Compatibility with negative physio traces

[m-miedema]

Peak detection returns an error if run on physio traces which have no positive values.

Expected Behavior

Ideally a large negative offset shouldn't break the peakfind_physio() operation!

Actual Behavior

I receive an error which outputs the following:

In [79]: data = operations.peakfind_physio(data, thresh=0.1, dist=100)
C:\Users\memie\miniconda3\envs\physiopy2023\lib\site-packages\peakdet\operations.py:131: RuntimeWarning: Mean of empty slice.
  cdist = np.diff(locs).mean() // 2
C:\Users\memie\miniconda3\envs\physiopy2023\lib\site-packages\numpy\core\_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[79], line 1
----> 1 data = operations.peakfind_physio(data, thresh=0.1, dist=100)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\peakdet\utils.py:40, in make_operation.<locals>.get_call.<locals>.wrapper(data, *args, **kwargs)
     37 params = sig.bind(data, *args, **kwargs).arguments
     39 # actually run function on data
---> 40 data = func(data, *args, **kwargs)
     42 # it shouldn't be, but don't bother appending to history if it is
     43 if data is None:

File ~\miniconda3\envs\physiopy2023\lib\site-packages\peakdet\operations.py:132, in peakfind_physio(data, thresh, dist)
    130 # second, more thorough peak detection
    131 cdist = np.diff(locs).mean() // 2
--> 132 heights = np.percentile(heights['peak_heights'], 1)
    133 locs, heights = signal.find_peaks(data[:], distance=cdist, height=heights)
    134 data._metadata['peaks'] = locs

File <__array_function__ internals>:200, in percentile(*args, **kwargs)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\lib\function_base.py:4205, in percentile(a, q, axis, out, overwrite_input, method, keepdims, interpolation)
   4203 if not _quantile_is_valid(q):
   4204     raise ValueError("Percentiles must be in the range [0, 100]")
-> 4205 return _quantile_unchecked(
   4206     a, q, axis, out, overwrite_input, method, keepdims)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\lib\function_base.py:4473, in _quantile_unchecked(a, q, axis, out, overwrite_input, method, keepdims)
   4465 def _quantile_unchecked(a,
   4466                         q,
   4467                         axis=None,
   (...)
   4470                         method="linear",
   4471                         keepdims=False):
   4472     """Assumes that q is in [0, 1], and is an ndarray"""
-> 4473     return _ureduce(a,
   4474                     func=_quantile_ureduce_func,
   4475                     q=q,
   4476                     keepdims=keepdims,
   4477                     axis=axis,
   4478                     out=out,
   4479                     overwrite_input=overwrite_input,
   4480                     method=method)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\lib\function_base.py:3752, in _ureduce(a, func, keepdims, **kwargs)
   3749             index_out = (0, ) * nd
   3750             kwargs['out'] = out[(Ellipsis, ) + index_out]
-> 3752 r = func(a, **kwargs)
   3754 if out is not None:
   3755     return out

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\lib\function_base.py:4639, in _quantile_ureduce_func(a, q, axis, out, overwrite_input, method)
   4637     else:
   4638         arr = a.copy()
-> 4639 result = _quantile(arr,
   4640                    quantiles=q,
   4641                    axis=axis,
   4642                    method=method,
   4643                    out=out)
   4644 return result

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\lib\function_base.py:4745, in _quantile(arr, quantiles, axis, method, out)
   4737 arr.partition(
   4738     np.unique(np.concatenate(([0, -1],
   4739                               previous_indexes.ravel(),
   4740                               next_indexes.ravel(),
   4741                               ))),
   4742     axis=DATA_AXIS)
   4743 if np.issubdtype(arr.dtype, np.inexact):
   4744     slices_having_nans = np.isnan(
-> 4745         take(arr, indices=-1, axis=DATA_AXIS)
   4746     )
   4747 else:
   4748     slices_having_nans = None

File <__array_function__ internals>:200, in take(*args, **kwargs)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\core\fromnumeric.py:190, in take(a, indices, axis, out, mode)
     93 @array_function_dispatch(_take_dispatcher)
     94 def take(a, indices, axis=None, out=None, mode='raise'):
     95     """
     96     Take elements from an array along an axis.
     97
   (...)
    188            [5, 7]])
    189     """
--> 190     return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)

File ~\miniconda3\envs\physiopy2023\lib\site-packages\numpy\core\fromnumeric.py:57, in _wrapfunc(obj, method, *args, **kwds)
     54     return _wrapit(obj, method, *args, **kwds)
     56 try:
---> 57     return bound(*args, **kwds)
     58 except TypeError:
     59     # A TypeError occurs if the object does have such a method in its
     60     # class, but its signature is not identical to that of NumPy's. This
   (...)
     64     # Call _wrapit from within the except clause to ensure a potential
     65     # exception has a traceback chain.
     66     return _wrapit(obj, method, *args, **kwds)

IndexError: cannot do a non-empty take from an empty axes.``

Steps to Reproduce the Problem

1. load in default RESP data or generate any kind of 'negative' data to read in as physio object
2. run operations.peakfind_physio() on this physio object (error seems replicable across different thresh, dist arguments)  

Specifications

- Python version: 3.9.16
- phys2bids version:  0.2.0rc1
- Platform: Windows (Anaconda Powershell Prompt, Ipython)

Possible solution

The easiest thing could be to check and possibly change offsets in the data on read-in, but the better thing seems like finding where the issue is coming from in the percentile call.

[smoia]

I cannot reproduce the bug. Which version of numpy are you using?

[m-miedema]

I'm using 1.24.2

[me-pic]

Same bug, also with numpy==1.24.2

[smoia]

I don't think it's numpy the problem here. Looking into scipy instead.