Пример #1
0
    def execute(self, processes):
        summary_outname = self.params["outdir"] + "theory_fit.dat"
        print "opening: ", self.params["summary_file"]
        print "to summary: ", summary_outname
        cross_summary = h5py.File(self.params["summary_file"], "r")
        # this has: kvec, power_1d, power_1d_cov

        print "using col:%d of %s" % (self.params["pk_column"],
                                      self.params["theory_curve"])

        theory_curve = np.genfromtxt(self.params["theory_curve"])
        theory_curve = theory_curve[:, self.params["pk_column"]]
        #print theory_curve

        bin_center = cross_summary["kvec"]["k_1d_center"].value
        (kmin, kmax) = tuple(self.params["krange"])
        restrict = np.where(np.logical_and(bin_center > kmin,
                                           bin_center < kmax))
        res_slice = slice(min(restrict[0]), max(restrict[0]) + 1)
        #restrict_alt = np.where(restrict)[0][np.newaxis, :]
        #restricted_cov = covmock[restrict_alt][0]

        summary_outfile = open(summary_outname, "w")
        for treatment in cross_summary["power_1d"]:

            pwr = cross_summary["power_1d"][treatment].value
            cov = cross_summary["power_1d_cov"][treatment].value

            amplitude = utils.ampfit(pwr[res_slice],
                                     cov[res_slice, res_slice],
                                     theory_curve[res_slice])

            nmodes = float(treatment.split("modes")[0])
            snr = amplitude['amp'] / amplitude['error']
            outstr = "%d " % nmodes
            outstr += "%(amp)10.5f %(error)10.5f " % amplitude
            outstr += "%(chi2)10.5f %(dof)10.5f %(pte)10.5f " % amplitude
            outstr += "%10.5f" % snr

            print outstr
            summary_outfile.write(outstr + "\n")
Пример #2
0
    def execute(self, processes):
        summary_outname = self.params["outdir"] + "theory_fit.dat"
        print "opening: ", self.params["summary_file"]
        print "to summary: ", summary_outname
        cross_summary = h5py.File(self.params["summary_file"], "r")
        # this has: kvec, power_1d, power_1d_cov

        print "using col:%d of %s" % (self.params["pk_column"],
                                      self.params["theory_curve"])

        theory_curve = np.genfromtxt(self.params["theory_curve"])
        theory_curve = theory_curve[:, self.params["pk_column"]]
        #print theory_curve

        bin_center = cross_summary["kvec"]["k_1d_center"].value
        (kmin, kmax) = tuple(self.params["krange"])
        restrict = np.where(
            np.logical_and(bin_center > kmin, bin_center < kmax))
        res_slice = slice(min(restrict[0]), max(restrict[0]) + 1)
        #restrict_alt = np.where(restrict)[0][np.newaxis, :]
        #restricted_cov = covmock[restrict_alt][0]

        summary_outfile = open(summary_outname, "w")
        for treatment in cross_summary["power_1d"]:

            pwr = cross_summary["power_1d"][treatment].value
            cov = cross_summary["power_1d_cov"][treatment].value

            amplitude = utils.ampfit(pwr[res_slice], cov[res_slice, res_slice],
                                     theory_curve[res_slice])

            nmodes = float(treatment.split("modes")[0])
            snr = amplitude['amp'] / amplitude['error']
            outstr = "%d " % nmodes
            outstr += "%(amp)10.5f %(error)10.5f " % amplitude
            outstr += "%(chi2)10.5f %(dof)10.5f %(pte)10.5f " % amplitude
            outstr += "%10.5f" % snr

            print outstr
            summary_outfile.write(outstr + "\n")
Пример #3
0
def make_xcorr_plotdata():
    # find the mean brightness used in simulations
    corrobj = corr21cm.Corr21cm()
    T_b_sim = corrobj.T_b(1420. / 800. - 1)
    print T_b_sim

    #splt.process_batch_correlations(xcorr_real_param,
    #                multiplier=1., cross_power=True)
    ##splt.process_batch_correlations(xcorr_sim_param, cross_power=True,
    ##                                multiplier=1./(T_b_sim/1.e3))
    #splt.process_batch_correlations(xcorr_sim_param, cross_power=True,
    #                                multiplier=1./(T_b_sim))
    #splt.process_batch_correlations(xcorr_loss_sim_param,
    #                multiplier=1./T_b_sim*1.e-3, cross_power=True)
    #splt.process_batch_correlations(xcorr_variants_param,
    #                multiplier=1., cross_power=True)

    #splt.plot_batch_correlations(xcorr_real_param,
    #                        dir_prefix="plots/xcorr_real/",
    #                        color_range=[-0.04, 0.04], cross_power=True)
    #splt.plot_batch_correlations(xcorr_sim_param,
    #                        dir_prefix="plots/xcorr_sim/",
    #                        color_range=[-0.04, 0.04], cross_power=True)
    #splt.plot_batch_correlations(xcorr_loss_param,
    #                        dir_prefix="plots/xcorr_loss/",
    #                        color_range=[-0.04, 0.04], cross_power=True)
    #splt.plot_batch_correlations(xcorr_loss_sim_param,
    #                        dir_prefix="plots/xcorr_loss_sim/",
    #                        color_range=[-0.04, 0.04], cross_power=True)
    #splt.plot_batch_correlations(xcorr_variants_param,
    #                        dir_prefix="plots/xcorr_variants/",
    #                        color_range=[-0.04, 0.04], cross_power=True)

    # find the real xcorr signal with errors
    xcorr_data = splt.batch_correlations_statistics(xcorr_real_param,
                                                    randtoken="rand",
                                                    include_signal=True)
    # find the simulated xcorr signal with errors
    xcorr_sim_data = splt.batch_correlations_statistics(xcorr_sim_param,
                                                        randtoken="rand",
                                                        include_signal=False)

    # find the compensation function from simulations
    compmode = splt.batch_compensation_function(xcorr_loss_sim_param)

    lagl = xcorr_data[0]
    lagc = xcorr_data[1]
    lagr = xcorr_data[2]
    xcorr_null = xcorr_data[3]
    xcorr_signal = xcorr_data[6]
    xcorr_cov = xcorr_data[5]
    xcorr_err = xcorr_data[4]
    xcorr_sim = xcorr_sim_data[3]
    xcorr_sim_err = xcorr_sim_data[4]
    xcorr_sim_cov = xcorr_sim_data[5]
    # 0=0, 5=1, 10=2, 15=3, etc.
    compensation = compmode[3, :]

    (amp, amp_err) = utils.ampfit(xcorr_signal, xcorr_cov + xcorr_sim_cov,
                                  xcorr_sim)
    print amp, amp_err

    # now convert from the 1e-3 Omega_HI in simulations to 0.5e-3
    #xcorr_sim /= 2.
    #xcorr_sim_err /= 2.
    xcorr_sim *= amp
    xcorr_sim_err *= amp

    for correntry in zip(lagl, lagc, lagr, xcorr_signal, xcorr_null, xcorr_err,
                         xcorr_sim, xcorr_sim_err, compensation):
        print "%5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g" % correntry