Time-Series Models#

Table of Models#

Time-Series Models currently implemented in the package#
Model	Description
`TimeSeriesModels`	Base class for time series models
`LinearModel`	Linear time series model
`QuadraticModel`	Quadratic time series model
`CubicModel`	Cubic time series model
`AnnualSinusoidalModel`	Annual sinusoidal time series model
`AnnualSemiannualSinusoidal`	Annual and semiannual sinusoidal time series model
`FreezeThawCycleModel`	Freeze-thaw cycle time series model
`FreezeThawCycleModelWithVelocity`	Freeze-thaw cycle time series model with velocity

Classes#

TimeSeriesModels#

class faninsar.NSBAS.tsmodels.TimeSeriesModels(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: object

Base class for time series models

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize TimeSeriesModels

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property unit: str#: unit of date_spans in time series model

property dates: DatetimeIndex#: dates of SAR acquisitions

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property param_names: list[str]#: parameter names in time series model

LinearModel#

class faninsar.NSBAS.tsmodels.LinearModel(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

Linear model

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize LinearModel

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

QuadraticModel#

class faninsar.NSBAS.tsmodels.QuadraticModel(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

Quadratic model

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize QuadraticModel

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

CubicModel#

class faninsar.NSBAS.tsmodels.CubicModel(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

Cubic model

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize CubicModel

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

AnnualSinusoidalModel#

class faninsar.NSBAS.tsmodels.AnnualSinusoidalModel(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

A sinusoidal model with annual period

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize AnnualSinusoidalModel

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

AnnualSemiannualSinusoidal#

class faninsar.NSBAS.tsmodels.AnnualSemiannualSinusoidal(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

A compose sinusoidal model that contains annual and semi-annual periods

__init__(dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize AnnualSemiannualSinusoidal

Parameters:

dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

FreezeThawCycleModel#

class faninsar.NSBAS.tsmodels.FreezeThawCycleModel(ftc: FreezeThawCycle, dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

A pure Freeze-thaw cycle model without velocity

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

__init__(ftc: FreezeThawCycle, dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize FreezeThawCycleModel

Parameters:

ftc (FreezeThawCycle) –
Freeze-thaw cycle instance. The dates in ftc should cover the dates of SAR acquisitions.

Warning

The first date in ftc should be earlier than the thawing onset of the first year in the time series model.
dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

FreezeThawCycleModelWithVelocity#

class faninsar.NSBAS.tsmodels.FreezeThawCycleModelWithVelocity(ftc: FreezeThawCycle, dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Bases: TimeSeriesModels

A Freeze-thaw cycle model with velocity

property G_br: ndarray#: bottom right block of the design matrix G in NSBAS inversion

property date_spans: ndarray#: date spans of SAR acquisitions in unit of year or day

property dates: DatetimeIndex#: dates of SAR acquisitions

property param_names: list[str]#: parameter names in time series model

property unit: str#: unit of date_spans in time series model

__init__(ftc: FreezeThawCycle, dates: DatetimeIndex | Sequence[datetime], unit: Literal['year', 'day'] = 'day')#

Initialize FreezeThawCycleModelWithVelocity

Parameters:

ftc (FreezeThawCycle) – Freeze-thaw cycle instance. The dates in ftc should cover the dates of SAR acquisitions.
dates (pd.DatetimeIndex | Sequence[datetime]) – Dates of SAR acquisitions. This can be easily obtained by accessing Pairs.dates.
unit (Literal["year", "day"], optional) – Unit of day spans in time series model, by default “day”.

Time-Series Models#

Table of Models#

Classes#