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loop like a pro, make parameter studies fun: set up and run a parameter study/sweep/scan, save a database

Project description


This package helps you to set up and run parameter studies.

Mostly, you'll start with a script and a for-loop and ask "why do I need a package for that"? Well, soon you'll want housekeeping tools and a database for your runs and results. This package exists because sooner or later, everyone doing parameter scans arrives at roughly the same workflow and tools.

This package deals with commonly encountered boilerplate tasks:

  • write a database of parameters and results automatically
  • make a backup of the database and all results when you repeat or extend the study
  • append new rows to the database when extending the study
  • simulate a parameter scan
  • experimental: support for managing batch runs, e.g. on remote HPC systems, including basic git workflow support

Otherwise, the main goal is to not constrain your flexibility by building a complicated framework -- we provide only very basic building blocks. All data structures are really simple (dicts), as are the provided functions. The database is a normal pandas DataFrame.

Getting started

A simple example: Loop over two parameters 'a' and 'b' in a nested loop (grid), calculate and store the result of a calculation for each parameter combination.

import random
import psweep as ps

def func(pset):
    return {'result': random.random() * pset['a'] * pset['b']}

if __name__ == '__main__':
    a = ps.plist('a', [1,2,3])
    b = ps.plist('b', [77,88])
    params = ps.pgrid(a,b)
    df = ps.run_local(func, params)

pgrid produces a list params of parameter sets (dicts {'a': ..., 'b': ...}) to loop over:

[{'a': 1, 'b': 77},
 {'a': 1, 'b': 88},
 {'a': 2, 'b': 77},
 {'a': 2, 'b': 88},
 {'a': 3, 'b': 77},
 {'a': 3, 'b': 88}]

and a database of results (pandas DataFrame df, pickled file calc/ by default):

                               a   b                               _run_id  \
2021-03-19 22:58:17.015223265  1  77  bb32aade-6ac9-4ac2-9019-3093c5f00965
2021-03-19 22:58:17.020342588  1  88  bb32aade-6ac9-4ac2-9019-3093c5f00965
2021-03-19 22:58:17.023407698  2  77  bb32aade-6ac9-4ac2-9019-3093c5f00965
2021-03-19 22:58:17.025991917  2  88  bb32aade-6ac9-4ac2-9019-3093c5f00965
2021-03-19 22:58:17.028507948  3  77  bb32aade-6ac9-4ac2-9019-3093c5f00965
2021-03-19 22:58:17.030908823  3  88  bb32aade-6ac9-4ac2-9019-3093c5f00965

                                                           _pset_id _calc_dir  \
2021-03-19 22:58:17.015223265  e6fbfe50-8c14-49f3-b9f7-83268c73f34d      calc
2021-03-19 22:58:17.020342588  d8599b90-8475-4bfb-96b5-4e7068195057      calc
2021-03-19 22:58:17.023407698  85948244-46a3-4b19-bd1c-2d59e32328a6      calc
2021-03-19 22:58:17.025991917  a775be5e-bc53-4c3a-a237-7151b279f1bd      calc
2021-03-19 22:58:17.028507948  b0ad166f-6bd8-4d64-a82a-7e8e8ac91b20      calc
2021-03-19 22:58:17.030908823  e91309d4-21b4-4d45-9fb6-cb84ffb31e24      calc

                                                  _time_utc  \
2021-03-19 22:58:17.015223265 2021-03-19 22:58:17.015223265
2021-03-19 22:58:17.020342588 2021-03-19 22:58:17.020342588
2021-03-19 22:58:17.023407698 2021-03-19 22:58:17.023407698
2021-03-19 22:58:17.025991917 2021-03-19 22:58:17.025991917
2021-03-19 22:58:17.028507948 2021-03-19 22:58:17.028507948
2021-03-19 22:58:17.030908823 2021-03-19 22:58:17.030908823

                                                             _pset_sha1  \
2021-03-19 22:58:17.015223265  281c595edf7ffa124ac3ef82f26de3f8eb8517f2
2021-03-19 22:58:17.020342588  088056f830758f949823ddc52bf8527f3e727b45
2021-03-19 22:58:17.023407698  b59bc3ae98e8b93de34135d751ed5090c3f087c5
2021-03-19 22:58:17.025991917  52434b66dd37763d09280480ff449d66ffcdcd4f
2021-03-19 22:58:17.028507948  f6295cdfab520d9ede7e709bc68d4d65ffc45848
2021-03-19 22:58:17.030908823  35af18a51c425b09097af78d55ebdd91588c6f3c

                               _pset_seq  _run_seq     result
2021-03-19 22:58:17.015223265          0         0  35.276105
2021-03-19 22:58:17.020342588          1         0  30.183059
2021-03-19 22:58:17.023407698          2         0  50.545904
2021-03-19 22:58:17.025991917          3         0  59.443434
2021-03-19 22:58:17.028507948          4         0  78.027465
2021-03-19 22:58:17.030908823          5         0  62.461030

You see the columns 'a' and 'b', the column 'result' (returned by func) and a number of reserved fields for book-keeping such as


as well as the df.index also holding a time stamp.

Observe that one call ps.run_local(func, params) creates one _run_id -- a UUID identifying this run, where by "run" we mean one loop over all parameter combinations. Inside that, each call func(pset) creates a UUID _pset_id and a new row in the DataFrame (the database). In addition we also add sequential integer IDs _run_seq and _pset_seq for convenience, as well as an additional hash _pset_sha1 over the input dict (pset in the example) to func().


The basic data structure for a param study is a list of "parameter sets" or short "psets", each of which is a dict.

params = [{'a': 1, 'b': 'lala'},  # pset 1
          {'a': 2, 'b': 'zzz'},   # pset 2
          ...                     # ...

Each pset contains values of parameters ('a' and 'b') which are varied during the parameter study.

You need to define a callback function func, which takes exactly one pset such as:

{'a': 1, 'b': 'lala'}

and runs the workload for that pset. func must return a dict, for example:

{'result': 1.234}

or an updated 'pset':

{'a': 1, 'b': 'lala', 'result': 1.234}

We always merge (dict.update) the result of func with the pset, which gives you flexibility in what to return from func. In particular, you are free to also return an empty dict if you record results in another way (see the save_data_on_disk example later).

The psets form the rows of a pandas DataFrame, which we use to store the pset and the result from each func(pset).

The idea is now to run func in a loop over all psets in params. You do this using the ps.run_local() helper function. The function adds some special columns such as _run_id (once per ps.run_local() call) or _pset_id (once per pset). Using ps.run_local(... poolsize=...) runs func in parallel on params using multiprocessing.Pool.

This package offers some very simple helper functions which assist in creating params. Basically, we define the to-be-varied parameters ('a' and 'b') and then use something like itertools.product to loop over them to create params, which is passed to ps.run_local() to actually perform the loop over all psets.

>>> from itertools import product
>>> import psweep as ps
>>> a=ps.plist('a', [1,2,3])
>>> b=ps.plist('b', ['xx', 'yy'])
>>> a
[{'a': 1}, {'a': 2}, {'a': 3}]
>>> b
[{'b': 'xx'}, {'b': 'yy'}]
>>> ps.itr2params(product(a,b))
[{'a': 1, 'b': 'xx'},
 {'a': 1, 'b': 'yy'},
 {'a': 2, 'b': 'xx'},
 {'a': 2, 'b': 'yy'},
 {'a': 3, 'b': 'xx'},
 {'a': 3, 'b': 'yy'}]

The last pattern is so common, that we have a function for it: pgrid().

>>> ps.pgrid(a,b)
[{'a': 1, 'b': 'xx'},
 {'a': 1, 'b': 'yy'},
 {'a': 2, 'b': 'xx'},
 {'a': 2, 'b': 'yy'},
 {'a': 3, 'b': 'xx'},
 {'a': 3, 'b': 'yy'}]

The logic of the param study is entirely contained in the creation of params. E.g., if parameters shall be varied together (say a and b), then instead of

>>> product(a,b,c)


>>> product(zip(a,b), c)

The nesting from zip() is flattened in itr2params() and pgrid()

>>> c=ps.plist('c', [None, 1.2, 'X'])
>>> ps.pgrid(zip(a,b),c)
[{'a': 1, 'b': 'xx', 'c': None},
 {'a': 1, 'b': 'xx', 'c': 1.2},
 {'a': 1, 'b': 'xx', 'c': 'X'},
 {'a': 2, 'b': 'yy', 'c': None},
 {'a': 2, 'b': 'yy', 'c': 1.2},
 {'a': 2, 'b': 'yy', 'c': 'X'}]

If you want a parameter which is constant, use a list of length one:

>>> const=ps.plist('const', [1.23])
>>> ps.pgrid(zip(a,b), c, const)
[{'a': 1, 'b': 'xx', 'c': None, 'const': 1.23},
 {'a': 1, 'b': 'xx', 'c': 1.2,  'const': 1.23},
 {'a': 1, 'b': 'xx', 'c': 'X',  'const': 1.23},
 {'a': 2, 'b': 'yy', 'c': None, 'const': 1.23},
 {'a': 2, 'b': 'yy', 'c': 1.2,  'const': 1.23},
 {'a': 2, 'b': 'yy', 'c': 'X',  'const': 1.23}]

So, as you can see, the general idea is that we do all the loops before running any workload, i.e. we assemble the parameter grid to be sampled before the actual calculations. This has proven to be very practical as it helps detecting errors early.

You are, by the way, of course not restricted to use simple nested loops over parameters using pgrid() (which uses itertools.product). You are totally free in how to create params, be it using other fancy stuff from itertools or explicit loops. Of course you can also define a static params list

params = [
    {'a': 1,    'b': 'xx', 'c': None},
    {'a': 1,    'b': 'yy', 'c': 1.234},
    {'a': None, 'b': 'xx', 'c': 'X'},

or read params in from an external source such as a database from a previous study, etc.

The point is: you generate params, we run the study.

The database

By default, ps.run_local() writes a database calc/ (a pickled DataFrame) with the default calc_dir='calc'. You can turn that off using save=False if you want. If you run ps.run_local() again

>>> ps.run_local(func, params)
>>> ps.run_local(func, other_params)

it will read and append to that file. The same happens in an interactive session when you pass in df again, in which case we don't read it from disk:

# default is df=None -> create empty df
# save=False: don't write db to disk, optional
>>> df_run_0 = ps.run_local(func, params, save=False)
>>> df_run_0_and_1 = ps.run_local(func, other_params, save=False, df=df_run_0)

_pset_id, _pset_seq, _run_id, _run_seq, _pset_sha1 and repeated runs

See examples/*

It is important to get the difference between the two special fields _run_id and _pset_id, the most important one being _pset_id.

Both are random UUIDs. They are used to uniquely identify things.

Once per ps.run_local() call, a _run_id and _run_seq is created. Which means that when you call ps.run_local() multiple times using the same database as just shown, you will see multiple (in this case two) _run_id and _run_seq values.

                             _run_id                              _pset_id  _run_seq  _pset_seq
8543fdad-4426-41cb-ab42-8a80b1bebbe2  08cb5f7c-8ce8-451f-846d-db5ac3bcc746         0          0
8543fdad-4426-41cb-ab42-8a80b1bebbe2  18da3840-d00e-4bdd-b29c-68be2adb164e         0          1
8543fdad-4426-41cb-ab42-8a80b1bebbe2  bcc47205-0919-4084-9f07-072eb56ed5fd         0          2
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  809064d6-c7aa-4e43-81ea-cebfd4f85a12         1          3
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  ef5f06d4-8906-4605-99cb-2a9550fdd8de         1          4
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  162a7b8c-3ab5-41bb-92cd-1e5d0db0842f         1          5

Each ps.run_local() call in turn calls func(pset) for each pset in params. Each func invocation creates a unique _pset_id and increment the integer counter _pset_seq. Thus, we have a very simple, yet powerful one-to-one mapping and a way to refer to a specific pset.

An interesting special case (see examples/ is when you call ps.run_local() multiple times using the exact same params,

>>> ps.run_local(func, params)
>>> ps.run_local(func, params)

which is perfectly fine, e.g. in cases where you just want to sample more data for the same psets in params over and over again. In this case, you will have as above two unique _run_ids but two sets of the same _pset_sha1.

                             _run_id                              _pset_id  _run_seq  _pset_seq                                _pset_sha1  a    result
8543fdad-4426-41cb-ab42-8a80b1bebbe2  08cb5f7c-8ce8-451f-846d-db5ac3bcc746         0          0  e4ad4daad53a2eec0313386ada88211e50d693bd  1  0.381589
8543fdad-4426-41cb-ab42-8a80b1bebbe2  18da3840-d00e-4bdd-b29c-68be2adb164e         0          1  7b7ee754248759adcee9e62a4c1477ed1a8bb1ab  2  1.935220
8543fdad-4426-41cb-ab42-8a80b1bebbe2  bcc47205-0919-4084-9f07-072eb56ed5fd         0          2  9e0e6d8a99c72daf40337183358cbef91bba7311  3  2.187107
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  809064d6-c7aa-4e43-81ea-cebfd4f85a12         1          3  e4ad4daad53a2eec0313386ada88211e50d693bd  1  0.590200
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  ef5f06d4-8906-4605-99cb-2a9550fdd8de         1          4  7b7ee754248759adcee9e62a4c1477ed1a8bb1ab  2  1.322758
969592bc-65e6-4315-9e6b-5d64b6eaa0b3  162a7b8c-3ab5-41bb-92cd-1e5d0db0842f         1          5  9e0e6d8a99c72daf40337183358cbef91bba7311  3  1.639455

This is a very powerful tool to filter the database for calculations that used the same pset, e.g. an exact repetition of one experiment. But since we use UUIDs for _pset_id, those calculations can still be distinguished.

Best practices

The following workflows and practices come from experience. They are, if you will, the "framework" for how to do things. However, we decided to not codify any of these ideas but to only provide tools to make them happen easily, because you will probably have quite different requirements and workflows.

Please also have a look at the examples/ dir where we document these and more common workflows.

Save data on disk, use UUIDs

Assume that you need to save results from a func() call not only in the returned dict from func (or even not at all!) but on disk, for instance when you call an external program which saves data on disk. Consider this toy example (examples/save_data_on_disk/

#!/usr/bin/env python3

import os
import subprocess
import psweep as ps

def func(pset):
    fn = os.path.join(pset["_calc_dir"], pset["_pset_id"], "output.txt")
    cmd = (
        f"mkdir -p $(dirname {fn}); "
        f"echo {pset['a']} {pset['a']*2} {pset['a']*4} > {fn}"
    ), shell=True)
    return {"cmd": cmd}

if __name__ == "__main__":
    params = ps.plist("a", [1, 2, 3, 4])
    df = ps.run_local(func, params)

In this case, you call an external program (here a dummy shell command) which saves its output on disk. Note that we don't return any output from the external command in func's return statement. We only update the database row added for each call to func by returning a dict {"cmd": cmd} with the shell cmd we call in order to have that in the database.

Also note how we use the special fields _pset_id and _calc_dir, which are added in ps.run_local() to pset before func is called.

After the run, we have four dirs for each pset, each simply named with _pset_id:

├── 63b5daae-1b37-47e9-a11c-463fb4934d14
│   └── output.txt
├── 657cb9f9-8720-4d4c-8ff1-d7ddc7897700
│   └── output.txt
├── d7849792-622d-4479-aec6-329ed8bedd9b
│   └── output.txt
├── de8ac159-b5d0-4df6-9e4b-22ebf78bf9b0
│   └── output.txt

This is a useful pattern. History has shown that in the end, most naming conventions start simple but turn out to be inflexible and hard to adapt later on. I have seen people write scripts which create things like:


i.e. encode the parameter values in path names, because they don't have a database. Good luck parsing that. I don't say this cannot be done -- sure it can (in fact the example above easy to parse). It is just not fun -- and there is no need to. What if you need to add a "column" for parameter 'c' later? Impossible (well, painful at least). This approach makes sense for very quick throw-away test runs, but gets out of hand quickly.

Since we have a database, we can simply drop all data in calc/<_pset_id> and be done with it. Each parameter set is identified by a UUID that will never change. This is the only kind of naming convention which makes sense in the long run.


An example of a simple post-processing script that reads data from disk (examples/save_data_on_disk/

df = ps.df_read("calc/")

# Filter database
df = df[df.a > 0 & ~df.a.isna()]

arr = np.array(
    [np.loadtxt(f"calc/{pset_id}/output.txt") for pset_id in df._pset_id.values]

df["mean"] = arr.mean(axis=1)

cols = ["a", "mean", "_pset_id"]

ps.df_write(df, "calc/")

Iterative extension of a parameter study

See examples/multiple_local_1d_scans/ and examples/*repeat*.

You can backup old calc dirs when repeating calls to ps.run_local() using the backup keyword.

df = ps.run_local(func, params, backup=True)

This will save a copy of the old calc_dir to something like


That way, you can track old states of the overall study, and recover from mistakes, e.g. by just

$ rm -r calc
$ mv calc.bak_2021-03-19T2* calc

For any non-trivial work, you won't use an interactive session. Instead, you will have a driver script (say which defines params and starts ps.run_local(). Also in a common workflow, you won't define params and run a study once. Instead you will first have an idea about which parameter values to scan. You will start with a coarse grid of parameters and then inspect the results and identify regions where you need more data (e.g. more dense sampling). Then you will modify params and run the study again. You will modify multiple times, as you refine your study.

Use git

Instead or in addition to using backup, we recommend a git-based workflow to at least track changes to (instead of manually creating backups such as,, ...). We recommend to create a .gitignore such as

# ignore backups

# ignore simulate runs

# ignore the whole calc/ dir, track only scripts

# or just ignore potentially big files coming from a simulation you run

You can even go crazy with git-lfs here. Again, we don't enforce a specific workflow. We just provide basic building blocks to create your own.

Simulate / Dry-Run: look before you leap

See examples/

When you fiddle with finding the next good params and even when using backup and/or git, appending to the old database might be a hassle if you find that you made a mistake when setting up params. You need to abort the current run, copy the backup over or use git to go back.

Instead, while you tinker with params, use another calc_dir, e.g.

# only needed so that we can copy the old database over
$ mkdir -p calc.simulate
$ cp calc/ calc.simulate/
df = ps.run_local(func, params, calc_dir='calc.simulate')

But what's even better: keep everything as it is and just set simulate=True, which performs exactly the two steps above.

df = ps.run_local(func, params, simulate=True)

It will copy only the database, not all the (possible large) data in calc/ to calc.simulate/ and run the study there. Additionally , it will not call call func() to run any workload. So you still append to your old database as in a real run, but in a safe separate dir which you can delete later.

Advanced: Give runs names for easy post-processing

See examples/

Post-processing is not the scope of the package. The database is a pandas DataFrame and that's it. You can query it and use your full pandas Ninja skills here, e.g. "give me all psets where parameter 'a' was between 10 and 100, while 'b' was constant, ...". You get the idea.

To ease post-processing, it can be useful practice to add a constant parameter named "study" or "scan" to label a certain range of runs. If you, for instance, have 5 runs (meaning 5 calls to ps.run_local()) where you scan values for parameter 'a' while keeping parameters 'b' and 'c' constant, you'll have 5 _run_id values. When querying the database later, you could limit by _run_id if you know the values:

>>> df_filtered = df[(df._run_id=='afa03dab-071e-472d-a396-37096580bfee') |
                     (df._run_id=='e813db52-7fb9-4777-a4c8-2ce0dddc283c') |

This doesn't look like fun. It shows that the UUIDs (_run_id and _pset_id) are rarely meant to be used directly, but rather to programmatically link psets and runs to other data (as shown above in the "Save data on disk" example). You can also use the integer IDs _run_seq and _pset_seq instead. But still, you need to know to which parameter values they correspond to.

When possible, you could limit by the constant values of the other parameters:

>>> df_filtered = df[(df.b==10) & (df.c=='foo')]

Much better! This is what most post-processing scripts will do. In fact, we have a shortcut function

>>> conds = [df.b==10, df.c=='foo']
>>> df_filtered = ps.df_filter_conds(df, conds)

which is useful in post-processing scripts where conds is created programmatically.

But when you have a column "study" which has the value 'a' all the time, it is just

>>> df = df['a']

You can do more powerful things with this approach. For instance, say you vary parameters 'a' and 'b', then you could name the "study" field 'scan=a:b' and encode which parameters (thus column names) you have varied. Later in the post-processing

>>> study = 'scan=a:b'
# cols = ['a', 'b']
>>> cols = study.split('=')[1].split(':')
>>> values = df[cols].values

So in this case, a naming convention is useful in order to bypass possibly complex database queries. But it is still flexible -- you can change the "study" column at any time, or delete it again.

Pro tip: You can manipulate the database at any later point and add the "study" column after all runs have been done.

Super Pro tip: Make a backup of the database first!

Batch runs

We have experimental support for managing calculations on remote systems such as HPC clusters with a batch system like SLURM. It is basically a modernized and stripped-down version of pwtools.batch. Note that we don't use any method like DRMAA to automatically dispatch jobs to clusters. Our design revolves around maximal user control of each step of the workflow.

See ps.prep_batch() and examples/batch/. The workflow is based on template files. In the templates, we use (for now) the standard library's string.Template, where each $foo is replaced by a value contained in a pset, so $param_a, $param_b, as well as $_pset_id and so forth.

We piggy-back on the run_local() workflow from above to use all it's power and flexibility to create batch scripts using template files. Again, you have full control over every part of the workflow. We just automate the boring stuff.

Workflow summary:

  • define params to be varied as shown above (probably in a script, say
  • in that script, call ps.prep_batch(params), which does
    • use templates/calc/*: scripts that you want to run in each batch job
    • use templates/machines/<mycluster>/jobscript: batch job script
    • read templates/machines/<mycluster>/info.yaml: machine-specific info (e.g. command to submit the jobscript)
    • define func() that will create a dir named calc/<_pset_id> for each batch job, replace placeholders such as $param_a from psets (including special ones such as $_pset_id)
    • call run_local(func, params)
    • create a script calc/run_<mycluster>.sh to submit all jobs

We replace running jobs locally (i.e. what ps.run_local() would do) with some small helper scripts:

  • use bin/psweep-push <mycluster> to rsync calc/ to a cluster
  • ssh to cluster; execute calc/run_<mycluster>.sh, wait ...
  • use bin/psweep-pull <mycluster> to rsync results back

Now suppose that each of our batch jobs produces a output file, then we have the same post-processing setup as in save_data_on_disk, namely

├── 63b5daae-1b37-47e9-a11c-463fb4934d14
│   └── output.txt
├── 657cb9f9-8720-4d4c-8ff1-d7ddc7897700
│   └── output.txt
├── d7849792-622d-4479-aec6-329ed8bedd9b
│   └── output.txt
├── de8ac159-b5d0-4df6-9e4b-22ebf78bf9b0
│   └── output.txt

The only difference is that the outputs were not calculated and written locally but rsynced from the cluster.

And then post-processing is (almost) as before:

  • analyze results, run post-processing script(s) on calc/, read in output.txt for each _pset_id
  • when extending the study (modify params, call again), we use the same features shown above
    • append to database
    • create new unique calc/<_pset_id> without overwriting anything
    • additionally: write a new calc/run_<mycluster>.sh with old submit commands still in there, but commented out

git support

Use prep_batch(..., git=True) to have some basic git support such as automatic commits in each call. Make sure to create .gitignore first, else we'll track calc/ as well, which you may safely do when data in calc is small.


$ pip install psweep

Dev install of this repo:

$ pip install -e .

See also


cd src/psweep/tests

Project details

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