def test_doas_matrix_benchmark(benchmark):
model = DOASModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2'],
'parameters': ['j.1', 'j.0']
},
},
'oscillation': {
'osc1': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
},
'osc2': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
}
},
'megacomplex': {
'm1': {
'oscillation': ['osc1', 'osc2'],
}
},
'irf': {
'irf1': {
'type': 'gaussian',
'center': ['irf.center'],
'width': ['irf.width']
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['m1'],
'irf': 'irf1',
}
}
})
parameter = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False
}],
['0', 0, {
'vary': False
}],
],
'osc': [
['freq', 16],
['rate', 0.3],
],
'irf': [['center', 0.5], ['width', 0.3]],
})
time = np.arange(0, 6, 0.001)
dataset = model.dataset['dataset1'].fill(model, parameter)
benchmark(calculate_doas_matrix, dataset, 0, time)
def test_doas_matrix_benchmark(benchmark):
model = DOASModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2'],
'parameters': ['j.1', 'j.0']
},
},
'oscillation': {
'osc1': {'frequency': 'osc.freq', 'rate': 'osc.rate'},
'osc2': {'frequency': 'osc.freq', 'rate': 'osc.rate'}
},
'megacomplex': {
'm1': {
'oscillation': ['osc1', 'osc2'],
}
},
'irf': {
'irf1': {'type': 'gaussian', 'center': ['irf.center'], 'width': ['irf.width']},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['m1'],
'irf': 'irf1',
}
}
})
parameter = ParameterGroup.from_dict({
'j': [
['1', 1, {'vary': False}],
['0', 0, {'vary': False}],
],
'osc': [
['freq', 16],
['rate', 0.3],
],
'irf': [['center', 0.5], ['width', 0.3]],
})
time = np.arange(0, 6, 0.001)
dataset = model.dataset['dataset1'].fill(model, parameter)
benchmark(calculate_doas_matrix, dataset, time, 0)
class OneOscillationWithIrf:
sim_model = DOASModel.from_dict({
"oscillation": {
"osc1": {
"frequency": "osc.freq",
"rate": "osc.rate"
}
},
"megacomplex": {
"m1": {
"oscillation": ["osc1"]
}
},
"shape": {
"sh1": {
"type": "gaussian",
"amplitude": "shape.amps.1",
"location": "shape.locs.1",
"width": "shape.width.1",
},
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"megacomplex": ["m1"],
"shape": {
"osc1": "sh1"
},
"irf": "irf1",
}
},
})
model = DOASModel.from_dict({
"oscillation": {
"osc1": {
"frequency": "osc.freq",
"rate": "osc.rate"
}
},
"megacomplex": {
"m1": {
"oscillation": ["osc1"]
}
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"megacomplex": ["m1"],
"irf": "irf1",
}
},
})
wanted_parameter = ParameterGroup.from_dict({
"osc": [
["freq", 25],
["rate", 0.1],
],
"shape": {
"amps": [7],
"locs": [5],
"width": [4]
},
"irf": [["center", 0.3], ["width", 0.1]],
})
parameter = ParameterGroup.from_dict({
"osc": [
["freq", 25],
["rate", 0.1],
],
"irf": [["center", 0.3], ["width", 0.1]],
})
time = np.arange(0, 3, 0.01)
spectral = np.arange(0, 10)
axis = {"time": time, "spectral": spectral}
wanted_clp = ["osc1_cos", "osc1_sin"]
wanted_shape = (300, 2)
class OneOscillationWithSequentialModel:
sim_model = DOASModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2"],
"parameters": ["j.1", "j.0"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.2",
}
}
},
"oscillation": {
"osc1": {
"frequency": "osc.freq",
"rate": "osc.rate"
}
},
"megacomplex": {
"m1": {
"k_matrix": ["k1"],
"oscillation": ["osc1"],
}
},
"shape": {
"sh1": {
"type": "gaussian",
"amplitude": "shape.amps.1",
"location": "shape.locs.1",
"width": "shape.width.1",
},
"sh2": {
"type": "gaussian",
"amplitude": "shape.amps.2",
"location": "shape.locs.2",
"width": "shape.width.2",
},
"sh3": {
"type": "gaussian",
"amplitude": "shape.amps.3",
"location": "shape.locs.3",
"width": "shape.width.3",
},
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["m1"],
"shape": {
"osc1": "sh1",
"s1": "sh2",
"s2": "sh3",
},
"irf": "irf1",
}
},
})
model = DOASModel.from_dict({
"initial_concentration": {
"j1": {
"compartments": ["s1", "s2"],
"parameters": ["j.1", "j.0"]
},
},
"k_matrix": {
"k1": {
"matrix": {
("s2", "s1"): "kinetic.1",
("s2", "s2"): "kinetic.2",
}
}
},
"oscillation": {
"osc1": {
"frequency": "osc.freq",
"rate": "osc.rate"
}
},
"megacomplex": {
"m1": {
"k_matrix": ["k1"],
"oscillation": ["osc1"],
}
},
"irf": {
"irf1": {
"type": "gaussian",
"center": "irf.center",
"width": "irf.width"
},
},
"dataset": {
"dataset1": {
"initial_concentration": "j1",
"megacomplex": ["m1"],
"irf": "irf1",
}
},
})
wanted_parameter = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
["0", 0, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [
["1", 0.2],
["2", 0.01],
],
"osc": [
["freq", 25],
["rate", 0.1],
],
"shape": {
"amps": [0.07, 2, 4],
"locs": [5, 2, 8],
"width": [4, 2, 3]
},
"irf": [["center", 0.3], ["width", 0.1]],
})
parameter = ParameterGroup.from_dict({
"j": [
["1", 1, {
"vary": False,
"non-negative": False
}],
["0", 0, {
"vary": False,
"non-negative": False
}],
],
"kinetic": [
["1", 0.2],
["2", 0.01],
],
"osc": [
["freq", 25],
["rate", 0.1],
],
"irf": [["center", 0.3], ["width", 0.1]],
})
time = np.arange(-1, 5, 0.01)
spectral = np.arange(0, 10)
axis = {"time": time, "spectral": spectral}
wanted_clp = ["osc1_cos", "osc1_sin", "s1", "s2"]
wanted_shape = (600, 4)
class OneOscillation():
sim_model = DOASModel.from_dict({
'oscillation': {
'osc1': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
}
},
'megacomplex': {
'm1': {
'oscillation': ['osc1']
}
},
'shape': {
'sh1': {
'type': "gaussian",
'amplitude': "shape.amps.1",
'location': "shape.locs.1",
'width': "shape.width.1",
},
},
'dataset': {
'dataset1': {
'megacomplex': ['m1'],
'shape': {
'osc1': 'sh1'
}
}
}
})
model = DOASModel.from_dict({
'oscillation': {
'osc1': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
}
},
'megacomplex': {
'm1': {
'oscillation': ['osc1']
}
},
'dataset': {
'dataset1': {
'megacomplex': ['m1']
}
}
})
wanted_parameter = ParameterGroup.from_dict({
'osc': [
['freq', 25.5],
['rate', 0.1],
],
'shape': {
'amps': [7],
'locs': [5],
'width': [4]
},
})
parameter = ParameterGroup.from_dict({
'osc': [
['freq', 20],
['rate', 0.3],
],
})
time = np.arange(0, 3, 0.01)
spectral = np.arange(0, 10)
axis = {'time': time, 'spectral': spectral}
wanted_clp = ['osc1_cos', 'osc1_sin']
wanted_shape = (300, 2)
class OneOscillationWithSequentialModel():
sim_model = DOASModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2'],
'parameters': ['j.1', 'j.0']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s2", "s1"): 'kinetic.1',
("s2", "s2"): 'kinetic.2',
}
}
},
'oscillation': {
'osc1': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
}
},
'megacomplex': {
'm1': {
'k_matrix': ['k1'],
'oscillation': ['osc1'],
}
},
'shape': {
'sh1': {
'type': "gaussian",
'amplitude': "shape.amps.1",
'location': "shape.locs.1",
'width': "shape.width.1",
},
'sh2': {
'type': "gaussian",
'amplitude': "shape.amps.2",
'location': "shape.locs.2",
'width': "shape.width.2",
},
'sh3': {
'type': "gaussian",
'amplitude': "shape.amps.3",
'location': "shape.locs.3",
'width': "shape.width.3",
},
},
'irf': {
'irf1': {
'type': 'gaussian',
'center': 'irf.center',
'width': 'irf.width'
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['m1'],
'shape': {
'osc1': 'sh1',
's1': 'sh2',
's2': 'sh3',
},
'irf': 'irf1',
}
}
})
model = DOASModel.from_dict({
'initial_concentration': {
'j1': {
'compartments': ['s1', 's2'],
'parameters': ['j.1', 'j.0']
},
},
'k_matrix': {
"k1": {
'matrix': {
("s2", "s1"): 'kinetic.1',
("s2", "s2"): 'kinetic.2',
}
}
},
'oscillation': {
'osc1': {
'frequency': 'osc.freq',
'rate': 'osc.rate'
}
},
'megacomplex': {
'm1': {
'k_matrix': ['k1'],
'oscillation': ['osc1'],
}
},
'irf': {
'irf1': {
'type': 'gaussian',
'center': 'irf.center',
'width': 'irf.width'
},
},
'dataset': {
'dataset1': {
'initial_concentration': 'j1',
'megacomplex': ['m1'],
'irf': 'irf1',
}
}
})
wanted_parameter = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
['0', 0, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [
["1", 0.2],
["2", 0.01],
],
'osc': [
['freq', 25],
['rate', 0.1],
],
'shape': {
'amps': [0.07, 2, 4],
'locs': [5, 2, 8],
'width': [4, 2, 3]
},
'irf': [['center', 0.3], ['width', 0.1]],
})
parameter = ParameterGroup.from_dict({
'j': [
['1', 1, {
'vary': False,
'non-negative': False
}],
['0', 0, {
'vary': False,
'non-negative': False
}],
],
'kinetic': [
["1", 0.2],
["2", 0.01],
],
'osc': [
['freq', 25],
['rate', 0.1],
],
'irf': [['center', 0.3], ['width', 0.1]],
})
time = np.arange(-1, 5, 0.01)
spectral = np.arange(0, 10)
axis = {'time': time, 'spectral': spectral}
wanted_clp = ['osc1_cos', 'osc1_sin', 's1', 's2']
wanted_shape = (600, 4)