Treat multiple Buffers as a single contiguous Buffer.
Buffer collection utilities.
[](https://discord.gg/poimandres)
Creates a buffer around a GeoJSON feature.
Treat a collection of Buffers as a single contiguous partially mutable Buffer.
Modern Buffer API polyfill without footguns
Safer Node.js Buffer API
Node.js Buffer API, for the browser
Get the byte length of an ArrayBuffer, even in engines without a `.byteLength` method.
Is this value a JS ArrayBuffer?
Reads / writes floats / doubles from / to buffers in both modern and ancient browsers.
smart-buffer is a Buffer wrapper that adds automatic read & write offset tracking, string operations, data insertions, and more.
Is this value a JS SharedArrayBuffer?
Get the ArrayBuffer out of a TypedArray, robustly.
Get the ArrayBuffer out of a DataView, robustly.
Determine if an object is a Buffer
Pass in a string, array, Buffer, Data View, or Uint8Array, and get a Buffer back.
JSON parse & stringify that supports binary via bops & base64
A [ponyfill](https://ponyfill.com) for `Buffer.from`, uses native implementation if available.
A pure javascript CRC32 algorithm that plays nice with binary data
Convert a typed array to a Buffer without a copy
minimal implementation of a PassThrough stream
Read/write IEEE754 floating point numbers from/to a Buffer or array-like object
Source code handling classes for webpack
Ruby's contemporary test coverage tools all lie, exaggerating coverage through false-positives and creating a false sense of security; minitest-coverage tries to address this. Coverage Analysis Tools rely on tracing facilities built into ruby’s VM. You run your tests, and collect data. Seems simple, but that’s a very flawed approach that buffers your coverage numbers up falsely. I’ve witnessed false coverage by as much as 60%, but it could be even worse. Worse, the tracing facilities currently make it impossible to get truly accurate numbers. Even so, they can be improved to be much more accurate.
FatTable is a gem that treats tables as a data type. It provides methods for constructing tables from a variety of sources, building them row-by-row, extracting rows, columns, and cells, and performing aggregate operations on columns. It also provides as set of SQL-esque methods for manipulating table objects: select for filtering by columns or for creating new columns, where for filtering by rows, order_by for sorting rows, distinct for eliminating duplicate rows, group_by for aggregating multiple rows into single rows and applying column aggregate methods to ungrouped columns, a collection of join methods for combining tables, and more. Furthermore, FatTable provides methods for formatting tables and producing output that targets various output media: text, ANSI terminals, ruby data structures, LaTeX tables, Emacs org-mode tables, and more. The formatting methods can specify cell formatting in a way that is uniform across all the output methods and can also decorate the output with any number of footers, including group footers. FatTable applies formatting directives to the extent they makes sense for the output medium and treats other formatting directives as no-ops. FatTable can be used to perform operations on data that are naturally best conceived of as tables, which in my experience is quite often. It can also serve as a foundation for providing reporting functions where flexibility about the output medium can be quite useful. Finally FatTable can be used within Emacs org-mode files in code blocks targeting the Ruby language. Org mode tables are presented to a ruby code block as an array of arrays, so FatTable can read them in with its .from_aoa constructor. A FatTable table can output as an array of arrays with its .to_aoa output function and will be rendered in an org-mode buffer as an org-table, ready for processing by other code blocks.
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