import numpy as np
import topomod


@topomod
def polynominal_background_correction(self, order):
    data = self.data
    x_means = np.mean(data, axis=0)
    x_i = np.arange(len(x_means))
    x_fit = np.polyfit(x_i, x_means, order)
    data = data - np.poly1d(x_fit)(x_i)
    y_means = np.mean(data, axis=1)
    y_i = np.arange(len(y_means))
    y_fit = np.polyfit(y_i, y_means, order)
    data = (data.T - np.poly1d(y_fit)(y_i)).T
    return self.clone(data=data)

3678e80458e4f443ce07082ab5e31a8a65e45dcf
<mask token>

<mask token>


@topomod
def polynominal_background_correction(self, order):
    data = self.data
    x_means = np.mean(data, axis=0)
    x_i = np.arange(len(x_means))
    x_fit = np.polyfit(x_i, x_means, order)
    data = data - np.poly1d(x_fit)(x_i)
    y_means = np.mean(data, axis=1)
    y_i = np.arange(len(y_means))
    y_fit = np.polyfit(y_i, y_means, order)
    data = (data.T - np.poly1d(y_fit)(y_i)).T
    return self.clone(data=data)

import numpy as np
import topomod


@topomod
def polynominal_background_correction(self, order):
    data = self.data
    x_means = np.mean(data, axis=0)
    x_i = np.arange(len(x_means))
    x_fit = np.polyfit(x_i, x_means, order)
    data = data - np.poly1d(x_fit)(x_i)
    y_means = np.mean(data, axis=1)
    y_i = np.arange(len(y_means))
    y_fit = np.polyfit(y_i, y_means, order)
    data = (data.T - np.poly1d(y_fit)(y_i)).T
    return self.clone(data=data)
