streaming-percentiles - npm Package Compare versions

Comparing version 1.0.0 to 2.1.0

package.json

 {
   "name": "streaming-percentiles",
-  "version": "1.0.0",
+  "version": "2.1.0",
   "description": "Implementations of various streaming percentile algorithms",
@@ -15,24 +15,19 @@ "keywords": [
   "license": "MIT",
-  "main": "build/streamingPercentiles.v1.js",
+  "main": "streamingPercentiles.v1.js",
   "module": "index",
   "repository": {
     "type": "git",
-    "url": "git+https://github.com/sengelha/streaming-percentiles-js.git"
+    "url": "git+https://github.com/sengelha/streaming-percentiles-cpp.git"
   },
   "bugs": {
-    "url": "https://github.com/sengelha/streaming-percentiles-js/issues"
+    "url": "https://github.com/sengelha/streaming-percentiles-cpp/issues"
   },
   "scripts": {
-    "pretest": "rm -rf build && mkdir build && rollup --banner \"$(preamble)\" -f umd -n streamingPercentiles -o build/streamingPercentiles.v1.js -- index.js",
-    "test": "tape 'test/**/*-test.js' && eslint index.js src",
-    "prepare": "npm run test && uglifyjs -b beautify=false,preamble=\"'$(preamble)'\" build/streamingPercentiles.v1.js -c -m -o build/streamingPercentiles.v1.min.js",
-    "postpublish": "git push && git push --tags && zip -j build/streamingPercentiles.v1.zip -- LICENSE README.md build/streamingPercentiles.v1.js build/streamingPercentiles.v1.min.js && echo \"Don't forget to publish to sengelha.github.io\""
+    "test": "exit 0",
+    "prepare": "npm run test",
+    "postpublish": "echo \"Don't forget to push, push tags, and publish to sengelha.github.io\""
   },
   "devDependencies": {
-    "eslint": "4",
-    "package-preamble": "0.1.0",
-    "rollup": "0.43",
-    "tape": "4",
-    "uglify-es": "3"
+    "package-preamble": "0.1.0"
   }
 }

README.md

@@ -1,6 +0,8 @@
-# Streaming Percentiles
+# streaming-percentiles-cpp
 
-This is a reusable JavaScript library with implementations of various
-percentile algorithms on streams of data.  These algorithms all
-calculate only approximate percentiles, not exact percentiles.
+This is a C++ library with implementations of various
+percentile algorithms on streams of data.  Using the
+magic of [emscripten](https://github.com/kripken/emscripten),
+this package also cross-compiles a JavaScript version of
+the streaming percentiles library as well.
 
@@ -10,77 +12,68 @@ For more on streaming percentiles, see [Calculating Percentiles on
 
-## Installing
+## Obtaining the Library
 
-If you use NPM, `npm install streaming-percentiles`.  Otherwise,
-download the [latest release
-binaries](https://sengelha.github.io/streaming-percentiles-js/streamingPercentiles.v1.zip)
-or the [latest release source
-code](https://github.com/sengelha/streaming-percentiles-js/releases/latest).
-You can also load directly from
-[unpkg.com](https://unpkg.com/streaming-percentiles/).
+You can download pre-built versions of the library from the
+[streaming-percentiles-cpp releases
+page](https://github.com/sengelha/streaming-percentiles-cpp/releases).
+Otherwise see [CONTRIBUTING.md](CONTRIBUTING.md) for instructions on how
+to compile the library from source.
 
-For convenience, you can also use the [latest release
-binaries](https://sengelha.github.io/streaming-percentiles-js/streamingPercentiles.v1.zip)
-directly from a web browser:
+## Usage Example
 
-```html
-<script src="//sengelha.github.io/streaming-percentiles-js/streamingPercentiles.v1.min.js"></script>
-<script>
-var gk = new streamingPercentiles.GK(0.1);
-...
-</script>
-```
+### C++
 
-## Example
-
 Here's a simple example on how to use the Greenwald-Khanna streaming
-percentile algorithm:
+percentile algorithm from C++:
 
-```javascript
-var sp = require('streaming-percentiles');
+```cpp
+#include <stmpct/gk.hpp>
 
-// epsilon is allowable error.  As epsilon becomes smaller, the
-// accuracy of the approximations improves, but the class consumes
-// more memory.
-var epsilon = 0.1;
-var gk = new sp.GK(epsilon);
-for (var i = 0; i < 1000; ++i)
-    gk.insert(Math.random());
-var p50 = gk.quantile(0.5); // Approx. median
-var p95 = gk.quantile(0.95); // Approx. 95th percentile
+using namespace stmpct;
+
+double epsilon = 0.1;
+gk g(epsilon);
+for (int i = 0; i < 1000; ++i)
+    g.insert(rand());
+double p50 = g.quantile(0.5); // Approx. median
+double p95 = g.quantile(0.95); // Approx. 95th percentile
 ```
 
-## API Reference
+### JavaScript
 
-### class GK(*epsilon*)
+#### Node.JS
 
-Construct an object which implements the Greenwald-Khanna streaming
-percentile algorithm with allowable error *epsilon*.
+Here's how to use the library from Node.JS:
+```javascript
+var sp = require('./streamingPercentiles.v1.min.js');
 
-Example:
-```javascript
-var sp = require('streaming-percentiles');
-var gk = new sp.GK(0.1);
+var epsilon = 0.1;
+var g = new sp.GK(epsilon);
+for (var i = 0; i < 1000; ++i)
+    g.insert(Math.random());
+var p50 = g.quantile(0.5); // Approx. median
+var p95 = g.quantile(0.95); // Approx. 95th percentile
 ```
 
-### *gk*.insert(*value*)
+#### Browser
 
-Logs the observation of a value.
-
-Example:
-```javascript
-gk.insert(Math.random());
+Here's how to use the library from a browser.  Note that the
+default module name is streamingPercentiles:
+```html
+<script src="streamingPercentiles.v1.min.js"></script>
+<script>
+var epsilon = 0.1;
+var gk = new streamingPercentiles.GK(epsilon);
+for (var i = 0; i < 1000; ++i)
+    g.insert(Math.random());
+var p50 = g.quantile(0.5);
+</script>
 ```
 
-### *gk*.quantile(*phi*)
+## API Reference
 
-Compute the approximate quantile at *phi*.  For example, the 95th
-percentile corresponds to *phi* = 0.95.
+Coming soon!
 
-Example:
-```javascript
-var p50 = gk.quantile(0.5);
-```
+## Contributing
 
-## License
-
-This project is licensed under the MIT License.
+If you are interested in contributing to the library, please see
+[CONTRIBUTING.md](CONTRIBUTING.md).

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Improved metrics

Total package byte prevSize

820637

increased by285.76%

Dev dependency count

1

decreased by-80%

Lines of code

24911

increased by9444.44%

Number of low maintenance alerts

1

decreased by-50%

Worsened metrics

Number of package files

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Number of lines in readme file

79

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