def calculate_E_no_bias(E_no_bias_all, qtanh_all, umb_params, N_k_bias):
with open("umbrella_params", "r") as fin:
params = fin.readline().split()
qtanh_center = float(params[0])
kumb = float(params[1])
gamma = float(params[2])
n_frames_toss = int(params[4])/1000
umb_params.append([qtanh_center, kumb, n_frames_toss])
if not os.path.exists("qtanh.npy"):
pairs = np.loadtxt("umbrella_params", usecols=(0,1), dtype=int, skiprows=1) - 1
r0 = np.loadtxt("umbrella_params", usecols=(2,), skiprows=1)
widths = (2./gamma)*np.ones(len(pairs))
qtanhsum_obs = observables.TanhContactSum("conf.gro", pairs, 1.2*r0, widths)
qtanh_temp = observables.calculate_observable(["traj.xtc"], qtanhsum_obs)[0][n_frames_toss:]
np.save("qtanh.npy", qtanh_temp)
else:
qtanh_temp = np.load("qtanh.npy")
# collect needed info for this umbrella
Ebias = 0.5*kumb*((qtanh_temp - qtanh_center)**2)
Epot = np.loadtxt("Epot.dat", usecols=(1,))[n_frames_toss:]
E_no_bias_all.append(Epot - Ebias)
qtanh_all.append(qtanh_temp)
N_k_bias.append(qtanh_temp.shape[0])
def calculate_E_no_bias(E_no_bias_all, qtanh_all, umb_params, N_k_bias):
with open("umbrella_params", "r") as fin:
params = fin.readline().split()
qtanh_center = float(params[0])
kumb = float(params[1])
gamma = float(params[2])
n_frames_toss = int(params[4]) / 1000
umb_params.append([qtanh_center, kumb, n_frames_toss])
if not os.path.exists("qtanh.npy"):
pairs = np.loadtxt(
"umbrella_params", usecols=(0, 1), dtype=int, skiprows=1) - 1
r0 = np.loadtxt("umbrella_params", usecols=(2, ), skiprows=1)
widths = (2. / gamma) * np.ones(len(pairs))
qtanhsum_obs = observables.TanhContactSum("conf.gro", pairs, 1.2 * r0,
widths)
qtanh_temp = observables.calculate_observable(
["traj.xtc"], qtanhsum_obs)[0][n_frames_toss:]
np.save("qtanh.npy", qtanh_temp)
else:
qtanh_temp = np.load("qtanh.npy")
# collect needed info for this umbrella
Ebias = 0.5 * kumb * ((qtanh_temp - qtanh_center)**2)
Epot = np.loadtxt("Epot.dat", usecols=(1, ))[n_frames_toss:]
E_no_bias_all.append(Epot - Ebias)
qtanh_all.append(qtanh_temp)
N_k_bias.append(qtanh_temp.shape[0])
traj = md.load("traj.xtc", top="conf.gro")
qtanh = md.compute_distances(traj, np.array([[0,57]]))
with open("umbrella_params", "r") as fin:
params = fin.readline().split()
q0 = float(params[0])
kumb = float(params[1])
gamma = float(params[2])
n_frames_toss = int(params[4])/1000
pairs = np.loadtxt("umbrella_params", usecols=(0,1), dtype=int, skiprows=1) - 1
r0 = np.loadtxt("umbrella_params", usecols=(2,), skiprows=1)
widths = (2./gamma)*np.ones(len(pairs))
qtanhsum_obs = observables.TanhContactSum("conf.gro", pairs, 1.2*r0, widths)
qtanh = observables.calculate_observable(["traj.xtc"], qtanhsum_obs)
np.save("qtanh.npy", qtanh)
else:
qtanh = np.load("qtanh.npy")
n, bins = np.histogram(qtanh, bins=n_bins)
mid_bin = 0.5*(bins[1:] + bins[:-1])
pmf = -np.log(n)
pmf -= pmf.min()
plt.plot(mid_bin, pmf, label="$Q_0 = {}$".format(Q0[i]))
os.chdir("..")
plt.xlim(0, 150)
nn_pairs = np.loadtxt("%s/pairwise_params" % dir, usecols=(0,1))[2*n_native_pairs + 1::2] - 1
nn_r0 = np.loadtxt("%s/pairwise_params" % dir, usecols=(4,))[2*n_native_pairs + 1::2]
nn_r0_cont = nn_r0 + 0.1
widths = 0.05
top = "%s/Native.pdb" % dir
if all([ (os.path.exists("%s/Atanh_0_05.dat" % x.split("/")[0]) & os.path.exists("%s/Qtanh_0_05.dat" % x.split("/")[0])) \
for x in trajfiles ]):
qtanh = [ np.loadtxt("%s/Qtanh_0_05.dat" % x.split("/")[0]) for x in trajfiles ]
Atanh = [ np.loadtxt("%s/Atanh_0_05.dat" % x.split("/")[0]) for x in trajfiles ]
else:
qtanhsum_obs = observables.TanhContactSum(top, pairs, r0_cont, widths)
qtanh = observables.calculate_observable(trajfiles, qtanhsum_obs, saveas="Qtanh_0_05.dat")
Atanhsum_obs = observables.TanhContactSum(top, nn_pairs, nn_r0_cont, widths)
Atanh = observables.calculate_observable(trajfiles, Atanhsum_obs, saveas="Atanh_0_05.dat")
q = np.concatenate(qtanh)
A = np.concatenate(Atanh)
n,bin_edges = np.histogram(q,bins=40)
mid_bin = 0.5*(bin_edges[1:] + bin_edges[:-1])
A_bin_avg = np.zeros(len(bin_edges) - 1,float)
dA2_bin_avg = np.zeros(len(bin_edges) - 1,float)
for i in range(len(bin_edges) - 1):
frames_in_this_bin = ((q > bin_edges[i]) & (q <= bin_edges[i+1]))
if any(frames_in_this_bin):
A_bin_avg[i] = np.mean(A[frames_in_this_bin])
import simulation.calc.observables as observables
if __name__ == "__main__":
trajfiles = [ x.rstrip("\n") for x in open("ticatrajs","r").readlines() ]
dir = os.path.dirname(trajfiles[0])
nat_pairs = np.loadtxt("%s/native_contacts.ndx" % dir, skiprows=1, dtype=int) - 1
n_native_pairs = nat_pairs.shape[0]
top = "%s/Native.pdb" % dir
with open(top, "r") as fin:
n_residues = len(fin.readlines()) - 1
end_pairs = np.array([[0, n_residues - 1]])
r_obs = observables.Distances(top,end_pairs)
r = np.concatenate(observables.calculate_observable(trajfiles, r_obs))
# Get probability density of end-to-end distance of the unfolded state
qtanh = np.concatenate([ np.loadtxt("%s/Qtanh_0_05.dat" % os.path.dirname(x)) for x in trajfiles ])
minima = np.loadtxt("Qtanh_0_05_profile/minima.dat")[0]
U = minima + 0.1*n_native_pairs
n, bins = np.histogram(r[qtanh < U],bins=40,density=True)
mid_bin = 0.5*(bins[1:] + bins[:-1])
if not os.path.exists("r1N_distribution"):
os.mkdir("r1N_distribution")
os.chdir("r1N_distribution")
np.savetxt("r1N_vs_bin.dat", n)
np.savetxt("mid_bin.dat", mid_bin)
r0_cont = r0 + 0.1
nn_pair_type = np.loadtxt("%s/pairwise_params" % dir, usecols=(3,), dtype=int)[2*n_native_pairs + 1::2]
nn_pairs = np.loadtxt("%s/pairwise_params" % dir, usecols=(0,1))[2*n_native_pairs + 1::2] - 1
nn_eps = np.loadtxt("%s/model_params" % dir)[2*n_native_pairs + 1::2]
nn_r0 = np.loadtxt("%s/pairwise_params" % dir, usecols=(4,))[2*n_native_pairs + 1::2]
nn_r0_cont = nn_r0 + 0.1
widths = 0.05
top = "%s/Native.pdb" % dir
if all([ os.path.exists("%s/Qtanh_0_05.dat" % x.split("/")[0]) for x in trajfiles ]):
qtanh = [ np.loadtxt("%s/Qtanh_0_05.dat" % x.split("/")[0]) for x in trajfiles ]
else:
qtanhsum_obs = observables.TanhContactSum(top, pairs, r0_cont, widths)
qtanh = observables.calculate_observable(trajfiles, qtanhsum_obs, saveas="Qtanh_0_05.dat")
if all([ os.path.exists("%s/Enonnative.dat" % x.split("/")[0]) for x in trajfiles ]):
Enn = [ np.loadtxt("%s/Enonnative.dat" % x.split("/")[0]) for x in trajfiles ]
else:
Enn_obs = observables.PairEnergySum(top, nn_pairs, nn_pair_type, nn_eps, nn_pair_params)
Enn = observables.calculate_observable(trajfiles, Enn_obs, saveas="Enonnative.dat")
q = np.concatenate(qtanh)
E = np.concatenate(Enn)
n,bin_edges = np.histogram(q,bins=40)
mid_bin = 0.5*(bin_edges[1:] + bin_edges[:-1])
E_bin_avg = np.zeros(len(bin_edges) - 1,float)
dE2_bin_avg = np.zeros(len(bin_edges) - 1,float)
with open("umbrella_params", "r") as fin:
params = fin.readline().split()
q0 = float(params[0])
kumb = float(params[1])
gamma = float(params[2])
n_frames_toss = int(params[4]) / 1000
pairs = np.loadtxt(
"umbrella_params", usecols=(0, 1), dtype=int, skiprows=1) - 1
r0 = np.loadtxt("umbrella_params", usecols=(2, ), skiprows=1)
widths = (2. / gamma) * np.ones(len(pairs))
qtanhsum_obs = observables.TanhContactSum("conf.gro", pairs,
1.2 * r0, widths)
qtanh = observables.calculate_observable(["traj.xtc"],
qtanhsum_obs)
np.save("qtanh.npy", qtanh)
else:
qtanh = np.load("qtanh.npy")
n, bins = np.histogram(qtanh, bins=n_bins)
mid_bin = 0.5 * (bins[1:] + bins[:-1])
pmf = -np.log(n)
pmf -= pmf.min()
plt.plot(mid_bin, pmf, label="$Q_0 = {}$".format(Q0[i]))
os.chdir("..")
plt.xlim(0, 150)
