From 0f2e29fbe106a7b50dffeb6f056aba4c107b82a7 Mon Sep 17 00:00:00 2001 From: Jeremy Freeman Date: Thu, 7 Apr 2016 10:16:54 -0400 Subject: [PATCH] Update README.md --- README.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/README.md b/README.md index d25c9fb3..2d1bebfa 100644 --- a/README.md +++ b/README.md @@ -79,15 +79,6 @@ Thunder doesn't require Spark and can run locally without it, but Spark and Thun Once you have a running cluster with a valid `SparkContext` — this is created automatically as the variable `sc` if you call the `pyspark` executable — you can pass it as the `engine` to any of Thunder's loading methods, and this will load your data in distributed `'spark'` mode. In this mode, all operations will be parallelized, and chained operations will be lazily executed. -Here's an example where we load distributed `series` data (in this case random data) and use parallelized versions of `detrend()` and `convolve()` and `max()`, and then call `toarray()` to return a local [`numpy`](https://github.com/numpy/numpy) array. - -```python -import thunder as td - -data = td.series.fromrandom(engine=sc) -ts = data.detrend().convolve(signal).max().toarray() -``` - ## contributing Thunder is a community effort! The codebase so far is due to the excellent work of the following individuals: