## Simple pendulum python code

To understand the concepts in this post, you should have a basic knowledge of calculus and be familiar with programming logic. The first step to simulating any sort of physical problem is to write out the analytic equations of motion. We will use the Lagrangian methoda particularly powerful way to analyze complex systems.

Using it for the case of a simple pendulum is probably overkill, but I like the elegance in its approach. Our first step is to define our problem.

Coding a Numerical Solution to the Simple Pendulum Problem using Python

A schematic of a simple pendulum is presented below:. The Lagrangian approach to doing this is as follows:. There will always be as many generalized coordinates as there are degrees of freedom in your system. What is the minimum set of information I can tell them so that they can completely replicate my system? In the case of a simple pendulum, I could tell them the and positions of the mass, of course, but I could also just tell them the angle the pendulum makes with the vertical.

One is fewer than two, and so the generalized coordinate for our simple pendulum is simplycorresponding to one degree of freedom.

The next step is to write out our energies in terms of the angle. The kinetic energy is easily written by remembering the relation for circular motion and noting that in our case, the radius is simply the length of the pendulum:.

Now we have our final analytic equation of motion. Note that the mass terms cancel out, suggesting that the motion of a pendulum is independent of its mass. At this stage, many introductory physics courses will take the small-angle approximation in order to obtain the equation for simple harmonic motionwhich can be solved analytically.

We have a second-order ordinary differential equation, which we can write as two first-order ordinary differential equations:. To numerically solve a system, the first step is discretizing it. The usual way to do this is by writing out the Taylor series for a continuous function and truncating it at some term. In our case, we want to find an approximation to given our known initial condition.

### inverted-pendulum

Expanding aboutgives, to the first order:. There is a question though — for the right hand side, at what time value should we compute and? This is a non-trivial thing to decide, but for now we will compute these values at the past valueelse we end up with multiple unknowns to solve for.

We now have a method for determining and given the initial conditions and where. The only things we need to specify ahead of time, aside from the constants, are the initial conditions. The relevant code, written in Python, looks like this:. Running this code for initial conditions andwe immediately see a problem, though:.

The total energy is increasing without bound! In other words, the energy of the system is not conserved. You can see this by the ever-increasing displacement of our pendulum, which paradoxically swings higher and higher with every rotation. The problem lies in our crude first-order approximation we made earlier when discretizing our solution. After all, our equation of motion is nonlinear, making linear approximations especially ineffective at solving it.

In lieu of using higher-order methods, we can just apply a simple modification called the Euler-Cromer methodwhich is guaranteed to conserve energy. We simply use the updated value of the angular velocity as it becomes available:. You can see that in the second line has been simply changed to. Running the simulation again, we get the results:. The pendulum now properly returns to its initial displacement after each cycle. We see that although the total energy still fluctuates a bit due to our first-order approximationit is now properly bounded.

Like Like. You are commenting using your WordPress.Updated 31 Dec For small angles, equation of motion of a simple pendulum as derived from the Newton's second law is a simple ordinary differential equation which can be solved numerically. One such numerical technique is the Euler-Cromer method. At the endthe code also calculates and animates the Kinetic Energy, Potential Energy and the Total energy as a function of time along with the motion of pendulum.

The units are arbitrary and energies are normalized to 1. Axis are scaled accordingly to give a better view. Sathyanarayan Rao Animation of a Simple Pendulum using the Euler-Cromer numerical method. Retrieved April 12, Learn About Live Editor. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.

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## Modeling a Pendulum's Swing Is Way Harder Than You Think

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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to write simple pendulum simulation in Pygame. The point is that I am trying to simulate directly the forces on the pendulum gravity and tension rather than solving the differential equation that describes the motion. First I wrote a function that get a vector, rotate the axis-system by some angle, and return this vector's components in the new, rotated axis-system; the code of this function is fine and it works as expected. Each tick of the simulation I rotate the gravity vector by the angle between the pendulum and the rope, and get the new components - one in the direction of the rope, and one is orthogonal to it.

After I calculate it, I rotate the acceleration vector back to the normal coordinates system, and integrate. However, the resulting behavior is not as intended. What can be the reason? What I fixed was: 1 since you've imported numpy, you should use it, and write things in terms of the vectors; 2 it's an unreasonable demand on yourself to write everything and have it work immediately; so you need to plot intermediate results, etc, like here I plot a as well, so you can see whether it makes sense; 3 your whole "rotation" approach is confusing; instead think of component parts; which I calculate here directly it's shorter, easier to read and understand, etc ; 4 in all simulations where you use a time step, you should explicitly use dt so you can change the timestep without changing other parameters.

Now if you watch it you can see it looks almost reasonable. Notice though that the acceleration never goes upward, so the ball just falls while it oscillates. The reason for this is that you did not include the tension of the rope into the forces on the ball. I'll leave that part to you. If you want to do a simulation, I think you're doing it the hard way. I'd start with the equation of motion, see Equation 20here. Then you should implement a finite differences scheme in the time direction, and step through time.

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Asked 7 years ago. Active 3 years, 10 months ago. Viewed 5k times. Clock pygame. QUIT: pygame. CalcForce p. What unexpected behavior are your seeing?The line that should be noted here is highlighted above.

They are used in the next section. The first lines of code here define system parameters. Then you can see that a new trep system is created. The frames of the system are defined using the methods that were imported above.

The Frame documentation has a through explanation of how to create and use these frames. It is defined by the configuration parameter theta and is named pendulumShoulder.

The system.

### Animate a pendulum

Gravity can work in any direction relative to the world frame. Trep can handle other types of potentials as well. See the Potential documentation for information on the trep. Potential base class, and see the trep.

Damping is applied to the entire system with the trep. Damping method. Note that trep can also apply unique damping values to individual configurations or set default values for all configurations — see the Damping documentation.

An input is configured for the system by adding a configuration force with the trep. ConfigForce method.

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Specifically this adds an input to the configuration variable theta the configuration variable for the pendulumShoulder frame with the name theta-torque. See Forces for a list of the available force types, and trep. Force for the documentation on the base class. Here a new variational integrator object mvi is created using our system instance of a trep. It is then initialized with a set of two time points and configurations using trep.

The first two arguments are the current time and configuration and the next two are the next time and configuration. Trep calculates the discrete generalized momentum from these two pairs. You can also initialize the variational integrator with a single time, configuration, and momentum using the trep.

The system is simulated forward in time using a simple while loop. First, two lists are initialized to hold all of the time values and configuration values for the simulation.

Next, it enters a loop that says to continue until the variational integrator reaches the final time. In each interation of the loop the system is integrated forward by one time step. Then the new values are append to the storage vectors. The variational integrator object has attributes for times, configurations, and discrete generalized momenta at both time points of the current integration e.

The trep. The second argument specifies the input over that time period. You can see above that the input is set to zero. Only the system object, the list of times, and the list of configurations are needed to create the visualization.Python Matplotlib Tips: Draw electric field lines with changing line color according to the electric potential. Tips for drawing efficient figures using python matplotlib pyplot.

You can brush up them by adding some additional options and settings. Example code for python animation: combine 3D and 2D animations in one figure using python, matplotlib.

Python Matplotlib Tips: Animate zoomed plot of crowded data by updating xlim using matplotlib. This code shows how to animate the zoomed subplot of original crowded subplot using Python and matplotlib. The result is:. Python Matplotlib Tips: Draw electric field lines with changing line color according to the electric potential Tips for drawing efficient figures using python matplotlib pyplot.

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Funcanimation This code shows how to animate the zoomed subplot of original crowded subplot using Python and matplotlib. Check the documents. Parameters func : callable y, t0, Computes the derivative of y at t0. The initial value point should be the first element of this sequence. Dfun : callable y, t0, These give the number of lower and upper non-zero diagonals in this banded matrix.

Defaults to 1. The following code plots both components. In this page, the second order differential equation for the angle theta of a pendulum acted on by gravity with friction is solved in imitaion of the official document. The equations can be written as follows:. Secondly, define the constants, the boundary condition, and time range for the simulation. Thirdly, solve the equation using odeint function. Show the output.

Convert the result to an animation. Save the animation in the mp4 and gif format using ffmpeg and imagemagick, respectively. Reduce the size of the GIF file using imagemagick. Finaly, show the animation in the jupyter notebook. Your browser does not support the video tag. Newer Post Older Post Home.In a previous InstructableI shared my excitement about the potential of the Arduino in the classroom and explored the use of Arduino as a data-logger in a simple toy-system where we measured water pollution.

In this Instructable, we'll build a classic and important physical system, the simple pendulum. We'll use an Arduino and a potentiometer to measure the amplitude of the pendulum's motion and Python to read and visualize our data. The project here is designed in such a way that the pendulum arm can be easily swapped out for arms of different lengths or even compound pendulum arms. So, once built, the basic system allows for a full range of experiments - from measuring the period of a simple pendulum to studying the nonlinear dynamics of a double pendulum.

Before we begin, let's take a quick look at the completed system. In the pictures above you'll see a front and side view of the completed system. On the front, you'll see the pendulum arm, rotating on a bearing, suspended on a rigid rod.

Attached to the pendulum arm is a simple gear that rotates around the same axis as the pendulum. This gear drives an identical gear attached to a potentiometer mounted adjacent to the pendulum arm. Moving to the rear, you'll see the potentiometer wired to an Arduino mounted to the back of the L-shaped wood supports that hold the entire setup. As the pendulum arm moves back and forth, the potentiometer is turned through an identical, but opposite angle as the pendulum.

Readings from the potentiometer are captured with the Arduino and fed to a Python script that stores and plots the motion of the pendulum. Did you use this instructable in your classroom? Add a Teacher Note to share how you incorporated it into your lesson. To construct the gears you'll need access to a laser cutter or the ability to otherwise construct or salvage two identical gears from something else.

The svg file for the gears appears below. For the pendulum arm, I used the same basic construction technique as in this project. Note that while here, I've specified a flat aluminum bar as the material from which to make the pendulum arm, you can use other materials such as wood or polycarbonate.

The aluminum bar specified has the correct width to be used with the bearings, so make sure any alternative you choose has the same or larger width. For this step, you'll need your gears, so either laser cut using the SVG file provided, or have the gears you intend to use at hand. The center of the hole should be about 15 mm from the top of the bar and centered across the width.

Once you've drilled the hole, check the fit of your bearing. At this point, the bearing should be too big to fit in the hole. Using the Dremel and grinder bit, carefully increase the radius of your hole until the bearing can be gently tapped into place.Create your free Platform account to download ActivePython or customize Python with the packages you require and get automatic updates.

This application will allow users of Django-powered websites keep track of the time they spend on different projects. It's a work in process. By modern I simply mean a version with the newforms-admin functionality. If you're running on Django 1. Download django-pendulum using one of the following methods:.

Install django-pendulum using pip :. Download the latest. Use python setup. Clone django-pendulum using one of the official repositories:. The easiest way to ensure that you have successfully installed Pendulum is to execute a command such as:.

If that displays the version of Pendulum that you tried to install, you're good to roll.

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Run manage. This creates a few tables in your database that are necessary for operation. Next, you should add an entry to your main urls. For example:. The next thing you will want to do is configure Pendulum for the active Django sites. Do this by going into the Django admin and clicking the "add" link next to Pendulum Configurations. The first step in the configuration is to choose which site this particular configuration will apply to.

The decision is easy if you only have one site :. Next, you must choose what kind of "accounting period" you wish to use.