您最好将标题数据保留在 dict 中。你真的需要它作为一个数组吗？ （如果是这样，为什么？将标头放在 numpy 数组中有一些优点，但它比简单的dict，并且不那么灵活。）
一个缺点是dict是它的键没有可预测的顺序。如果您需要以常规顺序（类似于 C 结构体）将标头写回到磁盘，那么您需要单独存储字段的顺序及其值。如果是这种情况，您可能会考虑使用有序字典（collections.OrderedDict）或者只是组合一个简单的类来保存标头数据并在那里存储顺序。
除非有充分的理由将其放入 numpy 数组中，否则您可能不想这样做。
然而，结构化数组将保留标头的顺序，并使将其二进制表示形式写入磁盘变得更容易，但它在其他方面不灵活。
如果你确实想让标头成为一个数组，你可以这样做：
import numpy as np
# Lists can be modified, but preserve order. That's important in this case.
names = ['Name1', 'Name2', 'Name3']
# It's "S3" instead of "a3" for a string field in numpy, by the way
formats = ['S3', 'i4', 'f8']
# It's often cleaner to specify the dtype this way instead of as a giant string
dtype = dict(names=names, formats=formats)
# This won't preserve the order we're specifying things in!!
# If we iterate through it, things may be in any order.
header = dict(Name1='abc', Name2=456, Name3=3.45)
# Therefore, we'll be sure to pass things in in order...
# Also, np.array will expect a tuple instead of a list for a structured array...
values = tuple(header[name] for name in names)
header_array = np.array(values, dtype=dtype)
# We can access field in the array like this...
print header_array['Name2']
# And dump it to disk (similar to a C struct) with
header_array.tofile('test.dat')
另一方面，如果您只想访问标头中的值，只需将其保留为dict。这样就更简单了。
根据听起来你在做什么，我会做这样的事情。我使用 numpy 数组读取标头，但标头值实际上存储为类属性（以及标头数组）。
这看起来比实际情况更复杂。
我只是定义两个新类，一个用于父文件，一个用于框架。您可以使用更少的代码来完成同样的事情，但这为您提供了更复杂的事情的基础。
import numpy as np
class SonarFile(object):
# These define the format of the file header
header_fields = ('num_frames', 'name1', 'name2', 'name3')
header_formats = ('i4', 'f4', 'S10', '>I4')
def __init__(self, filename):
self.infile = open(filename, 'r')
dtype = dict(names=self.header_fields, formats=self.header_formats)
# Read in the header as a numpy array (count=1 is important here!)
self.header = np.fromfile(self.infile, dtype=dtype, count=1)
# Store the position so we can "rewind" to the end of the header
self.header_length = self.infile.tell()
# You may or may not want to do this (If the field names can have
# spaces, it's a bad idea). It will allow you to access things with
# sonar_file.Name1 instead of sonar_file.header['Name1'], though.
for field in self.header_fields:
setattr(self, field, self.header[field])
# __iter__ is a special function that defines what should happen when we
# try to iterate through an instance of this class.
def __iter__(self):
"""Iterate through each frame in the dataset."""
# Rewind to the end of the file header
self.infile.seek(self.header_length)
# Iterate through frames...
for _ in range(self.num_frames):
yield Frame(self.infile)
def close(self):
self.infile.close()
class Frame(object):
header_fields = ('width', 'height', 'name')
header_formats = ('i4', 'i4', 'S20')
data_format = 'f4'
def __init__(self, infile):
dtype = dict(names=self.header_fields, formats=self.header_formats)
self.header = np.fromfile(infile, dtype=dtype, count=1)
# See discussion above...
for field in self.header_fields:
setattr(self, field, self.header[field])
# I'm assuming that the size of the frame is in the frame header...
ncols, nrows = self.width, self.height
# Read the data in
self.data = np.fromfile(infile, self.data_format, count=ncols * nrows)
# And reshape it into a 2d array.
# I'm assuming C-order, instead of Fortran order.
# If it's fortran order, just do "data.reshape((ncols, nrows)).T"
self.data = self.data.reshape((nrows, ncols))
你会像这样使用它：
dataset = SonarFile('input.dat')
for frame in dataset:
im = frame.data
# Do something...
