How to use exists_file method in Airtest

Best Python code snippet using Airtest

search_data.py

Source:search_data.py Github

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...47 save_data(dictionary, 'dictionary')48 save_data(bow, 'bow')49 return corpus, dictionary, bow50def exists_data_files():51 return exists_file('corpus') and exists_file('dictionary') and exists_file('bow')52def exists_file(name):53 return path.exists('res/pickle/' + name + '.pkl')54def load_data_files():55 return load_file('corpus'), load_file('dictionary'), load_file('bow')56def save_data(data, name):57 pkl.dump(data, open('res/pickle/' + name + '.pkl', "wb"), protocol=pkl.HIGHEST_PROTOCOL)58def load_file(name):59 return pkl.load(open('res/pickle/' + name + '.pkl', "rb"))60def split_space(text):61 return text.translate(str.maketrans('', '', string.punctuation)).split(' ') if text != "" else []62def split_underscore(tokens):63 return [word for token in tokens for word in token.split('_')]64def handle_camel_case(tokens):65 words = []66 for token in tokens:67 matches = finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)', token)68 words += [m.group(0) for m in matches]69 return words70def normalize_tokens(tokens):71 return [token.lower() for token in tokens]72def filter_stopwords(tokens):73 for token in tokens:74 if token in ['test', 'tests', 'main']:75 return []76 return tokens77def normalize_query(query):78 return query.strip().lower().split()79def query_frequency(query, bow, dictionary):80 return filter_results(create_top_5_result_tuples(get_freq_model(bow, dictionary)[dictionary.doc2bow(query)]))81def get_freq_model(bow, dictionary):82 return load_file('model_freq') if exists_file('model_freq') else create_freq_model(bow, dictionary)83def create_freq_model(bow, dictionary):84 model = SparseMatrixSimilarity(bow, num_features=len(dictionary.token2id))85 save_data(model, 'model_freq')86 return model87def query_tfidf(query, bow, dictionary):88 model = get_tfidf_model(bow)89 matrix = get_tfidf_matrix(model, bow, dictionary)90 return filter_results(create_top_5_result_tuples(matrix[model[dictionary.doc2bow(query)]]))91def get_tfidf_model(bow):92 return load_file('model_tfidf') if exists_file('model_tfidf') else create_tfidf_model(bow)93def create_tfidf_model(bow):94 model = TfidfModel(bow)95 save_data(model, 'model_tfidf')96 return model97def get_tfidf_matrix(model, bow, dictionary):98 return load_file('matrix_tfidf') if exists_file('matrix_tfidf') else create_tfidf_matrix(model, bow, dictionary)99def create_tfidf_matrix(model, bow, dictionary):100 matrix = SparseMatrixSimilarity(model[bow], num_features=len(dictionary.token2id))101 save_data(matrix, 'matrix_tfidf')102 return model103def query_lsi(query, bow, dictionary):104 model = get_lsi_model(bow, dictionary)105 matrix = get_lsi_matrix(model, bow)106 vector = model[dictionary.doc2bow(query)]107 result = abs(matrix[vector])108 embedding = [[value for _, value in vector]] + [[value for _, value in model[bow][i]] for i, value in109 sorted(enumerate(result), key=lambda x: x[1], reverse=True)[:5]]110 return filter_results(create_top_5_result_tuples(result)), embedding111def get_lsi_model(bow, dictionary):112 return load_file('model_lsi') if exists_file('model_lsi') else create_lsi_model(bow, dictionary)113def create_lsi_model(bow, dictionary):114 model = LsiModel(bow, id2word=dictionary, num_topics=300)115 save_data(model, 'model_lsi')116 return model117def get_lsi_matrix(model, bow):118 return load_file('matrix_lsi') if exists_file('matrix_lsi') else create_lsi_matrix(model, bow)119def create_lsi_matrix(model, bow):120 matrix = MatrixSimilarity(model[bow])121 save_data(matrix, 'matrix_lsi')122 return matrix123def create_top_5_result_tuples(arrg):124 return sorted(enumerate(arrg), key=lambda x: x[1], reverse=True)[:5]125def filter_results(tuples):126 return [i for i, v in tuples]127def query_doc2vec(query, corpus):128 model = get_doc2vec_model(get_doc2vec_corpus(corpus))129 vector = model.infer_vector(query)130 similar = model.docvecs.most_similar([vector], topn=5)131 return [index for (index, _) in similar], \132 [list(vector)] + [list(model.infer_vector(corpus[index])) for index, _ in similar]133def get_doc2vec_corpus(corpus):134 return [TaggedDocument(simple_preprocess(' '.join(element)), [index])135 for index, element in enumerate(corpus)]136def get_doc2vec_model(corpus):137 return load_file('model_doc2vec') if exists_file('model_doc2vec') else create_doc2vec_model(corpus)138def create_doc2vec_model(corpus):139 model = Doc2Vec(vector_size=300, min_count=2, epochs=77)140 model.build_vocab(corpus)141 model.train(corpus, total_examples=model.corpus_count, epochs=model.epochs)142 save_data(model, 'model_doc2vec')143 return model144def create_result_dataframe(queries_dictionary, df):145 for key, values in queries_dictionary.items():146 for index in values:147 row = df.iloc[index]148 yield [row["name"], row["file"], row["line"], row["type"], row["comment"], key]149def print_results(df):150 grouped = df.groupby(['search'])151 for key, item in grouped:...

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test_import_particles.py

Source:test_import_particles.py Github

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1import os2import sys3import unittest4import numpy as np5import h5py as h56sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))7from import_toolkit.cluster import Cluster8np.set_printoptions(suppress=True)9class TestMixin(unittest.TestCase):10 def test_particle_group_number(self):11 # Read in celr_e | halo 0 | z=012 path = '/cosma5/data/dp004/dc-pear3/data/eagle/halo_00/data/particledata_029_z000p000'13 with h5.File(os.path.join(path, 'eagle_subfind_particles_029_z000p000.0.hdf5'), 'r') as f:14 hd5set = f['/PartType1/GroupNumber']15 group_number = hd5set[...]16 print(f"\n{' celr_e | halo 0 | z=0 ':-^60}")17 print(f"Particles with group number < 0: {len(np.where(group_number<0)[0])} particles found.")18 print(f"Particles with group number = 0: {len(np.where(group_number==0)[0])} particles found.")19 print(f"Particles with group number = 1: {len(np.where(group_number==1)[0])} particles found.")20 # Read in celr_b | halo 0 | z=021 path = '/cosma5/data/dp004/dc-pear3/data/bahamas/halo_00/data/particledata_029'22 with h5.File(os.path.join(path, 'eagle_subfind_particles_029.0.hdf5'), 'r') as f:23 hd5set = f['/PartType1/GroupNumber']24 group_number = hd5set[...]25 print(f"\n{' celr_b | halo 0 | z=0 ':-^60}")26 print(f"Particles with group number < 0: {len(np.where(group_number < 0)[0])} particles found.")27 print(f"Particles with group number = 0: {len(np.where(group_number == 0)[0])} particles found.")28 print(f"Particles with group number = 1: {len(np.where(group_number == 1)[0])} particles found.")29 # Read in macsis | halo 0 | z=030 path = '/cosma5/data/dp004/dc-hens1/macsis/macsis_gas/halo_0000/data/particledata_022'31 with h5.File(os.path.join(path, 'eagle_subfind_particles_022.0.hdf5'), 'r') as f:32 hd5set = f['/PartType1/GroupNumber']33 group_number = hd5set[...]34 print(f"\n{' macsis | halo 0 | z=0 ':-^60}")35 print(f"Particles with group number < 0: {len(np.where(group_number < 0)[0])} particles found.")36 print(f"Particles with group number = 0: {len(np.where(group_number == 0)[0])} particles found.")37 print(f"Particles with group number = 1: {len(np.where(group_number == 1)[0])} particles found.")38 # Read in ceagle | halo 0 | z=039 path = '/cosma5/data/dp004/C-EAGLE/Complete_Sample/CE_00/data/particledata_029_z000p000'40 group_number = np.zeros(0, dtype=np.int)41 file_index = 042 while file_index > -1:43 try:44 with h5.File(os.path.join(path, f'eagle_subfind_particles_029_z000p000.{str(file_index)}.hdf5'),45 'r') as f:46 hd5set = f['/PartType1/GroupNumber']47 group_number = np.concatenate((group_number, hd5set[...]), axis=0)48 file_index += 149 except:50 file_index = -151 print(f"\n{' ceagle | halo 0 | z=0 ':-^60}")52 print(f"Particles with group number < 0: {len(np.where(group_number < 0)[0])} particles found.")53 print(f"Particles with group number = 0: {len(np.where(group_number == 0)[0])} particles found.")54 print(f"Particles with group number = 1: {len(np.where(group_number == 1)[0])} particles found.")55 def test_filenames(self):56 # Read in celr_e | halo 0 | z=057 path = '/cosma5/data/dp004/dc-pear3/data/eagle'58 exists_dir = os.path.isdir(os.path.join(path, 'halo_00/data/particledata_029_z000p000'))59 exists_file = os.path.isfile(os.path.join(path, 'halo_00/data/particledata_029_z000p000',60 'eagle_subfind_particles_029_z000p000.0.hdf5'))61 print(f"\n{' celr_e | halo 0 | z=0 ':-^60}")62 print(f"Data directory exists: {exists_dir}.")63 print(f"Data file exists: {exists_file}.")64 # Read in celr_b | halo 0 | z=065 path = '/cosma5/data/dp004/dc-pear3/data/bahamas'66 exists_dir = os.path.isdir(os.path.join(path, 'halo_00/data/particledata_029'))67 exists_file = os.path.isfile(os.path.join(path, 'halo_00/data/particledata_029',68 'eagle_subfind_particles_029.0.hdf5'))69 print(f"\n{' celr_b | halo 0 | z=0 ':-^60}")70 print(f"Data directory exists: {exists_dir}.")71 print(f"Data file exists: {exists_file}.")72 # Read in macsis | halo 0 | z=073 path = '/cosma5/data/dp004/dc-hens1/macsis/macsis_gas'74 exists_dir = os.path.isdir(os.path.join(path, 'halo_0000/data/particledata_022'))75 exists_file = os.path.isfile(os.path.join(path, 'halo_0000/data/particledata_022',76 'eagle_subfind_particles_022.0.hdf5'))77 print(f"\n{' macsis | halo 0 | z=0 ':-^60}")78 print(f"Data directory exists: {exists_dir}.")79 print(f"Data file exists: {exists_file}.")80 # Read in ceagle | halo 0 | z=081 path = '/cosma5/data/dp004/C-EAGLE/Complete_Sample'82 exists_dir = os.path.isdir(os.path.join(path, 'CE_00/data/particledata_029_z000p000'))83 print(f"\n{' ceagle | halo 0 | z=0 ':-^60}")84 print(f"Data directory exists: {exists_dir}.")85 collection_exists_file = []86 file_index = 087 exists_file = True88 while exists_file:89 exists_file = os.path.isfile(os.path.join(path, f'CE_00/data/particledata_029_z000p000',90 f'eagle_subfind_particles_029_z000p000.{str(file_index)}.hdf5'))91 collection_exists_file.append(exists_file)92 print(f"Data file {file_index:03d} exists: {exists_file}.")93 file_index += 194 print(f"{' SOFTWARE TEST ':=^60}")95 for sim in ['celr_e', 'celr_b', 'macsis', 'ceagle']:96 cluster = Cluster(simulation_name=sim, clusterID=0, redshift='z000p000')97 print(f"\n {sim}{' | halo 0 | z=0 ':-^60}")98 # print("cluster.groups_filePaths", cluster.groups_filePaths(), sep='\n')99 # Check the files exist100 for file in cluster.groups_filePaths():101 print(os.path.isfile(file), file)102 # print("cluster.partdata_filePaths", cluster.partdata_filePaths(), sep='\n')103 # Check the files exist104 for file in cluster.partdata_filePaths():105 print(os.path.isfile(file), file)106if __name__ == '__main__':...

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ImageNET.py

Source:ImageNET.py Github

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1from DL_img_utils import tar_unzip2from DL_img_utils import ano_img3from DL_img_utils import Annotation_folder_check4from DL_img_utils import delete_error_img5from DL_img_utils import class_update6from DL_img_utils import delete_error_ano7import os8import argparse9from DL_img_utils import File_matome10from DL_img_utils import trans_YOLO_Data11import shutil12def Img_DL(class_name,limit,mode):13 #クラスのリストを読み込む14 #fileobj = open("./DL_img_utils/TEST_LIST.txt", "r", encoding="utf_8")15 fileobj = open("./DL_img_utils/ANO_LIST.txt", "r", encoding="utf_8")16 exists_file = 017 wnid_list=[]18 while True:19 line = fileobj.readline()20 tmp_class_list = line.split()21 class_list = []22 #,をけす処理23 for s in tmp_class_list:24 if ',' in s:25 text = s.replace(',', '')26 class_list.append(text)27 else:28 class_list.append(s)29 #class_name = class_name + " "30 31 #print(class_name,"//")32 if line:33 if class_name in class_list:34 wnid=line.split()[0]35 if os.path.exists("./DL_img_utils/Annotation_all/"+wnid+".tar.gz"):36 exists_file = 137 wnid_list.append(wnid)38 print(wnid)39 #Anotationがはいってる圧縮ファイルを解凍40 tar_unzip.main(wnid)41 #アノテーションファイル内に不正なファイルがあるかを確認42 Annotation_folder_check.main(wnid)43 #画像をDL44 ano_img.main(wnid,limit=limit,verbose=False)45 #エラー画像を削除46 delete_error_img.main(wnid)47 #アノテーションのエラーを削除48 delete_error_ano.main(wnid)49 else:50 break51 return exists_file52#引数のクラスIDを参照して画像をDLする53def ALL_DL(wnid,class_name):54 if os.path.exists("./DL_img_utils/Annotation_all/"+wnid+".tar.gz"):55 #アノテーションファイル内に不正なファイルがあるかを確認56 Annotation_folder_check.main(wnid)57 #画像をDL58 ano_img.main(wnid,limit=500,verbose=False)59 #エラー画像を削除60 delete_error_img.main(wnid)61 #アノテーションのエラーを削除62 delete_error_ano.main(wnid)63 64 #ファイルをまとめる65 print("===========================")66 print("===ファイルを整理しています==")67 print("===========================")68 File_matome.main(wnid,class_name,limit=500)69 """70 fileobj = open("./DL_img_utils/Class.txt", "r", encoding="utf_8")71 #重複確認72 Duplicate = False73 while True:74 line = fileobj.readline()75 if line:76 if line == class_name:77 Duplicate = True 78 else:79 break80 fileobj.close()81 #クラスリストに追加書き込み82 if Duplicate == False: 83 with open("./DL_img_utils/Class.txt", mode='a') as f:84 print("ImageNet_line90:class_name:",class_name)85 f.write('{}\n'.format(class_name))86 """87 88 trans_YOLO_Data.main(class_name)89 90def main():91 #画像のDL数を設定92 limit = opt.limit93 #クラスのリストを読み込む94 fileobj = open("./DL_img_utils/TEST_LIST.txt", "r", encoding="utf_8")95 #クラス名を取得96 class_name=input("クラス名:")97 #class_name="remote"98 #クラス名が一致するwnidを保存するリスト99 #指定されたクラスの画像が存在したかを確認するフラグ100 exists_file = 0101 wnid_list=[]102 while True:103 line = fileobj.readline()104 class_list = line.split()105 if line:106 if class_name in class_list:107 wnid=line.split()[0]108 if os.path.exists("./DL_img_utils/Annotation_all/"+wnid+".tar.gz"):109 exists_file = 1110 wnid_list.append(wnid)111 print(wnid)112 #Anotationがはいってる圧縮ファイルを解凍113 tar_unzip.main(wnid)114 #アノテーションファイル内に不正なファイルがあるかを確認115 Annotation_folder_check.main(wnid)116 #画像をDL117 ano_img.main(wnid,verbose=False)118 #エラー画像を削除119 delete_error_img.main(wnid)120 #アノテーションのエラーを削除121 delete_error_ano.main(wnid)122 else:123 break124 #ファイルをまとめる125 if opt.mode == 0 and exists_file == 1:126 print("===========================")127 print("===ファイルを整理しています==")128 print("===========================")129 #File_matome.main(wnid_list[0],class_name,0)130 with open("./DL_img_utils/Class.txt", mode='a') as f:131 f.write('{}\n'.format(class_name))132 trans_YOLO_Data.main()133 #不要なファイルを削除134 #shutil.rmtree('./DL_img_utils/Annotations')135 #shutil.rmtree('./DL_img_utils/img')136 elif exists_file == 0:137 print("指定された画像のクラスはまだ実装されていません")138if __name__ == "__main__":139 parser = argparse.ArgumentParser()140 parser.add_argument('--limit', type=int, default=0, help='Number DL imags')141 parser.add_argument('--mode', type=int, default=0, help='0 is DL.1 is 試し')142 opt = parser.parse_args()...

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