notebook.community

Edit and run



In [1]:

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

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

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



In [27]:

    
pre = "/Users/weilu/Research/server/mar_2018/05_week/"
temp = 280
location = pre + "/eighth_with_real_distance/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), block=[-20,-10,0.6,0.7], end=(26,22), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmax=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# # plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x10a84e8d0>






    Out[27]:





[<matplotlib.lines.Line2D at 0x1a17941390>]



In [31]:

    
pre = "/Users/weilu/Research/server/mar_2018/05_week/"
temp = 280
location = pre + "/eighth_with_real_distance/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), block=[-20,-10,0.55,0.7], end=(26,22), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmax=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# # plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x10a84e8d0>






    Out[31]:





[<matplotlib.lines.Line2D at 0x10ce1c2e8>]



In [36]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/eighth/rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_1_27_Mar_231139.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[36]:





<seaborn.axisgrid.FacetGrid at 0x1a1f7e1278>



In [37]:

    
# rerun 1 
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)









    Out[37]:





<matplotlib.axes._subplots.AxesSubplot at 0x10c468d30>



In [32]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/eighth/rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_3_27_Mar_231139.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[32]:





<seaborn.axisgrid.FacetGrid at 0x1a47d19390>



In [78]:

    
a = np.loadtxt("/Users/weilu/Research/server/apr_2018/TMHC2/test/rnative.dat")



In [79]:

    
a.shape









    Out[79]:





(156, 156)



In [108]:

    
a.min()









    Out[108]:





0.0



In [81]:

    
plt.imshow(a)









    Out[81]:





<matplotlib.image.AxesImage at 0x10c44e320>



In [82]:

    
table = {"A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"}



In [86]:

    
scTMHC2_seq = "MTRTEIIRELERSLRLQLVLAIFLMALLIVLLWLQQNGSSNNNVNYLLIVILVLVLVIVALAVIQKYLVEQLKRQADPTDDSRTEIIRELERSLRLQLVLAIFLMALLIVLLWLQQNGSSNNNVNYLLIVILVLVLVIVALAVTQKYLVEQLKRQD"



In [88]:

    
t = "MTRTEIIRELERSLRLQLVLAIFLMALLIVLLWLQQNGSSNNNVNYLLIVILVLVLVIVALAVIQKYLVEQLKRQADPTDDSRTEIIRELERSLRLQLVLAIFLMALLIVLLWLQQNGSSNNNVNYLLIVILVLVLVIVALAVTQKYLVEQLKRQD"



In [98]:

    
list(set(t))









    Out[98]:





['K',
 'R',
 'T',
 'Q',
 'Y',
 'G',
 'S',
 'P',
 'V',
 'F',
 'L',
 'W',
 'M',
 'A',
 'I',
 'N',
 'E',
 'D']



In [106]:

    
"G"*156









    Out[106]:





'GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG'



In [89]:

    
scTMHC2_seq == t









    Out[89]:





True



In [100]:

    
from collections import defaultdict



In [101]:

    
>>> s = t
>>> d = defaultdict(int)
>>> for k in s:
...     d[k] += 1
...
>>> d.items()









    Out[101]:





dict_items([('M', 3), ('T', 5), ('R', 10), ('E', 8), ('I', 15), ('L', 38), ('S', 7), ('Q', 12), ('V', 20), ('A', 9), ('F', 2), ('W', 2), ('N', 10), ('G', 2), ('Y', 4), ('K', 4), ('D', 4), ('P', 1)])



In [ ]:

    
for i in scTMHC2_seq:
    dic[i] += 1



In [91]:

    
for i in scTMHC2_seq:
    if i not in table:
        print(i)



In [75]:

    
start = 2
end = 38
helix1 = " ".join([str(i) for i in list(range(start*3-2, end*3+1))])
start = 43
end = 75
helix2 = " ".join([str(i) for i in list(range(start*3-2, end*3+1))])
start = 80
end = 117
helix3 = " ".join([str(i) for i in list(range(start*3-2, end*3+1))])
start = 123
end = 154
helix4 = " ".join([str(i) for i in list(range(start*3-2, end*3+1))])



In [76]:

    
print(helix1)
print(helix2)
print(helix3)
print(helix4)









    



4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351
367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462



In [33]:

    
# rerun 3
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)









    Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x10ce5e278>



In [35]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)









    Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4b7c3f28>



In [34]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/eighth/force_0.03_rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_3_30_Mar_135549.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[34]:





<seaborn.axisgrid.FacetGrid at 0x1a2129a390>



In [ ]:

i235d



In [133]:

    
plt.plot(range(len(f)), i235d,range(len(f)), orignal_2, range(len(f)), i255d)









    Out[133]:





[<matplotlib.lines.Line2D at 0x1a47e38940>,
 <matplotlib.lines.Line2D at 0x1a47e38a90>,
 <matplotlib.lines.Line2D at 0x1a47e38f28>]



In [112]:

    
origianl = f



In [132]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week/"
temp = 260
location = pre + "/sixth_i235d/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
# location2 = location + f"evpb-{temp}.dat"
# (xi,yi,zi) = plot2d(location2, zmax=100)
# 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)
i235d = f



In [22]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week/"
temp = 260
location = pre + "/sixth_orignal/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
# location2 = location + f"evpb-{temp}.dat"
# (xi,yi,zi) = plot2d(location2, zmax=100)
# 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)
orignal_2 = f

higher force



In [31]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week/"
temp = 260
location = pre + "/sixth_orignal/_280-350/2d_z_qw/force_0.4/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
# location2 = location + f"evpb-{temp}.dat"
# (xi,yi,zi) = plot2d(location2, zmax=100)
# 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 [4]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week/"
temp = 260
location = pre + "/sixth_i255d/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
# location2 = location + f"evpb-{temp}.dat"
# (xi,yi,zi) = plot2d(location2, zmax=100)
# 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)
i255d = f



In [25]:

    
pre = "/Users/weilu/Research/server/mar_2018/05_week/"
temp = 260
location = pre + "/sixth_i235d/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmax=100)
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)









    



---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-25-de5f98cb66b4> in <module>()
      7 # plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
      8 location2 = location + f"evpb-{temp}.dat"
----> 9 (xi,yi,zi) = plot2d(location2, zmax=100)
     10 plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
     11 # plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)

~/opt/notebook/notebookFunctions.py in plot2d(location, temp, zmin, zmax, xlabel, ylabel, title, outname)
     84 def plot2d(location, temp="450", zmin=0, zmax=30, xlabel="xlabel", ylabel="ylabel", title="", outname=None):
     85     titlefontsize = 28
---> 86     data = np.loadtxt(location)
     87     xi, yi, zi = getxyz(data, zmin=zmin, zmax=zmax)
     88     # plt.contour(xi, yi, zi, 50, linewidths=0.25,colors='k')

~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)
    896                 fh = iter(open(fname, 'U'))
    897             else:
--> 898                 fh = iter(open(fname))
    899         else:
    900             fh = iter(fname)

FileNotFoundError: [Errno 2] No such file or directory: '/Users/weilu/Research/server/mar_2018/05_week//sixth_i235d/_280-350/2d_z_qw/force_0.2/evpb-260.dat'



In [262]:

    
t = np.loadtxt(location2)



In [263]:

    
tt = np.where(np.isnan(t), 32, t)



In [259]:

    
t = t[~np.isnan(t).any(axis=1)]



In [264]:

    
plt.scatter(tt[:,1], tt[:,2], tt[:,3])









    Out[264]:





<matplotlib.collections.PathCollection at 0x1a4a6186a0>



In [238]:

    
plt.scatter(t[:,1], t[:,2], t[:,3])









    Out[238]:





<matplotlib.collections.PathCollection at 0x1a3c42f9b0>



In [269]:

    
pre = "/Users/weilu/Research/server/mar_2018/05_week"
temp = 260
location = pre + "/sixth_i235d/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False, zmax=32)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
# location2 = location + f"evpb-{temp}.dat"
# (xi,yi,zi) = plot2d(location2, zmax=100)
# 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)

Ninth freeEnergy

apply 0.06 force, check force 0.05 later. q 0.3, z=-20 is a region I hope the expected distance to be 80.



In [329]:

    
t = np.loadtxt(location2)



In [333]:

    
plt.scatter(t[:,1], t[:,2], t[:,3])









    Out[333]:





<matplotlib.collections.PathCollection at 0x1a2eceae10>



In [339]:

    
tt









    Out[339]:





array([[  5.00000000e+00,  -4.06340000e+01,   1.48000000e-01, ...,
          0.00000000e+00,  -1.30005700e+03,  -1.32762600e+03],
       [  6.00000000e+00,  -4.06340000e+01,   1.75000000e-01, ...,
          0.00000000e+00,  -1.31851000e+03,  -1.34470900e+03],
       [  3.40000000e+01,  -3.89110000e+01,   1.21000000e-01, ...,
          0.00000000e+00,  -1.20744800e+03,  -1.23625200e+03],
       ..., 
       [  8.75000000e+02,   9.32900000e+00,   1.48000000e-01, ...,
          0.00000000e+00,  -1.46979300e+03,  -1.48809600e+03],
       [  8.76000000e+02,   9.32900000e+00,   1.75000000e-01, ...,
          0.00000000e+00,  -1.37493100e+03,  -1.40183000e+03],
       [  8.77000000e+02,   9.32900000e+00,   2.02000000e-01, ...,
          0.00000000e+00,  -1.38906000e+03,  -1.41404800e+03]])



In [342]:

    
res = 30
xi = np.linspace(min(t[:,1]), max(t[:,1]), res)
yi = np.linspace(min(t[:,2]), max(t[:,2]), res)



In [344]:

    
yi









    Out[344]:





array([ 0.013     ,  0.03989655,  0.0667931 ,  0.09368966,  0.12058621,
        0.14748276,  0.17437931,  0.20127586,  0.22817241,  0.25506897,
        0.28196552,  0.30886207,  0.33575862,  0.36265517,  0.38955172,
        0.41644828,  0.44334483,  0.47024138,  0.49713793,  0.52403448,
        0.55093103,  0.57782759,  0.60472414,  0.63162069,  0.65851724,
        0.68541379,  0.71231034,  0.7392069 ,  0.76610345,  0.793     ])



In [341]:

    
xi









    Out[341]:





array([-40.634     , -38.91113793, -37.18827586, -35.46541379,
       -33.74255172, -32.01968966, -30.29682759, -28.57396552,
       -26.85110345, -25.12824138, -23.40537931, -21.68251724,
       -19.95965517, -18.2367931 , -16.51393103, -14.79106897,
       -13.0682069 , -11.34534483,  -9.62248276,  -7.89962069,
        -6.17675862,  -4.45389655,  -2.73103448,  -1.00817241,
         0.71468966,   2.43755172,   4.16041379,   5.88327586,
         7.60613793,   9.329     ])



In [356]:

    
mask = np.ones((res,res))*32
zi = t[:,3]
index_list = t[:,0]
count = 0
for i in range(res):
    for j in range(res):
        pos = i*res + j
        if count < len(index_list):
            if pos == int(index_list[count]):
                mask[i][j] = zi[count]
                count += 1



In [359]:

    
plt.imshow(mask.T)









    Out[359]:





<matplotlib.image.AxesImage at 0x1a488a67b8>



In [353]:

    
index_li









    Out[353]:





(440,)



In [338]:

    
t = np.loadtxt(location2)
tt = np.where(np.isnan(t), 32, t)
# t = t[~np.isnan(t).any(axis=1)] 
t = tt
plt.scatter(t[:,1], t[:,2], c=t[:,3], cmap="jet")
plt.colorbar()









    Out[338]:





<matplotlib.colorbar.Colorbar at 0x1a443e7a20>



In [223]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 330
location = pre + "/ninth_freeEnergy_7_less_temp/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x10a84e8d0>






    Out[223]:





[<matplotlib.lines.Line2D at 0x1a2b5923c8>]



In [209]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 350
location = pre + "/ninth_freeEnergy_7_less_temp/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x10a84e8d0>






    Out[209]:





[<matplotlib.lines.Line2D at 0x1a2767d2b0>]



In [102]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/ninth_freeEnergy_5_less_temp/_280-350/2d_z_qw/less_bias_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x1a13027940>






    Out[102]:





[<matplotlib.lines.Line2D at 0x1a4cb1ecf8>]



In [89]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/ninth_freeEnergy_5_less_temp/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x1a13027940>






    Out[89]:





[<matplotlib.lines.Line2D at 0x1a543aa2b0>]



In [95]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/ninth_freeEnergy_5/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x1a13027940>






    Out[95]:





[<matplotlib.lines.Line2D at 0x1a5437a400>]



In [20]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 320
location = pre + "/ninth_freeEnergy_5_less_temp/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.2], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x1a13027940>






    Out[20]:





[<matplotlib.lines.Line2D at 0x1a1e629320>]



In [379]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/ninth_freeEnergy_5_less_temp/_280-350/2d_z_qw/high_temp/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.0,0.1], plot1d=True, save=False)
# 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=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x112feeda0>






    Out[379]:





[<matplotlib.lines.Line2D at 0x1a49248710>]



In [321]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 290
location = pre + "/ninth_freeEnergy_5_less_temp/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-20,-15,0.6,0.7], plot1d=True, save=False)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmax=100)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)









    



<matplotlib.colors.LinearSegmentedColormap object at 0x112feeda0>






    Out[321]:





[<matplotlib.lines.Line2D at 0x1a526be5f8>]



In [ ]:

    
block = (0.6, )



In [173]:

    
np.searchsorted(xi, 20)









    Out[173]:





2



In [172]:

    
xi









    Out[172]:





array([ 16.497     ,  19.26941379,  22.04182759,  24.81424138,
        27.58665517,  30.35906897,  33.13148276,  35.90389655,
        38.67631034,  41.44872414,  44.22113793,  46.99355172,
        49.76596552,  52.53837931,  55.3107931 ,  58.0832069 ,
        60.85562069,  63.62803448,  66.40044828,  69.17286207,
        71.94527586,  74.71768966,  77.49010345,  80.26251724,
        83.03493103,  85.80734483,  88.57975862,  91.35217241,
        94.12458621,  96.897     ])

Rerun 7



In [122]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/ninth/force_0.06_rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_7_04_Apr_231330.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])



In [123]:

    
rerun7 = data
# data["BiasTo"] = data["BiasTo"].apply(pd.to_numeric)



In [124]:

    
data.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[124]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cb1fa90>



In [154]:

    
data.query("Temp == 300 and DisReal > 60").shape









    Out[154]:





(28983, 43)



In [125]:

    
data.query("Temp == 300").plot.hexbin("TotalE", "Qw", cmap="seismic", sharex=False)









    Out[125]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a22c04a90>



In [139]:

    
data.query("Temp == 300 and Qw > 0.6").plot.hexbin("TotalE", "DisReal", cmap="seismic", sharex=False)









    Out[139]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cea14a8>



In [134]:

    
data.query("Temp == 300 and Qw > 0.6")["TotalE"].hist(bins=50)
data.query("Temp == 300 and Qw > 0.6")["TotalE"].mean()









    Out[134]:





-908.9993475062817



In [148]:

    
data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[148]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cbd5940>



In [164]:

    
data.query("Temp == 300 and Qw < 0.6")["z_h6"].hist(bins=50)









    Out[164]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a20134da0>



In [203]:

    
data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60")["TotalE"].hist(bins=50)
print(data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60")["TotalE"].mean())
data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60").shape









    



-858.5170866171323






    Out[203]:





(7175, 43)

Two part here,

One with DisReal > 60



In [204]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")["TotalE"].hist(bins=50)
print(data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60").shape)
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")["TotalE"].mean()









    



(6221, 43)






    Out[204]:





-875.0166693690952



In [195]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")

DisReal < 60



In [190]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")["TotalE"].hist(bins=50)
data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")["TotalE"].mean()









    Out[190]:





-896.6218664373583



In [206]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["TotalE"].describe().query("count > 100")









    Out[206]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      30.0
      0
      394.0
      -890.251209
      19.283854
      -947.544810
      -904.379298
      -888.767989
      -877.038622
      -838.763927
    
    
      3
      620.0
      -901.879032
      18.444902
      -961.730918
      -914.355321
      -901.868031
      -888.899866
      -845.904847
    
    
      5
      114.0
      -889.103539
      17.625567
      -935.345576
      -901.511543
      -888.432567
      -878.658842
      -850.719708
    
    
      7
      332.0
      -885.430943
      17.821426
      -932.626242
      -897.484021
      -885.885062
      -873.832352
      -835.073611
    
    
      8
      588.0
      -899.772309
      18.880098
      -957.719562
      -912.416068
      -900.452462
      -888.016278
      -823.522148
    
    
      32.0
      3
      632.0
      -899.654913
      18.584577
      -951.211078
      -912.247703
      -899.309120
      -887.062842
      -843.420836
    
    
      6
      194.0
      -883.791805
      17.641747
      -927.904285
      -896.698596
      -883.547391
      -872.018513
      -829.492370
    
    
      9
      558.0
      -901.007225
      19.587102
      -947.474355
      -913.153758
      -901.538200
      -888.388642
      -837.419089
    
    
      10
      157.0
      -887.753531
      19.533920
      -945.888816
      -900.551720
      -887.865169
      -873.062984
      -839.553259
    
    
      34.0
      0
      303.0
      -901.857320
      18.758789
      -956.152877
      -914.247053
      -900.549990
      -888.930325
      -857.552025
    
    
      5
      206.0
      -881.787809
      17.559785
      -935.028185
      -893.783233
      -884.337537
      -868.700505
      -817.040406
    
    
      6
      499.0
      -886.639411
      19.204203
      -956.004474
      -898.319677
      -886.362851
      -873.585164
      -831.227083
    
    
      8
      439.0
      -886.771852
      18.936983
      -935.404168
      -900.759851
      -888.298555
      -873.989461
      -819.988017
    
    
      9
      166.0
      -875.682184
      18.760039
      -920.377910
      -887.859687
      -874.233890
      -864.202902
      -833.039203
    
    
      10
      468.0
      -889.802760
      19.740321
      -940.281849
      -903.157731
      -890.244557
      -876.323514
      -839.329975
    
    
      11
      390.0
      -889.543798
      20.667989
      -945.690371
      -902.701796
      -889.173510
      -876.046579
      -827.609107
    
    
      36.0
      6
      745.0
      -898.672030
      17.722600
      -944.592028
      -911.162059
      -899.124975
      -887.156187
      -838.921809
    
    
      9
      178.0
      -887.958891
      19.361262
      -933.458611
      -901.874268
      -887.386495
      -874.446654
      -839.795967
    
    
      10
      751.0
      -899.442786
      18.264277
      -948.574290
      -911.876551
      -899.434582
      -887.730034
      -842.397713
    
    
      38.0
      0
      190.0
      -887.070169
      19.997046
      -932.513527
      -899.400700
      -887.331952
      -873.157369
      -837.380043
    
    
      1
      803.0
      -897.982131
      18.899288
      -948.417071
      -910.692875
      -898.353706
      -884.968253
      -845.413398
    
    
      2
      292.0
      -884.828799
      18.548164
      -929.632641
      -895.771273
      -886.483368
      -872.085254
      -822.687180
    
    
      3
      284.0
      -888.637338
      17.777921
      -935.713625
      -900.609155
      -888.930495
      -875.283016
      -841.905732
    
    
      9
      202.0
      -888.994218
      18.454132
      -940.793974
      -900.077167
      -890.945484
      -876.725354
      -840.839724
    
    
      40.0
      0
      332.0
      -881.066871
      18.798838
      -931.016997
      -894.962503
      -880.563670
      -868.173569
      -824.507881
    
    
      1
      421.0
      -884.327925
      18.959000
      -934.258436
      -897.606460
      -886.138082
      -871.902673
      -829.521160
    
    
      2
      433.0
      -886.459083
      18.767784
      -937.516092
      -899.308611
      -886.358190
      -874.860867
      -837.242895
    
    
      3
      415.0
      -886.970365
      19.547075
      -932.519441
      -900.623635
      -888.358786
      -873.599376
      -823.503081
    
    
      6
      266.0
      -874.557970
      18.951226
      -920.341500
      -886.050496
      -874.460368
      -862.144591
      -811.120793
    
    
      8
      303.0
      -885.330511
      17.744929
      -934.041834
      -897.705181
      -884.941002
      -874.706651
      -834.326246
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      66.0
      0
      121.0
      -901.122000
      18.884814
      -950.234870
      -914.512399
      -900.147314
      -891.314805
      -847.123991
    
    
      1
      116.0
      -895.673453
      20.593193
      -943.158672
      -909.286231
      -892.700447
      -882.176605
      -843.592094
    
    
      2
      122.0
      -893.386943
      16.639179
      -940.563389
      -903.356458
      -890.835211
      -882.714439
      -860.893218
    
    
      5
      146.0
      -892.489449
      18.084460
      -939.473970
      -903.558047
      -892.728915
      -881.652826
      -829.790894
    
    
      9
      113.0
      -900.106981
      19.429993
      -947.721683
      -913.266159
      -901.885025
      -886.696900
      -849.944626
    
    
      68.0
      2
      114.0
      -900.485431
      18.094734
      -955.734272
      -911.996865
      -900.974327
      -892.588273
      -840.315608
    
    
      4
      117.0
      -899.122680
      18.390827
      -937.082189
      -912.868173
      -897.244308
      -887.730809
      -849.170993
    
    
      5
      122.0
      -899.202206
      18.096502
      -942.277956
      -912.256825
      -899.429781
      -886.356515
      -849.526101
    
    
      6
      108.0
      -899.350525
      19.895123
      -948.184033
      -912.749381
      -900.136850
      -887.966586
      -852.250856
    
    
      70.0
      0
      128.0
      -894.078248
      19.151928
      -932.680240
      -906.530740
      -893.622555
      -882.169923
      -845.602397
    
    
      1
      116.0
      -892.546498
      19.361662
      -941.066566
      -905.893757
      -890.558962
      -878.897157
      -854.799927
    
    
      2
      102.0
      -886.921617
      19.749254
      -931.882107
      -899.941331
      -886.186594
      -874.562656
      -839.546481
    
    
      4
      117.0
      -900.586853
      15.987925
      -931.885593
      -911.162284
      -900.690096
      -891.125473
      -843.020882
    
    
      6
      151.0
      -897.635438
      19.035434
      -948.544722
      -910.327936
      -898.369543
      -884.971073
      -850.162537
    
    
      9
      144.0
      -899.479657
      18.821801
      -961.859476
      -910.614437
      -899.454667
      -887.326860
      -836.591334
    
    
      11
      129.0
      -896.937018
      18.231441
      -931.164155
      -912.345856
      -898.066799
      -882.549609
      -854.551788
    
    
      72.0
      5
      843.0
      -889.735359
      18.385546
      -945.165975
      -901.767226
      -889.610041
      -876.941197
      -834.454650
    
    
      7
      887.0
      -890.804077
      19.030764
      -950.500121
      -903.870195
      -890.725187
      -878.413063
      -825.277069
    
    
      74.0
      2
      563.0
      -889.161729
      19.215481
      -945.370722
      -901.770235
      -888.700659
      -876.424557
      -820.053293
    
    
      4
      486.0
      -891.617002
      18.427406
      -942.722054
      -903.735952
      -891.787370
      -879.126522
      -821.591513
    
    
      9
      260.0
      -876.031653
      18.858182
      -933.126162
      -889.470288
      -876.422794
      -862.308188
      -799.717821
    
    
      10
      573.0
      -892.888059
      20.179539
      -966.404766
      -907.757438
      -893.599110
      -879.050420
      -830.427500
    
    
      76.0
      0
      177.0
      -899.930496
      17.860655
      -951.523665
      -911.374667
      -899.719720
      -887.991498
      -849.665731
    
    
      1
      172.0
      -898.798099
      16.600835
      -936.725827
      -909.445908
      -901.499495
      -887.709274
      -857.098197
    
    
      4
      258.0
      -889.555351
      19.880130
      -946.994701
      -902.343378
      -888.851546
      -875.051645
      -841.475881
    
    
      5
      120.0
      -898.296793
      20.845806
      -947.423702
      -911.565177
      -899.877191
      -883.328435
      -841.984704
    
    
      9
      261.0
      -888.241465
      18.550261
      -947.615076
      -901.440237
      -889.035516
      -877.595380
      -836.922891
    
    
      78.0
      8
      986.0
      -889.553024
      19.162510
      -943.750324
      -902.323468
      -889.685562
      -875.594160
      -832.961716
    
    
      82.0
      5
      109.0
      -875.688598
      23.427553
      -934.032782
      -892.162279
      -874.180832
      -858.468330
      -823.612029
    
    
      84.0
      0
      131.0
      -881.062538
      20.784376
      -922.191985
      -896.345220
      -881.796198
      -868.651078
      -825.547022
    
  

119 rows × 8 columns



In [205]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")









    Out[205]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      30.0
      0
      394.0
      0.489531
      0.043844
      0.358303
      0.461404
      0.492923
      0.520311
      0.594439
    
    
      3
      620.0
      0.485340
      0.029477
      0.408019
      0.466635
      0.484666
      0.505224
      0.578709
    
    
      5
      114.0
      0.444789
      0.047450
      0.344071
      0.408037
      0.429610
      0.479605
      0.575462
    
    
      7
      332.0
      0.524273
      0.028805
      0.454118
      0.505165
      0.523628
      0.544429
      0.598160
    
    
      8
      588.0
      0.488453
      0.031958
      0.389779
      0.464817
      0.488286
      0.510258
      0.579697
    
    
      32.0
      3
      632.0
      0.487750
      0.030249
      0.410464
      0.467053
      0.487285
      0.507899
      0.585787
    
    
      6
      194.0
      0.521501
      0.028685
      0.441429
      0.500648
      0.521954
      0.539422
      0.589675
    
    
      9
      558.0
      0.486652
      0.027890
      0.405111
      0.468676
      0.486566
      0.504700
      0.576786
    
    
      10
      157.0
      0.522495
      0.031685
      0.437480
      0.504383
      0.519569
      0.541603
      0.595622
    
    
      34.0
      0
      303.0
      0.488812
      0.028451
      0.418062
      0.468822
      0.487874
      0.509373
      0.565705
    
    
      5
      206.0
      0.443994
      0.035986
      0.322582
      0.427907
      0.446196
      0.465365
      0.543804
    
    
      6
      499.0
      0.522046
      0.029040
      0.428436
      0.503733
      0.523642
      0.541547
      0.588953
    
    
      8
      439.0
      0.524791
      0.029219
      0.430359
      0.505522
      0.526952
      0.545704
      0.594427
    
    
      9
      166.0
      0.496579
      0.026986
      0.408038
      0.480445
      0.496830
      0.514039
      0.578228
    
    
      10
      468.0
      0.413349
      0.022430
      0.344875
      0.399962
      0.413567
      0.427569
      0.482350
    
    
      11
      390.0
      0.410743
      0.022368
      0.350805
      0.396504
      0.408812
      0.424746
      0.482739
    
    
      36.0
      6
      745.0
      0.483690
      0.030461
      0.394670
      0.463188
      0.482566
      0.503314
      0.585620
    
    
      9
      178.0
      0.414196
      0.020174
      0.364020
      0.401110
      0.413120
      0.428199
      0.467040
    
    
      10
      751.0
      0.484260
      0.030414
      0.404256
      0.463737
      0.483324
      0.504644
      0.582029
    
    
      38.0
      0
      190.0
      0.408572
      0.024124
      0.338022
      0.391332
      0.406393
      0.424663
      0.472623
    
    
      1
      803.0
      0.481138
      0.030067
      0.398346
      0.460458
      0.480804
      0.501786
      0.571299
    
    
      2
      292.0
      0.520303
      0.028539
      0.445324
      0.500611
      0.520745
      0.542394
      0.587540
    
    
      3
      284.0
      0.423729
      0.041731
      0.363189
      0.396241
      0.413268
      0.431222
      0.575946
    
    
      9
      202.0
      0.415782
      0.022594
      0.363667
      0.400491
      0.415146
      0.429986
      0.500137
    
    
      40.0
      0
      332.0
      0.442155
      0.023612
      0.370792
      0.426444
      0.444496
      0.456481
      0.507021
    
    
      1
      421.0
      0.518245
      0.030308
      0.421739
      0.499133
      0.519452
      0.538511
      0.591126
    
    
      2
      433.0
      0.407968
      0.022324
      0.351707
      0.392262
      0.407573
      0.422140
      0.473064
    
    
      3
      415.0
      0.411648
      0.021136
      0.323997
      0.398344
      0.410570
      0.425364
      0.472014
    
    
      6
      266.0
      0.351914
      0.017503
      0.300588
      0.341483
      0.352147
      0.361913
      0.418239
    
    
      8
      303.0
      0.443511
      0.021982
      0.363102
      0.428339
      0.443220
      0.459870
      0.503529
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      66.0
      0
      121.0
      0.575636
      0.017451
      0.524251
      0.560832
      0.577432
      0.591042
      0.599985
    
    
      1
      116.0
      0.577491
      0.021303
      0.495074
      0.569036
      0.584397
      0.592174
      0.599894
    
    
      2
      122.0
      0.467920
      0.031502
      0.386007
      0.445745
      0.470909
      0.490302
      0.548944
    
    
      5
      146.0
      0.467205
      0.025945
      0.404986
      0.451272
      0.465407
      0.485017
      0.535565
    
    
      9
      113.0
      0.579406
      0.016709
      0.511979
      0.570995
      0.583537
      0.590994
      0.599611
    
    
      68.0
      2
      114.0
      0.576348
      0.019013
      0.507565
      0.564166
      0.581423
      0.591033
      0.599923
    
    
      4
      117.0
      0.580757
      0.017576
      0.521178
      0.572045
      0.586170
      0.594763
      0.599066
    
    
      5
      122.0
      0.574987
      0.018805
      0.529303
      0.564137
      0.578293
      0.591018
      0.599944
    
    
      6
      108.0
      0.580065
      0.015938
      0.539403
      0.571373
      0.582882
      0.592905
      0.599633
    
    
      70.0
      0
      128.0
      0.462869
      0.026190
      0.395824
      0.447547
      0.460828
      0.479186
      0.534200
    
    
      1
      116.0
      0.466758
      0.026348
      0.388508
      0.450535
      0.467621
      0.483546
      0.525900
    
    
      2
      102.0
      0.464029
      0.027526
      0.391007
      0.447468
      0.463903
      0.481800
      0.542475
    
    
      4
      117.0
      0.575161
      0.021442
      0.482658
      0.565996
      0.579825
      0.591280
      0.599993
    
    
      6
      151.0
      0.577447
      0.017979
      0.519017
      0.565755
      0.581196
      0.592861
      0.599645
    
    
      9
      144.0
      0.576912
      0.019614
      0.518649
      0.567711
      0.582269
      0.592874
      0.599916
    
    
      11
      129.0
      0.580298
      0.017659
      0.505153
      0.572408
      0.586535
      0.592519
      0.599641
    
    
      72.0
      5
      843.0
      0.464094
      0.025547
      0.389147
      0.446118
      0.463628
      0.481530
      0.543629
    
    
      7
      887.0
      0.467385
      0.027932
      0.393338
      0.448422
      0.466138
      0.485082
      0.568665
    
    
      74.0
      2
      563.0
      0.466495
      0.026990
      0.401119
      0.449493
      0.464952
      0.483591
      0.561554
    
    
      4
      486.0
      0.466071
      0.028482
      0.375480
      0.447966
      0.466290
      0.483575
      0.550493
    
    
      9
      260.0
      0.496669
      0.028378
      0.404756
      0.478535
      0.496790
      0.515455
      0.580669
    
    
      10
      573.0
      0.470901
      0.028174
      0.383592
      0.452267
      0.470268
      0.488985
      0.579701
    
    
      76.0
      0
      177.0
      0.577569
      0.020088
      0.503613
      0.564750
      0.583074
      0.593591
      0.599979
    
    
      1
      172.0
      0.576932
      0.018177
      0.516327
      0.566479
      0.579116
      0.592593
      0.599804
    
    
      4
      258.0
      0.461957
      0.085142
      0.345313
      0.394413
      0.419904
      0.569953
      0.599947
    
    
      5
      120.0
      0.579387
      0.015364
      0.531898
      0.570654
      0.582667
      0.591257
      0.599300
    
    
      9
      261.0
      0.465503
      0.026522
      0.393174
      0.449805
      0.465494
      0.483787
      0.533802
    
    
      78.0
      8
      986.0
      0.463921
      0.027725
      0.365707
      0.444712
      0.463058
      0.482697
      0.557890
    
    
      82.0
      5
      109.0
      0.422596
      0.086911
      0.280349
      0.363041
      0.388452
      0.430845
      0.597187
    
    
      84.0
      0
      131.0
      0.425814
      0.070791
      0.274786
      0.387266
      0.447998
      0.476494
      0.558342
    
  

119 rows × 8 columns



In [197]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal < 60")["Qw"].hist(bins=50)
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal < 60")["Qw"].mean()









    Out[197]:





0.5200406120854402



In [ ]:

    
t = data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")



In [149]:

    
t = data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")



In [145]:

    
data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3").plot.hexbin("z_h4", "Qw", cmap="seismic", sharex=False)









    Out[145]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a191c06a0>



In [6]:

    
t = data.query("Temp < 350 and DisReal > 60 and Qw > 0.18")
t.hist("Lipid1")









    Out[6]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a21e912e8>]], dtype=object)



In [9]:

    
data.query("Temp < 350").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a21e913c8>



In [20]:

    
rerun7.query("Temp < 350")["Step"].count()









    Out[20]:





540000



In [17]:

    
t = rerun5.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t["Step"].count()









    Out[17]:





53939



In [18]:

    
t = rerun7.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t["Step"].count()









    Out[18]:





52305



In [10]:

    
t = data.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t.plot.hexbin("Lipid1", "z_h1", cmap="seismic", sharex=False)









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a215447b8>



In [283]:

    
data.query("Temp < 350 and DisReal > 60 and Qw > 0.18").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[283]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a59b3e198>



In [277]:

    
data.query("Qw > 0.58").groupby("Temp")["DisReal"].describe()



In [220]:

    
t = data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35 and DisReal > 60")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")



In [216]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35 and DisReal > 60").plot.hexbin("z_h4", "TotalE", cmap="seismic", sharex=False)









    Out[216]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4b46f390>



In [196]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").hist("DisReal", bins=50)









    Out[196]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a4a99ada0>]], dtype=object)



In [193]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[193]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4a625cc0>



In [40]:

    
data.query("Temp == 300").plot.hexbin("z_h3", "z_h6", cmap="seismic", sharex=False)









    Out[40]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a3ca9f828>



In [42]:

    
a = data.query("(z_h3 < -10) or (z_h6 < -10)")
a.query("Temp == 300").plot.hexbin("z_h3", "z_h6", cmap="seismic", sharex=False)









    Out[42]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a299f2588>



In [66]:

    
data.columns









    Out[66]:





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



In [88]:

    
data.query("Temp <= 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[88]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a43e3fd30>



In [87]:

    
data.query("Temp <= 300 and DisReal > 60").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[87]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a259706d8>



In [91]:

    
data.columns









    Out[91]:





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



In [94]:

    
data.query("Temp == 300 and DisReal > 60").plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)









    Out[94]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a431b42b0>



In [96]:

    
data.query("Temp == 300").plot.hexbin("abs_z_average", "z_h6", cmap="seismic", sharex=False)









    Out[96]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a435ea860>



In [126]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and Lipid1 > -1").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[126]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a25a95b38>



In [130]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60").shape









    Out[130]:





(35124, 43)



In [129]:

    
t = data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and Lipid1 > -1")
print(t.shape)
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")









    



(11439, 43)






    Out[129]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      72.0
      8
      538.0
      77.631154
      4.978481
      64.034331
      74.265102
      77.679578
      81.148599
      92.402795
    
    
      74.0
      5
      671.0
      79.352226
      4.992393
      64.103251
      75.905704
      79.439485
      82.716725
      94.980508
    
    
      8
      597.0
      80.370943
      4.771834
      63.743749
      77.312220
      80.329319
      83.590274
      94.733364
    
    
      76.0
      2
      609.0
      82.491103
      4.934288
      66.209328
      79.096101
      82.395038
      85.988688
      95.370163
    
    
      80.0
      7
      570.0
      84.330678
      4.942327
      65.585395
      81.343990
      84.503898
      87.671476
      98.049908
    
    
      8
      583.0
      84.765906
      4.744009
      71.252220
      81.699702
      84.902951
      87.689288
      99.617263
    
    
      82.0
      7
      591.0
      86.381836
      4.886817
      74.129515
      82.791748
      86.400281
      89.636735
      101.037585
    
    
      9
      515.0
      86.697468
      4.841471
      71.817455
      83.436906
      86.703616
      90.212749
      101.873019
    
    
      88.0
      1
      503.0
      91.928638
      4.759968
      75.885403
      88.872399
      91.672946
      95.171128
      105.171102
    
    
      90.0
      11
      817.0
      92.899936
      4.619934
      79.678197
      89.964819
      92.984239
      95.992677
      107.310590
    
    
      98.0
      10
      540.0
      98.684912
      4.785347
      85.058600
      95.322567
      98.759484
      102.196297
      111.325873



In [128]:

    
t = data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and z_h6 < -10")
print(t.shape)
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")









    



(15583, 43)






    Out[128]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      100.0
      11
      544.0
      84.293570
      3.425672
      75.519237
      81.961637
      84.397217
      86.622628
      94.611569
    
    
      72.0
      5
      548.0
      73.933853
      4.387406
      61.049616
      71.126700
      74.101592
      77.014725
      86.079380
    
    
      6
      570.0
      77.566640
      4.805078
      63.593634
      74.509323
      77.478254
      80.691643
      92.106523
    
    
      78.0
      5
      520.0
      82.549008
      4.721425
      70.541042
      79.102200
      82.721582
      85.779293
      95.670568
    
    
      10
      540.0
      82.675814
      4.635266
      68.840841
      79.520502
      82.781726
      85.958293
      96.702022
    
    
      80.0
      0
      568.0
      79.695028
      4.399605
      67.366476
      76.710162
      79.807395
      82.590351
      96.141058
    
    
      84.0
      8
      530.0
      83.894069
      5.306648
      70.917908
      80.118754
      83.161541
      87.110482
      100.301417
    
    
      11
      553.0
      86.657368
      4.707773
      73.011489
      83.747428
      86.888678
      89.551823
      100.057218
    
    
      86.0
      11
      531.0
      87.787863
      4.444919
      76.416774
      84.737965
      87.608177
      90.635270
      102.053831
    
    
      88.0
      2
      595.0
      84.620395
      4.761268
      70.773094
      81.346685
      84.557121
      87.582304
      98.051481
    
    
      92.0
      2
      564.0
      91.000057
      5.468401
      78.150078
      87.060316
      90.448845
      94.965484
      108.250962
    
    
      96.0
      7
      963.0
      95.072311
      5.525327
      77.822069
      91.490362
      94.993429
      98.830402
      110.993673



In [125]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and z_h6 < -10").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[125]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a43fc2908>



In [124]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[124]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a44fa2fd0>



In [120]:

    
t = data.query("Temp == 300 and Qw < 0.6 and Lipid1 < -1 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")



In [114]:

    
data.query("Temp == 300 and Qw < 0.6 and Lipid1 < -1 and DisReal < 60").plot.hexbin("z_h1", "Qw", cmap="seismic", sharex=False)









    Out[114]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4573e7b8>



In [85]:

    
data.query("Temp <= 300 and DisReal > 60").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[85]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4631c438>



In [64]:

    
data.query("Temp == 300").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[64]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a471194e0>



In [34]:

    
a = data.query("(z_h6 > -10 and z_h1 < -10) or (z_h6 < -10 and z_h1 > -10)")



In [ ]:



In [35]:

    
a.query("Temp == 300").plot.hexbin("z_h1", "z_h6", cmap="seismic", sharex=False)









    Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a22079908>

I hope we don't see this, for rerun 3.

(h6 inside, h1 outside)



In [59]:

    
rerun1.query("Temp <= 300 and z_h1 < -10")["DisReal"].count()









    Out[59]:





23027



In [60]:

    
rerun1.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a44ad06d8>



In [300]:

    
rerun5.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[300]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4c006d30>



In [7]:

    
data.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a5239eb70>

Good, decrease in number



In [61]:

    
data.query("Temp <= 300")["DisReal"].count()









    Out[61]:





270000



In [62]:

    
data.query("Temp <= 300 and z_h1 < -10")["DisReal"].describe()









    Out[62]:





count    15664.000000
mean        86.338072
std         16.724329
min         23.835159
25%         84.224392
50%         91.396990
75%         96.274063
max        115.018263
Name: DisReal, dtype: float64



In [63]:

    
data.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a42dd2e10>

enhance h1, h2 connection.

check if it increase the unfolding force, and if the helix 5,6 unfold first. (how strong h1,h2 bond needs to be)



In [27]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/05_week/unfold_strengthen_h1_h2/03_Apr_233639.feather")



In [76]:

    
data.query("Steps < 4e7 and Qw > 0.1").plot.hexbin("Steps", "Qw", by="Temp", cmap="cool", sharex=False)









    Out[76]:





<matplotlib.axes._subplots.AxesSubplot at 0x10d9f6cc0>



In [16]:

    
data.query("Folder == 'force_3_' and Steps < 3e7 and Qw > 0.1").plot.hexbin("Steps", "Qw", by="Temp", cmap="cool", sharex=False)









    Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4d733a20>



In [28]:

    
data["Run"] = data["Run"].apply(pd.to_numeric)



In [29]:

    
fg = sns.FacetGrid(data=data.query("Steps < 4e7 and Qw > 0.1 and Folder=='force_6_' and Run ==0"), hue='Run', size=8, aspect=1.61)
fg.map(plt.scatter, 'Steps', 'Qw').add_legend()









    Out[29]:





<seaborn.axisgrid.FacetGrid at 0x1a2f93b128>



In [77]:

    
fg = sns.FacetGrid(data=data.query("Steps < 4e7 and Qw > 0.1"), hue='Folder', size=8, aspect=1.61)
fg.map(plt.scatter, 'Steps', 'Qw').add_legend()









    Out[77]:





<seaborn.axisgrid.FacetGrid at 0x1a4ff850f0>



In [79]:

    
a = data.query("(z_h6 > -10 and z_h1 < -10) or (z_h6 < -10 and z_h1 > -10)")



In [80]:

    
a.query("Folder == 'force_6_'").plot.hexbin("z_h1", "z_h6", cmap="cool", sharex=False)









    Out[80]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a23c9e198>



In [15]:

    
data["Folder"].unique()









    Out[15]:





array(['force_2_mem_1.0_', 'force_1_mem_1.0_', 'force_3_',
       'force_2_mem_0.8_', 'force_1_mem_0.8_', 'force_4_'], dtype=object)



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
72.0	8	251.0	0.331380	0.016914	0.300520	0.318481	0.329844	0.343734	0.402180
74.0	8	115.0	0.350842	0.024570	0.300538	0.334939	0.349419	0.361610	0.435940
74.0	11	184.0	0.352030	0.025777	0.300973	0.334212	0.350647	0.364934	0.442350
78.0	2	582.0	0.350725	0.021947	0.301000	0.335213	0.349560	0.365497	0.418287
80.0	1	383.0	0.347422	0.021195	0.303433	0.331633	0.346739	0.360955	0.411149
	7	478.0	0.349377	0.022634	0.300982	0.332810	0.347720	0.363692	0.418313
	8	382.0	0.346878	0.021583	0.300855	0.330785	0.346393	0.360646	0.421669
	9	396.0	0.349728	0.021193	0.301145	0.333405	0.349832	0.365705	0.411630
84.0	7	361.0	0.347062	0.020163	0.300202	0.332871	0.345651	0.360076	0.416170
88.0	1	489.0	0.387654	0.031921	0.300672	0.365318	0.389423	0.410119	0.472375
88.0	10	459.0	0.399617	0.030593	0.318703	0.381806	0.400317	0.419547	0.487651
90.0	9	204.0	0.344613	0.020983	0.300634	0.329445	0.344976	0.358986	0.404646
90.0	10	225.0	0.398894	0.027372	0.316385	0.383034	0.398304	0.415516	0.468483
92.0	8	471.0	0.372714	0.032957	0.302890	0.347729	0.370922	0.396583	0.470442
92.0	11	198.0	0.341023	0.020253	0.304259	0.325441	0.339567	0.353536	0.428579
96.0	5	383.0	0.339173	0.020122	0.300796	0.324488	0.338162	0.350960	0.411800
96.0	9	393.0	0.340088	0.019831	0.302228	0.325773	0.339094	0.352400	0.399826

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
100.0	10	156.0	0.401507	0.029608	0.315298	0.384423	0.401515	0.419898	0.469629
72.0	1	256.0	0.377285	0.022454	0.301294	0.363600	0.377906	0.393576	0.439035
78.0	7	164.0	0.449923	0.035584	0.345792	0.427605	0.446752	0.475710	0.532827
80.0	0	162.0	0.446442	0.032028	0.354827	0.430338	0.449124	0.465840	0.529778
	5	229.0	0.448336	0.035905	0.327640	0.422755	0.448652	0.473249	0.536800
	11	358.0	0.460356	0.033694	0.369025	0.437515	0.459280	0.483573	0.570592
82.0	2	107.0	0.450985	0.039338	0.364041	0.420962	0.451839	0.477923	0.568188
84.0	8	734.0	0.367220	0.022367	0.306020	0.351989	0.367830	0.383073	0.427086
86.0	6	189.0	0.359501	0.025159	0.310352	0.342112	0.359164	0.375743	0.425219
88.0	2	316.0	0.357779	0.024044	0.301202	0.341398	0.357336	0.373844	0.431721
	4	507.0	0.445021	0.036512	0.335681	0.419464	0.446287	0.471036	0.558018
	5	481.0	0.443088	0.034372	0.339516	0.420437	0.440849	0.468509	0.547236
	8	191.0	0.362609	0.022720	0.307835	0.348507	0.363370	0.380009	0.418722
92.0	7	181.0	0.318419	0.017204	0.300058	0.305806	0.313089	0.327235	0.380311
92.0	10	113.0	0.378277	0.052080	0.300115	0.332064	0.378333	0.423989	0.472322
96.0	3	109.0	0.313523	0.010522	0.300059	0.305449	0.312272	0.319603	0.346076
96.0	7	854.0	0.334134	0.017192	0.300080	0.321745	0.333039	0.345030	0.404080
98.0	1	488.0	0.334744	0.019960	0.300052	0.320059	0.333613	0.347594	0.409914
	6	574.0	0.337025	0.019014	0.300169	0.324376	0.335988	0.349475	0.407215
	7	423.0	0.400406	0.031782	0.312027	0.377341	0.402923	0.421179	0.500205
	9	311.0	0.338310	0.021854	0.300088	0.320835	0.335578	0.354701	0.404305

	count	mean	std	min	25%	50%	75%	max
Temp
280	34395.0	42.940793	5.531507	24.547152	38.811300	42.836657	47.382027	59.594937
290	22489.0	42.784274	5.330718	21.744802	38.877496	42.851639	46.927034	57.014944
300	11801.0	43.579822	5.151529	25.652358	39.889707	44.021430	47.580846	58.152470
310	5845.0	44.908710	4.777508	27.114228	41.968282	45.678484	48.428579	55.947778
320	2639.0	45.515907	4.648256	26.487897	42.868365	46.234237	48.820234	56.778925
335	574.0	45.595006	4.176323	31.826684	42.863389	46.208786	48.586064	54.507667
350	82.0	45.239074	4.337481	32.754204	42.069960	46.152603	48.677832	52.897284
365	5.0	46.504712	3.444580	41.733659	45.157007	46.514402	48.106523	51.011969

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
60.0	5	113.0	66.512154	3.886832	60.147610	63.580878	66.285957	69.405557	77.179416
68.0	11	180.0	73.756180	6.057438	60.749281	69.398390	73.617739	77.737753	92.431294
70.0	0	469.0	74.191685	5.318893	60.414137	70.405310	74.056265	78.122878	89.513138
74.0	2	101.0	76.827775	5.457357	66.320898	72.989188	76.996523	80.230168	89.134214
74.0	11	144.0	75.486415	4.963238	62.432900	71.845102	74.953984	79.007245	87.323786
76.0	3	235.0	78.447992	5.253855	66.259977	74.608759	78.511404	81.494020	95.362849
80.0	5	217.0	79.799922	4.855510	67.250980	76.633111	80.259033	83.007123	96.753831
80.0	11	198.0	79.955361	5.238624	65.716158	76.483398	80.330906	83.365813	93.809286
82.0	3	114.0	82.243958	3.904281	72.643975	79.513151	81.941932	85.159661	91.372623
84.0	6	416.0	85.558974	4.732438	70.555359	82.467007	85.292550	88.945509	98.381981
84.0	8	415.0	86.876651	6.218563	70.161543	82.480690	87.035292	91.161424	104.109995
86.0	6	199.0	91.192025	5.494117	75.331115	88.039038	91.072395	94.250562	106.096975
88.0	2	262.0	90.216668	5.130045	75.261177	86.876216	90.352033	93.509445	104.125687
88.0	5	351.0	87.891460	5.725054	73.764376	83.802720	87.646519	91.993305	105.204918
92.0	2	542.0	89.927684	5.273472	70.467923	86.323840	89.760363	93.542206	105.406497
94.0	1	106.0	90.242444	6.574085	77.091854	84.989310	90.004269	95.004842	106.691579
96.0	7	812.0	96.576466	5.142513	79.397742	93.199597	96.335172	100.062148	110.993673
98.0	1	818.0	97.598809	4.302016	86.262144	94.911896	97.646100	100.322980	112.100967
98.0	6	441.0	97.337894	5.101396	81.527405	94.271081	97.383584	100.831160	109.210235

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	11	501.0	34.550022	3.497956	24.170195	32.341842	34.733842	36.927544	44.118947
32.0	8	523.0	35.867688	3.378665	24.100050	33.797883	35.808496	38.302579	45.959715
32.0	10	645.0	36.086853	2.985883	26.805858	34.255239	35.990810	38.118236	47.008506
34.0	6	514.0	32.001355	3.933645	19.487454	29.375800	32.185595	34.763219	43.226268
36.0	6	683.0	36.538337	3.075776	26.712718	34.600427	36.581137	38.632251	45.696680
	9	535.0	37.053009	3.487852	27.163052	34.778089	36.952146	39.300564	46.858792
	10	599.0	36.493984	3.050589	28.673133	34.313042	36.475634	38.632653	45.187989
38.0	0	542.0	38.292865	3.117689	29.507186	36.509181	38.429570	40.372452	47.251375
	1	714.0	37.156266	3.051513	27.119173	35.145635	37.288075	39.339564	46.587213
	2	646.0	38.042212	2.957526	29.438891	36.105246	37.955290	40.029292	47.525100
42.0	6	707.0	38.366364	2.976499	28.001329	36.458438	38.554256	40.285623	47.530492
42.0	9	636.0	38.318101	2.830133	30.080318	36.520623	38.279709	40.229305	45.076937
44.0	7	653.0	39.187825	2.832088	31.375651	37.427158	39.179752	41.124606	47.085678
46.0	5	582.0	39.461132	2.802785	29.656917	37.685658	39.756264	41.339765	46.331141
46.0	9	592.0	39.515688	2.656214	31.390946	37.743350	39.514984	41.435002	46.551716
48.0	2	532.0	39.997772	2.763595	32.714515	38.160923	40.078110	41.975311	47.424742
48.0	5	617.0	40.078830	2.917347	30.190290	38.052028	40.208077	42.158398	49.065847
50.0	4	572.0	40.516251	2.894581	29.218862	38.590035	40.625680	42.319545	49.911833
50.0	6	579.0	40.452561	2.723055	31.306359	38.851076	40.420034	42.158544	47.852737
52.0	6	585.0	42.891324	3.309122	34.838188	40.494285	42.791672	45.103617	53.407546
54.0	3	593.0	43.652043	3.418296	34.941781	41.392058	43.590167	45.989911	55.849975
54.0	8	504.0	43.468905	3.307635	33.128995	41.161186	43.479570	45.611239	52.045254
56.0	1	557.0	44.201995	3.411782	33.753458	41.816051	44.090526	46.363673	54.841648
56.0	7	542.0	44.164520	3.429523	32.235866	41.796897	44.247860	46.500655	55.349855
58.0	7	824.0	42.481098	2.637412	33.582897	40.764434	42.498365	44.173776	50.799169
60.0	8	608.0	42.638195	2.560345	34.202688	40.911645	42.578285	44.345535	49.555813
60.0	10	543.0	42.848207	2.621652	34.645227	41.066412	42.700465	44.606472	51.479163
62.0	0	597.0	43.277374	2.653459	32.995607	41.460755	43.142450	45.150519	53.672116
	7	634.0	43.225784	2.627363	35.509930	41.493669	43.174808	45.023711	50.970356
	9	684.0	43.219135	2.749727	34.961009	41.375080	43.101129	44.931003	53.977729
64.0	7	666.0	47.337949	2.991526	37.048099	45.439008	47.615525	49.520250	54.931101
64.0	10	588.0	46.540005	3.096186	38.317320	44.321463	46.640451	48.617557	54.942772
68.0	4	590.0	44.379569	2.656873	36.506250	42.625571	44.344091	46.021600	53.099925
70.0	1	678.0	45.143000	2.616832	36.997997	43.303478	45.142127	46.904454	53.147709
70.0	11	679.0	45.274321	2.628822	36.256916	43.455996	45.276506	47.127284	53.834286
74.0	1	549.0	50.903293	3.274476	40.958378	48.939727	50.828384	53.000226	59.523403
76.0	0	522.0	51.331612	3.403327	42.337286	49.037508	51.416541	53.997385	59.633690
76.0	4	526.0	51.618093	3.490510	40.795565	49.258330	51.623358	53.998894	59.458026

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	0	394.0	-890.251209	19.283854	-947.544810	-904.379298	-888.767989	-877.038622	-838.763927
	3	620.0	-901.879032	18.444902	-961.730918	-914.355321	-901.868031	-888.899866	-845.904847
	5	114.0	-889.103539	17.625567	-935.345576	-901.511543	-888.432567	-878.658842	-850.719708
	7	332.0	-885.430943	17.821426	-932.626242	-897.484021	-885.885062	-873.832352	-835.073611
	8	588.0	-899.772309	18.880098	-957.719562	-912.416068	-900.452462	-888.016278	-823.522148
32.0	3	632.0	-899.654913	18.584577	-951.211078	-912.247703	-899.309120	-887.062842	-843.420836
	6	194.0	-883.791805	17.641747	-927.904285	-896.698596	-883.547391	-872.018513	-829.492370
	9	558.0	-901.007225	19.587102	-947.474355	-913.153758	-901.538200	-888.388642	-837.419089
	10	157.0	-887.753531	19.533920	-945.888816	-900.551720	-887.865169	-873.062984	-839.553259
34.0	0	303.0	-901.857320	18.758789	-956.152877	-914.247053	-900.549990	-888.930325	-857.552025
	5	206.0	-881.787809	17.559785	-935.028185	-893.783233	-884.337537	-868.700505	-817.040406
	6	499.0	-886.639411	19.204203	-956.004474	-898.319677	-886.362851	-873.585164	-831.227083
	8	439.0	-886.771852	18.936983	-935.404168	-900.759851	-888.298555	-873.989461	-819.988017
	9	166.0	-875.682184	18.760039	-920.377910	-887.859687	-874.233890	-864.202902	-833.039203
	10	468.0	-889.802760	19.740321	-940.281849	-903.157731	-890.244557	-876.323514	-839.329975
	11	390.0	-889.543798	20.667989	-945.690371	-902.701796	-889.173510	-876.046579	-827.609107
36.0	6	745.0	-898.672030	17.722600	-944.592028	-911.162059	-899.124975	-887.156187	-838.921809
	9	178.0	-887.958891	19.361262	-933.458611	-901.874268	-887.386495	-874.446654	-839.795967
	10	751.0	-899.442786	18.264277	-948.574290	-911.876551	-899.434582	-887.730034	-842.397713
38.0	0	190.0	-887.070169	19.997046	-932.513527	-899.400700	-887.331952	-873.157369	-837.380043
	1	803.0	-897.982131	18.899288	-948.417071	-910.692875	-898.353706	-884.968253	-845.413398
	2	292.0	-884.828799	18.548164	-929.632641	-895.771273	-886.483368	-872.085254	-822.687180
	3	284.0	-888.637338	17.777921	-935.713625	-900.609155	-888.930495	-875.283016	-841.905732
	9	202.0	-888.994218	18.454132	-940.793974	-900.077167	-890.945484	-876.725354	-840.839724
40.0	0	332.0	-881.066871	18.798838	-931.016997	-894.962503	-880.563670	-868.173569	-824.507881
	1	421.0	-884.327925	18.959000	-934.258436	-897.606460	-886.138082	-871.902673	-829.521160
	2	433.0	-886.459083	18.767784	-937.516092	-899.308611	-886.358190	-874.860867	-837.242895
	3	415.0	-886.970365	19.547075	-932.519441	-900.623635	-888.358786	-873.599376	-823.503081
	6	266.0	-874.557970	18.951226	-920.341500	-886.050496	-874.460368	-862.144591	-811.120793
	8	303.0	-885.330511	17.744929	-934.041834	-897.705181	-884.941002	-874.706651	-834.326246
...	...	...	...	...	...	...	...	...	...
66.0	0	121.0	-901.122000	18.884814	-950.234870	-914.512399	-900.147314	-891.314805	-847.123991
	1	116.0	-895.673453	20.593193	-943.158672	-909.286231	-892.700447	-882.176605	-843.592094
	2	122.0	-893.386943	16.639179	-940.563389	-903.356458	-890.835211	-882.714439	-860.893218
	5	146.0	-892.489449	18.084460	-939.473970	-903.558047	-892.728915	-881.652826	-829.790894
	9	113.0	-900.106981	19.429993	-947.721683	-913.266159	-901.885025	-886.696900	-849.944626
68.0	2	114.0	-900.485431	18.094734	-955.734272	-911.996865	-900.974327	-892.588273	-840.315608
	4	117.0	-899.122680	18.390827	-937.082189	-912.868173	-897.244308	-887.730809	-849.170993
	5	122.0	-899.202206	18.096502	-942.277956	-912.256825	-899.429781	-886.356515	-849.526101
	6	108.0	-899.350525	19.895123	-948.184033	-912.749381	-900.136850	-887.966586	-852.250856
70.0	0	128.0	-894.078248	19.151928	-932.680240	-906.530740	-893.622555	-882.169923	-845.602397
	1	116.0	-892.546498	19.361662	-941.066566	-905.893757	-890.558962	-878.897157	-854.799927
	2	102.0	-886.921617	19.749254	-931.882107	-899.941331	-886.186594	-874.562656	-839.546481
	4	117.0	-900.586853	15.987925	-931.885593	-911.162284	-900.690096	-891.125473	-843.020882
	6	151.0	-897.635438	19.035434	-948.544722	-910.327936	-898.369543	-884.971073	-850.162537
	9	144.0	-899.479657	18.821801	-961.859476	-910.614437	-899.454667	-887.326860	-836.591334
	11	129.0	-896.937018	18.231441	-931.164155	-912.345856	-898.066799	-882.549609	-854.551788
72.0	5	843.0	-889.735359	18.385546	-945.165975	-901.767226	-889.610041	-876.941197	-834.454650
72.0	7	887.0	-890.804077	19.030764	-950.500121	-903.870195	-890.725187	-878.413063	-825.277069
74.0	2	563.0	-889.161729	19.215481	-945.370722	-901.770235	-888.700659	-876.424557	-820.053293
	4	486.0	-891.617002	18.427406	-942.722054	-903.735952	-891.787370	-879.126522	-821.591513
	9	260.0	-876.031653	18.858182	-933.126162	-889.470288	-876.422794	-862.308188	-799.717821
	10	573.0	-892.888059	20.179539	-966.404766	-907.757438	-893.599110	-879.050420	-830.427500
76.0	0	177.0	-899.930496	17.860655	-951.523665	-911.374667	-899.719720	-887.991498	-849.665731
	1	172.0	-898.798099	16.600835	-936.725827	-909.445908	-901.499495	-887.709274	-857.098197
	4	258.0	-889.555351	19.880130	-946.994701	-902.343378	-888.851546	-875.051645	-841.475881
	5	120.0	-898.296793	20.845806	-947.423702	-911.565177	-899.877191	-883.328435	-841.984704
	9	261.0	-888.241465	18.550261	-947.615076	-901.440237	-889.035516	-877.595380	-836.922891
78.0	8	986.0	-889.553024	19.162510	-943.750324	-902.323468	-889.685562	-875.594160	-832.961716
82.0	5	109.0	-875.688598	23.427553	-934.032782	-892.162279	-874.180832	-858.468330	-823.612029
84.0	0	131.0	-881.062538	20.784376	-922.191985	-896.345220	-881.796198	-868.651078	-825.547022

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
72.0	8	538.0	77.631154	4.978481	64.034331	74.265102	77.679578	81.148599	92.402795
74.0	5	671.0	79.352226	4.992393	64.103251	75.905704	79.439485	82.716725	94.980508
74.0	8	597.0	80.370943	4.771834	63.743749	77.312220	80.329319	83.590274	94.733364
76.0	2	609.0	82.491103	4.934288	66.209328	79.096101	82.395038	85.988688	95.370163
80.0	7	570.0	84.330678	4.942327	65.585395	81.343990	84.503898	87.671476	98.049908
80.0	8	583.0	84.765906	4.744009	71.252220	81.699702	84.902951	87.689288	99.617263
82.0	7	591.0	86.381836	4.886817	74.129515	82.791748	86.400281	89.636735	101.037585
82.0	9	515.0	86.697468	4.841471	71.817455	83.436906	86.703616	90.212749	101.873019
88.0	1	503.0	91.928638	4.759968	75.885403	88.872399	91.672946	95.171128	105.171102
90.0	11	817.0	92.899936	4.619934	79.678197	89.964819	92.984239	95.992677	107.310590
98.0	10	540.0	98.684912	4.785347	85.058600	95.322567	98.759484	102.196297	111.325873

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
100.0	11	544.0	84.293570	3.425672	75.519237	81.961637	84.397217	86.622628	94.611569
72.0	5	548.0	73.933853	4.387406	61.049616	71.126700	74.101592	77.014725	86.079380
72.0	6	570.0	77.566640	4.805078	63.593634	74.509323	77.478254	80.691643	92.106523
78.0	5	520.0	82.549008	4.721425	70.541042	79.102200	82.721582	85.779293	95.670568
78.0	10	540.0	82.675814	4.635266	68.840841	79.520502	82.781726	85.958293	96.702022
80.0	0	568.0	79.695028	4.399605	67.366476	76.710162	79.807395	82.590351	96.141058
84.0	8	530.0	83.894069	5.306648	70.917908	80.118754	83.161541	87.110482	100.301417
84.0	11	553.0	86.657368	4.707773	73.011489	83.747428	86.888678	89.551823	100.057218
86.0	11	531.0	87.787863	4.444919	76.416774	84.737965	87.608177	90.635270	102.053831
88.0	2	595.0	84.620395	4.761268	70.773094	81.346685	84.557121	87.582304	98.051481
92.0	2	564.0	91.000057	5.468401	78.150078	87.060316	90.448845	94.965484	108.250962
96.0	7	963.0	95.072311	5.525327	77.822069	91.490362	94.993429	98.830402	110.993673