def plot_f_R_g_R_g_ln_R_lognorm(mu_log, sigma_log, DNC, LWC_tot, R, m, xlim_m,
xlim_R, figsize, figname):
# f_m0 = compute_f_m0(m, DNC, LWC)
f_R0 = compute_f_R_lognormal_multi_modal(R, mu_log, sigma_log, DNC)
# f_m0 =
g_R0 = f_R0 * m
g_ln_R = g_R0 * R
#
# DNC_tot = DNC.sum()
f_R_max = f_R0.max()
g_R_max = g_R0.max()
g_lnR_max = g_ln_R.max()
# scale_f = 1E-9
# scale_gR = 1E9
fig, ax = plt.subplots(figsize=(cm2inch(figsize)), tight_layout=True)
# ax.plot(m, f_m0 * scale_f)
# ax.plot(R, f_R0 / DNC_tot, c = "k")
# ax.plot(R, g_R0 / LWC_tot, "--", c = "0.3")
ax.plot(R, f_R0 / f_R_max, c="k", label="$f_R$")
ax.plot(R, g_R0 / g_R_max, "--", c="0.3", label="$g_R$")
# ax.plot(m, g_ln_R)
# ax.set_xticks(np.arange(-10,41,10))
ax.set_xlim(xlim_m)
# ax.set_ylim((1E-6,1E12))
# ax.set_ylim((3,1E2))
# ax.set_xlabel("$T$ (K)")
# ax.set_xscale("log")
# ax.set_yscale("log")
# ax.set_xlabel("Droplet mass (kg)")
# ax.set_ylabel("$f_m$ ($\mathrm{kg^{-1}\,m^{-3}}$)")
ax.set_ylabel("Distributions (relative units)")
# ax_ = ax.twiny()
ax_ = ax
# ax2 = ax_.twiny()
# ax_.plot(R, g_ln_R / LWC_tot, ":", c = "0.3")
ax_.plot(R, g_ln_R / g_lnR_max, ":", c="0.3", label="$g_{\ln R}$")
# ax_.plot(R, g_ln_R*scale_gR)
ax_.set_xscale("log")
ax_.set_yscale("log")
ax_.set_xlim(xlim_R)
ax_.set_xlabel(r"Droplet radius ($\si{\micro\meter}$)")
ax_.legend()
# ax_ = ax.twinx()
# ax2 = ax_.twiny()
# ax2.plot(R, g_ln_R*scale_gR)
# ax2.set_xscale("log")
# ax2.set_yscale("log")
# ax.grid(which = "both", linestyle="--", linewidth=0.5, c = "0.5", zorder = 0)
fig.savefig(
figname,
# bbox_inches = 0,
bbox_inches='tight',
pad_inches=0.065)
def plot_f_m_g_m_g_ln_R(DNC, LWC, m, R, xlim_m, xlim_R, figsize, figname):
f_m0 = compute_f_m0(m, DNC, LWC)
g_m0 = f_m0 * m
g_ln_R = 3. * f_m0 * m * m
f_m_max = f_m0.max()
g_m_max = g_m0.max()
g_R_max = g_ln_R.max()
scale_f = 1E-9
scale_gR = 1E9
fig, ax = plt.subplots(figsize=(cm2inch(figsize)), tight_layout=True)
# ax.plot(m, f_m0 * scale_f)
# ax.plot(m, f_m0 / DNC, c = "k")
# ax.plot(m, g_m0 / LWC, "--", c = "0.3")
ax.plot(m, f_m0 / f_m_max, c="k", label="$f_m$")
ax.plot(m, g_m0 / g_m_max, "--", c="0.3", label="$g_m$")
# ax.plot(m, g_ln_R)
# ax.set_xticks(np.arange(-10,41,10))
ax.set_xlim(xlim_m)
# ax.set_ylim((1E-6,1E12))
# ax.set_ylim((3,1E2))
# ax.set_xlabel("$T$ (K)")
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel("Droplet mass (kg)")
# ax.set_ylabel("$f_m$ ($\mathrm{kg^{-1}\,m^{-3}}$)")
ax.set_ylabel("Distributions (relative units)")
ax_ = ax.twiny()
# ax2 = ax_.twiny()
# ax_.plot(R, g_ln_R / LWC, ":", c = "0.3")
ax_.plot(R, g_ln_R / g_R_max, ":", c="0.3", label="$g_{\ln R}$")
# ax_.plot(R, g_ln_R*scale_gR)
ax_.set_xscale("log")
ax_.set_xlim(xlim_R)
ax_.set_xlabel(r"Droplet radius ($\si{\micro\meter}$)")
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax_.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc="lower center")
# ax.legend()
# ax_ = ax.twinx()
# ax2 = ax_.twiny()
# ax2.plot(R, g_ln_R*scale_gR)
# ax2.set_xscale("log")
# ax2.set_yscale("log")
# ax.grid(which = "both", linestyle="--", linewidth=0.5, c = "0.5", zorder = 0)
fig.savefig(
figname,
# bbox_inches = 0,
bbox_inches='tight',
pad_inches=0.065)
def plot_phase_diagram(T_vl, e_vl, T_vi, e_vi, T_sl, e_sl, figname, figsize):
fig, ax = plt.subplots(figsize=(cm2inch(figsize)), tight_layout=True)
T_shift = -273.15
ax.plot(T_vl + T_shift, e_vl, c="k")
ax.plot(T_vi + T_shift, e_vi, "--", c="k")
ax.plot(T_sl + T_shift, e_sl, "-.", c="k")
# ax.annotate("triple point", (T_vi[-1] + T_shift, e_vi[-1]-0.5),
## ha='left',
# ha='center',
# va='top',
# xycoords='data',
# arrowprops=dict(facecolor='black',
# width = 0.5,
# headwidth = 2,
# shrink=0.05
# ),
# textcoords='data',
# xytext=(T_vi[-1] + T_shift, 2)
# )
ax.annotate("vapor", (20, 6), ha='center')
ax.annotate("solid", (-5, 15), ha='center')
ax.annotate("liquid", (12, 30), ha='center')
ax.annotate(r"$e_s(T)$", (22, 16), ha='center')
# ax.annotate("vapor")
ax.set_yscale("log")
ax.set_xticks(np.arange(-10, 41, 10))
ax.set_xlim((-10, 40))
ax.set_ylim((3, 1E2))
# ax.set_xlabel("$T$ (K)")
ax.set_xlabel("$T$ (°C)")
ax.set_ylabel("$p$ (hPa)")
# ax.grid(which = "both", linestyle="--", linewidth=0.5, c = "0.5", zorder = 0)
fig.savefig(
figname,
# bbox_inches = 0,
bbox_inches='tight',
pad_inches=0.065)
kernel_list = ['Hall_Bott', 'Ecol_const', 'Long_Bott'] #%% SET SIMULATION PARAS # choose simulation series (index of the lists/tuples above) SIM_N = 2 no_sims = 50 t_grid = 0 t_start = 7200 t_end = 10800 dt = 1. # advection time step (seconds) # 'with_collisions', 'wo_collisions' or 'spin_up' # simulation_mode = 'with_collisions' #%% SET PLOTTING PARAMETERS figsize_scalar_fields = cm2inch(13.7, 22.) figsize_spectra = cm2inch(13.3, 20.) figsize_tg_cells = cm2inch(6.6, 7) ### GRID FRAMES show_target_cells = True ### SPECTRA # if set to "None" the values are loaded from stored files no_rows = None no_cols = None # if set to "None" the values are loaded from stored files no_bins_R_p = None no_bins_R_s = None
def plot_rho_T_p_init(centers, corners, no_cells, density, temperature,
pressure, potential_temperature, saturation,
mixing_ratio_water_liquid, mixing_ratio_water_vapor,
no_ticks, figsize, figname):
rho0 = density[0, 0]
T0 = temperature[0, 0]
p0 = pressure[0, 0]
Theta0 = potential_temperature[0, 0]
r_v0 = np.average(mixing_ratio_water_vapor[:, 0])
r_l0 = np.average(mixing_ratio_water_liquid[:, -1])
# S0 = np.average( saturation[:,0] )
S0 = 1.
# r_l0 = r_l[0,0]
# S0 = r_l[0,0]
rho = density[0, :]
T = temperature[0, :]
p = pressure[0, :]
Theta = potential_temperature[0, :]
r_v = np.average(mixing_ratio_water_vapor, axis=0)
r_l = np.average(mixing_ratio_water_liquid, axis=0)
S = np.average(saturation, axis=0)
data0 = np.array((rho0, r_v0, T0, p0, Theta0))
data = np.array((rho, r_v, T, p, Theta))
names = [r"$\rho_\mathrm{dry}$", r"$r_v$", r"$T$", r"$p$", r"$\Theta$"]
ls = ["-", "--", ":", "-.", (0, (3, 1, 1, 1, 1, 1))]
z = centers[1][0, :]
fig, ax = plt.subplots(figsize=(cm2inch(figsize)), tight_layout=True)
# ax.plot(rho,z, c = "0.0")
# ax.plot(p,z, c = "0.2", linestyle = "dashed")
# ax.plot(T,z, c = "0.4", linestyle = "dashdot")
for n in range(len(data0)):
# ax.plot(data[n]/data0[n], z, label = "$r$")
ax.plot(data[n] / data0[n], z, label=names[n], c="k", linestyle=ls[n])
# ax.set_xlabel("Hallo")
ax2 = ax.twiny()
ax2.plot(r_l / r_l0, z, label="$r_l$", c="0.6")
ax2.plot(S, z, label="$S$", c="0.6", linestyle="dashdot")
# ax.set_xticks(np.linspace(corners[0][0,0], corners[0][-1,0], no_ticks[0]) )
ax.set_yticks(np.linspace(corners[1][0, 0], corners[1][0, -1],
no_ticks[1]))
# ax.set_xticks(corners[0][::tick_every_x,0])
# ax.set_yticks(corners[1][0,::tick_every_y])
# ax.set_xticks(corners[0][:,0], minor = True)
# ax.set_yticks(corners[1][0,:], minor = True)
ax.set_xlim(0.82, 1.03)
ax.set_ylim(corners[1][0, 0], corners[1][0, -1])
ax.set_xlabel(r'$\rho_\mathrm{dry}$, $r_v$, $T$, $p$, $\Theta$' +
' (relative values)')
# ax.set_xlabel('Atmospheric state variables (-)')
# ax.set_xlabel("$r$ (-)")
# ax.set_xlabel('$S$ (m)')
# ax.set_ylabel('Hallo (m)')
ax.set_ylabel('$z$ (m)')
ax.grid(linestyle="dashed", c="0.6")
ax.legend(loc='lower left', bbox_to_anchor=(0.06, -0.01))
ax2.set_xlabel("$S$ (-) and $r_l/r_{l,\mathrm{max}}$ (-)")
ax2.tick_params(axis='x', colors='0.5')
ax2.legend(loc='lower left', bbox_to_anchor=(0.34, -0.01))
# ax2.legend()
for n, d_ in enumerate(data0):
print(names[n], f"{d_:.4e}")
print(f"{r_l0*1000:.4}")
# fig.tight_layout()
fig.savefig(
figname,
# bbox_inches = 0,
bbox_inches='tight',
pad_inches=0.05)
def plot_velocity_field_centered(velocity, corners, centers, shifts, no_cells,
no_arrows, no_ticks, figsize, figname,
ARROW_SCALE, ARROW_WIDTH):
centered_u_field = ( velocity[0][0:-1,0:-1]\
+ velocity[0][1:,0:-1] ) * 0.5
centered_w_field = ( velocity[1][0:-1,0:-1]\
+ velocity[1][0:-1,1:] ) * 0.5
# no_cells = np.array((len(centered_u_field), len(centered_w_field)))
if no_ticks[0] < no_cells[0]:
# take no_major_xticks - 1 to get the right spacing
# in dimension of full cells widths
tick_every_x = no_cells[0] // (no_ticks[0] - 1)
else:
tick_every_x = 1
if no_ticks[1] < no_cells[1]:
tick_every_y = no_cells[1] // (no_ticks[1] - 1)
else:
tick_every_y = 1
if no_arrows[0] < no_cells[0]:
arrow_every_x = no_cells[0] // (no_arrows[0] - 1)
else:
arrow_every_x = 1
if no_arrows[1] < no_cells[1]:
arrow_every_y = no_cells[1] // (no_arrows[1] - 1)
else:
arrow_every_y = 1
fig, ax = plt.subplots(figsize=(cm2inch(figsize)), tight_layout=True)
sx = shifts[0]
sy = shifts[1]
ax.quiver(centers[0][sx::arrow_every_x, sy::arrow_every_y],
centers[1][sx::arrow_every_x, sy::arrow_every_y],
centered_u_field[sx::arrow_every_x, sy::arrow_every_y],
centered_w_field[sx::arrow_every_x, sy::arrow_every_y],
pivot='mid',
width=ARROW_WIDTH,
scale=ARROW_SCALE,
zorder=3)
# ax.axis('equal',"box")
ax.set_aspect('equal')
# ax.set_aspect('equal', 'box')
ax.set_xticks(np.linspace(corners[0][0, 0], corners[0][-1, 0],
no_ticks[0]))
ax.set_yticks(np.linspace(corners[1][0, 0], corners[1][0, -1],
no_ticks[1]))
# ax.set_xticks(corners[0][::tick_every_x,0])
# ax.set_yticks(corners[1][0,::tick_every_y])
# ax.set_xticks(corners[0][:,0], minor = True)
# ax.set_yticks(corners[1][0,:], minor = True)
ax.set_xlim(corners[0][0, 0], corners[0][-1, 0])
ax.set_ylim(corners[1][0, 0], corners[1][0, -1])
ax.grid(linestyle="dashed")
ax.set_xlabel('$x$ (m)')
ax.set_ylabel('$z$ (m)')
# ax2 = ax.twiny()
# ax2.set_xlabel("$S$ (-) and $r_l/r_{l,\mathrm{max}}$ (-)", color="white")
# fig.tight_layout()
# plt.gca().set_axis_off()
# plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
# hspace = 0, wspace = 0)
# plt.margins(0,0)
# plt.gca().xaxis.set_major_locator(plt.NullLocator())
# plt.gca().yaxis.set_major_locator(plt.NullLocator())
fig.savefig(
figname,
# bbox_inches = 0,
bbox_inches='tight',
pad_inches=0.065)
#x1 = np.hstack( ( np.array( (35) ), x1 ) ) x1b = np.array((35,35)) fit1b = np.array((1.,1.08)) x2 = np.linspace(80,100,10) fit2 = fit_exp1(x2,*p1) #fit2 = np.hstack( ( np.array( () ) , fit_exp1(x2,*p1) ) ) #%% #S = np.arange() # figsize in cm (x,y) fig_size = (7.5,6.0) fig, ax = plt.subplots(figsize=cm2inch(fig_size)) ax.plot(data1[0],data1[1],c="k") ax.plot(x1, fit1,":",c="k") ax.plot(x1b, fit1b,":",c="k") ax.plot(x2, fit2,c="k") #ax.plot(x1, fit1,":",c="0.6") #ax.plot(x1, fit1,"..",c="k") #ax.plot(data2[0],data2[1],c="k") #ax.plot(data_deli[0],data_deli[1],c="0.6") ax.plot(data_crys1[0],data_crys1[1],"--",c="k") ax.plot(data_crys2[0],data_crys2[1],"--",c="k") #ax.plot(data_crys[0],data_crys[1],c="0.4") ax.set_xlim((0.0,100.0)) ax.set_ylim((0.91,1.56))
TTFS = 10
LFS = 10
TKFS = 8
# LINEWIDTH, MARKERSIZE
LW = 1.2
MS = 2
# raster resolution for e.g. .png
DPI = 600
mpl.rcParams.update(generate_rcParams_dict(LW, MS, TTFS, LFS, TKFS, DPI))
if act_plot_moments_kappa_var_paper:
figsize = cm2inch(7., 12.0)
ref_data_path = "collision/ref_data/" \
+ f"{kernel_name}/"
moments_ref = np.loadtxt(ref_data_path + "Wang_2007_moments.txt")
times_ref = np.loadtxt(ref_data_path + "Wang_2007_times.txt")
data_dir = simdata_path + result_path_add
figname = fig_path \
+ f"{kernel_name}_moments_vs_time_" \
+ f"kappa_{kappa_list[0]}_{kappa_list[-1]}.pdf"
boxm.plot_moments_vs_time_kappa_var_paper(kappa_list, eta, dt, no_sims,
no_bins, kernel_name, gen_method,
dist, start_seed, moments_ref,
times_ref, data_dir, figsize,
figname, TTFS, LFS, TKFS)