In [1]:

    

import research as r
import scipy
import pandas


    

In [5]:

    

import research as r
import scipy
import pandas
    
def fitness_plots(path, treatment):
    D = r.load_files(r.find_files(path, treatment+"_.*/fitness.dat"))

    figure()
    for i,g in D.groupby('trial'):
        plot(g['update'], g['mean_fitness'])
    ylabel('Mean fitness')
    xlabel('Update')
    
    figure()
    r.quick_ciplot('update','mean_fitness', D)
    ylabel('Mean fitness')
    xlabel('Update')

    final = D[D['update']==D['update'].max()]
    print "\nDominant fitness:"
    print final.ix[final['max_fitness'].idxmax()]
    return D


    

In [8]:

    

D = fitness_plots('/Users/dk/research/var/rna/002-lags','ta.')


    

    




Dominant fitness:
update              100000
mean_generation    4970.71
min_fitness              0
mean_fitness        0.6269
max_fitness         0.6366
treatment              ta0
trial                    1
Name: 1000






    













    

In [6]:

    

D = fitness_plots('/Users/dk/research/var/rna/001-baseline','ta.')


    

    




Dominant fitness:
update               100000
mean_generation    5033.512
min_fitness               0
mean_fitness         0.6171
max_fitness          0.6366
treatment               ta0
trial                     1
Name: 1000






    













    

In [7]:

    

D = fitness_plots('/Users/dk/research/var/rna/001-baseline','tb.')


    

    




Dominant fitness:
update               100000
mean_generation    4969.586
min_fitness               0
mean_fitness         0.0219
max_fitness          0.0258
treatment               tb0
trial                    28
Name: 21020






    













    

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