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import xlrd
import sys
import numpy as np

book = xlrd.open_workbook(sys.argv[1])
sheet = book.sheet_by_name('Normalization 1')
headers = [c.value for c in sheet.row(0)]

# find columns
colidx = dict([(name, headers.index(name)) for name in ['Gene', 'rep1', 'rep2', 'rep3']])

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;;; from http://richardriley.net/projects/emacs/dotprogramming#sec-1.5
(when (load "flymake" t)
  (defun flymake-pycheckers-init ()
    (let* ((temp-file (flymake-init-create-temp-buffer-copy
                       'flymake-create-temp-inplace))
           (local-file (file-relative-name
                        temp-file
                        (file-name-directory buffer-file-name))))
      (list "/Users/tjones/elisp/pycheckers.sh"  (list local-file)))))

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    def recalculate_group(self, workspace, indexes):
        '''Recalculate all measurements once post_group has run
        
        workspace - the workspace being operated on
        indexes - the indexes of the group's image sets' measurements
        '''
        m = workspace.measurements
        object_name = self.object_name.value
        assert isinstance(m, cpmeas.Measurements)
        parent_object_numbers = m.get_all_measurements(

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#!/bin/sh

# TODO: check for virtualenv
# TODO: verify python is universal 32+64 bit

virtualenv -p /Library/Frameworks/Python.framework/Versions/2.7/bin/python --no-site-packages ${1}/cpdev
. ${1}/cpdev/bin/activate

pip install -U pip # to get git+https

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import cpscript
from scipy import ndimage

labels = cpscript.Cells.segmented
background_labels = np.array(labels == 0, dtype=int)

dt_labels = ndimage.distance_transform_edt(background_labels)

imVSVG_pixels = cpscript.imVSVG.pixel_data
imVSVG_dt_pixels = imVSVG_pixels * dt_labels

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##########################################################################
# Maximum Response filterbank from
# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
# based on several edge and bar filters.
# Adapted to Python by Andreas Mueller amueller@ais.uni-bonn.de
# Share and enjoy
#

import numpy as np
import matplotlib.pyplot as plt

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local grad = require 'autograd'
local nn = require 'nn'

local W1 = torch.FloatTensor(1, 1, 10, 10):normal()
local pooler = nn.SpatialMaxPooling(2, 2, 2, 2)
local unpooler = nn.SpatialMaxUnpooling(pooler)

-- not sure why these lines are necessary.
pooler.indices = torch.FloatTensor()
pooler.output = torch.FloatTensor()

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import sys
import h5py
import numpy as np

def pygame(h5, name, board, moves):
    for idx in range(board.shape[0]):
        for plane in range(board.shape[1]):
            if plane < 44:
                # different board orderings
                f = board[idx, plane, ...].T[:, ::-1]

thouis

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console