Direct ecitation coefficient fit

This notebook estracts the direct excitation coefficient from the set of 5 us-ALEX smFRET measurements.

What it does?

This notebook performs a weighted average of direct excitation coefficient fitted from each measurement.

Dependencies

This notebooks reads the file:



In [1]:

    
data_file = 'results/usALEX-5samples-PR-raw-dir_ex_aa-fit-AexAem.csv'

The data has been generated by running the template notebook usALEX-5samples-PR-raw-dir_ex_aa-fit-AexAem for each sample.

To recompute the PR data used by this notebook run the 8-spots paper analysis notebook.

Computation



In [2]:

    
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina'  # for hi-dpi displays

sns.set_style('whitegrid')
palette = ('Paired', 10)
sns.palplot(sns.color_palette(*palette))
sns.set_palette(*palette)



In [3]:

    
data = pd.read_csv(data_file).set_index('sample')
data









    Out[3]:






  
    
      
      n_bursts_aa
      dir_ex_S1p
      dir_ex_S_kde
      dir_ex_S2p
      dir_ex_S2pa
      dir_ex_S2p_w1
      dir_ex_S_kde_w1
      dir_ex_S_kde_w4
      dir_ex_S_kde_w5
      dir_ex_S2p_w5
      dir_ex_S2p_w5a
      S_2peaks_w5
      S_2peaks_w5_fiterr
    
    
      sample
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      7d
      630
      0.035049
      0.061121
      0.068715
      0.060454
      0.065912
      0.060895
      0.055075
      0.053741
      0.055369
      0.050042
      0.052465
      0.002288
    
    
      12d
      269
      0.037100
      0.060445
      0.092479
      0.041670
      0.073575
      0.060445
      0.057082
      0.054852
      0.057560
      0.044884
      0.054428
      0.003265
    
    
      17d
      608
      0.034677
      0.083424
      0.119491
      0.070370
      0.094961
      0.082720
      0.077354
      0.071352
      0.072895
      0.174377
      0.067942
      0.003098
    
    
      22d
      433
      0.026463
      0.086484
      0.111094
      0.053768
      0.082611
      0.085541
      0.071582
      0.064283
      0.061879
      0.046918
      0.058273
      0.003034
    
    
      27d
      129
      0.019777
      0.050862
      0.078734
      0.040582
      0.080239
      0.050420
      0.074807
      0.043406
      0.055485
      0.017062
      0.052569
      0.003970



In [4]:

    
data.columns









    Out[4]:





Index(['n_bursts_aa', 'dir_ex_S1p', 'dir_ex_S_kde', 'dir_ex_S2p',
       'dir_ex_S2pa', 'dir_ex_S2p_w1', 'dir_ex_S_kde_w1', 'dir_ex_S_kde_w4',
       'dir_ex_S_kde_w5', 'dir_ex_S2p_w5', 'dir_ex_S2p_w5a', 'S_2peaks_w5',
       'S_2peaks_w5_fiterr'],
      dtype='object')



In [5]:

    
d = data[[c for c in data.columns if c.startswith('dir')]]
d.plot(kind='line', lw=3, title='Direct Excitation Coefficient')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., frameon=False);



In [6]:

    
dir_ex_aa = np.average(data.dir_ex_S_kde_w5, weights=data.n_bursts_aa)
'%.5f' % dir_ex_aa









    Out[6]:





'0.06062'

Save coefficient to disk



In [7]:

    
with open('results/usALEX - direct excitation coefficient dir_ex_aa.csv', 'w') as f:
    f.write('%.5f' % dir_ex_aa)



In [8]:

	n_bursts_aa	dir_ex_S1p	dir_ex_S_kde	dir_ex_S2p	dir_ex_S2pa	dir_ex_S2p_w1	dir_ex_S_kde_w1	dir_ex_S_kde_w4	dir_ex_S_kde_w5	dir_ex_S2p_w5	dir_ex_S2p_w5a	S_2peaks_w5	S_2peaks_w5_fiterr
sample
7d	630	0.035049	0.061121	0.068715	0.060454	0.065912	0.060895	0.055075	0.053741	0.055369	0.050042	0.052465	0.002288
12d	269	0.037100	0.060445	0.092479	0.041670	0.073575	0.060445	0.057082	0.054852	0.057560	0.044884	0.054428	0.003265
17d	608	0.034677	0.083424	0.119491	0.070370	0.094961	0.082720	0.077354	0.071352	0.072895	0.174377	0.067942	0.003098
22d	433	0.026463	0.086484	0.111094	0.053768	0.082611	0.085541	0.071582	0.064283	0.061879	0.046918	0.058273	0.003034
27d	129	0.019777	0.050862	0.078734	0.040582	0.080239	0.050420	0.074807	0.043406	0.055485	0.017062	0.052569	0.003970