Handle all-NaN channels in calc_adc_params

[bemoody]

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 (numpy/numpy#8975, numpy/numpy#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.