In [1]:

    

import warnings
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

from skimage.io import imsave, imread


    

In [3]:

    

from os import mkdir
from os.path import exists


    

In [4]:

    

from thunder.images import fromtif, frombinary
from showit import image


    

In [4]:

    

# neurofinder data
directory = '/groups/freeman/freemanlab/from_tier2/Nick/neurofinder/neurofinder.02.00'
data = fromtif(directory + '/images', ext='tiff', engine=sc)


    

In [5]:

    

# kayvon data
directory = '/groups/freeman/freemanlab/from_tier2/Nick/kayvon/kd035_102616'
data = fromtif(directory + '/images', ext='tif', nplanes=1, engine=sc)


    

In [5]:

    

# large field data layer 4 data
directory = '/groups/freeman/freemanlab/from_tier2/Nick/mesoscope/000011'
data = frombinary(directory + '/images', engine=sc)


    

In [6]:

    

data


    

    
Out[6]:





Images
mode: spark
dtype: int16
shape: (9302, 884, 896)

In [9]:

    

data = data[:1000,:,:]


    

In [7]:

    

mean = data.mean().toarray()


    

In [8]:

    

image(mean, clim=(0, 2000));


    

In [17]:

    

imsave('/groups/svoboda/home/sofroniewn/data/tmp/mean.tif', mean.astype('float32'), plugin='tifffile', photometric='minisblack')


    

In [7]:

    

from mesoscope.registrations import register, register_blocks, register_blocks_piecewise


    

In [15]:

    

data_r, shifts = register(data)
mean_r = data_r.mean().toarray()


    

In [11]:

    

data_rb = register_blocks(data, size=(32, 32))
mean_rb = data_rb.mean().toarray()


    

In [12]:

    

data_rb = register_blocks(data_rb, size=(32, 32))
mean_rb = data_rb.mean().toarray()


    

In [16]:

    

image(mean_rb, clim=(0, 2000));


    

In [13]:

    

imsave('/groups/svoboda/home/sofroniewn/data/tmp/mean_rb232.tif', mean_rb.astype('float32'), plugin='tifffile', photometric='minisblack')


    

In [21]:

    

if not exists(directory + '/summary'):
    mkdir(directory + '/summary')


    

In [22]:

    

imsave(directory + '/summary/mean.tif', mean.astype('float32'), plugin='tifffile', photometric='minisblack')


    

In [20]:

    

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    from mesoscope.CC import CC
from mesoscope.models import overlay, filter_shape


    

In [21]:

    

diameter = 11
algorithm = CC(diameter=diameter, clip_limit=0.04, theshold=0.2, sigma_blur=1, boundary=(1,1))
unmerged = algorithm.fit(data)


    

    




/misc/local/python-2.7.11/lib/python2.7/site-packages/skimage/util/dtype.py:110: UserWarning: Possible precision loss when converting from float64 to uint16
  "%s to %s" % (dtypeobj_in, dtypeobj))
/misc/local/python-2.7.11/lib/python2.7/site-packages/skimage/util/dtype.py:110: UserWarning: Possible precision loss when converting from float64 to uint8
  "%s to %s" % (dtypeobj_in, dtypeobj))

In [24]:

    

model = unmerged.merge(overlap=0.20, max_iter=3, k_nearest=10)
model = filter_shape(model, min_diameter = 0.7*diameter, max_diameter = 1.3*diameter, min_eccentricity = 0.2)


    

In [16]:

    

localcorr = data.localcorr(diameter)


    

In [ ]:

    

imsave(directory + '/summary/localcorr.tif', localcorr.astype('float32'), plugin='tifffile', photometric='minisblack')


    

In [25]:

    

blend = overlay(model, image=localcorr, threshold=5, correct=False)
fig = plt.figure(figsize=[10,10])
ax = plt.axes()
image(blend, ax=ax)
plt.xlim([0, blend.shape[1]]);
plt.ylim([blend.shape[0], 0]);


    

In [58]:

    

if not exists(directory + '/regions'):
    mkdir(directory + '/regions')


    

In [59]:

    

model.save(directory + '/regions/regions-cc.json')


    

In [72]:

    

imsave(directory + '/summary/regions-lc.tif', (255*blend).astype('uint8'), plugin='tifffile', photometric='rgb')


    

In [23]:

    

from mesoscope.traces import dff


    

In [24]:

    

traces = dff(data, model)


    

In [26]:

    

traces.tobinary(directory + '/traces', overwrite=True)


    

In [27]:

    

t = traces.toarray()


    

In [28]:

    

plt.plot(t[0]);


    

In [17]:

    

image(localcorr);


    

    
Out[17]:





<matplotlib.image.AxesImage at 0x2b603e12cd10>






    

In [19]:

    

plt.plot(localcorr.mean(axis=0))


    

    
Out[19]:





[<matplotlib.lines.Line2D at 0x2b603df8d450>]






    

In [ ]: