Fixed spectral flux single frame problem. Changed imputer axis to correct one.

src/sppysound/analysis/SpectralFluxAnalysis.py

@@ -72,7 +72,7 @@ class SpectralFluxAnalysis(Analysis):
         # magnitude.
         rolled_mags = np.roll(magnitudes, 1, axis=0)[1:]
         sum_of_squares = np.sum((magnitudes[1:]-rolled_mags)**2., axis=1)
-        spectral_flux = np.sqrt(sum_of_squares) / (np.size(fft, axis=1)/2)
+        spectral_flux = np.sqrt(sum_of_squares) / (np.size(fft, axis=1))
 
         return spectral_flux
 
@@ -83,6 +83,8 @@ class SpectralFluxAnalysis(Analysis):
 
         # Get number of frames for time and frequency
         timebins = spcflux_frames.shape[0]
+        if not timebins:
+            return np.array([])
         # Create array ranging from 0 to number of time frames
         scale = np.arange(timebins+1)
         # divide the number of samples by the total number of frames, then

src/sppysound/config.py

@@ -48,8 +48,8 @@ matcher_weightings = {
     "centroid": 1.,
     "kurtosis": 1.,
     "skewness": 1.,
-    "variance": 3.,
-    "harm_ratio": 3.
+    "variance": 1.,
+    "harm_ratio": 1.
 }
 
 # Specifies the method for averaging analysis frames to create a single value

src/sppysound/database.py

@@ -426,9 +426,10 @@ class Matcher:
                 all_target_analyses[i] = target_data
 
 
-            imp = Imputer(axis=1)
-            nan_columns = np.all(np.isnan(all_target_analyses), axis=1)
-            all_target_analyses[nan_columns, :] = 0.
+            pdb.set_trace()
+            imp = Imputer(axis=0)
+            nan_columns = np.all(np.isnan(all_target_analyses), axis=0)
+            all_target_analyses[:, nan_columns] = 0.
             # Impute values for Nans
             all_target_analyses = imp.fit_transform(all_target_analyses)
 
@@ -450,8 +451,8 @@ class Matcher:
 
 
                 # Impute values for Nans
-                nan_columns = np.all(np.isnan(all_source_analyses), axis=1)
-                all_source_analyses[nan_columns, :] = 0.
+                nan_columns = np.all(np.isnan(all_source_analyses), axis=0)
+                all_source_analyses[:, nan_columns] = 0.
                 all_source_analyses = imp.fit_transform(all_source_analyses)
 
                 source_tree = spatial.cKDTree(all_source_analyses.T, leafsize=100)
@@ -725,8 +726,11 @@ class Matcher:
         # Find indexes within the range of each source sample index.
         x = np.logical_and(
             np.vstack(x)>=source_sample_indexes[:,0],
-            np.vstack(x)<source_sample_indexes[:,1]
+            np.vstack(x)<=source_sample_indexes[:,1]
         )
+
+        if not np.all(np.any(x, axis=1)):
+            raise ValueError("Not all match indexes have a corresponding sample index. This shouldn't happen...")
         x = x.reshape(mi_shape[0], mi_shape[1], x.shape[1])
         x = np.argmax(x, axis=2)
 

src/tests/audiofile_tests.py

@@ -600,9 +600,9 @@ class SpectralFluxAnalysisTests(globalTests):
 
     def test_GenerateSpectralFlux(self):
         x = np.vstack((self.equal_mag, self.peak))
-        output = analysis.SpectralFluxAnalysis.create_spcflux_analysis(x, 512)
+        output = analysis.SpectralFluxAnalysis.create_spcflux_analysis(x)
         x = np.vstack((self.peak, self.peak))
-        output1 = analysis.SpectralFluxAnalysis.create_spcflux_analysis(x, 512)
+        output1 = analysis.SpectralFluxAnalysis.create_spcflux_analysis(x)
         self.assertTrue(output[0] > output1[0])