In [2]:

    

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

import 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 [55]:

    

plt.plot(range(len(f)), i235d,range(len(f)), orignal_2, range(len(f)), i255d)
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/mutation.png")


    

In [51]:

    

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 [52]:

    

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 [53]:

    

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


    

In [40]:

    

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)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x11528fb70>






    
Out[40]:





[<matplotlib.lines.Line2D at 0x1a2f91e5c0>]






    













    













    













    

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)


    

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 [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     ])

In [376]:

    

pre = "/Users/weilu/Research/server/mar_2018/05_week"
temp = 290
location = pre + "/ninth_freeEnergy_3/_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[376]:





[<matplotlib.lines.Line2D at 0x1a46b78a58>]






    













    













    













    

In [57]:

    

pre = "/Users/weilu/Research/server/mar_2018/05_week"
temp = 280
location = pre + "/ninth_freeEnergy/_280-350/2d_z_qw/force_0.05/"
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-57-73b290df60a4> in <module>()
      3 location = pre + "/ninth_freeEnergy/_280-350/2d_z_qw/force_0.05/"
      4 location2 = location + f"pmf-{temp}.dat"
----> 5 path, f = shortest_path(location2, start=(1, 14), plot1d=True, save=False)
      6 # plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
      7 # plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)

~/opt/notebook/notebookFunctions.py in shortest_path(location, temp, start, end, res, zmin, zmax, xlabel, ylabel, title, save, plot1d, plot2d)
    147 
    148 def shortest_path(location, temp="450", start=(4,5), end=-1, res=30, zmin=0, zmax=30, xlabel="xlabel", ylabel="ylabel", title="", save=False, plot1d=True, plot2d=True):
--> 149     data = np.loadtxt(location)
    150     xi, yi, zi = getxyz(data, res=res, zmin=zmin, zmax=zmax)
    151     zi = np.where(np.isnan(zi), 50, zi)

~/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/ninth_freeEnergy/_280-350/2d_z_qw/force_0.05/pmf-280.dat'

In [138]:

    

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_1_31_Mar_182712.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 [139]:

    

rerun1 = data


    

In [ ]:

    




    

In [145]:

    

rerun1.query("Temp == 300 and DisReal > 60 and Qw > 0.2 and z_h6 < -10").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[145]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a52c4fa58>






    

In [ ]:

    

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.query("Temp == 320"), hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()


    

In [6]:

    

a.query("Temp == 300 and DisReal > 60 and DisReal < 90").plot.hexbin("z_h3", "z_h6", cmap="seismic", sharex=False)


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a229faa58>






    

In [37]:

    

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


    

    
Out[37]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a3cab86a0>






    

In [47]:

    

data = pd.read_feather("/Users/weilu/Research/server/mar_2018/sixth/rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_3_31_Mar_175942.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 [48]:

    

data.plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


    

    
Out[48]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a25d9b128>






    

In [49]:

    

data.query("Lipid1 > -1 and Qw > 0.3 and Temp==300").groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")


    

    
Out[49]:







  

    

      

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
    

      
BiasTo
      
Run
      

      

      

      

      

      

      

      

    
  
  

    

      
66.0
      
0
      
186.0
      
68.655783
      
6.069770
      
51.929840
      
64.680835
      
68.514751
      
72.338925
      
86.389636
    
    

      
70.0
      
5
      
447.0
      
71.610629
      
5.329203
      
53.209198
      
68.114881
      
71.951678
      
75.127247
      
87.952586
    
    

      
74.0
      
5
      
210.0
      
70.917855
      
5.260775
      
57.541260
      
68.013596
      
70.700056
      
74.053002
      
86.654484
    
    

      
86.0
      
3
      
206.0
      
84.545661
      
5.465229
      
69.445193
      
80.958664
      
85.056198
      
88.536706
      
98.376434
    
    

      
10
      
308.0
      
83.031149
      
4.920991
      
69.492482
      
79.694361
      
83.285074
      
86.126439
      
96.154586
    
  

In [41]:

    

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_3_01_Apr_144311.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 [42]:

    

rerun3 = data


    

In [43]:

    

data.plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)


    

    
Out[43]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a24e503c8>






    

In [46]:

    

data.query("Lipid1 > -1 and Qw > 0.3 and Temp==300").groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")


    

    
Out[46]:







  

    

      

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
    

      
BiasTo
      
Run
      

      

      

      

      

      

      

      

    
  
  

    

      
32.0
      
5
      
251.0
      
40.292235
      
5.489174
      
23.835159
      
36.310415
      
40.245681
      
43.478896
      
55.163690
    
    

      
34.0
      
5
      
427.0
      
44.520444
      
5.775708
      
29.743104
      
40.395054
      
44.587846
      
48.293983
      
61.693888
    
    

      
38.0
      
11
      
274.0
      
47.559146
      
5.457731
      
33.921784
      
43.546506
      
47.727943
      
51.198142
      
62.930494
    
    

      
44.0
      
9
      
176.0
      
53.074193
      
5.358604
      
38.866388
      
49.543183
      
52.910667
      
56.846417
      
71.040948
    
    

      
70.0
      
4
      
491.0
      
75.647939
      
5.247338
      
60.779750
      
72.205434
      
75.664376
      
78.897513
      
93.266709
    
    

      
72.0
      
8
      
493.0
      
77.361571
      
4.989067
      
59.765368
      
73.984486
      
77.373719
      
80.873908
      
92.141764
    
    

      
74.0
      
5
      
667.0
      
79.329147
      
4.986871
      
64.103251
      
75.847867
      
79.438294
      
82.698738
      
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
      
562.0
      
84.350539
      
4.959073
      
65.585395
      
81.343990
      
84.517576
      
87.745680
      
98.049908
    
    

      
8
      
579.0
      
84.737909
      
4.741354
      
71.252220
      
81.686929
      
84.882222
      
87.652842
      
99.617263
    
    

      
9
      
400.0
      
84.401657
      
6.255276
      
54.487521
      
81.209347
      
84.654996
      
88.362040
      
98.423390
    
    

      
88.0
      
1
      
471.0
      
91.795462
      
4.692006
      
75.885403
      
88.808540
      
91.562989
      
94.946491
      
104.763696
    
    

      
10
      
410.0
      
91.388021
      
5.199348
      
76.722241
      
87.640631
      
91.437215
      
94.909218
      
109.214974
    
    

      
90.0
      
9
      
252.0
      
92.393759
      
5.219676
      
75.182513
      
88.625179
      
92.940121
      
96.037827
      
104.600972
    
    

      
10
      
300.0
      
93.348850
      
4.670225
      
81.132513
      
90.162427
      
93.418551
      
96.657641
      
104.599181
    
    

      
92.0
      
8
      
417.0
      
97.186096
      
5.360534
      
83.360010
      
94.081603
      
97.227186
      
100.556798
      
112.103506
    
    

      
11
      
165.0
      
93.902001
      
4.733001
      
80.469762
      
90.909465
      
93.782722
      
96.981611
      
106.129501
    
    

      
96.0
      
5
      
374.0
      
97.038085
      
4.784950
      
85.480114
      
93.273428
      
97.224546
      
100.512618
      
109.939788
    
    

      
9
      
419.0
      
98.018156
      
4.413905
      
86.849407
      
95.165249
      
98.127517
      
100.761499
      
110.303018
    
  

In [31]:

    

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[31]:





<seaborn.axisgrid.FacetGrid at 0x1a349c4cc0>






    

In [202]:

    

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


    

In [199]:

    

data["Diff"] = data["DisReal"]-data["BiasTo"].apply(pd.to_numeric)


    

In [206]:

    

rerun1.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)


    

    
Out[206]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4b818b38>






    

In [197]:

    

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


    

    
Out[197]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4a947198>






    

In [371]:

    

rerun5Real.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)


    

    
Out[371]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a451b0240>






    

In [372]:

    

rerun5Real.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "Lipid1", cmap="seismic", sharex=False)


    

    
Out[372]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a48fc6550>






    

In [15]:

    

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_5_02_Apr_175521.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 [16]:

    

rerun5 = data
# data["BiasTo"] = data["BiasTo"].apply(pd.to_numeric)


    

In [68]:

    

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[68]:





<seaborn.axisgrid.FacetGrid at 0x1a20b9f4e0>






    

In [52]:

    

rerun5Real.query("Temp < 350").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[52]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a212f4630>






    

In [70]:

    

rerun5Real.query("Temp == 300 and z_h6 < -10").plot.hexbin("BiasTo", "Qw", cmap="seismic", sharex=False)


    

    
Out[70]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a5444fdd8>






    

In [54]:

    

rerun5Real.query("z_h6 < -15 and Qw < 0.19 and Temp < 350").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[54]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1ed249b0>






    

In [61]:

    

t = rerun5Real.query("BiasTo == '100.0' and Run == 0").plot.hexbin("Step", "Temp", cmap="seismic", sharex=False)


    

In [58]:

    

t = rerun5Real.query("BiasTo == '100.0' and Run == 0").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

In [57]:

    

t = rerun5Real.query("z_h6 < -15 and Qw < 0.19 and Temp < 350")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")


    

    
Out[57]:







  

    

      

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
    

      
BiasTo
      
Run
      

      

      

      

      

      

      

      

    
  
  

    

      
100.0
      
0
      
230.0
      
107.072088
      
5.153847
      
93.235307
      
103.400793
      
106.848808
      
110.632756
      
120.600588
    
    

      
1
      
339.0
      
107.043334
      
5.600557
      
91.117362
      
103.245228
      
106.877536
      
110.732350
      
124.544074
    
    

      
2
      
449.0
      
107.839734
      
5.617927
      
92.053543
      
103.806973
      
108.007643
      
111.226408
      
123.357252
    
    

      
3
      
400.0
      
106.830253
      
5.315044
      
93.020161
      
103.334509
      
106.501556
      
110.355048
      
126.184945
    
    

      
4
      
158.0
      
107.811192
      
4.786222
      
94.181941
      
104.437156
      
107.898609
      
111.401471
      
119.581089
    
    

      
6
      
744.0
      
107.591178
      
5.372206
      
90.837970
      
103.700418
      
107.572788
      
111.361550
      
124.064562
    
    

      
7
      
186.0
      
109.609476
      
4.720362
      
96.796927
      
106.599706
      
109.633136
      
112.447317
      
121.515520
    
    

      
8
      
362.0
      
107.620310
      
5.639909
      
89.253292
      
103.638417
      
107.085253
      
111.569685
      
123.243733
    
    

      
9
      
391.0
      
107.026119
      
5.565790
      
91.681514
      
103.697908
      
106.916169
      
110.867390
      
122.735879
    
    

      
11
      
216.0
      
105.590120
      
5.327599
      
88.764096
      
101.932631
      
105.708156
      
108.933231
      
117.549827
    
    

      
72.0
      
0
      
339.0
      
79.265596
      
5.690559
      
63.096307
      
75.411585
      
79.513764
      
83.162601
      
94.614666
    
    

      
78.0
      
3
      
166.0
      
84.380311
      
5.974263
      
71.283865
      
80.273110
      
84.147604
      
88.868709
      
101.296249
    
    

      
5
      
109.0
      
86.873984
      
5.737264
      
73.104251
      
83.072319
      
86.343598
      
90.091803
      
108.808916
    
    

      
6
      
121.0
      
85.279693
      
5.237668
      
70.989417
      
81.952301
      
85.561814
      
88.624054
      
97.884075
    
    

      
9
      
547.0
      
85.253129
      
6.150029
      
60.982805
      
81.607472
      
85.503989
      
89.381441
      
105.239871
    
    

      
10
      
187.0
      
85.301296
      
5.967258
      
71.117278
      
81.210871
      
84.818902
      
88.908395
      
99.846333
    
    

      
82.0
      
1
      
173.0
      
85.884242
      
4.373618
      
72.164111
      
83.270612
      
85.474863
      
88.406404
      
98.461885
    
    

      
3
      
159.0
      
86.842390
      
5.169531
      
75.263222
      
83.287231
      
86.523719
      
90.424681
      
101.599415
    
    

      
84.0
      
0
      
156.0
      
93.047780
      
5.116210
      
79.613867
      
89.526740
      
93.445192
      
96.671618
      
105.858604
    
    

      
5
      
281.0
      
92.413691
      
4.856579
      
78.820845
      
89.039614
      
92.540071
      
95.603369
      
106.493886
    
    

      
10
      
496.0
      
92.625104
      
4.875297
      
78.227571
      
89.252434
      
92.855612
      
95.950964
      
105.469970
    
    

      
11
      
165.0
      
87.448740
      
4.601817
      
68.126908
      
84.404328
      
87.573403
      
90.479759
      
99.116534
    
    

      
86.0
      
2
      
251.0
      
94.524562
      
5.635701
      
81.085311
      
91.247538
      
94.375071
      
98.292984
      
108.471354
    
    

      
3
      
244.0
      
93.408414
      
5.959654
      
77.583265
      
89.264098
      
93.806136
      
97.624417
      
107.011850
    
    

      
7
      
626.0
      
94.202531
      
5.532827
      
79.726481
      
90.665729
      
94.029748
      
98.139173
      
111.287618
    
    

      
10
      
239.0
      
94.797664
      
5.479752
      
78.249111
      
90.857005
      
95.047504
      
98.340450
      
109.806169
    
    

      
11
      
178.0
      
89.402917
      
5.024600
      
76.523367
      
86.481688
      
89.408867
      
92.857348
      
102.556737
    
    

      
90.0
      
0
      
243.0
      
96.956475
      
5.903734
      
82.500279
      
92.754985
      
97.044650
      
101.392487
      
116.384127
    
    

      
1
      
566.0
      
97.984208
      
5.462500
      
81.122583
      
94.113920
      
97.996140
      
101.826097
      
112.432023
    
    

      
2
      
579.0
      
97.900211
      
5.238162
      
77.083149
      
94.324371
      
97.811426
      
101.678932
      
111.945631
    
    

      
4
      
296.0
      
97.702412
      
5.673630
      
75.908231
      
94.081282
      
98.046794
      
101.398384
      
111.170978
    
    

      
5
      
234.0
      
97.757442
      
5.970999
      
80.150913
      
93.502057
      
97.876515
      
102.010565
      
112.344342
    
    

      
6
      
184.0
      
98.252066
      
5.750023
      
84.259257
      
94.351271
      
98.017863
      
101.950174
      
113.144814
    
    

      
7
      
268.0
      
98.671854
      
5.730473
      
77.847929
      
94.991306
      
98.660549
      
102.459253
      
115.351663
    
    

      
8
      
408.0
      
96.850653
      
5.664726
      
79.993956
      
92.892204
      
96.715015
      
100.616301
      
116.428001
    
    

      
92.0
      
1
      
315.0
      
99.976896
      
5.628666
      
83.091559
      
96.447123
      
100.006642
      
103.897363
      
113.489342
    
    

      
4
      
143.0
      
100.311195
      
5.625827
      
82.178171
      
97.128857
      
100.502532
      
104.368217
      
111.953441
    
    

      
6
      
274.0
      
99.499096
      
5.633787
      
81.196646
      
95.712738
      
99.680241
      
103.062175
      
114.059849
    
    

      
94.0
      
2
      
310.0
      
102.164023
      
4.959530
      
83.428131
      
98.919469
      
101.890004
      
105.692167
      
117.855877
    
    

      
3
      
995.0
      
101.058061
      
5.365371
      
83.172388
      
97.527977
      
100.792283
      
104.484260
      
120.253494
    
    

      
4
      
1309.0
      
101.787575
      
5.303878
      
86.526134
      
98.271980
      
101.855274
      
105.361320
      
120.292577
    
    

      
5
      
489.0
      
101.300986
      
5.653833
      
86.254887
      
96.981911
      
101.554410
      
105.010189
      
119.083281
    
    

      
7
      
918.0
      
100.604986
      
5.270230
      
85.558950
      
97.005727
      
100.622963
      
104.012274
      
119.213760
    
    

      
9
      
484.0
      
101.634491
      
5.464040
      
86.529547
      
97.859663
      
101.345062
      
105.202869
      
124.247219
    
    

      
10
      
703.0
      
100.391077
      
5.617240
      
83.827680
      
96.494296
      
100.764651
      
104.136632
      
117.552206
    
    

      
96.0
      
0
      
185.0
      
103.545191
      
5.737606
      
87.100828
      
98.857623
      
103.827285
      
107.637627
      
118.932397
    
    

      
1
      
521.0
      
103.049820
      
5.573078
      
89.386118
      
99.250161
      
102.707328
      
107.006924
      
120.421626
    
    

      
4
      
998.0
      
103.459632
      
5.629293
      
86.217056
      
99.555789
      
103.132477
      
107.217653
      
122.447667
    
    

      
6
      
444.0
      
102.711040
      
5.564602
      
82.770653
      
98.948561
      
102.964231
      
106.635865
      
120.547370
    
    

      
8
      
525.0
      
103.492121
      
5.239027
      
86.470044
      
99.960068
      
103.508069
      
106.547276
      
118.741354
    
    

      
10
      
126.0
      
104.043881
      
5.030920
      
91.554190
      
100.195912
      
104.055943
      
107.613296
      
115.383548
    
    

      
11
      
518.0
      
103.476490
      
5.523780
      
89.063803
      
99.656770
      
103.331352
      
107.358655
      
121.047127
    
    

      
98.0
      
2
      
326.0
      
105.193381
      
5.650755
      
86.618622
      
101.397177
      
104.790038
      
109.175442
      
123.354012
    
    

      
3
      
112.0
      
106.379511
      
4.649796
      
95.234224
      
103.479200
      
106.377168
      
109.468345
      
122.740330
    
    

      
5
      
283.0
      
104.724798
      
5.666254
      
89.484171
      
101.126413
      
104.713893
      
108.065489
      
123.686233
    
    

      
11
      
158.0
      
104.475030
      
5.454415
      
92.136594
      
100.482878
      
104.402892
      
108.496207
      
120.151856
    
  

In [280]:

    

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


    

    
Out[280]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a67fdd4e0>






    

In [281]:

    

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


    

    
Out[281]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a67fe97b8>






    

In [295]:

    

t = data.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")


    

    
Out[295]:







  

    

      

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
    

      
BiasTo
      
Run
      

      

      

      

      

      

      

      

    
  
  

    

      
56.0
      
10
      
1256.0
      
66.820079
      
4.296343
      
60.044115
      
63.368288
      
66.358890
      
69.530842
      
82.354064
    
    

      
70.0
      
4
      
1652.0
      
75.930618
      
5.086345
      
60.003176
      
72.480929
      
75.920739
      
79.491868
      
93.096026
    
    

      
72.0
      
8
      
2469.0
      
77.579076
      
4.987029
      
60.307852
      
74.230567
      
77.567436
      
81.038579
      
95.598008
    
    

      
74.0
      
5
      
2251.0
      
79.425321
      
5.214753
      
60.444895
      
76.004328
      
79.264136
      
82.858309
      
97.355344
    
    

      
8
      
2102.0
      
79.286566
      
5.314780
      
61.141134
      
75.739501
      
79.361913
      
83.064293
      
96.591458
    
    

      
76.0
      
2
      
1888.0
      
82.508506
      
5.156855
      
66.447159
      
78.990138
      
82.512010
      
86.102403
      
97.549200
    
    

      
78.0
      
2
      
2500.0
      
83.375966
      
5.096266
      
67.127944
      
80.057110
      
83.327756
      
86.768287
      
99.300269
    
    

      
80.0
      
1
      
2456.0
      
84.457202
      
4.977827
      
64.441522
      
81.200525
      
84.467712
      
87.792535
      
101.191245
    
    

      
2
      
118.0
      
83.773226
      
5.316375
      
71.852107
      
80.742306
      
84.004202
      
87.743108
      
96.204696
    
    

      
3
      
176.0
      
84.029784
      
5.384112
      
70.940725
      
80.358973
      
84.077892
      
87.302763
      
100.642210
    
    

      
7
      
2454.0
      
84.593961
      
5.023706
      
68.099753
      
81.288804
      
84.641437
      
87.922134
      
100.303462
    
    

      
8
      
2470.0
      
84.354282
      
5.015871
      
69.026456
      
80.998557
      
84.399900
      
87.806844
      
102.467243
    
    

      
9
      
2484.0
      
84.533552
      
4.944677
      
66.803309
      
81.270637
      
84.597032
      
87.626704
      
101.957387
    
    

      
82.0
      
7
      
1707.0
      
85.864871
      
5.135288
      
67.887647
      
82.529074
      
86.032246
      
89.285328
      
103.566591
    
    

      
9
      
1812.0
      
86.203190
      
4.994182
      
70.705391
      
82.993966
      
86.340866
      
89.518782
      
102.780962
    
    

      
84.0
      
7
      
2500.0
      
87.601858
      
4.936961
      
70.844029
      
84.358287
      
87.624615
      
91.013595
      
102.879505
    
    

      
88.0
      
0
      
319.0
      
91.019058
      
5.378250
      
73.192750
      
87.508919
      
90.893821
      
94.673136
      
106.747162
    
    

      
1
      
2480.0
      
91.372438
      
4.743301
      
74.005688
      
88.308472
      
91.318962
      
94.462678
      
105.837332
    
    

      
6
      
803.0
      
90.869652
      
4.956622
      
71.389748
      
87.735978
      
90.748577
      
94.088437
      
104.932534
    
    

      
9
      
764.0
      
87.907171
      
5.055785
      
75.019161
      
84.432165
      
88.019275
      
91.352074
      
103.313797
    
    

      
10
      
2474.0
      
92.073276
      
4.772843
      
75.886319
      
88.780522
      
92.066518
      
95.281773
      
112.263772
    
    

      
90.0
      
9
      
1202.0
      
92.427654
      
4.739478
      
77.523036
      
89.348735
      
92.513453
      
95.550816
      
107.962728
    
    

      
10
      
1203.0
      
93.081179
      
4.628795
      
79.619364
      
90.015158
      
93.014806
      
96.042680
      
109.446510
    
    

      
11
      
1132.0
      
92.375410
      
4.770930
      
75.469863
      
89.298786
      
92.639249
      
95.630375
      
105.954901
    
    

      
92.0
      
8
      
2500.0
      
95.244479
      
4.915523
      
77.309143
      
91.903495
      
95.204158
      
98.575696
      
110.788866
    
    

      
11
      
2500.0
      
94.053825
      
4.669505
      
77.182232
      
90.811113
      
94.150966
      
97.272661
      
109.083715
    
    

      
96.0
      
5
      
2500.0
      
97.013984
      
4.678806
      
80.594875
      
93.758124
      
97.092534
      
100.317067
      
114.679359
    
    

      
9
      
2500.0
      
97.215364
      
4.671423
      
81.498933
      
93.997105
      
97.139151
      
100.450282
      
114.352519
    
    

      
98.0
      
10
      
2118.0
      
98.473181
      
4.666779
      
83.685739
      
95.322738
      
98.410803
      
101.712885
      
115.530619
    
  

In [ ]:

    

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


    

In [386]:

    

t = rerun3.query("Temp < 350 and DisReal > 60 and Qw > 0.18")
t.hist("Lipid1")


    

    
Out[386]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a415dbda0>]], dtype=object)






    

In [387]:

    

t = rerun5Real.query("Temp < 350 and DisReal > 60 and Qw > 0.18")
t.hist("Lipid1")


    

    
Out[387]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a2ebeab38>]], dtype=object)






    

In [5]:

    

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 [14]:

    

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


    

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()


    

    
Out[277]:







  

    

      

      
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
    
  

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")


    

    
Out[220]:







  

    

      

      

      
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
    
    

      
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
    
    

      
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
    
    

      
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
    
    

      
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
    
    

      
6
      
441.0
      
97.337894
      
5.101396
      
81.527405
      
94.271081
      
97.383584
      
100.831160
      
109.210235
    
  

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)