28 ví dụ mã Python được tìm thấy liên quan đến "chuyển đổi tệp". Bạn có thể bỏ phiếu cho những người bạn thích hoặc bỏ phiếu cho những người bạn không thích và đi đến dự án gốc hoặc tệp nguồn bằng cách theo các liên kết trên mỗi ví dụ. convert files". You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
ví dụ 1
def convert_and_rename_subcortical_files(fol, new_fol, lookup): obj_files = glob.glob(op.join(fol, '*.srf')) utils.delete_folder_files(new_fol) for obj_file in obj_files: num = int(op.basename(obj_file)[:-4].split('_')[-1]) new_name = lookup.get(num, '') if new_name != '': utils.srf2ply(obj_file, op.join(new_fol, '{}.ply'.format(new_name))) verts, faces = utils.read_ply_file(op.join(new_fol, '{}.ply'.format(new_name))) np.savez(op.join(new_fol, '{}.npz'.format(new_name)), verts=verts, faces=faces) # copy_subcorticals_to_mmvt(new_fol, subject, mmvt_subcorticals_fol_name) # def copy_subcorticals_to_mmvt(subcorticals_fol, subject, mmvt_subcorticals_fol_name='subcortical'): # blender_fol = op.join(MMVT_DIR, subject, mmvt_subcorticals_fol_name) # if op.isdir(blender_fol): # shutil.rmtree(blender_fol) # utils.copy_filetree(subcorticals_fol, blender_fol)
Ví dụ 2
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs
Ví dụ 3
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString())
Ví dụ 4
def convert_skin_files(self): # get list of skins to convert skin_dirs = [d for d in os.listdir(self.skin_path) if os.path.isdir(os.path.join(self.skin_path, d))] for skin_dir in skin_dirs: if skin_dir != 'Default': target_dir = os.path.join(self.skin_path, skin_dir, '720p') skin_files = os.listdir(self.source_file_dir) convert_file = os.path.join(self.skin_path, skin_dir, 'convert.xml') if not os.path.exists(convert_file): print ('convert file does not exist: %s' %convert_file) continue for skin_file in skin_files: source_file = os.path.join(self.source_file_dir, skin_file) target_file = os.path.join(target_dir, skin_file) self.convert_skin_file(source_file, target_file, convert_file)
Ví dụ 5
def convert_examples_to_features_and_output_to_files( examples: List[str], max_seq_length: int, tokenizer: tx.data.GPT2Tokenizer, output_file: str, feature_types: Dict[str, Any], append_eos_token: bool = True): r"""Converts a set of examples to a `pickle` file.""" with tx.data.RecordData.writer(output_file, feature_types) as writer: for (_, example) in enumerate(examples): text_ids, length = tokenizer.encode_text( text=example, max_seq_length=max_seq_length, append_eos_token=append_eos_token) features = { "text_ids": text_ids, "length": length } writer.write(features)
Ví dụ 6
def convertSourceFiles(filenames): "Helper function - makes minimal PDF document" from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer, XPreformatted from reportlab.lib.styles import getSampleStyleSheet styT=getSampleStyleSheet()["Title"] styC=getSampleStyleSheet()["Code"] doc = SimpleDocTemplate("pygments2xpre.pdf") S = [].append for filename in filenames: S(Paragraph(filename,style=styT)) src = open(filename, 'r').read() fmt = pygments2xpre(src) S(XPreformatted(fmt, style=styC)) doc.build(S.__self__) print 'saved pygments2xpre.pdf'
Ví dụ 7
def convert_examples_to_features_and_output_to_files( examples: List[str], max_seq_length: int, tokenizer: tx.data.GPT2Tokenizer, output_file: str, feature_types: Dict[str, Any], append_eos_token: bool = True): r"""Converts a set of examples to a `pickle` file.""" with tx.data.RecordData.writer(output_file, feature_types) as writer: for (_, example) in enumerate(examples): text_ids, length = tokenizer.encode_text( text=example, max_seq_length=max_seq_length, append_eos_token=append_eos_token) features = { "text_ids": text_ids, "length": length } writer.write(features) # type: ignore
Ví dụ 8
def convertToMultipleFiles(fileDir, targetDir): destDir = genDestDir(targetDir) for _, dirnames, _ in os.walk(fileDir): valuesDirs = [di for di in dirnames if di.startswith("values")] for dirname in valuesDirs: workbook = pyExcelerator.Workbook() for _, _, filenames in os.walk(fileDir+'/'+dirname): xmlFiles = [fi for fi in filenames if fi.endswith(".xml")] for xmlfile in xmlFiles: ws = workbook.add_sheet(xmlfile) path = fileDir+'/'+dirname+'/' + xmlfile (keys, values) = XmlFileUtil.getKeysAndValues(path) for keyIndex in range(len(keys)): key = keys[keyIndex] value = values[keyIndex] ws.write(keyIndex, 0, key) ws.write(keyIndex, 1, value) filePath = destDir + "/" + getCountryCode(dirname) + ".xls" workbook.save(filePath) print "Convert %s successfully! you can see xls file in %s" % ( fileDir, destDir)
Ví dụ 9
def convertSourceFiles(filenames): "Helper function - makes minimal PDF document" from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer, XPreformatted from reportlab.lib.styles import getSampleStyleSheet styT=getSampleStyleSheet()["Title"] styC=getSampleStyleSheet()["Code"] doc = SimpleDocTemplate("pygments2xpre.pdf") S = [].append for filename in filenames: S(Paragraph(filename,style=styT)) src = open(filename, 'r').read() fmt = pygments2xpre(src) S(XPreformatted(fmt, style=styC)) doc.build(S.__self__) print('saved pygments2xpre.pdf')
Ví dụ 10
def convertFiles(self): files = [x for x in self.jsonFiles.getItems() if x[2] is True] outPath = self.exportPath.text().strip() if not len(self.outputPath.text().strip()) else self.outputPath.text().strip() if not os.path.exists(outPath): os.mkdir(outPath) parPool = mp.Pool(mp.cpu_count() - 1) inFiles = [file[1] for file in files] outFiles = [os.path.join(outPath, file[0] + '.obj') for file in files] progressBar = QtGui.QProgressDialog("Converting Files...", "Cancel", 0, len(outFiles), self) progressBar.setWindowModality(QtCore.Qt.WindowModal) progressBar.show() for i, _ in enumerate(parPool.imap_unordered(functools.partial(convert_to_obj.convertFile, cooked=self.cooked.isChecked()), zip(inFiles, outFiles))): progressBar.setValue(i) progressBar.setValue(len(outFiles))
Ví dụ 11
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 0
Ví dụ 12
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 1
Ví dụ 13
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 2
Ví dụ 14
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 3
Ví dụ 15
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 4
Ví dụ 16
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 5
Ví dụ 17
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 6
Ví dụ 18
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 7
Ví dụ 19
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 8
Ví dụ 20
def convert_files(self, paths, source=None, target=None, lang_chain=None, output=None, output_dir=None): """Convert a file by passing it through a chain of conversion functions.""" objs = [] for i, path in enumerate(paths): # Create the context object. context = self._create_context(path=path, source=source, target=target, lang_chain=lang_chain, output=output, output_dir=output_dir, ) logger.debug("Converting `%s` from %s to %s.", op.basename(context.path), context.source, context.target) obj = self._convert_from_context(context.path, context, is_path=True, do_append=i >= 1) objs.append(obj) return objs[0] if objs and len(objs) else objs 9
Ví dụ 21
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 0
Ví dụ 22
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 1
Ví dụ 23
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 2
Ví dụ 24
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 3
Ví dụ 25
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 4
Ví dụ 26
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 5
Ví dụ 27
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 6
Ví dụ 28
def convert_examples_to_features_and_output_to_files( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) tf_example = tf.train.Example( features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) 7