This document will describe some of the more advanced functionality that this module offers but which is not part of the “official” interface. Here, some of the features that will be implemented in the future will also be covered, along with unanswered questions about proper functionality. Also, common problems will be discussed, along with some solutions.

Here issues with numerically integrating code, choice of `dynamicsymbols` for
coordinate and speed representation, printing, differentiating, and
substitution will occur.

See Future Features: Code Output

Differentiation of very large expressions can take some time in SymPy; it is possible for large expressions to take minutes for the derivative to be evaluated. This will most commonly come up in linearization.

The Kane object is set up with the assumption that the generalized speeds are not the same symbol as the time derivatives of the generalized coordinates. This isn’t to say that they can’t be the same, just that they have to have a different symbol. If you did this:

```
>> KM.coords([q1, q2, q3])
>> KM.speeds([q1d, q2d, q3d])
```

Your code would not work. Currently, kinematic differential equations are required to be provided. It is at this point that we hope the user will discover they should not attempt the behavior shown in the code above.

This behavior might not be true for other methods of forming the equations of motion though.

The default printing options are to use sorting for `Vector` and `Dyad`
measure numbers, and have unsorted output from the `mprint`, `mpprint`, and
`mlatex` functions. If you are printing something large, please use one of
those functions, as the sorting can increase printing time from seconds to
minutes.

Differentiation of very large expressions can take some time in SymPy; it is possible for large expressions to take minutes for the derivative to be evaluated. This will most commonly come up in linearization.

There are two common issues with substitution in mechanics:

When subbing in expressions for

`dynamicsymbols`, sympy’s normal`subs`will substitute in for derivatives of the dynamic symbol as well:>>> from sympy.physics.mechanics import dynamicsymbols >>> x = dynamicsymbols('x') >>> expr = x.diff() + x >>> sub_dict = {x: 1} >>> expr.subs(sub_dict) Derivative(1, t) + 1

In this case,

`x`was replaced with 1 inside the`Derivative`as well, which is undesired.Substitution into large expressions can be slow.

If your substitution is simple (direct replacement of expressions with other
expressions, such as when evaluating at an operating point) it is recommended
to use the provided `msubs` function, as it is significantly faster, and
handles the derivative issue appropriately:

```
>>> from sympy.physics.mechanics import msubs
>>> msubs(expr, sub_dict)
Derivative(x(t), t) + 1
```

Currently, the linearization methods don’t support cases where there are non-coordinate, non-speed dynamic symbols outside of the “dynamic equations”. It also does not support cases where time derivatives of these types of dynamic symbols show up. This means if you have kinematic differential equations which have a non-coordinate, non-speed dynamic symbol, it will not work. It also means if you have defined a system parameter (say a length or distance or mass) as a dynamic symbol, its time derivative is likely to show up in the dynamic equations, and this will prevent linearization.

At a minimum, points need to have their velocities defined, as the acceleration can be calculated by taking the time derivative of the velocity in the same frame. If the 1 point or 2 point theorems were used to compute the velocity, the time derivative of the velocity expression will most likely be more complex than if you were to use the acceleration level 1 point and 2 point theorems. Using the acceleration level methods can result in shorted expressions at this point, which will result in shorter expressions later (such as when forming Kane’s equations).

Remember that the `Kane` object supports bodies which have time-varying
masses and inertias, although this functionality isn’t completely compatible
with the linearization method.

Operators were discussed earlier as a potential way to do mathematical
operations on `Vector` and `Dyad` objects. The majority of the code in this
module is actually coded with them, as it can (subjectively) result in cleaner,
shorter, more readable code. If using this interface in your code, remember to
take care and use parentheses; the default order of operations in Python
results in addition occurring before some of the vector products, so use
parentheses liberally.

This will cover the planned features to be added to this submodule.

A function for generating code output for numerical integration is the highest priority feature to implement next. There are a number of considerations here.

Code output for C (using the GSL libraries), Fortran 90 (using LSODA), MATLAB,
and SciPy is the goal. Things to be considered include: use of `cse` on large
expressions for MATLAB and SciPy, which are interpretive. It is currently unclear
whether compiled languages will benefit from common subexpression elimination,
especially considering that it is a common part of compiler optimization, and
there can be a significant time penalty when calling `cse`.

Care needs to be taken when constructing the strings for these expressions, as well as handling of input parameters, and other dynamic symbols. How to deal with output quantities when integrating also needs to be decided, with the potential for multiple options being considered.