honeybee_vtk.config module

Data json schema and validation.

class honeybee_vtk.config.Autocalculate(*, type: honeybee_vtk.config.ConstrainedStrValue = 'Autocalculate')[source]

Bases: pydantic.main.BaseModel

Config

alias of pydantic.main.BaseConfig

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, update: DictStrAny = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) Model
json(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model
type: honeybee_vtk.config.ConstrainedStrValue
class honeybee_vtk.config.DataConfig(*, identifier: str, object_type: honeybee_vtk.types.DataSetNames, unit: str, path: str, hide: bool = False, legend_parameters: honeybee_vtk.config.LegendConfig = LegendConfig(color_set=<ColorSets.ecotect: 'ecotect'>, min=Autocalculate(type='Autocalculate'), max=Autocalculate(type='Autocalculate'), hide_legend=False, orientation=<Orientation.horizontal: 'horizontal'>, width=0.45, height=0.05, position=[0.5, 0.1], color_count=Autocalculate(type='Autocalculate'), label_count=Autocalculate(type='Autocalculate'), decimal_count=<DecimalCount.default: 'default'>, preceding_labels=False, label_parameters=TextConfig(color=[0, 0, 0], size=0, bold=False), title_parameters=TextConfig(color=[0, 0, 0], size=0, bold=True)))[source]

Bases: pydantic.main.BaseModel

Config for simulation results you’d like to load on a honeybee-vtk model.

Config

alias of pydantic.main.BaseConfig

classmethod check_pos_against_width_height(v: honeybee_vtk.config.LegendConfig, values) honeybee_vtk.config.LegendConfig[source]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, update: DictStrAny = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) Model
json(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model
classmethod validate_folder_path(v: str) str[source]
hide: bool
identifier: str
legend_parameters: honeybee_vtk.config.LegendConfig
object_type: honeybee_vtk.types.DataSetNames
path: str
unit: str
class honeybee_vtk.config.LegendConfig(*, color_set: honeybee_vtk.legend_parameter.ColorSets = ColorSets.ecotect, min: Union[honeybee_vtk.config.Autocalculate, float] = Autocalculate(type='Autocalculate'), max: Union[honeybee_vtk.config.Autocalculate, float] = Autocalculate(type='Autocalculate'), hide_legend: bool = False, orientation: honeybee_vtk.legend_parameter.Orientation = Orientation.horizontal, width: honeybee_vtk.config.ConstrainedFloatValue = 0.45, height: honeybee_vtk.config.ConstrainedFloatValue = 0.05, position: types.ConstrainedListValue[honeybee_vtk.config.ConstrainedFloatValue] = [0.5, 0.1], color_count: Union[honeybee_vtk.config.Autocalculate, int] = Autocalculate(type='Autocalculate'), label_count: Union[honeybee_vtk.config.Autocalculate, int] = Autocalculate(type='Autocalculate'), decimal_count: honeybee_vtk.legend_parameter.DecimalCount = DecimalCount.default, preceding_labels: bool = False, label_parameters: honeybee_vtk.config.TextConfig = TextConfig(color=[0, 0, 0], size=0, bold=False), title_parameters: honeybee_vtk.config.TextConfig = TextConfig(color=[0, 0, 0], size=0, bold=True))[source]

Bases: pydantic.main.BaseModel

Config for the legend to be created from a dataset.

class Config[source]

Bases: object

validate_all = True
validate_assignment = True
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, update: DictStrAny = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) Model
json(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model
color_count: Union[honeybee_vtk.config.Autocalculate, int]
color_set: honeybee_vtk.legend_parameter.ColorSets
decimal_count: honeybee_vtk.legend_parameter.DecimalCount
height: honeybee_vtk.config.ConstrainedFloatValue
hide_legend: bool
label_count: Union[honeybee_vtk.config.Autocalculate, int]
label_parameters: honeybee_vtk.config.TextConfig
max: Union[honeybee_vtk.config.Autocalculate, float]
min: Union[honeybee_vtk.config.Autocalculate, float]
orientation: honeybee_vtk.legend_parameter.Orientation
position: types.ConstrainedListValue[honeybee_vtk.config.ConstrainedFloatValue]
preceding_labels: bool
title_parameters: honeybee_vtk.config.TextConfig
width: honeybee_vtk.config.ConstrainedFloatValue
class honeybee_vtk.config.TextConfig(*, color: List[honeybee_vtk.config.ConstrainedIntValue] = [0, 0, 0], size: honeybee_vtk.config.ConstrainedIntValue = 0, bold: bool = False)[source]

Bases: pydantic.main.BaseModel

Config for the text to be used in a legend.

This object applies to text for legend title and legend labels as well.

class Config[source]

Bases: object

validate_all = True
validate_assignment = True
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, update: DictStrAny = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) Model
json(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model
bold: bool
color: List[honeybee_vtk.config.ConstrainedIntValue]
size: honeybee_vtk.config.ConstrainedIntValue
honeybee_vtk.config.load_config(json_path: str, model: honeybee_vtk.model.Model, scene: honeybee_vtk.scene.Scene, validation: bool = False, legend: bool = False) honeybee_vtk.model.Model[source]

Mount data on model from config json.

Parameters
  • json_path – File path to the config json file.

  • model – A honeybee-vtk model object.

  • scene – A honeybee-vtk scene object.

  • validation – A boolean indicating whether to validate the data before loading.

  • legend – A boolean indicating whether to load legend parameters.

Returns

A honeybee-vtk model with data loaded on it.