API reference#

This page gives an overview of all ValEnsPy objects, functions, and methods. All classes and functions exposed in ValEnsPy.* are public API.

Modules#

`input`
`processing`
`diagnostic`

Pre-made Diagnostic#

Most users will use pre-made diagnostics. They are organized into four categories and listed below.

Model2Self#

See Model2Self for all shared functionality.

AnnualCycle(ds: Dataset)#

Annual Cycle - Model2Self

The annual cycle of the data.

Parameters:: ds (Dataset) – The data to calculate the annual cycle of.

See also

Model2Self, valenspy.diagnostic.functions.annual_cycle, valenspy.diagnostic.visualizations.plot_annual_cycle

Examples

>>> from valenspy.diagnostic import AnnualCycle
>>> result = AnnualCycle(ds)
>>> AnnualCycle.plot(result)

DiurnalCycle(ds: Dataset)#

Diurnal Cycle - Model2Self

The diurnal cycle of the data.

Parameters:: ds (Dataset) – The data to calculate the diurnal cycle of.

See also

Model2Self, valenspy.diagnostic.functions.diurnal_cycle, valenspy.diagnostic.visualizations.plot_diurnal_cycle

Examples

>>> from valenspy.diagnostic import DiurnalCycle
>>> result = DiurnalCycle(ds)
>>> DiurnalCycle.plot(result)

SpatialTimeMean(ds: Dataset)#

Spatial Mean - Model2Self

The spatial representation of the time mean of the data.

Parameters:: ds (Dataset) – The data to calculate the spatial mean of.

See also

Model2Self, valenspy.diagnostic.functions.spatial_time_mean, valenspy.diagnostic.visualizations.plot_map

Examples

>>> from valenspy.diagnostic import SpatialMean
>>> result = SpatialMean(ds)
>>> SpatialMean.plot(result)

TimeSeriesSpatialMean(ds: Dataset)#

Time Series - Model2Self

The time series of the data - if the data is spatial, the spatial mean is taken.

Parameters:: ds (Dataset) – The data to calculate the time series of the spatial mean of.

See also

Model2Self, valenspy.diagnostic.functions.time_series_spatial_mean, valenspy.diagnostic.visualizations.plot_time_series

Examples

>>> from valenspy.diagnostic import TimeSeries
>>> result = TimeSeries(ds)
>>> TimeSeries.plot(result)

TimeSeriesTrendSpatialMean(ds: Dataset, window_size, min_periods: int = None, center: bool = True, **window_kwargs)#

Time Series Trend - Model2Self

The time series trend of the data - if the data is spatial, the spatial mean is taken.

Parameters:

ds (Dataset) – The data to calculate the trend of.
window_size (Any) – The size - in number of time steps - of the window to use for the rolling average.
min_periods (int, default=None) – The minimum number of periods required for a value to be considered valid, by default None
center (bool, default=True) – If True, the value is placed in the center of the window, by default True
**window_kwargs (Any) – Description of window_kwargs.

See also

Model2Self, valenspy.diagnostic.functions.time_series_trend, valenspy.diagnostic.visualizations.plot_time_series

Examples

>>> from valenspy.diagnostic import TimeSeriesTrend
>>> result = TimeSeriesTrend(ds)
>>> TimeSeriesTrend.plot(result)

UrbanHeatIsland(ds: Dataset, urban_coord: tuple, rural_coord: tuple, projection=None)#

Urban Heat Island - Model2Self

The urban heat island as the difference in temperature between urban and rural areas.

Parameters:

ds (Dataset) – The data to calculate the urban heat island effect of.
urban_coord (tuple) – The coordinates of the urban area in the format (lat, lon).
rural_coord (tuple) – The coordinates of the rural area in the format (lat, lon).
projection (Any, default=None) – The projection used to convert the urban and rural coordinates to the dataset’s projection.

See also

Model2Self, valenspy.diagnostic.functions.urban_heat_island, valenspy.diagnostic.visualizations.plot_time_series

Examples

>>> from valenspy.diagnostic import UrbanHeatIsland
>>> result = UrbanHeatIsland(ds)
>>> UrbanHeatIsland.plot(result)

UrbanHeatIslandDiurnalCycle(ds: Dataset, urban_coord: tuple, rural_coord: tuple, projection=None)#

Urban Heat Island Diurnal Cycle - Model2Self

The diurnal cycle of the urban heat island.

Parameters:

ds (Dataset) – The data to calculate the urban heat island effect of.
urban_coord (tuple) – The coordinates of the urban area in the format (lat, lon).
rural_coord (tuple) – The coordinates of the rural area in the format (lat, lon).
projection (Any, default=None) – The projection used to convert the urban and rural coordinates to the dataset’s projection.

See also

Model2Self, valenspy.diagnostic.functions.urban_heat_island_diurnal_cycle, valenspy.diagnostic.visualizations.plot_diurnal_cycle

Examples

>>> from valenspy.diagnostic import UrbanHeatIslandDiurnalCycle
>>> result = UrbanHeatIslandDiurnalCycle(ds)
>>> UrbanHeatIslandDiurnalCycle.plot(result)

Model2Ref#

See Model2Ref for all shared functionality.

DiurnalCycleBias(ds: Dataset, ref: Dataset, calc_relative=False)#

Diurnal Cycle Bias - Model2Ref

The diurnal cycle bias of the data compared to the reference.

Parameters:

ds (Dataset) – The data to calculate the diurnal cycle bias of.
ref (Dataset) – The reference data to compare the data to.
calc_relative (Any, default=False) – If True, return the calc_relative bias, by default False

See also

Model2Ref, valenspy.diagnostic.functions.diurnal_cycle_bias, valenspy.diagnostic.visualizations.plot_diurnal_cycle

Examples

>>> from valenspy.diagnostic import DiurnalCycleBias
>>> result = DiurnalCycleBias(ds)
>>> DiurnalCycleBias.plot(result)

SpatialBias(ds: Dataset, ref: Dataset, calc_relative=False)#

Spatial Bias - Model2Ref

The spatial bias of the data compared to the reference.

Parameters:

ds (Dataset) – The data to calculate the spatial bias of.
ref (Dataset) – The reference data to compare the data to.
calc_relative (Any, default=False) – If True, return the relative bias, if False return the absolute bias, by default False

See also

Model2Ref, valenspy.diagnostic.functions.spatial_bias, valenspy.diagnostic.visualizations.plot_map

Examples

>>> from valenspy.diagnostic import SpatialBias
>>> result = SpatialBias(ds)
>>> SpatialBias.plot(result)

TemporalBias(ds: Dataset, ref: Dataset, calc_relative=False)#

Temporal Bias - Model2Ref

The temporal bias of the data compared to the reference.

Parameters:

ds (Dataset) – The data to calculate the temporal bias of.
ref (Dataset) – The reference data to compare the data to.
calc_relative (Any, default=False) – If True, return the relative bias, if False return the absolute bias, by default False

See also

Model2Ref, valenspy.diagnostic.functions.temporal_bias, valenspy.diagnostic.visualizations.plot_time_series

Examples

>>> from valenspy.diagnostic import TemporalBias
>>> result = TemporalBias(ds)
>>> TemporalBias.plot(result)

Ensemble2Self#

See Ensemble2Self for all shared functionality.

Ensemble2Ref#

See Ensemble2Self for all shared functionality.

MetricsRankings(dt_mod: DataTree, da_obs: Dataset, metrics=None, pss_binwidth=None)#

Metrics Rankings - Ensemble2Ref

The rankings of ensemble members with respect to several metrics when compared to the reference.

Parameters:

dt_mod (DataTree) – Description of dt_mod.
da_obs (Dataset) – Description of da_obs.
metrics (Any, default=None) – Description of metrics.
pss_binwidth (Any, default=None) – Description of pss_binwidth.

See also

Ensemble2Ref, valenspy.diagnostic.functions.calc_metrics_dt, valenspy.diagnostic.visualizations.plot_metric_ranking

Examples

>>> from valenspy.diagnostic import MetricsRankings
>>> result = MetricsRankings(ds)
>>> MetricsRankings.plot(result)