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scipy-data_fitting 0.1.0

Data fitting system with SciPy.

Package Documentation

Latest Version: 1.0.2


Documentation generated from source with pdoc for the latest version is hosted at

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( + '.json', meta=fit.metadata)

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

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.


This package is registered on the Python Package Index (PyPI) at

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


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



Source Repository

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

$ git clone


Generate documentation with pdoc by running

$ make docs


Run the tests with

$ make tests


Run an example with

$ python examples/

or run all the examples with

$ make examples


This code is licensed under the MIT license.


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.

File Type Py Version Uploaded on Size
scipy-data_fitting-0.1.0.tar.gz (md5) Source 2014-03-11 16KB
scipy_data_fitting-0.1.0-py3.3.egg (md5) Python Egg 3.3 2014-03-11 35KB