Better implementation of 'Measurement.fromWaveFile, using scipy, which is able to handle wave files with floating point data

lasp/device/lasp_daqconfig.py

@@ -88,7 +88,6 @@ class DAQConfiguration:
         en_input = self.input_channel_configs
         first_ch_enabled_found = False
         ch_disabled_found_after = False
-        print(en_input)
         for ch in en_input:
             if ch.channel_enabled:
                 first_ch_enabled_found = True

lasp/filter/colorednoise.py

@@ -11,6 +11,7 @@ import numpy as np
 
 __all__ = ['PinkNoise'] #, 'BrownianNoise', 'BlueNoise']
 
+
 def PinkNoise(fs, fstart=10, fend=None, N=3):
     """
     Creates SOS filter for pink noise. The filter has a flat response below

lasp/lasp_common.py

@@ -17,7 +17,6 @@ Common definitions used throughout the code.
 __all__ = [
     'P_REF', 'FreqWeighting', 'TimeWeighting', 'getTime', 'getFreq',
     'lasp_shelve', 'this_lasp_shelve', 'W_REF', 'U_REF', 'I_REF', 'dBFS_REF',
-    'SIUnits'
 ]
 
 # Reference sound pressure level

lasp/lasp_measurement.py

@@ -39,7 +39,7 @@ from contextlib import contextmanager
 import h5py as h5
 import numpy as np
 from .lasp_config import LASP_NUMPY_FLOAT_TYPE
-import wave
+from scipy.io import wavfile
 import os
 import time
 
@@ -537,31 +537,25 @@ class Measurement:
         if os.path.exists(newfn) and not force:
             raise RuntimeError(f'Measurement file name {newfn} already exists in path, set "force" to true to overwrite')
 
-        with wave.open(fn, 'r') as wf:
-            nchannels = wf.getnchannels()
-            samplerate = wf.getframerate()
-            sensitivity = np.ones(nchannels)
-            sampwidth = wf.getsampwidth()
-            nframes = wf.getnframes()
-            if sampwidth == 2:
-                dtype = np.int16
-            elif sampwidth == 1:
-                dtype = np.int8
-            elif sampwidth == 4:
-                dtype = np.int32
+        samplerate, data = wavfile.read(fn)
+        if data.ndim == 2:
+            nframes, nchannels = data.shape
+        else:
+            nchannels = 1
+            nframes = len(data)
+            data = data[:, np.newaxis]
+        sensitivity = np.ones(nchannels)
 
-            with h5.File(newfn, 'w') as hf:
-                hf.attrs['samplerate'] = samplerate
-                hf.attrs['nchannels'] = nchannels
-                hf.attrs['time'] = timestamp
-                hf.attrs['blocksize'] = 1
-                hf.attrs['sensitivity'] = sensitivity
-                data = np.frombuffer(
-                    wf.readframes(nframes), dtype=dtype).reshape(nframes, nchannels)
-                ad = hf.create_dataset('audio', (1, data.shape[0], data.shape[1]),
-                                   dtype=dtype,
-                                   maxshape=(1, data.shape[0], data.shape[1]),
-                                   compression='gzip')
-                ad[0] = data
+        with h5.File(newfn, 'w') as hf:
+            hf.attrs['samplerate'] = samplerate
+            hf.attrs['nchannels'] = nchannels
+            hf.attrs['time'] = timestamp
+            hf.attrs['blocksize'] = 1
+            hf.attrs['sensitivity'] = sensitivity
+            ad = hf.create_dataset('audio', (1, nframes, nchannels),
+                               dtype=data.dtype,
+                               maxshape=(1, nframes, nchannels),
+                               compression='gzip')
+            ad[0] = data
 
-            return Measurement(newfn)
+        return Measurement(newfn)