[numpy] low-pass filter high-pass filter

import pandas as pd
import dask.dataframe as dd
import numpy as np
import os
from sklearn.datasets import make_classification
import matplotlib.pyplot as plt

def high_pass(s, threshold=1e7):
    fourier = np.fft.rfft(s)
    frequencies = np.fft.rfftfreq(s.size, d=2e-2/s.size)
    fourier[frequencies < threshold] = 0
    return np.fft.irfft(fourier)

def low_pass(s, threshold=1e4):
    fourier = np.fft.rfft(s)
    frequencies =np.fft.rfftfreq(s.size, d=2e-2 / s.size)
    fourier[frequencies > threshold] = 0
    return np.fft.irfft(fourier)

# features
df = make_classification(n_samples = n_samples, n_features = 10)
df_raw = pd.DataFrame(df[0], columns = ['var1', 'var2', 'var3', 'var4', 'var5', 'var6', 'var7', 'var8', 'var9', 'var10'])

# class
df_raw['class'] = df[1]
del df

hf_signal_1 = high_pass(df_raw['var1'].values, threshold=1e4)

plt.plot(df_raw['var1'].values, color='lightgray')
plt.plot(hf_signal_1, color='black')

lf_signal_1 = low_pass(df_raw['var1'].values)
plt.plot(df_raw['var1'].values, color='lightgray')
plt.plot(lf_signal_1, color='black')

plt.plot(df_raw['var1'].values, color='black')
plt.plot(lf_signal_1 + hf_signal_1, color='lightgray')