https://github.com/data-exp-lab/yt-napari
A napari plugin for reading data from yt
https://github.com/data-exp-lab/yt-napari
Last synced: over 1 year ago
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A napari plugin for reading data from yt
- Host: GitHub
- URL: https://github.com/data-exp-lab/yt-napari
- Owner: data-exp-lab
- License: bsd-3-clause
- Created: 2022-01-14T23:51:24.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-02-17T21:25:45.000Z (over 1 year ago)
- Last Synced: 2025-02-17T22:27:25.886Z (over 1 year ago)
- Language: Python
- Size: 34.9 MB
- Stars: 4
- Watchers: 4
- Forks: 2
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- License: LICENSE
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README
# yt-napari
[](https://github.com/data-exp-lab/yt-napari/raw/main/LICENSE)
[](https://pypi.org/project/yt-napari)
[](https://python.org)
[](https://github.com/data-exp-lab/yt-napari/actions)
[](https://codecov.io/gh/data-exp-lab/yt-napari)
[](https://napari-hub.org/plugins/yt-napari)
[](https://yt-napari.readthedocs.io/en/latest/?badge=latest)
A [napari] plugin for loading data from [yt].
This readme provides a brief overview including:
1. [Installation](#Installation)
2. [Quick Start](#Quick-Start)
3. [Contributing](#Contributing)
Full documentation is available at [yt-napari.readthedocs.io].
## Installation
### 1. (optional) install `yt` and `napari`
If you skip this step, the installation in the following section will only install the minimal package requirements for `yt` or `napari`, in which case you will likely need to manually install some packages. So if you are new to either package, or if you are installing in a clean environment, it may be simpler to install these packages first.
For `napari`,
pip install napari[all]
will install `napari` with the default `Qt` backend (see [here](https://napari.org/tutorials/fundamentals/installation#choosing-a-different-qt-backend) for how to choose between `PyQt5` or `PySide2`).
For `yt`, you will need `yt>=4.0.1` and any of the optional dependencies for your particular workflow. If you know that you'll need more than the base `yt` install, you can install the full suite of dependent packages with
pip install yt[full]
See the [`yt` documentation](https://yt-project.org/doc/installing.html#leveraging-optional-yt-runtime-dependencies) for more information. If you're not sure which packages you'll need but don't want the full yt installation, you can proceed to the next step and then install any packages as needed (you will receive error messages when a required package is missing).
### 2. install `yt-napari`
You can install the `yt-napari` plugin with:
pip install yt-napari
If you are missing either `yt` or `napari` (or they need to be updated), the above installation will fetch and run a minimal installation for both.
To install the latest development version of the plugin instead, use:
pip install git+https://github.com/data-exp-lab/yt-napari.git
Note that if you are working off the development version, be sure to use the latest documentation
for reference: https://yt-napari.readthedocs.io/en/latest/
## Quick Start
After [installation](#Installation), there are three modes of using `yt-napari`:
1. jupyter notebook interaction ([jump down](#jupyter-notebook-interaction))
2. loading a json file from the napari gui ([jump down](#loading-a-json-file-from-the-napari-gui))
3. napari widget plugins ([jump down](#napari-widget-plugins))
### jupyter notebook interaction
`yt-napari` provides a helper class, `yt_napari.viewer.Scene` that assists in properly aligning new yt selections in the napari viewer when working in a Jupyter notebook.
```python
import napari
import yt
from yt_napari.viewer import Scene
from napari.utils import nbscreenshot
viewer = napari.Viewer()
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
yt_scene = Scene()
left_edge = ds.domain_center - ds.arr([40, 40, 40], 'kpc')
right_edge = ds.domain_center + ds.arr([40, 40, 40], 'kpc')
res = (600, 600, 600)
yt_scene.add_region(viewer,
ds,
("enzo", "Temperature"),
left_edge=left_edge,
right_edge=right_edge,
resolution=res)
yt_scene.add_region(viewer,
ds,
("enzo", "Density"),
left_edge=left_edge,
right_edge=right_edge,
resolution=res)
nbscreenshot(viewer)
```
`yt_scene.add_to_viewer` accepts any of the keyword arguments allowed by `viewer.add_image`. See the full documentation ([yt-napari.readthedocs.io]) for more examples, including additional helper methods for linking layer appearance.
Additionally, with `yt_napari`>= v0.2.0, you can use the `yt_napari.timeseries` module to help sample and load in selections from across datasets.
### loading a selection from a yt dataset interactively
`yt-napari` provides two ways to sample a yt dataset and load in an image layer into a Napari viewer: the yt Reader plugin and json file specification.
#### using the yt Reader plugin
To use the yt Reader plugin, click on `Plugins -> yt-napari: yt Reader`. From there, add a region or slice selector then specify a field type and field and bounds to sample between and then simply click "Load":
You can add multiple selections and load them all at once or adjust values and click "Load" again.
#### using the yt Time Series Reader plugin
To use the yt Time Series Reader plugin, click on `Plugins -> yt-napari: yt Time Series Reader`. Specify your file matching: use `file_pattern` to enter glob expressions or use `file_list` to enter a list of specific files.
Then add a slice or region to sample for each matched dataset file (note: be careful of memory here!):
#### using a json file and schema
`yt-napari` also provides the ability to load json that contain specifications for loading a file. Properly formatted files can be loaded from the napari GUI as you would load any image file (`File->Open`). The json file describes the selection process for a dataset as described by a json-schema. The following json file results in similar layers as the above examples:
```json
{"$schema": "https://raw.githubusercontent.com/data-exp-lab/yt-napari/main/src/yt_napari/schemas/yt-napari_0.0.1.json",
"datasets": [{"filename": "IsolatedGalaxy/galaxy0030/galaxy0030",
"selections": {"regions": [{
"fields": [{"field_name": "Temperature", "field_type": "enzo", "take_log": true},
{"field_name": "Density", "field_type": "enzo", "take_log": true}],
"left_edge": [460.0, 460.0, 460.0],
"right_edge": [560.0, 560.0, 560.0],
"resolution": [600, 600, 600]
}]},
"edge_units": "kpc"
}]
}
```
To help in filling out a json file, it is recommended that you use an editor capable of parsing a json schema and displaying hints. For example, in vscode, you will see field suggestions after specifying the `yt-napari` schema:
See the full documentation at [yt-napari.readthedocs.io] for a complete specification.
## Contributing
Contributions are very welcome! Development follows a fork and pull request workflow. To get started, you'll need a development installation and a testing environment.
### development environment
To start developing, fork the repository and clone your fork to get a local copy. You can then install in development mode along with
all the extra requirements for developing:
pip install -e .[full,dev]
### tests and style checks
Both bug fixes and new features will need to pass the existing test suite and style checks. While both will be run
automatically when you submit a pull request, it is helpful to run the test suites locally and run style checks
throughout development. For testing, you can use [tox] to test different python versions on your platform or
simply run `pytest` and rely on the github actions to test the additional python environments.
#### testing with tox
first install `tox` with:
pip install tox
And then from the top level of the `yt-napari` directory, run
tox
Tox will then run a series of tests in isolated environments. In addition to checking the terminal output for test results, the tox run will generate a test coverage report: a `coverage.xml` file and a `htmlcov` folder -- to view the results, open `htmlcov/index.html` in a browser.
#### testing with pytest
If you prefer a lighter weight test, you can also use `pytest` directly and rely on the Github CI to test different python versions and systems. To do so, first install `pytest` and some related plugins:
pip install pytest pytest-qt pytest-cov
Note that if you set up your dev environment with `pip install -e .[full,dev]` as suggested above, you'll arelady
have these dependencies.
To run the tests you can use the `pytest` command
pytest -v --cov=yt_napari --cov-report=html
Or the `taskipy` task:
task test
In addition to telling you whether or not the tests pass, the above command will write out a code coverage report to the `htmlcov` directory. You can open up `htmlcov/index.html` in a browser and check out the lines of code that were missed by existing tests.
#### style checks
For style checks, you can use [pre-commit](https://pre-commit.com/) to run checks as you develop. To set up `pre-commit`:
pip install pre-commit
pre-commit install
after which, every time you run `git commit`, some automatic style adjustments and checks will run. The same style checks will run when you submit a pull request, but it's often easier to catch them early.
After submitting a pull request, the `pre-commit.ci` bot will run the style checks. If style checks fail, you can have the bot attempt to auto-fix the failures by adding the following in a comment on it's own:
pre-commit.ci autofix
The bot will then commit changes to your pull request after which you will want to run `git pull` locally to update your local version of the branch before making further changes to the branch.
### building documentation locally
Documentation can be built using `sphinx` in two steps. First, update the api mapping with
sphinx-apidoc -f -o docs/source src/yt_napari/
This will update the `rst` files in `docs/source/` with the latest docstrings in `yt_napari`. Next, build the html documentation with
make html
### updating the pydantic models and schema
The schema versioning follows a `major.minor.micro` versioning pattern that matches the yt-napari versioning. Each yt-napari release should have an accompanying updated schema file, even if the contents of the schema file have not changed. On-disk schema are stored in `src/yt_napari/schemas/`, with copies in the documentation at `docs/_static`.
There are a number of utilities to help automate the management of schema in `repo_utilities/`. The easiest way to use these utitities is with `taskipy` from the command line. To list available scripts:
```commandline
task --list
```
Before a release, run
```commandline
task validate_release vX.X.X
```
where `vX.X.X` is the version of the upcoming release. This script will run through some checks that ensure:
* the on-disk schema matches the schema generated by the pydantic model
* the schema files in the documentation match the schema files in the package
If any of the checks fail, you will be advised to update the schema using `update_schema_docs`. If you
run without providing a version:
```commandline
task update_schema_docs
```
It will simply copy over the existing on-disk schema files to the documentation. If you run with a version:
```commandline
task update_schema_docs -v vX.X.X
```
It will write a schema file for the current pydantic model, overwriting any on-disk schema files for
the provided version.
### updating the sample data
The sample data utilizes another helper script: `repo_utilities/update_sample_data.py` that you can invoke
with `taskipy` as:
task update_sample_data
To adjust which sample datasets are included, go edit the `enabled` list in `repo_utilities/update_sample_data.py`. The names in `enabled` must match those accepted by `yt.load_sample`. In addition to enabling
a dataset, you may need to adjust the field settings for the sample dataset that you are adding: see the `sample_field` and `log_field` dictionaries.
When you run `update_sample_data`, a number of things happen:
1. The napari plugin manifest is updated. For every dataset in the `enabled` list, `yt_napari/napari.yaml` will include 2 entries: a new entry in `commands` and a new entry in `sample_data`.
2. For every dataset in the `enabled` list, a `json` file will be generated in `yt_napari/sample_data/` along with a single `yt_napari/sample_data/sample_registry.json`. These `json` files are used for actually loading the sample data.
3. `yt_napari/sample_data/_sample_data.py` will be rewritten and for every dataset in the `enabled` list, there will be a corresponding function. The function name maps to the python name in `yt_napari/napari.yaml` (the plugin manifest file). If `yt_napari/sample_data/_sample_data.py` is incorrect then the code generation in `repo_utilities/update_sample_data.py` should be updated, do not edit `yt_napari/sample_data/_sample_data.py` directly.
## License
Distributed under the terms of the [BSD-3] license,
"yt-napari" is free and open source software
## Issues
If you encounter any problems, please [file an issue] along with a detailed description.
## Funding
The yt-napari plugin project was funded with support from the Chan Zuckerberg Initiative through the napari Plugin Accelerator Grants project, [Enabling Access To Multi-resolution Data](https://chanzuckerberg.com/science/programs-resources/imaging/napari/enabling-access-to-multi-resolution-data/).
----------------------------------
This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[yt-napari.readthedocs.io]: https://yt-napari.readthedocs.io/en/stable/
[file an issue]: https://github.com/data-exp-lab/yt-napari/issues
[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
[yt]: https://yt-project.org/