Handle all-NaN channels in calc_adc_params

Bắt đầu trong wfdb/io/_signal.py tại calc_adc_params và lần theo cách xử lý nanmin/nanmax được wfdb.wrsamp sử dụng. Tái hiện ví dụ về một kênh có toàn bộ giá trị là NaN, sau đó xác minh rằng các kênh có toàn bộ giá trị là NaN không còn phát sinh cảnh báo hoặc bị lỗi, trong khi các kênh chứa giá trị vẫn giữ nguyên hành vi hiện có.

If all samples in a channel are NaN, calc_adc_params will fail:

>>> wfdb.wrsamp("xxx", fs=500, units=["mV"], sig_name=["I"], p_signal=numpy.array([[numpy.nan]]), fmt=["16"])
/home/bmoody/work/wfdb-python/wfdb/io/_signal.py:740: RuntimeWarning: All-NaN slice encountered
  minvals = np.nanmin(self.p_signal, axis=0)
/home/bmoody/work/wfdb-python/wfdb/io/_signal.py:741: RuntimeWarning: All-NaN slice encountered
  maxvals = np.nanmax(self.p_signal, axis=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/bmoody/work/wfdb-python/wfdb/io/record.py", line 2943, in wrsamp
    record.set_d_features(do_adc=1)
  File "/home/bmoody/work/wfdb-python/wfdb/io/_signal.py", line 470, in set_d_features
    self.adc_gain, self.baseline = self.calc_adc_params()
  File "/home/bmoody/work/wfdb-python/wfdb/io/_signal.py", line 787, in calc_adc_params
    baseline = int(np.floor(baseline))
ValueError: cannot convert float NaN to integer

A couple things are wrong here:

1. if pmin == np.nan doesn't do what you think.

2. nanmin and nanmax will give a RuntimeWarning if all samples in a channel are NaN.

(1) is easy to fix. (2) is a little weirder; have a look at the code of nanmin:

    if type(a) is np.ndarray and a.dtype != np.object_:
        # Fast, but not safe for subclasses of ndarray, or object arrays,
        # which do not implement isnan (gh-9009), or fmin correctly (gh-8975)
        res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
        if np.isnan(res).any():
            warnings.warn("All-NaN slice encountered", RuntimeWarning,
                          stacklevel=3)

In other words, for ordinary numeric numpy arrays, np.fmin.reduce gives what we want (minimum non-NaN value if there is one, otherwise NaN, and no warning.) It might not work if the array is something more exotic (e.g. a numpy-compatible array class created by some other python package.)

I think I understand the comment about object arrays (https://github.com/numpy/numpy/issues/8975, https://github.com/numpy/numpy/issues/9009), but I don't understand the "subclasses of ndarray" comment. When I try creating a trivial subclass of ndarray, fmin still appears to work as expected. So I don't see why the strict is np.ndarray is needed.