Ejemplo n.º 1
0
def plotDiagnostics(data, mu, xi, sigma, figfile):
    """
    Create a 4-panel diagnostics plot of the fitted distribution.

    :param data: :class:`numpy.ndarray` of observed data values (in units
                 of metres/second).
    :param float mu: Selected threshold value.
    :param float xi: Fitted shape parameter.
    :param float sigma: Fitted scale parameter.
    :param str figfile: Path to store the file (includes image format)

    """
    LOG.info("Plotting diagnostics")
    fig, ax = plt.subplots(2, 2)
    axes = ax.flatten()
    # Probability plots
    sortedmax = np.sort(data[data > mu])
    gpdf = fittedPDF(data, mu, xi, sigma)
    pp_x = sm.ProbPlot(sortedmax)
    pp_x.ppplot(xlabel="Empirical", ylabel="Model", ax=axes[0], line='45')
    axes[0].set_title("Probability plot")

    prplot = sm.ProbPlot(sortedmax,
                         genpareto,
                         distargs=(xi, ),
                         loc=mu,
                         scale=sigma)
    prplot.qqplot(xlabel="Model", ylabel="Empirical", ax=axes[1], line='45')
    axes[1].set_title("Quantile plot")

    ax2 = axes[2]
    rp = np.array(
        [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000])
    rate = float(len(sortedmax)) / float(len(data))
    rval = returnLevels(rp, mu, xi, sigma, rate)

    emprp = empiricalReturnPeriod(np.sort(data))
    ax2.semilogx(rp, rval, label="Fitted RP curve", color='r')
    ax2.scatter(emprp[emprp > 1],
                np.sort(data)[emprp > 1],
                color='b',
                label="Empirical RP",
                s=100)
    ax2.legend(loc=2)
    ax2.set_xlabel("Return period")
    ax2.set_ylabel("Return level")
    ax2.set_title("Return level plot")
    ax2.grid(True)
    maxbin = 4 * np.ceil(np.floor(data.max() / 4) + 1)
    sns.distplot(sortedmax,
                 bins=np.arange(mu, maxbin, 2),
                 hist=True,
                 axlabel='Wind speed (m/s)',
                 ax=axes[3])
    axes[3].plot(sortedmax, gpdf, color='r')
    axes[3].set_title("Density plot")
    plt.tight_layout()
    plt.savefig(figfile)
    plt.close()
Ejemplo n.º 2
0
def plotFit(data, mu, xi, sigma, title, figfile):
    """
    Plot a fitted distribution, with approximate 90% confidence interval
    and empirical return period values.

    :param data: :class:`numpy.ndarray` of observed data values.
    :param float mu: Selected threshold value.
    :param float xi: Fitted shape parameter.
    :param float sigma: Fitted scale parameter.
    :param str title: Title string for the plot.
    :param str figfile: Path to store the file (includes image format)

    """
    LOG.info("Plotting fitted return period curve")

    rp = np.array(
        [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000])
    rate = float(len(data[data > mu])) / float(len(data))
    rval = returnLevels(rp, mu, xi, sigma, rate)

    emprp = empiricalReturnPeriod(data)
    err = returnPeriodUncertainty(data, mu, xi, sigma, rp)

    sortedmax = np.sort(data)
    fig, ax1 = plt.subplots(1, 1, figsize=(12, 12))
    ax1.semilogx(rp, rval, label="Fitted RP curve")
    ax1.semilogx(rp,
                 rval + 1.96 * err,
                 label="90% CI",
                 linestyle='--',
                 color='0.5')
    ax1.semilogx(rp, rval - 1.96 * err, linestyle='--', color='0.5')

    ax1.scatter(emprp[emprp > 1],
                sortedmax[emprp > 1],
                s=100,
                color='r',
                label="Empirical RP")

    title_str = (
        title + "\n" +
        r"$\mu$ = {0:.2f}, $\xi$ = {1:.5f}, $\sigma$ = {2:.4f}".format(
            mu, xi, sigma))
    ax1.set_title(title_str)
    ax1.legend(loc=2)
    ax1.set_ylim((0, 100))
    ax1.set_xlim((1, 10000))
    ax1.set_ylabel('Wind speed (m/s)')
    ax1.set_xlabel('Return period (years)')
    ax1.grid(which='major')
    ax1.grid(which='minor', linestyle='--', linewidth=1)

    plt.savefig(figfile)
    plt.close()
Ejemplo n.º 3
0
def plotDiagnostics(data, mu, xi, sigma, figfile):
    """
    Create a 4-panel diagnostics plot of the fitted distribution.

    :param data: :class:`numpy.ndarray` of observed data values (in units
                 of metres/second).
    :param float mu: Selected threshold value.
    :param float xi: Fitted shape parameter.
    :param float sigma: Fitted scale parameter.
    :param str figfile: Path to store the file (includes image format)

    """
    LOG.info("Plotting diagnostics")
    fig, ax = plt.subplots(2, 2)
    axes = ax.flatten()
    # Probability plots
    sortedmax = np.sort(data[data > mu])   
    gpdf = fittedPDF(data, mu, xi, sigma)
    pp_x = sm.ProbPlot(sortedmax)
    pp_x.ppplot(xlabel="Empirical", ylabel="Model", ax=axes[0], line='45')
    axes[0].set_title("Probability plot")

    prplot = sm.ProbPlot(sortedmax, genpareto, distargs=(xi,),
                         loc=mu, scale=sigma)
    prplot.qqplot(xlabel="Model", ylabel="Empirical", ax=axes[1], line='45')
    axes[1].set_title("Quantile plot")

    ax2 = axes[2]
    rp = np.array([1, 2, 5, 10, 20, 50, 100, 200,
                   500, 1000, 2000, 5000, 10000])
    rate = float(len(sortedmax)) / float(len(data))
    rval = returnLevels(rp, mu, xi, sigma, rate)

    emprp = empiricalReturnPeriod(np.sort(data))
    ax2.semilogx(rp, rval, label="Fitted RP curve", color='r')
    ax2.scatter(emprp[emprp > 1], np.sort(data)[emprp > 1],
                color='b', label="Empirical RP", s=100)
    ax2.legend(loc=2)
    ax2.set_xlabel("Return period")
    ax2.set_ylabel("Return level")
    ax2.set_title("Return level plot")
    ax2.grid(True)
    maxbin = 4 * np.ceil(np.floor(data.max() / 4) + 1)
    sns.distplot(sortedmax, bins=np.arange(mu, maxbin, 2),
                 hist=True, axlabel='Wind speed (m/s)',
                 ax=axes[3])
    axes[3].plot(sortedmax, gpdf, color='r')
    axes[3].set_title("Density plot")
    plt.tight_layout()
    plt.savefig(figfile)
    plt.close()
Ejemplo n.º 4
0
def plotFit(data, mu, xi, sigma, title, figfile):
    """
    Plot a fitted distribution, with approximate 90% confidence interval
    and empirical return period values.

    :param data: :class:`numpy.ndarray` of observed data values.
    :param float mu: Selected threshold value.
    :param float xi: Fitted shape parameter.
    :param float sigma: Fitted scale parameter.
    :param str title: Title string for the plot.
    :param str figfile: Path to store the file (includes image format)

    """
    LOG.info("Plotting fitted return period curve")

    rp = np.array([1, 2, 5, 10, 20, 50, 100, 200,
                   500, 1000, 2000, 5000, 10000])
    rate = float(len(data[data > mu])) / float(len(data))
    rval = returnLevels(rp, mu, xi, sigma, rate)

    emprp = empiricalReturnPeriod(data)
    err = returnPeriodUncertainty(data, mu, xi, sigma, rp)

    sortedmax = np.sort(data)
    fig, ax1 = plt.subplots(1, 1, figsize=(12, 12))
    ax1.semilogx(rp, rval, label="Fitted RP curve")
    ax1.semilogx(rp, rval + 1.96 * err, label="90% CI",
                 linestyle='--', color='0.5')
    ax1.semilogx(rp, rval - 1.96 * err, linestyle='--', color='0.5')

    ax1.scatter(emprp[emprp > 1], sortedmax[emprp > 1], s=100,
                color='r', label="Empirical RP")

    title_str = (title + "\n" +
                 r"$\mu$ = {0:.2f}, $\xi$ = {1:.5f}, $\sigma$ = {2:.4f}".
                 format(mu, xi, sigma))
    ax1.set_title(title_str)
    ax1.legend(loc=2)
    ax1.set_ylim((0, 100))
    ax1.set_xlim((1, 10000))
    ax1.set_ylabel('Wind speed (m/s)')
    ax1.set_xlabel('Return period (years)')
    ax1.grid(which='major')
    ax1.grid(which='minor', linestyle='--', linewidth=1)

    plt.savefig(figfile)
    plt.close()