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98 lines
4.3 KiB
Markdown
98 lines
4.3 KiB
Markdown
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# TREXIO Python API
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[](https://mybinder.org/v2/gh/TREX-CoE/trexio-tutorials/HEAD)
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TREXIO provides a Python API, which enables interactive calls to the library.
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It facilitates the development of interfaces between different codes and
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can be used to convert data from one input/output file format into another.
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## Requirements
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- python3 (>= 3.6)
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- numpy (>= 1.17.3)
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## Installation from PyPI
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In short, you can run the following command:
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`pip install trexio`
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However, it is good practice to first check for updates of the build-system packages. This can be achieved by running
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`python3 -m pip install --upgrade pip setuptools wheel`
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**Note: we highly recommend to use virtual environments to avoid compatibility issues and to improve reproducibility.**
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For more details, see the corresponding part of the [Python documentation](https://docs.python.org/3/library/venv.html#creating-virtual-environments).
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## Additional requirements (for installation from source)
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- C compiler (gcc/icc)
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- HDF5 library (>= 1.8)
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- pkgconfig (Python package)
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## Installation from source
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1. Download the `trexio-<version>.tar.gz` file with the latest Python API
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2. `gzip -cd trexio-<version>.tar.gz | tar xvf -`
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3. `cd trexio-<version>`
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4. `pip3 install -r requirements.txt` (this installs all required python dependencies)
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5. Export custom environment variables needed for the installation following the procedure below and replacing `/path/to/hdf5/` with your paths.
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Steps (i) and (ii) can be skipped if HDF5 is properly configured for `pkg-config` (i.e. if executing `pkg-config --libs hdf5` returns a list of options).
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1. `export H5_CFLAGS=-I/path/to/hdf5/include`
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2. `export H5_LDFLAGS=-L/path/to/hdf5/lib`
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3. `source tools/set_NUMPY_INCLUDEDIR.sh`
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6. `pip3 install .` (this installs `trexio` in your environment)
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7. `cd test && python3 test_api.py` (this executes several tests that verify the installation)
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You are ready to go!
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**Note:**
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installation based on `pip` compiles its own C extension (shared library) called `pytrexio`.
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This extension is built from the TREXIO source files coupled to the wrapper code generated by [SWIG](http://www.swig.org/).
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The compiler options during this installation may differ from the ones used to compile the primary TREXIO API in C.
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Furthermore, custom compiler flags provided to `./configure` or `make` are not applied to the Python API.
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## Examples
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An interactive Jupyter notebook called `tutorial_benzene.ipynb` is provided in the `examples` directory.
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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).
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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).
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### Running the notebook
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The example notebook can be launched using the following command:
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`jupyter notebook tutorial_benzene.ipynb`
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### Additional steps needed to run a custom virtual environment in Jupyter notebooks
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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.
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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.
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This requires `ipykernel` python package, which usually comes together with the Jupyter installation. If this is not the case, run `pip install ipykernel`.
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You can install `myvenv` as a kernel by executing the following command:
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`python3 -m ipykernel install --user --name=myvenv`
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Now you can launch a Jupyter notebook. Once it is open, make sure that your virtual environment is selected as the current kernel.
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If this is not the case, try this:
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1. Press the `Kernel` button in the navigation panel
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2. In the output list of options select `Change kernel`
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3. Find the name of your virtual environment (e.g. `myvenv`) in the list and select it
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That's it, you have activated the custom virtual environment called `myvenv` in your notebook.
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To uninstall the kernel named `myvenv`, execute the following command:
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`jupyter kernelspec uninstall myvenv`
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