Updated app.py

app.py

@@ -58,6 +58,12 @@ if __name__ == '__main__':
     st.header('Savitzky-Golay Filter : ')
     st.write('Savitzky-Golay smoothing filters are typically used to "smooth out" a noisy signal. They are also called digital smoothing polynomial filters or least-squares smoothing filters. Savitzky-Golay filters perform better in some applications than standard averaging FIR filters, which tend to filter high-frequency content along with the noise. Savitzky-Golay filters are more effective at preserving high frequency signal components but less successful at rejecting noise.Savitzky-Golay filters are optimal in the sense that they minimize the least-squares error in fitting a polynomial to frames of noisy data.')
 
+    st.header('Moving Average Filter : ')
+    st.write('The moving average is the most common filter in Signal Processing , mainly because it is the easiest digital filter to understand and use. The moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response.This makes it the premier filter for time domain encoded signals.')
+
+    st.header('Gaussian Filter : ')
+    st.write('Gaussian filters are widely used for noise reduction due to their edge preserving properties. Gaussian filters are also used as smoothing filters. The Gaussian filter is a low-pass filter that removes the high-frequency components.')
+
     st.write('---')
 
 
@@ -83,7 +89,7 @@ if __name__ == '__main__':
         left,right = st.columns(2)
 
         with left:
-            st.subheader('Detrending Parameters')
+            st.subheader('Detrending')
             window_size_dt = st.slider('*Window Size for Detrending*',min_value=1,max_value=100,value=20,step=1)
             st.write('Window size is the number of samples to be considered for detrending.')
             trend_type = st.selectbox('*Trend Type*',['linear','constant','Dont Remove'])
@@ -111,8 +117,8 @@ if __name__ == '__main__':
         left,right = st.columns(2)
 
         with left:
-            st.subheader('Savitzky-Golay Filter Parameters')
-            window_size_sg = st.slider('*Window Size for SG filter*',min_value=1,max_value=100,value=20,step=1)
+            st.subheader('Savitzky-Golay Filter')
+            window_size_sg = st.slider('*Window Size for Savitzky-Golay filter*',min_value=1,max_value=100,value=20,step=1)
             st.write('Window size is the number of samples to be considered for filtering.')
             order = st.slider('*Polynomial Order*',min_value=1,max_value=10,value=3,step=1)
             st.write('Order is the order of the polynomial to be fitted to the window of data.')
@@ -141,7 +147,7 @@ if __name__ == '__main__':
         left,right = st.columns(2)
 
         with left:
-            st.subheader('Moving Average Filter Parameters')
+            st.subheader('Moving Average Filter')
             window_size_ma = st.slider('*Window Size for Moving Average filter*',min_value=1,max_value=100,value=20,step=1)
             st.write('Window size is the number of samples to be considered for filtering.')
         
@@ -168,7 +174,7 @@ if __name__ == '__main__':
         left,right = st.columns(2)
 
         with left:
-            st.subheader('Gaussian Filter Parameters')
+            st.subheader('Gaussian Filter')
             sigma_gf = st.slider('*Sigma*',min_value=0.1,max_value=10.0,value=3.0,step=0.1)
             st.write('Sigma is the standard deviation for Gaussian kernel.')
 
@@ -196,3 +202,5 @@ if __name__ == '__main__':
             ax5.set_ylabel('Amplitude')
             ax5.set_title('Gaussian Filtered Signal')
             st.pyplot(fig5)
+
+    st.write('---')