py_eddy_tracker.dataset.grid.GridDataset¶

class py_eddy_tracker.dataset.grid.GridDataset(filename, x_name, y_name, centered=None, indexs=None, unset=False, nan_masking=False)[source]¶

Bases: object

Class for basic tools on NetCDF Grid

Parameters:

filename (str) – Filename to load
x_name (str) – Name of longitude coordinates
y_name (str) – Name of latitude coordinates
centered (bool,None) – Allow to know how coordinates could be used with pixel
indexs (dict) – A dictionary that sets indexes to use for non-coordinate dimensions
unset (bool) – Set to True to create an empty grid object without file
nan_masking (bool) – Set to True to replace data.mask with isnan method result

Methods

`add_grid`	Add a grid in handler
`c_to_bounds`	Centered coordinates to bounds coordinates
`clean`
`copy`	Duplicate the variable from grid_in in grid_out
`eddy_identification`	Compute eddy identification on the specified grid
`get_amplitude`
`get_mask`
`get_uavg`	Compute geostrophic speed around successive contours Returns the average
`grid`	Give the grid required
`grid_tiles`	Give the grid tiles required, without buffer system
`high_filter`	Return the high-pass filtered grid, by substracting to the initial grid the low-pass filtered grid (default: order=1)
`is_circular`	Check grid circularity
`load`	Load variable (data).
`load_general_features`	Load attrs to be stored in object
`low_filter`	Return the low-pass filtered grid (default: order=1)
`populate`
`setup_coordinates`
`units`	Get unit from variable
`write`	Write dataset output with same format as input

Attributes

`x_c`
`y_c`
`x_bounds`
`y_bounds`
`centered`
`x_dim`
`y_dim`
`coordinates`
`filename`
`dimensions`
`indexs`
`variables_description`
`global_attrs`
`vars`
`contours`
`nan_mask`
`EARTH_RADIUS`
`GRAVITY`
`N`
`bounds`	Give bounds
`is_centered`	Give True if pixel is described with its center's position or a corner
`variables`

EARTH_RADIUS = 6370997.0¶

GRAVITY = 9.807¶

N = 1¶

add_grid(varname, grid)[source]¶

Add a grid in handler

Parameters:

varname (str) – name of the future grid
grid (array) – grid array

property bounds¶: Give bounds

static c_to_bounds(c)[source]¶

Centered coordinates to bounds coordinates

Parameters:: c (array) – centered coordinates to translate
Returns:: bounds coordinates

centered¶

clean()[source]¶

contours¶

coordinates¶

copy(grid_in, grid_out)[source]¶

Duplicate the variable from grid_in in grid_out

Parameters:

grid_in –
grid_out –

dimensions¶

eddy_identification(grid_height, uname, vname, date, step=0.005, shape_error=55, presampling_multiplier=10, sampling=50, sampling_method='visvalingam', pixel_limit=None, precision=None, force_height_unit=None, force_speed_unit=None, **kwargs)[source]¶

Compute eddy identification on the specified grid

Parameters:

grid_height (str) – Grid name of Sea Surface Height
uname (str) – Grid name of u speed component
vname (str) – Grid name of v speed component
date (datetime.datetime) – Date to be stored in object to date data
step (float,int) – Height between two layers in m
shape_error (float,int) – Maximal error allowed for outermost contour in %
presampling_multiplier (int) – Evenly oversample the initial number of points in the contour by nb_pts x presampling_multiplier to fit circles
sampling (int) – Number of points to store contours and speed profile
sampling_method (str) – Method to resample the stored contours, ‘uniform’ or ‘visvalingam’
pixel_limit ((int,int),None) – Min and max number of pixels inside the inner and the outermost contour to be considered as an eddy
precision (float,None) – Truncate values at the defined precision in m
force_height_unit (str) – Unit used for height unit
force_speed_unit (str) – Unit used for speed unit
kwargs (dict) – Arguments given to amplitude (mle, nb_step_min, nb_step_to_be_mle). Look at py_eddy_tracker.eddy_feature.Amplitude() The amplitude threshold is given by step*nb_step_min

Returns:

Return a list of 2 elements: Anticyclones and Cyclones

Return type:

py_eddy_tracker.observations.observation.EddiesObservations

Eddy detection : Med

Eddy detection : Gulf stream

Eddy detection and filter

Eddy detection on SLA and ADT

Eddy detection : Antartic Circumpolar Current

LAVD detection and geometric detection

Collocating external data

filename¶

static get_amplitude(contour, contour_height, data, anticyclonic_search=True, level=None, **kwargs)[source]¶

static get_mask(a)[source]¶

get_uavg(all_contours, centlon_e, centlat_e, original_contour, anticyclonic_search, level_start, pixel_min=3)[source]¶: Compute geostrophic speed around successive contours Returns the average