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)