Command-Line

To check the status of a running ESP instance, you can use the status entry point:

~$ python -m esp status

Connecting to esp with following credentials:
   host: localhost
   port: 8002
   email: admin@localhost
   cookies: None

Connection established!

As mentioned in the Usage section of the documentation, you can import YAML configuration for each model defined in the python client. Below is an example of how to import a Workflow via the command-line (see the Usage section for an example of the config file format):

~$ python -m esp import workflow /path/to/workflow.yml

You can also use the same entry point for importing experiment data into the ESP application. Here’s an example config definition for a project and related experiments (with data):

Miseq Sequencing:

  desc: Project container for MiSeq runs.

  tags:
    - illumina
    - miseq

  experiments:

    - MS001:
        submit: true
        workflow: Illumina Sequencing

        tags:
          - illumina
          - miseq

        samples:
          - ESP000001
          - ESP000002

        protocols:

          - Set Samples:
              complete: true
              data:
                Note: Ready to go!

          - Create Illumina Library:
              complete: true
              cols:
                - Index Type
                - I7 Index ID
                - I5 Index ID
              data:
                - ['Nextera DNA', 'N701', 'N501']
                - ['Nextera DNA', 'N702', 'N502']

          - Analyze Sequencing Results:
              run: true
              complete: true
              data:
                - ESP000001:
                    Reference: GRCh38
                - ESP000002:
                    Reference: GRCh37

    - MS002:
        submit: false
        workflow: Illumina Sequencing

        tags:
          - illumina
          - miseq

        samples:
            - ESP000001

To import this project, you can use an entry-point call with the same flavor:

~$ esp import project /path/to/project.yml

This will import the entire Project with related Experiment, Sample, and SampleSheet objects.

For more information on the import entry point, you can use the -h flag:

~$ python -m esp import -h

To do a bulk import, you can use the seed entry point:

~$ python -m esp seed /path/to/content.yml

For this type of import, all of your content can be defined in the same file if the models are explicitly declared:

---
- model: workflow
  data:
    My Workflow:
      desc: Definition for workflow.
      ...

- model: sample_type
  data: /path/to/sample_type.yml

- model: user
  data: ${CWD}/users.yml

Any type of importable model from the client can be included in this config. To see the list of models supported by the client, use the python -m esp import -h command.

During development, it’s useful to have real-time updates of changes you make to content config files. This is possible with the watch entrypoint. To use the watch entrypoint, include watch before your seed or import command:

~$ # with seed
~$ python -m esp watch seed /path/to/content.yml

~$ # with import
~$ python -m esp watch import workflow /path/to/workflow.yml

This will monitor the directory containing that seed file for changes and will run the command any time a change occurs. Notably, this helps shorten the develop/test cycle for content in the SDK, where make import isn’t necessary to re-seed an instance with content. With this the watch entrypoint will watch for changes made by developers and update their esp instance in real-time.

You can also include additional directories into the search path for the watch entrypoint. For example, to watch a content directory in the SDK and run seed each time a change is made in that directory, you can use:

~$ python -m esp watch --include=./content seed ./roles/content.yml

This makes the process of iterating on multi-part configuration for an instance more manageable (especially since you can comment out items in the seed file to cut down on imports during development/testing).

Along with imports, you can also export specific data. To export individual models, you can use:

~$ esp export workflow 'Illumina Sequencing' > Illumina-Sequencing.yml

Similarly, to export all data for a project, use:

~$ esp export project -n 'My Project' > My-Project.yml

For more information on the export entry point, you can use the -h flag:

~$ python -m esp export -h

Along with exporting a specific model, you can also do a full dump (export) of all content models in your instance. For example, if you use the client to run:

~$ esp dump

You will find a folder called content/ in your current directory with content models organized like the following:

.
├── chains
│   └── Whole-Genome-Sequencing.yml
├── files
│   ├── Spectramax_Template_field_descriptions.pdf
│   └── pn_010156.pdf
├── inventory
│   ├── Item-Types.yml
│   └── Sample-Types.yml
├── pipelines
│   ├── ABI-Zip-Process.yml
│   ├── Generate-NovaSeq-Runsheet.yml
├── reports
│   ├── flow_cell_layout.html
│   ├── held_samples.html
├── tasks
│   └── import_nanodrop.py
└── workflows
    ├── Bioanalyzer.yml
    ├── DNA-Isolation.yml
    ├── DNA-Quantification.yml
    ├── Library-Prep.yml
    ├── Whole-Genome-Bioinformatics.yml

This process will also create a seed file (see above) with a manifest of content definitions in content/seed/content.yml. You can change the default seed file location using the --seed argument. The root folder for the content dump can be set with the -r argument.

Similarly, you can dump all content for a specific model using the --model command-line argument:

~$ esp dump --model SampleType

Behavior is controlled by properties of conf, where conf has properties root_directory, model, seed, and modeldest.

For more specific information on the dump command, you can use the -h flag:

~$ python -m esp dump -h