| Name | trexio JSON |
| Version |
2.5.0
JSON |
| download |
| home_page | https://github.com/TREX-CoE/trexio |
| Summary | Python API of the TREXIO library |
| upload_time | 2024-09-11 08:09:54 |
| maintainer | None |
| docs_url | None |
| author | TREX-CoE |
| requires_python | >=3.6 |
| license | BSD |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# TREXIO Python API
[](https://badge.fury.io/py/trexio)
[](https://mybinder.org/v2/gh/TREX-CoE/trexio-tutorials/HEAD)
TREXIO provides a Python API, which enables interactive calls to the library.
It facilitates the development of interfaces between different codes and
can be used to convert data from one input/output file format into another.
## Requirements
- python3 (>= 3.8)
- numpy (>= 1.17.3)
## Installation from PyPI
In short, you can run the following command:
`pip install trexio`
However, it is good practice to first check for updates of the build-system packages. This can be achieved by running
`python -m pip install --upgrade pip setuptools build wheel`
**Note: we highly recommend to use virtual environments to avoid compatibility issues and to improve reproducibility.**
For more details, see the corresponding part of the [Python documentation](https://docs.python.org/3/library/venv.html#creating-virtual-environments).
## Additional requirements (for installation from source)
- C compiler (gcc/icc/clang)
- HDF5 library (>= 1.8)
- pkgconfig (Python package)
- build (Python package)
- pytest (Python package)
## Installation from source
1. Download the `trexio-<version>.tar.gz` file with the latest Python API
2. `gzip -cd trexio-<version>.tar.gz | tar xvf -`
3. `cd trexio-<version>`
4. `pip install -r requirements.txt` (this installs all required python dependencies)
5. Export custom environment variables needed for the installation following the procedure below and replacing `/path/to/hdf5/` with your paths.
The following two steps can be skipped if HDF5 is properly configured for `pkg-config` (i.e. if executing `pkg-config --libs hdf5` returns a list of options).
- `export H5_CFLAGS=-I/path/to/hdf5/include`
- `export H5_LDFLAGS=-L/path/to/hdf5/lib`
On MacOS where HDF5 is installed with homebrew (i.e. `brew install hdf5`), one can use the following:
- `export H5_CFLAGS="-I$(brew --prefix hdf5)/include"`
- `export H5_LDFLAGS="-L$(brew --prefix hdf5)/lib"`
6. `pip install .` (this installs `trexio` in your environment)
7. `cd test && python -m pytest -v test_api.py` (this executes several tests that verify the installation)
You are ready to go!
**Note:**
installation based on `pip` compiles its own C extension (shared library) called `pytrexio`.
This extension is built from the TREXIO source files coupled to the wrapper code generated by [SWIG](http://www.swig.org/).
The compiler options during this installation may differ from the ones used to compile the primary TREXIO API in C.
Furthermore, custom compiler flags provided to `./configure` or `make` are not applied to the Python API.
## Examples
An interactive Jupyter notebook called `tutorial_benzene.ipynb` is provided in the `examples` directory.
The notebook can be lauched either locally (see [next section](#Running-the-notebook) for details) or using [pre-built environment on Binder](https://mybinder.org/v2/gh/TREX-CoE/trexio-tutorials/HEAD?filepath=notebooks%2Ftutorial_benzene.ipynb).
Jupyter can be installed using `pip install jupyter`. If you are not familiar with it, feel free to consult the [Jupyter documentation](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html).
### Running the notebook
The example notebook can be launched using the following command:
`jupyter notebook tutorial_benzene.ipynb`
### Additional steps needed to run a custom virtual environment in Jupyter notebooks
In some cases, it may happen that the Jupyter kernels in the activated virtual environment (e.g. `myvenv`) still point to the system-wide python binaries and not to the environment ones.
This will result in `ImportError` when importing `trexio` in the notebook cell. In order to avoid this, the `myvenv` has to be installed as an additional kernel.
This requires `ipykernel` python package, which usually comes together with the Jupyter installation. If this is not the case, run `pip install ipykernel`.
You can install `myvenv` as a kernel by executing the following command:
`python3 -m ipykernel install --user --name=myvenv`
Now you can launch a Jupyter notebook. Once it is open, make sure that your virtual environment is selected as the current kernel.
If this is not the case, try this:
1. Press the `Kernel` button in the navigation panel
2. In the output list of options select `Change kernel`
3. Find the name of your virtual environment (e.g. `myvenv`) in the list and select it
That's it, you have activated the custom virtual environment called `myvenv` in your notebook.
To uninstall the kernel named `myvenv`, execute the following command:
`jupyter kernelspec uninstall myvenv`
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"description": "\n# TREXIO Python API\n\n[](https://badge.fury.io/py/trexio)\n[](https://mybinder.org/v2/gh/TREX-CoE/trexio-tutorials/HEAD)\n\nTREXIO provides a Python API, which enables interactive calls to the library.\nIt facilitates the development of interfaces between different codes and\ncan be used to convert data from one input/output file format into another.\n\n\n## Requirements\n\n- python3 (>= 3.8)\n- numpy (>= 1.17.3)\n\n## Installation from PyPI\n\nIn short, you can run the following command:\n\n`pip install trexio`\n\nHowever, it is good practice to first check for updates of the build-system packages. This can be achieved by running\n\n`python -m pip install --upgrade pip setuptools build wheel`\n\n**Note: we highly recommend to use virtual environments to avoid compatibility issues and to improve reproducibility.**\nFor more details, see the corresponding part of the [Python documentation](https://docs.python.org/3/library/venv.html#creating-virtual-environments).\n\n\n## Additional requirements (for installation from source)\n\n- C compiler \t (gcc/icc/clang)\n- HDF5 library\t(>= 1.8)\n- pkgconfig (Python package)\n- build (Python package)\n- pytest (Python package)\n\n## Installation from source\n\n1. Download the `trexio-<version>.tar.gz` file with the latest Python API\n2. `gzip -cd trexio-<version>.tar.gz | tar xvf -`\n3. `cd trexio-<version>`\n4. `pip install -r requirements.txt` (this installs all required python dependencies)\n5. Export custom environment variables needed for the installation following the procedure below and replacing `/path/to/hdf5/` with your paths.\nThe following two steps can be skipped if HDF5 is properly configured for `pkg-config` (i.e. if executing `pkg-config --libs hdf5` returns a list of options).\n - `export H5_CFLAGS=-I/path/to/hdf5/include`\n - `export H5_LDFLAGS=-L/path/to/hdf5/lib`\nOn MacOS where HDF5 is installed with homebrew (i.e. `brew install hdf5`), one can use the following:\n - `export H5_CFLAGS=\"-I$(brew --prefix hdf5)/include\"`\n - `export H5_LDFLAGS=\"-L$(brew --prefix hdf5)/lib\"`\n6. `pip install .` (this installs `trexio` in your environment)\n7. `cd test && python -m pytest -v test_api.py` (this executes several tests that verify the installation)\n\nYou are ready to go!\n\n**Note:**\ninstallation based on `pip` compiles its own C extension (shared library) called `pytrexio`.\nThis extension is built from the TREXIO source files coupled to the wrapper code generated by [SWIG](http://www.swig.org/).\nThe compiler options during this installation may differ from the ones used to compile the primary TREXIO API in C.\nFurthermore, custom compiler flags provided to `./configure` or `make` are not applied to the Python API.\n\n\n## Examples\n\nAn interactive Jupyter notebook called `tutorial_benzene.ipynb` is provided in the `examples` directory.\nThe notebook can be lauched either locally (see [next section](#Running-the-notebook) for details) or using [pre-built environment on Binder](https://mybinder.org/v2/gh/TREX-CoE/trexio-tutorials/HEAD?filepath=notebooks%2Ftutorial_benzene.ipynb).\n\nJupyter can be installed using `pip install jupyter`. If you are not familiar with it, feel free to consult the [Jupyter documentation](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html).\n\n\n### Running the notebook\n\nThe example notebook can be launched using the following command:\n\n`jupyter notebook tutorial_benzene.ipynb`\n\n\n### Additional steps needed to run a custom virtual environment in Jupyter notebooks\n\nIn some cases, it may happen that the Jupyter kernels in the activated virtual environment (e.g. `myvenv`) still point to the system-wide python binaries and not to the environment ones.\nThis will result in `ImportError` when importing `trexio` in the notebook cell. In order to avoid this, the `myvenv` has to be installed as an additional kernel.\nThis requires `ipykernel` python package, which usually comes together with the Jupyter installation. If this is not the case, run `pip install ipykernel`.\nYou can install `myvenv` as a kernel by executing the following command:\n\n`python3 -m ipykernel install --user --name=myvenv`\n\nNow you can launch a Jupyter notebook. Once it is open, make sure that your virtual environment is selected as the current kernel.\nIf this is not the case, try this:\n\n1. Press the `Kernel` button in the navigation panel\n2. In the output list of options select `Change kernel`\n3. Find the name of your virtual environment (e.g. `myvenv`) in the list and select it\n\nThat's it, you have activated the custom virtual environment called `myvenv` in your notebook.\n\nTo uninstall the kernel named `myvenv`, execute the following command:\n\n`jupyter kernelspec uninstall myvenv`\n",
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