remove debug, fix cf32 typo

Author: Teque5

sigmf/convert/blue.py

@@ -145,7 +145,7 @@ def detect_endian(data):
     elif endianness == "IEEE":
         return ">"
     else:
-        raise SigMFConversionError(f"Unexpected endianness: {endianness}")
+        raise SigMFConversionError(f"Unsupported endianness: {endianness}")
 
 
 def read_hcb(file_path):
@@ -313,7 +313,7 @@ def read_extended_header(file_path, h_fixed):
     return entries
 
 
-def data_loopback(blue_path: Path, out_path: Path, h_fixed: dict) -> np.ndarray:
+def data_loopback(blue_path: Path, out_path: Path, h_fixed: dict) -> None:
     """
     Write SigMF data file from BLUE file samples.
 
@@ -356,41 +356,26 @@ def data_loopback(blue_path: Path, out_path: Path, h_fixed: dict) -> np.ndarray:
 
     # Determine destination path for SigMF data file
     dest_path = out_path.with_suffix(".sigmf-data")
-    print("#" * 80)
-    print("Writing SigMF data to:", dest_path)
-    print("#" * 80)
 
     # read raw samples
     raw_samples = np.fromfile(blue_path, dtype=np_dtype, offset=HEADER_SIZE_BYTES, count=elem_count)
 
     if is_complex:
-        # complex data: already in IQIQIQ... format or native complex
-        if np_dtype == np.complex64:
+        # check if data is already complex or needs deinterleaving
+        if np.iscomplexobj(raw_samples):
             # already complex, no reassembly needed
             samples = raw_samples
         else:
             # reassemble interleaved IQ samples
             samples = raw_samples[::2] + 1j * raw_samples[1::2]
-            log.debug(f"Deinterleaved {len(raw_samples)} samples into {len(samples)} complex samples")
     else:
         # scalar data
         samples = raw_samples
 
-    # print('dbug', samples.dtype, len(samples), get_normalization_factor(fmt))
-
-    # normalize if needed
-    # if should_normalize:
-    #     norm_factor = get_normalization_factor(fmt)
-    #     if norm_factor:
-    #         samples /= norm_factor
-
     # save out as SigMF IQ data file
     samples.tofile(dest_path)
     log.info("wrote %s", dest_path)
 
-    # return the IQ data if needed for further processing if needed
-    return samples
-
 
 def construct_sigmf(
     out_path: Path, h_fixed: dict, h_keywords: dict, h_adjunct: dict, h_extended: list, is_metadata_only: bool = False
@@ -627,7 +612,7 @@ def blue_to_sigmf(
     implement a function that instead writes metadata only for a non-conforming
     dataset using the HEADER_BYTES_KEY and TRAILING_BYTES_KEY in most cases.
     """
-    log.debug(f"convert {blue_path}")
+    log.debug(f"read {blue_path}")
 
     blue_path = Path(blue_path)
     if out_path is None:
@@ -650,7 +635,7 @@ def blue_to_sigmf(
 
     # write to SigMF data file only if samples exist
     if not is_metadata_only:
-        _ = data_loopback(blue_path, out_path, h_fixed)
+        data_loopback(blue_path, out_path, h_fixed)
     else:
         log.info("skipping data file creation for zero-sample BLUE file")
 

tests/test_convert.py

@@ -91,25 +91,20 @@ def test_blue_to_sigmf(self):
             # # plot stft of RF data for visual inspection
             # import matplotlib.pyplot as plt
             # from scipy.signal import spectrogram
-            # from swiftfox import summary
+            # from swiftfox import summary, smartspec
 
+            # if meta.get_global_field("core:metadata_only"):
+            #     print("Metadata only file, skipping plot.")
+            #     continue
             # samples = meta.read_samples()
-            # plt.figure(figsize=(10, 10))
-            # summary(samples, detail=0.1, samp_rate=meta.get_global_field("core:sample_rate"))
+            # # plt.figure(figsize=(10, 10))
+            # summary(samples, detail=0.1, samp_rate=meta.get_global_field("core:sample_rate"), title=sigmf_path.name)
             # plt.figure()
-            # plt.plot(samples.real)
-            # plt.plot(samples.imag)
-
-            # freqs, times, spec = spectrogram(samples, fs=meta.get_global_field("core:sample_rate"), nperseg=1024)
+            # # plt.plot(samples.real)
+            # # plt.plot(samples.imag)
+            # # plt.figure()
+            # spec = smartspec(samples, detail=0.5, samp_rate=meta.get_global_field("core:sample_rate"))
             # # use imshow to plot spectrogram
 
-            # plt.figure()
-            # plt.imshow(
-            #     10 * np.log10(spec), aspect="auto", extent=[times[0], times[-1], freqs[0], freqs[-1]], origin="lower"
-            # )
-            # plt.colorbar(label="Intensity [dB]")
-            # plt.ylabel("Frequency [Hz]")
-            # plt.xlabel("Time [s]")
-            # plt.title(f"Spectrogram of {bluefile.name}")
             # plt.show()
             self.assertIsInstance(meta, sigmf.SigMFFile)