Setup



In [1]:

    
%matplotlib inline









    



/usr/local/python-2.7.6/lib/python2.7/site-packages/matplotlib-1.5.1-py2.7-linux-x86_64.egg/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')



In [2]:

    
from numpy import array
import matplotlib.pyplot as plt
import seaborn as sns









    



/usr/local/python-2.7.6/lib/python2.7/site-packages/matplotlib-1.5.1-py2.7-linux-x86_64.egg/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))



In [3]:

    
import thunder
from showit import image, tile
import matplotlib.animation as animation



In [4]:

    
import json



In [5]:

    
from os.path import join, exists
from os import mkdir, makedirs



In [6]:

    
from numpy import save



In [7]:

    
from pandas import DataFrame

Set directory and session information



In [8]:

    
directory = '/tier2/freeman/Nick/lfov.calibration'



In [25]:

    
key = '2016-04-24-6-vision'
name = 'anm-0330549'



In [26]:

    
path = join(directory, 'reprocessed', name, key)
print exists(path)









    



True

Create savepath



In [27]:

    
savepath = join(path, 'traces')
if not exists(savepath):
    makedirs(savepath)

Load data



In [28]:

    
data = thunder.images.frombinary(join(path, 'images'), engine=sc)



In [29]:

    
data.cache();

Load sources



In [30]:

    
from regional import many



In [31]:

    
tmp = []
for i in range(7):
    with open(join(path, 'sources', 'sources-%04i.json' % i), 'r') as fid:
        tmp.append(json.load(fid))
regions = many(tmp)



In [32]:

    
regions









    Out[32]:





regions
count: 7

Create extraction model



In [33]:

    
from extraction.model import ExtractionModel



In [34]:

    
sp = ExtractionModel(regions)

Get and save raw fluorescent traces



In [35]:

    
traces = sp.transform(data)



In [36]:

    
traces.tobinary(join(savepath, 'raw'))

Plot traces



In [37]:

    
raw = DataFrame(traces.toarray().T)



In [38]:

    
plt.plot(raw[0]);



In [39]:

    
plt.plot(raw.mean(axis=1));



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