Python GraphArtist.histogram方法代码示例

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
def plot_arrival_times():
    graph = GraphArtist()

    figure()
    sim = data.root.showers.E_1PeV.zenith_22_5
    t = get_front_arrival_time(sim, 20, 5, pi / 8)
    n, bins = histogram(t, bins=linspace(0, 50, 201))
    mct = monte_carlo_timings(n, bins, 100000)
    n, bins, patches = hist(mct, bins=linspace(0, 20, 101), histtype='step')
    graph.histogram(n, bins, linestyle='black!50')

    mint = my_t_draw_something(data, 2, 100000)
    n, bins, patches = hist(mint, bins=linspace(0, 20, 101), histtype='step')
    graph.histogram(n, bins)

    xlabel("Arrival time [ns]")
    ylabel("Number of events")

    graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
    graph.set_ylabel("Number of events")
    graph.set_xlimits(0, 20)
    graph.set_ylimits(min=0)
    graph.save('plots/SIM-T')

    print(median(t), median(mct), median(mint))

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
def plot_nearest_neighbors(data, limit=None):
    global coincidences
    hisparc_group = data.root.hisparc.cluster_kascade.station_601
    kascade_group = data.root.kascade

    coincidences = KascadeCoincidences(data, hisparc_group, kascade_group,
                                       ignore_existing=True)

    #dt_opt = find_optimum_dt(coincidences, p0=-13, limit=1000)
    #print dt_opt

    graph = GraphArtist(axis='semilogy')
    styles = iter(['solid', 'dashed', 'dashdotted'])

    uncorrelated = None
    figure()
    #for shift in -12, -13, dt_opt, -14:
    for shift in -12, -13, -14:
        print "Shifting", shift
        coincidences.search_coincidences(shift, dtlimit=1, limit=limit)
        print "."
        dts = coincidences.coincidences['dt']
        n, bins, p = hist(abs(dts) / 1e9, bins=linspace(0, 1, 101),
                          histtype='step', label='%.3f s' % shift)
        n = [u if u else 1e-99 for u in n]
        graph.histogram(n, bins, linestyle=styles.next() + ',gray')
        if uncorrelated is None:
            uncorrelated = n, bins

    y, bins = uncorrelated
    x = (bins[:-1] + bins[1:]) / 2
    f = lambda x, N, a: N * exp(-a * x)
    popt, pcov = curve_fit(f, x, y)
    plot(x, f(x, *popt), label=r"$\lambda = %.2f$ Hz" % popt[1])
    graph.plot(x, f(x, *popt), mark=None)

    yscale('log')
    xlabel("Time difference [s]")
    graph.set_xlabel(r"Time difference [\si{\second}]")
    ylabel("Counts")
    graph.set_ylabel("Counts")
    legend()
    graph.set_ylimits(min=10)
    utils.saveplot()
    graph.save('plots/MAT-nearest-neighbors')

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
def plot_front_passage():
    sim = data.root.showers.E_1PeV.zenith_0.shower_0
    leptons = sim.leptons
    R = 40
    dR = 2
    low = R - dR
    high = R + dR
    global t
    t = leptons.read_where('(low < core_distance) & (core_distance <= high)',
                          field='arrival_time')

    n, bins, patches = hist(t, bins=linspace(0, 30, 31), histtype='step')

    graph = GraphArtist()
    graph.histogram(n, bins)
    graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
    graph.set_ylabel("Number of leptons")
    graph.set_ylimits(min=0)
    graph.set_xlimits(0, 30)
    graph.save('plots/front-passage')

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
    def plot_gamma_landau_fit(self):
        events = self.data.root.hisparc.cluster_kascade.station_601.events
        ph0 = events.col('integrals')[:, 0]

        bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
        n, bins = np.histogram(ph0, bins=bins)
        x = (bins[:-1] + bins[1:]) / 2

        p_gamma, p_landau = self.full_spectrum_fit(x, n, (1., 1.),
                                                   (5e3 / .32, 3.38 / 5000, 1.))
        print "FULL FIT"
        print p_gamma, p_landau

        n /= 10
        p_gamma, p_landau = self.constrained_full_spectrum_fit(x, n, p_gamma, p_landau)
        print "CONSTRAINED FIT"
        print p_gamma, p_landau

        plt.figure()
        print self.calc_charged_fraction(x, n, p_gamma, p_landau)

        plt.plot(x * VNS, n)
        self.plot_landau_and_gamma(x, p_gamma, p_landau)
        #plt.plot(x, n - self.gamma_func(x, *p_gamma))
        plt.xlabel("Pulse integral [V ns]")
        plt.ylabel("Count")
        plt.yscale('log')
        plt.xlim(0, 30)
        plt.ylim(1e1, 1e4)
        plt.legend()
        utils.saveplot()

        graph = GraphArtist('semilogy')
        graph.histogram(n, bins * VNS, linestyle='gray')
        self.artistplot_landau_and_gamma(graph, x, p_gamma, p_landau)
        graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
        graph.set_ylabel("Count")
        graph.set_xlimits(0, 30)
        graph.set_ylimits(1e1, 1e4)
        artist.utils.save_graph(graph, dirname='plots')

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
    def plot_full_spectrum_fit_in_density_range(self, sel, popt, low, high):
        bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
        n, bins = np.histogram(sel, bins=bins)
        x = (bins[:-1] + bins[1:]) / 2

        p_gamma, p_landau = self.constrained_full_spectrum_fit(x, n, popt[:2], popt[2:])

        plt.figure()
        plt.plot(x * VNS, n, label="data")
        self.plot_landau_and_gamma(x, p_gamma, p_landau)

        y_charged = self.calc_charged_spectrum(x, n, p_gamma, p_landau)
        plt.plot(x * VNS, y_charged, label="charged particles")

        plt.yscale("log")
        plt.xlim(0, 50)
        plt.ylim(ymin=1)
        plt.xlabel("Pulse integral [V ns]")
        plt.ylabel("Count")
        plt.legend()
        suffix = "%.1f-%.1f" % (low, high)
        suffix = suffix.replace(".", "_")
        utils.saveplot(suffix)

        n = np.where(n > 0, n, 1e-99)
        y_charged = np.where(y_charged > 0, y_charged, 1e-99)

        graph = GraphArtist("semilogy")
        graph.histogram(n, bins * VNS, linestyle="gray")
        self.artistplot_alt_landau_and_gamma(graph, x, p_gamma, p_landau)
        graph.histogram(y_charged, bins * VNS)
        graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
        graph.set_ylabel("Count")
        graph.set_title(
            r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$" % (low, high)
        )
        graph.set_xlimits(0, 30)
        graph.set_ylimits(1e0, 1e4)
        artist.utils.save_graph(graph, suffix, dirname="plots")

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
def plot_R():
    graph = GraphArtist(width=r'.45\linewidth')

    n, bins, patches = hist(data.root.simulations.E_1PeV.zenith_22_5.shower_0.coincidences.col('r'), bins=100, histtype='step')
    graph.histogram(n, bins, linestyle='black!50')

    shower = data.root.simulations.E_1PeV.zenith_22_5.shower_0
    ids = shower.observables.get_where_list('(n1 >= 1) & (n3 >= 1) & (n4 >= 1)')
    R = shower.coincidences.read_coordinates(ids, field='r')
    n, bins, patches = hist(R, bins=100, histtype='step')
    graph.histogram(n, bins)

    xlabel("Core distance [m]")
    ylabel("Number of events")

    print("mean", mean(R))
    print("median", median(R))

    graph.set_xlabel(r"Core distance [\si{\meter}]")
    graph.set_ylabel("Number of events")
    graph.set_xlimits(min=0)
    graph.set_ylimits(min=0)
    graph.save('plots/SIM-R')

# 需要导入模块: from artist import GraphArtist [as 别名]
# 或者: from artist.GraphArtist import histogram [as 别名]
def plot_pulseheight_histogram(data):
    events = data.root.hisparc.cluster_kascade.station_601.events
    ph = events.col("pulseheights")

    s = landau.Scintillator()
    mev_scale = 3.38 / 340
    count_scale = 6e3 / 0.32

    clf()
    n, bins, patches = hist(ph[:, 0], bins=arange(0, 1501, 10), histtype="step")
    x = linspace(0, 1500, 1500)
    plot(x, s.conv_landau_for_x(x, mev_scale=mev_scale, count_scale=count_scale))
    plot(x, count_scale * s.landau_pdf(x * mev_scale))
    ylim(ymax=25000)
    xlim(xmax=1500)

    # Remove one statistical fluctuation from data.  It is not important
    # for the graph, but it detracts from the main message
    index = bins.searchsorted(370)
    n[index] = mean([n[index - 1], n[index + 1]])

    graph = GraphArtist()
    n_trunc = where(n <= 100000, n, 100000)
    graph.histogram(n_trunc, bins, linestyle="gray")
    graph.add_pin("data", x=800, location="above right", use_arrow=True)
    graph.add_pin("$\gamma$", x=90, location="above right", use_arrow=True)
    graph.plot(x, s.conv_landau_for_x(x, mev_scale=mev_scale, count_scale=count_scale), mark=None)
    graph.add_pin("convolved Landau", x=450, location="above right", use_arrow=True)
    graph.plot(x, count_scale * s.landau_pdf(x * mev_scale), mark=None, linestyle="black")
    graph.add_pin("Landau", x=380, location="above right", use_arrow=True)

    graph.set_xlabel(r"Pulseheight [\adc{}]")
    graph.set_ylabel(r"Number of events")
    graph.set_xlimits(0, 1400)
    graph.set_ylimits(0, 21000)
    graph.save("plots/plot_pulseheight_histogram")