Add OS independent paths for Allen dataset

cebra/datasets/allen/ca_movie.py

@@ -21,7 +21,7 @@
 
 import glob
 import hashlib
-import os
+import pathlib
 
 import h5py
 import joblib
@@ -66,9 +66,9 @@ def __init__(
         num_neurons=10,
         seed=111,
         area="VISp",
-        frame_feature_path=get_datapath(
-            "allen/features/allen_movies/vit_base/8/movie_one_image_stack.npz/testfeat.pth"
-        ),
+        frame_feature_path=pathlib.Path(_DEFAULT_DATADIR) / "allen" /
+        "features" / "allen_movies" / "vit_base" / "8" /
+        "movie_one_image_stack.npz" / "testfeat.pth",
         pca=False,
         load=None,
     ):
@@ -116,16 +116,17 @@ def _get_pseudo_mice(self, area: str):
             area: The visual cortical area to sample the neurons. Possible options: VISp, VISpm, VISam, VISal, VISl, VISrl.
 
         """
+
         self.area = area
-        list_mice = glob.glob(
-            get_datapath(
-                f"allen/visual_drift/data/calcium_excitatory/{area}/*"))
-        exp_containers = [
-            int(mice.split(f"{area}/")[1].replace(".mat", ""))
-            for mice in list_mice
-        ]
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "visual_drift" / "data" / "calcium_excitatory" / str(area)
+        list_mice = path.glob("*.mat")
+        exp_containers = [int(file.stem) for file in list_mice]
+
         ## Load summary file
-        summary = pd.read_csv(get_datapath("allen/data_summary.csv"))
+        summary = pd.read_csv(
+            pathlib.Path(_DEFAULT_DATADIR) / "allen" / "data_summary.csv")
         ## Filter excitatory neurons in V1
         area_filtered = summary[(summary["exp"].isin(exp_containers)) &
                                 (summary["target"] == area) &
@@ -169,9 +170,10 @@ def _convert_to_nums(string):
             indices2.sort()
             indices3.sort()
             indices = [indices1, indices2, indices3]
-            matfile = get_datapath(
-                f"allen/visual_drift/data/calcium_excitatory/{area}/{exp_container}.mat"
-            )
+            matfile = pathlib.Path(
+                _DEFAULT_DATADIR
+            ) / "allen" / "visual_drift" / "data" / "calcium_excitatory" / str(
+                area) / f"{exp_container}.mat"
             traces = scipy.io.loadmat(matfile)
             for n, i in enumerate(seq_sessions):
                 session = traces["filtered_traces_days_events"][n, 0][
@@ -214,9 +216,10 @@ class AllenCaMoviePreLoadDataset(AllenCaMovieDataset):
     """
 
     def __init__(self, num_neurons, seed):
-        preload = get_datapath(
-            f"allen_preload/allen-movie1-ca-{num_neurons}-{seed}.jl")
-        if not os.path.isfile(preload):
+        preload = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen_preload" / f"allen-movie1-ca-{num_neurons}-{seed}.jl"
+        if not preload.is_file():
             print("The dataset is not yet preloaded.")
             preload = None
         super().__init__(num_neurons=num_neurons, seed=seed, load=preload)

cebra/datasets/allen/ca_movie_decoding.py

@@ -22,6 +22,7 @@
 import glob
 import hashlib
 import os
+import pathlib
 
 import h5py
 import joblib
@@ -41,6 +42,8 @@
 from cebra.datasets.allen import SEEDS
 from cebra.datasets.allen import SEEDS_DISJOINT
 
+_DEFAULT_DATADIR = get_datapath()
+
 
 @parametrize(
     "allen-movie-{num_movie}-ca-{cortex}-{num_neurons}-{split_flag}-{test_repeat}-{seed}",
@@ -107,9 +110,9 @@ def _get_video_features(self, num_movie="one"):
 
         """
 
-        frame_feature_path = get_datapath(
-            f"allen/features/allen_movies/vit_base/8/movie_{num_movie}_image_stack.npz/testfeat.pth"
-        )
+        frame_feature_path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "features" / "allen_movies" / "vit_base" / "8" / f"movie_{num_movie}_image_stack.npz" / "testfeat.pth"
         frame_feature = torch.load(frame_feature_path)
         return frame_feature
 
@@ -171,15 +174,14 @@ def _get_pseudo_mice(self, area, num_movie):
 
         """
 
-        list_mice = glob.glob(
-            get_datapath(
-                f"allen/visual_drift/data/calcium_excitatory/{area}/*"))
-        exp_containers = [
-            int(mice.split(f"{area}/")[1].replace(".mat", ""))
-            for mice in list_mice
-        ]
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "visual_drift" / "data" / "calcium_excitatory" / str(area)
+        list_mice = path.glob("*.mat")
+        exp_containers = [int(file.stem) for file in list_mice]
         ## Load summary file
-        summary = pd.read_csv(get_datapath("allen/data_summary.csv"))
+        summary = pd.read_csv(
+            pathlib.Path(_DEFAULT_DATADIR) / "allen" / "data_summary.csv")
         ## Filter excitatory neurons in V1
         area_filtered = summary[(summary["exp"].isin(exp_containers)) &
                                 (summary["target"] == area) &
@@ -223,9 +225,10 @@ def _convert_to_nums(string):
             indices2.sort()
             indices3.sort()
             indices = [indices1, indices2, indices3]
-            matfile = get_datapath(
-                f"allen/visual_drift/data/calcium_excitatory/{area}/{exp_container}.mat"
-            )
+            matfile = pathlib.Path(
+                _DEFAULT_DATADIR
+            ) / "allen" / "visual_drift" / "data" / "calcium_excitatory" / str(
+                area) / f"{exp_container}.mat"
             traces = scipy.io.loadmat(matfile)
             for n, i in enumerate(seq_sessions):
                 session = traces["filtered_traces_days_events"][n, 0][
@@ -325,10 +328,10 @@ def _get_pseudo_mice(self, area, num_movie):
             area: The visual cortical area to sample the neurons. Possible options: VISp, VISpm, VISam, VISal, VISl, VISrl.
 
         """
-
-        list_mice = glob.glob(
-            get_datapath(
-                f"allen/visual_drift/data/calcium_excitatory/{area}/*"))
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "visual_drift" / "data" / "calcium_excitatory" / str(area)
+        list_mice = path.glob("*")
 
         def _get_neural_data(num_movie, mat_file):
             mat = scipy.io.loadmat(mat_file)

cebra/datasets/allen/combined.py

@@ -23,7 +23,6 @@
 
 import glob
 import hashlib
-import os
 
 import h5py
 import joblib

cebra/datasets/allen/make_neuropixel.py

@@ -22,7 +22,7 @@
 
 import argparse
 import glob
-import os
+import pathlib
 
 import h5py
 import joblib as jl
@@ -32,6 +32,8 @@
 
 from cebra.datasets import get_datapath
 
+_DEFAULT_DATADIR = get_datapath()
+
 
 def _filter_units(
     unit_ids: npt.NDArray[np.int64],
@@ -153,13 +155,13 @@ def _spike_counts(bin_edges: npt.NDArray[np.float64], units: list):
 
 
 def read_neuropixel(
-    path: str = "/shared/neuropixel/*/*.nwb",
+    path: str = pathlib.Path("/shared/neuropixel/"),
     cortex: str = "VISp",
     sampling_rate: float = 120.0,
 ):
     """Load 120Hz Neuropixels data recorded in the specified cortex during the movie1 stimulus.
 
-    The Neuropixels recordin is filtered and transformed to spike counts in a bin size specified by the sampling rat.
+    The Neuropixels recording is filtered and transformed to spike counts in a bin size specified by the sampling rat.
 
     Args:
         path: The wildcard file path where the neuropixels .nwb files are located.
@@ -168,7 +170,7 @@ def read_neuropixel(
 
     """
 
-    files = glob.glob(path)
+    files = path.glob("*/*.nwb")
     sessions = {}
     len_recording = []
     session_frames = []
@@ -238,7 +240,8 @@ def read_neuropixel(
     parser = argparse.ArgumentParser()
     parser.add_argument("--data-path", default="/shared/neuropixel", type=str)
     parser.add_argument("--save-path",
-                        default=get_datapath("allen_movie1_neuropixel/VISp/"),
+                        default=pathlib.Path(_DEFAULT_DATADIR) /
+                        "allen_movie1_neuropixel" / "VISp",
                         type=str)
     parser.add_argument("--sampling-rate", default=120, type=float)
     parser.add_argument("--cortex", default="VISp", type=str)
@@ -255,17 +258,14 @@ def read_neuropixel(
             "neural": sessions_dic,
             "frames": session_frames
         },
-        os.path.join(
-            args.save_path,
-            f"neuropixel_sessions_{int(args.sampling_rate)}_filtered.jl"),
+        Path(args.save_path) /
+        f"neuropixel_sessions_{int(args.sampling_rate)}_filtered.jl",
     )
     jl.dump(
         {
             "neural": pseudo_mice,
             "frames": pseudo_mice_frames
         },
-        os.path.join(
-            args.save_path,
-            f"neuropixel_pseudomouse_{int(args.sampling_rate)}_filtered.jl",
-        ),
+        Path(args.save_path) /
+        f"neuropixel_pseudomouse_{int(args.sampling_rate)}_filtered.jl",
     )

cebra/datasets/allen/neuropixel_movie.py

@@ -19,6 +19,7 @@
 import glob
 import hashlib
 import os
+import pathlib
 
 import h5py
 import joblib
@@ -38,6 +39,8 @@
 from cebra.datasets.allen import NUM_NEURONS
 from cebra.datasets.allen import SEEDS
 
+_DEFAULT_DATADIR = get_datapath()
+
 
 @parametrize(
     "allen-movie1-neuropixel-{num_neurons}-{seed}-10ms",
@@ -70,10 +73,10 @@ def _get_pseudo_mice(self, area="VISp"):
 
         """
         self.area = area
-        list_recording = joblib.load(
-            get_datapath(
-                f"allen/allen_movie1_neuropixel/{area}/neuropixel_pseudomouse_120_filtered.jl"
-            ))
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "allen_movie1_neuropixel" / area / "neuropixel_pseudomouse_120_filtered.jl"
+        list_recording = joblib.load(path)
         pseudo_mice = list_recording["neural"]
 
         return pseudo_mice.transpose(1, 0)
@@ -87,10 +90,9 @@ def _get_index(self, frame_feature):
             frame feature: The video frame feature.
 
         """
-
-        list_recording = joblib.load(
-            get_datapath(
-                f"allen/allen_movie1_neuropixel/{self.area}/neuropixel_pseudomouse_120_filtered.jl"
-            ))
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "allen_movie1_neuropixel" / self.area / "neuropixel_pseudomouse_120_filtered.jl"
+        list_recording = joblib.load(path)
         frames_index = list_recording["frames"]
         return frame_feature[frames_index]

cebra/datasets/allen/neuropixel_movie_decoding.py

@@ -19,6 +19,7 @@
 import glob
 import hashlib
 import os
+import pathlib
 
 import h5py
 import joblib
@@ -40,6 +41,8 @@
 from cebra.datasets.allen import SEEDS
 from cebra.datasets.allen import SEEDS_DISJOINT
 
+_DEFAULT_DATADIR = get_datapath()
+
 
 @parametrize(
     "allen-movie-{num_movie}-neuropixel-{cortex}-{num_neurons}-{split_flag}-10-{seed}",
@@ -87,11 +90,10 @@ def _get_pseudo_mice(self, cortex: str, num_movie: str = "one"):
         Args:
             cortex: The visual cortical area.
         """
-
-        data = joblib.load(
-            get_datapath(
-                f"allen/allen_movie1_neuropixel/{cortex}/neuropixel_pseudomouse_120_filtered.jl"
-            ))
+        path = pathlib.Path(
+            _DEFAULT_DATADIR
+        ) / "allen" / "allen_movie1_neuropixel" / cortex / "neuropixel_pseudomouse_120_filtered.jl"
+        data = joblib.load(path)
         return data
 
     def _split(self, pseudo_mice, frame_feature):
@@ -148,25 +150,23 @@ class AllenNeuropixelMovie120HzCorticesDisjointDataset(
 
     """
 
-    def __init__(
-        self,
-        group,
-        num_neurons,
-        seed=111,
-        cortex="VISp",
-        split_flag="train",
-        frame_feature_path=get_datapath(
-            "allen/features/allen_movies/vit_base/8/movie_one_image_stack.npz/testfeat.pth"
-        ),
-    ):
+    def __init__(self,
+                 group,
+                 num_neurons,
+                 seed=111,
+                 cortex="VISp",
+                 split_flag="train",
+                 frame_feature_path=pathlib.Path(_DEFAULT_DATADIR) / "allen" /
+                 "features" / "allen_movies" / "vit_base" / "8" /
+                 "movie_one_image_stack.npz" / "testfeat.pth"):
         self.split_flag = split_flag
         self.seed = seed
         self.group = group
         self.num_neurons = num_neurons
         data = joblib.load(
-            get_datapath(
-                f"allen/allen_movie1_neuropixel/{cortex}/neuropixel_pseudomouse_120_filtered.jl"
-            ))
+            pathlib.Path(_DEFAULT_DATADIR) / "allen" /
+            "allen_movie1_neuropixel" / cortex /
+            "neuropixel_pseudomouse_120_filtered.jl")
         pseudo_mice = data["neural"].T
         self.neurons_indices = self._sample_neurons(pseudo_mice)
         self.movie_len = pseudo_mice.shape[1]