from src.wrappers.OsipiBase import OsipiBase
from src.original.OGC_AmsterdamUMC.LSQ_fitting import fit_least_squares, fit_least_squares_array
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"
id_ref = "reference method in https://doi.org/10.1002/mrm.28852"
# 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
# Supported inputs in the standardized class
supported_bounds = True
supported_initial_guess = True
supported_thresholds = False
supported_dimensions = 1
supported_priors = False
def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True):
"""
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)
super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues=bvalues, bounds=bounds, initial_guess=initial_guess)
self.OGC_algorithm = fit_least_squares
self.OGC_algorithm_array = fit_least_squares_array
self.fitS0=fitS0
self.use_initial_guess = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
self.use_bounds = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
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def ivim_fit(self, signals, bvalues, **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_
"""
bounds = ([self.bounds["D"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["S0"][0]],
[self.bounds["D"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["S0"][1]])
initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
bvalues=np.array(bvalues)
fit_results = self.OGC_algorithm(bvalues, signals, p0=initial_guess, bounds=bounds, fitS0=self.fitS0)
results = {}
results["D"] = fit_results[0]
results["f"] = fit_results[1]
results["Dp"] = fit_results[2]
return results