本文整理汇总了Python中glue.core.data.Data.compute_histogram方法的典型用法代码示例。如果您正苦于以下问题：Python Data.compute_histogram方法的具体用法？Python Data.compute_histogram怎么用？Python Data.compute_histogram使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类glue.core.data.Data的用法示例。
在下文中一共展示了Data.compute_histogram方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_indexed
# 需要导入模块: from glue.core.data import Data [as 别名]
# 或者: from glue.core.data.Data import compute_histogram [as 别名]
def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :], label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :], label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
manual.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))示例2: setup_class
# 需要导入模块: from glue.core.data import Data [as 别名]
# 或者: from glue.core.data.Data import compute_histogram [as 别名]
class TestIndexedData:
def setup_class(self):
x = +np.arange(2520).reshape((3, 4, 5, 6, 7))
y = -np.arange(2520).reshape((3, 4, 5, 6, 7))
self.data = Data(x=x, y=y, label='Test data')
self.x_id, self.y_id = self.data.main_components
self.subset_state = self.x_id >= 1200
def test_identity(self):
# In this test, we don't actually slice any dimensions
derived = IndexedData(self.data, (None,) * 5)
assert derived.label == 'Test data[:,:,:,:,:]'
assert derived.shape == self.data.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in self.data.main_components]
assert derived.get_kind(self.x_id) == self.data.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(None), slice(1, 4), slice(0, 7, 2))]:
assert_equal(derived.get_data(self.x_id, view=view),
self.data.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
self.data.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), 3, 4, (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
self.data.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
self.data.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
self.data.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
self.data.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
self.data.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
self.data.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))
def test_indexed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
manual = Data()
manual.add_component(self.data[self.x_id][:, 2, :, 4, :], label=self.x_id)
manual.add_component(self.data[self.y_id][:, 2, :, 4, :], label=self.y_id)
assert derived.label == 'Test data[:,2,:,4,:]'
assert derived.shape == manual.shape
assert [str(c) for c in derived.main_components] == [str(c) for c in manual.main_components]
assert derived.get_kind(self.x_id) == manual.get_kind(self.x_id)
for view in [None, (1, slice(None), slice(1, 4))]:
assert_equal(derived.get_data(self.x_id, view=view),
manual.get_data(self.x_id, view=view))
assert_equal(derived.get_mask(self.subset_state, view=view),
manual.get_mask(self.subset_state, view=view))
bounds = [2, (-5, 5, 10), (-3, 3, 10)]
assert_equal(derived.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id),
manual.compute_fixed_resolution_buffer(bounds=bounds, target_cid=self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id),
manual.compute_statistic('mean', self.x_id))
assert_equal(derived.compute_statistic('mean', self.x_id, axis=2),
manual.compute_statistic('mean', self.x_id, axis=2))
assert_equal(derived.compute_statistic('mean', self.x_id, subset_state=self.subset_state),
manual.compute_statistic('mean', self.x_id, subset_state=self.subset_state))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30]))
assert_equal(derived.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state),
manual.compute_histogram([self.x_id], range=[(0, 1000)], bins=[30], subset_state=self.subset_state))
def test_numerical_values_changed(self):
# Here we slice two of the dimensions and then compare the results to a
# manually sliced dataset.
derived = IndexedData(self.data, (None, 2, None, 4, None))
data_collection = DataCollection([self.data, derived])
#.........这里部分代码省略.........