SUAVE is imported directly first, which allows us to grab individual SUAVE classes later on without importing each one at the top. # visualizations of the aircraft's performance throughout the mission. # Matplotlib's pyplot can be used to generate a large variety of plots. # mathematical functions and is faster than native Python, especially when using vectorized # Numpy is a commonly used mathematically computing package. The second is matplotlib, which allows plotting of the results. The first is numpy, which is a common numerical programming package used in scientific computing. The first is generally used Python libraries, which are not specific to SUAVE. This would be to identify you as a key person to check with if someone has questions about the file. A line only needs to be added to the modified section when significant changes are made. If you plan to add your other files to the main SUAVE repository, a similar header should be created with matching style. The header contains the file name and information on who created it and when it was last modified.
#Aku shaper set nose and tail rocker tutorial code#
Now we will step through the code to explain how it works and where changes can be made to create a different vehicle. For Boeing aircraft, much of this information can be found in their airport planning documentation. For most common aircraft, parameters can be found online or extracted from available three views. The values shown come from a variety of sources. If you have trouble with this step, first check the installation instructions and then refer to the forum with any questions.Ĭollecting Information on the Chosen Vehicle:įor this tutorial, we will be analyzing a Boeing 737-800. If everything is running properly, you should have several performance plots and matching. Remember that SUAVE is only compatible with Python 3. Python tut_mission_B737_800.py Note that depending on your system settings you may need to use "python3" or another command instead.
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