Sunday, October 1, 2017

Hot enough for ya?


This September in Oakland has been hot. Here are some ways to put it in perspective.

1. The average temperature was 78.3° F. This might not seem like much in Sacramento or San Diego, but that is the warmest average month not only this year so far, but the warmest since 2011, which is the arbitrary year when I started measuring things this way.

2. According to Weather Underground's daily average of the past fifteen years, September 2017 was 3.9° F. warmer than the average. 3.9° above the fifteen year benchmark is definitely warm, but it is by no means a record over the years I have been using to measure things this way. For example, the winter of 2015 was way above average, with January at 3.5° warmer, February at 5.1° warmer and March at 5.7° warmer. Still, 3.9° warmer than the benchmark is by no means an average month. In 2017 so far, September and May are tied for first with 3.9°.

3. September 1st and 2nd were both 101° F in Oakland. Again, it's a matter of perspective. In Sacramento, days over 100° are an inconvenience. In Oakland, two days in a row over 100° is a sign of the apocalypse.

We also have a statistical method to tell us if a month is unusually hot or not using t-scores, a relative of z-scores. The formula for the two is the same, the average times the square root of the number of days in the month divided by the month's standard deviation. (This formula is almost what we want, and it is exactly correct if mux = 0.) Using this test data, we can get a p-value, the beloved precious of scientific researchers everywhere. If the p-value is less than .05, this is usually a sign your paper can possibly be published.

Using this method, September 2017 was not unusually above the average of the last fifteen Septembers. (Note: May 2017 had the same raw score of 3.9° above average and it produced a p-value high enough to let us reject the null hypothesis. May was unusually hot using this method, September, not so much.)

Why did September fail in rejecting the null hypothesis, which is to say it does not seem unusually warm using this test? The answer is in the standard deviation, a commonly used method to measure how spread out a data set is. If there isn't much deviation in a set, a 3.9° difference would definitely impress the t-score test. What happened is that September was warm in a very weird way, several days way warmer than average, but thirteen days out of thirty, it was actually slightly cooler than average. (By "way warmer", the early heat wave was 26° F. warmer than average for two days and there were five more days in September were the temperature was 10° warmer than average or more.) But in the middle of the month, there was a ten day "cool snap", when temperatures were cooler than average by -1° to -6°. These big swings meant for a higher standard deviation, the highest of the year at 9.384. In comparison, the month of May did let us reject the null hypothesis because the standard deviation was "only" 7.865, which is the second highest standard deviation of the year.

Okay, Matty Boy, what does this have to do with the price of tea in China? Well, if it isn't my old pal Hypothetical Question Asker! This is just an example of statistical methods sometimes producing confounding results. I have no philosophical qualms about the t-score test in general, though the arbitrary threshold of .05 to decide whether we accept or reject the null hypothesis is fairly coming under question these days in research circles. My other quibble about this work that I am doing is whether we should think of a month as a period of time that measures climate or if it should be still considered just weather. The method I hit upon earlier in September argues that climate should use time spans of a year. Shorter spans like season or half years might make sense, but my general feeling is a month is too short.

In any case, I saw some weird numbers and decided to write about 700 words about them.

Don't hate. This is how I roll.

Any questions?

(Seriously, the comments are perfect for questions.)

Saturday, September 16, 2017

An idea for linking weather and climate


There is a difference between weather and climate, but as a mathematician I wish the demarcation point was better defined. Weather tends to deal with localities or small regions and time periods ranging from a day to several days. Climate usually discusses large regions or even the entire globe over longer periods of time. Climate scientists have decided a month is the smallest reasonable length of time when talking about climate. If I had a say, I'd prefer a season or a half year to be the smallest useful unit, where a half year would begin on the first day of spring, either in the Northern or Southern hemisphere, which means either late March or late September. This would argue that years should start on one of these dates, but that's too much to ask.

Whatever units of time are used, the numbers make a solid case that the surface temperature of both the land and the sea are getting warmer over time. The data does not show a constant rise, each day warmer than the last or even each year warmer than the last, but the trend over time is upward using any standard mathematical measure of a data set.

The idea I present today is currently in the hypothesis phase, as I have only done a little bit of data from a single weather station. I chose the Oakland Airport stations because... well, I'm from Oakland. The data did what I expected more or less, but for this to be fully fleshed out, I need to get a programming language on my computer and take a rip into a very large data set.

Here is my methodology.

1. Take the daily data from a baseline set of years for a single weather station. Climate scientists right now are using 1981-2010 as the standard baseline, so I used that set as well.

2. Using that set, get an average temperature and a standard deviation for each day. If I have any quibble with this method, I would say February 29 is getting the short end of the stick, as there were only seven leap years in the set instead of thirty for every other date. In practice in the Oakland data set, the average and the standard deviation for Leap Day are not out of line with the other nearby days.

The Excel data for 2013

3. Input the daily data from any year and get 365 or 366 z-scores. The numbers on the left are the z-scores from 2013, one of the warmest of the recent years but by no means the record holder. Cells with red backgrounds and borders are the z-scores greater than 3, which makes that day very unusually hot for that data set. The z-scores in red with no border are over 2 but under 3, so they are unusually hot. Two days are marked in green, they were unusually cold, which are z-scores under -2 but greater than -3. No days in 2013 had z-scores under -3.

4. A high z-score is not crazy hot by human standards, just crazy hot in context. For example, December 29th (ahem, my birthday) was 72° F in 2013, which right-thinking people would regard as "a nice day". The thing is, it is not normal for the weather to be that nice on December 29, as I can remember with some clarity. This example counts as a very unusually hot day.

5. Show the data from a weather station as the average temperature for the year and the number of days in each of four categories: Very unusually cold, unusually cold, unusually hot and very unusually hot. That's the what graph below shows for six years in the 1960s and six years in the 2010s.
Comparing the 1960s to the 2010s in terms of unusually warm and cold days

Okay, let's take a look at the data year by year.

1961: This is the warmest year in the 1960s in our Oakland Airport data, and the average is exactly the same as 2011, the coldest year in 21st Century set. 1961 holds the record with 18 days that are very unusually hot for that particular day of the year, but if we rank the years by (# of warm days) - (# of cold days), it has the highest number in the 1960s with 30, but would still be outranked by five of the six measured year in the 2010s.

1962: 1962 turns colder than 1961 and there are only 25 unusually hot days this year, with 8 unusually cold.

1963: 1963 is colder still, and the number of cold days is greater than the number of warm days by our measuring standards.

1964: 1964 is the only year on our list with no very unusually hot or very unusually cold days.

1965: The coldest year of the twelve on the list, it is dead last on the (warm days) - (cold days) ranking system at -5. It is also dead last in total number of unusual days with 16.

1966: 1966 warms up slightly in comparison to 1965, but as the chart shows, all its entries are about the size of Trump's fingers.

2011: The year most like 2011 is 1961, but most noticeably, it starts the 21st Century trend of no very unusually cold days.

2012: 2012 is only a little warmer than 2011 and the bars are unimpressive by 21st Century standards, but it is the first year on the list with no unusually cold days whatsoever.

2013: And now it gets warm. In terms of bar heights, 2013 is most like 2011, even though the average temperature is 2.25 degrees hotter. This is the most noticeable instance of the imperfect correlation between average yearly temperature and number of unusual days, but that actually makes me happy with the data set. Perfect correlation in naturally occurring data is suspicious in such a simple measuring system.

2014: And now it gets hot. The first of three years in a row with an average temperature in Oakland over 68° F, 2014 has the highest average temperature, the most unusually warm days and zero unusually cold days.

2015: Compared to 2014, 2015 is a reversion to the mean, but it has the second highest number of unusually hot days, the second highest number of very unusually hot days, the second highest total of unusually hot and very unusually hot combined and no unusually cold days at all.

2016: Again, we see the numbers shrinking from the 2014 peak, but still higher than 2013, which had been the highest on the list when it was posted.

To repeat myself here in the conclusion, this is an interesting hypothesis, but it needs more data. I took a very large climate data set and whipped it into shape back in 2013, so this only a matter of me applying myself once more, as well as buying a programming language package for the new computer. You may have read the book How to Be Your Own Best Friend. I must now write yet another chapter in my unpublished tome How to Be Your Own Overworked, Underpaid Grad Student. If I do put this in the pipeline of my many long-term projects, I will likely start a new blog showing the data.

Wish me luck. Or mutter to yourself that this mofo is crazy. Whatevs.

Sunday, August 13, 2017

We by Yevgeny Zamyatin

Most of the books I read are recently published, but occasionally I read an older book to continue my education, as I like to put it. This week I read the 1921 novel We by Yevgeny Zamyatin, this edition translated by Natasha Randall in 2006. Some reviews call it one of the first dystopian novels, but that isn't accurate. After Edward Bellamy's successful 1888 utopian novel Looking Backward: 2000-1887, both utopias and dystopias became the fashion for many years to come. Most of these books and their authors are now forgotten, but in English, readers will still recognize 1895's The Time Machine by H.G. Wells, and will know the name of Jack London, though his 1908 book The Iron Heel about the future dystopian struggles leading to utopian socialism is not nearly as popular as his thrilling boy's adventures.

The book We is compact, but for me it was hard to keep focused. It is written in the first person, so the narrator has to describe what is everyday to him (or her) in ways that will make sense to people like his readers who have never seen this world. There are other such books, but it's a tricky proposition. Zamyatin decides to describe things in mathematical terms and colors. As a mathematician, much of his mathematics irritates the hell out of me. For example, his narrator D-503, a mathematically trained engineer in charge of building an interplanetary spaceship, has a particular distaste for the square root of -1, which he calls "irrational". We usually call this number imaginary, but technically he is right. The number we often call i is not the ratio of two integers. Mathematicians call it algebraic.

What irritates me more is that an engineer should know that this very odd idea is of immense practical value in electrical engineering. Using both real and "imaginary" numbers together creates complex numbers and this system very cleanly represents the physical fact that electric currents naturally produce counter-currents that run in a perpendicular direction. Electrical engineers find this idea so useful, then call the square root of -1 j instead of i, so any reference to imaginary is erased. The great mathematician Gauss hated that "imaginary number" was already stuck in the mathematical vocabulary even in his era a century before Zamyatin, and wanted positive to be replaced by direct, negative by inverse and the imaginary directions to be coined lateral and inverse lateral. It is completely possible Zamyatin was never taught this.

Now that I have indulged myself to two paragraphs of mathematical quibbling, let me get to my complaints as a reader of speculative fiction. It's hard to understand some never seen world when the writing relies heavily on bad mathematical descriptions and a made up language of his own personal feelings about colors. Worse still, the first person narrator has a breakdown in the middle of the story where he believes he has died, and several chapters after this point are later to be understood as dreams or hallucinations caused by fever.

The world where D-503 lives is a city made of glass where all lives are supposed to be completely visible to everyone else to make sure everyone is doing exactly what they should be, but there is an exception for when people have sex.  The sex component is excessively important to the plot, and anyone who has seen through Hugh Hefner's idea of utopia can see it for the juvenile male fantasy it is. People can have sex with anyone who can agree to have sex with them, and men are completely free from the burdens of fatherhood. It also presents women who have once given consent and wish to rescind it as horrible and duplicitous creatures. It never assumes to a man he might not be an ideal lover.

In short, if you have never read We, you have my leave to never read it. The book has fans that range from Garry Kasparov, the former world chess champion who is strongly capitalist and just as strongly anti-Putin, to Noam Chomsky, the renowned linguist whose political views are sometimes described as libertarian socialist. Chomsky has said We is superior to Nineteen Eighty Four, which he considers wooden. Just to add a little more interest to reading this book I have said you shouldn't read, Orwell considers it completely superior to Brave New World.

Here's where I stand on these provisos to my bold and underlined main position above. Huxley and Orwell did not get along and I am 100% on Team Orwell. As a prose stylist, Orwell runs rings around Huxley and Zamyatin, though I will admit I cannot read Zamyatin in the original Russian, which is my problem, not his. A point on which I agree with Orwell that We is better than Brave New World is both books have characters who are considered great poets in morally empty times. What would such a poet write? Zamyatin gives examples, Huxley does not.

Point to Zamyatin.

More importantly than any political position or literary merit, Orwell understood the connection between politics of any stripe and lying. Here are his six rules of writing, from his essay Politics and the English Language.

  1. Never use a metaphor, simile or other figure of speech which you are used to seeing in print. (Many of Orwell's examples are now thankfully out of date. The best modern example is the completely meaningless cliche "thoughts and prayers".)
  2. Never use a long word where a short one will do.
  3. If it is possible to cut a word out, always cut it out.
  4. Never use the passive where you can use the active.
  5. Never use a foreign phrase, a scientific word, or a jargon word if you can think of an everyday English equivalent.
  6. Break any of these rules sooner than say anything outright barbarous.
 To summarize, if you are intrigued by my description or the testimonials, by all means read We. If you want to take my advice instead, find some Orwell you haven't read, especially his collections of essays. In particular, Shooting an Elephant should be at least as famous as The Declaration of Independence or the preamble to The Constitution.

It's a Sunday, so I will write: Here endeth the lesson.

It's a cliche, but any other way of writing it is barbarous.

 

Saturday, August 5, 2017

Trump and the polls


Many stories have been written this year saying Trump voters are still happy with Trump. Almost none have been written about Clinton voters still pissed that she won by nearly 3,000,000 votes and over 2% of the popular votes and Trump was still installed as president by the seriously anti-democratic Electoral College system.

In contrast to these anecdotes, polls  make the attempt to gauge the general public opinion using something approximating scientific methods. The big failure of poll-based prediction in 2016 makes me less confident in these numbers, but statistical methods never promise certainty. That said, the polling numbers for Trump's popularity after six months in office show a public growing quickly disenchanted.

I follow 22 different polling companies, getting their results from the Pollster page funded by The Huffington Post, but I have more confidence in looking at six companies that poll every week or even more often. The two tracking polls that update almost every day are Gallup and Rasmussen. The four polls that give weekly numbers are Politico, SurveyMonkey, YouGov and Ipsos/Reuters. I never consider any one polling company to be the most reliable, but I would rank these six at least as reliable as the companies that poll only two or three times a month or even less and much more reliable than the very sporadic pollsters.

The graph speaks for itself. While there are ups and downs in the average written in blue and the median written in red, the general trend is downhill. Since the middle of July, the numbers have taken a steep fall. On July 11, Trump's net popularity averaged -11 percentage points and the median was -13.5 points. As August began, those numbers sunk to -19.7 on average and a median of -21.5 percentage points.

On the left is slope graph for the six companies, showing their net numbers on January 31 and July 31. Two points are difficult to read due to exact overlap. On the far right, both YouGov and Ipsos/Reuters had Trump at -1 point net in January, while Rasmussen and Gallup now concur that Trump is at -22 percentage points when the unfavorable number is subtracted from favorable.

The first and most obvious point is that everything is downhill. Politico, represented by the light blue line at the top, has been consistently the kindest to Trump, but currently even they have his net favorable numbers at -10 percentage points, worse than even Gallup had at the end of January. The steepest fall is the yellow line, representing Rasmussen, a poll well known throughout this century as being very kind to conservatives. In January, only Politico and Rasmussen gave Trump a net favorable score. Now, Rasmussen is tied with Gallup giving Trump a -22 point rating, only surpassed in the negative direction by Ipsos/Reuters at -24.

Let me repeat that no poll is perfect and even a collection of polls won't always give us an accurate read. For example, in last year's polls of Pennsylvania, not even one company gave Trump the lead, which made his win there all the more shocking. But having written that, I present this data as an antidote to anecdotes. For all the reporters who can find Trump voters still happy with their choice, the polling companies can find large masses of voters who realize they made a horrible mistake in November.

Thursday, August 3, 2017

Climate Change in Oakland, 2015 to 2017


Longtime readers will know that I love to collect data. Many blog posts have had a collection of data as the jump off point, but there are times when I collect data hoping to see a pattern and none becomes apparent, or I see some trend but I'm of several minds about how to present it.

One type of data set I have been collecting for two and a half years concerns the average temperature in Oakland. The website Weather Underground publishes not only the daily temperature highs and lows, but compares each day to the average over the last fifteen years. I have used this data in my statistics classes, showing how to take large sets and input them in calculators using frequency tables. Most Texas Instruments calculators will balk at a data set with 365 or 366 values, but because of repetition of values, we can get all the data in the set and the important statistics from these samples, notably the five number summary - an old school way to look at outliers - and also average and standard deviation, the more modern way to discuss what numbers on a list are remarkably high or remarkably low.

This is a dot-plot of the 366 days of 2016 in Oakland, each day listed as the number of degrees above or below the average for the previous fifteen years. The tallest stack of dot is at zero degrees. This represents the mode of the set. Obviously, there are a lot more dots to the left of the tallest stack than there are to the right. The other two famous measures of center, the mean and the median, are not so apparent from this graph. The median is 2 and the average is about 2.604, with a standard deviation of 5.659. Simply put, the more commonly used measures of center say the temperature in 2016 is warmer than the rest of the century.

You might say this is evidence of climate change in Oakland. I am not 100% convinced. Here are my reasons.

1. Should I trust the average daily temperatures given by the website? The averages stay the same for weeks at a time, not even wobbling by a degree. That smells like they are averaging not just all the single day temperatures for example, but maybe taking the average of several days in a row, then averaging that over fifteen years. Not sure this is kosher.


2. Should I trust the t-score method and the p-value it produces? The t-score test uses average/(standard deviation) x sqrt(size of set) as the test statistic. In this case, that would be
2.604/5.659 x sqrt(366) ~= 8.803. This is a crazy big number for a t-score and it produces a p-value so small it has to be written in scientific notation, 2.705 x 10 ^ -17. Written in regular notation, this is 0.0000000000000002705, which is crazy close to zero. A paper publishes with a p-value this small is basically saying, "I'm right, so shut the fuck up."

But let me note here that statistics is math mixed with opinion, and not every statistician loves the t-score/p-value method used with a data set like this. Most notably, W. Edwards Deming, the famously practical statistician credited with turning the Japanese economy around after World War II, argued that if there was any difference between any two sets, all you needed was a large enough sample size to prove that difference significant. In this case, the large sample size gives us a multiple in the formula of sqrt(366), which is about 19. Since a t-score of 3 will give us a very impressive p-value, having this relatively large number in the formula guarantees an impressive p-value.

3. How should we think about a year in terms of climate change data? A hot or cold day is not climate change. I am skeptical about counting a month as a long enough time to have meaning, though Dr Michael E. Mann often tweets about a month being the hottest or second hottest (fill in the month in question) in history. Mann is not an alarmist, as was made clear when he poured cold water on the New York magazine article from earlier this year that was all doom and gloom. While not an alarmist, he does want to keep climate change in the news, and it is a slow moving process, at least from the standpoint of the 24 hour news cycle.

But I have no problem about thinking a year is a length of time where we can talk about the numbers as having meaning when discussing climate change. Personally, I am uncertain as to whether years should be the basic unit of measure or should be clumped into groups to have clearer meaning. My simile is this. A year has meaning, but if we compare it to grammar, is a year a sentence or a word or merely a letter? When I wrote my math blog about climate change, I argued that we should look at periods of time between strong El Niño years that included a strong La Niña year as the basic unit.

So those are my provisos and quibbles. Here is the data.

2015: The temperature in 2015 was 2.605° F warmer than the average of the previous fifteen years and the standard deviation was 5.659° F. With a sample of 365 days, this data set makes a very convincing argument that things are getting warmer. Using the average and standard deviation method, an unusually cold day would be 9° F lower than average. That happened once. An unusually hot day would be 14° F higher than average. That happened seventeen times, and very unusually hot days wound be over 20° F hotter than average, which happened three times.

2016: The temperature was 2.242° F warmer than the fifteen year average and the standard deviation was 5.447° F. It didn't warm up quite as much as 2015, but the lower standard deviation would mean the t-score/p-value number would again be hard to argue against. There were no days that count as unusually cold (again, 9° F colder than average), but eighteen days at 14° F hotter than average and six days above 19° F hotter than average.

First seven months of 2017: So far, the average temperature is 2.321° F warmer than the previous fifteen average with a standard deviation of 5.480° F. No days have been unusually cold so far, twelve have been unusually hot and three have been very unusually hot. The cutoff points for unusually hot and very unusually hot are 14° F above average and 19° F above average, respectively. These thresholds are unchanged from the 2016 numbers, which is not surprising because the averages and standard deviations are so similar.

Conclusion: Here in Oakland it's getting warmer. 2015 shows the largest change upward, but note that 2015 is part of the last fifteen year average when measuring 216 and 2017. I'd love to get more raw data from a weather station that has produced data continuously for a few decades and I have an idea of how to achieve that. I also want to come up with a good way to define a heat wave and I think I have the start of an idea I need to flesh out.

Tomorrow, another math-y blog post, this time about Trump's approval numbers.

Sunday, February 26, 2017

Bil Paxton 1955-2017

Bill Paxton, the Texas-born actor who has been working steadily since the 1980s with many of his best known roles in James Cameron films, has died at the age of 61 from complications during surgery.

It's remarkable how many well-known projects Paxton worked on in his career. His first big breaks included small roles in Stripes and Terminator, his first work with James Cameron. He moved on to the nasty older brother in Weird Science and first major role in a Cameron film as Private Hudson in Aliens. His career is a steady progression up the cast list, from a bit part in Commando, to a featured role in Predator 2 to starring in Twister. He was also seen in movies with great ensemble casts like Tombstone, Apollo 13 and Titanic. Possibly my favorite film of his after Tombstone is Sam Raimi's A Simple Plan, co-starring Bridget Fonda and Billy Bob Thornton, about three acquaintances finding millions of dollars in lost cash. This century, a lot of his best work has been on TV, including his starring role in HBO's Big Love, a recurring character on Marvel's Agents of S.H.I.E.L.D. and Training Day, a new series that just started showing on Amazon Prime.

This one came as a surprise to me, in part because Paxton is my age and also because he was still working so consistently. Thinking back on his work, I remember his character for Agents of S.H.I.E.L.D. calling Ruth Negga's character "Flowers". I also remember the interaction between him and the bodybuilder Vasquez in Aliens.

Hudson:, Hey, Vasquez, have you ever been mistaken for a man.
Vasquez: No. Have you?

Best wishes to the family and friends of Bill Paxton, from a fan. He is never to be forgotten.

Saturday, February 18, 2017

Norse Mythology by Neil Gaiman

Neil Gaiman is on a roll. Now in his fifties, he is sometimes still described as a "cult favorite", which is to say he is not as well known as J.K. Rowling, Stephen King or George R.R. Martin. Some adaptations of his earlier works will debut this year, an indie movie version of How To Talk to Girls at Parties and a TV mini-series of American Gods on the Starz network. On Twitter, he was surprised his latest effort Norse Mythology opened at #1 on The New York Times bestseller list this week. I bought my copy last week and I have already finished the slender volume, not even 300 pages with a large font. If you prefer audiobooks, he is his own narrator on this one and he has a lovely voice. Here is my review.

In the introduction, Gaiman admits his first taste of the Norse myths was in the Marvel Comics version, where Thor is the star and Loki is but one of many villains, with Odin usually in the background. His re-telling of the original stories, mixing together the two main sources of the myths, the Poetic Vedda and the Prose Vedda, changes the billing among these three, giving Loki his rightful place as the character who drives the story in most of the sagas, though it is hard to ever say he is the hero. Of course Odin and Thor have a lot to do as well, but we also meet Thor's wife Sif, known for her lovely hair; Heimdall, the gatekeeper; Idunn, the goddess who owns the apples of immortality that give the gods their very long lives; Balder, the most beautiful and beloved of the gods; and the giants, monsters and ancient gods that will come to destroy all nine worlds in the End Times known as Ragnarok.

It is good to read these stories in winter, because the cold and the storms are a near constant companion in these tales from people who lived so far north. Gaiman writes at a fine pace for stories of adventure and magic, and adds his own magic of humor and compassion even for the monsters and villains.

If you love Neil Gaiman, you should certainly read (or listen to) Norse Mythology. If you do not know him but the topic sounds interesting, this would be a fine introduction.

Friday, January 27, 2017

John Hurt, 1940-2017

John Hurt, one of the greatest British actors of an incredibly great generation, has died a week after his 77th birthday. He is pictured here as the emperor Caligula in I, Claudius, welcoming a horse that he has made a senator onto the Senate floor. Hurt played a lot of great roles, but I, Claudius was the first time he showed up on my radar as a callow American youth. Other British actors of his generation who were in I, Claudius include Brian Blessed, Derek Jacobi, Sian Phillips, Patrick Stewart and John Rhys-Davies. If you've never seen it, find a copy in the library or buy it or steal it if necessary. The production values are weak by today's standards, but the writing and acting are second to none.


Another great project many people haven't seen is the 1984 version of Nineteen Eighty-Four, in which Hurt plays the protagonist Winston Smith. A British TV version in the 1950s starred the gaunt and haunted Peter Cushing, The 1950s American version starred the hefty and clueless Edmond O'Brien. Let's just say the casting directors in one of these two countries actually read the book before casting.

Without checking imdb.com, the other projects I know I saw Hurt in are Alien and a parody scene of Alien in Spaceballs, Harry Potter, The Elephant Man, Only Lovers Left Alive, Snowpiercer, The Naked Civil Servant and V for Vendetta. I decided to show pictures from a TV mini-series about an obviously insane character being given absolute power and an unhappy cog in the machinery of a vicious totalitarian government where the truth means less than nothing.

I wonder why I chose those?

It's a puzzlement.

Best wished to the family and friends of John Hurt, from a fan. May he never be forgotten.

Wednesday, January 25, 2017

Mary Tyler Moore 1936-2017


I find myself unable to be clever talking about Mary Tyler Moore. I loved her and that was that. We shared a birthday, so she was 19 when I was born, and even before I knew that I always thought she was a wonder. She could sing, she could dance, she was a brilliant comedian, and she was a low flying angel. That's quite the combination.

A lot of people are remembering comedy scenes, most notably the funeral of Chuckles the Clown, but the scene that I remember today is Dick Van Dyke and Miss Moore singing Mountain Greenery.



Best wishes to the family and friends of Mary Tyler Moore, from a brokenhearted fan. May she never be forgotten. 

Sunday, January 22, 2017

Two obiturary tributes to two bands that didn't quite make it


Another pop music obituary from the 1970s is in the news today. Philip Overend Watts, guitarist first and bassist later for Mott the Hoople, is dead at 69. No one back in the day would have dreamed of putting them on the same bill with the sister group The Roches, who lost Maggie Roche this week, but both groups wrote brilliant songs about the death of the rock and roll dream.

With the rule of ladies first, here are The Roches with Mr. Sellack, a song about getting back in the job market once the dream is over.


Mott the Hoople had more success in Great Britain, or maybe it was easier to get to a certain level of success as a rock band versus a folk rock band. This is The Ballad of Mott the Hoople, in memory of Philip Overend Watts. This is a live recording in Zurich from 1972. They weren't truly at the end, but they certainly saw it coming. Oddly enough, The Roches survived as a group much longer after Mr. Sellack than Mott the Hoople did after this song.



 As a mathematician, I see two projectiles in the air that with different trajectories. As a man 61 years old who can still hit all the notes he could when he was 30 and many with more power and clarity, I think of heights I never reached.

I love both songs though they make me sad. Your mileage may vary.

Best wishes to the family and friends both Maggie Roche and Philip Overend Watts , from a heart stricken fan. May they never be forgotten.

Saturday, January 21, 2017

Maggie Roche 1951-2017

Maggie Roche, one of three sisters comprising the musical group The Roches, has died at the age of 65 of cancer.

I owned several of their albums back in the day, including The Roches, their first record as a trio produced by Robert Fripp, I also picked up Keep On Doing and Speak. I saw them live once in San Francisco in the late 1980s. As you listen to their harmonies, Maggie has the lowest of the three voices, a contralto that almost qualifies as a baritone.

By coincidence, I saw the movie 20th Century Women last night, which takes place in 1979. It didn't feel much like the 1979 I experienced, but the Roches' first album certainly takes me back.

Off the first album, here is The Hammond Song.


Also from The Roches, Maggie's composition The Married Men, later recorded by Phoebe Snow.


And from Keep On Doing, another of Maggie's songs, Losing True.


Best wishes to the family and friends of Maggie Roche, from a fan. She will never be forgotten.

Monday, January 16, 2017

The Ahlgrimm Harlequin

Using the same pieces as before but moving the colors around, here is the Ahlgrimm Harlequin.

Sunday, January 15, 2017

Dick Gautier 1931-2017

Dick Gautier, the actor whose best known role was as Hymie the Robot on Get Smart!, has died at the age of 85 after a long illness. On TV, he was usually in comedies and was a regular in two short lived series, Mr. Terrific in 1967  - as the best friend of the main character -  and When Things Were Rotten, Mel Brooks' parody of Robin Hood that aired for 13 episodes in 1975. He later became a regular on several TV game shows.

One of the reasons I like obituaries is finding out things I did know about people. After leaving the Navy, Gautier worked as a nightclub singer and Broadway musical actor, including playing Conrad Birdie in the first run of Bye Bye Birdie on Broadway, a show whose cast included Dick Van Dyke, Chita Rivera, Paul Lynde, Michael J. Pollard and Charles Nelson Reilly, as well as Broadway stalwarts Susan Watson and Kay Medford. Besides that, Gautier was a talented cartoonist and did a lot of voice acting, most notably as Rodimus Prime/Hot Rod on the 1980s cartoon Transformers.

Best wishes to the family and friends of Dick Gautier, from a fan. He is never to be forgotten.

Saturday, January 14, 2017

The Ahlgrimm Cube

I haven't been posting pictures of the OctTetra pieces recently because having pictures online could make the patent process more difficult. But today I throw caution to the wind to present a new shape I call The Ahlgrimm Cube, in honor of my CAD programmer and collaborator Dörte Ahlgrimm. She was playing around with the pieces you can see which are called Cylinder Wedges, rounded versions of the Wedge, which can most easily be described as half a pyramid. If the blue pieces were Wedges instead of Cylinder Wedge, we would have Size Two Corner, and if we we replaced all the wedges the shape would be a Size Two Cube, where all the faces would be flat. The half blue half green face on the lower right gives you a good idea of the shape of all the faces, which are kind of like square throw pillows with a button in the middle.

It's been a while since I've been playing with OctTetra on a regular basis, but it is my plan for the winter and spring to see if I can take the next steps to making the toy now in prototype into a viable product.

Saturday, January 7, 2017

Semi-slipped My Mind Saturdays
Fleetwood Mac Oh Well

I ran errands yesterday and did not get back home in time to put up a Half Forgotten Fridays post, so it's a Semi-slipped My Mind Saturday post instead.

Fleetwood Mac is by no means forgotten, but their original line-up before the additions of Stevie Nicks, Lindsey Buckingham, Bob Welch and Christie McVie is definitely obscure. The closest thing they had to a hit in the 1960s is this long song in two very distinct parts, Oh Well, written and sung by Peter Green. It starts with one of the greatest guitar hooks in rock history, breaking into a hard driving instrumental section that could easily be identified as early heavy metal. Then it breaks into a completely different melody on acoustic guitar and recorders, little wooden flutes famous for going out of tune after about a month of use due to saliva and heat warping the little bastards. I know, I used to play the recorder.

Then the symphonic section with flamenco guitar begins, which could be fairly considered a distinct third part.

I fucking loved this song in high school, when it was only FM radio that would play it. As a single, it had to be broken into Part 1 and Part 2, but FM radio would play whole thing straight through. I would lie in bed in the morning hoping the DJ would play it before I had to go to school. The only thing I can compare it to when I was a kid was the Traffic album John Barleycorn Must Die.

Peter Green had schizophrenia and the drugs didn't help. He has been in and out mental institutions much of his life. Still, he wrote some great songs and other musicians could see how damned good he was. His other great contribution to rock history is writing Black Magic Woman, turned into a hit by Santana. Oh Well has been covered by a whole passel of musicians, including Tom Petty & the Heartbreakers, Joe Jackson, Ratt, and The Black Crowes with guest guitarist Jimmy Page. Musicians loved the hell out of this, but it's public reception is tiny compared to the album Rumors took off.

Here's Fleetwood Mac, led by the musical genius Peter Green, playing the original version of is composition Oh Well.

 


Thursday, January 5, 2017

Math Thursday:
The math of life and death, part 3
Death by overdose

A major culprit in the increasing death rate are drug overdoses. This study from the CDC follows the number of deaths over the five year period from 2010 to 2014 for ten drugs: Six opioids (fenatyl, heroin, hydrocodone, oxycodone, methadone and morphine), two stimulants (cocaine and methamphetamine) and two benzodiapazines (Alprazolam and Diazepam). The rate of deaths from these causes, which are difficult to separate in many overdose cases, rose from 12.4 per 100,000 in 2010 to 14.8 per 100,000 in 2014. The drugs listed separately as causes all have been under 2.0 per 100,000 except for heroin, which rose steadily throughout the five year study from 1.0 per 100,000 in 2010 to 3.5 per 100,000 in 2014. In 2015, the numbers for heroin continued to rise and for the first time in any recent year, more people died from heroin overdose than from gun homicides, though the difference in the reported numbers - 12,989 to 12,979 - could be over-turned on a recount.

What's going on? Let me be the first to say I don't really know, but the old, often discredited idea of a "gateway drug" is re-surfacing here. It is assumed that the prescription opioids are introducing people to the opiate experience and they are more likely to take heroin after experiencing Oxycontin or some other doctor prescribed painkiller. I have no personal experience of this phenomenon, so I was surprised to find that heroin is much cheaper than the prescription drugs. We have assumed illegal drugs were an urban phenomenon for maybe a century now, but the deaths we are seeing now are definitely not limited to the cities or even the suburbs. When Rush Limbaugh was outed as an Oxycontin abuser, I first learned of its nickname Hillbilly Heroin. Obviously, some enterprising job creator has been able to introduce real heroin to real hillbillies.

The epidemic is most prevalent among whites and is also seen in the African American male demographic.  It is much less common among African American females and both genders of the Latino community. The current prevailing assumption is doctors prescribing pain relief in ways that show both racial and sexual bias, which actually hurts white males by this measurement rather than helps them. Many commentators linked these death statistics to the alleged spring of Trump's victory, the set upon white working class. We now get a four year experiment, possibly longer though I certainly hope not, as to whether having a guy in the White House who is "on their side" will see a drop in these numbers. Given that heroin is the leading edge of the problem, my assumption is that no slogans or cheering rallies or increased policing will make much difference. Like with the numbers in the early 1990s at the height of the AIDS epidemic, the at risk population is going to have to figure out how to pull out of this tailspin by themselves.