Spell check

BBPs/2-manual_verification.py

@@ -109,8 +109,8 @@ for file in range(len(audio_paths)):
 		    width=[0,max_length])[0]
 
 		Magnitude_audio = stft(signal_high, n_fft=nfft, hop_length=hop_length)
-		#spectre = amplitude_to_db(np.abs(Magnitude_audio))
-		spectre = pcen(np.abs(Magnitude_audio) * (2**31))
+		#spectrum = amplitude_to_db(np.abs(Magnitude_audio))
+		spectrum = pcen(np.abs(Magnitude_audio) * (2**31))
 
 		# show ICI of each BBP
 		n_BBP = np.unique(BBP_all[use,-1])
@@ -143,7 +143,7 @@ for file in range(len(audio_paths)):
 				axs[0].axis(xmin=lower, xmax=upper)
 				axs[0].tick_params('x', labelbottom=False, bottom=False)
 
-				axs[1].imshow(spectre[::-1][:,int(lower/hop_length):int(upper/hop_length)], 
+				axs[1].imshow(spectrum[::-1][:,int(lower/hop_length):int(upper/hop_length)], 
 				    aspect='auto', interpolation='none', cmap='jet', 
 				    extent=(0,(upper-lower)/sr,0,int(sr/2)))
 				axs[1].set_title("Spectrogram (dB scale)")
@@ -195,7 +195,7 @@ for file in range(len(audio_paths)):
 
 			plt.close('all')
 		del signal, signal_high, signal_peaks, tk_signal,\
-		Magnitude_audio, spectre
+		Magnitude_audio, spectrum
 
 		dict_annot[audio_paths[file]] = BBP_manual
 		# Temp save (in case of a crash)

Whistles/1-Identification_of_whistles.py

@@ -71,14 +71,14 @@ for file in range(len(audio_paths)):
 	# resample
 	signal_dec = resample(signal, int(((stop-start)*new_sr)))
 
-	# extract spectral informations
+	# extract spectrum informations
 	Magnitude_audio = stft(signal_dec, n_fft=nfft, hop_length=hop_length)
-	spectre = np.copy(np.abs(Magnitude_audio[f_min:,:]))
-	# PCEN could replace spectrogram in very noisy recordings
-	#spectre_pcen = pcen(np.abs(Magnitude_audio) * (2**31), bias=10)[f_min:,:]
+	spectrum = np.copy(np.abs(Magnitude_audio[f_min:,:]))
+	# PCEN could replace spectrum in very noisy recordings
+	#spectrum_pcen = pcen(np.abs(Magnitude_audio) * (2**31), bias=10)[f_min:,:]
 
 	# Selection algorithm
-	max_loc_per_bin_check1 = get_local_maxima(spectre, spectre, nrg_rap)[1]
+	max_loc_per_bin_check1 = get_local_maxima(spectrum, spectrum, nrg_rap)[1]
 	trajectories = get_trajectories(max_loc_per_bin_check1, dist_f=dist_f, dist_t=dist_t)
 	final_traj = select_trajectories(trajectories, taille_traj_min, min_acce, max_acce, verbose=0)
 	corrected_traj = sparsity_ridoff(final_traj, error_thresh=sparsity)

Whistles/WhistleUtils.py

@@ -112,11 +112,11 @@ def get_csv(csv_folder, slash="\\"):
     return data_frame, sorted_names
 
 #%% Trajectories algorithms
-def get_local_maxima(spectrogram, spectrogram2, hardness, threshold=10e-5):
+def get_local_maxima(spectrum, spectrum2, hardness, threshold=10e-5):
     """
     Parameters
     ----------
-    spectrogram : NUMPY ARRAY 
+    spectrum : NUMPY ARRAY 
         Spectrogram (float values) of an audio signal. 
     hardness : INT or FLOAT
         Number of times a value has to be above the geometric mean in order to be kept.
@@ -128,21 +128,21 @@ def get_local_maxima(spectrogram, spectrogram2, hardness, threshold=10e-5):
     local_max2 : NUMPY ARRAY
         Spectrogram with 1 & 0 indicating local maxima above hardness*geom_mean
     """
-    local_max1 = np.zeros(spectrogram.shape, dtype=int)
-    local_max2 = np.zeros(spectrogram.shape, dtype=int)
-    geom_mean = gmean(gmean(spectrogram, axis=1))
-    geom_mean0 = gmean(spectrogram, axis=0)
-    geom_mean1 = gmean(spectrogram, axis=1)
-    # geom_mean0, geom_mean1 = gmean(spectrogram), gmean(spectrogram)
-
-    for each_bin in range(spectrogram.shape[1]):
-        for freq in range(1, spectrogram.shape[0]-1):
-            if (spectrogram2[freq, each_bin] > spectrogram2[freq-1, each_bin]) and \
-            (spectrogram2[freq, each_bin] > spectrogram2[freq+1, each_bin]):
+    local_max1 = np.zeros(spectrum.shape, dtype=int)
+    local_max2 = np.zeros(spectrum.shape, dtype=int)
+    geom_mean = gmean(gmean(spectrum, axis=1))
+    geom_mean0 = gmean(spectrum, axis=0)
+    geom_mean1 = gmean(spectrum, axis=1)
+    # geom_mean0, geom_mean1 = gmean(spectrum), gmean(spectrum)
+
+    for each_bin in range(spectrum.shape[1]):
+        for freq in range(1, spectrum.shape[0]-1):
+            if (spectrum1[freq, each_bin] > spectrum2[freq-1, each_bin]) and \
+            (spectrum2[freq, each_bin] > spectrum2[freq+1, each_bin]):
                 local_max1[freq, each_bin] = 1
-                if (spectrogram[freq, each_bin] > (threshold)):
-                    if (spectrogram[freq, each_bin] > (geom_mean0[each_bin]*hardness)):
-                        if (spectrogram[freq, each_bin] > (geom_mean1[freq]*hardness)):
+                if (spectrum[freq, each_bin] > (threshold)):
+                    if (spectrum[freq, each_bin] > (geom_mean0[each_bin]*hardness)):
+                        if (spectrum[freq, each_bin] > (geom_mean1[freq]*hardness)):
                             local_max2[freq, each_bin] = 1
 
     return local_max1, local_max2