Design System

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.

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!

Package relax is a framework of pluggable components to build RESTful API's. It provides a thin layer over “net/http“ to serve resources, without imposing a rigid structure. It is meant to be used along “http.ServeMux“, but will work as a replacement as it implements “http.Handler“. The framework is divided into components: Encoding, Filters, Routing, Hypermedia and, Resources. These are the parts of a complete REST Service. All the components are designed to be pluggable (replaced) through interfaces by external packages. Relax provides enough built-in functionality to assemble a complete REST API. The system is based on Resource Oriented Architecture (ROA), and had some inspiration from Heroku's REST API.

Package xdg implements the XDG Base Directory Specification, with adjustments for macOS and Windows. Supported operating systems: all Unix flavors, macOS, and Windows. It was created with these design goals/principles:

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/

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/

Package isl provides a Go Input Sanitization Library (goisl). goisl is inspired by WordPress's escaping and sanitization system and is designed to help developers securely process user input by providing modular, testable sanitization and escaping functions for common use cases like emails, file names, URLs, and HTML. Custom hooks are supported to support just about any sanitization scenario imagineable. See README.md for examples and usage.

Package bolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

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/

yxxhero/tail provides a Go library that emulates the features of the BSD `tail` program. The library comes with full support for truncation/move detection as it is designed to work with log rotation tools. The library works on all operating systems supported by Go, including POSIX systems like Linux and *BSD, and MS Windows. Go 1.9 is the oldest compiler release supported.

Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. doc.go Package render provides a flexible and extensible rendering system for Go applications. This package is designed to handle various output formats (JSON, Text, HTML templates, etc.) in a consistent way, while maintaining type safety and providing rich configuration options. Basic usage: The package supports multiple renderer types: Key features: Each renderer implements the Renderer interface: The package uses the options pattern for configuration, making it easy to extend and customize rendering behavior without breaking existing code. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. Copyright 2025 The Nanoninja Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.

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/

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.

Package bolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

Package bubo provides a framework for building and orchestrating AI agents with a focus on reliability, extensibility, and maintainable workflows. The package implements a robust foundation for creating AI-powered applications through several key abstractions: A typical workflow involves creating agents, defining their capabilities, and orchestrating their interactions: The package is built around several core concepts: 1. Execution Engine (execution.go) 2. Hooks (hook.go) 3. Promises (promise.go) 4. Tasks (task.go) 5. Knots (knot.go) Bubo integrates with several backend systems: The examples directory contains various usage patterns: See the examples directory for complete implementations. 1. Agent Design 2. Workflow Management 3. Tool Implementation 4. Error Handling The package is designed to be thread-safe when used correctly: For more information about specific components, see their respective documentation: Package bubo provides a framework for building conversational AI agents that can interact in a structured manner. It supports multi-agent conversations, structured output, and flexible execution contexts. The execution package is a core component that handles: Key concepts: Package bubo provides a framework for building conversational AI agents that can interact in a structured manner. It supports multi-agent conversations, structured output, and flexible execution contexts. Package bubo provides a framework for building conversational AI agents that can interact in a structured manner. It supports multi-agent conversations, structured output, and flexible execution contexts. Package bubo provides a framework for building conversational AI agents that can interact in a structured manner. It supports multi-agent conversations, structured output, and flexible execution contexts. Package bubo provides a framework for building conversational AI agents that can interact in a structured manner. It supports multi-agent conversations, structured output, and flexible execution contexts.

Package decimal implements immutable decimal floating-point numbers. It is specifically designed for transactional financial systems and adheres to the principles set by ANSI X3.274-1996. Decimal is a struct with three fields: The numerical value of a decimal is calculated as follows: This approach allows the same numeric value to have multiple representations, for example, 1, 1.0, and 1.00, which represent the same value but have different scales and coefficients. The range of a decimal is determined by its scale. Here are the ranges for frequently used scales: Subnormal numbers are not supported to ensure peak performance. Consequently, decimals between -0.00000000000000000005 and 0.00000000000000000005 inclusive, are rounded to 0. Special values such as NaN, Infinity, or negative zeros are not supported. This ensures that arithmetic operations always produce either valid decimals or errors. Each arithmetic operation occurs in two steps: The operation is initially performed using uint64 arithmetic. If no overflow occurs, the exact result is immediately returned. If overflow occurs, the operation proceeds to step 2. The operation is repeated with at least double precision using big.Int arithmetic. The result is then rounded to 19 digits. If no significant digits are lost during rounding, the inexact result is returned. If any significant digit is lost, an overflow error is returned. Step 1 improves performance by avoiding performance impact associated with big.Int arithmetic. It is expected that, in transactional financial systems, most arithmetic operations will compute an exact result during step 1. The following rules determine the significance of digits during step 2: All transcendental functions are always computed with at least double precision using big.Int arithmetic. The result is then rounded to 19 digits. If no significant digits are lost during rounding, the inexact result is returned. If any significant digit is lost, an overflow error is returned. The following rules determine the significance of digits: Unlike many other decimal libraries, this package does not provide an explicit mathematical context. Instead, the context is implicit and can be approximately equated to the following settings: The equality of Etiny and Emin implies that this package does not support subnormal numbers. For all operations the result is the one that would be obtained by computing the exact mathematical result with infinite precision and then rounding it to 19 digits using half-to-even rounding. This method ensures that rounding errors are evenly distributed between rounding up and down. In addition to implicit rounding, the package provides several methods for explicit rounding: See the documentation for each method for more details. All methods are panic-free and pure. Errors are returned in the following cases: Division by Zero: Unlike Go's standard library, Decimal.Quo, Decimal.QuoRem, Decimal.Inv, Decimal.AddQuo, Decimal.SubQuo, do not panic when dividing by 0. Instead, they return an error. Invalid Operation: Decimal.PowInt returns an error if 0 is raised to a negative power. Decimal.Sqrt return an error if the square root of a negative decimal is requested. Decimal.Log returns an error when calculating the natural logarithm of a non-positive decimal. Overflow: Unlike standard integers, decimals do not "wrap around" when exceeding their maximum value. For out-of-range values, methods return an error. Errors are not returned in the following cases: A. JSON The package integrates seamlessly with standard encoding/json through the implementation of encoding.TextMarshaller and encoding.TextUnmarshaler interfaces. Below is an example structure: This package marshals decimals as quoted strings, ensuring the preservation of the exact numerical value. Below is an example OpenAPI schema: B. XML The package integrates with standard encoding/xml via the implementation of encoding.TextMarshaller and encoding.TextUnmarshaler interfaces. Below is an example structure: "xs:decimal" type can represent decimals in XML schema. It is possible to impose restrictions on the length of the decimals using the following type: C. Protocol Buffers Protocol Buffers provide two formats to represent decimals. The first format represents decimals as numerical strings. The main advantage of this format is that it preserves trailing zeros. To convert between this format and decimals, use Parse and Decimal.String. Below is an example of a proto definition: The second format represents decimals as a pair of integers: one for the integer part and another for the fractional part. This format does not preserve trailing zeros and rounds decimals with more than nine digits in the fractional part. For conversion between this format and decimals, use NewFromInt64 and Decimal.Int64 with a scale argument of "9". Below is an example of a proto definition: D. SQL The package integrates with the standard database/sql via the implementation of sql.Scanner and driver.Valuer interfaces. To ensure accurate preservation of decimal scales, it is essential to choose appropriate column types: Below are the reasons for these preferences: PostgreSQL: Always use DECIMAL without precision or scale specifications, that is, avoid DECIMAL(p) or DECIMAL(p, s). DECIMAL accurately preserves the scale of decimals. SQLite: Prefer TEXT, since DECIMAL is just an alias for binary floating-point numbers. TEXT accurately preserves the scale of decimals. MySQL: Use DECIMAL(19, d), as DECIMAL is merely an alias for DECIMAL(10, 0). The downside of this format is that MySQL automatically rescales all decimals: it rounds values with more than d digits in the fractional part (using half away from zero) and pads with trailing zeros those with fewer than d digits in the fractional part. To prevent automatic rescaling, consider using VARCHAR(22), which accurately preserves the scale of decimals. This example demonstrates the advantage of decimals for financial calculations. It computes the sum 0.1 + 0.2 using both decimal and float64 arithmetic. In decimal arithmetic, the result is exactly 0.3, as expected. In float64 arithmetic, the result is 0.30000000000000004 due to floating-point inaccuracy. This example calculates an approximate value of π using the Leibniz formula. The Leibniz formula is an infinite series that converges to π/4, and is given by the equation: 1 - 1/3 + 1/5 - 1/7 + 1/9 - 1/11 + ... = π/4. This example computes the series up to the 500,000th term using decimal arithmetic and returns the approximate value of π. This example implements a simple calculator that evaluates mathematical expressions written in postfix notation. The calculator can handle basic arithmetic operations such as addition, subtraction, multiplication, and division.

Package skogul is a framework for receiving, processing and forwarding data, typically metric data or event-oriented data, at high throughput. It is designed to be as agnostic as possible with regards to how it transmits data and how it receives it, and the processors in between need not worry about how the data got there or how it will be treated in the next chain. This means you can use Skogul to receive data on a influxdb-like line-based TCP interface and send it on to postgres - or influxdb - without having to write explicit support, just set up the chain. The guiding principles of Skogul is: - Make as few assumptions as possible about how data is received - Be stupid fast End users should only need to worry about the cmd/skogul tool, which comes fully equipped with self-contained documentation. Adding new logic to Skogul should also be fairly easy. New developers should focus on understanding two things: 1. The skogul.Container data structure - which is the heart of Skogul. 2. The relationship from receiver to handler to sender. The Container is documented in this very package. Receivers are where data originates within Skogul. The typical Receiver will receive data from the outside world, e.g. by other tools posting data to a HTTP endpoint. Receivers can also be used to "create" data, either test data or, for example, log data. When skogul starts, it will start all receivers that are configured. Handlers determine what is done with the data once received. They are responsible for parsing raw data and optionally transform it. This is the only place where it is allowed to _modify_ data. Today, the only transformer is the "templater", which allows a collection of metrics which share certain attributes (e.g.: all collected at the same time and from the same machine) to provide these shared attributes in a template which the "templater" transformer then applies to all metrics. Other examples of transformations that make sense are: - Adding a metadata field - Removing a metadata field - Removing all but a specific set of fields - Converting nested metrics to multiple metrics or flatten them Once a handler has done its deed, it sends the Container to the sender, and this is where "the fun begins" so to speak. Senders consist of just a data structure that implements the Send() interface. They are not allowed to change the container, but besides that, they can do "whatever". The most obvious example is to send the container to a suitable storage system - e.g., a time series database. So if you want to add support for a new time series database in Skogul, you will write a sender. In addition to that, many senders serve only to add internal logic and pass data on to other senders. Each sender should only do one specific thing. For example, if you want to write data both to InfluxDB and MySQL, you need three senders: The "MySQL" and "InfluxDB" senders, and the "dupe" sender, which just takes a list of other senders and sends whatever it receives on to all of them. Today, Senders and Receivers both have an identical "Auto"-system, found in auto.go of the relevant directories. This is how the individual implementations are made discoverable to the configuration system, and how documentation is provided. Documentation for the settings of a sender/receiver is handled as struct tags. Once more parsers/transformers are added, they will likely also use a similar system.

Package hotkey provides the basic facility to register a system-level global hotkey shortcut so that an application can be notified if a user triggers the desired hotkey. A hotkey must be a combination of modifiers and a single key. Note platform specific details: On macOS, due to the OS restriction (other platforms does not have this restriction), hotkey events must be handled on the "main thread". Therefore, in order to use this package properly, one must start an OS main event loop on the main thread, For self-contained applications, using mainthread package. is possible. It is uncessary or applications based on other GUI frameworks, such as fyne, ebiten, or Gio. See the "examples" for more examples. On Linux (X11), when AutoRepeat is enabled in the X server, the Keyup is triggered automatically and continuously as Keydown continues. On Linux (X11), some keys may be mapped to multiple Mod keys. To correctly register the key combination, one must use the correct underlying keycode combination. For example, a regular Ctrl+Alt+S might be registered as: Ctrl+Mod2+Mod4+S. If this package did not include a desired key, one can always provide the keycode to the API. For example, if a key code is 0x15, then the corresponding key is `hotkey.Key(0x15)`. THe following is a minimum example:

Package geom2d provides a comprehensive set of tools for computational geometry in two dimensions. The geom2d package is built around core types like Point, LineSegment, Circle, Rectangle, and PolyTree, supporting a wide range of operations including transformations, boolean geometry, and spatial relationships. Designed for both performance and clarity, geom2d leverages Go generics to handle various numeric types and provides intuitive APIs for working with 2D geometric data. This library assumes a standard Cartesian coordinate system where the x-axis increases to the right and the y-axis increases upward. This system is commonly referred to as a right-handed Cartesian coordinate system. All geometric operations and relationships (e.g., clockwise or counterclockwise points) are based on this convention. The library includes support for the following 2D geometric types: geom2d leverages Go’s generics, allowing you to use the library with different numeric types (int, float32, float64, etc.). This flexibility ensures the library can adapt to various applications, from integer-based grids to floating-point precision computations. geom2d incorporates an epsilon parameter in many of its relationship methods to handle floating-point precision issues. This allows you to control the tolerance for comparisons, making the library robust for real-world applications where precision errors can occur. This library provides methods to compute relationships between geometric types using a standardized set of relationships: RelationshipDisjoint, RelationshipIntersection, RelationshipContainedBy, RelationshipContains, and RelationshipEqual. geom2d builds upon the work of others and is grateful for the foundations they have laid. Specifically:

nxadm/tail provides a Go library that emulates the features of the BSD `tail` program. The library comes with full support for truncation/move detection as it is designed to work with log rotation tools. The library works on all operating systems supported by Go, including POSIX systems like Linux and *BSD, and MS Windows. Go 1.9 is the oldest compiler release supported.

Package conl implements CONL parsing and serializing. CONL is a post-modern, human-centric configuration language. It is designed to be a easy to read, easy to edit, and easy to parse, in that order. It uses a JSON-like structure of scalars, maps, and lists; an INI-like type system to defer scalar parsing until runtime; and a (simplified) YAML-like syntax for indentation. Like the builtin json package, CONL can automatically convert between Go types and CONL values. For example, you could parse the above document into a struct defined in Go as: If your type implements the encoding.TextMarshaler and encoding.TextUnmarshaler then CONL will use that to convert between a scalar and your type, otherwise scalars are parsed using the strconv package. Package conl supports a very similar set of Go types to encoding/json. In particular, any string, number, or boolean value can be serialized; as can any struct, map, array, or slive of such values. On the flip side, channels and functions cannot be serialized. Unlike json, conl allows map keys to be numbers, bools, or arrays or structs of those types in addition to strings.

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: