In [1]:

    

import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir

import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
sys.path.insert(0,'..')
from notebookFunctions import *
# from .. import notebookFunctions

%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6.180)    #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2


    

In [3]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second_combine/_280-350/2d_z_qw/quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[3]:





[<matplotlib.lines.Line2D at 0x1a18910c18>]






    













    













    













    

In [27]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 330
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[27]:





[<matplotlib.lines.Line2D at 0x1a1ab78d68>]






    













    













    













    

In [10]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 320
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[10]:





[<matplotlib.lines.Line2D at 0x1a194750b8>]






    













    













    













    

In [35]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.17/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
f_original =f
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[35]:





[<matplotlib.lines.Line2D at 0x1a199b0198>]






    













    













    













    

In [64]:

    

plt.plot(f)
plt.plot(f_original)


    

    
Out[64]:





[<matplotlib.lines.Line2D at 0x1a1e1dd470>]






    

In [50]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 320
location = pre + "/second_enhance_n/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
f_original =f
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[50]:





[<matplotlib.lines.Line2D at 0x1a18e684a8>]






    













    













    













    

In [49]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
f_original =f
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[49]:





[<matplotlib.lines.Line2D at 0x1a1beff9e8>]






    













    













    













    

In [67]:

    

plt.plot(f)
plt.plot(f_original)


    

    
Out[67]:





[<matplotlib.lines.Line2D at 0x1a1b844780>]






    

In [73]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_combine/_280-350/2d_z_qw/quick_force_0.15/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
f_original =f
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[73]:





[<matplotlib.lines.Line2D at 0x1a1db4be80>]






    













    













    













    

In [72]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.15_original/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
f_original =f
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[72]:





[<matplotlib.lines.Line2D at 0x1a1c120978>]






    













    













    













    

In [66]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.15_stronger/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[66]:





[<matplotlib.lines.Line2D at 0x1a18536dd8>]






    













    













    













    

In [41]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n2/_280-350/2d_z_qw/quick_force_0.15_strongest/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[41]:





[<matplotlib.lines.Line2D at 0x1a198d9828>]






    













    













    













    

In [62]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.15/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[62]:





[<matplotlib.lines.Line2D at 0x1a1d72c5f8>]






    













    













    













    

In [81]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second_long/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=40,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a0c820ef0>






    
Out[81]:





[<matplotlib.lines.Line2D at 0x1a1cde4630>]






    













    













    













    

In [82]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week/second_long/_280-350/1d_qw/dist_force_0.15/pmf-300.dat"
data = pd.read_table(pre, skiprows=2, sep="\s+", names=["i", "x", "f", "df", "e", "s"])
data.plot("x", "f")


    

    
Out[82]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1d9d8208>






    

In [77]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week/second_long/_280-350/1d_qw/dist_force_0.15/pmf-310.dat"
data = pd.read_table(pre, skiprows=2, sep="\s+", names=["i", "x", "f", "df", "e", "s"])
data.plot("x", "f")


    

    
Out[77]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1da3f828>






    

In [76]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week/second_long/_280-350/1d_qw/dist_force_0.1/pmf-310.dat"
data = pd.read_table(pre, skiprows=2, sep="\s+", names=["i", "x", "f", "df", "e", "s"])
data.plot("x", "f")


    

    
Out[76]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1c2744e0>






    

In [52]:

    

data = np.loadtxt(pre)


    

In [53]:

    

data


    

    
Out[53]:





array([[  0.00000000e+00,   7.73670000e+01,   5.00300000e+00,
          0.00000000e+00,  -1.33031700e+03,  -1.33532000e+03],
       [  1.00000000e+00,   8.24200000e+01,   3.88800000e+00,
          0.00000000e+00,  -1.36260100e+03,  -1.36648900e+03],
       [  2.00000000e+00,   8.74730000e+01,   2.75000000e+00,
          0.00000000e+00,  -1.40574600e+03,  -1.40849600e+03],
       [  3.00000000e+00,   9.25250000e+01,   3.68600000e+00,
          0.00000000e+00,  -1.37118100e+03,  -1.37486700e+03],
       [  4.00000000e+00,   9.75780000e+01,   3.95900000e+00,
          0.00000000e+00,  -1.35351900e+03,  -1.35747800e+03],
       [  5.00000000e+00,   1.02630000e+02,   3.54600000e+00,
          0.00000000e+00,  -1.38224800e+03,  -1.38579500e+03],
       [  6.00000000e+00,   1.07683000e+02,   4.99000000e+00,
          0.00000000e+00,  -1.33185800e+03,  -1.33684700e+03],
       [  7.00000000e+00,   1.12735000e+02,   3.12300000e+00,
          0.00000000e+00,  -1.38714700e+03,  -1.39026900e+03],
       [  8.00000000e+00,   1.17788000e+02,   4.59700000e+00,
          0.00000000e+00,  -1.31421300e+03,  -1.31881000e+03],
       [  9.00000000e+00,   1.22841000e+02,   3.97400000e+00,
          0.00000000e+00,  -1.30354300e+03,  -1.30751700e+03],
       [  1.00000000e+01,   1.27893000e+02,   3.13200000e+00,
          0.00000000e+00,  -1.31147000e+03,  -1.31460200e+03],
       [  1.10000000e+01,   1.32946000e+02,   2.83500000e+00,
          0.00000000e+00,  -1.30342900e+03,  -1.30626400e+03],
       [  1.20000000e+01,   1.37998000e+02,   2.19400000e+00,
          0.00000000e+00,  -1.30367700e+03,  -1.30587100e+03],
       [  1.30000000e+01,   1.43051000e+02,   1.96500000e+00,
          0.00000000e+00,  -1.30170000e+03,  -1.30366500e+03],
       [  1.40000000e+01,   1.48103000e+02,   1.24200000e+00,
          0.00000000e+00,  -1.31167300e+03,  -1.31291500e+03],
       [  1.50000000e+01,   1.53156000e+02,   1.96300000e+00,
          0.00000000e+00,  -1.30330700e+03,  -1.30526900e+03],
       [  1.60000000e+01,   1.58209000e+02,   1.72600000e+00,
          0.00000000e+00,  -1.29875800e+03,  -1.30048300e+03],
       [  1.70000000e+01,   1.63261000e+02,   1.51700000e+00,
          0.00000000e+00,  -1.29600000e+03,  -1.29751700e+03],
       [  1.80000000e+01,   1.68314000e+02,   1.59300000e+00,
          0.00000000e+00,  -1.29826800e+03,  -1.29986100e+03],
       [  1.90000000e+01,   1.73366000e+02,   1.86600000e+00,
          0.00000000e+00,  -1.29922000e+03,  -1.30108600e+03],
       [  2.00000000e+01,   1.78419000e+02,   1.97100000e+00,
          0.00000000e+00,  -1.31227700e+03,  -1.31424700e+03],
       [  2.10000000e+01,   1.83471000e+02,   2.83500000e+00,
          0.00000000e+00,  -1.27933300e+03,  -1.28216800e+03],
       [  2.20000000e+01,   1.88524000e+02,   2.43800000e+00,
          0.00000000e+00,  -1.28872200e+03,  -1.29116000e+03],
       [  2.30000000e+01,   1.93577000e+02,   2.69800000e+00,
          0.00000000e+00,  -1.27453800e+03,  -1.27723600e+03],
       [  2.40000000e+01,   1.98629000e+02,   2.43100000e+00,
          0.00000000e+00,  -1.26975800e+03,  -1.27218900e+03],
       [  2.50000000e+01,   2.03682000e+02,   1.88300000e+00,
          0.00000000e+00,  -1.27636800e+03,  -1.27825000e+03],
       [  2.60000000e+01,   2.08734000e+02,   2.03400000e+00,
          0.00000000e+00,  -1.26121900e+03,  -1.26325300e+03],
       [  2.70000000e+01,   2.13787000e+02,   1.66700000e+00,
          0.00000000e+00,  -1.26413300e+03,  -1.26580000e+03],
       [  2.80000000e+01,   2.18839000e+02,   1.25300000e+00,
          0.00000000e+00,  -1.26645300e+03,  -1.26770600e+03],
       [  2.90000000e+01,   2.23892000e+02,   1.16100000e+00,
          0.00000000e+00,  -1.27048100e+03,  -1.27164300e+03],
       [  3.00000000e+01,   2.28945000e+02,   1.34500000e+00,
          0.00000000e+00,  -1.27397300e+03,  -1.27531800e+03],
       [  3.10000000e+01,   2.33997000e+02,   1.76000000e+00,
          0.00000000e+00,  -1.26421200e+03,  -1.26597200e+03],
       [  3.20000000e+01,   2.39050000e+02,   2.19900000e+00,
          0.00000000e+00,  -1.26620200e+03,  -1.26840100e+03],
       [  3.30000000e+01,   2.44102000e+02,   2.91900000e+00,
          0.00000000e+00,  -1.25560700e+03,  -1.25852600e+03],
       [  3.40000000e+01,   2.49155000e+02,   3.22700000e+00,
          0.00000000e+00,  -1.26196400e+03,  -1.26519100e+03],
       [  3.50000000e+01,   2.54207000e+02,   4.03700000e+00,
          0.00000000e+00,  -1.25857200e+03,  -1.26260900e+03],
       [  3.60000000e+01,   2.59260000e+02,   5.18300000e+00,
          0.00000000e+00,  -1.25560800e+03,  -1.26079100e+03],
       [  3.70000000e+01,   2.64313000e+02,   6.39400000e+00,
          0.00000000e+00,  -1.25293200e+03,  -1.25932700e+03],
       [  3.80000000e+01,   2.69365000e+02,   7.60800000e+00,
          0.00000000e+00,  -1.24919800e+03,  -1.25680700e+03],
       [  3.90000000e+01,   2.74418000e+02,   8.72500000e+00,
          0.00000000e+00,  -1.25167000e+03,  -1.26039500e+03],
       [  4.00000000e+01,   2.79470000e+02,   9.79600000e+00,
          0.00000000e+00,  -1.24807800e+03,  -1.25787300e+03],
       [  4.10000000e+01,   2.84523000e+02,   1.13570000e+01,
          0.00000000e+00,  -1.23561900e+03,  -1.24697600e+03],
       [  4.20000000e+01,   2.89575000e+02,   1.23860000e+01,
          0.00000000e+00,  -1.24009600e+03,  -1.25248200e+03],
       [  4.30000000e+01,   2.94628000e+02,   1.38830000e+01,
          0.00000000e+00,  -1.23951100e+03,  -1.25339400e+03],
       [  4.40000000e+01,   2.99681000e+02,   1.56680000e+01,
          0.00000000e+00,  -1.24072700e+03,  -1.25639500e+03],
       [  4.50000000e+01,   3.04733000e+02,   1.81660000e+01,
          0.00000000e+00,  -1.22723800e+03,  -1.24540400e+03],
       [  4.60000000e+01,   3.09786000e+02,   2.04850000e+01,
          0.00000000e+00,  -1.23134200e+03,  -1.25182700e+03],
       [  4.70000000e+01,   3.14838000e+02,   2.31810000e+01,
          0.00000000e+00,  -1.23344800e+03,  -1.25662900e+03],
       [  4.80000000e+01,   3.19891000e+02,   2.62260000e+01,
          0.00000000e+00,  -1.22542200e+03,  -1.25164900e+03],
       [  4.90000000e+01,   3.24944000e+02,   2.96670000e+01,
          0.00000000e+00,  -1.12898400e+03,  -1.15865100e+03]])

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pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 320
location = pre + "/second_enhance_n/_280-350/2d_z_qw/quick_force_0.15_stronger/"
location2 = location + f"pmf-{temp}.dat"


    

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