from src.wrappers.OsipiBase import OsipiBase
from src.original.OGC_AmsterdamUMC.LSQ_fitting import fit_least_squares
import numpy as np
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class OGC_AmsterdamUMC_biexp(OsipiBase):
"""
Bi-exponential fitting algorithm by Oliver Gurney-Champion, Amsterdam UMC
"""
# I'm thinking that we define default attributes for each submission like this
# And in __init__, we can call the OsipiBase control functions to check whether
# the user inputs fulfil the requirements
# Some basic stuff that identifies the algorithm
id_author = "Oliver Gurney Champion, Amsterdam UMC"
id_algorithm_type = "Bi-exponential fit"
id_return_parameters = "f, D*, D, S0"
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=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=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(OGC_AmsterdamUMC_biexp, self).__init__(bvalues, bounds, initial_guess, fitS0)
self.OGC_algorithm = fit_least_squares
self.initialize(bounds, initial_guess, fitS0)
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def initialize(self, bounds, initial_guess, fitS0):
if bounds is None:
self.bounds=([0, 0, 0.005, 0.7],[0.005, 1.0, 0.2, 1.3])
else:
self.bounds=bounds
if initial_guess is None:
self.initial_guess = [0.001, 0.001, 0.01, 1]
else:
self.initial_guess = initial_guess
self.fitS0=fitS0
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def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
"""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_
"""
if initial_guess is not None and len(initial_guess) == 4:
self.initial_guess = initial_guess
bvalues=np.array(bvalues)
fit_results = self.OGC_algorithm(bvalues, signals, p0=self.initial_guess, bounds=self.bounds, fitS0=self.fitS0)
D = fit_results[0]
f = fit_results[1]
Dstar = fit_results[2]
return f, Dstar, D