schrodinger.application.transforms.enumerators module

class schrodinger.application.transforms.enumerators.SmilesTransformPair(smi: str, transform: str)

Bases: object

smi: str

transform: str

__init__(smi: str, transform: str) → None

class schrodinger.application.transforms.enumerators.Fragment(core_smarts: str, max_mol_wt: float = inf, max_fragments: int = 500)

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

Fragment input molecules while maintaining a core substructure.

__init__(core_smarts: str, max_mol_wt: float = inf, max_fragments: int = 500)

Parameters

• core_smarts – the core smarts string used for fragment matching

• max_mol_wt – the maximum molecular weight of the fragments

• max_fragments – the maximum number of fragments to generate

expand(inp_mols: apache_beam.pvalue.PCollection[rdkit.Chem.rdchem.Mol]) → apache_beam.pvalue.PCollection[rdkit.Chem.rdchem.Mol]

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.

class schrodinger.application.transforms.enumerators.SubstructureSubstitute(core_smarts: str, transforms_path: Optional[pathlib.Path] = None, cliques_path: Optional[pathlib.Path] = None, sample_size: int = 500000, n_pair_bonds: int = 3, n_apply_bonds: int = 1)

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

A PTransform that returns all match-molecular-pair transformed molecules based on fragment-cliques in cliques_path, while protecting the core_smarts.

In case not all transforms that one wants to apply may be expressed by cliques, combinations with the fragments in the optional transforms_path will always be generated.

Note that only the first occurrence of the core_smarts in the molecule determines what part is protected. This means that if more than one match is possible, the others will never be protected allowing the first one to be modified.

__init__(core_smarts: str, transforms_path: Optional[pathlib.Path] = None, cliques_path: Optional[pathlib.Path] = None, sample_size: int = 500000, n_pair_bonds: int = 3, n_apply_bonds: int = 1)

Parameters

• core_smarts – the core smarts string used for fragment matching

• transforms_path – optional json file of the list of transforms that are always to be applied (first). Set to None to use the default file.

• cliques_path – the optional json file (if gzipped must end with ‘gz’) of the fragment cliques used for enumeration. Set to None to use the default file.

• sample_size – the maximum number of randomly sampled outputs to yield from the cliques_file.

• n_pair_bonds – the number of bonds beyond which atoms of the core are included for fragment matching (extension of the R-group atoms)

• n_apply_bonds – the number of bonds beyond which atoms of the core are protected.

expand(pcoll)

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.

class schrodinger.application.transforms.enumerators.CorelessSubstitute(transforms_path: Optional[pathlib.Path] = None, cliques_path: Optional[pathlib.Path] = None, sample_size: int = 500000)

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

A PTransform that returns unique sanitized products after applying the transformations based on fragment-cliques in cliques_path.

In case not all transforms that one wants to apply may be expressed by cliques, combinations with the fragments in the optional transforms_path will always be generated.

__init__(transforms_path: Optional[pathlib.Path] = None, cliques_path: Optional[pathlib.Path] = None, sample_size: int = 500000)

Parameters

• transforms_path – optional json file of the list of transforms that are always to be applied (first). Set to None to use the default file.

• cliques_path – the optional json file (if gzipped must end with ‘gz’) of the fragment cliques used for enumeration. Set to None to use the default file.

• sample_size – the maximum number of randomly sampled outputs to yield from the cliques_file.

expand(pcoll)

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.

class schrodinger.application.transforms.enumerators.Substitute(transform_smarts: apache_beam.pvalue.PCollection[str])

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

A PTransform that returns unique standardized molecules after applying the transform_smarts.

__init__(transform_smarts: apache_beam.pvalue.PCollection[str])

Parameters

transform_smarts – the reaction smarts for the tranformation

expand(pcoll)

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.

class schrodinger.application.transforms.enumerators.Decorate(core_smarts: str, rgroups: apache_beam.pvalue.PCollection[RGroup], property_ranges: Optional[Dict[str, List[float]]] = None)

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

A PTransform that enumerates unique sanitized molecules formed by replacing a hydrogen on a C, N, or O atom in the ligand with an R-group that was attached to an Ar.

__init__(core_smarts: str, rgroups: apache_beam.pvalue.PCollection[RGroup], property_ranges: Optional[Dict[str, List[float]]] = None)

Parameters

• core_smarts – the SMARTS that the products should have and needs to be part of the input molecule

• rgroups – the R-groups to use for decoration

• property_ranges – the optional property ranges for the products

expand(pcoll)

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.

class schrodinger.application.transforms.enumerators.Synthesize(core_smarts: str, depth: int = 1, max_products_per_route: int = 100, seed: Optional[int] = None)

Bases: apache_beam.typehints.decorators.WithTypeHints, apache_beam.transforms.display.HasDisplayData, Generic[apache_beam.transforms.ptransform.InputT, apache_beam.transforms.ptransform.OutputT]

Enumerates unique sanitized molecules from a combinatorial synthesis using routes based on the input molecules using the default reaction dictionary and reagent library.

If the maximum number of products is less than the total number of combinations the route synthesis will be done by random sampling, which may yield fewer products than requested, otherwise a systematic set of unique products will be yielded.

__init__(core_smarts: str, depth: int = 1, max_products_per_route: int = 100, seed: Optional[int] = None)

Parameters

• core_smarts – the SMARTS that the products should have and needs to be part of the input molecule

• depth – the maximum depth of the retrosynthetic routes to use

• max_products_per_route – the maximum number of products try to synthesize for each input molecule per route. Use 0 to force an exhaustive synthesis.

• seed – seed for random number generator. If None, the random number generator will not be seeded.

expand(pcoll)

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]

default_label() → str

default_type_hints()

display_data() → dict

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

classmethod from_runner_api(proto: Optional[beam_runner_api_pb2.PTransform], context: PipelineContext) → Optional[PTransform]

get_resource_hints() → Dict[str, bytes]

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs: Any) → Windowing

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)

property label

pipeline: Optional[Pipeline] = None

classmethod register_urn(urn, parameter_type, constructor=None)

runner_api_requires_keyed_input()

side_inputs: Sequence[pvalue.AsSideInput] = ()

to_runner_api(context: PipelineContext, has_parts: bool = False, **extra_kwargs: Any) → beam_runner_api_pb2.FunctionSpec

to_runner_api_parameter(unused_context: PipelineContext) → Tuple[str, Optional[Union[message.Message, bytes, str]]]

to_runner_api_pickled(unused_context: PipelineContext) → Tuple[str, bytes]

type_check_inputs(pvalueish)

type_check_inputs_or_outputs(pvalueish, input_or_output)

type_check_outputs(pvalueish)

with_input_types(input_type_hint)

Annotates the input type of a PTransform with a type-hint.

Args:

input_type_hint (type): An instance of an allowed built-in type, a custom

class, or an instance of a TypeConstraint.

Raises:

TypeError: If input_type_hint is not a valid type-hint.

See apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_output_types(type_hint)

Annotates the output type of a PTransform with a type-hint.

Args:

type_hint (type): An instance of an allowed built-in type, a custom class,

or a TypeConstraint.

Raises:

TypeError: If type_hint is not a valid type-hint. See

validate_composite_type_param() for further details.

Returns:

PTransform: A reference to the instance of this particular PTransform object. This allows chaining type-hinting related methods.

with_resource_hints(**kwargs) → apache_beam.transforms.ptransform.PTransform

Adds resource hints to the PTransform.

Resource hints allow users to express constraints on the environment where the transform should be executed. Interpretation of the resource hints is defined by Beam Runners. Runners may ignore the unsupported hints.

Args:

**kwargs: key-value pairs describing hints and their values.

Raises:

ValueError: if provided hints are unknown to the SDK. See

apache_beam.transforms.resources for a list of known hints.

Returns:

PTransform: A reference to the instance of this particular PTransform object.