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

In [96]:
data = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/include_h14_aug18.feather")
data = data.reset_index(drop=True)
# data["BiasedEnergy"] = data["TotalE"] + 0.2*data["AMH_4H"]
data["BiasedEnergy"] = data["Lipid"] + data["Rg"] + data["Membrane"] + data["AMH-Go"] + 0.2*data["AMH_4H"]
data["EnergyAllTerms"] = data["Lipid"] + data["Rg"] + data["Membrane"] + data["AMH-Go"]
data["BiasEnergy"] = 0.02 * (data["BiasTo"] - data["DisReal"])**2
data["Energy_with_all_bias"] = data["BiasEnergy"] + data["BiasedEnergy"]
data["z_h56"] = data["z_h5"] + data["z_h6"]

In [185]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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, title="without_perturbation", 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)


native


In [187]:
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 20 and DisReal < 40 and z_average > -3 and z_average < -1").reset_index(drop=True)
chosen = t_pos.sort_values("EnergyAllTerms").head(20)
# chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/native.csv")

transition state.

range dis58 to 65


In [172]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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), plot1d=0,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)
plt.ylim(-10, 10)
plt.xlim(0,150)


Out[172]:
(0, 150)

In [179]:
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 58 and DisReal < 65 and z_average > -3 and z_average < -2").reset_index(drop=True)
chosen = t_pos.sort_values("EnergyAllTerms").head(20)
# chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_trans.csv")

In [181]:
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 58 and DisReal < 65 and z_average > -3 and z_average < -2").reset_index(drop=True)

chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_trans_2.csv")

first intermediate state


In [174]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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), plot1d=0,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)
plt.ylim(-10, 10)
plt.xlim(0,150)


Out[174]:
(0, 150)

In [178]:
# this looks like the same as I1
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 70 and DisReal < 90 and z_average > -3 and z_average < -2").reset_index(drop=True)
# chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen = t_pos.sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_inter.csv")

In [182]:
# this looks like the same as I1
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 70 and DisReal < 90 and z_average > -3 and z_average < -2").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_inter_2.csv")

as expected. this is the I2 state.


In [94]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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), plot1d=0,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)
plt.ylim(-15, -5)
plt.xlim(100, 200)


Out[94]:
(100, 200)

In [100]:
t_pos = data.query("TempT <= 373 and DisReal > 140 and DisReal < 160 and z_average > -12 and z_average < -10").reset_index(drop=True)
chosen = t_pos.sort_values("EnergyAllTerms").head(n=10)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb.csv")

In [ ]:


In [ ]:


In [127]:
# this looks like the same as I1
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 80 and DisReal < 100 and z_average > -7 and z_average < -5").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_path.csv")

In [156]:
# this looks like the same as I1
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 80 and DisReal < 100 and z_average > -5 and z_average < -4").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_inter.csv")

In [126]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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), plot1d=0,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)
plt.ylim(-15, -2)
plt.xlim(50, 200)


Out[126]:
(50, 200)

In [121]:
t_pos = data.query("TempT <= 373 and TempT > 300 and DisReal > 100 and DisReal < 110 and z_average > -9 and z_average < -7").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_trans.csv")

In [122]:
t_pos["TempT"].unique()


Out[122]:
array([335, 373])

In [128]:
a = t_pos["TempT"]

In [132]:
a.astype(str).describe()


Out[132]:
count     17571
unique        2
top         335
freq      11735
Name: TempT, dtype: object

In [180]:
t_pos.hist("Lipid1")


Out[180]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23dbd940>]], dtype=object)

In [155]:
t_pos.plot.hexbin("EnergyAllTerms", "Qw", cmap="seismic", sharex=False)


Out[155]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1fef2f60>

In [102]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 330
location = pre + "/combined_more_rc/_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), plot1d=0,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)
plt.ylim(-8, 0)
plt.xlim(50, 200)


Out[102]:
(50, 200)

In [107]:
t_pos = data.query("TempT <= 373 and DisReal > 70 and DisReal < 90 and z_average > -3 and z_average < -1").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(20)
chosen.to_csv("/Users/weilu/Desktop/aug_no_perturb_inter.csv")

In [97]:
data.columns


Out[97]:
Index(['index', 'Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'Dis_h56',
       'Dis_h12', 'Dis_h34', 'z_average', 'abs_z_average', 'z_h1', 'z_h2',
       'z_h3', 'z_h4', 'z_h5', 'z_h6', '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', 'TempT',
       'BiasedEnergy', 'EnergyAllTerms', 'BiasEnergy', 'Energy_with_all_bias',
       'z_h56'],
      dtype='object')

In [81]:
# data["EnergyAllTerms"] = data["Lipid"] + data["Rg"] + data["Membrane"] + data["AMH-Go"]

In [69]:
a = data["BiasTo"].unique()

In [73]:
a.sort()

In [16]:
data["z_h5"].describe()


Out[16]:
count    1.400000e+06
mean    -1.013107e+01
std      7.589518e+00
min     -8.035579e+01
25%     -1.429092e+01
50%     -9.788820e+00
75%     -5.759859e+00
max      1.761438e+01
Name: z_h5, dtype: float64

In [74]:
a


Out[74]:
array([  40.,   42.,   44.,   46.,   48.,   50.,   52.,   54.,   56.,
         58.,   60.,   62.,   64.,   66.,   68.,   70.,   72.,   74.,
         76.,   78.,   80.,   82.,   84.,   86.,   88.,   90.,   92.,
         94.,   96.,   98.,  100.,  102.,  104.,  106.,  108.,  110.,
        112.,  118.,  124.,  130.,  136.,  142.,  148.,  154.,  166.,
        172.,  178.,  184.,  190.,  196.,  202.,  214.,  226.,  232.,
        238.,  244.,  250.,  256.,  262.,  268.,  274.,  280.,  292.,
        298.,  310.,  316.,  322.,  328.,  334.,  340.])

In [76]:
data.columns


Out[76]:
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',
       'BiasedEnergy', 'BiasEnergy', 'Energy_with_all_bias'],
      dtype='object')

In [3]:
pre = "/Users/weilu/Research/server/aug_2018/02_week"
temp = 370
location = pre + "/freeEnergy/combined_more_force/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"  # perturbation 1 is 20% here
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,21), end=(5, 35),save=False, title="56_z_dis", xlabel="Z56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_z_dis_1d.png", dpi=300)



In [8]:
pre = "/Users/weilu/Research/server/aug_2018/02_week"
temp = 370
location = pre + "/freeEnergy/combined_more_force/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"  # perturbation 1 is 20% here
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,21), end=(5, 35),save=False, plot1d=0, title="56_z_dis", xlabel="Z56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
# xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_z_dis_1d.png", dpi=300)
plt.ylim(20, 50)
plt.xlim(-30, 0)


Out[8]:
(-30, 0)

highest peak


In [11]:
pre = "/Users/weilu/Research/server/aug_2018/02_week"
temp = 370
location = pre + "/freeEnergy/combined_more_force/_280-350/56_z_dis/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"  # perturbation 1 is 20% here
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(35,21), end=(5, 35),save=False, plot1d=0, title="56_z_dis", xlabel="Z56", ylabel="Dis56", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
# xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/56_z_dis_1d.png", dpi=300)
plt.ylim(30, 45)
plt.xlim(-25, -5)
# plt.clim(0,18)


Out[11]:
(-25, -5)

In [85]:
t_pos = data.query("TempT <= 373 and Dis_h56 > 35 and Dis_h56 < 40 and z_h56 > -21 and z_h56 < -15").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(n=20)
chosen["Qw"]
chosen.to_csv("/Users/weilu/Desktop/56_pick_4.csv")

In [84]:
t_pos = data.query("TempT <= 373 and Dis_h56 > 34 and Dis_h56 < 36 and z_h56 > -17.5 and z_h56 < -13").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("EnergyAllTerms").head(n=20)
chosen["Qw"]
chosen.to_csv("/Users/weilu/Desktop/56_pick_3.csv")

In [75]:
chosen.reset_index(drop=True)


Out[75]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias z_h56
0 205771 -206.526754 -435.165264 -280.925968 -339.025200 78.0 63.657389 34.404482 -33.297904 -873.772332 ... 0.988092 -5.371964 -2.266104 -9.207419 -9.481446 -5.649049 -534.716923 4.114210 -530.602714 -15.130495
1 669665 -213.817289 -475.835985 -293.818377 -356.736882 64.0 43.730212 34.030910 -12.553862 -868.481529 ... -3.299235 -8.363496 -5.492851 -12.022936 -7.447466 -8.261984 -588.220711 8.217286 -580.003426 -15.709450
2 283120 -204.018309 -459.656885 -283.130006 -344.423257 68.0 49.920093 34.505552 -23.555234 -829.958499 ... -4.762669 -8.301740 -3.323001 -10.048845 -9.674083 -4.638432 -566.828910 6.537661 -560.291250 -14.312514
3 658091 -199.183084 -396.287236 -264.799119 -308.616074 92.0 87.721798 35.855518 79.450835 -824.835665 ... -4.587225 -9.208188 -6.244938 20.507098 2.822399 -19.660188 -495.848844 0.366060 -495.482784 -16.837789
4 60620 -199.676168 -454.639925 -273.480985 -333.195446 50.0 38.757816 34.965658 30.338165 -818.683616 ... -3.976965 -8.452456 -4.523642 -10.533293 -6.437975 -6.703347 -563.727620 2.527734 -561.199886 -13.141323
5 684403 -210.894077 -460.914599 -287.539112 -345.627484 58.0 43.576149 35.008740 42.659026 -813.823360 ... -0.305541 -4.943481 -2.742624 -8.600871 -10.370657 -3.915921 -567.676918 4.160949 -563.515969 -14.286577
6 415976 -206.028065 -414.036930 -271.489936 -315.983295 70.0 66.068932 34.077152 61.225535 -812.424508 ... -1.834335 -8.783356 -10.250210 -10.508513 -8.217124 -5.460828 -512.376854 0.309066 -512.067789 -13.677952
7 400068 -211.570356 -470.908026 -287.707077 -349.766760 54.0 38.687766 34.034328 16.705763 -806.664852 ... -2.352095 -6.845574 -4.663598 -11.093673 -6.853819 -7.271832 -579.867379 4.689290 -575.178088 -14.125651
8 383364 -210.312864 -431.085064 -280.972307 -343.463317 76.0 66.084182 34.637604 -46.325189 -798.281649 ... -1.307087 -8.569315 -5.028983 -10.628676 -4.506836 -8.866264 -535.507674 1.966469 -533.541205 -13.373100
9 562170 -197.819521 -398.804709 -255.616934 -299.842518 78.0 60.379217 35.354777 -52.893643 -785.831986 ... -2.060817 -6.711340 -8.943993 -8.014141 -6.201402 -6.912406 -495.066230 6.209840 -488.856390 -13.113809
10 28404 -193.682140 -388.144238 -255.590903 -294.537320 76.0 72.449290 35.388376 72.311438 -780.462631 ... 0.106582 -6.619300 -6.637837 -8.152669 -5.725185 -8.182194 -485.973379 0.252151 -485.721229 -13.907378
11 163928 -203.228799 -395.173568 -261.680939 -305.662043 108.0 108.159930 35.967964 6.323758 -777.501401 ... -4.966937 -10.450269 -21.263728 -17.052656 -9.187427 -5.027765 -485.534877 0.000512 -485.534365 -14.215191
12 383380 -194.546104 -416.308281 -264.428661 -324.416654 76.0 61.483757 34.518969 -31.282112 -773.152206 ... 1.363798 -5.759946 -3.529383 -9.219258 -7.617136 -6.116643 -515.819329 4.214426 -511.604903 -13.733779
13 583218 -200.487688 -406.670114 -264.692110 -312.626149 66.0 64.161588 34.714258 17.375501 -770.956518 ... -1.154738 -7.628014 -8.029008 -8.729565 -10.307170 -4.163497 -501.273271 0.067595 -501.205676 -14.470667
14 260702 -207.196391 -400.787148 -262.411959 -305.995567 82.0 80.079571 34.302985 -79.841347 -768.854037 ... 0.904244 -6.950901 -21.208352 -10.410316 -6.073351 -11.240301 -497.374905 0.073761 -497.301144 -17.313652
15 501203 -202.772908 -395.877655 -265.335183 -309.576669 110.0 100.073260 35.769859 -16.357524 -766.887222 ... -1.943209 -7.536486 -5.695417 19.122556 4.880667 -18.678766 -495.626752 1.970803 -493.655949 -13.798099
16 423491 -210.839538 -473.441868 -287.868686 -350.851955 50.0 39.513704 34.185214 -10.718160 -764.525660 ... -0.109038 -5.287299 -3.007117 -9.816366 -8.317306 -5.360327 -582.156088 2.199248 -579.956840 -13.677633
17 529707 -203.866052 -394.045918 -259.206685 -302.568076 108.0 107.657914 35.822867 -96.276398 -762.870186 ... -1.401792 -7.677874 -30.312803 -34.314475 -8.158968 -6.733149 -485.265763 0.002340 -485.263422 -14.892117
18 75223 -211.732644 -457.469264 -286.656254 -346.010487 56.0 42.049597 34.068949 -0.235090 -761.033425 ... -0.331265 -5.421671 -2.610465 -9.374173 -9.344143 -4.904374 -569.068853 3.892275 -565.176578 -14.248517
19 522855 -199.349016 -385.240175 -258.984676 -303.077653 108.0 105.902275 34.518031 12.759441 -759.822585 ... -2.884375 -8.341004 -22.477588 -18.382229 -9.287161 -4.769126 -480.037723 0.088009 -479.949714 -14.056287

20 rows × 54 columns


In [71]:
t_pos = data.query("TempT <= 373 and Dis_h56 > 34 and Dis_h56 < 36 and z_h56 > -17.5 and z_h56 < -13").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen["Qw"]
chosen.to_csv("/Users/weilu/Desktop/56_pick_2.csv")

In [67]:
tt.plot.hexbin("z_h56", "Dis_h56", cmap="seismic", sharex=False)


Out[67]:
<matplotlib.axes._subplots.AxesSubplot at 0x116297940>

In [47]:
t = data.query("TempT == 373 and Dis_h56 > 30 and Dis_h56 < 44 and z_h56 > -25 and z_h56 < -5")
t.plot.hexbin("z_h4", "Qw", cmap="seismic", sharex=False)


Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a4406ec50>

In [64]:
t = data.query("TempT  <= 373 and Dis_h56 > 30 and Dis_h56 < 44 and z_h56 > -25 and z_h56 < -5")
t.query("Lipid1 < -0.5").query("z_h4 > -14 and z_h4 < 0 and Qw > 0.3 and Qw < 0.5").plot.hexbin("z_h4", "Qw", cmap="seismic", sharex=False)


Out[64]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1cdd59b0>

In [65]:
tt = t.query("Lipid1 < -0.5").query("z_h4 > -14 and z_h4 < 0 and Qw > 0.3 and Qw < 0.5")

In [66]:



Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ba6de10>

In [55]:
chosen.query("Lipid1 < -0.5").sample(10)


Out[55]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias z_h56
1045723 365723 -177.490583 -435.090308 -240.188481 -300.739842 86.0 75.178809 30.046047 -74.516878 -811.932098 ... 2.332595 -7.810694 -2.951927 -9.355071 -5.162534 -3.680325 -538.759883 2.341964 -536.417919 -8.842859
1055126 375126 -186.138668 -441.869856 -250.113847 -309.595345 84.0 88.988007 30.579470 84.809400 -887.874766 ... 0.417075 -7.131181 -5.356357 -10.025998 -5.064601 -5.498214 -550.763094 0.497604 -550.265489 -10.562815
802964 122964 -176.442589 -431.852238 -239.812611 -304.777340 96.0 78.154358 30.809957 -59.654168 -819.057935 ... -3.245921 -7.204180 -2.727450 -8.784208 -4.297992 -4.085412 -534.923700 6.369339 -528.554361 -8.383404
686273 6273 -180.426172 -416.870709 -240.978647 -298.762870 86.0 80.225181 30.714065 -57.180397 -736.240533 ... -0.403599 -6.551786 -4.240690 -9.551176 -7.728732 -4.778035 -520.456928 0.666971 -519.789957 -12.506767
1046246 366246 -185.878138 -418.829884 -250.370739 -303.888273 86.0 91.192209 30.899110 -88.559140 -682.572602 ... 2.590478 -10.222196 -5.376165 -9.269123 -2.639307 -8.127113 -514.937135 0.539181 -514.397954 -10.766420
900588 220588 -209.606026 -419.335144 -273.758371 -319.135358 66.0 60.667818 30.482806 -41.039555 -707.742018 ... -1.773014 -7.761778 -7.168985 -8.621878 -1.408396 -4.804739 -521.946388 0.568643 -521.377745 -6.213135
689686 9686 -169.716715 -403.702354 -234.337197 -290.332177 86.0 79.324558 30.027269 75.665124 -704.175352 ... 1.799193 -7.829656 -4.143076 -10.041850 -7.530246 -3.999649 -506.890673 0.891230 -505.999443 -11.529894
1250046 570046 -177.185689 -413.638997 -243.357085 -294.911723 74.0 66.206880 32.613217 -35.792491 -680.279990 ... 3.237520 -7.514513 -2.900412 -9.989474 -8.183942 -4.819954 -512.791417 1.214654 -511.576763 -13.003896
1123770 443770 -208.949270 -432.537905 -284.878699 -340.202143 80.0 74.932308 43.692935 5.380437 -797.586019 ... -0.738259 -6.359267 -5.835008 -8.740340 -8.586018 -6.360778 -536.413901 0.513630 -535.900271 -14.946796
1379767 699767 -180.977907 -430.305738 -250.066503 -310.480074 90.0 78.098957 30.704097 -45.808969 -819.773980 ... 1.759968 -7.019486 -3.524204 -9.635709 -8.745567 -4.611351 -536.323479 2.832696 -533.490783 -13.356918

10 rows × 54 columns


In [60]:
chosen = t.query("z_h4 > -14 and z_h4 < 0 and Qw > 0.3 and Qw < 0.5").query("Lipid1 < -0.5")
tt = chosen.sample(20)
tt.to_csv("/Users/weilu/Desktop/56_pick.csv")

In [63]:
# tt = pd.read_csv("/Users/weilu/Desktop/56_pick.csv", index_col=0)
tt["Qw"]


Out[63]:
698564     0.315081
904185     0.287506
1238725    0.424124
1063360    0.447058
885779     0.472875
886971     0.466197
1063336    0.482005
1369498    0.391694
731268     0.281537
792767     0.417758
1063200    0.442043
767100     0.305568
1369538    0.430243
1369510    0.429782
1063196    0.348849
886923     0.440738
1238848    0.484849
1262598    0.288097
1063464    0.459882
904409     0.287843
Name: Qw, dtype: float64

In [42]:
t_pos = data.query("TempT == 373 and Dis_h56 > 34 and Dis_h56 < 36 and z_h56 > -17.5 and z_h56 < -15").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen["Qw"]


Out[42]:
36    0.583427
20    0.643865
21    0.453714
23    0.452299
27    0.239346
22    0.198777
25    0.232988
31    0.193848
11    0.201396
17    0.209142
6     0.170733
28    0.204436
32    0.198900
10    0.207266
5     0.208237
19    0.163910
12    0.215989
26    0.224578
8     0.166514
9     0.195063
Name: Qw, dtype: float64

In [44]:
t_pos = data.query("TempT == 373 and Dis_h56 > 34 and Dis_h56 < 36 and z_h56 > -17.5 and z_h56 < -15").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
# chosen = t_pos.query("z_h4 > -10 and z_h4 < 10").sort_values("AMH_4H").head(n=20)
# chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Desktop/56.csv")

In [46]:
chosen.plot.hexbin("z_h4", "Qw", cmap="seismic", sharex=False)


Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a446bc2b0>

In [33]:
t_pos.plot.hexbin("z_h4", "AMH_4H", cmap="seismic", sharex=False)


Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a43e94b70>

In [22]:
t_pos.hist("AMH_4H")


Out[22]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a3ab12e80>]], dtype=object)

In [40]:
t_pos = data.query("TempT == 373 and Dis_h56 > 38 and Dis_h56 < 40 and z_h56 > -21.5 and z_h56 < -18.5").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen


Out[40]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias z_h56
123 566177 -208.452850 -405.107662 -276.393967 -317.518577 78.0 70.996838 39.249649 63.212982 -686.018153 ... -2.844452 -7.241506 -7.672706 13.356365 6.700749 -27.289237 -504.204755 0.980886 -503.223869 -20.588487
124 568909 -206.470506 -405.400733 -274.494152 -318.701503 78.0 72.438864 39.891973 40.269436 -675.839730 ... -2.500933 -8.925370 -4.434192 22.682909 2.636014 -23.103922 -502.969772 0.618525 -502.351247 -20.467908
159 652919 -204.127923 -401.578468 -269.417912 -310.310974 92.0 88.996100 38.083575 -41.427238 -709.114966 ... -0.288356 -5.314795 -4.212801 19.817404 -0.089313 -18.834035 -502.129136 0.180468 -501.948668 -18.923349
161 654843 -203.036501 -398.302005 -267.393119 -312.061019 92.0 84.098699 38.315603 68.581040 -741.368141 ... -3.980811 -7.756068 -5.119282 20.158898 0.982415 -22.278174 -501.188962 1.248611 -499.940351 -21.295759
130 604129 -206.559139 -400.191278 -271.319842 -312.610458 94.0 91.224633 39.913302 9.588540 -748.353835 ... -2.367397 -6.172563 -1.683158 23.780741 1.179440 -22.134845 -500.073329 0.154053 -499.919276 -20.955405
59 205188 -202.901594 -400.468594 -259.013229 -303.853023 78.0 70.453726 38.756742 -48.515145 -691.326105 ... 0.009299 -8.782013 -8.194323 10.942435 4.102379 -23.191238 -500.616876 1.138925 -499.477951 -19.088858
69 244689 -201.094959 -399.359407 -263.096422 -307.188389 94.0 86.246140 39.485060 -46.573403 -711.240672 ... -3.172298 -8.355068 -8.134308 13.284462 1.236199 -20.340852 -499.091551 1.202447 -497.889104 -19.104653
81 294983 -205.451567 -397.213079 -269.309339 -309.009465 92.0 91.121556 38.522136 -53.150336 -708.687573 ... -2.509565 -6.797100 -4.645682 25.896559 -0.607430 -20.183526 -497.626716 0.015433 -497.611283 -20.790957
71 244761 -205.145935 -397.515048 -272.699621 -309.731260 94.0 99.447057 38.837079 -33.493467 -655.235940 ... -2.391227 -8.147805 -6.957627 18.790741 0.564051 -19.849196 -497.993115 0.593409 -497.399706 -19.285145
154 651215 -209.889487 -396.957517 -275.894714 -313.879513 92.0 91.725785 39.604987 -81.942728 -690.649549 ... 0.361325 -7.917342 -4.613136 28.175535 5.262908 -25.079817 -496.534612 0.001504 -496.533108 -19.816910
80 290683 -212.640282 -399.935314 -279.041437 -322.128416 92.0 80.522461 38.668953 -79.449374 -683.719765 ... -2.000437 -7.296029 -6.791213 24.267338 0.419306 -19.408367 -498.865184 2.634678 -496.230506 -18.989061
110 482158 -194.228763 -394.397142 -259.458905 -303.610447 96.0 88.497407 38.209764 -88.104278 -679.994071 ... -5.719451 -9.934423 -9.450510 14.332794 1.731400 -22.712084 -496.848964 1.125778 -495.723186 -20.980684
156 652579 -201.519299 -394.723684 -264.259763 -304.781236 92.0 80.787931 38.444094 -79.158630 -718.040490 ... 1.322420 -4.611200 -3.784113 16.807865 -1.169693 -19.210861 -497.001071 2.514210 -494.486861 -20.380554
125 569289 -200.983683 -395.298909 -260.356258 -305.358154 78.0 82.217005 38.219903 -11.785675 -702.655685 ... -2.201977 -8.297987 -5.584820 10.239050 1.367591 -22.003969 -493.269286 0.355663 -492.913624 -20.636378
67 222883 -191.649590 -390.764985 -253.135485 -298.643280 66.0 64.606484 39.463718 59.024563 -717.652169 ... -1.627439 -7.149227 -7.910973 15.764207 2.172696 -22.383948 -492.476570 0.038838 -492.437732 -20.211252
138 608438 -194.654641 -393.507492 -255.952425 -303.023677 94.0 92.752032 39.056761 -51.787589 -684.208517 ... -1.926053 -7.493664 -5.889250 -12.192113 -10.720044 -8.810883 -492.029459 0.031148 -491.998310 -19.530927
41 59781 -203.368844 -395.661849 -261.706091 -305.271318 70.0 73.616707 39.035597 -47.535233 -684.045111 ... -0.376157 -7.799804 -22.162220 7.554788 1.688650 -22.611365 -491.628955 0.261611 -491.367344 -20.922715
104 413833 -192.868374 -391.801050 -254.157315 -297.458737 70.0 67.937319 38.391006 -67.563169 -725.490575 ... -1.958505 -8.546299 -23.419822 9.093970 1.105293 -19.714364 -491.205827 0.085093 -491.120734 -18.609071
139 610763 -203.155528 -395.346192 -269.667099 -314.092286 104.0 88.982733 39.074742 -38.232106 -698.855284 ... -4.006730 -6.650078 -4.381398 15.501785 0.405100 -21.844769 -495.566938 4.510366 -491.056572 -21.439668
132 604937 -206.636159 -395.732599 -272.839927 -313.091399 94.0 86.321609 39.339254 -30.226935 -713.869202 ... -1.382056 -6.882689 -3.357089 16.755834 0.799854 -19.782268 -492.093670 1.179154 -490.914516 -18.982413

20 rows × 54 columns


In [28]:
t_pos = data.query("TempT == 373 and Dis_h56 > 38 and Dis_h56 < 40 and z_h56 > -21.5 and z_h56 < -18.5").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("AMH_4H").head(n=20)
chosen


Out[28]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias z_h56
80 290683 -212.640282 -399.935314 -279.041437 -322.128416 92.0 80.522461 38.668953 -79.449374 -683.719765 ... -2.000437 -7.296029 -6.791213 24.267338 0.419306 -19.408367 -498.865184 2.634678 -496.230506 -18.989061
124 568909 -206.470506 -405.400733 -274.494152 -318.701503 78.0 72.438864 39.891973 40.269436 -675.839730 ... -2.500933 -8.925370 -4.434192 22.682909 2.636014 -23.103922 -502.969772 0.618525 -502.351247 -20.467908
123 566177 -208.452850 -405.107662 -276.393967 -317.518577 78.0 70.996838 39.249649 63.212982 -686.018153 ... -2.844452 -7.241506 -7.672706 13.356365 6.700749 -27.289237 -504.204755 0.980886 -503.223869 -20.588487
118 562924 -209.550174 -394.623740 -274.764692 -315.867950 78.0 86.709249 39.247068 -19.053485 -725.305216 ... 1.750579 -6.428666 -3.559804 21.199637 0.168990 -20.346782 -488.396144 1.517021 -486.879123 -20.177792
139 610763 -203.155528 -395.346192 -269.667099 -314.092286 104.0 88.982733 39.074742 -38.232106 -698.855284 ... -4.006730 -6.650078 -4.381398 15.501785 0.405100 -21.844769 -495.566938 4.510366 -491.056572 -21.439668
154 651215 -209.889487 -396.957517 -275.894714 -313.879513 92.0 91.725785 39.604987 -81.942728 -690.649549 ... 0.361325 -7.917342 -4.613136 28.175535 5.262908 -25.079817 -496.534612 0.001504 -496.533108 -19.816910
132 604937 -206.636159 -395.732599 -272.839927 -313.091399 94.0 86.321609 39.339254 -30.226935 -713.869202 ... -1.382056 -6.882689 -3.357089 16.755834 0.799854 -19.782268 -492.093670 1.179154 -490.914516 -18.982413
130 604129 -206.559139 -400.191278 -271.319842 -312.610458 94.0 91.224633 39.913302 9.588540 -748.353835 ... -2.367397 -6.172563 -1.683158 23.780741 1.179440 -22.134845 -500.073329 0.154053 -499.919276 -20.955405
161 654843 -203.036501 -398.302005 -267.393119 -312.061019 92.0 84.098699 38.315603 68.581040 -741.368141 ... -3.980811 -7.756068 -5.119282 20.158898 0.982415 -22.278174 -501.188962 1.248611 -499.940351 -21.295759
141 616151 -205.949382 -392.637974 -271.509292 -311.872430 104.0 89.633480 38.168639 81.667576 -661.531847 ... -2.780466 -9.685048 -5.524410 19.434217 -1.627740 -17.458019 -490.876324 4.127938 -486.748386 -19.085759
159 652919 -204.127923 -401.578468 -269.417912 -310.310974 92.0 88.996100 38.083575 -41.427238 -709.114966 ... -0.288356 -5.314795 -4.212801 19.817404 -0.089313 -18.834035 -502.129136 0.180468 -501.948668 -18.923349
121 564644 -214.770148 -396.767347 -272.216957 -310.056810 78.0 63.943037 38.225522 39.642172 -681.887070 ... -0.739712 -7.400699 -18.487332 10.889166 2.729359 -23.467386 -494.662933 3.951964 -490.710968 -20.738028
86 299807 -201.483624 -387.228230 -264.384024 -309.911817 92.0 88.761169 39.833494 -62.302911 -651.468199 ... 1.391854 -6.157402 -5.502356 14.366697 4.550583 -23.403347 -483.994834 0.209800 -483.785033 -18.852764
71 244761 -205.145935 -397.515048 -272.699621 -309.731260 94.0 99.447057 38.837079 -33.493467 -655.235940 ... -2.391227 -8.147805 -6.957627 18.790741 0.564051 -19.849196 -497.993115 0.593409 -497.399706 -19.285145
109 481818 -200.543773 -396.729387 -263.819549 -309.176279 96.0 84.798414 39.222375 -75.819124 -706.414290 ... 0.078448 -5.803070 -4.659452 19.811645 1.644242 -22.285154 -491.813311 2.509510 -489.303800 -20.640912
81 294983 -205.451567 -397.213079 -269.309339 -309.009465 92.0 91.121556 38.522136 -53.150336 -708.687573 ... -2.509565 -6.797100 -4.645682 25.896559 -0.607430 -20.183526 -497.626716 0.015433 -497.611283 -20.790957
69 244689 -201.094959 -399.359407 -263.096422 -307.188389 94.0 86.246140 39.485060 -46.573403 -711.240672 ... -3.172298 -8.355068 -8.134308 13.284462 1.236199 -20.340852 -499.091551 1.202447 -497.889104 -19.104653
160 653071 -199.869135 -385.773780 -263.687534 -306.609066 92.0 95.856456 39.812357 -54.047109 -719.831244 ... -0.801285 -4.388010 -6.871987 13.714815 4.870416 -24.691689 -483.215942 0.297445 -482.918497 -19.821272
17 50225 -202.987396 -389.677061 -260.937864 -306.079661 70.0 72.197296 39.818879 45.984138 -679.793921 ... 0.236720 -6.047002 -5.925866 19.169437 -1.286561 -18.136774 -487.922640 0.096562 -487.826078 -19.423335
105 415442 -204.467341 -390.640938 -262.696142 -305.906866 70.0 60.338175 38.779925 -45.498437 -676.242858 ... -3.136352 -10.297197 -26.372291 9.282224 -0.865856 -19.892185 -484.260256 1.867017 -482.393239 -20.758041

20 rows × 54 columns


In [31]:
t_pos = data.query("TempT == 373 and Dis_h56 > 38 and Dis_h56 < 40 and z_h56 > -21.5 and z_h56 < -18.5").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen = t_pos.query("z_h4 > -10 and z_h4 < 10").sort_values("AMH_4H").head(n=20)
# chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Desktop/56_highest_barrier.csv")

In [38]:
t_pos = data.query("TempT == 373 and DisReal > 52 and DisReal < 57 and z_average > -4 and z_average < 0").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
# chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_pre.csv")

In [18]:
t_pos.hist("Lipid1",bins=50)


Out[18]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a405598d0>]], dtype=object)

at transition state


In [20]:
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=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", plot1d=False, zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.ylim(-8, 0)
plt.xlim(40, 80)
# plt.clim(0, 20)
# plt.colorbar()


Out[20]:
(40, 80)

In [51]:
t_pos = data.query("TempT == 373 and DisReal > 57 and DisReal <63 and z_average > -5 and z_average < -2").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5 and Lipid10 < -0.5").sort_values("Energy_with_all_bias").head(n=10)
# chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_transition.csv")

In [50]:
t_pos.query("Lipid1 < -0.5").plot.hexbin("Energy_with_all_bias", "Lipid10", cmap="seismic", sharex=False)


Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1a41e240>

In [41]:
chosen["Lipid10"]


Out[41]:
1809   -1.539335
1810   -1.492583
101    -2.004417
1824   -1.290158
114    -2.036322
715    -0.507082
106    -1.930354
1823   -0.967124
113    -1.639273
1825   -1.801742
42     -0.722760
43     -1.407829
41     -1.119277
Name: Lipid10, dtype: float64

In [29]:
t_pos.query("Lipid1 < -0.5 and Lipid6 < -0.5").sort_values("Energy_with_all_bias").head(n=20)


Out[29]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias
1809 307888 -195.630242 -435.155569 -256.611899 -309.100543 64.0 58.183612 19.117130 53.955815 -736.440977 ... -3.372721 -1.120181 -6.685898 -4.955295 -10.356982 -8.509524 -4.613997 -537.957084 0.676607 -537.280477
852 222680 -212.492575 -426.742858 -280.660929 -322.856403 66.0 61.794507 23.979250 -48.982575 -698.168487 ... -2.493581 -3.412816 -9.158446 -5.865533 -7.015594 -1.613935 -2.160496 -527.957597 0.353723 -527.603873
2184 418784 -202.883003 -426.597887 -268.332371 -314.045236 70.0 58.645122 26.143309 53.643294 -755.443350 ... -2.323231 -2.429134 -6.090470 -6.003408 -8.892851 -5.687133 -4.399457 -529.470401 2.578665 -526.891736
101 26825 -199.648358 -425.305810 -273.748964 -333.705235 76.0 62.819403 47.702800 53.283447 -723.012908 ... -4.260705 0.139279 -6.230079 -6.442975 -10.699556 -6.137595 -16.491721 -530.114741 3.474563 -526.640178
94 26038 -208.510674 -430.119814 -276.016418 -320.356026 76.0 59.524977 24.224513 58.393343 -728.250229 ... -2.747517 -1.340868 -5.378715 -5.859207 -7.833290 -7.282965 -4.424369 -532.032658 5.428528 -526.604131
979 224932 -200.999176 -424.964917 -268.205085 -314.214457 66.0 61.513355 25.585336 15.120150 -757.444224 ... -2.930676 0.095223 -6.989153 -9.343414 -8.763971 -4.947870 -5.669710 -526.520846 0.402600 -526.118246
855 222692 -212.117022 -428.949862 -274.930920 -315.779656 66.0 58.304995 25.048162 -50.516437 -696.965462 ... -2.739929 -0.467787 -8.357556 -5.537234 -9.871371 -5.828420 -4.298823 -527.272362 1.184262 -526.088100
54 23504 -203.173196 -427.494189 -269.880197 -315.403807 76.0 62.247608 12.586580 30.725986 -730.314199 ... -4.241929 -5.544556 -9.178300 -7.867663 -8.649030 -6.375447 -4.008420 -529.720337 3.782566 -525.937772
2715 580048 -208.429187 -424.416328 -273.843307 -318.042329 66.0 59.913774 25.402670 -20.440483 -707.163751 ... -2.957331 -2.383170 -7.417953 -9.693094 -6.990099 -3.684163 -4.300805 -526.572130 0.740843 -525.831287
831 222424 -208.500164 -420.544978 -274.221226 -318.412940 66.0 61.444471 18.582937 -30.424102 -764.721182 ... -2.475907 -2.184501 -9.018082 -6.716107 -9.031155 -4.425669 -5.255468 -526.152329 0.415057 -525.737272
3023 585741 -205.590513 -424.835540 -266.596363 -312.892384 66.0 61.510270 27.663459 61.236966 -748.585380 ... -3.567792 -1.822974 -8.392898 -7.492330 -10.293985 -5.360528 -6.608474 -525.238362 0.403153 -524.835208
2890 582938 -208.170719 -425.274958 -268.888721 -315.565898 66.0 60.382575 25.155048 44.218346 -740.279154 ... -2.072679 -1.298619 -6.579887 -10.298974 -6.881801 -7.026276 -4.282315 -524.557407 0.631109 -523.926298
2185 418788 -209.170727 -423.160869 -270.003824 -316.414544 70.0 60.011881 25.519077 57.156796 -740.899096 ... -2.901282 -1.588733 -7.268564 -5.158878 -9.573811 -7.027214 -4.388643 -525.269339 1.995250 -523.274089
2767 580969 -207.500873 -419.180389 -274.419193 -319.159743 66.0 62.168897 20.376856 -15.834948 -718.895511 ... -2.429632 -0.330907 -5.280038 -5.899951 -10.524730 -8.366222 -3.552831 -523.268301 0.293547 -522.974754
2889 582934 -199.444272 -426.226597 -261.854898 -308.421515 66.0 59.125702 23.532604 48.910934 -746.689716 ... -2.752155 -3.291837 -8.802374 -11.646004 -7.853802 -3.844650 -2.622916 -523.864884 0.945120 -522.919764
1824 380180 -208.061244 -418.957813 -275.330351 -334.298486 76.0 60.666659 39.362515 35.550336 -739.062584 ... -4.668732 -1.465789 -6.224099 -6.559817 -10.549062 -6.279480 -21.435293 -527.570428 4.702227 -522.868201
805 221914 -203.281412 -420.903618 -268.756059 -314.024096 66.0 62.989386 27.760758 27.292178 -723.455757 ... -3.337388 -0.618825 -7.664474 -7.720646 -10.784186 -6.799462 -7.976852 -522.263435 0.181276 -522.082159
950 224401 -199.417503 -425.681589 -261.475693 -307.181621 66.0 57.831081 24.689610 48.087210 -754.651713 ... -3.198339 -0.984886 -8.154063 -5.880041 -9.803197 -9.561973 -6.575451 -522.967061 1.334625 -521.632436
1162 228622 -204.136704 -422.966143 -271.332616 -309.340516 66.0 57.128272 18.050762 -37.118292 -729.601746 ... -2.350094 -0.952849 -8.304294 -8.363146 -8.623820 -3.682796 -2.957453 -522.966405 1.574151 -521.392254
980 224936 -202.859992 -418.675942 -267.070088 -314.213437 66.0 62.502711 25.801454 10.388485 -729.281251 ... -3.096155 -2.458667 -6.394231 -7.265577 -8.579678 -5.920386 -5.427298 -521.586025 0.244621 -521.341405

20 rows × 53 columns

post trainsition


In [52]:
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=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", plot1d=False, zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.ylim(-8, 0)
plt.xlim(40, 80)
# plt.clim(0, 20)
# plt.colorbar()


Out[52]:
(40, 80)

In [54]:
t_pos = data.query("TempT == 373 and DisReal > 63 and DisReal <72 and z_average > -6 and z_average < -3").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
# chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_post_transition.csv")

In [55]:
chosen


Out[55]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias
4010 446101 -211.480423 -442.463754 -286.204801 -345.759750 80.0 66.936457 50.557520 -64.268538 -733.547034 ... -4.976259 -1.394228 -6.643697 -4.078782 -9.780926 -9.066586 -15.639029 -550.266247 3.413123 -546.853124
2839 388904 -221.503388 -436.726296 -291.605144 -345.176318 76.0 65.741900 45.936472 -55.943603 -741.000204 ... -5.869313 -2.672310 -6.559123 -6.108043 -10.470321 -8.651620 -19.190212 -544.426074 2.104572 -542.321502
4015 446133 -213.112447 -439.145305 -289.273137 -347.660227 80.0 67.062016 57.472450 -47.939616 -738.877963 ... -5.763330 -3.748541 -8.568946 -3.402772 -11.275554 -8.917268 -18.321435 -544.521828 3.347829 -541.173999
4013 446125 -209.451212 -436.911669 -285.341764 -342.279061 80.0 69.031551 52.115058 -46.543365 -708.575044 ... -4.951642 -0.677398 -7.152916 -5.391908 -11.183478 -9.321433 -16.967458 -540.502155 2.406137 -538.096018
505 26769 -212.502047 -432.283242 -284.108653 -345.343903 76.0 69.887765 63.889698 60.153880 -762.170015 ... -4.411739 -1.505991 -6.577402 -3.687029 -10.050159 -10.331140 -15.308807 -537.751250 0.747188 -537.004061
569 28377 -211.101138 -428.622490 -285.091278 -342.449126 76.0 68.912845 45.648562 -10.651692 -707.005817 ... -5.963440 -1.719226 -7.853797 -6.875862 -10.784236 -7.533348 -18.696292 -537.018407 1.004555 -536.013852
239 22277 -202.186215 -430.985317 -277.861108 -338.611440 76.0 69.594168 46.242939 -52.808616 -740.588945 ... -5.500809 -2.048178 -6.209699 -3.764542 -10.130583 -9.405525 -19.748512 -535.671116 0.820694 -534.850423
4014 446129 -207.600808 -433.650512 -284.874895 -343.534088 80.0 66.615807 54.887784 -44.059781 -745.538545 ... -5.800334 -2.747858 -9.419299 -4.241098 -12.719748 -9.414943 -16.538970 -537.048270 3.582732 -533.465538
568 28365 -207.214669 -428.678001 -279.481647 -337.742500 76.0 70.805104 50.827103 2.186179 -729.899974 ... -3.491304 0.333993 -5.603651 -1.682577 -9.441809 -8.717918 -16.279346 -533.237648 0.539739 -532.697909
3637 416993 -213.974771 -432.285433 -283.488804 -325.225638 70.0 68.413256 20.869282 65.738819 -735.073151 ... -4.355289 -2.938984 -10.520999 -10.438956 -7.884285 -1.999664 -4.131186 -532.317022 0.050355 -532.266667
4020 446153 -218.924307 -434.669732 -290.140110 -343.611160 80.0 66.020107 53.977987 -60.667803 -721.806330 ... -5.440438 -0.303600 -7.152898 -5.623939 -10.568709 -12.964864 -15.789399 -535.204667 3.908748 -531.295918
543 27397 -212.084337 -432.648025 -287.607980 -343.430750 76.0 68.321083 50.241477 8.093328 -699.138528 ... -5.889171 -1.598715 -6.151286 -0.867239 -9.181193 -17.699486 -20.647653 -531.975144 1.179315 -530.795828
575 28429 -200.693359 -424.773057 -273.493757 -333.706773 76.0 71.413661 56.281308 -2.018128 -740.015782 ... -5.325413 -0.100392 -6.316062 -4.652214 -10.309873 -11.555529 -21.319595 -530.953067 0.420690 -530.532377
4016 446137 -214.962985 -427.105238 -284.235463 -334.491148 80.0 65.340862 59.125437 -46.143545 -746.707614 ... -5.360031 -2.405346 -8.336494 -4.800097 -8.650347 -6.013376 -22.375479 -534.732178 4.297807 -530.434371
4018 446145 -203.743942 -429.051506 -274.857001 -334.947562 80.0 66.895127 56.166531 -51.738827 -749.106874 ... -5.894091 -1.081719 -7.207967 -6.559293 -11.151075 -7.630941 -22.014183 -533.101654 3.434754 -529.666900
4022 446161 -203.667352 -425.190842 -276.798774 -334.102619 80.0 70.160395 51.383012 -59.064280 -747.133193 ... -5.591876 -2.371950 -7.043812 -4.908712 -11.345381 -9.428008 -18.492082 -531.488826 1.936356 -529.552469
4009 446097 -202.319146 -424.259724 -275.420463 -334.284197 80.0 70.695499 50.032869 -65.795765 -730.866002 ... -5.037388 -0.595712 -6.308466 -5.530896 -9.205614 -8.241701 -19.952801 -531.050822 1.731475 -529.319347
2186 228832 -211.483525 -427.413298 -276.233647 -318.928657 66.0 63.485843 24.180937 -63.033278 -756.898569 ... -3.951987 -2.624168 -7.198137 -7.236925 -10.475400 -9.036326 -5.986729 -529.084324 0.126420 -528.957904
512 26861 -215.737067 -422.403880 -289.170966 -345.337835 76.0 66.815424 72.045591 54.222840 -690.435599 ... -5.547676 -0.592264 -6.868703 -5.508089 -10.873647 -7.458156 -16.465050 -529.994269 1.687129 -528.307140
329 23948 -205.677384 -429.765714 -272.896064 -319.530844 76.0 69.921961 20.899628 16.125406 -705.054513 ... -3.892404 -0.945163 -7.583568 -7.171835 -10.770997 -9.203308 -4.550224 -528.595577 0.738851 -527.856726

20 rows × 53 columns

5-6 out region

zoom to 50 - 130


In [5]:
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=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", plot1d=False, zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.ylim(-15, 0)
plt.xlim(50, 120)
# plt.clim(0, 20)
# plt.colorbar()


Out[5]:
(50, 120)

The unwanted path 1


In [45]:
t = data.query("TempT == 373 and DisReal < 100 and Lipid1 < -0.3 and Lipid15 < -0.3 and DisReal > 50 and Lipid10 > -0.25")
t.plot.hexbin("Qw", "Lipid10", cmap="seismic", sharex=False)


Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x1187d8d30>

In [46]:
t.plot.hexbin("Qw", "DisReal", cmap="seismic", sharex=False)


Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x1187897b8>

In [58]:
t_pos = data.query("TempT == 373 and DisReal < 100 and Lipid1 < -0.3 and Lipid15 < -0.3 and DisReal > 50 and Lipid10 > -0.25")
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_path1.csv")

unwanted path 2, helix 1 out


In [57]:
t = data.query("TempT == 373 and Lipid1 > -0.5 and Qw > 0.3 and DisReal > 50")
t.plot.hexbin("Qw", "DisReal", cmap="seismic", sharex=False)


Out[57]:
<matplotlib.axes._subplots.AxesSubplot at 0x11d9ecdd8>

In [59]:
t_pos = data.query("TempT == 373 and Lipid1 > -0.5 and Qw > 0.3 and DisReal > 50")
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_path2.csv")

In [ ]:
t_pos = data.query("TempT == 373 and DisReal > 80 and DisReal < 100 and z_average > -8 and z_average < -4").reset_index(drop=True)
t_pos.plot.hexbin("Energy_with_all_bias", "Lipid1", cmap="seismic", sharex=False)

In [193]:
t_pos = data.query("TempT == 373 and DisReal > 80 and DisReal < 100 and z_average > -8 and z_average < -4").reset_index(drop=True)
chosen = t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_h56.csv")

In [197]:
t_pos.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=20)


Out[197]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias
7922 600066 -218.142307 -446.992027 -291.678962 -352.354453 94.0 93.045966 62.599341 -33.528161 -739.972991 ... -7.928949 -3.226523 -9.011228 -6.757330 -14.131343 -14.803465 -21.232008 -548.922365 0.018204 -548.904161
7921 600062 -212.394362 -443.980428 -286.857215 -349.705965 94.0 89.735826 62.417495 -38.614652 -761.752478 ... -6.611168 -2.650674 -6.301858 -6.033335 -9.673215 -10.093627 -20.069953 -544.805595 0.363664 -544.441931
9008 653790 -214.252021 -436.923250 -285.205326 -345.318451 92.0 82.772092 58.521394 69.845324 -737.297739 ... -4.478611 0.097867 -6.508623 -4.419184 -9.199424 -6.788752 -17.728036 -545.132696 1.703086 -543.429611
2077 240410 -220.312986 -437.590957 -290.558738 -346.437659 94.0 94.635741 65.483675 38.657025 -733.150749 ... -6.246558 -0.183788 -6.445593 -4.796985 -9.924937 -17.313959 -19.361403 -543.242774 0.008083 -543.234691
1049 28461 -210.956131 -441.325110 -279.734301 -341.120978 76.0 80.201087 62.110460 -32.345150 -746.558349 ... -7.025193 -3.172057 -7.528709 -4.070789 -9.033173 -12.842535 -25.990765 -543.154244 0.352983 -542.801261
2854 270713 -211.361449 -437.038069 -287.883144 -345.241391 98.0 97.091568 71.533853 -96.869755 -772.208512 ... -6.390107 -2.693870 -6.815748 -4.249246 -9.322100 -16.711451 -17.488690 -542.273375 0.016505 -542.256870
6716 446065 -212.398077 -440.203450 -284.330981 -344.442563 80.0 83.013829 67.293783 -82.875897 -711.944040 ... -5.828299 -1.815328 -5.904521 -4.346957 -10.804010 -6.916591 -20.667553 -541.813732 0.181663 -541.632068
6690 445885 -208.093119 -436.829162 -286.090277 -344.267325 80.0 81.883750 63.799134 -69.686281 -726.046091 ... -6.148379 -0.260000 -4.974692 -3.671368 -10.373978 -14.422206 -21.623450 -540.727587 0.070970 -540.656617
7776 563495 -208.908227 -437.178089 -282.820087 -343.123115 78.0 82.207193 69.044239 -80.949390 -753.426505 ... -5.874535 -1.388256 -6.618534 -3.846508 -8.456262 -12.426866 -21.845310 -540.876040 0.354010 -540.522030
8522 619536 -209.730173 -435.139497 -285.360462 -343.690619 104.0 96.789610 67.003272 95.343535 -735.130488 ... -6.596803 -1.326120 -7.209749 -4.823753 -8.103709 -19.455100 -21.162817 -540.816427 1.039794 -539.776632
7778 563503 -208.436583 -436.837397 -283.238919 -343.463335 78.0 81.885067 56.982906 -76.685979 -724.020549 ... -7.114902 -1.477568 -6.612649 -3.199120 -10.453508 -16.777874 -25.190495 -539.798110 0.301875 -539.496235
1050 28473 -220.135601 -437.217229 -288.001001 -344.205189 76.0 82.991120 59.262175 -46.431646 -695.112952 ... -5.860363 -1.736144 -6.739031 -4.830234 -9.022601 -15.413089 -16.632460 -540.455567 0.977515 -539.478051
7781 563515 -206.870561 -438.635809 -281.591238 -338.710943 78.0 80.438295 56.187564 -74.028878 -732.064724 ... -5.460732 -0.545938 -4.768490 -3.701174 -9.765834 -11.239591 -19.515109 -539.522952 0.118906 -539.404046
1189 86301 -211.960730 -433.898772 -288.850065 -343.979583 80.0 82.855169 58.199864 76.025872 -760.927061 ... -5.386483 -0.389457 -6.562827 -5.948995 -9.201132 -6.871661 -16.303929 -538.857740 0.163040 -538.694700
8436 615664 -220.172608 -434.375236 -293.490150 -346.232024 104.0 97.984068 68.400507 97.231447 -723.571190 ... -6.078925 -3.106545 -7.654687 -4.219001 -9.031671 -13.851543 -17.208749 -538.912540 0.723829 -538.188711
1063 28753 -214.422406 -435.485956 -294.187917 -348.320962 76.0 80.309756 62.441801 -4.856663 -699.791206 ... -7.393932 -2.138212 -8.414449 -5.969417 -11.707585 -13.769266 -19.604829 -538.547087 0.371480 -538.175607
8007 605130 -212.480672 -435.874502 -281.993553 -342.886565 94.0 92.213645 68.460968 -20.396868 -762.221972 ... -4.706191 -0.564139 -5.381373 -4.617875 -8.572165 -12.342966 -15.949221 -538.147457 0.063821 -538.083636
2800 270421 -217.402294 -435.218341 -286.262799 -347.543004 98.0 97.090140 71.985975 -61.814981 -753.150084 ... -5.882859 -0.750126 -5.237230 -6.577398 -9.833065 -12.076057 -17.387600 -538.013094 0.016557 -537.996537
8527 619556 -214.674099 -439.749728 -294.699730 -349.979451 104.0 87.399507 67.526418 84.622477 -736.302262 ... -6.652500 -1.482072 -6.822858 -5.439398 -8.437352 -11.065111 -23.253700 -543.488331 5.511527 -537.976803
6329 387148 -206.661892 -435.570556 -282.275930 -342.914038 76.0 83.713251 60.623246 10.576354 -736.566069 ... -6.517047 -3.287793 -7.576438 -3.541026 -9.750046 -12.812499 -19.557466 -539.053267 1.189885 -537.863382

20 rows × 53 columns

notice two caveats

  1. At relative high temp(373), helix 6 is in the membrane half the time(energy term can pick out those I want)
  2. helix 1 could be seperated from the rest 5 helix.(energy term cannot pick them out)

In [198]:
t_pos["chosen"] = (t_pos["Lipid1"] < -0.5) &(t_pos["z_h6"] < -10) & (t_pos["z_h3"] > -15)
a = t_pos["chosen"]
a.value_counts()


Out[198]:
False    5743
True     4181
Name: chosen, dtype: int64

In [194]:
t_pos.hist("z_h6",bins=50)


Out[194]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a43ed3e80>]], dtype=object)

In [186]:
t_pos.hist("Lipid1",bins=50)


Out[186]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a43f49a20>]], dtype=object)

3-4 out region

zoom to 120 - 200


In [201]:
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=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", plot1d=False, zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
# plt.ylim(-15, 0)
# plt.xlim(50, 120)



In [203]:
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=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", plot1d=False, zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
plt.ylim(-20, -5)
plt.xlim(120, 200)


Out[203]:
(120, 200)

In [208]:
t_pos = data.query("TempT == 373 and DisReal > 140 and DisReal < 180 and z_average > -14 and z_average < -8").reset_index(drop=True)
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_h34.csv")

Next, additional state visible under high force


In [213]:
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=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)


zoom to 120 - 300


In [6]:
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=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)
plt.ylim(-20, -5)
plt.xlim(120, 300)


Out[6]:
(120, 300)

In [7]:
t_pos = data.query("TempT == 373 and DisReal > 220 and DisReal < 250 and z_average > -14 and z_average < -10").reset_index(drop=True)
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_h12.csv")

In [ ]:
# zoom z -3, dis 240

In [17]:
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=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)
plt.ylim(0, -5)
plt.xlim(220, 250)


Out[17]:
(220, 250)

In [34]:
t_pos = data.query("DisReal > 220 and DisReal < 250 and z_average > -5 and z_average < 0").reset_index(drop=True)
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun23.csv")

In [21]:
t_pos = data.query("TempT == 373 and DisReal > 220 and DisReal < 250 and z_average > -5 and z_average < 0").reset_index(drop=True)
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun23.csv")

In [22]:
chosen


Out[22]:
level_0 AMH AMH-Go AMH_3H AMH_4H BiasTo DisReal Dis_h56 Distance Energy ... z_average z_h1 z_h2 z_h3 z_h4 z_h5 z_h6 BiasedEnergy BiasEnergy Energy_with_all_bias
2 82690 NaN -358.690584 NaN -269.950237 226.0 228.080296 60.881548 -207.103784 -629.245592 ... -0.501580 -16.599387 9.431345 22.775684 23.027055 -1.901645 -26.003455 -443.678257 0.086553 -443.591704
5 82690 NaN -358.690584 NaN -269.950237 226.0 228.080296 60.881548 -207.103784 -629.245592 ... -0.501580 -16.599387 9.431345 22.775684 23.027055 -1.901645 -26.003455 -443.678257 0.086553 -443.591704
1 82678 NaN -358.722565 NaN -279.764687 226.0 225.462078 66.521893 -199.950740 -635.897974 ... -0.426415 -20.133379 11.798070 23.633232 23.358520 -2.560193 -24.700998 -437.024865 0.005787 -437.019078
4 82678 NaN -358.722565 NaN -279.764687 226.0 225.462078 66.521893 -199.950740 -635.897974 ... -0.426415 -20.133379 11.798070 23.633232 23.358520 -2.560193 -24.700998 -437.024865 0.005787 -437.019078
0 82486 NaN -352.076820 NaN -267.441646 226.0 229.430360 57.607434 -227.688271 -673.253001 ... -0.544515 -19.923898 6.243507 30.286311 19.081878 0.122172 -22.604769 -432.999507 0.235347 -432.764159
3 82486 NaN -352.076820 NaN -267.441646 226.0 229.430360 57.607434 -227.688271 -673.253001 ... -0.544515 -19.923898 6.243507 30.286311 19.081878 0.122172 -22.604769 -432.999507 0.235347 -432.764159

6 rows × 53 columns


In [35]:
chosen["DisReal"]


Out[35]:
87     228.080296
33     228.080296
74     220.901109
20     220.901109
92     220.055694
38     220.055694
68     233.440151
14     233.440151
86     225.462078
32     225.462078
99     227.547341
45     227.547341
53     223.464318
107    223.464318
61     222.563058
7      222.563058
60     223.597065
6      223.597065
96     224.409356
42     224.409356
Name: DisReal, dtype: float64

complete out


In [10]:
t_pos = data.query("TempT == 373 and DisReal > 260 and z_average < -16").reset_index(drop=True)
chosen = t_pos.sort_values("Energy_with_all_bias").head(n=20)
chosen.to_csv("/Users/weilu/Research/data/low_e_jun01_out.csv")