NOTE: Now maintained at https://github.com/ScrollPrize/villa/tree/main/vesuvius (this repository archived)

vesuvius

From Vesuvius Challenge, a Python library for accessing CT scans of ancient scrolls.

vesuvius allows direct access to scroll data without managing download scripts or storing terabytes of CT scans locally:

import vesuvius
import matplotlib.pyplot as plt

scroll = vesuvius.Volume("Scroll1")
img = scroll[1000,:,:]

plt.imshow(img)

Data is streamed in the background, only serving the requested regions.

The library provides tools for accessing, managing, and manipulating high-resolution volumetric data related to Vesuvius Challenge. It supports both remote and local data, with options for caching and normalization.

For a similar library in C, see vesuvius-c.

⚠️ vesuvius is in beta and the interface may change. Not all Vesuvius Challenge data is currently available - data will continue to be added to the library.

📓 Introductory notebooks

To get started, we recommend these notebooks that jump right in:

1. 📊 Scroll Data Access: an introduction to accessing scroll data using a few lines of Python!

2. ✒️ Ink Detection: load and visualize segments with ink labels, and train models to detect ink in CT.

3. 🧩 Volumetric instance segmentation cubes: how to access instance-annotated cubes with the Cube class, used for volumetric segmentation approaches.

vesuvius does:

• Data retrieval: Fetches volumetric scroll data, surface volumes of scroll segments, and annotated volumetric instance segmentation labels. Remote repositories and local files are supported.
• Data listing: Lists the available data on our data server.
• Data caching: Caches fetched data to improve performance when accessing remote repositories.
• Normalization: Provides options to normalize data values.
• Multiresolution: Accesses and manages data at multiple image resolutions.

vesuvius doesn't do:

• Remote data modification: The read-only library does not support modifying the original data.
• Complex analysis: While it provides access to data, it does not include built-in tools for complex data analysis or visualization.

Installation

vesuvius can be installed with pip. Then, before using the library for the first time, accept the license terms:

$ pip install vesuvius
$ vesuvius.accept_terms --yes

Usage

The library can be imported in Python:

import vesuvius

Listing

Listing files

To list the available files in the remote repository, use the following code:

from vesuvius import list_files

files = list_files()

The output of list_files is a dictionary that contains the paths to all the scroll volumes and segment surface volumes available in the data repository. The dictionary structure is as follows:

• The top-level keys are scroll_id.
• Under each scroll_id, there are keys for different energy levels.
• Under each energy, there are keys for different resolution levels.
• Under each resolution, there are keys for either segments or volume.

segments can contain segment_ids.

Here is a visual representation of what the dictionary can look like:

{
  'scroll_id1': {
    'energy1': {
      'resolution1': {
        'segments': {
          'segment_id1': 'path/to/segment_id1',
          'segment_id2': 'path/to/segment_id2'
        },
        'volume': 'path/to/volumes'
      },
      'resolution2': {
        'segments': {
          'segment_id1': 'path/to/segment_id1',
          'segment_id2': 'path/to/segment_id2'
        },
        'volume': 'path/to/volumes'
      }
    },
    'energy2': {
      'resolution1': {
        'segments': {
          'segment_id1': 'path/to/segment_id1',
          'segment_id2': 'path/to/segment_id2'
        },
        'volume': 'path/to/volume'
      },
    }
  },
  'scroll_id2': {
    'energy1': {
      'resolution1': {
        'segments': {
          'segment_id1': 'path/to/segment_id1',
          'segment_id2': 'path/to/segment_id2'
        },
        'volume': 'path/to/volumes'
      },
    }
  }
}

This structure allows you to access specific paths based on the scroll_id, energy, resolution, and segment_id of the data you are interested in. This function is automatically executed when the library is imported to constantly keep the list of available files updated.

Listing cubes

To list the available instance annotated volumetric cubes:

from vesuvius import cubes

available_cubes = cubes()

Similarly to list_files the output of cubes is a dictionary:

{
  'scroll_id1': {
    'energy1': {
      'resolution1': {
        'z1_y1_x1': 'path/to/z1_y1_x1',
        'z2_y2_x2': 'path/to/z2_y2_x2'
        }
      }
    }
}

z_y_x are the coordinates in the relative scroll volume of the origin of the reference frame of the selected cube.

Importing and using Volume

The Volume class is used for accessing volumetric data, both for scrolls and surface volume of segments.

Example usage

from vesuvius import Volume
# Basic usage
scroll = Volume(type="Scroll1") # this is going to access directly the canonical scroll 1 volume

# Basic usage specifying scan metadata
scroll = Volume(type="scroll", scroll_id=1, energy=54, resolution=7.91) # if you want to access a non canonical volume, you have to specify the scan metadata

# With cache (works only with remote repository)
scroll = Volume(type="scroll", scroll_id=1, energy=54, resolution=7.91, cache=True)

# Deactivate/activate caching (works only with remote repository)
scroll.activate_caching() # Don't need to do this if loaded the volume with cache=True
scroll.deactivate_caching()

# With normalization
scroll = Volume(type="scroll", scroll_id=1, energy=54, resolution=7.91, normalize=True)

# Visualize which subvolumes are available
scroll.meta()

# To print meta at initialization, use the argument verbose=True
scroll = Volume(type="Scroll1", verbose=True)

# To access shapes of multiresolution arrays
subvolume_index = 3  # third subvolume
shape = scroll.shape(subvolume_index)

# To access dtype
dtype = scroll.dtype

# Access data using indexing
data = scroll[:, :, :, subvolume_index]  # Access the entire third subvolume

# When only three or less indices are specified, you are automatically accessing to the main subvolume (subvolume_index = 0)

data = scroll[15] # equal to scroll[15,:,:,0]
data = scroll[15,12] # equal to scroll [15,12,:,0]

# Slicing is also permitted for the first three indices
data = scroll[20:300,12:18,20:40,2]

With local files

If you fully downloaded a scroll volume, or a segment, you can directly specify its local path on your device:

scroll = Volume(type="scroll", scroll_id=1, energy=54, resolution=7.91, domain="local", path="/path/to/54keV_7.91um.zarr")

Segments

You can access segments in a similar fashion:

from vesuvius import Volume
# Basic usage
segment = Volume("20230827161847") # access a segment specifying is unique timestamp

# Basic usage specifying scan metadata
segment = Volume(type="segment", scroll_id=1, energy=54, resolution=7.91, segment_id=20230827161847)

Constructor

Volume(
    type: Union[str, int],
    scroll_id: Optional[int] = None,
    energy: Optional[int] = None,
    resolution: Optional[float] = None,
    segment_id: Optional[int] = None,
    cache: bool = True,
    cache_pool: int = 1e10,
    normalize: bool = False,
    verbose: bool = True,
    domain: str = "dl.ash2txt",
    path: Optional[str] = None
)

• type: Type of volume, either 'scroll', 'scroll#' or 'segment'.
• scroll_id: Identifier for the scroll.
• energy: Energy level.
• resolution: Resolution level.
• segment_id: Identifier for the segment.
• cache: Enable caching.
• cache_pool: Cache pool size in bytes.
• normalize: Normalize the data.
• verbose: Enable verbose output.
• domain: Domain, either 'dl.ash2txt' or 'local'.
• path: Path to the local data.

Methods

• activate_caching(): Activates caching.
• deactivate_caching(): Deactivates caching.
• shape(subvolume_idx: int = 0): Returns the shape of the specified subvolume.

Importing and using Cube

The Cube class is used for accessing segmented cube data.

Example usage

from vesuvius import Cube

# Basic usage
cube = Cube(scroll_id=1, energy=54, resolution=7.91, z=2256, y=2512, x=4816, cache=True, cache_dir='/path/to/cache')  # with caching

# if caching=True but cache_dir is not selected, the instances will be automatically saved in $HOME / vesuvius / annotated-instances

cube = Cube(scroll_id=1, energy=54, resolution=7.91, z=2256, y=2512, x=4816, cache=False)  # without caching

# With normalization
cube = Cube(scroll_id=1, energy=54, resolution=7.91, z=2256, y=2512, x=4816, normalize=True)

# Deactivate/activate caching
cube.activate_caching(cache_dir=None)  # or define your own cache_dir
cube.deactivate_caching()

# To access the volume and the masks
volume, mask = cube[:, :, :]  # also works with slicing

Constructor

Cube(
    scroll_id: int,
    energy: int,
    resolution: float,
    z: int,
    y: int,
    x: int,
    cache: bool = True,
    cache_dir: Optional[os.PathLike] = None,
    normalize: bool = False
)

• scroll_id: Identifier for the scroll.
• energy: Energy level.
• resolution: Resolution level.
• z: Z-coordinate.
• y: Y-coordinate.
• x: X-coordinate.
• cache: Enable caching.
• cache_dir: Directory for cache.
• normalize: Normalize the data.

Methods

• load_data(): Loads data.
• activate_caching(): Activates caching.
• deactivate_caching(): Deactivates caching.

Additional notes

• Terms acceptance: Ensure that the terms are accepted before using the library.
• Caching: Caching is only supported with the remote repository.
• Normalization: The normalize parameter normalizes the data to the maximum value of the dtype.
• Local files: For local files, provide the appropriate path in the Volume constructor.