Create input_distributions.py and input_parameters.py

Change-Id: I2ade7f7d5105f03f30e5a8fc3a1b1e68449039c6

tns/extract_input/__init__.py

@@ -1,117 +1,4 @@
 ''' Module to extract morphometrics and TMD-input from a set of tree-shaped cells.'''
 
-import neurom as nm
-import tmd
-from tns.extract_input.from_TMD import persistent_homology_angles
-from tns.extract_input.from_neurom import soma_data, trunk_neurite, number_neurites
-from tns.extract_input import from_diameter
-
-
-def default_keys():
-    '''Returns the important keys for the distribution extraction'''
-    return {'soma': {},
-            'basal': {},
-            'apical': {},
-            'axon': {}}
-
-
-def distributions(filepath, neurite_types=None, threshold_sec=2,
-                  diameter_input_morph=False, feature='radial_distances'):
-    '''Extracts the input distributions from an input population
-    defined by a directory of swc or h5 files
-    threshold_sec: defines the minimum accepted number of terminations
-    diameter_input_morph: if provided it will be used for the generation
-                          of diameter model
-    feature: defines the TMD feature that will be used to extract the
-             persistence barcode: radial_distances, path_distances
-    '''
-    # Assume all neurite_types will be extracted if neurite_types is None
-    if neurite_types is None:
-        neurite_types = ['basal', 'apical', 'axon']
-
-    pop_tmd = tmd.io.load_population(filepath)
-    pop_nm = nm.load_neurons(filepath)
-
-    input_distributions = default_keys()
-    input_distributions['soma'].update(soma_data(pop_nm))
-
-    # Define the neurom neurite_types
-    neurom_types = {'basal': nm.BASAL_DENDRITE,
-                    'apical': nm.APICAL_DENDRITE,
-                    'axon': nm.AXON}
-
-    def fill_input_distributions(input_distr, neurite_type):
-        '''Helping function to avoid code duplication'''
-        nm_type = neurom_types[neurite_type]
-        input_distr[neurite_type].update(trunk_neurite(pop_nm, nm_type))
-        input_distr[neurite_type].update(number_neurites(pop_nm, nm_type))
-        input_distr[neurite_type].update(
-            persistent_homology_angles(pop_tmd,
-                                       threshold=threshold_sec,
-                                       neurite_type=neurite_type,
-                                       feature=feature))
-
-    for ntype in neurite_types:
-        fill_input_distributions(input_distributions, ntype)
-
-    # In order to create diameter model an exemplar morphology is required
-    # This is provided by diameter_input_morph
-    if diameter_input_morph:
-        neu_exemplar = nm.load_neuron(diameter_input_morph)
-        input_distributions["diameter"] = from_diameter.model(neu_exemplar)
-        input_distributions["diameter"]["method"] = 'M5'  # By default, diametrize from_tips
-
-    return input_distributions
-
-
-def parameters(origin=(0., 0., 0.), method='trunk', neurite_types=None):
-    '''Returns a default set of input parameters
-       to be used as input for synthesis.
-    '''
-    # Assume all neurite_types will be extracted if neurite_types is None
-    if neurite_types is None:
-        neurite_types = ['basal', 'apical', 'axon']
-
-    # Set up required fields
-    input_parameters = {'basal': {},
-                        'apical': {},
-                        'axon': {}}
-
-    input_parameters["origin"] = origin
-
-    if method == 'trunk':
-        branching = 'random'
-    elif method == 'tmd' or method == 'tmd_path':
-        branching = 'bio_oriented'
-
-    parameters_default = {"randomness": 0.15,
-                          "targeting": 0.12,
-                          "radius": 0.3,
-                          "orientation": None,
-                          "growth_method": method,
-                          "branching_method": branching,
-                          "modify": None}
-
-    if 'basal' in neurite_types:
-        input_parameters["basal"].update(parameters_default)
-        input_parameters["basal"].update({"tree_type": 3})
-
-    if 'apical' in neurite_types:
-        input_parameters["apical"].update(parameters_default)
-        input_parameters["apical"].update({"apical_distance": 0.0,
-                                           "tree_type": 4,
-                                           "branching_method": "directional",
-                                           "orientation": [(0., 1., 0.)], })
-        if method == 'tmd':
-            input_parameters["apical"]["growth_method"] = 'tmd_apical'
-        if method == 'tmd_path':
-            input_parameters["apical"]["growth_method"] = 'tmd_apical_path'
-
-    if 'axon' in neurite_types:
-        input_parameters["axon"].update(parameters_default)
-        input_parameters["axon"].update({"tree_type": 2,
-                                         "orientation": [(0., -1., 0.)], })
-
-    input_parameters['grow_types'] = neurite_types
-
-    return input_parameters
+from tns.extract_input.input_distributions import distributions
+from tns.extract_input.input_parameters import parameters

tns/extract_input/input_distributions.py

@@ -0,0 +1,64 @@
+"""Input distributions"""
+
+import neurom as nm
+import tmd
+from tns.extract_input.from_TMD import persistent_homology_angles
+from tns.extract_input.from_neurom import soma_data, trunk_neurite, number_neurites
+from tns.extract_input import from_diameter
+
+
+def default_keys():
+    '''Returns the important keys for the distribution extraction'''
+    return {'soma': {},
+            'basal': {},
+            'apical': {},
+            'axon': {}}
+
+
+def distributions(filepath, neurite_types=None, threshold_sec=2,
+                  diameter_input_morph=False, feature='radial_distances'):
+    '''Extracts the input distributions from an input population
+    defined by a directory of swc or h5 files
+    threshold_sec: defines the minimum accepted number of terminations
+    diameter_input_morph: if provided it will be used for the generation
+                          of diameter model
+    feature: defines the TMD feature that will be used to extract the
+             persistence barcode: radial_distances, path_distances
+    '''
+    # Assume all neurite_types will be extracted if neurite_types is None
+    if neurite_types is None:
+        neurite_types = ['basal', 'apical', 'axon']
+
+    pop_tmd = tmd.io.load_population(filepath)
+    pop_nm = nm.load_neurons(filepath)
+
+    input_distributions = default_keys()
+    input_distributions['soma'].update(soma_data(pop_nm))
+
+    # Define the neurom neurite_types
+    neurom_types = {'basal': nm.BASAL_DENDRITE,
+                    'apical': nm.APICAL_DENDRITE,
+                    'axon': nm.AXON}
+
+    def fill_input_distributions(input_distr, neurite_type):
+        '''Helping function to avoid code duplication'''
+        nm_type = neurom_types[neurite_type]
+        input_distr[neurite_type].update(trunk_neurite(pop_nm, nm_type))
+        input_distr[neurite_type].update(number_neurites(pop_nm, nm_type))
+        input_distr[neurite_type].update(
+            persistent_homology_angles(pop_tmd,
+                                       threshold=threshold_sec,
+                                       neurite_type=neurite_type,
+                                       feature=feature))
+
+    for ntype in neurite_types:
+        fill_input_distributions(input_distributions, ntype)
+
+    # In order to create diameter model an exemplar morphology is required
+    # This is provided by diameter_input_morph
+    if diameter_input_morph:
+        neu_exemplar = nm.load_neuron(diameter_input_morph)
+        input_distributions["diameter"] = from_diameter.model(neu_exemplar)
+        input_distributions["diameter"]["method"] = 'M5'  # By default, diametrize from_tips
+
+    return input_distributions

tns/extract_input/input_parameters.py

@@ -0,0 +1,54 @@
+"""Input parameters functions"""
+
+
+def parameters(origin=(0., 0., 0.), method='trunk', neurite_types=None):
+    '''Returns a default set of input parameters
+       to be used as input for synthesis.
+    '''
+    # Assume all neurite_types will be extracted if neurite_types is None
+    if neurite_types is None:
+        neurite_types = ['basal', 'apical', 'axon']
+
+    # Set up required fields
+    input_parameters = {'basal': {},
+                        'apical': {},
+                        'axon': {}}
+
+    input_parameters["origin"] = origin
+
+    if method == 'trunk':
+        branching = 'random'
+    elif method == 'tmd' or method == 'tmd_path':
+        branching = 'bio_oriented'
+
+    parameters_default = {"randomness": 0.15,
+                          "targeting": 0.12,
+                          "radius": 0.3,
+                          "orientation": None,
+                          "growth_method": method,
+                          "branching_method": branching,
+                          "modify": None}
+
+    if 'basal' in neurite_types:
+        input_parameters["basal"].update(parameters_default)
+        input_parameters["basal"].update({"tree_type": 3})
+
+    if 'apical' in neurite_types:
+        input_parameters["apical"].update(parameters_default)
+        input_parameters["apical"].update({"apical_distance": 0.0,
+                                           "tree_type": 4,
+                                           "branching_method": "directional",
+                                           "orientation": [(0., 1., 0.)], })
+        if method == 'tmd':
+            input_parameters["apical"]["growth_method"] = 'tmd_apical'
+        if method == 'tmd_path':
+            input_parameters["apical"]["growth_method"] = 'tmd_apical_path'
+
+    if 'axon' in neurite_types:
+        input_parameters["axon"].update(parameters_default)
+        input_parameters["axon"].update({"tree_type": 2,
+                                         "orientation": [(0., -1., 0.)], })
+
+    input_parameters['grow_types'] = neurite_types
+
+    return input_parameters