Commit c45eec07 authored by BO ZHANG's avatar BO ZHANG 🏀
Browse files

corrected imports from stats

parent 59ec96bd

csst/msc/_photometry/csst_photometry.py

+5 −5
Original line number Diff line number Diff line
@@ -31,7 +31,7 @@ from scipy.interpolate import UnivariateSpline 


# import ..magfluxconvert as magf

from .magfluxconvert import asinhpogson, fluxerr2magerr, magerr2fluxerr

import stats

from .stats import sigmaclip_limitsig, weighted_mean

# import system

from shutil import which
@@ -524,14 +524,14 @@ def magnitude_correction(fluxcalib, head, plot_name=None, magerr_lim=0.05, elp_l 
    else:

        print('isolated stars: ', mask.sum())

        magdiff = -np.transpose(apermag[mask, :].transpose() - apmag8[mask])

        diff_masked = stats.sigmaclip_limitsig(magdiff, sigma=sigma, maxiters=iters, axis=0)

        diff_masked = sigmaclip_limitsig(magdiff, sigma=sigma, maxiters=iters, axis=0)

        mask1 = mask

        mask = np.logical_not(np.any(diff_masked.mask, axis=1))

        nstar_aper = mask.sum()

        diff_masked = diff_masked[mask]

        for i in range(naper):

            weighterr = np.sqrt(apmag8err[mask1][mask] ** 2 + apermagerr[:, i][mask1][mask] ** 2)

            cor, _, corerr = stats.weighted_mean(diff_masked[:, i], weighterr, weight_square=False)

            cor, _, corerr = weighted_mean(diff_masked[:, i], weighterr, weight_square=False)

            corerr /= np.sqrt(nstar_aper)

            apercor[i] = cor

            apercor_std[i] = corerr
@@ -657,12 +657,12 @@ def magnitude_correction(fluxcalib, head, plot_name=None, magerr_lim=0.05, elp_l 
            else:

                print('isolated stars for ' + magkeys[i] + ':', mask.sum())

                magdiff = apmag8[mask] - kmag[mask]

                diff_masked = stats.sigmaclip_limitsig(magdiff, sigma=sigma, maxiters=iters, sig_limit=sig_limit)

                diff_masked = sigmaclip_limitsig(magdiff, sigma=sigma, maxiters=iters, sig_limit=sig_limit)

                mask1 = np.logical_not(diff_masked.mask)

                nstar_cor = mask1.sum()

                diff_masked = magdiff[mask1]

                weighterr = np.sqrt(kmagerr[mask][mask1] ** 2 + apmag8err[mask][mask1] ** 2)

                cor, _, corerr = stats.weighted_mean(diff_masked, weighterr, weight_square=False)

                cor, _, corerr = weighted_mean(diff_masked, weighterr, weight_square=False)

                corerr /= np.sqrt(nstar_cor)

                print('correction using stars:', nstar_cor)

                print([cor, corerr])