Further updates to SSB doc page

doc/spectral_analysis_ssb.md

@@ -18,6 +18,22 @@ As before, we will follow the three-stage workflow shown below.
 We will first generate the waveform to be analyzed, compute its FFT, and calculate various performance metrics by running spectral analysis. Please refer to the [leakage and spectral-analysis example](https://github.com/analogdevicesinc/genalyzer/blob/main/bindings/python/examples/gn_doc_spectral_analysis2.py) Python script to follow the discussion on this page.
 
 ## Leakage
+```{eval-rst} 
+.. mermaid::
+
+    graph LR;      
+        A[Generate/Import Waveform] --> B[Compute FFT];
+        B -->C1[Configure Spectral Analysis];
+
+        subgraph SA[Spectral Analysis]
+          C1[Configure] -->C2[Run];
+        end
+
+        style SA fill:#ffffff
+
+        style A fill:#9fa4fc        
+        style B fill:#9fa4fc
+```
 In the [leakage and spectral-analysis example](https://github.com/analogdevicesinc/genalyzer/blob/main/bindings/python/examples/gn_doc_spectral_analysis2.py) Python script, we generate the complex-sinusoidal waveform as follows:
 ```{code-block} python
 # signal configuration
@@ -40,16 +56,14 @@ A time-domain plot of the complex-sinusoidal tone is shown below for reference.
 
 Time-domain plot of a ``375 KHz`` complex sinusoidal tone sampled at ``4 MSPS``.
 ```
-As shown in this plot, the snapshot of the signal is aperiodic and data is sampled over an incomplete period at the end of the data record. This discontinuity causes _leakage_ resulting in a smearing effect that spreads the energy of a tone over a wider frequency range, with the appearance of frequency components that are not present in the original signal. This is readily seen in the FFT plot of the complex-sinusoidal tone shown below. 
+As shown in this plot, the snapshot of the signal is aperiodic and data is sampled over an incomplete period at the end. This discontinuity causes _leakage_ resulting in a smearing effect that spreads the energy of a tone over a wider frequency range, with the appearance of frequency components that are not present in the original signal. This is readily seen in the FFT plot of the complex-sinusoidal tone shown below. 
 ```{figure} figures/fft2.png
 
 FFT plot of a ``375 KHz`` complex sinusoidal tone sampled at ``4 MSPS``.
 ```
 
 ### Windowing To Reduce Leakage
-Note that in the FFT plot shown previously, the FFT of the time-domain waveform was computed as is with the underlying assumption that the waveform is periodic. In practice, since this assumption is generally invalid and the snapshot of a wavform contain incomplete periods rendering the signal aperiodic, a window function is applied to overcome leakage. A window function shapes the time-domain waveform such that the first and last samples are exactly zero, and the samples in between are multiplied according to a mathematical function that is symmetric around the center and tapers off away from the center. Thus, by applying a window function, an aperiodic waveform is forced to be periodic. When computing the FFT of a waveform to which a window function has been applied, a weighting factor must also be taken into account so that the correct FFT signal amplitude level is recovered after the windowing. Genalyzer takes this effect into account. 
-
-In the [leakage and spectral-analysis example](https://github.com/analogdevicesinc/genalyzer/blob/main/bindings/python/examples/gn_doc_spectral_analysis2.py) Python script, we used Blackman-Harris window as follows:
+Note that in the FFT plot shown previously, the FFT of the time-domain waveform was computed as is with the underlying assumption that the waveform is periodic. In practice, this assumption is generally invalid and the snapshot of a wavform contains incomplete periods rendering the signal aperiodic. Consequently, a window function is applied to overcome leakage. In the [leakage and spectral-analysis example](https://github.com/analogdevicesinc/genalyzer/blob/main/bindings/python/examples/gn_doc_spectral_analysis2.py) Python script, we used Blackman-Harris window as shown in the snippet below:
 ```{code-block} python
 # signal configuration
 npts = 30000 # number of points in the signal
@@ -79,8 +93,28 @@ qwfq = gn.quantize(awfq, fsr, qres, qnoise, code_fmt)
 # compute FFT
 fft_cplx = gn.fft(qwfi, qwfq, qres, navg, nfft, window, code_fmt)
 ```
-The FFT plot of the tone where Blackman-Harris window has been applied prior to computing the FFT is shown below for reference. 
-```{figure} figures/fft.png
+
+A window function shapes the time-domain waveform such that the first and last samples are exactly zero, and the samples in between are multiplied according to a mathematical function that is symmetric around the center and tapers off away from the center. Thus, an aperiodic waveform is forced to be periodic. As a result, the FFT plot below shows that leakage has been minimized and a sharp peak where the tone is expected is seen clearly. 
+```{figure} figures/fft3.png
 
 FFT plot of a ``375 KHz`` complex sinusoidal tone sampled at ``4 MSPS``.
+```
+```{note}
+When computing the FFT of a waveform to which a window function has been applied, a weighting factor must also be taken into account so that the correct FFT signal amplitude level is recovered after the windowing. Genalyzer takes this effect into account.
+```
+
+## Configuring Single Side Bins
+```{eval-rst} 
+.. mermaid::
+
+    graph LR;      
+        A[Generate/Import Waveform] --> B[Compute FFT];
+        B -->C1[Configure Spectral Analysis];
+
+        subgraph SA[Spectral Analysis]
+          C1[Configure] -->C2[Run];
+        end
+
+      style C1 fill:#9fa4fc
+      style SA fill:#ffffff
 ```