def catch_all(path): usage = {} with open('report.txt', 'r') as f: text = f.read() specialKeyFound = False tempSpecialKey = '' for i in range(0, len(text)): if text[i] == '[': specialKeyFound = True tempSpecialKey = text[i] elif specialKeyFound: if text[i] == ']': tempSpecialKey = tempSpecialKey + ']' count(tempSpecialKey, usage) specialKeyFound = False tempSpecialKey = '' else: tempSpecialKey = tempSpecialKey + text[i] else: count(text[i], usage) # Generando el reporte en JSON para visualizarlo en la página. with open('app/db.js', 'w') as f: cadena = json.dumps(usage) f.write("var db = " + cadena) return app.send_static_file(path)
def distribute_shape_data(): # data_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/data700_3/ele_type_data/" # files_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/data700_3/bpmn/" # imgs_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/data700_3/imgs/" data_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/ele_type_data/" files_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/files/" imgs_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/images/" # data_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/admission/ele_type_data/" # files_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/admission/bpmn/" # imgs_dir = "E:/diagrams/bpmn-io/bpmn2image/data0423/admission/images/" # if not os.path.exists(data_dir): # os.mkdir(data_dir) print("Start Counting ...") count.count(files_dir) print("Counting finished!") ele_type_list = count.statistic() make_type_dirs(ele_type_list, data_dir) all_shapes_label = count.all_shapes_label imgs = os.listdir(imgs_dir) # imgs.sort() i = 0 file_id = get_file_id(imgs[i]) # file_id = get_file_name(imgs[model_i]) file_path = imgs_dir + imgs[i] cur_img = cv.imread(file_path, cv.COLOR_BGR2GRAY) for shape_id, shape_label in enumerate(all_shapes_label): while shape_label[0] != file_id: i += 1 file_id = get_file_id(imgs[i]) print(file_id) file_path = imgs_dir + imgs[i] cur_img = cv.imread(file_path) shape_rec = shape_label[2] if shape_rec[2] * shape_rec[3] == 0: print(file_id) continue shape_rec = rh.dilate(shape_rec, 5) # print("{}:{}".format(str(shape_id)+"_"+file_id, shape_rec)) shape_img = rh.truncate(cur_img, shape_rec) shape_type = shape_label[1] shape_dir = "" for ele_type in ele_type_list: if ele_type.startswith(shape_type): shape_dir = ele_type break if len(shape_dir) > 0: shape_path = data_dir + shape_dir + "/" + str( shape_id) + "_" + file_id + ".png" cv.imwrite(shape_path, shape_img)
def test_empty_9x9(self): chess_board = [ [1,1,1], [1,0,1], [1,1,1] ] self.assertEqual(count(chess_board), {}, "Should be {}.")
def test_big_list(self): test_list = list(range(5436891)) result = count(test_list) expects = len(test_list) assert expects == result, 'Expected {} but got {}'.format( expects, result)
def test_empty_list(self): test_list = [] result = count(test_list) expects = len(test_list) assert expects == result, 'Expected {} but got {}'.format( expects, result)
def test_stress_20x20(self): """This test is just designed to measure the efficiency of the algorithm.""" chess_board = [ [0 for i in range(20)] for j in range(20) ] for i in range(100): self.assertEqual(count(chess_board), {}, "Should be {}.")
def test_count(self): lists = [[random.randint(1, 100) for _ in range(random.randint(1, 20))] for _ in range(random.randint(1, 20))] ans = _("The number of different elements of {} is {} and you returned {}.") for i in range(len(lists)): stu_ans = count.count(lists[i]) corr_ans = corr.count(lists[i]) self.assertEqual(corr_ans, stu_ans, ans.format(lists[i], corr_ans, stu_ans))
def test_full_9x9(self): chess_board = [ [1,1,1], [1,1,1], [1,1,1] ] self.assertEqual(count(chess_board), {2:4, 3:1}, "Should be {2:4, 3:1}.")
def test(): filename = 'testfile1.c' correct_count = 8 observed_count = count(filename) if correct_count == observed_count: print 'PASSED' else: print 'FAILED: Expected', correct_count, 'but got', observed_count
def on_submit_button_clicked(self, widget): """ draw result """ ss = self.entry.get_text() if ss != '' and ss in self.particle_lst and ss not in self.lst: self.lst.append(ss) n = len(self.lst) dic = frequency(self.lst) d = {'1':1, '2':2, '3':3.3} """ Here you shuld use dic After finished the func frequency""" picture(dic) if ss != '' and ss not in self.particle_lst: print(count.count(ss)) picture({ss: count.count(ss)})
def test(self): self.assertEqual(count(''), {}, 'should give empty dictionary if string is empty') self.assertEqual(count('aa'), {'a': 2}, 'should get as a result two A characters') self.assertEqual( count('aabb'), { 'a': 2, 'b': 2 }, 'should get as a result of two a characters and two b characters') self.assertEqual( count('aabb'), { 'b': 2, 'a': 2 }, 'should get as a result of two a characters and two b characters, showing that the result order does not matter' )
def test_mixed_5x5(self): chess_board = [ [0,1,1,1,1], [1,1,1,1,1], [1,1,1,1,1], [0,1,1,0,1], [1,1,1,1,1] ] self.assertEqual(count(chess_board), {3:2, 2:9}, "Should be {3:2, 2:9}.")
def generate_hap_seq_sets(self, pheno_seq, hap_seq_sets): """ hap_list [[a1,a2,a3],[a1,a2,a3], ... last consistent haplotype] set [h0 count,h1 count ... hn count] seq_set [[a1,a2,a3],[a1,a2,a3] ... g_sizeth haplotype] """ hap_list = self.generate_haps(pheno_seq) for set in count(len(hap_list), self.g_size + 1): if reduce(lambda c1, c2 : c1 + c2, set) == self.g_size: if phenotype.is_consistent(set, hap_list, pheno_seq): hap_seq_sets.append(phenotype.set_to_seq_set(set, hap_list))
def seqpos(self, chip_regions, width=600, margin=50): """Score motif on how centrally located they are within each ChIP region. ChIP regions should be given as a ChipRegions object. The results of SeqPos will be stored as properties of self.seqpos_results. Adapted from Cliff Meyer's ChIP_region.CentralMotifScan() method.""" ANTISENSE = 1 MOTIFMIN = 1e-3 if not chip_regions.preprocessed_regions: chip_regions.preprocess(width, margin) #process the chip-regions chip_regions.read_sequence(True) bgseqprob_mat = count.count(chip_regions.sequence) markov_order = 2 prob_option = _seq.MAX_OPTION #GRR: THIS IS THE ONLY numpy dependency, and it is b/c seqscan #expects self.pssm to be a numpy array! pssm = numpy.array(self.pssm, float) s_idx, start, end, orient, score = \ _seq.seqscan(chip_regions.sequence, pssm, bgseqprob_mat, markov_order, prob_option) #adjust score adj_score = map(lambda s: math.log(s + MOTIFMIN), score) #calculate the seqpos_results (stats) self.seqpos_stat(start, end, adj_score, width + margin) #generating the observed pssm #fracpos is the fractional position of each site/hit fracpos = [ abs(0.5 * (s + e) - (margin + width) / 2.0) / (width / 2.0) for (s, e) in zip(start, end) ] #retrieve sequences whose fracposition is in (0.0, 1.0] seq, dis = [], [] for j, elem in enumerate(fracpos): if elem <= 1.0: t = list(chip_regions.sequence[int(s_idx[j])]) dis.append(int(start[j]) - len(t) / 2) t = t[int(start[j]):int(end[j])] if orient[j] == ANTISENSE: seq.append(revcomp(t)) else: seq.append(t) self.seqpos_results['pssm'] = calc_pssm(seq) self.seqpos_results['seq'] = ["".join(t) for t in seq] self.seqpos_results['dis'] = dis
def testValidInput(self): self.assertEqual(count(0), 0) self.assertEqual(count(1), 1) self.assertEqual(count(10), 2) self.assertEqual(count(0x7FFFFFFF), 31) self.assertEqual(count(0xFFFFFFFF), 32) self.assertEqual(count(0x8000000), 1)
def seqpos(self, chip_regions, width=600, margin=50): """Score motif on how centrally located they are within each ChIP region. ChIP regions should be given as a ChipRegions object. The results of SeqPos will be stored as properties of self.seqpos_results. Adapted from Cliff Meyer's ChIP_region.CentralMotifScan() method.""" ANTISENSE = 1 MOTIFMIN = 1e-3 if not chip_regions.preprocessed_regions: chip_regions.preprocess(width, margin) #process the chip-regions chip_regions.read_sequence(True) bgseqprob_mat = count.count(chip_regions.sequence) markov_order = 2 prob_option = _seq.MAX_OPTION #GRR: THIS IS THE ONLY numpy dependency, and it is b/c seqscan #expects self.pssm to be a numpy array! pssm = numpy.array(self.pssm, float) s_idx, start, end, orient, score = \ _seq.seqscan(chip_regions.sequence, pssm, bgseqprob_mat, markov_order, prob_option) #adjust score adj_score = map(lambda s: math.log(s + MOTIFMIN), score) #calculate the seqpos_results (stats) self.seqpos_stat(start, end, adj_score, width + margin) #generating the observed pssm #fracpos is the fractional position of each site/hit fracpos = [abs(0.5*(s + e) - (margin + width)/2.0) / (width/2.0) for (s,e) in zip(start, end)] #retrieve sequences whose fracposition is in (0.0, 1.0] seq,dis = [],[] for j,elem in enumerate(fracpos): if elem <= 1.0: t = list(chip_regions.sequence[int(s_idx[j])]) dis.append(int(start[j])-len(t)/2) t = t[int(start[j]):int(end[j])] if orient[j] == ANTISENSE: seq.append(revcomp(t)) else: seq.append(t) self.seqpos_results['pssm'] = calc_pssm(seq) self.seqpos_results['seq'] = ["".join(t) for t in seq] self.seqpos_results['dis'] = dis
def findmaxchar(): f = open('w.log', 'r') s = f.read() str = s.replace("\t", "") str0 = str.replace("\n", "") str1 = unicode(str0.replace(" ", ""), 'UTF-8') print str1 dict = {} str2 = substr.substr(str1) print str2 str3 = list(str2) flag = count.count(str1, str3[0]) for i in range(str3.__len__()): dict[str3[i]] = count.count(str1, str3[i]) print json.dumps(dict, encoding="utf-8", ensure_ascii=False) for k, v in dict.items(): if flag < v: flag = v else: pass for k, v in dict.items(): if (v == flag): print k, v
def consensus(Motifs): k = len(Motifs[0]) count_mot = count(Motifs) consensus_string = "" for j in range(k): m = 0 frequentSymbol = "" for symbol in "ACGT": if count_mot[symbol][j] > m: m = count_mot[symbol][j] frequentSymbol = symbol consensus_string += frequentSymbol return consensus_string
def is_perfect(node): """Is this tree perfect? Math observation: a perfect tree always has a number of nodes that is equal to the 2 ** height - 1. >>> is_perfect(BNode(1)) True >>> is_perfect(BNode(2, BNode(1), BNode(3))) True >>> is_perfect(bst) False """ return count(node) == 2**height(node) - 1
def motifscan(self, motif): """Scan sequences with a motif. Motif must be provided as a Motif object. Hits will be returned as a ChipRegions object. Directly calls [Somebody]'s _seq program. """ SENSE = 0 self.read_sequence(True) # LEN: BINOCH UPGRADE bgseqprob_mat = count.count(self.sequence) markov_order = 2 prob_option = _seq.CUTOFF_OPTION #GRR: THIS IS THE ONLY numpy dependency, and it is b/c seqscan #expects self.pssm to be a numpy array! pssm = numpy.array(motif.pssm, float) s_idx, start, end, orient, score = \ _seq.seqscan(self.sequence, pssm, bgseqprob_mat, markov_order, prob_option) hits = ChipRegions(genome=self.genome,genome_dir=self.genome_dir) hits.genome = self.genome for i,idx in enumerate(s_idx): #sorry for the i vs idx confusion, but they're really different! hits.chrom.append(self.chrom[idx]) hits.chromStart.append(int(self.chromStart[idx] + int(start[i]))) hits.chromEnd.append(int(self.chromStart[idx] + int(end[i]))) if int(orient[i]) == SENSE: hits.strand.append("+") else: hits.strand.append("-") #hits.hitscore.append(score[idx])--not sure if i should use i/idx hits.hitscore.append(score[i]) # LEN: BINOCH UPGRADE END hits.read_sequence(True) return hits
def motifscan(self, motif): """Scan sequences with a motif. Motif must be provided as a Motif object. Hits will be returned as a ChipRegions object. Directly calls [Somebody]'s _seq program. """ SENSE = 0 self.read_sequence(True) # LEN: BINOCH UPGRADE bgseqprob_mat = count.count(self.sequence) markov_order = 2 prob_option = _seq.CUTOFF_OPTION #GRR: THIS IS THE ONLY numpy dependency, and it is b/c seqscan #expects self.pssm to be a numpy array! pssm = numpy.array(motif.pssm, float) s_idx, start, end, orient, score = \ _seq.seqscan(self.sequence, pssm, bgseqprob_mat, markov_order, prob_option) hits = ChipRegions() hits.genome = self.genome for i, idx in enumerate(s_idx): #sorry for the i vs idx confusion, but they're really different! hits.chrom.append(self.chrom[idx]) hits.chromStart.append(int(self.chromStart[idx] + int(start[i]))) hits.chromEnd.append(int(self.chromStart[idx] + int(end[i]))) if int(orient[i]) == SENSE: hits.strand.append("+") else: hits.strand.append("-") #hits.hitscore.append(score[idx])--not sure if i should use i/idx hits.hitscore.append(score[i]) # LEN: BINOCH UPGRADE END hits.read_sequence(True) return hits
def is_perfect_v2(node): """Is this tree perfect? Math observation: a perfect tree always has a number of nodes that is equal to the 2 ** height - 1. We don't need to get the height separately, though -- we can check if the # nodes is valid for any height: >>> is_perfect_v2(BNode(1)) True >>> is_perfect_v2(BNode(2, BNode(1), BNode(3))) True >>> is_perfect_v2(bst) False """ nnodes = count(node) return math.log(nnodes + 1, 2) == int(math.log(nnodes + 1, 2))
with open('None_recommend_list', 'a') as f3: f3.write(k + ':' + v + '\n') if __name__ == '__main__': start_time = time.time() # 1. 获取推荐列表(人名) path1 = '/Users/zhangwei/Desktop/sina_job/recommend/oid_name_type/20180114.txt' # 每次运行修改 path2 = './res_container/res14' # 每次运行修改 # {starname:url} full = [] full.append(star.get_mingxingurl_dict(path1)) full.append(star.get_yinyueurl_dict(path1)) # star_url = star.get_mingxingurl_dict(path) # 明星 # star_url = star.get_yinyueurl_dict(path) # 音乐 # star_url = star.getmingxingurl_test() # 测试用例 for i in full: recomend(i, path2) # 2. 获取oid推荐列表 path3 = mkdir.mkdir('./recommend_container/recommend8/') # 每次运行修改 print(type(path3)) name_oid.name_oid(path1, path2, path3) # 3. 增加反向关系,得到最终的列表 add.ad_re_relation(path3) # 4.统计结果 count.count(path3) end_time = time.time() print('程序运行了:' + str((end_time - start_time) / 60) + '分钟')
def test_count_zeros(): assert count.count([0, 0, 0], 0) == 3
def test_count_from_2_to_negative_5_should_give_2_1_0_1_2_3_4_5(): assert_equal(count(start=2, stop=-5), "2,1,0,-1,-2,-3,-4,-5")
def test_count_from_3_to_5_should_give_3_4_5(): assert_equal(count(start=3,stop=5), "3,4,5")
def testCount3(self): self.assertEqual(hw.count("z", "zyzzyzus"), 4)
def test_count_from_1_to_1_should_give_1(): assert_equal(count(start=1, stop=1), "1")
def test_count_5(self): self.assertEqual(11, count.count(1000, 2000, 91))
def testCount2(self): self.assertEqual(hw.count("z", "zyzzyva"), 3)
def test_count_1(self): self.assertEqual(4, count.count(3, 20, 5))
def test_count_3(self): self.assertEqual(25, count.count(1, 25, 1))
def test_sub(self): j = count(2,3) self.assertEqual(j.sub(),-1)
def test_sub2(self): j = count(71,46) self.assertEqual(j.sub(),25)
def testCount1(self): self.assertEqual(hw.count("b", "biology terms with z"), 1)
def test_count_string(): assert count.count(["a", "a", "a"], "a") == 3
def test_add2(self): j = count(41,76) self.assertEqual(j.add(),117)
def test_count_takes_two_argument(): count() count(start=3)
def test_add(self): j = count(2,3) self.assertEqual(j.add(),7,msg='is wrong')
a.align(pars) # pars['alignerIndexDir'] = alignerIndexDir2 # pars['flavor'] = 'salmon' # a.align(pars) # *** 05 quantify *** # cufflinks - generate quantifications from bams # pars['flavor'] = 'cufflinks' pars['flavor'] = 'stringtie' q.quantify(pars) # *** count *** # featureCounts pars['flavor'] = 'featureCounts' co.count(pars) # *** 10 qc *** # qorts - generate in case qc problems later pars['flavor'] = 'qorts' qc.qc(pars) # *** 20 clean *** # get rid of fastq files pars['flavor'] = 'standard' cl.clean(pars) # *** check to make sure sample has not already been successfully run *** # pars['flavor'] = 'salmon-bias' # check.isCompleted(pars)
def testCount5(self): self.assertEqual( hw.count(" ", "It has not escaped our notice that the specific pairing we have postulated"), 12 )
def test_count_2(self): self.assertEqual(111, count.count(1, 1000, 9))
def test_count_string(): assert count.count(["a","a","a"], "a") == 3 #type 'pytest' in terminal to go through the tests (within the directory)
def test_count_4(self): self.assertEqual(11, count.count(10, 90, 7))
EBOOKS_BASE_PATH = "/home/aleray/work/osp.work.annak/osp.work.annak.books/ebooks/txt/" if __name__ == '__main__': graph = rdflib.Graph() ns_stats = rdflib.Namespace("http://kavan.land/vocab/stats#") graph.namespace_manager.bind('stats', ns_stats) for url, filename in BOOKS.items(): subject = rdflib.URIRef(url) path = os.path.join(EBOOKS_BASE_PATH, filename) # generate bigrams bigrams = collocations(path) for bigram in bigrams: graph.add((subject, ns_stats.bigram, rdflib.Literal(" ".join(bigram)))) # generate stats stats = count(path) graph.add((subject, ns_stats.hasCharacterCount, rdflib.Literal(stats['cc']))) graph.add((subject, ns_stats.hasWordCount, rdflib.Literal(stats['wc']))) graph.add((subject, ns_stats.hasUniqueWordCount, rdflib.Literal(stats['uwc']))) graph.add((subject, ns_stats.hasDiversityIndice, rdflib.Literal(stats['idx']))) print(graph.serialize(format="turtle"))
path_res = mk.res_name_path('.\\res_container') path_recommend = mk.recommed_file_path('.\\recommend_container') # {starname:url} full = [] full.append(star.get_mingxingurl_dict(path)) full.append(star.get_yinyueurl_dict(path)) for i in full: p.apply_async(recomend, args=( i, path1, path2, path3, path_res, )) # recomend(i, path1, path2, path3, path_res) p.close() p.join() name_topic.name_oid(path, path_res, path_recommend) ad_re_relation.add_re(path_recommend) count.count(path_recommend) end_time = time.time() time = (end_time - start_time) / 60 print('0201号蜘蛛运行了:' + str(time) + '分钟') print('大概' + str(time / 60) + '小时') with open(path_recommend + 'recommend_count', 'a', encoding='utf-8') as f: f.write('0119号蜘蛛运行了:' + str(time) + '分钟')
''' Created on Jun 30, 2015 @author: nathaniel ''' import count print count.count() print count.count()