Data Fitting with SciPy

Built from makenew/python-package.

Description

Complete pipeline for easy data fitting with Python 3.

Check out the example fits on Fitalyzer. See the Fitalyzer README for details on how to use Fitalyzer for visualizing your fits.

Installation

This package is registered on the Python Package Index (PyPI) as scipy_data_fitting.

Add this line to your application's requirements.txt

scipy_data_fitting

and install it with

$ pip install -r requirements.txt

If you are writing a Python package which will depend on this, add this to your requirements in setup.py.

Alternatively, install it directly using pip with

$ pip install scipy_data_fitting

Documentation

Documentation is generated from source with pdoc. The latest version is hosted at pythonhosted.org/scipy-data_fitting/.

To get started quickly, check out the examples.

Then, refer to the source documentation for details on how to use each class.

Basic Usage

from scipy_data_fitting import Data, Model, Fit, Plot

# Load data from a CSV file.
data = Data('linear')
data.path = 'linear.csv'
data.error = (0.5, None)

# Create a linear model.
model = Model('linear')
model.add_symbols('t', 'v', 'x_0')
t, v, x_0 = model.get_symbols('t', 'v', 'x_0')
model.expressions['line'] = v * t + x_0

# Create the fit using the data and model.
fit = Fit('linear', data=data, model=model)
fit.expression = 'line'
fit.independent = {'symbol': 't', 'name': 'Time', 'units': 's'}
fit.dependent = {'name': 'Distance', 'units': 'm'}
fit.parameters = [
    {'symbol': 'v', 'guess': 1, 'units': 'm/s'},
    {'symbol': 'x_0', 'value': 1, 'units': 'm'},
]

# Save the fit result to a json file.
fit.to_json(fit.name + '.json', meta=fit.metadata)

# Save a plot of the fit to an image file.
plot = Plot(fit)
plot.save(fit.name + '.svg')
plot.close()

Controlling the fitting process

The above example will fit the line using the default algorithm scipy.optimize.curve_fit.

For a linear fit, it may be more desirable to use a more efficient algorithm.

For example, to use numpy.polyfit, one could set a fit_function and allow both parameters to vary,

fit.parameters = [
    {'symbol': 'v', 'guess': 1, 'units': 'm/s'},
    {'symbol': 'x_0', 'guess': 1, 'units': 'm'},
]
fit.options['fit_function'] = \
    lambda f, x, y, p0, **op: (numpy.polyfit(x, y, 1), )

Controlling the fitting process this way allows, for example, incorporating error values and computing and returning goodness of fit information.

See scipy_data_fitting.Fit.options for further details on how to control the fit and also how to use lmfit.

Development and Testing

Source Code

The scipy-data_fitting source is hosted on GitHub. Clone the project with

$ git clone https://github.com/razor-x/scipy-data_fitting.git

Requirements

You will need Python 3 with pip.

Install the development dependencies with

$ pip install -r requirements.devel.txt

Tests

Lint code with

$ python setup.py lint

Run tests with

$ python setup.py test

or

$ make test

Documentation

Generate documentation with pdoc by running

$ make docs

Examples

Run an example with

$ python examples/example_fit.py

or run all the examples with

$ make examples

Contributing

Please submit and comment on bug reports and feature requests.

To submit a patch:

1. Fork it (https://github.com/razor-x/scipy-data_fitting/fork).
2. Create your feature branch (git checkout -b my-new-feature).
3. Make changes. Write and run tests.
4. Commit your changes (git commit -am 'Add some feature').
5. Push to the branch (git push origin my-new-feature).
6. Create a new Pull Request.

License

This Python package is licensed under the MIT license.

Warranty

This software is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantibility and fitness for a particular purpose.