Plots¶

The mdpp.plots subpackage provides visualization functions. All plot functions accept an optional ax parameter and return a matplotlib Axes object.

Time Series¶

RMSD, RMSF, SASA, Radius of Gyration¶

from mdpp.plots import plot_rmsd, plot_rmsf, plot_sasa, plot_radius_of_gyration

ax = plot_rmsd(rmsd_result, label="backbone")
ax = plot_rmsf(rmsf_result)
ax = plot_sasa(sasa_result)
ax = plot_radius_of_gyration(rg_result)

Hydrogen bond counts and occupancy¶

from mdpp.plots import plot_hbond_counts, plot_hbond_occupancy

ax = plot_hbond_counts(hbond_result)
ax = plot_hbond_occupancy(hbond_result, top_n=10)

Distance time series¶

from mdpp.plots import plot_distances

ax = plot_distances(distance_result)

Native contacts Q(t)¶

from mdpp.plots import plot_native_contacts

ax = plot_native_contacts(native_contact_result)

Energy plots¶

Plot energy terms from parsed GROMACS output:

from mdpp.core import read_xvg
from mdpp.plots import plot_energy

df = read_xvg("energy.xvg")
ax = plot_energy(df, columns=["Potential", "Kinetic En."])  # columns selects which terms to plot

Matrix Plots¶

DCCM heatmap¶

from mdpp.plots import plot_dccm

ax = plot_dccm(dccm_result, cmap="RdBu_r")

Delta-RMSF¶

from mdpp.plots import plot_delta_rmsf

ax = plot_delta_rmsf(delta_rmsf_result)

Contact map¶

from mdpp.analysis.contacts import compute_contact_frequency
from mdpp.plots import plot_contact_map, contact_frequency_to_matrix

frequency, pairs = compute_contact_frequency(traj)
matrix = contact_frequency_to_matrix(frequency, pairs, n_residues=traj.n_residues)
ax = plot_contact_map(matrix)

Free Energy Surface¶

from mdpp.plots import plot_fes

ax = plot_fes(fes_result, cmap="coolwarm", contour_levels=15)

Scatter Plots¶

PCA / TICA projection¶

from mdpp.plots import plot_projection
import numpy as np

time = np.arange(len(pca_result.projections))
ax = plot_projection(pca_result, color_by=time, cmap="coolwarm")

Ramachandran plot¶

from mdpp.analysis.decomposition import featurize_backbone_torsions
from mdpp.plots import plot_ramachandran

torsions = featurize_backbone_torsions(traj, sincos_embedding=False)
ax = plot_ramachandran(torsions)

Molecule Drawing¶

Single molecule¶

from mdpp.plots import draw_mol
from rdkit import Chem

mol = Chem.MolFromSmiles("c1ccc(NC(=O)c2ccccc2)cc1")
img = draw_mol(mol, img_size=(400, 400))

With substructure highlighting¶

pattern = Chem.MolFromSmarts("c1ccccc1")
img = draw_mol(mol, pattern=pattern, highlight=True)

Grid of molecules¶

from mdpp.plots import draw_mols

mols = [Chem.MolFromSmiles(smi) for smi in smiles_list]
img = draw_mols(mols, mols_per_row=4)

3D Visualization¶

Interactive molecule view (py3Dmol)¶

from mdpp.plots import view_mol_3d

viewer = view_mol_3d(mol_with_conformer, style={"stick": {}})

Atom labels¶

from mdpp.plots import make_atom_labels_3d, view_mol_3d

labels = make_atom_labels_3d(
    mol,
    text_fn=lambda atom: atom.GetSymbol(),
    base_style={"fontSize": 12, "fontColor": "black"},
)
viewer = view_mol_3d(mol, labels=labels)

Trajectory viewer (nglview)¶

from mdpp.plots import view_traj_3d

widget = view_traj_3d(traj)

# Custom representations
widget = view_traj_3d(traj, representations=[
    {"type": "cartoon", "selection": "protein", "color": "sstruc"},
    {"type": "ball+stick", "selection": "ligand"},
])

Multi-Panel Figures¶

All plot functions work with matplotlib subplots:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(12, 10))
plot_rmsd(rmsd_result, ax=axes[0, 0])
plot_rmsf(rmsf_result, ax=axes[0, 1])
plot_dccm(dccm_result, ax=axes[1, 0])
plot_fes(fes_result, ax=axes[1, 1])
fig.tight_layout()