py_eddy_tracker.observations.groups.GroupEddiesObservations¶

class py_eddy_tracker.observations.groups.GroupEddiesObservations(size=0, track_extra_variables=None, track_array_variables=0, array_variables=None, only_variables=None, raw_data=False)[source]¶

Bases: py_eddy_tracker.observations.observation.EddiesObservations, abc.ABC

Methods

`add_fields`	Add a new field.
`add_rotation_type`
`align_on`	Align the time indices of two datasets.
`append`	Merge.
`basic_formula_ellipse_major_axis`	Give major axis in km with a given latitude
`bins_stat`	param str,array xname variable to compute stats on
`box_display`	Return values evenly spaced with few numbers
`build_var_list`
`circle_contour`	Set contours as circles from radius and center data.
`coherence`	Check coherence between two datasets.
`compare_units`
`concatenate`
`contains`	Return index of contour containing (x,y)
`copy`
`copy_data_to_zarr`	Copy with buffer for zarr.
`cost_function`	Return the cost function between two obs.
`cost_function_common_area`	How does it work on x bound ?
`create_particles`	create particles only inside speed contour.
`create_variable`
`create_variable_zarr`
`display`	Plot the speed and effective (dashed) contour of the eddies
`distance`	Use haversine distance for distance matrix between every self and other eddies.
`extract_with_area`	Extract geographically with a bounding box.
`extract_with_mask`	Extract a subset of observations.
`field_table`	Produce description table of the fields available in this object
`filled`	param matplotlib.axes.Axes ax matplotlib axe used to draw
`filled_by_interpolation`	Fill selected values by interpolation
`first_obs`	Get first obs of each trajectory.
`fix_next_previous_obs`
`fixed_ellipsoid_mask`
`format_label`
`from_netcdf`
`from_zarr`
`get_color`	Return colors as a cyclic list
`get_infos`
`get_missing_indices`	Find indexes where observations are missing
`grid_box_stat`	Get percentile of eddies in each bin
`grid_count`	Count the eddies in each bin (use all pixels in each contour)
`grid_stat`	Return the mean of the eddies’ variable in each bin
`hist`	Build histograms.
`index`	Return obs from self at the index.
`insert_observations`	Insert other obs in self at the given index.
`insert_virtual`	insert virtual observations on segments where observations are missing
`inside`	True for each point inside the effective contour of an eddy
`intern`
`interp_grid`	Interpolate a grid on a center or contour with mean, min or max method
`is_convex`	Get flag of the eddy’s convexity
`iter_on`	Yield observation group for each bin.
`keep_tracks_by_date`	Find tracks that exist at date date and lasted at least nb_days after.
`last_obs`	Get Last obs of each trajectory.
`load_file`	Load the netcdf or the zarr file.
`load_from_netcdf`	Load data from netcdf.
`load_from_zarr`	Load data from zarr.
`mask_from_polygons`	Return mask for all observations in one of polygons list
`mask_function`
`match`	Return index and score computed on the effective contour.
`merge`	Merge two datasets.
`merge_filters`	Compute an intersection between all filters after to evaluate each of them
`needed_variable`
`netcdf_create_dimensions`
`new_like`
`obs_dimension`
`parse_varname`
`post_process_link`
`propagate`	Fill virtual obs (C).
`re_reference_index`	Shift index with ref
`remove_fields`	Copy with fields listed remove
`reset`
`scatter`	Scatter data.
`set_global_attr_netcdf`
`set_global_attr_zarr`
`shifted_ellipsoid_degrees_mask`
`solve_conflict`
`solve_first`
`solve_function`
`solve_simultaneous`	Write something (TODO)
`time_sub_sample`	Time sub sampling
`to_netcdf`
`to_zarr`
`tracking`	Track obs between self and other
`write_file`	Write a netcdf or zarr with eddy obs.
`zarr_dimension`

Attributes

`COLORS`
`ELEMENTS`
`NB_COLORS`
`array_variables`
`dtype`	Return dtype to build numpy array.
`elements`	Return all the names of the variables.
`global_attr`
`nb_days`	Return period in days covered by the dataset
`obs`	Return observations.
`observations`
`only_variables`
`period`	Give the time coverage.
`period_`
`raw_data`
`shape`
`sign_legend`
`sign_type`
`track_array_variables`
`track_extra_variables`
`tracks`

array_variables¶

filled_by_interpolation(mask)[source]¶

Fill selected values by interpolation

Parameters: mask (array(bool)) – True if must be filled by interpolation

Track in python¶

abstract fix_next_previous_obs()[source]¶

abstract get_missing_indices(dt)[source]¶: Find indexes where observations are missing

insert_virtual()[source]¶: insert virtual observations on segments where observations are missing

keep_tracks_by_date(date, nb_days)[source]¶

Find tracks that exist at date date and lasted at least nb_days after.

Parameters

date (int,float) – date where the tracks must exist
nb_days (int,float) – number of times the tracks must exist. Can be negative

If nb_days is negative, it searches a track that exists at the date, but existed at least nb_days before the date

observations¶

only_variables¶

period_¶

raw_data¶

sign_type¶

track_array_variables¶

track_extra_variables¶