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plugin ¤

Plugin classes of Nendo Core.

NendoAnalysisPlugin ¤

Bases: NendoPlugin

Basic class for nendo analysis plugins.

Analysis plugins are plugins that analyze a track or a collection and add metadata and other properties to the track or collection. Decorate your methods with @NendoAnalysisPlugin.plugin_data to add the return values of your methods as plugin data to the track or collection.

Decorate your methods with @NendoAnalysisPlugin.run_track to run your method on a track and use @NendoAnalysisPlugin.run_collection to run your method on a collection.

Examples:

from nendo import Nendo, NendoConfig

class MyPlugin(NendoAnalysisPlugin):
    ...

    @NendoAnalysisPlugin.plugin_data
    def my_plugin_data_function_one(self, track):
        # do something analysis on the track
        return {"key": "value"}

    @NendoAnalysisPlugin.plugin_data
    def my_plugin_data_function_two(self, track):
        # do some more analysis on the track
        return {"key": "value"}

    @NendoAnalysisPlugin.run_track
    def my_run_track_function(self, track):
        my_plugin_data_function_one(track)
        my_plugin_data_function_two(track)

plugin_type property ¤

plugin_type: str

Return type of plugin.

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

plugin_data staticmethod ¤

plugin_data(
    func: Callable[
        [NendoPlugin, NendoTrack], Dict[str, Any]
    ]
) -> Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]

Decorator to enrich a NendoTrack with data from a plugin.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]

Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def plugin_data(
    func: Callable[[NendoPlugin, NendoTrack], Dict[str, Any]],
) -> Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]:
    """Decorator to enrich a NendoTrack with data from a plugin.

    Args:
        func: Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]: The function to register.

    Returns:
        Callable[[NendoPlugin, NendoTrack], Dict[str, Any]]: The wrapped function.
    """

    def wrapper(self, track: NendoTrack):
        try:
            f_result = func(self, track)
        except NendoError as e:
            raise NendoPluginRuntimeError(
                f"Error running plugin function: {e}",
            ) from None
        for k, v in f_result.items():
            track.add_plugin_data(
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                key=str(k),
                value=v,
            )
        return f_result

    return wrapper

run_collection staticmethod ¤

run_collection(
    func: Callable[
        [NendoPlugin, NendoCollection, Any], None
    ]
) -> Callable[[NendoPlugin, Any], NendoCollection]

Decorator to register a function as a collection running function for a NendoAnalysisPlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoCollection, Any], None]

Callable[[NendoPlugin, NendoCollection, Any], None]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_collection(
    func: Callable[[NendoPlugin, NendoCollection, Any], None],
) -> Callable[[NendoPlugin, Any], NendoCollection]:
    """Decorator to register a function as a collection running function for a `NendoAnalysisPlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoCollection, Any], None]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> NendoCollection:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if isinstance(track_or_collection, NendoCollection):
            func(self, track_or_collection, **kwargs)
            return self.nendo_instance.library.get_collection(
                track_or_collection.id,
            )

        tmp_collection = self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track_or_collection.id],
            collection_type="temp",
        )
        func(self, tmp_collection, **kwargs)
        return self.nendo_instance.library.get_track(track_or_collection.id)

    return wrapper

run_track staticmethod ¤

run_track(
    func: Callable[[NendoPlugin, NendoTrack, Any], None]
) -> Callable[
    [NendoPlugin, Any], Union[NendoTrack, NendoCollection]
]

Decorator to register a function as a track running function for a NendoAnalysisPlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoTrack, Any], None]

Callable[[NendoPlugin, NendoTrack, Any], None]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_track(
    func: Callable[[NendoPlugin, NendoTrack, Any], None],
) -> Callable[[NendoPlugin, Any], Union[NendoTrack, NendoCollection]]:
    """Decorator to register a function as a track running function for a `NendoAnalysisPlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoTrack, Any], None]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoTrack, NendoCollection]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if isinstance(track_or_collection, NendoTrack):
            func(self, track_or_collection, **kwargs)
            return self.nendo_instance.library.get_track(track_or_collection.id)
        [func(self, track, **kwargs) for track in track_or_collection.tracks()]
        return self.nendo_instance.library.get_collection(track_or_collection.id)

    return wrapper

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper

NendoEffectPlugin ¤

Bases: NendoPlugin

Basic class for nendo effects plugins.

Effects plugins are plugins that add effects to tracks or collections. Decorate your methods with @NendoGeneratePlugin.run_track to run your method on a track, use @NendoGeneratePlugin.run_collection to run your method on a collection and use @NendoGeneratePlugin.run_signal to run your method on a signal.

Examples:

from nendo import Nendo, NendoConfig

class MyPlugin(NendoGeneratePlugin):
    ...

    @NendoAnalysisPlugin.run_signal
    def my_effect_function(self, signal, sr, arg_one="foo"):
        # add some effect to the signal
        new_signal = apply_effect(signal, sr, arg_one)

        return new_signal, sr

plugin_type property ¤

plugin_type: str

Return type of plugin.

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

run_collection staticmethod ¤

run_collection(
    func: Callable[
        [NendoPlugin, NendoCollection, Any], NendoCollection
    ]
) -> Callable[[NendoPlugin, Any], NendoCollection]

Decorator to register a function as a collection running function for a NendoEffectPlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoCollection, Any], NendoCollection]

Callable[[NendoPlugin, NendoCollection, Any], NendoCollection]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_collection(
    func: Callable[[NendoPlugin, NendoCollection, Any], NendoCollection],
) -> Callable[[NendoPlugin, Any], NendoCollection]:
    """Decorator to register a function as a collection running function for a `NendoEffectPlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoCollection, Any], NendoCollection]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> NendoCollection:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if isinstance(track_or_collection, NendoCollection):
            return func(self, track_or_collection, **kwargs)

        tmp_collection = self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track_or_collection.id],
            collection_type="temp",
        )
        return func(self, tmp_collection, **kwargs)

    return wrapper

run_signal staticmethod ¤

run_signal(
    func: Callable[
        [NendoPlugin, ndarray, int, Any],
        Tuple[ndarray, int],
    ]
) -> Callable[
    [NendoPlugin, Any], Union[NendoTrack, NendoCollection]
]

Decorator to register a function as a signal running function for a NendoEffectPlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, ndarray, int, Any], Tuple[ndarray, int]]

Callable[[NendoPlugin, np.ndarray, int, Any], Tuple[np.ndarray, int]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoTrack]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_signal(
    func: Callable[[NendoPlugin, np.ndarray, int, Any], Tuple[np.ndarray, int]],
) -> Callable[[NendoPlugin, Any], Union[NendoTrack, NendoCollection]]:
    """Decorator to register a function as a signal running function for a `NendoEffectPlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, np.ndarray, int, Any], Tuple[np.ndarray, int]]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoTrack]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoTrack, NendoCollection]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        processed_tracks = []
        if isinstance(track_or_collection, NendoTrack):
            signal, sr = track_or_collection.signal, track_or_collection.sr
            new_signal, new_sr = func(self, signal, sr, **kwargs)

            # TODO update track instead of adding a new one to the library
            return self.nendo_instance.library.add_related_track_from_signal(
                new_signal,
                sr,
                related_track_id=track_or_collection.id,
            )

        for track in track_or_collection.tracks():
            new_signal, new_sr = func(
                self,
                track.signal,
                track.sr,
                **kwargs,
            )

            # TODO update track instead of adding a new one to the library
            processed_tracks.append(
                self.nendo_instance.library.add_related_track_from_signal(
                    new_signal,
                    track.sr,
                    related_track_id=track.id,
                ),
            )

        return self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track.id for track in processed_tracks],
            collection_type="temp",
        )

    return wrapper

run_track staticmethod ¤

run_track(
    func: Callable[
        [NendoPlugin, NendoTrack, Any],
        Union[NendoTrack, List[NendoTrack]],
    ]
) -> Callable[[NendoPlugin, Any], NendoTrack]

Decorator to register a function as a track running function for a NendoEffectPlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoTrack, Any], Union[NendoTrack, List[NendoTrack]]]

Callable[[NendoPlugin, NendoTrack, Any], Union[NendoTrack, List[NendoTrack]]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoTrack]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_track(
    func: Callable[
        [NendoPlugin, NendoTrack, Any],
        Union[NendoTrack, List[NendoTrack]],
    ],
) -> Callable[[NendoPlugin, Any], NendoTrack]:
    """Decorator to register a function as a track running function for a `NendoEffectPlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoTrack, Any], Union[NendoTrack, List[NendoTrack]]]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoTrack]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoTrack, NendoCollection]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if isinstance(track_or_collection, NendoTrack):
            return func(self, track_or_collection, **kwargs)

        processed_tracks = [
            func(self, track, **kwargs) for track in track_or_collection.tracks()
        ]
        return self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track.id for track in processed_tracks],
            collection_type="temp",
        )

    return wrapper

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper

NendoEmbeddingPlugin ¤

Bases: NendoPlugin

Basic class for nendo embedding plugins.

Embedding plugins are plugins that are used by nendo to embed NendoTracks or query strings into an n-dimensional vector space. To implement such a plugin, at least one function should exist that is annotated with the @run_text decorator. Alternatively or in addition to, any of the other decorators, @run_signal_and_text, @run_track, and @run_collection can be implemented and will be used when the plugin is directly called.

plugin_type property ¤

plugin_type: str

Return type of plugin.

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

run_collection staticmethod ¤

run_collection(
    func: Callable[
        [NendoPlugin, NendoCollection, Any], None
    ]
) -> Callable[
    [NendoPlugin, Any],
    Union[NendoEmbedding, List[NendoEmbedding]],
]

Decorator to register a function to create a NendoEmbedding from a collection.

This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoCollection, Any], None]

Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_collection(
    func: Callable[[NendoPlugin, NendoCollection, Any], None],
) -> Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]:
    """Decorator to register a function to create a `NendoEmbedding` from a collection.

    This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

    Args:
        func: Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoEmbedding, List[NendoEmbedding]]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        # TODO we currently have no way of handling embeddings of collections,
        # so we just return the function value(s) directly
        if isinstance(track_or_collection, NendoCollection):
            return func(self, track_or_collection, **kwargs)
        if track_or_collection is None:
            signal = kwargs.get("signal", None)
            sr = kwargs.get("sr", None)
            text = kwargs.get("text", None)
            track_or_collection = self.nendo_instance.library.add_track_from_signal(
                signal=signal if signal is not None else np.array([]),
                sr=sr if sr is not None else self.config.default_sr,
                meta={"text": text},
            )
            kwargs.pop("signal", None)
            kwargs.pop("sr", None)
            kwargs.pop("text", None)

        tmp_collection = self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track_or_collection.id],
            collection_type="temp",
        )
        return func(self, tmp_collection, **kwargs)

    return wrapper

run_signal_and_text staticmethod ¤

run_signal_and_text(
    func: Callable[
        [NendoPlugin, array, int, str, Any], None
    ]
) -> Callable[
    [NendoPlugin, Any],
    Union[NendoEmbedding, List[NendoEmbedding]],
]

Decorator to register a function as a function to create a NendoEmbedding from a given signal, sr, and text.

This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, array, int, str, Any], None]

Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_signal_and_text(
    func: Callable[[NendoPlugin, np.array, int, str, Any], None],
) -> Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]:
    """Decorator to register a function as a function to create a `NendoEmbedding` from a given signal, sr, and text.

    This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

    Args:
        func: Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoEmbedding, List[NendoEmbedding]]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if track_or_collection is None:
            signal = kwargs.get("signal", None)
            sr = kwargs.get("sr", None)
            text, embedding_vector = func(self, **kwargs)
            new_track = self.nendo_instance.library.add_track_from_signal(
                signal=signal if signal is not None else np.array([]),
                sr=sr if sr is not None else self.config.default_sr,
            )
            embedding = NendoEmbedding(
                track_id=new_track.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            return embedding
        if isinstance(track_or_collection, NendoTrack):
            kwargs["signal"] = track_or_collection.signal
            kwargs["sr"] = track_or_collection.sr
            kwargs["text"] = self.track_to_text(track=track_or_collection)
            text, embedding_vector = func(self, **kwargs)
            embedding = NendoEmbedding(
                track_id=track_or_collection.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            return embedding
        embeddings = []
        for track in track_or_collection.tracks():
            kwargs["signal"] = track.signal
            kwargs["sr"] = track.sr
            kwargs["text"] = self.track_to_text(track=track)
            text, embedding_vector = func(self, **kwargs)
            embedding = NendoEmbedding(
                track_id=track.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            embeddings.append(embedding)
        return embeddings

    return wrapper

run_text staticmethod ¤

run_text(
    func: Callable[
        [NendoPlugin, str, Any], Tuple[str, ndarray]
    ]
) -> Callable[
    [NendoPlugin, Any],
    Union[
        Tuple[str, ndarray],
        NendoEmbedding,
        List[NendoEmbedding],
    ],
]

Decorator to register a function that embeds a given text string into a vector space.

This decorator wraps the function and allows a plugin user to call the plugin with either a text, a track, or a collection.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, str, Any], Tuple[str, ndarray]]

Callable[[NendoPlugin, str, Any], Tuple[str, np.ndarray]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], Union[[str, ndarray], NendoEmbedding, List[NendoEmbedding]]]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_text(
    func: Callable[[NendoPlugin, str, Any], Tuple[str, np.ndarray]],
) -> Callable[
    [NendoPlugin, Any],
    Union[Tuple[str, np.ndarray], NendoEmbedding, List[NendoEmbedding]],
]:
    """Decorator to register a function that embeds a given text string into a vector space.

    This decorator wraps the function and allows a plugin user to call the plugin with either a text, a track, or a collection.

    Args:
        func: Callable[[NendoPlugin, str, Any], Tuple[str, np.ndarray]]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], Union[[str, np.ndarray], NendoEmbedding, List[NendoEmbedding]]]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(
        self,
        **kwargs: Any,
    ) -> Union[Tuple[str, np.ndarray], NendoEmbedding, List[NendoEmbedding]]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if track_or_collection is None:
            kwargs.pop("signal", None)
            kwargs.pop("sr", None)
            text, embedding_vector = func(self, **kwargs)
            return text, embedding_vector
        if isinstance(track_or_collection, NendoTrack):
            text, embedding_vector = func(
                self,
                text=self.track_to_text(track=track_or_collection),
                **kwargs,
            )
            embedding = NendoEmbedding(
                track_id=track_or_collection.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            return embedding
        processed_tracks = []
        for track in track_or_collection.tracks():
            text, embedding_vector = func(
                self,
                text=self.track_to_text(track=track),
                **kwargs,
            )
            embedding = NendoEmbedding(
                track_id=track.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            processed_tracks.append(embedding)
        return processed_tracks

    return wrapper

run_track staticmethod ¤

run_track(
    func: Callable[[NendoPlugin, NendoTrack, Any], None]
) -> Callable[
    [NendoPlugin, Any],
    Union[NendoEmbedding, List[NendoEmbedding]],
]

Decorator to register a function to create a NendoEmbedding from a track.

This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, NendoTrack, Any], None]

Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_track(
    func: Callable[[NendoPlugin, NendoTrack, Any], None],
) -> Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]:
    """Decorator to register a function to create a `NendoEmbedding` from a track.

    This decorator wraps the function and allows a plugin user to call the plugin with either a set of (signal, sr, text), a track or a collection.

    Args:
        func: Callable[[NendoPlugin, np.array, int, str, Any], None]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], Union[NendoEmbedding, List[NendoEmbedding]]]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoEmbedding, List[NendoEmbedding]]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if isinstance(track_or_collection, NendoTrack):
            text, embedding_vector = func(self, track_or_collection, **kwargs)
            embedding = NendoEmbedding(
                track_id=track_or_collection.id,
                user_id=self.nendo_instance.library.user.id,
                plugin_name=self.plugin_name,
                plugin_version=self.plugin_version,
                text=text,
                embedding=embedding_vector,
            )
            try:
                if self.config.replace_plugin_data is True:
                    existing_embeddings = (
                        self.nendo_instance.library.get_embeddings(
                            track_id=track_or_collection.id,
                            plugin_name=self.plugin_name,
                            plugin_version=self.plugin_version,
                        )
                    )
                    if len(existing_embeddings) > 0:
                        embedding_update = existing_embeddings[0]
                        embedding_update.text = text
                        embedding_update.embedding = embedding_vector
                        embedding = self.nendo_instance.library.update_embedding(
                            embedding_update,
                        )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            except AttributeError as e:  # noqa: F841
                self.logger.error(
                    "Failed to save the embedding to the library. "
                    "Please use a library plugin with vector support to "
                    "enable automatic storing of embeddings.",
                )
            return embedding
        if isinstance(track_or_collection, NendoCollection):
            embeddings = []
            for track in track_or_collection.tracks():
                text, embedding_vector = func(self, track, **kwargs)
                embedding = NendoEmbedding(
                    track_id=track.id,
                    user_id=self.nendo_instance.library.user.id,
                    plugin_name=self.plugin_name,
                    plugin_version=self.plugin_version,
                    text=text,
                    embedding=embedding_vector,
                )
                try:
                    if self.config.replace_plugin_data is True:
                        existing_embeddings = (
                            self.nendo_instance.library.get_embeddings(
                                track_id=track_or_collection.id,
                                plugin_name=self.plugin_name,
                                plugin_version=self.plugin_version,
                            )
                        )
                        if len(existing_embeddings) > 0:
                            embedding_update = existing_embeddings[0]
                            embedding_update.text = text
                            embedding_update.embedding = embedding_vector
                            embedding = (
                                self.nendo_instance.library.update_embedding(
                                    embedding_update,
                                )
                            )
                        else:
                            embedding = self.nendo_instance.library.add_embedding(
                                embedding=embedding,
                            )
                    else:
                        embedding = self.nendo_instance.library.add_embedding(
                            embedding=embedding,
                        )
                except AttributeError as e:  # noqa: F841
                    self.logger.error(
                        "Failed to save the embedding to the library. "
                        "Please use a library plugin with vector support to "
                        "enable automatic storing of embeddings.",
                    )
                embeddings.append(embedding)
            return embeddings
        signal = kwargs.get("signal", None)
        sr = kwargs.get("sr", None)
        text = kwargs.get("text", None)
        new_track = self.nendo_instance.library.add_track_from_signal(
            signal=signal if signal is not None else np.array([]),
            sr=sr if sr is not None else self.config.default_sr,
            meta={"text": text},
        )
        kwargs.pop("signal", None)
        kwargs.pop("sr", None)
        kwargs.pop("text", None)
        text, embedding_vector = func(self, track=new_track, **kwargs)
        embedding = NendoEmbedding(
            track_id=new_track.id,
            user_id=self.nendo_instance.library.user.id,
            plugin_name=self.plugin_name,
            plugin_version=self.plugin_version,
            text=text,
            embedding=embedding_vector,
        )
        try:
            if self.config.replace_plugin_data is True:
                existing_embeddings = self.nendo_instance.library.get_embeddings(
                    track_id=track_or_collection.id,
                    plugin_name=self.plugin_name,
                    plugin_version=self.plugin_version,
                )
                if len(existing_embeddings) > 0:
                    embedding_update = existing_embeddings[0]
                    embedding_update.text = text
                    embedding_update.embedding = embedding_vector
                    embedding = self.nendo_instance.library.update_embedding(
                        embedding_update,
                    )
                else:
                    embedding = self.nendo_instance.library.add_embedding(
                        embedding=embedding,
                    )
            else:
                embedding = self.nendo_instance.library.add_embedding(
                    embedding=embedding,
                )
        except AttributeError as e:  # noqa: F841
            self.logger.error(
                "Failed to save the embedding to the library. "
                "Please use a library plugin with vector support to "
                "enable automatic storing of embeddings.",
            )
        return embedding

    return wrapper

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper

track_to_text ¤

track_to_text(track: NendoTrack) -> str

Convert the given track into a string.

Can be used by embedding plugins to avoid implementing their own track-to-string conversion.

Parameters:

Name Type Description Default
track NendoTrack

The track to be converted.

 required

Returns:

Type Description
str

The string representation of the given track.
Source code in src/nendo/schema/plugin.py 
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def track_to_text(self, track: NendoTrack) -> str:
    """Convert the given track into a string.

    Can be used by embedding plugins to avoid implementing their own
    track-to-string conversion.

    Args:
        track (NendoTrack): The track to be converted.

    Returns:
        str: The string representation of the given track.
    """
    text = ""
    meta_items = [
        "artist",
        "album",
        "title",
        "genre",
        "year",
        "duration",
        "content",
    ]
    for i in meta_items:
        if track.get_meta(i) is not None:
            text += f"{i}: {track.get_meta(i)}; "
    for pd in track.get_plugin_data():
        text += f"{pd.key}: {pd.value}; "
    return text

NendoGeneratePlugin ¤

Bases: NendoPlugin

Basic class for nendo generate plugins.

Generate plugins are plugins that generate new tracks or collections, either from scratch or based on existing tracks or collections. Decorate your methods with @NendoGeneratePlugin.run_track to run your method on a track, use @NendoGeneratePlugin.run_collection to run your method on a collection and use @NendoGeneratePlugin.run_signal to run your method on a signal.

Examples:

from nendo import Nendo, NendoConfig

class MyPlugin(NendoGeneratePlugin):
    ...

    @NendoAnalysisPlugin.run_track
    def my_generate_track_function(self, track, arg_one="foo"):
        # generate some new audio

        # add audio to the nendo library
        return self.nendo_instance.library.add_related_track_from_signal(
            signal,
            sr,
            related_track_id=track.id,
        )

plugin_type property ¤

plugin_type: str

Return type of plugin.

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

run_collection staticmethod ¤

run_collection(
    func: Callable[
        [NendoPlugin, Optional[NendoCollection], Any],
        NendoCollection,
    ]
) -> Callable[[NendoPlugin, Any], NendoCollection]

Decorator to register a function as a collection running function for a NendoGeneratePlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, Optional[NendoCollection], Any], NendoCollection]

Callable[[NendoPlugin, NendoCollection, Any], NendoCollection]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_collection(
    func: Callable[[NendoPlugin, Optional[NendoCollection], Any], NendoCollection],
) -> Callable[[NendoPlugin, Any], NendoCollection]:
    """Decorator to register a function as a collection running function for a `NendoGeneratePlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoCollection, Any], NendoCollection]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> NendoCollection:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if track_or_collection is None:
            return func(self, **kwargs)

        if isinstance(track_or_collection, NendoCollection):
            return func(self, track_or_collection, **kwargs)

        tmp_collection = self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track_or_collection.id],
            collection_type="temp",
        )
        return func(self, tmp_collection, **kwargs)

    return wrapper

run_signal staticmethod ¤

run_signal(
    func: Callable[
        [
            NendoPlugin,
            Optional[ndarray],
            Optional[int],
            Any,
        ],
        Tuple[ndarray, int],
    ]
) -> Callable[
    [NendoPlugin, Any], Union[NendoTrack, NendoCollection]
]

Decorator to register a function as a signal running function for a NendoGeneratePlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, Optional[ndarray], Optional[int], Any], Tuple[ndarray, int]]

Callable[[NendoPlugin, np.ndarray, int, Any], Tuple[np.ndarray, int]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_signal(
    func: Callable[
        [NendoPlugin, Optional[np.ndarray], Optional[int], Any],
        Tuple[np.ndarray, int],
    ],
) -> Callable[[NendoPlugin, Any], Union[NendoTrack, NendoCollection]]:
    """Decorator to register a function as a signal running function for a `NendoGeneratePlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, np.ndarray, int, Any], Tuple[np.ndarray, int]]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoTrack, NendoCollection]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if track_or_collection is None:
            signal, sr = func(self, **kwargs)
            return self.nendo_instance.library.add_track_from_signal(
                signal,
                sr,
            )
        if isinstance(track_or_collection, NendoTrack):
            signal, sr = track_or_collection.signal, track_or_collection.sr
            new_signal, new_sr = func(self, signal, sr, **kwargs)
            return self.nendo_instance.library.add_related_track_from_signal(
                new_signal,
                sr,
                related_track_id=track_or_collection.id,
            )
        processed_tracks = []
        for track in track_or_collection.tracks():
            new_signal, new_sr = func(
                self,
                track.signal,
                track.sr,
                **kwargs,
            )
            processed_tracks.append(
                self.nendo_instance.library.add_related_track_from_signal(
                    new_signal,
                    track.sr,
                    related_track_id=track.id,
                ),
            )
        return self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track.id for track in processed_tracks],
            collection_type="temp",
        )

    return wrapper

run_track staticmethod ¤

run_track(
    func: Callable[
        [NendoPlugin, Optional[NendoTrack], Any],
        Union[NendoTrack, List[NendoTrack]],
    ]
) -> Callable[
    [NendoPlugin, Any], Union[NendoTrack, NendoCollection]
]

Decorator to register a function as a track running function for a NendoGeneratePlugin.

This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

Parameters:

Name Type Description Default
func Callable[[NendoPlugin, Optional[NendoTrack], Any], Union[NendoTrack, List[NendoTrack]]]

Callable[[NendoPlugin, NendoTrack, Any], Union[NendoTrack, List[NendoTrack]]]: The function to register.

 required

Returns:

Type Description
Callable[[NendoPlugin, Any], NendoCollection]

The wrapped function.
Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_track(
    func: Callable[
        [NendoPlugin, Optional[NendoTrack], Any],
        Union[NendoTrack, List[NendoTrack]],
    ],
) -> Callable[[NendoPlugin, Any], Union[NendoTrack, NendoCollection]]:
    """Decorator to register a function as a track running function for a `NendoGeneratePlugin`.

    This decorator wraps the function and allows a plugin user to call the plugin with either a collection or a track.

    Args:
        func: Callable[[NendoPlugin, NendoTrack, Any], Union[NendoTrack, List[NendoTrack]]]: The function to register.

    Returns:
        Callable[[NendoPlugin, Any], NendoCollection]: The wrapped function.
    """

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[NendoTrack, NendoCollection]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        processed_tracks = []
        if track_or_collection is None:
            track = func(self, **kwargs)

            # may be multiple tracks as a result
            if not isinstance(track, list):
                return track
            processed_tracks.extend(track)
        elif isinstance(track_or_collection, NendoTrack):
            track = func(self, track_or_collection, **kwargs)

            # may be multiple tracks as a result
            if not isinstance(track, list):
                return track
            processed_tracks.extend(track)
        else:
            for track in track_or_collection.tracks():
                processed_track = func(self, track, **kwargs)

                # may be multiple tracks as a result
                if isinstance(processed_track, list):
                    processed_tracks.extend(processed_track)
                else:
                    processed_tracks.append(processed_track)

        return self.nendo_instance.library.add_collection(
            name="tmp",
            track_ids=[track.id for track in processed_tracks],
            collection_type="temp",
        )

    return wrapper

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper

NendoLibraryPlugin ¤

Bases: NendoPlugin

Basic class for nendo library plugins.

plugin_type property ¤

plugin_type: str

Return type of plugin.

add_collection abstractmethod ¤

add_collection(
    name: str,
    user_id: Optional[Union[str, UUID]] = None,
    track_ids: Optional[List[Union[str, UUID]]] = None,
    description: str = "",
    collection_type: str = "collection",
    visibility: Visibility = Visibility.private,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection

Creates a new collection and saves it into the DB.

Parameters:

Name Type Description Default
track_ids List[Union[str, UUID]]

List of track ids to be added to the collection.

 None
name str

Name of the collection.

 required
user_id UUID

The ID of the user adding the collection.

 None
description str

Description of the collection.

 ''
collection_type str

Type of the collection. Defaults to "collection".

 'collection'
visibility str

Visibility of the track in multi-user settings. Defaults to "private".

 private
meta Dict[str, Any]

Metadata of the collection.

 None

Returns:

Type Description
NendoCollection

The newly created NendoCollection object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_collection(
    self,
    name: str,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    track_ids: Optional[List[Union[str, uuid.UUID]]] = None,
    description: str = "",
    collection_type: str = "collection",
    visibility: Visibility = Visibility.private,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection:
    """Creates a new collection and saves it into the DB.

    Args:
        track_ids (List[Union[str, uuid.UUID]]): List of track ids
            to be added to the collection.
        name (str): Name of the collection.
        user_id (UUID, optional): The ID of the user adding the collection.
        description (str): Description of the collection.
        collection_type (str): Type of the collection. Defaults to "collection".
        visibility (str, optional): Visibility of the track in multi-user settings.
            Defaults to "private".
        meta (Dict[str, Any]): Metadata of the collection.

    Returns:
        schema.NendoCollection: The newly created NendoCollection object.
    """
    raise NotImplementedError

add_plugin_data abstractmethod ¤

add_plugin_data(
    track_id: Union[str, UUID],
    key: str,
    value: Any,
    plugin_name: str,
    plugin_version: Optional[str] = None,
    user_id: Optional[Union[str, UUID]] = None,
    replace: Optional[bool] = None,
) -> NendoPluginData

Add plugin data to a NendoTrack and persist changes into the DB.

Parameters:

Name Type Description Default
track_id Union[str, UUID]

ID of the track to which the plugin data should be added.

 required
key str

Key under which to save the data.

 required
value str

Data to save.

 required
plugin_name str

Name of the plugin.

 required
plugin_version str

Version of the plugin. Defaults to None in which case the version will be inferred from the currently registered version of the plugin.

 None
user_id Union[str, UUID]

ID of user adding the plugin data.

 None
replace bool

Flag that determines whether the last existing data point for the given plugin name and -version is overwritten or not. Defaults to False.

 None

Returns:

Type Description
NendoPluginData

The saved plugin data as a NendoPluginData object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_plugin_data(
    self,
    track_id: Union[str, uuid.UUID],
    key: str,
    value: Any,
    plugin_name: str,
    plugin_version: Optional[str] = None,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    replace: Optional[bool] = None,
) -> NendoPluginData:
    """Add plugin data to a NendoTrack and persist changes into the DB.

    Args:
        track_id (Union[str, uuid.UUID]): ID of the track to which
            the plugin data should be added.
        key (str): Key under which to save the data.
        value (str): Data to  save.
        plugin_name (str): Name of the plugin.
        plugin_version (str, optional): Version of the plugin. Defaults to None
            in which case the version will be inferred from the currently
            registered version of the plugin.
        user_id (Union[str, uuid.UUID], optional): ID of user adding the
            plugin data.
        replace (bool, optional): Flag that determines whether
            the last existing data point for the given plugin name and -version
            is overwritten or not. Defaults to False.

    Returns:
        NendoPluginData: The saved plugin data as a NendoPluginData object.
    """
    raise NotImplementedError

add_related_collection(
    track_ids: List[Union[str, UUID]],
    collection_id: Union[str, UUID],
    name: str,
    description: str = "",
    user_id: Optional[Union[str, UUID]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection

Add a collection that is related to another NendoCollection.

Add a new collection with a relationship to and from the collection with the given collection_id.

Parameters:

Name Type Description Default
track_ids List[Union[str, UUID]]

List of track ids.

 required
collection_id Union[str, UUID]

Existing collection id.

 required
name str

Name of the new related collection.

 required
description str

Description of the new related collection.

 ''
user_id UUID

The ID of the user adding the collection.

 None
relationship_type str

Type of the relationship.

 'relationship'
meta Dict[str, Any]

Meta of the new related collection.

 None

Returns:

Type Description
NendoCollection

The newly added NendoCollection object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_related_collection(
    self,
    track_ids: List[Union[str, uuid.UUID]],
    collection_id: Union[str, uuid.UUID],
    name: str,
    description: str = "",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection:
    """Add a collection that is related to another `NendoCollection`.

    Add a new collection with a relationship to and from the collection
    with the given collection_id.

    Args:
        track_ids (List[Union[str, uuid.UUID]]): List of track ids.
        collection_id (Union[str, uuid.UUID]): Existing collection id.
        name (str): Name of the new related collection.
        description (str): Description of the new related collection.
        user_id (UUID, optional): The ID of the user adding the collection.
        relationship_type (str): Type of the relationship.
        meta (Dict[str, Any]): Meta of the new related collection.

    Returns:
        schema.NendoCollection: The newly added NendoCollection object.
    """
    raise NotImplementedError

add_related_track(
    file_path: Union[FilePath, str],
    related_track_id: Union[str, UUID],
    track_type: str = "str",
    user_id: Optional[Union[str, UUID]] = None,
    track_meta: Optional[Dict[str, Any]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack

Add track that is related to another NendoTrack.

Add the track found in the given path to the library and create a relationship in the new track that points to the track identified by related_to.

Parameters:

Name Type Description Default
file_path Union[FilePath, str]

Path to the file to add as track.

 required
related_track_id Union[str, UUID]

ID of the related track.

 required
track_type str

Track type. Defaults to "track".

 'str'
user_id Union[str, UUID]

ID of the user adding the track.

 None
track_meta dict

Dictionary containing the track metadata.

 None
relationship_type str

Type of the relationship. Defaults to "relationship".

 'relationship'
meta dict

Dictionary containing metadata about the relationship. Defaults to None in which case it'll be set to {}.

 None

Returns:

Type Description
NendoTrack

The track that was added to the Library
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_related_track(
    self,
    file_path: Union[FilePath, str],
    related_track_id: Union[str, uuid.UUID],
    track_type: str = "str",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    track_meta: Optional[Dict[str, Any]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack:
    """Add track that is related to another `NendoTrack`.

    Add the track found in the given path to the library and create a relationship
    in the new track that points to the track identified by related_to.

    Args:
        file_path (Union[FilePath, str]): Path to the file to add as track.
        related_track_id (Union[str, uuid.UUID]): ID of the related track.
        track_type (str, optional): Track type. Defaults to "track".
        user_id (Union[str, UUID], optional): ID of the user adding the track.
        track_meta (dict, optional): Dictionary containing the track metadata.
        relationship_type (str, optional): Type of the relationship.
            Defaults to "relationship".
        meta (dict, optional): Dictionary containing metadata about
            the relationship. Defaults to None in which case it'll be set to {}.

    Returns:
        NendoTrack: The track that was added to the Library
    """
    raise NotImplementedError

add_related_track_from_signal(
    signal: ndarray,
    sr: int,
    related_track_id: Union[str, UUID],
    track_type: str = "track",
    user_id: Optional[Union[str, UUID]] = None,
    track_meta: Optional[Dict[str, Any]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack

Add signal as track that is related to another NendoTrack.

Add the track represented by the provided signal to the library and create a relationship in the new track that points to the track passed as related_to.

Parameters:

Name Type Description Default
signal ndarray

Waveform of the track in numpy array form.

 required
sr int

Sampling rate of the waveform.

 required
related_track_id Union[str, UUID]

ID to which the relationship should point to.

 required
track_type str

Track type. Defaults to "track".

 'track'
user_id Union[str, UUID]

ID of the user adding the track.

 None
track_meta (dict

Dictionary containing the track metadata.

 None
relationship_type str

Type of the relationship. Defaults to "relationship".

 'relationship'
meta dict

Dictionary containing metadata about the relationship. Defaults to None in which case it'll be set to {}.

 None

Returns:

Type Description
NendoTrack

The added track with the relationship.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_related_track_from_signal(
    self,
    signal: np.ndarray,
    sr: int,
    related_track_id: Union[str, uuid.UUID],
    track_type: str = "track",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    track_meta: Optional[Dict[str, Any]] = None,
    relationship_type: str = "relationship",
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack:
    """Add signal as track that is related to another `NendoTrack`.

    Add the track represented by the provided signal to the library and create a
    relationship in the new track that points to the track passed as related_to.

    Args:
        signal (np.ndarray): Waveform of the track in numpy array form.
        sr (int): Sampling rate of the waveform.
        related_track_id (Union[str, uuid.UUID]): ID to which the relationship
            should point to.
        track_type (str, optional): Track type. Defaults to "track".
        user_id (Union[str, uuid.UUID], optional): ID of the user adding the track.
        track_meta  (dict, optional): Dictionary containing the track metadata.
        relationship_type (str, optional): Type of the relationship.
            Defaults to "relationship".
        meta (dict, optional): Dictionary containing metadata about
            the relationship. Defaults to None in which case it'll be set to {}.

    Returns:
        NendoTrack: The added track with the relationship.
    """
    raise NotImplementedError

add_track abstractmethod ¤

add_track(
    file_path: Union[FilePath, str],
    track_type: str = "track",
    copy_to_library: Optional[bool] = None,
    skip_duplicate: Optional[bool] = None,
    user_id: Optional[UUID] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack

Add the track given by path to the library.

Parameters:

Name Type Description Default
file_path Union[FilePath, str]

Path to the file to add as track.

 required
track_type str

Type of the track. Defaults to "track".

 'track'
copy_to_library bool

Flag that specifies whether the file should be copied into the library directory. Defaults to None.

 None
skip_duplicate bool

Flag that specifies whether a file should be added that already exists in the library, based on its file checksum. Defaults to None.

 None
user_id UUID

ID of user adding the track.

 None
meta dict

Metadata to attach to the track upon adding.

 None

Returns:

Type Description
NendoTrack

The track that was added to the library.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_track(
    self,
    file_path: Union[FilePath, str],
    track_type: str = "track",
    copy_to_library: Optional[bool] = None,
    skip_duplicate: Optional[bool] = None,
    user_id: Optional[uuid.UUID] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack:
    """Add the track given by path to the library.

    Args:
        file_path (Union[FilePath, str]): Path to the file to add as track.
        track_type (str): Type of the track. Defaults to "track".
        copy_to_library (bool, optional): Flag that specifies whether
            the file should be copied into the library directory.
            Defaults to None.
        skip_duplicate (bool, optional): Flag that specifies whether a
            file should be added that already exists in the library, based on its
            file checksum. Defaults to None.
        user_id (UUID, optional): ID of user adding the track.
        meta (dict, optional): Metadata to attach to the track upon adding.

    Returns:
        NendoTrack: The track that was added to the library.
    """
    raise NotImplementedError

add_track_from_signal abstractmethod ¤

add_track_from_signal(
    signal: ndarray,
    sr: int,
    track_type: str = "track",
    user_id: Optional[UUID] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack

Add a track to the library that is described by the given signal.

Parameters:

Name Type Description Default
signal ndarray

The numpy array containing the audio signal.

 required
sr int

Sample rate

 required
track_type str

Track type. Defaults to "track".

 'track'
user_id UUID

The ID of the user adding the track.

 None
meta Dict[str, Any]

Track metadata. Defaults to {}.

 None

Returns:

Type Description
NendoTrack

The added NendoTrack
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_track_from_signal(
    self,
    signal: np.ndarray,
    sr: int,
    track_type: str = "track",
    user_id: Optional[uuid.UUID] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoTrack:
    """Add a track to the library that is described by the given signal.

    Args:
        signal (np.ndarray): The numpy array containing the audio signal.
        sr (int): Sample rate
        track_type (str): Track type. Defaults to "track".
        user_id (UUID, optional): The ID of the user adding the track.
        meta (Dict[str, Any], optional): Track metadata. Defaults to {}.

    Returns:
        schema.NendoTrack: The added NendoTrack
    """
    raise NotImplementedError

add_track_to_collection abstractmethod ¤

add_track_to_collection(
    track_id: Union[str, UUID],
    collection_id: Union[str, UUID],
    position: Optional[int] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection

Creates a relationship from the track to the collection.

Parameters:

Name Type Description Default
collection_id Union[str, UUID]

Collection id.

 required
track_id Union[str, UUID]

Track id.

 required

Returns:

Type Description
NendoCollection

The updated NendoCollection object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_track_to_collection(
    self,
    track_id: Union[str, uuid.UUID],
    collection_id: Union[str, uuid.UUID],
    position: Optional[int] = None,
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection:
    """Creates a relationship from the track to the collection.

    Args:
        collection_id (Union[str, uuid.UUID]): Collection id.
        track_id (Union[str, uuid.UUID]): Track id.

    Returns:
        schema.NendoCollection: The updated NendoCollection object.
    """
    raise NotImplementedError

add_tracks abstractmethod ¤

add_tracks(
    path: Union[str, DirectoryPath],
    track_type: str = "track",
    user_id: Optional[Union[str, UUID]] = None,
    copy_to_library: Optional[bool] = None,
    skip_duplicate: bool = True,
) -> NendoCollection

Scan the provided path and upsert the information into the library.

Parameters:

Name Type Description Default
path Union[DirectoryPath, str]

Path to the directory to scan.

 required
track_type str

Track type. Defaults to "track".

 'track'
user_id UUID

The ID of the user adding the tracks.

 None
copy_to_library bool

Flag that specifies whether the file should be copied into the library directory. Defaults to None.

 None
skip_duplicate bool

Flag that specifies whether a file should be added that already exists in the library, based on its file checksum. Defaults to None.

 True

Returns:

Type Description
collection (NendoCollection

The collection of tracks that were added to the Library
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_tracks(
    self,
    path: Union[str, DirectoryPath],
    track_type: str = "track",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    copy_to_library: Optional[bool] = None,
    skip_duplicate: bool = True,
) -> NendoCollection:
    """Scan the provided path and upsert the information into the library.

    Args:
        path (Union[DirectoryPath, str]): Path to the directory to scan.
        track_type (str): Track type. Defaults to "track".
        user_id (UUID, optional): The ID of the user adding the tracks.
        copy_to_library (bool, optional): Flag that specifies whether
            the file should be copied into the library directory.
            Defaults to None.
        skip_duplicate (bool, optional): Flag that specifies whether a
            file should be added that already exists in the library, based on its
            file checksum. Defaults to None.

    Returns:
        collection (NendoCollection): The collection of tracks that were added to the Library
    """
    raise NotImplementedError

add_tracks_to_collection abstractmethod ¤

add_tracks_to_collection(
    track_ids: List[Union[str, UUID]],
    collection_id: Union[str, UUID],
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection

Creates a relationship from the track to the collection.

Parameters:

Name Type Description Default
track_ids List[Union[str, UUID]]

List of track ids to add.

 required
collection_id Union[str, UUID]

ID of the collection to which to add the track.

 required
meta Dict[str, Any]

Metadata of the relationship.

 None

Returns:

Type Description
NendoCollection

The updated NendoCollection object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def add_tracks_to_collection(
    self,
    track_ids: List[Union[str, uuid.UUID]],
    collection_id: Union[str, uuid.UUID],
    meta: Optional[Dict[str, Any]] = None,
) -> NendoCollection:
    """Creates a relationship from the track to the collection.

    Args:
        track_ids (List[Union[str, uuid.UUID]]): List of track ids to add.
        collection_id (Union[str, uuid.UUID]): ID of the collection to
            which to add the track.
        meta (Dict[str, Any], optional): Metadata of the relationship.

    Returns:
        NendoCollection: The updated NendoCollection object.
    """
    return NotImplementedError

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

collection_size abstractmethod ¤

collection_size(collection_id: Union[str, UUID]) -> int

Get the number of tracks in a collection.

Parameters:

Name Type Description Default
collection_id Union[str, UUID]

The ID of the collection.

 required

Returns:

Type Description
int

The number of tracks.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def collection_size(
    self,
    collection_id: Union[str, uuid.UUID],
) -> int:
    """Get the number of tracks in a collection.

    Args:
        collection_id (Union[str, uuid.UUID]): The ID of the collection.

    Returns:
        int: The number of tracks.
    """
    raise NotImplementedError

export_collection ¤

export_collection(
    collection_id: Union[str, UUID],
    export_path: str,
    filename_suffix: str = "_nendo",
    file_format: str = "wav",
) -> List[str]

Export the track to a file.

Parameters:

Name Type Description Default
collection_id Union[str, UUID]

The ID of the target collection to export.

 required
export_path str

Path to a directory into which the collection's tracks should be exported.

 required
filename_suffix str

The suffix which should be appended to each exported track's filename.

 '_nendo'
file_format str

Format of the exported track. Ignored if file_path is a full file path. Defaults to "wav".

 'wav'

Returns:

Type Description
List[str]

A list with all full paths to the exported files.
Source code in src/nendo/schema/plugin.py 
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def export_collection(
    self,
    collection_id: Union[str, uuid.UUID],
    export_path: str,
    filename_suffix: str = "_nendo",
    file_format: str = "wav",
) -> List[str]:
    """Export the track to a file.

    Args:
        collection_id (Union[str, uuid.UUID]): The ID of the target
            collection to export.
        export_path (str): Path to a directory into which the collection's tracks
            should be exported.
        filename_suffix (str): The suffix which should be appended to each
            exported track's filename.
        file_format (str, optional): Format of the exported track. Ignored if
            file_path is a full file path. Defaults to "wav".

    Returns:
        List[str]: A list with all full paths to the exported files.
    """
    raise NotImplementedError

export_track ¤

export_track(
    track_id: Union[str, UUID],
    file_path: str,
    file_format: str = "wav",
) -> str

Export the track to a file.

Parameters:

Name Type Description Default
track_id Union[str, UUID]

The ID of the target track to export.

 required
file_path str

Path to the exported file. Can be either a full file path or a directory path. If a directory path is given, a filename will be automatically generated and the file will be exported to the format specified as file_format. If a full file path is given, the format will be deduced from the path and the file_format parameter will be ignored.

 required
file_format str

Format of the exported track. Ignored if file_path is a full file path. Defaults to "wav".

 'wav'

Returns:

Type Description
str

The path to the exported file.
Source code in src/nendo/schema/plugin.py 
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def export_track(
    self,
    track_id: Union[str, uuid.UUID],
    file_path: str,
    file_format: str = "wav",
) -> str:
    """Export the track to a file.

    Args:
        track_id (Union[str, uuid.UUID]): The ID of the target track to export.
        file_path (str): Path to the exported file. Can be either a full
            file path or a directory path. If a directory path is given,
            a filename will be automatically generated and the file will be
            exported to the format specified as file_format. If a full file
            path is given, the format will be deduced from the path and the
            file_format parameter will be ignored.
        file_format (str, optional): Format of the exported track. Ignored if
            file_path is a full file path. Defaults to "wav".

    Returns:
        str: The path to the exported file.
    """
    raise NotImplementedError

filter_related_tracks(
    track_id: Union[str, UUID],
    direction: str = "to",
    filters: Optional[Dict[str, Any]] = None,
    search_meta: Optional[Dict[str, Any]] = None,
    track_type: Optional[Union[str, List[str]]] = None,
    user_id: Optional[Union[str, UUID]] = None,
    collection_id: Optional[Union[str, UUID]] = None,
    plugin_names: Optional[List[str]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Get tracks with a relationship to a track and filter the results.

Parameters:

Name Type Description Default
track_id Union[str, UUID]

ID of the track to be searched for.

 required
direction str

The relationship direction Can be either one of "to", "from", or "both". Defaults to "to".

 'to'
filters dict

Dictionary containing the filters to apply. Defaults to None.

 None
search_meta dict

Dictionary containing the keywords to search for over the track.resource.meta field. The dictionary's values should contain singular search tokens and the keys currently have no effect but might in the future. Defaults to {}.

 None
track_type Union[str, List[str]]

Track type to filter for. Can be a singular type or a list of types. Defaults to None.

 None
user_id Union[str, UUID]

The user ID to filter for.

 None
collection_id Union[str, UUID]

Collection id to which the filtered tracks must have a relationship. Defaults to None.

 None
plugin_names list

List used for applying the filter only to data of certain plugins. If None, all plugin data related to the track is used for filtering.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of tracks, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def filter_related_tracks(
    self,
    track_id: Union[str, uuid.UUID],
    direction: str = "to",
    filters: Optional[Dict[str, Any]] = None,
    search_meta: Optional[Dict[str, Any]] = None,
    track_type: Optional[Union[str, List[str]]] = None,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    collection_id: Optional[Union[str, uuid.UUID]] = None,
    plugin_names: Optional[List[str]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Get tracks with a relationship to a track and filter the results.

    Args:
        track_id (Union[str, UUID]): ID of the track to be searched for.
        direction (str, optional): The relationship direction
            Can be either one of "to", "from", or "both". Defaults to "to".
        filters (dict, optional): Dictionary containing the filters to apply.
            Defaults to None.
        search_meta (dict): Dictionary containing the keywords to search for
            over the track.resource.meta field. The dictionary's values
            should contain singular search tokens and the keys currently have no
            effect but might in the future. Defaults to {}.
        track_type (Union[str, List[str]], optional): Track type to filter for.
            Can be a singular type or a list of types. Defaults to None.
        user_id (Union[str, UUID], optional): The user ID to filter for.
        collection_id (Union[str, uuid.UUID], optional): Collection id to
            which the filtered tracks must have a relationship. Defaults to None.
        plugin_names (list, optional): List used for applying the filter only to
            data of certain plugins. If None, all plugin data related to the track
            is used for filtering.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of tracks, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

filter_tracks abstractmethod ¤

filter_tracks(
    filters: Optional[Dict[str, Any]] = None,
    search_meta: Optional[Dict[str, Any]] = None,
    track_type: Optional[Union[str, List[str]]] = None,
    user_id: Optional[Union[str, UUID]] = None,
    collection_id: Optional[Union[str, UUID]] = None,
    plugin_names: Optional[List[str]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Obtain tracks from the db by filtering over plugin data.

Parameters:

Name Type Description Default
filters dict

Dictionary containing the filters to apply. Defaults to None.

 None
search_meta dict

Dictionary containing the keywords to search for over the track.resource.meta field. The dictionary's values should contain singular search tokens and the keys currently have no effect but might in the future. Defaults to {}.

 None
track_type Union[str, List[str]]

Track type to filter for. Can be a singular type or a list of types. Defaults to None.

 None
user_id Union[str, UUID]

The user ID to filter for.

 None
collection_id Union[str, UUID]

Collection id to which the filtered tracks must have a relationship. Defaults to None.

 None
plugin_names list

List used for applying the filter only to data of certain plugins. If None, all plugin data related to the track is used for filtering.

 None
order_by str

Key used for ordering the results.

 None
order str

Ordering ("asc" vs "desc"). Defaults to "asc".

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of tracks, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def filter_tracks(
    self,
    filters: Optional[Dict[str, Any]] = None,
    search_meta: Optional[Dict[str, Any]] = None,
    track_type: Optional[Union[str, List[str]]] = None,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    collection_id: Optional[Union[str, uuid.UUID]] = None,
    plugin_names: Optional[List[str]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Obtain tracks from the db by filtering over plugin data.

    Args:
        filters (dict, optional): Dictionary containing the filters to apply.
            Defaults to None.
        search_meta (dict): Dictionary containing the keywords to search for
            over the track.resource.meta field. The dictionary's values
            should contain singular search tokens and  the keys currently have no
            effect but might in the future. Defaults to {}.
        track_type (Union[str, List[str]], optional): Track type to filter for.
            Can be a singular type or a list of types. Defaults to None.
        user_id (Union[str, UUID], optional): The user ID to filter for.
        collection_id (Union[str, uuid.UUID], optional): Collection id to
            which the filtered tracks must have a relationship. Defaults to None.
        plugin_names (list, optional): List used for applying the filter only to
            data of certain plugins. If None, all plugin data related to the track
            is used for filtering.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Ordering ("asc" vs "desc"). Defaults to "asc".
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of tracks, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

find_collections abstractmethod ¤

find_collections(
    value: str = "",
    collection_types: Optional[List[str]] = None,
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Find collections with a search term in the description or meta field.

Parameters:

Name Type Description Default
value str

Term to be searched for in the description and meta field.

 ''
collection_types List[str]

Collection types to filter for.

 None
user_id Union[str, UUID]

The user ID to filter for.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of collections, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def find_collections(
    self,
    value: str = "",
    collection_types: Optional[List[str]] = None,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Find collections with a search term in the description or meta field.

    Args:
        value (str): Term to be searched for in the description and meta field.
        collection_types (List[str], optional): Collection types to filter for.
        user_id (Union[str, UUID], optional): The user ID to filter for.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of collections, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

find_tracks abstractmethod ¤

find_tracks(
    value: str,
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Find tracks by searching for a string through the resource metadata.

Parameters:

Name Type Description Default
value str

The search value to filter by.

 required
user_id Union[str, UUID]

The user ID to filter for.

 None
order_by str

Name of the field by which to order. Defaults to None in which case no specific ordering will be applied.

 None
order str

Ordering direction. Defaults to "asc".

 'asc'
limit str

Pagination limit.

 None
offset str

Pagination offset.

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of tracks, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def find_tracks(
    self,
    value: str,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Find tracks by searching for a string through the resource metadata.

    Args:
        value (str): The search value to filter by.
        user_id (Union[str, UUID], optional): The user ID to filter for.
        order_by (str, optional): Name of the field by which to order.
            Defaults to None in which case no specific ordering will be applied.
        order (str, optional): Ordering direction. Defaults to "asc".
        limit (str, optional): Pagination limit.
        offset (str, optional): Pagination offset.

    Returns:
        Union[List, Iterator]: List or generator of tracks, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

get_collection abstractmethod ¤

get_collection(
    collection_id: uuid4, get_related_tracks: bool = True
) -> NendoCollection

Get a collection by its ID.

Parameters:

Name Type Description Default
collection_id uuid4

ID of the target collection.

 required
get_related_tracks bool

Flag that defines whether the returned collection should contain the related_tracks. Defaults to True.

 True

Returns:

Type Description
NendoCollection

The collection object.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def get_collection(
    self,
    collection_id: uuid.uuid4,
    get_related_tracks: bool = True,
) -> NendoCollection:
    """Get a collection by its ID.

    Args:
        collection_id (uuid.uuid4): ID of the target collection.
        get_related_tracks (bool, optional): Flag that defines whether the
            returned collection should contain the `related_tracks`.
            Defaults to True.

    Returns:
        NendoCollection: The collection object.
    """
    raise NotImplementedError

get_collection_tracks abstractmethod ¤

get_collection_tracks(
    collection_id: UUID, order: Optional[str] = "asc"
) -> List[NendoTrack]

Get all tracks of a collection.

Parameters:

Name Type Description Default
collection_id Union[str, UUID]

ID of the collection from which to get all tracks.

 required
order str

Ordering direction. Defaults to "asc".

 'asc'

Returns:

Type Description
List[NendoTrack]

List of tracks in the collection.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def get_collection_tracks(
    self,
    collection_id: uuid.UUID,
    order: Optional[str] = "asc",
) -> List[NendoTrack]:
    """Get all tracks of a collection.

    Args:
        collection_id (Union[str, uuid.UUID]): ID of the collection from which to
            get all tracks.
        order (str, optional): Ordering direction. Defaults to "asc".

    Returns:
        List[NendoTrack]: List of tracks in the collection.
    """
    raise NotImplementedError

get_collections abstractmethod ¤

get_collections(
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Get a list of collections.

Parameters:

Name Type Description Default
user_id Union[str, UUID]

The user ID to filter for.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of collections, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
@NendoPlugin.stream_output
def get_collections(
    self,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Get a list of collections.

    Args:
        user_id (Union[str, UUID], optional): The user ID to filter for.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results
            ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of collections, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

get_related_collections(
    collection_id: Union[str, UUID],
    direction: str = "to",
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Get collections with a relationship to the collection with collection_id.

Parameters:

Name Type Description Default
collection_id str

ID of the collection to be searched for.

 required
direction str

The relationship direction Can be either one of "to", "from", or "both". Defaults to "to".

 'to'
user_id Union[str, UUID]

The user ID to filter for.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of collections, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def get_related_collections(
    self,
    collection_id: Union[str, uuid.UUID],
    direction: str = "to",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Get collections with a relationship to the collection with collection_id.

    Args:
        collection_id (str): ID of the collection to be searched for.
        direction (str, optional): The relationship direction
            Can be either one of "to", "from", or "both". Defaults to "to".
        user_id (Union[str, UUID], optional): The user ID to filter for.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of collections, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

get_related_tracks(
    track_id: Union[str, UUID],
    direction: str = "to",
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]

Get tracks with a relationship to the track with track_id.

Parameters:

Name Type Description Default
track_id str

ID of the track to be searched for.

 required
direction str

The relationship direction Can be either one of "to", "from", or "both". Defaults to "to".

 'to'
user_id Union[str, UUID]

The user ID to filter for.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None

Returns:

Type Description
Union[List, Iterator]

List or generator of tracks, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def get_related_tracks(
    self,
    track_id: Union[str, uuid.UUID],
    direction: str = "to",
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: Optional[str] = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
) -> Union[List, Iterator]:
    """Get tracks with a relationship to the track with track_id.

    Args:
        track_id (str): ID of the track to be searched for.
        direction (str, optional): The relationship direction
            Can be either one of "to", "from", or "both". Defaults to "to".
        user_id (Union[str, UUID], optional): The user ID to filter for.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).

    Returns:
        Union[List, Iterator]: List or generator of tracks, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

get_track abstractmethod ¤

get_track(
    track_id: UUID,
    user_id: Optional[Union[str, UUID]] = None,
) -> NendoTrack

Get a single track from the library by ID.

If no track with the given ID was found, return None.

Parameters:

Name Type Description Default
track_id Any

The ID of the track to get.

 required
user_id uuid4

ID of user adding the plugin data.

 None

Returns:

Type Description
track (NendoTrack

The track with the given ID
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def get_track(
    self,
    track_id: uuid.UUID,
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> NendoTrack:
    """Get a single track from the library by ID.

    If no track with the given ID was found, return None.

    Args:
        track_id (Any): The ID of the track to get.
        user_id (uuid4, optional): ID of user adding the plugin data.

    Returns:
        track (NendoTrack): The track with the given ID
    """
    raise NotImplementedError

get_track_or_collection ¤

get_track_or_collection(
    target_id: Union[str, UUID]
) -> Union[NendoTrack, NendoCollection]

Return a track or a collection based on the given target_id.

Parameters:

Name Type Description Default
target_id Union[str, UUID]

The target ID to obtain.

 required

Returns:

Type Description
Union[NendoTrack, NendoCollection]

The track or the collection.
Source code in src/nendo/schema/plugin.py 
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def get_track_or_collection(
    self,
    target_id: Union[str, uuid.UUID],
) -> Union[NendoTrack, NendoCollection]:
    """Return a track or a collection based on the given target_id.

    Args:
        target_id (Union[str, uuid.UUID]): The target ID to obtain.

    Returns:
        Union[NendoTrack, NendoCollection]: The track or the collection.
    """
    target_id = ensure_uuid(target_id)
    collection = self.get_collection(target_id)
    if collection is not None:
        return collection

    # assume the id is a track id
    return self.get_track(target_id)

get_tracks abstractmethod ¤

get_tracks(
    user_id: Optional[Union[str, UUID]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
    load_related_tracks: bool = False,
) -> Union[List, Iterator]

Get tracks based on the given query parameters.

Parameters:

Name Type Description Default
user_id Union[str, UUID]

ID of user getting the tracks.

 None
order_by str

Key used for ordering the results.

 None
order str

Order in which to retrieve results ("asc" or "desc").

 'asc'
limit int

Limit the number of returned results.

 None
offset int

Offset into the paginated results (requires limit).

 None
load_related_tracks bool

Flag to control whether the related_tracks will be populated or not. Defaults to False.

 False

Returns:

Type Description
Union[List, Iterator]

List or generator of tracks, depending on the configuration variable stream_mode
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
@NendoPlugin.stream_output
def get_tracks(
    self,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    order_by: Optional[str] = None,
    order: str = "asc",
    limit: Optional[int] = None,
    offset: Optional[int] = None,
    load_related_tracks: bool = False,
) -> Union[List, Iterator]:
    """Get tracks based on the given query parameters.

    Args:
        user_id (Union[str, UUID], optional): ID of user getting the tracks.
        order_by (str, optional): Key used for ordering the results.
        order (str, optional): Order in which to retrieve results
            ("asc" or "desc").
        limit (int, optional): Limit the number of returned results.
        offset (int, optional): Offset into the paginated results (requires limit).
        load_related_tracks (bool, optional): Flag to control whether the
            `related_tracks` will be populated or not. Defaults to False.

    Returns:
        Union[List, Iterator]: List or generator of tracks, depending on the
            configuration variable stream_mode
    """
    raise NotImplementedError

library_size abstractmethod ¤

library_size(
    user_id: Optional[Union[str, UUID]] = None
) -> int

Get the number of all tracks in the library (per user).

Parameters:

Name Type Description Default
user_id Union[str, UUID]

The ID of the user. If not specified, the number of all tracks across all users is returned. Defaults to None.

 None

Returns:

Type Description
int

The number of tracks.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def library_size(
    self,
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> int:
    """Get the number of all tracks in the library (per user).

    Args:
        user_id (Union[str, uuid.UUID], optional): The ID of the user.
            If not specified, the number of all tracks across all users is
            returned. Defaults to None.

    Returns:
        int: The number of tracks.
    """
    raise NotImplementedError

load_blob abstractmethod ¤

load_blob(
    blob_id: UUID,
    user_id: Optional[Union[str, UUID]] = None,
) -> NendoBlob

Loads a blob of data into memory.

Parameters:

Name Type Description Default
blob_id UUID

The UUID of the blob.

 required
user_id Union[str, UUID]

ID of the user who's loading the blob.

 None

Returns:

Type Description
NendoBlob

The loaded blob.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def load_blob(
    self,
    blob_id: uuid.UUID,
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> NendoBlob:
    """Loads a blob of data into memory.

    Args:
        blob_id (uuid.UUID): The UUID of the blob.
        user_id (Union[str, uuid.UUID], optional): ID of the user
            who's loading the blob.

    Returns:
        schema.NendoBlob: The loaded blob.
    """
    raise NotImplementedError

remove_blob abstractmethod ¤

remove_blob(
    blob_id: UUID,
    remove_resources: bool = True,
    user_id: Optional[UUID] = None,
) -> bool

Deletes a blob of data.

Parameters:

Name Type Description Default
blob_id UUID

The UUID of the blob.

 required
remove_resources bool

If True, remove associated resources.

 True
user_id Union[str, UUID]

ID of the user who's removing the blob.

 None

Returns:

Type Description
success (bool

True if removal was successful, False otherwise
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def remove_blob(
    self,
    blob_id: uuid.UUID,
    remove_resources: bool = True,
    user_id: Optional[uuid.UUID] = None,
) -> bool:
    """Deletes a blob of data.

    Args:
        blob_id (uuid.UUID): The UUID of the blob.
        remove_resources (bool): If True, remove associated resources.
        user_id (Union[str, uuid.UUID], optional): ID of the user
            who's removing the blob.

    Returns:
        success (bool): True if removal was successful, False otherwise
    """
    raise NotImplementedError

remove_collection abstractmethod ¤

remove_collection(
    collection_id: UUID,
    user_id: Optional[Union[str, UUID]] = None,
    remove_relationships: bool = False,
) -> bool

Deletes the collection identified by collection_id.

Parameters:

Name Type Description Default
collection_id UUID

ID of the collection to remove.

 required
user_id Union[str, UUID]

The ID of the user.

 None
remove_relationships bool

If False prevent deletion if related tracks exist, if True delete relationships together with the object. Defaults to False.

 False

Returns:

Type Description
bool

True if deletion was successful, False otherwise.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def remove_collection(
    self,
    collection_id: uuid.UUID,
    user_id: Optional[Union[str, uuid.UUID]] = None,
    remove_relationships: bool = False,
) -> bool:
    """Deletes the collection identified by `collection_id`.

    Args:
        collection_id (uuid.UUID): ID of the collection to remove.
        user_id (Union[str, UUID], optional): The ID of the user.
        remove_relationships (bool, optional):
            If False prevent deletion if related tracks exist,
            if True delete relationships together with the object.
            Defaults to False.

    Returns:
        bool: True if deletion was successful, False otherwise.
    """
    raise NotImplementedError

remove_track abstractmethod ¤

remove_track(
    track_id: Union[str, UUID],
    user_id: Optional[Union[str, UUID]] = None,
    remove_relationships: bool = False,
    remove_plugin_data: bool = True,
    remove_resources: bool = True,
) -> bool

Delete track from library by ID.

Parameters:

Name Type Description Default
track_id Union[str, UUID]

The ID of the track to remove.

 required
user_id Union[str, UUID]

The ID of the user owning the track.

 None
remove_relationships bool

If False prevent deletion if related tracks exist, if True delete relationships together with the object. Defaults to False.

 False
remove_plugin_data bool

If False prevent deletion if related plugin data exist, if True delete plugin data together with the object. Defaults to True.

 True
remove_resources bool

If False, keep the related resources, e.g. files, if True, delete the related resources. Defaults to True.

 True

Returns:

Type Description
success (bool

True if removal was successful, False otherwise
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def remove_track(
    self,
    track_id: Union[str, uuid.UUID],
    user_id: Optional[Union[str, uuid.UUID]] = None,
    remove_relationships: bool = False,
    remove_plugin_data: bool = True,
    remove_resources: bool = True,
) -> bool:
    """Delete track from library by ID.

    Args:
        track_id (Union[str, uuid.UUID]): The ID of the track to remove.
        user_id (Union[str, UUID], optional): The ID of the user
            owning the track.
        remove_relationships (bool):
            If False prevent deletion if related tracks exist,
            if True delete relationships together with the object.
            Defaults to False.
        remove_plugin_data (bool):
            If False prevent deletion if related plugin data exist,
            if True delete plugin data together with the object.
            Defaults to True.
        remove_resources (bool):
            If False, keep the related resources, e.g. files,
            if True, delete the related resources.
            Defaults to True.

    Returns:
        success (bool): True if removal was successful, False otherwise
    """
    raise NotImplementedError

remove_track_from_collection abstractmethod ¤

remove_track_from_collection(
    track_id: Union[str, UUID],
    collection_id: Union[str, UUID],
) -> bool

Deletes a relationship from the track to the collection.

Parameters:

Name Type Description Default
collection_id Union[str, UUID]

Collection id.

 required
track_id Union[str, UUID]

Track id.

 required

Returns:

Type Description
success (bool

True if removal was successful, False otherwise.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def remove_track_from_collection(
    self,
    track_id: Union[str, uuid.UUID],
    collection_id: Union[str, uuid.UUID],
) -> bool:
    """Deletes a relationship from the track to the collection.

    Args:
        collection_id (Union[str, uuid.UUID]): Collection id.
        track_id (Union[str, uuid.UUID]): Track id.

    Returns:
        success (bool): True if removal was successful, False otherwise.
    """
    raise NotImplementedError

reset abstractmethod ¤

reset(
    force: bool = False,
    user_id: Optional[Union[str, UUID]] = None,
) -> None

Reset the nendo library.

Erase all tracks, collections and relationships. Ask before erasing.

Parameters:

Name Type Description Default
force bool

Flag that specifies whether to ask the user for confirmation of the operation. Default is to ask the user.

 False
user_id Union[str, UUID]

ID of the user who's resetting the library. If none is given, the configured nendo default user will be used.

 None
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def reset(
    self,
    force: bool = False,
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> None:
    """Reset the nendo library.

    Erase all tracks, collections and relationships.
    Ask before erasing.

    Args:
        force (bool, optional): Flag that specifies whether to ask the user for
            confirmation of the operation. Default is to ask the user.
        user_id (Union[str, uuid.UUID], optional): ID of the user
            who's resetting the library. If none is given, the configured
            nendo default user will be used.
    """
    raise NotImplementedError

store_blob abstractmethod ¤

store_blob(
    file_path: Union[FilePath, str],
    user_id: Optional[Union[str, UUID]] = None,
) -> NendoBlob

Stores a blob of data.

Parameters:

Name Type Description Default
file_path Union[FilePath, str]

Path to the file to store as blob.

 required
user_id Union[str, UUID]

ID of the user who's storing the file to blob.

 None

Returns:

Type Description
NendoBlob

The stored blob.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def store_blob(
    self,
    file_path: Union[FilePath, str],
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> NendoBlob:
    """Stores a blob of data.

    Args:
        file_path (Union[FilePath, str]): Path to the file to store as blob.
        user_id (Union[str, uuid.UUID], optional): ID of the user
            who's storing the file to blob.

    Returns:
        schema.NendoBlob: The stored blob.
    """
    raise NotImplementedError

store_blob_from_bytes abstractmethod ¤

store_blob_from_bytes(
    data: bytes, user_id: Optional[Union[str, UUID]] = None
) -> NendoBlob

Stores a data of type bytes to a blob.

Parameters:

Name Type Description Default
data bytes

The blob to store.

 required
user_id Union[str, UUID]

ID of the user who's storing the bytes to blob.

 None

Returns:

Type Description
NendoBlob

The stored blob.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def store_blob_from_bytes(
    self,
    data: bytes,
    user_id: Optional[Union[str, uuid.UUID]] = None,
) -> NendoBlob:
    """Stores a data of type `bytes` to a blob.

    Args:
        data (bytes): The blob to store.
        user_id (Union[str, uuid.UUID], optional): ID of the user
            who's storing the bytes to blob.

    Returns:
        schema.NendoBlob: The stored blob.
    """
    raise NotImplementedError

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper

update_collection abstractmethod ¤

update_collection(
    collection: NendoCollection,
) -> NendoCollection

Updates the given collection by storing it to the database.

Parameters:

Name Type Description Default
collection NendoCollection

The collection to store.

 required

Raises:

Type Description
NendoCollectionNotFoundError

If the collection with the given ID was not found.

Returns:

Type Description
NendoCollection

The updated collection.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def update_collection(
    self,
    collection: NendoCollection,
) -> NendoCollection:
    """Updates the given collection by storing it to the database.

    Args:
        collection (NendoCollection): The collection to store.

    Raises:
        NendoCollectionNotFoundError: If the collection with
            the given ID was not found.

    Returns:
        NendoCollection: The updated collection.
    """
    raise NotImplementedError

update_track abstractmethod ¤

update_track(track: NendoTrack) -> NendoTrack

Updates the given collection by storing it to the database.

Parameters:

Name Type Description Default
track NendoTrack

The track to be stored to the database.

 required

Raises:

Type Description
NendoTrackNotFoundError

If the track passed to the function does not exist in the database.

Returns:

Type Description
NendoTrack

The updated track.
Source code in src/nendo/schema/plugin.py 
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@abstractmethod
def update_track(
    self,
    track: NendoTrack,
) -> NendoTrack:
    """Updates the given collection by storing it to the database.

    Args:
        track (NendoTrack): The track to be stored to the database.

    Raises:
        NendoTrackNotFoundError: If the track passed to the function
            does not exist in the database.

    Returns:
        NendoTrack: The updated track.
    """
    raise NotImplementedError

verify ¤

verify(
    action: Optional[str] = None, user_id: str = ""
) -> None

Verify the library's integrity.

Parameters:

Name Type Description Default
action str

Default action to choose when an inconsistency is detected. Choose between (i)gnore and (r)emove.

 None
Source code in src/nendo/schema/plugin.py 
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def verify(self, action: Optional[str] = None, user_id: str = "") -> None:
    """Verify the library's integrity.

    Args:
        action (str, optional): Default action to choose when an
            inconsistency is detected. Choose between (i)gnore and (r)emove.
    """
    original_config = {}
    try:
        original_config["stream_mode"] = self.config.stream_mode
        original_config["stream_chunk_size"] = self.config.stream_chunk_size
        self.config.stream_mode = False
        self.config.stream_chunk_size = 16
        for track in self.get_tracks():
            if not self.storage_driver.file_exists(
                file_name=track.resource.file_name,
                user_id=user_id,
            ):
                action = (
                    action
                    or input(
                        f"Inconsistency detected: {track.resource.src} "
                        "does not exist. Please choose an action:\n"
                        "(i) ignore - (r) remove",
                    ).lower()
                )
                if action == "i":
                    self.logger.warning(
                        "Detected missing file "
                        f"{track.resource.src} but instructed "
                        "to ignore.",
                    )
                    continue
                if action == "r":
                    self.logger.info(
                        f"Removing track with ID {track.id} "
                        f"due to missing file {track.resource.src}",
                    )
                    self.remove_track(
                        track_id=track.id,
                        remove_plugin_data=True,
                        remove_relationships=True,
                        remove_resources=False,
                    )
        for library_file in self.storage_driver.list_files(user_id=user_id):
            file_without_ext = os.path.splitext(library_file)[0]
            if len(self.find_tracks(value=file_without_ext)) == 0:
                action = (
                    action
                    or input(
                        f"Inconsistency detected: File {library_file} "
                        "cannot be fonud in database. Please choose an action:\n"
                        "(i) ignore - (r) remove",
                    ).lower()
                )
                if action == "i":
                    self.logger.warning(
                        f"Detected orphaned file {library_file} "
                        f"but instructed to ignore.",
                    )
                    continue
                if action == "r":
                    self.logger.info(f"Removing orphaned file {library_file}")
                    self.storage_driver.remove_file(
                        file_name=library_file,
                        user_id=user_id,
                    )

    finally:
        self.config.stream_mode = original_config["stream_mode"]
        self.config.stream_chunk_size = original_config["stream_chunk_size"]

NendoUtilityPlugin ¤

Bases: NendoPlugin

Basic class for nendo utility plugins.

plugin_type property ¤

plugin_type: str

Return type of plugin.

batch_process staticmethod ¤

batch_process(func)

Decorator to run functions multithreaded in batches.

This decorator function transforms the given function to run in multiple threads. It expects that the first argument to the function is a list of items, which will be processed in parallel, in batches of a given size.

Source code in src/nendo/schema/core.py 
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@staticmethod
def batch_process(func):
    """Decorator to run functions multithreaded in batches.

    This decorator function transforms the given function to run
    in multiple threads. It expects that the first argument to the function
    is a list of items, which will be processed in parallel,
    in batches of a given size.
    """

    @functools.wraps(func)
    def wrapper(self, track=None, file_paths=None, *args, **kwargs):
        target = track or file_paths
        if isinstance(target, NendoTrack):
            return func(self, track=target, **kwargs)
        elif isinstance(target, list):  # noqa: RET505
            max_threads = self.config.max_threads
            batch_size = self.config.batch_size
            total = len(target)
            batches = [
                target[i : i + batch_size] for i in range(0, total, batch_size)
            ]
            start_time = time.time()
            futures = []

            def run_batch(batch_index, batch):
                try:
                    batch_start_time = time.time()
                    results = []
                    if track:
                        for _, item in enumerate(batch):
                            result = func(
                                self,
                                track=item,
                                *args,  # noqa: B026
                                **kwargs,
                            )
                            results.extend(result)
                    elif file_paths:
                        result = func(
                            self,
                            file_paths=batch,
                            *args,  # noqa: B026
                            **kwargs,
                        )
                        results.extend(result)
                    batch_end_time = time.time()
                    batch_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(batch_end_time - batch_start_time),
                    )
                    total_elapsed_time = batch_end_time - start_time
                    average_time_per_batch = total_elapsed_time / (batch_index + 1)
                    estimated_total_time = average_time_per_batch * len(batches)
                    estimated_total_time_print = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time),
                    )
                    remaining_time = time.strftime(
                        "%H:%M:%S",
                        time.gmtime(estimated_total_time - total_elapsed_time),
                    )
                    logger.info(
                        f"Finished batch {batch_index + 1}/{len(batches)}.\n"
                        f"Time taken for this batch: {batch_time} - "
                        f"Estimated total time: {estimated_total_time_print} - "
                        f"Estimated remaining time: {remaining_time}\n",
                    )
                    return results
                except NendoError as e:
                    logger.exception(
                        "Error processing batch %d: %s",
                        batch_index,
                        e,
                    )

            with ThreadPoolExecutor(max_workers=max_threads) as executor:
                for batch_index, batch in enumerate(batches):
                    futures.append(executor.submit(run_batch, batch_index, batch))

            all_results = []
            for future in as_completed(futures):
                result = future.result()
                if result:
                    all_results.extend(future.result())
            return all_results
        else:
            raise TypeError("Expected NendoTrack or list of NendoTracks")

    return wrapper

run_utility staticmethod ¤

run_utility(
    func: Callable[
        [NendoPlugin, Optional[NendoTrack], Any],
        Union[str, float, int, bool, List],
    ]
) -> Callable[
    [NendoPlugin, Any], Union[str, float, int, bool, List]
]

Run utility plugin.

Source code in src/nendo/schema/plugin.py 
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@staticmethod
def run_utility(
    func: Callable[
        [NendoPlugin, Optional[NendoTrack], Any],
        Union[str, float, int, bool, List],
    ],
) -> Callable[[NendoPlugin, Any], Union[str, float, int, bool, List]]:
    """Run utility plugin."""

    @functools.wraps(func)
    def wrapper(self, **kwargs: Any) -> Union[str, float, int, bool, List]:
        track_or_collection, kwargs = self._pop_track_or_collection_from_args(
            **kwargs,
        )
        if track_or_collection is None:
            return func(self, **kwargs)
        return func(self, track_or_collection, **kwargs)

    return wrapper

stream_output staticmethod ¤

stream_output(func)

Decorator to turn on streaming mode for functions.

The requirement for this decorator to work on a function is that it would normally return a list.

Source code in src/nendo/schema/core.py 
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@staticmethod
def stream_output(func):
    """Decorator to turn on streaming mode for functions.

    The requirement for this decorator to work on a function is that it would
    normally return a list.
    """

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        result = func(self, *args, **kwargs)
        if self.config.stream_mode:
            return result
        # function is yielding single tracks if stream_chunk_size == 1
        elif self.config.stream_chunk_size > 1:  # noqa: RET505
            return [track for chunk in result for track in chunk]
        else:
            return list(result)

    return wrapper