Chore: Apply Copier template (#57)

BlueBrain · Feb 16, 2023 · 5141f93 · 5141f93
1 parent f1d6a97
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diff --git a/.copier-answers.yml b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # Changes here will be overwritten by Copier
 
-_commit: 0.1.33
+_commit: 0.1.36
 _src_path: git@bbpgitlab.epfl.ch:neuromath/python-template.git
 author_email: ''
 author_name: Blue Brain Project, EPFL

diff --git a/.flake8 b/.flake8
@@ -1,5 +1,7 @@
 [flake8]
 ignore =
-	E203,  # E203: whitespace before ':'
-	W503  # W503: line break before binary operator
+	# E203: whitespace before ':'
+	E203,
+	# W503: line break before binary operator
+	W503
 max-line-length = 100
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@ default_language_version:
   python: python3.8
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -13,31 +13,31 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.0.0
+    rev: v9.4.0
     hooks:
         - id: commitlint
           stages:
             - commit-msg
           additional_dependencies: ['conventional-changelog-conventionalcommits']
   - repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
   - repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 23.1.0
     hooks:
       - id: black
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
+    rev: v2.2.2
     hooks:
       - id: codespell
         args: ["-x", ".codespellignorelines"]
   - repo: https://github.com/PyCQA/pydocstyle
-    rev: 6.1.1
+    rev: 6.3.0
     hooks:
       - id: pydocstyle
-        additional_dependencies: ["toml"]
+        additional_dependencies: ["tomli"]
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
diff --git a/neurots/astrocyte/context.py b/neurots/astrocyte/context.py
@@ -92,7 +92,6 @@ class SpaceColonizationContext:
     """
 
     def __init__(self, params):
-
         self.morphology_points = DynamicPointArray()
 
         if "endfeet_targets" in params:

diff --git a/neurots/astrocyte/grower.py b/neurots/astrocyte/grower.py
@@ -29,6 +29,7 @@
 from neurots.morphmath import sample
 from neurots.morphmath.utils import norm as vectorial_norm
 from neurots.morphmath.utils import normalize_vectors
+from neurots.utils import NeuroTSError
 
 L = logging.getLogger(__name__)
 
@@ -41,7 +42,7 @@ def _number_of_trees(tree_type, oris, distributions, random_generator=np.random)
         n_trees = len(oris)
 
     if tree_type == "basal" and n_trees < 2:
-        raise Exception(f"There should be at least 2 basal dendrites (got {n_trees})")
+        raise NeuroTSError(f"There should be at least 2 basal dendrites (got {n_trees})")
 
     return n_trees
 
@@ -63,9 +64,7 @@ def _ensure_endfeet_are_reached(cell, targets):
     distances, section_indices = KDTree(termination_points, copy_data=False).query(target_points)
 
     for distance, section_index, target_point in zip(distances, section_indices, target_points):
-
         if not np.isclose(distance, 0.0):
-
             L.warning(
                 "Target %s was not reached. Extending closest section to reach it.",
                 target_point,
@@ -175,7 +174,6 @@ def _create_process_trunks(self, tree_type, parameters, distributions):
         tree_type = "perivascular" if tree_type == "axon" else "perisynaptic"
 
         if tree_type == "perivascular":
-
             target_ids = np.array(parameters["target_ids"], dtype=np.int32)
             target_points = self.context.endfeet_targets.points[target_ids]
 
@@ -193,15 +191,13 @@ def _create_process_trunks(self, tree_type, parameters, distributions):
             ), "Number of targets is not equal to number of orientations"
 
             for target_id, trunk_point in zip(target_ids, trunk_points):
-
                 neurite_params = deepcopy(parameters)
                 neurite_params["target_id"] = target_id
                 neurite_params["distance_soma_target"] = vectorial_norm(
                     self.soma_grower.soma.center - self.context.endfeet_targets.points[target_id]
                 )
                 self._add_active_neurite(trunk_point, neurite_params, distributions)
         else:
-
             oris = parameters["orientation"]
 
             if oris is not None:
@@ -214,7 +210,6 @@ def _create_process_trunks(self, tree_type, parameters, distributions):
             )
 
             for i, trunk_point in enumerate(trunk_points):
-
                 neurite_params = deepcopy(parameters)
                 if "domain_distances" in neurite_params:
                     neurite_params["distance_to_domain"] = parameters["domain_distances"][i]

diff --git a/neurots/astrocyte/section.py b/neurots/astrocyte/section.py
@@ -55,7 +55,6 @@ def grow_to_target(start_point, start_direction, target_point, segment_length, p
 
     points = []
     while not in_squared_proximity(point, target_point, target_proximity):
-
         target_direction = from_to_direction(point, target_point)
         direction = (1.0 - p) * direction + p * target_direction
 

diff --git a/neurots/astrocyte/space_colonization.py b/neurots/astrocyte/space_colonization.py
@@ -237,7 +237,6 @@ def _colonization_split(section, angles, parameters, context):
     repulsion = _repulsion(morphology_points[ids], current_point, kill_distance)
 
     if section.process == "major":
-
         seed_ids = point_cloud.partial_ball_query(
             current_point,
             influence_distance,
@@ -254,7 +253,6 @@ def _colonization_split(section, angles, parameters, context):
                 section_direction, vectors_to_seeds, repulsion
             )
     else:
-
         seed_ids = point_cloud.upper_half_ball_query(
             current_point, influence_distance, section_direction
         )
@@ -266,7 +264,6 @@ def _colonization_split(section, angles, parameters, context):
             dir1, dir2 = _colonization_strategy_secondary(vectors_to_seeds, repulsion)
 
     if np.allclose(dir1, dir2):
-
         L.warning(
             "Splitting directions are identical. Use of fallback strategy to recalculate them."
         )
@@ -348,15 +345,13 @@ def _colonization_split_with_target_influence(section, angles, parameters, conte
     target_point = context.endfeet_targets.points[target_id]
 
     if in_squared_proximity(current_point, target_point, ENDFOOT_SQUARED_DISTANCE):
-
         # this section will become the endfoot section that will connect to the target
         process2 = "endfoot"
 
         # make the target invisible for the rest of the section growers
         context.endfeet_targets.active[target_id] = False
         dir2 = from_to_direction(current_point, target_point)
     else:
-
         max_target_distance = parameters["distance_soma_target"]
         if process1 == "major":
             dir1 = _add_attraction_bias(
@@ -367,7 +362,6 @@ def _colonization_split_with_target_influence(section, angles, parameters, conte
                 section.context.field,
             )
         else:
-
             process1 = _majorize_process(
                 process1,
                 section.stop_criteria["TMD"],

diff --git a/neurots/extract_input/from_neurom.py b/neurots/extract_input/from_neurom.py
@@ -105,7 +105,7 @@ def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
     Args:
         pop (neurom.core.population.Population): The given population.
         neurite_type (neurom.core.types.NeuriteType): Consider only the neurites of this type.
-        bins (int or list[int] or str, optional): The bins to use (this pararmeter is passed to
+        bins (int or list[int] or str, optional): The bins to use (this parameter is passed to
             :func:`numpy.histogram`).
 
     Returns:

diff --git a/neurots/extract_input/input_distributions.py b/neurots/extract_input/input_distributions.py
@@ -97,7 +97,6 @@ def distributions(
     elif (
         isinstance(diameter_model, str) and diameter_model == "default"
     ) or diameter_model is None:
-
         input_distributions["diameter"] = {}
         input_distributions["diameter"]["method"] = "default"
         input_distributions["diameter"]["diameter"] = build_diameter_models(

diff --git a/neurots/generate/diametrizer.py b/neurots/generate/diametrizer.py
@@ -20,6 +20,8 @@
 import numpy as np
 from morphio import SectionType
 
+from neurots.utils import NeuroTSError
+
 
 def section_filter(neuron, tree_type=None):
     """Filter all sections according to type."""
@@ -71,7 +73,7 @@ def redefine_diameter_section(section, diam_ind, diam_new):
         + section.diameters.tolist()[(diam_ind + 1) :]
     )
     if len(section.points) != len(section.diameters):
-        raise Exception("Mismatch in dimensions of diameters.")
+        raise NeuroTSError("Mismatch in dimensions of diameters.")
 
 
 def bifurcator(initial_diam, num_children, rall_ratio, siblings_ratio):
@@ -223,7 +225,6 @@ def diametrize_from_root(
         random_generator (numpy.random.Generator): The random number generator to use.
     """
     for r in root_section_filter(neuron, tree_type=neurite_type):
-
         model = model_params[r.type.name]  # Selected by the root type.
         trunk_diam = sample(model["trunk"], random_generator)
         min_diam = np.min(model["term"])
@@ -233,7 +234,6 @@ def diametrize_from_root(
 
         while active:
             for section in list(active):
-
                 if section.is_root:
                     taper = sample(model["trunk_taper"], random_generator)
                     init_diam = trunk_diam

diff --git a/neurots/generate/grower.py b/neurots/generate/grower.py
@@ -35,6 +35,7 @@
 from neurots.generate.tree import TreeGrower
 from neurots.morphmath import sample
 from neurots.morphmath.utils import normalize_vectors
+from neurots.utils import NeuroTSError
 from neurots.utils import convert_from_legacy_neurite_type
 from neurots.validator import validate_neuron_distribs
 from neurots.validator import validate_neuron_params
@@ -253,7 +254,6 @@ def _convert_orientation2points(self, orientation, n_trees, distr, params):
             ), "Orientations should have non-zero lengths"
             if params.get("trunk_absolute_orientation", False):
                 if len(orientation) == 1:
-
                     orientation = np.asarray(orientation[0], dtype=np.float64)
                     orientation /= np.linalg.norm(orientation)
 
@@ -271,7 +271,6 @@ def _convert_orientation2points(self, orientation, n_trees, distr, params):
                     raise ValueError("The orientation should contain exactly one point!")
             else:
                 if len(orientation) >= n_trees:
-
                     # TODO: pick orientations randomly?
                     orientations = normalize_vectors(np.asarray(orientation, dtype=np.float64))[
                         :n_trees
@@ -319,7 +318,6 @@ def _grow_trunks(self):
         legacy_mode = not isinstance(self.input_parameters[tree_types[0]]["orientation"], dict)
 
         if not legacy_mode:
-
             trunk_orientations_manager = self._trunk_orientations_class(
                 soma=self.soma_grower.soma,
                 parameters=self.input_parameters,
@@ -329,29 +327,30 @@ def _grow_trunks(self):
             )
 
         for type_of_tree in tree_types:
-
             params = self.input_parameters[type_of_tree]
             distr = self.input_distributions[type_of_tree]
 
             if legacy_mode:
-
                 n_trees = sample.n_neurites(distr["num_trees"], random_generator=self._rng)
 
                 if type_of_tree == "basal_dendrite" and n_trees < 2:
-                    raise Exception(f"There should be at least 2 basal dendrites (got {n_trees})")
+                    raise NeuroTSError(
+                        f"There should be at least 2 basal dendrites (got {n_trees})"
+                    )
 
                 orientation = params["orientation"]
                 points = self._convert_orientation2points(orientation, n_trees, distr, params)
 
             else:
-
                 orientations = trunk_orientations_manager.compute_tree_type_orientations(
                     type_of_tree
                 )
                 n_trees = len(orientations)
 
                 if type_of_tree == "basal_dendrite" and n_trees < 2:
-                    raise Exception(f"There should be at least 2 basal dendrites (got {n_trees})")
+                    raise NeuroTSError(
+                        f"There should be at least 2 basal dendrites (got {n_trees})"
+                    )
 
                 points = self.soma_grower.add_points_from_orientations(orientations)
 

diff --git a/neurots/generate/orientations.py b/neurots/generate/orientations.py
@@ -47,7 +47,6 @@ def _mode_{name}(self, values_dict, tree_type)
     """
 
     def __init__(self, soma, parameters, distributions, context, rng):
-
         self._soma = soma
         self._parameters = parameters
         self._distributions = distributions

diff --git a/neurots/generate/soma.py b/neurots/generate/soma.py
@@ -163,7 +163,6 @@ def add_points_from_orientations(self, vectors):
         new_points = []
 
         for vect in vectors:
-
             phi, theta = rotation.spherical_from_vector(vect)
             point = self.soma.point_from_trunk_direction(phi, theta)
             new_points.append(point)

diff --git a/neurots/morphmath/point_array.py b/neurots/morphmath/point_array.py
@@ -30,7 +30,6 @@ class DynamicPointArray:
     """
 
     def __init__(self, initial_capacity=100000, resize_factor=2.0):
-
         self._size = 0
         self._capacity = initial_capacity
         self._resize_factor = resize_factor

diff --git a/neurots/utils.py b/neurots/utils.py
@@ -56,7 +56,6 @@ def convert_from_legacy_neurite_type(data):
     """Convert legacy neurite type names, basal -> basal_dendrite and apical -> apical_dendrite."""
     old_data = deepcopy(data)
     for key, _data in old_data.items():
-
         if key == "apical":
             neurite_type_warning(key)
             data["apical_dendrite"] = data.pop("apical")

diff --git a/tests/astrocyte/test_context.py b/tests/astrocyte/test_context.py
@@ -37,7 +37,6 @@ def _input_params():
 
 
 def test_constructor():
-
     c = tested.SpaceColonizationContext(_input_params())
 
     npt.assert_equal(len(c.endfeet_targets.points), 2)
@@ -52,7 +51,6 @@ def test_constructor():
 
 
 def test_methods():
-
     c = tested.SpaceColonizationContext(_input_params())
 
     npt.assert_allclose(c.kill_distance(0.1), 15.0 * 0.1)

diff --git a/tests/astrocyte/test_grower.py b/tests/astrocyte/test_grower.py
@@ -80,7 +80,6 @@ def _parameters():
 
 
 def _distributions():
-
     path = _path / "bio_path_distribution.json"
     with open(path, "r", encoding="utf-8") as f:
         distributions = json.load(f)
@@ -216,7 +215,6 @@ def _legacy_rng():
 
 
 def _check_neurots_soma(soma):
-
     expected_points = np.array(
         [
             [-5.760930017396721, 14.022942101967232, -1.909993715025774],
-Original file line number
+Diff line change
@@ Expand Up / @@ -92,7 +92,6 @@ class SpaceColonizationContext: @@
         """
         def __init__(self, params):
             self.morphology_points = DynamicPointArray()
             if "endfeet_targets" in params:
@@ Expand Down @@