fjcommon

Some python helper libraries.

no_op.py

Class that silently ignores calls to any function and acts as a no-op context manager. Useful for quickly disabling some part of your code without re-writing/commenting out many parts.

Usage

from fjcommon.no_op import NoOp

foo = NoOp

foo.do_this().do_that()  # no effect

a = foo.get_me_this(18)  # returns NoOp, arguments ignored
a.more(spam=True)  # no effect, keyboard arguments ignored

with foo.enter_context() as t:  # t is NoOp
    t.run()  # no effect                                

configparser.py

Simple and Re-usable Configuration File Framework for tracking unique configuration files.

Example

File base.cf:

constrain network_type :: LINEAR, DNN
network_type = LINEAR

lr = 1e-5
batch_size_train = 25
batch_size_val = 0.5 * batch_size_train
conv_params = {'f': 5,
               'pad': 'zeros'}

File lr_sweep/lr_1e-6.cf:

use ../base
lr = 1e-6

File lr_sweep/lr_1e-4.cf:

use ../base
lr = 1e-4

Syntax

Uses configuration files with the following syntax:

use statment

The first line may contain a use statement, specifying a path to another config file, relative to the containing file. The specified file is parsed first. If a parameter is redefined in some file, the last definition is used.

use <RELATIVE_PATH>

The following lines may contain

constrain statement

constrain <PARAM_NAME> :: <CONSTRAIN_VAL_1>, <CONSTRAIN_VAL_2>, ...

parameter statement

<PARAM_NAME> = <PARAM_VALUE>

where <PARAM_VALUE> is a python expression that can reference any previously defined parameters (see note below about this). Can also be a multi-line statement by enclosing it in round brackets

value = (123+
         456)

or a multi-line dictionary or list definition.

To use environment variables, use $ENV_NAME$ (with two dollar signs):

value = "$DATA_DIR$/main"

Comments

Lines starting with # are ignored:

# <COMMENT>

Note on using previously defined variables

These variables should not be treated as placeholders. Example:

File base.cf:

batch_size_train = 25
batch_size_val = 0.5 * batch_size_train

File bigger_batches.cf:

use base.cf
batch_size_train = 50

In this case, when using bigger_batches.cf, batch_size_val = 0.5 * 25 still, because it simply uses the value defined in base.cf.