now including codeblocks (TM)

Doc/main.tex

@@ -7,11 +7,39 @@
 \usepackage{caption}
 \usepackage{graphicx}
 \usepackage{enumitem}
+\usepackage{listings}
+\usepackage{xcolor}
 
 \newenvironment{Figure}
   {\par\medskip\noindent\minipage{\linewidth}}
   {\endminipage\par\medskip}
 
+\definecolor{codegreen}{rgb}{0,0.6,0}
+\definecolor{codegray}{rgb}{0.5,0.5,0.5}
+\definecolor{codepurple}{rgb}{0.58,0,0.82}
+\definecolor{backcolour}{rgb}{0.97,0.97,0.95}
+
+\lstdefinestyle{mystyle}{
+  backgroundcolor=\color{backcolour},   
+  commentstyle=\color{codegreen},
+  keywordstyle=\color{magenta},
+  numberstyle=\tiny\color{codegray},
+  stringstyle=\color{codepurple},
+  basicstyle=\ttfamily\footnotesize,
+  breakatwhitespace=false,         
+  breaklines=true,                 
+  captionpos=b,                    
+  keepspaces=true,                 
+  numbers=left,                    
+  numbersep=5pt,                  
+  showspaces=false,                
+  showstringspaces=false,
+  showtabs=false,                  
+  tabsize=2
+}
+
+\lstset{style=mystyle}
+
 \graphicspath{{images/}}
 
 \title{B00st converter}
@@ -97,19 +125,44 @@
     mean over the period of 1 millisecond. Thus, the highest measured value can be
     subtracted from the lowest measured value.
 
+\begin{lstlisting}[language=Python, caption={Peak to peak function}]
+def PK_PK(all_data, ch):
+  output_pk = []
+  for data in all_data:
+      maximum = max(data[ch + 1])
+      minimum = min(data[ch + 1])
+      pk_pk = maximum-minimum
+      output_pk.append(pk_pk)
+  return output_pk
+\end{lstlisting}
+
 
   \subsubsection{Standard Deviation}\label{section:standard_devation}
-    The second metric used to measure noise was the standard deviation. 
-    Unlike, peak to peak it givesa better impression of the noise over a longer 
-    signal. SD can be calculated using equation \ref{eq:sd}.
+    The second metric used to measure noise was the standard deviation (SD). 
+    Unlike peak to peak, it gives a better impression of the average noise over 
+    a longer period. SD can be calculated using equation \ref{eq:sd}.
 
     \begin{equation}
       \label{eq:sd}
       \sigma = \sqrt{\frac{1}{N}\sum^{N-1}_{i=0}(x[i] - \mu)^2}
     \end{equation}
 
-    Where $x[i]$ is each voltage measurement, $\mu$ is the mean of the signal and
-    $N$ is the total amount of samples.
+    \noindent Where $x[i]$ is each voltage measurement, $\mu$ is the mean of the 
+    signal and $N$ is the total amount of samples.
+
+\begin{lstlisting}[language=Python, caption={SD function}]
+def SD(all_data, ch):
+  output_SD = []
+  for data in all_data:
+      N = len(data[ch + 1])
+      MU = sum(data[ch + 1])/N
+      x = 0
+      for val in data[ch + 1]:
+          x += (val-MU)**2
+      SD = sqrt((1/N) * x)
+      output_SD.append(SD)
+  return [output_load, output_SD]
+\end{lstlisting}
 
 
   \subsection{Ripple characteristics} \label{section:ripple}
@@ -142,6 +195,50 @@
     sample in the signal can be multiplied by the corresponding value in the window,
     preparing the signal for the FFT.
 
+\begin{lstlisting}[language=python, caption={Hamming function}]
+def window(y):
+  N = len(y)
+  hamming = []
+  for n in range(N):
+      hamming.append(
+        0.54 - 0.46 * cos(2*np.pi*(n/N)))
+  y = [hamming[i]*x for i, x in enumerate(y)]
+  return y
+\end{lstlisting}
+
+\begin{lstlisting}[language=python, caption={FFT function}]
+def Freq(all_data, ch):
+output_freq = []
+for data in all_data:
+    # FFT
+    # using realfft, 
+    # because the signal only has real parts 
+    y = np.fft.rfft(data[ch + 1])
+
+    # Window
+    y = window(y)
+
+    # Calculate the bins
+    dt = data[1][1] - data[1][0]
+    x = np.fft.rfftfreq(len(data[ch+1]), dt)
+    
+    # find maximum, max() and np.argmax() 
+    # are not playing nice with 
+    # imaginary numbers
+    maximum = 0
+    max_idx = 0
+    offset = 1  # Skip the first bin, DC offset
+    for idx, val in enumerate(y[offset:]):
+        mag = sqrt(val.real**2 + val.imag**2)
+        if mag > maximum:
+            maximum = mag
+            max_idx = idx + offset
+    
+    # get the frequency of the maximum
+    output_freq.append(x[max_idx])
+
+return [output_load, output_freq]
+\end{lstlisting}
 
   \subsection{Start up} \label{section:start_up}
     The last characteristics is the start up, specifically the different rise times
@@ -156,6 +253,18 @@
     filtered using a low pass filter, reducing the high frequencies from the 
     measurement. 
 
+\begin{lstlisting}[language=python, caption={LPF snippet}]
+initial = data[3][0]
+dt = data[1][1] - data[1][0]
+
+x_filter = [initial]
+for i in range(1, len(data[3])):
+    x_dot_filter = \
+      (data[3][i] - x_filter[i-1])/0.0002
+    x_filter.append(
+      x_filter[i-1] + x_dot_filter*dt)
+\end{lstlisting}
+
 
   \section{Results}
     In this section the results from section \ref{section:methodology} will be 
@@ -230,7 +339,7 @@
     Using the values from figure \ref{fig:schematic_full}, the resonating frequency
     of the circuit should be around $27KHz$. Thus, this cannot be the cause of
     the high frequency. As the frequency of the ripple is magnitudes higher
-    than the LC-circuit's resosonant frequency, what is seen is most likely the
+    than the LC-circuit's resonant frequency, what is seen is most likely the
     Self Resonating Frequency (SRF) of the inductor. Typically the SRF is
     $>10 MHz$, so that could be a probable source of the high frequencies.