import fastslide

# Open a slide using context manager (automatically closes when done)
with fastslide.FastSlide.from_file_path('slide.svs') as slide:
    # Get slide information
    print(f"Dimensions: {slide.dimensions}")  # (width, height) at level 0
    print(f"Levels: {slide.level_count}")     # Number of pyramid levels
    print(f"Resolution: {slide.mpp} µm/pixel")
    print(f"Format: {slide.format}")           # e.g., "SVS", "MRXS", "QPTIFF"

    # Read a region at full resolution (level 0)
    region = slide.read_region(
        location=(1000, 2000),  # (x, y) in level-native coordinates
        level=0,                 # pyramid level
        size=(512, 512)          # (width, height)
    )
    # region is a numpy array: shape (512, 512, 3), dtype uint8

import fastslide

# Open a slide without context manager
slide = fastslide.FastSlide.from_file_path('slide.mrxs')

try:
    # Work with the slide
    region = slide.read_region(location=(0, 0), level=0, size=(1024, 1024))

    # Get slide properties
    props = slide.properties
    print(f"Scanner: {props.get('scanner_model', 'Unknown')}")
    print(f"Magnification: {props.get('objective_magnification', 'N/A')}")

finally:
    # Always close the slide to release resources
    slide.close()

import fastslide

with fastslide.FastSlide.from_file_path('slide.tiff') as slide:
    # Get information about all pyramid levels
    print(f"Level count: {slide.level_count}")
    print(f"Level dimensions: {slide.level_dimensions}")
    print(f"Level downsamples: {slide.level_downsamples}")

    # Read the same region at different resolutions
    location = (10000, 15000)
    size = (256, 256)

    # Full resolution (level 0)
    region_l0 = slide.read_region(location=location, level=0, size=size)

    # 4× downsampled (level 2)
    # Convert coordinates to level 2 space
    x_l2, y_l2 = slide.convert_level0_to_level_native(
        location[0], location[1], level=2
    )
    region_l2 = slide.read_region(location=(x_l2, y_l2), level=2, size=size)

    # Find best level for a specific downsample factor
    best_level = slide.get_best_level_for_downsample(8.0)
    print(f"Best level for 8× downsample: {best_level}")

import fastslide
from PIL import Image

with fastslide.FastSlide.from_file_path('slide.svs') as slide:
    # Check what associated images are available
    associated = slide.associated_images
    print(f"Available images: {associated.keys()}")  # e.g., ['thumbnail', 'macro', 'label']

    # Read thumbnail (lazy loaded)
    if 'thumbnail' in associated:
        thumbnail = associated['thumbnail']  # numpy array

        # Convert to PIL Image and save
        img = Image.fromarray(thumbnail)
        img.save('thumbnail.png')

        # Get dimensions without loading
        dims = associated.get_dimensions('thumbnail')
        print(f"Thumbnail size: {dims}")

from torch.utils.data import DataLoader

# Each worker gets its own slide reader
dataloader = DataLoader(
    dataset,
    batch_size=32,
    num_workers=8,  # Safe for multi-worker loading
    shuffle=True
)

# Level 0: 10000 × 8000 px (full resolution)
# Level 1: 5000 × 4000 px (2× downsample)
# Level 2: 2500 × 2000 px (4× downsample)

# Read 512×512 region from level 2 at position (100, 200)
region = slide.read_region((100, 200), level=2, size=(512, 512))

# Convert coordinates between levels if needed
x0, y0 = slide.convert_level_native_to_level0(100, 200, level=2)
# Returns: (400, 800) - the level-0 equivalent