sparsestack 0.7.1

Python library to handle stacks of sparse COO arrays efficiently.

Memory efficient stack of multiple 2D sparse arrays.

Installation

Requirements

Python 3.10 or higher

Pip Install

Simply install using pip: pip install sparsestack

First code example

import numpy as np
from sparsestack import StackedSparseArray

# Create some fake data
scores1 = np.random.random((12, 10))
scores1[scores1 < 0.9] = 0  # make "sparse"
scores2 = np.random.random((12, 10))
scores2[scores2 < 0.75] = 0  # make "sparse"
sparsestack = StackedSparseArray(12, 10)
sparsestack.add_dense_matrix(scores1, "scores_1")

# Add second scores and filter
sparsestack.add_dense_matrix(scores2, "scores_2", join_type="left")

# Scores can be accessed using (limited) slicing capabilities
sparsestack[3, 4]  # => scores_1 and scores_2 at position row=3, col=4
sparsestack[3, :]  # => tuple with row, col, scores for all entries in row=3
sparsestack[:, 2]  # => tuple with row, col, scores for all entries in col=2
sparsestack[3, :, 0]  # => tuple with row, col, scores_1 for all entries in row=3
sparsestack[3, :, "scores_1"]  # => same as the one before

# Scores can also be converted to a dense numpy array:
scores2_after_merge = sparsestack.to_array("scores_2")

Adding data to a sparsestack-array

Sparsestack provides three options to add data to a new layer.

1. .add_dense_matrix(input_array) Can be used to add all none-zero elements of input_array to the sparsestack. Depending on the chosen join_type either all such values will be added (join_type="outer" or join_type="right"), or only those which are already present in underlying layers ("left" or "inner" join).
2. .add_sparse_matrix(input_coo_matrix) This method will expect a COO-style matrix (e.g. scipy) which has attributes .row, .col and .data. The join type can again be specified using join_type.
3. .add_sparse_data(row, col, data) This essentially does the same as .add_sparse_matrix(input_coo_matrix) but might in some cases be a bit more flexible because row, col and data are separate input arguments.

Accessing data from sparsestack-array

The collected sparse data can be accessed in multiple ways.

1. Slicing. sparsestack allows multiple types of slicing (see also code example above).

sparsestack[3, 4]  # => tuple with all scores at position row=3, col=4
sparsestack[3, :]  # => tuple with row, col, scores for all entries in row=3
sparsestack[:, 2]  # => tuple with row, col, scores for all entries in col=2
sparsestack[3, :, 0]  # => tuple with row, col, scores_1 for all entries in row=3
sparsestack[3, :, "scores_1"]  # => same as the one before

2. .to_array() Creates and returns a dense numpy array of size .shape. Can also be used to create a dense numpy array of only a single layer when used like .to_array(name="layerX").
   Carefull: Obviously by converting to a dense array, the sparse nature will be lost and all empty positions in the stack will be filled with zeros.
3. .to_coo(name="layerX") Returns a scipy sparse COO-matrix of the specified layer.