Deconvoluted makes performing integral transforms simple and pythonic!

Deconvoluted

Deconvoluted makes performing numerical integral transforms simple and pythonic!

Features

Fourier Transforms

As a first example, let’s perform a Fourier transform:

t = np.linspace(0, 10, 201)
f = np.sin(3 * 2 * np.pi * t)
F, nu = fourier_transform(f, t)

By default, Fourier transforms use Fourier coefficients a=0, b=-2pi. Using another convention is simple:

F, omega = fourier_transform(f, t, convention=(-1, 1))

As a physicist myself, I therefore switch the labelling of the output from nu for frequency, to omega for angular frequency.

Performing multidimensional transforms is just as easy. For example:

F_pq, p, q = fourier_transform(f_xy, x, y)

transforms both x and y at the same time. Transforming only one of the two variables can be done simply by setting those that shouldn’t transform to None:

F_py, p = fourier_transform(f_xy, x, None)
F_xq, q = fourier_transform(f_xy, None, y)

See the documentation for more examples!

History

0.1.1 (2019-06-05)

• Implemented support for different FT conventions.

0.1.0 (2019-06-03)

• First release on PyPI.

Project details

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