[ENH] simplify verbose tracking

AFQ/_fixes.py

@@ -3,11 +3,6 @@
 from scipy.special import lpmv, gammaln
 
 from tqdm import tqdm
-from dipy.align import Bunch
-from dipy.tracking.local_tracking import (LocalTracking,
-                                          ParticleFilteringTracking)
-from dipy.tracking.stopping_criterion import StreamlineStatus
-import random
 
 import math
 
@@ -43,80 +38,6 @@ def spherical_harmonics(m, n, theta, phi):
     return val
 
 
-TissueTypes = Bunch(OUTSIDEIMAGE=-1, INVALIDPOINT=0, TRACKPOINT=1, ENDPOINT=2)
-
-
-def _verbose_generate_tractogram(self):
-    """A streamline generator"""
-
-    # Get inverse transform (lin/offset) for seeds
-    inv_A = np.linalg.inv(self.affine)
-    lin = inv_A[:3, :3]
-    offset = inv_A[:3, 3]
-
-    F = np.empty((self.max_length + 1, 3), dtype=float)
-    B = F.copy()
-    for s in tqdm(self.seeds):
-        s = np.dot(lin, s) + offset
-        # Set the random seed in numpy and random
-        if self.random_seed is not None:
-            s_random_seed = hash(np.abs((np.sum(s)) + self.random_seed)) \
-                % (2**32 - 1)
-            random.seed(s_random_seed)
-            np.random.seed(s_random_seed)
-        directions = self.direction_getter.initial_direction(s)
-        if directions.size == 0 and self.return_all:
-            # only the seed position
-            if self.save_seeds:
-                yield [s], s
-            else:
-                yield [s]
-        directions = directions[:self.max_cross]
-        for first_step in directions:
-            stepsF, stream_status = self._tracker(s, first_step, F)
-            if not (self.return_all
-                    or stream_status == StreamlineStatus.ENDPOINT
-                    or stream_status == StreamlineStatus.OUTSIDEIMAGE):
-                continue
-            first_step = -first_step
-            stepsB, stream_status = self._tracker(s, first_step, B)
-            if not (self.return_all
-                    or stream_status == StreamlineStatus.ENDPOINT
-                    or stream_status == StreamlineStatus.OUTSIDEIMAGE):
-                continue
-            if stepsB == 1:
-                streamline = F[:stepsF].copy()
-            else:
-                parts = (B[stepsB - 1:0:-1], F[:stepsF])
-                streamline = np.concatenate(parts, axis=0)
-
-            # move to the next streamline if only the seed position
-            # and not return all
-            len_sl = len(streamline)
-            if len_sl >= self.min_length:
-                if len_sl <= self.max_length:
-                    if self.save_seeds:
-                        yield streamline, s
-                    else:
-                        yield streamline
-
-
-class VerboseLocalTracking(LocalTracking):
-    def __init__(self, *args, min_length=10, max_length=1000, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.min_length = min_length
-        self.max_length = max_length
-    _generate_tractogram = _verbose_generate_tractogram
-
-
-class VerboseParticleFilteringTracking(ParticleFilteringTracking):
-    def __init__(self, *args, min_length=10, max_length=1000, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.min_length = min_length
-        self.max_length = max_length
-    _generate_tractogram = _verbose_generate_tractogram
-
-
 def in_place_norm(vec, axis=-1, keepdims=False, delvec=True):
     """ Return Vectors with Euclidean (L2) norm
 

AFQ/tasks/tractography.py

@@ -47,8 +47,8 @@ def _meta_from_tracking_params(
         Parameters=dict(
             Units="mm",
             StepSize=tracking_params["step_size"],
-            MinimumLength=tracking_params["min_length"],
-            MaximumLength=tracking_params["max_length"],
+            MinimumLength=tracking_params["minlen"],
+            MaximumLength=tracking_params["maxlen"],
             Unidirectional=False),
         Timing=time() - start_time)
     return meta

AFQ/tests/test_api.py

@@ -497,12 +497,10 @@ def test_API_type_checking():
             preproc_pipeline='vistasoft',
             mapping_definition=IdentityMap(),
             tracking_params={
-                "n_seeds": 1,
+                "n_seeds": 10,
                 "random_seeds": True,
-                "directions": "prob",
-                "odf_model": "CSD"},
-            csd_sh_order=2,  # reduce CSD fit time
-            csd_response=((1, 1, 1), 1),
+                "directions": "det",
+                "odf_model": "DTI"},
             bundle_info=abd.default18_bd()["ARC_L", "ARC_R"])
         myafq.export("bundles")
     del myafq
@@ -630,7 +628,7 @@ def test_AFQ_pft():
             "stop_mask": stop_mask,
             "stop_threshold": "CMC",
             "tracker": "pft",
-            "max_length": 150,
+            "maxlen": 150,
         })
     sl_file = myafq.export("streamlines")["01"]
     dwi_file = myafq.export("dwi")["01"]
@@ -640,7 +638,7 @@ def test_AFQ_pft():
         bbox_valid_check=False,
         trk_header_check=False).streamlines
     for sl in sls:
-        # double the max_length, due to step size of 0.5
+        # double the maxlen, due to step size of 0.5
         assert len(sl) <= 300
 
 
@@ -782,7 +780,7 @@ def test_AFQ_data_waypoint():
 
     tracking_params = dict(odf_model="csd",
                            seed_mask=RoiImage(),
-                           n_seeds=100,
+                           n_seeds=200,
                            random_seeds=True,
                            rng_seed=42)
     segmentation_params = dict(filter_by_endpoints=False,
@@ -836,14 +834,14 @@ def test_AFQ_data_waypoint():
 
     seg_sft = aus.SegmentedSFT.fromfile(
         myafq.export("bundles"))
-    npt.assert_(len(seg_sft.get_bundle('SLF_R').streamlines) > 0)
+    npt.assert_(len(seg_sft.get_bundle('CST_L').streamlines) > 0)
 
     # Test bundles exporting:
     myafq.export("indiv_bundles")
     assert op.exists(op.join(
         myafq.export("results_dir"),
         'bundles',
-        'sub-01_ses-01_coordsys-RASMM_trkmethod-probCSD_recogmethod-AFQ_desc-SLFR_tractography.trk'))  # noqa
+        'sub-01_ses-01_coordsys-RASMM_trkmethod-probCSD_recogmethod-AFQ_desc-CSTL_tractography.trk'))  # noqa
 
     tract_profile_fname = myafq.export("profiles")
     tract_profiles = pd.read_csv(tract_profile_fname)
@@ -884,7 +882,7 @@ def test_AFQ_data_waypoint():
     # ROI mask needs to be put in quotes in config
     tracking_params = dict(odf_model="CSD",
                            seed_mask="RoiImage()",
-                           n_seeds=100,
+                           n_seeds=200,
                            random_seeds=True,
                            rng_seed=42)
     bundle_dict_as_str = (

AFQ/tests/test_tractography.py

@@ -24,7 +24,7 @@
 fdata = op.join(tmpdir.name, 'dti.nii.gz')
 make_tracking_data(fbval, fbvec, fdata)
 
-min_length = 20
+minlen = 20
 step_size = 0.5
 
 
@@ -36,7 +36,7 @@ def test_csd_local_tracking():
                         sh_order=sh_order, lambda_=1, tau=0.1, mask=None,
                         out_dir=tmpdir.name)
         for directions in ["det", "prob"]:
-            sl = track(
+            sls = track(
                 fname,
                 directions,
                 odf_model="CSD",
@@ -46,27 +46,29 @@ def test_csd_local_tracking():
                 n_seeds=seeds,
                 stop_mask=None,
                 step_size=step_size,
-                min_length=min_length,
+                minlen=minlen,
                 tracker="local").streamlines
 
-            npt.assert_(len(sl[0]) >= step_size * min_length)
+            for sl in sls:
+                npt.assert_(len(sl) >= minlen / step_size)
 
 
 def test_dti_local_tracking():
     fdict = fit_dti(fdata, fbval, fbvec)
     for directions in ["det", "prob"]:
-        sl = track(
+        sls = track(
             fdict['params'],
             directions,
             max_angle=30.,
             sphere=None,
             seed_mask=None,
             n_seeds=1,
             step_size=step_size,
-            min_length=min_length,
+            minlen=minlen,
             odf_model="DTI",
             tracker="local").streamlines
-        npt.assert_(len(sl[0]) >= min_length * step_size)
+        for sl in sls:
+            npt.assert_(len(sl) >= minlen / step_size)
 
 
 def test_pft_tracking():
@@ -89,7 +91,7 @@ def test_pft_tracking():
 
         for directions in ["det", "prob"]:
             for stop_threshold in ["ACT", "CMC"]:
-                sl = track(
+                sls = track(
                     fname,
                     directions,
                     max_angle=30.,
@@ -99,10 +101,12 @@ def test_pft_tracking():
                     stop_threshold=stop_threshold,
                     n_seeds=1,
                     step_size=step_size,
-                    min_length=min_length,
+                    minlen=minlen,
                     odf_model=odf,
                     tracker="pft").streamlines
-                npt.assert_(len(sl[0]) >= min_length * step_size)
+
+                for sl in sls:
+                    npt.assert_(len(sl) >= minlen / step_size)
 
     # Test error handling:
     with pytest.raises(RuntimeError):
@@ -116,7 +120,7 @@ def test_pft_tracking():
             stop_threshold=stop_threshold,
             n_seeds=1,
             step_size=step_size,
-            min_length=min_length,
+            minlen=minlen,
             tracker="pft")
 
     with pytest.raises(RuntimeError):
@@ -130,5 +134,5 @@ def test_pft_tracking():
             stop_threshold=None,  # Stop threshold needs to be a string!
             n_seeds=1,
             step_size=step_size,
-            min_length=min_length,
+            minlen=minlen,
             tracker="pft")

AFQ/tractography/tractography.py

@@ -2,6 +2,7 @@
 import numpy as np
 import nibabel as nib
 import logging
+from tqdm import tqdm
 
 import dipy.data as dpd
 from dipy.align import resample
@@ -15,8 +16,9 @@
 
 from nibabel.streamlines.tractogram import LazyTractogram
 
-from AFQ._fixes import (VerboseLocalTracking, VerboseParticleFilteringTracking,
-                        tensor_odf)
+from dipy.tracking.local_tracking import (LocalTracking,
+                                          ParticleFilteringTracking)
+from AFQ._fixes import tensor_odf
 
 
 def get_percentile_threshold(mask, threshold):
@@ -32,7 +34,7 @@ def get_percentile_threshold(mask, threshold):
 def track(params_file, directions="prob", max_angle=30., sphere=None,
           seed_mask=None, seed_threshold=0, thresholds_as_percentages=False,
           n_seeds=1, random_seeds=False, rng_seed=None, stop_mask=None,
-          stop_threshold=0, step_size=0.5, min_length=50, max_length=250,
+          stop_threshold=0, step_size=0.5, minlen=50, maxlen=250,
           odf_model="CSD", tracker="local", trx=False):
     """
     Tractography
@@ -95,9 +97,9 @@ def track(params_file, directions="prob", max_angle=30., sphere=None,
         Default: False
     step_size : float, optional.
         The size of a step (in mm) of tractography. Default: 0.5
-    min_length: int, optional
+    minlen: int, optional
         The miminal length (mm) in a streamline. Default: 20
-    max_length: int, optional
+    maxlen: int, optional
         The miminal length (mm) in a streamline. Default: 250
     odf_model : str, optional
         One of {"DTI", "CSD", "DKI"}. Defaults to use "DTI"
@@ -135,8 +137,8 @@ def track(params_file, directions="prob", max_angle=30., sphere=None,
 
     # We need to calculate the size of a voxel, so we can transform
     # from mm to voxel units:
-    min_length = int(min_length / step_size)
-    max_length = int(max_length / step_size)
+    minlen = int(minlen / step_size)
+    maxlen = int(maxlen / step_size)
 
     logger.info("Generating Seeds...")
     if isinstance(n_seeds, int):
@@ -192,7 +194,7 @@ def track(params_file, directions="prob", max_angle=30., sphere=None,
             stopping_criterion = ThresholdStoppingCriterion(stop_mask,
                                                             stop_threshold)
 
-        my_tracker = VerboseLocalTracking
+        my_tracker = LocalTracking
 
     elif tracker == "pft":
         if not isinstance(stop_threshold, str):
@@ -233,7 +235,7 @@ def track(params_file, directions="prob", max_angle=30., sphere=None,
 
         vox_sizes.append(np.mean(params_img.header.get_zooms()[:3]))
 
-        my_tracker = VerboseParticleFilteringTracking
+        my_tracker = ParticleFilteringTracking
         if stop_threshold == "CMC":
             stopping_criterion = CmcStoppingCriterion.from_pve(
                 pve_wm_data,
@@ -247,32 +249,34 @@ def track(params_file, directions="prob", max_angle=30., sphere=None,
                 pve_gm_data,
                 pve_csf_data)
 
-    logger.info("Tracking...")
+    logger.info(
+        f"Tracking with {len(seeds)} seeds, 2 directions per seed...")
 
     return _tracking(my_tracker, seeds, dg, stopping_criterion, params_img,
-                     step_size=step_size, min_length=min_length,
-                     max_length=max_length, random_seed=rng_seed,
+                     step_size=step_size, minlen=minlen,
+                     maxlen=maxlen, random_seed=rng_seed,
                      trx=trx)
 
 
 def _tracking(tracker, seeds, dg, stopping_criterion, params_img,
-              step_size=0.5, min_length=40, max_length=200,
+              step_size=0.5, minlen=40, maxlen=200,
               random_seed=None, trx=False):
     """
     Helper function
     """
     if len(seeds.shape) == 1:
         seeds = seeds[None, ...]
 
-    tracker = tracker(
+    tracker = tqdm(tracker(
         dg,
         stopping_criterion,
         seeds,
         params_img.affine,
         step_size=step_size,
-        min_length=min_length,
-        max_length=max_length,
-        random_seed=random_seed)
+        minlen=minlen,
+        maxlen=maxlen,
+        return_all=False,
+        random_seed=random_seed))
 
     if trx:
         return LazyTractogram(lambda: tracker,

AFQ/utils/bin.py

@@ -389,8 +389,8 @@ def generate_json(json_folder, overwrite=False,
                 "tckgen":{
                 "algorithm": "iFOD2",
                 "select": 1e6,
-                "max_length": 250,
-                "min_length": 30,
+                "maxlen": 250,
+                "minlen": 30,
                 "power":0.33
                 },
                 "sift2":{}

setup.cfg

@@ -28,7 +28,7 @@ python_requires = >=3.8
 install_requires =
     # core packages
     scikit_image>=0.14.2
-    dipy>=1.7.0,<1.8.0
+    dipy>=1.8.0,<1.9.0
     pandas
     pybids>=0.16.2
     templateflow>=0.8