def profile_plots(run_dir, run_out, run_ver, res_dir, name_list):
res_dir = res_dir + '/1D_plots/'
if util.chk_dir(res_dir) == False:
os.makedirs(res_dir) # make sure the results directory exists
file_d = run_dir + '%s%s.vtk' % (run_ver, run_out)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, mean_fields = rosie.getFields(out_f, name_list)
for field in mean_fields:
print '\n--> plotting field %s' % field
fig = plt.figure(figsize=(9, 3))
ax = fig.add_subplot(111) # ; ax.set_aspect(1, adjustable = 'box')
x = grid[:, 1] if run_out == "fra_mean" else grid[:, 2]
xmin, xmax = min(x), max(x)
ax.set_xlim(xmin, xmax)
y = mean_fields[field]
ymin, ymax = min(y), max(y)
ax.set_ylim(ymin, ymax)
field_name = field
if run_out == "fra_mean": ax.set_xlim(xmin, 10.0)
elif run_out == "prad_pdf":
field_name = "rad_" + field
ax.set_xscale('log')
elif run_out == "pvap_pdf":
field_name = "vap_" + field
ax.set_xscale('log')
plt.title('%s' % field_name)
ax.plot(x, y, 'b-', label=r'%s' % field_name)
ax.set_xlabel(r'%s' % field)
ax.set_ylabel(field_name)
plt.legend(loc='best')
plt.grid(which='both', axis='both', color='darkgrey')
plt.savefig(res_dir + '/%s.png' % field_name, format='png', dpi=300)
plt.close('all')
return []
def contour_plots(run_dir, run_out, run_ver, res_dir, name_list):
nan_list = [
'Crit_Radius', 'Crit_Radius_rms', 'part_number', 'part_radius',
'mass_loading'
]
res_dir = res_dir + '2D_contour/'
if util.chk_dir(res_dir) == False: os.makedirs(res_dir)
file_d = run_dir + '%s%s.vtk' % (run_ver, run_out)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, mean_fields = rosie.getFields(out_f, name_list)
x, y = grid[:, 1], grid[:, 2]
npts = 500
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
xi = np.linspace(xmin, xmax, npts)
yi = np.linspace(ymin, ymax, npts)
xi, yi = np.meshgrid(xi, yi) #, sparse=True)
for field in mean_fields:
print '\n--> plotting field %s' % field
fig = plt.figure(figsize=(9, 3))
ax = fig.add_subplot(111)
ax.set_aspect(1, adjustable='box')
cmap = cm.get_cmap('Blues_r')
ax.set_xlim(0.0, 99.0)
ax.set_ylim(0.0, 24.0)
func = lambda x, pos: "" if np.isclose(x, 0) else "%.f" % x
plt.gca().xaxis.set_major_formatter(ticker.FuncFormatter(func))
plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(func))
z = mean_fields[field]
lmin = min(z)
lmax = max(z)
if field in nan_list:
if field == 'part_number': z[z <= 1.e-1] = np.nan
elif field == 'part_radius': z[z <= 1.e-7] = np.nan
else: z[z <= 1.e-16] = np.nan
lmin = np.nanmin(z)
lmax = np.nanmax(z)
zi = griddata((x, y), z, (xi, yi), method='linear')
levels_n = 8
cm_format = None
my_cmap = copy.copy(cmap) # to init default values
if field in nan_list:
my_cmap.set_under(color='lightyellow')
zi = np.nan_to_num(zi) #, nan=-9999) # upgrade numpy
if field == 'Temperature':
lmin = 0.9
lmax = 1.3
elif field == 'Temperature_rms':
lmin = 0.0
lmax = 0.1
levels_n = 5
elif field == 'Y':
lmin = 0.0
lmax = 3.2e-3
oo_magn = -3
elif field == 'Sat_Ratio ':
lmin = 0.0
lmax = 900.0
levels_n = 6
elif field == 'Rho':
lmin = 0.7
lmax = 1.0
levels_n = 6
elif field == 'U_r':
lmin = -0.03
lmax = 0.03
levels_n = 6
oo_magn = -2
elif field == 'fp_div':
lmin = -1.0e-7
lmax = 0.0
levels_n = 5
oo_magn = -7
elif field == 'fp_vap':
lmin = 0.0
lmax = 5.0e-8
levels_n = 5
oo_magn = -8
elif field == 'Crit_Radius':
lmin = 1.0e-7
lmax = 9.0e-7
oo_magn = -7
elif field == 'Crit_Radius_rms':
lmin = 1.0e-7
lmax = 3.0e-7
levels_n = 6
oo_magn = -7
elif field == 'Nucl_rate':
lmin = 1.0e-16
lmax = 1.0e-5
levels_n = 6
oo_magn = -5
if run_out == 'flu_mean' and np.isclose(
max(mean_fields['Y']), 1.8e-3, rtol=1e-4, atol=1e-4):
if field == 'J_rate':
lmin = 0.0
lmax = 2.4
levels_n = 6
oo_magn = 0
elif field == 'fake_J_rate':
lmin = 0.0
lmax = 2.4e-1
levels_n = 6
oo_magn = -1
elif field == 'Jrate_rms':
lmin = 0.0
lmax = 0.8
levels_n = 6
oo_magn = 0
if run_out == 'flu_mean' and np.isclose(
max(mean_fields['Y']), 2.1e-3, rtol=1e-4, atol=1e-4):
if field == 'J_rate':
lmin = 0.0
lmax = 3.6
levels_n = 6
oo_magn = 0
elif field == 'fake_J_rate':
lmin = 0.0
lmax = 3.6
levels_n = 6
oo_magn = 0
elif field == 'Jrate_rms':
lmin = 0.0
lmax = 1.2
levels_n = 6
oo_magn = 0
if field == 'part_number':
lmin = 0.0
lmax = util.OOMRoundDown(lmax)
levels_n = 5
elif field == 'part_radius':
lmin = 0.0
lmax = util.OOMRoundUp(lmax)
levels_n = 5
oo_magn = util.OOMUp(lmax)
elif field == 'mass_loading':
lmin = 0.0
lmax = util.OOMRoundUp(lmax) / 10
levels_n = 5
oo_magn = util.OOMUp(lmax)
lcountour = np.linspace(lmin, lmax, levels_n + 1)
if field == 'Sat_Ratio': lcountour[0] = 1.0
elif field == 'Temperature_rms':
lcountour = np.insert(lcountour, 1, 0.01) #, axis = 0)
elif field in ['Y', 'U_r', 'part_radius', 'mass_loading', 'fp_div']:
cm_format = util.OOMFormatter(oo_magn, mathText=False)
elif field == 'fp_vap':
cm_format = util.OOMFormatter_eng(oo_magn, mathText=False)
elif field in ['J_rate', 'Jrate_rms', 'fake_J_rate']:
lcountour[0] = 1.e-3
cm_format = util.OOMFormatter(oo_magn, mathText=False)
elif field in ['Crit_Radius', 'Crit_Radius_rms']:
cm_format = util.OOMFormatter(oo_magn, mathText=False)
if field not in name_list: plt.title(field)
# CONTOUR: draws the boundaries of the isosurfaces & fill the contour plots
if run_out != 'prt_mean':
cp = plt.contour(yi,
xi,
zi,
levels=lcountour,
linewidths=0.5,
colors='black')
cp = plt.contourf(yi,
xi,
zi,
levels=lcountour,
vmin=lmin,
vmax=lmax,
extend='both',
cmap=my_cmap) #,cmap=cm.viridis)
plt.colorbar(cp, format=cm_format, orientation='horizontal')
fig.tight_layout() # upgrade plt: location='bottom'
plt.savefig(res_dir + '/%s.png' % field, format='png', dpi=300)
plt.close('all')
return []
def cfr_DNSvsExp(run_dir, plot_name, run_ver, res_dir):
name_list = []
file_list = []
res_dir = res_dir + '2D_scatter/'
if util.chk_dir(res_dir) == False: os.makedirs(res_dir)
x_exp = []
y_exp = []
x_dns_list = []
y_dns_list = []
x_dns_2w_shrt = []
y_dns_2w_shrt = []
y_dns_2w_shrt_norm = []
y_dns_2w_shrt_dime = []
x_dns_2w_long = []
y_dns_2w_long = []
y_dns_2w_long_norm = []
y_dns_2w_long_dime = []
x_dns_1w_long = []
y_dns_1w_long = []
y_dns_1w_long_norm = []
y_dns_1w_long_dime = []
Wvap = 278.34e-3
Wgas = 28.29e-3
Lref_exp = 0.00375 / 2.
Lref_dns = 0.00175 # pipe dimensional radius (DNS)
norm_exp = (Lref_exp * 1.e2)**3
norm_dns = (Lref_dns * 1.e2)**3
if plot_name == "fried2000F10.11":
exp_dataset = 'DBP_Re4700_d0375cm.csv'
dns_dataset = 'prt_mean'
for root, dirs, files in os.walk("%s" % run_dir):
for filename in files:
if re.match('%s' % exp_dataset, filename):
exp_dataset = '%s' % os.path.join(root, filename)
print '\n--> Reading', exp_dataset
# break #prevent decending into subfolders
with open(exp_dataset, 'r') as csvfile:
plots = csv.reader(csvfile, delimiter=';')
for row in plots:
x_exp.append(float(row[0]))
y_exp.append(float(row[1]))
y_exp_dime = [i * ((2.0)**3) for i in y_exp]
y_exp_norm = [i * norm_exp for i in y_exp_dime]
if re.match('%s%s.vtk' % (run_ver, dns_dataset), filename):
file_list.append(os.path.join(root, filename))
for dns_dataset in file_list:
file_d = dns_dataset
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, mean_fields = rosie.getFields(out_f, name_list)
x, y = grid[:, 1], grid[:, 2]
npts = 500
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
xi = np.linspace(xmin, xmax, npts)
yi = np.linspace(ymin, ymax, npts)
xi, yi = np.meshgrid(xi, yi)
z = mean_fields['part_number']
zi = griddata((x, y), z, (xi, yi), method='linear')
y_dns = util.ProbeAtLocation(zi, xi, yi, 0.1, 40.0)
if re.match('.+shrt.+', file_d):
if re.match('.+0w.+', file_d):
y_dns_0w_shrt.append(y_dns)
y_dns_0w_shrt_norm = y_dns_0w_shrt \
y_dns_0w_shrt_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_0w_shrt_norm]
elif re.match('.+1w.+', file_d):
y_dns_1w_shrt.append(y_dns)
y_dns_1w_shrt_norm = y_dns_1w_shrt \
y_dns_1w_shrt_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_1w_shrt_norm]
elif re.match('.+2w.+', file_d):
y_dns_2w_shrt.append(y_dns)
y_dns_2w_shrt_norm = y_dns_2w_shrt \
y_dns_2w_shrt_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_2w_shrt_norm]
elif re.match('.+long.+', file_d):
if re.match('.+0w.+', file_d):
y_dns_0w_long.append(y_dns)
y_dns_0w_long_norm = y_dns_0w_long \
y_dns_0w_long_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_0w_long_norm]
elif re.match('.+1w.+', file_d):
y_dns_1w_long.append(y_dns)
y_dns_1w_long_norm = y_dns_1w_long \
y_dns_1w_long_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_1w_long_norm]
elif re.match('.+2w.+', file_d):
y_dns_2w_long.append(y_dns)
y_dns_2w_long_norm = y_dns_2w_long \
y_dns_2w_long_dime = [(i / norm_dns )* ((0.8)**3) for i in y_dns_2w_long_norm]
file_d = dns_dataset.replace('prt_mean', 'flu_mean')
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, mean_fields = rosie.getFields(out_f, name_list)
x, y = grid[:, 1], grid[:, 2]
npts = 500
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
xi = np.linspace(xmin, xmax, npts)
yi = np.linspace(ymin, ymax, npts)
xi, yi = np.meshgrid(xi, yi)
z = mean_fields['Y']
zi = griddata((x, y), z, (xi, yi), method='linear')
x_dns = util.ProbeAtLocation(zi, xi, yi, 0.0, 0.1)
x_dns = x_dns / (x_dns + (1 - x_dns) * Wvap / Wgas)
if re.match('.+shrt.+', file_d):
if re.match('.+0w.+', file_d): x_dns_0w_shrt.append(x_dns)
elif re.match('.+1w.+', file_d): x_dns_1w_shrt.append(x_dns)
elif re.match('.+2w.+', file_d): x_dns_2w_shrt.append(x_dns)
elif re.match('.+long.+', file_d):
if re.match('.+0w.+', file_d): x_dns_0w_long.append(x_dns)
elif re.match('.+1w.+', file_d): x_dns_1w_long.append(x_dns)
elif re.match('.+2w.+', file_d): x_dns_2w_long.append(x_dns)
fig = plt.figure(figsize=(12, 6))
###############################################################
ax_dime = fig.add_subplot(121)
ax_dime.set_yscale('log')
xmin_dime, xmax_dime = 0., 5.e-4
ymin_dime, ymax_dime = 1., 1.e+6
ax_dime.xaxis.set_major_formatter(ticker.FormatStrFormatter('%.0e'))
ax_dime.set_xlim(xmin_dime, xmax_dime)
ax_dime.set_ylim(ymin_dime, ymax_dime)
plt.scatter(x_exp, y_exp_dime, c='b', label='experiments ')
plt.scatter(x_dns_2w_shrt, y_dns_2w_shrt_dime, c='r', label='2w DNS short')
plt.scatter(x_dns_2w_long, y_dns_2w_long_dime, c='m', label='2w DNS long ')
plt.scatter(x_dns_1w_long, y_dns_1w_long_dime, c='g', label='1w DNS long ')
plt.grid(True)
plt.legend(loc="lower right")
plt.title('dimensional results')
plt.xlabel('DBP inlet molar-franction')
plt.ylabel('Particle Number Density (#/cc)')
#plt.savefig(res_dir + '/dime_%s.eps'%plot_name, format='eps', dpi=300)
###############################################################
#fig= plt.figure(figsize=(6, 6))
ax_norm = fig.add_subplot(122)
ax_norm.set_yscale('log')
xmin_norm, xmax_norm = 0., 5.e-4
ymin_norm, ymax_norm = 1.e-2, 1.e+4
ax_norm.xaxis.set_major_formatter(ticker.FormatStrFormatter('%.0e'))
ax_norm.set_xlim(xmin_norm, xmax_norm)
ax_norm.set_ylim(ymin_norm, ymax_norm)
plt.scatter(x_exp, y_exp_norm, c='b', label='experiments ')
plt.scatter(x_dns_2w_shrt, y_dns_2w_shrt_norm, c='r', label='2w DNS short')
plt.scatter(x_dns_2w_long, y_dns_2w_long_norm, c='m', label='2w DNS long ')
plt.scatter(x_dns_1w_long, y_dns_1w_long_norm, c='g', label='1w DNS long ')
plt.grid(True)
plt.legend(loc="lower right")
plt.title('non-dimensional results')
plt.xlabel('DBP inlet molar-franction')
plt.ylabel('Particle Number Density (#/dV)')
fig.suptitle('DNS vs Experimental (Friedlander)', fontsize=14)
plt.savefig(res_dir + '/cfrDNSvsExp_%s.png' % plot_name,
format='png',
dpi=300)
plt.close('all')
return []
def cfr_profile_plots(run_dir, run_out, run_ver, res_dir):
res_dir = res_dir + '/1D_plots/'
if util.chk_dir(res_dir) == False:
os.makedirs(res_dir) # make sure the results directory exists
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111) # ; ax.set_aspect(1, adjustable = 'box')
xmin = 1.e+16
xmax = -1.e+16
ymin = 1.e+16
ymax = -1.e+16
name_list = []
for root, dirs, files in os.walk("%s" % run_dir):
for filename in files:
if re.match('%s%s*.*.vtk' % (run_ver, run_out), filename):
print '\n--> looking for %s' % filename
fname_end = filename.split("_")
prof_case = fname_end[-1]
prof_case = prof_case.rstrip('.vtk')
print '\n--> case %s' % prof_case
file_d = run_dir + '%s' % (filename)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, mean_fields = rosie.getFields(out_f, name_list)
for field in mean_fields:
if field == "pdf_38.0":
x = grid[:, 2]
xmin, xmax = 2.e-7, 2.e-6 # ; xmin, xmax = min(x), max(x)
ax.set_xlim(xmin, xmax)
ax.set_xscale('log') # ; ax.set_xlabel(r'%s'%field)
y = mean_fields[field]
y = y * (4 / 3 * 3.14 * np.power(x, 4))
x = 2 * x * 0.00175
# for i in range(len(y)):
#
# if i < (len(y)-1):
#
# delta_x = x[i+1] - x[i]
#
# else:
#
# delta_x = x[i] - x[i-1]
#
# y[i] = y[i]/(math.log10(x[i] + delta_x) - math.log10(x[i] - delta_x))
#
## print y[i]
ymin = min(np.append(y, ymin))
ymax = max(np.append(y, ymax))
ax.set_ylim(ymin, ymax) # ; ax.set_yscale('log')
field_name = field
if filename == "fra_mean": ax.set_xlim(xmin, 10.0)
elif filename == "prad_pdf" + prof_case + ".vtk":
field_name = "rad_" + field
ax.set_xscale('log') # ; ax.set_yscale('log')
elif filename == "pvap_pdf":
field_name = "vap_" + field
ax.set_xscale(
'log'
) # ax.plot(x,y,'b-',label=r'%s'%prof_case)
if prof_case == "x18":
ax.plot(
x, y, '#0d4186', label=r'1.8', linewidth=2.0
) # ax.plot(x,y,'#3470F0',label=r'%s'%prof_case)
if prof_case == "x21":
ax.plot(
x, y, '#0d4186', label=r'2.1', linewidth=2.0
) # ax.plot(x,y,'#3470F0',label=r'%s'%prof_case)
if prof_case == "x25":
ax.plot(
x, y, '#0d4186', label=r'2.5', linewidth=2.0
) # ax.plot(x,y,'#3470F0',label=r'%s'%prof_case)
if prof_case == "x32":
ax.plot(
x, y, '#0d4186', label=r'3.2', linewidth=2.0
) # ax.plot(x,y,'#3470F0',label=r'%s'%prof_case)
ax.yaxis.set_major_formatter(util.OOMFormatter(-10, mathText=False))
xvals = [8.0e-7, 7.5e-7, 8.0e-7, 9.0e-7]
util.labelLines(plt.gca().get_lines(),
align=False,
xvals=xvals,
color='k') #,fontsize=14)
plt.grid(which='both', axis='both', color='darkgrey')
fig.tight_layout()
plt.savefig(res_dir + '/cfr_%s.png' % field_name,
format='png',
dpi=300)
plt.close('all')
break #prevent decending into subfolders
return []
def norm_profile_plots(run_dir, run_out, norm_out, run_ver, res_dir):
res_dir = res_dir + '/1D_plots/'
if util.chk_dir(res_dir) == False:
os.makedirs(res_dir) # make sure the results directory exists
name_list = ['Uz_centline', 'R_halfw_Uz', 'Y_centline', 'R_halfw_Y']
file_d = run_dir + '%s%s.vtk' % (run_ver, norm_out)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
axi_grid, mean_axi_fields = rosie.getFields(out_f, name_list)
name_list = []
file_d = run_dir + '%s%s.vtk' % (run_ver, run_out)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
rad_grid, mean_rad_fields = rosie.getFields(out_f, name_list)
for field in mean_rad_fields:
fig = plt.figure(figsize=(9, 3))
ax = fig.add_subplot(111) # ; ax.set_aspect(1, adjustable = 'box')
if run_out == "fra_mean": x = rad_grid[:, 1]
y = mean_rad_fields[field]
rad_field = field.split("_", 2)
rad_field_name = rad_field[0]
rad_field_dist = float(rad_field[1])
axi_norm = mean_axi_fields[rad_field_name + "_centline"]
rad_norm = mean_axi_fields["R_halfw_" + rad_field_name]
for i in range(len(axi_norm)):
if np.isclose(axi_grid[i, 2], rad_field_dist, rtol=1e-2,
atol=1e-2): # use util.find_nearest
y = y / axi_norm[i]
x = x / rad_norm[i]
break
xmin, xmax = min(x), max(x)
ax.set_xlim(xmin, xmax)
ymin, ymax = min(y), max(y)
ax.set_ylim(ymin, ymax)
field_name = field
if run_out == "fra_mean": ax.set_xlim(xmin, 3.0)
# ax.set_ylim(xmin, 1.0)
plt.title('norm_%s' % field_name)
ax.plot(x, y, 'b-', label=r'norm_%s' % field_name)
ax.set_xlabel(r'%s' % field)
ax.set_ylabel(field_name)
plt.legend(loc='best')
plt.grid(which='both', axis='both', color='darkgrey')
plt.savefig(res_dir + '/norm_%s.png' % field_name,
format='png',
dpi=300)
plt.close('all')
return []
def scatter_plots(run_dir, run_ver, res_dir, name_list):
res_dir = res_dir + '/2D_scatter/'
if util.chk_dir(res_dir) == False: os.makedirs(res_dir)
z_name_list = ['J_rate', 'Rho']
xy_name_list = ['Temperature', 'Sat_ratio', 'Y_vapour', 'Rho']
for z_name in z_name_list:
for xy_name in list(combinations(xy_name_list, 2)):
x_name = xy_name[0]
y_name = xy_name[1]
if x_name == z_name or y_name == z_name: continue
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111) # ; ax.set_aspect(1, adjustable = 'box')
name_list = [x_name, y_name, z_name]
for root, dirs, files in os.walk("%s" % run_dir):
for filename in files:
if re.match('%sJE*.*.vtk' % run_ver, filename):
file_d = run_dir + '%s' % (filename)
out_f = rosie.getOutputVTKwithPointDataFromFile(file_d)
grid, inst_fields = rosie.getFields(out_f, name_list)
x = inst_fields[x_name]
y = inst_fields[y_name]
z = inst_fields[z_name]
# if x_name == 'Y_vapour' or x_name == 'Temperature': x = (x - min(x))/(max(x) - min(x)) # to restore asap
# if y_name == 'Y_vapour' or y_name == 'Temperature': y = (y - min(y))/(max(y) - min(y)) # to restore asap
xmin, xmax = min(x), max(x) # xmin, xmax = 1.e-3, 1.
ax.set_xlim(xmin, xmax)
ax.set_xlabel(r'%s' % x_name)
ymin, ymax = min(y), max(
y) # ymin, ymax = 1.e-3, 2.e+3
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'%s' % y_name) # ; ax.set_yscale('log')
if y_name == 'Sat_ratio':
ax.set_ylabel(r'Saturation ratio')
ax.set_xscale('log')
vmin = min(z)
vmax = max(z)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
if z_name in ['Rate_nucl', 'J_rate', 'Crit_rad']:
if z_name == 'Rate_nucl':
vmin = 1.0e-16
vmax = 1.0e-4
elif z_name == 'J_rate':
vmin = 1.0e-6
vmax = 1.0e+2
elif z_name == 'Crit_rad':
vmin = 1.0e-8
norm = colors.LogNorm(vmin=vmin, vmax=vmax)
sc = plt.scatter(x,
y,
s=0.05,
c=z,
norm=norm,
alpha=1.0,
linewidths=0.0,
edgecolors='face',
rasterized=True,
cmap=plt.cm.get_cmap('jet', 8))
cbar = plt.colorbar(sc)
cbar.set_label('%s' % z_name, rotation=270, labelpad=20)
if z_name == 'J_rate':
cbar.set_label('Nucleation rate',
rotation=270,
labelpad=15)
plt.savefig(res_dir + '/%s_%s_%s.png' %
(x_name[0:3:1], y_name[0:3:1], z_name[0:3:1]),
format='png',
dpi=300)
plt.close('all')
break #prevent decending into subfolders
return []