Esempi in Python per read_data_file

Esempio n. 1

File: ex_two_datasets_benchmark.py Progetto: AvratanuBiswas/pyglotaran-examples

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

Esempio n. 2

File: ex_spectral_guidance.py Progetto: AvratanuBiswas/pyglotaran-examples

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

Esempio n. 3

File: target_analysis_script.py Progetto: AvratanuBiswas/pyglotaran-examples

data_path = script_folder.joinpath("data/data.ascii")
# model_path = script_folder.joinpath(GLOBAL_MODEL)
# parameter_path = script_folder.joinpath(GLOBAL_PARAMS)
model_path = script_folder.joinpath(TARGET_MODEL)
parameter_path = script_folder.joinpath(TARGET_PARAMS)

result_name = str(model_path.stem).replace("model", "result")
output_folder = results_folder.joinpath(result_name)
print(f"- Using folder {output_folder.name} to read/write files for this run")

# %%
result_datafile = output_folder.joinpath("dataset1.nc")
if result_datafile.exists() and SKIP_FIT:
    print(f"Loading earlier fit results from: {result_datafile}")
else:
    dataset = read_data_file(data_path)
    model = read_model_from_yaml_file(model_path)
    parameter = read_parameters_from_yaml_file(parameter_path)
    scheme = Scheme(
        model,
        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)

Esempio n. 4

from glotaran.analysis.optimize import optimize
from glotaran.analysis.scheme import Scheme
from glotaran.io import read_data_file

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",

Esempio n. 5

File: target_analysis_script.py Progetto: AvratanuBiswas/pyglotaran-examples

from glotaran.analysis.optimize import optimize
from glotaran.analysis.scheme import Scheme
from glotaran.io import read_data_file

DATA_PATH = "data/demo_data_Hippius_etal_JPCC2007_111_13988_Figs5_9.ascii"
MODEL_PATH = "models/model.yml"
PARAMETERS_FILE_PATH = "models/parameters.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))
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": dataset},
                maximum_number_function_evaluations=10)

# %% Optimize the analysis scheme (and estimate parameters)
result = optimize(scheme)