3d trunk angles

.pylintrc

@@ -3,7 +3,8 @@ disable=
 	fixme,
 	invalid-name,
 	len-as-condition,
-	no-else-return
+	no-else-return,
+    broad-exception-raised
 
 [FORMAT]
 # Regexp for a line that is allowed to be longer than the limit.

docs/source/conf.py

@@ -79,11 +79,8 @@
 
 # autodoc settings
 autodoc_typehints = "signature"
-autodoc_default_options = {
-    "members": True,
-    "show-inheritance": True,
-}
-
+autodoc_default_options = {"members": True, "show-inheritance": True, "private-members": True}
+default_role = "py:obj"
 intersphinx_mapping = {
     "morphio": ("https://morphio.readthedocs.io/en/latest/", None),
     "neurom": ("https://neurom.readthedocs.io/en/stable/", None),

neurots/astrocyte/grower.py

@@ -98,16 +98,16 @@ def __init__(
         input_parameters,
         input_distributions,
         context,
+        skip_preprocessing=True,
         external_diametrizer=None,
-        skip_validation=False,
         rng_or_seed=np.random,
     ):
         super().__init__(
             input_parameters,
             input_distributions,
             context=SpaceColonizationContext(context),
             external_diametrizer=external_diametrizer,
-            skip_validation=skip_validation,
+            skip_preprocessing=skip_preprocessing,
             rng_or_seed=rng_or_seed,
         )
 

neurots/astrocyte/tree.py

@@ -45,7 +45,6 @@ def _initialize_algorithm(self):
             params=self.params,
             start_point=self.point,
             context=self.context,
-            skip_validation=self.skip_validation,
             random_generator=self._rng,
         )
 

neurots/extract_input/from_neurom.py

@@ -18,6 +18,9 @@
 import neurom as nm
 import numpy as np
 from neurom import stats
+from neurom.features.morphology import trunk_vectors
+
+from neurots.utils import PIA_DIRECTION
 
 
 def transform_distr(opt_distr):
@@ -99,7 +102,67 @@ def soma_data(pop):
     return {"size": transform_distr(ss)}
 
 
-def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
+def trunk_neurite_3d_angles(pop, neurite_type, bins):
+    """Extract 3d trunk angle data.
+
+    We extract non-projected, or 3d angles between the pia/apical and any neurite trunk.
+
+    If no apical dendrite is present, the entry `apical_3d_angles` will be absent.
+
+    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 parameter is passed to
+            :func:`numpy.histogram`).
+
+    Returns:
+        dict: A dictionary with the following structure:
+
+        .. code-block:: bash
+
+            {
+                "trunk": {
+                    "pia_3d_angles": {
+                        "data": {
+                            "bins": <bin values>,
+                            "weights": <weights>
+                        }
+                    },
+                    "apical_3d_angles": {
+                        "data": {
+                            "bins": <bin values>,
+                            "weights": <weights>
+                        }
+                    }
+                }
+            }
+    """
+    pia_3d_angles = []
+    apical_3d_angles = []
+    for morph in pop.morphologies:
+        vecs = trunk_vectors(morph, neurite_type=neurite_type)
+        pia_3d_angles += [nm.morphmath.angle_between_vectors(PIA_DIRECTION, vec) for vec in vecs]
+        if neurite_type.name != "apical_dendrite":
+            apical_ref_vec = trunk_vectors(morph, neurite_type=nm.APICAL_DENDRITE)
+            if len(apical_ref_vec) > 0:
+                apical_3d_angles += [
+                    nm.morphmath.angle_between_vectors(apical_ref_vec[0], vec) for vec in vecs
+                ]
+
+    def _get_hist(data):
+        """Return density histogram with bin centers."""
+        densities, _bins = np.histogram(data, bins=bins, density=True)
+        return densities, 0.5 * (_bins[1:] + _bins[:-1])
+
+    weights, _bins = _get_hist(pia_3d_angles)
+    data = {"pia_3d_angles": {"data": {"bins": _bins.tolist(), "weights": weights.tolist()}}}
+    if len(apical_3d_angles) > 0:
+        weights, _bins = _get_hist(apical_3d_angles)
+        data["apical_3d_angles"] = {"data": {"bins": _bins.tolist(), "weights": weights.tolist()}}
+    return {"trunk": data}
+
+
+def trunk_neurite_simple(pop, neurite_type, bins):
     """Extract the trunk data for a specific tree type.
 
     Args:
@@ -122,7 +185,7 @@ def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
                         }
                     },
                     "azimuth": {
-                        "inuform": {
+                        "uniform": {
                             "min": <min value>,
                             "max": <max value>
                         }
@@ -168,6 +231,30 @@ def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
     }
 
 
+def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
+    """Extract the trunk data for a specific tree type.
+
+    See docstring of :func:`trunk_neurite_simple` and :func:`trunk_neurite_3d_angles`
+    for more details on the extracted angles.
+
+    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 parameter is passed to
+            :func:`numpy.histogram`).
+        method (str): Method to use, either `simple` or `3d_angles`.
+
+    Returns:
+        dict: A dictionary with the trunk data.
+    """
+    trunk_data = trunk_neurite_simple(pop, neurite_type=neurite_type, bins=bins)
+    # adds 3d_angle related distributions
+    trunk_data["trunk"].update(
+        trunk_neurite_3d_angles(pop, neurite_type=neurite_type, bins=bins)["trunk"]
+    )
+    return trunk_data
+
+
 def number_neurites(pop, neurite_type=nm.BASAL_DENDRITE):
     """Extract the number of trees for a specific tree type from a given population.
 

neurots/extract_input/input_distributions.py

@@ -111,9 +111,9 @@ def distributions(
         else:
             type_feature = feature.get(neurite_type, "path_distances")
         nm_type = getattr(NeuriteType, neurite_type)
+
         input_distributions[neurite_type] = _append_dicts(
-            trunk_neurite(pop_nm, nm_type),
-            number_neurites(pop_nm, nm_type),
+            trunk_neurite(pop_nm, nm_type), number_neurites(pop_nm, nm_type)
         )
         if type_feature in ["path_distances", "radial_distances"]:
             _append_dicts(

neurots/extract_input/input_parameters.py

@@ -26,7 +26,7 @@ def parameters(
     neurite_types=None,
     feature="path_distances",
     diameter_parameters=None,
-):
+):  # pylint: disable=too-many-branches
     """Returns a default set of input parameters to be used as input for synthesis.
 
     Args:

neurots/generate/algorithms/tmdgrower.py

@@ -27,7 +27,6 @@
 from neurots.generate.algorithms.common import section_data
 from neurots.morphmath import sample
 from neurots.morphmath.utils import norm
-from neurots.preprocess.relevance_checkers import check_min_bar_length
 
 L = logging.getLogger(__name__)
 
@@ -40,7 +39,6 @@ class TMDAlgo(AbstractAlgo):
         params (dict): The parameters required for growth. It should include the
             ``branching_method`` selected from: ``|bio_oriented, symmetric, directional]``.
         start_point (list[float]): The first point of the trunk.
-        skip_validation (bool): If set to ``False``, the parameters and distributions and
             the "min_bar_length" parameter are validated.
         context (Any): An object containing contextual information.
         random_generator (numpy.random.Generator): The random number generator to use.
@@ -51,7 +49,6 @@ def __init__(
         input_data,
         params,
         start_point,
-        skip_validation=False,
         context=None,
         random_generator=np.random,
         **_,
@@ -64,9 +61,6 @@ def __init__(
         self.apical_section = None
         self.apical_point_distance_from_soma = 0.0
         self.persistence_length = self.barcode.get_persistence_length()
-        # Validate parameters and distributions
-        if not skip_validation:
-            check_min_bar_length(params, input_data, start_point, context)
 
     def select_persistence(self, input_data, random_generator=np.random):
         """Select the persistence.