With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports was published on SAS Users.

For beginners, how marketers can learn to create advanced data models from time series data

Using R programming, marketers can create a marketing mix model to determine how sustainable their audience channels are, and make better ad spend decisions. Here's how

Here's a new angle on GDPR, likely one of many angles people haven't yet considered: Does web session recording count as collecting PII?

Advanced analytics programs can incorporate geospatial data. Learn how such data can be used to augment local marketing plans

The marketing industry has never had greater access to data than it does today. However, data alone does not drive your marketing organization. Decisions do. And with all the recent hype regarding the potential of AI, a successful cross-channel campaign is propelled by a personalized, data-driven approach injected with machine [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 1] was published on Customer Intelligence Blog.

In part one of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. In part two, we will share perspectives on: How SAS Customer Intelligence 360 completes analytic's last mile. How campaign management processes can easily [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 2] was published on Customer Intelligence Blog.

In parts one and two of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. According to Daniel Newman (Futurum Research) and Wilson Raj (SAS) in the October 2019 research study Experience 2030: “Brands must [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 3] was published on Customer Intelligence Blog.

Digital transformation. Yup, I said it. It's over-hyped. But as SAS Chief Operating Officer and Chief Technology Officer Oliver Schabenberger says, "It's also real and powerful. Our world is being liquefied from physical assets into virtual assets, and analog processes into digital processes - the world is turning into bits [...]

SAS Customer Intelligence 360: Analytics as a guiding light was published on Customer Intelligence Blog.

In February, SAS was recognized as a Leader in the 2020 Gartner Magic Quadrant for Data Science & Machine Learning Platforms report. SAS is the only vendor to be a leader in this report for all seven years of its existence. According to us, the topic of the research is [...]

SAS Customer Intelligence 360: Analyst viewpoints was published on Customer Intelligence Blog.

Creative government workforce approaches come in many forms. Some time ago, the US Environmental Protection Agency (EPA) offered employees an interesting choice to incentivize them to stay. Employees could either telework from home one day per week or receive $1,000. As Jon Lemon, principal industry consultant at SAS told the [...]

The post How analytics can shape the government workforce appeared first on Government Data Connection.

January serves as National Slavery and Human Trafficking Awareness Month, culminating in the annual observation of National Freedom Day on February 1. For many, this month presents an opportunity to refocus efforts to slow, and ultimately stop, human trafficking around the globe, but especially here in the United States. The [...]

The post Analytics can help combat the horrors of human trafficking appeared first on Government Data Connection.

lawyers who has experience in Civil Practice 5-6yrs.We are looking for lawyers who has experience in Civil Practice 5-6yrs. Please share your detailed profile at email id - sk.h.analysis@gmail.com.

lawyers who has experience in Civil Practice 5-6yrs.We are looking for lawyers who has experience in Civil Practice 5-6yrs. Please share your detailed profile at email id - sk.h.analysis@gmail.com

  Mobile applications can behave differently between emulator and device and, as an app grows more and more complex, debugging performance bottlenecks can become extremely difficult. The GPA System Anal...

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There is a decreasing trend among the Indian students reaching the US to seek education over the years, as per a report. As per the Open Doors Report focusing on International Educational Exchange, the yearly growth of students from India, to the US…

Proper data prep means faster, better analytics. Guest blogger Jenine Milum shares tips.

The post 6 tips to prepare data for analytics appeared first on The Data Roundtable.

A business glossary improves data quality – one of the top five ways it makes analytics better.

The post Top 5 ways business glossaries make analytics better appeared first on The Data Roundtable.

Jeff Stander helps us understand the different options of preparing data for analytics.

The post 3 ways to prepare data for analytics: How to choose the best option appeared first on The Data Roundtable.

Jim Harris shows how data, analytics and humans work together to form the "insight equation."

The post Data, analytics and humans: The insight equation appeared first on The Data Roundtable.

The less distance from Kepler 1649 c's host red-dwarf star, scientists believe, puts the exoplanet in the habitable zone where liquid water could exist on the world's surface.

GUARDRAIL REPAIR, CANAL DISTRICT, OPEN END, FY20-FY22

An analysis of Covid-19 death cases by the health ministry also shows that 30% of the dead were in the 40-60 years age group, and just 7% were under 40 years.

Company faring better than peers on operational front due to its focus on exports; lower costs to aid margin; ‘Buy’ maintained.

ICMR, at present, is only using Watson for backend reporting, but it also needs to deploy it for data analysis and forecasting trends.

Government rejects Moody’s Analytics Comments on India

CSR In India 2019 is contributed by SUBAH, an enabler of CSR in India

Hi,

I was wondering if it is possible to save the coordinates of each stripe and row of the power grid 

and if it is possible to find out the effective resistance between two given points using Voltus

My goal is to built a resistance model of the power grid

Thanks

I have been using Voltus to do IR drop analysis but I got caught on one signal. It is negative. When I use:

set_pg_nets -net negsupply -voltage -5 -threshold -4.5 -package_net_name NEGSUP -force

Voltus dies with a backtrace. Looking at the beginning of the trace you see it suggests that the problem is it set maximum to -5 and minimum to 0. Is there another way to express a negative voltage supply for IR drop analysis?

The Triple Beat analysis is similar to Rapid IP2/IP3 analysis except that it uses three tones instead of two. It is used in cases where two closely-spaced small-signal inputs from a transmitter leak in to the receiver along with an intended small-signal RF input signal. (read more)

Have you ever manufactured a printed circuit board (PCB) without analyzing all the routed signal traces? Most designers will say “yes, all the time.” Trace widths and spacing are set by constraints,...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

In the former blog about Python and Specman: Specman: Python Is here!, we described the technical information around Specman-Python integration. Since Python provides so many easy to use existing libraries in various fields, it is very tempting to leverage these cool Python apps.

Coverage has always been the center of the verification methodology, however in the last few years it gets even more focus as people develop advanced utilities, usually using Machine Learning aids. Anyhow, any attempt to leverage your coverage usually starts with some analysis of the behavior and trends of some typical tests. Visualizing the data makes it easier to understand, analyze, and communicate. Fortunately, Python has many Visualization libraries.

In this blog, we show an example of how you can use the plotting Python library (matplotlib) to easily display coverage information during a run. In this blog, we use the Specman Coverage API to extract coverage data, and a Python module to display coverage grades interactively during a single run and the way to connect both.

Before we look at the example, if you have read the former blog about Specman and Python and were concerned about the fact that python3 is not supported, we are glad to update that in Specman 19.09, Python3 is now supported (in addition to Python2).

The Testcase
Let’s say I have a stable verification environment and I want to make it more efficient. For example: I want to check whether I can make the tests shorter while hardly harming the coverage. I am not sure exactly how to attack this task, so a good place to start is to visually analyze the behavior of the coverage on some typical test I chose. The first thing we need to do is to extract the coverage information of the interesting entities. This can be done using the old Coverage API. 

Coverage API
Coverage API is a simple interface to extract coverage information at a certain point. It is implemented through a predefined struct type named user_cover_struct. To use it, you need to do the following:

1. Define a child of user_cover_structusing like inheritance (my_cover_struct below).
2. Extend its relevant methods (in our example we extend only the end_group() method) and access the relevant members (you can read about the other available methods and members in cdnshelp).
3. Create an instance of the user_cover_structchild and call the predefined scan_cover() method whenever you want to query the data (even in every cycle). Calling this method will result in calling the methods you extended in step 2.  

 The code example below demonstrates these three steps. We chose to extend the end_group() method and we keep the group grade in some local variable. Note that we divide it by 100,000,000 to get a number between 0 to 1 since the grade in this API is an integer from 0 to 100,000,000. 

Pass the Data to a Python Module
After we have extracted the group grade, we need to pass the grade along with the cycle and the coverage group name (assuming there are a few) to a Python module. We will take a look at the Python module itself later. For now, we will first take a look at how to pass the information from the e code to Python. Note that in addition to passing the grade at certain points (addVal method), we need an initialization method (init_plot) with the number of cycles, so that the X axis can be drawn at the beginning, and end_plot() to mark interesting points on the plot at the end. But to begin with, let’s have empty methods on the Python side and make sure we can just call them from the e code.

 # plot_i.py
def init_plot(numCycles):
    print (numCycles)
def addVal(groupName,cycle,grade):
    print (groupName,cycle,grade)
def end_plot():
    print ("end_plot") 

And add the calls from e code:

 Before running this, note that you need to ensure that Specman finds the Python include and lib directories, and Python finds our Python module. To do this, you need to define a few environment variables: SPECMAN_PYTHON_INCLUDE_DIR, SPECMAN_PYTHON_LIB_DIR, and PYTHONPATH. 

 The Python Module to Draw the Plot
After we extracted the coverage information and ensured that we can pass it to a Python module, we need to display this data in the Python module. There are many code examples out there for drawing a graph with Python, especially with matplotlib. You can either accumulate the data and draw a graph at the end of the run or draw a graph interactively during the run itself- which is very useful especially for long runs.

Below is a code that draws the coverage grade of multiple groups interactively during the run and at the end of the run it prints circles around the maximum point and adds some text to it. I am new to Python so there might be better or simpler ways to do so, but it does the work. The cool thing is that there are so many examples to rely on that you can produce this kind of code very fast.

 In the example we used, we had two interesting entities: packet and state_machine, thus we had two equivalent coverage groups. When running our example connecting to the Python module, we get the following graph which is displayed interactively during the run.

When analyzing this specific example, we can see two things. First, packet gets to a high coverage quite fast and significant part of the run does not contribute to its coverage. On the other hand, something interesting happens relating to state_machine around cycle 700 which suddenly boosts its coverage. The next step would be to try to dump graphic information relating to other entities and see if something noticeable happens around cycle 700.

To run a complete example, you can download the files from: https://github.com/okirsh/Specman-Python/

Do you feel like analyzing the coverage behavior in your environment? We will be happy to hear about your outcomes and other usages of the Python interface.

Orit Kirshenberg
Specman team

All PCB designers know the importance of proper power delivery for successful board design. Integrated circuits need the power to turn on, and ICs with marginal power delivery will not operate reliably. Since power planes can...(read more)

Have you ever manufactured a printed circuit board (PCB) without analyzing all the routed signal traces? Most designers will say “yes, all the time.” Trace widths and spacing are set by constraints, and many designers simply don’t h...(read more)

Analog and Mixed-signal (AMS) designs are increasingly using active power management to minimize power consumption. Typical mixed-signal design uses several power domains and operate in a dozen or more power modes including multiple functional, standby and test modes. To save power, parts of design not active in a mode are shut down or may operate at reduced supply voltage when high performance is not required. These and other low power techniques are applied on both analog and digital parts of the design. Digital designers capture power intent in standard formats like Common Power Format (CPF), IEEE1801 (aka Unified Power Format or UPF) or Liberty and apply it top-down throughout design, verification and implementation flows. Analog parts are often designed bottom-up in schematic without upfront defined power intent. Verifying that low power intent is implemented correctly in mixed-signal design is very challenging. If not discovered early, errors like wrongly connected power nets, missing level shifters or isolations cells can cause costly rework or even silicon re-spin. 

Mixed-signal designers rely on simulation for functional verification. Although still necessary for electrical and performance verification, running simulation on so many power modes is not an effective verification method to discover low power errors. It would be nice to augment simulation with formal low power verification but a specification of power intent for analog/mixed-signal blocs is missing. So how do we obtain it? Can we “extract” it from already built analog circuit? Fortunately, yes we can, and we will describe an automated way to do so!

Virtuoso Power Manager is new tool released in the Virtuoso IC6.1.8 platform which is capable of managing power intent in an Analog/MS design which is captured in Virtuoso Schematic Editor. In setup phase, the user identifies power and ground nets and registers special devices like level shifters and isolation cells. The user has the option to import power intent into IEEE1801 format, applicable for top level or any of the blocks in design. Virtuoso Power Manager uses this information to traverse the schematic and extract complete power intent for the entire design. In the final stage, Virtuoso Power Manager exports the power intent in IEEE1801 format as an input to the formal verification tool (Cadence Conformal-LP) for static verification of power intent.

Cadence and Infineon have been collaborating on the requirements and validation of the Virtuoso Power Manager tool and Low Power verification solution on real designs. A summary of collaboration results were presented at the DVCon conference in Munich, in October of 2018.  Please look for the paper in the conference proceedings for more details. Alternately, can view our Cadence webinar on Verifying Low-Power Intent in Mixed-Signal Design Using Formal Method for more information.

Here is another blog in the multi-part series that aims at providing in-depth details of electromagnetic analysis in the Virtuoso RF solution. Read to learn about the nuances of port setup for electromagnetic analysis.(read more)

visit News18 Urdu for latest news, breaking news, news headlines and updates from Dhenkanal on politics, sports, entertainment, cricket, crime and more.

The Global TV Android and iOS applications send potentially sensitive information such as device model and resolution, mobile carrier, days since first use, days since last use, total number of app launches, number of app launches since upgrade, and previous app session length, unencrypted to both first (CNAME to third) and third party sites (Adobe Experience Cloud, ScorecardResearch). Global TV Android versions 2.3.2 and below and iOS versions 4.7.5 and below are affected.

The Citytv Video Android and iOS applications send potentially sensitive information such as device model and resolution, mobile carrier, days since first use, days since last use, total number of app launches, number of app launches since upgrade, and previous app session length, unencrypted to third party sites (Adobe Experience Cloud, ScorecardResearch). Citytv Video Android versions 4.08.0 and below and iOS versions 3.36 and below are affected.

ManageEngine EventLog Analyzer version 10.0 suffers from an information disclosure vulnerability.

Malware Analysis Part I - This guide is the first part of a series of three where we begin with setting up the very foundation of a analysis environment; the analysis station. It will give the reader a quick recap in the different phases of malware analysis along with a few examples. It will then guide the reader in how to build an analysis station optimized for these phases. Along with this, the guide also introduces a workflow that will give the reader a good kick-start in performing malware analysis on a professional basis, not only on a technical level.

This article explains the technical procedure and challenges involved in extracting data and artifacts from iPhone backups.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers.