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(([0, 3, 6, 9], [3, 3, 4]),
vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_points_histogram_explicit_bins(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, bins=[0, 1, 3], normed=False)
hist = Histogram(([0, 1, 3], [1, 3]),
vdims=('x_count', 'Count'))
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.]),
vdims=('x_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
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]),
vdims=('x_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
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]),
vdims=('x_count', 'Count'))
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)
hist = Histogram(([0, 1, 2, 3], [1, 2, 4]), vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_points_histogram_explicit_bins(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, bins=[0, 1, 3], normed=False)
hist = Histogram(([0, 1, 3], [1, 3]),
vdims=('x_count', 'Count'))
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.]),
vdims=('x_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
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]),
vdims=('x_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)
hist = Histogram(([0, 1, 2, 3], [1, 2, 4]),
vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_points_histogram_mean_weighted(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points,
num_bins=3,
weight_dimension='y',
mean_weighted=True)
hist = Histogram(([1., 4., 7.5], [0, 3, 6, 9]), vdims=['y'])
self.assertEqual(op_hist, hist)
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 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 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_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))
# 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(([0.25, 0.25, 0.5], [0., 1., 2., 3.]))
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)
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_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)
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_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_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 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_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_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_points_histogram_mean_weighted(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, weight_dimension='y', mean_weighted=True)
hist = Histogram(([1., 4., 7.5], [0, 3, 6, 9]), vdims=['y'])
self.assertEqual(op_hist, hist)
def test_dataset_histogram_empty_explicit_bins(self):
ds = Dataset([np.nan, np.nan], ['x'])
op_hist = histogram(ds, bins=[0, 1, 2])
hist = Histogram(([0, 1, 2], [0, 0]), vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
