diff --git a/requirements/base.pip b/requirements/base.pip
index 696c645..f543783 100644
--- a/requirements/base.pip
+++ b/requirements/base.pip
@@ -17,7 +17,7 @@ neurom>=3,<4
 pandas
 placement_algorithm>=2.2.0
 PyYAML
-region_grower>=0.3.1
+region_grower>=0.4.0
 scipy
 seaborn
 tmd
diff --git a/src/synthesis_workflow/circuit.py b/src/synthesis_workflow/circuit.py
index c8ea157..3714be9 100644
--- a/src/synthesis_workflow/circuit.py
+++ b/src/synthesis_workflow/circuit.py
@@ -3,18 +3,20 @@
 
 import numpy as np
 import pandas as pd
+import yaml
 from atlas_analysis.planes.planes import create_planes as _create_planes
 from brainbuilder.app.cells import _place as place
+from region_grower.atlas_helper import AtlasHelper
 from tqdm import tqdm
 from voxcell import CellCollection
 from voxcell.nexus.voxelbrain import LocalAtlas
 
 L = logging.getLogger(__name__)
-LEFT = 0
-RIGHT = 1
+LEFT = "left"
+RIGHT = "right"
 
 
-def halve_atlas(annotated_volume, axis=0, side=LEFT):
+def halve_atlas(annotated_volume, axis=2, side=LEFT):
     """Return the half of the annotated volume along the x-axis.
 
     The identifiers of the voxels located on the left half or the right half
@@ -24,8 +26,8 @@ def halve_atlas(annotated_volume, axis=0, side=LEFT):
         annotated_volume: integer array of shape (W, L, D) holding the annotation
             of a brain region.
         axis: (Optional) axis along which to halve. Either 0, 1 or 2.
-            Defaults to 0.
-        side: (Optional) Either LEFT or RIGHT, depending on which half is requested.
+            Defaults to 2.
+        side: (Optional) Either 'left' or 'right', depending on which half is requested.
             Defaults to LEFT.
 
     Returns:
@@ -85,6 +87,22 @@ def create_atlas_thickness_mask(atlas_dir):
     return brain_regions.with_data(np.logical_and(~too_thick, isocortex_mask).astype(np.uint8))
 
 
+def get_regions_from_composition(cell_composition_path):
+    """Get list of region regex from cell_composition."""
+    with open(cell_composition_path, "r") as comp_p:
+        cell_composition = yaml.safe_load(comp_p)
+
+    region_regex = "@"
+    for reg in set(entry["region"] for entry in cell_composition["neurons"]):
+        if reg[-1] != "|":
+            reg += "|"
+        if reg[0] == "@":
+            region_regex += reg[1:]
+        else:
+            region_regex += reg
+    return region_regex[:-1]
+
+
 def build_circuit(
     cell_composition_path,
     mtype_taxonomy_path,
@@ -109,7 +127,7 @@ def build_circuit(
         atlas_url=atlas_path,
         mini_frequencies_path=None,
         atlas_cache=None,
-        region=region,
+        region=region or get_regions_from_composition(cell_composition_path),
         mask_dset=mask,
         density_factor=density_factor,
         soma_placement="basic",
@@ -153,8 +171,12 @@ def circuit_slicer(cells, n_cells, mtypes=None, planes=None, hemisphere=None):
     if mtypes is not None:
         cells = slice_per_mtype(cells, mtypes)
 
-    if hemisphere is not None and "hemisphere" in cells:
-        cells = cells[cells.hemisphere == hemisphere]
+    # TODO: rough way to split hemisphere, maybe there is a better way, to investigate if needed
+    if hemisphere is not None:
+        if hemisphere == "left":
+            cells = cells[cells.z < cells.z.mean()]
+        if hemisphere == "right":
+            cells = cells[cells.z >= cells.z.mean()]
 
     if planes is not None:
         # between each pair of planes, select n_cells
@@ -198,31 +220,25 @@ def _get_principal_direction(points):
     return v[:, w.argmax()]
 
 
-def get_centerline_bounds(atlas, layer, region=None, central_layer=5, hemisphere=None):
+def get_centerline_bounds(layer):
     """Find centerline bounds using PCA of the voxell position of a given layer in the region."""
-    if region is not None:
-        mask = atlas.get_region_mask(region)
-        mask.raw = np.array(mask.raw, dtype=int)
-        layer.raw = layer.raw * mask.raw
-
-    _ids = np.where(layer.raw == central_layer)
-    ids = np.column_stack(_ids)
+    _ls = np.unique(layer.raw[layer.raw > 0])
+    central_layer = _ls[int(len(_ls) / 2)]
+    ids = np.column_stack(np.where(layer.raw == central_layer))
     points = layer.indices_to_positions(ids)
-
-    ids = np.array(ids)
-    points = np.array(points)
-    if hemisphere is not None:
-        if hemisphere == "left":
-            point_mask = points[:, 2] < points[:, 2].mean()
-        if hemisphere == "right":
-            point_mask = points[:, 2] > points[:, 2].mean()
-        ids = ids[point_mask]
-        points = points[point_mask]
-    direction = _get_principal_direction(points)
-    _align = points.dot(direction)
+    _align = points.dot(_get_principal_direction(points))
     return ids[_align.argmin()], ids[_align.argmax()]
 
 
+def get_local_bbox(annotation):
+    """Compute bbox where annotation file is strictly positive."""
+    ids = np.where(annotation.raw > 0)
+    dim = annotation.voxel_dimensions
+    return annotation.offset + np.array(
+        [np.min(ids, axis=1) * dim, (np.max(ids, axis=1) + 1) * dim]
+    )
+
+
 def create_planes(
     layer_annotation,
     plane_type="aligned",
@@ -254,6 +270,8 @@ def create_planes(
         centerline_axis (str): (for plane_type = aligned) axis along which to create planes
     """
     if plane_type == "centerline":
+        if centerline_first_bound is None and centerline_last_bound is None:
+            centerline_first_bound, centerline_last_bound = get_centerline_bounds(layer_annotation)
         centerline = np.array(
             [
                 layer_annotation.indices_to_positions(centerline_first_bound),
@@ -262,13 +280,10 @@ def create_planes(
         )
 
     elif plane_type == "aligned":
-        _n_points = 10
-        centerline = np.zeros([_n_points, 3])
-        bbox = layer_annotation.bbox
+        centerline = np.zeros([2, 3])
+        bbox = get_local_bbox(layer_annotation)
         centerline[:, centerline_axis] = np.linspace(
-            bbox[0, centerline_axis],
-            bbox[1, centerline_axis],
-            _n_points,
+            bbox[0, centerline_axis], bbox[1, centerline_axis], 2
         )
     else:
         raise Exception(f"Please set plane_type to 'aligned' or 'centerline', not {plane_type}.")
@@ -285,3 +300,28 @@ def create_planes(
     planes_select_ids += list(planes_all_ids[int(id_shift / 2) - 1 :: id_shift])
     planes_select_ids += list(planes_all_ids[int(id_shift / 2) + 1 :: id_shift])
     return [planes[i] for i in sorted(planes_select_ids)], centerline
+
+
+def get_layer_tags(atlas_dir, region_structure_path, region=None):
+    """Create a VoxelData with layer tags."""
+    atlas_helper = AtlasHelper(LocalAtlas(atlas_dir), region_structure_path=region_structure_path)
+
+    brain_regions = atlas_helper.brain_regions
+    layers_data = np.zeros_like(brain_regions.raw, dtype="uint8")
+
+    region_mask = None
+    if region is not None:
+        region_mask = atlas_helper.atlas.get_region_mask(region).raw
+
+    layer_mapping = {}
+    for layer_id, layer in enumerate(atlas_helper.layers):
+        layer_mapping[layer_id] = atlas_helper.region_structure["names"].get(layer, str(layer))
+        region_query = atlas_helper.region_structure["region_queries"].get(layer, None)
+        mask = atlas_helper.atlas.get_region_mask(region_query).raw
+        if region_mask is not None:
+            mask *= region_mask
+        layers_data[mask] = layer_id + 1
+        if not len(layers_data[mask]):
+            L.warning("No voxel found for layer %s.", layer)
+    brain_regions.raw = layers_data
+    return brain_regions, layer_mapping
diff --git a/src/synthesis_workflow/extra/insitu_validation.py b/src/synthesis_workflow/extra/insitu_validation.py
index a804e8b..7bf8d9f 100644
--- a/src/synthesis_workflow/extra/insitu_validation.py
+++ b/src/synthesis_workflow/extra/insitu_validation.py
@@ -17,7 +17,7 @@
 from voxcell import CellCollection
 from voxcell.nexus.voxelbrain import Atlas
 
-from synthesis_workflow.tools import get_layer_tags
+from synthesis_workflow.circuit import get_layer_tags
 
 matplotlib.use("Agg")
 
@@ -72,6 +72,7 @@ def plot_extents(results_df, pdf_name="extents.pdf", bins=200):
             pdf.savefig()
 
 
+# pylint: disable=too-many-locals
 def get_layer_morpho_counts(
     sonata_path,
     morphology_path,
@@ -81,6 +82,7 @@ def get_layer_morpho_counts(
     region=None,
     hemisphere="right",
     ext=".h5",
+    region_structure_path="region_structure.yaml",
 ):
     """For each layer, compute the fraction of crossing morphologies.
 
@@ -93,6 +95,7 @@ def get_layer_morpho_counts(
         region (str): is not None, must be the name of a region to filter morphologies
         hemisphere (str): left/right hemisphere
         ext (str): extension of morphology files
+        region_structure_path (str): path to region_structure.yaml file
 
     Returns:
         pandas.DataFrame: dafaframe with the counts, mtype and layers information
@@ -101,7 +104,7 @@ def get_layer_morpho_counts(
     cells = CellCollection.load(sonata_path).as_dataframe()
     morph_path = Path(morphology_path)
 
-    layer_annotation, _ = get_layer_tags(atlas_path)
+    layer_annotation, _ = get_layer_tags(atlas_path, region_structure_path)
 
     dfs = []
     for mtype in tqdm(cells.mtype.unique()):
@@ -205,6 +208,7 @@ def plot_layer_collage(
     shift=200,
     dpi=200,
     ext=".h5",
+    region_structure_path="region_structure.yaml",
 ):
     """Plot morphologies next to each other with points colored by layer.
 
@@ -221,6 +225,7 @@ def plot_layer_collage(
         shift (float): x-shift between morphologies
         dpi (int): dpi to save rasterized pdf
         ext (str): extension of morphology files
+        region_structure_path (str): path to region_structure.yaml file
     """
     if section_types is None:
         section_types = ["basal_dendrite", "apical_dendrite"]
@@ -228,8 +233,8 @@ def plot_layer_collage(
 
     cells = CellCollection.load(sonata_path).as_dataframe()
     morph_path = Path(morphology_path)
-    layer_annotation, _ = get_layer_tags(atlas_path)
-    atlas = AtlasHelper(Atlas().open(atlas_path))
+    layer_annotation, _ = get_layer_tags(atlas_path, region_structure_path)
+    atlas = AtlasHelper(Atlas().open(atlas_path), region_structure_path=region_structure_path)
 
     cmap = matplotlib.colors.ListedColormap(["C0", "C1", "C2", "C3", "C4", "C5", "C6"])
     with PdfPages(pdf_name) as pdf:
diff --git a/src/synthesis_workflow/synthesis.py b/src/synthesis_workflow/synthesis.py
index 1d02504..ff90007 100644
--- a/src/synthesis_workflow/synthesis.py
+++ b/src/synthesis_workflow/synthesis.py
@@ -8,11 +8,14 @@
 import matplotlib
 import numpy as np
 import pandas as pd
+import yaml
 from joblib import Parallel
 from joblib import delayed
 from morphio.mut import Morphology
 from neuroc.scale import ScaleParameters
 from neuroc.scale import scale_section
+from neurom import load_morphology
+from neurom.check.morphology_checks import has_apical_dendrite
 from neurom.core.dataformat import COLS
 from neurots import extract_input
 from placement_algorithm.app import utils
@@ -29,14 +32,25 @@
 matplotlib.use("Agg")
 
 
-def get_neurite_types(morphs_df, mtypes):
-    """Get the neurite types to consider for PC or IN cells."""
-    return {
-        mtype: ["basal", "axon"]
-        if morphs_df.loc[morphs_df.mtype == mtype, "morph_class"].tolist()[0] == "IN"
-        else ["basal", "apical", "axon"]
-        for mtype in mtypes
-    }
+def get_neurite_types(morphs_df):
+    """Get the neurite types to consider for PC or IN cells by checking if apical exists."""
+
+    def _get_morph_class(path):
+        return "PC" if has_apical_dendrite(load_morphology(path)) else "IN"
+
+    morphs_df["morph_class"] = morphs_df["path"].apply(_get_morph_class)
+    neurite_types = {}
+    for mtype, _df in morphs_df.groupby("mtype"):
+        morph_class = list(set(_df["morph_class"]))
+
+        if len(morph_class) > 1:
+            raise Exception("f{mtype} has not consistent morph_class, we stop here")
+
+        if morph_class[0] == "IN":
+            neurite_types[mtype] = ["basal"]
+        if morph_class[0] == "PC":
+            neurite_types[mtype] = ["basal", "apical"]
+    return neurite_types
 
 
 def apply_substitutions(original_morphs_df, substitution_rules=None):
@@ -72,16 +86,27 @@ def _build_distributions_single_mtype(
     trunk_method="simple",
 ):
     """Internal function for multiprocessing of tmd_distribution building."""
-    morphology_paths = morphs_df.loc[morphs_df.mtype == mtype, morphology_path].to_list()
-    kwargs = {
-        "neurite_types": neurite_types[mtype],
-        "diameter_input_morph": morphology_paths,
-        "diameter_model": diameter_model_function,
-    }
-    if trunk_method != "simple":
-        kwargs["trunk_method"] = trunk_method
+    data = {}
+    for neurite_type in neurite_types[mtype]:
+        if "use_axon" in morphs_df.columns:
+            morphology_paths = morphs_df.loc[
+                (morphs_df.mtype == mtype) & morphs_df.use_axon, morphology_path
+            ].to_list()
+        else:
+            morphology_paths = morphs_df.loc[morphs_df.mtype == mtype, morphology_path].to_list()
+        kwargs = {
+            "neurite_types": neurite_types[mtype],
+            "diameter_input_morph": morphology_paths,
+            "diameter_model": diameter_model_function,
+        }
+        if trunk_method != "simple":
+            kwargs["trunk_method"] = trunk_method
 
-    return mtype, extract_input.distributions(morphology_paths, **kwargs)
+        _data = extract_input.distributions(morphology_paths, **kwargs)
+        data[neurite_type] = _data[neurite_type]
+        data["diameter"] = _data["diameter"]
+        data["soma"] = _data["soma"]
+    return mtype, data
 
 
 def build_distributions(
@@ -90,7 +115,7 @@ def build_distributions(
     neurite_types,
     diameter_model_function,
     morphology_path,
-    cortical_thickness,
+    region_structure_path,
     nb_jobs=-1,
     joblib_verbose=10,
     trunk_method="simple",
@@ -102,7 +127,10 @@ def build_distributions(
         morphs_df (DataFrame): morphology dataframe with reconstruction to use
         diameter_model_function (function): diametrizer function (from diameter-synthesis)
         morphology_path (str): name of the column in morphs_df to use for paths to morphologies
-        cortical_thickness (list): list of cortical thicknesses for reference scaling
+        region_structure_path (str): path to region_structure yaml file with thicknesses
+        nb_jobs (int): number of jobs to run in parallal with joblib
+        joblib_verbose (int): verbose level of joblib
+        trunk_method (str): method to set trunk on soma for synthesis (simple|3d_angle)
 
     Returns:
         dict: dict to save to tmd_distribution.json
@@ -115,11 +143,15 @@ def build_distributions(
         morphology_path=morphology_path,
         trunk_method=trunk_method,
     )
+    thicknesses = []
+    if Path(region_structure_path).exists():
+        with open(region_structure_path, "r") as r_p:
+            region_structure = yaml.safe_load(r_p)
+        thicknesses = [
+            region_structure["thicknesses"][layer] for layer in region_structure["layers"]
+        ]
 
-    tmd_distributions = {
-        "mtypes": {},
-        "metadata": {"cortical_thickness": cortical_thickness},
-    }
+    tmd_distributions = {"mtypes": {}, "metadata": {"cortical_thickness": thicknesses}}
     for mtype, distribution in Parallel(nb_jobs, verbose=joblib_verbose)(
         delayed(build_distributions_single_mtype)(mtype) for mtype in mtypes
     ):
diff --git a/src/synthesis_workflow/tasks/circuit.py b/src/synthesis_workflow/tasks/circuit.py
index 671bc46..20845f3 100644
--- a/src/synthesis_workflow/tasks/circuit.py
+++ b/src/synthesis_workflow/tasks/circuit.py
@@ -6,31 +6,26 @@
 from atlas_analysis.planes.planes import load_planes_centerline
 from atlas_analysis.planes.planes import save_planes_centerline
 from voxcell import VoxelData
-from voxcell.nexus.voxelbrain import LocalAtlas
 
 from synthesis_workflow.circuit import build_circuit
 from synthesis_workflow.circuit import circuit_slicer
 from synthesis_workflow.circuit import create_atlas_thickness_mask
 from synthesis_workflow.circuit import create_planes
-from synthesis_workflow.circuit import get_centerline_bounds
+from synthesis_workflow.circuit import get_layer_tags
 from synthesis_workflow.circuit import halve_atlas
 from synthesis_workflow.circuit import slice_circuit
 from synthesis_workflow.tasks.config import AtlasLocalTarget
 from synthesis_workflow.tasks.config import CircuitConfig
 from synthesis_workflow.tasks.config import CircuitLocalTarget
-from synthesis_workflow.tasks.config import PathConfig
+from synthesis_workflow.tasks.config import GetCellComposition
+from synthesis_workflow.tasks.config import GetSynthesisInputs
 from synthesis_workflow.tasks.config import SynthesisConfig
-from synthesis_workflow.tasks.luigi_tools import BoolParameter
-from synthesis_workflow.tasks.luigi_tools import OptionalChoiceParameter
 from synthesis_workflow.tasks.luigi_tools import OptionalPathParameter
-from synthesis_workflow.tasks.luigi_tools import OptionalStrParameter
 from synthesis_workflow.tasks.luigi_tools import ParamRef
 from synthesis_workflow.tasks.luigi_tools import PathParameter
 from synthesis_workflow.tasks.luigi_tools import RatioParameter
 from synthesis_workflow.tasks.luigi_tools import WorkflowTask
 from synthesis_workflow.tasks.luigi_tools import copy_params
-from synthesis_workflow.tasks.utils import GetSynthesisInputs
-from synthesis_workflow.tools import get_layer_tags
 
 
 class CreateAtlasLayerAnnotations(WorkflowTask):
@@ -40,25 +35,19 @@ class CreateAtlasLayerAnnotations(WorkflowTask):
         default="layer_annotation.nrrd",
         description=":str: Path to save layer annotations constructed from atlas.",
     )
-    use_half = BoolParameter(
-        default=False,
-        description=":bool: Set to True to use half of the atlas (left or right hemisphere).",
-    )
-    half_axis = luigi.IntParameter(
-        default=0,
-        description=":int: Direction to select half of the atlas (can be 0, 1 or 2).",
-    )
-    half_side = luigi.IntParameter(
-        default=0,
-        description=":int: Side to choose to halve the atlas (0=left, 1=right).",
-    )
+
+    def requires(self):
+        return GetSynthesisInputs()
 
     def run(self):
         """ """
-        annotation, layer_mapping = get_layer_tags(CircuitConfig().atlas_path)
-
-        if self.use_half:
-            annotation.raw = halve_atlas(annotation.raw, axis=self.half_axis, side=self.half_side)
+        annotation, layer_mapping = get_layer_tags(
+            CircuitConfig().atlas_path,
+            self.input().pathlib_path / CircuitConfig().region_structure_path,
+            CircuitConfig().region,
+        )
+        if CircuitConfig().hemisphere is not None:
+            annotation.raw = halve_atlas(annotation.raw, side=CircuitConfig().hemisphere)
 
         annotation_path = self.output()["annotations"].path
         annotation.save_nrrd(annotation_path)
@@ -119,10 +108,6 @@ class CreateAtlasPlanes(WorkflowTask):
     atlas_planes_path = PathParameter(
         default="atlas_planes", description=":str: Path to save atlas planes."
     )
-    region = luigi.Parameter(default=None, description=":str: Name of region to consider")
-    hemisphere = luigi.Parameter(
-        default=None, description=":str: Hemisphere to considere (left/right)"
-    )
 
     def requires(self):
         """ """
@@ -130,17 +115,10 @@ def requires(self):
 
     def run(self):
         """ """
-
         if self.plane_count < 1:
             raise Exception("Number of planes should be larger than one")
 
         layer_annotation = VoxelData.load_nrrd(self.input()["annotations"].path)
-
-        atlas = LocalAtlas(CircuitConfig().atlas_path)
-        if self.centerline_first_bound is None and self.centerline_last_bound is None:
-            self.centerline_first_bound, self.centerline_last_bound = get_centerline_bounds(
-                atlas, layer_annotation, self.region, hemisphere=self.hemisphere
-            )
         planes, centerline = create_planes(
             layer_annotation,
             self.plane_type,
@@ -155,23 +133,16 @@ def run(self):
 
     def output(self):
         """ """
-        return AtlasLocalTarget(f"{self.atlas_planes_path}_{self.region}.npz")
+        if CircuitConfig().region is not None:
+            suffix = f"_{CircuitConfig().region}"
+        else:
+            suffix = ""
+        return AtlasLocalTarget(f"{self.atlas_planes_path}{suffix}.npz")
 
 
-@copy_params(
-    mtype_taxonomy_path=ParamRef(PathConfig),
-)
 class BuildCircuit(WorkflowTask):
-    """Generate cell positions and me-types from atlas, compositions and taxonomy.
-
-    Attributes:
-        mtype_taxonomy_path (str): Path to the taxonomy file (TSV).
-    """
+    """Generate cell positions and me-types from atlas, compositions and taxonomy."""
 
-    cell_composition_path = PathParameter(
-        default="cell_composition.yaml",
-        description=":str: Path to the cell composition file (YAML).",
-    )
     density_factor = RatioParameter(
         default=0.01,
         left_op=luigi.parameter.operator.lt,
@@ -181,16 +152,18 @@ class BuildCircuit(WorkflowTask):
         default=None, description=":str: Path to save thickness mask (NCx only)."
     )
     seed = luigi.IntParameter(default=None, description=":int: Pseudo-random generator seed.")
-    region = OptionalStrParameter(default=None, description=":str: Region to use.")
+    mtype_taxonomy_file = luigi.Parameter(
+        default="mtype_taxonomy.tsv",
+        description=":str: Filename of taxonomy file to provide to BrainBuilder",
+    )
 
     def requires(self):
         """ """
-        return GetSynthesisInputs()
+        return {"synthesis": GetSynthesisInputs(), "composition": GetCellComposition()}
 
     def run(self):
         """ """
-        cell_composition_path = self.input().pathlib_path / self.cell_composition_path
-        mtype_taxonomy_path = self.input().pathlib_path / self.mtype_taxonomy_path
+        mtype_taxonomy_path = self.input()["synthesis"].pathlib_path / self.mtype_taxonomy_file
 
         thickness_mask_path = None
         if self.mask_path is not None:
@@ -199,13 +172,13 @@ def run(self):
             thickness_mask_path = self.mask_path.stem
 
         cells = build_circuit(
-            cell_composition_path,
+            self.input()["composition"].path,
             mtype_taxonomy_path,
             CircuitConfig().atlas_path,
             self.density_factor,
             mask=thickness_mask_path,
             seed=self.seed,
-            region=self.region,
+            region=CircuitConfig().region,
         )
         cells.save(self.output().path)
 
@@ -231,11 +204,6 @@ class SliceCircuit(WorkflowTask):
     n_cells = luigi.IntParameter(
         default=10, description=":int: Number of cells per mtype to consider."
     )
-    hemisphere = OptionalChoiceParameter(
-        default=None,
-        choices=["left", "right"],
-        description=":str: The hemisphere side.",
-    )
 
     def requires(self):
         """ """
@@ -253,7 +221,7 @@ def run(self):
             n_cells=self.n_cells,
             mtypes=self.mtypes,
             planes=planes,
-            hemisphere=self.hemisphere,
+            hemisphere=CircuitConfig().hemisphere,
         )
 
         cells = slice_circuit(self.input()["circuit"].path, self.output().path, _slicer)
diff --git a/src/synthesis_workflow/tasks/config.py b/src/synthesis_workflow/tasks/config.py
index f628aa1..912e39e 100644
--- a/src/synthesis_workflow/tasks/config.py
+++ b/src/synthesis_workflow/tasks/config.py
@@ -1,12 +1,19 @@
 """Configurations for luigi tasks."""
 import logging
+import shutil
 import warnings
+from pathlib import Path
+from tempfile import TemporaryDirectory
 
 import luigi
+import yaml
+from git import Repo
 
 from synthesis_workflow.tasks.luigi_tools import ExtParameter
+from synthesis_workflow.tasks.luigi_tools import OptionalChoiceParameter
 from synthesis_workflow.tasks.luigi_tools import OptionalIntParameter
 from synthesis_workflow.tasks.luigi_tools import OutputLocalTarget
+from synthesis_workflow.tasks.luigi_tools import WorkflowTask
 
 # Add some warning filters
 warnings.filterwarnings("ignore", module="diameter_synthesis.build_diameters")
@@ -22,6 +29,72 @@
 logging.getLogger("neurots").propagate = False
 
 
+class GitClone(WorkflowTask):
+    """Task to clone a git repository."""
+
+    url = luigi.Parameter(
+        description=(
+            ":str: Url of repository. If None, git_synthesis_input_path should be an existing "
+            "folder."
+        )
+    )
+    dest = luigi.Parameter(description=":str: Path to the destination directory.")
+    branch = luigi.Parameter(default="main")
+
+    def run(self):
+        """ """
+        Repo.clone_from(self.url, self.output().path, branch=self.branch)
+
+    def output(self):
+        """ """
+        return OutputLocalTarget(self.dest)
+
+
+class GetSynthesisInputs(WorkflowTask):
+    """Task to get synthesis input files from a folder on git repository.
+
+    If no url is provided, this task will copy an existing folder to the target location.
+
+    Attributes:
+        local_synthesis_input_path (str): Path to local folder to copy these files.
+    """
+
+    url = luigi.OptionalParameter(
+        default=None,
+        description=(
+            ":str: Url of repository. If None, git_synthesis_input_path should be an "
+            "existing folder."
+        ),
+    )
+    version = luigi.OptionalParameter(
+        default=None, description=":str: Version of repo to checkout."
+    )
+    git_synthesis_input_path = luigi.Parameter(
+        default="synthesis_input",
+        description=":str: Path to folder in git repo with synthesis files.",
+    )
+    branch = luigi.Parameter(default="main")
+
+    def run(self):
+        """ """
+        if self.url is None:
+            shutil.copytree(self.git_synthesis_input_path, self.output().path)
+        else:
+            with TemporaryDirectory() as tmpdir:
+                dest = Path(tmpdir) / "tmp_repo"
+                # Note: can not be called with yield here because of the TemporaryDirectory
+                GitClone(url=self.url, dest=dest, branch=self.branch).run()
+                if self.version is not None:
+                    r = Repo(dest)
+                    r.git.checkout(self.version)
+                shutil.copytree(dest / self.git_synthesis_input_path, self.output().path)
+
+    def output(self):
+        """ """
+        # TODO: it would probably be better to have a specific target for each file
+        return OutputLocalTarget(PathConfig().local_synthesis_input_path)
+
+
 class DiametrizerConfig(luigi.Config):
     """Diametrizer configuration."""
 
@@ -88,10 +161,6 @@ class SynthesisConfig(luigi.Config):
         default="neurots_input/tmd_distributions.json",
         description=":str: The path to the TMD distributions.",
     )
-    cortical_thickness = luigi.ListParameter(
-        default=[165, 149, 353, 190, 525, 700],
-        description=":list(int): The list of cortical thicknesses.",
-    )
     mtypes = luigi.ListParameter(
         default=None,
         description=(
@@ -99,6 +168,9 @@ class SynthesisConfig(luigi.Config):
             "found mtypes are taken)."
         ),
     )
+    with_axons = luigi.BoolParameter(
+        default=False, description=":bool: Set to True to synthesize local axons"
+    )
 
 
 class CircuitConfig(luigi.Config):
@@ -108,16 +180,63 @@ class CircuitConfig(luigi.Config):
         default="circuit_somata.mvd3", description=":str: Path to the circuit somata."
     )
     atlas_path = luigi.Parameter(default=None, description=":str: Path to the atlas directory.")
+    cell_composition_path = luigi.Parameter(
+        default="cell_composition.yaml", description=":str: Path to cell_compoistion.yaml file."
+    )
+    region_structure_path = luigi.Parameter(
+        default="region_structure.yaml",
+        description=":str: Path to the file containing the layer structure data.",
+    )
+    region = luigi.OptionalParameter(default=None)
+    hemisphere = OptionalChoiceParameter(
+        default=None,
+        choices=["left", "right"],
+        description=":str: The hemisphere side.",
+    )
+
+
+class GetCellComposition(luigi.Task):
+    """Get cell_composition with adapted entries."""
+
+    new_cell_composition = luigi.Parameter(
+        default="new_cell_composition.yaml",
+        description=":str: Filename to the new cell composition file with modified entries.",
+    )
+
+    def requires(self):
+        """ """
+        return GetSynthesisInputs()
+
+    def run(self):
+        """ """
+        if not Path(CircuitConfig().cell_composition_path).exists():
+            cell_composition_path = str(
+                self.input().pathlib_path / CircuitConfig().cell_composition_path
+            )
+        else:
+            cell_composition_path = CircuitConfig().cell_composition_path
+
+        with open(cell_composition_path) as comp_p:
+            cell_comp = yaml.safe_load(comp_p)
+        if SynthesisConfig().mtypes is not None:
+            new_cell_comp = {"version": cell_comp["version"], "neurons": []}
+            for entry in cell_comp["neurons"]:
+                if entry["traits"]["mtype"] in SynthesisConfig().mtypes:
+                    new_cell_comp["neurons"].append(entry)
+        else:
+            new_cell_comp = cell_comp
+        with open(self.output().path, "w") as comp_p:
+            yaml.safe_dump(new_cell_comp, comp_p)
+
+    def output(self):
+        """ """
+        return OutputLocalTarget(self.new_cell_composition)
 
 
 class PathConfig(luigi.Config):
     """Morphology path configuration."""
 
     # Input paths
-    mtype_taxonomy_path = luigi.Parameter(
-        default="mtype_taxonomy.tsv",
-        description=":str: Path to the taxonomy file (TSV).",
-    )
     local_synthesis_input_path = luigi.Parameter(
         default="synthesis_input",
         description=":str: Path to the synthesis input directory.",
diff --git a/src/synthesis_workflow/tasks/synthesis.py b/src/synthesis_workflow/tasks/synthesis.py
index 71d5c4a..b17ef7e 100644
--- a/src/synthesis_workflow/tasks/synthesis.py
+++ b/src/synthesis_workflow/tasks/synthesis.py
@@ -10,6 +10,7 @@
 import yaml
 from diameter_synthesis.build_models import build as build_diameter_models
 from neurots import extract_input
+from placement_algorithm.app.compact_annotations import _collect_annotations
 from region_grower.synthesize_morphologies import SynthesizeMorphologies
 from region_grower.utils import NumpyEncoder
 
@@ -23,6 +24,8 @@
 from synthesis_workflow.tasks.circuit import SliceCircuit
 from synthesis_workflow.tasks.config import CircuitConfig
 from synthesis_workflow.tasks.config import DiametrizerConfig
+from synthesis_workflow.tasks.config import GetCellComposition
+from synthesis_workflow.tasks.config import GetSynthesisInputs
 from synthesis_workflow.tasks.config import MorphsDfLocalTarget
 from synthesis_workflow.tasks.config import PathConfig
 from synthesis_workflow.tasks.config import RunnerConfig
@@ -30,13 +33,12 @@
 from synthesis_workflow.tasks.config import SynthesisLocalTarget
 from synthesis_workflow.tasks.luigi_tools import BoolParameter
 from synthesis_workflow.tasks.luigi_tools import OptionalPathParameter
+from synthesis_workflow.tasks.luigi_tools import OutputLocalTarget
 from synthesis_workflow.tasks.luigi_tools import ParamRef
 from synthesis_workflow.tasks.luigi_tools import PathParameter
 from synthesis_workflow.tasks.luigi_tools import RatioParameter
 from synthesis_workflow.tasks.luigi_tools import WorkflowTask
 from synthesis_workflow.tasks.luigi_tools import copy_params
-from synthesis_workflow.tasks.utils import CreateAnnotationsFile
-from synthesis_workflow.tasks.utils import GetSynthesisInputs
 from synthesis_workflow.tools import find_case_insensitive_file
 from synthesis_workflow.tools import load_neurondb_to_dataframe
 
@@ -45,15 +47,8 @@
 L = logging.getLogger(__name__)
 
 
-@copy_params(
-    mtype_taxonomy_path=ParamRef(PathConfig),
-)
 class BuildMorphsDF(WorkflowTask):
-    """Generate the list of morphologies with their mtypes and paths.
-
-    Attributes:
-        mtype_taxonomy_path (str): Path to the mtype_taxonomy.tsv file.
-    """
+    """Generate the list of morphologies with their mtypes and paths."""
 
     neurondb_path = luigi.Parameter(description=":str: Path to the neuronDB file (XML).")
     morphology_dirs = luigi.DictParameter(
@@ -75,16 +70,12 @@ def run(self):
 
         L.debug("Build morphology dataframe from %s", neurondb_path)
 
-        mtype_taxonomy_path = self.input().pathlib_path / self.mtype_taxonomy_path
         morphs_df = load_neurondb_to_dataframe(
-            neurondb_path,
-            self.morphology_dirs,
-            mtype_taxonomy_path,
-            self.apical_points_path,
+            neurondb_path, self.morphology_dirs, self.apical_points_path
         )
 
-        # Remove duplicated morphologies in L23
-        morphs_df.drop_duplicates(subset=["name"], inplace=True)
+        # Remove possibly duplicated morphologies
+        morphs_df = morphs_df.drop_duplicates(subset=["name"])
 
         # Only use wanted mtypes
         if SynthesisConfig().mtypes is not None:
@@ -120,13 +111,13 @@ def run(self):
         substitution_rules_path = (
             self.input()["synthesis_input"].pathlib_path / self.substitution_rules_path
         )
-        with open(substitution_rules_path, "rb") as sub_file:
-            substitution_rules = yaml.full_load(sub_file)
+        df = pd.read_csv(self.input()["morphs_df"].path)
+        if substitution_rules_path.exists():
+            with open(substitution_rules_path, "rb") as sub_file:
+                substitution_rules = yaml.full_load(sub_file)
+            df = apply_substitutions(df, substitution_rules)
 
-        substituted_morphs_df = apply_substitutions(
-            pd.read_csv(self.input()["morphs_df"].path), substitution_rules
-        )
-        substituted_morphs_df.to_csv(self.output().path, index=False)
+        df.to_csv(self.output().path, index=False)
 
     def output(self):
         """ """
@@ -161,7 +152,10 @@ def run(self):
         """ """
         morphs_df = pd.read_csv(self.input()["morphologies"].path)
         mtypes = sorted(morphs_df.mtype.unique())
-        neurite_types = get_neurite_types(morphs_df, mtypes)
+        neurite_types = get_neurite_types(morphs_df)
+        if SynthesisConfig().with_axons:
+            for neurite_type in neurite_types.items():
+                neurite_type.append("axon")
 
         if self.input_tmd_parameters_path is not None:
             L.info("Using custom tmd parameters")
@@ -214,30 +208,31 @@ class BuildSynthesisDistributions(WorkflowTask):
 
     def requires(self):
         """ """
-        return ApplySubstitutionRules()
+        return {"rules": ApplySubstitutionRules(), "synthesis": GetSynthesisInputs()}
 
     def run(self):
         """ """
-        L.debug("reading morphs df from: %s", self.input().path)
-        morphs_df = pd.read_csv(self.input().path)
+        morphs_df = pd.read_csv(self.input()["rules"].path)
 
         mtypes = sorted(morphs_df.mtype.unique())
         L.debug("mtypes found: %s", mtypes)
 
-        neurite_types = get_neurite_types(morphs_df, mtypes)
+        neurite_types = get_neurite_types(morphs_df)
+        if SynthesisConfig().with_axons:
+            for neurite_type in neurite_types.items():
+                neurite_type.append("axon")
         L.debug("neurite_types found: %s", neurite_types)
 
         diameter_model_function = partial(
             build_diameter_models, config=DiametrizerConfig().config_model
         )
-
         tmd_distributions = build_distributions(
             mtypes,
             morphs_df,
             neurite_types,
             diameter_model_function,
             self.morphology_path,
-            SynthesisConfig().cortical_thickness,
+            self.input()["synthesis"].pathlib_path / CircuitConfig().region_structure_path,
             nb_jobs=self.nb_jobs,
             trunk_method=self.trunk_method,
         )
@@ -263,6 +258,26 @@ def output(self):
         return MorphsDfLocalTarget(self.axon_morphs_df_path)
 
 
+class CreateAnnotationsFile(WorkflowTask):
+    """Task to compact annotations into a single JSON file."""
+
+    annotation_dir = luigi.Parameter(description=(":str: Path to annotations folder."))
+    morph_db = luigi.OptionalParameter(default=None, description=":str: Path to MorphDB file.")
+    destination = luigi.Parameter(description=":str: Path to output JSON file.")
+
+    def run(self):
+        """ """
+        # pylint: disable=protected-access
+        annotations = _collect_annotations(self.annotation_dir, self.morph_db)
+
+        with open(self.destination, "w") as f:
+            json.dump(annotations, f, indent=4, sort_keys=True)
+
+    def output(self):
+        """ """
+        return OutputLocalTarget(self.destination)
+
+
 class BuildAxonMorphologies(WorkflowTask):
     """Run choose-morphologies to synthesize axon morphologies.
 
@@ -472,22 +487,34 @@ class Synthesize(WorkflowTask):
         description=":float: The std value of the scaling jitter to apply (in degrees).",
     )
     seed = luigi.IntParameter(default=0, description=":int: Pseudo-random generator seed.")
+    axon_method = luigi.ChoiceParameter(
+        default="reconstructed",
+        description=":str: The method used to handle axons.",
+        choices=["no_axon", "reconstructed", "synthesis"],
+    )
 
     def requires(self):
         """ """
 
-        return {
+        tasks = {
+            "synthesis_input": GetSynthesisInputs(),
             "substituted_cells": ApplySubstitutionRules(),
-            "circuit": SliceCircuit(),
             "tmd_parameters": AddScalingRulesToParameters(),
             "tmd_distributions": BuildSynthesisDistributions(),
-            "axons": BuildAxonMorphologies(),
+            "circuit": SliceCircuit(),
+            "composition": GetCellComposition(),
         }
+        if self.axon_method == "reconstructed":
+            tasks["axons"] = BuildAxonMorphologies()
+            tasks["axon_cells"] = BuildAxonMorphsDF(
+                neurondb_path=BuildAxonMorphologies().get_neuron_db_path("xml"),
+                morphology_dirs={"clone_path": BuildAxonMorphologies().axon_cells_path},
+            )
+
+        return tasks
 
     def run(self):
         """ """
-
-        axon_morphs_path = self.input()["axons"]["morphs"].path
         out_mvd3 = self.output()["out_mvd3"]
         out_morphologies = self.output()["out_morphologies"]
         out_apical_points = self.output()["apical_points"]
@@ -498,18 +525,18 @@ def run(self):
         else:
             debug_scales_path = None
 
-        # Get base-morph-dir argument value
-        if self.axon_morphs_base_dir is None:
-            axon_morphs_base_dir = get_axon_base_dir(
-                pd.read_csv(self.requires()["axons"].input()["axon_cells"].path),
-                "clone_path",
-            )
-        else:
-            axon_morphs_base_dir = self.axon_morphs_base_dir
-
-        L.debug("axon_morphs_base_dir = %s", axon_morphs_base_dir)
+        axon_morphs_path = None
+        axon_morphs_base_dir = None
+        if self.axon_method == "reconstructed":
+            axon_morphs_path = self.input()["axons"]["morphs"].path
+            if self.axon_morphs_base_dir is None:
+                axon_morphs_base_dir = get_axon_base_dir(
+                    pd.read_csv(self.input()["axon_cells"].path), "clone_path"
+                )
+            else:
+                axon_morphs_base_dir = self.axon_morphs_base_dir
+            L.debug("axon_morphs_base_dir = %s", axon_morphs_base_dir)
 
-        # Build arguments for region_grower.synthesize_morphologies.SynthesizeMorphologies
         kwargs = {
             "input_cells": self.input()["circuit"].path,
             "tmd_parameters": self.input()["tmd_parameters"].path,
@@ -526,9 +553,10 @@ def run(self):
             "max_drop_ratio": self.max_drop_ratio,
             "seed": self.seed,
             "nb_processes": self.nb_jobs,
+            "region_structure": self.input()["synthesis_input"].pathlib_path
+            / CircuitConfig().region_structure_path,
         }
-
-        if self.apply_jitter:
+        if self.axon_method == "reconstructed" and self.apply_jitter:
             kwargs["scaling_jitter_std"] = self.scaling_jitter_std
             kwargs["rotational_jitter_std"] = self.rotational_jitter_std
 
@@ -584,15 +612,15 @@ def run(self):
         """ """
         tmd_parameters = json.load(self.input()["tmd_parameters"].open("r"))
 
+        scaling_rules = {}
         if self.scaling_rules_path is not None:
             scaling_rules_path = (
                 self.input()["synthesis_input"].pathlib_path / self.scaling_rules_path
             )
-            L.debug("Load scaling rules from %s", scaling_rules_path)
-            with open(scaling_rules_path, "r") as f:
-                scaling_rules = yaml.full_load(f)
-        else:
-            scaling_rules = {}
+            if scaling_rules_path.exists():
+                L.debug("Load scaling rules from %s", scaling_rules_path)
+                with open(scaling_rules_path, "r") as f:
+                    scaling_rules = yaml.full_load(f)
 
         add_scaling_rules_to_parameters(
             tmd_parameters,
diff --git a/src/synthesis_workflow/tasks/utils.py b/src/synthesis_workflow/tasks/utils.py
deleted file mode 100644
index 2b883e1..0000000
--- a/src/synthesis_workflow/tasks/utils.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Utils luigi tasks."""
-import json
-import shutil
-from pathlib import Path
-from tempfile import TemporaryDirectory
-
-import luigi
-from git import Repo
-from placement_algorithm.app.compact_annotations import _collect_annotations
-
-from synthesis_workflow.tasks.config import PathConfig
-from synthesis_workflow.tasks.luigi_tools import OutputLocalTarget
-from synthesis_workflow.tasks.luigi_tools import WorkflowTask
-
-
-class GitClone(WorkflowTask):
-    """Task to clone a git repository."""
-
-    url = luigi.Parameter(
-        description=(
-            ":str: Url of repository. If None, git_synthesis_input_path should be an existing "
-            "folder."
-        )
-    )
-    dest = luigi.Parameter(description=":str: Path to the destination directory.")
-
-    def run(self):
-        """ """
-        Repo.clone_from(self.url, self.output().path)
-
-    def output(self):
-        """ """
-        return OutputLocalTarget(self.dest)
-
-
-class GetSynthesisInputs(WorkflowTask):
-    """Task to get synthesis input files from a folder on git it repository.
-
-    If no url is provided, this task will copy an existing folder to the target location.
-
-    Attributes:
-        local_synthesis_input_path (str): Path to local folder to copy these files.
-    """
-
-    url = luigi.OptionalParameter(
-        default=None,
-        description=(
-            ":str: Url of repository. If None, git_synthesis_input_path should be an "
-            "existing folder."
-        ),
-    )
-    version = luigi.OptionalParameter(
-        default=None, description=":str: Version of repo to checkout."
-    )
-    git_synthesis_input_path = luigi.Parameter(
-        default="synthesis_input",
-        description=":str: Path to folder in git repo with synthesis files.",
-    )
-
-    def run(self):
-        """ """
-        if self.url is None:
-            shutil.copytree(self.git_synthesis_input_path, self.output().path)
-        else:
-            with TemporaryDirectory() as tmpdir:
-                dest = Path(tmpdir) / "tmp_repo"
-                # Note: can not be called with yield here because of the TemporaryDirectory
-                GitClone(url=self.url, dest=dest).run()
-                if self.version is not None:
-                    r = Repo(dest)
-                    r.git.checkout(self.version)
-                shutil.copytree(dest / self.git_synthesis_input_path, self.output().path)
-
-    def output(self):
-        """ """
-        # TODO: it would probably be better to have a specific target for each file
-        return OutputLocalTarget(PathConfig().local_synthesis_input_path)
-
-
-class CreateAnnotationsFile(WorkflowTask):
-    """Task to compact annotations into a single JSON file."""
-
-    annotation_dir = luigi.Parameter(description=(":str: Path to annotations folder."))
-    morph_db = luigi.OptionalParameter(default=None, description=":str: Path to MorphDB file.")
-    destination = luigi.Parameter(description=":str: Path to output JSON file.")
-
-    def run(self):
-        """ """
-        # pylint: disable=protected-access
-        annotations = _collect_annotations(self.annotation_dir, self.morph_db)
-
-        with open(self.destination, "w") as f:
-            json.dump(annotations, f, indent=4, sort_keys=True)
-
-    def output(self):
-        """ """
-        return OutputLocalTarget(self.destination)
diff --git a/src/synthesis_workflow/tasks/vacuum_synthesis.py b/src/synthesis_workflow/tasks/vacuum_synthesis.py
index 3e5de44..6cb26ac 100644
--- a/src/synthesis_workflow/tasks/vacuum_synthesis.py
+++ b/src/synthesis_workflow/tasks/vacuum_synthesis.py
@@ -54,6 +54,11 @@ class VacuumSynthesize(WorkflowTask):
         description=":str: Diametrizer model to use.",
     )
     n_cells = luigi.IntParameter(default=10, description=":int: Number of cells to synthesize.")
+    axon_method = luigi.ChoiceParameter(
+        default="no_axon",
+        description=":str: The method used to handle axons.",
+        choices=["no_axon", "synthesis"],
+    )
 
     def requires(self):
         """ """
diff --git a/src/synthesis_workflow/tasks/validation.py b/src/synthesis_workflow/tasks/validation.py
index 6f281f6..e8f0177 100644
--- a/src/synthesis_workflow/tasks/validation.py
+++ b/src/synthesis_workflow/tasks/validation.py
@@ -16,6 +16,8 @@
 from synthesis_workflow.tasks.circuit import CreateAtlasLayerAnnotations
 from synthesis_workflow.tasks.circuit import CreateAtlasPlanes
 from synthesis_workflow.tasks.config import CircuitConfig
+from synthesis_workflow.tasks.config import GetCellComposition
+from synthesis_workflow.tasks.config import GetSynthesisInputs
 from synthesis_workflow.tasks.config import MorphsDfLocalTarget
 from synthesis_workflow.tasks.config import PathConfig
 from synthesis_workflow.tasks.config import RunnerConfig
@@ -315,6 +317,8 @@ def requires(self):
             "synthesis": Synthesize(),
             "planes": CreateAtlasPlanes(),
             "layers": CreateAtlasLayerAnnotations(),
+            "composition": GetCellComposition(),
+            "synthesis_input": GetSynthesisInputs(),
         }
 
     def run(self):
@@ -336,6 +340,7 @@ def run(self):
         planes = load_planes_centerline(planes_path)["planes"]
 
         layer_annotation_path = self.input()["layers"]["annotations"].path
+        layer_mappings = yaml.safe_load(self.input()["layers"]["layer_mapping"].open())
         L.debug("Load layer annotations from %s", layer_annotation_path)
         layer_annotation = VoxelData.load_nrrd(layer_annotation_path)
 
@@ -355,6 +360,9 @@ def run(self):
                 "linewidth": self.linewidth,
                 "diameter_scale": self.diameter_scale,
             },
+            layer_mappings=layer_mappings,
+            region_structure_path=self.input()["synthesis_input"].pathlib_path
+            / CircuitConfig().region_structure_path,
         )
 
     def output(self):
@@ -807,8 +815,6 @@ def run(self):
                 "linewidth": self.linewidth,
                 "diameter_scale": self.diameter_scale,
             },
-            region=self.region,
-            hemisphere=self.hemisphere,
             with_y_field=self.with_y_field,
             with_cells=self.with_cells,
         )
diff --git a/src/synthesis_workflow/tasks/workflows.py b/src/synthesis_workflow/tasks/workflows.py
index 75191ad..75174db 100644
--- a/src/synthesis_workflow/tasks/workflows.py
+++ b/src/synthesis_workflow/tasks/workflows.py
@@ -2,12 +2,12 @@
 import luigi
 import pandas as pd
 
+from synthesis_workflow.tasks.config import GetSynthesisInputs
 from synthesis_workflow.tasks.config import ValidationLocalTarget
 from synthesis_workflow.tasks.luigi_tools import BoolParameter
 from synthesis_workflow.tasks.luigi_tools import WorkflowTask
 from synthesis_workflow.tasks.luigi_tools import WorkflowWrapperTask
 from synthesis_workflow.tasks.synthesis import ApplySubstitutionRules
-from synthesis_workflow.tasks.utils import GetSynthesisInputs
 from synthesis_workflow.tasks.vacuum_synthesis import PlotVacuumMorphologies
 from synthesis_workflow.tasks.validation import MorphologyValidationReports
 from synthesis_workflow.tasks.validation import PlotCollage
diff --git a/src/synthesis_workflow/tools.py b/src/synthesis_workflow/tools.py
index f9d74dd..9a4f3c6 100644
--- a/src/synthesis_workflow/tools.py
+++ b/src/synthesis_workflow/tools.py
@@ -17,26 +17,10 @@
 from joblib import delayed
 from morph_tool.morphdb import MorphDB
 from morph_tool.utils import find_morph
-from voxcell import VoxelData
-from voxcell.nexus.voxelbrain import LocalAtlas
 
 L = logging.getLogger(__name__)
 
 
-def add_mtype_taxonomy(morphs_df, mtype_taxonomy):
-    """From a dict with mtype to morph_class, fill in the morphs_df dataframe.
-
-    Args:
-        morphs_df (pandas.DataFrame): the morphs_df DataFrame
-        mtype_taxonomy (pandas.DataFrame): with columns mtype and mClass
-    """
-    morphs_df = morphs_df[morphs_df["mtype"].isin(mtype_taxonomy.mtype)]
-    morphs_df["morph_class"] = morphs_df["mtype"].map(
-        lambda mtype: mtype_taxonomy.loc[mtype_taxonomy.mtype == mtype, "mClass"].to_list()[0]
-    )
-    return morphs_df
-
-
 def add_morphology_paths(morphs_df, morphology_dirs):
     """Same as the path loader of morph_tool.utils.neurondb_dataframe, but add multiple columns.
 
@@ -66,16 +50,13 @@ def add_apical_points(morphs_df, apical_points):
     return morphs_df
 
 
-def load_neurondb_to_dataframe(
-    neurondb_path, morphology_dirs=None, pc_in_types_path=None, apical_points_path=None
-):
+def load_neurondb_to_dataframe(neurondb_path, morphology_dirs=None, apical_points_path=None):
     """Loads morphology release to a dataframe.
 
     Args:
         neurondb_path (str): path to a neurondb.xml file
         morphology_dirs (dict): If passed, a column with the path to each morphology file
             will be added for each entry of the dict, where the column name is the dict key
-        pc_in_types_path (str): path to mtype_taxonomy.tsv file
         apical_points_path (str): path to JSON file containing apical points
     """
     morphs_df = MorphDB.from_neurondb(neurondb_path).df[
@@ -90,16 +71,9 @@ def load_neurondb_to_dataframe(
             apical_points = json.load(f)
         morphs_df = add_apical_points(morphs_df, apical_points)
 
-    if pc_in_types_path is not None:
-        mtype_taxonomy = pd.read_csv(pc_in_types_path, sep="\t")
-        morphs_df = add_mtype_taxonomy(morphs_df, mtype_taxonomy)
-
     return morphs_df
 
 
-L = logging.getLogger(__name__)
-
-
 def load_circuit(
     path_to_mvd3=None,
     path_to_morphologies=None,
@@ -164,21 +138,6 @@ def update_morphs_df(morphs_df_path, new_morphs_df):
     return pd.read_csv(morphs_df_path).merge(new_morphs_df, how="left")
 
 
-def get_layer_tags(atlas_dir):
-    """Creates a VoxelData with layer tags."""
-    atlas = LocalAtlas(atlas_dir)
-
-    names, ids = atlas.get_layers()  # pylint: disable=no-member
-    br = VoxelData.load_nrrd(Path(atlas_dir) / "brain_regions.nrrd")
-    layers = np.zeros_like(br.raw, dtype="uint8")
-    layer_mapping = {}
-    for layer_id, (ids_set, layer) in enumerate(zip(ids, names)):
-        layer_mapping[layer_id] = layer
-        layers[np.isin(br.raw, list(ids_set))] = layer_id + 1
-    br.raw = layers
-    return br, layer_mapping
-
-
 class IdProcessingFormatter(logging.Formatter):
     """Logging formatter class."""
 
diff --git a/src/synthesis_workflow/vacuum_synthesis.py b/src/synthesis_workflow/vacuum_synthesis.py
index 12df1ba..9615680 100644
--- a/src/synthesis_workflow/vacuum_synthesis.py
+++ b/src/synthesis_workflow/vacuum_synthesis.py
@@ -1,4 +1,6 @@
 """Functions for synthesis in vacuum to be used by luigi tasks."""
+import itertools
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -43,21 +45,10 @@ def _grow_morphology(
     vacuum_synth_morphs_df.loc[gid, "name"] = name
     vacuum_synth_morphs_df.loc[gid, "mtype"] = mtype
     vacuum_synth_morphs_df.loc[gid, vacuum_morphology_path] = morphology_path
-    # vacuum_synth_morphs_df.loc[gid, 'apical_point'] = grower.apical_points
 
     return vacuum_synth_morphs_df
 
 
-def _external_diametrizer(neuron, neurite_type, model_all, random_generator, diameter_params=None):
-    return build_diameters.build(
-        neuron,
-        model_all,
-        [neurite_type],
-        diameter_params,
-        random_generator,
-    )
-
-
 def grow_vacuum_morphologies(
     mtypes,
     n_cells,
@@ -111,16 +102,30 @@ def grow_vacuum_morphologies(
 
 def plot_vacuum_morphologies(vacuum_synth_morphs_df, pdf_filename, morphology_path):
     """Plot synthesized morphologies on top of each others."""
+    # pylint: disable=cell-var-from-loop
     with PdfPages(pdf_filename) as pdf:
         for mtype in tqdm(sorted(vacuum_synth_morphs_df.mtype.unique())):
+
+            ids = vacuum_synth_morphs_df[vacuum_synth_morphs_df.mtype == mtype].index
+            if len(ids) <= 5:
+                sqrt_n = len(ids)
+                sqrt_m = 1
+            else:
+                sqrt_n = int(np.sqrt(len(ids)) + 1)
+                sqrt_m = sqrt_n
             plt.figure()
             ax = plt.gca()
-            for gid in vacuum_synth_morphs_df[vacuum_synth_morphs_df.mtype == mtype].index:
+            for gid, (n, m) in zip(ids, itertools.product(range(sqrt_n), range(sqrt_m))):
                 morphology = load_morphology(vacuum_synth_morphs_df.loc[gid, morphology_path])
-                matplotlib_impl.plot_morph(morphology, ax, plane="zy")
+                matplotlib_impl.plot_morph(
+                    morphology.transform(lambda p: p + np.array([500 * n, 500 * m, 0])),
+                    ax,
+                    realistic_diameters=True,
+                    soma_outline=False,
+                )
             ax.set_title(mtype)
-            ax.set_rasterized(True)
-            plt.axis([-800, 800, -800, 2000])
+            plt.axis("equal")
+            plt.tight_layout()
             with DisableLogger():
                 pdf.savefig()
             plt.close()
diff --git a/src/synthesis_workflow/validation.py b/src/synthesis_workflow/validation.py
index 8001bad..9f2f4ba 100644
--- a/src/synthesis_workflow/validation.py
+++ b/src/synthesis_workflow/validation.py
@@ -24,6 +24,7 @@
 from joblib import delayed
 from matplotlib import cm
 from matplotlib.backends.backend_pdf import PdfPages
+from morph_tool.resampling import resample_linear_density
 from morphio.mut import Morphology
 from neurom import load_morphologies
 from neurom.apps import morph_stats
@@ -40,7 +41,7 @@
 from scipy.optimize import fmin
 from tmd.io.io import load_population
 from voxcell import CellCollection
-from voxcell import VoxelData
+from voxcell.exceptions import VoxcellError
 
 from synthesis_workflow.circuit import get_cells_between_planes
 from synthesis_workflow.fit_utils import clean_outliers
@@ -591,7 +592,7 @@ def get_plane_rotation_matrix(plane, current_rotation, target=None):
 
     current_direction = current_rotation.dot(target)
 
-    rotation_matrix = plane.get_quaternion().rotation_matrix
+    rotation_matrix = plane.get_quaternion().rotation_matrix.T
 
     def _get_rot_matrix(angle):
         """Get rotation matrix for a given angle along [0, 0, 1]."""
@@ -604,13 +605,30 @@ def _cost(angle):
     return _get_rot_matrix(angle).dot(rotation_matrix)
 
 
-def get_annotation_info(
-    annotation,
-    plane_origin,
-    rotation_matrix,
-    n_pixels=1024,
-    bbox=None,
-):
+def _get_plane_grid(annotation, plane_origin, rotation_matrix, n_pixels):
+    ids = np.vstack(np.where(annotation.raw > 0)).T
+    pts = annotation.indices_to_positions(ids)
+    pts = (pts - plane_origin).dot(rotation_matrix.T)
+    pts_dot_x = pts.dot([1.0, 0.0, 0.0])
+    pts_dot_y = pts.dot([0.0, 1.0, 0.0])
+    x_min = pts[np.argmin(pts_dot_x)][0]
+    x_max = pts[np.argmax(pts_dot_x)][0]
+    y_min = pts[np.argmin(pts_dot_y)][1]
+    y_max = pts[np.argmax(pts_dot_y)][1]
+
+    xs_plane = np.linspace(x_min, x_max, n_pixels)
+    ys_plane = np.linspace(y_min, y_max, n_pixels)
+    X, Y = np.meshgrid(xs_plane, ys_plane)
+    points = np.array(
+        [
+            np.array([x, y, 0]).dot(rotation_matrix) + plane_origin
+            for x, y in zip(X.flatten(), Y.flatten())
+        ]
+    )
+    return X, Y, points
+
+
+def get_annotation_info(annotation, plane_origin, rotation_matrix, n_pixels=1024):
     """Get information to plot annotation on a plane.
 
     Args:
@@ -619,56 +637,26 @@ def get_annotation_info(
         rotation_matrix (3*3 np.ndarray): rotation matrix to transform from real coordinates
             to plane coordinates
         n_pixels (int): number of pixel on each axis of the plane for plotting layers
-        bbox (ndarray): bbox to use, is None, annotation.bbox is used
     """
-    if bbox is None:
-        bbox = annotation.bbox
-
-    bbox_min = rotation_matrix.dot(bbox[0] - plane_origin)
-    bbox_max = rotation_matrix.dot(bbox[1] - plane_origin)
-
-    xs_plane = np.linspace(min(bbox_min[0], bbox_max[0]), max(bbox_min[0], bbox_max[0]), n_pixels)
-    ys_plane = np.linspace(min(bbox_min[1], bbox_max[1]), max(bbox_min[1], bbox_max[1]), n_pixels)
-
-    X, Y = np.meshgrid(xs_plane, ys_plane)
-    rot_T = rotation_matrix.T
-    points = [rot_T.dot([x, y, 0]) + plane_origin for x, y in zip(X.flatten(), Y.flatten())]
-
-    data = annotation.lookup(points, outer_value=-1).astype(float).reshape(n_pixels, n_pixels)
-    data[data < 1] = np.nan
+    X, Y, points = _get_plane_grid(annotation, plane_origin, rotation_matrix, n_pixels)
+    data = annotation.lookup(points, outer_value=0).astype(float).reshape(n_pixels, n_pixels)
     return X, Y, data
 
 
-def get_y_info(atlas, plane_origin, rotation_matrix, n_pixels=64, bbox=None):
+def get_y_info(annotation, atlas, plane_origin, rotation_matrix, n_pixels=64):
     """Get direction of y axis on a grid on the atlas planes."""
-    if bbox is None:
-        bbox = atlas.orientations.bbox
-
-    bbox_min = rotation_matrix.dot(bbox[0] - plane_origin)
-    bbox_max = rotation_matrix.dot(bbox[1] - plane_origin)
-
-    xs_plane = np.linspace(min(bbox_min[0], bbox_max[0]), max(bbox_min[0], bbox_max[0]), n_pixels)
-    ys_plane = np.linspace(min(bbox_min[1], bbox_max[1]), max(bbox_min[1], bbox_max[1]), n_pixels)
-
-    X, Y = np.meshgrid(xs_plane, ys_plane)
-
-    rot_T = rotation_matrix.T
-    points = np.array(
-        [rot_T.dot([x, y, 0]) + plane_origin for x, y in zip(X.flatten(), Y.flatten())]
-    )
-    # create a mask of outer voxels so it can lookup orientations safely
-    voxel_idx = atlas.orientations.positions_to_indices(points, strict=False)
-    outer_mask = np.any(voxel_idx == VoxelData.OUT_OF_BOUNDS, axis=-1)
-    points[outer_mask] = plane_origin
-
-    orientations = np.dot(atlas.orientations.lookup(points), [0.0, 1.0, 0.0]).dot(rotation_matrix.T)
-
-    if len(outer_mask[outer_mask]) > 0:
-        orientations[outer_mask] = len(outer_mask[outer_mask]) * [np.zeros(3)]
-
+    X, Y, points = _get_plane_grid(annotation, plane_origin, rotation_matrix, n_pixels)
+    orientations = []
+    for point in points:
+        try:
+            orientations.append(
+                rotation_matrix.dot(np.dot(atlas.orientations.lookup(point)[0], [0.0, 1.0, 0.0]))
+            )
+        except VoxcellError:
+            orientations.append([0.0, 1.0, 0.0])
+    orientations = np.array(orientations)
     orientation_u = orientations[:, 0].reshape(n_pixels, n_pixels)
     orientation_v = orientations[:, 1].reshape(n_pixels, n_pixels)
-
     return X, Y, orientation_u, orientation_v
 
 
@@ -682,8 +670,8 @@ def plot_cells(
     mtype=None,
     sample=10,
     plot_neuron_kwargs=None,
-    region=None,
-    hemisphere=None,
+    linear_density=None,
+    wire_plot=False,
 ):
     """Plot cells for collage."""
     if mtype is not None:
@@ -691,68 +679,38 @@ def plot_cells(
     else:
         cells = circuit.cells.get()
 
-    if len(cells) == 0:
-        raise Exception(f"No cell of that mtype ({mtype})")
-
     if plot_neuron_kwargs is None:
         plot_neuron_kwargs = {}
 
-    if region is not None:
-        if hemisphere is None:
-            cells = cells[(cells.region == f"{region}@left") & (cells.region == f"{region}@right")]
-        else:
-            cells = cells[cells.region == f"{region}@{hemisphere}"]
     _cells = get_cells_between_planes(cells, plane_left, plane_right)
 
+    gids = []
     if len(_cells.index) > 0:
         _cells = _cells.sample(n=min(sample, len(_cells.index)))
         gids = _cells.index
-    else:
-        gids = []
-
-    atlas = AtlasHelper(circuit.atlas)
-    vec = [0, 1, 0]
-    all_pos_orig = cells.loc[gids, ["x", "y", "z"]].values
-    all_orientations = atlas.orientations.lookup(all_pos_orig)
-    all_lookups = np.einsum("ijk, k", all_orientations, vec)
-    all_pos_final = all_pos_orig + all_lookups * 300
-    all_dist_plane_orig = all_pos_orig - plane_left.point
-    all_dist_plane_final = all_pos_final - plane_left.point
-    all_pos_orig_plane_coord = np.tensordot(all_dist_plane_orig, rotation_matrix.T, axes=1)
-    all_pos_final_plane_coord = np.tensordot(all_dist_plane_final, rotation_matrix.T, axes=1)
-
-    for num, gid in enumerate(gids):
-        morphology = neurom.core.Morphology(circuit.morph.get(gid, transform=True, source="ascii"))
+
+    def _wire_plot(morph, ax, lw=0.1):
+        for sec in morph.sections:
+            ax.plot(*sec.points.T[:2], c=matplotlib_impl.TREE_COLOR[sec.type], lw=lw)
+
+    for gid in gids:
+        m = circuit.morph.get(gid, transform=True, source="ascii")
+        if linear_density is not None:
+            m = resample_linear_density(m, linear_density)
+        morphology = neurom.core.Morphology(m)
 
         def _to_plane_coord(p):
             return np.dot(p - plane_left.point, rotation_matrix.T)
 
         # transform morphology in the plane coordinates
         morphology = morphology.transform(_to_plane_coord)
-
-        plt.plot(
-            [all_pos_orig_plane_coord[num, 0], all_pos_final_plane_coord[num, 0]],
-            [all_pos_orig_plane_coord[num, 1], all_pos_final_plane_coord[num, 1]],
-            c="0.5",
-            lw=0.1,
-        )
-        matplotlib_impl.plot_morph(morphology, ax, plane="xy", **plot_neuron_kwargs)
-
-
-def _get_region_mask(region, atlas, hemisphere=None):
-    """Get a region mask with hemisphere filter."""
-    region_mask = atlas.get_region_mask(region)
-    region_mask.raw = np.array(region_mask.raw, dtype=float)
-
-    if hemisphere is not None:
-        ids = np.array(np.where(region_mask.raw == 1)).T
-        pos = region_mask.indices_to_positions(ids)
-        if hemisphere == "left":
-            ids = ids[pos[:, 2] > pos[:, 2].mean()]
-        if hemisphere == "right":
-            ids = ids[pos[:, 2] < pos[:, 2].mean()]
-        region_mask.raw[tuple(ids.T.tolist())] = 0
-    return region_mask
+        if wire_plot:
+            ax.scatter(
+                *_to_plane_coord([_cells.loc[gid, ["x", "y", "z"]].values])[0, :2], c="k", s=5
+            )
+            _wire_plot(morphology, ax=ax)
+        else:
+            matplotlib_impl.plot_morph(morphology, ax, plane="xy", **plot_neuron_kwargs)
 
 
 # pylint: disable=too-many-arguments,too-many-locals
@@ -767,47 +725,37 @@ def _plot_collage(
     with_y_field=True,
     with_cells=True,
     plot_neuron_kwargs=None,
-    region=None,
-    hemisphere="right",
     figsize=(20, 20),
+    layer_mappings=None,
+    cells_linear_density=None,
+    cells_wire_plot=False,
+    region_structure_path="region_structure.yaml",
 ):
     """Internal plot collage for multiprocessing."""
     left_plane, right_plane = planes
     plane_point = left_plane.point
+    atlas = AtlasHelper(circuit.atlas, region_structure_path=region_structure_path)
     rotation_matrix = get_plane_rotation_matrix(
-        left_plane, AtlasHelper(circuit.atlas).orientations.lookup(plane_point)[0]
+        left_plane, atlas.orientations.lookup(plane_point)[0]
     )
 
-    fig = plt.figure(figsize=figsize)
-
-    if region is not None:
-        region_mask = _get_region_mask(region, circuit.atlas, hemisphere=hemisphere)
-
-        # get the bbox of the region
-        _pos = region_mask.indices_to_positions(np.array(np.where(region_mask.raw == 1)).T)
-        bbox = np.array([0.9 * np.min(_pos, axis=0), 1.1 * np.max(_pos, axis=0)])
-
-        # get the data to plot the mask
-        region_mask.raw = 1 - region_mask.raw
-        region_mask.raw[layer_annotation.raw < 1] = np.nan  # so that it is white outside atlas
-        X, Y, region_mask = get_annotation_info(
-            region_mask, plane_point, rotation_matrix, n_pixels, bbox=bbox
-        )
-
-        cmap = matplotlib.colors.ListedColormap(["k"])
-        plt.pcolormesh(X, Y, region_mask, shading="nearest", cmap=cmap, alpha=0.8)
-    else:
-        bbox = None
+    X, Y, layers = get_annotation_info(layer_annotation, plane_point, rotation_matrix, n_pixels)
+    layer_ids = np.array([-1] + list(layer_mappings.keys())) + 1
 
-    # get the data to plot the layers
-    X, Y, layers = get_annotation_info(
-        layer_annotation, plane_point, rotation_matrix, n_pixels, bbox=bbox
-    )
+    cmap = matplotlib.colors.ListedColormap(["0.8"] + [f"C{i}" for i in layer_mappings])
 
-    cmap = matplotlib.colors.ListedColormap(["C0", "C1", "C2", "C3", "C4", "C5"])
+    fig = plt.figure(figsize=figsize)
     plt.pcolormesh(X, Y, layers, shading="nearest", cmap=cmap, alpha=0.3)
-
-    plt.colorbar()
+    bounds = list(layer_ids - 0.5) + [layer_ids[-1] + 0.5]
+    norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
+    cbar = plt.colorbar(
+        matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm),
+        ticks=layer_ids,
+        boundaries=bounds,
+        shrink=0.3,
+        alpha=0.3,
+    )
+    cbar.ax.set_yticklabels(["outside"] + list(layer_mappings.values()))
 
     if with_cells:
         plot_cells(
@@ -819,13 +767,12 @@ def _plot_collage(
             mtype=mtype,
             sample=sample,
             plot_neuron_kwargs=plot_neuron_kwargs,
-            region=region,
-            hemisphere=hemisphere,
+            linear_density=cells_linear_density,
+            wire_plot=cells_wire_plot,
         )
-
     if with_y_field:
         X_y, Y_y, orientation_u, orientation_v = get_y_info(
-            AtlasHelper(circuit.atlas), left_plane.point, rotation_matrix, n_pixels_y, bbox=bbox
+            layer_annotation, atlas, left_plane.point, rotation_matrix, n_pixels_y
         )
         arrow_len = abs(X_y[0, 1] - X_y[0, 0]) * 0.5
         plt.quiver(
@@ -843,7 +790,6 @@ def _plot_collage(
     ax.set_aspect("equal")
     ax.set_rasterized(True)
     ax.set_title(f"plane coord: {left_plane.point}")
-    plt.axis([X[0, 0], X[0, -1], Y[0, 0], Y[-1, 0]])
 
     return fig
 
@@ -864,8 +810,10 @@ def plot_collage(
     n_pixels_y=20,
     plot_neuron_kwargs=None,
     with_cells=True,
-    region=None,
-    hemisphere=None,
+    layer_mappings=None,
+    cells_linear_density=None,
+    cells_wire_plot=False,
+    region_structure_path="region_structure.yaml",
 ):
     """Plot collage of an mtype and a list of planes.
 
@@ -884,8 +832,10 @@ def plot_collage(
         n_pixels_y (int): number of pixels for plotting y field
         plot_neuron_kwargs (dict): dict given to ``neurom.viewer.plot_neuron`` as kwargs
         with_cells (bool): plot cells or not
-        region (str): region to consider, if None, all atlas will be used
-        hemisphere (str): left/right hemisphere to consider
+        layer_mappings (dict): mapping between layer id and layer names to display
+        cells_linear_density (float): apply resampling to plot less points
+        cells_wire_plot (bool): if true, do not use neurom.view, but plt.plot
+        region_structure_path (str): path to region_structure.yaml file
     """
     ensure_dir(pdf_filename)
     with PdfPages(pdf_filename) as pdf:
@@ -900,8 +850,10 @@ def plot_collage(
             with_y_field=with_y_field,
             plot_neuron_kwargs=plot_neuron_kwargs,
             with_cells=with_cells,
-            region=region,
-            hemisphere=hemisphere,
+            layer_mappings=layer_mappings,
+            cells_linear_density=cells_linear_density,
+            cells_wire_plot=cells_wire_plot,
+            region_structure_path=region_structure_path,
         )
         for fig in Parallel(nb_jobs, verbose=joblib_verbose)(
             delayed(f)(planes) for planes in zip(planes[:-1:3], planes[2::3])
diff --git a/tests/conftest.py b/tests/conftest.py
index a15bec8..b39c837 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -13,8 +13,8 @@
 import numpy as np
 import pytest
 
+from synthesis_workflow.circuit import get_layer_tags
 from synthesis_workflow.tasks import config
-from synthesis_workflow.tools import get_layer_tags
 
 dir_content_diff.pandas.register()
 dir_content_diff_plugins.voxcell.register()
@@ -62,7 +62,9 @@ def small_O1(tmp_path):
     shutil.copyfile(DATA / "in_small_O1" / "metadata.json", atlas_dir / "metadata.json")
 
     # Add dummy cell density files for L1_DAC and L3_TPC:A
-    br, _ = get_layer_tags(atlas_dir)
+    br, _ = get_layer_tags(
+        atlas_dir, region_structure_path=DATA / "synthesis_input" / "region_structure.yaml"
+    )
     layer_tags = br.raw.copy()
     br.raw[np.where(layer_tags == 1)] = 1000
     br.raw[np.where(layer_tags != 1)] = 0
diff --git a/tests/data/in_small_O1/out/atlas/atlas_planes.npz b/tests/data/in_small_O1/out/atlas/atlas_planes.npz
new file mode 100644
index 0000000..9424121
Binary files /dev/null and b/tests/data/in_small_O1/out/atlas/atlas_planes.npz differ
diff --git a/tests/data/in_small_O1/out/atlas/atlas_planes_None.npz b/tests/data/in_small_O1/out/atlas/atlas_planes_None.npz
deleted file mode 100644
index 71c2fe0..0000000
Binary files a/tests/data/in_small_O1/out/atlas/atlas_planes_None.npz and /dev/null differ
diff --git a/tests/data/in_small_O1/out/circuit/sliced_circuit_somata.mvd3 b/tests/data/in_small_O1/out/circuit/sliced_circuit_somata.mvd3
index cc7d625..2275958 100644
Binary files a/tests/data/in_small_O1/out/circuit/sliced_circuit_somata.mvd3 and b/tests/data/in_small_O1/out/circuit/sliced_circuit_somata.mvd3 differ
diff --git a/tests/data/in_small_O1/out/morphs_df/axon_morphs_df.csv b/tests/data/in_small_O1/out/morphs_df/axon_morphs_df.csv
index 92e8317..667d5fa 100644
--- a/tests/data/in_small_O1/out/morphs_df/axon_morphs_df.csv
+++ b/tests/data/in_small_O1/out/morphs_df/axon_morphs_df.csv
@@ -1,5 +1,5 @@
-,name,layer,mtype,use_axon,path,clone_path,morph_class
-0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5,INT
-1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5,PYR
-2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5,PYR
-3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5,INT
+,name,layer,mtype,use_axon,path,clone_path
+0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5
+1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5
+2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5
+3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5
diff --git a/tests/data/in_small_O1/out/morphs_df/morphs_df.csv b/tests/data/in_small_O1/out/morphs_df/morphs_df.csv
index cd34f5a..2104c73 100644
--- a/tests/data/in_small_O1/out/morphs_df/morphs_df.csv
+++ b/tests/data/in_small_O1/out/morphs_df/morphs_df.csv
@@ -1,5 +1,5 @@
-,name,layer,mtype,use_axon,path,morphology_path,morph_class
-0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5,INT
-1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5,PYR
-2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5,PYR
-3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5,INT
+,name,layer,mtype,use_axon,path,morphology_path
+0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5
+1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5
+2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5
+3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5
diff --git a/tests/data/in_small_O1/out/morphs_df/substituted_morphs_df.csv b/tests/data/in_small_O1/out/morphs_df/substituted_morphs_df.csv
index 70ce60c..97110c4 100644
--- a/tests/data/in_small_O1/out/morphs_df/substituted_morphs_df.csv
+++ b/tests/data/in_small_O1/out/morphs_df/substituted_morphs_df.csv
@@ -1,5 +1,5 @@
-Unnamed: 0,name,layer,mtype,use_axon,path,morphology_path,morph_class
-0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5,INT
-1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5,PYR
-2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5,PYR
-3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5,INT
+Unnamed: 0,name,layer,mtype,use_axon,path,morphology_path
+0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5
+1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5
+2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5
+3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5
diff --git a/tests/data/in_small_O1/out/morphs_df/synth_morphs_df.csv b/tests/data/in_small_O1/out/morphs_df/synth_morphs_df.csv
index a7a6dab..7cea5f7 100644
--- a/tests/data/in_small_O1/out/morphs_df/synth_morphs_df.csv
+++ b/tests/data/in_small_O1/out/morphs_df/synth_morphs_df.csv
@@ -1,61 +1,61 @@
 etype,layer,morph_class,mtype,synapse_class,x,y,z,orientation,synth_morphology_path,name
-bNAC,1,INT,L1_DAC,INH,161.80154289988423,742.8768700945767,-786.4525935777549,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,106.80740739747216,625.1940988246093,122.11609153460836,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/e3e70682c2094cac629f6fbed82c07cd.asc,e3e70682c2094cac629f6fbed82c07cd
-cNAC,1,INT,L1_DAC,INH,458.990997635457,673.012202951677,731.1944995479603,"[[1. 0. 0.]
+bNAC,1,INT,L1_DAC,INH,56.910073861459296,640.7183297226001,606.9166995455139,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/cd613e30d8f16adf91b7584a2265b1f5.asc,cd613e30d8f16adf91b7584a2265b1f5
-cNAC,1,INT,L1_DAC,INH,-531.8607489396162,627.7596097731765,12.886056546632062,"[[1. 0. 0.]
+bNAC,1,INT,L1_DAC,INH,-609.1156281587262,781.5523818768568,15.941446344895553,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/d95bafc8f2a4d27bdcf4bb99f4bea973.asc,d95bafc8f2a4d27bdcf4bb99f4bea973
-cNAC,1,INT,L1_DAC,INH,-377.5682971025261,609.7844484494035,286.2191517421684,"[[1. 0. 0.]
+bNAC,1,INT,L1_DAC,INH,161.80154289988423,742.8768700945767,-786.4525935777549,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/216363698b529b4a97b750923ceb3ffd.asc,216363698b529b4a97b750923ceb3ffd
-cNAC,1,INT,L1_DAC,INH,-367.32991182317403,723.2744129279057,461.4464706476874,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,-178.31030156015254,766.3078203100101,-73.6677623262849,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/b8a1abcd1a6916c74da4f9fc3c6da5d7.asc,b8a1abcd1a6916c74da4f9fc3c6da5d7
-bNAC,1,INT,L1_DAC,INH,255.21924699224064,658.4476068955769,896.193637854723,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,-176.225817398436,650.2389457489262,794.258359969793,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/5bc8fbbcbde5c0994164d8399f767c45.asc,5bc8fbbcbde5c0994164d8399f767c45
-cNAC,1,INT,L1_DAC,INH,-457.94605333199013,655.7368791323917,886.0551173828792,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,240.67333458357484,655.2078276691971,-372.8347232393854,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/14a03569d26b949692e5dfe8cb1855fe.asc,14a03569d26b949692e5dfe8cb1855fe
-bNAC,1,INT,L1_DAC,INH,-322.59526673013613,633.3145152028642,-191.88986100120042,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,570.3888583540365,710.0226887312301,591.9482613744674,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/6513270e269e0d37f2a74de452e6b438.asc,6513270e269e0d37f2a74de452e6b438
-bNAC,1,INT,L1_DAC,INH,-4.904738894460706,623.3420255468096,-131.02317349222494,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,562.1478401499764,757.7228588604167,-576.2107178625491,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/6018366cf658f7a75ed34fe53a096533.asc,6018366cf658f7a75ed34fe53a096533
-cNAC,1,INT,L1_DAC,INH,240.67333458357484,655.2078276691971,-372.8347232393854,"[[1. 0. 0.]
+cNAC,1,INT,L1_DAC,INH,263.87617573665284,749.0305346548737,-301.05902227155684,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/4462ebfc5f915ef09cfbac6e7687a66e.asc,4462ebfc5f915ef09cfbac6e7687a66e
 cADpyr,3,PYR,L3_TPC:A,EXC,-159.16970916602543,437.74065725888875,680.9364971489199,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/7b89296c6dcbac5008577eb1924770d3.asc,7b89296c6dcbac5008577eb1924770d3
-cADpyr,3,PYR,L3_TPC:A,EXC,248.76763235382077,400.0545964896996,42.54017253550546,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-60.82030614353357,424.2178594126085,-274.9601787146428,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/db5b5fab8f4d3e27dda1494c73cf256d.asc,db5b5fab8f4d3e27dda1494c73cf256d
-cADpyr,3,PYR,L3_TPC:A,EXC,-337.83345635467424,428.8569957651696,187.4399917074843,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,248.76763235382077,400.0545964896996,42.54017253550546,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/87751d4ca8501e2c44dcda6a797d76de.asc,87751d4ca8501e2c44dcda6a797d76de
-cADpyr,3,PYR,L3_TPC:A,EXC,501.912289744869,451.8149813791174,684.2776862684843,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-78.35427390557902,416.6047824521246,892.2756610225367,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/e8d79f49af6d114c4a6f188a424e617b.asc,e8d79f49af6d114c4a6f188a424e617b
-cADpyr,3,PYR,L3_TPC:A,EXC,-173.27929681376816,487.73987891741194,-562.8081251487538,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-165.05597079514655,478.1479600234661,875.1021648856397,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/c15521b1b3dca50a9daa37e51b591d75.asc,c15521b1b3dca50a9daa37e51b591d75
-cADpyr,3,PYR,L3_TPC:A,EXC,92.72118073731167,402.895254902696,-310.4308708789796,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,295.91422519779735,410.32981550851383,-813.28328408948,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/8575062102fbcd4f357fbc5af71a1bfc.asc,8575062102fbcd4f357fbc5af71a1bfc
-cADpyr,3,PYR,L3_TPC:A,EXC,388.90599531897305,473.7278579714168,-299.4847035730976,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-588.2474357096362,497.5883868418533,-406.7438796142659,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/48f165d57b00c7f4781ef86f5c8cc1ab.asc,48f165d57b00c7f4781ef86f5c8cc1ab
-cADpyr,3,PYR,L3_TPC:A,EXC,-188.75726827876883,421.24343612940413,118.30332920799219,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-203.68027047396197,414.7801334065391,-274.3083356313331,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/4dad2986ce8349606a06e9ab85a0bcc1.asc,4dad2986ce8349606a06e9ab85a0bcc1
-cADpyr,3,PYR,L3_TPC:A,EXC,129.40766623831655,445.30942454488786,-550.6042166012777,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-599.8616650001001,424.7685022492316,331.8233509177062,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/72e63ac7a95383221f70d5dc2e675fc7.asc,72e63ac7a95383221f70d5dc2e675fc7
-cADpyr,3,PYR,L3_TPC:A,EXC,-165.05597079514655,478.1479600234661,875.1021648856397,"[[1. 0. 0.]
+cADpyr,3,PYR,L3_TPC:A,EXC,-171.7163147823918,433.9631044166199,160.02128681312934,"[[1. 0. 0.]
  [0. 1. 0.]
  [0. 0. 1.]]",synthesis/synthesized_morphologies/e539a78bc8eff3460b12ae6ead581e57.asc,e539a78bc8eff3460b12ae6ead581e57
diff --git a/tests/data/in_small_O1/out/synthesis/apical_points.yaml b/tests/data/in_small_O1/out/synthesis/apical_points.yaml
index 3feb04d..b92be2c 100644
--- a/tests/data/in_small_O1/out/synthesis/apical_points.yaml
+++ b/tests/data/in_small_O1/out/synthesis/apical_points.yaml
@@ -2,49 +2,49 @@
 216363698b529b4a97b750923ceb3ffd: null
 4462ebfc5f915ef09cfbac6e7687a66e: null
 48f165d57b00c7f4781ef86f5c8cc1ab:
-- -0.4680538475513458
-- 198.25662231445312
-- -8.388245582580566
+- -7.582686424255371
+- 191.33596801757812
+- -37.68757629394531
 4dad2986ce8349606a06e9ab85a0bcc1:
-- -4.403373718261719
-- 136.6024627685547
-- -7.630804061889648
+- 7.036743640899658
+- 192.56198120117188
+- 5.714080810546875
 5bc8fbbcbde5c0994164d8399f767c45: null
 6018366cf658f7a75ed34fe53a096533: null
 6513270e269e0d37f2a74de452e6b438: null
 72e63ac7a95383221f70d5dc2e675fc7:
-- -2.708533525466919
-- 199.09312438964844
-- -7.201815605163574
+- 22.429237365722656
+- 195.77279663085938
+- -18.43511390686035
 7b89296c6dcbac5008577eb1924770d3:
-- -15.83535099029541
-- 196.03489685058594
-- 1.0543631315231323
+- 6.158751487731934
+- 194.30145263671875
+- -11.033774375915527
 8575062102fbcd4f357fbc5af71a1bfc:
-- -4.238313674926758
-- 139.07034301757812
-- -6.794010162353516
+- 3.7870986461639404
+- 197.109619140625
+- -16.717546463012695
 87751d4ca8501e2c44dcda6a797d76de:
-- 12.631799697875977
-- 135.9075469970703
-- -4.667287826538086
+- -0.02146044373512268
+- 196.42794799804688
+- 14.575016021728516
 b8a1abcd1a6916c74da4f9fc3c6da5d7: null
 c15521b1b3dca50a9daa37e51b591d75:
-- 14.715275764465332
-- 132.83018493652344
-- -13.455227851867676
+- 2.402620315551758
+- 191.1907958984375
+- 17.717042922973633
 cd613e30d8f16adf91b7584a2265b1f5: null
 d95bafc8f2a4d27bdcf4bb99f4bea973: null
 db5b5fab8f4d3e27dda1494c73cf256d:
-- -5.762476921081543
-- 196.08946228027344
-- -0.19906087219715118
+- -9.867453575134277
+- 197.05661010742188
+- -12.531447410583496
 e3e70682c2094cac629f6fbed82c07cd: null
 e539a78bc8eff3460b12ae6ead581e57:
-- 0.6656373739242554
-- 137.11642456054688
-- -0.09093667566776276
+- 0.14039519429206848
+- 197.3130340576172
+- 5.079026222229004
 e8d79f49af6d114c4a6f188a424e617b:
-- -4.303969860076904
-- 139.21133422851562
-- 2.201572895050049
+- -4.53495454788208
+- 192.60043334960938
+- -3.3029251098632812
diff --git a/tests/data/in_small_O1/out/synthesis/neurots_input/tmd_distributions.json b/tests/data/in_small_O1/out/synthesis/neurots_input/tmd_distributions.json
index da8c70a..fe2b7cf 100644
--- a/tests/data/in_small_O1/out/synthesis/neurots_input/tmd_distributions.json
+++ b/tests/data/in_small_O1/out/synthesis/neurots_input/tmd_distributions.json
@@ -1,4624 +1,542 @@
 {
     "metadata": {
         "cortical_thickness": [
-            165,
-            149,
-            353,
-            190,
-            525,
-            700
+            200,
+            100,
+            100,
+            100,
+            100,
+            200
         ]
     },
     "mtypes": {
         "L1_DAC": {
-            "apical": {
+            "basal": {
                 "filtration_metric": "path_distances",
                 "num_trees": {
                     "data": {
                         "bins": [
-                            0
+                            3,
+                            4,
+                            5,
+                            6,
+                            7
                         ],
                         "weights": [
-                            2
-                        ]
-                    }
-                },
-                "persistence_diagram": [],
-                "trunk": {
-                    "absolute_elevation_deviation": {
-                        "data": {
-                            "bins": [
-                                0.016666666666666666,
-                                0.05,
-                                0.08333333333333334,
-                                0.11666666666666667,
-                                0.15,
-                                0.18333333333333335,
-                                0.21666666666666667,
-                                0.25,
-                                0.2833333333333333,
-                                0.31666666666666665,
-                                0.35,
-                                0.3833333333333333,
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-                            "max": 9.841953085089043e-17,
-                            "min": -0.0043574234703555705,
-                            "num_value": 64.0,
-                            "scale": 0.0011933960486203432
+                            "loc": -6.408057363246223e-18,
+                            "max": 6.408057363246223e-18,
+                            "min": -0.004344724677503109,
+                            "num_value": 25.0,
+                            "scale": 0.001989777898415923
                         },
                         "sequential": null
                     },
                     "basal": {
                         "distribution": "expon_rev",
                         "params": {
-                            "loc": -1.5910334685490776e-16,
-                            "max": 1.5910334685490776e-16,
-                            "min": -0.005049924016930163,
-                            "num_value": 56.0,
-                            "scale": 0.001477402518503368
+                            "loc": -1.595922966237103e-16,
+                            "max": 1.595922966237103e-16,
+                            "min": -0.0037232576403766872,
+                            "num_value": 25.0,
+                            "scale": 0.0019644065760076046
                         },
                         "sequential": null
                     }
@@ -6359,23 +1463,23 @@
                         "distribution": "exponnorm",
                         "params": {
                             "a": 0.3,
-                            "loc": 0.4407677174531953,
-                            "max": 0.6000000238418579,
-                            "min": 0.30057699233293533,
-                            "num_value": 42,
-                            "scale": 0.11020694467576098
+                            "loc": 0.3712710851761293,
+                            "max": 0.46310911774635316,
+                            "min": 0.3127447128295898,
+                            "num_value": 17,
+                            "scale": 0.04979673193593169
                         },
                         "sequential": null
                     },
                     "basal": {
                         "distribution": "exponnorm",
                         "params": {
-                            "a": 0.735877200480221,
-                            "loc": 0.3958188345943584,
-                            "max": 0.6000000357627868,
-                            "min": 0.30000001192092896,
-                            "num_value": 39,
-                            "scale": 0.09078264848451095
+                            "a": 1.0308705775946878,
+                            "loc": 0.34120020826448,
+                            "max": 0.4988087698817253,
+                            "min": 0.3039841204881668,
+                            "num_value": 18,
+                            "scale": 0.04537444854582963
                         },
                         "sequential": null
                     }
@@ -6384,12 +1488,12 @@
                     "apical": {
                         "distribution": "exponnorm",
                         "params": {
-                            "a": 0.3,
-                            "loc": 1.750200780295405,
-                            "max": 2.3495000898838043,
-                            "min": 1.4404999911785126,
-                            "num_value": 2,
-                            "scale": 0.4862531982490122
+                            "a": 1.3007585163212707,
+                            "loc": 1.3899999969093868,
+                            "max": 1.3899999856948853,
+                            "min": 1.3899999856948853,
+                            "num_value": 1,
+                            "scale": 1.2917495160173924e-27
                         },
                         "sequential": null
                     },
@@ -6397,11 +1501,11 @@
                         "distribution": "exponnorm",
                         "params": {
                             "a": 4.0,
-                            "loc": 1.1620941987531457,
-                            "max": 1.8140000224113464,
-                            "min": 1.2000000476837158,
-                            "num_value": 7,
-                            "scale": 0.06211353248078659
+                            "loc": 1.3742617033956122,
+                            "max": 1.7910000443458556,
+                            "min": 1.4039999842643738,
+                            "num_value": 3,
+                            "scale": 0.04733665039362104
                         },
                         "sequential": null
                     }
@@ -6410,8 +1514,8 @@
             "soma": {
                 "size": {
                     "norm": {
-                        "mean": 7.052670478820801,
-                        "std": 0.4954349994659424
+                        "mean": 6.557235240936279,
+                        "std": 0.0
                     }
                 }
             }
diff --git a/tests/data/in_small_O1/out/synthesis/sliced_circuit_morphologies.mvd3 b/tests/data/in_small_O1/out/synthesis/sliced_circuit_morphologies.mvd3
index 67df255..e6dc2dd 100644
Binary files a/tests/data/in_small_O1/out/synthesis/sliced_circuit_morphologies.mvd3 and b/tests/data/in_small_O1/out/synthesis/sliced_circuit_morphologies.mvd3 differ
diff --git a/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_config.json b/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_config.json
index 3fe13af..eb25948 100644
--- a/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_config.json
+++ b/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_config.json
@@ -1,31 +1,31 @@
 {
   "L1_DAC": {
     "basal_dendrite": {
-      "local_bifurcation_angles": {
+      "bifurcation_partitions": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_bifurcations": {
+      "local_bifurcation_angles": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_neurites": {
+      "number_of_bifurcations": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_sections_per_neurite": {
+      "number_of_neurites": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "bifurcation_partitions": {
+      "number_of_sections_per_neurite": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
@@ -89,31 +89,31 @@
   },
   "L3_TPC:A": {
     "basal_dendrite": {
-      "local_bifurcation_angles": {
+      "bifurcation_partitions": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_bifurcations": {
+      "local_bifurcation_angles": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_neurites": {
+      "number_of_bifurcations": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "number_of_sections_per_neurite": {
+      "number_of_neurites": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
         "threshold": 0.1
       },
-      "bifurcation_partitions": {
+      "number_of_sections_per_neurite": {
         "bins": 40,
         "criterion": "dist",
         "stat_test": "StatTests.ks",
diff --git a/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_results.json b/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_results.json
index 6f9fbc9..bdc1efb 100644
--- a/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_results.json
+++ b/tests/data/in_small_O1/out/validation/morphology_validation_reports/validation_results.json
@@ -11,23 +11,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.22782976827094475,
-              0.016086917813693955
+              0.2391934046345811,
+              0.009856629271629891
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 5.011741793559975,
+          "mean": 5.169239851058033,
           "median": 3.0,
           "sample_size": 352,
-          "std": 6.033799751563747
+          "std": 6.023913202741387
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.22782976827094475
+          "value": 0.2391934046345811
         }
       },
       "local_bifurcation_angles": {
@@ -47,10 +47,10 @@
           }
         },
         "test_summary_statistics": {
-          "mean": 0.18105730286566946,
-          "median": 0.18421144583120402,
+          "mean": 0.18368355913134726,
+          "median": 0.18448228167166003,
           "sample_size": 352,
-          "std": 0.0849479740889235
+          "std": 0.08513769086868977
         },
         "validation_criterion": {
           "criterion": "dist",
@@ -148,10 +148,10 @@
       },
       "principal_direction_extents": {
         "ref_summary_statistics": {
-          "mean": 221.66647868118002,
-          "median": 171.45850917326825,
+          "mean": 221.66647868118008,
+          "median": 171.45850917326828,
           "sample_size": 10,
-          "std": 178.35529502137229
+          "std": 178.3552950213723
         },
         "statistical_tests": {
           "StatTests.ks": {
@@ -163,10 +163,10 @@
           }
         },
         "test_summary_statistics": {
-          "mean": 154.3949219776213,
-          "median": 167.32538745757566,
+          "mean": 126.33782025262299,
+          "median": 133.71810110970014,
           "sample_size": 50,
-          "std": 60.91153444959894
+          "std": 61.69893569629977
         },
         "validation_criterion": {
           "criterion": "dist",
@@ -185,23 +185,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.4786096256684492,
-              7.510739254365386e-10
+              0.4743761140819964,
+              1.131568227261706e-09
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 0.8383033948957529,
-          "median": 0.7809544274404637,
+          "mean": 0.8487821998057786,
+          "median": 0.7597727790803787,
           "sample_size": 352,
-          "std": 0.5366114910349675
+          "std": 0.5405893665040464
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.4786096256684492
+          "value": 0.4743761140819964
         }
       },
       "section_branch_orders": {
@@ -214,23 +214,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.17933731960616822,
-              0.0024022490662272173
+              0.19163332284314166,
+              0.0009276870161595637
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 3.8938992042440317,
+          "mean": 3.9151193633952253,
           "median": 3.0,
           "sample_size": 754,
-          "std": 2.8470078146137068
+          "std": 2.8139873348152067
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.17933731960616822
+          "value": 0.19163332284314166
         }
       },
       "section_lengths": {
@@ -243,23 +243,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.17652744683720722,
-              0.002957207624508324
+              0.2428404441846873,
+              8.801771599212393e-06
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 33.925052642822266,
-          "median": 18.43250274658203,
+          "mean": 28.14206314086914,
+          "median": 14.805858612060547,
           "sample_size": 754,
-          "std": 39.770896911621094
+          "std": 34.99922561645508
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.17652744683720722
+          "value": 0.2428404441846873
         }
       },
       "section_path_distances": {
@@ -272,23 +272,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.34026435283010387,
-              5.064707231763582e-11
+              0.3880097109202895,
+              2.7225594103096117e-14
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 72.34872399124289,
-          "median": 60.95525538921356,
+          "mean": 60.90783993520376,
+          "median": 46.62140893936157,
           "sample_size": 754,
-          "std": 50.00799537294422
+          "std": 46.94835057033127
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.34026435283010387
+          "value": 0.3880097109202895
         }
       },
       "section_radial_distances": {
@@ -301,23 +301,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.2756372791440004,
-              2.445453919076133e-07
+              0.35154880187025134,
+              9.45473807012205e-12
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 60.2288932800293,
-          "median": 51.72051239013672,
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           "sample_size": 754,
-          "std": 41.16033172607422
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.2756372791440004
+          "value": 0.35154880187025134
         }
       },
       "section_tortuosity": {
@@ -330,23 +330,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.4334846918131547,
-              7.373936294357703e-18
+              0.4213909994155465,
+              7.234582823470782e-17
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 1.056825876235962,
-          "median": 1.0514146089553833,
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           "sample_size": 754,
-          "std": 0.04458966478705406
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.4334846918131547
+          "value": 0.4213909994155465
         }
       },
       "total_length": {
@@ -359,23 +359,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.7,
-              0.30303030303030304
+              0.8,
+              0.1818181818181818
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 2557.948915427923,
-          "median": 2637.074344098568,
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-          "std": 1044.906288124518
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         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.7
+          "value": 0.8
         }
       },
       "total_length_per_neurite": {
@@ -395,10 +395,10 @@
           }
         },
         "test_summary_statistics": {
-          "mean": 511.58978308558466,
-          "median": 460.86836671829224,
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-          "std": 385.3649739113055
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         "validation_criterion": {
           "criterion": "dist",
@@ -421,23 +421,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.19854041916167664,
-              0.20726651564746554
+              0.1751453488372093,
+              0.36256485385385534
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 3.8862275449101795,
+          "mean": 3.694721299372462,
           "median": 3.0,
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-          "std": 3.472376009279542
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.19854041916167664
+          "value": 0.1751453488372093
         }
       },
       "local_bifurcation_angles": {
@@ -451,16 +451,16 @@
           "StatTests.ks": {
             "results": [
               1.0,
-              2.0066895334767267e-37
+              3.4488112766546777e-34
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 0.12820994662704333,
-          "median": 0.11281647324406271,
-          "sample_size": 167,
-          "std": 0.07333343817934274
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         },
         "validation_criterion": {
           "criterion": "dist",
@@ -479,23 +479,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.3,
-              1.0
+              0.5,
+              0.7575757575757575
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 16.7,
-          "median": 16.0,
+          "mean": 12.9,
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           "sample_size": 10,
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         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.3
+          "value": 0.5
         }
       },
       "number_of_neurites": {
@@ -508,23 +508,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.0,
-              1.0
+              0.5,
+              0.7575757575757575
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 3.5,
-          "median": 3.5,
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           "sample_size": 10,
-          "std": 0.5
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         },
         "validation_criterion": {
           "criterion": "dist",
-          "status": "PASS",
+          "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.0
+          "value": 0.5
         }
       },
       "number_of_sections_per_neurite": {
@@ -537,23 +537,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.08571428571428572,
-              1.0
+              0.11904761904761904,
+              0.9999055895640336
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 10.542857142857143,
+          "mean": 9.6,
           "median": 9.0,
-          "sample_size": 35,
-          "std": 3.857037035585418
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+          "std": 3.431229128655406
         },
         "validation_criterion": {
           "criterion": "dist",
-          "status": "PASS",
+          "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.08571428571428572
+          "value": 0.11904761904761904
         }
       },
       "principal_direction_extents": {
@@ -566,23 +566,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.7714285714285715,
-              0.0006451845347865887
+              0.3904761904761905,
+              0.2792602417839609
             ],
-            "status": "FAIL"
+            "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 101.49535599358411,
-          "median": 103.2337146427756,
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-          "std": 19.961386104301198
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         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.7714285714285715
+          "value": 0.3904761904761905
         }
       },
       "remote_bifurcation_angles": {
@@ -595,23 +595,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.40550149700598803,
-              0.00016875243632900771
+              0.3687015503875969,
+              0.0013412802027484075
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 0.5866043144258016,
-          "median": 0.5313645707152134,
-          "sample_size": 167,
-          "std": 0.324866626359222
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.40550149700598803
+          "value": 0.3687015503875969
         }
       },
       "section_branch_orders": {
@@ -624,23 +624,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.14634146341463414,
-              0.1395894950704489
+              0.09722222222222222,
+              0.6122475409605344
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 2.5907859078590785,
+          "mean": 2.3958333333333335,
           "median": 2.0,
-          "sample_size": 369,
-          "std": 1.7601495625712718
+          "sample_size": 288,
+          "std": 1.650836277971461
         },
         "validation_criterion": {
           "criterion": "dist",
-          "status": "FAIL",
+          "status": "PASS",
           "threshold": 0.1,
-          "value": 0.14634146341463414
+          "value": 0.09722222222222222
         }
       },
       "section_lengths": {
@@ -653,23 +653,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.23966563609298064,
-              0.0017054803444536079
+              0.24667449139280126,
+              0.0015259917982022256
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 35.36289596557617,
-          "median": 23.26909065246582,
-          "sample_size": 369,
-          "std": 30.46419906616211
+          "mean": 72.07017517089844,
+          "median": 80.30104064941406,
+          "sample_size": 288,
+          "std": 57.026634216308594
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.23966563609298064
+          "value": 0.24667449139280126
         }
       },
       "section_path_distances": {
@@ -682,23 +682,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.42684835299057217,
-              2.5309810171951204e-10
+              0.14348591549295775,
+              0.17106590178194514
             ],
-            "status": "FAIL"
+            "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 59.14822071419176,
-          "median": 60.73670816421509,
-          "sample_size": 369,
-          "std": 35.81123096410863
+          "mean": 101.56173081778817,
+          "median": 128.83309268951416,
+          "sample_size": 288,
+          "std": 65.44143925097508
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.42684835299057217
+          "value": 0.14348591549295775
         }
       },
       "section_radial_distances": {
@@ -711,23 +711,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.42413832589030115,
-              3.408421377599884e-10
+              0.13399843505477307,
+              0.23258828404431103
             ],
-            "status": "FAIL"
+            "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 53.07786560058594,
-          "median": 54.04483413696289,
-          "sample_size": 369,
-          "std": 32.015254974365234
+          "mean": 89.84906768798828,
+          "median": 107.05494689941406,
+          "sample_size": 288,
+          "std": 58.2266731262207
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.42413832589030115
+          "value": 0.13399843505477307
         }
       },
       "section_tortuosity": {
@@ -740,23 +740,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.48696515134165425,
-              1.9082189099231458e-13
+              0.5249902190923318,
+              5.5779783591933655e-15
             ],
             "status": "FAIL"
           }
         },
         "test_summary_statistics": {
-          "mean": 1.0501981973648071,
-          "median": 1.0466011762619019,
-          "sample_size": 369,
-          "std": 0.028381353244185448
+          "mean": 1.0610288381576538,
+          "median": 1.0617560148239136,
+          "sample_size": 288,
+          "std": 0.03237013891339302
         },
         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.48696515134165425
+          "value": 0.5249902190923318
         }
       },
       "total_length": {
@@ -769,23 +769,23 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.8,
-              0.1818181818181818
+              0.5,
+              0.7575757575757575
             ],
             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 1304.8909575223922,
-          "median": 1308.22296667099,
+          "mean": 2075.6210257291796,
+          "median": 2079.040284395218,
           "sample_size": 10,
-          "std": 410.7839462521683
+          "std": 234.45267024547096
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.8
+          "value": 0.5
         }
       },
       "total_length_per_neurite": {
@@ -798,25 +798,25 @@
         "statistical_tests": {
           "StatTests.ks": {
             "results": [
-              0.4857142857142857,
-              0.10078541561211651
+              0.4380952380952381,
+              0.16234748635128146
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             "status": "PASS"
           }
         },
         "test_summary_statistics": {
-          "mean": 372.82598786354066,
-          "median": 358.7202024459839,
-          "sample_size": 35,
-          "std": 153.4661234360425
+          "mean": 691.8736752430598,
+          "median": 637.0880763530731,
+          "sample_size": 30,
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         "validation_criterion": {
           "criterion": "dist",
           "status": "FAIL",
           "threshold": 0.1,
-          "value": 0.4857142857142857
+          "value": 0.4380952380952381
         }
       }
     }
   }
-}
+}
\ No newline at end of file
diff --git a/tests/data/in_small_O1/out/validation/path_distance_fit.pdf b/tests/data/in_small_O1/out/validation/path_distance_fit.pdf
index e93ab3b..441a2b9 100644
Binary files a/tests/data/in_small_O1/out/validation/path_distance_fit.pdf and b/tests/data/in_small_O1/out/validation/path_distance_fit.pdf differ
diff --git a/tests/data/in_small_O1/out/validation/scales/statistics.pdf b/tests/data/in_small_O1/out/validation/scales/statistics.pdf
index 4f7915c..6626689 100644
Binary files a/tests/data/in_small_O1/out/validation/scales/statistics.pdf and b/tests/data/in_small_O1/out/validation/scales/statistics.pdf differ
diff --git a/tests/data/in_small_O1/out/validation/score_matrix_reports.pdf b/tests/data/in_small_O1/out/validation/score_matrix_reports.pdf
index f71f406..ea055b1 100644
Binary files a/tests/data/in_small_O1/out/validation/score_matrix_reports.pdf and b/tests/data/in_small_O1/out/validation/score_matrix_reports.pdf differ
diff --git a/tests/data/in_vacuum/out/morphs_df/morphs_df.csv b/tests/data/in_vacuum/out/morphs_df/morphs_df.csv
index cd34f5a..2104c73 100644
--- a/tests/data/in_vacuum/out/morphs_df/morphs_df.csv
+++ b/tests/data/in_vacuum/out/morphs_df/morphs_df.csv
@@ -1,5 +1,5 @@
-,name,layer,mtype,use_axon,path,morphology_path,morph_class
-0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5,INT
-1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5,PYR
-2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5,PYR
-3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5,INT
+,name,layer,mtype,use_axon,path,morphology_path
+0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5
+1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5
+2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5
+3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5
diff --git a/tests/data/in_vacuum/out/morphs_df/substituted_morphs_df.csv b/tests/data/in_vacuum/out/morphs_df/substituted_morphs_df.csv
index 70ce60c..97110c4 100644
--- a/tests/data/in_vacuum/out/morphs_df/substituted_morphs_df.csv
+++ b/tests/data/in_vacuum/out/morphs_df/substituted_morphs_df.csv
@@ -1,5 +1,5 @@
-Unnamed: 0,name,layer,mtype,use_axon,path,morphology_path,morph_class
-0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5,INT
-1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5,PYR
-2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5,PYR
-3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5,INT
+Unnamed: 0,name,layer,mtype,use_axon,path,morphology_path
+0,C270106A,1,L1_DAC,True,input_cells/C270106A.h5,input_cells/C270106A.h5
+1,C170797A-P2,3,L3_TPC:A,False,input_cells/C170797A-P2.h5,input_cells/C170797A-P2.h5
+2,rat_20160908_E3_LH2_cell2,3,L3_TPC:A,True,input_cells/rat_20160908_E3_LH2_cell2.h5,input_cells/rat_20160908_E3_LH2_cell2.h5
+3,sm080625a1-6_idD,1,L1_DAC,True,input_cells/sm080625a1-6_idD.h5,input_cells/sm080625a1-6_idD.h5
diff --git a/tests/data/in_vacuum/out/synthesis/neurots_input/tmd_distributions.json b/tests/data/in_vacuum/out/synthesis/neurots_input/tmd_distributions.json
index da8c70a..fe2b7cf 100644
--- a/tests/data/in_vacuum/out/synthesis/neurots_input/tmd_distributions.json
+++ b/tests/data/in_vacuum/out/synthesis/neurots_input/tmd_distributions.json
@@ -1,4624 +1,542 @@
 {
     "metadata": {
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-            149,
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                 "filtration_metric": "path_distances",
                 "num_trees": {
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@@ -6079,7 +1183,7 @@
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@@ -6103,17 +1207,17 @@
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                     "absolute_elevation_deviation": {
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                                 1,
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@@ -6283,24 +1387,24 @@
                     "apical": {
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@@ -6310,22 +1414,22 @@
                     "apical": {
                         "distribution": "expon_rev",
                         "params": {
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@@ -6334,22 +1438,22 @@
                     "apical": {
                         "distribution": "expon_rev",
                         "params": {
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@@ -6359,23 +1463,23 @@
                         "distribution": "exponnorm",
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                     "basal": {
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                         "params": {
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@@ -6384,12 +1488,12 @@
                     "apical": {
                         "distribution": "exponnorm",
                         "params": {
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+                            "a": 1.3007585163212707,
+                            "loc": 1.3899999969093868,
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                         "sequential": null
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@@ -6397,11 +1501,11 @@
                         "distribution": "exponnorm",
                         "params": {
                             "a": 4.0,
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-                            "num_value": 7,
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+                            "loc": 1.3742617033956122,
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@@ -6410,8 +1514,8 @@
             "soma": {
                 "size": {
                     "norm": {
-                        "mean": 7.052670478820801,
-                        "std": 0.4954349994659424
+                        "mean": 6.557235240936279,
+                        "std": 0.0
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                 }
             }
diff --git a/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_apical_dendrite.pdf b/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_apical_dendrite.pdf
index 1a289e5..2df42d3 100644
Binary files a/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_apical_dendrite.pdf and b/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_apical_dendrite.pdf differ
diff --git a/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_basal_dendrite.pdf b/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_basal_dendrite.pdf
index 8e6c2ab..e6b342b 100644
Binary files a/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_basal_dendrite.pdf and b/tests/data/in_vacuum/out/validation/morphometrics/morphometrics_basal_dendrite.pdf differ
diff --git a/tests/data/in_vacuum/out/validation/score_matrix_reports.pdf b/tests/data/in_vacuum/out/validation/score_matrix_reports.pdf
index 41e7f7c..ba7cec5 100644
Binary files a/tests/data/in_vacuum/out/validation/score_matrix_reports.pdf and b/tests/data/in_vacuum/out/validation/score_matrix_reports.pdf differ
diff --git a/tests/data/synthesis_input/region_structure.yaml b/tests/data/synthesis_input/region_structure.yaml
new file mode 100644
index 0000000..631cdb3
--- /dev/null
+++ b/tests/data/synthesis_input/region_structure.yaml
@@ -0,0 +1,28 @@
+layers:
+- 1
+- 2
+- 3
+- 4
+- 5
+- 6
+names:
+  1: layer 1
+  2: layer 2
+  3: layer 3
+  4: layer 4
+  5: layer 5
+  6: layer 6
+region_queries:
+  1: '@.*1$'
+  2: '@.*2[a|b]?$'
+  3: '@.*3$'
+  4: '@.*4$'
+  5: '@.*5$'
+  6: '@.*6[a|b]?$'
+thicknesses:
+  1: 200
+  2: 100
+  3: 100
+  4: 100
+  5: 100
+  6: 200