batchx-dev 1.0.91

Description:

batchxdev 1.0.91

Welcome to BatchX' python toolbox, or "the toolbox" for short.
The toolbox makes the life of BatchX (python) developers more pleasant.
To do so, it provides tools that help devs to work more effectively.
Table of Contents

Installation

Local
Inside Docker image


The manifest as single source of truth
bx-readme
CommandBuilder

Connecting CLI parameters to BatchX parameters
Understanding parameter types
Add action
Specify constraints



Installation
The toolbox is available on PyPi.
Local
Simple: do a pip install
python -m pip install batchx-dev

For reproducibility, it often makes sense to 'lock' yourself into a specific version.
This can be done as follows:
python -m pip install batchx-dev==1.0.63

Inside Docker image
Installation of the toolbox inside the docker image of a BatchX tool is almost identical to a local install, except for the fact that the pip install command now has to be part of the Dockerfile.
For the sake of argument, we assume that you are working on bringing a bioinformatics tool called biotool into BatchX, and that your directory structure looks like this,
biotool
├── Dockerfile
├── run_biotool.py
└── manifest
├── manifest.json
├── picture.png
└── readme.md

Suppose that, initially, biotool/Dockerfile looked as follows;
FROM amd64/python:3.10.4-bullseye

RUN python -m pip install biotool==2.4.2

RUN mkdir batchx
RUN chmod -R 777 /batchx

COPY run_biotool.py /batchx
ENTRYPOINT python /batchx/run_biotool.py

LABEL io.batchx.manifest=10
COPY manifest /batchx/manifest

installation of the toolbox inside the image requires the addition of a single line,
RUN python -m pip install batchx-dev==1.0.63

which yields a Dockerfile that looks like this:
FROM amd64/python:3.10.4-bullseye

RUN python -m pip install batchx-dev==1.0.63

RUN python -m pip install biotool==2.4.2

RUN mkdir batchx
RUN chmod -R 777 /batchx

COPY run_biotool.py /batchx
ENTRYPOINT python /batchx/run_biotool.py

LABEL io.batchx.manifest=10
COPY manifest /batchx/manifest

That's it.
Note that, in a Dockerfile, you really should 'pin' a specific version of the packages you install.
If you do not do so, the Docker image may differ depending on when it was last built.
The manifest as single source of truth
Ultimately, the manifest is of utmost importance in BatchX.
On an abstract level, much of what the toolbox achieves comes down to making your code understand the manifest properly.
Therefore, much of the explanations below are example-driven, showcasing different scenarios in which a manifest needs proper interpretation.
bx-readme
The toolbox comes with a bx-readme command that helps to keep the manifest.json and the readme.md in sync.
We assume the following directory structure,
biotool
├── Dockerfile
├── run_biotool.py
└── manifest
├── manifest.json
├── picture.png
└── readme.md
└── python-toolbox
├── manifest.json
├── ...

The manifest (i.e. manifest/manifest.json) is the single source of truth, but the human-readable readme (i.e. manifest/readme.md) repeats a lot of that information.
As a consequence, changes in the manifest need to be reflected in the readme and vice-versa.
This synchronisation process is prone to human error, which is why the toolbox steps in.
Quickstart
Ensure that your terminal is in the /manifest directory (cd manifest), and do,
bx-readme

This will likely do what you want.
This command looks at manifest.json and readme.md and does its best to improve both.
In-depth explanation
Run
bx-readme --help

for the latest information on the CLI.
Additionally, it is interesting to know that the bx-readme command internally relies on two objects:

the ManifestImprover and
the ReadmeImprover.

Those objects are responsible for exactly what their names suggest.
The bx-readme command is nothing more than a single script that initializes those objects, configures them, runs them and saves the results.
However, much more is possible beyond the "one size fits all approach" of that particular script.
To get an idea of all the possibilities, the best reference is to study their respective integration tests.

ManifestImprover integration test
ReadmeImprover integration test
ManifestImprover-ReadmeImprover end-to-end integration test

CommandBuilder
Apart from bx-readme, this toolbox also comes in handy when incorporating a bioinformatics tool into batchx.io.
In this section, we highlight some of its functionalities in that regard.
Initialize the CommandBuilder
The CommandBuilder relies on a few additional datastructures, namely Manifest and Filesystem objects.
from batchx_dev.toolbox import FileSystem, Manifest, CommandBuilder

fs = FileSystem(tool="biotool") # data-structure (approximately a dict) capturing the internal directory structure of a tool's docker image.
mf = Manifest(fs.mfp) # load manifest.json

cb = CommandBuilder(mf, tool="biotool", filesystem=fs) # Initialize the CommandBuilder

Connecting CLI parameters to BatchX parameters
Suppose the biotool CLI has an optional parameter --maximum-length
biotool --input genome.fasta --maximum-length 1000

which appears in the manifest as follows,
"maximumLength": {
"type": "integer",
"required": false,
"default": 250,
"description": "Maximum length (in bp) for which biotool makes sense."
},

Obviously, the biotool parameter (--maximum-length) has a different name than the BatchX parameter (maximumLength).
But, in order to pass the user-provided value for this parameter from BatchX (CLI or web interface) into the underlying biotool CLI, this obvious and trivial knowledge still needs to be represented explicitly somewhere in the wrapper script run_biotool.py.
This leads to code that looks like this,
maximum_length = parsed_json["maximumLength"]

...

command = "biotool "
if maximum_length is not None:
command += "--maximum_length {}".format(maximum_length)

which is not wrong, but this is a lot of code just to link maximum_length and maximumLength together.
In particular, note that this

parses a json;
looks up the user-provided value of the parameter of interest (maximumLength);
creates a new python variable (maximum_length) to house that value;
checks if that variable has an actual value (i.e. is not None);
if yes:

creates a substring --maximum_length;
injects the value of your python variable maximum_length into that string;
and finally, extends the command string with this substring.



Whereas the only thing you really had to do was to explain to your computer that maximumLength is a synonym for --maximum_length in this context.
Here, however, linking maximumLength and --maximum_length gets intertwined with value-passing and actual command-generation.
Mixing all of this is suboptimal, because value-passing and actual command-generation of the biotool CLI command is something that needs to happen anyway, for any parameter:
that part is perfectly suitable for automation.
The linking together is the only thing that requires actual human intervention here.
The toolbox allows you to do just that:
cb.add_command(cmd="maximum_length", key="maximumLength")

The commandbuilder now knows that maximumLength is a synonym for maximum_length, and upon command generation will do what it needs to do.
Example: Tool parameters to BatchX parameters
To illustrate, given this input.json,
"fasta": "some_genome.fasta",
"maximumLength": 200

this run_biotool.py implementation
from batchx_dev.toolbox import FileSystem, Manifest, CommandBuilder

fs = FileSystem(tool="biotool") # data-structure (approximately a dict) capturing the internal directory structure of a tool's docker image.
mf = Manifest(fs.mfp) # load manifest.json (mfp = manifest filepath)
ip = Input(fs.ifp) # load input.json (ifp = input filepath)

cb = CommandBuilder(mf, tool="biotool", filesystem=fs) # Initialize the CommandBuilder

cb.add_command(cmd="input", key="fasta")
cb.add_command(cmd="maximum_length", key="maximumLength")

cb.build_command(ip)

yields the following command:
biotool --input some_genome.fasta --maximum-length 200

Understanding parameter types
Suppose the biotool CLI has an optional flag --no-qc, which allows users to run biotool without its built-in quality control mechanism,
forcing it to produce more but potentially less relevant outputs.
Since it is a flag, its mere presence indicates that this option is active.
That is, this command
biotool --input genome.fasta --maximum-length 1000 --no-qc

runs biotool with quality control disabled, whereas this command
biotool --input genome.fasta --maximum-length 1000

runs biotool with quality control enabled.
Note the difference with "regular" command line arguments (i.e. "key-value style"), such as --input and --maximum-length that have to be followed by an actual value;
in flags, the value is implicit.
In a BatchX manifest, this flag can be described as follows,
"noQC": {
"type": "bool",
"required": false,
"default": false,
"description": "Flag that indicates whether or not biotool's internal quality control mechanism should be bypassed."
},

Apart from linking the biotool parameter (--no-qc) and the BatchX parameter (noQC), developers now must also encode particular logic to inject this parameter's value into the final command, i.e.:

if noQC==True, the flag must be present,
if noQC==False nothing needs to be added.

Obviously, this differs from how a command is built for regular (key-value style) arguments, which leads to code that looks like this,
maximum_length = parsed_json["maximumLength"]
no_qc = parsed_json["noQC"]

...

command = "biotool "
if maximum_length is not None:
command += "--maximum_length {}".format(maximum_length)
if no_qc:
command += "--no_qc"

again, nothing inherently wrong about this, but this intertwines proper understanding of the manifest and the parsing of the actual input.
Another disadvantage is the fact that readers of this code need to pay very close attention to figure out which parameters are flags and which are key-value.
The toolbox, on the other hand, simply allows you to explicitly state that a particular parameter is a flag,
cb.add_command(cmd="maximum_length", key="maximumLength")
cb.add_command(cmd="no-qc", key="noQC").set_kind(kind="flag")

The commandbuilder now knows that noQC is a flag (and so do readers of this code!), and takes this into account when generating commands.
Example: Understanding parameter types
To illustrate, given this input.json,
"fasta": "some_genome.fasta",
"maximumLength": 200,
"noQC": true

this run_biotool.py implementation
from batchx_dev.toolbox import FileSystem, Manifest, CommandBuilder

fs = FileSystem(tool="biotool") # data-structure (approximately a dict) capturing the internal directory structure of a tool's docker image.
ip = Input(fs.ifp) # load input.json
mf = Manifest(fs.mfp) # load manifest.json

cb = CommandBuilder(mf, tool="biotool", filesystem=fs) # Initialize the CommandBuilder

cb.add_command(cmd="input", key="fasta")
cb.add_command(cmd="maximum_length", key="maximumLength")
cb.add_command(cmd="no-qc", key="noQC").set_kind(kind="flag")

cb.build_command(ip)

yields the following command:
biotool --input some_genome.fasta --maximum-length 200 --no-qc

whereas given this input.json,
"fasta": "some_genome.fasta",
"maximumLength": 200,
"noQC": false

it generates this command instead:
biotool --input some_genome.fasta --maximum-length 200

Add actions
Example: Attaching an action to a parameter
To illustrate, given this input.json,
"fasta": "some_genome.fasta.gz",
"maximumLength": 2000,
"noQC": true

this run_biotool.py implementation
from batchx_dev.toolbox import FileSystem, Manifest, CommandBuilder

# define a useful function
def conditional_unzip_gzip(input, manifest, fp):
"""
For constraints & actions:
- First argument is always the Input object.
- Second argument is always the Manifest object.
- Third argument is always the value of the command with which the constraint is associated.
"""

def is_gz_file(fp: str | Path):
with open(fp, "rb") as f:
return f.read(2) == b"\x1f\x8b"

def unzip_gzip(gzip_fp, unzip_fp=None):
"""Unzips a gzipped file with pgiz."""
gzip_fp = Path(gzip_fp)
assert gzip_fp.suffix == ".gz", "Not the expected `.gz` extension!"

if unzip_fp is None:
unzip_fp = gzip_fp.parent / gzip_fp.stem # removes .gz suffix

print("Extracting target file {} using pigz".format(gzip_fp), flush=True)
with open(unzip_fp, "w") as f:
pigz = subprocess.call(["pigz", "-dc", str(gzip_fp)], stdout=f)
if pigz != 0:
sys.exit(pigz)
return unzip_fp

fp = Path(fp)
if fp.suffix == ".gz":
if is_gz_file(fp):
return unzip_gzip(fp)
else:
msg = """
Assumed that file {f} is gzipped.

However, upon closer inspection (via `is_gz_file`),
it turns out this is not the case.

Please resolve this issue.
""".format(
f=str(fp)
)
raise ValueError(msg)
else:
return fp

fs = FileSystem(tool="biotool") # data-structure (approximately a dict) capturing the internal directory structure of a tool's docker image.
ip = Input(fs.ifp) # load input.json
mf = Manifest(fs.mfp) # load manifest.json

cb = CommandBuilder(mf, tool="biotool", filesystem=fs) # Initialize the CommandBuilder

cb.add_command(cmd="input", key="fasta").add_action(conditional_unzip_gzip)
cb.add_command(cmd="maximum_length", key="maximumLength")
cb.add_command(cmd="no-qc", key="noQC").set_kind(kind="flag")

cb.build_command(ip)

yields the following command:
biotool --input some_genome.fasta --maximum-length 2000 --no-qc

where some_genome.fasta exists (although the input was some_genome.fasta.gz!), because the action you attached to that parameter took care of that via running the function conditional_gzip_unzip.
Specify constraints
Example: Specify constraints
To illustrate, given this input.json,
"fasta": "some_genome.fasta",
"maximumLength": 2000,
"noQC": true
"callLargeVariants": true

this run_biotool.py implementation
from batchx_dev.toolbox import FileSystem, Manifest, CommandBuilder

fs = FileSystem(tool="biotool") # data-structure (approximately a dict) capturing the internal directory structure of a tool's docker image.
ip = Input(fs.ifp) # load input.json
mf = Manifest(fs.mfp) # load manifest.json

cb = CommandBuilder(mf, tool="biotool", filesystem=fs) # Initialize the CommandBuilder

cb.add_command(cmd="input", key="fasta")
cb.add_command(cmd="maximum_length", key="maximumLength")
cb.add_command(cmd="no-qc", key="noQC").set_kind(kind="flag")

def large_variants_constraint(input, manifest, value, k: str="maximumLength", threshold: int=200):
"""
For constraints & actions:
- First argument is always the Input object.
- Second argument is always the Manifest object.
- Third argument is always the value of the command with which the constraint is associated.

Other arguments have to be keyword arguments, and can be passed whilst adding the
constraint to the CommandBuilder
"""
return ip.get(k) > threshold

cb.add_command(cmd="large-variants", key="callLargeVariants")
.set_kind(kind="flag")
.add_constraint(large_variants_constraint, threshold=1000)

cb.build_command(ip)

yields the following command:
biotool --input some_genome.fasta --maximum-length 2000 --no-qc --large-variants

Whereas the same implementation, given this input,
"fasta": "some_genome.fasta",
"maximumLength": 200,
"noQC": true
"callLargeVariants": true

would not produce any command at all and throw an error, due to the fact that "maximumLength": 200 is incompatible with the callLargeVariants flag.
Indeed, the callLargeVariants flag only makes sense if maximumLength > 1000.
Feature requests
Please add it to this project board.