energomera_hass_mqtt

Package to read data from Energomera energy meter and send over to HomeAssistant using MQTT.

class energomera_hass_mqtt.EnergomeraConfig(config_file=None, content=None)

Bases: object

Class representing configuration for EnergomeraHassMqtt.

Parameters:

config_file (str | None) – Name of configuration file
content (str | None) – Literal content representing the configuration

property of: ConfigSchema

Returns the configuration state

Returns:: Configuration state

property logging_level: int

Returns logging level suitable for logging library

Returns:: Logging level

interpolate()

Interpolates certain expressions in parameters section of the configuration. The method uses original configuration as read from the source, to access expressions to interpolate.

Supported expressions:

{{ energomera_prev_month }}: Previous month in meter’s format
{{ energomera_prev_day }}: Previous day in meter’s format

Return type:: None

exception energomera_hass_mqtt.EnergomeraConfigError

Bases: Exception

Exception thrown when configuration processing encounters an error.

add_note(note, /): Add a note to the exception

with_traceback(tb, /): Set self.__traceback__ to tb and return self.

exception energomera_hass_mqtt.EnergomeraMeterError

Bases: Exception

Exception thrown when the energy meter communication fails.

add_note(note, /): Add a note to the exception

with_traceback(tb, /): Set self.__traceback__ to tb and return self.

class energomera_hass_mqtt.EnergomeraHassMqtt(config, dry_run=False)

Bases: object

Communicates with Energomera energy meters using IEC 62056-21 (supersedes IEC 61107) and sends values of requested parameters to HomeAssisstant using MQTT.

Parameters:: config (EnergomeraConfig) – Configuration state

static calculate_bcc(bytes_data)

Calculates protocol BCC using Energomera non-standard modification of IEC 1155-78.

See http://www.energomera.ru/documentations/product/ce301_303_rp.pdf, page 126, for more details.

Parameters:: bytes_data (bytes) – The data to calculate BCC over
Return type:: bytes
Returns:: Calculated BCC

iec_read_values(address, additional_data=None)

Reads value(s) at selected address from the meter using IEC 62056-21 protocol.

Parameters:

address (str) – Address of meter parameter to read
additional_data (str | None) – Additional data to read the parameter with (argument to parameter’s address)

Return type:

List[DataSet]

Returns:

Parameter’s data received from the meter

property is_meter_ids_available: bool

Indicates whether meter IDs (model, version, serial number) are available.

Returns:: True if all the IDs are available

set_meter_ids(hello_response)

Stores meter’s model, serial number and software version.

Parameters:: hello_response (List[DataSet]) – Response to ‘HELLO’ command
Return type:: None

async iec_read_admin()

Primary method to loop over the parameters requested and process them.

Return type:: None

async finalize()

Performs finalization steps, that is - disconnecting MQTT client currently.

Return type:: None

async set_online_sensor(state, setup_only=False)

Adds a pseudo-sensor to HASS reflecting the communication state of meter - online or offline.

Parameters:: state (bool) – The sensor state
Return type:: None

async set_duration_sensor(value)

Adds a pseudo-sensor to HASS reflecting the duration of the meter cycle.

Return type:: None

class energomera_hass_mqtt.ConfigSchema(**data)

Bases: BaseModel

Class representing configuration schema.

validate_parameters()

Validates parameters section.

Return type:: ConfigSchema

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

Return type:: Self

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:: A copy of the model with included, excluded and updated fields as specified.

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

Return type:: Self

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Args:

_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,: this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Return type:: Self

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Args:

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.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False, polymorphic_serialization=None)

Return type:: dict[str, Any]

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False, polymorphic_serialization=None)

Return type:: str

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:

indent: Indentation to use in the JSON output. If None is passed, the output will be compact. ensure_ascii: If True, the output is guaranteed to have all incoming non-ASCII characters escaped.

If False (the default), these characters will be output as-is.

include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation', *, union_format='any_of')

Generates a JSON schema for a model class.

Return type:: dict[str, Any]

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. union_format: The format to use when combining schemas from unions together. Can be one of:

‘any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

schema_generator: To override the logic used to generate the JSON schema, as a subclass of: GenerateJsonSchema with your desired modifications

mode: The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Return type:: str

Args:

params: Tuple of types of the class. Given a generic class: Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Return type:: bool | None

Args:: force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.
Returns:: Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Return type:: Self

Args:

obj: The object to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)

Return type:: Self

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Args:

json_data: The JSON data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Return type:: Self

Args:

obj: The object containing string data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Modules

`config`	Module to instantiate configuration data for `EnergomeraHassMqtt` class from YAML files with defaults and schema validation.
`const`	Module to hold various constants
`exceptions`	Exceptions for the package.
`extra_sensors`	Package to provide additional sensors on top of IecToHassSensor.
`hass_mqtt`	Package to read data from Energomera energy meter and send over to HomeAssistant using MQTT.
`iec_hass_sensor`	Package provide single HASS sensor over MQTT from energy meter read using IEC protocol.
`main`	CLI interface to `EnergomeraHassMqtt` class
`mqtt_client`	The package provides additional functionality over aiomqtt.
`schema`	Module containing configuration file schema.