How the data looks like and is being used



In [1]:

    
%matplotlib inline
import os
import numpy as np
from matplotlib import pyplot as plt
import mpld3



In [2]:

    
import neuralyzer
from neuralyzer.im import smff



In [4]:

    
plt.rcParams['image.cmap'] = 'gray'
plt.rcParams['image.interpolation'] = 'none'
plt.rcParams['figure.figsize'] = (8,8)



In [3]:

    
#datafile = '/Users/michael/coding/RIKEN/data/140316/data5/data2/Image_0001_0001_channel0_13000_14000.tif'
#datafile = '/home/michael/datac/140316/data5/data2/Image_0001_0001_channel0.tif'
datafile = '/home/michael/datac/140316/data5/data2/Image_0001_0001_channel1_testdata.tif'
data = neuralyzer.get_data(datafile)









    



[ 2015-04-17 15:31:09 ] [ log ] [ INFO ] : NEURALYZER LOGGER CREATED
[ 2015-04-17 15:31:09 ] [ log ] [ DEBUG ] : stdoutloglevel: DEBUG
[ 2015-04-17 15:31:09 ] [ data_handler ] [ DEBUG ] : root_path set to /home/michael/lib/neuralyzer/notebooks/dev
[ 2015-04-17 15:31:09 ] [ data_handler ] [ DEBUG ] : loaded data from cache file: /home/michael/datac/140316/data5/data2/Image_0001_0001_channel1_testdata.tif.hdf5



In [5]:

    
print data.dtype
print data.shape
print data[0].shape









    



uint16
(1000, 256, 256)
(256, 256)

Mean Image and how the axis lay

In the numpy array data the axis are as follows:

data[time, y, x]

therefore, to get the image at time t i can simply do:

data[t]



In [6]:

    
mean = data.mean(axis=0)
# creating a white stripe of 20 pixels width along the first axis!!
mean[:,:20] = mean.max()
plt.imshow(mean)









    Out[6]:





<matplotlib.image.AxesImage at 0x43895d0>

Data Usage



In [8]:

    
datashape = data.shape
data = data.reshape((data.shape[0], data.shape[1]*data.shape[2])).T
data.shape









    Out[8]:





(65536, 1000)



In [10]:

    
# image at timepoint 0
plt.imshow(data[:,0].reshape((256, 256)))









    Out[10]:





<matplotlib.image.AxesImage at 0x47afb90>



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