How to use the vaex.dataset.DatasetArrays function in vaex

def test_export_concat(self):
		x1 = np.arange(1000, dtype=np.float32)
		x2 = np.arange(100, dtype=np.float32)
		self.x_concat = np.concatenate((x1, x2))

		dataset1 = vx.dataset.DatasetArrays("dataset1")
		dataset2 = vx.dataset.DatasetArrays("dataset2")
		dataset1.add_column("x", x1)
		dataset2.add_column("x", x2)

		self.dataset_concat = vx.dataset.DatasetConcatenated([dataset1, dataset2], name="dataset_concat")

		path_hdf5 = tempfile.mktemp(".hdf5")
		self.dataset_concat.export_hdf5(path_hdf5)

def test_virtual_columns_spherical(self):
		alpha = np.array([0.])
		delta = np.array([0.])
		distance = np.array([1.])
		dataset = vx.dataset.DatasetArrays()
		dataset.add_column("alpha", alpha)
		dataset.add_column("delta", delta)
		dataset.add_column("distance", distance)

		dataset.add_virtual_columns_spherical_to_cartesian("alpha", "delta", "distance", "x", "y", "z", radians=False)

		subspace = dataset("x", "y", "z")
		x, y, z = subspace.sum()

		self.assertAlmostEqual(x, 1)
		self.assertAlmostEqual(y, 0)
		self.assertAlmostEqual(z, 0)


		dataset.add_virtual_columns_cartesian_to_spherical("x", "y", "z", "theta", "phi", "r", radians=False)
		theta, phi, r = dataset("theta", "phi", "r").row(0)

ar1 = np.zeros((10, 2))
		ar2 = np.zeros((20))
		arrays = [ar1, ar2]
		N = len(arrays)
		datasets = [vx.dataset.DatasetArrays("dataset1") for i in range(N)]
		for dataset, array in zip(datasets, arrays):
			dataset.add_column("x", array)
		with self.assertRaises(ValueError):
			dataset_concat = vx.dataset.DatasetConcatenated(datasets, name="dataset_concat")


		ar1 = np.zeros((10))
		ar2 = np.zeros((20))
		arrays = [ar1, ar2]
		N = len(arrays)
		datasets = [vx.dataset.DatasetArrays("dataset1") for i in range(N)]
		for dataset, array in zip(datasets, arrays):
			dataset.add_column("x", array)
		dataset_concat = vx.dataset.DatasetConcatenated(datasets, name="dataset_concat")


		dataset_concat1 = vx.dataset.DatasetConcatenated(datasets, name="dataset_concat")
		dataset_concat2 = vx.dataset.DatasetConcatenated(datasets, name="dataset_concat")
		self.assertEqual(len(dataset_concat1.concat(dataset_concat2).datasets), 4)
		self.assertEqual(len(dataset_concat1.concat(datasets[0]).datasets), 3)
		self.assertEqual(len(datasets[0].concat(dataset_concat1).datasets), 3)
		self.assertEqual(len(datasets[0].concat(datasets[0]).datasets), 2)

def setUp(self):
		self.dataset = vaex.dataset.DatasetArrays("dataset")

		self.x = x = np.arange(10)
		self.y = y = x ** 2
		self.dataset.add_column("x", x)
		self.dataset.add_column("y", y)
		self.dataset.set_variable("t", 1.)
		self.dataset.add_virtual_column("z", "x+t*y")

		self.app = vx.ui.main.VaexApp()

def create_base_ds():
    dataset = vaex.dataset.DatasetArrays("dataset")
    x = np.arange(-2, 40, dtype=">f8").reshape((-1,21)).T.copy()[:,0]
    y = y = x ** 2
    ints = np.arange(-2,19, dtype="i8")
    ints[0] = 2**62+1
    ints[1] = -2**62+1
    ints[2] = -2**62-1
    ints[0+10] = 2**62+1
    ints[1+10] = -2**62+1
    ints[2+10] = -2**62-1
    dataset.add_column("x", x)
    dataset.add_column("y", y)
    # m = x.copy()
    m = np.arange(-2, 40, dtype=">f8").reshape((-1,21)).T.copy()[:,0]
    ma_value = 77777
    m[-1+10] = ma_value
    m[-1+20] = ma_value

#self.jobsManager = dataset.JobsManager()

		x = np.array([0., 1])
		y = np.array([-1., 1])
		self.datasetxy = vx.dataset.DatasetArrays("datasetxy")
		self.datasetxy.add_column("x", x)
		self.datasetxy.add_column("y", y)

		x1 = np.array([1., 3])
		x2 = np.array([2., 3, 4,])
		x3 = np.array([5.])
		self.x_concat = np.concatenate((x1, x2, x3))

		dataset1 = vx.dataset.DatasetArrays("dataset1")
		dataset2 = vx.dataset.DatasetArrays("dataset2")
		dataset3 = vx.dataset.DatasetArrays("dataset3")
		dataset1.add_column("x", x1)
		dataset2.add_column("x", x2)
		dataset3.add_column("x", x3)
		dataset3.add_column("y", x3**2)
		self.dataset_concat = vx.dataset.DatasetConcatenated([dataset1, dataset2, dataset3], name="dataset_concat")

		self.dataset_concat_dup = vx.dataset.DatasetConcatenated([self.dataset, self.dataset, self.dataset], name="dataset_concat_dup")

#self.jobsManager = dataset.JobsManager()

		x = np.array([0., 1])
		y = np.array([-1., 1])
		self.datasetxy = vx.dataset.DatasetArrays("datasetxy")
		self.datasetxy.add_column("x", x)
		self.datasetxy.add_column("y", y)

		x1 = np.array([1., 3])
		x2 = np.array([2., 3, 4,])
		x3 = np.array([5.])
		self.x_concat = np.concatenate((x1, x2, x3))

		dataset1 = vx.dataset.DatasetArrays("dataset1")
		dataset2 = vx.dataset.DatasetArrays("dataset2")
		dataset3 = vx.dataset.DatasetArrays("dataset3")
		dataset1.add_column("x", x1)
		dataset2.add_column("x", x2)
		dataset3.add_column("x", x3)
		dataset3.add_column("y", x3**2)
		self.dataset_concat = vx.dataset.DatasetConcatenated([dataset1, dataset2, dataset3], name="dataset_concat")

		self.dataset_concat_dup = vx.dataset.DatasetConcatenated([self.dataset, self.dataset, self.dataset], name="dataset_concat_dup")

for d in range(dimension):
			vaex.vaexfast.soneira_peebles(array[d], 0, 1, L[d], eta, max_level)
		for d, name in zip(list(range(dimension)), "x y z w v u".split()):
			self.add_column(name, array[d])
		if 0:
			order = np.zeros(N, dtype=np.int64)
			vaex.vaexfast.shuffled_sequence(order);
			for i, name in zip(list(range(dimension)), "x y z w v u".split()):
				#np.take(array[i], order, out=array[i])
				reorder(array[i], array[-1], order)
				self.addColumn(name, array=array[i])

dataset_type_map["soneira-peebles"] = SoneiraPeebles


class Zeldovich(DatasetArrays):
	def __init__(self, dim=2, N=256, n=-2.5, t=None, seed=None, scale=1, name="zeldovich approximation"):
		super(Zeldovich, self).__init__(name=name)

		if seed is not None:
			np.random.seed(seed)
		#sys.exit(0)
		shape = (N,) * dim
		A = np.random.normal(0.0, 1.0, shape)
		F = np.fft.fftn(A)
		K = np.fft.fftfreq(N, 1./(2*np.pi))[np.indices(shape)]
		k = (K**2).sum(axis=0)
		k_max = np.pi
		F *= np.where(np.sqrt(k) > k_max, 0, np.sqrt(k**n) * np.exp(-k*4.0))
		F.flat[0] = 0
		#pylab.imshow(np.where(sqrt(k) > k_max, 0, np.sqrt(k**-2)), interpolation='nearest')
		grf = np.fft.ifftn(F).real

if type.kind in ["i"]:
						masked_array.data[masked_array.mask] = 0
				self.add_column(clean_name, self.table[name].data)
			if type.kind in ["SU"]:
				self.add_column(clean_name, self.table[name].data)

		#dataset.samp_id = table_id
		#self.list.addDataset(dataset)
		#return dataset

	def read_table(self):
		self.table = astropy.table.Table.read(self.filename, format=self.format, **kwargs)

import astropy.io.votable
import string
class VOTable(DatasetArrays):
	def __init__(self, filename):
		DatasetArrays.__init__(self, filename)
		self.filename = filename
		self.path = filename
		votable = astropy.io.votable.parse(self.filename)

		self.first_table = votable.get_first_table()
		self.description = self.first_table.description

		for field in self.first_table.fields:
			name = field.name
			data = self.first_table.array[name]
			type = self.first_table.array[name].dtype
			clean_name = _python_save_name(name, self.columns.keys())
			if field.ucd:
				self.ucds[clean_name] = field.ucd