def optimize(
self,
parameter: ParameterGroup,
data: typing.Dict[str, typing.Union[xr.Dataset, xr.DataArray]],
nnls: bool = False,
verbose: bool = True,
max_nfev: int = None,
group_atol: int = 0,
) -> Result:
"""Optimizes the parameter for this model.
Parameters
----------
data :
A dictonary containing all datasets with their labels as keys.
parameter : glotaran.model.ParameterGroup
The initial parameter.
nnls :
If `True` non-linear least squaes optimizing is used instead of variable projection.
verbose :
If `True` feedback is printed at every iteration.
max_nfev :
Maximum number of function evaluations. `None` for unlimited.
group_atol :
The tolerance for grouping datasets along the global dimension.
"""
result = Result(self, data, parameter, nnls, atol=group_atol)
optimize(result, verbose=verbose, max_nfev=max_nfev)
return result
def dummy_result():
"""Dummy result for testing."""
model = suite.model
model.is_grouped = False
model.is_index_dependent = False
wanted_parameters = suite.wanted_parameters
data = {}
for i in range(3):
e_axis = getattr(suite, "e_axis" if i == 0 else f"e_axis{i+1}")
c_axis = getattr(suite, "c_axis" if i == 0 else f"c_axis{i+1}")
data[f"dataset{i+1}"] = simulate(
suite.sim_model, f"dataset{i+1}", wanted_parameters, {"e": e_axis, "c": c_axis}
)
scheme = Scheme(
model=suite.model,
parameters=suite.initial_parameters,
data=data,
maximum_number_function_evaluations=1,
)
yield optimize(scheme)
def test_optimization_full_model(index_dependent):
model = FullModel.model
model.megacomplex["m1"].is_index_dependent = index_dependent
print(model.validate())
assert model.valid()
parameters = FullModel.parameters
assert model.valid(parameters)
dataset = simulate(model, "dataset1", parameters, FullModel.coordinates)
scheme = Scheme(
model=model,
parameters=parameters,
data={"dataset1": dataset},
maximum_number_function_evaluations=10,
)
result = optimize(scheme, raise_exception=True)
assert result.success
optimized_scheme = result.get_scheme()
assert result.optimized_parameters == optimized_scheme.parameters
result_data = result.data["dataset1"]
assert "fitted_data" in result_data
for label, param in result.optimized_parameters.all():
if param.vary:
assert np.allclose(param.value, parameters.get(label).value, rtol=1e-1)
clp = result_data.clp
print(clp)
assert clp.shape == (4, 4)
assert all(np.isclose(1.0, c) for c in np.diagonal(clp))
def test_decay_model(suite, nnls):
model = suite.model
print(model.validate())
assert model.valid()
model.dataset_group_models["default"].link_clp = False
model.dataset_group_models["default"].method = (
"non_negative_least_squares" if nnls else "variable_projection")
wanted_parameters = suite.wanted_parameters
print(model.validate(wanted_parameters))
print(wanted_parameters)
assert model.valid(wanted_parameters)
initial_parameters = suite.initial_parameters
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
print(model.markdown(wanted_parameters))
dataset = simulate(model, "dataset1", wanted_parameters, suite.axis)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
data = {"dataset1": dataset}
scheme = Scheme(
model=model,
parameters=initial_parameters,
data=data,
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, wanted_parameters.get(label).value)
resultdata = result.data["dataset1"]
print(resultdata)
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-1)
assert "species_spectra" in resultdata
spectra = resultdata.species_spectra
assert "spectral_species" in spectra.coords
assert "spectral" in spectra.coords
assert spectra.shape == (suite.axis["spectral"].size, 3)
assert "species_concentration" in resultdata
concentration = resultdata.species_concentration
assert "species" in concentration.coords
assert "time" in concentration.coords
assert concentration.shape == (suite.axis["time"].size, 3)
def optimize_benchmark(model, parameters, dataset1, dataset2):
# %% Construct the analysis scheme
scheme = Scheme(
model,
parameters,
{
"dataset1": dataset1,
"dataset2": dataset2
},
maximum_number_function_evaluations=11,
non_negative_least_squares=True,
optimization_method="TrustRegionReflection",
)
# %% Optimize the analysis scheme (and estimate parameters)
result = optimize(scheme)
result2 = optimize(result.get_scheme())
return result, result2
def test_spectral_irf(suite):
model = suite.model
print(model.validate())
assert model.valid()
sim_model = suite.sim_model
print(sim_model.validate())
assert sim_model.valid()
wanted_parameters = suite.wanted_parameters
print(sim_model.validate(wanted_parameters))
print(wanted_parameters)
assert sim_model.valid(wanted_parameters)
initial_parameters = suite.initial_parameters
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
print(model.markdown(wanted_parameters))
dataset = sim_model.simulate("dataset1", wanted_parameters, suite.axis)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
data = {"dataset1": dataset}
scheme = Scheme(model=model,
parameters=initial_parameters,
data=data,
nfev=20)
result = optimize(scheme)
print(result.optimized_parameters)
for label, param in result.optimized_parameters.all():
assert np.allclose(param.value,
wanted_parameters.get(label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
print(resultdata)
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, atol=1e-14)
print(resultdata.fitted_data.isel(spectral=0).argmax())
print(resultdata.fitted_data.isel(spectral=-1).argmax())
assert (resultdata.fitted_data.isel(spectral=0).argmax() !=
resultdata.fitted_data.isel(spectral=-1).argmax())
assert "species_associated_spectra" in resultdata
assert "decay_associated_spectra" in resultdata
def test_fitting(suite):
model = suite.model
sim_model = suite.sim_model
est_axis = suite.e_axis
cal_axis = suite.c_axis
print(model.validate())
assert model.valid()
print(sim_model.validate())
assert sim_model.valid()
wanted = suite.wanted
print(wanted)
print(sim_model.validate(wanted))
assert sim_model.valid(wanted)
initial = suite.initial
print(initial)
print(model.validate(initial))
assert model.valid(initial)
dataset = simulate(sim_model, wanted, 'dataset1', {'e': est_axis, 'c': cal_axis})
print(dataset)
assert dataset.data.shape == (cal_axis.size, est_axis.size)
data = {'dataset1': dataset}
result = Result(model, data, initial, False)
optimize(result)
print(result.optimized_parameter)
print(result.data['dataset1'])
for _, param in result.optimized_parameter.all():
assert np.allclose(param.value, wanted.get(param.full_label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
assert np.array_equal(dataset.c, resultdata.c)
assert np.array_equal(dataset.e, resultdata.e)
assert dataset.data.shape == resultdata.data.shape
print(dataset.data[0, 0], resultdata.data[0, 0])
assert np.allclose(dataset.data, resultdata.data)
def test_spectral_model(suite):
model = suite.spectral_model
print(model.validate())
assert model.valid()
wanted_parameters = suite.spectral_parameters
print(model.validate(wanted_parameters))
print(wanted_parameters)
assert model.valid(wanted_parameters)
initial_parameters = suite.spectral_parameters
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
print(model.markdown(initial_parameters))
dataset = simulate(model, "dataset1", wanted_parameters, suite.axis,
suite.clp)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
data = {"dataset1": dataset}
scheme = Scheme(
model=model,
parameters=initial_parameters,
data=data,
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,
wanted_parameters.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 "species_associated_concentrations" in resultdata
assert resultdata.species_associated_concentrations.shape == (
suite.axis["time"].size,
len(suite.decay_compartments),
)
assert "species_spectra" in resultdata
assert resultdata.species_spectra.shape == (
suite.axis["spectral"].size,
len(suite.decay_compartments),
)
def recreate(self) -> Result:
"""Recrate a result from the initial parameters.
Returns
-------
Result :
The recreated result.
"""
from glotaran.analysis.optimize import optimize
return optimize(self.scheme)
def test_fitting(suite, index_dependend, grouped):
model = suite.model
def gr():
return grouped
model.grouped = gr
def id():
return index_dependend
model.index_dependend = id
sim_model = suite.sim_model
est_axis = suite.e_axis
cal_axis = suite.c_axis
print(model.validate())
assert model.valid()
print(sim_model.validate())
assert sim_model.valid()
wanted = suite.wanted
print(wanted)
print(sim_model.validate(wanted))
assert sim_model.valid(wanted)
initial = suite.initial
print(initial)
print(model.validate(initial))
assert model.valid(initial)
dataset = simulate(sim_model, 'dataset1', wanted, {'e': est_axis, 'c': cal_axis})
print(dataset)
assert dataset.data.shape == (cal_axis.size, est_axis.size)
data = {'dataset1': dataset}
scheme = Scheme(model=model, parameter=initial, data=data, nfev=5)
result = optimize(scheme)
print(result.optimized_parameter)
print(result.data['dataset1'])
for _, param in result.optimized_parameter.all():
assert np.allclose(param.value, wanted.get(param.full_label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
assert np.array_equal(dataset.c, resultdata.c)
assert np.array_equal(dataset.e, resultdata.e)
assert dataset.data.shape == resultdata.data.shape
print(dataset.data[0, 0], resultdata.data[0, 0])
assert np.allclose(dataset.data, resultdata.data)
def test_kinetic_model(suite, nnls):
model = suite.model
print(model.validate())
assert model.valid()
wanted_parameters = suite.wanted_parameters
print(model.validate(wanted_parameters))
print(wanted_parameters)
assert model.valid(wanted_parameters)
initial_parameters = suite.initial_parameters
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
print(model.markdown(initial_parameters))
dataset = model.simulate("dataset1", wanted_parameters, suite.axis,
suite.clp)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["pixel"].size)
data = {"dataset1": dataset}
scheme = Scheme(
model=model,
parameters=initial_parameters,
data=data,
maximum_number_function_evaluations=20,
non_negative_least_squares=nnls,
)
result = optimize(scheme)
print(result.optimized_parameters)
for label, param in result.optimized_parameters.all():
assert np.allclose(param.value,
wanted_parameters.get(label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
assert np.array_equal(dataset["time"], resultdata["time"])
assert np.array_equal(dataset["pixel"], resultdata["pixel"])
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 "species_associated_images" in resultdata
assert "decay_associated_images" in resultdata
if len(model.irf) != 0:
assert "irf" in resultdata
def test_kinetic_model(suite, nnls):
model = suite.model
print(model.validate())
assert model.valid()
model.dataset_group_models["default"].method = (
"non_negative_least_squares" if nnls else "variable_projection")
wanted_parameters = suite.wanted_parameters
print(model.validate(wanted_parameters))
print(wanted_parameters)
assert model.valid(wanted_parameters)
initial_parameters = suite.initial_parameters
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
print(model.markdown(wanted_parameters))
dataset = simulate(model, "dataset1", wanted_parameters, suite.axis)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
data = {f"dataset{i}": dataset for i in range(1, 5)}
scheme = Scheme(
model=model,
parameters=initial_parameters,
data=data,
maximum_number_function_evaluations=20,
)
result = optimize(scheme)
print(result.optimized_parameters)
for label, param in result.optimized_parameters.all():
print(label, param.value, wanted_parameters.get(label).value)
assert np.allclose(param.value,
wanted_parameters.get(label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
print(resultdata)
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)
def test_doas_model(suite):
print(suite.sim_model.validate())
assert suite.sim_model.valid()
print(suite.model.validate())
assert suite.model.valid()
print(suite.sim_model.validate(suite.wanted_parameter))
assert suite.sim_model.valid(suite.wanted_parameter)
print(suite.model.validate(suite.parameter))
assert suite.model.valid(suite.parameter)
dataset = simulate(suite.sim_model, "dataset1", suite.wanted_parameter,
suite.axis)
print(dataset)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
print(suite.parameter)
print(suite.wanted_parameter)
data = {"dataset1": dataset}
scheme = Scheme(
model=suite.model,
parameters=suite.parameter,
data=data,
maximum_number_function_evaluations=20,
)
result = optimize(scheme, raise_exception=True)
print(result.optimized_parameters)
for label, param in result.optimized_parameters.all():
assert np.allclose(param.value,
suite.wanted_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.fitted_data.shape
assert np.allclose(dataset.data, resultdata.fitted_data)
assert "damped_oscillation_cos" in resultdata
assert "damped_oscillation_sin" in resultdata
assert "damped_oscillation_associated_spectra" in resultdata
assert "damped_oscillation_phase" in resultdata
def test_spectral_irf(suite):
model = suite.model
print(model.validate())
assert model.valid()
parameters = suite.parameters
print(model.validate(parameters))
assert model.valid(parameters)
dataset = model.simulate("dataset1", parameters, suite.axis)
assert dataset.data.shape == (suite.axis["time"].size, suite.axis["spectral"].size)
data = {"dataset1": dataset}
scheme = Scheme(
model=model,
parameters=parameters,
data=data,
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"]
# print(resultdata)
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, atol=1e-14)
irf_max_at_start = resultdata.fitted_data.isel(spectral=0).argmax(axis=0)
irf_max_at_end = resultdata.fitted_data.isel(spectral=-1).argmax(axis=0)
print(f" irf_max_at_start: {irf_max_at_start}\n irf_max_at_end: {irf_max_at_end}")
# These should not be equal due to dispersion:
assert irf_max_at_start != irf_max_at_end
assert "species_associated_spectra" in resultdata
assert "decay_associated_spectra" in resultdata
def main():
# parameter_file = output_folder.joinpath("optimized_parameters.csv")
# if parameter_file.exists():
# print("Optimized parameters exists: please check")
# parameters = read_parameters_from_csv_file(str(parameter_file))
# else:
# parameters = read_parameters_from_yaml_file(script_folder.joinpath(PARAMETERS_FILE_PATH))
parameters = read_parameters_from_yaml_file(
script_folder.joinpath(PARAMETERS_FILE_PATH))
# %% Load in data, model and parameters
dataset1 = read_data_file(script_folder.joinpath(DATA_PATH1))
dataset2 = read_data_file(script_folder.joinpath(DATA_PATH2))
model = read_model_from_yaml_file(script_folder.joinpath(MODEL_PATH))
# %% Validate model and parameters
print(model.validate(parameters=parameters))
# %% Construct the analysis scheme
scheme = Scheme(
model,
parameters,
{
"dataset1": dataset1,
"dataset2": dataset2
},
optimization_method="Levenberg-Marquardt",
# maximum_number_function_evaluations=11,
non_negative_least_squares=True,
)
# %% Optimize the analysis scheme (and estimate parameters)
result = optimize(scheme)
# %% Basic print of results
print(result.markdown(True))
return result
def test_fitting(suite, index_dependent, grouped, weight):
model = suite.model
def gr():
return grouped
model.grouped = gr
def id():
return index_dependent
model.index_dependent = id
sim_model = suite.sim_model
est_axis = suite.e_axis
cal_axis = suite.c_axis
print(model.validate())
assert model.valid()
print(sim_model.validate())
assert sim_model.valid()
wanted = suite.wanted
print(wanted)
print(sim_model.validate(wanted))
assert sim_model.valid(wanted)
initial = suite.initial
print(initial)
print(model.validate(initial))
assert model.valid(initial)
dataset = simulate(sim_model, "dataset1", wanted, {
"e": est_axis,
"c": cal_axis
})
print(dataset)
if weight:
dataset["weight"] = xr.DataArray(np.ones_like(dataset.data) * 0.5,
coords=dataset.coords)
assert dataset.data.shape == (cal_axis.size, est_axis.size)
data = {"dataset1": dataset}
scheme = Scheme(model=model, parameter=initial, data=data, nfev=10)
result = optimize(scheme)
print(result.optimized_parameter)
print(result.data["dataset1"])
for _, param in result.optimized_parameter.all():
assert np.allclose(param.value,
wanted.get(param.full_label).value,
rtol=1e-1)
resultdata = result.data["dataset1"]
print(resultdata)
assert "residual" in resultdata
assert "residual_left_singular_vectors" in resultdata
assert "residual_right_singular_vectors" in resultdata
assert "residual_singular_values" in resultdata
assert np.array_equal(dataset.c, resultdata.c)
assert np.array_equal(dataset.e, resultdata.e)
assert dataset.data.shape == resultdata.data.shape
print(dataset.data[0, 0], resultdata.data[0, 0])
assert np.allclose(dataset.data, resultdata.data)
if weight:
assert "weight" in resultdata
assert "weighted_residual" in resultdata
assert "weighted_residual_left_singular_vectors" in resultdata
assert "weighted_residual_right_singular_vectors" in resultdata
assert "weighted_residual_singular_values" in resultdata
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_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)
parameter,
{"dataset1": dataset},
maximum_number_function_evaluations=9,
non_negative_least_squares=True,
)
print(model.validate(parameters=parameter))
# The problem is constructed automatically from the scheme by the optimize call,
# but can also be created manually for debug purposes:
test_problem = Problem(scheme)
# %%
start = timer()
# Warning: this may take a while (several seconds per iteration)
result = optimize(scheme, verbose=True)
end = timer()
print(f"Total time: {end - start}")
result.save(str(output_folder))
end2 = timer()
print(f"Saving took: {end2 - end}")
# %%
print(result.markdown(True))
# %%
res = result.data["dataset1"]
# Tip: print the xarray object to explore its content
print(res)
# define the analysis scheme to optimize
scheme = Scheme(
model,
parameters,
{
"dataset1": dataset1,
"dataset2": dataset2,
"dataset3": dataset3
},
maximum_number_function_evaluations=99,
non_negative_least_squares=True,
# optimization_method="Levenberg-Marquardt",
)
# optimize
result = optimize(scheme)
# %% Save results
result.save(str(output_folder))
# %% Plot results
# Set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
# TODO: enhance plot_overview to handle multiple datasets
result_datafile1 = output_folder.joinpath("dataset1.nc")
result_datafile2 = output_folder.joinpath("dataset2.nc")
result_datafile3 = output_folder.joinpath("dataset3.nc")
fig1 = plot_overview(result_datafile1, linlog=True, linthresh=1)
fig1.savefig(
output_folder.joinpath("plot_overview_sim3d_d1.pdf"),
# %% Construct the analysis scheme
scheme = Scheme(
model,
parameters,
{
"dataset1": dataset1,
"dataset2": dataset2
},
maximum_number_function_evaluations=11,
non_negative_least_squares=True,
optimization_method="TrustRegionReflection",
)
# %% Optimize the analysis scheme (and estimate parameters)
result = optimize(scheme)
print(result.markdown(True))
result2 = optimize(result.get_scheme())
print(result2.markdown(True))
# %% Basic print of results
print(result.markdown(True))
# %% Save the results
try:
save_result(result_path=str(output_folder),
result=result,
format_name="folder",
allow_overwrite=True)
except (ValueError, FileExistsError) as error:
print(f"catching error: {error}")
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
def test_coherent_artifact(spectral_dependence: str):
model_dict = {
"initial_concentration": {
"j1": {"compartments": ["s1"], "parameters": ["irf_center"]},
},
"megacomplex": {
"mc1": {"type": "decay", "k_matrix": ["k1"]},
"mc2": {"type": "coherent-artifact", "order": 3},
},
"k_matrix": {
"k1": {
"matrix": {
("s1", "s1"): "rate",
}
}
},
"irf": {
"irf1": {
"type": "spectral-multi-gaussian",
"center": ["irf_center"],
"width": ["irf_width"],
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["mc1", "mc2"],
"irf": "irf1",
},
},
}
parameter_list = [
["rate", 101e-4],
["irf_center", 10, {"vary": False, "non-negative": False}],
["irf_width", 20, {"vary": False, "non-negative": False}],
]
irf_spec = model_dict["irf"]["irf1"]
if spectral_dependence == "dispersed":
irf_spec["dispersion_center"] = "irf_dispc"
irf_spec["center_dispersion"] = ["irf_disp1", "irf_disp2"]
parameter_list += [
["irf_dispc", 300, {"vary": False, "non-negative": False}],
["irf_disp1", 0.01, {"vary": False, "non-negative": False}],
["irf_disp2", 0.001, {"vary": False, "non-negative": False}],
]
elif spectral_dependence == "shifted":
irf_spec["shift"] = ["irf_shift1", "irf_shift2", "irf_shift3"]
parameter_list += [
["irf_shift1", -2],
["irf_shift2", 0],
["irf_shift3", 2],
]
model = Model.from_dict(
model_dict.copy(),
megacomplex_types={
"decay": DecayMegacomplex,
"coherent-artifact": CoherentArtifactMegacomplex,
},
)
parameters = ParameterGroup.from_list(parameter_list)
time = np.arange(0, 50, 1.5)
spectral = np.asarray([200, 300, 400])
coords = {"time": time, "spectral": spectral}
dataset_model = model.dataset["dataset1"].fill(model, parameters)
dataset_model.overwrite_global_dimension("spectral")
dataset_model.set_coordinates(coords)
matrix = calculate_matrix(dataset_model, {"spectral": 1})
compartments = matrix.clp_labels
print(compartments)
assert len(compartments) == 4
for i in range(1, 4):
assert compartments[i] == f"coherent_artifact_{i}"
assert matrix.matrix.shape == (time.size, 4)
clp = xr.DataArray(
np.ones((3, 4)),
coords=[
("spectral", spectral),
(
"clp_label",
[
"s1",
"coherent_artifact_1",
"coherent_artifact_2",
"coherent_artifact_3",
],
),
],
)
axis = {"time": time, "spectral": clp.spectral}
data = simulate(model, "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-8)
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)
assert "coherent_artifact_response" in resultdata
if spectral_dependence == "none":
assert resultdata["coherent_artifact_response"].shape == (time.size, 3)
else:
assert resultdata["coherent_artifact_response"].shape == (spectral.size, time.size, 3)
assert "coherent_artifact_associated_spectra" in resultdata
assert resultdata["coherent_artifact_associated_spectra"].shape == (3, 3)
def dummy_result():
"""Dummy result for testing."""
print(SCHEME.data["dataset_1"])
yield optimize(SCHEME, raise_exception=True)
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
parameters = 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, parameters)
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, "dataset1", parameters, 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(
"dataset1", parameters, 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",
parameters,
clp=clp,
axes={
"time": time,
"spectral": np.array([1])
})
dataset = {"dataset1": data}
scheme = Scheme(model=model,
parameters=parameters,
data=dataset,
maximum_number_function_evaluations=20)
result = optimize(scheme)
for label, param in result.optimized_parameters.all():
if param.vary:
assert np.allclose(param.value,
parameters.get(label).value,
rtol=1e-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 np.allclose(
result_data.species_associated_spectra[0, 2].values,
rel2 * result_data.species_associated_spectra[0, 0].values,
)
plot_data.plot.line(x="time", aspect=2, size=5)
plot_data = dataset.data.sel(time=[1, 10, 20], method="nearest")
plot_data.plot.line(x="spectral", aspect=2, size=5)
dataset = gta.io.prepare_time_trace_dataset(dataset)
plot_data = dataset.data_singular_values.sel(singular_value_index=range(10))
plot_data.plot(yscale="log", marker="o", linewidth=0, aspect=2, size=5)
model = gta.read_model_from_yaml_file(script_dir.joinpath("model.yml"))
print(model)
parameters = gta.read_parameters_from_yaml_file(
script_dir.joinpath("parameters.yml"))
print(model.validate(parameters=parameters))
print(model)
print(parameters)
result = optimize(Scheme(model, parameters, {"dataset1": dataset}))
print(result)
print(result.optimized_parameters)
result_dataset = result.get_dataset("dataset1")
result_dataset
plot_data = result_dataset.residual_left_singular_vectors.sel(
left_singular_value_index=0)
plot_data.plot.line(x="time", aspect=2, size=5)
plot_data = result_dataset.residual_right_singular_vectors.sel(
right_singular_value_index=0)
plot_data.plot.line(x="spectral", aspect=2, size=5)
result_dataset.to_netcdf("dataset1.nc")
plt.show(block=True)
def test_optimization(suite, is_index_dependent, link_clp, weight, method):
model = suite.model
model.megacomplex["m1"].is_index_dependent = is_index_dependent
print("Link CLP:", link_clp)
print("Index dependent:", is_index_dependent)
sim_model = suite.sim_model
sim_model.megacomplex["m1"].is_index_dependent = is_index_dependent
print(model.validate())
assert model.valid()
print(sim_model.validate())
assert sim_model.valid()
wanted_parameters = suite.wanted_parameters
print(wanted_parameters)
print(sim_model.validate(wanted_parameters))
assert sim_model.valid(wanted_parameters)
initial_parameters = suite.initial_parameters
print(initial_parameters)
print(model.validate(initial_parameters))
assert model.valid(initial_parameters)
assert (
model.dataset["dataset1"].fill(model, initial_parameters).is_index_dependent()
== is_index_dependent
)
nr_datasets = 3 if issubclass(suite, ThreeDatasetDecay) else 1
data = {}
for i in range(nr_datasets):
global_axis = getattr(suite, "global_axis" if i == 0 else f"global_axis{i+1}")
model_axis = getattr(suite, "model_axis" if i == 0 else f"model_axis{i+1}")
dataset = simulate(
sim_model,
f"dataset{i+1}",
wanted_parameters,
{"global": global_axis, "model": model_axis},
)
print(f"Dataset {i+1}")
print("=============")
print(dataset)
if hasattr(suite, "scale"):
dataset["data"] /= suite.scale
if weight:
dataset["weight"] = xr.DataArray(
np.ones_like(dataset.data) * 0.5, coords=dataset.data.coords
)
assert dataset.data.shape == (model_axis.size, global_axis.size)
data[f"dataset{i+1}"] = dataset
scheme = Scheme(
model=model,
parameters=initial_parameters,
data=data,
maximum_number_function_evaluations=10,
clp_link_tolerance=0.1,
optimization_method=method,
)
model.dataset_group_models["default"].link_clp = link_clp
result = optimize(scheme, raise_exception=True)
print(result.optimized_parameters)
assert result.success
optimized_scheme = result.get_scheme()
assert result.optimized_parameters == optimized_scheme.parameters
for dataset in optimized_scheme.data.values():
assert "fitted_data" not in dataset
if weight:
assert "weight" in dataset
for label, param in result.optimized_parameters.all():
if param.vary:
assert np.allclose(param.value, wanted_parameters.get(label).value, rtol=1e-1)
for i, dataset in enumerate(data.values()):
resultdata = result.data[f"dataset{i+1}"]
print(f"Result Data {i+1}")
print("=================")
print(resultdata)
assert "residual" in resultdata
assert "residual_left_singular_vectors" in resultdata
assert "residual_right_singular_vectors" in resultdata
assert "residual_singular_values" in resultdata
assert np.array_equal(dataset.coords["model"], resultdata.coords["model"])
assert np.array_equal(dataset.coords["global"], resultdata.coords["global"])
assert dataset.data.shape == resultdata.data.shape
print(dataset.data[0, 0], resultdata.data[0, 0])
assert np.allclose(dataset.data, resultdata.data)
if weight:
assert "weight" in resultdata
assert "weighted_residual" in resultdata
assert "weighted_residual_left_singular_vectors" in resultdata
assert "weighted_residual_right_singular_vectors" in resultdata
assert "weighted_residual_singular_values" in resultdata
def test_multiple_groups():
wanted_parameters = ParameterGroup.from_list([101e-4])
initial_parameters = ParameterGroup.from_list([100e-5])
global_axis = np.asarray([1.0])
model_axis = np.arange(0, 150, 1.5)
sim_model_dict = {
"megacomplex": {
"m1": {
"is_index_dependent": False
},
"m2": {
"type": "global_complex"
}
},
"dataset": {
"dataset1": {
"initial_concentration": [],
"megacomplex": ["m1"],
"global_megacomplex": ["m2"],
"kinetic": ["1"],
}
},
}
sim_model = DecayModel.from_dict(sim_model_dict)
model_dict = {
"dataset_groups": {
"g1": {},
"g2": {
"residual_function": "non_negative_least_squares"
}
},
"megacomplex": {
"m1": {
"is_index_dependent": False
}
},
"dataset": {
"dataset1": {
"group": "g1",
"initial_concentration": [],
"megacomplex": ["m1"],
"kinetic": ["1"],
},
"dataset2": {
"group": "g2",
"initial_concentration": [],
"megacomplex": ["m1"],
"kinetic": ["1"],
},
},
}
model = DecayModel.from_dict(model_dict)
dataset = simulate(
sim_model,
"dataset1",
wanted_parameters,
{
"global": global_axis,
"model": model_axis
},
)
scheme = Scheme(
model=model,
parameters=initial_parameters,
data={
"dataset1": dataset,
"dataset2": dataset
},
maximum_number_function_evaluations=10,
clp_link_tolerance=0.1,
)
result = optimize(scheme, raise_exception=True)
print(result.optimized_parameters)
assert result.success
for label, param in result.optimized_parameters.all():
if param.vary:
assert np.allclose(param.value,
wanted_parameters.get(label).value,
rtol=1e-1)
def optimize_cmd(
dataformat: str,
data: typing.List[str],
out: str,
nfev: int,
nnls: bool,
yes: bool,
parameters_file: str,
model_file: str,
scheme_file: str,
):
"""Optimizes a model.
e.g.:
glotaran optimize --
"""
if scheme_file is not None:
scheme = util.load_scheme_file(scheme_file, verbose=True)
if nfev is not None:
scheme.nfev = nfev
else:
if model_file is None:
click.echo("Error: Neither scheme nor model specified", err=True)
sys.exit(1)
model = util.load_model_file(model_file, verbose=True)
if parameters_file is None:
click.echo("Error: Neither scheme nor parameter specified",
err=True)
sys.exit(1)
parameters = util.load_parameter_file(parameters_file, verbose=True)
if len(data) == 0:
click.echo("Error: Neither scheme nor data specified", err=True)
sys.exit(1)
dataset_files = {arg[0]: arg[1] for arg in data}
datasets = {}
for label in model.dataset:
if label not in dataset_files:
click.echo(f"Missing dataset for '{label}'", err=True)
sys.exit(1)
path = dataset_files[label]
datasets[label] = util.load_dataset_file(path,
fmt=dataformat,
verbose=True)
scheme = Scheme(
model=model,
parameters=parameters,
data=datasets,
non_negative_least_squares=nnls,
maximum_number_function_evaluations=nfev,
)
click.echo(scheme.validate())
click.echo(f"Use NNLS: {scheme.non_negative_least_squares}")
click.echo(
f"Max Nr Function Evaluations: {scheme.maximum_number_function_evaluations}"
)
click.echo(f"Saving directory: is '{out if out is not None else 'None'}'")
if yes or click.confirm(
"Do you want to start optimization?", abort=True, default=True):
# try:
# click.echo('Preparing optimization...', nl=False)
# optimizer = gta.analysis.optimizer.Optimizer(scheme)
# click.echo(' Success')
# except Exception as e:
# click.echo(" Error")
# click.echo(e, err=True)
# sys.exit(1)
try:
click.echo("Optimizing...")
result = optimize(scheme)
click.echo("Optimization done.")
click.echo(result.markdown(with_model=False))
click.echo("Optimized Parameter:")
click.echo(result.optimized_parameters.markdown())
except Exception as e:
click.echo(f"An error occurred during optimization: \n\n{e}",
err=True)
sys.exit(1)
if out is not None:
try:
click.echo(f"Saving directory is '{out}'")
if yes or click.confirm("Do you want to save the data?",
default=True):
save_result(result_path=out,
format_name="yml",
result=result)
click.echo("File saving successful.")
except Exception as e:
click.echo(f"An error occurred during saving: \n\n{e}",
err=True)
sys.exit(1)
click.echo("All done, have a nice day!")
def test_spectral_irf(suite):
model = suite.model
assert model.valid(), model.validate()
parameters = suite.parameters
assert model.valid(parameters), model.validate(parameters)
sim_model = deepcopy(model)
sim_model.dataset["dataset1"].global_megacomplex = ["mc2"]
dataset = simulate(sim_model, "dataset1", parameters, suite.axis)
assert dataset.data.shape == (suite.axis["time"].size,
suite.axis["spectral"].size)
data = {"dataset1": dataset}
scheme = Scheme(
model=model,
parameters=parameters,
data=data,
maximum_number_function_evaluations=20,
)
result = optimize(scheme)
for label, param in result.optimized_parameters.all():
assert np.allclose(param.value,
parameters.get(label).value), dedent(f"""
Error in {suite.__name__} comparing {param.full_label},
- diff={param.value-parameters.get(label).value}
""")
resultdata = result.data["dataset1"]
# print(resultdata)
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, atol=1e-14)
fit_data_max_at_start = resultdata.fitted_data.isel(spectral=0).argmax(
axis=0)
fit_data_max_at_end = resultdata.fitted_data.isel(spectral=-1).argmax(
axis=0)
if suite is NoIrfDispersion:
assert "center_dispersion_1" not in resultdata
assert fit_data_max_at_start == fit_data_max_at_end
else:
assert "center_dispersion_1" in resultdata
assert fit_data_max_at_start != fit_data_max_at_end
if abs(fit_data_max_at_start - fit_data_max_at_end) < 3:
warnings.warn(
dedent("""
Bad test, one of the following could be the case:
- dispersion too small
- spectral window to small
- time resolution (around the maximum of the IRF) too low"
"""))
for x in suite.axis["spectral"]:
# calculated irf location
model_irf_center = suite.model.irf["irf1"].center
model_dispersion_center = suite.model.irf["irf1"].dispersion_center
model_center_dispersion_coefficients = suite.model.irf[
"irf1"].center_dispersion_coefficients
calc_irf_location_at_x = _calculate_irf_position(
x, model_irf_center, model_dispersion_center,
model_center_dispersion_coefficients)
# fitted irf location
fitted_irf_loc_at_x = resultdata["irf_center_location"].sel(
spectral=x)
assert np.allclose(calc_irf_location_at_x,
fitted_irf_loc_at_x.values), dedent(f"""
Error in {suite.__name__} comparing irf_center_location,
- diff={calc_irf_location_at_x-fitted_irf_loc_at_x.values}
""")
assert "species_associated_spectra" in resultdata
assert "decay_associated_spectra" in resultdata
assert "irf_center" in resultdata
