Network basic manipulation

from matplotlib import pyplot as plt
from numpy import where

from py_eddy_tracker import data
from py_eddy_tracker.gui import GUI_AXES
from py_eddy_tracker.observations.network import NetworkObservations

Load data

Load data where observations are put in same network but no segmentation

n = NetworkObservations.load_file(data.get_demo_path("network_med.nc")).network(651)
i = where(
    (n.lat > 33)
    * (n.lat < 34)
    * (n.lon > 22)
    * (n.lon < 23)
    * (n.time > 20630)
    * (n.time < 20650)
)[0][0]
# For event use
n2 = n.relative(i, order=2)
n = n.relative(i, order=4)
n.numbering_segment()

Timeline

Display timeline with events A segment generated by a splitting is marked with a star

A segment merging in another is marked with an exagon

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
_ = n.display_timeline(ax)

Display timeline without event

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
_ = n.display_timeline(ax, event=False)

Timeline by mean latitude

Display timeline with the mean latitude of the segments in yaxis

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
ax.set_ylabel("Latitude")
_ = n.display_timeline(ax, field="latitude")

Timeline by mean Effective Radius

The factor argument is applied on the chosen field

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
ax.set_ylabel("Effective Radius (km)")
_ = n.display_timeline(ax, field="radius_e", factor=1e-3)

Timeline by latitude

Use method=”all” to display the consecutive values of the field

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.05, 0.92, 0.92])
ax.set_ylabel("Latitude")
_ = n.display_timeline(ax, field="lat", method="all")

You can filter the data, here with a time window of 15 days

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.05, 0.92, 0.92])
n_copy = n.copy()
n_copy.median_filter(15, "time", "latitude")
_ = n_copy.display_timeline(ax, field="lat", method="all")

Parameters timeline

Scatter is usefull to display the parameters’ temporal evolution

Effective Radius and Amplitude

kw = dict(s=25, cmap="Spectral_r", zorder=10)
fig = plt.figure(figsize=(15, 12))
ax = fig.add_axes([0.04, 0.54, 0.90, 0.44])
m = n.scatter_timeline(ax, "radius_e", factor=1e-3, vmin=50, vmax=150, **kw)
cb = plt.colorbar(
    m["scatter"], cax=fig.add_axes([0.95, 0.54, 0.01, 0.44]), orientation="vertical"
)
cb.set_label("Effective radius (km)")

ax = fig.add_axes([0.04, 0.04, 0.90, 0.44])
m = n.scatter_timeline(ax, "amplitude", factor=100, vmin=0, vmax=15, **kw)
cb = plt.colorbar(
    m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.44]), orientation="vertical"
)
cb.set_label("Amplitude (cm)")

Speed

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
m = n.scatter_timeline(ax, "speed_average", factor=100, vmin=0, vmax=40, **kw)
cb = plt.colorbar(
    m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical"
)
cb.set_label("Maximum speed (cm/s)")

Speed Radius

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
m = n.scatter_timeline(ax, "radius_s", factor=1e-3, vmin=20, vmax=100, **kw)
cb = plt.colorbar(
    m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical"
)
cb.set_label("Speed radius (km)")

Remove dead branch

Remove all tiny segments with less than N obs which didn’t join two segments

n_clean = n.copy()
n_clean.remove_dead_end(nobs=5, ndays=10)
n_clean = n_clean.remove_trash()
fig = plt.figure(figsize=(15, 12))
ax = fig.add_axes([0.04, 0.54, 0.90, 0.40])
ax.set_title(f"Original network ({n.infos()})")
n.display_timeline(ax)
ax = fig.add_axes([0.04, 0.04, 0.90, 0.40])
ax.set_title(f"Clean network ({n_clean.infos()})")
_ = n_clean.display_timeline(ax)

For further figure we will use clean path

n = n_clean

Change splitting-merging events

change event where seg A split to B, then A merge into B, to A split to B then B merge into A

fig = plt.figure(figsize=(15, 12))
ax = fig.add_axes([0.04, 0.54, 0.90, 0.40])
ax.set_title(f"Clean network ({n.infos()})")
n.display_timeline(ax)

clean_modified = n.copy()
# If it's happen in less than 40 days
clean_modified.correct_close_events(40)

ax = fig.add_axes([0.04, 0.04, 0.90, 0.40])
ax.set_title(f"resplitted network ({clean_modified.infos()})")
_ = clean_modified.display_timeline(ax)

Keep only observations where water could propagate from an observation

i_observation = 600
only_linked = n.find_link(i_observation)

fig = plt.figure(figsize=(15, 12))
ax1 = fig.add_axes([0.04, 0.54, 0.90, 0.40])
ax2 = fig.add_axes([0.04, 0.04, 0.90, 0.40])

kw = dict(marker="s", s=300, color="black", zorder=200, label="observation start")
for ax, dataset in zip([ax1, ax2], [n, only_linked]):
    dataset.display_timeline(ax, field="segment", lw=2, markersize=5, colors_mode="y")
    ax.scatter(n.time[i_observation], n.segment[i_observation], **kw)
    ax.legend()

ax1.set_title(f"full example ({n.infos()})")
ax2.set_title(f"only linked observations ({only_linked.infos()})")
_ = ax2.set_xlim(ax1.get_xlim()), ax2.set_ylim(ax1.get_ylim())

Keep close relative

When you want to investigate one particular observation and select only the closest segments

# First choose an observation in the network
i = 1100

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
n.display_timeline(ax)
obs_args = n.time[i], n.segment[i]
obs_kw = dict(color="black", markersize=30, marker=".")
_ = ax.plot(*obs_args, **obs_kw)

Colors show the relative order of the segment with regards to the chosen one

fig = plt.figure(figsize=(15, 6))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
m = n.scatter_timeline(
    ax, n.obs_relative_order(i), vmin=-1.5, vmax=6.5, cmap=plt.get_cmap("jet", 8), s=10
)
ax.plot(*obs_args, **obs_kw)
cb = plt.colorbar(
    m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical"
)
cb.set_label("Relative order")

You want to keep only the segments at the order 1

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
close_to_i1 = n.relative(i, order=1)
ax.set_title(f"Close segments ({close_to_i1.infos()})")
_ = close_to_i1.display_timeline(ax)

You want to keep the segments until order 2

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
close_to_i2 = n.relative(i, order=2)
ax.set_title(f"Close segments ({close_to_i2.infos()})")
_ = close_to_i2.display_timeline(ax)

You want to keep the segments until order 3

fig = plt.figure(figsize=(15, 5))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88])
close_to_i3 = n.relative(i, order=3)
ax.set_title(f"Close segments ({close_to_i3.infos()})")
_ = close_to_i3.display_timeline(ax)

Keep relatives to an event

When you want to investigate one particular event and select only the closest segments

First choose a merging event in the network

after, before, stopped = n.merging_event(triplet=True, only_index=True)
i_event = 7

then see some order of relatives

max_order = 1
fig, axs = plt.subplots(
    max_order + 2, 1, sharex=True, figsize=(15, 5 * (max_order + 2))
)
# Original network
ax = axs[0]
ax.set_title("Full network", weight="bold")
n.display_timeline(axs[0], colors_mode="y")
ax.grid(), ax.legend()

for k in range(0, max_order + 1):
    ax = axs[k + 1]
    ax.set_title(f"Relatives order={k}", weight="bold")
    # Extract neighbours of event
    sub_network = n.find_segments_relative(after[i_event], stopped[i_event], order=k)
    sub_network.display_timeline(ax, colors_mode="y")
    ax.legend(), ax.grid()
    _ = ax.set_ylim(axs[0].get_ylim())

Display track on map

# Get a simplified network
n = n2.copy()
n.remove_dead_end(nobs=50, recursive=1)
n = n.remove_trash()
n.numbering_segment()

Only a map can be tricky to understand, with a timeline it’s easier!

fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0.04, 0.06, 0.94, 0.88], projection=GUI_AXES)
n.plot(ax, color_cycle=n.COLORS)
ax.set_xlim(17.5, 27.5), ax.set_ylim(31, 36), ax.grid()
ax = fig.add_axes([0.08, 0.7, 0.7, 0.3])
_ = n.display_timeline(ax)

Get merging event

Display the position of the eddies after a merging

fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88], projection=GUI_AXES)
n.plot(ax, color_cycle=n.COLORS)
m1, m0, m0_stop = n.merging_event(triplet=True)
m1.display(ax, color="violet", lw=2, label="Eddies after merging")
m0.display(ax, color="blueviolet", lw=2, label="Eddies before merging")
m0_stop.display(ax, color="black", lw=2, label="Eddies stopped by merging")
ax.plot(m1.lon, m1.lat, marker=".", color="purple", ls="")
ax.plot(m0.lon, m0.lat, marker=".", color="blueviolet", ls="")
ax.plot(m0_stop.lon, m0_stop.lat, marker=".", color="black", ls="")
ax.legend()
ax.set_xlim(17.5, 27.5), ax.set_ylim(31, 36), ax.grid()
m1

3 observations from 20245.0 to 20762.0

Get splitting event

Display the position of the eddies before a splitting

fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88], projection=GUI_AXES)
n.plot(ax, color_cycle=n.COLORS)
s0, s1, s1_start = n.splitting_event(triplet=True)
s0.display(ax, color="violet", lw=2, label="Eddies before splitting")
s1.display(ax, color="blueviolet", lw=2, label="Eddies after splitting")
s1_start.display(ax, color="black", lw=2, label="Eddies starting by splitting")
ax.plot(s0.lon, s0.lat, marker=".", color="purple", ls="")
ax.plot(s1.lon, s1.lat, marker=".", color="blueviolet", ls="")
ax.plot(s1_start.lon, s1_start.lat, marker=".", color="black", ls="")
ax.legend()
ax.set_xlim(17.5, 27.5), ax.set_ylim(31, 36), ax.grid()
s1

4 observations from 20306.0 to 20908.0

Get birth event

Display the starting position of non-splitted eddies

fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88], projection=GUI_AXES)
birth = n.birth_event()
birth.display(ax)
ax.set_xlim(17.5, 27.5), ax.set_ylim(31, 36), ax.grid()
birth

2 observations from 19889.0 to 20223.0

Get death event

Display the last position of non-merged eddies

fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0.04, 0.06, 0.90, 0.88], projection=GUI_AXES)
death = n.death_event()
death.display(ax)
ax.set_xlim(17.5, 27.5), ax.set_ylim(31, 36), ax.grid()
death

3 observations from 20771.0 to 21191.0