update json to subsume end of range filtering

README.md

@@ -79,7 +79,7 @@ Many episodes in DROID contain significant pauses. This is an issue when trainin
 
 We provide `keep_ranges_1_0_1.json` which maps episode keys to a list of time step ranges that should *not* be filtered out. The episode keys uniquely identify each episode, and are defined as `f"{recording_folderpath}--{file_path}"`. We opt for this unique identifier because both pieces of information are found in the episodes' RLDS metadata, and thus is easy to compute (even with TensorFlow symbolic operations).
 
-To use this data, we recommend creating a `tf.lookup.StaticHashTable` identifying all frames that should not be filtered (with all other frames being filtered by default). Frames can be uniquely identified by simply concatenating their episode key with their time step within the episode.
+To use this data, we recommend creating a `tf.lookup.StaticHashTable` identifying all frames that should not be filtered (with all other frames being filtered by default). Frames can be uniquely identified by simply concatenating their episode key with their time step within the episode. See [here](https://github.com/Physical-Intelligence/openpi/blob/main/src/openpi/training/droid_rlds_dataset.py#L67) for an example implementation of how to use this `json` for filtering.
 
 This particular filter `json` is meant for `droid/1.0.1`, NOT `droid/1.0.0`. It was computed by finding all continuous sequences in episodes of non-idle actions that are at least of length 16 (1 second of wallclock time) that are not interrupted by 8 or more idle actions.
 

keep_ranges_1_0_1.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5046049ab62a2df2f802df89cf0888b720f852ce2557849417d40899c9a38bc8
-size 28573266
+oid sha256:efee695f8228fe19d4f01767b95ab612084c499c8a59421c5f3d2c7ad5f3625e
+size 22185018