AWS Lambda vs EC2- Comparison of AWS Compute Resources
Compare two of the widely used compute resources in software development: AWS Lambda vs EC2. Understand and analyze infrastructure management, performance and cost comparison. Get to know the evolution of AWS computing resources from EC2 to AWS Lambda and their potential use cases.
Cloud Foundry Foundation, a non-profit organisation that overlooks the open-source cloud computing projects, conducted a global survey recently consisting of 550 users. The survey results made it evident that 31% which is 169 of them are already using serverless architecture.
When further asked which platform they are using, 77% said AWS Lambda. FaaS services like AWS Lambda are becoming popular since they enable an organisation to build agile and scalable applications than server-based applications, for example, EC2.
EC2 requires management and provisioning of the environment. Each EC2 instances runs not just a full copy of an operating system, but a virtual copy of all the hardware that the operating system needs to run. In contrast, what AWS Lambda requires is enough system resources and dependencies to run a specific program.
Also, AWS Lambda enables you to create portable code blocks for easy development, testing and deployment. That’s a winning trifecta!
If that’s all there was to AWS Lambda vs EC2 then I’d be writing a death note for EC2. But, there’s a lot more to it than just serverless-less/full. Let’s discover.
- What is AWS Lambda?
- What is EC2?
- An evolution: From EC2 to AWS Lambda
- AWS Lambda vs EC2: Infrastructure Management
- AWS Lambda vs EC2: Performance Comparison
- AWS Lambda vs EC2: Cost Comparison
- Use cases: When to use AWS Lambda?
- Use cases: When to use EC2?
What is AWS Lambda?
AWS Lambda is an on-demand cloud computing resource offered in terms of function-as-a-service by AWS. Over time, AWS Lambda has changed the way we used to create, architect and run our applications.
The main difference between AWS Lambda vs EC2 (virtual server-based resources) is the responsibility of provisioning and use cases to name a few. AWS Lambda pricing is one of the biggest factors as well.
Before the emergence of agile solutions like AWS Lambda, operations teams had to allocate the resources based on the forecasting. They had to make sure that the computer and memory demands don’t overflow the limits their system can handle.
With the computing resources like AWS Lambda, the computing resources can scale and descends automatically based on real-time demands. Presently, AWS Lambda supports multiple languages and can be used within an application in multiple manner or back-end as a service.
The architecture of applications built using functions like AWS Lambda is popularly called serverless architecture. AWS Lambda is a splendid example of how the overhead of the operation team is going to be a distant memory.
What is EC2?
Amazon Elastic Compute Cloud (EC2) is a virtual cloud infrastructure service offered by AWS. This provides on-demand computing resources through which you can create powerful servers in the cloud.
The entire hardware of EC2 is fragmented into multiple resources which are offered in the form of instances which are scalable in terms of computing memory and processing power.
It also provides you with the flexible options to host your application onto more than one platforms with tight security for multi-model multi-tenant architecture. These instances can be accessed by the HTTP or HTTPS (API) which lets developers create applications just like an on-premise infrastructure.
With Amazon EC2, you have the facility of provisioning virtual machines as per your applications’ requirements. Such facility is provided on a utility based subscription model where the user is billed as per their consumption of resources.
An Evolution: From EC2 to AWS Lambda
With the onset of EC2 as IaaS, AWS wanted to remove the overhead of managing the infrastructure. With the EC2, the time to allocate a server reduced to a fraction along with features like automatic scaling, scheduled provisioning and monitoring & alerting system powered by CloudWatch.
When EC2 was launched, it prevailed with the far more volatile environment than what it is now. Some of the initial issues were the sudden outage, multi-tenant model per machine, failure in scheduled provisioning and disappearance of virtual machines at all. Some of the popular sites that have been affected due to this are Reddit, Foursquare, Rapportive and Heroku.
Then came Elastic Beanstalk (EB), which provided all these facilities in a pretty package. EB is available to work with multiple languages and frameworks. With EB, developers can easily upload their code to the virtual machine itself through the AWS console.
Apart from that, EB directly spins up EC2 instances for you while automatically balancing the load and giving you the direct endpoint to use at the end. Even with this, developers can still log into the AWS console and manually configure or tweak the allocation of instances.
With the launch of IaaS, infrastructure management has been removed. It has benefited tremendously to the number of organisations. However, many of the promised advantages have not been fulfilled yet. For example, provisioning and capacity planning is still a major issue based on the manual forecast. To get ahead of this, FaaS was launched.
Amazon released AWS Lambda which is available to work with multiple languages and frameworks. Like Elastic Beanstalk, you can upload your code packages directly to your functions. Unlike Elastic Beanstalk, the infrastructure is not available entirely for the development teams to tweak or manage.
However, the scaling is fully automatic as per the requirements. As said, Lambdas use ECS and these containers are not available to configure manually. On the other hand, Lambdas are exposed through API Gateway which functions as a URL router to your Lambdas.
AWS Lambda vs EC2: Infrastructure Management
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Setup & Management Environment
AWS Lambda: Whether you need to set up a multiple or single environment, you do not need to do much of a work. You are not required to spin up or provision containers or make them available for your applications, scaling is fully automated.
AWS Lambda might not appeal to someone already working with an on-demand development environment with containers and orchestration in place.
Amazon EC2: With EC2, setting up includes logging in via SSH and manually installing Apache and doing a git clone. Along with that, you need to install and configure all the required software in a manner which is automated and reproducible.
For EC2, instances come in two options, first are standards ones which serve data roughly the same as our desktop hard drive and second, advanced provisioning which will serve data much faster. Comparatively, this is a lot of work.
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Security
AWS Lambda: Serverless architecture abstracts away the patching and OS update manual work. With Lambda, you have the higher flexibility of workflows but at the same time, it increases the surface attack. What you need to consider is how to secure communication happening inside and outside your application.
Another thing is that the granularity of functions is really high. With an increasing number of functions, monitoring becomes hard which in turn is a threat to decaying functions.
However, vulnerability breaches are less likely to happen considering the stateless characteristic of the functions. Malicious agents grow over the time which isn’t possible considering the statelessness. More to that, functions are automatically scalable which gives good protection against DDOS attacks. The protection is facilitated by auto-scaling functionality but it also increases your bill.
Amazon EC2: With EC2, you’ll have to take care of the security layer at the instance level. Security layer decides and controls the traffic allowed to communicate with each instance. Each instance can have multiple security layers which dictates the allowed inbound traffic through certain protocols like TCP, UDP, ICMP, etc.
Creating policies which will have correct permissions is tiresome and a work of trial and error. This is especially true when you’re working with a growing team. Handling permissions for every specific business needs mean changing policies often and you end up increasing the unwanted granularity.
Meanwhile, with Lambda, this is entirely taken care of by AWS along with OS patches and system maintenance.
If we consider DDOS attacks, you’ll either have to opt for other services of AWS Shield or you can do it manually by using ELB to scale under the attack or limiting the rate of requests/minute from a particular id. EC2 definitely have security groups and firewalls but unfortunately, those are not enough to monitor resource-based traffic monitoring.
AWS Shield helps in automatically detect the type of AWS resource behind the elastic IP address and apply the relevant DDOS protections.
AWS Lambda vs EC2: Performance Comparison
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Timeout
AWS Lambda: As per the official documentation, AWS Lambda has the timeout of 300 seconds. This limits the type of tasks lambda can deal with. Long-running functions and complex tasks aren’t a good fit.
Another problem is the time limit imposed by the API Gateway for the function to invoke which presently is 30 seconds. Even if you manage to architect a solution which handles timeout sufficiently, you’ll have to constantly monitor and debug the issues when they inevitable happens.
Not all timeouts occur due to the 300 seconds limit. Some occur due to the bugs introduced or when you’re dealing with communication over external servers which takes too much time or answers the functions with the wrong data.
Amazon EC2: In comparison to AWS Lambda vs EC2, the later one have pretty flexible options. You can definitely work with long running tasks since instances are available for different types of requirements with different configurations. This makes EC2 a better option over Lambda.
However, EC2 service is not error free. Connection timeout is an issue due to overlapping security group rules and unidentified user key by the server. Another common error happens when you’re dealing with an insecure network over SSH.
This is especially applicable for newly created instances. For example, if you will try to SSH your new instance without waiting for its status checks to get complete, you will get an SSH timeout error. However, Lambda clearly lags while dealing with complex processing over EC2.
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Dependencies
AWS Lambda: External libraries are inevitable for any project and it is true for AWS Lambda too. When you’re dealing with heavy processing functions like image processing, video conversion, you’re bound to have dependencies for the application.
However, Lambda comes with a limit. Uploading your function dependencies with the package itself seems to be the logical solution. But Lambda has a package size limit of 50 MB. Although, it gives you the option of downloading the dependencies once your function is executed from its “/tmp” file storage.
More to that, “/tmp” file storage has a limit of 512 MB. Also, the higher the number of dependencies, more time will be required to execute the function successfully.
Amazon EC2: Managing dependencies in EC2 isn’t a big problem since it doesn’t have constraints when it comes to temporary storage. Though what you should consider is the size of software packages and corresponding instance CPU. This is because your CPU may undergo burden if it’s not configured for the same.
For example, the base of Amazon Linux Containers is already preloaded with many software packages that are required for executing the basic functionalities.
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Scalability
AWS Lambda: Scalability is one of the major benefits of Lambda as most of the part is automatic and is handles by AWS. It scales dynamically in response to the increased traffic. At the time of the surge, it increases the number of concurrent executing functions. The number of concurrent limits is set to 500 as of now.
But scaling isn’t as flawless as it is portrayed. In practical cases, we’ve faced errors during the creation of new Lambda functions. It is definitely great to have an automatic scaling system but it isn’t much fun when you can’t address these errors and work upon it.
For example, API Gateway timeout to invoke a lambda function is 30 seconds. And when you violate this limit, you’ll see a spike in 5XX error being returned from API Gateway. The only option you have is to retry the request until you succeed.
Another example, Lambda depends on Amazon EC2 to provide Elastic Network Interface for your VPC-enabled Lambda function. And hence, your function’s scalability also depends on EC2’s rate limits as they scale. You need to check whether your VPC-enables functions are allowed 500 concurrent invocations per minute or not.
Amazon EC2: With EC2, everything is under your control. Everything in control means you will have to manually configure the scalability features unlike Lambda and that might not be the novel idea. However, the positive side is you can make your scalability an error-free process. Also, EC2 Auto Scaling groups are there to help you out.
These groups help you to ensure that you have sufficient amount of instances available to handle the load. Simply create the Auto Scaling groups, add the instances and specify the number of minimum and maximum instances in each group.
AWS ensures that your group never goes above and beyond the mentioned number of instances by automatically launching and terminating them.
For example, this above mentioned Auto Scaling group has the minimum number of 1 instance (which will be active all the time) while the desired capacity is 2 instance (to meet the usual demands) and a maximum of 4 instances (to meet the unusual spike in traffic). More to this, there are no additional costs attach with Autoscaling Groups.
Conclusion: Fully automated scaling is definitely a dream come true but not at the cost of not being able to mitigate the errors. On the other side, manually configuring scalability environment is quite a work initially but once you have gained control through setup and tuning, it becomes automatic as well.
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Availability (On-demand vs Always available)
AWS Lambda: As discussed in the scalability section, Lambdas are available all the time. These are brought up and spun down automatically depending upon the requirements of the event triggers. Since you’re not paying for the idle time with AWS Lambda, you can save a lot of money, about which we will talk in the next section.
However, the debate over the function’s availability is going on. Here is one of the example, “I’m running is US-East-1 and I am getting 500 errors no matter what I do. It was working a few hours ago and I haven’t change any code. Even a new ‘hello world’ from the templates returns this response. The service shows healthy on dashboards.”
Such outcries are common as we give away the ability to manage infrastructure for other added benefits. In such cases, if your Lambda isn’t available, a potential solution would be to deploy your functions into multiple regions. Since you only pay for the executed functions, you only pay for your fallback deploys if there is a region outage.
Amazon EC2: With EC2 instances, you don’t get the benefit of on-demand availability. As discussed in the above section, auto scaling groups help in scalability but in the end, you will have to keep at least one instance up and running.
However, EC2 has also faced many outages. One such was in the US-East-1 region due to the network connectivity which led startups like Foursquare, Rapportive, Heroku and Reddit to the temporary unavailability. A similar outage before this one lasted for 48 hours.
Such examples raise questions over the reliability of cloud platforms and consumer technology.
As discussed the Lambda, replicating the EC2 instances for the multi-region availability is quite difficult. The most possible solution is to deploy a blue/green strategy but that will lead to double the number of running EC2 instances and let alone the extra cost.
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AWS Lambda vs EC2 Latency
AWS Lambda: The Cold startup is considered as one of the biggest issues with event-driven serverless functions. A cold start occurs when you trigger a function which has been inactive since a considerable time. This delay occurs from the cloud provider due to the time it takes to provision your selected runtime container and then executing your functions and hence this increases the function latency.
This may take more than 5 seconds which makes it impossible to guarantee less than 1 second triggering of function from API Gateway, DynamoDB, CloudWatch, etc. However, a potential solution can be pinging your function from time to time to keep it warm, as discussed we’ve discussed more about in the serverless performance.
However, this wouldn’t be a recommended solution as it comes at the cost of an increased bill. For example, if you have a scheduled function which consumes a lot of memory, CPU and time, Lambda’s cost might be higher even when compared to EC2.
Amazon EC2: Cold starts do not occur in EC2 instances typically unless you’re trying to start a new container. New instances have high latency and takes longer to be ready than existing instances because they typically run code on the first startup. For example, if you Stop the instance and Start it again, the instance will be available quite quickly.
However, the startup time invariable depends on some these issues- local filesystem mounts, remote file system mounts, regular OS initialization scripts, user data and cloud-init scripts, instance type and EBS volume type.
Moreover, the time taken to launch a new instance will be determined by running benchmarks for your particular use case and these points can only help in reducing the time.
Comparatively, this is a tiring task and Lambda seems to be the right choice. Also, consider an example, if you just have a couple of requests to process, running an entire EC2 instance would not be a wise choice.
AWS Lambda vs EC2: Cost Comparison
The setup costs for AWS Lambda vs EC2 is nil since both have free-tier available. However, getting started with AWS Lambda is comparatively easy. Saying that, let us examine what are the computing cost for two distinct use cases.
#1. For a low/infrequent traffic website/app use case, let’s assume the following specifications:
10,000 hits per day over 24 hours
Execution time: 200ms per hit at 512 GB
This will result in 432,000 requests per month and 2160 GB-sec of compute/month. Considering the above case, the total cost would be $0.31/month.
On the contrary, this is the 1/10th cost of what the smallest EC2 instance t2.nano will charge you. Higher EC2 prices is due to its running time for 24 hours (and hence the cost is multiplied with 24) which is not the case with AWS Lambda.
#2. For scalable architecture use case:
Lambda is independently scalable while the same is not the case with EC2. EC2 supports auto-scaling groups but with certain prerequisites.
To tackle the failover behaviour, the recommended number of instances to group together is three. Let’s say, we’re running two “nano” instances to handle the failover behaviour: $0.0059 * 24 * 30.5 = $4.31 for the virtual machine and $0.05 * 8 GB = $0.40.
So, the total cost of the two instances will be $9.42 (at $4.71 each). Autoscaling group would also need Elastic Load Balancer which would at least cost $0.0225 * 24 * 30.5 = $16.47 Can you compare now with AWS Lambda?
With Lambda, you pay per call and per GB second. Ignoring the call cost since it is $0.20/ 1 million requests. 1 million requests mean 0.4 requests per second in a month. AWS Lambda costs $0.00001667/GB-sec.
Both EC2 and Elastic Beanstalk will consume parts from of 512 MB memory of the OS and the container itself. Considering two instances similar instances like that would cost 2 * 256MB/1024MB * 60 * 60 * 24 * 30.5 = 1317600 GB-sec. The cost of this amount of GB-sec will be 1317600 * $0.00001667 = $21.96
This is more costly for obvious reasons. Apart from that, one should keep in mind that the traffic isn’t always distributed evenly and hence you might need more instances that you have anticipated. And this will further increase the cost.
Use Cases: When to use AWS Lambda?
#1. Low complex code: Lambda is perfectly suitable to execute code with no or few variables and 3rd party dependencies. It can quickly process easy tasks containing low complex code, for example, Alexa skills.
#2. Shorter executing time: There is no point for spinning up a container where your tasks are occasional and can be executed within minutes. For example, transforming image/video when uploaded to S3.
#3. Infrequent traffic: In typical scenarios, your servers would be sitting idle while you still pay for it. With the pay-per-function model, Lambda can help you in cutting down your computing cost.
#4. Real-time processing: AWS Lambda with AWS Kinesis works best for real-time batch processing. For example, writing batch data to dynamoDB, analysing logs, etc.
#5. Scheduled CRON jobs: You can use AWS Lambda function with scheduled events to function at a fixed scheduled time. You can specify this time or may use a CRON expression.
Use Cases: When to use EC2?
#1. High-performance computing: With the large number of EC2 instances you can create virtual servers to meet your demands, EC2 is perfectly suitable for processing complex tasks.
#2. Disaster recovery: EC2 can be used as a medium for disaster recovery for both active and passive environments. This is because it can be turned on quickly in case of an emergency which facilitates minimal downtime.
#3. Easy DevOps: DevOps processes are developed and are full-proof when it comes to EC2. On the contrary, when it comes to Lambda, it is still not mature and is an art only a few have mastered.
#4. Development/Testing: Since EC2 provides on-demand computing resources, organisations can deploy large scale testing environment without any upfront investment in the hardware.
#5. Secure Environment: EC2 has been known to provide outstanding security to some of the major clients from banking and healthcare. If security is your first priority, EC2 is for you.
Take Away!
Here we come to the final question: which one of these services is best suited for my application? Well, there is no direct answer. There are many factors which you need to analyze and one of the major ones is your specific use case.If you’re wasting your compute resources due to unpredictable traffic for your application but still want a scalable and cost friendly solution, AWS Lambda is for you. When not to use AWS Lambda? When you want to do complex processing and your process can’t be executed in the limited execution time.
Or maybe you want to run a complex application which has consistent traffic and want to operate in a tried and tested deployment environment, EC2 if for you. The only drawbacks are a complex setup environment and provisioning of servers.
The result- For either AWS Lambda vs EC2 or vice versa, both operate for a highly specific use case, however, one wasn’t sufficing the need which necessitated the invention of another.
Until then, let’s make the most of each of the services. If you’ve hands-on experience with either EC2 or Lambda or both, I’d love to hear from you.


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Fri Nov 7, 2008
Here are some recent article shorts. As usual, you can click on the thumbnails to read them. The one on the left is a feel-good piece on laughter yoga. The right piece touches on the depressing psychological and cultural effects of global warming. There’s also a teeny one in there on the dodgy air we have to breathe in airplanes (even Purell won’t save you this time).
Seeing these two articles juxtaposed like this evokes a stark contrast between Western frivolity and Aboriginal reality. Note how they’re both sitting in the same cross-legged position too. I almost wish the art department had actually planned it that way.


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Nearly two years after the launch of the MapSwipe app in July 2016, a massive 23,828 people have swiped through its satellite imagery, identifying squares containing buildings and/or roads. Together they’ve swiped 495,135 km2 - that’s very nearly the size of Spain.
MapSwipers are a truly international bunch, hailing as much from Tanzania, Denmark, Singapore or the Czech Republic as from the US and UK.
The app is being maintained by an equally international bunch of volunteers and staff from the Missing Maps community. And thanks to the hard work of volunteers, the MapSwipe app is now open source, so anyone with the relevant coding skills can access the code on Github and help to improve the app. Get in touch if you’re interested - particularly if you have React Native, Python, Firebase or general mobile app development skills!
When you’re lounging on the sofa swiping serenely over a remote part of the planet, it can be difficult to imagine how the data you’re producing is being used or who it’s helping. So I talked to Nell Gray, a qualitative research officer at MSF, about how they’re using MapSwipe data in a project in Port-au-Prince, Haiti.
In 2015 MSF opened the Pran Men’m (“Take My Hand”) clinic, which provides medical, psychological and social care for survivors of sexual violence.  Between May 2015 and Dec 2017 the clinic treated nearly 2,000 survivors, mostly women and girls, and 58% were minors (under 18). It was moving to hear Nell talk about the project; you can read stories here from some of the women and girls who have used the clinic.
“It’s a hugely capable and professional team providing care 24 hours a day to anybody who needs it, often in quite difficult cases. I was just really impressed by them and the work that they’re doing and the huge impact you can have on the lives of people experiencing sexual violence.” Rape survivors should be seen within 72 hours in order to receive effective HIV prevention and emergency contraception. Abortion is illegal in Haiti and although following intensive efforts HIV rates are falling they are still some of the highest outside Africa so the stakes are high for women.
Nell explained that in a large capital city one clinic isn’t accessible to everyone, so there are several outreach sites and MSF are currently carrying out a household survey to look at how they can increase access to services, particularly within the 72 hour window.
“We needed to pick random points to do the household survey to ensure the coverage of the survey is good. But the problem with Port-au-Prince is that some places are super densely populated and in some places you could drop a point and there’s no houses there at all. So we were grappling with how to manage that and it was at this point that somebody suggested MapSwipe. Within a couple of days it was done and the field team were super impressed and very happy at being able to work with that data rather than just a blank map of the city effectively.” You can see the MapSwipe data for the Haiti project here.
“We’re not asking about personal experiences or collecting information about incidence of sexual violence, it’s much more about understanding barriers to accessing care or support and how they can be overcome… There is often a question of whether it’s a knowledge issue, so that people aren’t aware that it’s a medical emergency for example or that there are health services available. Do people know about the 72 hour window? Or do they know that and it’s other barriers that stop them coming?”
The survey is also looking at whether particular groups of people or areas are more vulnerable. Because some of the factors may be geographical, having a good geographical starting point is important.
The results of the survey will help MSF carry out a participatory design process with local residents on improving access to services. “That could be anything from different health promotion strategies, different communication messages, different ways of communicating with people, to having services in different places and it would really help us to understand if knowledge is particularly low in an area and we need to focus awareness raising there, or if there are areas where services are particularly inaccessible where maybe we need to work with the Ministry of Health to build capacity in a hospital for example.”
The Haiti project used MapSwipe data directly. But on many projects - and this was its original purpose - MapSwipe is the first stage in preparing more detailed maps as it hugely cuts down the amount of time mappers have to spend scrolling through empty forest and scrub. Detailed mapping is mostly done by volunteers and anyone can get involved - find out how here. There may be a mapathon in your area you can go along to to help you get started.
You may be interested in the MapSwipe Analytics website, produced by the Heidelberg Institute for Geoinformation Technology. Here you can see the data visualised on a map or against the satellite imagery, download it to explore it yourself, watch results being submitted in real time or work through a tutorial.
Talking to humanitarian professionals revealed it was important to make the data as open and available as possible so anyone can refer to it when needed, without specialist equipment. So if you look at a project online you can see at a glance where the areas of likely population are. That could be pretty useful in an emergency.
There is an ongoing question whether machine learning could replace MapSwipe, but so far the answer is “not yet”. Researchers at Heidelberg have recently made significant progress but building styles and landscape appearance can vary hugely between different areas so there isn’t a one off solution. MapSwipe data makes great training data sets for these experiments though.
Now the code is open source there’s more opportunity to improve the app and we’ve been keeping an eye on feedback. For instance, lots of people have asked for the zoom function to be more stable, and for it to be clearer what scale the imagery is, so hopefully we can tackle some of these issues.
But what about MapSwipe 2.0? How could the app take a whole leap forward? One suggestion is that it could be used for a complete mapping process - I knocked up a quick prototype for this idea. Or should the user be able to indicate how many buildings are in a square, to give a better idea of population density? Or could MapSwipe be used to indicate where OpenStreetMap and the satellite imagery differ? These are just a few suggestions - let us know if you have more. We’re also looking for funding opportunities. You can get in touch with us via the feedback form (also accessible from the app).
Meanwhile I for one am going to keep swiping - as well as being relaxing and quite addictive as a process it really does make a difference on the ground. I’ve always liked the MapSwipe icon of a person waving as though saying “here I am, please put me on the map”. By putting on the map people like the women and girls who might need the services of Pran Men’m we can help make sure they get the help they need when they need it.
Nell added “I’d also add a huge thank you to all the MapSwipers. The team in the field didn’t realise that MapSwipe was an option. I was explaining it to them and they were just blown away that people were giving their time and energy to help the project and were even more blown away by the fact that it was super quick and effective. It was really hugely appreciated.”


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Hey stranger!


I attended school at Faith Ventures Christian Academy
I was an intern at Dr.Mattress in Ruiru between October 2020 and January 2021
I decided to take the software development course at Moringa School as a way of preparing myself for my next University course. This course will give me a working knowledge of how programs and websites are written.
Some of my Hobbies include:
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Over the years I have developed skills in various areas, such as:


This home page is just for helping developer to create Django Rest Framework APIs. You could learn more about APIs in general, Django Rest Framework in particular and other programming techiques reading my blog
You can use this home page in order to create basic Serializers, basic ModelViewSets and API urls with just entering the model name.


emoji-data-js
NodeJS library providing low level operations for dealing with Emoji glyphs in the Unicode standard. :cool:
EmojiData.js is like a swiss-army knife for dealing with Emoji encoding issues.
If all you need to do is translate :poop:
into :poop:, then there are plenty
of other libs out there that will probably do what you want. But once you are
dealing with Emoji as a fundamental part of your application, and you start to
realize the nightmare of [doublebyte encoding][doublebyte] or
[variants][variant], then this library may be your new best friend.
:raised_hands:
EmojiData.js is written by the same author as the Ruby emoji_data.rb gem, which is used in production by Emojitracker.com to parse well over 100M+ emoji glyphs daily. This version was written to provide all the same functionality while taking advantage of the crazy speed of the V8 runtime environment. :dizzy:
WORK IN PROGRESS - NOT RELEASED JUST YET
Installation
(Placeholder: won't work until this is released)
npm install emoji-data
Usage Examples
TODO
Testing
npm test


Space Shuttle Challenger
Code for this project is available on GitHub.
Introduction:
On January 28, 1986, space shuttle Challenger was due to takeoff from Kennedy Space Center on its tenth space mission to deploy satellites and observe Halley’s Comet among other things.
Space shuttle Challenger contained two Solid Rocket Boosters (SRBs) that boosts the shuttle into space. Each booster has three field joints, which contains two O-rings (secondary and primary). These O-rings are 37 foot circles of special rubber that help pressure seal the gas and exhaust.
It was an unusually cold morning that day, measuring 31 degrees Fahrenheit. Ice was visible around the launchpad and on the equipment. NASA had no experience launching at such cold temperatures with the coldest previous launch being 22 degrees warmer, at 53 degrees Fahrenheit. There was concern about the durability of the O-rings because they haven’t been tested at such low temperatures and could fail by erosion or blowby. However, there wasn’t enough support to abort and they decided to continue on with the mission.
73 seconds after liftoff, at an altitude of 9 miles, the O-ring on one of the boosters malfunctioned. The rubber O-rings had become stiff under the cold and failed to seal the joint, letting a plume of exhaust to leak out. Hot gases enveloped the cold hull of an external tank full of gases and ruptured it, causing the shuttle to be torn apart by aerodynamic forces.
The space shuttle had no escape hatch, so it went into a free fall and disintegrated going terminal velocity over the Atlantic Ocean near Cape Canaveral, Florida. The outcome of this terrible tragedy resulted in the death of 7 crewmembers and a nearly 3 year hiatus of the space program.
This tragedy could have possibly been prevented with enough statistical analysis using Bayesian Linear Regression by predicting the Number of O-rings experiencing thermal distress given historical data from 23 shuttle flights previous to the Challenger disaster.
Description of Dataset:
The dataset is found on the UCI Machine Learning Repository. The donor of the data is David Draper of UCLA.
- There are 23 instances with 4 attributes and a response variable.
- The four attributes are:
- The number of O-rings at risk on a given flight
- Launch temperature (degrees Fahrenheit)
- Leak-check pressure (psi)
- Temporal order of flight.
The Number of O-rings is always 6 and the temporal order of flight isn’t necessary since the flights are independent of each other. The data for Number of O-rings at risk on a given flight and Temporal order of flight is still listed in the dataset in OpenBUGS, but not used for the linear regression models.
As a result, there are really only two relevant attributes:
- The Launch Temperature (degrees F)
- Leak-Check pressure
- The pressure (in psi) at which safety testing for field joint leaks was performed, was available, but the relevance to the problem was unclear.
Therefore, I will run two linear regression models:
- One with 3 parameters – intercept, Launch Temperature (degrees F) and Leak-Check pressure and
- One with 2 parameters – intercept and Launch Temperature (degrees F).
The goal is to make a prediction of how many O-rings will experience thermal distress at 31 degrees Fahrenheit with Leak-Check pressure of 50, 100, and 200, and with temperature alone in the second model.
Bayesian Linear Regression vs OLS Linear Regression:
The Bayesian approach compared to OLS is that instead of maximizing the likelihood function alone, we assume prior distributions for the parameters and use Bayes theorem. Since I don’t have any estimates for the prior parameters, I will be setting normal non-informative priors on the beta coefficients and a gamma prior on the precision parameter.
By applying Bayes theorem, we obtain the posterior distribution of the parameters, which is an estimate that depends on both the data’s likelihood function and the prior belief. Bayesian Linear Regression doesn’t find the single “best” value of the model parameters, but determines the posterior distribution for the model parameters.
Depending on priors, Bayesian analysis is generally more accurate in small samples such as this problem with only 23 instances. With more information in the data, the priors are less influential.
Summary of Data:
Summary of the data shows:
- The mean launch temp is 69.57 degrees Fahrenheit, which is much warmer than the temperature on that day.
- Leak pressure is 152.2 psi on average, but it takes values of 50, 100, and 200.
- There are 7 occurrences of O-rings experiencing thermal distress, with 5 involving 1 O-ring and 2 involving 2 O-rings.
Analysis:
Both are sampled at 1,000,000 iterations and 1,000 burned.
With 3 parameters: Intercept, Launch Temperature (degrees F) and Leak-Check pressure
Beta[1] is intercept, beta[2] is Launch Temperature, and beta[3] is Leak-Check Pressure. From the 95% Credible Set, I can see that the intercept and launch temperature coefficients are statistically significant because the set doesn’t contain 0. Leak-Check pressure has a 95% Credible Set of (-6.623E-4, 0.00656) and barely includes 0 in the lower bound. It is possible that this coefficient isn’t significant. This Leak Pressure will be taken out in the 2 parameter model.
The mean response of the number of O-rings experiencing thermal distress when Launch temperature = 31 degrees Fahrenheit and Leak-check pressure (psi) = 50, 100, and 200 are as follows:
Leak-check pressure (psi) = 50, Thermal distress = 1.96, with 95% Credible Set (0.5725, 3.35)
Leak-check pressure (psi) = 100, Thermal distress = 2.11, with 95% Credible Set (0.7468, 3.466)
Leak-check pressure (psi) = 200, Thermal distress = 2.40, with 95% Credible Set (1.027, 3.766)
The 95% Credible Sets for prediction (new.TD_50, 100, 200) are much larger than the mean response, as expected, because there is more error in prediction.
Since the mean for coefficient beta[3] is 0.002943, it means that each unit of Leak-check pressure (psi) increases the number of O-rings experiencing thermal distress by 0.002943. From 50 psi to 200 psi, there is almost a .5 increase in O-rings experiencing thermal distress. A higher leak-check pressure is not better. This is a little strange and contrary to what I would believe - that checking leaks at a higher pressures should mean it’s more safe. However, this could also mean that they are expecting the rocket booster to withstand more pressure based on cargo or whatever and believe it’s necessary to test at higher pressures.
The R-squared = 0.2829 and the adjusted R-squared = 0.2112. The R-squared is a goodness-of-fit measure and explains the variability in the data. It’s small at .2 something. A higher R-squared is better and desired, but not possible with the given data. Adjusted R-squared modifies for the number of predictors in the model. The small R-squared could make the results more skeptical. The deviance is 39.19.
Still, it’s quite clear that regardless of the leak-check pressure, due to a very lower than normal temperature at launch of 31 degrees Fahrenheit, the chance of O-ring malfunction is very high and that the mission should have been aborted. The mean response of the number of O-rings experiencing thermal distress is around 2 for the three different values of leak-check pressure.
With 2 parameters: Intercept and Launch Temperature (degrees F)
Beta[1] is intercept and beta[2] is Launch Temperature. From the 95% Credible Set, I can see that the intercept and launch temperature coefficients are statistically significant because the set doesn’t contain 0.
The mean response of the number of O-rings experiencing thermal distress when Launch temperature = 31 degrees Fahrenheit is as follows:
Thermal distress = 2.245, with 95% Credible Set (0.8436, 3.668)
The 95% Credible Sets for prediction (new.TD) is much larger than the mean response as expected because there is more error in prediction.
The R-squared = 0.184 and the adjusted R-squared = 0.1452. The R-squared is a goodness-of-fit measure and explains the variability in the data. The R-squared values are much worse without Leak-check pressure as a variable. The R-squared is 0.184 vs 0.2829 and R-squared adjusted is 0.1452 vs. 0.2112. The deviance is also higher at 41.11 vs 39.19 and variance (sigma squared) is higher as well.
Due to a very lower than normal temperature at launch of 31 degrees Fahrenheit, the chance of O-ring malfunction is very high and that the mission should be aborted.
Conclusion:
In comparing the two models, the 3 parameter model with the intercept, launch temperature and leak-check pressure performs better than the 2 parameter model. The 3 parameter model explains much more of the variability in the data with the higher R-squared and R-squared adjusted value and has a slightly lower deviance. Having the leak-check pressure coefficient also explains a 0.5 variability in the number of O-rings experiencing thermal distress from 50 to 200 psi. With both models, it’s clear that with a launch temperature of 31 degrees Fahrenheit, the number of O-rings experiencing thermal distress is very high at around 2. If they performed regression using data from the 23 previous flights, they could have concluded there was a high chance of malfunction predicted and that the mission should not have been flown.


Contents
Documentation
This gallery contains examples of the many things you can do with the plasma properties package. Click on any image to see the full image and source code.
Computing A Single Transport Coefficient¶
Computing Multiple Transport Coefficients¶


Please update or install the free Flash plugin. It only takes a few seconds.
Don't want to update flash? Watch a video about what CameraSim can do instead.
(If you're on a mobile device, check out the other versions of CameraSim.)


"If you can't fly then run, if you can't run then walk, if you can't walk then crawl, but whatever you do you have to keep moving forward." -- Martin Luther King Jr.
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[gecode-users] staged search
Denys Duchier
denys.duchier at univ-orleans.fr
Thu Oct 9 00:19:30 CEST 2008
"Christian Schulte" <cschulte at kth.se> writes:
> That would more or less do the same I have been rambling on. However, it
> would require a new feature while branchings would already do the trick
> (more or less). For a user there should be no real visible difference.
 Is there an
example I can look at, that would help me understand how to do this?
Cheers,
--Denys
More information about the gecode-users
mailing list


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---
layout: post
title: What Is Your Rails Testing Stack?
tags:
- guard
- javascript
- Ruby
- test
status: publish
type: post
published: true
meta:
_edit_last: '1'
_clicky_goal: a:2:{s:2:"id";s:0:"";s:5:"value";s:0:"";}
---
Developing a Rails application is awesome. Why? Because the testing stack is really, really good! But, this is also a problem. There is so much to choose from, that it sometimes is really hard to figure out what is useful and what isn't.
I don't know about you, but my Gemfile tends to have a lot of stuff in the group :development, :test.
group :development, :test do
gem 'rspec-rails'
gem 'cucumber-rails', :require => false
gem 'database_cleaner'
gem 'capybara'
gem 'launchy'
gem 'rr'
gem 'awesome_print'
gem 'jasminerice'
gem 'guard-jasmine'
gem 'poltergeist'
end
Holy crap that's big! Of course, it also has some helpers only for development:
group :development do
gem 'guard'
gem 'libnotify'
gem 'guard-spork'
gem 'guard-cucumber'
gem 'guard-rspec'
gem 'guard-livereload'
gem 'spork'
gem 'pry-rails'
gem 'pry-stack_explorer'
gem 'pry-debugger'
end
Not too shaby, huh? So let me get into the details for each of those.
Guard
Guard is an amazing tool to automate nearly everything. Don't believe me? Take a look at all the possible Guard extensions on Github! So what does Guard do?
It basically is a system that runs in the background and can watch files. Extensions tell it (through a Guardfile
) that it should do something if a specific file pattern changes. For example, the Cucumber guard checks for this:
guard 'cucumber', :cli => "--drb" do
watch(%r{^features/.+\.feature$})
watch(%r{^features/support/.+$}) { 'features' }
watch(%r{^features/step_definitions/(.+)_steps\.rb$}) { |m| Dir[File.join("**/#{m[1]}.feature")][0] || 'features' }
end
It looks at all features in the features directory, and Guard will call the Cucumber guard if one of these files changes. In term, guard-cucumber will call Cucumber and show it to you on the console. Neat, isn't it?
Of course, this isn't just for Cucumber! I also use the RSpec guard (for RSpec tests, duh!), Spork and livereload.
Spork is a DRb (Distributed Ruby) server that preloads the Rails environment so the tests run faster. Adding this guard makes Guard start the DRb server for Cucumber and RSpec automatically when starting Guard itself.
Another very neat guard is guard-livereload. Thanks to an extension in Chrome, this guard will automatically reload the page I have open when I change the ERb or the CSS files. Makes fiddling with CSS a lot easier!
You Have To PRY It From My Cold Dead Hands
PRY is one of those tools that you just have to know (and love). It is an alternative to IRB, and way better! Just add a binding.pry to your code and Pry will open in that context when the code runs over it. It allows you to change into classes and objects to inspect them more closely. And edit the methods if you want to!
Some additions I added to Pry (and there are a few Pry plugins) are pry-debugger, so I can step through code and see what it really does. To complement Pry Debugger, I also use pry-stack_explorer. This plugin allows us to move up the stack and see the state of the code there.
Of course, in a perfect world, we never have to use one of these tools, but yeah... Real world and these things...
Testing tools
You might now say: "Hey! You said you will talk about testing tools, not support tools!" And you are right! (Though these support tools are amazing for testing!)
Yes, testing tools. For my testing needs I use the standard combo RSpec, Cucumber, database_cleaner, Capybara and rr stack. I don't think I have to explain these (Do I?
Additionally I use launchy to launch a browser window with the current state of the page to debug Cucumber steps.
I also make copious use of awesome_print, because it is, well, awesome! awesome_print can be used instead of puts or pp to output objects and have an easier time inspecting them. (Pry also can use awesome_print for it's built in printing habits!)
Last But Not Least: JavaScript Testing
Something I struggled with for a bit, but found a good solution for, is JavaScript testing. There are two aspects here: One is Cucumbers Javascript support. For some acceptance tests, I needed Javascript to run. Per default, Cucumber uses Selenium (with a real browser), which is slow and not awesome at all! And then there are the Javascript unit tests, that also need to run.
For the unit tests I settled on Jasmine. Why? Because it seems it even tries to be as close to RSpec as possible, so I don't have to learn yet another testing system. To run them, I of course, use a guard: guard-jasmine (Who's surprised?)
guard-jasmine uses PhantomJS to run the Javascript tests in a headless mode (without a user interface), which makes them fast (not incredibly fast, but at least fast).
Of course we also want to integrate the Jasmine unit tests with the asset pipeline so we can use CoffeeScript. Luckily, Brad Phelan solved that problem and released JasmineRice.
Now, this doesn't yet solve the Cucumber problem. For that, we can use Poltergeist. Poltergeist is a PhantomJS driver for Capybara. Do you see how this neatly matches up with guard-jasmine? Nice, isn't it? I just configured Capybara to use Poltergeist instead of Selenium, and now the tests run at an acceptable rate.
So, What's Your stack?
Yes, that is my "standard" Rails testing stack. I think this covers about everything I need. One thing that's not included (because I currently don't need it) is something like VCR. VCR records HTTP interactions, so that during testing the HTTP calls don't have to be made, but can be replayed by VCR. Which makes things even quicker.
Have I missed something? Added something superfluous? What do you use for your testing stack?
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The turtlegraphics module allows students to draw pictures using some simple Turtle graphics commands. For example, here is a Python program that uses Turtle graphics to draw a fractal known as the c-curve:
The Turtle class includes methods to
|
Download the turtlegraphics module View the turtlegraphics API |


token
Description
A token is essentially a word, a number or a special symbol such as :=. In a Turing program there are four kinds of tokens: keywords such as get, identifiers such as incomeTax, operators and special symbols, such as + and :=, and explicit constants, such as 1.5 and "Hello". Some keywords, such as index, are reserved and cannot be used in programs to name variables, procedures, etc.
A get statement, such as
uses token-oriented input. This means that white space (blanks, tabs, etc.) is skipped before reading the input item and after the item (up to the beginning of the next line). See the get statement for details.
get incomeTax
Example
In this example, the tokens are var, x, :, real, x, := and 9.84.
var x : real
x := 9.84


|
chigraph
master
Systems programming language written for beginners in LLVM
|
An instance of a node. More...
#include <chig/NodeInstance.hpp>
Public Member Functions | |
| NodeInstance (GraphFunction *func, std::unique_ptr< NodeType > nodeType, float posX, float posY, boost::uuids::uuid nodeID=boost::uuids::random_generator()()) | |
| Construct a nodeinstace with a type location and an ID, usually called from GraphFunction::insertNode. More... | |
| NodeInstance (NodeInstance &&)=default | |
| Move constructor. | |
| NodeInstance (const NodeInstance &other, boost::uuids::uuid id=boost::uuids::random_generator()()) | |
| Copy constructor. | |
| Context & | context () const |
| Get the containing Context object. | |
| GraphFunction & | function () const |
| Get the containing GraphFunction. | |
| boost::uuids::uuid | id () const |
| Get the ID of the instance, unique to the graph. More... | |
| GraphModule & | module () const |
| Get the containing GraphModule. | |
| void | setType (std::unique_ptr< NodeType > newType) |
| Set the type of the node instance. More... | |
| void | setX (float newX) |
| Set the X location of the instance. More... | |
| void | setY (float newY) |
| Set the Y location of the instance. More... | |
| std::string | stringId () const |
| Get the ID as a string. More... | |
| NodeType & | type () |
| Get the type of the instance. More... | |
| const NodeType & | type () const |
| Get the type of the instance, const version. More... | |
| float | x () const |
| Get the X location of the instance. More... | |
| float | y () const |
| Get the Y location of the instance. More... | |
Public Attributes | |
| std::vector< std::pair< NodeInstance *, size_t > > | inputDataConnections |
| The connections that go into this node, data. | |
| std::vector< std::vector< std::pair< NodeInstance *, size_t > > > | inputExecConnections |
| The connections that lead to this node, exec. | |
| std::vector< std::vector< std::pair< NodeInstance *, size_t > > > | outputDataConnections |
| The connections that lead out of this node, data. | |
| std::vector< std::pair< NodeInstance *, size_t > > | outputExecConnections |
| The connections that go out of this node, exec. | |
An instance of a node.
Definition at line 19 of file NodeInstance.hpp.
| chig::NodeInstance::NodeInstance | ( | GraphFunction * | func, |
| std::unique_ptr< NodeType > | nodeType, | ||
| float | posX, | ||
| float | posY, | ||
| boost::uuids::uuid | nodeID = boost::uuids::random_generator()() |
||
| ) |
Construct a nodeinstace with a type location and an ID, usually called from GraphFunction::insertNode.
| func | The function this nodeInstance belongs to |
| nodeType | The type that the node is |
| posX | The X location of the node |
| posY | The Y location of the node |
| nodeID | The unique ID for the node |
Definition at line 10 of file NodeInstance.cpp.
References inputDataConnections, inputExecConnections, outputDataConnections, outputExecConnections, and type().
|
inline |
Get the ID of the instance, unique to the graph.
Definition at line 61 of file NodeInstance.hpp.
Referenced by chig::GraphFunction::removeNode(), and setType().
| void chig::NodeInstance::setType | ( | std::unique_ptr< NodeType > | newType | ) |
Set the type of the node instance.
| newType | The new type |
Definition at line 50 of file NodeInstance.cpp.
References chig::NodeType::dataInputs(), chig::NodeType::dataOutputs(), chig::disconnectData(), chig::disconnectExec(), id(), inputDataConnections, inputExecConnections, outputDataConnections, outputExecConnections, and type().
|
inline |
Set the X location of the instance.
| newX | The new X coordinate |
Definition at line 55 of file NodeInstance.hpp.
|
inline |
Set the Y location of the instance.
| newY | The new Y coordinate |
Definition at line 58 of file NodeInstance.hpp.
|
inline |
Get the ID as a string.
Definition at line 65 of file NodeInstance.hpp.
Referenced by chig::disconnectData(), chig::disconnectExec(), chig::Debugger::inspectNodeOutput(), chig::Debugger::setBreakpoint(), and chig::validateFunctionConnectionsAreTwoWay().
|
inline |
Get the type of the instance.
Definition at line 43 of file NodeInstance.hpp.
Referenced by chig::connectData(), chig::connectExec(), chig::disconnectData(), chig::disconnectExec(), NodeInstance(), setType(), and chig::validateFunctionConnectionsAreTwoWay().
|
inline |
Get the type of the instance, const version.
Definition at line 46 of file NodeInstance.hpp.
|
inline |
Get the X location of the instance.
Definition at line 49 of file NodeInstance.hpp.
Referenced by NodeInstance().
|
inline |
Get the Y location of the instance.
Definition at line 52 of file NodeInstance.hpp.


I am telling it to everyone who listenes, podcasting is the future of radio. Sounds a bit far out for some, but now NPR proved my point. Mix your own is a system (via RWW) where you can mix together your own podcast right on npr.org, then subscribing to it via iTunes or whatever. Sure, people could do that themselves by subscribing to several small podcasts but what NPR does is so much better.
But one step back. Why is podcasting the future of radio?  I get in the car and the news is already over, I missed my favorite 1 minute comedy, and just this one day only sucky songs are on. It would be much better if I got into my car and it just played what I wanted to hear, freshly synced. All this is actually not a real problem if you think a few more years out. There is no reason that every radio will not be equiped with a 3G connection to sync in at fixed times. Sure you can do that with your iPhone/iPod, but that’s not the point and just not easy enough for the general population. Once everything can sync there is no problem anymore to not have the right podcast on the device at any time.
Of course there is still stuff like last.fm, but I am talking about the future of Radio not the future of consuming music, and I am actually more specifically meaning the future of Radio stations. I am very happy to have the right guy give me new and old music to listen to. I am very happy to get the right show from the radio on my device. Radio Stations should filter stuff for me, just like newspapers, but I want it right when and where I want it, not when they are sending it out. And this is exactly what NPR does with their system. I go on there and tell them what I am interested in and I get just that, at the time I want.
We already know that the focus is going to the consumer, and the focus is going to internet connected media.  Same goes for radio. Open up, remix, mash up, integrate. The NPR system also allows for integrating ads and I wouldn’t mind.
Rock on NPR. Thank you for an innovation that hopefullly many will follow.


Peter Garrett Joined Bad//Dreems For A Moving And Pointed ‘Like A Version’
by Jared RichardsAdelaide’s BAD//DREEMS bought in a stack of friends into the triple j studios for their Like A Version, including Peter Garrett.
The band needed a bit of help for their cover of choice — Warumpi Band’s 1985 anti-racism rock song ‘Blackfella/Whitefella’. In addition to Garrett, BAD//DREEMS enlisted Emily Wurramara and Don Nunggarrgalu & Brad Bara from Mambali, a nine piece from South East Arnhem Land and one of the standouts at this year’s Bigsound.
Related
The final product sees DREEMS’ lead singer Ben Marwe and Garrett alternate verses, with Wurramara on backing vocals. Bara takes over in the bridge over clapping sticks and a didgeridoo.
With so many people in the studio, there wasn’t a lot of room — but don’t worry, Garrett still did a lot of decidedly Garrett movements. It’s a stirring track surmised best by an early YouTube comment: “This is what Like a Version is all about.”
The timely cover arrives in a week of nation-wide protests after a 28-year-old police officer allegedly shot dead a 19-year-old Warlpiri man Kumanjayi Walker over the weekend. The officer has since been charged with murder.
BAD//DREEMS also performed a rendition on ‘Sonny’, their lead single from third album Doomsday Ballet, released last month. The four-piece just finished up a national tour, and are about to head to Europe. Watch them both below, and if you’re in the mood revisit our list of the best LAVs of the decade.


Warehouse Keeper Cliff:
Hey! You don't have enough medals to play. What happened?
Come back after gathering more medals.


The ending
(Charlotte) "A little tired, perhaps. But I feel peaceful.
Your success in the ring this morning was, to a small degree, any success.
Your future is assured.
You will live, secure and safe, Wilbur.
Nothing can harm you now. These autumn days will shorten and grow cold.
The leaves will shake loose from the trees and fall.
Christmas will come, then the snows of winter.
...
Wilbur remembered what Charlotte had told him - that the sac was waterproof and strong. It felt funny on his tongue and made him drool a bit. And of course he couldn't say anything. But as he was being shoved into the crate, he looked up at Charlotte and gave her a wink. She knew he was saying good-bye in the only way he could. And she knew her children were safe.
"Good-bye!" she whispered. Then she summoned all her strength and waved one of her front legs at him.
She never moved again. Next day, as the Ferris wheel was being taken apart and the race horses were being loaded into vans and the entertainers were packing up their belongings and driving away in their trailers, Charlotte died. The Fair Grounds were soon deserted. The sheds and buildings were empty and forlorn.
The infield was littered with bottles and trash. Nobody, of the hundreds of people that had visited the Fair, knew that a grey spider had played the most important part of all. No one was with her when she died.
Mr. Zuckerman took fine care of Wilbur all the rest of his days, and the pig was often visited by friends and admirers, for nobody ever forgot the year of his triumph and the miracle of the web. Life in the barn was very good - night and day, winter and summer, spring and fall, dull days and bright days. It was the best place to be, thought Wilbur, this warm delicious cellar, with the garrulous geese, the changing seasons, the heat of the sun, the passage of swallows, the nearness of rats, the sameness of sheep, the love of spiders, the smell of manure, and the glory of everything.
(End)


32. as-Sajdah: The Adoration
1
Alif. Lam. Mim
2
The revelation of the Scripture whereof there is no doubt is from the Lord of the Worlds.
3
Or say they: He hath invented it ? Nay, but it is the Truth from thy Lord, that thou mayst warn a folk to whom no warner came before thee, that haply they may walk aright.
4
Allah it is Who created the heavens and the earth, and that which is between them, in six Days. Then He mounted the Throne. Ye have not, beside Him, a protecting friend or mediator. Will ye not then remember ?
5
He directeth the ordinance from the heaven unto the earth; then it ascendeth unto Him in a Day, whereof the measure is a thousand years of that ye reckon.
6
Such is the Knower of the Invisible and the Visible, the Mighty, the Merciful,
7
Who made all things good which He created, and He began the creation of man from clay;
8
Then He made his seed from a draught of despised fluid;
9
Then He fashioned him and breathed into him of His Spirit; and appointed for you hearing and sight and hearts. Small thanks give ye!
10
And they say: When we are lost in the earth, how can we then be re-created ? Nay but they are disbelievers in the meeting with their Lord.
11
Say: The angel of death, who hath charge concerning you, will gather you, and afterward unto your Lord ye will be returned.
12
Couldst thou but see when the guilty hang their heads before their Lord, (and say): Our Lord! We have now seen and heard, so send us back; we will do right, now we are sure.
13
And if We had so willed, We could have given every soul its guidance, but the word from Me concerning evildoers took effect: that I will fill hell with the jinn and mankind together.
14
So taste (the evil of your deeds). Forasmuch as ye forgot the meeting of this your day, lo! We forget you. Taste the doom of immortality because of what ye used to do.
15
Only those believe in Our revelations who, when they are reminded of them, fall down prostrate and hymn the praise of their Lord, and they are not scornful,
16
Who forsake their beds to cry unto their Lord in fear and hope, and spend of that We have bestowed on them.
17
No soul knoweth what is kept hid for them of joy, as a reward for what they used to do.
18
Is he who is a believer like unto him who is an evil-liver ? They are not alike.
19
But as for those who believe and do good works, for them are the Gardens of Retreat - a welcome (in reward) for what they used to do.
20
And as for those who do evil, their retreat is the Fire. Whenever they desire to issue forth from thence, they are brought back thither. Unto them it is said: Taste the torment of the Fire which ye used to deny.
21
And verily We make them taste the lower punishment before the greater, that haply they may return.
22
And who doth greater wrong than he who is reminded of the revelations of his Lord, then turneth from them. Lo! We shall requite the guilty.
23
We verily gave Moses the Scripture; so be not ye in doubt of his receiving it; and We appointed it a guidance for the Children of Israel.
24
And when they became steadfast and believed firmly in Our revelations, We appointed from among them leaders who guided by Our command.
25
Lo! thy Lord will judge between them on the Day of Resurrection concerning that wherein they used to differ.
26
Is it not a guidance for them (to observe) how many generations We destroyed before them, amid whose dwelling places they do walk ? Lo! therein verily are portents! Will they not then heed ?
27
Have they not seen how We lead the water to the barren land and therewith bring forth crops whereof their cattle eat, and they themselves ? Will they not then see ?
28
And they say: When cometh this victory (of yours) if ye are truthful ?
29
Say (unto them): On the day of the victory the faith of those who disbelieve (and who then will believe) will not avail them, neither will they be reprieved.
30
So withdraw from them (O Muhammad), and await (the event). Lo! they (also) are awaiting (it).
-- Sura 32 --


{% extends 'tower_app/base.html'%} {% block content %}
Floor {{player.room.floor}}
{{player.name}}
HP: {{player.hp}}
Strength: {{player.strength}}
{{item.item_name}}
{% endfor %}{{rooms.9}}
{% if player.room.id in rm10ids %} directions_walk {% endif %}{{rooms.4}}
{% if player.room.id in rm5ids %} directions_walk{% endif %}{{rooms.8}}
{% if player.room.id in rm9ids %} directions_walk{% endif %}{{rooms.7}}
{% if player.room.id in rm8ids %} directions_walk{% endif %}{{rooms.6}}
{% if player.room.id in rm7ids %} directions_walk{% endif %}{{rooms.0}}
{% if player.room.id in rm1ids %} directions_walk{% endif %}{{rooms.1}}
{% if player.room.id in rm2ids %} directions_walk{% endif %}{{rooms.2}}
{% if player.room.id in rm3ids %} directions_walk{% endif %}{{rooms.3}}
{% if player.room.id in rm4ids %} directions_walk{% endif %}{{rooms.5}}
{% if player.room.id in rm6ids %} directions_walk {% endif %}Stairs
{% if player.room.id in stairids %} directions_walk{% endif %}Outside
{% if player.room.id in outsideids %} directions_walk {%endif%}{{player.room.room_name}} Room
{{player.room.description}}
{% if message %}{% endif%}{{message}}


---
# jekyll header
---
{% include header.html %}
{% include container-start.html %}
Open Source Tooling
The tools used to define the models and produce the logical models and the FHIR profiles are open source, developed as part of the Standard Health Record (SHR) Initiative. The SHR tooling consists of several elements:
- Clinical Information Modeling and Profiling Language (CIMPL). CIMPL (pronounced "simple") is a domain-specific language designed for the task of capturing requirements in a recognizable, human-readable form and defining detailed clinical information models. It has a formal grammar defined in ANTLR4. It has three types of definitional files: data elements, value sets, and profile mappings.
- Command Line Interface (CLI). CLI is the computer program that parses CIMPL files and produces outputs including CIMCORE, FHIR logical models, FHIR Profiles, and other artifacts.
- Clinical Information Modeling COmputable REpresentation (CIMCORE). CIMCORE is a computable health care modeling representation in JSON, expressing the class hierarchy, modeling constraints, value sets, and mappings. CIMCORE can be thought of as the "compiled form" of CIMPL, but it is explicitly designed for simplicity and cross-language interoperability. It is used as the central representation from which the various outputs (profiles, logical models, reference model browser) are produced.
The final form of the Implementation Guide (the html pages you see here) was produced using the standard FHIR Implementation Guide Publisher (IGPub).
{% include container-end.html %}
{% include footer.html %}


Entrepreneur and tech enthusiast with a passion for challenges
Hi, I'm Dominic! What gets you moving in the morning? For me the pungent smell of coffee and the endless opportunities to be challenged.
For a year, I have been working on various projects and dishing out projects to friends with exceptional skill. Some projects are, website development, mobile application development, logo design and more. I have a serious passion for working on new and challenging tasks.
My team can offer very valuable tips into starting your own business.
My team has strong skills in website design and are at the ready!
My team has experience in web based, android, and IOS application developement.
My team is strong in Photoshop, Illustrator and more to create the best logo for you!


AssemblyTron.Cloning Folder¶
Submodules¶
AssemblyTron.Cloning.IVA module¶
IVA Assembly with a one-pot PCR protocol
This script performs Homology dependent assembly (IVA) where all fragments for each assembly are amplified in one tube. This protocol has only been successfully validated with 2-fragment assemblies.
This script runs on the OT-2 via the run app. It calls CSVs which must be transferred to the OT-2 processor prior to running. This script is not designed to run a personal computer on the command line.
AssemblyTron.Cloning.IVA_separatepcrruns_gradient Script¶
Homology Dependent Assembly Protocol Script with separate PCRs
This script performs Homology dependent assembly where fragments for each assembly are amplified in separate tubes and uses a gradient thermocycler. This protocol is thoroughly validated and robust, so we recommend using it.
This script runs on the OT-2 via the run app. It calls CSVs which must be transferred to the OT-2 processor prior to running. This script is not designed to run a personal computer on the command line.
AssemblyTron.Cloning.Setup Script¶
Setup script for IVA Assembly with a one-pot PCR
This script walks the user through setup for IVA Assembly with a one-pot PCR. This script makes a dated directory in the wd, parses/transfers input files, allows users to customize parameters, and performs calculation and tracking steps for parsed design files. We recommend using either Setup_seppcr_gradient_24 or Setup_seppcr_gradient_96, since the one-pot IVA is highly error-prone and the protocol is not as thoroughly developed.
This script requires no arguments, but instead obtains all necessary information and files by user-friendly tkinter pop-up windows.
This script requires pandas and numpy to be installed in the python environment where running.
This script can also be called as a module by calling AssemblyTron.Cloning.Setup.
AssemblyTron.Cloning.Setup_seppcr_gradient_24 Script¶
Setup script for Homology Dependent Assembly with 24 tube capacity
This script walks the user through setup of a homology dependent assembly with up to 24 combined primers and templates. This script makes a dated directory in the wd, parses/transfers input files, allows users to customize parameters, runs the gradient PCR optimization algorithm, and performs calculation and tracking steps for parsed design files.
This script requires no arguments, but instead obtains all necessary information and files by user-friendly tkinter pop-up windows.
This script requires pandas and numpy to be installed in the python environment where running.
This script can also be called as a module by calling AssemblyTron.Cloning.Setup_seppcr_gradient_24.
AssemblyTron.Cloning.Setup_seppcr_gradient_96 Script¶
Setup script for Homology Dependent Assembly with 96 tube capacity
This script walks the user through setup of a homology dependent assembly with up to 96 combined primers and templates. This script makes a dated directory in the wd, parses/transfers input files, allows users to customize parameters, runs the gradient PCR optimization algorithm, and performs calculation and tracking steps for parsed design files.
This script requires no arguments, but instead obtains all necessary information and files by user-friendly tkinter pop-up windows.
This script requires pandas and numpy to be installed in the python environment where running.
This script can also be called as a module by calling AssemblyTron.Cloning.Setup_seppcr_gradient_96.
AssemblyTron.Cloning.dilution_24 Script¶
Dilution Script for up to 24 primers and templates
This script contains a protocol for diluting up to 24 primers and templates to concentrations specified in the AssemblyTron.Cloning.Setup_seppcr_gradient_24 module.
This script runs on the OT-2 via the run app. It calls CSVs which must be transferred to the OT-2 processor prior to running. This script is not designed to run a personal computer on the command line.
AssemblyTron.Cloning.dilution_96 Script¶
Dilution Script for up to 96 primers and templates
This script contains a protocol for diluting up to 96 primers and templates to concentrations specified in the AssemblyTron.Cloning.Setup_seppcr_gradient_96 module.
This script runs on the OT-2 via the run app. It calls CSVs which must be transferred to the OT-2 processor prior to running. This script is not designed to run a personal computer on the command line.


I went to Mexico, and it was fun.
Mexico rules!
I made Mexico last.
The Mexico skate scene is killer!


You will build a website centered around any idea you have.  You will want to choose a topic you are interested in as you will be developing this website over the course of the semester.
You can choose your choice for framework when building your website.


Daniel Ricciardo won't keep his "guard up" when Esteban Ocon joins Renault
by Louis ShawEsteban Ocon is set to replace Nico Hulkenberg at Renault next year. Daniel Ricciardo knows what it is like to have a young teammate from his time at Red Bull Racing but says that he won't keep his "guard up" when the Frenchman joins the team.
“He’s a slim boy, the slimmest driver I’ve ever seen in my life,” Ricciardo said to Sky Sports F1 when asked about his teammate for next season.
“I’ll be kind of going back to the scenario I had with Max. I’m now the old guy in the team but I’m not coming into it with my guard up [against him].
“I’m coming in knowing if we both do what we’ve got to do, we’ve got a chance to bring this team forward to where we both want it.”
Ricciardo will want the two drivers to help each other so that the team performs as well as possible. The Aussie will be able to help Ocon at the team and might even learn a thing or two from the Frenchman!


TOUR DE RUCHE
Copyright (c) 2004 Christian Roullier
This game is played with the following setup (the cells with black squares are not used):
Each player has one tower and four pawns.
VISIBILITY - A piece is visible if no other piece is over it. Otherwise, it is invisible.
TURN - On each turn, each player must move a friendly visible piece (or pass if it has no valid move):
A piece (tower or pawn) adjacent to 1 piece (of either color) moves to an adjacent cell.
A piece (tower or pawn) adjacent to 2 pieces (of either color) moves exactly 2 cells in a straight line.
A piece (tower or pawn) adjacent to 3 pieces (of either color) moves exactly 3 cells in a straight line.
The traveled cells must be empty except the destination cell which may be occupied. In that case, the moving piece moves over the piece(s) already there (which become invisible).
A stack is not moved as a unique piece, only the visible stone of that stack can move.
Isolated pieces or adjacent to more than 3 pieces cannot move.
The red cells cannot be crossed or moved to.
GOAL - Wins the player that moves his tower to the enemy tower initial cell.
A starting example Black moves a3 to b4 (it had one adjacent piece) making a stack of size 2.
White moved away its f5 to f6, preparing e4:e6 (if then, d6-e6, White replies f6:e6)
A winning move Black's move. He moves his tower from b3 to d3 (moving over a white pawn). On the next turn, he may move into the winning cell (since the tower is only adjacent to one piece, he can move to an adjacent cell).
Check http://jesweb.net/old/coin/concours04/ for more information about this and other games played on this small board.


Hey folks, This is Vishal Vijay. You can connect with me on +919995533909 and mail me @ 0vishalvijay0@gmail.com


|
iDreamSky Game Engine
v1.0.0
|
OrthographicCameraComponent. 更多...
#include <OrthographicCameraComponent.h>
公開方法(Public Methods) | |
| OrthographicCameraComponent (Component::Owner &owner_) | |
| OrthographicCameraComponent Constructor. 更多... | |
| virtual | ~OrthographicCameraComponent () |
| OrthographicCameraComponent Destructor. 更多... | |
| DECLARE_COMPONENT (OrthographicCameraComponent, CameraComponent, false) PROPERTY_CUSTOM_FUNC(float | |
| Set the Width of the near plane of the camera. 更多... | |
| getProjectionPlanes (l, r, b, t, n, f) | |
| getProjectionPlanes (l, r, b, t, n, f) | |
| setProjectionPlanes (-value/2, value/2, b, t, n, f) | |
| PROPERTY_CUSTOM_FUNC (float, Height,{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return t *2;},{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r,-value/2, value/2, n, f);}) | |
| Set the Height of the near plane of the camera. 更多... | |
| PROPERTY_CUSTOM_FUNC (float, NearPlane,{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return n;},{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r, b, t, value, f);}) | |
| Set the Near Plane of the camera. 更多... | |
| PROPERTY_CUSTOM_FUNC (float, FarPlane,{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return f;},{float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r, b, t, n, value);}) | |
| Set the Far Plane of the camera. 更多... | |
| void | setProjection (float left_, float right_, float bottom_, float top_, float nearPlane_, float farPlane_) |
| Set the coordinate of project plane of at the near plane of camera. 更多... | |
| void | getProjection (float &left_, float &right_, float &bottom_, float &top_, float &nearPlane_, float &farPlane_) const |
| Get the coordinate of project plane of at the near plane of camera. 更多... | |
| 公開方法(Public Methods) 繼承自 Magnum::CameraComponent | |
| CameraComponent (Component::Owner &owner_) | |
| CameraComponent Constructor. 更多... | |
| virtual | ~CameraComponent () |
| CameraComponent Destructor. 更多... | |
| DECLARE_COMPONENT (CameraComponent, Component, false) PROPERTY(int | |
| Set the Rendering Order of this Camera. 更多... | |
| PROPERTY (unsigned int, TargetLayer) | |
| Set the value of the TargetLayer of this Camera. 更多... | |
| PROPERTY (int, ClearFlags) | |
| Set the Clearing Flags of this Camera. 更多... | |
| PROPERTY (ColorRGBA, ClearColor) | |
| Set the Clear Color of this Camera. 更多... | |
| PROPERTY (float, ClearDepth) | |
| Set the Clear Depth of this Camera. 更多... | |
| PROPERTY (unsigned int, ClearStencil) | |
| Set the Clear Stencil of this Camera. 更多... | |
| PROPERTY (Vector4, Viewport) | |
| Set the Viewport of this Camera. 更多... | |
| void | setProjectionPlanes (float left_, float right_, float bottom_, float top_, float nearPlane_, float farPlane_) |
| Set the coordinate of project plane of at the near plane of camera. 更多... | |
| void | getProjectionPlanes (float &left_, float &right_, float &bottom_, float &top_, float &nearPlane_, float &farPlane_) const |
| Get the coordinate of project plane of at the near plane of camera. 更多... | |
| const Matrix4 & | getProjectionTransform () |
| Get the project transform of this camera. 更多... | |
| bool | isLayerMatched (unsigned int layer) const |
| Check if the parameter layer matches TargetLayer to this camera. 更多... | |
| 公開方法(Public Methods) 繼承自 Magnum::Component | |
| Component (Component::Owner &owner_) | |
| CLASS (Component, agm::reflection::NullClass) | |
| PROPERTY (bool, Enabled) | |
| Set the Enabled State of Owner, if the component is not active, it become non functional. 更多... | |
| PROPERTY (String, Name) | |
| Set the Name of Owner. 更多... | |
| Component::Owner & | getOwner () |
| const Component::Owner & | getOwner () const |
| virtual bool | isEditorModeActive () const =0 |
公開屬性 | |
| Width | |
| float | r |
| float | b |
| float | t |
| float | n |
| float | f |
| return r float | r |
| 公開屬性 繼承自 Magnum::CameraComponent | |
| Order | |
保護方法(Protected Methods) | |
| virtual void | onDebugRender (IComponentDebugRenderer &debugRenderer) |
| Handle Debuger Rendering Event of the Component. | |
| 保護方法(Protected Methods) 繼承自 Magnum::CameraComponent | |
| virtual bool | onConstruct () |
| Handle Construct Event of the Component. 更多... | |
| virtual void | onStart () |
| Handle Start Event of the Component. | |
| virtual void | onUpdate (float dt) |
| Handle Update Event of the Component. 更多... | |
| virtual void | onPause () |
| Handle Pause Event of the Component. | |
| virtual void | onResume () |
| Handle Resume Event of the Component. | |
| virtual void | onStop () |
| Handle Stop Event of the Component. | |
| virtual void | onDestruct () |
| Handle Destruct Event of the Component. | |
| void | inValidateProjectionTransform () |
| mark the member variable projectTransformValid is invalide and should be update before next render | |
額外的繼承成員 | |
| 公開型態 繼承自 Magnum::CameraComponent | |
| enum | ClearFlag { CLEAR_NONE = 0x00, CLEAR_COLOR = 0x01, CLEAR_DEPTH = 0x02, CLEAR_STENCIL = 0x04, CLEAR_SKYBOX = 0x08 } |
| 靜態公開方法(Static Public Methods) 繼承自 Magnum::CameraComponent | |
| static int | compare (const void *left, const void *right) |
OrthographicCameraComponent is a base class for all Orthographic Camera
| OrthographicCameraComponent::OrthographicCameraComponent | ( | Component::Owner & | owner_ | ) |
OrthographicCameraComponent Constructor.
To Construct the OrthographicCameraComponent for the owner.
| owner_,the | Component::Owner which owns this component. |
|
virtual |
OrthographicCameraComponent Destructor.
To Destruct OrthographicCameraComponent and remove from its owner.
| Magnum::OrthographicCameraComponent::DECLARE_COMPONENT | ( | OrthographicCameraComponent | , |
| CameraComponent | , | ||
| false | |||
| ) |
Set the Width of the near plane of the camera.
| value_,float,the | Width of the near plane of the camera Get the Width of the near plane of the camera |
| void OrthographicCameraComponent::getProjection | ( | float & | left_, |
| float & | right_, | ||
| float & | bottom_, | ||
| float & | top_, | ||
| float & | nearPlane_, | ||
| float & | farPlane_ | ||
| ) | const |
Get the coordinate of project plane of at the near plane of camera.
| left_,float,return | the left side frustum x coordinate in this camera space |
| right_,float,return | the right side frustum x coordinate in this camera space |
| bottom_,float,return | the bottom side frustum y coordinate in this camera space |
| top_,float,return | the top side frustum y coordinate in this camera space |
| nearPlane_,float,return | the near side frustum z coordinate in this camera space |
| farPlane_,float,return | the far side frustum z coordinate in this camera space |
| Magnum::OrthographicCameraComponent::PROPERTY_CUSTOM_FUNC | ( | float | , |
| Height | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return t *2;} | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r,-value/2, value/2, n, f);} | |||
| ) |
Set the Height of the near plane of the camera.
| value_,float,the | Height of the near plane of the camera Get the Height of the near plane of the camera |
| Magnum::OrthographicCameraComponent::PROPERTY_CUSTOM_FUNC | ( | float | , |
| NearPlane | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return n;} | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r, b, t, value, f);} | |||
| ) |
Set the Near Plane of the camera.
| value_,float,the | Near Plane of the camera Get the Near Plane of the camera |
| Magnum::OrthographicCameraComponent::PROPERTY_CUSTOM_FUNC | ( | float | , |
| FarPlane | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);return f;} | , | ||
| {float l;float r;float b;float t;float n;float f;getProjectionPlanes(l, r, b, t, n, f);setProjectionPlanes(l, r, b, t, n, value);} | |||
| ) |
Set the Far Plane of the camera.
| value_,float,the | Far Plane of the camera Get the Far Plane of the camera |
| void OrthographicCameraComponent::setProjection | ( | float | left_, |
| float | right_, | ||
| float | bottom_, | ||
| float | top_, | ||
| float | nearPlane_, | ||
| float | farPlane_ | ||
| ) |
Set the coordinate of project plane of at the near plane of camera.
| left_,float,the | left side frustum x coordinate in this camera space |
| right_,float,the | right side frustum x coordinate in this camera space |
| bottom_,float,the | bottom side frustum y coordinate in this camera space |
| top_,float,the | top side frustum y coordinate in this camera space |
| nearPlane_,float,the | near side frustum z coordinate in this camera space |
| farPlane_,float,the | far side frustum z coordinate in this camera space |
| float Magnum::OrthographicCameraComponent::r |
| return r float Magnum::OrthographicCameraComponent::r |


The technical reference includes resources to describe the commands and options that are available in the SKY UX CLI and configuration options for the SKY UX process.
For commands and options in the SKY UX CLI, see the CLI commands reference.
For a description of the default items that the template provides in a SKY UX single-page application, see the template items reference.
For guidance on naming conventions for files in SKY UX projects and where to place those files, see the file name and placement reference.
For settings that are available in the skyuxconfig.json
file in your project's directory, see the configuration options reference.
For helpers to make authenticated HTTP requests, see the helpers reference.
For information about unit tests and end-to-end tests in SKY UX projects, see the testing reference.
Next step » Frequently asked questions
Previous step » Get started


THE THOMAS CROWN AFFAIR A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): ** 1/2
Hollywood is a hotbed of ideas these days. Financial ones.
Having decided to marshal its resources on fewer films, the studio executives are hard at work searching for lucrative approaches. The safest path to riches? Remakes. Or so they seem to think.
After all, what's easier than remaking a movie that audiences have liked before? Start calling agents. Get yourself some bankable stars and a big name director, and start planning the marketing campaign.
As crass as this approach is, it does produce some modestly entertaining films, THE THOMAS CROWN AFFAIR, directed by DIE HARD's John McTiernan, being one. Pierce Brosnan (the current James Bond) and Rene Russo (TIN CUP) replace Steve McQueen and Faye Dunaway, who starred in the original. Although this is not particularly inspired casting, neither is a bad choice.
As suave billionaire Thomas Crown, Pierce Brosnan looks dashingly handsome, no surprise there. Crown is a kleptomaniac for the fun of it. He's the type who bets $100,000 on a single golf stroke. He wagers not because he thinks he'll make the putt but because he just enjoys the thrill of the gamble. He's also a playboy who draws women to him like flies to honey. With Brosnan's looks and charm, this is probably the easiest part of the story to believe.
The performance by the usually reliable Rene Russo is another matter entirely. This version of the movie focuses on the romantic aspects of the relationship between the rich thief and his insurance investigating nemesis, Catherine Banning (Russo). Russo, who has used her sexuality as a sideline in other films, is asked to make it a key part of this character.
Perhaps because action director McTiernan is unable to give her proper guidance or perhaps because of her harsh, heavy make-up, Russo delivers a performance that's sometimes funny, but too often stiff. The low points of the film are when she takes her clothes off. Trying without much success to recreate Elizabeth Shue's whiskey scene in LEAVING LAS VEGAS, Russo ends up appearing awkward, as if she'd never made love before.
The setup for the movie has a Trojan horse full of burglars delivered to what is clearly the Metropolitan Museum of Art in New York. (The press kit claims that it wasn't meant to be any real museum. Sure.) The crime goes awry, but between mergers and acquisitions, rich businessman Crown manages to steal a $100,000,000 painting anyway.
As the tough, by-the-book Detective Michael McCann, Denis Leary gives the funniest performance in the movie. When a tony insurance investigator (Banning) invades his turf, the detective is not pleased, especially when she uses effective but illegal and unethical techniques in going after Crown. "Do you have any idea how many flesh-eating lawyers this guy is going to have?" he asks her in frustration. The negative chemistry between the two is delicious. "So, are the laws of the United States completely unknown to you?" he asks her rhetorically.
As soon as Banning tells Crown that she knows he did it, the movie becomes a cat-and-mouse game. The script at its best finds especially clever ways for Crown to trick Banning and the cops and for her to discover his secrets. At its worst, as in the bizarre character of Crown's psychiatrist (Faye Dunaway), the movie becomes ridiculous. "Oh dear, Peter Pan decides to grow up and finds there's no place to land," his psychiatrist chides him in a typically asinine line. This subplot is a needless distraction in a film that needs more focus and energy. For work of a director known for his proclivity to blow things up, the movie moves at a remarkably languid pace.
Even if you can guess parts of the ending, the exact way the story gets resolved is a genuine treat. Full of inventiveness and audience pleasing twists, the story ends on an especially satisfying high note.
THE THOMAS CROWN AFFAIR runs 1:51. It is rated R for sex, nudity and language. If the few nude scenes had been deleted, which would have been a decided improvement, the movie would have been rated PG-13 and appropriate for kids 10 and up. As it is, the movie would be fine for those 15 and up.
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Register on Eventbrite
AT&T, United Way, UB ACM, InfoTech WNY, UB STOR, Buffalo Open Data and WNY Innovation Hotspot partnered together to come up with the all new WNY Civic Hack this coming fall.
This October 10 - 14, come join us to learn something new, build an app or start a company in our very own Western New York.
Teams of up to four people spend time working on innovative software and/or hardware solutions.
These projects can incorporate API's or platforms of all kinds which shall help the community advance.
Whether you want to develop the next big app, learn something new or just want to have fun, join us for what promises to be an exciting event!
This is a virtual event! You could submit hack on our devpost submission link
Community Air Quality developed by John O'Brien (Amherst) and Kris Eckert (Hamburg); The civic app analyses air quality from a broad range of qualitative and quantitative responses to illustrate the state of the air in a specific neighborhood. It allows a user to register their phone number in the general database and answer a small set of questions on the relationship between individual lung health and environmental factors (asthma, smoking, allergies), individuals receive regularly scheduled surveys to gather information on their neighborhood set-up and related health experiences (frequency of asthma attacks). Along with the surveys, the application is equipped with additional tools that can be accessed via special keywords. These tools can be used to report sources of pollution as well as associated concerns.
Thought4Food developed by Mary and Gregory Everhart (Kenmore); is a civic app that helps make it easier to donate food that cannot be sold or used. It helps those who donate, especially farmers, realize the potential tax credits available by calculating their potential credits based on the USDA commodity market value. This helps to demonstrate the value of donations by businesses or farmers and makes donation a more viable option.
Buffalo Snow developed by Jesse Thomson (Buffalo), a civic app that p[provides real time locations for municipal snowplows and weather data for what roads are clear throughout Western New York to provide drivers knowledge on road conditions and making winter travel safer.
Civic App Challenge is a marathon software product creation event which is going to be hosted in the heart of Buffalo. This event gives you the flexibilty to make any app and get sponsorship from our sponsors and make your application go LIVE!
WNYCivicHack starts October 10, 2015 (Saturday) 9 am. It goes until October 14, 2015 (Wednesday) till 11:59pm
You can sign-up for the event on EventBrite (registration is free). Entry is only permitted with a valid ticket, so don't forget to bring your ticket on the day of the event!
Sorry, we only issue one ticket per person. While you're most welcome to work in a team at the event, you team members will have to register individually
Anyone above the age of 18 can attend WNY Civic Hack!
This is a virual event. You could work from home