本文整理汇总了Python中zeobuilder.actions.composed.Parameters.interval_a方法的典型用法代码示例。如果您正苦于以下问题：Python Parameters.interval_a方法的具体用法？Python Parameters.interval_a怎么用？Python Parameters.interval_a使用的例子？那么, 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zeobuilder.actions.composed.Parameters的用法示例。
在下文中一共展示了Parameters.interval_a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: fn
# 需要导入模块: from zeobuilder.actions.composed import Parameters [as 别名]
# 或者: from zeobuilder.actions.composed.Parameters import interval_a [as 别名]
def fn():
context.application.model.file_open("test/input/silica_layer.zml")
parameters = Parameters()
parameters.interval_a = numpy.array([0.0, 3.0], float)
parameters.interval_b = numpy.array([0.0, 3.0], float)
UnitCellToCluster = context.application.plugins.get_action("UnitCellToCluster")
assert UnitCellToCluster.analyze_selection(parameters)
UnitCellToCluster(parameters)
crd_cluster = [node.transformation.t for node in context.application.model.universe.children]
context.application.model.file_open("test/input/silica_layer.zml")
parameters = Parameters()
parameters.repetitions_a = 3
parameters.repetitions_b = 3
SuperCell = context.application.plugins.get_action("SuperCell")
assert SuperCell.analyze_selection(parameters)
SuperCell(parameters)
crd_super = [node.transformation.t for node in context.application.model.universe.children]
# check that the number of atoms is the same
assert len(crd_super) == len(crd_cluster)
# check if the coordinates match
for c_super in crd_super:
for i in xrange(len(crd_cluster)):
c_cluster = crd_cluster[i]
delta = c_cluster - c_super
delta = context.application.model.universe.shortest_vector(delta)
if numpy.linalg.norm(delta) < 1e-3:
del crd_cluster[i]
break
assert len(crd_cluster) == 0示例2: default_parameters
# 需要导入模块: from zeobuilder.actions.composed import Parameters [as 别名]
# 或者: from zeobuilder.actions.composed.Parameters import interval_a [as 别名]
def default_parameters(cls):
result = Parameters()
universe = context.application.model.universe
if universe.cell.active[0]:
result.interval_a = numpy.array([0.0, universe.repetitions[0]], float)
if universe.cell.active[1]:
result.interval_b = numpy.array([0.0, universe.repetitions[1]], float)
if universe.cell.active[2]:
result.interval_c = numpy.array([0.0, universe.repetitions[2]], float)
return result