4dffil0sfvs.md

from scipy.signal import savgol_filter  

window_width = 25      # set number of points over which data is fit and smoothed equal to 25
                       # window_width must be an odd number
polynomial_order = 2   # set order of polynominal used to fit data equal to 2 
                       # polynomial_order must be less than window_width

data_spacing = 0.1     # data_spacing = x_1 - x_0 for data y(x_0), y(x_1), ....

smoothed_data   = savgol_filter(noisy_data, window_width, polynomial_order) #smooth data
data_derivative = savgol_filter(noisy_data, window_width, polynomial_order, deriv = 1, delta = 0.1) #take 1st derivative, assuming spacing in x = 0.1 for data points y(x_0), y(x_1), ...