Quickstart Guide
================
---------------------------------
Implementing to Existing Projects
---------------------------------

For users uninterested in cloning the source directory, they may use the functions simply by using pip to install the package.

.. code-block:: python

    pip install PythonDSSAnalysis

With this users can import the package into their current working files with:

.. code-block:: python

    from PythonDSSAnalysis import DSSHandler as DSS

Users can now call all the functions available by invoking the DSS module

.. code-block:: python

    betas = DSS.data_normalizer(someData)


----------------
Cloning the Repo
----------------
First, it is recommended the users clone the repository into a fresh directory with a virtual environment

.. code-block:: python

    git clone https://github.com/granthendrickson19/Python-DSS-Analysis.git

Now the user should navigate into the Python-DSS-Analysis working directory (where the pyproject.toml file is located) to install the dependencies

.. code-block:: python

    pip install .

This should install numpy and pyyaml as the two dependencies the project requires.


----------------
Running Examples
----------------
Included with the repository, is sample data, a sampleRun.py file which can be called from the command line, and two sample model files. Both models fit the sample data, one with a quadratic fit, the other with an exponential.

Users can quickly run the two example problems by switching their directory to the working directory (where sampleRun.py is located). Running sampleRun.py from the command line takes 3 flags:

1. -m the model file, this is the .yml files which should be structured as the sample files provided
2. -d the data file, This is time demarked data of a single parameter of interest the first column must be time, the second column is the parameter of interest. Upon reading the file. the sampleRun skips the first line assuming titles of the data.
3. -o This is the output file name, it will be created in the same directory at which the sampleRun.py file is invoked.

.. code-block:: python

    python3 sampleRun.py -d sampledata.csv -m samplemodel.yml -o quadraticOutput.csv

This will create a quadraticOutput.csv file with all the DSS parameters calculated, the users can run the sampledata again with the exponential fit very similarly

.. code-block:: python

    python3 sampleRun.py -d sampledata.csv -m samplemodelexp.yml -o exponentialOutput.csv