Collection Management

class Collection:
    def __init__(
        self,
        path,
        mode="r",
        driver=None,
        schema=None,
        crs=None,
        encoding=None,
        layer=None,
        vsi=None,
        archive=None,
        enabled_drivers=None,
        crs_wkt=None,
        ignore_fields=None,
        ignore_geometry=False,
        include_fields=None,
        wkt_version=None,
        allow_unsupported_drivers=False,
        **kwargs
    ):
        """
        Create a collection for a vector dataset.

        Parameters:
        - path: str or Path, dataset path
        - mode: str, 'r' (read), 'a' (append), or 'w' (write)
        - driver: str, OGR driver name
        - schema: dict, data schema (required for write mode)
        - crs: str or dict, coordinate reference system
        - encoding: str, text encoding for the dataset
        - layer: int or str, layer index or name for multi-layer datasets
        - vsi: str, virtual file system identifier
        - archive: str, archive file path
        - enabled_drivers: list, restrict to specific drivers
        - crs_wkt: str, CRS in WKT format
        - ignore_fields: list, field names to skip when reading
        - ignore_geometry: bool, skip geometry when reading
        - include_fields: list, only include these fields when reading
        - wkt_version: WktVersion, WKT format version
        - allow_unsupported_drivers: bool, allow experimental drivers
        - kwargs: dict, additional driver-specific options
        """
    
    def filter(self, *args, **kwds):
        """
        Return filtered iterator over records.
        
        Parameters:
        - args: positional arguments for spatial or attribute filters
        - kwds: keyword arguments for filtering options
        
        Returns:
        Iterator over filtered feature records
        """
    
    def items(self, *args, **kwds):
        """
        Return iterator over FID, record pairs.
        
        Parameters:
        - args: positional arguments for filtering
        - kwds: keyword arguments for filtering options
        
        Returns:
        Iterator over (feature_id, feature_record) tuples
        """
    
    def keys(self, *args, **kwds):
        """
        Return iterator over feature IDs.
        
        Parameters:
        - args: positional arguments for filtering
        - kwds: keyword arguments for filtering options
        
        Returns:
        Iterator over feature ID values
        """
    
    def write(self, record):
        """
        Write a single feature record.
        
        Parameters:
        - record: dict, feature record to write
        
        Raises:
        - ValueError: If record doesn't match schema
        - UnsupportedOperation: If collection not opened for writing
        """
    
    def writerecords(self, records):
        """
        Write multiple feature records efficiently.
        
        Parameters:
        - records: iterable of dict, feature records to write
        
        Raises:
        - ValueError: If any record doesn't match schema
        - UnsupportedOperation: If collection not opened for writing
        """
    
    def close(self):
        """Close the collection and release resources."""
    
    def flush(self):
        """Flush pending writes to disk."""
    
    def validate_record(self, record):
        """
        Validate a feature record against the collection schema.
        
        Parameters:
        - record: dict, feature record to validate
        
        Returns:
        bool: True if valid
        
        Raises:
        - SchemaError: If record doesn't match schema
        """
    
    @property
    def driver(self):
        """Get the OGR driver name."""
    
    @property
    def schema(self):
        """Get the data schema dictionary."""
    
    @property
    def crs(self):
        """Get the coordinate reference system."""
    
    @property
    def bounds(self):
        """Get spatial bounds as (minx, miny, maxx, maxy)."""
    
    @property
    def meta(self):
        """Get metadata dictionary."""
    
    @property 
    def profile(self):
        """Get profile dictionary (alias for meta)."""
    
    @property
    def closed(self):
        """Check if collection is closed."""

import fiona
from fiona.collection import Collection

# Reading with iteration
with Collection('data.shp', 'r') as collection:
    print(f"Schema: {collection.schema}")
    print(f"CRS: {collection.crs}")
    print(f"Bounds: {collection.bounds}")
    
    # Iterate over all features
    for feature in collection:
        print(f"Feature {feature['id']}: {feature['properties']}")

# Writing new collection
schema = {
    'geometry': 'Point',
    'properties': {
        'name': 'str:50',
        'population': 'int',
        'elevation': 'float'
    }
}

with Collection('cities.geojson', 'w', driver='GeoJSON',
                schema=schema, crs='EPSG:4326') as collection:
    
    cities = [
        {
            'geometry': {'type': 'Point', 'coordinates': [-122.4, 37.8]},
            'properties': {'name': 'San Francisco', 'population': 875000, 'elevation': 52.0}
        },
        {
            'geometry': {'type': 'Point', 'coordinates': [-74.0, 40.7]},
            'properties': {'name': 'New York', 'population': 8400000, 'elevation': 10.0}
        }
    ]
    
    # Write multiple records efficiently
    collection.writerecords(cities)

# Field filtering on read
with Collection('detailed_data.shp', 'r', 
                include_fields=['name', 'category']) as collection:
    for feature in collection:
        # Only specified fields are loaded, reducing memory usage
        print(feature['properties'])  # Only has 'name' and 'category'

class BytesCollection(Collection):
    def __init__(self, bytesbuf, **kwds):
        """
        Create collection from bytes buffer.
        
        Parameters:
        - bytesbuf: bytes, buffer containing dataset data
        - kwds: dict, additional Collection parameters
        """

from fiona.collection import BytesCollection
import requests

# Read GeoJSON from HTTP response
response = requests.get('https://example.com/data.geojson')
geojson_bytes = response.content

with BytesCollection(geojson_bytes) as collection:
    print(f"Driver: {collection.driver}")
    print(f"Feature count: {len(collection)}")
    for feature in collection:
        print(f"Feature: {feature['id']}")

# Work with shapefile bytes (from zip archive)
import zipfile
import io

with zipfile.ZipFile('data.zip', 'r') as archive:
    shp_bytes = archive.read('data.shp')
    shx_bytes = archive.read('data.shx') 
    dbf_bytes = archive.read('data.dbf')
    
    # Create virtual file system paths
    vsi_path = f'/vsimem/temp.shp'
    with BytesCollection(shp_bytes, vsi=vsi_path) as collection:
        # Process shapefile from memory
        for feature in collection:
            process_feature(feature)

# Convert between formats in memory
def convert_bytes_format(input_bytes, from_driver, to_driver):
    """Convert geospatial data between formats using bytes."""
    output_buffer = io.BytesIO()
    
    # Read from input bytes
    with BytesCollection(input_bytes) as input_collection:
        schema = input_collection.schema
        crs = input_collection.crs
        
        # Write to output buffer  
        with fiona.open(output_buffer, 'w', driver=to_driver, 
                       schema=schema, crs=crs) as output:
            for feature in input_collection:
                output.write(feature)
    
    return output_buffer.getvalue()

# Example: Convert GeoJSON bytes to Shapefile bytes
geojson_bytes = b'{"type": "FeatureCollection", "features": [...]}'
shapefile_bytes = convert_bytes_format(geojson_bytes, 'GeoJSON', 'ESRI Shapefile')

# Use field filtering to reduce memory usage
with fiona.open('large_dataset.shp', 'r',
                include_fields=['id', 'name']) as collection:
    # Only loads specified fields, faster and uses less memory
    for feature in collection:
        process_minimal_feature(feature)

# Skip geometry for attribute-only processing
with fiona.open('attribute_analysis.shp', 'r',
                ignore_geometry=True) as collection:
    # Much faster when you don't need spatial data
    for feature in collection:
        analyze_attributes(feature['properties'])

# Batch writing for better performance
features = generate_many_features()  # Generator or large list

with fiona.open('output.geojson', 'w', **write_params) as collection:
    # Write in batches rather than one-by-one
    collection.writerecords(features)