diff --git a/mosqito/functions/variant_filter/__init__.py b/mosqito/functions/variant_filter/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mosqito/functions/variant_filter/variant_filter.py b/mosqito/functions/variant_filter/variant_filter.py
new file mode 100644
index 00000000..6f6b16d0
--- /dev/null
+++ b/mosqito/functions/variant_filter/variant_filter.py
@@ -0,0 +1,137 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec 12 12:28:15 2020
+
+@author: josem
+"""
+
+import scipy.signal as sig
+from mosqito.functions.shared.load import load
+
+# FIR filter definition
+#Parameters:
+# Required input definitions are as follows;
+# data:   The data to be filtered
+# fs:     Sampling frecuency
+# fc:   The centerline frequency to be filtered
+# band:   The bandwidth around the centerline frequency that you wish to filter
+#Returns
+# Filtered data
+
+def fir_notch_filter(data, fs, fc, band):
+    #Order of the FIR filter
+    numtaps = 999
+    nyq = fs / 2    
+   
+    #Lower and upper filter cut-off frequencies are determined
+    if fc < band / 2:
+        band = fc / 2 
+        low = (fc - band / 2) / nyq
+        high = (fc + band / 2) / nyq
+    else:
+        low = (fc - band / 2) / nyq
+        high = (fc + band / 2) / nyq
+    
+    #The coefficients of a lowpass and a highpass filters are obtained, using Hamming window
+    h_l = sig.firwin(
+        numtaps,low, window = 'blackman', pass_zero='lowpass'
+    )
+    h_h = sig.firwin(
+        numtaps,high, window = 'blackman', pass_zero='highpass'
+    )
+
+    #Filters the data applying the obtained coefficients
+    filtered_data = sig.filtfilt(h_l, 1, data, padlen=0) + sig.filtfilt(h_h, 1, data, padlen=0)
+
+    return filtered_data
+
+# IIR filter definition
+#Parameters:
+# data:   The data to be filtered
+# fs:     Sampling frecuency
+# fc:   The centerline frequency to be filtered
+# band:   The bandwidth around the centerline frequency that you wish to filter
+#Return:
+# Filtered data
+
+def iir_notch_filter(data, fs, fc, band):
+   #Determines the quality factor
+    Q = fc / band  
+    
+    #The coefficients of a band remover filter are obtained
+    b, a = sig.iirnotch(fc, Q, fs = fs)
+    
+    #Filters the data applying the obtained coefficients
+    filtered_data = sig.filtfilt(b, a, data, padlen=0)
+    
+    return filtered_data
+
+# Variant filter definition
+#Parameters:
+# signal:     Signal to be filtered
+# track:   Signal to track the harmonics
+# ftype:   Type of filter. 'FIR' or 'IIR'
+# harmonic_order:   Order of the harmonic to filter
+#Returns:
+# Original signal
+# Filtered signal
+# Sampling frequency
+
+def variant_filter(original_signal, signal_tracking, ftype, harmonic_order, att):
+    #Load original and tracking data
+    if(isinstance(signal_tracking, str)):
+        signal_tracking, fs = load(False, signal_tracking, calib = 1)
+    
+    if(isinstance(original_signal, str)):
+        original_signal, fs = load(False, original_signal, calib = 2 * 2**0.5 )   
+    
+    if not(isinstance(signal_tracking, str) or isinstance(original_signal, str)):
+        fs = 48000
+    
+    #Band selector
+    if(ftype == 'fir'):
+        if(att == 3):
+            band = 25     #Atenuation = 3 dB (FIR)
+        if (att == 6):
+            band = 130  #Atenuation = 6 dB (FIR and IIR)
+        if(att == 9):
+            band = 330    #Atenuation = 9 dB (FIR and IIR)
+        if(att == 12):
+            band = 600   #Atenuation = 12 dB (FIR)
+    if(ftype == 'iir'):
+        if(att == 3):
+            band = 55    #Atenuation = 3 dB (IIR)
+        if(att == 6):
+            band = 150  #Atenuation = 6 dB (FIR and IIR)
+        if(att == 9):
+            band = 330    #Atenuation = 9 dB (FIR and IIR)
+        if(att == 12):
+            band = 500   #Atenuation = 12 dB (IIR)
+                
+    i = int(fs / 16)    #Segment size
+    j = 0               
+    h = int(fs / 32)    #Segment size to overlap
+    filtered_signal = []
+    
+    while i <= len(signal_tracking):
+        
+        #Value of RPM
+        rpm = signal_tracking[j]
+        
+        #The center frequency is calculated
+        if(rpm == 0):
+            fc = 1
+        else: 
+            fc = (harmonic_order * rpm) / 6
+        
+        #The filter is applied
+        if ftype == 'fir':   
+            filtered_signal[j:i] = fir_notch_filter(original_signal[j:i], fs, fc, band)
+                      
+        if ftype == 'iir':
+            filtered_signal[j:i] = iir_notch_filter(original_signal[j:i], fs, fc, band) 
+            
+        j = i - h
+        i = int(j + fs / 16)
+    
+    return original_signal, filtered_signal, fs
\ No newline at end of file
diff --git a/mosqito/tests/variant_filter/__init__.py b/mosqito/tests/variant_filter/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mosqito/tests/variant_filter/final_test_variant_filter.py b/mosqito/tests/variant_filter/final_test_variant_filter.py
new file mode 100644
index 00000000..52e39399
--- /dev/null
+++ b/mosqito/tests/variant_filter/final_test_variant_filter.py
@@ -0,0 +1,184 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec 12 12:28:15 2020
+
+@author: josem
+"""
+
+import numpy as np
+import matplotlib as mpl
+import scipy.signal as sig
+import pylab as plt
+from mosqito.functions.shared.load import load
+from matplotlib import gridspec
+from scipy.io.wavfile import write
+from mosqito.functions.variant_filter.variant_filter import variant_filter
+
+#Filter parameters
+harmonic_order = 1     #harmonics 1, 2, 3, ...
+att = 12
+
+signal = "mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.UFF"
+track = "mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.UFF"
+
+#Aplication of the variant_filter function
+original_signal, fir_filtered_signal, Fs = variant_filter(
+        signal, track,'fir', harmonic_order, att
+)
+original_signal, iir_filtered_signal, Fs = variant_filter(
+        signal, track,'iir', harmonic_order, att
+)
+
+#Scale signals to integers to write a wav file
+scale = np.max([np.max(np.abs(original_signal)), np.max(np.abs(iir_filtered_signal))])
+scaled_o = np.int16(original_signal / scale * 32767)
+scaled_fir = np.int16(fir_filtered_signal / scale * 32767)
+scaled_iir = np.int16(iir_filtered_signal / scale * 32767)
+
+write('motor_signal.wav', Fs, scaled_o)
+write('FIR_filtered_motor_signal_'+str(harmonic_order)+' harmonic.wav', Fs, scaled_fir)
+write('IIR_filtered_motor_signal_'+str(harmonic_order)+' harmonic.wav', Fs, scaled_iir)
+
+#RPM signal
+rpm_signal, fs = load(False, track, calib = 1)
+l_rpm=len(rpm_signal)
+t_rpm = np.arange(0, l_rpm)/ fs # Time interval in seconds
+
+#Plotting results
+
+#Average spectrum of 24000 samples
+plt.figure()
+
+f, Pxx_spec = sig.welch(
+    original_signal[1200000:1224000], fs, 'flattop', 1024, scaling='spectrum'
+)
+
+plt.semilogy(f, np.sqrt(Pxx_spec), color = 'darkred')
+plt.xlabel('frequency [Hz]')
+plt.ylabel('Linear spectrum [V RMS]')
+
+f, Pxx_spec = sig.welch(
+    fir_filtered_signal[1200000:1224000], fs,'flattop', 1024, scaling='spectrum'
+)
+plt.semilogy(f, np.sqrt(Pxx_spec), color = 'darkblue', linestyle='--')
+plt.xlabel('frequency [Hz]')
+plt.ylabel('Linear spectrum [V RMS]')
+
+f, Pxx_spec = sig.welch(
+    iir_filtered_signal[1200000:1224000], fs,'flattop', 1024, scaling='spectrum'
+)
+axes = plt.axes()
+axes.set_xlim([0,3000])
+plt.semilogy(f, np.sqrt(Pxx_spec), color='darkgreen', linestyle='--')
+plt.xlabel('frequency [Hz]')
+plt.ylabel('Linear spectrum [V RMS]')
+plt.title('Power Spectrum')
+plt.show()
+
+#Plot Spectrogram original signal
+NFFT = 4096
+noverlap = NFFT / 2
+vmin = 20*np.log10(np.max(original_signal)) - 70
+
+#Set height ratios for subplots
+gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) 
+
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    original_signal, 
+    NFFT=NFFT,                                 
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=plt.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Original Signal')
+plt.axis([0,37,0,3000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+norm = mpl.colors.Normalize(vmin=-25, vmax=30)
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(cmap = plt.cm.jet, norm=norm),
+    cax=cax).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal FIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    fir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=plt.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('FIR Filtered signal')
+plt.axis([0,37 ,0,3000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal IIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    iir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=plt.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('IIR Filtered signal')
+plt.axis([0,37 ,0,3000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
\ No newline at end of file
diff --git a/mosqito/tests/variant_filter/ramp_test_signal.py b/mosqito/tests/variant_filter/ramp_test_signal.py
new file mode 100644
index 00000000..662f23e3
--- /dev/null
+++ b/mosqito/tests/variant_filter/ramp_test_signal.py
@@ -0,0 +1,239 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 13 17:52:53 2021
+
+@author: josem
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import pylab as pylab
+import scipy.signal as sig
+from scipy.signal import chirp
+from matplotlib import gridspec
+from scipy.io.wavfile import write
+from mosqito.functions.variant_filter.variant_filter import variant_filter
+
+
+#White noise definition
+#Parameters:
+#   rhp - spectral noise density unit/SQRT(Hz)
+#   sr  - sample rate
+#   n   - no of points
+#   mu  - mean value, optional
+#Returns:
+#   n points of noise signal with spectral noise density of rho
+
+def white_noise(rho, sr, n, mu=0):
+    sigma = rho * np.sqrt(sr/2)
+    noise = np.random.normal(mu, sigma, n)
+    return noise
+
+#Filter parameters
+harmonic_order = 2     #harmonics 1, 2, 3, ...
+att = 6
+
+#Parameters
+rho=1
+fs = 48000
+n = 480000
+period = n/fs
+
+#White noise generation
+white_noise = white_noise(rho, fs, n, mu=0)
+
+#Generating frequency signal
+time = np.linspace(0, period, n)
+
+#Acceleration ramp from 0 to 20000 Hz
+freq = 80*chirp(
+    time, 
+    f0=20, 
+    f1=20000, 
+    t1=10, 
+    method='linear'
+) 
+
+#Adding noise to the signal
+
+noise_plus_signal = white_noise + freq
+
+#Tracking signal
+f = 20 + time*1000
+
+rpm_signal = [len(white_noise)]
+
+rpm_signal = f*6
+
+
+l_rpm=len(rpm_signal)
+t_rpm = np.arange(0, l_rpm)/fs # Intervalo de tiempo en segundos
+
+#Filtering signal
+
+original_signal, iir_filtered_signal, Fs = variant_filter(
+    noise_plus_signal, rpm_signal,'iir', harmonic_order, att
+)
+original_signal, fir_filtered_signal, Fs = variant_filter(
+    noise_plus_signal, rpm_signal,'fir', harmonic_order, att
+)
+
+#Scale both signals to integers to write a wav file
+scale = np.max([np.max(np.abs(original_signal)), np.max(np.abs(iir_filtered_signal))])
+scaled_o = np.int16(original_signal / scale * 32767)
+scaled_fir = np.int16(fir_filtered_signal / scale * 32767)
+scaled_iir = np.int16(iir_filtered_signal / scale * 32767)
+
+write('ramp_test_signal.wav', Fs, scaled_o)
+  
+write('IIR_filtered_ramp_test_signal.wav.wav', Fs, scaled_iir)
+write('FIR_filtered_ramp_test_signal.wav.wav', Fs, scaled_fir)
+
+#Plotting results
+
+#Average spectrum of 24000 samples
+
+plt.figure()
+f, Pxx_spec_o = sig.welch(
+    original_signal[120000:144000], fs, 'flattop', 1024, scaling='spectrum'
+)
+or_m_data = 20*np.log10(np.sqrt(Pxx_spec_o))
+plt.plot(
+    f, or_m_data, color = 'darkred',label="Original"
+)
+
+f, Pxx_spec_fir = sig.welch(
+    fir_filtered_signal[120000:144000], fs,'flattop', 1024, scaling='spectrum'
+)
+fir_m_data = 20*np.log10(np.sqrt(Pxx_spec_fir))
+plt.plot(
+    f, fir_m_data, color = 'darkblue', linestyle='--', label="FIR"
+)
+
+f, Pxx_spec_iir = sig.welch(
+    iir_filtered_signal[120000:144000], fs,'flattop', 1024, scaling='spectrum'
+)
+iir_m_data = 20*np.log10(np.sqrt(Pxx_spec_iir))
+plt.plot(
+    f, iir_m_data, color='darkgreen', linestyle='--', label="IIR"
+)
+
+plt.xlabel('Frequency [Hz]')
+plt.ylabel('dBV')
+plt.title('Spectrum')
+plt.legend()
+plt.show()
+
+#Plot Spectrogram of the noise and signals
+NFFT = 4096
+noverlap = NFFT/2
+vmin = 20*np.log10(np.max(noise_plus_signal)) - 60
+
+#Set height ratios for subplots
+gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) 
+
+ax1 = plt.subplot(gs[0])
+plt.specgram(
+    noise_plus_signal, 
+    NFFT=NFFT,
+    Fs=fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Original Signal')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+norm = mpl.colors.Normalize(vmin=-10, vmax=20)
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal IIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    iir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('IIR Filtered signal')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal FIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    fir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('FIR Filtered signal')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
\ No newline at end of file
diff --git a/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.UFF b/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.UFF
new file mode 100644
index 00000000..224032c1
--- /dev/null
+++ b/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.UFF
@@ -0,0 +1,242958 @@
+    -1
+    58                                                                          
+id1                                                                             
+NONE                                                                            
+NONE                                                                            
+NONE                                                                            
+NONE                                                                            
+    1         0    0         0 rpm_profile         0   0 rpm_profile         0   0
+         4    971776         1  0.00000e+00  3.90625e-05  0.00000e+00
+        17    0    0    0 NONE                 NONE                
+        19    0    0    0 NONE                 NONE                
+         0    0    0    0 NONE                 NONE                
+         0    0    0    0 NONE                 NONE                
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
+   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01   7.50953387476e+01
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diff --git a/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.UFF b/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.UFF
new file mode 100644
index 00000000..cb90b795
--- /dev/null
+++ b/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.UFF
@@ -0,0 +1,161977 @@
+    -1                                                                          
+    58                                                                          
+NONE                                                                            
+NONE                                                                            
+07/aug/2020 11:36:00                                                            
+NONE                                                                            
+NONE                                                                            
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+        15    0    0    0 Acoustic pressure    Pa                  
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+    -1                                                                          
diff --git a/mosqito/tests/variant_filter/signals/vocal_test_signal_movie.wav b/mosqito/tests/variant_filter/signals/vocal_test_signal_movie.wav
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diff --git a/mosqito/tests/variant_filter/signals/vocal_test_signal_singing.wav b/mosqito/tests/variant_filter/signals/vocal_test_signal_singing.wav
new file mode 100644
index 00000000..88b02981
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diff --git a/mosqito/tests/variant_filter/signals/vocal_test_signal_voice.wav b/mosqito/tests/variant_filter/signals/vocal_test_signal_voice.wav
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index 00000000..483e3bd4
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diff --git a/mosqito/tests/variant_filter/test_variant_filter.py b/mosqito/tests/variant_filter/test_variant_filter.py
new file mode 100644
index 00000000..d84350ce
--- /dev/null
+++ b/mosqito/tests/variant_filter/test_variant_filter.py
@@ -0,0 +1,134 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jan 10 17:16:18 2021
+
+@author: josem
+"""
+
+# Add MoSQITo to the Python path
+# import sys
+# sys.path.append('..')
+
+import time
+import pylab as plt
+import numpy as np
+# import sounddevice as sd
+from matplotlib import gridspec
+from scipy.io.wavfile import write
+from mosqito.functions.variant_filter.variant_filter import variant_filter
+from mosqito.functions.shared.load import load
+
+# Filter parameters
+armonic_order = 1  # harmonics 1, 2, 3, ...
+ftype = 1  # Filter selector: 0-->FIR, 1-->IIR
+band = 40  # The bandwidth around the centerline frequency that you wish to filter
+signal = "mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.UFF"
+track = "mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.UFF"
+
+# Aplication of the variant_filter function
+original_signal, filtered_signal, Fs = variant_filter(
+    signal, track, ftype, armonic_order, band
+)
+"""
+#Playback of original and filtered signal
+sd.play(original_signal, Fs)
+time.sleep(40)
+sd.play(filtered_signal, Fs)
+"""
+
+# Scale both signals to integers to write a wav file
+scale = np.max([np.max(np.abs(original_signal)), np.max(np.abs(filtered_signal))])
+scaled_o = np.int16(original_signal / scale * 32767)
+scaled_f = np.int16(filtered_signal / scale * 32767)
+
+write("original_signal_.wav", Fs, scaled_o)
+write("filtered_signal_" + str(armonic_order) + ".wav", Fs, scaled_f)
+
+# RPM signal
+rpm_signal, fs = load(False, track, calib=1)
+l_rpm = len(rpm_signal)
+t_rpm = np.arange(0, l_rpm) / fs  # Intervalo de tiempo en segundos
+
+# Plots
+
+# Plot Spectrogram original signal
+NFFT = 1024
+noverlap = NFFT / 2
+vmin = 20 * np.log10(np.max(original_signal)) - 70
+
+# Set height ratios for sublots
+gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
+
+ax1 = plt.subplot(gs[0])
+plt.specgram(
+    original_signal,
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin,
+    noverlap=noverlap,
+    cmap=plt.cm.jet,
+    window=plt.window_none,
+)
+plt.ylabel("Frequency [Hz]")
+plt.title("Original Signal")
+plt.axis([0, 37, 0, 3000])
+ax1.tick_params(axis="x", direction="in")
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel("Time [s]")
+plt.ylabel("RPM")
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(cax=cax).set_label("DEP [dB/Hz]", labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+# Plot Spectrogram filtered signal
+ax1 = plt.subplot(gs[0])
+pxx, freq, t, cax = plt.specgram(
+    filtered_signal,
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin,
+    noverlap=noverlap,
+    cmap=plt.cm.jet,
+    window=plt.window_none,
+)
+plt.ylabel("Frequency [Hz]")
+plt.title("Filtered signal")
+plt.axis([0, 37, 0, 3000])
+ax1.tick_params(axis="x", direction="in")
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel("Time [s]")
+plt.ylabel("RPM")
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(cax=cax).set_label("DEP [dB/Hz]", labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+# Plot Phase of a band of 5Hz between 995Hz and 1000Hz
+ax3 = plt.subplot(2, 1, 1)
+plt.angle_spectrum(original_signal, Fs=Fs)
+plt.ylabel("Phase [Rad]")
+plt.xlabel("Frequency [Hz]")
+plt.axis([995, 1000, -3.2, 3.2])
+plt.title("Original Signal Phase")
+
+ax4 = plt.subplot(2, 1, 2)
+plt.angle_spectrum(filtered_signal, Fs=Fs)
+plt.ylabel("Phase [Rad]")
+plt.xlabel("Frequency [Hz]")
+plt.axis([995, 1000, -3.2, 3.2])
+plt.title("Filtered Signal Phase")
+
+plt.tight_layout()
+plt.show()
diff --git a/mosqito/tests/variant_filter/tones_test_signal.py b/mosqito/tests/variant_filter/tones_test_signal.py
new file mode 100644
index 00000000..ba49cd2a
--- /dev/null
+++ b/mosqito/tests/variant_filter/tones_test_signal.py
@@ -0,0 +1,201 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 13 17:52:53 2021
+
+@author: josem
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pylab as pylab
+import matplotlib as mpl
+from scipy import signal
+from matplotlib import gridspec
+from scipy.io.wavfile import write
+from mosqito.functions.variant_filter.variant_filter import variant_filter
+
+#White noise definition
+#Parameters:
+#   rhp - spectral noise density unit/SQRT(Hz)
+#   sr  - sample rate
+#   n   - no of points
+#   mu  - mean value, optional
+#Returns:
+#   n points of noise signal with spectral noise density of rho
+
+def white_noise(rho, sr, n, mu=0):
+    sigma = rho * np.sqrt(sr/2)
+    noise = np.random.normal(mu, sigma, n)
+    return noise
+
+#Parameters
+rho=1
+fs = 48000
+n = 480000
+period = n/fs
+time = np.linspace(0, period, n)
+
+#Filter parameters
+harmonic_order = 1     #harmonics 1, 2, 3, ...
+att = 6
+
+#White noise generation
+white_noise = white_noise(rho, fs, n, mu=0)
+#Pure tones generation
+tone_1 = 500*np.sin(2*np.pi*1000*time)
+
+tone_2 = 500*np.sin(2*np.pi*5000*time)
+
+tone_3 = 500*np.sin(2*np.pi*10000*time)
+
+
+#Adding the tone to the noise
+noise_plus_signal = []
+noise_plus_signal[0:168000] = white_noise + tone_1
+
+noise_plus_signal[168001:384000] = white_noise + tone_2
+
+noise_plus_signal[384001:480000] = white_noise + tone_3
+
+#Generating tracking signal
+rpm_signal = [480000]
+rpm_signal[0:169000] = np.full(169001, 6000)
+rpm_signal[169001:385000] = np.full(215999, 30000)
+rpm_signal[385001:480000] = np.full(95999, 60000)
+
+l_rpm=len(rpm_signal)
+t_rpm = np.arange(0, l_rpm)/fs # Intervalo de tiempo en segundos
+
+#Filtering signal
+
+original_signal, filtered_signal_fir, Fs = variant_filter(
+    noise_plus_signal, rpm_signal,'fir', harmonic_order, att
+)
+original_signal, filtered_signal_iir, Fs = variant_filter(
+    noise_plus_signal, rpm_signal,'iir', harmonic_order, att
+)
+
+#Scale both signals to integers to write a wav file
+scale = np.max([np.max(np.abs(noise_plus_signal)), np.max(np.abs(filtered_signal_fir))])
+scaled_o = np.int16(original_signal / scale * 32767)
+scaled_fir = np.int16(filtered_signal_fir / scale * 32767)
+scaled_iir = np.int16(filtered_signal_iir / scale * 32767)
+
+write('tones_test_signal.wav', Fs, scaled_o)
+write('FIR_filtered_tones_test_signal.wav', Fs, scaled_fir)
+write('IIR_filtered_tones_test_signal.wav', Fs, scaled_iir)
+
+#Plotting results
+
+#Plot Spectrogram of the noise and signals
+NFFT = 4096
+noverlap = NFFT/2
+vmin = 20*np.log10(np.max(noise_plus_signal)) - 70
+
+#Set height ratios for subplots
+gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) 
+
+ax1 = plt.subplot(gs[0])
+plt.specgram(
+    noise_plus_signal, 
+    NFFT=NFFT,
+    Fs=fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Original Signal')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+norm = mpl.colors.Normalize(vmin=-25, vmax=30)
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ), 
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal FIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    filtered_signal_fir, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Filtered signal FIR')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ), 
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal IIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    filtered_signal_iir, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin,
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Filtered signal IIR')
+plt.axis([0,10,0,20000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet, 
+        norm=norm
+    ), 
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
\ No newline at end of file
diff --git a/mosqito/tests/variant_filter/vocal_test_signal.py b/mosqito/tests/variant_filter/vocal_test_signal.py
new file mode 100644
index 00000000..534669a0
--- /dev/null
+++ b/mosqito/tests/variant_filter/vocal_test_signal.py
@@ -0,0 +1,221 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec 12 12:28:15 2020
+
+@author: josem
+"""
+
+import numpy as np
+import pylab as plt
+import matplotlib as mpl
+import pylab as pylab
+from scipy.signal import chirp
+import scipy.signal as sig
+from mosqito.functions.shared.load import load
+from matplotlib import gridspec
+from scipy.io.wavfile import write
+from mosqito.functions.variant_filter.variant_filter import variant_filter
+
+#Filter parameters
+harmonic_order = 2     #harmonics 1, 2, 3, ...
+att = 12
+
+#Signal parameters
+fs = 48000
+n = 480000
+period = n/fs
+
+#Generating frequency signal
+time = np.linspace(0, period, n)
+
+#Acceleration ramp from 0 to 15000 Hz
+freq = 400*chirp(
+    time, 
+    f0=20, 
+    f1=15000, 
+    t1=10, 
+    method='linear'
+) 
+
+#Import data of the wav signal
+signal_path = "mosqito/tests/variant_filter/signals/vocal_test_signal_voice.wav"
+signal, fs = load(False, signal_path, calib = 2 * 2**0.5)
+signal = np.float64(signal[:,1])
+
+#Adding noise to the signal
+signal_plus_ramp = signal[0:n] + freq
+
+#Tracking signal
+f = 20 + time*750
+
+rpm_signal = [len(signal)]
+
+rpm_signal = f*6
+
+l_rpm=len(rpm_signal)
+t_rpm = np.arange(0, l_rpm)/fs # Intervalo de tiempo en segundos
+
+#Aplication of the variant_filter function
+signal_plus_ramp, fir_filtered_signal, Fs = variant_filter(
+    signal_plus_ramp, rpm_signal,'fir', harmonic_order, att
+)
+signal_plus_ramp, iir_filtered_signal, Fs = variant_filter(
+    signal_plus_ramp, rpm_signal,'iir', harmonic_order, att
+)
+
+#Scale both signals to integers to write a wav file
+scale = np.max([np.max(np.abs(signal_plus_ramp)), np.max(np.abs(iir_filtered_signal))])
+scaled_o = np.int16(signal_plus_ramp / scale * 32767)
+scaled_fir = np.int16(fir_filtered_signal / scale * 32767)
+scaled_iir = np.int16(iir_filtered_signal / scale * 32767)
+
+write('vocal_&_freq_signal.wav', Fs, scaled_o)
+write('FIR_filtered_vocal_&_freq_signal.wav', Fs, scaled_fir)
+write('IIR_filtered_vocal_&_freq_signal.wav', Fs, scaled_iir)
+
+#Plotting results
+    
+#Average spectrum of 24000 samples
+plt.figure()
+
+f, Pxx_spec = sig.welch(
+    signal_plus_ramp[400000:424000], fs, 'flattop', 1024, scaling='spectrum'
+)
+
+plt.semilogy(
+    f, np.sqrt(Pxx_spec), color = 'darkred', label='original'
+)
+
+f, Pxx_spec = sig.welch(
+    fir_filtered_signal[400000:424000], fs,'flattop', 1024, scaling='spectrum'
+)
+plt.semilogy(
+    f, np.sqrt(Pxx_spec), color = 'darkblue', linestyle='-.', label='FIR'
+)
+
+f, Pxx_spec = sig.welch(
+    iir_filtered_signal[400000:424000], fs,'flattop', 1024, scaling='spectrum'
+)
+plt.semilogy(
+    f, np.sqrt(Pxx_spec), color='darkgreen', linestyle='--', label='IIR'
+)
+
+axes = plt.axes()
+axes.set_xlim([0,15000])
+plt.xlabel('Frequency [Hz]')
+plt.ylabel('V RMS')
+plt.title('Spectrum')
+plt.legend()
+plt.show()
+
+#Plot Spectrogram of the noise and signals
+NFFT = 4096
+noverlap = NFFT/2
+vmin = 20*np.log10(np.max(signal_plus_ramp)) - 90
+
+#Set height ratios for subplots
+gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) 
+
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    signal_plus_ramp, 
+    NFFT = NFFT,
+    Fs = fs,
+    vmin = vmin, 
+    noverlap = noverlap,
+    cmap = plt.cm.jet, 
+    window = pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Original Signal')
+plt.axis([0,10,0,15000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+norm = mpl.colors.Normalize(vmin=-25, vmax=30)
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet,
+        norm=norm
+    ), 
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+#Plot Spectrogram filtered signal FIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    fir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+    )
+plt.ylabel('Frequency [Hz]')
+plt.title('Filtered signal FIR')
+plt.axis([0,10,0,15000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet,
+        norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
+
+#Plot Spectrogram filtered signal IIR
+ax1 = plt.subplot(gs[0])
+pxx,  freq, t, cax = plt.specgram(
+    iir_filtered_signal, 
+    NFFT=NFFT,
+    Fs=Fs,
+    vmin=vmin, 
+    noverlap=noverlap,
+    cmap = plt.cm.jet, 
+    window=pylab.window_none
+)
+plt.ylabel('Frequency [Hz]')
+plt.title('Filtered signal IIR')
+plt.axis([0,10,0,15000])
+ax1.tick_params(axis='x', direction='in')
+
+ax2 = plt.subplot(gs[1], sharex=ax1)
+plt.setp(ax1.get_xticklabels(), visible=False)
+plt.plot(t_rpm, rpm_signal)
+plt.xlabel('Time [s]')
+plt.ylabel('RPM')
+
+plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)
+
+cax = plt.axes([0.95, 0.38, 0.025, 0.52])
+plt.colorbar(
+    mpl.cm.ScalarMappable(
+        cmap = plt.cm.jet
+        ,norm=norm
+    ),
+    cax=cax
+).set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)
+
+plt.show()
\ No newline at end of file
diff --git a/mosqito/validations/variant_filter/filtered_signals/1st_armonic_filtered_IIR.wav b/mosqito/validations/variant_filter/filtered_signals/1st_armonic_filtered_IIR.wav
new file mode 100644
index 00000000..d3773db0
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diff --git a/mosqito/validations/variant_filter/filtered_signals/2nd_armonic_filtered_IIR.wav b/mosqito/validations/variant_filter/filtered_signals/2nd_armonic_filtered_IIR.wav
new file mode 100644
index 00000000..40743e22
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diff --git a/mosqito/validations/variant_filter/filtered_signals/3rd_armonic_filtered_IIR.wav b/mosqito/validations/variant_filter/filtered_signals/3rd_armonic_filtered_IIR.wav
new file mode 100644
index 00000000..f96cf4a1
Binary files /dev/null and b/mosqito/validations/variant_filter/filtered_signals/3rd_armonic_filtered_IIR.wav differ
diff --git a/mosqito/validations/variant_filter/filtered_signals/original_signal_.wav b/mosqito/validations/variant_filter/filtered_signals/original_signal_.wav
new file mode 100644
index 00000000..f9f3afa5
Binary files /dev/null and b/mosqito/validations/variant_filter/filtered_signals/original_signal_.wav differ
diff --git a/setup.py b/setup.py
index 01987039..b64d6350 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@
         requirements
     ).splitlines()  # remove endline in each element
 
-tests_require = ["pytest>=5.4.1","pandas", "openpyxl"]
+tests_require = ["pytest>=5.4.1", "pandas", "openpyxl"]
 
 setuptools.setup(
     name="mosqito",
@@ -40,7 +40,5 @@
     python_requires=python_requires,
     install_requires=install_requires,
     tests_require=tests_require,
-     extras_require={
-        'testing': tests_require
-    },
+    extras_require={"testing": tests_require},
 )
diff --git a/tutorials/tuto_variant_filter.ipynb b/tutorials/tuto_variant_filter.ipynb
new file mode 100644
index 00000000..4384db10
--- /dev/null
+++ b/tutorials/tuto_variant_filter.ipynb
@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Variant Filter\n",
+    "\n",
+    "This tutorial explains how to use MoSQITo to filter the harmonic frequencies generated by a rotating electromagnetic motor. Two .wav or .uff signals that contain a recording of the engine acceleration ramp and a tracking signal that provides the variation of RPMs will be needed. There are two ways to provide the signals to the filter: as paths to the file or as data already loaded.\n",
+    "\n",
+    "## Load signals\n",
+    "\n",
+    "An Audio object is first created by importing an audio file. In this example, the signal is imported from a .uff file. The tutorial Audio signal basic operations gives more information about the syntax of the import and the other supported file types. It also explains how to plot the time signal, compute and plot its 1/3 octave band spectrum, compute its overall level, etc.\n",
+    "\n",
+    "### Load original signal from a .wav file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Signal resampled to 48 kHz to allow calculation.\n",
+      "Signal resampled to 48 kHz to allow calculation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import load class\n",
+    "from mosqito.functions.shared.load import load\n",
+    "\n",
+    "# Load the original signal\n",
+    "original_signal, fs = load(False,\n",
+    "    \"C:/josema_rep/MoSQITo/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_Track1_Rec0.uff\",\n",
+    "    calib=2 * 2 ** 0.5\n",
+    ")\n",
+    "\n",
+    "# Load the tracking signal\n",
+    "tracking_signal, fs = load(False,\n",
+    "    \"C:/josema_rep/MoSQITo/mosqito/tests/variant_filter/signals/RUN5_RunUp_60s_RPM-profile_Rec0.uff\",\n",
+    "    calib=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determine the filter parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filter parameters\n",
+    "harmonic_order = 1     #harmonics 1, 2, 3, ...\n",
+    "att = 6               #Attenuation in 3, 6, 9, 12 dB\n",
+    "filter_type = 'fir'    #Type of filter: 'fir' or 'iir'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply the filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mosqito.functions.variant_filter.variant_filter import variant_filter\n",
+    "\n",
+    "#Application of the variant filter\n",
+    "original_signal, fir_filtered_signal, Fs = variant_filter(\n",
+    "        original_signal, tracking_signal,filter_type, harmonic_order, att\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save signals in .wav files\n",
+    "Saving both the original and the filtered signals in .wav files allows us to listen to them and appreciate the filtering carried out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.io.wavfile import write\n",
+    "import numpy as np\n",
+    "\n",
+    "#Scale both signals to integers to write a wav file\n",
+    "scale = np.max([np.max(np.abs(original_signal)), np.max(np.abs(fir_filtered_signal))])\n",
+    "scaled_o = np.int16(original_signal / scale * 32767)\n",
+    "scaled_fir = np.int16(fir_filtered_signal / scale * 32767)\n",
+    "\n",
+    "write('motor_signal.wav', Fs, scaled_o)\n",
+    "write('FIR_filtered_motor_signal_'+str(harmonic_order)+' harmonic.wav', Fs, scaled_fir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show the results\n",
+    "The results of the filtering will be displayed on a three-dimensional spectrogram where the axes correspond to time, frequency, and amplitude. For this, the specgram function of the Pylab library cluster will be used, including numpy, scipy, sympy, pandas, matplotlib, ipython. This will allow us to use python as if it were matlab. Data are split into NFFT length segments and the spectrum of each section is computed. The windowing function window is applied to each segment, and the amount of overlap of each segment is specified with noverlap.The spectrogram is plotted as a colormap.\n",
+    "\n",
+    "The X axis will correspond to the time variable, the Y axis the frequency and the Z axis, corresponding to the color of the spectrogram, it will be the amplitude of the signal.\n",
+    "\n",
+    "Additionally, the tracking signal will be shown below the spectrogram to be able to visualize the relationship between the increase in the speed of rotation with the evolution of the harmonics generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab as plt\n",
+    "import matplotlib as mpl\n",
+    "from matplotlib import gridspec\n",
+    "\n",
+    "#Time interval in seconds of the tracking signal\n",
+    "l_rpm=len(tracking_signal)\n",
+    "t_rpm = np.arange(0, l_rpm)/ fs \n",
+    "\n",
+    "#Set plot number of samples\n",
+    "NFFT = 4096\n",
+    "noverlap = NFFT / 2\n",
+    "\n",
+    "#Set minimun shown value\n",
+    "vmin = 20*np.log10(np.max(original_signal)) - 70\n",
+    "\n",
+    "#Set height ratios for subplots\n",
+    "gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) \n",
+    "\n",
+    "#Plot Spectrogram original signal\n",
+    "ax1 = plt.subplot(gs[0])\n",
+    "pxx,  freq, t, cax = plt.specgram(\n",
+    "    original_signal, \n",
+    "    NFFT=NFFT,                                 \n",
+    "    Fs=Fs,\n",
+    "    vmin=vmin, \n",
+    "    noverlap=noverlap,\n",
+    "    cmap = plt.cm.jet, \n",
+    "    window=plt.window_none\n",
+    ")\n",
+    "plt.ylabel('Frequency [Hz]')\n",
+    "plt.title('Original Signal')\n",
+    "plt.axis([0,37,0,3000])\n",
+    "ax1.tick_params(axis='x', direction='in')\n",
+    "\n",
+    "ax2 = plt.subplot(gs[1], sharex=ax1)\n",
+    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
+    "plt.plot(t_rpm, tracking_signal)\n",
+    "plt.xlabel('Time [s]')\n",
+    "plt.ylabel('RPM')\n",
+    "plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)\n",
+    "\n",
+    "norm = mpl.colors.Normalize(vmin=-25, vmax=30)\n",
+    "cax = plt.axes([0.95, 0.38, 0.025, 0.52])\n",
+    "plt.colorbar(\n",
+    "    mpl.cm.ScalarMappable(\n",
+    "        cmap = plt.cm.jet, \n",
+    "        norm=norm\n",
+    "    ),\n",
+    "    cax=cax\n",
+    ").set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "#Plot Spectrogram filtered signal FIR\n",
+    "ax1 = plt.subplot(gs[0])\n",
+    "pxx,  freq, t, cax = plt.specgram(\n",
+    "    fir_filtered_signal, \n",
+    "    NFFT=NFFT,\n",
+    "    Fs=Fs,\n",
+    "    vmin=vmin, \n",
+    "    noverlap=noverlap,\n",
+    "    cmap = plt.cm.jet, \n",
+    "    window=plt.window_none\n",
+    ")\n",
+    "plt.ylabel('Frequency [Hz]')\n",
+    "plt.title('FIR Filtered signal')\n",
+    "plt.axis([0,37 ,0,3000])\n",
+    "ax1.tick_params(axis='x', direction='in')\n",
+    "\n",
+    "ax2 = plt.subplot(gs[1], sharex=ax1)\n",
+    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
+    "plt.plot(t_rpm, tracking_signal)\n",
+    "plt.xlabel('Time [s]')\n",
+    "plt.ylabel('RPM')\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.1, right=0.94, top=0.9, hspace=0.05)\n",
+    "\n",
+    "cax = plt.axes([0.95, 0.38, 0.025, 0.52])\n",
+    "plt.colorbar(\n",
+    "    mpl.cm.ScalarMappable(\n",
+    "        cmap = plt.cm.jet, \n",
+    "        norm=norm\n",
+    "    ),\n",
+    "    cax=cax\n",
+    ").set_label('PSD [dB/Hz]', labelpad=-20, y=1.1, rotation=0)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}