class TestGpuGridGaussianCompute_64x64(TestCase):
def setUp(self):
self.grid_size = getDimensions(self)
a = np.array([[3, 3], [5, 5], [3, 5], [5, 3], [4, 4]]).astype(np.float32).reshape(5, 2)
self.sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (3, 5, 3, 5))
self.new = GpuGridGaussian(test_tile, self.grid_size, self.sigma)
self.new.compute_grid()
def doCleanups(self):
self.new.clean_cuda()
def test_shape(self):
print self.new.get_grid_data()
new_data = self.new.get_grid_data()
new_data.shape == self.grid_size
def test_max_point_value(self):
g = gaussian_normal_sigma(self.sigma)
new_data = self.new.get_grid_data()
diff = math.fabs(new_data.max() - g)
assert diff < .00001
def test_min_point_value(self):
new_data = self.new.get_grid_data()
assert new_data.min() < .001
class TestGpuGridGaussianStudyWindowSize(TestCase):
def setUp(self):
a = np.array([[750, 5], [0, 0], [1500, 0], [1500, 10], [0, 10], [237, 2], [237, 2], [1234, 7], [400, 3]]).astype(np.float32).reshape(9, 2)
self.grid_size = (1024, 1024)
self.sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1500, 0, 10))
self.new = GpuGridGaussian(test_tile, self.grid_size, self.sigma)
self.new.compute_grid()
def doCleanups(self):
self.new.clean_cuda()
def test_shape(self):
new_data = self.new.get_grid_data()
assert new_data.shape == self.grid_size
def test_max_point_value(self):
g = gaussian_normal_sigma(self.sigma) * 2.425
new_data = self.new.get_grid_data()
diff = math.fabs(new_data.max() - g)
print diff
assert diff < .1
def test_min_point_value(self):
new_data = self.new.get_grid_data()
assert new_data.min() < .001
class TestRandomGpuGridGaussianComputeComplex_1024x1024(TestCase):
def setUp(self):
self.grid_size = getDimensions(self)
a = np.array(np.random.random(400)).astype(np.float32).reshape(200, 2)
self.sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.new = GpuGridGaussian(test_tile, self.grid_size, self.sigma)
self.new.compute_grid()
def doCleanups(self):
self.new.clean_cuda()
def test_shape(self):
new_data = self.new.get_grid_data()
assert new_data.shape == self.grid_size
def test_max_greater_than(self):
new_data = self.new.get_grid_data()
assert new_data.max() > gaussian_normal_sigma(self.sigma)
def test_min_greater_than(self):
new_data = self.new.get_grid_data()
assert new_data.min() > gaussian_normal_sigma(self.sigma)
class TestGpuGridGaussianCompute_32x32(TestCase):
def setUp(self):
self.size = getDimensions(self)
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.new = GpuGridGaussian(test_tile, self.size, sigma)
self.new.compute_grid()
self.old = GpuGaussianOld(a, (0, 1, 0, 1), self.size, sigma)
self.old.compute_grid()
def doCleanups(self):
self.new.clean_cuda()
self.old.clean_cuda()
def test_equiv_arrays(self):
new_data = self.new.get_grid_data()
old_data = self.old.get_grid_data()
assert np.array_equiv(new_data, old_data)
def test_equal_arrays(self):
new_data = self.new.get_grid_data()
old_data = self.old.get_grid_data()
assert np.array_equal(new_data, old_data)
def test_size(self):
new_data = self.new.get_grid_data()
assert new_data.shape == self.size
class TestGpuGridGaussianCompute_2x2(TestCase):
def setUp(self):
self.size = getDimensions(self)
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.new = GpuGridGaussian(test_tile, self.size, sigma)
self.new.compute_grid()
self.old = GpuGaussianOld(test_tile.get_Data()[0].getDataSet(), test_tile.get_View().view(), self.size, sigma)
self.old.compute_grid()
def doCleanups(self):
self.new.clean_cuda()
self.old.clean_cuda()
def test_equiv_arrays(self):
new_data = self.new.get_grid_data()
old_data = self.old.get_grid_data()
assert np.array_equiv(new_data, old_data)
def test_equal_arrays(self):
new_data = self.new.get_grid_data()
old_data = self.old.get_grid_data()
assert np.array_equal(new_data, old_data)
def test_size(self):
new_data = self.new.get_grid_data()
assert new_data.shape == self.size
old_data = self.old.get_grid_data()
assert old_data.shape == self.size
def setUp(self):
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.g = GpuGridGaussian(test_tile, (32, 32), .1)
def setUp(self):
self.size = getDimensions(self)
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.new = GpuGridGaussian(test_tile, self.size, sigma)
self.new.compute_grid()
self.old = GpuGaussianOld(a, (0, 1, 0, 1), self.size, sigma)
self.old.compute_grid()
def setUp(self):
self.size = getDimensions(self)
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
self.sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.new = GpuGridGaussian(test_tile, self.size, self.sigma)
self.new.compute_grid()
def setUp(self):
a = np.array([[750, 5], [0, 0], [1500, 0], [1500, 10], [0, 10], [237, 2], [237, 2], [1234, 7], [400, 3]]).astype(np.float32).reshape(9, 2)
self.grid_size = (1024, 1024)
self.sigma = .1
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1500, 0, 10))
self.new = GpuGridGaussian(test_tile, self.grid_size, self.sigma)
self.new.compute_grid()
def setUp(self):
self.grid_size = getDimensions(self)
a = np.array([[3, 3], [5, 5], [3, 5], [5, 3], [4, 4]]).astype(np.float32).reshape(5, 2)
sigma = .07
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (3, 5, 3, 5))
self.new = GpuGridGaussian(test_tile, self.grid_size, sigma)
self.new.compute_grid()
self.old = GpuGaussianOld(test_tile.get_Data()[0].getDataSet(), test_tile.get_View().view(), self.grid_size, sigma)
self.old.compute_grid()
class TestGpuGridGaussianNoCompute(TestCase):
def setUp(self):
a = np.array([[0, 0], [1, 1], [0, 1], [1, 0], [.5, .5]]).astype(np.float32).reshape(5, 2)
test_set = CellTypeDataSet("", a, rgb=(0, .5, .5))
# These are the individual tiles that will have information about the dataset
test_tile = ViewTile((test_set,), (0, 1, 0, 1))
self.g = GpuGridGaussian(test_tile, (32, 32), .1)
def doCleanups(self):
self.g.clean_cuda()
def test_array_equiv(self):
image = self.g.get_grid_data()
compare = np.zeros_like(image)
assert np.array_equiv(image, compare)
def test_array_equal(self):
image = self.g.get_grid_data()
compare = np.zeros_like(image)
assert np.array_equal(image, compare)
print str(gc_data.shape[0]) + " Granule cell spikes, and"
print str(bc_data.shape[0]) + " Basket cell spikes."
mea_set = CellTypeDataSet("Cell # \n(MEA 0 - 6599, ", mea_data, rgb=(0, 0, 0.5))
lea_set = CellTypeDataSet("\nLEA 660 - 11199)", lea_data, rgb=(0.5, 0, 0))
gc_set = CellTypeDataSet("GC Cell Septotemporal Position (mm)", gc_data, rgb=(0, 0.5, 0.5))
bc_set = CellTypeDataSet("Basket Cells", bc_data, rgb=(0.5, 0, 0.5))
mea_lea_tile = ViewTile((mea_set, lea_set), (tstart, tstop, 0, sum(numCells[0:2])))
gc_tile = ViewTile((gc_set,), (tstart, tstop, 0, 10))
bc_tile = ViewTile((bc_set,), (tstart, tstop, 0, 10))
# Values are normalized from [0-1500, 0-n] to grid of [0-1, 0-1]. Sigma choice should be between 0-1
sigma = 0.001
gc = GpuGridGaussian(gc_tile, (1024, 1024), sigma)
gc.compute_grid()
gc.save_image("tmp/gc_data_gaussian_sigma=" + str(sigma) + ".bin")
gc.show_image()
gc.clean_cuda()
bc = GpuGridGaussian(bc_tile, (1024, 1024), sigma)
bc.compute_grid()
bc.save_image("tmp/bc_data_gaussian_sigma=" + str(sigma) + ".bin")
bc.show_image()
bc.clean_cuda()
mea_lea = GpuGridGaussian(mea_lea_tile, (1024, 1024), sigma)
mea_lea.compute_grid()
mea_lea.save_image("tmp/mea_lea_data_gaussian_sigma=" + str(sigma) + ".bin")
mea_lea.show_image()