def main(): config = ConfigLoader("config.yml") data = config.get_data("data")[0] phsp = config.get_data("phsp")[0] bg = config.get_data("bg")[0] config.set_params("final_params.json") mbc_idx = config.get_data_index("mass", "R_BC") # ("particle", "(D, K)", "m") mbd_idx = config.get_data_index("mass", "R_BD") # ("particle", "(D0, K, pi)", "m") data_idx = [mbc_idx, mbd_idx] data_cut = np.array([data_index(data, idx) for idx in data_idx]) adapter = AdaptiveBound(data_cut**2, [[2, 2], [3, 3], [2, 2]]) bound = adapter.get_bounds() cal_chi2_config(config, adapter, data, phsp, data_idx, bg=bg, data_cut=data_cut) draw_dalitz(data_cut, bound)
def main(): config = ConfigLoader("config.yml") decay = config.get_amplitude().decay_group data = config.get_data("phsp") ret = [] for i in data: cached_amp = build_angle_amp_matrix(decay, i) ret.append(data_to_numpy(cached_amp)) idx = config.get_data_index("angle", "R_BC/B") ang = data_index(data[0], idx) np.savez("phsp.npz", ret) for k, v in ret[0].items(): for i, amp in enumerate(v): w = np.abs(amp)**2 w = np.sum(np.reshape(w, (amp.shape[0], -1)), axis=-1) plt.hist( np.cos(ang["beta"]), weights=w, bins=20, histtype="step", label="{}: {}".format(k, i), ) plt.savefig("angle_costheta.png")
def main(): config = ConfigLoader("config.yml") config.set_params("final_params.json") amp = config.get_amplitude() data = config.get_data("data_origin")[0] phsp = config.get_data("phsp_plot")[0] phsp_re = config.get_data("phsp_plot_re")[0] print("data loaded") amps = amp(phsp_re) pw = amp.partial_weight(phsp_re) re_weight = phsp_re["weight"] re_size = config.resolution_size amps = sum_resolution(amps, re_weight, re_size) pw = [sum_resolution(i, re_weight, re_size) for i in pw] m_idx = config.get_data_index("mass", "R_BC") m_phsp = data_index(phsp, m_idx).numpy() m_data = data_index(data, m_idx).numpy() m_min, m_max = np.min(m_phsp), np.max(m_phsp) scale = m_data.shape[0] / np.sum(amps) get_hist = lambda m, w: Hist1D.histogram( m, weights=w, range=(m_min, m_max), bins=100) data_hist = get_hist(m_data, None) phsp_hist = get_hist(m_phsp, scale * amps) pw_hist = [] for i in pw: pw_hist.append(get_hist(m_phsp, scale * i)) ax2 = plt.subplot2grid((4, 1), (3, 0), rowspan=1) ax = plt.subplot2grid((4, 1), (0, 0), rowspan=3, sharex=ax2) data_hist.draw_error(ax, label="data") phsp_hist.draw(ax, label="fit") for i, j in zip(pw_hist, config.get_decay()): i.draw_kde(ax, label=str(j.inner[0])) (data_hist - phsp_hist).draw_pull(ax2) ax.set_ylim((1, None)) ax.legend() ax.set_yscale("log") ax.set_ylabel("Events/{:.1f} MeV".format((m_max - m_min) * 10)) ax2.set_xlabel("M( R_BC )") ax2.set_ylabel("pull") ax2.set_xlim((1.3, 1.7)) ax2.set_ylim((-5, 5)) plt.setp(ax.get_xticklabels(), visible=False) plt.savefig("m_R_BC_fit.png")
def main(): Nbins = 64 config = ConfigLoader("config.yml") # config = MultiConfig(["config.yml"]).configs[0] config.set_params("final_params.json") name = "R_BC" idx = config.get_data_index("mass", "R_BC") # idx_costheta = (*config.get_data_index("angle", "DstD/D*"), "beta") datas, phsps, bgs, _ = config.get_all_data() amp = config.get_amplitude() get_data = lambda x: data_index(x, idx).numpy() # get_data = lambda x: np.cos(data_index(x, idx_costheta).numpy()) plot_mass(amp, datas, bgs, phsps, get_data, name, Nbins)
def main(): sigma = 0.005 config = ConfigLoader("config.yml") decay = config.get_decay() m0 = decay.top.get_mass() m1, m2, m3 = [i.get_mass() for i in decay.outs] print("mass: ", m0, " -> ", m1, m2, m3) phsp = PhaseSpaceGenerator(m0, [m1, m2, m3]) p1, p2, p3 = phsp.generate(100000) angle = cal_angle_from_momentum({"B": p1, "C": p2, "D": p3}, decay) amp = config.get_amplitude() m_idx = config.get_data_index("mass", "R_BC") m_BC = data_index(angle, m_idx) R_BC = decay.get_particle("R_BC1") m_R, g_R = R_BC.get_mass(), R_BC.get_width() # import matplotlib.pyplot as plt # x = np.linspace(1.3, 1.7, 1000) # amp = R_BC.get_amp({"m": x}).numpy() # plt.plot(x, np.abs(amp)**2) # plt.show() print("mass: ", m_R, "width: ", g_R) amp_s2 = resolution_bw(m_BC, m_R, g_R, sigma, m1 + m2, m0 - m3) print("|A|*R: ", amp_s2) cut_data = simple_selection(angle, amp_s2) ps = [data_index(cut_data, ("particle", i, "p")).numpy() for i in "BCD"] np.savetxt("data/data_origin.dat", np.transpose(ps, (1, 0, 2)).reshape((-1, 4))) p1, p2, p3 = phsp.generate(100000) np.savetxt("data/phsp.dat", np.transpose([p1, p2, p3], (1, 0, 2)).reshape((-1, 4))) p1, p2, p3 = phsp.generate(50000) np.savetxt( "data/phsp_plot.dat", np.transpose([p1, p2, p3], (1, 0, 2)).reshape((-1, 4)), )
def main(): Nbins = 72 config = ConfigLoader("config.yml") config.set_params("final_params.json") error_matrix = np.load("error_matrix.npy") idx = config.get_data_index("mass", "R_BC") datas = config.get_data("data") phsps = config.get_data("phsp") bgs = config.get_data("bg") amp = config.get_amplitude() var = amp.trainable_variables get_data = lambda x: data_index(x, idx).numpy() m_all = np.concatenate([get_data(i) for i in datas]) m_min = np.min(m_all) - 0.1 m_max = np.max(m_all) + 0.1 binning = np.linspace(m_min, m_max, Nbins + 1) get_hist = lambda x, w: Hist1D.histogram( get_data(x), bins=Nbins, range=(m_min, m_max), weights=w ) data_hist = [get_hist(i, i.get("weight")) for i in datas] bg_hist = [get_hist(i, np.abs(i.get("weight"))) for i in bgs] phsp_hist = [] for dh, bh, phsp in zip(data_hist, bg_hist, phsps): m_phsp = data_index(phsp, idx).numpy() y_frac, grads, w_error2 = binning_gradient( binning, amp, phsp, m_phsp, var ) error2 = np.einsum("ij,jk,ik->i", grads, error_matrix, grads) # error parameters and error from integration sample weights yerr = np.sqrt(error2 + w_error2) n_fit = dh.get_count() - bh.get_count() phsp_hist.append(n_fit * Hist1D(binning, y_frac, yerr)) total_data = reduce(operator.add, data_hist) ax = plt.subplot2grid((4, 1), (0, 0), rowspan=3) total_data.draw(ax, label="data") total_data.draw_error(ax) total_bg = reduce(operator.add, bg_hist) total_bg.draw_bar(ax, label="back ground", color="grey", alpha=0.5) total_fit = reduce(operator.add, phsp_hist + bg_hist) total_fit.draw(ax, label="fit") total_fit.draw_error(ax) ax.set_ylim((0, None)) ax.set_xlim((m_min, m_max)) ax.legend() ax.set_ylabel(f"Events/ {(m_max-m_min)/Nbins:.3f} GeV") ax2 = plt.subplot2grid((4, 1), (3, 0), rowspan=1) (total_data - total_fit).draw_pull(ax2) plt.setp(ax.get_xticklabels(), visible=False) ax2.set_ylim((-5, 5)) ax2.set_xlim((m_min, m_max)) ax2.axhline(0, c="black", ls="-") ax2.axhline(-3, c="r", ls="--") ax2.axhline(3, c="r", ls="--") ax2.set_xlabel("$M(BC)$/GeV", loc="right") plt.savefig("fit_full_error.png")