MindTrace-Cognitive-Pattern-Analyzer

MindTrace is a comprehensive cognitive analysis system that analyzes personal text data (notes, chats, journal entries, code commits) to extract patterns about thinking style, emotional trends, productivity cycles, and behavioral drift over time.

Stars: 7
Language: Python
License: MIT
Updated: 2026-04-06

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MindTrace — Cognitive Pattern Analyzer

Overview

MindTrace is a comprehensive cognitive analysis system that analyzes personal text data (notes, chats, journal entries, code commits) to extract patterns about thinking style, emotional trends, productivity cycles, and behavioral drift over time.

This is Version 2.0 — HYPER-ADVANCED EDITION with enterprise features including distributed computing, ML pipelines, LLM integration, and cloud-native deployment.

Features

Core Analysis

Feature	Description
Sentiment Analysis	VADER + TextBlob hybrid sentiment detection
Emotion Detection	Joy, Anger, Sadness, Fear, Surprise, Anticipation
Topic Modeling	LDA for extracting key topics
Named Entity Recognition	spaCy NER for entity extraction
Word Embeddings	Semantic similarity using word2vec
Time Series Analysis	Trend detection, cycle analysis, forecasting
Anomaly Detection	Isolation Forest for outlier detection
Clustering	K-Means, t-SNE for thinking style clustering

Hyper-Advanced Features

Feature	Technology
GraphQL API	Strawberry GraphQL with subscriptions
REST API	FastAPI with async support
WebSocket API	Real-time streaming analysis
Redis Caching	Distributed caching layer
Apache Spark	Big data processing
Prometheus Monitoring	Production metrics
LangChain + LLM	GPT-4, Claude integration
Vector Databases	Pinecone, Weaviate semantic search
Knowledge Graph	NetworkX cognitive graphs
GPU Acceleration	CuPy CUDA support
Kubernetes	Cloud-native deployment
Distributed Computing	Ray cluster support
Kafka Streaming	Real-time event processing
Cloud Storage	S3, GCS integration

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        MindTrace v2.0                           │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │   Jupyter   │  │    REST     │  │   GraphQL   │              │
│  │  Notebook   │  │     API     │  │     API     │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │  WebSocket  │  │    CLI      │  │   Plugins   │              │
│  │     API     │  │  Interface  │  │    System    │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
├─────────────────────────────────────────────────────────────────┤
│                        Core Engine                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │    NLP      │  │   Pattern   │  │   Time      │              │
│  │  Analyzer   │  │  Detector   │  │   Series    │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │  Clustering │  │   Vector    │  │ Knowledge   │              │
│  │             │  │   Search    │  │    Graph    │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
├─────────────────────────────────────────────────────────────────┤
│                     Storage & Compute                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │   SQLite    │  │   Spark     │  │     GPU     │              │
│  │  Database   │  │  Cluster    │  │  Acceleration│              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │   Redis     │  │  Pinecone   │  │  Weaviate   │              │
│  │   Cache     │  │             │  │             │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
├─────────────────────────────────────────────────────────────────┤
│                     Cloud & DevOps                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │ Kubernetes  │  │ Prometheus  │  │    Kafka    │              │
│  │ Deployment  │  │  Monitoring │  │  Streaming  │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │     S3      │  │    GCS      │  │    Docker   │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
└─────────────────────────────────────────────────────────────────┘

Quick Start

Installation

# Clone repository
git clone https://github.com/yourusername/MindTrace-Cognitive-Pattern-Analyzer.git
cd MindTrace-Cognitive-Pattern-Analyzer

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Download spaCy model
python -m spacy download en_core_web_sm

Basic Usage (Jupyter)

from mindtrace import MindTraceDashboard

# Initialize dashboard
dashboard = MindTraceDashboard()

# Load data
dashboard.load_data("sample_data.csv")

# Run analysis
results = dashboard.run_analysis()

# Generate report
report = dashboard.generate_report()

REST API

# Start API server
python api.py

# API available at http://localhost:8000
# Docs at http://localhost:8000/docs

GraphQL API

# Start GraphQL server
python graphql_api.py

# GraphQL playground at http://localhost:8001

WebSocket API

# Start WebSocket server
python websocket_api.py

# Connect to ws://localhost:8002/ws/{client_id}

CLI

# Analyze text
mindtrace analyze "I love working on cognitive patterns"

# Batch analyze
mindtrace batch --file data.csv

# Start server
mindtrace serve --port 8000

API Endpoints

REST API (`api.py`)

Method	Endpoint	Description
POST	`/analyze`	Analyze single text
POST	`/analyze/batch`	Bulk analyze
GET	`/results/{id}`	Get analysis result
GET	`/patterns`	Get detected patterns
GET	`/trends`	Get emotional trends

GraphQL API (`graphql_api.py`)

query {
  analysis(id: 1) {
    id
    sentiment
    topics { topic_id keywords }
    emotions { date sentiment_score }
  }
}

mutation {
  analyzeText(userId: 1, text: "Hello world") {
    id
    sentiment
  }
}

Configuration

Environment Variables

# API Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
PINECONE_API_KEY=...
WEAVIATE_API_KEY=...

# Database
DATABASE_URL=postgresql://user:pass@localhost:5432/mindtrace

# Redis
REDIS_URL=redis://localhost:6379

# Kafka
KAFKA_BOOTSTRAP_SERVERS=localhost:9092

# Cloud
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
GOOGLE_APPLICATION_CREDENTIALS=...

Config File (`config.ini`)

[analysis]
sentiment_threshold = 0.05
emotion_weights = joy:1.0, anger:0.8

[api]
host = 0.0.0.0
port = 8000
workers = 4

[cache]
ttl = 3600
enabled = true

[spark]
master = spark://localhost:7077

Module Reference

Module	File	Description
Core	`mindtrace/__init__.py`	Main analyzer class
NLP	`advanced_nlp.py`	Topic modeling, NER, embeddings
Visualization	`advanced_viz.py`	Radar, sunburst charts
Export	`export_module.py`	JSON, CSV, Excel export
Database	`database.py`	SQLite storage
API	`api.py`	FastAPI REST API
CLI	`cli.py`	Command-line interface
Tests	`test_mindtrace.py`	Unit tests
Plugins	`plugins.py`	Plugin system
Validation	`validator.py`	Data validation
Metrics	`metrics.py`	Metrics calculator
Distributed	`distributed.py`	Ray distributed
Streaming	`stream.py`	Kafka integration
Cloud	`cloud_storage.py`	S3/GCS
ML Pipeline	`ml_pipeline.py`	sklearn pipelines
Model Serving	`model_serving.py`	ML model serving

Hyper-Advanced Modules

Module	File	Description
GraphQL	`graphql_api.py`	GraphQL with subscriptions
Redis	`redis_cache.py`	Distributed caching
Spark	`spark_integration.py`	Apache Spark processing
Monitoring	`prometheus_metrics.py`	Prometheus metrics
K8s	`kubernetes/`	Kubernetes manifests
LangChain	`langchain_llm.py`	LLM integration
Vector DB	`vector_db.py`	Pinecone/Weaviate
Knowledge Graph	`knowledge_graph.py`	NetworkX graphs
WebSocket	`websocket_api.py`	Real-time API
GPU	`gpu_acceleration.py`	CuPy CUDA

Deployment

Docker

# Build image
docker build -t mindtrace:latest .

# Run container
docker run -p 8000:8000 mindtrace:latest

Docker Compose

# Start all services
docker-compose up -d

# Services:
# - API: http://localhost:8000
# - GraphQL: http://localhost:8001
# - PostgreSQL: localhost:5432
# - Redis: localhost:6379
# - Kafka: localhost:9092

Kubernetes

# Apply manifests
kubectl apply -f kubernetes/manifests.yaml

# Scale deployment
kubectl scale deployment mindtrace-api --replicas=10

# Check status
kubectl get pods -n mindtrace

Sample Data Format

CSV

timestamp,text,source
2024-01-01 10:30:00,Working on project proposal,notes
2024-01-02 14:15:00,Feeling excited about the new feature,chat
2024-01-03 09:00:00,Completed sprint planning,journal

TXT

2024-01-01 10:30:00 | Working on project proposal
2024-01-02 14:15:00 | Feeling excited about the new feature

Output Examples

Sentiment Timeline

Line chart showing sentiment over time
Moving average overlay
Anomaly markers

Emotion Radar

Hexagonal radar chart
6 emotion dimensions
Comparison between periods

Thinking Style Clusters

t-SNE visualization
Cluster labels
Key terms per cluster

Knowledge Graph

Interactive network
Entity relationships
Temporal patterns

Performance

Metric	Value
Analysis Speed	1000 texts/min (CPU)
Batch Processing	10,000 texts/min (Spark)
API Latency	<50ms p95
Memory Usage	<500MB (base)
GPU Support	CuPy CUDA acceleration

Testing

# Run unit tests
pytest test_mindtrace.py -v

# Run with coverage
pytest --cov=. --cov-report=html

# Run linting
ruff check .
mypy .

Roadmap

License

MIT License — see LICENSE file

Author

MindTrace — Cognitive Pattern Analyzer

Version: 2.0.0 (Hyper-Advanced Edition)
Python: 3.10+
Built with ❤️ for cognitive science and AI