andrewgiessel

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

import numpy as np

def makeGaussian(size, fwhm = 3, center=None):
    """ Make a square gaussian kernel.

    size is the length of a side of the square
    fwhm is full-width-half-maximum, which
    can be thought of as an effective radius.
    """

andrewgiessel

PREFIX=$HOME
VERSION=1.2.3

# Install Protocol Buffers
wget http://protobuf.googlecode.com/files/protobuf-2.4.1.tar.bz2
tar -xf protobuf-2.4.1.tar.bz2 
cd protobuf-2.4.1
./configure --prefix=$PREFIX
make
make install

andrewgiessel

import scipy

def plot_mean_and_sem(array, axis=1):
    mean = array.mean(axis=axis)
    sem_plus = mean + scipy.stats.sem(array, axis=axis)
    sem_minus = mean - scipy.stats.sem(array, axis=axis)
    
    fill_between(np.arange(mean.shape[0]), sem_plus, sem_minus, alpha=0.5)
    plot(mean)

andrewgiessel

import numpy as np
def histeq(im,nbr_bins=256):

   #get image histogram
   imhist,bins = np.histogram(im.flatten(),nbr_bins,normed=True)
   cdf = imhist.cumsum() #cumulative distribution function
   cdf = 255 * cdf / cdf[-1] #normalize

   #use linear interpolation of cdf to find new pixel values
   im2 = np.interp(im.flatten(),bins[:-1],cdf)

andrewgiessel

from scipy.signal import butter, lfilter

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq

    b, a = butter(order, [low, high], btype='band')
    y = lfilter(b, a, data)
    return y

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 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

from PySide import QtCore, QtGui
import sys
matplotlib.rcParams['backend.qt4']='PySide'

from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure

class MatplotlibWidget(FigureCanvas):

    def __init__(self, parent=None):