alexbw

from numpy import *
from scipy import optimize

def n_gaussians(parameters):
    """
    Returns a summed gaussian function with the given parameters,
    so parameters is a list with repeated motif [height, center_x, center_y, a, b, c]
    for each gaussian. So, two gaussians would be defined by a length-12 list.
    
    Guided by: http://code.google.com/p/agpy/source/browse/trunk/agpy/gaussfitter.py

alexbw

class RepeatingTimer(object):
	"""
	USAGE:
	from time import sleep
	def myFunction(inputArgument):
		print(inputArgument)
	
	r = RepeatingTimer(0.5, myFunction, "hello")
	r.start(); sleep(2); r.interval = 0.05; sleep(2); r.stop()
	"""

alexbw

class Kalman:
	"""
	USAGE:
	
	# e.g., tracking an (x,y) point over time
	k = Kalman(state_dim = 6, obs_dim = 2)
	
	# when you get a new observation — 
	someNewPoint = np.r_[1,2]
	k.update(someNewPoint)

alexbw

import SimpleCV as scv
import scipy.ndimage as ndimage

radius = 5

# Grab a little blobby image from the interwebs (it's a picture of a mouse, taken with the Microsoft Kinect)
scv_img = scv.Image("http://i.imgur.com/mGg0V.png")

# Grab a grayscale layer of the image
img = scv_img.getNumpy()[:,:,0]

alexbw

import numpy as np
import cPickle as pickle
import joblib
from moseq.train import ARHMM, train_model
from moseq.train.util import whiten_all
from collections import OrderedDict
from syllables import analysis

# Load the data
with open("/data/efs/drugs/alldoses/dataset.pkl","r") as f:

alexbw

======================================================================
ERROR: test_eye_complex64 (test_linalg.test_linalg)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/dattalab/Downloads/scikits.cuda/tests/test_linalg.py", line 21, in setUp
    linalg.init()
  File "/home/dattalab/Downloads/scikits.cuda/scikits/cuda/misc.py", line 136, in init
    cublas.cublasInit()
  File "/home/dattalab/Downloads/scikits.cuda/scikits/cuda/cublas.py", line 235, in cublasInit
    _libcublas_ctx = cublasCreate()

alexbw

def f(x):
  assert x != 0, 'Do not pass zero!'
  return x * x

alexbw

def f(n):
  z = []
  # We ask you to tell us the element dtype of the list
  autograph.set_element_type(z, tf.int32)
  for i in range(n):
    z.append(i)
  # when you're done with the list, stack it
  # (this is just like np.stack)
  return autograph.stack(z) 

alexbw

def f(n):
  if n >= 0:
    while n < 5:
      n += 1
      print(n)
  return n

alexbw

def collatz(a):
    counter = 0
    while a != 1:
        if a % 2 == 0:
            a = a // 2
        else:
            a = 3 * a + 1
        counter = counter + 1
    return counter