Quick Start

Get copick installed, open your first project, and see what you can do with it — in a few minutes.

Install

copick runs on Python 3.10+ on Linux, macOS, and Windows. Install it with pip. The all extra pulls in the fsspec backends copick is tested against (local, s3, smb, ssh); a separate smb extra is also available.

pip install "copick[all]"

Open your first project

A copick project is described by a small JSON config file. The copick config commands build one for you — no hand-editing required. Pick the tab that matches where your data lives.

copick config dataportal --dataset-id 10301 --overlay ./overlay --output config.json

# 1. Create a local project and declare the objects you'll annotate.
#    --objects format: name,is_particle,[radius],[pdb_id]  (repeat per object)
copick config filesystem \
    --config config.json \
    --overlay-root ./my_project \
    --objects ribosome,True,150,7P6Z \
    --objects membrane,False \
    --proj-name my-project --proj-description "My cryo-ET dataset"

# 2. Import a tomogram (file type and voxel size are read from the MRC header;
#    the run is named after the file unless you pass --run).
copick add tomogram TS_001.mrc --config config.json --tomo-type wbp

# 3. Or batch-import a whole folder of MRCs (run name taken from each filename).
copick add tomogram "tomograms/*.mrc" --config config.json --tomo-type wbp

See your data

With a config.json in hand, here are three ways to dig in.

Browse in the terminal. Explore runs, tomograms, picks, meshes, and segmentations in an interactive TUI:

copick browse --config config.json

Script it with Python. The object-oriented API mirrors the data model — root → runs → voxel spacings → tomograms, plus picks/meshes/segmentations:

"""Print all objects and runs in a copick project."""

import copick

# Initialize the root object from a configuration file
root = copick.from_file("path/to/config.json")

# List all available objects
obj_info = [(o.name, o.label) for o in root.pickable_objects]

print("Pickable objects in this project:")
for name, label in obj_info:
    print(f"  {name}: {label}")

# Execute a function on each run in the project
runs = root.runs

print("Runs in this project:")
for run in runs:
    print(f"Run: {run.name}")
    # Do something with the run

Visualize and annotate. Open the project in a 3D viewer to inspect tomograms and create picks. See the ChimeraX-copick tutorial, or the ecosystem for napari-copick, CellCanvas, and the web viewer.

What can you do?

copick ships a toolbox of processing tools — convert between picks, segmentations, and meshes, clean up and filter annotations, fit surfaces, and more — all from the command line. Scroll through a few, or browse the full gallery.

Browse all processing tools

Next steps

Other storage backends

The two paths above cover local files and the CZ cryoET Data Portal, but a copick project's data and overlay can live almost anywhere — pick the setup that matches your storage:

• Local / Shared — data on a local or shared filesystem.
• AWS S3 / SSH — data on object storage or a remote server.
• CZ cryoET Data Portal — read from the portal API and write to any fsspec overlay.
• mlcroissant — read from a standards-compliant Croissant 1.1 manifest + CSV sidecars under a Croissant/ subdirectory. Live auto-sync writes keep the manifest up to date as you annotate.