Network Analysis

from matplotlib import pyplot as plt
from numpy import ma

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

n = NetworkObservations.load_file(
    get_remote_demo_sample(
        "eddies_med_adt_allsat_dt2018_err70_filt500_order1/Anticyclonic_network.nc"
    )
)

Parameters

step = 1 / 10.0
bins = ((-10, 37, step), (30, 46, step))
kw_time = dict(cmap="terrain_r", factor=100.0 / n.nb_days, name="count")
kw_ratio = dict(cmap=plt.get_cmap("YlGnBu_r", 10))

Functions

def start_axes(title):
    fig = plt.figure(figsize=(13, 5))
    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=GUI_AXES)
    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
    ax.set_aspect("equal")
    ax.set_title(title, weight="bold")
    return ax


def update_axes(ax, mappable=None):
    ax.grid()
    if mappable:
        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))

All

Display the % of time each pixel (1/10°) is within an anticyclonic network

ax = start_axes("")
g_all = n.grid_count(bins)
m = g_all.display(ax, **kw_time, vmin=0, vmax=75)
update_axes(ax, m).set_label("Pixel used in % of time")

Network longer than 10 days

Display the % of time each pixel (1/10°) is within an anticyclonic network which total lifetime in longer than 10 days

ax = start_axes("")
n10 = n.longer_than(10)
g_10 = n10.grid_count(bins)
m = g_10.display(ax, **kw_time, vmin=0, vmax=75)
update_axes(ax, m).set_label("Pixel used in % of time")

Ratio

Ratio between the longer and total presence

ax = start_axes("")
g_ = g_10.vars["count"] * 100.0 / g_all.vars["count"]
m = g_10.display(ax, **kw_ratio, vmin=50, vmax=100, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

Blue = mostly short networks

Network longer than 20 days

Display the % of time each pixel (1/10°) is within an anticyclonic network which total lifetime is longer than 20 days

ax = start_axes("")
n20 = n.longer_than(20)
g_20 = n20.grid_count(bins)
m = g_20.display(ax, **kw_time, vmin=0, vmax=75)
update_axes(ax, m).set_label("Pixel used in % of time")

Ratio

Ratio between the longer and total presence

ax = start_axes("")
g_ = g_20.vars["count"] * 100.0 / g_all.vars["count"]
m = g_20.display(ax, **kw_ratio, vmin=50, vmax=100, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

Now we will hide pixel which are used less than 365 times

g_ = ma.array(
    g_20.vars["count"] * 100.0 / g_all.vars["count"], mask=g_all.vars["count"] < 365
)
ax = start_axes("")
m = g_20.display(ax, **kw_ratio, vmin=50, vmax=100, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

Now we will hide pixel which are used more than 365 times

ax = start_axes("")
g_ = ma.array(
    g_20.vars["count"] * 100.0 / g_all.vars["count"], mask=g_all.vars["count"] >= 365
)
m = g_20.display(ax, **kw_ratio, vmin=50, vmax=100, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

Coastal areas are mostly populated by short networks

All merging

Display the occurence of merging events

ax = start_axes("")
g_all_merging = n.merging_event().grid_count(bins)
m = g_all_merging.display(ax, **kw_time, vmin=0, vmax=1)
update_axes(ax, m).set_label("Pixel used in % of time")

Ratio merging events / eddy presence

ax = start_axes("")
g_ = g_all_merging.vars["count"] * 100.0 / g_all.vars["count"]
m = g_all_merging.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

Merging in networks longer than 10 days, with dead end remove (shorter than 10 observations)

ax = start_axes("")
n10_ = n10.copy()
n10_.remove_dead_end(nobs=10)
merger = n10_.merging_event()
g_10_merging = merger.grid_count(bins)
m = g_10_merging.display(ax, **kw_time, vmin=0, vmax=1)
update_axes(ax, m).set_label("Pixel used in % of time")

Merging in networks longer than 10 days

ax = start_axes("")
merger = n10.merging_event()
g_10_merging = merger.grid_count(bins)
m = g_10_merging.display(ax, **kw_time, vmin=0, vmax=1)
update_axes(ax, m).set_label("Pixel used in % of time")

Ratio merging events / eddy presence

ax = start_axes("")
g_ = ma.array(
    g_10_merging.vars["count"] * 100.0 / g_10.vars["count"],
    mask=g_10.vars["count"] < 365,
)
m = g_10_merging.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

All splitting

Display the occurence of splitting events

ax = start_axes("")
g_all_splitting = n.splitting_event().grid_count(bins)
m = g_all_splitting.display(ax, **kw_time, vmin=0, vmax=1)
update_axes(ax, m).set_label("Pixel used in % of time")

Ratio splitting events / eddy presence

ax = start_axes("")
g_ = g_all_splitting.vars["count"] * 100.0 / g_all.vars["count"]
m = g_all_splitting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")

splitting in networks longer than 10 days

ax = start_axes("")
g_10_splitting = n10.splitting_event().grid_count(bins)
m = g_10_splitting.display(ax, **kw_time, vmin=0, vmax=1)
update_axes(ax, m).set_label("Pixel used in % of time")

ax = start_axes("")
g_ = ma.array(
    g_10_splitting.vars["count"] * 100.0 / g_10.vars["count"],
    mask=g_10.vars["count"] < 365,
)
m = g_10_splitting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
update_axes(ax, m).set_label("Pixel used in % all atlas")