folder_name, path_name_second)):
xD = file_name[file_name.find('_xD') + 3:-4]
z = fr.z_str_to_num(xD)
xD_name.append(xD)
xD_value.append(z)
data.update({
(case, z):
np.genfromtxt(file_name, delimiter=',', names=True)
})
xD_name = list(set(xD_name))
xD_value = sorted(list(set(xD_value)))
expr = {}
for xD in xD_name:
file_name = '../pmCDEFarchives/pmD.stat/D{0}.Yave'.format(xD)
expr.update({fr.z_str_to_num(xD): fr.sf_expr_read(file_name)})
for z in xD_value:
expr[z]['r'] /= z
for case in case_name:
data[(case, z)]['r'] /= z
# plot
# use TEX for interpreter
plt.rc('text', usetex=True)
# use serif font
plt.rc('font', family='serif')
# figure and axes parameters
# total width is fixed
plot_width = 19.0
subplot_h = 4.0
# two vars at least, because axes would be 1D vector for one var
var = ['Z', 'T', 'CO']
# import data
xD_value = []
data = {}
expr = {}
for filename in glob.glob('mean_xD*.csv'):
pos = filename.find('.csv')
xD = filename[7:pos]
z = fr.z_str_to_num(xD)
if z >= 7.5 and z <= 45.0:
xD_value.append(z)
data.update({z: np.genfromtxt(filename, delimiter=',', names=True)})
exp_name = '../../../pmCDEFarchives/pmD.stat/D{}.Yave'.format(xD)
expr.update({z: fr.sf_expr_read(exp_name)})
xD_value.sort()
for z in xD_value:
data[z]['r'] /= z
expr[z]['r'] /= z
# plot
# use TEX for interpreter
plt.rc('text', usetex=True)
# use serif font
plt.rc('font', family='serif')
# figure and axes parameters
# total width is fixed
plot_width = 19.0
subplot_h = 4.0
margin_left = 2.0
data_tmp = np.maximum(data[:, loc_rms], 0.0)
data[:, loc_rms] = np.sqrt(data_tmp)
data[:, 1:3] /= U_REF
simu.update({case: data})
# experiment scalar
folder_exp = '../pmCDEFarchives/pmD.stat/D'
scalar_name = ['T', 'Z']
expr = {'z': []}
for name in scalar_name:
expr.update({name: []})
expr.update({name + 'rms': []})
for file_name in glob.glob('{}*.Yave'.format(folder_exp)):
data_tmp = fr.sf_expr_read(file_name)
if file_name[len(folder_exp):-5].isdigit():
if 0.0 in data_tmp['r']:
expr['z'].append(fr.z_str_to_num(file_name[len(folder_exp):-5]))
i = np.where(data_tmp['r'] == 0.0)[0]
"""
elif -0.04 in data_tmp['r']:
i = np.where(data_tmp['r'] == -0.04)[0]
"""
for name in scalar_name:
expr[name].append(data_tmp[name][i])
expr[name + 'rms'].append(data_tmp[name + 'rms'][i])
# experiment velocity
file_name = '../TUD_LDV_DEF/TUD_LDV_D.axial'
expu = np.genfromtxt(file_name, skip_header=13)