def reverse_complement(context, dataset, sort_result=False, listing=False):
"""
Runs reverse_complement(pattern). The input variable 'pattern' is read from the DATASET
argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.ReverseComplementDataset(dataset, challenge)
pattern = data.pattern
correct_result = get_correct_result(data, challenge)
args = [pattern]
func = bioinfo1.reverse_complement
cli_output(challenge, correct_result, sort_result, listing, func, args)
def minimum_skew(context, dataset, sort_result=False, listing=False):
"""
Runs minimum_skew(genome). The input variable 'genome' is read from the DATASET argument, where
DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.MinimumSkewDataset(dataset, challenge)
genome = data.genome
correct_result = get_correct_result(data, challenge)
args = [genome]
func = bioinfo1.minimum_skew
cli_output(challenge, correct_result, sort_result, listing, func, args)
def median_string(context, dataset, sort_result=True, listing=False):
"""
Runs median_string(dna, k). The input variables 'dna' and 'k'
are read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.MedianString(dataset, challenge)
dna = data.dna
k = data.k
correct_result = get_correct_result(data, challenge)
args = dna, k
func = bioinfo1.median_string
cli_output(challenge, correct_result, sort_result, listing, func, args)
def frequent_words(context, dataset, sort_result=False, listing=False):
"""
Runs frequent_words_by_sorting(text, k). The input variables 'text' and 'k' are read from the
DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.FrequentWordsDataset(dataset, challenge)
text = data.text
k = data.k
correct_result = get_correct_result(data, challenge)
args = text, k
func = bioinfo1.frequent_words_by_sorting
cli_output(challenge, correct_result, sort_result, listing, func, args)
def number_to_pattern(context, dataset, sort_result=False, listing=False):
"""
Runs number_to_pattern(number, k). The input variables 'number' and 'k' are read from the
DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.NumberToPatternDataset(dataset, challenge)
number = data.number
k = data.k
correct_result = get_correct_result(data, challenge)
args = number, k
func = bioinfo1.number_to_pattern
cli_output(challenge, correct_result, sort_result, listing, func, args)
def neighbors(context, dataset, sort_result=True, listing=True):
"""
Runs neighbors(pattern, d). The input variables 'pattern' and 'd' are read from the DATASET
argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.NeighborsDataset(dataset, challenge)
pattern = data.pattern
d = data.d
correct_result = get_correct_result(data, challenge)
args = pattern, d
func = bioinfo1.neighbors
cli_output(challenge, correct_result, sort_result, listing, func, args)
def hamming_distance(context, dataset, sort_result=False, listing=False):
"""
Runs hamming_distance(string1, string2). The input variables 'string1' and 'string2' are read
from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.HammingDistanceDataset(dataset, challenge)
string1 = data.string1
string2 = data.string2
correct_result = get_correct_result(data, challenge)
args = string1, string2
func = bioinfo1.hamming_distance
cli_output(challenge, correct_result, sort_result, listing, func, args)
def greedy_motif_search(context, dataset, sort_result=False, listing=False):
"""
Runs greedy_motif_search(dna, k, t). The input variables 'dna', 'k' and 't'
are read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.GreedyMotifSearch(dataset, challenge)
dna = data.dna
k = data.k
t = data.t
correct_result = get_correct_result(data, challenge)
args = dna, k, t
func = bioinfo1.greedy_motif_search
cli_output(challenge, correct_result, sort_result, listing, func, args)
def approx_count(context, dataset, sort_result=False, listing=False):
"""
Runs approx_pattern_count(pattern, text, d). The input variables 'pattern', 'text' and 'd' are
read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.ApproxCountDataset(dataset, challenge)
pattern = data.pattern
text = data.text
d = data.d
correct_result = get_correct_result(data, challenge)
args = pattern, text, d
func = bioinfo1.approx_pattern_count
cli_output(challenge, correct_result, sort_result, listing, func, args)
def pattern_matching(context, dataset, sort_result=False, listing=False):
"""
Runs pattern_matching_problem(pattern, genome). The input variables 'pattern' and 'genome' are
read from the DATASET argument, where DATASET is the text file containing the input data. This
function is only available in Code Challenge mode.
"""
challenge = context.obj['CHALLENGE']
data = dsr.PatternMatchingDataset(dataset, challenge)
pattern = data.pattern
genome = data.genome
correct_result = get_correct_result(data, challenge)
args = pattern, genome
func = bioinfo1.pattern_matching_problem
cli_output(challenge, correct_result, sort_result, listing, func, args)
def pattern_count(context, dataset, sort_result=False, listing=False):
"""
Runs pattern_count(text, pattern). The input variables 'text' and 'pattern' are read from the
DATASET argument, where DATASET is the text file containing the input data.
"""
import ipdb
ipdb.set_trace()
challenge = context.obj['CHALLENGE']
data = dsr.PatternCountDataset(dataset, challenge)
text = data.text
pattern = data.pattern
correct_result = get_correct_result(data, challenge)
args = text, pattern
func = bioinfo1.pattern_count
cli_output(challenge, correct_result, sort_result, listing, func, args)
def motif_enumeration(context, dataset, sort_result=True, listing=False):
"""
Runs motif_enumeration(dna, k, d). The input variables 'dna', 'k' and 'd' are read from the
DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.MotifEnumerationDataset(dataset, challenge)
dna = data.dna
k = data.k
d = data.d
correct_result = get_correct_result(data, challenge)
args = dna, k, d
func = bioinfo1.motif_enumeration
cli_output(challenge, correct_result, sort_result, listing, func, args)
def clump_finding(context, dataset, sort_result=False, listing=False):
"""
Runs better_clump_finding(genome, k, l, t). The input variables 'genome', 'k', 'l', and 't' are
read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.ClumpFindingDataset(dataset, challenge)
genome = data.genome
k = data.k
l = data.l
t = data.t
correct_result = get_correct_result(data, challenge)
args = genome, k, l, t
func = bioinfo1.better_clump_finding
cli_output(challenge, correct_result, sort_result, listing, func, args)
def gibbs_sampler(context, dataset, sort_result=False, listing=False):
"""
"""
challenge = context.obj['CHALLENGE']
if challenge:
listing = True
data = dsr.GibbsSampler(dataset, challenge)
dna = data.dna
k = data.k
t = data.t
n = data.n
correct_result = get_correct_result(data, challenge)
args = dna, k, t, n
# func = bioinfo1.gibbs_sampler
func = bioinfo1.gibbs_sampler_loop
cli_output(challenge, correct_result, sort_result, listing, func, args)
def frequent_words_mismatches(context,
dataset,
sort_result=False,
listing=False):
"""
Runs frequent_words_with_mismatches(pattern, k, d). The input variables 'pattern', 'k' and 'd'
are read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.FrequentWordsMismatchesDataset(dataset, challenge)
text = data.text
k = data.k
d = data.d
correct_result = get_correct_result(data, challenge)
args = text, k, d
func = bioinfo1.frequent_words_with_mismatches_sorting
cli_output(challenge, correct_result, sort_result, listing, func, args)
def distance_between_pattern_and_string(context,
dataset,
sort_result=True,
listing=False):
"""
Runs distance_between_pattern_and_string(pattern, dna). The input variables 'pattern' and 'dna'
are read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.DistanceBetweenPatternAndString(dataset, challenge)
pattern = data.pattern
dna = data.dna
correct_result = get_correct_result(data, challenge)
args = pattern, dna
func = bioinfo1.distance_between_pattern_and_strings
cli_output(challenge, correct_result, sort_result, listing, func, args)
def profile_most_probable_kmer(context,
dataset,
sort_result=False,
listing=False):
"""
Runs profile_most_probable_kmer(text, k, profile). The input variables 'text', 'k' and 'profile'
are read from the DATASET argument, where DATASET is the text file containing the input data.
"""
challenge = context.obj['CHALLENGE']
data = dsr.ProfileMostProbableKmer(dataset, challenge)
text = data.text
k = data.k
profile = data.profile
correct_result = get_correct_result(data, challenge)
args = text, k, profile
func = bioinfo1.profile_most_probable_kmer
cli_output(challenge, correct_result, sort_result, listing, func, args)
