formatting

Author: Teque5

docs/source/quickstart.rst

@@ -90,14 +90,14 @@ For fixed-point datasets, you can control whether samples are automatically scal
 .. code-block:: python
 
     import sigmf
-    
+
     # Default behavior: autoscale fixed-point data to [-1.0, 1.0] range
     handle = sigmf.fromfile("fixed_point_data.sigmf")
     samples = handle.read_samples()  # Returns float32/complex64
-    
+
     # Disable autoscaling to access raw integer values
-    handle_raw = sigmf.fromfile("fixed_point_data.sigmf", autoscale=False)  
+    handle_raw = sigmf.fromfile("fixed_point_data.sigmf", autoscale=False)
     raw_samples = handle_raw.read_samples()  # Returns original integer types
-    
+
     # Both slicing and read_samples() respect the autoscale setting
     assert handle[0:10].dtype == handle.read_samples(count=10).dtype

sigmf/sigmffile.py

@@ -221,7 +221,7 @@ def __next__(self):
     def __getitem__(self, sli):
         mem = self._memmap[sli]  # matches behavior of numpy.ndarray.__getitem__()
 
-        # original behavior: always apply _return_type conversion if set
+        # apply _return_type conversion if set
         if self._return_type is None:
             # no special conversion needed
             if not self.autoscale:
@@ -253,7 +253,7 @@ def __getitem__(self, sli):
                     # floating-point data, no scaling needed
                     return mem
 
-        # handle complex data type conversion (original behavior)
+        # handle complex data type conversion
         if self._memmap.ndim == 2:
             # num_channels == 1
             ray = mem[:, 0].astype(self._return_type) + 1.0j * mem[:, 1].astype(self._return_type)