Turbulance Language Reference

Overview

The Turbulance programming language is designed around the principle of Scientific Method Encoding—a paradigm where scientific reasoning becomes a first-class programming construct. This reference provides comprehensive documentation of language syntax, semantics, and usage patterns.

Basic Syntax
Data Types
Variables and Constants
Functions
Scientific Constructs
Control Flow
Pattern Matching
Biological Operations
Metacognitive Features
Standard Library

Basic Syntax

Comments

# Single line comment
# Multi-line comments use multiple # symbols

Statements

Statements in Turbulance end with newlines and use indentation for scope:

statement_one
statement_two
    indented_block
    another_indented_statement

Data Types

Primitive Types

Numeric Types

item integer_value = 42
item float_value = 3.14159
item scientific_notation = 6.022e23

Boolean Type

item is_active = true
item is_complete = false

String Type

item message = "Hello, biological computing!"
item multiline = """
    This is a multiline string
    that preserves formatting
"""

None Type

item empty_value = none

Collection Types

Arrays

item numbers = [1, 2, 3, 4, 5]
item mixed_array = [1, "text", true, 3.14]
item nested_arrays = [[1, 2], [3, 4], [5, 6]]

Dictionaries

item properties = {
    "energy_level": 2.5,
    "stability": 0.87,
    "active": true
}

Scientific Types

Evidence

item evidence_piece = Evidence {
    id: "exp_001",
    source: "biological_sensor",
    value: 0.85,
    confidence: 0.92,
    timestamp: current_time()
}

Pattern

item efficiency_pattern = pattern("high_energy", oscillatory)
item stability_pattern = pattern("steady_state", temporal)

Variables and Constants

Variable Declaration

item variable_name = initial_value
item energy_level = 0.0
item molecule_count = harvest_energy("atp_synthesis")

Multiple Assignment

item x, y, z = [1, 2, 3]
item energy, efficiency = process_molecule("glucose")

Variable Scope

Variables follow lexical scoping rules:

item global_var = "accessible everywhere"

funxn example_function():
    item local_var = "only accessible in function"
    
    within "scope_block":
        item block_var = "only accessible in this block"
        # Can access global_var and local_var here
    
    # block_var not accessible here

Functions

Function Declaration

funxn function_name(parameter1, parameter2):
    # function body
    return result

Examples

Simple Function

funxn add_numbers(a, b):
    return a + b

item result = add_numbers(5, 10)

Biological Processing Function

funxn optimize_metabolism(substrate, target_efficiency):
    item current_efficiency = process_molecule(substrate)
    item energy_yield = harvest_energy("glycolysis")
    
    given current_efficiency < target_efficiency:
        item adjustment = target_efficiency - current_efficiency
        adjust_metabolic_rate(adjustment)
    
    return [current_efficiency, energy_yield]

Function with Default Parameters

funxn create_demon(demon_type="metabolic", threshold=2.5):
    item demon = BiologicalMaxwellDemon {
        type: demon_type,
        energy_threshold: threshold,
        state: "inactive"
    }
    return demon

Scientific Constructs

Propositions and Motions

Basic Proposition

proposition EnergyEfficiency:
    motion HighConversion("System achieves >90% energy conversion")
    motion StableOperation("Maintains consistent performance")
    motion ThermodynamicCompliance("Respects thermodynamic laws")

Proposition with Evidence Requirements

proposition MetabolicOptimization:
    motion ATPMaximization("Maximize ATP synthesis rate")
    motion WasteMinimization("Minimize metabolic waste products")
    
    # Evidence requirements
    requires_evidence from ["biosensor_array", "metabolic_analyzer"]
    
    # Support conditions
    given atp_rate > 0.9:
        support ATPMaximization with_weight(0.95)
    
    given waste_level < 0.1:
        support WasteMinimization with_weight(0.8)

Evidence Collection

Basic Evidence Collector

evidence ExperimentalData from "sensor_network":
    collect energy_measurements
    collect efficiency_metrics
    validate data_quality

Advanced Evidence Collection

evidence ComprehensiveAnalysis from "multi_sensor_array":
    collect_batch:
        - temperature_readings
        - pressure_measurements  
        - chemical_concentrations
        - quantum_coherence_data
    
    validation_rules:
        - thermodynamic_consistency
        - measurement_uncertainty < 0.05
        - temporal_coherence > 0.9
    
    processing_pipeline:
        1. raw_data_filtering
        2. noise_reduction
        3. statistical_analysis
        4. confidence_calculation

Goal Systems

Simple Goal

goal OptimizePerformance:
    description: "Achieve optimal system performance"
    success_threshold: 0.95
    metrics:
        efficiency: 0.0
        stability: 0.0
        throughput: 0.0

Complex Goal with Subgoals

goal SystemOptimization:
    description: "Complete system optimization with multiple objectives"
    success_threshold: 0.9
    
    subgoals:
        EnergyEfficiency:
            weight: 0.4
            threshold: 0.95
        
        ProcessingSpeed:
            weight: 0.3  
            threshold: 0.85
        
        Reliability:
            weight: 0.3
            threshold: 0.98
    
    constraints:
        - energy_consumption < max_energy_budget
        - temperature < critical_temperature
        - error_rate < 0.01

Control Flow

Conditional Statements

Basic Conditional

given condition:
    execute_if_true()
otherwise:
    execute_if_false()

Multiple Conditions

given energy_level > 0.8:
    operate_at_full_capacity()
given energy_level > 0.5:
    operate_at_reduced_capacity()
otherwise:
    enter_conservation_mode()

Complex Conditions

given (temperature < 310.0) and (pressure > 1.0) and not system_overload:
    continue_normal_operation()
otherwise:
    trigger_safety_protocols()

Loops

While Loop

item counter = 0
while counter < 10:
    process_iteration(counter)
    counter = counter + 1

For Loop

for element in collection:
    process_element(element)

for i in range(10):
    perform_optimization_step(i)

Scientific Iteration Loop

optimize_until goal_achieved:
    item current_performance = measure_system_performance()
    item adjustment = calculate_optimization_step()
    apply_adjustment(adjustment)
    
    # Loop continues until goal is achieved
    check_goal_progress("SystemOptimization")

Pattern Matching

Basic Pattern Matching

item data = collect_sensor_data()
item efficiency_pattern = pattern("high_efficiency", oscillatory)

given data matches efficiency_pattern:
    apply_efficiency_optimization()
otherwise:
    investigate_anomaly()

Advanced Pattern Matching

within "pattern_analysis":
    item patterns = {
        "efficiency": pattern("optimal_performance", temporal),
        "stability": pattern("steady_state", spatial),
        "anomaly": pattern("irregular_behavior", emergent)
    }
    
    for pattern_name, pattern_def in patterns.items():
        item match_result = sensor_data matches pattern_def
        given match_result:
            record_pattern_match(pattern_name, match_result.confidence)

Pattern Types

# Temporal patterns - time-based sequences
item temporal_pattern = pattern("growth_cycle", temporal)

# Spatial patterns - geometric or structural
item spatial_pattern = pattern("molecular_arrangement", spatial)

# Oscillatory patterns - periodic behavior
item oscillatory_pattern = pattern("metabolic_rhythm", oscillatory)

# Emergent patterns - complex system behavior
item emergent_pattern = pattern("collective_behavior", emergent)

Biological Operations

Molecular Processing

# Basic molecule processing
item energy_yield = process_molecule("glucose")
item products = process_molecule("substrate", enzyme="catalase")

# Advanced molecular processing with parameters
item processing_result = process_molecule("complex_substrate") {
    temperature: 310.0,
    ph_level: 7.4,
    concentration: 0.1,
    catalyst: "biological_enzyme_x"
}

Energy Harvesting

# Energy harvesting from various sources
item atp_energy = harvest_energy("atp_synthesis")
item glycolysis_energy = harvest_energy("glycolysis_pathway")
item photosynthetic_energy = harvest_energy("light_harvesting_complex")

# Energy harvesting with efficiency monitoring
item energy_data = harvest_energy("krebs_cycle") {
    monitor_efficiency: true,
    target_yield: 0.9,
    adaptive_optimization: true
}

Information Extraction

# Extract information from biological processes
item metabolic_info = extract_information("metabolic_state")
item genetic_info = extract_information("gene_expression")
item structural_info = extract_information("protein_conformation")

# Information extraction with processing
item processed_info = extract_information("cellular_state") {
    processing_method: "shannon_entropy",
    noise_filtering: true,
    confidence_threshold: 0.8
}

Membrane Operations

# Basic membrane state updates
update_membrane_state("high_permeability")
update_membrane_state("selective_transport")

# Advanced membrane control
configure_membrane {
    permeability: 0.7,
    selectivity: {
        "Na+": 0.9,
        "K+": 0.8,
        "Cl-": 0.6
    },
    transport_rate: 2.5,
    energy_requirement: 1.2
}

Metacognitive Features

Reasoning Monitoring

metacognitive ReasoningTracker:
    track_reasoning("optimization_process")
    track_reasoning("pattern_recognition")
    track_reasoning("decision_making")
    
    # Confidence evaluation
    item current_confidence = evaluate_confidence()
    
    # Bias detection
    item bias_detected = detect_bias("confirmation_bias")
    item availability_bias = detect_bias("availability_heuristic")

Adaptive Behavior

metacognitive AdaptiveLearning:
    # Monitor system performance
    item performance_metrics = monitor_performance()
    
    # Adapt based on performance
    given performance_metrics.accuracy < 0.8:
        adapt_behavior("increase_evidence_collection")
    
    given performance_metrics.efficiency < 0.7:
        adapt_behavior("optimize_processing_pipeline")
    
    # Learn from past decisions
    analyze_decision_history()
    update_decision_strategies()

Confidence Management

metacognitive ConfidenceManager:
    # Track confidence over time
    confidence_history = []
    
    funxn update_confidence():
        item current_confidence = evaluate_confidence()
        confidence_history.append(current_confidence)
        
        # Adaptive confidence thresholds
        given current_confidence < 0.6:
            increase_evidence_requirements()
        
        given current_confidence > 0.95:
            reduce_computational_overhead()

Standard Library

Mathematical Functions

# Basic math
item result = abs(-5)      # Absolute value
item power = pow(2, 8)     # Power function
item sqrt_val = sqrt(16)   # Square root
item log_val = log(10)     # Natural logarithm

# Statistical functions
item mean_val = mean([1, 2, 3, 4, 5])
item std_dev = stdev(data_array)
item correlation = corr(data_x, data_y)

Scientific Functions

# Thermodynamic calculations
item entropy_change = calculate_entropy_change(initial_state, final_state)
item free_energy = gibbs_free_energy(enthalpy, entropy, temperature)

# Information theory
item shannon_entropy = shannon(probability_distribution)
item mutual_information = mutual_info(signal_x, signal_y)
item information_gain = info_gain(dataset, attribute)

Biological Utility Functions

# Molecular calculations
item molecular_weight = calculate_mw("C6H12O6")  # Glucose
item binding_affinity = calculate_ka(concentration, bound_fraction)

# Metabolic pathway analysis
item pathway_flux = analyze_flux("glycolysis", metabolite_concentrations)
item enzyme_efficiency = calculate_kcat_km(enzyme_data)

String and Data Processing

# String operations
item formatted = format("Energy level: {:.2f} kJ/mol", energy_value)
item sequence = reverse("ATCGGCAT")
item length = len(data_array)

# Data structure operations
item sorted_data = sort(measurements, key="timestamp")
item filtered_data = filter(data, lambda x: x.confidence > 0.8)
item mapped_data = map(data, lambda x: x * conversion_factor)

Advanced Features

Quantum Operations

quantum_state qubit_system:
    amplitude: 1.0
    phase: 0.0
    coherence_time: 1000.0

# Quantum gate operations
apply_hadamard(qubit_system)
apply_cnot(control_qubit, target_qubit)

# Quantum measurement
item measurement_result = measure(qubit_system)
item entanglement_degree = measure_entanglement(qubit_pair)

Parallel Processing

# Parallel execution
parallel_execute:
    task_1: process_molecule_batch(batch_1)
    task_2: process_molecule_batch(batch_2)
    task_3: analyze_patterns(sensor_data)

# Wait for all tasks to complete
item results = await_all_tasks()

Error Handling

try:
    item result = risky_biological_operation()
catch BiologicalError as e:
    handle_biological_failure(e)
    item result = fallback_operation()
catch QuantumDecoherenceError:
    restore_quantum_coherence()
    retry_operation()
finally:
    cleanup_resources()

Language Conventions

Naming Conventions

Variables: snake_case (e.g., energy_level, processing_rate)
Functions: snake_case (e.g., process_molecule, harvest_energy)
Propositions: PascalCase (e.g., EnergyEfficiency, SystemOptimization)
Constants: UPPER_CASE (e.g., MAX_ENERGY, BOLTZMANN_CONSTANT)

Code Organization

# Import statements at the top
import biological_utils
import quantum_operations

# Constants
item TEMPERATURE_THRESHOLD = 310.0
item MAX_ITERATIONS = 1000

# Function definitions
funxn main():
    # Main program logic
    pass

# Execute main function
main()

Best Practices

Use descriptive variable names that reflect biological or scientific meaning
Group related operations within within blocks for clarity
Always validate evidence before making scientific conclusions
Monitor confidence levels and adapt behavior accordingly
Document complex propositions with clear motion descriptions
Use metacognitive features to ensure robust reasoning

This completes the comprehensive Turbulance Language Reference. For implementation examples and advanced usage patterns, see the Examples section.