notebook.community

Edit and run



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
from Bio.PDB.Polypeptide import one_to_three
from Bio.PDB.Polypeptide import three_to_one
from Bio.PDB.PDBParser import PDBParser
from pyCodeLib import *
from small_script.myFunctions import *
from collections import defaultdict
%matplotlib inline
# plt.rcParams['figure.figsize'] = (10,6.180)    #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2



In [3]:

    
plt.rcParams['figure.figsize'] = [16.18033, 10]    #golden ratio
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['figure.dpi'] = 100



In [4]:

    
pre = "/Users/weilu/Research/server/may_2019/multi_iter0/multiLetter/gammas/"
g = np.load(pre+ "filtered_gamma.npy")



In [6]:

    
g.shape









    Out[6]:





(10320,)



In [8]:



In [13]:

    
# direct_contact = np.zeros((16,20,20))
# protein_contact = np.zeros((16,20,20))
# water_contact = np.zeros((16,20,20))
# burial_gamma = np.zeros((4,3,20))

# c = 0
# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 direct_contact[4*ii+jj][i][j] = g[c]
#                 direct_contact[4*ii+jj][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 protein_contact[4*ii+jj][i][j] = g[c]
#                 protein_contact[4*ii+jj][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 water_contact[4*ii+jj][i][j] = g[c]
#                 water_contact[4*ii+jj][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#         for i in range(3):
#             for j in range(20):
#                 burial_gamma[ii][i][j] = g[c]
#                 c += 1



In [27]:

    
# direct_contact_2 = np.zeros((16,20,20))
# protein_contact_2 = np.zeros((16,20,20))
# water_contact_2 = np.zeros((16,20,20))
# burial_gamma = np.zeros((4,3,20))

# c = 0
# for ii in range(4):
#     for jj in range(ii, 4):
#         for i in range(20):
#             for j in range(i, 20):
#                 direct_contact_2[4*ii+jj][i][j] = g[c]
#                 direct_contact_2[4*ii+jj][j][i] = g[c]
#                 direct_contact_2[4*jj+ii][i][j] = g[c]
#                 direct_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(ii, 4):
#         for i in range(20):
#             for j in range(i, 20):
#                 protein_contact_2[4*ii+jj][i][j] = g[c]
#                 protein_contact_2[4*ii+jj][j][i] = g[c]
#                 protein_contact_2[4*jj+ii][i][j] = g[c]
#                 protein_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(ii, 4):
#         for i in range(20):
#             for j in range(i, 20):
#                 water_contact_2[4*ii+jj][i][j] = g[c]
#                 water_contact_2[4*ii+jj][j][i] = g[c]
#                 water_contact_2[4*jj+ii][i][j] = g[c]
#                 water_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#         for i in range(3):
#             for j in range(20):
#                 burial_gamma[ii][i][j] = g[c]
#                 c += 1



In [39]:

    
# # incorrect, should not use anymore.
# direct_contact_2 = np.zeros((16,20,20))
# protein_contact_2 = np.zeros((16,20,20))
# water_contact_2 = np.zeros((16,20,20))
# burial_gamma = np.zeros((4,3,20))

# c = 0
# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 direct_contact_2[4*ii+jj][i][j] = g[c]
#                 direct_contact_2[4*ii+jj][j][i] = g[c]
#                 direct_contact_2[4*jj+ii][i][j] = g[c]
#                 direct_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 protein_contact_2[4*ii+jj][i][j] = g[c]
#                 protein_contact_2[4*ii+jj][j][i] = g[c]
#                 protein_contact_2[4*jj+ii][i][j] = g[c]
#                 protein_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#     for jj in range(4):
#         for i in range(20):
#             for j in range(i, 20):
#                 water_contact_2[4*ii+jj][i][j] = g[c]
#                 water_contact_2[4*ii+jj][j][i] = g[c]
#                 water_contact_2[4*jj+ii][i][j] = g[c]
#                 water_contact_2[4*jj+ii][j][i] = g[c]
#                 c += 1

# for ii in range(4):
#         for i in range(3):
#             for j in range(20):
#                 burial_gamma[ii][i][j] = g[c]
#                 c += 1



In [65]:

    
pre = "/Users/weilu/Research/server/may_2019/multi_iter0/multiLetter_symmetric/gammas/"
g = np.load(pre+ "filtered_gamma.npy")



In [113]:

    
direct_contact = np.zeros((80,80))
protein_contact = np.zeros((80,80))
water_contact = np.zeros((80,80))
# burial_gamma = np.zeros((4,3,20))
burial_gamma = np.zeros((3,80))
c = 0
for i in range(80):
    for j in range(i, 80):
        direct_contact[i][j] = g[c]
        direct_contact[j][i] = g[c]
        c += 1


for i in range(80):
    for j in range(i, 80):
        protein_contact[i][j] = g[c]
        protein_contact[j][i] = g[c]
        c += 1


for i in range(80):
    for j in range(i, 80):
        water_contact[i][j] = g[c]
        water_contact[j][i] = g[c]
        c += 1

# not used in the future
for ii in range(4):
        for i in range(3):
            for j in range(20):
                burial_gamma[i][ii*20+j] = g[c]
                c += 1



In [84]:

    
21%20









    Out[84]:





1



In [88]:

    
40//20









    Out[88]:





2



In [95]:

    
from collections import defaultdict



In [98]:

    
c = 0
dct = defaultdict(list)
for i in range(80):
    for j in range(i, 80):
        res1_neighbor_type = i // 20
        res2_neighbor_type = j // 20
        res1 = i % 20
        res2 = j % 20

        dct[(res1_neighbor_type,res2_neighbor_type)].append(g[c])
        c += 1
for i in range(80):
    for j in range(i, 80):
        res1_neighbor_type = i // 20
        res2_neighbor_type = j // 20
        res1 = i % 20
        res2 = j % 20
        dct[(res1_neighbor_type,res2_neighbor_type)].append(g[c])
        c += 1
for i in range(80):
    for j in range(i, 80):
        res1_neighbor_type = i // 20
        res2_neighbor_type = j // 20
        res1 = i % 20
        res2 = j % 20
        dct[(res1_neighbor_type,res2_neighbor_type)].append(g[c])
        c += 1
# for ii in range(4):
#         for i in range(3):
#             for j in range(20):
#                 burial_gamma[i][ii*20+j] = g[c]
#                 c += 1



In [100]:

    
np.savetxt("/Users/weilu/Research/server/may_2019/openMM_multiLetter/gammas/ni_0_nj_0", dct[(0,0)])



In [102]:

    
for i in range(4):
    for j in range(i, 4):
        np.savetxt(f"/Users/weilu/Research/server/may_2019/openMM_multiLetter/gammas/ni_{i}_nj_{j}", dct[(i,j)])



In [103]:

    
pwd









    Out[103]:





'/Users/weilu/opt/notebook/Optimization'



In [104]:

    
c = 0
dct = defaultdict(list)
for i in range(80):
    for j in range(i, 80):
        res1_neighbor_type = i // 20
        res2_neighbor_type = j // 20
        res1 = i % 20
        res2 = j % 20

        dct[(res1_neighbor_type,res2_neighbor_type)].append(g[c])
        c += 1



In [107]:

    
len(dct[(0,1)])









    Out[107]:





400



In [114]:

    
dct = defaultdict(list)
for ii in range(4):
    for jj in range(ii, 4):
        for i in range(20):
            for j in range(i, 20):
                tmp = direct_contact[20*ii+i][20*jj+j]
                dct[(ii,jj)].append(tmp)
for ii in range(4):
    for jj in range(ii, 4):
        for i in range(20):
            for j in range(i, 20):
                tmp = protein_contact[20*ii+i][20*jj+j]
                dct[(ii,jj)].append(tmp)
for ii in range(4):
    for jj in range(ii, 4):
        for i in range(20):
            for j in range(i, 20):
                tmp = water_contact[20*ii+i][20*jj+j]
                dct[(ii,jj)].append(tmp)



In [116]:

    
for i in range(4):
    for j in range(i, 4):
        np.savetxt(f"/Users/weilu/Research/server/may_2019/openMM_multiLetter/gammas/correct_ni_{i}_nj_{j}", dct[(i,j)])



In [115]:

    
len(dct[(0,1)])









    Out[115]:





630



In [93]:

    
len(ni0_nj0)









    Out[93]:





210



In [94]:

    
plot_contact_well(ni0_nj0, inferBound=True, invert_sign=False)



In [83]:

    
plot_contact_well(g[:210], inferBound=True, invert_sign=False)



In [ ]:

    
direct_contact



In [78]:

    
burial_gamma[0]









    Out[78]:





array([ 0.36879388, -0.09751481, -0.20870196, -0.496142  , -0.00829436,
       -0.120054  , -0.26308542, -0.28426693,  0.14372142,  0.0123174 ,
       -0.04423901, -0.18026432, -0.200955  , -0.08123462, -0.29402321,
       -0.11284752,  0.02640513, -0.13191867, -0.21287602, -0.05340107,
       -0.29328326, -0.76270868, -0.77889434, -1.04303934, -0.19901381,
       -0.83369622, -1.03053435, -0.45400601, -0.33622764, -0.23641592,
       -0.43640123, -1.04979994, -0.52401767, -0.33042205, -0.86388863,
       -0.73243846, -0.54927641, -0.43845696, -0.55158207, -0.31474666,
        0.00589466, -0.4971077 , -0.53475314, -0.67368371, -0.2212954 ,
       -0.60934034, -0.85487773, -0.40923972, -0.17354116, -0.27760054,
       -0.42051743, -0.72739378, -0.49069792, -0.39657133, -0.27420799,
       -0.36072062, -0.25246973, -0.64098155, -0.60981448, -0.35291224,
       -0.32663561, -0.60409931, -0.70739314, -0.89904132, -0.30509155,
       -0.77712105, -1.0463444 , -0.74418751, -0.36635213, -0.42738952,
       -0.58602457, -0.9098682 , -0.6605612 , -0.52251148, -0.70056253,
       -0.59787357, -0.44345416, -0.73458332, -0.71487795, -0.51418723])



In [79]:

    
bb[:,0,:]









    Out[79]:





array([[ 0.36879388, -0.09751481, -0.20870196, -0.496142  , -0.00829436,
        -0.120054  , -0.26308542, -0.28426693,  0.14372142,  0.0123174 ,
        -0.04423901, -0.18026432, -0.200955  , -0.08123462, -0.29402321,
        -0.11284752,  0.02640513, -0.13191867, -0.21287602, -0.05340107],
       [-0.29328326, -0.76270868, -0.77889434, -1.04303934, -0.19901381,
        -0.83369622, -1.03053435, -0.45400601, -0.33622764, -0.23641592,
        -0.43640123, -1.04979994, -0.52401767, -0.33042205, -0.86388863,
        -0.73243846, -0.54927641, -0.43845696, -0.55158207, -0.31474666],
       [ 0.00589466, -0.4971077 , -0.53475314, -0.67368371, -0.2212954 ,
        -0.60934034, -0.85487773, -0.40923972, -0.17354116, -0.27760054,
        -0.42051743, -0.72739378, -0.49069792, -0.39657133, -0.27420799,
        -0.36072062, -0.25246973, -0.64098155, -0.60981448, -0.35291224],
       [-0.32663561, -0.60409931, -0.70739314, -0.89904132, -0.30509155,
        -0.77712105, -1.0463444 , -0.74418751, -0.36635213, -0.42738952,
        -0.58602457, -0.9098682 , -0.6605612 , -0.52251148, -0.70056253,
        -0.59787357, -0.44345416, -0.73458332, -0.71487795, -0.51418723]])



In [74]:

    
bb = burial_gamma



In [67]:

    
c









    Out[67]:





9960



In [68]:

    
g.shape









    Out[68]:





(9960,)



In [80]:

    
pre = "/Users/weilu/Research/server/may_2019/openMM_multiLetter/symmetric/"
np.save(pre+"/direct.npy", direct_contact)
np.save(pre+"/protein.npy", protein_contact)
np.save(pre+"/water.npy", water_contact)
np.save(pre+"/burial.npy", burial_gamma)



In [70]:

    
sum(direct_contact-direct_contact.T)









    Out[70]:





array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])



In [71]:

    
sum(water_contact-water_contact.T)









    Out[71]:





array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])



In [72]:

    
sum(protein_contact-protein_contact.T)









    Out[72]:





array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])



In [ ]:



In [40]:

    
a = (water_contact-water_contact_2)



In [41]:

    
a.shape









    Out[41]:





(16, 20, 20)



In [42]:

    
a.sum(axis=1).sum(axis=1)









    Out[42]:





array([ 0.        , 10.35640198, 57.60409142, 57.58835188,  0.        ,
        0.        , 12.38775027, -2.57383682,  0.        ,  0.        ,
        0.        ,  1.24468822,  0.        ,  0.        ,  0.        ,
        0.        ])



In [43]:

    
pre = "/Users/weilu/Research/server/may_2019/openMM_multiLetter/expand_contact/"
np.save(pre+"/direct.npy", direct_contact_2)
np.save(pre+"/protein.npy", protein_contact_2)
np.save(pre+"/water.npy", water_contact_2)
np.save(pre+"/burial.npy", burial_gamma)



In [21]:

    
pre = "/Users/weilu/Research/server/may_2019/openMM_multiLetter/"
np.save(pre+"/direct.npy", direct_contact)
np.save(pre+"/protein.npy", protein_contact)
np.save(pre+"/water.npy", water_contact)
np.save(pre+"/burial.npy", burial_gamma)



In [23]:

    
np.load(pre+"direct.npy").shape









    Out[23]:





(16, 20, 20)



In [26]:

    
np.load(pre+"burial.npy").shape









    Out[26]:





(4, 3, 20)



In [3]:

    
data = np.loadtxt("/Users/weilu/Research/server/may_2019/rewrite_phis_computation_reading/phis/proteins_name_list_phi_pairwise_contact_well4.5_6.5_5.0_10phi_density_mediated_contact_well6.5_9.5_5.0_10_2.6_7.0phi_burial_well4.0_phi_decoy_all_summary.txt")



In [4]:

    
data.shape









    Out[4]:





(468000, 690)



In [5]:

    
d2 = np.sum(data, axis=0)



In [8]:

    
plt.plot(d2)
plt.yscale("log")



In [48]:

    
folder = "/Users/weilu/Research/server/may_2019/multi_iter0/multiLetter_symmetric"



In [44]:

    
i = 0
location = f"{folder}/proteins_name_list/proteins_name_list_{i}.txt"
with open(location, "r") as f:
    a = f.readlines()
size = len(a)
# print(size)
pre = f"{folder}/sub_gamma"
base = pre + f"/proteins_name_list_{i}_phi_pairwise_contact_multiLetter_well4.5_6.5_5.0_10phi_density_mediated_contact_multiLetter_well6.5_9.5_5.0_10_2.6_7.0phi_burial_multiLetter_well4.0"
a_name = base + "_A"
a_prime_name = base + "_A_prime"
std_half_b_name = base + "_std_half_B"
half_b = base + "_half_B"
other_half_b = base + "_other_half_B"

A = size * np.loadtxt(a_name)
A_prime = size * np.loadtxt(a_prime_name)
std_half_B = np.loadtxt(std_half_b_name)
half_B = np.loadtxt(half_b)
other_half_B = np.loadtxt(other_half_b)



In [49]:

    
count = 1
for i in range(1, 362):
    location = f"{folder}/proteins_name_list/proteins_name_list_{i}.txt"
    with open(location, "r") as f:
        a = f.readlines()
    size = len(a)
    # print(size)
    pre = f"{folder}/sub_gamma"
    base = pre + f"/proteins_name_list_{i}_phi_pairwise_contact_multiLetter_well4.5_6.5_5.0_10phi_density_mediated_contact_multiLetter_well6.5_9.5_5.0_10_2.6_7.0phi_burial_multiLetter_well4.0"
    a_name = base + "_A"
    a_prime_name = base + "_A_prime"
    std_half_b_name = base + "_std_half_B"
    half_b = base + "_half_B"
    other_half_b = base + "_other_half_B"
    try:
        std_half_B = np.loadtxt(std_half_b_name)
        A += size * np.loadtxt(a_name)
        A_prime += size * np.loadtxt(a_prime_name)
        half_B += np.loadtxt(half_b)
        other_half_B += np.loadtxt(other_half_b)
        count += 1
        print(i, "done")
    except:
        print(i, "not found")









    



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In [50]:

    
count









    Out[50]:





350



In [51]:

    
folder = "/Users/weilu/Research/server/may_2019/multi_iter0/multiLetter_symmetric"
pre = f"{folder}/gammas/"
n = count*5
A /= n
A_prime /= n
std_half_B /= n
half_B /= n
other_half_B /= n

np.save(pre+ "A", A)
np.save(pre+ "A_prime", A_prime)
np.save(pre+ "std_half_B", std_half_B)
np.save(pre+ "half_B", half_B)
np.save(pre+ "other_half_B", other_half_B)



In [52]:

    
B = half_B - other_half_B
gamma = np.dot(np.linalg.pinv(B), A)



In [53]:

    
gamma









    Out[53]:





array([ 0.47007102,  0.03780206,  0.44285095, ..., -1.34694915,
       -1.21084701, -1.94769826])



In [54]:

    
lamb, P = np.linalg.eig(B)
lamb, P = sort_eigenvalues_and_eigenvectors(lamb, P)



In [55]:

    
plt.plot(lamb)
plt.yscale("log")



In [56]:

    
plt.plot(lamb[8000:])
plt.yscale("log")



In [57]:

    
cutoff_mode = 9500
filtered_lamb = np.copy(lamb)
filtered_B_inv, filtered_lamb, P = get_filtered_B_inv_lambda_and_P(
    filtered_lamb, cutoff_mode, P)

filtered_gamma = np.dot(filtered_B_inv, A)



In [58]:

    
filtered_gamma









    Out[58]:





array([ 0.48526619,  0.04773866,  0.45574379, ..., -0.89607113,
       -0.77330225, -1.51224099])



In [59]:

    
plt.plot(gamma)
plt.plot(filtered_gamma)









    Out[59]:





[<matplotlib.lines.Line2D at 0x1add3350f0>]



In [60]:

    
plt.plot(gamma-filtered_gamma)









    Out[60]:





[<matplotlib.lines.Line2D at 0x1add0c9518>]



In [61]:

    
pre = f"{folder}/gammas/"
np.save(pre+ "filtered_gamma", filtered_gamma)



In [62]:

    
A.dot(filtered_gamma)









    Out[62]:





48.17029503500869



In [64]:

    
A.dot(filtered_gamma)/(filtered_gamma.dot(B).dot(filtered_gamma))**0.5









    Out[64]:





6.946067912255422



In [63]:

    
A_prime.dot(filtered_gamma)









    Out[63]:





-107.73767675764009

not too bad. each phi has at least 10^3 entry from 4.7e5.



In [ ]:



In [ ]:

    
len(training_set)



In [ ]:

    
total_phis



In [ ]:

    
num_decoys