How to use the pyvista.Table function in pyvista

def test_table_init(tmpdir):
    """Save some delimited text to a file and read it"""
    filename = str(tmpdir.mkdir("tmpdir").join('tmp.%s' % 'csv'))
    nr, nc = 50, 3
    arrays = np.random.rand(nr, nc)

    # Create from 2D array
    table = pyvista.Table(arrays)
    assert table.n_rows == nr
    assert table.n_columns == nc
    assert table.n_arrays == nc

    assert len(table.row_arrays) == nc
    for i in range(nc):
        assert np.allclose(arrays[:,i], table[i])


    # create from dictionary
    array_dict = {}
    for i in range(nc):
        array_dict['foo{}'.format(i)] = arrays[:, i].copy()
    table = pyvista.Table(array_dict)
    assert table.n_rows == nr
    assert table.n_columns == nc

def test_table_repr():
    nr, nc = 50, 3
    arrays = np.random.rand(nr, nc)
    table = pyvista.Table(arrays)
    text = table._repr_html_()
    assert isinstance(text, str)
    text = table.__repr__()
    assert isinstance(text, str)
    text = table.__str__()
    assert isinstance(text, str)

# Create from 2D array
    table = pyvista.Table(arrays)
    assert table.n_rows == nr
    assert table.n_columns == nc
    assert table.n_arrays == nc

    assert len(table.row_arrays) == nc
    for i in range(nc):
        assert np.allclose(arrays[:,i], table[i])


    # create from dictionary
    array_dict = {}
    for i in range(nc):
        array_dict['foo{}'.format(i)] = arrays[:, i].copy()
    table = pyvista.Table(array_dict)
    assert table.n_rows == nr
    assert table.n_columns == nc

    assert len(table.row_arrays) == nc
    for i in range(nc):
        assert np.allclose(arrays[:,i], table['foo{}'.format(i)])

    dataset = examples.load_hexbeam()
    array_dict = dataset.point_arrays
    table = pyvista.Table(array_dict)
    assert table.n_rows == dataset.n_points
    assert table.n_columns == len(array_dict)

    assert len(table.row_arrays) == len(array_dict)
    for name in table.keys():
        assert np.allclose(dataset[name], table[name])

def test_table_row_uint8():
    n = 50
    table = pyvista.Table()
    arr = np.zeros(n, np.uint8)
    table.row_arrays['arr'] = arr
    arr[:] = np.arange(n)
    assert np.allclose(table.row_arrays['arr'], np.arange(n))

def test_table_row_np_bool():
    n = 50
    table = pyvista.Table()
    bool_arr = np.zeros(n, np.bool)
    table.row_arrays['bool_arr'] = bool_arr
    bool_arr[:] = True
    assert table.row_arrays['bool_arr'].all()
    assert table._row_array('bool_arr').all()
    assert table._row_array('bool_arr').dtype == np.bool

def test_fail_plot_table():
    """Make sure tables cannot be plotted"""
    table = pyvista.Table(np.random.rand(50, 3))
    with pytest.raises(TypeError):
        pyvista.plot(table)
    with pytest.raises(TypeError):
        plotter = pyvista.Plotter(off_screen=OFF_SCREEN)
        plotter.add_mesh(table)

This example will demonstrate how to reshape an input table as though it were a 2D array.

This filter will take a `vtkTable` object and reshape it. This filter essentially treats `vtkTable`s as 2D matrices and reshapes them using `numpy.reshape` in a C contiguous manner. Unfortunately, data fields will be renamed arbitrarily because VTK data arrays require a name.

This example demos :class:`PVGeo.filters.ReshapeTable`

"""
import numpy as np
import pyvista as pv
import PVGeo
from PVGeo.filters import ReshapeTable

###############################################################################
# Create some input table
t0 = pv.Table()
# Populate the tables
arrs = [None, None, None]
n = 400
ncols = 2
nrows = int(n * len(arrs) / ncols)
titles = ('Array 0', 'Array 1', 'Array 2')
arrs[0] = np.random.random(n)
arrs[1] = np.random.random(n)
arrs[2] = np.random.random(n)

t0[titles[0]] = arrs[0]
t0[titles[1]] = arrs[1]
t0[titles[2]] = arrs[2]

###############################################################################
# Use the filter to reshape the table

def table_to_data_frame(table):
    """Converts a vtkTable to a pandas DataFrame"""
    if not isinstance(table, vtk.vtkTable):
        raise _helpers.PVGeoError('Input is not a vtkTable')
    if not isinstance(table, pv.Table):
        table = pv.Table(table)
    return table.to_pandas()

"""Wrap any given VTK data object to its appropriate PyVista data object.

    Other formats that are supported include:
    * 2D :class:`numpy.ndarray` of XYZ vertices
    * 3D :class:`numpy.ndarray` representing a volume. Values will be scalars.

    """
    wrappers = {
        'vtkUnstructuredGrid': pyvista.UnstructuredGrid,
        'vtkRectilinearGrid': pyvista.RectilinearGrid,
        'vtkStructuredGrid': pyvista.StructuredGrid,
        'vtkPolyData': pyvista.PolyData,
        'vtkImageData': pyvista.UniformGrid,
        'vtkStructuredPoints': pyvista.UniformGrid,
        'vtkMultiBlockDataSet': pyvista.MultiBlock,
        'vtkTable': pyvista.Table,
        # 'vtkParametricSpline': pyvista.Spline,
    }
    # Otherwise, we assume a VTK data object was passed
    if hasattr(vtkdataset, 'GetClassName'):
        key = vtkdataset.GetClassName()
    elif vtkdataset is None:
        return None
    elif isinstance(vtkdataset, np.ndarray):
        if vtkdataset.ndim == 1 and vtkdataset.shape[0] == 3:
            return pyvista.PolyData(vtkdataset)
        if vtkdataset.ndim > 1 and vtkdataset.ndim < 3 and vtkdataset.shape[1] == 3:
            return pyvista.PolyData(vtkdataset)
        elif vtkdataset.ndim == 3:
            mesh = pyvista.UniformGrid(vtkdataset.shape)
            mesh['values'] = vtkdataset.ravel(order='F')
            mesh.active_scalars_name = 'values'

This example will demonstrate how to to merge to `vtkTable` objects with the
same number of rows into a single `vtkTable`.

This example demos :class:`PVGeo.filters.CombineTables`

Please note that this example only works on version of PyVista>=0.22.0
"""
import numpy as np
import pyvista as pv
import PVGeo
from PVGeo.filters import CombineTables

###############################################################################
# Create some input tables
t0 = pv.Table()
t1 = pv.Table()

# Populate the tables
n = 100
titles = ('Array 0', 'Array 1', 'Array 2')
arr0 = np.random.random(n) # Table 0
arr1 = np.random.random(n) # Table 0
t0[titles[0]] = arr0
t0[titles[1]] = arr1
arr2 = np.random.random(n) # Table 1
t1[titles[2]] = arr2
arrs = [arr0, arr1, arr2]

###############################################################################
print(t0)

###############################################################################