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import Graphics.Element exposing (..)
import Window
import Text
import Signal

main = Signal.map view Window.dimensions

view (w, h) = 
  (w, h)
  |> toString

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import Signal
import Graphics.Element exposing (..)
import Color exposing (..)
import Mouse
import Window
import Text

type alias Model = 
  { titles: List String
  , accepted: List Int

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import Graphics.Element exposing (..)
import Graphics.Collage exposing (..)
import Window
import Signal
import Color exposing (..)


main = 
  Signal.map (view x) Window.dimensions

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import Graphics.Element exposing (..)
import Graphics.Collage exposing (..)
import Window
import Signal
import Color exposing (..)


main = 
  Signal.map (view x) Window.dimensions

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# Download or retrieve dataset in SOFT format
soft_filepath = "./GSE11882.soft.gz"
gse = GEOparse.get_GEO(filepath = soft_filepath)


# Parse metadata for individual samples

# [String] -> Dict (individual_id, brain_region, gender, age)
def parse_characteristics(c):

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import pandas as pd
from pandas import DataFrame as DF
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from IPython.display import display
import IPython.html.widgets as widgets
from scipy.cluster.hierarchy import \
    linkage,\
    leaves_list,\

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import numpy as np

mag = np.linalg.norm
r = np.random.random

def cosdist(x,y):
    return sum(x * y) / (mag(x) * mag(y))

def repeat(f,n):
    return [f() for _ in range(n)]

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# source("http://bioconductor.org/biocLite.R")
library("affy")
library("frma")
library("hgu133plus2frmavecs")
library("parallel")

# String -> String
change.file.extension <- function(filename, extension){
  # Strip file extensions from filename, 
  #  and replace with given extension

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from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# String -> [String]
def parse_abstracts(filename):
    """ Given a text file containing PubMed abstracts, 
        return these abstracts as a list of strings. """
    f = open(filename).read()
    lines = f.split("\n")
    docs = [[]]

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# Wikipedia: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Two-sample_Kolmogorov.E2.80.93Smirnov_test
# Scipy source: https://github.com/scipy/scipy/blob/v0.15.1/scipy/stats/stats.py#L3966 

def ks_2samp(data1, data2):
    data1, data2 = map(asarray, (data1, data2)) 
    n1 = data1.shape[0] # n1 and n2 reassigned below, and can be removed here. 
    n2 = data2.shape[0] # maybe this is used for error message, in case inputs aren't lists. But len provides a good error message. 
    n1 = len(data1) # Avoid repetition with list comprehension or map
    n2 = len(data2)
    data1 = np.sort(data1) # Avoid repetition with list comprehension or map