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%matplotlib inline


    

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import numpy as np
import scipy as sp
import pylab as plt
import tomotools
import os


    

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tomo_objects = [
                'a12f2b09-b505-4b5b-be81-55db317f5197',
                '98f78673-92ec-4b60-a784-7d5d8280bc4b',
                '5514c001-5748-4029-885e-b27ccb684682',
                '23c3a8a9-048d-408a-b676-24a564ab553c',
                'f88e99e9-23ae-40af-8e50-5f32229cd929',
                '14ee3d50-6b99-481f-b61b-34496a82a616',
                '392b7a4b-36f6-4d30-8ca1-0cf5ee0c6de4',
                '0f21251a-1ed6-4b89-a36c-db8078e30aec',
                'efee4c82-2e1d-487a-9b2f-95e0cc7dd34d',
                'd4c33336-ab3f-4539-9aac-3a32a0ee905b',
                '1c3e2f04-239c-4793-ab62-d6ca1af73a12',
                '6fb4e2f3-8893-40d9-9246-ef37565a9592',
                '173f4f99-0a99-46c3-a3aa-681fa84cca0b'
                ]


    

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storage_dir = '/diskmnt/a/makov/robotom/'

plt.figure(figsize=(14,10))
for to in tomotools.log_progress(tomo_objects):
    tomo_info = tomotools.get_tomoobject_info(to) 
    experiment_id = tomo_info['_id']
    print(tomo_info['specimen'], experiment_id)
    data_file = os.path.join(storage_dir, experiment_id,'amira.raw')
    data = np.fromfile(data_file, dtype='float32')
    
    x,y = np.histogram(data,bins=1000);
    plt.semilogy(y[:-1],x, label=tomo_info['specimen'])

plt.xlim([0, 3])
plt.grid()
plt.legend(loc=0)


    

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plt.imshow(data.reshape(817,536,536)[200])


    

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data.shape[0]/536./536


    

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