def polynomial_fit()

in scheme_adapters/polyfit_adapter/polyfit_adapter.py [0:0]

• 34 lines of code
• 11 McCabe index (conditional complexity)

    def polynomial_fit(self, x, y, min_range, max_range, degree):
        x, y = zip(*sorted(zip(x, y)))
        x = list(x)
        y = list(y)
        mini = 10000000
        for el in y:
            if el != -1000 and mini > el:
                mini = el

        for i in range(len(y)):
            if y[i] == -1000:
                y[i] = mini
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', np.RankWarning)
            poly = np.poly1d(np.polyfit(x, y, degree))
            poly_coeff = np.polyfit(x, y, degree)
            num = int(len(poly_coeff))
            derivative = []
            for exp in range(0, len(poly_coeff) - 1, 1):
                num = num - 1
                derivative.append(poly_coeff[exp] * num)
            roots = np.roots(derivative)
            roots = np.append(roots, min_range)
            roots = np.append(roots, max_range)
            best_val = -10000000000
            best_point = -1
            for el in roots:
                if np.iscomplex(el):
                    continue
                if max_range >= el >= min_range:
                    if best_val < poly(el):
                        best_val = max(best_val, poly(el))
                        best_point = np.absolute(el)
            print("Best point:", best_point)
        return best_val, best_point