This guide documents a complete tmux configuration optimized for Fish shell and Alacritty terminal on Linux systems.
Ensure you have the following installed:
- tmux
| #!/usr/bin/env uv --script | |
| # /// script | |
| # dependencies = [ | |
| # "numpy", | |
| # "squidpy>=1.6.5", | |
| # "scikit-network", | |
| # ] | |
| # python_version = ">=3.12" | |
| # /// |
| #!/bin/bash | |
| set -e | |
| INPUT="$1" | |
| EXT="${INPUT##*.}" | |
| BASENAME=$(basename "$INPUT" ."$EXT") | |
| INPUT_DIR=$(dirname "$(realpath "$INPUT")") | |
| TMPDIR=$(mktemp -d) |
| # /// script | |
| # dependencies = [ | |
| # "numpy", | |
| # "pandas", | |
| # "statsmodels", | |
| # "scikit-learn", | |
| # "scikit-bio", | |
| # "matplotlib", | |
| # ] | |
| # /// |
| #! python3.7 | |
| import argparse | |
| import os | |
| import numpy as np | |
| import speech_recognition as sr | |
| import whisper | |
| import torch | |
| from datetime import datetime, timedelta |
| """ | |
| Re-analyze GeoMx data from Rendeiro et al. 2021 doi:s41586-021-03475-6 | |
| Requirements (Python 3.10+): | |
| pip install \ | |
| "anndata" \ | |
| "matplotlib>=3.8.3" \ | |
| "numpy>=1.26.4" \ | |
| "pandas>=2.1.0" \ | |
| "scanpy" \ |
| """ | |
| Download file from Google Drive URL | |
| """ | |
| import os | |
| import time | |
| import click | |
| from selenium import webdriver | |
| from selenium.webdriver.common.by import By | |
| from selenium.webdriver.firefox.options import Options as FirefoxOptions |
| oil | saturated | polyunsaturated (omega 3 & 6) | monounsaturated | trans fat | other fat | omega 3 | omega 6 | yield, litres per hectare | smoke point | flavour | cooking conditions | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| almond | 0.08 | 0.17 | 0.7 | 0.0 | 0.05 | 0.0 | 0.17 | 1380 | 230 | 2 | >230 °C (Deep-frying) | |
| beef tallow | 0.5 | 0.04 | 0.42 | 0.0 | 0.04 | 0.01 | 0.03 | - | 220 | 1 | 200-229 °C (Stir-frying) | |
| flaxseed | 0.09 | 0.66 | 0.2 | 0.0 | 0.05 | 0.59 | 0.13 | 506 | 107 | 2 | <150 °C (Dressings) | |
| apricot kernel | 0.06 | 0.29 | 0.6 | 0.0 | 0.05 | 0.0 | 0.29 | 680 | 257 | 0 | >230 °C (Deep-frying) | |
| argan | 0.18 | 0.37 | 0.43 | 0.0 | 0.02 | 0.02 | 0.35 | 0.06 | 218 | 2 | 200-229 °C (Stir-frying) | |
| avocado | 0.12 | 0.14 | 0.71 | 0.0 | 0.03 | 0.01 | 0.13 | 2638 | 269 | 1 | >230 °C (Deep-frying) | |
| brazil nut | 0.25 | 0.36 | 0.39 | 0.0 | 0.0 | 0.0 | 0.36 | 2392 | 195 | 2 | 150-199 °C (Light saute) | |
| butter | 0.51 | 0.03 | 0.21 | 0.033 | 0.25 | 0.0 | 0.03 | - | 167 | 2 | 150-199 °C (Light saute) | |
| butter, clarified (ghee) | 0.65 | 0.03 | 0.32 | 0.0 | 0.0 | 0.0 | 0.03 | - | 235 | 2 | >230 °C (Deep-frying) |
| """ | |
| Use dataloaders with nuclei coordinates for training. | |
| """ | |
| from functools import partial | |
| import requests | |
| import h5py | |
| from tqdm import tqdm | |
| import numpy as np |