def make_plot(netcdf_dir, netcdf_pattern, out_filename):
time_filename_list = partmc.get_time_filename_list(netcdf_dir,
netcdf_pattern)
ccn_array = np.zeros([len(time_filename_list), 4])
i_counter = 0
for [time, filename, key] in time_filename_list:
print time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
activated_1 = (s_crit < config.s_crit_1)
number_act_1 = sum(1 / particles.comp_vols[activated_1])
activated_2 = (s_crit < config.s_crit_2)
number_act_2 = sum(1 / particles.comp_vols[activated_2])
activated_3 = (s_crit < config.s_crit_3)
number_act_3 = sum(1 / particles.comp_vols[activated_3])
ccn_array[i_counter, 0] = time
ccn_array[i_counter, 1] = number_act_1
ccn_array[i_counter, 2] = number_act_2
ccn_array[i_counter, 3] = number_act_3
i_counter += 1
print ccn_array
np.savetxt(out_filename, ccn_array)
def check_num(in_dir, in_filename, in_file_pattern, out_filename, counter):
time_filename_list = partmc.get_time_filename_list(in_dir, in_file_pattern)
id_p_array = np.array([16388, 10, 33311, 9212, 451, 11769])
d = np.zeros((len(id_p_array),len(time_filename_list)))
seconds = np.zeros(len(time_filename_list))
i_count = 0
for [time, filename, key] in time_filename_list:
print time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
ncf.close()
wet_diameters = particles.diameters()
id_list = list(particles.ids)
for i in range(0,6):
i_index = id_list.index(id_p_array[i])
d[i,i_count] = wet_diameters[i_index]
seconds[i_count] = i_count
i_count = i_count + 1
plt.figure()
plt.semilogy(seconds, d[0,:], 'b-', label = 'act1_2')
plt.semilogy(seconds, d[1,:], 'g-', label = 'not_1_2')
plt.semilogy(seconds, d[2,:], 'r-', label = 'act2_not_act1')
plt.semilogy(seconds, d[3,:], 'b-', label = 'act1_2')
plt.semilogy(seconds, d[4,:], 'g-', label = 'not_1_2')
plt.semilogy(seconds, d[5,:], 'r-', label = 'act2_not_act1')
plt.xlabel("time (s)")
plt.ylabel("diameter (m)")
plt.legend(loc = 'upper left')
fig = plt.gcf()
fig.savefig(out_filename)
def make_plot(netcdf_pattern, aging_ss, output_pkl):
particle_set = {}
netcdf_dir = "/Users/nriemer/subversion/partmc/trunk/scenarios/1_urban_plume/out"
time_filename_list = partmc.get_time_filename_list(netcdf_dir,
netcdf_pattern)
for [time, filename, key] in time_filename_list:
print time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
removed_info = partmc.aero_removed_info_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
bc = particles.masses(include=["BC"])
no3 = particles.masses(include=["NO3"])
so4 = particles.masses(include=["SO4"])
nh4 = particles.masses(include=["NH4"])
dry_mass = particles.masses(exclude=["H2O"])
bc_frac = bc / dry_mass
no3_frac = no3 / dry_mass
so4_frac = so4 / dry_mass
nh4_frac = nh4 / dry_mass
solute_frac = (no3 + so4 + nh4) / dry_mass
kappas = particles.kappas()
comp_vols = particles.comp_vols
h2o = particles.masses(include=["H2O"])
for id in particle_set.keys():
while particle_set[id].current_id in removed_info.ids:
i = np.nonzero(
removed_info.ids == particle_set[id].current_id)[0][0]
if removed_info.actions[i] != removed_info.AERO_INFO_COAG:
particle_set[id].remove_time = time
particle_set[id].current_id = 0
else:
particle_set[id].current_id = removed_info.other_ids[i]
for i in range(len(particles.ids)):
id = particles.ids[i]
if id not in particle_set:
particle_set[id] = Struct()
particle_set[id].current_id = id
particle_set[id].emit_time = time
particle_set[id].emit_diam = dry_diameters[i]
particle_set[id].emit_s_crit = s_crit[i]
particle_set[id].emit_kappa = kappas[i]
particle_set[id].remove_time = -1
particle_set[id].aged_flag = False
particle_set[id].aging_time = -1
particle_set[id].min_s_crit = s_crit[i]
particle_set[id].emit_bc_fraction = bc_frac[i]
particle_set[id].emit_comp_vols = comp_vols[i]
particle_set[id].aging_kappa = -1
particle_set[id].aging_h2o = -1
particle_set[id].aging_no3_fraction = -1
particle_set[id].aging_so4_fraction = -1
particle_set[id].aging_nh4_fraction = -1
particle_set[id].aging_solute_fraction = -1
particle_set[id].aging_diameter = -1
for id in particle_set.keys():
if particle_set[id].current_id in particles.ids:
i = np.nonzero(
particles.ids == particle_set[id].current_id)[0][0]
if s_crit[i] <= particle_set[id].min_s_crit:
particle_set[id].min_s_crit = s_crit[i]
if (s_crit[i] <= aging_ss) and (particle_set[id].aged_flag
== False):
particle_set[id].aging_time = time
particle_set[id].aged_flag = True
particle_set[id].aging_kappa = kappas[i]
particle_set[id].aging_diameter = dry_diameters[i]
particle_set[id].aging_h2o = h2o[i]
particle_set[id].aging_no3_fraction = no3_frac[i]
particle_set[id].aging_so4_fraction = so4_frac[i]
particle_set[id].aging_nh4_fraction = nh4_frac[i]
particle_set[id].aging_solute_fraction = solute_frac[i]
output = open(output_pkl, 'wb')
pickle.dump(particle_set, output)
output.close()
#!/usr/bin/env python2.5
import scipy.io
import sys
import numpy as np
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
sys.path.append("../../tool")
import partmc
import config
netcdf_dir = "../../scenarios/2_urban_plume2/out/"
netcdf_pattern = "urban_plume_nc_0001_(.*).nc"
time_filename_list = partmc.get_time_filename_list(netcdf_dir, netcdf_pattern)
ccn_cn_array = np.zeros([len(time_filename_list), 4])
i_counter = 0
age_by_id = {}
for [time, filename, key] in time_filename_list:
print time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
activated_1 = (s_crit < config.s_crit_1)
activated_id_set = set(particles.ids[activated_1])
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)
import sys
import numpy as np
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
sys.path.append("../../tool")
import partmc
import config
i_loop_max = config.i_loop_max
#for counter in ["10K_wei\\+1", "10K_flat", "10K_wei-1", "10K_wei-2", "10K_wei-3", "10K_wei-4","1K_wei\\+1", "1K_flat", "1K_wei-1", "1K_wei-2", "1K_wei-3", "1K_wei-4"]:
#for counter in ["10K_mfa", "1K_mfa"]:
for counter in ["100K_wei\\+1", "100K_flat", "100K_wei-1", "100K_wei-2", "100K_wei-3", "100K_wei-4", "100K_mfa"]:
netcdf_pattern = "urban_plume_wc_%s_0001_(.*).nc" % counter
time_filename_list = partmc.get_time_filename_list(config.netcdf_dir, netcdf_pattern)
time_array = np.zeros([len(time_filename_list)])
num_avg = np.zeros([len(time_filename_list), i_loop_max])
mass_avg = np.zeros([len(time_filename_list), i_loop_max])
num_std = np.zeros([len(time_filename_list), i_loop_max])
mass_std = np.zeros([len(time_filename_list), i_loop_max])
array_num = np.zeros([len(time_filename_list), i_loop_max])
array_mass = np.zeros([len(time_filename_list), i_loop_max])
num_avg_overall = np.zeros([i_loop_max])
mass_avg_overall = np.zeros([i_loop_max])
num_std_overall = np.zeros([i_loop_max])
def check_num(in_dir1, in_filename1, in_file_pattern, indir2, in_filename2,
out_filename1, out_filename2, out_filename3, counter):
ncf = scipy.io.netcdf.netcdf_file(in_dir1 + in_filename1, 'r')
particles1 = partmc.aero_particle_array_t(ncf)
ncf.close()
ncf = scipy.io.netcdf.netcdf_file(in_dir2 + in_filename2, 'r')
particles2 = partmc.aero_particle_array_t(ncf)
particles2.aero_data.kappa[17] = 0.1
particles2.aero_data = particles1.aero_data
ncf.close()
final_wet_diameters = particles1.diameters()
is_activated1 = (final_wet_diameters > 3e-6)
sum_tot1 = sum(1 / particles1.comp_vols) * particles1.comp_vols[0]
num_act1 = sum(
1 / particles1.comp_vols[is_activated1]) * particles1.comp_vols[0]
id_list_act1 = particles1.ids[is_activated1]
is_not_activated1 = np.logical_not(is_activated1)
id_list_not_act1 = particles1.ids[is_not_activated1]
ccn_cn_ratio1 = num_act1 / sum_tot1
env_state_history = partmc.read_history(partmc.env_state_t, in_dir1,
in_file_pattern)
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))
]
maximum_ss = (max(rh) - 1) * 100.
max_index = np.argmax(np.array(rh))
time_index = time[max_index]
time_filename_list = partmc.get_time_filename_list(in_dir1,
in_file_pattern)
max_filename = partmc.find_filename_at_time(time_filename_list, time_index)
ncf = scipy.io.netcdf.netcdf_file(max_filename, 'r')
max_env_state = partmc.env_state_t(ncf)
ncf.close()
max_wet_diameters = np.zeros_like(final_wet_diameters)
max_critical_ratio = np.zeros_like(final_wet_diameters)
for [time, filename, key] in time_filename_list:
print 'time filename key ', time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
wet_diameters = particles.diameters()
max_wet_diameters = np.maximum(max_wet_diameters, wet_diameters)
critical_diameters = particles.critical_diameters(env_state)
critical_ratio = wet_diameters / critical_diameters
max_critical_ratio = np.maximum(max_critical_ratio, critical_ratio)
max_wet_ratio = max_wet_diameters / final_wet_diameters
s_crit = (particles2.critical_rel_humids(max_env_state) - 1) * 100
is_activated2 = (s_crit <= maximum_ss)
id_list_act2 = particles2.ids[is_activated2]
is_not_activated2 = np.logical_not(is_activated2)
id_list_not_act2 = particles2.ids[is_not_activated2]
sum_tot2 = sum(1 / particles2.comp_vols) * particles2.comp_vols[0]
num_act2 = sum(
1 / particles2.comp_vols[is_activated2]) * particles2.comp_vols[0]
ccn_cn_ratio2 = num_act2 / sum_tot2
set_act1 = set(id_list_act1)
set_not_act1 = set(id_list_not_act1)
set_act2 = set(id_list_act2)
set_not_act2 = set(id_list_not_act2)
act_1_2 = (set_act1 & set_act2)
not_1_2 = (set_not_act1 & set_not_act2)
act1_not_act2 = (set_act1 & set_not_act2)
act2_not_act1 = (set_act2 & set_not_act1)
print 'act_1_2 ', act_1_2
print 'not_1_2 ', not_1_2
print 'act1_not_act2 ', act1_not_act2
print 'act2_not_act1 ', act2_not_act1
diam_by_id1 = dict(zip(particles1.ids, particles1.dry_diameters()))
diam_by_id2 = dict(zip(particles2.ids, particles2.dry_diameters()))
scrit_by_id1 = dict(
zip(particles1.ids,
(particles1.critical_rel_humids(max_env_state) - 1) * 100))
scrit_by_id2 = dict(
zip(particles2.ids,
(particles2.critical_rel_humids(max_env_state) - 1) * 100))
oc_by_id1 = dict(
zip(
particles1.ids,
particles1.masses(include=["BC"]) /
particles1.masses(exclude=["H2O"])))
oc_by_id2 = dict(
zip(
particles2.ids,
particles2.masses(include=["BC"]) /
particles2.masses(exclude=["H2O"])))
wet_ratio_by_id1 = dict(zip(particles1.ids, max_wet_ratio))
critical_ratio_by_id1 = dict(zip(particles1.ids, max_critical_ratio))
diam_act_1_2 = [diam_by_id2[id] for id in act_1_2]
scrit_act_1_2 = [scrit_by_id2[id] for id in act_1_2]
oc_act_1_2 = [oc_by_id2[id] for id in act_1_2]
wet_ratio_act_1_2 = [wet_ratio_by_id1[id] for id in act_1_2]
critical_ratio_act_1_2 = [critical_ratio_by_id1[id] for id in act_1_2]
diam_not_1_2 = [diam_by_id2[id] for id in not_1_2]
scrit_not_1_2 = [scrit_by_id2[id] for id in not_1_2]
oc_not_1_2 = [oc_by_id2[id] for id in not_1_2]
wet_ratio_not_1_2 = [wet_ratio_by_id1[id] for id in not_1_2]
critical_ratio_not_1_2 = [critical_ratio_by_id1[id] for id in not_1_2]
diam_act1_not_act2 = [diam_by_id2[id] for id in act1_not_act2]
scrit_act1_not_act2 = [scrit_by_id2[id] for id in act1_not_act2]
oc_act1_not_act2 = [oc_by_id2[id] for id in act1_not_act2]
wet_ratio_act1_not_act2 = [wet_ratio_by_id1[id] for id in act1_not_act2]
critical_ratio_act1_not_act2 = [
critical_ratio_by_id1[id] for id in act1_not_act2
]
diam_act2_not_act1 = [diam_by_id2[id] for id in act2_not_act1]
scrit_act2_not_act1 = [scrit_by_id2[id] for id in act2_not_act1]
oc_act2_not_act1 = [oc_by_id2[id] for id in act2_not_act1]
wet_ratio_act2_not_act1 = [wet_ratio_by_id1[id] for id in act2_not_act1]
critical_ratio_act2_not_act1 = [
critical_ratio_by_id1[id] for id in act2_not_act1
]
plt.figure()
plt.semilogy(seconds, d[0, :], 'b-', label='act1_2')
plt.semilogy(seconds, d[1, :], 'g-', label='not_1_2')
plt.semilogy(seconds, d[2, :], 'r-', label='act2_not_act1')
plt.semilogy(seconds, d[3, :], 'b-', label='act1_2')
plt.semilogy(seconds, d[4, :], 'g-', label='not_1_2')
plt.semilogy(seconds, d[5, :], 'r-', label='act2_not_act1')
plt.xlabel("time (s)")
plt.ylabel("diameter (m)")
plt.legend(loc='upper left')
fig = plt.gcf()
fig.savefig('diameters.pdf')
plt.figure()
plt.loglog(diam_act_1_2, scrit_act_1_2, 'bx')
plt.loglog(diam_not_1_2, scrit_not_1_2, 'gx')
plt.loglog(diam_act1_not_act2, scrit_act1_not_act2, 'kx')
plt.loglog(diam_act2_not_act1, scrit_act2_not_act1, 'rx')
plt.axhline(y=maximum_ss)
plt.xlabel("diameter (m)")
plt.ylabel("critical supersaturation (%)")
fig = plt.gcf()
fig.savefig(out_filename1)
plt.figure()
plt.semilogx(diam_act_1_2, wet_ratio_act_1_2, 'bx')
plt.semilogx(diam_not_1_2, wet_ratio_not_1_2, 'gx')
plt.semilogx(diam_act1_not_act2, wet_ratio_act1_not_act2, 'kx')
plt.semilogx(diam_act2_not_act1, wet_ratio_act2_not_act1, 'rx')
plt.xlabel("diameter (m)")
plt.ylabel("wet ratio")
fig = plt.gcf()
fig.savefig(out_filename2)
plt.figure()
plt.loglog(diam_act_1_2, critical_ratio_act_1_2, 'bx')
plt.loglog(diam_not_1_2, critical_ratio_not_1_2, 'gx')
plt.loglog(diam_act1_not_act2, critical_ratio_act1_not_act2, 'kx')
plt.loglog(diam_act2_not_act1, critical_ratio_act2_not_act1, 'rx')
plt.xlabel("diameter (m)")
plt.ylabel("critial ratio")
fig = plt.gcf()
fig.savefig(out_filename3)
def make_plot(netcdf_pattern, aging_ss, output_file_data, output_file_count):
global error_detect
netcdf_dir = "/Users/nriemer/subversion/partmc/trunk/local_scenarios/aging_comp/run_100K_60min/out"
time_filename_list = partmc.get_time_filename_list(netcdf_dir,
netcdf_pattern)
data_array = np.zeros([len(time_filename_list), 18])
count_array = np.zeros([len(time_filename_list), 18], dtype=int)
fresh_bc_ids_prev = set()
aged_bc_ids_prev = set()
all_bc_ids_prev = set()
all_ids_prev = set()
small_ids_prev = set()
comp_vol_prev = 0
for (i_counter, [time, filename, key]) in enumerate(time_filename_list):
print time, filename, key
ncf = scipy.io.netcdf.netcdf_file(filename, 'r')
particles = partmc.aero_particle_array_t(ncf)
removed_info = partmc.aero_removed_info_t(ncf)
env_state = partmc.env_state_t(ncf)
ncf.close()
dry_diameters = particles.dry_diameters()
s_crit = (particles.critical_rel_humids(env_state) - 1) * 100
comp_vol = particles.comp_vols[0]
bc = particles.masses(include=["BC"])
is_bc = (bc > 0)
total_number_bc = sum(1 / particles.comp_vols[is_bc])
is_fresh_bc = ((bc > 0) & (s_crit > aging_ss))
is_aged_bc = ((bc > 0) & (s_crit <= aging_ss))
is_small = (dry_diameters < 5e-8)
number_fresh_bc = sum(1 / particles.comp_vols[is_fresh_bc])
number_aged_bc = sum(1 / particles.comp_vols[is_aged_bc])
fresh_bc_ids = set(particles.ids[is_fresh_bc])
aged_bc_ids = set(particles.ids[is_aged_bc])
all_bc_ids = set(particles.ids[is_bc])
all_ids = set(particles.ids)
small_ids = set(particles.ids[is_small])
# calculate things here
n_f_emit_ids = fresh_bc_ids - all_ids_prev # get the emitted that are fresh
n_a_emit_ids = aged_bc_ids - all_ids_prev # get the emitted that are aged
n_f_dilute_ids = set()
n_f_f_cond_ids = set()
n_f_a_cond_ids = set()
n_f_f_coag_ids = set()
n_f_a_coag_ids = set()
n_f_coag_ids = set()
n_a_dilute_ids = set()
n_a_f_cond_ids = set()
n_a_a_cond_ids = set()
n_a_f_coag_ids = set()
n_a_a_coag_ids = set()
n_a_coag_ids = set()
other_ids_set = set(removed_info.other_ids)
for id_prev in all_bc_ids_prev: # where did everybody go?
coag_happened = False
id_current = id_prev
while id_current in removed_info.ids:
i = np.nonzero(removed_info.ids == id_current)[0][0]
action = removed_info.actions[i]
other_id = removed_info.other_ids[i]
if action == removed_info.AERO_INFO_DILUTION:
id_current = 0
if action == removed_info.AERO_INFO_HALVED:
id_current = 0
if action == removed_info.AERO_INFO_COAG:
id_current = other_id
coag_happened = True
if id_current in other_ids_set:
coag_happened = True
if id_current == 0:
if id_prev in fresh_bc_ids_prev:
n_f_dilute_ids.add(id_prev)
if id_prev in aged_bc_ids_prev:
n_a_dilute_ids.add(id_prev)
else:
if id_prev in fresh_bc_ids_prev:
if id_current in fresh_bc_ids:
if coag_happened:
n_f_f_coag_ids.add(id_prev)
else:
n_f_f_cond_ids.add(id_prev)
elif id_current in aged_bc_ids:
if coag_happened:
n_f_a_coag_ids.add(id_prev)
else:
n_f_a_cond_ids.add(id_prev)
else:
raise Exception("Current not fresh or aged")
if id_prev in aged_bc_ids_prev:
if id_current in fresh_bc_ids:
if coag_happened:
n_a_f_coag_ids.add(id_prev)
else:
n_a_f_cond_ids.add(id_prev)
elif id_current in aged_bc_ids:
if coag_happened:
n_a_a_coag_ids.add(id_prev)
else:
n_a_a_cond_ids.add(id_prev)
else:
raise Exception("Current not fresh or aged")
for id_current in all_bc_ids:
if id_current in all_ids_prev: # check if it wasn't emitted
if id_current in other_ids_set:
if id_current in fresh_bc_ids:
n_f_coag_ids.add(id_current)
if id_current in aged_bc_ids:
n_a_coag_ids.add(id_current)
n_f_emit = len(n_f_emit_ids)
n_f_dilute = len(n_f_dilute_ids)
n_f_f_cond = len(n_f_f_cond_ids)
n_f_a_cond = len(n_f_a_cond_ids)
n_f_f_coag = len(n_f_f_coag_ids)
n_f_a_coag = len(n_f_a_coag_ids)
n_f_coag = len(n_f_coag_ids)
n_f_a_cond_small = len(n_f_a_cond_ids & small_ids_prev)
n_f_a_coag_small = len(n_f_a_coag_ids & small_ids_prev)
n_a_emit = len(n_a_emit_ids)
n_a_dilute = len(n_a_dilute_ids)
n_a_f_cond = len(n_a_f_cond_ids)
n_a_a_cond = len(n_a_a_cond_ids)
n_a_f_coag = len(n_a_f_coag_ids)
n_a_a_coag = len(n_a_a_coag_ids)
n_a_coag = len(n_a_coag_ids)
n_f = len(fresh_bc_ids)
n_a = len(aged_bc_ids)
n_f_prev = len(fresh_bc_ids_prev)
n_a_prev = len(aged_bc_ids_prev)
n_f_small = len(fresh_bc_ids & small_ids)
n_aging = n_f_a_cond + n_f_a_coag
n_aging_small = n_f_a_cond_small + n_f_a_coag_small
data_array[i_counter, 0] = time
data_array[i_counter, 1] = total_number_bc
data_array[i_counter, 2] = number_fresh_bc
data_array[i_counter, 3] = number_aged_bc
data_array[i_counter, 4] = n_f_emit / comp_vol
data_array[i_counter, 5] = n_a_emit / comp_vol
if (comp_vol_prev != 0):
data_array[i_counter, 6] = n_f_dilute / comp_vol_prev
data_array[i_counter, 7] = n_f_f_cond / comp_vol
data_array[i_counter, 8] = n_f_a_cond / comp_vol
data_array[i_counter, 9] = n_f_f_coag / comp_vol
data_array[i_counter, 10] = n_f_a_coag / comp_vol
data_array[i_counter, 11] = n_f_coag / comp_vol
if (comp_vol_prev != 0):
data_array[i_counter, 12] = n_a_dilute / comp_vol_prev
data_array[i_counter, 13] = n_a_f_cond / comp_vol
data_array[i_counter, 14] = n_a_a_cond / comp_vol
data_array[i_counter, 15] = n_a_f_coag / comp_vol
data_array[i_counter, 16] = n_a_a_coag / comp_vol
data_array[i_counter, 17] = n_a_coag / comp_vol
count_array[i_counter, 0] = time
count_array[i_counter, 1] = len(all_bc_ids)
count_array[i_counter, 2] = n_f #len(fresh_bc_ids)
count_array[i_counter, 3] = n_a #len(aged_bc_ids)
count_array[i_counter, 4] = n_f_emit #len(n_f_emit_ids)
count_array[i_counter, 5] = n_a_emit #len(n_a_emit_ids)
count_array[i_counter, 6] = n_f_dilute #len(n_f_dilute_ids)
count_array[i_counter, 7] = n_f_f_cond #len(n_f_f_cond_ids)
count_array[i_counter, 8] = n_f_a_cond #len(n_f_a_cond_ids)
count_array[i_counter, 9] = n_f_f_coag #len(n_f_f_coag_ids)
count_array[i_counter, 10] = n_f_a_coag #len(n_f_a_coag_ids)
count_array[i_counter, 11] = n_f_coag #len(n_f_coag_ids)
count_array[i_counter, 12] = n_a_dilute #len(n_a_dilute_ids)
count_array[i_counter, 13] = n_a_f_cond #len(n_a_f_cond_ids)
count_array[i_counter, 14] = n_a_a_cond #len(n_a_a_cond_ids)
count_array[i_counter, 15] = n_a_f_coag #len(n_a_f_coag_ids)
count_array[i_counter, 16] = n_a_a_coag #len(n_a_a_coag_ids)
count_array[i_counter, 17] = n_a_coag #len(n_a_coag_ids)
# The following is checking code #
lhs_f = n_f - n_f_prev
rhs_f = n_f_emit - n_f_dilute + n_a_f_cond + n_f_coag - n_f_f_coag - n_f_a_cond - n_f_a_coag
lhs_a = n_a - n_a_prev
rhs_a = n_a_emit - n_a_dilute + n_f_a_cond + n_a_coag - n_a_a_coag - n_a_f_cond - n_a_f_coag
loss_f = n_f_dilute + n_f_f_coag + n_f_a_coag + n_f_f_cond + n_f_a_cond
loss_f_ids = n_f_dilute_ids | n_f_f_coag_ids | n_f_a_coag_ids | n_f_f_cond_ids | n_f_a_cond_ids
gain_f = n_f_emit + n_f_coag + n_f_f_cond + n_a_f_cond
gain_f_ids = n_f_emit_ids | n_f_coag_ids | n_f_f_cond_ids | n_a_f_cond_ids
loss_a = n_a_dilute + n_a_f_coag + n_a_a_coag + n_a_f_cond + n_a_a_cond
loss_a_ids = n_a_dilute_ids | n_a_f_coag_ids | n_a_a_coag_ids | n_a_f_cond_ids | n_a_a_cond_ids
gain_a = n_a_emit + n_a_coag + n_f_a_cond + n_a_a_cond
gain_a_ids = n_a_emit_ids | n_a_coag_ids | n_f_a_cond_ids | n_a_a_cond_ids
coag_loss_f = n_f_f_coag + n_a_f_coag
coag_loss_f_ids = n_f_f_coag_ids | n_a_f_coag_ids
coag_loss_a = n_a_a_coag + n_f_a_coag
coag_loss_a_ids = n_a_a_coag_ids | n_f_a_coag_ids
print "budget f %8d %8d %8d" % (lhs_f, rhs_f, lhs_f - rhs_f)
if (lhs_f - rhs_f) != 0: set_error()
print "budget a %8d %8d %8d" % (lhs_a, rhs_a, lhs_a - rhs_a)
if (lhs_a - rhs_a) != 0: set_error()
print "loss f %8d %8d %8d" % (loss_f, n_f_prev, loss_f - n_f_prev)
if (loss_f - n_f_prev) != 0: set_error()
print "gain f %8d %8d %8d" % (gain_f, n_f, gain_f - n_f)
if (gain_f - n_f) != 0: set_error()
print "loss a %8d %8d %8d" % (loss_a, n_a_prev, loss_a - n_a_prev)
if (loss_a - n_a_prev) != 0: set_error()
print "gain a %8d %8d %8d" % (gain_a, n_a, gain_a - n_a)
if (gain_a - n_a) != 0: set_error()
# We don't check equation (12) in Riemer et al., 2010, Aerosol Sci. because it can
# happen that we emit a BC particle and it coagulates before we detect it.
print "loss f ids ", loss_f_ids - fresh_bc_ids_prev, fresh_bc_ids_prev - loss_f_ids
if (len(loss_f_ids - fresh_bc_ids_prev)) != 0: set_error()
if (len(fresh_bc_ids_prev - loss_f_ids)) != 0: set_error()
print "gain f ids ", gain_f_ids - fresh_bc_ids, fresh_bc_ids - gain_f_ids
if (len(gain_f_ids - fresh_bc_ids)) != 0: set_error()
if (len(fresh_bc_ids - gain_f_ids)) != 0: set_error()
print "loss a ids ", loss_a_ids - aged_bc_ids_prev, aged_bc_ids_prev - loss_a_ids
if (len(loss_a_ids - aged_bc_ids_prev)) != 0: set_error()
if (len(aged_bc_ids_prev - loss_a_ids)) != 0: set_error()
print "gain a ids ", gain_a_ids - aged_bc_ids, aged_bc_ids - gain_a_ids
if (len(gain_a_ids - aged_bc_ids)) != 0: set_error()
if (len(aged_bc_ids - gain_a_ids)) != 0: set_error()
print "gain f intersection ", n_f_emit_ids & n_f_coag_ids, n_f_emit_ids & n_f_f_cond_ids, \
n_f_emit_ids & n_a_f_cond_ids, n_f_coag_ids & n_f_f_cond_ids, \
n_f_coag_ids & n_a_f_cond_ids, n_f_f_cond_ids & n_a_f_cond_ids
if (len(n_f_emit_ids & n_f_coag_ids) !=0 or len(n_f_emit_ids & n_f_f_cond_ids) !=0 or \
len(n_f_emit_ids & n_a_f_cond_ids) !=0 or len(n_f_coag_ids & n_f_f_cond_ids) !=0 or \
len(n_f_coag_ids & n_a_f_cond_ids) !=0 or len( n_f_f_cond_ids & n_a_f_cond_ids)):
set_error()
print "gain a intersection ", n_a_emit_ids & n_a_coag_ids, n_a_emit_ids & n_f_a_cond_ids, \
n_a_emit_ids & n_a_a_cond_ids, n_a_coag_ids & n_f_a_cond_ids, n_a_coag_ids & n_a_a_cond_ids, \
n_f_a_cond_ids & n_a_a_cond_ids
if (len(n_a_emit_ids & n_a_coag_ids) !=0 or len(n_a_emit_ids & n_f_a_cond_ids) !=0 or \
len(n_a_emit_ids & n_a_a_cond_ids) !=0 or len(n_a_coag_ids & n_f_a_cond_ids) !=0 or len(n_a_coag_ids & n_a_a_cond_ids) !=0 or \
len(n_f_a_cond_ids & n_a_a_cond_ids)!=0):
set_error()
print "loss f intersection ", n_f_dilute_ids & n_f_f_coag_ids, n_f_dilute_ids & n_f_a_coag_ids, \
n_f_dilute_ids & n_f_f_cond_ids, n_f_dilute_ids & n_f_a_cond_ids, \
n_f_f_coag_ids & n_f_a_coag_ids, n_f_f_coag_ids & n_f_f_cond_ids, n_f_f_coag_ids & n_f_a_cond_ids, \
n_f_a_coag_ids & n_f_f_cond_ids, n_f_a_coag_ids & n_f_a_cond_ids, \
n_f_f_cond_ids & n_f_a_cond_ids
if (len(n_f_dilute_ids & n_f_f_coag_ids) !=0 or len(n_f_dilute_ids & n_f_a_coag_ids) !=0 or \
len(n_f_dilute_ids & n_f_f_cond_ids) !=0 or len(n_f_dilute_ids & n_f_a_cond_ids) !=0 or \
len(n_f_f_coag_ids & n_f_a_coag_ids) !=0 or len(n_f_f_coag_ids & n_f_f_cond_ids) !=0 or len(n_f_f_coag_ids & n_f_a_cond_ids) !=0 or \
len(n_f_a_coag_ids & n_f_f_cond_ids) !=0 or len(n_f_a_coag_ids & n_f_a_cond_ids) !=0 or \
len(n_f_f_cond_ids & n_f_a_cond_ids) !=0):
set_error()
print "loss a intersection ", n_a_dilute_ids & n_a_f_coag_ids, n_a_dilute_ids & n_a_a_coag_ids, \
n_a_dilute_ids & n_a_f_cond_ids, n_a_dilute_ids & n_a_a_cond_ids, \
n_a_f_coag_ids & n_a_a_coag_ids, n_a_f_coag_ids & n_a_f_cond_ids, n_a_f_coag_ids & n_a_a_cond_ids, \
n_a_a_coag_ids & n_a_f_cond_ids, n_a_a_coag_ids & n_a_a_cond_ids, \
n_a_f_cond_ids & n_a_a_cond_ids
if (len(n_a_dilute_ids & n_a_f_coag_ids) !=0 or len(n_a_dilute_ids & n_a_a_coag_ids) !=0 or \
len(n_a_dilute_ids & n_a_f_cond_ids) !=0 or len(n_a_dilute_ids & n_a_a_cond_ids) !=0 or \
len(n_a_f_coag_ids & n_a_a_coag_ids) !=0 or len(n_a_f_coag_ids & n_a_f_cond_ids) !=0 or len(n_a_f_coag_ids & n_a_a_cond_ids) !=0 or \
len(n_a_a_coag_ids & n_a_f_cond_ids) !=0 or len(n_a_a_coag_ids & n_a_a_cond_ids) !=0 or \
len(n_a_f_cond_ids & n_a_a_cond_ids) !=0):
set_error()
print "gain f/ gain a intersection ", gain_f_ids & gain_a_ids
if (len(gain_f_ids & gain_a_ids) != 0): set_error()
print "loss f/ loss a intersection ", loss_f_ids & loss_a_ids
if (len(loss_f_ids & loss_a_ids) != 0): set_error()
fresh_bc_ids_prev = fresh_bc_ids
aged_bc_ids_prev = aged_bc_ids
all_bc_ids_prev = all_bc_ids
all_ids_prev = all_ids
small_ids_prev = small_ids
comp_vol_prev = comp_vol
# Checking code ends #
np.savetxt(output_file_data, data_array)
np.savetxt(output_file_count, count_array, fmt='%d')