Visualize trajectories with Plotly

Data visualisation is integrated with the Plotly library as long as it is installed as an optional dependency.

You may install traffic with the plotly option, the full option, or install Plotly manually:

# at install time
pip install traffic[plotly]  # or full

# with poetry
poetry install traffic -E plotly  # or -E full

# or simply manually
pip install plotly
conda install -c conda-forge plotly

traffic provides the same interface as the plotly.express() module on the Flight and Traffic classes. All kwargs arguments are passed directly to the corresponding method.

• with plotly.express.line_mapbox():

  from traffic.data.samples import belevingsvlucht
  
  belevingsvlucht.line_mapbox(color="callsign")
  

• with plotly.express.scatter_mapbox():

  from traffic.data.samples import belevingsvlucht
  
  fig = belevingsvlucht.scatter_mapbox(
      color="altitude", width=600, height=600, zoom=6
  )
  fig.update_layout(margin=dict(l=50, r=0, t=40, b=40))
  

• as animations (perform resampling in advance and limit yourself to few points):

  from traffic.data.samples import belevingsvlucht
  
  belevingsvlucht.resample("1 min").scatter_mapbox(
      color="vertical_rate",
      range_color=[-4000, 4000],
      animation_frame="timestamp",
      width=600,
      height=600,
      zoom=6,
  )
  

It is also possible to combine elements by constructing a Scattermapbox object:

from traffic.data import airports

import plotly.graph_objects as go

# fig = go.Figure()  # if necessary, we can initiate a Figure and fill it later

fig = belevingsvlucht.resample("1 min").scatter_mapbox(
    color="vertical_rate",
    range_color=[-2000, 2000],
    animation_frame="timestamp",
    width=600,
    height=600,
    zoom=6,
)

fig.add_trace(
    belevingsvlucht.Scattermapbox(
        mode="lines",
        line=dict(color="#f58518", width=1),
        showlegend=False,
    )
)

fig.update_layout(
    width=600,
    height=600,
    margin=dict(l=50, r=0, t=40, b=40),
    mapbox=dict(
        style="carto-positron",
        zoom=7,
        center=airports["EHLE"].latlon_dict,
    ),
)

Or by combining several traces:

from traffic.data import airports
from traffic.data.samples import quickstart

subset = quickstart[["TVF22LK", "EJU53MF", "TVF51HP", "TVF78YY", "VLG8030"]]
subset = subset.resample("10s").eval()
assert subset is not None


fig = subset.scatter_mapbox(
    color="callsign",
    hover_data="altitude",
    animation_frame="timestamp",
    center=airports["LFPO"].latlon_dict,
)
fig = fig.add_traces(subset.line_mapbox(
        color="callsign",
    ).data)
fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
fig.show()

Similar functions are available and bound with plotly.express.line_geo() and plotly.express.scatter_geo():

from traffic.data import airports
from traffic.data.samples import belevingsvlucht

fig = belevingsvlucht.line_geo(
    scope="europe",
    projection="conic conformal",
    center=airports["EHLE"].latlon_dict,
)
fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
fig.update_geos(resolution=50, fitbounds="locations")
fig