|
| 1 | +""" |
| 2 | +Plots size distributions from updraft and downdraft |
| 3 | + from above cloud base and from below cloud base |
| 4 | +""" |
| 5 | +import numpy as np |
| 6 | +import h5py as h5 |
| 7 | +import Gnuplot as gp |
| 8 | +import math as mt |
| 9 | + |
| 10 | +# all the collision kernels |
| 11 | +kernels = {"out_hall_pinsky_stratocumulus"} |
| 12 | + |
| 13 | +ub_cover = {"out_hall_pinsky_stratocumulus" : 0} |
| 14 | +ua_cover = {"out_hall_pinsky_stratocumulus" : 0} |
| 15 | +db_cover = {"out_hall_pinsky_stratocumulus" : 0} |
| 16 | +da_cover = {"out_hall_pinsky_stratocumulus" : 0} |
| 17 | + |
| 18 | +# left and right edges of bins for dry and wet radius |
| 19 | +dry_edges = np.fromiter( (1e-6 * 10**(-3 + i * .1) for i in xrange(41)), dtype="float") |
| 20 | +wet_edges = np.fromiter( (1e-6 * 10**(-3 + i * .1) for i in xrange(56)), dtype="float") |
| 21 | +num_dry = dry_edges.size - 1 |
| 22 | +num_wet = wet_edges.size - 1 |
| 23 | + |
| 24 | +for kernel in kernels: |
| 25 | + |
| 26 | + # helper arrays for storing initial and cutoff dry radii as well as final and cutoff wet radii |
| 27 | + n_dry_ub = np.zeros(num_dry) |
| 28 | + n_dry_ua = np.zeros(num_dry) |
| 29 | + n_dry_db = np.zeros(num_dry) |
| 30 | + n_dry_da = np.zeros(num_dry) |
| 31 | + |
| 32 | + n_wet_ub = np.zeros(num_wet) |
| 33 | + n_wet_ua = np.zeros(num_wet) |
| 34 | + n_wet_db = np.zeros(num_wet) |
| 35 | + n_wet_da = np.zeros(num_wet) |
| 36 | + |
| 37 | + for run in {"seed_44"}: |
| 38 | + # open hdf5 files with data |
| 39 | + h5f_cst = h5.File('data/case_base/' + kernel + '/const.h5', 'r') |
| 40 | + h5f_ini = h5.File('data/case_base/' + kernel + '/timestep0000000000.h5', 'r') |
| 41 | + h5f_fin = h5.File('data/case_base/' + kernel + '/timestep0000011800.h5', 'r') |
| 42 | + |
| 43 | + x_grid = h5f_cst['X'][:-1][:-1] |
| 44 | + y_grid = h5f_cst['Y'][:-1][:-1] |
| 45 | + |
| 46 | + idx_ub = np.where((x_grid > 1 ) & (x_grid < 37) & (y_grid > 15) & (y_grid < 45)) |
| 47 | + idx_ua = np.where((x_grid > 1 ) & (x_grid < 37) & (y_grid > 45) & (y_grid < 70)) |
| 48 | + idx_db = np.where((x_grid > 39) & (x_grid < 76) & (y_grid > 15) & (y_grid < 45)) |
| 49 | + idx_da = np.where((x_grid > 39) & (x_grid < 76) & (y_grid > 45) & (y_grid < 70)) |
| 50 | + |
| 51 | + # count dry cloudy and rainy grid cells |
| 52 | + ub_cover[kernel] += idx_ub[0].size |
| 53 | + ua_cover[kernel] += idx_ua[0].size |
| 54 | + db_cover[kernel] += idx_db[0].size |
| 55 | + da_cover[kernel] += idx_da[0].size |
| 56 | + |
| 57 | + for i in range(num_dry-1): # first bin id for total conc |
| 58 | + name = "rd_rng" + str(i+1).zfill(3) + "_mom0" |
| 59 | + tmp_ini = 1e-6 * h5f_ini[name][:] |
| 60 | + tmp_fin = 1e-6 * h5f_fin[name][:] |
| 61 | + |
| 62 | + n_dry_ub[i] += tmp_fin[idx_ub].sum() / idx_ub[0].size / (mt.log(dry_edges[i+1], 10) - mt.log(dry_edges[i], 10)) |
| 63 | + n_dry_ua[i] += tmp_fin[idx_ua].sum() / idx_ua[0].size / (mt.log(dry_edges[i+1], 10) - mt.log(dry_edges[i], 10)) |
| 64 | + n_dry_db[i] += tmp_fin[idx_db].sum() / idx_db[0].size / (mt.log(dry_edges[i+1], 10) - mt.log(dry_edges[i], 10)) |
| 65 | + n_dry_da[i] += tmp_fin[idx_da].sum() / idx_da[0].size / (mt.log(dry_edges[i+1], 10) - mt.log(dry_edges[i], 10)) |
| 66 | + |
| 67 | + for i in range(num_wet-2): # first two bins are for total cloud and rain water conc |
| 68 | + name = "rw_rng" + str(i+2).zfill(3) + "_mom0" |
| 69 | + tmp_fin = 1e-6 * h5f_fin[name][:] |
| 70 | + |
| 71 | + n_wet_ub[i] += tmp_fin[idx_ub].sum() / idx_ub[0].size / (mt.log(wet_edges[i+1], 10) - mt.log(wet_edges[i], 10)) |
| 72 | + n_wet_ua[i] += tmp_fin[idx_ua].sum() / idx_ua[0].size / (mt.log(wet_edges[i+1], 10) - mt.log(wet_edges[i], 10)) |
| 73 | + n_wet_db[i] += tmp_fin[idx_db].sum() / idx_db[0].size / (mt.log(wet_edges[i+1], 10) - mt.log(wet_edges[i], 10)) |
| 74 | + n_wet_da[i] += tmp_fin[idx_da].sum() / idx_da[0].size / (mt.log(wet_edges[i+1], 10) - mt.log(wet_edges[i], 10)) |
| 75 | + |
| 76 | + # close hdf5 files |
| 77 | + h5f_ini.close() |
| 78 | + h5f_fin.close() |
| 79 | + |
| 80 | + ub_cover[kernel] = ub_cover[kernel] / 76. / 76 * 100 |
| 81 | + ua_cover[kernel] = ua_cover[kernel] / 76. / 76 * 100 |
| 82 | + db_cover[kernel] = db_cover[kernel] / 76. / 76 * 100 |
| 83 | + da_cover[kernel] = da_cover[kernel] / 76. / 76 * 100 |
| 84 | + |
| 85 | + print kernel, " ", ub_cover[kernel] |
| 86 | + print kernel, " ", ua_cover[kernel] |
| 87 | + print kernel, " ", db_cover[kernel] |
| 88 | + print kernel, " ", da_cover[kernel] |
| 89 | + |
| 90 | + # plotting |
| 91 | + ymin = 0.001 |
| 92 | + ymax = 1000 |
| 93 | + xmin = 0.001 |
| 94 | + xmax = 5 |
| 95 | + |
| 96 | + g = gp.Gnuplot() |
| 97 | + g('reset') |
| 98 | + g('set term svg dynamic enhanced font "Verdana, 14"') |
| 99 | + g('set output "plots/' + kernel + '_aerosol_ud.svg" ') |
| 100 | + g('set logscale xy') |
| 101 | + g('set key samplen 1.2') |
| 102 | + g('set xtics rotate by 65 right (.01, .1, 1, 10, 100)') |
| 103 | + g('set ytics (.001, .01, .1, 1, 10, 100, 1000)') |
| 104 | + g('set xlabel "particle radius [um]"') |
| 105 | + g('set ylabel "dN/dlog_{10}(r) [mg^{-1} per log_{10}(size interval)]"') |
| 106 | + g('set xrange [' + str(xmin) + ':' + str(xmax) + ']') |
| 107 | + g('set yrange [' + str(ymin) + ':' + str(ymax) + ']') |
| 108 | + g('set grid') |
| 109 | + g('set nokey') |
| 110 | + |
| 111 | + plot_rd_ub = gp.PlotItems.Data(dry_edges[:-1] * 1e6 , n_dry_ub, with_="steps lw 4 lc rgb 'black'") |
| 112 | + plot_rd_ua = gp.PlotItems.Data(dry_edges[:-1] * 1e6 , n_dry_ua, with_="steps lw 4 lc rgb 'green'") |
| 113 | + plot_rd_db = gp.PlotItems.Data(dry_edges[:-1] * 1e6 , n_dry_db, with_="steps lw 4 lc rgb 'red'") |
| 114 | + plot_rd_da = gp.PlotItems.Data(dry_edges[:-1] * 1e6 , n_dry_da, with_="steps lw 4 lc rgb 'blue'") |
| 115 | + |
| 116 | + g.plot(plot_rd_ub, plot_rd_ua, plot_rd_db, plot_rd_da) |
| 117 | + |
| 118 | + # plotting |
| 119 | + ymin = 0.001 |
| 120 | + ymax = 1000 |
| 121 | + xmin = 0.001 |
| 122 | + xmax = 500 |
| 123 | + |
| 124 | + g = gp.Gnuplot() |
| 125 | + g('reset') |
| 126 | + g('set term svg dynamic enhanced font "Verdana, 14"') |
| 127 | + g('set output "plots/' + kernel + '_rain_ud.svg" ') |
| 128 | + g('set logscale xy') |
| 129 | + g('set key samplen 1.2') |
| 130 | + g('set xtics rotate by 65 right (.01, .1, 1, 10, 100)') |
| 131 | + g('set ytics (.001, .01, .1, 1, 10, 100, 1000)') |
| 132 | + g('set xlabel "particle radius [um]"') |
| 133 | + g('set ylabel "dN/dlog_{10}(r) [mg^{-1} per log_{10}(size interval)]"') |
| 134 | + g('set xrange [' + str(xmin) + ':' + str(xmax) + ']') |
| 135 | + g('set yrange [' + str(ymin) + ':' + str(ymax) + ']') |
| 136 | + g('set grid') |
| 137 | + g('set nokey') |
| 138 | + |
| 139 | + plot_rw_ub = gp.PlotItems.Data(wet_edges[:-1] * 1e6 , n_wet_ub, with_="steps lw 4 lc rgb 'black'") |
| 140 | + plot_rw_ua = gp.PlotItems.Data(wet_edges[:-1] * 1e6 , n_wet_ua, with_="steps lw 4 lc rgb 'green'") |
| 141 | + plot_rw_db = gp.PlotItems.Data(wet_edges[:-1] * 1e6 , n_wet_db, with_="steps lw 4 lc rgb 'red'") |
| 142 | + plot_rw_da = gp.PlotItems.Data(wet_edges[:-1] * 1e6 , n_wet_da, with_="steps lw 4 lc rgb 'blue'") |
| 143 | + |
| 144 | + g.plot(plot_rw_ub, plot_rw_ua, plot_rw_db, plot_rw_da) |
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