Python shutil.rmtree() Examples
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code examples of shutil.rmtree().
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Example #1
Source File: test_gluon_utils.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 7 votes |
def test_multiprocessing_download_successful(): """ test download with multiprocessing """ tmp = tempfile.mkdtemp() tmpfile = os.path.join(tmp, 'README.md') process_list = [] # test it with 10 processes for i in range(10): process_list.append(mp.Process( target=_download_successful, args=(tmpfile,))) process_list[i].start() for i in range(10): process_list[i].join() assert os.path.getsize(tmpfile) > 100, os.path.getsize(tmpfile) # check only one file we want left pattern = os.path.join(tmp, 'README.md*') assert len(glob.glob(pattern)) == 1, glob.glob(pattern) # delete temp dir shutil.rmtree(tmp)
Example #2
Source File: _7zip.py From multibootusb with GNU General Public License v2.0 | 6 votes |
def test_extraction(): import shutil src = 'c:/Users/shinj/Downloads/clonezilla-live-2.5.2-31-amd64.iso' tmp_dir = 'c:/Users/shinj/Documents/tmp' for subdir, pattern in [ ('single_string', 'EFI/'), ('single_list', ['EFI/']), ('multi', ['EFI/', 'syslinux/']), ('all', None) ]: dest_dir = os.path.join(tmp_dir, subdir) if os.path.exists(dest_dir): shutil.rmtree(dest_dir) os.mkdir(dest_dir) args = [src, dest_dir] if pattern is not None: args.append(pattern) print ('Calling extract_iso(%s)' % args) extract_iso(*args)
Example #3
Source File: mainHelp.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 6 votes |
def save_dictionary(config): """ :param config: config :return: """ if config.save_dict is True: if os.path.exists(config.dict_directory): shutil.rmtree(config.dict_directory) if not os.path.isdir(config.dict_directory): os.makedirs(config.dict_directory) config.word_dict_path = "/".join([config.dict_directory, config.word_dict]) config.label_dict_path = "/".join([config.dict_directory, config.label_dict]) print("word_dict_path : {}".format(config.word_dict_path)) print("label_dict_path : {}".format(config.label_dict_path)) save_dict2file(config.create_alphabet.word_alphabet.words2id, config.word_dict_path) save_dict2file(config.create_alphabet.label_alphabet.words2id, config.label_dict_path) # copy to mulu print("copy dictionary to {}".format(config.save_dir)) shutil.copytree(config.dict_directory, "/".join([config.save_dir, config.dict_directory])) # load data / create alphabet / create iterator
Example #4
Source File: workflow.py From wechat-alfred-workflow with MIT License | 6 votes |
def _delete_directory_contents(self, dirpath, filter_func): """Delete all files in a directory. :param dirpath: path to directory to clear :type dirpath: ``unicode`` or ``str`` :param filter_func function to determine whether a file shall be deleted or not. :type filter_func ``callable`` """ if os.path.exists(dirpath): for filename in os.listdir(dirpath): if not filter_func(filename): continue path = os.path.join(dirpath, filename) if os.path.isdir(path): shutil.rmtree(path) else: os.unlink(path) self.logger.debug('deleted : %r', path)
Example #5
Source File: __init__.py From ALF with Apache License 2.0 | 6 votes |
def do_iteration(self, mutation_fn, aggression): """ This method is called with an output mutation filename and a real aggression (see :ref:`aggression`) indicating the amount of aggression the fuzzing algorithm should use. *mutation_fn* is unique for every invokation of :meth:`do_iteration`. It is an error for this method not to write the mutated template to *mutation_fn* before returning a result. If a result is not found, the mutation filename may be written to if needed, but it is not required. If a notable result is found, it should be returned as a :class:`FuzzResult` instance. This will be stored and reported to the ALF central server at the next check-in interval. A return of None indicates a result was not found. The filenames of any temporary files or folders created during execution can be safely removed using :func:`alf.delete`. This is safer than using :func:`os.remove` or :func:`shutil.rmtree` directly. *mutation_fn* does not need to be deleted, it is cleaned up automatically. """ raise NotImplementedError()
Example #6
Source File: utils_test.py From neural-pipeline with MIT License | 6 votes |
def test_creation(self): if os.path.exists(self.base_dir): shutil.rmtree(self.checkpoints_dir, ignore_errors=True) try: FileStructManager(base_dir=self.base_dir, is_continue=False) except FileStructManager.FSMException as err: self.fail("Raise error when base directory exists: [{}]".format(err)) self.assertFalse(os.path.exists(self.base_dir)) try: FileStructManager(base_dir=self.base_dir, is_continue=False) except FileStructManager.FSMException as err: self.fail("Raise error when base directory exists but empty: [{}]".format(err)) os.makedirs(os.path.join(self.base_dir, 'new_dir')) try: FileStructManager(base_dir=self.base_dir, is_continue=False) except: self.fail("Error initialize when exists non-registered folders in base directory") shutil.rmtree(self.base_dir, ignore_errors=True)
Example #7
Source File: cats_and_dogs.py From vergeml with MIT License | 6 votes |
def __call__(self, args, env): samples_dir = env.get('samples-dir') for label in ("cat", "dog"): dest = os.path.join(samples_dir, label) if os.path.exists(dest): raise VergeMLError("Directory {} already exists in samples dir: {}".format(label, dest)) print("Downloading cats and dogs to {}.".format(samples_dir)) src_dir = self.download_files([(_URL, "catsdogs.zip")], env) path = os.path.join(src_dir, "catsdogs.zip") print("Extracting data.") zipf = zipfile.ZipFile(path, 'r') zipf.extractall(src_dir) zipf.close() for file, dest in (("PetImages/Dog", "dog"), ("PetImages/Cat", "cat")): shutil.copytree(os.path.join(src_dir, file), os.path.join(samples_dir, dest)) shutil.rmtree(src_dir) # WTF? os.unlink(os.path.join(samples_dir, "cat", "666.jpg")) os.unlink(os.path.join(samples_dir, "dog", "11702.jpg")) print("Finished downloading cats and dogs.")
Example #8
Source File: unique_objects.py From vergeml with MIT License | 6 votes |
def __call__(self, args, env): samples_dir = env.get('samples-dir') print("Downloading unique objects to {}.".format(samples_dir)) src_dir = self.download_files([_URL], env=env, dir=env.get('cache-dir')) path = os.path.join(src_dir, "ObjectsAll.zip") zipf = zipfile.ZipFile(path, 'r') zipf.extractall(src_dir) zipf.close() for file in os.listdir(os.path.join(src_dir, "OBJECTSALL")): shutil.copy(os.path.join(src_dir, "OBJECTSALL", file), samples_dir) shutil.rmtree(src_dir) print("Finished downloading unique objects.")
Example #9
Source File: straight_dope_test_utils.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 6 votes |
def _download_straight_dope_notebooks(): """Downloads the Straight Dope Notebooks. Returns: True if it succeeds in downloading the notebooks without error. """ logging.info('Cleaning and setting up notebooks directory "{}"'.format(NOTEBOOKS_DIR)) shutil.rmtree(NOTEBOOKS_DIR, ignore_errors=True) cmd = [GIT_PATH, 'clone', GIT_REPO, NOTEBOOKS_DIR] proc, msg = _run_command(cmd) if proc.returncode != 0: err_msg = 'Error downloading Straight Dope notebooks.\n' err_msg += msg logging.error(err_msg) return False return True
Example #10
Source File: test_image.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
def teardownClass(cls): if cls.IMAGES_DIR: print("cleanup {}".format(cls.IMAGES_DIR)) shutil.rmtree(cls.IMAGES_DIR)
Example #11
Source File: projectfilefolderhandle.py From CAMISIM with Apache License 2.0 | 5 votes |
def remove_directory_temp(self): """ Delete temporary data @return: Nothing @rtype: None """ if os.path.exists(self._tmp_dir): assert os.path.isdir(self._tmp_dir) shutil.rmtree(self._tmp_dir)
Example #12
Source File: test_models.py From mlearn with BSD 3-Clause "New" or "Revised" License | 5 votes |
def tearDown(self): # Remove the directory after the test shutil.rmtree(self.test_dir)
Example #13
Source File: monitoring_test.py From neural-pipeline with MIT License | 5 votes |
def test_metrics_processing(self): def check_data(): self.assertIn('d', data) self.assertIn('lv1', data) self.assertIn('lv2', data['lv1']) self.assertIn('a', data['lv1']) self.assertIn('b', data['lv1']) self.assertIn('c', data['lv1']['lv2']) self.assertAlmostEqual(data['d'], values[-1] * 4, delta=1e-5) self.assertAlmostEqual(data['lv1']['a'], values[-1], delta=1e-5) self.assertAlmostEqual(data['lv1']['b'], values[-1] * 2, delta=1e-5) self.assertAlmostEqual(data['lv1']['lv2']['c'], values[-1] * 3, delta=1e-5) self.metrics_processing(with_final_file=False) shutil.rmtree('data') values = self.metrics_processing(with_final_file=True) expected_out = os.path.join('data', 'monitors', 'metrics_log', 'metrics.json') self.assertTrue(os.path.exists(expected_out) and os.path.isfile(expected_out)) with open(expected_out, 'r') as file: data = json.load(file) check_data() shutil.rmtree('data') values = self.metrics_processing(with_final_file=True, final_file='my_metrics.json') expected_out = os.path.join('my_metrics.json') self.assertTrue(os.path.exists(expected_out) and os.path.isfile(expected_out)) with open(expected_out, 'r') as file: data = json.load(file) check_data()
Example #14
Source File: test_data.py From mlearn with BSD 3-Clause "New" or "Revised" License | 5 votes |
def tearDownClass(cls): os.chdir(CWD) shutil.rmtree(cls.test_dir)
Example #15
Source File: core.py From neuropythy with GNU Affero General Public License v3.0 | 5 votes |
def tmpdir(prefix='npythy_tempdir_', delete=True): ''' tmpdir() creates a temporary directory and yields its path. At python exit, the directory and all of its contents are recursively deleted (so long as the the normal python exit process is allowed to call the atexit handlers). tmpdir(prefix) uses the given prefix in the tempfile.mkdtemp() call. The option delete may be set to False to specify that the tempdir should not be deleted on exit. ''' path = tempfile.mkdtemp(prefix=prefix) if not os.path.isdir(path): raise ValueError('Could not find or create temp directory') if delete: atexit.register(shutil.rmtree, path) return path
Example #16
Source File: mainHelp.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def load_data(config): """ :param config: config :return: batch data iterator and alphabet """ print("load data for process or pkl data.") train_iter, dev_iter, test_iter = None, None, None alphabet = None start_time = time.time() if (config.train is True) and (config.process is True): print("process data") if os.path.exists(config.pkl_directory): shutil.rmtree(config.pkl_directory) if not os.path.isdir(config.pkl_directory): os.makedirs(config.pkl_directory) train_iter, dev_iter, test_iter, alphabet = preprocessing(config) config.pretrained_weight = pre_embed(config=config, alphabet=alphabet) elif ((config.train is True) and (config.process is False)) or (config.test is True): print("load data from pkl file") # load alphabet from pkl # alphabet_dict = pcl.load(path=os.path.join(config.pkl_directory, config.pkl_alphabet)) alphabet_dict = torch.load(f=os.path.join(config.pkl_directory, config.pkl_alphabet)) print(alphabet_dict.keys()) alphabet = alphabet_dict["alphabet"] # load iter from pkl # iter_dict = pcl.load(path=os.path.join(config.pkl_directory, config.pkl_iter)) iter_dict = torch.load(f=os.path.join(config.pkl_directory, config.pkl_iter)) print(iter_dict.keys()) train_iter, dev_iter, test_iter = iter_dict.values() # train_iter, dev_iter, test_iter = iter_dict["train_iter"], iter_dict["dev_iter"], iter_dict["test_iter"] # load embed from pkl config.pretrained_weight = None if os.path.exists(os.path.join(config.pkl_directory, config.pkl_embed)): # embed_dict = pcl.load(os.path.join(config.pkl_directory, config.pkl_embed)) embed_dict = torch.load(f=os.path.join(config.pkl_directory, config.pkl_embed)) print(embed_dict.keys()) embed = embed_dict["pretrain_embed"] config.pretrained_weight = embed end_time = time.time() print("All Data/Alphabet/Iterator Use Time {:.4f}".format(end_time - start_time)) print("***************************************") return train_iter, dev_iter, test_iter, alphabet
Example #17
Source File: filemap.py From neuropythy with GNU Affero General Public License v3.0 | 5 votes |
def setup_cache_deletion(cache_path, actual_cache_path, cache_delete): ''' The filemap.setup_cache_deletion requirement ensures that the cache directory will be deleted at system exit, if required. ''' if cache_delete is True and cache_path is actual_cache_path: atexit.register(shutil.rmtree, cache_path) return True
Example #18
Source File: core.py From neuropythy with GNU Affero General Public License v3.0 | 5 votes |
def cache_root(custom_directory): ''' dataset.cache_root is the root directory in which the given dataset has been cached. ''' if custom_directory is not None: return None elif config['data_cache_root'] is None: # we create a data-cache in a temporary directory path = tempfile.mkdtemp(prefix='npythy_data_cache_') if not os.path.isdir(path): raise ValueError('Could not find or create cache directory') config['data_cache_root'] = path atexit.register(shutil.rmtree, path) return config['data_cache_root']
Example #19
Source File: test_gluon_utils.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
def test_download_successful(): """ test download with one process """ tmp = tempfile.mkdtemp() tmpfile = os.path.join(tmp, 'README.md') _download_successful(tmpfile) assert os.path.getsize(tmpfile) > 100, os.path.getsize(tmpfile) pattern = os.path.join(tmp, 'README.md*') # check only one file we want left assert len(glob.glob(pattern)) == 1, glob.glob(pattern) # delete temp dir shutil.rmtree(tmp)
Example #20
Source File: mainHelp.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def get_params(config, alphabet): """ :param config: config :param alphabet: alphabet dict :return: """ # get algorithm config.learning_algorithm = get_learning_algorithm(config) # save best model path config.save_best_model_path = config.save_best_model_dir if config.test is False: if os.path.exists(config.save_best_model_path): shutil.rmtree(config.save_best_model_path) # get params config.embed_num = alphabet.word_alphabet.vocab_size config.char_embed_num = alphabet.char_alphabet.vocab_size config.class_num = alphabet.label_alphabet.vocab_size config.paddingId = alphabet.word_paddingId config.char_paddingId = alphabet.char_paddingId config.label_paddingId = alphabet.label_paddingId config.create_alphabet = alphabet print("embed_num : {}, char_embed_num: {}, class_num : {}".format(config.embed_num, config.char_embed_num, config.class_num)) print("PaddingID {}".format(config.paddingId)) print("char PaddingID {}".format(config.char_paddingId))
Example #21
Source File: goldstandardassembly.py From CAMISIM with Apache License 2.0 | 5 votes |
def _close(self): if self.validate_dir(self._temp_merges_bam_directory, silent=True): shutil.rmtree(self._temp_merges_bam_directory) self._logger = None
Example #22
Source File: test_calcs.py From mlearn with BSD 3-Clause "New" or "Revised" License | 5 votes |
def tearDownClass(cls): os.chdir(CWD) shutil.rmtree(cls.test_dir)
Example #23
Source File: ncbitaxonomy.py From CAMISIM with Apache License 2.0 | 5 votes |
def __del__(self): super(NcbiTaxonomy, self).__del__() if self.validate_dir(self._tmp_dir, silent=True): import shutil shutil.rmtree(self._tmp_dir) self.tmp_dir = None
Example #24
Source File: ncbitaxonomy.py From CAMISIM with Apache License 2.0 | 5 votes |
def __exit__(self, type, value, traceback): super(NcbiTaxonomy, self).__exit__(type, value, traceback) if self.validate_dir(self._tmp_dir, silent=True): import shutil shutil.rmtree(self._tmp_dir) self.tmp_dir = None
Example #25
Source File: anim.py From CAMISIM with Apache License 2.0 | 5 votes |
def __exit__(self, type, value, traceback): super(ANIm, self).__exit__(type, value, traceback) if not self._debug: shutil.rmtree(self._tmp_dir) # def get_total_organism_length(self): # self.get_organism_lengths(self._reference_gid_to_location) # self.get_organism_lengths(self._query_gid_to_location) # Construct a command-line for NUCmer reference_id, candidate_id
Example #26
Source File: mgcluster.py From CAMISIM with Apache License 2.0 | 5 votes |
def __exit__(self, type, value, traceback): super(MGCluster, self).__exit__(type, value, traceback) if not self._debug: shutil.rmtree(self._tmp_dir)
Example #27
Source File: dataloader.py From models with MIT License | 5 votes |
def inflate_data_sources(input): import zipfile import tempfile import shutil import os dirpath = tempfile.mkdtemp() # make sure the directory is empty shutil.rmtree(dirpath) os.makedirs(dirpath) # load and extract zip file zf = zipfile.ZipFile(input) zf.extractall(dirpath) extracted_folders = os.listdir(dirpath) return {k.split(".")[0]: os.path.join(dirpath, k) for k in extracted_folders}
Example #28
Source File: LogMetric.py From nmp_qc with MIT License | 5 votes |
def _remove(path): """ param <path> could either be relative or absolute. """ if os.path.isfile(path): os.remove(path) # remove the file elif os.path.isdir(path): import shutil shutil.rmtree(path) # remove dir and all contains
Example #29
Source File: Plotter.py From nmp_qc with MIT License | 5 votes |
def _remove(path): """ param <path> could either be relative or absolute. """ if os.path.isfile(path): os.remove(path) # remove the file elif os.path.isdir(path): import shutil shutil.rmtree(path) # remove dir and all contains
Example #30
Source File: test_label.py From mlimages with MIT License | 5 votes |
def test_label_dir(self): # prepare the images p = env.get_data_folder() api = ImagenetAPI(p, limit=3) api.gather("n01316949", include_subset=True) folder = "work_animal" # make labeled data machine = LabelingMachine(p) lf_fixed = machine.label_dir(0, path_from_root=folder, label_file=os.path.join(p, "test_label_fixed.txt")) lf_auto, label_def = machine.label_dir_auto(path_from_root=folder, label_file=os.path.join(p, "test_label_auto.txt")) f_count = 0 for im in lf_fixed.fetch(): self.assertEqual(0, im.label) f_count += 1 label_and_path = {} a_count = 0 with open(label_def) as f: path_label = [ln.strip().split() for ln in f.readlines()] for pl in path_label: label_and_path[int(pl[0])] = pl[1] for im in lf_auto.fetch(): path = os.path.dirname(im.path) rel_path = machine.file_api.to_rel(path) self.assertTrue(im.label in label_and_path) self.assertEqual(os.path.join(label_and_path[im.label], ""), os.path.join(rel_path, "")) a_count += 1 else: im = None # release reference self.assertGreater(f_count, 0) self.assertEquals(f_count, a_count) shutil.rmtree(api.file_api.to_abs(folder)) os.remove(lf_fixed.path) os.remove(lf_auto.path) os.remove(label_def)