def write_boundary_layers(cases, time_resolved = False):
import article2_time_averaged_routines as tar
from os.path import isfile,splitext,split
import pandas as pd
for case in cases.iterrows():
print case[1].x_loc, case[1].file
df_pickle_name = "ReservedData/{0}_{1}_{2}_{3}_{4}.p".format(
splitext(split(case[1].file)[1])[0],
case[1].x_loc,
case[1].rotation,
case[1].height_correction,
case[1].x_corr
)
if isfile(df_pickle_name):
df = pd.read_pickle(df_pickle_name)
else:
df = tar.get_wall_normal_line(
pickle_file = case[1].file,
x_loc = case[1].x_loc,
variable = ['u','v',"u_rms","v_rms",],
plot = False,
rotation_angle = case[1].rotation,
trust_y_min = case[1].y_trust_min,
height_correction = case[1].height_correction,
streamwise_correction = case[1].x_corr,
)
df = tar.get_dimensionless_inner_variables(
df,
correction = 0,
Cf = case[1].Cf,
)
df['x_loc'] = case[1].x_loc
if time_resolved:
bl_file.replace('.csv','_TR.csv')
tar.write_boundary_layers(df, boundary_layers_file = bl_file)
def plot_cases(cases, plot_name = '', schematic = '', time_resolved = False):
import article2_time_averaged_routines as tar
import matplotlib.pyplot as plt
from matplotlib import rc
import seaborn as sns
from numpy import linspace,log,diff
import pandas as pd
from matplotlib.cbook import get_sample_data
from os.path import isfile,split,splitext
#if time_resolved:
# import tr_case_dict_overall_correction as case_dict
# cases = case_dict.return_case_df()
markeredgewidth = 2
markerfacecolor = 'none'
markersize = 12
mew = 4 # Marker edge width
""" Cite
Nagano1998
Lee2008
"""
rc('text',usetex=True)
rc('font',weight='normal')
sns.set_context('paper')
sns.set(font='serif',font_scale=3.0,style='whitegrid')
rc('font',family='serif', serif='Linux Libertine')
figsize = (7,6)
fig_nondim , axes_nondim = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_u , axes_u = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_v , axes_v = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_diff , axes_diff = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_diff_rms , axes_diff_rms = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_u_rms , axes_u_rms = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_v_rms , axes_v_rms = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_w , axes_w = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
fig_reyn , axes_reyn = plt.subplots(1,1,sharex=True,sharey=True,
figsize=figsize )
df_all_cases = pd.DataFrame()
Um_df = pd.DataFrame()
for case in cases.iterrows():
print "Processing {0}".format(case[1].file)
df_pickle_name = "ReservedData/{0}_{1}_{2}_{3}_{4}.p".format(
splitext(split(case[1].file)[1])[0],
case[1].x_loc,
case[1].rotation,
case[1].height_correction,
case[1].x_corr
)
if isfile(df_pickle_name):
df = pd.read_pickle(df_pickle_name)
else:
df = tar.get_wall_normal_line(
pickle_file = case[1].file,
x_loc = case[1].x_loc,
variable = ['u','v','w',"u_rms","v_rms",
'Reynold_stress_uv' ],
plot = False,
rotation_angle = case[1].rotation,
trust_y_min = case[1].y_trust_min,
height_correction = case[1].height_correction,
streamwise_correction = case[1].x_corr,
)
U_e, delta_99, delta_displacement, delta_momentum = \
tar.get_boundary_layer_values( df )
color, marker, cmap = get_color_and_marker( case[1].file )
df = tar.get_dimensionless_inner_variables(
df,
correction = 0,
Cf = case[1].Cf,
)
if 'Sr20R21_phi0_alpha0_U20_loc00' in case[1].file:
print 'applying correction'
df = df[ df.y > 0.8 ]
axes_nondim.plot(
df.y_plus,
df.u_plus,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth = markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
axes_u.plot(
#df.u/20.,
df.u/U_e,
df.y/delta_99,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
Um_df = pd.concat([ Um_df,
pd.DataFrame( data = {
(splitext(split(case[1].file)[1])[0], 'u') : df.u/U_e,
(splitext(split(case[1].file)[1])[0], 'u_real') : df.u,
(splitext(split(case[1].file)[1])[0], 'v_real') : df.v,
(splitext(split(case[1].file)[1])[0], 'ybl') : df.y/delta_99,
(splitext(split(case[1].file)[1])[0], 'y_real') : df.y
}, index = range(len(df.u.values)) ) ] , axis = 1
)
if 'Sr20R21_phi0_alpha0_U20_loc05' in case[1].file:
print 'applying correction'
df = df[ df.y > 0.8 ]
axes_u_rms.plot(
df.u_rms/U_e,
df.y/delta_99,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')],
ls = ''
)
#axes_u_rms.fill_betweenx(
# df.y/delta_99,
# df.u_rms/U_e * 0.98,
# df.u_rms/U_e * 1.02,
# color = 'k',#[float(f) for f in case[1].color.split(',')],
# alpha = 0.3
#)
axes_v_rms.plot(
df.v_rms/U_e,
df.y/delta_99,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')],
ls = ''
)
#axes_v_rms.fill_betweenx(
# df.y/delta_99,
# df.v_rms/U_e * 0.98,
# df.v_rms/U_e * 1.02,
# color = 'k',#[float(f) for f in case[1].color.split(',')],
# alpha = 0.5
#)
axes_diff.plot(
df.y.values[0:-1],
diff(df.u)/diff(df.y),
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
axes_diff_rms.plot(
df.y.values[0:-1],
diff(df.u_rms)/diff(df.y),
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
axes_v.plot(
df.v/U_e,
df.y/delta_99,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
axes_w.plot(
df.w/U_e,
df.y/delta_99,
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
axes_reyn.plot(
-df.Reynold_stress_uv/U_e**2 * 1000,
df.y/delta_99,
ls = '',
label = case[1].case_name,
marker = case[1].marker,
markeredgewidth=markeredgewidth,
markerfacecolor = markerfacecolor,
markeredgecolor = color,
markersize = markersize,
mew = mew,
color = [float(f) for f in case[1].color.split(',')]
)
df_all_cases = df_all_cases.append(
df,
ignore_index=True
)
if not isfile(df_pickle_name):
df.to_pickle(df_pickle_name)
# Plot u_plus = y_plus law
line = axes_nondim.plot(
linspace(1e0,1.2e1),
linspace(1e0,1.2e1),
'--'
)
# Plot u_plus = 1/0.4 * ln y_plus law
axes_nondim.plot(
linspace(8e0,1e3),
log(linspace(8e0,1e3))/0.4+5,
'--',
color = line[0].get_color()
)
axes_nondim.set_xlabel("$y^+$")
axes_nondim.set_ylabel("$u^+$")
axes_nondim.set_xscale('log')
axes_nondim.set_xlim(1e0, 1e3)
axes_nondim.xaxis.grid(True, which='minor')
list_of_axes = [
axes_u,
axes_v,
axes_w,
axes_v_rms,
axes_u_rms,
axes_diff,
axes_diff_rms,
axes_nondim,
axes_reyn
]
list_of_figures = [
fig_u,
fig_v,
fig_w,
fig_v_rms,
fig_u_rms,
fig_diff,
fig_diff_rms,
fig_nondim,
fig_reyn
]
if not schematic:
for ax in list_of_axes:
ax.legend(
bbox_to_anchor = (0., 1.02, 1.0, .102),
loc = 3,
ncol = 2,
mode = "expand",
borderaxespad = 0.
)
if schematic:
im = plt.imread( get_sample_data( schematic ) )
for fig in list_of_figures:
if fig == fig_v_rms or fig == fig_u_rms or fig == fig_w\
or fig == fig_reyn:
newax = fig.add_axes([0.55, 0.55, 0.3, 0.3], anchor = 'SW',
zorder=15)
else:
newax = fig.add_axes([0.175, 0.55, 0.3, 0.3], anchor = 'SW',
zorder=15)
newax.imshow(im)
newax.axis('off')
ylabel = r'$y / \delta$'
axes_u.set_xlabel("$\overline u/u_e$")
axes_u.set_ylabel(ylabel)
axes_u.set_xlim(0, 1.1)
axes_v.set_xlabel("$\overline v/u_e$")
axes_v.set_ylabel(ylabel)
axes_w.set_xlabel("$\overline w/u_e$")
axes_w.set_ylabel(ylabel)
axes_w.set_xlim(-0.04, 0.04)
axes_w.set_xticks([-0.04, -0.02, 0, 0.02, 0.04])
axes_u_rms.set_xlabel("$u'_\\textrm{rms}/u_e$")
axes_u_rms.set_ylabel(ylabel)
axes_u_rms.set_xlim(0,0.12)
axes_v_rms.set_xlabel("$v'_\\textrm{rms}/u_e$")
axes_v_rms.set_ylabel(ylabel)
axes_v_rms.set_xlim(0,0.12)
axes_diff.set_xlabel("$y$ [mm]")
axes_diff.set_ylabel("$\\partial \overline u/ \\partial y$")
axes_diff.set_yscale("log")
axes_diff_rms.set_xlabel("$y$ [mm]")
axes_diff_rms.set_ylabel("$\\partial u'_\\textrm{rms}/ \\partial y$")
axes_reyn.set_xlabel(r"$-\overline{u'v'}/u_e^2 \times 10^{-3}$")
axes_reyn.set_ylabel(ylabel)
axes_reyn.set_xlim(-1.0, 6.0)
axes_reyn.set_ylim(0, 1.5)
axes_u.set_ylim(0, 1.5)
axes_v.set_ylim(0, 1.5)
axes_w.set_ylim(0, 1.5)
axes_u_rms.set_ylim(0, 1.5)
axes_v_rms.set_ylim(0, 1.5)
axes_diff.set_xlim(left = 0)
axes_diff_rms.set_xlim(left = 0)
fig_u.savefig(
'images/{0}_BoundaryLayerAnalysis_u.png'.format(plot_name),
bbox_inches='tight'
)
fig_v.savefig(
'images/{0}_BoundaryLayerAnalysis_v.png'.format(plot_name),
bbox_inches='tight'
)
fig_w.savefig(
'images/{0}_BoundaryLayerAnalysis_w.png'.format(plot_name),
bbox_inches='tight'
)
fig_u_rms.savefig(
'images/{0}_BoundaryLayerAnalysis_urms.png'.format(plot_name),
bbox_inches='tight'
)
fig_v_rms.savefig(
'images/{0}_BoundaryLayerAnalysis_vrms.png'.format(plot_name),
bbox_inches='tight'
)
fig_diff.savefig(
'images/{0}_BoundaryLayerAnalysis_dudy.png'.format(plot_name),
bbox_inches='tight'
)
fig_diff_rms.savefig(
'images/{0}_BoundaryLayerAnalysis_durmsdy.png'.format(plot_name),
bbox_inches='tight'
)
fig_reyn.savefig(
'images/{0}_BoundaryLayerAnalysis_ReynoldsStressUV.png'\
.format(plot_name),
bbox_inches='tight'
)
fig_nondim.savefig(
'images/{0}_NonDimensionalBoundaryLayerAnalysis_u.png'.format(plot_name),
bbox_inches='tight'
)
tar.write_to_csv_with_units(
df_all_cases,
"{0}_BoundaryLayerData_StereoPIV.csv".format(plot_name),
)
Um_df.to_pickle( 'Um.p' )
def write_wall_normal_lines_to_csv():
import pandas as pd
import article2_time_averaged_routines as tar
import case_dict_overall_correction as case_dict
cases_df = case_dict.return_case_df()
case_STE = cases_df[
(cases_df.file == 'data/STE_phi0_alpha0_U20_loc00.dat_rotated.p') &\
(cases_df.x_loc == 0) ]
case_z00_x40 = cases_df[
(cases_df.file == 'data/Sr20R21_phi0_alpha0_U20_loc00.dat_rotated.p')&\
(cases_df.x_loc == 40) ]
case_z00_x20 = cases_df[
(cases_df.file == 'data/Sr20R21_phi0_alpha0_U20_loc00.dat_rotated.p')&\
(cases_df.x_loc == 20) ]
case_z00_x00 = cases_df[
(cases_df.file == 'data/Sr20R21_phi0_alpha0_U20_loc00.dat_rotated.p')&\
(cases_df.x_loc == 0) ]
case_z05 = cases_df[
(cases_df.file == 'data/Sr20R21_phi0_alpha0_U20_loc05.dat_rotated.p')&\
(cases_df.x_loc == 20) ]
case_z10 = cases_df[
(cases_df.file == \
'data/Sr20R21_phi0_alpha0_U20_loc10_even_newer.dat.p')&\
(cases_df.x_loc == 0) ]
cases = case_STE
cases = cases.append(case_z00_x00)
cases = cases.append(case_z00_x20)
cases = cases.append(case_z00_x40)
cases = cases.append(case_z05)
cases = cases.append(case_z10)
df_cases = pd.DataFrame()
for case in cases.iterrows():
df = tar.get_wall_normal_line(
pickle_file = case[1].file,
x_loc = case[1].x_loc,
variable = ['u','v',"u_rms","v_rms",],
plot = False,
rotation_angle = case[1].rotation,
trust_y_min = case[1].y_trust_min,
height_correction = case[1].height_correction,
streamwise_correction = case[1].x_corr,
)
tar.write_to_csv_with_units(
df,
'{0}_x{1:.2f}_z{2}'.format(
df.trailing_edge.unique()[0],
df.x.unique()[0],
df.z.unique()[0],
)
)
df_cases = df_cases.append(df, ignore_index = True)
tar.write_to_csv_with_units(
df_cases,
'Serrations_BoundaryLayerData_StereoPIV_v2.csv'
)
