py_eddy_tracker.observations.tracking.TrackEddiesObservations¶

class py_eddy_tracker.observations.tracking.TrackEddiesObservations(*args, **kwargs)[source]¶

Bases: py_eddy_tracker.observations.observation.EddiesObservations

Class to practice Tracking on observations

Methods

`add_distance`	Add a field of distance (m) between two consecutive observations, 0 for the last observation of each track
`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
`box_display`	Return value evenly spaced with few numbers
`build_var_list`
`circle_contour`	Set contours as a circles from radius and center data.
`close_tracks`	Get close trajectories from another atlas.
`coherence`	Check coherence between two datasets.
`compare_units`
`compute_index`	If obs are not sorted by track, __first_index_of_track will be unusable
`concatenate`
`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 ?
`count_by_track`	Count by track
`create_variable`
`create_variable_zarr`
`display`	Plot the speed and effective (dashed) contour of the eddies
`display_shape`	This function will draw the shape of each trajectory
`distance`	Use haversine distance for distance matrix between every self and other eddies.
`distance_to_next`	return array of distance in m, 0 when next obs if from another track
`empty_dataset`
`extract_first_obs_in_box`
`extract_ids`
`extract_in_direction`
`extract_longer_eddies`	Select the trajectories longer than nb_min
`extract_toward_direction`	Get trajectories going in the same direction
`extract_with_area`	Extract geographically with a bounding box.
`extract_with_length`	Return the observations within trajectories lasting between [b0:b1]
`extract_with_mask`	Extract a subset of observations
`extract_with_period`	Extract within a time period
`filled`	param matplotlib.axes.Axes ax matplotlib axe used to draw
`filled_by_interpolation`	Filled selected values by interpolation
`first_obs`	Get first obs of each trajectory.
`fixed_ellipsoid_mask`
`follow_obs`	Associate the observations to the segments
`format_label`
`from_netcdf`
`from_zarr`
`get_azimuth`	Return azimuth for each track.
`get_infos`
`get_mask_from_id`
`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 index.
`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.
`iter_track`	Yield track
`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.
`loess_filter`
`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
`needed_variable`
`netcdf_create_dimensions`
`new_like`
`next_obs`	Forward association of observations to the segments
`normalize_longitude`	Normalize all longitude
`obs_dimension`
`parse_varname`
`plot`	This function will draw path of each trajectory
`position_filter`
`post_process_link`
`previous_obs`	Backward association of observations to the segments
`propagate`	Filled virtual obs (C).
`re_reference_index`
`reset`
`scatter`	Scatter data.
`set_global_attr_netcdf`	Set global attr
`set_global_attr_zarr`
`set_tracks`	Will split one group (network) in segments
`shape_polygon`	Get the polygon enclosing each trajectory.
`shifted_ellipsoid_degrees_mask`
`solve_conflict`
`solve_first`
`solve_function`
`solve_simultaneous`	Write something (TODO)
`split_network`	Return each group (network) divided in segments
`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`
`age`	Return age in % for each observation, will be [0:100]
`array_variables`
`dtype`	Return dtype to build numpy array.
`elements`	Return all the names of the variables.
`global_attr`
`index_from_track`
`lifetime`	Return lifetime for each observation
`nb_days`	Return period days cover by dataset
`nb_obs_by_track`
`nb_tracks`	Will count and send number of track
`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`

ELEMENTS = ['lon', 'lat', 'radius_s', 'radius_e', 'speed_area', 'effective_area', 'amplitude', 'speed_average', 'time', 'shape_error_e', 'shape_error_s', 'nb_contour_selected', 'num_point_e', 'num_point_s', 'height_max_speed_contour', 'height_external_contour', 'height_inner_contour', 'cost_association']¶

NOGROUP = 0¶

add_distance()[source]¶: Add a field of distance (m) between two consecutive observations, 0 for the last observation of each track

property age¶: Return age in % for each observation, will be [0:100]

close_tracks(other, nb_obs_min=10, **kwargs)[source]¶

Get close trajectories from another atlas.

Parameters

other (self) – Atlas to compare
nb_obs_min (int) – Minimal number of overlap for one trajectory
kwargs (dict) – keyword arguments for match function

Returns

return other atlas reduce to common track with self

Warning

It could be a costly operation for huge dataset

compute_index()[source]¶: If obs are not sorted by track, __first_index_of_track will be unusable

classmethod concatenate(observations)[source]¶

count_by_track(mask)[source]¶

Count by track

Parameters: mask (array[bool]) – Mask of boolean count +1 if true
Returns: Return count by track
Return type: array

display_shape(ax, ref=None, intern=False, **kwargs)[source]¶

This function will draw the shape 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
intern (bool) – If True use speed contour instead of effective contour
kwargs (dict) – keyword arguments for Axes.plot

Returns

matplotlib mappable

distance_to_next()[source]¶

Returns: array of distance in m, 0 when next obs if from another track
Return type: array

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

empty_dataset()[source]¶

extract_first_obs_in_box(res)[source]¶

extract_ids(tracks)[source]¶

extract_in_direction(direction, value=0)[source]¶

extract_longer_eddies(nb_min, nb_obs, compress_id=True)[source]¶: Select the trajectories longer than nb_min

extract_toward_direction(west=True, delta_lon=None)[source]¶

Get trajectories going in the same direction

Parameters

west (bool) – Only eastward eddies if True return westward
delta_lon (None,float) – Only eddies with more than delta_lon span in longitude

Returns

Only eastern eddy

Return type

__class__

extract_with_length(bounds)[source]¶

Return the observations within trajectories lasting between [b0:b1]

Parameters: bounds ((int,int)) – length min and max of the desired trajectories, if -1 this bound is not used
Returns: Return all trajectories having length between bounds
Return type: TrackEddiesObservations

Display fields¶

Display Tracks¶

extract_with_mask(mask, full_path=False, remove_incomplete=False, compress_id=False, reject_virtual=False)[source]¶

Extract a subset of observations

Parameters

mask (array(bool)) – mask to select observations
full_path (bool) – extract the full trajectory if only one part is selected
remove_incomplete (bool) – delete trajectory if not fully selected
compress_id (bool) – resample trajectory number to use a smaller range
reject_virtual (bool) – if only virtual are selected, the trajectory is removed

Returns

same object with the selected observations

Return type

self.__class__

extract_with_period(period, **kwargs)[source]¶

Extract within a time period

Parameters

period ((int,int)) – two dates to define the period, must be specify from 1/1/1950
kwargs (dict) – look at extract_with_mask()

Returns

Return all eddy tracks which are in bounds

Return type

TrackEddiesObservations

filled_by_interpolation(mask)[source]¶

Filled selected values by interpolation

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

Track in python¶

classmethod follow_obs(i_next, track_id, used, ids, *args, **kwargs)[source]¶: Associate the observations to the segments

format_label(label)[source]¶

get_azimuth(equatorward=False)[source]¶

Return azimuth for each track.

Azimuth is computed with first and last observation

Parameters: equatorward (bool) – If True, Poleward are positive and equatorward negative
Return type: array

get_mask_from_id(tracks)[source]¶

property index_from_track¶

iter_track()[source]¶: Yield track

property lifetime¶: Return lifetime for each observation

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

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

property nb_obs_by_track¶

property nb_tracks¶: Will count and send number of track

static next_obs(i_current, ids, x, y, time_s, time_e, time_ref, window, **kwargs)[source]¶: Forward association of observations to the segments

normalize_longitude()[source]¶

Normalize all longitude

Normalize longitude field and in the same range : - longitude_max - contour_lon_e (how to do if in raw) - contour_lon_s (how to do if in raw)

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

matplotlib mappable

position_filter(median_half_window, loess_half_window)[source]¶

static previous_obs(i_current, ids, x, y, time_s, time_e, time_ref, window, **kwargs)[source]¶: Backward association of observations to the segments

static re_reference_index(index, ref)[source]¶

set_global_attr_netcdf(h_nc)[source]¶: Set global attr

set_tracks(x, y, ids, window, **kwargs)[source]¶

Will split one group (network) in segments

Parameters

x (array) – coordinates of group
y (array) – coordinates of group
ids (ndarray) – several fields like time, group, …
windows (int) – number of days where observations could missed

shape_polygon(intern=False)[source]¶

Get the polygon enclosing each trajectory.

The polygon merges the non-overlapping bounds of the specified contours

Parameters: intern (bool) – If True use speed contour instead of effective contour
Return type: list(array, array)

split_network(intern=True, **kwargs)[source]¶: Return each group (network) divided in segments