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ftmq.query

Source code in ftmq/query.py 
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class Query:
    def __init__(
        self,
        filters: Iterable[F] | None = None,
        aggregations: Iterable[Aggregation] | None = None,
        aggregator: Aggregator | None = None,
        sort: Sort | None = None,
        slice: Slice | None = None,
        table: Table | None = None,
    ):
        self.filters = set(ensure_list(filters))
        self.aggregations = set(ensure_list(aggregations))
        self.aggregator = aggregator
        self.sort = sort
        self.slice = slice
        self.table = table

    def __getitem__(self, value: Any) -> Self:
        """
        Implement list-like slicing. No negative values allowed.

        Examples:
            >>> q[1]
            # 2nd element (0-index)
            >>> q[:10]
            # first 10 elements
            >>> q[10:20]
            # next 10 elements

        Returns:
            The updated `Query` instance
        """
        if isinstance(value, int):
            if value < 0:
                raise ValueError(f"Invalid slicing: `{value}`")
            return self._chain(slice=slice(value, value + 1))
        if isinstance(value, slice):
            if value.step is not None:
                raise ValueError(f"Invalid slicing: `{value}`")
            return self._chain(slice=value)
        raise NotImplementedError

    def __bool__(self) -> bool:
        """
        Detect if any filter, ordering or slicing is defined

        Examples:
            >>> bool(Query())
            False
            >>> bool(Query().where(dataset="my_dataset"))
            True
        """
        return bool(self.to_dict())

    def __hash__(self) -> int:
        """
        Generate a unique key of the current state, useful for caching
        """
        return hash(repr(self.to_dict()))

    def _chain(self, **kwargs):
        # merge current state
        new_kwargs = self.__dict__.copy()
        for key, new_value in kwargs.items():
            old_value = new_kwargs[key]
            if old_value is None:
                new_kwargs[key] = new_value
            # "remove" old value:
            elif new_value is None:
                new_kwargs[key] = None
            # overwrite order by
            elif key == "sort":
                new_kwargs[key] = new_value
            # combine iterables and dicts
            elif is_listish(old_value):
                new_kwargs[key] = sorted(set(old_value) | set(new_value))
            elif is_mapping(old_value):
                new_kwargs[key] = {**old_value, **new_value}
            else:  # replace
                new_kwargs[key] = new_value
        return self.__class__(**new_kwargs)

    def _get_lookups(self, filters: set[F]) -> dict[str, Any]:
        data = {}
        for fi in filters:
            for k, v in fi.to_dict().items():
                current = data.get(k)
                if is_listish(current):
                    data[k].append(v)
                else:
                    data[k] = v
        return data

    @property
    def lookups(self) -> dict[str, Any]:
        """
        The current filter lookups as dictionary
        """
        return self._get_lookups(self.filters)

    @property
    def limit(self) -> int | None:
        """
        The current limit (inferred from a slice)
        """
        if self.slice is None:
            return None
        if self.slice.start and self.slice.stop:
            return self.slice.stop - self.slice.start
        return self.slice.stop

    @property
    def offset(self) -> int | None:
        """
        The current offset (inferred from a slice)
        """
        return self.slice.start if self.slice else None

    @property
    def sql(self) -> Sql:
        """
        An object of this query used for sql interfaces
        """
        return Sql(self)

    @property
    def ids(self) -> set[IdFilter]:
        """
        The current id filters
        """
        return {f for f in self.filters if isinstance(f, IdFilter)}

    @property
    def datasets(self) -> set[DatasetFilter]:
        """
        The current dataset filters
        """
        return {f for f in self.filters if isinstance(f, DatasetFilter)}

    @property
    def dataset_names(self) -> set[str]:
        """
        The names of the current filtered datasets
        """
        names = set()
        for f in self.datasets:
            names.update(ensure_list(f.value))
        return names

    @property
    def schemata(self) -> set[SchemaFilter]:
        """
        The current schema filters
        """
        return {f for f in self.filters if isinstance(f, SchemaFilter)}

    @property
    def schemata_names(self) -> set[str]:
        """
        The names of the current filtered schemas
        """
        names = set()
        for f in self.schemata:
            names.update(ensure_list([s.name for s in f.schemata]))
        return names

    @property
    def origins(self) -> set[OriginFilter]:
        """
        The current filtered origins
        """
        return {f for f in self.filters if isinstance(f, OriginFilter)}

    @property
    def origin_names(self) -> set[str]:
        """
        The names of the current filtered origins
        """
        names = set()
        for f in self.origins:
            names.update(ensure_list(f.value))
        return names

    @property
    def countries(self) -> set[str]:
        """
        The current filtered countries
        """
        names = set()
        for f in self.properties:
            if f.key == "country":
                names.update(ensure_list(f.value))
        return names

    @property
    def reversed(self) -> set[ReverseFilter]:
        """
        The current reverse lookup filters
        """
        return {f for f in self.filters if isinstance(f, ReverseFilter)}

    @property
    def properties(self) -> set[PropertyFilter]:
        """
        The current property lookup filters
        """
        return {f for f in self.filters if isinstance(f, PropertyFilter)}

    def discard(self, f_cls: F) -> None:
        filters = list(self.filters)
        for f in filters:
            if isinstance(f, f_cls):
                self.filters.discard(f)

    def to_dict(self) -> dict[str, Any]:
        """
        Dictionary representation of the current object

        Example:
            ```python
            q = Query().where(dataset__in=["d1", "d2"])
            assert q.to_dict() == {"dataset__in": {"d1", "d2"}}
            q = q.where(schema="Event").where(schema__in=["Person", "Organization"])
            assert q.to_dict() == {
                    "dataset__in": {"d1", "d2"},
                    "schema": "Event",
                    "schema__in": {"Organization", "Person"},
                }
            ```
        """
        data = self.lookups
        if self.sort:
            data["order_by"] = self.sort.serialize()
        if self.slice:
            data["limit"] = self.limit
            data["offset"] = self.offset
        if self.aggregations:
            data["aggregations"] = self.get_aggregator().to_dict()
        return data

    def where(self, **lookup: Any) -> Self:
        """
        Add another lookup to the current `Query` instance.

        Example:
            ```python
            q = Query().where(dataset="my_dataset")
            q = q.where(schema="Payment")
            q = q.where(date__gte="2024-10", date__lt="2024-11")
            q = q.order_by("amountEur", ascending=False)
            ```

        Args:
            **lookup: A dataset lookup `dataset="my_dataset"`
            **lookup: A schema lookup `schema="Person"`
            **lookup: `schema_include_descendants=True`: Include schema descendants for
                given schema lookup
            **lookup: `schema_include_matchable=True`: Include matchable schema for
                given schema lookup
            **lookup: A property=value lookup (with optional comparators):
                `name__startswith="Ja"`

        Returns:
            The updated `Query` instance
        """
        schema_include_descendants = lookup.pop("schema_include_descendants", False)
        schema_include_matchable = lookup.pop("schema_include_matchable", False)
        prop = lookup.pop("prop", None)
        value = lookup.pop("value", None)
        comparator = lookup.pop("comparator", None)
        if prop is not None:
            if value is None:
                raise ValueError("No lookup value specified")
            f = PropertyFilter(prop, value, comparator)
            self.filters.discard(f)  # replace existing property filter with updated one
            self.filters.add(f)

        properties: dict[str, Any] = {}
        for key, value in lookup.items():
            meta = False
            for f_key, f in FILTERS.items():
                if key.startswith(f_key):
                    if value is None:
                        self.discard(f)
                    else:
                        key, comparator = parse_comparator(key)
                        kwargs = {}
                        if key == "schema":
                            kwargs = {
                                "schema_include_matchable": schema_include_matchable,
                                "schema_include_descendants": schema_include_descendants,
                            }
                        self.filters.add(f(value, comparator, **kwargs))
                    meta = True
                    break
            if not meta:
                properties[key] = value

        # parse arbitrary `date__gte=2023` stuff
        for key, val in properties.items():
            for prop, value, comparator in parse_unknown_filters((key, val)):
                f = PropertyFilter(prop, value, comparator)
                self.filters.discard(
                    f
                )  # replace existing property filter with updated one
                self.filters.add(f)

        return self._chain()

    def order_by(self, *values: str, ascending: bool | None = True) -> Self:
        """
        Add or update the current sorting.

        Args:
            *values: Fields to order by
            ascending: Ascending or descending

        Returns:
            The updated `Query` instance.
        """
        self.sort = Sort(values=values, ascending=ascending)
        return self._chain()

    def aggregate(
        self,
        func: Aggregations,
        *props: Properties,
        groups: Properties | list[Properties] | None = None,
    ) -> Self:
        for prop in props:
            self.aggregations.add(
                Aggregation(func=func, prop=prop, group_props=ensure_list(groups))
            )
        return self._chain()

    def get_aggregator(self) -> Aggregator:
        return Aggregator(aggregations=self.aggregations)

    def apply(self, entity: Entity) -> bool:
        """
        Test if a entity matches the current `Query` instance.
        """
        if not self.filters:
            return True
        return all(f.apply(entity) for f in self.filters)

    def apply_iter(self, entities: Entities) -> Entities:
        """
        Apply the current `Query` instance to a generator of entities and return
        a generator of filtered entities

        Example:
            ```python
            entities = [...]
            q = Query().where(dataset="my_dataset", schema="Company")
            for entity in q.apply_iter(entities):
                assert entity.schema.name == "Company"
            ```

        Yields:
            A generator of `EntityProxy` or a sub-type
        """
        if not self:
            yield from entities
            return

        entities = (p for p in entities if self.apply(p))
        if self.sort:
            entities = self.sort.apply_iter(entities)
        if self.slice:
            entities = islice(
                entities, self.slice.start, self.slice.stop, self.slice.step
            )
        if self.aggregations:
            self.aggregator = self.get_aggregator()
            entities = self.aggregator.apply(entities)
        yield from entities

countries property

The current filtered countries

dataset_names property

The names of the current filtered datasets

datasets property

The current dataset filters

ids property

The current id filters

limit property

The current limit (inferred from a slice)

lookups property

The current filter lookups as dictionary

offset property

The current offset (inferred from a slice)

origin_names property

The names of the current filtered origins

origins property

The current filtered origins

properties property

The current property lookup filters

reversed property

The current reverse lookup filters

schemata property

The current schema filters

schemata_names property

The names of the current filtered schemas

sql property

An object of this query used for sql interfaces

__bool__()

Detect if any filter, ordering or slicing is defined

Examples:

>>> bool(Query())
False
>>> bool(Query().where(dataset="my_dataset"))
True
Source code in ftmq/query.py 
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def __bool__(self) -> bool:
    """
    Detect if any filter, ordering or slicing is defined

    Examples:
        >>> bool(Query())
        False
        >>> bool(Query().where(dataset="my_dataset"))
        True
    """
    return bool(self.to_dict())

__getitem__(value)

Implement list-like slicing. No negative values allowed.

Examples:

>>> q[1]
# 2nd element (0-index)
>>> q[:10]
# first 10 elements
>>> q[10:20]
# next 10 elements

Returns:

Type Description
Self

The updated Query instance
Source code in ftmq/query.py 
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def __getitem__(self, value: Any) -> Self:
    """
    Implement list-like slicing. No negative values allowed.

    Examples:
        >>> q[1]
        # 2nd element (0-index)
        >>> q[:10]
        # first 10 elements
        >>> q[10:20]
        # next 10 elements

    Returns:
        The updated `Query` instance
    """
    if isinstance(value, int):
        if value < 0:
            raise ValueError(f"Invalid slicing: `{value}`")
        return self._chain(slice=slice(value, value + 1))
    if isinstance(value, slice):
        if value.step is not None:
            raise ValueError(f"Invalid slicing: `{value}`")
        return self._chain(slice=value)
    raise NotImplementedError

__hash__()

Generate a unique key of the current state, useful for caching

Source code in ftmq/query.py 
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def __hash__(self) -> int:
    """
    Generate a unique key of the current state, useful for caching
    """
    return hash(repr(self.to_dict()))

apply(entity)

Test if a entity matches the current Query instance.

Source code in ftmq/query.py 
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def apply(self, entity: Entity) -> bool:
    """
    Test if a entity matches the current `Query` instance.
    """
    if not self.filters:
        return True
    return all(f.apply(entity) for f in self.filters)

apply_iter(entities)

Apply the current Query instance to a generator of entities and return a generator of filtered entities

Example 
entities = [...]
q = Query().where(dataset="my_dataset", schema="Company")
for entity in q.apply_iter(entities):
    assert entity.schema.name == "Company"

Yields:

Type Description
Entities

A generator of EntityProxy or a sub-type
Source code in ftmq/query.py 
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def apply_iter(self, entities: Entities) -> Entities:
    """
    Apply the current `Query` instance to a generator of entities and return
    a generator of filtered entities

    Example:
        ```python
        entities = [...]
        q = Query().where(dataset="my_dataset", schema="Company")
        for entity in q.apply_iter(entities):
            assert entity.schema.name == "Company"
        ```

    Yields:
        A generator of `EntityProxy` or a sub-type
    """
    if not self:
        yield from entities
        return

    entities = (p for p in entities if self.apply(p))
    if self.sort:
        entities = self.sort.apply_iter(entities)
    if self.slice:
        entities = islice(
            entities, self.slice.start, self.slice.stop, self.slice.step
        )
    if self.aggregations:
        self.aggregator = self.get_aggregator()
        entities = self.aggregator.apply(entities)
    yield from entities

order_by(*values, ascending=True)

Add or update the current sorting.

Parameters:

Name Type Description Default
*values str

Fields to order by

 ()
ascending bool | None

Ascending or descending

 True

Returns:

Type Description
Self

The updated Query instance.
Source code in ftmq/query.py 
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def order_by(self, *values: str, ascending: bool | None = True) -> Self:
    """
    Add or update the current sorting.

    Args:
        *values: Fields to order by
        ascending: Ascending or descending

    Returns:
        The updated `Query` instance.
    """
    self.sort = Sort(values=values, ascending=ascending)
    return self._chain()

to_dict()

Dictionary representation of the current object

Example 
q = Query().where(dataset__in=["d1", "d2"])
assert q.to_dict() == {"dataset__in": {"d1", "d2"}}
q = q.where(schema="Event").where(schema__in=["Person", "Organization"])
assert q.to_dict() == {
        "dataset__in": {"d1", "d2"},
        "schema": "Event",
        "schema__in": {"Organization", "Person"},
    }
Source code in ftmq/query.py 
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def to_dict(self) -> dict[str, Any]:
    """
    Dictionary representation of the current object

    Example:
        ```python
        q = Query().where(dataset__in=["d1", "d2"])
        assert q.to_dict() == {"dataset__in": {"d1", "d2"}}
        q = q.where(schema="Event").where(schema__in=["Person", "Organization"])
        assert q.to_dict() == {
                "dataset__in": {"d1", "d2"},
                "schema": "Event",
                "schema__in": {"Organization", "Person"},
            }
        ```
    """
    data = self.lookups
    if self.sort:
        data["order_by"] = self.sort.serialize()
    if self.slice:
        data["limit"] = self.limit
        data["offset"] = self.offset
    if self.aggregations:
        data["aggregations"] = self.get_aggregator().to_dict()
    return data

where(**lookup)

Add another lookup to the current Query instance.

Example 
q = Query().where(dataset="my_dataset")
q = q.where(schema="Payment")
q = q.where(date__gte="2024-10", date__lt="2024-11")
q = q.order_by("amountEur", ascending=False)

Parameters:

Name Type Description Default
**lookup Any

A dataset lookup dataset="my_dataset"

 {}
**lookup Any

A schema lookup schema="Person"

 {}
**lookup Any

schema_include_descendants=True: Include schema descendants for given schema lookup

 {}
**lookup Any

schema_include_matchable=True: Include matchable schema for given schema lookup

 {}
**lookup Any

A property=value lookup (with optional comparators): name__startswith="Ja"

 {}

Returns:

Type Description
Self

The updated Query instance
Source code in ftmq/query.py 
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def where(self, **lookup: Any) -> Self:
    """
    Add another lookup to the current `Query` instance.

    Example:
        ```python
        q = Query().where(dataset="my_dataset")
        q = q.where(schema="Payment")
        q = q.where(date__gte="2024-10", date__lt="2024-11")
        q = q.order_by("amountEur", ascending=False)
        ```

    Args:
        **lookup: A dataset lookup `dataset="my_dataset"`
        **lookup: A schema lookup `schema="Person"`
        **lookup: `schema_include_descendants=True`: Include schema descendants for
            given schema lookup
        **lookup: `schema_include_matchable=True`: Include matchable schema for
            given schema lookup
        **lookup: A property=value lookup (with optional comparators):
            `name__startswith="Ja"`

    Returns:
        The updated `Query` instance
    """
    schema_include_descendants = lookup.pop("schema_include_descendants", False)
    schema_include_matchable = lookup.pop("schema_include_matchable", False)
    prop = lookup.pop("prop", None)
    value = lookup.pop("value", None)
    comparator = lookup.pop("comparator", None)
    if prop is not None:
        if value is None:
            raise ValueError("No lookup value specified")
        f = PropertyFilter(prop, value, comparator)
        self.filters.discard(f)  # replace existing property filter with updated one
        self.filters.add(f)

    properties: dict[str, Any] = {}
    for key, value in lookup.items():
        meta = False
        for f_key, f in FILTERS.items():
            if key.startswith(f_key):
                if value is None:
                    self.discard(f)
                else:
                    key, comparator = parse_comparator(key)
                    kwargs = {}
                    if key == "schema":
                        kwargs = {
                            "schema_include_matchable": schema_include_matchable,
                            "schema_include_descendants": schema_include_descendants,
                        }
                    self.filters.add(f(value, comparator, **kwargs))
                meta = True
                break
        if not meta:
            properties[key] = value

    # parse arbitrary `date__gte=2023` stuff
    for key, val in properties.items():
        for prop, value, comparator in parse_unknown_filters((key, val)):
            f = PropertyFilter(prop, value, comparator)
            self.filters.discard(
                f
            )  # replace existing property filter with updated one
            self.filters.add(f)

    return self._chain()