Home

Awesome

PyPI version

fastremap

Renumber and relabel Numpy arrays at C++ speed and physically convert rectangular Numpy arrays between C and Fortran order using an in-place transposition.

import fastremap

uniq, cts = fastremap.unique(labels, return_counts=True) # may be much faster than np.unique
labels, remapping = fastremap.renumber(labels, in_place=True) # relabel values from 1 and refit data type
ptc = fastremap.point_cloud(labels) # dict of coordinates by label

labels = fastremap.refit(labels) # resize the data type of the array to fit extrema
labels = fastremap.refit(labels, value=-35) # resize the data type to fit the value provided

# remap all occurances of 1 -> 2
labels = fastremap.remap(labels, { 1: 2 }, preserve_missing_labels=True, in_place=True)

labels = fastremap.mask(labels, [1,5,13]) # set all occurances of 1,5,13 to 0
labels = fastremap.mask_except(labels, [1,5,13]) # set all labels except 1,5,13 to 0

mapping = fastremap.component_map([ 1, 2, 3, 4 ], [ 5, 5, 6, 7 ]) # { 1: 5, 2: 5, 3: 6, 4: 7 }
mapping = fastremap.inverse_component_map([ 1, 2, 1, 3 ], [ 4, 4, 5, 6 ]) # { 1: [ 4, 5 ], 2: [ 4 ], 3: [ 6 ] }

fastremap.transpose(labels) # physically transpose labels in-place
fastremap.ascontiguousarray(labels) # try to perform a physical in-place transposition to C order
fastremap.asfortranarray(labels) # try to perform a physical in-place transposition to F order

minval, maxval = fastremap.minmax(labels) # faster version of (np.min(labels), np.max(labels))

# computes number of matching adjacent pixel pairs in an image
num_pairs = fastremap.pixel_pairs(labels)  
n_foreground = fastremap.foreground(labels) # number of nonzero voxels

# computes the cutout.tobytes(order) of each chunk and returns
# the binaries indexed by fortran order in the order specified (C or F)
# If the input image is F contiguous and F is requested, or C and C order,
# and the image is larger than a single chunk, this will be significantly
# faster than iterating and using tobytes.
binaries = fastremap.tobytes(labels, (64,64,64), order="F")

All Available Functions

pip Installation

pip install fastremap

If not, a C++ compiler is required.

pip install numpy
pip install fastremap --no-binary :all:

Manual Installation

A C++ compiler is required.

sudo apt-get install g++ python3-dev 
mkvirtualenv -p python3 fastremap
pip install numpy

# Choose one:
python setup.py develop  
python setup.py install

The Problem of Remapping

Python loops are slow, so Numpy is often used to perform remapping on large arrays (hundreds of megabytes or gigabytes). In order to efficiently remap an array in Numpy you need a key-value array where the index is the key and the value is the contents of that index.

import numpy as np 

original = np.array([ 1, 3, 5, 5, 10 ])
remap = np.array([ 0, -5, 0, 6, 0, 0, 2, 0, 0, 0, -100 ])
# Keys:            0   1  2  3  4  5  6  7  8  9    10

remapped = remap[ original ]
>>> [ -5, 6, 2, 2, -100 ]

If there are 32 or 64 bit labels in the array, this becomes impractical as the size of the array can grow larger than RAM. Therefore, it would be helpful to be able to perform this mapping using a C speed loop. Numba can be used for this in some circumstances. However, this library provides an alternative.

import numpy as np
import fastremap 

mappings = {
  1: 100,
  2: 200,
  -3: 7,
}

arr = np.array([5, 1, 2, -5, -3, 10, 6])
# Custom remapping of -3, 5, and 6 leaving the rest alone
arr = fastremap.remap(arr, mappings, preserve_missing_labels=True) 
# result: [ 5, 100, 200, -5, 7, 10, 6 ]

The Problem of Renumbering

Sometimes a 64-bit array contains values that could be represented by an 8-bit array. However, similarly to the remapping problem, Python loops can be too slow to do this. Numpy doesn't provide a convenient way to do it either. Therefore this library provides an alternative solution.

import fastremap
import numpy as np

arr = np.array([ 283732875, 439238823, 283732875, 182812404, 0 ], dtype=np.int64) 

arr, remapping = fastremap.renumber(arr, preserve_zero=True) # Returns uint8 array
>>> arr = [ 1, 2, 1, 3, 0 ]
>>> remapping = { 0: 0, 283732875: 1, 439238823: 2, 182812404: 3 }

arr, remapping = fastremap.renumber(arr, preserve_zero=False) # Returns uint8 array
>>> arr = [ 1, 2, 1, 3, 4 ]
>>> remapping = { 0: 4, 283732875: 1, 439238823: 2, 182812404: 3 }

arr, remapping = fastremap.renumber(arr, preserve_zero=False, in_place=True) # Mutate arr to use less memory
>>> arr = [ 1, 2, 1, 3, 4 ]
>>> remapping = { 0: 4, 283732875: 1, 439238823: 2, 182812404: 3 }

The Problem of In-Place Transposition

When transitioning between different media, e.g. CPU to GPU, CPU to Network, CPU to disk, it's often necessary to physically transpose multi-dimensional arrays to reformat as C or Fortran order. Tranposing matrices is also a common action in linear algebra, but often you can get away with just changing the strides.

An out-of-place transposition is easy to write, and often faster, but it will spike peak memory consumption. This library grants the user the option of performing an in-place transposition which trades CPU time for peak memory usage. In the special case of square or cubic arrays, the in-place transpisition is both lower memory and faster.

import fastremap
import numpy as np 

arr = np.ones((512,512,512), dtype=np.float32)
arr = fastremap.asfortranarray(x)

arr = np.ones((512,512,512), dtype=np.float32, order='F')
arr = fastremap.ascontiguousarray(x)

C++ Usage

The in-place matrix transposition is implemented in ipt.hpp. If you're working in C++, you can also use it directly like so:

#include "ipt.hpp"

int main() {

  int sx = 128;
  int sy = 124;
  int sz = 103;
  int sw = 3;

  auto* arr = ....;

  // All primitive number types supported
  // The array will be modified in place, 
  // so these functions are void type.
  ipt::ipt<int>(arr, sx, sy);            // 2D
  ipt::ipt<float>(arr, sx, sy, sz);      // 3D
  ipt::ipt<double>(arr, sx, sy, sz, sw); // 4D

  return 0;
}

--
Made with <3