adc: replace NaNs before converting array to integers.

When converting physical to digital sample arrays, we must replace NaN
values (which represent a missing sample) with the appropriate
invalid-sample sentinel value.

Attempting to convert a floating-point NaN to an integer, as was done
here, is implementation-defined behavior (and is controlled, to an
extent, by the global numpy configuration.)  We don't want to be
dependent on the hardware or the global numpy configuration, and for
efficiency it's best to avoid triggering floating-point errors to
begin with.

So instead of converting the floating-point array to integers, and
fixing up the integer array after the fact, we want to replace the
floating-point values *first*, and then convert to integers.

Author: Benjamin Moody

wfdb/io/_signal.py

@@ -539,8 +539,8 @@ def adc_inplace_1d(ch_p_signal, adc_gain, baseline, d_nan):
             np.multiply(ch_p_signal, adc_gain, ch_p_signal)
             np.add(ch_p_signal, baseline, ch_p_signal)
             np.round(ch_p_signal, 0, ch_p_signal)
+            ch_p_signal[ch_nanlocs] = d_nan
             ch_d_signal = ch_p_signal.astype(intdtype, copy=False)
-            ch_d_signal[ch_nanlocs] = d_nan
             return ch_d_signal
 
         # Convert a 2D physical signal array to digital.  Note that the
@@ -550,12 +550,11 @@ def adc_inplace_2d(p_signal):
             np.multiply(p_signal, self.adc_gain, p_signal)
             np.add(p_signal, self.baseline, p_signal)
             np.round(p_signal, 0, p_signal)
-            d_signal = p_signal.astype(intdtype, copy=False)
-
             if nanlocs.any():
-                for ch in range(d_signal.shape[1]):
+                for ch in range(p_signal.shape[1]):
                     if nanlocs[:, ch].any():
-                        d_signal[nanlocs[:, ch], ch] = d_nans[ch]
+                        p_signal[nanlocs[:, ch], ch] = d_nans[ch]
+            d_signal = p_signal.astype(intdtype, copy=False)
             return d_signal
 
         # Do inplace conversion and set relevant variables.