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subgrounds.subgraph.fieldpath
__call__
Sets field arguments and expand subfields. The updated FieldPath is returned. Example: >>> aaveV2 = sg.load_subgraph( ... "https://api.thegraph.com/subgraphs/name/aave/protocol-v2" ... ) >>> query = aaveV2.Query.borrows( ... first=10, ... order_by=aaveV2.Borrow.timestamp, ... order_direction="desc", ... selection=[ ... aaveV2.Borrow.id, ... aaveV2.Borrow.timestamp, ... aaveV2.Borrow.amount ... ] ... ) Returns: The updated field path if :attr:`selection` is not specified, or a list of fieldpaths when :attr:`selection` is specified.
def __call__(self, **kwargs: Any) -> Any: """Sets field arguments and expand subfields. The updated FieldPath is returned. Example: >>> aaveV2 = sg.load_subgraph( ... "https://api.thegraph.com/subgraphs/name/aave/protocol-v2" ... ) >>> query = aaveV2.Query.borrows( ... first=10, ... order_by=aaveV2.Borrow.timestamp, ... order_direction="desc", ... selection=[ ... aaveV2.Borrow.id, ... aaveV2.Borrow.timestamp, ... aaveV2.Borrow.amount ... ] ... ) Returns: The updated field path if :attr:`selection` is not specified, or a list of fieldpaths when :attr:`selection` is specified. """ selection = kwargs.pop("selection", []) return self._set_arguments(kwargs, selection)
(self, **kwargs: Any) -> Any
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708,968
subgrounds.subgraph.fieldpath
__eq__
null
def __eq__(self, value: FieldPath | Any) -> Filter | bool: if FieldPath.__test_mode: # Purely used for testing so that assertEqual works return ( self._subgraph == value._subgraph and self._type == value._type and self._path == value._path ) else: return Filter.mk_filter(self, Filter.Operator.EQ, value)
(self, value: Union[subgrounds.subgraph.fieldpath.FieldPath, Any]) -> subgrounds.subgraph.filter.Filter | bool
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708,969
subgrounds.subgraph.fieldpath
__floordiv__
null
def __floordiv__(self, other: Any) -> SyntheticField: return SyntheticField( operator.floordiv, typeref_of_binary_op("div", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,970
subgrounds.subgraph.fieldpath
__ge__
null
def __ge__(self, value: Any) -> Filter: return Filter.mk_filter(self, Filter.Operator.GTE, value)
(self, value: Any) -> subgrounds.subgraph.filter.Filter
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708,971
subgrounds.subgraph.fieldpath
__getattribute__
null
def __getattribute__(self, __name: str) -> Any: # Small hack to get code completion to work while allowing updates to FieldPath # (i.e.: setting arguments) try: match super().__getattribute__(__name): case FieldPath() | SyntheticField() | None: return self._select(__name) case value: return value except AttributeError: return self._select(__name)
(self, _FieldPath__name: str) -> Any
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708,972
subgrounds.subgraph.fieldpath
__gt__
null
def __gt__(self, value: Any) -> Filter: return Filter.mk_filter(self, Filter.Operator.GT, value)
(self, value: Any) -> subgrounds.subgraph.filter.Filter
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708,973
subgrounds.subgraph.fieldpath
__init__
null
def __init__( self, subgraph: Subgraph, root_type: TypeRef.T, type_: TypeRef.T, path: list[tuple[dict[str, Any] | None, TypeMeta.FieldMeta]], ) -> None: self._subgraph = subgraph self._root_type = root_type self._type = type_ self._path = path # Add fields as attributes if leaf is object match self._subgraph._schema.type_of(self._leaf): case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta() as type_ if len( self._path ) < FPATH_DEPTH_LIMIT: # We generate fieldpaths up to depth 8 for fmeta in type_.fields: path = self._path.copy() path.append((None, fmeta)) super().__setattr__( fmeta.name, FieldPath( subgraph=self._subgraph, root_type=self._root_type, type_=fmeta.type_, path=path, ), ) case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta() as type_ if len( self._path ) == FPATH_DEPTH_LIMIT: for fmeta in type_.fields: # NOTE: We set the attribute to None on purpose since we want code # completion to work while avoiding infinite loops caused by cycles # in the GraphQL schema. The attribute itself will be initialized # on __getattribute_ super().__setattr__(fmeta.name, None) case _: pass
(self, subgraph: 'Subgraph', root_type: 'TypeRef.T', type_: 'TypeRef.T', path: 'list[tuple[dict[str, Any] | None, TypeMeta.FieldMeta]]') -> 'None'
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708,974
subgrounds.subgraph.fieldpath
__le__
null
def __le__(self, value: Any) -> Filter: return Filter.mk_filter(self, Filter.Operator.LTE, value)
(self, value: Any) -> subgrounds.subgraph.filter.Filter
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0.03759809583425522, 0.00656696455553174, 0.012380274012684822, 0.06767657399177551 ]
708,975
subgrounds.subgraph.fieldpath
__lt__
null
def __lt__(self, value: Any) -> Filter: return Filter.mk_filter(self, Filter.Operator.LT, value)
(self, value: Any) -> subgrounds.subgraph.filter.Filter
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708,976
subgrounds.subgraph.fieldpath
__mod__
null
def __mod__(self, rhs: Any) -> SyntheticField: return SyntheticField( operator.mod, typeref_of_binary_op("mod", self._type, rhs), [self, rhs] )
(self, rhs: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,977
subgrounds.subgraph.fieldpath
__mul__
null
def __mul__(self, other: Any) -> SyntheticField: return SyntheticField( operator.mul, typeref_of_binary_op("mul", self._type, other), [self, other] )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,978
subgrounds.subgraph.fieldpath
__ne__
null
def __ne__(self, value: Any) -> Filter: return Filter.mk_filter(self, Filter.Operator.NEQ, value)
(self, value: Any) -> subgrounds.subgraph.filter.Filter
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708,979
subgrounds.subgraph.fieldpath
__neg__
null
def __neg__(self) -> SyntheticField: return SyntheticField( operator.neg, type_ref_of_unary_op("neg", self._type), self )
(self) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,980
subgrounds.subgraph.fieldpath
__pow__
null
def __pow__(self, rhs: Any) -> SyntheticField: return SyntheticField( operator.pow, typeref_of_binary_op("pow", self._type, rhs), [self, rhs] )
(self, rhs: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,981
subgrounds.subgraph.fieldpath
__radd__
null
def __radd__(self, other: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.add(y, x), typeref_of_binary_op("add", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,982
subgrounds.subgraph.fieldpath
__repr__
null
def __repr__(self) -> str: vars = f"{self._subgraph._url}, {self._root_type.name}, {self._name_path()}" return f"FieldPath({vars})"
(self) -> str
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0.041141804307699203, -0.0301870945841074, -0.022367333993315697 ]
708,983
subgrounds.subgraph.fieldpath
__rfloordiv__
null
def __rfloordiv__(self, other: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.floordiv(y, x), typeref_of_binary_op("div", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,984
subgrounds.subgraph.fieldpath
__rmod__
null
def __rmod__(self, lhs: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.mod(y, x), typeref_of_binary_op("mod", self._type, lhs), [self, lhs], )
(self, lhs: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,985
subgrounds.subgraph.fieldpath
__rmul__
null
def __rmul__(self, other: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.mul(y, x), typeref_of_binary_op("mul", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,986
subgrounds.subgraph.fieldpath
__rpow__
null
def __rpow__(self, lhs: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.pow(y, x), typeref_of_binary_op("pow", self._type, lhs), [self, lhs], )
(self, lhs: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,987
subgrounds.subgraph.fieldpath
__rsub__
null
def __rsub__(self, other: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.sub(y, x), typeref_of_binary_op("sub", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,988
subgrounds.subgraph.fieldpath
__rtruediv__
null
def __rtruediv__(self, other: Any) -> SyntheticField: return SyntheticField( lambda x, y: operator.truediv(y, x), typeref_of_binary_op("div", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,989
subgrounds.subgraph.fieldpath
__str__
null
def __str__(self) -> str: return ".".join(self._path | map(lambda ele: ele[1].name))
(self) -> str
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708,990
subgrounds.subgraph.fieldpath
__sub__
null
def __sub__(self, other: Any) -> SyntheticField: return SyntheticField( operator.sub, typeref_of_binary_op("sub", self._type, other), [self, other] )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,991
subgrounds.subgraph.fieldpath
__truediv__
null
def __truediv__(self, other: Any) -> SyntheticField: return SyntheticField( operator.truediv, typeref_of_binary_op("div", self._type, other), [self, other], )
(self, other: Any) -> subgrounds.subgraph.fieldpath.SyntheticField
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708,992
subgrounds.subgraph.fieldpath
_auto_select
null
def _auto_select(self) -> FieldPath | list[FieldPath]: match self._subgraph._schema.type_of_typeref(self._leaf.type_): case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta() as obj: return list( fieldpaths_of_object(self._subgraph, obj) | map(partial(FieldPath._extend, self)) ) case _: return self
(self) -> subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath]
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708,993
subgrounds.subgraph.fieldpath
_extend
Extends the current :class:`FieldPath` with the :class:`FieldPath` :attr:`ext`. :attr:`ext` must start where the current :class:`FieldPath` ends. Args: ext: The :class:`FieldPath` representing the extension Raises: TypeError: [description] TypeError: [description] TypeError: [description] Returns: A new :class:`FieldPath` containing the initial current :class:`FieldPath` extended with :attr:`ext`
def _extend(self, ext: FieldPath) -> FieldPath: """Extends the current :class:`FieldPath` with the :class:`FieldPath` :attr:`ext`. :attr:`ext` must start where the current :class:`FieldPath` ends. Args: ext: The :class:`FieldPath` representing the extension Raises: TypeError: [description] TypeError: [description] TypeError: [description] Returns: A new :class:`FieldPath` containing the initial current :class:`FieldPath` extended with :attr:`ext` """ match self._leaf: case TypeMeta.FieldMeta() as fmeta: match self._schema.type_of_typeref(fmeta.type_): case TypeMeta.ObjectMeta(name=name) | TypeMeta.InterfaceMeta( name=name ): if name == ext._root_type.name: return FieldPath( subgraph=self._subgraph, root_type=self._root_type, type_=ext._type, path=self._path + ext._path, ) else: raise TypeError( f"extend: FieldPath {ext} does not start at the" f" same type from where FieldPath {self} ends" ) case _: raise TypeError(f"extend: FieldPath {self} is not object field") case _: raise TypeError(f"extend: FieldPath {self} is not an object field")
(self, ext: subgrounds.subgraph.fieldpath.FieldPath) -> subgrounds.subgraph.fieldpath.FieldPath
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708,994
subgrounds.subgraph.fieldpath
_extract_data
Extract the data corresponding to the current :class:`FieldPath` from the dictionary :attr:`data`. Args: data: Data dictionary that contains the data corresponding to the current :class:`FieldPath`. Returns: Data corresponding to the current :class:`FieldPath`.
def _extract_data( self, data: dict[str, Any] | list[dict[str, Any]] ) -> list[Any] | Any: """Extract the data corresponding to the current :class:`FieldPath` from the dictionary :attr:`data`. Args: data: Data dictionary that contains the data corresponding to the current :class:`FieldPath`. Returns: Data corresponding to the current :class:`FieldPath`. """ return extract_data(self._name_path(use_aliases=True), data)
(self, data: dict[str, typing.Any] | list[dict[str, typing.Any]]) -> Union[list[Any], Any]
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708,995
subgrounds.subgraph.fieldpath
_hash
null
@staticmethod def _hash(msg: str) -> str: h = blake2b(digest_size=8) h.update(msg.encode("UTF-8")) return "x" + h.hexdigest()
(msg: str) -> str
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708,996
subgrounds.subgraph.fieldpath
_merge
Returns a Selection tree containing all selection paths in `fpaths`. Note: Assumes that all fieldpaths in `fpaths` belong to the same subgraph Args: fpaths: _description_ Returns: _description_
@staticmethod def _merge(fpaths: list[FieldPath]) -> list[Selection]: """Returns a Selection tree containing all selection paths in `fpaths`. Note: Assumes that all fieldpaths in `fpaths` belong to the same subgraph Args: fpaths: _description_ Returns: _description_ """ query = reduce(Query.add, fpaths | map(FieldPath._selection), Query()) return query.selection
(fpaths: list[subgrounds.subgraph.fieldpath.FieldPath]) -> list[subgrounds.query.Selection]
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0.03715522959828377, -0.025078002363443375, 0.02534441277384758 ]
708,997
subgrounds.subgraph.fieldpath
_name
Generates the name of the current :class:`FieldPath` using the names of the fields it selects. If :attr:`use_aliases` is True, then if a field has an automatically generated alias, the alias will be used. Args: use_aliases: Flag indicating wether of not to use the fields' automatically generated alias (if present). Defaults to False. Returns: The generated name of the current :class:`FieldPath`.
def _name(self, use_aliases: bool = False) -> str: """Generates the name of the current :class:`FieldPath` using the names of the fields it selects. If :attr:`use_aliases` is True, then if a field has an automatically generated alias, the alias will be used. Args: use_aliases: Flag indicating wether of not to use the fields' automatically generated alias (if present). Defaults to False. Returns: The generated name of the current :class:`FieldPath`. """ return "_".join(self._name_path(use_aliases=use_aliases))
(self, use_aliases: bool = False) -> str
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708,998
subgrounds.subgraph.fieldpath
_name_path
Returns a list of strings correspoding to the names of all fields selected in the current :class:`FieldPath`. If :attr:`use_aliases` is True, then if a field has an automatically generated alias, the alias will be returned. Args: use_aliases: Flag indicating wether of not to use the fields' automatically generated alias (if present). Defaults to False. Returns: List of field names selected in the current :class:`FieldPath`
def _name_path(self, use_aliases: bool = False) -> list[str]: """Returns a list of strings correspoding to the names of all fields selected in the current :class:`FieldPath`. If :attr:`use_aliases` is True, then if a field has an automatically generated alias, the alias will be returned. Args: use_aliases: Flag indicating wether of not to use the fields' automatically generated alias (if present). Defaults to False. Returns: List of field names selected in the current :class:`FieldPath` """ def gen_alias(ele: tuple[dict[str, Any] | None, TypeMeta.FieldMeta]) -> str: if ele[0] != {} and ele[0] is not None: return FieldPath._hash(ele[1].name + str(ele[0])) else: return ele[1].name return list( self._path | map(lambda ele: gen_alias(ele) if use_aliases else ele[1].name) )
(self, use_aliases: bool = False) -> list[str]
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708,999
subgrounds.subgraph.fieldpath
_select
Returns a new FieldPath corresponding to the FieldPath `self` extended with an additional selection on the field named `name`. Args: name: The name of the field to expand on the leaf of `fpath` Raises: TypeError: [description] TypeError: [description] TypeError: [description] Returns: A new FieldPath containing `fpath` extended with the field named `name`
def _select(self, name: str) -> FieldPath: """Returns a new FieldPath corresponding to the FieldPath `self` extended with an additional selection on the field named `name`. Args: name: The name of the field to expand on the leaf of `fpath` Raises: TypeError: [description] TypeError: [description] TypeError: [description] Returns: A new FieldPath containing `fpath` extended with the field named `name` """ match self._schema.type_of_typeref(self._type): # If the FieldPath fpath case TypeMeta.EnumMeta() | TypeMeta.ScalarMeta(): raise TypeError( f"FieldPath: path {self} ends with a scalar field!" f" cannot select field {name}" ) case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta() as obj: field = obj.field(name) match self._schema.type_of_typeref(field.type_): case ( TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta() | TypeMeta.EnumMeta() | TypeMeta.ScalarMeta() ): # Copy current path and append newly selected field path = self._path.copy() path.append((None, field)) # Return new FieldPath return FieldPath( subgraph=self._subgraph, root_type=self._root_type, type_=field.type_, path=path, ) case _: raise TypeError( f"FieldPath: field {name} is not a valid field for object" f" {self._type.name} at path {self}" ) case _: raise TypeError( f"FieldPath: Unexpected type {self._type.name}" f" when selection {name} on {self}" )
(self, name: str) -> subgrounds.subgraph.fieldpath.FieldPath
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709,000
subgrounds.subgraph.fieldpath
_selection
Returns a selection or list of selections corresponding to the current :class:`FieldPath`. Returns: Selection | list[Selection]: _description_
def _selection(self) -> Selection | list[Selection]: """Returns a selection or list of selections corresponding to the current :class:`FieldPath`. Returns: Selection | list[Selection]: _description_ """ def f( path: list[tuple[dict[str, Any] | None, TypeMeta.FieldMeta]] ) -> list[Selection]: match path: case [ (args, TypeMeta.FieldMeta() as fmeta), *rest, ] if args == {} or args is None: return [Selection(fmeta, selection=f(rest))] case [(args, TypeMeta.FieldMeta() as fmeta), *rest]: return [ Selection( fmeta, # TODO: Revisit this alias=FieldPath._hash(fmeta.name + str(args)), arguments=arguments_of_field_args( self._subgraph._schema, fmeta, args ), selection=f(rest), ) ] case []: return [] assert False # Suppress mypy missing return statement warning return f(self._path)[0]
(self) -> subgrounds.query.Selection | list[subgrounds.query.Selection]
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709,001
subgrounds.subgraph.fieldpath
_set_arguments
Set the arguments to the leaf of the current :class:`FieldPath`. The method returns the :attr:`self`. Args: args: _description_ selection: _description_. Defaults to []. Returns: _description_
def _set_arguments( self, args: dict[str, Any], selection: list[FieldPath] = [] ) -> FieldPath | list[FieldPath]: """Set the arguments to the leaf of the current :class:`FieldPath`. The method returns the :attr:`self`. Args: args: _description_ selection: _description_. Defaults to []. Returns: _description_ """ def fmt_arg(name, raw_arg): match (name, raw_arg): case ("where", [Filter(), *_] as filters): return Filter.to_dict(filters) case ("orderBy", FieldPath() as fpath): if paths := fpath._name_path(): return "__".join(paths) raise Exception( f"Cannot use empty paths as orderBy argument {fpath}" ) case _: return raw_arg match self._leaf: case TypeMeta.FieldMeta(): args = {key: fmt_arg(key, val) for key, val in args.items()} self._path[-1] = (args, self._path[-1][1]) if len(selection) > 0: return list(selection | map(partial(FieldPath._extend, self))) else: return self case _: raise TypeError(f"Unexpected type for FieldPath {self}")
(self, args: dict[str, typing.Any], selection: list[subgrounds.subgraph.fieldpath.FieldPath] = []) -> subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath]
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709,002
subgrounds.subgraph.subgraph
Subgraph
Subgraph(url: 'str', schema: 'SchemaMeta', transforms: 'list[DocumentTransform]' = [<subgrounds.transform.transforms.TypeTransform object at 0x7f8aea508400>, <subgrounds.transform.transforms.TypeTransform object at 0x7f8aea5091e0>], is_subgraph: 'bool' = True) -> 'None'
class Subgraph: _url: str _schema: SchemaMeta _transforms: list[DocumentTransform] = field(default_factory=list) _is_subgraph: bool = True def __init__( self, url: str, schema: SchemaMeta, transforms: list[DocumentTransform] = DEFAULT_SUBGRAPH_TRANSFORMS, is_subgraph: bool = True, ) -> None: self._url = url self._schema = schema self._transforms = transforms self._is_subgraph = is_subgraph # Add objects as attributes for key, obj in self._schema.type_map.items(): match obj: case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta(): super().__setattr__(key, Object(self, obj)) case _: pass def _add_synthetic_field( self, object_: TypeMeta.ObjectMeta | TypeMeta.InterfaceMeta, name: str, sfield: SyntheticField, ) -> None: fmeta = TypeMeta.FieldMeta( name=name, description="", args=[], type=sfield._type ) object_.fields.append(fmeta) sfield_fpath = FieldPath( self, TypeRef.Named(name=object_.name, kind="OBJECT"), sfield._type, [(None, fmeta)], ) logger.debug( "Subgraph: Adding SyntheticField at FieldPath" f" {sfield_fpath._root_type.name}.{sfield_fpath._name_path()}" ) transform = LocalSyntheticField( self, fmeta, object_, sfield._f, sfield._default, list(sfield._deps | map(FieldPath._selection)), ) self._transforms = [transform, *self._transforms]
(url: 'str', schema: 'SchemaMeta', transforms: 'list[DocumentTransform]' = [<subgrounds.transform.transforms.TypeTransform object at 0x7f8aea508400>, <subgrounds.transform.transforms.TypeTransform object at 0x7f8aea5091e0>], is_subgraph: 'bool' = True) -> 'None'
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709,003
subgrounds.subgraph.subgraph
__eq__
null
""" Subgraph module that defines various classes to manipulate requests and subgraphs. This module is the glue that connects the lower level modules (i.e.: :module:`query`, :module:`schema`, :module:`transform`, :module:`pagination`) to the higher toplevel modules (i.e.: :module:`subgrounds`). """ from __future__ import annotations import logging import warnings from dataclasses import dataclass, field from pipe import map from subgrounds.schema import SchemaMeta, TypeMeta, TypeRef from subgrounds.transform import ( DEFAULT_SUBGRAPH_TRANSFORMS, DocumentTransform, LocalSyntheticField, ) from .fieldpath import FieldPath, SyntheticField from .object import Object logger = logging.getLogger("subgrounds") warnings.simplefilter("default") @dataclass class Subgraph: _url: str _schema: SchemaMeta _transforms: list[DocumentTransform] = field(default_factory=list) _is_subgraph: bool = True def __init__( self, url: str, schema: SchemaMeta, transforms: list[DocumentTransform] = DEFAULT_SUBGRAPH_TRANSFORMS, is_subgraph: bool = True, ) -> None: self._url = url self._schema = schema self._transforms = transforms self._is_subgraph = is_subgraph # Add objects as attributes for key, obj in self._schema.type_map.items(): match obj: case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta(): super().__setattr__(key, Object(self, obj)) case _: pass def _add_synthetic_field( self, object_: TypeMeta.ObjectMeta | TypeMeta.InterfaceMeta, name: str, sfield: SyntheticField, ) -> None: fmeta = TypeMeta.FieldMeta( name=name, description="", args=[], type=sfield._type ) object_.fields.append(fmeta) sfield_fpath = FieldPath( self, TypeRef.Named(name=object_.name, kind="OBJECT"), sfield._type, [(None, fmeta)], ) logger.debug( "Subgraph: Adding SyntheticField at FieldPath" f" {sfield_fpath._root_type.name}.{sfield_fpath._name_path()}" ) transform = LocalSyntheticField( self, fmeta, object_, sfield._f, sfield._default, list(sfield._deps | map(FieldPath._selection)), ) self._transforms = [transform, *self._transforms]
(self, other)
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709,004
subgrounds.subgraph.subgraph
__init__
null
def __init__( self, url: str, schema: SchemaMeta, transforms: list[DocumentTransform] = DEFAULT_SUBGRAPH_TRANSFORMS, is_subgraph: bool = True, ) -> None: self._url = url self._schema = schema self._transforms = transforms self._is_subgraph = is_subgraph # Add objects as attributes for key, obj in self._schema.type_map.items(): match obj: case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta(): super().__setattr__(key, Object(self, obj)) case _: pass
(self, url: str, schema: subgrounds.schema.SchemaMeta, transforms: list[subgrounds.transform.base.DocumentTransform] = [<subgrounds.transform.transforms.TypeTransform object at 0x7f8aea508400>, <subgrounds.transform.transforms.TypeTransform object at 0x7f8aea5091e0>], is_subgraph: bool = True) -> NoneType
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709,006
subgrounds.subgraph.subgraph
_add_synthetic_field
null
def _add_synthetic_field( self, object_: TypeMeta.ObjectMeta | TypeMeta.InterfaceMeta, name: str, sfield: SyntheticField, ) -> None: fmeta = TypeMeta.FieldMeta( name=name, description="", args=[], type=sfield._type ) object_.fields.append(fmeta) sfield_fpath = FieldPath( self, TypeRef.Named(name=object_.name, kind="OBJECT"), sfield._type, [(None, fmeta)], ) logger.debug( "Subgraph: Adding SyntheticField at FieldPath" f" {sfield_fpath._root_type.name}.{sfield_fpath._name_path()}" ) transform = LocalSyntheticField( self, fmeta, object_, sfield._f, sfield._default, list(sfield._deps | map(FieldPath._selection)), ) self._transforms = [transform, *self._transforms]
(self, object_: subgrounds.schema.TypeMeta.ObjectMeta | subgrounds.schema.TypeMeta.InterfaceMeta, name: str, sfield: subgrounds.subgraph.fieldpath.SyntheticField) -> NoneType
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709,007
subgrounds.client.sync
Subgrounds
null
class Subgrounds(SubgroundsBase): @cached_property def _client(self): """Cached client""" return httpx.Client(http2=HTTP2_SUPPORT, timeout=self.timeout) def load( self, url: str, save_schema: bool = False, cache_dir: str | None = None, is_subgraph: bool = True, ) -> Subgraph: if cache_dir is not None: warnings.warn("This will be depreciated", DeprecationWarning) try: loader = self._load(url, save_schema, is_subgraph) url, query = next(loader) # if this fails, schema is loaded from cache data = self._fetch(url, {"query": query}) loader.send(data) except StopIteration as e: return e.value assert False def load_subgraph( self, url: str, save_schema: bool = False, cache_dir: str | None = None ) -> Subgraph: """Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the subgraph with all its entities. Args: url The url of the API. save_schema: Flag indicating whether or not the schema should be cached to disk. Returns: Subgraph: A generated class representing the subgraph and its entities """ return self.load(url, save_schema, cache_dir, True) def load_api( self, url: str, save_schema: bool = False, cache_dir: str | None = None ) -> Subgraph: """Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the GraphQL endpoint with all its entities. Args: url: The url of the API. save_schema: Flag indicating whether or not the schema should be saved to disk. Returns: A generated class representing the subgraph and its entities """ return self.load(url, save_schema, cache_dir, False) def execute( self, req: DataRequest, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> DataResponse: """Executes a :class:`DataRequest` and returns a :class:`DataResponse`. Args: req: The :class:`DataRequest` object to be executed. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`DataResponse` object representing the response """ try: executor = self._execute(req, pagination_strategy) doc = next(executor) while True: data = self._fetch( doc.url, {"query": doc.graphql, "variables": doc.variables} ) doc = executor.send(DocumentResponse(url=doc.url, data=data)) except StopIteration as e: return e.value def execute_iter( self, req: DataRequest, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[DocumentResponse]: """Same as `execute`, except that an iterator is returned which will iterate the data pages. Args: req: The :class:`DataRequest` object to be executed pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the :class:`DocumentResponse` pages. ⚠️ DOES NOT apply global transforms across multiple documents or their pages. Since we yield each page as we get it, it's not possible to accurately perform the transforms since we don't collect the pages. This means transforms expecting multiple documents or pages of documents will be inaccurate. """ with suppress(StopIteration): executor = self._execute_iter(req, pagination_strategy) while True: doc = cast(Document, next(executor)) data = self._fetch( doc.url, {"query": doc.graphql, "variables": doc.variables} ) yield cast( DocumentResponse, executor.send(DocumentResponse(url=doc.url, data=data)), ) def query_json( self, fpaths: FieldPath | list[FieldPath], pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> list[dict[str, Any]]: """Equivalent to ``Subgrounds.execute(Subgrounds.mk_request(fpaths), pagination_strategy)``. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: The reponse data """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) req = self.mk_request(fpaths) data = self.execute(req, pagination_strategy) return [doc.data for doc in data.responses] def query_json_iter( self, fpaths: FieldPath | list[FieldPath], pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[dict[str, Any]]: """Same as `query_json` returns an iterator over the response data pages. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: list[dict[str, Any]]: The reponse data """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) req = self.mk_request(fpaths) responses = self.execute_iter(req, pagination_strategy) for resp in responses: yield resp.data def query_df( self, fpaths: FieldPath | list[FieldPath], columns: list[str] | None = None, concat: bool = False, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> pd.DataFrame | list[pd.DataFrame]: """Same as :func:`Subgrounds.query` but formats the response data into a Pandas DataFrame. If the response data cannot be flattened to a single query (e.g.: when querying multiple list fields that return different entities), then multiple dataframes are returned ``fpaths`` is a list of :class:`FieldPath` objects that indicate which data must be queried. ``columns`` is an optional argument used to rename the dataframes(s) columns. The length of ``columns`` must be the same as the number of columns of *all* returned dataframes. ``concat`` indicates whether or not the resulting dataframes should be concatenated together. The dataframes must have the same number of columns, as well as the same column names and types (the names can be set using the ``columns`` argument). Args: fpaths: One or more `FieldPath` objects that should be included in the request columns: The column labels. Defaults to None. merge: Whether or not to merge resulting dataframes. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`pandas.DataFrame` containing the rep
(timeout: int = 30, headers: dict[str, typing.Any] = <factory>, global_transforms: list[subgrounds.transform.base.RequestTransform] = <factory>, subgraphs: dict[str, subgrounds.subgraph.subgraph.Subgraph] = <factory>, schema_cache: pathlib.Path = PosixPath('schemas')) -> None
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709,008
subgrounds.client.sync
__enter__
null
def __enter__(self): self._client.__enter__() return self
(self)
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709,010
subgrounds.client.sync
__exit__
null
def __exit__(self, *args): self._client.__exit__(*args)
(self, *args)
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709,016
subgrounds.client.sync
_fetch
null
def _fetch(self, url: str, blob: dict[str, Any]) -> dict[str, Any]: resp = self._client.post( url, json=blob, headers=default_header(url) | self.headers ) resp.raise_for_status() try: raw_data = resp.json() except JSONDecodeError: raise ServerError( f"Server ({url}) did not respond with proper JSON" f"\nDid you query a proper GraphQL endpoint?" f"\n\n{resp.content}" ) if (data := raw_data.get("data")) is None: raise GraphQLError(raw_data.get("errors", "Unknown Error(s) Found")) return data
(self, url: str, blob: dict[str, typing.Any]) -> dict[str, typing.Any]
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709,019
subgrounds.client.sync
execute
Executes a :class:`DataRequest` and returns a :class:`DataResponse`. Args: req: The :class:`DataRequest` object to be executed. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`DataResponse` object representing the response
def execute( self, req: DataRequest, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> DataResponse: """Executes a :class:`DataRequest` and returns a :class:`DataResponse`. Args: req: The :class:`DataRequest` object to be executed. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`DataResponse` object representing the response """ try: executor = self._execute(req, pagination_strategy) doc = next(executor) while True: data = self._fetch( doc.url, {"query": doc.graphql, "variables": doc.variables} ) doc = executor.send(DocumentResponse(url=doc.url, data=data)) except StopIteration as e: return e.value
(self, req: subgrounds.query.DataRequest, pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> subgrounds.query.DataResponse
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709,020
subgrounds.client.sync
execute_iter
Same as `execute`, except that an iterator is returned which will iterate the data pages. Args: req: The :class:`DataRequest` object to be executed pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the :class:`DocumentResponse` pages. ⚠️ DOES NOT apply global transforms across multiple documents or their pages. Since we yield each page as we get it, it's not possible to accurately perform the transforms since we don't collect the pages. This means transforms expecting multiple documents or pages of documents will be inaccurate.
def execute_iter( self, req: DataRequest, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[DocumentResponse]: """Same as `execute`, except that an iterator is returned which will iterate the data pages. Args: req: The :class:`DataRequest` object to be executed pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the :class:`DocumentResponse` pages. ⚠️ DOES NOT apply global transforms across multiple documents or their pages. Since we yield each page as we get it, it's not possible to accurately perform the transforms since we don't collect the pages. This means transforms expecting multiple documents or pages of documents will be inaccurate. """ with suppress(StopIteration): executor = self._execute_iter(req, pagination_strategy) while True: doc = cast(Document, next(executor)) data = self._fetch( doc.url, {"query": doc.graphql, "variables": doc.variables} ) yield cast( DocumentResponse, executor.send(DocumentResponse(url=doc.url, data=data)), )
(self, req: subgrounds.query.DataRequest, pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> collections.abc.Iterator[subgrounds.query.DocumentResponse]
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709,022
subgrounds.client.sync
load
null
def load( self, url: str, save_schema: bool = False, cache_dir: str | None = None, is_subgraph: bool = True, ) -> Subgraph: if cache_dir is not None: warnings.warn("This will be depreciated", DeprecationWarning) try: loader = self._load(url, save_schema, is_subgraph) url, query = next(loader) # if this fails, schema is loaded from cache data = self._fetch(url, {"query": query}) loader.send(data) except StopIteration as e: return e.value assert False
(self, url: str, save_schema: bool = False, cache_dir: Optional[str] = None, is_subgraph: bool = True) -> subgrounds.subgraph.subgraph.Subgraph
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709,023
subgrounds.client.sync
load_api
Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the GraphQL endpoint with all its entities. Args: url: The url of the API. save_schema: Flag indicating whether or not the schema should be saved to disk. Returns: A generated class representing the subgraph and its entities
def load_api( self, url: str, save_schema: bool = False, cache_dir: str | None = None ) -> Subgraph: """Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the GraphQL endpoint with all its entities. Args: url: The url of the API. save_schema: Flag indicating whether or not the schema should be saved to disk. Returns: A generated class representing the subgraph and its entities """ return self.load(url, save_schema, cache_dir, False)
(self, url: str, save_schema: bool = False, cache_dir: Optional[str] = None) -> subgrounds.subgraph.subgraph.Subgraph
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709,024
subgrounds.client.sync
load_subgraph
Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the subgraph with all its entities. Args: url The url of the API. save_schema: Flag indicating whether or not the schema should be cached to disk. Returns: Subgraph: A generated class representing the subgraph and its entities
def load_subgraph( self, url: str, save_schema: bool = False, cache_dir: str | None = None ) -> Subgraph: """Performs introspection on the provided GraphQL API ``url`` to get the schema, stores the schema if ``save_schema`` is ``True`` and returns a generated class representing the subgraph with all its entities. Args: url The url of the API. save_schema: Flag indicating whether or not the schema should be cached to disk. Returns: Subgraph: A generated class representing the subgraph and its entities """ return self.load(url, save_schema, cache_dir, True)
(self, url: str, save_schema: bool = False, cache_dir: Optional[str] = None) -> subgrounds.subgraph.subgraph.Subgraph
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709,027
subgrounds.client.sync
query
Executes one or multiple ``FieldPath`` objects immediately and returns the data (as a tuple if multiple ``FieldPath`` objects are provided). Args: fpaths: One or more ``FieldPath`` object(s) to query. unwrap: Flag indicating whether or not, in the case where the returned data is a list of one element, the element itself should be returned instead of the list. Defaults to ``True``. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: The ``FieldPath`` object(s) data Example: .. code-block:: python >>> from subgrounds import Subgrounds >>> sg = Subgrounds() >>> univ3 = sg.load_subgraph( ... 'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3') # Define price SyntheticField >>> univ3.Swap.price = abs(univ3.Swap.amount0) / abs(univ3.Swap.amount1) # Construct FieldPath to get price of last swap on ETH/USDC pool >>> eth_usdc_last = univ3.Query.swaps( ... orderBy=univ3.Swap.timestamp, ... orderDirection='desc', ... first=1, ... where=[ ... univ3.Swap.pool == '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8' ... ] ... ).price # Query last price FieldPath >>> sg.query(eth_usdc_last) 2628.975030015892
def query( self, fpaths: FieldPath | list[FieldPath], unwrap: bool = True, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> str | int | float | bool | list | tuple | None: """Executes one or multiple ``FieldPath`` objects immediately and returns the data (as a tuple if multiple ``FieldPath`` objects are provided). Args: fpaths: One or more ``FieldPath`` object(s) to query. unwrap: Flag indicating whether or not, in the case where the returned data is a list of one element, the element itself should be returned instead of the list. Defaults to ``True``. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: The ``FieldPath`` object(s) data Example: .. code-block:: python >>> from subgrounds import Subgrounds >>> sg = Subgrounds() >>> univ3 = sg.load_subgraph( ... 'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3') # Define price SyntheticField >>> univ3.Swap.price = abs(univ3.Swap.amount0) / abs(univ3.Swap.amount1) # Construct FieldPath to get price of last swap on ETH/USDC pool >>> eth_usdc_last = univ3.Query.swaps( ... orderBy=univ3.Swap.timestamp, ... orderDirection='desc', ... first=1, ... where=[ ... univ3.Swap.pool == '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8' ... ] ... ).price # Query last price FieldPath >>> sg.query(eth_usdc_last) 2628.975030015892 """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) blob = self.query_json(fpaths, pagination_strategy=pagination_strategy) def f(fpath: FieldPath) -> dict[str, Any]: data = fpath._extract_data(blob) if type(data) == list and len(data) == 1 and unwrap: return data[0] return data data = tuple(fpaths | map(f)) if len(data) == 1: return data[0] return data
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], unwrap: bool = True, pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> str | int | float | bool | list | tuple | None
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709,028
subgrounds.client.sync
query_df
Same as :func:`Subgrounds.query` but formats the response data into a Pandas DataFrame. If the response data cannot be flattened to a single query (e.g.: when querying multiple list fields that return different entities), then multiple dataframes are returned ``fpaths`` is a list of :class:`FieldPath` objects that indicate which data must be queried. ``columns`` is an optional argument used to rename the dataframes(s) columns. The length of ``columns`` must be the same as the number of columns of *all* returned dataframes. ``concat`` indicates whether or not the resulting dataframes should be concatenated together. The dataframes must have the same number of columns, as well as the same column names and types (the names can be set using the ``columns`` argument). Args: fpaths: One or more `FieldPath` objects that should be included in the request columns: The column labels. Defaults to None. merge: Whether or not to merge resulting dataframes. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`pandas.DataFrame` containing the reponse data. Example: .. code-block:: python >>> from subgrounds import Subgrounds >>> sg = Subgrounds() >>> univ3 = sg.load_subgraph( ... 'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3') # Define price SyntheticField >>> univ3.Swap.price = abs(univ3.Swap.amount0) / abs(univ3.Swap.amount1) # Query last 10 swaps from the ETH/USDC pool >>> eth_usdc = univ3.Query.swaps( ... orderBy=univ3.Swap.timestamp, ... orderDirection='desc', ... first=10, ... where=[ ... univ3.Swap.pool == '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8' ... ] ... ) >>> sg.query_df([ ... eth_usdc.timestamp, ... eth_usdc.price ... ]) swaps_timestamp swaps_price 0 1643213811 2618.886394 1 1643213792 2618.814281 2 1643213792 2617.500494 3 1643213763 2615.458495 4 1643213763 2615.876574 5 1643213739 2615.352390 6 1643213678 2615.205713 7 1643213370 2614.115746 8 1643213210 2613.077301 9 1643213196 2610.686563
def query_df( self, fpaths: FieldPath | list[FieldPath], columns: list[str] | None = None, concat: bool = False, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> pd.DataFrame | list[pd.DataFrame]: """Same as :func:`Subgrounds.query` but formats the response data into a Pandas DataFrame. If the response data cannot be flattened to a single query (e.g.: when querying multiple list fields that return different entities), then multiple dataframes are returned ``fpaths`` is a list of :class:`FieldPath` objects that indicate which data must be queried. ``columns`` is an optional argument used to rename the dataframes(s) columns. The length of ``columns`` must be the same as the number of columns of *all* returned dataframes. ``concat`` indicates whether or not the resulting dataframes should be concatenated together. The dataframes must have the same number of columns, as well as the same column names and types (the names can be set using the ``columns`` argument). Args: fpaths: One or more `FieldPath` objects that should be included in the request columns: The column labels. Defaults to None. merge: Whether or not to merge resulting dataframes. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: A :class:`pandas.DataFrame` containing the reponse data. Example: .. code-block:: python >>> from subgrounds import Subgrounds >>> sg = Subgrounds() >>> univ3 = sg.load_subgraph( ... 'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3') # Define price SyntheticField >>> univ3.Swap.price = abs(univ3.Swap.amount0) / abs(univ3.Swap.amount1) # Query last 10 swaps from the ETH/USDC pool >>> eth_usdc = univ3.Query.swaps( ... orderBy=univ3.Swap.timestamp, ... orderDirection='desc', ... first=10, ... where=[ ... univ3.Swap.pool == '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8' ... ] ... ) >>> sg.query_df([ ... eth_usdc.timestamp, ... eth_usdc.price ... ]) swaps_timestamp swaps_price 0 1643213811 2618.886394 1 1643213792 2618.814281 2 1643213792 2617.500494 3 1643213763 2615.458495 4 1643213763 2615.876574 5 1643213739 2615.352390 6 1643213678 2615.205713 7 1643213370 2614.115746 8 1643213210 2613.077301 9 1643213196 2610.686563 """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) json_data = self.query_json(fpaths, pagination_strategy=pagination_strategy) return df_of_json(json_data, fpaths, columns, concat)
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], columns: Optional[list[str]] = None, concat: bool = False, pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> pandas.core.frame.DataFrame | list[pandas.core.frame.DataFrame]
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709,029
subgrounds.client.sync
query_df_iter
Same as `query_df` except returns an iterator over the response data pages Args: fpaths: One or more `FieldPath` objects that should be included in the request columns: The column labels. Defaults to None. merge: Whether or not to merge resulting dataframes. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the response data pages, each as a :class:`pandas.DataFrame`.
def query_df_iter( self, fpaths: FieldPath | list[FieldPath], pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[pd.DataFrame | list[pd.DataFrame]]: """Same as `query_df` except returns an iterator over the response data pages Args: fpaths: One or more `FieldPath` objects that should be included in the request columns: The column labels. Defaults to None. merge: Whether or not to merge resulting dataframes. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the response data pages, each as a :class:`pandas.DataFrame`. """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) for page in self.query_json_iter(fpaths, pagination_strategy): yield df_of_json(page, fpaths, None, False)
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> collections.abc.Iterator[pandas.core.frame.DataFrame | list[pandas.core.frame.DataFrame]]
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709,030
subgrounds.client.sync
query_iter
Same as `query` except an iterator over the resonse data pages is returned. Args: fpath: One or more ``FieldPath`` object(s) to query. unwrap: Flag indicating whether or not, in the case where the returned data is a list of one element, the element itself should be returned instead of the list. Defaults to ``True``. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the ``FieldPath`` object(s)' data pages
def query_iter( self, fpaths: FieldPath | list[FieldPath], unwrap: bool = True, pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[str | int | float | bool | list[Any] | tuple | None]: """Same as `query` except an iterator over the resonse data pages is returned. Args: fpath: One or more ``FieldPath`` object(s) to query. unwrap: Flag indicating whether or not, in the case where the returned data is a list of one element, the element itself should be returned instead of the list. Defaults to ``True``. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: An iterator over the ``FieldPath`` object(s)' data pages """ def f(fpath: FieldPath, blob: dict[str, Any]) -> dict[str, Any]: data = fpath._extract_data(blob) if type(data) == list and len(data) == 1 and unwrap: return data[0] return data fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) for page in self.query_json_iter(fpaths, pagination_strategy): data = tuple(fpaths | map(functools.partial(f, blob=page))) if len(data) == 1: yield data[0] else: yield data data = tuple(fpaths | map(functools.partial(f, blob=page))) if len(data) == 1: yield data[0] else: yield data
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], unwrap: bool = True, pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> collections.abc.Iterator[str | int | float | bool | list[typing.Any] | tuple | None]
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709,031
subgrounds.client.sync
query_json
Equivalent to ``Subgrounds.execute(Subgrounds.mk_request(fpaths), pagination_strategy)``. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: The reponse data
def query_json( self, fpaths: FieldPath | list[FieldPath], pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> list[dict[str, Any]]: """Equivalent to ``Subgrounds.execute(Subgrounds.mk_request(fpaths), pagination_strategy)``. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: The reponse data """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) req = self.mk_request(fpaths) data = self.execute(req, pagination_strategy) return [doc.data for doc in data.responses]
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> list[dict[str, typing.Any]]
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709,032
subgrounds.client.sync
query_json_iter
Same as `query_json` returns an iterator over the response data pages. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: list[dict[str, Any]]: The reponse data
def query_json_iter( self, fpaths: FieldPath | list[FieldPath], pagination_strategy: Type[PaginationStrategy] | None = LegacyStrategy, ) -> Iterator[dict[str, Any]]: """Same as `query_json` returns an iterator over the response data pages. Args: fpaths: One or more :class:`FieldPath` objects that should be included in the request. pagination_strategy: A Class implementing the :class:`PaginationStrategy` ``Protocol``. If ``None``, then automatic pagination is disabled. Defaults to :class:`LegacyStrategy`. Returns: list[dict[str, Any]]: The reponse data """ fpaths = list([fpaths] | traverse | map(FieldPath._auto_select) | traverse) req = self.mk_request(fpaths) responses = self.execute_iter(req, pagination_strategy) for resp in responses: yield resp.data
(self, fpaths: subgrounds.subgraph.fieldpath.FieldPath | list[subgrounds.subgraph.fieldpath.FieldPath], pagination_strategy: Optional[Type[subgrounds.pagination.strategies.PaginationStrategy]] = <class 'subgrounds.pagination.strategies.LegacyStrategy'>) -> collections.abc.Iterator[dict[str, typing.Any]]
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709,033
subgrounds.subgraph.fieldpath
SyntheticField
SyntheticField(f: 'Callable', type_: 'TypeRef.T', deps: 'list[FieldPath | SyntheticField] | FieldPath | SyntheticField', default: 'Any' = None) -> 'None'
class SyntheticField(FieldOperatorMixin): STRING: ClassVar[TypeRef.Named] = TypeRef.Named(name="String", kind="SCALAR") INT: ClassVar[TypeRef.Named] = TypeRef.Named(name="Int", kind="SCALAR") FLOAT: ClassVar[TypeRef.Named] = TypeRef.Named(name="Float", kind="SCALAR") BOOL: ClassVar[TypeRef.Named] = TypeRef.Named(name="Boolean", kind="SCALAR") _counter: ClassVar[int] = 0 _f: Callable _type: TypeRef.T _default: Any _deps: list[FieldPath] def __init__( self, f: Callable, type_: TypeRef.T, deps: list[FieldPath | SyntheticField] | FieldPath | SyntheticField, default: Any = None, ) -> None: deps = list([deps] | traverse) def mk_deps( deps: list[FieldPath | SyntheticField], f: Callable, acc: list[tuple[Callable | None, int]] = [], ) -> tuple[Callable, list[FieldPath]]: """If all dependencies are field paths, then this function does nothing. If the dependencies contain one or more other synthetic fields, as is the case when chaining binary operators, then the synthetic field tree is flattened to a single synthetic field containing all leaf dependencies. Args: deps: Initial dependencies for synthetic field f: Function to apply to the values of those dependencies acc: Accumulator. Defaults to []. Returns: A tuple containing the potentially modified function and dependency list. """ match deps: case []: def new_f(*args): new_args = [] _counter = 0 for f_, deps in acc: match (f_, deps): case (None, FieldPath()): new_args.append(args[_counter]) _counter += 1 case ( None, int() | float() | str() | bool() as constant, ): new_args.append(constant) case (f_, list() as deps): new_args.append( f_(*args[_counter : _counter + len(deps)]) ) _counter += len(deps) return f(*new_args) new_deps = [] for _, deps in acc: match deps: case FieldPath() as dep: new_deps.append(dep) case int() | float() | str() | bool(): pass case list() as deps: new_deps = new_deps + deps return (new_f, new_deps) case [SyntheticField(_f=inner_f, _deps=inner_deps), *rest]: acc.append((inner_f, inner_deps)) return mk_deps(rest, f, acc) case [FieldPath() as dep, *rest]: acc.append((None, dep)) return mk_deps(rest, f, acc) case [int() | float() | str() | bool() as constant, *rest]: acc.append((None, constant)) return mk_deps(rest, f, acc) case _ as deps: raise TypeError(f"mk_deps: unexpected argument {deps}") (f, deps) = mk_deps(deps, f) self._f = f self._type = type_ self._default = ( default if default is not None else SyntheticField.default_of_type(type_) ) self._deps = deps SyntheticField._counter += 1 @staticmethod def default_of_type(type_: TypeRef.T): match type_.name: case "String": return "" case "Int": return 0 case "Float": return 0.0 case "Boolean": return False case _: return 0 @classmethod def constant(cls, value: str | int | float | bool) -> SyntheticField: """Returns a constant ``SyntheticField`` with value ``value``. Useful for injecting additional static data to a schema or merging entities. Args: value: The constant field's value Returns: The constant ``SyntheticField`` Example: .. code-block:: python >>> from subgrounds import Subgrounds, SyntheticField >>> sg = Subgrounds() >>> univ3 = sg.load_subgraph( ... "https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3" ... ) # Create constant SyntheticFields >>> univ3.Mint.tx_type = SyntheticField.constant('MINT') >>> univ3.Burn.tx_type = SyntheticField.constant('BURN') # Last 10 mints and burns >>> mints = univ3.Query.mints( ... first=10, ... orderBy=univ3.Mint.timestamp, ... orderDirection='desc' ... ) >>> burns = univ3.Query.burns( ... first=10, ... orderBy=univ3.Burn.timestamp, ... orderDirection='desc' ... ) # Query mints and burns. Notice that we merge the two entity tables by # setting `concat=True` and overwriting the column names (columns must # match the `FieldPaths`) >>> df = sg.query_df( ... [ ... mints.transaction.id, ... mints.timestamp, ... mints.tx_type, ... mints.origin, ... mints.amountUSD, ... burns.transaction.id, ... burns.timestamp, ... burns.tx_type, ... burns.origin, ... burns.amountUSD, ... ], ... columns=['tx_hash', 'timestamp', 'tx_type', 'origin', 'amount_USD'], ... concat=True .. ) # Sort the DataFrame (output is truncated) >>> df.sort_values(by=['timestamp'], ascending=False) tx_hash timestamp tx_type origin amount_USD 0xcbe1... 1656016553 MINT 0x3435.... 7.879784e+03 0xddda... 1656016284 MINT 0xc747.... 5.110840e+04 0xa767... 1656016284 BURN 0xd40d.... 2.804573e+05 0xc132... 1656015853 MINT 0xc747.... 5.122569e+04 0x1444... 1656015773 MINT 0xd11a.... 8.897983e+03 0x3315... 1656015693 MINT 0xb7dd.... 0.000000e+00 0xcc71... 1656015278 BURN 0xa7c4.... 1.254942e+06 0x7bbf... 1656015111 MINT 0xac56.... 3.432075e+04 0xea21... 1656014785 BURN 0x0709.... 2.059106e+04 0x3bd3... 1656014120 MINT 0x5099.... 2.517578e+03 0x1ea5... 1656014018 BURN 0x5099.... 0.000000e+00 0xb9d3... 1656013998 MINT 0x22df.... 8.365750e+04 0xc5e3... 1656013946 BURN 0xac56.... 3.363809e+04 0x7c73... 1656013913 MINT 0x4ce6.... 4.837287e+04 0x95cf... 1656013728 BURN 0x4ce6.... 5.110010e+04 0x76dd... 1656013599 MINT 0x234a.... 5.363896e+03 0x47e5... 1656013580 BURN 0xaf0f.... 0.000000e+00 0xe20e... 1656013455 BURN 0xaf0f.... 0.000000e+00 0xac3e... 1656013237 BURN 0x665d.... 2.254100e+05 0x01c3... 1656013034 BURN 0x0084.... 0.000000e+00 """ match value: case str(): return cls(lambda: value, cls.STRING, []) case int(): return cls(lambda: value, cls.INT, []) case float(): return cls(l
(f: 'Callable', type_: 'TypeRef.T', deps: 'list[FieldPath | SyntheticField] | FieldPath | SyntheticField', default: 'Any' = None) -> 'None'
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709,036
subgrounds.subgraph.fieldpath
__eq__
null
from __future__ import annotations import logging import operator import warnings from dataclasses import dataclass from datetime import datetime from functools import partial, reduce from hashlib import blake2b from typing import TYPE_CHECKING, Any, Callable, ClassVar from pipe import map, traverse from typing_extensions import Self # 3.10 support from subgrounds.query import Query, Selection, arguments_of_field_args from subgrounds.schema import SchemaMeta, TypeMeta, TypeRef from subgrounds.utils import extract_data from .filter import Filter if TYPE_CHECKING: from .subgraph import Subgraph logger = logging.getLogger("subgrounds") warnings.simplefilter("default") FPATH_DEPTH_LIMIT: int = 4 def typeref_of_binary_op( op: str, t1: TypeRef.T, t2: int | float | str | bool | FieldPath | SyntheticField ): def f_typeref(t1, t2): match (op, TypeRef.root_type_name(t1), TypeRef.root_type_name(t2)): case ("add", "String" | "Bytes", "String" | "Bytes"): return TypeRef.Named(name="String", kind="SCALAR") case ( "add" | "sub" | "mul" | "div" | "pow" | "mod", "BigInt" | "Int", "BigInt" | "Int", ): return TypeRef.Named(name="Int", kind="SCALAR") case ( "add" | "sub" | "mul" | "div" | "pow", "BigInt" | "Int", "BigDecimal" | "Float", ): return TypeRef.Named(name="Float", kind="SCALAR") case ( "add" | "sub" | "mul" | "div" | "pow", "BigDecimal" | "Float", "BigInt" | "Int" | "BigDecimal" | "Float", ): return TypeRef.Named(name="Float", kind="SCALAR") case _ as args: raise Exception( f"typeref_of_binary_op: f_typeref: unhandled arguments {args}" ) def f_const(t1, const): match (op, TypeRef.root_type_name(t1), const): case ("add", "String" | "Bytes", str()): return TypeRef.Named(name="String", kind="SCALAR") case ( "add" | "sub" | "mul" | "div" | "pow" | "mod", "BigInt" | "Int", int(), ): return TypeRef.Named(name="Int", kind="SCALAR") case ("add" | "sub" | "mul" | "div" | "pow", "BigInt" | "Int", float()): return TypeRef.Named(name="Float", kind="SCALAR") case ( "add" | "sub" | "mul" | "div" | "pow", "BigDecimal" | "Float", int() | float(), ): return TypeRef.Named(name="Float", kind="SCALAR") case _ as args: raise Exception( f"typeref_of_binary_op: f_typeref: unhandled arguments {args}" ) match t2: case int() | float() | str() | bool() as constant: return f_const(t1, constant) case FieldPath() | SyntheticField() as field: return f_typeref(t1, field._type)
(self, other)
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709,038
subgrounds.subgraph.fieldpath
__init__
null
def __init__( self, f: Callable, type_: TypeRef.T, deps: list[FieldPath | SyntheticField] | FieldPath | SyntheticField, default: Any = None, ) -> None: deps = list([deps] | traverse) def mk_deps( deps: list[FieldPath | SyntheticField], f: Callable, acc: list[tuple[Callable | None, int]] = [], ) -> tuple[Callable, list[FieldPath]]: """If all dependencies are field paths, then this function does nothing. If the dependencies contain one or more other synthetic fields, as is the case when chaining binary operators, then the synthetic field tree is flattened to a single synthetic field containing all leaf dependencies. Args: deps: Initial dependencies for synthetic field f: Function to apply to the values of those dependencies acc: Accumulator. Defaults to []. Returns: A tuple containing the potentially modified function and dependency list. """ match deps: case []: def new_f(*args): new_args = [] _counter = 0 for f_, deps in acc: match (f_, deps): case (None, FieldPath()): new_args.append(args[_counter]) _counter += 1 case ( None, int() | float() | str() | bool() as constant, ): new_args.append(constant) case (f_, list() as deps): new_args.append( f_(*args[_counter : _counter + len(deps)]) ) _counter += len(deps) return f(*new_args) new_deps = [] for _, deps in acc: match deps: case FieldPath() as dep: new_deps.append(dep) case int() | float() | str() | bool(): pass case list() as deps: new_deps = new_deps + deps return (new_f, new_deps) case [SyntheticField(_f=inner_f, _deps=inner_deps), *rest]: acc.append((inner_f, inner_deps)) return mk_deps(rest, f, acc) case [FieldPath() as dep, *rest]: acc.append((None, dep)) return mk_deps(rest, f, acc) case [int() | float() | str() | bool() as constant, *rest]: acc.append((None, constant)) return mk_deps(rest, f, acc) case _ as deps: raise TypeError(f"mk_deps: unexpected argument {deps}") (f, deps) = mk_deps(deps, f) self._f = f self._type = type_ self._default = ( default if default is not None else SyntheticField.default_of_type(type_) ) self._deps = deps SyntheticField._counter += 1
(self, f: Callable, type_: subgrounds.schema.TypeRef.T, deps: list[subgrounds.subgraph.fieldpath.FieldPath | subgrounds.subgraph.fieldpath.SyntheticField] | subgrounds.subgraph.fieldpath.FieldPath | subgrounds.subgraph.fieldpath.SyntheticField, default: Optional[Any] = None) -> NoneType
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709,044
subgrounds.subgraph.fieldpath
__repr__
null
def fieldpaths_of_object( subgraph: Subgraph, object_: TypeMeta.ObjectMeta | TypeMeta.InterfaceMeta ): """Returns generator of FieldPath objects that selects all non-list fields of GraphQL Object of Interface :attr:`object_`. Args: schema: _description_ object_: _description_ Yields: _description_ """ for fmeta in object_.fields: if not fmeta.type_.is_list and len(fmeta.arguments) == 0: match subgraph._schema.type_of_typeref(fmeta.type_): case TypeMeta.ObjectMeta() | TypeMeta.InterfaceMeta(): yield subgraph.__getattribute__(object_.name).__getattribute__( fmeta.name ).id case _: yield subgraph.__getattribute__(object_.name).__getattribute__( fmeta.name )
(self)
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709,053
subgrounds.subgraph.fieldpath
default_of_type
null
@staticmethod def default_of_type(type_: TypeRef.T): match type_.name: case "String": return "" case "Int": return 0 case "Float": return 0.0 case "Boolean": return False case _: return 0
(type_: subgrounds.schema.TypeRef.T)
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709,130
blabber
StarterGenerator
null
class StarterGenerator: def _shuffle_starters(self): """Resets the generator's internal starter queue.""" self.starters = random.sample( list(self.original_starters), len(self.original_starters) ) def __init__(self): self.original_starters = set() data_dir = Path(__file__).parent / "data" for topic_path in data_dir.glob("*.txt"): with open(topic_path, encoding="utf-8") as topic_file: self.original_starters.update( line.strip() for line in topic_file.readlines() ) self._shuffle_starters() def starter(self): """Gets a fresh starter from the queue.""" # If the queue is out of starters, re-shuffle if len(self.starters) == 0: self._shuffle_starters() # Get a random starter while also removing it from the queue return self.starters.pop()
()
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709,131
blabber
__init__
null
def __init__(self): self.original_starters = set() data_dir = Path(__file__).parent / "data" for topic_path in data_dir.glob("*.txt"): with open(topic_path, encoding="utf-8") as topic_file: self.original_starters.update( line.strip() for line in topic_file.readlines() ) self._shuffle_starters()
(self)
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709,132
blabber
_shuffle_starters
Resets the generator's internal starter queue.
def _shuffle_starters(self): """Resets the generator's internal starter queue.""" self.starters = random.sample( list(self.original_starters), len(self.original_starters) )
(self)
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709,133
blabber
starter
Gets a fresh starter from the queue.
def starter(self): """Gets a fresh starter from the queue.""" # If the queue is out of starters, re-shuffle if len(self.starters) == 0: self._shuffle_starters() # Get a random starter while also removing it from the queue return self.starters.pop()
(self)
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709,135
bzutech.bzutechapi.BzuTechAPI
BzuTech
null
class BzuTech: def __init__(self, email: str, password: str) -> None: self.email = email self.password = password self._token = None self._operatorid = None self._contratoid = None self.dispositivos = None self.httpheaders = {} async def start(self) -> bool: if await self._async_auth(): self.httpheaders["Authorization"] = "Bearer " + self._token contract = await self._async_set_contrato() got_devices = await self._async_set_dispositivos() return contract and got_devices else: return False async def _async_auth(self) -> bool: url = "https://back-prd.bzutech.com.br/auth/login/" data = {"operador_email": self.email, "password": self.password} client = ClientSession() try: async with client.post(url=url, data=data) as resp: resposta = await resp.json() await client.close() self._token = resposta["tokens"]["access"] self._operatorid = resposta["id"] return True except(KeyError): return False async def _async_set_contrato(self) -> bool: """""" url = "https://back-prd.bzutech.com.br/operador/navbar/" + str(self._operatorid) client = ClientSession() async with client.get(url=url, headers = self.httpheaders) as resp: resposta = await resp.json() await client.close() self._contratoid = resposta["empresas"][0]["contratos_id"] return True async def _async_set_dispositivos(self) -> bool: url = "https://back-prd.bzutech.com.br/dispositivos/listar/" + str( self._contratoid ) dispositivos = {} client = ClientSession() async with client.get(url=url, headers = self.httpheaders) as resp: resposta = await resp.json() for disp in resposta: if disp["status_dispositivo"] == 1: chipid = int(disp["boot_chip_id"]) dispname = ( disp["dispnum"] if disp["dispname"] == None else disp["dispname"] ) dispositivo = Device(chipid, self.httpheaders, dispname) await dispositivo.initialize() dispositivos[str(chipid)] = dispositivo self.dispositivos = dispositivos await client.close() return True def get_token(self) -> str: return self._token def get_device_names(self) -> list: return list(self.dispositivos.keys()) def get_sensors(self, chipid: str) -> list: if chipid in self.get_device_names(): return list(self.dispositivos[chipid].get_sensor_names()) return ["-1"] def get_sensors_on(self, chipid: str, port: int) -> list: if chipid in self.get_device_names(): list = self.dispositivos[chipid].get_sensor_names_on(port) return list return ["-1"] def get_endpoint_on(self, chipid: str, port: int) -> str: if(chipid in self.get_device_names()): return self.dispositivos[chipid].get_ep_on(port) return "-1" def get_reading(self, chipid: str, sensorname:str): return self.dispositivos[chipid].get_readings(sensorname.upper()) async def send_reading(self, sensoref:str, DeviceID: str, reading: float, date: str): url = "https://back-dev.bzutech.com.br/logs/home_assistence/" data = '{"bci":"'+DeviceID+'", "date":"'+ date +'", "data":\"[{\'ref\':\''+ sensoref +'\',\'med\':'+ str(reading) +'}]\"}' print(data) data = json.loads(data) client = ClientSession() async with client.post(url, json=data) as resp: await client.close() print(resp.text) return (data, resp.text) def get_apiversion(self): return "2.3.3"
(email: str, password: str) -> None
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709,136
bzutech.bzutechapi.BzuTechAPI
__init__
null
def __init__(self, email: str, password: str) -> None: self.email = email self.password = password self._token = None self._operatorid = None self._contratoid = None self.dispositivos = None self.httpheaders = {}
(self, email: str, password: str) -> NoneType
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709,138
bzutech.bzutechapi.BzuTechAPI
_async_set_contrato
def __init__(self, email: str, password: str) -> None: self.email = email self.password = password self._token = None self._operatorid = None self._contratoid = None self.dispositivos = None self.httpheaders = {}
(self) -> bool
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709,140
bzutech.bzutechapi.BzuTechAPI
get_apiversion
null
def get_apiversion(self): return "2.3.3"
(self)
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709,141
bzutech.bzutechapi.BzuTechAPI
get_device_names
null
def get_device_names(self) -> list: return list(self.dispositivos.keys())
(self) -> list
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709,142
bzutech.bzutechapi.BzuTechAPI
get_endpoint_on
null
def get_endpoint_on(self, chipid: str, port: int) -> str: if(chipid in self.get_device_names()): return self.dispositivos[chipid].get_ep_on(port) return "-1"
(self, chipid: str, port: int) -> str
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709,143
bzutech.bzutechapi.BzuTechAPI
get_reading
null
def get_reading(self, chipid: str, sensorname:str): return self.dispositivos[chipid].get_readings(sensorname.upper())
(self, chipid: str, sensorname: str)
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709,144
bzutech.bzutechapi.BzuTechAPI
get_sensors
null
def get_sensors(self, chipid: str) -> list: if chipid in self.get_device_names(): return list(self.dispositivos[chipid].get_sensor_names()) return ["-1"]
(self, chipid: str) -> list
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709,145
bzutech.bzutechapi.BzuTechAPI
get_sensors_on
null
def get_sensors_on(self, chipid: str, port: int) -> list: if chipid in self.get_device_names(): list = self.dispositivos[chipid].get_sensor_names_on(port) return list return ["-1"]
(self, chipid: str, port: int) -> list
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709,146
bzutech.bzutechapi.BzuTechAPI
get_token
null
def get_token(self) -> str: return self._token
(self) -> str
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709,149
bzutech.device.Device
Device
null
class Device: def __init__(self, chipid: int, token: dict, dispname: str = "" ): self.chipid = chipid self.dispname = dispname self.httpheaders = token self.sensores = {} async def _async_set_sensores(self): url = "https://back-prd.bzutech.com.br/dispositivos/canais-list/" + str( self.chipid ) sensores = [] client = ClientSession() async with client.get(url, headers = self.httpheaders) as resp: resposta = await resp.json() await client.close() for sensor in resposta: if sensor["ultima_medicao_sensor"] != None: sensores.append( Sensor ( self.chipid, sensor["sensor_nome"].upper(), sensor["apelido_canal"], self.httpheaders ) ) return sensores async def initialize(self): self.sensores = await self._async_set_sensores() flemis = {} for sensor in self.sensores: flemis[sensor.apelido] = sensor self.sensores = flemis def get_sensor_names(self): return list(self.sensores.keys()) def get_sensor_names_on(self, port: str): sensores = [] for sensor in self.get_sensor_names(): if sensor[-1] == port: sensores.append(sensor) return sensores def get_ep_on(self, port:int): sensors = self.get_sensor_names_on(str(port)) for sensor in sensors: if "ADS7878" in sensor: return "EP400" if "RTZSBZ" in sensor: return "EP300" if "SGP30" in sensor: return "EP200" if "DOOR" in sensor: return "EP121" if "SHT30" in sensor: return "EP111" async def get_readings(self, nome_sensor): return await self.sensores[nome_sensor].get_leitura() def get_chipid(self): return self.chipid
(chipid: int, token: dict, dispname: str = '')
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709,150
bzutech.device.Device
__init__
null
def __init__(self, chipid: int, token: dict, dispname: str = "" ): self.chipid = chipid self.dispname = dispname self.httpheaders = token self.sensores = {}
(self, chipid: int, token: dict, dispname: str = '')
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709,152
bzutech.device.Device
get_chipid
null
def get_chipid(self): return self.chipid
(self)
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709,153
bzutech.device.Device
get_ep_on
null
def get_ep_on(self, port:int): sensors = self.get_sensor_names_on(str(port)) for sensor in sensors: if "ADS7878" in sensor: return "EP400" if "RTZSBZ" in sensor: return "EP300" if "SGP30" in sensor: return "EP200" if "DOOR" in sensor: return "EP121" if "SHT30" in sensor: return "EP111"
(self, port: int)
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709,155
bzutech.device.Device
get_sensor_names
null
def get_sensor_names(self): return list(self.sensores.keys())
(self)
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709,156
bzutech.device.Device
get_sensor_names_on
null
def get_sensor_names_on(self, port: str): sensores = [] for sensor in self.get_sensor_names(): if sensor[-1] == port: sensores.append(sensor) return sensores
(self, port: str)
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709,158
bzutech.sensor.Sensor
Sensor
null
class Sensor: def __init__(self, chipid: int, canal: str, apelido: str, token:dict) -> None: self.chipid = "11000"[0 : 8 - len(str(chipid))] + str(chipid) self.apelido = canal self.httpheaders = token self.porta = canal[-1] self.last_reading = 0 tipos_sensores = { "TMP": "Temperatura", "HUM": "Humidade", "VOT": "Voltagem", "CO2": "CO2", "CUR": "Corrente", "LUM": "Luminosidade", "PIR": "", "DOOR": "Porta", "DOR": "DOR", "M10": "M10", "M25": "M25", "M40": "M40", "SND": "SOM", "M01": "M01", "C01": "C01", "VOC": "Gases Volateis", "DOS": "DOS", "VOA": "Voltagem", "VOB": "Voltagem", "VOC": "Voltagem", "CRA": "Corrente", "CRB": "Corrente", "CRC": "Corrente", "VRA": "Voltagem", "VRB": "Voltagem", "VRC": "Voltagem", "C05": "C05", "C01": "C01", "C25": "C25", "C40": "C40", "C10": "C10", "UPT": "UPTIME", "DBM": "WIFI", "BAT": "BATTERY", "MEM": "MEMORY" } cod_sensores = { "AHT10-TMP": "01", "SCT013-CUR": "02", "SHT30-TMP": "03", "SR602-DOOR": "04", "TSL2561-LUM": "05", "ZMPT-CUR": "06", "AHT10-HUM": "07", "SHT30-HUM": "08", "ZMPT-VOT": "09", "SR602-PIR": "10", "BH1750-LUM": "11", "SCHNE-VOT": "12", "S16L201D-PIR": "13", "SCHNE-CUR": "14", "SPS30-M01": "64", "SPS30-M25": "66", "SPS30-M40": "68", "SPS30-M10": "70", "SHT20-TMP": "20", "DOOR-DOR": "23", "AHT20-TMP": "22", "SHT26-HUM": "85", "SHT24-HUM": "84", "SHT25-TMP": "83", "RTZSBZ-SND": "82", "SGP30-VOC": "60", "SGP30-CO2": "62", "SPS30-C01": "87", "DOOR-DOS": "86", "SPS30-C05": "100", "SPS30-C10": "99", "SPS30-C25": "97", "SPS30-C40": "98", "ADS7878-VOA": "88", "ADS7878-VOB": "89", "ADS7878-VOC": "90", "ADS7878-CRA": "91", "ADS7878-CRB": "92", "ADS7878-CRC": "93", "ADS7878-VRA": "94", "ADS7878-VRB": "95", "ADS7878-VRC": "96", "GATEWAY-DBM": "101", "GATEWAY-MEM": "102", "GATEWAY-BAT": "103", "GATEWAY-UPT": "104" } try: self.tipo = tipos_sensores[canal.split("-")[1]] self.sensorref = canal[-1] + "00" + cod_sensores[canal[0:-2]] except KeyError: self.tipo = tipos_sensores["PIR"] self.sensorref = canal[-1] + "00" + cod_sensores["SR602-PIR"] async def get_leitura(self): url = ( "https://back-prd.bzutech.com.br/logs/ultima_medicao/" + self.chipid + "/" + self.sensorref ) client = ClientSession( ) async with client.get(url, headers = self.httpheaders) as resp: resposta = await resp.json() await client.close() if("medicao" in resposta): self.last_reading = int(resposta["medicao"]) / 1000000 print(resposta) return self.last_reading
(chipid: int, canal: str, apelido: str, token: dict) -> None
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709,159
bzutech.sensor.Sensor
__init__
null
def __init__(self, chipid: int, canal: str, apelido: str, token:dict) -> None: self.chipid = "11000"[0 : 8 - len(str(chipid))] + str(chipid) self.apelido = canal self.httpheaders = token self.porta = canal[-1] self.last_reading = 0 tipos_sensores = { "TMP": "Temperatura", "HUM": "Humidade", "VOT": "Voltagem", "CO2": "CO2", "CUR": "Corrente", "LUM": "Luminosidade", "PIR": "", "DOOR": "Porta", "DOR": "DOR", "M10": "M10", "M25": "M25", "M40": "M40", "SND": "SOM", "M01": "M01", "C01": "C01", "VOC": "Gases Volateis", "DOS": "DOS", "VOA": "Voltagem", "VOB": "Voltagem", "VOC": "Voltagem", "CRA": "Corrente", "CRB": "Corrente", "CRC": "Corrente", "VRA": "Voltagem", "VRB": "Voltagem", "VRC": "Voltagem", "C05": "C05", "C01": "C01", "C25": "C25", "C40": "C40", "C10": "C10", "UPT": "UPTIME", "DBM": "WIFI", "BAT": "BATTERY", "MEM": "MEMORY" } cod_sensores = { "AHT10-TMP": "01", "SCT013-CUR": "02", "SHT30-TMP": "03", "SR602-DOOR": "04", "TSL2561-LUM": "05", "ZMPT-CUR": "06", "AHT10-HUM": "07", "SHT30-HUM": "08", "ZMPT-VOT": "09", "SR602-PIR": "10", "BH1750-LUM": "11", "SCHNE-VOT": "12", "S16L201D-PIR": "13", "SCHNE-CUR": "14", "SPS30-M01": "64", "SPS30-M25": "66", "SPS30-M40": "68", "SPS30-M10": "70", "SHT20-TMP": "20", "DOOR-DOR": "23", "AHT20-TMP": "22", "SHT26-HUM": "85", "SHT24-HUM": "84", "SHT25-TMP": "83", "RTZSBZ-SND": "82", "SGP30-VOC": "60", "SGP30-CO2": "62", "SPS30-C01": "87", "DOOR-DOS": "86", "SPS30-C05": "100", "SPS30-C10": "99", "SPS30-C25": "97", "SPS30-C40": "98", "ADS7878-VOA": "88", "ADS7878-VOB": "89", "ADS7878-VOC": "90", "ADS7878-CRA": "91", "ADS7878-CRB": "92", "ADS7878-CRC": "93", "ADS7878-VRA": "94", "ADS7878-VRB": "95", "ADS7878-VRC": "96", "GATEWAY-DBM": "101", "GATEWAY-MEM": "102", "GATEWAY-BAT": "103", "GATEWAY-UPT": "104" } try: self.tipo = tipos_sensores[canal.split("-")[1]] self.sensorref = canal[-1] + "00" + cod_sensores[canal[0:-2]] except KeyError: self.tipo = tipos_sensores["PIR"] self.sensorref = canal[-1] + "00" + cod_sensores["SR602-PIR"]
(self, chipid: int, canal: str, apelido: str, token: dict) -> NoneType
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709,165
click.decorators
decorator
null
def option( *param_decls: str, cls: t.Optional[t.Type[Option]] = None, **attrs: t.Any ) -> t.Callable[[FC], FC]: """Attaches an option to the command. All positional arguments are passed as parameter declarations to :class:`Option`; all keyword arguments are forwarded unchanged (except ``cls``). This is equivalent to creating an :class:`Option` instance manually and attaching it to the :attr:`Command.params` list. For the default option class, refer to :class:`Option` and :class:`Parameter` for descriptions of parameters. :param cls: the option class to instantiate. This defaults to :class:`Option`. :param param_decls: Passed as positional arguments to the constructor of ``cls``. :param attrs: Passed as keyword arguments to the constructor of ``cls``. """ if cls is None: cls = Option def decorator(f: FC) -> FC: _param_memo(f, cls(param_decls, **attrs)) return f return decorator
(f: ~FC) -> ~FC
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709,167
click.decorators
decorator
null
def argument( *param_decls: str, cls: t.Optional[t.Type[Argument]] = None, **attrs: t.Any ) -> t.Callable[[FC], FC]: """Attaches an argument to the command. All positional arguments are passed as parameter declarations to :class:`Argument`; all keyword arguments are forwarded unchanged (except ``cls``). This is equivalent to creating an :class:`Argument` instance manually and attaching it to the :attr:`Command.params` list. For the default argument class, refer to :class:`Argument` and :class:`Parameter` for descriptions of parameters. :param cls: the argument class to instantiate. This defaults to :class:`Argument`. :param param_decls: Passed as positional arguments to the constructor of ``cls``. :param attrs: Passed as keyword arguments to the constructor of ``cls``. """ if cls is None: cls = Argument def decorator(f: FC) -> FC: _param_memo(f, cls(param_decls, **attrs)) return f return decorator
(f: ~FC) -> ~FC
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709,171
cligj
geojson_type_bbox_opt
GeoJSON bbox output mode
def geojson_type_bbox_opt(default=False): """GeoJSON bbox output mode""" return click.option( '--bbox', 'geojson_type', flag_value='bbox', default=default, help="Output as GeoJSON bounding box array(s).")
(default=False)
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709,172
cligj
geojson_type_collection_opt
GeoJSON FeatureCollection output mode
def geojson_type_collection_opt(default=False): """GeoJSON FeatureCollection output mode""" return click.option( '--collection', 'geojson_type', flag_value='collection', default=default, help="Output as GeoJSON feature collection(s).")
(default=False)
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709,173
cligj
geojson_type_feature_opt
GeoJSON Feature or Feature sequence output mode
def geojson_type_feature_opt(default=False): """GeoJSON Feature or Feature sequence output mode""" return click.option( '--feature', 'geojson_type', flag_value='feature', default=default, help="Output as GeoJSON feature(s).")
(default=False)
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709,175
cligj.features
normalize_feature_inputs
Click callback that normalizes feature input values. Returns a generator over features from the input value. Parameters ---------- ctx: a Click context param: the name of the argument or option value: object The value argument may be one of the following: 1. A list of paths to files containing GeoJSON feature collections or feature sequences. 2. A list of string-encoded coordinate pairs of the form "[lng, lat]", or "lng, lat", or "lng lat". If no value is provided, features will be read from stdin. Yields ------ Mapping A GeoJSON Feature represented by a Python mapping
def normalize_feature_inputs(ctx, param, value): """Click callback that normalizes feature input values. Returns a generator over features from the input value. Parameters ---------- ctx: a Click context param: the name of the argument or option value: object The value argument may be one of the following: 1. A list of paths to files containing GeoJSON feature collections or feature sequences. 2. A list of string-encoded coordinate pairs of the form "[lng, lat]", or "lng, lat", or "lng lat". If no value is provided, features will be read from stdin. Yields ------ Mapping A GeoJSON Feature represented by a Python mapping """ for feature_like in value or ('-',): try: with click.open_file(feature_like, encoding="utf-8") as src: for feature in iter_features(iter(src)): yield feature except IOError: coords = list(coords_from_query(feature_like)) yield { 'type': 'Feature', 'properties': {}, 'geometry': { 'type': 'Point', 'coordinates': coords}}
(ctx, param, value)
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709,185
evidently.pipeline.column_mapping
ColumnMapping
ColumnMapping(target: Optional[str] = 'target', prediction: Union[str, int, Sequence[str], Sequence[int], NoneType] = 'prediction', datetime: Optional[str] = 'datetime', id: Optional[str] = None, numerical_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, datetime_features: Optional[List[str]] = None, target_names: Union[List[Union[int, str]], Dict[Union[int, str], str], NoneType] = None, task: Optional[str] = None, pos_label: Union[str, int, NoneType] = 1, text_features: Optional[List[str]] = None, embeddings: Optional[Dict[str, List[str]]] = None, user_id: Optional[str] = 'user_id', item_id: Optional[str] = 'item_id', recommendations_type: Union[evidently.pipeline.column_mapping.RecomType, str] = <RecomType.SCORE: 'score'>)
class ColumnMapping: target: Optional[str] = "target" prediction: Optional[Union[str, int, Union[Sequence[str], Sequence[int]]]] = "prediction" datetime: Optional[str] = "datetime" id: Optional[str] = None numerical_features: Optional[List[str]] = None categorical_features: Optional[List[str]] = None datetime_features: Optional[List[str]] = None target_names: Optional[TargetNames] = None task: Optional[str] = None pos_label: Optional[Union[str, int]] = 1 text_features: Optional[List[str]] = None embeddings: Optional[Embeddings] = None user_id: Optional[str] = "user_id" item_id: Optional[str] = "item_id" recommendations_type: Union[RecomType, str] = RecomType.SCORE @property def recom_type(self) -> RecomType: if isinstance(self.recommendations_type, str): return RecomType(self.recommendations_type) return self.recommendations_type def is_classification_task(self) -> bool: return self.task == TaskType.CLASSIFICATION_TASK def is_regression_task(self) -> bool: return self.task == TaskType.REGRESSION_TASK
(target: Optional[str] = 'target', prediction: Union[str, int, Sequence[str], Sequence[int], NoneType] = 'prediction', datetime: Optional[str] = 'datetime', id: Optional[str] = None, numerical_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, datetime_features: Optional[List[str]] = None, target_names: Union[List[Union[int, str]], Dict[Union[int, str], str], NoneType] = None, task: Optional[str] = None, pos_label: Union[str, int, NoneType] = 1, text_features: Optional[List[str]] = None, embeddings: Optional[Dict[str, List[str]]] = None, user_id: Optional[str] = 'user_id', item_id: Optional[str] = 'item_id', recommendations_type: Union[evidently.pipeline.column_mapping.RecomType, str] = <RecomType.SCORE: 'score'>) -> None
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0.020340049639344215, 0.003643385134637356, 0.007391616236418486, 0.04514080286026001 ]
709,186
evidently.pipeline.column_mapping
__eq__
null
from dataclasses import dataclass from enum import Enum from typing import Dict from typing import List from typing import Optional from typing import Sequence from typing import Union class TaskType: REGRESSION_TASK: str = "regression" CLASSIFICATION_TASK: str = "classification" RECOMMENDER_SYSTEMS: str = "recsys"
(self, other)
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709,189
evidently.pipeline.column_mapping
is_classification_task
null
def is_classification_task(self) -> bool: return self.task == TaskType.CLASSIFICATION_TASK
(self) -> bool
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709,190
evidently.pipeline.column_mapping
is_regression_task
null
def is_regression_task(self) -> bool: return self.task == TaskType.REGRESSION_TASK
(self) -> bool
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709,191
evidently.pipeline.column_mapping
TaskType
null
class TaskType: REGRESSION_TASK: str = "regression" CLASSIFICATION_TASK: str = "classification" RECOMMENDER_SYSTEMS: str = "recsys"
()
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709,192
evidently.nbextension
_jupyter_nbextension_paths
null
def _jupyter_nbextension_paths(): return [{"section": "notebook", "src": "nbextension/static", "dest": "evidently", "require": "evidently/extension"}]
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709,196
biglist._biglist
Biglist
null
class Biglist(BiglistBase[Element]): registered_storage_formats = { 'json': serializer.JsonSerializer, 'pickle': serializer.PickleSerializer, 'pickle-zstd': serializer.ZstdPickleSerializer, } DEFAULT_STORAGE_FORMAT = 'pickle-zstd' @classmethod def register_storage_format( cls, name: str, serializer: type[serializer.Serializer], ) -> None: """ Register a new serializer to handle data file dumping and loading. This class has a few serializers registered out of the box. They should be adequate for most applications. Parameters ---------- name Name of the format to be associated with the new serializer. After registering the new serializer with name "xyz", one can use ``storage_format='xyz'`` in calls to :meth:`new`. When reading the object back from persistence, make sure this registry is also in place so that the correct deserializer can be found. serializer A subclass of `upathlib.serializer.Serializer <https://github.com/zpz/upathlib/blob/main/src/upathlib/serializer.py>`_. Although this class needs to provide the ``Serializer`` API, it is possible to write data files in text mode. The registered 'json' format does that. """ good = string.ascii_letters + string.digits + '-_' assert all(n in good for n in name) if name.replace('_', '-') in cls.registered_storage_formats: raise ValueError(f"serializer '{name}' is already registered") name = name.replace('_', '-') cls.registered_storage_formats[name] = serializer @classmethod def new( cls, path: PathType | None = None, *, batch_size: int | None = None, storage_format: str | None = None, serialize_kwargs: dict | None = None, deserialize_kwargs: dict | None = None, init_info: dict = None, **kwargs, ) -> Self: """ Parameters ---------- path Passed on to :meth:`BiglistBase.new`. batch_size Max number of data elements in each persisted data file. There's no good default value for this parameter, although one is provided (currently the default is 1000), because the code of :meth:`new` doesn't know the typical size of the data elements. User is recommended to specify the value of this parameter. In choosing a value for ``batch_size``, the most important consideration is the size of each data file, which is determined by the typical size of the data elements as well as ``batch_size``, which is the upper bound of the the number of elements in each file. There are several considerations about the data file sizes: - It should not be so small that the file reading/writing is a large overhead relative to actual processing of the data. This is especially important when ``path`` is cloud storage. - It should not be so large that it is "unwieldy", e.g. approaching 1GB. - When :meth:`__iter__`\\ating over a :class:`Biglist` object, there can be up to (by default) 4 files-worth of data in memory at any time, where 4 is ``self._n_read_threads`` plus 1. - When :meth:`append`\\ing or :meth:`extend`\\ing to a :class:`Biglist` object at high speed, there can be up to (by default) 9 times ``batch_size`` data elements in memory at any time, where 9 is ``self._n_write_threads`` plus 1. See :meth:`_flush` and :class:`~biglist._biglist.Dumper`. Another consideration is access pattern of elements in the :class:`Biglist`. If there are many "jumping around" with random element access, large data files will lead to very wasteful file loading, because to read any element, its hosting file must be read into memory. (After all, if the application is heavy on random access, then :class:`Biglist` is **not** the right tool.) The performance of iteration is not expected to be highly sensitive to the value of ``batch_size``, as long as it is in a reasonable range. A rule of thumb: it is recommended to keep the persisted files between 32-128MB in size. (Note: no benchmark was performed to back this recommendation.) storage_format This should be a key in :data:`registered_storage_formats`. If not specified, :data:`DEFAULT_STORAGE_FORMAT` is used. serialize_kwargs Additional keyword arguments to the serialization function. deserialize_kwargs Additional keyword arguments to the deserialization function. ``serialize_kwargs`` and ``deserialize_kwargs`` are rarely needed. One use case is ``schema`` when storage format is "parquet". See :class:`~biglist._biglist.ParquetSerializer`. ``serialize_kwargs`` and ``deserialize_kwargs``, if not ``None``, will be saved in the "info.json" file, hence they must be JSON serializable, meaning they need to be the few simple native Python types that are supported by the standard ``json`` library. (However, the few formats "natively" supported by Biglist may get special treatment to relax this requirement.) If this is not possible, the solution is to define a custom serialization class and register it with :meth:`register_storage_format`. **kwargs additional arguments are passed on to :meth:`BiglistBase.new`. Returns ------- Biglist A new :class:`Biglist` object. """ if not batch_size: batch_size = 1000 warnings.warn( 'The default batch-size, 1000, may not be optimal for your use case; consider using the parameter ``batch_size``.' ) else: assert batch_size > 0 if storage_format is None: storage_format = cls.DEFAULT_STORAGE_FORMAT if storage_format.replace('_', '-') not in cls.registered_storage_formats: raise ValueError(f"invalid value of `storage_format`: '{storage_format}'") init_info = { **(init_info or {}), 'storage_format': storage_format.replace('_', '-'), 'storage_version': 3, # `storage_version` is a flag for certain breaking changes in the implementation, # such that certain parts of the code (mainly concerning I/O) need to # branch into different treatments according to the version. # This has little relation to `storage_format`. # version 0 designator introduced on 2022/3/8 # version 1 designator introduced on 2022/7/25 # version 2 designator introduced in version 0.7.4. # version 3 designator introduced in version 0.7.7. 'batch_size': batch_size, 'data_files_info': [], } if serialize_kwargs: init_info['serialize_kwargs'] = serialize_kwargs if deserialize_kwargs: init_info['deserialize_kwargs'] = deserialize_kwargs obj = super().new(path, init_info=init_info, **kwargs) # type: ignore return obj def __init__( self, *args, **kwargs, ): """ Please see the base class for additional documentation. """ super().__init__(*args, **kwargs) self.keep_files: bool = True """Indicates whether the persisted files should be kept or deleted when the object is garbage-collected.""" self._append_buffer: list = [] self._append_files_buffer: list = [] self._file_dumper = None self._n_write_threads = 4 """This value affects memory demand during quick "appending" (and
(*args, **kwargs)
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709,197
biglist._biglist
__del__
null
def __del__(self) -> None: if getattr(self, 'keep_files', True) is False: self.destroy(concurrent=False) else: self._warn_flush() self.flush()
(self) -> NoneType
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709,198
biglist._biglist
__getitem__
Access a data item by its index; negative index works as expected. Items not yet "flushed" are not accessible by this method. They are considered "invisible" to this method. Similarly, negative ``idx`` operates in the range of flushed items only. .. versionchanged:: 0.7.4 In previous versions, the accessible items include those that are not yet flushed.
def __getitem__(self, idx: int) -> Element: """ Access a data item by its index; negative index works as expected. Items not yet "flushed" are not accessible by this method. They are considered "invisible" to this method. Similarly, negative ``idx`` operates in the range of flushed items only. .. versionchanged:: 0.7.4 In previous versions, the accessible items include those that are not yet flushed. """ return super().__getitem__(idx)
(self, idx: int) -> ~Element
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709,199
biglist._biglist
__getstate__
null
def __getstate__(self): return (self.path,)
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709,200
biglist._biglist
__init__
Please see the base class for additional documentation.
def __init__( self, *args, **kwargs, ): """ Please see the base class for additional documentation. """ super().__init__(*args, **kwargs) self.keep_files: bool = True """Indicates whether the persisted files should be kept or deleted when the object is garbage-collected.""" self._append_buffer: list = [] self._append_files_buffer: list = [] self._file_dumper = None self._n_write_threads = 4 """This value affects memory demand during quick "appending" (and flushing/dumping in the background). If the memory consumption of each batch is large, you could manually set this to a lower value, like:: lst = Biglist(path) lst._n_write_threads = 4 """ self._serialize_kwargs = self.info.get('serialize_kwargs', {}) self._deserialize_kwargs = self.info.get('deserialize_kwargs', {}) if self.storage_format == 'parquet' and 'schema_spec' in self._serialize_kwargs: # Build the schema so that it does not need to be done each time the function # ``ParquetSerializer.serialize`` is called. Maybe this does not matter. assert 'schema' not in self._serialize_kwargs kk = copy.deepcopy(self._serialize_kwargs) kk['schema'] = make_parquet_schema(kk['schema_spec']) del kk['schema_spec'] self._serialize_kwargs = kk _biglist_objs.add(self) # For back compat. if self.info.get('storage_version', 0) < 3: # This is not called by ``new``, instead is opening an existing dataset. # Usually these legacy datasets are in a "read-only" mode, i.e., you should # not append more data to them. If you do, the back-compat code below # may not be totally reliable if the dataset is being used by multiple workers # concurrently. if 'data_files_info' not in self.info: # added in 0.7.4 if self.storage_version == 0: # This may not be totally reliable in every scenario. # The older version had a parameter `lazy`, which is gone now. # After some time we may stop supporting this storage version. (7/27/2022) # However, as long as older datasets are in a "read-only" status, # this is fine. try: data_info_file = self.path / 'datafiles_info.json' data_files = data_info_file.read_json() # A list of tuples, (file_name, item_count) except FileNotFoundError: data_files = [] elif self.storage_version == 1: # Starting with storage_version 1, data file name is # <timestamp>_<uuid>_<itemcount>.<ext> # <timestamp> contains a '.', no '_'; # <uuid> contains '-', no '_'; # <itemcount> contains no '-' nor '_'; # <ext> may contain '_'. files0 = (v.name for v in self.data_path.iterdir()) files1 = (v.split('_') + [v] for v in files0) files2 = ( (float(v[0]), v[-1], int(v[2].partition('.')[0])) # timestamp, file name, item count for v in files1 ) files = sorted(files2) # sort by timestamp if files: data_files = [ (v[1], v[2]) for v in files ] # file name, item count else: data_files = [] else: pass # `storage_version == 2 already has `data_files_info` in `self.info`, # but its format may be bad, hence it's included below. if data_files: data_files_cumlength = list( itertools.accumulate(v[1] for v in data_files) ) data_files_info = [ (filename, count, cumcount) for (filename, count), cumcount in zip( data_files, data_files_cumlength ) ] # Each element of the list is a tuple containing file name, item count in file, and cumsum of item counts. else: data_files_info = [] self.info['data_files_info'] = data_files_info with self._info_file.lock() as ff: ff.write_json(self.info, overwrite=True) else: # Added in 0.7.5: check for a bug introduced in 0.7.4. # Convert full path to file name. # Version 0.7.4 was used very briefly, hence very few datasets # were created by that version. data_files_info = self.info['data_files_info'] if data_files_info: new_info = None if os.name == 'nt' and '\\' in data_files_info[0][0]: new_info = [ (f[(f.rfind('\\') + 1) :], *_) for f, *_ in data_files_info ] elif '/' in data_files_info[0][0]: new_info = [ (f[(f.rfind('/') + 1) :], *_) for f, *_ in data_files_info ] if new_info: self.info['data_files_info'] = new_info with self._info_file.lock() as ff: ff.write_json(self.info, overwrite=True)
(self, *args, **kwargs)
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709,201
biglist._biglist
__iter__
Iterate over all the elements. Items that are not yet "flushed" are invisible to this iteration. .. versionchanged:: 0.7.4 In previous versions, this iteration includes those items that are not yet flushed.
def __iter__(self) -> Iterator[Element]: """ Iterate over all the elements. Items that are not yet "flushed" are invisible to this iteration. .. versionchanged:: 0.7.4 In previous versions, this iteration includes those items that are not yet flushed. """ self._warn_flush() return super().__iter__()
(self) -> collections.abc.Iterator[~Element]
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709,202
biglist._biglist
__len__
Number of data items in this biglist. If data is being appended to this biglist, then this method only includes the items that have been "flushed" to storage. Data items in the internal memory buffer are not counted. The buffer is empty upon calling :meth:`_flush` (internally and automatically) or :meth:`flush` (explicitly by user). .. versionchanged:: 0.7.4 In previous versions, this count includes items that are not yet flushed.
def __len__(self) -> int: """ Number of data items in this biglist. If data is being appended to this biglist, then this method only includes the items that have been "flushed" to storage. Data items in the internal memory buffer are not counted. The buffer is empty upon calling :meth:`_flush` (internally and automatically) or :meth:`flush` (explicitly by user). .. versionchanged:: 0.7.4 In previous versions, this count includes items that are not yet flushed. """ self._warn_flush() return super().__len__()
(self) -> int
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