"float“对象不是iretable错误；但是我看不到我在迭代什么？

Question

我正在尝试重写fast5 (HDF5)的数据文件到abf格式，我有以下代码，应该能够重写给定的fast5文件到一个通用的数据类型重写到abf的另一个工具。但是，下面的代码会在数字化步骤中产生错误

from uuid import uuid4
import numpy as np
from fast5_research import BulkFast5

filename='test1.fast5'
mean, stdv, n = 40.0, 2.0, 10000
raw_data = np.random.laplace(mean, stdv/np.sqrt(2))


# example of how to digitize data
start, stop = int(min(raw_data - 1)), int(max(raw_data + 1))
rng = stop - start
digitisation = 8192.0
bins = np.arange(start, stop, rng / digitisation)


# np.int16 is required, the library will refuse to write anything other
raw_data = np.digitize(raw_data, bins).astype(np.int16)



# The following are required meta data
channel_id = {
    'digitisation': digitisation,
    'offset': 0,
    'range': rng,
    'sampling_rate': 4000,
    'channel_number': 1,
    }
read_id = {
    'start_time': 0,
    'duration': len(raw_data),
    'read_number': 1,
    'start_mux': 1,
    'read_id': str(uuid4()),
    'scaling_used': 1,
    'median_before': 0,
}
tracking_id = {
    'exp_start_time': '1970-01-01T00:00:00Z',
    'run_id': str(uuid4()).replace('-',''),
    'flow_cell_id': 'FAH00000',
}
context_tags = {}

with Fast5.New(filename, 'w', tracking_id=tracking_id, context_tags=context_tags, channel_id=channel_id) as h:
    h.set_raw(raw_data, meta=read_id, read_number=1)

带错误

/usr/lib/python3/dist-packages/apport/report.py:13: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import fnmatch, glob, traceback, errno, sys, atexit, locale, imp, stat
Traceback (most recent call last):
  File "/home/archie/PycharmProjects/Fast5read/Fast5rewrite.py", line 11, in <module>
    start, stop = int(min(raw_data - 1)), int(max(raw_data + 1))
TypeError: 'float' object is not iterable

当我寻求帮助时，我应该迭代一个范围，但我不知道迭代在哪里，也不知道如何修复这个错误。

Answer 1

start, stop = int(min(raw_data - 1)), int(max(raw_data + 1))

min()和max()是期望将迭代器作为参数的函数。它们分别在这些迭代器中找到最小和最大的元素。

根据您调用np.random.laplace()的方式，它将返回一个浮点值，这不是一个迭代值。

Answer 2

如果你想要一个来自numpy.random.laplace的迭代值，你必须在(可选的)第三个参数中指定你想返回的数组的形状。

https://numpy.org/doc/stable/reference/random/generated/numpy.random.laplace.html

例如，抽取10000个样本：

mean, stdv, n = 40.0, 2.0, 10000
raw_data = np.random.laplace(mean, stdv/np.sqrt(2),n)
    
# example of how to digitize data
start, stop = int(min(raw_data - 1)), int(max(raw_data + 1))
rng = stop - start
digitisation = 8192.0
bins = np.arange(start, stop, rng / digitisation)