Charts

pmcontrols turns its results into the standard project-control charts. The plots are optional: the validated statistics stay dependency-free, and the plotting code lives behind the plot extra.

pip install "pmcontrols[plot]"

Every chart function returns the matplotlib (figure, axes), so you can restyle it, drop it onto an existing axis, embed it in a report, or save it in any format matplotlib supports. The numbers come straight from the results, so a chart always matches the validated output.

Gantt chart

import pmcontrols as pm

r = pm.cpm(activities)
fig, ax = pm.gantt(r)
fig.savefig("schedule.png", dpi=150)

Each activity is a bar from its earliest start to its earliest finish. Critical activities (zero total float) are red, the rest blue, and the light grey bar behind a non-critical activity is its total float. A zero-duration activity is drawn as a diamond (a milestone). Works on a cpm or crash result.

Network diagram

fig, ax = pm.network_diagram(pm.cpm(activities))

An activity-on-node view: nodes are activities (placed by earliest start), arrows are precedence, and the critical path is red. It reads the precedence straight from the result's provenance, so a cpm or crash result is all it needs.

Earned value S-curve

pmb = pm.plan(periods, pv)
r = pm.evm(pmb, ev=30_000, ac=35_000, at=4)
fig, ax = pm.evm_curve(pmb, r)

The classic earned-value chart: the planned-value curve, the earned value and actual cost at the status date, the budget at completion, and the earned-schedule forecast running from the current earned value to the forecast completion (IEAC).

Criticality and the completion distribution

These read a pert result. The criticality bars need only the standard result; the histogram needs the Monte Carlo sample, so run pert with keep_samples=True.

r = pm.pert(activities, keep_samples=True)
pm.criticality(r)        # per-activity share of runs on the critical path
pm.mc_distribution(r)    # histogram of completion times with p50/p80/p95

criticality highlights the activities that drive schedule risk, including near-critical ones a deterministic critical path hides. mc_distribution shows the spread of completion times with the key percentiles marked.