assignment 6 animation python

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Assignment 6: ‘Automated Animation’ Part 2

Posted on March 23, 2016 by littlexowlxstudios

So from the last post of what we could potentially do with coding, we can now move on to the bare basics of a little Python coding to make a character out of primitive shapes and eventually animate it. At the start we were given some code to create this little creature as an example to help us for when we create out characters:

As you can see from above when you write in the code and press the play button and hopefully your amazing code will turn into something like this alien. So what have we got so far?

Size- This stands for your canvas size.

rectMode(CENTER)- The rectangle in the middle of the alien will remain the centre point of the shapes. You can also use this mode for other shapes like the Ellipses.

Rect- Stands for rectangles , although you can make squares too with the right code.

Ellipses- These are the circles and just like the rectangles, you can turn these into ovals like the eyes of the character above.

Line- See those spindly legs? That’s your lines and can make great arms or legs for your character.

All these commands are quite nice but first we need to know how to type them up in a certain format for them to appear. If we look at the one of the rect commands:

rect(100,100,20,100) The first two 100 stands for co-ordinates of where your placing you rectangle whilst the 20,100 and stand for width and length. This rule when placing primitive shapes applies to ellipses, rectangles, lines, triangles ect So the main thing with coding is literally just having a play around to see where and how big you want your shapes to be.

Meet my tiny little turtle! For some reason I had drawn more designs on another sheet of paper but I can’t find them….This ranged from horses, wolves and dragons which seems relatively mad but whenever I draw animals, I use shapes to start off the body pose and they also act like the foundations of the proportions of the animal’s anatomy. Yet as this was my first experience of using shapes via Python to make something, the turtle won all being a mix of ellipses, rectangles and one triangle.

Here is my little turtle! I had fun creating this little guy but if there was one major problem- it was the tail! The tail is made from a triangle and follows a weird pattern to be coded.

triangle (200,150,155,120,100,120). 200,155 and 100 follows the triangle’s co-ordinates whilst 150,120 and 120 are the length, width and depth of your triangle across the board. After this I decided to follow more of what we were taught in class, like repeating the same creation and moving it across the board.

He’s multiplied!!! In order to do this we used an indentation written like this:

for i in range(4): This is asking for the rest of the code to be repeated 4 times, but in order for them to appear like mine have, we have to translate them (move them) across the co-ordinates with the command at the bottom of your character’s code:

translate(100,0)

So what’s next? Well according to the powerpoint we were given, all that’s left was to move my turtle around and make it follow the cursor!

Up above is all the code I used to create the video of my moving turtle that you’ll find below. What I’ve asked for this to do is make the turtle (which I’ve defined with the turtle’s code using def turtle. This defines any character and makes work neater!), follow my mouse button within the canvas which has been defined at the top at setup, and then for the turtle to follow it’s been defined under draw. What I’ve also done is filled in the parts of my turtle using the RGB system and here’s my final result:

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Go to the end to download the full example code

Animations using Matplotlib #

Based on its plotting functionality, Matplotlib also provides an interface to generate animations using the animation module. An animation is a sequence of frames where each frame corresponds to a plot on a Figure . This tutorial covers a general guideline on how to create such animations and the different options available.

Animation classes #

The animation process in Matplotlib can be thought of in 2 different ways:

FuncAnimation : Generate data for first frame and then modify this data for each frame to create an animated plot.

ArtistAnimation : Generate a list (iterable) of artists that will draw in each frame in the animation.

FuncAnimation is more efficient in terms of speed and memory as it draws an artist once and then modifies it. On the other hand ArtistAnimation is flexible as it allows any iterable of artists to be animated in a sequence.

FuncAnimation #

The FuncAnimation class allows us to create an animation by passing a function that iteratively modifies the data of a plot. This is achieved by using the setter methods on various Artist (examples: Line2D , PathCollection , etc.). A usual FuncAnimation object takes a Figure that we want to animate and a function func that modifies the data plotted on the figure. It uses the frames parameter to determine the length of the animation. The interval parameter is used to determine time in milliseconds between drawing of two frames. Animating using FuncAnimation would usually follow the following structure:

Plot the initial figure, including all the required artists. Save all the artists in variables so that they can be updated later on during the animation.

Create an animation function that updates the data in each artist to generate the new frame at each function call.

Create a FuncAnimation object with the Figure and the animation function, along with the keyword arguments that determine the animation properties.

Use animation.Animation.save or pyplot.show to save or show the animation.

The update function uses the set_* function for different artists to modify the data. The following table shows a few plotting methods, the artist types they return and some methods that can be used to update them.

Covering the set methods for all types of artists is beyond the scope of this tutorial but can be found in their respective documentations. An example of such update methods in use for Axes.scatter and Axes.plot is as follows.

ArtistAnimation #

ArtistAnimation can be used to generate animations if there is data stored on various different artists. This list of artists is then converted frame by frame into an animation. For example, when we use Axes.barh to plot a bar-chart, it creates a number of artists for each of the bar and error bars. To update the plot, one would need to update each of the bars from the container individually and redraw them. Instead, animation.ArtistAnimation can be used to plot each frame individually and then stitched together to form an animation. A barchart race is a simple example for this.

Animation writers #

Animation objects can be saved to disk using various multimedia writers (ex: Pillow, ffpmeg , imagemagick ). Not all video formats are supported by all writers. There are 4 major types of writers:

PillowWriter - Uses the Pillow library to create the animation.

HTMLWriter - Used to create JavaScript-based animations.

Pipe-based writers - FFMpegWriter and ImageMagickWriter are pipe based writers. These writers pipe each frame to the utility ( ffmpeg / imagemagick ) which then stitches all of them together to create the animation.

File-based writers - FFMpegFileWriter and ImageMagickFileWriter are examples of file-based writers. These writers are slower than their pipe-based alternatives but are more useful for debugging as they save each frame in a file before stitching them together into an animation.

Saving Animations #

To save animations using any of the writers, we can use the animation.Animation.save method. It takes the filename that we want to save the animation as and the writer , which is either a string or a writer object. It also takes an fps argument. This argument is different than the interval argument that FuncAnimation or ArtistAnimation uses. fps determines the frame rate that the saved animation uses, whereas interval determines the frame rate that the displayed animation uses.

Below are a few examples that show how to save an animation with different writers.

Pillow writers:

HTML writers:

FFMpegWriter:

Imagemagick writers:

(the extra_args for apng are needed to reduce filesize by ~10x)

Total running time of the script: (0 minutes 7.472 seconds)

Download Jupyter notebook: animations.ipynb

Download Python source code: animations.py

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How to Create Animations in Python?

Animations are a great way to make Visualizations more attractive and user-appealing. It helps us to demonstrate Data Visualization in a Meaningful Way. Python helps us to create Animation Visualization using existing powerful Python libraries. Matplotlib is a very popular Data Visualisation Library and is the commonly used for the graphical representation of data and also for animations using inbuilt functions we will see in this article how to animating graphs using Pandas in Python and creating animated graphs with Pandas in Python.

Create Animations in Python

There are two ways of Creating Animation using Matplotlib in Python : 

Table of Content

• Using pause() function
• Using FuncAnimation() function

Using pause() Function

The pause() function in the pyplot module of the Matplotlib library is used to pause for interval seconds mentioned in the argument. Consider the below example in which we will create a simple linear graph using matplotlib and show Animation in it:

Example 1: Animated Plotting with Matplotlib in Python

In this example , below Python code uses Matplotlib to create an animated graph. Basically its generates points in a loop, updating the plot in real-time with a brief pause after each iteration then xlim and ylim functions set the graph’s axis limits, and plt.show() displays the final animated plot.

Similarly, you can use the pause() function to create Animation in various plots.

Using FuncAnimation() Function

This FuncAnimation() Function does not create the Animation on its own, but it creates Animation from series of Graphics that we pass. Now there are Multiple types of Animation you can make using the FuncAnimation function: 

• Linear Graph Animation

Bar Plot Race Animation in Python

Scatter plot animation  in python.

• Horizontal Movement in Bar Chart Race

Linear Graph Animation:

In this example, we are creating a simple linear graph that will show an animation of a Line. Similarly, using FuncAnimation, we can create many types of Animated Visual Representations. We just need to define our animation in a function and then pass it to FuncAnimation with suitable parameters.

    Output:

In this example, we are creating a simple Bar Chart animation that will show an animation of each bar. 

In this example, we will Animate Scatter Plot in python using the random function. We will be Iterating through the animation_func and while iterating we will plot random values of the x and y-axis.

Horizontal Movement in Bar Chart Race:

In this example , we are creating animated graphs with Pandas in Python , as below Python code utilizes the Matplotlib library to create a real-time animated plot. It generates a sequence of points in a loop and updates the graph with a brief pause after each iteration, showcasing a dynamic representation of the data.

Dataset can be download from here: city_populations

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CS4620 PA6 Animation

Out: Friday November 13, 2015

Due: Friday November 20, 2015 at 11:59pm

Introduction

Problem 1: keyframe animation, getting started, the interface, what you will do, interpolation overview, naive approach, the more accurate approach, decomposing transformations, converting between rotation matrices and quaternions, linear interpolation, spherical linear interpolation.

What to Submit

Matplotlib Animation Tutorial

Matplotlib version 1.1 added some tools for creating animations which are really slick. You can find some good example animations on the matplotlib examples page. I thought I'd share here some of the things I've learned when playing around with these tools.

Basic Animation

The animation tools center around the matplotlib.animation.Animation base class, which provides a framework around which the animation functionality is built. The main interfaces are TimedAnimation and FuncAnimation , which you can read more about in the documentation . Here I'll explore using the FuncAnimation tool, which I have found to be the most useful.

First we'll use FuncAnimation to do a basic animation of a sine wave moving across the screen:

Let's step through this and see what's going on. After importing required pieces of numpy and matplotlib , The script sets up the plot:

Here we create a figure window, create a single axis in the figure, and then create our line object which will be modified in the animation. Note that here we simply plot an empty line: we'll add data to the line later.

Next we'll create the functions which make the animation happen. init() is the function which will be called to create the base frame upon which the animation takes place. Here we use just a simple function which sets the line data to nothing. It is important that this function return the line object, because this tells the animator which objects on the plot to update after each frame:

The next piece is the animation function. It takes a single parameter, the frame number i , and draws a sine wave with a shift that depends on i :

Note that again here we return a tuple of the plot objects which have been modified. This tells the animation framework what parts of the plot should be animated.

Finally, we create the animation object:

This object needs to persist, so it must be assigned to a variable. We've chosen a 100 frame animation with a 20ms delay between frames. The blit keyword is an important one: this tells the animation to only re-draw the pieces of the plot which have changed. The time saved with blit=True means that the animations display much more quickly.

We end with an optional save command, and then a show command to show the result. Here's what the script generates:

This framework for generating and saving animations is very powerful and flexible: if we put some physics into the animate function, the possibilities are endless. Below are a couple examples of some physics animations that I've been playing around with.

Double Pendulum

One of the examples provided on the matplotlib example page is an animation of a double pendulum. This example operates by precomputing the pendulum position over 10 seconds, and then animating the results. I saw this and wondered if python would be fast enough to compute the dynamics on the fly. It turns out it is:

Here we've created a class which stores the state of the double pendulum (encoded in the angle of each arm plus the angular velocity of each arm) and also provides some functions for computing the dynamics. The animation functions are the same as above, but we just have a bit more complicated update function: it not only changes the position of the points, but also changes the text to keep track of time and energy (energy should be constant if our math is correct: it's comforting that it is). The video below lasts only ten seconds, but by running the script you can watch the pendulum chaotically oscillate until your laptop runs out of power:

Particles in a Box

Another animation I created is the elastic collisions of a group of particles in a box under the force of gravity. The collisions are elastic: they conserve energy and 2D momentum, and the particles bounce realistically off the walls of the box and fall under the influence of a constant gravitational force:

The math should be familiar to anyone with a physics background, and the result is pretty mesmerizing. I coded this up during a flight, and ended up just sitting and watching it for about ten minutes.

This is just the beginning: it might be an interesting exercise to add other elements, like computation of the temperature and pressure to demonstrate the ideal gas law, or real-time plotting of the velocity distribution to watch it approach the expected Maxwellian distribution. It opens up many possibilities for virtual physics demos...

Summing it up

The matplotlib animation module is an excellent addition to what was already an excellent package. I think I've just scratched the surface of what's possible with these tools... what cool animation ideas can you come up with?

Edit: in a followup post , I show how these tools can be used to generate an animation of a simple Quantum Mechanical system.

How to create animations in Python

Today we are going to learn how to create animations in Python, to make our visualizations much more impressive and we can give more information in a visual and impressive way. In this post, you will learn how to create all kinds of animations in Python from simple animations to animated graphics like bar chart races. Sound interesting to you? Well, let’s get to it! Betsson app

How animations work in Python

To create our animations we will use the FuncAnimation function inside matplolib . A fundamental aspect to be able to create our animations is to understand that this function does not create whole animations with interpolatio n , but simply limits itself to creating animations from a series of graphics that we pass to it .

This is very important, as it is a very different approach from other packages like R’s gganimate (if you don’t know how it works, here you have the tutorial).

In fact, to create animations in Python using FuncAnimation you simply have to pass a function that has as input value a number that refers to a frame and returns the graphic corresponding to that frame .

This means that to create animations in Python we must prepare the data very well. Let’s see how to do it.

Estructura de datos para crear animaciones en Python

Although you can create graphs from data with a very different shape, in my opinion, to make it easier to create the visualization the data must be in tidy format, that is:

• Each variable must be in a column.
• Each observation of that variable must be a different row.

Let’s see an example with the gapminder dataset, which is what we’ll use as an example:

Now that we know how the data must be to create an animation in Python, let’s see how to create different animations in Python!

To create animations in Python we will use the animation functions of the matplotlib module. Therefore, creating an animation is very simple and similar to creating graphics with matplotlib. We simply have to create:

• fig : it is the object that we will use to paint our graph.
• func : it is a function that must return the state of the animation for each frame. Basically what we have to do is create a function that returns all the graphs. Following the example of the line chart animation mentioned above, the function must return, in the first iteration a linechart with the first year, in the second interaction a linechart with the first two years, and so on for all the years.
• interval : is the delay in milliseconds between the different frames of the animation.
• frames : the number of images on which to base the chart. This will depend on how many “states” the animation has. If we have an animation with data in 5 different states (let’s imagine, 5 years), the number of frames will be 5, while if we have data of 100 years, the number of frames will be 100.

With these arguments, we can create all kinds of animations. Now, this can be somewhat complex (especially the update part), so I would always recommend first creating the graphic that we want and, from that, generating the animation.

In any case, we already have all the basics, so let’s see how to create animations in Python!

How to create linechart animation

As I said, the easiest way to create an animation is to first create a graphic that looks like what we want to animate. In this case it is very simple, we simply have to create a GDP per Capita linechart for the countries Spain, Italy and the United States.

Now that we have our graph, to create a line animation, we will simply have to create a function that, for each iteration, creates the line graph but for the data that we have available.

In this way, in the first iteration the line graph must create only one point for the year 1952, in the second iteration it will create the graph with the first two points (1952, 1957), and so on until completing the entire graph .

Luckily, creating this iteration having already created the graph is quite simple, since we will simply have to use the indices to define the data that the graph should take.

With this, we have already created everything we need for our animation. Now we simply have to call it using the FuncAnimation function that I have previously explained. In this tense, face

We already have our linechart animation created with Python! Simple, right? Now let’s see how to create barchart animations!

How to create a barchart animation in Python

A good practice to make creating our barchart (and all animations beyond linecharts) easier is to filter the data within the iteration function itself. This will make it much easier for us to create animations and it will make understanding them much easier.

Anyway, as always when we want to create an animation, we must start by graphing what we want to achieve. So, in this case I am going to create a very simple barchart in which we see how the GDP per Capita has evolved in different countries.

Tip 1. Clean the previous graph

If we create the graph inside our ax object, the graphs will “pile up”, which can make our data not the real one. Worst of all, if you don’t use transparency in the chart, it depends on the type of chart you may not even realize.

To avoid this, in each iteration we must call ax.clear (), in such a way that it clears the result from the previous frame.

Tip 2. Filter your data in the update function

Making an animation for a single year is relatively easy. However, doing it for many years seems somewhat more complex. That is why to facilitate the creation of animations in Python I recommend:

• Create a list with all the possible states of the animation. In my case the states are the years, so I create a list with all the years that I can plot.
• Filter the complete dataset based on the state within the update function itself.

These two steps will make it much easier for you to create animations.

So, I’m going to create the update function of my barplot animation taking into account the two previous points:

Python barplot animation ready! As you can see, filtering the data within the update function itself makes everything much easier.

Now that we have more control over the animations, we are going to create a somewhat more complex but much more impressive animation: we are going to animate a scatter plot in Python. Let’s get to it!

How to animate Scatter Plot in Python

Once again, to create the animation of the scatter plot, first of all, create the graph for a single year. For this we are going to follow exactly the same cases as to create the barplot animation: first we filter the data and then we create the graph.

In this case, since there are many countries, I will color the countries based on the continent and, in addition, I will give them transparency:

Now that we have our graph mounted, now we must convert it into a function to animate it. In this case, it is essential that before each frame we clean the previous content of the graphic inside the object ax, since otherwise the animation will not look good.

Beyond that, the procedure to create the scatter plot animation is the same as the one previously explained to create other types of animations in Python:

We have our animated scatter plot! Now let’s go through the last of the animations that we are going to learn to create in Python: a barchart race animation.

How to create barplot race animations in Python

The barchart race animation is very similar to the barplot animation that we have done previously. The main difference lies in the fact that, in the barplot race, the data are ordered, in such a way that we can see how the top of X observations for a variable has evolved (it can be from stock market valuation to use of video games or, as in our case , the GDP per Capita of the countries).

So, to create our barplot race we will need to have, for each of the years, what is the ranking of the countries. For this, I recommend using the rank method of pandas , since we can obtain the rankings in a very simple way.

Once we have the ranking, we will simply have to filter the data to keep the number of observations that interest us, in my case 10.

Finally, once we have our data filtered, we will only have to create a horizontal bar chart where the vertical axis is the ranking. Also, to make the chart more understandable, we will change the name of the tick to the name of the country. We will do this with the tick_label parameter of the barh function.

So, let’s see what it would be like for a single case:

Perfect! We already have our base created. Now it only remains to create our animation function. In this case, I recommend that the ranking and selection of the countries be done within the update function itself, since it greatly facilitates understanding and allows you to take advantage of the code of our base graph.

So, the update function of our barplot race is the following:

Barplot race created! We have already seen how to create different types of animations in Python. However, they don’t seem like they are entirely visual, as they simply simply put one graphic on top of the other. So, let’s see how we can improve the fluidity of our animations in Python. Let’s get to it!

How to improve the fluency of Python animations

As I explained earlier in this post, the FuncAnimation function is limited to creating the animation by putting the images generated by our update function. As our images change from year to year, our animation will make little “jumps”.

So, for our animation to be much more fluid, we will have to create more data between each of the states we have. In this way we will achieve:

• Have intermediate data, so the animation jump will not be so great.
• Have many more frames, in such a way that for the same duration of the animation, it will have more fps (frames per second) making it look much smoother.

To create this objective, we are going to do the following:

• Create more observations between the data we already have. These observations will be empty.
• Imputing data to these new empty observations by interpolation between states.

It sounds complex, but it’s easier than it sounds. Let’s see how to do it:

Create more observations between the data we already have

Creating more observations than we already have between the current states is very simple. We simply have to start with an index that goes from 0 to the number of observations we have. We can achieve this with the reset_index method.

Once our data is like this, we can simply change the current index of the data by multiplying each index by the number of frames between states that we want to create. If we want to create 10 frames, multiplying the old index by 10, the second observation (index 1) will have the index 10 and in between, many empty variables will have been created.

In any case, for the interpolation to work well, we must have the data in the proper format, which is:

• Each row must be a state, a year in my case.
• Each column must be the observation that we are going to graph, in my case, a country.

So, this is what we should do:

As you can see, each column is a country and I have created 30 new observations among the states that I already had. Once this is done, we can see how to impute that new data through interpolation.

Imputing data to those new empty observations by interpolation between the states

To impute the empty data, we are going to use interpolation. This can be done with the interpolate method of pandas . There are different interpolation methods (you can find the method here ) and each one will give a different effect, as you can see in the following animation made by Nicholas A Rossi ( link ).

In our case we are going to make it easy, leaving the default values of the method, that is, applying a linear interpolation. Although it is the simplest, the change will be important, you just have to see the difference between using linear interpolation and not using interpolation in the animation.

So, we can interpolate our data as follows:

Finally, now that we have our data interpolated, we are going to change the shape of our dataframe so that it continues to maintain the shape it had before, that is, that both the year, the country, and the GDP per Capita are variable. We can achieve this with the pandas melt method.

If you notice, we have a dataframe exactly the same as the one we had when we did the animation of the barchart race previously, only now we have much more intermediate data. So, we simply have to replicate the code from before to get good results:

Improved animation! Now it looks much better, right? However, there is one thing that perhaps could be improved further: the intermediate states. And it is that, although the graphs are animated, the changes of position are still jumps. Let’s see how to create that horizontal movement of our animations in Python.

How to create horizontal movement in barchart race

The reason the positions continue to “jump” is that even though we have interpolated the data from the graph, we have not interpolated the data from the positions. So we can create an interpolation of the position data and join our previous dataset.

To do this, we will first have to keep the ranking of each country for each year. Once we have that dataframe, the process will be exactly the same as before: we pivot, interpolate and unpin the pivot with a melt.

Important: for this method to work we must use the same interpolation system that we have used previously.

Now that we have this information, we must join it with the information we had in the previous dataset:

As you can see, the rankings are not round numbers but they are slightly modified in each state. So now we can create the animation again. In this case, as we already have the calculated ranking, we will not need to recalculate it:

We already have our animation created in Python! As you can see, the interpolation has made our animation much more attractive and seem much more fluid than it was at the beginning. And all, with just a few lines of code! Isn’t it fantastic? But that’s not all, let’s see another little trick that will allow us to improve our animations in Python a lot.

Avoid jumps in animations

A typical problem in animations, as has happened in the animation of the scatter plot, is that there are jumps between frames. This is because the axes of the graph change, making the content of the graph appear different when, in fact, it is not.

Fixing this is pretty easy. Simply, for each frame, the maximum value of the X and Y axes must be set. This value will be the maximum that the graph will reach in the entire series. In this way, we will be able to avoid those jerks.

This is especially important to apply when animating graphics like the scatter plot. However, when animating the linechart, it is not recommended to apply it as it greatly reduces the visual impact of the animation.

So, we are going to redo the animation of the scatter plot, but this time applying greater fluidity through interpolation and avoiding jumps by setting the scales.

In this case, we will have to apply the interpolation to the three variables that are used in the animation. So, to facilitate the process, we will create a function that does the interpolation for us.

Now that we have the dataset, we can create the animation. To set the limits of the animation we simply have to use the set_xlim method.

Without a doubt creating animations in Python is something that will allow you to create highly visual graphics that generate much more impact. This is something basic that will allow you from generating much more powerful to be able to explain processes in a simpler way, as I did with the k-Mean algorithm in this post .

Also, if you are used to working with pandas and matplotlib and you understand the workings behind the animation functions, it is something very simple to do.

I hope you liked this post. If so, I encourage you to subscribe to keep up to date with all the posts that I am uploading. In any case, see you next time!

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