Embedded Files
Overview
Overview
In the previous section we scanned apt.dat to extract some basic airport and runway information for PACD, our example airport. In this section we will continue building the airport profile by searching for nearby navaids and waypoints. We will make use of the two geodataframe tables we created earlier: gdf_navaids and gdf_waypoints.
Recall we have been building dictionary airport_data, which has these keys:
airport_data keys:'icao_code''apt_name''city''country''region_code''apt_elev_ft''apt_coords''runways''search_area''navaids''landing_aids''waypoints'
Create Circle Polygon around airport
Create Circle Polygon around airport
We will create a 30 nautical mile search radius around the airport:
Search the polygon for navaids and waypoints
Search the polygon for navaids and waypoints
The inspection printout from the bottom of the codeblock is:
(2, 7)(39, 5)waypoint subtype counts:subtypeterminal 26enroute 13Name: count, dtype: int64
Meaning, there are 2 navaids and 39 waypoints in the area around the airport. Here are the dataframe results (top 10 results only):
Plot
Plot
We can use matplotlib to make a quick plot of our results. Note that I am only showing the enroute waypoints to avoid cluttering up the plot. Note also that NDBs are in blue and VORs are in magenta. The code is below the plot.
The code I wrote to make the plot is pretty ugly. Note at the top that we set the projection mode to polar.
Chapter Summary
Chapter Summary
In this chapter we have extracted waypoint, VOR and NDB data from the X-Plane 11 data files, and organized them into dataframes. We have also built functions to lookup airport and runway data on demand from the data files.
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