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How to use path_glob method in Behave

Best Python code snippet using behave

hadoop.py

Source:hadoop.py

1# Copyright 2009-2012 Yelp and Contributors2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import logging15import posixpath16import re17from subprocess import Popen18from subprocess import PIPE19from subprocess import CalledProcessError20try:21    from cStringIO import StringIO22    StringIO  # quiet "redefinition of unused ..." warning from pyflakes23except ImportError:24    from StringIO import StringIO25from mrjob.fs.base import Filesystem26from mrjob.parse import is_uri27from mrjob.parse import urlparse28from mrjob.util import cmd_line29from mrjob.util import read_file30log = logging.getLogger('mrjob.fs.hadoop')31# used by mkdir()32HADOOP_FILE_EXISTS_RE = re.compile(r'.*File exists.*')33# used by ls()34HADOOP_LSR_NO_SUCH_FILE = re.compile(35    r'^lsr: Cannot access .*: No such file or directory.')36# used by rm() (see below)37HADOOP_RMR_NO_SUCH_FILE = re.compile(r'^rmr: hdfs://.*$')38class HadoopFilesystem(Filesystem):39    """Filesystem for URIs accepted by ``hadoop fs``. Typically you will get40    one of these via ``HadoopJobRunner().fs``, composed with41    :py:class:`~mrjob.fs.local.LocalFilesystem`.42    """43    def __init__(self, hadoop_bin):44        """:param hadoop_bin: path to ``hadoop`` binary"""45        super(HadoopFilesystem, self).__init__()46        self._hadoop_bin = hadoop_bin47    def can_handle_path(self, path):48        return is_uri(path)49    def invoke_hadoop(self, args, ok_returncodes=None, ok_stderr=None,50                       return_stdout=False):51        """Run the given hadoop command, raising an exception on non-zero52        return code. This only works for commands whose output we don't53        care about.54        Args:55        ok_returncodes -- a list/tuple/set of return codes we expect to56            get back from hadoop (e.g. [0,1]). By default, we only expect 0.57            If we get an unexpected return code, we raise a CalledProcessError.58        ok_stderr -- don't log STDERR or raise CalledProcessError if stderr59            matches a regex in this list (even if the returncode is bad)60        return_stdout -- return the stdout from the hadoop command rather61            than logging it. If this is False, we return the returncode62            instead.63        """64        args = self._hadoop_bin + args65        log.debug('> %s' % cmd_line(args))66        proc = Popen(args, stdout=PIPE, stderr=PIPE)67        stdout, stderr = proc.communicate()68        log_func = log.debug if proc.returncode == 0 else log.error69        if not return_stdout:70            for line in StringIO(stdout):71                log_func('STDOUT: ' + line.rstrip('\r\n'))72        # check if STDERR is okay73        stderr_is_ok = False74        if ok_stderr:75            for stderr_re in ok_stderr:76                if stderr_re.match(stderr):77                    stderr_is_ok = True78                    break79        if not stderr_is_ok:80            for line in StringIO(stderr):81                log_func('STDERR: ' + line.rstrip('\r\n'))82        ok_returncodes = ok_returncodes or [0]83        if not stderr_is_ok and proc.returncode not in ok_returncodes:84            raise CalledProcessError(proc.returncode, args)85        if return_stdout:86            return stdout87        else:88            return proc.returncode89    def du(self, path_glob):90        """Get the size of a file, or None if it's not a file or doesn't91        exist."""92        try:93            stdout = self.invoke_hadoop(['fs', '-dus', path_glob],94                                        return_stdout=True)95        except CalledProcessError:96            raise IOError(path_glob)97        try:98            return sum(int(line.split()[1])99                       for line in stdout.split('\n')100                       if line.strip())101        except (ValueError, TypeError, IndexError):102            raise IOError(103                'Unexpected output from hadoop fs -du: %r' % stdout)104    def ls(self, path_glob):105        components = urlparse(path_glob)106        hdfs_prefix = '%s://%s' % (components.scheme, components.netloc)107        try:108            stdout = self.invoke_hadoop(109                ['fs', '-lsr', path_glob],110                return_stdout=True,111                ok_stderr=[HADOOP_LSR_NO_SUCH_FILE])112        except CalledProcessError:113            raise IOError("Could not ls %s" % path_glob)114        for line in StringIO(stdout):115            line = line.rstrip('\r\n')116            fields = line.split(' ')117            # Throw out directories118            if fields[0].startswith('d'):119                continue120            # Try to figure out which part of the line is the path121            # Expected lines:122            # -rw-r--r--   3 dave users       3276 2010-01-13 14:00 /foo/bar # HDFS123            # -rwxrwxrwx   1          3276 010-01-13 14:00 /foo/bar # S3124            path_index = None125            for index, field in enumerate(fields):126                if len(field) == 5 and field[2] == ':':127                    path_index = (index + 1)128            if not path_index:129                raise IOError("Could not locate path in string '%s'" % line)130            path = line.split(' ', path_index)[-1]131            # handle fully qualified URIs from newer versions of Hadoop ls132            # (see Pull Request #577)133            if is_uri(path):134                yield path135            else:136                yield hdfs_prefix + path137    def _cat_file(self, filename):138        # stream from HDFS139        cat_args = self._hadoop_bin + ['fs', '-cat', filename]140        log.debug('> %s' % cmd_line(cat_args))141        cat_proc = Popen(cat_args, stdout=PIPE, stderr=PIPE)142        def stream():143            for line in cat_proc.stdout:144                yield line145            # there shouldn't be any stderr146            for line in cat_proc.stderr:147                log.error('STDERR: ' + line)148            returncode = cat_proc.wait()149            if returncode != 0:150                raise IOError("Could not stream %s" % filename)151        return read_file(filename, stream())152    def mkdir(self, path):153        try:154            self.invoke_hadoop(155                ['fs', '-mkdir', path], ok_stderr=[HADOOP_FILE_EXISTS_RE])156        except CalledProcessError:157            raise IOError("Could not mkdir %s" % path)158    def path_exists(self, path_glob):159        """Does the given path exist?160        If dest is a directory (ends with a "/"), we check if there are161        any files starting with that path.162        """163        try:164            return_code = self.invoke_hadoop(['fs', '-ls', path_glob],165                                             ok_returncodes=[0,-1,255])166            return (return_code == 0)167        except CalledProcessError:168            raise IOError("Could not check path %s" % path_glob)169    def path_join(self, dirname, filename):170        return posixpath.join(dirname, filename)171    def rm(self, path_glob):172        if not is_uri(path_glob):173            super(HadoopFilesystem, self).rm(path_glob)174        if self.path_exists(path_glob):175            # hadoop fs -rmr will print something like:176            # Moved to trash: hdfs://hdnamenode:54310/user/dave/asdf177            # to STDOUT, which we don't care about.178            #179            # if we ask to delete a path that doesn't exist, it prints180            # to STDERR something like:181            # rmr: <path>182            # which we can safely ignore183            try:184                self.invoke_hadoop(185                    ['fs', '-rmr', path_glob],186                    return_stdout=True, ok_stderr=[HADOOP_RMR_NO_SUCH_FILE])187            except CalledProcessError:188                raise IOError("Could not rm %s" % path_glob)189    def touchz(self, dest):190        try:191            self.invoke_hadoop(['fs', '-touchz', dest])192        except CalledProcessError:...

detector.py

Source:detector.py

1#2# Licensed under the Apache License, Version 2.0 (the "License");3# you may not use this file except in compliance with the License.4# You may obtain a copy of the License at5#6#     http://www.apache.org/licenses/LICENSE-2.07#8# Unless required by applicable law or agreed to in writing, software9# distributed under the License is distributed on an "AS IS" BASIS,10# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.11# See the License for the specific language governing permissions and12# limitations under the License.13#14"""Detect Thermos tasks on disk15This module contains the TaskDetector, used to detect Thermos tasks within a given checkpoint root.16"""17import functools18import glob19import os20import re21from abc import abstractmethod22from twitter.common.lang import Compatibility, Interface23from apache.thermos.common.constants import DEFAULT_CHECKPOINT_ROOT24from apache.thermos.common.path import TaskPath25class PathDetector(Interface):26  @abstractmethod27  def get_paths(self):28    """Get a list of valid checkpoint roots."""29class FixedPathDetector(PathDetector):30  def __init__(self, path=DEFAULT_CHECKPOINT_ROOT):31    if not isinstance(path, Compatibility.string):32      raise TypeError('FixedPathDetector path should be a string, got %s' % type(path))33    self._paths = [path]34  def get_paths(self):35    return self._paths[:]36class ChainedPathDetector(PathDetector):37  def __init__(self, *detectors):38    for detector in detectors:39      if not isinstance(detector, PathDetector):40        raise TypeError('Expected detector %r to be a PathDetector, got %s' % (41            detector, type(detector)))42    self._detectors = detectors43  def get_paths(self):44    def iterate():45      for detector in self._detectors:46        for path in detector.get_paths():47          yield path48    return list(set(iterate()))49def memoized(fn):50  cache_attr_name = '__memoized_' + fn.__name__51  @functools.wraps(fn)52  def memoized_fn(self, *args):53    if not hasattr(self, cache_attr_name):54      setattr(self, cache_attr_name, {})55    cache = getattr(self, cache_attr_name)56    try:57      return cache[args]58    except KeyError:59      cache[args] = rv = fn(self, *args)60      return rv61  return memoized_fn62class TaskDetector(object):63  """64    Helper class in front of TaskPath to detect active/finished/running tasks. Performs no65    introspection on the state of a task; merely detects based on file paths on disk.66  """67  class Error(Exception): pass68  class MatchingError(Error): pass69  def __init__(self, root):70    self._root_dir = root71    self._pathspec = TaskPath()72  @memoized73  def __get_task_ids_patterns(self, state):74    path_glob = self._pathspec.given(75        root=self._root_dir,76        task_id="*",77        state=state or '*'78    ).getpath('task_path')79    path_regex = self._pathspec.given(80        root=re.escape(self._root_dir),81        task_id="(\S+)",82        state='(\S+)'83    ).getpath('task_path')84    return path_glob, re.compile(path_regex)85  def get_task_ids(self, state=None):86    path_glob, path_regex = self.__get_task_ids_patterns(state)87    for path in glob.glob(path_glob):88      try:89        task_state, task_id = path_regex.match(path).groups()90      except Exception:91        continue92      if state is None or task_state == state:93        yield (task_state, task_id)94  @memoized95  def __get_process_runs_patterns(self, task_id, log_dir):96    path_glob = self._pathspec.given(97        root=self._root_dir,98        task_id=task_id,99        log_dir=log_dir,100        process='*',101        run='*'102    ).getpath('process_logdir')103    path_regex = self._pathspec.given(104        root=re.escape(self._root_dir),105        task_id=re.escape(task_id),106        log_dir=log_dir,107        process='(\S+)',108        run='(\d+)'109    ).getpath('process_logdir')110    return path_glob, re.compile(path_regex)111  def get_process_runs(self, task_id, log_dir):112    path_glob, path_regex = self.__get_process_runs_patterns(task_id, log_dir)113    for path in glob.glob(path_glob):114      try:115        process, run = path_regex.match(path).groups()116      except Exception:117        continue118      yield process, int(run)119  def get_process_logs(self, task_id, log_dir):120    for process, run in self.get_process_runs(task_id, log_dir):121      for logtype in ('stdout', 'stderr'):122        path = (self._pathspec.with_filename(logtype).given(root=self._root_dir,123                                                            task_id=task_id,124                                                            log_dir=log_dir,125                                                            process=process,126                                                            run=run)127                                                     .getpath('process_logdir'))128        if os.path.exists(path):129          yield path130  def get_checkpoint(self, task_id):131    return self._pathspec.given(root=self._root_dir, task_id=task_id).getpath('runner_checkpoint')132  @memoized133  def __get_process_checkpoints_patterns(self, task_id):134    path_glob = self._pathspec.given(135        root=self._root_dir,136        task_id=task_id,137        process='*'138    ).getpath('process_checkpoint')139    path_regex = self._pathspec.given(140        root=re.escape(self._root_dir),141        task_id=re.escape(task_id),142        process='(\S+)',143    ).getpath('process_checkpoint')144    return path_glob, re.compile(path_regex)145  def get_process_checkpoints(self, task_id):146    path_glob, path_regex = self.__get_process_checkpoints_patterns(task_id)147    for path in glob.glob(path_glob):148      try:149        process, = path_regex.match(path).groups()150      except Exception:151        continue...

scans_to_tfrecords.py

Source:scans_to_tfrecords.py

1import argparse2from pathlib import Path3import numpy as np4from scipy.ndimage import zoom5import tensorflow as tf6from tqdm import tqdm7import nrrd8from pydicom import dcmread9import nibabel as nib10def preprocess_scan(scan, downsample):11    "Apply some preprocessing to the image"12    if downsample != 1:13        z, y, x = scan.shape14        scan = zoom(scan, (48 / z, 256 / y, 256 / x), order=5)15        # scan = zoom(scan, 1 / downsample)16    scan = scan.astype(np.float32)17    return scan18def scan_to_example(scan):19    "Convert a scan (a NumPy array) to an Example class"20    z, y, x = scan.shape21    scan_raw = scan.tostring()22    scan_features = {23        "z": tf.train.Feature(int64_list=tf.train.Int64List(value=[z])),24        "y": tf.train.Feature(int64_list=tf.train.Int64List(value=[y])),25        "x": tf.train.Feature(int64_list=tf.train.Int64List(value=[x])),26        "scan_raw": tf.train.Feature(27            bytes_list=tf.train.BytesList(value=[scan_raw])28        ),29    }30    return tf.train.Example(features=tf.train.Features(feature=scan_features))31def split_into_subsequences(data, s):32    """33    Split the input sequence into sublist of size s.34    >>> s = "abcdefg"35    >>> split_into_subsequences(s, 2)36    ['ab', 'cd', 'ef', 'g']37    """38    return [data[x : x + s] for x in range(0, len(data), s)]39def save_scan(writer, scan):40    """Serialize the scan in a tfrecord file.41    If the scan is not e regular volume (the first axis42    has length 0) then return doing nothing.43    """44    if scan.shape[0] < 1:45        return46    example = scan_to_example(scan)47    writer.write(example.SerializeToString())48def convert_nrrd(path_glob, output_dir_name, downsample):49    nrrd_files = [str(f) for f in Path(".").glob(path_glob)]50    nrrd_files = split_into_subsequences(nrrd_files, 10)51    output_dir = Path(output_dir_name)52    output_dir.mkdir()53    for i, chunk in tqdm(54        enumerate(nrrd_files, start=1), total=len(nrrd_files)55    ):56        tfrecord_fname = str(output_dir / f"{i:02}.tfrecord")57        with tf.io.TFRecordWriter(tfrecord_fname) as writer:58            for fname in chunk:59                scan, _ = nrrd.read(fname, index_order="C")60                scan = preprocess_scan(scan, downsample)61                save_scan(writer, scan)62def convert_dicom(path_glob, output_dir_name, downsample):63    dcm_directories = list(Path(".").glob(path_glob))64    dcm_directories = split_into_subsequences(dcm_directories, 10)65    output_dir = Path(output_dir_name)66    output_dir.mkdir()67    for i, chunk in tqdm(68        enumerate(dcm_directories, start=1), total=len(dcm_directories)69    ):70        tfrecord_fname = str(output_dir / f"{i:02}.tfrecord")71        with tf.io.TFRecordWriter(tfrecord_fname) as writer:72            for dcm_dir in chunk:73                dcm_files = Path(dcm_dir).glob("*.dcm")74                dcm_slices = [dcmread(str(f)) for f in dcm_files]75                is_volume = (76                    hasattr(dcm_slices[0], "SliceLocation")77                    and len(dcm_slices) >= 478                )79                if is_volume:80                    dcm_slices = sorted(81                        dcm_slices, key=lambda x: x.SliceLocation82                    )83                    if all(84                        s.pixel_array.shape == (512, 512) for s in dcm_slices85                    ):86                        scan = np.stack([s.pixel_array for s in dcm_slices])87                        scan = preprocess_scan(scan, downsample)88                        save_scan(writer, scan)89def convert_nifti(path_glob, output_dir_name, downsample):90    nifti_files = [str(f) for f in Path(".").glob(path_glob)]91    nifti_files = split_into_subsequences(nifti_files, 10)92    output_dir = Path(output_dir_name)93    output_dir.mkdir()94    for i, chunk in tqdm(95        enumerate(nifti_files, start=1), total=len(nifti_files)96    ):97        tfrecord_fname = str(output_dir / f"{i:02}.tfrecord")98        with tf.io.TFRecordWriter(tfrecord_fname) as writer:99            for fname in chunk:100                nib_obj = nib.load(fname)101                scan = nib_obj.get_fdata().T  # transpose to obtain C order102                scan = preprocess_scan(scan, downsample)103                save_scan(writer, scan)104if __name__ == "__main__":105    import doctest106    doctest.testmod()107    parser = argparse.ArgumentParser(108        description="Convert multiple CT scans to a single tfrecord file"109    )110    parser.add_argument("file_type", choices=["nrrd", "dicom", "nifti"])111    parser.add_argument(112        "path_glob",113        help="Glob that identifies all the nrrd/dicom/nifti files to convert (must be inside quotes)",114    )115    parser.add_argument(116        "-d", "--downsample", type=float, default=1, help="Downscaling factor"117    )118    parser.add_argument(119        "output_dir",120        help="Name of the directory where to store the tfrecords files",121    )122    args = parser.parse_args()123    if args.file_type == "nrrd":124        convert_nrrd(args.path_glob, args.output_dir, args.downsample)125    if args.file_type == "dicom":126        convert_dicom(args.path_glob, args.output_dir, args.downsample)127    if args.file_type == "nifti":...

base.py

Source:base.py

1# Copyright 2009-2012 Yelp and Contributors2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import logging15log = logging.getLogger('mrjob.fs')16class Filesystem(object):17    """Some simple filesystem operations that are common across the local18    filesystem, S3, HDFS, and remote machines via SSH. Different runners19    provide functionality for different filesystems via their20    :py:attr:`~mrjob.runner.MRJobRunner.fs` attribute. The ``hadoop`` and21    ``emr`` runners provide support for multiple protocols using22    :py:class:`~mrjob.job.composite.CompositeFilesystem`.23    Protocol support:24    * :py:class:`mrjob.fs.hadoop.HadoopFilesystem`: ``hdfs://``, others25    * :py:class:`mrjob.fs.local.LocalFilesystem`: ``/``26    * :py:class:`mrjob.fs.s3.S3Filesystem`: ``s3://bucket/path``,27      ``s3n://bucket/path``28    * :py:class:`mrjob.fs.ssh.SSHFilesystem`: ``ssh://hostname/path``29    """30    def cat(self, path_glob):31        """cat all files matching **path_glob**, decompressing if necessary"""32        for filename in self.ls(path_glob):33            for line in self._cat_file(filename):34                yield line35    def du(self, path_glob):36        """Get the total size of files matching ``path_glob``37        Corresponds roughly to: ``hadoop fs -dus path_glob``38        """39        raise NotImplementedError40    def ls(self, path_glob):41        """Recursively list all files in the given path.42        We don't return directories for compatibility with S3 (which43        has no concept of them)44        Corresponds roughly to: ``hadoop fs -lsr path_glob``45        """46        raise NotImplementedError47    def _cat_file(self, path):48        raise NotImplementedError49    def mkdir(self, path):50        """Create the given dir and its subdirs (if they don't already51        exist).52        Corresponds roughly to: ``hadoop fs -mkdir path``53        """54        raise NotImplementedError55    def path_exists(self, path_glob):56        """Does the given path exist?57        Corresponds roughly to: ``hadoop fs -test -e path_glob``58        """59        raise NotImplementedError60    def path_join(self, dirname, filename):61        raise NotImplementedError62    def rm(self, path_glob):63        """Recursively delete the given file/directory, if it exists64        Corresponds roughly to: ``hadoop fs -rmr path_glob``65        """66        raise NotImplementedError67    def touchz(self, path):68        """Make an empty file in the given location. Raises an error if69        a non-zero length file already exists in that location.70        Correponds to: ``hadoop fs -touchz path``71        """72        raise NotImplementedError73    def md5sum(self, path_glob):74        """Generate the md5 sum of the file at ``path``"""...

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