from src.wrappers.OsipiBase import OsipiBase
from super_ivim_dc.source.Classsic_ivim_fit import fit_least_squares_BOBYQA
import numpy as np
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class TCML_TechnionIIT_lsqBOBYQA(OsipiBase):
"""
TCML_TechnionIIT_lsqBOBYQA fitting algorithm by Moti Freiman and Noam Korngut, TechnionIIT
"""
# 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 = "Moti Freiman and Noam Korngut, TechnIIT"
id_algorithm_type = "Bi-exponential fit, BOBYQO"
id_return_parameters = "f, D*, D, S0"
id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
# 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?
# Supported inputs in the standardized class
supported_bounds = True
supported_initial_guess = True
supported_thresholds = 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(TCML_TechnionIIT_lsqBOBYQA, self).__init__(bvalues=bvalues, bounds=bounds, initial_guess=initial_guess)
self.fit_least_squares = fit_least_squares_BOBYQA
self.fitS0=fitS0
self.initialize(bounds, initial_guess, fitS0)
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def initialize(self, bounds, initial_guess, fitS0):
if bounds is None:
self.use_bounds = {"f": False, "Dp": False, "D": False}
else:
self.use_bounds = {"f": True, "Dp": True, "D": True}
if initial_guess is None:
self.use_initial_guess = {"f": False, "Dp": False, "D": False}
else:
self.use_initial_guess = {"f": True, "Dp": True, "D": True}
self.fitS0=fitS0
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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_
"""
bvalues=np.array(bvalues)
bounds = ([self.bounds["D"][0], self.bounds["Dp"][0], self.bounds["f"][0], self.bounds["S0"][0]],
[self.bounds["D"][1], self.bounds["Dp"][1], self.bounds["f"][1], self.bounds["S0"][1]])
initial_guess = [self.initial_guess["D"], self.initial_guess["Dp"], self.initial_guess["f"], self.initial_guess["S0"]]
fit_results = self.fit_least_squares(bvalues, np.array(signals)[:,np.newaxis], bounds, initial_guess.copy())
def get_scalar(val):
"""Convert value to Python scalar, handling numpy arrays."""
if isinstance(val, np.ndarray):
return float(val.item())
return float(val)
results = {}
if fit_results[0].size > 0:
results["D"] = get_scalar(fit_results[0])
results["f"] = get_scalar(fit_results[2])
results["Dp"] = get_scalar(fit_results[1])
else:
results["D"] = 0
results["f"] = 0
results["Dp"] = 0
results = self.D_and_Ds_swap(results)
return results