py_eddy_tracker.observations.network.NetworkObservations¶

class py_eddy_tracker.observations.network.NetworkObservations(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

Methods

`add_fields`	Add a new field.
`add_rotation_type`
`align_on`	Align the time indexes of two datasets.
`append`	Merge.
`basic_formula_ellips_major_axis`	Give major axis in km with a given latitude
`bins_stat`	param str,array xname variable to compute stats on
`birth_event`
`box_display`	Return value evenly spaced with few numbers
`build_var_list`
`circle_contour`	Set contours as a circles from radius and center data.
`coherence`	Check coherence between two datasets.
`compare_units`
`concatenate`
`connexions`
`contains`	Return index of contour which contain (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_variable`
`create_variable_zarr`
`death_event`
`display`	Plot the speed and effective (dashed) contour of the eddies
`display_timeline`	Must be call on only one network
`distance`	Use haversine distance for distance matrix between every self and other eddies.
`event_timeline`
`extract_event`
`extract_segment`
`extract_with_area`	Extract geographically with a bounding box.
`extract_with_mask`	Extract a subset of observations.
`filled`	param matplotlib.axes.Axes ax matplotlib axe used to draw
`first_obs`	Get first obs of each trajectory.
`fixed_ellipsoid_mask`
`format_label`
`from_netcdf`
`from_split_network`	Build a NetworkObservations object with Group dataset and indexs
`from_zarr`
`fully_connected`
`get_infos`
`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.
`infos`
`insert_observations`	Insert other obs in self at the index.
`insert_virtual`
`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.
`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_function`
`match`	Return index and score computed on the effective contour.
`median_filter`
`merge`	Merge two datasets.
`merge_filters`	Compute an intersection between all filters after to evaluate each of them
`merging_event`
`needed_variable`
`netcdf_create_dimensions`
`new_like`
`obs_dimension`
`obs_relative_order`
`only_one_network`	Raise a warning or error? if there are more than one network
`parse_varname`
`plot`	This function will draw path of each trajectory
`post_process_link`
`propagate`	Filled virtual obs (C).
`relative`
`remove_dead_branch`
`reset`
`scatter`	Scatter data.
`scatter_timeline`	Must be call on only one network
`segment_relative_order`
`segment_track_array`
`set_global_attr_netcdf`
`set_global_attr_zarr`
`shifted_ellipsoid_degrees_mask`
`solve_conflict`
`solve_first`
`solve_function`
`solve_simultaneous`	Write something (TODO)
`spliting_event`
`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`
`NOGROUP`
`array_variables`
`dtype`	Return dtype to build numpy array.
`elements`	Return all the names of the variables.
`global_attr`
`nb_days`	Return period days cover by 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`

NOGROUP = 0¶

birth_event()[source]¶

connexions()[source]¶

death_event()[source]¶

display_timeline(ax, event=True, field=None, method=None)[source]¶: Must be call on only one network

property elements¶: Return all the names of the variables.

event_timeline(ax, field=None, method=None)[source]¶

extract_event(indices)[source]¶

extract_segment(segments)[source]¶

extract_with_mask(mask)[source]¶

Extract a subset of observations.

Parameters: mask (array(bool)) – mask to select observations
Returns: same object with selected observations
Return type: self

classmethod from_split_network(group_dataset, indexs, **kwargs)[source]¶

Build a NetworkObservations object with Group dataset and indexs

Parameters

group_dataset (TrackEddiesObservations) – Group dataset
indexs – result from split_network

return NetworkObservations

fully_connected()[source]¶

infos(label='')[source]¶

insert_virtual()[source]¶

median_filter(half_window, xfield, yfield, inplace=True)[source]¶

merging_event()[source]¶

obs_relative_order(i_obs)[source]¶

only_one_network()[source]¶: Raise a warning or error? if there are more than one network

plot(ax, ref=None, **kwargs)[source]¶

This function will draw path of each trajectory

Parameters

ax (matplotlib.axes.Axes) – ax to draw
ref (float,int) – if defined, all coordinates will be wrapped with ref like west boundary
kwargs (dict) – keyword arguments for Axes.plot

Returns

a list of matplotlib mappables

relative(i_obs, order=2, direct=True, only_past=False, only_future=False)[source]¶

remove_dead_branch(nobs=3)[source]¶

scatter_timeline(ax, name, factor=1, event=True, **kwargs)[source]¶: Must be call on only one network

segment_relative_order(seg_origine)[source]¶

segment_track_array()[source]¶

spliting_event()[source]¶