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]:

    

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


    

In [5]:

    

from numpy import save


    

In [6]:

    

from skimage.io import imsave


    

In [ ]:

    

from mesoscope.utils import normalize


    

In [8]:

    

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


    

In [9]:

    

key = '2016-04-18-long'
name = 'anm-0330549'


    

In [10]:

    

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


    

    




True

In [11]:

    

savepath = join(directory, 'reprocessed', name, key, 'summary')
if not exists(savepath):
    makedirs(savepath)


    

In [13]:

    

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


    

In [14]:

    

data.cache();


    

In [15]:

    

mean = data.mean().toarray().astype('float32')


    

In [16]:

    

img = mean[:,:]
fig = plt.figure(figsize=[10,10])
ax = plt.axes()
im = image(img, clim=(0, 3.5*img.mean()), ax=ax)


    

In [17]:

    

imsave(savepath+'/mean.tif', mean, plugin='tifffile', photometric='minisblack')
#imsave(savepath+'/mean-norm.tif', normalize(mean), plugin='tifffile', photometric='minisblack')


    

In [19]:

    

std = data.std().toarray().astype('float32')


    

In [20]:

    

img = std[:,:]
fig = plt.figure(figsize=[10,10])
ax = plt.axes()
im = image(img, clim=(0, 3.5*img.mean()), ax=ax)


    

In [21]:

    

imsave(savepath+'/std.tif', std, plugin='tifffile', photometric='minisblack')
#imsave(savepath+'/std-norm.tif', normalize(std), plugin='tifffile', photometric='minisblack')


    

In [22]:

    

maximum = data.max().toarray().astype('float32')


    

In [23]:

    

img = maximum[:,:]
fig = plt.figure(figsize=[10,10])
ax = plt.axes()
im = image(img, clim=(0, 3.5*img.mean()), ax=ax)


    

In [24]:

    

imsave(savepath+'/maximum.tif', maximum, plugin='tifffile', photometric='minisblack')
#imsave(savepath+'/maximum-norm.tif', normalize(maximum), plugin='tifffile', photometric='minisblack')


    

In [25]:

    

data.shape


    

    
Out[25]:





(14607, 992, 1024)

In [26]:

    

localcorr = data.localcorr((4, 4)).astype('float32')


    

In [27]:

    

img = localcorr[:,:]
fig = plt.figure(figsize=[10,10])
ax = plt.axes()
im = image(img, clim=(0, 3.5*img.mean()), ax=ax)


    

In [28]:

    

imsave(savepath+'/localcorr.tif', localcorr, plugin='tifffile', photometric='minisblack')
#imsave(savepath+'/localcorr-norm.tif', normalize(localcorr), plugin='tifffile', photometric='minisblack')


    

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