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| Original file line number | Diff line number | Diff line change |
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| """Glotaran Analysis Package | ||
| This package contains functions for model simulation and fitting. | ||
| """ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| """This package contains functions for dataset analysis.""" | ||
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| from typing import Tuple | ||
| import numpy as np | ||
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| from glotaran.model.dataset import Dataset | ||
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| def dataset_svd(dataset: Dataset) -> Tuple[np.array, np.array, np.array]: | ||
| """Returns the singular value decomposition of a dataset. | ||
| Parameters | ||
| ---------- | ||
| dataset : glotaran.model.dataset.Dataset | ||
| Returns | ||
| ------- | ||
| tuple : | ||
| (lsv, svals, rsv) | ||
| lsv : np.array | ||
| left singular values | ||
| svals : np.array | ||
| singular values | ||
| rsv : np.array | ||
| right singular values | ||
| """ | ||
| lsv, svals, rsv = np.linalg.svd(dataset.data().T) | ||
| return lsv, svals, rsv.T |
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| """This package contains the FitResult object""" | ||
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| import numpy as np | ||
| from dataclasses import dataclass | ||
| from typing import Dict, List | ||
| from lmfit.minimizer import MinimizerResult | ||
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| from glotaran.model.dataset import Dataset | ||
| from glotaran.model.parameter_group import ParameterGroup | ||
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| @dataclass | ||
| class DatasetResult: | ||
| """DatasetResult holds the fitresults for the data.""" | ||
| label: str | ||
| compartments: List[str] | ||
| non_linear_matrix: np.ndarray | ||
| conditionally_non_linear_parameter: np.ndarray | ||
| data: Dataset | ||
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| class FitResult: | ||
| """The result of a fit.""" | ||
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| def __init__(self, | ||
| lm_result: MinimizerResult, | ||
| dataset_results: Dict[str, DatasetResult], | ||
| ): | ||
| """ | ||
| Parameters | ||
| ---------- | ||
| lm_result: MinimizerResult | ||
| dataset_results: dict(str, DatasetResult) | ||
| Returns | ||
| ------- | ||
| """ | ||
| self._lm_result = lm_result | ||
| self._dataset_results = dataset_results | ||
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| @property | ||
| def best_fit_parameter(self) -> ParameterGroup: | ||
| """The best fit parameters.""" | ||
| return ParameterGroup.from_parameter_dict(self._lm_result.params) | ||
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| def get_dataset(self, label: str): | ||
| """get_dataset returns the DatasetResult for the given dataset. | ||
| Parameters | ||
| ---------- | ||
| label : str | ||
| The label of the dataset. | ||
| Returns | ||
| ------- | ||
| dataset_result: DatasetResult | ||
| The result for the dataset. | ||
| """ | ||
| return self._dataset_results[label] | ||
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| def __str__(self): | ||
| string = "# Fitresult\n\n" | ||
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| # pylint: disable=invalid-name | ||
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| ll = 32 | ||
| lr = 13 | ||
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| string += "Optimization Result".ljust(ll-1) | ||
| string += "|" | ||
| string += "|".rjust(lr) | ||
| string += "\n" | ||
| string += "|".rjust(ll, "-") | ||
| string += "|".rjust(lr, "-") | ||
| string += "\n" | ||
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| string += "Number of residual evaluation |".rjust(ll) | ||
| string += f"{self._lm_result.nfev} |".rjust(lr) | ||
| string += "\n" | ||
| string += "Number of variables |".rjust(ll) | ||
| string += f"{self._lm_result.nvarys} |".rjust(lr) | ||
| string += "\n" | ||
| string += "Number of datapoints |".rjust(ll) | ||
| string += f"{self._lm_result.ndata} |".rjust(lr) | ||
| string += "\n" | ||
| string += "Negrees of freedom |".rjust(ll) | ||
| string += f"{self._lm_result.nfree} |".rjust(lr) | ||
| string += "\n" | ||
| string += "Chi Square |".rjust(ll) | ||
| string += f"{self._lm_result.chisqr:.6f} |".rjust(lr) | ||
| string += "\n" | ||
| string += "Reduced Chi Square |".rjust(ll) | ||
| string += f"{self._lm_result.redchi:.6f} |".rjust(lr) | ||
| string += "\n" | ||
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| string += "\n" | ||
| string += "## Best Fit Parameter\n\n" | ||
| string += f"{self.best_fit_parameter}" | ||
| string += "\n" | ||
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| return string |
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