def test_randomNorm():
N = 10; M = 10; dN = 6
l = 1; d = l*2e-3
np.random.seed(7)
sim = Simulation(N, dN, M, l, d, random_fun='normal', test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_vertices"]) == 98
def test_random10():
N = 10; M = 10; dN = 6
l = 1; d = l*2e-3
np.random.seed(4)
sim = Simulation(N, dN, M, l, d, random_fun='uni square', test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_vertices"]) == 108
def test_randomNormTable():
N = 10; M = 10; dN = 6
l = 1; d = l*2e-3
np.random.seed(7)
vertices = np.random.normal(0,l,size=(N,2))
sim = Simulation(N, dN, M, l, d, vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_vertices"]) == 98
def test_random10Table():
N = 10; M = 10; dN = 6
l = 1; d = l*2e-3
np.random.seed(4)
vertices = np.random.random(size=(N,2))*2*l-l
sim = Simulation(N, dN, M, l, d, vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_vertices"]) == 108
def test_classStraight():
vertices = [np.array([0, 1])]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N, dN, M, l, d, vertices=vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
def test_classTwoToPlay():
vertices = [np.array([1, 1]), np.array([1, -1])]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N, dN, M, l, d, vertices=vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_segments_index"]) == 102
def test_classSimultaneous_growing(): #powstają dziwne ścieżki oraz pętle
vertices = [np.array([1, 1]), np.array([1, -1]), np.array([0.5, 0.5])]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N, dN, M, l, d, vertices=vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_segments_index"]) == 91
def test_random_mini():
l = 1
d = l * 2e-3
N = 10
M = 10
dN = 6
np.random.seed(4)
vertices = RPwC(l, 4, 0.4)
sim = Simulation(N, dN, M, l, d, vertices=vertices.T, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_vertices"]) == 82
def test_simultaneous_breach(): #jak wyżej
vertices = [
np.array([1, 1]),
np.array([1, -1]),
np.array([0.45, 0.5]),
np.array([0.45, -0.5])
]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N, dN, M, l, d, vertices=vertices, test=True)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["animation_segments_index"]) == 82
def test_classAsymetricLoop():
vertices = [np.array([0.5, 0])]
segments = [(0, 0), (0.75, 0)]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N,
dN,
M,
l,
d,
vertices=vertices,
test=True,
initial_segments=segments)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["segments"]) == 377
def test_classLackOfLoopsCentered():
vertices = [np.array([0, 1])]
segments = [(0, 0), (2e-3, 0), (-2e-3, 0), (2 * 2e-3, 0), (-2 * 2e-3, 0)]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N,
dN,
M,
l,
d,
vertices=vertices,
test=True,
initial_segments=segments)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["segments"]) == 505
def test_classLoopsVsMulti(
): #dwie ścieżki nachodzą na siebie. Nie powoduje błędów
vertices = [np.array([0.5, 0]), np.array([0.2, 0])]
segments = [(0, 0), (0.75, 0)]
N = len(vertices)
M = 10
dN = len(vertices)
l = 1
d = l * 2e-3
sim = Simulation(N,
dN,
M,
l,
d,
vertices=vertices,
test=True,
initial_segments=segments)
output = sim.run()
assert len(output["active_vertices"]) == 0
assert len(output["segments"]) > 300
def main():
# N_tab = [5000, 3500, 2000, 1000, 500, 200]
N_tab = [1500, 1200, 600, 300]
N_tab = N_tab[::-1]
M = 10
dN = 6
l = 1
d = l * 2e-3
alpha = 1.0
for j in tqdm(range(100)):
np.random.seed(j * 3)
name = f'seed{j*3}'
# array = RPwC(l, 9, alpha, 0.009).T
array = random_vertex(1, 'uni_square', max(N_tab))
for i in range(len(N_tab)):
N = N_tab[i]
folder_anim = f'../wyniki/symulacje/wyniki_z_kwietnia/uniform/n{N}/anim'
folder_data = f'../wyniki/symulacje/wyniki_z_kwietnia/uniform/n{N}/data'
sim = Simulation(N, dN, M, l, d, vertices=array)
animation_segments, animation_index, animation_vertices, lines = sim.run(
)
save_dict = {
'animation_segments': animation_segments,
'animation_index': animation_index,
'animation_vertices': animation_vertices,
'lines': lines
}
save_sim(folder_data, name, save_dict)
# s = 0
tuple_of_arguments = (animation_segments[:animation_index[-1]],
animation_vertices[-1], folder_anim,
f'{name}.png', None)
save_pic(tuple_of_arguments)
