Thomas Aarholt thomasaarholt

## languages.toml
[[language]]
name = "python"
scope = "source.python"
injection-regex = "python"
file-types = ["py","pyi","py3","pyw",".pythonstartup",".pythonrc"]
shebangs = ["python"]
roots = [".", "pyproject.toml", "pyrightconfig.json"]
comment-token = "#"
language-servers = ["pyright", "ruff"]
indent = { tab-width = 4, unit = "    " }

## branch.sh
branch () {
    branches=$(git for-each-ref --sort=-committerdate refs/heads/ --format='%(color:cyan)%(refname:short)|%(color:bold green)%(committerdate:relative)|%(color:magenta)%(authorname)%(color:reset)' --color=always|column -ts'|') &&
    branch=$(echo "$branches" | fzf --multi --ansi --height=20%)
    if [ -z "${branch}" ];
        then return 0
        else git switch $(echo "$branch" | sed "s/ .*//")
    fi
}

## venv.sh
venv () {
    RED='\033[0;31m'
    NC='\033[0m' # No Color
    # create virtualenv
    if [ ! -d .venv ] # if venv does not exist already
    then
        python -m venv .venv --system-site-packages
    else
        echo -e "${RED}.venv${NC} already exists!"
        return 0

## train_test_split_polars.py

def train_test_split(
    df: pl.DataFrame, train_fraction: float = 0.75
) -> Tuple[pl.DataFrame, pl.DataFrame]:
    """Split polars dataframe into two sets.

    Args:
        df (pl.DataFrame): Dataframe to split
        train_fraction (float, optional): Fraction that goes to train. Defaults to 0.75.

## lightgbm_gpu.sh
module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0 Boost/1.74.0-GCC-10.2.0

pip install lightgbm --install-option=--gpu

# If running from notebook, ensure that the following contains Boost:
# import os
# os.environ["LD_LIBRARY_PATH"].split(":")

## xgboost_gpu.sh
# must not use gcc version > 10
module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0

# Clone with recursive! Otherwise you get dmlc error later on!
git clone --recursive https://github.com/dmlc/xgboost
cd xgboost/python-package

# clean out any old build files that may break things if try to recompile
rm -rf build

## docker_terminal.sh
# From a directory with a Dockerfile
# Example Dockerfile with "Dockerfile" as filename
FROM python:3.10-slim-buster
RUN pip install numpy


# Then, run the following

docker build -t your_image_name .

## cmap.py
from matplotlib.colors import LinearSegmentedColormap

def black_to_color(high_color: tuple, low_color: tuple = (0,0,0), name: str = "my_color", steps=256):
    "Get linear colormap from one rgb color to another, defaulting from black"

    r1,g1,b1 = low_color
    r2,g2,b2 = high_color

    cdict = {
        'red':    [(0.0,  r1, r1),

## gist:4c63d9f15a6895a4153e9bfa98f75360
cat /root/.jupyter/lab/user-settings/@jupyterlab/notebook-extension/tracker.jupyterlab-settings
{
    // Notebook
    // @jupyterlab/notebook-extension:tracker
    // Notebook settings.
    // **************************************

    // Code Cell Configuration
    // The configuration for all code cells.
    "codeCellConfig": {

## check_docker_running.sh
if docker info > /dev/null 2>&1; then
    : # pass
else
    echo "Docker Daemon is not running. Please open it and retry."
    exit -1
fi
	[[language]]
	name = "python"
	scope = "source.python"
	injection-regex = "python"
	file-types = ["py","pyi","py3","pyw",".pythonstartup",".pythonrc"]
	shebangs = ["python"]
	roots = [".", "pyproject.toml", "pyrightconfig.json"]
	comment-token = "#"
	language-servers = ["pyright", "ruff"]
	indent = { tab-width = 4, unit = " " }
	branch () {
	branches=$(git for-each-ref --sort=-committerdate refs/heads/ --format='%(color:cyan)%(refname:short)\|%(color:bold green)%(committerdate:relative)\|%(color:magenta)%(authorname)%(color:reset)' --color=always\|column -ts'\|') &&
	branch=$(echo "$branches" \| fzf --multi --ansi --height=20%)
	if [ -z "${branch}" ];
	then return 0
	else git switch $(echo "$branch" \| sed "s/ .*//")
	fi
	}
	venv () {
	RED='\033[0;31m'
	NC='\033[0m' # No Color
	# create virtualenv
	if [ ! -d .venv ] # if venv does not exist already
	then
	python -m venv .venv --system-site-packages
	else
	echo -e "${RED}.venv${NC} already exists!"
	return 0

	def train_test_split(
	df: pl.DataFrame, train_fraction: float = 0.75
	) -> Tuple[pl.DataFrame, pl.DataFrame]:
	"""Split polars dataframe into two sets.

	Args:
	df (pl.DataFrame): Dataframe to split
	train_fraction (float, optional): Fraction that goes to train. Defaults to 0.75.
	module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0 Boost/1.74.0-GCC-10.2.0

	pip install lightgbm --install-option=--gpu

	# If running from notebook, ensure that the following contains Boost:
	# import os
	# os.environ["LD_LIBRARY_PATH"].split(":")
	# must not use gcc version > 10
	module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0

	# Clone with recursive! Otherwise you get dmlc error later on!
	git clone --recursive https://github.com/dmlc/xgboost
	cd xgboost/python-package

	# clean out any old build files that may break things if try to recompile
	rm -rf build
	# From a directory with a Dockerfile
	# Example Dockerfile with "Dockerfile" as filename
	FROM python:3.10-slim-buster
	RUN pip install numpy


	# Then, run the following

	docker build -t your_image_name .
	from matplotlib.colors import LinearSegmentedColormap

	def black_to_color(high_color: tuple, low_color: tuple = (0,0,0), name: str = "my_color", steps=256):
	"Get linear colormap from one rgb color to another, defaulting from black"

	r1,g1,b1 = low_color
	r2,g2,b2 = high_color

	cdict = {
	'red': [(0.0, r1, r1),
	cat /root/.jupyter/lab/user-settings/@jupyterlab/notebook-extension/tracker.jupyterlab-settings
	{
	// Notebook
	// @jupyterlab/notebook-extension:tracker
	// Notebook settings.
	// **************************************

	// Code Cell Configuration
	// The configuration for all code cells.
	"codeCellConfig": {
	if docker info > /dev/null 2>&1; then
	: # pass
	else
	echo "Docker Daemon is not running. Please open it and retry."
	exit -1
	fi