import os
from timer import timer
# import tecplot as tp
# import plt2pandas as p2p
import numpy as np
import pandas as pd
import sys
from glob import glob
import variable_analysis as fv
from triaxial_field import TriField as tf
path = "/media/weibo/VID2/BFS_M1.7TS/"
pathV = path + "Vortex/"
flow = tf()
dirs = glob(pathV + '*.h5')
xloc = np.arange(0.125, 30.0 + 0.125, 0.125)
tp_time = np.arange(900, 1000 + 5.0, 5.0)
y = np.linspace(-3.0, 0.0, 151)
z = np.linspace(-8.0, 8.0, 161)
Xtilt1, Xtilt2, Xstret, Xdilate, Xtorque = np.zeros((5, np.size(xloc), 2))
Ytilt1, Ytilt2, Ystret, Ydilate, Ytorque = np.zeros((5, np.size(xloc), 2))
Ztilt1, Ztilt2, Zstret, Zdilate, Ztorque = np.zeros((5, np.size(xloc), 2))
nms1 = ['x', 'tilt1_p', 'tilt1_n', 'tilt2_p', 'tilt2_n', 'stretch_p',
'stretch_n', 'dilate_p', 'dilate_n', 'torque_p', 'torque_n']
enstro = False
x_out = False
y_out = True
ax.ticklabel_format(axis="z", style="sci", scilimits=(-2, 2))
ax.view_init(elev=50, azim=-20)
ax.axes.xaxis.labelpad = 1
ax.axes.yaxis.labelpad = 6
ax.axes.zaxis.labelpad = 0.1
ax.tick_params(axis='x', which='major', pad=1)
ax.tick_params(axis='y', which='major', pad=0.1)
ax.tick_params(axis='z', which='major', pad=0.1)
plt.tight_layout(pad=0.5, w_pad=0.5, h_pad=1)
#plt.savefig(pathF + "3dContourFWPSD.svg")
plt.show()
"""
save wall plane data and plot
"""
# %% plot Cf in x-z plane
time_ave = tf()
filename = glob(path + 'TP_data_1123.h5')
time_ave.load_3data(pathT, FileList=filename, NameList='h5')
# time_ave.load_3data(path+'TP_data_02087427/')
# time_ave._data_field.to_hdf(path+'TP_data_1124.h5', 'w', format='fixed')
time_ave.add_walldist(3.0)
# filename = glob(pathT + 'MeanFlow_*')
# time_ave.load_3data(pathT, FileList=filename, NameList='h5')
df1 = time_ave.TriData.query("x>0 & walldist>0")
df1a = df1.loc[df1['walldist'] == np.min(
df1['walldist'])] # np.min(df1['walldist'])*2
df2 = time_ave.TriData.query("x<=0 & y>=0 & walldist>0")
df2a = df2.loc[df2['walldist'] == np.min(
df2['walldist'])] # on the wall=0.5*dy
df = pd.concat([df1a, df2a])
df.to_hdf(pathT + 'WallValue.h5', 'w', format='fixed')
# %% Load Data for time-averaged results
path4 = "/media/weibo/Data1/BFS_M1.7L/Slice/"
MeanFlow = DataPost()
MeanFlow.UserDataBin(pathF + "SolTime995.00.h5")
# %% Preprocess the data in the xy plane (z=0.0)
MeanFlowZ0 = MeanFlow.DataTab.loc[MeanFlow.DataTab["z"] == 0.0]
MeanFlowZ1 = MeanFlow.DataTab.loc[MeanFlow.DataTab["z"] == 0.5]
MeanFlowZ2 = MeanFlow.DataTab.loc[MeanFlow.DataTab["z"] == -0.5]
MeanFlowZ3 = pd.concat((MeanFlowZ1, MeanFlowZ2))
MeanFlowZ4 = MeanFlowZ3.groupby(["x", "y"]).mean().reset_index()
MeanFlowZ5 = MeanFlowZ4.loc[MeanFlowZ4["y"] > 3.0]
MeanFlowZ0 = pd.concat((MeanFlowZ0, MeanFlowZ5), sort=False)
MeanFlowZ0.to_hdf(pathF + "MeanFlowZ0.h5", "w", format="fixed")
# %% Load data
fluc_flow = tf()
fluc_flow.load_3data(
path, FileList=path+"/ZFluctuation_900.0.h5", NameList='h5'
)
# %% Plot contour of the fluctuation flow field in the xy plane
zslice = fluc_flow.TriData.loc[fluc_flow.TriData['z']==0.0]
x, y = np.meshgrid(np.unique(zslice.x), np.unique(zslice.y))
omegaz = griddata((zslice.x, zslice.y), zslice.vorticity_3, (x, y))
corner = (x < 0.0) & (y < 0.0)
omegaz[corner] = np.nan
fig, ax = plt.subplots(figsize=(6.4, 3.2))
matplotlib.rc("font", size=textsize)
plt.tick_params(labelsize=numsize)
val1 = -0.2
val2 = 0.2
lev1 = np.linspace(val1, val2, 21)
ax.semilogx(freq1 * Lr1, psd1[:, ind1], "k-", linewidth=1.0)
# ax.scatter(Fre1*Lr1, FPSD1, s=10, marker='o',
# facecolors='w', edgecolors='C7', linewidths=0.8)
ax.semilogx(freq3 * Lr3, psd3[:, ind3], "k:", linewidth=1.5)
# ax.set_xscale('log')
ax.yaxis.offsetText.set_fontsize(numsize)
plt.tick_params(labelsize=numsize)
plt.tight_layout(pad=0.5, w_pad=0.8, h_pad=1)
plt.savefig(pathC + "VarFWPSD_com.svg", bbox_inches="tight", pad_inches=0.1)
plt.show()
# %%############################################################################
"""
Development of variables with spanwise direction
"""
# %% load data
TimeFlow0 = tf()
TimeFlow0.load_3data(path0T, FileList=path0 + 'TP_912.plt') #, NameList='plt')
TimeFlow1 = tf()
TimeFlow1.load_3data(path1T, FileList=path1 + 'TP_912.plt') #, NameList='plt')
# %% save data
xyloc = [[-0.1875, 0.03125], [0.203125, 0.0390625], [0.59375, -0.171875],
[2.0, 0.46875]]
for i in range(np.shape(xyloc)[0]):
file = path0P + 'spanwise_' + str(xyloc[i][0]) + '.dat'
var0 = TimeFlow0.TriData
df = var0.loc[(var0['x'] == xyloc[i][0]) & (var0['y'] == xyloc[i][1])]
val0 = df.drop_duplicates(subset='z', keep='first')
val0.to_csv(file, sep=' ', index=False, float_format='%1.8e')
file = path1P + 'spanwise_' + str(xyloc[i][0]) + '.dat'
var1 = TimeFlow1.TriData