DataBehandlings class | Python Fiddle

#IMPORT ALLE MODULER
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
from scipy import signal
from scipy import stats
plt.style.use('seaborn')

class Databehandling:
    def __init__(self, data):
        self.data = data

def Differential(self, nrolling):
        """ Udregner et rullende mean efterfulgt af første grads (Velocity) og andengrads differentiering (Acceleration) """
        self.x_rolling = pd.Series(self.data).rolling(nrolling).mean().dropna()

self.time = np.array([i / 1000 for i in range(0, len(self.x_rolling), 1)]) #Tidsvektor
        self.t_length = len(self.time)

dt = np.diff(self.time)
        dx = np.diff(self.x_rolling)
        self.velocity = dx/dt
        dv = np.diff(self.velocity)
        self.acceleration = dv/dt[0:self.t_length-2]
        
        return [self.x_rolling, self.time, self.velocity, self.acceleration]

def LowpassFilter(self, velocity, acceleration, 
                        sampling_frequency=1000, cutoff_frequency=3, orden=4):
        """ Laver et lowpass butterworth filter, og benytter på data.
        velocity og acceleration kommer fra differential funktionen.
        Hvis intet andet er nævnt, vil sampling_frequency være på 1000Hz, cutoff på 3Hz og være et 4.ordens filter. """
        #LAV BUTTERWORTH FILTER
        fs = sampling_frequency
        fc = cutoff_frequency
        low_pass = fc / fs
        w = fc / (fs/2)
        b, a = sp.signal.butter(orden, w, btype='lowpass')
        
        self.vel = signal.filtfilt(b, a, velocity)
        self.acc = signal.filtfilt(b, a, acceleration)
        
        return [self.vel, self.acc]

def Diskriptiv_Statistik(self):
        """ udregnet forskellige diskriptiv statistik som gennemsnit, max værdi, min værdi """
        vel_mean = np.mean(self.vel)
        acc_mean = np.mean(self.acc)
        
        vel_max = np.max(self.vel)
        acc_max = np.max(self.acc)
        
        vel_min = np.min(self.vel)
        acc_min = np.min(self.acc)
        
        return [vel_mean, vel_max, vel_min, acc_mean, acc_max, acc_min]
        
    def LinearRegModel(self, vel, acc, time):
        """linear regression med dertilhørende 
        hældning, intercept, r værdi, p værdi og std error """
        vel_lm = slope, intercept, r_value, p_value, std_err = stats.linregress(time[0:self.t_length-1], vel)
        acc_lm = slope, intercept, r_value, p_value, std_err = stats.linregress(time[0:self.t_length-2], acc)
        
        return [vel_lm, acc_lm]

def Plot(self, x_rolling, time, vel, acc):
        fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True)

ax1.plot(self.time, self.x_rolling, 'b')
        ax1.set_title('Position')
        ax2.plot(self.time[0:self.t_length-1], self.vel, 'g')
        ax2.set_title('Velocity')
        ax3.plot(self.time[0:self.t_length-2], self.acc, 'r')
        ax3.set_title('Acceleration')
        plt.grid(True)
        plt.show()

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