Add alternative apical point definition

* Modifies apical point distance to smoothed version
* Adds parameter 'has_apical_tuft' for apical trees
* Replaces definition of apical point for untufted cells
* If 'has_apical_tuft' is False, the 70% of total lenght is defined as apical point distance

Change-Id: Idb3c8e41c080235b963c435b9a6dc7c3d4449401

setup.py

@@ -16,7 +16,7 @@
     install_requires=[
         'matplotlib>=1.3.1',
         'morph-tool>=0.1.12',
-        'tmd>=2.0.5',
+        'tmd>=2.0.8',
         'neurom @ git+https://git@github.com/BlueBrain/NeuroM.git@mut_morphio#egg=neurom-2.0.0',
         'enum-compat>=0.0.1',
         'scipy>=0.13.3',

tests/data/bio_gradient_path_params.json

@@ -9,6 +9,7 @@
            "tree_type": 3,
            "step_size": {"norm":{"mean":1.0, "std":0.2}}},
 "apical": {"metric":"path_distances",
+           "has_apical_tuft": true,
            "randomness": 0.0,
            "targeting": 0.2,
            "radius": 0.3,

tests/data/bio_path_params.json

@@ -9,6 +9,7 @@
            "tree_type": 3,
            "step_size": {"norm":{"mean":1.0, "std":0.2}}},
 "apical": {"metric":"path_distances",
+           "has_apical_tuft": true,
            "randomness": 0.0,
            "targeting": 0.2,
            "radius": 0.3,

tests/data/bio_radial_params.json

@@ -9,6 +9,7 @@
            "tree_type": 3,
            "step_size": {"norm":{"mean":1.0, "std":0.2}}},
 "apical": {"metric":"radial_distances",
+           "has_apical_tuft": true,
            "randomness": 0.0,
            "targeting": 0.2,
            "radius": 0.3,

tests/data/dummy_params.json

@@ -1,5 +1,6 @@
 { "apical": {
         "branching_method": "directional",
+        "has_apical_tuft": true,
         "growth_method": "tmd_apical",
         "metric": "path_distances",
         "modify": null,

tests/data/expected_bio_rat_L5_TPC_B_with_params4_persistence_diagram.json

@@ -0,0 +1,166 @@
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tests/data/params1.json

@@ -1,5 +1,6 @@
 {"apical": {"branching_method": "directional",
             "growth_method": "tmd",
+            "has_apical_tuft": true,
             "metric": "path_distances",
             "modify": null,
             "orientation": [[0.0,

tests/data/params2.json

@@ -1,5 +1,6 @@
 {"apical": {"branching_method": "directional",
             "growth_method": "tmd_apical",
+            "has_apical_tuft": true,
             "metric": "path_distances",
             "modify": null,
             "orientation": [[0.0,

tests/data/params3.json

@@ -2,6 +2,7 @@
             "bias_length": 100,
             "branching_method": "directional",
             "growth_method": "tmd_gradient",
+            "has_apical_tuft": true,
             "metric": "path_distances",
             "modify": null,
             "orientation": [[0.0,

tests/data/params4.json

@@ -0,0 +1,29 @@
+{"apical": {"branching_method": "directional",
+            "growth_method": "tmd_apical",
+            "has_apical_tuft": false,
+            "metric": "path_distances",
+            "modify": null,
+            "orientation": [[0.0,
+                             1.0,
+                             0.0]],
+            "radius": 0.3,
+            "randomness": 0.15,
+            "targeting": 0.12,
+            "tree_type": 4,
+            "step_size": {"norm":{"mean":1.0, "std":0.2}}},
+"axon": {},
+"basal": {"branching_method": "bio_oriented",
+          "growth_method": "tmd",
+          "metric": "path_distances",
+          "modify": null,
+          "orientation": null,
+          "radius": 0.3,
+          "randomness": 0.15,
+          "targeting": 0.12,
+          "tree_type": 3,
+          "step_size": {"norm":{"mean":1.0, "std":0.2}}},
+"grow_types": ["basal", "apical"],
+"origin": [0.0,
+           0.0,
+           0.0],
+"diameter_params": {"method": "default"}}

tests/test_algo.py

@@ -23,6 +23,7 @@ def _setup_test(Algo, Grower):
         parameters = json.load(f)['basal']
     parameters['bias_length'] = 0.5
     parameters['bias'] = 0.5
+    parameters["has_apical_tuft"] = True
 
     np.random.seed(42)
     algo = Algo(distributions, parameters, [0, 0, 1])

tests/test_neuron_functional.py

@@ -43,7 +43,7 @@ def _load_inputs(distributions, parameters):
     return distributions, params
 
 
-def _test_full(feature, distributions, parameters, ref_cell, ref_persistence_diagram):
+def _test_full(feature, distributions, parameters, ref_cell, ref_persistence_diagram, save=False):
 
     np.random.seed(0)
     distributions, params = _load_inputs(join(_path, distributions), join(_path, parameters))
@@ -53,14 +53,16 @@ def _test_full(feature, distributions, parameters, ref_cell, ref_persistence_dia
         out_neuron = os.path.join(folder, 'test_output_neuron_.h5')
         n.write(out_neuron)
         # For checking purposes, we can output the cells as swc
-        # n.write(ref_cell.replace('.h5', 'NEW.h5'))
+        if save:
+            n.write(ref_cell.replace('.h5', 'NEW.h5'))
 
         if ref_persistence_diagram is not None:
             # Load with TMD and extract radial persistence
             n0 = tmd.io.load_neuron(out_neuron)
 
             actual_persistence_diagram = tmd.methods.get_persistence_diagram(n0.apical[0], feature=feature)
-            # print(actual_persistence_diagram)
+            if save:
+                print(actual_persistence_diagram)
 
             with open(join(_path, ref_persistence_diagram)) as f:
                 expected_persistence_diagram = json.load(f)
@@ -120,3 +122,9 @@ def test_bio_rat_l5_tpc():
                'params3.json',
                'expected_bio_rat_L5_TPC_B_with_params3.h5',
                'expected_bio_rat_L5_TPC_B_with_params3_persistence_diagram.json')
+
+    _test_full('path_distances',
+               'bio_rat_L5_TPC_B.json',
+               'params4.json',
+               'expected_bio_rat_L5_TPC_B_with_params4.h5',
+               'expected_bio_rat_L5_TPC_B_with_params4_persistence_diagram.json')

tns/extract_input/from_neurom.py

@@ -36,7 +36,7 @@ def trunk_neurite(pop, neurite_type=nm.BASAL_DENDRITE, bins=30):
     angles = [i for a in angles for i in a]
     heights, bins = np.histogram(angles, bins=bins)
 
-    # Extract trunk relative orientations to reshample
+    # Extract trunk relative orientations to resample
     actual_bins = (bins[1:] + bins[:-1]) / 2.
 
     return {"trunk": {"orientation_deviation": {"data":

tns/generate/algorithms/tmdgrower.py

@@ -192,9 +192,14 @@ def initialize(self):
         Initializes the tree grower and
         computes the apical distance using the input barcode.
         """
-        from tmd.Topology.analysis import find_apical_point_distance
+        from tmd.Topology.analysis import find_apical_point_distance_smoothed as ap_dist
         stop, num_sec = super(TMDApicalAlgo, self).initialize()
-        self.apical_point_distance_from_soma = find_apical_point_distance(self.ph_angles)
+        if self.params['has_apical_tuft']:
+            self.apical_point_distance_from_soma = ap_dist(self.ph_angles)
+        else:
+            # If cell does not have a tuft return the 70% of its length instead
+            # this will result in a point very close to the proximal apical point.
+            self.apical_point_distance_from_soma = 0.7 * np.nanmax(self.ph_angles)
         return stop, num_sec
 
     def bifurcate(self, current_section):

tns/generate/grower.py

@@ -3,6 +3,7 @@
 '''
 import copy
 import logging
+
 from morphio.mut import Morphology  # pylint: disable=import-error
 from tns.generate.soma import SomaGrower
 from tns.morphmath import sample
@@ -72,8 +73,8 @@ def next(self):
         for grower in list(self.active_neurites):
             if grower.end():
                 # If tree is an apical, the apical points get appended at the end of growth
-                # This will ensure that for each apical tree the relevant apical point,
-                # as a set of 3D coordinates (x,y,z) will be exposed to the user.
+                # This will ensure that for each apical tree a relevant apical point,
+                # will be exposed to the user as a set of 3D coordinates (x,y,z).
                 if 'apical' in self.input_parameters['grow_types'] and \
                    grower.type == self.input_parameters['apical']['tree_type']:
                     self.apical_points.append(grower.growth_algo.apical_point)