powerSupply is an example of creating a service that requires a somewhat complicated top level PVStructure. Look at the code for details.
For more information about EPICS generally, please refer to the home page of the Experimental Physics and Industrial Control System.
This is an example of creating a service that requires a somewhat complicated top level PVStructure.
The example has a top level pvStructure:
structure alarm_t alarm time_t timeStamp structure power double value structure voltage double value structure current double value
It is designed to be accessed via a channelPutGet request. The client sets power.value and voltage.value When the record processes it computes current.value. In addition the timeStamp is set to the time when process is called.
The example can be run on linux as follows:
mrk> pwd /home/epicsv4/master/exampleCPP/powerSupply mrk> bin/$EPICS_HOST_ARCH/powerSupplyMain
To start powerSupply as part of a V3IOC:
mrk> pwd /home/hg/exampleCPP/powerSupply/iocBoot/powerSupply mrk> ../../../bin/$EPICS_HOST_ARCH/powerSupply st.cmd
mrk> pwd /home/epicsv4/master/exampleCPP/powerSupply mrk> bin/$EPICS_HOST_ARCH/powerSupplyMonitor
mrk> pwd /home/epicsv4/master/exampleCPP/powerSupply mrk> bin/$EPICS_HOST_ARCH/powerSupplyClient
NOTE: The client ends by requesting a voltage of 0. This results in the power supply record thowing an exception. This results in problems including the powerSupplyMonitor no longer updating. Needs investigation.
The directory layout is:
powerSupply configure ExampleRELEASE.local ... src pv powerSupplyRecord.h powerSupplyRecord.cpp powerSupplyInclude.dbd powerSupplyMain.cpp powerSupplyClient.cpp powerSupplyMonitor.cpp powerSupplyRegister.cpp powerSupplyRegister.dbd ioc Db ... src powerSupplyInclude.dbd powerSupplyMain.cpp iocBoot powerSupply st.cmd ...where
powerSupplyCreateRecord powerSupplyMultiple commands can be issued to create multiple powerSupply records.
If only a main program is desired then the directory layout is:
powerSupply configure ExampleRELEASE.local ... src pv powerSupplyRecord.h code powerSupplyRecord.cpp powerSupplyMain.cpp
Thus if only a main program is required the directory layout is simple.
Also many sites will want to build the src directory in an area separate from where the iocs are build.
The example resides in src The implementation is in powerSupply.cpp.
The description consists of
class PowerSupplyRecord; typedef std::tr1::shared_ptrPowerSupplyRecordPtr; class PowerSupplyRecord : public PVRecord { public: POINTER_DEFINITIONS(PowerSupplyRecord); static PowerSupplyRecordPtr create( std::string const & recordName); virtual ~PowerSupplyRecord(); virtual void destroy(); virtual bool init(); virtual void process(); private: PowerSupplyRecord(std::string const & recordName, epics::pvData::PVStructurePtr const & pvStructure); epics::pvData::PVDoublePtr pvCurrent; epics::pvData::PVDoublePtr pvPower; epics::pvData::PVDoublePtr pvVoltage; epics::pvData::PVAlarm pvAlarm; };
where
The implementation of create method is:
PowerSupplyRecordPtr PowerSupplyRecord::create( std::string const & recordName) { StandardFieldPtr standardField = getStandardField(); FieldCreatePtr fieldCreate = getFieldCreate(); PVDataCreatePtr pvDataCreate = getPVDataCreate(); StructureConstPtr topStructure = fieldCreate->createFieldBuilder()-> add("alarm",standardField->alarm()) -> add("timeStamp",standardField->timeStamp()) -> addNestedStructure("power") -> add("value",pvDouble) -> endNested()-> addNestedStructure("voltage") -> add("value",pvDouble) -> endNested()-> addNestedStructure("current") -> add("value",pvDouble) -> endNested()-> createStructure(); PVStructurePtr pvStructure = pvDataCreate->createPVStructure(topStructure); PowerSupplyRecordPtr pvRecord( new PowerSupplyRecord(recordName,pvStructure)); if(!pvRecord->init()) pvRecord.reset(); return pvRecord; }This:
The private constructor method is:
PowerSupplyRecord::PowerSupplyRecord( std::string const & recordName, epics::pvData::PVStructurePtr const & pvStructure) : PVRecord(recordName,pvStructure) { }The example is very simple. Note that it calls the base class constructor.
The destructor and destroy methods are:
PowerSupplyRecord::~PowerSupplyRecord() { } void PowerSupplyRecord::destroy() { PVRecord::destroy(); }The destructor has nothing to do. The destroy method, which is virtual, just calls the destroy method of the base class. A more complicated example can clean up any resources it used but must call the base class destroy method.
The implementation of init is:
bool PowerSupplyRecord::init() { initPVRecord(); PVStructurePtr pvStructure = getPVStructure(); PVFieldPtr pvField; bool result; pvField = pvStructure->getSubField("timeStamp"); if(!pvField) { cerr << "no timeStamp" << endl; return false; } result = pvTimeStamp.attach(pvField); if(!result) { cerr << "no timeStamp" << endl; return false; } pvField = pvStructure->getSubField("alarm"); if(!pvField) { cerr << "no alarm" << endl; return false; } result = pvAlarm.attach(pvField); if(!result) { cerr << "no alarm" << endl; return false; } pvCurrent = pvStructure->getSubField;("current.value"); if(!pvCurrent) { cerr << "no current\n"; return false; } pvVoltage = pvStructure->getSubField ("voltage.value"); if(!pvVoltage) { cerr << "no current\n"; return false; } pvPower = pvStructure->getSubField ("power.value"); if(!pvPower) { cerr << "no power\n"; return false; } alarm.setMessage("bad voltage"); alarm.setSeverity(majorAlarm); pvAlarm.set(alarm); return true; }
The implementation of process is:
void PowerSupplyRecord::process() { double voltage = pvVoltage->get(); double power = pvPower->get(); if(voltage<1e-3 && voltage>-1e-3) { alarm.setMessage("bad voltage"); alarm.setSeverity(majorAlarm); pvAlarm.set(alarm); throw std::runtime_error("bad voltage exception"); } double current = power/voltage; pvCurrent->put(current); pvAlarm.get(alarm); if(alarm.getSeverity()!=noAlarm) { alarm.setMessage(""); alarm.setSeverity(noAlarm); pvAlarm.set(alarm); } PVRecord::process(); }
The main program is:
int main(int argc,char *argv[]) { PVDatabasePtr master = PVDatabase::getMaster(); ChannelProviderLocalPtr channelProvider = getChannelProviderLocal(); PVRecordPtr pvRecord; string recordName; recordName = "powerSupply"; pvRecord = PowerSupplyRecord::create(recordName); master->addRecord(pvRecord); ServerContext::shared_pointer ctx = startPVAServer("local",0,true,true); string str; while(true) { cout << "Type exit to stop: \n"; getline(cin,str); if(str.compare("exit")==0) break; } ctx->destroy(); return 0; }This:


1 killed, 2 wounded in shooting at San Diego restaurant
SAN DIEGO (AP) - Police searched for a gunman who shot three employees - killing one - at a fast food restaurant in San Diego on Wednesday.
The shooting happened around 5:30 p.m. at a Church’s Chicken in the Otay Mesa area, officials said.
The suspected gunman had been in the restaurant earlier, when he tried to pay with a counterfeit $100 bill, San Diego Police Officer John Buttle told KNSD-TV .
He came back minutes later and opened fire on employees behind the counter, Buttle said.
One victim died at a hospital. Two others were hospitalized in unknown condition.
The gunman was described by witnesses as a thin man in his 30s, wearing a light blue sweater, red sunglasses and a San Diego Chargers beanie. He was possibly driving a dark blue sedan, police said.
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Never again will you miss a local sporting event!
Create groups in which to invite other local players. Easily setup events at locations for strong group communications.
Notifications are sent to players before each event is to be held. This can be done automatically once players have accepted the event invitations.
Create events and then let PlaySpontaneous do the rest. Focus more on playing instead of setting up events.


| Class and Description |
|---|
| BatfishCombinedParser |
| Class and Description |
|---|
| BatfishCombinedParser |
| BgpTableExtractor |
| BgpTableFormat |
| GrammarSettings
Settings for parsers and lexers
|
| Class and Description |
|---|
| GrammarSettings
Settings for parsers and lexers
|
| Class and Description |
|---|
| BatfishANTLRErrorStrategy
Intended as a replacement for default ANTLR parser error recovery strategy.
|
| BatfishANTLRErrorStrategy.BatfishANTLRErrorStrategyFactory
Construct a factory for making instances of
BatfishANTLRErrorStrategy with supplied
separatorToken and minimumRequiredSeparatorText . |
| BatfishCombinedParser |
| BatfishExtractor |
| BatfishGrammarErrorListener |
| BatfishLexer |
| BatfishLexerErrorListener |
| BatfishLexerRecoveryStrategy
Used by
BatfishLexer to recover from lexing errors. |
| BatfishParser |
| BatfishParserErrorListener |
| BgpTableFormat |
| GrammarSettings
Settings for parsers and lexers
|
| RoutingTableFormat |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| ControlPlaneExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| GrammarSettings
Settings for parsers and lexers
|
| RoutingTableExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| GrammarSettings
Settings for parsers and lexers
|
| RoutingTableExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| BatfishExtractor |
| GrammarSettings
Settings for parsers and lexers
|
| RoutingTableExtractor |
| Class and Description |
|---|
| BatfishCombinedParser |
| Class and Description |
|---|
| BgpTableFormat |
| Class and Description |
|---|
| BatfishCombinedParser |
| BgpTableFormat |
| GrammarSettings
Settings for parsers and lexers
|
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Page No. -1- CC No. 153/01 PS. Hauz Khas Date : 21.01.2015 ORDER
In brief, the facts of the case are that complainant is the resident of Shahpur jaat and proposed accused M/s East West Linkers is running an industrial unit at premises no. 241 to 245 Shahpur jaat which is purely a residential area. The M/s East West Linkers is owned by Smt. Preet Singh. It is alleged that accused persons are carrying out illegal industrial activity in the premises without obtaining consent from DPCC or any other authority. It is further alleged that accused persons are using steam generator (boiler) in the process which was causing air pollution. It is further alleged that DPCC had knowledge of this wrong and illegal activity but did not took any action. Complainant relied upon certified copy of WS filed in suit no. 466/08 to show this.
It is further alleged that the above mentioned generators were used on roof top without any permission. It is also alleged that ground water is extracted without permission. It is further alleged that accused persons are also violating the direction of Hon'ble Supreme Court in MC Mehta V/s UOI, that "no industrial activity shall be carried out in the non conforming area". On these facts, complainant wants that accused persons be summoned for contravention of section 3 and 7 of Environment Protection Act as same are punishable under section 15 of Environment Protection Act.
It is pertinent to note here that cognizance was declined by this court Page No. -2- on the ground that notice has not been served as per provision contained in the Act, but that order was set aside and revisional court directed this court to proceed on the facts of the case and decide upon the issue of cognizance on facts of the case.
Thereafter, court took cognizance and complainant produced himself as witness and deposed that accused no.2 is running a factory at Shahpur Jat and accused no.1's company which has its office at Panchsheel shopping complex. During evidence, complainant proved the notice which is Ex. CW1/D. He also relied upon WS filed on behalf of DPCC in suit no. 466/08 alongwith inspection report and affidavit of Asst. Environment Engineer D.K. Shrivastav. During his PSE he deposed on line of his complaint.
After evidence, inquiry was first marked to PS Hauz Khas u/s 202 Cr.P.C. But on request of complainant, the inquiry was directed to be conducted by DPCC which is the specialized agency. DPCC filed its report and its report stated that present complaint is not maintainable as mandatory notice was not given. It however also admitted that the area where this generator was running is residential / non conforming area and the Department of Industries is responsible for taking action against a unit operating in non-conforming area. It was further reported that all the industries operating in non conforming areas are required to be closed down. In the annexure 2, which is request letter written by Environment Engineer to Commissioner of Industries, it is mentioned that the unit has MCD license for the activity of storage and sale of readymade garments. During inspection conducted on 26.02.14, it was found that one steam Page No. -3- generator for steam pressing is installed at roof top and at the premises, finishing and packing of garments is being done. It was further found that two generators were operating at the premises in question. The report is silent with regard to permission of use of water for the above purpose. However, during inspection, it was found that no water or air pollution was being caused and the affluent which are discharged are domestic in nature. It was also found that no dyeing or bleaching or use of chemical was observed during inspection.
Before deciding as to whether proposed accused persons are deserved to be summoned , court was required to satisfy itself on following points :
1. Whether this court has jurisdiction to deal with this matter or not ;
2. Whether prescribed notice was given or not ;
3. Whether ingredients of section 3 and 7 are satisfied or not. As far as 1st point as to whether this court has jurisdiction to deal with the present matter. Environment Act prescribed that court of Metropolitan Magistrate has jurisdiction to deal with the matter. This court has territorial jurisdiction of places whether the industry in question is allegedly being run. However during court proceedings, it was brought to the notice that there is one circular of Hon'ble High Court accordingly to which court of Ld. ACMM of Special Act only has power to deal with the cases under Environment Act.
However, the complainant argued that , that circular is prospective in nature. He further argued that circular deal with the cases which are filed by the DPCC and the present matter is filed by the individual after Page No. -4- giving notice to the DPCC. Perusal of language used in the circular dt. 23.2.2012 shows that it deals with the cases which are filed by DPCC. This complaint is filed by an individual, not by DPCC. Hence this court came to the conclusion that this court has jurisdiction to try and proceed with this matter.
2nd question which came is before court whether notice was u/s. 19 of Environment Protection Act was proper notice. There is bar for the court to take cognizance of any offence on private complaints except on prior notice being given as is prescribed under Environment Protection Act.
In the present matter, complainant proved prima facie during his deposition that he gave notice and revisional court vide order dt. 30.08.2011 has held that notice as valid.
3rd question before court was whether the alleged industry is creating any pollution or not and whether this pollution is exceeding the limit which is prescribed under the Environment Act.
Complainant could not point out what is the prescribed limit of pollution in the area. In order to prove that industry was creating pollution, complainant relied upon inspection report which was filed by DPCC in suit no. 466/08. However, inspection report is silent regarding the quantum of pollution and degree of pollution. Without bringing on record the degree of air pollution, it cannot be said that offence u/s. 7 is complete. Complainant failed to bring on record any other evidence to show that pollution which was created by the industry in question was exceeding the limit which is prescribed by rules. The report on which reliance has been placed is silent with respect to the degree of pollution.
Page No. -5-Further, during inspection , which was carried out by DPCC u/s. 202 CrPC, and it was found that now, industry is not creating any air pollution. Thus, as complainant failed to bring on record the exact degree of pollution, it can be said that he failed to prove that section 7 of Environment Protection Act is violated.
Complainant had also pleaded that action of the proposed accused also fall within the parameter of section 3 and which is punishable u/s.
15. This section shows that Central Govt may make any rule or can do anything necessary for purpose of securing the effective implementation of the provisions of this act.
He submitted that it has come in report of DPCC that company is running in non confirming area and in non confirming area, no industry can run in view of the judgment of Hon'ble Supreme Court passed in case titled as M.C.Mehta v Union of India in WP No. 4677/1985. Perusal of report filed by the DPCC shows that , this industry is running in non confirming area in violation of judgment of Hon'ble Supreme Court.
He submitted that by operating in non confirming area is violation of judgment of Hon'ble Supreme Court and it is also punishable u/s. 21 of Air Pollution of Prevention of Pollution Act 1981.
The court considered the provisions of section 3 of Environment Act as well as section 21 of Air Pollution Prevention Act 1981.
As far as proceedings against the accused for offence under Air Pollution Prevention Act, there is requirement of giving separate prior notice to DPCC under Air Pollution Prevention Act and in absence thereof, this court cannot take cognizance as contents of that notice is different Page No. -6- from the notice given under Environment Act.
As far as section 3 of Environment Act is concerned, section 3 empowers the Central Govt to prescribe the area in which running of any other industries is prohibited or allowed.
In M.C.Mehta v Union of India in WP No. 4677/85, Hon'ble Supreme Court banned the running of industry in non confirming area but also gave directions to Govt of India to categorize and identify the industry which can operate in non confirming area, meaning thereby that there was not blanket ban for the industries to run in non confirming area. The Government has permitted certain kind of industry to run in non confirming area.
As per report of DPCC, the area where industry in question is run in non confirming area, but they cannot take any action & Department of Industry is responsible for this, they have already requested to Department of Industry in this regard. As per notification dt. 7th February'2007 of Central Government for Master Plan 2021, there are 121 industries which are permitted to operate in non confirming area. Further the additional conditions under which these 121 industries can operate are that number of worker should not exceed more than 5 and the electricity load should not be more than 11KV.
In the present case, as per the report of DPCC the number of workers is around 50 and the electricity load is 150 KV. Further condition is that all industries should registered itself. But, as per report of DPCC, no permission / licence / sanction has been applied for registration except MCD Licence which is also not for the purpose. Further Environment Page No. -7- Protection Act Rules 1986 prescribe certain condition which every industry has to follow e.g., one condition is every industry is required to file the annual pollution report. As the industry is running without permission or any other kind of approval, it is natural inference these conditions and rules are also not followed.
In the present case, industry is pertaining to readymade garment finishing. As per list industry w.e.t., readygarment is allowed, but shall not be doing washing. Further the number of workers shall not exceed 5 and electricity shall not exceed 11 KW which as discussion is above in 50 & 150KW respectively.
In view of above discussion it is quite clear that industry in question is operating in violation of section 3 which is punishable u/s. 15.
As per the complaint the owner of industry in question is Smt. Preet Singh. As per report of DPCC, this is a private limited company, but in its reports, it is stated that it is being run by Smt. Preet Singh meaning thereby Smt. Preet Singh is responsible for running and managing the industry.
Accordingly, in view of the above discussions, this court find her / accused no. 1 prima facie liable for violating the section 3 of Environment Protection Act, which is punishable u/s. 15 of that Act. Hence, both these persons shall be summoned on filing of PF & RC to face the trial u/s. 3 read with section 15 of Environment Protection Act on filing of PF / RC for 24.4.2015.
(Ankit Singla) MM-03/SD/New Delhi 21.01.2015 Page No. -8- Page No. -9- Page No. -10- Page No. -11- Page No. -12- Page No. -13- Page No. -14- Page No. -15- Page No. -16-


Space Shooter based on Cron built with Crafty
Move: WASD, Aim/Fire: Mouse, Cycle Weapon: Shift (or key 1-3), Fire bombs: Space
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Developed on Chromium 38 on Arch Linux
Music: *
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Accommodation
Limited accommodation is available in the NITK guest house on a sharing basis and on advance payment. However, accommodation in local hotels could be arranged on request. List of Hotels with their contact numbers is given below:
| SNo. | Name of the HOTEL | Address | Contact No. | Distance from NITK campus (km) |
|---|---|---|---|---|
| 1 | Red Rock Residency | Near NITK Beach, Mallamar Beach Road, Mukka-575021 http://www.redrockresidency.com/ |
0824 2477191 | 0.5 |
| 2 | Shrinivas Boarding & Lodging | Balaji Tower, above Bank of Baroda, N.H.17 Surathkal -575014 http://www.shrinivaslodge.com/ |
0824 2478833 | 1.5 |
| 3 | HOTEL Sadanand | Subhashitha Nagar, Surathkal-575014 http://hotelsadanand.co.in/ |
0824 4284111 | 1.5 |
| 4 | Sai Suraj International | Surathkal Junction -575014 http://hotelsaisuraj.com/ |
0824 2478531 | 1.0 |
| 5 | Shree Krishna Palace | Surathkal, Karnataka 575014 See in OYO rooms |
072101 07876 | 1.5 |
| 6 | HOTEL Maharaja International | Market Road, Surathkal, Karnataka 575014 | 0824 4272755 | 1.5 |
| 7 | BMS Hotel | Panvel - Kochi - Kanyakumari Highway, Lohith Nagar, Derebail, Mangaluru, Karnataka 575006 http://hotelbmsmangalore.com/ |
078999 17277 | 14 |
| 8 | Ginger(A TATA Enterprise) | Opp. NH-66, KottaraChowki, Mangalore 575 006 https://www.gingerhotels.com/ginger-mangalore |
+91<PHONE_NUMBER> | 15.0 |
| 9 | Kavitha | Near Infosys, Urwa stores, Mangalore 575 006 http://kavitharesidency.com/hotel/ |
0824 2450380 (4 lines), 4280380, 4280382 | 15.0 |
| 10 | Goldfinch Hotel | Bunts Hostel Road, Near Jyoti Circle, Mangaluru, Karnataka 575003 https://www.goldfinchhotels.com/Mangalore/about_gf.php |
0824 4245678 | 18.1 |


Cyberpunk 2077 is an open-world, action-adventure story set in Night City, a megalopolis obsessed with power, glamour and body modification. You play as V, a mercenary outlaw going after a one-of-a-kind implant that is the key to immortality.
PUBG: NEW STATE
With next-generation mobile graphics, PUBG: NEW STATE delivers the full battle royale experience straight into players' hands. ▶Realistic and Dynamic Gunplay. Enjoy realistic gunplay optimized for mobile gaming. Master different weapons and make each of them your own with weapon customization..
Grand Theft Auto V official website - Rockstar Games
The Grand Theft Auto V: Premium Edition includes the complete GTAV story, Grand Theft Auto Online and all existing gameplay upgrades and content. You'll also get the Criminal Enterprise Starter Pack, the fastest way to jumpstart your criminal empire in GTA Online.


Created by Reece Pieri (M087496). 16/10/2020.
CSV Read & Write is an application designed to assist in the modification of CSV files.
Please forward feedback and bug reports to
To load a CSV file, go to File > Load CSV.
An Open File dialog will appear and allow the user to select a CSV file to load.
Data from the CSV will be loaded into the output table and file information will be displayed.
To save a CSV file, go to File > Save CSV.
The currently opened CSV file will be saved.
To save a CSV file to a new file/location, go to File > Save CSV As.
A Save File dialog will appear and allow the user to select a file to overwrite or another location to save the file to.
To modify a row, select a row from the output table. The data from the selected row will appear in the text boxes of the modify table.
After making changes to the data in the modify table click the "Modify" button to replace the data in the selected row of the output table.
To add rows, click the "Add Row" button. A new blank row will be appended to the output table.
To delete rows, select a row from the output table and click the "Delete Row" button. The selected row will be removed.
To clear all rows from the output table, go to Edit > Clear Data. All rows in the output table will be deleted.


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The Lean Testing methodology is backed up by a simple library, a set of nuGet packages named LeanTest.Net.
LeanTest.Net is little more than an implementation of a Builder pattern. In fact, the functionality of LeanTest.Net is so simple that it could almost serve as a morning coding kata for an experienced developer.
You can use LeanTest.Net if you understand that a test essentially declares data which is passed as initial context to external dependencies.
However, it can still be useful to understand what goes on under the hood, as it makes it more obvious how to write simple and elegant tests, and also to reason about what functionality existing tests cover.
This text is aimed at those who want to understand what is going on inside LeanTest.Net.
Let's assume that we want to write tests targeting a piece of software which has an external dependency represented by the interface IMyExternalService
.
With Lean Testing, we mock as little as possible and when we do mock, we always mock at the boundary of external dependencies. We write tests in terms of the relevant state of such external dependencies.
Thus, in this case, it makes sense to have a mock implement IMyExternalService
, then pass relevant state to it in each test.
In essence, all that our Builder pattern does is to pass data from a test to a mock via an internal context data store,
| Test arrange part | Context data store | Mock (the receiver) |
|---|---|---|
| _contextBuilder .WithData(new My()) .Build(); |
My: <one instance> | internal class MockForDataMy : IMockForData<My>, IMyExternalService { public voidWithData(My data) { } public void Build(Type type) {} // ... } |
It is core to Lean testing that we pass data between a test and a mock this way.
We do not simply instantiate an instance of a mock and feed it with context data, as that would tightly couple tests with mocks. By avoiding such a tight coupling, we can write tests which emphasize what is being tested, rather than how it is being tested. This not only means that test code is not coupled to a specific mock implementation, it also means that the test code does not depend on a mock being implemented at all. The test in the example above declares that it depends on having a My data in the context, but the test does not care whether the data is passed to a mock or a database.
Declaring data needed for a test, without explicitly stating how that data is supposed to be used, is what allows us to write tests in one consistent way, with any kind of mocking strategy, running in-process or out-of-process, mocking a database or not, running with immediate external dependencies or a full environment.
In short, there is one consistent way to write tests at any level and there is one consistent way to reason about functional test coverage.
Note that the test uses the type of the instance of data passed to the mock in order to decide the receiver, i.e. in order to identify the mock which will receive the data. We expect representations of external dependencies to use strong typing.
Also note how a mock declares what type of data it is prepared to receive simply by implementing the interface IMockForData<>
. Build()
on the context builder will pass data of type T
from the internal data store by asking the DI container for implementations of IMockForData<T>
. LeanTest.Net will never use reflection to discover receivers of data, it will rely on receivers to be registered with the DI container and tests to declare relevant data.
In the example above we had a piece of data which was passed to a single mock. It is not always that simple; in fact, the possibility of having multiple receivers with a consistent view of test data is one major benefit of our flavour of the Builder pattern.
One example usage of multiple receivers could be a test which declares that the financial Forex instrument EURUSD must be known and available. The external dependencies could be a number of backend services which each is responsible for handling a single aspect of that instrument (such as pricing, margin calculations etc.). We would expect that if one of these know about EURUSD, then the others will also.
Another example is when time itself is an external dependency. If the code under test has logic around the current time and also run code regularly on a thread as spun up by a timer, then you would expect such code to have the same notion of time. In other words, if a test declares that the current time is Star Wars day 2020, then you expect that both the current time and the timer logic would reflect that.
Treating time as an external dependency is a coding pattern which is documented in Lean Test Coding Patterns, the example functionality being a write-cache which flushes at regular times. From the details of the documented code pattern it follows that a single DateTime
instance declared in a test will be passed to the two
relevant mocks,
| Test arrange part | Context data store | Mock |
|---|---|---|
| _contextBuilder .WithData(new DateTime(2020, 5, 4)) .Build(); |
DateTime: <one instance> | internal class MockForDataDateTime : IDateTime, IMockForData<DateTime> { public void WithData(DateTime data) { } public void Build(Type type) { } // ... } internal class MockForDataTimer : ITimer { public void WithData(DateTime data) { } public void Build(Type type) { } // ... } |
In the examples above we passed data to mocks. It is not always that simple; actually, not hardcoding that data is passed to any given mock is one major benefit of our flavour of the Builder pattern.
LeanTest.Net's Builder pattern supports multiple builders. At the time of writing, a mock-for-data builder and a state handler builder are provided out-of-the-box.
The difference between these two builders is only conceptual. In short,
IMyExternalDependency
, along with IMockForData<T>
for all relevant data types T
.IStateHandler<T>
for all relevant data types T
. A state handler will never substitute production code.Since mocks substitute production code, we will mock as little as possible, thereby putting as much code under test as possible. And when we mock, we will try to not mock away any production code which is relevant to put under test. Key to this is to define façade interfaces representing external dependencies; these interfaces are defined such that a production code implementation contains no logic, only pass-through to the actual external dependency.
State handlers are typically used to handle data in database(s), but can also be used to handle other state, such as data in a distributed cache or a file system. In either case, a state handler must take full responsibility for handling the data. For a SQL database, that would involve deleting/inserting data in a way which respects referential integrity. Naturally, such destructive behaviour assumes ownership of the database.
A special use of state handlers is for shared environments, in which destructive actions are not come-il-faut. The best we can do in such an environment is to be explicit about our assumptions, checking and ensuring whenever possible. Tests may still fail for all the reasons which tests fail in shared environments, but at least we can more easily determine the assumption(s) that fail and do corrective actions immediately.
Naturally, the principle of having multiple receivers applies across multiple builders. Returning to an earlier example, a test could declare that EURUSD must be known and available; this instrument can then be passed to a mock implementation as well as to a table in the test target's own database.
In the examples above, we have declared data in tests using WithData
, then sent data to mocks and state handlers, via the context store and the builders.
There is obviously a connection between WithData
/Build
in a test, and WithData
/Build
in mocks and state handlers, and it is quite possible
to write tests without understanding the finer details of that connection.
However, for those who want to dig a bit deeper in order to excel at writing tests, here are the finer details.
The interfaces for mocks and state handlers, IMockForData<T>
and IStateHandler<T>
implement PreBuild
and PostBuild
in addition to WithData
and Build
,
/// Declare data of type <c>T</c>.
void WithData(T data);
/// Called before build only once for the instance, allows you to prepare to populate state.
void PreBuild();
/// Use the declared data to populate state, called after all data of type <c>type</c> has been put to the instance with <c>WithData</c>.
void Build(Type type);
/// Called after build, only once for the instance.
void PostBuild();
We implement either IMockForData<T>
or IStateHandler<T>
, but not both in a single class.
It is often a good idea to implement either of the interfaces for several types T
1, T
2, ... T
n in a single class. For example, a state handler which handles data in a SQL database, will be able to insert and delete records corresponding to T
1, T
2, ... T
n respecting relational constraints in the database.
In general, the data life-cycle is,
WithData
in a test will add data to the internal context data store; no data is passed to any mock or state handler.Build
in a test will initiate a sequence of calls which will pass the data in the internal context data store to relevant mocks and state handlers. For each instance which implements either IMockForData<T
1>
, IMockForData<T
2>
, ... , IMockForData<T
n>
or IStateHandler<T
1>
, IStateHandler<T
2>
, ..., IStateHandler<T
n>
,
PreBuild
is called once, allowing you to prepare to use the data which is coming. This allows you to delete any T
1, T
2, ... T
n data stored in the instance, so that it is entirely clean. A state handler which handles data in a SQL database will delete all records, a mock will clear any internal data structures.
WithData
is called once per piece of data in the internal context data store, for each type T
1, T
2, ... T
n of data. This is where you would store the data in your own internal data structures in the mock or state handler.Build
is called once per type T
1, T
2, ... T
n of data in the internal context data store. This is where you would commit the data from the WithData
phase if you need to do that per data type. If on the other hand, you do not need per-data type processing, or perhaps you require cross-data type processing, then you will do your processing in PostBuild
instead.PostBuild
is called once, allowing you to clean-up. This is where you would do cross-data type work, such as storing data in a relational database, assuming that there are relational constraints. This is also where you would dispose resources needed during the earlier phases.Note that the above text describes the full flexibility of the data life-cycle, whereas most mock and state handler implementations will be much simpler.
Most implementations will simply receive one or more pieces of data which are returned by methods of a mocked interface. In such cases, PreBuild
will be a no-op, WithData
will be a single line (an assignment), and both Build
and PostBuild
will be no-ops. Here is the full implementation of the time mock mentioned above (and described in details in Lean Test Coding Patterns)
WithData
stores a DateTime
instance and the two methods of the IDateTime
mocked interface are implemented simply by returning that instance or calling Ticks
on it respectively,public class MockForDataDateTime : IDateTime, IMockForData<DateTime>
{
// The two methods of the IDateTime mocked interface:
public DateTime UtcNow { get; private set; } = DateTime.UtcNow;
public long Ticks => UtcNow.Ticks;
public void WithData(DateTime data) => UtcNow = data;
public void PreBuild() {} // Can be omitted entirely.
public void Build(Type type) {} // Can be omitted entirely.
public void PostBuild() {} // Can be omitted entirely.
}
Most tests call WithData
multiple times, then call Build
once on a context builder. This is because most test cases are simple enough that a single call to Build is sufficient.
But the entire data life-cycle is traversed every time a test calls Build and this allows us to handle more advanced test cases.
Some examples of advanced cases involve testing of time based functionality. Such tests will typically not be simple AAA tests, with a single arrange, a single act and a single assert. Rather, it is common to set the state of the test target, perhaps do a bit of acting, change the current time, act a bit more etc.
Again, we can use our write-cache as an example. We arrange data, act in a way that puts data in the cache, assert that the cache has not been flushed (as no time has passed yet), then arrange that time passes past the flush delay, then finally assert that the cache has flushed as expected.
Here is the test using the extended AAA,
[TestMethod]
public void PutMustUpdateWhenTimeBetweenCacheFlushesHasElapsed()
{
DateTime startDateTime = DateTime.UtcNow;
_contextBuilder
.WithData<UserSettingsRow>(TestData.UserSettingsRows.User1InitialSettings)
.WithData(startDateTime)
.Build();
string pre = _reader.Query(new UserSettingsQuery { UserId = 1, AppId = 42, Path = "text" });
_target.PutUserSettings(new JValue("New value."), "text");
string afterPutButBeforeExpiry = _reader.Query(new UserSettingsQuery { UserId = 1, AppId = 42, Path = "text" });
_contextBuilder.WithClearDataStore()
.WithData(startDateTime + TimeSpan.FromMilliseconds(UserSettingsWriteCache.DefaultMillisecondsBetweenCacheFlushes + 1))
.Build();
string actual = _reader.Query(new UserSettingsQuery {UserId = 1, AppId = 42, Path = "text" });
MultiAssert.Aggregate(
() => Assert.AreEqual(@"""This is a string.""", pre),
() => Assert.AreEqual(@"""This is a string.""", afterPutButBeforeExpiry),
() => Assert.AreEqual(@"""New value.""", actual));
}
Note that with the magic of MultiAssert
we put all assert statements at the end of the test.
Also note the use of WithClearDataStore
, which clears all data which was passed in WithData
statements in the first arrange block. The effect of this is that only data from the following WithData
statement will be passed in the data life-cycle, so the UserSettingsRow
above will not be passed again. In other words, WithData
in mocks and state handlers will not be called again for the type UserSettingsRow
. However, PreBuild
and PostBuild
will still be called, since the data types are known to LeanTest.Net.
This is important to bear in mind when using multiple build calls, as it might influence the details of how mocks and state handlers must be implemented.
LeanTest.Net only knows about mocks and state handlers that you explicitly expose to it. This is the only way; LeanTest.Net will never magically find mocks and state handlers using reflection. This is what we call the no magic principle.
You expose your preferred IoC container by implementing a simple interface (or simply get the implementation from the LeanTest.DI.DotNetCore package if you use .NET Core/.NET 5),
public interface IIocContainer
{
T Resolve<T>() where T : class;
T TryResolve<T>() where T : class;
IEnumerable<T> TryResolveAll<T>() where T : class;
}
Your implementation is passed to LeanTest.Net initialization once per test suite. Note that it is wrapped in a factory which ensures that a new IoC container instance, along with new mock and state handler instances, are created before each test is run.
The consequence of this is that each test will start with an empty context data store, empty (i.e., no data passed yet) mocks and state handlers.
It also means that the builders for mocks and state handlers are empty in the sense that they do not know of any types for which to call PreBuild
/Build
/PostBuild
on mocks and state handlers yet.
This is a consequence of our no magic principle, which is exactly what is needed in most cases.
At rare occasions you may wish to have slightly more low-level control of the steps in the data life-cycle for certain data types.
We have already mentioned WithClearDataStore
, which you use when you want to avoid passing data again to mocks and state handlers in subsequent calls to Build
. This way, you control how WithData
and Build
is called on mocks and state handlers.
The data life-cycle also controls when PreBuild
and PostBuild
are called; in short, these will be called on instances which handle a type for which data has been declared. For example, if data for the type T
1 has been declared, then PreBuild
and PostBuild
will be called on implementations of IMockForData<T
1>
and IStateHandler<T
1>
. However, if you don't have data for T
2 in a given test, but still want PreBuild
and PostBuild
to be called on implementations of IMockForData<T
2>
and IStateHandler<T
2>
, then you simply declare empty T
2 data,
_contextBuilder
.WithData<T2>()
// ...
Experimental, not implemented yet: With the example above in mind, you may wish to go the opposite way; you may want the builders to forget about the type T
1, effectively stopping them from calling PreBuild
and PostBuild
on implementations of IMockForData<T
1>
and IStateHandler<T
1>
. For this you use WithClearBuilders
.
_contextBuilder
.WithClearBuilders()
// ...
Note that WithClearBuilders
imply WithClearDataStore
. Also note that WithClearBuilders
and WithClearDataStore
are never needed until the second call to Build
on the context builder, as a test will always start with a clear data store and clear builders.


Berlin
Your Points of Interest Information
Recommended Points
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Last Saved Points
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Gallery
Each of the examples below were built both test as well as illustrate Plato's generality and ease-of-use. For each example, you may (1) compile and view the Plato form and (2) edit the declarative description of that form. For those examples drawn from real-world applications, you can also (3) view the original. You may also (4) collaboratively contribute data to the resulting forms (for some of the examples).
For some examples, compilation may take 5-10 seconds, during which the browser's status line will read "Contacting <server>".
- A bite-sized example, small enough for perusing the generated code. There are two widgets, p and q, and the only constraint says that p(x) implies q(x).
Compile |
Edit
With selector widgets.
Compile
|
Edit
With multi-value textboxes
- On its website, Toyota allows customers to investigate the various options available for each of its cars. This Plato example encapsulates that functionality for the 2009 Toyota Sequoia. (Toyota recently updated their website for the 2010 models, so the Original link below no longer has the same content as the Plato example.)
Compile |
Edit |
Original
Whereas the original forces users to choose a repair strategy when choosing an invalid option combination, the Plato version allows users to choose arbitrary options and then highlights the combinations that are invalid.
Compile |
Edit
The small version from the introductory page.
- Many shopping cart applications ask for shipping and billing addresses, and many offer the ability to automatically populate the billing address with the already-entered shipping address. This example illustrates such functionality.
Compile |
Edit
This example was developed to illustrate (1) the use of text fields in place of selectors (which at first seem problematic because of their infinite domains) and (2) the automatic population of widget values.
- Feature-based programming involves code modules that can be combined only in certain ways. Plato can be used to generate an interface that allows customers to choose among modules and be informed whether or not the selected modules can be combined. More Info
Compile |
Edit
This version uses checkboxes and radio buttons, ensuring all widgets have values at all times. It illustrates the benefits and drawbacks of using GUI elements with no 'undefined' value.
Compile |
Edit
|
Collaborate
This version was designed to enable people to collaboratively make a feature selection. In place of checkboxes and radio buttons, it employs selectors.
- Scientists sometimes write software and make it available via the web. Here is an example of a biochemistry tool for morphing structures.
Compile
| Edit
| Original
This example illustrates (1) the built-in functions (e.g. +, *, >) available in Plato and (2) the JavaScript-only extension of Plato to hide widgets based on constraints. A work in progress.
Comments and suggestions to tlh@uchicago.edu


Preparation time
overnight
Cooking time
30 mins to 1 hour
Serves
Makes 16
Dietary
These tasty vegetarian burgers use quinoa and halloumi for a healthier alternative to meat or takeaways.
Each burger provides 122 kcal, 6.5g protein, 11g carbohydrate (of which 1.3g sugars), 6g fat (of which 3.5g saturates), 0.5g fibre and 0.5g salt.
Put the quinoa in a bowl and cover with approximately 500ml/18fl oz cold water. Add a pinch of salt. Set aside to soak overnight (or for at least 8 hours). Drain well.
Bring the stock to the boil in a large saucepan. Add the quinoa, stir once, then simmer for 10-12 minutes, or until tender. Remove from the heat and drain well. Set aside to cool.
Preheat the oven to 190C/170C Fan/Gas 5.
In a large mixing bowl, beat together the eggs, gram flour, herbs, chilli, and garlic until smooth and well combined. Stir in the spring onions, halloumi and quinoa. Season with salt and pepper. Add a little more gram flour or water if needed.
Shape the burger mixture into 16 equal-shaped patties in your hands.
Heat the ghee in a large frying pan over a medium-high heat. When the fat is smoking, add the burger patties, shape with a spoon in the pan. You may need to do this in batches. Fry for 3-4 minutes, or until a golden-brown crust forms, then gently flip with a spatula and fry the other side.
Transfer the fried burgers to a roasting tray. When all of the burgers have been fried, cook them in the oven for a further 12-15 minutes, or until cooked through.
Serve with a green salad with red onion and avocado.


element_n
is a meta-function
similar to the element
.
The only difference between this element
and element_n
is that
element_n
operates using
the member's name instead of its index.
namespace boost { namespace bitfields { template<typename BitfieldTuple, typename Name> struct element_n; }}
|
Parameter |
Description |
|---|---|
|
|
The |
|
|
The |
| Note | |
|---|---|
If there is a need for retrieving a |
|
Precondition |
Description |
Enforcement |
|---|---|---|
|
|
The |
Enforced by static assert. |
|
BitfieldTuple must be a |
Means that if something other then a |
Documented requirement only. Not enforced. |
N
is used for type element_n
within the following interface
documentation.
|
Operation |
Description |
|---|---|
|
|
This is the type which would be returned from the |
This meta-function is included along with the <boost/integer/bitfield_tuple.hpp> header file. There is no need to directly include the element header file within your code.
#include <boost/integer/bitfield_tuple/element.hpp>
Basic example of how to use the element_n
meta-function to retrieve the return type of the get
member or free function.
#include <boost/integer/bitfield_tuple.hpp> using namespace boost::bitfields; struct i; struct red; struct green; struct blue; typedef bitfield_tuple< member<unsigned int,red,5>, member<unsigned int, green, 6>, member<unsinged int, blue,5> > rgb565; // write a function whose return type is the proxy reference type // which would be returned from calling get<0>() or get<red>() element_n<rgb565,red>::type my_func(rgb565 bft) { return bft.get<red>(); // this could be replaced with get<0>() } // make a const version of my_func element_n<const rgb565,red>::type my_func(rgb565 const& bft) { return bft.get<red>(); // this could be replaced with get<0>() }
How to use element_n to make views from a bitfield_tuple
type. const
still applies
to the views, if one would like a const
view into something simply supply the const
qualifier to the BitfieldTuple template parameter and that's all that
needs to be done.
#include <boost/integer/bitfield_tuple.hpp> #include <cassert> using namespace boost::bitfields; struct red; struct green; struct blue; typedef bitfield_tuple< storage<std::size_t>, member<char,red,4>, member<unsigned char, green,5>, member<int, salmon, 16> > test_tuple_1; int main() { typedef element_n<test_tuple_1,red>::type view_red; std::size_t storage = 0; // constructs the red view type over storage // which is simply std::size_t. view_red red_v(storage); assert(red_v == 0); red_v = 3; assert(red_v == 3); return 0; }


About Us
What We Do
In addition to offering traditional and established medical care services, Gurukripa is distinguished with its tertiary and quaternary services. This means Gurukripa has the personnel and facilities to provide advanced medical inquiry and treatments that are not widely accessible in this region.
Whether a patient is coming in for a routine or a specialized procedure, they will always benefit from Gurukripa’s unique ability to quickly implement the newest discoveries and advancements in medicine. A few of Gurukripa’s many highly specialized services include:
- Organ Transplant
- Trauma and Burn Care
- Neonatal Intensive Care Unit
- Comprehensive Neuromedicine
- Complex Cardiology
- Cancer Service
Many of Gurukripa’s specialty programs consistently rank among “America’s Best Hospitals” and earned top honors in the first-ever “Best Regional Hospitals” 2010-11 ranking according to U.S. News & World Report. With 739 beds, most patients come from Monroe County but over 35 percent come from surrounding counties in the Upstate New York and Finger Lakes area.
How We Do It
Gurukripa is an exemplary teaching hospital with advanced scientific proficiencies, robust patient care services, and formidable community relations. These qualities and the dedicated staff who support them are precisely what elevate Gurukripa’s reputation. As a teaching hospital, all of Gurukripa’s medical clinicians are clinician scholars with faculty appointments (at the University of Rochester School of Medicine and Dentistry) who supervise residents and participate in the education of residents and students. The result is a learning environment immersed in research, education, community, and innovation—Medicine of the Highest Order.
Gurukripa is also characterized by having medical clinicians who are comfortable working with many other specialists in an interdisciplinary, team-based environment. Familiarity and ease with this level of collaboration allows staff to harness the powerful interaction of three distinct but interrelated missions: research, education, and patient care. The fruit of this interaction is found in not only how clinicians teach but also how they practice. Indeed, all of Gurukripa’s staff is committed to consistently delivering a high quality patient experience. This includes working to build partnerships with patients and their families along with identifying ways to empower patients with information and the tools they need to make informed decisions about their health care.
What This Means for You
As a result of Gurukripa’s approach, each and every patient has an extensive number of specialists who can be seamlessly integrated into their health care team. A condition is never isolated. Patients always have a team of people and specialists taking care of them, all supported with the highest technology. That technology also applies to communication. All of a patient’s medical information is consolidated into a single medical record thus eliminating potential for miscommunication.
This means that if you go in for a standard procedure and there is a complication, a specialist is on hand to be instantly included into your health care team. Moreover, that specialist represents the most advanced medical care and research. When you combine this expertise with Gurukripa’s philosophy of developing a meaningful partnership with patients and families the result is nothing short of a transformational patient experience.


J. K. Rowling, born in 1965, British author, who writes about the magical adventures of a boy wizard named Harry Potter.
The Harry Potter book series dominated bestseller lists beginning in the late 1990s, attracting millions of fans worldwide among children and adults alike.
Like that of her own character, Harry Potter, J.K. Rowling's life has the luster of a fairy tale. Divorced, living on public assistance in a tiny Edinburgh flat with her infant daughter, Rowling wrote Harry Potter and the Sorcerer's Stone at a table in a cafe during her daughter's naps — and it was Harry Potter that rescued her. Joanne Kathleen Rowling entered the world in Chipping Sodbury General Hospital in Bristol, England, a fitting beginning for someone who would later enjoy making up strange names for people, places and games played on flying broomsticks. Her younger sister Di was born just under two years later.
Rowling remembers that she always wanted to write and that the first story she actually wrote down, when she was five or six, was a story about a rabbit called Rabbit. Many of her favorite memories center around reading—hearing The Wind in the Willows read aloud by her father when she had the measles, enjoying the fantastic adventure stories of E. Nesbit, reveling in the magical world of C. S. Lewis's Narnia, and her favorite story of all, The Little White Horse by Elizabeth Goudge.
train ride from Manchester to London in the summer of 1990, the idea came to her of a boy who is a wizard and doesn't know it. He attends a school for wizardry--she could see him very plainly in her mind. By the time the train pulled into King's Cross Station four hours later, many of the characters and the early stages of the plot were fully formed in her head. The story took further shape as she continued working on it in pubs and cafes over her lunch hours. In 1992 Rowling left off working in offices and moved to Portugal to teach English as a Second Language. In spite of her students making jokes about her name (this time they called her "Rolling Stone"), she enjoyed teaching. She worked afternoons and evenings, leaving mornings free for writing. After her marriage to a Portuguese TV journalist ended in divorce, Rowling returned to Britain with her infant daughter and a suitcase full of Harry Potter notes and chapters. She settled in Edinburgh to be near her sister and set out to finish the book before looking for a teaching job. Wheeling her daughter's carriage around the city to escape their tiny, cold apartment, she would duck into coffee shops to write when the baby fell asleep.
In this way she finished the book and started sending it to publishers. It was rejected several times before she found an London agent, chosen because she liked his name-- Christopher Little, who sold the manuscript to Bloomsbury Children's Books. Rowling was working as a French teacher when she heard that her book about the boy wizard had been accepted for publication. Harry Potter and the Philosopher's Stone was published in June 1997 and achieved almost instant success. With the publication of the American edition, retitled Harry Potter and the Sorcerer's Stone, in 1998, Rowling's books continued to make publishing history. Harry Potter climbed to the top of all the bestseller lists for children's and adult books.


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
The Structural Mechanics Application contains a series of structural elements, as well as solid elements, constitutive laws and the corresponding strategies and solvers within Kratos Multiphysics.
The application includes tests to check the proper functioning of the application


Nokia US seasonal offers: Big discounts on Nokia 9 PureView, 7.1, 4.2, 2.2 & 3.1 Plus
by NayanNokia Mobile is offering steep discounts on select Nokia smartphones in the US in what it has termed as “Seasonal offers”. The offers cover Nokia 9 PureView, Nokia 7.1, Nokia 4.2, Nokia 2.2 and Nokia 3.1 Plus.
Nokia 9 PureView is discounted to just USD 399 at BestBuy and B&H PhotoVideo. On BestBuy one can even buy Nokia 9 PureView for just $349 with instant activation.
Nokia 7.1 is priced at $209 at BestBuy with instant activation.
Similarly Nokia 2.2 is discounted to $129, Nokia 3.1 Plus to $79 and Nokia 4.2 to $159.
Check all the seasonal offers here.


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[gecode-users] Bugs in Gecode/J
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Tue Apr 15 08:07:11 CEST 2008
Hi,
When the JVM crashes it leaves a file called hs_err_pid????.log
(replace ???? by some string of numbers), which contains a lot of very
interesting information. In particular, it contains the current stack
of the JVM, as well as as much of the current native stack as
possible. To debug what has happened, these are the files to look at.
If you need help with a particular error, then you should attach such
a file.
Cheers,
Mikael
On Mon, Apr 14, 2008 at 11:01 AM, Malcolm Ryan <malcolmr at cse.unsw.edu.au> wrote:
> My Gecode/J constraint program is occasionally crashing with the
> message:
>
> pure virtual method called
> terminate called without an active exception
>
> and occasionally with:
>
> Invalid memory access of location 00000000 eip=00000000
>
> I can't say for sure, but I don't think they are happening in my code.
> I'm having trouble tracking down where in the code this is happening
> (as they are not proper Java exceptions and can't be caught). I added
> some debug printfs and the errors changed. Go figure.
>
> Any suggestions?
>
> Malcolm
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
More information about the gecode-users
mailing list


All Classes
Account
APIClient
APIException
Auth
Capabilities
Client
Computer
CreateAccountJson
CreateAccountObject
Download
Options
Printer
PrintJob
PrintJobJson
Scale
State
Whoami


Senior Ameutar Mathematician
I like to build stuff using math.
- Strong software design skill
- Knowledge of multiple stacks
Core Competencies
- Full Stack Development
- Front End Development
- Server Side programming
- Hybrid Mobile Development
- Savvy Problem Solver
- Muscles
Certifications and Technical Proficiences
- Platforms Linux, LAMP, MEAN, NodeJS
- Databases MySQL, MongoDB
- Tools VS Code, SSH, Gulp, Git
- Languages HTML, CSS, SASS, JavaScript, ES6, PHP, BASH, SQL TypeScript
Professional Experience
Emplyeer 1 - Chicago, IL June - Present
About the employeer
Job Title
- Job duty 1
- Job duty 2
Key Contributions
- Contribution 1
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display_image/[1,3]
create_image/[2,3] | - create an image from a GIF file
display_image/[1,3] | - display an image
FORMS
create_image(ImagePath, ImageName)
create_image(Interp, ImagePath, ImageName)
display_image(ImageName)
display_image(Interp, ImageName, Options)
DESCRIPTION
These routines provide simple access from ALS Prolog to the image routines of
Tk. The current versions support GIF images, but the routines can be extended
to any of the types Tk supports. To display images, one must specify a path
to the image file, and must first produce an internal Tk form of the image.
This is done with:
create_image(ImagePath, ImageName)
:-
create_image(tcli, ImagePath, ImageName).
create_image(Interp, ImagePath, ImageName)
Assume that
pow_wow_dance.gif
is a file in the current directory. Then the call
?- create_image(‘pow_wow_dance.gif’, pow_wow).
will create the internal form of this image and associate the name pow_wow
with it. Display of images which have been created is accomplished with:
display_image(ImageName)
:-
display_image(tcli, ImageName, []).
display_image(Interp, ImageName, Options)
Thus, the call
?-display_image(pow_wow).
produces


The Whole Story
We want to show the world what flower power smells like
Life without plants and flowers is unthinkable. The awakening of nature in the spring is a delight for the eyes. The bees are hard working and with their buzzing they complement the spring atmosphere. Flowers reward us for our efforts in the garden with its color, scent and beauty. It's a wonderful feeling when the plants we care for bloom. Enjoy the view as the spring sun keeps you warm. That is why many say that spring is the most beautiful season! Read more here
Best flowers on FER
Life begins the day you start a garden. - A Chinese proverb
News, Events & Such
Beautiful new flowers
January 25, 2020
New arrivals in our store! Many more beautiful new flowers are now available. We can't wait for you to check them out.
Read moreFlower workshop on Tuesday
January 25, 2020
Workshop on proper care of various flower species. Focused on the beautiful orchids and how to maintain your blue orchid.
Read more


type: page_en slug: home url: index.html title: Python in Education ---
Master the basics, progress to the fun stuff and achieve coding greatness with lessons for learners of all ages, levels and abilities.
get startedInspire the next generation of Python programmers with fun resources, tested in the field by teachers and members of the global Python community.
fun resourcesDiscover all the technical details, designs and source code needed to build something amazing, create a cool project or make a contribution to our educational efforts.
let's go!It's used all over the world by all sorts of people, from professional coders to complete beginners. It's very easy to learn. Use Python to code cool stuff on your micro:bit, Raspberry Pi or other computing device.
Start here for an introduction to Python, for intermediate material that'll help you stretch your wings, and for advanced features that'll have you building your world-changing project from the comfort of your own home in no time at all!
try out a tutorialThe BBC micro:bit is a small programmable device. The aim is to inspire a new generation of digital creators, makers and coders. Thanks to the amazing MicroPython project, it runs a full re-implementation of Python 3.
The device consists of two buttons, a 5x5 LED matrix, I/O pins, an accelerometer, compass, radio, micro-USB port, battery connector and ARM microprocessor.
code the future with micro:bitThe Raspberry Pi is a unique phenomenon. It is the most successful computing-in-education project in history with over 6 million units delivered around the world.
But it's not just about the hardware! Thanks to the amazing work of the Raspberry Pi Foundation's education team, Python is widely used on the device to do all sorts of fun stuff such as program games (like Minecraft), travel into space (to the International Space Station) and control peripherals plugged into the Pi via its GPIO pins.
enjoy a slice of raspberry piTeachers and programmers from all over the world have created fun, engaging and challenging resources for teachers to use in their classrooms. These range from simple tasks for beginners to advanced projects to challenge the most advanced students. They include lesson plans, code, books, worksheets and online interactive courses.
Most importantly, many of these resources are free for people to use, adopt, adapt and contribute to.
discover compelling and creative resourcesCommunity is Python's secret weapon.
The Python community value openness, actively engage in outreach and give up their time for educational support (this site was written by volunteers). As one person put it, "I can for the programming language, but stayed for the community".
 There's a good chance there's one near you very soon.
If you're looking for support, advice or just some friendly encouragement, get involved and say "hello".
meet and make friends with your local communityPython has comprehensive and excellent documentation. In fact, many in the Python community spend lots of effort making sure Python projects have comprehensive, detailed and easy to understand technical documentation.
Need to know how to do something? Read the docs! Looking for the best way to use some code? Read the docs! Want to learn something new so you can impress your programming buddies with your knowledge? Read the docs!
read the docs!Python is free software: it is given away for free (nobody pays to use Python), you are also free to use it as you see fit and the Python community actively encourages people to contribute code for the benefit of others.
For this to work you need access to the source code: the instructions for the computer that tell it how to do the stuff you want it to do.
Perhaps you already know this stuff and you're just looking for a cool educational project to which you can contribute. If so, you've come to the right place!
hack on code


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{% else %}Frequently Asked Questions
{% endif %}What is this app?
Thanks for asking! This is an applcication that helps you meet new friends and form study groups here at Boston University!
How does it work?
It's simple! You provide us with some basic contact information, your courses, and when you're free, while we take care of the rest! If we find you a match, we'll send you an email and you can work on your coursework together!
Who made this?
This project was developed by Zach Halvorson and Michael Hendrick in the Fall of 2018. Created with the goal of bringing Boston University students together, WinWin represents what it means to create an inclusive community in the chaotic environment that is college.
Dashboard


{% include head.html %}
We want to continue from the success of the Hack Brexit hackathon on the 23-4 July to bring together technical and non-technical people to create tech solutions to drive dialog, unity and positive action beyond Brexit. We want to sustainably support and scale up projects that have a clear positive impact on society and respond to one of the three key themes we chose for the weekend:
Fortnightly meeting for the teams within Hack Brexit's accelerator programme to present and discuss their progress. We've taken forward two of the projects from July's hackathon to participate in our open-source accelerator programme:
What the fact read the wiki here
Ministers under the influence read the wiki here
We look forward to seeing you there!
Hack Brexit Team


6
Megiorni (2011) Zhang (2010) Akar (2002) Barbosa (2016) D+L Overall (-squared = 0.0%, p = 0.600)
M-H Overall
NOTE: Weights are from random effects analysis
s S
%
Events, Events, Weight
OR (95% Cl) AA
2.10 (1.05, 4.20) 17/102 1.44 (0.78, 2.68) 11/132 0.89 (0.28, 2.82) 7/65 2.04 (0.63, 6.59) 5/33 1.61 (1.08, 2.41) 40/332
1.60 (1.07, 2.39)
Controls (D+L)
20/230 33.72
1571/26501 42.33 6/50 12.09 9/112 11.86
1606/26893 100.00


simulation::random - Pseudo-random number generators
This package consists of commands to generate pseudo-random number generators. These new commands deliver
numbers that are distributed normally, uniformly, according to a Pareto or Gumbel distribution and so on
coordinates of points uniformly spread inside a sphere or a rectangle
For example:
set p [::simulation::random::prng_Normal -1.0 10.0]
produces a new command (whose name is stored in the variable "p") that generates normally distributed numbers with a mean of -1.0 and a standard deviation of 10.0.
The package defines the following public procedures for discrete distributions:
Create a command (PRNG) that generates numbers with a Bernoulli distribution: the value is either 1 or 0, with a chance p to be 1
Chance the outcome is 1
Create a command (PRNG) that generates numbers 0 to n-1 with equal probability.
Number of different values (ranging from 0 to n-1)
Create a command (PRNG) that generates numbers according to the Poisson distribution.
Mean number per time interval
The package defines the following public procedures for continuous distributions:
Create a command (PRNG) that generates uniformly distributed numbers between "min" and "max".
Minimum number that will be generated
Maximum number that will be generated
Create a command (PRNG) that generates triangularly distributed numbers between "min" and "max". If the argument min is lower than the argument max, then smaller values have higher probability and vice versa. In the first case the probability density function is of the form f(x) = 2(1-x) and the other case it is of the form f(x) = 2x.
Minimum number that will be generated
Maximum number that will be generated
Create a command (PRNG) that generates numbers distributed according to a symmetric triangle around the mean of "min" and "max".
Minimum number that will be generated
Maximum number that will be generated
Create a command (PRNG) that generates exponentially distributed numbers with a given minimum value and a given mean value.
Minimum number that will be generated
Mean value for the numbers
Create a command (PRNG) that generates normally distributed numbers with a given mean value and a given standard deviation.
Mean value for the numbers
Standard deviation
Create a command (PRNG) that generates numbers distributed according to Pareto with a given minimum value and a given distribution steepness.
Minimum number that will be generated
Steepness of the distribution
Create a command (PRNG) that generates numbers distributed according to Gumbel with a given minimum value and a given scale factor. The probability density function is:
P(v) = exp( -exp(f*(v-min)))
Minimum number that will be generated
Scale factor for the values
Create a command (PRNG) that generates numbers distributed according to the chi-squared distribution with df degrees of freedom. The mean is 0 and the standard deviation is 1.
Degrees of freedom
The package defines the following public procedures for random point sets:
Create a command (PRNG) that generates (x,y)-coordinates for points uniformly spread over a disk of given radius.
Radius of the disk
Create a command (PRNG) that generates (x,y,z)-coordinates for points uniformly spread over the surface of a sphere of given radius.
Radius of the disk
Create a command (PRNG) that generates (x,y,z)-coordinates for points uniformly spread within a ball of given radius.
Radius of the ball
Create a command (PRNG) that generates (x,y)-coordinates for points uniformly spread over a rectangle.
Length of the rectangle (x-direction)
Width of the rectangle (y-direction)
Create a command (PRNG) that generates (x,y,z)-coordinates for points uniformly spread over a block
Length of the block (x-direction)
Width of the block (y-direction)
Depth of the block (z-direction)
Mathematics
Copyright © 2004 Arjen Markus <arjenmarkus@users.sourceforge.net>


Regression - RDD-based API
Isotonic regression
Isotonic regression
belongs to the family of regression algorithms. Formally isotonic regression is a problem where
given a finite set of real numbers $Y = {y_1, y_2, ..., y_n}$
representing observed responses
and $X = {x_1, x_2, ..., x_n}$
the unknown response values to be fitted
finding a function that minimizes
\begin{equation}
f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2
\end{equation}
with respect to complete order subject to
$x_1\le x_2\le ...\le x_n$
where $w_i$
are positive weights.
The resulting function is called isotonic regression and it is unique.
It can be viewed as least squares problem under order restriction.
Essentially isotonic regression is a
monotonic function
best fitting the original data points.
spark.mllib
supports a
pool adjacent violators algorithm
which uses an approach to
parallelizing isotonic regression.
The training input is an RDD of tuples of three double values that represent
label, feature and weight in this order. Additionally, IsotonicRegression algorithm has one
optional parameter called $isotonic$ defaulting to true.
This argument specifies if the isotonic regression is
isotonic (monotonically increasing) or antitonic (monotonically decreasing).
Training returns an IsotonicRegressionModel that can be used to predict labels for both known and unknown features. The result of isotonic regression is treated as piecewise linear function. The rules for prediction therefore are:
- If the prediction input exactly matches a training feature then associated prediction is returned. In case there are multiple predictions with the same feature then one of them is returned. Which one is undefined (same as java.util.Arrays.binarySearch).
- If the prediction input is lower or higher than all training features then prediction with lowest or highest feature is returned respectively. In case there are multiple predictions with the same feature then the lowest or highest is returned respectively.
- If the prediction input falls between two training features then prediction is treated as piecewise linear function and interpolated value is calculated from the predictions of the two closest features. In case there are multiple values with the same feature then the same rules as in previous point are used.
Examples
Data are read from a file where each line has a format label,feature i.e. 4710.28,500.00. The data are split to training and testing set. Model is created using the training set and a mean squared error is calculated from the predicted labels and real labels in the test set.
Refer to the IsotonicRegression
Scala docs and IsotonicRegressionModel
Scala docs for details on the API.
import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel}
import org.apache.spark.mllib.util.MLUtils
val data = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_isotonic_regression_libsvm_data.txt").cache()
// Create label, feature, weight tuples from input data with weight set to default value 1.0.
val parsedData = data.map { labeledPoint =>
(labeledPoint.label, labeledPoint.features(0), 1.0)
}
// Split data into training (60%) and test (40%) sets.
val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0)
val test = splits(1)
// Create isotonic regression model from training data.
// Isotonic parameter defaults to true so it is only shown for demonstration
val model = new IsotonicRegression().setIsotonic(true).run(training)
// Create tuples of predicted and real labels.
val predictionAndLabel = test.map { point =>
val predictedLabel = model.predict(point._2)
(predictedLabel, point._1)
}
// Calculate mean squared error between predicted and real labels.
val meanSquaredError = predictionAndLabel.map { case (p, l) => math.pow((p - l), 2) }.mean()
println(s"Mean Squared Error = $meanSquaredError")
// Save and load model
model.save(sc, "target/tmp/myIsotonicRegressionModel")
val sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
Data are read from a file where each line has a format label,feature i.e. 4710.28,500.00. The data are split to training and testing set. Model is created using the training set and a mean squared error is calculated from the predicted labels and real labels in the test set.
Refer to the IsotonicRegression
Java docs and IsotonicRegressionModel
Java docs for details on the API.
import scala.Tuple2;
import scala.Tuple3;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.mllib.regression.IsotonicRegression;
import org.apache.spark.mllib.regression.IsotonicRegressionModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(
jsc.sc(), "data/mllib/sample_isotonic_regression_libsvm_data.txt").toJavaRDD();
// Create label, feature, weight tuples from input data with weight set to default value 1.0.
JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(point ->
new Tuple3<>(point.label(), point.features().apply(0), 1.0));
// Split data into training (60%) and test (40%) sets.
JavaRDD<Tuple3<Double, Double, Double>>[] splits =
parsedData.randomSplit(new double[]{0.6, 0.4}, 11L);
JavaRDD<Tuple3<Double, Double, Double>> training = splits[0];
JavaRDD<Tuple3<Double, Double, Double>> test = splits[1];
// Create isotonic regression model from training data.
// Isotonic parameter defaults to true so it is only shown for demonstration
IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training);
// Create tuples of predicted and real labels.
JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(point ->
new Tuple2<>(model.predict(point._2()), point._1()));
// Calculate mean squared error between predicted and real labels.
double meanSquaredError = predictionAndLabel.mapToDouble(pl -> {
double diff = pl._1() - pl._2();
return diff * diff;
}).mean();
System.out.println("Mean Squared Error = " + meanSquaredError);
// Save and load model
model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
IsotonicRegressionModel sameModel =
IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
Data are read from a file where each line has a format label,feature i.e. 4710.28,500.00. The data are split to training and testing set. Model is created using the training set and a mean squared error is calculated from the predicted labels and real labels in the test set.
Refer to the IsotonicRegression
Python docs and IsotonicRegressionModel
Python docs for more details on the API.
import math
from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel
from pyspark.mllib.util import MLUtils
# Load and parse the data
def parsePoint(labeledData):
return (labeledData.label, labeledData.features[0], 1.0)
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_isotonic_regression_libsvm_data.txt")
# Create label, feature, weight tuples from input data with weight set to default value 1.0.
parsedData = data.map(parsePoint)
# Split data into training (60%) and test (40%) sets.
training, test = parsedData.randomSplit([0.6, 0.4], 11)
# Create isotonic regression model from training data.
# Isotonic parameter defaults to true so it is only shown for demonstration
model = IsotonicRegression.train(training)
# Create tuples of predicted and real labels.
predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
# Calculate mean squared error between predicted and real labels.
meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
print("Mean Squared Error = " + str(meanSquaredError))
# Save and load model
model.save(sc, "target/tmp/myIsotonicRegressionModel")
sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")


Mr..John Chukwu Chartered Bank (CB). Lagos-Nigeria Attn: Sir/Madam. In order to transfer out Twenty Two Million United States Dollars from Chartered Bank (CB). I wish to ask for your assistance, believing that you will never let me down either now or in future. I got your e-mail address from the e-mail directory in the internet. I am Mr..John Chukwu, the Chief Auditor of Chartered Bank (CB). There was an account opened in this bank in 1983 and since 1989 nobody has operated on this account till date. After my long investigation to my greatest suprise I came to find out that the owner of the account who is Mr.Peter B. Andreas, a foreigner, crude oil exporter, who died in 1990. This account has no beneficiary, infact no other person knows about this account or anything about the account, and my investigation proved to me as well that this company does not exist anymore in NIgeria. The amount involved is USD 22M, which i seek your consent to transfer to a safe foreign account. I do not know any foreigner that i can trust either than you, I am only contacting you as a foreigner because this money can not be claimed by a Nigerian because the rightful owner of the account (Mr. Peter) is a foreigner. I know that this mail will come to you as a surprise as we do not know each other before hand. we will be needed to sign an Agreement as a security so that we cannot betray the trust that will be given to eachother in this legitimate and joint venture, which demand utmost secrecy and confidential. I am contacting you because of the need to involve a foreigner with foreign account and foreign beneficiary. I need your full co-operation for us to have a successful transaction. Because the management is ready to approve this payment to any foreigner who has correct information of this account, which I will give to you immediately i receive your positive reply. With my position now in the office I can authorise the immediate transfer of this money to any foreign reliable account which you can provide with assurance that this money will be intact pending my physical arrival in your country for sharing and further investment. I will destroy all documents used in this transaction immediately we must have transferred this money to your nominated account sucessfully, so as to avoid any document trace by any person. I will apply for annual leave to get visa to your country immediately you confirm me that you are willing to assist me. I will also use my position and influence to effect legal approvals and onward transfer of this money to your account with appropriate clearance forms of the ministries and foreign exchange departments. At the conclusion of this business, you will be given 20% of the total amount, 75% will be for me, while 5% will be for expenses both parties might have incurred during the process of transferring. Awaiting your urgent reply through my private mail box<EMAIL_ADDRESS>and please do not FORGET TO INCLUDE YOUR PRIVATE FAX AND TELEPHONE NUMBERS FOR EASY COMMUNICATION. Thank you and God bless. Yours truly, MrJohn Chukwu


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In 2004 Google engineers tried lots of methods to parallelize their processes.
They implemented Map and Reduce. Which drastically increased the efficiency for finding documents
and processing the whole internet. The algorithm worked by using a list of Map functions supplied to the algorithm.
The algorithm would then create key value lists that would be fed into the reduce functions.
The language used for this project was Go. Go maps well to threaded server management
For a couple of reasons, 1. It's garbage collected to avoid thread waiting for collecting memory
Go is faster than Java because it is compiled. And Go was the supplied starter code and I did not want
To start this from scratch.
Map Reduce Diagram from Google Paper


RRtoolbox.tools package¶
Submodules¶
RRtoolbox.tools.lens module¶
-
RRtoolbox.tools.lens.
drawCircle
(array, cnt, color=0)[source]¶ project circle over array.
Parameters: - array – array to draw circle
- cnt – contours of segmentation to fit circle
- color – color of lens
Returns: array
-
RRtoolbox.tools.lens.
drawEllipse
(array, cnt, color=0)[source]¶ project ellipse over array.
Parameters: - array – array to draw ellipse
- cnt – contours of segmentation to fit ellipse
- color – color of lens
Returns: array
-
RRtoolbox.tools.lens.
fitLens
(img, mask, color=0, asEllipse=False, addmask=False)[source]¶ Place lens-like object in image.
Parameters: - img – image to place lens
- mask – mask to fit lens
- color – color of the lens
- asEllipse – True to fit lens as a ellipse, False to fit circle.
- addmask – return additional mask parameter
Returns: image with simulated lens
-
RRtoolbox.tools.lens.
simulateLens
(img, threshfunc=None, pshape=(300, 300), color=0, asEllipse=True)[source]¶ Place lens-like object in image.
Parameters: - img – image to place lens.
- threshfunc – function to segment retinal area and get its mask.
- pshape – shape to resize processing image to increase performance.
- color – color of the lens.
- asEllipse – True to fit lens as a ellipse, False to fit circle.
Returns: image with simulated lens.
RRtoolbox.tools.segmentation module¶
-
RRtoolbox.tools.segmentation.
find_optic_disc_watershed
(img, P)[source]¶ Find optic disk in image using a watershed method.
Parameters: - img – BGR image
- P – gray image
Returns: optic_disc, Crs, markers, watershed
-
RRtoolbox.tools.segmentation.
get_beta_params_Otsu
(P)[source]¶ Automatically find parameters for alpha masks using Otsu threshold value.
Parameters: P – gray image Returns: beta1 for minimum histogram value, beta2 for Otsu value
-
RRtoolbox.tools.segmentation.
get_beta_params_hist
(P)[source]¶ Automatically find parameters for bright alpha masks using a histogram analysis method.
Parameters: P – gray image Returns: beta1 for minimum valley left of body, beta2 for brightest valley right of body where the body starts at the tallest peak in the histogram.
-
RRtoolbox.tools.segmentation.
get_bright_alpha
(backgray, foregray, window=None)[source]¶ Get alpha transparency for merging foreground to background gray image according to brightness.
Parameters: - backgray – background image. (as float)
- foregray – foreground image. (as float)
- window – window used to customizing alfa. It can be a binary or alpha mask, values go from 0 for transparency to any value where the maximum is visible i.e a window with all the same values does nothing. A binary mask can be used, where 0 is transparent and 1 is visible. If not window is given alfa is not altered and the intended alpha is returned.
Returns: alfa mask
-
RRtoolbox.tools.segmentation.
get_layered_alpha
(back, fore)[source]¶ Get bright alpha mask (using Otsu method)
Parameters: - back – BGR background image
- fore – BGR foreground image
Returns: alpha mask
-
RRtoolbox.tools.segmentation.
layeredfloods
(img, gray=None, backmask=None, step=1, connectivity=4, weight=False)[source]¶ Create an alpha mask from an image using a weighted layered flooding algorithm,
Parameters: - img – BGR image
- gray – Gray image
- backmask – background mask
- step – step to increase upDiff in the floodFill algorithm. If weight is True step also increases the weight of the layers.
- connectivity – pixel connectivity of 4 or 8 to use in the floodFill algorithm
- weight – Increase progressively the weight of the layers using the step parameter.
Returns: alpha mask
-
RRtoolbox.tools.segmentation.
retina_markers_thresh
(P)[source]¶ Retinal markers thresholds to find background, retinal area and optic disc with flares based in the histogram.
Parameters: P – gray image Returns: min,b1,b2,max where:
black background < min b1 > retina < b2 flares > max
-
RRtoolbox.tools.segmentation.
retinal_mask
(img, biggest=False, addalpha=False)[source]¶ Obtain the mask of the retinal area in an image. For a simpler and lightweight algorithm see
retinal_mask_watershed()
.Parameters: - img – BGR or gray image
- biggest – True to return only biggest object
- addalpha – True to add additional alpha mask parameter
Returns: - if addalpha:
binary mask, alpha mask
- else:
binary mask
-
RRtoolbox.tools.segmentation.
retinal_mask_watershed
(img, parameters=(10, 30, None), addMarkers=False)[source]¶ Quick and simple watershed method to obtain the mask of the retinal area in an image. For a more robust algorithm see
retinal_mask()
.Parameters: - img – BGR or gray image
- parameters – tuple of parameters to pass to
filterFactory()
- addMarkers – True to add additional Marker mask. It contains 0 for unknown areas, 1 for background and 2 for retinal area.
Returns: - if addMarkers:
binary mask, Markers mask
- else:
binary mask
RRtoolbox.tools.selectors module¶
-
class
RRtoolbox.tools.selectors.
EntropyPlot
(images, win='Entropy tests', func=None)[source]¶ Bases:
RRtoolbox.lib.plotter.Plotim
Plot entropy test
-
RRtoolbox.tools.selectors.
entropy
(imlist, loadfunc=None, invert=False)[source]¶ Entropy function modified from:
Yan Liu, Feihong Yu, An automatic image fusion algorithm for unregistered multiply multi-focus images, Optics Communications, Volume 341, 15 April 2015, Pages 101-113, ISSN 0030-4018, http://dx.doi.org/10.1016/j.optcom.2014.12.015. (http://www.sciencedirect.com/science/article/pii/S0030401814011559)
Parameters: imlist – list of path to images or arrays Returns: sortedD,sortedImlist,D,fns - where sortedD is the ranking of the Entropy test, D = [D0,...,DN] D0>DN
- sortedImlist is fns sorted to match sortedD, D is the list of the absolute difference between entropy and the root mean square, D = ||E-RMS||


The Diversity Advocacy Coalition's (DAC) mission is to ....
Better the MHSHS community. Lorem ipsum dolor sit amet, consectetur adipisicing elit. Aliquid, suscipit, rerum quos facilis repellat architecto commodi officia atque nemo facere eum non illo voluptatem quae delectus odit vel itaque amet.
In June 2020, Principal Froner recommended a summer reading for school community as one way to prepare to engage in deep and critical discussion within our community when we return in the fall. He selected Between the World and Me by Ta-Nehisi Coates. Dr. Froner. "There were numerous inspiring and thought provoking books recommended from our diversity committee but the following is not only relevant to the mission at hand but is a National Book Award winner and Pulitzer Prize Finalist. Reading Ta-Nehisi Coates’s memoir is a reminder that classrooms do not need to separate the most rigorous college standards from inclusive and diverse content."
Pick this up at your local (Black-owned, if you can) bookstore and dive deep into this beautifully crafted, painfully honest letter from a father to his Black son.
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