py_eddy_tracker.dataset.grid.RegularGridDataset

class py_eddy_tracker.dataset.grid.RegularGridDataset(*args, **kwargs)[source]

Bases: py_eddy_tracker.dataset.grid.GridDataset

Class only for regular 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 which set indexs to use for non-coordinate dimensions

  • unset (bool) – Set to True to create an empty grid object without file

Methods

add_grid

Add a grid in handler

add_uv

Compute a u and v grid

add_uv_lagerloef

bbox_indice

bessel_band_filter

bessel_high_filter

param str grid_name

grid to filter, data will replace original one

bessel_low_filter

c_to_bounds

Centred coordinates to bounds coordinates

check_order

clean_land

Function to remove all land pixel

compute_finite_difference

compute_pixel_path

Give a series of indexes which describe the path between to position

compute_stencil

Apply stencil ponderation on field.

contour

param matplotlib.axes.Axes ax

matplotlib axes use to draw

convolve_filter_with_dynamic_kernel

param str grid

grid name

copy

Duplicate the variable from grid_in in grid_out

display

param matplotlib.axes.Axes ax

matplotlib axes use to draw

eddy_identification

Compute eddy identification on the pecified grid

estimate_kernel_shape

finalize_kernel

get_amplitude

get_pixels_in

Get indices of pixels in contour.

get_step_in_km

get_uavg

Calculate 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 grid high-pass filtered, by substracting to the grid the low-pass filter (default: order=1)

init_speed_coef

Draft

interp

Compute z over lons, lats

is_circular

Check if the grid is circular

kernel_bessel

wave_length in km order must be int

kernel_lanczos

Not really operational wave_length in km order must be int

lanczos_high_filter

lanczos_low_filter

load

Load variable (data).

load_general_features

Load attrs to be stored in object

low_filter

Return the grid low-pass filtered (default: order=1)

nearest_grd_indice

normalize_x_indice

regrid

Interpolate another grid at the current grid position

setup_coordinates

spectrum_lonlat

speed_coef_mean

Some nan can be computed over contour if we are near border, something to explore

units

Get unit from variable

with_array

Geo matrix data must be ordered like this (X,Y) and masked with numpy.ma.array

write

Write dataset output with same format as input

Attributes

EARTH_RADIUS

GRAVITY

N

bounds

Give bounds

centered

contours

coordinates

dimensions

filename

global_attrs

indexs

interpolators

is_centered

Give True if pixel is described with its center’s position or a corner

speed_coef

variables

variables_description

vars

x_bounds

x_c

x_dim

x_size

xstep

Only for regular grid with no step variation

y_bounds

y_c

y_dim

ystep

Only for regular grid with no step variation
add_uv(grid_height, uname='u', vname='v', stencil_halfwidth=4)[source]

Compute a u and v grid

Parameters

  • grid_height (str) – grid name where the funtion will apply stencil method

  • uname (str) – future name of u

  • vname (str) – future name of v

  • stencil_halfwidth (int) – largest stencil could be apply (max: 4)

add_uv_lagerloef(grid_height, uname='u', vname='v', schema=15)[source]
bbox_indice(vertices)[source]
bessel_band_filter(grid_name, wave_length_inf, wave_length_sup, **kwargs)[source]
bessel_high_filter(grid_name, wave_length, order=1, lat_max=85, **kwargs)[source]
Parameters
bessel_low_filter(grid_name, wave_length, order=1, lat_max=85, **kwargs)[source]
static check_order(order)[source]
clean_land()[source]

Function to remove all land pixel

compute_finite_difference(data, schema=1, mode='reflect', vertical=False)[source]
compute_pixel_path(x0, y0, x1, y1)[source]

Give a series of indexes which describe the path between to position

compute_stencil(data, stencil_halfwidth=4, mode='reflect', vertical=False)[source]

Apply stencil ponderation on field.

Parameters

  • data (array) – array where apply stencil

  • stencil_halfwidth (int) – from 1 t0 4, maximal stencil used

  • mode (str) – convolution mode

  • vertical (bool) – if True, method apply a vertical convolution

Returns

gradient array from stencil application

Return type

array

Short story, how to get stencil coefficient for stencil (3 points, 5 points and 7 points)

Taylor’s theorem:

\[f(x \pm h) = f(x) \pm f'(x)h + \frac{f''(x)h^2}{2!} \pm \frac{f^{(3)}(x)h^3}{3!} + \frac{f^{(4)}(x)h^4}{4!} \pm \frac{f^{(5)}(x)h^5}{5!} + O(h^6)\]

If we stop at O(h^2), we get classic differenciation (stencil 3 points):

\[f(x+h) - f(x-h) = f(x) - f(x) + 2 f'(x)h + O(h^2)\]
\[f'(x) = \frac{f(x+h) - f(x-h)}{2h} + O(h^2)\]

If we stop at O(h^4), we will get stencil 5 points:

(1)\[f(x+h) - f(x-h) = 2 f'(x)h + 2 \frac{f^{(3)}(x)h^3}{3!} + O(h^4)\]
(2)\[f(x+2h) - f(x-2h) = 4 f'(x)h + 16 \frac{f^{(3)}(x)h^3}{3!} + O(h^4)\]

If we multiply equation (1) by 8 and substract equation (2), we get:

\[8(f(x+h) - f(x-h)) - (f(x+2h) - f(x-2h)) = 16 f'(x)h - 4 f'(x)h + O(h^4)\]
\[f'(x) = \frac{f(x-2h) - 8f(x-h) + 8f(x+h) - f(x+2h)}{12h} + O(h^4)\]

If we stop at O(h^6), we will get stencil 7 points:

(3)\[f(x+h) - f(x-h) = 2 f'(x)h + 2 \frac{f^{(3)}(x)h^3}{3!} + 2 \frac{f^{(5)}(x)h^5}{5!} + O(h^6)\]
(4)\[f(x+2h) - f(x-2h) = 4 f'(x)h + 16 \frac{f^{(3)}(x)h^3}{3!} + 64 \frac{f^{(5)}(x)h^5}{5!} + O(h^6)\]
(5)\[f(x+3h) - f(x-3h) = 6 f'(x)h + 54 \frac{f^{(3)}(x)h^3}{3!} + 486 \frac{f^{(5)}(x)h^5}{5!} + O(h^6)\]

If we multiply equation (3) by 45 and substract equation (4) multiply by 9 and add equation (5), we get:

\[45(f(x+h) - f(x-h)) - 9(f(x+2h) - f(x-2h)) + (f(x+3h) - f(x-3h)) = 90 f'(x)h - 36 f'(x)h + 6 f'(x)h + O(h^6)\]
\[f'(x) = \frac{-f(x-3h) + 9f(x-2h) - 45f(x-h) + 45f(x+h) - 9f(x+2h) +f(x+3h)}{60h} + O(h^6)\]

…

contour(ax, name, factor=1, ref=None, **kwargs)[source]
Parameters
convolve_filter_with_dynamic_kernel(grid, kernel_func, lat_max=85, extend=False, **kwargs_func)[source]
Parameters

  • grid (str) – grid name

  • kernel_func (func) – function of kernel to use

  • lat_max (float) – absolute latitude above no filtering apply

  • extend (bool) – if False, only non masked value will return a filtered value

  • kwargs_func (dict) – look at kernel_func

Returns

filtered value

Return type

array

display(ax, name, factor=1, ref=None, **kwargs)[source]
Parameters
estimate_kernel_shape(lat, wave_length, order)[source]
finalize_kernel(kernel, order, half_x_pt, half_y_pt)[source]
get_pixels_in(contour)[source]

Get indices of pixels in contour.

Parameters

contour (vertice,Path) – Contour which enclosed some pixels

Returns

Indices of grid in contour

Return type

array[int],array[int]

get_step_in_km(lat, wave_length)[source]
init_speed_coef(uname='u', vname='v')[source]

Draft

interp(grid_name, lons, lats, method='bilinear')[source]

Compute z over lons, lats

Parameters

  • grid_name (str) – Grid to be interpolated

  • lons – new x

  • lats – new y

  • method (str) – Could be ‘bilinear’ or ‘nearest’

Returns

new z

is_circular()[source]

Check if the grid is circular

kernel_bessel(lat, wave_length, order=1)[source]

wave_length in km order must be int

kernel_lanczos(lat, wave_length, order=1)[source]

Not really operational wave_length in km order must be int

lanczos_high_filter(grid_name, wave_length, order=1, lat_max=85, **kwargs)[source]
lanczos_low_filter(grid_name, wave_length, order=1, lat_max=85, **kwargs)[source]
nearest_grd_indice(x, y)[source]
normalize_x_indice(indices)[source]
regrid(other, grid_name, new_name=None)[source]

Interpolate another grid at the current grid position

Parameters

  • other (RegularGridDataset) –

  • grid_name (str) – variable name to interpolate

  • new_name (str) – name used to store, if None method will use current ont

setup_coordinates()[source]
spectrum_lonlat(grid_name, area=None, ref=None, **kwargs)[source]
speed_coef_mean(contour)[source]

Some nan can be computed over contour if we are near border, something to explore

classmethod with_array(coordinates, datas, variables_description=None, **kwargs)[source]

Geo matrix data must be ordered like this (X,Y) and masked with numpy.ma.array

x_size
property xstep

Only for regular grid with no step variation

property ystep

Only for regular grid with no step variation