Configuration and Options

from tiledbsoma.options import (
    SOMATileDBContext, TileDBCreateOptions, TileDBWriteOptions, ConfigDict
)

class SOMATileDBContext:
    def __init__(self, config=None):
        """
        Initialize TileDB context for SOMA operations.
        
        Parameters:
        - config: dict or ConfigDict, TileDB configuration parameters
        """
    
    @property
    def config(self):
        """
        Get current TileDB configuration.
        
        Returns:
        ConfigDict: Current configuration settings
        """
    
    def __enter__(self):
        """Context manager entry."""
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        """Context manager exit."""
        pass

import tiledbsoma
from tiledbsoma.options import SOMATileDBContext

# Basic context usage
with SOMATileDBContext() as ctx:
    with tiledbsoma.open("experiment.soma", context=ctx) as exp:
        # Operations use the specified context
        data = exp.obs.read().concat()

# Context with custom configuration
config = {
    "sm.memory_budget": 2**30,  # 1GB memory budget
    "sm.tile_cache_size": 2**28,  # 256MB tile cache
    "vfs.s3.max_parallel_ops": 16,  # S3 parallel operations
    "vfs.s3.multipart_part_size": 50*1024*1024  # 50MB parts
}

with SOMATileDBContext(config=config) as ctx:
    # Create objects with custom settings
    with tiledbsoma.DataFrame.create(
        "high_perf_dataframe.soma",
        schema=schema,
        context=ctx
    ) as df:
        df.write(data)

class ConfigDict:
    def __init__(self, config_dict=None):
        """
        Initialize configuration dictionary.
        
        Parameters:
        - config_dict: dict, initial configuration parameters
        """
    
    def __getitem__(self, key):
        """Get configuration parameter."""
        
    def __setitem__(self, key, value):
        """Set configuration parameter."""
        
    def update(self, other):
        """Update configuration with another dict or ConfigDict."""
        
    def items(self):
        """Iterate over configuration items."""

from tiledbsoma.options import ConfigDict, SOMATileDBContext

# Create configuration
config = ConfigDict({
    "sm.memory_budget": 1024**3,  # 1GB
    "sm.tile_cache_size": 512*1024**2,  # 512MB
})

# Update configuration
config.update({
    "vfs.num_threads": 8,
    "sm.compute_concurrency_level": 4
})

# Use with context
with SOMATileDBContext(config=config) as ctx:
    # Context uses the configuration
    pass

class TileDBCreateOptions:
    def __init__(self, **kwargs):
        """
        Initialize TileDB creation options.
        
        Parameters:
        - **kwargs: TileDB-specific creation parameters
        
        Common Parameters:
        - tile_order: str, tile order ("row-major" or "col-major")
        - cell_order: str, cell order ("row-major" or "col-major")
        - capacity: int, tile capacity
        - sparse: bool, whether array is sparse
        - allows_duplicates: bool, whether duplicates are allowed
        - offsets_compression: str, compression for offsets
        - offsets_compression_level: int, compression level for offsets
        - validity_compression: str, compression for validity data
        - validity_compression_level: int, compression level for validity
        """
    
    def __getitem__(self, key):
        """Get creation option."""
        
    def __setitem__(self, key, value):
        """Set creation option."""
        
    def get(self, key, default=None):
        """Get creation option with default."""

import tiledbsoma
from tiledbsoma.options import TileDBCreateOptions
import pyarrow as pa

# Create options for high-performance sparse array
create_opts = TileDBCreateOptions(
    capacity=100000,  # Large tile capacity
    tile_order="row-major",
    cell_order="row-major",
    allows_duplicates=False,
    # Compression settings
    offsets_compression="lz4",
    offsets_compression_level=1,
    validity_compression="rle"  # Run-length encoding
)

# Use options when creating arrays
with tiledbsoma.SparseNDArray.create(
    "optimized_matrix.soma",
    type=pa.float32(),
    shape=(10000, 5000),
    platform_config=create_opts
) as array:
    # Array created with optimized settings
    pass

# Create options for dataframes
df_create_opts = TileDBCreateOptions(
    capacity=10000,
    tile_order="row-major",
    offsets_compression="zstd",
    offsets_compression_level=3
)

with tiledbsoma.DataFrame.create(
    "optimized_dataframe.soma",
    schema=schema,
    platform_config=df_create_opts
) as df:
    df.write(data)

class TileDBWriteOptions:
    def __init__(self, **kwargs):
        """
        Initialize TileDB write options.
        
        Parameters:
        - **kwargs: TileDB-specific write parameters
        
        Common Parameters:
        - batch_size: int, number of elements per write batch
        - memory_budget: int, memory budget for writes in bytes
        - check_coord_dups: bool, check for coordinate duplicates
        - check_coord_oob: bool, check for out-of-bounds coordinates
        - dedup_coords: bool, deduplicate coordinates
        """
    
    def __getitem__(self, key):
        """Get write option."""
        
    def __setitem__(self, key, value):
        """Set write option."""
        
    def get(self, key, default=None):
        """Get write option with default."""

import tiledbsoma
from tiledbsoma.options import TileDBWriteOptions
import numpy as np
import pyarrow as pa

# Write options for large bulk loads
write_opts = TileDBWriteOptions(
    batch_size=50000,  # Large batches
    memory_budget=2**30,  # 1GB memory budget
    check_coord_dups=False,  # Skip duplicate check for performance
    check_coord_oob=False,   # Skip bounds check for performance
    dedup_coords=False       # Skip deduplication
)

# Use write options for bulk data loading
with tiledbsoma.open("large_matrix.soma", mode="w") as array:
    # Generate large dataset
    n_nonzero = 1000000
    cell_ids = np.random.randint(0, 50000, n_nonzero)
    gene_ids = np.random.randint(0, 20000, n_nonzero)
    values = np.random.exponential(2.0, n_nonzero)
    
    coords = pa.table({
        "soma_dim_0": cell_ids,
        "soma_dim_1": gene_ids
    })
    data = pa.table({
        "soma_data": values
    })
    
    # Write with optimized settings
    array.write((coords, data), platform_config=write_opts)

# Write options for safety-first approach
safe_write_opts = TileDBWriteOptions(
    check_coord_dups=True,
    check_coord_oob=True,
    dedup_coords=True,
    memory_budget=512*1024**2  # 512MB
)

with tiledbsoma.open("safe_dataframe.soma", mode="w") as df:
    df.write(data, platform_config=safe_write_opts)

from tiledbsoma.options import SOMATileDBContext, ConfigDict

# S3 configuration
s3_config = ConfigDict({
    # S3 settings
    "vfs.s3.aws_access_key_id": "your_access_key",
    "vfs.s3.aws_secret_access_key": "your_secret_key",
    "vfs.s3.region": "us-west-2",
    "vfs.s3.max_parallel_ops": 16,
    "vfs.s3.multipart_part_size": 100*1024*1024,  # 100MB
    "vfs.s3.use_virtual_addressing": "true",
    
    # Performance settings
    "sm.memory_budget": 4*1024**3,  # 4GB
    "sm.tile_cache_size": 1024**3,  # 1GB
    "vfs.num_threads": 16
})

with SOMATileDBContext(config=s3_config) as ctx:
    # Work with S3-stored data
    with tiledbsoma.open("s3://my-bucket/experiment.soma", context=ctx) as exp:
        data = exp.obs.read().concat()

# Azure Blob Storage configuration
azure_config = ConfigDict({
    "vfs.azure.storage_account_name": "myaccount",
    "vfs.azure.storage_account_key": "mykey",
    "vfs.azure.max_parallel_ops": 16,
    "sm.memory_budget": 2*1024**3,
    "vfs.num_threads": 8
})

from tiledbsoma.options import SOMATileDBContext, TileDBCreateOptions, TileDBWriteOptions

# High-performance local configuration
local_config = ConfigDict({
    "sm.memory_budget": 8*1024**3,  # 8GB memory
    "sm.tile_cache_size": 2*1024**3,  # 2GB cache
    "sm.compute_concurrency_level": 8,
    "vfs.num_threads": 16,
    "sm.io_concurrency_level": 4
})

# Optimized creation options
create_opts = TileDBCreateOptions(
    capacity=100000,
    tile_order="row-major",
    offsets_compression="lz4",
    offsets_compression_level=1
)

# Optimized write options
write_opts = TileDBWriteOptions(
    batch_size=100000,
    memory_budget=4*1024**3,
    check_coord_dups=False
)

# Combined usage
with SOMATileDBContext(config=local_config) as ctx:
    with tiledbsoma.SparseNDArray.create(
        "fast_array.soma",
        type=pa.float32(),
        shape=(100000, 50000),
        context=ctx,
        platform_config=create_opts
    ) as array:
        # Fast bulk loading
        array.write(data, platform_config=write_opts)

# Configuration for memory-limited environments
low_memory_config = ConfigDict({
    "sm.memory_budget": 256*1024**2,  # 256MB
    "sm.tile_cache_size": 64*1024**2,  # 64MB
    "vfs.num_threads": 2,
    "sm.compute_concurrency_level": 1
})

conservative_write_opts = TileDBWriteOptions(
    batch_size=1000,
    memory_budget=128*1024**2,  # 128MB
    check_coord_dups=True,
    check_coord_oob=True
)

with SOMATileDBContext(config=low_memory_config) as ctx:
    # Memory-efficient operations
    with tiledbsoma.open("data.soma", context=ctx) as obj:
        # Process in small batches
        for batch in obj.read(batch_size=1000):
            # Process batch
            pass

import tiledbsoma
from tiledbsoma.options import SOMATileDBContext

# Context sharing across operations
config = {"sm.memory_budget": 2*1024**3}

with SOMATileDBContext(config=config) as ctx:
    # Create experiment with shared context
    with tiledbsoma.Experiment.create("exp.soma", context=ctx) as exp:
        # All operations share the same context
        exp.add_new_dataframe("obs", schema=obs_schema)
        
        with exp.obs as obs_df:
            obs_df.write(obs_data)
    
    # Read operations with same context
    with tiledbsoma.open("exp.soma", context=ctx) as exp:
        data = exp.obs.read().concat()