Use natural sort order when storing data log to csv

[MichaelClerx]

Makes it easier to load multicell data without having to re-order CSV columns, see #695

[MbNicolas]

Here is the code I have used to sort the datalog in a natural way!
This is my original datalog in the csv file :
in :
import pandas as pd
df=pd.read_csv("propagation_normale.csv")
df.head()

out :

engine.time 0.membrane.V 1.membrane.V 10.membrane.V 100.membrane.V 101.membrane.V 102.membrane.V 103.membrane.V 104.membrane.V 105.membrane.V ... 90.membrane.V 91.membrane.V 92.membrane.V 93.membrane.V 94.membrane.V 95.membrane.V 96.membrane.V 97.membrane.V 98.membrane.V 99.membrane.V
0 0.0 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 ... -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000
1 0.1 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 ... -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009
2 0.2 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 ... -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295
3 0.3 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 ... -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400
4 0.4 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 ... -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898

And here is what I get after natural sorting :

in :
import pandas as pd
from natsort import natsorted #for natural sorting
name="reentree_3boucles.csv"
df=pd.read_csv(name) #lecture du fichier csv dans un dataframe
#natsorting dataframe according to column labels and reindexing
df1=df.reindex(natsorted(df.columns),axis=1)

#save dataframe to csv file
df1.to_csv('natsorted_'+name)
df1.head() #affichage des 5 premières lignes du dataframe

out :

0.membrane.V 1.membrane.V 2.membrane.V 3.membrane.V 4.membrane.V 5.membrane.V 6.membrane.V 7.membrane.V 8.membrane.V 9.membrane.V ... 495.membrane.V 496.membrane.V 497.membrane.V 498.membrane.V 499.membrane.V 500.membrane.V 501.membrane.V 502.membrane.V 503.membrane.V engine.time
0 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 ... -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 -87.000000 0.0
1 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 ... -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 -86.748009 0.1
2 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 ... -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 -86.505295 0.2
3 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 ... -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 -86.271400 0.3
4 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 ... -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 -86.045898 0.4

And now the csv output is fine for plotting out of myokit!