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{
 "metadata": {
  "name": "Fitting SciLife"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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{
 "metadata": {
  "name": "Lab 1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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from random import shuffle

slices = [1,2,3] * 4 + [20, 25, 30] * 2
shuffle(slices)

fig = plt.figure(figsize=[10, 10])
ax = fig.add_subplot(111)

cmap = plt.cm.prism
colors = cmap(np.linspace(0., 1., len(slices)))

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# Copyright: Luis Pedro Coelho <[email protected]>, 2012
# License: MIT
import numpy as np


def read_roi(fileobj):
    """
    points = read_roi(fileobj)

    Read ImageJ's ROI format

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def estimate_size_factor(counts): 
 
    #COUNTS = GENES (rows) x CELLS (columns) 
    genes = counts[:, 0]
    #ONLY COUNTS WITHOUT HEADER NOR GENES
    counts = numpy.array(counts[1:, 1:], dtype = int) 
 
    #DONT REMOVE INFITE VALUES AS IT CAUSES PROBLEMS
    log_counts = numpy.log(counts)
 

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

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