Before you start

Do this once, then every CLI bookmark is copy → edit ★ → run. A git clone is not required after pip install habitat-analysis.

Habitat analysis is the core (Guide: Habitat Guide). These steps only set up the terminal, config/, and the demo imaging pack.

1. Terminal + env

Open a conda terminal first. On Windows: Start (Win10 often bottom-left; Win11 often bottom-center) → Anaconda3 → Anaconda Prompt, or search Anaconda Prompt — not plain CMD/PowerShell. Details and screenshots: Installation.

conda activate habit          # prompt must show (habit)
habit --version

2. Work directory + demo configs

Pick any folder you own as <work_dir>. Materialize the bundled demo YAML tree (shipped inside the wheel; not demo_data):

mkdir D:\my_habit_work        # Windows — use your path
cd D:\my_habit_work
habit copy-demo-config --dest .

# macOS / Linux
mkdir -p ~/my_habit_work && cd ~/my_habit_work
habit copy-demo-config --dest .

ls config                     # or: Test-Path config

Python:

from habit.utils.demo_config_utils import copy_demo_config
copy_demo_config(r"D:/my_habit_work")

Commands below assume your shell cwd is this <work_dir>.

3. Demo data (first run)

Packs are split — habitat-only users need imaging; add ML only for habit model / habit cv. Fetch imaging once; HABIT prints the path and the folder tree (that tree is the contract for your own data).

Imaging — from <work_dir>:

habit fetch-demo --work-dir .

# same thing in Python
from habit.datasets import fetch_demo
DATA = fetch_demo()          # prints DATA, subjects, series, example files

  • Cache: ~/.habit_data/demo-data-v1/preprocessed (override with HABIT_DATA). Later calls do not download again.

  • --work-dir . links <work_dir>/demo_data/preprocessed to that cache so shipped YAML paths keep working.

  • Modalities in the pack: pre_contrast / LAP / PVP / delay_3min

  • Preprocessed tree is already there — skip preprocess the first time

  • Backup share if GitHub is blocked: Download preprocessed.zip (code 9bi3)

Tabular ML (ml_data.zip, optional):

  • Download ml_data.zip — extract code: atnp

  • Extract to demo_data/ml_data/ (e.g. breast_cancer_dataset.csv)

  • If zip top level is ml_data/, extract into demo_data/

4. Paths in YAML

  • Most shipped configs: relative paths resolve from the YAML file’s directory (hence ../../demo_data/...).

  • Documents with version: '1.0' / *_v1.yaml: paths as written; run from <work_dir> or use absolute paths.

  • Prefer D:/data/...; quote only if the path has spaces.

5. Safe YAML edits

  • Spaces for indent (no Tab); key: value; lowercase true / false

  • First run: change only ★ MUST EDIT fields

Validate without running:

habit check-config --config config/habitat/config_habitat_two_step.yaml

Next: 1. Data In — pick directory, loose NIfTI, SimpleITK, or NumPy. The same DATA / MODALITIES / ROI knobs appear in every gallery script. DICOM is Preprocessing, not a load route.