imops 0.5.0.post1

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Imops

Efficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines

Install

pip install imops  # default install with Cython backend
pip install imops[numba]  # additionally install Numba backend

Features

Fast Radon transform

from imops import radon, inverse_radon

Fast linear/bilinear/trilinear zoom

from imops import zoom, zoom_to_shape

# fast zoom with optional fallback to scipy's implementation
y = zoom(x, 2, axis=[0, 1])
# a handy function to zoom the array to a given shape 
# without the need to compute the scale factor
z = zoom_to_shape(x, (4, 120, 67))

Works faster only for ndim<=3, dtype=float32 or float64, output=None, order=1, mode='constant', grid_mode=False

Fast 1d linear interpolation

from imops import interp1d  # same as `scipy.interpolate.interp1d`

Works faster only for ndim<=3, dtype=float32 or float64, order=1 or 'linear'

Padding

from imops import pad, pad_to_shape

y = pad(x, 10, axis=[0, 1])
# `ratio` controls how much padding is applied to left side:
# 0 - pad from right
# 1 - pad from left
# 0.5 - distribute the padding equally
z = pad_to_shape(x, (4, 120, 67), ratio=0.25)

Cropping

from imops import crop_to_shape

# `ratio` controls the position of the crop
# 0 - crop from right
# 1 - crop from left
# 0.5 - crop from the middle
z = crop_to_shape(x, (4, 120, 67), ratio=0.25)

Backends

For zoom, zoom_to_shape, interp1d, radon, inverse_radon you can specify which backend to use. Backend can be specified by a string or by an instance of Backend class. The latter allows you to customize some backend options:

from imops import Cython, Numba, Scipy, zoom

y = zoom(x, 2, backend='Cython')
y = zoom(x, 2, backend=Cython(fast=False))  # same as previous
y = zoom(x, 2, backend=Cython(fast=True))  # -ffast-math compiled cython backend
y = zoom(x, 2, backend=Scipy())  # use scipy original implementation
y = zoom(x, 2, backend='Numba')
y = zoom(x, 2, backend=Numba(parallel=True, nogil=True, cache=True))  # same as previous

Also backend can be specified globally or locally:

from imops import imops_backend, set_backend, zoom

set_backend('Numba')  # sets Numba as default backend
with imops_backend('Cython'):  # sets Cython backend via context manager
    zoom(x, 2)

Note that for Numba backend setting num_threads argument has no effect for now and you should use NUMBA_NUM_THREADS environment variable. Available backends:

	Scipy	Cython	Numba
zoom	✓	✓	✓
zoom_to_shape	✓	✓	✓
interp1d	✓	✓	✓
radon	✗	✓	✗
inverse_radon	✗	✓	✗

Acknowledgements

Some parts of our code for radon/inverse radon transform as well as the code for linear interpolation are inspired by the implementations from scikit-image and scipy.