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"""Tests for non-spatially referenced objects"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
import pytest
import vtk
import pyvista as pv
from pyvista import examples
if TYPE_CHECKING:
from pytest_mock import MockerFixture
try:
import pandas as pd
except ImportError:
pd = None
def test_table_init(tmpdir):
"""Save some delimited text to a file and read it"""
filename = str(tmpdir.mkdir('tmpdir').join('tmp.csv'))
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
# Create from 2D array
table = pv.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(ValueError): # noqa: PT011
pv.Table(np.random.default_rng().random((100, 2, 3)))
# Create from 1D array
table = pv.Table(arrays[:, 0])
assert table.n_rows == nr
assert table.n_columns == 1
assert len(table.row_arrays) == 1
assert np.allclose(arrays[:, 0], table[0])
# create from dictionary
array_dict = {}
for i in range(nc):
array_dict[f'foo{i}'] = arrays[:, i].copy()
table = pv.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[f'foo{i}'])
dataset = examples.load_hexbeam()
array_dict = dict(dataset.point_data)
table = pv.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([f'a{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 = pv.Table(reader.GetOutput(), deep=True)
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pv.Table)
table = pv.Table(reader.GetOutput(), deep=False)
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pv.Table)
# Test wrap
table = pv.wrap(reader.GetOutput())
assert isinstance(table, vtk.vtkTable)
assert isinstance(table, pv.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):
pv.Table('foo')
def test_table_row_arrays():
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
table = pv.Table()
for i in range(nc):
table[f'foo{i}'] = arrays[:, i]
assert table.n_columns == nc
assert table.n_rows == nr
for i in range(nc):
assert np.allclose(table[f'foo{i}'], arrays[:, i])
# Multi component
table = pv.Table()
table['multi'] = arrays
assert table.n_columns == 1
assert table.n_rows == nr
assert np.allclose(table[0], arrays)
assert np.allclose(table['multi'], arrays)
del table['multi']
assert table.n_columns == 0
dataset = examples.load_hexbeam()
array_dict = dataset.point_data
# Test dict methods
table = pv.Table()
table.update(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])
for i, array in enumerate(table.values()):
name = table.keys()[i]
assert np.allclose(dataset[name], array)
for name, array in table.items():
assert np.allclose(dataset[name], array)
n = table.n_arrays
array = table.pop(table.keys()[0])
assert isinstance(array, np.ndarray)
assert table.n_arrays == n - 1
array = table.get(table.keys()[0])
assert isinstance(array, np.ndarray)
assert table.n_arrays == n - 1
del table[table.keys()[0]]
assert table.n_arrays == n - 2
def test_table_row_np_bool():
n = 50
table = pv.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_row_uint8():
n = 50
table = pv.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_repr():
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
table = pv.Table(arrays)
text = table._repr_html_()
assert isinstance(text, str)
text = table.__repr__()
assert isinstance(text, str)
text = table.__str__()
assert isinstance(text, str)
@pytest.mark.skipif(pd is None, reason='Requires Pandas')
def test_table_pandas():
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
df = pd.DataFrame()
for i in range(nc):
df[f'foo{i}'] = arrays[:, i].copy()
table = pv.Table(df)
assert table.n_rows == nr
assert table.n_columns == nc
for i in range(nc):
assert np.allclose(table.row_arrays[f'foo{i}'], arrays[:, i])
assert df.equals(table.to_pandas())
def test_table_iter():
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
table = pv.Table(arrays)
for i, array in enumerate(table):
assert np.allclose(array, arrays[:, i])
@pytest.mark.parametrize('preference', ['row', None])
def test_get_data_range_table(preference):
nr, nc = 50, 3
arrays = np.random.default_rng().random((nr, nc))
table = pv.Table(arrays)
nanmin, nanmax = (
table.get_data_range(preference=preference) if preference else table.get_data_range()
)
assert nanmin == np.nanmin(arrays[:, 0])
assert nanmax == np.nanmax(arrays[:, 0])
def test_from_dict_raises(mocker: MockerFixture):
m = mocker.MagicMock()
m.ndim = 1
with pytest.raises(
ValueError, match='Dictionary must contain only NumPy arrays with maximum of 2D.'
):
pv.Table(dict(a=m))
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