hillfit 0.1.7
Model for fitting data with the Hill equation, and exporting the contents
License: MIT
Keywords: biochemistry,systems biology,computational biology,data science,Hill equation

Fitting the Hill Equation to Modeled or Experimental Data
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Background
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The `Hill equation <https://www.physiologyweb.com/calculators/hill_equation_interactive_graph.html>`_ is defined as follows:

y = bottom + ((top - bottom) * x\ :sup:`nH`\ ) / (EC50\ :sup:`nH`\ + x\ :sup:`nH`\)

where *bottom* is the minimum activity; *top* is maximum activity; *EC50* is the half-maximum effective dose; and *nH* is the Hill coefficient. The variables *x* & *y* are the stimuli dose and the cellular or tissue response. The `hillfit` module applies this biological equation, and is available with the `MIT License <https://opensource.org/licenses/MIT>`_\.

Usage
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installation
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The following command are executed in a command prompt/terminal environment::

 pip install hillfit

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__init__
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The data environment, in a `Python IDE <https://www.simplilearn.com/tutorials/python-tutorial/python-ide>`_, is defined: 

.. code-block:: python

 import hillfit
 hf = hillfit.HillFit(x_data, y_data, bottom_param=True)

- *x_data* & *y_data* ``list`` or ``ndarray``: specifies the x-values & y-values, respectively, of the raw data that will be fitted with the Hill equation.
- *bottom_param* ``bool``: specifies whether the accessory ``bottom`` parameter of the Hill equation will be employed in the regression.

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fitting()
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The parameterized data is fitted to the Hill equation, with the following arguments and their default values:

.. code-block:: python

 hf.fitting(x_label='x', y_label='y', title='Fitted Hill equation', sigfigs=6, log_x=False, print_r_sqr=True, 
            generate_figure=True, view_figure=True, export_directory=None, export_name=None, figure=True, curve_fit_kws:Optional[dict]=None)

- *x_label* & *y_label* ``str``: specifies the x-axis & y-axis labels, respectively, that will be applied to the regression plot for the raw data points and the fitted Hill equation.
- *title* ``str``: specifies the title of the regression plot for the raw data points and the fitted Hill equation.
- *sigfigs* ``int``: specifies the number of `significant figures <https://en.wikipedia.org/wiki/Significant_figures>`_ that will be used in printed instances of the fitted Hill equation.
- *log_x* ``bool``: specifies whether the x-axis of the regression plot will be converted into a logarithmic scale.
- *generate_figure* & *view_figure* ``bool``: specifies whether the regression plot will be generated and printed in the Python environment, respectively.
- *print_r_sqr* ``bool``: specifies whether the coefficient of determination (R\ :sup:`2`\) regression plot will be printed in the Python environment.
- *export_name* & *export_directory* ``str``: optionally specifies the folder and the directory to where the contents folder will be exported, where `None` design a unique folder name for the information in the current working directory.
- *figure* ``bool``: specifies whether a figure will be exported with the fitted content.
- *curve_fit_kws* ``Optional[dict]``: Keyword arguments to pass to `scipy.optimize.curve_fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.htmlgit>`_.

-----------------------------
Accessible content
-----------------------------
Many data sets and exported components of the fitted information are accessible through the ``hillfit`` model object. 

- *top*, *bottom*, *ec50*, & *nH* ``float``: The fitted parameters of the Hill equation are accessible via ``hf.top``, ``hf.bottom``, ``hf.ec50``, & ``hf.nH``, respectively.
- *R*\ :sup:`2`\ ``float``: The coefficient of determination of the regression figure is available via ``hf.r_2``.
- *fitted_xs* & *fitted_ys* ``list``: The x- and y-values of the fitted Hill equation are accessible via ``hf.x_fit`` & ``hf.y_fit``, respectively.
- *fitted_equation* ``str``: The fitted Hill equation, in an amenable string format for the `eval() built-in function <https://pythongeeks.org/python-eval-function/>`_ of Python that allows the user to directly execute the string as a function for an "x" variable, is accessible via ``hf.equation``.
- *figure* & *ax* ``matplotlib.figure``: The `fig` and `ax` objects of the regression figure are available via ``hf.figure`` & ``hf.ax``, respectively. This allows users to edit the figures after the simulation is conducted.
- *x_data* & *y_data* ``ndarray``: The arrays of original data are available via ``hf.x_data`` & ``hf.y_data``, respectively.


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export()
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The fitted Hill equation, with its data points and parameters, and the regression information are exported to a designated folder through the following syntax and arguments:

.. code-block:: python

   hf.export(export_directory=None, export_name=None, figure=True)

- *export_name* & *export_directory* ``str``: optionally specifies the folder and the directory to where the contents folder will be exported, where `None` design a unique folder name for the information in the current working directory.
- *figure* ``bool``: specifies whether a figure will be exported with the fitted content.

Execution
+++++++++++

Hillfit is executed through the following example sequence of the aforementioned functions, which is exemplified in the `example Notebook of our GitHub repository <./examples>`_:

.. code-block:: python

   import hillfit
   hf = hillfit.HillFit(x_data, y_data)
   hf.fitting(x_label='test_x', y_label='test_y', title='Fitted Hill equation', generate_figure=False, export_name="example_contents")