# Install Python dependencies for all tools
pip install -r requirements.txt

# For visualization tools, also install:
pip install plotly networkx

# Ensure Redis with RediSearch is running (for generation/testing tools)
redis-server --loadmodule /path/to/redisearch.so

# Generate 3 indexes with different insertion orders (10K base docs, sparse size 100)
./generate_numeric_trees.py --docs-per-index 10000 --sparse-size 100

# Generate with smaller dataset for quick testing
./generate_numeric_trees.py --docs-per-index 1000 --sparse-size 50

# Generate with larger sparse size for more extreme sparsing effect
./generate_numeric_trees.py --docs-per-index 5000 --sparse-size 200

# Clean up existing indexes before creating new ones
./generate_numeric_trees.py --docs-per-index 10000 --sparse-size 100 --cleanup

# Run benchmark tests on all 3 indexes
./benchmark_numeric_tree.py --iterations 100

# Run benchmark with specific parameters
./benchmark_numeric_tree.py --iterations 50 --range-size 100

# Run benchmark with custom settings
./benchmark_numeric_tree.py --iterations 200

# Parse a RediSearch numeric tree dump file
./parse_numeric_tree.py dump_numidxtree.txt tree.json

# Fast parsing mode (disable assertions for large files)
./parse_numeric_tree.py dump_numidxtree.txt tree.json --fast

# Create interactive visualization
./visualize_numeric_tree.py tree.json

# Create visualization with custom spacing
./visualize_numeric_tree.py tree.json 3.0

# Show tree information only (no visualization)
./visualize_numeric_tree.py tree.json info

# Generate indexes with different insertion orders
./generate_numeric_trees.py --docs-per-index 10000 --sparse-size 100 --cleanup

# Run benchmark tests to compare performance across insertion orders
./benchmark_numeric_tree.py --iterations 200

# Run benchmark tests on intersection queries
./benchmark_numeric_tree.py --iterations 100

# Compare how tree structure affects performance

# Test different sparse sizes for the sparsed index
for sparse_size in 10 50 100 200 500; do
    ./generate_numeric_trees.py --docs-per-index 5000 --sparse-size $sparse_size --cleanup
    ./benchmark_numeric_tree.py --iterations 50
done

# Test how insertion order effects scale with dataset size
for docs in 1000 5000 10000 50000; do
    ./generate_numeric_trees.py --docs-per-index $docs --sparse-size 100 --cleanup
    ./benchmark_numeric_tree.py --iterations 100
done

✓ Connected to Redis at localhost:6379
✓ Created index: numeric_idx_1 with field: value_1
Populating index numeric_idx_1 with 10000 documents...
✓ Populated numeric_idx_1 with 10000 documents

Index Summary:
  numeric_idx_1: 10000 docs, IDs: 1-999901, Values: 0-1000
  numeric_idx_2: 10000 docs, IDs: 103-999823, Values: 1000-2000

UNION Query Statistics:
  Total queries: 100
  Execution time (ms):
    Mean: 2.45
    Median: 2.31
    Min: 1.89
    Max: 4.12
    Std Dev: 0.67
  Result counts:
    Mean: 1247.3
    Median: 1198.0
    Min: 0
    Max: 3456

# Connect to remote Redis instance
./generate_numeric_trees.py --redis-host redis.example.com --redis-port 6380 --redis-db 1
./test_numeric_queries.py --redis-host redis.example.com --redis-port 6380 --redis-db 1

# Generate large dataset for stress testing (always creates exactly 3 indexes)
./generate_numeric_trees.py --docs-per-index 100000 --sparse-size 1000

# Smaller datasets for quick iteration
./generate_numeric_trees.py --docs-per-index 1000 --sparse-size 10

# Check if Redis is running
redis-cli ping

# Check if RediSearch module is loaded
redis-cli MODULE LIST

# Clean up existing indexes before creating new ones
./generate_numeric_trees.py --cleanup --docs-per-index 1000 --sparse-size 10

# Check Redis memory usage
redis-cli INFO memory

# Create 3 indexes with different insertion orders
./generate_numeric_trees.py --docs-per-index 10000 --sparse-size 100

# Measure query performance across different tree structures
./benchmark_numeric_tree.py --iterations 100

# From Redis CLI, dump each index's tree structure
redis-cli FT.DEBUG DUMP_NUMIDXTREE numeric_idx_sequential price > sequential_tree.txt
redis-cli FT.DEBUG DUMP_NUMIDXTREE numeric_idx_random price > random_tree.txt
redis-cli FT.DEBUG DUMP_NUMIDXTREE numeric_idx_sparsed price > sparsed_tree.txt

# Parse each tree structure
./parse_numeric_tree.py sequential_tree.txt sequential.json
./parse_numeric_tree.py random_tree.txt random.json
./parse_numeric_tree.py sparsed_tree.txt sparsed.json

# Create interactive visualizations
./visualize_numeric_tree.py sequential.json
./visualize_numeric_tree.py random.json
./visualize_numeric_tree.py sparsed.json