diff --git a/particles/generators.py b/particles/generators.py
index af0e2716..059c90ef 100644
--- a/particles/generators.py
+++ b/particles/generators.py
@@ -1,533 +1,342 @@
 '''
-@file matching
-@author Kevin Li, Michael Schenk, Adrian Oeftiger
-@date 17.10.2014
-@brief Module for matching transverse and longitudinal distributions
-@copyright CERN
+@author Kevin Li, Michael Schenk, Adrian Oeftiger, Stefan Hegglin
+@date 30.03.2015
+@brief module for generating & matching particle distributions
 '''
-from __future__ import division
 
-from abc import ABCMeta, abstractmethod
-import sys
+from __future__ import division
 
 import numpy as np
-from numpy.random import normal, uniform, RandomState
-
-from scipy.constants import c, e
-from scipy.optimize import brentq, brenth, bisect, newton
 
 from particles import Particles
 from ..trackers.rf_bucket import RFBucket
+from scipy.optimize import brentq, brenth, bisect, newton
 from ..cobra_functions.pdf_integrators_2d import quad2d
 from . import Printing
-
 from functools import partial
+from scipy.constants import e, m_p, c
+
+
+def generate_Gaussian6DTwiss(macroparticlenumber, intensity, charge, mass,
+                             circumference, gamma,
+                             alpha_x, alpha_y, beta_x, beta_y, beta_z,
+                             epsn_x, epsn_y, epsn_z,
+                             dispersion_x=None, dispersion_y=None):
+    """ Convenience wrapper which generates a 6D gaussian phase space
+    with the specified parameters
+    Args:
+        the usual suspects
+    Returns: A particle instance with the phase space matched to the arguments
+    """
+    beta = np.sqrt(1.-gamma**(-2))
+    p0 = np.sqrt(gamma**2 -1) * m_p * c
+    eps_geo_x = epsn_x/(beta*gamma)
+    eps_geo_y = epsn_y/(beta*gamma)
+    eps_geo_z = epsn_z * e / (4. * np.pi * p0)
+    return ParticleGenerator(macroparticlenumber=macroparticlenumber,
+                             intensity=intensity, charge=charge, mass=mass,
+                             circumference=circumference, gamma=gamma,
+                             distribution_x=gaussian2D(eps_geo_x),
+                             distribution_y=gaussian2D(eps_geo_y),
+                             distribution_z=gaussian2D(eps_geo_z),
+                             linear_matcher_x=transverse_linear_matcher(
+                                 alpha_x, beta_x, dispersion_x),
+                             linear_matcher_y=transverse_linear_matcher(
+                                 alpha_y, beta_y, dispersion_y),
+                             linear_matcher_z=transverse_linear_matcher(
+                                 alpha=0., beta=beta_z)
+                             ).generate()
+
+
+def transverse_linear_matcher(alpha, beta, dispersion=None):
+    ''' Return a transverse matcher with the desired parameters.
+    Args:
+        alpha: Twiss parameter
+        beta: Twiss parameter
+        dispersion: (optional) only use in combination with a longitudinal
+                    phase space
+    Returns: Matcher(closure) taking two parameters: coords and direction
+    '''
+    sqrt = np.sqrt
+    # build the M matrix: only depends on twiss parameters for the
+    # special case of alpha0=0, beta0=1 and phi = 0 (=2pi)
+    # Wiedemann, Particle Accelerator Physics 3rd edition, p. 170
+    M = np.zeros(shape=(2, 2))
+    M[0, 0] = sqrt(beta)
+    M[0, 1] = 0
+    M[1, 0] = -alpha/sqrt(beta)
+    M[1, 1] = sqrt(1./beta)
+
+    def _transverse_linear_matcher(beam, direction):
+        '''Match the coords specified by direction list
+        Args:
+            coords: a Particle instance
+            direction: list (len>=2) specifying which coordinates to match
+                       the first element corresponds to space, the second to
+                       the momentum coordinate. e.g. ['x', 'xp']
+        Returns:
+            Nothing, transforms coords dictionary in place
+        Raises:
+            KeyError: If dispersion!=None and coords['dp] doesn't exist
+        '''
+        #match TODO check if formula correct
+        space_coords = getattr(beam, direction[0])
+        space_coords_copy = space_coords.copy()
+        momentum_coords = getattr(beam, direction[1])
+        space_coords =    (M[0, 0]*space_coords + # copy if not using +=, *=..
+                           M[0, 1]*momentum_coords)
+        momentum_coords = (M[1 ,0]*space_coords_copy +
+                           M[1, 1]*momentum_coords)
+        # add dispersion effects, raise exception of coords['dp'] inexistent
+        if dispersion:
+            try:
+                space_coords += dispersion * getattr(beam, 'dp')
+            except KeyError:
+                print ('Dispersion in the transverse phase space depends on' +
+                       'dp, however no longitudinal phase space was specified. '+
+                       'No matching performed')
+                raise
+        setattr(beam, direction[0], space_coords)
+        setattr(beam, direction[1], momentum_coords)
+
+
+    return _transverse_linear_matcher
+
+
+
+def longitudinal_linear_matcher(Qs, eta, C):
+    '''Return simple longitudinal matcher
+    Internally calls the transverse linear matcher with beta=beta_z
+    and alpha = 0.
+    beta_z = |eta| * C / (2*pi*Qs)t p
+    Args:
+        Qs: synchroton tune
+        eta: slippage factor (zeroth order),
+             is \alpha_c - gamma^2 (\alpha_c = momentum compaction factor)
+        C: circumference
+    Returns:
+        A matcher with the specified Qs, eta (closure)
+    '''
+    beta_z = np.abs(eta) * C / (2. * np.pi * Qs)
+    internal_transverse_matcher = transverse_linear_matcher(alpha=0.,
+                                                             beta=beta_z)
+
+    def _longitudinal_linear_matcher(beam, *args, **kwargs):
+        '''Match the beam to the specified parameters:
+        Qs, eta, beam.circumference
+        Args:
+            beam: provides beam.z, beam.dp, beam.beta, beam.circumference
+        Returns:
+            nothing, modifies beam in place
+        '''
+        internal_transverse_matcher(beam, direction=['z', 'dp'])
+    return _longitudinal_linear_matcher
+
+def RF_bucket_distribution(rfbucket, sigma_z=None, epsn_z=None):
+    '''Return a distribution function which generates particles
+    which are matched to the specified bucket and target emittance or std
+    Specify only one of sigma_z, epsn_z
+    Args:
+        rfbucket: An object of type RFBucket
+        sigma_z:target std
+        epsn_z: target normalized emittance in z-direction
+    Returns:
+        A matcher with the specified bucket properties (closure)
+    Raises:
+        ValueError: If neither or both of sigma_z, epsn_z are specified
+    '''
+    rf_bucket_matcher_impl = RFBucketMatcher(rfbucket, StationaryExponential,
+                                             sigma_z=sigma_z, epsn_z=epsn_z)
+    def _RF_bucket_matcher(n_particles):
+        z, dp, _, _ = rf_bucket_matcher_impl.generate(n_particles)
+        return [z, dp]
+    return _RF_bucket_matcher
+
+def cut_distribution(distribution, is_accepted):
+    """ Generate coordinates according to some distribution inside the region
+    specified by is_accepted. (Wrapper for distributions, based on RF_cut..)
+    Args:
+        is_accepted: function taking two parameters (z,dp) and returns an
+                     array [0,1] specifying whether the coordinate lies
+                     inside the desired phase space volume. Possible sources
+                     are the rfbucket.make_is_accepted(...)
+        sigma_z: std of the normally distributed z coordinates
+        sigma_dp: std of the normally distributed dp coordinates
+    Returns:
+        A matcher with the specified bucket properties (closure)
+    """
+    def _cut_distribution(n_particles):
+        '''Removes all particles for which is_accepted(x,x') is false
+        and redistributes them until all lie inside the bucket
+        '''
+        z = np.zeros(n_particles)
+        dp = np.zeros(n_particles)
+        new_coords = distribution(n_particles)
+        z = new_coords[0]
+        dp = new_coords[1]
+        mask_out = ~is_accepted(z, dp)
+        while mask_out.any():
+            n_gen = np.sum(mask_out)
+            new_coords = distribution(n_gen)
+            z[mask_out] = new_coords[0]
+            dp[mask_out] = new_coords[1]
+            mask_out = ~is_accepted(z, dp)
+        return [z, dp]
+    return _cut_distribution
 
 class ParticleGenerator(Printing):
-    '''Factory to provide Particle instances according to certain
-    distributions (which are implemented in inheriting classes via
-    the self.distribute() function).
-    The Particle instance is obtained via ParticleGenerator.generate() .
+    '''Factory to generate Particle instances according to distributions
+    specified by the parameters in the initializer.
+    The Particle instance can be generated via the .generate() method
     '''
-    __metaclass__ = ABCMeta
-
     def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, coords_n_momenta,
-                 *args, **kwargs):
-        '''coords_n_momenta is a list with the name strings of the
-        coordinates and conjugate momenta.
+                 circumference, gamma, distribution_x=None,
+                 distribution_y=None, distribution_z=None,
+                 linear_matcher_x=None, linear_matcher_y=None,
+                 linear_matcher_z=None):
+        '''
+        Specify the distribution for each phase space seperately. Only
+        the phase spaces for which a distribution has been specified
+        will be generated.
+        Args:
+            distribution_[x,y,z]: a function which takes the n_particles
+                as a parameter and returns a list-like object containing
+                a 2D phase space. result[0] should stand for the spatial,
+                result[1] for the momentum coordinate
+            linear_matcher_[x,y,z]: a function which takes a Particles object
+                and a direction (ie. ['x', 'xp'], ['y, 'yp'], ['z', 'dp'])
+                and transforms the specified beams coordinates
         '''
         self.macroparticlenumber = macroparticlenumber
-        self.particlenumber_per_mp = intensity / macroparticlenumber
+        self.intensity = intensity
         self.charge = charge
         self.mass = mass
         self.circumference = circumference
         self.gamma = gamma
-        self.coords_n_momenta = coords_n_momenta
-
-    @abstractmethod
-    def distribute(self):
-        '''Implement the specific distribution of this generator.
-        The self.coords_n_momenta may be used for the attribute names.
-        Return a coords_n_momenta_dict to instantiate Particles.
-        '''
-        pass
+        # bind the generator and methods
+        self.distribution_x = distribution_x
+        self.distribution_y = distribution_y
+        self.distribution_z = distribution_z
+        self.linear_matcher_x = linear_matcher_x
+        self.linear_matcher_y = linear_matcher_y
+        self.linear_matcher_z = linear_matcher_z
 
     def generate(self):
-        '''Generate the Particles instance (factory method).'''
-        coords_n_momenta_dict = self.distribute()
-        return Particles(macroparticlenumber=self.macroparticlenumber,
-                         particlenumber_per_mp=self.particlenumber_per_mp,
-                         charge=self.charge, mass=self.mass,
-                         circumference=self.circumference,
-                         gamma=self.gamma,
-                         coords_n_momenta_dict=coords_n_momenta_dict)
-
-    def update(self, beam):
-        '''Update the given Particles instance with the coordinate and
-        momentum distribution configured in this ParticleGenerator.
-        Attention: overwrites existing coordinate / momentum attributes
-        in the Particles instance.
-        '''
-        coords_n_momenta_dict = self.distribute()
-        beam.update(coords_n_momenta_dict)
-
-
-class ImportDistribution(ParticleGenerator):
-    '''Create a Particles instance from given distributed arrays.'''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, coords_n_momenta_dict,
-                 *args, **kwargs):
-        '''Directly uses the given dictionary coords_n_momenta_dict
-        with the coordinate and conjugate momentum names as keys
-        and their corresponding arrays as values.
+        ''' Returns a particle  object with the parameters specified
+        in the constructor of the Generator object
         '''
-        self.coords_n_momenta_dict = coords_n_momenta_dict
-        super(ImportDistribution, self).__init__(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, coords_n_momenta_dict.keys(),
-            *args, **kwargs)
+        coords = self._create_phase_space()
+        particles = Particles(self.macroparticlenumber,
+                              self.intensity/self.macroparticlenumber,
+                              self.charge, self.mass, self.circumference,
+                              self.gamma,
+                              coords_n_momenta_dict=coords)
+        self._linear_match_phase_space(particles)
+        return particles
 
-    def distribute(self):
-        return self.coords_n_momenta_dict
-
-
-class HEADTAILcoords(object):
-    '''The classic HEADTAIL phase space.'''
-    coordinates = ('x', 'xp', 'y', 'yp', 'z', 'dp')
-    transverse = coordinates[:4]
-    longitudinal = coordinates[-2:]
-
-
-class Uniform3D(ParticleGenerator):
-    '''Uniform grid along 3D cuboid in classic HEADTAIL phase space
-    with coordinates (x, xp, y, yp, z, dp).
-    All conjugate momenta (xp, yp, dp) are zero.
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, xextent, yextent, zextent,
-                 *args, **kwargs):
-        '''Take the extents in all three spatial dimensions.'''
-        super(Uniform3D, self).__init__(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, HEADTAILcoords.coordinates,
-            *args, **kwargs)
-        self.xextent = xextent
-        self.yextent = yextent
-        self.zextent = zextent
-
-    def distribute(self):
-        x  = uniform(-self.xextent, self.xextent, self.macroparticlenumber)
-        y  = uniform(-self.yextent, self.yextent, self.macroparticlenumber)
-        z  = uniform(-self.zextent, self.zextent, self.macroparticlenumber)
-        xp = np.zeros(self.macroparticlenumber)
-        yp = np.zeros(self.macroparticlenumber)
-        dp = np.zeros(self.macroparticlenumber)
-        return {'x': x, 'xp': xp, 'y': y, 'yp': yp, 'z': z, 'dp': dp}
-
-
-class Gaussian(ParticleGenerator):
-    '''Provide a Particle instance with Gaussian distributions for
-    all coordinates and conjugate momenta.
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, coords_n_momenta_with_sigmas,
-                 *args, **kwargs):
-        '''coords_n_momenta_with_sigmas is a dict: the keys are the
-        name strings of the coordinates and conjugate momenta,
-        the values are the respective sigmas.
-        e.g.: coords_n_momenta_with_sigmas = {'x': 1e-6, 'xp': 1.2e-6}
-        '''
-        # FUTURE IDEA?
-        # COORDINATES AND CONJUGATE MOMENTA ARE GROUPED TOGETHER IN TUPLES!
-        # '''coords_n_momenta_with_sigmas is a dict with tuple keys
-        # (with the name strings of the coordinate and conjugate momentum)
-        # and tuple values (with the sigmas of the coordinate and the
-        # conjugate momentum).
-        # e.g.: coords_n_momenta_with_sigmas = {('x', 'xp'): (1e-6, 1e-6)}
-        # '''
-        # if any(len(key) is not 2 for key in
-        #        coords_n_momenta_with_sigmas.key()):
-        #     raise ValueError("the dictionary coords_n_momenta_with_sigmas" +
-        #                      " takes tuple keys with two entries for the" +
-        #                      " coordinate and the respective momentum.")
-        # if any(len(value) is not 2 for value in
-        #        coords_n_momenta_with_sigmas.values()):
-        #     raise ValueError("the dictionary coords_n_momenta_with_sigmas" +
-        #                      " takes tuple values with two entries for the" +
-        #                      " two sigmas of the coordinate and the" +
-        #                      " respective momentum.")
-
-        coords_n_momenta = coords_n_momenta_with_sigmas.keys()
-        # # flatten:
-        # coords_n_momenta = list(itertools.chain.from_iterable(coords_n_momenta))
-        super(Gaussian, self).__init__(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, coords_n_momenta, *args, **kwargs)
-        self.coords_n_momenta_with_sigmas = coords_n_momenta_with_sigmas
-
-    def distribute(self):
-        '''Create the Gaussian distribution for all coordinates and
-        conjugate momenta saved in self.coords_n_momenta_with_sigmas.
-        '''
-        coords = self.coords_n_momenta
-        sigs = self.coords_n_momenta_with_sigmas
-        n = self.macroparticlenumber
-        return {coord: normal(0, sigs[coord], n) for coord in coords}
-
-
-class Gaussian6D(Gaussian):
-    '''The classic HEADTAIL phase space generator with coordinates
-    (x, xp, y, yp, z, dp).
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, sigma_x, sigma_xp,
-                 sigma_y, sigma_yp, sigma_z, sigma_dp, *args, **kwargs):
-        '''Take the sigmas of all directions to generate the Gaussian
-        phase space distribution.
-        '''
-        coords_n_momenta_with_sigmas = {
-            'x': sigma_x, 'xp': sigma_xp,
-            'y': sigma_y, 'yp': sigma_yp,
-            'z': sigma_z, 'dp': sigma_dp}
-        super(Gaussian6D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, coords_n_momenta_with_sigmas, *args, **kwargs)
-
-
-class Gaussian2DTwiss(Gaussian):
-    '''Generates a coordinate and conjugate momentum pair according
-    to the TWISS '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, coords,
-                 alpha, beta, epsn_geo, *args, **kwargs):
-        '''coords: indicate the name tuple with the strings of the
-        coordinate and conjugate momentum name in this order,
-        e.g. coords=('x', 'xp').
-        Uses the geometric emittance (in the transverse plane it would
-        be epsn_geo_{x,y} = epsn_{x,y} / betagamma while in the
-        longitudinal plane epsn_geo_z = epsn_z * e / p0) to calculate
-        the correspondingly matched coordinate and its conjugate
-        momentum.
-        '''
-        if not np.allclose(alpha, 0., atol=1e-15):
-            raise NotImplementedError("alpha != 0 is not yet taken into" +
-                                      " account")
-        coordssig = self.coords_n_momenta_with_sigmas(coords, epsn_geo, beta)
-        super(Gaussian2DTwiss, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, coordssig, *args, **kwargs)
-
-    @staticmethod
-    def coords_n_momenta_with_sigmas(coords, epsn_geo, beta):
-        return {
-            coords[0]: np.sqrt(epsn_geo * beta), # sigma_u generalised coord
-            coords[1]: np.sqrt(epsn_geo / beta)  # sigma_u' conjugate momentum
-            }
-
-
-class Gaussian6DTwiss(Gaussian):
-    '''The classic HEADTAIL phase space generator with coordinates
-    (x, xp, y, yp, z, dp) using the optics resp. TWISS parameters.
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, alpha_x, beta_x, epsn_x,
-                 alpha_y, beta_y, epsn_y, beta_z, epsn_z, *args, **kwargs):
-        '''Take the TWISS parameters at the point of injection along
-        with the emittances to generate the Gaussian phase space
-        distribution.
+    def update(self, beam):
+        '''Updates the beam coordinates specified in the constructor of the
+        Generator object. Existing coordinates will be overriden, new ones
+        will be added. Calls beam.update()
         '''
-        betagamma = np.sqrt(gamma**2 - 1)
-        p0 = betagamma * mass * c
-        epsn_geo_x = epsn_x / betagamma
-        epsn_geo_y = epsn_y / betagamma
-        epsn_geo_z = epsn_z * e / (4 * np.pi * p0)
-
-        # super(Gaussian6Dtwiss, self).__init__(
-        #     macroparticlenumber, intensity, charge, mass,
-        #     circumference, gamma,
-        #     epsn_geo_x * beta_x, epsn_geo_x / beta_x,
-        #     epsn_geo_y * beta_y, epsn_geo_y / beta_y,
-        #     epsn_geo_z * beta_z, epsn_geo_z / beta_z)
-
-        # the following is equivalent but consistently uses Gaussian2DTwiss:
-        get_sigmas = Gaussian2DTwiss.coords_n_momenta_with_sigmas
-        coordssig = {}
-        coordssig.update(get_sigmas(('x', 'xp'), epsn_geo_x, beta_x))
-        coordssig.update(get_sigmas(('y', 'yp'), epsn_geo_y, beta_y))
-        coordssig.update(get_sigmas(('z', 'dp'), epsn_geo_z, beta_z))
-
-        super(Gaussian6DTwiss, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, coordssig, *args, **kwargs)
-
-
-class MatchTransverseMap(Gaussian):
-    '''Transverse phase space (x, xp, y, yp) is generated with the
-    optics resp. TWISS parameters taken from a TransverseMap instance.
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, transverse_map,
-                 epsn_x, epsn_y, *args, **kwargs):
-        '''Uses the transverse_map to extract the optics parameters.'''
-        betagamma = np.sqrt(gamma**2 - 1)
-        epsn_geo_x = epsn_x / betagamma
-        epsn_geo_y = epsn_y / betagamma
-        alpha_x, beta_x, alpha_y, beta_y = transverse_map.get_injection_optics()
-
-        if not np.allclose([alpha_x, alpha_y], [0., 0.], atol=1e-15):
-            raise NotImplementedError("alpha != 0 is not yet taken into" +
-                                      " account")
-
-        get_sigmas = Gaussian2DTwiss.coords_n_momenta_with_sigmas
-        coordssig = {}
-        coordssig.update(get_sigmas(('x', 'xp'), epsn_geo_x, beta_x))
-        coordssig.update(get_sigmas(('y', 'yp'), epsn_geo_y, beta_y))
-        super(MatchTransverseMap, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, coordssig, *args, **kwargs)
-
-
-class MatchLinearLongMap(Gaussian):
-    '''Longitudinal phase space (z, dp) is generated with the epsn_z
-    or sigma_z, Qs (synchroton tune) and eta (slippage factor) taken
-    from a LongitudinalMap instance.
+        coords = self._create_phase_space()
+        beam.update(coords)
+        self._linear_match_phase_space(beam)
+
+    def _create_phase_space(self):
+        coords = {}
+        if self.distribution_x:
+            x_phase_space = self.distribution_x(self.macroparticlenumber)
+            coords.update({'x': np.ascontiguousarray(x_phase_space[0]),
+                           'xp': np.ascontiguousarray(x_phase_space[1])})
+            assert len(coords['x']) == len(coords['xp'])
+
+        if self.distribution_y:
+            y_phase_space = self.distribution_y(self.macroparticlenumber)
+            coords.update({'y': np.ascontiguousarray(y_phase_space[0]),
+                           'yp': np.ascontiguousarray(y_phase_space[1])})
+            assert len(coords['y']) == len(coords['yp'])
+        if self.distribution_z:
+            z_phase_space = self.distribution_z(self.macroparticlenumber)
+            coords.update({'z': np.ascontiguousarray(z_phase_space[0]),
+                           'dp': np.ascontiguousarray(z_phase_space[1])})
+            assert len(coords['z']) == len(coords['dp'])
+
+        return coords
+
+    def _linear_match_phase_space(self, beam):
+        #NOTE: keep this ordering (z as first, as x,y dispersion effects
+        #depend on the dp coordinate!
+        if self.linear_matcher_z:
+            self.linear_matcher_z(beam, ['z', 'dp'])
+        if self.linear_matcher_x:
+            self.linear_matcher_x(beam, ['x', 'xp'])
+        if self.linear_matcher_y:
+            self.linear_matcher_y(beam, ['y', 'yp'])
+
+def import_distribution2D(coords):
+    '''Return a closure which generates the phase space specified
+    by the coords list
+    Args:
+        coords: list containing the coordinates to use
+            coords[0] is the space, coords[1] the momentum coordinate
     '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, longitudinal_map,
-                 epsn_z=None, sigma_z=None, *args, **kwargs):
-        '''Uses the longitudinal_map to extract the Qs and eta for the
-        beta_z.
-        '''
-        self.check_long_input(epsn_z, sigma_z)
-        p0 = np.sqrt(gamma**2 - 1) * mass * c
-        eta = longitudinal_map.eta(0, gamma)
-        try:
-            Qs = longitudinal_map.Qs
-        except AttributeError as exc:
-            raise ValueError('"' + self.__name__ + '" expects a ' +
-                             'longitudinal_map with a Qs attribute ' +
-                             'yielding the linear synchroton frequency. ' +
-                             'However, the given "' + exc.message
-                             )
-        beta_z = np.abs(eta) * circumference / (2 * np.pi * Qs)
-        if sigma_z is None:
-            sigma_z = np.sqrt(epsn_z * beta_z / (4*np.pi) * e/p0)
-        sigma_dp = sigma_z / beta_z
-        super(MatchLinearLongMap, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, {'z': sigma_z, 'dp': sigma_dp}, *args, **kwargs
-            )
-
-    @staticmethod
-    def check_long_input(epsn_z, sigma_z):
-        '''Check that exactly one of epsn_z and sigma_z is given,
-        the other one should be None.
+    assert len(coords[0]) == len(coords[1])
+    def _import_distribution2D(n_particles):
+        '''Return the specified coordinates
+        Args:
+            n_particles: number of particles, must be equal len(coords[0/1])
         '''
-        if (epsn_z is None and sigma_z is None) or (epsn_z and sigma_z):
-            raise ValueError('***ERROR: exactly one of sigma_z and ' +
-                             ' epsn_z is required!')
-
-
-class MatchGaussian6D(ParticleGenerator):
-    '''The classic HEADTAIL phase space generator with coordinates
-    (x, xp, y, yp, z, dp) using the given epsn_x, epsn_y and either
-    epsn_z or sigma_z, as well as the optics resp. TWISS parameters
-    taken from a TransverseMap instance and a LongitudinalMap instance.
+        assert len(coords[0]) == n_particles
+        return coords
+
+    return _import_distribution2D
+
+def gaussian2D(emittance_geo):
+    '''Closure which generates a gaussian distribution with the given
+    geometrical emittance. Uncorrelated and symmetrical.
+    Args:
+        -emittance_geo: geometrical emittance (normalized emittance/betagamma
+                        for transverse, emittance*e/(4*pi*p0) for longitudinal)
+    Returns:
+        A function generating a 2d gaussian with the desired parameters
     '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma,
-                 transverse_map, epsn_x, epsn_y,
-                 longitudinal_map, epsn_z=None, sigma_z=None,
-                 *args, **kwargs):
-        '''Uses the transverse_map to extract the optics parameters
-        and the longitudinal_map to extract the Qs and eta for the
-        beta_z.
-        '''
-        self._transverse_matcher = MatchTransverseMap(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, transverse_map,
-            epsn_x, epsn_y, *args, **kwargs)
-        self._longitudinal_matcher = MatchLinearLongMap(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, longitudinal_map,
-            epsn_z, sigma_z, *args, **kwargs)
-        super(MatchGaussian6D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, HEADTAILcoords.coordinates, *args, **kwargs)
-
-    def distribute(self):
-        '''Create the Gaussian distribution for all 6D HEADTAIL
-        coordinates and conjugate momenta in both the transverse and
-        longitudinal plane.
-        '''
-        coords_n_momenta_dict = self._transverse_matcher.distribute()
-        coords_n_momenta_dict.update(self._longitudinal_matcher.distribute())
-        return coords_n_momenta_dict
-
 
-class MatchRFBucket6D(ParticleGenerator):
-    '''The classic HEADTAIL phase space generator with coordinates
-    (x, xp, y, yp, z, dp) using the given epsn_x, epsn_y and either
-    epsn_z or sigma_z, as well as the optics resp. TWISS parameters
-    taken from a TransverseMap instance and the Hamiltonian from an
-    RFBucket instance.
+    def _gaussian2D(n_particles):
+        std = np.sqrt(emittance_geo) # bc. std = sqrt(emittance_geo)
+        coords = [np.random.normal(loc=0., scale=std, size=n_particles),
+                  np.random.normal(loc=0., scale=std, size=n_particles)]
+        return coords
+    return _gaussian2D
+
+def gaussian2D_asymmetrical(sigma_u, sigma_up):
+    ''' Closure which generates a gaussian distribution with the given
+    standard deviations. No correlation between u and up
+    Args:
+        - sigma_u: standard deviation of the marginal spatial distribution
+        - sigma_up: standard deviation of the marginal momentum distribution
+    Returns:
+        A function generating a 2d gaussian with the desired parameters
     '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma,
-                 transverse_map, epsn_x, epsn_y,
-                 rf_bucket, epsn_z=None, sigma_z=None,
-                 *args, **kwargs):
-        '''Uses the transverse_map to extract the optics parameters
-        and the rf_bucket to match the longitudinal distribution.
-        '''
-        self._transverse_matcher = MatchTransverseMap(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, transverse_map,
-            epsn_x, epsn_y, *args, **kwargs)
-        self._rf_bucket_matcher = MatchRFBucket2D(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, rf_bucket,
-            epsn_z, sigma_z, *args, **kwargs)
-        super(MatchRFBucket6D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, HEADTAILcoords.coordinates, *args, **kwargs)
-
-    def distribute(self):
-        '''Create the Gaussian distribution for all 6D HEADTAIL
-        coordinates and conjugate momenta in both the transverse and
-        longitudinal plane.
-        '''
-        coords_n_momenta_dict = self._transverse_matcher.distribute()
-        coords_n_momenta_dict.update(self._rf_bucket_matcher.distribute())
-        return coords_n_momenta_dict
-
-
-class CutRFBucket6D(ParticleGenerator):
-    '''The classic HEADTAIL phase space generator with coordinates
-    (x, xp, y, yp, z, dp) using the given epsn_x, epsn_y as well as
-    the optics resp. TWISS parameters taken from a TransverseMap
-    instance. The longitudinal phase space is initialised as a
-    bi-gaussian with given sigma_z and sigma_dp which is then cut along
-    the function given by is_accepted. The usual choices for
-    is_accepted are either RFBucket.is_in_separatrix or
-    RFBucket.is_accepted . The former strictly follows the separatrix
-    of the bucket (the equihamiltonian with a value of 0), while with
-    the latter, tighter boundaries can be used (equihamiltonian lying
-    inside the RFBucket) as a result of which particles are not
-    initialised too close to the separatrix. This option is usually
-    preferred as it avoids bucket leakage and particle losses which may
-    occur as a consequence of the unmatched initialisation. The
-    RFBucket.is_accepted method must be created first however, by
-    calling the RFBucket.make_is_accepted(margin) method with a certain
-    value for the margin.
+    def _gaussian2D(n_particles):
+        coords = [np.random.normal(loc=0., scale=sigma_u, size=n_particles),
+                  np.random.normal(loc=0., scale=sigma_up, size=n_particles)]
+        return coords
+    return _gaussian2D
+
+def uniform2D(low, high):
+    '''Closure which generates a uniform distribution for the space coords.
+    All momenta are 0.
     '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma,
-                 transverse_map, epsn_x, epsn_y,
-                 sigma_z, sigma_dp, is_accepted,
-                 *args, **kwargs):
-        '''Uses the transverse_map to extract the optics parameters
-        and the rf_bucket to match the longitudinal distribution.
-        '''
-        self._transverse_matcher = MatchTransverseMap(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma, transverse_map,
-            epsn_x, epsn_y, *args, **kwargs)
-        self._rf_bucket_matcher = CutRFBucket2D(
-            macroparticlenumber, intensity, charge, mass,
-            circumference, gamma,
-            sigma_z, sigma_dp, is_accepted, *args, **kwargs)
-        super(CutRFBucket6D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, HEADTAILcoords.coordinates, *args, **kwargs)
-
-    def distribute(self):
-        '''Create the Gaussian distribution for all 6D HEADTAIL
-        coordinates and conjugate momenta in both the transverse and
-        longitudinal plane.
+    def _uniform2D(n_particles):
+        '''Create a 2xn_particles ndarray. The first row will be uniformly
+        distributed random numbers between high,low. The second row will
+        be zero (equals zero momentum)
         '''
-        coords_n_momenta_dict = self._transverse_matcher.distribute()
-        coords_n_momenta_dict.update(self._rf_bucket_matcher.distribute())
-        return coords_n_momenta_dict
-
-
-# possible TODO: incorporate RFBucketMatcher class
-class MatchRFBucket2D(ParticleGenerator):
-    '''Longitudinal phase space (z, dp) is generated with the epsn_z
-    or sigma_z and the Hamiltonian from the given RFBucket instance..
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, rf_bucket,
-                 epsn_z=None, sigma_z=None, *args, **kwargs):
-        '''Uses the RFBucket rf_bucket to match the longitudinal
-        distribution.
-        '''
-        self.rf_bucket = rf_bucket
-        self.epsn_z = epsn_z
-        self.sigma_z = sigma_z
-        MatchLinearLongMap.check_long_input(epsn_z, sigma_z)
-        super(MatchRFBucket2D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, HEADTAILcoords.longitudinal, *args, **kwargs)
-
-    def distribute(self):
-        '''Create the longitudinal distribution from an RFBucketMatcher
-        and return it.
-        '''
-        rf_bucket_matcher = RFBucketMatcher(
-            self.rf_bucket, StationaryExponential, self.sigma_z, self.epsn_z)
-        z, dp, _, _ = rf_bucket_matcher.generate(self.macroparticlenumber)
-        return {'z': z, 'dp': dp}
-
-
-class CutRFBucket2D(ParticleGenerator):
-    '''For HEADTAIL style matching into RF bucket.
-    The argument is_accepted takes a function (i.e. reference to a
-    function). The usual choices are RFBucket.is_in_separatrix or
-    RFBucket.is_accepted . The former strictly follows the separatrix
-    of the bucket (the equihamiltonian with a value of 0), while with
-    the latter, tighter boundaries can be used (equihamiltonian lying
-    inside the RFBucket) as a result of which particles are not
-    initialised too close to the separatrix. This option is usually
-    preferred as it avoids bucket leakage and particle losses which may
-    occur as a consequence of the unmatched initialisation.
-    The RFBucket.is_accepted method must be created first however, by
-    calling the RFBucket.make_is_accepted(margin) method with a certain
-    value for the margin.
-
-    BY KEVIN: NEEDS TO BE CLEANED UP BY ADRIAN!
-    '''
-    def __init__(self, macroparticlenumber, intensity, charge, mass,
-                 circumference, gamma, sigma_z, sigma_dp,
-                 is_accepted, *args, **kwargs):
-
-        self.sigma_z = sigma_z
-        self.sigma_dp = sigma_dp
-        self.is_accepted = is_accepted
-
-        super(CutRFBucket2D, self).__init__(
-            macroparticlenumber, intensity, charge, mass, circumference,
-            gamma, HEADTAILcoords.longitudinal, *args, **kwargs)
-
-    def distribute(self):
-
-        z = normal(0, self.sigma_z, self.macroparticlenumber)
-        dp = normal(0, self.sigma_dp, self.macroparticlenumber)
-        self._redistribute(z, dp)
-
-        return {'z': z, 'dp': dp}
-
-    def _redistribute(self, z, dp):
-
-        mask_out = ~self.is_accepted(z, dp)
-        while mask_out.any():
-            n_gen = np.sum(mask_out)
-            z[mask_out] = normal(0, self.sigma_z, n_gen)
-            dp[mask_out] = normal(0, self.sigma_dp, n_gen)
-            mask_out = ~self.is_accepted(z, dp)
-            self.prints('Reiterate on non-accepted particles')
-
+        coords = np.zeros(shape=(2, n_particles))
+        coords[0] = np.random.uniform(low=low, high=high, size=n_particles)
+        return coords
+    return _uniform2D
 
 class RFBucketMatcher(Printing):
 
@@ -538,7 +347,6 @@ def __init__(self, rfbucket, psi, sigma_z=None, epsn_z=None):
         hmax = rfbucket.h_sfp(make_convex=True)
         self.psi_object = psi(hamiltonian, hmax)
         self.psi = self.psi_object.function
-
         if sigma_z and not epsn_z:
             self.variable = sigma_z
             self.psi_for_variable = self.psi_for_bunchlength_newton_method
@@ -550,6 +358,7 @@ def __init__(self, rfbucket, psi, sigma_z=None, epsn_z=None):
                              "distribution! (Don't provide both sigma_z " +
                              "and epsn_z!)")
 
+
     def psi_for_emittance_newton_method(self, epsn_z):
 
         # Maximum emittance
@@ -562,12 +371,14 @@ def psi_for_emittance_newton_method(self, epsn_z):
             epsn_z = epsn_max*0.99
         self.prints('*** Maximum RMS emittance ' + str(epsn_max) + 'eV s.')
 
+
         def get_zc_for_epsn_z(ec):
             self._set_psi_epsn(ec)
             emittance = self._compute_emittance(self.rfbucket, self.psi)
 
             self.prints('... distance to target emittance: ' +
                         '{:.2e}'.format(emittance-epsn_z))
+
             return emittance-epsn_z
 
         try:
@@ -584,6 +395,7 @@ def get_zc_for_epsn_z(ec):
         sigma = self._compute_sigma(self.rfbucket, self.psi)
         self.prints('--> Bunch length:' + str(sigma))
 
+
     # @profile
     def psi_for_bunchlength_newton_method(self, sigma):
 
@@ -597,6 +409,7 @@ def psi_for_bunchlength_newton_method(self, sigma):
             sigma = sigma_max*0.99
         self.prints('*** Maximum RMS bunch length ' + str(sigma_max) + 'm.')
 
+
         def get_zc_for_sigma(zc):
             '''Width for bunch length'''
             self._set_psi_sigma(zc)
@@ -606,6 +419,7 @@ def get_zc_for_sigma(zc):
 
             self.prints('... distance to target bunch length: ' +
                         '{:.4e}'.format(length-sigma))
+
             return length-sigma
 
         zc_bar = newton(get_zc_for_sigma, sigma)
@@ -642,6 +456,7 @@ def generate(self, macroparticlenumber):
         lx = (xmax - xmin)
         ly = (ymax - ymin)
 
+        uniform = np.random.uniform
         n_gen = macroparticlenumber
         u = xmin + lx * uniform(size=n_gen)
         v = ymin + ly * uniform(size=n_gen)
@@ -717,3 +532,9 @@ def function(self, z, dp):
         psi = np.exp(self.H(z, dp).clip(min=0)/self.H0) - 1
         psi_norm = np.exp(self.Hmax/self.H0) - 1
         return psi/psi_norm
+
+class HEADTAILcoords(object):
+    '''The classic HEADTAIL phase space.'''
+    coordinates = ('x', 'xp', 'y', 'yp', 'z', 'dp')
+    transverse = coordinates[:4]
+    luongitudinal = coordinates[-2:]
diff --git a/testing/interactive-tests/ApertureNLossesTest.ipynb b/testing/interactive-tests/ApertureNLossesTest.ipynb
index a59ab27b..4b4e5e33 100644
--- a/testing/interactive-tests/ApertureNLossesTest.ipynb
+++ b/testing/interactive-tests/ApertureNLossesTest.ipynb
@@ -36,7 +36,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-88-g84ecef5e18-dirty\n",
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
       "\n",
       "\n"
      ]
@@ -44,7 +44,7 @@
    ],
    "source": [
     "from PyHEADTAIL.particles.particles import Particles\n",
-    "from PyHEADTAIL.particles.generators import Gaussian6DTwiss\n",
+    "from PyHEADTAIL.particles.generators import generate_Gaussian6DTwiss\n",
     "from PyHEADTAIL.trackers.simple_long_tracking import RFSystems\n",
     "import PyHEADTAIL.aperture.aperture as aperture"
    ]
@@ -131,31 +131,15 @@
    "outputs": [],
    "source": [
     "def generate_bunch(n_particles):\n",
-    "    bunch = Gaussian6DTwiss(\n",
-    "        n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "        inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "        inj_alpha_y, inj_beta_y, epsn_y,\n",
-    "        beta_z, epsn_z\n",
-    "        ).generate()\n",
+    "    bunch = generate_Gaussian6DTwiss(\n",
+    "        n_particles, intensity, e, m_p, circumference, gamma=gamma,\n",
+    "        alpha_x=inj_alpha_x, beta_x=inj_beta_x, epsn_x=epsn_x,\n",
+    "        alpha_y=inj_alpha_y, beta_y=inj_beta_y, epsn_y=epsn_y,\n",
+    "        beta_z=beta_z, epsn_z=epsn_z\n",
+    "        )\n",
     "    return bunch"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def check_macroparticlenumber(bunch):   \n",
-    "    if bunch.macroparticlenumber == len(bunch.x) == len(bunch.y):\n",
-    "        print 'bunch.macroparticlenumber was updated correctly.'\n",
-    "        print 'bunch.macroparticlenumber new:', bunch.macroparticlenumber\n",
-    "    else:\n",
-    "        print 'bunch.macroparticlenumber was not updated.'"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -163,19 +147,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bunch.macroparticlenumber was updated correctly.\n",
-      "bunch.macroparticlenumber new: 922\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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8xHv0ak4kQpLpq8mN+VrV97hVV9ZkeADoSM13APtHKTMvKdM+hroASFqfmu2O\niO7KwrWGNufhq4Y+JCD321nuG3T5HxRzHr6qaomwyVz94IlECLnpqx+chMmwXC0Bb9u8LNN7tJrv\n8SYjqZPcT2w1kTUZPgwskDQfOAh8HFidV+ZuYA2wUdJS4GhEHJZ0ZAx1AYiI9RnjNCstBEc7ennj\nwXZa+0Yvb1bYTcD5DD9NelP9wpk8kk5Q9+C8pM9Wc/uZ7jOMiD5yiW4T8CRwZ0TskHSFpCuSMvcB\nz0jaA9wOfLpU3Szx2AR38Nyb6U2dYeqdlltWjW0NCF58ay/PLezhuYU9vPjWXo5PzV3N+OJbe/nh\np9bFLc9O4fl3vF5yuwEcnzr2HwgmoJveDz2pr8k9bbllk9oAox/TvinwzPItpd6jyW+DFwNbkn/D\nfi+s6nvcqirTBTTjwRfQNJeqXkBTwbb0ifOeZ95Dpw5bOHjbw0tze9m9cn3c+6XP5S7O+fbyoQsv\nYOTFM6HkatOBvFscKP7bVIOoyQU06Y+aWrR5tIuc8i+O6W+Dvqlw9MxefvYb6wFYcO963ri/Hcjd\nAtM3bWDoiuLkdoys71FfQFMdDXU16XhwMrTxNOICh+T+tkIfWEO3bZxyaNrghyVA/gdd0RvcxYc5\n6/57uPAz7cx8mrz74YJX39IHgulH2mh5XbQm9/v1tcNrM/tpPd5Cf/sAU3paaXsNWvqGb+PYjAGm\nvtSC8pLxQHJCSJFL8ulbSwY/DgYAWhj63XWgDfrbc/cctvUOfyGOnxKoT7T2gvpPnG8ajOGV019L\n32A/9Hq86dkZTP9564gvC8dmDDD1Fy3DrvDsnZq76lORS2LRAm2vn7gKdPCez7f8+PyheywH60YL\nvDSvlwPv21rJrRLWmJwMzWpsPL+5D92TtvCfzuR9N8+ipe94xU9bqfCpMnXvjReoM5btuIfV3JwM\nzcys6VU7N/hB3WZm1vScDM3MrOk5GZqZWdNzMjQzs6bnZGhmZk3PydDMzJqek6GZmTU9J0MzM2t6\nFSdDSTMlbZG0S9JmSTOKlOuStFPSbknXpJZfIukJSf2Szqk0DjMzs6yy9AzXAlsi4mzggWR+GEmt\nwG1AF7AIWC1pYbL6cXJPd//XDDGYmZllliUZXgRsSKY3AB8pUGYJsCci9kZEL7ARWAUQETsjYleG\n/ZuZmVVFlmQ4KyIOJ9OHgVkFyswF9qXm9yfLzMzMGkbJke4lbQFmF1i1Lj0TESGp0BO/G/sp4GZm\nZoySDCOLy8PLAAAgAElEQVRiWbF1kg5Lmh0RhySdDjxXoNgBoCM130Gud1gWSetTs90R0V3uNszM\nbOKS1Al01mz7lQ7hJOkLwJGIuFHSWmBGRKzNK9MGPAVcABwEHgJWR8SOVJmtwJ9ExCNF9uMhnMzM\nbJhGGsLpBmCZpF3Ah5J5JM2RdC9ARPQBa4BNwJPAnYOJUNLFkvYBS4F7Jd2fIRYzM7OKeXBfMzOb\ncBqpZ2hmZjYpOBmamVnTczI0M7Om52RoZmZNz8nQzMyanpOhmZk1PSdDMzNrek6GZmbW9JwMzcys\n6TkZmplZ06s4GUqaKWmLpF2SNkuaUaRcl6SdknZLuia1/IuSdkh6TNJ3Jb2p0ljMzMyyyNIzXAts\niYizgQeS+WEktQK3AV3AImC1pIXJ6s3AOyPi3cAu4NoMsZiZmVUsSzK8CNiQTG8APlKgzBJgT0Ts\njYheYCOwCiAitkTEQFJuGzAvQyxmZmYVy5IMZ0XE4WT6MDCrQJm5wL7U/P5kWb7fB+7LEIuZmVnF\nSo50L2kLMLvAqnXpmYgISYXGghp1fChJ64DjEfEPo5U1MzOrhZLJMCKWFVsn6bCk2RFxSNLpwHMF\nih0AOlLzHeR6h4PbuAxYAVxQKg5J61Oz3RHRXaq8mZlNLpI6gc6abb/SwX0lfQE4EhE3SloLzIiI\ntXll2oCnyCW7g8BDwOqI2CGpC7gJ+M2IeL7Efjy4r5mZDVPt3JAlGc4Evg2cAewFPhYRRyXNAb4S\nESuTchcCtwCtwB0R8flk+W5gCvBCssl/j4hPF9iPk6GZmQ3TMMlwvDgZmplZvmrnBj+BxszMmp6T\noZmZNT0nQzMza3pOhmZm1vScDM3MrOk5GZqZWdNzMjQzs6bnZGhmZk3PydDMzJqek6GZmTW9ipOh\npJmStkjaJWmzpBlFynVJ2ilpt6RrUsv/UtJjkh6V9ICkjkL1zczMai1Lz3AtsCUizgYeSOaHkdQK\n3AZ0AYuA1ZIWJqu/EBHvjoj3AHcBn80Qy4SVDEsyaU3m9k3mtoHbN9FN9vZVW5ZkeBGwIZneAHyk\nQJklwJ6I2BsRvcBGYBVARLycKncKUHQYp0mus94B1FhnvQOooc56B1BjnfUOoMY66x1AjXXWO4CJ\npOTgvqOYFRGHk+nDwKwCZeYC+1Lz+4HzBmckXQ/8LtADLM0Qi5mZWcVK9gyT3wQfL/DvonS5yI0D\nVWgsqJLjQ0XEuog4A/gG8Fdlxm5mZlYVWQb33Ql0RsQhSacDWyPiHXlllgLrI6Irmb8WGIiIG/PK\nnQHcFxG/UmA/jT3gopmZ1UU1xzPMcpr0buD3gBuT/+8qUOZhYIGk+cBB4OPAagBJCyJid1JuFbC9\n0E48sK+ZmdValp7hTODbwBnAXuBjEXFU0hzgKxGxMil3IXAL0ArcERGfT5b/I/DLQD/wNPCpiHgu\nW3PMzMzKV3EyNDMzmyzq8gSaKtyw/0VJO5Kb9r8r6U2pddcm5XdKWj4e7SkQd9b2XSLpCUn9ks5J\nLZ8v6Zik7cm/vxmP9hSIuybtS9ZNhuNXsH49j1+xWPPK3Jqsf0zS4tHqjvV1Gg81at96SftTx6tr\nPNpSSMb2fU3SYUmP55WfLMevWPvKO34RMe7/gC8Af5pMXwPcUKBMK7AHmA+0A48CC5N1y4CWZPqG\nwfrkbux/NCk/P6nfMgHb9w7gbGArcE6qznzg8Xocs3Fq32Q5fgXr1+v4lYo1VWYFuYvYIHf7039U\n2s5J1L7PAlfVo03Val8y/xvA4vz33mQ4fqO0r6zjV69nk2a9YX9LRAwk5bYB85LpVcC3IqI3IvaS\ne4GX1KYJJWVt386I2DUukVamVu2bFMdvjPXHU6lYBw3FHBHbgBmSZo9St1HaWav2ATTCBXxZ2kdE\n/AB4scB2J8PxK9U+KOP41SsZVnrD/twC5X4fuC+ZnpOUG61OrVWzffnOTLr83ZLOzxhnpWrVvsly\n/ErVr8fxG8uxKFZmTom6Y3mdxkOt2gfwmeS03B11PI2YpX2lTIbjN5oxH78st1aUJGkLMLvAqnXp\nmYgIFb6XcNQreyStA45HxD+UKFaTK4TGo30FHAQ6IuLF5Le2uyS9M4Y/2q4q6tS+QibK8VOBZfn1\nx+34jRJrMWP5Fj2Wdo63arYv7cvAXyTTfwncBPzXMrdRDZW2b8zHY4Iev9HqlXX8apYMI2JZsXXJ\nj52z48QN+4VuqTgApEey6CDVa5B0GbnzyBeUqDMvWVZ1tW5fkX0eB44n0z+S9DSwAPhRufGPph7t\nK1BnIh2/dKwF64/n8Rsl1kLHolB79pP7DaesdtZBNds3VDdSt3pJ+ipwT/VCLkul7Rvtb2eiH7+S\n7Sv3+NXrNOngDfswhhv2JU0hd8P+3ZC78gj4b8CqiHgtb7uXSpoi6UxyHzQP1agNpWRqX56hb0OS\nTlNuJBAkvY1c+56pZuBjVJP2MXmOX8H6dTx+YzkWdwP/JYltKXA0OYVWdjvroCbtSxLEoIuBx6mP\nLO0rZTIcv6LKPn7jcbVQ/j9gJvB9YBewGZiRLJ8D3JsqdyHwFLkLKa5NLd8NPEvuqTXbgb9Jrbsu\nKb8T+PAEbd/F5M6PHwMOAfcnyz8K/CRp8yPAysnUvkl0/IrV/+16Hb9CsQJXAFekytyWrH+M4Vf5\nltXOOh2zWrTv74AfJ+XvIvcb20Rs37fInaJ/Pfm7u3ySHb9i7Svr+PmmezMza3r1Ok1qZmbWMJwM\nzcys6TkZmplZ03MyNDOzpudkaGZmTS9zMqz0aeOSpkraJulRSU9K+nzWWMzMzCqRKRkmNxDfBnSR\nG3FgtaSFeWVWAGdFxALgk+QekUPkbpb/YES8B3gX8ME6PmvTzMyaWNaeYaVPG5+VzPckZaaQG8bj\nhYzxmJmZlS1rMqz0aePzINezlPQouSemb42IJzPGY2ZmVrasyTDT08Yjoj85TToP+ICkzozxmJmZ\nlS3rqBVVedp4RPxC0r3AuUB3el0dhxUxM7MGFhHVG3w548NV24Cngfnkfvd7FFiYV2YFcF8yvRT4\nj2T6NE48wHga8K/ABQX2EfV6eGwFr8f6escwWeOdSLFOtHgdq+OdaLEm8UY1t5epZxgRfZLWAJvI\nXQBzR0TskHRFsv72iLhP0gpJe4BXgcuT6qcDGyS1kDtd+82IeCBLPGZmZpXIPLhvRNwP3J+37Pa8\n+TUF6j0OnJN1/2ZmZln5CTTV1V3vAMrUXe8AytBd7wDK1F3vAMrQXe8AytBd7wDK1F3vAMrQXe8A\n6qnhxzOUFFHNH0nNzGzCq3ZucM/QzMyanpOhmZk1PSdDMzNrek6GZmbW9JwMzcys6TkZmplZ03My\nNDOzpudkaGZmTS9zMpTUJWmnpN2SrilS5tZk/WOSFifLOiRtlfSEpJ9I+sOssZiZmVUiUzKU1Arc\nBnQBi4DVkhbmlVkBnBURC4BPAl9OVvUCfxwR7yQ3msWV+XXNzMzGQ9ae4RJgT0TsjYheYCOwKq/M\nRcAGgIjYBsyQNCsiDkXEo8nyV4AdwJyM8ZiZmZUtazKcC+xLze9Plo1WZl66gKT5wGJgW8Z4zMzM\nypY1GY71Kd/5D1MdqifpFOAfgT9KeohmZmbjKut4hgeAjtR8B7meX6ky85JlSGoH/gn4+4i4q9hO\nJK1PzXZHRHflIZuZ2UQjqRPorNn2swzhJKkNeAq4ADgIPASsjogdqTIrgDURsULSUuCWiFgqSeR+\nSzwSEX9cYh8ewsnMzIapdm7I1DOMiD5Ja4BNQCtwR0TskHRFsv72iLhP0gpJe4BXgcuT6r8O/A7w\nY0nbk2XXRsQ/Z4nJzMysXB7c18zMJhwP7mtmZlZlToZmZtb0nAzNzKzpORmamVnTczI0M7Om52Ro\nZmZNz8nQzMyanpOhmZk1PSdDMzNrek6GZmbW9DInQ0ldknZK2i3pmiJlbk3WPyZpcWr51yQdlvR4\n1jjMzMwqlSkZSmoFbgO6gEXAakkL88qsAM6KiAXAJ4Evp1Z/PalrZmZWN1l7hkuAPRGxNyJ6gY3A\nqrwyF5EbqomI2AbMkDQ7mf8B8GLGGMzMzDLJmgznAvtS8/uTZeWWMTMzq5usyXCs4z/lD7PR2ONG\nmZlZU8k0uC9wAOhIzXeQ6/mVKjMvWTZmktanZrsjoruc+mZmNrFJ6gQ6a7b9LIP7SmoDngIuAA4C\nDwGrI2JHqswKYE1ErJC0FLglIpam1s8H7omIXy2yDw/ua2ZmwzTU4L4R0QesATYBTwJ3RsQOSVdI\nuiIpcx/wjKQ9wO3ApwfrS/oW8CBwtqR9ki7PEo+ZmVklMvUMx4N7hmZmlq+heoZmZmaTgZOhmZk1\nPSdDMzNrek6GZmbW9JwMzSYwrbzyOn3ivOf1ifOe18orr2uUbZlNNFlvujezOtHKK69j8devp/1Y\nbsGsx6/XSoh7v/S5em4rK6288jrmPHwVAAfPvbkeMVjzcc/QrA6q0gub8/BVQ8kLoP0YQ0mkntvK\nYCgpz3voVOY9dCqLv359rXup5RwL954nL/cMzcZZTXthM3fP1CfOe34sPaphPbApL0+rdJdV7ckV\nT8oFt5l138WOxVAsqe1q5ZXXcc5Xr6fteG797Efr1nu26vNN99YQqvWBWmg7Eh8Grk6K3MSKK9+b\nLsN9X3okvT6CTVnj1sorr2P+1nWccmgar8w+xt4PXj+07hPnPc+8h04dtqHXTwl+vuiFUm3Xyiuv\nY/aja+k7aSo/vPJZZjz7b3zozy4b+iAPTjwSv28K/OgP1pXcVjoJDCQVW5LPg4EWeOZDu5n60sz8\ntpXcTu802H75umGvQ7HXZ87DVw0l4eNvOMbBc29mzsNXjXhtnlvYw/E3HCu4jXRy6psCOy/ezIyf\nvjddttR7q+CxeG5hD2/eO/3Ea9MCT16ymbf8+HzesmN6ftn40pMnF3qNSxn1dSkQa7nbmuyqnRuc\nDG3cFP0ABIZ/MLfA87/ck04gY95+ejt9U+DIgtfpn3ISAMdOg70f6OcDn2sdlkBefwM8+dsw+8cw\n41l4ec7rPPuBvyj64T+Y5F5/Qx9vONQ+9GE8IPjFGb30Tu/l1N3Tae07UbG/DR755Lq490uf05WL\nXh3xoTqodxp895t72PHRNRFsGvaapbc5IHjmQwO89V9baOtNgsvbVl8rHFn4Or3TXxl6nQc/NE8+\n/Ebe/Gz7iP2nE2p6erBtr856iaNnPjKUcKa8PG1EW3pmwg8/dYR33HUKv7TjJFoGTrwGvVODlj7R\n9trIH2kGk9kv37182DHUAEPt7m+DIwtyyfHk597Em/cOP7s1wIntFtpe7zR46qLNpeN/czD9xeGv\n5kAL9E4PTnpF+WXjxhfG/HPT0PvntKemD70uyRcIgFJfLEZsZ/B9MfPp6UPvwRJ1JpuGS4aSuoBb\ngFbgqxFxY4EytwIXAj3AZRGxvYy6ToY1VpVTTYP1n/j4Pfz7VbnxKt938wEWf/VjnHJoGv3tA5z8\n89ahnseg3mnw/Nn9nP5Y64gNJ72b/J5bfs+uZI8rX/oDvtTygVZ4/uyeoYQ9/chvsuDe5Ux5pXD9\nsehvgecXvc6bnj2JqS+XKNeWe10gd5ownVRLxVxy323AALQOlF+31H7TyafS2NKOT4Vdq3LJasrL\n05jx02lMea3wlsayj9dOCabmJbB0vf42UP+JHnHfFBhoiYL77J0C7ceHL3v9lOAXHcdof7Wdk15u\nyz8LkKZLLt3Eou8sH0qCafuXHAEY8f7dv+RIfGXbacO2k/+lr8C28usA5J8hKecMSCNqqGQoqZXc\nqBW/RW5Yph9SetSK84C/joilY6mb1J+0ybARTm+Mdpqrovobv5ebXn0RQ99YS+mZCdNfKLzuhfl9\n3PrTXmAab98EF1wLsx9j6ANl8BRWz6n/wrv/7n+M+OZeDf0tuf1NyndhAxoQHDz3eeY8clrBxFGO\ncpJyJP+K9fOC3PtttC8Ug1/i4ERP/OiZjxRNhHDiVHCh0+f97aST7Khf+vYvOTJ0yhnSPwV8Dxj8\nbfgYcPFoCXG0z6h6foY1WjJ8H/DZiOhK5tcCRMQNqTJ/C2yNiDuT+Z3kxqQ6c7S6yfJJmQyrkoSq\n8RtboT+sIt8sC9YvdMrv6WW5/9++ZWxBHFwMv7STgt90A/jPxfDkR6HzLwon11CuJ1esB2UlLd8D\nVz+Ym77p/bD5rPrGA2TrudZiO4P6pkDr8dG3+dzCnmGnL0eL47VTgv1L9/C2/7VgKGHm1xlMsvO3\nrit6mr3YqeHcqff8I7slguXFQhrRky30e3D+Z1j6FHSNk2O1c0PWq0nnAvtS8/uB88ZQZi4wZwx1\nJ68yr5pLa5R7wnTJpZt4Z5E/ymnPj20j/clb8OfvgNN2wpS8hChgznY4fXvxDxOFE2GFlu+B722E\n6cnLd/7P4OJLGyAhVusjrtpfo8eSCAFO3TWd1v6xxzH1FfH27y8o+HvtoLbjMH/rOmY+XfhvDuCF\nt/cw46fvHfHZ8mtfeis7PjqGwFO/a75zx/RhMeR/RhX6DEsnzzreq1qJrPcZjrVbOel6dnVVzXvC\nDp57M31TTsz3TWHoYosStPLK61j0neUjjuxAC/xiDsx+dGz7V38u2c3ZnmtHsXeU30E1cfWDJxIh\n5KYHe4lWwFjehwHDEmEl2y62n1MOTRv1p4eZu2eOWNb2+mvAMd6+CX53Ofzustxv+vkhDH7Rfkte\nIiyk0O046dPAdbpXtVJZe4YHgI7UfAe5Hl6pMvOSMu1jqAuApPWp2e6I6K4s3AZy8NybmfX48FMM\nY0hCDWPOw1cV/P3jxfnBe76pERfKFLvQIl3OCc8mssG3cq3exwG09hbfet8Uhp2aHdQ7DQ695wbe\nd/OCYbfinPG/L9PKp3cP67nlf9FOG2ip62eUpE5yP7HVRNZk+DCwQNJ84CDwcWB1Xpm7gTXARklL\ngaMRcVjSkTHUBSAi1meMs+Hk7n9ixI29Y6pczUQ65+Grhv3xtB0f8+naEQZa4PgbgpaBkX+w0Qrk\nfVuu9u85Vrab3p87NTrYO+xpyy2zChw/ZeStF9Uy+Ldy0isj7yd94e25i2+K3Sbyk9V/NnThTYU/\nzQxeqDbsMyp37+f0EeXSvzFWMXkmnaDuwXlJn63WtiHjadKI6COX6DYBTwJ3RsQOSVdIuiIpcx/w\njKQ9wO3Ap0vVzRLPRJLlApi490ufY/vl69i/5Aj7lxypy31FB8+9eejyf8j9EWz98356T351RNmB\nFugr8ICT3mkM20a+xr4FdlLYfFbyG+Hbcv8a4vfCiap/5G2bI0SR6WJePyWX0PJPofa8Odi/5Ag/\n+oN18aUnT46vbDtt6MEEaS8seKGsz4ZCf9fPLezh4f97XXxn44dLlu2dlruyu56fSxlkfhxbRNwP\n3J+37Pa8+TVjrdsMqnEBTFI2+xutwl7mUM82/USUHR9dwxv+872cvn34DfRPXrIZgHfeuXzYBQL/\nftURXpt5D4u/+rFhNyEPtMChd+euID3zX2DGngHevLdl2NNRVIVbHco9rTVJe7Kbz2rgBDiW1zzr\ncanG6c3eafDM8i3DruIsJD+pDYgRPymk/aIj1+ubnnfF9wsLXhhxxfdof8tj+Fsv54xVprNbDchP\noKmDrLczVD2eKt8rVPRRU5dcuom3bc7dd/HM8i3pb5pDdY6f/Eb+9zXtPD3sS+gWVlzZPew5mvmn\ng0bcNE9uQakPmr4p0P3/wPtvLn6f43iapMl2VKXa3S9oHeUz6uVZ/Ux7sbXohSUDwItvz03PfHrk\nvg7/yusc/pV+fnXj6BeN5Ds+FZ5715ERj36b8vI0Tts5veT7D3I9v5NeKb5+8J7Bcp9MAw13X2C1\nNdR9huPBybC5JE/JKHlzcMHX78W35h6DlnoWKJD71vqmvTM45bnWgh90T/8W7LnwG8MuLMh/Tmda\nlp7KgGCgnTHfe1bu9vPLUOH2YfhvP5UY65N+AHpmDDD1Fy0FX+/+ttFvmyn29Jb+Vjhydg/b/+Db\nQ09FWnnFTN771fcWunfuxKPSUkksBL0nQWvvyCtEBwQPf6r481/z79MbvGo7/ei0F+f3lHw0Xzq2\nSZLEqqXR7jO0Skz0K0lrKIJNEhdT6rFRhV6/3SvXF/mAONErXfhPy0d8sLb3vBwPXnW5Vj69e9gT\nO4CCz5B89td387YHFpz4sGTkh/vRt/Yy/Uj7iG/8P1/0Os/+5l+U1cPN1zcFtl/2CGdtehdv3N8O\nAT2n9TP9hdZhzy198mO5m59HezwdjHwk2eDN02/bvGzEMzrzHZ8aINF+jGGnwA/82vPM/eFpI9oy\n4vmpU+An/9d/B2DBveuZ8Wz7sO38tHM38/91wbBeX/o1Sv/t5L8nCvaebkcr2womluQik6toiRPH\nRDHU8xvx/Nz8C0ryxHc2flgrT71uxPsqf/7Ne4c/MP0XHb28Ovul/Nioxs8iVpR7hnXib3rZVPx0\n/zKf+lNwFIz8oY/yE9oYT20VjOfZX9/NnEfO4qSXNSJx98yEzV/cHNt/f/iFDKXiLPQMy4GW3Adu\n7/TeYQ9LH61+oZ7NYM+lwCnwks/ihGFXOg61o9B28kcAee5d/1boKSfV+JsqddamVn+z/iyojE+T\nmmVQk99HiyS9seyr5FA++dv9X//jG/HgVZeXHV+BkUIqfewfUNYFE0P1Z+6eOaKX2YA/DWR9TKKN\nHydDswbjHsPoJlKSmUyv+2TmZGhmE5KTjFWTk6GZmTW9aueGrA/qNjMzm/CcDM3MrOk5GZqZWdOr\nOBlKmilpi6RdkjZLmlGkXJeknZJ2S7omtfwSSU9I6pd0TqVxmJmZZZWlZ7gW2BIRZwMPJPPDSGoF\nbgO6gEXAakkLk9WPAxcD/5ohBjMzs8yyJMOLgA3J9AbgIwXKLAH2RMTeiOgFNgKrACJiZ0TsyrB/\nMzOzqsiSDGdFxOFk+jAwq0CZucC+1Pz+ZJmZmVnDKPmgbklbgNkFVq1Lz0RESCp0w2Jj38RoZmbG\nKMkwIpYVWyfpsKTZEXFI0unAcwWKHQA6UvMd5HqHZZG0PjXbHRHd5W7DzMwmLkmdQGfNtl/pE2gk\nfQE4EhE3SloLzIiItXll2oCngAuAg8BDwOqI2JEqsxX4k4h4pMh+/AQaMzMbppGeQHMDsEzSLuBD\nyTyS5ki6FyAi+oA1wCbgSeDOwUQo6WJJ+4ClwL2S7s8Qi5mZWcX8bFIzM5twGqlnaGZmNik4GZqZ\nWdNzMjQzs6bnZGhmZk3PydDMzJqek6GZmTU9J0MzM2t6ToZmZtb0nAzNzKzpORmamVnTqzgZSpop\naYukXZI2S5pRpFyXpJ2Sdku6JrX8i5J2SHpM0nclvanSWMzMzLLI0jNcC2yJiLOBB5L5YSS1ArcB\nXcAiYLWkhcnqzcA7I+LdwC7g2gyxmJmZVSxLMrwI2JBMbwA+UqDMEmBPROyNiF5gI7AKICK2RMRA\nUm4bMC9DLGZmZhXLkgxnRcThZPowMKtAmbnAvtT8/mRZvt8H7ssQi5mZWcVKjnQvaQswu8CqdemZ\niAhJhcaCGnV8KEnrgOMR8Q+jlTUzM6uFkskwIpYVWyfpsKTZEXFI0unAcwWKHQA6UvMd5HqHg9u4\nDFgBXFAqDknrU7PdEdFdqryZmU0ukjqBzpptv9LBfSV9ATgSETdKWgvMiIi1eWXagKfIJbuDwEPA\n6ojYIakLuAn4zYh4vsR+PLivmZkNU+3ckCUZzgS+DZwB7AU+FhFHJc0BvhIRK5NyFwK3AK3AHRHx\n+WT5bmAK8EKyyX+PiE8X2I+ToZmZDdMwyXC8OBmamVm+aucGP4HGzMyanpOhmZk1PSdDMzNrek6G\nZmbW9JwMzcys6TkZmplZ03MyNDOzpudkaGZmTc/J0MzMmp6ToZmZNb2Kk6GkmZK2SNolabOkGUXK\ndUnaKWm3pGtSy/9S0mOSHpX0gKSOQvXNzMxqLUvPcC2wJSLOBh5I5oeR1ArcBnQBi4DVkhYmq78Q\nEe+OiPcAdwGfzRBLQ0iGGJkwJlK8EylWmFjxOtbamUjxTqRYayFLMrwI2JBMbwA+UqDMEmBPROyN\niF5gI7AKICJeTpU7BSg6jNME0lnvAMrUWe8AytBZ7wDK1FnvAMrQWe8AytBZ7wDK1FnvAMrQWe8A\n6qnk4L6jmBURh5Ppw8CsAmXmAvtS8/uB8wZnJF0P/C7QAyzNEIuZmVnFSvYMk98EHy/w76J0uciN\nA1VoLKiS40NFxLqIOAP4BvBXZcZuZmZWFVkG990JdEbEIUmnA1sj4h15ZZYC6yOiK5m/FhiIiBvz\nyp0B3BcRv1JgP4094KKZmdVFNcczzHKa9G7g94Abk//vKlDmYWCBpPnAQeDjwGoASQsiYndSbhWw\nvdBOPLCvmZnVWpae4Uzg28AZwF7gYxFxVNIc4CsRsTIpdyFwC9AK3BERn0+W/yPwy0A/8DTwqYh4\nLltzzMzMyldxMjQzM5ssGvIJNJLWS9ovaXvy78LUumuTG/h3SlpezzjzSbpa0kDSax5c1lDxlnrY\nQaPFCiDpi5J2JDF/V9KbUusaKl5Jl0h6QlK/pHPy1jVUrFD8gRiNQtLXJB2W9Hhq2Zge9jHeJHVI\n2poc/59I+sNkeaPGO1XStuRz4ElJg2fsGjJeyN23nuSDe5L56sYaEQ33j9wN+FcVWL4IeBRoB+YD\ne4CWesebxNYB/DPwU2Bmo8YLvCE1/Rngq40aaxLXssE4gBuAGxo1XuAdwNnAVuCc1PJGjLU1iWN+\nEtejwMJ6H++8GH8DWAw8nlr2BeBPk+lrBt8P9f4HzAbek0yfAjwFLGzUeJN4pif/twH/AZzf4PFe\nBfx/wN21eC80ZM8wUejCmVXAtyKiNyL2kvtjXjKuURV3M/CnecsaLt4o/rCDhosVICK2RMRAMrsN\nmJdMN1y8EbEzInYVWNVwsVLigRiNIiJ+ALyYt3gsD/sYdxFxKCIeTaZfAXaQu8+6IeMFiIieZHIK\nuQoLqloAAAMwSURBVC9HL9Kg8UqaB6wAvsqJ3FDVWBs5GX4mOTV2R6r7O4fcjfuD9pN7w9WVpFXA\n/oj4cd6qRo33ekk/+//bOXfXqIIoDn8/waAgIiLGIilSpFPwUQgiiIj4QBS7NEKif4BVwOx2WliJ\nVtpoYyA22kRUfKC9oHmgIiqCiKB2YqGk8FjMXPe6ZNeVrDsj93zV7tzl7neH3Xvmzpw5wChwNjZn\n6drEceB2fP0/+Bbk6LpYQYzUTp3QSbGPpMTs+S2EwVu2vpKWSZoleD0ys+fk63seGAd+lNq66rqU\nrRVLQtJ9wtRCM3XgEnA6vj8DnANOtDhVTzKA/uA7AZTXgdptB/nnvm1ca2Z208zqQF3SKUKm71iL\nU6Xu25qZFesDdWDBzKbanCp53/7FqVJnrqX+/iVjZpbbPmRJq4AbwEkz+yo1bgW5+cYZl81xHf6u\npN1Nx7PwlXQI+GxmM63qp3bDNVkwNLO9nXxO0mWguMl8IKzNFQzEtn9OK19JG4EhYC7+8AeAJ5K2\nk8i3074Fpmg8aWXXtwWSRglTJHtKzbn3bZlkfduGZqdBfn96zZVPkjZYo9hHNtuxJC0nBMJJMyv2\nXWfrW2BmXyTdAraRp+8O4LCkg8AKYLWkSbrsmuU0abywgqNAkU02DYxI6pM0BAwDj3vtV8bMnplZ\nv5kNmdkQ4YayNT6+Z+crabj0tlzsIDtXCBmPhOmRI2b2vXQoS98S5dmBHF1/FcSQ1EcoiDGd2KkT\nimIf0LrYR89RGAlfAV6Y2YXSoVx91xXLT5JWEhLVZsjQ18xqZjYY768jwEMzO0a3XVNnCLXIGroK\nzANz8QL7S8dqhASEl8C+1K6LuL8lZpPm6AtcJwwuZgmj2PW5ukan18A7wh91BriYqy9h4PYe+AZ8\nBO7k6hqdDhCyHt8AE6l9FvG7RqhctRD7dQxYCzwAXgH3gDWpPaPrTsJ61mzpt7o/Y99NwNPoOw+M\nx/YsfUveu2hkk3bV1TfdO47jOJUny2lSx3Ecx+klHgwdx3GcyuPB0HEcx6k8Hgwdx3GcyuPB0HEc\nx6k8Hgwdx3GcyuPB0HEcx6k8Hgwdx3GcyvMT9slDk7GsKoMAAAAASUVORK5CYII=\n",
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Z6mJmZla1QUe6l7QOmFZi1op0IiJCUqmxoIYcH0rSCuBoRHxjqLxmZmYjYdBgGBGXlpsn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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb20208b6d0>"
+       "<matplotlib.figure.Figure at 0x10e772550>"
       ]
      },
      "metadata": {},
@@ -191,7 +167,6 @@
     "\n",
     "apt_x = aperture.RectangularApertureX(x_low=-0.004, x_high=0.005)\n",
     "apt_x.track(bunch)\n",
-    "check_macroparticlenumber(bunch)\n",
     "\n",
     "plot_phase_space(bunch, ax0, ax1, ax2, col='g')\n",
     "plt.show()"
@@ -204,19 +179,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bunch.macroparticlenumber was updated correctly.\n",
-      "bunch.macroparticlenumber new: 944\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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AdQXVXYwlYFJFJaf8rJrwTjo3PnIsEULu9Y2PNLh32Mrah+C9dwwnw2KdwJ+T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\n",
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cI5W51HIB7QOl53ceh1I/YNqAc7dP5Wf2rNP8//YncPXkgqVXAv2UPi5eAHxd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\nzcws9/5/SZKclr8uCzQAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb201d8ce50>"
+       "<matplotlib.figure.Figure at 0x10e75b2d0>"
       ]
      },
      "metadata": {},
@@ -232,7 +199,6 @@
     "\n",
     "apt_y = aperture.RectangularApertureY(y_low=-0.005, y_high=0.005)\n",
     "apt_y.track(bunch)\n",
-    "check_macroparticlenumber(bunch)\n",
     "\n",
     "plot_phase_space(bunch, ax0, ax1, ax2, col='g')\n",
     "plt.show()"
@@ -245,19 +211,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bunch.macroparticlenumber was updated correctly.\n",
-      "bunch.macroparticlenumber new: 903\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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/qOQDvn1gZAIY6EgSXMfhZDqAwXbYNxf6jz3EjCePGTXoobdhqSRTz/p5Fm+D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\ngfcBvbnLGjisiJmZNbGIqN3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8DPxpRDwxxHLchZOZmQ0wlrpw+jRwhaQtwHvSYSTNkvQAQER0A8uBdcBzwH19gVDStZJ2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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb20208b210>"
+       "<matplotlib.figure.Figure at 0x10ed98f10>"
       ]
      },
      "metadata": {},
@@ -273,7 +231,6 @@
     "\n",
     "apt_z = aperture.RectangularApertureZ(z_low=-15, z_high=25)\n",
     "apt_z.track(bunch)\n",
-    "check_macroparticlenumber(bunch)\n",
     "\n",
     "plot_phase_space(bunch, ax0, ax1, ax2, col='g')\n",
     "plt.show()"
@@ -286,19 +243,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bunch.macroparticlenumber was updated correctly.\n",
-      "bunch.macroparticlenumber new: 522\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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1sGBV056p52+Bb6+GSWlMPvtXcPGHawiI9SagrRvaXoBz/yqZ1h8QG2EiSeux\nGpNJgul3gMcpDqQTKb46Ucvfgo0CWYPhDmBOQXoOSQuvUp7ZaZ5xQygLgKQVBcnOiOisrbpWorT1\nNpH+1mC56WP3C+CslU0bCCFpEU4qaJxO6k6mNTwYFhJw1o2NDoaQXE6dxKFXJgYzgfKBtLCVO/b/\nFpqUpHb6W+h1lzUYPgjMk3QssBP4ELCkJM+dwFJgtaSFwJ6I2CVp9xDKAhARKzLW00be7kZXwHJp\nH/2XO/+NylcoyikNpD34FbSmkDaCOnvTkq6t5/IzHeSI6CYJdGuAJ4DbImKDpMskXZbmuRt4WtIW\n4BbgE5XKZqmPVW0lyZdHr33ptIGmZ1k2JA/QPFQmb+1PcdXL+iuhp3m/81aeBXsLfrrubUumNZUA\n1l/RiDXvBH4GrIPkEmZ6GfNnVS5nP0kgvThd1jrgr8n+t2CjQKanSUeCnyYdXsP4AE2lZX8J+L10\n+r8Bqzn06b/dJA9ZDdV+4BWSpw4Ln/kcurM/FbznWtGSPgjZ0wIvHgfj9sLkX2d7TrWS0ic5B3D+\nZrjyJ8nnmh6gqVM9Biz78B/AnV8eaonSp00Hs59DH4TpBq4d6PWMAZ5o7rUP+BRwCcml0WeAa8qd\n5366tDk11asVI8HBMB/KfeFIXEPxwws9wKskj9v3vV84yBfYccA04ABwI8nDO7+XLrP3C/kZYGkE\na3Th5dcw/ZFldE84jJ9e/gwbLvkSsIKzrxvHWTdCywF4dRrsPwJaurs4MPk1fnnOAeasP4IpW1uZ\n9HwLLd3Qtq/8SwU9wG+O6eLA5B56Wsexb+pBNv/OvzH1ybdy8jdPo+UAHJgMbfvhtdf30LZPvO55\n0XUY7Dy9iznrxyXvLAr2HZks97XXH0Q9Pbxu1zhau+DgeHhlRtA16QDjX2rh9dvH9QX40j/33rp1\nTYSfXLGbZxe8yJn/MJMJL03gjRtaaTvQn6/rMHjtyKRu+6YEIjhsTwt7joF7r4enLugNMO29xxE4\nDVhOEpB6gF/27usyx72zoGzh597zoehHVASXlh73CudB2WUOVt6al4Oh5Uoz/CqvpQ5FL+7vOe4h\npvzyNAB2nn7jUF9Ur5fSTgSG/KL8EMo1w/GxfHIwNDOz3Kt3bGjeJwbMzMxGiIOhmZnlnoOhmZnl\nnoOhmZnlnoOhmZnlnoOhmZnlnoOhmZnlnoOhmZnlXs3BUNJUSeskbZK0VtKUAfJ1SNooabOkqwqm\nf0DS45LExbA3AAAgAElEQVQOSjq1XFkzM7ORkKVluAxYFxEnAvem6SKSWoGbgQ7gJGCJpPnp7MdI\neof/UYY6mJmZZZYlGF4ErEo/rwLeXybPGcCWiNgaEV0koxMsBoiIjRGxKcP6zczM6iJLMJwWEbvS\nz7tIRgYoNQvYVpDenk4zMzNrGhVHupe0DpheZtbywkREhKRyPX43dy/gZmZmDBIMI+K8geZJ2iVp\nekQ8K2kG8FyZbDuAOQXpOSStw6pIWlGQ7IyIzmqXYWZmo5ekdvrHo6z/8msdwknSZ4HdEXGDpGXA\nlIhYVpKnDXgSOBfYCTwALImIDQV57gP+IiIeGmA9HsLJzMyKNNMQTp8BzpO0CXhPmkbSTEl3AURE\nN7AUWAM8AdzWGwglXSxpG7AQuEvSPRnqYmZmVjMP7mtmZqNOM7UMzczMxgQHQzMzyz0HQzMzyz0H\nQzMzyz0HQzMzyz0HQzMzyz0HQzMzyz0HQzMzyz0HQzMzyz0HQzMzy72ag6GkqZLWSdokaa2kKQPk\n65C0UdJmSVcVTP87SRskPSrpW5JeX2tdzMzMssjSMlwGrIuIE4F703QRSa3AzUAHcBKwRNL8dPZa\n4OSIeDuwCbg6Q13MzMxqliUYXgSsSj+vAt5fJs8ZwJaI2BoRXcBqYDFARKyLiJ403/3A7Ax1MTMz\nq1mWYDgtInaln3cB08rkmQVsK0hvT6eV+kPg7gx1MTMzq1nFke4lrQOml5m1vDARESGp3FhQg44P\nJWk5cCAivjZYXjMzs+FQMRhGxHkDzZO0S9L0iHhW0gzguTLZdgBzCtJzSFqHvcv4GLAIOLdSPSSt\nKEh2RkRnpfxmZja2SGoH2odt+bUO7ivps8DuiLhB0jJgSkQsK8nTBjxJEux2Ag8ASyJig6QOYCXw\n7oh4vsJ6PLivmZkVqXdsyBIMpwLfAI4GtgIfjIg9kmYC/xwRF6b53gvcBLQCt0bE9en0zcB44IV0\nkT+JiE+UWY+DoZmZFWmaYDhSHAzNzKxUvWODe6AxM7PcczA0M7PcczA0M7PcczA0M7PcczA0M7Pc\nczA0M7PcczA0M7PcczA0M7PcczA0M7PcczA0M7PcqzkYSpoqaZ2kTZLWSpoyQL4OSRslbZZ0VcH0\nT0p6VNIjku6VNKdceTMzs+GWpWW4DFgXEScC96bpIpJagZuBDuAkYImk+ensz0bE2yPiFOAO4NoM\ndRnT0qFLcs37wPsAvA/A+2C4ZAmGFwGr0s+rgPeXyXMGsCUitkZEF7AaWAwQES8X5DscGHAYJxu+\nMbxGkfZGV6AJtDe6Ak2gvdEVaALtja7AWFRxcN9BTIuIXennXcC0MnlmAdsK0tuBM3sTkq4DPgLs\nBRZmqIuZmVnNKrYM03uCj5X5d1FhvkjGgSo3FlTF8aEiYnlEHA18Gfj7KutuZmZWF1kG990ItEfE\ns5JmAPdFxJtL8iwEVkRER5q+GuiJiBtK8h0N3B0RbymznuYecNHMzBqinuMZZrlMeifwB8AN6f93\nlMnzIDBP0rHATuBDwBIASfMiYnOabzHwcLmVeGBfMzMbbllahlOBbwBHA1uBD0bEHkkzgX+OiAvT\nfO8FbgJagVsj4vp0+jeBNwEHgaeAP4mI57JtjpmZWfVqDoZmZmZjRUN6oKnDC/sfkPS4pIOSTi2Y\nfqykfZIeTv/940hsTy2Gax+k865O82+UdP5wb0sWddgPZcuPhnNhoG0qyfO5dP6jkhYMVnao+7NZ\nDNM+WCFpe8Gx7xiJbalVxn3wL5J2SXqsJH+ezoOB9kF150FEjPg/4LPAX6afrwI+UyZPK7AFOBYY\nBzwCzE/nvRk4EbgPOLWgzLHAY43YpibaByel+cal5bYALY3e3mHcD2XLN/u5UGmbCvIsInmwDJJX\nkv6z1v3RjP+GcR9cC1zR6O0b7n2Qpv8bsKD0XM/LeTDIPqjqPGhU36RZX9jfGBGbRqSmw2e49sFi\n4OsR0RURW0lOsjPqXfk6yrQfhli+GVXapl592xYR9wNTJE0fpOxo2h/DtQ8ARsuDd1n2ARHx78CL\nZZabl/Og0j6AKs6DRgXDWl/YnzWEZR+XNok7JZ2dsZ7Dabj2wcw0XzVlGinrfqhUvpnPhaEc24Hy\nzKxQdij7s1kM1z4A+F/p5bRbm/wSYZZ9UElezoPBDPk8yPJqRUWS1gHTy8xaXpiIiFD5dwlrebJn\nJzAnIl5M76PdIenkKO76bcQ0aB+U09CnpIZhP6jMtNLyTXUulDHUYzKUX7ZD2R/NqJ77oNAXgL9N\nP38SWAn8UZXLGCm17oMhH9cxfB4MVq6q82DYgmFEnDfQvPRm5/Tof2G/3CsVO4DCkSzmUNziKbfO\nA8CB9PPPJD0FzAN+Vm3966ER+6BMmdnptIYZhv1QuE1lyzfbuVDGUI5tue3eTnJfpar90aTquQ/6\nykbBK1qSvgh8t35Vrrta98Fgf9N5OA8q7oNqz4NGXSbtfWEfhvDCvqTxJC/s31kmX9+vBUlHKRkp\nA0nHk3z5PV3PitfRsOyDdP6HJY2XdBzJPnigftWuu6z7oWz5UXAuDOXY3gl8FPp6c9qTXvqqen80\nqWHZB+mXf6+LgcdoXln2QSV5OQ8GVPV50KCnh6YC3wc2AWuBKen0mcBdBfneCzxJ8hDI1QXTLya5\nfrwPeBa4J51+CfALkt5sHgIubMT2NXIfpPOuSfNvBC5o9LYO834YqPzvNvu5UG6bgMuAywry3JzO\nf5Tip4ar2h/N+m+Y9sFXgJ+n+e8guX/W8G0dpn3wdZJbAvvT74NLc3geDLQPqjoP/NK9mZnlXqMu\nk5qZmTUNB0MzM8s9B0MzM8s9B0MzM8s9B0MzM8u9zMGw1t7GJR0m6X5Jj0h6QtL1WetiZmZWi0zB\nMH2p+Wagg2S0hCWS5pfkWQTMjYh5wMdJusghIl4DzomIU4C3Aec0Yf+RZmaWA1lbhrX2Nj4tTe9N\n84wnGcbjhYz1MTMzq1rWYFhrb+OzIWlZSnqEpFf1+yLiiYz1MTMzq1rWYJipt/GIOJheJp0NvEtS\ne8b6mJmZVS3rqBV16W08In4j6S7gdKCzcF6TDz1iZmYNEhF1G8Q5azDs622cpKPUDwFLSvLcCSwF\nVhf2Ni7pKKA7IvZImgicB/xNuZXUc4PrSdKKiFjR6HqMNt5vtfF+q433W+2aed/Vu6GUKRhGRLek\npcAakgdgbo2IDZIuS+ffEhF3S1okaQvwKnBpWnwGsEpSC8nl2q9GxL1Z6mNmZlaLzIP7RsQ9wD0l\n024pSS8tU+4x4NSs6zczM8vKPdBk09noCoxSnY2uwCjV2egKjFKdja7AKNbZ6AqMlKYfz1BSNOs9\nQzMza4x6xwa3DM3MLPccDM3MLPccDM3MLPccDM3MLPccDM3MLPccDM3MLPccDM3MLPccDM3MLPcy\nB0NJHZI2Stos6aoB8nwunf+opAXptDmS7pP0uKRfSPrTrHUxMzOrRaZgKKkVuBnoAE4ClkiaX5Jn\nETA3IuYBHwe+kM7qAv48Ik4GFgKXl5Y1MzMbCVlbhmcAWyJia0R0AauBxSV5LgJWAUTE/cAUSdMi\n4tmIeCSd/gqwAZiZsT5mZmZVyxoMZwHbCtLb02mD5ZldmCEdD3EBcH/G+piZmVUtazAcai/fpZ2p\n9pWTdDjwTeDP0haimZnZiMo6nuEOYE5Beg5Jy69SntnpNCSNA24H/jUi7hhoJZJWFCQ7I6Kz9iqb\nmdloI6kdaB+25WcZwklSG/AkcC6wE3gAWBIRGwryLAKWRsQiSQuBmyJioSSR3EvcHRF/XmEdHsLJ\nzMyK1Ds2ZGoZRkS3pKXAGqAVuDUiNki6LJ1/S0TcLWmRpC3Aq8ClafF3Ar8P/FzSw+m0qyPie1nq\nZGZmVi0P7mtmZqOOB/c1MzOrMwdDMzPLPQdDMzPLPQdDMzPLPQdDMzPLPQdDMzPLPQdDMzPLPQdD\nMzPLPQdDMzPLPQdDMzPLvczBUFKHpI2SNku6aoA8n0vnPyppQcH0f5G0S9JjWethZmZWq0zBUFIr\ncDPQAZwELJE0vyTPImBuRMwDPg58oWD2l9KyZmZmDZO1ZXgGsCUitkZEF7AaWFyS5yKSoZqIiPuB\nKZKmp+l/B17MWAczM7NMsgbDWcC2gvT2dFq1eczMzBomazAc6vhPpcNsNPe4UWZmliuZBvcFdgBz\nCtJzSFp+lfLMTqcNmaQVBcnOiOispryZmY1uktqB9mFbfpbBfSW1AU8C5wI7gQeAJRGxoSDPImBp\nRCyStBC4KSIWFsw/FvhuRLx1gHV4cF8zMyvSVIP7RkQ3sBRYAzwB3BYRGyRdJumyNM/dwNOStgC3\nAJ/oLS/p68B64ERJ2yRdmqU+ZmZmtcjUMhwJbhmamVmppmoZmpmZjQUOhmZmlnsOhmZmlnsOhmZm\nlnsOhmZmlnsOhmZmlnsOhjZm6cLLr9Efn/m8/vjM53Xh5dc0uj7VkrhAYm3674JG1wcGrtNQ6tp3\nPP7onS/ppNs3N9N2mfk9QxsxuvDya5j54BUA7Dz9xrjr858e1nUt+NJ1jNuXTOiaCA9furwe6xyJ\n7UiDxLeBiemkfcDFEaxJ512ZTl8ZwZp6r7+aOqWfy9a1r2y547H62/DUBT3AX0cwbOeCjU31jg0O\nhjYi6h2cBgpIfYHio+9p5/j7xhUVem7+Xg5M3ldYpmg5j3/ou/zkit4RVVYeEnjeceMOFnzxgxz1\n5CRaeoq2A6C0Prrw8ms49r7lHP7sRF6Zvo+t51xXuL3lgpouvPwapj+yjK6Jh7P+f4unihpO64CV\nzP3e/8c7bkz6Ff7JFd1s6fidQwJP73p/c3QPP/rrX7LhkqXVBs3S+qWfzyvJti79v3/6CWvgnTd0\nMf7Vl/r2xR+98yWOXj+5qORrk+GFubDvKNjS8eVYf0XZHqj66jH/9uN4x43TaOk+wM7Tb+Tuzz9E\nuf03Qj+4rLEcDC2zar4wqm2FDBik/vjM55n9wBuKMm8/Yzc7T7+xYlArs96ygfVbX93ChkteYu73\n3sY7bmxjxkMw6YXiygX946f0tMBvju5m8s422g70L+eHy+Gk22HKM/DSrC6+fwNsee84TlgDH76Y\nvnUW2vWW/Ux9akJRfR75yEMs+PJpfcsG6AF+c0wXByb30NM6jr2/1cIv3w3H/TCp3AvHP8QpXz2t\naDk/XJ7Mn/g8THgJJu9M6tC7HV0T4Rv/96XYdOHr+/bNqV+8rni9gmdPgcd+/8sc+dTmSgG6bxnT\nH1lG16TJ/LK9f3/sObqHez/TUhKgX+ZN33mZd//NTKY8A90T4PBnoSX66/eDT32Zufd8jBO+f+i+\n66tjC/xmThddk7r6ph2YvC/9gfIhTlgzsWj/97TAs2+He6+nrz4nfnc3H/jQG4b6g2u4A6cD8/Bq\numAoqQO4CWgFvhgRN5TJ8zngvcBe4GMR8XAVZR0M66SvxVCmZVPuD7XMZbEe4BHgmpJWUxLk5t9+\nBL/7kbnlvozKBsNfv72Hoza1lOZPf/F/B5iQ5twPLO4NiGWXtfsE2H8ETH+4/054YfAr/FxJab7u\n8fD1O+GslXDCuqGVqXV9ZZcj0CB/o0+dB19duxx4iI+eexfH/6C1bL7u8aBuaO3pn3awDZ47+SDq\n6WH8q21Mek6Mf7U/mJXWqbcqL8+ABy5PAuX0R/rzl7P7BHj4UnjP/6HvvBuq3kA+4SV4w1OHzu+/\n3AofOf/QY1R4NWDPcQ8x5ZenAfDaES9w/A/mDfR3UBTICssNMagN52X6vnU06HJ5s2iqYCiplWTU\nit8mGZbpp1QeteJM4B8iYuFQyqblHQyHqNIv0UP+OAvtPTJ4Yd4LRS2zCy+/hqM2rCDaxrH+Skpa\nA/uATwF/RX+ghI+fCjMfPnTZv1jyVwBF6+9pgQOvg8NeLs7/3Py9/OMTG4FTgeSS21krgQge/H+e\nYsMlS/mTt3yHaY9PKCo31OBTi71Tk/9LW5rN4qUZ8Mp0mLoFxr9SOTDVUzUBnyHmrWVdT50H66+E\nS/7HoceoR+UDe7nl/eqsl+PW/zhCZ934Jd7zVx/rO1dLryg88YG18X9XV3zwZ6ArIfHP9x/VlydD\ny7HSPeWhLmO0a7Zg+A7g2ojoSNPLACLiMwV5/gm4LyJuS9MbScakOm6wsul0B8MhGOyXaNk/zlK9\nlxunPPPjoi+Dnhb4wd/Cj5cXBKeeKLqndcIa+L33lv8i7hE8fe5mXvdfx3Pk060Vv7AD+PUCeOKS\nQ1sdQdIied1/QWt3dTuoHoYz4A7g/C1w5frk88qzYO3cMpkaUK+msvsEOGJn+UvY1Xj6nC6+8oP3\n8ZHzvjfoJd3bvrWTJxdPAJ4hvVJSmKXs31sabCF7y1FiLWXu30Zw/lDKjwX1jg1ZB/edBWwrSG8H\nzhxCnlnAzCGUbVrDdYmi5l+LMx+8oujLYNy+3gc6hn5ZZtw+OP2WuRBzi5bV0pNc4pq6GU75Sm9w\nEsf9ILmEde/1cO7VAwe4loATvj9vSF/YImldznj40C94AUf8esibU3cNCITfXg2T0rh/9q/g4g+X\nCYh5DoQAk3fAuNeyLaOnBf7jL1uBewbdoS09sPgPZ/Lr02D9lW/gqQu+IxVcxhcXMP8vX+R3P1J8\n//JHf3WYxAURrKnL32sV8n5JdSiyBsOhNivH1J9rmUsUZ0vZL1Ec8mtx2mPX6UIY0gMA41+eWC5P\nn52n38i0x4ovU3ZNggmvlFvyoZNaemDBquJZLZEErg9fDAfL36aqtMi65h+DrlzfHwgh+Xzl+gFa\nh3mW9SpBAE+9B866sYWzboRfvhuO/jFlL5P2mvRCcn/ymB/C1++cwFMXXAl999G/zYZLJrL68PQq\nCsllXBjHR9/zXf3xqy8N+vc6uJXA2RRfJl1ZLuNwfV+NNVmD4Q5gTkF6DkkLr1Ke2WmecUMoC4Ck\nFQXJzojorK26dXMlhffKks9XQsaTq4pfi4cEzu7xyb/CJyN3nn5jb/7kUX+KHv8HOGQZE59PPhfe\na+lb6QD1HrcPDk4eYOYgRtMlvtFU1zzZc0zxZdJqH2gSMPf7/fOP/jE89kE45avJj0BVKN92ILkq\n0n9Pvf+74akLKLqNkDwNOw54w2B/r4NJH2C7mKG19obn+2qESWonucU2LLIGwweBeZKOBXYCHwKW\nlOS5E1gKrJa0ENgTEbsk7R5CWQAiYkXGeo49pYGz7UDZ9+gKi6TpksfogemPLKNt32R+6/H+B2AO\ntkHPwf6A2NNS+UnAvW8M2var7497qIGj0hdNzq08K7k02ts63NuWTLMCB9uSp4hfAl73HPSMS86n\n15U8SPPyDJi4Z+D7ioXn37h98ObvFp/vInkYbMLLOqQlOuUZGKBV1ueslVT79zqYNPiNqoCWRdoI\n6uxNS7q2nsvPFAwjolvSUpID0grcGhEbJF2Wzr8lIu6WtEjSFuBV4NJKZbPUZwQN+RJFVUovZVb5\na5EDk/cVPq02FL0BUn/0zpdoO9DfvGvtPvSx9Dd/+/yi99cKHbFd/PQTm/mtx+cByaWmBV+CqU8N\nHuhGSyAc4XqunZvcIxz0AZqxqtKPpCD5+2h77dAnmLvHw8GW4ldIJu2Gzv+TvLNZ7h3UoXhh3guM\nf3kiv7VhUtH0l2fuL2iVlX43dJF8vx3a9eUgf69973t2TziMn17+TC0dJwxQp/p8X40xWVuGRMQ9\nwD0l024pSS8datnRoMpLFENfbplLmQP+WswaOEu1dA8Q5VJ73/BD7r1+J2/9148x5Rlo2wvjCx5a\naDsAc9ZP5Yv3LweWAZOLnj6d+Hz599GyvKNXq3KXgJvU2rk5C4CFelqS82CgKxLjB2jltR2A/YcH\nra+oaNpxP4Svrk3Oyf/xO8X3GgvPua6J8Ph/P7QDhN6/r6lP9Xdq0D0ennnX3/Yt5tDvhjcAp7L+\nyuL7kAXLK/fQ3CG3Qeb851xum/Rd6b3vq/a7Zri+r8Ya90AzitWzh4uy9yCh+J5G/wvxV/LRc8/h\n+B8U/5gqeI8qvWn/aeAYYDwwuex7iKWPxZcLhD3ptNKXvyulKZnXdRi8PAtenfYy416dxIxHB3ni\nZwjLbRZZ6ngwfam/tN1S7Y+U4fpRs/uEQ68uDGW5e48MJr1YnCvpnABgN3/ylsMPeVe15H3bAXtT\nqq4Hp/5XIHp/GKq7i+fnrygb9Hr/zmY+eMUhr2Yk9c/V6xOVNNV7hiPBwXDkHPJ0aunloMJgV8V7\nUn1Ps5V2qdXbe8j4V7bwzs8eOeB6d6bvHZ59Q1J2zzHw9LkPccy/z+/rVuyZ/7aBk7952iGXv/ZO\nhdu/1vsgw/IIki+g079w3aCtw71T4Vfv3MG8e2Ydcp+o9Av5oGDP8TDll8WX5wZTSy85hXpakich\nT/hBfwuqR0nPNb31GGgdvb3rQPIQyNTNycwXT+jhyfe9wtk3HFHUAvrlu4ofNOld13Nv6WLX2+Gt\nXxtX1KPLj67pYd49rzLt54cz4ZVDt6z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uBmZmqIuZmVnNsgTDqRGxJ/2+B5haJs8MYEdBemc6rNT7gfUZ6mJmZlazij3dS9oETCsz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\nIRQvOsMQQgjFi84whBBC8f4HwVrRC329avcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb2018d2250>"
+       "<matplotlib.figure.Figure at 0x10ee05a50>"
       ]
      },
      "metadata": {},
@@ -306,9 +255,9 @@
     },
     {
      "data": {
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FCbdL+NI4fOZVcz21ziVblbvwkq2VuP7u3+2NRhusy14wlnufw3R5ebtPP/NqvvugVLd3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\n+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rpY2e/WnOz7+mnmiR1ISmeUtqVbqOV424+k2UfmtiXwD+LAbL5gJz4aZhIZbc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CAIgiCEICIpCIIgCCGISAqCIAhCCCKSgiAIghCCiKQgCIIghCAiKQiCIAghiEgKgiAIQggikoIg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\nEEQkBUEQBCGE/w0qZ4mey9MErQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb2019cdd90>"
+       "<matplotlib.figure.Figure at 0x10eb240d0>"
       ]
      },
      "metadata": {},
@@ -326,7 +275,6 @@
     "\n",
     "apt_xy = aperture.CircularApertureXY(radius=0.003)\n",
     "apt_xy.track(bunch)\n",
-    "check_macroparticlenumber(bunch)\n",
     "\n",
     "plot_phase_space(bunch, ax0, ax1, ax2, col='g')\n",
     "\n",
@@ -342,19 +290,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bunch.macroparticlenumber was updated correctly.\n",
-      "bunch.macroparticlenumber new: 486\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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hDSQ7oWOHypuOdzA8AEhcBXyJgR1sH7AOuKp/R5am6d9hPkZyoFTu7EVP+tdB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\nOVUHkcPQiqHt5d3aJV0NLAKeK2+6+cDvJV0HvEL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OdzAcZBLLgKXAMcB/AHuAfUAr8Gm6TjqdwGciuDEvb+4kPi1Nl+szs4MkgDUP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\nImLk/ReZ7MJPo35JCAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb20208b910>"
+       "<matplotlib.figure.Figure at 0x10ee0bc90>"
       ]
      },
      "metadata": {},
@@ -362,9 +302,9 @@
     },
     {
      "data": {
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C06I1nztz8ve1F5vuwtRpjQB/7IX3ngNeGOpnPZzVcqQSTyVSGZ6qQohIjlFK79ALW4qV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\nfquUekxr/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mCA5rSz/mYjSbXvKa9d+m9w+XS+Nmr2IM3uf16W3Wdzk8RRvK+VUKuaAapVLm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Bws/pUQn/4Apo6Bb9b5l2RzU8p7wbotVShh/SftS9w1DeadbE/iP3AEyIlQVaOE5F5jWb9ardlM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\nwa0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb201bd45d0>"
+       "<matplotlib.figure.Figure at 0x10e965b90>"
       ]
      },
      "metadata": {},
@@ -386,7 +326,6 @@
     "\n",
     "apt_xy = aperture.EllipticalApertureXY(x_aper=x_aper, y_aper=y_aper)\n",
     "apt_xy.track(bunch)\n",
-    "check_macroparticlenumber(bunch)\n",
     "\n",
     "plot_phase_space(bunch, ax0, ax1, ax2, col='g')\n",
     "\n",
@@ -407,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -416,7 +355,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 3.75 ms per loop\n"
+      "100 loops, best of 3: 3.32 ms per loop\n"
      ]
     }
    ],
@@ -429,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -438,7 +377,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 4.72 ms per loop\n"
+      "100 loops, best of 3: 4.48 ms per loop\n"
      ]
     }
    ],
@@ -484,7 +423,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/DetunersCythonTest.ipynb b/testing/interactive-tests/DetunersCythonTest.ipynb
index 94b41c4f..40c555ec 100644
--- a/testing/interactive-tests/DetunersCythonTest.ipynb
+++ b/testing/interactive-tests/DetunersCythonTest.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -24,9 +24,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
+      "\n",
+      "\n",
       "No module named PySussix\n",
       "trying PySUSSIX...\n"
      ]
+    },
+    {
+     "ename": "ImportError",
+     "evalue": "dlopen(../../../PySUSSIX/pysussix.so, 2): Library not loaded: /usr/local/lib/gcc/4.9/libgfortran.3.dylib\n  Referenced from: /Users/stefan/CERN/PySUSSIX/PySussix.so\n  Reason: image not found",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-cbc2a4e5dbb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0mem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'trying PySUSSIX...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mPySUSSIX\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSussix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/stefan/CERN/PySUSSIX/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mPySussix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/stefan/CERN/PySUSSIX/PySussix.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpysussix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: dlopen(../../../PySUSSIX/pysussix.so, 2): Library not loaded: /usr/local/lib/gcc/4.9/libgfortran.3.dylib\n  Referenced from: /Users/stefan/CERN/PySUSSIX/PySussix.so\n  Reason: image not found"
+     ]
     }
    ],
    "source": [
@@ -49,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -130,12 +146,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)\n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    #print bunch.sigma_z()\n",
     "\n",
     "    return bunch"
@@ -348,7 +364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/DetunersTest.ipynb b/testing/interactive-tests/DetunersTest.ipynb
index 4f3b0fca..c7272e32 100644
--- a/testing/interactive-tests/DetunersTest.ipynb
+++ b/testing/interactive-tests/DetunersTest.ipynb
@@ -129,12 +129,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)\n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    #print bunch.sigma_z()\n",
     "\n",
     "    return bunch"
@@ -376,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/GeneratorTest.ipynb b/testing/interactive-tests/GeneratorTest.ipynb
index d2df2f54..1e52d14b 100644
--- a/testing/interactive-tests/GeneratorTest.ipynb
+++ b/testing/interactive-tests/GeneratorTest.ipynb
@@ -35,7 +35,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-122-g056083c437-dirty\n",
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
       "\n",
       "\n"
      ]
@@ -45,7 +45,8 @@
     "from PyHEADTAIL.particles.particles import Particles\n",
     "import PyHEADTAIL.particles.generators as generators\n",
     "from PyHEADTAIL.trackers.transverse_tracking import TransverseMap\n",
-    "from PyHEADTAIL.trackers.simple_long_tracking import RFSystems, LinearMap"
+    "from PyHEADTAIL.trackers.simple_long_tracking import RFSystems, LinearMap\n",
+    "import PyHEADTAIL.cobra_functions.stats as st"
    ]
   },
   {
@@ -66,7 +67,18 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta: 0.915961012299\n",
+      "eta: -0.100049107837\n",
+      "Qs: 0.000254822142096\n",
+      "beta_z: 9810.60617229\n"
+     ]
+    }
+   ],
    "source": [
     "# general simulation parameters\n",
     "n_turns = 20000\n",
@@ -75,10 +87,10 @@
     "\n",
     "# machine parameters\n",
     "circumference = 157.\n",
-    "inj_alpha_x = 0\n",
-    "inj_alpha_y = 0\n",
-    "inj_beta_x = 5.9 # in [m]\n",
-    "inj_beta_y = 5.7 # in [m]\n",
+    "inj_alpha_x = 0#-1.2\n",
+    "inj_alpha_y = 0#15\n",
+    "inj_beta_x = 5.#5.9 # in [m]\n",
+    "inj_beta_y = 31.#5.7 # in [m]\n",
     "Qx = 5.1\n",
     "Qy = 6.1\n",
     "gamma_tr = 4.05\n",
@@ -98,13 +110,17 @@
     "# calculations\n",
     "gamma = 1 + e * Ekin / (m_p * c**2)\n",
     "beta = np.sqrt(1 - gamma**-2)\n",
+    "print('beta: ' + str(beta))\n",
     "eta = alpha_c_array[0] - gamma**-2\n",
+    "print('eta: ' + str(eta))\n",
     "if eta < 0:\n",
     "    phi_offset = np.pi - phi_offset\n",
     "Etot = gamma * m_p * c**2 / e\n",
     "p0 = np.sqrt(gamma**2 - 1) * m_p * c\n",
     "Qs = np.sqrt(np.abs(eta) * V_rf / (2 * np.pi * beta**2 * Etot))\n",
+    "print('Qs: ' + str(Qs))\n",
     "beta_z = np.abs(eta) * circumference / (2 * np.pi * Qs)\n",
+    "print('beta_z: ' + str(beta_z))\n",
     "turn_period = circumference / (beta * c)\n",
     "\n",
     "# BETATRON\n",
@@ -147,11 +163,100 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.09520838516e-06\n",
+      "1.09520838516e-06\n",
+      "0.0133665725141\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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FI7E7JttFofGrIaxv2Wql2wK02PfthIa1k9CAdtp5N4WGeDuhoRYKM7V2EBro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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10277fa10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "emittance x new: 2.45182772349e-06\n",
+      "emittance y new: 2.51484116678e-06\n",
+      "\n",
+      "\n",
+      "emittance z new: 1.19483239906\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "egeox = epsn_x/(beta*gamma)\n",
+    "egeoy = epsn_y/(beta*gamma)\n",
+    "egeoz = epsn_z*e/(4*np.pi*p0)\n",
+    "print(egeox)\n",
+    "print(egeoy)\n",
+    "print(egeoz)\n",
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.gaussian2D(egeox),\n",
+    "                                     distribution_y=generators.gaussian2D(egeoy),\n",
+    "                                     distribution_z=generators.gaussian2D(egeoz),\n",
+    "                                     linear_matcher_x=generators.transverse_linear_matcher(inj_alpha_x, inj_beta_x),\n",
+    "                                     linear_matcher_y=generators.transverse_linear_matcher(inj_alpha_y, inj_beta_y),\n",
+    "                                     linear_matcher_z=generators.longitudinal_linear_matcher(Qs, eta, circumference)).generate()\n",
+    "                                         #generators.transverse_linear_matcher(1., beta_z)).generate()\n",
+    "plot_phase_space(bunch_new)\n",
+    "\n",
+    "print('emittance x new: '+ str(bunch_new.epsn_x()))\n",
+    "print('emittance y new: '+ str(bunch_new.epsn_y()))\n",
+    "\n",
+    "#print('beta x: '+ str(bunch.beta_Twiss_x()))\n",
+    "#print('beta x new: '+ str(bunch_new.beta_Twiss_x()))\n",
+    "#print('beta y: '+ str(bunch.beta_Twiss_y()))\n",
+    "#print('beta y new: '+ str(bunch_new.beta_Twiss_y()))\n",
+    "\n",
+    "#print('alpha x new: '+ str(bunch_new.alpha_Twiss_x()))\n",
+    "#print('alpha y new: '+ str(bunch_new.alpha_Twiss_y()))\n",
+    "\n",
+    "\n",
+    "#longitudinal stuff\n",
+    "print('\\n')\n",
+    "print('emittance z new: '+ str(bunch_new.epsn_z()))\n",
+    "#print('beta_z via cf: ' + str(st.get_beta(bunch.z, bunch.dp, None)))\n",
+    "#print('beta_z new via cf: ' + str(st.get_beta(bunch_new.z, bunch_new.dp, None)))\n",
+    "#print('alpha_z via cf: ' + str(st.get_alpha(bunch.z, bunch.dp, None)))\n",
+    "#print('alpha_z new via cf: ' + str(st.get_alpha(bunch_new.z, bunch_new.dp, None)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9E7Cz7DPvTI28FuubFL5YX3nCW33Xp+GQButX/n0RORk4GjouBDrsVheg/3no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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564f4a23d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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AoYQpsO9W1edFpJOwvvbbTS513QoReYDwAf1nLcn/UtWPJvLwzsJxHGeSVFVncSDwzsJx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\nAABJRU5ErkJggg==\n",
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9E7Cz7DPvTI28FuubFL5YX3nCW33Xp+GQButX/n0RORk4GjouBDrsVheg/3no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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ece4550>"
+       "<matplotlib.figure.Figure at 0x7f564f4a2090>"
       ]
      },
      "metadata": {},
@@ -159,18 +264,72 @@
     }
    ],
    "source": [
-    "bunch = generators.Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, \n",
-    "    beta_z, epsn_z\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "#check the convenience functions. when setting the seed, they should be exactly the same\n",
+    "np.random.seed(1)\n",
+    "bunch1 = generators.generate_Gaussian6DTwiss(n_particles, intensity,\n",
+    "        e, m_p, circumference, gamma,\n",
+    "        alpha_x=0.1, alpha_y = -1.9, beta_x=81.1, beta_y=31.2,\n",
+    "        beta_z=beta_z, epsn_x = 3.75e-6, epsn_y=4.2e-5, epsn_z=0.1,\n",
+    "        dispersion_x=1, dispersion_y=-2.5)\n",
+    "np.random.seed(1)\n",
+    "bunch2 = generators.ParticleGenerator(n_particles, intensity,\n",
+    "             e, m_p, circumference, gamma,\n",
+    "             distribution_x=generators.gaussian2D(3.75e-6/(gamma*beta)),\n",
+    "             distribution_y=generators.gaussian2D(4.2e-5/(gamma*beta)),\n",
+    "             distribution_z=generators.gaussian2D(0.1*e/(4*np.pi*p0)),\n",
+    "             linear_matcher_x=generators.transverse_linear_matcher(\n",
+    "                 0.1, 81.1, 1.),\n",
+    "             linear_matcher_y=generators.transverse_linear_matcher(\n",
+    "                 -1.9, 31.2, -2.5),\n",
+    "             linear_matcher_z=generators.longitudinal_linear_matcher(\n",
+    "                 Qs, eta, circumference)).generate()\n",
+    "plot_phase_space(bunch1)\n",
+    "plot_phase_space(bunch2)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " turn 0\n",
+      "emittance z new: 1.19471291582\n",
+      "\n",
+      "\n",
+      " turn 1\n",
+      "emittance z new: 1.19471291582\n",
+      "\n",
+      "\n",
+      " turn 2\n",
+      "emittance z new: 1.19471291582\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#create a longitudinal only beam and track it, see if emittance grows ...\n",
+    "longitudinal = LinearMap(alpha_c_array, circumference, Qs)\n",
+    "for i in range(3):\n",
+    "    print('\\n turn ' + str(i))\n",
+    "    print('emittance z new: '+ str(bunch_new.epsn_z()))\n",
+    "    #print('beta_z via cf: ' + str(st.get_beta(bunch.z, bunch.dp, None)))\n",
+    "    #print('beta_z new via cf: ' + str(st.get_beta(bunch_new.z, bunch_new.dp, None)))\n",
+    "    #print('alpha_z via cf: ' + str(st.get_alpha(bunch.z, bunch.dp, None)))\n",
+    "    #print('alpha_z new via cf: ' + str(st.get_alpha(bunch_new.z, bunch_new.dp, None)))\n",
+    "    longitudinal.track(bunch_new)\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -181,16 +340,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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AoYQpsO9W1edFpJOwvvbbTS513QoReYDwAf1nLcn/UtWPJvLwzsJxHGeSVFVncSDwzsJx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\nAABJRU5ErkJggg==\n",
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OLKLHipjT7Swcx3ESVNvOolY2P7qNEkt9qOqKUno6juO80bGX6O7ILSJfqmT8WcYsVgGn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\nKCkNvtIvtzUAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ece4150>"
+       "<matplotlib.figure.Figure at 0x7f564f651190>"
       ]
      },
      "metadata": {},
@@ -198,25 +357,27 @@
     }
    ],
    "source": [
-    "bunch = generators.ImportDistribution(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    bunch.get_coords_n_momenta_dict()\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.import_distribution2D([bunch_new.x, bunch_new.xp]),\n",
+    "                                     distribution_y=generators.import_distribution2D([bunch_new.y, bunch_new.yp]),\n",
+    "                                     distribution_z=generators.import_distribution2D([bunch_new.z, bunch_new.dp])).generate()\n",
+    "plot_phase_space(bunch_new)     "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8OXBs1fva53Z8RfsAnwY+XSjztfT+vex+F15Xbbs3TH1qz28D96Xy15Odd698X2velteQ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\nSjHW1Qrg5PT6ZOD6NmWHhbL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a0gX9XH9deRaPp+cO5SOyzQAWkj1WwszMGqbOPItXAt+RNETWaX0vIm6rEo+Z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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ece42d0>"
+       "<matplotlib.figure.Figure at 0x7f564f2ebfd0>"
       ]
      },
      "metadata": {},
@@ -224,16 +385,19 @@
     }
    ],
    "source": [
-    "bunch = generators.Uniform3D(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    2e-3, 2e-3, 30\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "\n",
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.uniform2D(-2e-3, 2e-3),\n",
+    "                                     distribution_y=generators.uniform2D(-2e-3, 2e-3),\n",
+    "                                     distribution_z=generators.uniform2D(-30, 30)).generate()\n",
+    "plot_phase_space(bunch_new)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -246,45 +410,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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CGRvj+TGeeebJnHfe/wLOioWeDvQmws4gNGSfITyqnbH7B1qciGQDspywjNjp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\n",
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gjYXjOM7YqRkPbhHpFJEtIrJdRDaLSHsRu2Uisk1EdkQOeXb8UyLygIjcLyI/EpHD0sI7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\nC8dxHGdU/i+C0GoiMFU09gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578eb7a810>"
+       "<matplotlib.figure.Figure at 0x7f564f247c90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "emittance x new: 2.48104717957e-06\n",
+      "emittance y new: 2.49411271893e-06\n",
+      "\n",
+      "\n",
+      "emittance z new: 1.17877412812\n"
+     ]
     }
    ],
    "source": [
-    "bunch = generators.MatchGaussian6D( # implicitly tests MatchLinearLongMap and MatchTransverseMap\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    transverse, epsn_x, epsn_y, longitudinal, epsn_z\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.gaussian2D(egeox),\n",
+    "                                     distribution_y=generators.gaussian2D(egeoy),\n",
+    "                                     distribution_z=generators.gaussian2D(egeoz),\n",
+    "                                     linear_matcher_x=generators.transverse_linear_matcher(inj_alpha_x, inj_beta_x),\n",
+    "                                     linear_matcher_y=generators.transverse_linear_matcher(inj_alpha_y, inj_beta_y),\n",
+    "                                     linear_matcher_z=generators.longitudinal_linear_matcher(Qs, eta, circumference)).generate()\n",
+    "plot_phase_space(bunch_new)\n",
+    "print('emittance x new: '+ str(bunch_new.epsn_x()))\n",
+    "print('emittance y new: '+ str(bunch_new.epsn_y()))\n",
+    "\n",
+    "#print('beta x: '+ str(bunch.beta_Twiss_x()))\n",
+    "#print('beta x new: '+ str(bunch_new.beta_Twiss_x()))\n",
+    "#print('beta y: '+ str(bunch.beta_Twiss_y()))\n",
+    "#print('beta y new: '+ str(bunch_new.beta_Twiss_y()))\n",
+    "\n",
+    "#print('alpha x new: '+ str(bunch_new.alpha_Twiss_x()))\n",
+    "#print('alpha y new: '+ str(bunch_new.alpha_Twiss_y()))\n",
+    "\n",
+    "\n",
+    "#longitudinal stuff\n",
+    "print('\\n')\n",
+    "print('emittance z new: '+ str(bunch_new.epsn_z()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "rfsystems = RFSystems(circumference, [harmonic], [V_rf], [phi_offset],\n",
+    "                      alpha_c_array, gamma)\n",
+    "rfsystems2 = RFSystems(circumference, [harmonic], [V_rf], [phi_offset],\n",
     "                      alpha_c_array, gamma)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -293,96 +490,150 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "*** Maximum RMS emittance 4.50589180434eV s.\n",
+      "\n",
+      "*** Maximum RMS emittance: 4.50589180434eV s.\n",
       "... distance to target emittance: 7.72e-02\n",
       "... distance to target emittance: 7.75e-02\n",
       "... distance to target emittance: -4.08e-05\n",
+      "\n",
       "--> Emittance: 1.20000000151\n",
-      "--> Bunch length:11.8086653152\n"
+      "--> Bunch length: 11.8086653152\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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J8wWU9RRhuO9Ghtbx5xn62Z1J+LwOpthfB+ywdMWqi0KuyclKqeMsyrX40ZOE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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ec1dc10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'bunch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-18-fe052935bb8c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m                                      ).generate()\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mplot_phase_space\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbunch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mplot_phase_space\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbunch_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'emittance x: '\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbunch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsn_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'bunch' is not defined"
+     ]
     }
    ],
    "source": [
-    "bunch = generators.MatchRFBucket6D( # implicitly tests MatchRFBucket2D and MatchTransverseMap\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    transverse, epsn_x, epsn_y, rfsystems.get_bucket(gamma=gamma), epsn_z\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.gaussian2D(egeox),\n",
+    "                                     distribution_y=generators.gaussian2D(egeoy),\n",
+    "                                     distribution_z=generators.RF_bucket_distribution(rfsystems2.get_bucket(gamma),epsn_z=epsn_z),\n",
+    "                                     linear_matcher_x=generators.transverse_linear_matcher(inj_alpha_x, inj_beta_x),\n",
+    "                                     linear_matcher_y=generators.transverse_linear_matcher(inj_alpha_y, inj_beta_y),\n",
+    "                                     ).generate()\n",
+    "\n",
+    "plot_phase_space(bunch_new)\n",
+    "print('emittance x new: '+ str(bunch_new.epsn_x()))\n",
+    "print('emittance y new: '+ str(bunch_new.epsn_y()))\n",
+    "\n",
+    "#print('beta x: '+ str(bunch.beta_Twiss_x()))\n",
+    "#print('beta x new: '+ str(bunch_new.beta_Twiss_x()))\n",
+    "#print('beta y: '+ str(bunch.beta_Twiss_y()))\n",
+    "#print('beta y new: '+ str(bunch_new.beta_Twiss_y()))\n",
+    "\n",
+    "#print('alpha x new: '+ str(bunch_new.alpha_Twiss_x()))\n",
+    "#print('alpha y new: '+ str(bunch_new.alpha_Twiss_y()))\n",
+    "\n",
+    "\n",
+    "#longitudinal stuff\n",
+    "print('\\n')\n",
+    "print('emittance z new: '+ str(bunch_new.epsn_z()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "*** Maximum RMS emittance 4.50589180434eV s.\n",
+      "\n",
+      "*** Maximum RMS emittance: 4.50589180434eV s.\n",
       "... distance to target emittance: 7.72e-02\n",
       "... distance to target emittance: 7.75e-02\n",
       "... distance to target emittance: -4.08e-05\n",
+      "\n",
       "--> Emittance: 1.20000000151\n",
-      "--> Bunch length:11.8086653152\n"
+      "--> Bunch length: 11.8086653152\n"
      ]
     },
     {
      "data": {
-      "image/png": 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MwzD2P2Mai3L7WojIB3CfMT86Xz7mZ2EYhlGYqvWzgOy+FiKyGrcqzQpVfTJPHuZnYRiG\nMUFK7WeR1Vh0AD/ALdv1MPBuVX1WRObi1tc+zsulrlshIg/gPpj/tE/yv1X1I4k8zFgYhmFMkKoy\nFvsDMxaGYRgTp2o8uEWkQ0SuF5H7ReQ6EWnLI7daRLaKyAPBIc+Hf1ZE7hKRO0XkpyIyPy2+YRiG\nUXkqufjRNFV93sudCrxKVf8xJR97szAMw5ggVfNmQXaHvOcjuRYgdYDbMAzDqDxjTp0tQLEOeUeF\nAxHZCLwPt8J9TwZdDMMwjDJS0cWPVPUs4Cw/7fZLwMlpcuZnYRiGUZiq9bMQka3Aysgh7wZVXZqQ\n6QHOUdXV/ngDsE9Vz0/IHQRco6pHpORjYxaGYRgTpJrGLDYD6/z+OuDHKTK3AQeLyEIRaQDe4+Mh\nIgdHcicAd2TQxTAMwygjWd4ssjrk/RB4JbAX+B3wYVUd9bkQe7MwDMOYONX0ZgEpYxLJxY+8TNj2\nRXLvVNUjge8C78AtjjRpKWdfYSkxPUvLZNBzMugIpme1k8VYZF34CO+I14tbbW9SM1kuINOztEwG\nPSeDjmB6VjsV87PwfBH4VAYdDMMwjP1AFmORaeEjETkBeExV786gg2EYhrEfKDjAPYafxWWq2h7J\nPq2qHYn4a4HVqvpP/vgknFPep4AtQK+qPicivwdeq6pPpehgo9uGYRhFUMoB7oJOeWVc+GgxsBC4\nS0TArb39KxF5XXJGVCkLaxiGYRRHRfwsVPXXqtqlqotUdRHOgCxLmzprGIZhVJ4sxuI8oFdE7gfe\n4o8RkbkicjWAqu4BPgYMAvcCV6rqfSlpWVeTYRhGFVP1ix8ZhmEYlSerU17RlGDxpM+LyH1+AaV/\nF5HW6NwGL79VRFZVUMd3ichvRGSviCyLwheKyE4RucNvXy9Wx3Lq6c+VpC5LpGdq/FLVZ758EzJf\n8efvEpHuYnXOQpn0PEdEHovqcHWF9fy2Hxe9JyFfbfWZT8+S1mexOorIfBG5wd/fvxaR0yL5idWl\nqlZkAz4HfMrvnwmclyJTCzyIGwyvB+4EDvXneoEav39eiI9z/rvTyy/08WsqpONS4BDgBtyYTIiz\nELiniuoyn54lq8sS6ZkavxT1WSjfSOZY3Acvwc3q+2WxOlehnmcDZ5TwmixaT3/8N0B38n+tpvoc\nQ8+S1WfG/3w28Gq/34JbjG5pMXVZsTcLsi+edL2qhs+H3IybUYU/f4Wq7lbVh3GV/LoK6bhVVe8v\nMu9q0LOUdZlZz3HGL5axHEhH5K+qNwNtIjJ7P+tcLj0BSjnzMIueqOrPgWdS0q2m+iykJ5SuPovV\nsUtVt6vqnT78BeA+vK8bE6zLShqLTE59CT4IXOP353q5seLsbx2TLPKvp1tE5I1F6lduPUtZl5Bd\nz0Lxs9bneOonn8zcInUuhnLpCXCq78K4pATdO1n0LEQ11edYlKo+i9VxXiwgIgtxb0E3+6AJ1WVm\nY1GoL833h90jIk+KyC7fr/yAiBwfydQCtwNTU5Ifc/RdRM4ChlT18gJiedOJdExux49IwL2rTXiB\npzxsA+arajdwBnC5iEwrFKFCeqZRMJ0y6Clpcon4E67PceSbj/E8LY5H52IppZ4x/wYsAl4N/Am4\nYILxkxSr57jrp8L1OVa8UtZnZh1FpAX4IXC6f8MYKTiOusyyrGr8ocC34hzwbhWRzeqnx6pqr4gc\nC3xMVY8VkaOAL6vqZj8oNBv4e+AhYElKFvmc+kL+H8D11R1dIM48H5aKls/xMC+qOgQM+f3bReR3\nwME4o1k1eqbEKViXZdIzzjM1fjH1OY580+onTbfHcP3IE9I5A6XUcziuRj5OIvIt4KoK6Vnw+qJ6\n6nOs+6CU9ZlJRxGpB34EfE9VY3+4CdVl1jeLovvScE59p+Ia+6eA7SnpF1o8aTXwSeAEVX0pirMZ\neK+INIjIIlyjcUuR5cu0wFOCYasvIp3e0CIir/A6PlSkjmXTk9LWZSn0TI1fovocT/1sBt7v8+kB\nnvWv8RPWOQNl0dM3FoF3APeQjSx6FqKa6jMvJa7PonUUEQEuAe5V1QtT4oy/Losdofcj6O/ELXQU\njk8CvpqQuQp4Q3T8E2AZ0IHrJ3sEuBW41p+fC1wdyb8NN4L/ILAhCn/Ax73Db1+Pzn3ay28FjslQ\nvg6v7/3AdUDbBHV8B64fcSfOGIYyrgV+7fX+FXBcxv+hLHqWsi5LpGe++GtKUZ9p+QIfAj4UyXzN\nn7+LkTPHJqRzxnosh57fAe728j/G9WdXUs8rcN2Lu/y1eXKV1mc+PUtan8XqCLwRt47QneTaytXF\n1GXWSl7L+IzF8uj4J8BrgLcD/9uHrQSuypOH2mabbbbZNvEtqyGNt6zdUFn60t4AHC/ui7NXAG8R\nke+kZVLKApdrO/vssyuug+lpek5WHU3P0m+lJquxKLYvbbuqflpV56v7kOB7gZ+p6vsz6mMYhmGU\ngUyzoVR1j4iEDwXWApeo6n0i8iF//huqeo2IHCsiDwIvAifnSy6LLoZhGEb5yGQsPHEf2T5wRiIh\ns4+cMVAAEZkC3Ag0Ag3Af5ZAl4qxcuXKSqswLkzP0jIZ9JwMOoLpWe1k+uqsn674WyI/C+BEjT5D\nnsfPosefa1bVHSJSB9wEfEJVb0rkoeXofzMMwziQERG0hIvHVdLPAlXd4WUacN1YT2fUxzAMwygD\nWY1Fpm+WiEitiNyJ87e4QVXvzaiPYRiGUQayGovx9g+lfrNEVfeq6qtxxuNNIrIyoz6GYRhGGcg6\nwF2S76qo6l/ELcX6WmBLMpNzzjlneH/lypUv2wEmwzCMfGzZsoUtW7aULf2sA9x1uAHuo3Eu77dQ\neIC7B7hQVXtEpBPYo6rPikgTbvrtZ1T1p4k8bIDbMAxjgpR6gLuSfhZzgMtEpAbXHfbdpKEwDMMw\nqoOK+VkAzwJ/AWb5sF0l0MUwDMMoA5kGuKP1LFbj1ms+UUQOTcgcCyxR1YOBU3CLggDsBj6uqocD\nPcBHk3ENwzCM6qBifhaavjbs3Iz6GIZhGGWgon4WARm9NqxhGIZRRVTUzwLGXhvWMAzDqDwV9bMo\nsDbsCMzPwjAMozAHsp+F4MYynlLVjxfIw/wsDMMwJsiB5GexHLcM690icocP26CqA1l0MgzDMEpP\npjeL/YG9WRiGYUycavtEOSKyWkS2isgDInJmHpmv+PN3iUh3FP5tEXlcRO7JqodhGIZRPirplAdw\nqY9rGIZhVDGVcsqb7Y9/DjyTUQfDMAyjzFTKKS8pYxiGYVQxWf0sMjvljQfzszAMwyhMtftZ9ADn\nqOpqf7wB2Keq50cyFwFbVPX7/ngrsEJVH/fHC4GrVPXIPHnYbCjDMIwJUm2zoW4DDhaRhSLSALwH\n2JyQ2Qy8H4aNy7PBUBiGYRiTg0zGQlX3AMEp717gyuCUFznmXQM85J3yvgF8JMQXkSuA/wccIiKP\nisjJozIxDMMwKk4lFz8CN0uqG+fZ/S1VvbQE+hhVzODgIBs2nMsjjzzGggWzOffcf+WYY45hcHCQ\nCy64mIceeohHH/0DQ0NCQ8M+ZsxoZdeuWhYsmMfatb1ceumVPPzwdmAfTU37eOGF3T7lfUAj7vIa\n8sdNwF6/Dfnz9f74eaAZaPBxXgSm+Hj1UTq1fhNgj9/Ux1Vgp0/jJaAlSmuq12vI59fo09mNW+er\n1usX5JsS56d4PXb49GuAXdTWtlBbu5OhoXpAWLy4nR07lD/96VlgHw0Nwpve9Bpuv/33AJxxxsmc\nddZZGf4xw3BkHbOoxX0b6q24jwPeSuFvQx0FfNl/G2rMuD6+jVlkJDTEAH19p4xonJ988nGgjs7O\nGXnPATz33J95/PFnEKlnyZJFrF3by4033s6TTz7Fc8/9mWee2QXs4emnH8M1moJrQEMDHBrVBlxj\nWI9rAHfiGsipwFe8xp8AVgDX4xrQsIhinZfH57HTnw9pTgNmAg/gGuc9Pp9d5Brm54DpXr+/AK0+\nvRC+h5yRaPByoQFvHNZjFUP0sROo4QKauG74/JCPF/Td57fQ4O+IdJ3tZR7DGa/pPt4zvj4afNzn\nIz3/Eum/K0prKe4LOt9M5BnK1hzVRSjXC74edzDSmAWDWB+VQ4G91NQ00NzcyPr1HwVg48avs3Pn\nTmAnNTVTmTq1mTPP/BAAX/yie/b72799I9u2PQ/krj+j/JR6zAJVLXoDXg8MRMfrgfUJmYuA90TH\nW3F3yZhxfbgaxTMwMKBNTV0KmxQ2aVNTl/b39/uwPoXOcZ2DZoWO6Hi6wlKF5f5cj9+afdzlXiYZ\nP5nudIVWv69+64vCNnn56T683u+v9Wl2+vDpCR3W+rDFCm1eJsjFefdF+8sjXVsTciHPNl1Fn75I\nfVBWX6ReV9HsdWv2snEZQ7nbFGqjfJP1EHRsLnAu1r8zqu9NCu3+P1kQxY/TS8uz0+u1NpFms0JL\n4j8PabRH/0O+umxOPdfU1KUDAwOVvi1eFvi2M1MbH29ZjcU7gW9GxycBX03IXAW8ITr+CfAaYO1Y\ncdWMRWZ6e9ckGuJN2tGx2IdN5FzPKFkXNjXRAHUq/JVvgNLij84T5o0zrzVRXosjueUpOnQkwroU\nVqSkuyban5Un/x7f2M1Q2KSD9Gp0UhV0kMN9nj0p8eNyd2j+eljjt3zx0+L0+HLFx61eX02klxY/\n1FG7wkCUxljl6MxzLtYj/Vxv75pK3xYvC0ptLCrlZzEhzM+iWpmL60LpB9ZF4WfgukXGywIg/qzY\n/RPUYzvwhYQOfSlhn51gujG/AA7OEL+cxEvIzAX+GfdCPx5qyNXRxbgPLoR0xopnVBPl9rPI+mbR\nw8iupA3AmQmZi4D3Rsdbga7xxFV7s8hMvm6ompp2TXZL1NS0T6Abqss/iR6R8oQZun26EvFbU9Lt\n9OkM+KfRGeq6RGYmZEJXRqOO7oZK02FWSthSHV83VJuGt4jcuVyZiu+GatXSd0OF7sD4P4nfYMbq\nhlqnI98GQprhP0n+VyGNdWPUpXVDVRqqrBuqDvgdsBA3anYncGhC5ljgGs0Zl1+ON66asSgJAwMD\n2tu7Rnt71wzfqM5gzPANzRFaUzND+/v7R8h3dy/X7u4V2t29QhcvPkxbWuZoU9McFemIGobmlEYj\nbhB7fMN1mLqGvk1dn3qX348blVbNGYM+dUagXWGO13Oal1/uG/Mp6vrVW1J0aEwJm+p1a/d6Heb3\n2314h98PDXOrP24ZVaZVtOggooPU6ipa1BmKIBe65mb7NBb4sjb6rT5qlMOYTX2ki2huvKBVAR8+\nz+vc6veXermpmjPEwdjN82FtCjVert3XX5vmxh3i+mlJ+U+CkW9VaFGR+Pxar2ObwhStqZmh06bN\n13Xr1mld3VSvwzytqWnU7u4VI64/o/yU2lhkXs9CRN4GXEhu8aNz48WPvEz4Mu2LwMmqenu+uCnp\na1YdjXTSZklNJF6YCfWHP/yB3bsbqK2t5c1vfhVPPbWbe+75FXv2uCmodXXK9OltPPvss+zbtwf3\nd9fhZkuFKae15KaZ1pCbLfUCuZlM+/z5KeRm80z153aQm7oaZvk8j5spJLiZU6HXdU+U5hBuBhA+\nzm5yXSwvkpuh9JyPE6cRfmt8HmEabphZFWYlhSmwYUpsnQ/b5/N7iZGzsup8XpCb8RXqJEz7nUZu\n9tJun3+9D9vj0whTfcPMsL2MnCW2G6inrq4GGGLPnkbq6pSamr3s3dtAQ0MdS5e+Ynh6c2A8102x\n15ZROko9G6poYyEiHcCVuA7nh4F3q+qzKXKryRmEb6n/FIiIvAs4B9e5/T+CAUmJb8bCeFlgDaxR\nSqrJWHwOeFJVP+cXPWpX1fUJmby+FCKyFPdo9Q2gz4yFYRhG6aimb0MNr1Phf/8uRSbvehequlVV\nJzrtxTAMw6gAWYxFl+Y+CPg4boZTElvLwjAM4wCgoJ+FiFyP87ZOMuJjM6qqIpLWV1SS/iPzszAM\nwyhM1a5n4delWKmq20VkDnCDqi5NyIxnvYsbsDELwzCMklJNYxabybl+rgN+nCIznvUuIKOHt2EY\nhlFeshiL84BeEbkfeIs/RkTmisjVkH+9Cy/3DhF5FOeod7WIXJtBF8MwDKOMFG0sVPVp4N3AIzgv\n7B+ISJsUSJLqAAAgAElEQVSqblPV4yK5a4HTcZ5AH/TTbFHV/8DNjnoYN0C+U0RaMQzDMKqOrF8D\nWw9cr6qHAD/1xyPwvhbBg/sw4EQROdSfvg44XFVfhft63IaM+hiGYRhlIKuxyOprcb2q7vNyNwPz\nMupjGIZhlIGsxqKUvhYfBK7JqI9hGIZRBsZcz2J/+FqIyFnAkKpePpasYRiGsf8Z01ioam++cyLy\nuIjMjnwtnkgR+yMwPzqeT7Rai4h8APcZ86Pz5WNOeYZhGIWpWqc8GP6Y4FOqer6IrAfaUj4mWIf7\nmODRwDbgFnIfE1wNXACsUNUn8+RhTnmGYRgTpGq+OuuV6QB+ABxE9JlyEZmLW1/7OC+Xum6FiDyA\nWwTgaZ/kf6vqRxJ5mLEwDMOYIFVjLEqwnsVncbOpFHgK+ICqPpoS34yFYRjGBKkmY5F1PYtpqvq8\nlzsVeJWq/mNKPmYsDMMwJkg1fRsqq4/F85FcC5A6ZmEYhmFUnjFnQxWgWB+Lo8KBiGwE3odboLgn\ngy6GYRhGGSn4ZiEi14vIPSnb8bGc7yeasI+Fqp6lqgcBm4AvTVB3wzAMYz9R8M2i3D4WEZdTwHvb\n/CwMwzAKU7V+FiXwsThYVR/wcqcCr1PV96XkYwPchmEYE6SaZkNl9bH4IfBKYC/wO+DDqjrq7cSM\nhWEYxsSpGmOxvzBjYRiGMXGqZuqsiHT4AfD7ReQ6EWnLI7daRLaKyANh4aPE+T4R2effVCYt5ewr\nLCWmZ2mZDHpOBh3B9Kx2svhZZF34CBGZD/TiVtub1EyWC8j0LC2TQc/JoCOYntVOxZzyPF8EPpVB\nB8MwDGM/kMVYZFr4SEROAB5T1bsz6GAYhmHsBwoOcI+x8NFlqtoeyT6tqiPGHURkLbBaVf/JH5+E\n8+D+FLAF6FXV50Tk98BrVfWpFB1sdNswDKMISjnAXSmnvMXAQuAuEQG39vavROR1yemzpSysYRiG\nURxZuqE2A+v8/jrgxykytwEHi8hCEWkA3gNsVtVfq2qXqi5S1UU4A7Iszc/CMAzDqDxZjMV5QK+I\n3A+8xR8jInNF5GoAVd0DfAwYBO4FrlTV+1LSsq4mwzCMKqbqnfIMwzCMypPlzSITWZ36ROTzInKf\niNwlIv8uIq3RuQ1efquIrKqgju8Skd+IyF4RWRaFLxSRnSJyh9++XqyO5dTTnytJXZZIz9T4parP\nsRxIvcxX/Pm7RKS7WJ2zUCY9zxGRx6I6XF1hPb/tx0XvSchXW33m07Ok9VmsjiIyX0Ru8Pf3r0Xk\ntEh+YnWpqhXZgM8Bn/L7ZwLnpcjUAg/iBsPrgTuBQ/25XqDG758X4uOc/+708gt9/JoK6bgUOAS4\nATcmE+IsBO6porrMp2fJ6rJEeqbGL0V9Fso3kjkWuMbvHwX8slidq1DPs4EzSnhNFq2nP/4boDv5\nv1ZTfY6hZ8nqM+N/Pht4td9vwX3YdWkxdVmxNwuyr7R3varu83I342ZU4c9foaq7VfVhXCW/rkI6\nblXV+4vMuxr0LGVdZtZznPGLZSwH0hH5q+rNQJuIzN7POpdLT4BSzjzMoieq+nPgmZR0q6k+C+kJ\npavPYnXsUtXtqnqnD38BuA/v68YE67KSxiKTU1+CD5JbD2MuI9fMyBdnf+uYZJF/Pd0iIm8sUr9y\n61nKuoTsehaKn7U+x1M/+WTmFqlzMZRLT4BTfRfGJSXo3smiZyGqqT7HolT1WayO82IBEVmIewu6\n2QdNqC4zG4tCfWmSW2nvSRHZ5fuVH5BopT1x34+6HZiakvyYo+8ichYwpKqXFxDLm46UeTXAPGwD\n5qtqN3AGcLmITCsUoUJ6plEwnTLoKWlyifgTrs9x5JuP8TwtjkfnYimlnjH/BiwCXg38CbhggvGT\nFKvnuOunwvU5VrxS1mdmHUWkBfghcLp/wxgpOI66zLIGd/yhwLfiHPBuFZHN6qfHqmqviBwLfExV\njxWRo4Avq+pmPyg0G/h74CFgSUoWBVfaE5EP4Prqji4QZ54PS0X332qAcZ5DwJDfv11EfgccjDOa\nVaNnSpyCdVkmPeM8U+MXU5/jyDetftJ0ewzXjzwhnTNQSj2H42rk4yQi3wKuqpCeBa8vqqc+x7oP\nSlmfmXQUkXrgR8D3VDX2h5tQXWZ9syi6Lw3n1HcqrrF/Ctiekn6qUx+4Nxrgk8AJqvpSFGcz8F4R\naRCRRbhG45Yiy1e042GK3LDVF5FOb2gRkVd4HR8qUsey6Ulp67IUeqbGL1F9jqd+NgPv9/n0AM/6\n1/gJ65yBsujpG4vAO4B7yEYWPQtRTfWZlxLXZ9E6iogAlwD3quqFKXHGX5fFjtD7EfR34lbFC8cn\nAV9NyFwFvCE6/gmwDOjA9ZM9AtwKXOvPzwWujuTfhhvBfxDYEIU/4OPe4bevR+c+7eW3AsdkKF+H\n1/d+4Drc0rET0fEduH7EnThjGMq4Fvi11/tXwHEZ/4ey6FnKuiyRnvnirylFfablC3wI+FAk8zV/\n/i5GzhybkM4Z67Ecen4HuNvL/xjXn11JPa/AdS/u8tfmyVVan/n0LGl9Fqsj8EZgH24GVWgrVxdT\nl1kreS3jMxbLo+OfAK8B3g78bx+2ErgqTx5qm2222WbbxLeshjTesnZDZelLewNwvLgvzl4BvEVE\nvpOWSSkLXG3b2WefXXEdrHxWvpdb2V4O5Ss1WY1FsX1p21X106o6X92HBN8L/ExV359RH8MwDKMM\nZJoNpap7RCR8KLAWuERV7xORD/nz31DVa0TkWBF5EHgRODlfcll0MQzDMMpHJmPhifvI9oEzEgmZ\nfeSMgQKIyBTgRqARaAD+swS6TDpWrlxZaRXKipVv8nIglw0O/PKVmkxfnfXTFX9L5GcBnKjRZ8jz\n+Fn0+HPNqrpDROqAm4BPqOpNiTy0HP1vhmEYBzIigpZw8bhK+lmgqju8TAOuG+vpjPoYhmEYZSCr\nscj0zRIRqRWRO3H+Fjeo6r0Z9TEMwzDKQNYxi/H2D6V+s0RV9wKvFrcWxaCIrFTVLcnI55xzzvD+\nypUrra/RMAwjwZYtW9iyZUvZ0s86ZtEDnKOqq/3xBmCfqp4fyVwEbFHV7/vjrcAKTbjLi8i/AjtV\n9QuJcBuzMAzDmCDVNmaR5ZslnZJb6awJt5jRHRn1MQzDMMpAJf0s5gCXiUgNzmh9V1V/mkUfwzAM\nozxUzM8CeBb4CzDLh+0qgS6GYRhGGcjUDRWtZ7Eat17ziSJyaELmWGCJqh4MnIJbFARgN/BxVT0c\n6AE+moxrGIZhVAcV87PQ9LVh52bUxzAMwygDFfWzCMjotWENwzCMKiKrscjkZwFjrw1rGIZhVJ6s\nA9zlWht2BOaUZxiGUZhqd8qrw31I8Gjc0oK3UPhDgj3Ahara49eGvQx4SlU/XiAPc8ozDMOYIKV2\nyqukn8Vy3DKsd4tIcMbboKoDWXQyDMMwSk/F/CxU9SYR2QQcBzyhqkeWQBfDMAyjDFTSzwLgUh/X\nMAzDqGIq5Wcx2x//HHgmow6GYRhGmamUn0VSxjAMw6hiKu5nYRiGYVQ/FfWzGC/mZ2EYhlGYA9bP\nIjq/ELgq32wo87MwDMOYOAeSnwUicgWwApghIo8C/0tVL82ik1FZBgcHueCCiwHo6zuFY445JjUc\nGD5esWIZN954+6j9ED9fmuPVI05z7txpXHXVTQD87d++kbvvfpAHH3yUXbt2smfPECJ1zJrVSnPz\nFJ55Zhft7dPYseNp/vznl2hqmsKZZ57CWWedxcaNGzn//It58cW/UFdXQ21tHXV1TSxZsohzz90w\nSsfBwUFOPvmf+dOfngOgvn6Igw5aBMAzz+xgwYJ5eeNt2HAujzzyGAsWzGbt2rdx44238+STjwN1\ndHbOGC7T7t1DzJrVxitecfC462ms+nvyyaeAPXR2dmX6P4zJT6Y3i/3BgfZmMZEbbTyyyUbx0ksv\n55FHnqSpaQpr1ryZbdueHxE/X+OTFv+oow7hF7+4i507X6KmpoFFi+Zz8snv9I3VyEYEYMOGz3Ln\nnXehegRuuZI/IVKDmyhXB7QAbYj8kdrafezZ82XgKmAAaAcage3knmFqcC46LwHNPmwXsBdo8HI7\ngXrcs8pOL7cLN0wmuOGxBv/7LDAd+Cfc880fgRd8mOCWV9nrj/f4vKf4/V3AVJ/Oc15mp9cZ3Bf3\nO3wZf+/Dgs47fVrNuF7YJ7z8Xr/V+vP4331Ak8/rRR8vyIlPb4ov/z6vW4P/bYzSqAeeQWQaUId7\nyBzy5ZnidcDH3Utt7U727p3mw54FOn3dh7p8yetQC3wFAJHT+exnP8lrX/taNmw4lwcffIg9e16i\nrm4KS5YcxLnn/iu33XYbX/zipezevZuWlhp27aodde0lDVFsDPOdc4w0ZPuDyWIsS/1mgapm2nB+\nEluBB4Az88h8xZ+/C+ieYFw9UBgYGNCmpi6FPoUeramZof39/cPnenvXaG/vGh0YGNCBgQFtaGhT\n6FFYqiLtOmfOIdrSMkc7OhZrf3+/rlu3TqFdoVNhlkKtwnSFTX6brtCiUO/lWhLnOxUaFRb7/fqU\n+FMUOhJhnQoLFJp9mq0+/ak+jWQ+QY9ahTYv3+LjTPH7yxWO8OcafZw+X/6pCvP9b7vfGv1vrcIM\nr2NNlH6jT7clked0H6fTb0GnGVG8Nl+OZp9/T1TWJi8TdJnh6z6k3ebTbFeYp7A20jXoHddNm9cj\n/D+hLtf68rf5sk3x6R0R6djh86qN0g/12O51Djr1Rf9FvcLMqO7n+bRinZv9f9yW+B9b/b76bVOU\n16ZI5zavZ43mrtF5kU5T/f4RUfhUHy/Ug9O5qalL161bpzU17T4svh5bfdodunjxq3VgYGDU/bRu\n3Trt6Fg8fN+U5h7eNKxbyLPa8G1n5jY+bFkNRS3wILAQ9xhzJ3BoQuZY4Bq/fxTwy/HG1YzGItkA\n78/zaY1/R8dif3PkbkCRFl28+EgVyYXV1c3QuroWf1OEm73H38RtCrP9zdUc3TTNUbrhRu7zjVlI\na0bKjR7feMn4oSGYpc6grPBptvoGZ6qObPg6vB7JBqVP0w1Vnw8jaqiW+/gzNWdEOvy5ED80SNNS\nGqkOHyfUT9zYtSocpiMNUWjU4uPDfNk6E/ouHU53FdN1kMN1kMN11bAh6fB6h/8rlC/ouFZzjfpC\nhXW+XKGRD/n1RXUbG8xw/TT6a2CxDw8Na18UJ1xnwUh0KQxE/3Od5hrltTrauDd6fRYrzFE4SEOj\n7PTpV1jj9xf4OEdG/31Ip1nd9dOpuQeE8FCSlJ0e/e8LvA6H+DoK10/aNdzmy9Cj0KINDR0paYd6\naNSGhllaVzdL58xZqN3dK/Le32n3e1PTnFH59/aumUCrtP+oNmPxemAgOl4PrE/IXAS8JzreCswe\nT1zNYCzGegIo3fmRbwkDAwPa3b1Ca2pmDF+gDQ1t2tAwU0c2PAN+6/QXeVoj3qcjG6z4og8334C/\nccOTW0in3zckR0RppeXTEx2nnV+qIxutYBCm+7SD/IA/nqG5hnamP2706fSoa2AGfHqLfaPQqjlj\nmHzqnurDQ9kG1DV8cV32J8KCgQ2GMK1hieWbNdcIhePQKIb/qcfLLNBVLNUXqQ+J6ovU6yqW+jLF\nbyOhsQ6Na7JBbk4cr43Kd4SPOzNxHfRputFtV9dY9yXKFhuJNf78PM09wfeoe2uLr5sun158rcX/\nTWyUQjnD20qfz2eNjrzmOvx/uVRzb0bxtTM9qrfwRpssZ3gTSbuGw/2W735ak+c/cGWsqWkf9dYR\nP/D19/drXV2rpl1TZizGZyzeCXwzOj4J+GpC5irgDdHxT4DXAGvHiqsZjEVv75qCf2ppzo+8MUXa\nfNdR8kbNd/GuifbTboC08DUJmUOiGyDc0OFJPjY0XZozKsmbJaSfbKy7NPc2ETdAoYyLNdeAx8Yw\n+RYRv21s8rIh71iftKfG0BiFJ7q0OlmcEpav0ejRkUY1aSSDAY91HdkVM8hUjSKrgg4yU0e+HcRv\nIz15dOxJHM+LyhfeIpJlzndNhAY537UWG/40Q7NJRzemsaGJ9R997bv/d15K+ZdHehzht+SDRlq9\ndWl6fS2P9A06BqNU6H5ak+c/WDxcxpqaGSO6seKHQVe34YFgZLlfLt1QWf0sdJxymQZZqtfP4hfA\n+cA6wF1+Q0MXDR87Ls4Td1u0f0oizr8A/0BuoDQfLwBP4oaE1gGbcYO3PwbagP5Eupv98f+HGzjt\nBS4BwqzlG3z8PuBg3FdaLsYNBp8fpXWR/50NfAJYAnw+cf6fCxyHsN8DX0hJN2auj3shcBruE2RJ\ndqWENeE+bBx//f5M3DPJt1PkAxcndAp6xccfTIk3Dzc4n4x7BvAH3OB3PgZ9Hi8C9+K+kDMbt9Lw\nRJiLe3FPsg13TUHuWglsxpXnNNygffL8xV6fJCOvfcf/YnT5L432n8UNwH8F+CZwug9/JCXeJ1Ly\nBOgC3oar10Nw/+c3cddtuF+S99Np/vwtKenN9LInsW/fwVxwwcUcc8wxXHDBxezceRLwPV9OyNVh\nuC+20dLSXDUD3OX2s6iUU95juHGKseICI43FeOnrO4WbblrHzp3uuKnpTPr6Livp+Z/+9H+ybx9j\nsI2Ghq3AJxkaciENDZ/k8MMPAeA3v/kkQ0Ofx130fbhqeAl3Qa5j5E0TLvrLfPhLKfkdibtp7k/V\nBf4LNyNmLm6Bwo3kboIQ/+3Af+AaP2H037KcnEG7J+V8sSzHlTFwJq6s23Gzd3bhGtPTI5mP+/DT\nE/FOBb4IvIWRDcv3cDOLYvl7vAyMNOLpXEATb2QHzf5ZaQdwAW/BNfpJ9uJmMz2ZKNtpuFlJn/Bl\n/EIU/lGcgbzTn4+vg0WJdML5YAh/lDj/cZyB/QfyG8kjvZ47Us5t83msiNJdBPwsRbY+JWwXI6/X\nV/nwLuApnJFMe5bcyehy7sBdI/NwddeIu9Z7gW8B/+jlvoCri3+ho6OVp5/ehTNukP/66gM+wMgH\ntDSDeAauvo4HTmP9+k+l6F4Zkg/Sn/nMZ0qbQZbXEpyx+R1ukLqBsQe4e8gNcI8ZVzN0Q6mWf4C7\nv7/fz9Bwr6QNDTNHdEPV1LRrd/fy4QHutLTCGIdIGFfYpHV1rdrR8Vf+dXuB5ma3rNWR/cGtmhs/\nSPYlT9fRs5jaNTdIG3c/hQHVOK3wuh/6rpNjAkGX5Zob4CzUDZUci0jrhgoziabryD7z6VEaoWzx\nuEAYAwmzcPqifEMZwrhIGFdIznQKx8mumhk6emxBdBXNOsgCHaRWV9GouS6YOG74f6bqyJlBHX6r\nj8ofd40EXWcl9GpT1+3Y6MOO0Ny4VKjLPnX9/WHMJq7D5Gy5eJJBrY6eqRXqPXSlhTGDNl+W1kRa\naQPlC6K4y3X0NRp0SM5walQ30SCMuS1QmKI1NR3ReGBcZ0u1pmaGzpnzCp02bf6ImU/9/f3Ds6He\n+ta3am3tTM2NR+W6yOJxyYGBAZ/PyP+mqWmO1tXN0mnT5meeWVVuqKYxC6cPb8N5cT+IW7wI4EPA\nhyKZr/nzdwHLCsVNSb8c9Vgy0mY9FTIw400nGdbf3z9iwF2kTRcvPkx7e9dod3e3humjU6a0eMPT\nornpna1RAxKmsSYbtTAFtV1huk8jTLkN4xJh9ktyFlSYPRNPgwxxw3TR0OjFA42hEWzV3GB2i9c1\nNPRdUSMUwlqiOMlB2Hiaak20X+sbnVDWMKU21FUwRmE672zNGax4FtNBXu94OnKTT6dNAf8bphKH\n/2G5uv7xMJY0O/pPksYizBSa57dWHWm0wjhD+F8X+3RCox6mDS+N4ocpzY1R3HB91Pu6ao50Sk7f\nja+bMA05bVpynH5c//Up4S3qxqJma+6aatfcjLTcrLJ4ADo5+SR+KBvvvTae+MmHwWqeJptG1RgL\nXCfs9bj+juuAtjxyqb4UwLuA3+Def5cVyKcM1Tg5KdYQpaXR3b1cFy9+tXZ0LNbu7uXa39+fmnZ/\nf/+Iab0NDTN1YGBA+/v7ddq0g7S2doY2NXXqnDmv0KamOVpbO1MbGlp1ypQObWrq1GnTDtLu7hXe\noCXfNtqjRiM0Url59iJxwxWe0MNU0dgYtEXphaf4adFxmNqanG4bGuZwrjGKE94KgkGp19wbQWj8\n5kf6hqfkeZqbVhwa0thwBl+QUKa0p/lQhtC4Bj2CT8w8L1Prf+dpblZZyC/UR2joQ1412tQ0V6dM\n6dCGhi6trZ2pNTWNmjOaoeGf48vTph0dM7W/v1+7u5erSJhB1aMNDW2j3pLHuj7dm/Ry7ehYrIsX\nHzlq6mryAWm8U9OLvQ/Gil+Ke65SlNpYFO3BLSKfA55U1c+JyJlAu6quT8jU+jeHt+LGLm7FfztK\nRJbi3Ey/AfSp6u158tFidTRKQ6k8Vjdu3DjsyTtrVguveMXSgp8EifNJOzeWXvnibNjwWR55ZPvw\nJzaAYc/g7dv/zBNPPIPqEF1d7cye/Vd0dnaN+EzIsmWLuP3237N79xAtLbXs2lVLe3sz06eHQeyJ\nfR5jcHCQj350PY88so0pU2pZv/6jAHzxi5cC7rMk27Y9n+q5XOhTKbfddhvnn38xO3bspL5+L4ce\neijnnvuvY/5/E/0fjOqk1B7cWYzFVmCFqj7uFzPaoqpLEzKvB85W1dX+eD2Aqp4XydyAGQvDMIyS\nUmpjkWU9iy5VfdzvP46b3pDEFj4yDMM4ACg4dVZErsdN+E5yVnygqioiaY//JXklqF4/C8MwjOqg\natez8N1QK1V1u4jMAW5I6YbqAc6JuqE2APtU9fxIxrqhDMMwSkw1dUMFd2D8749TZG4DDhaRhSLS\nALzHx0tSus/oGoZhGCUni7E4D+gVkftxbrLnAYjIXBG5GkBV9wBhcaR7gSvVr6InIu/wCx71AFeL\nyLUZdDEMwzDKSNHGQlWfBt6N+7DLQuAHItKmqttU9bhI7lrctxX2AB/002xR1f8Avg88jBsg3yki\nrcXqYxiGYZSPLG8W4D4rfr2qHgL81B+PwPtafA3nnHcYcKKIHOpPXwccrqqvwjn3bcioj2EYhlEG\nshqL43Ff4sL//l2KzOuAB1X1YXXra34fOAFAVa9X1fApvptxXwgzDMMwqoysxqKUvhYfBK7JqI9h\nGIZRBsb8RPn+8LUQkbOAIVW9PO28+VkYhmEUpmr9LKA0vhYi8gHcIg1Hq+qoBRrMz8IwDGPiVJOf\nBWT0tRCR1cAngRPSDIVhGIZRHWR9s+gAfgAchJsC+25VfVZE5uLW1z7Oy70Nty5mLXCJqp7rwx/A\nLXz0tE/yv1X1I4k87M3CMAxjglTTV2c7gCuBBUSGIkVuNTlD8a2o++mzuNlUiltf8QOq+mhKfDMW\nhmEYE6SajEXW9SymqerzXu5U4FWq+o8p+ZixMAzDmCDVNGaR1cfi+UiuBbeivWEYhlGFjDl1tgDF\n+lgcFQ5EZCPwPmAH7htRhmEYRhVS8M1CRK4XkXtStuNjubDea0oSBfuPVPUsVT0I2AR8aYK6G4Zh\nGPuJgm8Wqtqb75yIPC4isyMfiydSxP4IzI+O5+PeLpJcTgHvbXPKMwzDKEzVOuX5Ae6nVPV8v7Z2\nW8oAdx1ugPtoYBtwC7kB7oNV9QEvdyrwOlV9X0o+NsBtGIYxQappNlRWH4sfAq8E9gK/Az6sqqPe\nTsxYGIZhTJxqmg0FKWMSyfUsvEzY9kVy71TVI4HvAu/ArXdhGIZhVCFZjEXWtSwQkflAL24BpZcl\n5exjrAasfJOXA7lscOCXr9RUzM/C80XgUxl0mPQc6BeslW/yciCXDQ788pWaLMYi01oWInIC8Jiq\n3p1BB8MwDGM/UHDqbLnWshCRJuDTuC6o4eDCqhqGYRiVIstsqKLXsgCuxo1z7PCi83A+Ga9LzojK\nY4QMwzCMMaiWqbOZ/CwScr8HXqOqT2MYhmFUHVnGLM4DekXkfuAt/hgRmSsiVwOo6h7gY8AgcC9w\nZdJQeOztwTAMo4rJtPiRYRiG8fIgq1NeWRGRU0XkPhH5tYicH4VvEJEHRGSriKyqpI5ZEJE+Ednn\nveFD2KQvm4h83v9vd4nIv4tIa3Ru0pcP3KJevgwP+PVcJjUiMl9EbhCR3/j77TQf3uE/KHq/iFwn\nIm2V1rVYRKRWRO4Qkav88YFUtjYR+aG/7+4VkaNKXb6qNRYi8macL8dfq+oRwBd8+GG4dbwPwzn7\nfV1EqrYc+UhzSDxQygZcBxyuqq8C7gc2wIFTvrGcTScpu4GPq+rhuOUCPurLNKbz7STidFx3eOhO\nOZDK9mXgGlU9FPhrYCslLl8136gfBs71znyo6p99+AnAFaq6W1UfBh7EOf9NNtIcEg+Isqnq9aoa\nPu1yM262Gxwg5WNsZ9NJh6puV9U7/f4LwH04n6jxON9WPSIyDzgW+Ba5afoHStlagb9R1W+DGytW\n1b9Q4vJVs7E4GHiTiPxSRLaIyGt9+FxGfuZ82NFvslDAIXHSly2FD5L7/PyBUr68zqYHAiKyEOjG\nGfrxON9OBr4EfJLo+3QcOGVbBPxZRC4VkdtF5JsiMpUSly/LSnmZGcPprw63rnePiPwP3BduX5En\nqaobpR+jbBuAuL++0FzoqisbFCzfp1U19AmfBQyp6uUFkqrK8o3BZNR5XIhIC/Aj4HRVfV4kd2kW\ncL6takTk7cATqnqHiKxMk5msZfPUAcuAj6nqrSJyIYkup1KUr6LGYozFlT4M/LuXu9UPBHcyekGl\n4NBXVeQrm4gcgXsSuMvfiPOAX4nIUUySskHh/w5ARD6Ae+0/OgqeNOUbg/Eu6jWpEJF6nKH4rqr+\n2AePZ5GzaucNwPEiciwwBZguIt/lwCgbuGvvMVW91R//EPdAur2U5avmbqgf4/w3EJFDgAZVfRLY\nDLxXRBpEZBGuu+qWyqk5MVT116rapaqLVHUR7o9e5l8XJ3XZAiKyGvfKf4KqvhSdOiDKB9wGHCwi\nC0TkP9kAAAD9SURBVEWkATdov7nCOmVC3JPLJcC9qnphdGozsM7vr8Pdl5MKVf20qs7399t7gZ/5\nhdYmfdnAjTcBj/p2EuCtwG+Aqyhh+Sr6ZjEG3wa+LSL3AEPA+wFU9V4R+QFuVsMe4COTfHWkYd0P\noLJ9FWgArvdvT/+tqh85UMqnqntEJDibhkW90pxNJxPLgZOAu0XkDh+2Aeds+wMR+Qf8ImeVUa+k\nhGvuQCrbqcD/8Q8vvwNOxl2bJSufOeUZhmEYY1LN3VCGYRhGlWDGwjAMwxgTMxaGYRjGmJixMAzD\nMMbEjIVhGIYxJmYsDMMwjDExY2EYhmGMiRkLwzAMY0z+fwcQaqfvCFjWAAAAAElFTkSuQmCC\n",
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Q3x5CQTiZ0sikaLQShAJ8F7FRY4TKRgiFalRR9FGqXCONk+xYrx2PCt4dFkc0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\n7NNCaMV5KwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ec27310>"
+       "<matplotlib.figure.Figure at 0x7f564f074050>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "emittance x new: 2.49267329426e-06\n",
+      "emittance y new: 2.44591505198e-06\n",
+      "\n",
+      "\n",
+      "emittance z new: 1.19996898796\n"
+     ]
     }
    ],
    "source": [
-    "bunch = generators.Gaussian6DTwiss(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, beta_z, epsn_z\n",
-    "    ).generate()\n",
-    "generators.MatchRFBucket2D(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    rfsystems.get_bucket(gamma=gamma), epsn_z=epsn_z\n",
-    "    ).update(bunch)\n",
-    "plot_phase_space(bunch)"
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                     intensity=intensity, charge=e, mass=m_p,\n",
+    "                                     circumference=circumference, gamma=gamma,\n",
+    "                                     distribution_x=generators.gaussian2D(egeox),\n",
+    "                                     distribution_y=generators.gaussian2D(egeoy),\n",
+    "                                     distribution_z=generators.gaussian2D(egeoz),\n",
+    "                                     linear_matcher_x=generators.transverse_linear_matcher(inj_alpha_x, inj_beta_x),\n",
+    "                                     linear_matcher_y=generators.transverse_linear_matcher(inj_alpha_y, inj_beta_y),\n",
+    "                                     linear_matcher_z=generators.longitudinal_linear_matcher(Qs, eta, circumference)).generate()\n",
+    "\n",
+    "\n",
+    "\n",
+    "generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                             intensity=intensity, charge=e, mass=m_p,\n",
+    "                             circumference=circumference, gamma=gamma,\n",
+    "                             distribution_z=generators.RF_bucket_distribution(rfsystems2.get_bucket(gamma),epsn_z=epsn_z),\n",
+    "                             ).update(bunch_new)\n",
+    "\n",
+    "plot_phase_space(bunch_new)\n",
+    "print('emittance x new: '+ str(bunch_new.epsn_x()))\n",
+    "print('emittance y new: '+ str(bunch_new.epsn_y()))\n",
+    "\n",
+    "#print('beta x: '+ str(bunch.beta_Twiss_x()))\n",
+    "#print('beta x new: '+ str(bunch_new.beta_Twiss_x()))\n",
+    "#print('beta y: '+ str(bunch.beta_Twiss_y()))\n",
+    "#print('beta y new: '+ str(bunch_new.beta_Twiss_y()))\n",
+    "\n",
+    "#print('alpha x new: '+ str(bunch_new.alpha_Twiss_x()))\n",
+    "#print('alpha y new: '+ str(bunch_new.alpha_Twiss_y()))\n",
+    "\n",
+    "\n",
+    "#longitudinal stuff\n",
+    "print('\\n')\n",
+    "print('emittance z new: '+ str(bunch_new.epsn_z()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reiterate on non-accepted particles\n",
-      "Reiterate on non-accepted particles\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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XiNPI0GbQvRP4LJHWwB2DjDsH+FEiv7T07iR8Zf8LFp4c1AfBn/TEQeb7I2D2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\n0RDTTnJ+FVUWVk14C6sm/AuOfEIi3wf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83BvnSWyOxwa8GAFUXXs634rsOqrKrvBGBZMrrWS6yp/+0xN53lbT/AsvlGG/ByUzK7aV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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f578ec27e10>"
+       "<matplotlib.figure.Figure at 0x7f564fa2ae10>"
       ]
      },
      "metadata": {},
@@ -390,141 +641,73 @@
     }
    ],
    "source": [
-    "sigma_z = np.sqrt(beta_z * e * epsn_z / (4. * np.pi * p0))\n",
-    "sigma_dp = sigma_z / beta_z\n",
-    "\n",
-    "bunch = generators.CutRFBucket6D( # implicitly tests CutRFBucket2D as well.\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    transverse, epsn_x, epsn_y, sigma_z, sigma_dp,\n",
-    "    rfsystems.get_bucket(gamma=gamma).make_is_accepted(0.6)\n",
-    "    ).generate()\n",
-    "plot_phase_space(bunch)"
+    "plt.scatter(bunch_new.z, bunch_new.dp)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test passed.\n",
-      "\n",
-      "Error message:\n",
-      "lengths of given phase space coordinate arrays do not coincide with self.macroparticlenumber.\n"
-     ]
-    }
-   ],
-   "source": [
-    "coords_n_momenta_dict = bunch.get_coords_n_momenta_dict()\n",
-    "coords_n_momenta_dict['x'] = np.append(coords_n_momenta_dict['x'], 0)\n",
-    "try:\n",
-    "    bunch = generators.ImportDistribution(\n",
-    "        n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "        coords_n_momenta_dict\n",
-    "        ).generate()\n",
-    "    print ('test FAILED. Expected ValueError was not raised!')\n",
-    "except ValueError as exc:\n",
-    "    print ('test passed.\\n')\n",
-    "    print ('Error message:\\n' + exc.message)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+     "data": {
+      "image/png": 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cDNIKPQItQ6BPwks/h6mnQ2MLDP4ZBnpgygzofhGaW6H+FbBzCKYOguyCbbdD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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564edba950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test I passed.\n",
-      "Error message:\n",
-      "One or more of the specified coordinates or momenta already exist and cannot be added. Use self.update(...) for this purpose.\n",
+      "emittance x new: 2.54820001855e-06\n",
+      "emittance y new: 2.53093525581e-06\n",
       "\n",
-      "Test II passed.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Test particles.add method.\n",
-    "# Test I: Should not be allowed to overwrite existing coords with .add method.\n",
-    "# Test II: Should be able to add 'new' coords.\n",
-    "bunch = generators.Gaussian6DTwiss(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, \n",
-    "    beta_z, epsn_z\n",
-    "    ).generate()\n",
-    "x = np.random.randn(bunch.macroparticlenumber)\n",
-    "xp = np.random.randn(bunch.macroparticlenumber)\n",
-    "xx = np.random.randn(bunch.macroparticlenumber)\n",
-    "xxp = np.random.randn(bunch.macroparticlenumber)\n",
-    "coords_momenta_dict_to_add = {'x': x, 'xp': xp, 'xx': xx, 'xxp': xxp}\n",
-    "\n",
-    "# Try to add coords_n_momenta that already exist in the bunch.\n",
-    "try:\n",
-    "    bunch.add(coords_momenta_dict_to_add)\n",
-    "    print ('Test FAILED. Expected ValueError was not raised!')\n",
-    "except ValueError as exc:\n",
-    "    print ('Test I passed.')\n",
-    "    print ('Error message:\\n' + exc.message)\n",
-    "    print ('')\n",
-    "    \n",
-    "# Add coords_n_momenta which are new to the bunch. Should not give error message.\n",
-    "try:\n",
-    "    bunch.add({'xx': xx, 'xxp': xxp})\n",
-    "    bunch.xx\n",
-    "    bunch.xxp\n",
-    "    print ('Test II passed.\\n')\n",
-    "except Exception as exc:\n",
-    "    print ('Test II FAILED. No error expected, but got message:\\n' + exc.message)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test passed.\n",
-      "\n"
+      "\n",
+      "emittance z new: 1.21270337756\n"
      ]
     }
    ],
    "source": [
-    "# Test particles.update method.\n",
-    "bunch = generators.Gaussian6DTwiss(\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, \n",
-    "    beta_z, epsn_z\n",
-    "    ).generate()\n",
-    "x = np.random.randn(bunch.macroparticlenumber)\n",
-    "xp = np.random.randn(bunch.macroparticlenumber)\n",
-    "xx = np.random.randn(bunch.macroparticlenumber)\n",
-    "xxp = np.random.randn(bunch.macroparticlenumber)\n",
-    "coords_momenta_dict_to_update = {'x': x, 'xp': xp, 'xx': xx, 'xxp': xxp}\n",
-    "\n",
-    "try:\n",
-    "    bunch.update(coords_momenta_dict_to_update)\n",
-    "    bunch.xx\n",
-    "    bunch.xxp\n",
-    "    print ('Test passed.\\n')\n",
-    "except Exception as exc:\n",
-    "    print ('Test FAILED. No error expected, but got message:\\n' + exc.message)    "
+    "sigma_z = np.sqrt(beta_z * e * epsn_z / (4. * np.pi * p0))\n",
+    "sigma_dp = sigma_z / beta_z\n",
+    "\n",
+    "\n",
+    "bunch_new = generators.ParticleGenerator(macroparticlenumber=n_particles,\n",
+    "                                         intensity=intensity, charge=e, mass=m_p,\n",
+    "                                         circumference=circumference, gamma=gamma,\n",
+    "                                         distribution_x=generators.gaussian2D(egeox),\n",
+    "                                         distribution_y=generators.gaussian2D(egeoy),\n",
+    "                                         distribution_z=generators.cut_distribution(\n",
+    "                                                            generators.gaussian2D_asymmetrical(sigma_z, sigma_dp),\n",
+    "                                                            rfsystems.get_bucket(gamma).make_is_accepted(0.)),\n",
+    "                                         linear_matcher_x=generators.transverse_linear_matcher(inj_alpha_x, inj_beta_x),\n",
+    "                                         linear_matcher_y=generators.transverse_linear_matcher(inj_alpha_y, inj_beta_y),\n",
+    "                                         ).generate()\n",
+    "\n",
+    "plot_phase_space(bunch_new)\n",
+    "print('emittance x new: '+ str(bunch_new.epsn_x()))\n",
+    "print('emittance y new: '+ str(bunch_new.epsn_y()))\n",
+    "\n",
+    "#print('beta x: '+ str(bunch.beta_Twiss_x()))\n",
+    "#print('beta x new: '+ str(bunch_new.beta_Twiss_x()))\n",
+    "#print('beta y: '+ str(bunch.beta_Twiss_y()))\n",
+    "#print('beta y new: '+ str(bunch_new.beta_Twiss_y()))\n",
+    "\n",
+    "#print('alpha x new: '+ str(bunch_new.alpha_Twiss_x()))\n",
+    "#print('alpha y new: '+ str(bunch_new.alpha_Twiss_y()))\n",
+    "\n",
+    "\n",
+    "#longitudinal stuff\n",
+    "print('\\n')\n",
+    "print('emittance z new: '+ str(bunch_new.epsn_z()))"
    ]
   },
   {
@@ -553,7 +736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/MonitorTest.ipynb b/testing/interactive-tests/MonitorTest.ipynb
index 39d4c5ea..bdb14f27 100644
--- a/testing/interactive-tests/MonitorTest.ipynb
+++ b/testing/interactive-tests/MonitorTest.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-122-g056083c437-dirty\n",
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
       "\n",
       "\n"
      ]
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -130,12 +130,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) # WITH OR WITHOUT 4 PIjQuery202047649151738733053_1414145430832?\n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    print bunch.sigma_z()\n",
     "    \n",
     "    return bunch"
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -206,7 +206,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0621143131729\n"
+      "0.0598522977141\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-39c3aff5b0d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mslice_monitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbunch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0mslice_set_pmon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbunch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munibin_slicer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0marrays_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'slidx'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice_set_pmon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_index_of_particle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'zz'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbunch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mparticle_monitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbunch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marrays_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -290,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/PyCUDA_longitudinal.ipynb b/testing/interactive-tests/PyCUDA_longitudinal.ipynb
index 64b14d19..6d6d0921 100644
--- a/testing/interactive-tests/PyCUDA_longitudinal.ipynb
+++ b/testing/interactive-tests/PyCUDA_longitudinal.ipynb
@@ -25,7 +25,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-87-g2d87c2cf6b\n",
+      "PyHEADTAIL v1.3.0-10-ge651b0b162-dirty\n",
       "\n",
       "\n"
      ]
@@ -39,7 +39,7 @@
     "from scipy.constants import c, e, m_p, epsilon_0\n",
     "import numpy as np\n",
     "\n",
-    "from PyHEADTAIL.particles.generators import Gaussian6D\n",
+    "from PyHEADTAIL.particles.generators import ParticleGenerator, gaussian2D, gaussian2D_asymmetrical\n",
     "import PyHEADTAIL.trackers.simple_long_tracking as long_tracking\n",
     "from PyHEADTAIL.particles.slicing import UniformBinSlicer\n",
     "from PyHEADTAIL.spacecharge.spacecharge import LongSpaceCharge\n",
@@ -109,8 +109,10 @@
    "outputs": [],
    "source": [
     "def make_bunch(macroparticlenumber):\n",
-    "    return Gaussian6D(macroparticlenumber, intensity, e, m_p, circ, gamma, \n",
-    "                      sigx, sigxp, sigy, sigyp, sigz, sigdp).generate()\n",
+    "    return ParticleGenerator(macroparticlenumber, intensity, e, m_p, circ, gamma,\n",
+    "                             distribution_x=gaussian2D_asymmetrical(sigx, sigxp),\n",
+    "                             distribution_y=gaussian2D_asymmetrical(sigy, sigyp),\n",
+    "                             distribution_z=gaussian2D_asymmetrical(sigz, sigdp)).generate()\n",
     "\n",
     "longitudinal_map = long_tracking.RFSystems(circ, h, V, phi, [alpha], gamma)\n",
     "slicer = UniformBinSlicer(n_slices, n_sigma_z=3)\n",
@@ -170,7 +172,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 131 ms per loop\n"
+      "10 loops, best of 3: 155 ms per loop\n"
      ]
     }
    ],
@@ -189,7 +191,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 58.6 ms per loop\n"
+      "10 loops, best of 3: 54.1 ms per loop\n"
      ]
     }
    ],
@@ -217,6 +219,8 @@
     "\n",
     "bunch = make_bunch(macroparticlenumber)\n",
     "\n",
+    "\n",
+    "\n",
     "bunch.z = gpuarray.to_gpu(bunch.z.astype(np.float64))\n",
     "bunch.dp = gpuarray.to_gpu(bunch.dp.astype(np.float64))\n",
     "\n",
@@ -255,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -264,17 +268,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 17.3 ms per loop\n"
+      "The slowest run took 37.09 times longer than the fastest. This could mean that an intermediate result is being cached \n",
+      "1 loops, best of 3: 23.5 ms per loop\n"
      ]
     }
    ],
    "source": [
+    "bunch.x = gpuarray.to_gpu(bunch.x.astype(np.float64))\n",
+    "bunch.y = gpuarray.to_gpu(bunch.y.astype(np.float64))\n",
     "%timeit longitudinal_map.track(bunch)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -283,11 +290,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 15.6 ms per loop\n"
+      "The slowest run took 8.28 times longer than the fastest. This could mean that an intermediate result is being cached \n",
+      "1 loops, best of 3: 45.6 ms per loop\n"
      ]
     }
    ],
    "source": [
+    "bunch.x = bunch.x.get() # cobra_functions cannot handle gpuarrays...\n",
+    "bunch.y = bunch.y.get() # cobra_functions cannot handle gpuarrays...\n",
     "%timeit long_sc.track(bunch)"
    ]
   },
@@ -317,7 +327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/RFBucketHamiltonianDeformation.ipynb b/testing/interactive-tests/RFBucketHamiltonianDeformation.ipynb
index ef9b5836..47330675 100644
--- a/testing/interactive-tests/RFBucketHamiltonianDeformation.ipynb
+++ b/testing/interactive-tests/RFBucketHamiltonianDeformation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -11,9 +11,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-82-geaa287af3e\n",
-      "\n",
-      "\n"
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
      ]
     }
    ],
@@ -51,7 +50,7 @@
     "sys.path.append(\"../../../\")\n",
     "\n",
     "# from PyHEADTAIL.particles.particles import Particles\n",
-    "from PyHEADTAIL.particles.generators import MatchRFBucket2D, Gaussian\n",
+    "from PyHEADTAIL.particles.generators import ParticleGenerator, RF_bucket_distribution, gaussian2D, gaussian2D_asymmetrical\n",
     "from PyHEADTAIL.particles.slicing import SliceSet, UniformBinSlicer\n",
     "from PyHEADTAIL.trackers.simple_long_tracking import RFSystems\n",
     "from PyHEADTAIL.trackers.rf_bucket import RFBucket\n",
@@ -69,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -108,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -129,16 +128,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ia9myZdghlEtJ8a9cCV98AXPm2LZgAaxeDb/8Aps3Q6VKULky7LYb7Lkn7L03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\nfMJdskSkFtAfGI11bDvnsDe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2e6ztsQ0It2Pk4ajbAUeQ+pB+bPNsuVHVR2I94FzgJODnMTIqhCSvuzM8P/0Z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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f95e23bf7d0>"
+       "<matplotlib.figure.Figure at 0x105283b10>"
       ]
      },
      "metadata": {},
@@ -146,9 +145,9 @@
     },
     {
      "data": {
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YiAfe9HE/StWdrs0UdAo/W8Dq8/OoOpRIbNeDRI79AKKS7Y4VsE641IaIZACP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\nhFJKqVr9H34dl++AhisWAAAAAElFTkSuQmCC\n",
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H6rQbiiMvtuwO/AA8CgQCU4Gu/lwc7KQFQtUppjTwJ0wA+eltr3A6i3JOauI4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\nJRX/ZVuZOAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f95e230f3d0>"
+       "<matplotlib.figure.Figure at 0x10575ce90>"
       ]
      },
      "metadata": {},
@@ -196,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -205,10 +204,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "beam.mean_z    = 6.031100e-02\n",
+      "beam.mean_z    = 6.043679e-02\n",
       "rfbucket.z_sfp = 6.015758e-02\n",
       "\n",
-      "beam.epsn_z    = 1.249137e-01\n",
+      "beam.epsn_z    = 1.253972e-01\n",
       "nominal epsn_z = 1.252499e-01\n"
      ]
     }
@@ -222,8 +221,14 @@
     "p_increment = dp_increment * p0\n",
     "\n",
     "slicer = UniformBinSlicer(n_slices, n_sigma_z=4)\n",
-    "beam = Gaussian(macroparticlenumber, intensity, e, m_p, circ, gamma, \n",
-    "                {'z': sigz, 'dp': sigdp, 'x': sigx, 'y': sigy}).generate()\n",
+    "# beam = Gaussian(macroparticlenumber, intensity, e, m_p, circ, gamma, \n",
+    "#                 {'z': sigz, 'dp': sigdp, 'x': sigx, 'y': sigy}).generate()\n",
+    "\n",
+    "beam = ParticleGenerator(macroparticlenumber, intensity, e, m_p, circ, gamma, \n",
+    "                        distribution_x=gaussian2D(sigx),\n",
+    "                        distribution_y=gaussian2D(sigy),\n",
+    "                        distribution_z=gaussian2D_asymmetrical(sigz, sigdp)).generate()\n",
+    "\n",
     "\n",
     "long_sc = LongSpaceCharge(slicer, 5e-2, circ)\n",
     "\n",
@@ -249,16 +254,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DA7QkiivFE/H4K51J43vz5Zdw9932VqfJT5YuhV69IDQU6tc38qLCU9jz4EbU\nPsVVryiCx6zh0xcm07O7Gxs3gotefHII9BqhjVBKeSmlApVSgUAxwN+crmW+308pdVgpVQlARGKB\n74DxSqkHlFINgR/Ndafb6TU0d4CI8OLnS0j1PkOUeyopM93o3aM3a9dqI1gUuOsuY8NMhhEEKBPg\nQfsN7TnkVQrfhDIc2nSIh6d8zJYtEB5uL6Wa3KINofXcA+wyXxWAV82fZ5jvewN1uHnddTgwC5gJ\n7ABqAV1F5KKtxVnOpzsDzqZ3TfAaHpsynpAl9RjbawYeS1N48uluwM0xKh0FZ+tfcHzNNWtCcPCN\ndIZet1JubHqgGbtfKMsWHy/WbLkEwM7dqXZQmT2O3r+ZKUi92hBaiYiEiIiL+XK1+NzJfH+2Of+0\nRZ00ERkhIv4i4iki7UVkl/3eQpMXUtNTGbt2PH+8/xJu98xiwQcv8UDXB+wtS+NA1Gvkwm/7G5P4\nw9+4n+wDxaIZOW8+SalJ9pamsQK9RuhE6DXCgicpNYlHf3+UpetPo35fSPCOc3QM6GhvWRoH448/\n4NFHoUQJSEyEYi1/JjnOk47vfsOixxfh4+mDyQSrV0OXLo45i1CY0WuEGk0eiY6J5vN+n7NmwwlK\nnOvFgz28tRHUZEnGuuF48/a5ae+1w/VSc66su8LvTf7kuUei6dYNunWDU6fsp1OTNdoQFhL0/L9t\n2LzZCJd1IfICC9osoP3S9oyY/iumlZ15pGc5e8uzGkft39vhbJot9d51F8yfDy+9BG++Cf171aDE\nteq8s2IEdY7XoteiPTxQL5z77oO9e+2v1xnQa4QajR24dg3atYM1646xpPUS6obWJaZkHH+WqEWz\nZq506WJvhRpHxdUVHn8cPD1h8mRwd4eg+1y58tL97HPxomw6NP7uOD3SDrFnt17acDT0GqETodcI\n85cDB6Bz42tM9FlG9WhfostEw2et+PwnfzZssLc6jbPx7bcwYgT06Z1M06ivabm8JQCbq5bh/dNN\n7ayuaKHXCDUaK/lx5Sb6cY7q0b5cqXyFDls7cNHkT7169lamcUZ69ID4eBj+bjHe/OdNVr+9mgT3\nJP6KcmPqtqn2lqexQBvCQoKe/889aWkwdCjExgr3PryTyYtWMdMbFpf34tNivXlvki/79hkbIRxB\nb25wNr3gfJpz0luzJuzfD4GB4OHqwSeff8K2edvZbirHayMiaTvoL66lpBWMWApf/9oSbQg1RZZd\nu+Cbb6DOLG+MAAAgAElEQVReh/1sXtgCdr5Eh96X+SryHmo08SAsDGbMQI8INXmmUaMbn12UC588\n/BGfL9iES2wNNv3Rgg5j3uPspRj7CdQAeo3QqdBrhLbl/U+i+GrGZa6dq4PrXf+QHtqLDRtgwABY\nssTw9WrcGA4dgjp17K1WU5jYeHojXZ/bRlJsMVwOPMvMx87w+IfVKVG7hL2lFUr0GqENUUrdr5Ta\no5S6ppQKU0q9ZUWdcKWUKdO1riD0arJGBGZO2kH5iXvwaPEJ5Z95izW/16Z4cWMb/NGjxs/69eH4\ncW0ENbanbbW2zHrncdjxCg28d1BtzkU2N9lK5G+RALz8Mpw/Dz/9BDF6wJjvaENoJUqpu4HFwFKg\nKTAaI6D24ByqCjABqGhx9bG1Pj3/bx3p6elMfeoPqr+bQLNExciYlhye/hEdGtbn9Gnw9b35xIDq\n1e2rN684m15wPs13qvehrpWoXMVEjVdXsL7yCVyTFIeeOMTeAaHM+i6dkBB44w3YtMkmcotc/+YG\nbQit521gq4iMEpEjIvITMAV4z4q6CSISaXFF569UTVacO36OOU3n0Gi+H+4IoR0u89bSIZTxLANA\nOefxl9cUAtzdITzMhcUfjKPM7/F8VTGeFJd0ouZcZCq7+HdmIjExcOSIvZUWfvQaoZUopU5hnEE4\n1iKvM7AS4/Dd89nUCwOKA+7ABWA18ImIXM2DBr1GmEcWb1iPKSidMukQ7aK4+JYrgz9vZ29ZGs11\nxn13mF/+F8nIyERKpRRnkKkN0S7FGDzY8EnU5B29Rmg7KmIYMksy0v63qTcFeBroiDGd2h3YpJQq\nbmuBmltJTkvmvVXv0W/SHFa5ebFNeTP17ua88Jk2ghrH4u0B9YmIbcdrri15r/NcrlKMik0OEHq4\n4FwsiiraEOYzIvKliKwRkYMi8gfQE+Pcwn62fI6e/7+VVfv3UqP/RD7b+Bly6BEi3zrBG9casXJr\nKVxdc9eW7t/8x9k021qvpyds2+rCoiVleO3TQJTfUc7XGseG3ZH8d2ALJhNs3GicbuEIevObgtTr\nlnMRjZkIbh35VbC4ZxUiEqaUigSq51XIwIEDCQgIAMDHx4fAwMDr9zJ+eYKCghw6nZ9648Pi2Vh+\nPxOGdIKz91LhmebEX+zMrFFubN7keHrzI+1senXaSJ8/H4JS8FqrV2m+4TD9p20nLHY/PVo1o2Sx\naFpHLaf2gHJMm93ZIfQ6cjokJITZs2djDXqN0EqUUnOB6iLSziJvIvCIiNTMRTtVgFPAsyIyN5ca\n9Brhbbh2+hpbXtsCS+BNXy/21tpGddN91C9fDy9PV/78094KNZrckZyWTLX6V7lcbTqPJcYxeGtv\nLrt40O6PulTs52dveU6DXiO0HZOBlkqpsUqp+kqpAcBrGK4RACil+imlDiul/M3p1kqpYUqp5kqp\n6kqp7sASIBz4yw7vUCg4eBC+//5GOu5cKot6H2JD7Q2wBJKVUMf3DKvnN2Dokw34719XHn3Ufno1\nmrxSzK0Yodv82Tm3HzH3hxJaORQ/UwqHHzrA9oe3kxKZYm+JhQJtCK1ERHYAfYFewB6MjS8jRcTi\nKxlvjPU/d3M6GWMtcCVwBJgKbALaiEgeZ/qzJvOUmKOTV70REUYw4/feg/R0uBxymS21NuDzTyRu\nqW6sdivJh40qMXPP/XSqeR9duxprL7162UevvXA2veB8mgtKr68vBPo35e8P/iZxXiLf3buUJBQJ\nCxNYWWUzi2dY542l+zd7tCHMBSKyTEQCRaS4iNQQka8y3Z8tIq4ictqc3i0ibUWkrLlObRF5VUQi\n7fMGzs/778NTT0GlSsIH322m+dSviU9PY5vvZb4ftoDWIX5s21cPby9jU26TJhAeDiVL2le3RnOn\nuLm48U77d5i4bCjvNyrB9vJnOWkqxlPfbGD8gn+5fMVkb4lOi14jdCKK+hrhjh3QuzfMXrWZ5944\nS8T67pDmSbkypxj6RQQfPNvu+lqARlOYmTgRVm26wPZ/PYlWgCi8ahzi54WR9L2rN0opIiPBy8u4\nijo5rRFqQ+hEFFVDeO3UNcI/O8PHS4qxr+dcDlceCUd7opbMonmbGPYF1yE6WlFCxyvWFBHWroXu\n3eGeloLJ5xihxWYTtaML+JyiXtBuJlbqy98hranZtgQjR9pbrf3Rm2WKCIVx/j/+QDxb+x1iY8AW\nLkw7R7dLRzjsPxLvYt58OOgeTh335Kcv6zJoUP4bwcLYv46Gs2m2p94WLSA1FZoFKjb+XZfz8z5i\n4o9HKCZliJgxDPf3TTyxbBsuX+4ianuU3fXmhYLUq/0INXZnwwbYtg3efttIJ0WksL7bfjwOxJlL\nCCsbrmVp10V82vlThrYcindxb+NWeZg2zS6yNRq7UbIkNGxoHPoLUNytOMPve5lXtiXw1qhVbPjO\nl07x6bS+EsvelnuJbxJPXJ84CLKrbIdFT406EYV1anT4cPj8c/jnHzgde5K3B1Xkh+SdlJF0lvsk\nsrLDep4b7suQu4dQqlgpe8vVaByCdeuMg399fW/Oj4iASpWgdf0ztIw4Tde4ZEqaXFjTdA2nRp9i\nSIshtK7SOsv19ORkY6RZ2DaX6TXCQkRhM4SmNBOSIvR4KI2E4kfZvdODayWOwF0LqVNuCxd/2YIU\n92TzJlca3qUnLzQaa6ld23Azuv8BE1O/D6fkuhgOtPqSQxXDYdcgVOjj1Gtxnjk/FqeRhy/u5dxx\nK+XGp58a53H+/LO938C26DXCIoIjz/+fOQOnTxufJV2IXhfND/1+YE2FNXz6zHhWbT3P5qoPcy3R\nBcI682J/P34bM4+gbj6QXoy76tnfCDpy/2aFs+kF59PsyHofegg6dYL7e7qw5M+ahFZuxpmNA/Fa\nvBS3mPrIm1U47DaPlu3i+an7QtaVWc+6VttJmXWKo4tjSb1mf1cMvUaoKVQMHw5H1ybyRZsDXFsR\nj2eCC3HE4YYbxf5rDSkV6NmqHv6l40g4WZzvH/kcgC5dIDb25oNyNRpNzkyceOOziwssWAC7d7vS\nu3dp0mlO8NkfmHfPPP6alk7Cum5IejKmbQl0JozOhBHi48KaR1pQr4sXAwdCSoqxjl+9OlStarfX\nyjf01KgTYe+p0dRU4zBRazl9NYI1x9fz0n29qNXkZ6ZtqQfAOTfYUDKNjckNOZLuR516igP7jL/J\n0tO5fjJEYiJcvAg1atj6TTQaDUBcchyLjyzmrw1/c3XYaJqrqzQ3xeF9zYun79tD8s6BfLFsIdt/\n7sumNd5cvar4amg8T3/ghXJ1Hp9dvUZYiLC3IezcGZ5+Gp5/3khv2gQ1q5koGZXI/j+iid9/hpST\nV/mm/3r2Ru4h4s934Epd8AmHgUEMWDyZhEBPer9WmrkfPIKfrzteXpCQAHNzFX5co9HYmu9/SGbH\nyWOkN/uO9Tu3cqz4Dpi/EDzi4Nj9VBjRiSD1AEM+7k66J5RpVxqfdmVxDfSmSrdSuBbP5dlmBYg2\nhDZCKXU/MB6oj3Hs0v+JyOQc6rgD44D+gA+wE3hDRHblUUO2hjAkJOT6UST5wZkzxgJ8zZrCmq2R\nTJ5+mervXqOeisMtk5xnStTgbLoPrpX2Ub3pKdreXZphLwTQuHzj67+Qa9aE0K5dELGxEB8P5lOl\nHJb87l9b42x6wfk0F2a9IsKByAMsWLeHeT+WJbLiXOICfuWuM3cx6rdxVI4vc1P5+DqJRP3oQdTh\nJrw2yA8XZaxnWM7w5KfenMjJEOo1QitQSt0NLAYmAo8DrYHvlFKJIjL9NlUnYRjBgUAY8C6wSil1\nl4hctKXGPXv23PEvTUwMeLmmE3M4mvP7znPl4BXijsSxNPAyq7ZXotQ9iRwNC6BSq3A425pvy63E\n5VI1zrmnc6x4GifLpLH/UnNaDwjn1VcSaVL5XnzLtM/yWfv27aFTpyD8/MDPCU6TsUX/FiTOphec\nT3Nh1quUonGFxjR+tDGfPgom6cGhS++z8fRGQvosY9diL7xXt6Wx60WanKvP3qhUpjxSGWLceWfO\nZKo2PEuZlKa4L+7E8I67KN3QiwqNKlCtWTW8a3mjXHKeVi3I/tWG0DreBraKyChz+ohSqiHwHpCl\nIVRKlQYGA0NF5B9z3nPAOWAI8IktBUZHZx+BPt2UTlRcFJcjLnP1wlUiXGIZ+WYzHh6xHOV7gnMx\nFzhzKZagoSNonx5zU91SlGJvSE1O+EZB94+gTRjFD7xC/ScWc6KqH/tO+2NKrMd7r5elRpWMoIaZ\nzy/OnV5HROvNf5xNc1HS66JcaFS+EY3KN2Iwg6EPJKUmse/iPlbtPkrwtwHUKbOJ6IDBXFoxkLBl\n/QiLrkF/wii70AcWwlXzf8mu6fwdeIxjbyykYsmKlCvuz4WtHejSK4YTSxpyf9dr+FUuw/lL50k3\npePqkv9TrtoQWkdbYEamvP+AYUqpSiJyPos6LYBiwPKMDBExKaVWAu2yKG81/zwUhgcmfEuaOB5q\notLzFTlxAkwmUAp+/x327oW2J3aRsPwEXklelEi5EYNsatVEjl525X+feMG5FyC+IpS8SGvvnaRG\neXO+RAJnr/kT4SlE+6XS85N/eS3Qn9q+/0dt39qULVH2TuRrNJpCgKe7J62qtKJVlVaM6g3QHniZ\n6I+iOXTpEMevhHDoT2HWN82pW2Uv3hGl8I/3oGxyca7ub8vKNfOgwhLYNQhWd+PX/zvFoJg0UsZE\ncZ4kLnCBJeM3EVcsmTLveNLt47asXQutWkHp0hAXB3PfvEp9YqnZwJVUV1c8vV1w9VSUal6KEnWt\nj7uo1witQCmVDLwqIjMt8hoC+4F7RGRnFnWeAn4BPEQkzSJ/EtBTRBrlQYf+x9JoNJo8oh3qNRqN\nRqPJAj01ah0R3LrwVcHiXnZ1ACoCZzPVy2oqNUey+2tGo9FoNHlHjwitYyPQPVNeDyA8m/VBMFwl\nks3lAFBKuQBdgA35IVKj0Wg0uUcbQuuYDLRUSo1VStVXSg0AXgMmZBRQSvVTSh1WSlUCEJFY4Dtg\nvFLqAfOa4o8YG2hu53Kh0Wg0mgJET41agYjsUEr1xXCoH4Yx7TlSRL63KOYN1OHmPh0OpAAzMRzq\ndwBdbe1DqNFoNJq8o3eNajQajaZIo6dGNRqNRlOk0YZQo9FoNEUabQg1Go1GU6TRhlCj0Wg0RRpt\nCDUajUZTpNGGUKPRaDRFGm0INRqNRlOk0YZQo9FoNEUabQg1Go1GU6SxuSFUSt2vlNqjlLqmlApT\nSr1lRR13pdREpdR5pVSiUmq9Uqp5FuXeVUqdMre9SynVNYsyA5VSR8xlQs3nAuZao1KqlVJqk1Iq\nyaxrvDlodsb9B5VSy5RSEUqpBKXUAaXU61m046+UWqCUijFf85RS5XLqE41Go9EUDDY1hEqpu4HF\nwFKgKTAaI+j04ByqTgKeB14C7gFOAquUUhlHHaGUetPc3ihz2yuBJUqpxhZl+mLE9fwWaGL+PEcp\nZXkCRI4alVJVze2HAs2Bl4HBwDgLzR0xTqXoCzQ0v8P/lFLvWrTjAvwDVMc4daIbUBdYlEN/aDQa\njaagEBGbXcCvwIZMeROBsNvUKQ0kAS9Y5LlgBLb+2JxWwDlgbKa624BZFulNwC+ZyiwAgnOjESO4\n9ulMZV4B4gHP27zL18AOi3Q3wATUschrYM7raMu+15e+9KUvfeXtsvXUaFtgeaa8/4DqGccTZUEL\njKOJrtcTERPGiKydOSsA42DczG0vzyijlPIA7s7m+a2VUhmH2lqjsS2wIosyJYBm2bwHQBkMY5lB\nW+CkiByzeLdDGAf1tkOj0Wg0dsfWxzBVBC5kystI+5P1yez+mcplcBFjWjKnMhn3/DDeJ6vnFwN8\ngStWaqwIrL9NmVtQSgUBTwD9LLL9s3hWRlsVs2rndiil9FEhGo1Gk0dERGWVr3eN2gClVGvgL4yp\n3KWZb9tBkkaj0WisxNaGMIJbR0wVLO5lVwduHSFV4MYI0poyl4G0bMokA1dzodHq9zCPBFcAn4nI\n/zLVichCT8Z7ZNcfOZLVHPeAAQPsPs+em0vr1XqdXbPW6zx6c8LWhnAj0D1TXg8gXESymhYF2Ilh\nqCx3drpg7LLcYM4KxzB4PTLV7ZFRRkRSgO3ZlNksN3rDGo0bga4W64oZZRKA3RY6H8DYffqxiEzI\n4t02ADWUUrUt6jQAqli8m0aj0WjsiK0N4WSgpVJqrFKqvlJqAPAacN1IKKX6KaUOZ2xMEZFY4DsM\nF4YHlFINgR8x1vWmm8sIhnvCW0qpp81tTwAam5+ZwUTgcaXU60qpekqptzHW7D7LjUZgGuANzFBK\nNVRK9QHGAFNEJMn8Ho9iTIdOAn5VSlU0X5Y+gquAXcAvSql7lFKtgDkYhnldnno4GwICAmzZXL6j\n9eYvzqYXnE+z1pu/FKheWw9ngfuBPcA1IAx4M9P9gUA6UM0izw3DWEVguFKsB5pn0fa7wClz27uA\nrlmUGQAcwRhlhgJP5VajuUwrjJFhklnXOEBZ3A82v4cp03UyUzsVMVw4YoEYYB7gl8e+FeOf7FaC\ng4OzzHdUtN78xdn0ijifZq03f7GlXovvziy/W229axQRWQYsu8392cDsTHlpwAjzdbu2J2KM+m5X\n5ifgpzvRaC6zFcP9Ibv7992uvkW5C8Bj1pTVaDQaTcGjd41qNBqNpkijxIodNRrHIMOPUP+baTQa\njfVk7HuUgvIjLEJBtysqpeaag22nKqVWZtFGgFLKlMU1Jqc+0TgWqanQty/8+qu9lWg0Glujg27n\nPeh2MYxINV9g7A693TCtD8ammYzrs9uUzRMhISG2bjJfcTa9w4eHcP48vPsubNpkbzU542z9C86n\nWevNXwpSr603y7wNbBWRUeb0EbM7xHuYXSEyo5QqjWFkhorIP+a85zCCbA8BPjH78w0HvhSRX8xV\nRyil7jM/8zlz3rvAfBH52pz+wuyyMIIb8UWt0fgyEC0ig8zpUKXUh8BEpdQYEUkSkVPA62a9HYHK\nt+mXKBGJvM19jQMjYowE//0XVq2CWbPg3nvtrUqj0dgKm64RKqVOATNEZKxFXmeM0VUVycKp3mzM\nVmO4U5y1yJ8D+ItIV6VUDeAE0EFENliUGQM8KSJ1zEG344HnLYwlSqlBwDdACRERazQqpdYCx0Tk\nBYsytYBjQDsRuWlMoJSaDVQWka6Z8gMwRrdnMAJ2h2OcfjHFvFM2V+g1QvsQFgZt28K5c3D+PDRu\nDGfPQokS9lam0WisoaDXCHMKaJ0V1gTUtlXQbWs15uU9siIOYyT7ONAZw5n+Y4yAARonYeNGwxAq\nBZUrG4Zw7Vp7q9JoNLbC5n6EmhuISMYaYgb7lFKxwCyl1AgRyVO80YEDB16PuuDj40NgYCAAQUFB\n1+fVg4KCABw27Ux6FyyAatUADL01a8KaNUH07OkY+rJKZ+Q5ih5r0pm121uP1uvcekNCQpg9ezZW\nkZ2nfV4ujKm/DzPldcKIuFIpmzr3me9XyZQ/B1hh/lzDXKZdpjJjgKPmzx5ACtA/U5nnMaLDKGs1\nAiHAD5nK1DSXuTeLd5gNrLSyj+qb22mdh/7VkWXsQOPGItOmBV9Pr18v0ry57Z9z9KjI1q0iJtOd\nt+VM/ZuBs2nWevOXgowsY2tDOJesT38/eZs62Z1Qfx74yJxWGIfZZnVC/Y8W6Y1kfUL9mtxoxNgd\nejrDeJrzXsGY6rzlhHqzIVxhZR89lZXht7JutoZQkz+kpIgUKyaSlHQjLzlZpFQpkStXbPecuXNF\n/PxEatQQGTXKdu1qNJqcDaGtp0YnA5uUUmOBXzDidb4GvJlRQCnVD/gf0ElEzotIrFIqI+h2BMaI\nbTiZgm4rpSaZy4RinFgxECPodsbOTjAM2h9KqW0YJ8o/gBF0u1duNGIE3X4NI+j2ZKAWmYJum98l\n0PzRFyillGqKYTz3mO8PxDgaajdGXNP2Zo2/i8XGII3jEhYGlSpB8eI38jw8oE0bWLfO8C3MK6tW\nwY4dsG0b7N0Lq1eDtzc0bw7DhoGPz53r12g0VpCdhczrRREJum0uYxlsOyMAd7rF/WeAfRgjyXjz\n52GAex77Vk+NFjB//y3So8eteidMEBk6NO/tjhwpUquWyPDhIj/8IBIVdeNe//4in3+e97ZFnKd/\nLXE2zVpv/qKDbmdf12GCbpvL3HbXrYj8DPx8uzIax+bIEahX79b8Tp3gueduzbeGo0dh+nQ4fBj8\n/G69/+ijMGUKvPNO3trXaDS5Q8cadSK0H2HB8+KLxlTlyy/fnJ+WZhixI0egQoWs62bHgAGGcR05\nMuv7MTFQpQpERoKnZ950azSaG+hYo3aMNWou56+UWqCUijFf89TNh/dqHJjsRoRubtCxIwQHZ10v\nJT2FY1eO8d/x/5i2fRpj1o7h7f/e5pn5LzPvjwR2+b/CM389w0tLXuL9Ve/zxaYv+HX/r+yK2IWr\nZzyNGztHKDeNpjBg06lRizieEzGcyFsD3ymlEkUkyxBrZiYB/THWD8Mw1gJXKaXuEpGL5rYzYo2+\nBGzHcItYopS6R0T2m8tkxBp9B/gX6I0Ra/SqiCy3VqNFrNHfMTbj1MVwglfA+2bNlrFGHwNcs+gP\nF+AfjA0zXTD+8PgWWEQO0665JSQk5LovjTPgLHozDGFWejt1Mgzhw4+msiN8B0f+OMKF4xeIPR2L\nKdJEieQSuKW7MeqpUTcq7XsKKofw5+lp9P29L6dLneZk+ZOc9z2PycV0vZh36a8YOjWAV0udJSgg\niAblGlz/q9YanKV/LXE2zVpv/lKQenWs0fyNNdoFaAbUE5Fj5rLPAAeUUh1FRMcncWCioyEhwdg1\neuzYjXwR4eClg5wtu4tfFnfh14B6JCcm8++4fwmQgFvaGdRoEBXLVKRM8TLMXvEYHZ4/TZtuP1P5\nk8oo80xNWrE0ImtEsrvmbr67+ztiKv1BzH9f8tq/xrbUmmVq0qduHx5p8Aj3Vr03V0ZRo9HcHh1r\nNAuNNow1+gnwtIjUzpR/GpguIpanWeSIXiMsWLZuNdYGd+2CxNOJ7PxhJxf/vshHT31EaGIomBR8\nfgFebEm92sV55+93KOHqz8ZNdxGZ6knj1m68+rYrZXv6olwVkZFQt64Rs7S4SufkyJMkHU0iYX8C\nyWeTAXAv507L8y3ZF3GYdnfV5YGpQ1kXuYhLiZcMUYllKB/2Bv061mb0gM5ULFnRjj2k0TgHOa0R\n2npEmFOMzlsMIbePI9rcijK5iTV6xUqNFTFcOLIrYy3+WTwroy39DWYH4uNhzRro0cPwB7wdR0NN\ntHU/zYIW2yi/qzwAfvhRrk45Lt9zmfvr3M+53lep57KPb14rTdTT0KIFjJgI/fsbz3DfByPNXqy/\n/Qa9e4OXF4Ardb6qc/1ZqVGpxKyLIfVqKu5u7rSo2phOHeHJkt/z2zPT2Hx8M/8c/oevRgYReU0x\nfVkgP55txrNt7mfYvcOo71c/fzpMoykC2HyzjOYWCmQOyzIunzNgD70REdCwobFbs1UrSErKutyF\n+At8sekLzr6/lIe3hVN+V3m2u2xnW9NtbH9vO2NGjyHinQhm953N9/+rz7yfSnPwIDzxBPTqBYMH\nG8Zu7lyYPBn27YNr14zPL76Y9TPdy7jj96Af/s/d+DurRw/4+29wdXGlxpoadH+6F0PP1eGvH0zU\n7LCN1DXv88PuH2gwtQEPL3iY0Euh1+s62+8DOJ9mrTd/KUi9th4RRnDriKmCxb3s6oAxQrKMtlKB\nGyNIyzLHsylzGWNTSuaRVgUM5/qrudCYl/fIigiMUycyUzGX7dxEdkG3wbGC5t4ubQ+977wDbduG\n8OKLMHVqEF98Ae3aGfc7dOzAf8f/Y8ycMWw9uxUJEFp4/EW850Zi7ougwcMNePm5l1m7di0SKbg2\nMPZGnToVwqOPQqtWQTz5JPTuHUJIiPG8atVgyJAQunY10s2agcl0435Oeh99FN5/P4SnnoJqmyug\n4tOozBJOdVf8MbAXfTd3odEDq1h56l8WspBFhxfR060nAwMH4lfCr8D7V6d12pHSIXYMuq1jjd6c\n3xUj2kxti7wG5rwOeehfHWs0j+zeLVK5skhCgpE+eVKkTBmRE+cvy8SQiVLjqxrCaITRiNsYN+k7\nv69UrxMlm7cmW9V+enr29375RWT0aJELF3Kvu2tXkXnzRJYuFelZPVr23r9XggmWYILlP5e1svj/\n4uVc7DkZvGSwuH7iKoxGSowrIRM3TJSUtJTcP1CjKYSQQ2QZWxvCuzFOgBiLccrCACAReMmiTD/g\nMBanUWDE/4zEiA3a0GxYrgAVLMq8ASQAT5vbnmA2oI0tyjwIpGLs5qyHsUM0FeieS41VgBgMV4yG\nQB+MEef4TO8baL7+BjYDTYFAi/sK2AFsAe7BCNu2A9iYx/7VhjCPPP+8yPjxxmeTySQbT2+UBndv\nkRdrT5PfS/4upYeXloCvAmTC+glyMf6ipKWJeHqKxMXZV/eKFSLlyhnXqlVGXsy2GNn7wF5ZXnW7\n9Ol946iK0Euh0nd+3+sGvcm0JrL5zGY7KddoHIcCNYTG83Ss0UxlKmKMSmPNxnUe4JfHvtWxRvPA\npUsiPj4ip84nyPc7vpf2E9rLkDZDZJnrf9dHV8vHLpe09LTrdY4cEQkIsI/ezGzcKLJmza35MRdS\nxddX5NQpI33unDF6nLdlpVR4tYIwGlGjlbzx7xuSmJJYsKLzgP4dzl+Kst6cDKGONZp9mTuONWou\ncwHD4V5jJyZNiaJKy+MEzulO602t+WDpB3ikG1tGdxUrQ5dZATR50vumOgcPGhtrHIF77806v3QF\nN555BsaPh7fegp49jdBsdcMb8X/PTWH3vbuYtHkSX2/9mpUnVzL3obkEVgzMujGNpgijY406EUXV\njzA21jgOqWlT6+uICMHhwXy9eSp/v/IlPN4PKu2mb3pfXh/7OmX7lqXGqBp8MKcUfn7w4Yc31x87\nFgbp35IAACAASURBVOLi4LP/b++8w6Mqujj8TgpJKKFIKKH3EkA6UkVFQFAQEMWCBEGwofCpKCiC\ngKL4fWJFBUVABEQsqCg9oUoXAUFa6ITeQno53x9zl2w2m2QTNo3M+zz3yd65Z2bPHZadnTvn/OZd\n996Luzl7Vg+ABw7A++/D473jWVFmE4UTE/C/zZ+4l+IIDg9m34V9eF2rQuUVa0i6XIlFixSNGrn2\nHleuwMiR8NFH4OOTvfdjMGQHOa41ajC4k8uXtZRZ27bw+ecZ20fGRfLF1i9o9Gkj7pp9F7/84IMq\ncYzHugaxafAmfhr/E62PtqbRj40o1qwYffvCggWp28lLM8L0KFMG1q6FvXth8GDwKupJwuPVuObt\nzdWNV4l5IIZ5f8zj1dKvkrDydcIKLaJEt/9x991JHDvm2ntMmABff613zDAYbkrSemaa1YPU628j\nXKjjjX7keQoduHIja4TB6DXCGFxfI0zlI3qNcAN6jfAU8Dbg4WBTG70BcCRwDr2hb2G761VJuY5o\nO8ZnsW8L3BrhqFEi/fuL7N8vUqpU2sErYRfD5MWlL0q10dXk0TsflXnF50mTl5tK2eqnZc4PaW8l\nn5goEhgosndvyvKGDUW2bs28v3mBqCiRkoVXyO6Rh2VN0TUSQohsuO0v8S8RK8Xf0NGxJXu8LXUb\nRMr58+m3deGCXl9dulSkTJn0o2NvlPzUxyLG3+wmJ9cI3TojtBO0XoyOoByH3lV+aAZV30OLaA9B\nR1eGoUW3r29wYye6/ZrV9nK06HZDOxub6PZUoJH1erZSqmtmfLQT3d6LVrd5Gq2H+padTVG0NFwc\n0Bq9DtgV+MrJ/fVAB83Yjjz+wC1vcO0aTJsGY8dCrVrQoYNOUreRJEksO7SMB2Y+wNBBQyn7TFmm\nT5rO4FWDKXelHKP3LqDyLWV5pFepNN/DwwMeeAC+/z65LDISwsKgXr1svLlsxM8PGrfyZH2NqrQ6\n1IoKwyoQWq8GgwYWYseIVTQr34xLTUZzoORnNGh+hU2b0m4rNBRat4bOnbUSz5EjOXUXBkMOktYI\nmZUDmIvzHL3D6dRJK48wHBhrnSu0CLezPMKv7c434DyPMCQzPqJnf8ccbJ5Ba5n6WedD0LPXYnY2\n3dAzvqqSckbY1k39W6DSJz78UOSBB5LPlywRad5c5HTEaZm0dpJU/7C6MA559M5Hr0d/hniHyK5e\nu2T7tAtSpnSS7NiR8fusXSvSoEHK9+nQwf33k5MsXCjSsaN+nZCgcyh37dLn0fHRMuSXIcJYhD4P\nSeHiEbL49wSn7Tz7rMjkyfp1t24iP/2UeV/69BEZP177YTDkBuTkjBAdZbnEoWwpUEUpFZhGnWZo\nLdDr9UQkCT0ja2cVVUUrvTi2vcRmY4luN0/j/W9TyXL9rvjYFljmxKYwejcJm80GEYmws1mOHvgc\n4/zmKqXOKaW2KKVGKKXcHq17s5GQoCXJXnpJnydJEqrcKnbuu0rFN9ozauUowi6FUbl4ZW4dcit+\nLf2oNbUWbU615Z+HGnDPmFK8/4FyKcCmTRsdGLN1qz5fuVKvS+Zn7rsP/v0X9uyBH3/U0aQNGuhr\nvl6+fHHfF8xoPYP//tWCCrcPpNeDkRw7fS1VO6tWJfdFw4awa1fm/Ni/H9atgzlzYM2aG7wpgyGb\ncPdAmJGgtTNcEdR2l+i2qz66YpNKUFtE4tFSbjabCPT2UQ+hpdZmA2PRexu6FUfpsrxORv7OnQuV\nKwqlIg8wZ+AcZleZzcXbzxJXcz6Ju++nR50eLH5kMWHPh/HyQy/TalMrfB+sQI/HvHnrLfjpJ3j0\nUdd88fCAZ56Bjz/W584GwvzWvxs2hDJkiI72fOUVmDQptU2739vRLKwZn/36FIMLr6RD/+84cTVZ\n5TA8HE6fBpuCX1YGwh9/hN69oVs32LIlfdv81sfG3+wlJ/01M5Osk2EOg4jYNu61sVMpdRX4Win1\niohkSW/0ZtIa/e23UH76Cfz9OxIcDJcuhXLuYhT7h1bmJc8TLL7rXwAa05g4rzjuq7+bPZvvZlG/\nySnaa9GiI/fcA5Urh/L++9C6deb8GTy4I7VqwQMPhHLmjNYOdeZvXuk/V/p3xAh48slQmjeHO+5I\nbV9tQjU2n9rMuR/O0fdsYzquKMNDvR8j+OlHebLPk4SEQP36oaxdq+0bNdLap65qpQLMnh3KoEFQ\nvnxHfvwx7/SPOb/5z0NzUWv0CDDGoexO9OPCwDTq3GFdr+hQPhtLvxOoZtm0c7AZD+y3XhdCB648\n5mDzBHoNUrnqIxAKfOVgU92yaWOdzwRWOth4o4W/U0Wq2tnUtdq5LQv9e1OtEcbH63Wsvn1FxryR\nICVLx0jFFttEld4nU4v+JiGEyPzi8+Xjzh/LH9P+kJhrMRIfL1K6tMiRIynbGjFC5KGHRJKSnL+X\nK6xbJ1Knjri0rngzcXX7VdnYbKOEECIrWCn1/lNPQg6HyODBIh99lGwXGytSqJBInIsSptHRWqYu\nOlrkwAGRSpWyx3+DISPI4TXC9UAXh7KuwBFxsimvxTa0HJp9ZKcHend32ya8R9ApDF0d6na12YhI\nHLAlDZs/rc5w1cf1wN1264o2myjgLzub1kqpYnY2d6MfN69P414heY/FE+nY3NQkxSVxccVFFty+\nH//zx/Dr9wSfFA7g0qCynKj8HtLlBdY+9y1H5x+lc3hnnlv6HF2f7IpPER+8vOD++2HhwuT2Nm/W\nj1I/+QRuZOP2tm31ulpmEvdvBoo1KUbLTS25HFydhWWKsNd/L13mdOHXpddSPCIuVAjKlYMTLn5y\nd+yAunXB1xdq1NBRwKed7c5pMOQ2aY2QWTkoQKLbQBH0DhW/olM17kDnJM61swkGHrPaqGGdnwW+\ny2L/5ps8wgMHRIYMEXnhBa2BGRMeIyennZTtPbbLysIrZQpTJIQQebrxqOsi0UGfBsm4kHFy8MLB\ndNtesUKkXj09M4mN1Tl/336bvfeT1/o3I7Lib0yMSPnySdLv07eEpxoJxU7Ip5umprBp39657qkz\nPvpIfwZcrVsQ+jg3Kcj+ksGM0K1rhCKy1crlext4CZ0CMVpEptmZFQdqkXJ98mX04PQlUAK9Q8Pd\nInLGru0PlVI+VttlgT1ADxHZZWezSCk1GBiNzk0MAwaIyNLM+CgiJ5RSnYH3LV8uA18Ar9vZRCql\nOgEfo3eeiAa+Rw++NhLRIgDV0CkgYehUjQ8z7s38y8GDcPvtehPaixeFtrfHMarL99T+tCIAHnhw\n0ieWf0qe41TXv5lwxwT61OtDvQDXEvfuvBMqVYJRo/TspFYtePjh7LyjgoGPDwwbptixehQNzt7P\n7lYf8uwf73Em8jSvVn0Vv2p+VK0KR4+61t6WLdC+ffJ5lSqu1zUYchKjNZqPyA2t0WnT4PXX9ePC\nr7/WYfjOSIpNImJrBBFHYrnrreI077EV1fIzQo+EcnLhCMqeaMQwv81srr2ZmIYBrJv6ESs2htO6\nbo0s+RUWBi++CMWKaR99fW/gJg3XuXwZevXSEnNjFs5ieOgTND3QlHfnvkvV0VWZmVQVD28Pxo3L\nuK369fUja1ss12uv6cerY8dm6y0YDKnISGvUDIT5iJweCH/5BZ57Dn79Fb77Dlav1koj3t6QGJPI\nuYXnuLL1Cmc3nSX+r3g8Yj244pVIr9rnkb799BwYKOMbiPpmJU1u9eSLKQE81LMEPXvCq6/myG0Y\nskBSkk4r+fnfn1kwYgGDlwzGAw+ia/izuEl9Pvk+/V8eERFQvjxcuqQ/L6B/sGzcCDPcnjxkMKSP\nEd0uIDiG+d8op07B04OTmP9uJI0aCRMnQpGiSbw09gzf/P0NI5aOYPfA3YR/GE7ixkQ8Yj04XPw8\noR4lKN9zPH3q9+Gjrh+x86mdhI88zp41dYk/U4vqgSUICoJWrdzrb3bj7v7Nbm7UXw/rm+H+uvcz\n4OMBjHpiFOeKncPv0FW6/byViysuplt/2zZo1Ch5EISMH40WtD7OaYy/aeP2PEKlVDf0+ltd9Prb\nRyIyJYM63mgdz8fQa4TbgBdEZLuD3UjgWZLXCF8RkeUONsHAKKAKOnhlgojMzayPSqlW6CCeJsAl\ndADP66JVb2w2tdFrhO3QATcLgRdFJMrOpjx6TdAWqfo78LyInEuvT3KD09+c5trf17i65xphodF8\nExNL3CPwxNVZbE7czL6gOJZ/sBGixkDJo8S1jOOa7zWiakfhWaEBS95/lc9+DOd/3XbhoVL+xipV\nCpYv1zMNT089szTkD7rU7ILfG348HPgwz89+lVbHGrJ3wF5uO3gbnn6eTuts2QLNm6csM2uEhjxL\nWlE0WTnQEZnx6EGtDjoiMxoYmkG9D9BRmfeiIyxnoRVa7KNGh6MHm8estt9F7x5hHzV6PzqP7wX0\nzhAvWuddM+MjUAm9o/xXQD10NOoFYJKdTVHgOKmjRufZ2XigB/VNaDHxVtb5+iz2b5bzCKPPxsnX\no67Ii03PSJc20dKybZQ0anFZat16VirWPy7VO6yX2aV+S9bsJERWqBUyp+QcqfFUjeu7nZfqMUlu\nqfOPTFg1SVYcWiGXoi/J1q0i5cvr3dENNy+bTmySUuPLyQB1QJ4YNVgi4yLTtO3bV2T27JRlkZE6\nDzE7d7AwGJxBBlGjbl0jVErNBSqLSDu7sslAXxGplkYdf7RU2jAR+dIq80CLbH8uIm9a+Xwn0ALb\nr9vV3Qz8IyIDrfMNQJiIPGZnswAIEJE7XPVRKfU2OjG/sp3NM+iIzwARiVZKDUEP4GXF0hu1Zpq/\nAdVE5KgVeboEqCMiByyb+sBu4A4RWZ3J/hWAs2eFgABdZvv3U04S6HaN3kXYD2H4nPDDN6rQ9fKX\nO81ha4VDoBLBI1EXnqvH/Vvvo0jkLRyvsJvEe36gRAMf6lesT1BAEA3KNKBeQD18PQtzzz1QpAj8\n5z96ljd1KkyfrvP7DDc3O8/spEntAJIGtaR9w2r89shv+Pv4p7AR0bO/5cuhTp2U9cuU0fmFgWkp\nDxsM2UBOrxEa0W1rtwk7mzDbIGjd2x70oN6OLLKo/DaWl9vE+jLrWeO7hnMLzjl9nn5tTzTF9xfH\nN6oQ0Z4JHCp7iDV11xBXdQtVGh+mdbsEHugayCsPt+eLsS15dl0RJhypwMrdg1n3v+X8NvA3Jt89\nmQGNB9AssBmFvQvj4aGDaGrU0FGbp0/rhPbMDoJmvSJ7yS5/G5VtRKM6JSgd14K1x9bSaXYnLkan\nXC/ctUs//q5dO3X9ypVJc0Ng08fZi/E3bdy9RpiRWLUzdZn0BLWbumCTGdHtCy76WA69OXBaNra/\nqUS3lVIX07Oxa6uck3KXqJkYAWf0812AhCsJetUUSEyEkBC9b9/OlXUJqHOSji/uo0lHP1r6t6R8\nsfKM8RvjdAbpKj4+8N57Wa5uyOfUreHH4w0/55PYv9lyagsdZ3Zkef/lxHwSg28lXxaFlaVnT+cq\nPxUq6EAsg+FGSUzUnzGPdKZziZGJnN6QelcVR4zodtZx9ZnyDYh+OWdat2lUrFSN1RsUh46XpO9f\n0L5oR154Ab75JpSAABg6tCNvveXP/v3bgRJ0rNURyFuiuOY8f54rBZHnOrLm+TW0HtOaXYd38WjY\no7z+39fZwQ72lCzH0B8eAlSq+hDK6tXQu3fq9m1Cybl9f66eG39v/HzVqlC+/RYWLepIqVJw112h\n3HcfdOjUnIqNKvLxdx9zMfoiw/sNxzOhOPO/XM7hhZcpcrIZV47HcyZxAwFFEunQqC13r6rHxo3J\n7c+YEcq7r02n2ukLZIS71wiPoMWqJ9iV3QmsQItqp/otqJS6A73Te2UROWFXPhsoJyKdlVLVgENA\nBxFZZ2czHugnIrWtR6PXgCdEZI6dzRPAp0BhERFXfFRKhQKHRGSQnU114CBa+HuDUmomUElE7rKz\n8UYH3jwuInOVUm+i1xpTZI0rpY6h1z/fdqVf7eqlyiNctw6++UbnazVuDH36pF6XMRjcyeef6/SI\n6dPhXOQ5Os/pzI7TO3hi5xM89nN/VBKUfzqQ2h/XQnmm/B04cSJERcHbmfrkG24mEpMSOXrlKEcu\nH+GT/5Zgc0hZ2j77GVH7kkj47XZKHq+CR/GjfBt4DiLLwtUKFImtTVKCFy3LRDLuaOr9vC4W9uOZ\nYq146in9+H3xYli/Hl4fHEPLJf/QapsOYU5rjdDdM0KboPUEu7LMiG7bB8t0Aj63bI6QLLq9zq5u\nCtFtpZRNdHuOg40z0e30fFwP9FdKKbt6zkS3P1RKFbNbJ3QU3V4HjFFK1RSRg9a91UdrmdrfR5Zp\n104foaGhdr+68z7G3+wlO/2tWhV++EG/DigSQMiAEO759h5myAzCVg9lzJVowj87RcL5eOp9Uw8P\nn+RnV4GBWpghp33ODoy/6XMl5gq7zu5i3/l97L+wn30X9N9Dlw4RlxgHB++m1I9fMsH/d6qNaItf\nvJ9V8zSXryXxa9lFBDaoSGCFJMbd+yTt6jQgMcKHsFGBFCpfCO9bvPEq6YV3KW+8y3iz0luLNhw6\nBJ07w6xZULSoL0xsluFzOXcPhFOADUqpiejBqBXwHDr1AQClVC9gEnCniJwSkatKqc+Bt5VS4ehB\n72X0ut4XoGNelVLvWTZ70YNnMNAQuD5rQ0d1LrSiSZeiRbx7odMyXPYR+Mwqm66UmoIWzB6PzjeM\ntmzmAmPQu8+/BtyCnnnOFxFbttQKYDswRyk1DD1IfooemNe43KsGQx7CMR+whG8JlvdfTpuR7xDq\n6UviwJcZP/9Nrmy4Qvz5eHwq+Fy3NWuENx8iwtErR9lxegc7Tu/g7zN/s+P0Dk6eP0nFCxWpeq4q\ngRcDWdR+0fUBqWxMOy7+Mo9mL31O3Umt8Yj3IKl8Ej5BPpSoX4JadUrwc91nuOPOO1K8l5e/F7U/\ndRKFhc67+zCLKs5ul1hzkqz+oYh8YHc9GJ2fV01EjlllXui8vsdJFt3OakL9ALTodlW0yLUrCfUp\nfLRsWqFFt5uiRbdnoBPqxc7GPqH+uui23WCJUqoc8BF6RinohPphInI+/Z5MTW5ojRoMjkRFaYGE\nqKjkQAURaNwkCe8732Jb8Te49cKtfNLjE9p1ShkcvWsX9OuntUwN+ZPo+Gi2hW9jw/EN/HniT/48\n/idnIq39EU40Y+xPo6lxtQSB8QpPu6nY6vfg9m63EH2yJs8M8WPUKBgyBK5uvopfTT+8S3mn8Y43\njtEavYkwA6Ehr1C5so5OrmGtfu/cCT16wL6DcTz206Ms3LOQwt6F+aXfL9xV/foyOhcvQvXqWtzb\nkPMkJsJXX+l/r+bNoWdPLVi/ejVs2KB/2HTtCq1a6YjMuDhYuiKOjfsPEF/qV47u/YuovVFUOluJ\nue3mcqXIFQBKJNXA548ZRB9tyIz43dxyKRHxAAL9iCtfmHDvInyXWJENewsRGAjjxsFDD+XcfZuB\n8CYivYHQrFdkL8bflNx7LwwapHeqABg9Wn/JvvsuJCQlMPiXwcz6exY+nj583/d77qtzH6Bnjn5+\ncOGCFmXISZ/dTX70d8aMjoSFwQMP6B8yq1ZBfDw0awZ33aVfL1gg4BWLb9ljHPw7gGcSd9MmNp6A\nhJR5CqsmrqJG9xpUkvY883A17rlHMWkSxG6/gmcxTwrXLpxifTgr/rqrf3NcdFsp1U0ptUMpFaOU\nOqyUGuFCHW+l1GSl1CmlVJRSaq1SqqkTu5FKqaNW29uVUnc7sQlWSu2zbPYqpR7Jio9KqVZKqQ1K\nqWjLr7etIB57m9pKqaVKqUil1Dml1GdKqcJ216sqpZKcHOMz6hODIS/TsKF+zAl6cPvuu+Rf+F4e\nXszoOYNnmj9DbGIsvRf05rvd3wFwaupJ6peJJTw8lxwvwKxercUvli2D4cNh0SI9M4+IEBZ/cZn2\n3Zdzrf0w4p+tTlj7juwpN5q44OYUq7qcgAQP4r2SOORZBM97SlHtrWqM6jeKxmoAj99XnaefVkyZ\nomeXxdsUp2jDojc0COY4aWmvZeXAaI06ao1WRSvN3AuUsTuKZLF/s6w1ajC4k2+/FenTR7/eskWk\nZk2RpKSUNklJSTJy2UhhHOLxpofMHTNXQgiRH3z/lJD50TnvdAEmPl6kenUR26bvSUlJcmbtGVk8\ncLH8VO4nCSFEenftLYxDGIeUfa+sPPHzEzJv1zw5tv2YRB6IlKTEJJkzR6RcOZHvvxf58kuRgACR\n+fNz9dZcAqM1mitao9VF5IhSqio6YKe9iNhSKrKMWSM05BV279Y5q/v2wcsva7WhiRNT24kIb619\nizEhYygWVYw5P8/Bf78/CaV9aLuxMX41/FJXMridWbP0xtrLVsaxcsZKYt+MpcTJEtevX/G7wvJ7\nl1N8aHF61OlBiwotUu0gY2PpUpg8WW+xNX48tGyZU3eRdTJ6NOruGeFRdGSlfdld6FlRYBp17rCu\nV3Qonw0st15Xs2zaOdiMBw5YrwsBcegBzN5mEHrGp1z1EVgNfOlgU8OyaWOdzwJWONh4o2egj0jK\nGeFR4BywBRgBeGWxf9OcEYbYfurlE4y/2Ut2+xsXJ1K0qMihQyIVK4rs3Jm+/fRt08XzTU8p8moR\n+arkSgkhRNYHrpfIf5N3sDB9nD3ExSVJhapR0uqJp6TkOyWl4cCGEkKILCy6UMZ3Gi9ffPqFHDp7\nKLfdTIU7+5cMZoRGa9S5j+7SGo1A50SuRz/WvR2dyN8EnSpiMORLvL1h2DBo0wZuu02vGabH4KaD\nKV+0PA8ufJDnGi/l/R2dqHsK/g3+lyYbmtyQ9q3BOScvn2T2rtl8OO0iZ5Lu42Ti5xADFZtXZE/Q\nHjr370yfMn1y2808QYYDoVJqHPBGBmbjRKSgBYBk+HxSRC4A/7Mr2qmUugp8rZR6RUTcFjKQn6LX\nwPib3eSEv6++Clu3wiefuGbfvXZ3QgeEctfurxhe7gIT6h7nwS8evD4Imj6+ceIS41j852L2TtlL\n7eW1ef/xzzn/y1pueWw4Q3qP4tGGjxJUJii33XSJnOxfV2aEH6NVVNLDlhweTvJsyEZZu2vOsJWX\nQ68D2tc75cTmYBo259GPJR13dSiLlnCz7RXjio+u2lSyN7C0RkuR9r2C3qQXoEoGdmkSHBxM1apV\nAShRogSNGzfOMyK65rzgnPv7w+jRoRw8CBUrulY/8kAkw4Lu4v3NVRjZ5WHe/exdJt4xkaf6PuXy\n+8+fD/7+HRkzBrZtyzv9kZvnMZ51WbLsNy58v5pGBxrRJqkNAK0XPc/xBpvY8vECvDy8CA0NJXRP\naK77mxPnoaGhzJw5E5dI65lpVg7gW2CdQ9lkdABLWnX80Wt4g+3KPNAD3BvWuS1YZqJD3c3ADLvz\n9cAcB5sFwKrM+IiOKD2Gta5olT2DFvX2s86fRD/uLGZn0w29Jlglnft9BCdroi72r1kjzCWMv+5j\n/36RqtUTpP2M9sI4xGeCj8z5e45LPm/YoKMWu3YVGTUq+31ND1f8jYsTmTZN5No119v980+RgQP1\n3/RISkqSVWGrpNGAr+T+Qv9ICCESQoisZJXMb7ZYXul+Who1TJJLl1z3Ny+Rk2uE7h4Im6MDViai\n5csGWIPFEDubXsC/2AXPoPU/z6K1QYOAmej1PPv0iReASOBRq+13rAHUPn2iJzo14nl0asR/rPMu\nmfSxInAFLQIeBPRAzzjftrMpYg2WjukTc+1sgtHpHkHoYJtg6z6/y2L/moEwlzD+uo+ICBFfX5GY\n+FgZ8suQ6yH7/d7rJ/EJ8XJ53eU067ZvLzJ7th4kgoJy0GknZNTHSUkijz0mUqmSSKNGIrGxGbd5\n9KhIqVIir78uUrq0yPbtqW0iYiNk6uapUv/T+sKICoLfeakS3FqW+SyTb9ttkp7NrkmNGiJPPSVy\n7pzr/uY18u1AKMmzoh3oHL/DwHCH68FAIjqFwVbmhc4LDLcGt7VAUydtj0RHYMagxazvdmIzANiH\nfhy6FyuCMzM+Wjat0DPMaMuvt7CbIVo2tdHi3pHWQPkZ1ozRut4f2IkOmrlmvX4J8M5i35o8QsNN\ngb+/yMWLelbz6eZPxfNNT2EcMr7XeAkhRA6+fFCSElMmJm7bpiNU4+JEEhJ0DltYWNZ9OHlSZOpU\nHfmaHWzaJFKtmkhkpEiHDiILF2Zcp18/kbFj9esvvtD1bPmZe8/slTc+fEP8J/lf//FQuPXX0u7h\ndXLq6imJvxqfPTdyE5DRQGgk1vIRJo/QcLNQvz58/z0EWXEbIYdDeHDhg7Rc15IRv43AK8mL0veX\npt6cengW8QRgwABd75VXdJ3gYGjRAp59Nms+vPgiLFkChQvDltRb3N0wQ4boLatGj9Z7hs6dC3/8\nkbb9sWPQpIn+W6SIlqxr2jiJ4C4bSdy5gSobqxAQEcDgoYMp36o8TzZ4gRc692HPHkV5x4gGQwpy\nXGLNkDvYFonzC8bf7CWv+1u5Mhw5knx+R7U7mFp/Ktd6XWPkYyOJ8I3g/M/n2d5mO1H7ozh9Gn75\nBZ58MrlOq1bw11+pmnaJuDiYM0fvqxgWBidPZr6N9Pr42jU90AcH6/M+fWDjRjh7Nu32vvwSHnlE\nD4IXoi4w99lvGH9gGU3+F0fz5c0JiAggsnQk826bx9qBa/Hc/wC33eb6IJjXPxOO5KS/ZiA0GAw5\nTq1acOBAyrKAIgGsfHwl3Qd057nBz3G81HEid0ay46kdTJ2qtUxLlUq2t9c7zSx//AF16+rjnnvg\nt9+yfi/O+P57aN9eb0QMetbZqZPeOd0RESEhQe8K0b7XXgYtGkTFKRVZcnAJxWN9Oenhzb7OEdRc\nU5NuZ7vRIrgFALNn61mywQ2k9cw0Kwep195GuFDHGx21eQodtHIj64PB6PXBGFxfH0zlI3p9cAN6\nffAUeu9CD7vr5dDRp7vRwTjL07i38uio1SvWMQ8t0ZbV/jVrhIabgg8+EHn22bSvLz+0XGq89eoU\n2QAAFcNJREFUVUNebPqiVHiqiRQuHin79ieksLl0SaRIEZHExMy//9NPi/z3v/r1vHkiPXtmrn50\ntMjMmSIxMc6vt2kj8vPPKctmzxbp1UskITpBLq64KIdePSRbmm2Rvc/slRc/XCPFauy8vvbHOKT3\nx71l8e+L5YMPEqR375RtnTghUrKkSFRU5vwuqJBTwTIULMHtKujNdgcCfwDLnNyXB7ANnTfYwhpc\ntwHrb6CPzUBouClYvFikc2fn144c0QEml6IvSfCPTwj1Fgpt3pUW01rI5hObU9hWqiRy8GDm379O\nneSIzIMHRSpXzlz9KVNEbrlFpHFjLWhtz549OsUjLi5l+ck/I2WS1y5Z7bf6eqpDCCHyTdlvhFq/\nCfc/LsUnFZfhfwyX/ef3X6937ZoODNqzJ7mtd94RGTw4cz4XZHJyIJyL8/y8w+nUSSuHMBwYa50r\ntAC3sxzCr+3ON+A8hzAkMz5as79jDjYpcggdrs10NiMEOqPzBWvZldW3ym7PYh+b9IlcwvjrXvbt\n0xGV9qxaFSI9e+q0gZIlRYYOFeneXaRBiwsS+E6N6zOlgT8PlGM7j0n08Wjp1k3kp58y994nTuhB\nzDaTTEzUuqm2fLuMiI4WKV9eZNq0EOnQQc/07HnxRZFXX01dL+ZkzPXBb0HVBfJ026elxWMtxHdo\nA/Esel4+WvulRMRGOH3PyZNFevTQr6Oi9MCdUZ6hI3n9M+FITqZPuHONsC2wxKFsKVBFKRWYRp1m\naB3Q6/VEJAlYDth2h6iKfsTo2PYSm41SqhB6tufs/W9TyUKGrvjYFljmxKYwWiPUVdqik/Svr4SI\nyB60MEC7NGsZDAWAatV0gEpcXHLZ77/rsvBw+PtvqFIFuneHDStL8e/wv3il7St4e3jz9Y6vWfDA\nAtbXXE+PiwfYszUhU++9YgV07Kh3Ywf9t0EDvWu7K2zYoH2rVQvGjtW7biQm6msxMbB4ZgwDB6aM\n7A67FMaEfyfwXtN/6dtoPw8GP8i0ztOo0qMKd0csZfSIUgxrN4iihYo6fc9hw/R66LRpMGECNG2q\nNV4N7sGdotsFSXDbFVKJctu15SgDd8PYJIbyC8bf7CWv++vtDZUqweHDUKcOJCTAvHkdWbQIvLz0\ntVGj7GsU451O7zCoySBe+vUlIrwi8Iz1pM7Gk1zbdoq9UoYaI2pQqHShDN/711/1AGvPrbfqgbBD\nh4x937hRi4137NgREQgIgO/mCV1KXmTdK6f49MIFyoQ35nzFBH7Y8wPzds9j9dHVunLrOnh9G8Lb\nd14kuPEAos6Wp9XT8OWe9N/T11cH+Nx7L5QpoyNeM0te/0w4kpP+pjsQGsHtGyZbJPWN1qg5vxnO\nb70VZs0KpXNnuHSpI1WrQkREKKGhadc/ueskI6qOwON3D6ZPn06pTytQ7XI5ir7dmLBpYYR/Fk7N\n0jXTrL9sWShLlsDUqSmvN2rUkb/+cs3/xYth+HB9vnzhMnoVuUihgVXYlRDLYXaQ5CGsnLGcyWsm\nk3BIz1Z9a/nSt35fmsY05fMV/1Ll+KuUbQvt+ofSqxeUKZNxf9WpA9On6/Nq1bL/3ye/n4e6S2sU\nuAUdeJLeUcqyPQKMcah/J1nfi3CZ9Tq9vQj3W68L4XwvwidIuRdhhj4CocBXDjbVsduL0OHaTJyv\nEb4JHHJSfgwYnV6/p/PvYdYIcwnjr/uZPl3koYf0644dRV57LSTTbfy8bY00LXRKJtWcJMG3Bwvj\nkGZfNJP31r8nxy4fS2X/++8irVunbmftWpFWrTJ+v6QkkTJlRI4d0318/JPj19f95qj10i/ov1L8\n5eLCOMTzTU/p8k0XmfnXTLkcnSwbt3atXmPs1Uv7Eh2d6dvOEvnhM2FPntmPUPQ2QhcyGEttrAe6\noPfbs9EVOCIizh6Lgo6ijLXs7Hen7wR8btkcQT+y7Aqsc2h7neVnnFJqi1U2x8HmT6sjXPVxPdBf\nKaXs6nVFy6illb7rTOplHTBGKVVTRA5a91YfrWO6zom9wVCg6NYNRo6E1avh6FF47bXMt9GzaXsG\nFYO2P/fh5OGPKLarGNvCt7EtfBsvL3+ZJuWaELw/mPoR9anZpRaTJwXy1PPeqdoJCoI9e0AEnG2N\nmHAtAa+iXuzaf5VEfJh39CMWrfqZ457HGdBgAH80+YPt1bbj6eVJhyod6Fu/L33q9SGgSECqttq1\ng88/h9274bnn9GNPQ+7iNok1pVRzdOTmZPRg1AqtvTlcRKZZNr2AScCdtoFHKTUFLaQ9ED3ovQzc\nB9QXkTOWzQvoaM4h6MEzGJ0m0VJEdlk2PYGF6LSJpWgB73eBe0VkaSZ8rAj8A3yPFgOvAcwAponI\naLv7bWy9HA8EAE+h+3OHdV2hd6RPAIaho2E/BWJFpG0W+9hIrBluKtq00UEgU6bA4MFZa6NtW3j7\nbbj9doiOj+aPg38wf/d8ft3/KzEJMUyd9hn1TtW9bn+p3CViq8Zy+bnL3NL4Fkr4lsDP24/erVoy\n7ad/KB52mbjTcUQfiSZufxy+//hS+Exhhk4cSthfQbD1KXgseZExsFggXWt0pXvt7nSq3gl/H/8b\n7RaDm8lIYs2tWqNKqW7oAasuOgXiQxH5wO56MDo/r5qIHLPKvNB5fY8DJYCtwAsist2h7ZHAs+h9\nAfcAr4jIcgebAcBodKRpGDBBROZmxkfLphXwPjpg5zJ6IHzdboaIUirJroqg1wNFRDztbMqh8w27\nWja/A8NE5DxZwAyEhpuNyEg4d05rcmaVJ57QEZRDhqQsj46PZv2xDXzW15+Afd7U8z5K3Ug/fBJ0\nQE3wM8EcLXM0ucLspXDbB8xe8jCVLqbYapQ4zziGDhnK8QP9ucWjGvc/t5F2ldvRrnI7qpaoinI2\njTTkGXJ0IDRkL+kNhKGhyZtt5geMv9lLfvMXsu7zf/+r0y6mTEl97csvdcrB6tXg7ZPAvvB97N20\nl0v/XGJXo10cjzlORGwEMQkxHJj7ND4lLjLsYgzFIosRWzYWjyoeFGpaiICmATSo2ICxz9WkWzcP\n+vfPf31ckP3NaCB0Z/qEwWAw5DgNGuhdJJzx5Zfw5pvg5wfgRVCFIIJ6B0Hv1LZfCaxZAy8vTPu9\ndu/W65qGmwszI8xHmEejBkNqTp7UCeZnzqQsP3hQrx+ePKlzEzNi40a9pdO2bc6vx8VB8eJw8aJt\nYDXkF3J8GyalVDel1A6lVIxS6rBSaoQLdbyVUpOVUqeUUlFKqbVKqaZO7EYqpY5abW9XSt3txCZY\nKbXPstmrlHokKz4qpVoppTYopaItv962Ilpt18sppb5VSu1WSsUrpZY7aaOqUirJyWHyLg0GNxEY\nqAcpxy2O5s6FBx90bRAEvdfhv/8mq8Q4cuCA3j7KDII3H24dCK2ozEXAYuBWYBzwtlJqaAZV30Pn\n/A1BC1SHASuUUmXt2h5utfea1fZy4FelVEM7m/vRaRhTgUbW69lKqa6Z8VEpVclqfy86YOZpYCg6\nqMeGDzq15H/ACpynUNjogVaTsR3vZtAfmcaWSJpfMP5mL/nNX8i6z0rpLZl2704uE4Fvv4VHH3W9\nHX9/KF1aq904Y/du/T436m9uYfxNG3fPCP8DbBKR10Rkn4jMAj4GXk2rglLKHz3IvCoiv4nIP+hU\nilh0SoItFeFl4H0RmWO1/Qqw03pPGyOB+SLyoYjsF5H/AT8Cr2TSx6eByyIySET2isgiYAwwTCnl\nByAiR0XkeRH5Gi33ll7Y2CUROWt3RKbbiwaDIVM4aoVu26Zndq1aZb6df/5xfm33bn3dcPPh7oHQ\nCG87Z65S6pxSaotSaoSVMuJW8lM0GBh/s5v85i/cmM9t2uhAFxu22WBmsxqCglLOLO1xHAjzWx8b\nf9PG3QNhRqLWznBFVNtdwtuu+piV+3BGBHom+xBwF1o6biw6L9FgMLiJzp1h1SqIj9czwfnzM/dY\n1EaDBq4PhIabhwxnJkZ4O+tYEnX/syvaqZS6CnytlHpFRMKz0q4z0W1IFpq1vYa8JYJrf278Nf46\nnjv6ntn61avDZ5+FEhcHFSt2pHbtzPuTmBhqzSxTXm/RoiMnT8LJk6GcPu0ef3P6vKD5G+ou0W0x\nwts3JLydxv3Wtdq5zRV7h7pGdDuXMP5mPzfq85gxIvfdJ9KokcisWVlrIzFRb9p74kTK8j//1LvR\n25Pf+rgg+0sGotsZPhoVkQuiA0/SOy5a5jZRa3syI7wNpBDetolTHyFZeNux7evC22htT2c2zoS3\n0/NxPXC33bqizSazwtvOsKWFnHDR3iVsv4jyC8bf7CW/+Qs37vPo0cmb7Pbvn7U2PDygffuU642g\nz9u3T1mW3/rY+Js27g7amAJsUEpNJFnU+jlguM3AUXhbRK4qpT5HpzCEkyy87QN8AXoYV0q9Z9ns\nJVl4uyEwyO79JwMLlVKbSRbe7gXcmxkf0ULczwHTLVHwGujZ58ciEm13Lzbh7VJAMaXUraQU3g5G\ni27/BcQA7S0fvxcRtw6EBkNBx9cXfvpJv74R6c/bb9cD38MPJ5etXq01TQ03KWlNFbN6AN2AHegv\n/sPonR3srwcDiUBluzIvdG5dOPox5lqgqZO2RwJHrba3A3c7sRkA7EPPMvcCj2TWR8umFXpmGG35\n9RbW41U7myS7I9H21+56f3SKRwRwzXr9EuCdxb41j0ZzCeNv9pNXfP7nH5HAQJH4eH0eHy/i7y9y\n9mxKu7zir6sUZH/J4NGo28P4ReR39C4LaV2fiV5Tsy9LQOf6veKkir3dZPSMKj2bWcCsG/HRstmE\nTqNIzybdR8si8g3wTXo2BoMhb1G/PlSqBEuXQvfusG4dVKkCAam3FjTcJBit0XyE0Ro1GHKGr77S\nEm3LlunBsE8fePLJ3PbKkFVyVGu0AOmM9lRK/a6UCldKRVp6o887aae8UmqBUuqKdcxTSpnflQZD\nHqd/f52P2Lq11h8dMCC3PTJkJ24bCAuYzujt6PXD+4Eg6x4mKb15sK0dD+A3oAo6ArYzOtXk5wz6\nI0vY59zkB4y/2Ut+8xfyls+FCsHPP8N//gObN+tzR/KSv65g/E0bd84IC5LO6H9E5C0R2SQiR6x2\nvgQetGunE1qO7TER2WKtOfYHWiulbne5V11kx44d7m4yWzH+Zi/5zV/Iez6XKAH9+kGZMs6v5zV/\nM8L4mzbuHAgLus5oSXRkqI22QJiIHLC7tz3o/MF2uJnLly+7u8lsxfibveQ3fyH/+Wz8zV5y0l93\nDoQFVmdUKdUR6Id+RIqdrWM7trbKOWvHYDAYDLlAWnkVVmTiOFLmyjk73rBsY4HBDvWDLJtmabT/\niHXdy6H8PWC39bqNZVPTweZZ4Jr1OtCy6eRg090qv8VVH9E5iBMdbIpYNn2c3MNtwCVglEP5NGC9\nE/vN6MT8LOcRmsMc5jCHOTJ/ZDWP8GNgbgY2562/4aSeMZW1u+YMW3k5UkqOlUVLqjnaHEzD5jxa\nwcVxplUWPfjZJOBc8dHl+7Bmgr8Ab4vIOw51wtE7TjhSzrEdg8FgMOQe6Q6EondPuOBiWzYNzwl2\nZZnRGf0SUuiMfm7ZHCFZZ3SdXd0UOqNKKZvO6BwHG2c6o+n5uB7or5RSdvVS6YwqpboDC4DXRWSK\nk3tbB4xRStUUkYNWnfpARYf7cJm0cmAMBoPBcANk5RFdGo/tmqN3f5iI3mFhABAFDLGz6QX8i91O\nFGjtz7Pox5hBaNWZC0BZO5sX0APRo1bb76Clzxra2fQE4oHngTroCNF4oEsmfawIXEEPzEFAD/SM\n8207m75WO+PQs8Vy1hFgZ6OArcBGdFpIK+s81eNSc5jDHOYwR+4d7m2sgOiMAiEka4vaH2EO7ZRD\nzxqvWoPrPKB0bv+jm8Mc5jCHOZIPI7FmMBgMhgKNWyXWDAaDwWDIb5iB0GAwGAwFGjMQGgwGg6FA\nYwZCg8FgMBRozEBoMBgMhgKNGQgNBoPBUKAxA6HBYDAYCjRmIDQYDAZDgcYMhAaDwWAo0JiB0GAw\nGAwFGjMQ5lOUUt2UUjuUUjFKqcNKqREu1JmjlDqolIpSSp1XSi1TSrXOi/4qpSorpb5QSu23/D2u\nlJqhlArMi/5adV5TSq1VSl1VSiVll69Z9M1bKTVZKXXK6s+1Sqmm2eGfm/zNkb5M5/3zzef1Zv8u\ncFJ/pvWZeM1tTuW22Kk5Mn+gd9GIR4uB10GLjUcDQzOo9yzQFqgMNARmABFAxbzmL3orrm+Ae4Bq\nlt+bgJ2AR17z16r3JvAiWiA+CbtdVvKAbx+gd1G5F72ryiz0Pp1l3e1jfunLm+HzWhC+CxzqB6N3\n8TkBjHabXzn14TKH+w70ZsnrHMomA4cz2U5x60vmvnzibxPL36C87C/QMRsHwkz7BvhbXzaD7co8\n0DurjC2ofZldPtvVyfbPa0H6LgDqW5/ZGuidg9w2EJpHo/mTtsASh7KlQBVXH8UopXyBZ4BL6F+u\n2ckN+2tR0vob5Rav0sZd/mYHWfGtGeBjX09EkoDlQLvscNKOvNyXaZGfPq8F4rtAKVUYvaXdSyJy\nyN1OmYEwf1IOOO1QZjsvn15FpdQzSqkI9EbHzwP3iMhZ97uYgiz7a0MpVRR4H1goIofd6Jszbtjf\nbCQrvpV3sLNxJp067iIv92Va5KfPa0H5LvgU2Coi32aHU2YgzCMopcZZC8DpHW+44a3mALeS/Mts\nsVKqVh72F6VUEeAXIA4YlMU2csxfQ8HGHZ/XHMIt3wXZjVLqUaANel0zxSV3vYeXuxoy3DAfo5+f\np8d56284qX89lbW7liYichW4CoQBG5VSe4En0UEJmSFH/FVKFQcWA55AJxGJyKSfNnLE3xwgK77Z\nysuhgwzs651yn2tpvnde7cu0yAufV1fJC98FmSEr/nYCagKXlbo+9nkCbyqlXhORwjfqlJkR5hFE\n5IKI7M/guGiZrwe6ODTRFTgiIpn9YvO0jjznr1KqNBCCXsS/2/qPmyVysX/dTVZ82wbEWnYAKKU8\n0F8w67LDSTvycl+mRa5/XjNBrn8XZJKs+PsaOrL1VutojP4B9wl6/fvGyc4IIXNkz4EOQY4DJgJ1\n0SHIUcAQO5tewL9Aees8CHgZaIoOmW6BDpmOAZrmIX8DrfPywB50qHR19GzGdnjnNX+tssrWf9LB\nWF+G1nnJPODbFOAs0N36LMwELpAz6RN5si9vhs9rQfguSKOdI5j0CXMA3YAd1of3MDDc4XowkAhU\nts5rAH+gF6Zj0Y/IfgRa5lF/g60vwETrb5LdeYe85q9VNtPBT9vfx/OAb17Au+jHT9HA2uz+0ssP\nfXkzfF5v9u+CNNpwa/qEsho1GAwGg6FAYtYIDQaDwVCgMQOhwWAwGAo0ZiA0GAwGQ4HGDIQGg8Fg\nKNCYgdBgMBgMBRozEBoMBoOhQGMGQoPBYDAUaMxAaDAYDIYCjRkIDQaDwVCg+T9I7LI8ny0l7gAA\nAABJRU5ErkJggg==\n",
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U2e+jRyg2dgsY9+gykZK+9B2WhYeT1SZZhMf6v8ufS57MIDj5Tg63mmIi0RhyY8YIKwim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\nuYZ1hBYWFhYW5RrWEVpYWFhYlGv8H/WUYiyAnuQuAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f95e214a850>"
+       "<matplotlib.figure.Figure at 0x1054d90d0>"
       ]
      },
      "metadata": {},
@@ -292,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -301,8 +306,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.00300487437607\n",
-      "[ 0.06292674] [ 0.00300487]\n"
+      "0.00283598573786\n",
+      "[ 0.06108146] [ 0.00283599]\n"
      ]
     }
    ],
@@ -365,16 +370,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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se+gLwZrDKxXJ1pNsjDHG+MnWI1tZ9Psiov+M5qsdXxFzLgZ2Ax4niEtdU4rS+UpT8pqS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UJgN3ARWAX8IfAHwFfHtUkA9gCh2LZ+B7gP+M5v5VHcZKqqqmb7EG540oczJ304c9KHMyd9OHPS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NopUM/2Zwb2nRiAjueIytL5BzR2iEzb48NMJmNGLdqJbcv1LwOJXLaHt2CPlpPrL+1aLI9I+n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NpFykRpfqvA8P1gyN0lTagvpS0JzpOkxWOk97pLbgvbJN8lSCPPklwWHtB2rg6MhVthcVpB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bYrNy90idZNgPRvRoc1SzA1odFhdVZYUpFqi07KZRz3ExrS664oNroDhfqanmmmquarPl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5KUgW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\n2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K9ULxmFcC15KS/v94xdmgi8otJGXAlhHxQUkTgE/3lFtI+jNwb0TsVzN+HeBeYKuImCzp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\nZEYCNwGvB64FZtb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FmNWAfwMXk+p+1yHt8312RBxTjBlNahF3O3AsqdPFecD1EbH7IOc2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\n7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i5zzhUmfQisLie/Tb90NORQ4UHK8RG45TjYn9wfqaObglItod5kx5e8o5LcbyWBNLOi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NGPnPYINd03bOn9VZ9zKmdVlgQes8yMRNf5E6RVDp9qLDb90HM3DK/xQGQJRg90nyei4G5G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\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jPnRFOUqoSFYURSlFXFRwGDKc3gUYN4kfzjyWfTU2BoI8seesIDtazgUGIRV25gDPWMvU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\nySIiIiIiKf4/Eo3gazK03a0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f95e1f440d0>"
+       "<matplotlib.figure.Figure at 0x101aaaa10>"
       ]
      },
      "metadata": {},
@@ -450,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -459,49 +464,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "*** Maximum RMS bunch length 0.172438746718m.\n",
-      "... distance to target bunch length: -1.3959e-02\n",
-      "... distance to target bunch length: -1.3844e-02\n",
-      "... distance to target bunch length: -1.0074e-03\n",
-      "... distance to target bunch length: -9.3602e-05\n",
-      "... distance to target bunch length: -8.1241e-07\n",
-      "... distance to target bunch length: -6.6769e-10\n",
+      "*** Maximum RMS bunch length 0.177942536689m.\n",
+      "... distance to target bunch length: 6.8733e-04\n",
+      "... distance to target bunch length: 7.9535e-04\n",
+      "... distance to target bunch length: -3.7752e-06\n",
+      "... distance to target bunch length: 2.0452e-08\n",
+      "... distance to target bunch length: 5.1766e-13\n",
       "--> Bunch length: 0.115\n",
-      "--> Emittance: 0.112246667272\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/share/anaconda/lib/python2.7/site-packages/scipy/integrate/quadpack.py:321: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
-      "  warnings.warn(msg, IntegrationWarning)\n"
+      "--> Emittance: 0.100721159479\n"
      ]
     }
    ],
    "source": [
     "matched_beam = copy.deepcopy(beam)\n",
-    "matchor = MatchRFBucket2D(macroparticlenumber, intensity, e, m_p, circ, gamma, rfbucket, sigma_z=sigz)\n",
+    "#matchor = MatchRFBucket2D(macroparticlenumber, intensity, e, m_p, circ, gamma, rfbucket, sigma_z=sigz)\n",
+    "matchor = ParticleGenerator(macroparticlenumber, intensity, e, m_p, circ, gamma, \n",
+    "                            distribution_z=RF_bucket_distribution(rfbucket, sigma_z=sigz))\n",
+    "\n",
     "matchor.update(matched_beam)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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5y/DHf/Rh1I2jyJmqo57kEyoUklCccxx8+iAAAy8aSFUVvPMOnHWWz8HiwMKF\ncOgQbNrkdxKJNd0qFGZ2npmtN7N6M9thZj/sxjYlZhZoc3uz95FFOla9oZr6knrSh6aTf1I+r77q\nHRaboy/IXUpJgUsvhf/+b7+TSKzpslCY2RzgGeA5YDqwBLjdzK7uYlMH3AEMa3W7sDdhRbpy8G9e\na2LQxYOwVGPpUnU79cRXvgJPPKFJAuXTzHXxijCzx4GRzrlTWi27E/iyc25MJ9vtAB5yzv2s1yHN\nHHjdCiKdaa5rpvwf5fQZ3Yc+k3MYMQLefRfGdPhKldac8yYKfOIJmDPH0VzZTFq+jnmJVxY8gsM5\n16tDObrT9bQQeLHNspeAUWY2oottv2dmB82s2MzuMbMBIaUU6abUvqkMumgQOdNzeOstGDlSRaIn\nzOBrX4Mn76xm5YkrKbqgyO9IEgO681VhGLCvzbKW+8OBPR1sdy+wHigFJgK3AYvMbIZzrj6ErCI9\n8te/whe/6HeK+HPVVTBvSh/OramnbnsdRw4cIWNwht+xxEcRa1M6537d6u4mM1sDbAMuAf4cyj6X\nLFly9OeCggIKCgp6kVASWSAATz8NhYV+J4k/xxwDs09No2ZHP7KKyylbWsbwrw/3O5Z0U2FhIYVh\nfuF3Z4yiBHjYOffTVsvOBF4BjnXOddSiaG9fe4F7nHN39Cikxiikh5Yt82ZELVLPSUiWLoWXr9nN\nJSUfMPDCgUx9ZqrfkSQE0RyjWAYsarPsHKCkh0XiWGAIsKv78US6p3J1JY2HG4/ef/JJdTv1xjnn\neGdpA5S/XE5zbbPPicRP3SkUdwPzzOw2M5tgZlcC38M79BUAM7vEzN43s+HB+wvM7Fozm2Vmo8xs\nEfAsUAI8Hf7/hiQzF3Bs+sImlg9bTs3mGpqbvaN2Lr3U72TxKy0Nvn5dJqUDcskan0XDrga/I4mP\nuiwUzrnVwMXA+XiD00uAG51zv2u1Wj4wDkgP3m/AG4t4GdgC3AcsB05yztWGK7wIeHM7NexqIGNI\nBlkTsnj1Va+ffeJEv5PFt6uugh8yg4FPzSFrfJbfccRHXY5RxAKNUUhn3vvae5Q+WsrIG0Zy/M+P\n5/LLYf58uOYav5PFvxtugLo6uOcev5NIKMI1RqFCIXGtsbyRFSNWEKgPMO+DeTQPzeK44+CDD2Dw\nYL/Txb/du2HqVO/5HKgJZeNONAezRWJW6R9LCdQH6H9Wf7LGZvHUU3DaaSoS4XLMMd6ssr/+ddfr\nSuJSoZAxkGy8AAAOnElEQVS4lndyHkO+OoRjvncMAI88Aldc4W+mRHPjjXD//VCmi94lLXU9ScJ4\n/30oKICPPoIMnUgcVtd9qYbJu/dx4a39GXC2ZuKJF+p6Emnj/vvhG99QkYiEy8eWMfqdXez87V6/\no4gP1KKQhFBT400AuHYtjBrld5rEU7+znndGv0NzWgqnl59MWo5mlI0HalGItPLEE94V2lQkIqPP\nqD5kzs4jtSnA9ocP+B1HokyFQuJOc00zddvrjt53Du67D779bR9DJYHR3/EmBiy6Q91PyUaFQuLO\nvkf38e64d9nx4x0AvPYa1NfDorYzkklYDbl0CCk5qfTbV8mWN3WlgGSiQiFxxQUcH9/9MQQge1o2\nAHfeCddd513zWSInNTuVSX+eyNr/vYCbf9vH7zgSRRrMlrhy8O8HKb6omMxRmczfNp+iTSmcdx5s\n3w6ZmX6nSw41NXDiifC3v8HcuX6nkc5oMFuS0q67vFnqj/3+saSkpXDXXfDv/64iEU3Z2XDzzXDt\ntd74kCQ+FQqJG4ffPEzF2xWk9U9j+DeHs2ULvPQSXH2138mSzze+AVVV8PjjfieRaFDXk8SNhj0N\n7LprFxnDMxh5/UguuwymTYP//E+/kyWnd96BL3wB3nsP8vP9TiPt0eyxktQ2boTPfx62bYOcHL/T\nJKfKVZX89au7KVo0nrvvVedELNIYhSS1m2+G669XkfCLCzje/5f3Of6DUg49uofly/1OJJGkQiFx\np7DQm6pDJ9j5x1KMMbePAeBK28m3Lm+kpsbnUBIxKhQSV5qavKOcfvlL6NvX7zTJbdDFg8g/JZ+U\nyka+k7qd66/3O5FEigqFxLSKFRUcfvPw0fsPPgj9+8OXvuRjKAG8/u8THzgRyzAmbNvLh0+W89RT\nfqeSSNBgtsQs1+xYM3cN1euqmfTEJJpOHcKMGfDKK97RThIbSm4tYdevd5F28yQu+vlA3noLxo/3\nO5VA+AazNVewxKw99++hel01mcdmMuD8gVx8qTcuoSIRW0beMJJhVw6jz6g+3JbtHTL77rs60CCR\nqEUhMalhbwMrJ6ykubKZyU9N5vnKwdx9N6xcqQsTxTLn4JvfhP374emnITXV70TJTYfHSsJyzvHB\nNR/QXNnMwPMHcmD8IK69Fh59VEUi1pnBb38LtbXeQQf6bpcYVCgk5jTub6RqVRWpuamMuGMsX/qS\ncccdMH2638mkOzIy4MknYdnrzfzqV36nkXBQ15PEpMbDjVStr+Hq+/qRmwu//73fiaS7nHPsvnc3\nO+/ezbeaZvKdGzN0zotP1PUkCS29Xzo/faYf+/d7XRkSP1yjo/SPpTSW1PHA4GLu/nkz//VffqeS\n3lChkJh0113w8sveNQ/66Bo5cSUlI4Upz0whc2QmR9ZV8ti4zfzyFwFuvVVjFvFKhUJ811zfjGv+\n5BPkF7+ABx6AF17wTq6T+JM5IpNpL00jbUAa9a+V8deFW3luqeOf/9mbnlziiwqF+KqpsoniC4rZ\n8s0tBJodP/kJPPIIvPkmHHec3+mkN7InZDN16VRS+qZQ+3IZL/2pgexs76CEt97yO530hAazxTf1\nH9VTdH4RNUU1pA1O5w/zZrPpYB+eeQaGDvU7nYTLoX8cos/xfcgamwXA3//unThZUAC33w6jRvmb\nL5FpMFviWsWKCtbMW0NNUQ2po/ty88BZNA7oQ2GhikSiGfD5AUeLBMCFF8KWLd51t2fOhK99DTZs\n8C+fdE0tCom6Q/84RNHiIlyTo3JsP75/aDI/vjOdq67yTtiS5HHoEPzud3DvvXDssXDZZfBP/wQj\nRvidLDHoCncSt5rrmnlj8lpeq+jPqhnHc/9DKYwZ43cqibY9D+5h8JcGk94/naYmePVV+POf4Zln\nYOxYWLwYzj8fZs2CFPV9hCRqXU9mdp6ZrTezejPbYWY/7MY26WZ2p5ntMbNaM3vLzGb1JqjEP+fg\njTfgnAtT+Z7NYt4fxvLiKyoSyWjv7/ey9d+2smrKKg4uPUhaGixa5B3IsH+/d3h0dTVccYXXurjq\nKnjqKR0x5ZdOWxRmNgdYAdwJPAYsAO4HfuCce6CT7X4DXA58DdgBXA9cAEx0zpX2OKRaFHHJBRwN\nuxpIO6YPzz3nvflLS+GGG7wPAM3blLzqdtTx3lffo3JFJQD9zuzH6JtHk39q/tFvwS0+/BCee867\nLV8O8+d7LY3Fi2HcOD/Sx4+odD2Z2ePASOfcKa2W3Ql82TnX7vdAM8sDSoFrnHMPBZelALuB+51z\nt/Q4ZBwVisLCQgoKCvyO0aVI5Qw0BqhaXUX5P8rZ9fA+6g418+28+QwdncY118CXvwxpPZjcPh6e\nz3jICLGX0zU7Pv4/H1NySwnNFc0ATHl2CsU5xR3mrK72rkfSUjgyM2H2bG9QfMYMbwr6Y4+NzlhX\nrD2f7YlW19NC4MU2y14CRplZR8NNs4HM1ts55wLAy8ApHWyTMAoLC/2O0C2h5gwcCRBoCrT7uw2L\ni3gz5y3WnbyOkiUlNO+qp8lS+J9f1rJ8uTdQ2ZMi0Zuc0RQPGSH2clqqcdwPj2PBjgWM+tEosqdm\nM+DzA9rNeXDpQWreq6FvWjMXX+xd6XD3bnj+efjiF6GiAu65B+bOhYEDvUNvv/99ePhhWLWKiFzP\nO9aez0jq6m07DNjXZlnL/eHAnna2Gd5mvRalQETHKepK6mgqa/rM8sxRmWQM+mw/R7Ku31TVRPXG\navY+vJeq8gDDLhhA3vjPXoB65892cvitwzQdbvJuZU00HmxkzFPTqRnfn337vMMcN2+GjRth8TLH\nvGZHdf++5BX0Z9K/DmLgov5Yqg5lko6l909nzE/HMPrW0Z/pdgI4sv8IxRcUe3cMMo/LJPO4TLLG\nZTHxDxOYONH7EtJizwdNbL5vP7v2pfDRI8bbH8HevUbmoFRS5g9k6lSYPNkbJB83Dpqqm6h4swJS\nwFIMzPs3NSeVvPl5n8nTXNdMTXENDXsaqFrzyaCJZRo5Uz57tabm+mZqN9V+Znm41o+GrrqeGoDv\ntnQhBZdNBoqAuc65Ne1s88/A/wMynHNNrZbfBZzrnJvS45DBricREem5SHc97eWTFkKLoa1+19E2\n4LVG2m7XXgtERERiWFddT8uARcBPWy07ByhxznX0ob8GaAiu13ow+yy8I6Z6rLfVUEREQtdVi+Ju\nYJ6Z3WZmE8zsSuB7wB0tK5jZJWb2fsvgtnOuEq8g3G5mi4NdVb/HG+Du8JBaERGJTZ22KJxzq83s\nYuB24Fq8bqUbnXO/a7VaPjCuzb6uA47gtSj6AauBs0M5h0JERPwVF1N4iIiIfzSDioiIdEqFQkRE\nOqVCISIinVKhEBGRTqlQiIhIp1QoRESkUyoUIiLSKRUKERHplAqFiIh0SoVCREQ6FbOFwszOM7P1\nZlZvZjvM7Ifd2GasmT1lZqVmVm1ma83s0ljLGdzuNDN7zcyqgreVZjY61nK22v4RMwuY2U2Ryhh8\nnB7lNLORZvaAmW01s1oz22Vmv+/kCowRzRPcJt3M7jSzPcFMb5lZRC/aFWvPW7hytrN9TL4OW20X\ntfd1VD8jnXMxdwPmAI3Az4D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NYz1dZGbLzGy6mf3UzLYr4b5ESml/YEYlFomUhvKQkipVobgJ+CJwNDAWOB6YYGZdS7Q/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\nRCQrFQoREcnq/wPOeLWS/MyIfQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f95e23bf390>"
+       "<matplotlib.figure.Figure at 0x116b0fd90>"
       ]
      },
      "metadata": {},
@@ -541,7 +535,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/RFQTest.ipynb b/testing/interactive-tests/RFQTest.ipynb
index 91dc0023..3bb79d2e 100644
--- a/testing/interactive-tests/RFQTest.ipynb
+++ b/testing/interactive-tests/RFQTest.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -24,8 +24,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ERROR: This interactive test needs the PySussix module. Trying PySUSSIX...\n",
-      "PySUSSIX found\n"
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
+      "\n",
+      "\n",
+      "ERROR: This interactive test needs the PySussix module. Trying PySUSSIX...\n"
+     ]
+    },
+    {
+     "ename": "ImportError",
+     "evalue": "dlopen(../../../PySUSSIX/pysussix.so, 2): Library not loaded: /usr/local/lib/gcc/4.9/libgfortran.3.dylib\n  Referenced from: /Users/stefan/CERN/PySUSSIX/PySussix.so\n  Reason: image not found",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-862e12413d5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'ERROR: This interactive test needs the PySussix module. Trying PySUSSIX...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mPySUSSIX\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSussix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PySUSSIX found'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/stefan/CERN/PySUSSIX/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mPySussix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/stefan/CERN/PySUSSIX/PySussix.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpysussix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: dlopen(../../../PySUSSIX/pysussix.so, 2): Library not loaded: /usr/local/lib/gcc/4.9/libgfortran.3.dylib\n  Referenced from: /Users/stefan/CERN/PySUSSIX/PySussix.so\n  Reason: image not found"
      ]
     }
    ],
@@ -131,12 +146,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) \n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    \n",
     "    return bunch"
    ]
@@ -358,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/SlicingTest.ipynb b/testing/interactive-tests/SlicingTest.ipynb
index ec4b4f80..677f0755 100644
--- a/testing/interactive-tests/SlicingTest.ipynb
+++ b/testing/interactive-tests/SlicingTest.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -15,11 +15,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from scipy.constants import m_p, c, e\n",
@@ -33,22 +43,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "slideshow": {
      "slide_type": "-"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# HELPERS\n",
     "\n",
@@ -207,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -232,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -275,12 +277,12 @@
     "beta_z = np.abs(eta) * circumference / (2 * np.pi * Qs)\n",
     "turn_period = circumference / (beta * c)\n",
     "\n",
-    "bunch = generators.Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well\n",
-    "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, \n",
-    "    beta_z, epsn_z\n",
-    "    ).generate()"
+    "bunch = generators.generate_Gaussian6DTwiss(\n",
+    "        macroparticlenumber=n_particles, intensity=intensity, charge=e,\n",
+    "        gamma=gamma, mass=m_p, circumference=circumference,\n",
+    "        alpha_x=inj_alpha_x, beta_x=inj_beta_x, epsn_x=epsn_x,\n",
+    "        alpha_y=inj_alpha_y, beta_y=inj_beta_y, epsn_y=epsn_y,\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)"
    ]
   },
   {
@@ -304,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -314,29 +316,29 @@
      "output_type": "stream",
      "text": [
       "bunch._slice_sets {}\n",
-      "bunch._slice_sets {<PyHEADTAIL.particles.slicing.UniformBinSlicer object at 0x7f5a86e33310>: <PyHEADTAIL.particles.slicing.SliceSet object at 0x7f5a5dd9ff50>}\n",
+      "bunch._slice_sets {<PyHEADTAIL.particles.slicing.UniformBinSlicer object at 0x111a2bad0>: <PyHEADTAIL.particles.slicing.SliceSet object at 0x1117aa750>}\n",
       "\n",
       " UNIFORM BIN SLICE SET, NSIGMAZ=2, 10 SLICES \n",
       "\n",
       "slicer.config ('uniform_bin', 10, 2, None)\n",
-      "z_cut_head 22.8317097073\n",
-      "z_cut_tail -22.7509767668\n",
-      "z_centers [-20.47184244 -15.9135738  -11.35530515  -6.7970365   -2.23876785\n",
-      "   2.31950079   6.87776944  11.43603809  15.99430674  20.55257538]\n",
+      "z_cut_head 23.1031482231\n",
+      "z_cut_tail -22.9447282057\n",
+      "z_centers [-20.64233438 -16.03754674 -11.4327591   -6.82797146  -2.22318381\n",
+      "   2.38160383   6.98639147  11.59117912  16.19596676  20.8007544 ]\n",
       "n_slices 10\n",
-      "slice_widths [ 4.55826865  4.55826865  4.55826865  4.55826865  4.55826865  4.55826865\n",
-      "  4.55826865  4.55826865  4.55826865  4.55826865]\n",
-      "slice_positions [   0  323  929 1869 3217 4818 6337 7636 8640 9233 9548]\n",
-      "n_macroparticles_per_slice [ 323  606  940 1348 1601 1519 1299 1004  593  315]\n",
+      "slice_widths [ 4.60478764  4.60478764  4.60478764  4.60478764  4.60478764  4.60478764\n",
+      "  4.60478764  4.60478764  4.60478764  4.60478764]\n",
+      "slice_positions [   0  313  920 1837 3188 4741 6371 7698 8655 9239 9524]\n",
+      "n_macroparticles_per_slice [ 313  607  917 1351 1553 1630 1327  957  584  285]\n",
       "particles_within_cuts [   0    1    2 ..., 9997 9998 9999]\n",
-      "particle_indices_by_slice [  98  116  127 ..., 9901 9963 9970]\n",
+      "particle_indices_by_slice [  11   33   68 ..., 9917 9952 9986]\n",
       "  Particle_indices_of_slice <-> slice_index_of_particle PASSED\n",
-      "line_density_derivative_gauss [  1.67508141e-09   1.82602999e-09   2.19300847e-09   1.95328441e-09\n",
-      "   5.06079576e-10  -8.93774786e-10  -1.52415700e-09  -2.08646542e-09\n",
-      "  -2.03615445e-09  -1.64548701e-09]\n",
-      "line_density_derivative [  1.67509322e-09   1.82602918e-09   2.19597026e-09   1.95624844e-09\n",
-      "   5.06079400e-10  -8.93777654e-10  -1.52415726e-09  -2.08942723e-09\n",
-      "  -2.03911524e-09  -1.64549793e-09]\n",
+      "line_density_derivative_gauss [  1.72261001e-09   1.76949821e-09   2.17671282e-09   1.86031271e-09\n",
+      "   8.17365304e-10  -6.62095877e-10  -1.97163725e-09  -2.17378436e-09\n",
+      "  -1.96578210e-09  -1.75190693e-09]\n",
+      "line_density_derivative [  1.72262278e-09   1.76949687e-09   2.17964515e-09   1.86324505e-09\n",
+      "   8.17366931e-10  -6.62096510e-10  -1.97164138e-09  -2.17671552e-09\n",
+      "  -1.96871175e-09  -1.75191908e-09]\n",
       "bunch._slice_sets {}\n"
      ]
     }
@@ -364,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -378,17 +380,17 @@
       " UNIFORM CHARGE SLICE SET, NSIGMAZ=2, 10 SLICES \n",
       "\n",
       "slicer.config ('uniform_charge', 10, 2, None)\n",
-      "z_cut_head 22.8317097073\n",
-      "z_cut_tail -22.7509767668\n",
-      "z_centers [-18.10359359 -11.19154779  -7.28820889  -4.22534796  -1.44829763\n",
-      "   1.2797133    4.20637029   7.46197719  11.33020541  18.16302408]\n",
+      "z_cut_head 23.1031482231\n",
+      "z_cut_tail -22.9447282057\n",
+      "z_centers [-18.22980826 -11.2085133   -7.24591023  -4.10944373  -1.25441793\n",
+      "   1.46566137   4.19305029   7.30320493  11.27646814  18.31241094]\n",
       "n_slices 10\n",
-      "slice_widths [ 9.29476634  4.52932527  3.27735252  2.84836934  2.70573132  2.75029054\n",
-      "  3.10302343  3.40819037  4.32826607  9.33737126]\n",
-      "slice_positions [   0  955 1909 2863 3818 4773 5728 6683 7638 8593 9548]\n",
-      "n_macroparticles_per_slice [955 954 954 955 955 955 955 955 955 955]\n",
+      "slice_widths [ 9.42983989  4.61275004  3.3124561   2.96047691  2.74957468  2.69058391\n",
+      "  2.76419393  3.45611535  4.49041106  9.58147456]\n",
+      "slice_positions [   0  952 1905 2857 3810 4763 5715 6667 7619 8571 9524]\n",
+      "n_macroparticles_per_slice [952 953 952 953 953 952 952 952 952 953]\n",
       "particles_within_cuts [   0    1    2 ..., 9997 9998 9999]\n",
-      "particle_indices_by_slice [  32   44   46 ..., 9964 9970 9974]\n",
+      "particle_indices_by_slice [  11   21   33 ..., 9952 9966 9986]\n",
       "  Particle_indices_of_slice <-> slice_index_of_particle PASSED\n",
       "\n",
       "\n",
@@ -420,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -448,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -956,7 +958,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/TransverseGaussianSpaceCharge.ipynb b/testing/interactive-tests/TransverseGaussianSpaceCharge.ipynb
index cc8dcab9..07f3c9e0 100644
--- a/testing/interactive-tests/TransverseGaussianSpaceCharge.ipynb
+++ b/testing/interactive-tests/TransverseGaussianSpaceCharge.ipynb
@@ -2,21 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PyHEADTAIL v1.2.0-91-g43649bd563-dirty\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys, os\n",
     "BIN = os.path.abspath(\"../../..\")\n",
@@ -37,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -96,25 +86,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "bunch = generators.Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well\n",
+    "bunch = generators.generate_Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well\n",
     "    n_particles, intensity, e, m_p, circumference, gamma,\n",
-    "    inj_alpha_x, inj_beta_x, epsn_x,\n",
-    "    inj_alpha_y, inj_beta_y, epsn_y, \n",
-    "    beta_z, epsn_z\n",
-    "    ).generate()\n",
+    "    inj_alpha_x, inj_alpha_y,\n",
+    "    inj_beta_x, inj_beta_y, beta_z,\n",
+    "    epsn_x, epsn_y, epsn_z,\n",
+    "    )\n",
     "slicer = slicing.UniformBinSlicer(20, n_sigma_z=4)\n",
     "besc = TransverseGaussianSpaceCharge(slicer, circumference)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -125,16 +115,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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LOp1pBPwE+hB4jH5/It96663f3Z933nmHPl8CgZF0ue5gbGwlbtu27YTlJ0x4kU5nNB2O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USEREpIgCiYiIFFEgERGRIgokIiJSRIFERESKKJCIiEgRBRIRESmiQCIiIkUU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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f673f1bcb10>"
+       "<matplotlib.figure.Figure at 0x110c2aa50>"
       ]
      },
      "metadata": {},
@@ -150,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -159,7 +149,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.00260426460177 0.00247853634425\n"
+      "0.00262129230329 0.00250541304955\n"
      ]
     }
    ],
@@ -169,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -177,18 +167,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f64f53b2650>]"
+       "[<matplotlib.lines.Line2D at 0x110e35810>]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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dsXDhwoavzZw5EwaDAfHx8QAAf39/5OTkwMvLq/GJ2P3iFBRF3Bz96CNAp5Odxj5dvAj4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\nExGpCIs6EZGK/H+YDxFxVmw5nQAAAABJRU5ErkJggg==\n",
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0W9Km5cSzViTxNmBP0g4+Vp7fAyuS/o2ZDUnNRb3oerkAOLposfd0B7B+RIwFfgTMKi+i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\nWYX8f66ZpyFFQr0tAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f64f54d2cd0>"
+       "<matplotlib.figure.Figure at 0x110d46a10>"
       ]
      },
      "metadata": {},
@@ -201,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -214,16 +204,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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58cVtlJV9BcwkGEzjoYfu5t//nkqPHn/i8suvZtOmTTv0fRQXFzN8+OU0aXIA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\no9giNEb2hFxbluB2tExgvC97Ws6R+Ql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dZewE0tH8X++hkewV6Irtc9dPD/An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\n2LEjc+fOpWfPnjXqrF69mtmzZ7N69WpycnIYNWoUFRUVde2mYRiGEWL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6qnruSR5O82yVefn+saV/i/fOL9ybktr/dOl3cbTD6N0RgFAMamiIZPPctDWm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\nkJAIIYSohYRECCFELf5/1xQgMXdVDr4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f671b13c190>"
+       "<matplotlib.figure.Figure at 0x110e6fd90>"
       ]
      },
      "metadata": {},
@@ -237,16 +227,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Rcey01LG6RK/To8LdA4kxA6SOQiQqFjl1yPRJydh3+3R4q2pw+6kjWLn+P1JH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\nVl8QtLX+n18QNDQ0CNnZ2UJ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eH3GU29Bo22h2DCKjscjJcLr0gXysvfQ6HP36vTLtj7lm5wEAcbqiEHMwCk1W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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f671b0ee9d0>"
+       "<matplotlib.figure.Figure at 0x110fb8a50>"
       ]
      },
      "metadata": {},
@@ -398,7 +388,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/TransverseTrackingCythonTest.ipynb b/testing/interactive-tests/TransverseTrackingCythonTest.ipynb
index 7c7dc3ec..5126fe05 100644
--- a/testing/interactive-tests/TransverseTrackingCythonTest.ipynb
+++ b/testing/interactive-tests/TransverseTrackingCythonTest.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-122-g056083c437-dirty\n",
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
       "\n",
       "\n"
      ]
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -125,19 +125,19 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) \n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    \n",
     "    return bunch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -516,7 +516,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/TransverseTrackingTest.ipynb b/testing/interactive-tests/TransverseTrackingTest.ipynb
index 9cd9685a..642efd96 100644
--- a/testing/interactive-tests/TransverseTrackingTest.ipynb
+++ b/testing/interactive-tests/TransverseTrackingTest.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-122-g056083c437-dirty\n",
+      "PyHEADTAIL v1.2.0-153-gbc0ea69a06-dirty\n",
       "\n",
       "\n"
      ]
@@ -125,12 +125,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) \n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    \n",
     "    return bunch"
    ]
@@ -596,7 +596,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/interactive-tests/WakeTest.ipynb b/testing/interactive-tests/WakeTest.ipynb
index e0720d25..ef72f680 100644
--- a/testing/interactive-tests/WakeTest.ipynb
+++ b/testing/interactive-tests/WakeTest.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyHEADTAIL v1.2.0-116-g1f823ad631-dirty\n",
+      "PyHEADTAIL v1.3.0-6-gb39dca047d-dirty\n",
       "\n",
       "\n"
      ]
@@ -119,12 +119,12 @@
     "    epsn_y = 3.75e-6 # [m rad]\n",
     "    epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)\n",
     "    \n",
-    "    bunch = generators.Gaussian6DTwiss(\n",
+    "    bunch = generators.generate_Gaussian6DTwiss(\n",
     "        macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,\n",
     "        gamma=gamma, mass=m_p, circumference=C,\n",
     "        alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,\n",
     "        alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,\n",
-    "        beta_z=beta_z, epsn_z=epsn_z).generate()\n",
+    "        beta_z=beta_z, epsn_z=epsn_z)\n",
     "    # print ('bunch sigma_z=' + bunch.sigma_z())\n",
     "    \n",
     "    return bunch\n",
@@ -373,7 +373,7 @@
      "output_type": "stream",
      "text": [
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0c3e383350>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x10f8b0bd0>\n"
      ]
     }
    ],
@@ -413,7 +413,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f07bca4fd90>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x10f94b0d0>\n"
      ]
     }
    ],
@@ -453,7 +453,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f0788cb84d0>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x1146d8350>\n"
      ]
     }
    ],
@@ -494,7 +494,7 @@
      "output_type": "stream",
      "text": [
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f078a278f10>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x114196210>\n"
      ]
     }
    ],
@@ -536,8 +536,8 @@
      "text": [
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f078a0fdad0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f078a0fd190>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114058bd0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114058c90>\n"
      ]
     }
    ],
@@ -579,12 +579,12 @@
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0788c68a10>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0788c685d0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0788c68290>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0788c80d90>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0788c80890>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0788c80710>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114365d10>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114365dd0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114365e90>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114365f50>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114340050>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114340110>\n"
      ]
     }
    ],
@@ -627,10 +627,10 @@
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f078cfa2bd0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f078cfa2790>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f078cfa2490>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x7f0788a63310>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114247590>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x114247390>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114247090>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x114243f50>\n"
      ]
     }
    ],
@@ -671,11 +671,11 @@
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0787d72390>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f0787d72290>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0787d72150>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x7f0787d4dcd0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.ConstantWakeKickZ object at 0x7f0787d4db50>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x1166c5650>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x1166c5310>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x1166cde90>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x1166cdd90>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.ConstantWakeKickZ object at 0x1166cd6d0>\n"
      ]
     }
    ],
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -713,8 +713,8 @@
      "text": [
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0c3a359f90>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0c3a162f50>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x110246210>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x1166f6910>\n"
      ]
     }
    ],
@@ -755,10 +755,10 @@
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0788e0f590>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f0788e0fd50>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0788e0f290>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x7f0788e0ff50>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x1167e49d0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x1146a3ad0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x1146a3ed0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x113deeb10>\n"
      ]
     }
    ],
@@ -786,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -807,18 +807,18 @@
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
       "*** PyHEADTAIL WARNING! *** WARNING: Acceleration not handled properly by this kind of convolution due to changing bunch length!\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0c39f20b10>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0c39f203d0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0c39f06e90>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f0c39f06e10>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0c39f066d0>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x7f0c39f8ee90>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x7f0c3e36d950>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x7f0c39f2b190>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickXY object at 0x7f0c39f2bf10>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickYX object at 0x7f0c3e3dd410>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x7f0c39f62090>\n",
-      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x7f0c39f59810>\n"
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x1144b9a50>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114480d90>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x114480650>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x114382c50>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x114264f10>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x114212490>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickX object at 0x113f80850>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickY object at 0x1143b2cd0>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickXY object at 0x1143b2590>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.DipoleWakeKickYX object at 0x1143b2e10>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickX object at 0x1143b2d10>\n",
+      "<PyHEADTAIL.impedances.wake_kicks.QuadrupoleWakeKickY object at 0x1143b2bd0>\n"
      ]
     }
    ],
@@ -876,7 +876,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/testing/unittests/autoruntests/ApertureNLossesTest.py b/testing/unittests/autoruntests/ApertureNLossesTest.py
index 2d36c2b8..12bfdc33 100644
--- a/testing/unittests/autoruntests/ApertureNLossesTest.py
+++ b/testing/unittests/autoruntests/ApertureNLossesTest.py
@@ -8,7 +8,7 @@
 
 
 from PyHEADTAIL.particles.particles import Particles
-from PyHEADTAIL.particles.generators import Gaussian6DTwiss
+from PyHEADTAIL.particles.generators import generate_Gaussian6DTwiss
 from PyHEADTAIL.trackers.simple_long_tracking import RFSystems
 import PyHEADTAIL.aperture.aperture as aperture
 
@@ -72,12 +72,11 @@ def plot_phase_space(bunch, ax0, ax1, ax2, col):
     # In[8]:
 
     def generate_bunch(n_particles):
-        bunch = Gaussian6DTwiss(
+        bunch = generate_Gaussian6DTwiss(
             n_particles, intensity, e, m_p, circumference, gamma,
-            inj_alpha_x, inj_beta_x, epsn_x,
-            inj_alpha_y, inj_beta_y, epsn_y,
-            beta_z, epsn_z
-            ).generate()
+            inj_alpha_x, inj_alpha_y, inj_beta_x, inj_beta_y, beta_z,
+            epsn_x, epsn_y, epsn_z
+            )
         return bunch
 
 
diff --git a/testing/unittests/autoruntests/DetunersCythonTest.py b/testing/unittests/autoruntests/DetunersCythonTest.py
index 1914f09e..fb4f8480 100644
--- a/testing/unittests/autoruntests/DetunersCythonTest.py
+++ b/testing/unittests/autoruntests/DetunersCythonTest.py
@@ -41,13 +41,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, alpha_0,
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
-        #print bunch.sigma_z()
+            beta_z=beta_z, epsn_z=epsn_z)
 
         return bunch
 
diff --git a/testing/unittests/autoruntests/DetunersTest.py b/testing/unittests/autoruntests/DetunersTest.py
index 7272aff0..0429f105 100644
--- a/testing/unittests/autoruntests/DetunersTest.py
+++ b/testing/unittests/autoruntests/DetunersTest.py
@@ -42,12 +42,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, alpha_0,
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
         #print bunch.sigma_z()
 
         return bunch
diff --git a/testing/unittests/autoruntests/GeneratorTest.py b/testing/unittests/autoruntests/GeneratorTest.py
deleted file mode 100644
index 257b5f7b..00000000
--- a/testing/unittests/autoruntests/GeneratorTest.py
+++ /dev/null
@@ -1,198 +0,0 @@
-
-# coding: utf-8
-
-# In[1]:
-
-import sys, os
-BIN = os.path.dirname(__file__) # ./PyHEADTAIL/testing/unittests/
-BIN = os.path.abspath( BIN ) # absolute path to unittests
-BIN = os.path.dirname( BIN ) # ../ -->  ./PyHEADTAIL/testing/
-BIN = os.path.dirname( BIN ) # ../ -->  ./PyHEADTAIL/
-BIN = os.path.dirname( BIN ) # ../ -->  ./
-BIN = os.path.dirname( BIN ) # ../ -->  ./
-
-sys.path.append(BIN)
-
-
-# In[2]:
-
-import numpy as np
-from scipy.constants import m_p, c, e
-
-
-# In[3]:
-
-from PyHEADTAIL.particles.particles import Particles
-import PyHEADTAIL.particles.generators as generators
-from PyHEADTAIL.trackers.transverse_tracking import TransverseMap
-from PyHEADTAIL.trackers.simple_long_tracking import RFSystems, LinearMap
-
-
-# In[4]:
-
-# In[5]:
-def run():
-# general simulation parameters
-    n_turns = 3
-    n_particles = 3
-    n_segments = 1
-
-    # machine parameters
-    circumference = 157.
-    inj_alpha_x = 0
-    inj_alpha_y = 0
-    inj_beta_x = 5.9 # in [m]
-    inj_beta_y = 5.7 # in [m]
-    Qx = 5.1
-    Qy = 6.1
-    gamma_tr = 4.05
-    alpha_c_array = [gamma_tr**-2]
-    V_rf = 8e3 # in [V]
-    harmonic = 1
-    phi_offset = 0 # measured from aligned focussing phase (0 or pi)
-    pipe_radius = 5e-2
-
-    # beam parameters
-    Ekin = 1.4e9 # in [eV]
-    intensity = 1.684e12
-    epsn_x = 2.5e-6 # in [m*rad]
-    epsn_y = 2.5e-6 # in [m*rad]
-    epsn_z = 1.2 # 4pi*sig_z*sig_dp (*p0/e) in [eVs]
-
-    # calculations
-    gamma = 1 + e * Ekin / (m_p * c**2)
-    beta = np.sqrt(1 - gamma**-2)
-    eta = alpha_c_array[0] - gamma**-2
-    if eta < 0:
-        phi_offset = np.pi - phi_offset
-    Etot = gamma * m_p * c**2 / e
-    p0 = np.sqrt(gamma**2 - 1) * m_p * c
-    Qs = np.sqrt(np.abs(eta) * V_rf / (2 * np.pi * beta**2 * Etot))
-    beta_z = np.abs(eta) * circumference / (2 * np.pi * Qs)
-    turn_period = circumference / (beta * c)
-
-    # BETATRON
-    # Loop on number of segments and create the TransverseSegmentMap
-    # for each segment.
-    s = np.arange(0, n_segments + 1) * circumference / n_segments
-    alpha_x = inj_alpha_x * np.ones(n_segments)
-    beta_x  = inj_beta_x * np.ones(n_segments)
-    D_x     = np.zeros(n_segments)
-    alpha_y = inj_alpha_y * np.ones(n_segments)
-    beta_y  = inj_beta_y * np.ones(n_segments)
-    D_y     = np.zeros(n_segments)
-
-
-
-
-
-    # In[7]:
-
-    bunch = generators.Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well
-        n_particles, intensity, e, m_p, circumference, gamma,
-        inj_alpha_x, inj_beta_x, epsn_x,
-        inj_alpha_y, inj_beta_y, epsn_y,
-        beta_z, epsn_z
-        ).generate()
-
-
-    # In[8]:
-
-    # Gaussian6D
-
-
-    # In[9]:
-
-    bunch = generators.ImportDistribution(
-        n_particles, intensity, e, m_p, circumference, gamma,
-        bunch.get_coords_n_momenta_dict()
-        ).generate()
-
-
-    # In[10]:
-
-    bunch = generators.Uniform3D(
-        n_particles, intensity, e, m_p, circumference, gamma,
-        2e-3, 2e-3, 30
-        ).generate()
-
-    # In[11]:
-
-    transverse = TransverseMap(circumference, s, alpha_x, beta_x, D_x, alpha_y,
-                               beta_y, D_y, Qx, Qy)
-    longitudinal = LinearMap(alpha_c_array, circumference, Qs)
-
-    # In[12]:
-
-    bunch = generators.MatchGaussian6D( # implicitly tests MatchLinearLongMap and MatchTransverseMap
-        n_particles, intensity, e, m_p, circumference, gamma,
-        transverse, epsn_x, epsn_y, longitudinal, epsn_z
-        ).generate()
-
-
-    # In[13]:
-
-    rfsystems = RFSystems(circumference, [harmonic], [V_rf], [phi_offset],
-                          alpha_c_array, gamma)
-
-
-    # In[14]:
-
-    bunch = generators.MatchRFBucket6D( # implicitly tests MatchRFBucket2D and MatchTransverseMap
-        n_particles, intensity, e, m_p, circumference, gamma,
-        transverse, epsn_x, epsn_y, rfsystems.get_bucket(gamma=gamma), epsn_z
-        ).generate()
-
-
-    # In[15]:
-
-    bunch = generators.Gaussian6DTwiss(
-        n_particles, intensity, e, m_p, circumference, gamma,
-        inj_alpha_x, inj_beta_x, epsn_x,
-        inj_alpha_y, inj_beta_y, epsn_y, beta_z, epsn_z
-        ).generate()
-    generators.MatchRFBucket2D(
-        n_particles, intensity, e, m_p, circumference, gamma,
-        rfsystems.get_bucket(gamma=gamma), epsn_z=epsn_z
-        ).update(bunch)
-
-
-    # In[16]:
-
-    sigma_z = np.sqrt(beta_z * e * epsn_z / (4. * np.pi * p0))
-    sigma_dp = sigma_z / beta_z
-
-    bunch = generators.CutRFBucket6D( # implicitly tests CutRFBucket2D as well.
-        n_particles, intensity, e, m_p, circumference, gamma,
-        transverse, epsn_x, epsn_y, sigma_z, sigma_dp,
-        rfsystems.get_bucket(gamma=gamma).make_is_accepted(0.6)
-        ).generate()
-
-
-    # In[17]:
-
-    coords_n_momenta_dict = bunch.get_coords_n_momenta_dict()
-
-
-    # In[19]:
-
-    # Test particles.update method.
-    bunch = generators.Gaussian6DTwiss(
-        n_particles, intensity, e, m_p, circumference, gamma,
-        inj_alpha_x, inj_beta_x, epsn_x,
-        inj_alpha_y, inj_beta_y, epsn_y,
-        beta_z, epsn_z
-        ).generate()
-    x = np.random.randn(bunch.macroparticlenumber)
-    xp = np.random.randn(bunch.macroparticlenumber)
-    xx = np.random.randn(bunch.macroparticlenumber)
-    xxp = np.random.randn(bunch.macroparticlenumber)
-    coords_momenta_dict_to_update = {'x': x, 'xp': xp, 'xx': xx, 'xxp': xxp}
-    bunch.update(coords_momenta_dict_to_update)
-
-
-if __name__ == '__main__':
-    run()
-
-
-# In[ ]:
diff --git a/testing/unittests/autoruntests/MonitorTest.py b/testing/unittests/autoruntests/MonitorTest.py
index c018d95f..741ac2bd 100644
--- a/testing/unittests/autoruntests/MonitorTest.py
+++ b/testing/unittests/autoruntests/MonitorTest.py
@@ -104,12 +104,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, alpha_0,
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e) # WITH OR WITHOUT 4 PIjQuery202047649151738733053_1414145430832?
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
         return bunch
 
 
diff --git a/testing/unittests/autoruntests/RFQTest.py b/testing/unittests/autoruntests/RFQTest.py
index e4b44fd2..7dac382c 100644
--- a/testing/unittests/autoruntests/RFQTest.py
+++ b/testing/unittests/autoruntests/RFQTest.py
@@ -43,12 +43,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, linear_ma
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
 
         return bunch
 
diff --git a/testing/unittests/autoruntests/SlicingTest.py b/testing/unittests/autoruntests/SlicingTest.py
index f6331caf..9e7fb1d3 100644
--- a/testing/unittests/autoruntests/SlicingTest.py
+++ b/testing/unittests/autoruntests/SlicingTest.py
@@ -137,12 +137,12 @@ def clean_bunch(bunch):
     beta_z = np.abs(eta) * circumference / (2 * np.pi * Qs)
     turn_period = circumference / (beta * c)
 
-    bunch = generators.Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well
-        n_particles, intensity, ee, m_p, circumference, gamma,
-        inj_alpha_x, inj_beta_x, epsn_x,
-        inj_alpha_y, inj_beta_y, epsn_y,
-        beta_z, epsn_z
-        ).generate()
+    bunch = generators.generate_Gaussian6DTwiss( # implicitly tests Gaussian and Gaussian2DTwiss as well
+        n_particles, intensity, ee, m_p, circumference, gamma=gamma,
+        alpha_x=inj_alpha_x, beta_x=inj_beta_x, epsn_x=epsn_x,
+        alpha_y=inj_alpha_y, beta_y=inj_beta_y, epsn_y=epsn_y,
+        beta_z=beta_z, epsn_z=epsn_z
+        )
 
 
 
diff --git a/testing/unittests/autoruntests/TransverseTrackingCythonTest.py b/testing/unittests/autoruntests/TransverseTrackingCythonTest.py
index e1009b60..1689bb66 100644
--- a/testing/unittests/autoruntests/TransverseTrackingCythonTest.py
+++ b/testing/unittests/autoruntests/TransverseTrackingCythonTest.py
@@ -63,12 +63,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, linear_ma
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
 
         return bunch
 
diff --git a/testing/unittests/autoruntests/TransverseTrackingTest.py b/testing/unittests/autoruntests/TransverseTrackingTest.py
index afd57e10..30fadddf 100644
--- a/testing/unittests/autoruntests/TransverseTrackingTest.py
+++ b/testing/unittests/autoruntests/TransverseTrackingTest.py
@@ -61,12 +61,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, linear_ma
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
 
         return bunch
 
diff --git a/testing/unittests/autoruntests/WakeTest.py b/testing/unittests/autoruntests/WakeTest.py
index 2c70a849..3c1988e0 100644
--- a/testing/unittests/autoruntests/WakeTest.py
+++ b/testing/unittests/autoruntests/WakeTest.py
@@ -65,12 +65,12 @@ def generate_bunch(n_macroparticles, alpha_x, alpha_y, beta_x, beta_y, linear_ma
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
 
-        bunch = generators.Gaussian6DTwiss(
+        bunch = generators.generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=alpha_x, beta_x=beta_x, epsn_x=epsn_x,
             alpha_y=alpha_y, beta_y=beta_y, epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
         # print ('bunch sigma_z=' + bunch.sigma_z())
 
         return bunch
diff --git a/testing/unittests/test_cobra.py b/testing/unittests/test_cobra.py
index 48eaf48d..3aa98881 100644
--- a/testing/unittests/test_cobra.py
+++ b/testing/unittests/test_cobra.py
@@ -18,7 +18,7 @@
 from scipy.constants import c, e, m_p
 
 from PyHEADTAIL.particles.particles import Particles
-from PyHEADTAIL.particles.generators import Gaussian6DTwiss
+from PyHEADTAIL.particles.generators import generate_Gaussian6DTwiss
 import PyHEADTAIL.cobra_functions.stats as cf
 from PyHEADTAIL.trackers.simple_long_tracking import LinearMap
 
@@ -158,12 +158,12 @@ def generate_gaussian6dBunch(self,n_macroparticles, alpha_x, alpha_y, beta_x,
         epsn_x = 3.75e-6 # [m rad]
         epsn_y = 3.75e-6 # [m rad]
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
-        bunch = Gaussian6DTwiss(
+        bunch = generate_Gaussian6DTwiss(
             macroparticlenumber=n_macroparticles, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=C,
             alpha_x=0., beta_x=1., epsn_x=epsn_x,
             alpha_y=0., beta_y=1., epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
         # Scale to correct beta and alpha
         bunch.x *= np.sqrt(beta_x)
         bunch.xp = -alpha_x/np.sqrt(beta_x) * bunch.x + 1./np.sqrt(beta_x) * bunch.xp
diff --git a/testing/unittests/test_generators.py b/testing/unittests/test_generators.py
index 7e811b1a..459bd257 100644
--- a/testing/unittests/test_generators.py
+++ b/testing/unittests/test_generators.py
@@ -1,100 +1,192 @@
-#!/usr/bin/python
+'''
+@date:   31/03/2015
+@author: Stefan Hegglin
+Tests for generator
+'''
 from __future__ import division
 
+
 import sys, os
 BIN = os.path.dirname(__file__) # ./PyHEADTAIL/testing/unittests/
-BIN = os.path.abspath( BIN ) # absolute path to unittests
-BIN = os.path.dirname( BIN ) # ../ -->  ./PyHEADTAIL/testing/
-BIN = os.path.dirname( BIN ) # ../ -->  ./PyHEADTAIL/
-BIN = os.path.dirname( BIN ) # ../ -->  ./
+BIN = os.path.abspath(BIN) # absolute path to unittests
+BIN = os.path.dirname(BIN) # ../ -->  ./PyHEADTAIL/testing/
+BIN = os.path.dirname(BIN) # ../ -->  ./PyHEADTAIL/
+BIN = os.path.dirname(BIN) # ../ -->  ./
 sys.path.append(BIN)
 
 import unittest
 
 import numpy as np
-from scipy.constants import c, e, m_p
+import scipy.constants as constants
+from PyHEADTAIL.trackers.simple_long_tracking import RFSystems
+import PyHEADTAIL.particles.generators as gf
 
-from PyHEADTAIL.trackers.rf_bucket import RFBucket
-from PyHEADTAIL.trackers.transverse_tracking import TransverseMap
-from PyHEADTAIL.trackers.simple_long_tracking import LinearMap, RFSystems
-from PyHEADTAIL.particles.generators import (
-    ParticleGenerator,
-    Gaussian6D
-    )
 
 class TestParticleGenerators(unittest.TestCase):
-
+    '''Test class for the new ParticleGenerator (generator_functional.py)'''
     def setUp(self):
-        # general simulation parameters
-        self.macroparticlenumber = 10000
-        self.n_segments = 1
-
-        # machine parameters
-        self.C = 157.
-        self.inj_alpha_x = 0
-        self.inj_alpha_y = 0
-        self.inj_beta_x = 5.9 # in [m]
-        self.inj_beta_y = 5.7 # in [m]
-        self.Qx = 5.1
-        self.Qy = 6.1
-        self.gamma_tr = 4.05
-        self.alpha_array = [self.gamma_tr**-2]
-        self.V_rf = 8e3 # in [V]
-        self.harmonic = 1
-        self.phi_offset = 0 # measured from aligned focussing phase (0 or pi)
-
-        # beam parameters
-        self.Ekin = 1.4e9 # in [eV]
-        self.intensity = 1.684e12
-        self.epsn_x = 2.5e-6 # in [m*rad]
-        self.epsn_y = 2.5e-6 # in [m*rad]
-        self.epsn_z = 1.2 # 4pi*sig_z*sig_dp (*p0/e) in [eVs]
-
-        # calculations
-        self.gamma = 1 + e * self.Ekin / (m_p * c**2)
-        self.beta = np.sqrt(1 - self.gamma**-2)
-        self.betagamma = np.sqrt(self.gamma**2 - 1)
-        self.eta = self.alpha_array[0] - self.gamma**-2
-        if self.eta < 0:
-            self.phi_offset = np.pi - self.phi_offset
-        self.Etot = self.gamma * m_p * c**2 / e
-        self.p0 = np.sqrt(self.gamma**2 - 1) * m_p * c
-        self.Qs = np.sqrt(np.abs(self.eta) * self.V_rf /
-                          (2 * np.pi * self.beta**2 * self.Etot))
-        self.beta_z = np.abs(self.eta) * self.C / (2 * np.pi * self.Qs)
-        self.turn_period = self.C / (self.beta * c)
-
-        self.sigma_x = np.sqrt(self.epsn_x * self.inj_beta_x/ self.betagamma)
-        self.sigma_y = np.sqrt(self.epsn_y * self.inj_beta_y/ self.betagamma)
-        self.sigma_z = np.sqrt(self.epsn_z * self.beta_z /
-                               (4 * np.pi * self.p0/e))
-        self.sigma_xp = self.sigma_x / self.inj_beta_x
-        self.sigma_yp = self.sigma_y / self.inj_beta_y
-        self.sigma_dp = self.sigma_z / self.beta_z
-
-        # BETATRON
-        # Loop on number of segments and create the TransverseSegmentMap
-        # for each segment.
-        self.s = np.arange(0, self.n_segments + 1) * self.C / self.n_segments
-        self.alpha_x = self.inj_alpha_x * np.ones(self.n_segments)
-        self.beta_x  = self.inj_beta_x * np.ones(self.n_segments)
-        self.D_x     = np.zeros(self.n_segments)
-        self.alpha_y = self.inj_alpha_y * np.ones(self.n_segments)
-        self.beta_y  = self.inj_beta_y * np.ones(self.n_segments)
-        self.D_y     = np.zeros(self.n_segments)
-
-    def create_Gaussian6D(self):
-        return Gaussian6D(
-            self.macroparticlenumber, self.intensity, e, m_p,
-            self.C, self.gamma,
-            self.sigma_x, self.sigma_xp,
-            self.sigma_y, self.sigma_yp,
-            self.sigma_z, self.sigma_dp
-            )
-
-    def test_Gaussian6D_macroparticlenumber(self):
-        bunch = self.create_Gaussian6D().generate()
-        self.assertTrue(bunch.macroparticlenumber == self.macroparticlenumber)
+        np.random.seed(0)
+        self.nparticles = 1000
+        self.epsx = 0.5
+        self.intensity = 1e11
+        self.charge = constants.e
+        self.mass = constants.m_p
+        self.circumference = 99
+        self.gamma = 27.1
+        self.generator = gf.ParticleGenerator(
+            self.nparticles, self.intensity,
+            self.charge, self.mass, self.circumference, self.gamma,
+            distribution_x=gf.gaussian2D(0.5),
+            distribution_z=gf.gaussian2D(3.0),
+            linear_matcher_x=gf.transverse_linear_matcher(alpha=-0.7, beta=4, dispersion=0))
+        self.beam = self.generator.generate()
+
+    def tearDown(self):
+        pass
+
+    def test_particles_length(self):
+        '''Tests whether the coordinate arrays of the resulting beam
+        have the correct length'''
+        self.assertEqual(self.beam.x.size, self.nparticles,
+                         'Length of x-beam coordinate array not correct')
+
+    def test_particles_coordinates(self):
+        '''Tests whether only the coordinates specified in the initializer
+        are initialized in the beam (e.g. yp is not)
+        '''
+        with self.assertRaises(AttributeError):
+            self.beam.yp
+
+    def test_update_beam_with_existing_coords(self):
+        '''Tests whether updating already existing coords produces
+        beam coordinates of the correct size
+        '''
+        self.generator.update(self.beam)
+        self.assertEqual(self.beam.x.size, self.nparticles,
+                         'Updating existing coordinates leads to wrong' +
+                         'coordinate lengths')
+
+    def test_update_beam_with_new_coords(self):
+        '''Tests whether adding new coordinates to the beam
+        works as expected
+        '''
+        x_copy = self.beam.x.copy()
+        longitudinal_generator = gf.ParticleGenerator(
+            self.nparticles, self.intensity, self.charge,
+            self.mass, self.circumference, self.gamma,
+            distribution_z=gf.gaussian2D(3.0))
+        longitudinal_generator.update(self.beam)
+        self.assertEqual(self.beam.dp.size, self.nparticles,
+                         'Updating the beam with new coordinates leads to' +
+                         'faulty coordinates')
+        for n in xrange(self.nparticles):
+            self.assertAlmostEqual(x_copy[n], self.beam.x[n],
+                msg='Updating the beam with new coordinates invalidates' +
+                'existing coordinates')
+
+    def test_distributions(self):
+        '''Tests whether the specified distributions return the coords
+        in the correct format (dimensions). If new distributions are added,
+        add them to the test here!
+        '''
+        # Gaussian
+        dist = gf.gaussian2D(0.1)
+        self.distribution_testing_implementation(dist)
+
+        # Uniform
+        dist = gf.uniform2D(-2., 3.)
+        self.distribution_testing_implementation(dist)
+
+    def test_import_distribution(self):
+        '''Tests whether import_distribution produces coordinate arrays of the
+        correct size'''
+        nparticles = 5
+        coords = [np.linspace(-2, 2, nparticles),
+                  np.linspace(-3, 3, nparticles)]
+        import_generator = gf.ParticleGenerator(
+                nparticles, 1e11, constants.e, constants.m_p, 100, 10,
+                distribution_y=gf.import_distribution2D(coords))
+        beam = import_generator.generate()
+        self.assertEqual(len(beam.y), nparticles,
+                'import_generator produces coords with the wrong length')
+        self.assertEqual(len(beam.yp), nparticles,
+                'import_generator produces coords with the wrong length')
+
+    def test_rf_bucket_distribution(self):
+        '''Tests the functionality of the rf-bucket matchor'''
+        #SPS Q20 flattop
+        nparticles = 100
+        h1 = 4620
+        h2 = 4*4620
+        V1 = 10e6
+        V2 = 1e6
+        dphi1 = 0
+        dphi2 = 0
+        alpha = 0.00308
+        p_increment = 0
+        long_map = RFSystems(self.circumference, [h1, h2], [V1, V2],
+                [dphi1, dphi2], [alpha], self.gamma, p_increment)
+        bucket = long_map.get_bucket(gamma=self.gamma)
+        bunch = gf.ParticleGenerator(
+                nparticles, 1e11, constants.e, constants.m_p,
+                self.circumference, self.gamma,
+                distribution_z=gf.RF_bucket_distribution(bucket, epsn_z=0.002)).generate()
+
+    def test_cut_bucket_distribution(self):
+        '''Tests functionality of the cut-bucket matchor '''
+        nparticles = 100
+        h1 = 4620
+        h2 = 4*4620
+        V1 = 10e6
+        V2 = 1e6
+        dphi1 = 0
+        dphi2 = 0
+        alpha = 0.00308
+        p_increment = 0
+        long_map = RFSystems(self.circumference, [h1, h2], [V1, V2],
+                [dphi1, dphi2], [alpha], self.gamma, p_increment)
+        bucket = long_map.get_bucket(gamma=self.gamma)
+        is_accepted_fn = bucket.make_is_accepted(margin=0.)
+        bunch = gf.ParticleGenerator(
+                nparticles, 11, constants.e, constants.m_p,
+                self.circumference, self.gamma,
+                distribution_z=gf.cut_distribution(
+                is_accepted=is_accepted_fn,
+                distribution=gf.gaussian2D(0.01))).generate()
+        self.assertEqual(nparticles, len(bunch.z),
+                         'bucket_cut_distribution loses particles')
+        self.assertTrue(np.sum(is_accepted_fn(bunch.z, bunch.dp)) == nparticles,
+                        'not all particles generated with the cut RF matcher' +
+                        ' lie inside the specified separatrix')
+
+    def test_import_distribution_raises_error(self):
+        '''Tests whether the generation fails when the number of particles
+        and the size of the specified distribution list do not match
+        '''
+        nparticles = 10
+        coords = [np.linspace(-2, 2, nparticles+1),
+                  np.linspace(-3, 3, nparticles+1)]
+        import_generator = gf.ParticleGenerator(
+                nparticles, 1e11, constants.e, constants.m_p, 100, 10,
+                distribution_y=gf.import_distribution2D(coords))
+        with self.assertRaises(AssertionError):
+            beam = import_generator.generate()
+
+    def distribution_testing_implementation(self, distribution):
+        '''Call this method with the distribution as a parameter.
+        distribution(n_particles) should be a valid command
+        '''
+        distribution_size = 100
+        X = distribution(distribution_size)
+        x = X[0]
+        p = X[1]
+        self.assertEqual(x.size, distribution_size,
+                         'space-direction ([0]) of ' + str(distribution) +
+                         'has wrong dimension')
+        self.assertEqual(p.size, distribution_size,
+                         'momentum-direction ([1]) of ' + str(distribution) +
+                         'has wrong dimension')
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/testing/unittests/test_itest_autorun.py b/testing/unittests/test_itest_autorun.py
index e2933df6..d65ae2b8 100644
--- a/testing/unittests/test_itest_autorun.py
+++ b/testing/unittests/test_itest_autorun.py
@@ -19,7 +19,6 @@
 import autoruntests.ApertureNLossesTest as at
 import autoruntests.DetunersCythonTest as dct
 import autoruntests.DetunersTest as dt
-import autoruntests.GeneratorTest as gt
 import autoruntests.MonitorTest as mt
 import autoruntests.RFQTest as rt
 import autoruntests.SlicingTest as st
@@ -72,15 +71,6 @@ def test_detunerstest(self):
         except Exception, err:
             self.fail('DetunersTest threw an exception:\n' + str(err))
 
-    def test_generatortest(self):
-        '''Runs the autoruntests/GeneratorTest script and fails
-        the test if an exception is thrown
-        '''
-        try:
-            gt.run()
-        except Exception, err:
-            self.fail('GeneratorTest threw an exception:\n' + str(err))
-
     def test_monitortest(self):
         '''Runs the autoruntests/MonitorTest script and fails
         the test if an exception is thrown
diff --git a/testing/unittests/test_slicing.py b/testing/unittests/test_slicing.py
index 1c592e0b..c12dcb18 100644
--- a/testing/unittests/test_slicing.py
+++ b/testing/unittests/test_slicing.py
@@ -22,7 +22,7 @@
 from PyHEADTAIL.particles.slicing import UniformBinSlicer, UniformChargeSlicer
 from PyHEADTAIL.general.printers import AccumulatorPrinter
 from PyHEADTAIL.trackers.simple_long_tracking import LinearMap
-from PyHEADTAIL.particles.generators import Gaussian6DTwiss
+from PyHEADTAIL.particles.generators import generate_Gaussian6DTwiss
 
 
 def check_elements_equal(np_array1d):
@@ -193,17 +193,17 @@ def create_bunch_with_params(self,alpha_x, beta_x, disp_x, gamma):
         gamma_t = 1. / np.sqrt(linear_map.alpha_array[0])
         p0 = np.sqrt(gamma**2 - 1) * m_p * c
 
-        beta_z = np.abs((linear_map.eta(dp=0, gamma=gamma) * linear_map.circumference / 
+        beta_z = np.abs((linear_map.eta(dp=0, gamma=gamma) * linear_map.circumference /
                   (2 * np.pi * linear_map.Qs)))
 
         epsn_z = 4 * np.pi * sigma_z**2 * p0 / (beta_z * e)
         #print ('epsn_z: ' + str(epsn_z))
-        bunch = Gaussian6DTwiss(
+        bunch = generate_Gaussian6DTwiss(
             macroparticlenumber=10000, intensity=intensity, charge=e,
             gamma=gamma, mass=m_p, circumference=linear_map.circumference,
             alpha_x=0., beta_x=1., epsn_x=epsn_x,
             alpha_y=0., beta_y=1., epsn_y=epsn_y,
-            beta_z=beta_z, epsn_z=epsn_z).generate()
+            beta_z=beta_z, epsn_z=epsn_z)
         # Scale to correct beta and alpha
         xx = bunch.x.copy()
         yy = bunch.y.copy()
diff --git a/trackers/simple_long_tracking.py b/trackers/simple_long_tracking.py
index 16c62860..a5967707 100644
--- a/trackers/simple_long_tracking.py
+++ b/trackers/simple_long_tracking.py
@@ -281,6 +281,7 @@ def __init__(self, circumference, harmonic_list, voltage_list,
                  phi_offset_list, alpha_array, gamma_reference,
                  p_increment=0, phase_lock=True,
                  shrink_transverse=True, shrink_longitudinal=False,
+                 D_x=0, D_y=0,
                  *args, **kwargs):
         """
         The first entry in harmonic_list, voltage_list and
@@ -326,6 +327,13 @@ def __init__(self, circumference, harmonic_list, voltage_list,
         In this case take care about all Kick.p_increment attributes --
         highly non-trivial, as all other p_increment functionality
         in RFSystems is broken. So take care, you're on your own! :-)
+        - D_x, D_y: horizontal and vertical dispersion. These arguments
+          are passed to the Kicks class. Because both kicks are applied
+          consecutively, the dispersion will be the same for both kicks and
+          it is therefore sufficient to specify only one dispersion.
+          The dispersion must match the dispersion of the following transverse
+          map. See the docstring of the Kick class for a more detailed
+          description.
         """
 
         super(RFSystems, self).__init__(
@@ -339,7 +347,8 @@ def __init__(self, circumference, harmonic_list, voltage_list,
         if not shrink_transverse:
             self.track = self.track_no_transverse_shrinking
 
-        self._kicks = [Kick(alpha_array, self.circumference, h, V, dphi)
+        self._kicks = [Kick(alpha_array, self.circumference, h, V, dphi,
+                            D_x=D_x, D_y=D_y)
                       for h, V, dphi in
                       zip(harmonic_list, voltage_list, phi_offset_list)]
         self._elements = ( [Drift(alpha_array, 0.5 * self.circumference)]