adamltyson

import numpy as np

from brainrender import Scene
from brainrender.actors import Points

scene = Scene()

mos = scene.add_brain_region("MOs", alpha=0.15)

# coordinate from brainglobe_segmentation

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# /// script
# requires-python = "==3.12"
# dependencies = [
#     "napari[all]",
#     "brainreg[napari]",
# ]
# ///

import napari

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alias dblc='rm -rf docs/build && sphinx-build docs/source docs/build -b linkcheck'

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import numpy as np
import tifffile as tiff
from pathlib import Path

# Define the input and output directories
input_dir = Path('/home/adam/Desktop/blackcap/blackcap_stitched/2')
output_dir = Path('/home/adam/Desktop/blackcap/blackcap_stitched_padded/2')

# Ensure the output directory exists
output_dir.mkdir(parents=True, exist_ok=True)

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from pathlib import Path

from myterial import *
from rich import print

from brainrender import Scene
from brainrender import settings
settings.SHOW_AXES = False

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# To use, download GitHub usage report from the organisation
# billing tab then run e.g.:
# `python actions_billing_summary.py file.csv`

import sys
import pandas as pd

actions_csv = sys.argv[1]

df = pd.read_csv(actions_csv)

adamltyson

SWC/GCNU Neuroinformatics Unit tooling

Python

Setting up a project

• To set up a new Python project, use the SWC cookiecutter repository. This ensures that the required tooling is in place (e.g. CI) and also that we are all using a familiar project structure. Part of using this cookiecutter is keeping it updated (e.g. Python versions, best practices) and communicating those changes to all users (including those at SWC).

• Whenever possible, we will use pyproject.toml as the main configuration file for the project. Support for setup.cfg and setup.py will be deprecated in future python versions. This will also be the default option for the SWC cookiecutter.

Virtual environments

adamltyson

# import packages
import numpy as np
import pandas as pd
from skimage.measure import label

# assign arrays to simpler variables
labels = label(viewer.layers["Labels"].data[0])
red_data = viewer.layers["NAME_OF_RED_CHANNEL"].data

# intialise empty list

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import dask.array as da
import xarray as xr
import numpy as np
from imlib.IO.cells import cells_xml_to_df
from skimage.io import imread
from dask import delayed
from glob import glob
from pathlib import Path

adamltyson

sudo apt-get update
sudo apt-get install ca-certificates curl gnupg lsb-release
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
sudo usermod -aG docker $USER

sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose