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rant = data['r'][0][0]
zant = data['z'][0][0]
sep_z = data['sep_z']
sep_r = data['sep_r']
sep_ra = np.linalg.norm(np.array([sep_r-rant,sep_z-zant]),axis=0)

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import numpy as np 
def expand_time(time, shape, verbose=True):
    if verbose:
        print 'Old time shape:',time.shape,'new shape:',shape
    
    output = np.tile(time, shape[1]).reshape(np.fliplr([shape])[0]).T
    if verbose:
        print 'output time shape:',output.shape
    return output

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""" Calculate mean std of clipped array"""
import numpy as np
from scipy import interpolate

def calc_func_clipped(X, Y, func=np.mean, npoints=None, verbose=False):
    """ Calculates func of Y values, where Y is clipped to the longest continuous
    values of X to prevent aliasing
        
    """
    # Size of each data vector

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import logging
def func(verbose=False, log=False):
    if log:
        logging.basicConfig(level=getattr(logging, log.upper(), 10))
    logger = logging.getLogger()
    msg = "Message"
    logger.info(msg)
    if verbose:
        print msg  
        

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import numpy as np
import matplotlib.pyplot as plt

def getData(fd, sweepnr, sweepwidth=3000, rawdatatype='int16' ):
    byte_offset = sweepwidth * sweepnr * np.dtype(rawdatatype).itemsize
    fd.seek(byte_offset)
    sig = np.fromfile(fd,dtype=rawdatatype,count=sweepwidth)
    return sig

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from scipy import signal


def filtfilt_lowpass(sig,cutoff,Fs=1,order=5):
    """ Low pass butterworth filt filter
    """
    nyq = np.float(Fs)/2
    cutoffnorm = cutoff/nyq;
    b, a = signal.butter(order,cutoffnorm,btype='low');
    sig = signal.filtfilt(b,a,sig)

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def filtfilt_lowpass(sig,cutoff,Fs=1,order=5):
    """ Low pass butterworth filt filter
    """
    nyq = np.float(Fs)/2
    cutoffnorm = cutoff/nyq;
    b, a = signal.butter(order,cutoffnorm,btype='low');
    sig = signal.filtfilt(b,a,sig)
    return sig

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import scipy.signal

def freq_hilbert(sig,Fs):
    """ Calcultes the instantaneous frequency based on the hilbert 
    transform
    """
    sigorig = sig
    sig = sig.real
    ts = 1.0/Fs
    hilb = scipy.signal.hilbert(sig)

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def calc_moving_avg_std(x, window=10):
    """
    Calculates the moving average and standard deviation along the window
    
    Parameters
    ----------
    x : array
    window : int (default=10)
        Window size
        

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def convert_dict_keys_to_numpy(dictionary):
    """
    Converts each element in dictionary to numpy array
    
    Parameters:
    ----------
    dictionary : dict
        Dictionary where each element is a list that must be converted to array
    
    Returns: