update stable adn dev documentation

dev/_downloads/010b39bedbe86acef7710ea2f2054aab/combined_workflow_creation.py

@@ -10,12 +10,13 @@
 file is usually located in the ``dipy/workflows`` directory.
 """
 
+import os
+
 from dipy.workflows.combined_workflow import CombinedWorkflow
 
 ###############################################################################
 # ``CombinedWorkflow`` is the base class that will be extended to create our
 # combined workflow.
-
 from dipy.workflows.denoise import NLMeansFlow
 from dipy.workflows.segment import MedianOtsuFlow
 
@@ -25,24 +26,27 @@
 
 
 class DenoiseAndSegment(CombinedWorkflow):
-
     """
     ``DenoiseAndSegment`` is the name of our combined workflow. Note that
     it needs to extend CombinedWorkflow for everything to work properly.
     """
 
     def _get_sub_flows(self):
-        return [
-            NLMeansFlow,
-            MedianOtsuFlow
-        ]
+        return [NLMeansFlow, MedianOtsuFlow]
 
         """
         It is mandatory to implement this method if you want to make all the
         sub workflows parameters available in commandline.
         """
 
-    def run(self, input_files, out_dir='', out_file='processed.nii.gz'):
+    def run(
+        self,
+        input_files,
+        out_dir="",
+        out_denoised="processed.nii.gz",
+        out_mask="brain_mask.nii.gz",
+        out_masked="dwi_masked.nii.gz",
+    ):
         """
         Parameters
         ----------
@@ -53,8 +57,14 @@ def run(self, input_files, out_dir='', out_file='processed.nii.gz'):
         out_dir : string, optional
             Where the resulting file will be saved. (default '')
 
-        out_file : string, optional
-            Name of the result file to be saved. (default 'processed.nii.gz')
+        out_denoised : string, optional
+            Name of the denoised file to be saved.
+
+        out_mask : string, optional
+            Name of the Otsu mask file to be saved.
+
+        out_masked : string, optional
+            Name of the Otsu masked file to be saved.
         """
 
         """
@@ -70,13 +80,27 @@ def run(self, input_files, out_dir='', out_file='processed.nii.gz'):
 
         io_it = self.get_io_iterator()
 
-        for in_file, out_file in io_it:
+        for fnames in io_it:
+            in_fname = fnames[0]
+            _out_denoised = os.path.basename(fnames[1])
+            _out_mask = os.path.basename(fnames[2])
+            _out_masked = os.path.basename(fnames[3])
+
             nl_flow = NLMeansFlow()
-            self.run_sub_flow(nl_flow, in_file, out_dir=out_dir)
-            denoised = nl_flow.last_generated_outputs['out_denoised']
+            self.run_sub_flow(
+                nl_flow, in_fname, out_dir=out_dir, out_denoised=_out_denoised
+            )
+            denoised = nl_flow.last_generated_outputs["out_denoised"]
 
             me_flow = MedianOtsuFlow()
-            self.run_sub_flow(me_flow, denoised, out_dir=out_dir)
+            self.run_sub_flow(
+                me_flow,
+                denoised,
+                out_dir=out_dir,
+                out_mask=_out_mask,
+                out_masked=_out_masked,
+            )
+
 
 ###############################################################################
 # Use ``self.get_io_iterator()`` in every workflow you create. This creates

dev/_downloads/04aeb7a462ca420e7796b8df102567d2/linear_fascicle_evaluation.py

@@ -7,58 +7,58 @@
 challenges for diffusion MRI. One proposal for evaluation of tractography
 results is to use a forward model that predicts the signal from each of a set
 of streamlines, and then fit a linear model to these simultaneous predictions
-[Pestilli2014]_.
+:footcite:p:`Pestilli2014`.
 
 We will use streamlines generated using probabilistic tracking on CSA
 peaks. For brevity, we will include in this example only streamlines going
 through the corpus callosum connecting left to right superior frontal
 cortex. The process of tracking and finding these streamlines is fully
-demonstrated in the :ref:`streamline_tools` example. If this example has been
-run, we can read the streamlines from file. Otherwise, we'll run that example
-first, by importing it. This provides us with all of the variables that were
-created in that example:
+demonstrated in the
+:ref:`sphx_glr_examples_built_streamline_analysis_streamline_tools.py` example.
+If this example has been run, we can read the streamlines from file. Otherwise,
+we'll run that example first, by importing it. This provides us with all of the
+variables that were created in that example:
 
 """
+
 from os.path import join as pjoin
 
-import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1 import AxesGrid
-
-import dipy.core.optimize as opt
-from dipy.data import fetch_stanford_tracks
-from dipy.io.streamline import load_trk
-import dipy.tracking.life as life
-from dipy.viz import window, actor, colormap as cmap
+import numpy as np
 
 # We'll need to know where the corpus callosum is from these variables:
 from dipy.core.gradients import gradient_table
-from dipy.data import get_fnames
+import dipy.core.optimize as opt
+from dipy.data import fetch_stanford_tracks, get_fnames
 from dipy.io.gradients import read_bvals_bvecs
-from dipy.io.image import load_nifti_data, load_nifti
+from dipy.io.image import load_nifti, load_nifti_data
+from dipy.io.streamline import load_trk
+import dipy.tracking.life as life
+from dipy.viz import actor, colormap as cmap, window
 
-hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi')
-label_fname = get_fnames('stanford_labels')
-t1_fname = get_fnames('stanford_t1')
+hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames(name="stanford_hardi")
+label_fname = get_fnames(name="stanford_labels")
+t1_fname = get_fnames(name="stanford_t1")
 
 data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
 labels = load_nifti_data(label_fname)
 t1_data = load_nifti_data(t1_fname)
 bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
-gtab = gradient_table(bvals, bvecs)
+gtab = gradient_table(bvals, bvecs=bvecs)
 
 cc_slice = labels == 2
 
 # Let's now fetch a set of streamlines from the Stanford HARDI dataset.
-# Those streamlines were generated during the :ref:`streamline_tools` example.
+# Those streamlines were generated during the
+# :ref:`sphx_glr_examples_built_streamline_analysis_streamline_tools.py` example.
 # Read the candidates from file in voxel space:
 
 streamlines_files = fetch_stanford_tracks()
-lr_superiorfrontal_path = pjoin(streamlines_files[1],
-                                'hardi-lr-superiorfrontal.trk')
+lr_superiorfrontal_path = pjoin(streamlines_files[1], "hardi-lr-superiorfrontal.trk")
 
-candidate_sl_sft = load_trk(lr_superiorfrontal_path, 'same')
+candidate_sl_sft = load_trk(lr_superiorfrontal_path, "same")
 candidate_sl_sft.to_vox()
 candidate_sl = candidate_sl_sft.streamlines
 
@@ -73,10 +73,10 @@
 # Enables/disables interactive visualization
 interactive = False
 
-candidate_streamlines_actor = actor.streamtube(candidate_sl,
-                                               cmap.line_colors(candidate_sl))
-cc_ROI_actor = actor.contour_from_roi(cc_slice, color=(1., 1., 0.),
-                                      opacity=0.5)
+candidate_streamlines_actor = actor.streamtube(
+    candidate_sl, colors=cmap.line_colors(candidate_sl)
+)
+cc_ROI_actor = actor.contour_from_roi(cc_slice, color=(1.0, 1.0, 0.0), opacity=0.5)
 
 vol_actor = actor.slicer(t1_data)
 
@@ -90,9 +90,7 @@
 scene.add(cc_ROI_actor)
 scene.add(vol_actor)
 scene.add(vol_actor2)
-window.record(scene, n_frames=1,
-              out_path='life_candidates.png',
-              size=(800, 800))
+window.record(scene=scene, n_frames=1, out_path="life_candidates.png", size=(800, 800))
 if interactive:
     window.show(scene)
 
@@ -139,7 +137,7 @@
 # the voxels. The  expected contributions of the streamline are calculated
 # using a forward model, where each node of the streamline is modeled as a
 # cylindrical fiber compartment with Gaussian diffusion, using the diffusion
-# tensor model. See [Pestilli2014]_ for more detail on the model, and
+# tensor model. See :footcite:p:`Pestilli2014` for more detail on the model, and
 # variations of this model.
 
 fiber_fit = fiber_model.fit(data, candidate_sl, affine=np.eye(4))
@@ -151,10 +149,10 @@
 # redundant streamlines, and these streamlines will have $\beta_i$ that are 0.
 
 fig, ax = plt.subplots(1)
-ax.hist(fiber_fit.beta, bins=100, histtype='step')
-ax.set_xlabel('Fiber weights')
-ax.set_ylabel('# fibers')
-fig.savefig('beta_histogram.png')
+ax.hist(fiber_fit.beta, bins=100, histtype="step")
+ax.set_xlabel("Fiber weights")
+ax.set_ylabel("# fibers")
+fig.savefig("beta_histogram.png")
 
 ###############################################################################
 # .. rst-class:: centered small fst-italic fw-semibold
@@ -166,13 +164,12 @@
 # We use $\beta$ to filter out these redundant streamlines, and generate an
 # optimized group of streamlines:
 
-optimized_sl = [np.row_stack(candidate_sl)[np.where(fiber_fit.beta > 0)[0]]]
+optimized_sl = [np.vstack(candidate_sl)[np.where(fiber_fit.beta > 0)[0]]]
 scene = window.Scene()
-scene.add(actor.streamtube(optimized_sl, cmap.line_colors(optimized_sl)))
+scene.add(actor.streamtube(optimized_sl, colors=cmap.line_colors(optimized_sl)))
 scene.add(cc_ROI_actor)
 scene.add(vol_actor)
-window.record(scene, n_frames=1, out_path='life_optimized.png',
-              size=(800, 800))
+window.record(scene=scene, n_frames=1, out_path="life_optimized.png", size=(800, 800))
 if interactive:
     window.show(scene)
 
@@ -215,9 +212,10 @@
 # voxel.
 
 beta_baseline = np.zeros(fiber_fit.beta.shape[0])
-pred_weighted = np.reshape(opt.spdot(fiber_fit.life_matrix, beta_baseline),
-                           (fiber_fit.vox_coords.shape[0],
-                            np.sum(~gtab.b0s_mask)))
+pred_weighted = np.reshape(
+    opt.spdot(fiber_fit.life_matrix, beta_baseline),
+    (fiber_fit.vox_coords.shape[0], np.sum(~gtab.b0s_mask)),
+)
 mean_pred = np.empty((fiber_fit.vox_coords.shape[0], gtab.bvals.shape[0]))
 S0 = fiber_fit.b0_signal
 
@@ -226,10 +224,11 @@
 # to add back the mean and then multiply by S0 in every voxel:
 
 mean_pred[..., gtab.b0s_mask] = S0[:, None]
-mean_pred[..., ~gtab.b0s_mask] =\
-    (pred_weighted + fiber_fit.mean_signal[:, None]) * S0[:, None]
+mean_pred[..., ~gtab.b0s_mask] = (pred_weighted + fiber_fit.mean_signal[:, None]) * S0[
+    :, None
+]
 mean_error = mean_pred - fiber_fit.data
-mean_rmse = np.sqrt(np.mean(mean_error ** 2, -1))
+mean_rmse = np.sqrt(np.mean(mean_error**2, -1))
 
 ###############################################################################
 # First, we can compare the overall distribution of errors between these two
@@ -239,18 +238,26 @@
 # to without the model fit.
 
 fig, ax = plt.subplots(1)
-ax.hist(mean_rmse - model_rmse, bins=100, histtype='step')
-ax.text(0.2, 0.9, 'Median RMSE, mean model: %.2f' % np.median(mean_rmse),
-        horizontalalignment='left',
-        verticalalignment='center',
-        transform=ax.transAxes)
-ax.text(0.2, 0.8, 'Median RMSE, LiFE: %.2f' % np.median(model_rmse),
-        horizontalalignment='left',
-        verticalalignment='center',
-        transform=ax.transAxes)
-ax.set_xlabel('RMS Error')
-ax.set_ylabel('# voxels')
-fig.savefig('error_histograms.png')
+ax.hist(mean_rmse - model_rmse, bins=100, histtype="step")
+ax.text(
+    0.2,
+    0.9,
+    f"Median RMSE, mean model: {np.median(mean_rmse):.2f}",
+    horizontalalignment="left",
+    verticalalignment="center",
+    transform=ax.transAxes,
+)
+ax.text(
+    0.2,
+    0.8,
+    f"Median RMSE, LiFE: {np.median(model_rmse):.2f}",
+    horizontalalignment="left",
+    verticalalignment="center",
+    transform=ax.transAxes,
+)
+ax.set_xlabel("RMS Error")
+ax.set_ylabel("# voxels")
+fig.savefig("error_histograms.png")
 
 ###############################################################################
 # .. rst-class:: centered small fst-italic fw-semibold
@@ -265,37 +272,39 @@
 # and of the improvement with the model fit:
 
 vol_model = np.ones(data.shape[:3]) * np.nan
-vol_model[fiber_fit.vox_coords[:, 0],
-          fiber_fit.vox_coords[:, 1],
-          fiber_fit.vox_coords[:, 2]] = model_rmse
+vol_model[
+    fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]
+] = model_rmse
 vol_mean = np.ones(data.shape[:3]) * np.nan
-vol_mean[fiber_fit.vox_coords[:, 0],
-         fiber_fit.vox_coords[:, 1],
-         fiber_fit.vox_coords[:, 2]] = mean_rmse
+vol_mean[
+    fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]
+] = mean_rmse
 vol_improve = np.ones(data.shape[:3]) * np.nan
-vol_improve[fiber_fit.vox_coords[:, 0],
-            fiber_fit.vox_coords[:, 1],
-            fiber_fit.vox_coords[:, 2]] = mean_rmse - model_rmse
+vol_improve[
+    fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]
+] = mean_rmse - model_rmse
 sl_idx = 49
 fig = plt.figure()
 fig.subplots_adjust(left=0.05, right=0.95)
-ax = AxesGrid(fig, 111,
-              nrows_ncols=(1, 3),
-              label_mode="1",
-              share_all=True,
-              cbar_location="top",
-              cbar_mode="each",
-              cbar_size="10%",
-              cbar_pad="5%")
+ax = AxesGrid(
+    fig,
+    111,
+    nrows_ncols=(1, 3),
+    label_mode="1",
+    share_all=True,
+    cbar_location="top",
+    cbar_mode="each",
+    cbar_size="10%",
+    cbar_pad="5%",
+)
 ax[0].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
 im = ax[0].matshow(np.rot90(vol_model[sl_idx, :, :]), cmap=matplotlib.cm.hot)
 ax.cbar_axes[0].colorbar(im)
 ax[1].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
 im = ax[1].matshow(np.rot90(vol_mean[sl_idx, :, :]), cmap=matplotlib.cm.hot)
 ax.cbar_axes[1].colorbar(im)
 ax[2].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
-im = ax[2].matshow(np.rot90(vol_improve[sl_idx, :, :]),
-                   cmap=matplotlib.cm.RdBu)
+im = ax[2].matshow(np.rot90(vol_improve[sl_idx, :, :]), cmap=matplotlib.cm.RdBu)
 ax.cbar_axes[2].colorbar(im)
 for lax in ax:
     lax.set_xticks([])
@@ -327,9 +336,8 @@
 # References
 # ----------
 #
-# .. [Pestilli2014] Pestilli, F., Yeatman, J, Rokem, A. Kay, K. and Wandell
-#    B.A. (2014). Validation and statistical inference in living connectomes.
-#    Nature Methods 11: 1058-1063. doi:10.1038/nmeth.3098
+# .. footbibliography::
+#
 
 ###############################################################################
 # .. include:: ../../links_names.inc