Anom

class spy4cast.meteo.Anom(ds: Dataset, typ: Literal['map', 'ts'], st: bool = False, group_season: bool = True)

Bases: _Procedure

Procedure to create the anomaly of a spy4cast.dataset.Dataset

Parameters:

• ds (spy4cast.dataset.Dataset) – spy4cast.dataset.Dataset on to which perform the anomaly

• type ('map' or 'ts') –

  Perform the anomaly and outputing a map (will result in a series of maps)

  or ouputting a timeseries by doing the mean across space.

• st (bool, default=False) – Indicates whether to standarise the anomaly

• group_season (bool, default=True) – If True, group data points with the same season_id (defined below) and take the average. This creates a new dataset with only time dimension being season_id. This dataset is the one used to calculate anomalies. This is used when regions span multiple months (e.g. JUN-AUG) and you consider the mean during this season the variable. season_id is the common year of the region; or for regions like DEC-FEB that mix years, the year of the end of the region (FEB).

Attributes Summary

data	Data Matrix
ds	Dataset introduced
lat	Array of latitude values of the dataset passes in initialization if type is map
lon	Array of longitude values of the dataset passes in initialization if type is map
region	Region applied to the matrix.
time	Array of years of the time variable
type	Type of anomaly passed in initialization
var	Variable name
var_names	Returns the variables contained in the object (data, time, lat, lon, ...)

Methods Summary

from_xrarray(array[, st])	Function to calculate the anomalies on a xarray DataArray
load(prefix[, folder, zip_file, type])	Load an anom object from matrices and type
plot(*[, save_fig, show_plot, halt_program, ...])	Plot the anomaly map or time series

Attributes Documentation

data

Data Matrix

Return type:

xarray.DataArray

ds

Dataset introduced

lat

Array of latitude values of the dataset passes in initialization if type is map

Return type:

xarray.DataArray

lon

Array of longitude values of the dataset passes in initialization if type is map

Return type:

xarray.DataArray

region

Region applied to the matrix.

Return type:

spy4cast.stypes.Region

Note

If type is ts and initilization from ds was not run then a default time and region region is returned

Note

If type is map and initilization from ds was not run then a default time region is returned

time

Array of years of the time variable

Return type:

xarray.DataArray

type

Type of anomaly passed in initialization

Return type:

PlotType

var

Variable name

Return type:

str

var_names

Returns the variables contained in the object (data, time, lat, lon, …)

Methods Documentation

classmethod from_xrarray(array: DataArray, st: bool = False) → Anom

Function to calculate the anomalies on a xarray DataArray

The anomaly is the time variable minus the mean across all times of a given point

Parameters:

• array (xr.DataArray) – Array to process the anomalies. Must have a dimension called time

• st (bool, default=False) – Indicates whether the anomaly should standarized. Divide by the standard deviation

Raises:

• TypeError – If array is not an instance of xr.DataArray

• ValueError – If the number of dimension of the array is not either 3 (map) or 1 (time series)

Returns:

Anom object

Return type:

Anom

See also

npanom

classmethod load(prefix: str, folder: str = '.', zip_file: str | None = None, *, type: Literal['map', 'ts'] | None = None, **attrs: Any) → Anom

Load an anom object from matrices and type

Parameters:

• prefix (str) – Prefix of the files containing the information for the object

• folder (str) – Directory of the files

• zip_file (optional, str) – If provided folder will be searched inside of the zip file, that should conatin all the data.

• type ('map' or 'ts') – Type of anomaly

Return type:

Clim

plot(*, save_fig: bool = False, show_plot: bool = False, halt_program: bool = False, year: int | None = None, timestamp: str | Timestamp | datetime | None = None, cmap: str | None = None, color: Tuple[float, float, float] | None = None, folder: str = '.', name: str = 'anomaly.png', levels: int | ndarray[Any, dtype[float32]] | Sequence[float] | None = None, ticks: ndarray[Any, dtype[float32]] | Sequence[float] | None = None, figsize: Tuple[float, float] | None = None, plot_type: Literal['contour', 'pcolor'] | None = None, central_longitude: float | None = None, xlim: Tuple[float, float] | None = None) → Tuple[Tuple[Figure], Tuple[Tuple[Axes]]]

Plot the anomaly map or time series

Parameters:

• save_fig – Saves the fig using folder and name parameters

• show_plot – Shows the plot

• halt_program – Only used if show_plot is True. If True shows the plot if plt.show and stops execution. Else uses fig.show and does not halt program

• year – Plot the anomaly map for the last date of the season with this year

• timestamp – Plot the date which is closest to the timestamp

• cmap – Colormap for the map types

• color – Color of the line for ts types

• folder – Directory to save fig if save_fig is True

• name – Name of the fig saved if save_fig is True

• levels – Levels for the anomaly map

• ticks – Ticks for the anomaly map

• figsize – Set figure size. See plt.figure

• plot_type ({"contour", "pcolor"}, defaut = "pcolor") – Plot type for map. If contour it will use function ax.contourf, if pcolor ax.pcolormesh.

• central_longitude (float, optional) – Longitude used to center the map

• xlim (tuple[float, float], optional) – Xlim lim for the y map passed into ax.set_extent

Returns:

• figures (Tuple[plt.Figure]) – Figures objects from matplotlib. In this case just one figure

• ax (Tuple[Tuple[plt.Axes]]) – Tuple of axes in figure. In this case just one axes