diff --git a/.travis.yml b/.travis.yml
index be07e2d..d1b123a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -9,5 +9,20 @@ deploy:
     tags: true
     repo: cni/MRS
     all_branches: true
+
+env:
+  global:
+  - PIP_DEPS="coveralls pytest-cov flake8"
+
+python:
+  - '2.7'
+  - '3.6'
+
+install:
+  - travis_retry pip install $PIP_DEPS
+  - travis_retry pip install -r requirements.txt
+  - travis_retry pip install -e .
+
 script:
-- true
+  - flake8 --ignore N802,N806 `find . -name \*.py | grep -v setup.py | grep -v version.py | grep -v __init__.py | grep -v /doc/`
+  - py.test --pyargs MRS --cov-report term-missing --cov=MRS
diff --git a/MRS/analysis.py b/MRS/analysis.py
index 62b4d8a..c1c389e 100644
--- a/MRS/analysis.py
+++ b/MRS/analysis.py
@@ -7,18 +7,14 @@
 in :mod:`MRS.api`.
 
 """
-import os
 
 import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.mlab as mlab
 
 import nitime as nt
-import nitime.timeseries as nts
 import nitime.analysis as nta
 import scipy.fftpack as fft
 import scipy.integrate as spi
-from scipy.integrate import trapz, simps
+from scipy.integrate import trapz
 import scipy.stats as stats
 
 import MRS.leastsqbound as lsq
@@ -49,7 +45,7 @@ def bootstrap_stat(arr, stat=np.mean, n_iters=1000, alpha=0.05):
     stat_orig = stat(arr, 0)
 
     boot_arr = np.empty((arr.shape[-1] , n_iters))
-    for ii in xrange(n_iters):
+    for ii in range(n_iters):
         this_arr=arr[np.random.random_integers(0, arr.shape[0]-1, arr.shape[0])]
         boot_arr[:, ii] = stat(this_arr, 0)
 
@@ -96,22 +92,22 @@ def separate_signals(data, w_idx=[1, 2, 3]):
    return w_data, w_supp_data
 
 
-def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
+def coil_combine(data, w_idx=[1, 2, 3], coil_dim=2, sampling_rate=5000.):
     """
     Combine data across coils based on the amplitude of the water peak,
     according to:
 
     .. math::
-        
+
         X = \sum_{i}{w_i S_i}
 
    Where X is the resulting combined signal, $S_i$ are the individual coil
    signals and $w_i$ are calculated as:
 
    .. math::
-   
+
         w_i = mean(S_i) / var (S_i)
-        
+
     following [Hall2013]_. In addition, we apply a phase-correction, so that
     all the phases of the signals from each coil are 0
 
@@ -119,8 +115,8 @@ def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
     ----------
     data : float array
        The data as it comes from the scanner, with shape (transients, echos,
-    coils, time points) 
-    
+    coils, time points)
+
     w_idx : list
        The indices to the non-water-suppressed transients. Per default we take
         the 2nd-4th transients. We dump the first one, because it seems to be
@@ -131,21 +127,21 @@ def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
 
     sampling rate : float
         The sampling rate in Hz. Default : 5000.
-  
+
     References
     ----------
 
     .. [Hall2013] Emma L. Hall, Mary C. Stephenson, Darren Price, Peter
        G. Morris (2013). Methodology for improved detection of low
-       concentration metabolites in MRS: Optimised combination of signals from 
-       multi-element coil arrays. Neuroimage 86: 35-42.  
+       concentration metabolites in MRS: Optimised combination of signals from
+       multi-element coil arrays. Neuroimage 86: 35-42.
 
     .. [Wald1997] Wald, L. and Wright, S. (1997). Theory and application of
        array coils in MR spectroscopy. NMR in Biomedicine, 10: 394-410.
 
     .. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
        edition. Wiley (West Sussex, UK).
-    
+
     """
     w_data, w_supp_data = separate_signals(data, w_idx)
 
@@ -154,15 +150,15 @@ def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
     freqs_w = np.linspace(-sampling_rate/2.0,
                           sampling_rate/2.0,
                           w_data.shape[-1])
-    
+
     # To determine phase and amplitude, fit a Lorentzian line-shape to each
-    # coils data in each trial: 
+    # coils data in each trial:
     # No bounds except for on the phase:
-    bounds = [(None,None),
-              (0,None),
-              (0,None),
+    bounds = [(None, None),
+              (0, None),
+              (0, None),
               (-np.pi, np.pi),
-              (None,None),
+              (None, None),
               (None, None)]
 
     n_params = len(bounds)
@@ -170,13 +166,11 @@ def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
 
     # Let's fit a Lorentzian line-shape to each one of these:
     for repeat in range(w_data.shape[0]):
-       for echo in range(w_data.shape[1]):
-          for coil in range(w_data.shape[2]):
-             sig = fft_w[repeat, echo, coil]
-             # Use the private function to do this:
-             params[repeat, echo, coil] = _do_lorentzian_fit(freqs_w,
-                                                             sig, bounds)
-
+        for echo in range(w_data.shape[1]):
+            for coil in range(w_data.shape[2]):
+                sig = fft_w[repeat, echo, coil]
+                params[repeat, echo, coil] = _do_lorentzian_fit(freqs_w,
+                                                                sig, bounds)
 
     # The area parameter stands for the magnitude:
     area_w = params[..., 1]
@@ -184,32 +178,32 @@ def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
     # In each coil, we derive S/(N^2):
     s = np.mean(area_w.reshape(-1, area_w.shape[-1]), 0)
     n = np.var(area_w.reshape(-1, area_w.shape[-1]), 0)
-    amp_weight = s/n 
-    # Normalize to sum to 1: 
-    amp_weight = amp_weight / np.sum(amp_weight)    
-    
+    amp_weight = s/n
+    # Normalize to sum to 1:
+    amp_weight = amp_weight / np.sum(amp_weight)
+
     # Next, we make sure that all the coils have the same phase. We will use
     # the phase of the Lorentzian to align the phases:
     phase_param = params[..., 3]
-    zero_phi_w = np.mean(phase_param.reshape(-1, phase_param.shape[-1]),0)
+    zero_phi_w = np.mean(phase_param.reshape(-1, phase_param.shape[-1]), 0)
 
     # This recalculates the weight with the phase alignment (see page 397 in
     # Wald paper):
-    weight = amp_weight * np.exp(-1j * zero_phi_w) 
+    weight = amp_weight * np.exp(-1j * zero_phi_w)
 
     # Multiply each one of the signals by its coil-weights and average across
     # coils:
     na = np.newaxis  # Short-hand
 
-    # Collapse across coils for the combination in both the water 
+    # Collapse across coils for the combination in both the water
     weighted_w_data = np.mean(np.fft.ifft(np.fft.fftshift(
-       weight[na, na, :, na] * fft_w)), coil_dim)
+        weight[na, na, :, na] * fft_w)), coil_dim)
     weighted_w_supp_data = np.mean(np.fft.ifft(np.fft.fftshift(
-       weight[na, na, : ,na] * fft_w_supp)) , coil_dim)
+        weight[na, na, :, na] * fft_w_supp)), coil_dim)
 
     # Normalize each series by the sqrt(rms):
     def normalize_this(x):
-       return  x * (x.shape[-1] / (np.sum(np.abs(x))))
+        return x * (x.shape[-1] / (np.sum(np.abs(x))))
 
     weighted_w_data = normalize_this(weighted_w_data)
     weighted_w_supp_data = normalize_this(weighted_w_supp_data)
@@ -234,23 +228,23 @@ def get_spectra(data, filt_method=dict(lb=0.1, filt_order=256),
     filt_method : dict
         Details for the filtering method. A FIR zero phase-delay method is used
         with parameters set according to these parameters
-        
+
     spect_method : dict
-        Details for the spectral analysis. Per default, we use 
+        Details for the spectral analysis. Per default, we use
 
     line_broadening : float
         Linewidth for apodization (in Hz).
 
     zerofill : int
         Number of bins to zero fill with.
-        
+
     Returns
     -------
-    f : 
+    f :
          the center frequency of the frequencies represented in the
         spectra
 
-     spectrum_water, spectrum_water_suppressed: 
+     spectrum_water, spectrum_water_suppressed:
         The first spectrum is for the data with water not suppressed and
         the second spectrum is for the water-suppressed data.
 
@@ -262,37 +256,38 @@ def get_spectra(data, filt_method=dict(lb=0.1, filt_order=256),
     2. Apodizing/windowing. Optionally, this is done with line-broadening (see
     page 92 of Keeler2005_.
     3. Spectral analysis.
-        
+
     .. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
        edition. Wiley (West Sussex, UK).
 
     """
     if not isinstance(data, nt.TimeSeries):
-       data = nt.TimeSeries(data, sampling_rate=5000.0)  
+        data = nt.TimeSeries(data, sampling_rate=5000.0)
     if filt_method is not None:
         filtered = nta.FilterAnalyzer(data, **filt_method).fir
     else:
         filtered = data
-    if line_broadening is not None: 
-       lbr_time = line_broadening * np.pi  # Conversion from Hz to
-                                           # time-constant, see Keeler page 94 
+    if line_broadening is not None:
+        # Conversion from Hz to time-constant, see Keeler page 94
+        lbr_time = line_broadening * np.pi  #
     else:
-       lbr_time = 0
+        lbr_time = 0
 
     apodized = ut.line_broadening(filtered, lbr_time)
-   
+
     if zerofill is not None:
-         new_apodized = np.concatenate([apodized.data,
-                    np.zeros(apodized.shape[:-1] + (zerofill,))], -1)
+        new_apodized = np.concatenate([apodized.data,
+                                       np.zeros(apodized.shape[:-1] +
+                                                (zerofill,))], -1)
 
-         apodized = nt.TimeSeries(new_apodized,
-                                  sampling_rate=apodized.sampling_rate)
+        apodized = nt.TimeSeries(new_apodized,
+                                 sampling_rate=apodized.sampling_rate)
 
     S = nta.SpectralAnalyzer(apodized,
                              method=dict(NFFT=spect_method['NFFT'],
                                          n_overlap=spect_method['n_overlap']),
                              BW=spect_method['BW'])
-    
+
     f, c = S.spectrum_fourier
 
     return f, c
@@ -300,7 +295,7 @@ def get_spectra(data, filt_method=dict(lb=0.1, filt_order=256),
 
 def subtract_water(w_sig, w_supp_sig):
     """
-    Subtract the residual water signal from the 
+    Subtract the residual water signal from the
     Normalize the water-suppressed signal by the signal that is not
     water-suppressed, to get rid of the residual water peak.
 
@@ -318,531 +313,504 @@ def subtract_water(w_sig, w_supp_sig):
     version of the signal that is presumably just due to water.
 
     """
-    mean_nw = np.mean(w_supp_sig,0)
+    mean_nw = np.mean(w_supp_sig, 0)
     water_only = np.mean(w_sig - mean_nw, 0)
     mean_water = np.mean(w_sig, 0)
 
     scale_factor = water_only/mean_nw
 
-    corrected = w_supp_sig - water_only/scale_factor[...,0,np.newaxis]
+    corrected = w_supp_sig - water_only/scale_factor[..., 0, np.newaxis]
     return corrected
 
 
 def fit_lorentzian(spectra, f_ppm, lb=2.6, ub=3.6):
-   """
-   Fit a lorentzian function to spectra
+    """
+    Fit a lorentzian function to spectra
 
-   This is used in estimation of the water peak and for estimation of the NAA
-   peak.  
-   
-   Parameters
-   ----------
-   spectra : array of shape (n_transients, n_points)
-      Typically the sum of the on/off spectra in each transient.
+    This is used in estimation of the water peak and for estimation of the NAA
+    peak.
+
+    Parameters
+    ----------
+    spectra : array of shape (n_transients, n_points)
+       Typically the sum of the on/off spectra in each transient.
 
-   f_ppm : array
+    f_ppm : array
 
-   lb, ub: floats
-      In ppm, the range over which optimization is bounded
-   
-   """
-   # We are only going to look at the interval between lb and ub
-   idx = ut.make_idx(f_ppm, lb, ub)
-   n_points = np.abs(idx.stop - idx.start) 
-   n_params = 6
-   # Set the bounds for the optimization
-   bounds = [(lb,ub), #peak
-             (0,None), #area
-             (0,None), #hwhm
-             (-np.pi/2, np.pi/2), #phase
-             (None,None), #offset
-             (None, None)] #drift
-
-   model = np.empty((spectra.shape[0], n_points))
-   signal = np.empty((spectra.shape[0], n_points))
-   params = np.empty((spectra.shape[0], n_params))
-   for ii, xx in enumerate(spectra):
-      # We fit to the real spectrum:
-      signal[ii] = np.real(xx[idx])
-      params[ii] = _do_lorentzian_fit(f_ppm[idx], np.real(signal[ii]),
-                                      bounds=bounds)
-      
-      model[ii] = ut.lorentzian(f_ppm[idx], *params[ii])
-   
-   return model, signal, params
+    lb, ub: floats
+       In ppm, the range over which optimization is bounded
+
+    """
+    # We are only going to look at the interval between lb and ub
+    idx = ut.make_idx(f_ppm, lb, ub)
+    n_points = np.abs(idx.stop - idx.start)
+    n_params = 6
+    # Set the bounds for the optimization
+    bounds = [(lb, ub),  # peak
+              (0, None),  # area
+              (0, None),  # hwhm
+              (-np.pi/2, np.pi/2),  # phase
+              (None, None),  # offset
+              (None, None)]  # drift
+
+    model = np.empty((spectra.shape[0], n_points))
+    signal = np.empty((spectra.shape[0], n_points))
+    params = np.empty((spectra.shape[0], n_params))
+    for ii, xx in enumerate(spectra):
+        # We fit to the real spectrum:
+        signal[ii] = np.real(xx[idx])
+        params[ii] = _do_lorentzian_fit(f_ppm[idx], np.real(signal[ii]),
+                                        bounds=bounds)
+        model[ii] = ut.lorentzian(f_ppm[idx], *params[ii])
+
+    return model, signal, params
 
 
 def _do_lorentzian_fit(freqs, signal, bounds=None):
-   """
+    """
 
-   Helper function, so that Lorentzian fit can be generalized to different
-   frequency scales (Hz and ppm).
-   
-   """
-   # Use the signal for a rough estimate of the parameters for initialization:
-   max_idx = np.argmax(np.real(signal))
-   max_sig = np.max(np.real(signal))
-   initial_f0 = freqs[max_idx]
-   half_max_idx = np.argmin(np.abs(np.real(signal) - max_sig/2))
-   initial_hwhm = np.abs(initial_f0 - freqs[half_max_idx])
-   # Everything should be treated as real, except for the phase!
-   initial_ph = np.angle(signal[signal.shape[-1]/2.])
-
-   initial_off = np.min(np.real(signal))
-   initial_drift = 0
-   initial_a = (np.sum(np.real(signal)[max_idx:max_idx +
-                              np.abs(half_max_idx)*2]) ) * 2
-
-   initial = (initial_f0,
-              initial_a,
-              initial_hwhm,
-              initial_ph,
-              initial_off,
-              initial_drift)
-
-   params, _ = lsq.leastsqbound(mopt.err_func, initial,
-                                args=(freqs, np.real(signal), ut.lorentzian),
-                                bounds=bounds)
-   return params
+    Helper function, so that Lorentzian fit can be generalized to different
+    frequency scales (Hz and ppm).
+
+    """
+    # Use the signal for a rough estimate of the parameters for initialization:
+    max_idx = np.argmax(np.real(signal))
+    max_sig = np.max(np.real(signal))
+    initial_f0 = freqs[max_idx]
+    half_max_idx = np.argmin(np.abs(np.real(signal) - max_sig/2))
+    initial_hwhm = np.abs(initial_f0 - freqs[half_max_idx])
+    # Everything should be treated as real, except for the phase!
+    initial_ph = np.angle(signal[signal.shape[-1] // 2])
+
+    initial_off = np.min(np.real(signal))
+    initial_drift = 0
+    initial_a = (np.sum(np.real(signal)[max_idx:max_idx +
+                                        np.abs(half_max_idx)*2])) * 2
+
+    initial = (initial_f0,
+               initial_a,
+               initial_hwhm,
+               initial_ph,
+               initial_off,
+               initial_drift)
+
+    params, _ = lsq.leastsqbound(mopt.err_func, initial,
+                                 args=(freqs, np.real(signal), ut.lorentzian),
+                                 bounds=bounds)
+    return params
 
 
 def _two_func_initializer(freqs, signal):
-   """
-   This is a helper function for heuristic estimation of the initial parameters
-   used in fitting dual peak functions
+    """
+    This is a helper function for heuristic estimation of the initial parameters
+    used in fitting dual peak functions
 
-   _do_two_lorentzian_fit
-   _do_two_gaussian_fit
-   """
-   # Use the signal for a rough estimate of the parameters for initialization:
-   r_signal = np.real(signal)
-   # The local maxima have a zero-crossing in their derivative, so we start by
-   # calculating the derivative:
-   diff_sig = np.diff(r_signal)
-   # We look for indices that have zero-crossings (in the right direction - we
-   # are looking for local maxima, not minima!)
-   local_max_idx = []
-   for ii in range(len(diff_sig)-1):
-      if diff_sig[ii]>0 and diff_sig[ii+1]<0:
-        local_max_idx.append(ii)
-
-   # Array-ify it before moving on:
-   local_max_idx = np.array(local_max_idx)
-   # Our guesses for the location of the interesting local maxima is the two
-   # with the largest signals in them: 
-   max_idx = local_max_idx[np.argsort(r_signal[local_max_idx])[::-1][:2]]
-   # We sort again, so that we can try to get the first one to be the left peak:
-   max_idx = np.sort(max_idx)
-   if len(max_idx)==1:
+    _do_two_lorentzian_fit
+    _do_two_gaussian_fit
+    """
+    # Use the signal for a rough estimate of the parameters for initialization:
+    r_signal = np.real(signal)
+    # The local maxima have a zero-crossing in their derivative, so we start by
+    # calculating the derivative:
+    diff_sig = np.diff(r_signal)
+    # We look for indices that have zero-crossings (in the right direction - we
+    # are looking for local maxima, not minima!)
+    local_max_idx = []
+    for ii in range(len(diff_sig)-1):
+       if diff_sig[ii]>0 and diff_sig[ii+1]<0:
+         local_max_idx.append(ii)
+
+    # Array-ify it before moving on:
+    local_max_idx = np.array(local_max_idx)
+    # Our guesses for the location of the interesting local maxima is the two
+    # with the largest signals in them:
+    max_idx = local_max_idx[np.argsort(r_signal[local_max_idx])[::-1][:2]]
+    # We sort again, so that we can try to get the first one to be the
+    # left  peak:
+    max_idx = np.sort(max_idx)
+    if len(max_idx)==1:
       max_idx = [max_idx[0], max_idx[0]]
-   # And thusly: 
-   max_idx_1 = max_idx[0]
-   max_idx_2 = max_idx[1]
-   # A few of the rest just follow:
-   max_sig_1 = r_signal[max_idx_1]
-   max_sig_2 = r_signal[max_idx_2]
-   initial_amp_1 = max_sig_1
-   initial_amp_2 = max_sig_2
-   initial_f0_1 = freqs[max_idx_1]
-   initial_f0_2 = freqs[max_idx_2]
-   half_max_idx_1 = np.argmin(np.abs(np.real(signal) - max_sig_1/2))
-   initial_hwhm_1 = np.abs(initial_f0_1 - freqs[half_max_idx_1])
-   half_max_idx_2 = np.argmin(np.abs(np.real(signal) - max_sig_2/2))
-   initial_hwhm_2 = np.abs(initial_f0_2 - freqs[half_max_idx_2])
-   # Everything should be treated as real, except for the phase!
-   initial_ph_1 = np.angle(signal[max_idx_1])
-   initial_ph_2 = np.angle(signal[max_idx_2])
-   # We only fit one offset and one drift, for both functions together! 
-   initial_off = np.min(np.real(signal))
-   initial_drift = 0
-
-   initial_a_1 = (np.sum(np.real(signal)[max_idx_1:max_idx_1 +
-                                         np.abs(half_max_idx_1)*2]) ) * 2
-
-   initial_a_2 = (np.sum(np.real(signal)[max_idx_2:max_idx_2 +
-                                         np.abs(half_max_idx_2)*2]) ) * 2
-
-   return (initial_f0_1,
-           initial_f0_2,
-           initial_amp_1,
-           initial_amp_2,
-           initial_a_1,
-           initial_a_2,
-           initial_hwhm_1,
-           initial_hwhm_2,
-           initial_ph_1,
-           initial_ph_2,
-           initial_off,
-           initial_drift)
+    # And thusly:
+    max_idx_1 = max_idx[0]
+    max_idx_2 = max_idx[1]
+    # A few of the rest just follow:
+    max_sig_1 = r_signal[max_idx_1]
+    max_sig_2 = r_signal[max_idx_2]
+    initial_amp_1 = max_sig_1
+    initial_amp_2 = max_sig_2
+    initial_f0_1 = freqs[max_idx_1]
+    initial_f0_2 = freqs[max_idx_2]
+    half_max_idx_1 = np.argmin(np.abs(np.real(signal) - max_sig_1/2))
+    initial_hwhm_1 = np.abs(initial_f0_1 - freqs[half_max_idx_1])
+    half_max_idx_2 = np.argmin(np.abs(np.real(signal) - max_sig_2/2))
+    initial_hwhm_2 = np.abs(initial_f0_2 - freqs[half_max_idx_2])
+    # Everything should be treated as real, except for the phase!
+    initial_ph_1 = np.angle(signal[max_idx_1])
+    initial_ph_2 = np.angle(signal[max_idx_2])
+    # We only fit one offset and one drift, for both functions together!
+    initial_off = np.min(np.real(signal))
+    initial_drift = 0
+
+    initial_a_1 = (np.sum(np.real(signal)[max_idx_1:max_idx_1 +
+                                          np.abs(half_max_idx_1) * 2])) * 2
+
+    initial_a_2 = (np.sum(np.real(signal)[max_idx_2:max_idx_2 +
+                                          np.abs(half_max_idx_2) * 2])) * 2
+
+    return (initial_f0_1,
+            initial_f0_2,
+            initial_amp_1,
+            initial_amp_2,
+            initial_a_1,
+            initial_a_2,
+            initial_hwhm_1,
+            initial_hwhm_2,
+            initial_ph_1,
+            initial_ph_2,
+            initial_off,
+            initial_drift)
 
 
 def _do_two_lorentzian_fit(freqs, signal, bounds=None):
-   """
-
-   Helper function for the Two-Lorentzian fit
-   
-   """
-
+    """
 
-   initial = _two_func_initializer(freqs, signal)
-   # Edit out the ones we want: 
-   initial = (initial[0], initial[1],
-              initial[4], initial[5],
-              initial[6], initial[7],
-              initial[8], initial[9],
-              initial[10], initial[11])
-
-   # We want to preferntially weight the error on estimating the height of the
-   # individual peaks, so we formulate an error-weighting function based on
-   # these peaks, which is simply a two-gaussian bumpety-bump:
-   w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
-        ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
-
-   # Further, we want to also optimize on the individual lorentzians error, to
-   # restrict the fit space a bit more. For this purpose, we will pass a list
-   # of lorentzians with indices into the parameter list, so that we can do
-   # that (see mopt.err_func for the mechanics).
-   func_list = [[ut.lorentzian, [0,2,4,6,8,9],
-                 ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)],
-                [ut.lorentzian, [1,3,5,7,8,9],
-                 ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)]]
-   
-   params, _ = lsq.leastsqbound(mopt.err_func, initial,
-                                args=(freqs, np.real(signal),
-                                ut.two_lorentzian, w, func_list),
-                                bounds=bounds)
-   return params
+    Helper function for the Two-Lorentzian fit
 
+    """
 
-def _do_two_gaussian_fit(freqs, signal, bounds=None):
-   """
-   Helper function for the two gaussian fit
-   """
-   initial = _two_func_initializer(freqs, signal)
-   # Edit out the ones we want in the order we want them: 
-   initial = (initial[0], initial[1],
-              initial[6], initial[7],
-              initial[2], initial[3],
-              initial[10], initial[11])
 
-   # We want to preferntially weight the error on estimating the height of the
-   # individual peaks, so we formulate an error-weighting function based on
-   # these peaks, which is simply a two-gaussian bumpety-bump:
-   w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
-        ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
+    initial = _two_func_initializer(freqs, signal)
+    # Edit out the ones we want:
+    initial = (initial[0], initial[1],
+               initial[4], initial[5],
+               initial[6], initial[7],
+               initial[8], initial[9],
+               initial[10], initial[11])
+
+    # We want to preferntially weight the error on estimating the height of the
+    # individual peaks, so we formulate an error-weighting function based on
+    # these peaks, which is simply a two-gaussian bumpety-bump:
+    w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
+         ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
+
+    # Further, we want to also optimize on the individual lorentzians error, to
+    # restrict the fit space a bit more. For this purpose, we will pass a list
+    # of lorentzians with indices into the parameter list, so that we can do
+    # that (see mopt.err_func for the mechanics).
+    func_list = [[ut.lorentzian, [0, 2, 4, 6, 8, 9],
+                  ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)],
+                 [ut.lorentzian, [1, 3, 5, 7, 8, 9],
+                  ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)]]
+
+    params, _ = lsq.leastsqbound(mopt.err_func, initial,
+                                 args=(freqs, np.real(signal),
+                                       ut.two_lorentzian, w, func_list),
+                                 bounds=bounds)
+    return params
 
-   # Further, we want to also optimize on the individual gaussians error, to
-   # restrict the fit space a bit more. For this purpose, we will pass a list
-   # of gaussians with indices into the parameter list, so that we can do
-   # that (see mopt.err_func for the mechanics).
-   func_list = [[ut.gaussian, [0,2,4,6,7],
-                 ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)],
-                [ut.gaussian, [1,3,5,6,7],
-                 ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)]]
 
-   params, _ = lsq.leastsqbound(mopt.err_func, initial,
-                                args=(freqs, np.real(signal),
-                                ut.two_gaussian, w, func_list),
-                                bounds=bounds)
+def _do_two_gaussian_fit(freqs, signal, bounds=None):
+    """
+    Helper function for the two gaussian fit
+    """
+    initial = _two_func_initializer(freqs, signal)
+    # Edit out the ones we want in the order we want them:
+    initial = (initial[0], initial[1],
+               initial[6], initial[7],
+               initial[2], initial[3],
+               initial[10], initial[11])
+
+    # We want to preferntially weight the error on estimating the height of the
+    # individual peaks, so we formulate an error-weighting function based on
+    # these peaks, which is simply a two-gaussian bumpety-bump:
+    w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
+         ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
+
+    # Further, we want to also optimize on the individual gaussians error, to
+    # restrict the fit space a bit more. For this purpose, we will pass a list
+    # of gaussians with indices into the parameter list, so that we can do
+    # that (see mopt.err_func for the mechanics).
+    func_list = [[ut.gaussian, [0,2,4,6,7],
+                  ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)],
+                 [ut.gaussian, [1,3,5,6,7],
+                  ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)]]
+
+    params, _ = lsq.leastsqbound(mopt.err_func, initial,
+                                 args=(freqs, np.real(signal),
+                                 ut.two_gaussian, w, func_list),
+                                 bounds=bounds)
+
+    return params
 
-   return params
 
+def fit_two_lorentzian(spectra, f_ppm, lb=2.6, ub=3.6):
+    """
+    Fit a lorentzian function to the sum spectra to be used for estimation of
+    the creatine and choline peaks.
 
+    Parameters
+    ----------
+    spectra : array of shape (n_transients, n_points)
+       Typically the sum of the on/off spectra in each transient.
 
-def fit_two_lorentzian(spectra, f_ppm, lb=2.6, ub=3.6):
-   """
-   Fit a lorentzian function to the sum spectra to be used for estimation of
-   the creatine and choline peaks.
-   
-   Parameters
-   ----------
-   spectra : array of shape (n_transients, n_points)
-      Typically the sum of the on/off spectra in each transient.
+    f_ppm : array
 
-   f_ppm : array
+    lb, ub : floats
+       In ppm, the range over which optimization is bounded
 
-   lb, ub : floats
-      In ppm, the range over which optimization is bounded
-   
-   """
-   # We are only going to look at the interval between lb and ub
-   idx = ut.make_idx(f_ppm, lb, ub)
-   n_points = np.abs(idx.stop - idx.start) 
-   n_params = 10 # Lotsa params!
-   # Set the bounds for the optimization
-   bounds = [(lb,ub), #peak1 
-             (lb,ub), #peak2 
-             (0,None), #area1 
-             (0,None), #area2 
-             (0,ub-lb), #hwhm1 
-             (0,ub-lb), #hwhm2
-             (-np.pi/2, np.pi/2), #phase
-             (-np.pi/2, np.pi/2), #phase
-             (None,None), #offset
-             (None, None)] #drift 
-
-   model = np.empty((spectra.shape[0], n_points))
-   signal = np.empty((spectra.shape[0], n_points))
-   params = np.empty((spectra.shape[0], n_params))
-   for ii, xx in enumerate(spectra):
-      # We fit to the real spectrum:
-      signal[ii] = np.real(xx[idx])
-      params[ii] = _do_two_lorentzian_fit(f_ppm[idx], np.real(signal[ii]),
-                                      bounds=bounds)
-      
-      model[ii] = ut.two_lorentzian(f_ppm[idx], *params[ii])
-   
-   return model, signal, params
+    """
+    # We are only going to look at the interval between lb and ub
+    idx = ut.make_idx(f_ppm, lb, ub)
+    n_points = np.abs(idx.stop - idx.start)
+    n_params = 10  # Lotsa params!
+    # Set the bounds for the optimization
+    bounds = [(lb, ub),  # peak1
+              (lb, ub),  # peak2
+              (0, None),  # area1
+              (0, None),  # area2
+              (0, ub-lb),  # hwhm1
+              (0, ub-lb),  # hwhm2
+              (-np.pi/2, np.pi/2),  # phase
+              (-np.pi/2, np.pi/2),  # phase
+              (None, None),  # offset
+              (None, None)]  # drift
+
+    model = np.empty((spectra.shape[0], n_points))
+    signal = np.empty((spectra.shape[0], n_points))
+    params = np.empty((spectra.shape[0], n_params))
+    for ii, xx in enumerate(spectra):
+        # We fit to the real spectrum:
+        signal[ii] = np.real(xx[idx])
+        params[ii] = _do_two_lorentzian_fit(f_ppm[idx], np.real(signal[ii]),
+                                            bounds=bounds)
+
+        model[ii] = ut.two_lorentzian(f_ppm[idx], *params[ii])
+
+    return model, signal, params
 
 
 def fit_two_gaussian(spectra, f_ppm, lb=3.6, ub=3.9):
-   """
-   Fit a gaussian function to the difference spectra
+    """
+    Fit a gaussian function to the difference spectra
 
-   This is useful for estimation of the Glx peak, which tends to have two
-   peaks.
+    This is useful for estimation of the Glx peak, which tends to have two
+    peaks.
 
 
-   Parameters
-   ----------
-   spectra : array of shape (n_transients, n_points)
-      Typically the difference of the on/off spectra in each transient.
+    Parameters
+    ----------
+    spectra : array of shape (n_transients, n_points)
+       Typically the difference of the on/off spectra in each transient.
 
-   f_ppm : array
+    f_ppm : array
 
-   lb, ub : floats
-      In ppm, the range over which optimization is bounded
+    lb, ub : floats
+       In ppm, the range over which optimization is bounded
 
-   """
-   idx = ut.make_idx(f_ppm, lb, ub)
-   # We are only going to look at the interval between lb and ub
-   n_points = idx.stop - idx.start
-   n_params = 8
-   fit_func = ut.two_gaussian
-   # Set the bounds for the optimization
-   bounds = [(lb,ub), # peak 1 location
-             (lb,ub), # peak 2 location
-             (0,None), # sigma 1
-             (0,None), # sigma 2
-             (0,None), # amp 1
-             (0,None), # amp 2
-             (None, None), # offset
-             (None, None),  # drift
-             ]
-
-   model = np.empty((spectra.shape[0], n_points))
-   signal = np.empty((spectra.shape[0], n_points))
-   params = np.empty((spectra.shape[0], n_params))
-   for ii, xx in enumerate(spectra):
-      # We fit to the real spectrum:
-      signal[ii] = np.real(xx[idx])
-      params[ii] = _do_two_gaussian_fit(f_ppm[idx], np.real(signal[ii]),
-                                      bounds=bounds)
-
-      model[ii] = fit_func(f_ppm[idx], *params[ii])
-
-   return model, signal, params
+    """
+    idx = ut.make_idx(f_ppm, lb, ub)
+    # We are only going to look at the interval between lb and ub
+    n_points = idx.stop - idx.start
+    n_params = 8
+    fit_func = ut.two_gaussian
+    # Set the bounds for the optimization
+    bounds = [(lb, ub),  # peak 1 location
+              (lb, ub),  # peak 2 location
+              (0, None),  # sigma 1
+              (0, None),  # sigma 2
+              (0, None),  # amp 1
+              (0, None),  # amp 2
+              (None, None),  # offset
+              (None, None),   # drift
+              ]
+
+    model = np.empty((spectra.shape[0], n_points))
+    signal = np.empty((spectra.shape[0], n_points))
+    params = np.empty((spectra.shape[0], n_params))
+    for ii, xx in enumerate(spectra):
+        # We fit to the real spectrum:
+        signal[ii] = np.real(xx[idx])
+        params[ii] = _do_two_gaussian_fit(f_ppm[idx], np.real(signal[ii]),
+                                          bounds=bounds)
+
+        model[ii] = fit_func(f_ppm[idx], *params[ii])
+
+    return model, signal, params
 
 
 def _do_scale_fit(freqs, signal, model, w=None):
-   """
-   Perform a round of fitting to deal with over or under-estimation.
-   Scales curve on y-axis but preserves shape.
-
-   Parameters
-   ----------
-   freqs : array
-   signal : array
-      The signal that the model is being fit to
-   model : array
-      The model being scaled
-   w : array
-      weighting function
-
-   Returns
-   -------
-   scalefac : array of len(signal)
-      the scaling factor for each transient
-   scalemodel : array of model.shape
-      the scaled model
-   """
-   scalefac = np.empty(model.shape[0])
-   scalemodel = np.empty((model.shape[0], np.real(model).shape[1]))
-   scalesignal = np.empty((signal.shape[0], np.real(signal).shape[1]))
-   for ii, xx in enumerate(signal): # per transient
-      scalesignal[ii] = np.real(xx)
-#      ratio = np.empty(scalesignal[ii].shape[0])
-#      for ppm, trans in enumerate(scalesignal[ii]):
-#          ratio[ppm] = trans/model[ii][ppm]
-#      scalefac[ii] = np.mean(ratio,0)
-      scalefac[ii] = np.nanmean(scalesignal[ii],0)/np.nanmean(model[ii],0)
-      scalemodel[ii] = scalefac[ii] * model[ii]
-   return scalefac, scalemodel
-
-
-def scalemodel(model, scalefac):
-   """
-   Given a scale factor, multiply by model to get scaled model
+    """
+    Perform a round of fitting to deal with over or under-estimation.
+    Scales curve on y-axis but preserves shape.
 
-   Parameters
-   ----------
-   model : array
-      original model
-   scalefac : array of model.shape[0]
-      array of scalefactors
+    Parameters
+    ----------
+    freqs : array
+    signal : array
+       The signal that the model is being fit to
+    model : array
+       The model being scaled
+    w : array
+       weighting function
 
-   Returns
-   -------
-   scaledmodel : array
-      model scaled by scale factor
-   """
-   for ii, mm in enumerate(model):
-      scaledmodel[ii] = mm * scalefac[ii]
-   return scaledmodel
+    Returns
+    -------
+    scalefac : array of len(signal)
+       the scaling factor for each transient
+    scalemodel : array of model.shape
+       the scaled model
+    """
+    scalefac = np.empty(model.shape[0])
+    scalemodel = np.empty((model.shape[0], np.real(model).shape[1]))
+    scalesignal = np.empty((signal.shape[0], np.real(signal).shape[1]))
+    for ii, xx in enumerate(signal):  # per transient
+        scalesignal[ii] = np.real(xx)
+        scalefac[ii] = (np.nanmean(scalesignal[ii], 0) /
+                        np.nanmean(model[ii], 0))
+        scalemodel[ii] = scalefac[ii] * model[ii]
+    return scalefac, scalemodel
 
 
 def fit_gaussian(spectra, f_ppm, lb=2.6, ub=3.6):
-   """
-   Fit a gaussian function to the difference spectra to be used for estimation
-   of the GABA peak.
-   
-   Parameters
-   ----------
-   spectra : array of shape (n_transients, n_points)
+    """
+    Fit a gaussian function to the difference spectra to be used for estimation
+    of the GABA peak.
+
+    Parameters
+    ----------
+    spectra : array of shape (n_transients, n_points)
       Typically the difference of the on/off spectra in each transient.
 
-   f_ppm : array
+    f_ppm : array
 
-   lb, ub : floats
+    lb, ub : floats
       In ppm, the range over which optimization is bounded
-   
-   """
-   idx = ut.make_idx(f_ppm, lb, ub)
-   # We are only going to look at the interval between lb and ub
-   n_points = idx.stop - idx.start
-   n_params = 5
-   fit_func = ut.gaussian
-   # Set the bounds for the optimization
-   bounds = [(lb,ub), # peak location
-             (0,None), # sigma
-             (0,None), # amp
-             (None, None), # offset
-             (None, None)  # drift
-             ]
-
-   model = np.empty((spectra.shape[0], n_points))
-   signal = np.empty((spectra.shape[0], n_points))
-   params = np.empty((spectra.shape[0], n_params))
-   for ii, xx in enumerate(spectra):
-      # We fit to the real spectrum:
-      signal[ii] = np.real(xx[idx])
-      # Use the signal for a rough estimate of the parameters for
-      # initialization :
-      max_idx = np.argmax(signal[ii])
-      max_sig = np.max(signal[ii])
-      initial_f0 = f_ppm[idx][max_idx]
-      half_max_idx = np.argmin(np.abs(signal[ii] - max_sig/2))
-      # We estimate sigma as the hwhm:
-      initial_sigma = np.abs(initial_f0 - f_ppm[idx][half_max_idx])
-      initial_off = np.min(signal[ii])
-      initial_drift = 0
-      initial_amp = max_sig
-      
-      initial = (initial_f0,
-                 initial_sigma,
-                 initial_amp,
-                 initial_off,
-                 initial_drift)
-      
-      params[ii], _ = lsq.leastsqbound(mopt.err_func,
-                                       initial,
-                                       args=(f_ppm[idx],
-                                             np.real(signal[ii]),
-                                             fit_func), bounds=bounds)
-
-      model[ii] = fit_func(f_ppm[idx], *params[ii])
-   
-   return model, signal, params
+
+    """
+    idx = ut.make_idx(f_ppm, lb, ub)
+    # We are only going to look at the interval between lb and ub
+    n_points = idx.stop - idx.start
+    n_params = 5
+    fit_func = ut.gaussian
+    # Set the bounds for the optimization
+    bounds = [(lb, ub),  # peak location
+              (0, None),  # sigma
+              (0, None),  # amp
+              (None, None),  # offset
+              (None, None)   # drift
+              ]
+
+    model = np.empty((spectra.shape[0], n_points))
+    signal = np.empty((spectra.shape[0], n_points))
+    params = np.empty((spectra.shape[0], n_params))
+    for ii, xx in enumerate(spectra):
+        # We fit to the real spectrum:
+        signal[ii] = np.real(xx[idx])
+        # Use the signal for a rough estimate of the parameters for
+        # initialization :
+        max_idx = np.argmax(signal[ii])
+        max_sig = np.max(signal[ii])
+        initial_f0 = f_ppm[idx][max_idx]
+        half_max_idx = np.argmin(np.abs(signal[ii] - max_sig/2))
+        # We estimate sigma as the hwhm:
+        initial_sigma = np.abs(initial_f0 - f_ppm[idx][half_max_idx])
+        initial_off = np.min(signal[ii])
+        initial_drift = 0
+        initial_amp = max_sig
+
+        initial = (initial_f0,
+                   initial_sigma,
+                   initial_amp,
+                   initial_off,
+                   initial_drift)
+
+        params[ii], _ = lsq.leastsqbound(mopt.err_func,
+                                         initial,
+                                         args=(f_ppm[idx],
+                                               np.real(signal[ii]),
+                                               fit_func),
+                                         bounds=bounds)
+
+        model[ii] = fit_func(f_ppm[idx], *params[ii])
+
+    return model, signal, params
 
 
 def integrate(func, x, args=(), offset=0, drift=0):
-   """
-   Integrate a function over the domain x
+    """
+    Integrate a function over the domain x
 
-   Parameters
-   ----------
-   func : callable
-       A function from the domain x to floats. The first input to this function
-       has to be x, an array with values to evaluate for, running in monotonic
-       order  
-
-   x : float array
-      The domain over which to integrate, as sampled. This can be monotonically
-      decreasing or monotonically increasing.
-      
-   args : tuple
-       The parameters of func after x.
-
-   offset : 
-
-   Notes
-   -----
-   We apply the trapezoid rule for integration here, using
-   scipy.integrate.trapz.
-
-   See: http://en.wikipedia.org/wiki/Trapezoidal_rule
-   
-   """
-   # If it's monotonically decreasing (as is often the case here), we invert
-   # it, so that our results are strictly positive
-   if x[1]<x[0]:
-      x = x[::-1]
-   y = func(x, *args)
-   # Correct for offset and drift, if those are present and specified
-   # (otherwise default to 0 on both):
-   y = y - offset
-   y = y - drift * (x-x[0])
-   # Use trapezoidal integration on the corrected function: 
-   return spi.trapz(y, x)
+    Parameters
+    ----------
+    func : callable
+        A function from the domain x to floats. The first input to this
+        function has to be x, an array with values to evaluate for, running in
+        monotonic order
+
+    x : float array
+       The domain over which to integrate, as sampled. This can be
+       monotonically decreasing or monotonically increasing.
+
+    args : tuple
+        The parameters of func after x.
+
+    offset :
+
+    Notes
+    -----
+    We apply the trapezoid rule for integration here, using
+    scipy.integrate.trapz.
+
+    See: http://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    """
+    # If it's monotonically decreasing (as is often the case here), we invert
+    # it, so that our results are strictly positive
+    if x[1]<x[0]:
+        x = x[::-1]
+    y = func(x, *args)
+    # Correct for offset and drift, if those are present and specified
+    # (otherwise default to 0 on both):
+    y = y - offset
+    y = y - drift * (x-x[0])
+    # Use trapezoidal integration on the corrected function:
+    return spi.trapz(y, x)
 
 
 def simple_auc(spectrum, f_ppm, center=3.00, bandwidth=0.30):
-   """
-   Calculates area under the curve (no fitting)
+    """
+    Calculates area under the curve (no fitting)
 
-   Parameters
-   ----------
-   spectrum : array of shape (n_transients, n_points)
+    Parameters
+    ----------
+    spectrum : array of shape (n_transients, n_points)
       Typically the difference of the on/off spectra in each transient.
 
-   center, bandwidth : float
+    center, bandwidth : float
       Determine the limits for the part of the spectrum for which we want
       to calculate the AUC.
       e.g. if center = 3.0, bandwidth = 0.3, lower and upper bounds will be
       2.85 and 3.15 respectively (center +/- bandwidth/2).
 
-   Notes
-   -----
-   Default center and bandwidth are 3.0 and 0.3ppm respectively
+    Notes
+    -----
+    Default center and bandwidth are 3.0 and 0.3ppm respectively
     because of Sanacora 1999 pg 1045:
-   "The GABA signal was integrated over a 0.30-ppm bandwidth at 3.00ppm"
+    "The GABA signal was integrated over a 0.30-ppm bandwidth at 3.00ppm"
 
-   Ref: Sanacora, G., Mason, G. F., Rothman, D. L., Behar, K. L., Hyder, F.,
-   Petroff, O. A., ... & Krystal, J. H. (1999). Reduced cortical
-   {gamma}-aminobutyric acid levels in depressed patients determined by proton
-   magnetic resonance spectroscopy. Archives of general psychiatry, 56(11),
-   1043.
+    Ref: Sanacora, G., Mason, G. F., Rothman, D. L., Behar, K. L., Hyder, F.,
+    Petroff, O. A., ... & Krystal, J. H. (1999). Reduced cortical
+    {gamma}-aminobutyric acid levels in depressed patients determined by proton
+    magnetic resonance spectroscopy. Archives of general psychiatry, 56(11),
+    1043.
 
-   """
-   range = np.max(f_ppm)-np.min(f_ppm)
-   dx=float(range)/float(len(f_ppm))
-   
-   lb = np.floor((np.max(f_ppm)-float(center)+float(bandwidth)/2)/dx)
-   ub = np.ceil((np.max(f_ppm)-float(center)-float(bandwidth)/2)/dx)
-
-   auc = trapz(spectrum[ub:lb].real, dx=dx)
-
-   return auc, ub, lb   
+    """
+    range = np.max(f_ppm) - np.min(f_ppm)
+    dx = float(range) / float(len(f_ppm))
 
+    lb = np.floor((np.max(f_ppm)-float(center)+float(bandwidth)/2)/dx)
+    ub = np.ceil((np.max(f_ppm)-float(center)-float(bandwidth)/2)/dx)
 
+    auc = trapz(spectrum[ub:lb].real, dx=dx)
 
+    return auc, ub, lb
diff --git a/MRS/api.py b/MRS/api.py
index 028c4e1..6bd593e 100644
--- a/MRS/api.py
+++ b/MRS/api.py
@@ -17,7 +17,7 @@
     import MRS.freesurfer as fs
 except ImportError:
     warnings.warn("Nipype is not installed. Some functions might not work")
-    
+
 
 class GABA(object):
     """
@@ -26,7 +26,7 @@ class GABA(object):
 
     def __init__(self,
                  in_data,
-                 w_idx=[1,2,3],
+                 w_idx=[1, 2, 3],
                  line_broadening=5,
                  zerofill=100,
                  filt_method=None,
@@ -44,11 +44,11 @@ def __init__(self,
         w_idx : list (optional)
             The indices to the non-water-suppressed transients. Per default we
             take the 2nd-4th transients. We dump the first one, because it
-            seems to be quite different than the rest of them. 
+            seems to be quite different than the rest of them.
 
         line_broadening : float (optional)
            How much to broaden the spectral line-widths (Hz). Default: 5
-           
+
         zerofill : int (optional)
            How many zeros to add to the spectrum for additional spectral
            resolution. Default: 100
@@ -60,30 +60,30 @@ def __init__(self,
             How to derive spectra. Per default, a simple Fourier transform will
             be derived from apodized time-series, but other methods can also be
             used (see `nitime` documentation for details)
-            
+
         min_ppm, max_ppm : float
            The limits of the spectra that are represented
 
         sampling_rate : float
            The sampling rate in Hz.
-        
+
         """
         if isinstance(in_data, str):
             # The nifti files follow the strange nifti convention, but we want
             # to use our own logic, which is transients on dim 0 and time on
             # dim -1:
             self.raw_data = np.transpose(nib.load(in_data).get_data(),
-                                         [1,2,3,4,5,0]).squeeze()
+                                         [1, 2, 3, 4, 5, 0]).squeeze()
         elif isinstance(in_data, np.ndarray):
             self.raw_data = in_data
 
         w_data, w_supp_data = ana.coil_combine(self.raw_data, w_idx=w_idx,
                                                sampling_rate=sampling_rate)
         f_hz, w_supp_spectra = ana.get_spectra(w_supp_data,
-                                           line_broadening=line_broadening,
-                                           zerofill=zerofill,
-                                           filt_method=filt_method,
-                                           spect_method=spect_method)
+                                               line_broadening=line_broadening,
+                                               zerofill=zerofill,
+                                               filt_method=filt_method,
+                                               spect_method=spect_method)
 
         self.w_supp_spectra = w_supp_spectra
 
@@ -95,17 +95,17 @@ def __init__(self,
         self.w_supp_lorentz = np.zeros(w_supp_spectra.shape[:-1] + (6,))
         for ii in range(self.w_supp_lorentz.shape[0]):
             for jj in range(self.w_supp_lorentz.shape[1]):
-                self.w_supp_lorentz[ii,jj]=\
-                    ana._do_lorentzian_fit(f_hz, w_supp_spectra[ii,jj])
+                self.w_supp_lorentz[ii, jj] = \
+                    ana._do_lorentzian_fit(f_hz, w_supp_spectra[ii, jj])
 
         # We store the frequency offset for each transient/echo:
         self.freq_offset = self.w_supp_lorentz[..., 0]
 
         # But for now, we average over all the transients/echos for the
-        # correction: 
+        # correction:
         mean_freq_offset = np.mean(self.w_supp_lorentz[..., 0])
         f_hz = f_hz - mean_freq_offset
-    
+
         self.water_fid = w_data
         self.w_supp_fid = w_supp_data
         # This is the time-domain signal of interest, combined over coils:
@@ -123,7 +123,7 @@ def __init__(self,
         idx1 = np.argmin(np.abs(f_ppm - max_ppm))
         self.idx = slice(idx1, idx0)
         self.f_ppm = f_ppm
-    
+
         self.echo_off = spectra[:, 1]
         self.echo_on = spectra[:, 0]
 
@@ -131,7 +131,6 @@ def __init__(self,
         self.diff_spectra = self.echo_on - self.echo_off
         self.sum_spectra = self.echo_off + self.echo_on
 
-        
     def reset_fits(self):
         """
         This is used to restore the original state of the fits.
@@ -139,15 +138,14 @@ def reset_fits(self):
         for attr in ['creatine_params', 'creatine_model', 'creatine_signal',
                      'cr_idx', 'creatine_auc', 'gaba_params', 'gaba_model',
                      'gaba_signal', 'gaba_idx', 'gaba_auc', 'glx_params',
-                     'glx_model', 'glx_signal', 'glx_idx', 'glx_auc' ]:
+                     'glx_model', 'glx_signal', 'glx_idx', 'glx_auc']:
             if hasattr(self, attr):
                 self.__delattr__(attr)
 
-
     def fit_water(self, line_broadening=5, zerofill=100,
-                 filt_method=None, min_ppm=-5.0, max_ppm=5.0):
+                  filt_method=None, min_ppm=-5.0, max_ppm=5.0):
         """
-
+        Fit the water signal
         """
         # Get the water spectrum as well:
         f_hz, w_spectra = ana.get_spectra(self.water_fid,
@@ -155,7 +153,6 @@ def fit_water(self, line_broadening=5, zerofill=100,
                                           zerofill=zerofill,
                                           filt_method=filt_method)
 
-        f_ppm = ut.freq_to_ppm(f_hz)
         # Averaging across echos:
         self.water_spectra = np.mean(w_spectra, 1)
         model, signal, params = ana.fit_lorentzian(self.water_spectra,
@@ -168,10 +165,8 @@ def fit_water(self, line_broadening=5, zerofill=100,
         self.water_signal = signal
         self.water_params = params
         self.water_idx = ut.make_idx(self.f_ppm, min_ppm, max_ppm)
-        mean_params = stats.nanmean(params, 0)
         self.water_auc = self._calc_auc(ut.lorentzian, params, self.water_idx)
 
-
     def _calc_auc(self, model, params, idx):
         """
         Helper function to calculate the area under the curve of a model
@@ -204,25 +199,22 @@ def _calc_auc(self, model, params, idx):
         for t in range(auc.shape[0]):
             model1 = model(self.f_ppm[idx], *p[t])
             # This controls the amplitude in both the Gaussian and the
-            # Lorentzian: 
+            # Lorentzian:
             p[t, 1] = 0
             model0 = model(self.f_ppm[idx], *p[t])
             auc[t] = np.sum((model1 - model0) * delta_f)
         return auc
 
-    def _outlier_rejection(self, params, model, signal, ii):
+    def _outlier_rejection(self, params, model, signal, ii, zthresh=3.0):
         """
         Helper function to reject outliers
-
-        DRY!
-        
         """
         # Z score across repetitions:
         z_score = (params - np.mean(params, 0))/np.std(params, 0)
-        # Silence warnings: 
+        # Silence warnings:
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            outlier_idx = np.where(np.abs(z_score)>3.0)[0]
+            outlier_idx = np.where(np.abs(z_score) > zthresh)[0]
             nan_idx = np.where(np.isnan(params))[0]
             outlier_idx = np.unique(np.hstack([nan_idx, outlier_idx]))
             ii[outlier_idx] = 0
@@ -272,29 +264,32 @@ def fit_creatine(self, reject_outliers=3.0, fit_lb=2.7, fit_ub=3.5):
                                                                 model,
                                                                 signal,
                                                                 ii)
-            
+
         # We'll keep around a private attribute to tell us which transients
         # were good (this is for both creatine and choline):
         self._cr_transients = np.where(ii)
-        
+
         # Now we separate choline and creatine params from each other (remember
         # that they both share offset and drift!):
-        self.choline_params = params[:, (0,2,4,6,8,9)]
-        self.creatine_params = params[:, (1,3,5,7,8,9)]
-        
+        self.choline_params = params[:, (0, 2, 4, 6, 8, 9)]
+        self.creatine_params = params[:, (1, 3, 5, 7, 8, 9)]
+
         self.cr_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
 
         # We'll need to generate the model predictions from these parameters,
         # because what we're holding in 'model' is for both together:
         self.choline_model = np.zeros((self.creatine_params.shape[0],
-                                    np.abs(self.cr_idx.stop-self.cr_idx.start)))
+                                       np.abs(self.cr_idx.stop -
+                                              self.cr_idx.start)))
 
         self.creatine_model = np.zeros((self.choline_params.shape[0],
-                                    np.abs(self.cr_idx.stop-self.cr_idx.start)))
-        
+                                        np.abs(self.cr_idx.stop -
+                                               self.cr_idx.start)))
+
         for idx in range(self.creatine_params.shape[0]):
             self.creatine_model[idx] = ut.lorentzian(self.f_ppm[self.cr_idx],
-                                                     *self.creatine_params[idx])
+                                                     *self.creatine_params[idx]
+                                                     )
             self.choline_model[idx] = ut.lorentzian(self.f_ppm[self.cr_idx],
                                                     *self.choline_params[idx])
         self.creatine_signal = signal
@@ -305,12 +300,11 @@ def fit_creatine(self, reject_outliers=3.0, fit_lb=2.7, fit_ub=3.5):
                                           self.choline_params,
                                           self.cr_idx)
 
-
     def _fit_helper(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
                     fit_func):
         """
-        This is a helper function for fitting different segments of the spectrum
-        with Gaussian functions (GLX and GABA).
+        This is a helper function for fitting different segments of the
+        spectrum with Gaussian functions (GLX and GABA).
 
         Parameters
         ----------
@@ -370,18 +364,19 @@ def _fit_helper(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
         this_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
         return choose_transients, model, signal, params, this_idx
 
-
     def _xval_choose_funcs(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
-                           fitters=[ana.fit_gaussian,ana.fit_two_gaussian],
-                           funcs = [ut.gaussian, ut.two_gaussian]):
-        """ Helper function used to do split-half xvalidation to select among
-            alternative models"""
+                           fitters=[ana.fit_gaussian, ana.fit_two_gaussian],
+                           funcs=[ut.gaussian, ut.two_gaussian]):
+        """
+        Helper function used to do split-half xvalidation to select among
+        alternative models
+        """
 
         set1 = fit_spectra[::2]
         set2 = fit_spectra[1::2]
 
         errs = []
-        signal_select = [] 
+        signal_select = []
         # We can loop over functions and try each one out, checking the
         # error in each:
         for fitter in fitters:
@@ -390,13 +385,13 @@ def _xval_choose_funcs(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
             for this_set in [set1, set2]:
                 choose_transients, model, signal, params, this_idx =\
                     self._fit_helper(this_set, reject_outliers,
-                                    fit_lb, fit_ub, fitter)
+                                     fit_lb, fit_ub, fitter)
                 models.append(np.nanmean(model[choose_transients], 0))
                 signals.append(np.nanmean(signal[choose_transients], 0))
 
                 signal_select.append(signal[choose_transients])
-                
-            #Cross-validate!
+
+            # Cross-validate!
             errs.append(np.mean([ut.rmse(models[0], signals[1]),
                                  ut.rmse(models[1], signals[0])]))
         # We really only need to look at the first two:
@@ -405,9 +400,9 @@ def _xval_choose_funcs(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
         # Based on the errors, choose a function. Also report errors:
         return (fitters[np.argmin(errs)], funcs[np.argmin(errs)], np.min(errs),
                 signal_err)
-            
+
     def _xval_model_error(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
-                           fitter, func):
+                          fitter, func):
         """
         Helper function for calculation of split-half cross-validation model
         error and signal reliability.
@@ -415,8 +410,7 @@ def _xval_model_error(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
         """
         set1 = fit_spectra[::2]
         set2 = fit_spectra[1::2]
-        errs = []
-        signal_select = [] 
+        signal_select = []
         models = []
         signals = []
         for this_set in [set1, set2]:
@@ -427,17 +421,16 @@ def _xval_model_error(self, fit_spectra, reject_outliers, fit_lb, fit_ub,
             signals.append(np.nanmean(signal[choose_transients], 0))
 
             signal_select.append(signal[choose_transients])
-                
-        #Cross-validation error estimation:
+
+        # Cross-validation error estimation:
         model_err = np.mean([ut.rmse(models[0], signals[1]),
-                              ut.rmse(models[1], signals[0])])
+                             ut.rmse(models[1], signals[0])])
         # Also for the signal:
         signal_err = ut.rmse(np.nanmean(signal_select[0], 0),
                              np.nanmean(signal_select[1], 0))
         # Based on the errors, choose a function. Also report errors:
         return model_err, signal_err
 
-            
     def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
                  phase_correct=True, fit_func=None):
         """
@@ -465,7 +458,6 @@ def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
             callable function will be fit. Needs to conform to the conventions
             of `fit_gaussian`/`fit_two_gaussian` and
             `ut.gaussian`/`ut.two_gaussian`.
-
         """
         # We need to fit the creatine, so that we know which transients to
         # exclude in fitting this peak:
@@ -484,15 +476,21 @@ def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
                 # Silence warnings:
                 with warnings.catch_warnings():
                     warnings.simplefilter("ignore")
-                    fit_spectra[ii] = ut.phase_correct_zero(this_spec,
-                            self.creatine_params[self._cr_transients][ii, 3])
+                    fit_spectra[ii] = ut.phase_correct_zero(
+                                        this_spec,
+                                        self.creatine_params[
+                                            self._cr_transients][ii, 3])
 
         if fit_func is None:
             # Cross-validate!
-            fitter, self.gaba_func, self.gaba_model_err, self.gaba_signal_err=\
+            fitter, gaba_func, gaba_model_err, gaba_signal_err = \
                 self._xval_choose_funcs(fit_spectra,
                                         reject_outliers,
                                         fit_lb, fit_ub)
+            self.gaba_func = gaba_func
+            self.gaba_model_err = gaba_model_err
+            self.gaba_signal_err = gaba_signal_err
+
         # Otherwise, you had better supply a couple of callables that can be
         # used to fit these spectra!
         else:
@@ -501,7 +499,7 @@ def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
             self.gaba_model_err, self.gaba_signal_err = \
                 self._xval_model_error(fit_spectra, reject_outliers,
                                        fit_lb, fit_ub, fitter, self.gaba_func)
-        # Either way, we end up fitting to everything in the end: 
+        # Either way, we end up fitting to everything in the end:
         choose_transients, model, signal, params, this_idx = self._fit_helper(
                                          fit_spectra, reject_outliers,
                                          fit_lb, fit_ub, fitter)
@@ -511,10 +509,7 @@ def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
         self.gaba_signal = signal
         self.gaba_params = params
         self.gaba_idx = this_idx
-        mean_params = stats.nanmean(params, 0)
-
-        self.gaba_auc =  self._calc_auc(self.gaba_func, params, self.gaba_idx)
-
+        self.gaba_auc = self._calc_auc(self.gaba_func, params, self.gaba_idx)
 
     def fit_glx(self, reject_outliers=3.0, fit_lb=3.6, fit_ub=3.9,
                 fit_func=None):
@@ -550,10 +545,13 @@ def fit_glx(self, reject_outliers=3.0, fit_lb=3.6, fit_ub=3.9,
 
         if fit_func is None:
             # Cross-validate!
-            fitter, self.glx_func, self.glx_model_err, self.glx_signal_err=\
+            fitter, glx_func, glx_model_err, glx_signal_err = \
                 self._xval_choose_funcs(fit_spectra,
                                         reject_outliers,
                                         fit_lb, fit_ub)
+            self.glx_func = glx_func
+            self.glx_model_err = glx_model_err
+            self.glx_signal_err = glx_signal_err
         # Otherwise, you had better supply a couple of callables that can be
         # used to fit these spectra!
         else:
@@ -573,9 +571,7 @@ def fit_glx(self, reject_outliers=3.0, fit_lb=3.6, fit_ub=3.9,
         self.glx_signal = signal
         self.glx_params = params
         self.glx_idx = this_idx
-        mean_params = stats.nanmean(params, 0)
-
-        self.glx_auc =  self._calc_auc(self.glx_func, params, self.glx_idx)
+        self.glx_auc = self._calc_auc(self.glx_func, params, self.glx_idx)
 
 
     def fit_naa(self, reject_outliers=3.0, fit_lb=1.8, fit_ub=2.4,
@@ -596,7 +592,6 @@ def fit_naa(self, reject_outliers=3.0, fit_lb=1.8, fit_ub=2.4,
         self.naa_signal = signal
         self.naa_params = params
         self.naa_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
-        mean_params = stats.nanmean(params, 0)
         self.naa_auc = self._calc_auc(ut.lorentzian, params, self.naa_idx)
 
 
@@ -655,46 +650,48 @@ def fit_glx2(self, reject_outliers=3.0, fit_lb=3.6, fit_ub=3.9,
         # We'll need to generate the model predictions from these parameters,
         # because what we're holding in 'model' is for both together:
         self.glxp1_model = np.zeros((self.glxp1_params.shape[0],
-                                np.abs(self.glx2_idx.stop-self.glx2_idx.start)))
+                                     np.abs(self.glx2_idx.stop -
+                                            self.glx2_idx.start)))
 
         self.glxp2_model = np.zeros((self.glxp2_params.shape[0],
-                                np.abs(self.glx2_idx.stop-self.glx2_idx.start)))
+                                     np.abs(self.glx2_idx.stop -
+                                            self.glx2_idx.start)))
 
         for idx in range(self.glxp2_params.shape[0]):
             self.glxp2_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
                                                 *self.glxp2_params[idx])
-            self.glxp1_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
-                                                    *self.glxp1_params[idx])
+            self.glxp1_model[idx] = ut.gaussian(
+                                        self.f_ppm[self.glx2_idx],
+                                        *self.glxp1_params[idx])
 
         if scalefit:
             combinedmodel = self.glxp2_model + self.glxp1_model
             scalefac, scalemodel = ana._do_scale_fit(
-                self.f_ppm[self.glx2_idx], signal,combinedmodel)
+                self.f_ppm[self.glx2_idx], signal, combinedmodel)
             # Reject outliers:
-            scalemodel, signal, params, ii = self._rm_outlier_by_amp(params,
-                                                                scalemodel,
-                                                                signal,
-                                                                ii)
+            scalemodel, signal, params, ii = self._rm_outlier_by_amp(
+                                                        params,
+                                                        scalemodel,
+                                                        signal,
+                                                        ii)
             self.glx2_model = scalemodel
         else:
             self.glx2_model = self.glxp1_model + self.glxp2_model
 
-
         self.glx2_signal = signal
         self.glx2_auc = (
             self._calc_auc(ut.gaussian, self.glxp2_params, self.glx2_idx) +
             self._calc_auc(ut.gaussian, self.glxp1_params, self.glx2_idx))
 
-
-    def _rm_outlier_by_amp(self, params, model, signal, ii):
+    def _rm_outlier_by_amp(self, params, model, signal, ii, zthresh=2.0):
         """
         Helper function to reject outliers based on mean amplitude
         """
-        maxamps = np.nanmax(np.abs(model),0)
-        z_score = (maxamps - np.nanmean(maxamps,0))/np.nanstd(maxamps,0)
+        maxamps = np.nanmax(np.abs(model), 0)
+        z_score = (maxamps - np.nanmean(maxamps, 0)) / np.nanstd(maxamps, 0)
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            outlier_idx = np.where(np.abs(z_score)>2.0)[0]
+            outlier_idx = np.where(np.abs(z_score) > zthresh)[0]
             nan_idx = np.where(np.isnan(params))[0]
             outlier_idx = np.unique(np.hstack([nan_idx, outlier_idx]))
             ii[outlier_idx] = 0
@@ -704,7 +701,6 @@ def _rm_outlier_by_amp(self, params, model, signal, ii):
 
         return model, signal, params, ii
 
-
     def est_gaba_conc(self):
         """
         Estimate gaba concentration based on equation adapted from Sanacora
@@ -723,34 +719,31 @@ def est_gaba_conc(self):
 
         # estimate [GABA] according to equation9
         gaba_conc_est = self.gaba_auc / self.creatine_auc * 1.5 * 9.0
-        
-        self.gaba_conc_est = gaba_conc_est
 
+        self.gaba_conc_est = gaba_conc_est
 
     def voxel_seg(self, segfile, MRSfile):
         """
-        add voxel segmentation info
-        
+        Add voxel segmentation info.
+
         Parameters
         ----------
-        
+
         segfile : str
             Path to nifti file with segmentation info (e.g. XXXX_aseg.nii.gz)
-        
+
         MRSfile : str
-            Path to MRS nifti file 
+            Path to MRS nifti file
         """
         total, grey, white, csf, nongmwm, pGrey, pWhite, pCSF, pNongmwm =\
             fs.MRSvoxelStats(segfile, MRSfile)
-        
+
         self.pGrey = pGrey
         self.pWhite = pWhite
         self.pCSF = pCSF
         self.pNongmwm = pNongmwm
 
 
-
-
 class SingleVoxel(object):
     """
     Class for representation and analysis of single voxel (SV) -PROBE
@@ -768,7 +761,7 @@ def __init__(self, in_file, line_broadening=5, zerofill=100,
 
         line_broadening : float
            How much to broaden the spectral line-widths (Hz)
-           
+
         zerofill : int
            How many zeros to add to the spectrum for additional spectral
            resolution
@@ -778,13 +771,13 @@ def __init__(self, in_file, line_broadening=5, zerofill=100,
 
         fit_lb, fit_ub : float
            The limits for the part of the spectrum for which we fit the
-           creatine and GABA peaks. 
-        
+           creatine and GABA peaks.
+
         """
         self.raw_data = np.transpose(nib.load(in_file).get_data(),
-                                     [1,2,3,4,5,0]).squeeze()
+                                     [1, 2, 3, 4, 5, 0]).squeeze()
 
-        w_data, w_supp_data = ana.coil_combine(self.raw_data, w_idx = range(8),
+        w_data, w_supp_data = ana.coil_combine(self.raw_data, w_idx=range(8),
                                                coil_dim=1)
         # We keep these around for reference, as private attrs
         self._water_data = w_data
@@ -796,7 +789,7 @@ def __init__(self, in_file, line_broadening=5, zerofill=100,
                                         line_broadening=line_broadening,
                                         zerofill=zerofill,
                                         filt_method=filt_method)
-                                           
+
         self.f_hz = f_hz
         # Convert from Hz to ppm and extract the part you are interested in.
         f_ppm = ut.freq_to_ppm(self.f_hz)
@@ -804,4 +797,4 @@ def __init__(self, in_file, line_broadening=5, zerofill=100,
         idx1 = np.argmin(np.abs(f_ppm - max_ppm))
         self.idx = slice(idx1, idx0)
         self.f_ppm = f_ppm
-        self.spectra = spectra[:,self.idx]
+        self.spectra = spectra[:, self.idx]
diff --git a/MRS/corr.py b/MRS/corr.py
index 57e8272..31ef4c0 100644
--- a/MRS/corr.py
+++ b/MRS/corr.py
@@ -10,11 +10,10 @@
 from scipy import interpolate
 
 def naa_correct(G):
-
     """
     This function resets the fits and corrects shifts in the spectra.
     It uses uses the NAA peak at 2.0ppm as a guide to replaces the existing
-    f_ppm values! 
+    f_ppm values!
     """
     G.reset_fits()
 
@@ -22,54 +21,58 @@ def naa_correct(G):
     diff = np.mean(G.diff_spectra, 0)
     # find index of NAA peak in diff spectrum, that is between 3 and 1ppm
     temp_diff = np.mean(G.diff_spectra, 0)
-    temp_diff[slice(0,np.min(np.where(G.f_ppm<3)))]=0
-    temp_diff[np.max(np.where(G.f_ppm>1)):]=0 
+    temp_diff[slice(0, np.min(np.where(G.f_ppm < 3)))] = 0
+    temp_diff[np.max(np.where(G.f_ppm > 1)):] = 0
     idx = np.argmin(temp_diff)
     adjust_by=(float(idx)/len(diff))*(np.max(G.f_ppm)-
                                       np.min(G.f_ppm))
-    NAA_ppm = np.max(G.f_ppm)-adjust_by 
-    
+    NAA_ppm = np.max(G.f_ppm)-adjust_by
+
     # determine how far spectrum is shifted
     NAA_shift = 2.0-NAA_ppm
-    
+
     # correct
     G.f_ppm = G.f_ppm + NAA_shift
 
     # tag as corrected
     G.naa_corrected = True
-    
+
+
 def baseline_correct(G):
 
     """
-    This function zeroes the baseline from 2.5ppm upwards 
-   
+    This function zeroes the baseline from 2.5ppm upwards
+
     """
     # define ppm ranges that are known to be at baseline, get indices
-    baseidx =[]
-    baseidx.extend(range(np.min(np.where(G.f_ppm<5.0)),np.max(np.where(G.f_ppm>4.0))+1))
-    baseidx.extend(range(np.min(np.where(G.f_ppm<3.5)),np.max(np.where(G.f_ppm>3.2))+1))
-    baseidx.extend(range(np.min(np.where(G.f_ppm<2.8)),np.max(np.where(G.f_ppm>2.5))+1))
-
-    G.diff = np.mean(G.diff_spectra,0)
-    # find x and y values at those indices
-    yArr=np.real(G.diff[baseidx])
+    baseidx = []
+    baseidx.extend(range(np.min(np.where(G.f_ppm < 5.0)),
+                         np.max(np.where(G.f_ppm > 4.0))+1))
+    baseidx.extend(range(np.min(np.where(G.f_ppm < 3.5)),
+                         np.max(np.where(G.f_ppm > 3.2)) + 1))
+    baseidx.extend(range(np.min(np.where(G.f_ppm < 2.8)),
+                         np.max(np.where(G.f_ppm > 2.5))+1))
+
+    G.diff = np.mean(G.diff_spectra, 0)
+    # Find x and y values at those indices
+    yArr = np.real(G.diff[baseidx])
     baseppm = G.f_ppm[baseidx]
     # filter out anything above the new max
-    adjbaseppm =[baseppm[i] for i in np.where(baseppm<=np.max(G.f_ppm))[0]]
-    
+    adjbaseppm = [baseppm[i] for i in np.where(baseppm <= np.max(G.f_ppm))[0]]
+
     # spline
-    f = interpolate.interp1d(adjbaseppm[::-1], yArr[::-1], kind='linear', bounds_error=True, fill_value=0)
-    
-    fitidxmax = np.where(G.f_ppm<np.max(adjbaseppm))[0]
-    fitidxmin = np.where(G.f_ppm>np.min(adjbaseppm))[0]
+    f = interpolate.interp1d(adjbaseppm[::-1], yArr[::-1], kind='linear',
+                             bounds_error=True, fill_value=0)
+
+    fitidxmax = np.where(G.f_ppm < np.max(adjbaseppm))[0]
+    fitidxmin = np.where(G.f_ppm > np.min(adjbaseppm))[0]
     fitidx = list(set(fitidxmax) & set(fitidxmin))
-    
-    basefit = f(G.f_ppm[fitidx])#[::-1]
-    adjusted = G.diff[fitidx]-basefit#[::-1]
+
+    basefit = f(G.f_ppm[fitidx])
+    adjusted = G.diff[fitidx] - basefit
 
     G.diff_corrected = G.diff
     G.diff_corrected[fitidx] = adjusted
-    
+
     # tag as corrected
     G.baseline_corrected = True
-
diff --git a/MRS/data.py b/MRS/data.py
index cdec9c2..437d860 100644
--- a/MRS/data.py
+++ b/MRS/data.py
@@ -24,7 +24,7 @@ class FetcherError(Exception):
 
 import MRS
 
-data_folder = os.path.join(pjoin(os.path.expanduser('~'), '.mrs_data'))
+data_folder = os.path.join(pjoin(os.path.expanduser('~'), 'MRS_data'))
 
 def _log(msg):
     print(msg)
@@ -75,6 +75,7 @@ def fetch_data(files, folder):
     else:
         _log("Files successfully downloaded to %s" % (folder))
 
+
 def _get_file_md5(filename):
     """Compute the md5 checksum of a file"""
     md5_data = md5()
@@ -87,7 +88,7 @@ def _get_file_md5(filename):
 def check_md5(filename, stored_md5):
     """
     Computes the md5 of filename and check if it matches with the supplied
-    string md5 
+    string md5
 
     Input
     -----
@@ -99,10 +100,12 @@ def check_md5(filename, stored_md5):
     """
     computed_md5 = _get_file_md5(filename)
     if stored_md5 != computed_md5:
-        print ("MD5 checksum of filename", filename,
-               "failed. Expected MD5 was", stored_md5,
-               "but computed MD5 was", computed_md5, '\n',
-               "Please check if the data has been downloaded correctly or if the upstream data has changed.")
+        print("MD5 checksum of filename", filename,
+              "failed. Expected MD5 was", stored_md5,
+              "but computed MD5 was", computed_md5, '\n',
+              "Please check if the data has been downloaded correctly",
+              "or if the upstream data has changed.")
+
 
 def _get_file_data(fname, url):
     with contextlib.closing(urlopen(url)) as opener:
@@ -123,38 +126,38 @@ def fetch_from_sdr(folder=data_folder, data='test'):
     data : str
        Which data to download. Either 'test', which is data required for
        testing, or 'example', which is data needed for the example notebooks.
-       
+
     """
     url = "https://stacks.stanford.edu/file/druid:fn662rv4961/"
 
     if data == 'test':
         md5_dict = {'5182_1_1.nii.gz': '0656e59818538baa7d45311f2581bb4e',
-                '5182_15_1.nii.gz': 'a5a307b581620184baf868cd0df81f89',
-                'data.mat': 'a6275698f2220c65994354d412e6d82e',
-                'pure_gaba_P64024.nii.gz': 'f3e09ec0f00bd9a03910b19bfe731afb'}
+                    '5182_15_1.nii.gz': 'a5a307b581620184baf868cd0df81f89',
+                    'data.mat': 'a6275698f2220c65994354d412e6d82e',
+                    'pure_gaba_P64024.nii.gz':
+                    'f3e09ec0f00bd9a03910b19bfe731afb'}
 
     elif data == 'example':
         md5_dict = {'12_1_PROBE_MEGA_L_Occ.nii.gz':
                     'a0571606c1caa16a9d9b00847771bc94',
-                     '5062_2_1.nii.gz':
+                    '5062_2_1.nii.gz':
                     '6f77fb5134bc2841bdfc954390f0f4a4'}
-        
+
     if not os.path.exists(folder):
         print('Creating new directory %s' % folder)
         os.makedirs(folder)
-        
+
     for k, v in md5_dict.items():
         fname = pjoin(folder, k)
         if not os.path.exists(fname):
-            print('Downloading %s from SDR ...'%k)
+            print('Downloading %s from SDR ...' % k)
             _get_file_data(fname, url + k)
             check_md5(fname, v)
         else:
-            print('File %s is already in place. If you want to fetch it again, please first remove it from the folder %s ' % (fname, folder))
+            print('File %s is already in place' % fname,
+                  'If you want to fetch it again, ',
+                  'please first remove it from the folder',
+                   ' %s' % folder)
 
     print('Done.')
     print('Files copied in folder %s' % folder)
-        
-
-
-
diff --git a/MRS/freesurfer.py b/MRS/freesurfer.py
index 4ee690c..19894b6 100644
--- a/MRS/freesurfer.py
+++ b/MRS/freesurfer.py
@@ -1,23 +1,20 @@
 """
 freesurfer tools
 
-""" 
+"""
 
 import os
 import re
 import numpy as np
 import nibabel as nib
 import nipype.pipeline.engine as pe
-import nipype.interfaces.io as nio
 import nipype.interfaces.freesurfer as fs
-import nipype.interfaces.utility as util
-import nipype.interfaces.fsl as fsl   
 
 
-def reconall(subjfile, subjID=None, subjdir=None, runreconall=True): 
+def reconall(subjfile, subjID=None, subjdir=None, runreconall=True):
     """
     Carries out Freesurfer's reconall on T1 nifti file
-    
+
     WARNING: Reconall takes very long to run!!
 
     http://nipy.sourceforge.net/nipype/users/examples/smri_freesurfer.html
@@ -26,7 +23,7 @@ def reconall(subjfile, subjID=None, subjdir=None, runreconall=True):
     ----------
     subjfile: nifti file
         Path to subject's T1 nifti file
-    
+
     subjID: string
         optional name for subject's output folder
 
@@ -37,21 +34,21 @@ def reconall(subjfile, subjID=None, subjdir=None, runreconall=True):
 
     runreconall: boolean
         If set to true, runs reconall, otherwise just converts assorted mgz
-        files to nii 
-    """  
+        files to nii
+    """
     T1dir = os.path.dirname(subjfile)
     filename = os.path.basename(subjfile)
 
     # subject ID
-    if subjID==None:
-        m=re.search('(\w+?)_*_', subjfile)
-        subjID=m.group(0) + 'seg'        
+    if subjID is None:
+        m = re.search('(\w+?)_*_', subjfile)
+        subjID = m.group(0) + 'seg'
 
     # Tell freesurfer what subjects directory to use
-    if subjdir==None:
-        subjdir=T1dir
+    if subjdir is None:
+        subjdir = T1dir
     fs.FSCommand.set_default_subjects_dir(subjdir)
-    segdir=subjdir+'/'+subjID+'/'
+    segdir = subjdir + '/' + subjID + '/'
     print('saving to ' + subjdir)
 
     # check if file exists
@@ -72,11 +69,11 @@ def reconall(subjfile, subjID=None, subjdir=None, runreconall=True):
     if runreconall:
         # run recon-all
         reconall = pe.Node(interface=fs.ReconAll(), name='reconall')
-        reconall.inputs.subject_id = subjID 
+        reconall.inputs.subject_id = subjID
         reconall.inputs.directive = 'all'
         reconall.inputs.subjects_dir = subjdir
         reconall.inputs.T1_files = subjfile
-    
+
         wf.add_nodes([reconall])
         result = wf.run()
 
@@ -113,15 +110,15 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
     ----------
     segfile:  nifti file
         path to segmentation file with grey/white matter labels (freesurfer
-        aseg file converted from mgz). 
+        aseg file converted from mgz).
 
     MRSfile: nifti file
         path to MRSfile of MRS voxel. provide either this or center + dim
 
     center : integer array
         [x,y,z] where x, y and z are the coordinates of the point of
-        interest. Provide either MRSfile or center+dim 
-    
+        interest. Provide either MRSfile or center+dim
+
     dim : float array
         dimensions of voxel in mm. Provide either MRSfile or center+dim
 
@@ -132,13 +129,13 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
         arrays of freesurfer labels for gray, white, and csf areas
         respectively. Determines which areas are considered
         grey/white/csf. Some areas like brainstem were lumped with grey
-        matter. Did not include hypointensities. 
-   
+        matter. Did not include hypointensities.
+
     Returns
     -------
 
     raw number of voxels and proportions of grey, white, csf and
-    non-grey-or-white-matter 
+    non-grey-or-white-matter
 
     Notes
     -----
@@ -148,7 +145,7 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
     # subject ID
     if subjID==None:
         m=re.search('(\w+?)_*_',segfile)
-        subjID=m.group(0)[:-1] 
+        subjID=m.group(0)[:-1]
 
     # get segmentation file
     aseg = nib.load(segfile)
@@ -165,18 +162,18 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
 
         mrs = nib.load(MRSfile)
         mrs_data = mrs.get_data().squeeze()
-        mrs_aff = mrs.get_affine()        
+        mrs_aff = mrs.get_affine()
         # This applies the concatenation of the transforms from mrs space to
         # the T1 space. [0,0,0] is the center of the MRS voxel:
         center = np.round(np.dot(np.dot(np.linalg.pinv(aseg_aff), mrs_aff),
                                  [0,0,0,1]))[:3].astype(int)
-        
+
         dim=np.diagonal(mrs_aff)[:3]
     else: # no MRSfile
         if center==None or dim==None:
             msg = 'if no MRSfile is provided, provide center and '
             msg += 'dimensions of voxel'
-            raise ValueError()    
+            raise ValueError()
 
     # calculate beginning corner of MRS voxel
     print('Creating mask with center: [%s, %s, %s]'%(center[0],
@@ -184,7 +181,7 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
                                                     str(center[2])))
 
     print('and dimensions: [%s, %s, %s] mm'%(dim[0], dim[1], dim[2]))
-    
+
     # calculate roi mask with numpy
     # round up to nearest number of voxel units
     voxdim=np.zeros(3)
@@ -225,7 +222,7 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
     gmasked= mdata * gdata
     wmasked= mdata * wdata
     csfmasked= mdata * csfdata
-    
+
     # extract stats from a given segmentation
     total = np.sum(mdata)
     white = np.sum(wmasked)
@@ -236,7 +233,7 @@ def MRSvoxelStats(segfile, MRSfile=None, center=None, dim=None, subjID=None,
     # proportions
     pWhite = float(white) / total
     pGrey = float(grey) / total
-    pCSF = float(csf) / total 
+    pCSF = float(csf) / total
     pNongmwm = float(nongmwm) / total
 
     return (total, grey, white, csf,nongmwm, pGrey, pWhite,pCSF, pNongmwm)
diff --git a/MRS/tests/__init__.py b/MRS/tests/__init__.py
index e69de29..dc73b44 100644
--- a/MRS/tests/__init__.py
+++ b/MRS/tests/__init__.py
@@ -0,0 +1,2 @@
+import MRS.data as mrd
+mrd.fetch_from_sdr(data='test')
diff --git a/MRS/tests/test_analysis.py b/MRS/tests/test_analysis.py
index 20f0a57..fee83de 100644
--- a/MRS/tests/test_analysis.py
+++ b/MRS/tests/test_analysis.py
@@ -13,7 +13,7 @@
 import MRS.analysis as ana
 import MRS.data as mrd
 
-data_folder = os.path.join(os.path.join(os.path.expanduser('~'), '.mrs_data'))
+data_folder = mrd.data_folder
 file_name = os.path.join(data_folder, 'pure_gaba_P64024.nii.gz')
 
 def test_separate_signals():
@@ -26,14 +26,14 @@ def test_separate_signals():
     npt.assert_equal(w_data.shape[-1], data.shape[-1])
     npt.assert_equal(w_supp_data.shape[-1], data.shape[-1])
     npt.assert_array_equal(data[1], w_data[0])
-    
+
 def test_coil_combine():
     """
     Test combining of information from different coils
     """
     data = np.transpose(nib.load(file_name).get_data(), [1,2,3,4,5,0]).squeeze()
     w_data, w_supp_data = ana.coil_combine(data)
-    # Make sure that the time-dimension is still correct: 
+    # Make sure that the time-dimension is still correct:
     npt.assert_equal(w_data.shape[-1], data.shape[-1])
     npt.assert_equal(w_supp_data.shape[-1], data.shape[-1])
 
@@ -43,7 +43,7 @@ def test_coil_combine():
                                   np.zeros_like(w_data[:,:,0]),
                                   decimal=1)  # We're not being awfully strict
                                               # about it.
-        
+
 def test_get_spectra():
     """
     Test the function that does the spectral analysis
@@ -63,7 +63,7 @@ def test_get_spectra():
                                        sampling_rate=5000),
                                        line_broadening=5,
                                        zerofill=1000)
-    
+
 def test_bootstrap_stat():
     """
     Test simple bootstrapping statistics
diff --git a/MRS/tests/test_api.py b/MRS/tests/test_api.py
index 047e2d3..ba39bf2 100644
--- a/MRS/tests/test_api.py
+++ b/MRS/tests/test_api.py
@@ -1,9 +1,10 @@
 import os
+
 import MRS
 import MRS.api as api
-import os
+import MRS.data as mrd
 
-data_folder = os.path.join(os.path.join(os.path.expanduser('~'), '.mrs_data'))
+data_folder = mrd.data_folder
 file_name = os.path.join(data_folder, 'pure_gaba_P64024.nii.gz')
 
 def test_fitting():
diff --git a/MRS/tests/test_io.py b/MRS/tests/test_io.py
index 92586cf..45495ae 100644
--- a/MRS/tests/test_io.py
+++ b/MRS/tests/test_io.py
@@ -8,7 +8,7 @@
 import MRS
 import MRS.data as mrd
 
-data_folder = os.path.join(os.path.join(os.path.expanduser('~'), '.mrs_data'))
+data_folder = mrd.data_folder
 file_name = os.path.join(data_folder, 'pure_gaba_P64024.nii.gz')
 
 if not os.path.exists(file_name):
diff --git a/MRS/tests/test_qc.py b/MRS/tests/test_qc.py
index bbb0527..623ec70 100644
--- a/MRS/tests/test_qc.py
+++ b/MRS/tests/test_qc.py
@@ -3,7 +3,7 @@
 from nibabel.tmpdirs import InTemporaryDirectory
 
 
-import MRS
+import MRS.data as mrd
 try:
     import MRS.qc as qc
     has_nipype = True
@@ -11,7 +11,7 @@
     has_nipype = False
 
 
-data_folder = os.path.join(os.path.join(os.path.expanduser('~'), '.mrs_data'))
+data_folder = mrd.data_folder
 ref_file = os.path.join(data_folder, '5182_1_1.nii.gz')
 end_file = os.path.join(data_folder, '5182_15_1.nii.gz')
 
@@ -22,5 +22,3 @@ def test_motion():
     """
     with InTemporaryDirectory() as tmpdir:
         qcres=qc.motioncheck(ref_file, end_file, out_path='.', thres=5.0)
-
-
diff --git a/MRS/version.py b/MRS/version.py
index 3b50511..2abe1b6 100644
--- a/MRS/version.py
+++ b/MRS/version.py
@@ -2,7 +2,7 @@
 
 # Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
 _version_major = 0
-_version_minor = 1
+_version_minor = 2
 _version_micro = ''  # use '' for first of series, number for 1 and above
 _version_extra = 'dev'
 _version_extra = ''  # Uncomment this for full releases
@@ -32,7 +32,7 @@
 --------------------------------------------
 
 This library contains implementations of analysis of data acquired in
-magnetic resonance spectroscopy experiments (MRS). 
+magnetic resonance spectroscopy experiments (MRS).
 
 
 Copyright (c) 2013-, Ariel Rokem, Grace Tang.
@@ -59,7 +59,7 @@
 MICRO = _version_micro
 VERSION = __version__
 PACKAGES = ['MRS', 'MRS.leastsqbound', 'MRS.tests']
-BIN = 'bin/'            
+BIN = 'bin/'
 PACKAGE_DATA = {"MRS": ["LICENSE"]}
 
 REQUIRES = ["numpy", "matplotlib", "scipy",
diff --git a/ipynb/001-principles-of-MRS.ipynb b/ipynb/001-principles-of-MRS.ipynb
index a519f91..376c58a 100644
--- a/ipynb/001-principles-of-MRS.ipynb
+++ b/ipynb/001-principles-of-MRS.ipynb
@@ -4,12 +4,20 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arokem/source/MRS/MRS/api.py:19: UserWarning: Nipype is not installed. Some functions might not work\n",
+      "  warnings.warn(\"Nipype is not installed. Some functions might not work\")\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib\n",
@@ -27,7 +35,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -37,14 +44,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/matplotlib/__init__.py:1256: UserWarning:  This call to matplotlib.use() has no effect\n",
-      "because the backend has already been chosen;\n",
-      "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
+      "/Users/arokem/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: \n",
+      "This call to matplotlib.use() has no effect because the backend has already\n",
+      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
       "or matplotlib.backends is imported for the first time.\n",
       "\n",
-      "  warnings.warn(_use_error_msg)\n",
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/matplotlib/cbook.py:133: MatplotlibDeprecationWarning: The matplotlib.mpl module was deprecated in version 1.3. Use `import matplotlib as mpl` instead.\n",
-      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
+      "  warnings.warn(_use_error_msg)\n"
      ]
     }
    ],
@@ -103,7 +108,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -120,7 +124,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -146,7 +149,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -154,1490 +156,9 @@
    "outputs": [
     {
      "data": {
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E+1lTFAWVSgWVSoWioiLxcWEYu81mCyp2ijSMPVKxE5myQ0gHREwJaUFw1OF5Pmh9iud5\n8UJXVFQUs1c0WTFNVNScTid0Oh0YhsHy5cuh0WjEm4DQyS3CxdZms4kXbsHzVrjQa7Va5ObmkvaN\nBEj2sxZIdBi72+3GwMCA6OwUbR2VTNkhJAoRU0JKSC86Op0O5eXlKCwsBACMj49Dr9cjJycn7l5R\njuOSujjFG5l6vV50dXVhYmICDQ0NQW0dkZBGRtLnMwwjjkQTBnzzPC+6BQmRUaSbh0SYrxHRdDg7\nhfMn9vl8OHr0KFiWDfqssrOzg9LE0mHs8RY7kZYdAkDElJAkkXpFeZ6HzWaDTqeDTCZDc3Nz0PpX\nNFKpyI0VmUp9fevq6rBs2bIpF7tEL340TSMnJwcKhQI1NTVQPvoo5Pv3Y+SVV2D3+aaMRpNetIXU\nYyLMt0InobI7EygUCtA0PaXYKdIwdulnFWsYOzC1ZUcwsNBqtaRlZ4FAxJSQENF6RTmOQ1dXFyiK\nQkNDA/Lz8xN67VTFNJzY8DwPk8kEo9EY0dc3LfA8VD/+MQAg//RpaC6/HIsWLRKPIVzqkabpoEIn\n4aK9UEg1zZsq0Yax22w20T7R6XSKxU5S4wlhGDsw9UZsYmICMpkMWVlZpGVngUDElBAXQqGGtFdU\nuPB7PB50d3eLPqsNDQ0ZLSIStpUKMc/zGBsbQ2dnJwoLC6Ou1aaCuM47OCg+Jj9+HOzllwc9J1zq\nUahQtdvtYro4UoXqfCSTYprI35VSqURRUVHYYifBOjHWMHaO46BUKoNu3CK17AAIKnYiLTtzk/n5\nLSWklUiGCwzDwGAwYGRkBHV1dVCpVEmlLwVSqYyVRqaTk5PQ6XRQqVRYs2YNsrOzk3rNRPYr0+sv\nPGYwxLWtUqlEYWGhuMYMRK5QFS6wZrNZnD0618m0mE5nsVPoMHaO46DVakWxlaZ4yZSd+QkRU0JE\nhHQuy7IAgkeiGY1G9Pf3o6amBps3b4ZMJoPRaEypTSSVNK9MJoPP58OpU6fg9XrR1NQUdOGbbqjR\nUQAAn58PWU9P0q8T6aJtMplgsViC1vYEIwMhihWmtswVMimmyRa2RSNSxoFhGHR0dABAxGHsQv9y\ntGInAKRlZw5BxJQwhWi9ogMDAzAYDCgvL8emTZuCUpCpWvolu73X64XRaMTY2BhaWlriqtBNF2Ka\nNyCm7Lp1kHV3p30fQp9rXV2d+LgwtcVms4lTW+RyedAFW2gBmY3Mpcg0EWiahlKpRHFxMQoKCsT9\nRxrGLv28pMVOAKZ8dpFadpxOpyjQUpElZA4ipgSRaCIqOATl5+ejra0trCuQEB0mS6JrplI3pfLy\ncvA8n1EhBYLFlJfLwS1bBvnhwxnZdzgjA4ZhxLSjtAUk1NVpOtaPE2WuR6aJ7E9YCw83jF34vHp7\ne8MOY5d+XpGiWIPBgKVLl4pZJOG5JIrNHERMCeLdrslkgkqlQn5+vviFs1gs0Ov1yM7Ojrn+mGpk\nGu+aKc/zGBwchMFgEN2U3G439JJ1y0xDjY6CLy4GX1YGyuEA7HYgzpagdELTNPLz84MqqQW/W5vN\nBrPZjJ6enqDimVCnoEyRaTHN5LkJ69yxCLduHjqMXfi8YrlwKRQKcZ/CTWk8xhOkZSc9EDFdwIT2\nigoORhRFwW63Q6fTAYDoEBSLTKR5x8bGoNfrp0TIqXjzCiTrvCRGpsXF4AJTUaiREfAzIKbhiOR3\nKy2ekbbruN1uDA4Oik5B0xXRZTrNO5ORaSLEM4xdWuyUk5MjppCFvtZILTtkys70QcR0ASJ8oXw+\nX5Dhglwuh8vlwunTp+F0OtHY2Ciu+cTDdIqp1WqFTqeDQqFAS0sL1Gp10O/TIabJwvM8qIkJ8Pn5\n4AOFKNToKPglS9K2j0w7Bfl8PvT19YlpR2nKMV3tOgspzZsqFBV+GDvDMHA4HDCbzRgZGYHBYAga\nxi58XiqVKmaxE2nZSQ0ipguIaL2iXq8XIyMjcDgcWLFiBUpKShL+0shksqA1m0QJJ4gulwt6vR4e\njweNjY0Rh4an0qOaCmIEYLeDX7QIfCAylY2MIN3295k4P8EpqKamRnxM2mM5MjIitutIL9jS6tR4\nma8FSEDmxJumaeTl5UGpVGLZsmUAgoexS4udaJoOElgh6xBPy44U4aZXpVJBoVCQlp0AREwXCNF6\nRY1GI4aGhpCfn4+ioqKgAc6JkKqgSSNTn8+H7u5ujI+Po76+HsXFxVG/rKn0qKaCmOa12cA1NFyI\nTMfGMn4s00Wkdp1wF2ypFZ9QnRrpcyOR6fRAUeGHsft8PjFNLM06SIvTBH9i4XUiRbHnz59HZWWl\nmIomxU5ETOc90XpF+/r60Nvbi6qqKmzevFmMTBPB7WPx6w968H82VEMRaFZPFplMBoZh0NPTA5PJ\nhNraWjQ2Nsb1hZypyFTEbgev0YAPFP5QExMzdywZINIFW2rFJ507Ko2IBBMDEplmFoVCgYKCgqCl\nm9DiNOkw9lB/Yqm4hjo8RWrZCWc8Mdvel3RBxHSeEq3NRaiELSkpCeoVlcvlCaVpT/ZN4rt/Oovu\nMScq8rJwbVNe0mlewSDfbDajuro6YQ/dGY9MhepdtRq8QgHMczGNRCQrPqkvsWBiIFxgx8fHgyKi\n6SDT4pbpgqdkiWcY++jo6JRh7G63O+h1IkWxwmsJ9Pb24umnn8bu3bun98RmACKm84xIIgpcqITN\ny8vD+vXrp1jSxVtA5PGx+MW+bjzzDyPKtCr89o61uGhpEZxOZ1KCZjabodfrIZPJUFNTgyVJFO7M\nVAESRVHgfT5QLpe/epeiwOfnz9nIdDreQ7lcjry8vKD1biEzYrPZMD4+LkZEkdo/UiXTkWkm/xbT\nvS/BJCTaMHa3241z586B4zhx7Tx0IlI4gbVYLLDb7Wk93tkCEdN5glBYFJrOBS541SqVyrCVsALx\niKlhzIFv/OE0zg/Z8bl1Fbh/SyNys+i4t5cijGqTy+VYtWoVJiYmkjZ9SIeY2u12jI+PQ6vVJjTB\nRe5yAQD4wJriXBbTTCGTycQKU6HYSdr+ETqEXboOm0y7zmxMu6aLeHtaU0W6dm4ymbB+/XoAgNvt\nhs1mmzIRSSqwghOXzWaLq81uLkLEdI4jXas4fvw4VqxYIRorOBwO6HQ6cBwXl1dtrDTvnz8ewkN/\nboeSluFXt7Xg8qaSoN/HK6aCwYLL5UJjY6NoMGC1WlMyuk8Wj8cDvV4Ph8OBgoKCoAku0mrVcNZ8\nFEX5TRqACyYNBQVzVkxnch0zUvtHpMKZ0Ok60VydMh2ZzkaDiHQjnKPQYhWp2MlkMqGnpwcPPvgg\nysrKoFKp8M4776ClpQXl5eURX3/nzp148803UVpaijNnzkz5Pc/zuPfee/HWW29BrVbj2Wefxbp1\n6wAAzz33HH70ox8BAB588EHs2LEjnaceFiKmc5RwvaI0TYPjOLjdbnR2dsLhcKChoSHIXSUakVpb\nvAyHH73VgVeOmbCuJg8/uWkVFuVlhd0+mhj6fD4YDAaMjY2hvr5+SvtNpouIWJZFT08PhoeHsWTJ\nEixfvhwMw4jHJF3rk1rzSS/iFEVBFri4CyYNfH6+6NVLiEy8AhepcEZIOUpHoklTjhqNRox+Z2LN\nNFPMhJjG+txCP7Ply5fjyiuvxC9/+Uu0t7fjr3/9K37yk59gaGgI77zzTlhR/cIXvoBdu3bhjjvu\nCLuPt99+G3q9Hnq9HocPH8bdd9+Nw4cPY3x8HI888giOHj0KiqKwfv16bNu2LaGe+WQgYjrHiNYr\nSlEUuru7YbPZsHTpUjQ3Nyd0hywPU41rtntxz55TONE3iS99shb3XrEUCnn4i1IkMRXWx4QpM5s2\nbQp7YctUEZFg2N/T04PKykrxeEL3HWmtT3oRn5iYgMpkAgAMO52A2YxFWi0UAfeouHG5/EYPkv7O\nmWCuVNhGcgmSphwHBgbEdh2KoqBUKmG32+fdEPa5ksJWKpVQq9W45JJLcPfdd4uPR7rxuOSSS9AT\nZQLT66+/jjvuuAMURWHTpk2YmJjA4OAg9u3bh6uvvloMIq6++mrs3bsXt956a1rPJxQipnOISL2i\nguH76OgoqqqqIopVLELF9PyQDXe/dApmhxc//dxKbF0ZOSUDTF235HlenMlZVlaGjRs3RnXOSdVB\nKR7MZjN0Ol1Uw/5ohF7E7XY7LOfOAQDoggKMWixQsCxKzWacOnVKfG4s79usL30J9F/+AsexY2l1\nTprNpLviVerqFNqu09PTA4/HMyWFL12HTdcQ9tnqA5wuUjk/m82GioqKoMeSfS2TyYTq6mrx31VV\nVTCZTBEfn26ImM4BovWK9vf3o7e3FxUVFaioqEBRUVFKnqDCPvbpxvDNP5xGrorGiztbsaoy9mxQ\n6ZdifHwcOp0OWq0Wra2tcQ2znk4xdTgc6OjoAEVRWL16NXJyctLyuhRFQRZoEyiorERefT2UDQ2g\n//QnNDU0wBaIYoXCjLCmBi4XFG+8AQCg//IX+L72tbD7mW9kKgpWKpXIzs6GRqPBokWLAARXpg4N\nDcFut4NlWXHmqNTVKVEyHSlmWkxT2Z/NZovoYpYo4SLaSIWImfg7I2I6i4nWKzo0NITu7m4UFxdj\nw4YNUCgU6OzsTMnOTxCz104M4ME32rGsLBdP3bYGZdrYQijAsiyOHz8OiqKwatWqhERrOtpbvF4v\nOjs7YbVa0dTUFHXdJOmUo1CBHLhh4PPzQfE8srxeZJWUBHnfCqYGNpsNY2NjcDqdyO/sxPrA77lj\nxyJejDOxDjdX0ryp7iuSq5NgGB86hF16AxSrXWe+uy0xDJN0FG+z2WIWQsZLVVUV+vr6xH/39/ej\noqICVVVV2LdvX9Djl112WVr2GQ0iprOQaL2iQk+mRqPBunXrkJV1oRAoUdOFUGQyGd7q9uJ/dOew\neUkhdm9fjVxVfH8iQtGT2+3GypUrk1rsT2dkynEcjEYjBgYGUFdXh+XLl0/LhZuiKFAB71JeIqYA\nQFmt4v8LhDM1kJ0/DwDw1NSA//hjHDt2LKhBXqPRpPS5zlZmm52g8J5LZ44KpgOCq9PIyIjYrhPq\ncytEa4IhRaaYa5FpusR027Zt2L17N7Zv347Dhw8jLy8PixYtwpYtW/DAAw/AYrEAAN5991089thj\nadlnNIiYziKiiagwNYWm6YgRX7gCokT2/eP3OvE/Oh+2rizD459thpKOfbfLMAwMBgNGR0exdOlS\nTE5OJl01lw4xlUbtwqzT6b7QUILDi5DKDlT1UjYb4okl6YEB//OvugrqV19FW1vblDSkxWIBwzBw\nu93TOuR7vkamyUZvUgOD0Gkt4Sq91Wo1srKywDAMfD5fRoawzzUxjTfNe+utt2Lfvn0YGxtDVVUV\nHnnkEbEP/Stf+Qq2bt2Kt956C/X19VCr1fjd734HACgsLMT3v/99tLW1AQAeeuihuDsaUoGI6SxA\naFZ3Op1BA38BwOl0Qq/Xw+v1Rp2aAiQ/tYXneTz+jh6/O9iLy6tpPHHjSshk0S9y0vVaaYVuV1dX\nwvsXSFVMGYbBRx99BI1Gk1RxEZD4BZ6iqKlpXsH822aL7zWGh8EVFoJbsgTU5CQwOQl5Xl5QGtJs\nNsNisaC8vHzKkO9Up7fMFHNZuKMNYR8bG4PX68Xp06czMoSdZdm0FU/FA8MwSYup1WqNOzJ9+eWX\no/6eoij88pe/DPu7nTt3YufOnQkfXyoQMZ1BpIYLExMTMJlMWLlyJQC/kUBXVxesVqs4NSUWNE0H\n+WDGewyP7dXhuUN9+PzGalyeNxZVSHmeF8dwhXr7Sp+TzMUi2TVTp9MJnU4Hr9eLtWvXpuSwkuhx\nh03zCvuPV0yHhsCXl4MPVCDK+vrAhblpklagCoROb+nv7w9a50vUlm8uC1w0MrGuKPjcCr3ey5Yt\nCxrCHuoQFFqIluzxsSwbV4FfukhFvO12O3FAIqSPcL2iCoVCFFYhbSoYCcR7wUk0MpUK6R2bqvHA\nNY04eNAc8fkWiwU6nQ65ublT1msFROP3JC6SiUamPp8PXV1dsFgsaGxshMvlSluVbrxQFAUqJDJF\ngpGpLCCmXGWlf7v+fiBwUxXP/kOnt0jX+aS2fOm8gKeDTAt3ps6VZdmg3u9IQ9iFdVhpu06oq1M8\nojWXqodU7hk+AAAgAElEQVQ5jstoFJ1J5udZzWIi9YpSFAWbzYZDhw5FNTaIRqIFSD//e3eQkAoX\nttCLnN1uh16vB8/zaG5uDoqMQhEEMZkvd7xiynEcent7xTFtTU1NQZXOmUYmZAMCaeWE07xDQ+Ca\nmsTB4qnOQo20zhfpAj5TKeJMR6azKQpWKBQoLCwMWsuTDmEX+rOFIezSm6DQzyjTa6bJpnlndERi\nBiBimiEi9YryPA+TyQSDwQCWZXHRRRclfeeWSAHScwd78eR+A25cWxEkpNLI0uPxoLOzE3a7PW5b\nwlTWPWNtK00xl5aWBplAsBwPBwP0jTvB8BSUtAzZCjkKc5SQx1j/TQWKokB5veCVSiBwAU0ozcvz\noEZGwJWViYPFZdM0WDzSBVxaSGOz2eByuXDu3LmgC/h0RBPzLc0r3VcyYhNtCLvNZpuSxhc+G4/H\nk/HINJW08lxYz08GIqbTTLRe0ZGREXR2dqKwsBCtra04ceJESheteCPT108N4j/36nD18hI8+ull\nU/rvPB4PTCYTRkZGsGTJEqxYsSKhVHMqYhrp7nVychIdHR1Qq9VoXr0G50fdeP+fffjYZIVhzIE+\niws+lgfeOxy0nUJOoSIvC01luVhfk4/NS4vQWJqT1i+0zOu9kOIFgqp5Y+J0+tPEBQVATg54tTqj\nvr6hlokejwfnz59HdXW1aJnY3d2dNkMDKWR9NjbSNL7QrgMgaLqO1WpFe3s7FArFFFen6YhYk42E\nvV5vRqqbZwoiptNEtDYXYe1RrVZj7dq14pSXVNMg8Yjph51mfPdP57CprgBP3LgStMRnl+M4eL1e\nHD16NOlUcypiGs6b1+VyQa/Xw+r0oA/F+McZGz780yG4fP7n1RWr0VCSgyuaSuCdHEHD4mrkZivh\nZTg4vRwGJ93otThxZsCGd9tHAeixtCQH17cswi2tlcjLTu3LHRSZCsjl4HNy4hJTanISAMAHxIwv\nLg4rppkWnXC+t06nEzabDRaLBb29veL8UeG5UmP5RPaVCTIdmU73vqTzRm02G+rr66FQKMR2qtB2\nHek6bKo3QcmaNszn8WsAEdO0E01EhfmdMpks5tpjMsQqQNKP2HHv/3yM+pIc/HJ7C1QKuXjMo6Oj\n6OzsBM/zaGlpSbqxOlUxFWAYBt3d3TjWPYKjk2r8rdMOp3cS5VoVPrumApc0FGFtdT7y1RfE8Phx\nG5YvLxFvTkIZtrrxt44x/OX0EJ54vxNPHTDgtrYq3H1JnTiTNZljpny+4MgUgVRvPGIaGNUmmDtE\nElNgZtecpIYG0uPxeDxhjeWnWCaGEU0SmaYHIVKkaTrsYAbB1clsNgcNYZd+Rom06yQbmSbSFjMX\nIWKaJqRtLsIXV/jjFKIrt9sdNL8z3USLTM12L7784klkK+T49e1rRPGYmJgQo+R169aho6MjpQtB\nKsYRgP99NBqNeO+UEX8dpHF8wI1shRfXrizDTesqsa46L+KXPlYBUpk2C7e2VeHWtip0DNvxmw97\n8Mw/jXj91CAeuLYR1zaXRdw2GlPSvACQm5tcZFpSAmp4OKnjSAeJiA5FXZg/GssyMdyA7/kqcJmu\nWI0mbkK7Tm5urjjmTHoTZLfbMTQ0FDTQW+rqFO49S1ZM0+nLOxshYpoiQovLxMQENBoNpCPRvF4v\nurq6MDExIfaKTufFI5KYenws7tnjn/7ywhdbsSgvCw6HA3q9HizLYvny5WL6JR2WhMluPzo6ivZh\nB352qhcfD3tRkgv8+1X12N5aCW0c6dhEouKmslz8+MaVuGNTNR79Swe++Ycz+LDTjO9cVQe1Mv6v\nBUVRkHm9Yo+pAK/RxLdmKkSmEjGVhRmEHPUYOjshb28Hc911wCwo7ghnmcgwjHjxFgZ8u91uMAyD\ngoIC8QI+XVWpmRTuTPd9JnqjEOkmKJ4h7BqNJulqXqvVStK8hKkIw7kF27D29nZs2rQJgP/CIQyd\nrqurw7JlyzKyjhSugIfneTz85nmc6JvEL25ZjaaSLJw7dw5WqxUNDQ1BFzzhNVKJLJPZ3maz4aOP\n2/HKeS/298qQr+bwwDWN2N5aKaai4yGZ1pjVlXnYc2crfrGvG78+0IP2ASt+uX0linPjuxgKa6ZT\n0rxabXJp3sJCUObIvb5T4Dior70WsuFhOF9/Hezll8e/bRimS3Romp4y4PvMmTMoKSkBwzAYHByE\n3W6fssaXLsvE+bZmGko6PrN4hrAL85LPnz8fZAoSz1p5On15ZyNETJMgtFdUSG0KQ7D7+vpQXV2N\nzZs3J/SlEqLCdKaIXjlqwmsnB3H3JYuxVGXDkSP6qGYQ6YhM4xVTt9sNvV6Ptzsm8QedDy6Gw9Y6\nGo9uvwiaJNYwk+0zpeUyfGljOXLco/j5UQe2/+Yj3L9RjcWleeIFI5qDkCzcmmlublwtLkKaF4KY\n5ueDcrsBtxsIY4oxZd/nz0MWSAvTb7yRsphmGmFNVUBY40u3ZeJ8F9PpItwQ9sOHD6O+vl503TKZ\nTGHXyrOzs4PeByKmBJFIvaKAP0Vy8OBBlJWVhbXYiweaptMqpqf6J/GjtzrQVpWD9aohyOVVMQU+\nE5GpELnr+4bxcrcch3vdaKvNxyOfXo5h/amkhDTefYcipOInJyfxuU8sQ8syBl995Qx+/jGHn31a\nA5fLJU4KERyEhB+hsEbm9U6ZDgONBpTdHnP/4ppp4CIjTpyZmABfHn0YOwDIDx0CAHDV1ZCfPJnI\nqYdlpouCpGt80uelwzJxPq7PzgThXLcABE3XMZvNoimI3W7H0aNHYbfbURlw+YrF3r17ce+994Jl\nWdx11124//77g37/zW9+E3//+98B+O1ER0ZGMBHI8sjlcqxatQoAUFNTgzcCs4KnGyKmcRCtV1So\ngmVZFps3b06p7Fwul4NhmLSst5jtHux6+STyVMA3NuahZXlDXOmy6YxMBYMKo9GIfi4fP/uIgcPr\nwfe3NuH2DVWgKArD+qR3nVBkKmQR+vv7xVQ8AKxRe/HzzzXjKy+fxkPvDeJ3n18NRaB9SHAQCi2s\nWePxwMPzsNvtokUfr9GAslpjH/PEBPjcXCDw2SQqpjKdDnxODpht26B45hmA40TziNlOvMIdj2Wi\ntIhmpi0TF4KYhiPcWjnLsujp6cHx48dx9OhRvPHGG3jhhRfQ1NSEtWvX4lvf+taU94plWdxzzz14\n7733UFVVhba2Nmzbtg0rVqwQn/PTn/5U/P9f/OIXOHHihPjv7OxsnEzDjWWiEDGNQrQ2l4mJCej1\neqhUKrS0tODkyZMp92+lKmQClokJfOWFk7A4GTx/RwvW1pXE3ijAdEWmZrMZOp0O2rx87LcW4blD\n/Wgqy8Xvb1qJhtL0tAjFK6ajo6PQ6/VTXJSEi3tbbT7+Y9syfPuP7Xjir924/1P1AMI7CPl8Psh9\nPrBKJYxGIxwOB2QyGZZ5vSi32WCzWpGTmxvx4kpNTorFR8AFMYWQ/o2BrLsbXF0duPp6UG633zS/\noiKubcMx05FpvCRqmeh2u2EymabVzEAgk2Ka6XapRPcnl8uxdOlS/Nu//RtsNhsuueQSbN26FXq9\nHu3t7WHfp48++gj19fVYsmQJAGD79u14/fXXg8RUyssvv4xHHnkk8ZNJM0RMwxBNRAWfWo7jsGzZ\nMnEtQTAcSLWtJBUxdTqdcLlc2P3uGXw8yuChrU0JCWk6jiFUTO12Ozo6OiCXy1HX1IzvvdmJD7vG\ncVtbFe7f0jClwCiV9zHWjYDdbsf58+ehVCojGvULXLOiBKf6rXjhiAmbFhfgssaisM9TKBSQMQyU\n+flobm4G4E9jU3v3gmIY9Hd1wc4wUzxwcwWBjSCmVGCwcSwogwFcYyO42lr/e9DTAzYFMc0k0yHc\n4W54GIbB0aNHxcyIYGYgHb4er6l8PGRSTDPty5uKx7GwZkrTNJYvX47ly5eHfZ7JZEJ1YIISAFRV\nVeHw4cNhn2s0GmEwGHDFFVeIj7ndbrS2toKmadx///24/vrrkzreRCFiKiFar6jb7UZnZyccDgca\nGxunDMCmaRoMw6Sc5k1GyKQtOMNeJV4578EVTcW4bUNVwq8lk8nEAbzJILTGeL1edHZ2wmazobGx\nEeOMEjteOAXThAs/2rYcn1sffu0kmqVgLCJFptJjaWpqirvP99+urMNh4wR+uFeP1to85KrCf11k\nPp9ocg/4/xYUgXTkiqoq8CUlUzxw7YH11PWDg1BkZWFychK5ubmgAsIqVPlKz20KHOcXz2uuEcWU\n6ukBPvGJuM5vpslUFEzTNORyOaqqLnwfpFWqUlP5dFgmJuvNmwyZ3BeQ2vi1eAuQwn2HI/2d7Nmz\nBzfddFPQe9Db24uKigp0d3fjiiuuwKpVq7B06dKkjjkRiJgivIhKe0UNBgPMZjPq6+tRUlIyLVWw\nwAVBjheWZWE0GjE4OIi6ujrULGnAd39+APnZCvzHZ+L305Uil8vhdrsT3k7K6Ogo+vv7xarhE32T\n+PKLR6CQy/DsjvVorY0sZkJkmswFIlSIpdNl6urq4hpnJ73AK+QyPLK1Abc/exK79/eI6d4p+/X5\nwIdEubxQQGO1AiUlUzxwAf/nl82y8ObkiK0htMWCiwFM9PSAmZgIiphCLzLU+DgojwdcZSX4mhrw\nFAWZ0RjXexXP+U83mdxXKOGqVCNZJkqHe8djmSgdwTbdZHJfwv6SFe94TRuqqqrQ19cn/ru/vx8V\nEbIte/bsmTIgXHjukiVLcNlll+HEiRNETKcbIZ3r8Xggl8uDRFRYOB8aGkJtbS0aGxujfoESFcJw\nxCvIPM9jYGAAPT09qKiowKZNmyCXy/HDv5zHkJ3Dr7c3oDAnuQg52TVTnucxODiIzs5O5Obmisd0\nQD+Gr73yMcq1WXjm82tRVRDe6i/V/QMXhFhaGFZaWioeSzKsqtTixrXleOXYIG5vq0R1mOOXhXrz\nAhdmmtrtiBRny+VyyN1uqGpqxAIoLjBoXGGzwTQ8jM7OTnAcB4VCAZ7nYbFYxCkuglMSX14OqFTg\ni4pm1D0pUWZSTMMRyTJROtw71DJRSBFLLRPnc5o3WcMGIP7ItK2tDXq9HgaDAZWVldizZw9eeuml\nKc/r6OiAxWLB5s2bxccsFgvUajVUKhXGxsbwj3/8A9/5zneSOt5EWbBiKvSKWiwWmEwmNDc3ixfj\n/v5+9Pb2oqqqKu4LcSbElOd5jI2NiZNmNmzYIFboHjaM44WP+rG1PhvrqpIv6EkmwrZYLOjo6IBW\nq0VTUxPsdjvkcjneOjOE77x2FvUlOfjvz6+NywghlTSvTCaDy+XCsWPH4loXjZe7L67Fm6dH8OQB\nIx77zLKp+/X5wIbpMwUQsz2GcjgASZ+lTKUCn5sLLcuiqakJgP9vdWhoCCMjI0FTXBadOYMVACaz\ns6H0+cCXlYEaGkrpXBdKZBovkYZ7SyuJR0dHgywTPR4PHA7HhXXxaSTTYpqJNC9N09i9eze2bNkC\nlmWxc+dONDc346GHHkJrayu2bdsGwF94tH379qC/ofb2dnz5y18Wb8rvv//+iIVL6WbBianUcAHw\nFywIIjg0NITu7m6UlJRg48aNCTmvpCPNK4w/C4fVakVHRwdUKhXWrFkTZObu8DD47p/OobYwG59f\nnZcx0wWn04mOjg7wPI+VK1ciNzcXZrMZLMvizdND+NarZ7C+Jh+/um1N3L2j4SbHxIPX68Xw8DB8\nPh9Wr16dkgdo6EW+VKPC9tYK/P5wP7522WJU5AULNBXBThBAbBckhwO8JBICAsYNkmpemUyG7Oxs\n5OTkoKGhQTxG7tw5AICZpjF2+jRWZmVB2dODnp6etI1Jm04yJabTUfEazTJRWOZwOBxitCuNYtMp\nfjNlqp/stvH+PW7duhVbt24NeuzRRx8N+vfDDz88ZbtPfOITOH36dFLHlyoLUkyFijSKoqBQKOBy\nuXD48GFotVqsX78+qT7PdESmNE2LfpgCgkm+x+NBU1NT2Du7H7/XiYFJN174YivUrqFpF1Ofz4fO\nzk5MTEygsbEx6IIik8nwz14HfnzQhLbafDx9+1pkK+P/8iWa5pWui2q1WuTn5yctpNL+4VBub6vA\n84f78dKRAXzrqiUXfsEwkHHcVKN7Ic0bQ0wphwMII6YIKUAKd6xZAcGt2bABNRoNVI2NkB84gOzs\n7ClrfqFj0gToN9+E7MgReL/3PUCpzHi0mCkxzVShU0FBAZRKpRgNsSwrFjpNh2XiTESmyexvJice\nZYoFJ6bSddHJyUnodDo4nU5s2rQpaK0kUQTDhVSQRrc+nw9dXV2wWCxoaGgI6qWTctQ4gZeO9OML\nm2vQWpsPvX40JTGNFmFLhWvx4sVhPYcPG2144p8WrKrMw1O3rUlISIH4xVS6Liq4Tg0MDEzbl7Zc\nm4VPLS/BaycH8bXLFkNFB6IBIZOQTJqXZUG53eEj0xhiCgCy4WHwarU4jJwvK4NsdBRlpaXiIGlh\nzc9ms2FychJ9fX3wer3IyspCPs9j5W23AQC4mhowd94Zc59zkZk0UZDL5dBqtUE3wfFaJsZzUz8T\na6aptBDN9rR+KixIMRUmpjAMg8bGRpw9ezYlIQUuWAGmgiDIBoMBAwMDWLx4MZqamiL+AfpYDg+/\n2Y6KvCzce8VS8TXSbbrA8zyGh4fR3d0tCle4L/BR4wS+82c9qvNo/Ob/rInYShJr/7EEUTDazsrK\nCloXTTZFHC+fXVOOt8+NYp/ejC3LA+tnATGdkuYVqnmjiWkgCxFOTGUGQ8zjoYaHwZeWipNi+NJS\nv+m+xQIE+iyla35S9yCPxwP+uecuHMpLL6F9/XoolUr4fD64XK6EZlzOZmbb2mwilokqlSpIYEM/\nk7myZprpquOZYMGJqc1mw+nTp8NOTEkFmqYjrnfGA8/zMJvNGBoawpIlS+IqfHr2YC/0Iw48dVsL\n1IEIMNW129DtJycn0dHRAbVaHTUF3mN24p49p1CuUeGBzeq4RqaFI5oger1e6PV6OBwONDU1TUnn\nxnJA8vhYdI05oBu2Y9jqgc3DwMfy0GbRKFAr0VCag4biLGQrwn/pN9TmozRXiTdPD4tiSkWITIVo\nMVqalxJS+qE3cnl5QWumEbcfHfWLaQA+EI3KRkbASUwLpmwXGMGlam8Hn58P3403ouiVV7C8qQmD\nw8OiQ5Tb7Q6qWo1l+D9bmQv2fslYJmo0GvGmJ1Mka3c638evAQtQTDUaDTZu3Jj2C0IqIjY2Nga9\nXo/c3FwUFBSINlrR6Le4sHtfN65aVoIrmi5UGabLwcjlckGn08Hn8wXNOw2HxekfPE4B2H3zCtiH\nelLevxSO42A0GjEwMIClS5dixYrwPbThth2adGPv2WHs15txxGiBj70gtgo5BYVcBqf3wvtFyyi0\n1eZh2+oyfGpZCZT0hYuwXEbhUytK8D/HBuD0sv4bmAiRKeRy8Gp19DRvpMg0Ly8+X9/RUdGsAQD4\nwFJAvCPc5KdOgV29GlxLC6hnnkHW0BDy8vPh8/nEQifhYm61WmMa/s9W5qq9XyzLRCFNLIywCx2+\nPh0Ra7KRqdVqndcTY4AFKKZSVyMpqaaCkilAslqt0Ol0UCgUaGlpAU3TOHXqVFzb/sfbHZBRFL53\nbVPQ46k6GPE8D4fDgZMnT4omFdHwMhx27fkYpgkXnvvCetQWZeP0QHrSzDzPY2RkBF1dXVHTywJC\nZMrzPPbrzXj5SD8O6MfA8UB9SQ4+v7Eaq6vy0Fiai6r8LNHK0MdyMNu96Bi242DXGN7vGMN3X+/A\nT/9mwK5LF+Mzq8sgC/xtXFJfiBc+MuGjnglc1ljkT6sCUyNTBFK9UcQ0UmQqiinLAoHzDfe3SVks\nwNq1F7YTUrvj4xH3KcIwkJ09C99dd4EN2LrJzp8HAjN5BcJVrXq9Xtjtdlit1iDD/1CBnS3R4Hxr\n95FaJrIsKxbeCYVOUoetdFsmJptWttlsJDJdCKRjjmgiYiqt0G1sbBTTlSzLxhVV/rPLjL91jOHf\nr6pHRX5wiifZyFTaX0tRFDZt2hTXReFHb3XgqHECT9y0Eutr8oPajpJBEMRI66Kxtj3U68C33/sI\nZwdtKNEo8a8XL8aNaytQU6iOuJ1CLkN5XhbK87KwebEW916+GIcME/jlASMeelOHN08P4z+2LUO5\nVoXWmjyolXJ80DXu9+sV3KLCpb5yc6OneZ1OAGEiU+EO3moFJLaVUxyQJibAS38fELx4IlOqrw+U\n2w12+XJwAcMIeRgxDYdSqQxr+C8YzPf09IgCK13vy8nJmRGBnc+zTIX90TQ9xWErmmWiNIpNpH0q\nWdOG+T7LFFiAYhpOIAQhTEVM4xExn8+H7u5ujI+Po76+HsXFxUHHE9csUJbDY3t1qMzPwo5N1VN+\nn6iYSqtiS0pKsGnTJnz00UdxCelrJwbwyjET/vWTi3HdqnJx/6mIqZDS5Xk+7LpoJLpGHXjwdQOO\nm+yoLsjGf16/AttWl4vj0xJBRlG4aGkhPrGkAK+dHMLj73Xhtt+dwFPbV6KpLBdrq7Q43hdY0xTS\nvGEuSHysmaaB3/HqYKEXze4nJ4PEMgiPB5TTGTRHNZHIVNbT499m8WIgLw9cURGowPueTGQVyWBe\nENje3l5xgotwIWdZNiPik8nIdDb1fU6HZSJJ80ZmwYkpMLVQJV09opFeQ9pSEs2aMJ4v/P8eH4Bu\nxIH/unnVlIkrQGJiKjWCSNQt6PyQDQ+/eR4b6wpw7xUX1niTraiVrouWl5eHbbsJh5fh8OR+A/77\nHz3IomX4SlsBdl27NikRBfwXC2EpgKIo3Lh2EVZXanH3ntPY8ftT+M1tq7C2Wovd+42YdPlQKKR5\nw7x3vEYT1bRBTPPmhjhWCWb3k5MRrQiF1pkgsVWrwWdlxSemAQ9fYc2Vr6mBrLc35naJIPRdSodC\nCIb/VqsVXq8Xx44dCzI20Gq1aV/vy7S932wR03Aka5koFJ+lkuYlYroASJcVYOhr8DwvuiqVl5en\n5BELADY3g//6exdaa/OxZUVp2OfEExm63W6xWlOaZk7kOL72ysfIy1bgJzetBC0RrkQjAGFdtLOz\nE4sWLcLixYuhUCjiep0esxP//r9ncGbAis+0lONfN5TAZxtPSkhZloXBYMDg4CAAfypTq9VCo9Gg\nSqPB8zvW4IsvfIxdfziLb1xWBwA4PWDDpbHSvAMDkXcapQAJQNSKXmFEGx8yAYcvKoovzWs0gqdp\n8JX+6T1cdTVkATerhD5Dnhdbc+JBavg/NDSEtrY20djAarWGHZGm1WpTcg7KdJp3LpgoSInHMlEo\nPnO5XOju7k44dU8i0wVCOsQ0tD/SbDZDr9cjLy8PbW1tabF1e+YfRow7fHj69sim+9EiU6GHdXR0\nNOoEnFg88pfzME248fwX18fltxsJITLOzs5Ga2srVCoVent746qIfPvsMB740zko5BR+cctqfGpF\nKSwWCwatiVVTCj20XV1dqKysxIYNGwAEX0gGBwfhdrtxbwuNRw968PN93QCAzlEHLg1EpmHTvLm5\nkCXRGpOQmIakgfnCwvgiU4MBfFWVWODE19RA9t57fnGMB55H9i23QHbkCFx/+Qu4FPxPIxkbCJHS\n0NAQbDab6BwkiKtg+B/7UOdvmnc6xTtc8dnhw4dRWloKu92Ovr6+uC0T7XZ70IzS+ciCFNPpSPMK\nX1abzQadTge5XI5Vq1YlZQYR7stvtnvx3KFeXNtchlWVke/wwompMBTZaDSK5v2xvvCRLkBvnRnC\nnz8ewtcuX4L1NfHNBQ3F4/FAr9fD6XRi2bJlQRdRYR5qJDiOxy/2dePJ/Qasrc7Dzz63CuUBr9xE\nTfJtNhva29uhVqvFGx6hgCpcS4LH4wGTPYAH9vrHQx1uN+KqyQ4sBzDudCLL7Q5aa+I1mujVvJEK\nkIRMQbT2mHBpXiQgpkYjuLo68d9cdTUolwuU2QwqDms7+fvvg967FwCg/PGP4f7tb2NukwgymSys\nwAoFNSMjI6Lhf6jAhlrzzecCpEynlWUyWdjUfSTLRLvdjrGxMZjNZqxcuTKufezduxf33nsvWJbF\nXXfdhfvvvz/o988++yy+/e1vozKQVdm1axfuuusuAMBzzz2HH/3oRwCABx98EDt27EjHacfFghTT\nUNIhpm63Gy6XC+3t7WhsbIx7AHUoQhFS6J3drz8wwO1j8fUroveghoqR0MMaOmUmGsLNRqiYDlvd\nePjN81hdqcVXLl4c/0kFkM5fra+vR2lp6ZR9RGvt8TIcvv3aGew9O4Ib1i7CI9ctD+oDjXe9VjB/\nsNvtWLZsWdxpbpVKhU+vr8PRQR9eOzWEE2MUqir8aTG7zwdjRwc8Hg9UKhU0Gg1qZTLkBS4sYSMj\nux08RQHZwWPdhGreaJaCgmCGS/PKPv445rlQJhNYSTQppHvp4WGgKvZQefrNN8FrNGA+8xnQf/wj\nwDBAim0XsQhXUCO15hsbGxOt+aRDvn0+37yNTDPtgBSOaJaJJ06cwBtvvIEjR47gz3/+M55//nms\nW7cOGzduxFVXXTXltViWxT333IP33nsPVVVVaGtrw7Zt26ZMfrnllluwe/fuoMfGx8fxyCOP4OjR\no6AoCuvXr8e2bduChH86IWIKv5gm25vJMAy6u7sxNjYGmqbR1taW0hdXiCylX5DBSTdePmrCZ9dU\nYElx9EhX2N5ut6OjowNyuRwtLS1QqyO3hoQiCLr0osDzPB740zl4GQ7/94bmoHXSWEhTqRUVFdi8\neXPEC06kimaHx79O+4+ucXznUw3Y+YmaKe9zLAckjuPQ19cnDi6PZP4Qi3+7sg6vnRqCw8tCEbhx\nqVq6FFU1NaJVn81mg1uhQL7LhSMHD0IlSYNptVp/BCuY3IcegyCmkjTvlHONFJkWFACxIlOWBTUy\nIjomAQAX+H/5yEh8Yvr++2AuvxzMVVdB8cILkJ06BW79+pjbiceZJnMDqTXfokWLxNcWBHZ8fBzj\n4xnRf1cAACAASURBVOPw+Xyw2+3Izc0V18KnY6LOTLgtZbKHNl6Ez+Xiiy/GxRdfjK9+9av42te+\nhsLCQhw/fhw6nS6smH700Ueor68XjWu2b9+O119/Pa4xau+88w6uvvpqsaL86quvxt69e3HrrbfG\nfdypsCDFNPSPj6ZpuFyuhF5DemGuqanBpk2bcPjw4bSZP0i/6E/tN4DnedxzWV2ULf34fD44HA6c\nPXsWTU1NSUXI4fpuXz0xgA+7xvGDf1mGuhiCLsVqteL8+fNQq9Xiumg0wqVqbW4GX3rhBE71T+I/\nr1+BG9dWhN02WmRqNpuh0+lQXFyMjRs3ptQGlZetQLZCBpePg3FwAk3wOyBRuGDVl5WVBUVNDQCg\nbflyeLKzRSchk8kEj8eD5r4+FAeGGAcZm8vl4LXamGumPEWJlb8CfGGhX2glhg9TtjWbQXGcf6i4\nsJ1gRTg6GvPvlxobg6yvD96vfAXsxo3+Qz56NGExnS4RkFaslpeXY2hoCB6PByUlJWFbQqQVq0ql\nMqXjmgvWhcmSyrnZbDYUFBSgvr4e9fX1EZ9nMpmC1larqqpw+PDhKc979dVXceDAATQ2NuKnP/0p\nqqurw25rMpmSOt5kWJBiGkoiad5Q03fphTmcECZK6Jrn0KQbr50cwE3rKlGZnx1xO5Zl0dPTg6Gh\nIdA0jQ0bNiR9UQg9BrPdi//7rh6ttfnY3loZ12tIK4ZD10WjESqITi+LL794EqdNVvzXzf5Co0iE\nE2Jh5iqAhCN0AfmBA+Dz88GtXi0+9vXL6vD4e134R/uQX0yVSkx5twMtLzKHA1kFBcjKyhKrJXme\nh5KmgZycIGNz4eK+IjcXXJQIk5qY8AtpyMWNLyoCxXH+NdUI3tPCAHFpZCr8Pz0yEvP9kAVcuriW\nFvAVFX5j/vPnY24XdJwZLgqSy+Wi922kiTr9/f3weDxhR9bFe6yzIe06XaRybvFW84aLfkPf+09/\n+tO49dZboVKp8Ktf/Qo7duzA3/72t7i2nU4WpJiGi0zjEdPx8XHo9XpoNJqwpu/TIabP/NMIjgfu\nuqg27PN5nsfg4CAMBoOYQj106FBKf0ShqdbH39XB6WXxyHXLIJNFf12WZcX+wUjrovHu2+NjsWvP\nKZzom8ATN62KKqRAcJpXqFweGxubMnM1UbK+/GWwl18O95NPio9VF/iLnsbG/EVCkUwbAL/ZfejX\nnKIoyD0eyPLysHSpf+KP9OLuy8mBw2TCqY8+QlZWFpRKJTweD7xerz96sljCGjpIjRv4SGI6PAwA\n4CSRKbKzweflQT4yEvPzkgfElF29GqAosMuWQRYYVB4vmbb4CxdRRZuoI1RyDwwMwO12B7VKhZve\nIjCfI9NUjG3i7TOtqqpCX1+f+O/+/n5UVARnoqTf5S996Uu47777xG337dsXtO1ll12W1PEmw4IU\n01BiiandbodOpwNFUVi5cmXECt1UTeZDX2Pc4cX/HDNh2+pyVBVMjUrHx8eh0+nS2n4DBAvawe5x\nvH5qCHdfUof60tyI20jXRSmKiiulG23fHMfjvj+ewz+6xvHY9SuwdWVZXNuyLIuBgQEYDAZUVVVh\n48aNKV/c+JwcsSdUoCjH/14rWf9ae6TWGAARjRsohwOQRMrSi7uitBT5FIW2tja43W4MDw/DarXi\n7Nmz8Pl8WNfbC5VaDbPZHLT+FySmEc5HEFPpxBnAv24qHx2N8k74kZ0+7Td7CCwhcCtWQPHaawn1\nnPI8DypDA6M5jotbBKRpemnPpVRghekt4UwN5qqpfjykEpkKWZdYtLW1Qa/Xw2AwoLKyEnv27MFL\nL70U9JzBwUFxffyNN97A8oC39JYtW/DAAw/AEmgbe/fdd/HYY48ldbzJQMQUkcXU7Xajs7MTDocD\njY2NMavC0mX+IIjpcwd74WE4fOmTi4Oe43A4oNPpwPN80u038RyDl+Hw8JvnUVuYja9csjji84Ux\nbTk5OWhra8Pp06eT3rcQXf7X37vw9tlhfPvqetwQYY00FGEWpHAcydxchC2AyskB5XCA43nR8F6T\n5f/qKFkffHI6vIgIkWmE9hjKbr/gwxsCn5cHWX+/KLAFBQXweDxoamoCz/PI9vngKygIWv/Lzs5G\nqceDegDsyMjUtLNwjoKYlgXfoPABMY0VMcq6u8FJ1r24hgZQExNRo+EgWBa5t9yCiz78EJ6XXwZ7\n5ZWxt0mBdAhcuFapcKYGQiWxUHk8nRN1Mh0FJ+vLm4jo0zSN3bt3Y8uWLWBZFjt37kRzczMeeugh\ntLa2Ytu2bfj5z3+ON954AzRNo7CwEM8++ywAoLCwEN///vfR1tYGAHjooYeC7C2nmwUppqF/3AqF\nIkgEpeYGS5cuRXNzc1xfiHAuSIkiCLLDw+DFI/341PJSLC3xi6XX60VXVxcmJyfR2NgY9Q8llTSa\nICh7jvajx+zEr29fg6ww1oXSdVHpmLZYvaKx9v03gwO/OjKIm9dX4s4I6W0pXq8XOp0OTqcTarU6\nrsq/SAhffLuHwT69GUd6JrHDysFnHsBN//kB5BRQlKuEJjD4XMX44JEr4PAwyAuJfoTINKI/r9MJ\nPnCHPYUoBUgURUE+OQnU1YnFHMJwaaGQbuD0aZjKypCdnR1URaxQKEANDflFPNQTuKwMdJhij5A3\nCLKuLvgCFywg4O8LgOrpiUtM6VdfhfL99wEAqvvug/PIkYRclBJlulLK4UwNhMyMx+OZ9ok6MzEY\nPJX9xfsZbN26FVu3bg167NFHHxX//7HHHosYce7cuRM7d+5M+hhTYUGKaSgXUov+ySl9fX2orq6O\ny9xACk3TaUvz/vHkIGxuBndeVBvkW1tXVxfTtzbVKThyuRwWhwe793XjoqWFuLQh+AIpFDsNDw+H\ndVKKx7A/ErpRF359zIpNdQV46F+aop6n1PN46dKlKC4uxtGjR5Par0D7kA3PHuzDu+2j8AYGh38+\nW41yfgJ3X1wDhuUxZPPgQKe/OEjJ+uCmlTjUM4EtzeVBrxVXmjdCVoGPNSB8YiKox1QcLt3YCABY\nrNWioq0NLpdLbBExGo1gGAYtej1yCwsxPj4eZHLAl5eDHh2NGNEC/kpeymoFF1jnBS74+8qMxrgq\nehUvvAC2thaG7dtR//jj/raaNWtibpcsmYzgKIqCVqsNimAFw3+bzQaj0Rhk+J/KRJ2ZENNkrikc\nx83qebfpgogp/HeuPp8Phw4dQklJSdKtE+lK83q8Xjx/eBAtlVqU0S4cPHgmIW/fVCe3yGQy/O7I\nMGxuBvdtuWBdKPUarqysjHizkayYWl0+3PeGDlqVDD/93KqoHrtjY2PQ6XQoLS0V3xeO45JeR+qz\nuPD/3tXjnXMjUCvluGHNIvzLyhKsrtRCfXoRZKfH8FVJqtvmZvCJJ/4JJeODV67At/6kQ1VhDpoX\nSWY2xkjzwuGY4n4kwOflAZOTAMdNqdgFx0UsQEJeHni5HNT4+AWBDalgzXI4wJSWwmw2B5kc1CmV\nKHe7wUdryen2WylykgH2UjGNidUK+QcfwLlrF8Yvvhh4/HHQ774L7zSK6UzbCYYz/GcYRrRLlNry\nhY6si/Z9nysGEXa7fd7PMgUWqJhKv1gWiwU6nQ4Mw2Djxo0JTU4JJR1pXrlcjsO9dvSYnfjqmiyY\nzeaEi3lSLYQacrD44xkzblxbgaYyf3Q1OTmJ8+fPQ6PRxFyPTEZMeZ7Hd/90DkNWL75/US4Kc8K/\nvtPpxPnz5yGTybB27VpkS9yDYpk2hINhOTz9YQ+eOtADOQXcc8libF9fjny1ZP9q9QUf3QDyQFVz\nS7ES3j5/ZLfzhY+x++ZmtNX6I8ZYad6okWl+vr9Ax2ab0ksKm83fJxouzU9R4AsKIloKUhTljz7X\nrkVDQ4N/XwGTAwRSzhMdHRh2u8W5l8KPQqGArLMTAIIiU2g04gi3WMgPHgTFsvBcfDHYwkKwy5ZB\nHiu1nCKZnhoTj+DQNI38/PygPnDBbCV0wLdUYKW+t5mOTBmGictBLRSr1UrEdD4jLeJpbm7Gxx9/\nnFT1qRSapuEJzLdMFpZl8crJERRmUbjj8lUoyE9soguQupi+dMoCOUXh61cshdvthk6ng9frxYoV\nK+L6UiQjpv97fADvnx/Ft66sQ73aMuX3DMOgq6sL4+PjaGpqCrtenGj00W9x4duvncHx3klc21yG\n+7c0oDhnanaBz8kRfXQFfKz//BSMD/IsBVS0DOVaFe7933N46QtrsLhIDWRlgZfLw/vzsiwolyt6\nZIrAGLa8vKBzE92PIhhyxPLnDXU/EkwO5IEUcZVcjsK2tiCbPoPBAJZl0XToECrlcoxrtdBIWiX4\n2lrIDIaI+xSgP/wQvFIJ79q1oEZGwG7cCMXrr4ePwNNEpNaY6SAV4ZZO1JG+niCwUt9bIWplGCbl\nWczxwrJs0M1rvNhstoQnU81FFqSYchyH9vZ2LFmyRLwop7rOCKSW5hUGh39sGEL7OI9vXrk0KSEF\nUisA6h5z4G//n70zj5OjrtP/u/qc7p7pua/MZDKTzCSTTG4yOTgjEGKiBAW5PVFXXPmJ68qhKLru\norjLIgKK7i4qLGoAOYUFuQyEKyGTO5k7c0/PffR9VdXvj+rq9D09BxENz+vlSzIzdXR1VT3fz/U8\n7Q4+sTQb+2APbUND03aYmS6Z9oy5+dGLLWysyuWzG+dz6OBJCzFZlunv76ezszNcx56LlN2ejjH+\n36OHESWZuy5bzsUrlXpnou9PzsyMI0R/UCVTPxiMBEWJey+v4zMPHeTmp5r4/XVr0GkEyMpCSCRY\nn0TkPgyVTO32+BlVVbN3JmTqdCpdxCUlcb8KCzeMjsapCIHyXejvu49AWRnDExOc6OlBFEUsFgtL\ni4owNzVN+WLXvvsu0po1yCaTItCxbh3CQw8hdHQgR0a7qeB0omlsRFq7NqnKUyROZc1urqPgVIL/\nNpsNj8fDoUOHwt9DZCZhrgl2Nl6mH0amf6fQarWsW7cu6mdqR+9sbsCZRISRsoQLFiygyZ+PVhhI\nKpn3fp2Hil/s6kCvEdiY40CnK5x2ExZMj0wlSebWp4+jEeDHn6hDp9WGU7VqatlqtaYt0p8OnjjQ\nz+3PNlKZb+aX16xift4UqkhmM0IwCH4/hNLbflE5R10wgGTUE/on39tWzTefaOSRvb18fuN85Kys\nhGneZPZrKuQE+rzhbdXINMlqX87LS2r0HRZsKIoXwFD1eXUjI4m3FQQM3d3INTUsDkWxsizjcrkQ\n589H++qrHDpwADHCizTqxS5JaI4fJ3DVVeE6piq2r21qIpgOmTqdmDdvRtvSQuDKK/H+939Pucnf\nm2uMOnbj8XgwGAxUVlZGCf4PDw9HOerEpupnipm+H08HL1M4TckU3h8btunKEg4PD9PW1hZuepLQ\n8HxjG2tLDBRmzTzlPFMyPXjCxnNHBvjYIiNLFhRSGRp5mC6mQ6Z/3N/Pvq4JfvyJZczLyUCSJILB\nIEeOHMHr9aadWk4Xv32nmx+/2MJZi/L42RUrw/OiqRCOHp1OCGUyfGpkGvDjNyoEO+rys6W2kLMX\nDfA/b/dw+ZpSzAmiWuCkMXgSecMwUUaQqXq/qgSbLM1LXh7CwYMJfxWeMU0QmZKbi6zXo0sh3KDp\n7CSwdm3432rTjH7ZMjSBAOvKyxGLi8Mv9kirtLzJSdY4HDgXLiQYDCqRaW2tst/jx+FjH0t6XBWG\n++9H29JC8IIL0D/6KP6vfAUpZmEci792A9L7hchIcSrB/8hms8hxqekI/s8mMv0wzXsaYa46cdPZ\nh2qKnZGRwdq1a8NNTy8cG2TcHWTz8tmJMEyXTNW66P17xjHqNVy/uRrJk8JLcwqkS6ZjLj93vdLK\n+spcPrm6FEmS6OzsxOVyzUiKcCr89u0ufvznVrYuK+I/P7U8Ybewz+fD5/NhsVhOHjtEpoLbHW76\ncfqU71kX8BMIfX8TbkUN6WvnVnL1bw7wx4MDfDUzM3VkmplYVSpcM01gwzabmqmQRLABAI2GYH4+\nutHR+N8B2O0I4+NICRZZUkjUX+juRlNaGvdilyQJ8cknARgsLqYvVIeXJIk1ZWUEDx+e+mUtiuh/\n/WuCW7fi+fWvyVyyBP3//i++Kcj07y0yVTHV9UqWqlfHpVIJ/ifqH5kpmX7YgPR3jkSR6Uxt2CL3\nkYrEVNJSlWxiUx+PN/RRmm2kLn92BJLuaIwoinR0dDA0NEROaSXv9A9zdX05BVkZ2JzxTUDpIl0y\n/feXWnH7RX7w8dqw72pxcTEWiyU8yjFXeLyhLyWRiqLIiRMnGB4exmg04vF40Ol0WK1WSoNBSiAq\nwhxzKfeKQQzgMyrf46RHIdjl87JYOS+Lpw8NcH1mJkKCOVMhzcg04aypGpmmSPMKXq9Sl43Zf1jk\nPlFkCgQLCtANDyeUIlRTx3KIOKOOGTlrGnKSidpWoyEjNFZT/tGPkhkMMjg4yLx58/DX1KA5dowD\nBw6Em2tUkYnI7lXtm2+iGRjA95OfQFYWwYsuQvf88/juvjtl7fRUNiCdSrPumfR4JBuXihX89/v9\nYV9e9X+BQGDGZBqrr/v3iNOWTGMxF4ILyaLbSEWlZM08g3Yvb58Y46vnVCJLUzt3pMJUkWmkOH55\neTkbN27kntdOIMkyn9s4H40mMKs51XTIfF/XOE8dtPGFjWVMdDfh1OnCUfpgKHqaK7zROsL3n2vi\nnOr8OCKVZZmhoSHa2tooKyujvr4+/EJU5eI8oZfj8ffew+f1kpWVRdfgyTQvRqUO5Q6cvOaXrCzm\nX19sw2EwYXXa4k9KJdMkkSkpyFQYH0fWaMJzrLGI0ueNJdOhIWSdLvGMKhAsLERvs5FoWSmEyFRK\nQKZSyPoqWa0WQHPsGFJVFWRmIo+Ph2t/+jVrMLz1FutWr0bSaHC5XGENXIfDESbYmj/8gQyTCd+W\nLWiB4Ec/iv7JJ5X9Rjj6xJ3bKWxAOtWdw3MxGpOu4L/D4eDo0aNpCf5H4sM509MMc5HmjbUAk2WZ\n3t5euru7p1RUev7IILIMO1aVYmuZPZn6/f6Ev5uYmKC5uRmr1RqeF3X7RXbu62XL0iLm55lxOByz\nFn1IFeXLssydL7aQb9ayyTrBwoW1U+oezxTNg05ufOwIS4ozueeKaCEIp9NJU1MTRqMxPMsrimJ4\nIaLKxWlDjTHLKytxL12K3W5n8Ljikxh0OvCFNIMcDmfY1eXcmjx4EQYkA9mp0rzJLOF0OuTMzOQN\nSNnZSSX4osg0xuhbMzCgpHiT3IfBggJMR44kJFNNZ6ey/0S1dJMJqbAwTLiJoDl6FHH5cmUfEXVM\nqbYWIRBA6OhAU1MTflGrULtXLXv24Fi1igNNTciyTF5xMasB/2uvIdTVJSWWqNTr+Di6l19GPPPM\nuGszVzhVxP1+zpkmEvzfu3cvy5YtS0vwP/IafFgz/TtHIhu22c6IRkJNW+bl5aXVifrsYRvL51mp\nKrBga5ndsRONxng8nrA4RV1dHZkREdFTB/qZ9AT5/CYl4pjtnGqqNK8sy/zhzUaO9Du46dwSzj0z\nPd3jmcDpDfL1Rw9jMWr55TWryQzp6aozq+Pj49TW1k5poB5uQHK5MBqNFBYWIhvtmA0uMgQBnUUh\nRKfbw9GjRxl1BehyK8faMxpk4YSd4zYHtSWZYaH8cGSawqRAtlohwViNMDmZvPmIaDKN23ZwMM4t\nJhLB/Hy0Y2MJzcU13d3IFktisQiU9G9SFSS3G017O8HLLlP+NoZMIdTRGxKSiDquRoPV7cbY3g6f\n+xzr1q0Lz18GSkuRd+3i4NlnI8tyWODAarWGZzHDx5qYwLJ5M5qODqS8PNy7diVeGPyN4FSLNgiC\nkLbgv06no7e3l4GBAUZHR9My43jxxRe58cYbEUWRL33pS9x6661Rv7/77rv5n//5H3Q6HYWFhfz6\n179mQai8oNVqWbFiBQAVFRU8++yzc/jJ08NpS6ax0Ol0uGJUbmYCURTZt28fer0+bTPqtiEnjQNO\nvvPRxbM+PkSTYWSKuaamJspWCpSX2u/e62VFmZU185XV42y0dVNtPzExwbHjjfz3Hh+LCsx84SPv\nD5Gq2YHvPHOcnnEPD31uLcVWY1R6u6KigsWLF6d3fLUBKeL+6Jv0Mi/bqNQmQ80a79pEDo5oOdTn\nA5SFmdNoRudycuWD+ynN0vKZ1Xl8fGUpBWodNRWZJtHnFWJ0eeO2S0WmQ0PIKepXwYICBElCGBmJ\na1ISurqUFG+SayYtWBD2Oo2FpqkJQZKQEkWmoTEbTVMTXHxxwu21r7+unN955yl/G5q/5JxzyNm1\ni3VnnIEkywkVhLxeLzabjfk/+QlCdzfen/0M43e+g/H738f70ENJr8UHHR8UI/JEgv+BQABRFGlo\naODw4cN88YtfpKCggLVr13LhhRdyccz3LIoiX/va13j55ZcpLy+nvr6eHTt2RJlWrFmzhn379mE2\nm3nggQe4+eabefTRRwEwmUwcTNLBfqrwIZmGMNs0r8/nCzuoLF++fFppyz8dGUAjkJZnZzpQu4r7\n+vro7OwM10UTpZj390zSPuzijkuWhl9ucx2ZRqootctF9Ds6+PnHl4Ul+eYSamPZ4/v7+fPxIb61\npZr6ylwcDgeNjY0zsmeTE5Bpz7iH8hwT+P30+ZXHqHnIRWWeia9vrmRTVS67WkdxvWtCK0v86ML5\n/ObwKP/+5gh7e5zc1tJKDdDY00NmIBButon8jiLJNLJhblZkOjiIGDHaEotgaLElDA7Gkammuzvc\naJQIUkUFuueeS6hmpDl2DACxrk45x8hxFYsFacEChUyTQPvGG8g5OXG1UXHDBvSPPYbQ1YWmsjIs\ncFBWVqb8XhR577330I6PY965E9vWrTSvWEHtZZdR8sgjOJubMdfU/E0aen+QvVP1ej319fXU19dz\n+PBhHn74YcxmMwcPHgw7G0Vi7969VFdXszCk+XzVVVfxzDPPRJHpRz7ykfB/b9y4kUceeWSGn+b9\nwWlLponSvDMhU9VBZWBggEWLFqXtKB+Jl44PUV+ZG54tFQRhVg+K2+1mYGAAQRCmTDE/3tCHxahl\n+/KT3Z1zFZmqoy4DAwNUV1eTk5fPTfe+w+rybC5YUjj1jmYAQRDoHXdz559bFUWl9fNobGzEbrez\ndOnSGQ2Ph5t41NSsLNM77mV1uRXJ62PPiPKiuXBJAXdfdnJRMuYO8K5BkV/bsTCT7eur+OXuLn75\nZjdXjkjUAPOqq3G4XOFISp3btFqtLLBY0CcSUJicTFnvS0qmoogwPJwyzRsIRRfCwABEEpcso+nq\nIrBpU/LjVlQg+P1K9BvTLaw9ehTZbEauqgrtLnr2U1qyJCWZ6t56i+BZZ8WlnsX165X9791LMEHK\nVqvVotVqWfDqq2h8PrJ/8APW1NTg0esRHn6Y4G9/S8Oll0aJzKsp4g86wZ7KyHQ2XcpqzdRisbB5\n8+aEf9PX18f8UBMbQHl5OXtSaDY/+OCDbNu2Lfxvr9fLunXr0Ol03HrrrXziE5+Y0bnOBqctmcZi\numQaKXNXVlbGpk2b0Gg09PT0TMtEt33YxYkRN9euP3kjqZ3F0715PR4Pzc3N+Hw+cnJywg70yWD3\nBHjh2CCXrCrFbDh5vrMlU0EQcLlcvPvuu2G3G41GwzOHbPRPevneFNZqMItBe0Hgu88qL+Wvb8hj\n33vvUVlZOaVtXeS5x0EVrA+R6bDTjzco8VRDH3dJIvk5yiJo+/LoLu2iTAOuEJnicKAtKuJr51Ui\nCNDz2ijBDBPWnBysOTlRkZTT6cRut+PU6TAPDdHQ0IDRaMTn8ymNOFNEphgMSvNSDJkKIyOKQH6K\nsaNgqBYmDMU0wU1MKNZrqSLT0MtQ6OqKI1PNsWNIy5aFI9Y4Mq2tRf/66wlrtcLQEJr2dvxf+EL8\nMevqkC0WhUyvuCLpuemeegpx3Tqk2lq0QOaqVYhr1jBv3z5yfvzjKJH5WBcXtXvVYrGg7e5WatkJ\n6sYzdSyaKU41mc5UHc7r9U6p6Zvo2iV7Xh955BH27dvH66HUP0B3dzfz5s3jxIkTnH/++axYsYJF\n6cpTzhE+2Euv9xGziUzHxsbYs2cPdrud+vp6Kisrw8Q33RGbVxqVl9aFtScjtem6zwSDQVpbWzlw\n4ADl5eWsWLEiLeJ47sgg3oDE5WvLon4+mzqm0+mkubkZt9vNGWecwcKFC8Ndzv/zVhc1RRY21xSk\n3EdsV/R08FZfkD2dE1y5WIdF8LFhwwbKyspmV5s1GBTB+pCe7mvNiqiBIaj0vFaXKmSba47OAOSY\n9WEyjRRu+MrZC5hvlLBrlU7qSKhi5/Pnz8daUYHJ62XVqlXk5uYiSRIdJ07A+DgDIWP2wcFB3G53\n3PVKJNyQUrAhBDUy1cSMJ4VnTFOQaXjWNLajV5ajOnmVH0WTqVhbi+DzIYQ6hiOhusqICeZX0ekQ\nzzgD7XvvJT2vjIEBtAcOELjkkqifB7dtQ7N3L8LwcPi6l5eXs2zZMurr61mzZg2lpaWIokhPdzeO\nz3yGzJUrsSxZgv3hh+O63v8almgfFIGIZFC/56mev/Lycnp6esL/7u3tTTib+sorr3DHHXfw7LPP\nRglLqH+7cOFCNm/ezIEDB6Z9rrPFaUumsUiHTF0uF/v376erq4sVK1awdOnSuNrbdCPcl5uGWVFm\npST7pPVbujVLWZbp6+tjz549GI1GNm7cSEFBQdrbP3vYxuLiTJbPm/0MWCAQoLGxkaNHj7JgwQKy\ns7OjbvY3WkdpGXTypbMWoJmiVqqmuaeLMYebnY0eFuVo+OrWVdTW1s6N2LcgQEjJ6MSImzv+rNiQ\n3X9JNQBOFBItyoxWjck26XAalBRxJJlqNQJrc7U4dRk8dWgg6WHl7Gyw29FpteGa6vLqajSBAAXV\n1eTl5eHxeGhra+O9997jwIEDtLe3Mzw8jJiTA0nIVEpBpnJGBmJWVvhvVahduolmTFUkmzUVwjSO\njAAAIABJREFUBgbQjI0pkal6nARpXgBNc3PcfrXvvIOckZHUQFxcvx7N4cPhNHwsCt94A4Dgjh1R\nPw9u24Ygy2j//OeE20UubFa99x7lf/oTvuuuI1BbS8kttzDY0EBDQwP79u2jubmZ/v7+cHnjVOFU\nkel0sm2JMBWZ1tfX09raSkdHB36/n507d7Ij5vs6cOAAX/nKV3j22WfD87Cg2GiqkxgjIyO89dZb\nUbXWU4UP07whpIqG/H4/bW1t2O12Fi9enND+S8V0mncG7V6O9Nn55gXR6Yh09jE+Pk5zczM5OTlx\nddF0tu+f8HKgZ5J/umDRrKK2yFlaNZ3q8XgYGIgmid+8001ptpGPrUisvBOJ6aaZZVmmp6eHu145\ngcMPv7hqOdlzLKwtWyy4Jxx86XeHwz9bX6pEnQ5ZeYwKMmMWVhohKs0biTwC9JnN/OnIINfWR2cG\nwsfMzkYQxSiSUKUENXl5cR2U6pC93W7HaTSi6e7myKFD4TRlYX+/st9UZCrLiIWFcWneVIINYVgs\nSAUFcbOmmiNHlG1DowvqcRKRqbapCXH79qjtte+8ozRNJbFIFOvrEUQR7YEDiGefHff7ojfeQFy1\nKlyvDR9z5UqkkhJ0L71E8NOfTv65RBHDT3+KeMYZ+H/6U4SuLgxnnEHdn/6E7z//M2z0rb7UGxoa\nwlq56rU3m80f+BpsKsw0zZtuhkmn03H//fezdetWRFHkuuuuo66ujttvv51169axY8cObrrpJpxO\nJ5dffjlwcgSmsbGRr3zlK+H3xq233vohmZ5KpEMgkiTR1dVFf38/VVVVLF26dMrtphOZvtmuRA7n\nLY5Oe6YiQ7fbTUtLC5IksWLFioTzW+mQ6QvHlMhjNh3E4+PjNDU1kZeXx4YNG8IPWywZdo66eefE\nGP90waKEerixmA6ZqucQNGbzao/I5gVG6kqTqArNApLJzMGmfrxLJIw6DWvnWxFCq+FxdGQaNFF1\nZwBZBmeCNK/674zsLI7ZnNgmvZRmJzClj1RBinGRSVQzjZwBzKioQHPgAEuWLMFutzM5OQmHD5MJ\nHB0exgLhF31sg5pYUIA+ZjGk6exUaoVTdKknmjXVxnTyKtcmpiZutSKVlcU3IbndaA4dwn/jjUmP\nKUU0IcWSqdDXR3ZjI77bb4/fUBAIXnQR+qefhkAAkjTq6Z55Bk1HB55//VfFfL2yksDVVyu6wLfd\nhi4vj5ycHIxGI06nkxUrVoQJ1m6309XVhdvtDis+qf+L0n/+gGOmaV6Xy5XWjCnA9u3b2R6zkPrh\nD38Y/u9XXnkl4XZnnnkmR0ILtr8mTlsyTQVZlhkYGODEiROUlpaycePGtG+kaZFp2yiFWQaWFEe/\n/BPtIxgMcuLECUZHR6mpqYkamo5FOg/o/x0dZPk8KxVT2Y8lgNfrpbm5mWAwyMqVK+Mellg5wcca\n+tBphLRt5dKpmarjNoFAgJUrV/JvL3ei0QhcsdQ852k2WZbpC2oRHU7uvKSWf/rjMWqLMxFCKlMj\nQQ1l2fFRkwwna6YxkangdmPKVQjyYK89IZnKich0CpH78LZ5eWjGxsIqNkVFRRi1WmSrlaq6Oux2\ne5STiNlsxmq14na7CRYUYIxJt2o6OpTmoynuLamiAu3Ro9HbHjmipIAjzjmR7J60eHFcmlfb0IAQ\nDCaul6r7ys9HWrQIzd69cb/ThYb3Y+ulKsStWzE8/DDaPXsSRrXIMoZ77kGqriYY4WoT+PKXMTz8\nMPonniDw5S8r5x9RM9XpdOTk5JwUBAkGEZ54gkBnJ0Mf+QhdFgsulwutVhsmV6vVitls/kAS7EzT\nvH9LIvezdRf6kExjMD4+TktLC5mZmWGJuekg3TSvKMm83T7GR5YUxH2BkftQ66JdXV3Mnz+fDRs2\nzDpd1DPm5mi/nZsvilebUZFoPEcdAxocHEwoAKEiUoHJH5R48kA/5y8pSNtWLlXNNDJboJ5D56ib\nZw4N8On15RRYfHPeVfnYfhsrRB1LcmAgy4BflJUFkE+J7gcCWubnxM+tOn1BnMbQYiVWUtDlwjR/\nPkadhmM2J9vqigiIEidG3Iy7A+RZDCyJjEbVsYFxxYAgmci9CjkvTxHEj+iOFYaGkIuKwkLnkU4i\nqh6uy+VizGjEZLNx/Pjxk2nKtjakNWumvFZyRQXCCy9EzZpqjh4NizWE/y7Bi0uqrUX/0ENR22rf\neQdI0nwUAXH9erQvv6ykAyL2q3v2WZyVlcgJlJVAEYGQ9Xq0L72UkEy1u3ahPXgQ7333RXUZSytX\nIi5fjv6RRxKSaRQ8HkxXX43utdcAyPn5z3G/8ALS+vUEg8GwelBHRwdutztMsJEp4thr9dfoHJ5J\nmncmo4KnEjabjbfffpuLL754WrPniXDakmnszel2u/F6vbS3t8fJ7U0H6brPHOu3M+EJcHZ1ftzv\nVDIdGxujubmZ3NzcOTXHfq1ZmV28aGnyWU811apGiYODg7S3t1NWVjalYXhkmvaVpmHG3QGuWJe+\nDmqyNO/o6CjNzc0UFxdHZQt+tbsTg1bgH86pxNbRMqcvms5RN3e9coKdOVmUav280KPI+60qz4JW\nJZ05EtSxMAGZTrgDuPRKxBkXmbpcCJmZlGYb2ds5wQ+eb+HF48O4Irp7N0/28luACBu2Kb1MQ5Dz\n8hBkWRF4UGdHBwcTNh+pIyCZmZl4PB6ylixB99RTVOTkMCnLDHR3M6+ri96zzmKkqSnlLKZUUaF0\n5aqzpl4vmtZW/DGKN8nIVHC7EXp6wp3B2r/8BXH16inTy+L69ej/8AeEjg7k0OC/MDSE9u23Gfns\nZ4l/ykKwWhHPPBPdSy/hj0gpqjD89KdIJSUErroq9qIRuPZaMr79bTTHjyMtW5ZUeN74r/+K7rXX\n8P70p4hnn43p4ovJ+Oxncb/9Njqjkdzc3CiRl0AgEE4Rj4yM4Ha70el0cRZpp7pzeCZk43A4PtCR\nqZqF3LVrF2vXrk2Z8ZsKpy2ZgvIS8fv9YZ1Ws9lMXV3dlDNRqaDT6RIqfMTirVC99MyF8c1MoijS\n29uLyWRKmEadLd5oHWFhgZn5KVK8KqE5HA6ampowmUxpR+qRL8nnjgxQbDVyVoLPOdWxVajzs7Is\ns2bNmqjvZ8jh47kjA1y1rpyCTCODs5yRjTx/SZb53nMtGPUaqhcUQlsL+3smKcoyUJadEU7z+nR6\nFuTEX5cJTxBJoyWYYYr3NHW5kMxmOkeVe6VzzM2WpYWctTCXwkwDtkkfb/1ZIc4X3m5jUyhqCvub\npkGmAMLoaJhMNQMDiKtWTfn5A6Eo2Do8TOaqVQhuNxpJouiss9CXlsbNYkZGUdbQbJ+mqQmxpATN\noUMIohgX1SYiUzHUNKI9fJjgggXKbO6ePfi//vUpzzlKvCFEprrnnkOQZUbOOy85mQLBiy4i47bb\nFBKPEA7QHDiAbtcufD/8YcLmp+AVVyB/73vof/97fP/2bwlHVTRHj6J/4AH8n/88gS9+EQDv/fdj\n/tSnMDzwAP5vfCNuv3q9PiHBqhHsyMgILpcrrLqmXv9Ygfm5hGoqPl3Y7fYPXGSqdlxrNBrmzZvH\n5s2bufPOO3nssce49dZbqaqqmlFK+2+3vWwO0NXVxd69e8nMzGTjxo2YzeY5MQhPJ827r2ucxUUW\n8iwnV3vBYJDm5mZ6e3uxWq2sXbt2VkSaKEJz+0X2dI5z3hSznoIg0NTUxPHjx1m8eDHLly+fdsrb\n6Q3yRusIH11WNOU4TCTUaFiSJNrb28Pzs7FECvC7PT0EJZnPbpwfPu+5ikz/dGSQg712vnXBQow5\nVgSXi4aeSdbOz1ZeWl4vAD6tgZqC+JrnkENpUJIzMxOmef/cfXLR9fxX67nj4iVsryuifkEOO1YW\nc+fnlNTmweM9vNo6rhDQ6CiyIEwdmYZSuKp/KbKM0NeHXJa4czgSYkhJSNPRofx/mzIKxOLFcbOY\nq1evpqioiEAgQGdnJw2hhczYq68yMDCA9Oabyj5DZBc+v0SR6erVyCYT2t27AdDu3q3US88/f8pz\nlpYtQ87MDM+kAuh27kSsrsYzxfC+uHWr8vcvvxz1c8M99yBbrQnFIgDkwkKCW7ei27kTgsH4NK8k\nkfGNbyDn5OD7wQ9OHu+iiwhu2YL+Zz9LOs4TC70gkD86yoKCApYvX86qVauwWq3k5+fj8/lob2/n\nvffeY//+/Snnj2eKv4c078TEBBMTE2g0GnQ6HRqNho6ODnbv3s3GjRvJz8/nvvvuY9++fTPa/2kd\nmRqNxqgu1LmwYUtnH0FRYn/PJJ9YVQpE10UrKiqora1lIiK1NxOo0V3sCuvdjjECohzXQaxCkiR6\ne3uZmJhg0aJFaQtAJMKrzcMERJlt0+wYFgSBsbExjh8/Hm4AS5TS8gZEdu7r48LawnAj1VyRqcMb\n5O7XOlhZlsWOlcWKFJ7TxZDDz9qQIQChyFRvNlJkiV/Jdo8rZClYs6JGY4btXixuNz1+5TNV5pkS\n1pOFUF20Whfg/t19/PQCK8LoqBKVTvFik0JD7EJoHEYYHUXwepGmsB2TZTkcmYbJtLVV2WcCUtLp\ndNFR1PLliPPmkdvZybjXi3/XLtylpezv68MaSvlZrdbEHqNGI+LGjWFRe/0TTyDn5ExZLwVAq0Xc\nsAHdq6/ikyQ0zc3o3n0X1w9+gDBFOlSqqUGqrET35z8TuO465TO3tKB7+mn8//RP4a7qRAheey36\n559H+8orSGvXRt2n+v/9X7R79+J54IE4xSTfzTdj2bIF/W9+Q+CGG1J/tF27yPjHf0TT24tsNuP7\n0Y9wXXkler2evLy8qFE9NYK12+1RDi6RHqQziWBn2s37QSLTX/3qVzz44IOce+65ZGVlhYOVuro6\nzjnnHADeeOMNbDbFf3i6DUmnNZmWlpZGpQTngkzTUS9qGnTi9ousW5ATrotGWrWNjY3N2qhcjZBj\nH4A3WkcxG7ScUREf2YyOjtLS0kJ+fj6FhYXk5+fPKm30wrFBSrONrCpL38vQ7XYzNDSEyWQKm4Un\nwytNw0x4AlFSjLORQlTrJ8PDwzzVFmTMFeDnl9ehEQQlunQp0eXa+aHGoFBkWpyfeMShe9xLcZYB\nISsrHJk6fUH+38P7eF6WuWhdJUfrCjnW74jbFgCjEdlk4sx8Dd93Bjg0FKB+bCyctk35WUqVhZom\n9GIQQuoykWnMpNtmZSHl5yOEyFR79ChSSUlCCb1EkNaswXToEJVlZWQeOkTw4x+nLtRB7HA4sNls\n2O12MjIywi9btQ4YvOgiMr79bbSvv47u+eeVWmWaGZHA1Vdj+tKX0L78Mvo//AHZbMZ9+eVoplqY\nCgLBHTvQ//znCN3dyBUVGH70IzCbpyS64EUXIeXno//d75BWrw6TqTAygvH22wmeeSbBa66Jv0Yb\nNhA87zwMP/uZkv5NkkLVvvgipquvRqqpwXvvveiefpqMb3wDYyCANuSgE4lEBJvIIi3Sg9RqtU5p\n8j2bbt7Z1CHnErW1tYyNjfHGG29w5ZVX0tvby/j4OBaLhTfeeINPfvKTnHvuueG/n+6777Qm01jM\nVWQ6FRHu61Ie7gxHL11d2jirttm6tqj7SEQqezvHWbcgB4Pu5Ara4/HQFDJcVs/l2LFjs6o9+oIy\nb7aNcu36+WmleEVR5MSJE4yMjJCbm0tJSUlKIgV4Yn8/ZTkZbKg8WVuaaWTqdDppbGzEZDJhying\n6eNNbCwz4O5romFAS43HQ1kgQKERqguV78rjcGEC5hdnJjxm56ibijxTOM0blGT++clGhgeUjtz5\n5fl4/CImffKXlJydzXyND4tBw/4BP8LoKFI6L6fMTGSrNRyZanp7AdKKTAVBQF64MJze1Rw6hJRG\nrVWFeP756J9/Hv2vf40wMUFw+3YMBgMFBQXhF2tbWxsWiwWdTsfk5CQ9PT34/X6yVq6kPiMD88UX\nI2s0BK6/Pu3jBi+5BOmOOzB9+tMIPh++b39bWRQksLGLhf/669H/4hcKAW7bhv7JJ/HdcgvyVNfa\nYCB4zTXof/ELtF/9KpqQqIXxllvA6cT3058mHSfy33IL5u3blej0H/8x7veaY8cwXXcd0ooVuJ9/\nHrKyCFx7LaZPfYrs734Xy//+L4S8YFOfYrxFWiTBDg4ORpl8q4ubSIKdaZrX6XSGnWD+2ti2bRu7\nd+9m9+7dNDQ0sGjRIrZt20Z3dzc7d+5kzZo1LFu2bMZR+GlNpnPlHDOdfQSDQV4/1kOBSWD1kqqo\nG1zFXJBpIoPwMZef9mEXl6xU6mkqgQ0PD7N48eKoFeRsz6FpXCIgymxOkk5WEdkpXF5ezoYNG+jo\n6JiSEPsmPLzTMcYN5y2MIuvp6vrGGoVnZ2fzHy+14A3K3PKxFVQXWggGg0ivvArAWoufhpBfrdzU\nxXlAdbE57phBSaZ50MWVZ5QqUoQ2G//zVjdvnxjnrjOL4V6lljruDpJrSd6lLWdno7XbqSux0Drm\nVsg0hT5uJKTS0pNp3mlEpgDiypXoH38cXC40LS1x3bipENy+Hfmmm8i46SakvDyCSWqeRqORvLy8\n8IiVLMt4PB6G7ryT7Pvuo2fHDnrsdsxHj0alKZO+1I1GPL/7Hcbbb0datgz/TTcheb1pdb3K5eX4\nb70V47/9G/onn0Rctw7/t76V1uf133gj+gcfpPDOO+n/xS/Q/f736B9/XCHzFGYT4tlnEzznHAz3\n3EPgC1+Iik6FkRFMV16JnJWFZ+dOUDti9Xq8//VfmDZsoPL730e84IKkYhNRsNsRvF7kwkIQhJQE\na7fbGRgYCBOs1WrF4/EQCAQwGAzTitjsdjvZU4xxnSoYDAaWLl3KokWLWLBgASdOnKC/v5+LL76Y\nz33uc4ByD85UNvG0JtNY6HS6sMbjTJGMhCJl9zomgqyrKkxIpOp5vB8R8v5uJSJetyAHm83GiRMn\nknqdztY55uiISIZOc7K+mACR0WBkp3A62rwvHhtCluGSVdHyhOnq+sqyzNDQEG1tbcyfPz9sFO7y\nBXlsv40tSwuoLlSav3Q6HQOGLPKBj9cWsX79Snw+H7vtTwOQqVNGqoaGhsJ+mjaPBl9Qoq40Czkr\ni0BjE7/c3cXHlxexrTJ0fmYz4zY/83JSjA6EPE0rcjM4anMqDUgp/EijPuO8eWgiIlPZZAp3+aa6\nLoIgIK5di+HBB9E/8giCKCLW16d1TAC5rAz/t76F4d578YXSpcmOEwlBEJQMzXXXEbzuOkqBElnG\n7XZjt9sZHh7mxIkTiKIYZZeWmZkZfgFKy5fjefLJlMdJBv9NNyEtXYowODit9LJcVITve98j69vf\npnLHDgzt7QTPPRf/P//z1Me89VbMH/sYhvvuw3/zzcoPAwEyPvtZhKEh3C++GGfmLhcXM/zDH1Jy\nww347r4b/y23JN2/0NdHxje/ie6FFwAQ16zBe999cb6wkDyCtdvt4feF1+vFYDBEjemkShF/kGqm\n4+PjtLe309vby+joKLt27eLJJ5/k5ptv5pZbbsFqtX4o2jBX0Ol0uNLsrkuGRJGRWovMy8tjycq1\n2F56i2vmJb/B3q/I9L2uCQxaAW9/C2PWzJQm2bM9h2OjIvWVuRgTpDCDwSBtbW1MTExQW1t7UiUm\n4tynIsSXG4dYVpoVN96TTmTqdrtpbGzEYDDEjfs8e9iGwxvk0zF6uY12xX+0vkB5ZIxGIyNjiotM\neVUlmqwscnJycDgcjI6O8ufjIWcZ5wB2BDRjkxRlGfnO1mo41KBcB7OF/kkfW1LM+8pWpeko36zD\n7ZcQRkaiaqbegMgLx4bZ3T6G0xdkWWkWn1lfRr7FgFxWhqaxUbku7e1IVVVTKhipEDduBCDjppuQ\n9frE6kAp4P/ud/HfdlvS4yVSQEoEQRCwWCxYLBZKQ3VgSZLCIhM2mw2n04ksy1FauKrR+rScXASB\n4DQi8EgE/vEfGZ2YIP/llwl87Wv4brsN0pjLFM85h8Cll2K4807EtWsR160j4/rr0b35Jp7/+i+k\nJAsn+5YtmLZvx/qTnxDcti0hOWoaGjBdfTWC04nvW98CqxX9Aw9g3rIFz5NPIp511pTnp6bnDQYD\nK0PHUDWgHQ5HOIJVCTay/i0IwgeKTK+88koGBwfZsmULK1eu5Nprr+Xyyy+nurp6Ts7xtCbT9yPN\nGwm3201zSB5NrUW+3a68ZFM5tcxVzTRyH36/n7eabVRla1hRN7VJ9mwiU9ukF5tL5jNV0YP2sixj\ns9no6OhgwYIFLFmS2Nd0KkIctPs40DPJjefH12JSRaaRddna2tqoOT71/H63t5faYgury6Ovz8FJ\niR1AluhHAgbsXiYnQwuvEBmrykLFxcWMNknkmEZZv6yKdr+eFV4Ply2UaTpygLLjx1kCdPlkgpLM\nwvzk875ydjaajg50GoFMvwchEAiT6XtdE3zvuRb6JryUWo3kWwz89p0e/nR4kIc/t5qqhQsVjV27\nHU1LC2KE0HzS46k105oaxdqsoYHgJz8JMxnRSkHcs+m4jtS4Va23RFHE5XJht9vp7e0Nz8AaDIaw\nV+n7KjYvCAxfdRWB669PmnFKBu/dd2NubsZ86aXIOh1IEt677iIYKxQRAVEUGfze98hqaCDj+utx\n79oVRd66p54i4ytfQS4uxv3002HHnsA112Davh3TVVfhfumllGno6I938ruM1IBWEWmy0N/fzxNP\nPME777yDJEm88847WK1WysvLk0Z+L774IjfeeCOiKPKlL32JW2+9Ner3Pp+Pz372szQ0NJCfn8+j\njz5KZWiE68c//jEPPvggWq2We++9l62hUadY1NfX8+6773L06FEGBgZYvXo1K1euVBbGIyOzbpQ6\nrck0FnNFprIs09zczNjYGEuWLInqrDsa6txcVpqczGbj56lCbUCSJInu7m66e3rpmgxyzfr5aa3C\nZkPoDWo6ueLkcex2O01NTWRmZk6p5jRVqnZXyzAAW5YWxf0uWQPS0NAQra2tlJWVJZVkPGZz0Drk\n4vZt1VEPfd+ElyaHsk/VIPwvLWMYgwHl5RdTw5NlmXc7xtlQmYvGYGLfpIb1QR//cNEZSBoNYmiB\ndXDYAZjwjXTR2moPr+ojRxfknByEsTFEGXI9ivqSnJ/PU4cG+JfnW5ifa+JXV69gU1WOMhs84OS6\nRw7xwxdaeVB1Yjl2DKGjA+myy5Je00Tw/OY36P/4x6RzlrPBbHVQY6EN2dRF3tvBYJD+/n5GRkbo\n6uoKa+GGBSbmWOhgxn6meXm4X3oJ/UMPIQwNEbz00qR2c5HH0uXn4733XsxXXonx1lvx/cd/QDCI\n4cc/xnj33QQ3bsT7u98pddIQ5KIiPE8+ifmCCzBdfjnu115DLop/jqaLWIJdsWIF3d3dXHfddbS2\ntvLUU0/R09PDtddeyy0xaWlRFPna177Gyy+/THl5OfX19ezYsSPK+eXBBx8kNzeXtrY2du7cyS23\n3MKjjz7K8ePH2blzJ8eOHaO/v58LL7yQlpaWhHXP7373uwwMDNDe3s7Ro0fp6uqipaUFo9FIRkYG\n//Iv/xLVCDpdfEimEZgtmapWYC6XiwULFoTrcJFoGnBQlpNBjnlupAGTQavVMjExQVtbG4WFhRTX\nrMT/yl6WlqYn7ZUoTZwuDvdOYtDCwnwTgUCA1tZWnE4ntbW1aRG5RqNJKcn4zolxiq3GcE0zErFE\n7PF4aGxsRKvVcsYZZ6TsEH7+yAB6jcBFtdEr1JebhnGrVmohMt3VOsolOglC6axIAu8Y9TDk8LOx\nKoed+/pxakK1YJcLbU4OGaHrOpZVhm4oyNYNKwn6lLqg2llpMBiwWq1UZGeTOz6Ox+mhxKV0Ae/z\nGPj+cy1sWpjLf166lEzjyce4tiSTL55ZwT1/6aBrywJqAd3TTyNIElIanZ/qNQSQKyvTbsKZLuaa\nTBNBp9NhNpvJyckJd5RGKgm1t7dHzWEm6mKdDmZlDp6VNeUYTiTUjlNx2zb8N9yA4f77lbqo14tm\ndBT/5z6H7667EtZ95YoKPI8+innbNiVCff75pKM5MLMsgkajobKyEr/fz49+9CO0Wi2yLOMNjZNF\nYu/evVRXV4e/o6uuuopnnnkmikyfeeYZfhASvvjUpz7FDTfcgCzLPPPMM1x11VUYjUaqqqqorq5m\n7969bNq0Ke44JpOJqqoqqqqquPDCCwEYHByksbGRPXv2zFqu9bQm07lM80bOaGZlZVFSUpLwgWwf\ncbEoAQnMJdxuN/39/QiCEFYN+tNhRQlnaUn6ZJqOxnAiHO6zU5WjY2jAxsDAQNr2dZHHTi50L7On\nc4xzqhPPwKqLAEmS6OjoYHBwkCVLlkyZepMkmeePDnJuTT5WU/RD9VLjCGWlIXk+lwunL8jezgm+\nkalBTvCyeqdDIb3V5Vaue+QwXytXyFlwOhXlIrsSYe4dF1lWkonVkgGWjKgMhs/nU+Yys7LIBUaO\nN7PQq5QI/v24h9WrsvjZp5aRkaAmvXVZAff8pYM3pGyWZGZieOABYGqxeDh1AuqngkwTHSfVHKba\nxerxeDAajVERbDrqX7Mi02kicnzDd8cdimDFE09ARgbeq69G/MhHUm4vrV2L98EHybj2WjK+8hW8\nv/51UiGQmY6KqAjP3gpCQknCvr4+5kd0mZeXl7MnQskq9m90Oh3Z2dmMjo7S19fHxlB9X922r68v\n5fmo97ggCBQXF1NcXMzmzZtn9NkicVqTaSxmQqYul4vm5mY0Gk24Lrpv376Ec1miJNMx4mZTVfo6\ntdNBpE1bfn6+MjMZunkbBxwYdBoWFqSXxphpmjcgShzvt3N2ieJEEqkwlS5Spblbh12MuQJsTHIN\nBUHA6XTy7rvvplRPisUxm4NBu49vXVgd9fO+CS9H+h18si7UkORysbttjKAkM98kQCiKiST/V5pG\nqC40c7Tfwbg7wMYVIalDpxMZEFQyHRO5uj5xt7PRaKSwsBBtaL4z0+GkwqPoOTtyC/glmizEAAAg\nAElEQVTmYpHDBxrCTTdqV6tGo6E8x4TZoKXbESS4eTP6555Dqq5Oeyzmr0Fy7xfSIbhEXazqYsZu\nt9PX14fP58NkMkU12cQ27yXS5n2/EPW5BIHgJZcQTGIzlwzBj38c3x13kPGd76Dp7iZw3XUELr44\nzlRgpmSa7sIs0d+l45KTrKQz1X31ft13pzWZxl7U6dQqA4EA7e3tTExMsGTJkqhmFpWUY1ez/RMe\nfEEp7cg03RdOZGOPatM2ODgYJbjfMuhkUYE5LXNumFma1+/389LeY/hEmbrSTBYsWDCjQe9UNdPD\nvcoAfiIFJ6/XS3d3N6IocsYZZ0xLmHt32wiCAGctiibpl5uU+uzZqxSxA8Hp5E9HhijOMpCvk+PS\nvCNOP/t7JvmHsyp44uAAVfkmFi4Mje+ExAMEu52gJRO/JFC/YAortVCDTaCnD93wCD6tnm9fs5Ez\nF+WFu1rVF77T6QwLz2cbNQxNuvH98z+j6e3Ff9NNaV2Hv7fIdKbRorqYiZyB9Xq9OBwOxsfH6erq\nCou/q+Q6U5WgmWC20aKKwA03IJeUYPz+98m44QaC556LHEOmwWBwRs9xZASYCuXl5fSE5qABent7\nw41lsX9TXl5OMBhkcnKSvLy8tLY9VTityXQmULVre3p6qKysTNiRmiyqax9RRimqCqYmU3UfU93E\nk5OTNIVssSJHXWLPoXvMnXa9VN0+3W5eSZLo6emht7eXSVlZ3S/MN824GzhVmvdov52sDB0VeSeJ\nUvU4tdlsFBQUoNVqp+1wsbttlGWlWeRZDPhDmruyLPPM4UFWzsuitEx5qbrH7bw9NsbnN81H2OdD\njqnBvtYygiRDTZGFX77ZzbcuWIisU+4PYUyJLAWHA2eGhQydhvWVqQXrpdA4SO7kKCWOUex5RZwZ\nIvzIrtaykIC9KIo4HA6C4gg+t4s9ogbdPfco0evQUFRNUJRkXm8dpXXYxcoyK5uqUtuczSX+Wmne\nmUJNUZpMJopCDTtyaAZWdXKx2+0cOnQoakQnKyvrfSHYuSJTgOCnPkXw0ksR2tuRQwpOc3Esr9eb\n1nNYX19Pa2srHR0dlJWVsXPnTn7/+99H/c2OHTt46KGH2LRpE3/84x85//zzEQSBHTt2cM011/DN\nb36T/v5+WltbWR9jqnCq8CGZTgMjIyO0tLRQWFiYMn2ZLF3cE5pLrMyf+gZT95HsGKr9ksfjYdmy\nZXGegZFkGhQl+ia8bF2WvuB8uqMxqrZwQUEBGzduZP8bnQgClGUb3hcyPW5zsKw0K/yCVI9fVFTE\nxo0bwy+16cAXEDnca+fzm6JfJMdsTtqG3dy+rUbRyTUY6DwxgFgJFy8vUrR5Q8SkrsJfODZMZZ4p\nLHK/dVkB8qhSe1bJlMlJRrUmNi3MTSklCEBODkFjBiWOEebZh7EsrCBVvkCr1ZKTk4NXhHnFBaxf\nX00gEAinLNWaoEs2cP8BH61j/vC2N29ZyLqsU0dypyIl+n7WMSNnYEtKSnA6naxYsSKcIh4cHKSt\nrS08AxspMjHbc5pLMgVAo0luoD4LXd50vEx1Oh33338/W7duRRRFrrvuOurq6rj99ttZt24dO3bs\n4Itf/CKf+cxnqK6uJi8vj507dwJQV1fHFVdcwbJly9DpdPz85z8/ZdmBuM/xVznqBwSJXhpqijHy\nZne5XDQ1NaHVahPagMUiGZn2T3ox6jTkW6Ye5k4W3apRWH9/P4sWLaK4uDjh54iMLG12H0FJjorm\nZnp8FV6vl+bmZoLBYJS28IkRN+U5JkwG3YzJNFktRJJkWoacXLWuHJ/PR1NTE6IoRh0/XQWkSDQN\nKrq5q8qjU65PHRogQ6fho8tCqT6rFVvPEHWbMpVUvc8HIXk1WZbpHvOwr3uSGzdX8pfmUepKMymx\nZiCH3DSFUaWByDUywYQug48sTmMeURDozZ9H1VgfSyb70G1Zn5JMAcbdARw+kfIcJWrW6/VRNcFh\nh49P//YAE54g39iQQ601wM/3e7jvLye4+7wMrFY7JpNpRqm9dPG3FpmmA9WlSTVaj/y50+nE4XBE\npeMjI9hERutTHeuv0ew0HUxHsGH79u1s37496mc/jDBrz8jI4PHHH0+47W233cZtt9027fOba5zW\nZArxL26VCA0GA4FAgLa2NiYnJ+PqoqmQjIj6JryUZqfXdp9oH8PDw7S2tlJcXMzGjRtT3uCR26sR\n8XTINFl0GJlSra6uDqe8VPSMe6jIM81K9CHZtsNOH96ARCYe9u3bR01NTdzxZyJ0f6RPiWRXlJ18\n8D0Bkf87NsSWpQVkZSiPid+cSWBikotXhCJ8rzcqzfv0oQE0AmxenM+9uzr56jkhDd2sLGS9Pkym\njqExXBkWLlwy9ZB4QJQ4lD2fC9r3kun34F28eMpt2keU8Z3KBGIQoiTzracaGfcE+fWnV4XFQ/w5\nI3z98eP0OCFnfBybzfa+RFQqElqwvQ84laSTjLg1Gk24SSwyHe90OrHb7XR3d4dnYCOdXMxmc9Jr\nNOeRaQr8PXiZngqc9mQaC51ORyAQwGaz0dvbS2VlJbW1tdPztUsSmdomvZTlpHZCURFJhmpkrNPp\nprQlS7S9za7oDc/LTu/YsdurUNPcxcXFbNiwIeHDPOTwUVNkmdWcajIyPdY1BECRSUi6mJgJiTcP\nOskx6ymxnmwYe6VpBKdP5JMR2r/Dmgxy/G7qQ2QqeL3IWUrKOShKPHNkkLMX5dE/6UMGzqgIRbqC\ngJyfjzA6ijcgIo5PYK5ZFibpVHizfZzmwgVc0qh4fEp1dVNus787tDhIoLK1s6Gf/T127rh4SZQK\nl3pveGU9lZWVZGZmhiOqWFWhyJGRVC/8VDiVkempIlNIv1NUq9WSnZ0dJQIfDAbDIzodHR243e4o\nL9JIq7RT+blmStx2u/1DMj2dEQwGOXDgQJgwZrIi02q14SaWSNgmvSwuTk+ySqvV4vP5wkpKieTv\nUiGSzMZcyrnkpZFejtxeJSXVoi1ybjURRElmxOmnKMs4p5Gp3++nubmZg22KetSG5dVJH+6ZRKbd\nY24q88xR2z7aYKMiNyNMiOPuAH2ygQpdEKtKgn4/cujl9naPmyGHn9u2lnCwZxKtEC0ZKefnI4yN\n8WrzKFvddjSVpWmd26/e7EKoWsvNbzwMpDcrurdrgsVFFnJjhEFcviC/3N3FxqocLl4RHdGLknrN\nTpJcZESlQn3hOxyO8As/0rpLnck8VanVqXCqIuC5QJzROomt0gwGAz6fj+Hh4Sgd3PcLiSYT0sGH\nkelpBvUF6nQ6aW5uxuPxUFNTExbVngl0Ol3UWAoo9b4xd4CCzKkJTe0StNlsLFq0KKGS0lSIjCzH\nXH6MOg1mQ/qrS9XkvK2tjaGhobSED0ZdfkRJDpEps66ZqopSPT09LFq0COtkFtCW8hrORIqxZ9zD\nmvknu2qP9js41Gfn1osWoQld9ycO2qgzmFmNHTXnIHi9YYWZZxodVORmcF6NIvVXVWCOut5qZPrI\n251c5XVgqprHVJIYk54Ax2xOKF5E58234cvNZN4UcmeTngAN3ZN8Zn1Z3O8e3W9jwhPk6+dVxt1P\nA6HsRb4p9T0S+cIfcfpx+0VKMrXhBiebzRbu4owk2Fh1mQ/6aMwHBclmYPfv34/D4aC/vx+v10tG\nRkZUBJvMwGImmGma98PI9DSD3++ntbU1XBcdGRmZdS0iUZrX4QsiSjK55tQ3+cTERFgcf+HChVHK\nINNBNJkGyLek70MoyzIjIyM4HA5KSkrSFj5weJXPnG3So9GIM1ZQ0mg0+Hw+9uzZQ25ubjhDMHGk\nFb1WwJJiUTDdBiRZlhmw+8JpTlmW+e3bnZj1Gi5ZqaRzg5LMow02fpKXg9HWHyZTfD4wGjk+6KF5\n1M93tlaj1Qh0jnqoKYoef5Lz8/EfPIKtU1GiIg0x9McP2NQPRcY3bsDW0cFUE3QvN40QlORw01Tk\n53zigI0zKrKjasMqjg840QhQmpke8Ty0p5d7XusgKMn8YHsNl60pDeuyqjOZdrs9aibTYrGEX/an\nKmI8lQ1IpwpGoxGtVhuW34ucgZ2YmKCnp4dAIBAnMjFTubzZpHk/KF6mpwKnPZl2dnaSnZ0drotO\nTEzMWuw+Ub1RTbXGpt5UeL1eWlpa8Pv91NXVMT4+PmtnDXX7cbc/6XFjodZn9Xo9ZrOZqqqqtI/p\n8ivXzWLQotHIM4pM1cWNqp4U2RU56QmSbdKnfDlON83r8ouIkky2Scfk5CTvHDzGq61uzq/Qc/zQ\nfiwWCw0jGgbsPsorChGaT47dCC4XstnMzkOjZBoELllZjCjJ9E54uTBG31fOy0McHqFSVprB5Ckc\nKvxBid+/p3iRXrAksXRiIjxzeJDKfBNLSzKjfn6oz0H3uJd/ODt+jhBgX/ckS0syMemEKY/14vEh\n7nrlBBcsyadrzMPOhn4uW3MykxM5k1lcrCxIZFkOC0wMDg7icrloaIhWcJpuR2s6+FuPTBMh9v5O\nNgPr8Xiw2+2Mjo7S0dGBKIpRC5rMzMy0Is6ZijY4HA4qEsyt/r3itCfTJUuWRBHfXBlzx+5j3K1E\nabGkJkkSnZ2d2Gw2ampqKCwsRBAE7Hb7rI3KVXgC4pQp3kgpQrU++/bbb0/rOC6fch0tRh0ajTTt\nCLGvr4+uri4WLFiAy+WKIlL1cyTSoo3EdCNTNZp2jg/T3DzEUU8uouzmxu2rqcjNwOF0cvNfjjLf\nqkWjF5HtdpqbmrBmZ1PjdjMi6Xiz08FVy7MwG7RMuAMEJTlu/GnQlEOZc5IrQpwjTxGZPn9siGGn\nsgBbUpyZ8m9VHOmzc7DXzi1bFsUR4lvtY2gE+MjieBIfcfo52DPJFzbNB1L7+Tp9Qf7txTZWlmVx\n1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qvCUwbp4QqWzHDcktJTZFrcaFnzuumswUxMdN3BSIRtJfYlSX179dS3nfwdn07NXWf9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\nOpkE0+5pr0G8fv06Wlt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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1085ee3d0>"
+       "<matplotlib.figure.Figure at 0x10c142da0>"
       ]
      },
      "metadata": {},
@@ -1676,7 +197,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -1699,7 +219,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -1708,7 +227,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x109f3c810>"
+       "<matplotlib.text.Text at 0x1c15415eb8>"
       ]
      },
      "execution_count": 7,
@@ -1717,380 +236,9 @@
     },
     {
      "data": {
-      "image/png": [
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-       "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xu0HNV59/nvD10AAYms4EggyRYxsgMevwsFL6GFLxxf\n",
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-       "QYkkQHK4mZtBl2f+qH2gaXWfa3ftqq7fZ61ep7pqd/fznI3o5+zatUsRgZmZmTXTYbkDMDMzs3xc\n",
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-       "zAZd7hGBbwHLux2UtBI4MSIWA58Avl5WYGZmZk2QtRCIiFuAJ0dpsgpYl9reBsyWNLeM2MzMzJog\n",
-       "94jAWOYDO1ue7wIWZIrFzMxs4FS9EABQ23OviWxmZtYj03MHMIbdwMKW5wvSvleR5OLAzMwaJSLa\n",
-       "/1CelKoXAhuAC4GrJS0DnoqIPZ0a9uoXkot0+P8EL/4JMCuCF3LHM1mS1kbE2txxTNUg5DEIOcBg\n",
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SWBqU/wT462D+b4CfBPNLgceC+dnB365CYGrwNy0W9vsbjhPwbeAR4OlgeVi2hXr6PesY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\nzczMCuL/A44tNtW3YAoaAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10883b290>"
+       "<matplotlib.figure.Figure at 0x1c152b1cf8>"
       ]
      },
      "metadata": {},
@@ -2135,7 +283,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -2143,1832 +290,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmYVNWd//8+565V3Q00S0OzKIIsIougIogiKBjjEo1G\n",
-       "jc5MYpwkmmU0JpnJJCb5xclozOQ3JjFGo2M0RrMYjRrcN1wI7sENERGFRqGgaeimb213O+f7x7m3\n",
-       "6lb1VlW9VEOf1/PU09W36i613ff97IRzDolEIpFIJN1Dq30AEolEIpHsD0jBlEgkEomkBKRgSiQS\n",
-       "iURSAlIwJRKJRCIpASmYEolEIpGUgBRMiUQikUhKQAqmRCKRSCQlIAVTIpFIJJISkIIpkUgkEkkJ\n",
-       "SMGUSCQSiaQEpGBKJBKJRFICUjAlEolEIikBKZgSiUQikZSAFEyJRCKRSEpACqZEIpFIJCUgBVMi\n",
-       "kUgkkhKQgimRSCQSSQlIwZRIJBKJpASkYEokEolEUgJSMCUSiUQiKQEpmBKJRCKRlIAUTIlEIpFI\n",
-       "SkAKpkQikUgkJSAFUyKRSCSSEpCCKZFIJBJJCUjBlEgkEomkBKRgSiQSiURSAmq1D0AiqQaWZSmc\n",
-       "8wXZbHah7/vPAdgJYG9jYyOr9rFJJJLBCeGcV/sYJJIBw7IsAmAmgAs453PS6bTBOU8AIBAXkCkA\n",
-       "TwHYCiGizY2NjW61jlcikQwepIUpGTJYljUJwLnpdPrfY7HYvYSQVkII5Zx/FDxlFoAlEL+LXLgi\n",
-       "kUjsALAZwPsQIrqzsbExPcCHL5FIqowUTMkBj2VZowCcCWApgAxjbAyAfb7vj+Gcj4AQwSwAD4AP\n",
-       "YHtkdQKgBsBiACcA4ABoIpHYC+ADAO8B2BFso62xsVG6bCSSAxTpkpUcsFiWVQtgJYDTIIQuAYAl\n",
-       "k8nvKYqyzvf9IwC0AxgOIA0gCSAO4Ingue3dbN4EUBs8n0MIawbAFggR/QhCRHc3Njb6ff/qJBLJ\n",
-       "QCMFU3LAYVmWDuA4AOcCMCDEz+WcK7ZtL/Q872RK6Rumab5j27bv+/5WACMBzAMwB0ALgEYIt+zO\n",
-       "4JYI/u4B0FVikAYhojXBujx47scQIrol2MauxsbGbJ+/cIlE0q9IwZQcMFiWRQHMB3AhhADuApDl\n",
-       "nBPHcQ53XfckSmkzY+ygeDx+PaW0PpPJGL7vbwk2MQVCaH8f/F8LYFxwawz+1gFoRqGQNgPoKjGI\n",
-       "QghoLYSghtbobgCbIGKjoRhb0qUrkQxeZAxTst8TZL5OB/BZCNFrAdAEAK7rTnYcZyUAGIbxgKZp\n",
-       "Tclk8tvIJ/WQyKYYCmuTkxCCtjmyTEdeRMcDOBLAaABtKLREd0K4eRkAK7iFEACxYN2vALAhLNBk\n",
-       "IpHYDCGk24Nt7JGlLhLJ4EAKpmS/xrKsCRCu1/kQMcctAOB53hjHcVYyxsZomvaUrusbCCGh9cY5\n",
-       "56HLtFgwo/93hgNgW3ALUSBEM7REpwf3HRQKaAJCWDmEmKYhko2ywfZ0AIcCOCJ4DgD4iURiK4Sg\n",
-       "NiHv0pWlLhLJACMFU7JfYlnWSNd1LyWEHKeq6jaIuknu+36dbdvLGGMzVVVdE4vF7iaEFCfdhJZk\n",
-       "sfuTo7LuVz6E+3cXgDcjy0cg78o9AsAnIUQxaonGISxMQAjs3uAWogAYA+CQ4D4HgEQikYAoc9mM\n",
-       "fKlLqoJjl0gkJSIFU7JfYVlWDYCTAJzBGDucMaapqvoKY0y3bXuJ7/tHK4qyLh6P/4pS2lViTVeC\n",
-       "WeyS7S1twe3dyLI48i7dQyFcyCaAw5C3RMObAyHG+4JbCAm2swidl7psQr7UpVXGRSWSvkEKpmS/\n",
-       "wLIsDaKpwLkQ8b8EgEbOeaNt20e5rnsCpfTDWCx2s6Io+7rdGMBClywhJOqCLXbR9gdpAB8GN0CI\n",
-       "vwchdKGQzgXQABH3LI6LJoPjTAW3KCZE84WjkH8t2UQi8SGEiEZLXbz+eXkSyYGLFEzJoCbIfJ0H\n",
-       "kfk6BsLtuZtzDsbYGMbY4ZzzetM0/6Cq6s5StkkICWOVncUwB3ogAQ9uHwe3EApgFPJx0WOD+wwd\n",
-       "46KtwTbCeGgUDcBECAs2fM11iUSiHcBqCOEOXbqy1EUi6QYpmJJBSZD5eihE5uuhEPWPWwHAdd2J\n",
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5oYLBoBDVkdST1+tFT08PPB4PWltbJQUyiciXmPp8PhiNRni9XrS2tmbUy5pr8iGm2bQa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/nhjhNIjZWAzOUNcOPSbextlNpv3r5qIjk5qZwxxqYIgo9Jj+Xta6ah02q4a/luVlT7uGZ2Jnee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\nFEW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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a261650>"
+       "<matplotlib.figure.Figure at 0x1c1513b400>"
       ]
      },
      "metadata": {},
@@ -3988,7 +312,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -3998,14 +321,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/numpy/core/numeric.py:462: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "/Users/arokem/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n",
       "  return array(a, dtype, copy=False, order=order)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10a6e2ed0>]"
+       "[<matplotlib.lines.Line2D at 0x1c15837898>]"
       ]
      },
      "execution_count": 9,
@@ -4014,117 +337,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10886f390>"
+       "<matplotlib.figure.Figure at 0x1c1549b128>"
       ]
      },
      "metadata": {},
@@ -4140,16 +355,23 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arokem/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return array(a, dtype, copy=False, order=order)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10a7bc650>]"
+       "[<matplotlib.lines.Line2D at 0x1c15968128>]"
       ]
      },
      "execution_count": 10,
@@ -4158,102 +380,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a6a5110>"
+       "<matplotlib.figure.Figure at 0x1c151de128>"
       ]
      },
      "metadata": {},
@@ -4269,16 +398,23 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arokem/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return array(a, dtype, copy=False, order=order)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10a74e990>]"
+       "[<matplotlib.lines.Line2D at 0x1c157cbf98>]"
       ]
      },
      "execution_count": 11,
@@ -4287,131 +423,9 @@
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+      "image/png": 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a2b3A00Al8Li7rzCzh4A6d68tdZEiJdHWCj/9fPjg9A9fgrMvirsikaIpaCKvuy8DluXs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\nFPr/wtVYz4PhjYIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a43ef50>"
+       "<matplotlib.figure.Figure at 0x1c157cba20>"
       ]
      },
      "metadata": {},
@@ -4449,8 +463,27 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading 12_1_PROBE_MEGA_L_Occ.nii.gz from SDR ...\n",
+      "Downloading 5062_2_1.nii.gz from SDR ...\n",
+      "Done.\n",
+      "Files copied in folder /Users/arokem/MRS_data\n"
+     ]
+    }
+   ],
+   "source": [
+    "mrd.fetch_from_sdr(data='example')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -4463,20 +496,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 1400.\n",
-      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
+     "ename": "IndexError",
+     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-cca2b79a85e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGABA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/source/MRS/MRS/api.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, in_data, w_idx, line_broadening, zerofill, filt_method, spect_method, min_ppm, max_ppm, sampling_rate)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         w_data, w_supp_data = ana.coil_combine(self.raw_data, w_idx=w_idx,\n\u001b[0;32m---> 81\u001b[0;31m                                                sampling_rate=sampling_rate)\n\u001b[0m\u001b[1;32m     82\u001b[0m         f_hz, w_supp_spectra = ana.get_spectra(w_supp_data,\n\u001b[1;32m     83\u001b[0m                                            \u001b[0mline_broadening\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline_broadening\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/source/MRS/MRS/analysis.py\u001b[0m in \u001b[0;36mcoil_combine\u001b[0;34m(data, w_idx, coil_dim, sampling_rate)\u001b[0m\n\u001b[1;32m    176\u001b[0m              \u001b[0;31m# Use the private function to do this:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m              params[repeat, echo, coil] = _do_lorentzian_fit(freqs_w,\n\u001b[0;32m--> 178\u001b[0;31m                                                              sig, bounds)\n\u001b[0m\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/source/MRS/MRS/analysis.py\u001b[0m in \u001b[0;36m_do_lorentzian_fit\u001b[0;34m(freqs, signal, bounds)\u001b[0m\n\u001b[1;32m    387\u001b[0m    \u001b[0minitial_hwhm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_f0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_max_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m    \u001b[0;31m# Everything should be treated as real, except for the phase!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m    \u001b[0minitial_ph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m    \u001b[0minitial_off\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
      ]
     }
    ],
@@ -4486,9 +524,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -4507,9 +544,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -4532,1470 +568,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
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-       "AAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a7bc910>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_fid(np.linspace(1, 3.5, n_samples), (np.real(G.water_fid[0,0]), np.imag(G.water_fid[0,0])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
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-       "96zZnpS0HfhJ0qfAI2Az8ND2NUljtKc3KfNa2binv4ARYCHwo+27klaVuf9MlW49Z/tUh2fjDscD\n",
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-       "SIozImaaaflWKWkncBn4/i1N9wNf18qdVgv/97wlrQVuJTiL6B1ZQYuIiIjoMllBi4iIiOgyCdAi\n",
-       "IiIiukwCtIiIiIgukwAtIiIiosskQIuIiIjoMgnQIiIiIrrMC2RlFYjKPHy5AAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bb01e90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_fid_fft()"
    ]
@@ -6057,9 +649,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -6085,1899 +676,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
-    {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bb0b490>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_fid(np.linspace(1,3.5, n_samples), (np.real(G.w_supp_fid[0,0]), np.imag(G.w_supp_fid[0,0])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "OE4XxsWa4ziO4zhOF8bFmuM4juM4ThfGxZrjOI7jOE4X5v8DAN2hpwAXbFkAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a3d5bd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_fid_ft_w_suppressed()"
    ]
@@ -8072,9 +790,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -8099,345 +816,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "BfYBnb30r7C9CdhD1Qf9CWA1sEbSbYtaeURMk8CPiG4mbB8p42FgSxm/Upb7gU1lbGDqVZ1fAqdt\n",
-       "H3fVu/s4MPj/lxsRs0ngR0Q3nS/aEDA5xz5/lOUk8HvH9kn6/FXcEUtBAj8iurlB0sYyfgD4pIx3\n",
-       "dSw/XfSqImJeEvgR0c1XwJCkMWAl8GzZfqWkz4BHqa7XT3GX8UzrEbHI8nrciJhG0iBwwPaaf2z/\n",
-       "Drjd9pk66oqI+csMPyK6mWk2kBlCRJ/KDD8iIqIFMsOPiIhogQR+RERECyTwIyIiWiCBHxER0QIJ\n",
-       "/IiIiBZI4EdERLTAX8mvV7/O6IhhAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11cc0a190>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_resonances()"
    ]
@@ -8488,9 +873,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -8515,505 +899,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "lgAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11da63290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_two_echos()"
    ]
@@ -9031,31 +923,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 2200.\n",
-      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 1200.\n",
-      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 1800.\n",
-      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:83: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  return lambda x: sqrt((x - lower + 1.)**2 - 1)\n",
-      "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:87: RuntimeWarning: invalid value encountered in arcsin\n",
-      "  return lambda x: arcsin((2. * (x - lower) / (upper - lower)) - 1.)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "G.fit_gaba()\n",
     "G.fit_glx()"
@@ -9063,9 +937,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -9083,1297 +956,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
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-       "VtB/B5YAS81seZ+OPCOjB8hMISOjGvvdfW28/gFwd7z+Yfz/E9RNDsQsfhKv3wQOufvmKPO+GZhz\n",
-       "5YebkdE7yEwhI6Ma5aJgBrR1cszF+N8GXCh93sbVU3gyI6NTZKaQkVGN2VEmGuCfAC/E639Y+r+m\n",
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-       "wK3NatFnZAx0ZE0hI6MaVdJSlqAyPvLImkJGRkZGRg1ZU8jIyMjIqCEzhYyMjIyMGjJTyMjIyMio\n",
-       "ITOFjIyMjIwaMlPIyMjIyKghM4WMjIyMjBr+PzjRMT0uD/LwAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11de0aad0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot_gaba_spec()"
    ]
@@ -10442,9 +1027,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -10464,329 +1048,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "0H7OOm/3eIEAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d213b50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1)\n",
     "ax.plot(G.f_ppm[G.idx], stats.nanmean(G.sum_spectra[G._cr_transients, :], 1).squeeze()[G.idx])\n",
@@ -10809,476 +1077,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "1Xu28wAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x120184f90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(2)\n",
     "ax[0].matshow(np.real(G.diff_spectra[:, G.gaba_idx]), cmap=matplotlib.cm.Reds)\n",
@@ -11292,268 +1097,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
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-       "fj+S+p+hS1KdrQ98NTM3BB4EDqG4tdXCzHwJcALQeN/FqZm5NfB+invUfRbYCNg4IjbpauWSxh1D\n",
-       "l6Q6uy0z/1A+/z7wivL5KeWfpwJbl88TOKN8fjUwPzOvyeJeaNcA64x9uZLGM0OXpDprvHlsAIuH\n",
-       "afNE+edi4PGG9YuBSZ0tTZKeydAlqc7WioityudvBi4un+/d8Ofvu16VJDVh6JJUZzcAh0TEtcBK\n",
-       "wInl+pUj4krgPRTjt5bIQZ43W5akjopiOIMk1UtErAOckZkbD1j/V2DzzLy/F3VJ0mDs6ZJUZ82+\n",
-       "NfpNUlIl2dMlSZLUBfZ0SZIkdYGhS5IkqQsMXZIkSV1g6JIkSeoCQ5ckSVIXGLokSZK64P8DNxC5\n",
-       "wm1fO2oAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ddabe50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1)\n",
     "ax.plot(G.f_ppm[G.idx], stats.nanmean(G.diff_spectra[G._gaba_transients, G.idx], 1).squeeze())\n",
@@ -11665,25 +1215,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.23434054869982721"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stats.nanmean(G.gaba_auc)/stats.nanmean(G.creatine_auc)"
    ]
@@ -11697,22 +1235,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.76677718867328692"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "stats.nanmean(G.glx_auc)/stats.nanmean(G.creatine_auc)"
    ]
@@ -11720,32 +1245,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/setup.py b/setup.py
index 6bf475f..cac0cb9 100644
--- a/setup.py
+++ b/setup.py
@@ -11,9 +11,11 @@
 
 from distutils.core import setup
 
-# Get version and release info, which is all stored in nitime/version.py
+# Get version and release info, which is all stored in MRS/version.py
 ver_file = os.path.join('MRS','version.py')
-execfile(ver_file)
+with open(ver_file) as f:
+    exec(f.read())
+
 
 opts = dict(name=NAME,
             maintainer=MAINTAINER,