ENH: Nipype-less implementation (refactor) #16

Co-authored-by: Ariel Rokem <arokem@gmail.com>

…me once

I'm making progress on the integration with DIPY's regitration. However, I'm hitting the following issue: ```Python >>> EddyMotionEstimator.fit(data) Optimizing level 0 [max iter: 100] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-35-e34da2fc4915> in <module> ----> 1 EddyMotionEstimator.fit(data) ~/workspace/EddyMotionCorrection/emc/estimator.py in fit(dwdata, n_iter, align_kwargs, model, seed, **kwargs) 99 100 init_affine = dwdata.em_affines[i] if dwdata.em_affines else np.eye(4) --> 101 xform = registration.optimize( 102 predicted, # fixed 103 data_test[0], # moving ~/.anaconda/lib/python3.8/site-packages/dipy/align/imaffine.py in optimize(***failed resolving arguments***) 1099 self.options['maxiter'] = max_iter 1100 -> 1101 opt = Optimizer(self.metric.distance_and_gradient, 1102 self.params0, 1103 method=self.method, jac=True, ~/.anaconda/lib/python3.8/site-packages/dipy/core/optimize.py in __init__(self, fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options, evolution) 127 else: 128 --> 129 res = minimize(fun, x0, args, method, jac, hess, hessp, bounds, 130 constraints, tol, callback, options) 131 ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options) 615 **options) 616 elif meth == 'l-bfgs-b': --> 617 return _minimize_lbfgsb(fun, x0, args, jac, bounds, 618 callback=callback, **options) 619 elif meth == 'tnc': ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options) 304 iprint = disp 305 --> 306 sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps, 307 bounds=new_bounds, 308 finite_diff_rel_step=finite_diff_rel_step) ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/optimize.py in _prepare_scalar_function(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess) 259 # ScalarFunction caches. Reuse of fun(x) during grad 260 # calculation reduces overall function evaluations. --> 261 sf = ScalarFunction(fun, x0, args, grad, hess, 262 finite_diff_rel_step, bounds, epsilon=epsilon) 263 ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in __init__(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon) 74 75 self._update_fun_impl = update_fun ---> 76 self._update_fun() 77 78 # Gradient evaluation ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in _update_fun(self) 164 def _update_fun(self): 165 if not self.f_updated: --> 166 self._update_fun_impl() 167 self.f_updated = True 168 ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in update_fun() 71 72 def update_fun(): ---> 73 self.f = fun_wrapped(self.x) 74 75 self._update_fun_impl = update_fun ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in fun_wrapped(x) 68 def fun_wrapped(x): 69 self.nfev += 1 ---> 70 return fun(x, *args) 71 72 def update_fun(): ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/optimize.py in __call__(self, x, *args) 72 def __call__(self, x, *args): 73 """ returns the the function value """ ---> 74 self._compute_if_needed(x, *args) 75 return self._value 76 ~/.anaconda/lib/python3.8/site-packages/scipy/optimize/optimize.py in _compute_if_needed(self, x, *args) 66 if not np.all(x == self.x) or self._value is None or self.jac is None: 67 self.x = np.asarray(x).copy() ---> 68 fg = self.fun(x, *args) 69 self.jac = fg[1] 70 self._value = fg[0] ~/.anaconda/lib/python3.8/site-packages/dipy/align/imaffine.py in distance_and_gradient(self, params) 783 """ 784 try: --> 785 self._update_mutual_information(params, True) 786 except (AffineInversionError, AffineInvalidValuesError): 787 return np.inf, 0 * self.metric_grad ~/.anaconda/lib/python3.8/site-packages/dipy/align/imaffine.py in _update_mutual_information(self, params, update_gradient) 670 671 # Update the histogram with the current joint intensities --> 672 static_values, moving_values = self._update_histogram() 673 674 H = self.histogram # Shortcut to `self.histogram` ~/.anaconda/lib/python3.8/site-packages/dipy/align/imaffine.py in _update_histogram(self) 631 pts = sp_to_moving.dot(self.samples.T).T # Points on moving grid 632 pts = pts[..., :self.dim] --> 633 self.moving_vals, inside = self.interp_method(self.moving, pts) 634 self.moving_vals = np.array(self.moving_vals) 635 static_values = self.static_vals dipy/core/interpolation.pyx in dipy.core.interpolation.__pyx_fused_cpdef() TypeError: No matching signature found ```

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ENH: Nipype-less implementation (refactor) #16

ENH: Nipype-less implementation (refactor) #16

Commits on Apr 7, 2021