andrewgiessel

    def gaussian(self, height, center_x, center_y, width_x, width_y, rotation):
        """Returns a gaussian function with the given parameters"""
        width_x = float(width_x)
        width_y = float(width_y)

        rotation = np.deg2rad(rotation)
        center_x = center_x * np.cos(rotation) - center_y * np.sin(rotation)
        center_y = center_x * np.sin(rotation) + center_y * np.cos(rotation)

        def rotgauss(x,y):

andrewgiessel

import cPickle as pickle

def save(obj, filename):
    """Simple wrapper to pickle an object on disk
    
    :param: obj, any pickable object
    :param: filename, string representation of the file to save to

    """
    with open(filename, 'wb') as f:

andrewgiessel

import numpy as np
import matplotlib.pyplot as plt

def plot_array(npArray, axis=1, xlim=None, ylim=None):

    plt.figure()

    num_plots = npArray.shape[axis]
    side = np.ceil(np.sqrt(num_plots))
    for current_plot in range(1, num_plots+1):

andrewgiessel

from scipy.optimize import curve_fit

def exponential(t, A, tau, C):
    return A * np.exp(-tau * t) + C

start = None # redefine

ydata = data[start:]
xdata = np.arange(ydata.shape[0])
 

andrewgiessel

import numpy as np
def peakSort(values):
    values = np.array(values)
    low = np.floor(values.shape[0] / 2)
    high = low+1
    new = np.zeros_like(values)
    for i, item in enumerate(np.sort(values)[::-1]):
        if i % 2:
            new[high] = np.argwhere(values==item)
            high += 1

andrewgiessel

import numpy as np
def lifetimeSparseness(data):
    N = data.shape[0]
    top = (np.power(np.sum(data/N), 2))
    bottom = np.sum(np.power(data, 2) / N)
    return (1 -  top / bottom)

andrewgiessel

# good discussion here:  http://stackoverflow.com/questions/4308168/sigmoidal-regression-with-scipy-numpy-python-etc
# curve_fit() example from here: http://permalink.gmane.org/gmane.comp.python.scientific.user/26238
# other sigmoid functions here: http://en.wikipedia.org/wiki/Sigmoid_function

import numpy as np
import pylab
from scipy.optimize import curve_fit

def sigmoid(x, x0, k):
     y = 1 / (1 + np.exp(-k*(x-x0)))

andrewgiessel

andrewgiessel

on open dropped_dir
  tell application "Terminal"
		set dirname to do shell script "perl -e \"print quotemeta ('" & POSIX path of item 1 of dropped_dir & "');\""
		do script "cd " & dirname & "; ipython notebook --pylab; exit"
	end tell
end open

andrewgiessel

# found here: http://stackoverflow.com/questions/1589706/iterating-over-arbitrary-dimension-of-numpy-array

import numpy as np

def iterdim(a, axis=0) :
  a = np.asarray(a);
  leading_indices = (slice(None),)*axis
  for i in xrange(a.shape[axis]) :
    yield a[leading_indices+(i,)]