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.github/actions/setup-python/action.yaml

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shell: bash
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run: |
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poetry env use ${{ inputs.python-version }}
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poetry install --extras dev
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poetry install --with dev,test,docs

.gitignore

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.idea
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.vscode
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__pycache__
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.mypy_cache
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*.pyc
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dist
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.venv/
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.coverage
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coverage.xml
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.ipynb_checkpoints/

CONTRIBUTING.md

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You'll need:
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- [pipx](https://python-poetry.org/) for installing Python tools
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- [pipx](https://pipx.pypa.io/) for installing Python tools
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- [git](https://git-scm.com/) for version control
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Both of these can be installed using your distribution's package manager or [Homebrew](https://brew.sh/) on Mac.
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```bash
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poetry env use 3.12
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poetry install --extras dev
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poetry install --with dev,test,docs
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```
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This installs the runtime dependencies along with the `dev`, `test`, and `docs`
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[dependency groups](https://python-poetry.org/docs/managing-dependencies/#dependency-groups).
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If you only need to run tests, `poetry install --with test` is sufficient.
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**Recommended**: Use `poetry run` to run commands inside the virtual environment:
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```bash

README.md

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- [Linux setup guide](LINUX_SETUP.md)
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- [macOS setup guide](MACOS_SETUP.md)
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- [Windows setup guide](WINDOWS_SETUP.md)
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- [Google Colab (TPU) setup guide](docs/source/colab_tpu_runtime.rst)
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Detailed instructions can be found in the [Contributors guide](https://github.com/malariagen/malariagen-data-python/blob/master/CONTRIBUTING.md).
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## AI use policy and guidelines

docs/source/colab_tpu_runtime.rst

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Google Colab Installation Guide
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===============================
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Prerequisites
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-------------
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Before installing the package, configure the runtime environment
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correctly:
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1. Open a new notebook in Google Colab.
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2. Navigate to ``Runtime → Change runtime type``.
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3. Set the runtime configuration as follows:
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- **Runtime type:** Python 3
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- **Hardware accelerator:** TPU
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- **TPU type:** v2-8
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4. Click **Save** to apply the configuration.
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Using the recommended TPU configuration ensures compatibility with
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workflows that may require TPU-based computation.
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Installation Procedure
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----------------------
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In a new notebook cell, install the package:
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.. code-block:: bash
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!pip install malariagen_data
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After installation completes, verify that the package is available:
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.. code-block:: python
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import malariagen_data
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If the import executes without errors, the installation was successful.
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If dependency-related warnings or conflicts occur, follow one of the
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resolution options described below.
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Resolution Options
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------------------
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Resolution Option 1: Uninstall Panel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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If your notebook does not require ``panel``, uninstall it before
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installing ``malariagen_data``.
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.. code-block:: bash
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!pip uninstall -y panel
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!pip install malariagen_data
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Verify installation:
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.. code-block:: python
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import malariagen_data
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Resolution Option 2: Install Compatible Panel Version
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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If your workflow depends on ``panel``, install a compatible version:
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.. code-block:: bash
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!pip install panel==1.7.0
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!pip install malariagen_data
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Restart the runtime:
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``Runtime → Restart runtime``
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Then verify:
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.. code-block:: python
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import malariagen_data
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Resolution Option 3: Install Required Blinker Version
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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If a ``blinker`` version conflict occurs:
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.. code-block:: bash
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!pip install blinker==1.9.0 --ignore-installed
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!pip install malariagen_data
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Restart the runtime and verify:
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.. code-block:: python
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import malariagen_data
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Final Verification
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------------------
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After completing any of the procedures above:
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- Ensure that ``malariagen_data`` installs without dependency errors.
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- Confirm that ``import malariagen_data`` runs successfully.
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- Restart the runtime whenever core dependencies are modified.
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- Avoid mixing incompatible package versions within the same Colab session.

malariagen_data/adar1.py

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tqdm_class=tqdm_class,
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taxon_colors=TAXON_COLORS,
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virtual_contigs=None,
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gene_names=None,
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inversion_tag_path=None,
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unrestricted_use_only=unrestricted_use_only,
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surveillance_use_only=surveillance_use_only,

malariagen_data/adir1.py

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tqdm_class=tqdm_class,
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taxon_colors=TAXON_COLORS,
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virtual_contigs=None,
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gene_names=None,
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inversion_tag_path=None,
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unrestricted_use_only=unrestricted_use_only,
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surveillance_use_only=surveillance_use_only,

malariagen_data/af1.py

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taxon_colors=TAXON_COLORS,
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virtual_contigs=None,
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gene_names=None,
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inversion_tag_path=None,
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unrestricted_use_only=unrestricted_use_only,
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surveillance_use_only=surveillance_use_only,

malariagen_data/amin1.py

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virtual_contigs=None,
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inversion_tag_path=None,
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unrestricted_use_only=unrestricted_use_only,
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surveillance_use_only=surveillance_use_only,

malariagen_data/anoph/aim_data.py

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# to the superclass constructor.
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super().__init__(**kwargs)
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# Store possible values for the `aims` parameter.
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# TODO Consider moving this to data resource configuration.
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self._aim_ids = aim_ids
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self._aim_palettes = aim_palettes
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# Read AIM parameters from the JSON config, falling back to
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# constructor args for backward compatibility.
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config = self.config
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_aim_ids = config.get("AIM_IDS", None)
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if _aim_ids is not None:
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self._aim_ids: Optional[aim_params.aim_ids] = tuple(_aim_ids)
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else:
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self._aim_ids = aim_ids
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_aim_palettes = config.get("AIM_PALETTES", None)
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if _aim_palettes is not None:
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# Convert lists to tuples for each palette entry.
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self._aim_palettes: Optional[aim_params.aim_palettes] = {
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k: tuple(v)
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for k, v in _aim_palettes.items() # type: ignore
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}
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else:
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self._aim_palettes = aim_palettes
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# Set up caches.
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self._cache_aim_variants: Dict[str, xr.Dataset] = dict()
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return None
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else:
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return fig
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return fig

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