CppStressProfile/plotresults.py at main · PhysFoley/CppStressProfile · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
import numpy as np
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from scipy.integrate import simps

# put all the input files here if you broke the trajectory up
filenames = ["stress_profile.dat"] # e.g. ["file1.dat","file2.dat","file3.dat"]

# same as above but for standard error files
errfiles = ["profile_eom.dat"]

zvals = []
lat_profile = []
Szz_profile = []
n_files = len(filenames)

# read in the first file and populate the lists
with open(filenames[0],"r") as infile:
    for line in infile:
        t = line.strip().split(" ")
        if len(t) > 0:
            if t[0] == "z=":
                zvals.append(float(t[1]))
                lat_profile.append([])
                Szz_profile.append(0.0)
            else:
                t = [float(x) for x in t]
                lat_profile[-1].append( (0.5*(t[0]+t[1]) - t[2])/float(n_files) )
                Szz_profile[-1] += t[2]/float(n_files)

# now read in the rest of the data files and add their contributions
for fname in filenames[1:]:
    with open(fname,"r") as infile:
        z_index = -1
        kbn_index = 0
        for line in infile:
            t = line.strip().split(" ")
            if len(t) > 0:
                if t[0] == "z=":
                    z_index += 1
                    kbn_index = 0
                else:
                    t = [float(x) for x in t]
                    lat_profile[z_index][kbn_index] += (0.5*(t[0]+t[1]) - t[2])/float(n_files)
                    Szz_profile[z_index] += t[2]/float(n_files)
                    kbn_index += 1

lat_profile = np.array(lat_profile).transpose()
Szz_profile = np.array(Szz_profile)

# total lateral stress profile is the sum of the three contributions
# (kinetic, bonded, non-bonded)
tot_profile = lat_profile.sum(axis=0)

# now, read in error bar files
profile_err = [np.zeros(len(zvals)) for i in range(n_files)]
z_err = [np.zeros(len(zvals)) for i in range(n_files)]

for i in range(n_files):
    with open(errfiles[i],"r") as file:
        z_index = -1
        for line in file:
            t = line.strip().split(" ")
            if len(t) > 0:
                if t[0] == "z=":
                    z_index += 1
                else:
                    t = [float(x) for x in t]
                    profile_err[i][z_index] = np.sqrt( 0.25*(t[0]**2 + t[1]**2) + t[2]**2 )
                    z_err[i][z_index] = t[2]

tot_profile_err = []
tot_z_err = []
# this loop calculates the complete error bars based on all input files
for i in range(len(zvals)):
    p_sumsq = 0.0
    z_sumsq = 0.0
    for j in range(n_files):
        p_sumsq += profile_err[j][i]**2
        z_sumsq += z_err[j][i]**2
    tot_profile_err.append(np.sqrt(p_sumsq)/n_files)
    tot_z_err.append(np.sqrt(z_sumsq)/n_files)

tot_profile_err = np.array(tot_profile_err)
tot_z_err = np.array(tot_z_err)

half = int(np.floor(len(zvals)/2))

# Make some nice smooth cubic splines
p_cs = CubicSpline(zvals, tot_profile)
p_pe_cs = CubicSpline(zvals, tot_profile + tot_profile_err)
p_me_cs = CubicSpline(zvals, tot_profile - tot_profile_err)

z_cs = CubicSpline(zvals, Szz_profile)
z_pe_cs = CubicSpline(zvals, Szz_profile + tot_z_err)
z_me_cs = CubicSpline(zvals, Szz_profile - tot_z_err)

# integrate the stress profile for upper and lower leaflets using simpson's rule
lower_tension = simps(tot_profile[:half+1], zvals[:half+1])
upper_tension = simps(tot_profile[half:], zvals[half:])
full_tension = simps(tot_profile, zvals)

print("Upper leaflet tension: {}".format(upper_tension))
print("Lower leaflet tension: {}".format(lower_tension))
print("Total membrane tension: {}".format(full_tension))

plt.axhline(linewidth=1, color="black")
plt.axvline(linewidth=1, linestyle=":", color="black")

shift = zvals[half]

# z values for plotting the spline
zz = np.linspace(zvals[0], zvals[-1], 1000)

# plot the main splines
plt.plot(zz-shift, p_cs(zz) , zz-shift, z_cs(zz) )

#shade in the error regions
plt.fill_between(zz-shift, p_me_cs(zz), p_pe_cs(zz), alpha=0.5)
plt.fill_between(zz-shift, z_me_cs(zz), z_pe_cs(zz), alpha=0.5)

plt.xlabel(r"$z$ (from mid-plane) $[\sigma]$")
plt.ylabel(r"Lateral Stress $\partial\Sigma/\partial z$ $[\varepsilon/\sigma^3]$")

# plot the raw data
#plt.errorbar(zvals, tot_profile, yerr=tot_profile_err, linestyle="none")
#plt.errorbar(zvals, Szz_profile, yerr=tot_z_err, linestyle="none")

plt.show()