Design System

github.com/ryanbressler/CloudForest

Package CloudForest implements ensembles of decision trees for machine learning in pure Go (golang to search engines). It allows for a number of related algorithms for classification, regression, feature selection and structure analysis on heterogeneous numerical/categorical data with missing values. These include: Breiman and Cutler's Random Forest for Classification and Regression Adaptive Boosting (AdaBoost) Classification Gradiant Boosting Tree Regression Entropy and Cost driven classification L1 regression Feature selection with artificial contrasts Proximity and model structure analysis Roughly balanced bagging for unbalanced classification The API hasn't stabilized yet and may change rapidly. Tests and benchmarks have been performed only on embargoed data sets and can not yet be released. Library Documentation is in code and can be viewed with godoc or live at: http://godoc.org/github.com/ryanbressler/CloudForest Documentation of command line utilities and file formats can be found in README.md, which can be viewed fromated on github: http://github.com/ryanbressler/CloudForest Pull requests and bug reports are welcome. CloudForest was created by Ryan Bressler and is being developed in the Shumelivich Lab at the Institute for Systems Biology for use on genomic/biomedical data with partial support from The Cancer Genome Atlas and the Inova Translational Medicine Institute. CloudForest is intended to provide fast, comprehensible building blocks that can be used to implement ensembles of decision trees. CloudForest is written in Go to allow a data scientist to develop and scale new models and analysis quickly instead of having to modify complex legacy code. Data structures and file formats are chosen with use in multi threaded and cluster environments in mind. Go's support for function types is used to provide a interface to run code as data is percolated through a tree. This method is flexible enough that it can extend the tree being analyzed. Growing a decision tree using Breiman and Cutler's method can be done in an anonymous function/closure passed to a tree's root node's Recurse method: This allows a researcher to include whatever additional analysis they need (importance scores, proximity etc) in tree growth. The same Recurse method can also be used to analyze existing forests to tabulate scores or extract structure. Utilities like leafcount and errorrate use this method to tabulate data about the tree in collection objects. Decision tree's are grown with the goal of reducing "Impurity" which is usually defined as Gini Impurity for categorical targets or mean squared error for numerical targets. CloudForest grows trees against the Target interface which allows for alternative definitions of impurity. CloudForest includes several alternative targets: Additional targets can be stacked on top of these target to add boosting functionality: Repeatedly splitting the data and searching for the best split at each node of a decision tree are the most computationally intensive parts of decision tree learning and CloudForest includes optimized code to perform these tasks. Go's slices are used extensively in CloudForest to make it simple to interact with optimized code. Many previous implementations of Random Forest have avoided reallocation by reordering data in place and keeping track of start and end indexes. In go, slices pointing at the same underlying arrays make this sort of optimization transparent. For example a function like: can return left and right slices that point to the same underlying array as the original slice of cases but these slices should not have their values changed. Functions used while searching for the best split also accepts pointers to reusable slices and structs to maximize speed by keeping memory allocations to a minimum. BestSplitAllocs contains pointers to these items and its use can be seen in functions like: For categorical predictors, BestSplit will also attempt to intelligently choose between 4 different implementations depending on user input and the number of categories. These include exhaustive, random, and iterative searches for the best combination of categories implemented with bitwise operations against int and big.Int. See BestCatSplit, BestCatSplitIter, BestCatSplitBig and BestCatSplitIterBig. All numerical predictors are handled by BestNumSplit which relies on go's sorting package. Training a Random forest is an inherently parallel process and CloudForest is designed to allow parallel implementations that can tackle large problems while keeping memory usage low by writing and using data structures directly to/from disk. Trees can be grown in separate go routines. The growforest utility provides an example of this that uses go routines and channels to grow trees in parallel and write trees to disk as the are finished by the "worker" go routines. The few summary statistics like mean impurity decrease per feature (importance) can be calculated using thread safe data structures like RunningMean. Trees can also be grown on separate machines. The .sf stochastic forest format allows several small forests to be combined by concatenation and the ForestReader and ForestWriter structs allow these forests to be accessed tree by tree (or even node by node) from disk. For data sets that are too big to fit in memory on a single machine Tree.Grow and FeatureMatrix.BestSplitter can be reimplemented to load candidate features from disk, distributed database etc. By default cloud forest uses a fast heuristic for missing values. When proposing a split on a feature with missing data the missing cases are removed and the impurity value is corrected to use three way impurity which reduces the bias towards features with lots of missing data: Missing values in the target variable are left out of impurity calculations. This provided generally good results at a fraction of the computational costs of imputing data. Optionally, feature.ImputeMissing or featurematrixImputeMissing can be called before forest growth to impute missing values to the feature mean/mode which Brieman [2] suggests as a fast method for imputing values. This forest could also be analyzed for proximity (using leafcount or tree.GetLeaves) to do the more accurate proximity weighted imputation Brieman describes. Experimental support is provided for 3 way splitting which splits missing cases onto a third branch. [2] This has so far yielded mixed results in testing. At some point in the future support may be added for local imputing of missing values during tree growth as described in [3] [1] http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#missing1 [2] https://code.google.com/p/rf-ace/ [3] http://projecteuclid.org/DPubS?verb=Display&version=1.0&service=UI&handle=euclid.aoas/1223908043&page=record In CloudForest data is stored using the FeatureMatrix struct which contains Features. The Feature struct implements storage and methods for both categorical and numerical data and calculations of impurity etc and the search for the best split. The Target interface abstracts the methods of Feature that are needed for a feature to be predictable. This allows for the implementation of alternative types of regression and classification. Trees are built from Nodes and Splitters and stored within a Forest. Tree has a Grow implements Brieman and Cutler's method (see extract above) for growing a tree. A GrowForest method is also provided that implements the rest of the method including sampling cases but it may be faster to grow the forest to disk as in the growforest utility. Prediction and Voting is done using Tree.Vote and CatBallotBox and NumBallotBox which implement the VoteTallyer interface.

v0.0.0-20220205065429-8f151e494fd2 • 3 years ago

www.velocidex.com/golang/vfilter

The veloci-filter (vfilter) library implements a generic SQL like query language. Overview:: There are many applications in which it is useful to provide a flexible query language for the end user. Velocifilter has the following design goals: - It should be generic and easily adaptable to be used by any project. - It should be fast and efficient. An example makes the use case very clear. Suppose you are writing an archiving application. Most archiving tools require a list of files to be archived (e.g. on the command line). You launch your tool and a user requests a new flag that allows them to specify the files using a glob expression. For example, a user might wish to only select the files ending with the ".go" extension. While on a unix system one might use shell expansion to support this, on other operating systems shell expansion may not work (e.g. on windows). You then add the ability to specify a glob expression directly to your tool (suppose you add the flag --glob). A short while later, a user requires filtering the files to archive by their size - suppose they want to only archive a file smaller than a certain size. You studiously add another set of flags (e.g. --size with a special syntax for greater than or less than semantics). Now a user wishes to be able to combine these conditions logically (e.g. all files with ".go" extension newer than 5 days and smaller than 5kb). Clearly this approach is limited, if we wanted to support every possible use case, our tool would add many flags with a complex syntax making it harder for our users. One approach is to simply rely on the unix "find" tool (with its many obscure flags) to support the file selection problem. This is not ideal either since the find tool may not be present on the system (E.g. on Windows) or may have varying syntax. It may also not support every possible condition the user may have in mind (e.g. files containing a RegExp or files not present in the archive). There has to be a better way. You wish to provide your users with a powerful and flexible way to specify which files to archive, but we do not want to write complicated logic and make our tool more complex to use. This is where velocifilter comes in. By using the library we can provide a single flag where the user may specify a flexible VQL query (Velocidex Query Language - a simplified SQL dialect) allowing the user to specify arbirarily complex filter expressions. For example: SELECT file from glob(pattern=["*.go", "*.py"]) where file.Size < 5000 and file.Mtime < now() - "5 days" Not only does VQL allow for complex logical operators, but it is also efficient and optimized automatically. For example, consider the following query: SELECT file from glob(pattern="*") where grep(file=file, pattern="foobar") and file.Size < 5k The grep() function will open the file and search it for the pattern. If the file is large, this might take a long time. However velocifilter will automatically abort the grep() function if the file size is larger than 5k bytes. Velocifilter correctly handles such cancellations automatically in order to reduce query evaluation latency. Protocols - supporting custom types:: Velocifilter uses a plugin system to allow clients to define how their own custom types behave within the VQL evaluator. Note that this is necessary because Go does not allow an external package to add an interface to an existing type without creating a new type which embeds it. Clients who need to handle the original third party types must have a way to attach new protocols to existing types defined outside their own codebase. Velocifilter achieves this by implementing a registration systen in the Scope{} object. For example, consider a client of the library wishing to pass custom types in queries: Where both Foo and Bar are defined and produced by some other library which our client uses. Suppose our client wishes to allow addition of Foo objects. We would therefore need to implement the AddProtocol interface on Foo structs. Since Foo structs are defined externally we can not simply add a new method to Foo struct (we could embed Foo struct in a new struct, but then we would also need to wrap the bar field to produce an extended Bar. This is typically impractical and not maintainable for heavily nested complex structs). We define a FooAdder{} object which implements the Addition protocol on behalf of the Foo object. Now clients can add this protocol to the scope before evaluating a query: scope := NewScope().AddProtocolImpl(FooAdder{})

v0.0.0-20231014062339-d62b5a5877d2 • last year

gopkg.in/bluesuncorp/validator.v10

Package validator implements value validations for structs and individual fields based on tags. It can also handle Cross-Field and Cross-Struct validation for nested structs and has the ability to dive into arrays and maps of any type. see more examples https://github.com/go-playground/validator/tree/master/_examples Validator is designed to be thread-safe and used as a singleton instance. It caches information about your struct and validations, in essence only parsing your validation tags once per struct type. Using multiple instances neglects the benefit of caching. The not thread-safe functions are explicitly marked as such in the documentation. Doing things this way is actually the way the standard library does, see the file.Open method here: The authors return type "error" to avoid the issue discussed in the following, where err is always != nil: Validator only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom Validation functions can be added. Example: Cross-Field Validation can be done via the following tags: If, however, some custom cross-field validation is required, it can be done using a custom validation. Why not just have cross-fields validation tags (i.e. only eqcsfield and not eqfield)? The reason is efficiency. If you want to check a field within the same struct "eqfield" only has to find the field on the same struct (1 level). But, if we used "eqcsfield" it could be multiple levels down. Example: Multiple validators on a field will process in the order defined. Example: Bad Validator definitions are not handled by the library. Example: Baked In Cross-Field validation only compares fields on the same struct. If Cross-Field + Cross-Struct validation is needed you should implement your own custom validator. Comma (",") is the default separator of validation tags. If you wish to have a comma included within the parameter (i.e. excludesall=,) you will need to use the UTF-8 hex representation 0x2C, which is replaced in the code as a comma, so the above will become excludesall=0x2C. Pipe ("|") is the 'or' validation tags deparator. If you wish to have a pipe included within the parameter i.e. excludesall=| you will need to use the UTF-8 hex representation 0x7C, which is replaced in the code as a pipe, so the above will become excludesall=0x7C Here is a list of the current built in validators: Tells the validation to skip this struct field; this is particularly handy in ignoring embedded structs from being validated. (Usage: -) This is the 'or' operator allowing multiple validators to be used and accepted. (Usage: rgb|rgba) <-- this would allow either rgb or rgba colors to be accepted. This can also be combined with 'and' for example ( Usage: omitempty,rgb|rgba) When a field that is a nested struct is encountered, and contains this flag any validation on the nested struct will be run, but none of the nested struct fields will be validated. This is useful if inside of your program you know the struct will be valid, but need to verify it has been assigned. NOTE: only "required" and "omitempty" can be used on a struct itself. Same as structonly tag except that any struct level validations will not run. Allows conditional validation, for example if a field is not set with a value (Determined by the "required" validator) then other validation such as min or max won't run, but if a value is set validation will run. Allows to skip the validation if the value is nil (same as omitempty, but only for the nil-values). This tells the validator to dive into a slice, array or map and validate that level of the slice, array or map with the validation tags that follow. Multidimensional nesting is also supported, each level you wish to dive will require another dive tag. dive has some sub-tags, 'keys' & 'endkeys', please see the Keys & EndKeys section just below. Example #1 Example #2 Keys & EndKeys These are to be used together directly after the dive tag and tells the validator that anything between 'keys' and 'endkeys' applies to the keys of a map and not the values; think of it like the 'dive' tag, but for map keys instead of values. Multidimensional nesting is also supported, each level you wish to validate will require another 'keys' and 'endkeys' tag. These tags are only valid for maps. Example #1 Example #2 This validates that the value is not the data types default zero value. For numbers ensures value is not zero. For strings ensures value is not "". For booleans ensures value is not false. For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value when using WithRequiredStructEnabled. The field under validation must be present and not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if any of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if all of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must be present and not empty only when any of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only when all of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must not be present or not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must not be present or empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: This validates that the value is the default value and is almost the opposite of required. For numbers, length will ensure that the value is equal to the parameter given. For strings, it checks that the string length is exactly that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, len will ensure that the value is equal to the duration given in the parameter. For numbers, max will ensure that the value is less than or equal to the parameter given. For strings, it checks that the string length is at most that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, max will ensure that the value is less than or equal to the duration given in the parameter. For numbers, min will ensure that the value is greater or equal to the parameter given. For strings, it checks that the string length is at least that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, min will ensure that the value is greater than or equal to the duration given in the parameter. For strings & numbers, eq will ensure that the value is equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, eq will ensure that the value is equal to the duration given in the parameter. For strings & numbers, ne will ensure that the value is not equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, ne will ensure that the value is not equal to the duration given in the parameter. For strings, ints, and uints, oneof will ensure that the value is one of the values in the parameter. The parameter should be a list of values separated by whitespace. Values may be strings or numbers. To match strings with spaces in them, include the target string between single quotes. For numbers, this will ensure that the value is greater than the parameter given. For strings, it checks that the string length is greater than that number of characters. For slices, arrays and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gt will ensure that the value is greater than the duration given in the parameter. Same as 'min' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gte will ensure that the value is greater than or equal to the duration given in the parameter. For numbers, this will ensure that the value is less than the parameter given. For strings, it checks that the string length is less than that number of characters. For slices, arrays, and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lt will ensure that the value is less than the duration given in the parameter. Same as 'max' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lte will ensure that the value is less than or equal to the duration given in the parameter. This will validate the field value against another fields value either within a struct or passed in field. Example #1: Example #2: Field Equals Another Field (relative) This does the same as eqfield except that it validates the field provided relative to the top level struct. This will validate the field value against another fields value either within a struct or passed in field. Examples: Field Does Not Equal Another Field (relative) This does the same as nefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltefield except that it validates the field provided relative to the top level struct. This does the same as contains except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. This does the same as excludes except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. For arrays & slices, unique will ensure that there are no duplicates. For maps, unique will ensure that there are no duplicate values. For slices of struct, unique will ensure that there are no duplicate values in a field of the struct specified via a parameter. This validates that a string value contains ASCII alpha characters only This validates that a string value contains ASCII alphanumeric characters only This validates that a string value contains unicode alpha characters only This validates that a string value contains unicode alphanumeric characters only This validates that a string value can successfully be parsed into a boolean with strconv.ParseBool This validates that a string value contains number values only. For integers or float it returns true. This validates that a string value contains a basic numeric value. basic excludes exponents etc... for integers or float it returns true. This validates that a string value contains a valid hexadecimal. This validates that a string value contains a valid hex color including hashtag (#) This validates that a string value contains only lowercase characters. An empty string is not a valid lowercase string. This validates that a string value contains only uppercase characters. An empty string is not a valid uppercase string. This validates that a string value contains a valid rgb color This validates that a string value contains a valid rgba color This validates that a string value contains a valid hsl color This validates that a string value contains a valid hsla color This validates that a string value contains a valid E.164 Phone number https://en.wikipedia.org/wiki/E.164 (ex. +1123456789) This validates that a string value contains a valid email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all possibilities. This validates that a string value is valid JSON This validates that a string value is a valid JWT This validates that a string value contains a valid file path and that the file exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid file path and that the file exists on the machine and is an image. This is done using os.Stat and github.com/gabriel-vasile/mimetype This validates that a string value contains a valid file path but does not validate the existence of that file. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid url This will accept any url the golang request uri accepts but must contain a schema for example http:// or rtmp:// This validates that a string value contains a valid uri This will accept any uri the golang request uri accepts This validates that a string value contains a valid URN according to the RFC 2141 spec. This validates that a string value contains a valid bas324 value. Although an empty string is valid base32 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 value. Although an empty string is valid base64 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value according the RFC4648 spec. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value, but without = padding, according the RFC4648 spec, section 3.2. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid bitcoin address. The format of the string is checked to ensure it matches one of the three formats P2PKH, P2SH and performs checksum validation. Bitcoin Bech32 Address (segwit) This validates that a string value contains a valid bitcoin Bech32 address as defined by bip-0173 (https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki) Special thanks to Pieter Wuille for providing reference implementations. This validates that a string value contains a valid ethereum address. The format of the string is checked to ensure it matches the standard Ethereum address format. This validates that a string value contains the substring value. This validates that a string value contains any Unicode code points in the substring value. This validates that a string value contains the supplied rune value. This validates that a string value does not contain the substring value. This validates that a string value does not contain any Unicode code points in the substring value. This validates that a string value does not contain the supplied rune value. This validates that a string value starts with the supplied string value This validates that a string value ends with the supplied string value This validates that a string value does not start with the supplied string value This validates that a string value does not end with the supplied string value This validates that a string value contains a valid isbn10 or isbn13 value. This validates that a string value contains a valid isbn10 value. This validates that a string value contains a valid isbn13 value. This validates that a string value contains a valid UUID. Uppercase UUID values will not pass - use `uuid_rfc4122` instead. This validates that a string value contains a valid version 3 UUID. Uppercase UUID values will not pass - use `uuid3_rfc4122` instead. This validates that a string value contains a valid version 4 UUID. Uppercase UUID values will not pass - use `uuid4_rfc4122` instead. This validates that a string value contains a valid version 5 UUID. Uppercase UUID values will not pass - use `uuid5_rfc4122` instead. This validates that a string value contains a valid ULID value. This validates that a string value contains only ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains only printable ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains one or more multibyte characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains a valid DataURI. NOTE: this will also validate that the data portion is valid base64 This validates that a string value contains a valid latitude. This validates that a string value contains a valid longitude. This validates that a string value contains a valid U.S. Social Security Number. This validates that a string value contains a valid IP Address. This validates that a string value contains a valid v4 IP Address. This validates that a string value contains a valid v6 IP Address. This validates that a string value contains a valid CIDR Address. This validates that a string value contains a valid v4 CIDR Address. This validates that a string value contains a valid v6 CIDR Address. This validates that a string value contains a valid resolvable TCP Address. This validates that a string value contains a valid resolvable v4 TCP Address. This validates that a string value contains a valid resolvable v6 TCP Address. This validates that a string value contains a valid resolvable UDP Address. This validates that a string value contains a valid resolvable v4 UDP Address. This validates that a string value contains a valid resolvable v6 UDP Address. This validates that a string value contains a valid resolvable IP Address. This validates that a string value contains a valid resolvable v4 IP Address. This validates that a string value contains a valid resolvable v6 IP Address. This validates that a string value contains a valid Unix Address. This validates that a string value contains a valid MAC Address. Note: See Go's ParseMAC for accepted formats and types: This validates that a string value is a valid Hostname according to RFC 952 https://tools.ietf.org/html/rfc952 This validates that a string value is a valid Hostname according to RFC 1123 https://tools.ietf.org/html/rfc1123 Full Qualified Domain Name (FQDN) This validates that a string value contains a valid FQDN. This validates that a string value appears to be an HTML element tag including those described at https://developer.mozilla.org/en-US/docs/Web/HTML/Element This validates that a string value is a proper character reference in decimal or hexadecimal format This validates that a string value is percent-encoded (URL encoded) according to https://tools.ietf.org/html/rfc3986#section-2.1 This validates that a string value contains a valid directory and that it exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid directory but does not validate the existence of that directory. This is done using os.Stat, which is a platform independent function. It is safest to suffix the string with os.PathSeparator if the directory may not exist at the time of validation. This validates that a string value contains a valid DNS hostname and port that can be used to validate fields typically passed to sockets and connections. This validates that a string value is a valid datetime based on the supplied datetime format. Supplied format must match the official Go time format layout as documented in https://golang.org/pkg/time/ This validates that a string value is a valid country code based on iso3166-1 alpha-2 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-3 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-numeric standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid BCP 47 language tag, as parsed by language.Parse. More information on https://pkg.go.dev/golang.org/x/text/language BIC (SWIFT code) This validates that a string value is a valid Business Identifier Code (SWIFT code), defined in ISO 9362. More information on https://www.iso.org/standard/60390.html This validates that a string value is a valid dns RFC 1035 label, defined in RFC 1035. More information on https://datatracker.ietf.org/doc/html/rfc1035 This validates that a string value is a valid time zone based on the time zone database present on the system. Although empty value and Local value are allowed by time.LoadLocation golang function, they are not allowed by this validator. More information on https://golang.org/pkg/time/#LoadLocation This validates that a string value is a valid semver version, defined in Semantic Versioning 2.0.0. More information on https://semver.org/ This validates that a string value is a valid cve id, defined in cve mitre. More information on https://cve.mitre.org/ This validates that a string value contains a valid credit card number using Luhn algorithm. This validates that a string or (u)int value contains a valid checksum using the Luhn algorithm. This validates that a string is a valid 24 character hexadecimal string or valid connection string. Example: This validates that a string value contains a valid cron expression. This validates that a string is valid for use with SpiceDb for the indicated purpose. If no purpose is given, a purpose of 'id' is assumed. Alias Validators and Tags NOTE: When returning an error, the tag returned in "FieldError" will be the alias tag unless the dive tag is part of the alias. Everything after the dive tag is not reported as the alias tag. Also, the "ActualTag" in the before case will be the actual tag within the alias that failed. Here is a list of the current built in alias tags: Validator notes: A collection of validation rules that are frequently needed but are more complex than the ones found in the baked in validators. A non standard validator must be registered manually like you would with your own custom validation functions. Example of registration and use: Here is a list of the current non standard validators: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

v10.20.0 • 7 months ago

github.com/xumingpeng/validator/v10

Package validator implements value validations for structs and individual fields based on tags. It can also handle Cross-Field and Cross-Struct validation for nested structs and has the ability to dive into arrays and maps of any type. see more examples https://github.com/go-playground/validator/tree/master/_examples Validator is designed to be thread-safe and used as a singleton instance. It caches information about your struct and validations, in essence only parsing your validation tags once per struct type. Using multiple instances neglects the benefit of caching. The not thread-safe functions are explicitly marked as such in the documentation. Doing things this way is actually the way the standard library does, see the file.Open method here: The authors return type "error" to avoid the issue discussed in the following, where err is always != nil: Validator only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom Validation functions can be added. Example: Cross-Field Validation can be done via the following tags: If, however, some custom cross-field validation is required, it can be done using a custom validation. Why not just have cross-fields validation tags (i.e. only eqcsfield and not eqfield)? The reason is efficiency. If you want to check a field within the same struct "eqfield" only has to find the field on the same struct (1 level). But, if we used "eqcsfield" it could be multiple levels down. Example: Multiple validators on a field will process in the order defined. Example: Bad Validator definitions are not handled by the library. Example: Baked In Cross-Field validation only compares fields on the same struct. If Cross-Field + Cross-Struct validation is needed you should implement your own custom validator. Comma (",") is the default separator of validation tags. If you wish to have a comma included within the parameter (i.e. excludesall=,) you will need to use the UTF-8 hex representation 0x2C, which is replaced in the code as a comma, so the above will become excludesall=0x2C. Pipe ("|") is the 'or' validation tags deparator. If you wish to have a pipe included within the parameter i.e. excludesall=| you will need to use the UTF-8 hex representation 0x7C, which is replaced in the code as a pipe, so the above will become excludesall=0x7C Here is a list of the current built in validators: Tells the validation to skip this struct field; this is particularly handy in ignoring embedded structs from being validated. (Usage: -) This is the 'or' operator allowing multiple validators to be used and accepted. (Usage: rgb|rgba) <-- this would allow either rgb or rgba colors to be accepted. This can also be combined with 'and' for example ( Usage: omitempty,rgb|rgba) When a field that is a nested struct is encountered, and contains this flag any validation on the nested struct will be run, but none of the nested struct fields will be validated. This is useful if inside of your program you know the struct will be valid, but need to verify it has been assigned. NOTE: only "required" and "omitempty" can be used on a struct itself. Same as structonly tag except that any struct level validations will not run. Allows conditional validation, for example if a field is not set with a value (Determined by the "required" validator) then other validation such as min or max won't run, but if a value is set validation will run. Allows to skip the validation if the value is nil (same as omitempty, but only for the nil-values). This tells the validator to dive into a slice, array or map and validate that level of the slice, array or map with the validation tags that follow. Multidimensional nesting is also supported, each level you wish to dive will require another dive tag. dive has some sub-tags, 'keys' & 'endkeys', please see the Keys & EndKeys section just below. Example #1 Example #2 Keys & EndKeys These are to be used together directly after the dive tag and tells the validator that anything between 'keys' and 'endkeys' applies to the keys of a map and not the values; think of it like the 'dive' tag, but for map keys instead of values. Multidimensional nesting is also supported, each level you wish to validate will require another 'keys' and 'endkeys' tag. These tags are only valid for maps. Example #1 Example #2 This validates that the value is not the data types default zero value. For numbers ensures value is not zero. For strings ensures value is not "". For booleans ensures value is not false. For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value when using WithRequiredStructEnabled. The field under validation must be present and not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if any of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if all of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must be present and not empty only when any of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only when all of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must not be present or not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must not be present or empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: This validates that the value is the default value and is almost the opposite of required. For numbers, length will ensure that the value is equal to the parameter given. For strings, it checks that the string length is exactly that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, len will ensure that the value is equal to the duration given in the parameter. For numbers, max will ensure that the value is less than or equal to the parameter given. For strings, it checks that the string length is at most that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, max will ensure that the value is less than or equal to the duration given in the parameter. For numbers, min will ensure that the value is greater or equal to the parameter given. For strings, it checks that the string length is at least that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, min will ensure that the value is greater than or equal to the duration given in the parameter. For strings & numbers, eq will ensure that the value is equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, eq will ensure that the value is equal to the duration given in the parameter. For strings & numbers, ne will ensure that the value is not equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, ne will ensure that the value is not equal to the duration given in the parameter. For strings, ints, and uints, oneof will ensure that the value is one of the values in the parameter. The parameter should be a list of values separated by whitespace. Values may be strings or numbers. To match strings with spaces in them, include the target string between single quotes. For numbers, this will ensure that the value is greater than the parameter given. For strings, it checks that the string length is greater than that number of characters. For slices, arrays and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gt will ensure that the value is greater than the duration given in the parameter. Same as 'min' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gte will ensure that the value is greater than or equal to the duration given in the parameter. For numbers, this will ensure that the value is less than the parameter given. For strings, it checks that the string length is less than that number of characters. For slices, arrays, and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lt will ensure that the value is less than the duration given in the parameter. Same as 'max' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lte will ensure that the value is less than or equal to the duration given in the parameter. This will validate the field value against another fields value either within a struct or passed in field. Example #1: Example #2: Field Equals Another Field (relative) This does the same as eqfield except that it validates the field provided relative to the top level struct. This will validate the field value against another fields value either within a struct or passed in field. Examples: Field Does Not Equal Another Field (relative) This does the same as nefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltefield except that it validates the field provided relative to the top level struct. This does the same as contains except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. This does the same as excludes except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. For arrays & slices, unique will ensure that there are no duplicates. For maps, unique will ensure that there are no duplicate values. For slices of struct, unique will ensure that there are no duplicate values in a field of the struct specified via a parameter. This validates that a string value contains ASCII alpha characters only This validates that a string value contains ASCII alphanumeric characters only This validates that a string value contains unicode alpha characters only This validates that a string value contains unicode alphanumeric characters only This validates that a string value can successfully be parsed into a boolean with strconv.ParseBool This validates that a string value contains number values only. For integers or float it returns true. This validates that a string value contains a basic numeric value. basic excludes exponents etc... for integers or float it returns true. This validates that a string value contains a valid hexadecimal. This validates that a string value contains a valid hex color including hashtag (#) This validates that a string value contains only lowercase characters. An empty string is not a valid lowercase string. This validates that a string value contains only uppercase characters. An empty string is not a valid uppercase string. This validates that a string value contains a valid rgb color This validates that a string value contains a valid rgba color This validates that a string value contains a valid hsl color This validates that a string value contains a valid hsla color This validates that a string value contains a valid E.164 Phone number https://en.wikipedia.org/wiki/E.164 (ex. +1123456789) This validates that a string value contains a valid email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all possibilities. This validates that a string value is valid JSON This validates that a string value is a valid JWT This validates that a string value contains a valid file path and that the file exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid file path and that the file exists on the machine and is an image. This is done using os.Stat and github.com/gabriel-vasile/mimetype This validates that a string value contains a valid file path but does not validate the existence of that file. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid url This will accept any url the golang request uri accepts but must contain a schema for example http:// or rtmp:// This validates that a string value contains a valid uri This will accept any uri the golang request uri accepts This validates that a string value contains a valid URN according to the RFC 2141 spec. This validates that a string value contains a valid bas324 value. Although an empty string is valid base32 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 value. Although an empty string is valid base64 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value according the RFC4648 spec. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value, but without = padding, according the RFC4648 spec, section 3.2. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid bitcoin address. The format of the string is checked to ensure it matches one of the three formats P2PKH, P2SH and performs checksum validation. Bitcoin Bech32 Address (segwit) This validates that a string value contains a valid bitcoin Bech32 address as defined by bip-0173 (https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki) Special thanks to Pieter Wuille for providing reference implementations. This validates that a string value contains a valid ethereum address. The format of the string is checked to ensure it matches the standard Ethereum address format. This validates that a string value contains the substring value. This validates that a string value contains any Unicode code points in the substring value. This validates that a string value contains the supplied rune value. This validates that a string value does not contain the substring value. This validates that a string value does not contain any Unicode code points in the substring value. This validates that a string value does not contain the supplied rune value. This validates that a string value starts with the supplied string value This validates that a string value ends with the supplied string value This validates that a string value does not start with the supplied string value This validates that a string value does not end with the supplied string value This validates that a string value contains a valid isbn10 or isbn13 value. This validates that a string value contains a valid isbn10 value. This validates that a string value contains a valid isbn13 value. This validates that a string value contains a valid UUID. Uppercase UUID values will not pass - use `uuid_rfc4122` instead. This validates that a string value contains a valid version 3 UUID. Uppercase UUID values will not pass - use `uuid3_rfc4122` instead. This validates that a string value contains a valid version 4 UUID. Uppercase UUID values will not pass - use `uuid4_rfc4122` instead. This validates that a string value contains a valid version 5 UUID. Uppercase UUID values will not pass - use `uuid5_rfc4122` instead. This validates that a string value contains a valid ULID value. This validates that a string value contains only ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains only printable ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains one or more multibyte characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains a valid DataURI. NOTE: this will also validate that the data portion is valid base64 This validates that a string value contains a valid latitude. This validates that a string value contains a valid longitude. This validates that a string value contains a valid U.S. Social Security Number. This validates that a string value contains a valid IP Address. This validates that a string value contains a valid v4 IP Address. This validates that a string value contains a valid v6 IP Address. This validates that a string value contains a valid CIDR Address. This validates that a string value contains a valid v4 CIDR Address. This validates that a string value contains a valid v6 CIDR Address. This validates that a string value contains a valid resolvable TCP Address. This validates that a string value contains a valid resolvable v4 TCP Address. This validates that a string value contains a valid resolvable v6 TCP Address. This validates that a string value contains a valid resolvable UDP Address. This validates that a string value contains a valid resolvable v4 UDP Address. This validates that a string value contains a valid resolvable v6 UDP Address. This validates that a string value contains a valid resolvable IP Address. This validates that a string value contains a valid resolvable v4 IP Address. This validates that a string value contains a valid resolvable v6 IP Address. This validates that a string value contains a valid Unix Address. This validates that a string value contains a valid MAC Address. Note: See Go's ParseMAC for accepted formats and types: This validates that a string value is a valid Hostname according to RFC 952 https://tools.ietf.org/html/rfc952 This validates that a string value is a valid Hostname according to RFC 1123 https://tools.ietf.org/html/rfc1123 Full Qualified Domain Name (FQDN) This validates that a string value contains a valid FQDN. This validates that a string value appears to be an HTML element tag including those described at https://developer.mozilla.org/en-US/docs/Web/HTML/Element This validates that a string value is a proper character reference in decimal or hexadecimal format This validates that a string value is percent-encoded (URL encoded) according to https://tools.ietf.org/html/rfc3986#section-2.1 This validates that a string value contains a valid directory and that it exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid directory but does not validate the existence of that directory. This is done using os.Stat, which is a platform independent function. It is safest to suffix the string with os.PathSeparator if the directory may not exist at the time of validation. This validates that a string value contains a valid DNS hostname and port that can be used to validate fields typically passed to sockets and connections. This validates that a string value is a valid datetime based on the supplied datetime format. Supplied format must match the official Go time format layout as documented in https://golang.org/pkg/time/ This validates that a string value is a valid country code based on iso3166-1 alpha-2 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-3 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-numeric standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid BCP 47 language tag, as parsed by language.Parse. More information on https://pkg.go.dev/golang.org/x/text/language BIC (SWIFT code) This validates that a string value is a valid Business Identifier Code (SWIFT code), defined in ISO 9362. More information on https://www.iso.org/standard/60390.html This validates that a string value is a valid dns RFC 1035 label, defined in RFC 1035. More information on https://datatracker.ietf.org/doc/html/rfc1035 This validates that a string value is a valid time zone based on the time zone database present on the system. Although empty value and Local value are allowed by time.LoadLocation golang function, they are not allowed by this validator. More information on https://golang.org/pkg/time/#LoadLocation This validates that a string value is a valid semver version, defined in Semantic Versioning 2.0.0. More information on https://semver.org/ This validates that a string value is a valid cve id, defined in cve mitre. More information on https://cve.mitre.org/ This validates that a string value contains a valid credit card number using Luhn algorithm. This validates that a string or (u)int value contains a valid checksum using the Luhn algorithm. This validates that a string is a valid 24 character hexadecimal string or valid connection string. Example: This validates that a string value contains a valid cron expression. This validates that a string is valid for use with SpiceDb for the indicated purpose. If no purpose is given, a purpose of 'id' is assumed. Alias Validators and Tags NOTE: When returning an error, the tag returned in "FieldError" will be the alias tag unless the dive tag is part of the alias. Everything after the dive tag is not reported as the alias tag. Also, the "ActualTag" in the before case will be the actual tag within the alias that failed. Here is a list of the current built in alias tags: Validator notes: A collection of validation rules that are frequently needed but are more complex than the ones found in the baked in validators. A non standard validator must be registered manually like you would with your own custom validation functions. Example of registration and use: Here is a list of the current non standard validators: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

v10.11.0 • 3 years ago

github.com/vube/i18n

Package i18n offers the following basic internationalization functionality: There's more we'd like to add in the future, including: In order to interact with this package, you must first get a TranslatorFactory instace. Through the TranslatorFactory, you can get a Translator instance. Almost everything in this package is accessed through methods on the Translator struct. About the rules and messages paths: This package ships with built-in rules, and you are welcome to use those directly. However, if there are locales or rules that are missing from what ships directly with this package, or if you desire to use different rules than those that ship with this package, then you can specify additional rules paths. At this time, this package does not ship with built-in messages, other than a few used for the unit tests. You will need to specify your own messages path(s). For both rules and messages paths, you can specify multiple. Paths later in the slice take precedence over packages earlier in the slice. For a basic example of getting a TranslatorFactory instance: For simple message translation, use the Translate function, and send an empty map as the second argument (we'll explain that argument in the next section). You can also pass placeholder values to the translate function. That's what the second argument is for. In this example, we will inject a username into the translation. You can also translate strings with plurals. However, any one message can contain at most one plural. If you want to translate "I need 5 apples and 3 oranges" you are out of luck. The Pluralize method takes 3 arguments. The first is the message key - just like the Translate method. The second argument is a float which is used to determine which plural form to use. The third is a string representation of the number. Why two arguments for the number instead of one? This allows you ultimate flexibility in number formatting to use in the translation while eliminating the need for string number parsing. You can use the "FomatNumber", "FormatCurrency" and "FormatPercent" methods to do locale-based number formatting for numbers, currencies and percentages. If you need to sort a list of strings alphabetically, then you should not use a simple string comparison to do so - this will often result in incorrect results. "ȧ" would normally evaluate as greater than "z", which is not correct in any latin writing system alphabet. Use can use the Sort method on the Translator struct to do an alphabetic sorting that is correct for that locale. Alternatively, you can access the SortUniversal and the SortLocale functions directly without a Translator instance. SortUniversal does not take a specific locale into account when doing the alphabetic sorting, which means it might be slightly less accurate than the SortLocal function. However, there are cases in which the collation rules for a specific locale are unknown, or the sorting needs to be done in a local-agnostic way. For these cases, the SortUniversal function performs a unicode normalization in order to best sort the strings. In order to be flexible, these functions take a generic interface slice and a function for retrieving the value on which to perform the sorting. For example: When getting a Translator instance, the TranslatorFactory will automatically attempt to determine an appropriate fallback Translator for the locale you specify. For locales with specific "flavors", like "en-au" or "zh-hans", the "vanilla" version of that locale will be used if it exists. In these cases that would be "en" and "zh". When creating a TranslatorFactory instance, you can optionally specify a final fallback locale. This will be used if it exists. When determining a fallback, the the factory first checks the less specific versions of the specified locale, if they exist and will ultimate fallback to the global fallback if specified. All of the examples above conveniently ignore errors. We recommend that you DO handle errors. The system is designed to give you a valid result if at all possible, even in errors occur in the process. However, the errors are still returned and may provide you helpful information you might otherwise miss - like missing files, file permissions problems, yaml format problems, missing translations, etc. We recommend that you do some sort of logging of these errors.

v0.0.0-20170329174511-75e795684593 • 8 years ago

github.com/drand/kyber

Package kyber provides a toolbox of advanced cryptographic primitives, for applications that need more than straightforward signing and encryption. This top level package defines the interfaces to cryptographic primitives designed to be independent of specific cryptographic algorithms, to facilitate upgrading applications to new cryptographic algorithms or switching to alternative algorithms for experimentation purposes. This toolkits public-key crypto API includes a kyber.Group interface supporting a broad class of group-based public-key primitives including DSA-style integer residue groups and elliptic curve groups. Users of this API can write higher-level crypto algorithms such as zero-knowledge proofs without knowing or caring exactly what kind of group, let alone which precise security parameters or elliptic curves, are being used. The kyber.Group interface supports the standard algebraic operations on group elements and scalars that nontrivial public-key algorithms tend to rely on. The interface uses additive group terminology typical for elliptic curves, such that point addition is homomorphically equivalent to adding their (potentially secret) scalar multipliers. But the API and its operations apply equally well to DSA-style integer groups. As a trivial example, generating a public/private keypair is as simple as: The first statement picks a private key (Scalar) from a the suites's source of cryptographic random or pseudo-random bits, while the second performs elliptic curve scalar multiplication of the curve's standard base point (indicated by the 'nil' argument to Mul) by the scalar private key 'a'. Similarly, computing a Diffie-Hellman shared secret using Alice's private key 'a' and Bob's public key 'B' can be done via: Note that we use 'Mul' rather than 'Exp' here because the library uses the additive-group terminology common for elliptic curve crypto, rather than the multiplicative-group terminology of traditional integer groups - but the two are semantically equivalent and the interface itself works for both elliptic curve and integer groups. Various sub-packages provide several specific implementations of these cryptographic interfaces. In particular, the 'group/mod' sub-package provides implementations of modular integer groups underlying conventional DSA-style algorithms. The `group/nist` package provides NIST-standardized elliptic curves built on the Go crypto library. The 'group/edwards25519' sub-package provides the kyber.Group interface using the popular Ed25519 curve. Other sub-packages build more interesting high-level cryptographic tools atop these primitive interfaces, including: - share: Polynomial commitment and verifiable Shamir secret splitting for implementing verifiable 't-of-n' threshold cryptographic schemes. This can be used to encrypt a message so that any 2 out of 3 receivers must work together to decrypt it, for example. - proof: An implementation of the general Camenisch/Stadler framework for discrete logarithm knowledge proofs. This system supports both interactive and non-interactive proofs of a wide variety of statements such as, "I know the secret x associated with public key X or I know the secret y associated with public key Y", without revealing anything about either secret or even which branch of the "or" clause is true. - sign: The sign directory contains different signature schemes. - sign/anon provides anonymous and pseudonymous public-key encryption and signing, where the sender of a signed message or the receiver of an encrypted message is defined as an explicit anonymity set containing several public keys rather than just one. For example, a member of an organization's board of trustees might prove to be a member of the board without revealing which member she is. - sign/cosi provides collective signature algorithm, where a bunch of signers create a unique, compact and efficiently verifiable signature using the Schnorr signature as a basis. - sign/eddsa provides a kyber-native implementation of the EdDSA signature scheme. - sign/schnorr provides a basic vanilla Schnorr signature scheme implementation. - shuffle: Verifiable cryptographic shuffles of ElGamal ciphertexts, which can be used to implement (for example) voting or auction schemes that keep the sources of individual votes or bids private without anyone having to trust more than one of the shuffler(s) to shuffle votes/bids honestly. As should be obvious, this library is intended to be used by developers who are at least moderately knowledgeable about cryptography. If you want a crypto library that makes it easy to implement "basic crypto" functionality correctly - i.e., plain public-key encryption and signing - then [NaCl secretbox](https://godoc.org/golang.org/x/crypto/nacl/secretbox) may be a better choice. This toolkit's purpose is to make it possible - and preferably easy - to do slightly more interesting things that most current crypto libraries don't support effectively. The one existing crypto library that this toolkit is probably most comparable to is the Charm rapid prototyping library for Python (https://charm-crypto.com/category/charm). This library incorporates and/or builds on existing code from a variety of sources, as documented in the relevant sub-packages. This library is offered as-is, and without a guarantee. It will need an independent security review before it should be considered ready for use in security-critical applications. If you integrate Kyber into your application it is YOUR RESPONSIBILITY to arrange for that audit. If you notice a possible security problem, please report it to dedis-security@epfl.ch.

v1.2.0 • 2 years ago

go.uber.org/net/metrics

Package metrics is a telemetry client designed for Uber's software networking team. It prioritizes performance on the hot path and integration with both push- and pull-based collection systems. Like Prometheus and Tally, it supports metrics tagged with arbitrary key-value pairs. Like Prometheus, but unlike Tally, metric names should be relatively long and descriptive - generally speaking, metrics from the same process shouldn't share names. (See the documentation for the Root struct below for a longer explanation of the uniqueness rules.) For example, prefer "grpc_successes_by_procedure" over "successes", since "successes" is common and vague. Where relevant, metric names should indicate their unit of measurement (e.g., "grpc_success_latency_ms"). Counters represent monotonically increasing values, like a car's odometer. Gauges represent point-in-time readings, like a car's speedometer. Both counters and gauges expose not only write operations (set, add, increment, etc.), but also atomic reads. This makes them easy to integrate directly into your business logic: you can use them anywhere you'd otherwise use a 64-bit atomic integer. This package doesn't support analogs of Tally's timer or Prometheus's summary, because they can't be accurately aggregated at query time. Instead, it approximates distributions of values with histograms. These require more up-front work to set up, but are typically more accurate and flexible when queried. See https://prometheus.io/docs/practices/histograms/ for a more detailed discussion of the trade-offs involved. Plain counters, gauges, and histograms have a fixed set of tags. However, it's common to encounter situations where a subset of a metric's tags vary constantly. For example, you might want to track the latency of your database queries by table: you know the database cluster, application name, and hostname at process startup, but you need to specify the table name with each query. To model these situations, this package uses vectors. Each vector is a local cache of metrics, so accessing them is quite fast. Within a vector, all metrics share a common set of constant tags and a list of variable tags. In our database query example, the constant tags are cluster, application, and hostname, and the only variable tag is table name. Usage examples are included in the documentation for each vector type. This package integrates with StatsD- and M3-based collection systems by periodically pushing differential updates. (Users can integrate with other push-based systems by implementing the push.Target interface.) It integrates with pull-based collectors by exposing an HTTP handler that supports Prometheus's text and protocol buffer exposition formats. Examples of both push and pull integration are included in the documentation for the root struct's Push and ServeHTTP methods. If you're unfamiliar with Tally and Prometheus, you may want to consult their documentation:

v1.4.0 • last year

github.com/tovenja/cron/v3

Package cron implements a cron spec parser and job runner. To download the specific tagged release, run: Import it in your program as: It requires Go 1.11 or later due to usage of Go Modules. Callers may register Funcs to be invoked on a given schedule. Cron will run them in their own goroutines. A cron expression represents a set of times, using 5 space-separated fields. Month and Day-of-week field values are case insensitive. "SUN", "Sun", and "sun" are equally accepted. The specific interpretation of the format is based on the Cron Wikipedia page: https://en.wikipedia.org/wiki/Cron Alternative Cron expression formats support other fields like seconds. You can implement that by creating a custom Parser as follows. Since adding Seconds is the most common modification to the standard cron spec, cron provides a builtin function to do that, which is equivalent to the custom parser you saw earlier, except that its seconds field is REQUIRED: That emulates Quartz, the most popular alternative Cron schedule format: http://www.quartz-scheduler.org/documentation/quartz-2.x/tutorials/crontrigger.html Asterisk ( * ) The asterisk indicates that the cron expression will match for all values of the field; e.g., using an asterisk in the 5th field (month) would indicate every month. Slash ( / ) Slashes are used to describe increments of ranges. For example 3-59/15 in the 1st field (minutes) would indicate the 3rd minute of the hour and every 15 minutes thereafter. The form "*\/..." is equivalent to the form "first-last/...", that is, an increment over the largest possible range of the field. The form "N/..." is accepted as meaning "N-MAX/...", that is, starting at N, use the increment until the end of that specific range. It does not wrap around. Comma ( , ) Commas are used to separate items of a list. For example, using "MON,WED,FRI" in the 5th field (day of week) would mean Mondays, Wednesdays and Fridays. Hyphen ( - ) Hyphens are used to define ranges. For example, 9-17 would indicate every hour between 9am and 5pm inclusive. Question mark ( ? ) Question mark may be used instead of '*' for leaving either day-of-month or day-of-week blank. You may use one of several pre-defined schedules in place of a cron expression. You may also schedule a job to execute at fixed intervals, starting at the time it's added or cron is run. This is supported by formatting the cron spec like this: where "duration" is a string accepted by time.ParseDuration (http://golang.org/pkg/time/#ParseDuration). For example, "@every 1h30m10s" would indicate a schedule that activates after 1 hour, 30 minutes, 10 seconds, and then every interval after that. Note: The interval does not take the job runtime into account. For example, if a job takes 3 minutes to run, and it is scheduled to run every 5 minutes, it will have only 2 minutes of idle time between each run. By default, all interpretation and scheduling is done in the machine's local time zone (time.Local). You can specify a different time zone on construction: Individual cron schedules may also override the time zone they are to be interpreted in by providing an additional space-separated field at the beginning of the cron spec, of the form "CRON_TZ=Asia/Tokyo". For example: The prefix "TZ=(TIME ZONE)" is also supported for legacy compatibility. Be aware that jobs scheduled during daylight-savings leap-ahead transitions will not be run! A Cron runner may be configured with a chain of job wrappers to add cross-cutting functionality to all submitted jobs. For example, they may be used to achieve the following effects: Install wrappers for all jobs added to a cron using the `cron.WithChain` option: Install wrappers for individual jobs by explicitly wrapping them: Since the Cron service runs concurrently with the calling code, some amount of care must be taken to ensure proper synchronization. All cron methods are designed to be correctly synchronized as long as the caller ensures that invocations have a clear happens-before ordering between them. Cron defines a Logger interface that is a subset of the one defined in github.com/go-logr/logr. It has two logging levels (Info and Error), and parameters are key/value pairs. This makes it possible for cron logging to plug into structured logging systems. An adapter, [Verbose]PrintfLogger, is provided to wrap the standard library *log.Logger. For additional insight into Cron operations, verbose logging may be activated which will record job runs, scheduling decisions, and added or removed jobs. Activate it with a one-off logger as follows: Cron entries are stored in an array, sorted by their next activation time. Cron sleeps until the next job is due to be run. Upon waking:

v3.0.2 • 3 years ago

gopkg.in/inconshreveABLE/LOG15.v2

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v2.16.0 • 2 years ago

github.com/inconshreveable/Log15

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v2.16.0+incompatible • 2 years ago

gopkg.in/inconshreveable/LOG15.v2

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v2.16.0 • 2 years ago

gopkg.in/inconshreveable/Log15.v2

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v2.16.0 • 2 years ago

gopkg.in/inconshreveable/log15.v1

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are can be easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy function. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v1.0.0-20140523170446-a127215f557c • 11 years ago

go.mercari.io/datastore/v2

Package datastore has an abstract representation of (AppEngine | Cloud) Datastore. repository https://github.com/mercari/datastore Let's read https://cloud.google.com/datastore/docs/ or https://cloud.google.com/appengine/docs/standard/go/datastore/ . You should also check https://godoc.org/cloud.google.com/go/datastore or https://godoc.org/google.golang.org/appengine/v2/datastore as datastore original library. Japanese version https://github.com/mercari/datastore/blob/master/doc_ja.go Please see https://godoc.org/go.mercari.io/datastore/v2/clouddatastore or https://godoc.org/go.mercari.io/datastore/v2/aedatastore . Create a Client using the FromContext function of each package. Later in this document, notes on migration from each package are summarized. Please see also there. This package is based on the newly designed Cloud Datastore API. We are introducing flatten tags that only exist in Cloud Datastore, we need to be careful when migrating from AE Datastore. Details will be described later. If you are worried, you may have a clue to the solution at https://godoc.org/go.mercari.io/datastore/v2/clouddatastore . This package has three main objectives. We are forced to make functions that are not directly related to the value of the application for speed, stability and operation. Such functions can be abstracted and used as middleware. Let's think about this case. Put Entity to Datastore and set it to Memcache or Redis. Next, when getting from Datastore, Get from Memcache first, Get it again from Datastore if it fails. It is very troublesome to provide these operations for all Kind and all Entity operations. However, if the middleware intervenes with all Datastore RPCs, you can transparently process without affecting the application code. As another case, RPC sometimes fails. If it fails, the process often succeeds simply by retrying. For easy RET retry with all RPCs, it is better to implement it as middleware. Please refer to https://godoc.org/go.mercari.io/datastore/v2/dsmiddleware if you want to know the middleware already provided. The same interface is provided for AppEngine Datastore and Cloud Datastore. These two are compatible, you can run it with exactly the same code after creating the Client. For example, you can use AE Datastore in a production environment and Cloud Datastore Emulator in UnitTest. If you can avoid goapp, tests may be faster and IDE may be more vulnerable to debugging. You can also read data from the local environment via Cloud Datastore for systems running on AE Datastore. Caution. Although the storage bodies of RPCs of AE Datastore and Cloud Datastore are shared, there is a difference in expressiveness at the API level. Please carefully read the data written in AE Datastore carelessly on Cloud Datastore and do not update it. It may become impossible to read from the API of AE Datastore side. About this, we have not strictly tested. The operation of Datastore has very little latency with respect to RPC's network. When acquiring 10 entities it means that GetMulti one time is better than getting 10 times using loops. However, we are not good at putting together multiple processes at once. Suppose, for example, you want to query on Post Kind, use the list of Comment IDs of the resulting Post, and get a list of Comments. For example, you can query Post Kind and get a list of Post. In addition, consider using CommentIDs of Post and getting a list of Comment. This is enough Query + 1 GetMulti is enough if you write very clever code. However, after acquiring the data, it is necessary to link the Comment list with the appropriate Post. On the other hand, you can easily write a code that throws a query once and then GetMulti the Comment as many as Post. In summary, it is convenient to have Put or Get queued, and there is a mechanism to execute it collectively later. Batch() is it! You can find the example at https://godoc.org/go.mercari.io/datastore/v2/#pkg-examples . I love goon. So I made https://godoc.org/go.mercari.io/datastore/v2/boom which can be used in conjunction with this package. Here's an overview of what you need to do to migrate your existing code. from AE Datastore from Cloud Datastore from goon to boom

v2.2.0 • 2 years ago

gopkg.in/dedis/crypto.v3

Package kyber provides a toolbox of advanced cryptographic primitives, for applications that need more than straightforward signing and encryption. This top level package defines the interfaces to cryptographic primitives designed to be independent of specific cryptographic algorithms, to facilitate upgrading applications to new cryptographic algorithms or switching to alternative algorithms for experimentation purposes. This toolkits public-key crypto API includes a kyber.Group interface supporting a broad class of group-based public-key primitives including DSA-style integer residue groups and elliptic curve groups. Users of this API can write higher-level crypto algorithms such as zero-knowledge proofs without knowing or caring exactly what kind of group, let alone which precise security parameters or elliptic curves, are being used. The kyber.Group interface supports the standard algebraic operations on group elements and scalars that nontrivial public-key algorithms tend to rely on. The interface uses additive group terminology typical for elliptic curves, such that point addition is homomorphically equivalent to adding their (potentially secret) scalar multipliers. But the API and its operations apply equally well to DSA-style integer groups. As a trivial example, generating a public/private keypair is as simple as: The first statement picks a private key (Scalar) from a the suites's source of cryptographic random or pseudo-random bits, while the second performs elliptic curve scalar multiplication of the curve's standard base point (indicated by the 'nil' argument to Mul) by the scalar private key 'a'. Similarly, computing a Diffie-Hellman shared secret using Alice's private key 'a' and Bob's public key 'B' can be done via: Note that we use 'Mul' rather than 'Exp' here because the library uses the additive-group terminology common for elliptic curve crypto, rather than the multiplicative-group terminology of traditional integer groups - but the two are semantically equivalent and the interface itself works for both elliptic curve and integer groups. Various sub-packages provide several specific implementations of these cryptographic interfaces. In particular, the 'group/mod' sub-package provides implementations of modular integer groups underlying conventional DSA-style algorithms. The `group/nist` package provides NIST-standardized elliptic curves built on the Go crypto library. The 'group/edwards25519' sub-package provides the kyber.Group interface using the popular Ed25519 curve. Other sub-packages build more interesting high-level cryptographic tools atop these primitive interfaces, including: - share: Polynomial commitment and verifiable Shamir secret splitting for implementing verifiable 't-of-n' threshold cryptographic schemes. This can be used to encrypt a message so that any 2 out of 3 receivers must work together to decrypt it, for example. - proof: An implementation of the general Camenisch/Stadler framework for discrete logarithm knowledge proofs. This system supports both interactive and non-interactive proofs of a wide variety of statements such as, "I know the secret x associated with public key X or I know the secret y associated with public key Y", without revealing anything about either secret or even which branch of the "or" clause is true. - sign: The sign directory contains different signature schemes. - sign/anon provides anonymous and pseudonymous public-key encryption and signing, where the sender of a signed message or the receiver of an encrypted message is defined as an explicit anonymity set containing several public keys rather than just one. For example, a member of an organization's board of trustees might prove to be a member of the board without revealing which member she is. - sign/cosi provides collective signature algorithm, where a bunch of signers create a unique, compact and efficiently verifiable signature using the Schnorr signature as a basis. - sign/eddsa provides a kyber-native implementation of the EdDSA signature scheme. - sign/schnorr provides a basic vanilla Schnorr signature scheme implementation. - shuffle: Verifiable cryptographic shuffles of ElGamal ciphertexts, which can be used to implement (for example) voting or auction schemes that keep the sources of individual votes or bids private without anyone having to trust more than one of the shuffler(s) to shuffle votes/bids honestly. As should be obvious, this library is intended to be used by developers who are at least moderately knowledgeable about cryptography. If you want a crypto library that makes it easy to implement "basic crypto" functionality correctly - i.e., plain public-key encryption and signing - then [NaCl secretbox](https://godoc.org/golang.org/x/crypto/nacl/secretbox) may be a better choice. This toolkit's purpose is to make it possible - and preferably easy - to do slightly more interesting things that most current crypto libraries don't support effectively. The one existing crypto library that this toolkit is probably most comparable to is the Charm rapid prototyping library for Python (https://charm-crypto.com/category/charm). This library incorporates and/or builds on existing code from a variety of sources, as documented in the relevant sub-packages. This library is offered as-is, and without a guarantee. It will need an independent security review before it should be considered ready for use in security-critical applications. If you integrate Kyber into your application it is YOUR RESPONSIBILITY to arrange for that audit. If you notice a possible security problem, please report it to dedis-security@epfl.ch.

v3.1.0 • 2 years ago

gopkg.in/dedis/kyber.v3

Package kyber provides a toolbox of advanced cryptographic primitives, for applications that need more than straightforward signing and encryption. This top level package defines the interfaces to cryptographic primitives designed to be independent of specific cryptographic algorithms, to facilitate upgrading applications to new cryptographic algorithms or switching to alternative algorithms for experimentation purposes. This toolkits public-key crypto API includes a kyber.Group interface supporting a broad class of group-based public-key primitives including DSA-style integer residue groups and elliptic curve groups. Users of this API can write higher-level crypto algorithms such as zero-knowledge proofs without knowing or caring exactly what kind of group, let alone which precise security parameters or elliptic curves, are being used. The kyber.Group interface supports the standard algebraic operations on group elements and scalars that nontrivial public-key algorithms tend to rely on. The interface uses additive group terminology typical for elliptic curves, such that point addition is homomorphically equivalent to adding their (potentially secret) scalar multipliers. But the API and its operations apply equally well to DSA-style integer groups. As a trivial example, generating a public/private keypair is as simple as: The first statement picks a private key (Scalar) from a the suites's source of cryptographic random or pseudo-random bits, while the second performs elliptic curve scalar multiplication of the curve's standard base point (indicated by the 'nil' argument to Mul) by the scalar private key 'a'. Similarly, computing a Diffie-Hellman shared secret using Alice's private key 'a' and Bob's public key 'B' can be done via: Note that we use 'Mul' rather than 'Exp' here because the library uses the additive-group terminology common for elliptic curve crypto, rather than the multiplicative-group terminology of traditional integer groups - but the two are semantically equivalent and the interface itself works for both elliptic curve and integer groups. Various sub-packages provide several specific implementations of these cryptographic interfaces. In particular, the 'group/mod' sub-package provides implementations of modular integer groups underlying conventional DSA-style algorithms. The `group/nist` package provides NIST-standardized elliptic curves built on the Go crypto library. The 'group/edwards25519' sub-package provides the kyber.Group interface using the popular Ed25519 curve. Other sub-packages build more interesting high-level cryptographic tools atop these primitive interfaces, including: - share: Polynomial commitment and verifiable Shamir secret splitting for implementing verifiable 't-of-n' threshold cryptographic schemes. This can be used to encrypt a message so that any 2 out of 3 receivers must work together to decrypt it, for example. - proof: An implementation of the general Camenisch/Stadler framework for discrete logarithm knowledge proofs. This system supports both interactive and non-interactive proofs of a wide variety of statements such as, "I know the secret x associated with public key X or I know the secret y associated with public key Y", without revealing anything about either secret or even which branch of the "or" clause is true. - sign: The sign directory contains different signature schemes. - sign/anon provides anonymous and pseudonymous public-key encryption and signing, where the sender of a signed message or the receiver of an encrypted message is defined as an explicit anonymity set containing several public keys rather than just one. For example, a member of an organization's board of trustees might prove to be a member of the board without revealing which member she is. - sign/cosi provides collective signature algorithm, where a bunch of signers create a unique, compact and efficiently verifiable signature using the Schnorr signature as a basis. - sign/eddsa provides a kyber-native implementation of the EdDSA signature scheme. - sign/schnorr provides a basic vanilla Schnorr signature scheme implementation. - shuffle: Verifiable cryptographic shuffles of ElGamal ciphertexts, which can be used to implement (for example) voting or auction schemes that keep the sources of individual votes or bids private without anyone having to trust more than one of the shuffler(s) to shuffle votes/bids honestly. As should be obvious, this library is intended to be used by developers who are at least moderately knowledgeable about cryptography. If you want a crypto library that makes it easy to implement "basic crypto" functionality correctly - i.e., plain public-key encryption and signing - then [NaCl secretbox](https://godoc.org/golang.org/x/crypto/nacl/secretbox) may be a better choice. This toolkit's purpose is to make it possible - and preferably easy - to do slightly more interesting things that most current crypto libraries don't support effectively. The one existing crypto library that this toolkit is probably most comparable to is the Charm rapid prototyping library for Python (https://charm-crypto.com/category/charm). This library incorporates and/or builds on existing code from a variety of sources, as documented in the relevant sub-packages. This library is offered as-is, and without a guarantee. It will need an independent security review before it should be considered ready for use in security-critical applications. If you integrate Kyber into your application it is YOUR RESPONSIBILITY to arrange for that audit. If you notice a possible security problem, please report it to dedis-security@epfl.ch.

v3.1.0 • 2 years ago

github.com/xuexihuang/new_log15

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json //This package implements three Handlers that add debugging information to the //context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's //an example that adds the source file and line number of each logging call to //the context. // // h := log.CallerFileHandler(log.StdoutHandler) // log.Root().SetHandler(h) // ... // log.Error("open file", "err", err) // //This will output a line that looks like: // // lvl=eror t=2014-05-02T16:07:23-0700 msg="open file" err="file not found" caller=data.go:42 // //Here's an example that logs the call stack rather than just the call site. // // h := log.CallerStackHandler("%+v", log.StdoutHandler) // log.Root().SetHandler(h) // ... // log.Error("open file", "err", err) // //This will output a line that looks like: // // lvl=eror t=2014-05-02T16:07:23-0700 msg="open file" err="file not found" stack="[pkg/data.go:42 pkg/cmd/main.go]" // //The "%+v" format instructs the handler to include the path of the source file //relative to the compile time GOPATH. The github.com/go-stack/stack package //documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

v1.0.0 • 2 years ago

github.com/jjeffery/errors

Package errors provides simple error handling primitives that work well with structured logging. This package is inspired by the excellent github.com/pkg/errors package. A significant amount of code and documentation in this package has been adapted from that source. A key difference between this package and github.com/pkg/errors is that this package has been designed to suit programs that make use of structured logging. Some of the ideas in this package were proposed for package github.com/pkg/errors, but after a reasonable amount of consideration, were ultimately not included in that package. (See https://github.com/pkg/errors/issues/34 for details). If you are not using structured logging in your application and have no intention of doing so, you will probably be better off using the github.com/pkg/errors package in preference to this one. The traditional error handling idiom in Go is roughly akin to which applied recursively up the call stack results in error reports without context or debugging information. The errors package allows programmers to add context to the failure path in their code in a way that does not destroy the original value of the error. The `errors` package provides three operations which combine to form a simple yet powerful system for enhancing the value of returned errors: The `New` function is used to create an error. This function is compatible with the Go standard library `errors` package: The `Wrap` function returns an error that adds a message to the original error. This additional message can be useful for putting the original error in context. For example: The `With` function accepts a variadic list of alternating key/value pairs, and returns an error context that can be used to create a new error or wrap an existing error. One useful pattern is to create an error context that is used for an entire function scope: The errors returned by `New` and `Wrap` provide a `With` method that enables a fluent-style of error handling: Using errors.Wrap constructs a stack of errors, adding context to the preceding error. Depending on the nature of the error it may be necessary to reverse the operation of errors.Wrap to retrieve the original error for inspection. Any error value which implements this interface can be inspected by errors.Cause. errors.Cause will recursively retrieve the topmost error which does not implement causer, which is assumed to be the original cause. For example: Errors created by `errors.Wrap` and `errors.New` implement the following interface: The Keyvals method returns an array of alternating keys and values. The first key will always be "msg" and its value will be a string containing the message associated with the wrapped error. Example using go-kit logging (https://github.com/go-kit/kit/tree/master/log): GOOD ADVICE: Do not use the Keyvals method on an error to retrieve the individual key/value pairs associated with an error for processing by the calling program.

v1.0.3 • 6 years ago

github.com/One-com/gone/log

Package log is a drop-in replacement for the standard Go logging library "log" which is fully source code compatible support all the standard library API while at the same time offering advanced logging features through an extended API. The design goals of gonelog was: Out of the box the default logger with package level methods works like the standard library *log.Logger with all the standard flags and methods: Under the hood the default *log.Logger is however a log context object which can have key/value data and which generate log events with a syslog level and hands them of to a log Handler for formatting and output. The default Logger supports all this as well, using log level constants source code compatible with the "log/syslog" package through the github.com/One-com/gone/log/syslog package: Logging with key/value data is (in its most simple form) done by calling level specific functions. First argument is the message, subsequenct arguments key/value data: Earch *log.Logger object has a current "log level" which determines the maximum log level for which events are actually generated. Logging above that level will be ignored. This log level can be controlled: Calling Fatal*() and Panic*() will in addition to Fataling/panicing log at level ALERT. The Print*() methods will log events with a configurable "default" log level - which default to INFO. Per default the Logger *will* generate log event for Print*() calls even though the log level is lower. The Logger can be set to respect the actual log level also for Print*() statements by the second argument to SetPrintLevel() A new custom Logger with its own behavior and formatting handler can be created: A customer Logger will not per default spend time timestamping events or registring file/line information. You have to enable that explicitly (it's not enabled by setting the flags on a formatting handler). When having key/value data which you need to have logged in all log events, but don't want to remember to put into every log statement, you can create a "child" Logger: To simply set the standard logger in a minimal mode where it only outputs <level>message to STDOUT and let an external daemon supervisor/log system do the rest (including timestamping) just do: Having many log statements can be expensive. Especially if the arguments to be logged are resource intensive to compute and there's no log events generated anyway. There are 2 ways to get around that. The first is do do Lazy evaluation of arguments: The other is to pick an comma-ok style log function: Sometimes it can be repetitive to make a lot of log statements logging many attributes of the same kinda of object by explicitly accessing every attribute. To make that simpler, every object can implement the Logable interface by creating a LogValues() function returning the attributes to be logged (with keys). The object can then be logged by directly providing it as an argument to a log function: Loggers can have names, placing them in a global "/" separated hierarchy. It's recommended to create a Logger by mentioning it by name using GetLogger("logger/name") - instead of creating unnamed Loggers with NewLogger(). If such a logger exists you will get it returned, so you can configure it and set the formatter/output. Otherwise a new logger by that name is created. Libraries are encouraged to published the names of their Loggers and to name Loggers after their Go package. This works exactly like the Python "logging" library - with one exception: When Logging an event at a Logger the tree of Loggers by name are only traversed towards to root to find the first Logger having a Handler attached, not returning an error. The log-event is then sent to that handler. If that handler returns an error, the parent Logger and its Handler is tried. This allows to contruct a "Last Resort" parent for errors in the default log Handler. The Python behaviour is to send the event to all Handlers found in the Logger tree. This is not the way it's done here. Only one Handler will be given the event to log. If you wan't more Handlers getting the event, use a MultiHandler. Happy logging.

v1.0.1 • 5 years ago

github.com/ipfs/go-qringbuf

Package qringbuf provides a concurrency-friendly, zero-copy abstraction of io.ReadAtLeast(…) over a pre-allocated ring-buffer, populated asynchronously by a standalone goroutine. It is primarily designed for processing a series of consecutive sub-streams from a single io.Reader, each sub-stream in turn comprised of variable-length records. The buffer object DOES NOT ASSUME exclusive ownership of the supplied io.Reader, never reads more than instructed by an argument to StartFill(…), and exposes a standard sync.Mutex interface allowing pausing all operations when exclusive access of the underlying Reader is desired. In all cases below the background "collector" goroutine reading from the enclosed someIoReader into the ring buffer is guaranteed to: In code the basic usage looks roughly like this (error/flow handling elided): In addition one can operate over individual (sub)regions with "fearless concurrency": The specific technical guarantees made by an object of this package are: Unlike io.ReadAtLeast(…), errors from the underlying reader are always made available on NextRegion(…). As with the standard io.Read(…) semantics, an error can be returned together with a result. One should always check whether the *Region return value is nil first, before processing the error. See the documentation of io.Read(…) for an extended discussion. Changes of the NextRegion(…) "emitter" and collector positions are protected by a mutex on the qringbuf object. Calls modifying the buffer state will block until this lock can be obtained. The same mutex is exposed as part of the API, so one can pause the collector if a direct read and/or skip on the underlying io.Reader is needed. The *Region.{Reserve/Release}() functionality does not use the mutex, ensuring that an asynchronous Release() call can not be affected by the current state of the buffer. Reservation tracking is implemented as an atomically modified list of reservation counts, one int32 per SectorSize bytes of the buffer. The reservation system explicitly allows "recursive locking": you can hold an arbitrary number of reservations over a sector by repeatedly creating SubRegion(…) objects. Care must be taken to release every single reservation obtained previously, otherwise the collector will remain blocked forever. Follows an illustration of a contrived lifecycle of a hypothetical qringbuf object initialized with: Note that for brevity THE DIAGRAMS BELOW ARE DECIDEDLY NOT REPRESENTATIVE of a typical lifecycle. Normally BufferSize is an order of magnitude larger than MinRegion and MaxCopy, and the time spent waiting and copying data is insignificant in relation to all other possible states. Also outstanding async reservations typically trail the emitter very closely, so after a wrap the collector is virtually never blocked, contrary to what is depicted below. Instead the diagrams merely demonstrate the choices this library makes dealing with the "tricky parts" of maintaining the illusion of an arbitrary stream of contiguous bytes.

v0.0.0-20230822093825-b5cafb50c8ce • last year

github.com/mercari/datastore

Package datastore has an abstract representation of (AppEngine | Cloud) Datastore. repository https://github.com/mercari/datastore Let's read https://cloud.google.com/datastore/docs/ or https://cloud.google.com/appengine/docs/standard/go/datastore/ . You should also check https://godoc.org/cloud.google.com/go/datastore or https://godoc.org/google.golang.org/appengine/datastore as datastore original library. Japanese version https://github.com/mercari/datastore/blob/master/doc_ja.go Please see https://godoc.org/go.mercari.io/datastore/clouddatastore or https://godoc.org/go.mercari.io/datastore/aedatastore . Create a Client using the FromContext function of each package. Later in this document, notes on migration from each package are summarized. Please see also there. This package is based on the newly designed Cloud Datastore API. We are introducing flatten tags that only exist in Cloud Datastore, we need to be careful when migrating from AE Datastore. Details will be described later. If you are worried, you may have a clue to the solution at https://godoc.org/go.mercari.io/datastore/clouddatastore . This package has three main objectives. We are forced to make functions that are not directly related to the value of the application for speed, stability and operation. Such functions can be abstracted and used as middleware. Let's think about this case. Put Entity to Datastore and set it to Memcache or Redis. Next, when getting from Datastore, Get from Memcache first, Get it again from Datastore if it fails. It is very troublesome to provide these operations for all Kind and all Entity operations. However, if the middleware intervenes with all Datastore RPCs, you can transparently process without affecting the application code. As another case, RPC sometimes fails. If it fails, the process often succeeds simply by retrying. For easy RET retry with all RPCs, it is better to implement it as middleware. Please refer to https://godoc.org/go.mercari.io/datastore/dsmiddleware if you want to know the middleware already provided. The same interface is provided for AppEngine Datastore and Cloud Datastore. These two are compatible, you can run it with exactly the same code after creating the Client. For example, you can use AE Datastore in a production environment and Cloud Datastore Emulator in UnitTest. If you can avoid goapp, tests may be faster and IDE may be more vulnerable to debugging. You can also read data from the local environment via Cloud Datastore for systems running on AE Datastore. Caution. Although the storage bodies of RPCs of AE Datastore and Cloud Datastore are shared, there is a difference in expressiveness at the API level. Please carefully read the data written in AE Datastore carelessly on Cloud Datastore and do not update it. It may become impossible to read from the API of AE Datastore side. About this, we have not strictly tested. The operation of Datastore has very little latency with respect to RPC's network. When acquiring 10 entities it means that GetMulti one time is better than getting 10 times using loops. However, we are not good at putting together multiple processes at once. Suppose, for example, you want to query on Post Kind, use the list of Comment IDs of the resulting Post, and get a list of Comments. For example, you can query Post Kind and get a list of Post. In addition, consider using CommentIDs of Post and getting a list of Comment. This is enough Query + 1 GetMulti is enough if you write very clever code. However, after acquiring the data, it is necessary to link the Comment list with the appropriate Post. On the other hand, you can easily write a code that throws a query once and then GetMulti the Comment as many as Post. In summary, it is convenient to have Put or Get queued, and there is a mechanism to execute it collectively later. Batch() is it! You can find the example at https://godoc.org/go.mercari.io/datastore/#pkg-examples . I love goon. So I made https://godoc.org/go.mercari.io/datastore/boom which can be used in conjunction with this package. Here's an overview of what you need to do to migrate your existing code. from AE Datastore from Cloud Datastore from goon to boom

v1.8.2 • 2 years ago

github.com/Nik-u/pbc

Package pbc provides structures for building pairing-based cryptosystems. It is a wrapper around the Pairing-Based Cryptography (PBC) Library authored by Ben Lynn (https://crypto.stanford.edu/pbc/). This wrapper provides access to all PBC functions. It supports generation of various types of elliptic curves and pairings, element initialization, I/O, and arithmetic. These features can be used to quickly build pairing-based or conventional cryptosystems. The PBC library is designed to be extremely fast. Internally, it uses GMP for arbitrary-precision arithmetic. It also includes a wide variety of optimizations that make pairing-based cryptography highly efficient. To improve performance, PBC does not perform type checking to ensure that operations actually make sense. The Go wrapper provides the ability to add compatibility checks to most operations, or to use unchecked elements to maximize performance. Since this library provides low-level access to pairing primitives, it is very easy to accidentally construct insecure systems. This library is intended to be used by cryptographers or to implement well-analyzed cryptosystems. Cryptographic pairings are defined over three mathematical groups: G1, G2, and GT, where each group is typically of the same order r. Additionally, a bilinear map e maps a pair of elements — one from G1 and another from G2 — to an element in GT. This map e has the following additional property: If G1 == G2, then a pairing is said to be symmetric. Otherwise, it is asymmetric. Pairings can be used to construct a variety of efficient cryptosystems. The PBC library currently supports 5 different types of pairings, each with configurable parameters. These types are designated alphabetically, roughly in chronological order of introduction. Type A, D, E, F, and G pairings are implemented in the library. Each type has different time and space requirements. For more information about the types, see the documentation for the corresponding generator calls, or the PBC manual page at https://crypto.stanford.edu/pbc/manual/ch05s01.html. This package must be compiled using cgo. It also requires the installation of GMP and PBC. During the build process, this package will attempt to include <gmp.h> and <pbc/pbc.h>, and then dynamically link to GMP and PBC. Most systems include a package for GMP. To install GMP in Debian / Ubuntu: For an RPM installation with YUM: For installation with Fink (http://www.finkproject.org/) on Mac OS X: For more information or to compile from source, visit https://gmplib.org/ To install the PBC library, download the appropriate files for your system from https://crypto.stanford.edu/pbc/download.html. PBC has three dependencies: the gcc compiler, flex (http://flex.sourceforge.net/), and bison (https://www.gnu.org/software/bison/). See the respective sites for installation instructions. Most distributions include packages for these libraries. For example, in Debian / Ubuntu: The PBC source can be compiled and installed using the usual GNU Build System: After installing, you may need to rebuild the search path for libraries: It is possible to install the package on Windows through the use of MinGW and MSYS. MSYS is required for installing PBC, while GMP can be installed through a package. Based on your MinGW installation, you may need to add "-I/usr/local/include" to CPPFLAGS and "-L/usr/local/lib" to LDFLAGS when building PBC. Likewise, you may need to add these options to CGO_CPPFLAGS and CGO_LDFLAGS when installing this package. This package is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. For additional details, see the COPYING and COPYING.LESSER files. This example generates a pairing and some random group elements, then applies the pairing operation. This example computes and verifies a Boneh-Lynn-Shacham signature in a simulated conversation between Alice and Bob.

v0.0.0-20181205041846-3e516ca0c5d6 • 6 years ago

github.com/NiK-U/pbc

Package pbc provides structures for building pairing-based cryptosystems. It is a wrapper around the Pairing-Based Cryptography (PBC) Library authored by Ben Lynn (https://crypto.stanford.edu/pbc/). This wrapper provides access to all PBC functions. It supports generation of various types of elliptic curves and pairings, element initialization, I/O, and arithmetic. These features can be used to quickly build pairing-based or conventional cryptosystems. The PBC library is designed to be extremely fast. Internally, it uses GMP for arbitrary-precision arithmetic. It also includes a wide variety of optimizations that make pairing-based cryptography highly efficient. To improve performance, PBC does not perform type checking to ensure that operations actually make sense. The Go wrapper provides the ability to add compatibility checks to most operations, or to use unchecked elements to maximize performance. Since this library provides low-level access to pairing primitives, it is very easy to accidentally construct insecure systems. This library is intended to be used by cryptographers or to implement well-analyzed cryptosystems. Cryptographic pairings are defined over three mathematical groups: G1, G2, and GT, where each group is typically of the same order r. Additionally, a bilinear map e maps a pair of elements — one from G1 and another from G2 — to an element in GT. This map e has the following additional property: If G1 == G2, then a pairing is said to be symmetric. Otherwise, it is asymmetric. Pairings can be used to construct a variety of efficient cryptosystems. The PBC library currently supports 5 different types of pairings, each with configurable parameters. These types are designated alphabetically, roughly in chronological order of introduction. Type A, D, E, F, and G pairings are implemented in the library. Each type has different time and space requirements. For more information about the types, see the documentation for the corresponding generator calls, or the PBC manual page at https://crypto.stanford.edu/pbc/manual/ch05s01.html. This package must be compiled using cgo. It also requires the installation of GMP and PBC. During the build process, this package will attempt to include <gmp.h> and <pbc/pbc.h>, and then dynamically link to GMP and PBC. Most systems include a package for GMP. To install GMP in Debian / Ubuntu: For an RPM installation with YUM: For installation with Fink (http://www.finkproject.org/) on Mac OS X: For more information or to compile from source, visit https://gmplib.org/ To install the PBC library, download the appropriate files for your system from https://crypto.stanford.edu/pbc/download.html. PBC has three dependencies: the gcc compiler, flex (http://flex.sourceforge.net/), and bison (https://www.gnu.org/software/bison/). See the respective sites for installation instructions. Most distributions include packages for these libraries. For example, in Debian / Ubuntu: The PBC source can be compiled and installed using the usual GNU Build System: After installing, you may need to rebuild the search path for libraries: It is possible to install the package on Windows through the use of MinGW and MSYS. MSYS is required for installing PBC, while GMP can be installed through a package. Based on your MinGW installation, you may need to add "-I/usr/local/include" to CPPFLAGS and "-L/usr/local/lib" to LDFLAGS when building PBC. Likewise, you may need to add these options to CGO_CPPFLAGS and CGO_LDFLAGS when installing this package. This package is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. For additional details, see the COPYING and COPYING.LESSER files. This example generates a pairing and some random group elements, then applies the pairing operation. This example computes and verifies a Boneh-Lynn-Shacham signature in a simulated conversation between Alice and Bob.

v0.0.0-20181205041846-3e516ca0c5d6 • 6 years ago

github.com/nigeltao/taowm

Taowm is The Acutely Opinionated Window Manager. It is a minimalist, keyboard driven, low distraction, tiling window manager for someone who uses a computer primarily to run just two GUI programs: a web browser and a terminal emulator. To install taowm: This will install taowm in your $GOPATH, or under $GOROOT/bin if $GOPATH is empty. Run "go help gopath" to read more about $GOPATH. Taowm is designed to run from an Xsession session. Add this line to the end of your ~/.xsession file: where the path is wherever "go get" or "go install" wrote to. Again, run "go help gopath" for more information. Log out and log back in with the "Xsession" option. Some systems, such as Ubuntu 12.04 "Precise", do not offer an Xsession option by default. To enable it, create a new file /usr/share/xsessions/custom.desktop that contains: Taowm starts with each screen divided into two side-by-side frames, outlined in green. Frames can frame windows, but they can also be empty: closing a frame's window will not collapse that frame. The frame that contains the mouse pointer is the focused frame, and its border is brighter than other frames. Its window (if it contains one) will have the keyboard focus. Taowm is primarily keyboard driven, and all keyboard shortcuts involve first holding down the Caps Lock key, similar to how holding down the Control key followed by the 'N' key, in your web browser, creates a new browser window. The default Caps Lock behavior, CHANGING ALL TYPED LETTERS TO UPPER CASE, is disabled. Caps Lock and the Space key will open a new web browser window. Caps Lock and the Enter key will open a new terminal emulator window. Caps Lock and the Shift key and the '|' pipe key will lock the screen. Caps Lock and the Backspace key will close the window in the focused frame. Caps Lock and the Tab key will cycle through the frames. To quit taowm and return to the log in screen, hold down Caps Lock and the Shift key and hit the Escape key three times in quick succession. Normally, this will quit immediately. Some programs may ask for something before closing, such as a file name to write unsaved data to. In this case, taowm will quit in 60 seconds or whenever all such programs have closed, instead of quitting immediately, and the frame borders will turn red. If there are more windows than frames, then Caps Lock and the 'D' or 'F' key will cycle through hidden windows. Caps Lock and a number key like '1', '2', etc. will move the 1st, 2nd, etc. window to the focused frame. Caps Lock and the 'A' key will show a list of windows: the one currently in the focused frame is marked with a '+', other windows in other frames are marked with a '-', hidden windows that have not been seen yet are marked with an '@', and hidden windows that have been seen before are unmarked. In particular, newly created windows will not automatically be shown. Taowm prevents new windows from popping up and 'stealing' keyboard focus, a problem if the password you are typing into your terminal emulator accidentally gets written to a chat window that popped up at the wrong time. Instead, if there isn't an empty frame to accept a new window, taowm keeps that window hidden (and marked with an '@' in the window list) until you are ready to deal with it. If there are any such windows that have not been seen yet, the green frame borders will pulsate to remind you. Selected windows are also marked with a '#'; selection is described below. Caps Lock and the 'G' key will toggle the focused frame in occupying the entire screen. Caps Lock and Shift and the 'G' key will hide the window in the focused frame. Caps Lock and the '-' key, the '=' key or Shift and the '+' key will split the current frame horizontally, vertically, or merge a frame to undo a frame split respectively. A screen contains workspaces like a frame contains windows. Caps Lock and the 'T' key will create a new workspace, hiding the current one. Caps Lock and the 'E' or 'R' key will cycle through hidden workspaces. Caps Lock and Shift and the 'T' key will delete the current workspace, provided that it holds no windows and there is another hidden workspace to switch to. Caps Lock and the 'Q' key will show a list of workspaces (and their windows). Caps Lock and the '`' key will cycle through the screens. Caps Lock and the F1 key, F2 key, etc. will move the 1st, 2nd, etc. workspace to the current screen. Caps Lock and the 'S' key will select a window, or unselect a selected window. More than one window may be selected at a time. Caps Lock and Shift and the 'S' key will select or unselect all windows in the current workspace. Caps Lock and the 'W' key will migrate all selected windows to the current workspace and unselect them. Taowm also provides alternative ways to navigate within a program's window. Caps Lock and the 'H', 'J', 'K' or 'L' keys are equivalent to pressing the Left, Down, Up or Right arrow keys. Similarly, Caps Lock and the 'Y', 'U', 'B' or 'N' keys are equivalent to Home, Page Up, End or Page Down. The 'I' or 'M' keys are equivalent to a mouse wheel scrolling up or down, and the ',' or '.' keys are equivalent to the Backspace or Delete keys. Taowm provides similar shortcuts for other common actions. Caps Lock and the 'O' or 'P' keys will copy or paste, '/' or Shift-and-'?' will open or close a tab in the current window, 'C' or 'V' will cycle through tabs, 'Z' or 'X' will zoom in or out. By default, these keys will only work with the google-chrome web browser and the gnome-terminal terminal emulator. Making these work with other programs will require some customization. Customizing the keyboard shortcuts, web browser, terminal emulator, colors, etc., is done by editing config.go and re-compiling (and re-installing): run "go install github.com/nigeltao/taowm". When working on taowm, it can be run in a nested X server such as Xephyr. From the github.com/nigeltao/taowm directory under $GOPATH: The taowm mailing list is at http://groups.google.com/group/taowm Taowm is copyright 2013 The Taowm Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.

v0.0.0-20211014224427-4c67979f2966 • 3 years ago

gopkg.in/mix.v0

Sequence-based Go-native audio mixer for music apps See `demo/demo.go`: Play this Demo from the root of the project, with no actual audio playback Or export WAV via stdout `> demo/output.wav`: Game audio mixers are designed to play audio spontaneously, but when the timing is known in advance (e.g. sequence-based music apps) there is a demand for much greater accuracy in playback timing. Read the API documentation at https://godoc.org/gopkg.in/mix.v0 Mix seeks to solve the problem of audio mixing for the purpose of the playback of sequences where audio files and their playback timing is known in advance. Mix stores and mixes audio in native Go `[]float64` and natively implements Paul Vögler's "Loudness Normalization by Logarithmic Dynamic Range Compression" (details below) Charney Kaye https://charneykaye.com XJ Music Inc. https://xj.io Even after selecting a hardware interface library such as http://www.portaudio.com/ or https://www.libsdl.org/, there remains a critical design problem to be solved. This design is a music application mixer. Most available options are geared towards Game development. Game audio mixers offer playback timing accuracy +/- 2 milliseconds. But that's totally unacceptable for music, specifically sequence-based sample playback. The design pattern particular to Game design is that the timing of the audio is not know in advance- the timing that really matters is that which is assembled in near-real-time in response to user interaction. In the field of Music development, often the timing is known in advance, e.g. a sequencer, the composition of music by specifying exactly how, when and which audio files will be played relative to the beginning of playback. Ergo, mix seeks to solve the problem of audio mixing for the purpose of the playback of sequences where audio files and their playback timing is known in advance. It seeks to do this with the absolute minimal logical overhead on top of the audio interface. Mix takes maximum advantage of Go by storing and mixing audio in native Go `[]float64` and natively implementing Paul Vögler's "Loudness Normalization by Logarithmic Dynamic Range Compression" (see The Mixing Algorithm below) To the Mix API, time is specified as a time.Duration-since-epoch, where the epoch is the moment that mix.Start() was called. Internally, time is tracked as samples-since-epoch at the master out playback frequency (e.g. 48000 Hz). This is most efficient because source audio is pre-converted to the master out playback frequency, and all audio maths are performed in terms of samples. Inspired by the theory paper "Mixing two digital audio streams with on the fly Loudness Normalization by Logarithmic Dynamic Range Compression" by Paul Vögler, 2012-04-20. This paper is published at http://www.voegler.eu/pub/audio/digital-audio-mixing-and-normalization.html. There's a demo implementation of **mix** included in the `demo/` folder in this repository. Run it using the defaults: Or specify options, e.g. using WAV bytes to stdout for playback (piped to system native `aplay`) To show the help screen: Best efforts will be made to preserve each API version in a release tag that can be parsed, e.g. http://gopkg.in/mix.v0 Mix in good health!

v0.0.6 • 5 years ago

github.com/carbon-design-system/ibm-dotcom-library

github.com/alexpate/awesome-design-systems

github.com/carbon-design-system/carbon-charts

github.com/carbon-design-system/carbon-tutorial

github.com/brendandburns/designing-distributed-systems-labs

github.com/carbon-design-system/carbon-badges

github.com/carbon-design-system/carbon-sandbox

github.com/carbon-design-system/carbon-components-vue

github.com/carbon-design-system/carbon-addons-ics

github.com/carbon-design-system/ibm-dotcom-library-nextjs-template

github.com/klaufel/awesome-design-systems

github.com/carbon-design-system/gatsby-starter-carbon-theme

github.com/ryanbressler/CloudForest

www.velocidex.com/golang/vfilter

github.com/boltDb/bolt

github.com/BoltDb/bolt

gopkg.in/boltdb/bolt.v1

github.com/boltDB/bolt

gopkg.in/bluesuncorp/validator.v10

github.com/xumingpeng/validator/v10

github.com/vube/i18n

github.com/boltdb-go/bolt

github.com/drand/kyber

go.uber.org/net/metrics

github.com/tovenja/cron/v3

gopkg.in/inconshreveABLE/LOG15.v2

github.com/inconshreveable/Log15

gopkg.in/inconshreveable/LOG15.v2

gopkg.in/inconshreveable/Log15.v2

gopkg.in/inconshreveable/log15.v1

go.mercari.io/datastore/v2

golang.design/x/tgstore

github.com/ConsenSys/gnark

gopkg.in/dedis/crypto.v3

gopkg.in/dedis/kyber.v3

github.com/smallnest/resp3

github.com/lyft/cni-ipvlan-vpc-k8s

github.com/xuexihuang/new_log15

github.com/zephyrtronium/xirho

github.com/jjeffery/errors

github.com/c0mm4nd/dbolt

github.com/One-com/gone/log

github.com/ipfs/go-qringbuf

github.com/mercari/datastore

github.com/Nik-u/pbc

github.com/NiK-U/pbc

github.com/hooklift/govix

github.com/nigeltao/taowm

gopkg.in/mix.v0

github.com/srfrog/go-relax