Shiny red button gif11/14/2022 ![]()
Select ‘Glass’ button now, hold the Alt key with Retangular marquee tool selected. HOLD your ctrl key and click on layer ‘Buttons”s layer thumbnail. Select Retangular marquee tool, make sure you are selecting ‘Button’ layer. ![]() Your button should look something like this Step 3 – Create glossy effect Right click on the Blending Options for ‘Button’ layer On layer ‘Button’, select the Rounded rectangle toolĭraw a rectangle similar to the image below. You might want to double check with the defaults too. Step 1 – Creating the baseįire up a new canvas and adjust the following settings (marked in yellow) according to the image below. #Shiny red button gif how to# mean of first normal Bm = 12.5 # mean of second normal fig, ax = plt.subplots(figsize=(9,6)) # empty fig camera = Camera(fig) for j in range(10): plt.ylim((0, 0.2)) # setting up the limits (or else it will auto ajust plt.xlim((-50, 50)) A = np.random.normal(Am, std, size=(1000)) # creating the 1000-sized normals B = np.random.normal(Bm, std, size=(1000)) A_plot = sns.distplot(A, color='red') B_plot = sns.distplot(B, color='blue') plt.legend(( 'Real Mean A: '.format(np.mean(B)) )) ax.text(0.5, 1.01, "Standard Deviation = "+str(std), transform=ax.transAxes) # making the dynamic title camera.snap() # camera snapshot std += 1 # incrementing the std anim = camera.animate() # animating the plots HTML(anim.Looking to design some glossy looking Web 2.0 button? Here’s a simple Photoshop tutorial that gives you step by step how to get a nice looking red glossy button. I had to pass the calculated label as a tuple because I used two curves, if it was only one I could’ve used just a like plt.legend(), which is simple from celluloid import Camera # getting the camera import matplotlib.pyplot as plt import numpy as np import seaborn as sns from IPython.display import HTML std = 3 # start std Am = 15. I put the current standard deviation on the title and the real mean as the label of each curve. In this example I plotted two normal distributions with distinct means but the same standard deviation and then I changed this standard deviation to evaluate the impact it has on each curve. This is one of the most interesting aspects of celluloid, here we have the capacity to make the plot very dynamic. Image by Author Using dynamic labels and titles #Shiny red button gif codeLet’s create a simple plot just to demonstrate the basic usage of how to run the code in a Jupyter notebook, but we could also use the method save(‘filename.gif_or_mp4’) from celluloid import Camera # getting the camera import matplotlib.pyplot as plt import numpy as np from IPython.display import HTML # to show the animation in Jupyter fig, ax = plt.subplots() # creating my fig camera = Camera(fig)# the camera gets the fig we'll plot for i in range(10): ax.plot( * 5, c='black') # 5 element array from 0 to 9 camera.snap() # the camera takes a snapshot of the plot animation = camera.animate() # animation ready HTML(animation.to_html5_video()) # displaying the animation #Shiny red button gif installLet’s start by installing the library with $ pip install celluloid #Shiny red button gif seriesUsing only 50 lines of code to deal with Matplotlib Artists and ArtistAnimations Celluloid creates an animation from the series of images you want to plot into the Camera abstraction. ![]() Even if this makes the coding part harder and more complex, the result generally is much more efficient in communicating my findings and process.īut in Python, there’s always an easier and simpler way and to simplify the animating process, Celluloid was born. Lately, I’ve been growing to use GIFs and quick videos. ![]() ![]() I really enjoy working with data visualization and I always wonder what’s the best way to provide more direct and intuitive visual interactions when I have to explain some result or complex model. How to create amazing animations in seconds using Celluloid ![]()
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