def smoothen(self, width):
"""smoothen the data with a window function average
The window width is given in fraction of the number of datapoints"""
if width >= 0.0 and width <= 1.0:
ww = int(width*len(self.data[0]))
else:
print("ERROR: window width must be in [0.0,1.0]")
sys.exit(1)
for i, d in enumerate(self.data):
self.data[i] = ra.running_average(d, ww, win="gaussian")
def main():
# filenames = ["/home/oliver/SiSc/Courses/Thesis/iGRASP/liPASe/jubiofs/iGRASP/Complex/folded/citrate_bound/structure1/prod01.pdb",
# "/home/oliver/SiSc/Courses/Thesis/iGRASP/liPASe/jubiofs/iGRASP/Complex/folded/citrate_bound/structure2/prod01.pdb",
# "/home/oliver/SiSc/Courses/Thesis/iGRASP/liPASe/jubiofs/iGRASP/Complex/folded/citrate_bound/structure4/prod01.pdb",
# "/home/oliver/SiSc/Courses/Thesis/iGRASP/liPASe/jubiofs/iGRASP/Complex/folded/citrate_bound/structure6/prod01.pdb"]
filenames = ["/home/oliver/BigData/ThinkPad/citrate_bound/structure1/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_bound/structure2/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_bound/structure4/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_bound/structure6/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_free/structure1/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_free/structure2/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_free/structure4/prod01.pdb",
"/home/oliver/BigData/ThinkPad/citrate_free/structure6/prod01.pdb"]
# filenames = ["/home/oliver/BigData/prod01.pdb"]
pickleFileName = "complex_traj_collective_coords.pickle"
if os.path.isfile(pickleFileName):
start = time.time()
pickleFile = open(pickleFileName, 'r')
MDs = pickle.load(pickleFile)
pickleFile.close()
print "Read MDs with {} frames from pickled file in {:.2f} sec.".format(len(MDs[0].CitA_r), time.time()-start)
else:
MDs = []
for filename in filenames:
start = time.time()
MDs.append(Trajectory(filename))
MDs[-1].get_collective_coordinates()
MDs[-1].sm_CitA_r = 180.0/math.pi*ra.running_average(MDs[-1].CitA_r , int(0.02*len(MDs[-1].CitA_r)), 'gaussian')
MDs[-1].sm_CitA_theta = 180.0/math.pi*ra.running_average(MDs[-1].CitA_theta, int(0.02*len(MDs[-1].CitA_theta)), 'gaussian')
MDs[-1].sm_CitA_phi = 180.0/math.pi*ra.running_average(MDs[-1].CitA_phi , int(0.02*len(MDs[-1].CitA_phi)), 'gaussian')
MDs[-1].sm_Lip_r = 180.0/math.pi*ra.running_average(MDs[-1].CitA_r , int(0.02*len(MDs[-1].CitA_r)), 'gaussian')
MDs[-1].sm_Lip_theta = 180.0/math.pi*ra.running_average(MDs[-1].CitA_theta, int(0.02*len(MDs[-1].CitA_theta)), 'gaussian')
MDs[-1].sm_Lip_phi = 180.0/math.pi*ra.running_average(MDs[-1].CitA_phi , int(0.02*len(MDs[-1].CitA_phi)), 'gaussian')
MDs[-1].frames = []
print "Done with file {}\n in {:.2f} sec.".format(filename, time.time()-start)
pickleFile = open(pickleFileName, 'w')
pickle.dump(MDs, pickleFile)
pickleFile.close()
a = 10
for i in range(len(MDs)):
print "MDs[{}]".format(i)
print "\tCitA_theta = [",
for n in MDs[i].CitA_theta[:a]:
print "{:.2f}, ".format(n),
print "{:.2f}]".format(MDs[i].CitA_theta[a])
# i = 0
# plt.plot(MDs[i ].CitA_theta, MDs[i ].CitA_phi, 'r')
# plt.plot(MDs[i+4].CitA_theta, MDs[i+4].CitA_phi, 'b')
markerSize = 2;
plot = '2d'
if plot == '3d':
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(4):
ax.plot([0], [0], [0], 'o')
x = []; y = []; z = [];
for n in range(len(MDs[i].CitA_phi)):
# x.append(MDs[i].CitA_r[n] * math.sin(MDs[i].CitA_theta[n]) * math.cos(MDs[i].CitA_phi[n]) )
# y.append(MDs[i].CitA_r[n] * math.sin(MDs[i].CitA_theta[n]) * math.sin(MDs[i].CitA_phi[n]) )
# z.append(MDs[i].CitA_r[n] * math.cos(MDs[i].CitA_theta[n]) )
x.append(MDs[i].r[n][0])
y.append(MDs[i].r[n][1])
z.append(MDs[i].r[n][2])
ax.plot3D(x, y, z, '-')
i = i + 4
x = []; y = []; z = [];
for n in range(len(MDs[i].CitA_phi)):
# x.append(MDs[i].CitA_r[n] * math.sin(MDs[i].CitA_theta[n]) * math.cos(MDs[i].CitA_phi[n]) )
# y.append(MDs[i].CitA_r[n] * math.sin(MDs[i].CitA_theta[n]) * math.sin(MDs[i].CitA_phi[n]) )
# z.append(MDs[i].CitA_r[n] * math.cos(MDs[i].CitA_theta[n]) )
x.append(MDs[i].r[n][0])
y.append(MDs[i].r[n][1])
z.append(MDs[i].r[n][2])
ax.plot3D(x, y, z, '-')
xmax = max(abs(max(x)), abs(min(x)))
ymax = max(abs(max(y)), abs(min(y)))
zmax = max(abs(max(z)), abs(min(z)))
totalMax = max([xmax, ymax, zmax])
ax.set_xlim3d([-totalMax, totalMax])
ax.set_ylim3d([-totalMax, totalMax])
ax.set_zlim3d([-totalMax, totalMax])
plt.savefig('structure_{}_and_{}.png'.format(i-4, i), bbox_inches='tight')
plt.show()
elif plot == '2d':
max_theta = -1*float('inf')
min_theta = float('inf')
max_phi = -1*float('inf')
min_phi = float('inf')
for i in range(8):
theta = max(MDs[i].sm_CitA_theta)
phi = max(MDs[i].sm_CitA_phi)
if theta > max_theta:
max_theta = theta
if phi > max_phi:
max_phi = phi
theta = min(MDs[i].sm_CitA_theta)
phi = min(MDs[i].sm_CitA_phi)
if theta < min_theta:
min_theta = theta
if phi < min_phi:
min_phi = phi
theta_diff = max_theta - min_theta
phi_diff = max_phi - min_phi
for i in range(4):
plt.figure()
plt.plot(MDs[i ].sm_CitA_theta, MDs[i ].sm_CitA_phi, 'r', lw=1)
plt.plot(MDs[i+4].sm_CitA_theta, MDs[i+4].sm_CitA_phi, 'b', lw=1)
plt.plot(MDs[i ].sm_CitA_theta[0], MDs[i ].sm_CitA_phi[0], '^r', ms=15)
plt.plot(MDs[i+4].sm_CitA_theta[0], MDs[i+4].sm_CitA_phi[0], '^b', ms=15)
plt.title('structure {}'.format(i+1))
plt.xlabel('theta (degree)')
plt.ylabel('phi (degree)')
# plt.xlim([0, math.pi])
# plt.ylim([-math.pi, math.pi])
plt.xlim([-0.1*theta_diff+min_theta, 0.1*theta_diff+max_theta])
plt.ylim([-0.1*phi_diff +min_phi, 0.1*phi_diff +max_phi])
plt.legend(['citrate bound', 'citrate free'], loc='lower left')
plt.savefig('collecitve_coords_structure{}.png'.format(i+1), bbox_inches='tight')
plt.savefig('collecitve_coords_structure{}.pdf'.format(i+1), bbox_inches='tight')
plt.show()
filename = "testfile.txt"
# READ DATA
a = open(dir + filename, "r")
data = []
# CONVERT STRING IN DATA LIST TO FLOAT
with a as file:
for line in file:
for element in line.split():
data.append(float(element))
print data
# CALL FUNTION running_average TO CALCULATE RUNNING AVERAGE OF data
data_avg = []
n = 3
data_avg = running_average.running_average(data, n)
print data_avg
# PLOT THE DATA
plt.plot(data)
plt.xlabel('# of data')
plt.ylabel('data')
plt.show()
# PLOT THE DATA
plt.plot(data_avg)
plt.xlabel('# of data')
plt.ylabel('data running average')
plt.show()
