Python Path.mkdir方法代碼示例

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def __init__(self, env, rank=None):
        self.rank = rank
        if self.rank is not None:
            log_path = Path(env, f"rank{rank}").resolve()
        else:
            log_path = Path(env).resolve()
        logger.debug(f"using tensorboard on --logdir {str(log_path)}")
        try:
            Path.mkdir(log_path, parents=True, exist_ok=True)
        except OSError as e:
            if e.errno == errno.EEXIST:
                logger.warning(f'Tensorboard log directory already exists: {str(log_path)}')
                for f in log_path.rglob("*"):
                    f.unlink()
            else:
                raise

        from tensorboardX import SummaryWriter
        self.writer = SummaryWriter(str(log_path)) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def plot_tsne(ssvae, test_loader, use_cuda=False):
    xs = test_loader.dataset.test_data.float()
    ys = test_loader.dataset.test_labels
    z_mu, z_sigma = ssvae.guide_sample(xs, ys, len(test_loader))

    z_states = z_mu.data.cpu().numpy()
    classes = ys.cpu().numpy()

    logger.info("calculating T-SNE of z embedding..")
    if use_cuda:
        import t_sne_bhcuda.bhtsne_cuda as tsne_bhcuda
        files_dir = Path.cwd() / "tsne"
        Path.mkdir(files_dir, parents=True, exist_ok=True)
        z_embed = tsne_bhcuda.t_sne(z_states, no_dims=2, files_dir=files_dir, gpu_mem=0.9)
        z_embed = np.array([list(x) for x in z_embed])
    else:
        from sklearn.manifold import TSNE
        model_tsne = TSNE(n_components=2, random_state=0)
        z_embed = model_tsne.fit_transform(z_states)

    __plot_tsne_to_visdom(z_embed, classes)
    #__plot_tsne_to_matplotlib(z_embed, classes) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def get_elmo_model( model_dir, cpu, verbose ):
    
    weights_path = model_dir / 'weights.hdf5'
    options_path = model_dir / 'options.json'

    # if no pre-trained model is available, yet --> download it
    if not (weights_path.exists() and options_path.exists()):
        if verbose: 
            print('No existing model found. Start downloading pre-trained SeqVec (~360MB)...')
        import urllib.request
        Path.mkdir(model_dir)
        repo_link    = 'http://rostlab.org/~deepppi/embedding_repo/embedding_models/seqvec'
        options_link = repo_link +'/options.json'
        weights_link = repo_link +'/weights.hdf5'
        urllib.request.urlretrieve( options_link, options_path )
        urllib.request.urlretrieve( weights_link, weights_path )

    cuda_device = 0 if torch.cuda.is_available() and not cpu else -1
    return ElmoEmbedder( weight_file=weights_path, options_file=options_path, cuda_device=cuda_device ) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def test_fsdir_missing_space(tmp_path):
    fshome = os.environ["FREESURFER_HOME"]

    # fsaverage2 doesn't exist in source or destination, so can't copy
    with pytest.raises(FileNotFoundError):
        bintfs.BIDSFreeSurferDir(
            derivatives=str(tmp_path), spaces=["fsaverage2"], freesurfer_home=fshome
        ).run()

    subjects_dir = tmp_path / "freesurfer"

    # If fsaverage2 exists in the destination directory, no error is thrown
    Path.mkdir(subjects_dir / "fsaverage2")
    bintfs.BIDSFreeSurferDir(
        derivatives=str(tmp_path), spaces=["fsaverage2"], freesurfer_home=fshome
    ).run() 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def create_browser_output(fig, outfile, window):
    if outfile is None:
        outfile = "methylation_browser_{}.html".format(window.string)
    else:
        from pathlib import Path

        outfile = outfile.format(region=window.string)
        p = Path(outfile)
        Path.mkdir(p.parent, exist_ok=True, parents=True)

    if outfile.endswith(".html"):
        write_html_output(fig, outfile)
    else:
        try:
            fig.write_image(outfile)
        except ValueError as e:
            sys.stderr.write("\n\nERROR: creating the image in this file format failed.\n")
            sys.stderr.write("ERROR: creating in default html format instead.\n")
            sys.stderr.write("ERROR: additional packages required. Detailed error:\n")
            sys.stderr.write(str(e))
            write_html_output(fig, outfile) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def create_cache_dirs(cache_file: Path) -> None:
    """Create the ``__pycache__`` directories if needed for the ``cache_file``.

    Args:
        cache_file: Path to the cache_file

    Returns:
        None, creates the cache directory on disk if needed.
    """
    if not cache_file.parent.exists():
        # exists_ok shouldn't be needed with exists() check, suppressing FileExistsErrors
        Path.mkdir(cache_file.parent, parents=True, exist_ok=True) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def create_dir(name):
    if not Path.exists(name):
        Path.mkdir(name) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def create_directory(course_directory):
    course_dir = Path(course_directory)
    try:
        Path.mkdir(course_dir, mode=0o777)
    except FileExistsError as err:
        pass
    return course_dir 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def run_lithium(lithArgs, logPrefix, targetTime):  # pylint: disable=invalid-name,missing-param-doc,missing-return-doc
    # pylint: disable=missing-return-type-doc,missing-type-doc
    """Run Lithium as a subprocess: reduce to the smallest file that has at least the same unhappiness level.

    Returns a tuple of (lithlogfn, LITH_*, details).
    """
    deletableLithTemp = None  # pylint: disable=invalid-name
    if targetTime:
        # loop is being used by bot
        deletableLithTemp = tempfile.mkdtemp(prefix="fuzzbot-lithium")  # pylint: disable=invalid-name
        lithArgs = [f"--maxruntime={targetTime}", f"--tempdir={deletableLithTemp}"] + lithArgs
    else:
        # loop is being run standalone
        lithtmp = logPrefix.parent / f"{logPrefix.stem}-lith-tmp"
        Path.mkdir(lithtmp)
        lithArgs = [f"--tempdir={lithtmp}"] + lithArgs
    lithlogfn = (logPrefix.parent / f"{logPrefix.stem}-lith-out").with_suffix(".txt")
    print(f"Preparing to run Lithium, log file {lithlogfn}")
    print(" ".join(quote(str(x)) for x in runlithiumpy + lithArgs))
    with io.open(str(lithlogfn), "w", encoding="utf-8", errors="replace") as f:
        subprocess.run(runlithiumpy + lithArgs, check=False, stderr=subprocess.STDOUT, stdout=f)
    print("Done running Lithium")
    if deletableLithTemp:
        shutil.rmtree(deletableLithTemp)
    r = readLithiumResult(lithlogfn)  # pylint: disable=invalid-name

    with open(lithlogfn, "rb") as f_in:  # Replace the old gzip subprocess call
        with gzip.open(lithlogfn.with_suffix(".txt.gz"), "wb") as f_out:
            shutil.copyfileobj(f_in, f_out)
    lithlogfn.unlink()

    return r 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def make_dirs(dir_pathes: Union[str, List[str]]) -> None:
    """Create one or more directories."""
    if isinstance(dir_pathes, str):
        dir = Path(dir_pathes)
        Path.mkdir(dir, parents=True, exist_ok=True)

    elif isinstance(dir_pathes, list):
        for dir_path in dir_pathes:
            dir = Path(dir_path)
            Path.mkdir(dir, parents=True, exist_ok=True) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def crawl(site, crawl_page_limit=500, restart=True):
    print('Crawling up to %s pages from %s' % (crawl_page_limit, site))
    pickles = Path.cwd() / 'crawl'
    if not pickles.exists():
        Path.mkdir(pickles)
    unvisited_filename = pickles / 'unvisited.pkl'
    visited_filename = pickles / 'visited.pkl'
    alllinks_filename = pickles / 'alllinks.pkl'
    if restart:
        page_links = links(site)
        persist(set(page_links), unvisited_filename)
        persist(set(), visited_filename)
        persist(set(), alllinks_filename)
    msg = ''
    for i, alink in enumerate(persists(unvisited_filename)):
        unvisited = persists(unvisited_filename)
        unvisited.remove(alink)
        visited = persists(visited_filename)
        if crawl_page_limit != False and len(visited) > crawl_page_limit:
            print('Hit crawl page limit.')
            break
        visited.add(alink)
        new_links = links(alink.url)
        all_links = persists(alllinks_filename)
        all_links = all_links | set(new_links)
        persist(all_links, alllinks_filename)
        msg = "%s. Unvisited: %s, Visited: %s" % (i + 1, len(unvisited), len(visited))
        print(msg)
        for new_link in new_links:
            if new_link not in visited:
                visited.add(new_link)
                persist(visited, visited_filename)
        persist(unvisited, unvisited_filename)
    print('Done crawl')
    return all_links 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def visualize_setup(image_dir):
    assert viz.check_connection(), "visdom server is not working!"
    try:
        global result_dir
        Path.mkdir(Path(image_dir), parents=True, exist_ok=True)
        result_dir = image_dir
    except:
        raise() 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def mkdir(directory: Path) -> None:
    """
    Not Path.mkdir() for compatibility with Python 3.4.
    """
    makedirs(str(directory), exist_ok=True) 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def __init__(self, *, max_processes=20, loop=None, cache_dir=DFT_CACHE_DIR):
        self.semaphore = asyncio.Semaphore(value=max_processes, loop=loop)
        self.loop = loop
        if not cache_dir.exists():
            Path.mkdir(cache_dir)
        self.cache_dir = cache_dir 

# 需要導入模塊: from pathlib import Path [as 別名]
# 或者: from pathlib.Path import mkdir [as 別名]
def load():

    path = Path('.')
    global model
    global learn
    global classes
    model = CONFIG['model_name']
    # Check if we need to download Model file
    if CONFIG[model]['url'] != "":
        try:
            logging.info(f"Downloading model file from: {CONFIG[model]['url']}")
            urllib.request.urlretrieve(CONFIG[model]['url'], f"models/{model}.pth")
            logging.info(f"Downloaded model file and stored at path: models/{model}.pth")
        except HTTPError as e:
            logging.critical(f"Failed in downloading file from: {CONFIG[model]['url']}, Exception: '{e}'")
            sys.exit(4)

    init_data = ImageDataBunch.single_from_classes(
                                    path, CONFIG[model]['classes'], tfms=get_transforms(),
                                    size=CONFIG[model]['size']
                                ).normalize(imagenet_stats)
    classes = CONFIG[model]['classes']
    logging.info(f"Loading model: {CONFIG['model_name']}, architecture: {CONFIG[model]['arch']}, file: models/{model}.pth")
    learn = create_cnn(init_data, eval(f"models.{CONFIG[model]['arch']}"))
    learn.load(model, device=CONFIG[model]['device'])

    # Create direcotry to get feedback for this model
    Path.mkdir(Path(path_to(FEEDBACK_DIR, model)), parents=True, exist_ok=True)