Merge remote-tracking branch 'origin/develop' into develop

src/lasp/lasp_measurement.py

@@ -264,7 +264,7 @@ class Measurement:
                 try:
                     qtys_json = f.attrs['qtys']
                     # Load quantity data
-                    self._qtys = [Qty.from_json(qty_json) for qty_json in qtys_json] 
+                    self._qtys = [Qty.from_json(qty_json) for qty_json in qtys_json]
                 except KeyError:
                     # If quantity data is not available, this is an 'old'
                     # measurement file.
@@ -274,7 +274,6 @@ class Measurement:
                 self._qtys = [SIQtys.default() for i in range(self.nchannels)]
                 logging.debug(f'Physical quantity data not available in measurement file. Assuming {SIQtys.default}')
 
-
     def setAttribute(self, atrname, value):
         """
         Set an attribute in the measurement file, and keep a local copy in
@@ -395,7 +394,7 @@ class Measurement:
     def timestr(self):
         """
         Return a properly formatted string of the measurement time, in order of
-    
+
         year-month-day hour etc.
 
         """
@@ -426,7 +425,7 @@ class Measurement:
                 meansquare += np.sum(block**2, axis=0) / self.N
 
         avg = sum_/N
-        # In fact, this is not the complete RMS, as in includes the DC 
+        # In fact, this is not the complete RMS, as in includes the DC
         # If p = p_dc + p_osc, then rms(p_osc) = sqrt(ms(p)-ms(p_dc))
         if substract_average:
             meansquare -= avg**2
@@ -437,7 +436,7 @@ class Measurement:
         return self.rms(substract_average=True)
 
     def rawData(self, channels=None, **kwargs):
-        """Returns the raw data as stored in the measurement file, 
+        """Returns the raw data as stored in the measurement file,
         without any transformations applied
 
         args:
@@ -490,10 +489,10 @@ class Measurement:
             window: Window type
             overlap: Overlap percentage (value between 0.0 and up to and
             including 100.0)
-            weighting: 
+            weighting:
 
         Returns:
-            Cross-power-spectra. C[freq, ch_i, ch_j] = C_ij 
+            Cross-power-spectra. C[freq, ch_i, ch_j] = C_ij
 
         """
         nfft = kwargs.pop('nfft', 2048)
@@ -535,14 +534,14 @@ class Measurement:
 
         # Estimate noise power in block
         blocks = [signal[i*N:(i+1)*N] for i in range(Nblocks)]
-        
+
         if noiseCorrection:
-            # The difference between the measured signal in the previous block and 
+            # The difference between the measured signal in the previous block and
             # the current block
             en = [None] + [blocks[i] - blocks[i-1] for i in range(1,Nblocks)]
-            
+
             noise_est = [None] + [-np.average(en[i]*en[i+1]) for i in range(1,len(en)-1)]
-            
+
             # Create weighting coefficients
             sum_inverse_noise = sum([1/n for n in noise_est[1:]])
             c_n = [1/(ni*sum_inverse_noise) for ni in noise_est[1:]]
@@ -551,7 +550,7 @@ class Measurement:
 
         assert np.isclose(sum(c_n), 1.0)
         assert Nblocks-2 == len(c_n)
-        
+
         # Average signal over blocks
         avg = np.zeros((blocks[0].shape), dtype=float)
         for n in range(0, Nblocks-2):
@@ -576,7 +575,7 @@ class Measurement:
         CS = ps.compute(avg)
         freq = getFreq(self.samplerate, N)
 
-        return freq, CS 
+        return freq, CS
 
 
     @property
@@ -632,8 +631,8 @@ class Measurement:
             return False
 
 
-    def exportAsWave(self, fn=None, force=False, newsampwidth=None,
+    def exportAsWave(self, fn=None, force=False, dtype=None,
-                     normalize=True, **kwargs):
+                     normalize=False, **kwargs):
         """Export measurement file as wave. In case the measurement data is
         stored as floats, the values are scaled to the proper integer (PCM)
         data format.
@@ -645,9 +644,8 @@ class Measurement:
             force: If True, overwrites any existing files with the given name
             , otherwise a RuntimeError is raised.
 
-            newsampwidth: sample width in bytes with which to export the data.
+            dtype: if not None, convert data to this data type.
-            This should only be given in case the measurement data is stored as
+            Options are 'int16', 'int32', 'float32'.
-            floating point values, otherwise an error is thrown
 
             normalize: If set: normalize the level to something sensible.
         """
@@ -661,39 +659,49 @@ class Measurement:
         if os.path.exists(fn) and not force:
             raise RuntimeError(f'File already exists: {fn}')
 
-
         if not np.isclose(self.samplerate%1,0):
             raise RuntimeError(f'Sample rates should be approximately integer for exporting to Wave to work')
 
         # TODO: With VERY large measurment files, this is not possible! Is this
         # a theoretical case?
+        # TODO: add sensitivity? Then use self.data() instead of self.rawData()
         data = self.rawData(**kwargs)
-        if np.issubdtype(data.dtype, np.floating) and newsampwidth is None:
-            raise ValueError('Newsampwidth parameter should be given for floating point raw data')
 
         if normalize:
             # Scale back to maximum of absolute value
             maxabs = np.max(np.abs(data))
             data = data / maxabs  # "data /= maxabs" fails if dtpyes differ
 
-        if newsampwidth is not None:
+        if dtype==None:
-            # Convert to floats, then to new sample width
+            dtype = data.dtype  # keep existing
-            sensone = np.ones_like(self.sensitivity)
+            logging.debug(f"dtype not passed as arg; using dtype = {dtype}")
-            data = scaleBlockSens(data, sensone)
 
-            if newsampwidth == 2:
+        # dtype conversion
-                newtype = np.int16
+        if dtype=='int16':
-            elif newsampwidth == 4:
+            newtype = np.int16
-                newtype = np.int32
+            newsampwidth = 2
-            else:
+        elif dtype=='int32':
-                raise ValueError('Invalid sample width, should be 2 or 4')
+            newtype = np.int32
+            newsampwidth = 4
+        elif dtype=='float32':
+            newtype = np.float32
+        elif dtype=='float64':
+            newtype = np.float64
+        else:
+            logging.debug(f"cannot handle this dtype {dtype}")
+            pass
 
+        # Convert range to [-1, 1]
+        # TODO: this is wrong for float data where full scale > 1
+        sensone = np.ones_like(self.sensitivity)
+        data = scaleBlockSens(data, sensone)
+
+        if dtype=='int16' or dtype=='int32':
+            # Scale data to integer range and convert to integers
             scalefac = 2**(8*newsampwidth-1)-1
-
-            # Scale data to integer range, and then convert to integers
             data = (data*scalefac).astype(newtype)
 
-        wavfile.write(fn, int(self.samplerate), data)
+        wavfile.write(fn, int(self.samplerate), data.astype(newtype))
 
     @staticmethod
     def fromtxt(fn,