daguiam

print 'test gist'

daguiam

import matplotlib.pyplot as plt

fig, axes = plt.subplots(3, 4, sharex=True, sharey=True)
# add a big axes, hide frame
fig.add_subplot(111, frameon=False)
# hide tick and tick label of the big axes
plt.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
plt.xlabel("common X")
plt.ylabel("common Y")
plt.show()

daguiam

import matplotlib.pyplot as plt

fig, axes = plt.subplots(3, 4, sharex=True, sharey=True)
# add a big axes, hide frame
fig.add_subplot(111, frameon=False)
# hide tick and tick label of the big axes
plt.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
plt.xlabel("common X")
plt.ylabel("common Y")
plt.show()

daguiam

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:

daguiam

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
        

daguiam

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)

daguiam

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

daguiam

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)

daguiam

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

daguiam

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