In [1]:

    

import numpy as np
import os
from invivoinfer.vc_infer_trace import VCAnalysis
import matplotlib.pylab as plt
import seaborn as sns
%matplotlib inline
import imp
import logging
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
from IPython.display import clear_output


    

    




Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:

    

DATA_DIR = os.path.join('/'.join(imp.find_module("invivoinfer")[1].split('/')[:-1]), 'data')
TEST_DATA_PATH = os.path.join(DATA_DIR, 'test_trace.csv')
FIGURES_DIR = os.path.join('/'.join(imp.find_module("invivoinfer")[1].split('/')[:-1]), 'notebooks', 'figures')

trace = np.genfromtxt(TEST_DATA_PATH, delimiter=',')
dt = 0.0001


    

In [3]:

    

# FOR A FASTER TESTING USE 'config_testing.json'
vcanalysis = VCAnalysis(trace, dt, figures_folder=FIGURES_DIR, config='config.json')


    

    




INFO:root:Trace length: 10.2237 sec
INFO:root:We flip the trace to be positive, negative trace detected.

In [4]:

    

dt_trace = np.arange(0, dt*(len(trace)), dt)
plt.plot(dt_trace, vcanalysis.raw_trace)
plt.xlabel('Time (s)')
plt.ylabel('Current (pA)')


    

    
Out[4]:





<matplotlib.text.Text at 0x11206a910>






    

In [5]:

    

vcanalysis.run_analysis(save_pdf=True, distType='LogNormal')
clear_output()


    

In [6]:

    

vcanalysis.plot_mean_offset(save_pdf=True)


    

In [7]:

    

vcanalysis.plot_tau_estimation(save_pdf=True)


    

    




/Users/paolopuggioni/virtualenvironment/py_1/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):






    













    

In [8]:

    

tau1 = vcanalysis.param_priors['tau1'][0]
tau2 = vcanalysis.param_priors['tau2'][0]
print 'Tau1 = {} sec, Tau2 = {} msec'.format(tau1*1000, tau2*1000)


    

    




Tau1 = 7.63593652613e-05 sec, Tau2 = 0.718633914978 msec

In [9]:

    

vcanalysis.inference_results['map']['weights'].squeeze()


    

    
Out[9]:





A         64.727896
stdA      58.874846
freq    1326.418130
Name: 48, dtype: float64

In [10]:

    

vcanalysis.inference_results['map']['metrics']


    

    
Out[10]:





{'aic': 26.918003951815617, 'bic': 22.622064479654853}

In [11]:

    

vcanalysis.inference_results['mh']['weights'].describe()


    

    
Out[11]:






  

    

      

      
A
      
stdA
      
freq
    
  
  

    

      
count
      
2000.000000
      
2000.000000
      
2000.000000
    
    

      
mean
      
65.465157
      
54.213810
      
1458.694777
    
    

      
std
      
15.618751
      
7.310697
      
393.198510
    
    

      
min
      
36.302509
      
11.041378
      
535.812451
    
    

      
25%
      
54.761998
      
50.931254
      
1171.125926
    
    

      
50%
      
61.939383
      
54.510533
      
1446.408341
    
    

      
75%
      
73.059336
      
58.496313
      
1713.989609
    
    

      
max
      
141.470821
      
78.423618
      
2948.160083
    
  

In [12]:

    

vcanalysis.inference_results['mh']['dic']


    

    
Out[12]:





35.447256578884968

In [13]:

    

vcanalysis.plot_mc_results()


    

    




INFO:root:START PLOTTING FIGURES
/Users/paolopuggioni/virtualenvironment/py_1/lib/python2.7/site-packages/matplotlib/collections.py:650: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors_original != str('face'):
/Users/paolopuggioni/virtualenvironment/py_1/lib/python2.7/site-packages/matplotlib/axes/_axes.py:475: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
  warnings.warn("No labelled objects found. "






    






<matplotlib.figure.Figure at 0x112f0c3d0>






    

In [17]:

    

for el in ['A', 'stdA', 'freq']:
    vcanalysis.inference_results['mh']['weights'][el].plot()


    

In [ ]: