def get_unused_data(self, raw_data=False):
"""
Add in track object all the observations which aren't selected
Returns: Unused Eddies
"""
nb_dataset = len(self.datasets)
has_virtual = "virtual" in self[0].dtype.names
eddies = list()
for i, dataset in enumerate(self.datasets):
last_dataset = i == (nb_dataset - 1)
if has_virtual and not last_dataset:
m_in = ~self[i]["virtual"]
else:
m_in = slice(None)
if i == 0:
index_used = self[i]["in"]
elif last_dataset:
index_used = self[i - 1]["out"]
else:
index_used = unique(
concatenate((self[i - 1]["out"], self[i]["in"][m_in])))
logger.debug("Load file : %s", dataset)
if self.memory:
with open(dataset, "rb") as h:
current_obs = self.class_method.load_file(
h, raw_data=raw_data)
else:
current_obs = self.class_method.load_file(dataset,
raw_data=raw_data)
eddies.append(current_obs.index(index_used, reverse=True))
return EddiesObservations.concatenate(eddies)
def update_axes(ax, mappable=None):
ax.grid()
if mappable:
plt.colorbar(mappable, cax=ax.figure.add_axes([0.95, 0.05, 0.01, 0.9]))
# %%
# We load demo sample and take only first year.
#
# Replace by a list of filename to apply on your own dataset.
file_objects = get_remote_demo_sample(
"eddies_med_adt_allsat_dt2018/Anticyclonic_2010_2011_2012")[:365]
# %%
# Merge all identification datasets in one object
all_a = EddiesObservations.concatenate(
[EddiesObservations.load_file(i) for i in file_objects])
# %%
# We define polygon bound
x0, x1, y0, y1 = 15, 20, 33, 38
xs = np.array([[x0, x1, x1, x0, x0]], dtype="f8")
ys = np.array([[y0, y0, y1, y1, y0]], dtype="f8")
# Polygon object created for the match function use.
polygon = dict(contour_lon_e=xs,
contour_lat_e=ys,
contour_lon_s=xs,
contour_lat_s=ys)
# %%
# Geographic frequency of eddies
step = 0.125
# Movie period
t0 = (datetime(2005, 5, 1) - datetime(1950, 1, 1)).days
t1 = (datetime(2005, 6, 1) - datetime(1950, 1, 1)).days
# %%
# Get data from period and area
e = EddiesObservations.load_file(data.get_demo_path("network_med.nc"))
e = e.extract_with_mask((e.time >= t0) * (e.time < t1)).extract_with_area(
dict(llcrnrlon=25, urcrnrlon=35, llcrnrlat=31, urcrnrlat=37.5))
# %%
# Reproduce individual daily identification(for demonstration)
EDDIES_BY_DAYS = list()
for i, b0, b1 in e.iter_on("time"):
EDDIES_BY_DAYS.append(e.index(i))
# need for display
e = EddiesObservations.concatenate(EDDIES_BY_DAYS)
# %%
# Run network building group to intercept every step
n = MyNetwork.from_eddiesobservations(EDDIES_BY_DAYS, window=7)
_ = n.group_observations(minimal_area=True)
# %%
def update(frame):
i_current, i_match, gr = NETWORK_GROUPS[frame]
current = EDDIES_BY_DAYS[i_current]
x = flatten_line_matrix(current.contour_lon_e)
y = flatten_line_matrix(current.contour_lat_e)
current_contour.set_data(x, y)
match = EDDIES_BY_DAYS[i_match]