• Create and activate a new conda environment (Python >=3.9, but >=3.11 not yet tested), then install:

conda create -n neuralib python=3.9
conda activate neuralib
pip install neura-library

• If you wish to install all dependencies, run:

pip install neura-library[all]

• If you wish to install the minimal required dependencies according to usage purpose:

Choices in []: atlas, scanner, calimg, segmentation, model, track, gpu, profile, imagelib, tools, full Example of using the atlas module:

pip install neura-library[atlas]

• If installing in developer mode (Install pre-commit and linter check by ruff)

pip install neura-library[dev]
pre-commit install
ruff check .

• Install uv, run in Unix or git bash (Windows):

curl -LsSf https://astral.sh/uv/install.sh | sh

• Follow uv project structure doc:

uv init

• Make sure python version (>=3.9, but >=3.11 not yet tested), both in pyproject.py and .python-version

uv python install 3.9

• If you wish to install all dependencies, run:

uv add neura-library[all]

• If you wish to install the minimal required dependencies according to usage purpose:

Choices in []: atlas, scanner, calimg, segmentation, model, track, gpu, profile, imagelib, tools, full Example of using the atlas module:

uv add neura-library[atlas]

• If installing in developer mode (Install pre-commit and linter check by ruff)

uv add neura-library[dev]
pre-commit install
ruff check .

• Module for whole brain, slice view visualization and rois classification
  • neuralib.atlas.ccf: Customized hierarchical classification for the mouse brain atlas
  • neuralib.atlas.brainrender: cli-based wrapper for brainrender
  • neuralib.atlas.cellatlas: Volume and cell types counts for each brain region, refer to Cell Atlas
  • neuralib.atlas.ibl: Slice view plotting wrapper for ibllib and iblatlas

• Module for 2photon calcium imaging acquisition and result parsing
  • neuralib.calimg.scan_image: Data acquired from ScanImage ( under DEV)
  • neuralib.calimg.scanbox: Data acquired from Scanbox
  • neuralib.calimg.suite2p: Result parser for suite2p
  • neuralib.calimg.spikes: dF/F to spike activity (OASIS/Cascade)

• Module for neuronal segmentation
  • neuralib.segmentation.cellpose: Result Parser and batch running for cellpose
  • neuralib.segmentation.stardist: Result Parser and batch running for stardist

• neuralib.model.bayes_decoding: Position decoding using population neuronal activity

• neuralib.model.rastermap: Run and result parser for rastermap

• Morphological reconstruction data presentation

• Module for other open-source tools wrapper

  • neuralib.tracking.deeplabcut: Result parser for DeepLabCut

  • neuralib.tracking.facemap: Result parser for facemap

• neuralib.argp: Use argparse as dataclass field

• neuralib.persistence: caching the analyzed results (i.e., concatenation for statistic purpose)

• neuralib.bokeh_model: Interactive dashboard visualization

• neuralib.sqlp: Python functions to build a SQL (sqlite3) statement.

• neuralib.plot: Module for general plotting purpose

• neuralib.imglib: Image processing library (under DEV)

• neuralib.tools.gspread: Google spreadsheet API wrapper for read/write

• neuralib.tools.slack_bot: Real-time slack notification bot for analysis pipeline

• neuralib.util.cli_args: run script as subprocess

• neuralib.util.color_logging: logging with color format

• neuralib.util.gpu: OS-dependent gpu info

• neuralib.util.profile_test: simple benchmark profile testing

• neuralib.util.table: rich table visualization

• neuralib.util.segments: Epoch or value segments

• brender: see examples in api