"T",
"uu",
"uv",
"uw",
"vv",
"vw",
"ww",
"Q-criterion",
"L2-criterion",
"gradp",
]
timezone = np.arange(700, 999.75 + 0.25, 0.25)
x1x2 = [700, 1000]
StepHeight = 3.0
MeanFlow = pf()
#MeanFlow.load_data(path + 'inca_out/')
MeanFlow.load_meanflow(path)
MeanFlow.add_walldist(StepHeight)
# %% Load laminar data for comparison
path1 = "/media/weibo/VID2/BFS_M1.7L/MeanFlow/"
MeanFlowL = pf()
MeanFlowL.load_meanflow(path)
MeanFlowL.add_walldist(StepHeight)
# %%############################################################################
"""
boundary layer profile along streamwise direction
"""
# %% plot BL profile along streamwise
"y",
"z",
"u",
"v",
"w",
"rho",
"p",
"T",
"Q-criterion",
"L2-criterion",
]
StepHeight = 3.0
# %%
MeanFlow = pf()
MeanFlow.load_meanflow(path)
MeanFlow.add_walldist(StepHeight)
stat = MeanFlow.PlanarData
# %%############################################################################
"""
save coordinates of bubble line & max fluctuations points
"""
# %% save dividing line coordinates
dividing = np.loadtxt(pathM + "BubbleLine.dat", skiprows=1)[:-2, :]
x2 = np.arange(dividing[-1, 0], 50.0+0.125, 0.125)
y2 = np.ones(np.size(x2))*(-2.99342)
x3 = np.concatenate((dividing[:,0], x2), axis=0)
y3 = np.concatenate((dividing[:,1], y2), axis=0) # streamline
xx = np.zeros(np.size(x3))
pathT = path + "TimeAve/"
pathI = path + "Instant/"
pathV = path + "Vortex/"
pathSL = path + "Slice/"
matplotlib.rcParams["xtick.direction"] = "out"
matplotlib.rcParams["ytick.direction"] = "out"
textsize = 13
numsize = 10
matplotlib.rc("font", size=textsize)
# %% Load Data for time- spanwise-averaged results
# filter files
FileId = pd.read_csv(path + "StatList.dat", sep='\t')
filelist = FileId['name'].to_list()
pltlist = [os.path.join(path + 'TP_stat/', name) for name in filelist]
MeanFlow = pf()
MeanFlow.load_meanflow(path, FileList=pltlist)
# %% Load Data for time- spanwise-averaged results
MeanFlow = pf()
MeanFlow.load_meanflow(path)
x, y = np.meshgrid(np.unique(MeanFlow.x), np.unique(MeanFlow.y))
corner = (x < 0.0) & (y < 0.0)
# %%############################################################################
"""
Examination of the computational mesh
"""
# %% check mesh
temp = MeanFlow.PlanarData[['x', 'y']]
df = temp.query("x>=-5.0 & x<=5.0 & y>=-3.0 & y<=1.0")
font = {
"family": "Times New Roman", # 'color' : 'k',
"weight": "normal",
"size": "large",
}
matplotlib.rcParams["xtick.direction"] = "in"
matplotlib.rcParams["ytick.direction"] = "in"
textsize = 13
numsize = 10
# %%############################################################################
###
### load data
###
StepHeight = 3.0
MeanFlow0 = pf()
MeanFlow0.load_meanflow(path0)
MeanFlow0.add_walldist(StepHeight)
MeanFlow1 = pf()
MeanFlow1.load_meanflow(path1)
MeanFlow1.add_walldist(StepHeight)
MeanFlow2 = pf()
MeanFlow2.load_meanflow(path2)
MeanFlow2.add_walldist(StepHeight)
MeanFlow3 = pf()
MeanFlow3.load_meanflow(path3)
MeanFlow3.add_walldist(StepHeight)
# %%############################################################################
###
### skin friction & pressure coefficiency/turbulent kinetic energy along streamwise