def plotforces(pKa):
R = 8.3145 * 10**-3 # "kJ * mol⁻1 * K^-1"
T = 300
lambda_i = load.Col("lambda_dwp.dat", 1, 942, 2062)
V_bias = load.Col("lambda_dwp.dat", 0, 942, 2062)
pH = universe.get('ph_pH')
V_pH = []
for i in lambda_i:
V_pH.append(R * T * np.log(10) * (pKa - pH) * i)
V_bias = np.gradient(V_bias) # Take derivatives.
V_pH = np.gradient(V_pH)
V_comb = [V_bias[i] + V_pH[i] for i in range(0, len(lambda_i))]
plt.plot(lambda_i, V_bias, color="b", linestyle='--', label="$F_{bias}}$")
plt.plot(lambda_i, V_pH, color="b", linestyle=':', label="$F_{pH}$")
plt.plot(lambda_i, V_comb, color="b", label="$F_{combined}$")
plt.ylim(-0.1, 0.1)
plt.xlabel(r"$\lambda$-coordinate")
plt.ylabel("Force")
plt.grid()
plt.legend()
plt.show()
def plotpotentials(pKa):
R = 8.3145 * 10**-3 # "kJ * mol⁻1 * K^-1"
T = 300
lambda_i = load.Col("lambda_dwp.dat", 1, 942, 2062)
V_bias = load.Col("lambda_dwp.dat", 0, 942, 2062)
pH = universe.get('ph_pH')
V_pH = []
for i in lambda_i:
V_pH.append(R * T * np.log(10) * (pKa - pH) * i)
V_comb = []
for i in range(0, len(lambda_i)):
V_comb.append(V_bias[i] + V_pH[i])
plt.plot(lambda_i, V_bias, color="b", linestyle='--', label="$V_{bias}}$")
plt.plot(lambda_i, V_pH, color="b", linestyle=':', label="$V_{pH}$")
plt.plot(lambda_i, V_comb, color="b", label="$V_{combined}$")
plt.xlabel(r"$\lambda$-coordinate")
plt.ylabel(r"$V$ (kJ/mol)")
plt.grid()
plt.legend()
plt.show()
def plotlambda(plotBUF=False):
resnameList = [] # Get the names and such of all the ASPs and GLUs.
residList = []
for residue in universe.get('d_residues'):
if residue.d_resname in ["ASP", "ASPH", "ASPT", "GLU", "GLUH", "GLUT"]:
resnameList.append(residue.d_resname)
residList.append(residue.d_resid)
plt.figure()
for idx in range(1, len(resnameList) + 1):
t = load.Col("lambda_{0}.dat".format(idx), 1)
x = load.Col("lambda_{0}.dat".format(idx), 2)
plt.plot(t,
x,
label="%s-%s" % (resnameList[idx - 1], residList[idx - 1]),
linewidth=0.5)
if (plotBUF):
t = load.Col("lambda_{0}.dat".format(len(resnameList) + 1), 1)
x = load.Col("lambda_{0}.dat".format(len(resnameList) + 1), 2)
plt.plot(t, x, label="Buffer", linewidth=0.5)
plt.xlabel("Time (ps)")
plt.ylabel(r"$\lambda$-coordinate")
plt.ylim(-0.1, 1.1)
plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt.legend()
plt.grid()
plt.show()
def titrate(lambdaFileName, cutoff=0.80):
lambda_proto = 0
lambda_deproto = 0
for x in load.Col(lambdaFileName, 2):
if (x > cutoff):
lambda_deproto += 1
if (x < 1 - cutoff):
lambda_proto += 1
fraction = float(lambda_deproto) / (lambda_proto + lambda_deproto)
return fraction
def plothistogram(fname, bins=200):
from scipy.stats import gaussian_kde
data = load.Col(fname, 2)
plt.hist(data, density=True, bins=bins)
density = gaussian_kde(data)
xs = np.linspace(-0.1, 1.1, bins)
density.covariance_factor = lambda: .25
density._compute_covariance()
plt.plot(xs, density(xs), label="10 ns test")
plt.xlabel(r"$\lambda$-coordinate")
# plt.axis([-0.1, 1.1, 0, 2.5])
plt.xlim(-0.1, 1.1)
plt.show()
# The part where we do the loop to get the mean and standard deviations:
dVdlInitList = [ii / 10.0 for ii in range(-1, 12)]
dVdlMeanList = []
dVdlStdList = []
for init in dVdlInitList:
phbuilder.md.energy_tcouple()
phbuilder.md.energy_pcouple()
phbuilder.md.gen_mdp('MD', nsteps=50000, nstxout=10000)
phbuilder.md.gen_constantpH(ph_pH=4.25,
ph_lambdaM=0.0,
ph_nstout=1,
ph_barrierE=0.0,
cal=True,
lambdaInit=init)
os.system("./run.sh")
dVdlList = load.Col('lambda_1.dat', 3)
dVdlMeanList.append(numpy.mean(dVdlList))
dVdlStdList.append(numpy.std(dVdlList))
phbuilder.universe.add('ph_dvdl_initList', dVdlInitList)
phbuilder.universe.add('ph_dvdl_meanList', dVdlMeanList)
phbuilder.universe.add('ph_dvdl_stdList', dVdlStdList)
phbuilder.universe.inspect()
phbuilder.analyze.fitCalibration(order=5)
def compareLambdaFiles(namelist):
# If you accidentally put a string instead of a list, fix it.
if (type(namelist) == type("")):
namelist = [namelist]
# Define (size of) main figure.
fig = plt.figure(figsize=(24, 10))
# Define sub-plots.
plt1 = fig.add_subplot(2, 4, 1)
plt2 = fig.add_subplot(2, 4, 2)
plt3 = fig.add_subplot(2, 4, 3)
plt4 = fig.add_subplot(2, 4, 4)
plt5 = fig.add_subplot(2, 4, 5)
plt6 = fig.add_subplot(2, 4, 6)
plt7 = fig.add_subplot(2, 4, 7)
plt8 = fig.add_subplot(2, 4, 8)
# Get the data and plot.
for name in namelist:
time = load.Col(name, 1)
lambda_x = load.Col(name, 2)
lambda_dvdl = load.Col(name, 3)
lambda_temp = load.Col(name, 4)
lambda_vel = load.Col(name, 5)
F_coulomb = load.Col(name, 6)
F_corr = load.Col(name, 7)
F_bias = load.Col(name, 8)
F_ph = load.Col(name, 9)
plt1.plot(time,
lambda_x,
linewidth=0.5,
label="deprotonation = {:.2f}".format(titrate(name)))
plt2.plot(time,
lambda_temp,
linewidth=0.5,
label="mean = {:.1f} (K)".format(
sum(lambda_temp) / len(lambda_temp)))
plt3.hist(lambda_vel, density=True)
plt4.scatter(lambda_x, lambda_dvdl, s=5)
plt5.scatter(lambda_x, F_coulomb, s=5)
plt6.scatter(lambda_x, F_corr, s=5)
plt7.scatter(lambda_x, F_bias, s=5)
plt8.scatter(lambda_x, F_ph, s=5)
plt1.set_title("$\lambda$-coordinate vs time")
plt1.set_xlabel("Time (ps)")
plt1.set_ylabel("$\lambda$-coordinate")
plt1.set_ylim(-0.1, 1.1)
plt1.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt1.legend()
plt2.set_title("$\lambda$-temperature vs time")
plt2.set_xlabel("Time (ps)")
plt2.set_ylabel("$\lambda$-temperature (K)")
plt2.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt2.legend()
plt3.set_title("$\lambda$-velocity distribution")
plt3.set_xlabel("$\lambda$-velocity (km/s)")
plt4.set_title("Force (dV/dl) on $\lambda$-particle")
plt4.set_xlabel("$\lambda$-coordinate")
plt4.set_ylabel("dV/dl")
plt4.set_xlim(-0.1, 1.1)
plt5.set_title("Coulomb-force on $\lambda$-particle")
plt5.set_xlabel("$\lambda$-coordinate")
plt5.set_ylabel("$F_{Coulomb}$")
plt5.set_xlim(-0.1, 1.1)
plt6.set_title("Reference-force on $\lambda$-particle")
plt6.set_xlabel("$\lambda$-coordinate")
plt6.set_ylabel("$F_{corr}$")
plt6.set_xlim(-0.1, 1.1)
plt7.set_title("Bias-force on $\lambda$-particle")
plt7.set_xlabel("$\lambda$-coordinate")
plt7.set_ylabel("$F_{bias}$")
plt7.axis([-0.1, 1.1, -200, 200])
plt8.set_title("pH-force on $\lambda$-particle")
plt8.set_xlabel("$\lambda$-coordinate")
plt8.set_ylabel("$F_{pH}$")
plt8.set_xlim(-0.1, 1.1)
# Stuff we do in all subplots we can do in a loop:
for plot in [plt1, plt2, plt3, plt4, plt5, plt6, plt7, plt8]:
plot.grid()
fig.legend(namelist, loc="upper center")
# plt.tight_layout()
plt.show()
def glicphstates():
# EXPERIMENTAL DATA ON PROTONATION STATES AT VARIOUS PH ####################
biophys = { # also prevost2012
'ASP-13': 1,
'ASP-31': 1,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 1,
'ASP-86': 0,
'ASP-88': 0,
'ASP-91': 1,
'ASP-97': 1,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 1,
'ASP-153': 1,
'ASP-154': 1,
'ASP-161': 1,
'ASP-178': 1,
'ASP-185': 1,
'GLU-14': 1,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 0,
'GLU-69': 1,
'GLU-75': 0,
'GLU-82': 0,
'GLU-104': 1,
'GLU-147': 1,
'GLU-163': 1,
'GLU-177': 0,
'GLU-181': 1,
'GLU-222': 1,
'GLU-243': 0,
'GLU-272': 1,
'GLU-282': 1
}
nury2010 = { # this is also cheng2010, calimet2013
'ASP-13': 1,
'ASP-31': 1,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 1,
'ASP-86': 0,
'ASP-88': 0,
'ASP-91': 1,
'ASP-97': 1,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 1,
'ASP-153': 1,
'ASP-154': 1,
'ASP-161': 1,
'ASP-178': 1,
'ASP-185': 1,
'GLU-14': 1,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 0,
'GLU-69': 0,
'GLU-75': 0,
'GLU-82': 0,
'GLU-104': 1,
'GLU-147': 1,
'GLU-163': 1,
'GLU-177': 0,
'GLU-181': 1,
'GLU-222': 1,
'GLU-243': 0,
'GLU-272': 1,
'GLU-282': 1
}
fritsch2011 = {
'ASP-13': 0,
'ASP-31': 0,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 0,
'ASP-86': 0,
'ASP-88': 0,
'ASP-91': 0,
'ASP-97': 0,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 0,
'ASP-153': 0,
'ASP-154': 0,
'ASP-161': 0,
'ASP-178': 0,
'ASP-185': 0,
'GLU-14': 0,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 0,
'GLU-69': 0,
'GLU-75': 0,
'GLU-82': 0,
'GLU-104': 1,
'GLU-147': 0,
'GLU-163': 0,
'GLU-177': 0,
'GLU-181': 0,
'GLU-222': 1,
'GLU-243': 0,
'GLU-272': 0,
'GLU-282': 0
}
lev2017 = {
'ASP-13': 1,
'ASP-31': 1,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 1,
'ASP-86': 1,
'ASP-88': 1,
'ASP-91': 1,
'ASP-97': 1,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 1,
'ASP-153': 1,
'ASP-154': 1,
'ASP-161': 1,
'ASP-178': 1,
'ASP-185': 1,
'GLU-14': 1,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 0,
'GLU-69': 0,
'GLU-75': 0,
'GLU-82': 0,
'GLU-104': 1,
'GLU-147': 1,
'GLU-163': 1,
'GLU-177': 0,
'GLU-181': 1,
'GLU-222': 1,
'GLU-243': 0,
'GLU-272': 1,
'GLU-282': 1
}
nemecz2017 = { # also Hu2018
'ASP-13': 1,
'ASP-31': 1,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 1,
'ASP-86': 0,
'ASP-88': 0,
'ASP-91': 1,
'ASP-97': 1,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 1,
'ASP-153': 1,
'ASP-154': 1,
'ASP-161': 1,
'ASP-178': 1,
'ASP-185': 1,
'GLU-14': 1,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 1,
'GLU-69': 1,
'GLU-75': 1,
'GLU-82': 1,
'GLU-104': 1,
'GLU-147': 1,
'GLU-163': 1,
'GLU-177': 1,
'GLU-181': 1,
'GLU-222': 0,
'GLU-243': 0,
'GLU-272': 1,
'GLU-282': 1
}
ullman = { # unpublished
'ASP-13': 1,
'ASP-31': 1,
'ASP-32': 1,
'ASP-49': 1,
'ASP-55': 1,
'ASP-86': 1,
'ASP-88': 1,
'ASP-91': 1,
'ASP-97': 1,
'ASP-115': 1,
'ASP-122': 1,
'ASP-136': 1,
'ASP-145': 1,
'ASP-153': 1,
'ASP-154': 1,
'ASP-161': 1,
'ASP-178': 1,
'ASP-185': 1,
'GLU-14': 1,
'GLU-26': 0,
'GLU-35': 0,
'GLU-67': 0,
'GLU-69': 0,
'GLU-75': 0,
'GLU-82': 1,
'GLU-104': 1,
'GLU-147': 0,
'GLU-163': 0,
'GLU-177': 0,
'GLU-181': 1,
'GLU-222': 1,
'GLU-243': 0,
'GLU-272': 0,
'GLU-282': 1
}
# DIRECTORY STRUCTURE ######################################################
dirname = "lambdaplots"
if not os.path.isdir(dirname):
os.mkdir(dirname)
else:
os.system("rm -f {0}/*.png {0}/*.pdf".format(dirname))
# GET THE RESIDUE NUMBER, NAME, AND CHAIN OF ALL PROTO RESIDUES ############
resnameList = []
residList = []
chainList = []
for residue in universe.get('d_residues'):
if residue.d_resname in ["ASP", "GLU"]:
resnameList.append(residue.d_resname)
residList.append(residue.d_resid)
chainList.append(residue.d_chain)
# CREATE LAMBDA PLOT FOR EVERY INDIVIDUAL PROTONATABLE RESIDUE #############
# Loop through all the lambdas:
# for idx in range(1, len(resnameList) + 1):
# plt.figure(figsize=(8, 6))
# # Update user
# print("plotting {}/{}".format(idx, len(resnameList)), end='\r')
# # Load columns from .dat files
# t = load.Col("lambda_{0}.dat".format(idx), 1)
# x = load.Col("lambda_{0}.dat".format(idx), 2)
# # Analyze a running sim not all columns will be equal long so trim:
# if len(t) > len(x):
# t.pop()
# elif len(t) < len(x):
# x.pop()
# plt.plot(t, x, linewidth=0.5)
# # Title
# plt.title("{0}-{1} in chain {2} in {3}.pdb\npH={4}, nstlambda={5}, deprotonation={6:.2f}\n\
# Experimentally determined state for {0}-{1} at this pH = {7}".format(
# resnameList[idx-1],
# residList[idx-1],
# chainList[idx-1],
# universe.get('d_pdbName'),
# universe.get('ph_pH'),
# universe.get('ph_nstout'),
# titrate("lambda_{}.dat".format(idx)),
# expVals40["{0}-{1}".format(resnameList[idx-1], residList[idx-1])]
# ))
# # Axes and stuff
# plt.ylim(-0.1, 1.1)
# plt.xlabel("Time (ps)")
# plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
# plt.ylabel(r"$\lambda$-coordinate")
# plt.grid()
# # Save.
# fileName = "{}/{}_{}-{:03d}".format(dirname, chainList[idx-1], resnameList[idx-1], residList[idx-1])
# # plt.savefig("{}.pdf".format(fileName)); os.system("pdfcrop {0}.pdf {0}.pdf >> /dev/null 2>&1".format(fileName))
# plt.savefig("{}.png".format(fileName))
# # clf = clear the entire current figure. close = closes a window.
# plt.clf(); plt.close()
# CREATE HISTOGRAM PLOTS FOR COMBINED PROTO STATE OF ALL FIVE CHAINS #######
number_of_chains = len(set(chainList))
residues_per_chain = int(len(resnameList) / number_of_chains)
for ii in range(1, residues_per_chain + 1):
data = []
for jj in range(0, number_of_chains):
print(ii + residues_per_chain * jj, end=' ')
data += (load.Col(
'lambda_{}.dat'.format(ii + residues_per_chain * jj), 2, 49713,
124320))
print()
# PLOTTING STUFF #######################################################
plt.figure(figsize=(8, 6))
plt.hist(data, density=True, bins=200)
# Title
plt.title(
"{0}-{1} (all chains) in {2}.pdb\npH={3}, nstlambda={4}, deprotonation={5:.2f}"
.format(
resnameList[ii - 1], residList[ii - 1],
universe.get('d_pdbName'), universe.get('ph_pH'),
universe.get('ph_nstout'),
titrate("lambda_{}.dat".format(ii))
# expVals40["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]
))
# Axes and stuff
plt.axis([-0.1, 1.1, -0.1, 12])
plt.xlabel(r"$\lambda$-coordinate")
plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt.grid()
# Add green vertical line indicating experimental value
plt.vlines(x=biophys["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=12,
color='r',
linewidth=4.0,
label="biophysics.se/Prevost2012 = {}".format(
biophys["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])]))
plt.vlines(x=nury2010["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=10,
color='g',
linewidth=4.0,
label="Nury2010/Cheng2010/Calimet2013 = {}".format(
nury2010["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])]))
plt.vlines(x=fritsch2011["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=8,
color='b',
linewidth=4.0,
label="Fritsch2011 = {}".format(
fritsch2011["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])]))
plt.vlines(x=lev2017["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=6,
color='c',
linewidth=4.0,
label="Lev2017 = {}".format(lev2017["{0}-{1}".format(
resnameList[ii - 1], residList[ii - 1])]))
plt.vlines(x=nemecz2017["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=4,
color='m',
linewidth=4.0,
label="Nemecz2017/Hu2018 = {}".format(
nemecz2017["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])]))
plt.vlines(x=ullman["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])],
ymin=0,
ymax=2,
color='y',
linewidth=4.0,
label="Ullman (unpublished) = {}".format(
ullman["{0}-{1}".format(resnameList[ii - 1],
residList[ii - 1])]))
plt.legend()
# Save and clear
fileName = "{}/hist_{}-{:03d}".format(dirname, resnameList[ii - 1],
residList[ii - 1])
# plt.savefig("{}.pdf".format(fileName)); os.system("pdfcrop {0}.pdf {0}.pdf >> /dev/null 2>&1".format(fileName))
plt.savefig('{}.png'.format(fileName))
plt.clf()
plt.close()
import matplotlib.pyplot as plt
import load
def runningAverage(data):
Av = len(data) * [0]
Sum = 0
for idx in range(0, len(data)):
Sum += data[idx]
Av[idx] = Sum / (idx + 1)
return Av
t1 = load.Col("energy_single_0.002.log", 1)
t1 = [0.002 * x for x in t1]
E1 = load.Col("energy_single_0.002.log", 8)
t2 = load.Col("energy_double_0.002.log", 1)
t2 = [0.002 * x for x in t2]
E2 = load.Col("energy_double_0.002.log", 8)
t3 = load.Col("energy_single_0.00002.log", 1)
t3 = [0.00002 * x for x in t3]
E3 = load.Col("energy_single_0.00002.log", 8)
t4 = load.Col("energy_single_0.0000002.log", 1)
t4 = [0.0000002 * x for x in t4]
E4 = load.Col("energy_single_0.0000002.log", 8)
# print("time_get_distance = {:.4f}".format(time_get_distance))
# print("time_periodic = {:.4f}".format(time_periodic))
# print("time_LJ = {:.4f}".format(time_LJ))
# print("time_reduce = {:.4f}".format(time_reduce))
atom1 = len(sim_pos) * [0]
atom2 = len(sim_pos) * [0]
atom3 = len(sim_pos) * [0]
for i in range(0, len(sim_pos)):
atom1[i] = sim_pos[i][0]
atom2[i] = sim_pos[i][1]
atom3[i] = sim_pos[i][2]
plt.plot(atom1, '.', label='atom 1')
plt.plot(atom2, '.', label='atom 2')
plt.plot(atom3, '.', label='atom 3')
plt.xlabel(r'Step')
plt.ylabel(r'$x$-Position/Å')
plt.legend(frameon=False)
plt.show()
x = load.Col('energy.log', 1)
T = load.Col('energy.log', 2)
plt.plot(x, T, label="mean = {:.4f}".format(sum(T) / len(T)))
plt.legend()
plt.show()
