从字典写入numpy数组

从字典写入numpy数组

您最好将标头数据保留在dict中。您是否真的需要将其作为数组？（如果是这样，为什么？将标头包含在numpy数组中有一些优点，但是它比简单的更为复杂

dict

，并且不够灵活。）

a的一个缺点

dict

是其键没有可预测的顺序。如果您需要按常规顺序（类似于C结构）将标头写回到磁盘，则需要分别存储字段的顺序及其值。在这种情况下，您可以考虑使用有序的dict（

collections.OrderedDict

）或仅将一个简单的类放在一起以保存标头数据并在其中存储顺序。

除非有充分的理由将其放入numpy数组，否则您可能不希望这样做。

但是，结构化数组将保留标题的顺序，并使将其二进制表示形式写入磁盘更加容易，但是在其他方面则不灵活。

如果您确实想将标头设置为数组，则可以执行以下 *** 作：

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 wayformats = ['S3', 'i4', 'f8']# It's often cleaner to specify the dtype this way instead of as a giant stringdtype = 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) withheader_array.tofile('test.dat')

另一方面，如果您只想访问标头中的值，则将其保留为

dict

。这样比较简单。

---

根据听起来您正在做的事情，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）。

这看起来比实际要复杂。

我只是在定义两个新类，一个用于父文件，一个用于框架。您可以用更少的代码来完成相同的事情，但这为您完成更复杂的事情奠定了基础。

import numpy as npclass 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))

您将使用类似于以下内容的方法：

dataset = SonarFile('input.dat')for frame in dataset:    im = frame.data    # Do something...