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more clean cleanup and filename standardization
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nsheff committed Feb 7, 2024
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2 changes: 1 addition & 1 deletion docs/eido/writing-a-filter.md
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Expand Up @@ -25,7 +25,7 @@ def my_custom_filter(p, **kwargs):
out = re.sub('\n', '\n ', yaml.safe_dump(s.to_dict(), default_flow_style=False))
sys.stdout.write(out + "\n")
```
For reference you can check the signatures of the functions in [Built-in `eido` Plugins Documentation](plugin_api_docs.md). Importantly, if the function *requires* any arguments (always provided via `**kwargs`), the creator of the function should take care of handling missing/faulty input.
For reference you can check the signatures of the functions in [Built-in `eido` Plugins Documentation](code/plugin-api-docs.md). Importantly, if the function *requires* any arguments (always provided via `**kwargs`), the creator of the function should take care of handling missing/faulty input.

Next, we need to link that function in to the `eido` filter plugin system.

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6 changes: 3 additions & 3 deletions docs/geofetch/README.md
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Expand Up @@ -12,7 +12,7 @@

- Download either raw or processed data from either [SRA](https://www.ncbi.nlm.nih.gov/sra) or [GEO](https://www.ncbi.nlm.nih.gov/geo/)
- Produce a standardized [PEP](http://pepkit.github.io) sample table. This makes it really easy to run [looper](https://pepkit.github.io/docs/looper/)-compatible pipelines on public datasets by handling data acquisition and metadata formatting and standardization for you.
- Prepare a project to run with [sraconvert](sra_convert.md) to convert SRA files into FASTQ files.
- Prepare a project to run with [sraconvert](sra-convert.md) to convert SRA files into FASTQ files.

![](./img/pipeline.svg)

Expand Down Expand Up @@ -85,8 +85,8 @@ find_gse = Finder(filters='bed')
# get all projects that were found:
gse_list = find_gse.get_gse_all()
```
Find more information here: [GSE Finder](./gse_finder.md)
Find more information here: [GSE Finder](./gse-finder.md)


For more details, check out the [usage](usage.md) reference, [installation instructions](install.md), or head on over to the [tutorial for raw data](raw-data-downloading.md) and [tutorial for processed data](processed-data-downloading.md) for a detailed walkthrough.
For more details, check out the [usage](usage.md) reference, [installation instructions](install.md), or head on over to the [tutorial for raw data](code/raw-data-downloading.md) and [tutorial for processed data](code/processed-data-downloading.md) for a detailed walkthrough.

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2 changes: 1 addition & 1 deletion docs/looper/README.md
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Expand Up @@ -57,4 +57,4 @@ cd hello_looper-master
looper run project/project_config.yaml
```

Detailed explanation of results is in the [Hello world tutorial](hello-world.md).
Detailed explanation of results is in the [Hello world tutorial](code/hello-world.md).
4 changes: 2 additions & 2 deletions docs/looper/faq.md
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Expand Up @@ -3,7 +3,7 @@

## What kind of pipelines can `looper` run?

`Looper` can run samples through *any pipeline that runs on the command line*. The flexible [pipeline interface](../pipeline-interface) file allows `looper` to execute arbitrary shell commands. A pipeline may consist of scripts in languages like Perl, Python, or bash, or it may be built with a particular framework. Typically, we use Python pipelines built using the [`pypiper` package](http://pypiper.readthedocs.io), which provides some additional power to `looper`, but that's optional.
`Looper` can run samples through *any pipeline that runs on the command line*. The flexible [pipeline interface](pipeline-interface-specification.md) file allows `looper` to execute arbitrary shell commands. A pipeline may consist of scripts in languages like Perl, Python, or bash, or it may be built with a particular framework. Typically, we use Python pipelines built using the [`pypiper` package](http://pypiper.readthedocs.io), which provides some additional power to `looper`, but that's optional.


## Why isn't the `looper` executable available on `PATH`?
Expand All @@ -26,7 +26,7 @@ When using the `run` subcommand, for each sample being processed `looper` first

If you do in fact want to re-rerun a sample (maybe you've updated the pipeline, or you want to run restart a failed attempt), you can do so by just passing to `looper` at startup the `--ignore-flags` option; this will skip the flag check **for *all* samples**. If you only want to re-run or restart a few samples, it's best to just delete the flag files for the samples you want to restart, then use `looper run` as normal.

You may be interested in the [usage docs](../usage) for the `looper rerun` command, which runs any failed samples.
You may be interested in the [usage docs](usage.md) for the `looper rerun` command, which runs any failed samples.

## How can I resubmit a subset of jobs that failed?

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2 changes: 1 addition & 1 deletion docs/pephub/development.md
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Expand Up @@ -14,7 +14,7 @@ There are two components to PEPhub: a FastAPI backend, and a React frontend. As
uvicorn pephub.main:app --reload
```

The backend server should now be running at http://localhost:8000. If you wish to debug the backend server, we've provided a [`launch.json`](../.vscode/launch.json) file for VSCode. You can use this to debug the backend server.
The backend server should now be running at http://localhost:8000. If you wish to debug the backend server, the repository contains a `launch.json` file for VSCode. You can use this to debug the backend server.

## Frontend development
*Before begining, ensure you are using a `nodejs` version > 16.* To manage `node` versions, most people recommend [`nvm`](https://github.com/nvm-sh/nvm).
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