| Name | imops JSON |
| Version |
0.9.1
JSON |
| download |
| home_page | https://github.com/neuro-ml/imops |
| Summary | Efficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines |
| upload_time | 2024-12-05 02:26:17 |
| maintainer | None |
| docs_url | None |
| author | maxme1, vovaf709, talgat, alexeybelkov |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2022 NeuroML Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
image processing
fast
ndarray
data pipelines
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
numpy
scipy
dataclasses
scikit-image
connected-components-3d
fastremap
edt
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://codecov.io/gh/neuro-ml/imops)
[](https://pypi.org/project/imops/)
[](https://pypi.org/project/imops/)
# Imops
Efficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines.
- [Documentation](https://neuro-ml.github.io/imops/)
- [Benchmarks](https://neuro-ml.github.io/imops/benchmarks/)
# Install
```shell
pip install imops # default install with Cython backend
pip install imops[numba] # additionally install Numba backend
```
# How fast is it?
Time comparisons (ms) for Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz using 8 threads. All inputs are C-contiguous NumPy arrays. For morphology functions `bool` dtype is used and `float64` for all others.
| function / backend | Scipy() | Cython(fast=False) | Cython(fast=True) | Numba() |
|:----------------------:|:-----------:|:----------------------:|:---------------------:|:-----------:|
| `zoom(..., order=0)` | 2072 | 1114 | **867** | 3590 |
| `zoom(..., order=1)` | 6527 | 596 | **575** | 3757 |
| `interp1d` | 780 | 149 | **146** | 420 |
| `radon` | 59711 | 5982 | **4837** | - |
| `inverse_radon` | 52928 | 8254 | **6535** | - |
| `binary_dilation` | 2207 | 310 | **298** | - |
| `binary_erosion` | 2296 | 326 | **304** | - |
| `binary_closing` | 4158 | 544 | **469** | - |
| `binary_opening` | 4410 | 567 | **522** | - |
| `center_of_mass` | 2237 | **64** | **64** | - |
We use [`airspeed velocity`](https://asv.readthedocs.io/en/stable/) to benchmark our code. For detailed results visit [benchmark page](https://neuro-ml.github.io/imops/benchmarks/).
# Features
### Fast Radon transform
```python
from imops import radon, inverse_radon
```
### Fast 0/1-order zoom
```python
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<=4, dtype=float32 or float64 (and bool-int16-32-64-uint8-16-32 if order == 0), output=None, order=0 or 1, mode='constant', grid_mode=False`
### Fast 1d linear interpolation
```python
from imops import interp1d # same as `scipy.interpolate.interp1d`
```
Works faster only for `ndim<=3, dtype=float32 or float64, order=1`
### Fast 2d linear interpolation
```python
import numpy as np
from imops.interp2d import Linear2DInterpolator
n, m = 1024, 2
points = np.random.randint(low=0, high=1024, size=(n, m))
points = np.unique(points, axis=0)
x_points = points[: n // 2]
values = np.random.uniform(low=0.0, high=1.0, size=(len(x_points),))
interp_points = points[n // 2:]
num_threads = -1 # will be equal to num of CPU cores
# You can optionally pass your own triangulation as an np.array of shape [num_triangles, 3], element at (i, j) position is an index of a point from x_points
interpolator = Linear2DInterpolator(x_points, values, num_threads=num_threads, triangles=None)
# Also you can pass values to __call__ and rewrite the ones that were passed to __init__
interp_values = interpolator(interp_points, values + 1.0, fill_value=0.0)
```
### Fast binary morphology
```python
from imops import binary_dilation, binary_erosion, binary_opening, binary_closing
```
These functions mimic `scikit-image` counterparts
### Padding
```python
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
```python
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)
```
### Labeling
```python
from imops import label
# same as `skimage.measure.label`
labeled, num_components = label(x, background=1, return_num=True)
```
# Backends
For all heavy image routines except `label` 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:
```python
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:
```python
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:
| function / backend | Scipy | Cython | Numba |
|:-------------------:|:---------:|:---------:|:---------:|
| `zoom` | ✓ | ✓ | ✓ |
| `interp1d` | ✓ | ✓ | ✓ |
| `radon` | ✗ | ✓ | ✗ |
| `inverse_radon` | ✗ | ✓ | ✗ |
| `binary_dilation` | ✓ | ✓ | ✗ |
| `binary_erosion` | ✓ | ✓ | ✗ |
| `binary_closing` | ✓ | ✓ | ✗ |
| `binary_opening` | ✓ | ✓ | ✗ |
| `center_of_mass` | ✓ | ✓ | ✗ |
# 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`](https://github.com/scikit-image/scikit-image) and [`scipy`](https://github.com/scipy/scipy).
Also we used [`fastremap`](https://github.com/seung-lab/fastremap), [`edt`](https://github.com/seung-lab/euclidean-distance-transform-3d) and [`cc3d`](https://github.com/seung-lab/connected-components-3d) out of the box.
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"description": "[](https://codecov.io/gh/neuro-ml/imops)\n[](https://pypi.org/project/imops/)\n\n[](https://pypi.org/project/imops/)\n\n# Imops\n\nEfficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines.\n- [Documentation](https://neuro-ml.github.io/imops/)\n- [Benchmarks](https://neuro-ml.github.io/imops/benchmarks/)\n\n# Install\n\n```shell\npip install imops # default install with Cython backend\npip install imops[numba] # additionally install Numba backend\n```\n\n# How fast is it?\n\nTime comparisons (ms) for Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz using 8 threads. All inputs are C-contiguous NumPy arrays. For morphology functions `bool` dtype is used and `float64` for all others.\n| function / backend | Scipy() | Cython(fast=False) | Cython(fast=True) | Numba() |\n|:----------------------:|:-----------:|:----------------------:|:---------------------:|:-----------:|\n| `zoom(..., order=0)` | 2072 | 1114 | **867** | 3590 |\n| `zoom(..., order=1)` | 6527 | 596 | **575** | 3757 |\n| `interp1d` | 780 | 149 | **146** | 420 |\n| `radon` | 59711 | 5982 | **4837** | - |\n| `inverse_radon` | 52928 | 8254 | **6535** | - |\n| `binary_dilation` | 2207 | 310 | **298** | - |\n| `binary_erosion` | 2296 | 326 | **304** | - |\n| `binary_closing` | 4158 | 544 | **469** | - |\n| `binary_opening` | 4410 | 567 | **522** | - |\n| `center_of_mass` | 2237 | **64** | **64** | - |\n\nWe use [`airspeed velocity`](https://asv.readthedocs.io/en/stable/) to benchmark our code. For detailed results visit [benchmark page](https://neuro-ml.github.io/imops/benchmarks/).\n\n# Features\n\n### Fast Radon transform\n\n```python\nfrom imops import radon, inverse_radon\n```\n\n### Fast 0/1-order zoom\n\n```python\nfrom imops import zoom, zoom_to_shape\n\n# fast zoom with optional fallback to scipy's implementation\ny = zoom(x, 2, axis=[0, 1])\n# a handy function to zoom the array to a given shape \n# without the need to compute the scale factor\nz = zoom_to_shape(x, (4, 120, 67))\n```\nWorks faster only for `ndim<=4, dtype=float32 or float64 (and bool-int16-32-64-uint8-16-32 if order == 0), output=None, order=0 or 1, mode='constant', grid_mode=False`\n### Fast 1d linear interpolation\n\n```python\nfrom imops import interp1d # same as `scipy.interpolate.interp1d`\n```\nWorks faster only for `ndim<=3, dtype=float32 or float64, order=1`\n\n### Fast 2d linear interpolation\n```python\nimport numpy as np\nfrom imops.interp2d import Linear2DInterpolator\nn, m = 1024, 2\npoints = np.random.randint(low=0, high=1024, size=(n, m))\npoints = np.unique(points, axis=0)\nx_points = points[: n // 2]\nvalues = np.random.uniform(low=0.0, high=1.0, size=(len(x_points),))\ninterp_points = points[n // 2:]\nnum_threads = -1 # will be equal to num of CPU cores\n# You can optionally pass your own triangulation as an np.array of shape [num_triangles, 3], element at (i, j) position is an index of a point from x_points\ninterpolator = Linear2DInterpolator(x_points, values, num_threads=num_threads, triangles=None)\n# Also you can pass values to __call__ and rewrite the ones that were passed to __init__\ninterp_values = interpolator(interp_points, values + 1.0, fill_value=0.0)\n```\n\n### Fast binary morphology\n\n```python\nfrom imops import binary_dilation, binary_erosion, binary_opening, binary_closing\n```\nThese functions mimic `scikit-image` counterparts\n### Padding\n\n```python\nfrom imops import pad, pad_to_shape\n\ny = pad(x, 10, axis=[0, 1])\n# `ratio` controls how much padding is applied to left side:\n# 0 - pad from right\n# 1 - pad from left\n# 0.5 - distribute the padding equally\nz = pad_to_shape(x, (4, 120, 67), ratio=0.25)\n```\n\n### Cropping\n\n```python\nfrom imops import crop_to_shape\n\n# `ratio` controls the position of the crop\n# 0 - crop from right\n# 1 - crop from left\n# 0.5 - crop from the middle\nz = crop_to_shape(x, (4, 120, 67), ratio=0.25)\n```\n\n### Labeling\n\n```python\nfrom imops import label\n\n# same as `skimage.measure.label`\nlabeled, num_components = label(x, background=1, return_num=True)\n```\n\n# Backends\nFor all heavy image routines except `label` 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:\n```python\nfrom imops import Cython, Numba, Scipy, zoom\n\ny = zoom(x, 2, backend='Cython')\ny = zoom(x, 2, backend=Cython(fast=False)) # same as previous\ny = zoom(x, 2, backend=Cython(fast=True)) # -ffast-math compiled cython backend\ny = zoom(x, 2, backend=Scipy()) # use scipy original implementation\ny = zoom(x, 2, backend='Numba')\ny = zoom(x, 2, backend=Numba(parallel=True, nogil=True, cache=True)) # same as previous\n```\nAlso backend can be specified globally or locally:\n```python\nfrom imops import imops_backend, set_backend, zoom\n\nset_backend('Numba') # sets Numba as default backend\nwith imops_backend('Cython'): # sets Cython backend via context manager\n zoom(x, 2)\n```\nNote that for `Numba` backend setting `num_threads` argument has no effect for now and you should use `NUMBA_NUM_THREADS` environment variable.\nAvailable backends:\n| function / backend | Scipy | Cython | Numba |\n|:-------------------:|:---------:|:---------:|:---------:|\n| `zoom` | ✓ | ✓ | ✓ |\n| `interp1d` | ✓ | ✓ | ✓ |\n| `radon` | ✗ | ✓ | ✗ |\n| `inverse_radon` | ✗ | ✓ | ✗ |\n| `binary_dilation` | ✓ | ✓ | ✗ |\n| `binary_erosion` | ✓ | ✓ | ✗ |\n| `binary_closing` | ✓ | ✓ | ✗ |\n| `binary_opening` | ✓ | ✓ | ✗ |\n| `center_of_mass` | ✓ | ✓ | ✗ |\n\n# Acknowledgements\n\nSome parts of our code for radon/inverse radon transform as well as the code for linear interpolation are inspired by\nthe implementations from [`scikit-image`](https://github.com/scikit-image/scikit-image) and [`scipy`](https://github.com/scipy/scipy).\nAlso we used [`fastremap`](https://github.com/seung-lab/fastremap), [`edt`](https://github.com/seung-lab/euclidean-distance-transform-3d) and [`cc3d`](https://github.com/seung-lab/connected-components-3d) out of the box.\n",
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