cgeniepy.grid

Classes

GridOperation	A set of operations on grid/coordinate data
Interpolator	A univeral ineteprolator for cgeniepy that

Module Contents

class cgeniepy.grid.GridOperation

A set of operations on grid/coordinate data

get_genie_lon(N=36, edge=False, offset_start=-260)

get GENIE longitude in 10 degree resolution, if edge is False, then return midpoint

Parameters:

• N – number of grid points

• edge – if True, return edge points

• offset_start – the par_grid_lon_offset option in main configuration file

get_genie_lat(N=36, edge=False)

return cGENIE latitude in log-sine normally degree resolution, if edge is False, then return midpoint

get_genie_depth(N=16, edge=False, max_depth=5000)

calculate cGENIE vertical depth :param N: number of grid points :param edge: if True, return edge points, otherwise return midpoints

get_normal_lon(N=36, edge=False)

Normal longitude in 10 degree resolution (default), if edge is False, then return midpoint

lon_n2g(x, grid_lon_offset=-260)

Convert normal longitude (-180, 180) to GENIE longitude (-270, 90)

Parameters:

x – normal longitude

Returns:

GENIE longitude

lon_g2n(x)

Convert GENIE longitude to normal longitude. This is independent on the grid_offset_start option

Parameters:

x – GENIE longitude

Returns:

normal longitude

lon_e2n(x)

Convert eastern longitude to normal longitude

Parameters:

x – longitude in eastern degree

Returns:

normal longitude

lon_n2e(x)

Convert normal longitude (-180, 180) to longitude east(0,360)

Parameters:

x – normal longitude

Returns:

longitude in eastern degree

xr_n2g(data: xarray.Dataset, longitude='lon', *args, **kwargs) → xarray.Dataset

Apply longitude conversion method n2g for the input data (normal to GENIE)

Parameters:

• data – input data

• longitude – longitude coordinate name

Returns:

xr.Dataset

xr_g2n(data: xarray.Dataset, longitude='lon', *args, **kwargs) → xarray.Dataset

Apply longitude conversion method g2n for the input data (GENIE to normal)

Parameters:

• data – input data

• longitude – longitude coordinate name

Returns:

xr.Dataset

xr_e2n(data: xarray.Dataset, longitude='lon', *args, **kwargs) → xarray.Dataset

Apply longitude conversion method e2n for the input data (eastern to normal)

Parameters:

• data – input data

• longitude – longitude coordinate name

Returns:

xr.Dataset

xr_n2e(data: xarray.Dataset, longitude='lon', *args, **kwargs) → xarray.Dataset

Apply longitude conversion method n2e for the input data (normal to eastern)

Parameters:

• data – input data

• longitude – longitude coordinate name

Returns:

xr.Dataset

mask_Arctic_Med(array, policy='na')

mask Arctic and Meditterean Sea in cGENIE modern continent configuration

Parameters:

array – 36x36 GENIE array

GENIE_grid_mask(base='worjh2', basin='ALL', subbasin='', invert=False)

Get a modern GENIE 36x36 mask array from input data. The input array is flipped (left/right flip -> up/down flip) for easy recognition

Continent:

worjh2, worlg4, worbe2, GIteiiaa, GIteiiaa, p0055c

Basin:

Atlantic/Pacific/Indian/ALL/Tanzania

Subbasin:

N/S/ALL, ALL means Southern Ocean section included

Returns:

GENIE grid array where continent/ice cap is 0 and ocean is 1, default is ‘worjh2’

geniebin_lat(x, *args, **kwargs)

Categorize <latitude> into cGENIE grid bins

geniebin_lon(x, *args, **kwargs)

Categorize <longitude> into cGENIE grid bins

geniebin_depth(x, *args, **kwargs)

Categorize <depth> into cGENIE grid bins

normbin_lon(x, *args, **kwargs)

Categorize <longitude> into normal grid bins

haversine_distance(lat1, lon1, lat2, lon2)

Calculate the Haversine distance between corresponding pairs of points.

Parameters:

• lat1 – Array of latitudes for points 1

• lon1 – Array of longitudes for points 1

• lat2 – Array of latitudes for points 2

• lon2 – Array of longitudes for points 2

Returns:

Array of distances between corresponding pairs of points

geo_dis3d(point1, points2)

Calculate the 3D geographical distance between point1 and multiple points2.

Parameters:

• point1 – Tuple/list of coordinates (z, lat, lon) or (lat, lon)

• points2 – Numpy Array of shape (n, 3) containing coordinates (z, lat, lon) of points

Returns:

Array of distances between point1 and each point in points2

geo_dis2d(point1, points2)

Calculate the 2D geographical distance between point1 and multiple points2.

Parameters:

• point1 – Tuple/list of coordinates (z, lon, lat) or (lon, lat)

• points2 – Array of shape (n, 3) containing coordinates (z, lon, lat) of points

Returns:

Array of distances between point1 and each point in points2

static check_dimension(input)

check the presence of latitude, longitude, depth, and time in the input tuple

Parameters:

input – tuple of dimension names

Returns:

tuple of boolean values indicating the presence of latitude, longitude, depth, and time

Example

>>> input = ('lat', 'lon', 'depth', 'time')
>>> check_dimension(input)
(True, True, True, True)

dim_order(input)

Determine the order of dimensions in the input data array

Returns:

tuple of index in the input data

Example

>>> input = ('lat', 'lon', 'depth')
>>> dim_order(input) ## always follow time, depth, lat, lon
(2, 0, 1)

static set_coordinates(obj, index)

Set the coordinates of the input object based on the input index

Parameters:

• obj – object to set coordinates

• index – tuple of dimension names

class cgeniepy.grid.Interpolator(dims, coordinates, values, grid_number=200, method='r-linear')

A univeral ineteprolator for cgeniepy that can be used to interpolate data for both regular and irregular grid

dims

coords

values

grid_number = 200

gridded_coord = []

meshgrid

interp_function

gridded_data

new_coordinate(n)

create new coordinates for regridding

Parameters:

n – the resolution of the new grid

Returns:

a list of new coordinates

new_meshgrid(*args, **kwargs)

create meshgrid for regridding

Returns:

numpy meshgrid

interpolate_data(*args, **kwargs)

Use the interpolation function to get regridded values

Returns:

regridded data array

to_xarray()

Combine the regridded data with the new coordinates to create a xarray DataArray

Returns:

xarray DataArray

to_dataframe()

Convert the regridded data to a pandas DataFrame

Returns:

pandas DataFrame