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 scipy
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

no force, with force. with perturbation


In [350]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/same_temp/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [353]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/same_temp_no_force/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])


with and without force. no perturbation


In [354]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/same_temp/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [355]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/same_temp_no_force/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])


compare


In [363]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [364]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [365]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [367]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"perturbation-3-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [368]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"perturbation-4-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [ ]:


In [ ]:


In [ ]:

no ss


In [369]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0_no_ss/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [372]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0_no_ss/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])


../notebookFunctions.py:296: UserWarning: loadtxt: Empty input file: "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0_no_ss/perturbation-1-pmf-373.dat"
  data = np.loadtxt(location)
---------------------------------------------------------------------------
AxisError                                 Traceback (most recent call last)
<ipython-input-372-c6f8682057b6> in <module>()
      5 zmax=25
      6 res=40
----> 7 path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
      8 # print(getBound(location2, res=res, zmax=zmax))
      9 xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)

~/opt/notebook/notebookFunctions.py in shortest_path_2(location, temp, start, end, block, res, zmin, zmax, xlabel, ylabel, title, save, plot1d, plot2d)
    295 def shortest_path_2(location, temp="450", start=(4,5), end=-1, block=-1, res=30, zmin=0, zmax=30, xlabel="xlabel", ylabel="ylabel", title="AverageZ_Dis", save=False, plot1d=1, plot2d=True):
    296     data = np.loadtxt(location)
--> 297     xi, yi, zi = getxyz(data, res=res, zmin=zmin, zmax=zmax)
    298     zi = np.where(np.isnan(zi), 50, zi)
    299 

~/opt/notebook/notebookFunctions.py in getxyz(data, res, zmin, zmax, x, y, z, xmin, xmax, ymin, ymax)
    112 def getxyz(data, res=30, zmin=0, zmax=20, x=1, y=2, z=3, xmin=-1,xmax=-1,ymin=-1,ymax=-1):
    113     # data = np.where(np.isnan(data), zmax, data)
--> 114     data = data[~np.isnan(data).any(axis=1)]  # remove rows with nan
    115     if zmin == -1:
    116         zmin = data[:,3].min()

~/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py in _any(a, axis, dtype, out, keepdims)
     36 
     37 def _any(a, axis=None, dtype=None, out=None, keepdims=False):
---> 38     return umr_any(a, axis, dtype, out, keepdims)
     39 
     40 def _all(a, axis=None, dtype=None, out=None, keepdims=False):

AxisError: axis 1 is out of bounds for array of dimension 1

In [ ]:

force 0.1, no ss


In [ ]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 373
location = pre + "how_force_bias_added_4/_280-350/2d_zAverage_dis/force_0.0_no_ss/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])

In [ ]:


In [ ]:


In [351]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 370
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [ ]:


In [ ]:


In [ ]:


In [263]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/second_half/_280-350/2d_zAverage_dis/force_0.1/"
location1 = location + f"perturbation-1-pmf-{temp}.dat"
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/first_half/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
plot2d_side_by_side(location1, location2, xlabel="Distance", ylabel="AverageZ", title1="first", title2="second")
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/convergence_2d.png")



In [344]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 330
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin, _ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=1, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [342]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin, _ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=1, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [347]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/"
temp = 370
location = pre + "combined_more_force/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin,_ = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=0, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.xlim([20,280])
# plt.ylim([-25,10])



In [266]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=0, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.xlim([20,280])
plt.ylim([-25,10])


Out[266]:
(-25, 10)

In [269]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/second_half/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin_second, f_origin_second = shortest_path_2(location2, start=(16, 30), end=(26,0), title="second_half_AverageZ_Dis",save=False, plot1d=0, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.xlim([20,280])
plt.ylim([-25,10])


Out[269]:
(-25, 10)

In [271]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/first_half/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin_first, f_origin_first = shortest_path_2(location2, start=(22, 30), end=(33,0), title="first_half_AverageZ_Dis",save=False, plot1d=0, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.xlim([20,280])
plt.ylim([-25,10])


Out[271]:
(-25, 10)

In [273]:
len(f_origin_second)


Out[273]:
31

In [274]:
len(f_origin_first)


Out[274]:
31

In [275]:
len(f_origin)


Out[275]:
31

In [283]:
x_on_path = np.array(path_origin)
d = pd.DataFrame(data={"x":x_on_path, "y":f_origin})
# mean the dupliation
d = d.groupby("x").mean().reset_index().values
x_smooth = np.linspace(d[:,0].min(), d[:,0].max(), 200)
spl = scipy.interpolate.interp1d(d[:,0], d[:,1], kind="cubic")

In [284]:
x_on_path = np.array(path_origin_first)
d = pd.DataFrame(data={"x":x_on_path, "y":f_origin_first})
# mean the dupliation
d = d.groupby("x").mean().reset_index().values
x_smooth_first = np.linspace(d[:,0].min(), d[:,0].max(), 200)
spl_first = scipy.interpolate.interp1d(d[:,0], d[:,1], kind="cubic")

In [285]:
x_on_path = np.array(path_origin_second)
d = pd.DataFrame(data={"x":x_on_path, "y":f_origin_second})
# mean the dupliation
d = d.groupby("x").mean().reset_index().values
x_smooth_second = np.linspace(d[:,0].min(), d[:,0].max(), 200)
spl_second = scipy.interpolate.interp1d(d[:,0], d[:,1], kind="cubic")

In [287]:
plt.plot(x_smooth, spl(x_smooth), label="combined")
plt.plot(x_smooth_first, spl_first(x_smooth_first), label="first")
plt.plot(x_smooth_second, spl_second(x_smooth_second), label="second")
plt.legend()
plt.ylim(0,20)
plt.xlabel("End to end distance(Å)")
plt.ylabel("Free energy(kT)")
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/convergence_1d.png")



In [276]:
plt.plot(x_smooth, spl(x_smooth), label="combined")
plt.plot(x_smooth, spl_first(x_smooth), label="first")
plt.plot(x_smooth, spl_second(x_smooth), label="second")
plt.legend()
plt.ylim(0,20)
plt.xlabel("End to end distance(Å)")
plt.ylabel("Free energy(kT)")
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/convergence_1d.png")



In [335]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 330
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [150]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [3]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 390
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.0/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [4]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 360
location = pre + "/combined/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
zmax=40
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [290]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,20), end=(3, 30),save=False, xlabel="Z_h56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [11]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,20), end=(3, 30),save=False, xlabel="Z_h56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [ ]:
"/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/combined_more_force_fix_order/_280-350/56_z_dis/force_0.1/"

In [325]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/combined_more_force_fix_order/_280-350/56_z_dis/force_0.1/"
location1 = location + f"perturbation-1-pmf-{temp}.dat"
temp = 340
location = pre + "/combined_more_force_fix_order/_280-350/56_z_dis/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
plot2d_side_by_side(location1, location2, xlabel="D_h56", ylabel="Z_h56", title1="with perturbation", title2="without perturbation")
# plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_2d_compare.png")



In [331]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/combined/_280-350/56_z_dis/force_0.1/"
location1 = location + f"perturbation-2-pmf-{temp}.dat"
temp = 340
location = pre + "/combined/_280-350/56_z_dis/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
plot2d_side_by_side(location1, location2, xlabel="D_h56", ylabel="Z_h56", title1="with perturbation", title2="without perturbation")
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_2d_compare.png")



In [333]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 340
location = pre + "/combined/_280-350/56_z_dis/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin_2, f_origin_2 = shortest_path_2(location2, start=(35,20), end=(3, 30),save=False, xlabel="Z_h56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [311]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 370
location = pre + "/combined/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(33,20), end=(3, 30),save=False, xlabel="Z_h56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [317]:
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="with perturbation")

spl_2 = scipy.interpolate.interp1d(x, f_origin_2, kind="cubic")
plt.plot(x_smooth, spl_2(x_smooth), label="without perturbation")
plt.legend()
plt.ylabel("Free energy(kT)")
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_1d_compare")



In [306]:
len(f_origin_2)


Out[306]:
33

In [312]:
len(f_origin)


Out[312]:
33

In [302]:
plt.plot(f_origin_2)
plt.plot(f_origin)


Out[302]:
[<matplotlib.lines.Line2D at 0x1a5cf750f0>]

In [15]:
pre = "/Users/weilu/Research/server/aug_2018/01_week/freeEnergy"
temp = 340
location = pre + "/combined/_280-350/56_z_dis/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,20), end=(3, 30),save=False, xlabel="Z_h56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [2]:
data = pd.read_feather("/Users/weilu/Research/server/aug_2018/01_week/freeEnergy/all_data_folder/second_start_extended_combined_may19.feather")

In [21]:
data.query("TempT != 417 and DisReal > 50 \
           and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
        query("Lipid1 < -0.5 and Lipid7 <  -0.5").hist("TotalE",bins=50)


Out[21]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a5d5d17f0>]], dtype=object)

In [5]:
a.columns


Out[5]:
Index(['level_0', 'AMH', 'AMH-Go', 'AMH_3H', 'AMH_4H', 'BiasTo', 'DisReal',
       'Dis_h56', 'Distance', 'Energy', 'Lipid', 'Lipid1', 'Lipid10',
       'Lipid11', 'Lipid12', 'Lipid13', 'Lipid14', 'Lipid15', 'Lipid2',
       'Lipid3', 'Lipid4', 'Lipid5', 'Lipid6', 'Lipid7', 'Lipid8', 'Lipid9',
       'Membrane', 'Qw', 'Rg', 'Run', 'Step', 'Temp', 'TempT', 'TotalE',
       'abs_z_average', 'index', 'rg1', 'rg2', 'rg3', 'rg4', 'rg5', 'rg6',
       'rg_all', 'z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5', 'z_h6'],
      dtype='object')

In [61]:
data.query("TempT == 373 and BiasTo <= 110 and Step > 7e7")


Out[61]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... rg5 rg6 rg_all z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6
1040000 360000 -178.321133 -412.066149 -235.338918 -292.892214 86.0 79.004676 28.346208 34.986200 -714.949919 ... 0.668179 1.034435 6.755773 -1.033087 2.494844 -4.841397 -3.889325 -8.467838 -5.754722 -4.876540
1040004 360004 -177.200623 -410.571989 -235.760741 -294.325427 86.0 83.372854 18.937570 40.892598 -722.068300 ... 0.801884 1.374862 6.547033 -2.802593 0.773482 -9.611948 -6.093863 -9.556687 -7.412869 -4.627857
1040008 360008 -180.879480 -407.788823 -235.803323 -294.257476 86.0 89.521842 22.380344 46.390928 -709.820249 ... 0.645107 2.179880 7.059012 -2.171087 1.730007 -7.565345 -5.411125 -9.619961 -7.155922 -3.403780
1040012 360012 -171.128915 -409.410718 -233.914608 -289.781585 86.0 83.579512 21.361462 38.329813 -702.409638 ... 0.808295 1.102991 6.581213 -0.585338 3.456519 -5.765632 -2.172227 -7.773421 -3.901099 -3.266604
1040016 360016 -171.690895 -401.386354 -231.802732 -283.413267 86.0 81.770035 25.603405 36.290812 -666.730519 ... 0.890132 1.092686 6.349323 -0.737092 4.747721 -7.508631 -2.545017 -8.130889 -5.948760 -2.941567
1040020 360020 -175.212562 -411.063653 -238.114432 -294.465943 86.0 77.052335 23.153542 45.458047 -669.801538 ... 0.653753 0.887969 5.381157 -2.722350 -0.396683 -6.997332 -3.931108 -10.986878 -8.555905 -6.895371
1040024 360024 -175.051816 -412.711876 -238.117980 -294.301467 86.0 74.364323 23.750555 34.865682 -692.663837 ... 0.777575 1.194753 5.701887 -2.527804 -2.874243 -7.025268 -4.092978 -8.792953 -5.815720 -4.356905
1040028 360028 -175.528304 -416.805217 -237.790195 -291.876143 86.0 80.122468 24.758321 30.863065 -685.537891 ... 1.139500 1.076961 6.526219 -1.517133 0.844767 -8.072552 -3.069387 -9.641124 -3.768143 -5.261024
1040032 360032 -181.120685 -412.380862 -238.466680 -295.650334 86.0 86.451046 24.091012 45.483044 -729.879618 ... 1.204523 1.043938 5.959973 -1.920873 2.254641 -8.315136 -5.301401 -10.772393 -4.227035 -5.493539
1040036 360036 -178.814269 -416.989089 -239.063277 -299.583833 86.0 79.699798 28.826969 54.038287 -676.019765 ... 0.761742 1.273970 7.347845 -1.006030 1.550467 -6.880606 -4.333249 -8.250059 -7.076709 -3.398288
1040040 360040 -176.391959 -400.183354 -231.084472 -288.985686 86.0 82.508970 21.816418 59.951966 -717.737985 ... 0.893372 1.590578 7.466062 -1.061339 -0.482857 -5.617197 -3.676639 -8.151255 -6.530324 -2.009296
1040044 360044 -173.127191 -407.211710 -233.486166 -290.528843 86.0 89.073286 19.509020 61.372443 -732.020460 ... 0.888663 1.403316 6.374586 -3.019790 -2.001518 -9.246640 -5.224385 -10.428687 -6.205725 -4.419867
1040048 360048 -176.159288 -407.148601 -235.147875 -292.618584 86.0 84.169299 20.751592 58.872895 -658.352557 ... 1.406614 1.060973 8.238610 -1.921348 -0.921587 -5.368779 -4.396217 -7.841318 -5.161096 -4.431059
1040052 360052 -170.038768 -399.880548 -228.766385 -288.757755 86.0 80.844108 24.695191 54.629178 -679.401083 ... 1.049406 0.712622 5.815108 -0.935673 3.467459 -7.610551 -3.679222 -8.930749 -4.574644 -4.715171
1040056 360056 -175.358559 -410.868341 -237.888724 -293.781247 86.0 81.896552 29.124802 47.401280 -666.541728 ... 0.775782 0.951432 5.729818 -0.689007 5.821621 -8.154914 -3.963428 -9.973349 -3.317898 -6.159056
1040060 360060 -173.799222 -408.879832 -234.117546 -286.423280 86.0 78.737365 27.334346 38.533985 -706.780273 ... 0.753349 1.672872 6.416538 -0.531145 2.205442 -7.684658 -2.714931 -7.985872 -3.132108 -4.434411
1040064 360064 -181.488187 -421.780140 -245.059600 -303.228605 86.0 82.443252 27.788711 43.035056 -698.986005 ... 0.728864 1.973502 7.123840 -2.191063 -0.662584 -8.252737 -3.265645 -9.899119 -6.600647 -3.667702
1040068 360068 -179.589811 -430.280836 -241.887834 -300.967252 86.0 89.178602 27.827319 46.411033 -689.978286 ... 0.656411 1.600334 7.017798 -3.347456 -3.696312 -10.186573 -3.769876 -11.217579 -8.287199 -4.838538
1040072 360072 -172.521207 -422.922929 -234.609442 -294.265890 86.0 83.961348 27.631373 51.670522 -726.118718 ... 1.446974 0.812705 5.930572 -2.954025 -6.443165 -6.644847 -4.787638 -10.387694 -6.738329 -6.330282
1040076 360076 -170.824914 -403.499904 -228.189896 -282.589280 86.0 89.758417 31.261850 60.175974 -687.180015 ... 1.139087 0.961599 6.194648 -2.848726 -2.545971 -7.447305 -7.438930 -9.736082 -3.829788 -6.344437
1040080 360080 -172.353224 -417.926752 -234.880431 -290.191957 86.0 83.621083 24.833195 62.062661 -717.918247 ... 0.792342 1.044773 6.816250 -2.658299 1.223716 -8.967760 -4.845812 -10.239304 -5.225054 -5.782689
1040084 360084 -169.062843 -402.589216 -229.412124 -284.619397 86.0 92.735613 23.712617 64.805045 -659.914007 ... 0.621149 1.815832 8.079380 -2.138903 1.251279 -10.091001 -2.956303 -9.662481 -9.629761 -3.507532
1040088 360088 -166.263324 -397.886794 -227.395698 -281.576073 86.0 84.646715 24.955095 49.642607 -689.141098 ... 0.732721 2.464527 9.348828 -1.416447 1.779372 -8.370172 -1.761939 -9.114509 -5.580449 -4.205461
1040092 360092 -167.332323 -410.173596 -227.515182 -281.436701 86.0 87.072746 23.972244 50.353858 -709.219305 ... 0.586615 1.172001 6.513027 -1.603906 -0.796512 -7.491999 -3.433567 -8.355756 -8.771330 -3.805017
1040096 360096 -171.630686 -418.551968 -237.619118 -294.737442 86.0 89.672098 22.233028 47.692004 -703.749847 ... 0.624688 1.326772 6.375756 -2.064348 -2.380637 -8.491368 -1.961614 -10.468627 -7.508679 -4.598695
1040100 360100 -156.536762 -402.638659 -226.041474 -282.129511 86.0 88.169635 24.976922 53.151127 -715.129270 ... 1.039153 1.394437 7.792985 -1.478542 -0.803532 -8.203470 -2.130952 -9.678299 -6.178723 -3.905886
1040104 360104 -169.927578 -415.630097 -238.227771 -292.854323 86.0 80.709913 26.526297 41.992529 -683.099433 ... 0.967620 1.381514 8.716215 -0.627672 1.767935 -6.814654 -1.853433 -9.084486 -5.837010 -3.313316
1040108 360108 -171.596814 -421.522543 -240.124993 -293.557808 86.0 86.650605 25.637778 48.912595 -692.121311 ... 0.739439 1.233560 7.896235 -1.828815 -2.209892 -7.936734 -2.929359 -8.707512 -5.800156 -4.395571
1040112 360112 -170.300754 -421.634321 -238.065194 -297.333886 86.0 88.891380 26.061075 56.995997 -740.098528 ... 0.796858 1.510952 6.735176 -2.946861 -5.387177 -8.767923 -3.590466 -10.129330 -7.490393 -3.741897
1040116 360116 -175.875351 -422.493165 -244.371422 -300.027082 86.0 87.809170 29.832796 52.464183 -712.005605 ... 0.855994 1.553571 8.318154 -3.597512 -4.986482 -9.995631 -4.223783 -10.116652 -7.974839 -6.354995
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1399880 719880 -167.873191 -420.317560 -234.366110 -293.893035 102.0 105.129832 26.922379 -8.712567 -721.452942 ... 0.810585 0.995656 6.468342 -1.896878 -0.249820 -7.773691 -3.274044 -8.895507 -8.024724 -5.321279
1399884 719884 -178.802889 -439.933693 -246.790750 -303.078149 102.0 96.474555 25.804714 1.041885 -715.812440 ... 0.713994 0.910584 6.355773 -1.407569 -0.557523 -6.844717 -4.834720 -8.198662 -6.040549 -3.976762
1399888 719888 -176.863627 -437.054123 -243.442273 -304.619946 102.0 102.404041 26.591604 8.756516 -759.546484 ... 0.651169 1.673616 8.724481 -2.599015 -3.983663 -7.726893 -3.029118 -9.552410 -9.200229 -5.517792
1399892 719892 -176.208997 -427.695444 -242.080839 -299.354508 102.0 94.204510 19.900871 13.362841 -722.241615 ... 0.634370 1.355714 7.859970 -2.626734 -3.505683 -8.760510 -1.678004 -9.650746 -7.864757 -4.706013
1399896 719896 -175.488458 -430.201979 -243.006790 -304.148260 102.0 97.815834 25.025367 -5.390786 -711.899063 ... 0.684174 2.067361 8.206639 -2.767956 0.732283 -9.867537 -4.426434 -10.633748 -6.037644 -5.849769
1399900 719900 -177.767288 -429.752719 -244.980743 -303.460656 102.0 86.266374 23.054795 -0.025271 -757.041062 ... 0.949211 1.296958 7.096959 -2.199023 1.546567 -10.118112 -3.953230 -10.776679 -6.370534 -4.728151
1399904 719904 -172.165504 -410.013523 -236.884910 -294.103756 102.0 94.096201 26.288277 -16.917646 -703.475483 ... 1.752571 2.406353 9.733213 -1.902741 -1.495095 -8.550650 -4.205672 -9.204814 -6.287052 -3.421558
1399908 719908 -168.292244 -417.785675 -232.192267 -292.145625 102.0 92.532087 26.133086 -8.232180 -690.272283 ... 1.512481 1.629408 7.417447 -2.307195 -5.164502 -9.473234 -3.299571 -9.819056 -1.422691 -3.805257
1399912 719912 -174.844517 -429.255487 -242.624453 -303.818717 102.0 93.009973 27.600176 6.248464 -716.991288 ... 0.835384 1.795459 7.996992 -1.128325 -2.886495 -6.338056 0.080677 -9.413851 -3.291499 -4.038922
1399916 719916 -171.157617 -419.741687 -233.307850 -292.824429 102.0 87.286915 22.543370 -8.500810 -707.557635 ... 0.795607 1.386610 7.316347 -1.623106 0.628509 -6.943735 -1.168389 -7.851464 -8.789331 -4.253531
1399920 719920 -176.924966 -425.104031 -240.719726 -296.888741 102.0 103.103851 24.226896 -18.898382 -701.659721 ... 0.546435 1.898461 8.101888 -2.418715 0.914652 -8.192813 -4.518817 -10.448267 -8.766651 -5.033113
1399924 719924 -179.619157 -433.953855 -247.269573 -302.448105 102.0 97.640849 25.236140 -6.973110 -710.993057 ... 0.790202 0.882058 6.749890 -2.768696 -0.352342 -10.536011 -4.440945 -9.610149 -7.435535 -5.353405
1399928 719928 -168.807795 -420.215204 -236.145046 -293.984978 102.0 99.478372 30.185217 -12.988214 -711.364696 ... 0.790430 1.153922 6.390102 -2.253980 -0.549764 -7.266207 -4.704123 -10.206608 -4.987632 -5.638974
1399932 719932 -176.620854 -416.787586 -238.986422 -294.790658 102.0 88.785668 25.917881 -14.103072 -722.311156 ... 1.354204 1.265060 7.871237 -1.394978 -0.433176 -7.187144 -3.488160 -9.590172 -2.172991 -5.683134
1399936 719936 -176.110274 -428.249885 -242.227502 -300.997032 102.0 101.455216 29.682117 -6.394470 -754.493209 ... 0.854150 1.019850 6.411265 -1.451188 -0.719805 -5.824830 -3.691924 -9.331727 -5.030478 -4.300115
1399940 719940 -179.083509 -423.793892 -241.575534 -297.512667 102.0 97.549209 25.600382 -8.995992 -729.385310 ... 0.823946 1.002124 7.344599 -1.238961 -0.695822 -5.542229 -2.449473 -7.766112 -5.554014 -3.534813
1399944 719944 -174.740001 -430.785428 -240.747522 -295.247062 102.0 98.193574 24.649803 -20.969052 -710.228212 ... 0.776585 2.530289 9.866398 -1.054915 -1.585248 -7.295722 -0.935876 -7.634522 -4.610581 -1.762488
1399948 719948 -182.358432 -436.363036 -246.521078 -303.498691 102.0 100.526931 25.893450 -14.689841 -727.148151 ... 0.691536 1.693629 8.425749 -0.688771 0.815369 -7.753703 -1.970145 -7.611946 -4.526404 -2.527664
1399952 719952 -174.866835 -433.599151 -237.823821 -300.991038 102.0 98.364889 24.566608 -16.862624 -719.232667 ... 0.905176 1.185738 7.615932 -0.638713 3.500591 -5.843704 -1.634829 -8.872617 -5.318635 -4.348143
1399956 719956 -180.320241 -425.319689 -246.272825 -303.566914 102.0 98.591834 24.481151 -13.504563 -734.152998 ... 0.886513 0.906708 9.292772 -0.912170 0.571090 -5.721099 -1.153354 -8.805731 -5.241378 -2.621888
1399960 719960 -173.639013 -425.426029 -240.314975 -298.637223 102.0 104.498741 27.214486 -21.697987 -705.291143 ... 0.751882 1.468870 7.140713 -0.769137 -1.390923 -7.066251 -1.790164 -7.914039 -2.396733 -2.916757
1399964 719964 -176.342983 -430.065827 -241.872734 -297.662800 102.0 94.601480 24.014857 -20.152523 -757.699291 ... 1.578374 0.790147 7.283029 -0.831442 -1.645917 -4.860872 -2.568839 -7.431155 -2.523078 -3.777256
1399968 719968 -174.801217 -417.709223 -241.060070 -297.178644 102.0 92.608063 28.177941 -30.349731 -739.351893 ... 0.901874 1.002961 8.198290 -1.806930 0.094815 -6.692053 -5.498560 -10.311017 -3.362237 -5.599222
1399972 719972 -178.725030 -423.488574 -243.998078 -302.548105 102.0 96.554771 25.599314 -14.953278 -768.801626 ... 0.670990 1.274940 6.721008 -2.466561 4.213255 -9.400574 -6.523701 -10.939101 -6.741168 -6.460690
1399976 719976 -175.134080 -424.742064 -237.100356 -298.576695 102.0 92.634826 25.750346 4.858877 -734.720144 ... 0.725933 1.079715 5.953475 -2.298796 3.355892 -8.055047 -4.292885 -10.894707 -5.941350 -5.975201
1399980 719980 -180.838873 -422.128821 -243.769311 -300.386956 102.0 100.590423 22.496298 -0.025247 -734.805861 ... 0.778388 1.248211 6.998108 -2.384341 -0.510537 -7.444899 -3.000111 -10.165996 -5.424914 -4.879895
1399984 719984 -182.677336 -431.877114 -249.875599 -305.882569 102.0 85.560026 24.179652 8.871215 -730.945732 ... 0.628557 1.485071 7.052195 -1.286153 -0.547296 -6.717355 -0.715170 -8.234139 -6.267730 -2.602707
1399988 719988 -187.890123 -446.317112 -257.119450 -314.595805 102.0 100.997173 20.662907 9.305860 -750.598109 ... 0.776280 1.549393 7.889488 -1.488352 -2.559167 -7.074563 -1.225270 -9.301677 -4.545633 -3.928111
1399992 719992 -169.386925 -413.082833 -233.157375 -288.669058 102.0 96.031979 26.280834 3.976283 -749.949139 ... 1.543018 1.300873 8.176069 -1.709614 -1.889524 -8.307254 -3.298733 -8.742325 -4.117646 -3.367814
1399996 719996 -177.297319 -423.665643 -239.064086 -296.990102 102.0 87.471225 25.817823 2.633153 -732.767353 ... 1.145461 0.718796 7.150671 -2.570168 -1.718925 -8.955210 -6.736448 -10.122350 -1.541202 -6.733705

90000 rows × 50 columns


In [60]:
data.query("TempT == 373 and BiasTo <= 110 and Step <= 7e7")


Out[60]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... rg5 rg6 rg_all z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6
680000 0 -178.627890 -433.576377 -246.583490 -305.298335 86.0 77.046868 26.215912 -62.043353 -692.687622 ... 0.673049 0.919396 5.798037 -2.513710 0.375505 -9.194752 -5.344894 -10.214853 -8.348931 -6.222135
680004 4 -175.587111 -437.778327 -244.343016 -306.057510 86.0 84.641804 28.340007 -69.407894 -698.237107 ... 1.344175 0.593261 6.502283 -1.902340 -0.608567 -5.746729 -5.571338 -9.711419 -4.282059 -7.395059
680008 8 -165.881609 -423.144257 -234.028768 -292.861082 86.0 74.876910 25.385569 -64.309426 -749.615666 ... 1.215953 0.602852 8.183012 -1.017174 0.798023 -5.607535 -4.058465 -7.780378 -2.954611 -5.257975
680012 12 -167.368034 -414.262203 -234.216790 -291.858831 86.0 81.477400 26.715844 -68.012403 -726.209605 ... 0.865621 0.713431 7.213472 -1.083818 -0.083613 -6.680219 -3.086740 -7.537329 -2.432568 -4.509047
680016 16 -168.401126 -419.798745 -235.924695 -289.758702 86.0 86.972885 26.873403 -71.062836 -741.797971 ... 0.672385 1.019587 6.424920 -1.817486 0.078821 -9.732506 -2.095835 -8.339287 -4.815011 -4.539695
680020 20 -168.417105 -414.268271 -234.798836 -293.403483 86.0 80.572839 26.894212 -65.131907 -708.509981 ... 0.640655 1.520420 8.424260 -3.195074 -0.529479 -11.319999 -4.137169 -9.983859 -10.795163 -4.576906
680024 24 -177.606832 -426.642247 -244.850904 -302.197238 86.0 82.270854 24.647507 -55.766106 -738.639350 ... 1.323559 1.055352 8.960091 -2.046927 -0.972552 -10.169295 -2.807205 -9.228246 -7.467926 -4.107037
680028 28 -180.459864 -439.735869 -247.435036 -305.428322 86.0 80.314097 28.569759 -55.298821 -746.464243 ... 1.037181 1.447303 7.319102 -1.941985 0.159153 -8.898918 -4.028470 -10.119860 -3.383570 -4.982699
680032 32 -179.267787 -428.128942 -245.639078 -305.704172 86.0 79.921998 27.595099 -56.501167 -721.621219 ... 0.533845 1.046649 6.232830 -2.497712 -3.990375 -7.887636 -4.144892 -7.916204 -6.313308 -5.585080
680037 37 -173.709175 -429.203682 -239.784664 -296.327575 86.0 86.673514 25.572349 -59.753635 -761.847772 ... 0.937518 1.643031 8.069047 -1.070941 2.749444 -7.096093 -2.850150 -7.746216 -8.232665 -2.537201
680041 41 -181.397849 -433.267174 -249.133542 -308.396115 86.0 79.996530 28.375501 -54.671080 -745.754904 ... 0.693941 1.000800 6.559898 -0.901434 6.300291 -7.198008 -3.387504 -8.760150 -9.301067 -4.664306
680045 45 -170.807903 -421.794452 -239.953114 -299.399897 86.0 83.761704 24.508192 -62.021529 -713.617567 ... 0.905674 1.666285 6.670916 -1.748091 2.727831 -6.584626 -3.977139 -10.557416 -8.990149 -5.821967
680049 49 -174.072632 -430.605965 -240.470971 -300.495133 86.0 73.249581 23.858677 -52.663421 -705.741769 ... 0.585151 1.419662 7.087266 -2.898799 0.720171 -8.395684 -4.299279 -10.138884 -11.377393 -5.262938
680053 53 -183.067988 -437.526326 -250.709100 -310.138039 86.0 72.544445 25.152731 -50.433734 -706.165128 ... 0.737501 1.894636 8.132551 -3.272633 0.264180 -10.463897 -4.868414 -11.806258 -6.519880 -5.494872
680057 57 -177.646594 -433.073194 -244.586170 -304.116005 86.0 73.771414 24.894960 -54.685395 -734.101419 ... 0.783258 1.755530 7.922873 -2.367993 1.205303 -9.326395 -3.872550 -10.071499 -6.623933 -5.584688
680061 61 -169.310992 -426.334393 -239.982226 -303.932144 86.0 81.416961 23.663510 -58.574001 -708.929448 ... 0.711684 1.565314 8.137151 -0.475804 5.401942 -7.602438 -1.565347 -8.983180 -5.946696 -4.219178
680065 65 -177.864410 -421.762013 -243.662276 -304.457596 86.0 80.005193 25.738388 -59.950871 -684.808634 ... 0.792590 1.677071 6.842383 0.405313 4.268532 -4.901514 -2.497166 -7.558315 -2.870232 -2.851323
680069 69 -172.807236 -428.844653 -241.376326 -302.052337 86.0 73.603326 25.711705 -46.012294 -675.262390 ... 0.919871 1.127971 7.832089 -0.577113 -0.398513 -7.433651 -2.227880 -7.791227 -3.741178 -4.226190
680073 73 -167.891197 -415.284718 -235.025349 -290.018233 86.0 83.588321 24.813961 -55.251722 -689.008759 ... 0.912846 1.012170 6.319360 -0.568639 -0.208993 -5.149064 -3.772533 -7.574200 -4.056595 -4.296551
680077 77 -162.291277 -411.818137 -230.988379 -287.053193 86.0 80.464139 26.929268 -46.048159 -697.164945 ... 1.071982 1.116906 6.311088 -1.196534 -0.312192 -6.356926 -2.366787 -9.199642 -4.291848 -5.000103
680081 81 -174.351006 -421.494358 -239.134631 -292.957119 86.0 79.439663 27.102657 -48.887673 -736.042247 ... 0.977467 1.327099 8.167270 -2.008897 0.087121 -8.881415 -3.045696 -9.379493 -4.155787 -5.592324
680085 85 -177.668803 -417.377477 -243.759642 -296.566328 86.0 82.652477 22.444409 -49.844887 -735.276987 ... 0.783558 1.000792 6.778545 -2.515193 -0.128088 -9.244176 -4.760495 -9.639853 -7.447561 -4.580941
680089 89 -170.622078 -420.756207 -239.300694 -295.801134 86.0 86.789300 23.942679 -46.994700 -706.319657 ... 1.064017 0.707098 7.070533 -2.351125 -1.998373 -7.659630 -5.944035 -9.695609 -4.381162 -5.726317
680093 93 -178.343307 -421.983700 -243.039196 -302.234251 86.0 82.592678 24.320193 -41.912411 -707.593309 ... 1.585929 0.626126 7.159592 -1.349485 -0.287048 -7.193812 -5.174934 -7.923904 -2.544830 -5.202762
680097 97 -183.970071 -432.755516 -251.294367 -311.180009 86.0 82.764819 25.270548 -44.786741 -723.897059 ... 1.563171 1.128001 6.891107 -0.525293 -0.439146 -6.472813 -2.793318 -8.493326 -1.417257 -3.670667
680101 101 -181.122797 -425.271404 -246.880812 -305.869600 86.0 75.380470 23.064550 -37.575314 -746.296837 ... 0.736561 0.932424 6.588177 0.865909 -1.704513 -5.339777 -1.178962 -6.558756 2.258882 -1.328502
680105 105 -180.206489 -424.616331 -246.547665 -306.768218 86.0 81.266168 28.625813 -48.479376 -745.692735 ... 0.715490 0.883699 6.410821 0.250736 -0.356335 -4.643974 -3.902111 -8.199770 1.614698 -3.876967
680109 109 -172.906230 -412.476227 -237.808950 -296.737280 86.0 81.735155 18.555755 -35.748195 -703.960340 ... 1.156780 0.906065 6.108166 -1.095344 2.199232 -4.167308 -6.339922 -9.905547 -0.427039 -6.459992
680113 113 -171.647892 -408.875219 -235.051976 -292.107571 86.0 87.379765 23.612524 -40.831087 -665.479968 ... 1.336964 1.264103 6.339313 -1.192798 2.695925 -7.097114 -7.526562 -8.968586 -2.133536 -3.966697
680117 117 -174.045988 -416.049367 -239.766936 -296.387199 86.0 83.026696 26.190441 -38.081751 -696.329919 ... 0.838896 1.841936 7.086207 -0.475587 1.592877 -5.852673 -4.647440 -9.344295 -2.161598 -4.365486
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1039881 359881 -169.239872 -413.033116 -230.686340 -289.735055 102.0 95.984580 27.621016 -9.346732 -677.882277 ... 0.671354 1.823187 7.990326 -1.379964 2.144891 -7.582088 -1.625324 -8.021491 -9.883906 -1.968718
1039885 359885 -173.367835 -405.071555 -232.862340 -292.065083 102.0 99.692142 30.073374 -14.314371 -678.642007 ... 1.275475 2.466391 9.432734 -2.194855 -1.999249 -7.464181 -1.035693 -8.906041 -9.055224 -3.508659
1039889 359889 -176.303736 -417.281231 -234.936407 -289.201397 102.0 99.148833 25.608730 -7.449356 -683.783742 ... 1.147920 1.244300 6.173977 -2.636067 -4.112425 -6.420534 -3.271775 -9.848532 -7.434615 -5.775168
1039893 359893 -163.628783 -411.919935 -224.253288 -283.050245 102.0 99.405306 29.041790 -13.900390 -663.330040 ... 0.975502 1.406572 6.835516 -2.512110 -2.777126 -6.824069 -6.077053 -10.970709 -2.851308 -6.064603
1039897 359897 -168.257874 -413.422018 -230.645297 -291.518717 102.0 95.561047 24.087333 -9.490940 -732.149183 ... 1.525397 1.170476 8.409282 -1.649929 -1.426113 -7.991750 -5.051505 -8.522498 -4.043999 -3.878205
1039901 359901 -177.974412 -425.347136 -240.932171 -300.326284 102.0 96.305442 25.336245 -2.036311 -753.295849 ... 0.769295 2.050509 8.409934 -2.019804 -2.508578 -8.566508 -6.415799 -8.738057 -4.427827 -4.187574
1039905 359905 -179.190689 -425.528126 -240.585283 -295.908878 102.0 99.847300 25.787226 -10.374205 -748.401726 ... 0.599896 1.339053 7.056772 -3.470978 -3.305609 -9.394947 -5.749868 -10.530456 -7.386127 -5.603244
1039909 359909 -178.218801 -416.635587 -239.261869 -295.500880 102.0 99.583698 26.875357 -15.581626 -728.727858 ... 0.749134 1.068308 7.616512 -2.973647 -0.504898 -8.724683 -4.619878 -10.676332 -6.257319 -4.945323
1039913 359913 -178.953121 -419.440076 -239.278653 -299.822897 102.0 89.165316 28.356262 -13.370268 -710.547645 ... 1.290556 1.737041 7.840020 -1.244672 -1.415577 -6.971477 -2.950651 -8.262340 -2.847371 -3.658438
1039917 359917 -173.441204 -409.254842 -231.917506 -288.187509 102.0 93.312722 30.087831 7.816019 -715.505952 ... 0.848778 1.675915 7.436919 -0.895941 1.244742 -7.114721 -2.753456 -9.019173 -5.459730 -4.335111
1039921 359921 -174.806170 -408.246567 -233.899199 -288.568305 102.0 96.920838 25.821360 4.092448 -743.271538 ... 0.721228 1.245744 6.327796 -0.351639 3.051987 -8.676523 -3.783378 -9.125128 -0.170538 -3.359203
1039925 359925 -168.087047 -404.697750 -228.428761 -284.440149 102.0 100.128369 29.943892 -12.580401 -730.936655 ... 1.842634 1.141949 7.721007 -0.363450 2.297855 -6.050486 -3.931842 -7.396489 -1.019142 -2.818748
1039929 359929 -178.916261 -405.899629 -237.923707 -290.418746 102.0 97.259258 27.223855 -4.891240 -698.966987 ... 1.231746 0.889718 6.382003 -0.977020 -0.573819 -6.946696 -3.895578 -8.170539 0.710521 -3.879402
1039933 359933 -169.653175 -413.829651 -230.412329 -285.353883 102.0 103.907426 28.947607 6.664458 -707.601641 ... 1.176690 0.890388 6.129500 -0.608457 1.630646 -5.648146 -3.014991 -8.074527 -3.119182 -3.375126
1039937 359937 -173.629583 -419.801237 -234.806579 -293.125175 102.0 102.801680 29.825732 0.190055 -730.931830 ... 0.803408 0.979405 6.363360 -1.043789 0.813990 -5.572890 -3.004035 -9.127406 -3.026240 -4.892954
1039941 359941 -180.188729 -428.495728 -240.385059 -297.204968 102.0 102.263158 28.502926 -3.809436 -737.507822 ... 0.908107 1.084273 6.901306 -2.670474 0.534170 -8.999436 -3.928166 -9.139948 -6.104955 -6.145196
1039945 359945 -178.307059 -426.040099 -234.770656 -294.651040 102.0 101.973266 21.791867 -2.923736 -731.573368 ... 0.612827 1.424400 7.769424 -1.972635 0.405628 -6.493241 -1.461140 -8.731377 -7.699118 -3.129820
1039949 359949 -173.347063 -409.228025 -232.777315 -287.061660 102.0 103.597654 24.556797 -0.844272 -714.285182 ... 1.011601 0.790632 8.224482 -1.296231 -0.879212 -3.310151 -1.787665 -8.339322 -7.503532 -3.544181
1039953 359953 -171.475403 -423.306233 -236.225505 -293.913908 102.0 102.410311 25.988150 -8.273680 -718.817108 ... 0.913065 1.262877 7.530533 -0.990788 -0.802732 -4.559889 -0.056861 -8.824918 -4.529091 -5.190863
1039957 359957 -181.631833 -422.081632 -237.960353 -296.745791 102.0 100.915875 31.948672 -9.916686 -713.958768 ... 0.846685 1.981801 8.412854 -1.972628 0.671762 -7.325692 -2.189688 -9.003449 -5.600492 -5.004449
1039961 359961 -174.770106 -419.978088 -233.766735 -293.042033 102.0 100.051339 29.284660 -8.308492 -687.055711 ... 0.831791 1.557256 8.042489 -2.219613 0.380828 -6.737694 -4.229779 -10.743524 -6.970310 -5.360754
1039965 359965 -178.985048 -426.663102 -240.921031 -296.657055 102.0 97.727851 24.715379 -16.269113 -678.623430 ... 0.609036 1.527498 9.623195 -1.815603 3.842891 -11.082882 -4.149377 -8.828145 -6.927817 -3.926058
1039969 359969 -178.940993 -429.915660 -245.497712 -304.266948 102.0 96.528897 25.793271 -25.240799 -745.690927 ... 0.687096 0.965144 8.043665 -1.972490 2.906697 -11.069062 -4.491108 -9.793612 -7.205021 -4.727670
1039973 359973 -184.314223 -432.160385 -250.043999 -306.266919 102.0 96.091114 27.162615 -21.846878 -716.285218 ... 0.905849 1.412171 8.098941 -3.266625 -0.446121 -11.476785 -4.109162 -11.044577 -7.557792 -5.462893
1039977 359977 -174.645152 -428.749954 -241.790681 -301.429550 102.0 95.127612 25.509348 -21.965203 -774.693623 ... 0.705616 1.907915 8.028019 -2.776243 -2.399044 -9.554361 -3.592264 -10.932354 -5.936771 -4.545451
1039981 359981 -181.245089 -423.393076 -242.664172 -302.065294 102.0 101.979511 24.168312 -17.915273 -707.059251 ... 1.034243 1.442296 8.100239 -1.267996 -1.574603 -7.737187 -2.784066 -8.322691 -4.212866 -2.931605
1039985 359985 -175.217267 -418.320723 -237.621589 -293.396097 102.0 94.386763 26.006032 -14.385936 -696.254965 ... 1.492716 0.924952 7.316786 -1.006404 -0.983456 -5.109014 -5.278706 -7.726409 -1.513513 -3.922319
1039989 359989 -176.322303 -429.435074 -243.878019 -302.619012 102.0 88.548041 26.798391 -13.027291 -732.505308 ... 1.302437 1.194976 7.267441 -2.330793 -0.528921 -7.459661 -5.732551 -10.648159 -4.678821 -5.815536
1039993 359993 -180.651135 -431.770086 -249.533822 -308.599899 102.0 96.081604 23.434877 -13.058938 -661.833093 ... 0.752290 1.805562 7.402320 -3.510906 -0.324711 -10.935088 -7.082180 -11.357075 -7.603796 -6.286676
1039997 359997 -172.312386 -425.814856 -239.663534 -299.506622 102.0 86.075258 25.938455 -17.258269 -736.048442 ... 0.582028 1.226384 6.116850 -3.357511 -2.255255 -8.453330 -4.815731 -10.568172 -8.156458 -5.416252

90000 rows × 50 columns


In [65]:
part1 = pd.concat([data.query("BiasTo <= 110 and Step > 7e7"),data.query("BiasTo > 110 and Step > 5.5e7")])

In [66]:
part2 = pd.concat([data.query("BiasTo <= 110 and Step <= 7e7"),data.query("BiasTo > 110 and Step <= 5.5e7")])

In [121]:
part1.reset_index(drop=True).to_feather("/Users/weilu/Research/server/aug_2018/01_week/freeEnergy/all_data_folder/part1.feather")

In [122]:
part2.reset_index(drop=True).to_feather("/Users/weilu/Research/server/aug_2018/01_week/freeEnergy/all_data_folder/part2.feather")

In [98]:
data_topology = pd.read_feather("/Volumes/Wei_backup/GlpG/may_2018_back/03_week/all_data_folder/second_start_topologyrerun_5_20_May_231514.feather")

In [101]:
data_topology_pre = pd.read_feather("/Volumes/Wei_backup/GlpG/may_2018_back/03_week/all_data_folder/second_toplogy_may21.feather")

In [99]:
dic = {"T0":300, "T1":335, "T2":373, "T3":417, "T4":465, "T5":519, "T6":579, "T7":645, "T8":720, "T9":803, "T10":896, "T11":1000}

data_topology["TempT"] = data_topology["Temp"].apply(lambda x: dic[x])
data_topology["BiasTo"] = data_topology["BiasTo"].apply(pd.to_numeric)

In [102]:
dic = {"T0":300, "T1":335, "T2":373, "T3":417, "T4":465, "T5":519, "T6":579, "T7":645, "T8":720, "T9":803, "T10":896, "T11":1000}

data_topology_pre["TempT"] = data_topology_pre["Temp"].apply(lambda x: dic[x])
data_topology_pre["BiasTo"] = data_topology_pre["BiasTo"].apply(pd.to_numeric)

In [97]:
data["Step"].hist()


Out[97]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a57168438>

In [117]:
np.linspace(100, 340, 41)


Out[117]:
array([ 100.,  106.,  112.,  118.,  124.,  130.,  136.,  142.,  148.,
        154.,  160.,  166.,  172.,  178.,  184.,  190.,  196.,  202.,
        208.,  214.,  220.,  226.,  232.,  238.,  244.,  250.,  256.,
        262.,  268.,  274.,  280.,  286.,  292.,  298.,  304.,  310.,
        316.,  322.,  328.,  334.,  340.])

In [111]:
data_topology_all = pd.concat([data_topology, data_topology_pre])

In [103]:
data_topology_pre["Step"].hist()


Out[103]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a63eb7978>

In [100]:
data_topology["Step"].hist()


Out[100]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a570f9b38>

In [95]:
data_topology.query("TempT == 335").plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[95]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a5f5d8438>

In [86]:
part1.query("TempT == 335").plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[86]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1e787a58>

In [87]:
part2.query("TempT == 335").plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[87]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1e711358>

In [84]:
part1.query("TempT == 373").plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[84]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ece2a58>

In [85]:
part2.query("TempT == 373").plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[85]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ed2c358>

In [62]:
data.query("TempT == 373 and BiasTo <= 110 and Step <= 7e7").plot.hexbin("z_average", "DisReal", cmap="seismic", sharex=False)


Out[62]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a19965c18>

In [ ]:
data.query("TempT == 373 and BiasTo < ").plot.hexbin("z_average", "DisReal", cmap="seismic", sharex=False)

In [24]:
data.query("TempT == 373").plot.hexbin("z_average", "DisReal", cmap="seismic", sharex=False)


Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a14c7fd68>

In [ ]:
data.plot.hexbin("Qw", "BiasedEnergy", cmap="seismic", sharex=False)

In [20]:
data.plot.hexbin("Qw", "BiasedEnergy", cmap="seismic", sharex=False)


Out[20]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... rg5 rg6 rg_all z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6
0 2 NaN -379.148448 NaN -293.851227 154.0 143.058540 69.711825 128.695515 -749.055518 ... 1.956153 0.049067 4.030620 -10.697500 0.293701 -8.314213 -24.950958 -22.767507 -13.396043 -20.261706
1 3 NaN -391.029001 NaN -304.318676 154.0 155.769889 71.728932 -11.712890 -829.560495 ... 1.606369 0.155952 3.290609 -9.732590 -0.358111 -6.341261 -23.181296 -22.302062 -12.660480 -18.502729
2 6 NaN -363.446509 NaN -281.031500 154.0 158.979235 67.217110 -155.038714 -694.155744 ... 2.347773 0.831679 4.898764 -9.700146 0.401878 -7.492871 -23.159413 -26.033700 -7.668875 -18.479031
3 11 NaN -361.015236 NaN -275.708559 154.0 157.977030 69.591612 68.672889 -615.805485 ... 2.198832 0.051714 3.602357 -10.315623 1.509482 -7.803889 -25.095741 -24.129020 -12.041743 -19.436537
4 14 NaN -381.507444 NaN -294.193657 154.0 140.499767 64.592401 129.825200 -761.720212 ... 1.409658 0.005059 2.740206 -11.618918 -2.305991 -7.831763 -26.200711 -23.127587 -13.930792 -22.040452
5 15 NaN -387.069432 NaN -303.637469 154.0 154.654845 70.238808 -16.937939 -839.251167 ... 1.226198 0.064708 2.827011 -10.505651 -1.315608 -6.482535 -24.275326 -21.388367 -14.020198 -20.731112
6 18 NaN -368.386318 NaN -280.637399 154.0 149.907110 65.186854 -147.117606 -672.449483 ... 1.604890 0.290834 3.410223 -10.090597 -3.431608 -5.915535 -25.944815 -26.794577 -9.177802 -18.509398
7 23 NaN -366.233841 NaN -283.537719 154.0 156.380264 65.769387 63.411148 -585.507719 ... 1.540535 0.149440 3.012327 -11.182451 -3.218628 -5.954479 -25.724560 -24.124372 -13.395186 -21.000627
8 26 NaN -379.181391 NaN -292.995012 154.0 137.161147 59.675458 122.360272 -746.285175 ... 2.316822 0.019471 3.853485 -10.713631 -0.579149 -8.504878 -27.673935 -22.471133 -11.983866 -20.959535
9 27 NaN -383.172776 NaN -300.882996 154.0 157.401288 67.545758 -28.163941 -834.167980 ... 2.001552 0.227254 4.392032 -10.018806 -0.413807 -8.189500 -25.527823 -18.582207 -12.205976 -20.470501
10 30 NaN -358.333659 NaN -274.454024 154.0 156.632494 64.857297 -155.048021 -664.249513 ... 1.841544 0.376626 3.373460 -9.807359 1.356668 -9.038097 -27.611369 -23.981680 -10.045085 -17.486072
11 35 NaN -354.508855 NaN -259.901208 154.0 154.960874 67.651792 55.382485 -580.658474 ... 0.756111 0.051875 2.191659 -10.991559 -4.579441 -5.533328 -28.012641 -20.532497 -14.162903 -18.679767
12 38 NaN -386.938723 NaN -299.403434 154.0 139.353624 62.090424 126.640599 -742.133896 ... 0.954273 0.594127 3.548247 -10.749614 -0.672418 -8.741256 -26.398084 -21.571392 -14.543500 -17.018765
13 39 NaN -393.807775 NaN -308.845823 154.0 157.484650 67.085420 -32.689965 -808.614330 ... 2.214636 0.204042 5.379868 -10.364606 -3.251968 -9.366016 -23.476006 -17.898697 -11.600253 -19.887202
14 42 NaN -375.835081 NaN -289.687886 154.0 146.508213 67.446430 -146.105867 -653.051775 ... 2.009247 0.350681 4.758018 -10.330856 2.091507 -11.939955 -26.347366 -22.032796 -12.459356 -17.207012
15 47 NaN -366.269100 NaN -280.350393 154.0 159.262614 65.021349 55.022017 -569.618630 ... 0.463761 0.278620 2.709611 -11.127461 0.812290 -9.962810 -29.965812 -18.311633 -15.367888 -19.331788
16 50 NaN -382.885928 NaN -294.780145 154.0 141.742647 61.041280 128.805305 -733.194806 ... 0.451080 0.942226 3.876490 -11.674995 -3.185854 -9.998484 -26.124144 -26.267141 -15.076393 -16.735631
17 51 NaN -394.527672 NaN -308.785589 154.0 156.594000 68.170344 -32.385845 -825.098911 ... 2.140550 0.207640 4.429582 -10.833543 -3.177129 -12.501924 -22.538206 -21.281788 -12.443190 -18.751370
18 54 NaN -372.892559 NaN -288.157083 154.0 156.056831 74.049550 -155.593877 -654.676421 ... 2.175955 0.726480 4.924620 -11.115189 -2.533004 -9.823895 -26.123846 -25.929809 -13.436628 -17.137633
19 59 NaN -369.663674 NaN -279.410320 154.0 154.678566 63.067806 45.399781 -633.443273 ... 0.421268 0.050945 2.569137 -11.953950 -2.013482 -13.619220 -27.735707 -20.054507 -15.664256 -20.062515
20 62 NaN -379.900629 NaN -295.730849 154.0 140.412139 61.733084 127.903712 -744.149427 ... 0.728833 0.211049 2.331567 -12.039736 -2.490448 -12.068436 -25.881369 -28.716404 -14.844762 -19.938272
21 63 NaN -389.374064 NaN -305.207437 154.0 155.566212 73.650462 -34.285206 -807.142353 ... 2.067787 0.555381 4.831883 -10.512895 -2.089895 -9.019313 -24.505543 -24.279103 -12.192958 -18.910592
22 66 NaN -366.656317 NaN -283.133547 154.0 159.735336 74.752091 -159.017599 -676.568911 ... 2.129468 0.366059 4.513405 -11.194674 -0.642643 -12.995737 -27.421708 -26.735055 -13.421868 -20.102695
23 71 NaN -353.771681 NaN -266.246192 154.0 148.807238 63.298261 35.295209 -617.844074 ... 1.785119 0.239427 3.935404 -11.996087 -1.149399 -17.814256 -26.841584 -24.804137 -13.581365 -21.466465
24 74 NaN -383.735697 NaN -297.729186 154.0 146.242956 63.250914 133.476340 -762.719250 ... 1.805462 0.044575 3.524273 -11.067372 -2.005885 -7.937571 -27.148690 -25.083429 -12.741257 -20.260412
25 75 NaN -395.468131 NaN -311.502195 154.0 151.492555 77.822891 -35.560150 -809.935179 ... 3.286638 0.242297 6.790891 -9.691372 -0.203274 -7.680198 -24.826085 -24.199944 -9.579322 -19.992747
26 78 NaN -366.545705 NaN -284.668332 154.0 163.409110 64.173975 -162.101820 -655.110597 ... 2.589784 0.251617 5.001493 -10.556212 0.884704 -12.155480 -29.048294 -24.740444 -11.208712 -19.190635
27 83 NaN -357.447327 NaN -274.784251 154.0 147.262446 66.589021 25.854487 -594.380811 ... 2.864831 0.192486 5.066841 -11.043290 -2.173171 -13.530575 -27.332014 -25.075759 -10.037916 -19.119518
28 86 NaN -386.236689 NaN -302.613245 154.0 145.262333 67.387656 133.317951 -738.270727 ... 2.497988 0.003118 4.376855 -11.241211 0.454819 -6.754172 -27.299254 -26.811476 -11.869558 -23.230580
29 87 NaN -390.348400 NaN -307.976301 154.0 148.750549 72.596734 -31.541530 -797.295826 ... 2.854795 0.030244 5.935939 -9.589103 -0.035503 -9.291243 -24.344488 -23.785913 -8.422567 -22.206853
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1399970 719970 -171.057031 -355.857188 -230.653289 -276.439251 102.0 92.634793 23.985971 -4.446557 -566.613447 ... 1.250324 1.981658 5.404852 -7.132010 0.116247 -7.285447 -25.067470 -26.008650 -6.539274 -4.820620
1399971 719971 -175.612406 -437.291752 -241.550961 -302.391999 102.0 95.516759 24.945252 -28.441392 -848.117620 ... 1.021387 1.047110 7.479215 -1.689840 1.308467 -7.194774 -4.857301 -9.439820 -4.577103 -5.427067
1399972 719972 -178.725030 -423.488574 -243.998078 -302.548105 102.0 96.554771 25.599314 -14.953278 -768.801626 ... 0.670990 1.274940 6.721008 -2.466561 4.213255 -9.400574 -6.523701 -10.939101 -6.741168 -6.460690
1399973 719973 -177.911703 -439.222689 -244.905300 -305.333721 102.0 95.126958 24.810979 -22.955382 -786.060935 ... 0.699178 0.809209 7.314186 -2.124907 4.262438 -9.293881 -5.739718 -10.227891 -7.177069 -5.883781
1399974 719974 -174.989194 -350.890741 -231.629925 -270.649994 102.0 103.755382 19.880067 -8.324283 -566.847471 ... 0.831252 1.191068 4.032185 -8.657168 4.724263 -10.708529 -28.528736 -28.873790 -9.319561 -4.275865
1399975 719975 -181.236641 -436.643387 -246.622000 -308.150896 102.0 99.567245 26.935655 -21.512389 -860.076843 ... 0.635406 1.236063 6.722517 -2.081544 5.283848 -9.191359 -4.877358 -11.299551 -7.396885 -5.682646
1399976 719976 -175.134080 -424.742064 -237.100356 -298.576695 102.0 92.634826 25.750346 4.858877 -734.720144 ... 0.725933 1.079715 5.953475 -2.298796 3.355892 -8.055047 -4.292885 -10.894707 -5.941350 -5.975201
1399977 719977 -179.814828 -435.156403 -245.606418 -303.733607 102.0 94.887904 27.385640 -6.843862 -823.279564 ... 0.839215 1.088340 6.511031 -2.245549 1.839950 -8.354201 -4.087725 -10.435672 -6.858959 -5.094256
1399978 719978 -182.341784 -351.907471 -239.292396 -277.456314 102.0 97.186010 8.939649 3.601369 -555.255942 ... 0.668643 0.886641 3.683799 -10.033003 3.153622 -10.112552 -32.669453 -33.701318 -7.300882 -3.577476
1399979 719979 -178.182948 -439.725116 -244.045625 -305.154818 102.0 97.645865 25.286208 -3.410565 -843.956114 ... 0.829723 1.179853 6.523134 -2.275159 2.876769 -7.650321 -4.774146 -10.736497 -5.996398 -5.529201
1399980 719980 -180.838873 -422.128821 -243.769311 -300.386956 102.0 100.590423 22.496298 -0.025247 -734.805861 ... 0.778388 1.248211 6.998108 -2.384341 -0.510537 -7.444899 -3.000111 -10.165996 -5.424914 -4.879895
1399981 719981 -178.331740 -432.802522 -247.053636 -303.136874 102.0 94.795827 26.543677 -9.167217 -783.267248 ... 0.854507 1.090369 6.698716 -2.094262 -0.956475 -7.997445 -3.379182 -9.089184 -6.608137 -4.122130
1399982 719982 -175.706128 -349.206657 -229.173094 -270.314035 102.0 102.856846 18.337287 5.826294 -540.335605 ... 1.554903 1.000396 5.216737 -10.829160 -1.329106 -11.616417 -33.143344 -33.906793 -6.732055 -2.663132
1399983 719983 -182.958480 -439.526802 -250.142378 -308.503895 102.0 98.195959 27.578173 -3.173738 -835.156172 ... 1.266963 0.951107 6.881724 -2.172680 -0.521974 -7.638480 -3.466873 -9.220524 -5.095877 -4.779658
1399984 719984 -182.677336 -431.877114 -249.875599 -305.882569 102.0 85.560026 24.179652 8.871215 -730.945732 ... 0.628557 1.485071 7.052195 -1.286153 -0.547296 -6.717355 -0.715170 -8.234139 -6.267730 -2.602707
1399985 719985 -181.439519 -437.199641 -245.921224 -309.027420 102.0 91.716999 27.724606 -2.223027 -790.889463 ... 0.964946 1.792479 7.823137 -0.730680 -0.367054 -6.075972 -0.438665 -7.241001 -8.348529 -1.927863
1399986 719986 -168.978488 -349.117215 -227.955323 -269.320432 102.0 98.240706 19.791533 4.142120 -540.025817 ... 1.944093 0.793432 5.372266 -9.293027 1.259234 -7.958038 -31.474266 -33.371587 -7.485706 -1.792007
1399987 719987 -179.867089 -443.851010 -247.242764 -308.387107 102.0 88.817781 24.343320 10.699192 -846.415804 ... 0.839091 1.652025 7.697071 -0.903982 -0.491247 -6.555267 -0.704187 -7.994691 -7.571233 -2.060933
1399988 719988 -187.890123 -446.317112 -257.119450 -314.595805 102.0 100.997173 20.662907 9.305860 -750.598109 ... 0.776280 1.549393 7.889488 -1.488352 -2.559167 -7.074563 -1.225270 -9.301677 -4.545633 -3.928111
1399989 719989 -185.352486 -445.133829 -251.187393 -311.437316 102.0 97.888534 26.271401 2.964430 -815.309937 ... 0.753713 1.649015 9.043289 -1.150297 -1.251992 -7.951735 -0.944055 -7.732699 -7.116783 -3.270597
1399990 719990 -173.860199 -355.194962 -233.600775 -272.632439 102.0 111.569546 25.478314 0.029396 -579.030444 ... 0.926511 1.630800 5.518499 -9.050827 -2.069999 -8.279948 -30.417976 -32.815653 -6.280914 -1.899248
1399991 719991 -182.510362 -433.872195 -250.118250 -307.785057 102.0 100.278070 22.059778 5.283719 -890.654006 ... 0.605929 2.400309 8.882541 -1.495027 -1.889176 -6.930420 -0.599319 -9.651502 -6.806001 -2.793156
1399992 719992 -169.386925 -413.082833 -233.157375 -288.669058 102.0 96.031979 26.280834 3.976283 -749.949139 ... 1.543018 1.300873 8.176069 -1.709614 -1.889524 -8.307254 -3.298733 -8.742325 -4.117646 -3.367814
1399993 719993 -175.889921 -441.413190 -245.058937 -303.559043 102.0 89.352130 26.501929 -1.438488 -801.878348 ... 0.741905 1.735556 8.535762 -2.116905 -1.456958 -9.191511 -3.166552 -8.715021 -7.954109 -3.206232
1399994 719994 -168.879463 -347.217885 -226.905840 -267.441369 102.0 104.293977 19.361344 0.992685 -553.126973 ... 1.079893 1.779986 4.845644 -9.429993 -3.103134 -10.277525 -27.636810 -30.490544 -6.615511 -7.441021
1399995 719995 -173.936471 -437.140650 -243.018031 -303.168074 102.0 91.820353 25.529091 1.779432 -870.676086 ... 1.061244 1.353440 8.553501 -2.007328 -1.380797 -8.381024 -3.246383 -7.954931 -6.661481 -2.738108
1399996 719996 -177.297319 -423.665643 -239.064086 -296.990102 102.0 87.471225 25.817823 2.633153 -732.767353 ... 1.145461 0.718796 7.150671 -2.570168 -1.718925 -8.955210 -6.736448 -10.122350 -1.541202 -6.733705
1399997 719997 -181.882563 -446.843909 -248.540298 -307.764920 102.0 87.966854 23.750570 -3.941888 -800.153161 ... 0.793680 1.289033 7.330957 -3.002874 -1.402896 -8.809445 -6.023749 -11.029106 -6.796907 -6.306770
1399998 719998 -164.583767 -338.458234 -220.400634 -260.654172 102.0 95.226065 21.651543 0.025823 -522.071412 ... 1.449667 2.703288 6.053790 -10.150905 -2.552638 -10.181433 -32.516017 -33.022531 -5.470948 -9.127874
1399999 719999 -174.925017 -442.557153 -243.252067 -304.529014 102.0 91.504540 25.315192 1.900850 -887.570230 ... 0.922607 0.964643 6.491683 -2.644266 -0.725797 -9.156278 -5.902342 -10.657191 -3.958978 -6.524912

1400000 rows × 50 columns


In [13]:
perturbation_table = {3:"Decrease Go",
                      4:"Increase Go",
                        5:"Decrease Lipid",
                     6:"Increase Lipid",
                     7:"Decrease Membrane",
                     8:"Increase Membrane",
                     9:"Decrease Rg",
                     10:"Increase Rg"}
all_f = {}
all_location = {}

for i in perturbation_table:
#     print(i,perturbation_table[i])
    location_i = location + f"perturbation-{i}-pmf-{temp}.dat"
    path, f = plot_shortest_path(location_i, path_origin, save=False, 
                               xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res,
                              xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, plot2d=False)
#     all_path[i] = path_origin
    all_f[i] = f
    all_location[i] = location_i

In [14]:
i = 3
j = 4
title = "Go"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)



In [15]:
i = 5
j = 6
title = "Lipid"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
# plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)



In [16]:
i = 7
j = 8
title = "Membrane"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
# plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)



In [17]:
i = 9
j = 10
title = "Rg"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
# plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)


High force


In [148]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/higer_force_0.2/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(11, 36), end=(26,0),save=False, plot1d=2, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [149]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/more_bins/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(28,0),save=False, plot1d=2,xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [67]:
data = np.loadtxt(location2)
zmin = 0
z =3
xi, yi, zi = getxyz(data, zmin=zmin, zmax=zmax,res=res, z=z)
# V = ma.masked_array(zi, zi>40)
# zi = np.where(np.isnan(zi), 1e6, zi)
f_on_path = [zi[tuple(p)] for p in reversed(path_origin)]
distance_on_path = [xi[tuple(p)[1]] for p in reversed(path_origin)]
distance_on_path = np.array(distance_on_path)
x = np.arange(len(distance_on_path))
x_smooth = np.linspace(distance_on_path.min(), distance_on_path.max(), 200)
spl1 = scipy.interpolate.interp1d(distance_on_path, f_origin, kind="cubic")
plt.plot(x_smooth, spl1(x_smooth))
plt.xlabel("End to end distance(Å)")
plt.ylabel("Free energy(kT)")
# plt.ylim([0,20])
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/freeEnergy_Distance.png")


Enhance Go term


In [14]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.18/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(10, 35), end=(28,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [15]:
perturbation_table = {3:"Decrease 10% Lipid",
                      4:"Increase 10% Lipid",
                        5:"Decrease 20% Lipid",
                     6:"Increase 20% Lipid",
                     7:"Decrease Membrane",
                     8:"Increase Membrane",
                     9:"Decrease Rg",
                     10:"Increase Rg"}
all_f = {}
all_location = {}

for i in perturbation_table:
#     print(i,perturbation_table[i])
    location_i = location + f"perturbation-{i}-pmf-{temp}.dat"
    path, f = plot_shortest_path(location_i, path_origin, save=False, 
                               xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res,
                              xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, plot2d=False)
#     all_path[i] = path_origin
    all_f[i] = f
    all_location[i] = location_i

In [16]:
i = 3
j = 4
title = "Lipid"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)


Out[16]:
<matplotlib.legend.Legend at 0x1a1d0c2400>

In [17]:
i = 5
j = 6
title = "Lipid"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)


Out[17]:
<matplotlib.legend.Legend at 0x1a13463dd8>

In [18]:
i = 7
j = 8
title = "Membrane"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
x = np.arange(len(f_origin))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_origin, kind="cubic")

plt.plot(x_smooth, spl(x_smooth), label="original")

spl = scipy.interpolate.interp1d(x, all_f[i], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[i])

spl = scipy.interpolate.interp1d(x, all_f[j], kind="cubic")
plt.plot(x_smooth, spl(x_smooth), label=perturbation_table[j])
plt.ylim([0,20.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)


Out[18]:
<matplotlib.legend.Legend at 0x1a1d865dd8>

In [128]:
data = pd.read_feather("/Volumes/Wei_backup/GlpG/may_2018_back/03_week/all_data_folder/secondrerun_7_19_May_155517.feather")

In [131]:
data.columns


Out[131]:
Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'Dis_h56',
       'z_average', 'abs_z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5',
       'z_h6', 'AMH', 'AMH_3H', 'AMH_4H', 'Distance', 'AMH-Go', 'Membrane',
       'Rg', 'rg1', 'rg2', 'rg3', 'rg4', 'rg5', 'rg6', 'rg_all', 'Lipid',
       'Lipid1', 'Lipid2', 'Lipid3', 'Lipid4', 'Lipid5', 'Lipid6', 'Lipid7',
       'Lipid8', 'Lipid9', 'Lipid10', 'Lipid11', 'Lipid12', 'Lipid13',
       'Lipid14', 'Lipid15', 'TotalE', 'BiasTo'],
      dtype='object')

In [132]:
data_all = pd.read_feather("/Volumes/Wei_backup/GlpG/may_2018_back/03_week/all_data_folder/second_start_extended_combined_may19.feather")

In [138]:
data_all.query("Step > 7e7")


Out[138]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... rg5 rg6 rg_all z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6
1040000 360000 -178.321133 -412.066149 -235.338918 -292.892214 86.0 79.004676 28.346208 34.986200 -714.949919 ... 0.668179 1.034435 6.755773 -1.033087 2.494844 -4.841397 -3.889325 -8.467838 -5.754722 -4.876540
1040001 360001 -188.299728 -447.198046 -252.309590 -312.801516 86.0 76.611795 26.503922 66.355478 -863.891967 ... 0.826445 1.772088 6.904373 -0.743387 1.148260 -5.227564 -1.425519 -9.043892 -5.895529 -4.493921
1040002 360002 -171.687394 -407.066513 -237.428319 -289.086674 86.0 77.891836 27.000805 50.752370 -629.877271 ... 1.908471 1.135514 7.790810 -1.308511 0.446359 -7.046833 -3.049219 -8.155064 -5.602625 -3.843632
1040003 360003 -180.758189 -431.130772 -243.949674 -303.135304 86.0 76.310309 23.970832 76.250721 -808.283812 ... 0.805586 1.362957 6.343286 -0.404976 0.186463 -5.939964 -1.582849 -8.324221 -4.780361 -2.786373
1040004 360004 -177.200623 -410.571989 -235.760741 -294.325427 86.0 83.372854 18.937570 40.892598 -722.068300 ... 0.801884 1.374862 6.547033 -2.802593 0.773482 -9.611948 -6.093863 -9.556687 -7.412869 -4.627857
1040005 360005 -189.560915 -455.080660 -252.741874 -315.285649 86.0 77.138549 23.965611 67.909113 -875.315412 ... 0.626170 1.227854 6.671891 -2.526055 0.828463 -8.453980 -2.390166 -10.600602 -9.344658 -5.994914
1040006 360006 -165.960261 -394.927103 -228.087408 -282.374683 86.0 81.804196 22.570860 46.997684 -617.038543 ... 1.515393 1.330794 7.078147 -2.516296 2.376668 -9.006004 -5.647366 -11.014686 -7.808930 -6.237744
1040007 360007 -183.028184 -435.242788 -244.838200 -308.013220 86.0 79.692817 24.642406 79.354488 -815.887543 ... 0.674458 1.409087 7.566555 -2.113954 1.318628 -8.127702 -2.632928 -10.117539 -6.694920 -6.331474
1040008 360008 -180.879480 -407.788823 -235.803323 -294.257476 86.0 89.521842 22.380344 46.390928 -709.820249 ... 0.645107 2.179880 7.059012 -2.171087 1.730007 -7.565345 -5.411125 -9.619961 -7.155922 -3.403780
1040009 360009 -187.553033 -450.997601 -252.023328 -313.843100 86.0 80.956135 25.333072 69.330618 -871.712350 ... 0.758550 1.127856 6.378650 -1.968650 2.601144 -7.283513 -3.448582 -10.006650 -5.816201 -4.557311
1040010 360010 -172.830352 -414.926482 -233.740396 -295.186930 86.0 84.577011 24.441085 37.359667 -586.240081 ... 0.985306 2.220688 8.283865 -2.066397 4.528237 -8.056028 -5.015032 -10.432687 -8.763801 -5.193078
1040011 360011 -184.342861 -436.717905 -247.901471 -307.401887 86.0 77.370702 22.931313 77.359309 -812.321649 ... 0.646060 1.227642 6.626957 -1.918649 1.666958 -8.448751 -3.629818 -9.627210 -6.108897 -4.480972
1040012 360012 -171.128915 -409.410718 -233.914608 -289.781585 86.0 83.579512 21.361462 38.329813 -702.409638 ... 0.808295 1.102991 6.581213 -0.585338 3.456519 -5.765632 -2.172227 -7.773421 -3.901099 -3.266604
1040013 360013 -178.653114 -435.935315 -244.829481 -303.914015 86.0 73.943124 25.206944 65.826393 -885.108499 ... 0.866487 1.120154 7.060652 -0.000778 3.709484 -5.910061 -1.914833 -7.694260 -3.338004 -3.487737
1040014 360014 -175.434126 -413.236756 -238.225446 -293.994676 86.0 92.703817 24.439102 43.071717 -582.858892 ... 0.960122 1.129744 7.026546 -0.315268 4.046416 -8.336011 -2.794837 -8.394247 -2.373982 -3.743758
1040015 360015 -171.396612 -426.438976 -232.507480 -295.544332 86.0 83.172268 23.156169 83.016975 -821.543867 ... 0.740087 1.005653 6.665883 -0.195987 2.622665 -7.564743 -2.362413 -7.566946 -3.079646 -3.134826
1040016 360016 -171.690895 -401.386354 -231.802732 -283.413267 86.0 81.770035 25.603405 36.290812 -666.730519 ... 0.890132 1.092686 6.349323 -0.737092 4.747721 -7.508631 -2.545017 -8.130889 -5.948760 -2.941567
1040017 360017 -182.647056 -445.114764 -249.711719 -310.927585 86.0 82.436955 24.607747 73.825721 -890.930912 ... 0.722372 1.146925 6.988588 -0.596535 3.392905 -7.810212 -2.592294 -7.640068 -4.216691 -4.264674
1040018 360018 -162.398506 -400.052595 -228.691049 -285.020761 86.0 78.870965 25.218384 33.725197 -543.384521 ... 1.241233 0.727040 7.773756 -0.420305 3.507919 -8.828819 -3.501351 -7.907030 -1.555449 -4.396408
1040019 360019 -185.078408 -436.693767 -247.317409 -309.063807 86.0 80.099693 26.511083 79.167080 -799.419955 ... 0.690185 1.107500 6.945675 -0.347545 4.279291 -7.065227 -3.045929 -7.837259 -4.921402 -3.778740
1040020 360020 -175.212562 -411.063653 -238.114432 -294.465943 86.0 77.052335 23.153542 45.458047 -669.801538 ... 0.653753 0.887969 5.381157 -2.722350 -0.396683 -6.997332 -3.931108 -10.986878 -8.555905 -6.895371
1040021 360021 -186.392535 -437.935149 -249.730434 -310.458814 86.0 87.391587 27.837164 74.283489 -855.621336 ... 0.745375 1.698300 6.632165 -2.721245 -0.538624 -8.747517 -3.786496 -10.697388 -7.906758 -5.193006
1040022 360022 -171.459863 -406.824679 -232.760411 -289.837755 86.0 85.086993 20.200220 55.281060 -622.552580 ... 0.812567 0.888293 5.998297 -2.118040 -1.033433 -7.348048 -3.824403 -11.648821 -4.402861 -7.386115
1040023 360023 -175.360554 -430.164151 -238.660748 -299.659111 86.0 84.441425 23.650324 84.429128 -800.040833 ... 0.690610 1.160560 6.924249 -2.295857 0.821658 -9.350320 -3.961374 -10.012466 -7.159954 -5.790271
1040024 360024 -175.051816 -412.711876 -238.117980 -294.301467 86.0 74.364323 23.750555 34.865682 -692.663837 ... 0.777575 1.194753 5.701887 -2.527804 -2.874243 -7.025268 -4.092978 -8.792953 -5.815720 -4.356905
1040025 360025 -187.252779 -448.189637 -252.800951 -311.757692 86.0 74.656855 24.028541 59.992396 -865.830228 ... 0.917492 1.058570 6.239010 -2.579924 -2.703427 -8.640629 -4.256439 -9.983446 -3.416459 -5.028673
1040026 360026 -171.763851 -393.846217 -230.618802 -284.547427 86.0 85.458012 26.025567 44.159187 -612.625925 ... 1.018125 1.038773 6.891655 -1.741463 -2.885852 -6.468365 -3.996942 -9.575548 -3.815418 -3.678599
1040027 360027 -180.313984 -432.165890 -243.785017 -305.766050 86.0 81.408665 22.907431 81.049764 -808.138541 ... 0.757924 0.777829 5.694349 -2.166330 -0.843153 -8.070438 -3.344762 -9.808620 -2.818492 -5.848006
1040028 360028 -175.528304 -416.805217 -237.790195 -291.876143 86.0 80.122468 24.758321 30.863065 -685.537891 ... 1.139500 1.076961 6.526219 -1.517133 0.844767 -8.072552 -3.069387 -9.641124 -3.768143 -5.261024
1040029 360029 -178.168209 -431.205818 -242.524380 -302.576675 86.0 77.729291 26.137374 66.406624 -855.109816 ... 1.237022 0.854550 6.424251 -1.166513 0.681142 -7.161242 -3.929606 -9.438804 -1.565832 -5.495186
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1399970 719970 -171.057031 -355.857188 -230.653289 -276.439251 102.0 92.634793 23.985971 -4.446557 -566.613447 ... 1.250324 1.981658 5.404852 -7.132010 0.116247 -7.285447 -25.067470 -26.008650 -6.539274 -4.820620
1399971 719971 -175.612406 -437.291752 -241.550961 -302.391999 102.0 95.516759 24.945252 -28.441392 -848.117620 ... 1.021387 1.047110 7.479215 -1.689840 1.308467 -7.194774 -4.857301 -9.439820 -4.577103 -5.427067
1399972 719972 -178.725030 -423.488574 -243.998078 -302.548105 102.0 96.554771 25.599314 -14.953278 -768.801626 ... 0.670990 1.274940 6.721008 -2.466561 4.213255 -9.400574 -6.523701 -10.939101 -6.741168 -6.460690
1399973 719973 -177.911703 -439.222689 -244.905300 -305.333721 102.0 95.126958 24.810979 -22.955382 -786.060935 ... 0.699178 0.809209 7.314186 -2.124907 4.262438 -9.293881 -5.739718 -10.227891 -7.177069 -5.883781
1399974 719974 -174.989194 -350.890741 -231.629925 -270.649994 102.0 103.755382 19.880067 -8.324283 -566.847471 ... 0.831252 1.191068 4.032185 -8.657168 4.724263 -10.708529 -28.528736 -28.873790 -9.319561 -4.275865
1399975 719975 -181.236641 -436.643387 -246.622000 -308.150896 102.0 99.567245 26.935655 -21.512389 -860.076843 ... 0.635406 1.236063 6.722517 -2.081544 5.283848 -9.191359 -4.877358 -11.299551 -7.396885 -5.682646
1399976 719976 -175.134080 -424.742064 -237.100356 -298.576695 102.0 92.634826 25.750346 4.858877 -734.720144 ... 0.725933 1.079715 5.953475 -2.298796 3.355892 -8.055047 -4.292885 -10.894707 -5.941350 -5.975201
1399977 719977 -179.814828 -435.156403 -245.606418 -303.733607 102.0 94.887904 27.385640 -6.843862 -823.279564 ... 0.839215 1.088340 6.511031 -2.245549 1.839950 -8.354201 -4.087725 -10.435672 -6.858959 -5.094256
1399978 719978 -182.341784 -351.907471 -239.292396 -277.456314 102.0 97.186010 8.939649 3.601369 -555.255942 ... 0.668643 0.886641 3.683799 -10.033003 3.153622 -10.112552 -32.669453 -33.701318 -7.300882 -3.577476
1399979 719979 -178.182948 -439.725116 -244.045625 -305.154818 102.0 97.645865 25.286208 -3.410565 -843.956114 ... 0.829723 1.179853 6.523134 -2.275159 2.876769 -7.650321 -4.774146 -10.736497 -5.996398 -5.529201
1399980 719980 -180.838873 -422.128821 -243.769311 -300.386956 102.0 100.590423 22.496298 -0.025247 -734.805861 ... 0.778388 1.248211 6.998108 -2.384341 -0.510537 -7.444899 -3.000111 -10.165996 -5.424914 -4.879895
1399981 719981 -178.331740 -432.802522 -247.053636 -303.136874 102.0 94.795827 26.543677 -9.167217 -783.267248 ... 0.854507 1.090369 6.698716 -2.094262 -0.956475 -7.997445 -3.379182 -9.089184 -6.608137 -4.122130
1399982 719982 -175.706128 -349.206657 -229.173094 -270.314035 102.0 102.856846 18.337287 5.826294 -540.335605 ... 1.554903 1.000396 5.216737 -10.829160 -1.329106 -11.616417 -33.143344 -33.906793 -6.732055 -2.663132
1399983 719983 -182.958480 -439.526802 -250.142378 -308.503895 102.0 98.195959 27.578173 -3.173738 -835.156172 ... 1.266963 0.951107 6.881724 -2.172680 -0.521974 -7.638480 -3.466873 -9.220524 -5.095877 -4.779658
1399984 719984 -182.677336 -431.877114 -249.875599 -305.882569 102.0 85.560026 24.179652 8.871215 -730.945732 ... 0.628557 1.485071 7.052195 -1.286153 -0.547296 -6.717355 -0.715170 -8.234139 -6.267730 -2.602707
1399985 719985 -181.439519 -437.199641 -245.921224 -309.027420 102.0 91.716999 27.724606 -2.223027 -790.889463 ... 0.964946 1.792479 7.823137 -0.730680 -0.367054 -6.075972 -0.438665 -7.241001 -8.348529 -1.927863
1399986 719986 -168.978488 -349.117215 -227.955323 -269.320432 102.0 98.240706 19.791533 4.142120 -540.025817 ... 1.944093 0.793432 5.372266 -9.293027 1.259234 -7.958038 -31.474266 -33.371587 -7.485706 -1.792007
1399987 719987 -179.867089 -443.851010 -247.242764 -308.387107 102.0 88.817781 24.343320 10.699192 -846.415804 ... 0.839091 1.652025 7.697071 -0.903982 -0.491247 -6.555267 -0.704187 -7.994691 -7.571233 -2.060933
1399988 719988 -187.890123 -446.317112 -257.119450 -314.595805 102.0 100.997173 20.662907 9.305860 -750.598109 ... 0.776280 1.549393 7.889488 -1.488352 -2.559167 -7.074563 -1.225270 -9.301677 -4.545633 -3.928111
1399989 719989 -185.352486 -445.133829 -251.187393 -311.437316 102.0 97.888534 26.271401 2.964430 -815.309937 ... 0.753713 1.649015 9.043289 -1.150297 -1.251992 -7.951735 -0.944055 -7.732699 -7.116783 -3.270597
1399990 719990 -173.860199 -355.194962 -233.600775 -272.632439 102.0 111.569546 25.478314 0.029396 -579.030444 ... 0.926511 1.630800 5.518499 -9.050827 -2.069999 -8.279948 -30.417976 -32.815653 -6.280914 -1.899248
1399991 719991 -182.510362 -433.872195 -250.118250 -307.785057 102.0 100.278070 22.059778 5.283719 -890.654006 ... 0.605929 2.400309 8.882541 -1.495027 -1.889176 -6.930420 -0.599319 -9.651502 -6.806001 -2.793156
1399992 719992 -169.386925 -413.082833 -233.157375 -288.669058 102.0 96.031979 26.280834 3.976283 -749.949139 ... 1.543018 1.300873 8.176069 -1.709614 -1.889524 -8.307254 -3.298733 -8.742325 -4.117646 -3.367814
1399993 719993 -175.889921 -441.413190 -245.058937 -303.559043 102.0 89.352130 26.501929 -1.438488 -801.878348 ... 0.741905 1.735556 8.535762 -2.116905 -1.456958 -9.191511 -3.166552 -8.715021 -7.954109 -3.206232
1399994 719994 -168.879463 -347.217885 -226.905840 -267.441369 102.0 104.293977 19.361344 0.992685 -553.126973 ... 1.079893 1.779986 4.845644 -9.429993 -3.103134 -10.277525 -27.636810 -30.490544 -6.615511 -7.441021
1399995 719995 -173.936471 -437.140650 -243.018031 -303.168074 102.0 91.820353 25.529091 1.779432 -870.676086 ... 1.061244 1.353440 8.553501 -2.007328 -1.380797 -8.381024 -3.246383 -7.954931 -6.661481 -2.738108
1399996 719996 -177.297319 -423.665643 -239.064086 -296.990102 102.0 87.471225 25.817823 2.633153 -732.767353 ... 1.145461 0.718796 7.150671 -2.570168 -1.718925 -8.955210 -6.736448 -10.122350 -1.541202 -6.733705
1399997 719997 -181.882563 -446.843909 -248.540298 -307.764920 102.0 87.966854 23.750570 -3.941888 -800.153161 ... 0.793680 1.289033 7.330957 -3.002874 -1.402896 -8.809445 -6.023749 -11.029106 -6.796907 -6.306770
1399998 719998 -164.583767 -338.458234 -220.400634 -260.654172 102.0 95.226065 21.651543 0.025823 -522.071412 ... 1.449667 2.703288 6.053790 -10.150905 -2.552638 -10.181433 -32.516017 -33.022531 -5.470948 -9.127874
1399999 719999 -174.925017 -442.557153 -243.252067 -304.529014 102.0 91.504540 25.315192 1.900850 -887.570230 ... 0.922607 0.964643 6.491683 -2.644266 -0.725797 -9.156278 -5.902342 -10.657191 -3.958978 -6.524912

360000 rows × 50 columns


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]: