这可能很容易解决，但我不知道该怎么做。
我扩展了pandas.Series类，以便它可以包含我研究的数据集。这是我到目前为止编写的代码：
import pandas as pd
import numpy as np
from allantools import oadev
class Tombstone(pd.Series):
"""An extension of ``pandas.Series``, which contains raw data from a
tombstone test.
Parameters
----------
data : array-like of floats
The raw data measured in volts from a lock-in amplifier. If no scale
factor is provided, this data is presumed to be in units of °/h.
rate : float
The sampling rate in Hz
start : float
The unix time stamp of the start of the run. Used to create the index
of the Tombstone object. This can be calculated by
running ``time.time()`` or similar. If no value is passed, the index
of the Tombstone object will be in hours since start.
scale_factor : float
The conversion factor between the lock-in amplifier voltage and deg/h,
expressed in deg/h/V.
Attributes
----------
adev : 2-tuple of arrays of floats
Returns the Allan deviation in degrees/hour in a 2-tuple. The first
tuple is an array of floats representing the integration times. The
second tuple is an array of floats representing the allan deviations.
noise : float
The calculated angular random walk in units of °/√h taken from the
1-Hz point on the
Allan variance curve.
arw : float
The calculated angular random walk in units of °/√h taken from the
1-Hz point on the
Allan deviation curve.
drift : float
The minimum allan deviation in units of °/h.
"""
def __init__(self, data, rate, start=None, scale_factor=0, *args, **kwargs):
if start:
date_index = pd.date_range(
start=start*1e9, periods=len(data),
freq='%.3g ms' % (1000/rate), tz='UTC')
date_index = date_index.tz_convert('America/Los_Angeles')
else:
date_index = np.arange(len(data))/60/60/rate
super().__init__(data, date_index)
if scale_factor:
self.name = 'voltage'
else:
self.name = 'rotation'
self.rate = rate
@property
def _constructor(self):
return Tombstone
@property
def adev(self):
tau, dev, _, _ = oadev(np.array(self), rate=self.rate,
data_type='freq')
return tau, dev
@property
def noise(self):
_, dev, _, _ = oadev(np.array(self), rate=self.rate, data_type='freq')
return dev[0]/60
# alias
arw = noise
@property
def drift(self):
tau, dev, _, _ = oadev(np.array(self), rate=self.rate,
data_type='freq')
return min(dev)
我可以在Jupyter笔记本中运行它：
>>> t = Tombstone(np.random.rand(60), rate=10)
>>> t
0.000000 0.497036
0.000028 0.860914
0.000056 0.626183
0.000083 0.537434
0.000111 0.451693
...
最后一项的输出显示了pandas.Series的预期。
但是当我将61个元素传递给构造函数时，我得到一个错误
>>> t = Tombstone(np.random.rand(61), rate=10)
>>> t
TypeError: cannot concatenate a non-NDFrame object
即使使用大型数据集，我仍然可以毫无问题地运行命令：
>>> from matplotlib.pyplot import loglog, show
>>> t = Tombstone(np.random.rand(10000), rate=10)
>>> t.noise
>>> loglog(*t.adev); show()
但是当我要求Jupyter笔记本打印t时，我总是会遇到错误。
在浏览堆栈跟踪之后，似乎问题在于pandas尝试连接前几个元素和最后几个元素之间的省略号。运行下面的代码重现堆栈跟踪的最后几行：
>>> pd.concat(t.iloc[10:], t.iloc[:-10])
TypeError Traceback (most recent call last)
<ipython-input-12-86a3d2f95e07> in <module>()
----> 1 pd.concat(t.iloc[10:], t.iloc[:-10])
/Users/wheelerj/miniconda3/lib/python3.5/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
1332 keys=keys, levels=levels, names=names,
1333 verify_integrity=verify_integrity,
-> 1334 copy=copy)
1335 return op.get_result()
1336
/Users/wheelerj/miniconda3/lib/python3.5/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
1389 for obj in objs:
1390 if not isinstance(obj, NDFrame):
-> 1391 raise TypeError("cannot concatenate a non-NDFrame object")
1392
1393 # consolidate
TypeError: cannot concatenate a non-NDFrame object
我找到了一个解决方案，这应该适用于我的情况。我仍然认为有一种方法可以通过将切片表示为NDFrame对象来解决它。也许SO上的其他人可以解决这个问题。
如果我覆盖__repr__类中的Tombstone内置函数，
def __repr__(self):
ret = 'Tombstone('
ret += 'rate=%.3g' % self.rate
# etc...
ret += ')'
return ret
我可以运行以下内容：
>>> t = Tombstone(np.random.rand(61), rate=10)
>>> t
Tombstone(rate=10)