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Utilities

taxotagger.utils

download_from_url 

download_from_url(
    url: str,
    root: str | PathLike,
    overwrite_existing: bool = False,
    http_method: str = "GET",
    allow_http_redirect: bool = True,
) -> str

Download data from the given URL.

The output file name is determined by the URL and saved to the given root directory.

Parameters:

  • url (str) –

    The URL of the file to download.

  • root (str | PathLike) –

    The directory to save the file to.

  • overwrite_existing (bool, default: False ) –

    Whether to overwrite the existing file. Defaults to False.

  • http_method (str, default: 'GET' ) –

    The HTTP method to use. Defaults to "GET".

  • allow_http_redirect (bool, default: True ) –

    Whether to allow HTTP redirects. Defaults to True.

Returns:

  • str –

    The path to the downloaded file.

Examples:

Download the MycoAI-CNN model to the current directory

>>> url = "https://zenodo.org/records/10904344/files/MycoAI-CNN.pt"
>>> download_model(url, ".")
Source code in src/taxotagger/utils.py 
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def download_from_url(
    url: str,
    root: str | PathLike,
    overwrite_existing: bool = False,
    http_method: str = "GET",
    allow_http_redirect: bool = True,
) -> str:
    """Download data from the given URL.

    The output file name is determined by the URL and saved to the given `root` directory.

    Args:
        url: The URL of the file to download.
        root: The directory to save the file to.
        overwrite_existing: Whether to overwrite the existing file. Defaults to False.
        http_method: The HTTP method to use. Defaults to "GET".
        allow_http_redirect: Whether to allow HTTP redirects. Defaults to True.

    Returns:
        The path to the downloaded file.

    Examples:
        Download the MycoAI-CNN model to the current directory
        >>> url = "https://zenodo.org/records/10904344/files/MycoAI-CNN.pt"
        >>> download_model(url, ".")
    """
    fpath = Path(root) / Path(url).name

    if fpath.exists() and not overwrite_existing:
        return str(fpath)

    with open(fpath, "wb") as fh:
        with httpx.stream(http_method, url, follow_redirects=allow_http_redirect) as response:
            response.raise_for_status()
            print(f"Downloading {url} to {root}")
            total = int(response.headers.get("Content-Length", 0))

            with Progress() as progress:
                task = progress.add_task(f"[hot_pink]Downloading {fpath.name}", total=total)
                for chunk in response.iter_bytes():
                    fh.write(chunk)
                    progress.update(task, advance=len(chunk))

    return str(fpath)

load_model 

load_model(model_id: str, config: ProjectConfig) -> Any

Load the pretrained model with pytorch for the given model identifier.

Available models are defined in the default PRETRAINED_MODELS. If the model {model_id}.pt is not found in the cache, it will be downloaded from the predefined URL.

Parameters:

  • model_id (str) –

    The identifier of the model to load.

  • config (ProjectConfig) –

    The configurations for the project.

Returns:

  • Any –

    The pretrained model loaded with torch.load.

Examples:

>>> config = Config()
>>> model = load_model("MycoAI-CNN", config)
Source code in src/taxotagger/utils.py 
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def load_model(
    model_id: str,
    config: ProjectConfig,
) -> Any:
    """Load the pretrained model with pytorch for the given model identifier.

    Available models are defined in the default
    [`PRETRAINED_MODELS`][taxotagger.defaults.PRETRAINED_MODELS].
    If the model `{model_id}.pt` is not found in the cache, it will be downloaded from the
    predefined URL.

    Args:
        model_id: The identifier of the model to load.
        config: The configurations for the project.

    Returns:
        The pretrained model loaded with `torch.load`.

    Examples:
        >>> config = Config()
        >>> model = load_model("MycoAI-CNN", config)
    """
    # validate the model id
    if model_id not in PRETRAINED_MODELS:
        raise ValueError(
            f"Invalid model id {model_id}. Available models are {PRETRAINED_MODELS.keys()}"
        )

    # use cache or download the model
    model_dir = config.mycoai_home
    os.makedirs(model_dir, exist_ok=True)
    model_path = Path(model_dir) / f"{model_id}.pt"
    if not model_path.exists() or config.force_reload:
        download_from_url(PRETRAINED_MODELS[model_id], model_dir, config.force_reload)

    logger.info(f"Loading model [magenta]{model_id}[/magenta] from {model_path}")
    model = torch.load(model_path, map_location=config.device)
    return model

parse_unite_fasta_header 

parse_unite_fasta_header(header: str) -> list[str]

Parse metadata from a UNITE FASTA file header.

The header of a FASTA file must follow the formats:

  • the UNITE format: 
    >Accession|k__Kingdom;p__Phylum;c__Class;o__Order;f__Family;g__Genus;s__Species|SHIdentifier
  • only the accession: 
    >Accession

Note that the SHIdentifier (Species Hypothesis identifier) is optional.

Parameters:

  • header (str) –

    A string representing the header of a FASTA file from the UNITE database.

Returns:

  • list[str] –

    A list of parsed metadata in the following order: [Accession, Kingdom, Phylum, Class, Order, Family, Genus, Species, SH_ID]. Empty strings are returned for missing metadata.

Examples:

Parse the header of a UNITE FASTA file

>>> header = ">MH855962|k__Fungi;p__Basidiomycota;c__Agaricomycetes;o__Corticiales;f__Corticiaceae;g__Waitea;s__Waitea_circinata|SH1011630.09FU"
>>> parse_unite_fasta_header(header)
['MH855962', 'Fungi', 'Basidiomycota', 'Agaricomycetes', 'Corticiales', 'Corticiaceae', 'Waitea', 'Waitea_circinata', 'SH1011630.09FU']

Parse the header of a FASTA file with only the accession

>>> process_unite_fasta_header(">MH855962")
['MH855962', '', '', '', '', '', '', '', '']
Source code in src/taxotagger/utils.py 
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def parse_unite_fasta_header(header: str) -> list[str]:
    """Parse metadata from a UNITE FASTA file header.

    The header of a FASTA file must follow the formats:

    - the UNITE format:
        ```
        >Accession|k__Kingdom;p__Phylum;c__Class;o__Order;f__Family;g__Genus;s__Species|SHIdentifier
        ```
    - only the accession:
        ```
        >Accession
        ```

    Note that the `SHIdentifier` (Species Hypothesis identifier) is optional.

    Args:
        header: A string representing the header of a FASTA file from the UNITE database.

    Returns:
        A list of parsed metadata in the following order:
            `[Accession, Kingdom, Phylum, Class, Order, Family, Genus, Species, SH_ID]`.
            Empty strings are returned for missing metadata.

    Examples:
        Parse the header of a UNITE FASTA file
        >>> header = ">MH855962|k__Fungi;p__Basidiomycota;c__Agaricomycetes;o__Corticiales;f__Corticiaceae;g__Waitea;s__Waitea_circinata|SH1011630.09FU"
        >>> parse_unite_fasta_header(header)
        ['MH855962', 'Fungi', 'Basidiomycota', 'Agaricomycetes', 'Corticiales', 'Corticiaceae', 'Waitea', 'Waitea_circinata', 'SH1011630.09FU']

        Parse the header of a FASTA file with only the accession
        >>> process_unite_fasta_header(">MH855962")
        ['MH855962', '', '', '', '', '', '', '', '']
    """
    result = [""] * 9

    # Split the header into sections based on '|'
    header = header.lstrip(">")
    sections = header.split("|")

    # The accession is the first part
    result[0] = sections[0]

    # If there is a taxonomy section, process it
    if len(sections) > 1:
        taxonomy_parts = sections[1].split(";")
        taxonomy_map = {
            "k__": 1,  # Kingdom
            "p__": 2,  # Phylum
            "c__": 3,  # Class
            "o__": 4,  # Order
            "f__": 5,  # Family
            "g__": 6,  # Genus
            "s__": 7,  # Species
        }
        for part in taxonomy_parts:
            prefix, value = part[:3], part[3:]
            if prefix in taxonomy_map:
                result[taxonomy_map[prefix]] = value

    # If there is an SH ID section, add it
    if len(sections) > 2:
        result[8] = sections[2]

    return result