def make_plot(in_filename, out_filename, time, title):
ncf = scipy.io.netcdf.netcdf_file(in_filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
age = abs(particles.least_create_times / 3600. - time)
dry_diameters = particles.dry_diameters()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
x_axis = partmc.log_grid(min=1e-8, max=1e-6, n_bin=140)
y_axis = partmc.linear_grid(min=0, max=48, n_bin=96)
vals2d = partmc.multival_2d(dry_diameters, age, s_crit, x_axis, y_axis)
plt.clf()
plt.pcolor(x_axis.edges(),
y_axis.edges(),
vals2d.transpose(),
norm=matplotlib.colors.LogNorm(),
linewidths=0.1)
a = plt.gca()
a.set_xscale("log")
a.set_yscale("linear")
plt.axis([x_axis.min, x_axis.max, y_axis.min, y_axis.max])
plt.xlabel("dry diameter (m)")
plt.ylabel("age (h)")
cbar = plt.colorbar()
cbar.set_label("S_crit (%)")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
def get_plot_data_bc(filename, value_min=None, value_max=None):
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
diameters = particles.dry_diameters() * 1e6
comp_frac = particles.masses(include = ["BC"]) \
/ particles.masses(exclude = ["H2O"]) * 100
# hack to avoid landing just around the integer boundaries
comp_frac *= (1.0 + 1e-12)
h2o = particles.masses(include=["H2O"])
x_axis = partmc.log_grid(min=diameter_axis_min,
max=diameter_axis_max,
n_bin=num_diameter_bins * 2)
y_axis = partmc.linear_grid(min=bc_axis_min,
max=bc_axis_max,
n_bin=num_bc_bins * 2)
value = partmc.multival_2d(diameters, comp_frac, h2o, x_axis, y_axis)
if value_max == None:
value_max = value.max()
if value_min == None:
maxed_value = np.where(value > 0.0, value, value_max)
value_min = maxed_value.min()
#if value_max > 0.0:
# value = (log(value) - log(value_min)) \
# / (log(value_max) - log(value_min))
#value = value.clip(0.0, 1.0)
return (value, x_axis.edges(), y_axis.edges(), env_state, value_min,
value_max)
def make_plot(in_filename,out_filename):
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(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)