Module SimEconomica.Resources

Expand source code 
from typing import List, Tuple

from dataclasses import dataclass
from dataclasses_json import dataclass_json

from Data import RESOURCES_RAW, RECIPES_RAW

class BaseResource(object):
    pass


@dataclass_json
@dataclass
class Resource(BaseResource):
    Name: str


@dataclass_json
@dataclass
class Ingredient(object):
    ResourceID: int
    Quantity: int


@dataclass_json
@dataclass
class Recipe(object):
    Name: str
    JobsRequired: List[int]
    Inputs: List[Ingredient]
    Outputs: List[Ingredient]



RESOURCES = [Resource.from_dict(RESOURCES_RAW[idElement]) for idElement in range(len(RESOURCES_RAW))]
RECIPES = [
    Recipe(
        Name=RECIPES_RAW[idElement]["Name"],
        JobsRequired=RECIPES_RAW[idElement]["JobsRequired"],
        Inputs=[
            Ingredient(
                ResourceID=data[0],
                Quantity=data[1]
            ) for data in RECIPES_RAW[idElement]["Inputs"]
        ],
        Outputs=[
            Ingredient(
                ResourceID=data[0],
                Quantity=data[1]
            ) for data in RECIPES_RAW[idElement]["Outputs"]
        ]
    ) for idElement in range(len(RECIPES_RAW))
]

Classes

class BaseResource
Expand source code 
class BaseResource(object):
    pass

Subclasses

class Ingredient (ResourceID: int, Quantity: int)

Ingredient(ResourceID: int, Quantity: int)

Expand source code 
@dataclass_json
@dataclass
class Ingredient(object):
    ResourceID: int
    Quantity: int

Class variables

var Quantity : int
var ResourceID : int

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, NoneType], *, infer_missing=False) ‑> ~A
Expand source code 
@classmethod
def from_dict(cls: Type[A],
              kvs: Json,
              *,
              infer_missing=False) -> A:
    return _decode_dataclass(cls, kvs, infer_missing)
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
Expand source code 
@classmethod
def from_json(cls: Type[A],
              s: JsonData,
              *,
              parse_float=None,
              parse_int=None,
              parse_constant=None,
              infer_missing=False,
              **kw) -> A:
    kvs = json.loads(s,
                     parse_float=parse_float,
                     parse_int=parse_int,
                     parse_constant=parse_constant,
                     **kw)
    return cls.from_dict(kvs, infer_missing=infer_missing)
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Expand source code 
@classmethod
def schema(cls: Type[A],
           *,
           infer_missing: bool = False,
           only=None,
           exclude=(),
           many: bool = False,
           context=None,
           load_only=(),
           dump_only=(),
           partial: bool = False,
           unknown=None) -> SchemaType:
    Schema = build_schema(cls, DataClassJsonMixin, infer_missing, partial)

    if unknown is None:
        undefined_parameter_action = _undefined_parameter_action_safe(cls)
        if undefined_parameter_action is not None:
            # We can just make use of the same-named mm keywords
            unknown = undefined_parameter_action.name.lower()

    return Schema(only=only,
                  exclude=exclude,
                  many=many,
                  context=context,
                  load_only=load_only,
                  dump_only=dump_only,
                  partial=partial,
                  unknown=unknown)

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, NoneType]]
Expand source code 
def to_dict(self, encode_json=False) -> Dict[str, Json]:
    return _asdict(self, encode_json=encode_json)
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, NoneType] = None, separators: Tuple[str, str] = None, default: Callable = None, sort_keys: bool = False, **kw) ‑> str
Expand source code 
def to_json(self,
            *,
            skipkeys: bool = False,
            ensure_ascii: bool = True,
            check_circular: bool = True,
            allow_nan: bool = True,
            indent: Optional[Union[int, str]] = None,
            separators: Tuple[str, str] = None,
            default: Callable = None,
            sort_keys: bool = False,
            **kw) -> str:
    return json.dumps(self.to_dict(encode_json=False),
                      cls=_ExtendedEncoder,
                      skipkeys=skipkeys,
                      ensure_ascii=ensure_ascii,
                      check_circular=check_circular,
                      allow_nan=allow_nan,
                      indent=indent,
                      separators=separators,
                      default=default,
                      sort_keys=sort_keys,
                      **kw)
class Recipe (Name: str, JobsRequired: List[int], Inputs: List[Ingredient], Outputs: List[Ingredient])

Recipe(Name: str, JobsRequired: List[int], Inputs: List[SimEconomica.Resources.Ingredient], Outputs: List[SimEconomica.Resources.Ingredient])

Expand source code 
@dataclass_json
@dataclass
class Recipe(object):
    Name: str
    JobsRequired: List[int]
    Inputs: List[Ingredient]
    Outputs: List[Ingredient]

Class variables

var Inputs : List[Ingredient]
var JobsRequired : List[int]
var Name : str
var Outputs : List[Ingredient]

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, NoneType], *, infer_missing=False) ‑> ~A
Expand source code 
@classmethod
def from_dict(cls: Type[A],
              kvs: Json,
              *,
              infer_missing=False) -> A:
    return _decode_dataclass(cls, kvs, infer_missing)
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
Expand source code 
@classmethod
def from_json(cls: Type[A],
              s: JsonData,
              *,
              parse_float=None,
              parse_int=None,
              parse_constant=None,
              infer_missing=False,
              **kw) -> A:
    kvs = json.loads(s,
                     parse_float=parse_float,
                     parse_int=parse_int,
                     parse_constant=parse_constant,
                     **kw)
    return cls.from_dict(kvs, infer_missing=infer_missing)
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Expand source code 
@classmethod
def schema(cls: Type[A],
           *,
           infer_missing: bool = False,
           only=None,
           exclude=(),
           many: bool = False,
           context=None,
           load_only=(),
           dump_only=(),
           partial: bool = False,
           unknown=None) -> SchemaType:
    Schema = build_schema(cls, DataClassJsonMixin, infer_missing, partial)

    if unknown is None:
        undefined_parameter_action = _undefined_parameter_action_safe(cls)
        if undefined_parameter_action is not None:
            # We can just make use of the same-named mm keywords
            unknown = undefined_parameter_action.name.lower()

    return Schema(only=only,
                  exclude=exclude,
                  many=many,
                  context=context,
                  load_only=load_only,
                  dump_only=dump_only,
                  partial=partial,
                  unknown=unknown)

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, NoneType]]
Expand source code 
def to_dict(self, encode_json=False) -> Dict[str, Json]:
    return _asdict(self, encode_json=encode_json)
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, NoneType] = None, separators: Tuple[str, str] = None, default: Callable = None, sort_keys: bool = False, **kw) ‑> str
Expand source code 
def to_json(self,
            *,
            skipkeys: bool = False,
            ensure_ascii: bool = True,
            check_circular: bool = True,
            allow_nan: bool = True,
            indent: Optional[Union[int, str]] = None,
            separators: Tuple[str, str] = None,
            default: Callable = None,
            sort_keys: bool = False,
            **kw) -> str:
    return json.dumps(self.to_dict(encode_json=False),
                      cls=_ExtendedEncoder,
                      skipkeys=skipkeys,
                      ensure_ascii=ensure_ascii,
                      check_circular=check_circular,
                      allow_nan=allow_nan,
                      indent=indent,
                      separators=separators,
                      default=default,
                      sort_keys=sort_keys,
                      **kw)
class Resource (Name: str)

Resource(Name: str)

Expand source code 
@dataclass_json
@dataclass
class Resource(BaseResource):
    Name: str

Ancestors

Class variables

var Name : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, NoneType], *, infer_missing=False) ‑> ~A
Expand source code 
@classmethod
def from_dict(cls: Type[A],
              kvs: Json,
              *,
              infer_missing=False) -> A:
    return _decode_dataclass(cls, kvs, infer_missing)
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
Expand source code 
@classmethod
def from_json(cls: Type[A],
              s: JsonData,
              *,
              parse_float=None,
              parse_int=None,
              parse_constant=None,
              infer_missing=False,
              **kw) -> A:
    kvs = json.loads(s,
                     parse_float=parse_float,
                     parse_int=parse_int,
                     parse_constant=parse_constant,
                     **kw)
    return cls.from_dict(kvs, infer_missing=infer_missing)
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]
Expand source code 
@classmethod
def schema(cls: Type[A],
           *,
           infer_missing: bool = False,
           only=None,
           exclude=(),
           many: bool = False,
           context=None,
           load_only=(),
           dump_only=(),
           partial: bool = False,
           unknown=None) -> SchemaType:
    Schema = build_schema(cls, DataClassJsonMixin, infer_missing, partial)

    if unknown is None:
        undefined_parameter_action = _undefined_parameter_action_safe(cls)
        if undefined_parameter_action is not None:
            # We can just make use of the same-named mm keywords
            unknown = undefined_parameter_action.name.lower()

    return Schema(only=only,
                  exclude=exclude,
                  many=many,
                  context=context,
                  load_only=load_only,
                  dump_only=dump_only,
                  partial=partial,
                  unknown=unknown)

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, NoneType]]
Expand source code 
def to_dict(self, encode_json=False) -> Dict[str, Json]:
    return _asdict(self, encode_json=encode_json)
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, NoneType] = None, separators: Tuple[str, str] = None, default: Callable = None, sort_keys: bool = False, **kw) ‑> str
Expand source code 
def to_json(self,
            *,
            skipkeys: bool = False,
            ensure_ascii: bool = True,
            check_circular: bool = True,
            allow_nan: bool = True,
            indent: Optional[Union[int, str]] = None,
            separators: Tuple[str, str] = None,
            default: Callable = None,
            sort_keys: bool = False,
            **kw) -> str:
    return json.dumps(self.to_dict(encode_json=False),
                      cls=_ExtendedEncoder,
                      skipkeys=skipkeys,
                      ensure_ascii=ensure_ascii,
                      check_circular=check_circular,
                      allow_nan=allow_nan,
                      indent=indent,
                      separators=separators,
                      default=default,
                      sort_keys=sort_keys,
                      **kw)