Alexandra K. Diem

Personal Website.
Mitzis, I am so happy and grateful that I could ride this adventure with you nutters! I'm still amazed by how well we fit together and always looked out for each other. Always together from the start, always together across the finish line. Most of you I met for the first time on the way to the opening event and we left others with the impression that we'd been riding together for years. I couldn't have hoped for a better team spirit on this journey. I really hope that we get the chance to do something crazy like this again 😘❤️
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📸 @radrace photobox by @christophmannhardt
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#mitziandfriends #radrace #tourdefriends #tourdefriends3 #tdf #storyoftheday #oneobsession #photooftheday #worldbybike #outsideisfree #fromwhereiride #cycling #womencycling #ladieseditioncc #landevei #utno #vegtur Watch out, #mitzitrain coming through! 🚴‍♀️💨🔥
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#mitziandfriends #radrace #tourdefriends #tourdefriends3 #tdf #storyoftheday #oneobsession #photooftheday #worldbybike #outsideisfree #fromwhereiride #cycling #womencycling #ladieseditioncc #landevei #utno #vegtur And that's it for the 669 km and 9500 m elevation that made up this year's @radrace #tourdefriends3 from Augsburg to Feltre! I left it all out on the tracks, but I couldn't resist trying to muster up the last remaining power for a bike lift photo at the finish line 😅 #oneobsession I really don't know if I'm rather happy that we all made it as a team and always looked out for each other #mitziandfriends, or whether I'm rather sad that it's all over. How about a #tourdefriends in Norway soon? 😁
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#radrace #tdf #storyoftheday #photooftheday #worldbybike #outsideisfree #fromwhereiride #cycling #womencycling #ladieseditioncc #landevei #utno #vegtur #mitzipower ist too cool for you. #uffeuffe
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📸 photobox by @christophmannhardt
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#mitziandfriends #radrace #tourdefriends #tourdefriends3 #tdf #storyoftheday #oneobsession #photooftheday #worldbybike #outsideisfree #fromwhereiride #cycling #womencycling #ladieseditioncc #landevei #utno #vegtur

Managing simulation runs using pandas

At work I run a lot of simulations of the same code, but using slightly different parameters. Sometimes, simulation data can take up quite a bit of space, so often I want to store those data somewhere other than my laptop. I try my best to use sensible folder names, but often, six or so months later, I have to look into several folders to figure out which one contains the data I am looking for. The Python library pandas provides a much more elegant solution to this problem, that only requires me to store meta data in a text file, which I can easily keep on my laptop, that will point me to the folder in question.

The solution is based on the storing simulation parameters in a .cfg file. These look something like this:

[Simulation]
id = simulation0
solver = direct
debug = 0

[Parameter]
N = 2
TOL = 1e-7
rho = 1000 * kg/m**3
K = 1e-7 * m**2/Pa/s
phi = 0.1
beta = 1
qi = 0
qo = 0
tf = 0.5 * s
dt = 0.1 * s
theta = 0.5

Using pandas, we can create scripts that automatically filter this type of meta data for certain parameter values, so that we can quickly figure out, which name we gave to our data folder. This means that now it doesn’t matter anymore what we call our data folders and we can automate the naming process by for example using random numbers (or just simply count from 0).

We need to import the following libraries into Python:

import pandas
import glob
from configparser import ConfigParser

Glob returns a list of all paths fitting a pattern,

files = glob.glob("./data/*.cfg")
files

such that the output looks similar to this:

['./data/simulation1.cfg',
 './data/simulation3.cfg',
 './data/simulation0.cfg',
 './data/simulation2.cfg']

We initialise a ConfigParser to read the .cfg files and tell it which section(s) we are interested in:

config = ConfigParser()
config.optionxform = str
sections = ['Simulation', 'Parameter']

Create a dictionary of dictionaries holding all simulation parameters from the files. The truncated file name serves as the key for each parameter dictionary d in data.

data = {}
for file in files:
    config.read(file)
    d = {}
    for section in sections:
        options = config.items(section)
        for key, value in options:
            d[key] = value
    fname = file.split("/")[-1]
    data[fname] = d

Create a pandas table from the dictionary data

tab = pandas.DataFrame.from_dict(data, orient="index")

Now we can look at the values of a parameter for each file in our table:

tab.TOL
simulation0.cfg    1e-7
simulation1.cfg    1e-7
simulation2.cfg    1e-7
simulation3.cfg    1e-7
Name: TOL, dtype: object
tab.beta
simulation0.cfg      1
simulation1.cfg      1
simulation2.cfg    0.1
simulation3.cfg      1
Name: beta, dtype: object
tab.qi
simulation0.cfg      0
simulation1.cfg      0
simulation2.cfg      0
simulation3.cfg    0.1
Name: qi, dtype: object

We can also filter by parameter values

tab[tab.beta == '0.1']
N TOL rho K phi beta qi qo tf dt theta
simulation2.cfg 2 1e-7 1000 * kg/m**3 1e-7 * m**2/Pa/s 0.1 0.1 0 0 0.5 * s 0.1 * s 0.5
tab[tab.qi == '0']
N TOL rho K phi beta qi qo tf dt theta
simulation0.cfg 2 1e-7 1000 * kg/m**3 1e-7 * m**2/Pa/s 0.1 1 0 0 0.5 * s 0.1 * s 0.5
simulation1.cfg 2 1e-7 1000 * kg/m**3 1e-7 * m**2/Pa/s 0.2 1 0 0 0.5 * s 0.1 * s 0.5
simulation2.cfg 2 1e-7 1000 * kg/m**3 1e-7 * m**2/Pa/s 0.1 0.1 0 0 0.5 * s 0.1 * s 0.5