V 1.0.1 (#12)

* v-1.0.1

* Changed deployment to production

.gitignore

@@ -26,9 +26,10 @@ share/python-wheels/
 *.egg
 MANIFEST
 .github
-.github/*
-.github/workflows/
-.github/workflows/*
+
+# Don't ignore .github/workflows
+!.github/
+!.github/workflows/
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

README.md

@@ -78,7 +78,7 @@ This will upscale the MP3 file specified in the example and produce a FLAC file
 
 ## Spectrogram Results
 
-![Spectrogram Results](https://drive.google.com/uc?export=view&id=1Y-NHkwrSfrVexH1yYtzxcLOWQuLdvScL)
+![Spectrogram Results](https://drive.google.com/uc?export=view&id=1_QgMQ8FR1Rryyw22bBQa0EAEGIjw9eS_)
 
 ## How it works
 
@@ -106,16 +106,17 @@ The report titled "Fast Sparse Fourier Transformations for NMR Spectroscopy" by
 
 ericzo - beyond link(https://soundcloud.com/ericzomusic/free-electro-trap-anthem-beyond)
 
-## Run Tests
-
-Clone the repository and install the requirements. Test the packaged files:
-```
-python -m unittest discover -s fat_llama/tests
-```
 ## Changelog
 
 All notable changes to this project will be documented in this file.
 
+### [1.0.1] - 2024-07-26
+
+#### Changed
+
+- Updated `analytics.py` analysis and spectorgram results.
+- Updated `README.md` details.
+
 ### [1.0.0] - 2024-07-25
 
 #### Added

analysis.py

@@ -22,15 +22,15 @@ def read_flac(file_path):
 def normalize(signal):
     return signal / np.max(np.abs(signal))
 
-def compare_signals(mp3, flac):
+def compare_signals(mp3, flac, sample_rate):
     # Normalize signals
     mp3 = normalize(mp3)
     flac = normalize(flac)
 
     min_len = min(len(mp3), len(flac))
     mp3 = mp3[:min_len]
     flac = flac[:min_len]
-    time = np.arange(min_len)
+    time = np.arange(min_len) / sample_rate
 
     print(f"MP3 (first 10 samples): {mp3[:10]}")
     print(f"FLAC (first 10 samples): {flac[:10]}")
@@ -40,7 +40,7 @@ def compare_signals(mp3, flac):
     plt.plot(time, mp3, label='MP3')
     plt.plot(time, flac, label='FLAC')
     plt.legend()
-    plt.xlabel('Time')
+    plt.xlabel('Time (s)')
     plt.ylabel('Amplitude')
     plt.title('Waveform Comparison')
     plt.show()
@@ -50,7 +50,7 @@ def compare_signals(mp3, flac):
     plt.figure(figsize=(14, 7))
     plt.plot(time, difference_signal, label='Difference Signal')
     plt.legend()
-    plt.xlabel('Time')
+    plt.xlabel('Time (s)')
     plt.ylabel('Amplitude')
     plt.title('Difference Signal')
     plt.show()
@@ -60,21 +60,28 @@ def compare_signals(mp3, flac):
     print(f"Mean Squared Error: {mse}")
 
     # 4. Spectrogram Comparison
-    f1, t1, Sxx1 = signal.spectrogram(mp3)
-    f2, t2, Sxx2 = signal.spectrogram(flac)
+    nperseg = 2048
+    f1, t1, Sxx1 = signal.spectrogram(mp3, fs=sample_rate, nperseg=nperseg)
+    f2, t2, Sxx2 = signal.spectrogram(flac, fs=sample_rate, nperseg=nperseg)
 
     plt.figure(figsize=(14, 7))
     plt.subplot(2, 1, 1)
-    plt.pcolormesh(t1, f1, 10 * np.log10(Sxx1))
+    plt.pcolormesh(t1, f1, 10 * np.log10(Sxx1), vmin=-120, vmax=0)
     plt.title('Spectrogram of MP3')
-    plt.ylabel('Frequency [Hz]')
-    plt.xlabel('Time [sec]')
+    plt.ylabel('Frequency (Hz)')
+    plt.xlabel('Time (s)')
+    plt.colorbar(label='SNR (dB)')
+    plt.yscale('log')
+    plt.ylim(20, 25000)
 
     plt.subplot(2, 1, 2)
-    plt.pcolormesh(t2, f2, 10 * np.log10(Sxx2))
+    plt.pcolormesh(t2, f2, 10 * np.log10(Sxx2), vmin=-120, vmax=0)
     plt.title('Spectrogram of FLAC')
-    plt.ylabel('Frequency [Hz]')
-    plt.xlabel('Time [sec]')
+    plt.ylabel('Frequency (Hz)')
+    plt.xlabel('Time (s)')
+    plt.colorbar(label='SNR (dB)')
+    plt.yscale('log')
+    plt.ylim(20, 25000)
     plt.tight_layout()
     plt.show()
 
@@ -126,4 +133,5 @@ def compare_signals(mp3, flac):
         else:
             mp3 = resample(mp3, len(flac))
 
-    compare_signals(mp3, flac)
+    sample_rate = sample_rate_mp3  # Assuming both are the same after resampling
+    compare_signals(mp3, flac, sample_rate)

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='fat_llama',
-    version='1.0.0',
+    version='1.0.1',
     packages=find_packages(),
     install_requires=[
         'numpy',
@@ -28,7 +28,7 @@
     long_description_content_type='text/markdown',
     url='https://github.com/bkraad47/fat_llama',
     classifiers=[
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Developers',
         'License :: OSI Approved :: BSD License',
         'Programming Language :: Python :: 3.9',