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 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,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(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_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 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 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_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(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 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
x_axis = partmc.log_grid(min=diameter_axis_min,
max=diameter_axis_max,
n_bin=num_diameter_bins)
y_axis = partmc.linear_grid(min=bc_axis_min,
max=bc_axis_max,
n_bin=num_bc_bins)
# hack to avoid landing just around the integer boundaries
comp_frac *= (1.0 + 1e-12)
value = partmc.histogram_2d(diameters,
comp_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
value *= 100
value /= 1e6
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)
time_for_aging[i_counter] = -1
i_counter = i_counter + 1
emit_morning = ((emit_time < 6.) & (bc_frac_emit > 0))
emit_afternoon = (((emit_time > 11.) & (emit_time < 12.)) & (bc_frac_emit > 0))
emit_night = ((emit_time > 12) & (bc_frac_emit > 0))
bc_containing = (bc_frac_emit > 0)
# 2D Histogram plot
x_axis = partmc.log_grid(min=1e-3, max=1e1, n_bin=70)
y_axis = partmc.linear_grid(min=0, max=48, n_bin=48)
hist2d = partmc.histogram_2d(emit_diam[bc_containing],
time_for_aging[bc_containing],
x_axis,
y_axis,
weights=1 / emit_comp_vols[bc_containing])
hist2d_morning = partmc.histogram_2d(emit_diam[emit_morning],
time_for_aging[emit_morning],
x_axis,
y_axis,
weights=1 / emit_comp_vols[emit_morning])
hist2d_afternoon = partmc.histogram_2d(emit_diam[emit_afternoon],
time_for_aging[emit_afternoon],
x_axis,
y_axis,
weights=1 /
emit_comp_vols[emit_afternoon])
def make_plot(dir_name, in_files, out_filename1, out_filename2):
x_axis = partmc.log_grid(min=1e-9, max=1e-5, n_bin=70)
y_axis = partmc.log_grid(min=1e-3, max=1e2, n_bin=50)
x_centers = x_axis.centers()
y_centers = y_axis.centers()
counter = 0
hist_array = np.zeros([len(x_centers), len(y_centers), config.i_loop_max])
hist_average = np.zeros([len(x_centers), len(y_centers)])
hist_std = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm = np.zeros([len(x_centers), len(y_centers)])
for file in in_files:
ncf = Scientific.IO.NetCDF.NetCDFFile(dir_name + file)
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
hist2d = partmc.histogram_2d(dry_diameters,
s_crit,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist_array[:, :, counter] = hist2d
counter = counter + 1
hist_average = np.average(hist_array, axis=2)
hist_std = np.std(hist_array, axis=2)
hist_std_norm = hist_std / hist_average
hist_std_norm = np.ma.masked_invalid(hist_std_norm)
print "min, max", hist_average.min(), hist_average.max()
# dry_diameters_line = np.array([1e-9, 1e-5])
# kappa_line1 = np.array([0.01, 0.01])
# crit_ss_line1 = (partmc.critical_rel_humids(env_state,kappa_line1, dry_diameters_line)-1)*100.
# kappa_line2 = np.array([0.1, 0.1])
# crit_ss_line2 = (partmc.critical_rel_humids(env_state,kappa_line2, dry_diameters_line)-1)*100.
# kappa_line3 = np.array([2,2])
# crit_ss_line3 = (partmc.critical_rel_humids(env_state,kappa_line3, dry_diameters_line)-1)*100.
# kappa_line4 = np.array([0.001,0.001])
# crit_ss_line4 = (partmc.critical_rel_humids(env_state,kappa_line4, dry_diameters_line)-1)*100.
# print 'line ', kappa_line1, dry_diameters_line, crit_ss_line1
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_average.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e3, vmax=1e11),
linewidths=0.1)
# plt.plot(dry_diameters_line, crit_ss_line1, 'k-')
# plt.plot(dry_diameters_line, crit_ss_line2, 'k-')
# plt.plot(dry_diameters_line, crit_ss_line3, 'k-')
# plt.plot(dry_diameters_line, crit_ss_line4, 'k-')
a = plt.gca()
a.set_xscale("log")
a.set_yscale("log")
plt.grid()
plt.axis([5e-9, 5e-6, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("S_crit in %")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
fig = plt.gcf()
fig.savefig(out_filename1)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_std_norm.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e-2, vmax=10),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("log")
plt.grid()
plt.axis([5e-9, 5e-6, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("S_crit in %")
cbar = plt.colorbar()
cbar.set_label("CV")
fig = plt.gcf()
fig.savefig(out_filename2)
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
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)
(figure, axes, cbar_axes) = config_matplotlib.make_fig(colorbar=True,
right_margin=1,
top_margin=0.3)
axes.grid(True)
axes.grid(True, which='minor')
axes.minorticks_on()
axes.set_xscale('log')
axes.set_xbound(x_axis.min, x_axis.max)
axes.set_ybound(y_axis.min, y_axis.max)
xaxis = axes.get_xaxis()
yaxis = axes.get_yaxis()
xaxis.labelpad = 8
yaxis.labelpad = 8
#xaxis.set_major_formatter(matplotlib.ticker.LogFormatter())
#yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(5))
#yaxis.set_minor_locator(matplotlib.ticker.MaxNLocator(8))
axes.set_xlabel(r"dry diameter $D\ (\rm\mu m)$")
axes.set_ylabel(r"BC dry mass frac. $w_{{\rm BC},{\rm dry}}$")
hist2d = partmc.histogram_2d(dry_diameters,
bc_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
# plt.clf()
axes.set_xbound(x_axis.min, x_axis.max)
axes.set_ybound(y_axis.min, y_axis.max)
p = axes.pcolor(x_axis.edges(),
y_axis.edges(),
hist2d.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e8, vmax=1e11),
linewidths=0.1)
title = partmc.time_of_day_string(env_state)
axes.set_xbound(x_axis.min, x_axis.max)
axes.set_ybound(y_axis.min, y_axis.max)
axes.set_title(title)
figure.colorbar(p,
cax=cbar_axes,
format=matplotlib.ticker.LogFormatterMathtext())
cbar_axes.set_ylabel(r"number conc. $(\rm m^{-3})$")
#cbar_axes.set_ylim([1e8, 1e11])
#plt.title(title)
axes.set_xbound(x_axis.min, x_axis.max)
axes.set_ybound(y_axis.min, y_axis.max)
fig = plt.gcf()
fig.savefig(out_filename)
def make_plot(hour, f1, f2, f3, f4):
x_axis = partmc.log_grid(
min=1e-9, max=1e-5,
n_bin=80) # n_bin changed to 80. was 70 for the submitted paper
y_axis = partmc.linear_grid(min=0, max=1., n_bin=50)
x_centers = x_axis.centers()
y_centers = y_axis.centers()
hist_array_num = np.zeros([
len(x_centers),
len(y_centers), config.i_weighting_schemes, config.i_loop_max
])
hist_array_mass = np.zeros([
len(x_centers),
len(y_centers), config.i_weighting_schemes, config.i_loop_max
])
hist_array_pnum = np.zeros([
len(x_centers),
len(y_centers), config.i_weighting_schemes, config.i_loop_max
])
hist_average_num = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
hist_average_mass = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
hist_average_pnum = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
hist_var_num = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
hist_var_mass = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
weighting_factor_num = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
weighting_factor_mass = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
for (counter_weighting, counter) in enumerate([
"1K_wei+1", "1K_flat", "1K_wei-1", "1K_wei-2", "1K_wei-3",
"1K_wei-4"
]):
print "I'm doing ", counter
files = []
for i_loop in range(0, config.i_loop_max):
filename_in = config.netcdf_dir + "/urban_plume_wc_%s_0%03d_000000%02d.nc" % (
counter, i_loop + 1, hour)
files.append(filename_in)
for (counter_i_loop, file) in enumerate(files):
print "file ", file
ncf = scipy.io.netcdf.netcdf_file(file, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
bc = particles.masses(include=["BC"])
dry_mass = particles.masses(exclude=["H2O"])
bc_frac = bc / dry_mass
hist2d = partmc.histogram_2d(dry_diameters,
bc_frac,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist_array_num[:, :, counter_weighting, counter_i_loop] = 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_weighting, counter_i_loop] = hist2d
hist2d = partmc.histogram_2d(dry_diameters, bc_frac, x_axis,
y_axis)
hist_array_pnum[:, :, counter_weighting, counter_i_loop] = hist2d
hist_array_num = np.ma.masked_less_equal(hist_array_num, 0)
hist_array_mass = np.ma.masked_less_equal(hist_array_mass, 0)
hist_array_pnum = np.ma.masked_less_equal(hist_array_pnum, 0)
hist_average_num = np.average(hist_array_num, axis=3)
hist_average_mass = np.average(hist_array_mass, axis=3)
hist_average_pnum = np.average(hist_array_pnum, axis=3)
hist_var_num = np.var(hist_array_num, axis=3)
hist_var_mass = np.var(hist_array_mass, axis=3)
print "Calculated average and variance", counter
# weighting_factor_num = 1 / (hist_var_num / hist_average_pnum)
# weighting_factor_mass = 1 / (hist_var_mass / hist_average_pnum)
# new way of calculating weighting_factor after Matt discovered error, 6/25/2011
# weighting_factor_num = 1 / hist_var_num
# weighting_factor_mass = 1 / hist_var_mass
# TEST: weighting directly proportional to N_p
weighting_factor_num = hist_average_pnum
weighting_factor_mass = hist_average_pnum
weighting_factor_num_sum = np.sum(weighting_factor_num, axis=2)
weighting_factor_mass_sum = np.sum(weighting_factor_mass, axis=2)
hist_composite_num = np.zeros([len(x_centers), len(y_centers)])
hist_composite_mass = np.zeros([len(x_centers), len(y_centers)])
for i in range(0, config.i_weighting_schemes):
increment = weighting_factor_num[:, :,
i] / weighting_factor_num_sum * hist_average_num[:, :,
i]
# increment = increment.filled(0)
hist_composite_num += increment
print "hist_composite_num ", hist_composite_num
hist_composite_num = np.nan_to_num(hist_composite_num)
for i in range(0, config.i_weighting_schemes):
increment = weighting_factor_mass[:, :,
i] / weighting_factor_mass_sum * hist_average_mass[:, :,
i]
# increment = increment.filled(0)
hist_composite_mass += increment
hist_composite_mass = np.nan_to_num(hist_composite_mass)
np.savetxt(f1, x_axis.edges())
np.savetxt(f2, y_axis.edges())
np.savetxt(f3, hist_composite_num)
np.savetxt(f4, hist_composite_mass)
time_for_aging[i_counter] = (particle_set[id].aging_time - particle_set[id].emit_time) / 3600. else: time_for_aging[i_counter] = -1 i_counter = i_counter + 1 emit_morning = ((emit_time < 6.) & (bc_frac_emit > 0)) emit_afternoon = (((emit_time > 6.) & (emit_time < 12.)) & (bc_frac_emit > 0)) emit_night = ((emit_time > 12) & (bc_frac_emit > 0 )) bc_containing = (bc_frac_emit > 0) # 2D Histogram plot x_axis = partmc.log_grid(min=1e-3,max=1e1,n_bin=70) y_axis = partmc.linear_grid(min=0,max=1,n_bin=50) hist2d = partmc.histogram_2d(emit_diam[bc_containing], aging_solute_fraction[bc_containing], x_axis, y_axis, weights = 1 / emit_comp_vols[bc_containing]) hist2d_morning = partmc.histogram_2d(emit_diam[emit_morning], aging_solute_fraction[emit_morning], x_axis, y_axis, weights = 1 / emit_comp_vols[emit_morning]) hist2d_afternoon = partmc.histogram_2d(emit_diam[emit_afternoon], aging_solute_fraction[emit_afternoon], x_axis, y_axis, weights = 1 / emit_comp_vols[emit_afternoon]) hist2d = hist2d * 1e-6 hist2d_morning = hist2d_morning * 1e-6 hist2d_afternoon = hist2d_afternoon * 1e-6 (figure, axes_array, cbar_axes_array) = mpl_helper.make_fig_array(1,1, figure_width=5, top_margin=1, bottom_margin=0.45, left_margin=1.07, right_margin=0.65, vert_sep=0.3, horiz_sep=0.3,
def make_plot(dir_name, hour, out_filename1):
x_axis = partmc.log_grid(min=1e-9, max=1e-5, n_bin=70)
y_axis = partmc.log_grid(min=1e-3, max=1e2, n_bin=50)
x_centers = x_axis.centers()
y_centers = y_axis.centers()
hist_array = np.zeros([
len(x_centers),
len(y_centers), config.i_weighting_schemes, config.i_loop_max
])
hist_average = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
hist_var = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
weighting_factor = np.zeros(
[len(x_centers),
len(y_centers), config.i_weighting_schemes])
for (counter_weighting, counter) in enumerate([
"10K_wei+1", "10K_flat", "10K_wei-1", "10K_wei-2", "10K_wei-3",
"10K_wei-4"
]):
print "I'm doing ", counter
files = []
for i_loop in range(0, config.i_loop_max):
filename_in = "urban_plume_wc_%s_0%03d_000000%02d.nc" % (
counter, i_loop + 1, hour)
files.append(filename_in)
for (counter_i_loop, file) in enumerate(files):
print "file ", file
ncf = Scientific.IO.NetCDF.NetCDFFile(dir_name + file)
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
hist2d = partmc.histogram_2d(dry_diameters,
s_crit,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist_array[:, :, counter_weighting, counter_i_loop] = hist2d
hist_array = np.ma.masked_less_equal(hist_array, 0)
hist_average = np.average(hist_array, axis=3)
hist_var = np.var(hist_array, axis=3)
print "Calculated average and variance", counter
weighting_factor = 1 / hist_var
weighting_factor_sum = np.sum(weighting_factor, axis=2)
hist_composite = np.zeros([len(x_centers), len(y_centers)])
for i in range(0, config.i_weighting_schemes):
increment = weighting_factor[:, :,
i] / weighting_factor_sum * hist_average[:, :,
i]
increment = increment.filled(0)
hist_composite += increment
hist_composite = np.ma.masked_less_equal(hist_composite, 0)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_composite.transpose(),
norm=matplotlib.colors.LogNorm(),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("log")
plt.grid()
plt.axis([x_axis.min, x_axis.max, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("S_crit in %")
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
fig = plt.gcf()
fig.savefig(out_filename1)
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)
def make_plot(dir_name, in_files, out_filename1, out_filename2):
x_axis = partmc.log_grid(min=1e-9, max=1e-5, n_bin=70)
y_axis = partmc.log_grid(min=1e-3, max=1e2, n_bin=50)
x_centers = x_axis.centers()
y_centers = y_axis.centers()
counter = 0
hist_array = np.zeros([len(x_centers), len(y_centers), config.i_loop_max])
hist_average = np.zeros([len(x_centers), len(y_centers)])
hist_std = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm = np.zeros([len(x_centers), len(y_centers)])
for file in in_files:
ncf = Scientific.IO.NetCDF.NetCDFFile(dir_name + file)
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
hist2d = partmc.histogram_2d(dry_diameters,
s_crit,
x_axis,
y_axis,
weights=particles.masses(include=["BC"]) /
particles.comp_vols)
hist_array[:, :, counter] = hist2d
counter = counter + 1
hist_average = np.average(hist_array, axis=2)
hist_std = np.std(hist_array, axis=2)
hist_std_norm = hist_std / hist_average
hist_std_norm = np.ma.masked_invalid(hist_std_norm)
print 'hist_array ', hist2d.shape, hist_array[35, :, 0]
print 'hist_std ', hist_average[35, :], hist_std[35, :], hist_std_norm[
35, :]
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_average.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e-11, vmax=1e-7),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("log")
plt.grid()
plt.axis([5e-9, 5e-6, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("S_crit in %")
cbar = plt.colorbar()
cbar.set_label("mass density (kg m^{-3})")
fig = plt.gcf()
fig.savefig(out_filename1)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_std_norm.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e-2, vmax=10),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("log")
plt.grid()
plt.axis([5e-9, 5e-6, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("S_crit in %")
cbar = plt.colorbar()
cbar.set_label("std/avg")
fig = plt.gcf()
fig.savefig(out_filename2)
def make_plot(dir_name, in_files, out_filename1, out_filename2):
x_axis = partmc.log_grid(min=1e-9, max=1e-5, n_bin=70)
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 = np.zeros([len(x_centers), len(y_centers), config.i_loop_max])
hist_average = np.zeros([len(x_centers), len(y_centers)])
hist_std = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm = np.zeros([len(x_centers), len(y_centers)])
for file in in_files:
ncf = scipy.io.netcdf.netcdf_file(dir_name + 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[:, :, counter] = hist2d
counter = counter + 1
hist_average = np.average(hist_array, axis=2)
hist_std = np.std(hist_array, axis=2)
hist_std_norm = hist_std / hist_average
hist_std_norm = np.ma.masked_invalid(hist_std_norm)
print 'hist_std ', hist_average[35, :], hist_std[35, :], hist_std_norm[
35, :]
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_average.transpose(),
norm=matplotlib.colors.LogNorm(),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("linear")
plt.axis([5e-9, 5e-6, 0, 0.8])
plt.xlabel("dry diameter (m)")
plt.ylabel("BC mass fraction")
plt.grid(True)
plt.clim(1e8, 5e11)
cbar = plt.colorbar()
cbar.set_label("number density (m^{-3})")
fig = plt.gcf()
fig.savefig(out_filename1)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
hist_std_norm.transpose(),
norm=matplotlib.colors.LogNorm(vmin=1e-1, vmax=10),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("linear")
plt.axis([5e-9, 5e-6, 0, 0.8])
plt.xlabel("dry diameter (m)")
plt.ylabel("BC mass fraction")
plt.grid(True)
cbar = plt.colorbar()
cbar.set_label("CV")
fig = plt.gcf()
fig.savefig(out_filename2)
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
axes.set_xscale("log")
axes.set_yscale("linear")
axes.set_ylim(0, 48)
axes.set_ylabel(r"per-particle aging time / h")
axes.set_xlabel(r"dry diameter at emission $D_{\rm dry}$ / $\rm \mu m$")
axes.grid(True)
mpl_helper.remove_fig_array_axes(axes_array)
figure.savefig("aging_kappa_wc_01_1.pdf")
# 2D Histogram plot
x_axis = partmc.log_grid(min=1e-3,max=1e1,n_bin=70)
y_axis = partmc.linear_grid(min=0,max=48,n_bin=48)
hist2d = partmc.histogram_2d(emit_diam[emit_morning], time_for_aging[emit_morning], x_axis, y_axis, weights = 1 / emit_comp_vols[emit_morning])
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=1, bottom_margin=0.45,
left_margin=1.07, right_margin=0.65,
vert_sep=0.3, horiz_sep=0.3,
colorbar="shared", colorbar_location="top")
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=1e2, vmax=1e4), linewidths = 0.1)
axes.set_xscale("log")
axes.set_yscale("linear")
def make_plot(in_files, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10):
x_axis = partmc.log_grid(min=1e-9, max=1e-5, n_bin=70)
y_axis = partmc.log_grid(min=1e-3, max=1e2, 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_mass = np.zeros([len(x_centers), len(y_centers)])
hist_std_mass = np.zeros([len(x_centers), len(y_centers)])
hist_std_norm_mass = np.zeros([len(x_centers), len(y_centers)])
hist_array_kappas = np.zeros(
[len(x_centers), len(y_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)
env_state = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
kappas = particles.kappas()
print 'kappa max and min', kappas.max(), kappas.min()
kappa_const = np.ones([len(x_axis.edges())])
kappa_const1 = 1 * kappa_const
kappa_const2 = 0 * kappa_const
print "kappa_const ", kappa_const1, len(kappa_const1), len(
particles.dry_diameters())
crit_rhs1 = (partmc.critical_rel_humids(env_state, kappa_const1,
x_axis.edges()) - 1) * 100
crit_rhs2 = (partmc.critical_rel_humids(env_state, kappa_const2,
x_axis.edges()) - 1) * 100
print "crit_rhs ", crit_rhs1, crit_rhs2
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
dry_mass = particles.masses(exclude=["H2O"])
dry_diameters = particles.dry_diameters()
hist2d = partmc.histogram_2d(dry_diameters,
s_crit,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist_array_num[:, :, counter] = hist2d
hist2d = partmc.histogram_2d(dry_diameters,
s_crit,
x_axis,
y_axis,
weights=particles.masses(include=["BC"]) /
particles.comp_vols)
hist_array_mass[:, :, counter] = hist2d
hist2d = partmc.histogram_2d(dry_diameters,
kappas,
x_axis,
y_axis,
weights=1 / particles.comp_vols)
hist_array_kappas[:, :, 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)
hist_average_kappas = np.average(hist_array_kappas, axis=2)
hist_std_kappas = np.std(hist_array_kappas, axis=2)
hist_std_norm_kappas = hist_std_kappas / hist_average_kappas
hist_std_norm_kappas = np.ma.masked_invalid(hist_std_norm_kappas)
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)
np.savetxt(f7, hist_average_kappas)
np.savetxt(f8, hist_std_norm_kappas)
np.savetxt(f9, crit_rhs1)
np.savetxt(f10, crit_rhs2)
