Metadata-Version: 2.1
Name: LancasterAQ
Version: 1.0.0
Summary: Python package developed for the Lancaster Air Quality project
Home-page: https://github.com/lgouldsbrough/LancasterAQ
Author: Lily Gouldsbrough
Author-email: l.gouldsbrough@lancaster.ac.uk
License: LICENSE
Description: # LancasterAQ Package
        
        Python package developed for the Lancaster Air Quality project.
        
        Included are tools to:
        
        * Import the dataset
        * Convert the dataset to different formats
        
        ## Installation Instructions
        
        ### PyPI Installation
        
        ```bash
        $ pip install LancasterAQ
        ```
        
        ### Manual Installation
        
        #### Local install
        
        ```bash
        # clone from github
        $ git clone https://github.com/lgouldsbrough/LancasterAQ.git
        # change directory into project root
        $ cd LancasterAQ
        
        # regular install
        $ pip install .
        # or 
        # development install 
        # $ pip install -e .
        ```
        
        #### Directly install from GitHub
        
        ```bash
        # pip install from github
        pip install git+https://github.com/lgouldsbrough/LancasterAQ.git
        # or `python -m pip ...` for environment safety 
        ```
        
        ## Example notebook
        
        An introductory notebook can be found within the [examples folder](examples/introduction.ipynb).
        
        <sub>Note: requires [Matplotlib](https://matplotlib.org/stable/users/getting_started/index.html#installation-quick-start) 
        and [Seaborn](https://seaborn.pydata.org/installing.html)
        packages.</sub>
        
        ## Loading in the Lancaster AQ dataset
        ```python
        import LancasterAQ as laq
        
        # load tabular data
        data = laq.TabularObject()
        
        # OR load the graph object
        data = laq.GraphObject()
        ```
        A helper function is also available:
        ```python
        import LancasterAQ as laq
        # load tabular data
        data = laq.dataset('TabularObject')
        # OR load the graph object
        data = laq.dataset('GraphObject')
        
        ```
        
        ## Convert the tabular data to different formats
        To avoid implicit data copies replace the `data` object with the dataset function call.
        
        For example: replace `data.to_numpy()` with `laq.TabularObject().to_numpy()`
        ### Convert to a pandas dataframe
        
        ``` python
        data = data.to_pandas()
        ```
        
        ### Convert to a numpy array
        
        ``` python
        data = data.to_numpy()
        ```
        
        ## Convert the graph object to different formats
        
        To avoid implicit data copies replace the `data` object with the dataset function call.
        
        For example: replace `data.to_numpy()` with `laq.GraphObject().to_numpy()`
        
        #### Convert to a numpy sparse array
        
        ``` python
        # returns the graph adjacency matrix as a numpy array
        numpy_array = data.to_numpy()
        ```
        
        #### Convert to a dictionary of dictionaries
        
        ``` python
        # returns adjacency representation of graph as a dictionary of dictionaries
        dict_of_dicts = data.to_dict()
        ```
        
        #### Convert to an edge list
        
        ``` python
        # returns a list of edges in the graph
        edge_list = data.to_edgelist()
        ```
        
        #### Convert to a dictionary of lists
        
        ``` python
        # returns adjacency representation of graph as a dictionary of lists
        dict_of_lists = data.to_dict_of_lists()
        ```
        
        #### Convert to a scipy sparse array
        
        ``` python
        # returns the graph adjacency matrix as a scipy sparse array
        scipy_sparse_array = data.to_scipy()
        ```
        
        #### Convert to JSON
        
        ``` python
        # returns json object of graph
        data_json = data.to_json()
        ```
        
        
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: examples
