def test_columns_sort_vdim_hm(self):
xs_2 = np.array(self.xs_2)
columns = Columns(np.column_stack([self.xs, -xs_2]),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(np.column_stack([self.xs[::-1], -xs_2[::-1]]),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_2D_aggregate_partial_hm(self):
array = np.random.rand(11, 11)
columns = Columns({'x':self.xs, 'y':self.y_ints, 'z': array},
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns({'x':self.xs, 'z': np.mean(array, axis=1)},
kdims=['x'], vdims=['z']))
def test_columns_heterogeneous_aggregate(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
aggregated = Columns(pd.DataFrame([('F', 10., 0.8), ('M', 16.5, 0.7)],
columns=['Gender']+self.vdims),
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated)
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 = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'],
vdims=['y'])
def init_data(self):
self.xs = range(11)
self.xs_2 = [el**2 for el in self.xs]
self.y_ints = [i * 2 for i in range(11)]
self.columns_hm = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'],
vdims=['y'])
def test_columns_sort_heterogeneous_string(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
keys = [('F',12), ('M',10), ('M',16)]
values = [(10, 0.8), (15, 0.8), (18, 0.6)]
columns_sorted = Columns(zip(keys, values),
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)
def test_columns_1D_reduce_hm(self):
columns = Columns({
'x': self.xs,
'y': self.y_ints
},
kdims=['x'],
vdims=['y'])
self.assertEqual(columns.reduce('x', np.mean), 10)
def test_columns_heterogeneous_reduce(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([(10, 15, 0.8), (12, 10, 0.8), (16, 18, 0.6)],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(reduced_data, kdims=self.kdims[1:],
vdims=self.vdims)
self.assertEqual(columns.reduce(['Gender'], np.mean), reduced)
def test_columns_heterogeneous_reduce2d(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([d[1:] for d in self.column_data],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(pd.DataFrame([(14.333333333333334, 0.73333333333333339)], columns=self.vdims),
kdims=[], vdims=self.vdims)
self.assertEqual(columns.reduce(function=np.mean), reduced)
def test_columns_groupby(self):
group1 = {'Age': [10, 16], 'Weight': [15, 18], 'Height': [0.8, 0.6]}
group2 = {'Age': [12], 'Weight': [10], 'Height': [0.8]}
with sorted_context(False):
grouped = HoloMap(
[('M', Columns(group1, kdims=['Age'], vdims=self.vdims)),
('F', Columns(group2, kdims=['Age'], vdims=self.vdims))],
kdims=['Gender'])
self.assertEqual(self.table.groupby(['Gender']), grouped)
def test_columns_sort_vdim_hm(self):
xs_2 = np.array(self.xs_2)
columns = Columns(np.column_stack([self.xs, -xs_2]),
kdims=['x'],
vdims=['y'])
columns_sorted = Columns(np.column_stack([self.xs[::-1], -xs_2[::-1]]),
kdims=['x'],
vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_groupby(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
cols = self.kdims + self.vdims
group1 = pd.DataFrame(self.column_data[:2], columns=cols)
group2 = pd.DataFrame(self.column_data[2:], columns=cols)
grouped = HoloMap({'M': Columns(group1, kdims=['Age'], vdims=self.vdims),
'F': Columns(group2, kdims=['Age'], vdims=self.vdims)},
kdims=['Gender'])
self.assertEqual(columns.groupby(['Gender']), grouped)
def test_columns_2D_reduce_hm(self):
columns = Columns(
{
'x': self.xs,
'y': self.y_ints,
'z': [el**2 for el in self.y_ints]
},
kdims=['x', 'y'],
vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(140))
def init_data(self):
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.gender, self.age = ['M','M','F'], [10,16,12]
self.weight, self.height = [15,18,10], [0.8,0.6,0.8]
self.table = Columns({'Gender':self.gender, 'Age':self.age,
'Weight':self.weight, 'Height':self.height},
kdims=self.kdims, vdims=self.vdims)
super(HeterogeneousColumnTypes, self).init_data()
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns_ht = Columns({'x':self.xs, 'y':self.ys},
kdims=['x'], vdims=['y'])
def test_columns_sort_vdim_ht(self):
columns = Columns({
'x': self.xs,
'y': -self.ys
},
kdims=['x'],
vdims=['y'])
columns_sorted = Columns({
'x': self.xs[::-1],
'y': -self.ys[::-1]
},
kdims=['x'],
vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_2D_aggregate_partial_ht(self):
columns = Columns({
'x': self.xs,
'y': self.ys,
'z': self.zs
},
kdims=['x', 'y'],
vdims=['z'])
reduced = Columns({
'x': self.xs,
'z': self.zs
},
kdims=['x'],
vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean), reduced)
def test_columns_2D_aggregate_partial_hm(self):
z_ints = [el**2 for el in self.y_ints]
columns = Columns({
'x': self.xs,
'y': self.y_ints,
'z': z_ints
},
kdims=['x', 'y'],
vdims=['z'])
self.assertEqual(
columns.aggregate(['x'], np.mean),
Columns({
'x': self.xs,
'z': z_ints
}, kdims=['x'], vdims=['z']))
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 = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'],
vdims=['y'])
def test_columns_collapse_heterogeneous(self):
collapsed = HoloMap(
{
i: Columns(
{
'x': self.xs,
'y': self.ys * i
}, kdims=['x'], vdims=['y'])
for i in range(10)
},
kdims=['z']).collapse('z', np.mean)
expected = Columns({
'x': self.xs,
'y': self.ys * 4.5
},
kdims=['x'],
vdims=['y'])
self.compare_columns(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 init_data(self):
self.xs = range(11)
self.xs_2 = [el**2 for el in self.xs]
self.y_ints = [i*2 for i in range(11)]
self.columns_hm = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'], vdims=['y'])
def test_columns_2D_reduce_ht(self):
reduced = Columns(
{
'Weight': [14.333333333333334],
'Height': [0.73333333333333339]
},
kdims=[],
vdims=self.vdims)
self.assertEqual(self.table.reduce(function=np.mean), reduced)
def test_columns_slice_hm(self):
columns_slice = Columns(
{
'x': range(5, 9),
'y': [2 * i for i in range(5, 9)]
},
kdims=['x'],
vdims=['y'])
self.assertEqual(self.columns_hm[5:9], columns_slice)
def test_columns_collapse_heterogeneous(self):
collapsed = HoloMap(
{
i: Columns(
{
'x': self.xs,
'y': self.ys * i
}, kdims=['x'], vdims=['y'])
for i in range(10)
},
kdims=['z']).collapse('z', np.mean)
expected = Columns({
'x': self.xs,
'y': self.ys * 4.5
},
kdims=['x'],
vdims=['y'])
self.compare_columns(collapsed, expected)
def test_columns_reduce_ht(self):
reduced = Columns(
{
'Age': self.age,
'Weight': self.weight,
'Height': self.height
},
kdims=self.kdims[1:],
vdims=self.vdims)
self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced)
def test_column_aggregate_ht(self):
aggregated = Columns(
{
'Gender': ['M', 'F'],
'Weight': [16.5, 10],
'Height': [0.7, 0.8]
},
kdims=self.kdims[:1],
vdims=self.vdims)
self.compare_columns(self.table.aggregate(['Gender'], np.mean),
aggregated)
def test_columns_index_item_table(self):
indexed = Columns(
{
'Gender': ['F'],
'Age': [12],
'Weight': [10],
'Height': [0.8]
},
kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(self.table['F', 12], indexed)
def test_columns_dataframe_init_hm(self):
"Tests support for homogeneous DataFrames"
if pd is None:
raise SkipTest("Pandas not available")
columns = Columns(pd.DataFrame({
'x': self.xs,
'x2': self.xs_2
}),
kdims=['x'],
vdims=['x2'])
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_sort_string_ht(self):
columns_sorted = Columns(
{
'Gender': ['F', 'M', 'M'],
'Age': [12, 10, 16],
'Weight': [10, 15, 18],
'Height': [0.8, 0.8, 0.6]
},
kdims=self.kdims,
vdims=self.vdims)
self.assertEqual(self.table.sort(), columns_sorted)
def test_columns_value_dim_index(self):
row = self.table[:, :, 'Weight']
indexed = Columns(
{
'Gender': ['M', 'M', 'F'],
'Age': [10, 16, 12],
'Weight': [15, 18, 10]
},
kdims=self.kdims,
vdims=self.vdims[:1])
self.assertEquals(row, indexed)
def test_columns_index_row_gender_female(self):
indexed = Columns(
{
'Gender': ['F'],
'Age': [12],
'Weight': [10],
'Height': [0.8]
},
kdims=self.kdims,
vdims=self.vdims)
row = self.table['F', :]
self.assertEquals(row, indexed)
def test_columns_index_rows_gender_male(self):
row = self.table['M', :]
indexed = Columns(
{
'Gender': ['M', 'M'],
'Age': [10, 16],
'Weight': [15, 18],
'Height': [0.8, 0.6]
},
kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(row, indexed)
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dictionary', 'array']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.keys1 = [('M',10), ('M',16), ('F',12)]
self.values1 = [(15, 0.8), (18, 0.6), (10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.columns = Columns(dict(zip(self.xs, self.ys)),
kdims=['x'], vdims=['y'])
def test_columns_boolean_index(self):
row = self.table[np.array([True, True, False])]
indexed = Columns(
{
'Gender': ['M', 'M'],
'Age': [10, 16],
'Weight': [15, 18],
'Height': [0.8, 0.6]
},
kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(row, indexed)
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dataframe']
self.column_data = [('M',10, 15, 0.8), ('M',16, 18, 0.6),
('F',12, 10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys}),
kdims=['x'], vdims=['y'])
def init_data(self):
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.gender, self.age = ['M', 'M', 'F'], [10, 16, 12]
self.weight, self.height = [15, 18, 10], [0.8, 0.6, 0.8]
self.table = Columns(
{
'Gender': self.gender,
'Age': self.age,
'Weight': self.weight,
'Height': self.height
},
kdims=self.kdims,
vdims=self.vdims)
super(HeterogeneousColumnTypes, self).init_data()
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns_ht = Columns({
'x': self.xs,
'y': self.ys
},
kdims=['x'],
vdims=['y'])
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 = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'], vdims=['y'])
def test_columns_collapse_heterogeneous(self):
collapsed = HoloMap({i: Columns({'x':self.xs, 'y':self.ys*i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
expected = Columns({'x':self.xs, 'y':self.ys*4.5}, kdims=['x'], vdims=['y'])
self.compare_columns(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]))
class ColumnsDFrameTest(ComparisonTestCase):
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dataframe']
self.column_data = [('M',10, 15, 0.8), ('M',16, 18, 0.6),
('F',12, 10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys}),
kdims=['x'], vdims=['y'])
def tearDown(self):
Columns.datatype = self.datatype
def test_columns_range(self):
self.assertEqual(self.columns.range('y'), (0., 1.))
def test_columns_shape(self):
self.assertEqual(self.columns.shape, (11, 2))
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_sample(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_columns_df_construct(self):
self.assertTrue(isinstance(self.columns.data, pd.DataFrame))
def test_columns_tuple_list_construct(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertTrue(isinstance(self.columns.data, pd.DataFrame))
def test_columns_slice(self):
data = [('x', range(5, 9)), ('y', np.linspace(0.5, 0.8, 4))]
columns_slice = Columns(pd.DataFrame.from_items(data),
kdims=['x'], vdims=['y'])
self.assertEqual(self.columns[5:9], columns_slice)
def test_columns_index_row_gender(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['F',:]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_rows_gender(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['M',:]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[:2],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_row_age(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns[:, 12]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_single_row(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['F', 12]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_value1(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(columns['F', 12, 'Weight'], 10)
def test_columns_index_value2(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(columns['F', 12, 'Height'], 0.8)
def test_columns_sort_vdim(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': -self.ys}),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(pd.DataFrame({'x': self.xs[::-1], 'y': -self.ys[::-1]}),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_sort_heterogeneous_string(self):
columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims)
columns_sorted = Columns([self.column_data[i] for i in [2, 0, 1]],
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)
def test_columns_add_dimensions_value(self):
columns = self.columns.add_dimension('z', 1, 0)
self.assertEqual(columns.kdims[1], 'z')
self.compare_arrays(columns.dimension_values('z'), np.zeros(len(columns)))
def test_columns_add_dimensions_values(self):
columns = self.columns.add_dimension('z', 1, range(1,12))
self.assertEqual(columns.kdims[1], 'z')
self.compare_arrays(columns.dimension_values('z'), np.array(list(range(1,12))))
def test_columns_getitem_column(self):
self.compare_arrays(self.columns['y'], self.ys)
def test_columns_collapse(self):
collapsed = HoloMap({i: Columns(pd.DataFrame({'x': self.xs, 'y': self.ys*i}), kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
self.compare_columns(collapsed, Columns(pd.DataFrame({'x': self.xs, 'y': self.ys*4.5}), kdims=['x'], vdims=['y']))
def test_columns_1d_reduce(self):
self.assertEqual(self.columns.reduce('x', np.mean), np.float64(0.5))
def test_columns_2d_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))
def test_columns_2d_partial_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.reduce(['y'], np.mean),
Columns(pd.DataFrame({'x': self.xs, 'z': self.zs}),
kdims=['x'], vdims=['z']))
def test_columns_heterogeneous_reduce(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([(10, 15, 0.8), (12, 10, 0.8), (16, 18, 0.6)],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(reduced_data, kdims=self.kdims[1:],
vdims=self.vdims)
self.assertEqual(columns.reduce(['Gender'], np.mean), reduced)
def test_columns_heterogeneous_reduce2d(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([d[1:] for d in self.column_data],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(pd.DataFrame([(14.333333333333334, 0.73333333333333339)], columns=self.vdims),
kdims=[], vdims=self.vdims)
self.assertEqual(columns.reduce(function=np.mean), reduced)
def test_columns_groupby(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
cols = self.kdims + self.vdims
group1 = pd.DataFrame(self.column_data[:2], columns=cols)
group2 = pd.DataFrame(self.column_data[2:], columns=cols)
grouped = HoloMap({'M': Columns(group1, kdims=['Age'], vdims=self.vdims),
'F': Columns(group2, kdims=['Age'], vdims=self.vdims)},
kdims=['Gender'])
self.assertEqual(columns.groupby(['Gender']), grouped)
def test_columns_heterogeneous_aggregate(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
aggregated = Columns(pd.DataFrame([('F', 10., 0.8), ('M', 16.5, 0.7)],
columns=['Gender']+self.vdims),
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated)
def test_columns_2d_partial_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns(pd.DataFrame({'x': self.xs, 'z': self.zs}),
kdims=['x'], vdims=['z']))
def test_columns_array(self):
self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
def setUp(self):
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y'])
def test_columns_2d_partial_reduce(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.reduce(['y'], np.mean),
Columns((self.xs, self.zs), kdims=['x'], vdims=['z']))
def test_columns_2d_aggregate_partial(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns((self.xs, self.zs), kdims=['x'], vdims=['z']))
def test_columns_1d_reduce(self):
columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y'])
self.assertEqual(columns.reduce('x', np.mean), np.float64(0.5))
def test_columns_2d_reduce(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))
def test_columns_sort_heterogeneous_string(self):
columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims)
columns_sorted = Columns([self.column_data[i] for i in [2, 0, 1]],
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)
def test_columns_array_init_hm(self):
"Tests support for arrays (homogeneous)"
columns = Columns(np.column_stack([self.xs, self.xs_2]),
kdims=['x'],
vdims=['x2'])
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_ndelement_init_hm(self):
"Tests support for homogeneous NdElement (backwards compatibility)"
columns = Columns(
NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2']))
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_sort_vdim(self):
columns = Columns(OrderedDict(zip(self.xs, -self.ys)),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(OrderedDict(zip(self.xs[::-1], -self.ys[::-1])),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_double_zip_init(self):
columns = Columns(zip(zip(self.gender, self.age),
zip(self.weight, self.height)),
kdims=self.kdims,
vdims=self.vdims)
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_simple_zip_init(self):
columns = Columns(zip(self.xs, self.ys), kdims=['x'], vdims=['y'])
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_2d_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))
def test_columns_heterogeneous_reduce(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
reduced = Columns(zip([k[1:] for k in self.keys1], self.values1),
kdims=self.kdims[1:], vdims=self.vdims)
self.assertEqual(columns.reduce(['Gender'], np.mean), reduced)
class ColumnsNdElementTest(ComparisonTestCase):
"""
Test for the Chart baseclass methods.
"""
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dictionary', 'array']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.keys1 = [('M',10), ('M',16), ('F',12)]
self.values1 = [(15, 0.8), (18, 0.6), (10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.columns = Columns(dict(zip(self.xs, self.ys)),
kdims=['x'], vdims=['y'])
def tearDown(self):
Columns.datatype = self.datatype
def test_columns_sort_vdim(self):
columns = Columns(OrderedDict(zip(self.xs, -self.ys)),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(OrderedDict(zip(self.xs[::-1], -self.ys[::-1])),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_sort_heterogeneous_string(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
keys = [('F',12), ('M',10), ('M',16)]
values = [(10, 0.8), (15, 0.8), (18, 0.6)]
columns_sorted = Columns(zip(keys, values),
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)
def test_columns_shape(self):
self.assertEqual(self.columns.shape, (11, 2))
def test_columns_range(self):
self.assertEqual(self.columns.range('y'), (0., 1.))
def test_columns_odict_construct(self):
columns = Columns(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B'])
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_dict_construct(self):
self.assertTrue(isinstance(self.columns.data, NdElement))
def test_columns_ndelement_construct(self):
columns = Columns(NdElement(zip(self.xs, self.ys)))
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_items_construct(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_sample(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_columns_index_row_gender(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
row = table['F',:]
self.assertEquals(row, indexed)
def test_columns_index_rows_gender(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
row = table['M',:]
indexed = Columns(OrderedDict([(('M', 10), (15, 0.8)),
(('M', 16), (18, 0.6))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(row, indexed)
def test_columns_index_row_age(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table[:, 12], indexed)
def test_columns_index_item_table(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table['F', 12], indexed)
def test_columns_index_value1(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table['F', 12, 'Weight'], 10)
def test_columns_index_value2(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table['F', 12, 'Height'], 0.8)
def test_columns_getitem_column(self):
self.compare_arrays(self.columns['y'], self.ys)
def test_columns_add_dimensions_value(self):
table = self.columns.add_dimension('z', 1, 0)
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.zeros(len(table)))
def test_columns_add_dimensions_values(self):
table = self.columns.add_dimension('z', 1, range(1,12))
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12))))
def test_columns_collapse(self):
collapsed = HoloMap({i: Columns(dict(zip(self.xs, self.ys*i)), kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
self.compare_columns(collapsed, Columns(zip(zip(self.xs), self.ys*4.5),
kdims=['x'], vdims=['y']))
def test_columns_1d_reduce(self):
self.assertEqual(self.columns.reduce('x', np.mean), np.float64(0.5))
def test_columns_2d_reduce(self):
columns = Columns(zip(zip(self.xs, self.ys), self.zs),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))
def test_columns_2d_partial_reduce(self):
columns = Columns(zip(zip(self.xs, self.ys), self.zs),
kdims=['x', 'y'], vdims=['z'])
reduced = Columns(zip(zip(self.xs), self.zs),
kdims=['x'], vdims=['z'])
self.assertEqual(columns.reduce(['y'], np.mean), reduced)
def test_columns_heterogeneous_reduce(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
reduced = Columns(zip([k[1:] for k in self.keys1], self.values1),
kdims=self.kdims[1:], vdims=self.vdims)
self.assertEqual(columns.reduce(['Gender'], np.mean), reduced)
def test_columns_heterogeneous_reduce2d(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
reduced = Columns([((), (14.333333333333334, 0.73333333333333339))], kdims=[], vdims=self.vdims)
self.assertEqual(columns.reduce(function=np.mean), reduced)
def test_column_heterogeneous_aggregate(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
aggregated = Columns(OrderedDict([('M', (16.5, 0.7)), ('F', (10., 0.8))]),
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated)
def test_columns_2d_aggregate_partial(self):
columns = Columns(zip(zip(self.xs, self.ys), self.zs),
kdims=['x', 'y'], vdims=['z'])
reduced = Columns(zip(zip(self.xs), self.zs),
kdims=['x'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean), reduced)
def test_columns_array(self):
self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
def test_columns_sort_vdim(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': -self.ys}),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(pd.DataFrame({'x': self.xs[::-1], 'y': -self.ys[::-1]}),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_add_dimensions_value(self):
table = Columns((self.xs, self.ys),
kdims=['x'], vdims=['y'])
table = table.add_dimension('z', 1, 0)
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.zeros(len(table)))
def test_columns_dict_init(self):
columns = Columns(dict(zip(self.xs, self.ys)),
kdims=['A'],
vdims=['B'])
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_add_dimensions_values(self):
table = Columns((self.xs, self.ys),
kdims=['x'], vdims=['y'])
table = table.add_dimension('z', 1, range(1,12))
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12))))
def test_columns_zip_init(self):
columns = Columns(zip(self.gender, self.age, self.weight, self.height),
kdims=self.kdims,
vdims=self.vdims)
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_columns_heterogeneous_reduce2d(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
reduced = Columns([((), (14.333333333333334, 0.73333333333333339))], kdims=[], vdims=self.vdims)
self.assertEqual(columns.reduce(function=np.mean), reduced)
def test_columns_implicit_indexing_init(self):
columns = Columns(self.ys, kdims=['x'], vdims=['y'])
self.assertTrue(isinstance(columns.data, self.data_instance_type))
def test_column_heterogeneous_aggregate(self):
columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims,
vdims=self.vdims)
aggregated = Columns(OrderedDict([('M', (16.5, 0.7)), ('F', (10., 0.8))]),
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated)
def test_columns_2d_partial_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns(pd.DataFrame({'x': self.xs, 'z': self.zs}),
kdims=['x'], vdims=['z']))
class ColumnsNdArrayTest(ComparisonTestCase):
def setUp(self):
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y'])
def test_columns_shape(self):
self.assertEqual(self.columns.shape, (11, 2))
def test_columns_range(self):
self.assertEqual(self.columns.range('y'), (0., 1.))
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_values_construct(self):
columns = Columns(self.ys)
self.assertTrue(isinstance(columns.data, np.ndarray))
def test_columns_tuple_construct(self):
columns = Columns((self.xs, self.ys))
self.assertTrue(isinstance(columns.data, np.ndarray))
def test_columns_array_construct(self):
columns = Columns(np.column_stack([self.xs, self.ys]))
self.assertTrue(isinstance(columns.data, np.ndarray))
def test_columns_tuple_list_construct(self):
columns = Columns(zip(self.xs, self.ys))
self.assertTrue(isinstance(columns.data, np.ndarray))
def test_columns_sort_vdim(self):
columns = Columns((self.xs, -self.ys), kdims=['x'], vdims=['y'])
columns_sorted = Columns((self.xs[::-1], -self.ys[::-1]),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_index(self):
self.assertEqual(self.columns[5], self.ys[5])
def test_columns_slice(self):
columns_slice = Columns(zip(range(5, 9), np.linspace(0.5,0.8, 4)),
kdims=['x'], vdims=['y'])
self.assertEqual(self.columns[5:9], columns_slice)
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_getitem_column(self):
self.compare_arrays(self.columns['y'], self.ys)
def test_columns_sample(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_columns_add_dimensions_value(self):
table = Columns((self.xs, self.ys),
kdims=['x'], vdims=['y'])
table = table.add_dimension('z', 1, 0)
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.zeros(len(table)))
def test_columns_add_dimensions_values(self):
table = Columns((self.xs, self.ys),
kdims=['x'], vdims=['y'])
table = table.add_dimension('z', 1, range(1,12))
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12))))
def test_columns_collapse(self):
collapsed = HoloMap({i: Columns((self.xs, self.ys*i), kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
self.compare_columns(collapsed, Columns((self.xs, self.ys*4.5), kdims=['x'], vdims=['y']))
def test_columns_1d_reduce(self):
columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y'])
self.assertEqual(columns.reduce('x', np.mean), np.float64(0.5))
def test_columns_2d_reduce(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))
def test_columns_2d_partial_reduce(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.reduce(['y'], np.mean),
Columns((self.xs, self.zs), kdims=['x'], vdims=['z']))
def test_columns_2d_aggregate_partial(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns((self.xs, self.zs), kdims=['x'], vdims=['z']))
def test_columns_array(self):
self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
