这可能很容易解决，但我不知道该怎么做。
我已经扩展了pandas.Series类，以便它可以包含用于研究的数据集。这是我到目前为止写的代码：
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笔记本中运行此操作：
>>> 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
...
上一项的输出显示了预期的pandas.Series。
但是，当我将61个元素传递给构造函数时，我会收到一个错误
>>> t = Tombstone(np.random.rand(61), rate=10)
>>> t
TypeError: cannot concatenate a non-NDFrame object
即使有大数据集，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()
，但是当我向jupyter笔记本电脑询问漂亮打印t时，我总是会遇到错误。
2017-09-13 Update
戳破堆栈跟踪后，似乎问题在于熊猫试图将前几个元素和最后几个元素与两者之间的省略号进行连接时。在下面运行代码将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 a 修复程序，在我的情况下应该有效。我仍然认为可以通过将切片表示为NDFrame对象来解决它。也许其他人可以弄清楚。
如果我覆盖Tombstone班级内的__repr__内置功能，
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)
我认为问题在于您呼叫super().__init__()。pd.Series.__init__()还有许多您没有通过的其他参数。就我而言，我正在获取fastpath参数集，但没有处理。
如果我这样调整您的__init__()，它似乎有效：
def __init__(self, data=None, index=None, rate=None, start=None, scale_factor=0, *args, **kwargs):
if index is None and rate is not None:
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
else:
date_index=index
super().__init__(data, date_index, *args, **kwargs)
if scale_factor:
self.name = 'voltage'
else:
self.name = 'rotation'
self.rate = rate
您需要确保take和通过iloc返回类型的对象进行索引（在这种情况下为Tombstone）。