From 83fe6cc26598892991b748e801e6c5bcf9eb2755 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9my=20Neveu?= Date: Thu, 1 Oct 2026 17:37:40 +0200 Subject: [PATCH] remove deprecated median_absolute_deviation --- runMultiSpectra.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/runMultiSpectra.py b/runMultiSpectra.py index e123d67c7..66a7c5465 100644 --- a/runMultiSpectra.py +++ b/runMultiSpectra.py @@ -7,7 +7,7 @@ def filter_data(file_names): # pragma: no cover - from scipy.stats import median_absolute_deviation + from scipy.stats import median_abs_deviation D = [] chi2 = [] dx = [] @@ -34,20 +34,20 @@ def filter_data(file_names): # pragma: no cover plt.plot(k, D) # plt.plot(k, np.polyval(np.polyfit(k, reg, deg=1), k)) plt.axhline(np.median(D)) - plt.axhline(np.median(D) + 3 * median_absolute_deviation(D)) - plt.axhline(np.median(D) - 3 * median_absolute_deviation(D)) + plt.axhline(np.median(D) + 3 * median_abs_deviation(D, scale='normal')) + plt.axhline(np.median(D) - 3 * median_abs_deviation(D, scale='normal')) plt.grid() plt.title("D2CCD") plt.show() - filter_indices = np.logical_and(D > np.median(D) - 3 * median_absolute_deviation(D), - D < np.median(D) + 3 * median_absolute_deviation(D)) + filter_indices = np.logical_and(D > np.median(D) - 3 * median_abs_deviation(D, scale='normal'), + D < np.median(D) + 3 * median_abs_deviation(D, scale='normal')) if len(chi2) > 0: - filter_indices *= np.logical_and(chi2 > np.median(chi2) - 3 * median_absolute_deviation(chi2), - chi2 < np.median(chi2) + 3 * median_absolute_deviation(chi2)) - filter_indices *= np.logical_and(dx > np.median(dx) - 3 * median_absolute_deviation(dx), - dx < np.median(dx) + 3 * median_absolute_deviation(dx)) - filter_indices *= np.logical_and(regs > np.median(regs) - 3 * median_absolute_deviation(regs), - regs < np.median(regs) + 3 * median_absolute_deviation(regs)) + filter_indices *= np.logical_and(chi2 > np.median(chi2) - 3 * median_abs_deviation(chi2, scale='normal'), + chi2 < np.median(chi2) + 3 * median_abs_deviation(chi2, scale='normal')) + filter_indices *= np.logical_and(dx > np.median(dx) - 3 * median_abs_deviation(dx, scale='normal'), + dx < np.median(dx) + 3 * median_abs_deviation(dx, scale='normal')) + filter_indices *= np.logical_and(regs > np.median(regs) - 3 * median_abs_deviation(regs, scale='normal'), + regs < np.median(regs) + 3 * median_abs_deviation(regs, scale='normal')) plt.plot(k, D) plt.title("D2CCD") plt.plot(k[filter_indices], D[filter_indices], "ko")