Installation ============ It's easiest to install pdata from the `conda-forge `_ channel using `Conda `_. For example, you can create a new conda environment including pdata, `QCoDeS `_, and `JupyterLab `_ like this:: conda create -n pdatasandbox --channel conda-forge pdata qcodes jupyterlab Here, :code:`pdatasandbox` is an arbitrary name for the new environment. If needed, familiarize yourself with the basics of `Conda environments `_. You can also activate the `conda-forge channel `_ for an existing environment or all environments:: conda config --add channels conda-forge # Add --env if you want this only for the currently active conda environment conda config --set channel_priority strict # --env After which you can run (inside the appropriate conda environment):: conda install pdata Often, however, it's simpler and more robust to create a new conda environment and specify all the required packages in one go in the :code:`create` command, as in the :code:`pdatasandbox` example above. In general, you should consider conda environments disposable. pip install ----------- If you're not using Conda, download the latest version from `qithub `_ and install with pip. That is, run this in the root folder where :file:`setup.cfg` is:: pip install . Note that you also `need a C++ compiler `_. Alternatively, you can disable :code:`fast_parser` by setting :code:`FAST_PARSER_ENABLED=False` in :code:`setup.py`. Requirements ------------ Required packages are listed in :file:`setup.cfg`. Here are some of them: * `NumPy `_ * `jsondiff `_ * `pytz `_ * `uncertainties `_ * `requests `_, `ipykernel `_, `ipython `_, `notebook `_, `ipylab `_ (for storing .ipynb measurement scripts) * `ipywidgets `_ (for dataexplorer) * `matplotlib `_ (for dataexplorer) * `Setuptools `_ (for installation) * `Cython `_ (for fast_parser)