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Run parametric studies and scoop output files.

Project description

soops = scoop output of parametric studies

Utilities to run parametric studies in parallel using dask, and to scoop the output files produced by the studies into a pandas dataframe.


The latest release:

pip install soops

The source code of the development version in git:

git clone
cd soops
pip install .

or the development version via pip:

pip install git+


Install pytest:

pip install pytest

Install soops from sources (in the current directory):

pip install .

Run the tests:

pytest .


Before we begin - TL;DR:

  • Run a script in parallel with many combinations of parameters.
  • Scoop all the results in many output directories into a big DataFrame.
  • Work with the DataFrame.

A Script

Suppose we have a script that takes a number of command line arguments. The actual arguments are not so important, neither what the script does. Nevertheless, to have something to work with, let us simulate the Monty Hall problem in Python.

For the first reading of the example below, it is advisable not to delve in details of the script outputs and code listings and just read the text to get an overall idea. After understanding the idea, return to the details, or just have a look at the complete example script.

This is our script and its arguments:

$ python ./examples/ -h
usage: [-h] [--switch] [--host {random,first}] [--num int]
                     [--repeat int] [--seed int] [--plot-opts dict-like] [-n]

The Monty Hall problem simulator parameterizable with soops.


positional arguments:
  output_dir            output directory

optional arguments:
  -h, --help            show this help message and exit
  --switch              if given, the contestant always switches the door,
                        otherwise never switches
  --host {random,first}
                        the host strategy for opening doors
  --num int             the number of rounds in a single simulation [default:
  --repeat int          the number of simulations [default: 5]
  --seed int            if given, the random seed is fixed to the given value
  --plot-opts dict-like
                        matplotlib plot() options [default:
  -n, --no-show         do not call matplotlib show()
  --silent              do not print messages to screen

Basic Run

A run with the default parameters:

$ python examples/ output
monty_hall: num: 100
monty_hall: repeat: 5
monty_hall: switch: False
monty_hall: host strategy: random
monty_hall: elapsed: 0.004662119084969163
monty_hall: win rate: 0.25
monty_hall: elapsed: 0.0042096920078620315
monty_hall: win rate: 0.3
monty_hall: elapsed: 0.003894180990755558
monty_hall: win rate: 0.31
monty_hall: elapsed: 0.003928505931980908
monty_hall: win rate: 0.35
monty_hall: elapsed: 0.0035342529881745577
monty_hall: win rate: 0.31

produces some results:



Now we would like to run it for various combinations of arguments and their values, for example:

  • –num=[100,1000,10000]
  • –repeat=[10,20]
  • –switch either given or not
  • –seed either given or not, changing together with –seed
  • –host=[‘random’, ‘first’]

and then collect and analyze the all results. Doing this manually is quite tedious, but soops can help.

In order to run a parametric study, first we have to define a function describing the arguments of our script:

def get_run_info():
    # script_dir is added by soops-run, it is the normalized path to
    # this script.
    run_cmd = """
    {python} {script_dir}/ {output_dir}
    run_cmd = ' '.join(run_cmd.split())

    # Arguments allowed to be missing in soops-run calls.
    opt_args = {
        '--num' : '--num={--num}',
        '--repeat' : '--repeat={--repeat}',
        '--switch' : '--switch',
        '--host' : '--host={--host}',
        '--seed' : '--seed={--seed}',
        '--plot-opts' : '--plot-opts={--plot-opts}',
        '--no-show' : '--no-show',
        '--silent' : '--silent',

    output_dir_key = 'output_dir'
    is_finished_basename = 'wins.png'

    return run_cmd, opt_args, output_dir_key, is_finished_basename

The get_run_info() functions should provide four items:

  1. A command to run given as a string, with the non-optional arguments and their values (if any) given as str.format() keys.
  2. A dictionary of optional arguments and their values (if any) given as str.format() keys.
  3. A special format key, that denotes the output directory argument of the command. Note that the script must have an argument allowing an output directory specification.
  4. A function is_finished(pars, options), where pars is the dictionary of the actual values of the script arguments and options are soops-run options, see below. The dictionary contains the output directory argument of the script and the function should return True, whenever the results are already present in the given output directory. Instead of a function, a file name can be given, as in get_run_info() above. Then the existence of a file with the specified name means that the results are present in the output directory.

Run Parametric Study

Putting get_run_info() into our script allows running a parametric study using soops-run:

$ soops-run -h
usage: soops-run [-h] [--dry-run] [-r {0,1,2}]
                 [--generate-pars dict-like: class=class_name,par0=val0,...]
                 [-c key1+key2+..., ...]
                 [--compute-pars dict-like: class=class_name,par0=val0,...]
                 [-n int]
                 [--run-function {,psutil.Popen,os.system}]
                 [-t float] [--silent] [--shell] [-o path]
                 conf run_mod

Run parametric studies.

positional arguments:
  conf                  a dict-like parametric study configuration
  run_mod               the importable script/module with get_run_info()

optional arguments:
  -h, --help            show this help message and exit
  --dry-run             perform a trial run with no commands executed
  -r {0,1,2}, --recompute {0,1,2}
                        recomputation strategy: 0: do not recompute, 1:
                        recompute only if is_finished() returns False, 2:
                        always recompute [default: 1]
  --generate-pars dict-like: class=class_name,par0=val0,...
                        if given, generate values of parameters using the
                        specified function; the generated parameters must be
                        set to @generate in the parametric study
  -c key1+key2+..., ..., --contract key1+key2+..., ...
                        list of option keys that should be contracted to vary
                        in lockstep
  --compute-pars dict-like: class=class_name,par0=val0,...
                        if given, compute additional parameters using the
                        specified class
  -n int, --n-workers int
                        the number of dask workers [default: 2]
  --run-function {,psutil.Popen,os.system}
                        function for running the parameterized command
  -t float, --timeout float
                        if given, the timeout in seconds; requires setting
  --silent              do not print messages to screen
  --shell               run ipython shell after all computations
  -o path, --output-dir path
                        output directory [default: output]

In our case (the arguments with no value (flags) can be specified either as '@defined' or '@undefined'):

soops-run -r 1 -n 3 -c='--switch + --seed' -o output "python='python3', output_dir='output/study/%s', --num=[100,1000,10000], --repeat=[10,20], --switch=['@undefined', '@defined', '@undefined', '@defined'], --seed=['@undefined', '@undefined', 12345, 12345], --host=['random', 'first'], --silent=@defined, --no-show=@defined" examples/

This command runs our script using three dask workers (-n 3 option) and produces a directory for each parameter set:

$ ls output/study/
000-7a6b546a625c2d37569346a286f2b2b6/  024-6f9810a492faf793b80de2ec32dec4b1/
001-1daf48cede910a9c7c700fb78ce3aa2d/  025-a4d05c2889189c4e086f9d6f56e1ba1d/
002-57c1271f4b9cbe00742e3c97e0c14e24/  026-67a251e1c40f65bae8bbf621c4e1a987/
003-2f828633fa9eefa8eb8b40873882247d/  027-9e3d30603d2b382256f62fdf17bc23ae/
004-24f370388496173d8e1d7a9e574262e0/  028-6ff18af0333367a65ed131d210078653/
005-7893091a6fedc4ccdf7d73d803a91687/  029-54d77d99e74402a043af583ac1e14c4e/
006-70132dc423f26c78f1d2e33f0607820c/  030-4bad1e59de5b446e80a621fdfb5fb127/
007-7e5ecb11154e4c402caa51878e283e63/  031-d65b7afd4d43b3159b580cf6c974a26c/
008-201e1ab3e47d3b994f2d6532859ac301/  032-cd83aafc620d81b994f005c6a7b1d2c4/
009-35105e72d8ec2ddfd8adc8ffa8c1f088/  033-e065bfc2596f3b285877e36578d77cce/
010-ff68ea026e0efba0e4c2a71d64e12f2c/  034-0533ff015142c967f86b365076fcee18/
011-217e45abc1d2b188b0755fc6a550dfe9/  035-f127408b640dae1de6acc9bce1b68669/
012-d6adcade17e2d7d843cbd8e14aebf76a/  036-56654b678decdd2d77ecc07ead326ad7/
013-cdff71cb542f8159ff5c5a023c91f61c/  037-d3d16497570cb3f934e73c3f0c519822/
014-551f32ba477c7e8e8fad0769ac793d3c/  038-5b3b21be9e6dbbd5c7d8e031bd621717/
015-856ad0b4ee0273da8cd8ad3cf222077b/  039-d11e877087ec25fe2c8062708687204c/
016-7eb991928b39b40c98e7cb7970d0f15b/  040-5cf056a63f2e10ee78d599e097eb4d0e/
017-9a3f4b32f5ba30ec173dd651c9810c6e/  041-ca696dc0edbe70890f2dcbcfcf99fe47/
018-9067a6dbbb4afaf285f5c9101fa5fa73/  042-9962ccd67846d21245580de2c5e83bcc/
019-03a0123bd55725fdabec32e0aeff9d44/  043-18503a94bf6398644e2a32d3a93e9450/
020-266ed9d092128d8e3c3c2f78669a0425/  044-6c46f7a9e9cd0b50d914d6e2a188a64d/
021-00a156df6ccecab8d35c5bdc5ddb6c0e/  045-0af51ef33a80a99ac38bfbac10fea9b2/
022-91f0d18a4d9cd2e6721d937c9de4dbe9/  046-746823fee6450a294869dc9ca7396e15/
023-e3edef5a83fe941c75df4257ac056ca5/  047-f9046e62d8da3159dfcdebcf687092f3/

The directory names consist of an integer allowing an easy location and a MD5 hash of the run parameters. In each directory, there are four files:

$ ls output/study/000-7a6b546a625c2d37569346a286f2b2b6/
options.txt  output_log.txt  soops-parameters.csv  wins.png

three just like in the basic run above, and soops-parameters.csv, where the run parameters (mostly command line arguments) are stored by soops-run. For convenience, parameters of all runs are collected in all_parameters.csv in the soops-run output directory (output by default), using the data in all soops-parameters.csv files found.

Our example script also stores the values of command line arguments in options.txt for possible re-runs and inspection:

$ cat output/study/000-7a6b546a625c2d37569346a286f2b2b6/options.txt

command line

"examples/" "output/study/000-7a6b546a625c2d37569346a286f2b2b6" "--num=100" "--repeat=10" "--host=random" "--no-show" "--silent"


host: random
num: 100
output_dir: output/study/000-7a6b546a625c2d37569346a286f2b2b6
plot_opts: {'linewidth': 3, 'alpha': 0.5}
repeat: 10
seed: None
show: False
silent: True
switch: False

Show Parameters Used in Each Output Directory

Use soops-info to explain which parameters were used in the given output directories:

$ soops-info -h
usage: soops-info [-h] [-e dirname [dirname ...]] [--shell] run_mod

Get parametric study configuration information.

positional arguments:
  run_mod               the importable script/module with get_run_info()

optional arguments:
  -h, --help            show this help message and exit
  -e dirname [dirname ...], --explain dirname [dirname ...]
                        explain parameters used in the given output
  --shell               run ipython shell after all computations
$ soops-info examples/ -e output/study/000-7a6b546a625c2d37569346a286f2b2b6/
info: output/study/000-7a6b546a625c2d37569346a286f2b2b6/
info:      finished: True
info: *      --host: random
info: *   --no-show: @defined
info: *       --num: 100
info: * --plot-opts: @undefined
info: *    --repeat: 10
info: *      --seed: @undefined
info: *    --silent: @defined
info: *    --switch: @undefined
info: *      python: python3
info:    output_dir: output/study/000-7a6b546a625c2d37569346a286f2b2b6
info:    script_dir: examples

A * denotes a parameter used in the parameterization of the example script, other parameters are employed by soops-run.

Scoop Outputs of the Parametric Study

In order to use soops-scoop to scoop/collect outputs of our parametric study, a new function needs to be defined:

import soops.scoop_outputs as sc

def get_scoop_info():
    info = [
        ('options.txt', partial(
        ), True),
        ('output_log.txt', scrape_output),

    return info

The function for loading the 'options.txt' files is already in soops. The third item in the tuple, if present and True, denotes that the output contains input parameters that were used for the parameterization. This allows getting the parameterization in post-processing plugins, see below the plot_win_rates() function.

The function to get useful information from 'output_log.txt' needs to be provided:

def scrape_output(filename, rdata=None):
    out = {}
    with open(filename, 'r') as fd:
        repeat = rdata['repeat']
        for ii in range(4):

        elapsed = []
        win_rate = []
        for ii in range(repeat):
            line = next(fd).split()
            line = next(fd).split()

        out['elapsed'] = np.array(elapsed)
        out['win_rate'] = np.array(win_rate)

    return out

Then we are ready to run soops-scoop:

$ soops-scoop -h
usage: soops-scoop [-h] [-s column[,column,...]]
                   [--filter filename[,filename,...]] [--no-plugins]
                   [--use-plugins name[,name,...] | --omit-plugins
                   name[,name,...]] [-p module] [--plugin-args dict-like]
                   [--results filename] [--no-csv] [-r] [--write] [--shell]
                   [--debug] [-o path]
                   scoop_mod directories [directories ...]

Scoop output files.

positional arguments:
  scoop_mod             the importable script/module with get_scoop_info()
  directories           results directories

optional arguments:
  -h, --help            show this help message and exit
  -s column[,column,...], --sort column[,column,...]
                        column keys for sorting of DataFrame rows
  --filter filename[,filename,...]
                        use only DataFrame rows with given files successfully
  --no-plugins          do not call post-processing plugins
  --use-plugins name[,name,...]
                        use only the named plugins (no effect with --no-
  --omit-plugins name[,name,...]
                        omit the named plugins (no effect with --no-plugins)
  -p module, --plugin-mod module
                        if given, the module that has get_plugin_info()
                        instead of scoop_mod
  --plugin-args dict-like
                        optional arguments passed to plugins given as
                        plugin_name={key1=val1, key2=val2, ...}, ...
  --results filename    results file name [default: <output_dir>/results.h5]
  --no-csv              do not save results as CSV (use only HDF5)
  -r, --reuse           reuse previously scooped results file
  --write               write results files even when results were loaded
                        using --reuse option
  --shell               run ipython shell after all computations
  --debug               automatically start debugger when an exception is
  -o path, --output-dir path
                        output directory [default: .]

as follows:

$ soops-scoop examples/ output/study/ -s rdir -o output/study --no-plugins --shell


Python 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.13.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: df.keys()
Index(['rdir', 'rfiles', 'host', 'num', 'output_dir', 'plot_opts', 'repeat',
       'seed', 'show', 'silent', 'switch', 'elapsed', 'win_rate', 'time'],

In [2]: df.win_rate.head()
0    [0.32, 0.4, 0.38, 0.27, 0.31, 0.39, 0.25, 0.33...
1    [0.64, 0.67, 0.68, 0.67, 0.73, 0.62, 0.66, 0.7...
2    [0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.3...
3    [0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.6...
4    [0.28, 0.28, 0.35, 0.32, 0.29, 0.33, 0.29, 0.3...
Name: win_rate, dtype: object

In [3]: df.iloc[0]
rdir          ~/projects/soops/output/study/000-7a6b546a625c...
rfiles                            [options.txt, output_log.txt]
host                                                     random
num                                                         100
output_dir    output/study/000-7a6b546a625c2d37569346a286f2b2b6
plot_opts                        {'linewidth': 3, 'alpha': 0.5}
repeat                                                       10
seed                                                        NaN
show                                                      False
silent                                                     True
switch                                                    False
elapsed       [0.0031552709988318384, 0.0032349379907827824,...
win_rate      [0.32, 0.4, 0.38, 0.27, 0.31, 0.39, 0.25, 0.33...
time                                 2021-02-07 14:34:30.202971
Name: 0, dtype: object

The DataFrame with the all results is saved in output/study/results.h5 for reuse.

Post-processing Plugins

It is also possible to define simple plugins that act on the resulting DataFrame. First, define a function that will register the plugins:

def get_plugin_info():
    from soops.plugins import show_figures

    info = [plot_win_rates, show_figures]

    return info

The show_figures() plugin is defined in soops. The plot_win_rates() plugin allows plotting the all results combined:

def plot_win_rates(df, data=None, colormap_name='viridis'):
    import soops.plot_selected as sps

    df = df.copy()
    df['seed'] = df['seed'].where(df['seed'].notnull(), -1)

    uniques = sc.get_uniques(df, [key for key in data.multi_par_keys
                                  if key not in ['output_dir']])
    for key, val in uniques.items():
        output(key, val)

    selected = sps.normalize_selected(uniques)

    styles = {key : {} for key in selected.keys()}
    styles['seed'] = {'alpha' : [0.9, 0.1]}
    styles['num'] = {'color' : colormap_name}
    styles['repeat'] = {'lw' : np.linspace(3, 2,
                                           len(selected.get('repeat', [1])))}
    styles['host'] = {'ls' : ['-', ':']}
    styles['switch'] = {'marker' : ['x', 'o'], 'mfc' : 'None', 'ms' : 10}

    styles = sps.setup_plot_styles(selected, styles)

    fig, ax = plt.subplots(figsize=(8, 8))
    sps.plot_selected(ax, df, 'win_rate', selected, {}, styles)
    ax.set_xlabel('simulation number')
    ax.set_ylabel('win rate')
    fig.savefig(os.path.join(data.output_dir, 'win_rates.png'))

    return data

Then, running:

soops-scoop examples/ output/study/ -s rdir -o output/study -r

reuses the output/study/results.h5 file and plots the combined results:


It is possible to pass arguments to plugins using --plugin-args option, as follows:

soops-scoop examples/ output/study/ -s rdir -o output/study -r --plugin-args=plot_win_rates={colormap_name='plasma'}


  • The get_run_info(), get_scoop_info() and get_plugin_info() info function can be in different modules.
  • The script that is being parameterized need not be a Python module - any executable which can be run from a command line can be used.

Special Argument Values

  • '@defined' denotes that a value-less argument is present.
  • '@undefined' denotes that a value-less argument is not present.
  • '@arange([start,] stop[, step,], dtype=None)' denotes values obtained by calling numpy.arange() with the given arguments.
  • '@linspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)' denotes values obtained by calling numpy.linspace() with the given arguments.
  • '@generate' denotes an argument whose values are generated, in connection with --generate-pars option, see below.

Generated Arguments

Argument sequences can be generated using a function with the help of --generate-pars option. For example, the same results as above can be achieved by defining a function that generates --switch and --seed arguments values:

def generate_seed_switch(args, gkeys, dconf, options):
    args : Struct
        The arguments passed from the command line.
    gkeys : list
        The list of option keys to generate.
    dconf : dict
        The parsed parameters of the parametric study.
    options : Namespace
        The soops-run command line options.
    seeds, switches = zip(*product(args.seeds, args.switches))
    gconf = {'--seed' : list(seeds), '--switch' : list(switches)}
    return gconf

and then calling soops-run as follows:

soops-run -r 1 -n 3 -c='--switch + --seed' -o output/study2 "python='python3', output_dir='output/study2/%s', --num=[100,1000,10000], --repeat=[10,20], --switch=@generate, --seed=@generate, --host=['random', 'first'], --silent=@defined, --no-show=@defined" --generate-pars="function=generate_seed_switch, seeds=['@undefined', 12345], switches=['@undefined', '@defined']" examples/

Notice the special @generate values of --switch and --seed, and the use of --generate-pars: all key-value pairs, except the function name, are passed into :func:generate_seed_switch() in the args dict-like argument.

The combined results can again be plotted using:

soops-scoop examples/ output/study2/0* -s rdir -o output/study2/

Computed Arguments

By using --compute-pars option it is possible to define arguments depending on other arguments values in a more general way than with --contract. A callable class needs to be provided with the following structure:

class ComputePars:

    def __init__(self, args, par_seqs, key_order, options):
        Called prior to the parametric study to pre-compute reusable data.

    def __call__(self, all_pars):
        Called for each parameter set of the study.
        out = {}
        return out

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