GitLocker: The Coding Marketplace

Description:

galaxyparsec 1.16.0

Command-line utilities to assist in working with Galaxy servers.

Installation
$ pip install galaxy-parsec
$ parsec init
Python 3.6+ is supported

Questions?

Quick Start
This quick start demonstrates using parsec commands to manipulate Galaxy
histories and datasets. You will want to install jq
if you do not have it already.

Connect to a Galaxy server
To connect to a running Galaxy server, you will need an account on that Galaxy
instance and an API key for the account. Instructions on getting an API key can
be found at http://wiki.galaxyproject.org/Learn/API .
First initialize parsec:
$ parsec init
Once initialized, parsec will be usable from the command line. Please note that
an admin account is required for a few actions like creation of data libraries,
or access to user API keys. Your configuration must allow access to /api without
need for a username or password. More information can be found at
https://galaxyproject.org/admin/config/performance/production-server/

Introduction To Parsec
Parsec is a set of automatically generated wrappers for BioBlend functions. I
found myself writing a large number of small / one-off scripts that invoked
simple bioblend functions. These scripts were impossible to compose and use in
a linux-friendly manner. I copied and pasted code between all of these utility scripts.
Parsec is the answer to all of these problems. It extracts all of the
individual functions I was writing as separate CLI commands that can be piped
together, run in parallel, etc.
After installation, running parsec will present you with a list of sub-commands you can execute.
$ parsec
Usage: parsec [OPTIONS] COMMAND [ARGS]...

Command line wrappers around BioBlend functions. While this sounds
unexciting, with parsec and jq you can easily build powerful command line
scripts.

Options:
--version Show the version and exit.
-v, --verbose Enables verbose mode.
--galaxy_instance TEXT name of galaxy instance from ~/.planemo.yml
[required]
--help Show this message and exit.

Commands:
config
datasets
datatypes
folders
forms
...
Each of these commands has more commands under it:
$ parsec histories
Usage: parsec histories [OPTIONS] COMMAND [ARGS]...

Options:
--help Show this message and exit.

Commands:
create_dataset_collection Create a new dataset collection
create_history Create a new history, optionally setting
the...
create_history_tag Create history tag
delete_dataset Mark corresponding dataset as deleted.
delete_dataset_collection Mark corresponding dataset collection as...
delete_history Delete a history.
download_dataset Deprecated method, use...
download_history Download a history export archive.
export_history Start a job to create an export archive
for...
...

Viewing Histories and Datasets
To get information on the Histories currently in your account, call history get_histories, and we will pipe this to a jq command which selects the
first element from the JSON array.
$ parsec histories get_histories | jq '.[0]'
Parsec will respond with information about your first history
{
"name": "BuildID=Manual-2017.05.02T16:13 WF=PAP_2017_Comparative_(v1.0)_BOOTSTRAPPED Org=CCS Source=Jenkins",
"url": "/galaxy/api/histories/548c0777ac615645",
"annotation": null,
"model_class": "History",
"id": "548c0777ac615645",
"tags": [
"Automated",
"Annotation",
"BICH464"
],
"purged": false,
"published": false,
"deleted": false
}
This may not be all of the information you were expecting about your history.
In that case, you might want to call show_history which will show you more
details about a single history. You can either manually type parsec histories show_history 548c0777ac615645, or we can do this in batch:
$ parsec histories get_histories | jq '.[0].id' | xargs -n 1 parsec histories show_history
Which pulls out the first history, select the id attribute, before passing it to xargs.
If you have not used it before, xargs allows us to execute multiple
commands for some input data. Here we execute the command parsec histories show_history for each line of input (i.e. each ID returned to us from the jq call).
xargs -n 1 ensures that we will only pass a single ID to a
single call of show_history. If you were to use jq '.[].id' instead of
jq '.[0].id' it would output the IDs for every history you own. You could
then pipe this to xargs and run show_history on all of your histories!
{
"annotation": null,
"contents_url": "/galaxy/api/histories/548c0777ac615645/contents",
"create_time": "2017-05-02T16:18:21.285382",
"deleted": false,
"empty": false,
"genome_build": null,
"id": "548c0777ac615645",
"importable": true,
"model_class": "History",
"name": "BuildID=Manual-2017.05.02T16:13 WF=PAP_2017_Comparative_(v1.0)_BOOTSTRAPPED Org=CCS Source=Jenkins",
"published": false,
"purged": false,
"size": 34760258,
"slug": "buildidmanual-20170502t1613-wfpap2017comparativev10bootstrapped-orgccs-sourcejenkins",
"state": "ok",
"state_details": {
"discarded": 0,
"empty": 0,
"error": 0,
"failed_metadata": 0,
"new": 0,
"ok": 29,
"paused": 0,
"queued": 0,
"running": 0,
"setting_metadata": 0,
"upload": 0
},
"state_ids": {
"discarded": [
"a6cc986453fae8ba",
"f2f9b7b017f20578",
"70eb5af78c588bd1"
],
"empty": [],
"error": [
"d643e34e1114cc52",
"98ae3d35d73f82c9"
],
"failed_metadata": [],
"new": [],
"ok": [
"e510305efbee5f49",
"0d595b7c2b6e9b93",
"d04ac6f949ae266c",
"175f283ddaeca39c",
"b34432b8a0847c04",
"ea7ff5323ddebcb8",
"3e40a393efafc45c",
"7ce5ec5d51ef85cb",
"577e4242cdfbe1aa",
"193d15527d13f45e",
"4543f9456af7f0df",
"5e1293df75b4f95b",
"a57bae35eca5fbfe",
"6c306b2ed4533f1f",
"97c5f81b159505f0",
"64d1d8e46b4554bd",
"8e9432496d7e2b43",
"5c8579257c579aae",
"243ad216fbfa268e",
"8336d9eb27b27677",
"a1d4cc61bdba629d",
"7f93a80890822fa9",
"c479b351902302e2",
"36b60fb58ad24a71",
"041dd3cb6879f1f7",
"36992e90715c9c77",
"4bddfe152467e972",
"2d9f5c0c36d89e10",
"e53ad6f3133b2816"
],
"paused": [
"4a8143557292a233",
"b0f8a75aa6be2c1d"
],
"queued": [],
"running": [],
"setting_metadata": [],
"upload": []
},
"tags": [
"Automated",
"Annotation",
"BICH464"
],
"update_time": "2017-05-02T16:49:07.941097",
"url": "/galaxy/api/histories/548c0777ac615645",
"user_id": "f570ade6e7840ba0",
"username_and_slug": "u/helena-rasche/h/buildidmanual-20170502t1613-wfpap2017comparativev10bootstrapped-orgccs-sourcejenkins"
}
So much metadata to play with and filter on! Note that many of these commands
have additional flags, for example parsec histories show_history --help
will tell us that we can also pass the –contents option to retrieve a list of datasets in that history, even filtering on their visibility.
$ parsec histories show_history --help
Usage: parsec histories show_history [OPTIONS] HISTORY_ID

Get details of a given history. By default, just get the history meta
information.

Options:
--contents When ``True``, the complete list of datasets in the given
history.
--deleted TEXT Used when contents=True, includes deleted datasets in
history dataset list
--visible TEXT Used when contents=True, includes only visible datasets in
history dataset list
--details TEXT Used when contents=True, includes dataset details. Set to
'all' for the most information
Thus with a simple query
$ parsec histories show_history 548c0777ac615645 --contents --deleted True | jq -S '.[0]'
We see the first deleted dataset in the history.
{
"create_time": "2017-05-02T16:18:54.272050",
"dataset_id": "93c926a0dabafde3",
"deleted": true,
"extension": "fasta",
"hid": 30,
"history_content_type": "dataset",
"history_id": "548c0777ac615645",
"id": "d643e34e1114cc52",
"name": "Feature Sequence Export Unique on data 27 and data 20",
"purged": false,
"state": "error",
"type": "file",
"type_id": "dataset-d643e34e1114cc52",
"update_time": "2017-05-02T16:47:57.807506",
"url": "/galaxy/api/histories/548c0777ac615645/contents/d643e34e1114cc52",
"visible": true
}
This gives us a dictionary containing the History’s metadata. With contents=False (the default), we only get a list of ids of the datasets contained within the History; with contents=True we would get metadata on each dataset. We can also directly access more detailed information on a particular dataset by passing its id to the show_dataset method:
$ parsec datasets_show_dataset 10a4b652da44e82a
{
"accessible": true,
"annotation": null,
"api_type": "file",
"create_time": "2015-02-27T23:46:27.642906",
"data_type": "galaxy.datatypes.data.Text",
"dataset_id": "10a4b652da44e82a",
"deleted": false,
"display_apps": [],
"display_types": [],
"download_url": "/api/histories/f3c2b0f3ecac9f02/contents/10a4b652da44e82a/display",
"extension": "fastq",
"file_ext": "fastq",
"file_path": null,
"file_size": 16527060,
"genome_build": "dm3",
"hda_ldda": "hda",
"hid": 1,
"history_content_type": "dataset",
"history_id": "f3c2b0f3ecac9f02",
"id": "10a4b652da44e82a",
"meta_files": [],
"metadata_data_lines": 4,
"metadata_dbkey": "dm3",
"misc_blurb": "15.8 MB",
"misc_info": "uploaded fastqsanger file",
"model_class": "HistoryDatasetAssociation",
"name": "C1_R2_1.chr4.fq",
"purged": false,
"resubmitted": false,
"state": "ok",
"tags": [],
"type": "file",
"update_time": "2015-02-27T23:46:34.659590",
"url": "/api/histories/f3c2b0f3ecac9f02/contents/10a4b652da44e82a",
"uuid": "ccad6f3a-f75d-472f-9142-2d4c39ad1a35",
"visible": true,
"visualizations": []
}

On JQ
It is worth it to look at some of the things possible with JQ for a moment. The
above example may not be so exciting at first blush, but you can do incredible
things with the combination of parsec, jq, and xargs. Here are some examples to consider:

find all histories with a public link, but not published in the
shared-histories section, and print out their history name and the shared
link.
$ parsec histories get_histories | \
jq '.[].id' | \
xargs -n 1 parsec histories show_history | \
jq '. | select(.published == false) | select(.importable == true) | [.published, .importable, .id, .username_and_slug] | @tsv' -r

reset the API keys for 30 users at once.
$ parsec users get_users | \
jq '.[] | \
select(.username | contains("janedoe")) | .id' | \
xargs -n 1 parsec users create_user_apikey

download all of the OK datasets in a set of histories
$ parsec histories get_histories | \
jq '.[].id' | \ # Or other, more complex filtering?
xargs -n 1 parsec histories show_history | \ # Get history details
jq '.state_ids.ok[]' | \ # Find OK datasets
xargs -n 1 parsec datasets download_dataset --file_path '.' --use_default_filename # Download

View Workflows
Methods for accessing workflows are grouped under GalaxyInstance.workflows.*.
To get information on the Workflows currently in your account, use:
$ parsec workflows get_workflows
[
{
'id': 'e8b85ad72aefca86',
'name': u"TopHat + cufflinks part 1",
'url': '/api/workflows/e8b85ad72aefca86'
},
{
'id': 'b0631c44aa74526d',
'name': 'CuffDiff',
'url': '/api/workflows/b0631c44aa74526d'
}
]
For example, to further investigate a workflow, we can request:
$ parsec workflows show_workflow ded67e5aa1371841 | jq 'del(.steps)'
The workflow output is generally quite large as it embeds a full copy of the
workflow. In the above JQ command I have removed the steps attribute from
the output for brevity.
{
"annotation": "",
"model_class": "StoredWorkflow",
"latest_workflow_uuid": "94c40212-c4bb-43b7-a43b-eadc1a3b2894",
"id": "ded67e5aa1371841",
"url": "/galaxy/api/workflows/ded67e5aa1371841",
"deleted": false,
"tags": [],
"owner": "helena-rasche",
"name": "PAP 2017 Functional (v8.15)",
"inputs": {
"0": {
"value": "",
"uuid": "9397916e-afb7-4e48-b89e-d4c99bf202de",
"label": "Apollo Organism JSON File"
},
"2": {
"value": "",
"uuid": "eca835c6-328a-4698-a387-d0719b24d19d",
"label": "Genome Sequence"
},
"1": {
"value": "",
"uuid": "5511d038-e96b-49b2-998a-d037935f6e06",
"label": "Annotation Set"
}
},
"published": false
}

View Users
Methods for managing users are grouped under GalaxyInstance.users.*. User management is only available to Galaxy administrators, that is, the API key used to connect to Galaxy must be that of an admin account.
To get a list of users, call:
$ parsec users get_users
[
{
"username": "test",
"model_class": "User",
"email": "test@local.host",
"id": "f2db41e1fa331b3e"
},
...
]

In Depth Example
As a more detailed example, we’ll launch a simple workflow.

Step 1. What are the Inputs
$ parsec workflows show_workflow ded67e5aa1371841 | jq .inputs > inputs.json
In practice this file probably looks similar to this:
{
"0": {
"value": "",
"uuid": "9397916e-afb7-4e48-b89e-d4c99bf202de",
"label": "Apollo Organism JSON File"
},
"2": {
"value": "",
"uuid": "eca835c6-328a-4698-a387-d0719b24d19d",
"label": "Genome Sequence"
},
"1": {
"value": "",
"uuid": "5511d038-e96b-49b2-998a-d037935f6e06",
"label": "Annotation Set"
}
}

Step 2: Prepare History and Load Datasets
First, we’ll create a history to manage all of our work:
$ HISTORY_ID=$(parsec histories create_history | jq .id)
$ parsec histories update_history --name 'Parsec test'
Next we have to fetch some datasets. You could upload them:
$ parsec tools upload_file my-file.gff3 $HISTORY_ID
But in my case, I need to run a tool which produces them:
JOB_ID=$(parsec tools run_tool $HISTORY_ID edu.tamu.cpt2.webapollo.export \
'{"org_source|source_select": "direct", "org_source|org_raw": "Miro"}' | \
jq .id)

$ parsec jobs show_job .outputs $JOB_ID
By storing the job ID in a variable, we can make repeated requests to check on
it. The second parsec statement fetches the output datasets from this step.
{
"fasta_out": {
"id": "61513e15ce98c986",
"src": "hda",
"uuid": "0de1442b-c410-4a38-b9ca-49cff973d9b8"
},
"gff_out": {
"id": "62ee69adcf74378c",
"src": "hda",
"uuid": "887aaf6f-ed07-4ee8-a396-c16612f83d83"
},
"json_out": {
"id": "1f73e96543934ac8",
"src": "hda",
"uuid": "3be3d364-83c5-4a23-87fa-ebd8c27f2094"
}
}

Step 3: Invoking the Workflow
Remembering back to the inputs in step 1, we will match them up and create an inputs.json file

0 / organism json file => json_out
1 / genome sequence => gff_out
2 / annotation set => fasta_out

This gives us an inputs.json that looks like so:
{
"0": {
"id": "1f73e96543934ac8",
"src": "hda"
},
"1": {
"id": "62ee69adcf74378c",
"src": "hda"
},
"2": {
"id": "61513e15ce98c986",
"src": "hda"
}
}
We can now invoke our workflow using parsec!
Since the inputs is a JSON parameter, it can be supplied many different ways for your convenience. All of the following behave identically.
$ cat params.json | parsec jobs search_jobs -; # Stdin
$ parsec jobs search_jobs params.json; # Filename
$ parsec jobs search_jobs $(cat params.json); # String argument
Running the invocation:
$ parsec workflows invoke_workflow ded67e5aa1371841 --inputs inputs.json --history_id $HISTORY_ID
Produces a very succinct workflow launch output:
{
"uuid": "94246003-2f8b-11e7-9427-20474784cc00",
"state": "new",
"workflow_id": "3daf5606d767a471",
"id": "c7f60cfda02f0f46",
"update_time": "2017-05-02T23:03:39.693288",
"model_class": "WorkflowInvocation",
"history_id": "0d17c6f8cd8d49a5"
}
We can now use parsec to check on the status of all of the datasets:
$ parsec workflows show_invocation 3daf5606d767a471 c7f60cfda02f0f46 | jq '.steps[].state' | sort | uniq -c
3 "running"
72 "new"
3 null
1 "ok"
Or we can use one of the utility scripts to wait on that workflow to finish before continuing on to some other task:
$ parsec utils wait_on_invocation 3daf5606d767a471 c7f60cfda02f0f46 && ...

License
Copyright 2016-2021 Galaxy IUC
Licensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Support
This material is based upon work supported by the National Science Foundation under Grant Number (Award 1565146)