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 [2]:

    

data = np.loadtxt("/Users/weilu/Research/server/mar_2018/05_week/unfold_strengthen_h1_h2/force_2_mem_1.0_/simulation/0/rnative.dat")


    

In [3]:

    

data


    

    
Out[3]:





array([[  0.      ,   3.806121,   7.074538, ...,  26.010404,  29.297561,
         27.977447],
       [  3.806121,   0.      ,   3.805936, ...,  26.304308,  29.587635,
         28.318099],
       [  7.074538,   3.805936,   0.      , ...,  24.338368,  27.567935,
         26.395188],
       ..., 
       [ 26.010404,  26.304308,  24.338368, ...,   0.      ,   3.799961,
          5.562379],
       [ 29.297561,  29.587635,  27.567935, ...,   3.799961,   0.      ,
          3.788385],
       [ 27.977447,  28.318099,  26.395188, ...,   5.562379,   3.788385,
          0.      ]])

In [4]:

    

cutoff = 7
n = data.shape[0]
MAX_OFFSET = 6
remove_band = np.eye(n)
for i in range(1, MAX_OFFSET):
    remove_band += np.eye(n, k=i)
    remove_band += np.eye(n, k=-i)
data[remove_band==1] = np.max(data)


    

In [5]:

    

plt.imshow(data)


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x1a10aaae80>






    

In [7]:

    

data[data > 10] = 0


    

In [9]:

    

plt.imshow(data)


    

    
Out[9]:





<matplotlib.image.AxesImage at 0x1a15c16278>






    

In [14]:

    

a = (data < 7)[25:57]


    

In [15]:

    

a[:,25:57] = 0
plt.imshow(a)


    

    
Out[15]:





<matplotlib.image.AxesImage at 0x1a1593ea58>






    

In [16]:

    

a.sum()


    

    
Out[16]:





31

In [17]:

    

plt.imshow((data < cutoff)[25:57])


    

    
Out[17]:





<matplotlib.image.AxesImage at 0x1a15a364a8>






    

In [18]:

    

plt.imshow((data < cutoff)[:25])


    

    
Out[18]:





<matplotlib.image.AxesImage at 0x1a15ab6e80>






    

In [19]:

    

plt.imshow((data < cutoff)[:57,:57])


    

    
Out[19]:





<matplotlib.image.AxesImage at 0x1a15c18550>






    

In [56]:

    

(data < cutoff)[:25].sum()


    

    
Out[56]:





17

In [57]:

    

plt.imshow((data < cutoff)[160:])


    

    
Out[57]:





<matplotlib.image.AxesImage at 0x1a218c79e8>






    

In [58]:

    

(data < cutoff)[160:].sum()


    

    
Out[58]:





40

In [67]:

    

data = pd.read_table("/Users/weilu/opt/gremlin/protein/2xov/raptor.2xov.dat", skiprows=9, sep="\s+", names=["i","j","s", "ss","p"]).dropna()


    

In [65]:

    

181*181


    

    
Out[65]:





32761

In [75]:

    

data["i"] = data["i"].astype(int)
data["j"] = data["j"].astype(int)


    

In [96]:

    

# data.sort_values(by=["i","j"])


    

In [92]:

    

181*181/2


    

    
Out[92]:





16380.5

In [78]:

    

data.sort_values(by=["i","j"]).values[0]


    

    
Out[78]:





array([ 1.      ,  7.      ,  0.      ,  8.      ,  0.037144])

In [100]:

    




    

In [93]:

    

t = np.zeros((181,181))
for index, d in data.iterrows():
#     print(index)
    i = int(d["i"]) - 1
    j = int(d["j"]) - 1 
    p = d["p"]
#     print(i,j,p)
    t[i,j] = p
    t[j,i] = p


    

In [95]:

    

plt.imshow(t)


    

    
Out[95]:





<matplotlib.image.AxesImage at 0x1a21dd5198>






    

In [103]:

    

data = t
n = data.shape[0]
MAX_OFFSET = 6
remove_band = np.eye(n)
for i in range(1, MAX_OFFSET):
    remove_band += np.eye(n, k=i)
    remove_band += np.eye(n, k=-i)
data[remove_band==1] = np.min(data)


    

In [104]:

    

cutoff = 0.5
(data > cutoff)[:25].sum()


    

    
Out[104]:





45

In [105]:

    

(data > cutoff)[160:].sum()


    

    
Out[105]:





79

In [ ]: