Install PyChrono
PyChrono technology reference
Tutorials
gym-chrono: OpenAI Gym robotic environments based on PyChrono

This is an introduction on how to use PyChrono, that is Chrono::Engine for Python.

PyChrono is a Python module, an alternative way of creating applications based on Chrono::Engine that does not require any C++ programming. In fact, once you installed PyChrono in your Python environment, you will be able to use the easy Python scripting language to call a big part of the Chrono::Engine API functions, classes, methods, etc.

Advantages of Python programming vs. C++ programming:

Python is simple,
Python can be interpreted on the fly,
there are lot of third party modules for Python, for instance Matplotlib for plotting, Numpy for algebra, etc.,
a minimal installation is required.

Disadvantages of Python programming vs. C++ programming:

Python is slower than C++,
the Chrono::Engine Python module does not cover all the features of the C++ API.

The idea is that, once installed, you can open your Python IDE, import the ChronoEngine Python module(s) and start creating Chrono objects as in the following:

import pychrono as chrono
 
my_systemA = chrono.ChSystem()
my_vect1   = chrono.ChVectorD()
...

First steps with Python

After the installation, you are ready to use PyChrono from Python. To begin:

start your editor, for example Spyder.
create a new blank Python script file, for example 'test.py'

Now you can type Python programs in this new Python file, execute it, save it on disk, etc.

Let's see a first program.

First of all, you should use the import keyword to specify which Python modules must be load and used in your program. Most of the core functionalities of Chrono::Engine are in a module called pychrono, hence write:

import pychrono as chrono

Note that the as chrono is optional: but if you avoid it you must call all Chrono::Engine functions using the syntax pychrono.ChClassFoo..., whereas if you use as chrono you simply rename the namespace just like the C++ equivalent: chrono.ChClassFoo...

Let's create a 3D vector object:

my_vect1 = chrono.ChVectorD()

(Note that in this way all PyChrono classes are prefixed by the chrono word).

Modify the properties of that vector object; this is done using the **.** dot operator:

my_vect1.x=5
my_vect1.y=2
my_vect1.z=3

Some classes have build parameters, for example anothe vector can be built by passing the 3 coordinates for quick initialization:

my_vect2 = chrono.ChVectorD(3,4,5)

Most operator-overloading features that are available in C++ for the Chrono::Engine vectors and matrices are also available in Python, for example:

my_vect4 = my_vect1*10 + my_vect2

Member functions of an object can be called simply using the **.** dot operator, just like in C++:

my_len = my_vect4.Length()

print ('vector length =', my_len)

You can use most of the classes that you would use in C++, for example let's play with quaternions and vectors:

my_quat = chrono.ChQuaternionD(1,2,3,4)
my_qconjugate = ~my_quat
print ('quat. conjugate  =', my_qconjugate)
print ('quat. dot product=', my_qconjugate ^ my_quat)
print ('quat. product=',     my_qconjugate % my_quat)
my_vec = chrono.ChVectorD(1,2,3)
my_vec_rot = my_quat.Rotate(my_vec)

PyChrono linear algebra (ChMatrixDynamicD and ChVectorDynamicD) are interfaced with Python lists, this allows to use any third-party packege to perform linear algevra operation. In the following example we use NumPy:

mlist = [[1,2,3,4], [5,6,7,8], [9,10,11,12], [13,14,15,16]]
ma = chrono.ChMatrixDynamicD() 
ma.SetMatr(mlist)   # Create a Matrix from a list. Size is adjusted automatically.
npmat = np.asarray(ma.GetMatr()) # Create a 2D npy array from the list extracted from ChMatrixDynamic
w, v = LA.eig(npmat)  # get eigenvalues and eigenvectors using numpy
mb = chrono.ChMatrixDynamicD(4,4)
prod = v * npmat  
mb.SetMatr(v.tolist())    

If you want to know the list of methods and/or properties that are available in a class, you can simply use the code completion feature of IDEs like Spyder or VisualStudio Code: for example once you type chrono. you will see a pop-up window with a list of available classes, constants, etc.

Most classes behave like their C++ counterparts, so you are invited to look at the C++ API documentation to understand their features. Currently there is no SPhynx automated generation of Python API docs, so you should look at the C++ API docs.

First steps with Python

Further reading