def setup():
# %% 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))
parameters = read_parameters_from_yaml_file(
script_folder.joinpath(PARAMETERS_FILE_PATH))
return model, parameters, dataset1, dataset2
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
# print(dataset2)
data_path3 = script_folder.joinpath("equareaIRFsim8.ascii")
dataset3 = gta.io.read_data_file(data_path3)
# model inlezen + parameters
model_path = script_folder.joinpath("model.yml")
parameters_path = script_folder.joinpath("parameters.yml")
model = gta.read_model_from_yaml_file(model_path)
# if the optimized parameters from a previous run are available, use those
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 = gta.read_parameters_from_yaml_file(parameters_path)
print(model.validate(parameters=parameters))
# 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",
print(f"- Using folder {output_folder.name} to read/write files for this run")
result_datafile1 = output_folder.joinpath("dataset1.nc")
result_datafile2 = output_folder.joinpath("dataset2.nc")
# %%
dataset1 = read_data_file(data_path1) # CO in toluene
dataset2 = read_data_file(data_path2) # C2O in toluene
print(dataset1)
print(dataset2)
# %%
model = read_model_from_yaml_file(model_path)
parameter = read_parameters_from_yaml_file(parameter_path)
print(model.validate(parameters=parameter))
# %%
start = timer()
scheme = Scheme(
model,
parameter,
{
"dataset1": dataset1,
"dataset2": dataset2
},
maximum_number_function_evaluations=2,
)
result = optimize(scheme)
script_dir = Path(__file__).resolve().parent
print(f"Script folder: {script_dir}")
script_dir.cwd()
plot_data = dataset.data.sel(spectral=[620, 630, 650], method="nearest")
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)
DATA_PATH1 = "data/data1.ascii"
DATA_PATH2 = "data/data2.ascii"
MODEL_PATH = "models/model.yml"
PARAMETERS_FILE_PATH = "models/parameters.yml"
# %% Setup necessary (output) paths
results_folder, script_folder = setup_case_study(Path(__file__))
output_folder = results_folder.joinpath("target_analysis")
print(f"- Using folder {output_folder.name} to read/write files for this run")
# %% 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))
parameters = read_parameters_from_yaml_file(script_folder.joinpath(PARAMETERS_FILE_PATH))
# %% Validate model and parameters
print(model.validate(parameters=parameters))
# %% 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)
"parameters": "models/parameters.yml"
},
"with_penalties": {
"model": "models/model_equal_area_penalties.yml",
"parameters": "models/parameters_equal_area_penalties.yml",
},
}
# %% Setup necessary (output) paths
script_file = Path(__file__)
results_folder, script_folder = setup_case_study(script_file)
output_folder = results_folder.joinpath(script_file.stem)
print(f"- Using folder {output_folder.name} to read/write files for this run")
# %% Load in data, model and parameters
dataset = read_data_file(script_folder.joinpath(DATA_PATH))
for key, val in MODEL_PATHS.items():
model = read_model_from_yaml_file(script_folder.joinpath(val["model"]))
parameters = read_parameters_from_yaml_file(
script_folder.joinpath(val["parameters"]))
print(model.markdown(parameters=parameters))
scheme = Scheme(model, parameters, {"dataset1": dataset})
result = optimize(scheme)
# Second optimization with results of the first:
scheme2 = result.get_scheme()
result2 = optimize(scheme2)
simple_plot_overview(result.data["dataset1"], key)
simple_plot_overview(result2.data["dataset1"], key)
plt.show()