AWS Step Functions adds updates to ‘choice’ state

Developers can use AWS Step Functions to design and execute workflows that connect services such as AWS Lambda, AWS Fargate, and Amazon SageMaker into a rich application. A workflow consists of a series of steps, with the output of one step being the input to the next step. Application development becomes more intuitive using the AWS Step Functions, allowing developers to configure each applications with chain of functions such as a AWS Lambda function, or a function on the container that is developed stateless as a set of states.

Today, we are announcing enhancements of AWS Step Functions with updates to Amazon States Language (ASL). ASL is a JSON-based structured language that defines state machines and collections of states that can perform work (Task states), determines which state to transition to next (Choice state), and stops execution on error (Fail state). Today’s updates allow customers to write simplified workflow applications, increase flexibility within the state machine definition, reduce lambda calls, and reduce state transitions to save money.

If you access the AWS Step Functions management console, you’ll see new code snippets under the definition step.

Updates to Choice State

Choice State basically adds branch logic to the state machine. This update adds several new operators and provides additional selection that allow operators to simplify existing definitions or add dynamic behavior within state machine definitions.

Comparison Operator – supports a test for below values;
IsNull – null
IsString – string
IsNumeric – numeric
IsBoolean – boolean
IsTimestamp – timestamp
{
"Variable": "$.foo",
"IsNull|IsString|IsNumeric|IsBoolean|IsTimestamp": true|false
}
Existence Test – supports a test for the existence or non-existence of a particular field.
{
"Variable": "$.foo",
"IsPresent": true|false
}
Wildcarding – supports shell “glob” style wildcards, so customers can test for log-*.txt or LATEST.
{
"Variable": "$.foo",
"StringMatches": "log-*.txt"
}
Variable to Variable Comparison – allows for the comparison of an input field to another input field. Currently, the choice state allows for comparison to a fixed value.
{
"Variable": "$.foo",
"StringEqualsPath": "$.bar"
}

#application services #aws step functions #function

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AWS Step Functions adds updates to ‘choice’ state

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

John Smith

1657107416

Find the Best Restaurant Mobile App Development Company in Abu Dhbai

The era of mobile app development has completely changed the scenario for businesses in regions like Abu Dhabi. Restaurants and food delivery businesses are experiencing huge benefits via smart business applications. The invention and development of the food ordering app have helped all-scale businesses reach new customers and boost sales and profit.

As a result, many business owners are searching for the best restaurant mobile app development company in Abu Dhabi. If you are also searching for the same, this article is helpful for you. It will let you know the step-by-step process to hire the right team of restaurant mobile app developers.

Step-by-Step Process to Find the Best Restaurant App Development Company

Searching for the top mobile app development company in Abu Dhabi? Don't know the best way to search for professionals? Don't panic! Here is the step-by-step process to hire the best professionals.

#Step 1 – Know the Company's Culture

Knowing the organization's culture is very crucial before finalizing a food ordering app development company in Abu Dhabi. An organization's personality is shaped by its common beliefs, goals, practices, or company culture. So, digging into the company culture reveals the core beliefs of the organization, its objectives, and its development team.

Now, you might be wondering, how will you identify the company's culture? Well, you can take reference from the following sources –

Social media posts
App development process
About us Page
Client testimonials

#Step 2 - Refer to Clients' Reviews

Another best way to choose the On-demand app development firm for your restaurant business is to refer to the clients' reviews. Reviews are frequently available on the organization's website with a tag of "Reviews" or "Testimonials." It's important to read the reviews as they will help you determine how happy customers are with the company's app development process.

You can also assess a company's abilities through reviews and customer testimonials. They can let you know if the mobile app developers create a valuable app or not.

#Step 3 – Analyze the App Development Process

Regardless of the company's size or scope, adhering to the restaurant delivery app development process will ensure the success of your business application. Knowing the processes an app developer follows in designing and producing a top-notch app will help you know the working process. Organizations follow different app development approaches, so getting well-versed in the process is essential before finalizing any mobile app development company.

#Step 4 – Consider Previous Experience

Besides considering other factors, considering the previous experience of the developers is a must. You can obtain a broad sense of the developer's capacity to assist you in creating a unique mobile application for a restaurant business.

You can also find out if the developers' have contributed to the creation of other successful applications or not. It will help you know the working capacity of a particular developer or organization. Prior experience is essential to evaluating their work. For instance, whether they haven't previously produced an app similar to yours or not.

#Step 5 – Check for Their Technical Support

As you expect a working and successful restaurant mobile app for your business, checking on this factor is a must. A well-established organization is nothing without a good technical support team. So, ensure whatever restaurant mobile app development company you choose they must be well-equipped with a team of dedicated developers, designers, and testers.

Strong tech support from your mobile app developers will help you identify new bugs and fix them bugs on time. All this will ensure the application's success.

#Step 6 – Analyze Design Standards

Besides focusing on an organization's development, testing, and technical support, you should check the design standards. An appealing design is crucial in attracting new users and keeping the existing ones stick to your services. So, spend some time analyzing the design standards of an organization. Now, you might be wondering, how will you do it? Simple! By looking at the organization's portfolio.

Whether hiring an iPhone app development company or any other, these steps apply to all. So, don't miss these steps.

#Step 7 – Know Their Location

Finally, the last yet very crucial factor that will not only help you finalize the right person for your restaurant mobile app development but will also decide the mobile app development cost. So, you have to choose the location of the developers wisely, as it is a crucial factor in defining the cost.

Summing Up!!!

Restaurant mobile applications have taken the food industry to heights none have ever considered. As a result, the demand for restaurant mobile app development companies has risen greatly, which is why businesses find it difficult to finalize the right person. But, we hope that after referring to this article, it will now be easier to hire dedicated developers under the desired budget. So, begin the hiring process now and get a well-craft food ordering app in hand.

Noah Rowe

1597388160

AWS Step Functions adds updates to ‘choice’ state

Developers can use AWS Step Functions to design and execute workflows that connect services such as AWS Lambda, AWS Fargate, and Amazon SageMaker into a rich application. A workflow consists of a series of steps, with the output of one step being the input to the next step. Application development becomes more intuitive using the AWS Step Functions, allowing developers to configure each applications with chain of functions such as a AWS Lambda function, or a function on the container that is developed stateless as a set of states.

Today, we are announcing enhancements of AWS Step Functions with updates to Amazon States Language (ASL). ASL is a JSON-based structured language that defines state machines and collections of states that can perform work (Task states), determines which state to transition to next (Choice state), and stops execution on error (Fail state). Today’s updates allow customers to write simplified workflow applications, increase flexibility within the state machine definition, reduce lambda calls, and reduce state transitions to save money.

If you access the AWS Step Functions management console, you’ll see new code snippets under the definition step.

Updates to Choice State

Choice State basically adds branch logic to the state machine. This update adds several new operators and provides additional selection that allow operators to simplify existing definitions or add dynamic behavior within state machine definitions.

Comparison Operator – supports a test for below values;
IsNull – null
IsString – string
IsNumeric – numeric
IsBoolean – boolean
IsTimestamp – timestamp
{
"Variable": "$.foo",
"IsNull|IsString|IsNumeric|IsBoolean|IsTimestamp": true|false
}
Existence Test – supports a test for the existence or non-existence of a particular field.
{
"Variable": "$.foo",
"IsPresent": true|false
}
Wildcarding – supports shell “glob” style wildcards, so customers can test for log-*.txt or LATEST.
{
"Variable": "$.foo",
"StringMatches": "log-*.txt"
}
Variable to Variable Comparison – allows for the comparison of an input field to another input field. Currently, the choice state allows for comparison to a fixed value.
{
"Variable": "$.foo",
"StringEqualsPath": "$.bar"
}

#application services #aws step functions #function

Brad Hintz

1594446180

The “finally” block in AWS Step Functions state machine

If you’re not familiar with AWS Step Functions, it is a service where we can create state-machines to orchestrate asynchronous tasks in workflows without worrying too much about any underlying infrastructure and operations. I’ve been using it a lot recently, and I’d like to share the way I implement a finally block in a Step Functions state machine.

In a workflow, there could be some operations that need to be executed no matter what happens, for example, cleaning up temporary resources that are created at the beginning of the workflow. If we relate to Java language, it is similar to what we do with finally block.

try {
  // Do something
} catch (SomeException ex) {
  // Handle exception
} finally { 
  // The things that must happen no matter what
}

In AWS Step Functions, the default behavior when a state reports an exception is to fail the execution entirely, which means the rest of the state in the state machine will not be executed. Step Functions provides the feature to catch exceptions (please refer to Error Handling in Step Functions), but there is no explicit feature for the finally block.

I figured that we could use Catch field with the wildcard exception name States.ALL to implement the finally block. Let’s start with a simple example:

{
  "Comment": "An simple example",
  "StartAt": "DoSomething",
  "States": {
    "DoSomething": {
      "Type": "Task",
      "Resource": "arn:aws:states:${region}:${account}:activity:DoSomethingActivity",
      "Next": "Cleanup"
    },
    "Cleanup": {
      "Type": "Task",
      "Resource": "arn:aws:states:${region}:${account}:activity:CleanupActivity",
      "End": true
    }
  }
}

This is a state machine that has only one step then clean up, and we don’t have any error handling in it yet.

Image for post

State machine diagram

#aws-step-functions #state-machine #error-handling #programming #aws

Rory West

1619214480

ETL Orchestration on AWS with AWS Step Functions

In the latest years, the engineering, governance, and analysis of data has become a very common talking point.

The need for data-driven decision-making, in fact, has grown the need of collecting and analyzing data in many ways and AWS has shown a particular interest in this field developing multiple tools for achieving these business goals.

Before being able to allow the figure of the data analyst to explore and visualize the data, a crucial step is needed. This procedure is commonly identified as ETL (extract, transform, and load) and, usually, it’s far from being simple.

#aws-step-functions #aws #etl #aws-lambda