It's a question that countless home buyers grapple with as they contemplate where they want to purchase a home.
That question? Should I buy a home in an urban, or suburban community?
It's a good question, and a highly personal one at that. Ultimately, the choice you make is determined by two factors.
The first factor is the type of lifestyle you envision? The second factor — and this is a significant one — is, what stage of life are you presently in?
For example, if you're in your mid-twenties, environmentally-conscious and have secured your first job working for a firm downtown, then urban living is likely to appeal to you.
You can walk, bike or take public transit to work in a matter of minutes, a host of amenities (coffee shops, restaurants, shops, stores) are at your fingertips and you can enjoy a mature, walkable area that's loaded with character. During the evening, the area also comes alive with vibrant nightlife.
That said, living in an urban setting — Osborne Village is a prime example — can also have its downsides.
Accommodations – while trendy — tend to be pricier.
Not only that, but there's a good chance you'll pay top dollar for a condo that, while stylish, is (ahem) on the small side.
And if you do find an affordable (likely older) condo, it may not have a balcony to venture out on to, leaving you with no outdoor space for your barbeque and outdoor furniture.
Still, you can console yourself with the knowledge that you're limiting your carbon footprint by walking to work, and that you're in the middle of all the action — right where you want to be.
Those who don't enjoy being in the middle of all the action – i.e., the hustle and bustle of being downtown, constant traffic and the noise that comes part and parcel of living in a happening area – might want to look at living in a suburban area.
Again, it all comes down to lifestyle.
hectic traffic and noise at the end of a long work day.
If that's the case, a suburban community could be for you.
For starters, you'll have more personal space to enjoy with your own yard and a larger home.
Next, if you have kids, suburban areas tend to be more family-friendly. Because they're relatively isolated (Bridgwater Forest and Sage Creek are good examples), they tend to be safer, and offer parks for kids to play in just minutes from home.
Likewise, your kids — and their friends — can also play in your backyard, or downstairs in your basement's rec room. That's a luxury you won't find in a 600 sq. ft. condo in an urban community.
Of course, there are downsides associated with suburban living, too.
Because there's a good chance you'll be a fair distance from your workplace, there will be the grind of a grueling daily commute to deal with. Doubtless, you'll be spending more time in your car.
Wear and tear on your vehicles (with most moms and dads working these days) will also be an issue, so you'll need to figure more money into your budget for car repairs.
And don't forget you'll have to drive the kids longer distances to their activities on a regular basis.
However, there is a payoff at the end of a long day: you have a quiet, spacious refuge to return home to — a place where you can commune with your family and rejuvenate.
In the final analysis, choosing an urban or suburban lifestyle – and, hence, a home in such a setting — is highly personal.
Consequently, carefully assess your current stage of life. Then, take the time to envision the lifestyle that you want to pursue.
They will be more than happy to help you find the right home – and area – for you to put roots down in.


text:
(Pixabay)
Juan de Fuca Electoral Area and Our Place bring extreme weather shelter to Sooke
Move comes after years of no shelter for the community's homeless
Feb. 8, 2019 3:10 p.m.
The Juan de Fuca Electoral Area and Our Place have combined their efforts to finally bring an extreme weather shelter to Sooke.
The move comes just as Sooke descends into a period of cold and higher than average snowfall.
The shelter initiative has been under discussion at Our Place since September, when officials there became aware of the need for emergency shelter in the community.
"We understand that there are more than 100 people living outdoors, based on the point in time count, and no where for them to shelter from the weather," said Linda McLean, Our Place's chief operating officer.
"The problem we've had is finding a space for a shelter. There just didn't seem to be anyplace available."
RELATED: No shelter in Sooke
That's where Mike Hicks and the Juan de Fuca Electoral Area came into the picture.
Hicks, the Juan de Fuca Electoral Area director, said that he became aware of the problem and felt it was time for someone to act.
"We have this 1,000-square-foot meeting space available at our Butler Road local area services building where we have washrooms and it seemed that we needed to do something," said Hicks.
With the space made available, Our Place staff jumped into action and, since Monday, has been working out the logistics of the shelter.
"What's going to happen is that we will send out cots to the location, but we realize that this is a large area and it could be hard for people to get to the shelter. That's why we're sending a van out to make it all work," McLean said.
The Our Place van will be in the community and go to muster stations, where staff will pick up clients and transport them to the shelter.
"We'll be picking people up around 7 p.m. and the shelter will operate between 7:30 p.m. and 7 a.m.," McLean said.
"We'll be bringing in hot soup, sandwiches and coffee and tea and some breakfast bag lunches that we'll be providing to people as they leave in the morning."
The idea, McLean added, is to provide a warm, safe place to go in extreme weather.
RELATED: Still no shelter
Hicks added that JDF has also taken the initiative to provide dog kennels at the shelter so that those homeless people who have animals will not be left out in the cold.
The move comes after the nearly two years of no shelter beds available in Sooke, although a point in time count of homeless in July indicated a need for a shelter in the community.
The Sooke Baptist Church operated a shelter until 2015, when B.C. Housing ended the initiative.
mailto:tim.collins@sookenewsmirror.com
Victoria puppy saved with naloxone injection
UPDATE: Power has been restored to 2,300 people in Saanich
West Shore RCMP release sketch of Galloping Goose sexual assault suspect
Anyone who knows the man is asked to call West Shore RCMP
MISSING: Victoria police looking for 'high-risk' missing woman Clarissa Adamick
Adamick was last seen on July 12 and may be in the downtown Victoria area
Greater Victoria to see all 10 of the electric buses announced by federal-provincial governments
Prime Minister Justin Trudeau announced $79 million in joint funding for 118 new buses across B.C.
Gate at Mount Douglas Park smashed on Wednesday night
It is suspected that a motorist drove through after being locked in after 11 p.m.
VIDEO: 1,400 classic cars roll into Victoria for Deuce Days
The four-day festival highlights classic hot rods, with a special emphasis on cars built in 1932

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Adding a header to pdf in Python I am making a script that batch converts pdf from 1.5 or above to 1.4
After conversion, I need to prepend header tags that were previously there.
Whenever I do this through the Python script I wrote, the pdf returns an error
"There was an error while reading this Stream"
But when I do it manually wherein I open the pdf with Notepad++ and paste the headers manually, it works just fine.
What I've done to figure out the problem was to compare the ones that were manually added and the one where the script added the header.
Upon doing so, there seems to be a lot of changes that were made when I just pasted the header like
/CreationDate(D:20200728024434+08'00')
/ModDate(D:20200728024434+08'00')
/ID [<BCBBEC32B429CF4C4EC06D5317B6B1FE><BCBBEC32B429CF4C4EC06D5317B6B1FE>]

to
/CreationDate(D:20200728024353+08'00')
/ModDate(D:20200728024353+08'00')
/ID [<8C36C8F0530094A4C7058CC0845A2FDC><8C36C8F0530094A4C7058CC0845A2FDC>]

Is there some sort of fix that Notepad++ do whenever I edit the file and is there a way I can do the same automatically through my script so that I won't have to paste the headers manually.
EDIT: I made a mistake and Notepad++ wasn't really doing anything to the pdf. Is there a way that I can paste the headers automatically without it being corrupted?
In my python code, what I do is
    content = f.read()
    f.seek(0, 0)
    f.write(header1 + header2 + content)
    f.close()`

I found this way to add the headers in this article

redpajama_set_name: RedPajamaStackExchange


A question often asked of candidates is what they would seek to accomplish in the first 90 days of a new role, and how they'll define success at the 12-24 months mark.
a. What are the major issues?
Defining Success at 12-24 months.
At the 12-24 month mark there is a multitude of elements and indicators that should have gone into play in the initial year. However, many of these elements are ever evolving and are moving targets. Here are a few of the elements and indicators that I have used in the past.
Has the team adjusted to their roles and are they functional experts and mentors in their domains to the rest of the organization? It's one thing for an individual to come into a role and provide value and feel comfortable with both the business and surrounding environment. It's another when those individuals become curious teachers and mentors. I push my teams to be curious, to never stop learning, and to never stop teaching one another. This makes for stronger teams, better people, and long-term loyalty to organizations.
Have we identified bottlenecks and inefficiencies, and more importantly, what have we done about them? One of my mantras is for our team to question everything. If I develop an idea I expect to be pushed on my reasoning, and expect they'll be ready when I push them on theirs. I encourage the team to take this line of thinking outside of our group as well. The advantage of this way of thinking is that it forces people to think through ideas and initiatives in greater detail, and leads to great collaboration and comradery. This method has helped my groups identify and take meaningful action on a number of bottlenecks to help create greater efficiencies – from organizational flow, to user experiences, to reporting, and to clearer marketing campaigns.
Have we gotten smarter and do we understand variable mixes and the predictability of how they influence one another? It's a process with no end as there is always a new way to grow or leverage data. However, success should be measured on having become smarter and setting up foundational practices that allow us to continue to grow and learn about both our business and about our consumers. Part of this should be our ability to predict how changing certain marketing variables impacts sales and revenue.
A simple example is understanding the CPAs of each individual channel – if I pour marketing dollars into social retargeting ads, I should have an estimate of cost of that acquisition channel, but should also be cognizant if I'm working with a channel of diminishing returns (I can only retarget people that have shown interest, so as I convert individuals form this pool, CPA would be expected to rise in time and be finite in respect to its exponential growth capabilities).
A slightly more complex example may involve additional layers of the marketing mix. I served you a banner ad that made you aware of our brand. I continue to serve you banner ads to build awareness and recall and eventually you come across an influencer you follow that talks about my organization which then leads you to visit my website where I collect additional information from you. Over the course of the next several weeks I'm able to serve you an email, targeted social ads, and a follow-up phone call to get you to come in-store. After several visits and interactions with staff you make a purchase. Each of these channels deserves credit/ attribution for the end purchase. This "slightly more complex example" within itself is not overly complex, but this is the extent of complexity that most marketers will come across. This is shocking as there are additional marketing layers that, when addressed, separate good organizations from great ones.
A complex example. Consider for a moment if the influencer you followed hadn't been involved in the above marketing mix, and as a result your interest in my brand didn't spike, leading you not to visit my site, and subsequently never making a purchase. The point is that the order and the mix in which marketing channels are deployed and hit individuals are important, but few marketers focus on the individual components and variables within the mix. Marketers may understand this conceptually, but putting this into action IS HARD. Additionally, everyone is different – the marketing mix that gets you to convert is different than the mix that gets me to convert. Studying behavior and trends based on what data you can collect from an individual is paramount to help one make inferences to help increase downstream conversion. Getting to a point where we're collecting and identifying the data and trends that matter to this type of predictability is an important factor in being successful and moving in the right direction.
Have we identified the bottlenecks throughout the marketing funnel, have we decreased pain points and increased overall conversion percentages? Furthermore, do we understand why individuals are stalled in various portions of the marketing funnel? Knowing what variables we control and can manipulate (user experience, look & feel, interactions, etc.) and calibrating them such that we reduce pain points and increase conversion will be a strong indicator of success and moving in the right direction.
Has the database and market share increased? Ultimately one of the most important indicators for success will be increasing the database size, and continuing to carve out and increase market share.
How automated and turnkey are our processes now? Marketing campaigns can either be fun and impactful, or a drain on time and resources. Another indicator of success will be how turnkey and automated our practices have become (at least the ones that are able to be automated). Understanding how data informs strategy and how strategy gets executed efficiently (i.e. automation) should be a top priority and factor in evaluating success – make your life easy where you can.
Have we successfully built acquisition and engagement loops? Acquisition and engagement loops are processes that help organizations gain greater scale. For example, say the goal of a campaign is to grow a subscriber base to your organization's email database. And let's say one of the calls to action is to import their contact lists to be sent sign-up messaging. If 100 users sign up, on average ~30% will import their contacts (30 people). Of those 30, there is on average a 10X of the number of people who will receive a sign-up message (300 people). Of those 300, on average 40% will click the link (120 people), and 50% of those will sign up (60 additional sign-ups). In the end, creating an acquisition loop like this (if done correctly), should help grow 100 user sign-ups into ~166. These are easy wins that I think need to be set-up as part of the definition of overall success 12-24 months into the role.
Simply put, is marketing paying for itself? This will be a major measurement of true success. More specifically, will incremental revenue outweigh the costs of deployment and SG&A?


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Alonso wants stewards to "show a yellow or red card" for qualifying incidents
2021 Abu Dhabi Grand Prix
11th December 2021, 16:43 | Written by Dieter Rencken and RJ O'Connell
Fernando Alonso says penalties for impeding drivers during qualifying should be harsher and the stewards should be less hesitant to administer penalties.
The Alpine driver qualified 11th for the Abu Dhabi Grand Prix but the stewards are investigating whether he was impeded by Daniel Ricciardo at turn 16 during Q2. Alonso missed the cut to advance to Q3 by a hundredth of a second.
Alonso said the enforcement of the racing rules has deteriorated since his last race in Abu Dhabi three years ago. "[It is] definitely much worse than in 2018," he said.
"There are no rules out there. Not for the out-lap, there are no minimum times to respect. There are, on the first lap, a lot of strange movements off-track. I complained the first third of the championship. Then I changed attitude and then I was doing the same. And now there was someone impeding, today."
Following the Saudi Arabian Grand Prix Red Bull team principal Christian Horner said the sport missed the oversight of its late race director Charlie Whiting, who passed away in 2019. But Alonso does not feel his successor, Michael Masi, is to blame for a softening in the policing of incidents.
"We should be a little bit more aligned with the things that are right and the things that are wrong," he said. "It should be more black and white – you know, what is a penalty, what is not a penalty."
He believes the stewards "have to be harsh in some of the decisions" when necessary. "In football, when someone makes a tackle and there is a big penalty or whatever, there is a red card. Here they are always struggling to show a yellow card or a red card, so that's why we keep repeating the same bad things."
The lax enforcement of the rules means many drivers are causing problems by backing off before beginning their flying laps in qualifying, Alonso believes. "It's everybody," he said. "Everybody [does] that.
"We do that as well in the out-lap. You know, sometimes there is no way we can pass. And we are too slow, slower than what we want.
"But our team is very sharp on telling us when a fast car is coming, so I had zero investigations for impeding in the whole year. So you have to be also sharp, and the people that are not sharp, they are investigated. And normally, they get a penalty like that. But this is another thing, you know, it's very random."
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Dieter Rencken
Dieter Rencken has held full FIA Formula 1 media accreditation since 2000, during which period he has reported from over 300 grands prix, plus...
Read more about Dieter Rencken, find all their articles and get in touch with them
RJ O'Connell
Motorsport has been a lifelong interest for RJ, both virtual and 'in the carbon', since childhood. RJ picked up motorsports writing as a hobby...
Read more about RJ O'Connell, find all their articles and get in touch with them
Posted on 11th December 2021, 16:43 Author Dieter RenckenCategories 2021 Abu Dhabi Grand Prix, 2021 F1 season articles, F1 newsTags 2021 Abu Dhabi Grand Prix, Abu Dhabi Grand Prix, alonso, F1, f1 abu dhabi, F1 Abu Dhabi 2021, f1 yas marina, Fernando Alonso, formula 1, grand prix of abu dhabi
9 comments on "Alonso wants stewards to "show a yellow or red card" for qualifying incidents"
Martin Elliott
Firstly, its not just rules. Its offences and then penalties.
Even when thought to be clearly defined ie causing a collision, there is the added, but not formally approved/published guidance. In this example, did one driver predominantly or equally cause the decision.
Then the penalty. Only a few offences have a penalty clearly associated. As Michael Masi demonstrated, they pop up all through ISC, app & SR. So we know there is guidance, again in the 'magic' Stewards Handbook, to ensure consistency. So despite them being in the book, Masi can't tell the Stewards to issue more severe penalties, can he?
Then this philosophy of informal consistency is vetoed by 'First Lap' and 'Let them race' for minor incidents?
Then the last point, the penalty severity, is set by the actual incident. Forget actual consequences, or worse possible consequences. Add to that, the penalty can only be applied to the event.
Which seems inconsistent when adding time after a finish which doesn't change result, rather than 2 lines down in SR, giving any amount of grid places, let alone, as now 'threatened' removing Championship Points.
The 'rules' have followed FIA culture of growing in an organic reaction to perceived empirical problems. As a first solution it would be to recollate all 'rules' into a logical order. Strangely there are well laid out schemes for other motorsport series.
Then a critical review against current offence/penalty philosophies. This would, of course, include the Private Stewards Handbook.
Patrick (@paeschli)
Typical boomer saying it was better in the old days.
Steveetienne
Typical ignorant statement born out having no idea whatsoever of what you are commenting on. Fernando Alonso, having been born in 1981 is of the Generation X group. The Baby Boomer generation is generally defined as people being born between the years of 1946 to 1964 some 17 years at minimum before Alonso was born. The standard of knowledge on this site from many commentators is appalling.
Jason Blankenship (@jblank)
floodo1
12th December 2021, 6:04
he isnt wrong tho KEKW
Jose Lopes da Silva
Qualifying incidents need strong stewarding as the qualifying structure increases the odds for them to happen. In fact, these happen in pretty much every Saturday.
ruliemaulana (@ruliemaulana)
"It should be more black and white – you know, what is a penalty, what is not a penalty."
The last voice of reason in F1
F1oSaurus (@f1osaurus)
@ruliemaulana So was this really worth a penalty? Someone well ahead of Alonso starting his own flying lap. Alonso can't expect every driver to also make sure they have a 3s clear gap behind them so they don't cause even the slightest bother to him.
Captain Pie (@captainpie)
I wonder if at the next stage of Alonso's career, after racing, he can take the job of being a steward and revolutionise it. Someone needs to.
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Late at night, the attic lights are still on at my neighbour's farm. Sheepskins and lace petticoats are tugged free from warped wooden chests. Strings of Txistora are unhooked from old oak beams and brought downstairs where black bean stew bubbles on the stove and Amatxi, 83, stitches pink and blue streamers to a long conical hat – a whip at her side. There are whispers and secrets; tomorrow is Carnival. Here in my tiny Basque Pyrenean village of Ituren, now internationally recognised and protected by UNESCO, we have one of the oldest carnivals in Europe. It is pagan, raw and visceral. This is no public show case – it is a deeply private affair.
The next day we wait on the bridge, ears trained on the sounds of the mountain slopes above our head. A horn sounds in the distance; a long eerie whine that summons the Joaldunak in from the forests and down towards the village square. Marion, my young Basque daughter, grabs my hand as she tensely awaits the first deep rhythmic dirge of the bells that herald her father's arrival. The Joaldunak, clad in sheepskin with huge copper bells roped round their waists, high hats and ribbons, whips and lace petticoats, march solemnly out of the forest towards us. Our stomachs lurch. A huge carnival bear with ram's horn ears lunges at the crowd. The children run screaming to their parents' side. The forces of spring, fertility and hope battle those of winter, darkness and disease and the carnival fervour begins.
In the square the sound of the bells reaches a raucous crescendo and, as I look at the tense faces of the Basque villagers around me, I realise how deeply engrained this sound is on their cultural psyche, passed on generation to generation from their early pagan ancestors to this day. I look down at Marion's pale Basque face by my side and realise, oddly, that I am the only outsider here; this is my daughter's cultural heritage – not my own.


Before Hawthorne readies itself for the recently annexed Plum Creek property, before the company's less-than-certain approval from Alachua County, the timber giant is busy preparing to recruit companies that might want to move onto its land.
The effort includes an army of local, state and national economic development experts, planners and consultants, and trips to areas with model developments.
The effort also extends beyond Plum Creek's property as its recruitment plans are intertwined with county and even region-wide economic development strategies developed by the Gainesville-Area Chamber of Commerce to capitalize on the area's strengths, in particular to build on research and talent coming out of the University of Florida and job training out of the nation's top-ranked community college in Santa Fe College.


text:
Tag: Radio Caroline
The pirates return
It's time to leave Phil Sayer in peace. My daughter gave him a glorious send-off last Monday, with a funeral attended by nearly 400 people, celebrating his life with tears certainly, but also, nostalgia, humour and even laugh-out-loud moments. When were you last at a funeral which began with Monty Python's 'Galaxy Song'? Just before we try to resume normal service, here's a post I wrote two years ago, celebrating Phil's time on the pirate ship Radio Caroline.
Rest in peace, Phil.
Alex and Ben rush down the gangplank of the pirate ship.
This post probably won't make much sense if you're not from the UK. It won't make sense even if you're British if you're not at least in your mid- 50's. You won't know of a world where your radio listening choices were limited to the Home Service (much like Radio 4), the Light Programme (much like Radio 2) and the Third Programme ( much like…. yes, Radio 3). What's missing from this list? Yes, indeed, Radio One.
If you were a teenager before the mid 1960s, you weren't going to get much joy listening out for a diet of pop music by choosing the BBC. The only option was to tune in to the commercial Radio Luxembourg. The amount of music it offered grew rapidly throughout the '60s, but anyone from my generation will remember the commercials too…
Author margaret21Posted on April 30, 2016 April 30, 2016 Format GalleryCategories MusicTags Liverpool, Phil Sayer, Pirate Radio, Radio Caroline11 Comments on The pirates return
If you were a teenager before the mid 1960s, you weren't going to get much joy listening out for a diet of pop music by choosing the BBC. The only option was to tune in to the commercial Radio Luxembourg. The amount of music it offered grew rapidly throughout the '60s, but anyone from my generation will remember the commercials too. Hands up anyone who can remember Horace Batchelor's 'Infra-Draw' method for winning the football pools, turning the previously obscure Bristol suburb of Keynsham, 'spelt K-E-Y-N-S-H-A-M' into a household name?
I'd listen whilst allegedly doing my homework, but in 1964, along came another listening choice, broadcast, for goodness' sake, from a ship anchored four miles off the coast of southern England. This was Radio Caroline. By broadcasting from the waters, it avoided the need to be licensed, and that's why it quickly became known as a 'pirate' radio station. The single southern ship was augmented by others broadcasting from around the British coast . The tag-line was 'Your all-day music station', and like Radio Luxembourg and certain American stations, the top 40 was the usual diet, presented by names who later became the mainstay of mainstream radio: Tony Blackburn, Simon Dee, Johnny Walker……..
By insisting that the broadcasts potentially interfered with radio messages to shipping, legislation succeeded in making the pirates illegal in 1967. They continued nevertheless, but their hey day was over, because the BBC gave in. Radio One was born (and Two, Three, Four, and later Five), and most young people exchanged patchy reception from the pirates for the more certain transmissions of the BBC.
Why the history lesson? Because this last month celebrated the 50th birthday of Radio Caroline. It still broadcasts via the internet, but up in Liverpool, for one month only Radio Caroline North set itself up on Lightship LV23, in Albert Dock, for a 1960s and 1970s nostalgia-fest. DJs from the era were wheeled out to have fun and give listeners fun too, playing old favourites and patently enjoying themselves as they chatted over the airwaves.
And one of those presenters was my son-in-law, Phil Sayer. If you lived near Manchester in the 1980s, you might remember him as one of the presenters of BBC North West, and later he was on radio stations such as Piccadilly Radio and Smooth FM. These days he and my daughter run a successful voice-over business, but both still enjoy a chance to do a spot of work on the radio when the chance arises.
We all made a trip to Liverpool on Radio Caroline North's last Saturday: daughter Elinor, the twins, Malcolm and me. On the way over in the car we demanded, and got, a mention on air. Once on board ship we clambered up narrow stairways to get to the cramped studio with its blast-from-the-past transmitting equipment, and found ourselves in the company of radio-geeks and nostalgia seekers from all over the north-west . Fun all round. And here are some pictures as a souvenir.
LV23 dwarfed by Liverpool old and new.
View through a porthole on board.
Phil's gone off air and has just handed over.
Remember all these? And old money too?
Another porthole view.
The pirate flag.
Out on deck.
A view of the Albert Dock.
Goodbye, Radio Caroline!
Author margaret21Posted on April 30, 2014 April 13, 2017 Categories EnglandTags Phil Sayer, Piccadilly Radio, Radio Caroline, Radio Luxembourg, Smooth FM17 Comments on The pirates return


Adjustable lumbar cushioning supports your lower back and enhances the natural curve of your spine. The back cushion is contoured to support the natural curvature of the spine, helping promote posture and relaxation. Raise or lower the back cushion to place the support right where you need it most.
Metal frame construction ensures durability.
41 1/2"H x 25"W x 26"D.


Historic Marker #205 - Vanceburg, Ky.
In 1775, Col. Robert Patterson, Wm. McConnell, David Perry and Stephen Lowry brought the first horses (9) and cattle (14) into northern Kentucky. Animals were brought by boat from Ft. Pitt and driven overland from here to the early inland settlements.
For those who read of the Patterson Cabin, you know the importance of Col. Robert Patterson. William McConnell, for whom Lexington's McConnell Springs was named, is another key leader in early Kentucky history. Yes, these men would go on to establish much of the Bluegrass. And they brought with them the first horses and cattle into northern Kentucky. OK, these may not be the ancestors of Man-O-War or Seabiscuit, but the species that has so contributed to our state and it image first arrived by way of boat from Pittsburgh near this spot in Vanceburg.


text:
cash flows for Baldwin Company
1.The statement of cash flows for Baldwin Company shows what happens in the Cash account during the year. It can be seen as a summary of the sources and uses of cash (sources of cash are added, uses of cash are subtracted). Please answer which of the following is true if Baldwin's inventory goes up:Select: 1
It is a source of cash, and will be shown in the investing section as an addition.
It is a use of cash, and will be shown in the investing section as a subtraction.
It is a source of cash and will be shown in the operating section as an addition.
It is a use of cash, and will be shown in the operating section as a subtraction.


Much debate surrounds microwaves in our day and age. Are they potentially harmful? Most definitely lethal? Nuclear? Radioactive? In league with the Russians?
Rest assured, microwaves do not have telepathy, nor do they swear illegal dictatorship, nor do they sell candy-canes on the corner of 44th Street. And yet it remains hypothesized that Microwaves aren't the most beneficial or courteous way to vaporize food. Naturally, this knowledge would likely start an American revolution if word ever spread around, so to take preemptive measures, alternatives have been theorized but not yet introduced to the legal markets.
One of the most regarded theoretical alternatives to the microwave is the Food-O-Zap, and economists predict that when (if) it hits markets, the Food-O-Zap will be the top-selling object in America, and, due to it's inevitable international popularity, will extract American economy out of it's several trillion dollars in Federal debt; the only drawback would be the hazardous increase of unwanted microwaves in recycling facilities around nation. Stay tuned – you don't want to be the only person in America who owns a microwave when the time comes to replace it with a brand new Food-O-Zap!
Speaking of which, I wrote a short poem summarizing the drawbacks of the microwave, and the introduction of the Food-O-Zap as a long-awaited-for alternative!
And worst of all, the flu.
Perhaps munch on a rose petal.
As an unused brown golf cleat.
And we won't bug you more.
Be aware of your surroundings. Stay tuned for signs of food-zapping life starting up in American consumerism. When the day comes, jump on the bandwagon. Buy the Food-O-Zap and throw away that Microwave.
Until then? Watch your favorite news channel and feel free to use that microwave for easy-to-heat TV-food!


text:
Achchu Mechchu is a love story with different touch: Tharun
oi-Shekhar Hooli
By Shekhar Hooli
| Published: Friday, July 29, 2011, 11:19 [IST]
Many people in Gandhi Nagar were wondering where the budding Kannada actor, Tharun Chandra, went missing after the release of his movie Gaana Bajaana, the songs of which are still ruling the chart of a few FM radio stations in Bangalore. The Love Guru star answers that he was shooting for his next project, which is titled Achchu Mechchu. The calm star of Sandalwood has silently completed the filming and is now gearing up for its audio release.
Tharun's Achchu Mechchu introduces Bangalore-based ad filmmaker Himayath Khan, who is trained in Mumbai as a director. Actress Archana Gupta, who stared in Golden star Ganesh's Circus, has teamed up with Gaana Bajaana actor. Recently, Tharun was available for an exclusive chat with Oneindia about his film Achchu Mechchu. Here are the excerpts of Tharun's exclusive interview.
How was the response for Gaana Bajaana? And where were you all these days after its release?
The response for Gaana Bajaana was really good, but it had to compete with two other Kannada films. Though the movie have not suffered losses, it was not up to our exceptions. I can say it is moderate hit. After this movie, I took up a project with Himayath Khan, which was on long waiting in my kitty. I have recently finished the filming for Achchu Mechchu.
What would you like to tell about the film Achchu Mechchu?
Achchu Mechchu is a comedy-cum-romantic story and producer Lokesh MV, who has got state award for his Tulu movie, has written the story and screenplay and Himayath Khan has directed it. I can't tell you the story because it has been weaved on one particular aspect of love, which has been touched by anyone so far and revealing which would take away the curiosity of audience. So I rather leave it to the viewers to enjoy it on screen. It has all the commercial elements like love story, sentiments, comedy music, fights etc. It will be a perfect entertainer for youths.
What do you say about the shooting of the film?
We have shot the film in Bangalore, Goa and Sakaleshpur for 35-40 days. Nagesh has handled the camera work for Achchu Mechchu. He has delivered an excellent job and outcome of his work is superb.
What is take on the music of the movie?
AM Neel, who has composed the music for couple of Kannada films, has scored music for Achchu Mechchu. There are five songs and he has composed good music for all kinds of tracks like love, sentimental and peppy. They have come out really well. One song is like awesome and you will like it immediately after hearing it for the first time. You will like another couple of songs after seeing their videos and remaining two will impress you as you go on listening. We are planning to release the audio within a month.
How was your association with Himayath Khan and Archana Gupta on the sets?
Himayath is very good filmmaker, who has already made name in ad making in Mumbai. He is very friendly, cooperative and knowledgeable person. I have had a great experience with him. To talk about Archana, I had a negative feeling that she is quite egoistic, but she proved to be very extrovert as we went on shooting Achchu Mechchu. She was very friendly and cooperative on- as well as off-screen not only with me but also with all the crew members. I had really totally different and wonderful experience with her.
What is your take on fight and comedy?
There is one fight sequence and Kaurava Venkatesh has composed it in a wonderful way. As for as comedy is concerned, there are a host of comedians like Sadhu Kokila, Achyuth Kumar, Shobhraj and Veena Sundar. This comic element will be one among major highlights of the film. I am sure that both stunt and comedy sequences will impress the audience.
Story first published: Friday, July 29, 2011, 11:19 [IST]
More THARUN CHANDRA Stories
Tharun teams up with Sanjjana for Ondu Kshanadalli
Tharun Chandra to star Dinesh Babu's Ondhu Kshanadalli
Gaana Bajaana earns Rs. 90 lakhs at Box Office
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Darshan's Kranti TV Rights Sold To Udaya TV For This Whopping Price! To Stream On This Platform
Urfi Javed Bashes Kangana Ranaut Over 'Country Loves Only Khans' Remark; The Latter Responds


If you have ridden, walked, run, hiked a trail in our area, chances are that Thomas Jenkins had a hand in designing, building, or maintaining the trail. Thomas took the lead project manager role for all three of our $500,000 worth of Recreational Trails Program Grants in the George Washington National Forest. Thomas has also led our Massanutten Western Slope trail building efforts for over a decade.
Congrats Thomas! A well deserved recognition for the many years of hard work and commitment to building more trails for all of us to enjoy.
Bicycle Coalition Member Jess Daddio profiled Thomas in a post on the REI Co-Op Journal as one of the "Toughest Trail Builders Around." If you have been up on the new "Puzzler" trail this year at the Western Slope you have seen the magnificent rock work and grand vision that Thomas is making a reality by empowering our volunteer trail builders.
Wedged between the Blue Ridge Mountains to the east and the Allegheny Mountains to the west, Virginia's Shenandoah Valley contains a unique blend of fertile fields and craggy ridgelines. That diversity in terrain has turned Harrisonburg, Virginia, from a quaint college town to a bustling biking mecca; less than 30 minutes away are some of the largest national forests in the east, the George Washington and Jefferson National Forests.
What this area lacks in elevation—one of the highest points, Reddish Knob, is just under 4,400 feet—it makes up for twofold in the sheer number of rocks. Mountain biking here is notoriously technical, which is what initially drew Shenandoah Valley Bicycle Coalition cofounder and bike shop owner Thomas Jenkins to Harrisonburg (aka "Rocktown"). As a trail builder, his rock sidewalks and rock bridges have become hallmarks of the local singletrack, but hand-building trail out of sandstone boulder fields (where rocks can weigh upwards of two tons) presents its own set of challenges.
One of the most impressive of Jenkins and the coalition's hand-build projects is the 2,000 Hours Trail located on Massanutten Resort's Western Slopes. Named for the 2,000 person-hours it took to complete the mile-long trail, "2K" is a masterpiece of buttery berms, seamless step-ups and step-downs, and those iconic rock sidewalks.
← 12/7: Ride Bicycles in the Harrisonburg Holiday Parade!


text:
Putin Needs Better Solution To Western Sanctions Than Trying To Fix Western Elections
by JamesiGeorgia | 07/30/2018 11:03 AM
Cyber warfare when waged covertly by a corporation, organization or individual is an act of terrorism and an act war when committed by a state. Russian President Putin has his Military Intelligence GRU hackers working every day to not only flip another election by literally hacking the voting data and changing votes in November.
Did Russia Affect the 2016 Election? It's Now Undeniable
Russian Hacks on U.S. Voting System Wider Than Previously Known
Russia hacked voting systems in 39 states before the 2016 presidential election
Putin's hackers are now both focused on changing votes and figuring out how to shut down our …nations electrical grid… communications networks… and how to wipe out all of our bank accounts and even shutting down Wall Street for a few weeks according to Dan Coats the Director of National Security and a new warning from U.S. national security agencies. Why is Putin so desperate to change U.S. foreign policy and have U.S. sanctions lifted?
Russia's economy is STILL the world's eleventh-largest. Following a period of recession, it managed to return to growth last year despite Western sanctions widening. Russian President Vladimir Putin, who secured a fourth term in the March 18, oversaw a period of economic growth during his first two terms in office (2000-2008), boosting incomes across Russia. Historians agree those years during his first term are looked back on as a welcome change from the financial chaos of the 1980's and 1990s when many Russians saw their savings evaporated by inflation and financial crisis. But Putin's third term in the Kremlin, which began in 2012, was an entirely different, Russia's population saw a decline in ordinary people's quality of life.
Western Sanctions Continue to Take Their Toll
The far-reaching sanctions imposed following Russia's annexation of Crimea hurt the Russian economy at a time that its economy was hit rapidly falling oil prices, which considerably reduced the country's revenue from oil exports.
This month Putin and his political allies in its congress were forced to consider cuts in the country's retirement pension system
This past Wednesday, about 200 people in Moscow defied the ban on political rallies and came to demonstrate outside of one of Putin's offices. They handed in a petition demanding an end to the changes and calling for a national referendum. The petition said…
"Putin is personally responsible for stealing from the Russian people," Sergey Udaltsov told the crowd. Udaltsov previously served four years in a Russian prison for organizing mass protests against Putin in 2012 that led to violent confrontations with police.
Political activist and Putin critic Sergey Udaltsov spoke to a gathering of media and protestors outside the presidential offices while serving the petition. He's no stranger to standing up to Vladimir Putin. Sergey spent four years in Russian prison for organizing mass protests against Putin in 2012.
Tens of thousands of Russians in more than 30 smaller communities also came out to local protests. Some carried signs blaming the pension issue on government spending, especially the cost of vanity projects such as hosting the World Cup, which will run to an estimated $13 billion. Russia also recently opened a bridge to the contested Crimean peninsula at the cost of more than $5 billion.
Putin is boxed in and time is running out at home. He needs to get out of the Western sanctions penalty box. His best bet is to buy his way out. It should take…
· $450B to Ukraine over 30 years. Paid in Natural Gas.
· Allow Ukraine to join the EU without penalty.
· Agree via iron clad treaty that he will keep his hands off the Baltic States
· A commitment and agreement on no nukes in Iran, and denuclearization of North Korea with agreement to support sanctions imposed on the hermit state.
· Adherence and renewal of nuclear treaties.
· A peace deal that divides Syria into Assad controlled region and the free Syrian region.
By paying for Crimea and the eastern potions now occupied by Russian backed terrorist' occupiers, Putin could conceivable side step and invasion that is likely never be reversed by using a land purchase using natural gas as payment. It just might work.


text:
Four For Friday (40)
6th Mar 2020 3rd Nov 2020 ~ Shefaly Yogendra
Women CEOs remain rare.
Despite being "..just as likely as men to possess the mix of skills and charisma that predicted a future chief executive. The female CEO candidates were still 28% less likely to become CEOs than their male peers, the study, conducted by researchers at the University of Chicago and Copenhagen Business School, found". The feeder roles on the way to CEO seem dominated by men. But is that the whole explanation? Not quite, according to this article in the Wall Street Journal (link may be paywalled):
Men were three times as likely as women to have been encouraged to consider a P&L role and twice as likely to have been promoted or selected for leadership training in the past two years. Nearly half the men reported getting detailed advice at work on how to chart their path to a P&L job, compared with 15% of women surveyed. More men than women said they had a strategic support network that included sponsors who championed them for promotions—among those in the early stages of their careers, nearly twice as many men said so, 79% vs. 45% of women.
A big obstacle for women is they are more likely to develop deep expertise in one area, say, marketing or finance, and move up the ranks boxed in certain roles, said Korn Ferry's Ms. Stevenson and others. "Women get trained around people; men get trained around the guts of the business"—in key operations or production roles, she said.
The perils of being pigeonholed resonate even with women who have broken through to the top. "You hear it more in discussions about women, 'They're really good at that, let's keep them there,' " said Ellen Kullman, 64, former chief executive of DuPont Co. and, as of November, CEO of 3-D printing startup Carbon. She, along with Ms. Bickford of Evercore, is a co-chair of the Paradigm for Parity coalition.
For nonwhite women, the trek to the top is even steeper. Women of color were less likely to say their bosses gave them opportunities to manage people and projects or helped them navigate corporate politics, in a 2019 survey of 329 major companies and more than 68,000 of their employees. They made up just 4% of C-suite roles, compared with 10% for men of color, according to the research by McKinsey & Co. and LeanIn.Org, a nonprofit that promotes the advancement of women at work.
Last week I had a meandering first conversation with someone and we instantly connected on the topic of the "digital native". I mentioned to him why the binary did not resonate with me way back in 2007 when it was introduced and why I saw some as "naturalised digital citizens". Teaching some of the soi disant natives quickly shows that using a tool does not mean understanding the tool. So when I came across the idea that a new type of digital native was now in evidence, I clicked through to this article in The Conversation.
Group 1 developers were on average 22 years old and had begun regularly using social media before the age of 11. Group 2a were those of a similar age who did not use the technology so young. And group 2b comprised older developers (on average 34 years old) who usually had more experience with social media, but that also came after the start of adolescence.
We found that group 1 developers understood the goals of software development differently. They put more emphasis on challenging established norms and transforming how users interact with technology, expecting it to fit seamlessly into people's lives. This could help explain the popular stereotype of precocious social media users becoming developers of disruptive but intuitive apps.
Group 1 developers also favoured continuous experimentation with new technologies and noted how this could contribute to creating new standards and models in the industry. And they expected that the software they develop should help society. As one participant said:
The world is increasingly running on software products, and in many cases, those products have social and ethical implications. Software developers can engage in initiatives like hackathons to align the software we develop to social issues that need to be addressed.
This is where a surprising finding emerged. Group 2b of older software developers with significantly more experience also shared a desire for software to have social impact. But group 2a of younger developers who had not been precocious social media users did not share this emphasis.
Where does this desire to change the world come from? For the older, group 2b developers, their age and experience might explain why they want their work to have a broader impact. However, for the younger developers in group 1, perhaps the precocious use of social media might have given them an early taste of making a social impact online.
How do you imagine your work will be shaped by AI? Not at all? A little? A lot? Most white collar workers especially those with specialised skills think they are immune. They may be, within their own working lives, but their work is not, which is likely to change. This New Yorker article has some fascinating insights from research.
"Patents are a reflection of the things that inventors think are going to be important innovations that will make money in the future," Webb told me. "The reason you patent something is that you think you could make some money off of this innovation, and you want the right to create that product and not have other people do it instead."
As a way of testing the effectiveness of this research method, Webb looked back at the previous thirty years or so of patents in software and industrial robotics, to see if the predictions about employment and wage decline one would have found then had panned out. They had: software patents often referred to "recording," "storing," and "producing information," while robot-related patents talked about "cleaning," "moving," "welding," and "assembling." The words correlated most strongly with the tasks of packers and packagers, hoist and winch operators, machine operators, and those who worked in warehouses—for example, people driving forklifts. "It turns out that the jobs that were highly exposed to those technologies experienced declines in employment and in wages over the next thirty years," Webb said. That, to him, suggested that software and industrial robots were replacing human labor in those fields (although there were other forces in effect, as well, such as the offshoring of factories).
Webb then analyzed A.I. patent filings and found them using verbs such as "recognize," "detect," "control," "determine," and "classify," and nouns like "patterns," "images," and "abnormalities." The jobs that appear to face intrusion by these newer patents are different from the more manual jobs that were affected by industrial robots: intelligent machines may, for example, take on more tasks currently conducted by physicians, such as detecting cancer, making prognoses, and interpreting the results of retinal scans, as well as those of office workers that involve making determinations based on data, such as detecting fraud or investigating insurance claims. People with bachelor's degrees might be more exposed to the effects of the new technologies than other educational groups, as might those with higher incomes. The findings suggest that nurses, doctors, managers, accountants, financial advisers, computer programmers, and salespeople might see significant shifts in their work. Occupations that require high levels of interpersonal skill seem most insulated. (Notably, jobs at the very top of the earning scale, such as C.E.O., are not shown to be deeply changed.)
Has the coronavirus sent you and your team working from home? Do you worry about loneliness and isolation? I wrote about it three years ago:
..the gig economy did not create workplace isolation. It is an existential condition of human beings to seek both camaraderie and company, and solitude: the former perhaps to generate ideas and to rejuvenate the self, the latter to reflect, create, and indeed, rejuvenate.
How do you manage the balance though? Ryan Holmes, Hootsuite's CEO has crowdsourced and compiled some excellent ideas and practices here. My favourites are these:
The cupcake principle. "Make sure everyone gets the celebratory cupcake. Even the individual remote workers. We have so many food delivery services now, there is no excuse for everyone to not take part in a celebration." — Thera Martens, Marketing Director at Visier
Face time (not just Facetime). "Get remote teams together in person wherever you can — no virtual replacement for that. We budget bringing people from North America, South Africa and Europe together twice a year. Then, enshrine virtual connections as sacred, and model as leaders. — Laurie Bennett, Founding Partner at Within People
Random (virtual) coffee. "Can't go wrong with Random Coffee. Create pairings once a month with those participating across the company, and they meet up for coffee/drink/chat. It gets people who would otherwise not chat to one another to connect and learn about different parts of the company." — Tara-Lee Houkamau, Senior Business Analyst at Oranga Tamariki
Fun thing to learn while you are here: Good things are possible with surveillance that is, if we surveil things that we need to care for. This Tech Republic article lists IoT technologies already in use.
Posted in talent, technology	businessfutureleadershipsocietywomenwork
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Tags / Recovery
Salvaging AirAsia Flight 8501
By Donal Husni
Indonesia AirAsia Flight 8501 (QZ8501/AWQ8501) was a scheduled international passenger flight, operated by AirAsia Group affiliate Indonesia AirAsia, from Surabaya, Indonesia, to Singapore. On 28 December 2014, the aircraft operating the route, an Airbus A320-216, crashed into the Java Sea during bad weather, killing all 155 passengers and seven crew on board. Two days after the crash, debris from the aircraft and human remains were found floating in the Java Sea. Searchers located wreckage on the sea floor beginning on 3 January, and the flight data recorder and cockpit voice recorder were recovered by 13 January. The search for bodies ended in March 2015 after recovery of 106 of the 162 bodies.
Search and Rescue Air Asia QZ8501
Pangkalan Bun, Kalimantan
On March 18th 2015, after almost 3 months of search and rescue activity, an Indonesia SAR officer announces the closing of the operation, with total 97 victim found and identified. The rest of the victims remain lost at sea and it is impossible to find them as their bodies decompose and become very hard to find. The families of the victims threw flowers into the sea as their condolences to their loved ones.
Money found on the fuselage of Air Asia QZ8501 is handed over to the Indonesian Air Asia Director before he hands it over to the family of victim to whom it belongs.
A coffin is prepared at Kalimantan Hospital for a victim of the AirAsia plane crash.
The Indonesian Air Force prepare to transfer the body of a crash victim to Surabaya Airport.
Victims of AirAsia QZ8501 on an air plane as they are transferred to Surabaya for identification.
Indonesian Air Force mechanics fix a broken helicopter during the search and rescue operation for AirAsia Flight 8501.
The search and rescue team pray before the beginning their mission.
A high tech Russian aircraft arrives at Pangkalan Bun-Kalimantan in order to aid the search and rescue mission for AirAsia Flight 8501.
An American Helicopter delivers crash victims from the sea at Pangkalan Bun-Kalimantan.
A daily press conference is held by the Indonesian officer in charge of the evacuation of AirAsia crash victims at Pangkalan Bun.
An Indonesian Air Force Helicopter partakes in the search and rescue mission.
Indonesian search and rescue teams standby to collect the bodies of victims of the Air Asia crash as they arrive from sea at Pangkalan Bun-Kalimantan.
A victim of the Air Asia plane crash in a body bag before being put in a coffin at Kalimantan Hospital.
Victim of the Air Asia plane crash at Pangkalan Bun to be transfer to Surabaya.
A Russian search and rescue team take part in the AirAsia search mission.
An officer prepares the diving tools for the search and rescue teams working on the AirAsia 8501 recovery mission in the Java Sea.
An object, suspected to be part of the lost Air Asia Plane, is spotted in the Java Sea.
Indonesian Navy Seals prepare before diving into the Java Sea for the search and rescue mission.
Journalist arrive at Pangkalan Bun-Kalimantan to cover the event.
Air Asia Director, Tony Hernandez (middle) at Pangkalan Bun-Kalimantan coordination with Indonesian authorities for the evacuation.
Indonesian Air Force search the Java Sea to find the exact location of the downed Air Asia airliner.
An Indonesia Air Force Hercules Plane searches the Java Sea for missing AirAsia flight 8501.
President Joko Widodo of the Indonesian Air Force arrives from Irian Jaya to give "total all out" instruction to the Indonesian Search and Rescue team in finding the victims and fuselage of Air Asia QZ8501.
Death Surge: Bangladesh's Capsizing R...
May 2014-January 2015
Located on the delta of the Ganges river, Bangladesh is a country of waterways. There are around 300 rivers in Bangladesh, which make up 24,140 km of waterways. For this reason, river transportation is used more than road or rail transportation.
However, poorly designed transport boats, poor maintenance, recruitment of unskilled boat drivers, and the overloading of transport vessels are resulting in catastrophic boat disasters which kill thousands. It has been reported that in last 15 years there have been 573 boat accidents. These accidents have claimed the lives of more than 5000 people, and left around 1000 people missing and unaccounted for.
River transport is particularly popular amongst Bangladesh's poor, as it is much cheaper than overland transport. As a result, most victims of riverboat disasters come from poor backgrounds, as they have no choice but to travel by boat. Many of those who require transport in the first place are the primary breadwinners of a family, as they need to travel to and from work. Therefore, those most affected by these tragic disasters are some of Bangladesh's most vulnerable. Many of those killed leave behind dependent and impoverished families.
Navy divers have recover the body of a dead child who was killed the MV Miraj-4 passenger vessel capsized on the Meghna River near Munshigonj, Dhaka. On that sunken passenger vessel 54 people died and 250 went missing.


The high school years—freshman to senior—are when you learn that church can be fun and transformative at the same time. Our heart is to make Student Impact the kind of place where no one stands alone. Come exactly as you are!
Student Impact meetings at Willow Huntley are known as Impact: 7 and Impact: 11, where students experience a time of worship, creative elements, a message and small group time.
Impact: 7 is on Thursday at 7 p.m., at Leggee Elementary School (Yes, it's an elementary school. But the room is great!).
Impact: 11 is on Sunday at 11 a.m., at Huntley High School.
Student Impact is a place for high schoolers to bring their non-churched friends. Impact offers games, engaging teaching, and small groups, which are designed for going deeper into community and tackling deep questions.
We believe there are three important questions teenagers should ask when is comes to God: Is there a God? Who is God? What does He want to do in my life?
Pat is the Student Impact Director at Willow Creek Huntley.


(1) Regular weekly and often twice weekly meetings are held between the administrator and the contractor. Since 30 January, eight meetings have been held and weekly meetings will continue to take place for the foreseeable future.
(2) The Ministry of Justice are receiving daily management information from the contractor concentrating on priority areas of interpreter availability and fulfilment of bookings. I have no plans to place these reports in the Library. On 20 March 2011,Official Report, column 672W, I confirmed that the Chief Statistician of the Ministry of Justice is considering what interpreter data can be published by the end of May 2012.


text:
"""
Created on 2017-8-24

@author: cheng.li
"""

import bisect
import datetime as dt
from typing import Iterable
from typing import Union

import numpy as np
import pandas as pd
from simpleutils.asserts import require
from PyFin.DateUtilities import Period
from PyFin.api import BizDayConventions
from PyFin.api import DateGeneration
from PyFin.api import advanceDateByCalendar
from PyFin.api import makeSchedule

from alphamind.data.engines.sqlengine import SqlEngine
from alphamind.data.engines.sqlengine import total_risk_factors
from alphamind.data.engines.universe import Universe
from alphamind.data.processing import factor_processing
from alphamind.data.transformer import Transformer
from alphamind.utilities import alpha_logger
from alphamind.utilities import map_freq

def _merge_df(engine, names, factor_df, target_df, universe, dates, risk_model, neutralized_risk):
    risk_df = engine.fetch_risk_model_range(universe, dates=dates, risk_model=risk_model)[1]
    used_neutralized_risk = list(set(total_risk_factors).difference(names))
    risk_df = risk_df[['trade_date', 'code'] + used_neutralized_risk].dropna()
    target_df = pd.merge(target_df, risk_df, on=['trade_date', 'code']).dropna()

    if neutralized_risk:
        train_x = pd.merge(factor_df, risk_df, on=['trade_date', 'code'])
        train_y = target_df.copy()

        risk_exp = train_x[neutralized_risk].values.astype(float)
        x_values = train_x[names].values.astype(float)
        y_values = train_y[['dx']].values
    else:
        risk_exp = None
        train_x = factor_df.copy()
        train_y = target_df.copy()
        x_values = train_x[names].values.astype(float)
        y_values = train_y[['dx']].values

    codes = train_x['code'].values
    date_label = pd.DatetimeIndex(factor_df.trade_date).to_pydatetime()
    dates = np.unique(date_label)
    return target_df, dates, date_label, risk_exp, x_values, y_values, train_x, train_y, codes

def prepare_data(engine: SqlEngine,
                 factors: Union[Transformer, Iterable[object]],
                 start_date: str,
                 end_date: str,
                 frequency: str,
                 universe: Universe,
                 benchmark: int,
                 warm_start: int = 0,
                 fit_target: Union[Transformer, object] = None):
    if warm_start > 0:
        p = Period(frequency)
        p = Period(length=-warm_start * p.length(), units=p.units())
        start_date = advanceDateByCalendar('china.sse', start_date, p).strftime('%Y-%m-%d')

    dates = makeSchedule(start_date,
                         end_date,
                         frequency,
                         calendar='china.sse',
                         dateRule=BizDayConventions.Following,
                         dateGenerationRule=DateGeneration.Forward)

    dates = [d.strftime('%Y-%m-%d') for d in dates]

    horizon = map_freq(frequency)

    if isinstance(factors, Transformer):
        transformer = factors
    else:
        transformer = Transformer(factors)

    factor_df = engine.fetch_factor_range(universe,
                                          factors=transformer,
                                          dates=dates).sort_values(['trade_date', 'code'])
    alpha_logger.info("factor data loading finished")

    if fit_target is None:
        target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
    else:
        one_more_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
        target_df = engine.fetch_factor_range_forward(universe, factors=fit_target,
                                                      dates=dates + [one_more_date])
        target_df = target_df[target_df.trade_date.isin(dates)]
        target_df = target_df.groupby('code').apply(lambda x: x.fillna(method='pad'))
    alpha_logger.info("fit target data loading finished")

    industry_df = engine.fetch_industry_range(universe, dates=dates)
    alpha_logger.info("industry data loading finished")
    benchmark_df = engine.fetch_benchmark_range(benchmark, dates=dates)
    alpha_logger.info("benchmark data loading finished")

    df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
    df = pd.merge(df, benchmark_df, on=['trade_date', 'code'], how='left')
    df = pd.merge(df, industry_df, on=['trade_date', 'code'])
    df['weight'] = df['weight'].fillna(0.)
    df.dropna(inplace=True)

    return dates, df[['trade_date', 'code', 'dx']], df[
        ['trade_date', 'code', 'weight', 'industry_code', 'industry'] + transformer.names]

def batch_processing(names,
                     x_values,
                     y_values,
                     groups,
                     group_label,
                     batch,
                     risk_exp,
                     pre_process,
                     post_process,
                     codes):
    train_x_buckets = {}
    train_y_buckets = {}
    train_risk_buckets = {}
    predict_x_buckets = {}
    predict_y_buckets = {}
    predict_risk_buckets = {}
    predict_codes_bucket = {}

    for i, start in enumerate(groups[:-batch]):
        end = groups[i + batch]

        left_index = bisect.bisect_left(group_label, start)
        right_index = bisect.bisect_left(group_label, end)

        this_raw_x = x_values[left_index:right_index]
        this_raw_y = y_values[left_index:right_index]

        if risk_exp is not None:
            this_risk_exp = risk_exp[left_index:right_index]
        else:
            this_risk_exp = None

        train_x_buckets[end] = pd.DataFrame(factor_processing(this_raw_x,
                                                              pre_process=pre_process,
                                                              risk_factors=this_risk_exp,
                                                              post_process=post_process),
                                            columns=names)

        train_y_buckets[end] = factor_processing(this_raw_y,
                                                 pre_process=pre_process,
                                                 risk_factors=this_risk_exp,
                                                 post_process=post_process)

        train_risk_buckets[end] = this_risk_exp

        left_index = bisect.bisect_right(group_label, start)
        right_index = bisect.bisect_right(group_label, end)

        sub_dates = group_label[left_index:right_index]
        this_raw_x = x_values[left_index:right_index]
        this_codes = codes[left_index:right_index]

        if risk_exp is not None:
            this_risk_exp = risk_exp[left_index:right_index]
        else:
            this_risk_exp = None

        ne_x = factor_processing(this_raw_x,
                                 pre_process=pre_process,
                                 risk_factors=this_risk_exp,
                                 post_process=post_process)

        inner_left_index = bisect.bisect_left(sub_dates, end)
        inner_right_index = bisect.bisect_right(sub_dates, end)
        predict_x_buckets[end] = pd.DataFrame(ne_x[inner_left_index:inner_right_index],
                                              columns=names)
        if risk_exp is not None:
            predict_risk_buckets[end] = this_risk_exp[inner_left_index:inner_right_index]
        else:
            predict_risk_buckets = None
        predict_codes_bucket[end] = this_codes[inner_left_index:inner_right_index]

        this_raw_y = y_values[left_index:right_index]
        if len(this_raw_y) > 0:
            ne_y = factor_processing(this_raw_y,
                                     pre_process=pre_process,
                                     risk_factors=this_risk_exp,
                                     post_process=post_process)
            predict_y_buckets[end] = ne_y[inner_left_index:inner_right_index]

    return train_x_buckets, \
           train_y_buckets, \
           train_risk_buckets, \
           predict_x_buckets, \
           predict_y_buckets, \
           predict_risk_buckets, \
           predict_codes_bucket

def fetch_data_package(engine: SqlEngine,
                       alpha_factors: Iterable[object],
                       start_date: str,
                       end_date: str,
                       frequency: str,
                       universe: Universe,
                       benchmark: int,
                       warm_start: int = 0,
                       batch: int = 1,
                       neutralized_risk: Iterable[str] = None,
                       risk_model: str = 'short',
                       pre_process: Iterable[object] = None,
                       post_process: Iterable[object] = None,
                       fit_target: Union[Transformer, object] = None) -> dict:
    alpha_logger.info("Starting data package fetching ...")
    transformer = Transformer(alpha_factors)
    names = transformer.names
    dates, target_df, factor_df = prepare_data(engine,
                                               transformer,
                                               start_date,
                                               end_date,
                                               frequency,
                                               universe,
                                               benchmark,
                                               warm_start + batch,
                                               fit_target=fit_target)

    target_df, dates, date_label, risk_exp, x_values, y_values, train_x, train_y, codes = \
        _merge_df(engine, names, factor_df, target_df, universe, dates, risk_model,
                  neutralized_risk)

    alpha_logger.info("data merging finished")

    target_df['weight'] = train_x['weight']
    target_df['industry'] = train_x['industry']
    target_df['industry_code'] = train_x['industry_code']

    if neutralized_risk:
        for i, name in enumerate(neutralized_risk):
            target_df.loc[:, name] = risk_exp[:, i]

    alpha_logger.info("Loading data is finished")

    train_x_buckets, train_y_buckets, train_risk_buckets, predict_x_buckets, predict_y_buckets, predict_risk_buckets, predict_codes_bucket \
        = batch_processing(names,
                           x_values,
                           y_values,
                           dates,
                           date_label,
                           batch,
                           risk_exp,
                           pre_process,
                           post_process,
                           codes)

    alpha_logger.info("Data processing is finished")

    ret = dict()
    ret['x_names'] = names
    ret['settlement'] = target_df[target_df.trade_date >= start_date]

    train_x_buckets = {k: train_x_buckets[k] for k in train_x_buckets if
                       k.strftime('%Y-%m-%d') >= start_date}
    train_y_buckets = {k: train_y_buckets[k] for k in train_y_buckets if
                       k.strftime('%Y-%m-%d') >= start_date}
    train_risk_buckets = {k: train_risk_buckets[k] for k in train_risk_buckets if
                          k.strftime('%Y-%m-%d') >= start_date}

    predict_x_buckets = {k: predict_x_buckets[k] for k in predict_x_buckets if
                         k.strftime('%Y-%m-%d') >= start_date}
    predict_y_buckets = {k: predict_y_buckets[k] for k in predict_y_buckets if
                         k.strftime('%Y-%m-%d') >= start_date}
    if neutralized_risk:
        predict_risk_buckets = {k: predict_risk_buckets[k] for k in predict_risk_buckets if
                                k.strftime('%Y-%m-%d') >= start_date}
    else:
        predict_risk_buckets = None
    predict_codes_bucket = {k: predict_codes_bucket[k] for k in predict_codes_bucket if
                            k.strftime('%Y-%m-%d') >= start_date}

    ret['train'] = {'x': train_x_buckets, 'y': train_y_buckets, 'risk': train_risk_buckets}
    ret['predict'] = {'x': predict_x_buckets, 'y': predict_y_buckets, 'risk': predict_risk_buckets,
                      'code': predict_codes_bucket}
    return ret

def fetch_train_phase(engine,
                      alpha_factors: Union[Transformer, Iterable[object]],
                      ref_date,
                      frequency,
                      universe,
                      batch=1,
                      neutralized_risk: Iterable[str] = None,
                      risk_model: str = 'short',
                      pre_process: Iterable[object] = None,
                      post_process: Iterable[object] = None,
                      warm_start: int = 0,
                      fit_target: Union[Transformer, object] = None) -> dict:
    if isinstance(alpha_factors, Transformer):
        transformer = alpha_factors
    else:
        transformer = Transformer(alpha_factors)

    p = Period(frequency)
    p = Period(length=-(warm_start + batch) * p.length(), units=p.units())

    start_date = advanceDateByCalendar('china.sse', ref_date, p, BizDayConventions.Following)
    dates = makeSchedule(start_date,
                         ref_date,
                         frequency,
                         calendar='china.sse',
                         dateRule=BizDayConventions.Following,
                         dateGenerationRule=DateGeneration.Backward)

    horizon = map_freq(frequency)

    factor_df = engine.fetch_factor_range(universe, factors=transformer, dates=dates)
    if fit_target is None:
        target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
    else:
        one_more_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
        target_df = engine.fetch_factor_range_forward(universe, factors=fit_target,
                                                      dates=dates + [one_more_date])
        target_df = target_df[target_df.trade_date.isin(dates)]
        target_df = target_df.groupby('code').apply(lambda x: x.fillna(method='pad'))

    df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()

    target_df, factor_df = df[['trade_date', 'code', 'dx']], df[
        ['trade_date', 'code'] + transformer.names]

    target_df, dates, date_label, risk_exp, x_values, y_values, _, _, codes = \
        _merge_df(engine, transformer.names, factor_df, target_df, universe, dates, risk_model,
                  neutralized_risk)

    if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
        require(len(dates) >= 2, ValueError,
                "No previous data for training for the date {0}".format(ref_date))
        end = dates[-2]
        start = dates[-batch - 1] if batch <= len(dates) - 1 else dates[0]
    else:
        end = dates[-1]
        start = dates[-batch] if batch <= len(dates) else dates[0]

    index = (date_label >= start) & (date_label <= end)
    this_raw_x = x_values[index]
    this_raw_y = y_values[index]
    this_code = codes[index]
    if risk_exp is not None:
        this_risk_exp = risk_exp[index]
    else:
        this_risk_exp = None

    ne_x = factor_processing(this_raw_x,
                             pre_process=pre_process,
                             risk_factors=this_risk_exp,
                             post_process=post_process)

    ne_y = factor_processing(this_raw_y,
                             pre_process=pre_process,
                             risk_factors=this_risk_exp,
                             post_process=post_process)

    ret = dict()
    ret['x_names'] = transformer.names
    ret['train'] = {'x': pd.DataFrame(ne_x, columns=transformer.names), 'y': ne_y,
                    'code': this_code}

    return ret

def fetch_predict_phase(engine,
                        alpha_factors: Union[Transformer, Iterable[object]],
                        ref_date,
                        frequency,
                        universe,
                        batch=1,
                        neutralized_risk: Iterable[str] = None,
                        risk_model: str = 'short',
                        pre_process: Iterable[object] = None,
                        post_process: Iterable[object] = None,
                        warm_start: int = 0,
                        fillna: str = None,
                        fit_target: Union[Transformer, object] = None):
    if isinstance(alpha_factors, Transformer):
        transformer = alpha_factors
    else:
        transformer = Transformer(alpha_factors)

    p = Period(frequency)
    p = Period(length=-(warm_start + batch - 1) * p.length(), units=p.units())

    start_date = advanceDateByCalendar('china.sse', ref_date, p, BizDayConventions.Following)
    dates = makeSchedule(start_date,
                         ref_date,
                         frequency,
                         calendar='china.sse',
                         dateRule=BizDayConventions.Following,
                         dateGenerationRule=DateGeneration.Backward)

    horizon = map_freq(frequency)

    factor_df = engine.fetch_factor_range(universe, factors=transformer, dates=dates)

    if fillna:
        factor_df = factor_df.groupby('trade_date').apply(
            lambda x: x.fillna(x.median())).reset_index(
            drop=True).dropna()
    else:
        factor_df = factor_df.dropna()

    if fit_target is None:
        target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
    else:
        one_more_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
        target_df = engine.fetch_factor_range_forward(universe, factors=fit_target,
                                                      dates=dates + [one_more_date])
        target_df = target_df[target_df.trade_date.isin(dates)]
        target_df = target_df.groupby('code').apply(lambda x: x.fillna(method='pad'))

    names = transformer.names

    if neutralized_risk:
        risk_df = engine.fetch_risk_model_range(universe, dates=dates, risk_model=risk_model)[1]
        used_neutralized_risk = list(set(neutralized_risk).difference(names))
        risk_df = risk_df[['trade_date', 'code'] + used_neutralized_risk].dropna()
        train_x = pd.merge(factor_df, risk_df, on=['trade_date', 'code'])
        train_x = pd.merge(train_x, target_df, on=['trade_date', 'code'], how='left')
        risk_exp = train_x[neutralized_risk].values.astype(float)
    else:
        train_x = pd.merge(factor_df, target_df, on=['trade_date', 'code'], how='left')
        risk_exp = None

    train_x.dropna(inplace=True, subset=train_x.columns[:-1])
    x_values = train_x[names].values.astype(float)
    y_values = train_x[['dx']].values.astype(float)

    date_label = pd.DatetimeIndex(train_x.trade_date).to_pydatetime()
    dates = np.unique(date_label)

    if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
        end = dates[-1]
        start = dates[-batch] if batch <= len(dates) else dates[0]

        left_index = bisect.bisect_left(date_label, start)
        right_index = bisect.bisect_right(date_label, end)
        this_raw_x = x_values[left_index:right_index]
        this_raw_y = y_values[left_index:right_index]
        sub_dates = date_label[left_index:right_index]

        if risk_exp is not None:
            this_risk_exp = risk_exp[left_index:right_index]
        else:
            this_risk_exp = None

        ne_x = factor_processing(this_raw_x,
                                 pre_process=pre_process,
                                 risk_factors=this_risk_exp,
                                 post_process=post_process)

        ne_y = factor_processing(this_raw_y,
                                 pre_process=pre_process,
                                 risk_factors=this_risk_exp,
                                 post_process=post_process)

        inner_left_index = bisect.bisect_left(sub_dates, end)
        inner_right_index = bisect.bisect_right(sub_dates, end)

        ne_x = ne_x[inner_left_index:inner_right_index]
        ne_y = ne_y[inner_left_index:inner_right_index]

        left_index = bisect.bisect_left(date_label, end)
        right_index = bisect.bisect_right(date_label, end)

        codes = train_x.code.values[left_index:right_index]
    else:
        ne_x = None
        ne_y = None
        codes = None

    ret = dict()
    ret['x_names'] = transformer.names
    ret['predict'] = {'x': pd.DataFrame(ne_x, columns=transformer.names, index=codes), 'code': codes,
                      'y': ne_y.flatten()}

    return ret

redpajama_set_name: RedPajamaGithub


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text:
HomeProfile in VICE: The First US Official to Resign Over Afghanistan Is Fighting to Help Whistleblowers
Profile in VICE: The First US Official to Resign Over Afghanistan Is Fighting to Help Whistleblowers
November 29, 2014 MatthewHoh Interviews, Recovery, War and ConflictAdam Barnett, Afghanistan, Alcohol Abuse, Depression, ExposeFacts, Glenn Greenwald, Iraq, James Risen, Joe Sandler Clarke, London, Marine Corps, Moral Injury, Norman Solomon, Peace, PTSD, Recovery, Richard Holbrooke, Suicide, VICE, War Resisters International
I've liked VICE for a bit of time now, so it was pretty cool to have been profiled by VICE UK while I was in London. Much appreciation for to Joe Sandler Clarke and Adam Barnett for their time and effort telling my story.
The First US Official to Resign Over Afghanistan Is Fighting to Help Whistleblowers
It's five years since Matthew Hoh became the first US official to resign in protest over the government's handling of the Afghanistan war, resulting in a PR disaster for the US government.
"After I resigned, I was in a bar and it just so happened that I was sitting next to an editor from the Washington Post. We got talking and he told me to call the foreign affairs desk the next day." He did and a few hours later, Post journalist Karen DeYoung was on the phone. They spoke for six hours and within days, his resignation letter was on the front page.
In the letter, Hoh explained he had lost confidence in the tactics being used in the conflict, and that he had no idea why it was going on. He wrote, "My resignation is based not upon how we are pursuing this war, but why and to what end."
Today, sitting in his hotel room in central London, wearing a War Resisters International badge and with leather elbow patches sown onto his jacket, he admits he is surprised by his journey from Marine Corps captain to peace activist. "I never planned any of this," he says. "In a year I went from thinking I would have 35 years in the government before getting a PhD and teaching at a small college somewhere to saying, 'Fuck you, I am not doing this anymore. It's wrong.'"
The years since he resigned have been marked by the current administration embarking on what Glenn Greenwald has called "the most aggressive and vindictive assault on whistleblowers of any president in American history." Of the 11 times the Espionage Act has been used to prosecute whistleblowers who have leaked information to journalists, seven have been under Obama. Pulitzer Prize–winning journalist James Risen is to this day facing possible prosecution for refusing to reveal the identity of his one of sources to the authorities.
Hoh now fears that if he had blown the whistle today as he had done in 2009, he would be facing prosecution. This explains his motivation for becoming an advisory board member at ExposeFacts, a new website led by veteran journalist and activist Norman Solomon. The project is designed as a place for people to leak information safely, while also offering better protection to whistleblowers and campaigning to shield reporters from state surveillance. It already has the backing of a host of Pulitzer Prize winners and Daniel Ellsberg, the man who leaked the Pentagon Papers to the New York Times.
The day before I meet him, Hoh was part of panel of former intelligence workers at the launch of ExposeFacts that told the world's media that they were fighting back against the Obama administrations "war on journalism and whistleblowing."
They aim to provide technology for secure, anonymous whistleblowing, and to push the actions of whistleblowers "to the forefront of the public consciousness."
Having enlisted for the Marines in the heady days before 9/11, initially Hoh's military career was "just like the brochure said it would be." He was stationed in Okinawa, Japan, with his days spent training, traveling the world, and hanging out at the base's private beach.
Hardworking and intelligent, with supreme self-confidence and an inherent curiosity about the world, Hoh enjoyed what he describes as a "Forrest Gump-like" rise through the ranks.
By the time US forces invaded Iraq, he was working for the Secretary of the Navy. By 2004, he was leading reconstruction projects in Iraq, handing out money to political leaders and making arrangements, ostensibly so the country's devastated athletics facilities could be rebuilt. He would travel with his own security team, with a pistol tucked into his suit pocket and $25 million in cash.
"It was part Scarface, part Lawrence of Arabia," he recalls. "But it was very instructive to me about the folly of war."
Throughout the conflict, Hoh was skeptical about the reasons for going to war and the mission itself. "I certainly doubted why we were there and could see it wasn't adding up. I was doing all I could to do it right," he says. "But it doesn't matter how much honor you possess if a war is morally fraught."
He worked with a group of women in Baghdad and the memory of them haunts him still. They were modern and educated. They wore hijabs that matched their mascara and believed in the US mission.
"We gave them this hope and this promise and then we gave them a hell that you and I can't even imagine," he says. "I know one of them is still alive, but that is something that has haunted me ever since. I don't know if they were blown up in a car bomb, or if they were raped, or if their families were killed. That's where a lot of my moral injury comes from."
After a period spent moving from one prestigious desk job to the next, Hoh was back in Iraq in 2007. He was with a small group of men when the helicopter they were traveling in crashed over the Persian Gulf. "It was kind of ironic because you go to the desert and almost die in the water," he says. "Four guys died, including one who was a friend of mine and I could not save any of them. It crushed me. I had survivor's guilt."
On returning home he could barely function. While spending a day at the beach in Delaware, he had a flashback. "It came over me as soon as I went in the water. All the stereotypical PTSD symptoms you hear about not liking fireworks, or not being in crowds, they're all a joke, compared to this moral injury. It's just blackness," he says.
"The alcohol became key. I was always a big drinker, but this was different. It was the only way I could get through the day. My days in this period consisted of getting up, going to work, leaving work as soon as possible, getting home, working out, drinking, blacking out by 10 PM and then doing it all again."
Two years later, figuring that if he was going to die, it may as well be in Afghanistan, he went back to fight. He was the State Department's senior representative in Zabul province, an area which had seen some of the fiercest fighting of the war. But five months into his year-long contract, he was done with the military.
"I didn't believe any of what was being said. That we were there to protect ourselves from another 9/11 and all that stuff. It just wasn't true," he remembers.
That's when he resigned and before long he was being chased by journalists who wanted to hear of his disaffection. "It was a huge deal," recalls Hoh. "I had three TV news trucks outside my house and 75 media requests, the day after it broke."
Despite US Envoy Richard Holbrooke telling him that he understood his misgivings about the war and that his letter was being "taken seriously," after news of his resignation went public Hoh found himself cut off from the Washington establishment. A Wikipedia page about him that downplayed his role in the State Department and featured a clip of him being used in an al Qaeda propaganda video surfaced online. For more than two years, he couldn't find work and had no money coming in. He found himself selling cars for a few months just to get by.
Being frozen out took its toll. By 2011, suicide had become a daily obsession. He would plan it meticulously, figuring out when and how he would do it, how he would tell his family. "The only thing I didn't do was buy a gun," he says.
Ultimately, it was through the support of family and an ex-girlfriend who forced him into therapy that he was able to dig himself out of that feeling. A sense of having a greater purpose helped too. Every time he saw a politician lie on TV, or when he read a newspaper article he knew to be untrue, he kept wanting to speak out. "I was out in public and doing media, so I felt like I couldn't kill myself," he says. "People would say, 'You're gonna listen to what that guy thinks about the war?! He shot himself in the head!' I had this cause, this purpose and I could not discredit that by killing myself."
Hoh is now 41. Having left Washington vowing never to return, he lives in Raleigh, North Carolina and earns $48,000 a year through his job the Center for International Policy. If he had stayed in the military, he says, he would be earning more than double that.
He's turned his back on a career, a high salary, an institution, and a way of life—now he's determined to help others who want to do the same. For all he's lost by speaking out, he's also gained a tremendous amount. "I'm very happy," Hoh tells me later. "With the moral injury, the PTSD, the depression, the suicidality, I have my bad periods, but I'm getting through. I don't own a gun, I don't keep alcohol in my house, I see my psychologist every week, I take medication. I manage it like you would manage high blood pressure. I'm just happy that I can express my own thoughts and think my own way. That's worth more than any amount of money."
← Journalism, Whistleblowing and the Security State
Interview with Op-Ed News and Joan Brunwasser →


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Eirian grew up in South Wales and became a Christian when he was 13 on a summer youth camp in North Wales. He has been married to Portia since 2014 and they have twin boys. Eirian moved to Exeter in 2013 to train as a solicitor and qualified in 2015. However he enjoys camping, body boarding, cooking and watching rugby far more.