Reduce time it takes to run the examples

[moorepants]

Here is the current timing from a clean build:

computation time summary:
    - ../examples-gallery/plot_pendulum_swing_up_variable_duration.py:   110.06 sec   0.0 MB
    - ../examples-gallery/plot_drone.py:                                  75.58 sec   0.0 MB
    - ../examples-gallery/plot_two_link_pendulum_on_a_cart.py:            75.11 sec   0.0 MB
    - ../examples-gallery/plot_sliding_block.py:                          59.82 sec   0.0 MB
    - ../examples-gallery/plot_parallel_park.py:                          58.78 sec   0.0 MB
    - ../examples-gallery/plot_pendulum_swing_up_fixed_duration.py:       56.85 sec   0.0 MB
    - ../examples-gallery/plot_one_legged_time_trial.py:                  44.69 sec   0.0 MB
    - ../examples-gallery/plot_vyasarayani.py:                            11.22 sec   0.0 MB
    - ../examples-gallery/plot_betts2003.py:                              10.89 sec   0.0 MB

Reducing the time it takes to create animations is an easy adjusted, for example. We can safely skip generating a frame for every time instance by skipping frames with a minor adjustment to the code. Here is an example:

https://github.com/csu-hmc/opty/blob/master/examples-gallery/plot_drone.py#L299-L300

[moorepants]

Another way to reduce time is to give better initial guesses. We can even store a solution and give that as a guess.

[Peter230655]

Another way to reduce time is to give better initial guesses. We can even store a solution and give that as a guess.

How can I store a solution, so it does not get lost when the program is closed?
I thought about this often, but never came up with an idea... :-(
(I mean short of printing the solution and then typüing it into the program...)

[moorepants]

You can save any value to a txt file with many different methods. NumPy has builtin ways to do this: https://numpy.org/doc/stable/reference/routines.io.html

[Peter230655]

You can save any value to a txt file with many different methods. NumPy has builtin ways to do this: https://numpy.org/doc/stable/reference/routines.io.html

Amazing how this works - and it is two lines of code initially, no additional line in the final simulation. (After your hint, chatGPT told me the lines) I once modeled a drone with propellers, the one that wiggled and it cut down the simulation time from over one minute to 16 sec. - still wiggles, though :-)

[moorepants]

Here is the output from a recent build on the readthedocs system, which is slower than running it locally:

computation time summary:
    - ../examples-gallery/plot_parallel_park.py:                             128.56 sec   0.0 MB
    - ../examples-gallery/plot_pendulum_swing_up_variable_duration.py:       127.54 sec   0.0 MB
    - ../examples-gallery/plot_pendulum_swing_up_fixed_duration.py:          124.13 sec   0.0 MB
    - ../examples-gallery/plot_sliding_block.py:                             105.60 sec   0.0 MB
    - ../examples-gallery/plot_ball_rolling_on_spinning_disc.py:              95.81 sec   0.0 MB
    - ../examples-gallery/plot_drone.py:                                      64.82 sec   0.0 MB
    - ../examples-gallery/plot_one_legged_time_trial.py:                      63.60 sec   0.0 MB
    - ../examples-gallery/plot_two_link_pendulum_on_a_cart.py:                49.41 sec   0.0 MB
    - ../examples-gallery/plot_crane_moving_a_load.py:                        33.36 sec   0.0 MB
    - ../examples-gallery/plot_non_contiguous_parameter_identification.py:    12.81 sec   0.0 MB
    - ../examples-gallery/plot_vyasarayani.py:                                12.40 sec   0.0 MB
    - ../examples-gallery/plot_betts2003.py:                                  11.66 sec   0.0 MB

[Peter230655]

If I understand correctly, this is what I tried with the examples that have animations. Should be in the PRs.