Refresh PyPI project metadata/readme after package rename to extropy-run.

Rename PyPI package to extropy-run and publish first extropy-run release.

Publish extropy package to PyPI after repo/package rename.

Highlights

• Added per-run token usage and cost tracking in simulation outputs (meta.json).
• Added schema-driven categorical null phrasing (null_options / null_phrase) for persona rendering.
• Added dependency auto-inference during constraint binding and related validator coverage.
• Removed checked-in study artifacts from the core package repo.

Validation

• ruff check .
• ruff format --check .
• pytest -q (637 passed)

What's New

Simulation Dynamics

• Added propagation damping controls (decay_per_hop, max_hops) and bounded spread behavior in simulation propagation.
• Added option-level friction support for categorical outcomes to better model behavior persistence under social pressure.
• Improved state handling for public/private dynamics and stabilization behavior in simulation engine.

Validation & Runtime Correctness

• Fixed scenario validation result construction so errors/warnings are preserved and surfaced correctly.
• Fixed scenario file-reference validation to resolve relative paths against the scenario file location.
• Fixed validator/runtime contract for spread modifiers (edge_weight is now recognized in validation).
• Fixed boolean expression consistency in safe evaluation (true/false now handled consistently at runtime).
• Fixed expression syntax false-positives for valid escaped apostrophes and string literals.

Network Config Reliability

• Fixed network config generation so degree multiplier condition values are typed correctly (boolean/number/string) instead of string-only.
• Removed legacy preset network config from runtime; network behavior is now fully config-driven.

CLI

• Improved scenario detection in entropy validate to handle scenario.yaml filenames directly.

Full Changelog: v0.1.3...v0.1.4

What's New

• Chat Completions API support for Azure OpenAI models (DeepSeek-V3.2, Kimi-K2.5, gpt-5-mini)
• simulation.api_format config key (auto-defaults: chat_completions for Azure, responses for OpenAI)
• Async reasoning timeouts (30s/20s) to prevent batch hangs
• Fix: rate limit overrides now applied to both pivotal and routine limiters
• Defensive input validation: rescale 0-1 conviction scores, clamp out-of-range sentiment
• DeepSeek-V3.2 and Kimi-K2.5 pricing added

What's New

Azure OpenAI Support

• New provider: azure_openai — works in both pipeline and simulation zones
• Reuses OpenAIProvider by swapping in Azure SDK clients at construction time
• Configure via: entropy config set simulation.provider azure_openai
• Env vars: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT
• Default API version: 2025-03-01-preview (required for Responses API)

Progress Display Fixes

• Fix stale data in progress display when 0 agents to reason
• Cap position names at 40 chars to prevent layout overflow
• Type AgentDoneCallback with ReasoningResponse instead of Any
• Remove redundant avg_sentiment/avg_conviction properties

Tests

• 600 tests across 15 test files

What's Changed

New Features

• entropy estimate - Predict simulation cost (LLM calls, tokens, USD) without running it
• Adaptive network calibration - Binary search for target average degree
• Claude Code skill - Pipeline assistance integration

Bug Fixes

• Fix relative path resolution in scenario files - commands now work from any directory
• Fix float_to_conviction returning string instead of float
• Fix rate limiter 429 storms - staggered task launches, per-model splitting, concurrency caps
• Fix async HTTP client cleanup before event loop shutdown
• Register missing persona command
• Add missing simulation config keys (pivotal_model, routine_model, rate_tier)

Improvements

• Code cleanup: performance, reliability, tests
• CI: enable uv cache, add workflow_dispatch triggers
• 158 new simulation validation tests

Full Changelog: v0.1.0...v0.1.1

Initial public release of entropy-predict.

Predictive intelligence through agent-based population simulation.

• 7-step pipeline: spec → extend → sample → network → persona → scenario → simulate
• Two-pass LLM reasoning (role-play + classification) to eliminate central tendency bias
• Categorical conviction system with memory traces
• Token bucket rate limiter with provider-aware defaults
• Two-zone config: mix providers for pipeline (Claude) and simulation (OpenAI)
• Persona system: first-person agent narratives with z-score relative positioning
• Network propagation with edge-typed spread modifiers

pip install entropy-predict