jwintersinger

These are the proportions of variants possessing features used in each question
for the various questions comprising ECE1508 assignment 1.

jwintersinger

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]

jwintersinger

51763946673

jwintersinger

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.

jwintersinger

#!/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

jwintersinger

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):

jwintersinger

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

jwintersinger

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
10314 in-paralogs from PRJEB506.munged.fa

jwintersinger

#!/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

jwintersinger

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):