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,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.log_grid(min=1e-3,max=1e2,n_bin=100)
vals2d = partmc.multival_2d(dry_diameters, s_crit, age, x_axis, y_axis)
plt.clf()
plt.pcolor(x_axis.edges(), y_axis.edges(), vals2d.transpose(), 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("S_crit (%)")
cbar = plt.colorbar()
cbar.set_label("age (h)")
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_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_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(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(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()
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_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(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(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 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_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 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(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)
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)
if particle_set[id].aging_time != -1:
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 > 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],
import numpy as np
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
sys.path.append("../../tool")
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
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,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 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
import numpy as np
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
sys.path.append("../../tool")
import partmc
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
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)
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)
# Licensed under the GNU General Public License version 2 or (at your
# option) any later version. See the file COPYING for details.
import os, sys, math
import numpy as np
import scipy.io
sys.path.append("../../tool")
import partmc
import config
import config_filelist
data_base_dir = "data"
data_type = "diam_num"
x_axis = partmc.log_grid(min=config.diameter_axis_min,
max=config.diameter_axis_max,
n_bin=config.num_diameter_bins)
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
value = partmc.histogram_1d(dry_diameters,
x_axis,
def make_plot(filename_in_080, filename_in_100, filename_in_130,
filename_in_150, dir_cloud, in_file_pattern):
## calculate critical supersaturation for each particle
print filename_in_100
ncf = scipy.io.netcdf.netcdf_file(filename_in_080, 'r')
particles = partmc.aero_particle_array_t(ncf)
particles.sort_by_id()
env_state_080s = partmc.env_state_t(ncf)
ncf.close()
ncf = scipy.io.netcdf.netcdf_file(filename_in_100, 'r')
particles = partmc.aero_particle_array_t(ncf)
particles.sort_by_id()
env_state_100s = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
ncf = scipy.io.netcdf.netcdf_file(filename_in_130, 'r')
particles = partmc.aero_particle_array_t(ncf)
particles.sort_by_id()
env_state_130s = partmc.env_state_t(ncf)
ncf.close()
ncf = scipy.io.netcdf.netcdf_file(filename_in_150, 'r')
particles = partmc.aero_particle_array_t(ncf)
particles.sort_by_id()
env_state_150s = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
## calculate time series for RH and maximum RH that occurs
print dir_cloud, in_file_pattern
env_state_history = partmc.read_history(partmc.env_state_t, dir_cloud,
in_file_pattern)
env_state_init = env_state_history[0][1]
time = [env_state_history[i][0] for i in range(len(env_state_history))]
rh = [
env_state_history[i][1].relative_humidity
for i in range(len(env_state_history))
]
temp = [
env_state_history[i][1].temperature
for i in range(len(env_state_history))
]
maximum_ss = (max(rh) - 1) * 100.
print 'maximum ss ', maximum_ss
rh_final = rh[-1]
## calculate time series for each particle
print dir_cloud, in_file_pattern
time_filename_list = partmc.get_time_filename_list(dir_cloud,
in_file_pattern)
rh_c = np.zeros((len(dry_diameters), len(time_filename_list)))
rh_c_final = np.zeros(len(dry_diameters))
d_c = np.zeros((len(dry_diameters), len(time_filename_list)))
d = np.zeros((len(dry_diameters), len(time_filename_list)))
rh_eq = np.zeros((len(dry_diameters), len(time_filename_list)))
kappas = np.zeros((len(dry_diameters), len(time_filename_list)))
dry_diam = np.zeros((len(dry_diameters), len(time_filename_list)))
ids = np.zeros((len(dry_diameters), len(time_filename_list)), dtype=int)
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)
particles.sort_by_id()
env_state = partmc.env_state_t(ncf)
ncf.close()
rh_c[:, i_count] = particles.critical_rel_humids(env_state)
d_c[:, i_count] = particles.critical_diameters(env_state)
d[:, i_count] = particles.diameters()
rh_eq[:, i_count] = particles.equilib_rel_humids(env_state)
kappas[:, i_count] = particles.kappas()
dry_diam[:, i_count] = particles.dry_diameters()
ids[:, i_count] = particles.ids
seconds[i_count] = i_count
i_count = i_count + 1
d_max = d.max(axis=1)
rh_c_final = rh_c[:, -1]
d_final = d[:, -1]
d_c_final = d_c[:, -1]
## find the particles in the four categories
## 1. particles that do not activate
not_activate = np.logical_and(np.all(rh < rh_c, axis=1),
np.all(d < d_c, axis=1))
id_list_not_activate = particles.ids[not_activate]
print "id_list_not_activate ", id_list_not_activate
plot_id = id_list_not_activate[0]
plot_index = partmc.find_nearest_index(particles.ids, plot_id)
diam_not_activate = d[plot_index, :]
rh_eq_not_activate = rh_eq[plot_index, :]
ids_not_activate = ids[plot_index, :]
rh_c_not_activate = rh_c[plot_index, :]
d_c_not_activate = d_c[plot_index, :]
dry_diam_not_activate = dry_diam[plot_index, 0]
kappa_not_activate = kappas[plot_index, 0]
wet_diameters_not_activate = partmc.log_grid(
min=1.1 * dry_diam_not_activate,
max=100 * dry_diam_not_activate,
n_bin=100).edges()
equilib_rhs_not_activate_grid_080 = partmc.equilib_rel_humids(
env_state_080s, kappa_not_activate, dry_diam_not_activate,
wet_diameters_not_activate)
equilib_rhs_not_activate_grid_100 = partmc.equilib_rel_humids(
env_state_100s, kappa_not_activate, dry_diam_not_activate,
wet_diameters_not_activate)
equilib_rhs_not_activate_grid_130 = partmc.equilib_rel_humids(
env_state_130s, kappa_not_activate, dry_diam_not_activate,
wet_diameters_not_activate)
equilib_rhs_not_activate_grid_150 = partmc.equilib_rel_humids(
env_state_150s, kappa_not_activate, dry_diam_not_activate,
wet_diameters_not_activate)
## 2. evaporation type
evaporation = np.logical_and(
np.logical_and(np.any(rh > rh_c, axis=1), np.all(d < d_c, axis=1)),
(rh_final < rh_c_final))
id_list_evaporation = particles.ids[evaporation]
print "id_list_evaporation ", id_list_evaporation
# plot_id = id_list_evaporation[0]
rh_c_init = rh_c[:, 0]
rh_c_init_evaporation = np.ma.masked_where(np.logical_not(evaporation),
rh_c_init)
plot_index = rh_c_init_evaporation.argmin()
# plot_index = partmc.find_nearest_index(particles.ids, plot_id)
diam_evaporation = d[plot_index, :]
ids_evaporation = ids[plot_index, :]
rh_eq_evaporation = rh_eq[plot_index, :]
rh_c_evaporation = rh_c[plot_index, :]
d_c_evaporation = d_c[plot_index, :]
dry_diam_evaporation = dry_diam[plot_index, 0]
kappa_evaporation = kappas[plot_index, 0]
wet_diameters_evaporation = partmc.log_grid(min=1.1 * dry_diam_evaporation,
max=100 * dry_diam_evaporation,
n_bin=100).edges()
equilib_rhs_evaporation_grid_080 = partmc.equilib_rel_humids(
env_state_080s, kappa_evaporation, dry_diam_evaporation,
wet_diameters_evaporation)
equilib_rhs_evaporation_grid_100 = partmc.equilib_rel_humids(
env_state_100s, kappa_evaporation, dry_diam_evaporation,
wet_diameters_evaporation)
equilib_rhs_evaporation_grid_130 = partmc.equilib_rel_humids(
env_state_130s, kappa_evaporation, dry_diam_evaporation,
wet_diameters_evaporation)
equilib_rhs_evaporation_grid_150 = partmc.equilib_rel_humids(
env_state_150s, kappa_evaporation, dry_diam_evaporation,
wet_diameters_evaporation)
## 3. deactivation type
deactivation = np.logical_and(
np.logical_and(np.any(rh > rh_c, axis=1), np.any(d > d_c, axis=1)),
(d_final < d_c_final))
id_list_deactivation = particles.ids[deactivation]
print "id_list_deactivation ", id_list_deactivation
# plot_id = id_list_deactivation[0]
rh_c_init = rh_c[:, 0]
rh_c_init_deactivation = np.ma.masked_where(np.logical_not(deactivation),
rh_c_init)
plot_index = rh_c_init_deactivation.argmin()
# plot_index = partmc.find_nearest_index(particles.ids, plot_id)
diam_deactivation = d[plot_index, :]
ids_deactivation = ids[plot_index, :]
rh_eq_deactivation = rh_eq[plot_index, :]
rh_c_deactivation = rh_c[plot_index, :]
d_c_deactivation = d_c[plot_index, :]
dry_diam_deactivation = dry_diam[plot_index, 0]
kappa_deactivation = kappas[plot_index, 0]
wet_diameters_deactivation = partmc.log_grid(
min=1.1 * dry_diam_deactivation,
max=100 * dry_diam_deactivation,
n_bin=100).edges()
equilib_rhs_deactivation_grid_080 = partmc.equilib_rel_humids(
env_state_080s, kappa_deactivation, dry_diam_deactivation,
wet_diameters_deactivation)
equilib_rhs_deactivation_grid_100 = partmc.equilib_rel_humids(
env_state_100s, kappa_deactivation, dry_diam_deactivation,
wet_diameters_deactivation)
equilib_rhs_deactivation_grid_130 = partmc.equilib_rel_humids(
env_state_130s, kappa_deactivation, dry_diam_deactivation,
wet_diameters_deactivation)
equilib_rhs_deactivation_grid_150 = partmc.equilib_rel_humids(
env_state_150s, kappa_deactivation, dry_diam_deactivation,
wet_diameters_deactivation)
## 4. inertial type
inertial = np.logical_and(
np.logical_and(np.any(rh > rh_c, axis=1), np.all(d < d_c, axis=1)),
(rh_final > rh_c_final))
id_list_inertial = particles.ids[inertial]
print "id_list_inertial ", id_list_inertial
plot_id = id_list_inertial[0]
plot_index = partmc.find_nearest_index(particles.ids, plot_id)
diam_inertial = d[plot_index, :]
ids_inertial = ids[plot_index, :]
rh_eq_inertial = rh_eq[plot_index, :]
rh_c_inertial = rh_c[plot_index, :]
d_c_inertial = d_c[plot_index, :]
dry_diam_inertial = dry_diam[plot_index, 0]
kappa_inertial = kappas[plot_index, 0]
wet_diameters_inertial = partmc.log_grid(min=1.1 * dry_diam_inertial,
max=100 * dry_diam_inertial,
n_bin=100).edges()
equilib_rhs_inertial_grid_080 = partmc.equilib_rel_humids(
env_state_080s, kappa_inertial, dry_diam_inertial,
wet_diameters_inertial)
equilib_rhs_inertial_grid_100 = partmc.equilib_rel_humids(
env_state_100s, kappa_inertial, dry_diam_inertial,
wet_diameters_inertial)
equilib_rhs_inertial_grid_130 = partmc.equilib_rel_humids(
env_state_130s, kappa_inertial, dry_diam_inertial,
wet_diameters_inertial)
equilib_rhs_inertial_grid_150 = partmc.equilib_rel_humids(
env_state_150s, kappa_inertial, dry_diam_inertial,
wet_diameters_inertial)
np.savetxt("seconds.txt", seconds)
np.savetxt("rh.txt", rh)
np.savetxt("diam_not_activate.txt", diam_not_activate)
np.savetxt("diam_evaporation.txt", diam_evaporation)
np.savetxt("diam_deactivation.txt", diam_deactivation)
np.savetxt("diam_inertial.txt", diam_inertial)
np.savetxt("rh_eq_not_activate.txt", rh_eq_not_activate)
np.savetxt("rh_eq_evaporation.txt", rh_eq_evaporation)
np.savetxt("rh_eq_deactivation.txt", rh_eq_deactivation)
np.savetxt("rh_eq_inertial.txt", rh_eq_inertial)
np.savetxt("ids_not_activate.txt", ids_not_activate)
np.savetxt("ids_evaporation.txt", ids_evaporation)
np.savetxt("ids_deactivation.txt", ids_deactivation)
np.savetxt("ids_inertial.txt", ids_inertial)
np.savetxt("rh_c_not_activate.txt", rh_c_not_activate)
np.savetxt("rh_c_evaporation.txt", rh_c_evaporation)
np.savetxt("rh_c_deactivation.txt", rh_c_deactivation)
np.savetxt("rh_c_inertial.txt", rh_c_inertial)
np.savetxt("d_c_not_activate.txt", d_c_not_activate)
np.savetxt("d_c_evaporation.txt", d_c_evaporation)
np.savetxt("d_c_deactivation.txt", d_c_deactivation)
np.savetxt("d_c_inertial.txt", d_c_inertial)
np.savetxt("wet_diameters_not_activate.txt", wet_diameters_not_activate)
np.savetxt("wet_diameters_evaporation.txt", wet_diameters_evaporation)
np.savetxt("wet_diameters_deactivation.txt", wet_diameters_deactivation)
np.savetxt("wet_diameters_inertial.txt", wet_diameters_inertial)
np.savetxt("equilib_rhs_not_activate_grid_080.txt",
equilib_rhs_not_activate_grid_080)
np.savetxt("equilib_rhs_evaporation_grid_080.txt",
equilib_rhs_evaporation_grid_080)
np.savetxt("equilib_rhs_deactivation_grid_080.txt",
equilib_rhs_deactivation_grid_080)
np.savetxt("equilib_rhs_inertial_grid_080.txt",
equilib_rhs_inertial_grid_080)
np.savetxt("equilib_rhs_not_activate_grid_100.txt",
equilib_rhs_not_activate_grid_100)
np.savetxt("equilib_rhs_evaporation_grid_100.txt",
equilib_rhs_evaporation_grid_100)
np.savetxt("equilib_rhs_deactivation_grid_100.txt",
equilib_rhs_deactivation_grid_100)
np.savetxt("equilib_rhs_inertial_grid_100.txt",
equilib_rhs_inertial_grid_100)
np.savetxt("equilib_rhs_not_activate_grid_130.txt",
equilib_rhs_not_activate_grid_130)
np.savetxt("equilib_rhs_evaporation_grid_130.txt",
equilib_rhs_evaporation_grid_130)
np.savetxt("equilib_rhs_deactivation_grid_130.txt",
equilib_rhs_deactivation_grid_130)
np.savetxt("equilib_rhs_inertial_grid_130.txt",
equilib_rhs_inertial_grid_130)
np.savetxt("equilib_rhs_not_activate_grid_150.txt",
equilib_rhs_not_activate_grid_150)
np.savetxt("equilib_rhs_evaporation_grid_150.txt",
equilib_rhs_evaporation_grid_150)
np.savetxt("equilib_rhs_deactivation_grid_150.txt",
equilib_rhs_deactivation_grid_150)
np.savetxt("equilib_rhs_inertial_grid_150.txt",
equilib_rhs_inertial_grid_150)
