class: center, middle, inverse, title-slide # Selection in self pollinated crops ## Mass selection
Pure line selection
Pedigree selection
Bulk population
Single seed descent ### Deependra Dhakal ### 2019/04/15 (updated: 2020-09-29) ---  .pull-left[

Self pollinating crop (Tomato)

 ] .pull-right[

Self pollinating crop (Pea)

 ] --- class: inverse, center, middle # Breeding self pollinated crops --- # Mass selection - Mass selection (originally described by Danish biologist W. Johansen, 1903) is an example of selection from a biologically variable population in which differences are genetic in origin. - Often described as the oldest method of breeding self-pollinated plant species. - Provided that there is sufficient genetic variation, this method of selection is applicable to both self- and cross-pollinated species. - Common method of seed saving of best appearing plants is comparable to mass selection. - Major objective: Population improvement by increasing gene frequencies of desirable genes. ---

Mass selection in self pollinated crops

 --- - General procedure in mass selection is to rogue out off-types or plants with undesirable traits (*Negative mass selection*). - The specific strategies for retaining representative individuals for the population vary according to species, traits of interest, or creativity of the breeder. - Rouging out and bulking appears to be the basic strategy of mass selection, some breeders may rather select and advance a large number of plants that are desirable and uniform for the trait(s) of interest (*positive mass selection*). - Where applicable, single pods from each plant may be picked and bulked for planting. For cereal species, the heads may be picked and bulked. --- - The breeder plants the heterogeneous population in the field, looks for off-types to remove and discard. - A mechanical device (e.g., using a sieve to determine which size of grain would be advanced) may be used, or selection may be purely on visual basis according to the breeder's visual evaluation. - Further, selection may be based on targeted traits (*direct selection*) or indirectly by selecting a trait correlated with the trait to be improved. - Year 1: If the objective is to purify an established cultivar, seed of selected plants may be progeny rowed to confirm the purity of the selected plants prior to bulking. This would make a cycle of mass selection have a two-year duration instead of one year. The original cultivar needs to be planted alongside for comparison. - Year 2: Evaluate composite seed in replicated trial, using original cultivar as check. This test may be conducted at different locations and over several years. The seed is bulk-harvested. --- - Contamination from outcrossing may produce heterozygotes in the population. Mass selection is most effective if the expression of the trait of interest is conditioned by additive gene action. - With Masss selection, - In self-pollinated populations, the persistence of inbreeding will alter population gene frequencies by reducing heterozygosity from one generation to the next. - In cross-pollinated populations, gene frequencies are expected to remain unchanged unless the selection ofplants was biased enough to change the frequency of alleles that control the trait of interest. - Mass selection is based on plant phenotype. Consequently, it is most effective if the trait of interest has high heritability. - Selection is effective when environment is uniform. For example, When selecting for disease resistance, the method is more effective if the pathogen is uniformly present throughout the field without "hot spots". - Correlated response to selection for secondary traits. --- ## Advantages - It is rapid, simple, and straightforward. Large populations can be handled and one generation per cycle can be used. - It is inexpensive to conduct. - The cultivar is phenotypically fairly uniform even though it is a mixture of pure lines. ## Disadvantages - To be most effective, the traits of interest should have high heritability. - Because selection is based on phenotypic values, optimal selection is achieved if it is conducted in a uniform environment. - Phenotypic uniformity is less than in cultivars produced by pure line selection. - With dominance, heterozygotes are indistinguishable from homozygous dominant genotypes. Without progeny testing, the selected heterozygotes will segregate in the next generation. --- class: inverse, middle, center # Pure line selection --- - A pure line suggests that a cultivar has identical alleles at all loci. - The theory of the pure line was developed in 1903 by the Danish botanist Johannsen.

The development of the pure line theory by Johannsen

 --- - Johannsen demonstrated that a mixed population of self-pollinated species could be sorted out into genetically pure lines. - These lines were subsequently non-responsive to selection within each of them . - Selection is a passive process, since it eliminates variation but does not create it. - Lines are used; - as cultivars or as parents in hybrid production (inbred lines). - in the development of genetic stock (containing specific genes such as disease resistance, nutritional quality) and synthetic and multiline cultivars. - Line cultivars have a very narrow genetic base and tend to be uniform in traits of interest (e.g., height, maturity). --- ## Pureline theory - Lines that are genetically different may be successfully isolated from within a population of mixed genetic types. - Any variation that occurs within a pure line is not heritable but due to environmental factors only. Consequently, as Johansen's bean study showed, further selection within the line is not effective. --- ## Application - Cultivars for mechanized production that must meet a certain specification for uniform operation by farm machines (e.g., uniform maturity, uniform height for uniform location of economic part). - Cultivars developed for a discriminating market that puts a premium on eye-appeal (e.g., uniform shape, size). - Cultivars for the processing market (e.g., with demand for certain canning qualities, texture). - Advancing "sports" that appear in a population (e.g., a mutant flower for ornamental use). - Improving newly domesticated crops that have some variability. - The pure-line selection method is also an integral part of other breeding method,s such as the pedigree selection and bulk population selection. --- ## Procedure .small-font[.pull-left[ - The pure-line selection in breeding entails repeated cycles of selfing following the initial selection from a mixture of homozygous lines. - Natural populations of self-pollinated species consist of mixtures of homozygous lines with transient heterozygosity originating from mutations and outcrossing. - Steps: - Year 1: The first step is to obtain a variable base population (e.g., introductions, segregating populations from crosses, land race) and space plant it in the first year, select, and harvest desirable individuals. - Year 2: Grow progeny rows of selected plants. Rogue out any variants. Harvest selected progenies individually. These are experimental strains. - Year 3-6: Conduct preliminary yield trials of the experimental strains including appropriate check cultivars. - Year 7-10: Conduct advanced yield trials at multilocations. Release highest yielding line as new cultivar. ]] .pull-right[

Generalized steps in breeding by pure-line selection

 ] --- ## Genetic issues - Pure-line breeding produces cultivars with a narrow genetic base and, hence, that are less likely to produce stable yields over a wider range of environments. Such cultivars are more prone to being wiped out by pathogenic outbreaks. - Pure-line cultivars depend primarily on phenotypic plasticity for production response and stability across environments. --- ## Advantages - It is a rapid breeding method. - The method is inexpensive to conduct. The base population can be a landrace. The population size selected is variable and can be small or large, depending on the objective. - The cultivar developed by this method has great "eye appeal" (because of the high uniformity of, e.g., harvesting time, height, etc.). ## Disadvantages - The purity of the cultivar may be altered through admixture, natural crossing with other cultivars, and mutations. Such off-type plants should be rogued out to maintain cultivar purity. - The cultivar has a narrow genetic base and, hence, is susceptible to devastation from adverse environmental factors because of uniform response. - A new genotype is not created. Rather, improvement is limited to the isolation of the most desirable or best genotype from a mixed population. - The method promotes genetic erosion because most superior pure lines are identified and multiplied to the exclusion of other genetic variants. - Progeny rows takes up more resources (time, space, funds). --- class: inverse, middle, center # Pedigree selection ---

Generalized steps in breeding by pedigree selection

 --- class: inverse, middle, center # Bulk population ---

Generalized steps in breeding by bulk selection

 --- class: inverse, center, middle # Single seed descent --- - The method of single seed descent was born out of a need to speed up the breeding program by rapidly inbreeding a population prior to beginning individual plant selection and evaluation, while reducing a loss of genotypes during the segregating generations. - The concept was first proposed by C.H. Goulden in 1941 when he attained the F6 generation in two years by reducing the number of generations grown from a plant to one or two, while conducting multiple plantings per year, using the greenhouse and the off season. - However, it was C.A. Brim who, in 1966, provided a formal description of the procedure of single seed descent, calling it a modified pedigree method. - A large F1 population is generated to ensure adequate recombination among parental chromosomes. - The method allows the breeder to advance the maximum number of F2 plants through the F5 generation. - This is achieved by advancing *one* randomly selected seed per plant through the early segregating stages. - The focus on the early stages of the procedure is on attaining homozygosity as rapidly as possible, without selection. - Then, each plant is used to establish a family to help breeders in selection and to increase seed for subsequent yield trails. - Discriminating among plants starts after attainment of homozygosity. --- ## Steps - Year 1. Crossing is used to create the base population. Cross selected parents to generate adequate number of F1 for the production of a large F2 population. - Year 2. About 50-100 F1 plants are grown in a greenhouse ground bench or in pots. They may also be grown in the field. Harvest identical F1 crosses and bulk. - Year 3. About, 2000-3000 F2 plants are grown. At maturity, a single seed per plant is harvested and bulked for planting F3. Subsequently, F2 is spaced enough to allow each plant to produce only a few seeds. - Year 4-6. Single pods per plant are harvested to plant the F4. The F5 is space-planted in the field, harvesting seed from only superior plants to grow progeny rows in the F6 generation. - Year 7. Superior rows are harvested to grow preliminary yield trails in the F7 - Year 8 and later. Yield trials are conducted in the F8 to F10 generations. The most superior line is increased in the F11 and F12 as a new cultivar. --- ## Comments - If the sample is too small, superior genetic combinations may be lost because only one seed from each plant is used. - It may be advantageous to use progeny rows prior to yield testing to produce sufficient seed as well as to help in selecting superior families. - The breeder may choose to impose some artificial selection pressure by excluding undesirable plants from contributing to the subsequent generations (in the early generations). This is effective for qualitative traits. - Record keeping is minimal and so are other activities such as harvesting, especially in early generations. --- # Backcross breeding - This application in plants was first proposed by H.V. Harlan and M.N. Pope in 1922. - The rationale of backcross breeding is to replace a specific undesirable gene with a desirable alternative, while preserving all other qualities (adaptation, productivity, etc.) of an adapted cultivar (or breeding line). - Instead of inbreeding the F1 as normally done, it is repeatedly crossed with the desirable parent to retrieve (by "modified inbreeding") the desirable genotype. - The adapted and highly desirable parent is called the *recurrent parent* in the crossing program, while the source of the desirable gene missing in the adapted parent is called the *donor parent*. - Even though the chief role of the donor parent is to supply the missing gene, it should not be significantly deficient in other desirable traits. An inferior recurrent parent will still be inferior after the gene transfer. --- class: inverse, middle, center ## Comparison of backcross breeding types --- ## Dominant gene transfer .small-font[.pull-left[ - Cross Wild (W) × Adapted (A) `\(\longrightarrow\)` F1 = 50% genetic contribution of each parent. - F1 x A `\(\longrightarrow\)` BC1 =75% genetic contribution of A (Golden Promise) and 25% genetic contribution of W (wild type); - Screen BC1 population and select plants with good resistance to disease (i.e. Rr types). - Use the resistant plants in cross of BC1 x A `\(\longrightarrow\)` BC2 = 87.5% genetic contribution of A and 12.5% genetic contribution of W; - Screen BC2 population and select plants with good resistance to disease (i.e. Rr types). - Use the resistant plants in cross of BC2 x A `\(\longrightarrow\)` BC3 = 93.75% genetic contribution of A and 6.25% genetic contribution of W; - Screen BC3 population and select plants with good resistance to disease (i.e. Rr types). - Use the resistant plants in cross of BC3 x A `\(\longrightarrow\)` BC4 = 96.87% genetic contribution of A and 3.12% genetic contribution of W. - Now, self pollinate BC4 plants to produce BC4F2 and identify lines with homozygous disease resistance (RR). These plants should have 96.87% of the adaptated cultivar with the resistance gene. However, this may be somewhat overestimated due to linkage drag pooling a high percentage of the wild type when selecting for the resistance gene. ]] .pull-right[

Generalized steps in breeding a dominant trait by the backcross method.

 ] --- ## Recessive gene transfer

Generalized steps in breeding a recessive trait by the backcross method.

 --- ## Genetic issues - In theory, the BC3 genotype will be 93.75% **identical to the recurrent parent**. The mathematical relationship for the recovery of the recurrent parent is presented by W. Allard is: $$ 1-\left(\frac{1}{2}\right)^{m+1} $$ where `\(m\)` is the number of generations of selfing or backcrosses. - Same relation also gives the proportion of homozygous individuals (includes both homozygous dominant and recessive individuals) in any generations of recurrent selfing. For example, for `\(3^{th}\)` BC generation, $$ `\begin{aligned} 1-\left(\frac{1}{2}\right)^{m+1} &=& 1-\left(\frac{1}{2}\right)^{3+1} \\ ~ &=& 1-\left(\frac{1}{16}\right) \\ ~ &=& 0.9375 \end{aligned}` $$ --- - In another way, the proportion of the donor genes is reduced by 50% following each generation of backcrossing. This is obtained by the relationship `\(\left(\frac{1}{2}\right)^{m+1}\)`, where m is the number of crosses and backcross to the parent. For example, in the BC4, the value is `\(\left(\frac{1}{2}\right)^{5} = 3.125\%\)`. ??? - The proportion of completely homozygotes plants for any number, n, of genes in a particular backcross generation is: `$$\left[\frac{\left(2^{m-1}\right)}{2^m}\right]^{n}$$` Where `\(n\)` is the number of genes. As an instance, with two gene difference between between recurrent parent (AABB) and donor parent (aabb), the BC1 (m = 1; n = 2) shall have four genptypes viz. AABB; AABb; AaBB and AaBb in equal proportion. As expected, on an average 75\% alleles of recurrent parent, at both loci individually as well as jointly, are recovered in BC1 but only 25% plants are completely homozygous for both the genes (AABB) of the recurrent parent. The 25% plants each have 3 alleles (2A, 1B and 1A and 2B) of recurrent parent while remaining 25% have only 2 alleles (AB) in common with the recurrent parent. The above formula gives the number of individuals homozygous for genes of recurrent parent because all the complete homozygotes in backcross resemble only to the recurrent parent. --- - Because of cytoplasmic inheritance, it is sometimes critical which of the two parents is used as female. - Linkage drag, backcrossing is more effective in breaking linkages over selfing, especially where heritability is low for the undesirable trait. - For a given number of genes, certain number of individuals is needed for a chance to recover the desired genes in a BC program. - When the trait is governed by a dominant gene, it is easy to identify plants carrying the desired gene. - When the desired trait is conditioned by a recessive gene, an additional step is needed after each backcross to produce an F2 generation in order to identify the recessive trait. --- ## Genetic advance in BC breeding depends on - Heritability of the trait - Sustainable intensity of trait expression - Availability of selection aids - Number of backcrosses of marker --- ## Congruency backcross - It is a modification of the standard backcross procedure whereby multiple backcrosses, alternating between the two parents in the cross (instead of restricted to the recurrent parent), are used. - The technique has been used to overcome the interspecific hybridization barrier of hybrid sterility, genotypic incompatibility, and embryo abortion that occurs in simple interspecific crosses. --- ## Some inquiries about backcross breeding method (dominant gene) 1. Why selection is not done on generations prior to `\(BC_3\)` ? - Because recurrent parents genotype will not have been adequately fixed or introgressed and selection may not effectively discriminate useful (high yield, adapted) genotypes. 2. Why selection for lines in BC breeding not be delayed after `\(BC_3\)` or further? - Because adequate material will have been achieved and progressing to lower generations without selecting will add to the cost of maintaining large amount of germplasm. 3. Why selection should be preceded by progeny testing ? - In Self pollinated species, population structure promotes highly homozygous individuals. So without progeny testing, selecting for heterozygous individuals will not reflect true population structure. Selection is best carried out in homozygous individuals. --- 4. Why not backcross in `\(5^{th}\)` year (again) ? - Because progeny testing should be done here to enable selection as early as possible. 5. Why "RR x rr" cross of `\(8^{th}\)` year is called backcross ? - Normally heterozygote when crossed with either parent is called backcross. In `\(8^{th}\)` generation, "RR" is crossed to the recurrent parent. Note, however, that "RR" is not fully homozygous for all other loci.


If you recently read about AWS Cloud Development Kit (CDK) you might ask yourself what it does and how it works in detail. Essentially it’s a new way of describing your infrastructure on AWS. It allows you to use programming languages to model your application.
This has a lot of benefits. For instance you can make use of construct libraries to avoid reinventing patterns.
You can write tests against your constructs to ensure security and other requirements and catch common mistakes.
This all sounds pretty good, but for many new starters and also engineers working since years with AWS infrastructure this can be challenging to learn.
To lower the entry barrier and allow engineers to collaboratively improve their CDK skills and solve problems together Philipp Garbe and me built https://play-with-cdk.com It’s a tool to write CDK code in the browser and immediately get the resulting CloudFormation output, which can then be deployed into your AWS account. It’s extremely easy to get started with CDK and share code in seconds.
To get started just open https://play-with-cdk.com and add some cdk code to the template shown in the editor.
Then press the run button on the right or use the Cmd / Cntrl + S
shortcut.
Your YAML CloudFormation output will show up on the bottom.
You will also find a button to share a link to your code and the well-known Lauch Stack
button which will lead you to the CloudFormation service in your AWS account.
To enhance this learning experience we are planning to add common application patterns and walkthroughs soon.
We want this to be a place where you can learn and try using CDK as easy as possible.
So go ahead, try it out and share your constructs with others. We are also happy to receive feedback and ideas. Just use the GitHub link on the top right and drop us an issue.


Enable, disable features, add new ones, delete existing and test the outcome of your changes over the application immediately.
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| Name | Description | Created on | Active | |||
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| {{feature.key}} | {{feature.description}} | {{feature.creation}} | {{feature.enabled}} |


A bit of History
Did you know?: Java was originally called Oak, a symbol of strength, and the national tree of many countries like U.S.A., France, Germany and Romania.
Java had it's beginning thanks to James Gosling and colleagues at Sun Microsystems, in June 1991. With the promise of "Write Once, Run Anywhere", Java proposed the posibility to be run in multiple platforms, by compiling it to a bytecode, which is then run by a Java Virtual Machine.
There were five primary goals in the creation of the Java language:
- Use the Object-oriented programming methodology.
- Allow the same program to be executed on multiple operating systems.
- Contain built-in support for using computer networks.
- Designed to execute code from remote sources securely.
- Easy to use, by selecting what was considered the good parts of other object-oriented languages.
Programming Paradigms
For each programming language, we have a set of concepts, principles, and rules; which we refer to as a Programming paradigm. The Java programming language is based on four main paradigms: Imperative, object-oriented, declarative, and functional. Lets go through each of them and the major concepts that describe these paradigms.
Imperative Programming
In this paradigm, statements are written to change the state of the program. The program is a set of instructions that is run on the processing unit, and it changes the state. The name imperative implies the fact that the instructions dictate how the program operates. This essencially means that the code runs in an orderly manner, step by step, until there are no more lines to execute.
Object-Oriented Programming
Objects are the main elements of an object-oriented programming (OOP) language. An object holds both the state and the behavior. There are 4 fundamental principles(which we have already discussed heavily in class):
- Encapsulation
- Abstraction
- Inheritance
- Polymorphism
Functional Programming
In functional programming terminology, functions are similar to mathematical functions, which produces an output that typically depends only on its arguments. Each time a functional programming function is called with the same arguments, the same result is achieved. Functional programming contains the following key concepts:
- Functions as first class objects:
This means that you can create an "instance" of a function, and have a variable reference that function instance. Functions can also be passed as parameters to other functions.
- Pure functions:
- Higher order functions:
Java Uses & Applications
Among the main uses for Java, we can list the following:
- Android Apps: Using Android Studio and Java SDK.
- Embedded Systems: Several devices, such as SIM cards, blue-ray disk players, utility meters and televisions, use embedded Java technologies.
- Web Applications: Servlets, Struts or JSPs.
- Enterprise Applications: Java Enterprise Edition (Java EE) is a popular platform that provides API and runtime environment for scripting and running enterprise software.
IntelliJ IDEA (JAVA IDE)
My personal recommendation would be IntelliJ IDEA. You can download and use the Community Edition for free. It's installation is fairly simple and straight-forward.
It's also fairly easy to use. At the left section, you will have your project folders and files. In the middle, the main editor allows you to open and modify the code. Finally, at the bottom, you will have your terminal, where your standard text outputs will show. You can run your program by clicking the green play button at the top right.


{% extends "layout.html" %} {% block body %}
Default project:
✓ Expanded to all user's accounts across projects
Validation: refresh
Validated of cohort members. are valid, are invalid.


EVENT HORIZON (1997)
Directed by Paul Anderson Starring : Lawrence Fishburne Sam Neill Kathleen Quinlan Running Time : 1hr 50mins Rating : ***1/2 out of **** (at your own risk!)
EVENT HORIZON is dreadful horror.=20
No, it is not a bad film, in fact it is so good in achieving its = purpose, I actually wished for the film to end itself quickly. EVENT = HORIZON is not your run-of-the-mill sci-fi film, I'm sure many who have = watched this will agree with me. It is not even original in that sense, = and it does borrow heavily from films like ALIEN, HELLRAISER and even = BLADE RUNNER. The magic of this film lies in its unorthodox setting and = methodical build-up that makes it wonderfully horrifying.
The story brings us to the year 2050, where space travel have since = enormously progressed. The EVENT HORIZON is actually a ship built for a = secret experimental purpose; it has the ability to fold time and space = thus making it possible for travelling distances not reachable in a = man's lifetime. The ship disappeared as soon as it went into operation = but after 7 years, it reappeared again within the orbit of Neptune. A = search and rescue team led by Miller (Fishburne) is given the task to = check for survivors of the EVENT HORIZON. The designer of the ship, Dr. = Weir (Neill) follows along to satisfy his curiosity on what actually = happened to his otherwise technologically impressive ship.
 While in ALIEN, the crew has to cope with a = ferocious creature while encased within tight spaces, in EVENT HORIZON, = the forces which goes against the crew is undefined but yet so powerful = and horrifying. The story unfolds itself slowly and the suspense buildup = is superb, up till the point where I really felt like saying out loud = `enough! I can't stand this anymore!'. There are more than enough jolts = to send your adrenalin pumping overtime. The story in itself is not = exactly something very exciting and also, the acting is only at most, = average by most commercial standards. Even so, the essence of horror is = carried out very well in this film, thanks to its well-paced = storytelling, disturbing set designs and some really gory sequences = (which incidentally, has been quite generously removed by our censorship = board).=20
Director Paul Anderson (who did MORTAL KOMBAT) and his team of = `atmosphere' creators have done well in keeping EVENT HORIZON within the = bounds of the term `horror'. This picture did not do very well in the US = box-office most probably for the dread-factor impact it has on most = audiences; people will not see it again and not many would recommend = their friend to watch this. Although on the face of it, EVENT HORIZON is = mostly molded for mass-entertainment; many thrills, spills and suspense = to keep audiences in focus. However, the offbeat horror element which it = ultimately convey audiences is very disturbing. My friend and I could = not say a word to each other for quite a while after going through the = `experience' of EVENT HORIZON. I guess it was just the immense feeling = of dread which the film had cast upon us.
Be forewarned, this film is not for the faint-hearted nor for pure = entertainment. Be prepared to be disturbed if you should decide to watch = this film! EVENT HORIZON is highly recommended=85=85but at your own = risk! It has been a long time since good-horror embraced the big screen, = and EVENT HORIZON is one.
------=_NextPart_000_01BDB37A.450CF120--
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Kan 11 Poll: Blue&White, Likud, Gain a Seat Each, Habayit Hayehudi Disappears
by David IsraelA poll done for Kan 11 News on Sunday by the Canter Institute shows a small strengthening of both Blue&White and Likud, at the expense of smaller parties in their respective blocs. This does not change the relationship between the blocs.
The poll gives 35 seats to Blue&White, and 34 to Likud.
The Joint Arab List holds on to its 13 seats.
Both United Torah Judaism and Shas get 8 seats each.
Avigdor Liberman’s Israel Beiteinu is clipped down by one seat to 7.
A Naftali Bennett-Ayelet Shaked New Right party gets 6 seats – up from 3.
Labor-Gesher goes down to 5 seats from its current 6.
The Democratic camp (Meretz) is also down by a seat, from 5 to 4.
Both Habayit Hayehudi and Otzma Yehudit disappear from the political map – Habayit Hayehudi loses its current 4 seats and falls below the threshold vote.
The left-center bloc remains, according to the survey results, at its current size of 44 seats.
The rightwing bloc is strengthened by one mandate and rises to 56 at the expense of Israel Beiteinu.
In response to the question whom they would prefer as the next prime minister, 41% of respondents preferred Benjamin Netanyahu, with Benny Gantz trailing with 38%, within the margin of error.
Interestingly, according to the survey, 17% of Blue&White voters prefer Gideon Sa’ar over Gantz for prime minister.
The survey was conducted on Sunday, with 556 respondents selected out of 1,271 who were asked to participate online. The margin of error was +-4.3%.


|
AbstractMulti Expression Programming (MEP) is a method for automatic generation of computer programs. Particularly, MEP can be used for generating mathematical expressions for data analysis (regression, classification and time series). MEP differentiates from other GP techniques by encoding multiple solutions in the same chromosome. MEP strengthsMEP has several strong advantages compared to other techniques:
Representation of solutionsMEP representation is not specific. Linear and tree-based representations have been tested (see the list of papers). Here we exemplify with a linear representation.
ExampleConsider a representation where the numbers on the left positions stand for gene labels. Labels do not belong to the chromosome, as they are provided only for explanation purposes. For this example we use the set of functions: F = {+, *}, and the set of terminals: T = {a, b, c, d}. An example of chromosome using the sets F and T is given below:
The maximum number of symbols in MEP chromosome is given by the formula: Number_of_Symbols = (n + 1) * (Number_of_Genes - 1) + 1, where n is the number of arguments of the function with the greatest number of arguments. The maximum number of effective symbols is achieved when each gene (excepting the first one) encodes a function symbol with the highest number of arguments. The minimum number of effective symbols is equal to the number of genes and it is achieved when all genes encode terminal symbols only.  This translation represents the phenotypic transcription of the MEP chromosomes. Phenotypic translation is obtained by parsing the chromosome top-down. A terminal symbol specifies a simple expression. A function symbol specifies a complex expression obtained by connecting the operands specified by the argument positions with the current function symbol. For instance, genes 1, 2, 4 and 5 in the previous example encode simple expressions formed by a single terminal symbol. These expressions are: E1 = a, E2 = b, E4 = c, E5 = d, Gene 3 indicates the operation + on the operands located at positions 1 and 2 of the chromosome. Therefore gene 3 encodes the expression: E3 = a + b. Gene 6 indicates the operation + on the operands located at positions 4 and 5. Therefore gene 6 encodes the expression: E6 = c + d. Gene 7 indicates the operation * on the operands located at position 3 and 5. Therefore gene 7 encodes the expression: E7 = (a + b) * d. E7 is the last expression encoded by in chromosome. There is neither practical nor theoretical evidence that one of these expressions is better than the others. This is why each MEP chromosome is allowed to encode a number of expressions equal to the chromosome length (number of genes). The chromosome described above encodes the following expressions: E1 = a, E2 = b, E3 = a + b, E4 = c, E5 = d, E6 = c + d, E7 = (a + b) * d. The value of these expressions may be computed by reading the chromosome top down. Partial results are computed by dynamic programming and are stored in a conventional manner. Due to its multi expression representation, each MEP chromosome may be viewed as a forest of trees rather than as a single tree, which is the case of Genetic Programming. As MEP chromosome encodes more than one problem solution, it is interesting to see how the fitness is assigned. The chromosome fitness is usually defined as the fitness of the best expression encoded by that chromosome. For instance, if we want to solve symbolic regression problems, the fitness of each sub-expression Ei may be computed using the formula: fitness(Ei) = sum(|obtainedk,i - targetk,i|), k=1,... ,nwhere obtainedk,i is the result obtained by the expression Ei for the fitness case k and targetk is the targeted result for the fitness case k. In this case the fitness needs to be minimized. The fitness of an individual is set to be equal to the lowest fitness of the expressions encoded in the chromosome: fitness(C) = min fitness(Ei).When we have to deal with other problems, we compute the fitness of each sub-expression encoded in the MEP chromosome. Thus, the fitness of the entire individual is supplied by the fitness of the best expression encoded in that chromosome. Why encoding multiple solutions within a chromosome?When you compute the value of an expression encoded as a GP tree you have the compute the value of all subtrees. This means that all GP subtrees can be viewed as a potential solution of the problem being solved. Most of the GP techniques considers only the one tree while ignoring all the other subtrees. However, the value/fitness for all subtrees is computed by GP. The biggest difference between MEP and other GP techniques is that MEP outputs the best subtree encoded in a chromosome. Note that the complexity (roughly speaking - the running time) is the same for MEP and other GP techniques encoding 1 solution/chromosome. The second reason for the this question is motivated by the No Free Lunch Theorems for Search. There is neither practical nor theoretical evidence that one of the solutions encoded in a chromosome is better than the others. More than that, Wolpert and McReady proved that we cannot use the search algorithm's behavior so far for a particular test function to predict its future behavior on that function. How to efficiently encode multiple solutions within a chromosome is sometimes difficult. There is no general prescription on how to encode multiple solutions in the same chromosome. In most cases this involves creativity and imagination. However, some general suggestions can be given. In order to obtain some benefits from encoding more than one solution in a chromosome we have to spend a similar effort (computer time, memory etc) as in the case of encoding a single solution in a chromosome. For instance, if we have 10 solutions encoded in chromosome and the time needed to extract and decode these solutions is 10 times larger than the time needed to extract and process one solution we got nothing. In this case we can not talk about an useful encoding. We have to be careful when we want to encode a multiple solutions in a variable length chromosome (for instance in a standard GP chromosome), because this kind of chromosome will tend to increase its size in order to encode more and more solutions. And this could lead to bloat. Usually encoding multiple solutions in a chromosome might require storing the partial results. Sometimes this can be achieved by using the Dynamic Programming technique. This kind of model is employed by the Multi Expression Programming. Genetic operatorsCrossoverOne cutting point or uniform crossover have been tested with similar results. The cut points are chosen between instructions not inside them. One-point recombination operator in MEP representation is similar to the corresponding binary representation operator. One crossover point is randomly chosen and the parent chromosomes exchange the sequences at the right side of the crossover point. MutationEach symbol (terminal, function of function pointer) in the chromosome may be the target of the mutation operator. Some symbols in the chromosome are changed by mutation. To preserve the consistency of the chromosome, its first gene must encode a terminal symbol. We may say that the crossover operator occurs between genes and the mutation operator occurs inside genes. If the current gene encodes a terminal symbol, it may be changed into another terminal symbol or into a function symbol. In the later case, the positions indicating the function arguments are randomly generated. If the current gene encodes a function, the gene may be mutated into a terminal symbol or into another function (function symbol and pointers towards ar- guments). Main algorithmThe MEP algorithm starts by creating a random population of individuals. The following steps are repeated until a given number of generations is reached:
Read moreRead more about MEP and MEP for symbolic regression problems here. Read more about MEP for classification problems here. |


I go out on a limb to reach out to her.

She saws away all our connections,
Memories and ties.

What have I done to deserve this?
This hate
This disrespect
This cruelty
Nothing
Sure I've made mistakes,
Everyone does.
I've slipped up a few times,
Everyone does.
No one is pure to the bone.

If she can't see that I'm trying,
Trying so hard
Then maybe,
Just maybe,
It's time to
Let
Her
Go.


0">
ID
Name
Species
Age
Diet
Location
Care #
Sex
Likes
Dislikes
{{animal.id}}
{{animal.name}}
{{animal.species}}
{{animal.age}}
{{animal.diet}}
{{animal.location}}
{{animal.caretakers}}
{{animal.sex}}
{{animal.likes}}
{{animal.dislikes}}
Filter by AGE:


See how our Lambda@Edge function generates the blockies in the public github repository for the project.
Read our blog post to understand why we made this feature, and why we wanted to make it publicly available.
Visit our home page to understand who we are and how we utilize Ethereum Blockchain technology.


{% include 'header.html' %}
OUR
LEADERS
Charles Kimpolo
Founder
Rahul De
Co-Founder
Charles Kimpolo
Founder
Rahul De
Co-Founder
{% include 'footer.html' %}


{% extends "get_together/emails/base.html" %} {% block content %}
Name: {{event.name|striptags}}
Date: {{event.local_start_time}}
Recurring: {% for rule in event.series.recurrences.rrules %}{{rule.to_text|capfirst}}{% endfor %}


Presenting to you Tech Bytes - DPS Bangalore South's first-ever Tech Magazine by the IT Council!
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Error 500
Sorry an error occured...
Our team is making a little
, next we will open the code to catch this bug
.
You should find a page without bugs here


An InputStream
handles byte data.
InputStream is an abstract class from which all
byte-oriented input streams are derived.
Its descendant classes are used for general-purpose input
(non-character input).
These streams are aimed at
delivering data to a program in groups of 8-bit bytes.
The bytes can be grouped into the size necessary for
the type of data.
For example,
if a disk file contains 32-bit int
data,
data can be delivered to the program in 4-byte groups
in the same format as Java primitive type int
.
We will be mostly concerned with DataInputStream
and FileInputStream
.


{% extends 'home/base.html' %} {% block body %}
The brain, while superficially elegant, is an ovewhelmingly complex device consisting of billions of microscopic cells firing in unique patterns to create what we observe as thought. Imaging techniques such as functional Magnetic Resonance Imaging (fMRI) allow us to see these activation patterns in real time at a fine spatial resolution. These images contain highly valuable information that can be leveraged to further our understanding of the brain.
Processing these graphs is a complex and expensive procedure. The initial investment in a processing pipeline may require hundreds of hours of labor to establish, and even using existing open source cutting edge tools requires a background in computer science and computer vision just to get the software installed and functioning properly; coupled with the extensive sets of options for processing, researchers looking to enter the computational neuroscience field may simply be barred by the learning curve to get involved.
The Functional Neurodata Graphs Service is a one-click pipeline that boasts a full suite of fMRI processing methods. It leverages cutting-edge open-source software with many novel algorithms, all combined in a simple-to-deploy docker container that functions on the cloud. The processing steps include template registration, nuisance correction, timeseries extraction, and much more. The pipeline outputs highly relevant connectome graphs along with quality assurance figures every step of the way.
Through FNGS, we hope to lower the barrier to entry of computational neuroscience. Our user-friendly UI coupled with the integration of AWS cloud services into the pipeline enables researchers to process their brain scans with lightning fast efficiency on the click of a button. Our research into high-quality fMRI processing algorithms ensures that users will be able to trust the easy-to-understand qualitative and quantitative outputs they receive.
For detailed code documentation of the core pipeline, refer to the Docs tab. For detailed explanations of the processing methods employed in the pipeline, refer to the About tab. For instructions and tutorials on how to use the service, see the link(s) below:
Local analyses using Docker container
Cloud analyses using Docker container
Below is a link to the data needed to follow the tutorials:


Auxilliary Tool:
A set of functions that are commonly used by many NMPT classes and executables.
More...
Functions | |
| void | printMat (const cv::Mat &mat) |
| Display a matrix to stdout in a multi line format easy to read. | |
| void | printMat (const CvMat &mat) |
| Display a matrix to stdout in a multi line format easy to read. | |
| double | randomNormal () |
| Get a single normally distributed random number. | |
| double | randomFloat () |
| Get a single random number distributed uniformly from 0-1. | |
| std::string | commaSeparatedFlattenedMat (const cv::Mat &mat) |
| Format a matrix in a single line, comma separated format suitable for output to a CSV file. | |
| int | getVideoCaptureFromCommandLineArgs (cv::VideoCapture &capture, const int argc, const char **argv) |
| Allows the source for an NMPT program to be specified from the command line. | |
| double | rectAreaOverlapRatio (cv::Rect r1, cv::Rect r2) |
| Computes the ratio of the area of the intersection of two rectangles to the area of the union. | |
| double | rectAreaIntersect (cv::Rect r1, cv::Rect r2) |
| Computes the area of the intersection of two rectangles. | |
| double | notfinite (double a) |
| Returns true if a is NaN or infinty. | |
| void | distNorm (cv::Mat &normDist, const cv::Mat &unnormDist) |
| Divides unnormDist by its L1-norm, effectively turning it into a probability distribution's histogram. The values of unnormDist should be all positive. If any values are NaN or inf, the probability mass is split uniformly between those values and all other values become 0. | |
| void | sample (cv::Mat &samples, const cv::Mat &histogram, cv::Size sampleSize=cv::Size(1, 1)) |
| Generate a matrix of samples from a probability histogram. | |
| int | sample (const cv::Mat &histogram) |
| Generate a single sample from a probability histogram. | |
| template<class mattype > | |
| void | map (cv::Mat &mat, mattype(*function)(mattype)) |
| Apply a function (in place) to every element of a matrix. The function must take an argument of the matrix's data type, and return a value of the matrix's data type. | |
| template<class mattype > | |
| int | contains (const cv::Mat &mat, mattype val) |
| Check to see if a matrix contains a certain value. | |
| double | nchoosek (int n, int k) |
| Quickly computer the n choose k function. The result is accurate through at least the range of 32-bit integers. | |
| int | any (CvMat *mat) |
| Checks to see if any member of mat is non-zero. | |
| template<class mattype > | |
| int | any (const cv::Mat &mat) |
| Checks to see if any member of mat is non-zero. | |
| void | fix (CvMat *mat) |
| Round the elements of mat towards zero. | |
| void | writeMatBinary (cv::FileStorage &storage, const std::string &name, const cv::Mat &mat) |
| Write the value of a matrix to a FileStorage in a Base64 format that is much more compact than OpenCV's default format. The data is keyed by the supplied name. | |
| void | readMatBinary (const cv::FileNode &storage, cv::Mat &mat) |
| Read the value of a matrix to a FileStorage in a Base64 format that is much more compact than OpenCV's default format. The data is keyed by the supplied name. If you attempt to read a matrix that was not saved with "writeMatBinary," this will probably crash your program. | |
| void | binaryToAscii (std::string &dest, const uchar *data, size_t bytes) |
| Read the value of a matrix to a FileStorage in a Base64 format that is much more compact than OpenCV's default format. The data is keyed by the supplied name. The data is uncompressed with zlib after writing. If you attempt to read a matrix that was not saved with "writeMatBinaryCompressed," this will probably crash your program. | |
| void | asciiToBinary (const std::string &data, uchar *dest, size_t bytes) |
| Converts a Base64 string to binary data. | |
| void | binaryToHex (std::string &dest, const uchar *data, size_t bytes) |
| Converts binary data to a non-standard hex format that can be written to a text file, e.g. the YAML format written by OpenCV. This format requires 1.5 the storage space of Base64. | |
| void | hexToBinary (const std::string &data, uchar *dest, size_t bytes) |
| Converts a non-standard hex format to binary data. This format requires 1.5 the storage space of Base64. | |
| void | splitString (std::vector< std::string > &dest, const std::string &data, size_t maxlen=512) |
| Splits a string into a collection of string vectors, each with maximum length maxlen. This is useful because OpenCV imposes a maximum string length when writing to a file, somewhere between 2048 and 4096. | |
| void | joinString (const std::vector< std::string > &data, std::string &dest) |
| Rejoins a vector of strings into a single string. | |
| void | rectangleRotated (cv::Mat &image, cv::Point center, cv::Size size, double angle, const cv::Scalar &color, int thickness=1, int lineType=8, int shift=0) |
| Uses zlib to compress data. The output dest is allocated in this function and dest_size is set to the number of bytes of the compressed data. Returns a zlib error code if compression fails. | |
| void | unIntegrate (const cv::Mat &src, cv::Mat &dest, int type=CV_8U) |
| Calculate derivative image. | |
| void | unSqIntegrate (const cv::Mat &src, cv::Mat &dest, int type=CV_8U) |
| Calculate sqrt of derivative image. | |
| cv::Mat | RBF (const cv::Mat &input, const cv::Mat &labels, const cv::Mat &weights, const cv::Mat &xqueries, double tau=.05, double eps=.001) |
| Compute RBF values at supplied query points, given input data and labels. |
Auxilliary Tool:
A set of functions that are commonly used by many NMPT classes and executables.
| void NMPTUtils::binaryToAscii | ( | std::string & | dest, |
| const uchar * | data, | ||
| size_t | bytes | ||
| ) |
Read the value of a matrix to a FileStorage in a Base64 format that is much more compact than OpenCV's default format. The data is keyed by the supplied name. The data is uncompressed with zlib after writing. If you attempt to read a matrix that was not saved with "writeMatBinaryCompressed," this will probably crash your program.
Converts binary data to a Base64 string that can be written to a text file, e.g. the YAML format written by OpenCV.
| int NMPTUtils::getVideoCaptureFromCommandLineArgs | ( | cv::VideoCapture & | capture, |
| const int | argc, | ||
| const char ** | argv | ||
| ) |
Allows the source for an NMPT program to be specified from the command line.
Returns 0 if there was an error, 1 if a video file was the source, or 2 if a camera was the source,
| cv::Mat NMPTUtils::RBF | ( | const cv::Mat & | input, |
| const cv::Mat & | labels, | ||
| const cv::Mat & | weights, | ||
| const cv::Mat & | xqueries, | ||
| double | tau = .05 , |
||
| double | eps = .001 |
||
| ) |
Compute RBF values at supplied query points, given input data and labels.
| input | NxM, N data points, M Dims |
| labels | NxO, N data points, O outputs |
| weights | Nx1, 1 weight per data point |
| xqueries | QxM, Q query points, M Dims |
| tau,: | variance of gaussian weighting window |
| eps,: | minimal attention paid to all points |
| void NMPTUtils::rectangleRotated | ( | cv::Mat & | image, |
| cv::Point | center, | ||
| cv::Size | size, | ||
| double | angle, | ||
| const cv::Scalar & | color, | ||
| int | thickness = 1 , |
||
| int | lineType = 8 , |
||
| int | shift = 0 |
||
| ) |
Uses zlib to compress data. The output dest is allocated in this function and dest_size is set to the number of bytes of the compressed data. Returns a zlib error code if compression fails.
Uncompress data that was previously compressed. The output dest is allocated in this function, to the amount specified by dest_size. This should be the size of the original uncompressed data, which presumably you know, because you compressed it. Returns a zlib error code if decompression fails. Draw a rotated rectangle on an image. The point is to be like opencv's cv::rectangle function, but to allow rotation.


Fri Oct 3, 2008
In this rocky economic climate, marketing yourself has never been more crucial if you want to survive the inevitable cutbacks. When it comes to technical writing, it’s a useful exercise to understand and appreciate what companies look for in a writer.
Lyndsey Amott, in an article on her website, stresses that industry knowledge should not be a primary deciding factor when mulling over suitable candidates. Her top three must-haves are:
1. design a document that looks good both on paper and on-line
2. design a document that can be updated easily
3. write instructions that are easy to understand
She goes on to say that the proof of a good writer lies in the way they organize their thoughts and that inexperienced writers shouldn’t necessarily be dismissed.
Technical writing consultant Jean Weber makes some good points in this article. She agrees that the writing is more important than the software or technical expertise. She advises companies to look for problem-solvers who keep digging until they find all the answers. But they should be somebody tactful, not abrasive.
Employers are always concerned with costs and staying on budget. If you can relay the value of creating relevant content in a specific context and to a specific audience, you’re on the right track. Remember that as a technical writer you’re the bridge between subject matter experts and the stakeholders, the marketers, and the end-users. Without you, knowledge could not be channeled into capital gain.
So, if you find your head on the chopping block, start thinking like your employer and ask yourself why they should keep you around. The answer could just save your neck.


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Line charts should be used to track one or more sets of data over time or to compare multiple sets of data.
The line charts that you can create with this plugin are highly customizable, can be animated, updated dynamically, and feature area fills, data points, and tooltips. Find out more below.
To create an line chart, assign a class of lineChart
to a div
element.
Data and options are assigned via the data-chartData
and data-chartOptions
attributes, respectively.
The data-chartData
attribute takes an array of JSON objects as its value, with each
object containing x and y keys with array values containing numerical data
points. Charts with dates along the x-axis will ignore the x key-value pair, so for those charts,
its possible to omit the x-axis information.
Chart options passed through the data-chartOptions
attribute must be contained in a
single-level JSON object, with each option key corresponding to a value. It's not necessary to
use the data-chartOptions
attribute if you only want to have the default chart. All of
the options relevant to line charts are detailed below.
| Chart Options | |||
|---|---|---|---|
| Option | Description | Default Value | Values |
animate |
Animates the chart. | false |
true , false |
animationDuration |
The duration of the chart animation in seconds. | 1 |
Any positive number |
axisColor |
The hex color of the axis lines. | #333 |
Any hex color code as a string |
aspectRatio |
The aspect ratio of the entire chart area, including labels and keys. | [16, 9] |
An array of 2 positive integers [x, y] |
colors |
The hex colors for each series of chart data. | ['#058DC7', '#A006C7', '#C7062D', '#06C740',
'#C7A006', '#EEE', '#CCC', '#999', '#666', '#333', '#000'] |
An array of hex codes [color1, color2, ...] |
dataLabels |
Labels for each series of data, will also show in the key. If no labels are provided, labels will be 'Series 1', 'Series 2', etc. | [] |
An array of strings equal to the number of series ['Label 1',
'Label 2', ...] |
dateEnd |
Sets the end date for a time-based chart.
xAxisIsDate must be enabled. |
null |
'now' or any date string |
dateFormat |
Formats the date by replacing placeholders in a string.
xAxisIsDate must be enabled. |
'%b %d' |
A string of any combination of options from the MooTools Date Object |
dateIntervalUnit |
Sets the inverval unit for a date.
xAxisIsDate must be enabled. |
'day' |
'year' , 'month' , 'week' ,
'day' , 'hour' , 'minute' , 'second' ,
'ms'
|
dateIntervalValue |
Sets the interval value for a date.
xAxisIsDate must be enabled. |
1 |
Any positive integer |
dateStart |
Sets the start date for a time-based chart.
xAxisIsDate must be enabled. |
null |
'now' or any date string |
decimalPrecisionX |
The number of decimal places shown on x-axis interval labels or pie tips; does not affect placement or other tip types. | 0 |
0 or any positive integer |
decimalPrecisionY |
The number of decimal places shown on y-axis interval labels; does not affect placement or any tips. | 0 |
0 or any positive integer |
gridColor |
The hex color of the grid lines. | '#DDD' |
Any hex color string |
keyPosition |
The position of the key. The showKey option must
also be enabled. |
'bottom' |
'top' , 'bottom' , 'left' ,
'right' |
keyWidthPercentage |
The width percentage of the key in relation to the entire chart area, applicable only for left and right key positions. | 12 |
Any positive integer |
maxX |
The maximum value of the x-axis. | null |
Any number |
maxY |
The maximum value of the y-axis. | null |
Any number |
minX |
The minimum value of the x-axis. | 0 |
Any number |
minY |
The minimum value of the y-axis. | 0 |
Any number |
showAxisLabels |
Toggles both the x and y axis labels. | false |
true , false |
showAxisLines |
Toggles both the x and y axis lines. | false |
true , false |
showGridX |
Toggles the x-axis grid lines. | false |
true , false |
showGridY |
Toggles the y-axis grid lines. | false |
true , false |
showIntervalLabels |
Toggles interval labels on both axises. | false |
true , false |
showIntervalLabelsX |
Toggles interval labels on the x-axis. | false |
true , false |
showIntervalLabelsY |
Toggles interval labels on the y-axis. | false |
true , false |
showKey |
Toggles the chart key. | false |
true , false |
showLineFill |
Toggles an area chart overlay for line charts. | false |
true , false |
showPoints |
Toggles individual data points for area and line charts. | false |
true , false |
showTicksX |
Toggles tick marks along the x-axis. | false |
true , false |
showTicksY |
Toggles tick marks along the y-axis. | false |
true , false |
showTips |
Toggles tooltips for all charts. Line charts and area charts
must have showPoints enabled for tips to show. |
false |
true , false |
showTitle |
Toggles the chart title. | false |
true , false |
textColor |
The color of all text in the chart. | "#000" |
Any hex color string |
title |
The title of the chart. | "Untitled Chart" |
Any string |
tickColor |
The color of all tick marks. | "#333" |
Any hex color string |
tipText |
An array of HTML text strings to customize the tip text, one string for each series. Placeholders in the string (surrounded by curly braces {}) can be replaced by values or labels from the chart. Placeholders options include: {x}, {y}, {xAxis}, {yAxis}, {piePercent} and {label}. | [] |
An array of HTML strings with placeholders, one string for each series |
xAxisLabel |
The label for the x-axis. | "x-axis" |
Any string |
xInterval |
The interval for the x-axis. | 25 |
Any positive number |
xAxisIsDate |
Toggles the date options for a chart. Any coordinate values
for the x-axis entered in the data-chartData attribute will be
ignored. |
false |
true , false |
xLabels |
An array of labels for the x-axis matching the number of
intervals. If no labels are entered, the chart will default to using numbers. To
skip labels for an interval, use null as a placeholder in the array. |
[] |
An array of any combination of numbers, strings, or nulls matching the number of intervals |
yAxisLabel |
The label for the y-axis. | "y-axis" |
Any string |
yInterval |
The interval for the y-axis. | 25 |
Any positive number |
yLabelWidthPercentage |
The width percentage (in relation to the entire chart area) of y interval labels, if they are enabled. | 5 |
Any positive number |
yLabels |
An array of labels for the y-axis matching the number of
intervals. If no labels are entered, the chart will default to using numbers. To
skip labels for an interval, use null as a placeholder in the array. |
[] |
An array of any combination of numbers, strings, or nulls matching the number of intervals |
Each chart type has an updateChart
event, which allows the chart to be updated
dynamically. The event is bound to the chart container element and can be fired using MooTools'
fireEvent
method.
New data can be passed directly to the chart via fireEvent
. If no new data is passed
through fireEvent
, then the plugin will pull the data and options from the data
attributes.
Note When the updateChart
event is fired, the plugin
will rerender the chart completely.
Examples of how to use the updateChart
event can be found in the
Dynamic Data example.
Inline line charts are small charts that can appear inline within the context of a block of text or other inline elements. Note Because inline charts are so small, they should generally only be used with a single data set and without many of the visual chart features enabled. Keep inline charts simple.
This is an inline line chart: It can appear within a paragraph.

This is an inline line chart:  It can appear within a paragraph.

This is the default line chart, with no options enabled. It expands to the full width of its container and shows data only.

In this example, the title, labels, ticks, and axis lines have all been enabled. Each of those elements can be toggled separately through the options object.
If your labels are longer, you can specify the width of the line used for labels along the
y-axis using the yLabelWidthPercentage
option. The default value for
yLabelWidthPercentage
is 5% of the total width of the chart canvas area, but
the option accepts any positive integer value between 1-100.
Note Chart text may not be perfect on mobile-sized screens, so choose what information you display carefully.

The keys in the charts plugin are all interactive. Clicking on the key text or color square will toggle the visibility of that series in the graph.
Keys can appear in four different positions: top, bottom, left, or right, with a default position of bottom. Choose the appropriate key position based on the length and number of your series labels.
Note The width of the left and right key
positions can be determined using the keyWidthPercentage
option, which
defaults to 15%. Because cross-browser support for SVG text-wrapping isn't particularly
fantastic, you'll need to use this option for especially longer labels.

<div class="lineChart" data-chartData="[ { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[0,1,6,3,17,12,24,38,15,87,75,45,23,12,73,17,45,87,99,34,20] }, { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[23,15,65,33,37,32,34,36,25,47,75,40,63,22,43,10,46,68,69,68,60] } ]" data-chartOptions="{ showAxisLabels: true, showAxisLines: true, showTicksY: true, showTicksX: true, showIntervalLabels: true, showTitle: true, showKey: true, keyPosition: 'right', title: 'Right Key' }">

Grid lines can be added to the chart as well, with granular control over both the X
and Y lines through the showGridX
and showGridY
options.
Grid line color is set through the gridColor
option.
<div class="lineChart" data-chartData="[ { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[0,1,6,3,17,12,24,38,15,87,75,45,23,12,73,17,45,87,99,34,20] }, { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[23,15,65,33,37,32,34,36,25,47,75,40,63,22,43,10,46,68,69,68,60] } ]" data-chartOptions="{ showAxisLabels: true, showAxisLines: true, showTicksY: true, showTicksX: true, showIntervalLabels: true, showTitle: true, showKey: true, title: 'Grid Lines', showGridX: true, showGridY: true }">

The line chart can display individual data points and area fills using the
showPoints
and showLineFill
options.
The tips for Series 1 in the demo are custom text, whereas the tips for Series
2 are the default tips. Tip text can be customized per data set and is set via the
tipText
option.
The tipText
option's value should be an array, with custom text as a series of
strings in the same order as the data sets. The string can take HTML and also has special
placeholders that will be replaced automatically.
| String | Replacement |
|---|---|
| {x} | The x-cordinate value. |
| {y} | The y-cordinate value. |
| {xAxis} | The name of the x-axis. |
| {yAxis} | The name of the y-axis. |
| {label} | The name of the series. |
<div class="lineChart" data-chartData="[ { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[0,1,6,3,17,12,24,38,15,87,75,45,23,12,73,17,45,87,99,34,20] }, { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[23,15,65,33,37,32,34,36,25,47,75,40,63,22,43,10,46,68,69,68,60] } ]" data-chartOptions="{ showAxisLabels: true, showAxisLines: true, showTicksY: true, showTicksX: true, showIntervalLabels: true, showTitle: true, showKey: true, title: 'Lines, Points, & Tips', showGridX: true, showGridY: true, showLineFill: true, showPoints: true, showTips: true, tipText: ['This is a custom tip. The value for the {xAxis} is: {x}'] }">

By default, the chart area will expand to accomodate all of the data with the largest view possible while still making the chart legible. Negative values in your series will add a one interval padding to the bottom of your chart below your lowest value, while charts with no negative numbers will not add the bottom interval. All charts add a one interval padding to the top of the chart. This is done for asthetic purposes, adding visual whitespace to the chart.
Using the maxX
, minX
, maxY
, and minY
options, you can override the default behavior and give your chart custom ranges.
<div class="lineChart" data-chartData="[ { x:[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[0,1,6,3,-17,12,24,38,-15,87,75,45,23,12,73,17,-45,87,99,34,20] }, { x:[-25,-20,-15,-10,-5,0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100], y:[0,12,80,22,30,23,15,65,33,37,32,34,-36,25,47,75,-40,63,22,43,10,46,68,69,68,60] } ]" data-chartOptions="{ showAxisLabels: true, showAxisLines: true, showTicksY: true, showTicksX: true, showIntervalLabels: true, showTitle: true, showKey: true, title: 'Negative Values & Custom Ranges', showGridX: true, showGridY: true, maxX: 150, minX: -50 }">

Decimal values can be used in any chart, but in most cases, you will probably want to adjust
the intervals along at least one of the axises. To do so, use the xInterval
and yInterval
options to establish custom intervals.
By default, the interval labels will show only integers, so any decimal interval labels will
be rounded to the nearest integer. To show decimal interval labels, use the
decimalPrecisionX
and decimalPrecisionY
options to specify the
number of decimal places to be shown. Note This will only
affect the interval labels; the data points and tips will display the full number as
specified in the series.

Charts can make use of dates along along the x-axis. When using dates, it is not necessary
to enter the x-coordinate in the series (if you do, it will be ignored). To enable dates along
the x-axis, set the xAxisIsDate
option to true
.
The date range is set using the dateStart
and dateEnd
options,
both of which accept any date string accepted by the default JavaScript
Date
object. The default value for both options is the current date and time, so at least
one option will need to be specified.
Date intervals are controlled by two options: dateIntervalUnit
and
dateIntervalValue
. The default value for the dateIntervalUnit
option is day
, but other options include:
year
month
week
day
hour
minute
second
ms
The dateIntervalValue
option controls the frequency of the interval in the
chosen date unit. The default value is 1.
The date label format is highly customizable and can feature almost any unit of time in a
custom string. The format is set via the dateFormat
option. All date
replacement strings must be preceded by the percent sign, %
. The replacement
string formats can be found in the MooTools More
Date.format method
documentation.
If the labels or values are displaying incorrectly, make sure that you have a date range that corresponds to at least the number of data points.
The length of the animation can also be controlled with the animationDuration
option, which defaults to a 1 second animation.
Note Animations do not currently work in Internet Explorer
as it does not support declarative animations. The chart will simply ignore the
animate
option and display a static chart in Internet Explorer.
Animation has also been disabled in Safari until further testing can be performed and a fix can be found.  The value for the {xAxis} is: {x}'], animate: true }">

Refresh Animation

The chart can be updated dynamically using data generated via script or retrieved via AJAX.
To update the chart, fire the updateChart
event on the chart container
element. New data can either be passed in as parameters via the fireEvent
function or you can choose to dynamically update the data-chartData
attribute and the data will be automatically detected when the updateChart
event is fired.
Notes When the updateChart
event is fired, the
entire chart is re-rendered. Animations should not be used with dynamic data with a high
refresh rate.
If you want to have an empty series like the initial state of the demo, you must define
both the maxX
and maxY
options, otherwise the result will be an
error.
Choose the data refresh rate in milliseconds:

Start Data

Clear Data

Choose the data refresh rate in milliseconds:
 <form> <input type="text" id="dataSpeed" name="dataSpeed" class="slider" data-range="10,1000" data-increment="10" data-start="500"> </form>
VM UI Framework is created, owned, maintained by Virtuosi Media, Inc. © 2012-2013.


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/74858warnings.html ---
There isn't any way to suppress all warnings or specific warnings from lean, is there?
In the editor? Or in compiler output in the terminal?
In the terminal
preferably, from the command line
You could just pipe via grep...
Completely customisable, but maybe not exactly user friendly
What kind of filtering are you looking for?
Often I want to suppress the many "warning: imported file uses sorry" messages, and sometimes I want to ignore the messages about using sorry as well
That seems like something that grep
could do for you.