Merge pull request #167 from geometric-intelligence/fix_pyproject

Fix pyproject

neurometry/curvature/datasets/gridcells.py

@@ -4,9 +4,9 @@
 import pandas as pd
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
-import geomstats.backend as gs  # noqa: E402
+import geomstats.backend as gs
 
-import neurometry.curvature.datasets.structures as structures  # noqa: E402
+import neurometry.curvature.datasets.structures as structures
 
 
 # TODO

neurometry/curvature/datasets/synthetic.py

@@ -4,15 +4,15 @@
 import os
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
-import geomstats.backend as gs  # noqa: E402
-import numpy as np  # noqa: E402
-import pandas as pd  # noqa: E402
-import skimage  # noqa: E402
-import torch  # noqa: E402
-from geomstats.geometry.special_orthogonal import SpecialOrthogonal  # noqa: E402
-from torch.distributions.multivariate_normal import MultivariateNormal  # noqa: E402
-
-from neurometry.topology.persistent_homology import (  # noqa: E402
+import geomstats.backend as gs
+import numpy as np
+import pandas as pd
+import skimage
+import torch
+from geomstats.geometry.special_orthogonal import SpecialOrthogonal
+from torch.distributions.multivariate_normal import MultivariateNormal
+
+from neurometry.topology.persistent_homology import (
     cohomological_circular_coordinates,
     cohomological_toroidal_coordinates,
 )

neurometry/curvature/datasets/utils.py

@@ -140,7 +140,7 @@ def load(config):
     elif config.dataset_name == "three_place_cells_synthetic":
         dataset, labels = load_three_place_cells()
     print(f"Dataset shape: {dataset.shape}.")
-    if type(dataset) == np.ndarray:
+    if isinstance(dataset, np.ndarray):
         dataset_torch = torch.from_numpy(dataset)
     else:
         dataset_torch = dataset

neurometry/curvature/evaluate.py

@@ -5,13 +5,13 @@
 import torch
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
-import geomstats.backend as gs  # noqa: E402
-from geomstats.geometry.base import ImmersedSet  # noqa: E402
-from geomstats.geometry.euclidean import Euclidean  # noqa: E402
-from geomstats.geometry.pullback_metric import PullbackMetric  # noqa: E402
-from geomstats.geometry.special_orthogonal import SpecialOrthogonal  # noqa: E402
+import geomstats.backend as gs
+from geomstats.geometry.base import ImmersedSet
+from geomstats.geometry.euclidean import Euclidean
+from geomstats.geometry.pullback_metric import PullbackMetric
+from geomstats.geometry.special_orthogonal import SpecialOrthogonal
 
-from neurometry.curvature.datasets.synthetic import (  # noqa: E402
+from neurometry.curvature.datasets.synthetic import (
     get_s1_synthetic_immersion,
     get_s2_synthetic_immersion,
     get_t2_synthetic_immersion,
@@ -200,8 +200,8 @@ def _compute_curvature_error_s1(thetas, curv_norms_learned, curv_norms_true):
     """Compute "error" of learned curvature profile given true profile for S1."""
     curv_norms_learned = np.array(curv_norms_learned)
     curv_norms_true = np.array(curv_norms_true)
-    diff = np.trapz((curv_norms_learned - curv_norms_true) ** 2, thetas)
-    normalization = np.trapz(curv_norms_learned**2, thetas) + np.trapz(
+    diff = np.trapezoid((curv_norms_learned - curv_norms_true) ** 2, thetas)
+    normalization = np.trapezoid(curv_norms_learned**2, thetas) + np.trapezoid(
         curv_norms_true**2, thetas
     )
 

neurometry/datasets/synthetic.py

@@ -1,12 +1,12 @@
 import os
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
-import geomstats.backend as gs  # noqa: E402
-import torch  # noqa: E402
-from geomstats.geometry.euclidean import Euclidean  # noqa: E402
-from geomstats.geometry.hypersphere import Hypersphere  # noqa: E402
-from geomstats.geometry.klein_bottle import KleinBottle  # noqa: E402
-from geomstats.geometry.product_manifold import ProductManifold  # noqa: E402
+import geomstats.backend as gs
+import torch
+from geomstats.geometry.euclidean import Euclidean
+from geomstats.geometry.hypersphere import Hypersphere
+from geomstats.geometry.klein_bottle import KleinBottle
+from geomstats.geometry.product_manifold import ProductManifold
 
 
 def synthetic_neural_manifold(

neurometry/dimension/dimension.py

@@ -13,7 +13,7 @@
 import neurometry.datasets.synthetic as synthetic
 
 os.environ["GEOMSTATS_BACKEND"] = "pytorch"
-import geomstats.backend as gs  # noqa: E402
+import geomstats.backend as gs
 
 
 def skdim_dimension_estimation(

pyproject.toml

@@ -51,8 +51,8 @@ dependencies = [
     "clu",
     "labml-helpers",
     "giotto-ph @ git+https://github.com/alibayeh/giotto-ph.git",
-    "pyflagser @ git+https://github.com/alibayeh/pyflagser.git",
-    "giotto-tda @ git+https://github.com/alibayeh/giotto-tda.git",
+    "pyflagser @ git+https://github.com/franciscoeacosta/pyflagser.git",
+    "giotto-tda @ git+https://github.com/franciscoeacosta/giotto-tda.git",
     "geomstats @ git+https://github.com/geomstats/geomstats.git",
     "torchlens @ git+https://github.com/johnmarktaylor91/torchlens.git",
     "netrep @ git+https://github.com/ahwillia/netrep.git",