def spline_plot_cycles(cart_coords, energies):
num_cycles = energies.shape[1]
fig, ax = plt.subplots()
colors = matplotlib.cm.Greys(np.linspace(.2, 1, num=num_cycles))
# for cycle, color in zip(energies, colors):
for i, (cycle, color) in enumerate(zip(energies, colors)):
ax.plot(cycle, "o-", color=color)
ax.set_title("COS image energies")
kwargs = {
"ls": ":",
"color": "darkgrey",
}
# Try to spline the last cycle to get an estimate for the spliend HEI
try:
last_cycle = energies[-1]
num_images = last_cycle.size
spl = splrep(np.arange(num_images), last_cycle)
# Calculate interpolated values
x2 = np.linspace(0, num_images, 100)
y2 = splev(x2, spl)
# Only consider maxima
peak_inds, _ = peakdetect(y2, lookahead=2)
if not peak_inds:
ax.plot(x2, y2)
else:
peak_inds = np.array(peak_inds)[:, 0].astype(int)
peak_xs = x2[peak_inds]
peak_ys = y2[peak_inds]
ax.plot(x2, y2, peak_xs, peak_ys, "x")
for px, py in zip(peak_xs, peak_ys):
ax.axhline(y=py, **kwargs)
line = matplotlib.lines.Line2D([px, px], [0, py], **kwargs)
ax.add_line(line)
except TypeError:
print("Not enough images for splining!")
# Always draw a line at the minimum y=0
ax.axhline(y=0, **kwargs)
ax.set_xlabel("Image")
ax.set_ylabel(UNIT_DEKJMOL)
return fig, ax
def plot_afir():
with open("image_results.yaml") as handle:
res = yaml.load(handle.read(), Loader=yaml.loader.Loader)
afir_ens = [_["energy"] for _ in res]
true_ens = [_["true_energy"] for _ in res]
afir_ens = np.array(afir_ens) * AU2KJPERMOL
afir_ens -= afir_ens.min()
true_ens = np.array(true_ens) * AU2KJPERMOL
true_ens -= true_ens.min()
afir_forces = np.linalg.norm([_["forces"] for _ in res], axis=1)
true_forces = np.linalg.norm([_["true_forces"] for _ in res], axis=1)
afir_forces = np.array(afir_forces)
true_forces = np.array(true_forces)
fig, (en_ax, forces_ax) = plt.subplots(nrows=2, sharex=True)
style1 = "r--"
style2 = "g--"
style3 = "bo-"
l1 = en_ax.plot(afir_ens, style1, label="AFIR")
l2 = en_ax.plot(true_ens, style2, label="True")
en_ax2 = en_ax.twinx()
l3 = en_ax2.plot(true_ens + afir_ens, style3, label="Sum")
en_ax2.tick_params(axis="y", labelcolor="blue")
lines = l1 + l2 + l3
labels = [l.get_label() for l in lines]
en_ax.legend(lines, labels, loc=0)
en_ax.set_title("Energies")
en_ax.set_ylabel("$\Delta$E kJ / mol")
forces_ax.set_title("||Forces||")
l1 = forces_ax.plot(afir_forces, style1, label="AFIR")
l2 = forces_ax.plot(true_forces, style2, label="True")
forces_ax2 = forces_ax.twinx()
l3 = forces_ax2.plot(true_forces + afir_forces, style3, label="Sum")
forces_ax2.tick_params(axis="y", labelcolor="blue")
lines = l1 + l2 + l3
labels = [l.get_label() for l in lines]
forces_ax.legend(lines, labels, loc=0)
peak_inds, _ = peakdetect(true_ens, lookahead=2)
print(f"Peaks: {peak_inds}")
try:
peak_xs, peak_ys = zip(*peak_inds)
highest = np.argmax(peak_ys)
en_ax.scatter(peak_xs, peak_ys, s=100, marker="X", c="k", zorder=10)
en_ax.scatter(peak_xs[highest],
peak_ys[highest],
s=150,
marker="X",
c="k",
zorder=10)
en_ax.axvline(peak_xs[highest], c="k", ls="--")
forces_ax.axvline(peak_xs[highest], c="k", ls="--")
except ValueError as err:
print("Peak-detection failed!")
# fig.legend(loc="upper right")
plt.tight_layout()
plt.show()
def plot_energies():
keys = ("energy", "cart_coords")
(energies, coords), num_cycles, num_images = load_results(keys)
if isinstance(num_images, list):
print("Please use --aneb instead of --energies")
return
lengths = np.array([len(e) for e in energies])
equal_lengths = lengths == lengths[-1]
# Hack to support growing string calculations
energies = np.array([e for e, l in zip(energies, equal_lengths) if l])
coords = np.array([c for c, l in zip(coords, equal_lengths) if l])
num_cycles, num_images = energies.shape
energies -= energies.min()
energies *= AU2KJPERMOL
# Static plot of path with equally spaced images
fig, ax = plt.subplots()
colors = matplotlib.cm.Greys(np.linspace(.2, 1, num=num_cycles))
for cycle, color in zip(energies, colors):
ax.plot(cycle, "o-", color=color)
ax.set_title("Energies")
kwargs = {
"ls": ":",
"color": "darkgrey",
}
try:
last_cycle = energies[-1]
spl = splrep(np.arange(num_images), last_cycle)
# Calculate interpolated values
x2 = np.linspace(0, num_images, 100)
y2 = splev(x2, spl)
# Only consider maxima
peak_inds, _ = peakdetect(y2, lookahead=2)
if not peak_inds:
ax.plot(x2, y2)
else:
peak_inds = np.array(peak_inds)[:, 0].astype(int)
peak_xs = x2[peak_inds]
peak_ys = y2[peak_inds]
ax.plot(x2, y2, peak_xs, peak_ys, "x")
for px, py in zip(peak_xs, peak_ys):
ax.axhline(y=py, **kwargs)
line = matplotlib.lines.Line2D([px, px], [0, py], **kwargs)
ax.add_line(line)
except TypeError:
print("Not enough images for splining!")
# Always draw a line at the minimum y=0
ax.axhline(y=0, **kwargs)
ax.set_xlabel("Image")
ax.set_ylabel("dE / kJ mol⁻¹")
fig2, ax2 = plt.subplots()
last_energies = energies[-1].copy()
xs = np.arange(len(last_energies))
ax2.plot(xs, last_energies, "o-")
ax2.set_xlabel("Image")
ax2.set_ylabel("$\Delta$E / kJ mol⁻¹")
ax2.set_title(f"Cycle {len(energies)-1}")
first_image_en = last_energies[0]
last_image_en = last_energies[-1]
max_en_ind = last_energies.argmax()
max_en = last_energies[max_en_ind]
print("Barrier heights using actual energies (not splined) from "
f"cycle {energies.shape[0]-1}.")
print(f"\tHighest energy image (HEI) at index {max_en_ind} (0-based)")
first_barr = max_en - first_image_en
print(f"\tBarrier between first image and HEI: {first_barr:.1f} kJ mol⁻¹")
last_barr = max_en - last_image_en
print(f"\tBarrier between last image and HEI: {last_barr:.1f} kJ mol⁻¹")
# Also do an animation
plotter = Plotter(coords, energies, "ΔE / au", interval=250, save=False)
# This calls plt.show()
plotter.animate()
def plot_afir(h5_fn="afir.h5", h5_group="afir"):
with h5py.File(h5_fn, "r") as handle:
group = handle[h5_group]
cycles = group.attrs["cur_cycle"] + 1
afir_ens = group["energy"][:cycles]
true_ens = group["true_energy"][:cycles]
afir_forces = group["forces"][:cycles]
true_forces = group["true_forces"][:cycles]
afir_ens *= AU2KJPERMOL
afir_ens -= afir_ens.min()
true_ens *= AU2KJPERMOL
true_ens -= true_ens.min()
afir_forces = np.linalg.norm(afir_forces, axis=1)
true_forces = np.linalg.norm(true_forces, axis=1)
fig, (en_ax, forces_ax) = plt.subplots(nrows=2, sharex=True)
style1 = "r--"
style2 = "g--"
style3 = "bo-"
l1 = en_ax.plot(afir_ens, style1, label="AFIR")
l2 = en_ax.plot(true_ens, style2, label="True")
en_ax2 = en_ax.twinx()
l3 = en_ax2.plot(true_ens + afir_ens, style3, label="Sum")
en_ax2.tick_params(axis="y", labelcolor="blue")
lines = l1 + l2 + l3
labels = [l.get_label() for l in lines]
en_ax.legend(lines, labels, loc=0)
en_ax.set_title("Energies")
en_ax.set_ylabel(UNIT_DEKJMOL)
forces_ax.set_title("||Forces||")
l1 = forces_ax.plot(afir_forces, style1, label="AFIR")
l2 = forces_ax.plot(true_forces, style2, label="True")
forces_ax2 = forces_ax.twinx()
l3 = forces_ax2.plot(true_forces + afir_forces, style3, label="Sum")
forces_ax2.tick_params(axis="y", labelcolor="blue")
lines = l1 + l2 + l3
labels = [l.get_label() for l in lines]
forces_ax.legend(lines, labels, loc=0)
forces_ax.set_xlabel("Cycle")
peak_inds, _ = peakdetect(true_ens, lookahead=2)
if peak_inds:
print(f"Peaks:")
print("\tCycle: Energy / kJ mol⁻¹")
print()
for at, energy in peak_inds:
print(f"\t{at}: {energy:.2f}")
try:
peak_xs, peak_ys = zip(*peak_inds)
highest = np.argmax(peak_ys)
en_ax.scatter(peak_xs, peak_ys, s=100, marker="X", c="k", zorder=10)
en_ax.scatter(peak_xs[highest],
peak_ys[highest],
s=150,
marker="X",
c="k",
zorder=10)
en_ax.axvline(peak_xs[highest], c="k", ls="--")
forces_ax.axvline(peak_xs[highest], c="k", ls="--")
except ValueError as err:
print("Peak-detection failed!")
# fig.legend(loc="upper right")
plt.tight_layout()
plt.show()