本文整理汇总了Python中django.contrib.gis.shortcuts.numpy.dtype方法的典型用法代码示例。如果您正苦于以下问题：Python numpy.dtype方法的具体用法？Python numpy.dtype怎么用？Python numpy.dtype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django.contrib.gis.shortcuts.numpy的用法示例。
在下文中一共展示了numpy.dtype方法的3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: datatype
# 需要导入模块: from django.contrib.gis.shortcuts import numpy [as 别名]
# 或者: from django.contrib.gis.shortcuts.numpy import dtype [as 别名]
def datatype(self, as_string=False):
"""
Returns the GDAL Pixel Datatype for this band.
"""
dtype = capi.get_band_datatype(self._ptr)
if as_string:
dtype = GDAL_PIXEL_TYPES[dtype]
return dtype 示例2: data
# 需要导入模块: from django.contrib.gis.shortcuts import numpy [as 别名]
# 或者: from django.contrib.gis.shortcuts.numpy import dtype [as 别名]
def data(self, data=None, offset=None, size=None, as_memoryview=False):
"""
Reads or writes pixel values for this band. Blocks of data can
be accessed by specifying the width, height and offset of the
desired block. The same specification can be used to update
parts of a raster by providing an array of values.
Allowed input data types are bytes, memoryview, list, tuple, and array.
"""
if not offset:
offset = (0, 0)
if not size:
size = (self.width - offset[0], self.height - offset[1])
if any(x <= 0 for x in size):
raise ValueError('Offset too big for this raster.')
if size[0] > self.width or size[1] > self.height:
raise ValueError('Size is larger than raster.')
# Create ctypes type array generator
ctypes_array = GDAL_TO_CTYPES[self.datatype()] * (size[0] * size[1])
if data is None:
# Set read mode
access_flag = 0
# Prepare empty ctypes array
data_array = ctypes_array()
else:
# Set write mode
access_flag = 1
# Instantiate ctypes array holding the input data
if isinstance(data, (bytes, six.memoryview)) or (numpy and isinstance(data, numpy.ndarray)):
data_array = ctypes_array.from_buffer_copy(data)
else:
data_array = ctypes_array(*data)
# Access band
capi.band_io(self._ptr, access_flag, offset[0], offset[1],
size[0], size[1], byref(data_array), size[0],
size[1], self.datatype(), 0, 0)
# Return data as numpy array if possible, otherwise as list
if data is None:
if as_memoryview:
return memoryview(data_array)
elif numpy:
return numpy.frombuffer(
data_array, dtype=numpy.dtype(data_array)).reshape(size)
else:
return list(data_array)
else:
self.source._flush() 示例3: data
# 需要导入模块: from django.contrib.gis.shortcuts import numpy [as 别名]
# 或者: from django.contrib.gis.shortcuts.numpy import dtype [as 别名]
def data(self, data=None, offset=None, size=None, shape=None, as_memoryview=False):
"""
Reads or writes pixel values for this band. Blocks of data can
be accessed by specifying the width, height and offset of the
desired block. The same specification can be used to update
parts of a raster by providing an array of values.
Allowed input data types are bytes, memoryview, list, tuple, and array.
"""
if not offset:
offset = (0, 0)
if not size:
size = (self.width - offset[0], self.height - offset[1])
if not shape:
shape = size
if any(x <= 0 for x in size):
raise ValueError('Offset too big for this raster.')
if size[0] > self.width or size[1] > self.height:
raise ValueError('Size is larger than raster.')
# Create ctypes type array generator
ctypes_array = GDAL_TO_CTYPES[self.datatype()] * (shape[0] * shape[1])
if data is None:
# Set read mode
access_flag = 0
# Prepare empty ctypes array
data_array = ctypes_array()
else:
# Set write mode
access_flag = 1
# Instantiate ctypes array holding the input data
if isinstance(data, (bytes, six.memoryview)) or (numpy and isinstance(data, numpy.ndarray)):
data_array = ctypes_array.from_buffer_copy(data)
else:
data_array = ctypes_array(*data)
# Access band
capi.band_io(self._ptr, access_flag, offset[0], offset[1],
size[0], size[1], byref(data_array), shape[0],
shape[1], self.datatype(), 0, 0)
# Return data as numpy array if possible, otherwise as list
if data is None:
if as_memoryview:
return memoryview(data_array)
elif numpy:
# reshape() needs a reshape parameter with the height first.
return numpy.frombuffer(
data_array, dtype=numpy.dtype(data_array)
).reshape(tuple(reversed(size)))
else:
return list(data_array)
else:
self._flush()