| DOSE: T+ 0:00 |
50 mg |
oral |
AMT |
(capsule) |
| T+ 4:00 |
25 mg |
oral |
AMT |
(capsule) |
| T+ 6:00 |
25 mg |
oral |
AMT |
(capsule) |
Ok..here's the rendition of my AMT(
aplpha-methyltryptamine) experience.
Those involved: subjects a, b, c
T-0: A & B ate ~50mg. AMT. subject C decides not to and goes to work.(4PM friday)
T-0:45: feel a slight discomfort. Nothing like 5-MeO-DiPT (foxy).
T-1:20: So much about the 2-3 hours to come on. Maybe there was no cut so when the capsule dissolved, there we go...
T-2:30: I feel like I've been on some kind of speed for a while. I want to do something but the focus with amphetamines simply isn't there. A&B are actually bored.
T-3: It really only feels like an hour. really I'm not sure about times after the first 2-3 entries. A&B are now convinced that they are on MDMA(although they know they aren't). Subject B grabs pacifier and cleans the house. A giggles and sighs.
T-4: The tripping begins. Still have a somewhat rolling feeling, but there are definitely pronounced tracers. A&B take ~25mg more each. (total = ~75 ea.)
T-4:45: The strong rolling feeling in the gut comes again along with a teeny bit of discomfort, presumably from the second dose.
T-5:30: Tracer are intensifying. Pupils have overtaken the eyes (esp. of subj A), subj A is annoyed by frequent urination/dehydration (not as bad as DXM however).
T-6: subj C returns. the previous plan of her driving elsewhere is nixed due to numerous reasons, ~50mg is ingested via capsule. A&B take ~25mg more, putting their total up to ~100mg. (partially to stay awake with subj C, who has excess energy as it is).
T-7: Subj C has thrown up twice. She tends to throw up at least once on every drug. subj C (a shorty) is already getting the rolling effect.
T-8: Wow. Has it really been 8 hrs? how many times has subj A gone to the bathroom by now? it must be in the hundreds. luckily no one else has this problem quite as badly. Subj A is still compalaining about his fast food burrito dinner. It was simply gross. Also, Agenda Suicide (The Faint is played repeatedly by the overly dorky Subj A) For the past while, Subj A has been lost in a room filled with tracers.
T-10: Still tripping strong. Subj's A, B, & C spend lots of time talking, in various couch piles, subj. A on bottom (always!) Subj A&C are now staring intently at a pillowcase covered in blue and yelow stars. It is also covered in lots of electricity, sparkles, snowflakes, etc.
T-12: Still talking, but subj B loses ability to participate in convos or even remember how sentences started. Perhaps it was mean, but Subj B&C play with this for a few hours as they ramble on and tease him about not remembering. He forgets. ('I have this condition you see'...)
T-14: numerous observations/insights are made using some twisted sense of logic, but still valid. ie, If someone has cold feet, then they are dead. if they are dead. and dead is as goth as it gets. others are made, most forgotten. ('I have this condition you see'....)
This continued, with the minds of A&B becoming mushier until the trip ended at somewhere between T-22 to T-26 hrs. It was difficult to tell when one was done due to the sleep dep/mental exhaustion. This is something that all subjects will probably do again, but probably leave the dosages around 50mg to avoid the brain mush experienced afterwards.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This vignette provides best practices for credential management to improve the ease and portability of code across analysts.
With access to data PEPFAR’s DATIM, USAID Google Drive, and, the USAID’s Data Development Commons, analysts have different credentials to all these systems. Passwords should never be stored in scripts and its best not to include user names as well. Using glamr
will systematize USAID analysts code. User names will be called the same thing and will be stored in the same location across machines.
Let’s go through an example of how this might be used, staring by loading the packages we’ll need.
Before using credentials you’ll need to store then on your local OS. The glamr
package utilize keyring
to store and access the usernames and passwords, using some wrappers around keyring
functions.
Let’s start by storing our USAID email address, which we will use to access Google Drive. You’ll only be storing your email, not password. This function also allows you to store your email in your .Rprofile, allowing googledrive::drive_auth()
and googlesheets4::gs4_auth()
to run without you having to enter your email.
<EMAIL_ADDRESS>Next up we’ll store our DATIM credentials. For this, you’ll need to enter you DATIM user name. When you run the code, you will get a prompt/pop-up window in RStudio to enter your password.
set_datim("rshah")
Lastly, if you have access to Amazon Web Services (s3) for the DDC, you can also store your s3 access and secret keys.
set_s3access(access = "ABDCEDFF",
secret = "MLIZD998SD")
These functions have now passed your credentials in your operating system’s credential manager - Credential Store on Windows and Keychain on MacOS. We can use keyring
to see all the “services”, or accounts, stored there.
keyring::key_list()
Without using the glamr
package, analysts would have had to write out their username and authenticate. Prior to saving and pushing the code to GitHub, the analyst would have to remove their email. Another analyst would have to review the code and find where to put their email in manually if running themselves.
library(googlesheets4)
#email
email <-<EMAIL_ADDRESS>#authenticate
gs4_auth(email)
#specific Google Sheet Unique ID (found at the end of the URL)
sheet_id <- '5mD3ndk08Sdd3dn1dm29smD'
#read directly into R (similar to read_csv or read_xlsx)
df <- read_sheet(as_sheets_id(sheet_id), "Sheet1")
We faced a similar issue with DATIM credentials, where we had to either write out our username and remove prior to pushing to GitHub or some analysts stored this information in scripts in different locations on their different machines, using different object name, eg user
, myuser
, datim_acct
, datim_login
, etc.
#DATIM user
user <- "rshah"
#pull DATIM table of OU/country UIDs and sub-national unit levels
ou_table <- datim_outable(user, mypwd(user))
The old way of doing things was inefficient and posed a risk of posting credentials accidentally. The previous method required storing the username in your code and then using it to pull from an encrypted local file that stored the password associated with the username using glamr::mypwd()
. Each analyst would have to change the username if they ran the code. The analyst, now, will perform the same task, but won’t have to write our their username in the code since its loaded into the session with load_secrets()
and always assigned/called the same thing.
Now that they’re stored after using set_email()
and set_datim()
, we can load up our credentials at the beginning of our code and be available for your current R session. In addition to storing your email, load_secrets()
will authenticate with Google Drive if you have the googledrive
and/or googlesheets4
packages.
Your account information is stored for the session in Options
and can be accessed directly via getOptions("email")
or getOptions("datim")
. We also have two wrapper functions to pull your DATIM information since you may need to include that in an API request - datim_user()
and datim_pwd()
How does this help? Instead of having to manually enter your USAID email, it can be loaded automatically and already authenticated for Google API by running load_secrets()
. The user can also specify which authentication they want to provide in a session, rather than loading all of them. For example, you could just specify to authenticate for use with Google Drive and Sheets - load_secrets("email")
.
library(googlesheets4)
library(glamr)
#setup session
load_secrets()
#specific Google Sheet Unique ID (found at the end of the URL)
sheet_id <- '5mD3ndk08Sdd3dn1dm29smD'
#read directly into R (similar to read_csv or read_xlsx)
df <- read_sheet(as_sheets_id(sheet_id), "Sheet1")
#pull DATIM table of OU/country UIDs and sub-national unit levels
ou_table <- datim_outable(datim_user(), datim_pwd())


Chapter 5 Logistic Regression
We’ve asked you thus far to take our word regarding Generalized Linear Models (GLMs). In this chapter, we’re going to take a look at a certain type of data that we know violates our assumptions: binomial data. Here we’ll examine binomial data, see why ordinary least sqares falls apart, and consider some alternative methods (spoiler: it’s going to be one of our generalized linear models).
5.1 What is Binomial Data?
5.1.1 Refresher: Bernoulli and Binomial
Bernoulli and Binomial random variables are some of the most important to consider, because they get at real the roots of representing the stata of the world. A Bernoulli random variable considers the simplest possible datum: whether something is, or it isn’t. Numerically, we represent this as 1 or 0, just like computer data. And while I can use this to measure yes/no type data, such as “does a person own at least one pet?”, an astute observer will realise the same idea can apply to a variety concepts including:
- success / failure,
- wins / losses,
- heads / tails,
- exists / doesn’t exist
- has / doesn’t have
- is a member of a group of interest / is not a member
- or even, was my prediction realized?
Often, whatever resulted in creating this single Bernoulli instance might be something that repeats. In fact, if we hope to apply statistics to it, there needs to be many instances. If we can assume a constant probability across all iterations of the events we’re interested in, but want to ask questions about them in aggregate, that’s Bernoulli Binomial (or just binomial for short). Some examples of questions that fall into binomial data would be:
- is this strategy effective (i.e. does it affect the rates of success or failure across many trials?)
- is this baseball team better than another (i.e. do they win more games, not just a game?)
- if I bet money on getting four heads in a row, what kind of odds makes that a good gamble?
- if a mailcarrier is moving to a new route, if I know how many homes are \(\mu\)-probable to have an angry dog, what’s the liklihood that mail carrier will encounter an angry dog?
- if \(\mu\)-proportion of students have a medical condition I’m interested in, how many students will I need to talk to in order for it to be more likely than not I’ll talk to x of them? Way more likely than not? Almost certain?
- if my predictions are p-percent accurate, and the cost of failure is m dollars, is it worth spending more money for more accurate predictions?
Hopefully you’ll agree with me that questions of this type are actually extremely common.
5.1.2 Representing the Bernoulli distribution
Different texts use different language, so it’s to explain how we’ll be talking about Bernoulli and binomial distributions here. First, here’s the /Bernoulli distribution/ probability mass function.
\[\mathcal{P}(y;\mu)=\mu^{y}(1-\mu)^{1-\mu},\] where \(y\in\{0, 1\}, 0\leq\mu\leq1\).
This might be a bit different than what you’ve seen before, so lets talk about each variable.
- \(\mathcal{P}\): (\(\texttt{\\mathcal\{P\}}\) in TeX) is the probability mass function of a certain outcome. \(\mathcal{P}(y;\mu)\) then is the probability of event \(y\) when the success has a probability of \(\mu\).
- \(y\): is the number of successes. Since this is a single trial, it can only be 0 or 1 (described by the statement \(y\in\{0, 1\}\)).
- Basically, it lets us choose whether we’re predicting success or predicting failure all in one function.
- If we’re interested in the liklihood of an event not occuring \(y=0\), \(\mu^0=1\), and the PMF simplifies to \(\mathcal{P(1,\mu)}=1-\mu\).
- Conversely, if we’re interetsed in an event occuring, \(y-1\), \((1-\mu)^{(1-1)}=1\), and the PMF simplifies to \(\mathcal{P(1,\mu)}=\mu\).
- \(\mu\): (\(\texttt{\\mu}\) in TeX) is the probability of the event. But wait, isn’t \(\mu\) the mean, and also the expected value? Yes, exactly! Those are all the same in this PMF, so we use the same variable. Because many text books need you to consider those ideas separatey so you can discover that relationship, you might see it recorded elsewhere as \(p\), but since demonstrating that is beyond our scope here, we’ll use \(\mu\) to reduce complexity.
- It complicates using the formula elsewhere, but we could rewrite this as: \[ \mathcal{P}(y;\mu)= \begin{cases} \mu&\textrm{if }y=1\\ 1-\mu&\textrm{if }y=0\\ \end{cases}, 0\leq\mu\leq1 \]
5.1.3 Representing the binomial distribution
Intuitively, you likely recall that if \(P(\textrm{heads on one coinflip})=\frac{1}{2}\), then \[P(\textrm{heads twice on two coinflips})=P(\textrm{heads on one coinflip})^2=\frac{1}{2}\times\frac{1}{2}=\frac{1}{4}\]. However, just like the above PMF accounts for yes and no, our binomial PMF \(\mathcal{P}\) needs to account for yes and no, how many yesses we’re looking for, and how many trials there are overall. So here, we’ll use:
\[\mathcal{P}(y;\mu,m)=\binom{m}{my}\mu^y(1-\mu)^{m(1-y)}\] where \(y={0, 1/m, 2/m, ..., 1}, m \textrm{ is a positive integer, and }0<\mu<1.\)
Like before, this format might be a bit different than what you may have seen elsewhere, so let’s break it down.
- A reminder, \(\binom{n}{k}\), (written
\texttt{\binom{n}{k}}
in TeX) is a combination \(n\) choose \(k\). More detail here. - \(\mathcal{P}\) (\(\texttt{\\mathcal\{P\}}\) in TeX) is the probability mass function (PMF) aka probability function. \(\mathcal{P}(y;\mu,m)\) then is the probability function of a binomial distribution for event \(y\) in the case of \(m\) Bernoulli trials with each trial having a probability of \(\mu\).
- \(m\) here is simply the number of trials easy.
- \(y\) here measures the proportion of successes (and actually it really is above as well–I’ll get to that in a moment).
- For example, if there are 4 trials and we want to see two successes, \(y/m=2/4=0.5\)
- This interacts interestingly in the combinatorial component. That \(my\) will always equal the numerator of \(y\). In the previous example then, the choose part of \(m\) choose \(my\) (aka \(\binom{m}{my}\)) will equal \(\binom{4}{2}\).
- If \(y=0\), \(\binom{m}{m0}=\binom{m}{0}=1\) (any choose 0, like any number raised to 0 equals 1). So, looking at this formula, if I want to know the odds of not getting any heads on four coin flips, then
- \[\mathcal{P}(y;\mu,m)=\binom{m}{my}\mu^y(1-\mu)^{m(1-y)}=\binom{4}{4\times0}\mu^0(1-\mu)^{4(1-0)}=1\times1\times(1-\mu)^4\]
- So assuming a fair coin where \(\mu=\frac{1}{2}\), then \(\mathcal{P}(y;\mu,m)=\mu^4=(\frac{1}{2})^4=\frac{1}{16}\), which fits our expectations. Neat!
5.1.4 What does a bunch of binomial data look like then?
possibilities <- c(0,1) # either heads (1) or tails (0).
# c(...) makes a list of whatever you include in the ...
probabilities <- c(0.5, 0.5)
# a 50% chance of heads, a 50% chance of tails.
(flip_data = sample(x = possibilities, size = 10, replace=TRUE, prob=probabilities))
## [1] 1 0 0 1 0 0 1 0 0 1
# x is the vector of possible outcomes.
# size is the number of coin flips I want.
# we are flipping coin, not drawing from a deck, so ne
# second flip is just as likely to get heads or tails
# as the first: we *do* replace.
# prob is a vector of probabilities for each x
# encasing the whole thing in parentheses shows the
# value of the variable you set as you set it
So zeros and ones, we expect that (sorry if you’re the 2 in \(2^{10}\) readers who see all zeroes or ones). Lets check a simple histogram.


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Usefull fucntions to manipulate CSS rules.
It insert a new style sheet element on the page.
It will be inserted where the js code is placed (head or body)
and will override other CSS style sheets placed before.
jCSSRule(selector,property,value)
or jCSSRule(selector,{ property:value })
to add
new rules for the selector.
jCSSRule(selector,property)
to get the value for that key.
jCSSRule(selector,property,null)
will remove the rule and use
the default one defined in the css.
jCSSRule("*",null)
will remove all previous rules.
If you better like the jQuery syntaxe, use the jQuery plugin
jCSSRule("body",{ background:xxx });
jCSSRule("h1,h2",{ color:xxx, background:xxx });
jCSSRule("button",{ background:xxx });
jCSSRule("*",null);


{% load staticfiles i18n %}
The planner found a solution for your query.
The planner found a plan but is not the most appropriate for your needs. Try stretching the time between places. Or increasing the running time for the planner.


Jon Rahm lands $5 million jackpot with victory in Dubai
DUBAI – Jon Rahm is $5 million richer after winning both the season-ending DP World Tour Championship and the Race to Dubai title on Sunday.
The world No. 5 got up and down from a greenside bunker for birdie at the 18th hole to win the tournament by a stroke from Tommy Fleetwood, who also would have become European No. 1 with a win on the Earth Course.
Rahm, who started the final round tied for the lead with Mike Lorenzo-Vera, led by six shots after birdying five of his first seven holes.
However, Fleetwood — playing one group ahead — birdied five of his last seven holes, with his tap-in birdie at No. 18 giving him a 7-under 65 and tying for the lead with Rahm as the Spaniard was putting on the 17th green.
Rahm’s birdie putt there came up a few centimeters short so he needed a birdie on the par-5 last to finish ahead of Fleetwood. His drive was perfect, his approach found the bunker, but he chipped out to 4 feet and rolled in the putt for a 68 as Fleetwood watched in the scorer’s tent.
Rahm, who was 19 under overall, earned $3 million for winning the event, and a bonus of $2 million for winning the Race to Dubai.
Rahm is only the second Spaniard to finish a season as European Tour’s No. 1 player, after the late Seve Ballesteros.
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All Senior Analysts Sheila Dugan Katherine Klosek Matt Pazoles Matt Raifman All Analysts Lena Geraghty Nick Hadjigeorge Eric Reese


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Event Location:
Microsoft head office
3012 William Nicol Drive
Bryanston, Gauteng, South Africa
Abstract:
In this session, we will cover the complete set of features and capabilities in SQL Server 2019. Want your queries to run faster with no application changes or watch your database recover in seconds? Need to connect to Oracle, CosmosDB, or Azure without moving data? Making a move to SQL Server 2019 could be the right choice for you. SQL Server 2019 represents the next evolution of the leading database platform in the industry. "This is not your grandpa's SQL Server" says Travis Wright. Whether it is Big Data Clusters with Hadoop and Spark, Java, Linux, Containers, or Kubernetes. Pedro Lopes says it still is "Your grandpa's SQL Server" because of Intelligent Performance, Mission Critical Availability, and Security. Who is right? Come to this session and see for yourself.
Speaker(s): Bob Ward,
Abstract:
BI is certainly about interactive reports and clever analysis, but for it to be truly valuable, your insights must easily be turned into actions. In this session we’ll explore how combining Power BI and two other Power Platform apps, namely Microsoft Flow and Microsoft PowerApps, we can turn BI into “insights AND action”. We will walk through a popular use case of a Sales Rep visiting a client, and integrating Flow, PowerApps and Power BI on a tablet one can vastly improve customer satisfaction, significantly reduce workload and administration, and improve sales volumes. Here are some of the things we’ll cover: • Validate stock levels on the spot to avoid ordering an out-of-stock item • Place orders directly from Power BI using Flow and PowerApps to integrate with your ERP system • Don’t miss out on any lost opportunities by viewing past order history using insightful Power BI visuals • Trigger workflows automatically instead of completing the admin later • CRM Integration
Speaker(s): Sven Vosse,
Abstract:
You’re comfortable with picking the right visualization for a specific data set, and making it look good. You know the basics of design and can craft a report that is pleasing on the eye. Now it’s time to take your data presentation skills to the next level. In this session, we will learn how to design for accessibility, allowing more people to benefit from our work. We’ll learn the difference between designing for users to explore information, and for users to consume insights. This will lead us into the basics of constructing a data story. From choosing the relevant data points, to removing all unnecessary distractions and constructing a cohesive narrative. Every data story is unique, but the techniques for effective data presentation are universal. Improve your reports, dashboards, and slide decks by learning the art and science of quality data visualization.
Speaker(s): James McGillivray,
Abstract:
Moving to the cloud should not be difficult. Most organizations make the mistake of wanting to modernize their applications and databases on-prem first before moving to cloud. This is normally very expensive, leads to delays and often abandonment of projects. In this session we will look at : How to take the guess work out of developing a cloud roadmap. Look at how cost effective cloud will be in a very accurate manner. How to assess applications and their dependencies. Reduce time to cloud by telling exactly which lines of code to change in your applications and databases. Effort estimation to complete each task. Including 3 migration options in Microsoft Azure for your applications and databases.
Speaker(s): Jody Roberts,
Abstract:
Data Science is the latest Data Buzz word, but what does this mean for us the Analysts and Developers. Well, this is just another opportunity to grow and learn a new skill. The great thing about the era we live in is that information is everywhere but can be a little overwhelming for someone who is just starting off in a new field or wants to learn a new skill. In this session, we will use Python in an easy to understand layout by using Notebooks as our Interface and pandas as our data manipulation library in Python. This session is for everyone that is just starting off in Python. We will learn how to import/export data into Python as well as manipulate and analyse data. Finally, we will look at how to bring this back into our SQL Server world.
Speaker(s): Jean-Pierre Voogt,
Abstract:
In this session, we will unpack how to do big data analytics using Power BI, Azure Databricks and Azure Data Lake. This session with cover a brief introduction to Azure Databricks and Power BI composite models and unpack what it takes to do analytics over trillions of rows of the data.
Speaker(s): Kathryn Varrall,
Abstract:
Have you ever been thinking that you would like to be able to do more in SQL Server than what T-SQL allows you to do? If that is the case, the this session is for you! Here we look at the SQL Server Extensibility Framework, and we see how we can call out to external languages from inside SQL Server. We look at R, Python and Java, and what we can do from SQL Server having access to those languages.
Speaker(s): Niels Berglund,
Abstract:
In the age of Self-Service analytics. We often have to deal with "raw" data. Using Power Query and M language one can define a step by step process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The final transformed dataset is available one can use it to create visualizations that can be added to Power BI reports and publish to the Power BI service. M stands for Data Mashup and M is a functional language. In this session, I will demonstrate around 30 Things that one can do with Power Query
Speaker(s): Sandesh Nagaraj,
Abstract:
Migrating SQL workloads to Azure can be a daunting task. Come learn from the SQL product group about the tools and services that are available to help you assess, plan, and migrate your entire SQL Server stack.
Speaker(s): Anna Hoffman,
Abstract:
Data Science is a new field in technology, and has begun to mature, splintering off into many disciplines. Like any complex role, there isn't one single skillset and knowledge-base required for Data Science. Buck Woody, an Applied Data Scientist from the SQL Server Product Group at Microsoft explains Data Science using a "day" to show you the processes and procedures a Data Scientist uses, and how you can learn the skills, math, languages and platforms involved in a Data Science role.
Speaker(s): Buck Woody,
Abstract:
At the recent Build conference, Microsoft announced the general availability of Azure SQL DB hyperscale. What is this 'hyperscale' thing all about? In this talk I'll show how the hyperscale architecture differs from a normal database architecture and why, in theory, it promises near infinite scalability for transactional workloads
Speaker(s): Gail Shaw,
Abstract:
One of the models that CosmosDB offers is the Gremlin graph API. In this presentation I’m going to take you through the basics of what CosmosDB is, show how to create and configure a database. Once we've got a database, we'll look at the the gremlin api, including how to create and query the graphs.
Speaker(s): Gail Shaw,
Abstract:
Have you ever started a warehouse or ETL project and realized that the data wasn't as "clean" as you were told? If only you had profiled your data before you started then you wouldn't have to rework design elements, change code or redesign your database. In this session we will talk about what data profiling is, why you should do it and how you can do it with tools that are already included in the SQL Server BI stack.
Speaker(s): Angela Henry,
Abstract:
There's a wealth of data available on the internet which can be used for data augmentation or to create entirely new datasets. The data might all be on one page (if you're lucky!) or distributed across multiple (possibly hundreds or thousands) pages. Web scraping allows you to gather those data and consolidate them in a convenient format. This will be a hands-on tutorial in R, so bring your machine and follow along. Session code: https://github.com/datawookie/talks/tree/master/2019-09-07-sql-saturday-johannesburg
Speaker(s): Andrew Collier,
Abstract:
It sounds more difficult than it is, but using Azure's Resource Manager deployment engine and preconfigured templates, we can deploy a fully functional Kubernetes cluster in Azure with persistent storage. The Templates use CNAB, Cloud Native Application Bundles to achieve this. We take a deeper look at how this is deployed and how you can use containerisation for your Highly Available Data Workloads.
Speaker(s): Warren du Toit,
Abstract:
This session will be presented by the Microsoft Azure SQL Database Product Group and will cover a deep dive into the architecture for Azure SQL Database Managed Instance Azure SQL Database Managed Instance is a new deployment option for Azure SQL Database, that enables easy SQL server applications migration from on-premises to a fully managed platform as a service. Managed Instance provides SQL Server application compatibility, security isolation with VNET support.
Speaker(s): Alain Dormehl,
Abstract:
They're just numbers, right? A date's a date. It's just string data, who cares? I can't tell you how many times I've heard these phrases. This session will help you understand why choosing the correct data type for your data is so important. It affects data quality, storage and performance. It can even produce incorrect query results.
Speaker(s): Angela Henry,
Abstract:
There is a fight for attention these days in the IT space. Are you doing everything that you could be doing to make sure that you stand out from your peers? Are you sure that you are on the correct path for you to achieve your end career goals? In order to be seen as a Thought Leader in your chosen IT discipline these days, you need to be developing and nurturing your personal brand. This is going to be vitally important for you in your next career move.
Speaker(s): Vanessa Raath,
Abstract:
In this session we shall take a look at Azure Machine Learning Studio. We will import data sets, create models and train them. We will go over the fundamentals of Azure Machine Learning and what it has to offer.
Speaker(s): Jeanne Combrinck,
Abstract:
With the advent of the tabular model in SQL Server 2012, another analytical option has opened to the BI Specialist. We shall be looking at what critical factors should be analyzed prior to opting for the model and that done, how to extract data from a tabular model using Data Analysis Expressions (DAX). We shall discuss many of the often used functions, discuss their 'overloads' (utilizing several practical examples) and see how to turn the extracted data into valuable information that may be used by decision makers to help the enterprise achieve its corporate goals.
Speaker(s): Steve Simon,
Abstract:
Do you want to boost your IT career, or thinking about starting a new career? Then Microsoft Certifications are for you. In this session, we will look at the different types of certification paths and what you can do to pass the exams. I will take you on the journey to selecting the right exam for you and assist you to pass these exams with confidence.
Speaker(s): Vernon Du Preez,
Abstract:
In this session you will learn about use cases for paginated reports, working with sub-reports in SSRS to build reusable sections which will form part of a dynamic which can be used to create a structured invoice or statement. We will then dive into the paginated report functionality in Power BI and learn more about SSRS features that are now available in Power BI.
Speaker(s): Arohan Naidoo,
Abstract:
The new release of SQL Server; SQL Server 2019 includes Apache Spark and Hadoop Distributed File System (HDFS) for scalable compute and storage. This new architecture that combines together the SQL Server database engine, Spark, and HDFS into a unified data platform is called a “big data cluster.” This session gives you an overview of what a SQL Server Big Data Cluster is, and what you can do with it. We look at the various components, (Kubernetes, Spark, HDFS, PolyBase, etc.), and what you can do with them.
Speaker(s): Niels Berglund,
Abstract:
"Data is the new oil" - Everyone It's a statement we've all heard before. No doubt people are realizing just how valuable data is becoming. Ultimately though for data to be valuable it needs to be usable. With the growth of new technologies like AI, Data Lakes, IOT to name a few the modelling of data is even more important than ever. Stop your data lakes from turning into data swamps. This session will cover the basic terminology and methods of modelling data. It will also show some of the most common models and what they are best suited for. Go from Zoolander too Right Said Fred and be Too Sexy for your data in 50 minutes.
Speaker(s): Tinus Visagie,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: https://za.linkedin.com/in/niels-berglund-0122593
Contact: https://nielsberglund.com
Niels works as Software Architect at Derivco, where he develops software as well as "dreaming" up new and innovative ways to use existing and new technology. Before joining Derivco, Niels worked as researcher/instructor at Developmentor where he was responsible for the database curriculum. He was a co-author of the first SQL Server 2005 book for developers: quot; A First Look at SQL 2005 for Developersquot;. He has presented at all leading technical conferences; Tech-ED, SQL PASS, DevWeek, Ignite the Tour, etc., and is also a Microsoft Data Platform MVP.
Jean-Pierre is an SQL Developer and Data Analysis Team Lead from South Africa. He is MCSA (Microsoft Certified Solutions Associate) and owner of a bachelor’ degree in Software Engineering with good experience with Database design, Data warehousing and development. He has a great passion for SQL server and he enjoys solving complex business problems. Jean-Pierre speaks at the Johannesburg SQL User Group, trying to give back to the SQL community as much as possible. He loves to tinker with SQL Server and see how he can approach a problem with a different angle.
LinkedIn: https://za.linkedin.com/in/niels-berglund-0122593
Contact: https://nielsberglund.com
Niels works as Software Architect at Derivco, where he develops software as well as "dreaming" up new and innovative ways to use existing and new technology. Before joining Derivco, Niels worked as researcher/instructor at Developmentor where he was responsible for the database curriculum. He was a co-author of the first SQL Server 2005 book for developers: quot; A First Look at SQL 2005 for Developersquot;. He has presented at all leading technical conferences; Tech-ED, SQL PASS, DevWeek, Ignite the Tour, etc., and is also a Microsoft Data Platform MVP.
LinkedIn: https://www.linkedin.com/in/datawookie/
Contact: https://datawookie.netlify.com/blog/
Itinerant Data Scientist; Geek; Runner.
Contact: http://aka.ms/bobsql
Bob Ward is a Principal Architect for the Microsoft Azure Data SQL Server team, which owns the development for all SQL Server versions. Bob has worked for Microsoft for 26+ years on every version of SQL Server shipped from OS/2 1.1 to SQL Server 2019 including Azure SQL. Bob is a well-known speaker on SQL Server, often presenting talks on new releases, internals, and performance at events such as PASS Summit, Red Hat Summit, Microsoft Ready, SQLBits, SQLIntersection, Microsoft Inspire, and Microsoft Ignite. You can follow him at @bobwardms or linkedin.com/in/bobwardms. Bob is the author of the books Pro SQL Server on Linux and SQL Server 2019 Revealed available from Apress Media. Twitter: http://twitter.com/bobwardms.
Contact: http://sqlinthewild.co.za
Gail Shaw is a Database Specialist focusing on database performance tuning and database recovery, with a particular interest in topics such as indexing strategies, execution plans, and writing T-SQL code that performs well and scales gracefully. Gail holds a Microsoft Certified Master certification for SQL Server 2008 and is a Data Platform MVP. She's a frequent poster on the SQLServerCentral forums, a Pluralsight author, writes articles for both SQLServerCentral.com and Simple-Talk.com, and has spoken at SQLSaturdays, SQL Bits and at the PASS Community Summit. Gail has been responsible for extending the lifespan of many an application, by performance-tuning their databases and providing technical guidance on all things SQL Server related.
Warren is a Senior Solutions Architect focused on Cloud with emphasis on Open Source and Networking and PaaS workloads at Britehouse (Dimension Data). Warren has an extensive Open Source and Cloud Networking background, having built global grids for global cloud providers. Warren consults to Enterprise Customers and System Integrators, with a focus of either assisting customers move to the cloud or helping System Integrators build their own cloud offering. When Warren is not customer focused, he tries to spend significant time-sharing knowledge on how to drive Open Source and Azure based workloads. Warren has travelled extensively presenting on Microsoft’s behalf at events around the world like Microsoft Ignite. Warren is a multiple year M
Contact: http://sqlinthewild.co.za
Gail Shaw is a Database Specialist focusing on database performance tuning and database recovery, with a particular interest in topics such as indexing strategies, execution plans, and writing T-SQL code that performs well and scales gracefully. Gail holds a Microsoft Certified Master certification for SQL Server 2008 and is a Data Platform MVP. She's a frequent poster on the SQLServerCentral forums, a Pluralsight author, writes articles for both SQLServerCentral.com and Simple-Talk.com, and has spoken at SQLSaturdays, SQL Bits and at the PASS Community Summit. Gail has been responsible for extending the lifespan of many an application, by performance-tuning their databases and providing technical guidance on all things SQL Server related.
LinkedIn: https://www.linkedin.com/in/amthomas46/
Anna is a Data and Applied Scientist on the Azure Data (SQL) product team at Microsoft. In her time at Microsoft, Anna has worked in Microsoft Consulting Services, AI Engineering, and now SQL Engineering. She spends most of her time working on Azure SQL and participating in the community to democratize intelligence over all your data, from ground to cloud. You can follow her on Twitter at @AnalyticAnna.
LinkedIn: http://www.linkedin.com/in/angelahenrydba
Contact: http://SQLSwimmer.com
Angela is a Solution Architect, living in High Point, NC and loves what she does. She's worked with all versions of SQL Server worn all the hats that come with dealing with SQL Server throughout the years: developer, administrator, data architect and BI developer. She has her MSCE in Data and Analytics (f.k.a., Business Intelligence), is a Data Platform MVP, and a Microsoft Certified Trainer (MCT). She is the former chapter leader for the PASS Triad SQL User Group in Greensboro, NC. In her spare time you can probably find her in or at the pool, she's an avid US Masters Swimmer, Coach and Instructor.
LinkedIn: http://za.linkedin.com/in/sandeshnagaraj
A Certified Data Architect with around 15 years of experience in Data warehouse, Business Intelligence and Database implementation. Sandesh holds MCITP SQL Server in BI, Data Architect certified from Microstrategy etc. He has worked extensively as BI Consultant/Technical Specialist/BI Architect/Sales Engineer/Manager. Sandesh actively participates and speaks at Johannesburg SQL Server User and SQL Saturday events.
LinkedIn: http://www.linkedin.com/in/stephenrsimon/
Contact: http://www.sqlservercentral.com/blogs/simon_says/
Steve Simon is a Database Solutions Architect with Business Knowledge Services Inc. He has been involved with database design and analysis for over 35 years. Steve has presented papers at numerous PASS Summits and PASS SQL Rallies. He is also regular presenter on the SQL Saturday circuit and he is a contributor on SQLShack.com
Sven Vosse is the lead consultant at Seidor Analytics, a specialist BI Training and Consulting company based in Jhb. Seidor Analytics serves clients of all sizes in all industries and focuses mainly on Power BI and SAP Analytics Cloud. Sven is also the Johannesburg Power BI User Group Leader
LinkedIn: http://www.linkedin.com/in/tinusvisagie
Contact: https://tvisagieblog.wordpress.com/
Started my IT career as a software developer just in time for Y2K. After 13 years of software development I moved my focus to data and have been doing data development in various forms for 8 years. Currently working at one of the big banks developing data marts and defining best practice development patterns.
Arohan is a Business Intelligence developer working at Entelect primarily on the Microsoft BI stack who has an interest in using Big Data and IoT to find insights into ways to improve daily life as well as focused research on using technology to enhance learning.
LinkedIn: https://za.linkedin.com/in/jeannecombrinck
Contact: http://thesqlgirl.com
Senior BI Developer at Britehouse. In her spare time she can be found spending time with her animals, doing SQL puzzles and writing on her blog http://thesqlgirl.com/.
LinkedIn: https://www.linkedin.com/in/vanessaraath
Contact: https://www.vanessaraath.com
I am determined to bring the ‘Human Element’ back into today’s Recruitment Process as I believe it has been lacking for too long now! I believe that people are the core building blocks of every business but that doesn’t mean that we can treat every ‘block’ the same. I have over 13 years’ experience in the Recruitment Industry and I have specialised in the IT Applications space for most of this time. I have recently decided to flex my entrepreneurial muscle and I have launched my own Sourcing Training Business. I am passionate about raising the standard of Sourcing in Africa.
LinkedIn: http://www.linkedin.com/in/buckwoody
Contact: http://buckwoody.wordpress.com/
Born helpless, naked, and unable to provide for himself, Buck Woody overcame these amazing handicaps to become a software developer, data professional, teacher, and a productive invertebrate member of society.
LinkedIn: http://www.linkedin.com/in/angelahenrydba
Contact: http://SQLSwimmer.com
Angela is a Solution Architect, living in High Point, NC and loves what she does. She's worked with all versions of SQL Server worn all the hats that come with dealing with SQL Server throughout the years: developer, administrator, data architect and BI developer. She has her MSCE in Data and Analytics (f.k.a., Business Intelligence), is a Data Platform MVP, and a Microsoft Certified Trainer (MCT). She is the former chapter leader for the PASS Triad SQL User Group in Greensboro, NC. In her spare time you can probably find her in or at the pool, she's an avid US Masters Swimmer, Coach and Instructor.
LinkedIn: https://www.linkedin.com/in/kathryn-varrall-87212132
I have been doing what I love most for the last 15 years and that is working with data, specifically building data warehouse and business intelligence solutions and doing advanced analytics. I now work for Microsoft as a Technical Solutions Professional in the Advanced Analytics and AI space assisting and guiding customers in their data journey
LinkedIn: https://www.linkedin.com/in/vernon-du-preez-a8001723
I am an MCSE Certified Data Analyst with a love for SQL. I enjoy learning something new every day, even if it is something unimportant. I like to see most issues as challenges, thus making issues fun to solve.
LinkedIn: http://za.linkedin.com/pub/jody-roberts/7/b29/193/Edit
Contact: http://JodyWP.wordpress.com
Jody Roberts is a Microsoft Data Platform MVP based in Cape Town, South Africa. He specializes in SQL Server architecture, disaster recovery (DR), and server optimization. Jody serves as a PASS Regional Mentor responsible for the Middle East amp; Africa, Chapter Lead of the SQLCape user group, and Co-Lead of the PASS Cloud Virtual Chapter. Jody is a Principal Consultant at Northern Data.
LinkedIn: https://za.linkedin.com/in/thejimmyrsa
Contact: https://jimbabwe.co.za/blog-posts/
James McGillivray, Data Management and Analytics MCSE, has been working in Business Intelligence for over a decade. He spends his working hours building solutions that allow people to make informed decisions quickly and accurately. James is a director at Cobalt Analytics. Before his career in data, James was an avid musical theatre student and musician. This love for performance has developed into a passion for developing and delivering presentations that are informative and enjoyable. James speaks frequently at the Johannesburg SQL User Group, and has delivered sessions at numerous SQL Saturdays, PASS Summit, Power BI World Tour and SQL Bits.
LinkedIn: https://za.linkedin.com/pub/alain-dormehl/7b/903/446
MCSA and MCT in SQL Data Platforms, I am passionate about all things data. I also have a particular love for new innovative solutions to deal with the big data problem. I have been using SQL for 9 years professionally from SQL 2003 and more recently SQL 2016 CTP2.1. I have also been fortunate enough to work and develop extensively on SQL 2012 Parallel Data Warehouse through a Microsoft Analytic Platforms System.
The following is a list of sponsors that helped fund the event.


This essay is about my fisrt coding project with my tutor, Mr. Ezekiel, he is a website developer and he is taking me on web developer and programing ,it had been a few days since he started coming and this last day have been great. He taught my so many things including UI/UX, currently he is teaching how to code.
I have leant alot from his few teaching and hope to learn more. It is because of his help that i was able to code this letter you are reading now. My thanks and apreciation to Mr. Ezekiel, my tutor, my teacher and my friend.


In Belgium, pop art was represented by Paul Van Hoeydonck, whose sculpture Fallen Astronaut was left on the moon during one of the moon missions. Internationally recognized artists such as Marcel Broodthaers and Panamarenko are indebted to the pop art movement; Broodthaers's great influence was George Segal. Another well-known artist, Roger Raveel, mounted a birdcage with a real live pigeon in one of his paintings. By the end of the 1960s and early 1970s, pop art references disappeared from the work of these artists when they started to adopt a more critical attitude towards America because of the Vietnam War's increasingly gruesome character. Panamarenko, however, has retained the irony inherent in the pop art movement up to the present day.


95
Let Us Sing Songs of Praise
1 Oh come, let us sing to the LORD;
let us make a joyful noise to the rock of our salvation!
2 Let us come into his presence with thanksgiving;
let us make a joyful noise to him with songs of praise!
3 For the LORD is a great God,
and a great King above all gods.
4 In his hand are the depths of the earth;
the heights of the mountains are his also.
5 The sea is his, for he made it,
and his hands formed the dry land.
6 Oh come, let us worship and bow down;
let us kneel before the LORD, our Maker!
7 For he is our God,
and we are the people of his pasture,
and the sheep of his hand.
Today, if you hear his voice,
8 do not harden your hearts, as at Meribah,
as on the day at Massah in the wilderness,
9 when your fathers put me to the test
and put me to the proof, though they had seen my work.
10 For forty years I loathed that generation
and said, “They are a people who go astray in their heart,
and they have not known my ways.”
11 Therefore I swore in my wrath,
“They shall not enter my rest.”


--- body_id: holidays ---
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Chinese Literature in English
September 11th
Questions from last time?
The Analects 論語
Historical Background
Confucius 孔丘/孔夫子
The Analects
The Model of Governance
Relation with Deities
Ritual 禮
Power Dynamics
The Gentleman 君子
Humanity 仁
Filial Piety 孝
Key Terms
The reading
Open Discussion


White-Tailed Deer in Athens
Deer are so numerous around Athens, in our suburbs and farms, that it helps for us to be vigilant while driving, especially at dawn and dusk when lighting can be tricky. You can learn more than the casual observer by clicking the buttons below...
Taxonomy
| Order |
Artiodactyla |
| Family |
Cervidae |
| Genus |
Odocoileus |
| Species |
virginianus |
Diet
- In the wild, deer eat a wide variety of vegetable matter. Leaves, shoots, grasses and legumes all make up the diet. Acorns are a major diet component.
- At Bear Hollow, the deer eat a mix of horse sweet feed, lettuce, carrots and other vegetables. Strawberries and grapes are a favorite treat.
Range and Habitat
- Mostly wooded areas, but predominantly the edge habitat (where two habitats meet). Also found in urban environments, river corridors, swamps and mountains.
- In North America, found in the entire eastern half of the continent.
- In Georgia, they range statewide and are found in every county.
Physical Features
- Coat reddish brown during the spring and summer, but turns brownish gray in the late fall and winter.
- Underside of tail is white.
- Body length is about seven feet from nose to rump, and deer stand a little over three feet tall at the shoulder.
- Bucks weigh in at between 120 and 230 pounds, and does weigh between 90 and 200 pounds.
Life Span
- In the wild, deer may live to be almost ten years old, the average being five or six years.
- In captivity, deer can live much longer – regularly to 14, but occasionally as many as 20 years.
Reproduction
- Females enter ‘rut’ in the late fall and go through the winter breeding with the dominant male in the area.
- Mature bucks will spar with each other to determine their place in the hierarchy. They ram each other and enter pushing matches with their antlers.
- One to three fawns are born to a female several months later, in May or June.
Social Behavior
- Deer have numerous scent glands. They use these to communicate with other deer in places called rubs (on trees or rocks) and scrapes (in cleared places on the ground).
- Considered to be a social animal that usually travels in small groups. Generally separated out by the sexes. Does travel in small units, often with fawns, but bucks either travel alone or in small bachelor group.
- Deer communicate with various audible noises including grunts, bleats and snorting. Additionally, when a white-tail deer is spooked it will raise its tail to warn the other deer in the area that can see it.
Conservation
- White tails are a species of little conservation concern. They are usually so numerous that most states have hunting regulations in place for their harvest.
- A century ago the deer population was severely depressed for various reasons. Conservation efforts have since been so successful that, combined with the lack of natural predators for deer, there are now 30 million deer estimated to live in the US. Hunting and population control methods are necessary to control this species which threatens farmers’ crops (being excellent fence jumpers), native fauna (given that they can decimate plant populations and even destroy attempts at re-foresting which then drives out other native fauna, even bears, by depriving them of their food source), as well as human and deer life due to the high number of vehicular crashes caused by deer. These concerns actually make deer one of the most dangerous animals in North America because of the potential damage they can cause by their sheer numbers.
Interpretive Information
- Fawns are born with light colored spots all over their body, and no scent. This helps them to hide in the leaf litter while their mother is out looking for food.
- Bucks re-grow their antlers every year.
- Deer are ruminants with a four chambered stomach. This allows them to eat a wide variety of food in a hurry then digest it later.
- They are prey to many animals in the zoo, including (potentially) the American alligator, the Black Bear, and the Bobcats. Bald Eagles and Golden Eagles can even prey on fawns. However, their most effective natural predators are wolves, cougars and American alligators.
Bear Hollow’s White Tailed Deer
- Male: Rocky
- Female: Ginger (pictured above courtesy of Wade Sheldon)
- Born in June 2005, she was raised in someone’s house as a pet.
- Arrived at Bear Hollow in the late summer of 2005.
- Past Deer:
- Red Buck – Born in July 1991 and orphaned while very young. He was mauled by a pack of dogs as a fawn and was rescued and rehabilitated and then given to Bear Hollow.
Credits and References
- Photo © Wade Sheldon
- General research by Sherrie Hines
- Where the Wild Things Were: Life, Death, and Ecological Wreckage in a Land of Vanishing Predators by William Stolzenburg


Home
Locate The Law :: Alabama
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Here you will find a summary of the data in our Progress Index. Progress
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categories.
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Why are Michael Bloomberg and Deval Patrick flirting with jumping into the 2020 presidential primary this late in the game? This week on “The Argument,” the columnists debate what the pair’s maneuvers say about the state of the race and whether either stands a chance of becoming the Democratic nominee. Michelle Goldberg notes that Bloomberg’s fiscally conservative, socially liberal politics aren’t shared by much of the wider electorate. The best recourse for Democrats seeking a moderate alternative to Joe Biden is to back one of the other moderates already in the race, argues Ross Douthat. And David Leonhardt thinks Biden’s weaknesses as a candidate could give Patrick an opening to siphon off the African-American voters who currently back the former vice president.
Then, the anonymous author of a controversial Times op-ed is out with a new book about the resistance inside the Trump administration. The columnists discuss whether that resistance has been effective in constraining the president.
And finally, Ross doesn’t recommend communing with spirits from beyond the grave, but he does recommend a spooky read that may challenge your preconceptions about the supernatural.
Background Reading:
- Ross on whether social progressivism can win over swing voters, Amy Klobuchar and Pete Buttigieg as moderate alternatives to Biden, and why conscientious conservatives should work for Trump (from 2016)
- Michelle on the relative unpopularity of “socially liberal, fiscally conservative,” mounting anxiety about the 2020 field, Biden’s up-close weaknesses as a candidate and why Republicans speaking out against Trump matters
- David on the case for Bloomberg’s candidacy and patriots who have spoken up about Trump
Ross Douthat
I’ve been an Op-Ed columnist since 2009, and I write about politics, religion, pop culture, sociology and the places where they all intersect. I’m a Catholic and a conservative, in that order, which means that I’m against abortion and critical of the sexual revolution, but I tend to agree with liberals that the Republican Party is too friendly to the rich. I was against Donald Trump in 2016 for reasons specific to Donald Trump, but in general I think the populist movements in Europe and America have legitimate grievances and I often prefer the populists to the “reasonable” elites. I’ve written books about Harvard, the G.O.P., American Christianity and Pope Francis; I’m working on one about decadence. Benedict XVI was my favorite pope. I review movies for National Review and have strong opinions about many prestige television shows. I have three small children, two girls and a boy, and I live in New Haven with my wife.
Michelle Goldberg
I’ve been an Op-Ed columnist at The New York Times since 2017, writing mainly about politics, ideology and gender. These days people on the right and the left both use “liberal” as an epithet, but that’s basically what I am, though the nightmare of Donald Trump’s presidency has radicalized me and pushed me leftward. I’ve written three books, including one, in 2006, about the danger of right-wing populism in its religious fundamentalist guise. (My other two were about the global battle over reproductive rights and, in a brief detour from politics, about an adventurous Russian émigré who helped bring yoga to the West.) I love to travel; a long time ago, after my husband and I eloped, we spent a year backpacking through Asia. Now we live in Brooklyn with our son and daughter.
David Leonhardt
I’ve worked at The Times since 1999 and have been an Op-Ed columnist since 2016. I caught the journalism bug a very long time ago — first as a little kid in the late 1970s who loved reading the Boston Globe sports section and later as a teenager working on my high school and college newspapers. I discovered that when my classmates and I put a complaint in print, for everyone to see, school administrators actually paid attention. I’ve since worked as a metro reporter at The Washington Post and a writer at Businessweek magazine. At The Times, I started as a reporter in the business section and have also been a Times Magazine staff writer, the Washington bureau chief and the founding editor of The Upshot.
My politics are left of center. But I’m also to the right of many Times readers. I think education reform has accomplished a lot. I think two-parent families are good for society. I think progressives should be realistic about the cultural conservatism that dominates much of this country. Most of all, however, I worry deeply about today’s Republican Party, which has become dangerously extreme. This country faces some huge challenges — inequality, climate change, the rise of China — and they’ll be very hard to solve without having both parties committed to the basic functioning of American democracy.
How do I listen?
Tune in on iTunes , Google Play , Spotify , Stitcher or wherever you listen to podcasts. Tell us what you think at<EMAIL_ADDRESS>Follow Michelle Goldberg ( @michelleinbklyn ), Ross Douthat ( @DouthatNYT ) and David Leonhardt ( @DLeonhardt ) on Twitter.
This week’s show was produced by Kristin Schwab for Transmitter Media and edited by Sara Nics. Our executive producer is Gretta Cohn. We had help from Tyson Evans, Phoebe Lett, Ian Prasad Philbrick and Francis Ying. Our theme is composed by Allison Leyton-Brown.


{% extends "layouts/govuk-registers.html" %} {% set section = "index" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Department for Business, Energy and Industrial Strategy Tasks Manage contacts Check uploads Tasks Next data submission deadline: 31 July 2017 Set up account Completed Read through the main changes to publishing data Upload a list of contacts {% endblock %}
Next data submission deadline: 31 July 2017


Please annotate the following query:
Link to GitHub
0 - Irrelevant
1 - Weak match
2 - Strong match
3 - Exact match
Skip


{% extends "layouts/base.html" %} {% block title %}Displaying maths{% endblock title %} {% block head %} {% endblock head %} {% block content %}
Maths is displayed using LaTeX. A good place to learn LaTeX is the Art of Problem Solving LaTeX Guide.
Write in-line maths between dollar signs, like so:
$e^{\pi i} + 1 = 0$
Which produces: $e^{\pi i} + 1 = 0$.
Larger formulae and equations should be written in display mode, like so:
\[ \int_0^1 e^{-x^2} dx \]
Which produces:
\[ \int_0^1 e^{-x^2} dx \]
LaTeX is purely a typesetting language and is ill-suited for representing meaning in addition to layout. For this reason, dynamic or randomised maths expressions must be written in JME syntax and converted to LaTeX. Numbas provides two new LaTeX commands to do this for you.
To substitute the result of an expression into a LaTeX expression, use the \var
command. Its parameter is a JME expression, which is evaluated and then converted to LaTeX.
For example,
\[ \var{2^3} \]
produces
\[ 8 \]
and if a variable called x
has been defined to have the value 3
,
\[ 2^{\var{x}} \]
produces
\[ 2^{3} \]
This simple substitution doesn’t always produce attractive results, for example when substituted variables might have negative values. If $y = -4$,
\[ \var{x} + \var{y} \]
produces
\[ 3 + -4 \]
To deal with this, and other more complicated substitutions, there is the \simplify
command.
The main parameter of the \simplify
command is a JME expression. It is not evaluated – it is converted into LaTeX as it stands. For example,
\[ \simplify{ x + (-1/y) } \]
produces
\[ x - \frac{1}{y} \]
Variables can be substituted in by enclosing them in curly braces. For example,
\[ \simplify{ {x} + {y} } \]
produces
\[ 3 - 4 \]
The \simplify
command automatically rearranges expressions, according to a set of simplification rules, to make them look more natural. Sometimes you might not want this to happen, for example while writing out the steps in a worked solution.
The set of rules to be used is defined in a list enclosed in square brackets before the main argument of the \simplify
command. You can control the \simplify
command’s behaviour by switching rules on or off.
For example, in
\[ \simplify{ 1*x } \]
I have not given a list of rules to use, so they are all switched on. The unitFactor
rule cancels the redundant factor of $1$ to produce
\[ x \]
while in
\[ \simplify[!unitFactor]{ 1*x } \]
I have turned off the unitFactor
rule, leaving the expression as it was:
\[ 1 x \]
When a list of rules is given, the list is processed frim left to right. Initially, no rules are switched on.
Sets of rules can be given names in the exam’s rulesets section, so they can be turned on or off in one go.


This routine is essentially the same as the Font.Draw procedure in the Font module. All coordinates are based on the canvas and all drawing is clipped to the canvas drawing surface. If the canvas is in "xor mode", all the drawing will be done with "xor" set. (See View.Set for more information about "xor".)
The widgetID must specify a canvas widget.
This means that you can only call the procedure by calling GUI.FontDraw, not by calling FontDraw.


Getting Started: Building and Running Clang
This page gives you the shortest path to checking out Clang and demos a few options. This should get you up and running with the minimum of muss and fuss. If you like what you see, please consider getting involved with the Clang community.
A Word of Warning
While this work aims to provide a fully functional C/C++/ObjC front-end, it is still relatively new and under heavy development. Currently we believe clang to be very usable as a C and Objective-C compiler, however there is no real C++ support yet (this is obviously a big project). Additionally, for C and Objective-C:
- The semantic analyzer does not produce all of the warnings it should.
- We don't consider the API to be stable yet, and reserve the right to change fundamental things.
- Only the X86-32 and X86-64 targets have been well tested.
If you run into problems, please file bugs in LLVM Bugzilla or bring up the issue on the Clang development mailing list.
Building Clang and Working with the Code
On Unix-like Systems
If you would like to check out and build Clang, the current procedure is as follows:
- Get the required tools.
- See Getting Started with the LLVM System - Requirements.
- Note also that Python is needed for running the test suite. Get it at: http://www.python.org/download
- Checkout LLVM:
- Change directory to where you want the llvm directory placed.
- svn co http://llvm.org/svn/llvm-project/llvm/trunk llvm
- Checkout Clang:
- cd llvm/tools
- svn co http://llvm.org/svn/llvm-project/cfe/trunk clang
- Build LLVM and Clang:
- cd .. (back to llvm)
- ./configure
- make
- This builds both LLVM and Clang for debug mode.
- Note: For subsequent Clang development, you can just do make at the clang directory level.
- If you intend to work on Clang C++ support, you may need to tell it how to find your C++ standard library headers. If Clang cannot find your system libstdc++ headers, please follow these instructions:
- 'touch empty.cpp; gcc -v empty.cpp -fsyntax-only' to get the path.
- Look for the comment "FIXME: temporary hack: hard-coded paths" in clang/lib/Frontend/InitHeaderSearch.cpp and change the lines below to include that path.
- Try it out (assuming you add llvm/Debug/bin to your path):
- clang-cc --help
- clang-cc file.c -fsyntax-only (check for correctness)
- clang-cc file.c -ast-dump (internal debug dump of ast)
- clang-cc file.c -ast-view (set up graphviz and rebuild llvm first)
- clang-cc file.c -emit-llvm (print out unoptimized llvm code)
- clang-cc file.c -emit-llvm -o - | llvm-as | opt -std-compile-opts | llvm-dis (print out optimized llvm code)
- clang-cc file.c -emit-llvm -o - | llvm-as | opt -std-compile-opts | llc > file.s (output native machine code)
Note: Here clang-cc is the "low-level" frontend executable that is similar in purpose to cc1. Clang also has a high-level compiler driver that acts as a drop-in replacement for gcc.
Note that the C front-end uses LLVM, but does not depend on llvm-gcc. If you encounter problems with building Clang, make sure you have the latest SVN version of LLVM. LLVM contains support libraries for Clang that will be updated as well as development on Clang progresses.
Simultaneously Building Clang and LLVM:
Once you have checked out Clang into the llvm source tree it will build along with the rest of llvm. To build all of LLVM and Clang together all at once simply run make from the root LLVM directory.
Note: Observe that Clang is technically part of a separate Subversion repository. As mentioned above, the latest Clang sources are tied to the latest sources in the LLVM tree. You can update your toplevel LLVM project and all (possibly unrelated) projects inside it with make update. This will run svn update on all subdirectories related to subversion.
Using Visual Studio
The following details setting up for and building Clang on Windows using Visual Studio:
- Get the required tools:
- Subversion. Source code control program. Get it from: http://subversion.tigris.org/getting.html
- cmake. This is used for generating Visual Studio solution and project files. Get it from: http://www.cmake.org/cmake/resources/software.html
- Visual Studio 2005 (VS 2008 may work also - cmake outputs VS2005 project files)
- Python. This is needed only if you will be running the tests (which is essential, if you will be developing for clang). Get it from: http://www.python.org/download
- GnuWin32 tools These are also necessary for running the tests. (Note that the grep from MSYS or Cygwin doesn't work with the tests because of embedded double-quotes in the search strings. The GNU grep does work in this case.) Get them from http://getgnuwin32.sourceforge.net.
- Checkout LLVM:
- svn co http://llvm.org/svn/llvm-project/llvm/trunk llvm
- Checkout Clang:
- cd llvm\tools
- svn co http://llvm.org/svn/llvm-project/cfe/trunk clang
- Run cmake to generate the Visual Studio solution and project files:
- cd .. (Change directory back to the llvm top.)
- cmake .
- The above, if successful, will have created an LLVM.sln file in the llvm directory.
- Build Clang:
- Open LLVM.sln in Visual Studio.
- Build the "clang-cc" project for just the compiler front end. Alternatively, build the "clang" project for the compiler driver (note that the driver is currently broken on Windows), or the "ALL_BUILD" project to build everything, including tools.
- Try it out (assuming you added llvm/debug/bin to your path). (See the running examples from above.)
- See Hacking on clang - Testing using Visual Studio on Windows for information on running regression tests on Windows.
Note that once you have checked out both llvm and clang, to synchronize to the latest code base, use the svn update command in both the llvm and llvm\tools\clang directories, as they are separate repositories.
High-Level Compiler Driver (Drop-in Substitute for GCC)
While the clang-cc executable is a low-level frontend executable that can perform code generation, program analysis, and other actions, it is not designed to be a drop-in replacement for GCC's cc. For this purpose, use the high-level driver, aptly named clang. Here are some examples of how to use the high-level driver:
$ cat t.c #include <stdio.h> int main(int argc, char **argv) { printf("hello world\n"); } $ clang t.c $ ./a.out hello world
Examples of using Clang
The high-level driver clang is designed to understand most of GCC's options, and the lower-level clang-cc executable also directly takes many of GCC's options. You can see which options clang-cc accepts with 'clang-cc --help'. Here are a few examples of using clang and clang-cc:
$ cat ~/t.c typedef float V __attribute__((vector_size(16))); V foo(V a, V b) { return a+b*a; }
Preprocessing:
$ clang ~/t.c -E # 1 "/Users/sabre/t.c" 1 typedef float V __attribute__((vector_size(16))); V foo(V a, V b) { return a+b*a; }
Type checking:
$ clang -fsyntax-only ~/t.c
GCC options:
$ clang -fsyntax-only ~/t.c -pedantic /Users/sabre/t.c:2:17: warning: extension used typedef float V __attribute__((vector_size(16))); ^ 1 diagnostic generated.
Pretty printing from the AST:
$ clang-cc ~/t.c -ast-print typedef float V __attribute__(( vector_size(16) )); V foo(V a, V b) { return a + b * a; }
Code generation with LLVM:
$ clang-cc ~/t.c -emit-llvm -o - | llvm-as | opt -std-compile-opts | llvm-dis define <4 x float> @foo(<4 x float> %a, <4 x float> %b) { entry: %mul = mul <4 x float> %b, %a %add = add <4 x float> %mul, %a ret <4 x float> %add } $ clang-cc ~/t.c -emit-llvm -o - | llvm-as | opt -std-compile-opts | llc -march=ppc32 -mcpu=g5 .. _foo: vmaddfp v2, v3, v2, v2 blr $ clang-cc ~/t.c -emit-llvm -o - | llvm-as | opt -std-compile-opts | llc -march=x86 -mcpu=yonah .. _foo: mulps %xmm0, %xmm1 addps %xmm0, %xmm1 movaps %xmm1, %xmm0 ret


How to start mining with Minergate Admin Edition miner?
To start mining with Minergate Console miner:
- Download new Minergate Miner Admin Edition which mines in background mode.
- To start using it launch miner, choose settings and close the window. Then press WIN+R -> services.msc -> start Minergate service.
- Optionally you can choose startup type.


A-Star
A* (A star) pathfinding algorithm implementation in Swift 5.0 (Playground included) using protocol oriented programming.
Add pathfinding to your graph just by implementing the 3 requirements of the Graph protocol.
Playground
- To use the playground: open AStar.xcodeproj
- Build the macOS framework (select the
AStar macOS
targer and presscommand
+b
) - Click on Playground.playground
Swift Package Manager
You can now add this package to your project using SPM 🎉
Using Xcode
Xcode 11 has a new interface to include packages into your existing projects.
- Click
File
→Swift Packages
→Add Package Dependency...
- Search for
AStar
and select this repository. - Follow the on screen instructions
Manualy editing package file
To include it in your package manually, add the following dependency to your Package.swift
file.
.package(url: "https://github.com/Dev1an/A-Star.git", .branch("master"))
Use import AStar
to access the APIs.


Stores excess solar energy which can be used to power select parts of your home during power outages
Save money by using stored solar power to offset peak energy rates
Every LG Chem battery comes with a 10-year warranty backed by LG
Want to go solar and pay nothing out of pocket? No problem. We offer a solar lease, which can start at $0 down. Want to buy and own your system? We can do that, too.
Sunrun understands that everyone’s financial situation is different, so we give you a choice of payment options.


| MATLAB 文件帮助: cv.SVM/trainAuto | 查看 cv.SVM/trainAuto 的代码 | Index |
trainAuto Trains an SVM with optimal parameters
status = model.trainAuto(samples, responses)
status = model.trainAuto(csvFilename, [])
[...] = model.trainAuto(..., 'OptionName', optionValue, ...)
KFold
subsets. One subset is used to train
the model, the others form the test set. So, the SVM
algorithm is executed KFold
times. default 10CGrid, GammaGrid, NuGrid, PGrid, CoeffGrid, DegreeGrid Iteration grid for the corresponding SVM parameter. A structure that represents the logarithmic grid range of SVM parameters. It is used for optimizing model accuracy by varying model parameters, the accuracy estimate being computed by cross-validation. It accepts a struct having the fields below. It also accepts a 3-element vector in which each parameter is specified in the same order as the supported struct:
The grid determines the following iteration sequence of
the model parameter values: minVal * logStep.^(0:n)
,
where n
is the maximal index satisfying
minVal*logStep^n < maxVal
. The grid is logarithmic, so
logStep
must always be greater then 1.
Defaults are:
struct('minVal',0.1, 'maxVal',500, 'logStep',5 )
struct('minVal',1e-5, 'maxVal',0.6, 'logStep',15)
struct('minVal',0.01, 'maxVal',100, 'logStep',7 )
struct('minVal',0.01, 'maxVal',0.2, 'logStep',3 )
struct('minVal',0.1, 'maxVal',300, 'logStep',14)
struct('minVal',0.01, 'maxVal',4, 'logStep',7 )
The method trains the SVM model automatically by choosing the
optimal parameters C
, Gamma
, P
, Nu
, Coef0
, Degree
of
an SVM model. Parameters are considered optimal when the
cross-validation estimate of the test set error is minimal.
If there is no need to optimize a parameter, the corresponding
grid step should be set to any value less than or equal to 1.
For example, to avoid optimization in gamma, set logStep = 0
in GammaGrid
, and minVal
, maxVal
as arbitrary numbers. In
this case, the value of the parameter Gamma
is taken for
gamma.
And, finally, if the optimization in a parameter is required but
the corresponding grid is unknown, you may set it by name to
obtain the default grid of that parameter. To generate a grid,
for example, for gamma, set GammaGrid='Gamma'
.
This function works for the classification (C_SVC
or NU_SVC
)
as well as for the regression (EPS_SVR
or NU_SVR
). For
ONE_CLASS
, no optimization is made and the usual SVM with the
specified parameters is executed.
| Access | public |
| Sealed | false |
| Static | false |


WITNESS LIST Appropriations Committee April 3, 1997-1:30P SB 1706 For: Cane, Mark (Amtrak) Elsbree, Amy (Amtrak) Hare, Deborah (Amtrak) Hennes, Anthony (City of Temple) Kariel, Audrey (City of Marshall) Padilla, Jesse (Rail Passenger Service) Robb, Edmund (Chamber of Commerce) Robb, Martha (City Commissioner) Slayton, Jim (City of Mineola) Todd, Bruce (City of Austin) Williams, Tony (City of Marshall) On: Burnett, William (TXDOT) Griebel, Thomas (TXDOT) SB 1898 For: Cane, Mark (Amtrak) Ellis, Greg (Edwards Aquifer) Elsbree, Amy (Amtrak) Hare, Deborah (Amtrak) Hennes, Anthony (City of Temple) Kariel, Audrey (City of Marshall) Labatt, Weir (Edwards Aquifer) Padilla, Jesse (Rail Passenger Service) Robb, Edmund (Chamber of Commerce) Robb, Martha (City Commissioner) Slayton, Jim (City of Mineola) Todd, Bruce (City of Austin) Williams, Tony (City of Marshall) On: Armstrong, Julia (Attorney Generals Off) Bates, Dale (TNRCC) Berriozabal, Manuel (U.T.S.A.) Bomar, George (TNRCC) Burnett, William (TXDOT) Denton, Judy (TX.Dept Of Human Svcs.) Graham, Weldon (TYC) Griebel, Thomas (TXDOT) Halfman, Bobby (TX.Dept Of Human Svcs.) Hill, Valerie (SAO) Hine, Jim (TX.Dept.Of Regulatory) Holderman, Terry (SAO) McNutt, David (TDCJ) Phillips, Jim (TNRCC) Purcell, Carolyn (Dept. Of Info. Resource) Reynolds, Carl (TDCJ) Robinson, Steve (TYC) Serna, Ed (Dept. Of Info. Resource) Thigpen, Drew (TX.Dept.Of Regulatory) Trimble, Terry (TX.Dept Of Human Svcs.) Vega, Jorge (Attorney General) Williamson, Diana (TX.Dept.Of Human Svs)


[{'created':<PHONE_NUMBER>,
'creator': 'alright',
'deadline':<PHONE_NUMBER>,
'name': '20vs22',
'options': ['20', '22'],
'question': 'Should LABS launch with 20 or 22 notary nodes? If >50% of the '
'current 20 notary nodes decide 22, we will hold a week-long '
'provably fair lottery for the two additional positions '
'beginning at the deadline time of this poll. The lottery '
'mechanism can be found in discord pins of #kmdlabs discord '
'channel. ',
'txid': '68c78190c91f45d7dacff72281226a075a9a14f0ea60efd4edda15c6edea947a'}]
is this deadline the same exact number for you guys? @gt @CrisF


This page is here to display my Cultural and Technical Blogs.
Its a 2 out of 10 in the CSS Styling Department.
It will progress with time!


This is a demo of the
Maltypart
multipart library.
Text:
Image:
Submit
Request
Content-Type:
Body:
Response
Status:
Body:
Result
Text:
Image:


fontwr is a tool that helps front-end developers download fonts, convert them, and transform into font-face! There are more than 800 fonts available in our repository and growing!
fontwr.json
hold all font settings, such as font family,
font-style, and formats (woff2, woff, eot, ttf). If you download a project, just run
fontwr install
to get all fonts. Whether is a new project, just run commands
in your terminal to setup everything!
$ npm install fontwr -g
$ fontwr get roboto
Fetch all Roboto fonts family, prompt user what fonts to be downloaded and what formats.
$ fontwr get opensans -g
Install the font into system's font directory. Doesn't convert and generate font-face.
$ fontwr list
List all available fonts.
{
"fonts": {
"Roboto-Regular": {
"family": "roboto",
"format": [".woff2", ".woff", ".eot"]
},
"OpenSans-Light": {
"family": "opensans",
"format": [".woff2", ".woff"]
}
},
"systemFonts":{
"Nexa-Light": {
"format": [".woff2"]
},
"Comic Sans MS": {
"format": [".woff2"]
}
},
"output": {
"fonts": "fonts/",
"fontface": "css/"
}
}


No water at Zimbabwe President’s State residence
by Staff ReporterZimbabwe’s State House and Zimbabwe House have been severely affected by the worsening water woes in Harare it has been revealed.
Sources from Harare told us that the water shortage has not spared the President’s official residence.
“The situation is bad in Harare to an extent that even the President of Zimbabwe is affected by the matter. Recently when we were at State House, we saw those black drums that were full of water and we were told that the Presidential staffers are buying the drums and using them for all the hygiene related services in State house.”
On Monday President Emmerson Mnangagwa toured the Morton Jeffrey Water Treatment plant to get an appreciation of the problems at the City Water Works.
Source – Byo24


04 Feb 2015
Wave Equation with PML
Last post, I wrote some doodle work on solving the wave equation under FEM
framework. And we did not consider the boundary condition, since the equation assumes valid for all plane. Now if truncated, PML
is a way to save it, just with tiny modification on our previous work.
Formulation is not as simple as frequency domain using a trivial transformation to introduce absorption term. We need to use ifft
to transform our Helmholtz equation back to time domain.
Think about equation:
$$u_{tt} - \nabla\cdot(\mu\nabla u) = f$$
using Laplace transform
$$\tilde{u} = \int_0^{\infty} \exp(st) u(x, t)\mathrm{d}t$$
Thus we got Helmholtz, $s$ is complex.
$$s^2 \tilde{u} = \nabla\cdot(\mu\nabla\tilde{u}) +\tilde{f}$$
making coordinate transform as we do with Helmholtz:
$$x \rightarrow \tilde{x} = x + \frac{1}{s} \int^R \sigma(x)\mathrm{d}x$$
with conductivity $\sigma$, which is positive outside domain and vanish inside.
Extend $\tilde{u}$ onto transformed space,
$$s^2\tilde{u} = \tilde{\nabla}\cdot(\mu\tilde{\nabla}\tilde{u}) + \tilde{f}$$
The transforming back,
$$\begin{aligned}(s^2 + s(\sigma_x + \sigma_y) + \sigma_x \sigma_y) \tilde{u}= \nabla\cdot(\mu\nabla \tilde{u}) + \frac{\partial}{\partial x}(\mu \frac{\sigma_y - \sigma_x}{s + \sigma_x} \tilde{u}_x) + \frac{\partial}{\partial y}(\mu \frac{\sigma_x - \sigma_y}{s + \sigma_y} \tilde{u}_y) + f \end{aligned}$$
$s$ means $\frac{\partial}{\partial t}$, therefore $u_{tt} + (\sigma_x + \sigma_y) u_t + \sigma_x \sigma_y u = \nabla\cdot(\mu\nabla u) + \nabla\cdot\phi + f $
where $\phi_t = A\phi + \mu B {\nabla}u$ and $A = diag(-\sigma_x, -\sigma_y)$, $B = diag(\sigma_y -\sigma_x, \sigma_x - \sigma_y)$. Initial $\phi$ as 0.
Using finite difference is acceptable. Central scheme with time, and work carefully with gradient on spatial grid.
Or use FEM
on spatial and RK4
for time integration. It should be fast on constructing the matrices if time-independent. Then it would be easy to calculate
$$\zeta' = f(\zeta)$$
each step involves 4 calls on the function $f$, and each call involves ~10 sparse-matrix-dense-vector
multiplication, timing takes 0.05 seconds each for a matrix size of 18000 or so. That is about 1~2 seconds for one time step!! Unless we can find a way to reduce the calculations.
Hooray! By profiling the calculations, multiplication is just too fast and mldivide
is taking most of time. But we know one thing, everything is going to be applied the same operation mldivide
at last, do it at one pass with reduce the overhead at each round, the real calculation time would be only 1e-3
or so, but overhead will make it 1e-1
somehow.
Another thing is calculating the vector-mass matrix like
$$\int_{\Omega} \phi_j (A\nabla \phi_i )\mathrm{d}x$$
It seems taking twice the timing than just stiffness or mass. However, we know one thing, as a sparse matrix COO-format
: $I, J$ are fixed during all calculations like this, then avoiding getting extra work will make things move faster.
Update: examples are included in femex/example/PML
on github. Performance is reasonable.
Problematic part: The previous overhead argument is kind of wrong in Matlab, horzcat
creates a pretty large copy of the input, that will allocates lots of memory in advance.
Another thing is the ode45
, it stores all solutions. That is a large cost, and porting them into plot
function also takes a lot of time.
Workaround: try to write a function in C using GSL to make this faster, or write some function with C to avoid storing the solution. Or simply add a callback..


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General information for developers¶
The PyClaw repository is hosted on Github at http://github.com/clawpack/pyclaw.
Guidelines for contributing¶
When preparing contributions, please follow the guidelines in Contributing to PyClaw code development. Also:
- If the planned changes are substantial or will be backward-incompatible, it’s best to discuss them on the claw-dev Google group before starting.
- Make sure all tests pass and all the built-in examples run correctly.

- It may be wise to have one of the maintainers look at your changes before they are complete (especially if the changes will necessitate modifications of tests and/or examples).
- If your changes are not backward-compatible, your pull request should include instructions for users to update their own application codes.
Bugs¶
If you find a bug, post an issue with as much explanation as possible on the Issue tracker at https://github.com/clawpack/pyclaw/issues. If you’re looking for something useful to do, try tackling one of the issues listed there.
Developer communication¶
As PyClaw is part of the family of Clawpack codes, developer communication takes place on the google group at http://groups.google.com/group/claw-dev/.
Dependencies¶
It is almost always okay to add “optional dependencies”, for example, creating an optional feature or application that is only enabled when the software dependencies for that feature are satisfied. petclaw is an example of this, as it provides an optional means of running many PyClaw applications in parallel.
In general, introduction of general dependencies should be avoided. If you wish to make a change that will introduce a new dependency (including depending on a more recent version of a particular package), it should be discussed on the claw-dev Google group first.
New versions of existing dependencies will typically be adopted either when new functionality provides an important benefit for PyClaw or when the currently supported version is deemed to be substantially outdated.
Running the tests¶
The PyClaw test suite is built around nosetests for automatic test discovery, with supplementary functionality from the pyclaw.util module. To run the complete test suite with helpful output, issue the following command at the top-level of the pyclaw source directory:
nosetests -vs
To run the parallel versions of the tests (if petsc4py is installed), run:
mpirun -n 4 nosetests -vs
(Replace 4 with the number of processes you’d like test on)
Try prime numbers if you’re really trying to break things!
Running serial tests simultaneously¶
When running the tests, if your machine has multiple cores you can take advantage of them by doing:
nosetests -vs --processes=2
(replace “2” with the number of processes you want to spawn). However, using large numbers of processes occasionally causes spurious failure of some tests due to issues with the operating system. If you see this behavior, it’s best to run the tests in serial or with a small number of processes.
Running a specific test¶
The PyClaw tests are associated with particular applications in the examples/ sub- directory of the primary repository directory. If you want to run tests for a specific application, simply specify the directory containing the application you are interested in:
nosetests -vs examples/acoustics_3d_variable
You can also specify a single file to run the tests it contains.
Doctests¶
Several of the main PyClaw modules also have doctests (tests in their docstrings). You can run them by executing the corresponding module:
cd $PYCLAW/src/pyclaw
python grid.py
python state.py
If the tests pass, you will see no output. You can get more output by using the -v option:
python state.py -v
Writing New Tests¶
If you contribute new functionality to PyClaw, it is expected that you will also write at least one or two new tests that exercise your contribution, so that further changes to other parts of PyClaw or your code don’t break your feature. You do not have to use any of the functionality offered by pyclaw.util, but it may simplify your test-writing and allow you to check more cases than you would easily specify by hand.
The most important function in pyclaw.util is pyclaw.util.gen_variants(), which allows you to perform combinatorial testing without manually specifying every feature you’d like to perform. Currently, gen_variants() can multiplicatively exercise kernel_languages (Fortran or Python) and pure PyClaw or PetClaw implementations. This allows you to write one function that tests four variants.
Another function provided by util is pyclaw.util.test_app(). The test_app() function will run an application as if started from the command line with the specified keyword arguments passed in. This is useful for testing specific code that does not necessarily work with petclaw, for example, and is not expected to.
You will notice that both gen_variants() and test_app() require a verifier method as an argument. These functions both effectively run tests and verify output with the following function calls:
output = application(**kwargs)
check_values = verifier(output)
The verifier method needs to return None if there is no problem with the output, or a sequence of three values describing what was expected, what it received, and more details about the error. A very simple verifier method that you can use is pyclaw.util.check_diff(), which can use either an absolute tolerance or a relative tolerance to compare an expected value against the test output from the application.
See examples/acoustics_1d_homogeneous/test_acoustics.py for a comprehensive example of how to use gen_variants() and check_diff(). See examples/shallow_sphere/test_shallow_sphere.py for an example that uses test_app() and also loads a known solution from disk using numpy.
Catching errors with Pyflakes and Pylint¶
Pyflakes and Pylint are Python packages designed to help you catch errors or poor coding practices. To run pylint on the whole PyClaw package, do:
cd $PYCLAW
pylint -d C pyclaw
The -d option suppresses a lot of style warnings, since PyClaw doesn’t generally conform to PEP8. To run pylint on just one module, use something like:
pylint -d C pyclaw.state
Since pylint output can be long, it’s helpful to write it to an html file and open that in a web browser:
pylint -d C pyclaw.state -f html > pylint.html
Pyflakes is similar to pylint but aims only to catch errors. If you use Vim, there is a nice extension package pyflakes.vim that will catch errors as you code and underline them in red.
Checking test coverage¶
You can use nose to see how much of the code is covered by the current suite of tests and track progress if you add more tests
nosetests --with-coverage --cover-package=pyclaw --cover-html
This creates a set of html files in ./cover, showing exactly which lines of code have been tested.


{% extends "./common/layout.html" %} {% block headscript %} {% endblock %} {% block body %}
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NAME
CoordinateReferenceSystemType
NON_IGNORABLE
CollationAlternate
NamedCoordinateReferenceSystem
geojson
NettyStreamFactoryFactory
connection
name
BuildInfo
BuildInfo
MongoDatabase
namespace
MongoCollection
ne
Filters
near
Filters
nearSphere
Filters
nearest
ReadPreference
newBuilder
Document
Document
nin
Filters
noCursorTimeout
FindObservable
GridFSFindObservable
nonAtomic
MapReduceObservable
nor
Filters
not
Filters
notEqual
Filters


More...
|
| | Builder (TAjaxProxy component) |
| |
| virtual TBuilder | Headers (Action< ParameterCollection > action) |
| | Any headers to add to the Ajax request. Defaults to undefined. More...
|
| |
| virtual TBuilder | Json (bool json) |
| | Send params as JSON object More...
|
| |
| virtual TBuilder | Xml (bool xml) |
| | Send params as XML object More...
|
| |
| virtual TBuilder | ActionMethods (Action< CRUDMethods > action) |
| | Mapping of action name to HTTP request method. In the basic AjaxProxy these are set to 'GET' for 'read' actions and 'POST' for 'create', 'update' and 'destroy' actions. The Ext.data.proxy.Rest maps these to the correct RESTful methods. More...
|
| |
| virtual TBuilder | GetMethod (Action< JFunction > action) |
| | Returns the HTTP method name for a given request. By default this returns based on a lookup on actionMethods. More...
|
| |
| virtual TBuilder | Password (string password) |
| | Most oData feeds require basic HTTP authentication. This configuration allows you to specify the password. More...
|
| |
| virtual TBuilder | UseDefaultXhrHeader (bool useDefaultXhrHeader) |
| | Set this to false to not send the default Xhr header (X-Requested-With) with every request. This should be set to false when making CORS (cross-domain) requests. Defaults to true. More...
|
| |
| virtual TBuilder | Username (string username) |
| | Most oData feeds require basic HTTP authentication. This configuration allows you to specify the username.. More...
|
| |
| virtual TBuilder | WithCredentials (bool withCredentials) |
| | This configuration is sometimes necessary when using cross-origin resource sharing. Defaults to false. More...
|
| |
| | Builder () |
| |
| | Builder (AjaxProxy component) |
| |
| | Builder (AjaxProxy.Config config) |
| |
◆ Builder() [1/4]
◆ Builder() [2/4]
◆ Builder() [3/4]
◆ Builder() [4/4]
◆ ActionMethods()
Mapping of action name to HTTP request method. In the basic AjaxProxy these are set to 'GET' for 'read' actions and 'POST' for 'create', 'update' and 'destroy' actions. The Ext.data.proxy.Rest maps these to the correct RESTful methods.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ GetMethod()
Returns the HTTP method name for a given request. By default this returns based on a lookup on actionMethods.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ Headers()
Any headers to add to the Ajax request. Defaults to undefined.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ Json()
Send params as JSON object
◆ operator Builder()
◆ Password()
Most oData feeds require basic HTTP authentication. This configuration allows you to specify the password.
◆ UseDefaultXhrHeader()
Set this to false to not send the default Xhr header (X-Requested-With) with every request. This should be set to false when making CORS (cross-domain) requests. Defaults to true.
◆ Username()
Most oData feeds require basic HTTP authentication. This configuration allows you to specify the username..
◆ WithCredentials()
This configuration is sometimes necessary when using cross-origin resource sharing. Defaults to false.
◆ Xml()
Send params as XML object
The documentation for this class was generated from the following file:


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