Executed: Mon Mar 27 11:48:22 2017

Duration: 34 seconds.

Analysis of 5 smFRET samples

In this notebook:

Per-sample FRET histograms
Collapsed (merged channels) FRET histograms
FRET vs distance plot (multi-spot vs usALEX)
D-only fraction plots
Burst size vs measurement (corrected and uncorrected)
Background (per CH) vs measuremets
Burst-per-second (per CH) vs measuremet
Burst accumulation vs time (per CH and all-CH)

USAGE TIP: to comment-out a code cell, select all (CTRL+a) and hit CTRL+/.

FRET fitting remarks

In this notebook we fit the Proximity Ratio histogram using different models.

Load FRETBursts software



In [1]:

    
from fretbursts import *
sns = init_notebook()









    



 - Optimized (cython) burst search loaded.
 - Optimized (cython) photon counting loaded.
--------------------------------------------------------------
 You are running FRETBursts (version 0.5.9).

 If you use this software please cite the following paper:

   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET
   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 

--------------------------------------------------------------



In [2]:

    
import os
import pandas as pd
from IPython.display import display
from IPython.display import display, Math



In [3]:

    
import ipywidgets as widgets
from ipywidgets import interact, interactive, fixed



In [4]:

    
import lmfit
print('lmfit version:', lmfit.__version__)









    



lmfit version: 0.9.5

8-spot paper plot style



In [5]:

    
PLOT_DIR = './figure/'



In [6]:

    
import matplotlib as mpl
from cycler import cycler

bmap = sns.color_palette("Set1", 9)
colors = np.array(bmap)[(1,0,2,3,4,8,6,7), :]
mpl.rcParams['axes.prop_cycle'] = cycler('color', colors)
colors_labels = ['blue', 'red', 'green', 'violet', 'orange', 'gray', 'brown', 'pink', ]
for c, cl in zip(colors, colors_labels):
    locals()[cl] = tuple(c) # assign variables with color names
sns.palplot(colors)

Data files

Data folder:



In [7]:

    
data_dir = './data/multispot/'

Check that the folder exists:



In [8]:

    
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir

List of data files in data_dir:



In [9]:

    
from glob import glob
file_list = sorted(glob(data_dir + '*.hdf5'))



In [10]:

    
labels = ['7d', '12d', '17d', '22d', '27d', 'DO']
files_dict = {lab: fname for lab, fname in zip(sorted(labels), file_list)}
files_dict









    Out[10]:





{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/12d_New_30p_320mW_steer_3.hdf5',
 '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/17d_100p_320mW_steer_1.hdf5',
 '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/22d_30p_320mW_steer_1.hdf5',
 '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/27d_50p_320mW_steer_1.hdf5',
 '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/7d_New_150p_320mW_steer_3.hdf5',
 'DO': '/Users/anto/Google Drive/notebooks/multispot_paper/data/multispot/DO12_No2_50p_320mW_steer_1.hdf5'}

Parameters from file

Load the leakage coefficient from disk (computed in Multi-spot 5-Samples analyis - Leakage coefficient fit):



In [11]:

    
leakage_coeff_fname = 'results/Multi-spot - leakage coefficient KDE wmean DexDem.csv'
leakage = np.loadtxt(leakage_coeff_fname, ndmin=1)

print('Leakage coefficient:', leakage)









    



Leakage coefficient: [ 0.0334]

Load the direct excitation coefficient ($d_{dirT}$) from disk (computed in usALEX - Corrections - Direct excitation physical parameter):



In [12]:

    
dir_ex_coeff_fname = 'results/usALEX - direct excitation coefficient dir_ex_t beta.csv'
dir_ex_t = np.loadtxt(dir_ex_coeff_fname, ndmin=1)

print('Direct excitation coefficient (dir_ex_t):', dir_ex_t)









    



Direct excitation coefficient (dir_ex_t): [ 0.04932]

Parameters

Analysis parameters:



In [13]:

    
gamma_sel = 0.44     # Used to compute burst size during burst selection
donor_ref = False    # False -> gamma correction is: g*nd + na
                     # True  -> gamma correction is: nd + na/g

hist_weights = 'size'

## Background fit parameters
bg_kwargs_auto = dict(fun=bg.exp_fit,
                 time_s = 30,
                 tail_min_us = 'auto',
                 F_bg=1.7,
                 )

## Burst search
F=6
dither = False
size_th = 30    # Burst size threshold (selection on corrected burst sizes)

## FRET fit parameters
bandwidth = 0.03        # KDE bandwidth
E_range = {'7d':  (0.7, 1.0), '12d': (0.4, 0.8), '17d': (0.2, 0.4), 
           '22d': (0.0, 0.1), '27d': (0.0, 0.1), 'DO': (0.0, 0.1)}
E_axis_kde = np.arange(-0.2, 1.2, 0.0002)

Processing and plot options:



In [14]:

    
# Data load options
reload_data = 1
burst_search = 1
delete_ph_times = 0

# Plot output options save_figure = 0 nosuptitle = False plt.rc('savefig', dpi=75) # Changes the figure size in the notebook savefig_kwargs = dict(dpi=200, bbox_inches='tight') # default save-figure options fret_plot_kw = dict(bins = np.r_[-0.2:1.201:bandwidth], fit_color='k', fit_alpha=0.9, fit_lw=2, #fit_fillcolor='#888888', sharey = False)

Utility functions



In [15]:

    
def print_fit_report(E_pr, gamma=1, leakage=0, dir_ex_t=0, math=True):
    """Print fit and standard deviation for both corrected and uncorrected E
    Returns d.E_fit.
    """
    E_corr = fretmath.correct_E_gamma_leak_dir(E_pr, gamma=gamma, leakage=leakage, dir_ex_t=dir_ex_t)
    
    E_pr_mean = E_pr.mean()*100
    E_pr_delta = (E_pr.max() - E_pr.min())*100
    
    E_corr_mean = E_corr.mean()*100
    E_corr_delta = (E_corr.max() - E_corr.min())*100
    if math:
        display(Math(r'\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = %.1f\%% \qquad'
                     '\Delta E_{fit} = %.2f \%%' % \
                     (E_pr_mean, E_pr_delta)))
        display(Math(r'\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = %.1f\%% \qquad'
                     '\Delta E_{fit} = %.2f \%%' % \
                     (E_corr_mean, E_corr_delta)))
    else:
        print('Pre-gamma  E (delta, mean):  %.2f  %.2f' % (E_pr_mean, E_pr_delta))
        print('Post-gamma E (delta, mean):  %.2f  %.2f' % (E_corr_mean, E_corr_delta))

5-samples processing



In [16]:

    
df = pd.DataFrame(index=['7d', '12d', '17d', '22d', '27d'], columns=range(8), dtype=float)
df.index.name = 'Sample'
df.columns.name = 'Channel'

E_pr_fret = df.copy()
E_pr_fret_sig = df.copy()
nbursts = df.copy()

7bp sample



In [17]:

    
data_id = '7d'
if reload_data:
    d7 = loader.photon_hdf5(files_dict[data_id])
    d7.calc_bg(**bg_kwargs_auto)
if burst_search:
    d7.burst_search(m=10, F=F, dither=dither)
    if delete_ph_times: d7.delete('ph_times_m')









    



 - Calculating BG rates ... [DONE]
 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   [DONE Counting D/A]



In [18]:

    
gamma_sel, donor_ref









    Out[18]:





(0.44, False)



In [19]:

    
dfs7 = Sel(d7, select_bursts.size, th1=30, gamma=gamma_sel, donor_ref=donor_ref)
dx = dfs7



In [20]:

    
nbursts.loc[data_id] = dx.num_bursts



In [21]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_two_gaussians(add_bridge=True, p2_center=0.8)
fitter.fit_histogram()



In [22]:

    
E_pr_fret.loc[data_id] = fitter.params['p2_center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      7d
      0.866766
      0.872245
      0.838805
      0.84337
      0.835858
      0.858535
      0.84036
      0.846814
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 85.0\% \qquad\Delta E_{fit} = 3.64 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 92.7\% \qquad\Delta E_{fit} = 1.93 \%$$



In [23]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='p2_center', show_fit_value=True);



In [24]:

    
fig, axes = plt.subplots(4, 2, figsize=(14, 12), sharex=True, sharey=True)
fig.subplots_adjust(left=0.08, right=0.96, top=0.93, bottom=0.07,
                    wspace=0.06, hspace=0.15)

for ich, ax in enumerate(axes.ravel()):
    mfit.plot_mfit(fitter, ich=ich, ax=ax)

Compare the effect of burst-size weights



In [25]:

    
# bext.bursts_fitter(dx, weights=None)
# dx.E_fitter.fit_histogram(mfit.factory_two_gaussians())
# dplot(dx, hist_fret, weights=None, show_model=True, show_fit_stats=True, fit_from='p2_center');
# ylim(0, 6)
# fig_no_w = gcf()
# plt.close(fig_no_w)

# bext.bursts_fitter(dx, weights='size', gamma=0.43)
# dx.E_fitter.fit_histogram(mfit.factory_two_gaussians())
# dplot(dx, hist_fret, weights='size', gamma=0.43,
#       show_model=True, show_fit_stats=True, fit_from='p2_center');
# fig_w = gcf()
# plt.close(fig_w)

# def _plot(weights=False):
#     if weights:
#         display(fig_w)
#     else:
#         display(fig_no_w)

# interact(_plot, weights=False);

12bp sample



In [26]:

    
data_id = '12d'
if reload_data: 
    d12 = loader.photon_hdf5(files_dict[data_id])
    d12.calc_bg_cache(**bg_kwargs_auto)
if burst_search:
    d12.burst_search(m=10, F=F, dither=dither)
    if delete_ph_times: d12.delete('ph_times_m')









    



 * No cached BG rates, recomputing:
 - Calculating BG rates ... [DONE]
 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   [DONE Counting D/A]



In [27]:

    
dfs12 = Sel(d12, select_bursts.size, th1=30, gamma=gamma_sel, donor_ref=donor_ref)
dx = dfs12



In [28]:

    
nbursts.loc[data_id] = dx.num_bursts



In [29]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_two_gaussians(add_bridge=True, p2_center=0.65)
fitter.fit_histogram()
E_pr_fret.loc[data_id] = fitter.params['p2_center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      12d
      0.58912
      0.580325
      0.572867
      0.574415
      0.563134
      0.555029
      0.565811
      0.563246
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 57.0\% \qquad\Delta E_{fit} = 3.41 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 74.3\% \qquad\Delta E_{fit} = 2.76 \%$$



In [30]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='p2_center', show_fit_value=True);

17bp sample



In [31]:

    
data_id = '17d'
if reload_data:
    d17 = loader.photon_hdf5(files_dict[data_id])
    d17.calc_bg_cache(**bg_kwargs_auto)
if burst_search:
    d17.burst_search(m=10, F=F, dither=dither)
    if delete_ph_times: d17.delete('ph_times_m')









    



 * No cached BG rates, recomputing:
 - Calculating BG rates ... [DONE]
 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   [DONE Counting D/A]



In [32]:

    
dfs17 = Sel(d17, select_bursts.size, th1=30, gamma=gamma_sel, donor_ref=donor_ref)
dx = dfs17



In [33]:

    
nbursts.loc[data_id] = dx.num_bursts



In [34]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_two_gaussians(add_bridge=False, p2_center=0.4)
fitter.fit_histogram()
E_pr_fret.loc[data_id] = fitter.params['p2_center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      17d
      0.297846
      0.287886
      0.276148
      0.279504
      0.283104
      0.284872
      0.271156
      0.271861
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 28.2\% \qquad\Delta E_{fit} = 2.67 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 43.4\% \qquad\Delta E_{fit} = 3.74 \%$$



In [35]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='p2_center', show_fit_value=True);

22bp sample



In [36]:

    
data_id = '22d'
if reload_data:
    d22 = loader.photon_hdf5(files_dict[data_id])
    d22.calc_bg_cache(**bg_kwargs_auto)
if burst_search:
    d22.burst_search(m=10, F=F, dither=dither)
    if delete_ph_times: d22.delete('ph_times_m')









    



 * No cached BG rates, recomputing:
 - Calculating BG rates ... [DONE]
 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   [DONE Counting D/A]



In [37]:

    
dfs22 = Sel(d22, select_bursts.size, th1=30, gamma=gamma_sel, donor_ref=donor_ref)
dx = dfs22



In [38]:

    
nbursts.loc[data_id] = dx.num_bursts



In [39]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_gaussian()
fitter.fit_histogram()
E_pr_fret.loc[data_id] = fitter.params['center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      22d
      0.133968
      0.131691
      0.124933
      0.126694
      0.119514
      0.124942
      0.122458
      0.12983
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 12.7\% \qquad\Delta E_{fit} = 1.45 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 17.1\% \qquad\Delta E_{fit} = 2.95 \%$$



In [40]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='center', show_fit_value=True);

27bp sample



In [41]:

    
data_id = '27d'
if reload_data:
    d27 = loader.photon_hdf5(files_dict[data_id])
    d27.calc_bg_cache(**bg_kwargs_auto)
if burst_search:
    d27.burst_search(m=10, F=F, dither=dither)#, ph_sel=Ph_sel(Dex='Dem'))
    if delete_ph_times: d27.delete('ph_times_m')









    



 * No cached BG rates, recomputing:
 - Calculating BG rates ... [DONE]
 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   [DONE Counting D/A]



In [42]:

    
dfs27 = Sel(d27, select_bursts.size, th1=30, gamma=gamma_sel, donor_ref=donor_ref)
dx = dfs27



In [43]:

    
nbursts.loc[data_id] = dx.num_bursts



In [44]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_asym_gaussian()
fitter.fit_histogram()
E_pr_fret.loc[data_id] = fitter.params['center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      27d
      0.078874
      0.073995
      0.073122
      0.079337
      0.071889
      0.073837
      0.073261
      0.081081
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 7.6\% \qquad\Delta E_{fit} = 0.92 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 5.9\% \qquad\Delta E_{fit} = 2.16 \%$$



In [45]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='center', show_fit_value=True);



In [46]:

    
fitter = bext.bursts_fitter(dx)
fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
fitter.model = mfit.factory_gaussian()
fitter.fit_histogram()
E_pr_fret.loc[data_id] = fitter.params['center']
display(E_pr_fret.loc[[data_id]])
print_fit_report(E_pr_fret.loc[data_id], gamma=0.42, leakage=leakage, dir_ex_t=dir_ex_t)









    






  
    
      Channel
      0
      1
      2
      3
      4
      5
      6
      7
    
    
      Sample
      
      
      
      
      
      
      
      
    
  
  
    
      27d
      0.087183
      0.091312
      0.079124
      0.08268
      0.077164
      0.084574
      0.077923
      0.082638
    
  








    





$$\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = 8.3\% \qquad\Delta E_{fit} = 1.41 \%$$






    





$$\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = 7.6\% \qquad\Delta E_{fit} = 3.25 \%$$



In [47]:

    
dplot(dx, hist_fret, show_model=True, 
      show_fit_stats=True, fit_from='center', show_fit_value=True);

5-samples: global analysis

Proximity ratios fitted from multispot data:



In [48]:

    
E_pr_fret = E_pr_fret.round(6)
E_pr_fret



In [49]:

    
nbursts = nbursts.astype(int)
nbursts



In [50]:

    
E_pr_fret.to_csv('results/Multi-spot - dsDNA - PR - all_samples all_ch.csv')
nbursts.to_csv('results/Multi-spot - dsDNA - nbursts - all_samples all_ch.csv')



In [51]:

    
norm = (E_pr_fret.T - E_pr_fret.mean(1))#/E_pr_fret.mean(1)
norm_rel = (E_pr_fret.T - E_pr_fret.mean(1))/E_pr_fret.mean(1)
norm.plot()
norm_rel.plot()









    Out[51]:





<matplotlib.axes._subplots.AxesSubplot at 0x1393dfac8>



In [52]:

    
mch_plot_bg(dx)

NOTE: The 27d and DO samples have a trend that correlates with the A-ch background. For these samples could be beneficial to use a D-only burst search.

NOTE 2: Like observed during the leakage fit, even a D-only burst search results in +2% offset (DO) in CH1, this cannot be correlation with the A-background and must be slightly different gamma in the spot.

Corrected $E$ from μs-ALEX data:



In [53]:

    
data_file = 'results/usALEX-5samples-E-corrected-all-ph.csv'
data_alex = pd.read_csv(data_file).set_index('sample')#[['E_pr_fret_kde']]
data_alex.round(6)









    Out[53]:






  
    
      
      n_bursts_all
      n_bursts_do
      n_bursts_fret
      E_kde_w
      E_gauss_w
      E_gauss_w_sig
      E_gauss_w_err
      E_gauss_w_fiterr
      S_kde
      S_gauss
      S_gauss_sig
      S_gauss_err
      S_gauss_fiterr
      E_pr_do_kde
      nt_mean
    
    
      sample
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      7d
      1172
      587
      542
      0.9290
      0.924307
      0.060130
      0.002583
      0.001782
      0.5520
      0.550744
      0.099172
      0.004260
      0.002501
      0.0022
      22.227823
    
    
      12d
      1307
      329
      948
      0.7398
      0.727043
      0.092062
      0.002990
      0.001995
      0.5758
      0.558745
      0.105387
      0.003423
      0.003823
      0.0154
      22.002399
    
    
      17d
      2489
      464
      1964
      0.4268
      0.422624
      0.112952
      0.002549
      0.002008
      0.5462
      0.539604
      0.112457
      0.002538
      0.002187
      0.0124
      21.193171
    
    
      22d
      2054
      320
      1672
      0.1774
      0.181067
      0.077181
      0.001888
      0.001536
      0.5458
      0.547034
      0.114990
      0.002812
      0.002676
      0.0000
      23.016538
    
    
      27d
      791
      160
      589
      0.0826
      0.083971
      0.069303
      0.002856
      0.001585
      0.5208
      0.561290
      0.114456
      0.004716
      0.003679
      -0.0088
      16.830070



In [54]:

    
E_alex = data_alex.E_gauss_w
E_alex









    Out[54]:





sample
7d     0.924307
12d    0.727043
17d    0.422624
22d    0.181067
27d    0.083971
Name: E_gauss_w, dtype: float64

Merging the channels

Merge the data of the different channels:



In [55]:

    
dfs7c = dfs7.collapse()
dfs12c = dfs12.collapse()
dfs17c = dfs17.collapse()
dfs22c = dfs22.collapse()
dfs27c = dfs27.collapse()

Define samples lists

Define list of results and labels:



In [56]:

    
d_samples = [dfs7, dfs12, dfs17, dfs22, dfs27]#, dfso]
d_samples_c = [dfs7c, dfs12c, dfs17c, dfs22c, dfs27c ]
d_labels = ['7d', '12d', '17d', '22d', '27d']#, 'DO']
CH = np.arange(8)
CH_labels = ['CH%d' % i for i in CH]
dist_s_bp = [7, 12, 17, 22, 27]

Print a summary of current processed data:



In [57]:

    
def print_params(d_samples, d_labels, status=False):
    print('Sample              Model             Ph_sel')
    for dx, name in zip(d_samples, d_labels):
        print("%3s %25s %35s" % (name, dx.E_fitter.model.name, dx.ph_sel))
    if status:
        print()
        for dx, name in zip(d_samples, d_labels):
            print(dx.status())



In [58]:

    
print_params(d_samples, d_labels, 1)









    



Sample              Model             Ph_sel
 7d ((Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_')) + Model(bridge_function, prefix='br_'))                                 all
12d ((Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_')) + Model(bridge_function, prefix='br_'))                                 all
17d (Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_'))                                 all
22d           Model(gaussian)                                 all
27d           Model(gaussian)                                 all

multispot_7d_New_150p_320mW_steer_3 BS_all L10 m10 MR44 G1.000 BGexp-30s bg Lk0.000size_th30Ga_0.4
multispot_12d_New_30p_320mW_steer_3 BS_all L10 m10 MR36 G1.000 BGexp-30s bg Lk0.000size_th30Ga_0.4
multispot_17d_100p_320mW_steer_1 BS_all L10 m10 MR41 G1.000 BGexp-30s bg Lk0.000size_th30Ga_0.4
multispot_22d_30p_320mW_steer_1 BS_all L10 m10 MR37 G1.000 BGexp-30s bg Lk0.000size_th30Ga_0.4
multispot_27d_50p_320mW_steer_1 BS_all L10 m10 MR40 G1.000 BGexp-30s bg Lk0.000size_th30Ga_0.4

Plot FRET vs distance

fontsize = 12 text_pos = {'7d': (10.5, 0.92), '12d': (15.5, 0.74), '17d': (16, 0.43), '22d': (21, 0.18), '27d': (29, 0.15)} text_kwargs = dict(ha='right', va='center', bbox=dict(facecolor='white', edgecolor='white'), zorder=2, fontsize=fontsize-1, ) dfun = lambda E_fit: 100*(E_fit.max()-E_fit.min()) fig, ax = plt.subplots() ax.plot(dist_s_bp, E_fret_mch, '+', lw=2, mew=1.2, ms=8, zorder=4) ax.plot(dist_s_bp, E_alex, '-', lw=3, mew=0, alpha=0.5, color='r', zorder=3) plt.title('Multi-spot smFRET dsDNA, Gamma = %.2f' % multispot_gamma) plt.xlabel('Distance in base-pairs', fontsize=fontsize); plt.ylabel('E', fontsize=fontsize) plt.ylim(0, 1); plt.xlim(0, 30) plt.grid(True) plt.legend(['CH1','CH2','CH3','CH4','CH5','CH6','CH7','CH8', u'μsALEX'], fancybox=True, prop={'size':fontsize-1}, #loc='upper right', bbox_to_anchor=(1.05, 1)) loc='best') if deviance_plot: for sample in E_fret_mch.index: delta = dfun(E_fret_mch.loc[sample]) text_kwargs.update( s="%.1f%%" % delta, #s="$\Delta = %.1f%%$" % delta, x=text_pos[sample][0], y=text_pos[sample][1], ) ax.text(**text_kwargs) #ax.set_axisbelow(True) #if save_figure: plt.savefig("FRET vs distance - %s SPW F%d.png" % (fret_gen_fit_func.__name__, F)) #plt.savefig(PLOT_DIR+"FRET vs distance - Gpost_th30.png", dpi=200, bbox_inches='tight')

Channel	0	1	2	3	4	5	6	7
Sample
7d	953	363	1018	1543	1413	656	1648	1844
12d	926	576	1112	1474	1349	824	1510	1575
17d	740	493	778	1026	1005	642	1066	1160
22d	322	251	354	495	354	282	438	470
27d	630	430	736	910	933	619	933	1039

Channel	0	1	2	3	4	5	6	7
Sample
7d	0.866766	0.872245	0.838805	0.84337	0.835858	0.858535	0.84036	0.846814

Channel	0	1	2	3	4	5	6	7
Sample
12d	0.58912	0.580325	0.572867	0.574415	0.563134	0.555029	0.565811	0.563246

Channel	0	1	2	3	4	5	6	7
Sample
17d	0.297846	0.287886	0.276148	0.279504	0.283104	0.284872	0.271156	0.271861

Channel	0	1	2	3	4	5	6	7
Sample
22d	0.133968	0.131691	0.124933	0.126694	0.119514	0.124942	0.122458	0.12983

Channel	0	1	2	3	4	5	6	7
Sample
27d	0.078874	0.073995	0.073122	0.079337	0.071889	0.073837	0.073261	0.081081

Channel	0	1	2	3	4	5	6	7
Sample
27d	0.087183	0.091312	0.079124	0.08268	0.077164	0.084574	0.077923	0.082638

	n_bursts_all	n_bursts_do	n_bursts_fret	E_kde_w	E_gauss_w	E_gauss_w_sig	E_gauss_w_err	E_gauss_w_fiterr	S_kde	S_gauss	S_gauss_sig	S_gauss_err	S_gauss_fiterr	E_pr_do_kde	nt_mean
sample
7d	1172	587	542	0.9290	0.924307	0.060130	0.002583	0.001782	0.5520	0.550744	0.099172	0.004260	0.002501	0.0022	22.227823
12d	1307	329	948	0.7398	0.727043	0.092062	0.002990	0.001995	0.5758	0.558745	0.105387	0.003423	0.003823	0.0154	22.002399
17d	2489	464	1964	0.4268	0.422624	0.112952	0.002549	0.002008	0.5462	0.539604	0.112457	0.002538	0.002187	0.0124	21.193171
22d	2054	320	1672	0.1774	0.181067	0.077181	0.001888	0.001536	0.5458	0.547034	0.114990	0.002812	0.002676	0.0000	23.016538
27d	791	160	589	0.0826	0.083971	0.069303	0.002856	0.001585	0.5208	0.561290	0.114456	0.004716	0.003679	-0.0088	16.830070