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import torch
import joblib
import numpy as np
import mdtraj as md
import matplotlib.pyplot as plt
import pyemma.coordinates as coor
from .utils import compute_dihedral
from matplotlib.colors import LinearSegmentedColormap
class Plot:
def __init__(self, args, mds):
self.device = args.device
self.save_dir = args.save_dir
self.molecule = args.molecule
self.start_state = args.start_state
self.num_samples = args.num_samples
self.start_position = mds.start_position
self.target_position = mds.target_position
self.energy_function = mds.energy_function
def __call__(self):
positions, potentials = [], []
for i in range(self.num_samples):
position = np.load(f"{self.save_dir}/positions/{i}.npy").astype(np.float32)
potential = self.energy_function(position)[1]
positions.append(torch.from_numpy(position).to(self.device))
potentials.append(potential)
self.paths(positions)
def paths(self, positions):
zorder = 32
circle_size = 500
saddle_size = 2400
custom_colors_1 = ["#05009E", "#6B67EE", "#50B2D7", "#B0ADF1"]
custom_colors_2 = ["#05009E", "#6B67EE", "#50B2D7", "#F7EFFF"]
custom_cmap_1 = LinearSegmentedColormap.from_list("my_cmap", custom_colors_1)
custom_cmap_2 = LinearSegmentedColormap.from_list("my_cmap", custom_colors_2)
if self.molecule == "aldp":
angle_1 = [6, 8, 14, 16]
angle_2 = [1, 6, 8, 14]
plt.clf()
plt.close()
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111)
plt.xlim([-np.pi, np.pi])
plt.ylim([-np.pi, np.pi])
with open("./data/aldp/landscape.dat") as f:
lines = f.readlines()
dims = [90, 90]
locations = torch.zeros((int(dims[0]), int(dims[1]), 2))
data = torch.zeros((int(dims[0]), int(dims[1])))
i = 0
for line in lines[1:]:
splits = line[0:-1].split(" ")
vals = [y for y in splits if y != ""]
x = float(vals[0])
y = float(vals[1])
val = float(vals[-1])
locations[i // 90, i % 90, :] = torch.tensor([x, y])
data[i // 90, i % 90] = val
i = i + 1
xs = np.arange(-np.pi, np.pi + 0.1, 0.1)
ys = np.arange(-np.pi, np.pi + 0.1, 0.1)
x, y = np.meshgrid(xs, ys)
inp = torch.tensor(np.array([x, y])).view(2, -1).T
loc = locations.view(-1, 2)
distances = torch.cdist(inp, loc.double(), p=2)
index = distances.argmin(dim=1)
a = torch.div(index, locations.shape[0], rounding_mode="trunc")
b = index % locations.shape[0]
z = data[a, b]
z = z.view(y.shape[0], y.shape[1])
plt.contourf(xs, ys, z, levels=100, zorder=0, cmap=custom_cmap_2)
# changed
cm = custom_cmap_2
"""ax.set_prop_cycle(
color=[cm(1.0 * i / len(positions)) for i in range(len(positions))]
)"""
for position in positions:
psi = compute_dihedral(position[:, angle_1, :]).detach().cpu().numpy()
phi = compute_dihedral(position[:, angle_2, :]).detach().cpu().numpy()
# changed to white paths
ax.plot(
phi,
psi,
marker="o", linestyle="None", markersize=2, alpha=1.0,
markerfacecolor="white",
markeredgecolor="none", # no edge
markeredgewidth=0,
)
#mark endpoint
end_phi, end_psi = phi[-1], psi[-1]
ax.scatter(
[end_phi], [end_psi],
s=70, c="#D577FF", edgecolors="w", linewidths=0.8,
zorder=zorder + 1, marker="o" # use "D" or "*" if you prefer
)
start_psi = (
compute_dihedral(self.start_position[:, angle_1, :])
.detach()
.cpu()
.numpy()
)
start_phi = (
compute_dihedral(self.start_position[:, angle_2, :])
.detach()
.cpu()
.numpy()
)
target_psi = (
compute_dihedral(self.target_position[:, angle_1, :])
.detach()
.cpu()
.numpy()
)
target_phi = (
compute_dihedral(self.target_position[:, angle_2, :])
.detach()
.cpu()
.numpy()
)
phis_saddle = [-0.035, -0.017]
psis_saddle = [1.605, -0.535]
"""ax.scatter(
phis_saddle,
psis_saddle,
edgecolors="black",
c="w",
zorder=zorder,
s=saddle_size,
marker="*",
)"""
ax.scatter(
start_phi,
start_psi,
edgecolors="w",
c="#9793F8",
zorder=zorder,
s=circle_size,
marker="*",
)
ax.scatter(
target_phi,
target_psi,
edgecolors="w",
c="#9793F8",
zorder=zorder,
s=circle_size,
marker="*",
)
plt.xlabel("\u03A6", fontsize=35, fontweight="medium")
plt.ylabel("\u03A8", fontsize=35, fontweight="medium")
else:
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111)
cm = plt.get_cmap("gist_rainbow")
"""ax.set_prop_cycle(
color=[cm(1.0 * i / len(positions)) for i in range(len(positions))]
)"""
pmf = np.load(f"./data/{self.molecule}/pmf.npy")
xs = np.load(f"./data/{self.molecule}/xs.npy")
ys = np.load(f"./data/{self.molecule}/ys.npy")
plt.pcolormesh(xs, ys, pmf.T, cmap=custom_cmap_1)
tica_model = joblib.load(f"./data/{self.molecule}/tica_model.pkl")
feat = coor.featurizer(f"./data/{self.molecule}/{self.start_state}.pdb")
feat.add_backbone_torsions(cossin=True)
for position in positions:
traj = md.Trajectory(
position.cpu().numpy(),
md.load(f"./data/{self.molecule}/{self.start_state}.pdb").topology,
)
feature = feat.transform(traj)
tica = tica_model.transform(feature)
ax.plot(
tica[:, 0],
tica[:, 1],
marker="o",
linestyle="None",
markersize=2,
alpha=1.0,
markerfacecolor="white",
markeredgecolor="none", # no edge
markeredgewidth=0,
)
end_x, end_y = tica[-1, 0], tica[-1, 1]
ax.scatter(
[end_x], [end_y],
s=70, c="#D577FF", edgecolors="w", linewidths=0.8,
zorder=zorder + 1, marker="o"
)
start_position = md.Trajectory(
self.start_position.cpu().numpy(),
md.load(f"./data/{self.molecule}/{self.start_state}.pdb").topology,
)
feature = feat.transform(start_position)
start_tica = tica_model.transform(feature)
ax.scatter(
start_tica[:, 0],
start_tica[:, 1],
edgecolors="w",
c="#9793F8",
zorder=zorder,
s=circle_size,
marker="*",
)
target_position = md.Trajectory(
self.target_position.cpu().numpy(),
md.load(f"./data/{self.molecule}/{self.start_state}.pdb").topology,
)
feature = feat.transform(target_position)
target_tica = tica_model.transform(feature)
ax.scatter(
target_tica[:, 0],
target_tica[:, 1],
edgecolors="w",
c="#9793F8",
zorder=zorder,
s=circle_size,
marker="*",
)
plt.xlabel("TIC 1", fontsize=35, fontweight="medium")
plt.ylabel("TIC 2", fontsize=35, fontweight="medium")
plt.xlim(xs.min(), xs.max())
plt.ylim(ys.min(), ys.max())
plt.tick_params(
left=False,
right=False,
labelleft=False,
labelbottom=False,
bottom=False,
)
plt.tight_layout()
plt.savefig(f"{self.save_dir}/paths.png", dpi=300, bbox_inches="tight")
plt.show()
plt.close()
return fig
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