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 [21]:

    

data = pd.read_feather("/Users/weilu/Research/server/apr_2018/third/force_0.02_rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_1_16_Apr_143519.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])
rerun1 = data
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[21]:





<seaborn.axisgrid.FacetGrid at 0x1a4ffaf470>






    

In [22]:

    

rerun1.query("Temp == 365").plot.hexbin("DisReal", "Rg", cmap="seismic", sharex=False)


    

    
Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1e4c14a8>






    

In [31]:

    

rerun1.query("Temp == 365").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


    

    
Out[31]:





<matplotlib.axes._subplots.AxesSubplot at 0x115ea9978>






    

In [23]:

    

rerun1.query("Temp == 365").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


    

    
Out[23]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2190ec50>






    

In [7]:

    

rerun1.query("Temp == 300 and DisReal > 45 and DisReal < 60").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x1148dbe80>






    

In [47]:

    

pre = "/Users/weilu/Research/server/apr_2018/03_week/all_data_folder/third_with_h56rerun_1_24_Apr_230220.feather"
data = pd.read_feather(pre)


    

In [48]:

    

data.columns


    

    
Out[48]:





Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'Dis_h56',
       '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 [ ]:

    




    

In [42]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 310
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/super_quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x1122b50b8>






    
Out[42]:





[<matplotlib.lines.Line2D at 0x117327278>]






    













    













    













    

In [17]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 310
location = pre + "/third/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x1122b50b8>






    
Out[17]:





[<matplotlib.lines.Line2D at 0x1a1edc2908>]






    













    













    













    

In [36]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 340
location = pre + "/third/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(25,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x1122b50b8>






    
Out[36]:





[<matplotlib.lines.Line2D at 0x1a21d2f470>]






    













    













    













    

In [37]:

    

f


    

    
Out[37]:





[8.2872653673163601,
 4.4673688155922067,
 1.7365352323838075,
 1.1310359820089964,
 1.1428920539730145,
 1.4403283358320835,
 2.2472848575712119,
 3.2155307346326882,
 3.4803973013493268,
 3.792016491754131,
 7.3559805097451401,
 11.717202398800598,
 11.782916041979011,
 10.920929535232382,
 8.7602338830584667,
 7.6382908545727135,
 7.3663943028485752,
 7.2526536731634179,
 7.3941274362818596,
 7.9572908545727126,
 8.3009775112443762,
 8.7356056971514242,
 10.780898050974512]

In [38]:

    

f_on_path


    

    
Out[38]:





[26.941385307346359,
 30.696973013493253,
 30.447131934032985,
 30.68845577211394,
 30.838632683658169,
 31.003184407796098,
 31.281014992503742,
 32.129712143928039,
 33.628517241379306,
 34.193422788605702,
 36.744098950524837,
 70.045364317841091,
 71.290425787106443,
 73.648332833583197,
 73.690346326836561,
 75.531101949025498,
 78.740425787106446,
 80.215896551724128,
 81.360451274362816,
 81.300182908545722,
 87.572427286356799,
 93.164787106446781,
 90.193644677661169]

In [41]:

    

plt.plot(f_on_path, f)


    

    
Out[41]:





[<matplotlib.lines.Line2D at 0x1a201215c0>]






    

In [39]:

    

from scipy.interpolate import CubicSpline
import scipy
x = np.array(range(len(f)))
spl1 = scipy.interpolate.interp1d(x, f, kind="cubic")
# plt.plot(x_smooth1, spl1(x_smooth1))
f_on_path
x_smooth = np.linspace(x.min(), x.max(), 200)
spl = scipy.interpolate.interp1d(x, f_on_path, kind="cubic")
# plt.plot(x_smooth, spl(x_smooth))
# Create axes
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, x_smooth, spl1(x_smooth), spl(x_smooth), 'r', 'b')

color_y_axis(ax1, 'r')
color_y_axis(ax2, 'b')
# plt.show()
# plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/freeEnergy_Distance.png")


    

In [27]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 310
location = pre + "/third/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x10ee51898>






    
Out[27]:





[<matplotlib.lines.Line2D at 0x1a21b26e48>]






    













    













    













    

In [30]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 360
location = pre + "/third/_280-350/2d_z_qw/temp_350/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x10ee51898>






    
Out[30]:





[<matplotlib.lines.Line2D at 0x1a22db2d30>]






    













    













    













    

In [32]:

    

pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 360
location = pre + "/third/_280-350/2d_z_qw/temp_350_and_365/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# 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 0x10ee51898>






    
Out[32]:





[<matplotlib.lines.Line2D at 0x1a24de40b8>]






    













    













    













    

In [ ]: