synapse-lang 2.0.0

A revolutionary programming language for scientific computing with parallel execution and uncertainty quantification

Synapse Programming Language

Created by Michael Benjamin Crowe

A proprietary programming language designed for deep scientific reasoning and enhanced parallel thought processing pipelines.

Dark / light adaptive inline logo example:

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/MichaelCrowe11/synapse-lang/master/branding/dark-inline.svg">
  <img alt="Synapse-Lang" width="140" src="https://raw.githubusercontent.com/MichaelCrowe11/synapse-lang/master/branding/light-inline.svg" />
</picture>

Overview

Synapse is a domain-specific language that combines:

• Parallel execution streams for simultaneous hypothesis testing
• Uncertainty quantification built into the type system
• Scientific reasoning chains with formal logic constructs
• Native tensor operations for high-dimensional data
• Hypothesis-driven programming paradigm

Key Features

1. Uncertainty-Aware Computing

uncertain measurement = 42.3 ± 0.5
uncertain temperature = 300 ± 10
// Uncertainty propagates automatically through calculations

2. Parallel Thought Streams

parallel {
    branch A: test_hypothesis_1()
    branch B: test_hypothesis_2()
    branch C: control_experiment()
}

3. Reasoning Chains

reason chain ScientificMethod {
    premise P1: "Observable phenomenon exists"
    derive D1 from P1: "Hypothesis can be formed"
    conclude: D1 => "Experiment validates or refutes"
}

Project Structure

synapse-lang/
├── LANGUAGE_SPEC.md          # Complete language specification
├── synapse_interpreter.py     # Core interpreter implementation
├── test_synapse.py           # Test suite
├── examples/
│   ├── quantum_simulation.syn    # Quantum mechanics example
│   ├── climate_model.syn        # Climate modeling example
│   └── drug_discovery.syn       # Drug discovery pipeline
└── README.md                 # This file

Running Tests

cd synapse-lang
python test_synapse.py

Example Programs

Quantum Simulation

Demonstrates parallel evolution of quantum states with uncertainty:

experiment DoubleSlitSimulation {
    parallel {
        branch slit_A: evolve_wavefunction(path="A")
        branch slit_B: evolve_wavefunction(path="B")
    }
    synthesize: compute_interference(slit_A, slit_B)
}

Climate Modeling

Complex system analysis with ensemble runs:

parallel {
    branch RCP2.6: model_pathway(emissions="low")
    branch RCP4.5: model_pathway(emissions="moderate")
    branch RCP8.5: model_pathway(emissions="high")
}

Drug Discovery

Molecular simulation pipeline with parallel screening:

pipeline DrugDiscovery {
    stage VirtualScreening parallel(64) {
        fork {
            path ligand_docking: autodock_vina
            path ml_prediction: graph_neural_network
        }
    }
}

Implementation Status

✅ Completed:

• Basic lexer/tokenizer
• Token types for scientific operators
• Uncertain value arithmetic with error propagation
• Parallel execution framework
• Variable storage and retrieval
• Example scientific programs

🚧 In Progress:

• Full parser implementation
• Advanced reasoning chains
• Tensor operations
• Symbolic mathematics
• Pipeline execution

Design Philosophy

Synapse is designed to express scientific thinking naturally:

1. Hypothesis-first: Start with assumptions, derive conclusions
2. Parallel exploration: Test multiple theories simultaneously
3. Uncertainty-native: Propagate measurement errors automatically
4. Reasoning chains: Build formal logical arguments
5. Pipeline-oriented: Structure complex workflows

Branding & Assets

The project now includes an initial logo/identity kit (v1) under branding/LOGO_KIT.html featuring:

• Synapse‑Lang horizontal + stacked lockups
• Icon-only badge (simplified path & nodes)
• Favicon simplification (no ± token for clarity at 16–32px)
• Seed marks for Qubit‑Flow and QNet (shared gradient family; QNet uses emerald accent)
• Palette & typography reference (Inter + JetBrains Mono)

Quick Usage (Markdown badge / inline HTML)

<!-- Inline SVG icon example -->
<svg width="20" height="20" viewBox="0 0 256 256" aria-hidden="true">
    <defs>
        <linearGradient id="synG" x1="0%" y1="0%" x2="100%" y2="100%">
            <stop offset="0%" stop-color="#43E5FF"/>
            <stop offset="100%" stop-color="#7A5CFF"/>
        </linearGradient>
    </defs>
    <path d="M64 56 C128 56, 128 120, 192 120 C224 120, 224 160, 192 176 C160 192, 112 184, 88 208" fill="none" stroke="url(#synG)" stroke-width="18" stroke-linecap="round"/>
    <circle cx="64" cy="56" r="16" fill="#0B0F14"/>
    <circle cx="192" cy="120" r="16" fill="#0B0F14"/>
    <circle cx="88" cy="208" r="16" fill="#0B0F14"/>
</svg>
<span style="font:600 14px 'JetBrains Mono', monospace; vertical-align:middle;">Synapse‑Lang</span>

Guidelines: keep gradient stroke, avoid arbitrary recoloring of node fills except to invert for dark backgrounds; maintain minimum horizontal lockup width of 160px or switch to icon.

Asset Inventory

• branding/light-inline.svg – Inline horizontal lockup (light / default)
• branding/dark-inline.svg – Inline horizontal lockup tuned for dark backgrounds
• branding/LOGO_KIT.html – Interactive reference + download kit
• scripts/optimize_svgs.py – Batch optimizer (uses svgo if available, else trims whitespace)

Optimizing SVG Assets

Optional deep optimization via svgo (Node.js). Fallback lightweight trim always works.

Install (optional):

npm install -g svgo

Run optimization (auto-modifies files):

python -m synapse_lang.scripts.optimize_svgs

CI / check mode (fails if further optimization possible):

python -m synapse_lang.scripts.optimize_svgs --check

Output lists per-file bytes saved plus total.