def make_plot(in_filename, out_filename, time, title):
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()
age = abs(particles.least_create_times / 3600. - time)
dry_diameters = particles.dry_diameters()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
x_axis = partmc.log_grid(min=1e-8, max=1e-6, n_bin=140)
y_axis = partmc.linear_grid(min=0, max=48, n_bin=96)
vals2d = partmc.multival_2d(dry_diameters, age, s_crit, x_axis, y_axis)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
vals2d.transpose(),
norm=matplotlib.colors.LogNorm(),
linewidths=0.1)
a = plt.gca()
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("age (h)")
cbar = plt.colorbar()
cbar.set_label("S_crit (%)")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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())
def check_num(in_dir, in_filename, in_file_pattern, out_filename, counter):
time_filename_list = partmc.get_time_filename_list(in_dir, in_file_pattern)
id_p_array = np.array([16388, 10, 33311, 9212, 451, 11769])
d = np.zeros((len(id_p_array),len(time_filename_list)))
seconds = np.zeros(len(time_filename_list))
i_count = 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)
ncf.close()
wet_diameters = particles.diameters()
id_list = list(particles.ids)
for i in range(0,6):
i_index = id_list.index(id_p_array[i])
d[i,i_count] = wet_diameters[i_index]
seconds[i_count] = i_count
i_count = i_count + 1
plt.figure()
plt.semilogy(seconds, d[0,:], 'b-', label = 'act1_2')
plt.semilogy(seconds, d[1,:], 'g-', label = 'not_1_2')
plt.semilogy(seconds, d[2,:], 'r-', label = 'act2_not_act1')
plt.semilogy(seconds, d[3,:], 'b-', label = 'act1_2')
plt.semilogy(seconds, d[4,:], 'g-', label = 'not_1_2')
plt.semilogy(seconds, d[5,:], 'r-', label = 'act2_not_act1')
plt.xlabel("time (s)")
plt.ylabel("diameter (m)")
plt.legend(loc = 'upper left')
fig = plt.gcf()
fig.savefig(out_filename)
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 = particles.masses(include = ["BC"])
dry_mass = particles.masses(exclude = ["H2O"])
bc_frac = bc / dry_mass
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=0.8,n_bin=40)
hist2d = partmc.histogram_2d(dry_diameters, bc_frac, 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()
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("BC mass fraction")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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
comp_frac = particles.masses(include = ["BC"]) \
/ particles.masses(exclude = ["H2O"]) * 100
# hack to avoid landing just around the integer boundaries
comp_frac *= (1.0 + 1e-12)
h2o = particles.masses(include=["H2O"])
x_axis = partmc.log_grid(min=diameter_axis_min,
max=diameter_axis_max,
n_bin=num_diameter_bins * 2)
y_axis = partmc.linear_grid(min=bc_axis_min,
max=bc_axis_max,
n_bin=num_bc_bins * 2)
value = partmc.multival_2d(diameters, comp_frac, h2o, x_axis, y_axis)
if value_max == None:
value_max = value.max()
if value_min == None:
maxed_value = np.where(value > 0.0, value, value_max)
value_min = maxed_value.min()
#if value_max > 0.0:
# value = (log(value) - log(value_min)) \
# / (log(value_max) - log(value_min))
#value = value.clip(0.0, 1.0)
return (value, x_axis.edges(), y_axis.edges(), env_state, value_min,
value_max)
def make_plot(in_filename,out_filename,time,title):
ncf = scipy.io.netcdf.netcdf_file(in_filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
ncf.close()
age = abs(particles.least_create_times / 3600. - time)
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 = 48, n_bin=49)
hist2d = partmc.histogram_2d(dry_diameters, age, 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()
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("age (h)")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
def make_plot(netcdf_dir, netcdf_pattern, out_filename):
time_filename_list = partmc.get_time_filename_list(netcdf_dir,
netcdf_pattern)
ccn_array = np.zeros([len(time_filename_list), 4])
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()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
activated_1 = (s_crit < config.s_crit_1)
number_act_1 = sum(1 / particles.comp_vols[activated_1])
activated_2 = (s_crit < config.s_crit_2)
number_act_2 = sum(1 / particles.comp_vols[activated_2])
activated_3 = (s_crit < config.s_crit_3)
number_act_3 = sum(1 / particles.comp_vols[activated_3])
ccn_array[i_counter, 0] = time
ccn_array[i_counter, 1] = number_act_1
ccn_array[i_counter, 2] = number_act_2
ccn_array[i_counter, 3] = number_act_3
i_counter += 1
print ccn_array
np.savetxt(out_filename, ccn_array)
def make_plot(in_filename,out_filename,title):
print in_filename
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()
s_crit = (particles.critical_rel_humids(env_state) - 1)*100
x_axis = partmc.log_grid(min=1e-8,max=1e-6,n_bin=70)
y_axis = partmc.log_grid(min=1e-3,max=1e2,n_bin=50)
hist2d = partmc.histogram_2d(dry_diameters, s_crit, 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()
a.set_xscale("log")
a.set_yscale("log")
plt.axis([x_axis.min, x_axis.max, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("critical supersaturation (%)")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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
scrit = (particles.critical_rel_humids(env_state) - 1) * 100 # in %
value = partmc.histogram_2d(dry_diameters, scrit, x_axis, y_axis,
weights = 1 / particles.comp_vols,
only_positive=False)
# do not account for y_axis in % as it is a log-scale
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())
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
comp_frac = particles.masses(include = ["BC"]) \
/ particles.masses(exclude = ["H2O"]) * 100 # in %
# hack to avoid landing just around the integer boundaries
comp_frac *= (1.0 + 1e-12)
value = partmc.histogram_2d(dry_diameters,
comp_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols,
only_positive=False)
value *= 100 # account for y_axis scaling in %
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())
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()
oin = particles.masses(include = ["BC"])
dry_mass = particles.masses(exclude = ["H2O"])
oin_frac = oin / dry_mass
dry_diameters = particles.dry_diameters()
print oin.max()
x_axis = partmc.log_grid(min=1e-8,max=1e-4,n_bin=100)
y_axis = partmc.log_grid(min=1e-19,max=6e-15,n_bin=30)
hist2d = partmc.histogram_2d(dry_diameters, oin, 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()
a.set_xscale("log")
a.set_yscale("log")
plt.axis([1e-8, 1e-4, 1e-19, 6e-15])
plt.xlabel("dry diameter (m)")
plt.ylabel("BC mass")
plt.grid(True)
# plt.clim(1e6, 1e12)
cbar = plt.colorbar()
cbar.set_label(r"number density ($\rm m^{-3}$)")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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)
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)
def make_plot(dir_name, in_filename, out_filename):
ncf = scipy.io.netcdf.netcdf_file(dir_name + in_filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
ncf.close()
bc = particles.masses(include=["BC"])
dry_mass = particles.masses(exclude=["H2O"])
bc_frac = bc / dry_mass
dry_diameters = particles.dry_diameters() * 1e6
x_axis = partmc.log_grid(min=1e-3, max=1e1, n_bin=100)
y_axis = partmc.linear_grid(min=0, max=0.8, n_bin=40)
hist2d = partmc.histogram_2d(dry_diameters,
bc_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist2d = hist2d * 1e-6
print hist2d[36, :]
(figure, axes_array,
cbar_axes_array) = mpl_helper.make_fig_array(1,
1,
figure_width=5,
top_margin=0.5,
bottom_margin=0.45,
left_margin=0.65,
right_margin=1,
vert_sep=0.3,
horiz_sep=0.3,
colorbar="shared",
colorbar_location="right")
axes = axes_array[0][0]
cbar_axes = cbar_axes_array[0]
p = axes.pcolor(x_axis.edges(),
y_axis.edges(),
hist2d.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e3, vmax=1e5),
linewidths=0.1)
axes.set_xscale("log")
axes.set_yscale("linear")
axes.set_ylabel(r"BC mass fraction $w_{\rm BC}$")
axes.set_xlabel(r"dry diameter $D$/ $\rm \mu m$")
axes.set_ylim(0, 0.8)
axes.set_xlim(5e-3, 1e0)
axes.grid(True)
cbar = figure.colorbar(p,
cax=cbar_axes,
format=matplotlib.ticker.LogFormatterMathtext(),
orientation='vertical')
cbar_axes.xaxis.set_label_position('top')
cbar.set_label(r"number conc. $n(D,w_{\rm BC})$ / $\rm cm^{-3}$")
mpl_helper.remove_fig_array_axes(axes_array)
figure.savefig(out_filename)
def make_plot(in_dir, in_filename_wc, in_filename_nc, title, out_filename_wc,
out_filename_nc):
print 'file ', in_dir + in_filename_wc
ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename_wc, 'r')
particles_wc = partmc.aero_particle_array_t(ncf)
ncf.close()
ncf = scipy.io.netcdf.netcdf_file(in_dir + in_filename_nc, 'r')
particles_nc = partmc.aero_particle_array_t(ncf)
ncf.close()
# so4_wc = particles_wc.masses(include = ["SO4"])/particles_wc.aero_data.molec_weights[0]
# nh4_wc = particles_wc.masses(include = ["NH4"])/particles_wc.aero_data.molec_weights[3]
# no3_wc = particles_wc.masses(include = ["NO3"])/particles_wc.aero_data.molec_weights[1]
so4_nc = particles_nc.masses(
include=["SO4"]) / particles_nc.aero_data.molec_weights[0]
nh4_nc = particles_nc.masses(
include=["NH4"]) / particles_nc.aero_data.molec_weights[3]
no3_nc = particles_nc.masses(
include=["NO3"]) / particles_nc.aero_data.molec_weights[1]
# plt.scatter(nh4_wc, 2*so4_wc+no3_wc)
# a = plt.gca() # gets the axis
# a.set_xscale("log") # x axis log
# a.set_yscale("log") # y axis log
# plt.axis([1e-25, 1e-15, 1e-25, 1e-15]) # axis limit
# plt.title(title)
# fig = plt.gcf()
# fig.savefig(out_filename_wc)
plt.scatter(nh4_nc, 2 * so4_nc + no3_nc)
a = plt.gca() # gets the axis
a.set_xscale("log") # x axis log
a.set_yscale("log") # y axis log
plt.axis([1e-25, 1e-15, 1e-25, 1e-15]) # axis limit
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename_nc)
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)
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]
dry_mass = particles.masses(exclude=["H2O"])
so4_frac = so4 / dry_mass
ion_ratio = (2 * so4 + no3) / nh4
is_neutral = (ion_ratio < 2)
print 'neutral ', sum(is_neutral), ion_ratio[is_neutral]
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=1.0, n_bin=50)
hist2d = partmc.histogram_2d(dry_diameters,
so4_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
plt.clf()
plt.semilogx(dry_diameters, ion_ratio, 'rx')
fig = plt.gcf()
fig.savefig('figs/t.pdf')
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist2d.transpose(),
norm=matplotlib.colors.LogNorm(),
linewidths=0.1)
a = plt.gca()
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("SO4 mass fraction")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
def make_plot(in_files, f1, f2, f3, f4, f5, f6):
x_axis = partmc.log_grid(min=1e-10,max=1e-4,n_bin=100)
y_axis = partmc.linear_grid(min=0,max=1.,n_bin=50)
x_centers = x_axis.centers()
y_centers = y_axis.centers()
counter = 0
hist_array_num = np.zeros([len(x_centers),len(y_centers),config.i_loop_max])
hist_average_num = np.zeros([len(x_centers), len(y_centers)])
hist_std_num = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm_num = np.zeros([len(x_centers), len(y_centers)])
hist_array_mass = np.zeros([len(x_centers),len(y_centers),config.i_loop_max])
hist_average_num = np.zeros([len(x_centers), len(y_centers)])
hist_std_num = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm_num = np.zeros([len(x_centers), len(y_centers)])
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()
bc = particles.masses(include = ["BC"])
dry_mass = particles.masses(exclude = ["H2O"])
bc_frac = bc / dry_mass
dry_diameters = particles.dry_diameters()
hist2d = partmc.histogram_2d(dry_diameters, bc_frac, x_axis, y_axis, weights = 1 / particles.comp_vols)
hist_array_num[:,:,counter] = hist2d
hist2d = partmc.histogram_2d(dry_diameters, bc_frac, x_axis, y_axis, weights = particles.masses(include=["BC"]) / particles.comp_vols)
hist_array_mass[:,:,counter] = hist2d
counter = counter+1
hist_average_num = np.average(hist_array_num, axis = 2)
hist_std_num = np.std(hist_array_num, axis = 2)
hist_std_norm_num = hist_std_num / hist_average_num
hist_std_norm_num = np.ma.masked_invalid(hist_std_norm_num)
hist_average_mass = np.average(hist_array_mass, axis = 2)
hist_std_mass = np.std(hist_array_mass, axis = 2)
hist_std_norm_mass = hist_std_mass / hist_average_mass
hist_std_norm_mass = np.ma.masked_invalid(hist_std_norm_mass)
np.savetxt(f1, x_axis.edges())
np.savetxt(f2, y_axis.edges())
np.savetxt(f3, hist_average_num)
np.savetxt(f4, hist_std_norm_num)
np.savetxt(f5, hist_average_mass)
np.savetxt(f6, hist_std_norm_mass)
def make_plot(in_filename,out_filename):
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()
bc = particles.masses(include = ["BC"])
dry_mass = particles.masses(exclude = ["H2O"])
bc_frac = bc / dry_mass
so4 = particles.masses(include = ["SO4"])
inorg = particles.masses(include = ["SO4", "NO3", "NH4"])
inorg_frac = inorg / dry_mass
kappas = particles.kappas()
wet_diameters = particles.diameters()
dry_diameters = particles.dry_diameters() * 1e6
x_axis = partmc.log_grid(min=1e-2,max=1e0,n_bin=90)
y_axis = partmc.linear_grid(min=0,max=0.8,n_bin=40)
vals = partmc.multival_2d(dry_diameters, bc_frac, kappas, x_axis, y_axis, rand_arrange=False)
vals_pos = np.ma.masked_less_equal(vals, 0)
vals_zero = np.ma.masked_not_equal(vals, 0)
plt.clf()
if vals_zero.count() > 0:
plt.pcolor(x_axis.edges(), y_axis.edges(), vals_zero.transpose(), cmap=matplotlib.cm.gray, linewidths = 0.1)
if vals_pos.count() > 0:
plt.pcolor(x_axis.edges(), y_axis.edges(), vals_pos.transpose(), linewidths = 0.1)
title = partmc.time_of_day_string(env_state)
a = plt.gca()
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 (\mu m)")
plt.ylabel("BC dry mass fraction")
cbar = plt.colorbar()
plt.clim(0, 0.6)
cbar.set_label("kappa")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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())
def make_plot(dir_name,in_filename,out_filename):
ncf = scipy.io.netcdf.netcdf_file(dir_name+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
s_crit = (particles.critical_rel_humids(env_state) - 1)*100
x_axis = partmc.log_grid(min=1e-3,max=1e1,n_bin=100)
y_axis = partmc.log_grid(min=1e-3,max=1e2,n_bin=50)
hist2d = partmc.histogram_2d(dry_diameters, s_crit, x_axis, y_axis, weights = particles.num_concs)
hist2d = hist2d * 1e-6
(figure, axes_array, cbar_axes_array) = mpl_helper.make_fig_array(1,1, figure_width=5,
top_margin=0.5, bottom_margin=0.45,
left_margin=0.65, right_margin=1,
vert_sep=0.3, horiz_sep=0.3,
colorbar="shared", colorbar_location="right")
axes = axes_array[0][0]
cbar_axes = cbar_axes_array[0]
p = axes.pcolor(x_axis.edges(), y_axis.edges(), hist2d.transpose(),
norm = matplotlib.colors.LogNorm(vmin=1e3, vmax=1e5), linewidths = 0.1)
axes.set_xscale("log")
axes.set_yscale("log")
axes.set_ylabel(r"crit. supersat. $S_{\rm c}$")
axes.set_xlabel(r"dry diameter $D$/ $\rm \mu m$")
axes.set_ylim(1e-3,10)
axes.set_xlim(5e-3, 1e0)
axes.grid(True)
cbar = figure.colorbar(p, cax=cbar_axes, format=matplotlib.ticker.LogFormatterMathtext(),
orientation='vertical')
cbar_axes.xaxis.set_label_position('top')
cbar.set_label(r"number conc. $n(D,S_{\rm c})$ / $\rm cm^{-3}$")
mpl_helper.remove_fig_array_axes(axes_array)
figure.savefig(out_filename)
plt.close()
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)
def process_data(in_filename_list):
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()
time_since_midnight = env_state.start_time_of_day \
+ env_state.elapsed_time
value = (1 / particles.comp_vols).sum()
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)
return [time_since_midnight, total_value]
def process_data(in_filename_list):
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()
time_since_midnight = env_state.start_time_of_day \
+ env_state.elapsed_time
value = (particles.raw_masses / particles.comp_vols).sum(1)
value *= 1e3 # kg m^{-3} to mg cm^{-3}
if total_value is None:
total_value = value
else:
total_value += value
total_value /= len(in_filename_list)
return np.concatenate((np.array([time_since_midnight]), total_value))
def make_plot(in_dir, in_filename, title, out_filename):
print 'file ', in_dir+in_filename
ncf = scipy.io.netcdf.netcdf_file(in_dir+in_filename, 'r')
particles= partmc.aero_particle_array_t(ncf)
ncf.close()
bc = particles.masses(include = ["BC"])/particles.aero_data.molec_weights[0]
oc = particles.masses(include = ["OC"])/particles.aero_data.molec_weights[3]
no3 = particles.masses(include = ["NO3"])/particles.aero_data.molec_weights[1]
plt.scatter(bc,oc)
a = plt.gca() # gets the axis
a.set_xscale("log") # x axis log
a.set_yscale("log") # y axis log
plt.axis([1e-20, 1e-15, 1e-20, 1e-15]) # axis limit
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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)
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)
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)
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)
def get_plot_data(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
comp_frac = particles.masses(include = ["BC"]) \
/ particles.masses(exclude = ["H2O"]) * 100
# hack to avoid landing just around the integer boundaries
comp_frac *= (1.0 + 1e-12)
h2o = particles.masses(include=["H2O"]) * 1e18 # kg to fg
x_axis = partmc.log_grid(min=diameter_axis_min,
max=diameter_axis_max,
n_bin=num_diameter_bins * 2)
y_axis = partmc.linear_grid(min=bc_axis_min,
max=bc_axis_max,
n_bin=num_bc_bins * 2)
value = partmc.multival_2d(diameters, comp_frac, h2o, x_axis, y_axis)
return (value, x_axis.edges(), y_axis.edges(), env_state)
def make_plot(netcdf_pattern, aging_ss, output_pkl):
particle_set = {}
netcdf_dir = "/Users/nriemer/subversion/partmc/trunk/scenarios/1_urban_plume/out"
time_filename_list = partmc.get_time_filename_list(netcdf_dir,
netcdf_pattern)
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)
removed_info = partmc.aero_removed_info_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
bc = particles.masses(include=["BC"])
no3 = particles.masses(include=["NO3"])
so4 = particles.masses(include=["SO4"])
nh4 = particles.masses(include=["NH4"])
dry_mass = particles.masses(exclude=["H2O"])
bc_frac = bc / dry_mass
no3_frac = no3 / dry_mass
so4_frac = so4 / dry_mass
nh4_frac = nh4 / dry_mass
solute_frac = (no3 + so4 + nh4) / dry_mass
kappas = particles.kappas()
comp_vols = particles.comp_vols
h2o = particles.masses(include=["H2O"])
for id in particle_set.keys():
while particle_set[id].current_id in removed_info.ids:
i = np.nonzero(
removed_info.ids == particle_set[id].current_id)[0][0]
if removed_info.actions[i] != removed_info.AERO_INFO_COAG:
particle_set[id].remove_time = time
particle_set[id].current_id = 0
else:
particle_set[id].current_id = removed_info.other_ids[i]
for i in range(len(particles.ids)):
id = particles.ids[i]
if id not in particle_set:
particle_set[id] = Struct()
particle_set[id].current_id = id
particle_set[id].emit_time = time
particle_set[id].emit_diam = dry_diameters[i]
particle_set[id].emit_s_crit = s_crit[i]
particle_set[id].emit_kappa = kappas[i]
particle_set[id].remove_time = -1
particle_set[id].aged_flag = False
particle_set[id].aging_time = -1
particle_set[id].min_s_crit = s_crit[i]
particle_set[id].emit_bc_fraction = bc_frac[i]
particle_set[id].emit_comp_vols = comp_vols[i]
particle_set[id].aging_kappa = -1
particle_set[id].aging_h2o = -1
particle_set[id].aging_no3_fraction = -1
particle_set[id].aging_so4_fraction = -1
particle_set[id].aging_nh4_fraction = -1
particle_set[id].aging_solute_fraction = -1
particle_set[id].aging_diameter = -1
for id in particle_set.keys():
if particle_set[id].current_id in particles.ids:
i = np.nonzero(
particles.ids == particle_set[id].current_id)[0][0]
if s_crit[i] <= particle_set[id].min_s_crit:
particle_set[id].min_s_crit = s_crit[i]
if (s_crit[i] <= aging_ss) and (particle_set[id].aged_flag
== False):
particle_set[id].aging_time = time
particle_set[id].aged_flag = True
particle_set[id].aging_kappa = kappas[i]
particle_set[id].aging_diameter = dry_diameters[i]
particle_set[id].aging_h2o = h2o[i]
particle_set[id].aging_no3_fraction = no3_frac[i]
particle_set[id].aging_so4_fraction = so4_frac[i]
particle_set[id].aging_nh4_fraction = nh4_frac[i]
particle_set[id].aging_solute_fraction = solute_frac[i]
output = open(output_pkl, 'wb')
pickle.dump(particle_set, output)
output.close()
i_loop_max = config.i_loop_max
i_ens_max = config.i_ens_max
netcdf_dir = "/home/nriemer/subversion/partmc/branches/nriemer/local_scenarios/brownian_test_paper2/out/"
array_num_init = np.zeros([i_loop_max*i_ens_max])
array_mass_init = np.zeros([i_loop_max*i_ens_max])
array_num_end = np.zeros([i_loop_max*i_ens_max])
array_mass_end = np.zeros([i_loop_max*i_ens_max])
for i_loop in range (0, i_ens_max*i_loop_max):
filename = "brownian_part_%05d_00000001.nc" % (i_loop+1)
print filename
ncf = scipy.io.netcdf.netcdf_file(netcdf_dir+filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
total_number = sum(1/particles.comp_vols)
total_dry_mass = sum(particles.masses()/particles.comp_vols)
array_num_init[i_loop] = total_number
array_mass_init[i_loop]= total_dry_mass
filename = "brownian_part_%05d_00000002.nc" % (i_loop+1)
print filename
ncf = scipy.io.netcdf.netcdf_file(netcdf_dir+filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
def grid_box_histogram(time):
# filename prefix
dir = config.data_dir
prefix = config.file_prefix
filename = '%s_%08i.nc' %(prefix,time)
# make grids
diam_axis = partmc.log_grid(min=1e-9,max=1e-6,n_bin=60)
bc_axis = partmc.linear_grid(min=0,max=1.,n_bin=50)
# load the file
ncf = scipy.io.netcdf.netcdf_file(config.data_dir+'/'+filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
ncf.close()
# compute the values
bc = particles.masses(include = ["BC"])
dry_mass = particles.masses(exclude = ["H2O"])
bc_frac = bc / dry_mass
dry_diameters = particles.dry_diameters()
# 2D histogram
hist_2d_bc = partmc.histogram_2d(dry_diameters, bc_frac, diam_axis, bc_axis,
weights = 1 / particles.comp_vols)
# convert
hist_2d_bc /= 1e6
# create the figure
width_in = 4.0
(figure, axes, cbar_axes) = mpl_helper.make_fig(figure_width=width_in,
colorbar=True,left_margin=.7,right_margin=1.1, top_margin=0.3,
bottom_margin=.65, colorbar_height_fraction=0.8)
# Data min and max
# we want this to be fixed for all time
data_min = 10**2 #min(data_mins)
data_max = 10**6 #max(data_maxes)
norm = matplotlib.colors.LogNorm(vmin=data_min, vmax=data_max)
p = axes.pcolormesh(diam_axis.edges()/1e-6, bc_axis.edges()*100,
hist_2d_bc.transpose(),norm = norm, linewidths = 0.1, edgecolors='None')
# make the plot pretty
axes.set_xscale('log')
axes.set_yscale('linear')
xlabel = r'diameter $(\mu \rm m)$'
ylabel = r'BC mass fraction'
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
axes.set_xlim(.005,1)
axes.set_ylim(0,80)
axes.set_yticks([0,20,40,60,80])
axes.grid(True)
# colorbar
cbar = figure.colorbar(p, cax=cbar_axes,
format=matplotlib.ticker.LogFormatterMathtext(),
orientation='vertical')
kwargs = {}
kwargs["format"] = matplotlib.ticker.LogFormatterMathtext()
cmappable = matplotlib.cm.ScalarMappable(norm=norm)
cmappable.set_array(numpy.array([hist_2d_bc.min(), hist_2d_bc.max()]))
cbar_label = r"num. conc. $(\rm cm^{-3})$"
cbar.set_label(cbar_label)
cbar.solids.set_edgecolor("face")
# Save figure and print name
fig_name = '%s/bc_plot_%08i.pdf' % \
(config.fig_dir, time)
figure.savefig(fig_name)
# print name in case we need it
print fig_name
return