In [1]:

    

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


    

In [2]:

    

sns.set(font_scale=1.3)
sns.set_style('whitegrid')
colors = np.array(sns.color_palette("Set1", 9))[(1,0,2,3,4,8,6,7), :]
sns.set_palette(colors, 8)


    

In [3]:

    

save_figures = True

def savefig(filename, **kwargs):
    if not save_figures:
        return
    import os
    dir_ = 'figures/'
    kwargs_ = dict(dpi=300, bbox_inches='tight')
                   #frameon=True, facecolor='white', transparent=False)
    kwargs_.update(kwargs)
    plt.savefig(dir_ + filename, **kwargs_)
    print('Saved: %s' % (dir_ + filename))


    

In [4]:

    

fs = [f for f in Path('results').glob('singlespot_*_histfit_*window30s*')]
[f.stem for f in fs]


    

    
Out[4]:





['singlespot_bubble-bubble_ALEX_150uWGreen_100uWRed_Runoff_kinetics_RT_1_histfit_ampl_only__window30s_step10s',
 'singlespot_bubble-bubble_ALEX_150uWGreen_100uWRed_Runoff_kinetics_RT_2_histfit_ampl_only__window30s_step10s',
 'singlespot_bubble-bubble_ALEX_150uWGreen_100uWRed_Runoff_kinetics_RT_3_histfit_ampl_only__window30s_step10s']

In [5]:

    

df_alex = pd.read_csv(fs[0], index_col=0)
df_alex.head()


    

    
Out[5]:






  

    

      

      
p1_center
      
p1_sigma
      
p2_amplitude
      
p2_center
      
p2_sigma
      
tmean
      
tstart
      
tstop
      
kinetics
    
  
  

    

      
-585.0
      
0.559
      
0.1267
      
0.1258
      
0.902
      
0.0875
      
-585.0
      
-600
      
-570
      
0.1258
    
    

      
-575.0
      
0.559
      
0.1267
      
0.1367
      
0.902
      
0.0875
      
-575.0
      
-590
      
-560
      
0.1367
    
    

      
-565.0
      
0.559
      
0.1267
      
0.1609
      
0.902
      
0.0875
      
-565.0
      
-580
      
-550
      
0.1609
    
    

      
-555.0
      
0.559
      
0.1267
      
0.2044
      
0.902
      
0.0875
      
-555.0
      
-570
      
-540
      
0.2044
    
    

      
-545.0
      
0.559
      
0.1267
      
0.1832
      
0.902
      
0.0875
      
-545.0
      
-560
      
-530
      
0.1832
    
  

In [6]:

    

fm = [f for f in Path('results').glob('multispot_*_histfit_*window30s*')]
[f.stem for f in fm]


    

    
Out[6]:





['multispot_2015-07-29_bubble-bubble-open-complex-run-off-kinetics-600mW-steer110_7_histfit_ampl_only__window30s_step1s',
 'multispot_2015-07-30_bubble-bubble-run-off-kinetics-800mW-steer110_8_histfit_ampl_only__window30s_step1s',
 'multispot_2015-07-31_bubble-bubble-run-off-kinetics-800mW-steer110_12_histfit_ampl_only__window30s_step1s']

In [7]:

    

df_mspot = pd.read_csv(fm[1], index_col=0)
df_mspot.index.name = 'Time (s)'
df_mspot.head()


    

    
Out[7]:






  

    

      

      
p1_center
      
p1_sigma
      
p2_amplitude
      
p2_center
      
p2_sigma
      
p3_amplitude
      
p3_center
      
p3_sigma
      
tmean
      
tstart
      
tstop
      
kinetics
    
    

      
Time (s)
      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
-885.0
      
0.071
      
0.0576
      
0.6834
      
0.617
      
0.2057
      
0.1264
      
0.936
      
0.0513
      
-885.0
      
-900
      
-870
      
0.1561
    
    

      
-884.0
      
0.071
      
0.0576
      
0.6970
      
0.617
      
0.2057
      
0.1200
      
0.936
      
0.0513
      
-884.0
      
-899
      
-869
      
0.1469
    
    

      
-883.0
      
0.071
      
0.0576
      
0.6978
      
0.617
      
0.2057
      
0.1207
      
0.936
      
0.0513
      
-883.0
      
-898
      
-868
      
0.1474
    
    

      
-882.0
      
0.071
      
0.0576
      
0.6964
      
0.617
      
0.2057
      
0.1163
      
0.936
      
0.0513
      
-882.0
      
-897
      
-867
      
0.1431
    
    

      
-881.0
      
0.071
      
0.0576
      
0.6916
      
0.617
      
0.2057
      
0.1165
      
0.936
      
0.0513
      
-881.0
      
-896
      
-866
      
0.1441
    
  

In [8]:

    

fig, ax = plt.subplots(figsize=(7, 5))
sty = dict(lw=0)
df_mspot.kinetics[::10].plot(ms=8, marker='o', zorder=2)
df_alex.kinetics.plot(ms=7, marker='s', zorder=1)
plt.ylabel('Run-off fraction')
plt.title('Multi-spot vs single-spot kinetics')
plt.xlim(-200, 600)
plt.legend(['Multispot', 'μs-ALEX'])
savefig('Multi-spot vs single-spot kinetics.png')


    

    




Saved: figures/Multi-spot vs single-spot kinetics.png






    

In [ ]: