本文整理汇总了Python中netCDF4.Dataset.set_auto_maskandscale方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.set_auto_maskandscale方法的具体用法？Python Dataset.set_auto_maskandscale怎么用？Python Dataset.set_auto_maskandscale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netCDF4.Dataset的用法示例。
在下文中一共展示了Dataset.set_auto_maskandscale方法的4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: read_dataset
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import set_auto_maskandscale [as 别名]
def read_dataset(pathname):
"""
:type pathname: str
:rtype : Dataset
"""
dataset = Dataset(pathname)
dataset.set_auto_maskandscale(True)
return dataset示例2: _check_product_can_be_opened
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import set_auto_maskandscale [as 别名]
def _check_product_can_be_opened(self):
try:
dataset = Dataset(self.source_pathname)
dataset.set_auto_maskandscale(False)
self.report['product_can_be_opened_check'] = 0
return dataset
except:
self.report['product_can_be_opened_check'] = 1
filename = os.path.basename(self.source_pathname)
self.report['product_can_be_opened_check_failed_for'] = filename
raise VerificationError示例3: read_mmd
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import set_auto_maskandscale [as 别名]
def read_mmd(sensor, pathname):
"""
:type sensor: str
:type pathname: str
:rtype : tuple
"""
mmd = Dataset(pathname)
mmd.set_auto_maskandscale(True)
nx = len(mmd.dimensions['atsr.nx'])
ny = len(mmd.dimensions['atsr.ny'])
m_ids = mmd.variables['matchup.id'][:]
mmd.variables[sensor + '.matchup_elem'].set_auto_maskandscale(False)
mmd.variables[sensor + '.matchup_line'].set_auto_maskandscale(False)
m_elems = mmd.variables['atsr.3.matchup_elem'][:]
m_lines = mmd.variables['atsr.3.matchup_line'][:]
m_source_filenames = chartostring(mmd.variables[sensor + '.l1b_filename'][:])
return mmd, nx, ny, m_ids, m_source_filenames, m_elems, m_lines示例4: runTest
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import set_auto_maskandscale [as 别名]
def runTest(self):
# Note: The default behaviour is to to have both auto-masking and auto-scaling activated.
# This is already tested in tst_scaled.py, so no need to repeat here. Instead,
# disable auto-masking and auto-scaling altogether.
f = Dataset(self.testfile, "r")
# Neither scaling and masking enabled
f.set_auto_maskandscale(False)
v0 = f.variables['var0']
v1 = f.groups['Group1'].variables['var1']
v2 = f.groups['Group2'].variables['var2']
self.assertFalse(v0.scale)
self.assertFalse(v0.mask)
self.assertFalse(v1.scale)
self.assertFalse(v1.mask)
self.assertFalse(v2.scale)
self.assertFalse(v2.mask)
# No auto-masking, but auto-scaling
f.set_auto_maskandscale(True)
f.set_auto_mask(False)
self.assertTrue(v0.scale)
self.assertFalse(v0.mask)
self.assertTrue(v1.scale)
self.assertFalse(v1.mask)
self.assertTrue(v2.scale)
self.assertFalse(v2.mask)
f.close()