Ethan Hughes

Ethan Hughes

1592467200

Vue Pixel Art is an easy way to draw your Pixel Arts

Vue Pixel Art

Vue Pixel Art is an easy way to draw your Pixel Arts and get the CSS code generated from it.

This project is refactored from CSS Collection - DotGen

Credits: @bc_rikko

Instructions

  • Click on a square to paint. If it is already painted, that square will be transparent again.
  • Hold mouse down and drag your mouse everywhere in the grid to paint.
  • The button “Paint/Erase activated” only works when you use mouse down and drag.
  • Size can’t be greater than 100.

Usage

Recommended resolution: >= 925 x 768.

Go to https://vue-pixel-art.now.sh and have fun!

Development

This project follow the Puzzle Pattern guidelines and uses Standard Code Style.

Fork the project and enter this commands in your terminal:

git clone https://github.com/YOUR_GITHUB_USERNAME/vue-pixel-art.git
cd vue-pixel-art
npm install
npm run serve

The default port is 8080.

TODO

  • [ ] Add Unit tests with Jest.
  • [ ] Add Redo/Undo.
  • [x] Add E2E tests with Cypress.
  • [x] Add mouse drag and paint.
  • [x] Add Vuejs pixel logo on README and favicon.ico.
  • [x] Deploy.

Download Details:

Author: guastallaigor

Live Demo: https://vue-pixel-art.now.sh/

GitHub: https://github.com/guastallaigor/vue-pixel-art

#vuejs #javascript #vue #vue-js

What is GEEK

Buddha Community

Vue Pixel Art is an easy way to draw your Pixel Arts
Luna  Mosciski

Luna Mosciski

1600583123

8 Popular Websites That Use The Vue.JS Framework

In this article, we are going to list out the most popular websites using Vue JS as their frontend framework.

Vue JS is one of those elite progressive JavaScript frameworks that has huge demand in the web development industry. Many popular websites are developed using Vue in their frontend development because of its imperative features.

This framework was created by Evan You and still it is maintained by his private team members. Vue is of course an open-source framework which is based on MVVM concept (Model-view view-Model) and used extensively in building sublime user-interfaces and also considered a prime choice for developing single-page heavy applications.

Released in February 2014, Vue JS has gained 64,828 stars on Github, making it very popular in recent times.

Evan used Angular JS on many operations while working for Google and integrated many features in Vue to cover the flaws of Angular.

“I figured, what if I could just extract the part that I really liked about Angular and build something really lightweight." - Evan You

#vuejs #vue #vue-with-laravel #vue-top-story #vue-3 #build-vue-frontend #vue-in-laravel #vue.js

Ethan Hughes

Ethan Hughes

1592467200

Vue Pixel Art is an easy way to draw your Pixel Arts

Vue Pixel Art

Vue Pixel Art is an easy way to draw your Pixel Arts and get the CSS code generated from it.

This project is refactored from CSS Collection - DotGen

Credits: @bc_rikko

Instructions

  • Click on a square to paint. If it is already painted, that square will be transparent again.
  • Hold mouse down and drag your mouse everywhere in the grid to paint.
  • The button “Paint/Erase activated” only works when you use mouse down and drag.
  • Size can’t be greater than 100.

Usage

Recommended resolution: >= 925 x 768.

Go to https://vue-pixel-art.now.sh and have fun!

Development

This project follow the Puzzle Pattern guidelines and uses Standard Code Style.

Fork the project and enter this commands in your terminal:

git clone https://github.com/YOUR_GITHUB_USERNAME/vue-pixel-art.git
cd vue-pixel-art
npm install
npm run serve

The default port is 8080.

TODO

  • [ ] Add Unit tests with Jest.
  • [ ] Add Redo/Undo.
  • [x] Add E2E tests with Cypress.
  • [x] Add mouse drag and paint.
  • [x] Add Vuejs pixel logo on README and favicon.ico.
  • [x] Deploy.

Download Details:

Author: guastallaigor

Live Demo: https://vue-pixel-art.now.sh/

GitHub: https://github.com/guastallaigor/vue-pixel-art

#vuejs #javascript #vue #vue-js

Dylan  Iqbal

Dylan Iqbal

1561523460

Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

Python Matplotlib cheat sheet

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.

Matplotlib 

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt

Figure 

>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))

Axes 

>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot

>>> plt.show()

Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
      vmin=-2,
      vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 

 y-axis      

                           x-axis 

Workflow 

The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          [5,15,25],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )

Markers 

>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")

Linestyles 

>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           -2.1, 
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 
connectionstyle="arc3"),)

Mathtext 

>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis

Legends 

>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements

Ticks 

>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 
              length=10)

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
             hspace=0.3,
             left=0.125,
             right=0.9,
             top=0.9,
             bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#matplotlib #cheatsheet #python

Edna  Bernhard

Edna Bernhard

1598573522

ART + AI

The idea that AI can infiltrate the field of art is frightening and rightfully so. While it has been no secret that AI can definitely replace blue-collar jobs and possibly threaten white-collar jobs, the idea that it can impact the livelihood of artists isn’t one that the media has foretold, nor have dystopian movies explored. However, we can see early traces of AI in art. It has slowly seeped into written literature, journalism, paintings and even music.

Having said that, this isn’t a novel (😉) idea. Sometime in the 90s, a music theory professor trained a program to write Bach-styled compositions. Then, to his students, he played both the real and computer-generated versions. To them, both were indistinguishable. Since then, technology has rapidly improved to a state that AI can create music of its own.

#ai #art #artificial-intelligence #art-and-ai #is-ai-art-really-art #is-art-unique-to-humans #creativity #future

Teresa  Bosco

Teresa Bosco

1598685221

Vue File Upload Using vue-dropzone Tutorial

In this tutorial, I will show you how to upload a file in Vue using vue-dropzone library. For this example, I am using Vue.js 3.0. First, we will install the Vue.js using Vue CLI, and then we install the vue-dropzone library. Then configure it, and we are ready to accept the file. DropzoneJS is an open source library that provides drag and drops file uploads with image previews. DropzoneJS is lightweight doesn’t depend on any other library (like jQuery) and is  highly customizable. The  vue-dropzone is a vue component implemented on top of Dropzone.js. Let us start Vue File Upload Using vue-dropzone Tutorial.

Vue File Upload Using vue-dropzone

First, install the Vue using Vue CLI.

#vue #vue-dropzone #vue.js #dropzone.js #dropzonejs #vue cli