confsmooth 1.0.0

The algorithm smooths functions preserving local peaks. To distinguish peaks from noise it needs noise standard deviation

Confidence Based Smoothing

The algorithm smooths functions preserving local peaks. To distinguish peaks from noise it needs noise standard deviation.

Installation

pip install --upgrade confsmooth

Usage

import os, shutil, statsmodels
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.nonparametric.kernel_regression import KernelReg
from confsmooth import confsmooth
from scipy.signal import savgol_filter

x = np.linspace(-2,2,500)
real_noise_level = 0.002
y = 1 - np.power(np.abs(x)+0.01,0.01) + np.random.normal(0, real_noise_level, len(x))

# estimate noise standard deviation
savgol = savgol_filter(y,21,1)
noise_level = np.std(y - savgol)
print('Estimated noise level =', noise_level, 'Real noise level =', real_noise_level)

smoothed_conf = confsmooth(y, noise_level, confidence=0.995, deg=2)

kr = KernelReg(endog=y, exog=x, var_type='c', bw=[0.1])
smoothed_kr, _ = kr.fit(x)

fig,ax = plt.subplots()
ax.plot(x, y, label='initial')
ax.plot(x, savgol, label='savgol')
ax.plot(x, smoothed_kr, label='Kernel regr')
ax.plot(x, smoothed_conf, label='confsmooth')
ax.legend()
plt.show()