andrewgiessel

from scipy.optimize import curve_fit

def linear(x, m, b):
    return m*x + b

x = np.arange(0,400,1)
y = linear(x, -0.2, 3)
yn = y + np.random.normal(size=len(x))*2
yn[50:350] *= np.random.normal(size=300)

andrewgiessel

// most recent update:  04/22/2014

// one liner
javascript:function go(){var asdf = window.location.host.replace(/\./g, "-");window.location = window.location.protocol+'//'+asdf+'.ezp1.harvard.edu'+window.location.pathname+window.location.search;}go();void(0); 

// expanded

function go() { 
    var asdf = window.location.host.replace(/\./g, "-");   // replace all the periods with dashes (uses regexp)
    window.location = window.location.protocol+'//' + 

andrewgiessel

#!/usr/bin/env python

import subprocess
import time
import re
import os
import sys

# launch the server and get the jobid

andrewgiessel

import numpy as np

def contiguous_regions(condition):
    """Finds contiguous True regions of the boolean array "condition". Returns
    three 1d arrays:  start indicies, stop indicies and lengths of contigous regions
    """

    d = np.diff(condition)
    idx, = d.nonzero() 
    idx += 1 # need to shift indices because of diff

andrewgiessel

import numpy as np
import scipy.signal
import matplotlib.pyplot as plt

n=256
data = np.random.random(n)
data_fft = np.fft.fft(data)

# see also : http://mpastell.com/2010/01/18/fir-with-scipy/

andrewgiessel

# imports
import numpy as np
import scipy as scipy

# construct static 2d distance tree
size = 256
x, y = np.mgrid[0:size, 0:size]
distance_tree = spatial.KDTree(zip(x.ravel(), y.ravel()))

# simple use case

andrewgiessel

import tables
import numpy as np

# Store "all_data" in a chunked array...
# from: http://stackoverflow.com/questions/8843062/python-how-to-store-a-numpy-multidimensional-array-in-pytables

f = tables.openFile('all_data.hdf', 'w')
atom = tables.Atom.from_dtype(all_data.dtype)
filters = tables.Filters(complib='blosc', complevel=5)
ds = f.createCArray(f.root, 'all_data', atom, all_data.shape, filters=filters)

andrewgiessel

# setup colormaps with alpha backgrounds

imshow(np.random.random((10,10)), cmap='Reds')
imshow(np.random.random((10,10)), cmap='Blues')
close('all')

import copy
red_alpha = copy.copy(mpl.cm.Reds)
blue_alpha = copy.copy(mpl.cm.Blues)

andrewgiessel

{
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  "name": "replace text in files"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

andrewgiessel

import mahotas

def excludePixels(image, size_cutoff=1):
    labeled_image = mahotas.label(image)[0]

    for label_id in range(labeled_image.max()+1):
        label_id_index = labeled_image == label_id
        if label_id_index.sum() <= size_cutoff:
            labeled_image[label_id_index] = 0