def plot_individual_energy(data, plot_trace_kwargs=None, save=True):
"""
Individual plot of the Delta G time evolution
:param data: np.array
:param plot_trace_kwargs: kwargs for msmexplorer's plot_trace function
:param save: Bool, save image or not
:return ax, side_ax: The main and side axis produced by plot_trace
"""
if plot_trace_kwargs is None:
plot_trace_kwargs = {}
# actual values
ax, side_ax = plot_trace(data=data, window=1, alpha=0.2,
**plot_trace_kwargs)
# mean and std lines in side_ax
side_ax.axhline(y=data.mean())
side_ax.axhline(y=data.mean() - data.std(), ls='--', lw=0.8)
side_ax.axhline(y=data.mean() + data.std(), ls='--', lw=0.8)
# annotate side_ax with actual values
side_ax.annotate(f"\u03bc = {data.mean():.02f}", xy=(0.55, 0.90), xycoords='axes fraction')
side_ax.annotate(f"\u03c3 = {data.std():.02f}", xy=(0.55, 0.85), xycoords='axes fraction')
# moving avg in ax
plot_trace(data=data, window=max(1, int(len(data) / 100)),
ax=ax, alpha=1,
**plot_trace_kwargs)
ax.set(title=args.plot_title, xlabel='Time (ns)',
ylabel='$\Delta G (kcal \cdot mol^{-1}$)')
formatter = FuncFormatter(to_ns)
ax.xaxis.set_major_formatter(formatter)
if save:
f = plt.gcf()
f.savefig((args.output_file + '_individual.pdf'))
f.savefig((args.output_file + '_individual.png'), dpi=300)
return ax, side_ax
def plot_individual_energy(data, plot_trace_kwargs=None, save=True):
"""
Individual plot of the Delta G time evolution
:param data: np.array
:param plot_trace_kwargs: kwargs for msmexplorer's plot_trace function
:param save: Bool, save image or not
:return ax, side_ax: The main and side axis produced by plot_trace
"""
if plot_trace_kwargs is None:
plot_trace_kwargs = {}
# actual values
ax, side_ax = plot_trace(data=data,
window=1,
alpha=0.2,
**plot_trace_kwargs)
# mean and std lines in side_ax
side_ax.axhline(y=data.mean())
side_ax.axhline(y=data.mean() - data.std(), ls='--', lw=0.8)
side_ax.axhline(y=data.mean() + data.std(), ls='--', lw=0.8)
# annotate side_ax with actual values
side_ax.annotate(f"\u03bc = {data.mean():.02f}",
xy=(0.55, 0.90),
xycoords='axes fraction')
side_ax.annotate(f"\u03c3 = {data.std():.02f}",
xy=(0.55, 0.85),
xycoords='axes fraction')
# moving avg in ax
plot_trace(data=data,
window=max(1, int(len(data) / 100)),
ax=ax,
alpha=1,
**plot_trace_kwargs)
ax.set(title=args.plot_title,
xlabel='Time (ns)',
ylabel='$\Delta G (kcal \cdot mol^{-1}$)')
formatter = FuncFormatter(to_ns)
ax.xaxis.set_major_formatter(formatter)
if save:
f = plt.gcf()
f.savefig((args.output_file + '_individual.pdf'))
f.savefig((args.output_file + '_individual.png'), dpi=300)
return ax, side_ax
def plot_rsmd(traj_list, fout=None):
rmsd_list = [mdtraj.rmsd(traj, traj, 0) * 10 for traj in traj_list]
ax, side_ax = msme.plot_trace(rmsd_list[0], ylabel='RMSD (Å)', xlabel='Time (ns)',
label=args.Trajectories[0][:-3],
**next(palette_cycled))
formatter = FuncFormatter(to_ns)
ax.xaxis.set_major_formatter(formatter)
if len(rmsd_list) > 1:
for i, rmsd in enumerate(rmsd_list[1:]):
msme.plot_trace(rmsd, ylabel='RMSD (Å)', xlabel='Time (ns)', ax=ax,
side_ax=side_ax,
label=args.Trajectories[i + 1][:-3],
**next(palette_cycled))
if len(rmsd_list) > 5:
ax.legend_.remove()
sns.despine()
f = plt.gcf()
f.savefig(fout)
""" Trace Plot ========== """ from msmbuilder.example_datasets import FsPeptide from msmbuilder.featurizer import RMSDFeaturizer import msmexplorer as msme # Load Fs Peptide Data traj = FsPeptide().get().trajectories[0] # Calculate RMSD featurizer = RMSDFeaturizer(reference_traj=traj[0]) rmsd = featurizer.partial_transform(traj).flatten() # Plot Trace msme.plot_trace(rmsd, label='traj0', xlabel='Timestep', ylabel='RMSD (nm)')
def plot_tic_array(tica_trajs,
nrows,
ncols,
ts=0.2,
subplot_kwargs={},
trace_kwargs={},
free_energy_kwargs={}):
'''
Plot a nrows x cols array of the tIC projections, starting from the 1st one.
Parameters
----------
tica_trajs: list, or np.ndarray
The tica transformed trajectory(ies)
nrows: int
Number of rows
ncols: int
Number of cols
ts: float or int (default: 0.2)
Timestep (in microseconds) between each frame in the trajectory
subplot_kwargs: dict, optional
Arguments to pass to plt.subplots
trace_kwargs: dict, optional
Arguments to pass to msme.plot_trace
free_energy_kwargs: dict, optional
Arguments to pass to msme.plot_free_energy
Returns
-------
ax : matplotlib axis
matplotlib figure axis
'''
def convert_to_mus(x, pos):
'function for formatting the x axis of time trace plot'
return x * ts
if isinstance(tica_trajs, list):
tica_trajs = np.concatenate(tica_trajs)
elif not isinstance(tica_trajs, np.ndarray):
raise ValuError('tica_trajs must be of type list or np.ndarray')
fig, ax_list = plt.subplots(nrows=nrows, ncols=ncols, **subplot_kwargs)
for i in range(nrows):
for j in range(ncols):
if (nrows == 1) and (ncols == 1):
# User just passed a 1x1 array...
ax = ax_list
elif nrows == 1:
ax = ax_list[j]
elif ncols == 1:
ax = ax_list[i]
else:
ax = ax_list[i][j]
ax.grid(False)
if i == j:
msme.plot_trace(tica_trajs[:, i], ax=ax, **trace_kwargs)
formatter = FuncFormatter(convert_to_mus)
ax.xaxis.set_major_formatter(formatter)
ax.grid(False, axis='y')
ax.set_xlabel('Time ($\mu$s)')
else:
msme.plot_free_energy(tica_trajs,
obs=(j, i),
ax=ax,
**free_energy_kwargs)
# Bottom row
if i == (nrows - 1):
ax.set_xlabel('tIC{}'.format(j + 1))
# First column
if j == 0:
ax.set_ylabel('tIC{}'.format(i + 1))
fig.tight_layout()
return fig