def test_input(self):
def pairwise_test(points_1, points_2):
pass
rate = ((), )
bounds = 1
pairwisef = pairwise_test
mix_prob = 1
iterations = 1
burn_in = 1
with self.assertRaises(TypeError):
out = PyPgen.MaPP(rate=rate,
bounds=bounds,
pairwisef=pairwisef,
mix_prob=mix_prob,
iterations=iterations,
burn_in=burn_in)
rate = 1
with self.assertRaises(TypeError):
out = PyPgen.MaPP(rate=rate,
bounds=bounds,
pairwisef=pairwisef,
mix_prob=mix_prob,
iterations=iterations,
burn_in=burn_in)
def test_basic(self):
rate = 5
bounds = [1, 10]
out = PyPgen.HPP_temporal(rate=rate, bounds=bounds)
self.assertTrue(np.amin(out) > bounds[0])
self.assertTrue(np.amax(out) < bounds[1])
plt.title(
str(len(out)) + " samples of a temporal HPP of rate " + str(rate) +
" in 10s")
plt.plot(out, np.arange(len(out)), ".k")
plt.xlabel("Time [s]")
plt.ylabel("Count")
plt.savefig(os.path.join(path2res, "HPP_temporal_test.png"),
bbox_inches='tight',
dpi=100,
edgecolor='w')
# plt.show()
plt.close("all")
realizations = 3
rate = 5
bounds = [1, 10]
out = PyPgen.HPP_temporal(rate=rate,
bounds=bounds,
realizations=realizations)
self.assertTrue(len(out) == realizations)
for i in np.arange(realizations):
self.assertTrue(np.amin(out[i]) > bounds[0])
self.assertTrue(np.amax(out[i]) < bounds[1])
def test_typecheck(self):
with self.assertRaises(TypeError):
PyPgen.NHPP(
((), ),
((), ),
((), ),
)
def test_2Dscalarsampleplot(self):
rate = 2
realizations = 1
bounds = [[0, 10], [0, 10]]
dimensions = len(bounds)
out = PyPgen.HPP_rate(rate=rate,
bounds=bounds,
realizations=realizations)
self.assertTrue(np.amin(out[:, 0]) > bounds[0][0])
self.assertTrue(np.amax(out[:, 0]) < bounds[0][1])
self.assertTrue(np.amin(out[:, 1]) > bounds[1][0])
self.assertTrue(np.amax(out[:, 1]) < bounds[1][1])
plt.title(
str(len(out)) + " samples of a HPP of rate " + str(rate) +
" in 10 unit square")
plt.scatter(out[:, 0], out[:, 1], c="k")
plt.xlabel("X")
plt.ylabel("Y")
plt.savefig(os.path.join(path2res, "HPP_rate_2Dtest.png"),
bbox_inches='tight',
dpi=100,
edgecolor='w')
# plt.show()
plt.close("all")
def test_realizations_samples(self):
samples = [20, 10]
realizations = 1
bounds = [[0, 10], [0, 10]]
dimensions = len(bounds)
with self.assertRaises(ValueError):
out = PyPgen.HPP_samples(samples=samples,
bounds=bounds,
realizations=realizations)
realizations = 3
bounds = [[0, 10], [0, 10]]
dimensions = len(bounds)
with self.assertRaises(ValueError):
out = PyPgen.HPP_samples(samples=samples,
bounds=bounds,
realizations=realizations)
def test_halfsinus3D(self):
phase_1 = 0
phase_2 = 0
phase_3 = 0
period_1 = 1
period_2 = 2
period_3 = 3
amplitude = 1
offset = 1
rate_max = amplitude + amplitude + amplitude + offset
dimensions = 3
def rate_halfsinus3D(x_1, x_2, x_3):
sins = np.sin(2.*np.pi*x_1/period_1 + phase_1) + \
np.sin(2.*np.pi*x_2/period_2 + phase_2) + \
np.sin(2.*np.pi*x_3/period_3 + phase_3)
out = amplitude * (sins * (sins > 0)) + offset
return (out)
bounds = [[1, 10], [10, 20], [20, 21]]
out = PyPgen.NHPP(rate_halfsinus3D, rate_max, bounds)
self.assertTrue(out.shape[-1] == dimensions)
self.assertTrue(len(out.shape) > 1)
self.assertTrue(np.amin(out[:, 0]) > bounds[0][0])
self.assertTrue(np.amax(out[:, 0]) < bounds[0][1])
self.assertTrue(np.amin(out[:, 1]) > bounds[1][0])
self.assertTrue(np.amax(out[:, 1]) < bounds[1][1])
self.assertTrue(np.amin(out[:, 2]) > bounds[2][0])
self.assertTrue(np.amax(out[:, 2]) < bounds[2][1])
def test_2Dscalarrealizations(self):
rate = 2
realizations = 3
bounds = [[0, 10], [10, 0]]
dimensions = len(bounds)
out = PyPgen.HPP_rate(rate=rate,
bounds=bounds,
realizations=realizations)
self.assertTrue(len(out) == realizations)
for i in np.arange(realizations):
self.assertTrue(np.amin(out[i][:, 0]) > bounds[0][0])
self.assertTrue(np.amax(out[i][:, 0]) < bounds[0][1])
self.assertTrue(np.amin(out[i][:, 1]) > bounds[1][1])
self.assertTrue(np.amax(out[i][:, 1]) < bounds[1][0])
def test_3D(self):
samples = [3, 4, 5]
realizations = 3
bounds = [[0, 10], [0, 10]]
dimensions = len(bounds)
out = PyPgen.HPP_samples(samples=samples,
bounds=bounds,
realizations=realizations)
self.assertTrue(len(out) == realizations)
for i in np.arange(realizations):
self.assertTrue(np.amin(out[i][:, 0]) > bounds[0][0])
self.assertTrue(np.amax(out[i][:, 0]) < bounds[0][1])
self.assertTrue(np.amin(out[i][:, 1]) > bounds[1][0])
self.assertTrue(np.amax(out[i][:, 1]) < bounds[1][1])
self.assertTrue(len(out[i]) == samples[i])
def test_basic(self):
shape = 3
scale = 1
bounds = [1, 10]
dimensions = 1
realizations = 3
def info(size):
return (np.random.gamma(shape=shape, scale=scale, size=size))
out = PyPgen.MPP(info=info, bounds=bounds, realizations=realizations)
self.assertTrue(len(out) == realizations)
for i in np.arange(realizations):
self.assertTrue(np.amin(out[i][:, 0]) > bounds[0])
self.assertTrue(np.amax(out[i][:, 0]) < bounds[1])
self.assertTrue(out[i].shape[-1] == dimensions)
def test_halfsinus1D(self):
phase = 0
period = 2
amplitude = 3
offset = 1
rate_max = amplitude + offset
dimensions = 1
def rate_halfsinus1D(time):
sins = np.sin(2. * np.pi * time / period + phase)
out = amplitude * (sins * (sins > 0)) + offset
return (out)
bounds = [1, 10]
out = PyPgen.NHPP(rate_halfsinus1D, rate_max, bounds)
self.assertTrue(out.shape[-1] == dimensions)
self.assertTrue(len(out.shape) > 1)
self.assertTrue(np.amin(out) > bounds[0])
self.assertTrue(np.amax(out) < bounds[1])