

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

Go



In [38]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 340
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/temp_340/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-1, zmax=-300)
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 0x1162deef0>






    Out[38]:





[<matplotlib.lines.Line2D at 0x11b86dd30>]

Lipid



In [39]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 340
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/temp_340/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-500, zmax=500, z=4)
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 0x1162deef0>






    Out[39]:





[<matplotlib.lines.Line2D at 0x1a24a16208>]

Membrane



In [41]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 340
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/temp_340/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-500, zmax=500, z=5)
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 0x1162deef0>






    Out[41]:





[<matplotlib.lines.Line2D at 0x11b875320>]

Rg



In [42]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 340
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/temp_340/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-500, zmax=500, z=6)
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 0x1162deef0>






    Out[42]:





[<matplotlib.lines.Line2D at 0x11b1fedd8>]



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [28]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 330
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/super_quick_2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-1, zmax=-300)
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 0x1162deef0>






    Out[28]:





[<matplotlib.lines.Line2D at 0x1a24440ba8>]



In [21]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 290
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(28,22), 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, zmin=-1, 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 0x1162deef0>






    Out[21]:





[<matplotlib.lines.Line2D at 0x11b4f2278>]



In [17]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 260
location = pre + "/third_expectedEnergy/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,22), 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, zmin=-1, zmax=-1)
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 0x1162deef0>






    Out[17]:





[<matplotlib.lines.Line2D at 0x11b89e320>]



In [5]:

    
data = np.loadtxt(location2)



In [7]:

    
data.shape









    Out[7]:





(508, 4)



In [8]:

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



In [13]:

    
data[:,3].shape









    Out[13]:





(367,)



In [15]:

    
data[:,3].max()









    Out[15]:





176.0



In [16]:

    
data[:,3].min()









    Out[16]:





29.120000000000001



In [ ]: