Metadata-Version: 2.1
Name: sfsimodels
Version: 0.7.16
Summary: A set of standard models for assessing structural and geotechnical problems
Home-page: https://github.com/eng-tools/sfsimodels
Author: Maxim Millen
Author-email: mmi46@uclive.ac.nz
License: MIT
Description: .. image:: https://travis-ci.org/eng-tools/sfsimodels.svg?branch=master
           :target: https://travis-ci.org/eng-tools/sfsimodels
           :alt: Testing Status
        
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           :target: https://pypi.python.org/pypi/sfsimodels
           :alt: PyPi version
           
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           :target: https://coveralls.io/github/eng-tools/sfsimodels
        
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            :target: https://github.com/eng-tools/sfsimodels/blob/master/LICENSE
            :alt: License
        
        **********
        sfsimodels
        **********
        
        A set of python objects to represent physical objects for assessing structural and geotechnical problems
        
        Attempting to solve the `Liskov Substitution Principle <https://en.wikipedia.org/wiki/Liskov_substitution_principle>`_
        problem for combining independently developed source
        code in the fields of structural and geotechnical engineering.
        
        Model inheritance system
        ========================
        
        Every object contains a `type`, a `base_type` and a list of `ancestor_types`.
        
         - `type` is the current type of the class or instance of the class
         - `base_type` is what class should be considered as for standard operations such as saving and loading.
         - `ancestor_types` is a list of the `type` of the ancestors of the class
        
        
        Generation of new custom models
        ===============================
        
        It is easiest to create a new object by inheriting from `sm.CustomObject`, as this contains the default parameters
        needed for loading and saving the model.
        
        If you chose not to use the default custom object, you must set the object `base_type` parameter to `"custom_object"`.
        
        Loading a custom object
        =======================
        
        pass a dictionary to the `custom_object` parameter in the `sm.load_json` function, where the dictionary contains:
        `custom={"<base_type>-<type>": Object}`.
        
        
        Installation
        ============
        
        .. code:: bash
            pip install sfsimodels
        
        Saving and loading models
        =========================
        
        .. code-block:: python
            structure = models.Structure()  # Create a structure object
            structure.id = 1  # Assign it an id
            structure.name = "sample building"  # Assign it a name and other parameters
            structure.h_eff = 10.0
            structure.t_fixed = 1.0
            structure.mass_eff = 80000.
            structure.mass_ratio = 1.0  # Set vertical and horizontal masses are equal
        
            ecp_output = files.Output()  # Create an output object
            ecp_output.add_to_dict(structure)  # Add the structure to the output object
            ecp_output.name = "test data"
            ecp_output.units = "N, kg, m, s"  # Set the units
            ecp_output.comments = ""
        
            p_str = json.dumps(ecp_output.to_dict(), skipkeys=["__repr__"], indent=4)  # Assign it to a json string
            objs = files.loads_json(p_str)  # Load a json string and convert to a dictionary of objects
            assert ct.isclose(structure.mass_eff, objs['buildings'][1].mass_eff)  # Access the object
        
        
        How do I get set up?
        ====================
        
        1. Run ``pip install -r requirements.txt``
        
        Testing
        =======
        
        Tests are run with pytest
        
        * Locally run: ``pytest`` on the command line.
        
        * Tests are run on every push using travis, see the ``.travis.yml`` file
        
        
        Deployment
        ==========
        
        To deploy the package to pypi.com you need to:
        
         1. Push to the *pypi* branch. This executes the tests on circleci.com
        
         2. Create a git tag and push to github, run: ``trigger_deploy.py`` or manually:
        
         .. code:: bash
            git tag 0.5.2 -m "version 0.5.2"
            git push --tags origin pypi
        
        
        Documentation
        =============
        
        At http://sfsimodels.readthedocs.io/en/latest/
Keywords: geotechnical structural soil
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.3
Provides-Extra: test
