Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/Rubick65/dcemapper/llms.txt

Use this file to discover all available pages before exploring further.

DCEMapper uses a Conda-managed environment to ensure consistent, reproducible builds across machines. All Python packages — including the GUI framework, neuroimaging libraries, and scientific computing tools — are pinned to specific versions in environment.yml, so the application behaves identically regardless of where it is installed.

​
Prerequisites

Before setting up the environment, ensure the following tools are available on your system:
  • Conda or Miniconda — the environment targets Python 3.13.9, which is specified directly in environment.yml. Any recent Conda or Miniconda distribution will resolve this version automatically.
  • Git — required to clone the DCEMapper repository from GitHub.

​
Creating the Environment

1

Clone the repository

Download the DCEMapper source code from GitHub and move into the project directory:
git clone https://github.com/Rubick65/dcemapper.git
cd dcemapper
2

Create the Conda environment

Use the provided environment.yml file to create the environment with all pinned dependencies:
conda env create -f environment.yml
Conda will resolve and install all conda-managed system packages as well as the pip subsection automatically.
3

Activate the environment

The environment name defined in environment.yml is base. Activate it with:
conda activate base
4

Run the application

Launch DCEMapper by running the main PyQt6 interface module:
python -m src.ui.interface.PyQT_interface
The main application window will open, ready to load NIfTI or Bruker data.
The name: field in environment.yml is set to base, which installs everything into Conda’s default base environment. If you prefer an isolated, named environment (recommended on shared machines), edit the name: field before running conda env create. For example, change name: base to name: dcemapper, then activate with conda activate dcemapper.

​
environment.yml Overview

The environment.yml file at the root of the repository fully describes the DCEMapper runtime. Its main sections are: 
name: base
channels:
  - defaults
dependencies:
  - python=3.13.9
  # ... conda-managed system packages ...
  - pip:
      - PyQt6==6.10.2
      - nibabel==5.4.0
      - numpy==2.4.3
      # ... remaining pip packages ...
SectionDescription
name: baseTargets Conda’s base environment. Change this to use an isolated named environment.
channels: defaultsPulls conda packages exclusively from the Anaconda default channel.
dependencies:Conda-managed system-level packages, including Python 3.13.9 and core Conda tooling.
pip:Python application packages installed via pip. This subsection contains all of DCEMapper’s direct runtime dependencies.
The prefix: field at the bottom of the file records the absolute path of the environment on the machine where environment.yml was generated. Conda ignores this field when creating the environment on a new machine, so it does not need to be changed.

​
Updating the Environment

If environment.yml changes (for example, after pulling an upstream update that pins a new package version), synchronise your local environment with: 
conda env update -f environment.yml --prune
The --prune flag removes any packages that are no longer listed in the file, keeping the environment clean and consistent with the pinned specification.
For a full list of Python packages installed via the pip: subsection — including their roles in the application and their pinned versions — see Dependencies.

Build docs developers (and LLMs) love

Get started for freeTalk to us