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scitrack

Project description

CI Status codecov Using Black Formatting Python 3.6+

About scitrack

One of the critical challenges in scientific analysis is to track all the elements involved. This includes the arguments provided to a specific application (including default values), input data files referenced by those arguments and output data generated by the application. In addition to this, tracking a minimal set of system specific information.

scitrack is a simple package aimed at researchers writing scripts, or more substantial scientific software, to support the tracking of scientific computation. The package provides elementary functionality to support logging. The primary capabilities concern generating checksums on input and output files and facilitating logging of the computational environment.

To see some projects using scitrack, see the “Used by” link at the top of the project GitHub page.

Installing

For the released version:

$ pip install scitrack

For the very latest version:

$ pip install git+https://github.com/HuttleyLab/scitrack

Or clone it:

$ git clone git@github.com:HuttleyLab/scitrack.git

And then install:

$ pip install ~/path/to/scitrack

CachingLogger

There is a single object provided by scitrack, CachingLogger. This object is basically a wrapper around the Python standard library logging module. On invocation, CachingLogger captures basic information regarding the system and the command line call that was made to invoke the application.

In addition, the class provides convenience methods for logging both the path and the md5 hexdigest checksum [1] of input/output files. A method is also provided for producing checksums of text data. The latter is useful for the case when data are from a stream or a database, for instance.

All logging calls are cached until a path for a logfile is provided. The logger can also, optionally, create directories.

Simple instantiation of the logger

Creating the logger. Setting create_dir=True means on creation of the logfile, the directory path will be created also.

from scitrack import CachingLogger
LOGGER = CachingLogger(create_dir=True)
LOGGER.log_file_path = "somedir/some_path.log"

The last assignment triggers creation of somedir/some_path.log.

Capturing a programs arguments and options

scitrack will write the contents of sys.argv to the log file, prefixed by command_string. However, this only captures arguments specified on the command line. Tracking the value of optional arguments not specified, which may have default values, is critical to tracking the full command set. Doing this is now easy with the simple statement LOGGER.log_args(). The logger can also record the versions of named dependencies.

Here’s one approach to incorporating scitrack into a command line application built using the click command line interface library. Below we create a simple click app and capture the required and optional argument values.

import click

from scitrack import CachingLogger

LOGGER = CachingLogger()


@click.command()
@click.option("-i", "--infile", type=click.Path(exists=True))
@click.option("-t", "--test", is_flag=True, help="Run test.")
def main(infile, test):
    # capture the local variables, at this point just provided arguments
    LOGGER.log_args()
    LOGGER.log_versions("numpy")
    LOGGER.input_file(infile)
    LOGGER.log_file_path = "some_path.log"


if __name__ == "__main__":
    main()

The CachingLogger.write() method takes a message and a label. All other logging methods wrap log_message(), providing a specific label. For instance, the method input_file() writes out two lines in the log.

  • input_file_path, the absolute path to the intput file
  • input_file_path md5sum, the hex digest of the file

output_file() behaves analogously. An additional method text_data() is useful for other data input/output sources (e.g. records from a database). For this to have value for arbitrary data types requires a systematic approach to ensuring the text conversion is robust across platforms.

The log_args() method captures all local variables within a scope.

The log_versions() method captures versions for the current file and that of a list of named packages, e.g. LOGGER.log_versions(['numpy', 'sklearn']).

Some sample output

2020-05-25 13:32:07 Eratosthenes:98447      INFO    system_details : system=Darwin Kernel Version 19.4.0: Wed Mar  4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
2020-05-25 13:32:07 Eratosthenes:98447      INFO    python : 3.8.2
2020-05-25 13:32:07 Eratosthenes:98447      INFO    user : gavin
2020-05-25 13:32:07 Eratosthenes:98447      INFO    command_string : ./demo.py -i /Users/gavin/repos/SciTrack/tests/sample-lf.fasta
2020-05-25 13:32:07 Eratosthenes:98447      INFO    params : {'infile': '/Users/gavin/repos/SciTrack/tests/sample-lf.fasta', 'test': False}
2020-05-25 13:32:07 Eratosthenes:98447      INFO    version : __main__==None
2020-05-25 13:32:07 Eratosthenes:98447      INFO    version : numpy==1.18.4
2020-05-25 13:32:07 Eratosthenes:98447      INFO    input_file_path : /Users/gavin/repos/SciTrack/tests/sample-lf.fasta
2020-05-25 13:32:07 Eratosthenes:98447      INFO    input_file_path md5sum : 96eb2c2632bae19eb65ea9224aaafdad

Other useful functions

Two other useful functions are get_file_hexdigest and get_text_hexdigest.

Reporting issues

Use the project issue tracker.

[1]The hexdigest serves as a unique signature of a files contents.

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