Module `futureexpert.checkin`

Contains the models with the configuration for CHECK-IN.

Classes

class BaseConfig (**data: Any)

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class BaseConfig(BaseModel):
    """Basic Configuaration for all models."""
    model_config = ConfigDict(extra='forbid')

Basic Configuaration for all models.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

pydantic.main.BaseModel

Class variables

var model_config

class CheckInResult (**data: Any)

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class CheckInResult(BaseModel):
    """Result of the CHECK-IN.

    Parameters
    ----------
    time_series: builtins.list[futureexpert.shared_models.TimeSeries]
        Time series values.
    version_id: builtins.str
        Id of the time series version. Used to identifiy the time series
    """
    time_series: list[TimeSeries]
    version_id: str

Result of the CHECK-IN.

Parameters

time_series : builtins.list[TimeSeries]: Time series values.
version_id : builtins.str: Id of the time series version. Used to identifiy the time series

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

pydantic.main.BaseModel

Class variables

var model_config
var time_series : list[TimeSeries]
var version_id : str

class Column (**data: Any)

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class Column(BaseConfig):
    """Base model for the different column models
    (`DateColumn`, `ValueColumn` and `GroupColumn`).

    Parameters
    ----------
    name: builtins.str
        The original name of the column.
    name_new: typing.Optional[builtins.str]
        The new name of the column.
    """
    name: str
    name_new: Optional[str] = None

Base model for the different column models (DateColumn, ValueColumn and GroupColumn).

Parameters

name : builtins.str: The original name of the column.
name_new : typing.Optional[builtins.str]: The new name of the column.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Subclasses

DateColumn
GroupColumn
ValueColumn

Class variables

var model_config
var name : str
var name_new : str | None

class DataDefinition (**data: Any)

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class DataDefinition(BaseConfig):
    """Model for the input parameter needed for the first CHECK-IN step.
    Every single column in your data must be accounted for. Each column must either be assigned a type (`date_column`,
    `value_columns`, `group_columns`) or be explicitly marked for removal in `remove_columns`.

    Parameters
    ----------
    date_column: futureexpert.checkin.DateColumn
        Definition of the date column. Must be a single column that contains the complete date information.
    value_columns: builtins.list[futureexpert.checkin.ValueColumn]
        Definitions of the value columns. Not all columns defined here must be used for time series creation;
        selecting a subset or combining is possible in a later step.
    group_columns: builtins.list[futureexpert.checkin.GroupColumn]
        Definitions of the group columns. Not all columns defined here must be used for time series creation; selecting
        a subset is possible in a later step. Grouping information can also be used to create hierarchical levels.
    remove_rows: typing.Optional[builtins.list[builtins.int]]
        Indexes of the rows to be removed before validation. Note: If the raw data was committed as pandas data frame
        the header is the first row (row index 0).
    remove_columns: typing.Optional[builtins.list[builtins.int]]
        Indexes of the columns to be removed before validation. Any column that is not assigned a type must be listed here.
    """
    date_column: DateColumn
    value_columns: list[ValueColumn]
    group_columns: list[GroupColumn] = []
    remove_rows: Optional[list[int]] = []
    remove_columns: Optional[list[int]] = []

Model for the input parameter needed for the first CHECK-IN step. Every single column in your data must be accounted for. Each column must either be assigned a type (date_column, value_columns, group_columns) or be explicitly marked for removal in remove_columns.

Parameters

date_column : DateColumn: Definition of the date column. Must be a single column that contains the complete date information.
value_columns : builtins.list[ValueColumn]: Definitions of the value columns. Not all columns defined here must be used for time series creation; selecting a subset or combining is possible in a later step.
group_columns : builtins.list[GroupColumn]: Definitions of the group columns. Not all columns defined here must be used for time series creation; selecting a subset is possible in a later step. Grouping information can also be used to create hierarchical levels.
remove_rows : typing.Optional[builtins.list[builtins.int]]: Indexes of the rows to be removed before validation. Note: If the raw data was committed as pandas data frame the header is the first row (row index 0).
remove_columns : typing.Optional[builtins.list[builtins.int]]: Indexes of the columns to be removed before validation. Any column that is not assigned a type must be listed here.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Class variables

var date_column : DateColumn
var group_columns : list[GroupColumn]
var model_config
var remove_columns : list[int] | None
var remove_rows : list[int] | None
var value_columns : list[ValueColumn]

class DateColumn (**data: Any)

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class DateColumn(Column):
    """Model for the date columns.

    Parameters
    ----------
    format: builtins.str
        The format of the date.
    name: builtins.str

    name_new: typing.Optional[builtins.str]

    """
    format: str

Model for the date columns.

Parameters

format : builtins.str: The format of the date.
name : builtins.str
name_new : typing.Optional[builtins.str]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

Column
BaseConfig
pydantic.main.BaseModel

Class variables

var format : str
var model_config

class FileSpecification (**data: Any)

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class FileSpecification(BaseConfig):
    """Specify the format of the CSV file.

    Parameters
    ----------
    delimiter: typing.Optional[builtins.str]
        The delimiter used to separate values.
    decimal: typing.Optional[builtins.str]
        The decimal character used in decimal numbers.
    thousands: typing.Optional[builtins.str]
        The thousands separator used in numbers.
    """
    delimiter: Optional[str] = ','
    decimal: Optional[str] = '.'
    thousands: Optional[str] = None

Specify the format of the CSV file.

Parameters

delimiter : typing.Optional[builtins.str]: The delimiter used to separate values.
decimal : typing.Optional[builtins.str]: The decimal character used in decimal numbers.
thousands : typing.Optional[builtins.str]: The thousands separator used in numbers.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Class variables

var decimal : str | None
var delimiter : str | None
var model_config
var thousands : str | None

class FilterSettings (**data: Any)

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class FilterSettings(BaseConfig):
    """Model for the filters.

    Parameters
    ----------
    type: typing.Literal['exclusion', 'inclusion']
        The type of filter: `exclusion` or `inclusion`.
    variable: builtins.str
        The columns name to be used for filtering.
    items: builtins.list[builtins.str]
        The list of values to be used for filtering.
    """
    type: Literal['exclusion', 'inclusion']
    variable: str
    items: list[str]

Model for the filters.

Parameters

type : typing.Literal['exclusion', 'inclusion']: The type of filter: exclusion or inclusion.
variable : builtins.str: The columns name to be used for filtering.
items : builtins.list[builtins.str]: The list of values to be used for filtering.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Class variables

var items : list[str]
var model_config
var type : Literal['exclusion', 'inclusion']
var variable : str

class GroupColumn (**data: Any)

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class GroupColumn(Column):
    """Model for the group columns.

    Parameters
    ----------
    dtype_str: typing.Optional[typing.Literal['Character']]
        The data type of the column.
    name: builtins.str

    name_new: typing.Optional[builtins.str]

    """
    dtype_str: Optional[Literal['Character']] = None

Model for the group columns.

Parameters

dtype_str : typing.Optional[typing.Literal['Character']]: The data type of the column.
name : builtins.str
name_new : typing.Optional[builtins.str]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

Column
BaseConfig
pydantic.main.BaseModel

Class variables

var dtype_str : Literal['Character'] | None
var model_config

class NewValue (**data: Any)

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class NewValue(BaseConfig):
    """Model for the value data.

    Parameters
    ----------
    first_variable: builtins.str
        The first variable name.
    operator: typing.Literal['x', '+', '-']
        The operator that will be used to do the math operation
        between the first and second variable.
    second_variable: builtins.str
        The second variable name.
    new_variable: builtins.str
        The new variable name.
    unit: typing.Optional[builtins.str]
        The unit.
    """
    first_variable: str
    operator: Literal['x', '+', '-']
    second_variable: str
    new_variable: str
    unit: Optional[str] = None

Model for the value data.

Parameters

first_variable : builtins.str: The first variable name.
operator : typing.Literal['x', '+', '-']: The operator that will be used to do the math operation between the first and second variable.
second_variable : builtins.str: The second variable name.
new_variable : builtins.str: The new variable name.
unit : typing.Optional[builtins.str]: The unit.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Class variables

var first_variable : str
var model_config
var new_variable : str
var operator : Literal['x', '+', '-']
var second_variable : str
var unit : str | None

class TimeSeriesVersion (**data: Any)

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class TimeSeriesVersion(BaseModel):
    """Time series version created in CHECK-IN.

    Parameters
    ----------
    version_id: builtins.str
        Id of the time series version. Used to identifiy the time series.
    description: typing.Optional[builtins.str]
        Description of the time series version.
    creation_time_utc: datetime.datetime
        Time of the creation.
    keep_until_utc: datetime
        Last day where the data is stored until it is deleted.
    keep_until_utc: datetime.datetime

    """
    version_id: str
    description: Optional[str]
    creation_time_utc: datetime
    keep_until_utc: datetime

    @pydantic.model_validator(mode="after")
    def fix_time_stamps(self) -> Self:
        last_day = self.keep_until_utc.date()
        self.creation_time_utc = self.creation_time_utc.replace(microsecond=0)
        self.keep_until_utc = datetime.combine(last_day, datetime.min.time())

        return self

Time series version created in CHECK-IN.

Parameters

version_id : builtins.str: Id of the time series version. Used to identifiy the time series.
description : typing.Optional[builtins.str]: Description of the time series version.
creation_time_utc : datetime.datetime: Time of the creation.
keep_until_utc : datetime: Last day where the data is stored until it is deleted.
keep_until_utc : datetime.datetime

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

pydantic.main.BaseModel

Class variables

var creation_time_utc : datetime.datetime
var description : str | None
var keep_until_utc : datetime.datetime
var model_config
var version_id : str

Methods

def fix_time_stamps(self) ‑> Self

Expand source code

@pydantic.model_validator(mode="after")
def fix_time_stamps(self) -> Self:
    last_day = self.keep_until_utc.date()
    self.creation_time_utc = self.creation_time_utc.replace(microsecond=0)
    self.keep_until_utc = datetime.combine(last_day, datetime.min.time())

    return self

class TsCreationConfig (**data: Any)

Expand source code

class TsCreationConfig(BaseConfig):
    """Configuration for the creation of time series.

    Parameters
    ----------
    value_columns_to_save: builtins.list[builtins.str]
        Value columns that should be saved.
    time_granularity: typing.Literal['yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly', 'halfhourly']
        Target granularity of the time series.
    description: typing.Optional[builtins.str]
        A short description of the time series.
    start_date: typing.Optional[builtins.str]
        Dates before this date are excluded.
    end_date: typing.Optional[builtins.str]
        Dates after this date are excluded.
    grouping_level: builtins.list[builtins.str]
        Names of group columns that should be used as the grouping level.
    save_hierarchy: builtins.bool
        If true, interpretes the given grouping levels as levels of a hierarchy and saves all hierachy levels.
        Otherwise, no hierarchy levels are implied and only the single level with the given grouping is saved.
        e.g. if grouping_level is ['A', 'B', 'C'] time series of grouping 'A', 'AB' and 'ABC' is saved.
        For later filtering use {'grouping.A': {'$exists': True}}
    filter: builtins.list[futureexpert.checkin.FilterSettings]
        Settings for including or excluding values during time series creation.
    new_variables: builtins.list[futureexpert.checkin.NewValue]
        New value column that is a combination of two other value columns.
    missing_value_handler: typing.Literal['keepNaN', 'setToZero']
        Strategy how to handle missing values during time series creation.
    """
    value_columns_to_save: list[str]
    time_granularity: Literal['yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly', 'halfhourly']
    description: Optional[str] = None
    grouping_level: list[str] = []
    start_date: Optional[str] = None
    end_date: Optional[str] = None
    save_hierarchy: bool = False
    filter: list[FilterSettings] = []
    new_variables: list[NewValue] = []
    missing_value_handler: Literal['keepNaN', 'setToZero'] = 'keepNaN'

Configuration for the creation of time series.

Parameters

value_columns_to_save : builtins.list[builtins.str]: Value columns that should be saved.
time_granularity : typing.Literal['yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly', 'halfhourly']: Target granularity of the time series.
description : typing.Optional[builtins.str]: A short description of the time series.
start_date : typing.Optional[builtins.str]: Dates before this date are excluded.
end_date : typing.Optional[builtins.str]: Dates after this date are excluded.
grouping_level : builtins.list[builtins.str]: Names of group columns that should be used as the grouping level.
save_hierarchy : builtins.bool: If true, interpretes the given grouping levels as levels of a hierarchy and saves all hierachy levels. Otherwise, no hierarchy levels are implied and only the single level with the given grouping is saved. e.g. if grouping_level is ['A', 'B', 'C'] time series of grouping 'A', 'AB' and 'ABC' is saved. For later filtering use {'grouping.A': {'$exists': True}}
filter : builtins.list[FilterSettings]: Settings for including or excluding values during time series creation.
new_variables : builtins.list[NewValue]: New value column that is a combination of two other value columns.
missing_value_handler : typing.Literal['keepNaN', 'setToZero']: Strategy how to handle missing values during time series creation.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

BaseConfig
pydantic.main.BaseModel

Class variables

var description : str | None
var end_date : str | None
var filter : list[FilterSettings]
var grouping_level : list[str]
var missing_value_handler : Literal['keepNaN', 'setToZero']
var model_config
var new_variables : list[NewValue]
var save_hierarchy : bool
var start_date : str | None
var time_granularity : Literal['yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly', 'halfhourly']
var value_columns_to_save : list[str]

class ValueColumn (**data: Any)

Expand source code

class ValueColumn(Column):
    """Model for the value columns.

    Parameters
    ----------
    min: typing.Optional[builtins.int]
        The set minimum value of the column.
    max: typing.Optional[builtins.int]
        The set maximum value of the column.
    dtype_str: typing.Optional[typing.Literal['Numeric', 'Integer']]
        The data type of the column.
    unit: typing.Optional[builtins.str]
        The unit of the column.
    name: builtins.str

    name_new: typing.Optional[builtins.str]

    """
    min: Optional[int] = None
    max: Optional[int] = None
    dtype_str: Optional[Literal['Numeric', 'Integer']] = None
    unit: Optional[str] = None

Model for the value columns.

Parameters

min : typing.Optional[builtins.int]: The set minimum value of the column.
max : typing.Optional[builtins.int]: The set maximum value of the column.
dtype_str : typing.Optional[typing.Literal['Numeric', 'Integer']]: The data type of the column.
unit : typing.Optional[builtins.str]: The unit of the column.
name : builtins.str
name_new : typing.Optional[builtins.str]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

Column
BaseConfig
pydantic.main.BaseModel

Class variables

var dtype_str : Literal['Numeric', 'Integer'] | None
var max : int | None
var min : int | None
var model_config
var unit : str | None