Merge pull request dipy#3257 from deka27/keyword-reconst

NF: Applying Decorators in Module (Reconst)

dipy/direction/peaks.py

@@ -540,7 +540,11 @@ def peaks_from_model(
     """
     if return_sh and (B is None or invB is None):
         B, invB = sh_to_sf_matrix(
-            sphere, sh_order_max, sh_basis_type, return_inv=True, legacy=legacy
+            sphere,
+            sh_order_max=sh_order_max,
+            basis_type=sh_basis_type,
+            return_inv=True,
+            legacy=legacy,
         )
 
     num_processes = determine_num_processes(num_processes)
@@ -597,7 +601,7 @@ def peaks_from_model(
         if not mask[idx]:
             continue
 
-        odf = model.fit(data[idx]).odf(sphere)
+        odf = model.fit(data[idx]).odf(sphere=sphere)
 
         if return_sh:
             shm_coeff[idx] = np.dot(odf, invB)

dipy/reconst/base.py

@@ -10,6 +10,8 @@
 
 """
 
+from dipy.testing.decorators import warning_for_keywords
+
 
 class ReconstModel:
     """Abstract class for signal reconstruction models"""
@@ -25,7 +27,8 @@ def __init__(self, gtab):
         """
         self.gtab = gtab
 
-    def fit(self, data, mask=None, **kwargs):
+    @warning_for_keywords()
+    def fit(self, data, *, mask=None, **kwargs):
         return ReconstFit(self, data)
 
 

dipy/reconst/cache.py

@@ -1,4 +1,5 @@
 from dipy.core.onetime import auto_attr
+from dipy.testing.decorators import warning_for_keywords
 
 
 class Cache:
@@ -65,7 +66,8 @@ def cache_set(self, tag, key, value):
         """
         self._cache[(tag, key)] = value
 
-    def cache_get(self, tag, key, default=None):
+    @warning_for_keywords()
+    def cache_get(self, tag, key, *, default=None):
         """Retrieve a value from the cache.
 
         Parameters

dipy/reconst/cross_validation.py

@@ -5,9 +5,11 @@
 import numpy as np
 
 import dipy.core.gradients as gt
+from dipy.testing.decorators import warning_for_keywords
 
 
-def coeff_of_determination(data, model, axis=-1):
+@warning_for_keywords()
+def coeff_of_determination(data, model, *, axis=-1):
     r"""Calculate the coefficient of determination for a model prediction,
     relative to data.
 
@@ -145,7 +147,7 @@ class for which prediction is conducted. That is, the Fit object that gets
             err_str += "do not have an implementation of model prediction"
             err_str += " and do not support cross-validation"
             raise ValueError(err_str)
-        this_predict = S0[..., None] * this_fit.predict(left_out_gtab, S0=1)
+        this_predict = S0[..., None] * this_fit.predict(gtab=left_out_gtab, S0=1)
 
         idx_to_assign = np.where(~gtab.b0s_mask)[0][~fold_mask]
         prediction[..., idx_to_assign] = this_predict[..., np.sum(gtab.b0s_mask) :]

dipy/reconst/csdeconv.py

@@ -26,6 +26,7 @@
 )
 from dipy.reconst.utils import _mask_from_roi, _roi_in_volume
 from dipy.sims.voxel import single_tensor
+from dipy.testing.decorators import warning_for_keywords
 from dipy.utils.deprecator import deprecate_with_version, deprecated_params
 
 
@@ -36,9 +37,11 @@
     since="1.2",
     until="1.4",
 )
+@warning_for_keywords()
 def auto_response(
     gtab,
     data,
+    *,
     roi_center=None,
     roi_radius=10,
     fa_thr=0.7,
@@ -161,7 +164,8 @@ class AxSymShResponse:
 
     """
 
-    def __init__(self, S0, dwi_response, bvalue=None):
+    @warning_for_keywords()
+    def __init__(self, S0, dwi_response, *, bvalue=None):
         self.S0 = S0
         self.dwi_response = dwi_response
         self.bvalue = bvalue
@@ -183,10 +187,12 @@ def on_sphere(self, sphere):
 
 class ConstrainedSphericalDeconvModel(SphHarmModel):
     @deprecated_params("sh_order", new_name="sh_order_max", since="1.9", until="2.0")
+    @warning_for_keywords()
     def __init__(
         self,
         gtab,
         response,
+        *,
         reg_sphere=None,
         sh_order_max=8,
         lambda_=1,
@@ -310,13 +316,14 @@ def fit(self, data, **kwargs):
             dwi_data,
             self._X,
             self.B_reg,
-            self.tau,
+            tau=self.tau,
             convergence=self.convergence,
             P=self._P,
         )
         return SphHarmFit(self, shm_coeff, None)
 
-    def predict(self, sh_coeff, gtab=None, S0=1.0):
+    @warning_for_keywords()
+    def predict(self, sh_coeff, *, gtab=None, S0=1.0):
         """Compute a signal prediction given spherical harmonic coefficients
         for the provided GradientTable class instance.
 
@@ -362,8 +369,9 @@ def predict(self, sh_coeff, gtab=None, S0=1.0):
 
 class ConstrainedSDTModel(SphHarmModel):
     @deprecated_params("sh_order", new_name="sh_order_max", since="1.9", until="2.0")
+    @warning_for_keywords()
     def __init__(
-        self, gtab, ratio, reg_sphere=None, sh_order_max=8, lambda_=1.0, tau=0.1
+        self, gtab, ratio, *, reg_sphere=None, sh_order_max=8, lambda_=1.0, tau=0.1
     ):
         r"""Spherical Deconvolution Transform (SDT)
         :footcite:p:`Descoteaux2009`.
@@ -457,7 +465,7 @@ def fit(self, data, **kwargs):
             # normalize ODF
             odf_sh /= Z
             shm_coeff, num_it = odf_deconv(
-                odf_sh, self.R, self.B_reg, self.lambda_, self.tau
+                odf_sh, self.R, self.B_reg, lambda_=self.lambda_, tau=self.tau
             )
             # print 'SDT CSD converged after %d iterations' % num_it
 
@@ -487,7 +495,8 @@ def estimate_response(gtab, evals, S0):
 
 
 @deprecated_params("n", new_name="l_values", since="1.9", until="2.0")
-def forward_sdt_deconv_mat(ratio, l_values, r2_term=False):
+@warning_for_keywords()
+def forward_sdt_deconv_mat(ratio, l_values, *, r2_term=False):
     r"""Build forward sharpening deconvolution transform (SDT) matrix
 
     Parameters
@@ -569,7 +578,8 @@ def _solve_cholesky(Q, z):
     return f
 
 
-def csdeconv(dwsignal, X, B_reg, tau=0.1, convergence=50, P=None):
+@warning_for_keywords()
+def csdeconv(dwsignal, X, B_reg, *, tau=0.1, convergence=50, P=None):
     r"""Constrained-regularized spherical deconvolution (CSD).
 
     Deconvolves the axially symmetric single fiber response function `r_rh` in
@@ -737,7 +747,8 @@ def csdeconv(dwsignal, X, B_reg, tau=0.1, convergence=50, P=None):
     return fodf_sh, _num_it
 
 
-def odf_deconv(odf_sh, R, B_reg, lambda_=1.0, tau=0.1, r2_term=False):
+@warning_for_keywords()
+def odf_deconv(odf_sh, R, B_reg, *, lambda_=1.0, tau=0.1, r2_term=False):
     r"""ODF constrained-regularized spherical deconvolution using
     the Sharpening Deconvolution Transform (SDT).
 
@@ -840,9 +851,11 @@ def odf_deconv(odf_sh, R, B_reg, lambda_=1.0, tau=0.1, r2_term=False):
 
 
 @deprecated_params("sh_order", new_name="sh_order_max", since="1.9", until="2.0")
+@warning_for_keywords()
 def odf_sh_to_sharp(
     odfs_sh,
     sphere,
+    *,
     basis=None,
     ratio=3 / 15.0,
     sh_order_max=8,
@@ -925,7 +938,8 @@ def odf_sh_to_sharp(
     return fodf_sh
 
 
-def mask_for_response_ssst(gtab, data, roi_center=None, roi_radii=10, fa_thr=0.7):
+@warning_for_keywords()
+def mask_for_response_ssst(gtab, data, *, roi_center=None, roi_radii=10, fa_thr=0.7):
     """Computation of mask for single-shell single-tissue (ssst) response
         function using FA.
 
@@ -1055,7 +1069,8 @@ def response_from_mask_ssst(gtab, data, mask):
     return _get_response(S0s, lambdas)
 
 
-def auto_response_ssst(gtab, data, roi_center=None, roi_radii=10, fa_thr=0.7):
+@warning_for_keywords()
+def auto_response_ssst(gtab, data, *, roi_center=None, roi_radii=10, fa_thr=0.7):
     """Automatic estimation of single-shell single-tissue (ssst) response
         function using FA.
 
@@ -1093,7 +1108,9 @@ def auto_response_ssst(gtab, data, roi_center=None, roi_radii=10, fa_thr=0.7):
     `ratio` (more details are available in the description of the function).
     """
 
-    mask = mask_for_response_ssst(gtab, data, roi_center, roi_radii, fa_thr)
+    mask = mask_for_response_ssst(
+        gtab, data, roi_center=roi_center, roi_radii=roi_radii, fa_thr=fa_thr
+    )
     response, ratio = response_from_mask_ssst(gtab, data, mask)
 
     return response, ratio
@@ -1114,9 +1131,11 @@ def _get_response(S0s, lambdas):
 
 
 @deprecated_params("sh_order", new_name="sh_order_max", since="1.9", until="2.0")
+@warning_for_keywords()
 def recursive_response(
     gtab,
     data,
+    *,
     mask=None,
     sh_order_max=8,
     peak_thr=0.01,

dipy/reconst/cti.py

@@ -16,6 +16,7 @@
 )
 from dipy.reconst.multi_voxel import multi_voxel_fit
 from dipy.reconst.utils import cti_design_matrix as design_matrix
+from dipy.testing.decorators import warning_for_keywords
 
 
 def from_qte_to_cti(C):
@@ -135,7 +136,8 @@ def split_cti_params(cti_params):
     return evals, evecs, kt, ct
 
 
-def cti_prediction(cti_params, gtab1, gtab2, S0=1):
+@warning_for_keywords()
+def cti_prediction(cti_params, gtab1, gtab2, *, S0=1):
     """Predict a signal given correlation tensor imaging parameters.
 
     Parameters
@@ -198,7 +200,7 @@ def cti_prediction(cti_params, gtab1, gtab2, S0=1):
 class CorrelationTensorModel(ReconstModel):
     """Class for the Correlation Tensor Model"""
 
-    def __init__(self, gtab1, gtab2, fit_method="WLS", *args, **kwargs):
+    def __init__(self, gtab1, gtab2, *args, fit_method="WLS", **kwargs):
         """Correlation Tensor Imaging Model.
 
         See :footcite:p:`NetoHenriques2020` for further details about the model.
@@ -254,7 +256,8 @@ def __init__(self, gtab1, gtab2, fit_method="WLS", *args, **kwargs):
         self.weights = fit_method in {"WLS", "WLLS", "UWLLS"}
 
     @multi_voxel_fit
-    def fit(self, data, mask=None):
+    @warning_for_keywords()
+    def fit(self, data, *, mask=None):
         """Fit method of the CTI model class.
 
         Parameters
@@ -278,7 +281,8 @@ def fit(self, data, mask=None):
 
         return CorrelationTensorFit(self, params)
 
-    def predict(self, cti_params, S0=1):
+    @warning_for_keywords()
+    def predict(self, cti_params, *, S0=1):
         """Predict a signal for the CTI model class instance given parameters
 
         Parameters
@@ -307,7 +311,7 @@ def predict(self, cti_params, S0=1):
             Predicted signal based on the CTI model
         """
 
-        return cti_prediction(cti_params, self.gtab1, self.gtab2, S0)
+        return cti_prediction(cti_params, self.gtab1, self.gtab2, S0=S0)
 
 
 class CorrelationTensorFit(DiffusionKurtosisFit):
@@ -341,7 +345,8 @@ def ct(self):
         """
         return self.model_params[..., 27:48]
 
-    def predict(self, gtab1, gtab2, S0=1):
+    @warning_for_keywords()
+    def predict(self, gtab1, gtab2, *, S0=1):
         """Given a CTI model fit, predict the signal on the vertices of a
         gradient table
 
@@ -360,7 +365,7 @@ def predict(self, gtab1, gtab2, S0=1):
         S : numpy.ndarray
             Predicted signal based on the CTI model
         """
-        return cti_prediction(self.model_params, gtab1, gtab2, S0)
+        return cti_prediction(self.model_params, gtab1, gtab2, S0=S0)
 
     @property
     def K_aniso(self):
@@ -515,7 +520,8 @@ def K_micro(self):
         return micro_K
 
 
-def params_to_cti_params(result, min_diffusivity=0):
+@warning_for_keywords()
+def params_to_cti_params(result, *, min_diffusivity=0):
     # Extracting the diffusion tensor parameters from solution
     DT_elements = result[:6]
     evals, evecs = decompose_tensor(
@@ -536,8 +542,9 @@ def params_to_cti_params(result, min_diffusivity=0):
     return cti_params
 
 
+@warning_for_keywords()
 def ls_fit_cti(
-    design_matrix, data, inverse_design_matrix, weights=True, min_diffusivity=0
+    design_matrix, data, inverse_design_matrix, *, weights=True, min_diffusivity=0
 ):
     r"""Compute the diffusion kurtosis and covariance tensors using an
     ordinary or weighted linear least squares approach