def load_data(self, path, FileList=None, NameList=None):
# nfiles = np.size(os.listdir(path))
if FileList is None:
infile = glob(path + '*.plt')
else:
infile = FileList
if NameList is None:
# ext_name = os.path.splitext(infile)
df = p2p.ReadAllINCAResults(path, FileName=infile)
elif NameList == 'h5':
df = pd.read_hdf(infile)
else:
df = p2p.ReadINCAResults(path, VarList=NameList, FileName=infile)
df = df.drop_duplicates(keep='last')
grouped = df.groupby(['x', 'y', 'z'])
df = grouped.mean().reset_index()
self._data_field = df
def load_3data(self, path, FileList=None, NameList=None):
# nfiles = np.size(os.listdir(path))
if FileList is None:
infile = glob(path + '*.plt')
else:
infile = FileList
if NameList is None:
df = p2p.ReadAllINCAResults(path, FileName=infile, SpanAve=None)
elif NameList == 'h5':
if np.size(FileList) == 1:
df = pd.read_hdf(infile)
else:
num = np.size(FileList)
df = pd.concat([pd.read_hdf(FileList[i]) for i in range(num)])
df.reset_index()
else:
df = p2p.ReadINCAResults(path,
VarList=NameList,
FileName=infile,
SpanAve=False)
# df = df.drop_duplicates(keep='last')
self._data_field = df
'v',
'w',
'p',
'rho',
'vorticity_1',
'vorticity_2',
'vorticity_3',
'L2-criterion',
'T',
]
start = time.time()
volume = [(-100.0, 80.0), (0.0, 40.0), (-8, 8)]
dxyz = [0.25, 0.125, 0.125]
df0, stime = p2p.ReadINCAResults(pathin, VarList, SubZone=volume)
xval = np.arange(volume[0][0], volume[0][1] + dxyz[0], dxyz[0])
yval = np.arange(volume[1][0], volume[1][1] + dxyz[1], dxyz[1])
zval = np.arange(volume[2][0], volume[2][1] + dxyz[2], dxyz[2])
df1 = df0[df0.x.isin(xval)]
df2 = df1[df1.y.isin(yval)]
df3 = df2[df2.z.isin(zval)]
stime = np.around(stime, decimals=2)
filename = "TP_data_" + str(stime)
df3.to_hdf(pathout + filename + ".h5", 'w', format='fixed')
# %% save to tecplot format
# in front of the step
df1 = df3.query("x<=0.0 & y>=0.0")
p2p.frame2tec3d(df1, pathout, filename + 'A', zname=1, stime=time)
# %%############################################################################
"""
Streamwise wavenumber and growth rate by spanwise-averaged data
"""
# %% extract probe data along spanwise
VarList = [
'x', 'y', 'z', 'u', 'v', 'w', 'p', 'vorticity_1', 'vorticity_2',
'vorticity_3', 'Q-criterion', 'L2-criterion', '|grad(rho)|', 'T', '|gradp|'
]
path1 = path + "TP_data_01386484/"
equ = ['{|gradp|}=sqrt(ddx({p})**2+ddy({p})**2+ddz({p})**2)']
p2p.ReadINCAResults(path1,
VarList,
SavePath=path,
OutFile="SpanwiseFlow",
Equ=equ)
# %% extract probe data along spanwise
pathSW = path + 'Spanwise/'
x0 = np.arange(-40.0, 0.0 + 0.5, 0.5)
y0 = 0.001953125 # 0.00390625 #
fm = pd.read_hdf(pathSW + "SpanwiseFlow.h5")
for j in range(np.size(x0)):
filenm = pathSW + 'spanwise_' + str(x0[j]) + '.dat'
var = fm.loc[(fm['x'] == x0[j]) & (fm['y'] == y0), ['z', 'u', 'p']].values
df = pd.DataFrame(data=var, columns=['z', 'u', 'p'])
df.to_csv(filenm, sep=' ', index=False, float_format='%1.8e')
base = pd.read_csv(pathL + 'baseline_' + yloc + '.dat',
sep=' ',
'{vx} = ddx({v})',
'{vy} = ddy({v})',
'{vz} = ddz({v})',
'{wx} = ddx({w})',
'{wy} = ddy({w})',
'{wz} = ddz({w})',
'{rhox} = ddx({rho})',
'{rhoy} = ddy({rho})',
'{rhoz} = ddz({rho})',
'{px} = ddx({p})',
'{py} = ddy({p})',
'{pz} = ddz({p})',
]
FoldPath = "/media/weibo/VID2/BFS_M1.7TS1/bubble/" # TS/Slice/TP_2D_S_10/"
OutFolder = "/media/weibo/VID2/BFS_M1.7TS1/" # TS/Slice/TP_2D_S_10/"
subzone = [(-40.0, 30.0), (-3.0, 2.0)]
# subzone = [(-10.0, 30.0), (-3.0, 1.0)]
dirs = os.listdir(FoldPath)
for i in range(np.size(dirs)):
# files = pd.read_csv(OutFolder + 'ZoomZone1.dat', header=None)[0]
# infiles = [os.path.join(filedir, name) for name in files]
filedir = FoldPath + dirs[i] + '/'
outfile = 'TP_data'
with timer("Read " + dirs[i]):
DataFrame, time = \
p2p.ReadINCAResults(filedir, VarList, SubZone=subzone, Equ=equ,
SavePath=OutFolder, OutFile=outfile)
# p2p.ReadINCAResults(FoldPath, VarList, FileName=FoldPath + 'TP_912.plt',
# Equ=equ, SavePath=OutFolder, OutFile='TP_912')
OutFolder3 = "/media/weibo/Data1/BFS_M1.7L_0505/Slice/5C/"
OutFolder4 = "/media/weibo/Data1/BFS_M1.7L_0505/Slice/5D/"
OutFolder5 = "/media/weibo/Data1/BFS_M1.7L_0505/Slice/5E/"
OutFolder6 = "/media/weibo/Data1/BFS_M1.7L_0505/Slice/5F/"
NoBlock = 606
# dirs1 = os.listdir(FoldPath)
subzone = [(-40.0, 70.0), (-3.0, 10.0)]
dirs = os.scandir(FoldPath)
for folder in dirs:
# path = FoldPath+folder.name+"/"
file = FoldPath + folder.name
outfile = os.path.splitext(folder.name)[0]
with timer("Read " + folder.name + " data"):
DataFrame, time = \
p2p.ReadINCAResults(FoldPath, VarList, FileName=file, SubZone=subzone,
SavePath=OutFolder, OutFile=outfile, Equ=equ)
#DataFrame.to_hdf(OutFolder + "SolTime" + "%0.2f"%time + '.h5',
# 'w', format='fixed')
#df1 = p2p.SaveSlice(DataFrame, time, 2.0, OutFolder1)
#df2 = p2p.SaveSlice(DataFrame, time, 1.5, OutFolder1)
#df3 = p2p.SaveSlice(DataFrame, time, 1.0, OutFolder1)
#df4 = p2p.SaveSlice(DataFrame, time, 0.5, OutFolder1)
#df5 = p2p.SaveSlice(DataFrame, time, 0.0, OutFolder1)
#df6 = p2p.SaveSlice(DataFrame, time, -2.0, OutFolder1)
#DataFrame.to_csv(FoldPath + "MeanFlow.dat", sep="\t", index=False,
# header=VarList, float_format='%.10e')
# %% Extract Data for 3D DMD
VarList = ['x', 'y', 'z', 'u', 'v', 'w', 'p', '|gradp|']
VarList = [
'x',
'y',
'z',
'u',
'v',
'w',
'p',
'rho',
'vorticity_1',
'vorticity_2',
'vorticity_3',
'T',
]
df, time = p2p.ReadINCAResults(path1, VarList)
df.to_hdf(path2 + "TP_data_" + str(time) + ".h5", 'w', format='fixed')
# df = pd.read_hdf(path2 + "TP_data_901.h5")
df2 = df.groupby(['x', 'y']).transform(lambda x: (x - x.mean()))
df1 = pd.concat([df[['x', 'y']], df2], axis=1)
df1.to_hdf(path2 + 'ZFluctuation_' + str(time) + '.h5', 'w', format='fixed')
cube = [(-10.0, 20.0), (-3.0, 30.0), (-8, 8)]
df_zone = p2p.extract_zone(path1, cube, filename=path + "ZoneInfo1.dat")
p2p.save_tec_index(df, df_zone, filename=path + "ReadList1.dat")
# %% Save fluctuations flow as tecplot .dat
varname = [
'u',
'v',
'w',
'p',
