([0.2, 0.3, 0.6, 1.2]), 0.1, np.array([0.5, 0.5, 1.3, 1.3]), 1.1932900895106002),
(np.array([0.2, 0.3, 0.6, 1.2]), np.array([0.1, 0.1, 0.3, 0.3]), np.array([0.5, 0.5, 1.3, 1.3]), 1.7155483581674074),
# fmt:on
]
],
)
def test_multi_observation_limits(data, min_obs, max_obs, loglik):
fit = DwelltimeModel(data, 1, min_observation_time=min_obs, max_observation_time=max_obs)
Expand All @@ -54,15 +54,15 @@ def test_invalid_multi_dwelltime_parameters():
ValueError,
match=re.escape(
r"Size of minimum observation time array (2) must be equal to that of dwelltimes (3)"
)
),
):
DwelltimeModel(data, min_observation_time=np.array([0.1, 0.2]), max_observation_time=10.0)

with pytest.raises(
ValueError,
match=re.escape(
r"Size of maximum observation time array (4) must be equal to that of dwelltimes (3)"
)
),
):
DwelltimeModel(data, min_observation_time=1.0, max_observation_time=np.array([1, 2, 3, 4]))

Expand All @@ -71,7 +71,7 @@ def test_invalid_multi_dwelltime_parameters():
match=re.escape(
r"When providing an array of discretization timesteps, the number of "
r"discretization timesteps (2) should equal the number of dwell times provided (3)."
)
),
):
DwelltimeModel(data, min_observation_time=10, discretization_timestep=np.array([10, 1]))

Expand All @@ -80,7 +80,7 @@ def test_invalid_multi_dwelltime_parameters():
match=re.escape(
r"To use a continuous model, specify a discretization timestep of None. Do not pass "
r"zero as this leads to an invalid probability mass function."
)
),
):
DwelltimeModel(data, min_observation_time=10, discretization_timestep=np.array([10, 0, 10]))

Expand Down Expand Up @@ -136,22 +136,18 @@ def test_fit_parameters(exponential_data):
np.array([0.1, 0.1, 0.3, 0.3]),
np.array([0.5, 0.5, 1.3, 1.3]),
None,
(0.3497069, 1.3528150)
(0.3497069, 1.3528150),
),
(0.2, 1.4, 0.2, (0.247748, 1.463575)),
(
np.array([0.2, 0.2, 0.4, 0.4]),
1.4,
np.array([0.2, 0.2, 0.4, 0.4]),
(0.21762, 1.448516)
),
]
(np.array([0.2, 0.2, 0.4, 0.4]), 1.4, np.array([0.2, 0.2, 0.4, 0.4]), (0.21762, 1.448516)),
],
)
def test_bootstrap_multi(min_obs, max_obs, ref_ci, time_step):
np.random.seed(123)
data = np.array([0.2, 0.3, 0.6, 1.2])
fit = DwelltimeModel(
data, 1,
data,
1,
min_observation_time=min_obs,
max_observation_time=max_obs,
discretization_timestep=time_step,
Expand Down Expand Up @@ -233,7 +229,7 @@ def test_dwelltime_profiles(exponential_data, exp_name, reference_bounds, reinte
dataset["data"],
n_components=2,
**dataset["parameters"].observation_limits,
discretization_timestep=dataset["parameters"].dt
discretization_timestep=dataset["parameters"].dt,
)

profiles = fit.profile_likelihood(max_chi2_step=0.25)
Expand All @@ -252,7 +248,7 @@ def test_dwelltime_profiles(exponential_data, exp_name, reference_bounds, reinte
match=re.escape(
"Significance level (0.001) cannot be chosen lower or equal than the minimum profiled "
"level (0.01)."
)
),
):
profiles.get_interval("amplitude", 0, 0.001)

Expand All @@ -267,11 +263,11 @@ def test_dwelltime_profile_plots(n_components):
max_observation_time=1e4,
)
profiles = fit.profile_likelihood(num_steps=2) # Keep it short
plt.close('all')
plt.close("all")
profiles.plot()
np.testing.assert_allclose(plt.gca().get_lines()[-1].get_data()[-1][-1], 22.415292)

plt.close('all')
plt.close("all")
profiles.plot(alpha=0.5)
np.testing.assert_allclose(plt.gca().get_lines()[-1].get_data()[-1][-1], 19.02877)

Expand All @@ -288,7 +284,7 @@ def test_dwelltime_profiles_dunders(exponential_data):
r"DwelltimeProfiles({'amplitude 0': <lumicks.pylake.fitting.profile_likelihood"
)

ref_keys = ['amplitude 0', 'amplitude 1', 'lifetime 0', 'lifetime 1']
ref_keys = ["amplitude 0", "amplitude 1", "lifetime 0", "lifetime 1"]
for key, ref_key in zip(profiles.keys(), ref_keys):
assert key == ref_key
assert profiles[key] is profiles.profiles[key]
Expand Down Expand Up @@ -371,12 +367,12 @@ def test_plots(exponential_data):
dataset = exponential_data["dataset_2exp"]
fit = DwelltimeModel(dataset["data"], 1, **dataset["parameters"].observation_limits)
fit.hist()
plt.close('all')
plt.close("all")

np.random.seed(123)
bootstrap = fit.calculate_bootstrap(iterations=2)
bootstrap.hist()
plt.close('all')
plt.close("all")

with pytest.warns(DeprecationWarning):
bootstrap.plot()
Expand Down Expand Up @@ -405,7 +401,8 @@ def test_integration_dwelltime_fixing_parameters(exponential_data):
dataset = exponential_data["dataset_2exp"]
initial_params = np.array([0.2, 0.2, 0.5, 0.5])
pars, log_likelihood = _exponential_mle_optimize(
2, dataset["data"],
2,
dataset["data"],
**dataset["parameters"].observation_limits,
initial_guess=initial_params,
fixed_param_mask=[False, True, False, True],
Expand All @@ -417,7 +414,10 @@ def test_integration_dwelltime_fixing_parameters(exponential_data):
"dataset, n_components, ref_discrete, ref_continuous",
[
(
"dataset_1exp_discrete", 1, [1, 1.512417], [1, 1.462961], # True value: [1, 1.5]
"dataset_1exp_discrete",
1,
[1, 1.512417],
[1, 1.462961], # True value: [1, 1.5]
),
(
"dataset_2exp_discrete", # True values: [0.4, 0.6, 1.5, 5]
Expand Down Expand Up @@ -476,7 +476,7 @@ def fit_data(dt):
1, np.array([0.3, 0.5]), [False, False],
[False, True], None, 0, [1.0, 0.5],
],
# fmt:off
# fmt:on
],
)
def test_parameter_fixing(
Expand All @@ -503,7 +503,7 @@ def test_parameter_fixing(
np.testing.assert_allclose(constraints["args"], free_amplitudes)
np.testing.assert_allclose(
constraints["fun"](np.arange(2 * n_components) / (2 * n_components), free_amplitudes),
ref_const_fun
ref_const_fun,
)
else:
assert not constraints
Expand All @@ -514,7 +514,7 @@ def test_invalid_models():
ValueError,
match=re.escape(
"Length of fixed parameter mask (4) is not equal to the number of model parameters (6)"
)
),
):
_handle_amplitude_constraint(
2, np.array([1, 0.0001, 0.0, 0.3, 0.3, 0.3]), np.array([True, True, False, False])
Expand All @@ -524,7 +524,7 @@ def test_invalid_models():
_handle_amplitude_constraint(
3,
np.array([1, 0.0001, 0.1, 0.3, 0.3, 0.3]),
np.array([True, True, False, False, False, False])
np.array([True, True, False, False, False, False]),
)

# If all amplitudes are fixed, they have to be 1.
Expand All @@ -537,15 +537,17 @@ def test_invalid_models():
_handle_amplitude_constraint(
3,
np.array([0.25, 0.25, 0.5, 0.3, 0.3, 0.3]),
np.array([True, True, True, False, True, True])
np.array([True, True, True, False, True, True]),
)


@pytest.mark.parametrize(
"multiple_dt", [False, True],
"multiple_dt",
[False, True],
)
@pytest.mark.parametrize(
"dt", [None, 0.01, 0.0001], # 0.1,
"dt",
[None, 0.01, 0.0001], # 0.1,
)
@pytest.mark.parametrize(
"params, t, min_observation_time, max_observation_time",
Expand All @@ -568,12 +570,13 @@ def test_invalid_models():
[[0.4, 0.6, 1e-2, 1.0], np.arange(0.0, 10.0, 0.1), 0, 1.0 + np.arange(0.0, 10.0, 0.1) * 2],
# Mix of infinity and values for t_max
[[0.4, 0.6, 1e-2, 1.0], np.array([1.0, 2.0, 3.0]), 0, np.array([3.0, np.inf, 5.0])],
]
],
)
def test_analytic_gradient_exponential(
params, t, min_observation_time, max_observation_time, dt, multiple_dt
):
dt = dt * np.arange(1, len(t) + 1) if (multiple_dt and dt is not None) else dt

def fn(params):
return np.atleast_1d(
_exponential_mixture_log_likelihood(
Expand Down Expand Up @@ -621,6 +624,7 @@ def store_args(*args, **kwargs):

def test_dwelltime_exponential_no_free_params(monkeypatch):
"""When fitting a single parameter, we don't need to optimize"""

def stop(*args, **kwargs):
raise StopIteration

Expand Down

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