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These are the proportions of variants possessing features used in each question for the various questions comprising ECE1508 assignment 1. |
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Initial: | |
[[0 1 1 0 1] | |
[0 0 1 0 1] | |
[0 0 0 0 0] | |
[0 1 1 0 1] | |
[0 0 0 0 0]] | |
Top: | |
[[0 1 1 0 1] | |
[0 0 1 0 1] |
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51763946673 |
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Olympic Weightlifting Level I (eight one-hour classes) | |
This 8 week class is designed for true beginners with little to no previous weight training experience. Participants will learn the basics of weightlifting starting with the Olympic squat and deadlift and moving to the power snatch and power clean in a progressive manner. By the end of the session, participants will gain hip and shoulder flexibility along with leg and core strength. | |
Fundamentals of Weight Training (six one-hour classes) | |
Learn to train effectively and safely following appropriate exercise progressions under the supervision of a certified trainer. Participants will be introduced to a series of fundamental exercises commonly used in training programs such as squats, lunges and rows. Participants will learn how to create a personalized program using a variety of training methods and be familiarized with most of the equipment in the weight room. |
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#!/usr/bin/env python3 | |
''' | |
For each query listed in a BLAST XML results file, determine how much of the | |
query sequence is covered by the "best" hit. The best hit is deemed to be the | |
one in which the summed bitscores of its compatible HSPs is highest. Mutually | |
compatible HSPs are deemed to be ones that neither overlap nor "cross" each | |
other. | |
Usage: | |
cat blast_results.xml | find-best-hit.py |
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import random | |
recur_calls = 0 | |
dp_calls = 0 | |
def make_board(n, max_val_mag): | |
board = [] | |
for i in range(n): | |
board.append([]) | |
for j in range(n): |
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Configurator/Accordion/Canvas###21 | |
Configurator/Accordion/Canvas###27 | |
Configurator/Accordion/Canvas###31 | |
Configurator/Accordion/Canvas###42 | |
Configurator/Accordion/Canvas###45 | |
Configurator/Accordion/Canvas###48 | |
Configurator/ActionScript/ScriptFile/desc###100 | |
Configurator/ActionScript/ScriptFile/desc###104 | |
Configurator/ActionScript/ScriptFile/desc###108 | |
Configurator/ActionScript/ScriptFile/desc###112 |
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24775 sequences in file PRJEB506.munged.fa | |
24942 sequences in file PRJNA205202.munged.fa | |
26312 sequences in file PRJNA13758.munged.fa | |
15408 sequences PRJEB506.munged.fa have homologs in dataset PRJNA205202.munged.fa | |
11651 sequences PRJNA205202.munged.fa have homologs in dataset PRJEB506.munged.fa | |
71943 PRJEB506.munged.fa-PRJEB506.munged.fa matches | |
25919 PRJNA205202.munged.fa-PRJNA205202.munged.fa matches | |
################################### | |
7008 groups of orthologs |
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#!/usr/bin/env python2 | |
# Efficient means of determining which ranges from set overlap a given query | |
# range. See http://www.cise.ufl.edu/~sahni/cop5536/powerpoint/lec34.ppt for | |
# details. | |
class Node(object): | |
def __init__(self, intervals): | |
self._olap = [] | |
self.l = None |
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from HTSeq import GenomicInterval | |
from collections import defaultdict | |
class IntervalCmp(object): | |
''' | |
Implements GenomicInterval comparison functions that ignore strandedness, | |
unlike the methods included in HTSeq. | |
''' | |
@staticmethod | |
def overlaps(a, b): |