Loading Graph
=============
This notebook takes a dataset from a file or (Geo)DataFrame as well as
graphs of processed flights and visualises the graph, as well as
checking individual flights or handovers to extract a confidence value.
Before using this notebook ensure you have dumped some flight data using
``demo-export-flights.ipynb`` and processed them using
``demo-process-flights.ipynb``.
.. code:: ipython3
from flight_processing import DataConfig
from flight_processing.data import AirspaceGraph, FlightDownloader
import geopandas
from datetime import datetime, timedelta
from dateutil import parser
import logging
logger = logging.getLogger()
logger.setLevel(level=logging.INFO)
logging.getLogger('traffic').disabled = True
Example 1: known dataset from file, graph output in standard location
---------------------------------------------------------------------
This example loads data from the expected location as defined in
``~/.config/flight_processing/flight_processing.conf``.
.. code:: ipython3
airspacegraph = AirspaceGraph("switzerland")
airspacegraph.load_graphs("2020-03-05 00:00", "2020-04-24 00:00")
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Loading airspace dataset from disk at location /mnt/cold_data/josh/processing/regions_switzerland_wkt.json.
INFO:flight_processing.data.airspace_graph:Initialising AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Successfully loaded airspaces, 152 in total.
INFO:flight_processing.data.airspace_graph:Loading matrix of handovers.
INFO:flight_processing.data.airspace_graph:Loading 1200 saved NPZ files, from 2020-03-05 00:00:00 to 2020-04-24 00:00:00.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200305/0000.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200305/0100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200305/0200.npz.
...
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200423/2100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200423/2200.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland/20200423/2300.npz.
INFO:flight_processing.data.airspace_graph:Building graph from matrix.
INFO:flight_processing.data.data_utils:Generating new graph from dataframe.
INFO:flight_processing.data.airspace_graph:Computing adjusted weights.
.. code:: ipython3
airspacegraph.visualise_graph()
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Drawing graph using holoviews.
.. raw:: html
.. code:: ipython3
airspacegraph.draw_graph_map(logscale=True)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Downloading terrain data from Stamen.
INFO:flight_processing.data.airspace_graph:Plotting airspace boundaries on map.
INFO:flight_processing.data.airspace_graph:Extracting edge weights from graph.
INFO:flight_processing.data.airspace_graph:Plotting graph on map.
.. image:: demo_airspace_graph_files/demo_airspace_graph_5_1.png
.. code:: ipython3
# Cleanup
del airspacegraph
Example 2: known dataset from file, custom graph location
---------------------------------------------------------
We’re doing the same sort of thing as example 1, but calling
``load_graph_files`` instead of ``load_graphs``, which allows us to
input a custom file location instead of time bounds.
.. code:: ipython3
template = "/mnt/cold_data/josh/processing/graphs/switzerland/{date}/{time}.npz"
template_date = "%Y%m%d"
template_time = "%H%M"
start_time = parser.parse("2020-03-05 00:00")
time_delta = timedelta(hours=2)
count = 50 * 12
files = []
for i in range(count):
time = start_time + (i * time_delta)
file = template.format(
date = time.strftime(template_date),
time = time.strftime(template_time)
)
files.append(file)
airspacegraph = AirspaceGraph("switzerland")
airspacegraph.load_graph_files(files)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Loading airspace dataset from disk at location /mnt/cold_data/josh/processing/regions_switzerland_wkt.json.
INFO:flight_processing.data.airspace_graph:Initialising AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Successfully loaded airspaces, 152 in total.
INFO:flight_processing.data.airspace_graph:Loading matrix of handovers.
INFO:flight_processing.data.airspace_graph:Building graph from matrix.
INFO:flight_processing.data.data_utils:Generating new graph from dataframe.
INFO:flight_processing.data.airspace_graph:Computing adjusted weights.
.. code:: ipython3
airspacegraph.visualise_graph()
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Drawing graph using holoviews.
.. raw:: html
.. code:: ipython3
airspacegraph.draw_graph_map(logscale=True)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Downloading terrain data from Stamen.
INFO:flight_processing.data.airspace_graph:Plotting airspace boundaries on map.
INFO:flight_processing.data.airspace_graph:Extracting edge weights from graph.
INFO:flight_processing.data.airspace_graph:Plotting graph on map.
.. image:: demo_airspace_graph_files/demo_airspace_graph_10_1.png
.. code:: ipython3
# Cleanup
del airspacegraph
Example 3: Custom dataset, graph output in standard location
------------------------------------------------------------
In this example we use a custom DataConfig as in example 2 of
``demo-export-flights.ipynb`` and ``demo-process-flights.ipynb``. We
also specify a custom dataset location.
.. code:: ipython3
dataset = DataConfig("switzerland-custom", minlon=5.3, maxlon=10.7, minlat=45.5, maxlat=48, detail=6)
location = "/mnt/cold_data/josh/processing/regions_switzerland_wkt.json"
airspacegraph = AirspaceGraph(dataset, dataset_location=location)
airspacegraph.load_graphs("2020-03-05 00:00", "2020-03-06 00:00")
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Loading airspace dataset from disk at location /mnt/cold_data/josh/processing/regions_switzerland_wkt.json.
INFO:flight_processing.data.airspace_graph:Initialising AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Successfully loaded airspaces, 152 in total.
INFO:flight_processing.data.airspace_graph:Loading matrix of handovers.
INFO:flight_processing.data.airspace_graph:Loading 24 saved NPZ files, from 2020-03-05 00:00:00 to 2020-03-06 00:00:00.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/0000.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/0100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/0200.npz.
...
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/2100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/2200.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/switzerland-custom/20200305/2300.npz.
INFO:flight_processing.data.airspace_graph:Building graph from matrix.
INFO:flight_processing.data.data_utils:Generating new graph from dataframe.
INFO:flight_processing.data.airspace_graph:Computing adjusted weights.
.. code:: ipython3
airspacegraph.visualise_graph()
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Drawing graph using holoviews.
.. raw:: html
.. code:: ipython3
airspacegraph.draw_graph_map(logscale=True)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Downloading terrain data from Stamen.
INFO:flight_processing.data.airspace_graph:Plotting airspace boundaries on map.
INFO:flight_processing.data.airspace_graph:Extracting edge weights from graph.
INFO:flight_processing.data.airspace_graph:Plotting graph on map.
.. image:: demo_airspace_graph_files/demo_airspace_graph_15_1.png
.. code:: ipython3
# Cleanup
del airspacegraph
Example 4: Processing handovers and flights
-------------------------------------------
In this example we load our graph and use it to obtain a confidence
rating at a given point and for a given handover. We also do the same
for all the potential handovers on a given flight.
We are using the UK dataset here - dump and process some flights for
this dataset if you have not already done so.
.. code:: ipython3
airspacegraph = AirspaceGraph("uk")
airspacegraph.load_graphs("2020-01-01 00:00", "2020-01-02 00:00")
downloader = FlightDownloader("uk")
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Loading airspace dataset from disk at location /mnt/cold_data/josh/processing/regions_uk_wkt.json.
INFO:flight_processing.data.airspace_graph:Initialising AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Successfully loaded airspaces, 616 in total.
INFO:flight_processing.data.airspace_graph:Loading matrix of handovers.
INFO:flight_processing.data.airspace_graph:Loading 24 saved NPZ files, from 2020-01-01 00:00:00 to 2020-01-02 00:00:00.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/0000.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/0100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/0200.npz.
...
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/2100.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/2200.npz.
INFO:flight_processing.data.airspace_graph:Loading saved graph from location /mnt/cold_data/josh/processing/graphs/uk/20200101/2300.npz.
INFO:flight_processing.data.airspace_graph:Building graph from matrix.
INFO:flight_processing.data.data_utils:Generating new graph from dataframe.
INFO:flight_processing.data.airspace_graph:Computing adjusted weights.
.. code:: ipython3
airspacegraph.draw_graph_map(logscale=True)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Downloading terrain data from Stamen.
INFO:flight_processing.data.airspace_graph:Plotting airspace boundaries on map.
INFO:flight_processing.data.airspace_graph:Extracting edge weights from graph.
INFO:flight_processing.data.airspace_graph:Plotting graph on map.
.. image:: demo_airspace_graph_files/demo_airspace_graph_19_1.png
Setup
~~~~~
First we need to (optionally) set the values and weights used for the
functions that follow.
.. code:: ipython3
average_weight = airspacegraph.average_edge_weight(median=True)
print("Median edge weight: {}".format(average_weight))
airspacegraph.set_confidence_values(
distance_zero = 5000,
distance_one = 3000,
minimum_weight = average_weight,
minimum_weight_adjusted = 0.05,
confidence_distance = 1.0,
confidence_distance_modifier = 0.8,
confidence_weight = 1.0,
confidence_weight_adjusted = 1.0
)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Computing median edge weight in graph.
.. parsed-literal::
Median edge weight: 24
Test a single point
~~~~~~~~~~~~~~~~~~~
Next we can test a point - for each airspace contained within the point
we are given a number of nearby airspaces, and information on the
confidence of that potential handover.
.. code:: ipython3
long = -0.453350
lat = 51.470569
alt = 6000
airspacegraph.test_point(long, lat, alt)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Getting airspaces at the given point using AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Getting airspaces near to the given point using AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Computing confidence in a handover to each nearby airspace.
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
...
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
.. parsed-literal::
[{'distance1': 0.0,
'distance2': 869.841552734375,
'confidence': 1.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 1.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 506,
'airspace2': 264,
'name1': 'LONDON TMA 3',
'name2': 'LONDON LUTON CTA 4'},
{'distance1': 0.0,
'distance2': 869.841552734375,
'confidence': 1.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 1.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 506,
'airspace2': 262,
'name1': 'LONDON TMA 3',
'name2': 'LONDON LUTON CTA 4'},
{'distance1': 0.0,
'distance2': 869.841552734375,
'confidence': 1.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 1.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 506,
'airspace2': 263,
'name1': 'LONDON TMA 3',
'name2': 'LONDON LUTON CTA 4'},
...
{'distance1': 0.0,
'distance2': 126746.1953125,
'confidence': 0.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 0.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 513,
'airspace2': 314,
'name1': 'LONDON TMA 3',
'name2': 'MIDLANDS CTA'},
{'distance1': 0.0,
'distance2': 126746.1953125,
'confidence': 0.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 0.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 513,
'airspace2': 313,
'name1': 'LONDON TMA 3',
'name2': 'MIDLANDS CTA'},
{'distance1': 0.0,
'distance2': 126746.1953125,
'confidence': 0.0,
'weight': 0,
'weight_adjusted': 0,
'confidence_distance': 0.0,
'confidence_weight': 0.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 513,
'airspace2': 311,
'name1': 'LONDON TMA 3',
'name2': 'MIDLANDS CTA'}]
Look at a single airspace
~~~~~~~~~~~~~~~~~~~~~~~~~
We can get information about a specific airspace:
.. code:: ipython3
a1 = "LONDON TMA 3"
airspacegraph.get_airspace(a1)
.. parsed-literal::
name LONDON TMA 3
lateral_limits 514021N 0004916W -514542N 0005550W -515021N 00...
upper_limit 19500
lower_limit 3500
class_of_airspace A
unit LONDON CONTROL (SWANWICK)
callsign LONDON CONTROL
language English
hours H24
frequency 118.825
purpose None
remarks Parts of the following ATS Routes are incorpor...
wkt MULTIPOLYGON (((-0.4916 51.4021, -0.555 51.454...
geometry (POLYGON ((-0.4916 51.4021, -0.555 51.4542, -0...
total_weight 46450
Name: 504, dtype: object
.. code:: ipython3
airspacegraph.get_airspace(a1).geometry
.. image:: demo_airspace_graph_files/demo_airspace_graph_26_0.svg
.. code:: ipython3
airspacegraph.graph["LONDON TMA 3"]
.. parsed-literal::
AtlasView({'LONDON STANSTED CTA 1': {'weight': 1960, 'weight_adjusted': 0.04219590958019376}, 'SOUTHERN CTA': {'weight': 19350, 'weight_adjusted': 0.41657696447793324}, 'UPPER AIRSPACE CTA': {'weight': 4620, 'weight_adjusted': 0.09946178686759957}, 'LONDON TMA 1': {'weight': 17100, 'weight_adjusted': 0.3681377825618945}, 'LONDON TMA 5': {'weight': 220, 'weight_adjusted': 0.004736275565123789}, 'LONDON TMA 6': {'weight': 1000, 'weight_adjusted': 0.021528525296017224}, 'LONDON TMA 7': {'weight': 910, 'weight_adjusted': 0.019590958019375673}, 'LONDON TMA 9': {'weight': 160, 'weight_adjusted': 0.003444564047362756}, 'LONDON TMA 16': {'weight': 650, 'weight_adjusted': 0.013993541442411194}, 'LONDON TMA 18': {'weight': 200, 'weight_adjusted': 0.004305705059203444}, 'LONDON TMA 19': {'weight': 280, 'weight_adjusted': 0.0060279870828848225}})
Test a handover
~~~~~~~~~~~~~~~
Given two airspaces, we can get information on the confidence of a
handover between those two airspaces at a given position:
.. code:: ipython3
a2 = "LONDON TMA 19"
airspacegraph.test_handover(long, lat, alt, a1, a2)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on distance from airspace borders.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
.. parsed-literal::
{'distance1': 0.0,
'distance2': 6929.1259765625,
'confidence': 1.0,
'weight': 280,
'weight_adjusted': 0.0060279870828848225,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0}
Test a flight
~~~~~~~~~~~~~
Finally, we can test an entire flight, giving us a list of airspace
handovers which could have occurred along the flight and the model’s
confidence in that handover.
.. code:: ipython3
# Download some flights
flights = downloader.download_flights("2019-01-01 00:00", "2019-01-01 01:00")
flights
.. parsed-literal::
INFO:flight_processing.data.flight_downloader:Downloading flights between 2019-01-01 00:00:00 and 2019-01-01 01:00:00 from OpenSky.
INFO:root:Sending request between time 2019-01-01 00:00:00+00:00 and 2019-01-01 01:00:00+00:00 and hour 2019-01-01 00:00:00+00:00 and 2019-01-01 01:00:00+00:00
INFO:paramiko.transport:Connected (version 2.0, client OpenSSH_7.6p1)
INFO:paramiko.transport:Authentication (password) successful!
INFO:root:Sending request: select time, icao24, lat, lon, velocity, heading, vertrate, callsign, onground, alert, spi, squawk, baroaltitude, geoaltitude, lastposupdate, lastcontact, hour from state_vectors_data4 where hour>=1546300800.0 and hour<1546304400.0 and time>=1546300800.0 and time<1546304400.0 and lon>=-11 and lon<=6 and lat>=48 and lat<=61.5
INFO:root:Reading request in cache /mnt/cold_data/josh/traffic_cache/opensky/25a2c24888155b039a81e36a56c86679
INFO:numexpr.utils:Note: NumExpr detected 32 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO:numexpr.utils:NumExpr defaulting to 8 threads.
.. raw:: html
Traffic with 34 identifiers | | count |
| icao24 | callsign | |
| 44c46f |
LGGRU10 |
3599 |
| 44c46d |
LGGRU1 |
3599 |
| 484ad0 |
TFL554 |
3413 |
| 3c4582 |
BOX317 |
2932 |
| c058d5 |
ACA2082 |
2785 |
| 8004e0 |
AIC144 |
2466 |
| 4952c1 |
TAP746 |
2171 |
| 4074d2 |
NRS7409 |
1939 |
| 4073a0 |
EZY6950 |
1730 |
| 7380c5 |
ELY028 |
1582 |
.. code:: ipython3
# Isolate one specific flight
flight = flights[9]
flight
.. parsed-literal::
INFO:root:Loading @junzis aircraft database
.. raw:: html
Flight EZY6950- aircraft: 4073a0 · 🇬🇧 G-UZHC (A20N)
- from: 2019-01-01 00:00:01+00:00
- to: 2019-01-01 00:28:50+00:00
.. code:: ipython3
handovers = airspacegraph.test_flight(flight)
handovers
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Getting all handovers along the flight.
INFO:flight_processing.data.airspace_graph:Converting flight to arrays of coordinates.
INFO:flight_processing.data.airspace_graph:Processing flight using AirspaceHandler C++ object.
INFO:flight_processing.data.airspace_graph:Computing confidence values for each handover.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
INFO:flight_processing.data.airspace_graph:Getting edge weights.
INFO:flight_processing.data.airspace_graph:Computing confidence based on non-position-based data sources.
.. parsed-literal::
[{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 457,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 458,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 459,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 460,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 461,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 462,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 463,
'airspace2': 602,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 457,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 458,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 459,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 460,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 461,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 462,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 463,
'airspace2': 601,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 457,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 458,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 459,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 460,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 461,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 462,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'},
{'distance1': None,
'distance2': None,
'confidence': 1.0,
'weight': 966,
'weight_adjusted': 0.011811023622047244,
'confidence_distance': 0.0,
'confidence_weight': 1.0,
'confidence_weight_adjusted': 0.0,
'airspace1': 463,
'airspace2': 603,
'name1': 'UPPER AIRSPACE CTA',
'name2': 'SCOTTISH TMA 3'}]
Visualisation
~~~~~~~~~~~~~
We can plot this flight on a map along with the airspaces it intersected
along the way.
.. code:: ipython3
regions = set()
for h in handovers:
regions.add(int(h['airspace1']))
regions.add(int(h['airspace2']))
regions
.. parsed-literal::
{457, 458, 459, 460, 461, 462, 463, 601, 602, 603}
.. code:: ipython3
airspacegraph.draw_graph_map(flight=flight, subset=regions)
.. parsed-literal::
INFO:flight_processing.data.airspace_graph:Downloading terrain data from Stamen.
INFO:flight_processing.data.airspace_graph:Plotting airspace boundaries on map.
INFO:flight_processing.data.airspace_graph:Extracting edge weights from graph.
INFO:flight_processing.data.airspace_graph:Plotting graph on map.
INFO:flight_processing.data.airspace_graph:Plotting flight(s) on map.
INFO:flight_processing.data.airspace_graph:Plotting subset of airspaces on map.
.. image:: demo_airspace_graph_files/demo_airspace_graph_36_1.png
.. code:: ipython3
# Cleanup
del airspacegraph