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توضیحات

Python package for Augmented Interval List

ویژگی	مقدار
سیستم عامل	-
نام فایل	ailist-1.0.4
نام	ailist
نسخه کتابخانه	1.0.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Kyle S. Smith
ایمیل نویسنده	kyle.smith@stjude.org
آدرس صفحه اصلی	https://github.com/kylessmith/ailist
آدرس اینترنتی	https://pypi.org/project/ailist/
مجوز	GPL2

# Augmented Interval List [![Build Status](https://travis-ci.org/kylessmith/ailist.svg?branch=master)](https://travis-ci.org/kylessmith/ailist) [![PyPI version](https://badge.fury.io/py/ailist.svg)](https://badge.fury.io/py/ailist) [![Coffee](https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee&color=ff69b4)](https://www.buymeacoffee.com/kylessmith) Augmented interval list (AIList) is a data structure for enumerating intersections between a query interval and an interval set. AILists have previously been shown to be faster than interval tree, NCList, and BEDTools. This implementation is a Python wrapper of the one used in the original [AIList library][AIList_github]. Additonal wrapper functions have been created which allow easy user interface. All citations should reference to [original paper][paper]. For full usage and installation [documentation][AIList_docs] ## Install If you dont already have numpy and scipy installed, it is best to download `Anaconda`, a python distribution that has them included. ``` https://continuum.io/downloads ``` Dependencies can be installed by: ``` pip install -r requirements.txt ``` PyPI install, presuming you have all its requirements installed: ``` pip install ailist ``` ## Benchmark Test numpy random integers: ```python # ailist version: 0.1.7 from ailist import AIList # ncls version: 0.0.53 from ncls import NCLS # numpy version: 1.18.4 import numpy as np # pandas version: 1.0.3 import pandas as pd # quicksect version: 0.2.2 import quicksect # Set seed np.random.seed(100) # First values starts1 = np.random.randint(0, 100000, 100000) ends1 = starts1 + np.random.randint(1, 10000, 100000) ids1 = np.arange(len(starts1)) values1 = np.ones(len(starts1)) # Second values starts2 = np.random.randint(0, 100000, 100000) ends2 = starts2 + np.random.randint(1, 10000, 100000) ids2 = np.arange(len(starts2)) values2 = np.ones(len(starts2)) ``` | Library | Function | Time (µs) | | --- | --- | --- | | ncls | single overlap | 1170 | | pandas | single overlap | 924 | | quicksect | single overlap | 550 | | ailist | single overlap | 73 | | Library | Function | Time (s) | Max Memory (GB) | | --- | --- | --- | --- | | ncls | bulk overlap | 151 s | >50 | | ailist | bulk overlap | 17.8 s | ~9 | ## Usage ```python from ailist import AIList import numpy as np i = AIList() i.add(15, 20) i.add(10, 30) i.add(17, 19) i.add(5, 20) i.add(12, 15) i.add(30, 40) # Print intervals i.display() # (15-20) (10-30) (17-19) (5-20) (12-15) (30-40) # Find overlapping intervals o = i.intersect(6, 15) o.display() # (5-20) (10-30) (12-15) # Find index of overlaps i.intersect_index(6, 15) # array([3, 1, 4]) # Now i has been constructed/sorted i.display() # (5-20) (10-30) (12-15) (15-20) (17-19) (30-40) # Can be done manually as well at any time i.construct() # Iterate over intervals for x in i: print(x) # Interval(5-20, 3) # Interval(10-30, 1) # Interval(12-15, 4) # Interval(15-20, 0) # Interval(17-19, 2) # Interval(30-40, 5) # Interval comparisons j = AIList() j.add(5, 15) j.add(50, 60) # Subtract regions s = i - j #also: i.subtract(j) s.display() # (15-20) (15-30) (15-20) (17-19) (30-40) # Common regions i + j #also: i.common(j) # AIList # range: (5-15) # (5-15, 3) # (10-15, 1) # (12-15, 4) # AIList can also add to from arrays starts = np.arange(10,1000,100) ends = starts + 50 ids = starts values = np.ones(10) i.from_array(starts, ends, ids, values) i.display() # (5-20) (10-30) (12-15) (15-20) (17-19) (30-40) # (10-60) (110-160) (210-260) (310-360) (410-460) # (510-560) (610-660) (710-760) (810-860) (910-960) # Merge overlapping intervals m = i.merge(gap=10) m.display() # (5-60) (110-160) (210-260) (310-360) (410-460) # (510-560) (610-660) (710-760) (810-860) (910-960) # Find array of coverage c = i.coverage() c.head() # 5 1.0 # 6 1.0 # 7 1.0 # 8 1.0 # 9 1.0 # dtype: float64 # Calculate window protection score w = i.wps(5) w.head() # 5 -1.0 # 6 -1.0 # 7 1.0 # 8 -1.0 # 9 -1.0 # dtype: float64 # Filter to interval lengths between 3 and 20 fi = i.filter(3,20) fi.display() # (5-20) (10-30) (15-20) (30-40) # Query by array i.intersect_from_array(starts, ends, ids) # (array([ 10, 10, 10, 10, 10, 10, 10, 110, 210, 310, 410, 510, 610, # 710, 810, 910]), # array([ 5, 2, 0, 4, 10, 1, 3, 110, 210, 310, 410, 510, 610, # 710, 810, 910])) ``` ## Original paper > Jianglin Feng, Aakrosh Ratan, Nathan C Sheffield; Augmented Interval List: a novel data structure for efficient genomic interval search, Bioinformatics, btz407, https://doi.org/10.1093/bioinformatics/btz407 [AIList_github]: https://github.com/databio/AIList [paper]: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz407/5509521 [AIList_docs]: https://www.biosciencestack.com/static/ailist/docs/index.html

نحوه نصب

نصب پکیج whl ailist-1.0.4:

pip install ailist-1.0.4.whl

نصب پکیج tar.gz ailist-1.0.4:

pip install ailist-1.0.4.tar.gz