RocketPy is a trajectory simulation for High-Power Rocketry built by Projeto Jupiter. The code is written as a Python library and allows for a complete 6 degrees of freedom simulation of a rocket's flight trajectory, including high fidelity variable mass effects as well as descent under parachutes. Weather conditions, such as wind profile, can be imported from sophisticated datasets, allowing for realistic scenarios. Furthermore, the implementation facilitates complex simulations, such as multi-stage rockets, design and trajectory optimization and dispersion analysis.

You can preview RocketPy's main functionalities by browsing through a sample notebook either in Google Colab or in MyBinder!

Then, you can read the Getting Started section to get your own copy!

These instructions will get you a copy of RocketPy up and running on your local machine.

The following is needed in order to run RocketPy:

• Python >= 3.0
• Numpy >= 1.0
• Scipy >= 1.0
• Matplotlib >= 3.0
• requests
• netCDF4 >= 1.4 (optional, requires Cython)

All of these packages, with the exception of netCDF4, should be automatically installed when RocketPy is installed using either pip or conda.

However, in case the user wants to install these packages manually, they can do so by following the instructions bellow:

The first 4 prerequisites come with Anaconda, but Scipy might need updating. The nedCDF4 package can be installed if there is interest in importing weather data from netCDF files. To update Scipy and install netCDF4 using Conda, the following code is used:

$ conda install "scipy>=1.0"
$ conda install -c anaconda "netcdf4>=1.4"

Alternatively, if you only have Python 3.X installed, the packages needed can be installed using pip:

$ pip install "numpy>=1.0"
$ pip install "scipy>=1.0"
$ pip install "matplotlib>=3.0"
$ pip install "netCDF4>=1.4"
$ pip install "requests"

Although Jupyter Notebooks are by no means required to run RocketPy, they are strongly recommend. They already come with Anaconda builds, but can also be installed separately using pip:

$ pip install jupyter

To get a copy of RocketPy using pip, just run:

$ pip install rocketpyalpha

Alternatively, the package can also be installed using conda:

$ conda install -c conda-forge rocketpy

If you want to downloaded it from source, you may do so either by:

• Downloading it from RocketPy's GitHub page
  • Unzip the folder and you are ready to go
• Or cloning it to a desired directory using git:
  • $ git clone https://github.com/giovaniceotto/RocketPy.git

The RockeyPy library can then be installed by running:

$ python setup.py install 

You can find RocketPy's documentation at Read the Docs.

In order to run your first rocket trajectory simulation using RocketPy, you can start a Jupyter Notebook and navigate to the nbks folder. Open Getting Started - Examples.ipynb and you are ready to go.

Otherwise, you may want to create your own script or your own notebook using RocketPy. To do this, let's see how to use RocketPy's four main classes:

• Environment - Keeps data related to weather.
• SolidMotor - Keeps data related to solid motors. Hybrid motor suport is coming in the next weeks.
• Rocket - Keeps data related to a rocket.
• Flight - Runs the simulation and keeps the results.

A typical workflow starts with importing these classes from RocketPy:

from rocketpy import Environment, Rocket, SolidMotor, Flight

Then create an Environment object. To learn more about it, you can use:

help(Environment)

A sample code is:

Env = Environment(
    railLength=5.2,
    latitude=32.990254,
    longitude=-106.974998,
    elevation=1400,
    date=(2020, 3, 4, 12) # Tomorrow's date in year, month, day, hour UTC format
) 

Env.setAtmosphericModel(type='Forecast', file='GFS')

This can be followed up by starting a Solid Motor object. To get help on it, just use:

help(SolidMotor)

A sample Motor object can be created by the following code:

Pro75M1670 = SolidMotor(
    thrustSource="../data/motors/Cesaroni_M1670.eng",
    burnOut=3.9,
    grainNumber=5,
    grainSeparation=5/1000,
    grainDensity=1815,
    grainOuterRadius=33/1000,
    grainInitialInnerRadius=15/1000,
    grainInitialHeight=120/1000,
    nozzleRadius=33/1000,
    throatRadius=11/1000,
    interpolationMethod='linear'
)

With a Solid Motor defined, you are ready to create your Rocket object. As you may have guessed, to get help on it, use:

help(Rocket)

A sample code to create a Rocket is:

Calisto = Rocket(
    motor=Pro75M1670,
    radius=127/2000,
    mass=19.197-2.956,
    inertiaI=6.60,
    inertiaZ=0.0351,
    distanceRocketNozzle=-1.255,
    distanceRocketPropellant=-0.85704,
    powerOffDrag='../data/calisto/powerOffDragCurve.csv',
    powerOnDrag='../data/calisto/powerOnDragCurve.csv'
)

Calisto.setRailButtons([0.2, -0.5])

NoseCone = Calisto.addNose(length=0.55829, kind="vonKarman", distanceToCM=0.71971)

FinSet = Calisto.addFins(4, span=0.100, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956)

Tail = Calisto.addTail(topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656)

You may want to add parachutes to your rocket as well:

def drogueTrigger(p, y):
    return True if y[5] < 0 else False

def mainTrigger(p, y):
    return True if y[5] < 0 and y[2] < 800 else False

Main = Calisto.addParachute('Main',
                            CdS=10.0,
                            trigger=mainTrigger, 
                            samplingRate=105,
                            lag=1.5,
                            noise=(0, 8.3, 0.5))

Drogue = Calisto.addParachute('Drogue',
                              CdS=1.0,
                              trigger=drogueTrigger, 
                              samplingRate=105,
                              lag=1.5,
                              noise=(0, 8.3, 0.5))

Finally, you can create a Flight object to simulate your trajectory. To get help on the Flight class, use:

help(Flight)

To actually create a Flight object, use:

TestFlight = Flight(rocket=Calisto, environment=Env, inclination=85, heading=0)

Once the TestFlight object is created, your simulation is done! Use the following code to get a summary of the results:

TestFlight.info()

To seel all available results, use:

TestFlight.allInfo()

• Numpy
• Scipy
• Matplotlib
• netCDF4

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us. - Still working on this!

Still working on this!

• Giovani Hidalgo Ceotto

See also the list of contributors who participated in this project.

This project is licensed under the MIT License - see the LICENSE.md file for details

Still working on this!