import numpy as np
from src.wrappers.OsipiBase import OsipiBase
from src.original.PV_MUMC.two_step_IVIM_fit import fit_least_squares_array, fit_least_squares
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class PV_MUMC_biexp(OsipiBase):
"""
Bi-exponential fitting algorithm by Paulien Voorter, Maastricht University
"""
# Some basic stuff that identifies the algorithm
id_author = "Paulien Voorter MUMC"
id_algorithm_type = "Bi-exponential fit"
id_return_parameters = "f, D*, D"
id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
# Algorithm requirements
required_bvalues = 4
required_thresholds = [0,0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
required_bounds = False
required_bounds_optional = True # Bounds may not be required but are optional
required_initial_guess = False
required_initial_guess_optional = True
accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
"""
Everything this algorithm requires should be implemented here.
Number of segmentation thresholds, bounds, etc.
Our OsipiBase object could contain functions that compare the inputs with
the requirements.
"""
super(PV_MUMC_biexp, self).__init__(bvalues, None, bounds, None)
self.PV_algorithm = fit_least_squares
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def ivim_fit(self, signals, bvalues=None):
"""Perform the IVIM fit
Args:
signals (array-like)
bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
Returns:
_type_: _description_
"""
fit_results = self.PV_algorithm(bvalues, signals)
f = fit_results[1]
Dstar = fit_results[2]
D = fit_results[0]
return f, Dstar, D