cryohub

cryohub is a library for reading and writing Cryo-ET data based on the cryotypes specification.

Installation

pip install cryohub

Usage

cryohub provides granular I/O functions such as read_star and read_mrc, which will all return objects following the cryotypes specification.

from cryohub.reading import read_star
poseset = read_star('/path/to/file.star')

A higher level function called read adds some magic to the IO procedure, guessing file formats and returning a list of cryotypes.

from cryohub import read
data = read('/path/to/file.star', '/path/to/directotry/', lazy=False, name_regex=r'tomo_\d+')

See the help for each function for more info.

Similarly to the read_* functions, cryohub provides a series of write_* functions, and a magic higher level write funtion.

from cryohub import write
write([poseset1, poseset2], 'particles.tbl')

From the command line

cryohub can be used as a conversion tool between all available formats:

cryohub convert input_file.star output_file.tbl

If instead you just need to quickly inspect your data but want something more powerful than just reading text files or headers, this command will land you in an ipython shell with the loaded data collected in a list called data:

cryohub view path/to/files/* /other/path/to/file.star

print(data[0])

Features

Currently cryohub is capable of reading images in the following formats:

• .mrc (and the .mrcs, .st, .map variants)
• Dynamo .em
• EMAN2 .hdf

and particle data in the following formats:

• Relion .star
• Dynamo .tbl
• Cryolo .cbox and .box
• EMAN2 .json[^1]

Writer functions currently exist for:

• .mrc
• EMAN2 .hdf
• Dynamo .em
• Relion .star
• Dynamo .tbl

[^1]: EMAN2 uses the center of the tomogram as the origin for particle coordinates. This means that when opening a tomogram, you'll have to recenter the particles based on its dimensions. To do so automatically, you can use the center_on_tomo argument to provide the hdf file with the tomogram you want to use.

Image data

When possible (and unless disabled), cryohub loads images lazily using dask. The resulting objects can be treated as normal numpy array, except one needs to call array.compute() to apply any pending operations and return the result.

Contributing

Contributions are more than welcome! If there is a file format that you wish were supported in reading or writing, simply open an issue about it pointing to the specification. Alternatively, feel free to open a PR with your proposed implementation; you can look at the existing functions for inspiration.