Installation and Configuration

Installation

The entire package can be installed in any Python environment (v3.10 or later) via pip:

pip install microlensing_photometry

This allows all modules of the pipeline to be used as Python libraries.

In order to run the pipeline scripts, it is easiest to clone this repository:

git clone https://github.com/LCOGT/microlensing_photometry

The package includes a `pyproject.toml` file which can be used to streamline the creation of a new Python virtual environment in which to run the software.

> python3 -m venv venv
> source venv/bin/activate
venv> pip install poetry
venv> cd microlensing_photometry/
venv> poetry install

Once this is done, all pipeline scripts can be run from within the virtual environment using:

> source venv/bin/activate
venv> poetry run python <path/name_of_script> <args>

External Package Dependencies

The data management software depends on `fpack` to handle compressed data. This is available from HEASARC.

Configuration

All applications within this package are configured using YAML files. Template configuration files are included within the repository for reference - these can be found in:

./microlensing_photometry/data_management/configuration/
                        |─ example_data_download.yaml

and

./microlensing_photometry/image_reduction/configuration/
            |- example_reduction_configuration.yaml
            |- example_reduction_manager_configuration.yaml

These should be copied to a location outside the directory containing the package’s code to avoid confusion or accidental uploads to Github.

Each local config file should then be updated as follows to match the user’s local set-up.

Setting up the data reduction directory structure

The pipeline applications require an initial set of data directories to be available:

<root_path>/<data_reduction_dir>/
            |- <data_download_dir>
            |- <config_dir>
            |- <log_dir>

For more information about the required directories, see Data Directory Structure.

Prefect Server

The pipelines in this package use the Prefect library to orchestrate the workflow (for more information, see Prefect Workflows). Before you run any of the pipelines you need to start the prefect server as follows:

> source /<path>/venv/bin/activate
venv> prefect server start