Update to scilpy 2.0

dwi_ml/data/processing/streamlines/data_augmentation.py

@@ -9,8 +9,7 @@
 import numpy as np
 
 from scilpy.tractograms.streamline_operations import \
-    resample_streamlines_step_size
-from scilpy.utils.streamlines import compress_sft
+    resample_streamlines_step_size, compress_sft
 
 
 def resample_or_compress(sft, step_size_mm: float = None,

dwi_ml/io_utils.py

@@ -21,6 +21,7 @@ def add_resample_or_compress_arg(p: ArgumentParser):
         help="Step size to resample the data (in mm). Default: None")
     g.add_argument(
         '--compress', type=float, metavar='r', const=0.01, nargs='?',
+        dest='compress_th',
         help="Compression ratio. Default: None. Default if set: 0.01.\n"
              "If neither step_size nor compress are chosen, streamlines "
              "will be kept \nas they are.")

dwi_ml/testing/projects/tt_visu_colored_sft.py

@@ -7,7 +7,7 @@
 from dipy.io.stateful_tractogram import StatefulTractogram
 from dipy.io.streamline import save_tractogram
 
-from scilpy.viz.utils import get_colormap
+from scilpy.viz.color import get_lookup_table
 
 from dwi_ml.testing.projects.tt_visu_utils import (
     get_visu_params_from_options,
@@ -234,7 +234,7 @@ def color_sft_x_y_projections(
 
 def _color_sft_from_dpp(sft, key, cmap='viridis', vmin=None, vmax=None,
                         prepare_fig: bool = False, title=None, **kw):
-    cmap = get_colormap(cmap)
+    cmap = get_lookup_table(cmap)
     tmp = [np.squeeze(sft.data_per_point[key][s]) for s in range(len(sft))]
     data = np.hstack(tmp)
 

dwi_ml/testing/projects/tt_visu_submethods.py

@@ -65,8 +65,8 @@ def load_data_run_model(parser, args, model: AbstractTransformerModel,
         # Resampling streamlines to a fixed step size, if any
         logging.debug("            Resampling: {}".format(args.step_size))
         sft = resample_streamlines_step_size(sft, step_size=args.step_size)
-    if args.compress:
-        logging.debug("            Compressing: {}".format(args.compress))
+    if args.compress_th:
+        logging.debug("            Compressing: {}".format(args.compress_th))
         sft = compress_sft(sft)
 
     # To tensor

dwi_ml/testing/projects/tt_visu_utils.py

@@ -7,7 +7,7 @@
 from scipy.ndimage import zoom
 from tqdm import tqdm
 
-from scilpy.io.fetcher import get_home as get_scilpy_folder
+from scilpy import get_home as get_scilpy_folder
 
 
 THRESH_IMPORTANT = {

dwi_ml/unit_tests/utils/data_and_models_for_tests.py

@@ -5,7 +5,8 @@
 
 import torch
 
-from scilpy.io.fetcher import fetch_data, get_home
+from scilpy import get_home
+from scilpy.io.fetcher import fetch_data
 
 from dwi_ml.data.processing.streamlines.post_processing import \
     compute_directions

requirements.txt

@@ -5,17 +5,10 @@
 # Main dependency: scilpy
 #    Scilpy and comet_ml both require requests. In comet: >=2.18.*,
 #    which installs a version >2.28. Adding request version explicitely.
-#
-#    Changed many times the scilpy version. Tried to have latest fixed version,
-#    but currently, working on beluga only when installing manually. But using
-#    the master is not good; changes too fast for us. Using a fixed commit for
-#    now.
-#    To using a commit preceding all recent changes in scilpy's test data
-#    management: d20d3d4917d40f698aa975f64a575dda34e0c89c
 # -------
 requests==2.28.*
-dipy==1.8.0
--e git+https://github.com/scilus/scilpy.git@d20d3d4917d40f698aa975f64a575dda34e0c89c#egg=scilpy
+dipy==1.9.*
+scilpy==2.0
 
 # -------
 # Other important dependencies
@@ -30,17 +23,17 @@ jupyter>=1.0.0
 IProgress>=0.4     # For jupyter with tdqm
 nested_lookup==0.2.25  # For lists management
 pynvml>=11.5.0
-scikit-image
 
 # -------
-# Necessary but should be installed with scilpy (Last check: 01/2024):
+# Necessary but should be installed with scilpy (Last check: 04/2024):
 # -------
 future==0.18.*
 h5py==3.7.*   # h5py must absolutely be >2.4: that's when it became thread-safe
 matplotlib==3.6.*   # Hint: If matplotlib fails, you may try to install pyQt5.
 nibabel==5.2.*
 numpy==1.23.*
 scipy==1.9.*
+scikit-image==0.22.*
 
 
 # --------------- Notes to developers

scripts_python/dwiml_compute_loss_copy_previous.py

@@ -63,7 +63,7 @@ def main():
     # 1. Prepare fake model
     dg_args = check_args_direction_getter(args)
     model = CopyPrevDirModel(args.dg_key, dg_args, args.skip_first_point,
-                             args.step_size, args.compress)
+                             args.step_size, args.compress_th)
     model.set_context('visu')
 
     # 2. Load data through the tester

scripts_python/dwiml_create_hdf5_dataset.py

@@ -88,7 +88,7 @@ def prepare_hdf5_creator(args):
     # Instantiate a creator and perform checks
     creator = HDF5Creator(Path(args.dwi_ml_ready_folder), args.out_hdf5_file,
                           training_subjs, validation_subjs, testing_subjs,
-                          groups_config, args.step_size, args.compress,
+                          groups_config, args.step_size, args.compress_th,
                           args.enforce_files_presence,
                           args.save_intermediate, intermediate_subdir)
 

scripts_python/dwiml_visualize_noise_on_streamlines.py

@@ -68,7 +68,7 @@ def main():
     subj_sft_data = subj_data.sft_data_list[streamline_group_idx]
     sft = subj_sft_data.as_sft()
 
-    sft = resample_or_compress(sft, args.step_size, args.compress)
+    sft = resample_or_compress(sft, args.step_size, args.compress_th)
     sft.to_vox()
     sft.to_corner()
 

scripts_python/l2t_track_from_model.py

@@ -101,7 +101,7 @@ def prepare_tracker(parser, args):
             dataset=subset, subj_idx=0, model=model, mask=tracking_mask,
             seed_generator=seed_generator, nbr_seeds=nbr_seeds,
             min_len_mm=args.min_length, max_len_mm=args.max_length,
-            compression_th=args.compress, nbr_processes=args.nbr_processes,
+            compression_th=args.compress_th, nbr_processes=args.nbr_processes,
             save_seeds=args.save_seeds, rng_seed=args.rng_seed,
             track_forward_only=args.track_forward_only,
             step_size_mm=args.step_size, algo=args.algo, theta=theta,
@@ -130,7 +130,7 @@ def main():
     assert_outputs_exist(parser, args, args.out_tractogram)
 
     verify_streamline_length_options(parser, args)
-    verify_compression_th(args.compress)
+    verify_compression_th(args.compress_th)
     verify_seed_options(parser, args)
 
     tracker, ref = prepare_tracker(parser, args)

scripts_python/l2t_train_model.py

@@ -73,7 +73,7 @@ def init_from_args(args, sub_loggers_level):
         # INPUTS: verifying args
         model = Learn2TrackModel(
             experiment_name=args.experiment_name,
-            step_size=args.step_size, compress_lines=args.compress,
+            step_size=args.step_size, compress_lines=args.compress_th,
             # PREVIOUS DIRS
             prev_dirs_embedding_key=args.prev_dirs_embedding_key,
             prev_dirs_embedded_size=args.prev_dirs_embedded_size,

scripts_python/tt_track_from_model.py

@@ -106,7 +106,7 @@ def prepare_tracker(parser, args):
             dataset=subset, subj_idx=0, model=model, mask=tracking_mask,
             seed_generator=seed_generator, nbr_seeds=nbr_seeds,
             min_len_mm=args.min_length, max_len_mm=args.max_length,
-            compression_th=args.compress, nbr_processes=args.nbr_processes,
+            compression_th=args.compress_th, nbr_processes=args.nbr_processes,
             save_seeds=args.save_seeds, rng_seed=args.rng_seed,
             track_forward_only=args.track_forward_only,
             step_size_mm=args.step_size, algo=args.algo, theta=theta,
@@ -138,7 +138,7 @@ def main():
     assert_outputs_exist(parser, args, args.out_tractogram)
 
     verify_streamline_length_options(parser, args)
-    verify_compression_th(args.compress)
+    verify_compression_th(args.compress_th)
     verify_seed_options(parser, args)
 
     tracker, ref = prepare_tracker(parser, args)

scripts_python/tt_train_model.py

@@ -101,7 +101,7 @@ def init_from_args(args, sub_loggers_level):
     with Timer("\n\nPreparing model", newline=True, color='yellow'):
         model = cls(
             experiment_name=args.experiment_name,
-            step_size=args.step_size, compress_lines=args.compress,
+            step_size=args.step_size, compress_lines=args.compress_th,
             # Concerning inputs:
             max_len=args.max_len, nb_features=args.nb_features,
             positional_encoding_key=args.position_encoding,