From e9465b2ea29cc35793dd8df5aad48e8786a9f194 Mon Sep 17 00:00:00 2001 From: Damien Irving Date: Mon, 24 Jun 2024 14:56:35 +1000 Subject: [PATCH] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e369e0d..81a6d1e 100644 --- a/README.md +++ b/README.md @@ -73,10 +73,10 @@ $ python adjust.py -h ### Command line At the command line, QDC and/or ECDFm can be achieved by running the following scripts: -1. `train.py` to calculate the adjustment factors between an *historical* and *reference* dataset - (in QDC the reference dataset is a future model simulation; in ECDFm it is observations) +1. `train.py` to calculate the adjustment factors between an *historical* model dataset and a *reference* dataset + (for QDC the reference dataset is a future model simulation; for ECDFm it is observations) 1. `adjust.py` to apply the adjustment factors to the *target* data - (in QDC the target data is observations; in ECDFm it is a model simulation) + (for QDC the target data is observations; for ECDFm it is a model simulation) See the files named `docs/example_*.md` for detailed worked examples using these two command line programs. @@ -89,7 +89,7 @@ Starting with historical (`ds_hist`), reference (`ds_ref`) and target (`ds_targe containing the variable of interest (`hist_var`, `ref_var` and `target_var`) you can import the relevant functions from the scripts mentioned above. For instance, -a QDC workflow would look something like this: +a typical workflow would look something like this: ```python