File I/O and Data Processing

def download_file(url, filename=None, **kwargs):
    """
    Download file from URL.
    
    Args:
        url (str): URL to download from
        filename (str): Local filename for downloaded file
        **kwargs: Download options (headers, timeout, verify, etc.)
        
    Returns:
        str: Path to downloaded file
    """

def download_from_url(url, out_file, **kwargs):
    """
    Advanced download with progress tracking.
    
    Args:
        url (str): Source URL
        out_file (str): Output file path  
        **kwargs: Advanced options (chunk_size, progress, resume, etc.)
        
    Returns:
        str: Path to downloaded file
    """

def download_folder(url, out_dir, **kwargs):
    """
    Download entire folder from URL.
    
    Args:
        url (str): Folder URL
        out_dir (str): Output directory
        **kwargs: Download options (recursive, filters, etc.)
        
    Returns:
        list: List of downloaded file paths
    """

def read_raster(filename, **kwargs):
    """
    Read raster file into memory.
    
    Args:
        filename (str): Path to raster file
        **kwargs: Read options (bands, window, masked, etc.)
        
    Returns:
        tuple: (array, metadata) - raster data and metadata
    """

def write_raster(array, filename, **kwargs):
    """
    Write raster array to file.
    
    Args:
        array (numpy.ndarray): Raster data array
        filename (str): Output file path
        **kwargs: Write options (crs, transform, compress, etc.)
        
    Returns:
        str: Path to written raster file
    """

def raster_info(filename):
    """
    Get raster file information.
    
    Args:
        filename (str): Path to raster file
        
    Returns:
        dict: Raster metadata (bands, CRS, bounds, etc.)
    """

def read_netcdf(filename, variables=None, **kwargs):
    """
    Read NetCDF file.
    
    Args:
        filename (str): Path to NetCDF file
        variables (list): Variables to read
        **kwargs: NetCDF read options (time, bbox, etc.)
        
    Returns:
        xarray.Dataset: NetCDF data as xarray Dataset
    """

def read_vector(filename, **kwargs):
    """
    Read vector file into GeoDataFrame.
    
    Args:
        filename (str): Path to vector file
        **kwargs: Read options (bbox, rows, columns, etc.)
        
    Returns:
        gpd.GeoDataFrame: Vector data as GeoDataFrame
    """

def write_vector(gdf, filename, **kwargs):
    """
    Write GeoDataFrame to vector file.
    
    Args:
        gdf (gpd.GeoDataFrame): GeoDataFrame to write
        filename (str): Output file path
        **kwargs: Write options (driver, crs, etc.)
        
    Returns:
        str: Path to written vector file
    """

def vector_info(filename):
    """
    Get vector file information.
    
    Args:
        filename (str): Path to vector file
        
    Returns:
        dict: Vector metadata (geometry_type, CRS, bounds, feature_count, etc.)
    """

def read_lidar(filename, **kwargs):
    """
    Read LiDAR data file.
    
    Args:
        filename (str): Path to LiDAR file (.las, .laz)
        **kwargs: LiDAR read options (classification, returns, etc.)
        
    Returns:
        dict: LiDAR points and metadata
    """

def csv_to_geojson(filename, lat='latitude', lon='longitude', **kwargs):
    """
    Convert CSV file to GeoJSON.
    
    Args:
        filename (str): Path to CSV file
        lat (str): Latitude column name
        lon (str): Longitude column name
        **kwargs: Conversion options (crs, properties, etc.)
        
    Returns:
        str: Path to GeoJSON file
    """

def csv_to_gdf(filename, lat='latitude', lon='longitude', **kwargs):
    """
    Convert CSV to GeoDataFrame.
    
    Args:
        filename (str): Path to CSV file
        lat (str): Latitude column name
        lon (str): Longitude column name
        **kwargs: Conversion options (crs, etc.)
        
    Returns:
        gpd.GeoDataFrame: CSV data as GeoDataFrame
    """

def csv_to_shp(filename, output, lat='latitude', lon='longitude', **kwargs):
    """
    Convert CSV to shapefile.
    
    Args:
        filename (str): Path to CSV file
        output (str): Output shapefile path
        lat (str): Latitude column name
        lon (str): Longitude column name
        **kwargs: Conversion options (crs, etc.)
        
    Returns:
        str: Path to created shapefile
    """

def geojson_to_gdf(filename, **kwargs):
    """
    Convert GeoJSON to GeoDataFrame.
    
    Args:
        filename (str): Path to GeoJSON file
        **kwargs: Conversion options
        
    Returns:
        gpd.GeoDataFrame: GeoJSON as GeoDataFrame
    """

def shp_to_geojson(filename, **kwargs):
    """
    Convert shapefile to GeoJSON.
    
    Args:
        filename (str): Path to shapefile
        **kwargs: Conversion options (crs, precision, etc.)
        
    Returns:
        str: Path to GeoJSON file
    """

def vector_to_geojson(filename, **kwargs):
    """
    Convert any vector format to GeoJSON.
    
    Args:
        filename (str): Path to vector file
        **kwargs: Conversion options
        
    Returns:
        str: Path to GeoJSON file
    """

def image_to_cog(filename, output=None, **kwargs):
    """
    Convert image to Cloud Optimized GeoTIFF.
    
    Args:
        filename (str): Input image path
        output (str): Output COG path
        **kwargs: COG creation options (compress, overview, blocksize, etc.)
        
    Returns:
        str: Path to COG file
    """

def numpy_to_cog(array, filename, **kwargs):
    """
    Convert NumPy array to COG.
    
    Args:
        array (numpy.ndarray): Input array
        filename (str): Output COG path
        **kwargs: COG options (crs, transform, nodata, etc.)
        
    Returns:
        str: Path to COG file
    """

def array_to_image(array, filename, **kwargs):
    """
    Convert NumPy array to image file.
    
    Args:
        array (numpy.ndarray): Input array
        filename (str): Output image path
        **kwargs: Image options (format, quality, etc.)
        
    Returns:
        str: Path to image file
    """

def images_to_gif(images, output, **kwargs):
    """
    Convert image sequence to animated GIF.
    
    Args:
        images (list): List of image file paths
        output (str): Output GIF path
        **kwargs: GIF options (duration, loop, optimize, etc.)
        
    Returns:
        str: Path to GIF file
    """

def extract_archive(filename, output_dir=None, **kwargs):
    """
    Extract compressed archive.
    
    Args:
        filename (str): Path to archive file (.zip, .tar.gz, etc.)
        output_dir (str): Extraction directory
        **kwargs: Extraction options (members, password, etc.)
        
    Returns:
        list: List of extracted file paths
    """

def create_archive(files, output, **kwargs):
    """
    Create compressed archive from files.
    
    Args:
        files (list): List of file paths to archive
        output (str): Output archive path
        **kwargs: Archive options (compression, password, etc.)
        
    Returns:
        str: Path to created archive
    """

def compress_file(filename, **kwargs):
    """
    Compress single file.
    
    Args:
        filename (str): File to compress
        **kwargs: Compression options (level, format, etc.)
        
    Returns:
        str: Path to compressed file
    """

def get_metadata(filename):
    """
    Get comprehensive metadata from geospatial file.
    
    Args:
        filename (str): Path to geospatial file
        
    Returns:
        dict: Complete metadata including CRS, bounds, format info, etc.
    """

def get_crs(filename):
    """
    Get coordinate reference system from file.
    
    Args:
        filename (str): Path to geospatial file
        
    Returns:
        pyproj.CRS: Coordinate reference system
    """

def get_bounds(filename):
    """
    Get spatial bounds from file.
    
    Args:
        filename (str): Path to geospatial file
        
    Returns:
        list: Bounding box [minx, miny, maxx, maxy]
    """

def file_size(filename):
    """
    Get file size in human-readable format.
    
    Args:
        filename (str): Path to file
        
    Returns:
        str: File size (e.g., '15.3 MB')
    """

import leafmap

# Download a file
leafmap.download_file(
    'https://example.com/data.tif',
    'local_data.tif'
)

# Read raster data
array, metadata = leafmap.read_raster('local_data.tif')

print(f"Raster shape: {array.shape}")
print(f"CRS: {metadata['crs']}")

# Read vector data
gdf = leafmap.read_vector('vector_data.shp')
print(f"Features: {len(gdf)}")

import leafmap

# Convert CSV with coordinates to GeoJSON
leafmap.csv_to_geojson(
    'points.csv',
    lat='lat_column',
    lon='lon_column',
    output='points.geojson'
)

# Convert shapefile to GeoJSON
leafmap.shp_to_geojson(
    'boundaries.shp',
    output='boundaries.geojson'
)

# Convert to GeoDataFrame for analysis
gdf = leafmap.geojson_to_gdf('points.geojson')

# Process and save back
gdf_processed = gdf[gdf['value'] > 100]  # Filter
leafmap.write_vector(gdf_processed, 'filtered_points.gpkg')

import leafmap
import numpy as np

# Read raster
array, metadata = leafmap.read_raster('input.tif')

# Process array (example: threshold)
processed = np.where(array > 0.5, 1, 0)

# Convert to COG
leafmap.numpy_to_cog(
    processed,
    'processed.tif',
    crs=metadata['crs'],
    transform=metadata['transform'],
    compress='lzw'
)

# Create visualization
leafmap.array_to_image(
    processed,
    'visualization.png'
)

import leafmap
import glob

# Find all shapefiles
shapefiles = glob.glob('data/*.shp')

# Convert all to GeoJSON
geojson_files = []
for shp in shapefiles:
    geojson = leafmap.shp_to_geojson(shp)
    geojson_files.append(geojson)

print(f"Converted {len(geojson_files)} files")

# Combine all into single GeoDataFrame
import geopandas as gpd
combined = gpd.GeoDataFrame()

for geojson in geojson_files:
    gdf = leafmap.geojson_to_gdf(geojson)
    combined = gpd.concat([combined, gdf])

# Save combined data
leafmap.write_vector(combined, 'combined_data.gpkg')

import leafmap

# Read NetCDF file
dataset = leafmap.read_netcdf(
    'climate_data.nc',
    variables=['temperature', 'precipitation']
)

print(f"Variables: {list(dataset.variables)}")
print(f"Time range: {dataset.time.min().values} to {dataset.time.max().values}")

# Extract single time slice
temp_slice = dataset['temperature'].isel(time=0)

# Convert to raster
leafmap.numpy_to_cog(
    temp_slice.values,
    'temperature.tif',
    crs='EPSG:4326',
    bounds=[-180, -90, 180, 90]
)

import leafmap

# Read LiDAR file
lidar_data = leafmap.read_lidar(
    'pointcloud.las',
    classification=[2, 3, 4, 5]  # Ground, low/medium/high vegetation
)

print(f"Points: {len(lidar_data['points'])}")
print(f"Classifications: {set(lidar_data['classification'])}")

# Create DEM from ground points
ground_points = lidar_data['points'][lidar_data['classification'] == 2]

# Process to raster (would need additional gridding)
# This is a simplified example
dem_array = process_points_to_grid(ground_points)  # Custom function

leafmap.numpy_to_cog(
    dem_array,
    'dem.tif',
    crs='EPSG:32612'  # UTM zone
)

download_options = {
    'headers': {'User-Agent': 'leafmap'},  # Custom headers
    'timeout': 300,                        # Timeout in seconds
    'verify': True,                        # SSL verification
    'chunk_size': 8192,                    # Download chunk size
    'resume': True,                        # Resume interrupted downloads
    'progress': True                       # Show progress bar
}

raster_options = {
    'bands': [1, 2, 3],                   # Specific bands to read
    'window': ((0, 512), (0, 512)),       # Spatial window
    'masked': True,                       # Return masked array
    'dtype': 'float32',                   # Data type
    'compress': 'lzw',                    # Compression method
    'tiled': True,                        # Tiled output
    'blockxsize': 512,                    # Tile width
    'blockysize': 512                     # Tile height
}

vector_options = {
    'bbox': [-180, -90, 180, 90],         # Bounding box filter
    'rows': slice(0, 1000),               # Row slice
    'columns': ['name', 'value'],         # Column selection
    'driver': 'GeoJSON',                  # Output driver
    'precision': 6,                       # Coordinate precision
    'drop_z': True                        # Drop Z coordinates
}