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testndmapping.py
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testndmapping.py
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from collections import OrderedDict
from holoviews.core import Dimension
from holoviews.core.ndmapping import MultiDimensionalMapping
from holoviews.element.comparison import ComparisonTestCase
from holoviews import HoloMap, Dataset
import numpy as np
class DimensionTest(ComparisonTestCase):
def test_dimension_init(self):
Dimension('Test dimension')
Dimension('Test dimension', cyclic=True)
Dimension('Test dimension', cyclic=True, type=int)
Dimension('Test dimension', cyclic=True, type=int, unit='Twilight zones')
def test_dimension_call(self):
dim1 = Dimension('Test dimension')
dim2 = dim1(cyclic=True)
self.assertEqual(dim2.cyclic,True)
dim3 = dim1('New test dimension', unit='scovilles')
self.assertEqual(dim3.name, 'New test dimension')
self.assertEqual(dim3.unit, 'scovilles')
def test_dimension_pprint(self):
dim = Dimension('Test dimension', cyclic=True, type=float, unit='Twilight zones')
self.assertEqual(dim.pprint_value_string(3.23451), 'Test dimension: 3.2345 Twilight zones')
self.assertEqual(dim.pprint_value_string(4.23441), 'Test dimension: 4.2344 Twilight zones')
class NdIndexableMappingTest(ComparisonTestCase):
def setUp(self):
self.init_items_1D_list = [(1, 'a'), (5, 'b')]
self.init_item_list = [((1, 2.0), 'a'), ((5, 3.0), 'b')]
self.init_item_odict = OrderedDict([((1, 2.0), 'a'), ((5, 3.0), 'b')])
self.dimension_labels = ['intdim', 'floatdim']
self.dim1 = Dimension('intdim', type=int)
self.dim2 = Dimension('floatdim', type=float)
self.time_dimension = Dimension
def test_idxmapping_init(self):
MultiDimensionalMapping()
def test_idxmapping_init_item_odict(self):
MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, self.dim2])
def test_idxmapping_init_item_list(self):
MultiDimensionalMapping(self.init_item_list, kdims=[self.dim1, self.dim2])
def test_idxmapping_init_dimstr(self):
MultiDimensionalMapping(self.init_item_odict, kdims=self.dimension_labels)
def test_idxmapping_init_dimensions(self):
MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, self.dim2])
def test_idxmapping_dimension_labels(self):
idxmap = MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, 'floatdim'])
self.assertEqual([d.name for d in idxmap.kdims], self.dimension_labels)
def test_idxmapping_ndims(self):
dims = [self.dim1, self.dim2, 'strdim']
idxmap = MultiDimensionalMapping(kdims=dims)
self.assertEqual(idxmap.ndims, len(dims))
def test_idxmapping_key_len_check(self):
try:
MultiDimensionalMapping(initial_items=self.init_item_odict)
raise AssertionError('Invalid key length check failed.')
except KeyError:
pass
def test_idxmapping_nested_update(self):
data1 = [(0, 'a'), (1, 'b')]
data2 = [(2, 'c'), (3, 'd')]
data3 = [(2, 'e'), (3, 'f')]
ndmap1 = MultiDimensionalMapping(data1, kdims=[self.dim1])
ndmap2 = MultiDimensionalMapping(data2, kdims=[self.dim1])
ndmap3 = MultiDimensionalMapping(data3, kdims=[self.dim1])
ndmap_list = [(0.5, ndmap1), (1.5, ndmap2)]
nested_ndmap = MultiDimensionalMapping(ndmap_list, kdims=[self.dim2])
nested_ndmap[(0.5,)].update(dict([(0, 'c'), (1, 'd')]))
self.assertEquals(list(nested_ndmap[0.5].values()), ['c', 'd'])
nested_ndmap[1.5] = ndmap3
self.assertEquals(list(nested_ndmap[1.5].values()), ['e', 'f'])
def test_idxmapping_unsorted(self):
data = [('B', 1), ('C', 2), ('A', 3)]
ndmap = MultiDimensionalMapping(data, sort=False)
self.assertEquals(ndmap.keys(), ['B', 'C', 'A'])
def test_idxmapping_unsorted_clone(self):
data = [('B', 1), ('C', 2), ('A', 3)]
ndmap = MultiDimensionalMapping(data, sort=False).clone()
self.assertEquals(ndmap.keys(), ['B', 'C', 'A'])
def test_idxmapping_groupby_unsorted(self):
data = [(('B', 2), 1), (('C', 2), 2), (('A', 1), 3)]
grouped = MultiDimensionalMapping(data, sort=False, kdims=['X', 'Y']).groupby('Y')
self.assertEquals(grouped.keys(), [1, 2])
self.assertEquals(grouped.values()[0].keys(), ['A'])
self.assertEquals(grouped.last.keys(), ['B', 'C'])
def test_idxmapping_reindex(self):
data = [((0, 0.5), 'a'), ((1, 0.5), 'b')]
ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2])
reduced_dims = ['intdim']
reduced_ndmap = ndmap.reindex(reduced_dims)
self.assertEqual([d.name for d in reduced_ndmap.kdims], reduced_dims)
def test_idxmapping_redim(self):
data = [((0, 0.5), 'a'), ((1, 0.5), 'b')]
ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2])
redimmed = ndmap.redim(intdim='Integer')
self.assertEqual(redimmed.kdims, [Dimension('Integer', type=int),
Dimension('floatdim', type=float)])
def test_idxmapping_redim_range_aux(self):
data = [((0, 0.5), 'a'), ((1, 0.5), 'b')]
ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2])
redimmed = ndmap.redim.range(intdim=(-9,9))
self.assertEqual(redimmed.kdims, [Dimension('intdim', type=int, range=(-9,9)),
Dimension('floatdim', type=float)])
def test_idxmapping_redim_type_aux(self):
data = [((0, 0.5), 'a'), ((1, 0.5), 'b')]
ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2])
redimmed = ndmap.redim.type(intdim=str)
self.assertEqual(redimmed.kdims, [Dimension('intdim', type=str),
Dimension('floatdim', type=float)])
def test_idxmapping_add_dimension(self):
ndmap = MultiDimensionalMapping(self.init_items_1D_list, kdims=[self.dim1])
ndmap2d = ndmap.add_dimension(self.dim2, 0, 0.5)
self.assertEqual(list(ndmap2d.keys()), [(0.5, 1), (0.5, 5)])
self.assertEqual(ndmap2d.kdims, [self.dim2, self.dim1])
def test_idxmapping_apply_key_type(self):
data = dict([(0.5, 'a'), (1.5, 'b')])
ndmap = MultiDimensionalMapping(data, kdims=[self.dim1])
self.assertEqual(list(ndmap.keys()), [0, 1])
def test_setitem_nested_1(self):
nested1 = MultiDimensionalMapping([('B', 1)])
ndmap = MultiDimensionalMapping([('A', nested1)])
nested2 = MultiDimensionalMapping([('B', 2)])
ndmap['A'] = nested2
self.assertEqual(ndmap['A'], nested2)
def test_setitem_nested_2(self):
nested1 = MultiDimensionalMapping([('B', 1)])
ndmap = MultiDimensionalMapping([('A', nested1)])
nested2 = MultiDimensionalMapping([('C', 2)])
nested_clone = nested1.clone()
nested_clone.update(nested2)
ndmap.update({'A': nested2})
self.assertEqual(ndmap['A'].data, nested_clone.data)
class HoloMapTest(ComparisonTestCase):
def setUp(self):
self.xs = range(11)
self.y_ints = [i*2 for i in range(11)]
self.ys = np.linspace(0, 1, 11)
self.columns = Dataset(np.column_stack([self.xs, self.y_ints]),
kdims=['x'], vdims=['y'])
def test_holomap_redim(self):
hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z'])
redimmed = hmap.redim(x='Time')
self.assertEqual(redimmed.dimensions('all', True),
['z', 'Time', 'y'])
def test_holomap_redim_nested(self):
hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z'])
redimmed = hmap.redim(x='Time', z='Magnitude')
self.assertEqual(redimmed.dimensions('all', True),
['Magnitude', 'Time', 'y'])
def test_columns_collapse_heterogeneous(self):
collapsed = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
expected = Dataset({'x':self.xs, 'y': self.ys * 4.5}, kdims=['x'], vdims=['y'])
self.compare_dataset(collapsed, expected)
def test_columns_sample_homogeneous(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 10, 20]))
def test_holomap_map_with_none(self):
hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z'])
mapped = hmap.map(lambda x: x if x.range(1)[1] > 0 else None, Dataset)
self.assertEqual(hmap[1:10], mapped)