Metadata-Version: 2.1
Name: pyEvalData
Version: 1.4.2
Summary: Python module to evaluate experimental data
Home-page: https://github.com/dschick/pyEvalData
Author: Daniel Schick
Author-email: schick.daniel@gmail.com
License: MIT
Description: # Welcome to pyEvalData
        
        This is a Python module to read and evaluate experimental data.
        It can handle raw data from different sources such as
        [spec](https://certif.com/content/spec/),
        [hdf5](https://www.hdfgroup.org/solutions/hdf5/),
        [NeXus](https://www.nexusformat.org/) files which are common data formats at
        synchrotrons, FELs, as well as in a growing number of laboratories.
        The evaluation provides common functionalities such as binning,
        error calculation, and advanced data manipulation via algebraic expressions as
        well as pre- and post-data-filters.
        Furthermore, advanced wrapper functions allow for plotting and fitting
        sequences of one or multiple scans in dependence of an external parameter.
        
        A minimal code example would look like this:
        
        ```python
        import pyEvalData as ped
        # define your data source
        spec = ped.io.Spec(file_name='data.spec')
        # initialize the evaluation
        ev = ped.Evaluation(spec)
        # define the x- and y-data
        ev.xcol = 'motor1'
        ev.clist = ['ct1', 'ct2', 'ct1/ct2']
        # create a plot for scans 1-3
        ev.plot_scans([1, 2, 3])
        ```
        
        Please follow the [user guide](https://pyevaldata.readthedocs.io/en/latest/user_guide.html) and [examples](https://pyevaldata.readthedocs.io/en/latest/examples.html) for your first steps with `pyEvalData`.
        
        ## Features
        
        - reading of several pre-defined raw data formats
          * [spec](https://certif.com/content/spec/)
          * [hdf5](https://www.hdfgroup.org/solutions/hdf5/)
          * [NeXus](https://www.nexusformat.org/)
          * user-defined text files
          * camera images (Dectris Pilatus, Princeton MTE, Greateyes, ...)
          * composite sources
        - easy implementation of new raw data formats using an `interface class`
        - common methods for plotting and fitting of experimental data, including:
          * data binning
          * error calculation
          * data manipulation via algebraic expressions
          * common data pre- and post-filters
        
        ## Installation
        
        You can either install directly from
        [pypi.org](https://www.pypi.org/project/pyEvalData) using the command
        
            $ pip install pyEvalData
        
        or if you want to work on the latest develop release you can clone 
        `pyEvalData` from the main git repository:
        
            $ git clone https://github.com/dschick/pyEvalData.git pyEvalData
        
        To work in editable mode (source is only linked 
        but not copied to the python site-packages), just do:
        
            $ pip install -e ./pyEvalData
        
        Or to do a normal install with
        
            $ pip install ./pyEvalData
        
        Optionally, you can also let pip install directly from the repository: 
        
            $ pip install git+https://github.com/dschick/pyEvalData.git
        
        You can have the following optional installation to enable unit tests, as well
        as building the documentation:
        
            $ pip install pyEvalData[testing]
            $ pip install pyEvalData[documentation]
            
        ## Contribute & Support
        
        If you are having issues please let us know via the
        [issue tracker](https://github.com/dschick/pyEvalData/issues).
        
        You can contribute to the project via pull-requests following the
        [GitHub flow concept](https://docs.github.com/en/get-started/quickstart/github-flow).
        
        ## License
        
        The project is licensed under the MIT license.
        
Keywords: data evaluation analysis SPEC h5 NeXus
Platform: UNKNOWN
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: testing
Provides-Extra: documentation
