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_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)
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_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)
def test_table_pandas():
nr, nc = 50, 3
arrays = np.random.rand(nr, nc)
df = pd.DataFrame()
for i in range(nc):
df['foo{}'.format(i)] = arrays[:, i].copy()
table = pyvista.Table(df)
assert table.n_rows == nr
assert table.n_columns == nc
for i in range(nc):
assert np.allclose(table.row_arrays['foo{}'.format(i)], arrays[:, i])
assert df.equals(table.to_pandas())
def test_scalars_dict_update():
mesh = examples.load_uniform()
n = len(mesh.point_arrays)
arrays = {
'foo': np.arange(mesh.n_points),
'rand': np.random.random(mesh.n_points)
}
mesh.point_arrays.update(arrays)
assert 'foo' in mesh.array_names
assert 'rand' in mesh.array_names
assert len(mesh.point_arrays) == n + 2
# Test update from Table
table = pyvista.Table(arrays)
mesh = examples.load_uniform()
mesh.point_arrays.update(table)
assert 'foo' in mesh.array_names
assert 'rand' in mesh.array_names
assert len(mesh.point_arrays) == n + 2
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]
###############################################################################
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)
def test_table_iter():
nr, nc = 50, 3
arrays = np.random.rand(nr, nc)
table = pyvista.Table(arrays)
for i, array in enumerate(table):
assert np.allclose(array, arrays[:, i])
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])
with pytest.raises(AssertionError):
pyvista.Table(np.random.rand(100))
with pytest.raises(AssertionError):
pyvista.Table(np.random.rand(100, 2, 3))
# 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])
# Create from vtkTable object
h = '\t'.join(['a{}'.format(i) for i in range(nc)])
np.savetxt(filename, arrays, delimiter='\t', header=h, comments='')
reader = vtk.vtkDelimitedTextReader()
reader.SetFileName(filename)
reader.DetectNumericColumnsOn()
reader.SetFieldDelimiterCharacters('\t')
reader.SetHaveHeaders(True)
reader.Update()
# Test init
table = pyvista.Table(reader.GetOutput(), deep=True)
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pyvista.Table)
table = pyvista.Table(reader.GetOutput(), deep=False)
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pyvista.Table)
# Test wrap
table = pyvista.wrap(reader.GetOutput())
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pyvista.Table)
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[i])
with pytest.raises(TypeError):
pyvista.Table("foo")
return
