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Data fitting system with SciPy.

Project description

Documentation

Documentation generated from source with pdoc for the latest version is hosted at packages.python.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 <http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html>`__.

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

For example, to use `numpy.polyfit <http://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html>`__, 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 <http://packages.python.org/scipy-data_fitting/#scipy_data_fitting.Fit.options>`__ for further details on how to control the fit and also how to use lmfit.

Installation

This package is registered on the Python Package Index (PyPI) at pypi.python.org/pypi/scipy-data_fitting.

Add this line to your application’s requirements.txt:

scipy-data_fitting

And then execute:

$ pip install -r requirements.txt

Or install it yourself as:

$ pip install scipy-data_fitting

Instead of the package name scipy-data_fitting, you can use this repository directly with

git+https://github.com/razor-x/scipy-data_fitting.git@master#egg=scipy-data_fitting

Development

Source Repository

The source is hosted at GitHub. Fork it on GitHub, or clone the project with

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

Documentation

Generate documentation with pdoc by running

$ make docs

Tests

Run the tests with

$ make tests

Examples

Run an example with

$ python examples/example_fit.py

or run all the examples with

$ make examples

License

This code 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.

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