soops = scoop output of parametric studies
Utilities to run parametric studies in parallel, and to scoop the output files produced by the studies into a pandas dataframe.
Contents
- Installation
- Testing
- Example
- A Script
- Basic Run
- Parameterization
- Run Parametric Study
- Using Parametric Study Configuration Files
- Show Parameters Used in Each Output Directory
- Scoop Outputs of the Parametric Study
- Post-processing Plugins
- Notes
- Special Argument Values
- Generated Arguments
- Computed Arguments
- Find Runs with Given Parameters
- Additional Utilities
- See Also
The latest release:
pip install soops
Optionally, dask.distributed can be used to run the studies instead of the default concurrent.futures:
pip install soops[dask]
The source code of the development version in git:
git clone https://github.com/rc/soops.git cd soops pip install . # or pip install .[dask]
or the development version via pip:
pip install git+https://github.com/rc/soops.git
Install pytest:
pip install pytest
Install soops from sources (in the current directory):
pip install .
Run the tests (in any directory):
python -c "import soops; soops.test()"
Run tests in the source directory without installing soops:
export PYTHONPATH=. python -c "import soops; soops.test()" # or pytest soops/tests
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.
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 soops/examples/monty_hall.py -h
usage: monty_hall.py [-h] [--switch] [--host {random,first}] [--num INT]
[--repeat INT] [--seed INT] [--plot-opts STR] [-n]
[--silent]
output_dir
The Monty Hall problem simulator parameterized with soops.
https://en.wikipedia.org/wiki/Monty_Hall_problem
<snip>
positional arguments:
output_dir output directory
options:
-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 [default: random]
--num INT the number of rounds in a single simulation [default:
100]
--repeat INT the number of simulations [default: 5]
--seed INT if given, the random seed is fixed to the given value
[default: None]
--plot-opts STR matplotlib plot() options [default:
linewidth=3,alpha=0.5]
-n, --no-show do not call matplotlib show()
--silent do not print messages to screen
A run with the default parameters:
$ python soops/examples/monty_hall.py 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:
opts = so.Struct(
output_dir=(None, 'output directory'),
switch=(False, 'if given, the contestant always switches the door,'
' otherwise never switches'),
host=(('random', 'first'), 'the host strategy for opening doors'),
num=(100, 'the number of rounds in a single simulation'),
repeat=(5, 'the number of simulations'),
seed=([None, 42], 'if given, the random seed is fixed to the given value'),
plot_opts=('linewidth=3,alpha=0.5', 'matplotlib plot() options'),
show=(True, 'do not call matplotlib show()', ),
silent=(False, 'do not print messages to screen'),
)
def get_run_info():
# script_dir is added by soops-run, it is the normalized path to
# this script.
run_cmd = """
{python} {script_dir}/monty_hall.py {output_dir}
"""
run_cmd = ' '.join(run_cmd.split())
opt_args = so.build_opt_args(opts, omit=['--plot-opts'],
return_defaults=True)
output_dir_key = 'output_dir'
is_finished_basename = 'wins.png'
return run_cmd, opt_args, output_dir_key, is_finished_basenameThe get_run_info() functions should provide four items:
- A command to run given as a string, with the non-optional arguments and
their values (if any) given as
str.format()keys. - A dictionary of optional arguments constructed using so.build_opt_args() from opts. Note that opts data are used also to build automatically the command line options in parse_args(), see the example script.
- 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.
- 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.
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}] [-n int]
[--run-function {subprocess.run,psutil.Popen,os.system}]
[-t float]
[--generate-pars dict-like: function=function_name,par0=val0,... or str]
[-c key1+key2+..., ...]
[--compute-pars dict-like: class=class_name,par0=val0,...]
[-s str] [--silent] [--shell] [-o path]
conf run_mod
Run parametric studies.
positional arguments:
conf a dict-like parametric study configuration or a study
configuration file name
run_mod the importable script/module with get_run_info()
options:
-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]
-n int, --n-workers int
the number of dask workers [default: 2]
--run-function {subprocess.run,psutil.Popen,os.system}
function for running the parameterized command
[default: subprocess.run]
-t float, --timeout float
if given, the timeout in seconds; requires setting
--run-function=psutil.Popen
--generate-pars dict-like: function=function_name,par0=val0,... or str
if given, generate values of parameters using the
specified function; the generated parameters must be
set to @generate in the parametric study
configuration. Alternatively, a section key in a study
configuration file.
-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
-s str, --study str study key when parameter sets are given by a study
configuration file
--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" soops/examples/monty_hall.py
This command runs our script using three dask workers (-n 3 option) and
produces a directory for each parameter set:
$ ls output/study/ 000-a96da9ba9ee27be055166a3f64f641d2 024-04c03c243eea170aaf2bcb6ad27b3553 001-683dca8ed500db306362a9aed10876f6 025-59e26f89a5c4ff98b7e6bb3ab23369f8 002-ed389f6f0b8f8656891dd2785f1eca9d 026-2217691056bf0ab11e64e6125abefb3a 003-79cacb84c4fb358ba7431c60ee0bd6b9 027-a6d738c5a732b6c4cb130fa5a36cc525 004-b9eec14c504e6a2d2b23448e7e4073e5 028-b6e0b030fda9d3d2eff55b6e29b3438b 005-18248bf1c448246a741e8c0e9099934e 029-b4754c22669dbcad1e2a66f3bc94ced1 006-b4c8cd0dec50799eb77dbfff487f2608 030-2af86aa5329a5b69c9b5feaf989e9652 007-c3ce48559a177812a9934f76cf45bd72 031-30ee10ccca40840a52c574defe622c51 008-e15c5fdad6d3c6a4ef09e97d3d43ff40 032-03d1cf2e9d1a7b3c1692d52003669099 009-889ac21d981a4aaee5ecb338d3845bea 033-d92c598ca65e423b71b4c52b0c99ca45 010-8fcac713a518bfac737f72efd9c7a72f 034-b2bd1faf276b8072f97a6ed6ed8a4a3a 011-20daf27250fd56ecbccc3cd674229ad6 035-2f676e229524da9f68b07024d2db9463 012-7acbff8c9aebc0e51dc401eb1166fe41 036-7c97e794bfd442cf4062291755220fd8 013-90701b1a193e4bcb3302348a0a89f26d 037-d8e72d98da5130bbab551f1fc6e6ee18 014-c25408ecfd430a8550962fe2aab6be7e 038-d0759f20f4f5029d2a7dd872bda4d330 015-94ef45703ab2c96dd05add8a78b9d24f 039-372d0de1c0aee25861e60d96f13700ac 016-1a3a94fbad26651b1cce1f031b414841 040-6c390a413d099e4e41d00bbc79e36e23 017-7f8d639a85f5e61685d0c919e4053270 041-ebff32cd18b128a5e9eb790b2d61682e 018-2e38d5032b32c9e8c6c1f34c54ad5f07 042-fceb9e65b951a4b44000589cd7cd5fb1 019-bbf0acb5c529bbb75dad3b9e0814af1d 043-ff77579f63caa2569105f2c1d0edd157 020-e4df1cc5ad45307f39c6a03235b3857d 044-f16c4a6c32cb350a004a9618254d79a4 021-2f3ecfddc555d626a31bf812982bc877 045-cae7631f8cb0bbb257bf11d8d38b2611 022-9d90968a994089d8cacfd3a092b083f9 046-97299823f49756637785d68a432f81d3 023-8ffc3ecfdc3cd5cedc47d06ad1f61c46 047-82e6500591a10f967a7be1035407151a
The directory names consist of an integer allowing an easy location and a MD5 hash of the run parameters. In each directory, there are five files:
$ ls output/study/000-* options.txt output_log.txt run.py soops-parameters.csv wins.png
three just like in the basic run above, soops-parameters.csv, where the run parameters (mostly command line arguments) are stored by soops-run and run.py script that can be used to manually rerun the 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-a96da9ba9ee27be055166a3f64f641d2/options.txt
command line
------------
"soops/examples/monty_hall.py" "output/study/000-a96da9ba9ee27be055166a3f64f641d2" "--host=random" "--num=100" "--repeat=10" "--no-show" "--silent"
options
-------
host: random
num: 100
output_dir: output/study/000-a96da9ba9ee27be055166a3f64f641d2
plot_opts: {'linewidth': 3, 'alpha': 0.5}
repeat: 10
seed: None
show: False
silent: True
switch: False
Instead of providing the parameter sets on the command line, a study configuration file can be used. Then the same parametric study as above can be run using:
soops-run -r 1 -n 3 -c='--switch + --seed' --study=study -o output soops/examples/studies.cfg soops/examples/monty_hall.py
where soops/examples/studies.cfg contains:
[study] 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
Several studies can be stored in a single file, see soops/examples/studies.cfg. See also the docstring of soops/examples/monty_hall.py for more examples.
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
directory/directories
--shell run ipython shell after all computations
$ soops-info soops/examples/monty_hall.py -e output/study/000-* info: output/study/000-a96da9ba9ee27be055166a3f64f641d2/ info: finished: True info: iset: 0 info: * --host: random info: * --no-show: @defined info: * --num: 100 info: * --repeat: 10 info: * --seed: @undefined info: * --silent: @defined info: * --switch: @undefined info: * python: python3 info: output_dir: output/study/000-a96da9ba9ee27be055166a3f64f641d2 info: script_dir: soops/examples
A * denotes a parameter used in the parameterization of the example script, other parameters are employed by soops-run.
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(
sc.load_split_options,
split_keys=None,
), True),
('output_log.txt', scrape_output),
]
return infoThe 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):
next(fd)
elapsed = []
win_rate = []
for ii in range(repeat):
line = next(fd).split()
elapsed.append(float(line[-1]))
line = next(fd).split()
win_rate.append(float(line[-1]))
out['elapsed'] = np.array(elapsed)
out['win_rate'] = np.array(win_rate)
return outThen 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 | -u] [--write]
[--write-after-plugins] [--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. On "Argument list too long"
system error, enclose the directories matching pattern
in "", it will be expanded using glob.glob().
options:
-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
scooped
--no-plugins do not call post-processing plugins
--use-plugins name[,name,...]
use only the named plugins (no effect with --no-
plugins)
--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
-u, --update update previously scooped results file with results in
new directories. Results in previously existing
directories are reused (as with -r) without any
contents checking.
--write write results files even when results were loaded
using --reuse option
--write-after-plugins
write the pandas HDF5 results file again after plugins
were applied
--shell run ipython shell after all computations
--debug automatically start debugger when an exception is
raised
-o path, --output-dir path
output directory [default: .]
as follows:
$ soops-scoop soops/examples/monty_hall.py output/study/ -s rdir -o output/study --no-plugins --shell
<snip>
Python 3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.39.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: df.keys()
Out[1]:
Index(['rdir', 'rfiles', 'host', 'num', 'output_dir', 'plot_opts', 'repeat',
'seed', 'show', 'silent', 'switch', 'elapsed', 'win_rate', 'time'],
dtype='object')
In [2]: df.win_rate.head()
Out[2]:
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]
Out[3]:
rdir ~/projects/soops/output/study/000-a96da9ba9ee2...
rfiles [options.txt, output_log.txt]
host random
num 100
output_dir output/study/000-a96da9ba9ee27be055166a3f64f641d2
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 2026-10-07 14:05:33.537072
Name: 0, dtype: object
The DataFrame with the all results is saved in output/study/results.h5
for reuse.
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 infoThe 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']])
output('parameterization:')
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.tight_layout()
fig.savefig(os.path.join(data.output_dir, 'win_rates.png'))
return dataThen, running:
soops-scoop soops/examples/monty_hall.py 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 soops/examples/monty_hall.py 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.
'@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 callingnumpy.arange()with the given arguments.'@linspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)'denotes values obtained by callingnumpy.linspace()with the given arguments.'@generate'denotes an argument whose values are generated, in connection with--generate-parsoption, see below.
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):
"""
Parameters
----------
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 gconfand 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']" soops/examples/monty_hall.py
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 soops/examples/monty_hall.py output/study2/0* -s rdir -o output/study2/
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.
"""
pass
def __call__(self, all_pars):
"""
Called for each parameter set of the study.
"""
out = {}
return outFor very large parametric studies, it might be impractical to view all_parameters.csv directly when searching a directory of a run with given parameters. The soops-find script can be used instead:
$ soops-find -h
usage: soops-find [-h] [-q pandas-query-expression]
[--engine {numexpr,python}] [-m {truncated,full,single}]
[-k KEY] [--shell]
directories [directories ...]
Find parametric studies with parameters satisfying a given query.
Option-like parameters are transformed to valid Python attribute names removing
initial dashes and replacing other dashes by underscores. For example
'--output-dir' becomes 'output_dir'.
positional arguments:
directories one or more root directories with sub-directories
containing parametric study results
options:
-h, --help show this help message and exit
-q pandas-query-expression, --query pandas-query-expression
pandas query expression applied to collected
parameters
--engine {numexpr,python}
pandas query evaluation engine [default: numexpr]
-m {truncated,full,single}, --mode {truncated,full,single}
output mode [default: truncated]
-k KEY, --key KEY column key. If given, forces "single" output mode
[default: output_dir]
--shell run ipython shell after all computations
Without options, it loads all parameter sets found in given directories into a DataFrame and launches the ipython shell:
$ soops-find output/study
find: 48 parameter sets stored in `apdf` DataFrame
find: column names:
Index(['finished', 'iset', 'host', 'no_show', 'num', 'repeat', 'seed',
'silent', 'switch', 'python', 'output_dir', 'script_dir'],
dtype='object')
Using matplotlib backend: gtk3agg
Python 3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]
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The --query option can be used to limit the search, for example:
$ soops-find output/study -q "num==1000 & repeat==20 & seed==12345"
- dict-like classes with attribute access, various utilities: soops.base
- Command line options made simple: soops.cliargs
- LaTeX report generation utilities: soops.formatting
- Timer class: soops.timing