> For the complete documentation index, see [llms.txt](https://gitbook.bergelsonlab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gitbook.bergelsonlab.com/data-pipeline/code.md).

# Code

Where to find code that help working with the Seedlings data, so that you don't have to reinvent the wheel

There are two principal ways that code for working with Seedlings is distributed:

* Default one. Through two libraries: `blabr` and `blabpy`. 
  * Neither of the libraries has any narrative documentation at the moment, most of the functions are documented though. So, a good way to find if something already exists is a GitHub search. 
  * Many of the functions are not too robust. If something isn't working as expected, feel free to open an issue on GitHub or slack the lab technician.
  * Zhenya thinks that all the code that is run more than once should be moved to these two libraries.
* Old one. An assortment of `*.py` scripts that live in several GitHub repositories that are cloned to `Fas_Phyc-PEB-Lab/Seedlings/Scripts_and_Apps/Github/seedlings`. Many of the instructions in the Seedlings chapter will point you to specific scripts. Avoid using those scripts, use `blabpy` as much as possible. Do not rely on the clones in `Scripts_and_Apps` either. They often contain uncommitted changes that either should have been committed or should have been undone - your guess is as good as mine. So, if you do want to use one of those scripts:
  * Clone the repo to your computer.
  * Try running the script.
  * If it works - great!
  * If it doesn't, see if the clone in `Scripts_and_Apps` has changes that might be useful. If those changes *are* useful, commit them, push them, update the clone in `Scripts_and_Apps`.

### blabr

GitHub repo [here](https://github.com/BergelsonLab/blabr).

R package. It is not on CRAN, so it has to be installed from GitHub with

```
remotes::install_github('BergelsonLab/blabr')
```

Here are some functions and modules one might find useful:

* `get_blab_share_path` finds the location of the BLab share.
* module `get_data` has functions to download specific version from our data repositories on GitHub (currently, we might move them later). 
  * `get_all_basiclevel` downloads a specific version of the `all_basiclevel dataset`.
* `big_aggregate` aggregates information in `all_basiclevel` (see `get_all_basiclevel`) outputting a big dataframe.
* `blabr:::make_new_global_basic_level` adds a column with the global (corpus-wide) basic level information.
* module `lena` contains functions to calculate annotations metrics for a set of intervals and then select intervals with the most `X`.
* module `seedlings` has functions to read sparse code csvs - csv versions of annotations with an extra `basic_level` column.

### blabpy

GitHub repo [here](https://github.com/BergelsonLab/blabpy).

Python package. It is on PyPI and can be installed with

```
pip install blabpy
```

Here are some functions and modules one might find useful:

* Annotations-to-all\_basiclevel pipeline:
  * `blabpy.seedlings.cha` functions to read/write/extract information from the `.cha` files (CLAN's CHAT annotation files for audio recordings).
  * `blabpy.seedlings.opf` - same but for the datavyu's `.opf` files with video annotations.
  * `blabpy.seedlings.merge` functions that combine new sparse code csvs with data from the existing ones.
  * `blabpy.seedlings.gather` functions to assemble the `all_basiclevel` dataset.
  * `blabpy.seedlings.pipeline` putting all of the above together so that it can be run on the whole corpus at once.
* `blabpy.seedlings.listened_time` - functions that help figure out how much of the recordings have been listened to and annotated.
* `blabpy.seedlings.paths` - functions that help locate certain types of files. Not always consistent signature - raising issues is highly appreciated.