Data & ReproGIS & Remote SensingK-Dense-AI/claude-scientific-skillsData & Reproduction
SI

SimPy

Maintainer K-Dense Inc. · Last updated April 1, 2026

SimPy is a process-based discrete-event simulation framework based on standard Python. Use SimPy to model systems where entities (customers, vehicles, packets, etc.) interact with each other and compete for shared resources (servers, machines, bandwidth, etc.) over time. **Core capabilities:** - Process modeling using Python generator functions - Shared resource management (servers, containers, stores) - Event-drive….

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Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/simpy

Maintainer
K-Dense Inc.
License
MIT license
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Process modeling using Python generator functions.
  • Shared resource management (servers, containers, stores).
  • Event-driven scheduling and synchronization.
  • Real-time simulations synchronized with wall-clock time.
  • Comprehensive monitoring and data collection.

Source Doc

Excerpt From SKILL.md

When to Use This Skill

Use the SimPy skill when:

  1. Modeling discrete-event systems - Systems where events occur at irregular intervals
  2. Resource contention - Entities compete for limited resources (servers, machines, staff)
  3. Queue analysis - Studying waiting lines, service times, and throughput
  4. Process optimization - Analyzing manufacturing, logistics, or service processes
  5. Network simulation - Packet routing, bandwidth allocation, latency analysis
  6. Capacity planning - Determining optimal resource levels for desired performance
  7. System validation - Testing system behavior before implementation

Not suitable for:

  • Continuous simulations with fixed time steps (consider SciPy ODE solvers)
  • Independent processes without resource sharing
  • Pure mathematical optimization (consider SciPy optimize)

Basic Simulation Structure

import simpy

def process(env, name):
    """A simple process that waits and prints."""
    print(f'{name} starting at {env.now}')
    yield env.timeout(5)
    print(f'{name} finishing at {env.now}')

## Start processes

env.process(process(env, 'Process 1'))
env.process(process(env, 'Process 2'))

Use cases

  • Use SimPy for GIS and remote-sensing workflows.
  • Apply SimPy to earth observation and spatial analysis tasks.
  • Use simpy for GIS and remote-sensing workflows.
  • Apply simpy to earth observation and spatial analysis tasks.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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