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(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()
num_avg_overall = np.zeros([config.i_loop_max])
mass_avg_overall = np.zeros([config.i_loop_max])
f1 = "data/ensemble_norm_num_brownian_60s.txt"
f2 = "data/ensemble_norm_mass_brownian_60s.txt"
#f3 = "data/ensemble_norm_num_brownian_1200s.txt"
#f4 = "data/ensemble_norm_mass_brownian_1200s.txt"
num_avg_overall = np.loadtxt(f1) / 1e6
mass_avg_overall = np.loadtxt(f2)*1e9
#num_avg_overall_6s = np.loadtxt(f3) / 1e6
#mass_avg_overall_6s = np.loadtxt(f4)*1e9
(figure, axes_array) = mpl_helper.make_fig_array(1,2, figure_width=config.figure_width_double,
left_margin=0.75, horiz_sep=0.6, share_y_axes = False)
axes = axes_array[0][0]
large_time_step = axes.plot(num_avg_overall, 'b-')
axes.set_xscale("log")
axes.set_yscale("log")
axes.set_xlim([0, 100])
axes.set_ylim([num_avg_overall.min(), num_avg_overall.max()])
axes.grid(True)
axes.set_xlabel(r"ensemble size")
axes.set_ylabel(r" $\| \langle n_{\rm p}(24\, {\rm h}) - n_{\rm sect}(24 \, {\rm h}) \rangle \|_2$")
#axes.legend((large_time_step, small_time_step), (r"60 s", r"1200 s"))
axes = axes_array[0][1]
particle_set[id].emit_time) / 3600.
else:
time_for_aging[i_counter] = -1
i_counter = i_counter + 1
emit_morning = (emit_time < 6.)
emit_afternoon = ((emit_time > 6.) & (emit_time < 12.))
emit_night = (emit_time > 12)
# Scatter plot, colored by diameter at time of aging
(figure, axes_array,
cbar_axes_array) = mpl_helper.make_fig_array(3,
1,
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[2][0]
cbar_axes = cbar_axes_array[0]
p = axes.scatter(emit_diam[emit_morning],
time_for_aging[emit_morning],
c=aging_no3_fraction[emit_morning],
norm=matplotlib.colors.Normalize(vmin=0, vmax=1),
s=2,
linewidths=0)
axes.set_xscale("log")
axes.set_yscale("linear")
axes.set_xlim(1e-9, 1e-6)
y_array_scrit = np.loadtxt("data/2d_scrit_compo_12_y_values_corrected2.txt")
num_bc = np.loadtxt("data/2d_bc_compo_12_average_num_corrected2.txt") / 1e6
num_scrit = np.loadtxt(
"data/2d_scrit_compo_12_average_num_corrected2.txt") / 1e6
mass_bc = np.loadtxt("data/2d_bc_compo_12_average_mass_corrected2.txt") * 1e9
mass_scrit = np.loadtxt(
"data/2d_scrit_compo_12_average_mass_corrected2.txt") * 1e9
(figure, axes_array, cbar_axes_array) = mpl_helper.make_fig_array(
2,
2,
figure_width=config.figure_width_double,
left_margin=0.7,
right_margin=0.1,
top_margin=0.8,
vert_sep=1.5,
horiz_sep=0.3,
colorbar="individual",
colorbar_location="top")
axes = axes_array[1][0]
cbar_axes = cbar_axes_array[1][0]
p = axes.pcolor(x_array_bc,
y_array_bc,
num_bc.transpose(),
linewidths=0.1,
norm=matplotlib.colors.LogNorm(vmin=num_bc_min,
vmax=num_bc_max))
axes.set_xscale("log")
import mpl_helper
import matplotlib
import config
bc_std_overall = np.zeros([21, 4]) # 21 for weighting schemes, 4 for ss-values
ccn_std_overall = np.zeros([21, 4])
i_counter = 0
for counter in ["ss1", "ss2", "ss3", "ss4"]:
f1 = "data/ccn_std_overall_%s.txt" % counter
f2 = "data/bc_std_overall_%s.txt" % counter
ccn_std_overall[:, i_counter] = np.loadtxt(f1)
bc_std_overall[:, i_counter] = np.loadtxt(f2)
i_counter += 1
(figure, axes_array) = mpl_helper.make_fig_array(
1, 2, figure_width=config.figure_width_double, vert_sep=0.2, axis_ratio=1)
axes = axes_array[0][0]
axes.set_xscale("log")
axes.set_yscale("log")
axes.set_ylim(1e-3, 1)
axes.set_xlabel(
r"avg. CCN coeff. var. $\overline{{\rm CV}(N_{\rm CCN}(S_{\rm env}))}$")
axes.set_ylabel(
r"avg. scav. BC mass coeff. var. $\overline{{\rm CV}(M_{\rm CCN}^{\rm BC}(S_{\rm env}))}$"
)
axes.grid()
axes.plot(ccn_std_overall[0:6, 2], bc_std_overall[0:6, 2], 'r-x')
axes.plot(ccn_std_overall[6:12, 2], bc_std_overall[6:12, 2], 'g-x')
axes.plot(ccn_std_overall[12:18, 2], bc_std_overall[12:18, 2], 'b-x')
axes.plot(ccn_std_overall[18, 2], bc_std_overall[18, 2], 'ro')
axes.plot(ccn_std_overall[19, 2], bc_std_overall[19, 2], 'go')
num_std3 = np.loadtxt("data/1d_10K_wei-4_12_e_bars_num.txt") / 1e6
mass_avg1 = np.loadtxt("data/1d_10K_wei+1_12_hist_array_gav_mass.txt") * 1e9
mass_avg2 = np.loadtxt("data/1d_10K_wei-1_12_hist_array_gav_mass.txt") * 1e9
mass_avg3 = np.loadtxt("data/1d_10K_wei-4_12_hist_array_gav_mass.txt") * 1e9
mass_std1 = np.loadtxt("data/1d_10K_wei+1_12_e_bars_mass.txt") * 1e9
mass_std2 = np.loadtxt("data/1d_10K_wei-1_12_e_bars_mass.txt") * 1e9
mass_std3 = np.loadtxt("data/1d_10K_wei-4_12_e_bars_mass.txt") * 1e9
(figure, axes_array) = mpl_helper.make_fig_array(
3,
2,
figure_width=config.figure_width_double,
top_margin=0.41,
bottom_margin=0.45,
left_margin=1.07,
right_margin=0.65,
horiz_sep=0.3,
vert_sep=0.3,
share_y_axes=False)
axes = axes_array[2][0]
axes.errorbar(x_array, num_avg1, num_std1, fmt='b-')
axes.set_xscale("log")
axes.set_yscale("log")
axes.set_ylabel(r"number conc. $n(D_{\rm dry})$ / $\rm cm^{-3}$")
axes.set_ylim(1e-3, 1e5)
axes.grid(True)
axes.text(-0.3,
0.5,
l2 = axes.plot(wet_diameter_inertial * 1e6, equilibrium_rhs_inertial_grid_080)
l3 = axes.plot(wet_diameter_inertial * 1e6, equilibrium_rhs_inertial_grid_100)
l4 = axes.plot(wet_diameter_inertial * 1e6, equilibrium_rhs_inertial_grid_130)
l5 = axes.plot(wet_diameter_inertial * 1e6, equilibrium_rhs_inertial_grid_150)
axes.grid(True)
axes.set_xscale("log")
axes.set_xlabel(r"diameter / $\rm \mu m$")
axes.set_ylabel(r"rh")
axes.set_xlim(1, 100)
axes.set_ylim(1, 1.003)
axes.legend((l1, l2, l3, l4, l5),
("env rh", "eq rh 080", "eq rh 100", "eq rh 130", "eq rh 150"),
loc='center left')
figure.savefig("figs/rh_versus_d_inertial.pdf")
(figure, axes_array) = mpl_helper.make_fig_array(2, 1, share_x_axes=True)
axes = axes_array[0][0]
l1 = axes.plot(seconds, diam_not_activate * 1e6)
l2 = axes.plot(seconds, d_c_not_activate * 1e6)
axes.grid(True)
axes.set_xlabel(r"time / s")
axes.set_ylabel(r"diameter / $\rm \mu m$")
axes.legend((l1, l2), ("wet diameter", "critical diameter"),
loc='center right')
axes = axes_array[1][0]
l1 = axes.plot(seconds, rh)
l2 = axes.plot(seconds, rh_c_not_activate)
l3 = axes.plot(seconds, rh_eq_not_activate)
axes.grid(True)
axes.set_ylim(1, 1.005)
x_array_bc = np.loadtxt("data/2d_bc_10K_wei-1_12_x_values.txt") * 1e6
y_array_bc = np.loadtxt("data/2d_bc_10K_wei-1_12_y_values.txt") * 100
x_array_scrit = np.loadtxt("data/2d_scrit_10K_wei-1_12_x_values.txt") * 1e6
y_array_scrit = np.loadtxt("data/2d_scrit_10K_wei-1_12_y_values.txt")
num_bc_std = np.loadtxt("data/2d_bc_10K_wei-1_12_hist_std_norm_num.txt")
num_scrit_std = np.loadtxt("data/2d_scrit_10K_wei-1_12_hist_std_norm_num.txt")
num_bc_std = np.ma.masked_invalid(num_bc_std)
num_scrit_std = np.ma.masked_invalid(num_scrit_std)
(figure, axes_array, cbar_axes_array) \
= mpl_helper.make_fig_array(1,2, figure_width=config.figure_width_double,
left_margin=0.5,right_margin=0.2,top_margin=0.8,
vert_sep=0.2, horiz_sep=0.7,
colorbar="individual", colorbar_location="top", share_y_axes=False)
print "check"
print num_bc_std.max(), num_bc_std.min()
print num_scrit_std.max(), num_scrit_std.min()
axes = axes_array[0][0]
cbar_axes = cbar_axes_array[0][0]
p = axes.pcolor(x_array_bc,
y_array_bc,
num_bc_std.transpose(),
norm=matplotlib.colors.LogNorm(vmin=5e-2, vmax=10),
linewidths=0.1)
axes.set_xscale("log")
axes.set_yscale("linear")