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 [7]:
data = pd.read_feather("/Users/weilu/Research/server/apr_2018/sixth/rg_0.15_lipid_1.0_mem_1_go_0.8_long/rerun_3_18_Apr_205152.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}
dic = {"T0":280, "T1":300, "T2":325, "T3":350, "T4":375, "T5":400, "T6":450, "T7":500, "T8":550, "T9":600, "T10":650, "T11":700}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
rerun3 = 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[7]:
<seaborn.axisgrid.FacetGrid at 0x1a1fd4c470>

In [36]:
rerun3.query("Temp==300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x10c187518>

In [39]:
rerun3.columns


Out[39]:
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 [38]:
rerun3.query("Temp==300").plot.hexbin("DisReal", "AMH-Go", cmap="seismic", sharex=False)


Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a15a82e10>

In [37]:
rerun3.query("Temp==300").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)


Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a4c18d9b0>

In [15]:
rerun3.plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x10c60fe48>

In [40]:
pre = "/Users/weilu/Research/server/apr_2018/"
temp = 300
location = pre + "02_week/sixth_long/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(25,13), save=False, xlabel="z_H6", ylabel="Qw", zmax=40)
# 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=420)
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 0x108f86e48>
Out[40]:
[<matplotlib.lines.Line2D at 0x1a1b136be0>]

In [28]:
pre = "/Users/weilu/Research/server/may_2018/"
temp = 300
location = pre + "/sixth_long/_280-350/2d_z_qw/super_quick_force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(25,13), save=False, xlabel="z_H6", ylabel="Qw", zmax=40)
# 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=420)
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 0x108f86e48>
Out[28]:
[<matplotlib.lines.Line2D at 0x1a4c1dbb38>]

I probably should work with 1d distance free Energy.


In [30]:
pre = "/Users/weilu/Research/server/may_2018/"
temp = 300
location = pre + "/sixth_long/_280-350/2d_z_qw/super_quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 10), end=(25,24), save=False, xlabel="z_H6", ylabel="Qw", zmax=40)
# 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=220)
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 0x108f86e48>
Out[30]:
[<matplotlib.lines.Line2D at 0x1a1b6ec860>]

In [29]:
pre = "/Users/weilu/Research/server/may_2018/"
temp = 300
location = pre + "/sixth_long/_280-350/2d_z_qw/super_quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 10), end=(25,24), save=False, xlabel="z_H6", ylabel="Qw", zmax=40)
# 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=220)
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 0x108f86e48>
Out[29]:
[<matplotlib.lines.Line2D at 0x1a1905f6d8>]