In [2]:
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 [124]:
data = pd.read_feather("/Users/weilu/Research/server/may_2018/03_week/all_data_folder/second_start_extended_combined_may19.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["BiasEnergy"] = 0.02 * (data["BiasTo"] - data["DisReal"])**2
data["Energy_with_all_bias"] = data["BiasEnergy"] + data["BiasedEnergy"]

In [ ]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350//"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)

In [163]:
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)


5-6 out region

zoom to 50 - 130


In [165]:
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[165]:
(50, 120)

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
  2. helix 1 could be seperated from the rest 5 helix.

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, visible under high force

zoom to 120 - 300


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)



In [212]:
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[212]:
(120, 300)

In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


In [ ]:


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", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [28]:
data.columns


Out[28]:
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'],
      dtype='object')

In [ ]:
t = data.query("TempT == 373 and DisReal <56 and DisReal > 52")

In [19]:
t.plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x10f2e4fd0>

In [22]:
h1_out = t.query("Lipid1 > -0.5")

In [23]:
h1_out.shape


Out[23]:
(1460, 51)

In [24]:
t.shape


Out[24]:
(2070, 51)

In [25]:
h1_not_out =  t.query("Lipid1 <= -0.5")

In [32]:
h1_not_out.plot.hexbin("BiasedEnergy", "Qw", cmap="seismic", sharex=False)


Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a202dd9e8>

according to this Qw vs Energy. we can seperate to two group. (high and low)


In [62]:
t_pre = data.query("TempT == 373 and DisReal <56 and DisReal > 52").reset_index(drop=True)
t_pre.sort_values("BiasedEnergy").head(n=10).to_csv("/Users/weilu/Research/data/may28_pre_lowE2.csv")

In [66]:


In [30]:
h1_not_out_high_q = h1_not_out.query("Qw > 0.4")

In [31]:
h1_not_out_high_q.shape


Out[31]:
(114, 51)

In [ ]:
h1_not_out_high_q.to_csv("/Users/weilu/Research/data/may28_pre_highQ.csv")

In [34]:
h1_not_out_low_energy = h1_not_out.query("BiasedEnergy < -820")

In [35]:
h1_not_out_low_energy.to_csv("/Users/weilu/Research/data/may28_pre_lowE.csv")

In [36]:
h1_not_out_low_energy.shape


Out[36]:
(66, 51)

In [55]:
chosen = data.query("TempT == 373 and DisReal <56 and DisReal > 52 and TotalE < -780").reset_index(drop=True)

In [57]:
chosen["withoutGo"] = chosen["TotalE"] - chosen["AMH-Go"]

In [59]:
chosen.columns


Out[59]:
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', 'withoutGo'],
      dtype='object')

lipid1 out is energetically similar


In [58]:
chosen.plot.hexbin("withoutGo", "Lipid1", cmap="seismic", sharex=False)


Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a24c546d8>

In [53]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and TotalE < -780").plot.hexbin("TotalE", "Lipid1", cmap="seismic", sharex=False)


Out[53]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2485d748>

In [52]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and AMH_4H < -320").plot.hexbin("TotalE", "Lipid1", cmap="seismic", sharex=False)


Out[52]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2486ac18>

In [51]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and AMH_4H < -320").plot.hexbin("AMH_4H", "Lipid1", cmap="seismic", sharex=False)


Out[51]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a24487160>

In [46]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and BiasedEnergy < -850").to_csv("/Users/weilu/Research/data/may28_pre_lowE2.csv")

In [47]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and BiasedEnergy < -850").plot.hexbin("BiasedEnergy", "Lipid1", cmap="seismic", sharex=False)


Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a216c8a90>

In [40]:
data.query("TempT == 373 and DisReal <56 and DisReal > 52 and BiasedEnergy < -820").plot.hexbin("TotalE", "Qw", cmap="seismic", sharex=False)


Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a210df160>

Middle part


In [75]:
data.columns


Out[75]:
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'],
      dtype='object')

In [ ]:
t_middle = data.query("TempT == 373 and DisReal <64 and DisReal > 56")

In [82]:
t_middle.sort_values("Energy_with_all_bias").head(n=10)


Out[82]:
968976     0.002038
987300     0.484228
862553     0.002067
817889     0.002087
987200     0.619495
1340261    0.393771
1348542    0.002085
861733     0.002075
818114     0.690917
960362     0.002076
Name: Lipid1, dtype: float64

In [84]:
t_middle.sort_values("Energy_with_all_bias").head(n=10)


Out[84]:
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
968976 288976 -185.150523 -452.855140 -255.166642 -314.562816 68.0 62.760565 24.956958 -21.646766 -817.007286 ... -1.011145 -0.561827 -10.006342 -3.576899 -7.729485 -2.628495 -3.042307 -891.943372 0.549034 -891.394338
987300 307300 -191.477959 -462.454677 -266.727788 -328.154309 64.0 63.107126 26.771274 29.612238 -813.296817 ... -1.100404 1.740113 -7.581102 -0.008847 -8.402791 -6.478215 -3.044261 -890.086085 0.015944 -890.070141
862553 182553 -177.932338 -445.751715 -249.383109 -310.686959 60.0 57.244524 22.712414 12.335953 -808.602263 ... -1.790858 2.368893 -8.777372 -2.267988 -8.486011 -7.601503 -3.794057 -883.633192 0.151853 -883.481339
817889 137889 -187.600558 -433.647181 -252.892214 -306.058929 62.0 59.070395 27.794529 -56.709284 -807.070217 ... 0.011100 1.474549 -4.677597 -3.736879 -6.626902 -2.519906 -4.405690 -879.482537 0.171652 -879.310885
987200 307200 -187.627924 -448.626452 -258.117104 -318.333319 64.0 61.600435 26.826586 9.301599 -801.503674 ... -2.033945 -0.423518 -7.824309 -3.283775 -9.585157 -6.004256 -5.084711 -876.776931 0.115158 -876.661772
1340261 660261 -193.476936 -453.977439 -262.047565 -326.592460 64.0 63.735667 25.762269 -63.592103 -797.027768 ... -3.856957 -1.527055 -8.141128 -4.659882 -10.594385 -8.724989 -4.978763 -871.576904 0.001397 -871.575507
1348542 668542 -182.890138 -424.305940 -244.947614 -299.861460 64.0 60.263618 24.438730 59.882642 -799.074728 ... -0.974776 2.166633 -6.337763 -4.073608 -9.451097 -3.074651 -6.681425 -870.817154 0.279211 -870.537943
861733 181733 -182.906244 -442.675172 -250.206101 -309.693097 60.0 58.086352 27.439114 -22.834862 -794.993919 ... -3.361718 -1.036232 -8.213383 -6.630186 -9.495162 -4.368721 -6.002130 -867.992943 0.073241 -867.919702
818114 138114 -185.156753 -445.076764 -257.262259 -319.596854 62.0 56.352389 25.842610 36.796611 -795.365808 ... -2.127698 -1.366890 -8.337652 -0.969612 -10.005213 -5.915076 -4.852799 -868.529782 0.637910 -867.891872
960362 280362 -185.383967 -443.292820 -257.209624 -312.033323 68.0 61.723127 22.775846 -40.973911 -793.124810 ... -0.403986 2.174375 -8.155180 0.536708 -6.893027 -6.565490 -1.551007 -867.347392 0.787983 -866.559410

10 rows × 53 columns


In [83]:
t_middle.query("Lipid1 < -0.5").sort_values("Energy_with_all_bias").head(n=10)


Out[83]:
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
701186 21186 -209.133908 -418.951106 -270.329612 -313.703091 76.0 62.014634 25.631543 59.764704 -795.730168 ... -2.723255 -4.000245 -7.756103 -8.994198 -5.593929 -0.685517 -3.082913 -864.700700 3.911809 -860.788891
904080 224080 -213.655212 -423.941326 -272.243852 -318.180096 66.0 60.718511 24.335414 60.465456 -787.438196 ... -3.716866 -1.914587 -9.157983 -5.587579 -7.893231 -2.351856 -1.651036 -856.196257 0.557883 -855.638375
1267493 587493 -197.666069 -409.827156 -259.923175 -301.627741 66.0 61.820953 21.048411 59.039681 -785.420694 ... 0.116077 -0.864105 -6.006079 -3.170596 -6.960827 -2.538990 -2.241102 -852.035309 0.349289 -851.686021
1269092 589092 -198.248522 -403.671063 -256.030194 -296.299067 66.0 60.412923 22.405400 -54.367456 -784.194851 ... -5.039440 1.112002 -7.104066 -20.386384 -10.007655 -6.785298 -4.197061 -849.353453 0.624309 -848.729144
1261253 581253 -201.116930 -413.832648 -258.702356 -304.689194 66.0 61.414392 26.005718 13.321524 -780.344135 ... -2.961339 -2.644498 -9.656189 -12.192673 -6.613459 -0.593869 -0.676587 -848.976545 0.420556 -848.555989
906069 226069 -203.865362 -414.742325 -264.843559 -309.038487 66.0 60.759228 25.615073 23.097784 -778.462800 ... -2.984441 -3.419661 -9.565722 -8.099691 -8.470239 -6.105927 -4.251687 -847.279984 0.549314 -846.730670
901220 221220 -200.730946 -410.319419 -261.402907 -306.431810 66.0 61.064203 18.522741 55.609889 -775.898991 ... -2.732144 -4.542499 -7.893796 -5.047760 -9.848075 -1.950217 -6.329883 -845.157923 0.487242 -844.670681
1268781 588781 -193.717584 -413.213506 -255.746521 -300.545073 66.0 57.706235 25.848698 -26.947205 -774.013570 ... -1.580429 -1.522628 -8.591993 -5.867868 -7.853529 -3.782479 -2.911377 -841.701263 1.375731 -840.325533
1269789 589789 -198.723167 -418.898969 -262.766255 -310.140125 66.0 58.114019 22.732688 -49.795578 -773.186298 ... -1.438265 -1.323346 -6.461957 -8.688956 -8.747420 -1.896451 -3.515479 -841.465646 1.243774 -840.221872
1267353 587353 -211.698489 -419.647987 -274.232408 -314.965327 66.0 62.623772 22.023728 61.549801 -769.182771 ... -2.006364 -1.901208 -7.703248 -6.128692 -5.920078 -2.112978 -2.154064 -840.199936 0.227978 -839.971958

10 rows × 53 columns


In [ ]:
# t_pre = data.query("TempT == 373 and DisReal <56 and DisReal > 52").reset_index(drop=True)
# t_pre.sort_values("BiasedEnergy").head(n=10).to_csv("/Users/weilu/Research/data/may28_pre_lowE2.csv")

In [68]:
t_middle.sort_values("BiasedEnergy").head(n=10)["Lipid1"]


Out[68]:
968976     0.002038
987300     0.484228
862553     0.002067
817889     0.002087
987200     0.619495
1340261    0.393771
1348542    0.002085
818114     0.690917
861733     0.002075
960362     0.002076
Name: Lipid1, dtype: float64

In [38]:
t_middle.shape


Out[38]:
(11303, 51)

In [39]:
t_middle.plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a20236cc0>

In [45]:
t_middle.plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2109f240>

In [91]:
t_middle.query("Lipid1 < -0.5").plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


Out[91]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a24111c50>

Post part


In [136]:
t_pos = data.query("TempT == 373 and DisReal > 64 and DisReal < 70")

In [137]:
t_pos.plot.hexbin("DisReal", "z_average", cmap="seismic", sharex=False)


Out[137]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a68a994a8>

In [145]:
t_pos.plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)


Out[145]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a208ce438>

In [151]:
t_pos.plot.hexbin("DisReal", "z_h3", cmap="seismic", sharex=False)


Out[151]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a4435e748>

In [152]:
t_pos = data.query("TempT == 373 and DisReal > 64 and DisReal < 70").reset_index(drop=True)
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()

In [155]:


In [158]:
t_pos.groupby("chosen").mean()


Out[158]:
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
chosen
False 349854.403006 -185.296016 -411.394756 -247.143209 -298.450955 70.665836 67.084624 26.229786 1.054884 -709.251579 ... -2.600236 -0.767343 -7.674342 -7.848627 -8.184398 -5.089119 -5.934283 -510.509885 0.868006 -509.641879
True 282352.453920 -197.144026 -392.655104 -260.105525 -306.311400 74.605227 67.432956 48.774796 8.319537 -693.805555 ... -1.844080 -1.313915 -7.404818 -5.608807 8.525941 -2.816140 -20.574323 -490.153804 1.524713 -488.629091

2 rows × 52 columns


In [ ]:


In [157]:



Out[157]:
False    13640
True       727
Name: chosen, dtype: int64

In [154]:
t_pos["chosen"].count()


Out[154]:
14367

In [ ]:
t_pos.plot.hexbin("DisReal", "z_average", cmap="seismic", sharex=False)

In [87]:
t_pos.plot.hexbin("z_average", "Qw", cmap="seismic", sharex=False)


Out[87]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2519d400>

In [88]:
t_pos.plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


Out[88]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2cc96f60>

In [90]:
t_pos.query("Lipid1 < -0.5").plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


Out[90]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2cfac9b0>

In [92]:
t_pos.query("Lipid1 < -0.5").plot.hexbin("Energy_with_all_bias", "Qw", cmap="seismic", sharex=False)


Out[92]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a240dec88>

In [98]:
t_pos.query("Lipid1 < -0.5 and z_average < -4").sort_values("Energy_with_all_bias").head(n=10)["z_average"]


Out[98]:
706769    -4.411739
739437    -4.677910
709840    -4.129936
1060008   -5.584049
1093669   -4.351164
1012509   -4.085650
1123500   -5.016386
1060532   -6.330104
1125877   -4.989070
1060480   -5.000276
Name: z_average, dtype: float64

In [ ]:
t_pos.query("Lipid1 < -0.5 and z_average < -4").sort_values("Energy_with_all_bias")

In [138]:
t_pos.query("z_average < -4").sort_values("AMH_4H").head(n=10)


Out[138]:
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
1126273 446273 -218.375632 -437.165472 -295.945947 -355.891479 80.0 65.790700 62.497227 -55.052450 -719.666303 ... -7.767960 -0.742286 -6.063817 -6.246092 -10.192611 -16.107114 -18.833197 -537.935705 4.038084 -533.897621
1125849 445849 -218.177119 -431.653287 -298.179687 -354.934807 80.0 65.065334 53.971524 -50.825090 -706.837964 ... -9.413110 -0.849681 -7.509167 -7.642640 -11.495064 -22.853939 -26.234604 -532.652897 4.460885 -528.192012
1067216 387216 -216.679415 -425.721697 -292.856272 -350.828395 76.0 68.253689 43.873366 23.764883 -717.941721 ... -6.716317 -0.461650 -5.590795 -5.203010 -9.972778 -14.301081 -26.770710 -535.653821 1.200107 -534.453714
1126133 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
1126101 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
706769 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
706861 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
1068904 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
707049 27049 -211.259988 -429.056623 -288.146556 -344.858163 76.0 67.429033 38.711934 54.304548 -704.662968 ... -6.145018 -1.765843 -6.806651 -6.149995 -10.130540 -8.774747 -18.825244 -532.794951 1.469230 -531.325722
1126153 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

10 rows × 53 columns


In [139]:
t_pos.query("z_average < -4").sort_values("BiasedEnergy").head(n=10)


Out[139]:
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
717013 37013 -184.821189 -454.752292 -257.960787 -321.510281 72.0 64.547573 25.192301 55.070622 -745.525301 ... -5.317478 -0.677545 -10.466153 -6.055538 -10.961284 -6.478105 -5.562260 -557.292471 1.110773 -556.181697
715996 35996 -192.483234 -452.971870 -260.330769 -322.082205 72.0 67.064870 24.807108 50.403206 -711.212103 ... -4.130182 1.260293 -11.112324 -6.063463 -12.594077 -11.839025 -7.879270 -555.861394 0.487110 -555.374284
899947 219947 -182.435981 -447.984790 -256.753479 -321.780414 74.0 66.942706 26.704031 -65.166301 -729.291099 ... -4.567757 -1.445567 -7.965119 -3.471961 -10.597827 -9.456817 -5.981019 -553.639176 0.996108 -552.643068
1126101 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
981429 301429 -184.372016 -444.186864 -251.549499 -311.673000 64.0 66.886782 21.113763 58.218896 -738.111443 ... -4.479701 -4.108172 -6.707405 -6.369209 -9.742018 -6.940032 -5.918885 -546.233613 0.166670 -546.066942
898173 218173 -184.787413 -443.305889 -256.907756 -313.972940 74.0 65.386871 23.673697 58.521074 -736.644738 ... -4.362766 -1.923334 -10.804873 -4.647711 -10.420182 -9.520949 -4.127454 -544.858784 1.483720 -543.375064
1126133 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
1068904 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
1173548 493548 -185.265449 -443.647653 -252.216356 -313.877744 62.0 64.941020 23.171403 62.723999 -698.580721 ... -4.182046 0.688321 -11.117311 -4.146008 -11.009482 -8.317644 -6.230584 -543.783287 0.172992 -543.610295
1075412 395412 -175.813211 -443.149054 -245.412530 -309.455651 72.0 64.761869 24.170295 -5.002363 -739.255565 ... -4.615607 -2.501809 -9.223600 -3.682824 -9.812103 -7.694580 -5.017458 -543.534173 1.047811 -542.486363

10 rows × 53 columns


In [ ]:
t_pos["Energy_with_all_bias"] = t["Lipid"] + t["Rg"] + t["Membrane"] + t["AMH-Go"]

In [122]:
t_pos.columns


Out[122]:
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 [115]:
t_pos.query("Lipid1 < -0.5 and z_average < -4").sort_values("Energy_with_all_bias").head(n=10)


Out[115]:
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
706769 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 -838.006747 0.747188 -837.259559
739437 59437 -196.030115 -387.054015 -257.419911 -300.193306 70.0 68.915638 48.912577 -0.996450 -770.617953 ... -4.677910 1.688017 -8.503781 -22.746647 9.408182 -0.678420 -27.738414 -832.256738 0.023517 -832.233221
709840 29840 -199.024849 -408.722619 -265.652053 -317.687485 76.0 66.412493 48.923734 9.731951 -763.470920 ... -4.129936 0.161948 -6.968308 -4.509131 -7.866892 -11.967710 -17.647050 -833.872146 1.838406 -832.033740
1060008 380008 -205.177507 -410.711785 -272.345876 -319.908173 76.0 67.163034 55.193395 6.191250 -763.017734 ... -5.584049 -2.154063 -9.166598 -8.109790 -10.043909 -8.057730 -18.807800 -832.920760 1.561839 -831.358921
1093669 413669 -197.495777 -388.618359 -255.195028 -298.446911 70.0 64.979684 43.130653 -63.230556 -766.819052 ... -4.351164 -1.189091 -7.201623 -20.683600 3.730482 -0.608185 -21.278041 -828.359515 0.504072 -827.855444
1012509 332509 -196.531048 -395.716664 -248.078482 -291.601372 90.0 69.820594 25.900637 -46.896724 -772.067345 ... -4.085650 -0.925995 -7.186946 -20.912458 -9.351533 -1.412594 -6.199589 -835.941761 8.144169 -827.797593
1123500 443500 -197.078658 -407.761645 -249.470402 -292.499780 80.0 68.150903 23.497658 -31.948016 -763.937178 ... -5.016386 -0.737543 -5.703099 -21.721631 -9.948837 -3.943697 -6.441064 -828.303029 2.808022 -825.495007
1060532 380532 -218.381075 -415.740553 -272.046754 -320.986063 76.0 67.711041 45.171521 43.852187 -758.254947 ... -6.330104 -0.560204 -6.850070 -5.351418 -8.599004 -14.943284 -24.908974 -826.567362 1.374137 -825.193225
1125877 445877 -209.106105 -424.949267 -278.493055 -334.499184 80.0 69.209617 67.819513 -57.103592 -753.865733 ... -4.989070 -1.360069 -6.731906 -3.835646 -8.928388 -9.682420 -18.847038 -827.129819 2.328647 -824.801172
1060480 380480 -201.740818 -416.641561 -267.129982 -321.890984 76.0 65.268530 58.376447 51.630428 -754.389351 ... -5.000276 -1.071173 -9.341406 -1.744067 -10.372312 -11.079897 -14.759797 -826.404009 2.303289 -824.100720

10 rows × 53 columns


In [117]:
t_pos.query("Lipid1 < -0.5 and z_average < -4").sort_values("Energy_with_all_bias").head(n=5).to_csv("/Users/weilu/Research/data/may28_post_lowE.csv")

In [102]:
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", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)



In [48]:
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, title="high_force_AverageZ_Dis", 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 [ ]:


In [5]:
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, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [114]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_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, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [104]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"perturbation-10-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [110]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"perturbation-9-pmf-{temp}.dat"
zmax=30
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [111]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.1/"
location2 = location + f"perturbation-9-pmf-{temp}.dat"
zmax=30
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [112]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"perturbation-9-pmf-{temp}.dat"
zmax=30
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [113]:
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-9-pmf-{temp}.dat"
zmax=30
res=40
path_origin, f_origin = shortest_path_2(location2, title="high_force_AverageZ_Dis", start=(10, 35), end=(28,1),save=True, 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 [ ]: