Exemplo n.º 1
0
def make_plot(in_dir, in_files, title, out_filename, error):
    x_axis = partmc.log_grid(min=1e-8, max=1e-5, n_bin=3)
    x_centers = x_axis.centers()
    counter = 0
    hist_array = np.zeros([len(x_centers), config.i_loop_max])
    error = np.zeros([3])
    for file in in_files:
        ncf = scipy.io.netcdf.netcdf_file(in_dir + file, 'r')
        particles = partmc.aero_particle_array_t(ncf)
        ncf.close()

        dry_diameters = particles.dry_diameters()
        hist = partmc.histogram_1d(dry_diameters,
                                   x_axis,
                                   weights=1 / particles.comp_vols)
        hist_array[:, counter] = hist
        counter = counter + 1
    plt.clf()
    for i_loop in range(0, config.i_loop_max):
        plt.loglog(x_axis.centers(), hist_array[:, i_loop], 'k')
        plt.errorbar(x_axis.centers(), np.average(hist_array, axis=1),
                     np.std(hist_array, axis=1))
        avg = np.average(hist_array, axis=1)
        std = np.std(hist_array, axis=1)
        error = std / avg
        print 'avg and std ', avg, std, error
    plt.axis([1e-8, 1e-5, 1e4, 1e11])
    plt.xlabel("dry diameter (m)")
    plt.ylabel("number density (m^{-3})")
    plt.title(title)
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 2
0
def process_data(in_filename_list, out_filename):
    total_value = None
    for in_filename in in_filename_list:
        ncf = scipy.io.netcdf.netcdf_file(in_filename, 'r')
        particles = partmc.aero_particle_array_t(ncf)
        env_state = partmc.env_state_t(ncf)
        ncf.close()

        dry_diameters = particles.dry_diameters() * 1e6  # m to um
        masses = particles.masses() * 1e9  # kg to ug

        value = partmc.histogram_1d(dry_diameters,
                                    x_axis,
                                    weights=masses / particles.comp_vols)
        value /= 1e6  # m^{-3} to cm^{-3}

        if total_value is None:
            total_value = value
        else:
            total_value += value
    total_value /= len(in_filename_list)
    np.savetxt(out_filename, total_value)
    mask = np.ma.make_mask(total_value <= 0.0)
    masked_total_value = np.ma.array(total_value, mask=mask)
    return (masked_total_value.min(), masked_total_value.max())
Exemplo n.º 3
0
def make_plot(in_dir, in_filename1, in_filename2, in_filename3, out_filename,
              title, ccn_cn_i, ccn_cn_j):
    print in_filename1, in_filename2, in_filename3
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename1, 'r')
    particles1 = partmc.aero_particle_array_t(ncf)
    ncf.close()
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename2, 'r')
    particles2 = partmc.aero_particle_array_t(ncf)
    ncf.close()
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename3, 'r')
    particles3 = partmc.aero_particle_array_t(ncf)
    ncf.close()

    x_axis = partmc.log_grid(min=1e-10, max=1e-4, n_bin=30)
    x_centers = x_axis.centers()

    wet_diameters1 = particles1.diameters()
    wet_diameters2 = particles2.diameters()
    wet_diameters3 = particles3.diameters()

    hist1 = partmc.histogram_1d(wet_diameters1,
                                x_axis,
                                weights=1 / particles1.comp_vols)
    hist2 = partmc.histogram_1d(wet_diameters2,
                                x_axis,
                                weights=1 / particles2.comp_vols)
    hist3 = partmc.histogram_1d(wet_diameters3,
                                x_axis,
                                weights=1 / particles3.comp_vols)

    is_activated = (wet_diameters3 > 2e-6)
    sum_tot = sum(1 / particles3.comp_vols) * 1e-6
    num_act = sum(1 / particles3.comp_vols[is_activated]) * 1e-6
    print title, num_act, sum_tot, num_act / sum_tot * 100

    ccn_cn_ratio[ccn_cn_i, ccn_cn_j] = num_act / sum_tot

    plt.clf()
    plt.semilogx(x_axis.centers(), hist1, label='0 min')
    plt.semilogx(x_axis.centers(), hist2, label='2 mins')
    plt.semilogx(x_axis.centers(), hist3, label='10 mins')
    plt.legend(loc='upper left')
    plt.xlabel("wet diameter (m)")
    plt.ylabel("number density (m^{-3})")
    plt.title(title)
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 4
0
def make_plot(in_dir, in_filename1, in_filename2, in_filename3, out_filename):
    print in_filename1, in_filename2, in_filename3
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename1, 'r')
    particles1 = partmc.aero_particle_array_t(ncf)
    ncf.close()
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename2, 'r')
    particles2 = partmc.aero_particle_array_t(ncf)
    ncf.close()
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename3, 'r')
    particles3 = partmc.aero_particle_array_t(ncf)
    ncf.close()

    x_axis = partmc.log_grid(min=1e-10, max=1e-4, n_bin=50)
    x_centers = x_axis.centers()

    dry_diameters1 = particles1.dry_diameters()
    dry_diameters2 = particles2.dry_diameters()
    dry_diameters3 = particles3.dry_diameters()

    hist1 = partmc.histogram_1d(dry_diameters1,
                                x_axis,
                                weights=particles1.masses(exclude=["H2O"]) /
                                particles1.comp_vols)
    hist2 = partmc.histogram_1d(dry_diameters2,
                                x_axis,
                                weights=particles2.masses(exclude=["H2O"]) /
                                particles2.comp_vols)
    hist3 = partmc.histogram_1d(dry_diameters3,
                                x_axis,
                                weights=particles3.masses(exclude=["H2O"]) /
                                particles3.comp_vols)

    plt.clf()
    plt.loglog(x_axis.centers(), hist1, label='initial')
    plt.loglog(x_axis.centers(), hist2, label='6 hours')
    plt.loglog(x_axis.centers(), hist3, label='12 hours')
    plt.legend(loc='center right')
    plt.axis([5e-9, 1e-4, 1e-14, 1e-7])
    plt.grid(True)
    plt.xlabel("dry diameter (m)")
    plt.ylabel(r"mass concentration ($\rm kg \, m^{-3}$)")
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 5
0
def make_plot(in_files, f1, f2, f3, f4, f5):
    x_axis = partmc.log_grid(min=1e-10, max=1e-4, n_bin=100)
    x_centers = x_axis.centers()
    counter = 0
    hist_array_num = np.zeros([len(x_centers), config.i_loop_max])
    hist_array_mass = np.zeros([len(x_centers), config.i_loop_max])
    for file in in_files:
        ncf = scipy.io.netcdf.netcdf_file(config.netcdf_dir + '/' + file, 'r')
        particles = partmc.aero_particle_array_t(ncf)
        ncf.close()

        dry_diameters = particles.dry_diameters()
        hist = partmc.histogram_1d(dry_diameters,
                                   x_axis,
                                   weights=1 / particles.comp_vols)
        hist_array_num[:, counter] = hist
        hist = partmc.histogram_1d(dry_diameters,
                                   x_axis,
                                   weights=particles.masses(exclude=["H2O"]) /
                                   particles.comp_vols)
        hist_array_mass[:, counter] = hist

        counter = counter + 1

    hist_array_gav_num = np.exp(np.average(np.log(hist_array_num), axis=1))
    hist_array_gstd_num = np.exp(np.std(np.log(hist_array_num), axis=1))
    e_bar_top_num = hist_array_gav_num * hist_array_gstd_num
    e_bar_bottom_num = hist_array_gav_num / hist_array_gstd_num
    e_bars_num = np.vstack((hist_array_gav_num - e_bar_bottom_num,
                            e_bar_top_num - hist_array_gav_num))

    hist_array_gav_mass = np.exp(np.average(np.log(hist_array_mass), axis=1))
    hist_array_gstd_mass = np.exp(np.std(np.log(hist_array_mass), axis=1))
    e_bar_top_mass = hist_array_gav_mass * hist_array_gstd_mass
    e_bar_bottom_mass = hist_array_gav_mass / hist_array_gstd_mass
    e_bars_mass = np.vstack((hist_array_gav_mass - e_bar_bottom_mass,
                             e_bar_top_mass - hist_array_gav_mass))

    np.savetxt(f1, x_axis.centers())
    np.savetxt(f2, hist_array_gav_num)
    np.savetxt(f3, e_bars_num)
    np.savetxt(f4, hist_array_gav_mass)
    np.savetxt(f5, e_bars_mass)
Exemplo n.º 6
0
def make_plot(in_dir, in_filename, out_filename, out_data_name):
    print in_filename
    ncf = scipy.io.netcdf.netcdf_file(in_dir+in_filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    x_axis = partmc.log_grid(min=1e-9,max=1e-5,n_bin=100)
    x_centers = x_axis.centers() 

    diameters = particles.diameters()

    pure_bc = ((particles.masses(include = ["BC"]) > 0) & (particles.masses(include = ["SO4"]) == 0))
    pure_so4 =  ((particles.masses(include = ["SO4"]) > 0) & (particles.masses(include = ["BC"]) == 0))
    with_bc = (particles.masses(include = ["BC"]) > 0)
    with_so4 = (particles.masses(include = ["SO4"]) > 0)
    mixed_bc_so4 = ((particles.masses(include = ["SO4"]) > 0) &  (particles.masses(include = ["BC"]) > 0))

    hist = partmc.histogram_1d(diameters, x_axis, weights = 1 / particles.comp_vols) / 1e6
    hist_bc = partmc.histogram_1d(diameters[pure_bc], x_axis, weights = 1 / particles.comp_vols[pure_bc]) /1e6
    hist_so4 = partmc.histogram_1d(diameters[pure_so4], x_axis, weights = 1 / particles.comp_vols[pure_so4]) /1e6
    hist_mixed = partmc.histogram_1d(diameters[mixed_bc_so4], x_axis, weights = 1 / particles.comp_vols[mixed_bc_so4]) / 1e6

    plt.clf()
    plt.loglog(x_centers*1e6, hist,  'r-', label = 'total')
    plt.loglog(x_centers*1e6, hist_bc,  'k-', label = 'pure bc')
    plt.loglog(x_centers*1e6, hist_so4,  'b-', label = 'pure so4')
    plt.loglog(x_centers*1e6, hist_mixed,  'g-', label = 'mixed so4 and bc')
    plt.axis([1e-3, 2e-0, 1e-1, 1e4])
    plt.xlabel("dry diameter / micrometer")
    plt.ylabel("number density / cm^{-3}")
    plt.legend(loc = "upper left")
    plt.grid(True)
    fig = plt.gcf()
    fig.savefig(out_filename)
    np.savetxt("diameter_values.txt", x_centers*1e6)
    np.savetxt(out_data_name+"_total_acc_bc1.txt", hist)
    np.savetxt(out_data_name+"_bc_acc_bc1.txt", hist_bc)
    np.savetxt(out_data_name+"_so4_acc_bc1.txt", hist_so4)
    np.savetxt(out_data_name+"_mixed_acc_bc1.txt", hist_mixed)
Exemplo n.º 7
0
def get_plot_data_bc(filename, value_min=None, value_max=None):
    ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    env_state = partmc.env_state_t(ncf)
    ncf.close()

    diameters = particles.dry_diameters() * 1e6

    x_axis = partmc.log_grid(min=diameter_axis_min,
                             max=diameter_axis_max,
                             n_bin=num_diameter_bins)

    value = partmc.histogram_1d(diameters,
                                x_axis,
                                weights=1 / particles.comp_vols)
    value /= 1e6

    return (value, x_axis.centers())
Exemplo n.º 8
0
def make_plot(in_dir, in_files, title, out_filename):
    x_axis = partmc.log_grid(min=1e-10,max=1e-4,n_bin=100)
    x_centers = x_axis.centers()
    counter = 0
    hist_array = np.zeros([len(x_centers),config.i_loop_max])
    for file in in_files:
        ncf = scipy.io.netcdf.netcdf_file(in_dir+file, 'r')
        particles = partmc.aero_particle_array_t(ncf)
        ncf.close() 

        dry_diameters = particles.dry_diameters()
        hist = partmc.histogram_1d(dry_diameters, x_axis, weights = 1 / particles.comp_vols)
        hist_array[:,counter] = hist
        counter = counter+1
#    hist_array_av = np.average(hist_array,axis = 1)
#    hist_array_std = np.std(hist_array, axis = 1)
#    hist_array_std_clipped = np.minimum(hist_array_std, hist_array_av - 1)
#    e_bars = np.vstack((hist_array_std_clipped, hist_array_std))

    hist_array_gav = np.exp(np.average(np.log(hist_array),axis = 1))
    hist_array_gstd = np.exp(np.std(np.log(hist_array), axis = 1))
    e_bar_top = hist_array_gav * hist_array_gstd 
    e_bar_bottom = hist_array_gav / hist_array_gstd
    e_bars = np.vstack((hist_array_gav - e_bar_bottom, e_bar_top - hist_array_gav))


    plt.clf()
#    for i_loop in range(0,config.i_loop_max):
#        plt.loglog(x_axis.centers(), hist_array[:,i_loop], 'k')
    a = plt.gca() # gets the axis
    a.set_xscale("log") # x axis log
    a.set_yscale("log") # y axis log
    plt.errorbar(x_axis.centers(), hist_array_gav, e_bars)
    plt.axis([5e-9, 5e-6, 1e4, 1e11])
    plt.xlabel("dry diameter (m)")
    plt.ylabel("number density (m^{-3})")
#    plt.title(title)
    plt.grid(True)
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 9
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def make_plot(in_dir, in_filename, out_filename):
    print in_filename
    ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    x_axis = partmc.log_grid(min=1e-10, max=1e-4, n_bin=100)
    x_centers = x_axis.centers()

    dry_diameters = particles.dry_diameters()

    hist = partmc.histogram_1d(dry_diameters,
                               x_axis,
                               weights=1 / particles.comp_vols)

    plt.clf()
    plt.loglog(x_axis.centers(), hist)
    plt.axis([1e-10, 1e-4, 1e7, 1e15])
    plt.xlabel("dry diameter (m)")
    plt.ylabel("number density (m^{-3})")
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 10
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def make_plot(in_filename, out_filename, title):
    ncf = scipy.io.netcdf.netcdf_file(in_filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    bc_volume = particles.volumes(include=["BC"])
    bc = particles.masses(include=["BC"])
    dry_mass = particles.masses(exclude=["H2O"])
    bc_frac = bc / dry_mass
    coat_frac = 1 - bc / dry_mass

    is_bc = (bc_frac > 0.05)
    dry_diameters = particles.dry_diameters()

    core_diameters = (6 / math.pi * bc_volume)**(1. / 3.)
    coating_thickness = (dry_diameters - core_diameters) / 2.
    ratio = coating_thickness / dry_diameters

    print ratio.max()
    x_axis = partmc.linear_grid(min=0, max=ratio.max(), n_bin=50)

    hist1d = partmc.histogram_1d(coating_thickness[is_bc] /
                                 dry_diameters[is_bc],
                                 x_axis,
                                 weights=1 / particles.comp_vols[is_bc])
    print hist1d
    plt.clf()
    a = plt.gca()
    a.set_xscale("linear")
    a.set_yscale("log")
    plt.plot(x_axis.centers(), hist1d)
    plt.axis([x_axis.min, x_axis.max, 1e8, 1e12])
    plt.grid(True)
    plt.xlabel("coating thickness / dry diameter")
    plt.ylabel("number concentration (m^{-3})")
    plt.title(title)
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 11
0
netcdf_dir = "../../scenarios/4_nucleate/out/"

netcdf_pattern = "urban_plume_wc_0001_(.*).nc"
time_filename_list = partmc.get_time_filename_list(netcdf_dir, netcdf_pattern)

dist_array = np.zeros([len(time_filename_list), 100])
times = np.zeros([len(time_filename_list)])
i_counter = 0
diam_axis = partmc.log_grid(min=1e-10, max=1e-6, n_bin=100)
diam_axis_edges = diam_axis.edges()

for [time, filename, key] in time_filename_list:
    print time, filename, key
    ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    dry_diameters = particles.dry_diameters()
    hist = partmc.histogram_1d(dry_diameters,
                               diam_axis,
                               weights=particles.masses(include=["SO4"]) /
                               particles.comp_vols)
    dist_array[i_counter, :] = hist
    times[i_counter] = time
    i_counter += 1

np.savetxt("data/banana_so4_dist.txt", dist_array)
np.savetxt("data/banana_diam.txt", diam_axis_edges)
np.savetxt("data/banana_times.txt", times)
Exemplo n.º 12
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def make_plot(in_filename,out_filename,title):
    ncf = scipy.io.netcdf.netcdf_file(in_filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    so4 = particles.masses(include = ["SO4"])/particles.aero_data.molec_weights[0]
    nh4 =  particles.masses(include = ["NH4"])/particles.aero_data.molec_weights[3]
    no3 =  particles.masses(include = ["NO3"])/particles.aero_data.molec_weights[1]
    bc =  particles.masses(include = ["BC"])/particles.aero_data.molec_weights[18]
    oc =  particles.masses(include = ["OC"])/particles.aero_data.molec_weights[17]

    print 'min nh4 ', min(particles.masses(include = ["NH4"])), max(nh4), min(no3), max(no3)

    ion_ratio = (2*so4 + no3) / nh4

    is_neutral = (ion_ratio < 2)
    dry_diameters = particles.dry_diameters()

    x_axis = partmc.log_grid(min=1e-8,max=1e-6,n_bin=70)
    y_axis = partmc.linear_grid(min=0,max=30.0,n_bin=100)
    x_centers = x_axis.centers()

    bin_so4 = partmc.histogram_1d(dry_diameters, x_axis, weights = so4)
    bin_nh4 = partmc.histogram_1d(dry_diameters, x_axis, weights = nh4)
    bin_no3 = partmc.histogram_1d(dry_diameters, x_axis, weights = no3)
    
    print 'bin_so4 ', bin_so4[40]
    print 'bin_nh4 ', bin_nh4[40]
    print 'bin_no3 ', bin_no3[40]

    bin_ratio = (2*bin_so4 + bin_no3)/ bin_nh4
    np.isnan(bin_ratio) # checks which elements in c are NaN (produces array with True and False)
    bin_ratio[np.isnan(bin_ratio)] = 0 # replaces NaN with 0. useful for plotting
    print 'bin_ratio ', bin_ratio[40]

    diameter_bins = x_axis.find(dry_diameters)
    print 'diameter_bins ', diameter_bins
    is_40 = (diameter_bins == 40)
#    for i in range(len(dry_diameters)):
#        if diameter_bins[i] == 40:
#            print 'particle info', so4[i], nh4[i], no3[i], ion_ratio[i]
    so4_40 = so4[is_40]
    nh4_40 = nh4[is_40]
    no3_40 = no3[is_40]
    bc_40 = bc[is_40]
    oc_40 = oc[is_40]

    ion_ratio_40 = ion_ratio[is_40]
#    data = [(so4_40[i],nh4_40[i], no3_40[i], ion_ratio_40[i]) for i in range(len(so4_40)
    data = zip(so4_40, nh4_40, no3_40, bc_40, oc_40, ion_ratio_40)
    data.sort(key = lambda x: x[5])
    for (so,nh,no,bc,oc,ir) in data:
        print so,nh,no,bc,oc,ir

    print 'sums ', sum(so4[is_40]), sum(nh4[is_40]), sum(no3[is_40]), (2*sum(so4[is_40])+ sum(no3[is_40])) / sum(nh4[is_40])
    print 'sums/number ',  sum(so4[is_40])/len(so4_40), sum(nh4[is_40])/len(nh4_40), sum(no3[is_40])/len(no3_40)
    
    
    hist2d = partmc.histogram_2d(dry_diameters, ion_ratio, x_axis, y_axis, weights = 1/particles.comp_vols)

    plt.clf()
    plt.pcolor(x_axis.edges(), y_axis.edges(), hist2d.transpose(),norm = matplotlib.colors.LogNorm(), linewidths = 0.1)
    a = plt.gca()
    plt.semilogx(x_centers, bin_ratio, 'w-', linewidth = 3)
    plt.semilogx(x_centers, bin_ratio, 'k-', linewidth = 1)
    a.set_xscale("log")
    a.set_yscale("linear")
    plt.axis([x_axis.min, x_axis.max, y_axis.min, y_axis.max])
    plt.xlabel("dry diameter (m)")
    plt.ylabel("ion ratio")
    cbar = plt.colorbar()
    cbar.set_label("number density (m^{-3})")
    plt.title(title)
    fig = plt.gcf()
    fig.savefig(out_filename)
Exemplo n.º 13
0
import partmc

netcdf_dir = "../../scenarios/4_nucleate/out_wei-0_lowbg2/"
netcdf_pattern = "nucleate_wc_0001_(.*).nc"

time_filename_list = partmc.get_time_filename_list(netcdf_dir, netcdf_pattern)

size_dist_array = np.zeros([len(time_filename_list), 100])
times = np.zeros([len(time_filename_list)])
i_counter = 0
diam_axis = partmc.log_grid(min=1e-4, max=1e0, n_bin=100)
diam_axis_edges = diam_axis.edges()

for [time, filename, key] in time_filename_list:
    print time, filename, key
    ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
    particles = partmc.aero_particle_array_t(ncf)
    ncf.close()

    dry_diameters = particles.dry_diameters() * 1e6  # in micrometers
    hist = partmc.histogram_1d(dry_diameters,
                               diam_axis,
                               weights=1 / particles.comp_vols)
    size_dist_array[i_counter, :] = hist / 1e6  # in cm^{-3}
    times[i_counter] = time
    i_counter += 1

np.savetxt("data/banana_size_dist_wc_wei-0_lowbg2.txt", size_dist_array)
np.savetxt("data/banana_diam_wc_wei-0_lowbg2.txt", diam_axis_edges)
np.savetxt("data/banana_times_wc_wei-0_lowbg2.txt", times)
Exemplo n.º 14
0
for i_loop in range (0, i_loop_max):

    netcdf_pattern = "brownian_part_0%03d_(.*).nc"  % (i_loop+1)
    print netcdf_pattern
    time_filename_list = partmc.get_time_filename_list(config.netcdf_dir, netcdf_pattern)

    i_counter = 0
    for [time, filename, key] in time_filename_list:
        print time, filename, key
        ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
        particles = partmc.aero_particle_array_t(ncf)
        env_state = partmc.env_state_t(ncf)
        ncf.close()

        wet_diameters = particles.diameters()
        hist = partmc.histogram_1d(wet_diameters, x_axis, weights = 1 / particles.comp_vols)

#        total_number = sum(1/particles.comp_vols)
#        total_mass = sum(particles.masses()/particles.comp_vols)
        time_array[i_counter]= time / 3600.
        array_num[i_counter,i_loop,:]= hist

        hist = partmc.histogram_1d(wet_diameters, x_axis, weights = particles.masses() / particles.comp_vols)
        array_mass[i_counter,i_loop,:]= hist
        i_counter += 1

print "array_num ", array_num.shape

for i_ensemble in range(0, i_loop_max):
    print "i_ensemble ", i_ensemble
    num_avg[:,i_ensemble,:] = np.average(array_num[:,:i_ensemble+1,:], axis = 1)