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 [80]:
data1 = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/labelrerun_4_16_Aug_201552.feather")
data2 = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/labelrerun_5_16_Aug_201552.feather")
data3 = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/labelrerun_6_16_Aug_201635.feather")
data4 = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/labelrerun_7_16_Aug_201635.feather")
In [81]:
data = pd.concat([data1,data2,data3,data4])
In [82]:
dic = {"T0":300, "T1":335, "T2":373, "T3":417, "T4":465, "T5":519, "T6":579, "T7":645, "T8":720, "T9":803, "T10":896, "T11":1000}
data["TempT"] = data["Temp"].apply(lambda x: dic[x])
data["BiasTo"] = data["BiasTo"].apply(pd.to_numeric)
In [83]:
save_data = data.query("TempT <= 417")
chosen = save_data.query("not ((BiasTo == 100.0 or BiasTo == 106.0) and Step <= 6e7)")
In [84]:
chosen.columns = chosen.columns.str.strip()
In [85]:
chosen.reset_index().to_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/include_h14_aug18.feather")
In [86]:
chosen.shape
Out[86]:
(1400000, 48)
In [91]:
chosen
Out[91]:
Step
Run
Temp
Qw
Energy
DisReal
Dis_h56
Dis_h12
Dis_h34
z_average
...
Lipid9
Lipid10
Lipid11
Lipid12
Lipid13
Lipid14
Lipid15
TotalE
BiasTo
TempT
2
40004000
2
T2
0.140180
-711.707879
144.708940
69.781271
14.944107
60.970334
-12.214930
...
0.000103
9.934604e-11
1.571177e-10
8.128362e-12
1.321546e-04
1.876596e-07
9.906222e-05
-713.500279
154.0
373
3
40004000
3
T1
0.149747
-783.670958
149.101952
66.652354
15.125225
78.342728
-12.502948
...
0.000053
3.502296e-13
2.644780e-08
1.571675e-09
5.087124e-09
3.023050e-10
2.282875e-05
-784.901256
154.0
335
6
40004000
6
T0
0.132641
-828.826346
142.869187
66.214056
13.473236
75.530439
-10.780254
...
0.000007
5.548324e-09
1.154264e-06
2.283946e-08
1.740215e-05
5.449429e-09
1.133691e-06
-830.365418
154.0
300
11
40004000
11
T3
0.132584
-596.192621
150.066565
66.759818
16.756575
79.888166
-12.705062
...
0.000043
4.392087e-13
2.039428e-08
9.080129e-10
3.326615e-07
4.440192e-10
2.061765e-05
-598.002860
154.0
417
14
40008000
2
T2
0.123968
-691.195590
154.711894
69.646010
22.418261
68.066145
-10.445594
...
0.000203
1.090174e-11
3.705993e-10
3.669914e-11
7.051735e-04
4.124631e-07
2.053185e-04
-692.285939
154.0
373
15
40008000
3
T1
0.153474
-799.315081
160.000681
68.617129
8.746308
74.947723
-11.461473
...
0.000018
3.185255e-12
2.894876e-07
7.190074e-09
2.781553e-09
6.908610e-11
6.278797e-06
-800.592753
154.0
335
18
40008000
6
T0
0.125509
-798.081370
150.106381
66.651626
8.425028
79.590014
-9.615588
...
0.000008
2.336961e-08
1.939040e-05
8.772754e-08
3.086063e-05
8.016231e-09
6.651287e-06
-798.726183
154.0
300
23
40008000
11
T3
0.137473
-609.383603
146.046148
68.689535
13.937725
82.348475
-11.717372
...
0.000003
7.165527e-13
1.572877e-07
3.187303e-10
4.630028e-09
9.382360e-12
2.059486e-06
-610.616070
154.0
417
26
40012000
2
T2
0.124142
-649.455431
145.154715
64.195638
22.793577
69.985766
-10.831080
...
0.000069
3.723976e-13
2.895950e-09
9.528426e-11
2.840217e-05
8.800080e-09
1.075262e-03
-651.495130
154.0
373
27
40012000
3
T1
0.143983
-775.392560
154.217178
70.418173
15.104489
70.761005
-11.581905
...
0.000014
1.144590e-09
1.531694e-06
1.412354e-08
1.194543e-08
1.101472e-10
1.012369e-05
-776.969559
154.0
335
30
40012000
6
T0
0.144065
-822.837269
150.197113
66.520085
14.834091
78.159540
-9.893360
...
0.000026
2.078453e-09
5.663768e-06
1.051077e-07
3.472593e-07
6.444406e-09
1.756096e-05
-824.182853
154.0
300
35
40012000
11
T3
0.131046
-624.742562
146.725447
71.359273
20.373487
81.478906
-11.808728
...
0.000001
4.632549e-12
5.800477e-07
8.177280e-10
2.024582e-09
2.854175e-12
3.573750e-07
-626.210078
154.0
417
38
40016000
2
T2
0.120336
-643.486942
146.756962
58.785431
21.290891
70.895726
-12.010792
...
0.000011
4.595236e-15
1.680580e-09
9.254371e-12
3.126177e-07
3.107946e-11
7.913219e-04
-644.843810
154.0
373
39
40016000
3
T1
0.139100
-760.555946
145.857154
71.228401
13.105879
69.238890
-11.951340
...
0.000042
5.114133e-10
2.622149e-08
8.534364e-09
1.313966e-09
4.276594e-10
2.718064e-06
-761.304376
154.0
335
42
40016000
6
T0
0.154019
-815.873953
153.090106
65.433091
11.188248
74.949904
-10.281771
...
0.000191
1.228105e-08
5.311800e-07
3.695433e-07
3.395762e-07
2.362440e-07
2.540142e-05
-817.441351
154.0
300
47
40016000
11
T3
0.129233
-601.641108
147.603330
68.292696
13.672848
76.337505
-11.779336
...
0.000004
6.723490e-12
3.202842e-07
6.127587e-09
4.890617e-10
9.356589e-12
4.457161e-07
-602.979173
154.0
417
50
40020000
2
T2
0.123564
-663.914786
143.072389
61.222185
17.398631
69.667336
-12.087272
...
0.000084
3.774240e-16
5.360487e-10
2.187101e-11
1.811749e-07
5.905835e-11
5.380626e-03
-665.353657
154.0
373
51
40020000
3
T1
0.146165
-765.236026
153.796980
68.495053
18.569298
75.305595
-11.135469
...
0.001292
1.329075e-11
1.089386e-07
5.129373e-07
4.443137e-09
2.092051e-08
1.714764e-04
-766.481274
154.0
335
54
40020000
6
T0
0.147769
-806.581941
152.522383
59.460247
14.198154
76.322283
-9.662156
...
0.001180
4.094313e-08
6.675258e-06
1.038580e-05
1.704127e-06
2.651393e-06
6.551107e-03
-808.114734
154.0
300
59
40020000
11
T3
0.153329
-591.761180
152.352879
65.178468
17.160790
81.843366
-10.400482
...
0.000789
1.150248e-11
1.835273e-06
1.715798e-06
2.142412e-09
2.002942e-09
3.195788e-04
-593.279271
154.0
417
62
40024000
2
T2
0.130574
-713.565459
142.245828
64.185706
16.642493
70.878798
-11.064084
...
0.000768
3.838960e-16
1.986023e-10
7.364446e-11
5.075994e-07
1.493529e-09
4.287783e-03
-714.600539
154.0
373
63
40024000
3
T1
0.142469
-756.956755
158.239085
69.371018
19.743080
76.695782
-9.986665
...
0.001112
2.681130e-11
6.090258e-07
3.521268e-07
7.850013e-08
4.538724e-08
1.030983e-03
-758.399295
154.0
335
66
40024000
6
T0
0.140795
-817.860369
156.575708
62.264671
18.991464
82.946190
-8.447716
...
0.000866
1.592245e-08
4.912588e-06
2.184842e-06
5.890526e-06
2.619811e-06
8.572222e-03
-819.271309
154.0
300
71
40024000
11
T3
0.137565
-518.415233
157.116388
67.954064
22.703811
86.951791
-9.124735
...
0.001751
4.307026e-11
2.223910e-06
1.941939e-06
3.736614e-08
3.262845e-08
1.684753e-03
-520.007437
154.0
417
74
40028000
2
T2
0.110227
-685.374322
143.216009
69.972106
14.905007
68.992690
-11.617549
...
0.000332
3.043538e-17
4.805823e-11
7.728565e-12
6.044056e-08
2.124896e-10
3.355265e-04
-686.208169
154.0
373
75
40028000
3
T1
0.139979
-760.033754
151.840647
68.414207
14.970739
76.269349
-10.186055
...
0.000345
5.652247e-12
6.173761e-08
1.103665e-08
1.646820e-07
2.943971e-08
3.215601e-04
-761.463803
154.0
335
78
40028000
6
T0
0.125455
-792.772392
156.679846
62.150564
20.154311
83.681703
-9.509520
...
0.000206
2.846051e-10
2.494783e-07
2.593660e-08
2.163067e-06
2.248797e-07
1.971244e-04
-793.969882
154.0
300
83
40028000
11
T3
0.111375
-511.400628
161.619003
70.070703
23.371023
86.516866
-9.865027
...
0.000182
4.993011e-12
8.484696e-08
7.880514e-09
1.094280e-07
1.016358e-08
1.727112e-04
-512.023662
154.0
417
86
40032000
2
T2
0.119834
-670.942806
149.614205
70.192154
19.297020
67.833846
-13.381435
...
0.000012
4.117029e-18
1.255655e-10
7.478061e-13
6.842911e-11
4.075300e-13
1.242928e-05
-672.259340
154.0
373
87
40032000
3
T1
0.142437
-770.291504
147.499123
63.720867
13.475817
75.335757
-11.349672
...
0.000029
9.589639e-12
2.060107e-07
3.149254e-09
8.848190e-08
1.352609e-09
2.905761e-05
-771.608706
154.0
335
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1079907
79972000
3
T3
0.119829
-566.613447
92.634793
23.985971
29.417764
74.187856
-7.132010
...
0.002080
1.432411e-10
8.114348e-07
8.113728e-07
9.889655e-05
1.519229e-04
-1.713839e+00
-567.466889
102.0
417
1079911
79972000
7
T0
0.339597
-848.117620
95.516759
24.945252
82.236040
14.552386
-1.689840
...
0.047303
-1.366462e+00
5.702076e-01
-2.066059e+00
-1.806501e+00
-2.119394e+00
-2.324975e-01
-859.083990
102.0
300
1079916
79976000
0
T2
0.336862
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96.554771
25.599314
84.991723
11.463748
-2.466561
...
-0.012568
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7.891069e-01
-2.055963e+00
-1.407285e+00
-2.012579e+00
-1.318908e-01
-778.361071
102.0
373
1079918
79976000
2
T1
0.363101
-786.060935
95.126958
24.810979
83.766562
11.970968
-2.124907
...
-0.059175
-1.414625e+00
7.158597e-01
-2.103973e+00
-1.586282e+00
-2.075712e+00
1.371058e-01
-796.565146
102.0
335
1079919
79976000
3
T3
0.116297
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103.755382
19.880067
33.823862
75.469003
-8.657168
...
0.001984
6.249051e-13
3.839979e-08
3.858468e-08
1.269800e-05
1.249508e-05
-1.942396e+00
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102.0
417
1079923
79976000
7
T0
0.346187
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99.567245
26.935655
85.642508
11.516349
-2.081544
...
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7.734772e-01
-2.122273e+00
-1.363544e+00
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1.495140e-01
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102.0
300
1079928
79980000
0
T2
0.338325
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92.634826
25.750346
83.604608
14.029456
-2.298796
...
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4.469600e-01
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102.0
373
1079930
79980000
2
T1
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27.385640
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102.0
335
1079931
79980000
3
T3
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35.914649
72.313181
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...
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1.611186e-09
1.612580e-09
1.999619e-07
1.663583e-07
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102.0
417
1079935
79980000
7
T0
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25.286208
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300
1079940
79984000
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T2
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79984000
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T3
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1079947
79984000
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T0
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1079955
79988000
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T3
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1079959
79988000
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T0
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1079964
79992000
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T2
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1400000 rows × 48 columns
In [89]:
a.shape
Out[89]:
(1400000, 48)
In [88]:
a = pd.read_feather("/Users/weilu/Research/server/aug_2018/02_week/freeEnergy/all_data_folder/include_h14.feather")
In [78]:
a["Step"]
Out[78]:
0 50004000
1 50004000
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...
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1399994 79996000
1399995 79996000
1399996 80000000
1399997 80000000
1399998 80000000
1399999 80000000
Name: Step, Length: 1400000, dtype: int64
In [60]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 340
location = pre + "/combined_more_force_fix_order/_280-350/56_z_dis/no_subsample/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(35,20), end=(3, 30), title="AverageZ_Dis",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 [59]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 340
location = pre + "/combined_more_rc/_280-350/56_z_dis/no_subsample/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(35,20), end=(3, 30), title="AverageZ_Dis",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 [33]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/combined_more_rc/_280-350/2d_dis56_z14/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(18, 30), title="AverageZ_Dis", end=(27,1),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 [54]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 340
location = pre + "/combined_more_rc/_280-350/2d_dis56_z14/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(18, 30), title="AverageZ_Dis", end=(27,1),save=False, xlabel="Distance", ylabel="Z_h14", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [34]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/combined_more_rc/_280-350/2d_dis34_z14/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(18, 30), title="AverageZ_Dis", end=(27,1),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 [35]:
pre = "/Users/weilu/Research/server/aug_2018/02_week/freeEnergy"
temp = 370
location = pre + "/combined_more_rc/_280-350/2d_dis12_z14/force_0.1/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin_second = shortest_path_2(location2, start=(18, 30), title="AverageZ_Dis", end=(27,1),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 [ ]:
In [ ]:
In [ ]:
Content source: luwei0917/awsemmd_script
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