Plot Types¶

MPP can generate 15 types of plots, all accessible through both the CLI (-p <plot>) and the Python API (mpp.plot.<method>(out)).

Dendrogram¶

What it shows: The lumping tree with macrostate boundaries and a contact-fraction colorbar. A second panel shows the macrostate assignment as a color-coded grid.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p dendrogram -o dendrogram.pdf

API:

mpp.plot.dendrogram("dendrogram.pdf")

Implied Timescales¶

What it shows: Implied timescales of the microstate model and the macrostate model across multiple lag times.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p timescales -o timescales.pdf

API:

mpp.calc_timescales(n=5)
mpp.plot.implied_timescales("timescales.pdf")

Sankey Diagram¶

What it shows: Microstate flow between the macrostates of this lumping and the reference (T none) lumping.

CLI:

python -m MPP.run config.yml KL none -Z Z.npy -p sankey -o sankey.pdf

API:

mpp.plot.sankey("sankey.pdf")

Contact Representation¶

What it shows: Mean contact distances (or other features) per macrostate, using a cluster file to label contacts.

Requires: cluster_file key in YAML config (contact index file).

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p contacts -o contacts.pdf

API:

mpp.plot.contact_rep("path/to/cluster_file", "contacts.pdf")

Macrostate Trajectory¶

What it shows: The macrostate trajectory as a color-coded horizontal time series.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p macrotraj -o macrotraj.pdf

API:

mpp.plot.macrostate_trajectory("macrotraj.pdf")

Chapman-Kolmogorov Test¶

What it shows: Chapman-Kolmogorov test for the macrostate model. Compares propagated macrostate populations against direct estimates at multiples of the lag time.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p ck_test -o ck_test.pdf

API:

mpp.plot.ck_test("ck_test.pdf")

RMSD¶

What it shows: Per-macrostate C-alpha RMSD (or feature-space RMSD) relative to the mean structure of each macrostate.

Requires: Topology (.pdb) and trajectory (.xtc) files set on the Lumping object.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p rmsd -o rmsd.pdf

API:

mpp.topology_file = "structure.pdb"
mpp.xtc_trajectory_file = "trajectory.xtc"
mpp.plot.rmsd("rmsd.pdf")

Delta RMSD¶

What it shows: Per-macrostate RMSD relative to macrostate 0 (instead of each state's own mean). Highlights structural differences between macrostates.

Requires: Same as RMSD above.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p delta_rmsd -o delta_rmsd.pdf

API:

mpp.plot.delta_rmsd("delta_rmsd.pdf")

State Network¶

What it shows: A graph of macrostates as nodes, with edge widths proportional to transition probabilities.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p state_network -o state_network.pdf

API:

mpp.plot.state_network("state_network.pdf")

Transition Matrix¶

What it shows: The macrostate transition matrix as a heatmap. Entries below a threshold are shown as zero (white).

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p transition_matrix -o tmat.pdf

API:

mpp.plot.transition_matrix("tmat.pdf")

Transition Time¶

What it shows: Mean first-passage times between macrostates.

CLI:

python -m MPP.run config.yml T none -Z Z.npy -p transition_time -o transition_time.pdf

API:

mpp.plot.transition_time("transition_time.pdf")

Macrostate Trajectory (text output)¶

What it produces: Not a plot — writes the macrostate trajectory as a plain-text file (one integer per line, 1-based macrostate indices).

CLI:

python -m MPP.run config.yml T none -Z Z.npy \
    -p macrostate_trajectory -o macrostate_trajectory.txt

API:

mpp.save_macrostate_trajectory("macrostate_trajectory.txt", one_based=True)

Stochastic State Similarity¶

What it shows: Overlap of macrostate assignments between stochastic lumping runs. Only meaningful for n_runs > 1.

CLI:

python -m MPP.run config_stochastic.yml T none -Z Z.npy \
    -p stochastic_state_similarity -o state_similarity.pdf

API:

mpp.plot.stochastic_state_similarity("state_similarity.pdf")

Relative Implied Timescales¶

What it shows: Implied timescales of each stochastic run relative to the reference T none lumping. Only meaningful for n_runs > 1.

CLI:

python -m MPP.run config_stochastic.yml T none -Z Z.npy \
    -p relative_implied_timescales -o rel_timescales.pdf

API:

mpp.plot.relative_implied_timescales("rel_timescales.pdf")

Macro Feature¶

What it shows: Population-weighted mean feature per macrostate across all stochastic runs, with optional comparison to a reference lumping. Only meaningful for n_runs > 1.

CLI:

python -m MPP.run config_stochastic.yml T none -Z Z.npy \
    -p macro_feature -o macro_feature.pdf

API:

mpp.plot.macro_feature("macro_feature.pdf")