I am working on a small project in the lab with an Arduino Mega 2560 board. I want to average the signal (voltage) of the positive-slope portion (rise) of a triangle wave to try to remove as much noise as possible. My frequency is 20Hz and I am working with a data rate of 115200 bits/second (fastest recommended by Arduino for data transfer to a computer).
The raw signal looks like this:
My data is stored in a text file, with each line corresponding to a data point. Since I do have thousands of data points, I expect that some averaging would smooth the way my signal looks and make a close-to-perfect straight line in this case. However, other experimental conditions might lead to a signal where I could have features along the positive-slope portion of the triangle wave, such as a negative peak, and I absolutely do need to be able to see this feature on my averaged signal.
I am a Python beginner so I might not have the ideal approach to do so and my code might look bad for most of you. I would still like to get your hints / ideas on how I can improve my signal processing code to achieve a better noise removal by averaging the signal.
#!/usr/bin/python import matplotlib.pyplot as plt import math # *** OPEN AND PLOT THE RAW DATA *** data_filename = "My_File_Name" filepath = "My_File_Path" + data_filename + ".txt" # Open the Raw Data with open(filepath, "r") as f: rawdata = f.readlines() # Remove the \n rawdata = map(lambda s: s.strip(), rawdata) # Plot the Raw Data plt.plot(rawdata, 'r-') plt.ylabel('Lightpower (V)') plt.show() # *** FIND THE LOCAL MAXIMUM AND MINIMUM # Number of data points for each range datarange = 15 # This number can be changed for better processing max_i_range = int(math.floor(len(rawdata)/datarange))-3 #Declare an empty lists for the max and min min_list =  max_list =  min_list_index =  max_list_index =  i=0 for i in range(0, max_i_range): delimiter0 = i * datarange delimiter1 = (i+1) * datarange delimiter2 = (i+2) * datarange delimiter3 = (i+3) * datarange sumrange1 = sum(float(rawdata[i]) for i in range(delimiter0, delimiter1 + 1)) averagerange1 = sumrange1 / len(rawdata[delimiter0:delimiter1]) sumrange2 = sum(float(rawdata[i]) for i in range(delimiter1, delimiter2 + 1)) averagerange2 = sumrange2 / len(rawdata[delimiter1:delimiter2]) sumrange3 = sum(float(rawdata[i]) for i in range(delimiter2, delimiter3 + 1)) averagerange3 = sumrange3 / len(rawdata[delimiter2:delimiter3]) # Find if there is a minimum in range 2 if ((averagerange1 > averagerange2) and (averagerange2 < averagerange3)): min_list.append(min(rawdata[delimiter1:delimiter2])) # Find the value of all the minimum #Find the index of the minimum min_index = delimiter1 + [k for k, j in enumerate(rawdata[delimiter1:delimiter2]) if j == min(rawdata[delimiter1:delimiter2])] #  To use the first index out of the possible values min_list_index.append(min_index) # Find if there is a maximum in range 2 if ((averagerange1 < averagerange2) and (averagerange2 > averagerange3)): max_list.append(max(rawdata[delimiter1:delimiter2])) # Find the value of all the maximum #Find the index of the maximum max_index = delimiter1 + [k for k, j in enumerate(rawdata[delimiter1:delimiter2]) if j == max(rawdata[delimiter1:delimiter2])] #  To use the first index out of the possible values max_list_index.append(max_index) # *** PROCESS EACH RISE PATTERN *** # One rise pattern goes from a min to a max numb_of_rise_pattern = 50 # This number can be increased or lowered. This will average 50 rise patterns max_min_diff_total = 0 for i in range(0, numb_of_rise_pattern): max_min_diff_total = max_min_diff_total + (max_list_index[i]-min_list_index[i]) # Find the average number of points for each rise pattern max_min_diff_avg = abs(max_min_diff_total / numb_of_rise_pattern) # Find the average values for each of the rise pattern avg_position_value_list =  for i in range(0, max_min_diff_avg): sum_position_value = 0 for j in range(0, numb_of_rise_pattern): sum_position_value = sum_position_value + float( rawdata[ min_list_index[j] + i ] ) avg_position_value = sum_position_value / numb_of_rise_pattern avg_position_value_list.append(avg_position_value) #Plot the Processed Signal plt.plot(avg_position_value_list, 'r-') plt.title(data_filename) plt.ylabel('Lightpower (V)') plt.show()
At the end, the processed signal looks like this:
I would expect a straighter line, but I could be wrong. I believe that there are probably a lot of flaws in my code and there would certainly be better ways to achieve what I want.
I have included a link to a text file with some raw data if any of you want to have fun with it.