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)