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 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
masses = particles.masses() * 1e9 # kg to ug
value = partmc.histogram_1d(dry_diameters,
x_axis,
weights=masses / particles.comp_vols)
value /= 1e6 # m^{-3} to cm^{-3}
if total_value is None:
total_value = value
else:
total_value += value
total_value /= len(in_filename_list)
np.savetxt(out_filename, total_value)
mask = np.ma.make_mask(total_value <= 0.0)
masked_total_value = np.ma.array(total_value, mask=mask)
return (masked_total_value.min(), masked_total_value.max())
def make_plot(in_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_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_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_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 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_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):
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 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_volume = particles.volumes(include=["BC"])
bc = particles.masses(include=["BC"])
dry_mass = particles.masses(exclude=["H2O"])
bc_frac = bc / dry_mass
coat_frac = 1 - bc / dry_mass
is_bc = (bc_frac > 0.05)
dry_diameters = particles.dry_diameters()
core_diameters = (6 / math.pi * bc_volume)**(1. / 3.)
coating_thickness = (dry_diameters - core_diameters) / 2.
ratio = coating_thickness / dry_diameters
print ratio.max()
x_axis = partmc.linear_grid(min=0, max=ratio.max(), n_bin=50)
hist1d = partmc.histogram_1d(coating_thickness[is_bc] /
dry_diameters[is_bc],
x_axis,
weights=1 / particles.comp_vols[is_bc])
print hist1d
plt.clf()
a = plt.gca()
a.set_xscale("linear")
a.set_yscale("log")
plt.plot(x_axis.centers(), hist1d)
plt.axis([x_axis.min, x_axis.max, 1e8, 1e12])
plt.grid(True)
plt.xlabel("coating thickness / dry diameter")
plt.ylabel("number concentration (m^{-3})")
plt.title(title)
fig = plt.gcf()
fig.savefig(out_filename)
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
i_counter += 1
np.savetxt("data/banana_so4_dist.txt", dist_array)
np.savetxt("data/banana_diam.txt", diam_axis_edges)
np.savetxt("data/banana_times.txt", times)
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)
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
np.savetxt("data/banana_size_dist_wc_wei-0_lowbg2.txt", size_dist_array)
np.savetxt("data/banana_diam_wc_wei-0_lowbg2.txt", diam_axis_edges)
np.savetxt("data/banana_times_wc_wei-0_lowbg2.txt", times)
for i_loop in range (0, i_loop_max):
netcdf_pattern = "brownian_part_0%03d_(.*).nc" % (i_loop+1)
print netcdf_pattern
time_filename_list = partmc.get_time_filename_list(config.netcdf_dir, netcdf_pattern)
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()
wet_diameters = particles.diameters()
hist = partmc.histogram_1d(wet_diameters, x_axis, weights = 1 / particles.comp_vols)
# total_number = sum(1/particles.comp_vols)
# total_mass = sum(particles.masses()/particles.comp_vols)
time_array[i_counter]= time / 3600.
array_num[i_counter,i_loop,:]= hist
hist = partmc.histogram_1d(wet_diameters, x_axis, weights = particles.masses() / particles.comp_vols)
array_mass[i_counter,i_loop,:]= hist
i_counter += 1
print "array_num ", array_num.shape
for i_ensemble in range(0, i_loop_max):
print "i_ensemble ", i_ensemble
num_avg[:,i_ensemble,:] = np.average(array_num[:,:i_ensemble+1,:], axis = 1)
