def test_dataset_cumulative_histogram_dask(self):
import dask.array as da
ds = Dataset((da.from_array(np.array(range(10), dtype='f'), chunks=(3)),),
['x'], datatype=['dask'])
op_hist = histogram(ds, num_bins=3, cumulative=True, normed=True)
hist = Histogram(([0, 3, 6, 9], [0.3, 0.6, 1]),
vdims=('x_frequency', 'Frequency'))
self.assertIsInstance(op_hist.data['x_frequency'], da.Array)
self.assertEqual(op_hist, hist)
def setUp(self):
self.store_copy = OptionTree(sorted(Store.options().items()),
groups=['style', 'plot', 'norm'])
self.backend = 'matplotlib'
Store.current_backend = self.backend
Store.options(val=OptionTree(groups=['plot', 'style']))
options = Store.options()
self.default_plot = dict(plot1='plot1', plot2='plot2')
options.Histogram = Options('plot', **self.default_plot)
self.default_style = dict(style1='style1', style2='style2')
options.Histogram = Options('style', **self.default_style)
data = [np.random.normal() for i in range(10000)]
frequencies, edges = np.histogram(data, 20)
self.hist = Histogram(frequencies, edges)
super(TestStoreInheritance, self).setUp()
def test_dataset_weighted_histogram_dask(self):
import dask.array as da
ds = Dataset((da.from_array(np.array(range(10), dtype='f'), chunks=3),
da.from_array([i/10. for i in range(10)], chunks=3)),
['x', 'y'], datatype=['dask'])
op_hist = histogram(ds, weight_dimension='y', num_bins=3, normed=True)
hist = Histogram(([0, 3, 6, 9], [0.022222, 0.088889, 0.222222]),
vdims='y')
self.assertIsInstance(op_hist.data['y'], da.Array)
self.assertEqual(op_hist, hist)
def setUp(self):
self.xs = np.linspace(0, 2 * np.pi, 11)
self.hxs = np.arange(len(self.xs))
self.sin = np.sin(self.xs)
self.cos = np.cos(self.xs)
sine_data = np.column_stack((self.xs, self.sin))
cos_data = np.column_stack((self.xs, self.cos))
self.curve = Curve(sine_data)
self.path = Path([sine_data, cos_data])
self.histogram = Histogram((self.hxs, self.sin))
super(ElementConstructorTest, self).setUp()
def test_points_histogram_not_normed(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, normed=False)
# Make sure that the name and label are as desired
op_freq_dim = op_hist.get_dimension('x_frequency')
self.assertEqual(op_freq_dim.label, 'x Frequency')
# Because the operation labels are now different from the
# default Element label, change back before comparing.
op_hist = op_hist.redim(x_frequency='Frequency')
hist = Histogram(([3, 3, 4], [0, 3, 6, 9]))
self.assertEqual(op_hist, hist)
def test_histogram_operation_pd_period(self):
dates = pd.date_range('2017-01-01', '2017-01-04', freq='D').to_period('D')
op_hist = histogram(Dataset(dates, 'Date'), num_bins=4, normed=True)
hist_data = {
'Date': np.array([
'2017-01-01T00:00:00.000000', '2017-01-01T18:00:00.000000',
'2017-01-02T12:00:00.000000', '2017-01-03T06:00:00.000000',
'2017-01-04T00:00:00.000000'], dtype='datetime64[us]'),
'Date_frequency': np.array([
3.85802469e-18, 3.85802469e-18, 3.85802469e-18,
3.85802469e-18])
}
hist = Histogram(hist_data, kdims='Date', vdims=('Date_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
def test_points_histogram_cumulative(self):
arr = np.arange(4)
points = Points(arr)
op_hist = histogram(points, cumulative=True, num_bins=3, normed=False)
# Make sure that the name and label are as desired
op_freq_dim = op_hist.get_dimension('x_frequency')
self.assertEqual(op_freq_dim.label, 'x Frequency')
# Because the operation labels are now different from the
# default Element label, change back before comparing.
op_hist = op_hist.redim(x_frequency='Frequency')
hist = Histogram(([0, 1, 2, 3], [1, 2, 4]))
self.assertEqual(op_hist, hist)
def test_histogram_operation_datetime64(self):
dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M')
op_hist = histogram(Dataset(dates, 'Date'), num_bins=4, normed=True)
hist_data = {
'Date': np.array([
'2017-01-01T00:00:00.000000', '2017-01-01T18:00:00.000000',
'2017-01-02T12:00:00.000000', '2017-01-03T06:00:00.000000',
'2017-01-04T00:00:00.000000'], dtype='datetime64[us]'),
'Date_frequency': np.array([
3.85802469e-18, 3.85802469e-18, 3.85802469e-18,
3.85802469e-18])
}
hist = Histogram(hist_data, kdims='Date', vdims=('Date_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
def _process(self, tree, key=None):
preferences = tree[self.p.feature + 'Preference']
layout = Layout()
for s, p in self.p.projections:
if s not in preferences:
continue
featurepref = preferences[s]
if isinstance(featurepref, HoloMap):
featurepref = featurepref.last
feature = featurepref.value_dimensions[0]
feature_arr = featurepref.data.flat
cfs = tree.CFs[p]
deltas, weights = [], []
for k, cf in cfs.items():
preferred = featurepref[k]
weight_arr = cf.situated.data.flat
feature_slice = feature_arr[weight_arr > 0]
weight_slice = weight_arr[weight_arr > 0]
if feature.cyclic:
feature_delta = circular_dist(preferred, feature_slice,
feature.range[1])
else:
feature_delta = np.abs(feature_slice - preferred)
deltas.append(feature_delta)
weights.append(weight_slice)
deltas = np.concatenate(deltas)
weights = np.concatenate(weights)
bin_range = (0, feature.range[1] / 2.) if feature.cyclic else None
bins, edges = np.histogram(deltas,
range=bin_range,
bins=self.p.num_bins,
weights=weights,
normed=self.p.normalized)
# Construct Elements
label = ' '.join([s, p])
group = '%s Weight Distribution' % self.p.feature
histogram = Histogram(
bins,
edges,
group=group,
label=label,
kdims=[' '.join([self.p.feature, 'Difference'])],
vdims=['Weight'])
layout.WeightDistribution['_'.join([s, p])] = histogram
return layout
def setUp(self):
self.store_copy = OptionTree(sorted(Store.options().items()),
groups=['style', 'plot', 'norm'])
self.backend = 'matplotlib'
Store.current_backend = self.backend
Store.options(val=OptionTree(groups=['plot', 'style']))
options = Store.options()
self.default_plot = dict(plot1='plot1', plot2='plot2')
options.Histogram = Options('plot', **self.default_plot)
self.default_style = dict(style1='style1', style2='style2')
options.Histogram = Options('style', **self.default_style)
data = [np.random.normal() for i in range(10000)]
frequencies, edges = np.histogram(data, 20)
self.hist = Histogram(frequencies, edges)
super(TestStoreInheritance, self).setUp()
def test_histogram_operation_kwargs(self):
points = Points([float(j) for i in range(10) for j in [i] * (2 * i)])
op_hist = histogram(
points,
dimension='y',
normed=False,
num_bins=10,
bin_range=[0, 10],
)
hist = Histogram(
([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0
], [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]),
vdims=('y_count', 'Count'),
kdims='y')
# Check histogram
self.assertEqual(op_hist, hist)
# Check operation kwargs for histogram generated with operation
self.assertEqual(
op_hist._operation_kwargs, {
'dimension': 'y',
'normed': False,
'dynamic': False,
'bins':
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
})
# Test that operation_kwargs is preserved through clone
self.assertEqual(
op_hist.clone()._operation_kwargs, {
'dimension': 'y',
'normed': False,
'dynamic': False,
'bins':
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
})
# Check that operation kwargs is None for histogram generated directly
# from the Histogram constructor
self.assertIsNone(hist._operation_kwargs)
def test_histogram_range_y_explicit_upper(self):
r = Histogram([1,2,3], [0, 1, 2, 3],
vdims=[Dimension('y', range=(None, 4.))]).range(1)
self.assertEqual(r, (1., 4.))
def test_histogram_range_y(self):
r = Histogram([1,2,3], [0, 1, 2, 3]).range(1)
self.assertEqual(r, (1., 3.0))
def test_histogram_range_x_explicit_lower(self):
r = Histogram([1,2,3], [0, 1, 2, 3],
kdims=[Dimension('x', range=(-1, None))]).range(0)
self.assertEqual(r, (-1., 3.))
def test_histogram_range_x_explicit_upper(self):
r = Histogram([1,2,3], [0, 1, 2, 3],
kdims=[Dimension('x', range=(None, 4.))]).range(0)
self.assertEqual(r, (0, 4.))
def test_histogram_range_y_explicit(self):
r = Histogram(([0, 1, 2, 3], [1, 2, 3]),
vdims=[Dimension('y', range=(0, 4.))]).range(1)
self.assertEqual(r, (0., 4.))
def test_histogram_range_x_explicit(self):
r = Histogram(([0, 1, 2, 3], [1, 2, 3]),
kdims=[Dimension('x', range=(-1, 4.))]).range(0)
self.assertEqual(r, (-1., 4.))
def test_points_histogram(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3)
hist = Histogram(([0.1, 0.1, 0.133333], [0, 3, 6, 9]))
self.assertEqual(op_hist, hist)
def setUp(self):
self.values = [i for i in range(10)]
self.edges = [i for i in range(11)]
self.hist=Histogram(self.values, self.edges)
def test_hist_curve_int_edges_construct(self):
hist = Histogram(Curve(range(3)))
values = hist.dimension_values(1)
edges = hist.edges
self.assertEqual(values, np.arange(3))
self.assertEqual(edges, np.array([-.5, .5, 1.5, 2.5]))
def test_hist_yvalues_construct(self):
self.assertEqual(Histogram(self.sin), self.histogram)
def test_hist_zip_construct(self):
self.assertEqual(Histogram(list(zip(self.hxs, self.sin))),
self.histogram)
def test_points_histogram_not_normed(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, normed=False)
hist = Histogram(([3, 3, 4], [0, 3, 6, 9]))
self.assertEqual(op_hist, hist)
def test_points_histogram_bin_range(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, bin_range=(0, 3))
hist = Histogram(([0.25, 0.25, 0.5], [0., 1., 2., 3.]))
self.assertEqual(op_hist, hist)
def test_histogram_range_y_explicit_lower(self):
r = Histogram([1,2,3], [0, 1, 2, 3],
vdims=[Dimension('y', range=(0., None))]).range(1)
self.assertEqual(r, (0., 3.))
def test_histogram_range_x(self):
r = Histogram(([0, 1, 2, 3], [1, 2, 3])).range(0)
self.assertEqual(r, (0., 3.0))
def test_hist_curve_int_edges_construct(self):
hist = Histogram(Curve(range(3)))
values = hist.dimension_values(1)
edges = hist.edges
self.assertEqual(values, np.arange(3))
self.assertEqual(edges, np.array([-.5, .5, 1.5, 2.5]))
def test_hist_curve_int_edges_construct(self):
hist = Histogram(Curve(range(3)))
self.assertEqual(hist.data[0], np.arange(3))
self.assertEqual(hist.data[1], np.array([-.5, .5, 1.5, 2.5]))
def test_hist_array_construct(self):
self.assertEqual(Histogram(np.column_stack((self.hxs, self.sin))),
self.histogram)
def test_points_histogram_weighted(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, weight_dimension='y')
hist = Histogram(([0.022222, 0.088889, 0.222222], [0, 3, 6, 9]),
vdims=['y'])
self.assertEqual(op_hist, hist)
def test_hist_curve_construct(self):
hist = Histogram(Curve(([0.1, 0.3, 0.5], [2.1, 2.2, 3.3])))
values = hist.dimension_values(1)
edges = hist.edges
self.assertEqual(values, np.array([2.1, 2.2, 3.3]))
self.assertEqual(edges, np.array([0, 0.2, 0.4, 0.6]))
class TestStoreInheritance(ComparisonTestCase):
"""
Tests to prevent regression after fix in 71c1f3a that resolves
issue #43
"""
def setUp(self):
self.store_copy = OptionTree(sorted(Store.options().items()),
groups=['style', 'plot', 'norm'])
self.backend = 'matplotlib'
Store.current_backend = self.backend
Store.options(val=OptionTree(groups=['plot', 'style']))
options = Store.options()
self.default_plot = dict(plot1='plot1', plot2='plot2')
options.Histogram = Options('plot', **self.default_plot)
self.default_style = dict(style1='style1', style2='style2')
options.Histogram = Options('style', **self.default_style)
data = [np.random.normal() for i in range(10000)]
frequencies, edges = np.histogram(data, 20)
self.hist = Histogram(frequencies, edges)
super(TestStoreInheritance, self).setUp()
def lookup_options(self, obj, group):
return Store.lookup_options(self.backend, obj, group)
def tearDown(self):
Store.options(val=self.store_copy)
Store._custom_options = {k:{} for k in Store._custom_options.keys()}
super(TestStoreInheritance, self).tearDown()
def test_original_style_options(self):
self.assertEqual(self.lookup_options(self.hist, 'style').options,
self.default_style)
def test_original_plot_options(self):
self.assertEqual(self.lookup_options(self.hist, 'plot').options,
self.default_plot)
def test_plot_inheritance_addition(self):
"Adding an element"
hist2 = self.hist(plot={'plot3':'plot3'})
self.assertEqual(self.lookup_options(hist2, 'plot').options,
dict(plot1='plot1', plot2='plot2', plot3='plot3'))
# Check style works as expected
self.assertEqual(self.lookup_options(hist2, 'style').options, self.default_style)
def test_plot_inheritance_override(self):
"Overriding an element"
hist2 = self.hist(plot={'plot1':'plot_child'})
self.assertEqual(self.lookup_options(hist2, 'plot').options,
dict(plot1='plot_child', plot2='plot2'))
# Check style works as expected
self.assertEqual(self.lookup_options(hist2, 'style').options, self.default_style)
def test_style_inheritance_addition(self):
"Adding an element"
hist2 = self.hist(style={'style3':'style3'})
self.assertEqual(self.lookup_options(hist2, 'style').options,
dict(style1='style1', style2='style2', style3='style3'))
# Check plot options works as expected
self.assertEqual(self.lookup_options(hist2, 'plot').options, self.default_plot)
def test_style_inheritance_override(self):
"Overriding an element"
hist2 = self.hist(style={'style1':'style_child'})
self.assertEqual(self.lookup_options(hist2, 'style').options,
dict(style1='style_child', style2='style2'))
# Check plot options works as expected
self.assertEqual(self.lookup_options(hist2, 'plot').options, self.default_plot)
def test_style_transfer(self):
hist = self.hist.opts(style={'style1':'style_child'})
hist2 = self.hist.opts()
opts = Store.lookup_options('matplotlib', hist2, 'style').kwargs
self.assertEqual(opts, {'style1': 'style1', 'style2': 'style2'})
Store.transfer_options(hist, hist2, 'matplotlib')
opts = Store.lookup_options('matplotlib', hist2, 'style').kwargs
self.assertEqual(opts, {'style1': 'style_child', 'style2': 'style2'})
class TestStoreInheritance(ComparisonTestCase):
"""
Tests to prevent regression after fix in 71c1f3a that resolves
issue #43
"""
def setUp(self):
if 'matplotlib' not in Store.renderers:
raise SkipTest('Matplotlib backend not available.')
self.store_copy = OptionTree(sorted(Store.options().items()),
groups=Options._option_groups)
self.backend = 'matplotlib'
Store.current_backend = self.backend
Store.options(val=OptionTree(groups=['plot', 'style']))
options = Store.options()
self.default_plot = dict(plot1='plot1', plot2='plot2')
options.Histogram = Options('plot', **self.default_plot)
self.default_style = dict(style1='style1', style2='style2')
options.Histogram = Options('style', **self.default_style)
data = [np.random.normal() for i in range(10000)]
frequencies, edges = np.histogram(data, 20)
self.hist = Histogram(frequencies, edges)
super(TestStoreInheritance, self).setUp()
def lookup_options(self, obj, group):
return Store.lookup_options(self.backend, obj, group)
def tearDown(self):
Store.options(val=self.store_copy)
Store._custom_options = {k: {} for k in Store._custom_options.keys()}
super(TestStoreInheritance, self).tearDown()
def test_original_style_options(self):
self.assertEqual(
self.lookup_options(self.hist, 'style').options,
self.default_style)
def test_original_plot_options(self):
self.assertEqual(
self.lookup_options(self.hist, 'plot').options, self.default_plot)
def test_plot_inheritance_addition(self):
"Adding an element"
hist2 = self.hist.opts(plot={'plot3': 'plot3'})
self.assertEqual(
self.lookup_options(hist2, 'plot').options,
dict(plot1='plot1', plot2='plot2', plot3='plot3'))
# Check style works as expected
self.assertEqual(
self.lookup_options(hist2, 'style').options, self.default_style)
def test_plot_inheritance_override(self):
"Overriding an element"
hist2 = self.hist.opts(plot={'plot1': 'plot_child'})
self.assertEqual(
self.lookup_options(hist2, 'plot').options,
dict(plot1='plot_child', plot2='plot2'))
# Check style works as expected
self.assertEqual(
self.lookup_options(hist2, 'style').options, self.default_style)
def test_style_inheritance_addition(self):
"Adding an element"
hist2 = self.hist.opts(style={'style3': 'style3'})
self.assertEqual(
self.lookup_options(hist2, 'style').options,
dict(style1='style1', style2='style2', style3='style3'))
# Check plot options works as expected
self.assertEqual(
self.lookup_options(hist2, 'plot').options, self.default_plot)
def test_style_inheritance_override(self):
"Overriding an element"
hist2 = self.hist.opts(style={'style1': 'style_child'})
self.assertEqual(
self.lookup_options(hist2, 'style').options,
dict(style1='style_child', style2='style2'))
# Check plot options works as expected
self.assertEqual(
self.lookup_options(hist2, 'plot').options, self.default_plot)
def test_style_transfer(self):
if 'matplotlib' not in Store.renderers:
raise SkipTest("test_style_transfer requires matplotlib")
hist = self.hist.opts(style={'style1': 'style_child'})
hist2 = self.hist.opts()
opts = Store.lookup_options('matplotlib', hist2, 'style').kwargs
self.assertEqual(opts, {'style1': 'style1', 'style2': 'style2'})
Store.transfer_options(hist, hist2, 'matplotlib')
opts = Store.lookup_options('matplotlib', hist2, 'style').kwargs
self.assertEqual(opts, {'style1': 'style_child', 'style2': 'style2'})
def test_hist_curve_construct(self):
hist = Histogram(Curve(([0.1, 0.3, 0.5], [2.1, 2.2, 3.3])))
values = hist.dimension_values(1)
edges = hist.edges
self.assertEqual(values, np.array([2.1, 2.2, 3.3]))
self.assertEqual(edges, np.array([0, 0.2, 0.4, 0.6]))
