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ellipsoid.py
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ellipsoid.py
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from __future__ import division
import matplotlib
matplotlib.use('tkagg')
import matplotlib.pyplot as plt
import numpy as np
from numpy import linalg
import re
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering
import numpy as np
import scipy.cluster.hierarchy as sch
import math
import sys
__author__ = "Thu Nguyen"
__copyright__ = "Copyright (C) 2020 Thu Nguyen"
__license__ = "LGPL 2.1"
__version__ = "1.0"
class Ellipsoid:
def __init__(self): pass
def getMinElp(self, array_inp=None, err=0.01):
(N, dim) = np.shape(array_inp)
array_inp_trans = array_inp.T
dim = float(dim)
array_stack = np.vstack([np.copy(array_inp_trans), np.ones(N)])
array_stack_trans = array_stack.T
error = 1.0 + err
u = (1.0 / N) * np.ones(N)
while error > err:
v = np.dot(array_stack, np.dot(np.diag(u), array_stack_trans))
diag_vector = np.diag(np.dot(array_stack_trans , np.dot(linalg.inv(v), array_stack)))
max_ind = np.argmax(diag_vector)
max_val = diag_vector[max_ind]
step_size = (max_val - dim - 1.0) / ((dim + 1.0) * (max_val - 1.0))
new_u = (1.0 - step_size) * u
new_u[max_ind] += step_size
error = np.linalg.norm(new_u - u)
u = new_u
center_elp = np.dot(array_inp_trans, u)
center_mul = np.array([[a * b for b in center_elp] for a in center_elp])
A = linalg.inv(np.dot(array_inp_trans, np.dot(np.diag(u), array_inp)) - center_mul) / dim
U, s, rotation = linalg.svd(A)
r = 1.0/np.sqrt(s)
return (center_elp, r, rotation)
def plot(self, center_elp, r, rotation, ax, plotAxes=False, color='b', alpha=0.2):
u = np.linspace(0.0, 2.0 * np.pi, 100)
v = np.linspace(0.0, np.pi, 100)
x = r[0] * np.outer(np.cos(u), np.sin(v))
y = r[1] * np.outer(np.sin(u), np.sin(v))
z = r[2] * np.outer(np.ones_like(u), np.cos(v))
for i in range(len(x)):
for j in range(len(x)):
[x[i,j],y[i,j],z[i,j]] = np.dot([x[i,j],y[i,j],z[i,j]], rotation) + center_elp
if plotAxes:
axes = np.array([[r[0],0.0,0.0],
[0.0,r[1],0.0],
[0.0,0.0,r[2]]])
for i in range(len(axes)):
axes[i] = np.dot(axes[i], rotation)
for p in axes:
x_tmp = np.linspace(-p[0], p[0], 100) + center_elp[0]
y_tmp = np.linspace(-p[1], p[1], 100) + center_elp[1]
z_tmp = np.linspace(-p[2], p[2], 100) + center_elp[2]
ax.plot(x_tmp, y_tmp, z_tmp, color=color)
ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color=color, alpha=alpha)
def extract_coordinate(filename):
with open(filename) as file:
coor_list = []
lines = file.readlines()
for l in lines:
l = re.split('\s+', l)
coor_list.append(l[6:9])
return coor_list
def extract_cluster_files(cluster_out_file):
with open(cluster_out_file) as file:
cluster_dict = {}
lines = file.readlines()
for l in lines[1:len(lines)-4]:
l = re.split('\s+', l.strip())
cluster_nb = int(re.findall('[1-9][0-9]*', l[0])[0])
cluster_files = l[3:]
cluster_dict.update({cluster_nb: cluster_files})
return cluster_dict
def extract_matrix(matrix_file):
with open(matrix_file) as file:
matrix = []
lines = file.readlines()
for i in range(len(lines)):
l = re.split('\s+', lines[i].strip())
if(i%3==0):
matrix.append(float(l[1]))
elif (i%3==1):
matrix.append(float(l[2]))
else:
matrix.append(float(l[3]))
return matrix
def get_cc_matrix(cc_file):
cc_matrix=[]
with open(cc_file) as f:
lines = f.readlines()
for line in lines[1:]:
line = re.split('\s+',line.strip())
cc = float(line[2])
distance = math.sqrt(1-cc)
cc_matrix.append(distance)
return cc_matrix
if __name__ == '__main__':
#argv[1]: cctable.dat from Kamo outputs
#argv[2]: file contains coordinates of a specific residue of all structures
#argv[3]: CLUSTERS.txt from Kamo outputs
#argv[4]: height cutoff
matrix = get_cc_matrix(sys.argv[1])
z = sch.linkage(matrix, method='ward')
rs = extract_coordinate(sys.argv[2])
fig_dendro = plt.figure(figsize=(80, 50))
plt.rc('ytick', labelsize=20)
plt.ylabel('Height', fontsize=20)
sch.set_link_color_palette(['g', 'r', 'c', 'm', 'y'])
d = sch.dendrogram(z, color_threshold=float(sys.argv[4]))
color_cluster=d['color_list']
ivl = d['ivl']
print(ivl)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(len(rs)):
rs[i][0] = float(rs[i][0])
rs[i][1] = float(rs[i][1])
rs[i][2] = float(rs[i][2])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
kmeans = KMeans(n_clusters = 5)
kmeans.fit(rs)
range_0, range_1, range_2, range_3, range_4 = 1, 1, 1, 1, 1
for i in range (0, len(color_cluster)):
if(color_cluster[i]== 'g'):
range_0 += 1
elif(color_cluster[i]=='r'):
range_1 += 1
elif(color_cluster[i]=='c'):
range_2 += 1
elif(color_cluster[i]=='m'):
range_3 += 1
elif(color_cluster[i]=='y'):
range_4 += 1
else:
continue
s0=[int(ivl[i])+1 for i in range(0,range_0)]
s1=[int(ivl[i])+1 for i in range(range_0,range_0+range_1)]
s2=[int(ivl[i])+1 for i in range(range_0+range_1,range_0+range_1+range_2)]
s3=[int(ivl[i])+1 for i in range(range_0+range_1+range_2,range_0+range_1+range_2+range_3)]
s4=[int(ivl[i])+1 for i in range(range_0+range_1+range_2+range_3,range_0+range_1+range_2+range_3+range_4)]
s0_cluster=[]
s1_cluster=[]
s2_cluster=[]
s3_cluster=[]
s4_cluster=[]
cluster_dict = extract_cluster_files(sys.argv[3])
clusters = cluster_dict.keys()
for c0 in s0:
for cluster in clusters:
if str(c0) in cluster_dict[cluster] and (cluster not in s0_cluster):
s0_cluster.append(cluster)
for c1 in s1:
for cluster in clusters:
if str(c1) in cluster_dict[cluster] and (cluster not in s1_cluster):
s1_cluster.append(cluster)
for c2 in s2:
for cluster in clusters:
if str(c2) in cluster_dict[cluster] and (cluster not in s2_cluster):
s2_cluster.append(cluster)
for c3 in s3:
for cluster in clusters:
if str(c3) in cluster_dict[cluster] and (cluster not in s3_cluster):
s3_cluster.append(cluster)
for c4 in s4:
for cluster in clusters:
if str(c4) in cluster_dict[cluster] and (cluster not in s4_cluster):
s4_cluster.append(cluster)
s0_centroid=[]
s1_centroid=[]
s2_centroid=[]
s3_centroid=[]
s4_centroid=[]
for s0 in s0_cluster:
s0_centroid.append([rs[s0-1][0], rs[s0-1][1], rs[s0-1][2]])
for s1 in s1_cluster:
s1_centroid.append([rs[s1-1][0], rs[s1-1][1], rs[s1-1][2]])
for s2 in s2_cluster:
s2_centroid.append([rs[s2-1][0], rs[s2-1][1], rs[s2-1][2]])
for s3 in s3_cluster:
s3_centroid.append([rs[s3-1][0], rs[s3-1][1], rs[s3-1][2]])
for s4 in s4_cluster:
s4_centroid.append([rs[s4-1][0], rs[s4-1][1], rs[s4-1][2]])
s0_centroid = np.asarray(s0_centroid)
s1_centroid = np.asarray(s1_centroid)
s2_centroid = np.asarray(s2_centroid)
s3_centroid = np.asarray(s3_centroid)
s4_centroid = np.asarray(s4_centroid)
ellipsoid = Ellipsoid()
(center_0, radii_0, rotation_0) = ellipsoid.getMinElp(s0_centroid)
(center_1, radii_1, rotation_1) = ellipsoid.getMinElp(s1_centroid)
(center_2, radii_2, rotation_2) = ellipsoid.getMinElp(s2_centroid)
(center_3, radii_3, rotation_3) = ellipsoid.getMinElp(s3_centroid)
(center_4, radii_4, rotation_4) = ellipsoid.getMinElp(s4_centroid)
ax.scatter(s0_centroid[:,0], s0_centroid[:,1], s0_centroid[:,2], color='g', s=10)
ax.scatter(s1_centroid[:,0], s1_centroid[:,1], s1_centroid[:,2], color='r', s=10)
ax.scatter(s2_centroid[:,0], s2_centroid[:,1], s2_centroid[:,2], color='c', s=10)
ax.scatter(s3_centroid[:,0], s3_centroid[:,1], s3_centroid[:,2], color='m', s=10)
ax.scatter(s4_centroid[:,0], s4_centroid[:,1], s4_centroid[:,2], color='y', s=10)
ellipsoid.plot(center_0, radii_0, rotation_0, ax, plotAxes=True, color='g')
ellipsoid.plot(center_1, radii_1, rotation_1, ax, plotAxes=True, color='r')
ellipsoid.plot(center_2, radii_2, rotation_2, ax, plotAxes=True, color='c')
ellipsoid.plot(center_3, radii_3, rotation_3, ax, plotAxes=True, color='m')
ellipsoid.plot(center_4, radii_4, rotation_4, ax, plotAxes=True, color='y')
plt.title("C-alpha coordinate variances at residue 146")
plt.show()
#plt.savefig(sys.argv[4])
#plt.close(fig)