def test_equal_area_penalties(debug=False):
# %%
optim_spec = OptimizationSpec(nnls=True, max_nfev=999)
noise_spec = NoiseSpec(active=True, seed=1, std_dev=1e-8)
wavelengths = np.arange(650, 670, 2)
time_p1 = np.linspace(-1, 2, 50, endpoint=False)
time_p2 = np.linspace(2, 10, 30, endpoint=False)
time_p3 = np.geomspace(10, 50, num=20)
times = np.concatenate([time_p1, time_p2, time_p3])
irf_loc = float(times[20])
irf_width = float((times[1] - times[0]) * 10)
irf = IrfSpec(irf_loc, irf_width)
amplitude = 1
location1 = float(wavelengths[2]) # 2
location2 = float(wavelengths[-3]) # -3
width1 = float((wavelengths[1] - wavelengths[0]) * 5)
width2 = float((wavelengths[1] - wavelengths[0]) * 3)
shape1 = ShapeSpec(amplitude, location1, width1)
shape2 = ShapeSpec(amplitude, location2, width2)
dataset_spec = DatasetSpec(times, wavelengths, irf, [shape1, shape2])
wavelengths = dataset_spec.wavelengths
equ_interval = [(min(wavelengths), max(wavelengths))]
weight = 0.01
# %% The base model specification (mspec)
base = {
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2"],
"parameters": ["i.1", "i.2"],
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.2",
}
}
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
"irf": "irf1",
},
},
}
shape = {
"shape": {
"sh1": {
"type": "gaussian",
"amplitude": "shapes.amps.1",
"location": "shapes.locs.1",
"width": "shapes.width.1",
},
"sh2": {
"type": "gaussian",
"amplitude": "shapes.amps.2",
"location": "shapes.locs.2",
"width": "shapes.width.2",
},
}
}
dataset_shape = {
"shape": {
"s1": "sh1",
"s2": "sh2",
}
}
equ_area = {
"equal_area_penalties": [
{
"source": "s1",
"target": "s2",
"parameter": "rela.1",
"source_intervals": equ_interval,
"target_intervals": equ_interval,
"weight": weight,
},
],
}
mspec = ModelSpec(base, shape, dataset_shape, equ_area)
rela = 1.0 # relation between areas
irf = dataset_spec.irf
[sh1, sh2] = dataset_spec.shapes
pspec_base = {
"kinetic": [1e-1, 5e-3],
"i": [0.5, 0.5, {
"vary": False
}],
"irf": [["center", irf.location], ["width", irf.width]],
}
pspec_equa_area = {
"rela": [rela, {
"vary": False
}],
}
pspec_shape = {
"shapes": {
"amps": [sh1.amplitude, sh2.amplitude],
"locs": [sh1.location, sh2.location],
"width": [sh1.width, sh2.width],
},
}
pspec = ParameterSpec(pspec_base, pspec_equa_area, pspec_shape)
# derivates:
mspec_sim = dict(deepcopy(mspec.base), **mspec.shape)
mspec_sim["dataset"]["dataset1"].update(mspec.dataset_shape)
mspec_fit_wp = dict(deepcopy(mspec.base), **mspec.equ_area)
mspec_fit_np = dict(deepcopy(mspec.base))
model_sim = KineticSpectrumModel.from_dict(mspec_sim)
model_wp = KineticSpectrumModel.from_dict(mspec_fit_wp)
model_np = KineticSpectrumModel.from_dict(mspec_fit_np)
print(model_np)
# %% Parameter specification (pspec)
pspec_sim = dict(deepcopy(pspec.base), **pspec.shapes)
param_sim = ParameterGroup.from_dict(pspec_sim)
# For the wp model we create two version of the parameter specification
# One has all inputs fixed, the other has all but the first free
# for both we perturb kinetic parameters a bit to give the optimizer some work
pspec_wp = dict(deepcopy(pspec.base), **pspec.equal_area)
pspec_wp["kinetic"] = [v * 1.01 for v in pspec_wp["kinetic"]]
pspec_wp.update({"i": [[1, {"vary": False}], 1]})
pspec_np = dict(deepcopy(pspec.base))
param_wp = ParameterGroup.from_dict(pspec_wp)
param_np = ParameterGroup.from_dict(pspec_np)
# %% Print models with parameters
print(model_sim.markdown(param_sim))
print(model_wp.markdown(param_wp))
print(model_np.markdown(param_np))
# %%
simulated_data = model_sim.simulate(
"dataset1",
param_sim,
axes={
"time": times,
"spectral": wavelengths
},
noise=noise_spec.active,
noise_std_dev=noise_spec.std_dev,
noise_seed=noise_spec.seed,
)
# %%
simulated_data = prepare_time_trace_dataset(simulated_data)
# make a copy to keep an intact reference
data = deepcopy(simulated_data)
# %% Optimizing model without penalty (np)
dataset = {"dataset1": data}
scheme_np = Scheme(
model=model_np,
parameters=param_np,
data=dataset,
nnls=optim_spec.nnls,
nfev=optim_spec.max_nfev,
)
result_np = optimize(scheme_np)
print(result_np)
# %% Optimizing model with penalty fixed inputs (wp_ifix)
scheme_wp = Scheme(
model=model_wp,
parameters=param_wp,
data=dataset,
nnls=optim_spec.nnls,
nfev=optim_spec.max_nfev,
)
result_wp = optimize(scheme_wp)
print(result_wp)
if debug:
# %% Plot results
plt_spec = importlib.util.find_spec("matplotlib")
if plt_spec is not None:
import matplotlib.pyplot as plt
plot_overview(result_np.data["dataset1"], "no penalties")
plot_overview(result_wp.data["dataset1"], "with penalties")
plt.show()
# %% Test calculation
print(result_wp.data["dataset1"])
area1_np = np.sum(
result_np.data["dataset1"].species_associated_spectra.sel(
species="s1"))
area2_np = np.sum(
result_np.data["dataset1"].species_associated_spectra.sel(
species="s2"))
assert not np.isclose(area1_np, area2_np)
area1_wp = np.sum(
result_wp.data["dataset1"].species_associated_spectra.sel(
species="s1"))
area2_wp = np.sum(
result_wp.data["dataset1"].species_associated_spectra.sel(
species="s2"))
assert np.isclose(area1_wp, area2_wp)
input_ratio = result_wp.optimized_parameters.get(
"i.1") / result_wp.optimized_parameters.get("i.2")
assert np.isclose(input_ratio, 1.5038858115)
def test_spectral_relation():
model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2', 's3', 's4'],
'parameters': ['i.1', 'i.2', 'i.3', 'i.4']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): 'kinetic.1',
("s2", "s2"): 'kinetic.1',
("s3", "s3"): 'kinetic.1',
("s4", "s4"): 'kinetic.1',
}
}
},
'spectral_relations': [
{
'compartment': 's1',
'target': 's2',
'parameter': 'rel.1',
'interval': [(0, 2)],
},
{
'compartment': 's1',
'target': 's3',
'parameter': 'rel.2',
'interval': [(0, 2)],
},
],
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['mc1'],
},
},
})
print(model)
rel1, rel2 = 10, 20
parameter = ParameterGroup.from_dict({
'kinetic': [1e-4],
'i': [1, 2, 3, 4],
'rel': [rel1, rel2],
})
time = np.asarray(np.arange(0, 50, 1.5))
dataset = model.dataset['dataset1'].fill(model, parameter)
compartments, matrix = kinetic_image_matrix(dataset, time, 0)
assert len(compartments) == 4
assert matrix.shape == (time.size, 4)
reduced_compartments, relation_matrix = \
create_spectral_relation_matrix(model, parameter, compartments, matrix, 1)
print(relation_matrix)
assert len(reduced_compartments) == 2
assert relation_matrix.shape == (4, 2)
assert np.array_equal(relation_matrix, [
[1., 0.],
[10., 0.],
[20., 0.],
[0., 1.],
])
reduced_compartments, reduced_matrix = \
model.constrain_matrix_function(parameter, compartments, matrix, 1)
assert reduced_matrix.shape == (time.size, 2)
print(reduced_matrix[0, 0], matrix[0, 0], matrix[0, 1], matrix[0, 2])
assert np.allclose(
reduced_matrix[:, 0],
matrix[:, 0] + rel1 * matrix[:, 1] + rel2 * matrix[:, 2])
clp = xr.DataArray([[1., 10., 20., 1]],
coords=(('spectral', [1]), ('clp_label',
['s1', 's2', 's3', 's4'])))
data = model.simulate('dataset1',
parameter,
clp=clp,
axes={
'time': time,
'spectral': np.array([1])
})
result = model.optimize(parameter, {'dataset1': data}, max_nfev=1)
result_data = result.data['dataset1']
print(result_data.species_associated_spectra)
assert result_data.species_associated_spectra.shape == (1, 4)
assert result_data.species_associated_spectra[0, 1] == \
rel1 * result_data.species_associated_spectra[0, 0]
assert result_data.species_associated_spectra[0, 2] == \
rel2 * result_data.species_associated_spectra[0, 0]
def test_spectral_penalties():
model_without_penalty = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3"],
"parameters": ["i.1", "i.2", "i.3"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.1",
("s3", "s3"): "kinetic.1",
}
}
},
"spectral_relations": [
{
"compartment": "s1",
"target": "s2",
"parameter": "rel.1",
"interval": [(0, 2)],
},
],
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
},
},
})
weight = 0.1
model_with_penalty = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3"],
"parameters": ["i.1", "i.2", "i.3"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.1",
("s3", "s3"): "kinetic.1",
}
}
},
"equal_area_penalties": [
{
"compartment": "s2",
"target": "s3",
"parameter": "pen.1",
"interval": [(0, 2)],
"weight": weight,
},
],
"spectral_relations": [
{
"compartment": "s1",
"target": "s2",
"parameter": "rel.1",
"interval": [(0, 2)],
},
],
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
},
},
})
print(model_with_penalty)
rel1 = 2
pen = 0.5
parameter = ParameterGroup.from_dict({
"kinetic": [1e-4],
"i": [1, 1, 1],
"rel": [rel1],
"pen": [pen],
})
time = np.asarray(np.arange(0, 50, 1.5))
clp = xr.DataArray([[1.0, 2.0, 4]],
coords=(("spectral", [1]), ("clp_label",
["s1", "s2", "s3"])))
data = model_without_penalty.simulate("dataset1",
parameter,
clp=clp,
axes={
"time": time,
"spectral": np.array([1])
})
result_with_penalty = model_with_penalty.optimize(parameter,
{"dataset1": data},
max_nfev=1)
parameter_no_penalty = deepcopy(parameter)
del parameter_no_penalty["pen"]
result_without_penalty = model_without_penalty.optimize(
parameter_no_penalty, {"dataset1": data}, max_nfev=1)
result_data = result_with_penalty.data["dataset1"]
wanted_penalty = (
result_data.species_associated_spectra.sel(species="s2") -
result_data.species_associated_spectra.sel(species="s3") * pen)
wanted_penalty *= weight
wanted_penalty **= 2
wanted_penalty = np.sum(wanted_penalty.values)
additional_penalty = result_with_penalty.chisqr - result_without_penalty.chisqr
assert np.isclose(additional_penalty, wanted_penalty)
def test_coherent_artifact():
model_dict = {
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["2"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "1",
}
}
},
"irf": {
"irf1": {
"type": "gaussian-coherent-artifact",
"center": "2",
"width": "3",
"coherent_artifact_order": 3,
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
"irf": "irf1",
},
},
}
model = KineticSpectrumModel.from_dict(model_dict.copy())
parameters = ParameterGroup.from_list([
101e-4,
[10, {
"vary": False,
"non-negative": False
}],
[20, {
"vary": False,
"non-negative": False
}],
[30, {
"vary": False,
"non-negative": False
}],
])
time = np.asarray(np.arange(0, 50, 1.5))
irf = model.irf["irf1"].fill(model, parameters)
irf_same_width = irf.calculate_coherent_artifact(time)
model_dict["irf"]["irf1"]["coherent_artifact_width"] = "4"
model = KineticSpectrumModel.from_dict(model_dict)
irf = model.irf["irf1"].fill(model, parameters)
irf_diff_width = irf.calculate_coherent_artifact(time)
assert np.array_equal(irf_same_width[0],
irf_diff_width[0]) # labels the same
assert not np.array_equal(irf_same_width[1],
irf_diff_width[1]) # but content is not
data = model.dataset["dataset1"].fill(model, parameters)
compartments, matrix = kinetic_spectrum_matrix(data, time, 0)
assert len(compartments) == 4
for i in range(1, 4):
assert compartments[i] == f"coherent_artifact_{i}"
assert matrix.shape == (time.size, 4)
clp = xr.DataArray(
[[1, 1, 1, 1]],
coords=[
("spectral", [0]),
(
"clp_label",
[
"s1",
"coherent_artifact_1",
"coherent_artifact_2",
"coherent_artifact_3",
],
),
],
)
axis = {"time": time, "spectral": clp.spectral}
data = model.simulate("dataset1", parameters, axis, clp)
dataset = {"dataset1": data}
scheme = Scheme(model=model,
parameters=parameters,
data=dataset,
maximum_number_function_evaluations=20)
result = optimize(scheme)
print(result.optimized_parameters)
for label, param in result.optimized_parameters.all():
assert np.allclose(param.value, parameters.get(label).value, rtol=1e-1)
resultdata = result.data["dataset1"]
assert np.array_equal(data.time, resultdata.time)
assert np.array_equal(data.spectral, resultdata.spectral)
assert data.data.shape == resultdata.data.shape
assert data.data.shape == resultdata.fitted_data.shape
assert np.allclose(data.data, resultdata.fitted_data, rtol=1e-2)
assert "coherent_artifact_concentration" in resultdata
assert resultdata["coherent_artifact_concentration"].shape == (time.size,
3)
assert "coherent_artifact_associated_spectra" in resultdata
assert resultdata["coherent_artifact_associated_spectra"].shape == (1, 3)
class OneComponentOneChannel:
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["2"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "1",
}
}
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["2"]
},
},
"shape": {
"sh1": ["one"]
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "1",
}
}
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
"shape": {
"s1": "sh1"
},
},
},
})
initial_parameters = ParameterGroup.from_list(
[101e-4, [1, {
"vary": False,
"non-negative": False
}]])
wanted_parameters = ParameterGroup.from_list(
[101e-3, [1, {
"vary": False,
"non-negative": False
}]])
time = np.asarray(np.arange(0, 50, 1.5))
spectral = np.asarray([0])
axis = {"time": time, "spectral": spectral}
def test_spectral_relation():
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3", "s4"],
"parameters": ["i.1", "i.2", "i.3", "i.4"],
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.1",
("s3", "s3"): "kinetic.1",
("s4", "s4"): "kinetic.1",
}
}
},
"spectral_relations": [
{
"compartment": "s1",
"target": "s2",
"parameter": "rel.1",
"interval": [(0, 2)],
},
{
"compartment": "s1",
"target": "s3",
"parameter": "rel.2",
"interval": [(0, 2)],
},
],
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
},
},
})
print(model)
rel1, rel2 = 10, 20
parameter = ParameterGroup.from_dict({
"kinetic": [1e-4],
"i": [1, 2, 3, 4],
"rel": [rel1, rel2],
})
time = np.asarray(np.arange(0, 50, 1.5))
dataset = model.dataset["dataset1"].fill(model, parameter)
compartments, matrix = kinetic_image_matrix(dataset, time, 0)
assert len(compartments) == 4
assert matrix.shape == (time.size, 4)
reduced_compartments, relation_matrix = create_spectral_relation_matrix(
model, parameter, compartments, matrix, 1)
print(relation_matrix)
assert len(reduced_compartments) == 2
assert relation_matrix.shape == (4, 2)
assert np.array_equal(
relation_matrix,
[
[1.0, 0.0],
[10.0, 0.0],
[20.0, 0.0],
[0.0, 1.0],
],
)
reduced_compartments, reduced_matrix = model.constrain_matrix_function(
parameter, compartments, matrix, 1)
assert reduced_matrix.shape == (time.size, 2)
print(reduced_matrix[0, 0], matrix[0, 0], matrix[0, 1], matrix[0, 2])
assert np.allclose(
reduced_matrix[:, 0],
matrix[:, 0] + rel1 * matrix[:, 1] + rel2 * matrix[:, 2])
clp = xr.DataArray([[1.0, 10.0, 20.0, 1]],
coords=(("spectral", [1]), ("clp_label",
["s1", "s2", "s3", "s4"])))
data = model.simulate("dataset1",
parameter,
clp=clp,
axes={
"time": time,
"spectral": np.array([1])
})
result = model.optimize(parameter, {"dataset1": data}, max_nfev=1)
result_data = result.data["dataset1"]
print(result_data.species_associated_spectra)
assert result_data.species_associated_spectra.shape == (1, 4)
assert (result_data.species_associated_spectra[0, 1] == rel1 *
result_data.species_associated_spectra[0, 0])
assert (result_data.species_associated_spectra[0, 2] == rel2 *
result_data.species_associated_spectra[0, 0])
def test_spectral_constraint():
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2"],
"parameters": ["i.1", "i.2"],
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.2",
}
}
},
"spectral_constraints": [
{
"type": "zero",
"compartment": "s2",
"interval": (float("-inf"), float("inf"))
},
],
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1"],
},
},
})
print(model)
wanted_parameters = ParameterGroup.from_dict({
"kinetic": [1e-4, 1e-5],
"i": [1, 2],
})
initial_parameters = ParameterGroup.from_dict({
"kinetic": [2e-4, 2e-5],
"i": [1, 2, {
"vary": False
}],
})
time = np.asarray(np.arange(0, 50, 1.5))
dataset = model.dataset["dataset1"].fill(model, wanted_parameters)
compartments, matrix = kinetic_image_matrix(dataset, time, 0)
assert len(compartments) == 2
assert matrix.shape == (time.size, 2)
reduced_compartments, reduced_matrix = apply_spectral_constraints(
model, compartments, matrix, 1)
print(reduced_matrix)
assert len(reduced_compartments) == 1
assert reduced_matrix.shape == (time.size, 1)
reduced_compartments, reduced_matrix = model.constrain_matrix_function(
"dataset1", wanted_parameters, compartments, matrix, 1)
assert reduced_matrix.shape == (time.size, 1)
clp = xr.DataArray([[1.0, 10.0, 20.0, 1]],
coords=(("spectral", [1]), ("clp_label",
["s1", "s2", "s3", "s4"])))
data = model.simulate("dataset1",
wanted_parameters,
clp=clp,
axes={
"time": time,
"spectral": np.array([1])
})
dataset = {"dataset1": data}
scheme = Scheme(model=model,
parameters=initial_parameters,
data=dataset,
nfev=20)
# the resulting jacobian is singular
with pytest.warns(UserWarning):
result = optimize(scheme)
result_data = result.data["dataset1"]
print(result_data.clp_label)
print(result_data.clp)
# TODO: save reduced clp
# assert result_data.clp.shape == (1, 1)
print(result_data.species_associated_spectra)
assert result_data.species_associated_spectra.shape == (1, 2)
assert result_data.species_associated_spectra[0, 1] == 0
sim_model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3"],
"parameters": ["j.1", "j.0", "j.0"],
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s3", "s2"): "kinetic.2",
("s3", "s3"): "kinetic.3",
}
}
},
"megacomplex": {
"m1": {
"k_matrix": ["k1"],
}
},
"shape": {
"sh1": {
"type": "gaussian",
"amplitude": "shapes.amps.1",
"location": "shapes.locs.1",
"width": "shapes.width.1",
},
"sh2": {
"type": "gaussian",
"amplitude": "shapes.amps.2",
"location": "shapes.locs.2",
"width": "shapes.width.2",
},
"sh3": {
"type": "gaussian",
"amplitude": "shapes.amps.3",
"location": "shapes.locs.3",
"width": "shapes.width.3",
},
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["m1"],
"shape": {
"s1": "sh1",
"s2": "sh2",
"s3": "sh3",
},
"irf": "irf1",
}
},
})
class MultiIrfDispersion:
model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['j.1']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): 'kinetic.1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-multi-gaussian',
'center': ['irf.center'],
'width': ['irf.width'],
'dispersion_center': 'irf.dispcenter',
'center_dispersion': ['irf.centerdisp1', 'irf.centerdisp2'],
'width_dispersion': ['irf.widthdisp'],
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['j.1']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): 'kinetic.1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-multi-gaussian',
'center': ['irf.center'],
'width': ['irf.width'],
'dispersion_center': 'irf.dispcenter',
'center_dispersion': ['irf.centerdisp1', 'irf.centerdisp2'],
'width_dispersion': ['irf.widthdisp'],
},
},
'shape': {
'sh1': {
'type': "one",
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
'shape': {
's1': 'sh1'
}
},
},
})
initial = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [["1", 0.5], {
'non-negative': False
}],
'irf': [['center', 0.3], ['width', 0.1],
['dispcenter', 400, {
'vary': False
}], ['centerdisp1', 0.01], ['centerdisp2', 0.001],
['widthdisp', 0.025]],
})
wanted = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [
["1", 0.5],
],
'irf': [['center', 0.3], ['width', 0.1],
['dispcenter', 400, {
'vary': False
}], ['centerdisp1', 0.01], ['centerdisp2', 0.001],
['widthdisp', 0.025]],
})
time = np.arange(-1, 5, 0.2)
spectral = np.arange(300, 500, 25)
axis = {"time": time, "spectral": spectral}
class SimpleIrfDispersion:
model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['j.1']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): 'kinetic.1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-gaussian',
'center': 'irf.center',
'width': 'irf.width',
'dispersion_center': 'irf.dispcenter',
'center_dispersion': ['irf.centerdisp'],
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['j.1']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): 'kinetic.1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-gaussian',
'center': 'irf.center',
'width': 'irf.width',
'dispersion_center': 'irf.dispcenter',
'center_dispersion': ['irf.centerdisp'],
},
},
'shape': {
'sh1': {
'type': "one",
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
'shape': {
's1': 'sh1'
}
},
},
})
initial = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [["1", 0.5], {
'non-negative': False
}],
'irf': [['center', 0.3], ['width', 0.1],
['dispcenter', 400, {
'vary': False
}], ['centerdisp', 0.5]],
})
wanted = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [
["1", 0.5],
],
'irf': [['center', 0.3], ['width', 0.1], ['dispcenter', 400],
['centerdisp', 0.5]],
})
time_p1 = np.linspace(-1, 2, 50, endpoint=False)
time_p2 = np.linspace(2, 5, 30, endpoint=False)
time_p3 = np.geomspace(5, 10, num=20)
time = np.concatenate([time_p1, time_p2, time_p3])
spectral = np.arange(300, 500, 5)
axis = {"time": time, "spectral": spectral}
def test_coherent_artifact():
model_dict = {
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['2']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): '1',
}
}
},
'irf': {
'irf1': {
'type': 'gaussian-coherent-artifact',
'center': '2',
'width': '3',
'coherent_artifact_order': 3,
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['mc1'],
'irf': 'irf1',
},
},
}
model = KineticSpectrumModel.from_dict(model_dict.copy())
parameter = ParameterGroup.from_list([
101e-4,
[10, {
'vary': False,
'non-negative': False
}],
[20, {
'vary': False,
'non-negative': False
}],
[30, {
'vary': False,
'non-negative': False
}],
])
time = np.asarray(np.arange(0, 50, 1.5))
irf = model.irf['irf1'].fill(model, parameter)
irf_same_width = irf.calculate_coherent_artifact(time)
model_dict['irf']['irf1']['coherent_artifact_width'] = '4'
model = KineticSpectrumModel.from_dict(model_dict)
irf = model.irf['irf1'].fill(model, parameter)
irf_diff_width = irf.calculate_coherent_artifact(time)
assert not np.array_equal(irf_same_width, irf_diff_width)
dataset = model.dataset['dataset1'].fill(model, parameter)
compartments, matrix = kinetic_spectrum_matrix(dataset, time, 0)
assert len(compartments) == 4
for i in range(1, 4):
assert compartments[i] == f'coherent_artifact_{i}'
assert matrix.shape == (time.size, 4)
clp = xr.DataArray([[1, 1, 1, 1]],
coords=[('spectral', [0]),
('clp_label', [
's1',
'coherent_artifact_1',
'coherent_artifact_2',
'coherent_artifact_3',
])])
axis = {"time": time, "spectral": clp.spectral}
dataset = model.simulate('dataset1', parameter, axis, clp)
data = {'dataset1': dataset}
result = model.optimize(parameter, data, max_nfev=20)
print(result.optimized_parameter)
for label, param in result.optimized_parameter.all():
assert np.allclose(param.value, parameter.get(label).value, rtol=1e-1)
resultdata = result.data["dataset1"]
assert np.array_equal(dataset['time'], resultdata['time'])
assert np.array_equal(dataset['spectral'], resultdata['spectral'])
assert dataset.data.shape == resultdata.data.shape
assert dataset.data.shape == resultdata.fitted_data.shape
assert np.allclose(dataset.data, resultdata.fitted_data, rtol=1e-2)
assert 'coherent_artifact_concentration' in resultdata
assert resultdata['coherent_artifact_concentration'].shape == (time.size,
3)
assert 'coherent_artifact_associated_spectra' in resultdata
assert resultdata['coherent_artifact_associated_spectra'].shape == (1, 3)
class OneComponentOneChannelGaussianIrf:
model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['2']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): '1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-gaussian',
'center': '2',
'width': '3'
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1'],
'parameters': ['4']
},
},
'shape': {
'sh1': ['one']
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s1", "s1"): '1',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-gaussian',
'center': '2',
'width': '3'
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
'shape': {
's1': 'sh1'
}
},
},
})
initial = ParameterGroup.from_list(
[101e-4, 0.1, 5, [1, {
'vary': False,
'non-negative': False
}]])
wanted = ParameterGroup.from_list(
[101e-3, 0.3, 10, [1, {
'vary': False,
'non-negative': False
}]])
time = np.asarray(np.arange(-10, 50, 1.5))
spectral = np.asarray([0])
axis = {"time": time, "spectral": spectral}
class ThreeComponentSequential:
model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2', 's3'],
'parameters': ['j.1', 'j.0', 'j.0']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s2", "s1"): 'kinetic.1',
("s3", "s2"): 'kinetic.2',
("s3", "s3"): 'kinetic.3',
}
}
},
'irf': {
'irf1': {
'type': 'spectral-multi-gaussian',
'center': ['irf.center'],
'width': ['irf.width']
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2', 's3'],
'parameters': ['j.1', 'j.0', 'j.0']
},
},
'megacomplex': {
'mc1': {
'k_matrix': ['k1']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s2", "s1"): 'kinetic.1',
("s3", "s2"): 'kinetic.2',
("s3", "s3"): 'kinetic.3',
}
}
},
'shape': {
'sh1': {
'type': "gaussian",
'amplitude': "shape.amps.1",
'location': "shape.locs.1",
'width': "shape.width.1",
},
'sh2': {
'type': "gaussian",
'amplitude': "shape.amps.2",
'location': "shape.locs.2",
'width': "shape.width.2",
},
'sh3': {
'type': "gaussian",
'amplitude': "shape.amps.3",
'location': "shape.locs.3",
'width': "shape.width.3",
},
},
'irf': {
'irf1': {
'type': 'spectral-multi-gaussian',
'center': ['irf.center'],
'width': ['irf.width']
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'irf': 'irf1',
'megacomplex': ['mc1'],
'shape': {
's1': 'sh1',
's2': 'sh2',
's3': 'sh3'
}
},
},
})
initial = ParameterGroup.from_dict({
'kinetic': [
["1", 501e-3],
["2", 202e-4],
["3", 105e-5],
{
'non-negative': True
},
],
'irf': [['center', 1.3], ['width', 7.8]],
'j': [['1', 1, {
'vary': False,
'non-negative': False
}], ['0', 0, {
'vary': False,
'non-negative': False
}]],
})
wanted = ParameterGroup.from_dict({
'kinetic': [
["1", 501e-3],
["2", 202e-4],
["3", 105e-5],
],
'shape': {
'amps': [3, 1, 5],
'locs': [620, 670, 720],
'width': [10, 30, 50]
},
'irf': [['center', 1.3], ['width', 7.8]],
'j': [['1', 1, {
'vary': False,
'non-negative': False
}], ['0', 0, {
'vary': False,
'non-negative': False
}]],
})
time = np.asarray(np.arange(-10, 50, 1.0))
spectral = np.arange(600, 750, 5.0)
axis = {"time": time, "spectral": spectral}
class MultiIrfDispersion:
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["j.1"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
}
}
},
"irf": {
"irf1": {
"type": "spectral-multi-gaussian",
"center": ["irf.center"],
"width": ["irf.width"],
"dispersion_center": "irf.dispcenter",
"center_dispersion": ["irf.centerdisp1", "irf.centerdisp2"],
"width_dispersion": ["irf.widthdisp"],
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["j.1"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
}
}
},
"irf": {
"irf1": {
"type": "spectral-multi-gaussian",
"center": ["irf.center"],
"width": ["irf.width"],
"dispersion_center": "irf.dispcenter",
"center_dispersion": ["irf.centerdisp1", "irf.centerdisp2"],
"width_dispersion": ["irf.widthdisp"],
},
},
"shape": {
"sh1": {
"type": "one",
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
"shape": {
"s1": "sh1"
},
},
},
})
initial = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [["1", 0.5], {
"non-negative": False
}],
"irf": [
["center", 0.3],
["width", 0.1],
["dispcenter", 400, {
"vary": False
}],
["centerdisp1", 0.01],
["centerdisp2", 0.001],
["widthdisp", 0.025],
],
})
wanted = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [
["1", 0.5],
],
"irf": [
["center", 0.3],
["width", 0.1],
["dispcenter", 400, {
"vary": False
}],
["centerdisp1", 0.01],
["centerdisp2", 0.001],
["widthdisp", 0.025],
],
})
time = np.arange(-1, 5, 0.2)
spectral = np.arange(300, 500, 100)
axis = {"time": time, "spectral": spectral}
class ThreeComponentSequential:
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3"],
"parameters": ["j.1", "j.0", "j.0"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s3", "s2"): "kinetic.2",
("s3", "s3"): "kinetic.3",
}
}
},
"irf": {
"irf1": {
"type": "spectral-multi-gaussian",
"center": ["irf.center"],
"width": ["irf.width"],
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2", "s3"],
"parameters": ["j.1", "j.0", "j.0"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s3", "s2"): "kinetic.2",
("s3", "s3"): "kinetic.3",
}
}
},
"shape": {
"sh1": {
"type": "gaussian",
"amplitude": "shapes.amps.1",
"location": "shapes.locs.1",
"width": "shapes.width.1",
},
"sh2": {
"type": "gaussian",
"amplitude": "shapes.amps.2",
"location": "shapes.locs.2",
"width": "shapes.width.2",
},
"sh3": {
"type": "gaussian",
"amplitude": "shapes.amps.3",
"location": "shapes.locs.3",
"width": "shapes.width.3",
},
},
"irf": {
"irf1": {
"type": "spectral-multi-gaussian",
"center": ["irf.center"],
"width": ["irf.width"],
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
"shape": {
"s1": "sh1",
"s2": "sh2",
"s3": "sh3"
},
},
},
})
initial_parameters = ParameterGroup.from_dict({
"kinetic": [
["1", 501e-3],
["2", 202e-4],
["3", 105e-5],
{
"non-negative": True
},
],
"irf": [["center", 1.3], ["width", 7.8]],
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
["0", 0, {
"vary": False,
"non-negative": False
}],
],
})
wanted_parameters = ParameterGroup.from_dict({
"kinetic": [
["1", 501e-3],
["2", 202e-4],
["3", 105e-5],
],
"shapes": {
"amps": [3, 1, 5],
"locs": [620, 670, 720],
"width": [10, 30, 50]
},
"irf": [["center", 1.3], ["width", 7.8]],
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
["0", 0, {
"vary": False,
"non-negative": False
}],
],
})
time = np.asarray(np.arange(-10, 50, 1.0))
spectral = np.arange(600, 750, 5.0)
axis = {"time": time, "spectral": spectral}
class SimpleIrfDispersion:
model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["j.1"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
}
}
},
"irf": {
"irf1": {
"type": "spectral-gaussian",
"center": "irf.center",
"width": "irf.width",
"dispersion_center": "irf.dispcenter",
"center_dispersion": ["irf.centerdisp"],
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
},
},
})
sim_model = KineticSpectrumModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1"],
"parameters": ["j.1"]
},
},
"megacomplex": {
"mc1": {
"k_matrix": ["k1"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "kinetic.1",
}
}
},
"irf": {
"irf1": {
"type": "spectral-gaussian",
"center": "irf.center",
"width": "irf.width",
"dispersion_center": "irf.dispcenter",
"center_dispersion": ["irf.centerdisp"],
},
},
"shape": {
"sh1": {
"type": "one",
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"irf": "irf1",
"megacomplex": ["mc1"],
"shape": {
"s1": "sh1"
},
},
},
})
initial = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [["1", 0.5], {
"non-negative": False
}],
"irf": [
["center", 0.3],
["width", 0.1],
["dispcenter", 400, {
"vary": False
}],
["centerdisp", 0.5],
],
})
wanted = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [
["1", 0.5],
],
"irf": [["center", 0.3], ["width", 0.1], ["dispcenter", 400],
["centerdisp", 0.5]],
})
time_p1 = np.linspace(-1, 2, 50, endpoint=False)
time_p2 = np.linspace(2, 5, 30, endpoint=False)
time_p3 = np.geomspace(5, 10, num=20)
time = np.concatenate([time_p1, time_p2, time_p3])
spectral = np.arange(300, 500, 100)
axis = {"time": time, "spectral": spectral}
def test_spectral_penalties():
model_without_penalty = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2', 's3'],
'parameters': ['i.1', 'i.2', 'i.3']
},
},
'megacomplex': {
'mc1': {'k_matrix': ['k1']},
},
'k_matrix': {
"k1": {'matrix': {
("s1", "s1"): 'kinetic.1',
("s2", "s2"): 'kinetic.1',
("s3", "s3"): 'kinetic.1',
}}
},
'spectral_relations': [
{
'compartment': 's1',
'target': 's2',
'parameter': 'rel.1',
'interval': [(0, 2)],
},
],
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['mc1'],
},
},
})
weight = 0.1
model_with_penalty = KineticSpectrumModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2', 's3'],
'parameters': ['i.1', 'i.2', 'i.3']
},
},
'megacomplex': {
'mc1': {'k_matrix': ['k1']},
},
'k_matrix': {
"k1": {'matrix': {
("s1", "s1"): 'kinetic.1',
("s2", "s2"): 'kinetic.1',
("s3", "s3"): 'kinetic.1',
}}
},
'equal_area_penalties': [
{
'compartment': 's2',
'target': 's3',
'parameter': 'pen.1',
'interval': [(0, 2)],
'weight': weight
},
],
'spectral_relations': [
{
'compartment': 's1',
'target': 's2',
'parameter': 'rel.1',
'interval': [(0, 2)],
},
],
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['mc1'],
},
},
})
print(model_with_penalty)
rel1 = 2
pen = 0.5
parameter = ParameterGroup.from_dict({
'kinetic': [1e-4],
'i': [1, 1, 1],
'rel': [rel1],
'pen': [pen],
})
time = np.asarray(np.arange(0, 50, 1.5))
clp = xr.DataArray([[1., 2., 4]],
coords=(('spectral', [1]), ('clp_label', ['s1', 's2', 's3'])))
data = model_without_penalty.simulate('dataset1', parameter, clp=clp,
axes={'time': time, 'spectral': np.array([1])})
result_with_penalty = \
model_with_penalty.optimize(parameter, {'dataset1': data}, max_nfev=1)
result_without_penalty = \
model_without_penalty.optimize(parameter, {'dataset1': data}, max_nfev=1)
result_data = result_with_penalty.data['dataset1']
wanted_penalty = result_data.species_associated_spectra.sel(species='s2') - \
result_data.species_associated_spectra.sel(species='s3') * pen
wanted_penalty *= weight
wanted_penalty **= 2
wanted_penalty = np.sum(wanted_penalty.values)
additional_penalty = result_with_penalty.chisqr - result_without_penalty.chisqr
assert np.isclose(additional_penalty, wanted_penalty)
