def create_map(self,
projection: Union[str, Projection] = "EuroPP()") -> None:
if isinstance(projection, str):
if not projection.endswith(")"):
projection = projection + "()"
projection = eval(projection)
self.projection = projection
self.trajectories.clear()
with plt.style.context("traffic"):
self.fig.clear()
self.ax = self.fig.add_subplot(111, projection=self.projection)
projection_name = projection.__class__.__name__.split(".")[-1]
self.ax.add_feature(
countries(scale="10m" if projection_name not in
["Mercator", "Orthographic"] else "110m"))
if projection_name in ["Lambert93", "GaussKruger", "Amersfoort"]:
# Hardcoded projection list?! :o/
self.ax.add_feature(rivers())
self.fig.set_tight_layout(True)
self.ax.background_patch.set_visible(False)
self.ax.outline_patch.set_visible(False)
self.ax.format_coord = lambda x, y: ""
self.ax.set_global()
self.draw()
def plot_trajs(t, sector, proj=Lambert93()):
n_clusters_ = int(1 + t.data.cluster.max())
# -- dealing with colours --
color_cycle = cycle(
["#377eb8", "#ff7f00", "#4daf4a", "#f781bf"] +
["#a65628", "#984ea3", "#999999", "#e41a1c", "#dede00"])
colors = list(islice(color_cycle, n_clusters_))
colors.append("#aaaaaa") # color for outliers, if any
# -- dealing with the grid --
nb_cols = 5
nb_lines = (1 + n_clusters_) // nb_cols + ((
(1 + n_clusters_) % nb_cols) > 0)
def ax_iter(axes):
if len(axes.shape) == 1:
yield from axes
if len(axes.shape) == 2:
for ax_ in axes:
yield from ax_
with plt.style.context("traffic"):
fig, ax = plt.subplots(
nb_lines,
nb_cols,
subplot_kw=dict(projection=proj),
figsize=(15, 25),
)
for cluster, ax_ in tqdm(
zip(range(t.data.cluster.min(), n_clusters_), ax_iter(ax))):
ax_.add_feature(countries())
ax_.add_feature(rivers())
tc = t.query(f"cluster == {cluster}")
len_tc = len(tc)
tc = tc[sample(tc.flight_ids, min(50, len(tc)))]
tc.plot(ax_, color=colors[cluster])
vr = tc.data.vertical_rate.mean()
alt = tc.data.altitude.mean() // 100
evolution = "=" if abs(vr) < 200 else "↗" if vr > 0 else "↘"
ax_.set_title(f"{alt:.0f}FL{evolution}\nlen cluster:{len_tc}")
if sector is not None:
ax_.set_extent(sector)
sector.plot(ax_, lw=2)
def plot_trajs(tc, sector):
n_clusters_ = int(1 + tc.data.cluster.max())
# -- dealing with colours --
color_cycle = cycle(
["#377eb8", "#ff7f00", "#4daf4a", "#f781bf"] +
["#a65628", "#984ea3", "#999999", "#e41a1c", "#dede00"])
colors = list(islice(color_cycle, n_clusters_))
colors.append("#aaaaaa") # color for outliers, if any
# -- dealing with the grid --
nb_cols = 3
nb_lines = (1 + n_clusters_) // nb_cols + ((
(1 + n_clusters_) % nb_cols) > 0)
def ax_iter(axes):
if len(axes.shape) == 1:
yield from axes
if len(axes.shape) == 2:
for ax_ in axes:
yield from ax_
with plt.style.context("traffic"):
fig, ax = plt.subplots(nb_lines,
nb_cols,
subplot_kw=dict(projection=Lambert93()))
for cluster, ax_ in zip(range(-1, n_clusters_), ax_iter(ax)):
ax_.add_feature(countries())
ax_.add_feature(rivers())
tc.query(f"cluster == {cluster}").plot(ax_, color=colors[cluster])
ax_.set_extent(sector)
sector.plot(ax_, lw=2)
def plot_data(flights: Traffic, airport: Airport, output: Path,
arrival: int=1):
fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection=UTM((airport.lon + 180) // 6,
southern_hemisphere=(airport.lat > 0)))
ax.add_feature(countries(scale='50m'))
ax.add_feature(rivers(scale='50m'))
ax.add_feature(lakes(scale='50m'))
ax.gridlines()
ax.set_extent(airport.extent)
try:
from cartotools.osm import request, tags
request(f'{airport.icao} airport', **tags.airport).plot(ax)
except Exception:
pass
fd = flights.data
fd = fd[fd.longitude > airport.lon - 1]
fd = fd[fd.longitude < airport.lon + 1]
fd = fd[fd.latitude > airport.lat - 1]
fd = fd[fd.latitude < airport.lat + 1]
fd = fd.groupby('callsign').filter(
lambda f: arrival * f.vertical_rate[:-100].mean() < -10)
for flight in Traffic(fd):
flight.plot(ax)
fig.suptitle(f"{airport.name}")
fig.set_tight_layout(True)
logging.info(f"Image saved as {output}")
fig.savefig(output.as_posix())
def clusters_plot2d(
sector,
t,
nb_samples,
projection,
scaler=None,
plot_trajs=False,
plot_clust=None,
):
with plt.style.context("traffic"):
fig, ax = plt.subplots(subplot_kw=dict(projection=projection))
ax.add_feature(countries())
ax.add_feature(rivers())
ax.set_extent(
tuple(x - 0.5 + (0 if i % 2 == 0 else 1)
for i, x in enumerate(sector.extent)))
sector.plot(ax, lw=5)
clust_ids = sorted(t.data.cluster.unique())
if plot_clust is None:
plot_clust = clust_ids
for cid in plot_clust:
tc = t.query(f"cluster=={cid}")
if plot_trajs:
for flight in tc:
lat = list(flight.data["latitude"])
lon = list(flight.data["longitude"])
if cid != -1:
ax.plot(
lon,
lat,
color=C[cid],
transform=PlateCarree(),
lw=1,
alpha=0.5,
)
else:
ax.plot(
lon,
lat,
color="grey",
transform=PlateCarree(),
lw=0.5,
alpha=0.5,
)
if cid != -1:
cent = tc.centroid(nb_samples,
projection=projection,
transformer=scaler).data
lat = list(cent["latitude"])
lon = list(cent["longitude"])
ax.plot(lon, lat, color=C[cid], transform=PlateCarree(), lw=5)
ax.arrow(
lon[-5],
lat[-5],
lon[-1] - lon[-5],
lat[-1] - lat[-5],
color=C[cid],
transform=PlateCarree(),
lw=3,
head_width=0.1,
head_length=0.1,
)
import matplotlib.pyplot as plt
from traffic.drawing import countries, rivers, lakes, ocean, TransverseMercator
from traffic.data import eurofirs
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(111, projection=TransverseMercator(10, 45))
ax.set_extent((-20, 45, 30, 70))
ax.add_feature(countries(scale="50m"))
ax.add_feature(rivers(scale="50m"))
ax.add_feature(lakes(scale="50m"))
ax.add_feature(ocean(scale="50m"))
for key, fir in eurofirs.items():
fir.plot(ax, edgecolor="#3a3aaa", lw=2, alpha=.5)
if key not in ["ENOB", "LPPO", "GCCC"]:
fir.annotate(
ax,
s=key,
ha="center",
color="#3a3aaa",
fontname="Ubuntu",
fontsize=13,
)
fig.savefig("eurofirs.png", bbox_inches="tight", pad_inches=0, dpi=200)