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Adds a new skill covering polars-bio (v0.26.0), a high-performance library for genomic interval arithmetic and file I/O built on Polars, Arrow, and DataFusion. All code examples verified against the actual API at runtime. SKILL.md covers overlap, nearest, merge, coverage, complement, subtract, cluster, count_overlaps operations plus read/scan/write/sink for BED, VCF, BAM, CRAM, GFF, GTF, FASTA, FASTQ, SAM, and Hi-C pairs formats. References: interval_operations, file_io, sql_processing, pileup_operations, configuration, bioframe_migration. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
176 lines
4.7 KiB
Markdown
176 lines
4.7 KiB
Markdown
# Configuration
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## Overview
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polars-bio uses a global configuration system based on `set_option` and `get_option` to control execution behavior, coordinate systems, parallelism, and streaming modes.
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## set_option / get_option
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```python
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import polars_bio as pb
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# Set a configuration option
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pb.set_option("datafusion.execution.target_partitions", 8)
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# Get current value
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value = pb.get_option("datafusion.execution.target_partitions")
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```
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## Parallelism
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### DataFusion Target Partitions
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Controls the number of parallel execution partitions. Defaults to 1 (single-threaded).
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```python
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import os
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import polars_bio as pb
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# Use all available CPU cores
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pb.set_option("datafusion.execution.target_partitions", os.cpu_count())
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# Set specific number of partitions
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pb.set_option("datafusion.execution.target_partitions", 8)
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```
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**When to increase parallelism:**
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- Processing large files (>1GB)
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- Running interval operations on millions of intervals
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- Batch processing multiple chromosomes
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**When to keep default (1):**
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- Small datasets
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- Memory-constrained environments
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- Debugging (deterministic execution)
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## Coordinate Systems
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polars-bio defaults to 1-based coordinates (standard genomic convention).
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### Global Coordinate System
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```python
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import polars_bio as pb
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# Switch to 0-based half-open coordinates
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pb.set_option("coordinate_system", "0-based")
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# Switch back to 1-based (default)
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pb.set_option("coordinate_system", "1-based")
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# Check current setting
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print(pb.get_option("coordinate_system"))
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```
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### Per-File Override via I/O Functions
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I/O functions accept `use_zero_based` to set coordinate metadata on the resulting DataFrame:
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```python
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# Read with explicit 0-based metadata
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df = pb.read_bed("regions.bed", use_zero_based=True)
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```
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**Note:** Interval operations (overlap, nearest, etc.) do **not** accept `use_zero_based`. They read coordinate metadata from the DataFrames, which is set by I/O functions or the global option. When using manually constructed DataFrames, polars-bio warns about missing metadata and falls back to the global setting.
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### Setting Metadata on Manual DataFrames
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```python
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import polars_bio as pb
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# Set coordinate metadata on a manually created DataFrame
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pb.set_source_metadata(df, format="bed", path="")
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```
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### File Format Conventions
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| Format | Native Coordinate System | polars-bio Conversion |
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|--------|-------------------------|----------------------|
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| BED | 0-based half-open | Converted to configured system on read |
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| VCF | 1-based | Converted to configured system on read |
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| GFF/GTF | 1-based | Converted to configured system on read |
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| BAM/SAM | 0-based | Converted to configured system on read |
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## Streaming Execution Modes
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polars-bio supports two streaming modes for out-of-core processing:
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### DataFusion Streaming
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Enabled by default for interval operations. Processes data in batches through the DataFusion execution engine.
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```python
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# DataFusion streaming is automatic for interval operations
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result = pb.overlap(lf1, lf2) # Streams if inputs are LazyFrames
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```
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### Polars Streaming
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Use Polars' native streaming for post-processing operations:
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```python
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# Collect with Polars streaming
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result = lf.collect(streaming=True)
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```
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### Combining Both
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```python
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import polars_bio as pb
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# Scan files lazily (DataFusion streaming for I/O)
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lf1 = pb.scan_bed("large1.bed")
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lf2 = pb.scan_bed("large2.bed")
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# Interval operation (DataFusion streaming)
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result_lf = pb.overlap(lf1, lf2)
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# Collect with Polars streaming for final materialization
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result = result_lf.collect(streaming=True)
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```
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## Logging
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Control log verbosity for debugging:
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```python
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import polars_bio as pb
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# Set log level
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pb.set_loglevel("debug") # Detailed execution info
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pb.set_loglevel("info") # Standard messages
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pb.set_loglevel("warn") # Warnings only (default)
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```
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**Note:** Only `"debug"`, `"info"`, and `"warn"` are valid log levels.
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## Metadata Management
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polars-bio attaches coordinate system and source metadata to DataFrames produced by I/O functions. This metadata is used by interval operations to determine the coordinate system.
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```python
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import polars_bio as pb
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# Inspect metadata on a DataFrame
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metadata = pb.get_metadata(df)
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# Print metadata summary
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pb.print_metadata_summary(df)
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# Print metadata as JSON
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pb.print_metadata_json(df)
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# Set metadata on a manually created DataFrame
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pb.set_source_metadata(df, format="bed", path="regions.bed")
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# Register a DataFrame as a SQL table
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pb.from_polars("my_table", df)
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```
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## Complete Configuration Reference
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| Option | Default | Description |
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|--------|---------|-------------|
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| `datafusion.execution.target_partitions` | `1` | Number of parallel execution partitions |
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| `coordinate_system` | `"1-based"` | Default coordinate system (`"0-based"` or `"1-based"`) |
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