Remove Streaming Export for Large Datasets section

- Remove chunked export functionality
- Remove parallel export functionality
- Simplify export guide to focus on basic export operations

scientific-skills/tiledbvcf/references/export.md

@@ -130,112 +130,6 @@ processed_df = export_processed_variants(
 )
 ```
 
-## Streaming Export for Large Datasets
-
-### Chunked Export
-```python
-def streaming_export(ds, regions, samples, output_file, chunk_size=100000):
-    """Export large datasets in chunks to manage memory"""
-    import csv
-
-    total_variants = 0
-
-    with open(output_file, 'w', newline='') as f:
-        writer = None
-        header_written = False
-
-        for region in regions:
-            print(f"Processing region: {region}")
-
-            # Query region
-            df = ds.read(
-                attrs=["sample_name", "contig", "pos_start", "alleles", "fmt_GT"],
-                regions=[region],
-                samples=samples
-            )
-
-            if df.empty:
-                continue
-
-            # Process in chunks
-            for i in range(0, len(df), chunk_size):
-                chunk = df.iloc[i:i+chunk_size]
-
-                # Write header on first chunk
-                if not header_written:
-                    writer = csv.writer(f)
-                    writer.writerow(chunk.columns)
-                    header_written = True
-
-                # Write chunk data
-                for _, row in chunk.iterrows():
-                    writer.writerow(row.values)
-
-                total_variants += len(chunk)
-
-                if i + chunk_size < len(df):
-                    print(f"  Processed {i + chunk_size:,} variants...")
-
-    print(f"Exported {total_variants:,} variants to {output_file}")
-
-# Usage
-regions = [f"chr{i}" for i in range(1, 23)]  # All autosomes
-streaming_export(ds, regions, ds.sample_names(), "genome_wide_variants.csv")
-```
-
-### Parallel Export
-```python
-import multiprocessing as mp
-import os
-
-def export_region_chunk(args):
-    """Export single region - for parallel processing"""
-    dataset_uri, region, samples, output_dir = args
-
-    # Create separate dataset instance for each process
-    ds = tiledbvcf.Dataset(uri=dataset_uri, mode="r")
-
-    # Generate output filename
-    region_safe = region.replace(":", "_").replace("-", "_")
-    output_file = os.path.join(output_dir, f"variants_{region_safe}.tsv")
-
-    # Export region
-    ds.export_tsv(
-        uri=output_file,
-        regions=[region],
-        samples=samples,
-        tsv_fields=["CHR", "POS", "REF", "ALT", "S:GT", "S:DP"]
-    )
-
-    return region, output_file
-
-def parallel_export(dataset_uri, regions, samples, output_dir, n_processes=4):
-    """Export multiple regions in parallel"""
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Prepare arguments for parallel processing
-    args = [(dataset_uri, region, samples, output_dir) for region in regions]
-
-    # Export in parallel
-    with mp.Pool(n_processes) as pool:
-        results = pool.map(export_region_chunk, args)
-
-    # Combine results if needed
-    output_files = [output_file for _, output_file in results]
-    print(f"Exported {len(output_files)} region files to {output_dir}")
-
-    return output_files
-
-# Usage
-regions = [f"chr{i}:1-50000000" for i in range(1, 23)]  # First half of each chromosome
-output_files = parallel_export(
-    dataset_uri="my_dataset",
-    regions=regions,
-    samples=ds.sample_names()[:100],
-    output_dir="parallel_export",
-    n_processes=8
-)
-```
 
 ## Integration with Analysis Tools