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def test_cell():
"""Test `dycpm.Cell`"""
side = 100
volume = round(np.mean(dycpm.DIVISION_VOLUME_DISTRIBUTION) / 2)
partition = round(np.cbrt(volume));
margin = round(np.floor((side - partition) / 2));
cell = dycpm.Cell(
type_id=dycpm.EPI, parent_id=0, age=0, preferred_volume=volume,
division_volume=dycpm.sample_division_volume(),
voxel_ids=[round(
margin + (n - 1) % partition**2 % partition
+ side * (margin + (n - 1) % partition**2 // partition)
+ side**2 * (margin + (n - 1) // partition**2)
) for n in range(volume)]
)
assert 475144 in cell.voxel_ids
assert cell.type_id == dycpm.EPI
assert cell.type_id != dycpm.PRE
cell.type_id = dycpm.PRE
assert cell.type_id == dycpm.PRE
def test_state(tmp_path):
"""Test JSON-format export and import of a state"""
state = dycpm.State(
side=10, action_probabilities=3 * [0.3],
growth_rate=2 * [1.6], volume_elasticity=2 * [1],
active_exponents = [0.1, -1.2, 2.3, -3.4, 0.5, -0.6, 7.7],
surface_tension=5 * [10], cells={
1: {
'type_id': dycpm.EPI, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': 1,
"voxel_ids": [1]
}
}
)
assert state.side == 10
assert state.active_exponents == [0.1, -1.2, 2.3, -3.4, 0.5, -0.6, 7.7]
state.to_json_file(path)
assert isinstance(state.cells[1], dycpm.Cell)
state = dycpm.State.from_json_file(path)
assert state.side == 10
assert state.surface_tension[0] == 10
state.surface_tension = [20, 5, 10, 15, 10]
assert state.surface_tension[0] == 20
state.surface_tension[0] = 25
assert state.surface_tension[0] == 25
assert state.cells[1].type_id == dycpm.EPI
assert state.cells[1].type_id != dycpm.PRE
state.cells[1].type_id = dycpm.PRE
assert state.cells[1].type_id != dycpm.EPI
assert state.cells[1].type_id == dycpm.PRE
assert state.active_exponents == [0.1, -1.2, 2.3, -3.4, 0.5, -0.6, 7.7]
def test_simulation():
"""Test simulation flow"""
side = 100
volume = round(np.mean(dycpm.DIVISION_VOLUME_DISTRIBUTION) / 2)
partition = round(np.cbrt(volume));
margin = round(np.floor((side - partition) / 2));
state = dycpm.State(
time=0, side=side, action_probabilities=3 * [0.3], death_probability=1e-4,
growth_rate=2 * [1.6], volume_elasticity=2 * [1], surface_tension=5 * [10],
division_distribution=dycpm.DIVISION_VOLUME_DISTRIBUTION,
cells={
1: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': volume,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [round(
margin + (n - 1) % partition**2 % partition
+ side * (margin + (n - 1) % partition**2 // partition)
+ side**2 * (margin + (n - 1) // partition**2)
) for n in range(volume)]
}
}
)
assert 1 in state.cells
kernel = dycpm.Kernel(state)
assert kernel.cell_number == 1
for _ in range(2): kernel.advance()
state = kernel.state
assert state.time == 2
assert state.side == 100
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def test_lattice():
"""Test `State.lattice()"""
side = 100
volume = round(np.mean(dycpm.DIVISION_VOLUME_DISTRIBUTION) / 2)
partition = round(np.cbrt(volume));
margin = round(np.floor((side - partition) / 2));
state = dycpm.State(
time=0, side=side, action_probabilities=3 * [0.3], death_probability=1e-4,
growth_rate=2 * [1.6], volume_elasticity=2 * [1], surface_tension=5 * [10],
division_distribution=dycpm.DIVISION_VOLUME_DISTRIBUTION,
cells={
1: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': volume,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [round(
margin + (n - 1) % partition**2 % partition
+ side * (margin + (n - 1) % partition**2 // partition)
+ side**2 * (margin + (n - 1) // partition**2)
) for n in range(volume)]
}
}
)
lattice = state.lattice()
assert lattice.shape == (side, ) * 3
assert lattice[49, 49, 49] == 1
assert lattice[0, 0, 0] == 0
assert np.count_nonzero(lattice.ravel() == 1) == volume
def test_analysis_neighborIndex():
""" Test `dycpm.analysis.neighbor_index()`"""
state = dycpm.State(
time=0, side=10, action_probabilities=3 * [0.3], death_probability=1e-4,
growth_rate=2 * [1.6], volume_elasticity=2 * [1], surface_tension=5 * [10],
division_distribution=dycpm.DIVISION_VOLUME_DISTRIBUTION,
cells={
1: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 4]
},
2: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 5]
},
3: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 5 * 10 + 4]
}
}
)
assert dycpm.analysis.neighbor_index(state, [1], [2]) == 0.5
assert dycpm.analysis.neighbor_index(state, [1], [0, 2]) == 2 / 3
assert dycpm.analysis.neighbor_index(state, [2], [1]) == 1
assert dycpm.analysis.neighbor_index(state, [2], [0, 1]) == 1
""" Test `dycpm.analysis`"""
state = dycpm.State(
time=0, side=10, action_probabilities=3 * [0.3], death_probability=1e-4,
growth_rate=2 * [1.6], volume_elasticity=2 * [1], surface_tension=5 * [10],
division_distribution=dycpm.DIVISION_VOLUME_DISTRIBUTION,
cells={
1: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 4]
},
2: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 5]
},
3: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 5 * 10 + 4]
},
4: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [0 * 10**2 + 0 * 10 + 0]
},
5: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [1 * 10**2 + 0 * 10 + 0]
}
}
)
assert dycpm.analysis.neighbor_index(state, [1], [2]) == 0.5
assert dycpm.analysis.neighbor_index(state, [1], [0, 2]) == 2 / 3
assert dycpm.analysis.neighbor_index(state, [2], [1]) == 1 / 3
assert dycpm.analysis.neighbor_index(state, [2], [0, 1]) == 2 / 3
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def test_analysis_cellGraph():
""" Test `dycpm.analysis.cellGraph()`"""
state = dycpm.State(
time=0, side=10, action_probabilities=3 * [0.3], death_probability=1e-4,
growth_rate=2 * [1.6], volume_elasticity=2 * [1], surface_tension=5 * [10],
division_distribution=dycpm.DIVISION_VOLUME_DISTRIBUTION,
cells={
1: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 4]
},
2: {
'type_id': 1, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 4 * 10 + 5]
},
3: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [4 * 10**2 + 5 * 10 + 4]
},
4: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [0 * 10**2 + 0 * 10 + 0]
},
5: {
'type_id': 2, 'parent_id': 0, 'age': 0,
'preferred_volume': 1,
'division_volume': dycpm.sample_division_volume(),
"voxel_ids": [1 * 10**2 + 0 * 10 + 0]
}
}
)
epi = dycpm.analysis.CellGraph(state, dycpm.EPI)
pre = dycpm.analysis.CellGraph(state, dycpm.PRE)
assert len(epi.nodes) == 2
assert len(pre.nodes) == 3
assert nx.number_connected_components(epi) == 1
assert nx.number_connected_components(pre) == 2