src.wrappers package

Submodules

src.wrappers.OsipiBase module

class src.wrappers.OsipiBase.OsipiBase(bvalues=None, thresholds=None, bounds=None, initial_guess=None, algorithm=None, **kwargs)

Bases: object

The base class for OSIPI IVIM fitting

initialize()

Placeholder for subclass initialization

osipi_accepted_dimensions()

The array of accepted dimensions e.g. (1D, 2D, 3D, 4D, 5D, 6D) (True, True, False, False, False, False)

osipi_accepts_dimension(dim)

Query if the selection dimension is fittable

osipi_author()

Author identification

osipi_check_required_bounds()

Checks if input bounds fulfil the algorithm requirements

osipi_check_required_bvalues()

Minimum number of b-values required

osipi_check_required_initial_guess()

Checks if input initial guess fulfil the algorithm requirements

osipi_check_required_thresholds()

Checks if the number of input thresholds fulfil the algorithm requirements

osipi_fit(data, bvalues=None, **kwargs)

Fits the data with the bvalues Returns [S0, f, Dstar, D]

osipi_initiate_algorithm(algorithm, **kwargs)

Turns the class into a specified one by the input. This method can be used instead of specifically importing that class and initiating it.

WIP: Add args and kwargs!

Args:

algorithm (string): The name of the algorithm, should be the same as the file in the src/standardized folder without the .py extension.

osipi_print_requirements()

Prints the requirements of the algorithm. Attributes that are currently checked for are: required_bvalues (int):

The lowest number of b-values required.

required_thresholds (array-like)

1D array-like of two ints [least number of b-value thresholds, most number of b-value_thresholds].

required_bounds (bool):

Whether bounds are required or not.

required_bounds_optional (bool):

Whether bounds are optional or not

required_initial_guess (bool):

Whether an initial guess is required or not.

required_initial_guess_optional (bool):

Whether an initial guess is optional or not.

osipi_simple_bias_and_RMSE_test(SNR, bvalues, f, Dstar, D, noise_realizations=100)

src.wrappers.ivim_fit module

src.wrappers.ivim_fit.ivim_fit(authors=None, signals=None, bvalues=None, data=None, initial_guess=None, bounds=None)

wrapper function to use OSIPI code contributions for IVIM fit :param author: str, can be one of [] :param signals: numpy array containing signal intensities :param bvalues: numpy array containing corresponding b-values :param data: object containing signals and bvalues :param initial_guess: list of initial parameter estimates :param bounds: list containing list of lower parameter bounds and list of upper parameter bounds :return: numpy array of shape (signals.size, 4) with the D, Dp, f, S0 per voxel.

Module contents