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import numpy as np
import dask.array as da
import numpy as np
import time
import napari

scales = [1,2,4,8,16]
chunks = (128,) * 3
full_size = (8192,8192,8192)

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java -Xmx31g -XX:+UseConcMarkSweepGC -cp C:\Users\parkw\.m2\repository\org\janelia\saalfeldlab\n5-utils\0.0.5-SNAPSHOT\n5-utils-0.0.5-SNAPSHOT.jar;C:\Users\parkw\.m2\repository\net\imglib2\imglib2\5.8.0\imglib2-5.8.0.jar;^
C:\Users\parkw\.m2\repository\org\janelia\saalfeldlab\n5\2.1.4\n5-2.1.4.jar;^
C:\Users\parkw\.m2\repository\org\tukaani\xz\1.8\xz-1.8.jar;^
C:\Users\parkw\.m2\repository\org\lz4\lz4-java\1.5.0\lz4-java-1.5.0.jar;^
C:\Users\parkw\.m2\repository\com\google\code\gson\gson\2.8.5\gson-2.8.5.jar;^
C:\Users\parkw\.m2\repository\org\scijava\scijava-common\2.82.0\scijava-common-2.82.0.jar;^
C:\Users\parkw\.m2\repository\org\scijava\parsington\1.0.4\parsington-1.0.4.jar;^
C:\Users\parkw\.m2\repository\org\bushe\eventbus\1.4\eventbus-1.4.jar;^
C:\Users\parkw\.m2\repository\org\apache\commons\commons-compress\1.18\commons-compress-1.18.jar;^
C:\Users\parkw\.m2\repository\org\janelia\saalfeldlab\n5-imglib2\3.4.1\n5-imglib2-3.4.1.jar;^

d-v-b

[hyperlink to viewer](http://neuroglancer-demo.appspot.com/#!%7B%22dimensions%22:%7B%22x%22:%5B1e-9%2C%22m%22%5D%2C%22y%22:%5B1e-9%2C%22m%22%5D%2C%22z%22:%5B1e-9%2C%22m%22%5D%7D%2C%22position%22:%5B21676.611328125%2C2946.377685546875%2C21234.828125%5D%2C%22crossSectionOrientation%22:%5B0%2C0%2C1%2C0%5D%2C%22crossSectionScale%22:63.75196454781246%2C%22projectionOrientation%22:%5B-0.17717696726322174%2C-0.3176162838935852%2C-0.034147508442401886%2C0.9308931827545166%5D%2C%22projectionScale%22:73427.99372841888%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%22n5://https://janelia-cosem-dev.s3.amazonaws.com/macrophage.n5/fibsem/%22%2C%22shader%22:%22#uicontrol%20float%20upper%20slider%28min=0%2C%20max=1%2C%20step=0.001%2C%20default=1%29%5C%5Cn%5Cn#uicontrol%20float%20lower%20slider%28min=0%2C%20max=1%2C%20step=0.001%2C%20default=0%29%5C%5Cn%5Cnvoid%20main%28%29%20%7BemitGrayscale%28%28clamp%28toNormalized%28getDataValue%28%29%29%2C%20lower%2C%20upper%29%20-%20lower%29%20/%20%28upper%20-%20lower%29%29

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Introduction to COSEM

The COSEM (Cellular Organelle Segmentation in Electron Microscopy) project team at Janelia Research campus uses computer vision techniques to scalably detect subcellular structures in datasets generated with next-generation volumetric electron microscopy.

COSEM processes datasets that are acquired by members of the Hess lab at Janelia Research Campus using Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM). FIB-SEM is an electron microscopy technique that enables volumetric imaging of single cells and / or bulk tissue at nanometer isotropic resolution. For more information about FIB-SEM microscopy and its applications, see these publications: Xu et al., Hoffman et al..

FIB-SEM datasets are large (hundreds of gigabytes), dense, and extremely detailed, which poses a challenge for image pr

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bennettd@c11u24 (base) ➜  hot-knife git:(master) ✗ bash run-align-cosem-cos7.sh 
bsub -q spark32 -a "spark32(master,current)" -W 24:00 commandstring
Master submitted. Job ID is 72407758
vm9913
bsub -q spark32 -a "spark32(worker,current)" -J W72407758 -W 24:00 commandstring
Worker submitted. Job ID is 72407759
vm9913
bsub -q spark32 -a "spark32(worker,current)" -J W72407758 -W 24:00 commandstring
Worker submitted. Job ID is 72407760
vm9913

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run this:

@njit(parallel=True)
def stencil_test2(A):
    @stencil
    def fun (a):
        c = 2
        return 0.3 * (a[-c+1] + a[0] + a[c-1])
    B = fun(A)
    return B

d-v-b

import numpy as np
from scipy.ndimage.interpolation import shift
from dask import delayed
import dask.array as da
import napari
from itertools import product

# define a 2D periodic pattern
x = np.linspace(-np.pi, np.pi, 200) * 5
y = x

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Col1	Col2	Col3
d1	d2	d3
e1	e2	e3

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import numpy as np
from skimage import data
from napari import view
from napari.util import app_context
import dask.array as da

x = np.linspace(-np.pi, np.pi, 100)
x_s = np.sin(x ** 2)
x_c = np.cos(x ** 2)

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(napari) [bennettd@c11u25 ~]$ pip install pyqt5==5.10.1
Collecting pyqt5==5.10.1
  Downloading https://files.pythonhosted.org/packages/e4/15/4e2e49f64884edbab6f833c6fd3add24d7938f2429aec1f2883e645d4d8f/PyQt5-5.10.1-5.10.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (107.8MB)
    100% |████████████████████████████████| 107.8MB 165kB/s 
Requirement already satisfied: sip<4.20,>=4.19.4 in ./miniconda/envs/napari/lib/python3.6/site-packages/sip-4.19.8-py3.6-linux-x86_64.egg (from pyqt5==5.10.1) (4.19.8)
Installing collected packages: pyqt5
Successfully installed pyqt5-5.10.1
(napari) [bennettd@c11u25 ~]$ python napari/examples/nD_image.py 
(napari) [bennettd@c11u25 ~]$ ipython
Python 3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 09:07:38)