cgeniepy.array#

Classes#

GriddedData

Module Contents#

class cgeniepy.array.GriddedData(array=np.nan, attrs={})#
modify_in_place = False#
keep_attrs = True#

GriddedData is a class to store and compute GENIE netcdf data.

It stores data in xarray.DataArray format, and provides optimalised methods for GENIE model output to compute statistics.

data#
attrs#
__repr__()#
__getitem__(item)#

make GriddedData subscriptable like xarray.DataArray

__array__()#

return the numpy array of the data

interpolate(*args, **kwargs)#

Interpolate the GriddedData to a finer grid, mostly useful for GENIE plotting

Returns:

a GriddedData object with interpolated data

Example#

>>> model = GenieModel(path)
>>> var = model.get_var(target_var)
>>> var.interpolate(method='r-linear') ## regular linear interpolation
sel(*args, **kwargs)#

select grid points based on the given coordinates a wrapper to xarray sel method

Returns:

a GriddedData object with selected data

Examples:#

>>> model = GenieModel(path)
>>> var = model.get_var(target_var)
>>> var.search_grid(lon=XX, lat=XX, zt=XX, method='nearest')
isel(*args, **kwargs)#

select grid points based on the given index a wrapper to xarray isel method

Examples:#

>>> model = GenieModel(path)
>>> var = model.get_var(target_var)
>>> var.isel(lon=XX, lat=XX, zt=XX)
normalise_longitude(method='g2n', *args, **kwargs)#

Normalise GENIE’s longitude (eastern degree) to normal longitude (-180, 180)

Parameters:

method – normalise method, default is ‘g2n’ (GENIE to normal). A full list of methods are: - ‘g2n’: GENIE to normal - ‘n2g’: normal to GENIE - ‘e2n’: eastern to normal - ‘n2e’: normal to eastern

Example#

>>> Model = GenieModel("a path")
>>> Model.get_var('abc').normalise_longitude()
__add__(other)#

Allow GriddedData to be added by a number or another GriddedData

__radd__(other)#

Allow a number or another GriddedData to be added to this GriddedData

__sub__(other)#

Allow GriddedData to be subtracted by a number or another GriddedData

__rsub__(other)#

Allow a number or another GriddedData to be subtracted from this GriddedData

__truediv__(other)#

Allow GriddedData to be divided by a number or another GriddedData

NA/NA -> NA

__rtruediv__(other)#

Allow a number or another GriddedData to be divided by this GriddedData

__mul__(other)#

Allow GriddedData to be multiplied by a number or another GriddedData

__rmul__(other)#

Allow a number or another GriddedData to be multiplied by this GriddedData

__pow__(other)#

Allow GriddedData to be raised to a power

__lt__(other)#
__le__(other)#
__gt__(other)#
__ge__(other)#
__eq__(other)#
__ne__(other)#
max(*args, **kwargs)#

compute the maximum value of the array

min(*args, **kwargs)#

compute the minimal value of the array

sum(*args, **kwargs)#

compute the sum of the array

mean(*args, **kwargs)#

compute the mean of the array. Note this is not weighted mean, for weighted mean, use weighted_mean method

median(*args, **kwargs)#

compute the median of the array

std(*args, **kwargs)#

Compute the standard deviation using xarray’s API so named dimensions can be selected.

sd(*args, **kwargs)#

Backward-compatible alias for std.

variance(*args, **kwargs)#

compute the variance of the array

se(*args, **kwargs)#

compute the standard error of the mean

weighted(weights, *args, **kwargs)#

assign weights to the data

sel_modern_basin(basin, norm_lon_method='g2n')#

select modern basin from regionmask.defined_regions.ar6.ocean

The 58 defines marine regions from the sixth IPCC assessment report (AR6), Iturbide et al., (2020) ESSD

https://regionmask.readthedocs.io/en/stable/_images/plotting_ar6_all.png

Parameters:

  • basin – the basin index (int) or basin abbrev name (str), or a list of basin index or basin name

  • norm_lon_method – normalise longitude method, default is ‘g2n’

49: South Pacific

50: North Atlantic Ocean 51: Equatorial Atlantic Ocean 52: Southern Atlantic Ocean

53: North Indian Ocean 55: Equatorial Indian Ocean 56: South Indian Ocean

57: Southern Ocean 46: Arctic Ocean ——————————–

Example:#

>>> gd.sel_modern_basin(47) ## North Pacific
mask_basin(base, basin, subbasin)#

use pre-defined grid mask to select_basin, mostly used for cGENIE model

Parameters:

  • base – the base configuration name, e.g., worjh2, worlg4

  • basin – the basin name, e.g., Atlantic, Pacific, Indian

  • subbasin – N/S/ALL, ALL means Southern Ocean section included

Example#

>>> gd.mask_basin('worjh2', 'Atlantic'').mean()
save_ocn_data()#
search_point(point, ignore_na=False, to_genielon=False, **kwargs)#

search the nearest grid point to the given coordinates

Parameters:

  • point – a list/tuple of coordinate values in the same order to the data dimension (self.data.dims)

  • ignore_na – whether only check ocean data (which ignore the NA grids)

  • to_genielon – whether convert the input longitude to genie longitude, input point must be list if True

Example#

>>> lat, lon, depth = 10, 20, 30
>>> data.search_point((lat, lon, depth))
to_GriddedDataVis()#

convert GriddedData to GriddedDataVis

plot(*args, **kwargs)#

plot the data using GriddedDataVis

to_dataframe()#

convert GriddedData to pandas.DataFrame

to_ScatterData()#

convert GriddedData to ScatterData

fill_poles()#

Fill the poles with a given value, default is NaN