您最好将标题数据保存在 dict 中。你真的需要它作为一个数组吗？ （如果是，为什么？在 numpy 数组中包含标头有一些优点，但它比简单的 dict 更复杂，而且不够灵活。）
dict 的一个缺点是它的键没有可预测的顺序。如果您需要以常规顺序将标头写回磁盘（类似于 C 结构），则需要单独存储字段的顺序以及它们的值。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您的标题数据并将订单存储在那里。
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。
但是，结构化数组将保留标头的顺序，并且可以更轻松地将其二进制表示形式写入磁盘，但在其他方面不灵活。
如果你确实想让标题成为一个数组，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。这样更简单。
根据你正在做的事情，我会做这样的事情。我正在使用 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）。
这看起来比实际上更复杂。
我只是定义了两个新类，一个用于父文件，一个用于框架。你可以用更少的代码做同样的事情，但这为你做更复杂的事情奠定了基础。
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...