dj_helper_fn.py

Helper functions for manipulating information from DataJoint fetch calls.

fuzzy_get(index, names, sources)

Given lists of items/names, return item at index or by substring.

Source code in src/spyglass/utils/dj_helper_fn.py

def fuzzy_get(index: Union[int, str], names: List[str], sources: List[str]):
    """Given lists of items/names, return item at index or by substring."""
    if isinstance(index, int):
        return sources[index]
    for i, part in enumerate(names):
        if index in part:
            return sources[i]
    return None

unique_dicts(list_of_dict)

Remove duplicate dictionaries from a list.

Source code in src/spyglass/utils/dj_helper_fn.py

def unique_dicts(list_of_dict):
    """Remove duplicate dictionaries from a list."""
    return [dict(t) for t in {tuple(d.items()) for d in list_of_dict}]

deprecated_factory(classes, old_module='')

Creates a list of classes and logs a warning when instantiated

Parameters:

Name	Type	Description	Default
classes	list	list of tuples containing old_class, new_class	required

Returns:

Type	Description
list	list of classes that will log a warning when instantiated

Source code in src/spyglass/utils/dj_helper_fn.py

def deprecated_factory(classes: list, old_module: str = "") -> list:
    """Creates a list of classes and logs a warning when instantiated

    Parameters
    ---------
    classes : list
        list of tuples containing old_class, new_class

    Returns
    ------
    list
        list of classes that will log a warning when instantiated
    """

    if not isinstance(classes, list):
        classes = [classes]

    ret = [
        _subclass_factory(old_name=c[0], new_class=c[1], old_module=old_module)
        for c in classes
    ]

    return ret[0] if len(ret) == 1 else ret

dj_replace(original_table, new_values, key_column, replace_column)

Given the output of a fetch() call from a schema and a 2D array made up of (key_value, replace_value) tuples, find each instance of key_value in the key_column of the original table and replace the specified replace_column with the associated replace_value. Key values must be unique.

Parameters:

Name	Type	Description	Default
original_table		Result of a datajoint .fetch() call on a schema query.	required
new_values	list	List of tuples, each containing (key_value, replace_value).	required
replace_column	str	The name of the column where to-be-replaced values are located.	required

Returns:

Type	Description
original_table	Structured array of new table entries that can be inserted back into the schema

Source code in src/spyglass/utils/dj_helper_fn.py

def dj_replace(original_table, new_values, key_column, replace_column):
    """Given the output of a fetch() call from a schema and a 2D array made up
    of (key_value, replace_value) tuples, find each instance of key_value in
    the key_column of the original table and replace the specified
    replace_column with the associated replace_value. Key values must be
    unique.

    Parameters
    ----------
    original_table
        Result of a datajoint .fetch() call on a schema query.
    new_values : list
        List of tuples, each containing (key_value, replace_value).
    replace_column : str
        The name of the column where to-be-replaced values are located.

    Returns
    -------
    original_table
        Structured array of new table entries that can be inserted back into the schema
    """

    # check to make sure the new_values are a list or array of tuples and fix if not
    if isinstance(new_values, tuple):
        tmp = list()
        tmp.append(new_values)
        new_values = tmp

    new_val_array = np.asarray(new_values)
    replace_ind = np.where(
        np.isin(original_table[key_column], new_val_array[:, 0])
    )
    original_table[replace_column][replace_ind] = new_val_array[:, 1]
    return original_table

get_all_tables_in_stack(stack)

Get all classes from a stack of tables.

Source code in src/spyglass/utils/dj_helper_fn.py

def get_all_tables_in_stack(stack):
    """Get all classes from a stack of tables."""
    classes = set()
    for frame_info in stack:
        locals_dict = frame_info.frame.f_locals
        for obj in locals_dict.values():
            if not isinstance(obj, UserTable):
                continue  # skip non-tables
            if (name := obj.full_table_name) in PERIPHERAL_TABLES:
                continue  # skip common_nwbfile tables
            classes.add(name)
    return classes

get_fetching_table_from_stack(stack)

Get all classes from a stack of tables.

Source code in src/spyglass/utils/dj_helper_fn.py

def get_fetching_table_from_stack(stack):
    """Get all classes from a stack of tables."""
    classes = get_all_tables_in_stack(stack)
    if len(classes) > 1:
        classes = None  # predict only one but not sure, so return None
    return next(iter(classes)) if classes else None

get_nwb_table(query_expression, tbl, attr_name, *attrs, **kwargs)

Get the NWB file name and path from the given DataJoint query.

Parameters:

Name	Type	Description	Default
query_expression	query	A DataJoint query expression (e.g., join, restrict) or a table to call fetch on.	required
tbl	table	DataJoint table to fetch from.	required
attr_name	str	Attribute name to fetch from the table.	required
*attrs	list	Attributes from normal DataJoint fetch call.	()
**kwargs	dict	Keyword arguments from normal DataJoint fetch call.	{}

Returns:

Name	Type	Description
nwb_files	list	List of NWB file names.
file_path_fn	function	Function to get the absolute path to the NWB file.

Source code in src/spyglass/utils/dj_helper_fn.py

def get_nwb_table(query_expression, tbl, attr_name, *attrs, **kwargs):
    """Get the NWB file name and path from the given DataJoint query.

    Parameters
    ----------
    query_expression : query
        A DataJoint query expression (e.g., join, restrict) or a table to call fetch on.
    tbl : table
        DataJoint table to fetch from.
    attr_name : str
        Attribute name to fetch from the table.
    *attrs : list
        Attributes from normal DataJoint fetch call.
    **kwargs : dict
        Keyword arguments from normal DataJoint fetch call.

    Returns
    -------
    nwb_files : list
        List of NWB file names.
    file_path_fn : function
        Function to get the absolute path to the NWB file.
    """
    from spyglass.common.common_nwbfile import AnalysisNwbfile, Nwbfile

    kwargs["as_dict"] = True  # force return as dictionary
    attrs = attrs or query_expression.heading.names  # if none, all

    which = "analysis" if "analysis" in attr_name else "nwb"
    tbl_map = {  # map to file_name_str and file_path_fn
        "analysis": ["analysis_file_name", AnalysisNwbfile.get_abs_path],
        "nwb": ["nwb_file_name", Nwbfile.get_abs_path],
    }
    file_name_str, file_path_fn = tbl_map[which]

    # TODO: check that the query_expression restricts tbl - CBroz
    nwb_files = (
        query_expression * tbl.proj(nwb2load_filepath=attr_name)
    ).fetch(file_name_str)

    # Disabled #1024
    # if which == "analysis":  # log access of analysis files to log table
    #     AnalysisNwbfile().increment_access(
    #         nwb_files, table=get_fetching_table_from_stack(inspect.stack())
    #     )

    return nwb_files, file_path_fn

fetch_nwb(query_expression, nwb_master, *attrs, **kwargs)

Get an NWB object from the given DataJoint query.

Parameters:

Name	Type	Description	Default
query_expression	query	A DataJoint query expression (e.g., join, restrict) or a table to call fetch on.	required
nwb_master	tuple	Tuple (table, attr) to get the NWB filepath from. i.e. absolute path to NWB file can be obtained by looking up attr column of table table is usually Nwbfile or AnalysisNwbfile; attr is usually 'nwb_file_abs_path' or 'analysis_file_abs_path'	required
*attrs	list	Attributes from normal DataJoint fetch call.	()
**kwargs	dict	Keyword arguments from normal DataJoint fetch call.	{}

Returns:

Name	Type	Description
nwb_objects	list	List of dicts containing fetch results and NWB objects.

Source code in src/spyglass/utils/dj_helper_fn.py

def fetch_nwb(query_expression, nwb_master, *attrs, **kwargs):
    """Get an NWB object from the given DataJoint query.

    Parameters
    ----------
    query_expression : query
        A DataJoint query expression (e.g., join, restrict) or a table to call fetch on.
    nwb_master : tuple
        Tuple (table, attr) to get the NWB filepath from.
        i.e. absolute path to NWB file can be obtained by looking up attr column of table
        table is usually Nwbfile or AnalysisNwbfile;
        attr is usually 'nwb_file_abs_path' or 'analysis_file_abs_path'
    *attrs : list
        Attributes from normal DataJoint fetch call.
    **kwargs : dict
        Keyword arguments from normal DataJoint fetch call.

    Returns
    -------
    nwb_objects : list
        List of dicts containing fetch results and NWB objects.
    """
    kwargs["as_dict"] = True  # force return as dictionary

    tbl, attr_name = nwb_master
    if "analysis" in attr_name:
        file_name_attr = "analysis_file_name"
    else:
        file_name_attr = "nwb_file_name"

    if not attrs:
        attrs = query_expression.heading.names

    nwb_files, file_path_fn = get_nwb_table(
        query_expression, tbl, attr_name, *attrs, **kwargs
    )

    for file_name in nwb_files:
        file_path = file_path_fn(file_name)
        if not os.path.exists(file_path):  # retrieve the file from kachery.
            # This also opens the file and stores the file object
            get_nwb_file(file_path)

    query_table = query_expression * tbl.proj(nwb2load_filepath=attr_name)
    rec_dicts = query_table.fetch(*attrs, **kwargs)
    # get filepath for each. Use datajoint for checksum if local
    for rec_dict in rec_dicts:
        file_path = file_path_fn(rec_dict[file_name_attr])
        if file_from_dandi(file_path):
            # skip the filepath checksum if streamed from Dandi
            rec_dict["nwb2load_filepath"] = file_path
            continue

        # Full dict caused issues with dlc tables using dicts in secondary keys
        rec_only_pk = {k: rec_dict[k] for k in query_table.heading.primary_key}
        rec_dict["nwb2load_filepath"] = (query_table & rec_only_pk).fetch1(
            "nwb2load_filepath"
        )

    if not rec_dicts or not np.any(
        ["object_id" in key for key in rec_dicts[0]]
    ):
        return rec_dicts

    ret = []
    for rec_dict in rec_dicts:
        nwbf = get_nwb_file(rec_dict.pop("nwb2load_filepath"))
        # for each attr that contains substring 'object_id', store key-value: attr name to NWB object
        # remove '_object_id' from attr name
        nwb_objs = {
            id_attr.replace("_object_id", ""): _get_nwb_object(
                nwbf.objects, rec_dict[id_attr]
            )
            for id_attr in attrs
            if "object_id" in id_attr and rec_dict[id_attr] != ""
        }
        ret.append({**rec_dict, **nwb_objs})
    return ret

update_analysis_for_dandi_standard(filepath, age='P4M/P8M')

Function to resolve common nwb file format errors within the database

Parameters:

Name	Type	Description	Default
filepath	str	abs path to the file to edit	required
age	str	age to assign animal if missing, by default "P4M/P8M"	'P4M/P8M'

Source code in src/spyglass/utils/dj_helper_fn.py

def update_analysis_for_dandi_standard(
    filepath: str,
    age: str = "P4M/P8M",
):
    """Function to resolve common nwb file format errors within the database

    Parameters
    ----------
    filepath : str
        abs path to the file to edit
    age : str, optional
        age to assign animal if missing, by default "P4M/P8M"
    """
    from spyglass.common import LabMember

    LabMember().check_admin_privilege(
        error_message="Admin permissions required to edit existing analysis files"
    )
    file_name = filepath.split("/")[-1]
    # edit the file
    with h5py.File(filepath, "a") as file:
        sex_value = file["/general/subject/sex"][()].decode("utf-8")
        if sex_value not in ["Female", "Male", "F", "M", "O", "U"]:
            raise ValueError(f"Unexpected value for sex: {sex_value}")

        if len(sex_value) > 1:
            new_sex_value = sex_value[0].upper()
            logger.info(
                f"Adjusting subject sex: '{sex_value}' -> '{new_sex_value}'"
            )
            file["/general/subject/sex"][()] = new_sex_value

        # replace subject species value "Rat" with "Rattus norvegicus"
        species_value = file["/general/subject/species"][()].decode("utf-8")
        if species_value == "Rat":
            new_species_value = "Rattus norvegicus"
            logger.info(
                f"Adjusting subject species from '{species_value}' to "
                + f"'{new_species_value}'."
            )
            file["/general/subject/species"][()] = new_species_value

        if not (
            len(species_value.split(" ")) == 2 or "NCBITaxon" in species_value
        ):
            raise ValueError(
                "Dandi upload requires species either be in Latin binomial form"
                + " (e.g., 'Mus musculus' and 'Homo sapiens') or be a NCBI "
                + "taxonomy link (e.g., "
                + "'http://purl.obolibrary.org/obo/NCBITaxon_280675').\n "
                + f"Please update species value of: {species_value}"
            )

        # add subject age dataset "P4M/P8M"
        if "age" not in file["/general/subject"]:
            new_age_value = age
            logger.info(
                f"Adding missing subject age, set to '{new_age_value}'."
            )
            file["/general/subject"].create_dataset(
                name="age", data=new_age_value, dtype=STR_DTYPE
            )

        # format name to "Last, First"
        experimenter_value = file["/general/experimenter"][:].astype(str)
        new_experimenter_value = dandi_format_names(experimenter_value)
        if experimenter_value != new_experimenter_value:
            new_experimenter_value = new_experimenter_value.astype(STR_DTYPE)
            logger.info(
                f"Adjusting experimenter from {experimenter_value} to "
                + f"{new_experimenter_value}."
            )
            file["/general/experimenter"][:] = new_experimenter_value

    # update the datajoint external store table to reflect the changes
    _resolve_external_table(filepath, file_name)

dandi_format_names(experimenter)

Make names compliant with dandi standard of "Last, First"

Parameters:

Name	Type	Description	Default
experimenter	List	List of experimenter names	required

Returns:

Type	Description
List	reformatted list of experimenter names

Source code in src/spyglass/utils/dj_helper_fn.py

def dandi_format_names(experimenter: List) -> List:
    """Make names compliant with dandi standard of "Last, First"

    Parameters
    ----------
    experimenter : List
        List of experimenter names

    Returns
    -------
    List
        reformatted list of experimenter names
    """
    for i, name in enumerate(experimenter):
        parts = name.split(" ")
        new_name = " ".join(
            parts[:-1],
        )
        new_name = f"{parts[-1]}, {new_name}"
        experimenter[i] = new_name
    return experimenter

make_file_obj_id_unique(nwb_path)

Make the top-level object_id attribute of the file unique

Parameters:

Name	Type	Description	Default
nwb_path	str	path to the NWB file	required

Returns:

Type	Description
str	the new object_id

Source code in src/spyglass/utils/dj_helper_fn.py

def make_file_obj_id_unique(nwb_path: str):
    """Make the top-level object_id attribute of the file unique

    Parameters
    ----------
    nwb_path : str
        path to the NWB file

    Returns
    -------
    str
        the new object_id
    """
    from spyglass.common.common_lab import LabMember  # noqa: F401

    LabMember().check_admin_privilege(
        error_message="Admin permissions required to edit existing analysis files"
    )
    new_id = str(uuid4())
    with h5py.File(nwb_path, "a") as f:
        f.attrs["object_id"] = new_id
    _resolve_external_table(nwb_path, nwb_path.split("/")[-1])
    return new_id

populate_pass_function(value)

Pass function for parallel populate.

Note: To avoid pickling errors, the table must be passed by class, NOT by instance. Note: This function must be defined in the global namespace.

Parameters:

Name	Type	Description	Default
value	(table, key, kwargs)	Class of table to populate, key to populate, and kwargs for populate	required

Source code in src/spyglass/utils/dj_helper_fn.py

def populate_pass_function(value):
    """Pass function for parallel populate.

    Note: To avoid pickling errors, the table must be passed by class, NOT by instance.
    Note: This function must be defined in the global namespace.

    Parameters
    ----------
    value : (table, key, kwargs)
        Class of table to populate, key to populate, and kwargs for populate
    """
    table, key, kwargs = value
    return table.populate(key, **kwargs)

NonDaemonPool

Bases: Pool

NonDaemonPool. Used to create a pool of non-daemonized processes, which are required for parallel populate operations in DataJoint.

Source code in src/spyglass/utils/dj_helper_fn.py

class NonDaemonPool(multiprocessing.pool.Pool):
    """NonDaemonPool. Used to create a pool of non-daemonized processes,
    which are required for parallel populate operations in DataJoint.
    """

    # Explicitly set the start method to 'fork'
    # Allows the pool to be used in MacOS, where the default start method is 'spawn'
    multiprocessing.set_start_method("fork", force=True)

    def Process(self, *args, **kwds):
        proc = super(NonDaemonPool, self).Process(*args, **kwds)

        class NonDaemonProcess(proc.__class__):
            """Monkey-patch process to ensure it is never daemonized"""

            @property
            def daemon(self):
                return False

            @daemon.setter
            def daemon(self, val):
                pass

        proc.__class__ = NonDaemonProcess
        return proc