timegrid 0.1.3

Business-hours, availability, downtime, capacity, and SLA working-time calculations for Python, powered by Rust.

TimeGrid for Python

Business-hours, availability, downtime, capacity, and SLA working-time calculations for Python, powered by Rust.

TimeGrid turns schedule rules into a queryable operational timeline. Define working hours, lunch breaks, holidays, maintenance windows, capacity overrides, and named schedule entries; serialize the source definition as JSON; compile it once; then answer timestamp and range queries quickly.

Use it for factory calendars, machine availability, SLA clocks, business-hour math, capacity-aware slot search, and systems that need a dependable time layer before they schedule, simulate, dispatch, or report.

Not a scheduler. Not a simulator. Not a timezone database. TimeGrid is the availability and working-time engine those systems can depend on.

Install

pip install timegrid

30-Second Example

Calculate an SLA due date while skipping lunch, nights, weekends, and one-off closures:

from datetime import date, datetime, time
from timegrid import TimeGridCalendar, hours

calendar = (
    TimeGridCalendar.weekdays(time(9), time(17))
    .break_weekdays(time(12), time(13))
    .close(date(2026, 1, 1))
    .set_closed_window("company-offsite", datetime(2026, 1, 7, 13), datetime(2026, 1, 7, 17))
)

opened_at = datetime(2026, 1, 5, 11)
trace = calendar.at(opened_at).trace_work_duration(hours(10))

print(trace.result)
for step in trace.steps:
    print(step.window, step.duration)

Practical Examples

Runnable examples live in examples/:

python examples/sla_due_date.py
python examples/manufacturing_capacity_slot.py
python examples/fleet_batch_snapshot.py
python examples/json_schedule_template.py
python examples/overnight_shift.py

From a local checkout:

.\.venv\Scripts\python.exe examples\run_all.py

Example	What it shows
sla_due_date.py	Business-hour due dates and consumed working windows.
manufacturing_capacity_slot.py	Downtime, capacity boosts, and first slot with enough capacity.
fleet_batch_snapshot.py	Batch capacity and analysis queries across many compiled machine timelines.
json_schedule_template.py	JSON schedule templates plus per-machine exceptions.
overnight_shift.py	Open rules that cross midnight and breaks inside overnight shifts.

What It Answers

Question	API
Can this machine, team, or queue work now?	timeline.analyze(now).can_work
What is the effective capacity at this timestamp?	analysis.capacity
Which state window contains this timestamp?	analysis.current_window
When does the state change next?	analysis.next_transition
Why is this timestamp blocked or boosted?	calendar.at(now).analyze().matches
How much usable business time exists in a range?	timeline.get_working_duration(start, end)
Where is the first continuous slot with enough capacity?	timeline.find_first_slot(start, hours(2), 3)
What are many capacities at once?	timeline.get_capacities_at(instants)
What is the state of many machine timelines now?	TimeGridTimelineBatch(timelines).get_capacities_at(now)

Schedule Definitions

TimeGrid stores source rules, not compiled runtime state. That makes definitions easy to save, review, ship, and apply to many operational entities:

from datetime import datetime, time
from timegrid import TimeGridCalendar

template_json = (
    TimeGridCalendar.create()
    .open_weekdays(time(7), time(19))
    .break_weekdays(time(12), time(12, 45))
    .capacity(3)
    .to_json(indented=True)
)

template = TimeGridCalendar.from_json(template_json).to_definition()

machine = (
    template.to_calendar()
    .set_closed_window("machine-42-maintenance", datetime(2026, 1, 5, 15), datetime(2026, 1, 5, 17))
    .set_capacity_window("machine-42-overtime-crew", datetime(2026, 1, 5, 17), datetime(2026, 1, 5, 19), 5)
    .compile(datetime(2026, 1, 5), datetime(2026, 1, 6))
)

Compile For Repeated Reads

Use compile(start, end) when the same calendar will be queried many times. A compiled timeline is read-only and answers point and range queries by searching precomputed state segments.

from timegrid import TimeGridTimelineBatch, hours

timeline = calendar.compile(month_start, month_end)

point = timeline.analyze(now)
work = timeline.get_working_duration(day_start, day_end)
slot = timeline.find_first_slot(day_start, hours(4), 2)

capacities = timeline.get_capacities_at(instants)
analyses = timeline.analyze_many(instants)

fleet = TimeGridTimelineBatch(machine_timelines)
fleet_capacities = fleet.get_capacities_at(now)

API Style

Python code can use Python naming conventions while compatibility aliases preserve the original TimeGrid.NET-style names.

Surface	Python style	Compatibility style
Classes	TimeGridCalendar, TimeWindow	same
Methods and properties	snake_case	PascalCase
Constants	DayOfWeek.MONDAY	DayOfWeek.Monday

Why It Is Fast

Operation	Runtime strategy
Point analysis	Binary search over compiled state segments
Range analysis	Binary search, then scan touched segments only
Transition lookup	Segment boundary lookup
Working duration	Sum usable segment durations
Slot search	Scan continuous capacity-matching segments
Batch point queries	One Python call, Rust loop over compiled timelines
JSON	Rust serde source definition serialization

Local quick-query benchmark on CPython 3.12, Windows 10, Rust release wheel. The scenario matches the TimeGrid.NET QuickQueryPerf benchmark: 50,000 compiled state segments, 1,000 compiled machine timelines, 20,000 warmup calls, and 1,000,000 measured calls.

Common API, one Python call per query:

Scenario	Operation	Python/Rust	TimeGrid.NET
50,000 compiled state segments	get_capacity_at	0.153 us/query	0.052 us/query
50,000 compiled state segments	analyze	0.225 us/query	0.064 us/query
1,000 compiled machine timelines	get_capacity_at sweep	123.154 us	26.625 us
1,000 compiled machine timelines	analyze sweep	189.991 us	34.463 us

Batch API, one Python call per batch:

Scenario	Operation	Python/Rust
50,000 compiled state segments	get_capacities_at	0.076 us/query
50,000 compiled state segments	analyze_many	0.220 us/query
1,000 compiled machine timelines	TimeGridTimelineBatch.get_capacities_at sweep	28.342 us
1,000 compiled machine timelines	TimeGridTimelineBatch.analyze sweep	118.398 us

Measurements exclude compile time and run against already compiled timelines.

Good Fit

Use TimeGrid for	Use something else for
Business-hours and working-time calculations	Calendar UI rendering
Manufacturing availability and machine downtime	Queue dispatching
Capacity-aware slot search	Optimization solving
SLA clocks and operational reporting	Full timezone database behavior
JSON-backed schedule definitions	General-purpose date-range value objects
Scheduler or simulator availability layer	Running background jobs

Core Rules

• Time windows are half-open: [start, end).
• Capacity 0 means unavailable.
• Capacity overrides split the timeline into state segments.
• JSON stores source definitions, not compiled timelines.
• Compiled timelines are read-only and optimized for repeated analysis.
• Use naive datetime values consistently in your chosen local or UTC convention.

Direct APIs

calendar.can_work(now)
calendar.get_capacity_at(now)
calendar.get_windows_at(now)
calendar.get_previous_transition_time(now)
calendar.get_next_transition_time(now)
calendar.get_open_windows(start, end)
calendar.get_unavailable_windows(start, end)
calendar.get_state_windows(start, end)
calendar.get_working_duration(start, end)
calendar.find_first_slot(start, hours(2), 2)

timeline.get_capacities_at(instants)
timeline.analyze_many(instants)
TimeGridTimelineBatch(timelines).get_capacities_at(now)

Links

• PyPI: https://pypi.org/project/timegrid/
• Repository: https://github.com/code-gihan/timegrid-python
• Issues: https://github.com/code-gihan/timegrid-python/issues
• TimeGrid.NET: https://www.nuget.org/packages/TimeGrid.NET
• License: MIT

If TimeGrid gives you a wrong answer, a confusing error, or a missing API for a real scheduling or availability problem, please open a GitHub issue with the smallest calendar definition and query that reproduces it.