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The Entity class is an abstract class that is used to organize the classes in the hierarchy whose instances represent real-world entities such as companies, products, and industries. Each instance of these classes corresponds to a particular entity. For example, the instances of the class Company could represent individual corporate entities such as GM. Instances of the class Industry represent specific industries such as Autos.
The Entity class is a direct subclass of Object:
Object
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Entity
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|-- Classification
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|-- Currency
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|-- Universe
Since it is useful to be able to address individual entities, entity instances are explicitly named. To avoid conflicting names (e.g., a company, security, and account all named GM), separate naming dictionaries are created for each entity subclass. For example, to access the object representing the company GM, you would execute the expression:
Named Company GMand to access the object representing the account GM, you would execute the expression:
Named Account GMThe general form to get to a specific instance of an entity class is:
Named entityClass entityIdwhere entityClass is the name of an entity class and entityId is the name of the actual instance. Note that the entityId must start with a letter or the _ character. If it does not, you should use the \ (escape) character to precede the name. For example, if the id for US currency is 1, the expression:
Named Currency \1can be used to access this object. An alternative to using the escape character for individual special characters is to identify the entire code as a string using the form:
Named Currency at: "code" .For example:
Named Currency at: "1" .Additional information on valid identifiers is available.
All entities responds the the messages code and name. Initially, these values are the same. The value of name is usually changed from its initial value to a more descriptive name for the entity. An entity's name can be changed using the message setNameTo:. For example, the expression:
Named Currency US setNameTo: "U.S. Dollar" ;sets the value of the property name to the string supplied.
The properties shortName and sortCode are also initialized with the value of code. The shortName property is usually used to keep an abbreviated name for the entity that can be displayed in fewer characters than the full name. The sortCode property is usually used to store an alternative string used for sorting the entities in a class. These values can be set using the messages: setShortNameTo: and setSortCodeTo:. The property numericCode is defined for all entities. It is not initialized and can be used as needed to set a numeric value for instances that require one. The message setNumericCodeTo: can be used to set this value.
The message displayInfo is defined to display the entity's code and name followed by a carriage return. Some classes redefine the displayInfo message to display additional information. The message profile is defined to display a descriptive report for an entity. Some classes redefine this message to display additional information.
The message masterList returns the list of all non-default entities in a class. For example, to display the code and name for each Currency you could use:
Currency masterList do: [ code print: 10 ; name printNL ; ] ;
Each entity instance can have more than one name. For example, your installation may have named the currency instance for US as "USD" and "US". The expressions:
Named Currency USD and Named Currency UScan both be used to return the currency instance that represents the US currency.
The identifiers USD and US are both aliases for the object that represents the US currency instance. An entity can have any number of aliases associated with it. All entities have at least one alias. By default, the value of the code property for an entity is used as the first alias created. Additional aliases can be added using the message addAlias:. For example, the alias USA can be added to this entity using:
Named Currency US addAlias: "USA" ;The expression:
Named Currency US = Named Currency USAwould then return the value TRUE.
To display all the aliases associated with a specific entity you can display the list of strings stored in the aliases property. For example, to see the aliases defined for the US currency use:
Named Currency US aliases do: [ printNL ] ;
To delete an alias use the message deleteAlias:. For example:
Named Currency US deleteAlias: "USA" ;deletes the USA alias for the US currency. To display the aliases available for each currency, use:
Currency masterList sortUp: [ name ] . do: [ name print: 30 ; aliases do: [ " / " print ; print ; ] ; newLine print ; ] ;
The expression:
Entity createSubclass: "Company"is used to create a new subclass of the Entity class that represents company objects. Once created, this class can be accessed using:
Company or Named Company DefaultThe variations of the createSubclass messages have been redefined at the Entity class to perform the following additional functions:
Named Company and Named Company Default named and Company namedall return the naming dictionary for the Company class.
When a subclass of Entity is created, an initial instance representing the default instance for the class is also created. This instance is used by the Entity initialization messages to store values that are copied to new instances. This instance in all ways behaves like other instances in the class. To determine if an object is the default instance in its class, the isDefault and isntDefault messages are defined. Both return boolean (i.e., TRUE/FALSE) values. For example:
Named Currency US isDefault returns FALSE Named Currency US isntDefault returns TRUE Named Currency Default isDefault returns TRUE and Named Currency Default isntDefault returns FALSE.
Note that the message instanceList returns the list of all instances in a class including the default instance and the message masterList returns the list of all instances in a class excluding the default instance.
The expression:
Company createInstance: "XYZ"is used to create a new instance of the Company class named XYZ. Once created, this instance can be accessed using:
Named Company XYZThe variations of the createInstance messages have been redefined at the Entity class to perform the following additional functions:
Several additional messages have been define at Entity which are used to initialize properties in the new instance. Initial values can automatically be copied from the default instance. For example, define the following:
Company defineFixedProperty: 'property1' . defineFixedProperty: 'property2' ; Named Company Default :property1 <- 10 ; Named Company Default :property2 <- "xyz" ;The following technique can be used to automatically initialize these values in new instances:
Company createInitializationListFrom: NA ; Company updateInitializationListWith: [ :property1 ] ; Company updateInitializationListWith: [ :property2 ] ;The first statement defines an empty initialization list. You could supply an existing list here instead. The next statements add the two properties to the automatic initialization process. When new instances are created, the values for these properties will be copied from the default instance. For example:
Company createInstance: "GM"will create a new company instance named GM and automatically copy the values for property1 and property2. The expression:
Named Company GM property1will return the value 10 and the expression:
Named Company GM property2will return the string "xyz".
Suppose you needed to created several classes which represent simple tables of information that provide short and long names for various codes. For example, the class Rating could be defined to stores names for the ratings A through F and the class Status could be defined to store names for various status codes such as "Approved" and "Restricted".
To begin, create a subclass of Entity that will be the super class of all Table subclasses:
Entity createSubclass: "Table";Next, define the Rating class and its instances:
Table createSubclass: "Rating" ; Rating createInstance: "A" . setNameTo: "Excellent" ; Rating createInstance: "B" . setNameTo: "Good" ; Rating createInstance: "C" . setNameTo: "Fair" ; Rating createInstance: "D" . setNameTo: "Poor" ; Rating createInstance: "F" . setNameTo: "Failure" ;Finally, define the Status class and its instances:
Table createSubclass: "Status" ; Status createInstance: "A" . setNameTo: "Approved" ; Status createInstance: "R" . setNameTo: "Restricted" ; Status createInstance: "D" . setNameTo: "Discard" ;The A rating instance is accessed using:
Named Rating AThe A status instance is accessed using:
Named Status ANotice that although both the classes have instances named A these objects have distinct access paths.
To display all the instances in the Rating class including the default use:
Rating instanceList do: [ code print: 3 ; name printNL ] ;To display all the instances in the Status class excluding the default use:
Status masterList do: [ code print: 3 ; name printNL ] ;To display all the subclasses of the Table class use:
Table showInheritanceTo create a status property at the Company class and assign the approved status for GM use:
Company defineFixedProperty: 'status' ; Named Company GM :status <- Named Status A ;
| Copyright © 1997 Innovative Systems Techniques, Inc. | Updated June 19, 1997 |


Byetta and Bydureon are injectable drugs used to control blood sugar in people with Type 2 diabetes. They are linked to pancreatitis and pancreatic cancer, which have led to lawsuits.
Byetta (exenatide injection) is a twice-daily injectable drug approved by the U.S. Food and Drug Administration (FDA) for the treatment of Type 2 diabetes. The drug is designed to work with diet and exercise to control blood sugar. Manufactured by San Diego-based Amylin Pharmaceuticals, Byetta was approved by the FDA in 2005. In January 2012, the FDA approved a once-weekly exenatide injection called Bydureon – a longer-lasting version of Byetta. The FDA previously rejected it twice in 2010 because of evidence that the drug may cause heart rhythm abnormalities. In 2012, Bristol-Myers Squibb purchased Amylin for about $5.3 billion.
Exenatide is part of a class of drugs called glucagonlike peptide-1 (GLP-1) receptor agonists. These drugs mimic the effects of the body’s natural GLP-1, a hormone that reacts to high blood sugar by increasing insulin production. The synthetic hormone is derived from the saliva of a poisonous lizard called the Gila monster that is found in the southwestern United States.
Byetta generates the majority of Amylin’s yearly sales and in 2011 accounted for $517.7 million (83 percent) of the company’s $621.6 million net sales. Industry analysts expect sales of Bydureon to exceed $1 billion. The drug also costs more than many other oral diabetes medications currently on the market such as Actos (pioglitazone) or Januvia (sitagliptin). Byetta and Bydureon typically cost close to $500 a month, but actual prices may vary depending on the patient’s insurance coverage.
Doctors typically prescribe Byetta or Bydureon to overweight people with Type 2 diabetes, because the drug also seems to help with weight loss. People taking only Byetta lost an average of six pounds over 24 weeks, although the drug is not marketed as a weight-loss product.
In three 30-week clinical trials, people who took Byetta had an average reduction of 1 percent in A1C – the amount of glucose concentrated in a person’s blood – after three years of treatment, and the drug was effective in managing Type 2 diabetes. However, the drug once proclaimed as an incredible advancement in diabetes medication is now linked to disturbing side effects like pancreatitis, pancreatic cancer and thyroid cancer. People who took Byetta and their families have since pursued litigation against Amylin and Eli Lilly, who helped market the drug.
How Does Byetta Work?
Byetta is packaged in a glass cartridge inside a pen-injector. A doctor or physician’s assistant instructs patients how to inject it. The active ingredient, exenatide, is mixed in water along with an antimicrobial agent.
Two prefilled pens come with either a 5 mcg or 10 mcg dose, and each prefilled pen delivers a 30-day supply of Byetta that should be administered twice a day. Byetta can be used alone or with metformin, a sulfonylurea (Amaryl, Glocotrol), a thiazolidinedione (Actos, Avandia), or Lantus (insulin glargine). It can also be used with DPP-4 inhibitors like Januvia. It should not be used in people with Type 1 diabetes, however.
In healthy people, eating causes a rise in blood glucose and stimulates an insulin response from the pancreas. People with diabetes do not produce enough insulin to react to an increase in blood sugar. Byetta helps insulin production to regulate sugar levels.
| Byetta controls blood glucose in four ways: | |
|---|---|
| Glucose-dependent insulin secretion. Cells in the pancreas that respond to glucose are called beta cells. In studies, Byetta increased the responsiveness of beta cells and allowed for more insulin production. Once sugar levels stabilize, the insulin production stops. | |
| First-phase insulin response. The “first-phase insulin response” usually occurs within 10 minutes of raised blood glucose levels. In people with diabetes, defects in the beta cells prevent the insulin response. Byetta significantly increased the first-phase insulin response in patients taking the drug. | |
| Glucagon secretion. Byetta stops excess glucagon secretion during times of hyperglycemia. Glucagon is a hormone released by the pancreas that increases blood sugar and it is the opposite of insulin. | |
| Gastric emptying. By slowing gastric emptying after meals, Byetta slows the rate at which glucose enters the blood. |
Clinical Studies
Scientists conducted three 30-week, controlled clinical trials to test the effectiveness of Byetta in controlling blood glucose. In the trials, 1,446 patients whose blood sugar was poorly controlled while taking metformin alone, a sulfonylurea alone or a combination of the two were given Byetta or a placebo.
Results revealed that patients taking Byetta had reductions in HbA1C and reduced blood glucose, both during fasting and after eating. In addition, study participants in the Byetta group also lost weight.
Byetta for Weight Loss
Another way Byetta may help people with diabetes is by stimulating weight loss. Many people with Type 2 diabetes are overweight or obese. This increases their risk of diabetes-related complications such as heart disease or stroke. Along with lifestyle changes, Byetta is shown to help people with diabetes lose weight and better control their blood sugar levels.
In a May 2010 study published in The American Journal of Medicine, researchers recruited 194 overweight or obese patients with Type 2 diabetes. They administered Byetta to one group and a placebo to the other. People who took Byetta lost more weight than those who did not. According to Amylin’s research, the average amount of weight loss for people who took the drug with another diabetes medication was four pounds over 30 weeks. Those who took Byetta alone lost six pounds over 24 weeks.
Byetta is so effective at inducing weight loss that some people without diabetes are using it as a diet drug. Amylin and Eli Lilly say they do not encourage the “off-label” use – use not approved by the FDA – of the drug, but they funded studies of Byetta for weight loss in people with normal blood sugar. These studies showed Byetta is effective at promoting weight loss even in healthy individuals. However, the long-term use of exenatide has not been tested in people without diabetes, and doctors caution that it may be dangerous.
Serious Side Effects
There are reports of several side effects and complications related to Byetta and Bydureon, including some that require hospitalization and may be fatal. The most serious side effects are linked to damage of the pancreas, kidneys and thyroid.
Pancreatitis
Pancreatitis is a medical condition that causes the pancreas to become inflamed and swell. Severe cases can lead to hospitalization and even death – with a mortality rate of 10 to 30 percent. According to data gathered from the FDA and compiled by the Institute of Safe Medication Practices, there were more than 400 reports of acute and chronic pancreatitis in 2011 resulting from exenatide.
Data also reveal that pancreatitis is 49 times more likely to be reported in Byetta and Bydureon users than in patients who take other drugs in the same class.
In a 2011 study published in Gastroenterology, researchers found that people who took Byetta were six times more likely to report pancreatitis. The results were consistent with animal studies that also revealed an increased risk.
As a result of the increasing reports of pancreatitis in users of Byetta, the FDA required Amylin to conduct more postmarket studies on the drug in 2009, but results have yet to be reported.
| Symptoms of pancreatitis include: | |
|---|---|
| Severe pain in the upper left side or middle of the abdomen that worsens after eating and drinking, especially if food is high in fat | |
| Pain becomes more severe and lasts for several days and may radiate to the back | |
| Fever | |
| Nausea | |
| Vomiting | |
| Sweating | |
| Swollen abdomen | |
| Yellowing of the skin |
This is a serious condition, and anyone who exhibits signs of pancreatitis should seek medical attention immediately. Pancreatitis can also lead to pancreatic cancer.
Pancreatic Cancer
Pancreatic cancer is a cancer with a low survival rate because more than 80 percent of cases are diagnosed after tumors spread to other organs. Long-term survival rates are exceedingly low: about 95 percent of people succumb to the disease within five years.
Exenatide is linked to an increase in pancreatic cancer. The same 2011 study published in Gastroenterology revealed that there was an almost three-fold increase in reported cases of pancreatic cancer in users of Byetta.
Thyroid Cancer
The thyroid is a gland that is located in the neck, just above the collarbone. It makes hormones that are necessary for regulating the body’s metabolism, including how fast calories burn and how fast the heart beats. Depending on a number of factors, the thyroid may produce too many or too few hormones, causing weight gain or loss, rapid heartbeat and sensitivity to cold or hot temperatures. The thyroid can also develop cancer.
Clinical study results linked Byetta to thyroid cancer. One study conducted by Michael Elashoff and colleagues at the University of California, Los Angeles and published in Gastroenterology revealed that people who used Byetta were almost five times more likely to develop thyroid cancer than those who took another diabetes medication, Avandia (rosiglitazone).
A review conducted by doctors at National Taiwan University College of Medicine and published in Experimental Diabetes Research found that Byetta increased the number of cancerous cells in the thyroids of mice, and the FDA Adverse Event Reporting System database supported an increased risk of thyroid cancer associated with the drug.
Other Side Effects
In 2009, the FDA required Amylin to add the risk of kidney damage and failure to Byetta’s warning label. Exenatide was also linked to cardiovascular events such as heart attack. A 2008 study was done in Canada for risk of cardiovascular events, which Amylin attempted to withhold from the FDA. This delayed the release of Bydureon for several years, until the drug was approved in 2012 with the condition that Amylin conduct postmarket studies.
| The most common side effects reported with Byetta and Bydureon are nausea and stomach upset. Others include: | |
|---|---|
| Vomiting | Diarrhea |
| Jittery feelings | Dizziness |
| Headache | Weight loss |
| Weakness |
Byetta Lawsuits
Because of reports of hundreds of cases of pancreatitis and some fatal cases of pancreatic cancer, several individuals and families pursued litigation against Amylin and Eli Lilly for failure to warn, negligence and concealing the side effects of Byetta.
Among them is Guy Riley, surviving heir of Kathleen Riley. He filed a lawsuit on her behalf in the Superior Court of California in San Diego after she died from pancreatic cancer after taking Byetta with Januvia. His complaint alleges that Kathleen Riley suffered “severe physical, economic and emotional injuries” as a result of taking Byetta. The Rileys were unaware that Byetta carried a risk of pancreatic cancer because Amylin and Eli Lilly failed to warn the public.
If you or a loved one took Byetta or Bydureon and suffered from side effects like pancreatic cancer or pancreatitis, Drugwatch can help point you in the right direction. Our Patient Advocates are available any day of the week to answer your questions about Byetta, pancreatitis and pancreatic cancer. We can also provide information to help you decide if filing a lawsuit may be right for you.
Last modified: November 18, 2014


Reference - a library of standard reference drawings or details.
Select Reference from File Menu.
Reference drawings on the disk file have a *.Lb1 extension.
When you Get a reference drawing, the Origin is registered at the cursor position, and the entire drawing is in the Trap Buffer.


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| Edmund Charles Tarbell art links |
Edmund Charles Tarbell Works Online
Categorized & Annotated
Commercial Galleries: Galleries: We invite you to register and list your site (no charge for this service)
Original works by Edmund Charles Tarbell available for purchase at art galleries worldwide
Paintings in Museums and Public Art Galleries Worldwide:
Fine Arts Museums of San Francisco NEW!
Metropolitan Museum of Art, New York City NEW!
Metropolitan Museum of Art Timetable of Art History Across the Room, ca.1899
Museum of Fine Arts, Boston NEW! Paintings collection online
National Gallery of Art, Washington D.C. NEW! Includes a biography of the artist
Edmund Charles Tarbell at the Smithsonian American Art Museum, Washington D.C.
Arkell Museum at Canajoharie, New York NEW! Girl Crocheting, 1904
Cleveland Museum of Art, Ohio
Colby College Museum of Art, Maine Mother, Mercie, and Mary, 1918
Crocker Art Museum, California In the Station Waiting Room, Boston, ca.1915
Currier Gallery of Art, New Hampshire 4 works online
Harvard University Art Museums, Massachusetts Harvard have works in their collections, but unfortunately you have to type in the search yourself
Indianapolis Museum of Art, Indiana
Los Angeles County Museum of Art NEW!
Milwaukee Art Museum, Wisconsin Three Sisters - A Study in June Sunlight
Edmund Charles Tarbell at the National Portrait Gallery, Washington D.C. Portrait of Woodrow Wilson
Palmer Museum of Art at Pennsylvania State University Girl Reading, 1909
Philadelphia Museum of Art NEW!
Terra Foundation for American Art, Chicago NEW! In the Orchard, 1891
The Huntington Library, California
Worcester Art Museum, Massachusetts Arrangement in Pink and Gray (Afternoon Tea), 1894
National Art Databases and Museum Inventories:
Smithsonian American Art Museum National Art Inventories List of works nationwide from two sources: the Inventory of American Paintings Executed before 1914 and the Inventory of American Sculpture (only a few percent of listings have an accompanying image)
Art Market: (e.g. records of past sales at auction; sites providing examples of the artist's signature)
Sotheby's Sold Lot Archive (database goes as far back as 1998; images where permitted by copyright go about as far back as 2001)
Pictures from Image Archives:
Edmund Charles Tarbell at The Art Renewal Center
Edmund Charles Tarbell at The Athenaeum 78 works online
Bert Christensen's Cyberspace Home
Edmund Charles Tarbell at CGFA
The History Project at U.C. Davis
University of Michigan Fine Art Slides The Symbol of Spring
USC Annenberg School for Communication
Additional Image Search Tools:
(SafeSearch set to "strict"; go to Advanced Search (Flickr/Google) or Preferences (Bing) to change)
Miscellaneous Sites:
NewportArt Mother, Mercie and Mary
The Running Horse Schooling the Horses, 1903
Articles and Reference Sites:
Union List of Artist Names (Getty Museum) Reference sheet with basic information about the artist and pointers to other references.
Wikipedia, the "Open Content" Encyclopedia Biographical info
Antiques & Fine Art Magazine Portrait of a Woman in White, 1890s
Artnet Magazine Girl with Horse, 1892
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I just got an ounce of dried Leonotis leonurus.
Some net research has told me that the active principle is leonurine, the same alkaloid that is mildly psychoactive in the similar Leonurus sibiricus (marihuanilla). Addictive on the order of nicotine with regular usage, according to Gottleib. Alleged to produce a MJ-type euphoria. [Also, the plant contains marrubiin, a bitter principle good at increasing secretion flows (such as bile), and has expectorant properties. For headache, cough, etc, prepare dried foliage as a tea infusion. I'm unsure of the dosage.]
Smoking this plant produced MJ-like effects, only milder, and a somewhat larger amount must be taken. I just had about two joints worth, and got a buzz roughly equivalent to smoking half a bowl of average commercial grass (or sharing a pinner with a pal). Beware of the small thorns!!
I have read that the foliage is used by the Zulu as a medicinal tea, and that only the flowers + flowering tops of the plant are smoked. I'm going to try alcohol extraction to make 'wild dagga hash oil' and see how that works.
--later--
Tried smoking a joint or so of the plant with no effect. I read that the African specimens are stronger, so I'll definitely make the extraction.
--later--
Well, I made a 5x extract of the plant matter using pharmacy-grade 91% isopropyl. If I wasn't enthralled with the smoke's flavor before, it sure revolts me now. Extremely 'planty' in flavor; made me gag and nearly vomit. Let's put this down for now.
--later--
Tastes just as bad as previously. This is not even worthwhile as a drug for a desperation high. Ever since that first attempt, I have gotten no effects from this plant. I'll chalk the first experience up to the placebo effect.
Final opinion: Don't waste your time with this 'drug'.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Diego Maradona hits back after daughter claims legend is 'dying from the inside'
Gianinna Maradona sparked fear over the legendary star's health with a post on social media earlier in the week
by Ned Keating, Gerard CouzensDiego Maradona has denied his daughter's claims that the Argentina legend is 'dying from the inside', before insisting he will leave his children with nothing.
Fears were sparked about Maradona's health earlier this week after daughter Gianinna, voiced her concerns about her father's wellbeing.
The ex-wife of Manchester City forward Sergio Aguero took to social media to speak about Diego, posting: "He is not dying because his body has decided so, he is getting killed from the inside and does not realise it.
"I do not believe in the parameters of normal, but that is far from the reality of what he deserves. Pray for him. Please! Thank you!"
That led to immediate concerns being raised about the health of the former Barcelona attacker, who is currently in charge of Gimnasia de la Plata in his homeland.
However, the 59-year-old has hit back in an astonishing video in which he refutes his Gianinna's claims, before saying he will donate everything he has earned when he does die.
In the clip that was posted on his official YouTube account, Maradona said: "I want to tell you that I’m not dying at all, that I sleep peacefully because I’m working.
"I don't know what she meant to say or what she had interpreted.
"What I do know is that as you get older, people worry more about what you're going to leave behind than what you're doing.
"I tell you that I'm not going to leave anything behind, that I'm going to donate it.
"I'm not going to give away everything I earned by running during my life, I'm going to donate it."
Maradona returned to his homeland earlier this year to take up the manager's job at Gimnasia, agreeing a deal until the end of the season with the Primera Division outfit.


📅 (2023-05-27 14:57:17 UTC)
This result is black-listed as it is considered as too hard to reproduce / to solve. If you find a way to fix this package, please make a pull-request to github.com/coq/opam-coq-archive. The list of black-listed packages is in black_list.rb.
# Packages matching: installed # Name # Installed # Synopsis base-bigarray base base-num base Num library distributed with the OCaml compiler base-threads base base-unix base camlp5 7.14 Preprocessor-pretty-printer of OCaml conf-findutils 1 Virtual package relying on findutils conf-perl 2 Virtual package relying on perl coq 8.6 Formal proof management system num 0 The Num library for arbitrary-precision integer and rational arithmetic ocaml 4.03.0 The OCaml compiler (virtual package) ocaml-base-compiler 4.03.0 Official 4.03.0 release ocaml-config 1 OCaml Switch Configuration ocamlfind 1.9.6 A library manager for OCaml # opam file: opam-version: "2.0" maintainer: "Matej Košík<EMAIL_ADDRESS>homepage: "http://compcert.inria.fr/" dev-repo: "git+https://github.com/AbsInt/CompCert.git" bug-reports: "https://github.com/AbsInt/CompCert/issues" license: "INRIA Non-Commercial License Agreement" build: [ [ "./configure" "ia32-linux" {os = "linux"} "ia32-macosx" {os = "macos"} "ia32-cygwin" {os = "cygwin"} "-bindir" "%{bin}%" "-libdir" "%{lib}%/compcert" "-clightgen" ] [make "-j%{jobs}%"] ] install: [ [make "install"] ] remove: [ ["rm" "%{bin}%/ccomp"] ["rm" "%{bin}%/clightgen"] ["rm" "-R" "%{lib}%/compcert"] ["rm" "%{share}%/compcert.ini"] ["rm" "%{share}%/man/man1/ccomp.1"] ["sh" "-c" "rmdir -p %{share}%/man/man1 || true"] ] depends: [ "ocaml" "coq" {>= "8.6" & < "8.7~"} "menhir" {>= "20160303" & < "20180530"} ] synopsis: "The CompCert C compiler" authors: "Xavier Leroy<EMAIL_ADDRESS>url { src: "https://github.com/AbsInt/CompCert/archive/v3.1.tar.gz" checksum: "md5=e7efc9e44a52ffa8340789ae1bac6624" }
true
Dry install with the current Coq version:
opam install -y --show-action coq-compcert.3.1.0 coq.8.6
Dry install without Coq/switch base, to test if the problem was incompatibility with the current Coq/OCaml version:
true
opam list; echo; ulimit -Sv 4000000; timeout 4h opam install -y --deps-only coq-compcert.3.1.0 coq.8.6
opam list; echo; ulimit -Sv 16000000; timeout 8h opam install -y -v coq-compcert.3.1.0 coq.8.6
# Packages matching: installed # Name # Installed # Synopsis base-bigarray base base-num base Num library distributed with the OCaml compiler base-threads base base-unix base camlp5 7.14 Preprocessor-pretty-printer of OCaml conf-findutils 1 Virtual package relying on findutils conf-perl 2 Virtual package relying on perl coq 8.6 Formal proof management system menhir 20180528 LR(1) parser generator num 0 The Num library for arbitrary-precision integer and rational arithmetic ocaml 4.03.0 The OCaml compiler (virtual package) ocaml-base-compiler 4.03.0 Official 4.03.0 release ocaml-config 1 OCaml Switch Configuration ocamlbuild 0.14.2 OCamlbuild is a build system with builtin rules to easily build most OCaml projects ocamlfind 1.9.6 A library manager for OCaml [NOTE] Package coq is already installed (current version is 8.6). The following actions will be performed: - install coq-compcert 3.1.0 <><> Gathering sources ><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Processing 1/1: [coq-compcert.3.1.0: http] [coq-compcert.3.1.0] downloaded from https://github.com/AbsInt/CompCert/archive/v3.1.tar.gz Processing 1/1: <><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><> Processing 1/2: [coq-compcert: ./configure ia32-linux] + /home/bench/.opam/opam-init/hooks/sandbox.sh "build" "./configure" "ia32-linux" "-bindir" "/home/bench/.opam/ocaml-base-compiler.4.03.0/bin" "-libdir" "/home/bench/.opam/ocaml-base-compiler.4.03.0/lib/compcert" "-clightgen" (CWD=/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0) - Testing assembler support for CFI directives... - yes - Testing linker support for '-no-pie' option... - no - Testing Coq... version 8.6 -- good! - Testing OCaml... version 4.03.0 -- good! - WARNING: some Intel processors of the Skylake and Kaby Lake generations - have a hardware bug that can be triggered by this version of OCaml. - To avoid this risk, it is recommended to use OCaml 4.05. - Testing OCaml .opt compilers... yes - Testing Menhir... version 20180528 -- good! - Testing GNU make... version 4.2.1 (command 'make') -- good! - - CompCert configuration: - Target architecture........... x86 - Hardware model................ 32sse2 - Application binary interface.. standard - Endianness.................... little - Composite passing conventions. arguments: ints, return values: ref - OS and development env........ linux - C compiler.................... gcc -m32 - C preprocessor................ gcc - Assembler..................... gcc - Assembler supports CFI........ true - Assembler for runtime lib..... gcc -m32 -c - Linker........................ gcc - Linker needs '-no-pie'........ false - Math library.................. -lm - Binaries installed in......... /home/bench/.opam/ocaml-base-compiler.4.03.0/bin - Runtime library provided...... true - Library files installed in.... /home/bench/.opam/ocaml-base-compiler.4.03.0/lib/compcert - Standard headers provided..... true - Standard headers installed in. /home/bench/.opam/ocaml-base-compiler.4.03.0/lib/compcert/include - Build command to use.......... make - Processing 1/2: [coq-compcert: make] + /home/bench/.opam/opam-init/hooks/sandbox.sh "build" "make" "-j4" (CWD=/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0) - make[1]: Entering directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - ocamlopt -o tools/ndfun str.cmxa tools/ndfun.ml - menhir --coq cparser/Parser.vy - Preprocessing x86/ConstpropOp.vp - Preprocessing x86/SelectOp.vp - Preprocessing x86/SelectLong.vp - Preprocessing backend/SelectDiv.vp - Preprocessing backend/SplitLong.vp - Analyzing Coq dependencies - make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - make proof - make[1]: Entering directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - COQC lib/Axioms.v - COQC lib/Coqlib.v - COQC flocq/Core/Fcore_Zaux.v - COQC driver/Compopts.v - COQC cparser/validator/Alphabet.v - COQC cparser/validator/Tuples.v - COQC cparser/Cabs.v - COQC lib/Wfsimpl.v - COQC cparser/validator/Grammar.v - COQC flocq/Core/Fcore_Raux.v - COQC flocq/Core/Fcore_digits.v - COQC lib/Intv.v - COQC lib/Maps.v - COQC lib/Iteration.v - COQC lib/Parmov.v - COQC lib/UnionFind.v - COQC lib/FSetAVLplus.v - COQC lib/IntvSets.v - COQC lib/Decidableplus.v - COQC lib/BoolEqual.v - COQC common/Errors.v - COQC cparser/validator/Automaton.v - COQC flocq/Core/Fcore_defs.v - COQC lib/Lattice.v - COQC lib/Postorder.v - COQC common/Unityping.v - COQC cparser/validator/Interpreter.v - COQC cparser/validator/Validator_complete.v - COQC cparser/validator/Validator_safe.v - COQC flocq/Core/Fcore_float_prop.v - COQC flocq/Core/Fcore_rnd.v - COQC flocq/Calc/Fcalc_digits.v - COQC flocq/Calc/Fcalc_bracket.v - COQC flocq/Calc/Fcalc_ops.v - COQC cparser/validator/Interpreter_correct.v - COQC cparser/validator/Interpreter_safe.v - COQC flocq/Core/Fcore_generic_fmt.v - COQC cparser/validator/Interpreter_complete.v - COQC flocq/Calc/Fcalc_div.v - COQC flocq/Calc/Fcalc_sqrt.v - COQC flocq/Core/Fcore_ulp.v - COQC flocq/Prop/Fprop_Sterbenz.v - COQC cparser/validator/Main.v - COQC flocq/Core/Fcore_rnd_ne.v - COQC flocq/Core/Fcore_FIX.v - COQC cparser/Parser.v - COQC flocq/Core/Fcore_FLX.v - COQC flocq/Core/Fcore_FLT.v - COQC flocq/Core/Fcore_FTZ.v - COQC flocq/Core/Fcore.v - COQC flocq/Prop/Fprop_plus_error.v - COQC flocq/Appli/Fappli_double_round.v - COQC flocq/Calc/Fcalc_round.v - COQC flocq/Prop/Fprop_relative.v - COQC flocq/Appli/Fappli_rnd_odd.v - COQC flocq/Prop/Fprop_mult_error.v - COQC flocq/Appli/Fappli_IEEE.v - COQC flocq/Prop/Fprop_div_sqrt_error.v - COQC flocq/Appli/Fappli_IEEE_bits.v - COQC lib/Fappli_IEEE_extra.v - COQC x86_32/Archi.v - COQC lib/Integers.v - COQC lib/Ordered.v - COQC lib/Floats.v - COQC lib/Heaps.v - COQC common/AST.v - COQC common/Linking.v - COQC common/Values.v - COQC cfrontend/Ctypes.v - COQC common/Memdata.v - COQC common/Switch.v - COQC backend/Registers.v - COQC common/Memtype.v - COQC common/Memory.v - COQC common/Globalenvs.v - COQC cfrontend/Cop.v - COQC common/Events.v - COQC cfrontend/Csyntax.v - COQC cfrontend/Initializers.v - COQC common/Smallstep.v - COQC common/Separation.v - COQC x86/Op.v - COQC common/Behaviors.v - COQC backend/Cminor.v - COQC cfrontend/Csem.v - COQC cfrontend/Clight.v - COQC cfrontend/Csharpminor.v - COQC common/Determinism.v - COQC backend/Kildall.v - COQC cfrontend/Ctyping.v - COQC cfrontend/Cstrategy.v - COQC cfrontend/Initializersproof.v - COQC cfrontend/SimplExpr.v - COQC cfrontend/ClightBigstep.v - COQC cfrontend/SimplLocals.v - COQC cfrontend/Cshmgen.v - COQC cfrontend/Cminorgen.v - COQC cfrontend/SimplExprspec.v - COQC cfrontend/SimplLocalsproof.v - COQC cfrontend/Cshmgenproof.v - COQC cfrontend/Cminorgenproof.v - COQC cfrontend/Cexec.v - COQC cfrontend/SimplExprproof.v - COQC backend/CminorSel.v - COQC x86/Machregs.v - COQC backend/RTL.v - COQC x86/SelectOp.v - COQC backend/Locations.v - COQC backend/RTLgen.v - COQC backend/Inlining.v - COQC backend/Renumber.v - COQC backend/Liveness.v - COQC backend/ValueDomain.v - COQC backend/CSEdomain.v - COQC backend/Unusedglob.v - COQC backend/RTLgenspec.v - COQC x86/Conventions1.v - COQC backend/Inliningspec.v - COQC backend/Renumberproof.v - COQC x86/CombineOp.v - COQC backend/Unusedglobproof.v - COQC backend/SplitLong.v - COQC x86/SelectOpproof.v - COQC backend/RTLgenproof.v - COQC backend/Conventions.v - COQC x86/CombineOpproof.v - COQC backend/LTL.v - COQC x86/SelectLong.v - COQC backend/SplitLongproof.v - COQC backend/Tailcall.v - COQC backend/Inliningproof.v - COQC backend/RTLtyping.v - COQC backend/Tunneling.v - COQC backend/Linear.v - COQC backend/SelectDiv.v - COQC backend/Tailcallproof.v - COQC x86/ValueAOp.v - COQC x86/ConstpropOp.v - COQC backend/NeedDomain.v - COQC backend/Allocation.v - COQC backend/Tunnelingproof.v - COQC backend/Lineartyping.v - COQC backend/Linearize.v - COQC backend/CleanupLabels.v - COQC backend/Debugvar.v - COQC backend/Bounds.v - COQC backend/Selection.v - COQC backend/ValueAnalysis.v - COQC x86/ConstpropOpproof.v - COQC x86/NeedOp.v - COQC backend/Allocproof.v - COQC backend/Linearizeproof.v - COQC backend/CleanupLabelsproof.v - COQC backend/Debugvarproof.v - COQC x86/Stacklayout.v - COQC x86/SelectLongproof.v - COQC backend/Constprop.v - COQC backend/CSE.v - COQC backend/Deadcode.v - COQC backend/Mach.v - COQC x86/Asm.v - COQC backend/SelectDivproof.v - COQC backend/Constpropproof.v - COQC backend/CSEproof.v - COQC backend/Deadcodeproof.v - COQC backend/Stacking.v - COQC x86/Asmgen.v - COQC backend/Selectionproof.v - COQC backend/Stackingproof.v - COQC backend/Asmgenproof0.v - COQC x86/Asmgenproof1.v - COQC x86/Asmgenproof.v - COQC driver/Compiler.v - COQC driver/Complements.v - make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - make extraction - make[1]: Entering directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - rm -f extraction/*.ml extraction/*.mli - "coqtop" -R lib compcert.lib -R common compcert.common -R x86_32 compcert.x86_32 -R x86 compcert.x86 -R backend compcert.backend -R cfrontend compcert.cfrontend -R driver compcert.driver -R flocq compcert.flocq -R exportclight compcert.exportclight -R cparser compcert.cparser -batch -load-vernac-source extraction/extraction.v - Welcome to Coq 8.6 (May 2023) - File "/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0/extraction/extraction.v", lin [...] truncated tion/Errors.mli - OCAMLC extraction/Equalities.mli - OCAMLC extraction/Specif.mli - OCAMLC extraction/Memtype.mli - OCAMLC extraction/PeanoNat.mli - OCAMLC cparser/Machine.mli - OCAMLC cparser/C.mli - OCAMLC driver/Commandline.mli - OCAMLC extraction/Ring.mli - OCAMLC lib/Printlines.mli - OCAMLC driver/Driveraux.mli - OCAMLC driver/Linker.mli - OCAMLOPT lib/Json.ml - OCAMLC extraction/DecidableClass.mli - OCAMLC cparser/Unblock.mli - OCAMLC cparser/StructReturn.mli - OCAMLC cparser/Rename.mli - OCAMLC extraction/Tuples.mli - OCAMLC extraction/Streams.mli - OCAMLC extraction/Int31.mli - OCAMLC extraction/Cabs.mli - OCAMLOPT cparser/pre_parser_aux.ml - OCAMLC cparser/pre_parser_aux.ml - OCAMLOPT cparser/pre_parser_messages.ml - OCAMLC cparser/Cleanup.mli - OCAMLC cparser/Checks.mli - OCAMLC cparser/Elab.mli - OCAMLC cparser/Bitfields.mli - OCAMLC cparser/Parse.mli - OCAMLC extraction/UnionFind.mli - OCAMLC extraction/Compopts.mli - OCAMLC extraction/Compare_dec.mli - OCAMLC extraction/Mergesort.mli - OCAMLC lib/Tokenize.mli - OCAMLC driver/Assembler.mli - OCAMLOPT lib/Readconfig.ml - OCAMLOPT extraction/Datatypes.ml - OCAMLC extraction/Nat.mli - OCAMLOPT extraction/BinNums.ml - OCAMLC extraction/BinPosDef.mli - OCAMLC extraction/List0.mli - OCAMLOPT extraction/Bool.ml - OCAMLOPT lib/Responsefile.ml - OCAMLOPT extraction/Orders.ml - OCAMLC extraction/OrdersTac.mli - OCAMLOPT extraction/EquivDec.ml - OCAMLOPT extraction/BoolEqual.ml - OCAMLOPT extraction/String0.ml - OCAMLOPT extraction/Errors.ml - OCAMLOPT extraction/Equalities.ml - OCAMLC extraction/DecidableType.mli - OCAMLOPT extraction/Specif.ml - OCAMLOPT extraction/Memtype.ml - OCAMLOPT extraction/PeanoNat.ml - OCAMLOPT cparser/Machine.ml - OCAMLC cparser/Env.mli - OCAMLC cparser/Cprint.mli - OCAMLOPT driver/Commandline.ml - OCAMLC cparser/Cerrors.mli - OCAMLC cparser/Cutil.mli - OCAMLC cparser/Ceval.mli - OCAMLC cparser/Builtins.mli - OCAMLOPT extraction/Ring.ml - OCAMLOPT lib/Printlines.ml - OCAMLOPT extraction/DecidableClass.ml - OCAMLC cparser/Transform.mli - OCAMLOPT extraction/Tuples.ml - OCAMLOPT extraction/Streams.ml - OCAMLOPT extraction/Cabs.ml - OCAMLC cparser/pre_parser.mli - OCAMLC cparser/Cflow.mli - OCAMLOPT extraction/UnionFind.ml - OCAMLOPT extraction/Compare_dec.ml - OCAMLOPT extraction/Mergesort.ml - OCAMLOPT lib/Tokenize.ml - OCAMLOPT driver/Configuration.ml - OCAMLOPT extraction/Nat.ml - OCAMLOPT extraction/BinPosDef.ml - OCAMLC extraction/BinPos.mli - OCAMLOPT extraction/List0.ml - OCAMLOPT extraction/OrdersTac.ml - OCAMLC extraction/OrderedType.mli - OCAMLC extraction/OrdersFacts.mli - OCAMLOPT extraction/DecidableType.ml - OCAMLC extraction/FSetInterface.mli - OCAMLOPT cparser/Env.ml - OCAMLOPT cparser/Cprint.ml - OCAMLOPT cparser/Cerrors.ml - OCAMLC extraction/Alphabet.mli - OCAMLC extraction/FMapList.mli - OCAMLOPT cparser/pre_parser.ml - OCAMLC cparser/ErrorReports.mli - OCAMLOPT cparser/Cabshelper.ml - OCAMLOPT driver/Clflags.ml - OCAMLOPT extraction/BinPos.ml - OCAMLC extraction/Zpower.mli - OCAMLC extraction/BinNat.mli - OCAMLC extraction/Fcore_digits.mli - OCAMLOPT extraction/OrderedType.ml - OCAMLC extraction/OrdersAlt.mli - OCAMLOPT extraction/OrdersFacts.ml - OCAMLC extraction/MSetInterface.mli - OCAMLOPT extraction/FSetInterface.ml - OCAMLOPT cparser/Cutil.ml - OCAMLOPT backend/Fileinfo.ml - OCAMLC extraction/Grammar.mli - OCAMLOPT extraction/FMapList.ml - OCAMLOPT extraction/Compopts.ml - OCAMLOPT extraction/Zpower.ml - OCAMLOPT extraction/BinNat.ml - OCAMLC extraction/BinInt.mli - OCAMLOPT extraction/Fcore_digits.ml - OCAMLC extraction/Fcore_Zaux.mli - OCAMLC extraction/Fcore_FLT.mli - OCAMLC extraction/Zbool.mli - OCAMLC extraction/Int0.mli - OCAMLOPT extraction/OrdersAlt.ml - OCAMLOPT extraction/MSetInterface.ml - OCAMLC extraction/MSetAVL.mli - OCAMLC extraction/Decidableplus.mli - OCAMLC extraction/Automaton.mli - OCAMLC extraction/FMapAVL.mli - OCAMLC extraction/ZArith_dec.mli - OCAMLOPT extraction/BinInt.ml - OCAMLC extraction/Fcalc_bracket.mli - OCAMLC extraction/FSetAVL.mli - OCAMLOPT cparser/Ceval.ml - OCAMLOPT cparser/Builtins.ml - OCAMLOPT extraction/Decidableplus.ml - OCAMLC extraction/Validator_safe.mli - OCAMLC extraction/Interpreter_correct.mli - OCAMLC extraction/Interpreter.mli - OCAMLOPT cparser/ErrorReports.ml - OCAMLOPT cparser/Checks.ml - OCAMLOPT cparser/Cflow.ml - OCAMLC extraction/FSetAVLplus.mli - OCAMLOPT extraction/ZArith_dec.ml - OCAMLC extraction/Coqlib.mli - OCAMLOPT extraction/Fcore_Zaux.ml - OCAMLOPT extraction/Fcore_FLT.ml - OCAMLOPT extraction/Zbool.ml - OCAMLC extraction/Fcalc_round.mli - OCAMLC extraction/Maps.mli - OCAMLOPT extraction/Int0.ml - OCAMLC extraction/Iteration.mli - OCAMLOPT cparser/Transform.ml - OCAMLOPT cparser/Rename.ml - OCAMLC extraction/Interpreter_safe.mli - OCAMLOPT extraction/FMapAVL.ml - OCAMLC extraction/Validator_complete.mli - OCAMLC extraction/Postorder.mli - OCAMLC extraction/IntvSets.mli - OCAMLOPT extraction/Coqlib.ml - OCAMLOPT extraction/Fcalc_bracket.ml - OCAMLC extraction/Fappli_IEEE.mli - OCAMLOPT extraction/Maps.ml - OCAMLC extraction/Ordered.mli - OCAMLOPT extraction/MSetAVL.ml - OCAMLC extraction/Registers.mli - OCAMLC extraction/Lattice.mli - OCAMLOPT extraction/Iteration.ml - OCAMLC extraction/Heaps.mli - OCAMLC extraction/Unityping.mli - OCAMLOPT cparser/StructReturn.ml - OCAMLC extraction/Interpreter_complete.mli - OCAMLOPT cparser/PackedStructs.ml - OCAMLOPT extraction/Postorder.ml - OCAMLOPT extraction/IntvSets.ml - OCAMLOPT extraction/FSetAVLplus.ml - OCAMLOPT extraction/Fcalc_round.ml - OCAMLC extraction/Archi.mli - OCAMLC extraction/Fappli_IEEE_extra.mli - OCAMLC extraction/Fappli_IEEE_bits.mli - OCAMLOPT extraction/Ordered.ml - OCAMLOPT extraction/FSetAVL.ml - OCAMLOPT extraction/Lattice.ml - OCAMLC extraction/Kildall.mli - OCAMLOPT extraction/Unityping.ml - OCAMLC extraction/Main.mli - OCAMLOPT extraction/Fappli_IEEE.ml - OCAMLC extraction/Integers.mli - OCAMLOPT extraction/Registers.ml - OCAMLOPT extraction/Heaps.ml - OCAMLC extraction/Switch.mli - OCAMLC extraction/Floats.mli - OCAMLOPT extraction/Archi.ml - OCAMLOPT extraction/Fappli_IEEE_extra.ml - OCAMLOPT extraction/Fappli_IEEE_bits.ml - OCAMLC extraction/AST.mli - OCAMLC lib/Camlcoq.ml - OCAMLOPT x86/CBuiltins.ml - OCAMLC extraction/Parser.mli - OCAMLOPT extraction/Integers.ml - OCAMLC extraction/Op.mli - OCAMLOPT extraction/Kildall.ml - OCAMLC extraction/Cminor.mli - OCAMLC extraction/Values.mli - OCAMLC extraction/Ctypes.mli - OCAMLC extraction/Memdata.mli - OCAMLC common/Sections.mli - OCAMLC extraction/Events.mli - OCAMLC debug/DebugTypes.mli - OCAMLC extraction/CminorSel.mli - OCAMLC extraction/ValueDomain.mli - OCAMLC extraction/Csharpminor.mli - OCAMLC extraction/CSEdomain.mli - OCAMLOPT extraction/Floats.ml - OCAMLC extraction/Machregs.mli - OCAMLC extraction/RTL.mli - OCAMLC extraction/Memory.mli - OCAMLC debug/DwarfTypes.mli - OCAMLC extraction/Asm.mli - OCAMLC extraction/Determinism.mli - OCAMLC extraction/Unusedglob.mli - OCAMLOPT extraction/Switch.ml - OCAMLC extraction/SelectOp.mli - OCAMLC extraction/Renumber.mli - OCAMLC extraction/RTLgen.mli - OCAMLC extraction/Inlining.mli - OCAMLC extraction/ValueAOp.mli - OCAMLC extraction/Liveness.mli - OCAMLC extraction/NeedDomain.mli - OCAMLC extraction/ConstpropOp.mli - OCAMLC extraction/Cminorgen.mli - OCAMLC extraction/CombineOp.mli - OCAMLC backend/Asmexpandaux.mli - OCAMLC x86/AsmToJSON.mli - OCAMLOPT lib/Camlcoq.ml - OCAMLOPT extraction/AST.ml - OCAMLC extraction/Locations.mli - OCAMLC x86/Machregsaux.mli - OCAMLC extraction/LTL.mli - OCAMLC extraction/Mach.mli - OCAMLC extraction/Cop.mli - OCAMLC debug/Debug.mli - File "debug/Debug.mli", line 1: - Error: Corrupted compiled interface - lib/Camlcoq.cmi - make[2]: *** [Makefile.extr:117: debug/Debug.cmi] Error 2 - make[2]: *** Waiting for unfinished jobs.... - make[2]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - make[1]: *** [Makefile:164: ccomp] Error 2 - make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' - make: *** [Makefile:139: all] Error 2 [ERROR] The compilation of coq-compcert failed at "/home/bench/.opam/opam-init/hooks/sandbox.sh build make -j4". #=== ERROR while compiling coq-compcert.3.1.0 =================================# # context 2.0.5 | linux/x86_64 | ocaml-base-compiler.4.03.0 | file:///home/bench/run/opam-coq-archive/released # path ~/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0 # command ~/.opam/opam-init/hooks/sandbox.sh build make -j4 # exit-code 2 # env-file ~/.opam/log/coq-compcert-27526-e4db2f.env # output-file ~/.opam/log/coq-compcert-27526-e4db2f.out ### output ### # [...] # OCAMLC extraction/Mach.mli # OCAMLC extraction/Cop.mli # OCAMLC debug/Debug.mli # File "debug/Debug.mli", line 1: # Error: Corrupted compiled interface # lib/Camlcoq.cmi # make[2]: *** [Makefile.extr:117: debug/Debug.cmi] Error 2 # make[2]: *** Waiting for unfinished jobs.... # make[2]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' # make[1]: *** [Makefile:164: ccomp] Error 2 # make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.03.0/.opam-switch/build/coq-compcert.3.1.0' # make: *** [Makefile:139: all] Error 2 <><> Error report <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> +- The following actions failed | - build coq-compcert 3.1.0 +- - No changes have been performed # Run eval $(opam env) to update the current shell environment 'opam install -y -v coq-compcert.3.1.0 coq.8.6' failed. The middle of the output is truncated (maximum 20000 characters)
No files were installed.
true


I was born and raised in California my entire life, and would love to one day move to Texas. I've held many different kinds of jobs, but for a very long time, I wanted to be a developer as I love to bring things to life online. My American dream is to one day build a successful company. After LambdaSchool I plan to continue learning more languages, such as c#, c++, e-commerce, and more.
I'm Hannah-Marie Wieser, and i'm a Biology & Chinese double major at GaTech. In my free time, I bake for friends, grow herbs/veggies, & read fantasy novels.
Hi, Im Nerissa Taylor and I live in Washington DC. I am looking to change careers by learning Web Development. My favorite programming language thus far is React. My favorite food is pizza.
I’m David, Backend Architect. I started to write code because I wanted to create my own startup. When I’m not learning about programming I’ll be studying random things like psychology.
Originally from north Idaho, Mason Karsevar is a University of Washington graduate with degrees in American Ethnic Studies and Earth and Space Sciences.
I’m Gurpreet Dhaliwal and I live in San Francisco, CA. I’m currently studying to become a Full Stack Web Developer in hopes of changing careers. My hobbies when I am not coding are playing basketball and hiking.
Front-end Architect: Born and raised in the city known for the Indianapolis 500, only fast cars eclipse my love of programming. Came to Lambda School to secure my dream job and make a better life for my children and myself.


Somewhere in another dimension not in this timeframe.
Total cost of package is $8,000 and is available September 2020 through February 2021.
Total cost of package is $20,000 and is available September 2020 through February 2021.
Total cost of package is $100,000 and is available September 2020 through February 2021.
Comments
"It's been a bad time in 2020"
"Been stuck in the Corona!"
"Been in the wrong world!"


Gully Boy producer Ritesh Sidhwani: Maybe we’ll get the Oscar this time
by PTIBy: PTI | Mumbai | Updated: December 9, 2019 3:03:34 pm
Producer Ritesh Sidhwani said his team is trying “very hard” to give Gully Boy all the necessary push.
Producer Ritesh Sidhwani says the Gully Boy team is leaving no stone unturned to make sure the Ranveer Singh-starrer drama gets in the forefront at the Oscars race.
The film has been selected as India’s official entry in the International Feature Film category at the 92nd Academy Awards.
Sidhwani said the team is trying “very hard” to give Gully Boy all the necessary push.
“We are trying to bring the film to the forefront. We are doing everything within our power to make sure we leave no stone unturned in bringing the film there. The first shortlist will come out on 15th December, so we’re waiting for that,” Sidhwani told reporters.
“After that 10 out of 93 films will advance (in the best international film category). So hopefully, we’ll make it to the shortlist then maybe we’ll get the award this time. Maybe, it’ll be the first time for India, so fingers crossed,” he added.
Sidhwani was speaking at the red carpet of Star Screen Awards.
Directed by Zoya Akhtar, Gully Boy, produced by Sidhwani and Farhan Akhtar, featured Ranveer Singh as an up-and-coming rapper who sets out to achieve his dream by rhyming about his life on the Mumbai streets.
For all the latest Entertainment News, download Indian Express App
- Tags:
- Gully Boy
- Ritesh Sidhwani


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Guidance
Guidance for sixth-form and further education colleges on preparing their annual report and financial statements (‘accounts’).
PDF , 113KB , 13 pages
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The college accounts direction sets out the Skills Funding Agency (SFA) and Education Funding Agency (EFA) financial reporting requirements for sixth-form and further education colleges.
The accounts direction outlines the requirements for each college to:
The accounts direction is updated by SFA/EFA each year.
Colleges must refer to the college accounts direction 2016 to 2017 when preparing their accounts for all periods commencing on or after 1 August 2016.
SFA/EFA plan to publish the finance record template for 2016 to 2017 in due course.
The college accounts direction 2015 to 2016 sets out the financial reporting requirements for colleges for the year ending 31 July 2016.
This included a requirement to submit a completed finance record for 2015 to 2016.
Colleges should refer to the following resources in preparing their accounts:
Accounts direction handbook which sets out detailed guidance on the preparation of college accounts, including a model set of accounts
Post-16 audit code of practice which sets out a common standard for the provision of assurance in relation to funding of post-16 providers
Published: 2 March 2017


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Check in with yourself, take a breather and reframe with gratitude. Check out a variety of mindfullness events around you, garuanteed to refresh your mind.
Stress less, sleep better, stay balanced!
How many hours can you dedicate to your soul a month?


Next: Defaulting of Initialization Arguments, Previous: Initialization Arguments, Up: Object Creation and Initialization
Initialization arguments are checked for validity in each of the four situations that use them. An initialization argument may be valid in one situation and not another. For example, the system-supplied primary method for make-instance defined for the class standard-class checks the validity of its initialization arguments and signals an error if an initialization argument is supplied that is not declared as valid in that situation.
There are two means for declaring initialization arguments valid.
Initialization arguments that fill slots are declared as valid by the :initarg slot option to defclass. The :initarg slot option is inherited from superclasses. Thus the set of valid initialization arguments that fill slots for a class is the union of the initialization arguments that fill slots declared as valid by that class and its superclasses. Initialization arguments that fill slots are valid in all four contexts.
Initialization arguments that supply arguments to methods are declared as valid by defining those methods. The keyword name of each keyword parameter specified in the method’s lambda list becomes an initialization argument for all classes for which the method is applicable.
The presence of &allow-other-keys in the lambda list of an applicable method disables validity checking of initialization arguments.
Thus method inheritance controls the set of valid initialization arguments that supply arguments to methods. The generic functions for which method definitions serve to declare initialization arguments valid are as follows:
Making an instance of a class: allocate-instance, initialize-instance, and shared-initialize. Initialization arguments declared as valid by these methods are valid when making an instance of a class.
Re-initializing an instance: reinitialize-instance and shared-initialize. Initialization arguments declared as valid by these methods are valid when re-initializing an instance.
Updating an instance to conform to a redefined class: update-instance-for-redefined-class and shared-initialize. Initialization arguments declared as valid by these methods are valid when updating an instance to conform to a redefined class.
Updating an instance to conform to the definition of a different class: update-instance-for-different-class and shared-initialize. Initialization arguments declared as valid by these methods are valid when updating an instance to conform to the definition of a different class.
The set of valid initialization arguments for a class is the set of valid initialization arguments that either fill slots or supply arguments to methods, along with the predefined initialization argument :allow-other-keys. The default value for :allow-other-keys is nil.
Validity checking of initialization arguments is disabled if the value of the initialization argument :allow-other-keys is true.
Next: Defaulting of Initialization Arguments, Previous: Initialization Arguments, Up: Object Creation and Initialization


Ocala events
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Beyers Costin - Client Alerts
New Law Automatically Extends Subdivision Maps, Again . Easements by necessity are probably not available when the subject properties' . a real estate purchase contract to have violated the Subdivision Map Act because it allowed the .
http://www.beyerscostin.com/alerts/18.20.160. Final or parcel mapEasements and rights-of-way.
(a) The final or parcel map shall show the side lines of all easements to which the . in the manner required by the Subdivision Map Act, certificates executed, .
http://qcode.us/codes/sunnyvale/view.php?topic=18-18_20-18_20_160&frames=on
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5.0 EASEMENTS - TLMA
Section 66436 of the Subdivision Map Act states the circumstances that constitute Signature. Omissions are as follows: ? Rights-of-way, easements or other .
http://www.tlma.co.riverside.ca.us/trans/documents/5-Easements%20April%202010.pdf -
Chapter 16.16 DEDICATIONS AND RESERVATIONS
Pursuant to Section 66447 of the Subdivision Map Act, all dedications or offers of . C. Easements for public utilities or other public agencies may be dedicated to .
http://www.codepublishing.com/ca/poway/html/poway16/poway1616.html -
(Subdivision Map Act) - California Department of Transportation ...
whole or in part, to the project owner's or developer's relinquishment of easement rights to construct the improvements. Subdivision Map Act & Division 3. 7 .
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Exhibit A - Final Parcel Map No. 70768 - City of Calabasas
THE SANITARY SEWER EASEMENT AND THE EASEMENT FOR INGRESS AND . WITH MAPPING PROVISIONS OF THE SUBDIVISION MAP ACT AND I AM .
http://www.cityofcalabasas.com/pdf/agendas/council/2012/091212/item6-exhibit-a.pdf -
16.12 Miscellaneous Provisions
16.12.070 Subdivision Map Act provisions. . Easements for public utilities, sanitary sewers, storm drains or stormwater channels and slope rights shall be .
http://www.ci.glendale.ca.us/gmc/16.12.htm


Movie Memo
Version : 1.4 released 22/06/2014 For feedback and support<EMAIL_ADDRESS>Website : www.varunest.com
Licenses
Copyright © 2014 Movie Memo Permission is hereby granted, free of charge, to any person obtaining copy of this software (the Software), to deal in the Software without restriction, including without limitation the rights to use, copy, distribute, copies of Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions : The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE


Processes incoming complex data with a discrete FFT, showing the resulting signal spectra in a plot of frequency vs. decibel full-scale power (dbFS). Also contains a spectrograph view of the FFT results over time, with pixels colored according to frequency power level.


| new_node {rlang} | R Documentation |
Important: These functions are for expert R programmers only. You should only use them if you feel comfortable manipulating low level R data structures at the C level. We export them at the R level in order to make it easy to prototype C code. They don't perform any type checking and can crash R very easily (try to take the CAR of an integer vector — save any important objects beforehand!).
new_node(car, cdr = NULL) node_car(x) node_cdr(x) node_caar(x) node_cadr(x) node_cdar(x) node_cddr(x) node_poke_car(x, newcar) node_poke_cdr(x, newcdr) node_poke_caar(x, newcar) node_poke_cadr(x, newcar) node_poke_cdar(x, newcdr) node_poke_cddr(x, newcdr) node_tag(x) node_poke_tag(x, newtag)
car, newcar, cdr, newcdr |
The new CAR or CDR for the node. These can be any R objects. |
x |
A language or pairlist node. Note that these functions are barebones and do not perform any type checking. |
newtag |
The new tag for the node. This should be a symbol. |
Setters like node_poke_car()
invisibly return x
modified
in place. Getters return the requested node component.
duplicate()
for creating copy-safe objects and
base::pairlist()
for an easier way of creating a linked list of
nodes.


Name : Aman Hussain
Reg : 15BCE1077
Description : Color Space Based Image Retrieval System
Note : Source Code can be found at my github repo : https://github.com/AmanDaVinci/Color-Based-Image-Retrieval
import os
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder,filename))
if img is not None:
images.append(img)
return images
images = load_images_from_folder('./data')
print("Total Images : {}".format(len(images)))
Since images loaded by OpenCV are in BGR colorspace, whereas matplotlib expects images in RGB format we will convert change the color space from BGR to RGB.
%%time
images_rgb = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in images]
plt.imshow(images_rgb[25])
plt.imshow(images_rgb[30])
plt.imshow(images_rgb[35])
Study how the following CBIR Engines work:
Akiwi
Akiwi is a new website that’s designed to help us keyword photographs with minimal effort. It’s a semi-automated image tagging system that is easier than manually tagging and more accurate than automatic image recognition.
STEPS INVOLVED:
Chic Engine
Chic Engine- A smarter, faster, and easier to use fashion search engine where you search using images rather than keywords. This analyzes images rather than the text of the webpage they appear on.
Users can utilize the search engine to find pieces of clothing that appeals to them personally by providing an example image of the clothing (or fabric) they are interested in. Other search engines on the web do not yet have a visual search component. Chic Engine is aiming to be the first successful visual search engine online focusing in the fashion area.
Ex - When you upload a picture of a blue shirt, find all the blue shirts for you. No more guessing the right combinations of keywords to find what you want.
Image Hunter
Image Hunter is a prototype Content Based Image Retrieval system joint developed with the Ambient Intelligence Laboratory (Sardegna Ricerche). The system performs Query by Example searches, that can be interactively refined by Relevance Feedback mechanisms.
In general, this first stage doesn't provide high relevant results because it is an hard task to model the similarity semantic through low level visual features. At this stage the user can submit to the system a feedback, this feedback represents the relevance of the image with respect of the semantic desired. The system implements two degrees of relevance: relevant, not relevant.
These relevance degrees are subdued to the system that, after a computation, rearrange the research previously made in a way to present to the user more relevant images. In this way it is possible to find similar images in tagged images databases, and, above all, in not-tagged images databases or with meaningless keyword.
Google Image Search
Google Image Search- Google's Search by image is a feature that utilizes reverse image search and allows users to search for related images just by uploading an image or image URL. Google accomplishes this by analyzing the submitted picture. It is then compared with billions of other images in Google's databases before returning matching and similar results. It should be noted that when available, Google also uses metadata about the image such as description.
Baidu Image Search
Baidu Image Search- Baidu introduced "phonetic" or "pin-yin" search, which allows users to type in Chinese keywords using English alphabets. This feature is designed to skip the switching from English input to Chinese input, and for when the user is not sure of the written form of a keyword. Since there are many different ways to pronounce and state common words, and many different dialects, this was a huge addition for the search engine which greatly set it apart from all other search engines. It is load capacity friendly and is designed so it will not overload your site by visiting it too quickly.
Yandex
Yandex images lets you input the image via URL or by uploading directly from the computer. You can also filter search results by file size (Large, Medium, Small). Yandex developed this search engine to track duplicate images. It’s a hassle free reverse image search engine without the need of signing up, totally free and works smoothly.
Best Image Search Engine
Google image search is the best as, Google images have the largest database than most of the other sites. Google Images introduced the feature of reverse image search. The mechanism of reverse photo search here is via uploading an image from your computer or pasting the link of the image in the search bar itself, it works equally well both ways, or you can simply drag and drop the image in search bar. Google images use algorithms based on various attributes like shape, size, color and resolution to get the similar pictures.
Convert an image from RGB to HSV and back to RGB
%%time
images_hsv = [cv2.cvtColor(image, cv2.COLOR_BGR2HSV) for image in images]
plt.imshow(images_hsv[35])
Write the equations for conversion
The R,G,B values are divided by 255 to change the range from 0..255 to 0..1
R' = R/255
G' = G/255
B' = B/255
Cmax = max(R', G', B')
Cmin = min(R', G', B')
delta = Cmax - Cmin
Computing HSV values:
HUE
- 0, if delta = 0
- 60 * (G' - B') * mod6 / delta, if Cmax = R'
- 60 * (B' - R') + 2 / delta, if Cmax = G'
- 60 * (R' - G') + 4 / delta, if Cmax = B'
SATURATION
- 0, if Cmax = 0
- delta/cmax, if Cmax = 0
Value
- Cmax
Are they perceptually same?
RGB is an additive color model. It means that different proportions of Red, Blue and Green light can be used to produce color. The RGB color model was created specifically for display purposes (display screens, projectors etc).
However, HSV space transforms the RGB space into a more convenient representation. The "Whiteness" / "lightness" is a function of R, G and B when we view it in the cube while it's a separate dimension in the HSV space know as Value.
The Saturation or "colorfulness" is given by the distance of the color from the 3D diagonal of the RGB matrix cube. In the HSV model, the saturation is directly given as another dimension.
Show the RGB and HSV values of (100,100) pixel
print("Image dimension : {}".format(images_hsv[0].shape))
For RGB:
print("Red: {}".format(images_rgb[0][100,100,0]))
print("Blue: {}".format(images_rgb[0][100,100,1]))
print("Green: {}".format(images_rgb[0][100,100,2]))
For HSV:
print("Hue: {}".format(images_hsv[0][100,100,0]))
print("Saturation: {}".format(images_hsv[0][100,100,1]))
print("Value: {}".format(images_hsv[0][100,100,2]))
1) Image Dimensions Column
def get_dimension(image):
return image.shape
2) Mean of Color Channels
def get_mean(img):
mean_red = img[:,:,0].mean()
mean_blue = img[:,:,1].mean()
mean_green = img[:,:,2].mean()
return (mean_red, mean_blue, mean_green)
3) Moment of Color Channels
def get_moment(img):
moment_red = cv2.moments(img[:,:,0])['m00']
moment_blue = cv2.moments(img[:,:,1])['m00']
moment_green = cv2.moments(img[:,:,2])['m00']
return (moment_red, moment_blue, moment_green)
Image Database
imageDB = pd.DataFrame()
imageDB['image_matrix'] = images_rgb
imageDB['dimension'] = imageDB['image_matrix'].apply(get_dimension)
imageDB['mean'] = imageDB['image_matrix'].apply(get_mean)
imageDB['moment'] = imageDB['image_matrix'].apply(get_moment)
imageDB.head()
def euclidean_distance(x=(0,0,0), y=(0,0,0)):
return np.sqrt(np.square(x[0] - y[0])
+ np.square(x[1] - y[1])
+ np.square(x[2] - y[2]))
def query(query_img, DB, by='mean', rank=10):
if by == 'dimension':
dimension = get_dimension(query_img)
similarity_dimension = DB['dimension'].map(lambda x : euclidean_distance(dimension, x))
similarity_dimension = similarity_dimension.to_dict()
similar_images_dimension = sorted(similarity_dimension, key=similarity_dimension.get)
# displaying the similar images
f, axarr = plt.subplots(rank, sharex=True, figsize=(5,30))
f.suptitle('Similar Images by Dimension')
for i in range(rank):
similar_image = similar_images_dimension[i]
axarr[i].imshow(imageDB.iloc[similar_image][0])
elif by == 'mean':
# taking measures of the query image
mean = get_mean(query_img)
similarity_mean = DB['mean'].map(lambda x : euclidean_distance(mean, x))
similarity_mean = similarity_mean.to_dict()
similar_images_mean = sorted(similarity_mean, key=similarity_mean.get)
# displaying the similar images
f, axarr = plt.subplots(rank, sharex=True, figsize=(5,30))
f.suptitle('Similar Images by Mean Color')
for i in range(rank):
similar_image = similar_images_mean[i]
axarr[i].imshow(imageDB.iloc[similar_image][0])
elif by == 'moment':
# taking measures of the query image
moment = get_moment(query_img)
# finding the similarity measure
similarity_moment = DB['moment'].map(lambda x : euclidean_distance(moment, x))
# sorting the similar images
similarity_moment = similarity_moment.to_dict()
similar_images_moment = sorted(similarity_moment, key=similarity_moment.get)[:rank]
# displaying the similar images
f, axarr = plt.subplots(rank, sharex=True, figsize=(5,30))
f.suptitle('Similar Images by Color Moment')
for i in range(rank):
similar_image = similar_images_moment[i]
axarr[i].imshow(imageDB.iloc[similar_image][0])
Image Database
imageDB
Ranked By Image Dimension
query(images_rgb[13],imageDB, by='mean', rank=10)
Ranked By Image Color Mean
query(images_rgb[35],imageDB, by='mean', rank=10)
Ranked By Image Moment
query(images_rgb[25],imageDB, by='moment', rank=10)
Hence, we see that searching images by the mean of color channel returns the most visually similar results.


Introduction¶
SeisIO is a framework for working with univariate geophysical data. SeisIO is designed around three basic principles:
- Ease of use: you shouldn’t need a PhD to study geophysical data.
- Fluidity: working with data shouldn’t feel clumsy.
- Performance: speed and efficient memory usage matter.
The project is home to an expanding set of web clients, file format readers, and analysis utilities.
Overview¶
SeisIO stores data in minimalist containers that track the bare necessities for
analysis. New data are easily added with basic commands like +
. Unwanted
channels can be removed just as easily. Data can be saved to a native SeisIO
format or written to other supported file formats.
Installation¶
From the Julia prompt: press ]
to enter the Pkg environment, then type
add SeisIO; build; precompile
Dependencies should install automatically. To verify that everything works correctly, type
test SeisIO
and allow 10-20 minutes for tests to complete. To get started, exit the Pkg environment by pressing Backspace or Control + C, then type
using SeisIO
at the Julia prompt. You’ll need to repeat that last step whenever you restart Julia, as with any command-line interpreter (CLI) language.
Getting Started¶
The tutorial is designed as a gentle introduction for people less familiar with the Julia language. If you already have some familiarity with Julia, you probably want to start with one of the following topics:
- Working with Data: learn how to manage data using SeisIO
- Reading Data: learn how to read data from file
- Requesting Data: learn how to make web requests
Updating¶
From the Julia prompt: press ]
to enter the Pkg environment, then type
update
. Once updates finish, restart Julia to use them.
Getting Help¶
In addition to these documents, a number of help documents can be called at the Julia prompt. These commands are a useful starting point:
?chanspec # how to specify channels in web requests
?get_data # how to download data
?read_data # how to read data from file
?timespec # how to specify times in web requests and data processing
?seed_support # how much of the SEED data standard is supported?
?seis_www # list strings for data sources in web requests
?SeisData # information about SeisIO data types
?SeisIO.KW # SeisIO shared keywords and their meanings


fudge.gnd.covariances package¶
Submodules¶
fudge.gnd.covariances.base module¶
Base classes for covariances: matrix, axes.
-
class
fudge.gnd.covariances.base.
covarianceMatrix
(label, type='absolute', matrix=None, energyBounds=None, ENDFconversionFlag=None)[source]¶ Bases:
xData.ancestry.ancestry
Base class for all covariances. covarianceMatrix contains a label, a ‘gridded2d’ container with the matrix data + axes and a list of energy boundaries, and a covariance ‘type’ which might be ‘absolute’, ‘relative’ or ‘correlation’. Some details :
- Matrix data is stored in an
xData.gridded.gridded2d
class. May be diagonal, symmetric, sparse, etc - Symmetric matrices only require one set of energy bounds, but asymmetric matrices require bounds for both axes.
-
ENDFconversionFlag
= None¶ yes, this is a crutch to help when converting back to ENDF
-
check
(info)[source]¶ Check if uncertainty in the bounds passed into the checker. Requires specification of the data (“theData”) if the covariance is not relative. I was not creative when I coded this, so it will fail when theData.getValue( x ) doesn’t exist or is a function of more than one value.
-
convertAxesToUnits
(units)[source]¶ Converts all the axes’ units. The parameter
units
should be a list of units with the same length as self.axes
-
fix
(**kw)[source]¶ Fix uncertainty using the bounds passed into the fixer. Requires specification of the data (“theData”) if the covariance is not relative. I was not creative when I coded this, so it will fail when theData.getValue( x ) doesn’t exist or is a function of more than one value.
-
getColumnBounds
(unit=None)[source]¶ Get the bounds of the column. If unit is specified, return the bounds in that unit.
-
getCorrelationMatrix
()[source]¶ Returns the correlation matrix generated from self’s covariance matrix. This is essentially a copy of self, but renormalized by the uncertainty:
correlation[i,j] = covariance[i,j]/sqrt(covariance[i,i])/sqrt(covariance[j,j])We reuse the covariance matrix class so that we can do plotting, etc. If you have a correlation matrix, you can safely recover it provided you have the uncertainty vector.
Currently only works for a square covariance matrix and not a off-diagonal part of another covariance.
-
getRowBounds
(unit=None)[source]¶ Get the bounds of the row. If unit is specified, return the bounds in that unit.
-
getUncertaintyVector
(theData=None, relative=True)[source]¶ Get an XYs1d object containing uncertainty for this matrix. Convert relative/absolute if requested (if so, must also pass central values as theData)
Examples:
if the covariance matrix is relative and we want relative uncertainty vector, just do:
> matrix.getUncertaintyVector()
if we want the absolute matrix instead:
> matrix.getUncertaintyVector( theData=<XYs1d instance>, relative=False )
-
group
(groupBoundaries=(None, None), groupUnit=(None, None))[source]¶ Group the matrix in self
Parameters: - groupBoundaries – a 2 element list containing the group boundaries for the rows and columns (respectively) of the covariance to be regrouped rows go in the first element, columns in the second
- groupUnit – a 2 element list containing the units in which group boundaries are specified for the rows and columns (respectively) of the covariance to be regrouped
Returns: the regrouped matrix (an xData.array.full as the array in a gridded2d.matrix)
Note
We still need to do flux weighting
Regrouping Theory
Given a function , we write the grouped data using fudge’s
flat
interpolation scheme. We note that we could write this scheme as an expansion over basis functions:where the weight functions are
These weights are an orthogonal (bot not orthonormal) basis, with
So, to transform from basis to (which has group boundaries ), do:
where is the regrouped function coefficients and is the matrix
Applying regrouping theory to covariance matrices
When we are given a covariance matrix in ENDF, it is meant to be interpreted as a grouped covariance in both the direction of the matrix rows and the matrix columns. Therefore, we must regroup in both the row direction and the column direction. The ENDF format gives both the group boundaries for the rows and columns. In other words, ENDF gives us the following rule for evaluating the continuous row- column covariance:
Computing as before,
It is straightforward to generalize to the case where the row and column bases are different.
In the routine below, we abuse
xData.XYs1d
to specify the functions and use theXYs1d.groupOneFunction()
method to perform the integrals to get the regrouping matrix. We do this separately for the rows and the columns. The matrix multiplication that converts a covariance from one pair of bases (group structures) to another is accomplished using numpy.An explanation of fudge’s ‘flat’ interpolation
Suppose we have a function specified using fudge’s ‘flat’ interpolation. Then we have entries and a set of group boundaries and the following rule for interpolation:
- Below , evaluates to
- From , evaluates to
- From , evaluates to
- ...
- From , evaluates to
- ...
- From , evaluates to
- Above , evaluates to
-
label
¶
-
matrix
= None¶ a
xData.gridded.gridded2d
instance containing the matrix
-
moniker
= 'covarianceMatrix'¶
-
static
parseXMLNode
(element, xPath, linkData)[source]¶ Translate <covarianceMatrix> element from xml into python class.
-
plot
(title=None, scalelabel=None, xlim=None, ylim=None, xlog=False, ylog=False)[source]¶ Parameters: - title –
- scalelabel –
- xlim –
- ylim –
- xlog –
- ylog –
Returns:
-
toAbsolute
(rowData=None, colData=None)[source]¶ Rescales self (if it is a relative covariance) using XYs1d rowData and colData to convert self into an absolute covariance matrix.
Parameters: - rowData (XYs1d) – an XYs1d instance containing data to rescale covariance in the “row direction” if it isn’t given, we’ll compute it from the corresponding data in the reactionSuite
- colData (XYs1d) – an XYs1d instance containing data to rescale covariance in the “col direction” if it isn’t given, we’ll compute it from the corresponding data in the reactionSuite
Returns: a copy of self, but rescaled and with the type set to absoluteToken
-
toCovarianceMatrix
()[source]¶ Return self, converted to a base instance. Since it already is one, we just return a copy. :return:
-
toRelative
(rowData=None, colData=None)[source]¶ Rescales self (if it is a absolute covariance) using XYs1d rowData and colData to convert self into a relative covariance matrix.
Parameters: - rowData (XYs1d) – an XYs1d instance containing data to rescale covariance in the “row direction” if it isn’t given, we’ll compute it from the corresponding data in the reactionSuite
- colData (XYs1d) – an XYs1d instance containing data to rescale covariance in the “col direction” if it isn’t given, we’ll compute it from the corresponding data in the reactionSuite
Note
If the column axis is a link, only rowData is needed. If neither rowData nor colData are specified, you’d better hope that the covariance is already relative because this will throw an error.
Returns: a copy of self, but rescaled and with the type set to relativeToken
-
type
= None¶ ‘relative’ or ‘absolute’
- Matrix data is stored in an
fudge.gnd.covariances.covarianceSuite module¶
Module with containers for covariances in several different forms
-
class
fudge.gnd.covariances.covarianceSuite.
covarianceSuite
(projectile, target, evaluation, externalReactions=None, sections=None, modelParameterCovariances=None, style=None)[source]¶ Bases:
xData.ancestry.ancestry
All covariances for a target/projectile combination are stored in a
covarianceSuite
in gnd. ThecovarianceSuite
is stored in a separate file from the reactionSuite.Within the
covarianceSuite
, data is sorted intosections
(see the section module), each of which contains one section of the full covariance matrix.-
check
(**kwargs)[source]¶ Check all covariance sections, returning a list of warnings.
Parameters: - checkUncLimits (bool) – Should we check the uncertainty limits? (default: True)
- minRelUnc (float) – Minimum allowable relative uncertainty (default: 0.0)
- maxRelUnc (float) – Maximum allowable relative uncertainty (default: 10.0)
- theData – A reference to the data for this covariance. This is useful for converting between relative and absolute covariance (default: None)
- negativeEigenTolerance (float) – Ignore negative eigenvalues smaller than this (default: -1e-6)
- eigenvalueRatioTolerance (float) – Warn if smallest eigenvalue < this value * biggest (default: 1e-8)
- eigenvalueAbsoluteTolerance (float) – Warn if smallest eigenvalue < this value (default: 1e-14)
Return type:
-
convertUnits
(unitMap)[source]¶ unitMap is a dictionary with old/new unit pairs where the old unit is the key (e.g., { ‘eV’ : ‘MeV’, ‘b’ : ‘mb’ }).
-
externalReactions
= None¶ List of cross-material covariances
-
findEntity
(entityName, attribute=None, value=None)[source]¶ Overrides ancestry.findEntity. covarianceSuite contains more than one list, so may need to descend into those to find desired entity
-
fix
(**kwargs)[source]¶ Apply basic fixes to a covariance
Parameters: - removeNegativeEVs (bool) – Should we remove eigenspaces corresponding to negative eigenvalues? (Default: True)
- removeSmallEVs (bool) – Should we remove eigenspaces corresponding to small eigenvalues? (Default: False)
- fixUncLimits (bool) – Should we fix the uncertainties to lie within bounds imposed by minRelUnc and maxRelUnc? (Default: False)
- minRelUnc (float or None) – Minimum allowable uncertainty for this covariance
- maxRelUnc (float or None) – Maximum allowable uncertainty for this covariance
- theData (instance or None) – Reference to the data that accompanies this covariance so that we may convert between absolute and relative covariance as needed.
-
modelParameterCovariances
= None¶ List of parameterCovariance instances
-
moniker
= 'covarianceSuite'¶
-
static
parseXMLNode
(CSelement, xPath, linkData)[source]¶ Parse an XML node and load create a covarianceSuite based on its contents
-
projectile
= None¶ The projectile
-
sections
= None¶ List of section instances
-
styles
¶
-
target
= None¶ The target
-
fudge.gnd.covariances.distributions module¶
-
class
fudge.gnd.covariances.distributions.
LegendreLValue
(L1, L2, frame)[source]¶ Bases:
fudge.gnd.suites.suite
Represents one subsection of the Legendre coefficient covariance matrix: covariance between coefficients for two Legendre orders at various energies
-
L1
= None¶
-
L2
= None¶
-
frame
= None¶
-
moniker
= 'LegendreLValue'¶
-
-
class
fudge.gnd.covariances.distributions.
LegendreOrderCovarianceForm
(label=None, lvalues=None)[source]¶ Bases:
xData.ancestry.ancestry
Stores covariance between energy-dependent Legendre coefficients for a reaction. This class contains one or more LegendreLValue sections, each section containing the matrix between a pair of L-values
-
label
¶
-
lvalues
= None¶ the l values of course
-
moniker
= 'LegendreOrderCovariance'¶
-
fudge.gnd.covariances.mixed module¶
-
class
fudge.gnd.covariances.mixed.
mixedForm
(label=None, components=None)[source]¶ Bases:
xData.ancestry.ancestry
Covariance for a single quantity, stored as several separate matrices that must be summed together. In general, the energy bounds for these matrices can overlap (unlike regions1d cross section data).
-
addComponent
(covariance)[source]¶ Parameters: covariance – an instance of covariance (or inherited class)
-
components
= None¶ a Python list containing instances of
mixedForm
,summedCovariance
, andcovarianceMatrix
-
getColumnBounds
(unit=None)[source]¶ Get the bounds of the column. If unit is specified, return the bounds in that unit. Otherwise, take unit from the first sub-matrix.
-
getMatchingComponent
(rowBounds=None, columnBounds=None)[source]¶ Parameters: - rowBounds –
- columnBounds –
Returns:
-
getRowBounds
(unit=None)[source]¶ Get the bounds of the row. If unit is specified, return the bounds in that unit. Otherwise, take unit from first sub-matrix.
-
getUncertaintyVector
(theData=None, relative=True)[source]¶ Combines all subsections into single uncertainty vector, converting to relative if requested.
Returns: an XYs1d instance
-
label
¶
-
makeSafeBounds
()[source]¶ Go through all the components and make sure the bounds don’t overlap. If they do, it is likely a bug.
-
moniker
= 'mixed'¶
-
toAbsolute
(rowData=None, colData=None)[source]¶ Rescales self (if it is a relative covariance) using XYs1d rowData and colData to convert self into an absolute covariance matrix.
Parameters: - rowData – an XYs1d instance containing data to rescale covariance in the “row direction”
- colData – an XYs1d instance containing data to rescale covariance in the “col direction”
Note
If the column axis is set to ‘mirrorOtherAxis’, only rowData is needed. If neither rowData nor colData are specified, you’d better hope that the covariance is already absolute because this will throw an error.
Returns: a copy of self, but rescaled and with the type set to absoluteToken
-
toRelative
(label='composed', rowData=None, colData=None)[source]¶ Rescales self (if it is a absolute covariance) using XYs1d rowData and colData to convert self into a relative covariance matrix.
Parameters: - rowData – an XYs1d instance containing data to rescale covariance in the “row direction”
- colData – an XYs1d instance containing data to rescale covariance in the “col direction”
Note
If the column axis is set to ‘mirrorOtherAxis’, only rowData is needed. If neither rowData nor colData are specified, you’d better hope that the covariance is already relative because this will throw an error.
Returns: a copy of self, but rescaled and with the type set to relativeToken
-
fudge.gnd.covariances.modelParameters module¶
-
class
fudge.gnd.covariances.modelParameters.
loopOverResonanceParameters
(link=None, root=None, path=None, relative=False, nResonances=0, parametersPerResonance='')[source]¶ Bases:
xData.link.link
For resonance region covariances, we need a compact way to express many model inputs. Simplest is to specify a loop over the resonances
-
moniker
= 'loopOverResonanceParameters'¶
-
nResonances
¶
-
parametersPerResonance
¶
-
-
class
fudge.gnd.covariances.modelParameters.
parameterCovariance
(label, matrix, parameters_=None, type='relativeCovariance', ENDFconversionFlags='')[source]¶ Bases:
xData.ancestry.ancestry
Store covariances (or correlations, depending on ‘type’) between model parameters
-
moniker
= 'parameterCovariance'¶
-
-
class
fudge.gnd.covariances.modelParameters.
parameterLink
(label, link, nParameters=1, matrixStartIndex=0, parameterStartIndex=0, parameterStride=1, **kwargs)[source]¶ Bases:
xData.link.link
Establishes a link between one or more rows of a parameterCovariance and corresponding parameter(s). Supports linking to specific parameters inside a table or list.
and wish to give a 4x4 covariance matrix for elements in the 2nd and 4th column of the table, we can create a parameterLink pointing to the table, with ‘matrixStartIndex=0’, ‘nParameters=4’, ‘parameterStartIndex=1’, ‘parameterStride=2’.
The corresponding covariance matrix rows would then correspond to ‘B, D, F, H’.
-
moniker
= 'parameterLink'¶
-
-
class
fudge.gnd.covariances.modelParameters.
parameters
[source]¶ Bases:
fudge.gnd.suites.suite
-
moniker
= 'parameters'¶
-
nParameters
¶
-
-
class
fudge.gnd.covariances.modelParameters.
resonanceParameterCovariance
(label=None, matrix=None, inputParameters=None, type=None, ENDFconversionFlags='')[source]¶ Bases:
fudge.gnd.covariances.modelParameters.parameterCovariance
In the resonance region, covariances are given between resonance parameters (energy and widths). Generally, the dimension of the matrix is 3*(number of resonances) for light targets, and 4*(nres) for heavy targets (where the fission width must be given).
We also allow including the scattering radius in the covariance, although ENDF files currently only have room to list the uncertainty (variance) on the scattering radius.
-
static
parseXMLNode
(element, xPath, linkData)[source]¶ Translate <resonanceParameterCovariance> element from xml.
-
tag
= None¶ usually set to ‘resonanceParameterCovariance’
-
static
fudge.gnd.covariances.section module¶
A covarianceSuite is organized into sections, where each section contains either - a covariance matrix for a single reaction quantity (cross section, multiplicity, etc), or - a covariance matrix between two different quantities (off-diagonal block)
-
class
fudge.gnd.covariances.section.
columnData
(link=None, root=None, path=None, label=None, relative=False, **attributes)[source]¶ Bases:
xData.link.link
-
moniker
= 'columnData'¶
-
-
class
fudge.gnd.covariances.section.
externalReaction
(label, projectile, target, ENDF_MFMT=None)[source]¶ Bases:
xData.ancestry.ancestry
Covariance may relate this target with another material (‘cross-material covariance’). In this case, specify the other material and reaction here
-
ENDF_MFMT
= None¶ the ENDF MF & MT values, a tuple of form (MF, MT)
-
label
= None¶ an identifier str
-
moniker
= 'externalReaction'¶
-
-
class
fudge.gnd.covariances.section.
rowData
(link=None, root=None, path=None, label=None, relative=False, **attributes)[source]¶ Bases:
xData.link.link
-
moniker
= 'rowData'¶
-
-
class
fudge.gnd.covariances.section.
section
(label, rowData=None, columnData=None)[source]¶ Bases:
fudge.gnd.suites.suite
A covarianceSuite contains sections, where each section represents either a self-covariance for one quantity, or a cross-covariance between two quantities
More generally, the covarianceSuite can be thought of as a single covariance matrix with all covariance data for a target/projectile. It is broken into sections, where each section holds a chunk of the full matrix.
Within each section, covariance data can take multiple forms:
covarianceMatrix
is the most common, but ‘summed’, ‘mixed’ are also possible.- Valid values in the
forms
dictionary are: - mixedForm
- summedCovariance
- LegendreOrderCovarianceForm
- covarianceMatrix
-
columnData
= None¶ xData.link.link pointing to the corresponding data for the covariance column
-
label
= None¶ a str label that gets used on plots, etc.
-
moniker
= 'section'¶
-
rowData
= None¶ xData.link.link pointing to the corresponding data for the covariance row
- Valid values in the
fudge.gnd.covariances.summed module¶
-
class
fudge.gnd.covariances.summed.
summand
(link=None, root=None, path=None, label=None, relative=False, **attributes)[source]¶ Bases:
xData.link.link
-
moniker
= 'summand'¶
-
-
class
fudge.gnd.covariances.summed.
summedCovariance
(label, domainMin, domainMax, domainUnit='eV', pointerList=None, coefficients=None)[source]¶ Bases:
xData.ancestry.ancestry
Covariance matrix stored as sum/difference of other matrices.
-
domainMax
= None¶ upper bound of row/column direction.
-
domainMin
= None¶ lower bound of row/column direction.
-
getColumnBounds
(unit=None)[source]¶ Get the bounds of the column. If unit is specified, return the bounds in that unit.
-
getRowBounds
(unit=None)[source]¶ Get the bounds of the row. If unit is specified, return the bounds in that unit.
-
getUncertaintyVector
(theData=None, relative=True)[source]¶ Combine all subsections into single uncertainty vector, converting to relative if requested.
Returns: an XYs1d instance
-
label
¶
-
moniker
= 'sum'¶
-
toAbsolute
(rowData=None, colData=None)[source]¶ Rescales self (if it is a relative covariance) using XYs1d rowData and colData to convert self into an absolute covariance matrix.
Parameters: - rowData – an XYs1d instance containing data to rescale covariance in the “row direction”
- colData – an XYs1d instance containing data to rescale covariance in the “col direction”
Note
If the column axis is set to ‘mirrorOtherAxis’, only rowData is needed. If neither rowData nor colData are specified, you’d better hope that the covariance is already absolute because this will throw an error.
Returns: a copy of self, but rescaled and with the type set to absoluteToken
-
toCovarianceMatrix
(label='composed')[source]¶ Sum the parts to construct the covariance matrix. Note, each part must be converted to an absolute covariance before summing.
-
toRelative
(rowData=None, colData=None)[source]¶ Rescales self (if it is a absolute covariance) using XYs1d rowData and colData to convert self into a relative covariance matrix.
Parameters: - rowData – an XYs1d instance containing data to rescale covariance in the “row direction”
- colData – an XYs1d instance containing data to rescale covariance in the “col direction”
Note
If the column axis is set to ‘mirrorOtherAxis’, only rowData is needed. If neither rowData nor colData are specified, you’d better hope that the covariance is already relative because this will throw an error.
Returns: a copy of self, but rescaled and with the type set to relativeToken
-
fudge.gnd.covariances.tokens module¶
Define tokens to identify covariance elements in GND


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