This project was automatically generated using the LINCC-Frameworks python-project-template.
Look here for information on adler, the API and some example notebooks: https://adler.readthedocs.io/en/latest/
Before installing any dependencies or writing code, it's a great idea to create a virtual environment. If you have conda installed locally, you can run the following to create and activate a new environment.
>> conda create --name <env_name> python=3.10
>> conda activate <env_name>
Once you have created a new environment, you can install this project for local development. Git clone the repo and from the top directory run the following commands from within the adler folder:
>> pip install -e .'[dev]'
>> pre-commit install
WARNING: If you're installing on the RSP, then use the following pip command instead:
>> pip install --user -e .'[dev]'
Furthermore, to get the pre-commit hooks working on RSP, you will probably have to update git within your environment:
conda install git
If you're also working on the docs:
>> conda install pandoc
You can then test that everything works by running:
adler -s 8268570668335894776
This will currently print some phase curve information to the terminal.
One can also read from a local database, for example:
adler -s 8268570668335894776 -i tests/data/testing_database.db
Notes:
- The single quotes around
'[dev]'may not be required for your operating system. pre-commit installwill initialize pre-commit for this local repository, so that a set of tests will be run prior to completing a local commit. For more information, see the Python Project Template documentation on pre-commit- Install
pandocallows you to verify that automatic rendering of Jupyter notebooks into documentation for ReadTheDocs works as expected. For more information, see the Python Project Template documentation on Sphinx and Python Notebooks
- Copy notebook into
docs/notebooks(N.B. the notebook must have at least one section header* and be using the "Python 3 (ipykernel)" kernel, not some conda env kernel that may only be installed locally) - Update the toctree in the file
docs/notebooks.rst - Ensure necessary requirements are declared in
pyproject.tomlanddocs/requirements.txt. Also, make sure that the notebook being added to the docs is using the python3 (ipykernel) kernel, not some conda env kernel that may only be installed locally - To update the docs locally, from the
docsdir run:python -m sphinx -T -E -b html -d _build/doctrees -D language=en . ../_readthedocs/html
- Multiple section headers from a notebook will also show up in the table of contents.
If you are adding code that requires a new dependency, this needs to be included in pyproject.toml under the `[project]' section:
dependencies = [
"ipykernel", # Support for Jupyter notebooks
"numpy",
"lsst-rsp"
"your-dependency-here"
]
If you are adding code that should be run from the command line, this should be set up under [project.scripts]:
[project.scripts]
adler = "adler.adler:main"
my_command = "adler.module_folder.module_name:function_name"
The docstrings use the numpydoc format. This is the format expected by the LINCC Frameworks template and results in docstrings which compile neatly and automatically for the docs.
An example is below. Don't include the Returns section if your code returns nothing.
"""Here is a function that does some cool sciency stuff.
- Perhaps you want to add some bullet points.
- You can do that like this!
Parameters
-----------
arg1 : int
The first argument to the function.
arg2 : str
The second argument to the function. Default = "foo".
Returns
----------
return_value : float
The thing your function returns.
"""
For classes:
"""A class that contains some important science information.
Attibutes
-----------
attr1 : int
The first class attribute.
attr2: np.array
The second class attribute.
"""