Ejemplo n.º 1
0
def correlations():
    from data_load import gen_models, combine_models
    models = gen_models(cc = .95, quick = True)
    c, h, e = combine_models(models)
    fig, axes = plt.subplots(2, 2, figsize = (12,10), sharex = True, sharey = True)
    phases = ['lanina', 'neutneg', 'neutpos', 'elnino']

    cmap = mpl.cm.get_cmap('viridis')
    rgba = [cmap(0.2), cmap(0.5), cmap(0.9)]

    for phase, ax in zip(phases, axes.ravel()):
        ax.scatter(models[phase].clim_data, models[phase].hindcast, s = 30, color = cmap(0.0))
        ax.axis([0,550,0,550])
        if phase == 'allyears': title = 'All Years'
        if phase == 'lanina': title = 'La Nina'
        if phase == 'elnino': title = 'El Nino'
        if phase == 'neutpos': title = 'Neutral-Positive'
        if phase == 'neutneg': title = 'Neutral-Negative'
        ax.set_title(title, fontweight= 'bold')
        ax.text(400,20, 'r = %.2f' % models[phase].correlation)
    fig.text(0.5, 0.04, 'Observed MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20)
    fig.text(0.06, 0.5, 'Hindcast MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20, rotation = 'vertical')
    fig.suptitle('Hindcast Correlations by Phase', fontsize = 24, fontweight = 'bold')
    #fig.tight_layout()
    fig.savefig(EV['HOME']+'/Desktop/Feb20Response/images/phase_corr')
    plt.close(fig)

    fig, (ax1, ax2) = plt.subplots(1,2, figsize = (12,8), sharey = True)
    fig = plt.figure(figsize = (12,6))
    ax1 = fig.add_axes([.15, .2, .35, 0.6])
    ax2 = fig.add_axes([0.55, 0.2, 0.35, 0.6])

    ax1.scatter(c, h, s = 30, color = cmap(0.0))
    ax1.axis([0, 550, 0, 550])

    ax1.set_title('NIPA', fontweight = 'bold')
    ax1.text(400,20, 'r = %.2f' % np.corrcoef(c, h)[0,1])

    ax2.scatter(models['allyears'].clim_data, models['allyears'].hindcast, s= 30, color = cmap(0.0))
    ax2.axis([0, 550, 0, 550])
    ax2.set_yticks([])
    ax2.set_title('All-years', fontweight = 'bold')
    ax2.text(400, 20, 'r = %.2f' % models['allyears'].correlation)
    fig.text(0.5, 0.04, 'Observed MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20)
    fig.text(0.06, 0.5, 'Hindcast MAMJ Prcp (mm)', ha='center', va = 'center', fontsize = 20, rotation = 'vertical')
    fig.suptitle('NIPA vs One-Phase', fontsize = 24, fontweight = 'bold')
    fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/aycorr')
    plt.close(fig)
    return
Ejemplo n.º 2
0
def nmmecompare():
    from nmme import gen_ensembles
    from data_load import gen_models, combine_models, RPSS, HSS
    from scipy.stats import gaussian_kde as gk
    models = gen_models(cc = 0.95, quick = True)
    clim_data, hindcast, ensemble = combine_models(models)
    df = gen_ensembles()

    cmap = mpl.cm.get_cmap('viridis')
    rgba = [cmap(0.25), cmap(0.7)]
    idx = np.arange(1921,2011)>=1982
    x = 16
    dNIPA = gk(ensemble[:,idx][x], bw_method = 0.5)
    dNMME = gk(df.values[x], bw_method = 0.5)
    p_x = np.linspace(0, 600, 1000)
    fig, (ax1, ax2) = plt.subplots(1,2,sharey = True, figsize = (14,10))
    ax1.plot(p_x, dNMME.pdf(p_x)*1000, color = rgba[0], label = 'NMME', linewidth = 2)
    ax1.plot(p_x, dNIPA.pdf(p_x)*1000, color = rgba[1], label = 'NIPA', linewidth = 2)
    ax1.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[1], alpha = 0.5)
    ax1.fill_between(p_x, 0, dNMME.pdf(p_x)*1000, color = rgba[0],alpha = 0.5)
    ax1.vlines(clim_data[idx][x], 0, 6, linewidth = 4, color = 'k')
    ax1.set_title(str(clim_data.index.year[idx][x]),fontsize = 18, fontweight='bold')
    ax1.set_ylabel('Probability Density, $10^{-3}$', fontweight = 'bold')
    h, l = ax1.get_legend_handles_labels()
    ax1.legend(h, l)

    x = 25
    dNIPA = gk(ensemble[:,idx][x], bw_method = 0.5)
    dNMME = gk(df.values[x], bw_method = 0.5)
    p_x = np.linspace(0, 600, 1000)
    ax2.plot(p_x, dNMME.pdf(p_x)*1000, color = rgba[0], label = 'NMME', linewidth = 2)
    ax2.plot(p_x, dNIPA.pdf(p_x)*1000, color = rgba[1], label = 'NIPA', linewidth = 2)
    ax2.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[1], alpha = 0.5)
    ax2.fill_between(p_x, 0, dNMME.pdf(p_x)*1000, color = rgba[0],alpha = 0.5)
    ax2.vlines(clim_data[idx][x], 0, 6, linewidth = 4, color = 'k')
    ax2.set_title(str(clim_data.index.year[idx][x]), fontsize = 18, fontweight='bold')
    fig.text(0.33, 0.015, 'Total MAMJ Precipitation, mm')
    fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/nmmecompare')
    plt.close(fig)
    return
Ejemplo n.º 3
0
def timecompare():
    from data_load import gen_models, combine_models
    models = gen_models(cc = .95, quick = True)
    c, h, e = combine_models(models)
    cmap = mpl.cm.get_cmap('viridis')
    rgba = [cmap(0.0), cmap(0.5), cmap(0.8)]

    plt.plot(c, label = 'Observations',
                linewidth = 3,
                color = 'black')
    plt.plot(h, label = 'Hindcast',
                linewidth = 3,
                color = rgba[1])
    plt.plot(models['allyears'].hindcast,
                label = 'One-Phase',
                linewidth = 3,
                color = rgba[2])
    plt.legend()
    plt.ylim(0,600)
    plt.ylabel('Total MAMJ Precipitaton, mm', fontweight = 'bold')
    plt.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/time_series')
    return
Ejemplo n.º 4
0
def rpssexmp():
    from data_load import gen_models, combine_models
    from scipy.stats import gaussian_kde as gk
    cmap = mpl.cm.get_cmap('viridis')
    rgba = [cmap(0.8), cmap(0.2)]
    models = gen_models()
    c, h, e = combine_models(models)
    x, x2 = 15, 20
    fig, ax = plt.subplots(1, 1, figsize = (8,6))
    ax.set_ylabel('Probability Density, $10^{-3}$')
    ax.set_xlabel('Total MAMJ Precipitation, mm')
    dNIPA = gk(e[:, x], bw_method = 0.5)
    p_x = np.linspace(0,600,1000)
    ax.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = rgba[0], label = 'Bad')
    ax.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color = rgba[0], alpha = 0.5)
    dNIPA = gk(e[:, x2], bw_method = 0.5)
    ax.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = rgba[1], label = 'Worse')
    ax.fill_between(p_x, 0, dNIPA.pdf(p_x)*1000, color =rgba[1] , alpha = 0.5)
    ax.vlines(100, 0,8, linewidth = 2, label = 'obs')

    plt.legend()
    fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/rpss_exmp')
    return
Ejemplo n.º 5
0
from data_load import gen_models, combine_models, RPSS, HSS
import numpy as np
from matplotlib import pyplot as plt
models = gen_models(cc = 0.95, bc = 0.80, quick = True)
clim_data, hindcast, ensemble = combine_models(models)
for x in [10, 20, 30]:
    print RPSS(clim_data, ensemble, x, x)
    print HSS(clim_data, hindcast, x, x), '\n'
Ejemplo n.º 6
0
def terciles():
    from data_load import gen_models, combine_models
    from scipy.stats import gaussian_kde as gk

    models = gen_models(cc = 0.95, quick = True)
    c, h, e = combine_models(models)

    cmap = mpl.cm.get_cmap('viridis')
    rgba = [cmap(0.0), cmap(0.35), cmap(0.8)]

    idx = np.argsort(c)

    B = c[idx][24]
    A = c[idx][66]

    x = 20
    dNIPA = gk(e[:,x], bw_method = 0.5)
    dCLIM = gk(c, bw_method = 0.5)

    bclim = round(dCLIM.integrate_box(0,B),2)* 100
    aclim = round(dCLIM.integrate_box(A, 600),2)* 100
    nclim = round(dCLIM.integrate_box(B,A),2)* 100

    bnipa = round(dNIPA.integrate_box(0,B),2)* 100
    anipa = round(dNIPA.integrate_box(A, 600),2)* 100
    nnipa = round(dNIPA.integrate_box(B,A),2)* 100

    p_x = np.linspace(0,600,1000)
    below = p_x[p_x<B]
    middle = p_x[(p_x>=B) & (p_x<=A)]
    above = p_x[p_x>A]

    fig, (ax1, ax2) = plt.subplots(1,2,figsize = (12,6), sharey = True)

    ax1.plot(p_x, dCLIM.pdf(p_x)*1000, linewidth = 2, color = 'k')
    ax1.fill_between(below, 0, dCLIM.pdf(below)*1000, color = rgba[2], alpha = 0.5)
    ax1.fill_between(above, 0, dCLIM.pdf(above)*1000, color = rgba[0], alpha = 0.5)
    ax1.fill_between(middle, 0, dCLIM.pdf(middle)*1000, color = rgba[1], alpha = 0.5)
    line = '\n\nB: %.0f%%\nN: %.0f%%\nA: %.0f%%' % (bclim, nclim, aclim)
    ax1.text(400,3,line)
    ax1.set_title('Climatological PDF', fontsize = 22, fontweight = 'bold')
    ax1.set_ylabel('Probability Density, $10^{-3}$', fontweight = 'bold')

    ax2.plot(p_x, dCLIM.pdf(p_x)*1000, linewidth = 2,
                            color = 'grey', alpha = 0.7)
    ax2.fill_between(p_x, dCLIM.pdf(p_x)*1000, label = 'Climatology', color = 'grey', alpha = 0.2)
    ax2.plot(p_x, dNIPA.pdf(p_x)*1000, linewidth = 2, color = 'k')
    ax2.fill_between(below, 0, dNIPA.pdf(below)*1000, color = rgba[2],
                        label = 'Below', alpha = 0.5)
    ax2.fill_between(middle, 0, dNIPA.pdf(middle)*1000, color = rgba[1],
                        label = 'Normal', alpha = 0.5)
    ax2.fill_between(above, 0, dNIPA.pdf(above)*1000, color = rgba[0],
                        label = 'Above', alpha = 0.5)

    line = '\n\nB: %.0f%%\nN: %.0f%%\nA: %.0f%%' % (bnipa, nnipa, anipa)
    ax2.text(400,3,line)
    ax2.set_title('Forecast PDF', fontsize = 22, fontweight = 'bold')
    h, l = ax2.get_legend_handles_labels()
    fig.legend(h,l, loc = (0.395, 0.55))
    fig.text(0.33, 0.015, 'Total MAMJ Precipitation, mm')
    fig.savefig(EV['HOME'] + '/Desktop/Feb20Response/images/tercile')
    return