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Home > Foreign Affairs > 644 European lawmakers back Taiwan's participation in upcoming WHA
644 European lawmakers back Taiwan's participation in upcoming WHA
by Guest Contributor 2 years ago Nov 08, 2020 0 Comments
A total of 644 lawmakers from the European Union and elsewhere in Europe have called on the World Health Organization (WHO) to include Taiwan in its annual assembly, which is scheduled to resume virtually next week, writes Sylvia Teng.
The WHA meetings will take place virtually from Nov. 9-14 in light of the ongoing pandemic. In its previous virtual sessions in May, Taiwan was not invited.
"We urge you to invite Taiwan to participate in the WHA as an observer and fully include Taiwan into the WHO meetings, mechanisms and activities, which would be in the interest of all parties concerned," said a letter addressing WHO Director-General Tedros Ahano Adhanom Ghebreyesus. The letter sent on Thursday (Nov. 5) is the latest show of support from EU politicians for Taiwan's participation in the WHO's decision-making body.
The letter was initiated by the Formosa Club and comprised Taiwan friendship groups from the European Parliament, Germany, France, and Britain, among others. The 644 lawmakers are from a variety of parties.
In the letter, European lawmakers hailed Taiwan as exemplary for its containment of the novel coronavirus, thus limiting its impact on the economy. It also mentioned the donations of surgical masks and other medical and protective equipment Taiwan has made over the past few months to more than 80 countries, including many in Europe.
"Regrettably, despite considerable calls and support from the international community, Taiwan's participation in the WHO is still met with obstacles," the letter said. Calling the WHO's handling of Taiwan's affairs "unfair," the lawmakers said obstructing Taiwan's access to WHO is "not only detrimental to the fundamental health rights of Taiwan's 23 million citizens, but also creates a dangerous gap in the global disease prevention network."
Taiwan's Ministry of Foreign Affairs (MOFA) expressed gratitude on Twitter for the unprecedented bipartisan support from Europe for Taiwan's inclusion: "644 lawmakers from 25 countries are urging #Taiwan' meaningful participation in the @WHO & events like #WHA73." The ministry went on to say: "Please remember, @DrTedros, no one is safe from #COVID19 until everyone is safe."
The Author, Sylvia Teng, is a Staff Writer for Taiwan News, where this story first appeared.
Tagged @WHP #Taiwan #Tedros #WHA
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Wonder in Modern Life
Patrick Curry
Explores the profound human experience of enchantment, and discusses how it plays out in a wide range of contexts
Argues that modernist attempts to undermine or dismiss enchantment as a delusion are not only misguided but dangerous
Patrick Curry is a respected author and has been a lecturer at the Universities of Kent, Bath Spa and Trinity Saint David
This unique book explores how enchantment plays out in a wide range of contexts – in love, art, religion and learning, in food and drink, and perhaps most significantly in our relationship with the natural world.
Philosophy of Human Life; Mind, Body, Spirit
Enchantment is a profound human experience. When we encounter wonder, awe or amazement, that is enchantment. Enchantment can reveal profound truths, lead to deep values and become central to a life well-lived.
This unique book explores how enchantment plays out in a wide range of contexts -- in love, art, religion and learning, in food and drink, and perhaps most significantly in our relationship with the natural world.
Patrick Curry argues that modernist attempts to undermine or dismiss enchantment as a delusion are not only misguided but dangerous, potentially leading to a disengagement with our world that could have disastrous consequences for our future on this planet.
2. The Dynamics of Enchantment
3. Love
4. Art
8. Nature
9. Disenchantment
10. Technoscience
11. Enchantment as a Way of Life
A Bibliographical Note
'Patrick Curry suggests that the opposite of enchantment is not disenchantment but glamour. Like Jung when he stated that the opposite of love is not hatred but power, Curry shows how glamour is a fabricated, false, fake version of enchantment. His demonstration is extremely convincing. Reading him, one feels like opening one's eyes wider.'
-- Ginette Paris, author of The Wisdom of the Psyche
'Curry's Enchantment is simply an enchanting book, built on the wonder of our relationship with existence. It is lyrically written for all who have been enchanted, and draws on his own experiences. It encourages each of us to consider the wonder and love we are gifted -- and can return.'
-- Haydn Washington, author of A Sense of Wonder Towards Nature
'As Patrick Curry argues eloquently in his thoughtful examination of the human condition in these challenging modern times, "enchantment is an experience of wonder". This is no mere academic exercise, although he is surprisingly erudite in his choice of texts, writers and cultural icons to illustrate his thoughts on this vital feature of being fully human. Nor is this a fluffy 'New Age' extended essay on how much better this world would be if we were all nice to each other. Much of the material is philosophical or poetic in nature, drawn from inspirational writers such as J.R.R. Tolkien, Max Weber, and W.H. Auden. Although mainly positive in tone, Curry does not hold back from stating things as they are. He is passionately scathing in denunciation of elements in the present world that seek to enslave the human race as little more than robots.'
-- Magonia Review
'What could be more confounding than trying to understand 'enchantment', an uncanny power that by its very nature frustrates all comprehension? Yet in this small volume Patrick Curry accomplishes much. If we join him as he tracks several of the numberless styles of enchantment, if we pay heed to his insights and sometimes argue with him, we'll find ourselves slowly becoming more layered within ourselves, wiser, and maybe even wonder-struck.'
-- David Abram, author of The Spell of the Sensuous and Becoming Animal
'Understanding begins -- and ends -- in wonder. This book articulates simply, and in a personal and unpretentious manner, an urgently needed defence of wonder as indispensable to a true perception and just appreciation of the world. We neglect its message at our peril.'
-- Iain McGilchrist, author of The Master and his Emissary
'An impressive erudition and literary sensibility animate the pages of this thoughtful book. With a light poetic touch Curry traces the many ways in which enchantment lies at the core of human experience, even in a world that does its best to disabuse and disillusion us. Detaching it from ideology, Curry sees enchantment as an irrepressible mode of our being present to things, and vice versa. The enchanted moments cannot be sustained yet they lay their claim on us time and time again by virtue of the fact that we are alive and capable of wonder.'
-- Robert Pogue Harrison, author of Forests: the Shadow of Civilization
Dr Patrick Curry was born in Canada and has lived in London for over forty years. He is the author of numerous well-regarded scholarly and popular books on topics ranging from environmental ethics to cosmology and literature, and has been a lecturer at the Universities of Kent and Bath Spa. His books include Defending Middle-Earth: Tolkien, Myth and Modernity and Ecological Ethics. He is also editor-in-chief of an online journal, The Ecological Citizen. In 2019, he was elected a Companion of the Guild of St George.

redpajama_set_name: RedPajamaCommonCrawl


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*Featured/Spotlights, Competitions, Dressage, Events, USEF
Rain Fails to Dampen Riders' Spirits at United States Equestrian Federation Paralympic Selection Trials
June 13, 2012 Associate Editor Leave a comment
Rebeca Hart and Lord Ludger compete under a downpour of rain at the USEF Paralympic Selection Trials. Photo © Lindsay Y McCall
Gladstone, NJ – June 12, 2012 – The weather report for Tuesday, June 12, 2012 predicted 80% chance of rain at the United States Equestrian Team Foundation headquarters. Within an hour of competition at the United States Equestrian Federation (USEF) Paralympic Selection Trials/Para-Equestrian Dressage National Championships, rain began to crash upon the trees. Riders did not waiver from the schedule and welcomed the rain as a variable to take advantage of. As the second day concluded Rebecca Hart (Unionville, PA) inched ahead of Donna Ponessa (New Windsor, NY), and 2011 National Champion Jonathan Wentz (Richardson, TX). With only the Freestyle competition left to secure four spots on the United States Paralympic Team headed to London, riders hope to continue earning supreme scores. Currently Rebecca Hart, Donna Ponessa, Jonathan Wentz, and Dale Dedrick (Ann Arbor, MI) have remained in the top four of the standings.
Rebecca Hart, 26, and Lord Ludger excelled in the Grade II Team Test earning a 73.968% aboard the Holsteiner gelding. Hart showcased her test when the rain became a downpour but Hart remained determined and relaxed. "I owe my grippy gloves to sticky spray," chuckled Hart. "The reins get quite slippery in a rain like this and with two whips, and two reins it gets difficult to hold onto everything if it's wet. However, the rain was not a factor on how LoLu performed." Having all types of situations occur within an atmosphere like the Selection Trials gives the riders experiences they can grow from before heading to a large show like the Paralympics.
Under Blue Hill Farm's instruction with Missy and Jessica Ransehousen, Hart has achieved four National Champions, represented the U.S. at the 2008 Paralympics and 2010 Alltech FEI World Equestrian Games, and she has traveled the world including a trip to Australia for international Para-Equestrian competition. Riding continues to help Rebecca strengthen and condition her body against a rare genetic disease called Familial Spastic Paraplegia.
"Riding makes a huge difference with my disability," Hart explained. "Doctors call me an 'upside down paraplegic' because based on my scans they cannot see how I could even walk, let alone ride. However my physical therapy, training with a personal trainer, and constantly riding helps to strengthen and condition my muscles."
Donna Ponessa added, "I am way ahead of the curve with my disease progression like Rebecca and doctors say it is because of riding and my determination."
Ponessa has gone from a wide-eyed brand new Para-Equestrian in March 2011 to one of the top Para-Equestrians in the United States. Alongside FEI rider and trainer Wes Dunham, Ponessa has climbed the ranks to achieve supreme scores in the Grade Ia division.
On the first day of competition at the USET Headquarters Ponessa went one-two with Western Rose, owned by Wes Dunham. Western Rose was followed by PG Ganda, on loan from Vinceremos Thereaputic Riding Center. On the second day of competition the ex-Canadian show jumper, PG Ganda and Ponessa secured a 73% to take home the first place followed by Western Rose.
In Grade II, Jonathan Wentz, 21, replicated Ponessa on day one securing a one-two placing with Silvano, owned by Dr. Michele Miles in one and NTEC Richter Scale in second. During the individual competition NTEC Richter Scale, owned by Kai Handt, earned the win with a 72.899% followed by the ex-World Cup jumper, Silvano.
To be competing at the 2012 Paralympic Selection Trials is a goal Jonathan Wentz decided to capture from 13 years of age. After growing out of the therapeutic riding programs Wentz found Kai Handt and North Texas Equestrian Center. Handt and his wife run a successful Dressage barn in Wylie, TX and compete on the FEI level around the world. Handt enjoyed challenging Wentz each week when they began working together and he never went easy on 6'3″ young rider. Wentz has emulated Handt's desire strive for the best results and not accept an average performance.
Daled Dedrick has accompanied Wentz as a US Team Member as they campaigned over the 2011 year. Dedrick's mount Bonifatius has been essential to the United States Para-Equestrian Team. Dedrick has been a lifelong horsewoman and was even part of the 1986 U.S. Olympic Festival Team. Dedrick joined the Para-Equestrian competition in June 2011 as a Grade II rider and hit the ground running earning the 2011 USEF Para-Equestrian Dressage Reserve National Championship. Dedrick trains her grey 14-year-old Hanoverian gelding in Michigan with Rosalind Kinstler "Roz". Dedrick is currently ranked fourth among her competitors and has a very good chance she may earn a spot on the 2012 Paralympic Team.
In Grade III, Wendy Fryke of Parker, CO and Lateran achieved top scores during Tuesday's competition. The 2010 World Equestrian Games member continues to excel in front of the international judges. Fellow 2010 World Equestrian Games teammate Mary Jordan of Wells, ME and Deecie Denison's gelding, Sebastian, earned the top placing in Grade IV. Jordan went one-two riding her mare P. Sparrow Socks to a second place finish.
The 2012 Paralympic Selection Trials and 2012 USEF Para-Equestrian Dressage National Championships finalize competition on Wednesday with the exciting Freestyle Tests. Each horse and rider combination will complete their Grade's technical requirements using a previous selected piece of music. The Freestyle Test is the last 20% of each rider's overall score and will be evaluated by FEI Ground Jury members, Carlos Lopes (5*) POR, Anne Prain (5*) FRA, and Marc Urban (5*) BEL. An awards ceremony will follow Wednesday's competition at the USET Headquarters in Gladstone, NJ.
FAST FACTS: 2012 USEF Para-Equestrian Dressage Paralympic Selection Trials and National Championships
WHAT: Para-Equestrian Dressage Selection Trials for London Paralympics
WHEN: June 11-13, 2012
SHOW MANAGER: Kim Keenan-Stordahl
LIVE SCORING: http://usefnetwork.com/featured/2012USEFDressageFestivalofChampions/
LIVE TWEETING: Follow the USEF Paralympic Selection Trials and Para-Equestrian Dressage National Championships results from the USPEA Twitter Account and view the #PARAEQTRIALS from @uspea.
To view an online version of this press release with more results and photos, please click here.
About United States Para-Equestrian Association:
The USPEA is a network of riders, judges, national federation board members, and equestrian enthusiasts. The association gives athletes the ability to get involved and expand their knowledge and experience in the Para-Equestrian sport. The USPEA encourages para-athletes to participate in all disciplines under the para-equestrian umbrella.
The USPEA is a recognized affiliate of the United States Equestrian Federation (USEF) which serves as the National Governing Body for the equestrian sport. This relationship between the USPEA and USEF is to encourage para-equestrian competitors, leisure riders, coaches, fans and enthusiasts to network and get involved with the entire equestrian sport.
Ultimately the goal of the USPEA is to foster growth in the para-equestrian discipline. From growth in the number of participants to growth as a team, and growth in the experience and knowledge of all involved. From local horse shows to international Olympic Games, the USPEA will provide para-equestrians the knowledge of what they need to succeed. The USPEA connects with the International Olympic Committee (IOC), the Federation Equestre Internationale (FEI), the United States Dressage Federation (USDF), and USEF which provides Para-Equestrians the top equestrian resources.
In June 2010, the USPEA earned its 501 (c)(3) status which has encouraged supporters to help supply funding to the Para-Equestrian Team as a recognized affiliate of the United States Equestrian Federation (USEF).
For more information about the USPEA please visit www.USPEA.org or contact USPEA President Hope Hand by e-mail: Wheeler966@aol.com or by phone: (610)356-6481.
dressageequestrian competitionsequestrian disciplinesOlympic GamesPara-EquestrianUSEF Para-Equestrian Dressage Paralympic Selection TrialsUSPEA
Previous PostRenee Rains and Samir' Sandro Win Rock Star Freestyle Award from Cunningham & Cunningham at Gold Coast May ShowNext PostHart and Lord Ludger Lead 2012 National Para-Equestrian Championships


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Q: Consumer: Send offset again if middleware lost connection to external appilication Im new to spring cloud stream and kafka in general.
What is the best approach to stop the Consumer committing to the next offset if the external application doesn't get the last message. I have a middleware that manages communication between the consumer and the external application. I want to, at least, try to send the message 10 times with a 1 minute delay between (to the external application) if something goes wrong.
The Middleware is a simple post request application that filters for specific data in the kafka message.

A: If the exception is thrown, the binder will try 3 times by default and then throw the exception the kafka listener container.
You can use a ListenerContainerCustomizer bean to customize the listener container to add a SeekToCurrentErrorHandler.
See the spring-kafka documentation about stateful retry.

redpajama_set_name: RedPajamaStackExchange


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Q: What is the difference between areNotificationsEnabled and areNotificationsPaused Can I know what is the difference between areNotificationsEnabled and areNotificationsPaused

A: areNotificationsEnabled
The first method, i.e areNotificationsEnabled will be able to tell if the notifications we completely blocked by the user for your app or the package name you want
The user can block the notification in this way

areNotificationsPaused
The second method will be able to tell if the app was snoozed or paused in other terms, that is, a temporary stoppage of notifications from a certain app
The user can snooze notifications from the notification panel by sliding left or right partially and then set the time for which they want the notification to be snoozed

A: I would like to suggest you to go through the official docs for the same, it will help you alot, bi
areNotificationEnabled:-
https://developer.android.com/reference/androidx/core/app/NotificationManagerCompat
public boolean areNotificationsEnabled()

Returns whether notifications from the calling package are not blocked.
areNotificationPaused:-
https://developer.android.com/reference/android/app/NotificationManager
public boolean areNotificationsPaused()

Returns whether notifications from this package are temporarily hidden. This could be done because the package was marked as distracting to the user via PackageManager#setDistractingPackageRestrictions(String[], int) or because the package is PackageManager#setPackagesSuspended(String[], boolean, PersistableBundle, PersistableBundle, SuspendDialogInfo) suspended.


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Winter is finally on its way out in London, or at least it's now alternating between bone-chilling cold and warm sunshine. I think the season of cognac and whisky cocktails is on the wane, and I'm looking forward to Negronis and daiquiris and all manner of summery drinks. Maybe I'm a bit too strict with my seasons here, but I can't help associating those spirits with hunkering down and warming up. I'm so looking forward to the point where I'll want to cool down instead. But before spring really hits, here's a lovely Sidecar to celebrate the last of winter citrus and the fiery burn of brandy.
I love this cocktail – it's the perfect smoothing out of two intense flavours together, the Cointreau forming a bridge between them. Such a lovely way to say goodbye to the cold weather – something I sincerely hope I'm about to do!
Optionally, rub a wedge of lemon around the rim of your cocktail glass and press it into a saucer of fine sugar. Shake all ingredients over ice and strain into the (chilled) glass. Garnish with a twist of lemon peel.
Looks great. I am a big believer in that seasons don't matter when drinking spirits. They don't only grow in one season (like a strawberry or tomato), and they stay good for all 4. Drink negronis in winter! Free your spirits!
Certainly wouldn't want to be prescriptive about it, I guess my tastes do just change with the seasons!
Ahh, the sidecar. Such a wonderfully simple and overall wonderful drink. Looks great! Though I think you're not giving whiskey enough summertime credit. I can drink these all year round.
These do straddle the seasons well, I must admit.


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Q: When can a mathematician have $3$ friends? At a mathematics convention there are $n$ mathematicians. Each one has $3$ friends (friendship is symmetric). For what values of $n$ is this possible?

A: Base case $1$ if you have $4$ people make them all know each other.
Base case $2$ if you have $6$ imagine you have two columns of $3$ people
and make each person know every person in the opposite column.
Now for the general case if you have $4n$ people seperate them in $n$ groups
of $4$ people and apply case $1$
If you have $4n+2$ people apply case $2$ for $6$ people so now you are left with $4n -4= 4(n-1)$ people. And the previous can be applied.

A: For all even $n$ except $2$.
$n$ cannot be odd because if there are $k$ friendship-pairs, we must have $2k=3n$ (by counting person-pair incidences in two ways).
Also, clearly $n=2$ is impossible.
If $n>2$ (or $n=0$)  is even, arrange $n$ mathematicians in a circle and assume that everyone is friend with their left and their right neighbour and additionally with the one diametrically opposite.


Published in Forest Products Journal J.51 (1) pp. 43-50.
A multi-channel x-ray scanner is described that can be used to estimate lumber strength. The scanner measures lumber density at about 1-inch intervals along the full length and width of each board. The locations and approximate sizes of knots are identified by the presence of areas of high wood density. This detailed information is used to get an improved estimate of the strength of the lumber. Correlations of the destructively measured strength with the density-based estimated strength give r2 values around 0.70. This compares well with r2 values of around 0.55 for traditional bending-based lumber strength grading machines. The density-based data also indicate that destructive measurements of bending and compression strengths are also subject to uncertainty because less than the entire volume of each test board is fully loaded. Thus, observed differences between estimated and destructively measured strengths are due to uncertainties in both strength quantities, not just in the estimated strengths. A lumber strength grading machine based on the principles described here has been commercialized, and several machines are now operating successfully in sawmills.
Wood is a natural material with physical properties and characteristics that vary over a wide range. The uses for wood vary over a similarly wide range, and a challenge for the wood products manufacturer is to sort the material to match the various intended uses. For structural lumber, a reliably specified strength is a fundamental need. The traditional method for sorting lumber uses human visual grading based on established empirical rules for wood characteristics such as knots, wane, grain slope,etc. (9,13). Such grading is quite effective for sorting appearance-grade products; however, it is much less effective at identifying wood strength, mainly because strength-controlling features cannot be seen consistently by eye (5). For this reason, various mechanical methods for strength-grading lumber have been developed.
Bending stiffness measurement (6) is the most common mechanical lumber grading method. This method is based on the observation that stronger lumber pieces tend to have higher bending stiffness. In practical operation, lumber strength is estimated by measuring the bending stiffness along the length of each board, and then applying a statistical relationship to determine lumber strength. However, the relationship between lumber strength and bending stiffness is not a well-defined one. Correlation coefficients between estimated and measured strengths are quite modest, with typical r2 values in the range 0.50 to 0.60 (8). The associated uncertainty in the indicated lumber strength seriously impairs the ability to segregate lumber into distinct strength classes. Consequently, greatly varying strengths are present within a given lumber grade, combined with large overlaps in the strengths of nominally distinct grades of lumber. For safety, design strength has to be based on the weakest expected boards within a grade. Thus, the majority of stronger boards are undervalued (14).
There are two main reasons for the modest strength-indicating capability of the bending-stiffness method. The first is the low spatial resolution of the measurements. Bending stiffness is measured over a 36- to 48-inch span, with a main contribution from the central 12 to 20 inches (3). In comparison, the main strength-controlling features, the knots, are typically only 0.5 to 2 inches in diameter. The second reason is the entirely statistical nature of the lumber strength identification. There is no mechanistic consideration of how the structure of the lumber controls its strength. Thus, variations in raw material source and quality cause unknown deviations from the previously established statistical strength vs. stiffness correlations.
This paper describes an alternative method for estimating lumber strength based on x-ray measurements of lumber density (11,15). This method seeks to overcome the shortcomings of the bending-stiffness method by using a spatial measurement resolution of 1 inch, which is about the same scale as the main strength-controlling features, the knots. The method also uses the measured density data to characterize the lumber material properties and structure so that the strength identification procedure can include these features. Tests of the density measurement system have indicated an improved lumber strength identification capability. Typical r2 values are in the range 0.65 to 0.75. These results directly translate into improved yields of high-value products and less undervaluation of stronger material. A lumber strength grading machine incorporating an x-ray scanner of the type described here has now been commercialized (4). The design has proven to be robust in a sawmill environment, with industrial versions now operating in several North American sawmills.
In addition to its superior strengthestimation capabilities, x-ray scanning also has some operational advantages over bending-based lumber grading. There is no contact between the sensor and the wood. The lumber and the conveyor system are the only moving parts. Thus, maintenance costs are low and service reliability is high. The scanner is also very compact and can conveniently be installed behind a planer. This arrangement saves floor space and minimizes the need for special material infeed systems.
Figure 1 shows x-rays passing from a source, through a piece of lumber, and onto a detector. During passage through the lumber, a part of the radiation is absorbed by the wood, and only the remaining part reaches the detector. The amount of absorption depends on the density (specific gravity) of the part of wood through which the radiation passes. A comparison of measurements of the radiation reaching the detector with and without wood in place gives the amount of radiation absorbed by the wood. The local wood density can then be determined directly from these measurements.
Fig 1: Schematic diagram of a single-detector lumber density scanner.
During the initial tests done in this study, the radiation was provided by an Americium 241 source instead of an x-ray source. The radiations from these two source types are very similar and can be used interchangeably. For industrial use, an x-ray source is preferable because it avoids the use of nuclear materials and can be switched off easily. Also, the higher beam intensity allows measurements to be made at practical industrial speeds. Using the basic arrangement shown in Figure 1, a specific gravity resolution better than 0.01 was achieved in measurements along the length of 2 by 4 lumber pieces. The associated spatial resolution was 1 inch inlength and the full 3.5 inches in width. After initial tests to demonstrate the measurement concept and the wood strength grading potential, the 3-detector arrangement shown in Figure 2 was built. In this variation, the radiation passes through the thickness of the lumber instead of its width, and is subsequently measured by three separate detectors. This arrangement increases the spatial resolution of the density measurement to about 1 inch in both length and width.
Figure 3 shows a typical density profile measured along the length of a board, as measured by one of the radiation detectors in Figure 2 or the singledetector in Figure 1. The flat areas of the profile correspond to clear wood and the sharp peaks correspond to knots. The sharp measured density increases occur because the density of knot material is about twice that of clear wood. The local density represented by the solid line in Figure 2 is designated r.
The additional dashed line in Figure 3 shows the computed clear wood density, excluding the effects of knots. For clarity, this line has been displaced slightly downwards. It represents the estimated density profile of the board if no knots were present. The computed clear wood density, also called the reference density, equals the measured wood density in areas of clear wood. In areas containing knots, it is determined by interpolating between the adjacent clear wood areas. The reference density, designated 0, is useful when identifying clear wood properties and when measuring the sizes of the knot peaks.
Fig 2: Schematic diagram of a multiple-detector lumber density scanner. Fig 3: Typical measured density profile along the length of a board.
Clear wood strength. This property is a material characteristic of clear, straight-grained wood without any knots or grain distortions.
Wood structural factor. This factor describes the strength reduction from the idealized clear wood case caused by the non-uniform structure of practical lumber. Knots and the surrounding grain distortions are the main sources of non-uniform grain structure.
where Sc is the strength of clear wood of density 0.5 gm/cm 3 and r0 is the local clear wood mean density. When using the three-detector arrangement in Figure 2, r0 is the average of the set of three density readings at the given board cross section.
where S represents a summation of the quantities for all three density readings, and w i (i = 1,3) are weighting factors that describe the relative effects of knots within the three density measurement locations. For tension and compression strength predictions, the weighting factors w i are approximately equal because a board is uniformly stressed under such axial loadings. Location within a board therefore has only minor influence on the strength-reducing effect of knots. For bending strength predictions, the weighting factors w i for the two edges of the board are larger than the one for the center because the bending loading puts the highest stresses on these two regions. In all cases, the sum of all three weighting factors equals one.
These equations are combined to calculate an estimated board strength profile from the measured density profile(s). It is assumed that failure occurs within a single cross section. The estimated board strength is determined from the cross section with the minimum calculated strength. When comparing with actual strength tests, the strength calculation was limited to the particular area of each board that was significantly loaded. This was done to avoid confusing the strength calculation with data from parts of the boards that were not loaded during the subsequent destructive strength tests.
Table 1 summarizes the six groups of tension, compression, and bending strength estimation tests that were done using six groups of mill-run southern yellow pine 2 by 4s, 12 to 16 feet long, supplied from sawmills in Arkansas and Mississippi. All of the boards except group 4 were evaluated by the bending-stiffness method at their mill of origin using standard industrial testing machines (7). The boards were then density-scanned over their entire lengths, barring 2 inches on either end. The board transport system that was used did not allow scanning in these end regions. (The industrial x-ray scanner developed from the laboratory prototype scans full-length, including the end regions.) The boards in groups 1 to 3 were single profile-scanned as shown in Figure 1. The boards in groups 4 to 6 were three-profile-scanned corresponding to Figure 2. The laboratory system that was used for these tests had only one detector. Three-profile scanning was simulated by repeated single-scanning along three different paths.
The destructive tests to determine actual board strengths were done using American Society for Testing and Materials standard procedures (2). For the tension tests, the boards were tested full length. The 2-foot sections at either end of the boards were used for gripping, and were not subject to the full tensile load. Wherever possible, the board positions were slightly adjusted so that significant defects were not contained within the areas of stress concentration close to the boundaries of the grips.
For the bending tests (third-point loading), the load span was 73-1/2 inches, typically chosen to contain the most serious defect in the board. This choice was made visually and emphasized the major defects that were on or close to the tensile edge of each board. Such defects have a great influence on bending strength, and should represent the minimum bending strength of each board. To accommodate other testing needs, part of group 3 and all of group 4 were loaded at pre-determined locations, not related to the presence of defects. Since the region of significant bending load is a very small fraction of the total board length, this choice of load position can have a large influence on the measured strength value.
For the compression tests, the specimen length was 9 inches, chosen to contain the largest available visual defect in the undamaged material from group 5 remaining after completion of the bending tests. Because of this procedure, the specimens typically did not contain the largest defect originally in the board. However, the remaining visual defects were usually comparable in size to the original largest defect. In the same way as the bending test, choice of specimen position can have a large influence on the measured strength value.
Table 2 summarizes the densitybased strength estimates for the six groups of test boards listed in Table 1. For consistency of interpretation, all results are calculated for single-profile density measurements corresponding to Figure 1. For the boards in groups 4, 5, and 6, the three measured density profiles that were measured using the arrangement shown in Figure 2 were averaged to simulate the single-profile arrangement shown in Figure 1. In this and subsequent tables, the "optimal" coefficients S c , B, etc. are chosen to give the minimum value of the Root Mean Square Error (RMSE) between the measured and estimated strengths of the boards in that group. This calculation was done using Powell's minimization method (10).
Table 1: Board strength tests.
Table 2: Single-density profile board strength estimates.
Table 3 lists the corresponding strength estimates from the bending-stiffness measurements. The industrial bending-stiffness testing machines that were used reported both the average and the minimum value of Young's modulus measured along the length of each board. These two quantities are designated Eave and Emin. To allow maximum strength-estimation capability, both of these values were used in a multiple-regression calculation. Table 2 lists the corresponding regression coefficients. The boards in group 4 were not stiffness-tested at their originating sawmill. The bending stiffness of these boards was measured in joist configuration using a laboratory testing machine. These data were used in the single regression reported in Table 2.
Table 3: Board strength estimates from bending stiffness.
In Tables 2 and 3, the coefficient of determination, r2 , indicates if the estimated board strengths have a straight-line relationship with the actual measured board strengths. The Standard Error of the Estimate (SEE) indicates the size of the typical error between the estimated and measured board strengths. In all cases, the board strength estimates from the density measurements are significantly more accurate than those from the bending-stiffness measurements. The r2 values are 0.1 to 0.2 higher and SEE is 200 to 600 psi lower. The main reason for this superior strength-estimation accuracy is believed to be the finer spatial resolution of the density measurements compared with the bending-stiffness measurements.
The tension and compression tests have the most accurate strength estimates in Table 2. This is because the applied stresses are uniform for these loading types. Knot location within a cross section is therefore not a major factor. As a result, single-profile density measurements are sufficient in these cases.
Table 2 shows that single-profile density measurements are less effective for estimating bending strength, although still superior to bending-stiffness measurements. This occurs because bending loading creates highly non-uniform stresses within a cross section much greater at the edges than in the middle. Single-profile density measurements do not take into account this stress variation. The strength-estimation accuracy for board group 4 is anomalously low for both density- and bending-stiffnessbased results. This occurred because the experimental procedure for this board group was non-standard. To accommodate other testing needs, the bending load was applied at a fixed location on each board, independent of observed defect locations. Consequently, the measured bending strength often differed quite substantially from the bending strength at the weakest location on the board.
The calculated knot sensitivity factor (B) is greatest for tensile loading because knots at any point within a cross section can cause strength reductions. Knots are prime crack initiation sites for tensile failures. The factor B is smaller for bending loading because, for such loadings, only those knots close to the tensile edge cause significant strength reductions. The knot sensitivity factor is smallest for compressive loading because these failures occur by crushing of the wood material rather than by the crack propagation typical of tensile and bending failures. Crack initiation aroundknots is not an important factor affecting wood crushing strength.
The results presented in Tables 2 and 3 refer to the optimal choices of coefficients Sc , B, Eave , and Emin , and the corresponding r2 and SEE values for each group of boards. Ideally, the optimal coefficients should be similar for all groups of boards subject to the same loading. This similarity indicates that the optimal coefficients determined during a calibration test can be expected to also apply to other material not included in the calibration test. In practice, almost all boards that are to be graded are not included in the calibration test. Thus, stability of the optimal coefficients and insensitivity to small changes in their values are important features.
The r2 and SEE are statistical measures that describe the optimal fit (regression) between two independent quantities, here the measured and estimated strengths of groups of boards. However, for the non-optimal case of interest here, no regression has been done, so r2and SEE are not appropriate. The RMSE between the measured and estimated strengths of the boards in each group is a more meaningful measure. This quantity indicates typical board strength-estimation accuracy. In general, RMSE is larger than SEE. It equals SEE only when the calibration coefficients equal the optimal values for the boards being evaluated, and the regression line passes through the origin.
Tables 4 and 5 show the effects of applying the optimal regression coefficients from one board sample group to a different board group. The negligible increases in RMSE caused by the change of coefficients in Table 4 indicates high reliability in the density-based strength predictions. The larger increases in strength-prediction errors listed in Table 5 indicate much less reliable results when using bending stiffness as the strength predictor.
1 Tension 9,680 5.42 1,320 Optimal coeffs.
2 Tension 10,250 6.14 1,180 Optimal coeffs.
3 Bending 11,710 3.68 1,780 Optimal coeffs.
5 Bending 11,750 4.06 2,190 Optimal coeffs.
Table 4: Root mean square errors (RMSE) for density-based strength predictions.
1 Tension -3.07 .0602 -.0088 1,690 Optimal coeffs.
2 Tension -2.62 .0135 .0412 1,785 Optimal coeffs.
3 Bending -2.10 .0358 .0315 2,180 Optimal coeffs.
5 Bending -4.48 .0469 .0409 2,550 Optimal coeffs.
Table 5: Root mean square errors for bending-stiffness-based strength predictions.
Board groups 4, 5, and 6 were 3-profile-scanned corresponding to Figure 2. In the single-profile results reported in Table 2, the three density profiles had been averaged to simulate the single-profile arrangement in Figure 1. Here, the data from the three measured density profiles are used individually. Table 6 summarizes the strength estimates from the 3-profile density measurements with board groups 4, 5, and 6, and also the corresponding bending-stiffness results.
Table 6 expands on the data in Tables 2 and 3 to include the effects of the weights (wi). For bending loadings, weight w1 represents the compressive edge, w2 the center, and w3 the tensile edge of the board. Three calculationsare done for each of the board groups. The "1-profile" calculation uses equal weights to simulate the effect of singleprofile density scanning. This reproduces the results in Table 2. The "3-symmetric" calculation uses all three weights to represent 3-profile density scanning. In this calculation, weights w 1 and w3 are constrained to be the same, to correspond to the symmetrical case where the board can be loaded eitherway up. The final "3-unsymmetric" case is a general 3-profile calculation where the weights are allowed to be unsymmetrical. These results refer to loading the board a specific way up.
The sequence of calculations in Table 6 represents an increasing degree of detailed knowledge of the position of the knots in the board samples. For the bending strength calculations, this detailed knowledge allows increasingly more accurate predictions of board strength. The r2 values are increased by over 0.10 and SEE values are decreased by 300 to 400 psi. The large values of weights w1 and w3 in 3-profile calculations confirm the sensitivity of bending strength to knots close to the board edges, particularly the tensile edge. The negative values of w2 are unexpected. They occur because of the large strength sensitivity to edge knots.
The symmetric 3-profile case (3-symmetric) is probably the most suitable for practical bending strength estimations because it applies to loadings either way up. In general, service load direction is not known in advance. If the direction of service load can be specified, strength-prediction accuracy can be enhanced by using the unsymmetric 3-profile calculation instead.
Figure 4 shows comparisons of the measured and estimated strengths of the boards in group 5 determined using the bending-stiffness, 1-profile, and symmetric 3-profile density methods. The reduced scatter in Figures 4b and 4c clearly demonstrates the improved strength-estimation capabilities of the density method compared to the bend ing-stiffness method. The graphs also illustrate the further improvement that can be achieved by using multiple-profile scanning when estimating bending strength.
Table 6: Comparison of bending-stiffness and various density-based strength estimates.
For compression-strength calculations, detailed knowledge of knot position within a cross section provides no significant advantage because compression strength is relatively insensitive to knot position. For board sample group 6 in Table 6, all density-based calculations give similar r2 and SEE values. Tensile strength is also relatively insensitive to knot position within the board cross section. Multiple-profile measurements made during another study on a sample of Douglas-fir tested in tension showed similar insensitivity to knot position. Thus, for tension or compression strength predictions, 1-profile scanning or equal-weight multiple-profile scanning is sufficient.
The algorithm used to estimate board strength summarized in the earlier section "Strength-estimation method" is a conceptually simple mathematical procedure to model the two specified strength-controlling factors. The question arises as to whether significant improvements in board strength estimation can be achieved by developing a more sophisticated strength-estimation algorithm. This question was investigated bytrying several "enhancements" to the basic algorithm. These included non-linear variations of the various mathematical terms, inclusion of the influence of adjacent cross sections, and the use of additional coefficients. In general, most such changes had positive effects on r2 and SEE for the boards in the calibration group. However, they typically also had correspondingly negative effects on RMSE for cross-group calculations such as those shown in Table 4. These results suggested that, in general, the more highly "tuned" a strength calculation is to a particular group of boards, the less well it will be "tuned" to a different group of boards. This behavior renders an "enhanced" strength estimation less effective for practical use. However, this observation does not imply that the basic algorithm presented here is the final answer. Certainly, future conceptual advances can be expected to lead to algorithmic improvements.
A related question is how well density scanning can be expected to indicate board strength when it evaluates only two strength-controlling factors: knots and clear wood density. It does not indicate other important strength-controlling factors such as grain direction. However, in practice, even when board failure occurs because of other factors such as grain direction, the density-based method still gives reasonable strength estimates. This behavior probably occurs because board strength is a "weak link" process. That is, the weakest area of a board controls the strength of the entire board. When board failure occurs because another factor has caused the most serious area of weakness, it is likely that there are also other areas where knots have caused almost equal weaknesses. Of course, improvement in strength estimation can be expected if the ihother factorslo are explicitly included in the calculation.
When comparing measured and estimated wood strength data, the assumption is often implicitly made that the measured data are ideal and that all discrepancies are contained in the estimated data. In Figure 4, this corresponds to all the uncertainty being contained in the abscissa, and none in the ordinate. However, it is clear that the measured strength data do actually contain significant uncertainty. This uncertainty occurs because the standard tests used for determining bending and compression strengths only apply significant load to a very small fraction of each test board. To make the test represent all the board material, the test technician has to choose the loading zone according to a personal judgment of the weakest region in the board. This choice is a subjective one, and can vary substantially among different test personnel. The destructive nature of the strength testing often means that other potentially weak regions of a board cannot subsequently be tested to validate the original load position choice.
Fig 4:Measured and estimated bending strengths of board group 5 determined by different methods:a) bending stiffness; b) 1-profile density; and c) 3-profile density (symmetric).
The bending and compression strength test uncertainty becomes a significant issue when seeking to make strength predictions without knowing in advance where the boards are going to be loaded. This circumstance is likely to be the case in practical quality control procedures in sawmill operations. Such quality control procedures combine the uncertainties due to the test and the prediction method, and unfairly attribute them both to the prediction method. Misattribution of the uncertainty in board strength becomes increasingly significant when assessing the performance of higher accuracy strength-prediction procedures.
When load position is not known, the strength prediction is based on the calculated weakest location in a board. This calculation includes large areas of the board that are not subsequently loaded. Inclusion of data from such untested areas can detrimentally affect the strength prediction, especially if the load position choice is poor. Table 7 compares the strength predictions possible both with and without knowing the test load positions. The ihknown load positionln results come from calculations using density data only from within the region of applied load. The "unknown load position". calculations use density data from the entire length of the boards. Table 7 includes all six board groups, using 1-profile calculations for groups 1, 2, and 3, and unsymmetric 3-profile calculations for groups 4, 5, and 6.
For the bending test groups 3, 4, and 5, uncertainty of the load position impairs the strength-prediction accuracy by 0.06 to 0.18 for r2 and by 150 to 450 psi for SEE. This reduction in accuracy has two main causes: incorrect choice of weakest board location and strength-prediction uncertainty. The first type ofuncertainty occurs when the chosen load position does not contain the actual weakest location in the board. The second type occurs when the predicted weakest location differs from the actual weakest location. It is hard to separate these two uncertainties.
Board group 4 was tested in a non-standard way. The bending load was applied at a fixed location on each board, independent of observed defect locations. Because of this, the measured bending strength often differed quite substantially from the bending strength at the weakest location on the board. This effect caused the abnormally large difference in the results in Table 7 for known and unknown load positions.
The data for compression test group 6 also show a large difference between known and unknown load positions. The test uncertainties are magnified here because of the very small size of the tested region and also the limitation of having to take the specimens from the unbroken material remaining after bending testing. Care was taken not to include any material damaged by the previous bending testing.
The tension tests 1 and 2 are complementary to the bending and compression tests. Here, the entire length of the board is loaded during the test, and so there is no uncertainty associated with choice of load position. The known load position result in Table 7 refers to the strength prediction made using density data from around the actual break positions. The smallness of the difference between the known and unknown load positions suggests that the density method is successful in assessing the weakest board location.
Comparative calculations for the effect of knowing the loading position on bending-stiffness-based measurements could not be made here. The testing machines used in this study only reported the average and minimum E-values. Unfortunately, they did not provide the along-length measurements of E-profile needed for a load position analysis. Some recent models of industrial bending-stiffness testers do provide the ability to indicate E-profile. Based on the results shown in Table 7, some improvement in strength-estimation capability could be expected from such machines. However, the size of that improvement cannot be conjectured here.
Table 7: Strength predictions with and without knowledge of load position.
X-ray lumber density scanning gives board strength estimates that are significantly superior to those from bending-stiffness measurements. For several groups of southern yellow pine boards totaling over 800 pieces, correlations of estimated vs. destructively measured strength gave r2 values in the range 0.68 to 0.78, and SEE values in the range 900 to 1,800 psi. The corresponding results from bending-stiffness grading are r2 = 0.50 to 0.60 and SEE = 1,200 to 2,500 psi. This increase in lumber strength grading accuracy directly translates into increased yields of higher value stressrated products. The results presented in this study are for southern yellow pine lumber. Other tests with western species such as fir and hemlock also show strength-prediction capabilities superior to those from bending-stiffness grading, although with r2 and SEE values slightly inferior to those for the southern yellow pine considered here. This difference is probably due to the larger knot size typically found in southern yellow pine.
The x-ray method is equally successful for tension, compression, and bending strength estimations. All three of these strength values can be simultaneously estimated from the measured board density data. Multiple-profile density measurements significantly improve the accuracy of bending strength estimations. For tension and compression strength estimations, single-profile measurements are sufficient because in these cases the applied stresses are uniform within the lumber cross section.
The data presented here indicate a significant uncertainty in the measured bending and compression strengths. This uncertainty occurs because both tests use very localized loadings. The test technician must attempt to make the loaded part of each board represent the entire board by visual choice of the "weakest" board location. Although made conscientiously, this choice often appears not to be the best one. The test position uncertainty becomes a significant issue when endeavoring to make higher accuracy strength predictions. In measured vs. predicted strength comparisons, the measurement uncertainty is unfairly classified as increased prediction uncertainty.
American Society for Testing and Materials. 1993. Standard practice for establishing structural grades and related allowable properties for visually graded lumber. Standard D 245-93. ASTM, West Conshohocken,Pa.
__________. 1997. Standard test methods of static tests of lumber in structural sizes. Standard D 198-97. ASTM, West Consho-hocken, Pa.
Bechtel, F.K. and J.R. Allen. 1995. Intro-duction to the Metriguard Model 72000 LS Lumber Tester. MSR Lumber Producers Council Meeting in Vancouver, B.C, Can-ada. Metriguard is in Pullman, Wash.
CAE Newnes Ltd. X-ray Lumber Grader (XLG). Salmon Arm, B.C.
DeVisser, D., W. Galligan, and S. Suddarth. 1993. Comparing machine stress-rated and visually graded lumber used in walls. Wood Design Focus 4(3):3-7.
Hilbrand, H.C. and D.G. Miller. 1966. Ma-chine grading - theory and practice. Forest Prod. J. 16(11):28-40.
Metriguard, Inc. Continuous Lumber Tester (CLT). Pullman, Wash.
Müller, P.H. 1968. Mechanical stress-grading of structural timber in Europe, North America and Australia. Wood Sci. and Technology 2:43-72.
National Institute of Standards and Technol-ogy. 1994. American Softwood Lumber Standard. Voluntary Product Standard PS 20-94. NIST, Gaithersburg, Md.
Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. 1986. Numerical Recipes. Cambridge Univ. Press, Cam-bridge, Mass.
Schajer, G.S. 1989. Method and apparatus for estimating the strength of wood. U.S. Patent 4941357.
USDA Forest Service. 1987. Wood Hand-book: Wood as an Engineering Material. Agri. Handb. 72. USDAForest Serv., Forest Prod. Lab., Madison, Wis.
Western Wood Products Association. 1995. Standard grading rules for western lumber. WPPA, Portland, Oreg.
Winistorfer, S.G. and R.D. Theilen. 1997. Machine-graded lumber - design implications. Wood Design Focus 8(2):14-20.
Ziegler, G.A. 1997. Machine grading pro-cesses for softwood dimension lumber. Wood Design Focus 8(2):7-14.
The author is a Professor on leave from the Dept. of Mechanical Engineering, Univ. of British Columbia, Vancouver, B.C., Canada (currently at Weyerhaeuser Technology Center, Tacoma, WA98477). The author sincerely thanks Dr. Gerald Ziegler and Dr. Henry Montrey,of Weyerhaeuser Co., for their support and encouragement, and for permission to publish this work. Dr. R.O. Foschi kindly reviewed the manuscipt. A part of this paper was presented at ScanPro '98, Vancouver, Canada, November 4-6, 1998. This paper was received for publication in December 1999. Reprint No. 9075.


text:
from collections import defaultdict

from django.db import models
from django.core.exceptions import ImproperlyConfigured
from django.utils.functional import cached_property

from .. import settings
from .. import utils
from ..cache import CachedTranslation
from ..models import Translation

class Linguist(object):
    def __init__(self, *args, **kwargs):
        self.instance = kwargs.get("instance", None)
        self.identifier = kwargs.get("identifier", None)
        self.default_language = kwargs.get("default_language", None)
        self.default_language_field = kwargs.get("default_language_field", None)
        self.fields = kwargs.get("fields", None)
        self.decider = kwargs.get("decider", Translation)

        self.validate_args()

        self.fields = list(self.fields)

        self._language = None

    def validate_args(self):
        """
        Validates arguments.
        """
        from ..mixins import ModelMixin

        for arg in ("instance", "decider", "identifier", "fields", "default_language"):
            if getattr(self, arg) is None:
                raise AttributeError("%s must not be None" % arg)

        if not isinstance(self.instance, (ModelMixin,)):
            raise ImproperlyConfigured('"instance" argument must be a Linguist model')

        if not issubclass(self.decider, (models.Model,)):
            raise ImproperlyConfigured(
                '"decider" argument must be a valid Django model'
            )

    @property
    def active_language(self):
        """
        Returns active language.
        """
        # Current instance language (if user uses activate_language() method)
        if self._language is not None:
            return self._language

        # Current site language (translation.get_language())
        current = utils.get_language()
        if current in self.supported_languages:
            return current

        # Default language descriptor
        return self.default_language

    @property
    def language(self):
        return self.active_language

    @language.setter
    def language(self, value):
        self._language = value

    @cached_property
    def supported_languages(self):
        return utils.get_supported_languages()

    @property
    def cached_languages(self):
        langs = []
        for k, v in self.instance._linguist_translations.items():
            for lang in v.keys():
                if lang not in langs:
                    langs.append(lang)
        return langs

    @property
    def suffixed_fields(self):
        return utils.get_language_fields(self.fields)

    @property
    def cached_fields(self):
        return [k for k, v in self.instance._linguist_translations.items() if v]

    @property
    def cached_suffixed_fields(self):
        return [
            "%s_%s" % (field, lang)
            for field in self.cached_fields
            for lang in self.supported_languages
        ]

    @property
    def empty_fields(self):
        return list(set(self.fields) - set(self.cached_fields))

    @property
    def empty_suffixed_fields(self):
        return list(set(self.suffixed_fields) - set(self.cached_suffixed_fields))

    @property
    def translations(self):
        """
        Returns translations dictionary.
        """
        return self.instance._linguist_translations

    @property
    def translation_instances(self):
        """
        Returns translation instances.
        """
        return [
            instance
            for k, v in self.instance._linguist_translations.items()
            for instance in v.values()
        ]

    @property
    def translations_count(self):
        """
        Returns translations count.
        """
        return len(self.translation_instances)

    def get_cache(
        self,
        instance,
        translation=None,
        language=None,
        field_name=None,
        field_value=None,
    ):
        """
        Returns translation from cache.
        """
        is_new = bool(instance.pk is None)

        try:
            cached_obj = instance._linguist_translations[field_name][language]
            if not cached_obj.field_name:
                cached_obj.field_name = field_name
            if not cached_obj.language:
                cached_obj.language = language
            if not cached_obj.identifier:
                cached_obj.identifier = self.instance.linguist_identifier
        except KeyError:
            cached_obj = None

            if not is_new:
                if translation is None:
                    try:
                        translation = self.decider.objects.get(
                            identifier=self.instance.linguist_identifier,
                            object_id=self.instance.pk,
                            language=language,
                            field_name=field_name,
                        )
                    except self.decider.DoesNotExist:
                        pass

            if cached_obj is None:
                if translation is not None:
                    cached_obj = CachedTranslation.from_object(translation)
                else:
                    cached_obj = CachedTranslation(
                        instance=instance,
                        language=language,
                        field_name=field_name,
                        field_value=field_value,
                    )

            instance._linguist_translations[cached_obj.field_name][
                cached_obj.language
            ] = cached_obj

        return cached_obj

    def set_cache(
        self,
        instance=None,
        translation=None,
        language=None,
        field_name=None,
        field_value=None,
    ):
        """
        Add a new translation into the cache.
        """
        if instance is not None and translation is not None:
            cached_obj = CachedTranslation.from_object(translation)
            instance._linguist_translations[translation.field_name][
                translation.language
            ] = cached_obj
            return cached_obj

        if instance is None:
            instance = self.instance

        cached_obj = self.get_cache(
            instance,
            translation=translation,
            field_value=field_value,
            language=language,
            field_name=field_name,
        )

        if field_value is None and cached_obj.field_value:
            cached_obj.deleted = True

        if field_value != cached_obj.field_value:
            cached_obj.has_changed = True
            cached_obj.field_value = field_value

        return cached_obj

class DefaultLanguageDescriptor(object):
    """
    Default language descriptor.
    """

    def __get__(self, instance, instance_type=None):
        if not instance:
            return self

        # Meta default_language_field explicitly defined.
        if instance._linguist.default_language_field is not None:
            default_language = getattr(
                instance, instance._linguist.default_language_field
            )
            if callable(default_language):
                return default_language()
            return default_language

        # Meta default_language
        if instance._linguist.default_language is not None:
            return instance._linguist.default_language

        # Settings default
        return settings.DEFAULT_LANGUAGE

class CacheDescriptor(object):
    """
    Cache Descriptor.
    """

    def __init__(self, meta):
        self.identifier = meta.get("identifier", None)
        self.fields = meta.get("fields", None)
        self.default_language = meta.get("default_language", settings.DEFAULT_LANGUAGE)
        self.default_language_field = meta.get("default_language_field", None)
        self.decider = meta.get("decider", Translation)

    def __get__(self, instance, instance_type=None):
        if instance is None:
            return self

        try:
            return getattr(instance, "_linguist_cache")
        except AttributeError:
            linguist = Linguist(
                instance=instance,
                identifier=self.identifier,
                default_language=self.default_language,
                default_language_field=self.default_language_field,
                fields=self.fields,
                decider=self.decider,
            )

            setattr(instance, "_linguist_cache", linguist)
            setattr(instance, "_linguist_translations", defaultdict(dict))

        return instance._linguist_cache

class TranslationDescriptor(object):
    """
    Translation Field Descriptor.
    """

    def __init__(self, field, translated_field, language):
        self.field = field
        self.translated_field = translated_field
        self.language = language
        self.attname = utils.build_localized_field_name(self.field.name, language)
        self.name = self.attname
        self.verbose_name = utils.build_localized_verbose_name(
            field.verbose_name, language
        )
        self.column = None

    def __get__(self, instance, instance_type=None):
        if not instance:
            return self

        obj = instance._linguist.get_cache(
            instance=instance,
            language=self.language,
            field_name=self.translated_field.name,
        )
        return obj.field_value or ""

    def __set__(self, instance, value):
        if not instance:
            return self

        instance._linguist.set_cache(
            instance=instance,
            language=self.language,
            field_name=self.translated_field.name,
            field_value=value,
        )

    def db_type(self, connection):
        """
        Returning None will cause Django to exclude this field from the concrete
        field list (``_meta.concrete_fields``) resulting in the fact that syncdb
        will skip this field when creating tables in PostgreSQL.
        """
        return None

class TranslationField(object):
    """
    Proxy of original field.
    """

    def __init__(self, translated_field, language, *args, **kwargs):
        self.__dict__.update(translated_field.__dict__)

        self.translated_field = translated_field
        self.language = language
        self.descriptor_class = kwargs.pop("descriptor_class", TranslationDescriptor)

        # Suffix field with '_fr', '_en', etc.
        self.attname = utils.build_localized_field_name(
            self.translated_field.name, language
        )
        self.name = self.attname
        self.verbose_name = utils.build_localized_verbose_name(
            translated_field.verbose_name, language
        )

        # No concrete field Django < 1.8
        self.column = None

        # No concret field Django >= 1.8
        self.concrete = False

    def contribute_to_class(self, cls, name):
        self.model = cls
        self.name = name
        setattr(
            cls,
            self.name,
            self.descriptor_class(self, self.translated_field, self.language),
        )
        cls._meta.add_field(self)
        getattr(
            cls._meta, "virtual_fields", getattr(cls._meta, "private_fields", None)
        ).append(self)

    def db_type(self, connection):
        """
        Returning None will cause Django to exclude this field from the concrete
        field list (``_meta.concrete_fields``) resulting in the fact that syncdb
        will skip this field when creating tables in PostgreSQL.
        """
        return None

    def clone(self):
        from django.utils.module_loading import import_string

        name, path, args, kwargs = self.deconstruct()
        cls = import_string(path)
        return cls(*args, **kwargs)

    def deconstruct(self):
        name, path, args, kwargs = self.translated_field.deconstruct()
        if self.null is True:
            kwargs.update({"null": True})

        return str(self.name), path, args, kwargs

redpajama_set_name: RedPajamaGithub


text:
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