read_paths = { 'data': arr, 'Nbls': '%s/arrs/%s_%s_INS_Nbls.npym' % (args.inpath, obs, args.flag_choice), 'freq_array': '%s/metadata/%s_freq_array.npy' % (args.inpath, obs), 'pols': '%s/metadata/%s_pols.npy' % (args.inpath, obs), 'vis_units': '%s/metadata/%s_vis_units.npy' % (args.inpath, obs) } ins = INS(obs=obs, outpath=args.outpath, flag_choice=args.flag_choice, read_paths=read_paths) # ins.data[:, :, :82] = np.ma.masked # ins.data[:, :, -21:] = np.ma.masked ins.data_ms = ins.mean_subtract() ins.counts, ins.bins, ins.sig_thresh = ins.hist_make() cp.INS_plot(ins, **ms_plot_kwargs) mf = MF(ins, shape_dict=shape_dict, point=args.point, streak=args.streak, **mf_kwargs) for test in args.tests: getattr(mf, 'apply_%s_test' % test)(args.order) ins.save() cp.MF_plot(mf, **ms_plot_kwargs)
ylabel='Time (10 s)') plot_lib.image_plot(fig_mf, ax[1], ins.data_ms[:, 0, :, 0], freq_array=ins.freq_array[0], cbar_label='Deviation ($\hat{\sigma}$)', aspect=aspect, vmin=-5, vmax=5, cmap=cm.coolwarm, mask_color='black', ylabel='Time (10 s)') fig_mf.savefig('%s/%s_INS_MF.png' % (outpath, obs)) fig.savefig('%s/%s_INS.png' % (outpath, obs)) ins.counts, ins.bins = np.histogram(ins.data_ms[np.logical_not(ins.data.mask)], bins='auto') ins2.counts, ins2.bins = np.histogram(ins2.data_ms[np.logical_not( ins2.data.mask)], bins='auto') exp, var = util.hist_fit(ins2.counts, ins2.bins) N1 = np.sum(ins.counts) N2 = np.sum(ins2.counts) Nexp = np.sum(exp) pdf1 = ins.counts / (N1 * np.diff(ins.bins)) pdf2 = ins2.counts / (N2 * np.diff(ins2.bins)) pdf_exp = exp / (Nexp * np.diff(ins2.bins)) print(np.sum(pdf1 * np.diff(ins.bins))) print(np.sum(pdf2 * np.diff(ins2.bins))) print(np.sum(pdf_exp * np.diff(ins2.bins)))