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test_extract_input.py
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test_extract_input.py
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import os
import json
from nose import tools as nt
from nose.tools import assert_dict_equal
import neurom
import numpy as np
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises
from neurom import load_neurons
from neurom import stats
from tns import extract_input
from tns import TNSError
_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'test_data')
POP_PATH = os.path.join(_PATH, 'bio/')
NEU_PATH = os.path.join(_PATH, 'diam_simple.swc')
POPUL = load_neurons(POP_PATH)
NEU = load_neurons(NEU_PATH)
def test_num_trees():
target_numBAS = {'num_trees': {'data': {'bins': [4, 5, 6, 7, 8, 9],
'weights': [1, 0, 0, 0, 0, 1]}}}
target_numAX = {'num_trees': {'data': {'bins': [1], 'weights': [2]}}}
numBAS = extract_input.from_neurom.number_neurites(POPUL)
numAX = extract_input.from_neurom.number_neurites(POPUL, neurite_type=neurom.AXON)
assert_equal(numBAS, target_numBAS)
assert_equal(numAX, target_numAX)
def test_trunk_distr():
bins_BAS = [0.19391773616376634,
0.4880704446023673,
0.7822231530409682,
1.076375861479569,
1.3705285699181702,
1.6646812783567713,
1.9588339867953721,
2.2529866952339734,
2.547139403672574,
2.841292112111175]
absolute_elevation_deviation_BAS = {
"data": {
"weights": [2, 0, 0, 3, 1, 2, 1, 1, 1, 2],
}
}
bins_absolute_ele_dev_BAS = [-0.7718245274301169,
-0.6464835753472811,
-0.5211426232644452,
-0.39580167118160936,
-0.27046071909877345,
-0.1451197670159376,
-0.019778814933101796,
0.10556213714973406,
0.23090308923256997,
0.35624404131540577]
bins_absolute_ele_dev_APIC = [1.03738723]
target_trunkBAS = {'trunk':
{'azimuth': {'uniform': {'max': 0.0, 'min': np.pi}},
'orientation_deviation': {'data': {'weights': [4, 3, 1, 2, 0, 1, 0, 0, 0, 2]}},
'absolute_elevation_deviation': absolute_elevation_deviation_BAS}}
target_trunkAPIC = {'trunk': {'azimuth': {'uniform': {'max': 0.0, 'min': np.pi}},
'orientation_deviation': {'data': {'bins': [0.0], 'weights': [2]}},
'absolute_elevation_deviation': {'data': {'weights': [2]}}}}
trunkAP = extract_input.from_neurom.trunk_neurite(POPUL, neurite_type=neurom.APICAL_DENDRITE, bins=1)
trunkBAS = extract_input.from_neurom.trunk_neurite(POPUL, bins=10)
assert_array_almost_equal(trunkBAS['trunk']['orientation_deviation']['data']['bins'],
bins_BAS)
assert_array_almost_equal(trunkBAS['trunk']['absolute_elevation_deviation']['data']['bins'],
bins_absolute_ele_dev_BAS)
assert_array_almost_equal(trunkAP['trunk']['absolute_elevation_deviation']['data']['bins'],
bins_absolute_ele_dev_APIC)
del trunkBAS['trunk']['orientation_deviation']['data']['bins']
del trunkBAS['trunk']['absolute_elevation_deviation']['data']['bins']
del trunkAP['trunk']['absolute_elevation_deviation']['data']['bins']
assert_equal(trunkBAS, target_trunkBAS)
assert_equal(trunkAP, target_trunkAPIC)
def test_diameter_extract():
res = extract_input.from_diameter.model(NEU)
assert_equal(set(res.keys()), {'basal'})
expected = {'Rall_ratio': 1.5,
'siblings_ratio': 1.0,
'taper': [0.24, 0.1],
'term': [2.0, 2.0],
'trunk': [3.9],
'trunk_taper': [0.30]}
assert_equal(set(res['basal'].keys()), set(expected.keys()))
for key in expected.keys():
assert_array_almost_equal(res['basal'][key], expected[key])
assert_raises(TNSError, extract_input.from_diameter.model,
load_neurons(os.path.join(_PATH, 'simple.swc')))
# Test on Population
res = extract_input.from_diameter.model(POPUL)
assert_equal(set(res.keys()), {'axon', 'basal', 'apical'})
expected = {
'basal': {
'Rall_ratio': 1.5,
'siblings_ratio': 1.0,
'taper': [
0.003361, 0.009487, 0.009931, 0.016477,
0.023878, 0.024852, 0.027809, 0.027975
],
'term': [0.3] * 8,
'trunk': [0.6 , 0.6 , 0.72, 0.84, 1.2 , 1.5 , 1.8 , 2.4],
'trunk_taper': [
0, 3.036411e-02, 3.053287e-02, 5.059035e-02,
1.168936e-01, 1.172027e-01, 0.15, 2.121002e-01
]
},
'apical': {
'Rall_ratio': 1.5,
'siblings_ratio': 1.0,
'taper': [
0.010331, 0.02135 , 0.02264 , 0.033914,
0.035313, 0.041116, 0.055751, 0.056211
],
'term': [0.3] * 8,
'trunk': [1.57, 7.51],
'trunk_taper': [0.05324615, 0.65223652]
},
'axon': {
'Rall_ratio': 1.5,
'siblings_ratio': 1.0,
'taper': [
0.04079 , 0.055286, 0.092382, 0.099524,
0.11986 , 0.140346, 0.214172, 0.407058
],
'term': [0.12] * 8,
'trunk': [2.1, 3.0],
'trunk_taper': [0.0435508, 0.0717109]
}
}
for neurite_type in ['basal', 'apical', 'axon']:
for key in expected[neurite_type].keys():
try:
assert_equal(res[neurite_type].keys(), expected[neurite_type].keys())
if key in ['taper', 'term', 'trunk', 'trunk_taper']:
tested = sorted(res[neurite_type][key])[:8]
else:
tested = res[neurite_type][key]
assert_array_almost_equal(tested, expected[neurite_type][key])
except AssertionError:
raise AssertionError(f"Failed for res[{neurite_type}][{key}]")
def test_distributions():
filename = os.path.join(_PATH, 'bio/')
distr = extract_input.distributions(filename, feature='radial_distances')
assert_equal(set(distr.keys()), {'soma', 'basal', 'apical', 'axon', 'diameter'})
assert_equal(distr['basal']['num_trees'],
{'data': {'bins': [4, 5, 6, 7, 8, 9], 'weights': [1, 0, 0, 0, 0, 1]}})
assert_equal(distr['basal']['filtration_metric'], 'radial_distances')
distr = extract_input.distributions(filename, feature='path_distances')
assert_equal(distr['basal']['filtration_metric'], 'path_distances')
distr = extract_input.distributions(filename, feature='radial_distances', diameter_model=None)
assert_equal(set(distr.keys()), {'soma', 'basal', 'apical', 'axon', 'diameter'})
distr_M5 = extract_input.distributions(
filename, feature='radial_distances', diameter_model='M5'
)
assert_equal(set(distr_M5.keys()), {'soma', 'basal', 'apical', 'axon', 'diameter'})
def diam_method(pop):
return extract_input.from_diameter.model(pop)
distr_external = extract_input.distributions(
filename, feature='radial_distances', diameter_model=diam_method
)
assert_equal(set(distr_external.keys()), {'soma', 'basal', 'apical', 'axon', 'diameter'})
def test_transform_distr():
ss = stats.fit([1, 2], distribution='norm')
tss = extract_input.from_neurom.transform_distr(ss)
assert_equal(tss, {'norm': {'mean': 1.5, 'std': 0.5}})
ss = stats.fit([1, 2], distribution='uniform')
tss = extract_input.from_neurom.transform_distr(ss)
assert_equal(tss, {'uniform': {'min': 1, 'max': 2}})
ss = stats.fit([1, 2], distribution='expon')
tss = extract_input.from_neurom.transform_distr(ss)
assert_equal(tss, {'expon': {'loc': 1.0, 'lambda': 2.0}})
ss = stats.fit([1, 2], distribution='gamma')
tss = extract_input.from_neurom.transform_distr(ss)
assert_equal(tss, None)
def test_number_neurites():
pop = load_neurons(POP_PATH)
res = extract_input.from_neurom.number_neurites(pop)
assert_equal(
res,
{'num_trees': {'data': {'bins': [4, 5, 6, 7, 8, 9], 'weights': [1, 0, 0, 0, 0, 1]}}}
)
pop_cut = load_neurons(POP_PATH)
neurons = [neuron for neuron in pop_cut.neurons]
if len(neurons[0].neurites) > len(neurons[1].neurites):
smallest = 1
biggest = 0
else:
smallest = 0
biggest = 1
for i in list(range(2, len(neurons[biggest].root_sections) - 1))[::-1]:
neurons[biggest].delete_section(
neurons[biggest].root_sections[i], recursive=True
)
pop_cut = neurom.core.population.Population(neurons)
assert_equal(len(neurons), 2)
assert_equal(len(neurons[biggest].neurites), 3)
assert_equal(len(neurons[smallest].neurites), 6)
assert_equal(len([i for i in pop_cut.neurites]), 9)
res_cut = extract_input.from_neurom.number_neurites(pop_cut)
assert_equal(
res_cut,
{'num_trees': {'data': {'bins': [2, 3, 4],
'weights': [1, 0, 1]}}}
)
def test_parameters():
params = extract_input.parameters(
neurite_types=['basal', 'apical'], method='tmd', feature='radial_distances')
assert_equal(params,
{'basal': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': None,
'growth_method': 'tmd', 'branching_method': 'bio_oriented', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 3, 'metric': 'radial_distances'},
'apical': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, 1.0, 0.0)],
'growth_method': 'tmd_apical', 'branching_method': 'directional', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 4, 'metric': 'radial_distances'},
'axon': {}, 'origin': (0.0, 0.0, 0.0), 'grow_types': ['basal', 'apical'], 'diameter_params': {'method': 'default'}})
params_path = extract_input.parameters(
neurite_types=['basal', 'apical'], method='tmd')
assert_equal(params_path,
{'basal': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': None,
'growth_method': 'tmd', 'branching_method': 'bio_oriented', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 3, 'metric': 'path_distances'},
'apical': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, 1.0, 0.0)],
'growth_method': 'tmd_apical', 'branching_method': 'directional', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 4, 'metric': 'path_distances'},
'axon': {}, 'origin': (0.0, 0.0, 0.0), 'grow_types': ['basal', 'apical'], 'diameter_params': {'method': 'default'}})
params_axon = extract_input.parameters(neurite_types=['axon'], method='tmd')
assert_equal(params_axon,
{'basal': {},
'apical': {},
'axon': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, -1.0, 0.0)],
'growth_method': 'tmd', 'branching_method': 'bio_oriented', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 2, 'metric': 'path_distances'},
'origin': (0.0, 0.0, 0.0),
'grow_types': ['axon'], 'diameter_params': {'method': 'default'}
})
params_axon = extract_input.parameters(neurite_types=['axon'], method='trunk')
assert_equal(params_axon,
{'basal': {},
'apical': {},
'axon': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, -1.0, 0.0)],
'growth_method': 'trunk', 'branching_method': 'random', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 2, 'metric': 'path_distances'},
'origin': (0.0, 0.0, 0.0),
'grow_types': ['axon'], 'diameter_params': {'method': 'default'}
})
params_diameter = extract_input.parameters(
neurite_types=['axon'], method='trunk', diameter_parameters='M1'
)
assert_equal(params_diameter,
{'basal': {},
'apical': {},
'axon': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, -1.0, 0.0)],
'growth_method': 'trunk', 'branching_method': 'random', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 2, 'metric': 'path_distances'},
'origin': (0.0, 0.0, 0.0),
'grow_types': ['axon'], 'diameter_params': {'method': 'M1'}
})
params_diameter_dict = extract_input.parameters(
neurite_types=['axon'],
method='trunk',
diameter_parameters={'a': 1, 'b': 2},
)
assert_equal(params_diameter_dict,
{'basal': {},
'apical': {},
'axon': {'randomness': 0.15, 'targeting': 0.12, 'radius': 0.3, 'orientation': [(0.0, -1.0, 0.0)],
'growth_method': 'trunk', 'branching_method': 'random', 'modify': None,
"step_size": {"norm": {"mean": 1.0, "std": 0.2}},
'tree_type': 2, 'metric': 'path_distances'},
'origin': (0.0, 0.0, 0.0),
'grow_types': ['axon'], 'diameter_params': {'method': 'external', 'a': 1, 'b': 2}
})
assert_raises(
KeyError,
extract_input.parameters,
neurite_types=['axon'],
method='UNKNOWN METHOD',
)
assert_raises(
ValueError,
extract_input.parameters,
neurite_types=['axon'],
method='trunk',
diameter_parameters=object(),
)