From 6cf41629c3c3cde6f5127205bc093db842a9d011 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 16:25:55 +0100 Subject: [PATCH 01/35] wip --- src/impy/common.py | 8 +- src/impy/kinematics.py | 217 ++++++++++++--------------------------- src/impy/util.py | 108 ++++++++++++++++++- tests/test_kinematics.py | 95 ++++++++++++----- tests/test_util.py | 18 ++++ 5 files changed, 270 insertions(+), 176 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index 99099b6f..c731300c 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -9,7 +9,13 @@ """ from abc import ABC, abstractmethod import numpy as np -from impy.util import classproperty, select_parents, naneq, pdg2name, Nuclei +from impy.util import ( + classproperty, + select_parents, + naneq, + pdg2name, + Nuclei, +) from impy.constants import ( quarks_and_diquarks_and_gluons, long_lived, diff --git a/src/impy/kinematics.py b/src/impy/kinematics.py index bb4050e2..3d437f32 100644 --- a/src/impy/kinematics.py +++ b/src/impy/kinematics.py @@ -1,133 +1,38 @@ """ This module handles transformations between Lorentz frames and different inputs required by the low-level event generator interfaces. - - -@Hans @Sonia: we need to come up with some sort general handling -of different inputs. Hans suggested to mimic a similar behavior as for -colors in matplotlib. That one can initialize with different arguments, like -'pp' 7 TeV would be internally translated to 2212, 2212 and to 4-vectors -[0.,0.,+-3.499999TeV, 3.5TeV]. This assumes that the input interpreted as -center of mass total energy (not momentum) AND that the final state is -defined in center-of-mass as well. - -This was already the initial motivation but I have the impression that -the implementation is very "cooked up". We have to discuss this. - """ - import numpy as np from impy.util import ( TaggedFloat, - AZ2pdg, - is_AZ, energy2momentum, + momentum2energy, elab2ecm, + ecm2elab, mass, - pdg2name, - name2pdg, + process_particle, ) -from impy.constants import nucleon_mass +from impy.constants import nucleon_mass, MeV, GeV, TeV, PeV, EeV +from impy.util import CompositeTarget, EventFrame from particle import PDGID import dataclasses -from typing import Union, Tuple, Collection -from enum import Enum - - -EventFrame = Enum("EventFrame", ["CENTER_OF_MASS", "FIXED_TARGET", "GENERIC"]) - - -@dataclasses.dataclass(init=False) -class CompositeTarget: - """Definition of composite targets made of multiple (atomic) nuclei. - - Examples of such composite targets are Air, CO_2, HCl, C_2H_60. - """ - - label: str - components: Tuple[PDGID] - fractions: np.ndarray - - def __init__( - self, components: Collection[Tuple[Union[str, int], float]], label: str = "" - ): - """ - Parameters - ---------- - components : collection of (str|int, float) - The components of the targets. Each component is given by a string or PDGID - that identifies the element, and its relative amount in the material. - Amounts do not have to add up to 1, fractions are computed automatically. - label : str, optional - Give the target a name. This is purely cosmetic. - """ - - if len(components) == 0: - raise ValueError("components cannot be empty") - fractions = np.empty(len(components)) - c = [] - for i, (particle, amount) in enumerate(components): - fractions[i] = amount - p = _normalize_particle(particle) - if not p.is_nucleus: - raise ValueError(f"component {particle} is not a nucleus") - c.append(p) - self.label = label - self.components = tuple(c) - self.fractions = fractions / np.sum(fractions) - self.fractions.flags["WRITEABLE"] = False - - @property - def Z(self): - """Return maximum charge number.""" - # needed for compatibility with PDGID interface and for dpmjet initialization - return max(p.Z for p in self.components) - - @property - def A(self): - """Return maximum number of nucleons.""" - # needed for compatibility with PDGID interface and for dpmjet initialization - return max(p.A for p in self.components) - - @property - def is_nucleus(self): - return True - - def __int__(self): - """Return PDGID for heaviest of elements.""" - return int(max((c.A, c) for c in self.components)[1]) - - def average_mass(self): - return sum( - f * p.A * nucleon_mass for (f, p) in zip(self.fractions, self.components) - ) - - def __abs__(self): - return abs(int(self)) - - def __repr__(self): - components = [ - (pdg2name(c), amount) - for (c, amount) in zip(self.components, self.fractions) - ] - args = f"{components}" - if self.label: - args += f", label={self.label!r}" - return f"CompositeTarget({args})" - - -def _normalize_particle(x): - if isinstance(x, (PDGID, CompositeTarget)): - return x - if isinstance(x, int): - return PDGID(x) - if isinstance(x, str): - try: - return PDGID(name2pdg(x)) - except KeyError: - raise ValueError(f"particle with name {x} not recognized") - if is_AZ(x): - return PDGID(AZ2pdg(*x)) - raise ValueError(f"{x} is not a valid particle specification") +from typing import Union, Tuple + + +__all__ = ( + "EventFrame", + "CompositeTarget", + "MeV", + "GeV", + "TeV", + "PeV", + "EeV", + "EventKinematics", + "CenterOfMass", + "FixedTarget", + "TotalEnergy", + "KinEnergy", + "Momentum", +) @dataclasses.dataclass @@ -136,28 +41,38 @@ class EventKinematics: There are different ways to specify a particle collision. For instance the projectile and target momenta can be specified in the target rest frame, - the so called 'laboratory' frame, or the nucleon-nucleon center of mass frame + the so called 'laboratory' frame, or the nucleon-nucleon center-of-mass frame where the modulus of the nucleon momenta is the same but the direction inverted. Each event generator expects its arguments to be given in one or the other frame. This class allows the generator to pick itself the correct frame, while the user can specify the kinematics in the preferred form. - Args: - (float) ecm : :math:`\\sqrt{s}`, the center-of-mass energy - (float) plab : projectile momentum in lab frame - (float) elab : projectile energy in lab frame - (float) ekin : projectile kinetic energy in lab frame - (tuple) beam : Specification as tuple of 4-vectors (np.array)s - (tuple) particle1: particle name, PDG ID, or nucleus mass & charge (A, Z) - of the projectile - (tuple) particle2: particle name, PDG ID, or nucleus mass & charge (A, Z), - or CompositeTarget of the target - + Parameters + ---------- + particle1: str or int or (int, int) + Particle name, PDG ID, or nucleus mass & charge (A, Z) of projectile. + particle2: str or int or (int, int) or CompositeTarget + Particle name, PDG ID, nucleus mass & charge (A, Z), or CompositeTarget + of the target + ecm : float, optional + Center-of-mass energy :math:`\\sqrt{s}`. + plab : float, optional + Projectile momentum in lab frame. If the projectile is a nucleus, it is + the momentum per nucleon. + elab : float, optional + Projectile energy in lab frame. If the projectile is a nucleus, it is + the energy per nucleon. + ekin : float, optional + Projectile kinetic energy in lab frame. If the projectile is a nucleus, + it is the kinetic energy per nucleon. + beam : tuple of two floats + Specification as tuple of two momenta. If the projectile or target are + nuclei, it is the momentum per nucleon. """ frame: EventFrame - p1: PDGID - p2: Union[PDGID, CompositeTarget] + p1: Union[PDGID, Tuple[int, int]] + p2: Union[PDGID, Tuple[int, int], CompositeTarget] ecm: float # for ions this is nucleon-nucleon collision system beams: Tuple[np.ndarray, np.ndarray] _gamma_cm: float @@ -185,57 +100,61 @@ def __init__( if particle1 is None or particle2 is None: raise ValueError("particle1 and particle2 must be set") - self.p1 = _normalize_particle(particle1) - self.p2 = _normalize_particle(particle2) + self.p1 = process_particle(particle1) + self.p2 = process_particle(particle2) if isinstance(self.p1, CompositeTarget): raise ValueError("Only 2nd particle can be CompositeTarget") p2_is_composite = isinstance(self.p2, CompositeTarget) - m1 = mass(self.p1) + m1 = nucleon_mass if self.p1.is_nucleus else mass(self.p1) m2 = nucleon_mass if p2_is_composite or self.p2.is_nucleus else mass(self.p2) self.beams = (np.zeros(4), np.zeros(4)) - # Input specification in center of mass frame + # Input specification in center-of-mass frame if ecm is not None: - self.frame = EventFrame.CENTER_OF_MASS if frame is None else frame + self.frame = frame or EventFrame.CENTER_OF_MASS self.ecm = ecm - self.elab = 0.5 * (ecm**2 - m1**2 - m2**2) / m2 + self.elab = ecm2elab(ecm, m1, m2) self.plab = energy2momentum(self.elab, m1) # Input specification as 4-vectors elif beam is not None: if p2_is_composite: raise ValueError("beam cannot be used with CompositeTarget") - self.frame = EventFrame.GENERIC if frame is None else frame + self.frame = frame or EventFrame.GENERIC p1, p2 = beam self.beams[0][2] = p1 self.beams[1][2] = p2 - self.beams[0][3] = np.sqrt(m1**2 + p1**2) - self.beams[1][3] = np.sqrt(m2**2 + p2**2) + self.beams[0][3] = momentum2energy(p1, m1) + self.beams[1][3] = momentum2energy(p2, m2) s = np.sum(self.beams, axis=0) - self.ecm = np.sqrt(s[3] ** 2 - np.sum(s[:3] ** 2)) - self.elab = 0.5 * (self.ecm**2 - m1**2 + m2**2) / m2 + # We compute ecm with energy2momentum. It is not really energy to momentum, + # but energy2momentum(x, y) computes x^2 - y^2, which is what we need. Here, + # I use that px and py are always zero, if we ever change this, many formulas + # have to change in this class, like all the boosts + self.ecm = energy2momentum(s[3], s[2]) + self.elab = ecm2elab(self.ecm, m1, m2) self.plab = energy2momentum(self.elab, m1) # Input specification in lab frame elif elab is not None: if not (elab > m1): raise ValueError("projectile energy > projectile mass required") - self.frame = EventFrame.FIXED_TARGET if frame is None else frame + self.frame = frame or EventFrame.FIXED_TARGET self.elab = elab self.plab = energy2momentum(self.elab, m1) - self.ecm = np.sqrt(2.0 * self.elab * m2 + m2**2 + m1**2) + self.ecm = elab2ecm(self.elab, m1, m2) # self.ecm = np.sqrt((self.elab + m2)**2 - self.plab**2) elif ekin is not None: - self.frame = EventFrame.FIXED_TARGET if frame is None else frame + self.frame = frame or EventFrame.FIXED_TARGET self.elab = ekin + m1 self.plab = energy2momentum(self.elab, m1) self.ecm = elab2ecm(self.elab, m1, m2) elif plab is not None: - self.frame = EventFrame.FIXED_TARGET if frame is None else frame + self.frame = frame or EventFrame.FIXED_TARGET self.plab = plab - self.elab = np.sqrt(self.plab**2 + m1**2) + self.elab = momentum2energy(self.plab, m1) self.ecm = elab2ecm(self.elab, m1, m2) else: assert False # this should never happen @@ -275,7 +194,7 @@ def _fill_beams(self, m1, m2): elif self.frame == EventFrame.FIXED_TARGET: self.beams[0][2] = self.plab self.beams[1][2] = 0 - # set energyies + # set energies for b, m in zip(self.beams, (m1, m2)): b[3] = np.sqrt(m**2 + b[2] ** 2) diff --git a/src/impy/util.py b/src/impy/util.py index 21fdbbe5..0fa6de0b 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -8,9 +8,79 @@ import zipfile import shutil import numpy as np -from typing import Sequence, Set +from typing import Sequence, Set, Tuple, Collection, Union from particle import Particle, PDGID, ParticleNotFound, InvalidParticle from impy.constants import MeV, nucleon_mass +from enum import Enum +import dataclasses + +EventFrame = Enum("EventFrame", ["CENTER_OF_MASS", "FIXED_TARGET", "GENERIC"]) + + +@dataclasses.dataclass(init=False) +class CompositeTarget: + """Definition of composite targets made of multiple (atomic) nuclei. + + Examples of such composite targets are Air, CO_2, HCl, C_2H_60. + """ + + label: str + components: Tuple[PDGID] + fractions: np.ndarray + + def __init__( + self, components: Collection[Tuple[Union[str, int], float]], label: str = "" + ): + """ + Parameters + ---------- + components : collection of (str|int, float) + The components of the targets. Each component is given by a string or PDGID + that identifies the element, and its relative amount in the material. + Amounts do not have to add up to 1, fractions are computed automatically. + label : str, optional + Give the target a name. This is purely cosmetic. + """ + + if len(components) == 0: + raise ValueError("components cannot be empty") + fractions = np.empty(len(components)) + c = [] + for i, (particle, amount) in enumerate(components): + fractions[i] = amount + p = process_particle(particle) + if not p.is_nucleus: + raise ValueError(f"component {particle} is not a nucleus") + c.append(p) + self.label = label + self.components = tuple(c) + self.fractions = fractions / np.sum(fractions) + self.fractions.flags["WRITEABLE"] = False + + @property + def Z(self): + """Return maximum charge number.""" + # needed for compatibility with PDGID interface and for dpmjet initialization + return max(p.Z for p in self.components) + + @property + def A(self): + """Return maximum number of nucleons.""" + # needed for compatibility with PDGID interface and for dpmjet initialization + return max(p.A for p in self.components) + + @property + def is_nucleus(self): + return True + + def __int__(self): + """Return PDGID for heaviest of elements.""" + return int(max((c.A, c) for c in self.components)[1]) + + def average_mass(self): + return sum( + f * p.A * nucleon_mass for (f, p) in zip(self.fractions, self.components) + ) class Singleton(type): @@ -23,11 +93,45 @@ def __call__(cls, *args, **kwargs): def energy2momentum(E, m): + # numerically more stable way to compute E^2 - m^2 return np.sqrt((E + m) * (E - m)) +def momentum2energy(p, m): + # only a minor trick can be used here, add in order + # of increasing scale + a, b = (p, m) if p < m else (m, p) + return np.sqrt(a**2 + b**2) + + def elab2ecm(elab, m1, m2): - return np.sqrt(2.0 * elab * m2 + m2**2 + m1**2) + # ecm^2 = s = ((p1^2 + m1^2)^0.5 + (p2^2 + m2^2)^0.5)^2 - (p1 + p2)^2 + # with elab = (p1^2 + m1^2)^0.5, p2 = 0 + # = (elab + m2)^2 - p1^2 + # = (elab + m2)^2 - (elab^2 - m1^2) + # = elab^2 + 2 elab m2 + m2^2 - elab^2 + m1^2 + # = 2 elab m2 + m1^2 + m2^2 + # sum in order of increasing size to improve numerical accuracy + return np.sqrt(m1**2 + m2**2 + 2.0 * elab * m2) + + +def ecm2elab(ecm, m1, m2): + return 0.5 * (ecm**2 - m1**2 - m2**2) / m2 + + +def process_particle(x): + if isinstance(x, (PDGID, CompositeTarget)): + return x + if isinstance(x, int): + return PDGID(x) + if isinstance(x, str): + try: + return PDGID(name2pdg(x)) + except KeyError: + raise ValueError(f"particle with name {x} not recognized") + if is_AZ(x): + return PDGID(AZ2pdg(*x)) + raise ValueError(f"{x} is not a valid particle specification") def mass(pdgid): diff --git a/tests/test_kinematics.py b/tests/test_kinematics.py index 92266c0f..41b8a9aa 100644 --- a/tests/test_kinematics.py +++ b/tests/test_kinematics.py @@ -5,44 +5,91 @@ Momentum, EventFrame, CompositeTarget, + GeV, + MeV, ) -from impy.constants import GeV, MeV +from impy.constants import nucleon_mass +from impy.util import AZ2pdg, energy2momentum from particle import literals as lp from pytest import approx +import pytest +import numpy as np + + +def test_CompositeTarget_repr(): + t = CompositeTarget([("N", 3), ("O", 1)]) + assert t.A == 16 + assert t.Z == 8 + assert t.components == (1000070140, 1000080160) + assert int(t) == int(t.components[1]) + assert abs(t) == int(t.components[1]) + assert repr(t) == "CompositeTarget([('N14', 0.75), ('O16', 0.25)])" + + t = CompositeTarget([("N", 3), ("O", 1)], label="air") + assert repr(t) == "CompositeTarget([('N14', 0.75), ('O16', 0.25)], label='air')" def test_fixed_target(): - ft = FixedTarget(10, "proton", "proton") - assert ft.elab == 10 * GeV - assert ft.frame == EventFrame.FIXED_TARGET + x = 2 * GeV - ft = FixedTarget(10.0 * GeV, "proton", "proton") - assert ft.elab == 10 * GeV + ft = FixedTarget(TotalEnergy(x), "proton", "proton") + assert ft.plab < x + assert ft.elab == x assert ft.frame == EventFrame.FIXED_TARGET + # default is to interpret x as total energy + assert ft == FixedTarget(x, "proton", "proton") - ft = FixedTarget(TotalEnergy(2 * GeV), "proton", "proton") - assert ft.elab == 2 * GeV + ft = FixedTarget(KinEnergy(x), "proton", "proton") + et = x + (lp.proton.mass * MeV) + assert ft.elab == approx(et, rel=1e-3) assert ft.frame == EventFrame.FIXED_TARGET - ft = FixedTarget(KinEnergy(2 * GeV), "proton", "proton") - et = 2 + (lp.proton.mass * MeV) - assert ft.elab == approx(et) + ft = FixedTarget(Momentum(x), "proton", "proton") + et = (x**2 + (lp.proton.mass * MeV) ** 2) ** 0.5 + assert ft.plab == x + assert ft.elab > x + assert ft.elab == approx(et, rel=1e-3) assert ft.frame == EventFrame.FIXED_TARGET - ft = FixedTarget(Momentum(2 * GeV), "proton", "proton") - et = (2**2 + (lp.proton.mass * MeV) ** 2) ** 0.5 - assert ft.elab == approx(et) - assert ft.frame == EventFrame.FIXED_TARGET + ft = FixedTarget(x, "proton", "He") + assert ft.p1 == lp.proton.pdgid + assert ft.p2 == AZ2pdg(4, 2) + # check that ecm is in nucleon-nucleon collision system + p1 = np.array([energy2momentum(x, lp.proton.mass * MeV), x]) + p2 = np.array([0, nucleon_mass]) + ps = p1 + p2 + ecm = (ps[1] ** 2 - ps[0] ** 2) ** 0.5 + assert ft.ecm == approx(ecm, rel=1e-3) + x = 32 * GeV + ft = FixedTarget(x, "O", "He") + assert ft.p1 == AZ2pdg(16, 8) + assert ft.p2 == AZ2pdg(4, 2) + # check that ecm is in nucleon-nucleon collision system + p1 = np.array([energy2momentum(x, nucleon_mass), x]) + p2 = np.array([0, nucleon_mass]) + ps = p1 + p2 + ecm = (ps[1] ** 2 - ps[0] ** 2) ** 0.5 + assert ft.ecm == approx(ecm, rel=1e-3) + + # air = CompositeTarget([("N", 3), ("O", 1)]) + # ft = FixedTarget(Momentum(2 * GeV), "proton", air) + # assert ft.p1 == lp.proton.pdgid + # assert ft.p2 == air + # assert ft.p2.A == 16 + # # check that ecm is in nucleon-nucleon collision system + # p1 = np.array([(x**2 - nucleon_mass**2) ** 0.5, x]) + # p2 = np.array([0, nucleon_mass]) + # ps = p1 + p2 + # ecm = (ps[1] ** 2 - ps[0] ** 2) ** 0.5 + # assert ft.ecm == approx(ecm, rel=1e-3) + + +def test_fixed_target_bad_input(): + with pytest.raises(ValueError): + FixedTarget(0.1 * GeV, "p", "p") -def test_CompositeTarget_repr(): t = CompositeTarget([("N", 3), ("O", 1)]) - assert t.A == 16 - assert t.Z == 8 - assert t.components == (1000070140, 1000080160) - assert int(t) == int(t.components[1]) - assert abs(t) == int(t.components[1]) - assert repr(t) == "CompositeTarget([('N14', 0.75), ('O16', 0.25)])" - t = CompositeTarget([("N", 3), ("O", 1)], label="air") - assert repr(t) == "CompositeTarget([('N14', 0.75), ('O16', 0.25)], label='air')" + with pytest.raises(ValueError): + FixedTarget(100 * GeV, t, "p") diff --git a/tests/test_util.py b/tests/test_util.py index d8a7e38e..5ea730c6 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -2,6 +2,7 @@ import numpy as np from numpy.testing import assert_equal from particle import literals as lp +from pytest import approx def test_select_parents(): @@ -58,3 +59,20 @@ def test_Nuclei(): assert carbon in d assert lead not in d assert repr(d) == "Nuclei(a_min=1, a_max=20, z_min=0, z_max=1000)" + + +def test_ecm_elab_conversion(): + elab = 2.1 + m1 = 1.1 + m2 = 0.5 + ecm = util.elab2ecm(elab, m1, m2) + elab2 = util.ecm2elab(ecm, m1, m2) + assert elab2 == approx(elab) + + +def test_momentum_energy_conversion(): + p = 2.1 + m = 1.1 + en = util.momentum2energy(p, m) + p2 = util.energy2momentum(en, m) + assert p == approx(p2) From a85ba34768a20123864e2600ed136d00ef01c27e Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 16:41:02 +0100 Subject: [PATCH 02/35] fix --- src/impy/util.py | 54 +++++++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 23 deletions(-) diff --git a/src/impy/util.py b/src/impy/util.py index 0fa6de0b..bc50f611 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -77,19 +77,27 @@ def __int__(self): """Return PDGID for heaviest of elements.""" return int(max((c.A, c) for c in self.components)[1]) + # this allows us to use CompositeTarget in Set[int].__contains__ + def __hash__(self): + return self.__int__().__hash__() + def average_mass(self): return sum( f * p.A * nucleon_mass for (f, p) in zip(self.fractions, self.components) ) + def __abs__(self): + return abs(self.__int__()) -class Singleton(type): - _instances = {} - - def __call__(cls, *args, **kwargs): - if cls not in cls._instances: - cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) - return cls._instances[cls] + def __repr__(self): + components = [ + (pdg2name(c), amount) + for (c, amount) in zip(self.components, self.fractions) + ] + args = f"{components}" + if self.label: + args += f", label={self.label!r}" + return f"CompositeTarget({args})" def energy2momentum(E, m): @@ -119,21 +127,6 @@ def ecm2elab(ecm, m1, m2): return 0.5 * (ecm**2 - m1**2 - m2**2) / m2 -def process_particle(x): - if isinstance(x, (PDGID, CompositeTarget)): - return x - if isinstance(x, int): - return PDGID(x) - if isinstance(x, str): - try: - return PDGID(name2pdg(x)) - except KeyError: - raise ValueError(f"particle with name {x} not recognized") - if is_AZ(x): - return PDGID(AZ2pdg(*x)) - raise ValueError(f"{x} is not a valid particle specification") - - def mass(pdgid): m = Particle.from_pdgid(pdgid).mass if m is None: @@ -236,6 +229,21 @@ def AZ2pdg(A, Z): return PDGID(pdg_id) +def process_particle(x): + if isinstance(x, (PDGID, CompositeTarget)): + return x + if isinstance(x, int): + return PDGID(x) + if isinstance(x, str): + try: + return PDGID(name2pdg(x)) + except KeyError: + raise ValueError(f"particle with name {x} not recognized") + if is_AZ(x): + return PDGID(AZ2pdg(*x)) + raise ValueError(f"{x} is not a valid particle specification") + + def fortran_chars(array_ref, char_seq): """Helper to set fortran character arrays with python strings""" info(10, "Setting fortran array with", char_seq) @@ -626,7 +634,7 @@ def __init__( self._z_max = z_max self._other = set() - def __contains__(self, pdgid: int): + def __contains__(self, pdgid: Union[int, CompositeTarget]): if pdgid in self._other: return True if not isinstance(pdgid, PDGID): From 26c769e9b2b4084b253af658bd92e6a1d2790b0b Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 16:42:04 +0100 Subject: [PATCH 03/35] cleanup --- tests/test_kinematics.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/tests/test_kinematics.py b/tests/test_kinematics.py index 41b8a9aa..450b08c0 100644 --- a/tests/test_kinematics.py +++ b/tests/test_kinematics.py @@ -72,18 +72,6 @@ def test_fixed_target(): ecm = (ps[1] ** 2 - ps[0] ** 2) ** 0.5 assert ft.ecm == approx(ecm, rel=1e-3) - # air = CompositeTarget([("N", 3), ("O", 1)]) - # ft = FixedTarget(Momentum(2 * GeV), "proton", air) - # assert ft.p1 == lp.proton.pdgid - # assert ft.p2 == air - # assert ft.p2.A == 16 - # # check that ecm is in nucleon-nucleon collision system - # p1 = np.array([(x**2 - nucleon_mass**2) ** 0.5, x]) - # p2 = np.array([0, nucleon_mass]) - # ps = p1 + p2 - # ecm = (ps[1] ** 2 - ps[0] ** 2) ** 0.5 - # assert ft.ecm == approx(ecm, rel=1e-3) - def test_fixed_target_bad_input(): with pytest.raises(ValueError): From 9255d40464b347dab87e217809da22b548f8f362 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 16:54:57 +0100 Subject: [PATCH 04/35] bug-fix for epos --- src/impy/models/epos.py | 4 +-- .../test_generators/EposLHC_He_air_ft.pkl.gz | Bin 24163 -> 22294 bytes tests/test_generators.py | 27 +++++++++++------- 3 files changed, 18 insertions(+), 13 deletions(-) diff --git a/src/impy/models/epos.py b/src/impy/models/epos.py index 74cd9eaf..7f2f684f 100644 --- a/src/impy/models/epos.py +++ b/src/impy/models/epos.py @@ -80,9 +80,9 @@ def _cross_section(self, kin=None): ) def _set_kinematics(self, kin): - if self._frame == EventFrame.FIXED_TARGET: + if kin.frame == EventFrame.FIXED_TARGET: iframe = 2 - self._frame = kin.frame + self._frame = EventFrame.FIXED_TARGET else: iframe = 1 self._frame = EventFrame.CENTER_OF_MASS diff --git a/tests/data/test_generators/EposLHC_He_air_ft.pkl.gz b/tests/data/test_generators/EposLHC_He_air_ft.pkl.gz index 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z>fa`LDraXg-z@EcCM({Tjs{Wfle+M*87(xV0`Y4=;rqUgXD-&MYhkt@#GcjdUdV=me>jzHm^_X+fzKhWQi$D~6r0XMf7h z8=Bj{3Yt7$Y}uBap2oN4`ozx<%--ZklPg05V;o@(tMUNEOqiX!=^${gNMdU07Iyx APyhe` diff --git a/tests/test_generators.py b/tests/test_generators.py index ef679fe0..9bbd29a9 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -11,6 +11,7 @@ import pytest from .util import run_in_separate_process from impy.util import get_all_models, pdg2name +import impy import boost_histogram as bh import gzip import pickle @@ -137,9 +138,9 @@ def draw_comparison(fn, p_value, axes, values, val_ref, cov_ref): @pytest.mark.trylast +@pytest.mark.parametrize("frame", ("cms", "ft", "cms2ft", "ft2cms")) @pytest.mark.parametrize("target", ("p", "air")) @pytest.mark.parametrize("projectile", ("gamma", "pi-", "p", "He")) -@pytest.mark.parametrize("frame", ("cms", "ft", "cms2ft", "ft2cms")) @pytest.mark.parametrize("Model", get_all_models()) def test_generator(projectile, target, frame, Model): p1 = projectile @@ -171,17 +172,19 @@ def test_generator(projectile, target, frame, Model): path_ref = REFERENCE_PATH / fn.with_suffix(".pkl.gz") p_value = None if not path_ref.exists(): - print(f"{fn}: reference does not exist; generating... (but fail test)") - # check plots to see whether reference makes any sense before committing it - p_value = -1 # make sure test fails + # New reference is generated. Check plots to see whether reference makes + # any sense before committing it. values = run_in_separate_process(run_model, Model, kin, 50, timeout=10000) val_ref = np.reshape(np.mean(values, axis=0), -1) cov_ref = np.cov(np.transpose([np.reshape(x, -1) for x in values])) with gzip.open(path_ref, "wb") as f: pickle.dump((val_ref, cov_ref), f) - else: - with gzip.open(path_ref) as f: - val_ref, cov_ref = pickle.load(f) + values = np.reshape(h.values(), -1) + draw_comparison(fn, -1, h.axes, values, val_ref, cov_ref) + pytest.xfail(reason="reference does not exist; generated new one, check it") + + with gzip.open(path_ref) as f: + val_ref, cov_ref = pickle.load(f) # histogram sometimes contains extreme spikes # not reflected in cov, this murky formula @@ -190,9 +193,11 @@ def test_generator(projectile, target, frame, Model): for i, v in enumerate(values): cov_ref[i, i] = 0.5 * (cov_ref[i, i] + v) - if p_value is None: - p_value = compute_p_value(values, val_ref, cov_ref) + p_value = compute_p_value(values, val_ref, cov_ref) + + threshold = 1e-6 - draw_comparison(fn, p_value, h.axes, values, val_ref, cov_ref) + if not (p_value >= threshold) or impy.debug_level > 0: + draw_comparison(fn, p_value, h.axes, values, val_ref, cov_ref) - assert p_value >= 1e-6 + assert p_value >= threshold From f904713ae9a2f830d9ea597b355fdee2f953d2ce Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 17:11:14 +0100 Subject: [PATCH 05/35] cleanup --- src/impy/kinematics.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/impy/kinematics.py b/src/impy/kinematics.py index 3d437f32..c7882621 100644 --- a/src/impy/kinematics.py +++ b/src/impy/kinematics.py @@ -145,7 +145,6 @@ def __init__( self.elab = elab self.plab = energy2momentum(self.elab, m1) self.ecm = elab2ecm(self.elab, m1, m2) - # self.ecm = np.sqrt((self.elab + m2)**2 - self.plab**2) elif ekin is not None: self.frame = frame or EventFrame.FIXED_TARGET self.elab = ekin + m1 From 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Wed, 4 Jan 2023 13:49:44 +0100 Subject: [PATCH 07/35] fix wrong mass for proton,neutron --- src/impy/kinematics.py | 5 +++-- src/impy/util.py | 19 +++++++++++++++++++ tests/test_util.py | 22 ++++++++++++++++++++++ 3 files changed, 44 insertions(+), 2 deletions(-) diff --git a/src/impy/kinematics.py b/src/impy/kinematics.py index c7882621..9003892c 100644 --- a/src/impy/kinematics.py +++ b/src/impy/kinematics.py @@ -9,6 +9,7 @@ elab2ecm, ecm2elab, mass, + is_real_nucleus, process_particle, ) from impy.constants import nucleon_mass, MeV, GeV, TeV, PeV, EeV @@ -108,8 +109,8 @@ def __init__( p2_is_composite = isinstance(self.p2, CompositeTarget) - m1 = nucleon_mass if self.p1.is_nucleus else mass(self.p1) - m2 = nucleon_mass if p2_is_composite or self.p2.is_nucleus else mass(self.p2) + m1 = nucleon_mass if is_real_nucleus(self.p1) else mass(self.p1) + m2 = nucleon_mass if is_real_nucleus(self.p2) else mass(self.p2) self.beams = (np.zeros(4), np.zeros(4)) diff --git a/src/impy/util.py b/src/impy/util.py index bc50f611..f2a8dc23 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -73,6 +73,10 @@ def A(self): def is_nucleus(self): return True + @property + def is_hadron(self): + return False + def __int__(self): """Return PDGID for heaviest of elements.""" return int(max((c.A, c) for c in self.components)[1]) @@ -100,6 +104,21 @@ def __repr__(self): return f"CompositeTarget({args})" +def is_real_nucleus(pdgid: Union[int, PDGID, CompositeTarget]) -> bool: + """ + Return True if pdgid is a nucleus with A > 1. + + PDGID.is_nucleus is True also for proton and neutrons, + which is correct in some sense, but often we want to + handle only nuclei with A > 1 in the interface. + + Also works for CompositeTarget. + """ + if not isinstance(pdgid, PDGID): + pdgid = PDGID(pdgid) + return pdgid.A and pdgid.A > 1 + + def energy2momentum(E, m): # numerically more stable way to compute E^2 - m^2 return np.sqrt((E + m) * (E - m)) diff --git a/tests/test_util.py b/tests/test_util.py index 5ea730c6..fbe1fca8 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -76,3 +76,25 @@ def test_momentum_energy_conversion(): en = util.momentum2energy(p, m) p2 = util.energy2momentum(en, m) assert p == approx(p2) + + +def test_is_real_nucleus(): + proton = 2212 + neutron = 2112 + + assert not util.is_real_nucleus(proton) + assert not util.is_real_nucleus(neutron) + + proton2 = util.AZ2pdg(1, 1) + neutron2 = util.AZ2pdg(1, 0) + + assert not util.is_real_nucleus(proton2) + assert not util.is_real_nucleus(neutron2) + + deuterium = util.AZ2pdg(2, 1) + + assert util.is_real_nucleus(deuterium) + + mix = util.CompositeTarget([("p", 0.5), ("He", 0.5)]) + + assert util.is_real_nucleus(mix) From 0213fadaef40121f2564ebe8cb24034f1f0cfda2 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 4 Jan 2023 15:33:24 +0100 Subject: [PATCH 08/35] update urqmd reference --- .../UrQMD34_pi-_p_cms2ft.pkl.gz | Bin 17501 -> 17661 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff 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zrqpu&1hjzJ9&tSflYJm9n`ReDp?4`C>;-Udr3&e*ECl1_!d=8zRrV7ocQkiOJ6C;~ z>FG48s|FTmc1ikP&`o3EU2bfuXsiV@IuoidRZ>ACj0?SFI~I}&Hi3?m*}4z-9(;z_ z@n#&nqz2bO;vkTsDN1_@e6k|d9Cz~ewToDQoFm(6S878x=rQ zSK+3f2+#jF$XpwUXlrEq8;7Qe%V`IJuK6&lQzf9K|Ha_JpJ+fit#HgglC^UA_ID5c G@jn2iu^fQ_ From c752368b43f492c42dccc30ca1d08e76ac317e14 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 14:51:48 +0100 Subject: [PATCH 09/35] work in progress --- src/impy/common.py | 269 ++++++++++++++++------------------- src/impy/models/dpmjetIII.py | 4 +- src/impy/models/epos.py | 25 ++-- src/impy/models/phojet.py | 4 +- src/impy/models/pythia6.py | 4 +- src/impy/models/pythia8.py | 4 +- src/impy/models/qgsjet.py | 4 +- src/impy/models/sibyll.py | 8 +- src/impy/models/sophia.py | 4 +- src/impy/models/urqmd.py | 4 +- src/impy/util.py | 4 +- tests/test_epos.py | 43 +++--- tests/test_pythia6.py | 2 +- 13 files changed, 179 insertions(+), 200 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index c731300c..afe6daed 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -13,6 +13,7 @@ classproperty, select_parents, naneq, + name2pdg, pdg2name, Nuclei, ) @@ -26,7 +27,6 @@ import dataclasses import copy from typing import Tuple, Optional -from contextlib import contextmanager import warnings from particle import Particle @@ -444,12 +444,14 @@ class MCEvent(EventData, ABC): _jmohep = "jmohep" _jdahep = "jdahep" - def __init__(self, generator): + def __init__(self, generator, kinematics): """ Parameters ---------- generator: Generator instance. + kinematics: + Kinematics of the event. """ # used by _charge_init and generator-specific methods self._lib = generator._lib @@ -468,7 +470,7 @@ def __init__(self, generator): EventData.__init__( self, (generator.name, generator.version), - generator.kinematics, + kinematics, int(getattr(evt, self._nevhep)), self._get_impact_parameter(), self._get_n_wounded(), @@ -576,32 +578,19 @@ def seed(self): # ========================================================================= -# MCRun +# Model # ========================================================================= -class MCRun(ABC): - #: Prevent creating multiple classes within same python scope - _is_initialized = [] - _restartable = False - _set_final_state_particles_called = False +class Model(ABC): + _once_called = False _projectiles = standard_projectiles _targets = Nuclei() _ecm_min = 10 * GeV # default for many models nevents = 0 # number of generated events so far - def __init__(self, seed): + def __init__(self, seed, *args): import importlib from random import randint - if not self._restartable: - self._abort_if_already_initialized() - - assert hasattr(self, "_name") - assert hasattr(self, "_version") - assert hasattr(self, "_library_name") - assert hasattr(self, "_event_class") - assert hasattr(self, "_frame") - self._lib = importlib.import_module(f"impy.models.{self._library_name}") - if seed is None: self._seed = randint(1, 10000000) elif isinstance(seed, int): @@ -609,28 +598,43 @@ def __init__(self, seed): else: raise ValueError(f"Invalid seed {seed}") - if hasattr(self._lib, "init_rmmard"): - self._lib.init_rmmard(self._seed) - # TODO use rmmard for this, too, instead of numpy PRNG self._composite_target_rng = np.random.default_rng(self._seed) - def __call__(self, nevents): + if not self._once_called: + self._once_called = True + assert hasattr(self, "_name") + assert hasattr(self, "_version") + assert hasattr(self, "_library_name") + assert hasattr(self, "_event_class") + assert hasattr(self, "_frame") + self._lib = importlib.import_module(f"impy.models.{self._library_name}") + if hasattr(self._lib, "init_rmmard"): + self._lib.init_rmmard(self._seed) + # Run internal model initialization code + self._once(*args) + # Set standard long lived particles as stable + for pid in long_lived: + self._set_stable(pid, True) + else: + if hasattr(self._lib, "init_rmmard"): + self._lib.init_rmmard(self._seed) + + def __call__(self, kin, nevents): """Generator function (in python sence) which launches the underlying event generator and returns its the result (event) as MCEvent object """ - assert self._set_final_state_particles_called nretries = 0 - for nev in self._composite_plan(nevents): + for nev in self._composite_plan(kin, nevents): while nev > 0: if self._generate(): nretries = 0 self.nevents += 1 nev -= 1 - event = self._event_class(self) + event = self._event_class(self, kin) # boost into frame requested by user - self.kinematics.apply_boost(event, self._frame) + kin.apply_boost(event, self._frame) yield event continue nretries += 1 @@ -677,40 +681,35 @@ def targets(cls): """Supported targets (positive PDGIDs only, c.c. implied).""" return cls._targets - @abstractmethod - def _generate(self): - """The method to generate a new event. - - Returns: - bool : True if event was successfully generated and False otherwise. - """ - pass - - @abstractmethod - def _set_kinematics(self, kin): - # Set new combination of energy, momentum, projectile - # and target combination for next event. - - # Either, this method defines some derived variables - # that _generate_event() can use to generate new events - # without additional arguments, or, it can also set - # internal variables of the model. In both cases the - # important thing is that _generate_event remains argument-free. - - # This call must not update self.kinematics, only the - # generator-specific variables. - pass + @classmethod + def _validate_kinematics(cls, kin): + if abs(kin.p1) not in cls._projectiles: + raise ValueError( + f"projectile {pdg2name(kin.p1)}[{int(kin.p1)}] is not allowed, " + f"see {cls.pyname}.projectiles" + ) + if abs(kin.p2) not in cls._targets: + raise ValueError( + f"target {pdg2name(kin.p2)}[{int(kin.p2)}] is not among allowed, " + f"see {cls.pyname}.targets" + ) + if kin.ecm < cls._ecm_min: + raise ValueError( + f"center-of-mass energy {kin.ecm/GeV} GeV < " + f"minimum energy {cls._ecm_min/GeV} GeV" + ) - def _composite_plan(self, nevents): - kin = self.kinematics + def _composite_plan(self, kin, nevents): + self._validate_kinematics(kin) if isinstance(kin.p2, CompositeTarget): nevents = self._composite_target_rng.multinomial(nevents, kin.p2.fractions) - ek = copy.deepcopy(kin) - for c, k in zip(kin.p2.components, nevents): - ek.p2 = c - with self._temporary_kinematics(ek): - yield k + components = kin.p2.components + for c, nev in zip(components, nevents): + kin.p2 = c + self._set_kinematics(kin) + yield nev else: + self._set_kinematics(kin) yield nevents @property @@ -721,113 +720,95 @@ def random_state(self): def random_state(self, rng_state): rng_state._restore_state(self) - @property - def kinematics(self): - return self._kinematics + def stable(self, particle, stable=True): + """Prevent decay of an unstable particle. - @kinematics.setter - def kinematics(self, kin): - if abs(kin.p1) not in self._projectiles: - raise ValueError( - f"projectile {pdg2name(kin.p1)}[{int(kin.p1)}] is not allowed, " - f"see {self.pyname}.projectiles" - ) - if abs(kin.p2) not in self._targets: - raise ValueError( - f"target {pdg2name(kin.p2)}[{int(kin.p2)}] is not among allowed, " - f"see {self.pyname}.targets" - ) - if kin.ecm < self._ecm_min: - raise ValueError( - f"center-of-mass energy {kin.ecm/GeV} GeV < " - f"minimum energy {self._ecm_min/GeV} GeV" - ) - self._kinematics = kin - self._set_kinematics(kin) - - def set_stable(self, pdgid, stable=True): - """Prevent decay of unstable particles - - Args: - pdgid (int) : PDG ID of the particle - stable (bool) : If `False`, particle is allowed to decay + Parameters + ---------- + particle : str or int + Name or PDG ID of the particle. + stable : bool, optional + If true, particle is not decayed by the generator. + + Notes + ----- + Some generators (e.g. the QGSJet models) do not provide + a full particle history and do not allow one to set + certain resonances as stable. """ - p = Particle.from_pdgid(pdgid) + pid = name2pdg(particle) if isinstance(particle, str) else particle + p = Particle.from_pdgid(pid) if p.ctau is None or p.ctau == np.inf: - raise ValueError(f"{pdg2name(pdgid)} cannot decay") - if abs(pdgid) == 311: + raise ValueError(f"{pdg2name(pid)} cannot decay") + if abs(pid) == 311: + self._set_stable(311, stable) self._set_stable(130, stable) self._set_stable(310, stable) else: - self._set_stable(pdgid, stable) + self._set_stable(pid, stable) - def set_unstable(self, pdgid): - """Convenience funtion for `self.set_stable(..., stable=False)` + def maydecay(self, particle): + """Decay particle in event record. - Args: - pdgid(int) : PDG ID of the particle + Equivalent to `self.stable(particle, stable=False)` + + Parameters + ---------- + particle : str or int + Name or PDG ID of the particle. """ - self.set_stable(pdgid, False) + self.stable(particle, False) - def cross_section(self, kin=None): + def cross_section(self, kin): """Cross sections according to current setup. Parameters ---------- - kin : EventKinematics, optional - If provided, calculate cross-section for EventKinematics. - Otherwise return values for current setup. + kin : EventKinematics + Calculate cross-section for EventKinematics. """ - with self._temporary_kinematics(kin): - kin2 = self.kinematics - if isinstance(kin2.p2, CompositeTarget): - cross_section = CrossSectionData(0, 0, 0, 0, 0, 0, 0) - kin3 = copy.copy(kin2) - for component, fraction in zip(kin2.p2.components, kin2.p2.fractions): - kin3.p2 = component - # this calls cross_section recursively, which is fine - cs = self.cross_section(kin3) - for i, val in enumerate(dataclasses.astuple(cs)): - cross_section[i] += fraction * val - return cross_section - else: - return self._cross_section(kin) + if isinstance(kin.p2, CompositeTarget): + cross_section = CrossSectionData(0, 0, 0, 0, 0, 0, 0) + components = kin.p2.components + fractions = kin.p2.fractions + for component, fraction in zip(components, fractions): + kin.p2 = component + cs = self._cross_section(kin) + for i, val in enumerate(dataclasses.astuple(cs)): + cross_section[i] += fraction * val + return cross_section + else: + return self._cross_section(kin) @abstractmethod - def _cross_section(self, kin): + def _once(self, *args): + # This has to be implemented in the derived concrete class. pass @abstractmethod - def _set_stable(self, pidid, stable): - pass - - def _abort_if_already_initialized(self): - # The first initialization should not be run more than - # once. - message = """ - Don't run initialization multiple times for the same generator. This - is a limitation of fortran libraries since all symbols are by default - in global scope. Multiple instances can be created in mupliple threads - or "python executables" using Pool in multiprocessing etc.""" - - assert self._library_name not in self._is_initialized, message - self._is_initialized.append(self._library_name) + def _set_kinematics(self, kin): + # Set new combination of energy, momentum, projectile + # and target combination in the underlying model. - def _set_final_state_particles(self): - """Defines particles as stable for the default 'tau_stable' - value in the config.""" + # Either, this method defines some derived variables + # that _generate() can use to generate new events + # without additional arguments, or, it can also set + # internal variables of the model. In both cases the + # important thing is that _generate() remains argument-free. + pass - for pdgid in long_lived: - self._set_stable(pdgid, True) + @abstractmethod + def _set_stable(self, pid, stable): + # This has to be implemented in the derived concrete class. + pass - self._set_final_state_particles_called = True + @abstractmethod + def _cross_section(self, kin): + # This has to be implemented in the derived concrete class. + pass - @contextmanager - def _temporary_kinematics(self, kin): - if kin is None: - yield - else: - prev = copy.copy(self.kinematics) - self.kinematics = kin - yield - self.kinematics = prev + @abstractmethod + def _generate(self): + # This has to be implemented in the derived concrete class. + # Returns True if event was successfully generated and False otherwise. + pass diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index 3959144b..b0c39f77 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -1,4 +1,4 @@ -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.kinematics import EventFrame from impy.util import info, _cached_data_dir, fortran_chars, Nuclei from impy.constants import standard_projectiles, GeV @@ -37,7 +37,7 @@ def _get_n_wounded(self): # ========================================================================= # DpmjetIIIMCRun # ========================================================================= -class DpmjetIIIRun(MCRun): +class DpmjetIIIRun(Model): """Implements all abstract attributes of MCRun for the DPMJET-III series of event generators. diff --git a/src/impy/models/epos.py b/src/impy/models/epos.py index 7f2f684f..10cdce3a 100644 --- a/src/impy/models/epos.py +++ b/src/impy/models/epos.py @@ -1,6 +1,7 @@ import numpy as np from impy.kinematics import EventFrame -from impy.common import MCEvent, MCRun, CrossSectionData +from impy.constants import TeV +from impy.common import MCEvent, Model, CrossSectionData from impy.util import ( _cached_data_dir, fortran_array_insert, @@ -13,8 +14,8 @@ class EPOSEvent(MCEvent): """Wrapper class around EPOS particle stack.""" - def __init__(self, generator): - super().__init__(generator) + def __init__(self, generator, kinematics): + super().__init__(generator, kinematics) # EPOS sets parents of beam particles to (-1, -1). # We change it to (0, 0) self.parents[self.status == 4] = 0 @@ -30,7 +31,7 @@ def _get_n_wounded(self): return int(self._lib.cevt.npjevt), int(self._lib.cevt.ntgevt) -class EposLHC(MCRun): +class EposLHC(Model): """Implements all abstract attributes of MCRun for the EPOS-LHC series of event generators.""" @@ -45,10 +46,11 @@ class EposLHC(MCRun): + "/releases/download/zipped_data_v1.0/epos_v001.zip" ) - def __init__(self, evt_kin, *, seed=None): - import impy + def __init__(self, seed=None, *, ecm_max=1000 * TeV): + super().__init__(seed, ecm_max) - super().__init__(seed) + def _once(self, ecm_max): + from impy import debug_level self._lib.aaset(0) datdir = _cached_data_dir(self._data_url) @@ -56,18 +58,17 @@ def __init__(self, evt_kin, *, seed=None): lun = 6 # stdout self._lib.initepos( float(self._seed), - evt_kin.ecm, + ecm_max, datdir, len(datdir), - impy.debug_level, + debug_level, lun, ) - self._set_final_state_particles() self._lib.charge_vect = np.vectorize(self._lib.getcharge, otypes=[np.float32]) - self.kinematics = evt_kin - def _cross_section(self, kin=None): + def _cross_section(self, kin): + self._set_kinematics(kin) total, inel, el, dd, sd, _ = self._lib.xsection() return CrossSectionData( total=total, diff --git a/src/impy/models/phojet.py b/src/impy/models/phojet.py index d4152754..8337e8a2 100644 --- a/src/impy/models/phojet.py +++ b/src/impy/models/phojet.py @@ -1,4 +1,4 @@ -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.util import fortran_chars, _cached_data_dir from impy.kinematics import EventFrame from impy.constants import standard_projectiles @@ -59,7 +59,7 @@ def khard(self): # self._lib.poevt1.phep[0:4, 5])) -class PHOJETRun(MCRun): +class PHOJETRun(Model): """Implements all abstract attributes of MCRun for the PHOJET series of event generators. diff --git a/src/impy/models/pythia6.py b/src/impy/models/pythia6.py index cffba7a7..f6dd3db7 100644 --- a/src/impy/models/pythia6.py +++ b/src/impy/models/pythia6.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from particle import literals as lp from impy.kinematics import EventFrame from impy.constants import standard_projectiles @@ -14,7 +14,7 @@ def _charge_init(self, npart): return np.fromiter((self._lib.pychge(ki) / 3 for ki in k), np.double) -class Pythia6(MCRun): +class Pythia6(Model): """Implements all abstract attributes of MCRun for the EPOS-LHC series of event generators.""" diff --git a/src/impy/models/pythia8.py b/src/impy/models/pythia8.py index 72febc67..77443814 100644 --- a/src/impy/models/pythia8.py +++ b/src/impy/models/pythia8.py @@ -1,4 +1,4 @@ -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.util import _cached_data_dir, name2pdg from os import environ import numpy as np @@ -30,7 +30,7 @@ def _get_n_wounded(self): return hi.nPartProj, hi.nPartTarg -class Pythia8(MCRun): +class Pythia8(Model): _name = "Pythia" _version = "8.308" _library_name = "_pythia8" diff --git a/src/impy/models/qgsjet.py b/src/impy/models/qgsjet.py index 72fc507a..3519b244 100644 --- a/src/impy/models/qgsjet.py +++ b/src/impy/models/qgsjet.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.kinematics import EventFrame from impy.constants import standard_projectiles from impy.util import _cached_data_dir, Nuclei @@ -26,7 +26,7 @@ def _get_n_wounded(self): return self._lib.qgarr55.nwp, self._lib.qgarr55.nwt -class QGSJetRun(MCRun): +class QGSJetRun(Model): _name = "QGSJet" _frame = EventFrame.FIXED_TARGET _projectiles = standard_projectiles | Nuclei() diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index a09a4116..ab7dee34 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import MCRun, MCEvent, RMMARDState, CrossSectionData +from impy.common import Model, MCEvent, RMMARDState, CrossSectionData from impy.util import info, Nuclei from impy.kinematics import EventFrame import dataclasses @@ -47,7 +47,7 @@ def __eq__(self, other: object) -> bool: ) -class SIBYLLRun(MCRun): +class SIBYLLRun(Model): """Implements all abstract attributes of MCRun for the SIBYLL 2.1, 2.3 and 2.3c event generators.""" @@ -130,8 +130,8 @@ def _set_stable(self, pdgid, stable): sid = abs(self._lib.isib_pdg2pid(pdgid)) if abs(pdgid) == 311: info(1, "Ignores K0. Using K0L/S instead") - self.set_stable(130, stable) - self.set_stable(310, stable) + self.stable(130, stable) + self.stable(310, stable) return idb = self._lib.s_csydec.idb if sid == 0 or sid > idb.size - 1: diff --git a/src/impy/models/sophia.py b/src/impy/models/sophia.py index b498f355..65775720 100644 --- a/src/impy/models/sophia.py +++ b/src/impy/models/sophia.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.constants import nucleon_mass from impy.constants import microbarn from impy.kinematics import EventFrame @@ -37,7 +37,7 @@ def decayed_parent(self): return self._lib.schg.iparnt[: self.npart] -class Sophia20(MCRun): +class Sophia20(Model): """Implements all abstract attributes of MCRun for the Sophia event generator. """ diff --git a/src/impy/models/urqmd.py b/src/impy/models/urqmd.py index 11377c0c..d789b7cf 100644 --- a/src/impy/models/urqmd.py +++ b/src/impy/models/urqmd.py @@ -6,7 +6,7 @@ # The current settings are taken from CORSIKA and they are optimized for speed aparently. # The license of UrQMD is quite restrictive, they won't probably permit distributing it. -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import Model, MCEvent, CrossSectionData from impy.util import info, fortran_array_insert, fortran_array_remove, Nuclei from impy.kinematics import EventFrame from impy.constants import standard_projectiles, GeV @@ -23,7 +23,7 @@ def _get_impact_parameter(self): return self._lib.rsys.bimp -class UrQMD34(MCRun): +class UrQMD34(Model): """Implements all abstract attributes of MCRun for the UrQMD series of event generators. diff --git a/src/impy/util.py b/src/impy/util.py index f2a8dc23..da9336f2 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -582,7 +582,7 @@ def tolerant_string_match(a, b): def get_all_models(skip=None): from impy import models - from impy.common import MCRun + from impy.common import Model if skip is None: skip = [] @@ -595,7 +595,7 @@ def get_all_models(skip=None): if skip and obj in skip: continue try: - if issubclass(obj, MCRun): # fails if obj is not a class + if issubclass(obj, Model): # fails if obj is not a class result.append(obj) except TypeError: pass diff --git a/tests/test_epos.py b/tests/test_epos.py index 6bc4dd4b..e77369fa 100644 --- a/tests/test_epos.py +++ b/tests/test_epos.py @@ -3,28 +3,25 @@ from impy.constants import GeV import numpy as np from numpy.testing import assert_allclose -from .util import ( - reference_charge, - run_in_separate_process, -) +from .util import reference_charge import pytest from particle import literals as lp from functools import lru_cache def run_pp_collision(): - evt_kin = CenterOfMass(10 * GeV, "proton", "proton") - m = EposLHC(evt_kin, seed=4) - m.set_stable(lp.pi_0.pdgid, True) - for event in m(1): + m = EposLHC(seed=4) + m.stable("pi0", True) + kin = CenterOfMass(10 * GeV, "proton", "proton") + for event in m(kin, 1): pass return event def run_ab_collision(): - evt_kin = CenterOfMass(10 * GeV, (4, 2), (12, 6)) - m = EposLHC(evt_kin, seed=1) - for event in m(1): + kin = CenterOfMass(10 * GeV, (4, 2), (12, 6)) + m = EposLHC(seed=1) + for event in m(kin, 1): pass return event @@ -32,13 +29,13 @@ def run_ab_collision(): @pytest.fixture @lru_cache(maxsize=1) def event(): - return run_in_separate_process(run_pp_collision) + return run_pp_collision() @pytest.fixture @lru_cache(maxsize=1) def event_ion(): - return run_in_separate_process(run_ab_collision) + return run_ab_collision() def test_impact_parameter(event, event_ion): @@ -59,13 +56,13 @@ def test_n_wounded_ion(event_ion): def run_cross_section(p1, p2): - evt_kin = CenterOfMass(10 * GeV, p1, p2) - m = EposLHC(evt_kin, seed=1) - return m.cross_section() + m = EposLHC(seed=1) + kin = CenterOfMass(10 * GeV, p1, p2) + return m.cross_section(kin) def test_cross_section(): - c = run_in_separate_process(run_cross_section, "p", "p") + c = run_cross_section("p", "p") assert_allclose(c.total, 38.2, atol=0.1) assert_allclose(c.inelastic, 30.7, atol=0.1) assert_allclose(c.elastic, 7.4, atol=0.1) @@ -121,20 +118,20 @@ def test_parents(event): def run_set_stable(stable): - evt_kin = CenterOfMass(10 * GeV, "proton", "proton") - m = EposLHC(evt_kin, seed=4) + m = EposLHC(seed=4) for pid, s in stable.items(): - m.set_stable(pid, s) + m.stable(pid, s) print("stable", m._get_stable()) - for event in m(1): + kin = CenterOfMass(10 * GeV, "proton", "proton") + for event in m(kin, 1): pass return event def test_set_stable(): pid = lp.pi_0.pdgid - ev1 = run_in_separate_process(run_set_stable, {pid: True}) - ev2 = run_in_separate_process(run_set_stable, {pid: False}) + ev1 = run_set_stable({"pi_0": True}) + ev2 = run_set_stable({pid: False}) # ev1 contains final state pi0 assert np.any(ev1.pid[ev1.status == 1] == pid) diff --git a/tests/test_pythia6.py b/tests/test_pythia6.py index 5514efb1..8418243d 100644 --- a/tests/test_pythia6.py +++ b/tests/test_pythia6.py @@ -156,7 +156,7 @@ def run_pp_collision_copy(): evt_kin = CenterOfMass(1 * TeV, 2212, 2212) m = Pythia6(evt_kin, seed=4) - m.set_stable(lp.pi_0.pdgid, False) # needed to get nonzero vertices + m.stable(lp.pi_0.pdgid, False) # needed to get nonzero vertices for event in m(1): pass From 1f4982c1358964b80eb8c1567bbebf826ba15b21 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 16:37:46 +0100 Subject: [PATCH 10/35] ported sibyll --- src/impy/common.py | 10 ++-- src/impy/models/sibyll.py | 15 ++---- ....pkl.gz => DpmjetIII191-He-air-cms.pkl.gz} | Bin ...l.gz => DpmjetIII191-He-air-cms2ft.pkl.gz} | Bin ...t.pkl.gz => DpmjetIII191-He-air-ft.pkl.gz} | Bin ...l.gz => DpmjetIII191-He-air-ft2cms.pkl.gz} | Bin ...ms.pkl.gz => DpmjetIII191-He-p-cms.pkl.gz} | Bin ...pkl.gz => 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Sibyll23d-pi--air-ft.pkl.gz} | Bin ...pkl.gz => Sibyll23d-pi--air-ft2cms.pkl.gz} | Bin ..._cms.pkl.gz => Sibyll23d-pi--p-cms.pkl.gz} | Bin ...t.pkl.gz => Sibyll23d-pi--p-cms2ft.pkl.gz} | Bin ..._p_ft.pkl.gz => Sibyll23d-pi--p-ft.pkl.gz} | Bin ...s.pkl.gz => Sibyll23d-pi--p-ft2cms.pkl.gz} | Bin ...cms.pkl.gz => Sophia20-gamma-p-cms.pkl.gz} | Bin ....pkl.gz => Sophia20-gamma-p-cms2ft.pkl.gz} | Bin ...p_ft.pkl.gz => Sophia20-gamma-p-ft.pkl.gz} | Bin ....pkl.gz => Sophia20-gamma-p-ft2cms.pkl.gz} | Bin ...r_cms.pkl.gz => UrQMD34-He-air-cms.pkl.gz} | Bin ...ft.pkl.gz => UrQMD34-He-air-cms2ft.pkl.gz} | Bin ...air_ft.pkl.gz => UrQMD34-He-air-ft.pkl.gz} | Bin ...ms.pkl.gz => UrQMD34-He-air-ft2cms.pkl.gz} | Bin ...e_p_cms.pkl.gz => UrQMD34-He-p-cms.pkl.gz} | Bin ...s2ft.pkl.gz => UrQMD34-He-p-cms2ft.pkl.gz} | Bin ..._He_p_ft.pkl.gz => UrQMD34-He-p-ft.pkl.gz} | Bin ...2cms.pkl.gz => UrQMD34-He-p-ft2cms.pkl.gz} | Bin ...ir_cms.pkl.gz => UrQMD34-p-air-cms.pkl.gz} | Bin ...2ft.pkl.gz => UrQMD34-p-air-cms2ft.pkl.gz} | Bin ..._air_ft.pkl.gz => UrQMD34-p-air-ft.pkl.gz} | Bin ...cms.pkl.gz => UrQMD34-p-air-ft2cms.pkl.gz} | Bin ..._p_p_cms.pkl.gz => UrQMD34-p-p-cms.pkl.gz} | Bin ...ms2ft.pkl.gz => UrQMD34-p-p-cms2ft.pkl.gz} | Bin ...34_p_p_ft.pkl.gz => UrQMD34-p-p-ft.pkl.gz} | Bin ...t2cms.pkl.gz => UrQMD34-p-p-ft2cms.pkl.gz} | Bin ..._cms.pkl.gz => UrQMD34-pi--air-cms.pkl.gz} | Bin ...t.pkl.gz => UrQMD34-pi--air-cms2ft.pkl.gz} | Bin ...ir_ft.pkl.gz => UrQMD34-pi--air-ft.pkl.gz} | Bin ...s.pkl.gz => UrQMD34-pi--air-ft2cms.pkl.gz} | Bin ..._p_cms.pkl.gz => UrQMD34-pi--p-cms.pkl.gz} | Bin ...2ft.pkl.gz => UrQMD34-pi--p-cms2ft.pkl.gz} | Bin ...i-_p_ft.pkl.gz => UrQMD34-pi--p-ft.pkl.gz} | Bin ...cms.pkl.gz => UrQMD34-pi--p-ft2cms.pkl.gz} | Bin tests/test_generators.py | 50 +++++++++++------- tests/test_sibyll21.py | 18 +++---- 309 files changed, 52 insertions(+), 41 deletions(-) rename tests/data/test_generators/{DpmjetIII191_He_air_cms.pkl.gz => DpmjetIII191-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_air_cms2ft.pkl.gz => DpmjetIII191-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_air_ft.pkl.gz => DpmjetIII191-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_air_ft2cms.pkl.gz => DpmjetIII191-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_p_cms.pkl.gz => DpmjetIII191-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_p_cms2ft.pkl.gz => DpmjetIII191-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_p_ft.pkl.gz => DpmjetIII191-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_He_p_ft2cms.pkl.gz => DpmjetIII191-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_air_cms.pkl.gz => DpmjetIII191-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_air_cms2ft.pkl.gz => DpmjetIII191-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_air_ft.pkl.gz => DpmjetIII191-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_air_ft2cms.pkl.gz => DpmjetIII191-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_p_cms.pkl.gz => DpmjetIII191-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_p_cms2ft.pkl.gz => DpmjetIII191-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_p_ft.pkl.gz => DpmjetIII191-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_p_p_ft2cms.pkl.gz => DpmjetIII191-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_air_cms.pkl.gz => DpmjetIII191-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_air_cms2ft.pkl.gz => DpmjetIII191-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_air_ft.pkl.gz => DpmjetIII191-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_air_ft2cms.pkl.gz => DpmjetIII191-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_p_cms.pkl.gz => DpmjetIII191-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_p_cms2ft.pkl.gz => DpmjetIII191-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_p_ft.pkl.gz => DpmjetIII191-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191_pi-_p_ft2cms.pkl.gz => DpmjetIII191-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_air_cms.pkl.gz => DpmjetIII193-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_air_cms2ft.pkl.gz => DpmjetIII193-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_air_ft.pkl.gz => DpmjetIII193-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_air_ft2cms.pkl.gz => DpmjetIII193-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_p_cms.pkl.gz => DpmjetIII193-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_p_cms2ft.pkl.gz => DpmjetIII193-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_p_ft.pkl.gz => DpmjetIII193-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_He_p_ft2cms.pkl.gz => DpmjetIII193-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_air_cms.pkl.gz => DpmjetIII193-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_air_cms2ft.pkl.gz => DpmjetIII193-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_air_ft.pkl.gz => DpmjetIII193-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_air_ft2cms.pkl.gz => DpmjetIII193-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_p_cms.pkl.gz => DpmjetIII193-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_p_cms2ft.pkl.gz => DpmjetIII193-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_p_ft.pkl.gz => DpmjetIII193-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_p_p_ft2cms.pkl.gz => DpmjetIII193-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_air_cms.pkl.gz => DpmjetIII193-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_air_cms2ft.pkl.gz => DpmjetIII193-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_air_ft.pkl.gz => DpmjetIII193-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_air_ft2cms.pkl.gz => DpmjetIII193-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_p_cms.pkl.gz => DpmjetIII193-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_p_cms2ft.pkl.gz => DpmjetIII193-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_p_ft.pkl.gz => DpmjetIII193-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193_pi-_p_ft2cms.pkl.gz => DpmjetIII193-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_air_cms.pkl.gz => DpmjetIII306-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_air_cms2ft.pkl.gz => DpmjetIII306-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_air_ft.pkl.gz => DpmjetIII306-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_air_ft2cms.pkl.gz => DpmjetIII306-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_p_cms.pkl.gz => DpmjetIII306-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_p_cms2ft.pkl.gz => DpmjetIII306-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_p_ft.pkl.gz => DpmjetIII306-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_He_p_ft2cms.pkl.gz => DpmjetIII306-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_air_cms.pkl.gz => DpmjetIII306-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_air_cms2ft.pkl.gz => DpmjetIII306-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_air_ft.pkl.gz => DpmjetIII306-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_air_ft2cms.pkl.gz => DpmjetIII306-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_p_cms.pkl.gz => DpmjetIII306-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_p_cms2ft.pkl.gz => DpmjetIII306-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_p_ft.pkl.gz => DpmjetIII306-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_p_p_ft2cms.pkl.gz => DpmjetIII306-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_air_cms.pkl.gz => DpmjetIII306-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_air_cms2ft.pkl.gz => DpmjetIII306-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_air_ft.pkl.gz => DpmjetIII306-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_air_ft2cms.pkl.gz => DpmjetIII306-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_p_cms.pkl.gz => DpmjetIII306-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_p_cms2ft.pkl.gz => DpmjetIII306-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_p_ft.pkl.gz => DpmjetIII306-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306_pi-_p_ft2cms.pkl.gz => DpmjetIII306-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_air_cms.pkl.gz => EposLHC-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_air_cms2ft.pkl.gz => EposLHC-He-air-cms2ft.pkl.gz} (100%) create mode 100644 tests/data/test_generators/EposLHC-He-air-ft.pkl.gz rename tests/data/test_generators/{EposLHC_He_air_ft2cms.pkl.gz => EposLHC-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_p_cms.pkl.gz => EposLHC-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_p_cms2ft.pkl.gz => EposLHC-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_p_ft.pkl.gz => EposLHC-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_He_p_ft2cms.pkl.gz => EposLHC-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_air_cms.pkl.gz => EposLHC-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_air_cms2ft.pkl.gz => EposLHC-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_air_ft.pkl.gz => EposLHC-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_air_ft2cms.pkl.gz => EposLHC-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_p_cms.pkl.gz => EposLHC-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_p_cms2ft.pkl.gz => EposLHC-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_p_ft.pkl.gz => EposLHC-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_p_p_ft2cms.pkl.gz => EposLHC-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_air_cms.pkl.gz => EposLHC-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_air_cms2ft.pkl.gz => EposLHC-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_air_ft.pkl.gz => EposLHC-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_air_ft2cms.pkl.gz => EposLHC-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_p_cms.pkl.gz => EposLHC-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_p_cms2ft.pkl.gz => EposLHC-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_p_ft.pkl.gz => EposLHC-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC_pi-_p_ft2cms.pkl.gz => EposLHC-pi--p-ft2cms.pkl.gz} (100%) delete mode 100644 tests/data/test_generators/EposLHC_He_air_ft.pkl.gz rename tests/data/test_generators/{Phojet112_gamma_p_cms.pkl.gz => Phojet112-gamma-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_gamma_p_cms2ft.pkl.gz => Phojet112-gamma-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_gamma_p_ft.pkl.gz => Phojet112-gamma-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_gamma_p_ft2cms.pkl.gz => Phojet112-gamma-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_p_p_cms.pkl.gz => Phojet112-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_p_p_cms2ft.pkl.gz => Phojet112-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_p_p_ft.pkl.gz => Phojet112-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112_p_p_ft2cms.pkl.gz => Phojet112-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_p_p_cms.pkl.gz => Phojet191-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_p_p_cms2ft.pkl.gz => Phojet191-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_p_p_ft.pkl.gz => Phojet191-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_p_p_ft2cms.pkl.gz => Phojet191-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_pi-_p_cms.pkl.gz => Phojet191-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_pi-_p_cms2ft.pkl.gz => Phojet191-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_pi-_p_ft.pkl.gz => Phojet191-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191_pi-_p_ft2cms.pkl.gz => Phojet191-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_p_p_cms.pkl.gz => Phojet193-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_p_p_cms2ft.pkl.gz => Phojet193-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_p_p_ft.pkl.gz => Phojet193-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_p_p_ft2cms.pkl.gz => Phojet193-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_pi-_p_cms.pkl.gz => Phojet193-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_pi-_p_cms2ft.pkl.gz => Phojet193-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_pi-_p_ft.pkl.gz => Phojet193-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193_pi-_p_ft2cms.pkl.gz => Phojet193-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_p_p_cms.pkl.gz => Pythia6-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_p_p_cms2ft.pkl.gz => Pythia6-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_p_p_ft.pkl.gz => Pythia6-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_p_p_ft2cms.pkl.gz => Pythia6-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_pi-_p_cms.pkl.gz => Pythia6-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_pi-_p_cms2ft.pkl.gz => Pythia6-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_pi-_p_ft.pkl.gz => Pythia6-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6_pi-_p_ft2cms.pkl.gz => Pythia6-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_gamma_p_cms.pkl.gz => Pythia8-gamma-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_gamma_p_cms2ft.pkl.gz => Pythia8-gamma-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_gamma_p_ft.pkl.gz => Pythia8-gamma-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_gamma_p_ft2cms.pkl.gz => Pythia8-gamma-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_p_p_cms.pkl.gz => Pythia8-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_p_p_cms2ft.pkl.gz => Pythia8-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_p_p_ft.pkl.gz => Pythia8-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_p_p_ft2cms.pkl.gz => Pythia8-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_pi-_p_cms.pkl.gz => Pythia8-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_pi-_p_cms2ft.pkl.gz => Pythia8-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_pi-_p_ft.pkl.gz => Pythia8-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8_pi-_p_ft2cms.pkl.gz => Pythia8-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_air_cms.pkl.gz => QGSJet01d-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_air_cms2ft.pkl.gz => QGSJet01d-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_air_ft.pkl.gz => QGSJet01d-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_air_ft2cms.pkl.gz => QGSJet01d-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_p_cms.pkl.gz => QGSJet01d-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_p_cms2ft.pkl.gz => QGSJet01d-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_p_ft.pkl.gz => QGSJet01d-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_He_p_ft2cms.pkl.gz => QGSJet01d-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_air_cms.pkl.gz => QGSJet01d-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_air_cms2ft.pkl.gz => QGSJet01d-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_air_ft.pkl.gz => QGSJet01d-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_air_ft2cms.pkl.gz => QGSJet01d-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_p_cms.pkl.gz => QGSJet01d-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_p_cms2ft.pkl.gz => QGSJet01d-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_p_ft.pkl.gz => QGSJet01d-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_p_p_ft2cms.pkl.gz => QGSJet01d-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_air_cms.pkl.gz => QGSJet01d-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_air_cms2ft.pkl.gz => QGSJet01d-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_air_ft.pkl.gz => QGSJet01d-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_air_ft2cms.pkl.gz => QGSJet01d-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_p_cms.pkl.gz => QGSJet01d-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_p_cms2ft.pkl.gz => QGSJet01d-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_p_ft.pkl.gz => QGSJet01d-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d_pi-_p_ft2cms.pkl.gz => QGSJet01d-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_air_cms.pkl.gz => QGSJetII03-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_air_cms2ft.pkl.gz => QGSJetII03-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_air_ft.pkl.gz => QGSJetII03-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_air_ft2cms.pkl.gz => QGSJetII03-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_p_cms.pkl.gz => QGSJetII03-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_p_cms2ft.pkl.gz => QGSJetII03-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_p_ft.pkl.gz => QGSJetII03-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_He_p_ft2cms.pkl.gz => QGSJetII03-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_air_cms.pkl.gz => QGSJetII03-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_air_cms2ft.pkl.gz => QGSJetII03-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_air_ft.pkl.gz => QGSJetII03-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_air_ft2cms.pkl.gz => QGSJetII03-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_p_cms.pkl.gz => QGSJetII03-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_p_cms2ft.pkl.gz => QGSJetII03-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_p_ft.pkl.gz => QGSJetII03-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_p_p_ft2cms.pkl.gz => QGSJetII03-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_air_cms.pkl.gz => QGSJetII03-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_air_cms2ft.pkl.gz => QGSJetII03-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_air_ft.pkl.gz => QGSJetII03-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_air_ft2cms.pkl.gz => QGSJetII03-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_p_cms.pkl.gz => QGSJetII03-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_p_cms2ft.pkl.gz => QGSJetII03-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_p_ft.pkl.gz => QGSJetII03-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03_pi-_p_ft2cms.pkl.gz => QGSJetII03-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_air_cms.pkl.gz => QGSJetII04-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_air_cms2ft.pkl.gz => QGSJetII04-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_air_ft.pkl.gz => QGSJetII04-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_air_ft2cms.pkl.gz => QGSJetII04-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_p_cms.pkl.gz => QGSJetII04-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_p_cms2ft.pkl.gz => QGSJetII04-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_p_ft.pkl.gz => QGSJetII04-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_He_p_ft2cms.pkl.gz => QGSJetII04-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_air_cms.pkl.gz => QGSJetII04-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_air_cms2ft.pkl.gz => QGSJetII04-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_air_ft.pkl.gz => QGSJetII04-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_air_ft2cms.pkl.gz => QGSJetII04-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_p_cms.pkl.gz => QGSJetII04-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_p_cms2ft.pkl.gz => QGSJetII04-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_p_ft.pkl.gz => QGSJetII04-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_p_p_ft2cms.pkl.gz => QGSJetII04-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_air_cms.pkl.gz => QGSJetII04-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_air_cms2ft.pkl.gz => QGSJetII04-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_air_ft.pkl.gz => QGSJetII04-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_air_ft2cms.pkl.gz => QGSJetII04-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_p_cms.pkl.gz => QGSJetII04-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_p_cms2ft.pkl.gz => QGSJetII04-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_p_ft.pkl.gz => QGSJetII04-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04_pi-_p_ft2cms.pkl.gz => QGSJetII04-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_air_cms.pkl.gz => Sibyll21-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_air_cms2ft.pkl.gz => Sibyll21-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_air_ft.pkl.gz => Sibyll21-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_air_ft2cms.pkl.gz => Sibyll21-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_p_cms.pkl.gz => Sibyll21-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_p_cms2ft.pkl.gz => Sibyll21-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_p_ft.pkl.gz => Sibyll21-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_p_p_ft2cms.pkl.gz => Sibyll21-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_air_cms.pkl.gz => Sibyll21-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_air_cms2ft.pkl.gz => Sibyll21-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_air_ft.pkl.gz => Sibyll21-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_air_ft2cms.pkl.gz => Sibyll21-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_p_cms.pkl.gz => Sibyll21-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_p_cms2ft.pkl.gz => Sibyll21-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_p_ft.pkl.gz => Sibyll21-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21_pi-_p_ft2cms.pkl.gz => Sibyll21-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_air_cms.pkl.gz => Sibyll23-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_air_cms2ft.pkl.gz => Sibyll23-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_air_ft.pkl.gz => Sibyll23-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_air_ft2cms.pkl.gz => Sibyll23-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_p_cms.pkl.gz => Sibyll23-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_p_cms2ft.pkl.gz => Sibyll23-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_p_ft.pkl.gz => Sibyll23-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_p_p_ft2cms.pkl.gz => Sibyll23-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_air_cms.pkl.gz => Sibyll23-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_air_cms2ft.pkl.gz => Sibyll23-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_air_ft.pkl.gz => Sibyll23-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_air_ft2cms.pkl.gz => Sibyll23-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_p_cms.pkl.gz => Sibyll23-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_p_cms2ft.pkl.gz => Sibyll23-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_p_ft.pkl.gz => Sibyll23-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23_pi-_p_ft2cms.pkl.gz => Sibyll23-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_air_cms.pkl.gz => Sibyll23c-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_air_cms2ft.pkl.gz => Sibyll23c-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_air_ft.pkl.gz => Sibyll23c-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_air_ft2cms.pkl.gz => Sibyll23c-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_p_cms.pkl.gz => Sibyll23c-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_p_cms2ft.pkl.gz => Sibyll23c-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_p_ft.pkl.gz => Sibyll23c-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_p_p_ft2cms.pkl.gz => Sibyll23c-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_air_cms.pkl.gz => Sibyll23c-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_air_cms2ft.pkl.gz => Sibyll23c-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_air_ft.pkl.gz => Sibyll23c-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_air_ft2cms.pkl.gz => Sibyll23c-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_p_cms.pkl.gz => Sibyll23c-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_p_cms2ft.pkl.gz => Sibyll23c-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_p_ft.pkl.gz => Sibyll23c-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c_pi-_p_ft2cms.pkl.gz => Sibyll23c-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_air_cms.pkl.gz => Sibyll23d-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_air_cms2ft.pkl.gz => Sibyll23d-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_air_ft.pkl.gz => Sibyll23d-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_air_ft2cms.pkl.gz => Sibyll23d-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_p_cms.pkl.gz => Sibyll23d-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_p_cms2ft.pkl.gz => Sibyll23d-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_p_ft.pkl.gz => Sibyll23d-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_p_p_ft2cms.pkl.gz => Sibyll23d-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_air_cms.pkl.gz => Sibyll23d-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_air_cms2ft.pkl.gz => Sibyll23d-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_air_ft.pkl.gz => Sibyll23d-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_air_ft2cms.pkl.gz => Sibyll23d-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_p_cms.pkl.gz => Sibyll23d-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_p_cms2ft.pkl.gz => Sibyll23d-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_p_ft.pkl.gz => Sibyll23d-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23d_pi-_p_ft2cms.pkl.gz => Sibyll23d-pi--p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sophia20_gamma_p_cms.pkl.gz => Sophia20-gamma-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{Sophia20_gamma_p_cms2ft.pkl.gz => Sophia20-gamma-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sophia20_gamma_p_ft.pkl.gz => Sophia20-gamma-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{Sophia20_gamma_p_ft2cms.pkl.gz => Sophia20-gamma-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_air_cms.pkl.gz => UrQMD34-He-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_air_cms2ft.pkl.gz => UrQMD34-He-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_air_ft.pkl.gz => UrQMD34-He-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_air_ft2cms.pkl.gz => UrQMD34-He-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_p_cms.pkl.gz => UrQMD34-He-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_p_cms2ft.pkl.gz => UrQMD34-He-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_p_ft.pkl.gz => UrQMD34-He-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_He_p_ft2cms.pkl.gz => UrQMD34-He-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_air_cms.pkl.gz => UrQMD34-p-air-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_air_cms2ft.pkl.gz => UrQMD34-p-air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_air_ft.pkl.gz => UrQMD34-p-air-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_air_ft2cms.pkl.gz => UrQMD34-p-air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_p_cms.pkl.gz => UrQMD34-p-p-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_p_cms2ft.pkl.gz => UrQMD34-p-p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_p_ft.pkl.gz => UrQMD34-p-p-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_p_p_ft2cms.pkl.gz => UrQMD34-p-p-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_air_cms.pkl.gz => UrQMD34-pi--air-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_air_cms2ft.pkl.gz => UrQMD34-pi--air-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_air_ft.pkl.gz => UrQMD34-pi--air-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_air_ft2cms.pkl.gz => UrQMD34-pi--air-ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_p_cms.pkl.gz => UrQMD34-pi--p-cms.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_p_cms2ft.pkl.gz => UrQMD34-pi--p-cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_p_ft.pkl.gz => UrQMD34-pi--p-ft.pkl.gz} (100%) rename tests/data/test_generators/{UrQMD34_pi-_p_ft2cms.pkl.gz => UrQMD34-pi--p-ft2cms.pkl.gz} (100%) diff --git a/src/impy/common.py b/src/impy/common.py index afe6daed..ecf24e64 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -7,6 +7,7 @@ such as the rapidity :func:`MCEvent.y` or the laboratory momentum fraction :func:`MCEvent.xlab`. """ +from __future__ import annotations from abc import ABC, abstractmethod import numpy as np from impy.util import ( @@ -444,13 +445,13 @@ class MCEvent(EventData, ABC): _jmohep = "jmohep" _jdahep = "jdahep" - def __init__(self, generator, kinematics): + def __init__(self, generator: Model, kinematics: EventKinematics): """ Parameters ---------- - generator: + generator : Model Generator instance. - kinematics: + kinematics : EventKinematics Kinematics of the event. """ # used by _charge_init and generator-specific methods @@ -484,7 +485,7 @@ def __init__(self, generator, kinematics): ) @abstractmethod - def _charge_init(self, npart): + def _charge_init(self, npart: int): # override this in derived to get charge info ... @@ -767,6 +768,7 @@ def cross_section(self, kin): kin : EventKinematics Calculate cross-section for EventKinematics. """ + self._validate_kinematics(kin) if isinstance(kin.p2, CompositeTarget): cross_section = CrossSectionData(0, 0, 0, 0, 0, 0, 0) components = kin.p2.components diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index ab7dee34..dde66508 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -67,9 +67,10 @@ class SIBYLLRun(Model): ) } - def __init__(self, evt_kin, *, seed=None): + def __init__(self, seed=None): super().__init__(seed) + def _once(self): # setup logging import impy @@ -82,14 +83,7 @@ def __init__(self, evt_kin, *, seed=None): self._lib.rndmgas.iset = 0 self._lib.pdg_ini() - # This calls _set_event_kinematics which uses self._lib.isib_pdg2pid - # which works only after an initialization call to self._lib.pdg_ini() - self.kinematics = evt_kin - - self._set_final_state_particles() - - def _cross_section(self, kin=None): - kin = self.kinematics if kin is None else kin + def _cross_section(self, kin): if kin.p2.A > 1: # TODO figure out what this returns exactly: # self._lib.sib_sigma_hnuc @@ -125,6 +119,7 @@ def sigma_inel_air(self): def _set_kinematics(self, kin): self._production_id = self._lib.isib_pdg2pid(kin.p1) assert self._production_id != 0 + self._kin = kin def _set_stable(self, pdgid, stable): sid = abs(self._lib.isib_pdg2pid(pdgid)) @@ -142,7 +137,7 @@ def _set_stable(self, pdgid, stable): idb[sid - 1] = abs(idb[sid - 1]) def _generate(self): - kin = self.kinematics + kin = self._kin self._lib.sibyll(self._production_id, kin.p2.A, kin.ecm) self._lib.decsib() self._lib.sibhep() diff --git a/tests/data/test_generators/DpmjetIII191_He_air_cms.pkl.gz b/tests/data/test_generators/DpmjetIII191-He-air-cms.pkl.gz similarity index 100% rename from tests/data/test_generators/DpmjetIII191_He_air_cms.pkl.gz rename to tests/data/test_generators/DpmjetIII191-He-air-cms.pkl.gz diff --git a/tests/data/test_generators/DpmjetIII191_He_air_cms2ft.pkl.gz b/tests/data/test_generators/DpmjetIII191-He-air-cms2ft.pkl.gz similarity index 100% rename from tests/data/test_generators/DpmjetIII191_He_air_cms2ft.pkl.gz rename to tests/data/test_generators/DpmjetIII191-He-air-cms2ft.pkl.gz diff --git a/tests/data/test_generators/DpmjetIII191_He_air_ft.pkl.gz b/tests/data/test_generators/DpmjetIII191-He-air-ft.pkl.gz similarity index 100% rename from tests/data/test_generators/DpmjetIII191_He_air_ft.pkl.gz rename to tests/data/test_generators/DpmjetIII191-He-air-ft.pkl.gz diff --git a/tests/data/test_generators/DpmjetIII191_He_air_ft2cms.pkl.gz b/tests/data/test_generators/DpmjetIII191-He-air-ft2cms.pkl.gz similarity index 100% rename from tests/data/test_generators/DpmjetIII191_He_air_ft2cms.pkl.gz rename to tests/data/test_generators/DpmjetIII191-He-air-ft2cms.pkl.gz diff --git a/tests/data/test_generators/DpmjetIII191_He_p_cms.pkl.gz b/tests/data/test_generators/DpmjetIII191-He-p-cms.pkl.gz similarity index 100% rename from tests/data/test_generators/DpmjetIII191_He_p_cms.pkl.gz rename to 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zM5E!yL2a~pp$(^e01~6it#_b7pF0b}z%J(>FsvM?o9`g^jo4Nl@v=do* z3nMH^bCVGZ@E+gb1zPjddneY1Wr=eY1a6Xa9N0SFDBFMN4X1gwlgpo*Qc&LX?h)Es&8i|!80nfj0g8q|Wjm6>bt2v%m>^h&2Z5!2t1tmtc2X1rb^eWr2F&ndmSSP+L6Ay3#?yt4%5W1>WD_? zaC Date: Sat, 31 Dec 2022 16:45:30 +0100 Subject: [PATCH 11/35] ported pythia6 --- src/impy/models/pythia6.py | 12 +++---- tests/test_pythia6.py | 66 ++++++++++++++------------------------ 2 files changed, 29 insertions(+), 49 deletions(-) diff --git a/src/impy/models/pythia6.py b/src/impy/models/pythia6.py index f6dd3db7..fd97d067 100644 --- a/src/impy/models/pythia6.py +++ b/src/impy/models/pythia6.py @@ -26,9 +26,10 @@ class Pythia6(Model): _projectiles = standard_projectiles _targets = standard_projectiles - def __init__(self, evt_kin, *, seed=None, new_mpi=False): - super().__init__(seed) + def __init__(self, seed=None, *, new_mpi=False): + super().__init__(seed, new_mpi) + def _once(self, new_mpi): # setup logging lun = 6 # stdout self._lib.pydat1.mstu[10] = lun @@ -55,11 +56,8 @@ def __init__(self, evt_kin, *, seed=None, new_mpi=False): for isub in [11, 12, 13, 28, 53, 68, 92, 93, 94, 95, 96]: self._lib.pysubs.msub[isub - 1] = 1 - self.kinematics = evt_kin - - self._set_final_state_particles() - - def _cross_section(self, kin=None): + def _cross_section(self, kin): + self._set_kinematics(kin) s = self._lib.pyint7.sigt[0, 0] c = CrossSectionData( total=s[0], diff --git a/tests/test_pythia6.py b/tests/test_pythia6.py index 8418243d..17eda80b 100644 --- a/tests/test_pythia6.py +++ b/tests/test_pythia6.py @@ -8,7 +8,6 @@ from .util import reference_charge, run_in_separate_process import pytest import pickle -from particle import literals as lp from functools import lru_cache @@ -19,27 +18,27 @@ def test_name(): def run_collision(p1, p2): - evt_kin = CenterOfMass(100 * GeV, p1, p2) - m = Pythia6(evt_kin, seed=4) - for event in m(1): + kin = CenterOfMass(100 * GeV, p1, p2) + m = Pythia6(seed=4) + for event in m(kin, 1): pass return event # MCEvent is restored as EventData def run_cross_section(p1, p2): - evt_kin = CenterOfMass(10 * GeV, p1, p2) - m = Pythia6(evt_kin, seed=1) - return m.cross_section() + kin = CenterOfMass(10 * GeV, p1, p2) + m = Pythia6(seed=1) + return m.cross_section(kin) @pytest.fixture @lru_cache(maxsize=1) def event(): - return run_in_separate_process(run_collision, "p", "p") + return run_collision("p", "p") def test_cross_section(): - c = run_in_separate_process(run_cross_section, "p", "p") + c = run_cross_section("p", "p") assert_allclose(c.total, 38.4, atol=0.1) assert_allclose(c.inelastic, 31.4, atol=0.1) assert_allclose(c.elastic, 7.0, atol=0.1) @@ -90,29 +89,16 @@ def test_parents(event): assert sum(x[0] > 0 and x[1] > 0 for x in event.parents) > 0 -def run_is_view(): - evt_kin = CenterOfMass(10 * GeV, 2212, 2212) +def test_is_view(): + kin = CenterOfMass(10 * GeV, 2212, 2212) - m = Pythia6(evt_kin, seed=1) - for event in m(1): + m = Pythia6(seed=1) + for event in m(kin, 1): pass - return ( - event.px.flags["OWNDATA"], - event[:5].px.flags["OWNDATA"], - event.copy().px.flags["OWNDATA"], - ) - - -def test_is_view(): - ( - event_owndata, - sliced_owndata, - copy_owndata, - ) = run_in_separate_process(run_is_view) - assert event_owndata is False - assert sliced_owndata is False - assert copy_owndata is True + event.px.flags["OWNDATA"] is False + event[:5].px.flags["OWNDATA"] is False + event.copy().px.flags["OWNDATA"] is True def test_final_state(event): @@ -133,11 +119,11 @@ def test_final_state_charged(event): assert_equal(ev1, ev3) -def run_pickle(): - evt_kin = CenterOfMass(10 * GeV, 2212, 2212) +def test_pickle(event): + kin = CenterOfMass(10 * GeV, 2212, 2212) - m = Pythia6(evt_kin, seed=1) - for event in m(1): + m = Pythia6(seed=1) + for event in m(kin, 1): pass s = pickle.dumps(event) @@ -146,18 +132,14 @@ def run_pickle(): assert event == event2 -def test_pickle(event): - run_in_separate_process(run_pickle) - - def run_pp_collision_copy(): from impy.models.pythia6 import PYTHIA6Event from impy.common import EventData - evt_kin = CenterOfMass(1 * TeV, 2212, 2212) - m = Pythia6(evt_kin, seed=4) - m.stable(lp.pi_0.pdgid, False) # needed to get nonzero vertices - for event in m(1): + m = Pythia6(seed=4) + m.stable("pi_0", False) # needed to get nonzero vertices + kin = CenterOfMass(1 * TeV, 2212, 2212) + for event in m(kin, 1): pass event2 = event.copy() @@ -172,7 +154,7 @@ def run_pp_collision_copy(): assert event3 == event2 # just running this used to trigger a bug - list(m(1)) + list(m(kin, 1)) def test_event_copy(): From baf41b43e9f2aca562df4deacec3e7cae5536d42 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 16:50:10 +0100 Subject: [PATCH 12/35] ported pythia8 --- src/impy/models/pythia8.py | 11 ++++------- tests/test_pythia8.py | 36 +++++++++++++++++------------------- 2 files changed, 21 insertions(+), 26 deletions(-) diff --git a/src/impy/models/pythia8.py b/src/impy/models/pythia8.py index 77443814..f24b4ca8 100644 --- a/src/impy/models/pythia8.py +++ b/src/impy/models/pythia8.py @@ -43,15 +43,15 @@ class Pythia8(Model): name2pdg(x) for x in ("He4", "Li6", "C12", "O16", "Cu63", "Xe129", "Au197", "Pb208") } - _restartable = True _data_url = ( "https://github.com/impy-project/impy" + "/releases/download/zipped_data_v1.0/Pythia8_v002.zip" ) - def __init__(self, evt_kin, *, seed=None): + def __init__(self, seed=None): super().__init__(seed) + def _once(self): self._lib.hepevt = self._lib.Hepevt() datdir = _cached_data_dir(self._data_url) + "xmldoc" @@ -64,11 +64,8 @@ def __init__(self, evt_kin, *, seed=None): del environ["PYTHIA8DATA"] self._lib.pythia = self._lib.Pythia(datdir, True) - # must come last - self.kinematics = evt_kin - self._set_final_state_particles() - - def _cross_section(self, kin=None): + def _cross_section(self, kin): + self._set_kinematics(kin) st = self._lib.pythia.info.sigmaTot return CrossSectionData( st.sigmaTot, diff --git a/tests/test_pythia8.py b/tests/test_pythia8.py index 449f3c37..f4cec6e3 100644 --- a/tests/test_pythia8.py +++ b/tests/test_pythia8.py @@ -3,7 +3,7 @@ from impy.constants import GeV import numpy as np from numpy.testing import assert_allclose -from .util import reference_charge, run_in_separate_process +from .util import reference_charge import pytest from functools import lru_cache import sys @@ -15,23 +15,23 @@ def run_collision(energy, p1, p2): - evt_kin = CenterOfMass(energy, p1, p2) - m = Pythia8(evt_kin, seed=4) - for event in m(1): + kin = CenterOfMass(energy, p1, p2) + m = Pythia8(seed=4) + for event in m(kin, 1): pass return event def run_cross_section(energy, p1, p2): - evt_kin = CenterOfMass(energy, p1, p2) - m = Pythia8(evt_kin, seed=1) - return m.cross_section() + m = Pythia8(seed=1) + kin = CenterOfMass(energy, p1, p2) + return m.cross_section(kin) @pytest.fixture @lru_cache(maxsize=1) # Pythia8 initialization is very slow def event(): - return run_in_separate_process(run_collision, 10 * GeV, "p", "p") + return run_collision(10 * GeV, "p", "p") def test_impact_parameter(event): @@ -44,7 +44,7 @@ def test_n_wounded(event): def test_cross_section(): - c = run_in_separate_process(run_cross_section, 10 * GeV, "p", "p") + c = run_cross_section(10 * GeV, "p", "p") assert_allclose(c.total, 38.4, atol=0.1) assert_allclose(c.inelastic, 31.3, atol=0.1) assert_allclose(c.elastic, 7.1, atol=0.1) @@ -100,9 +100,7 @@ def test_nuclear_collision(): # The test takes ages because the initialization is extremely long, # and Pythia seldom raises the success flag unless Ecm > TeV are used. - event = run_in_separate_process( - run_collision, 2000 * GeV, "p", (4, 2), timeout=1000 - ) + event = run_collision(2000 * GeV, "p", (4, 2)) assert event.pid[0] == 2212 assert event.pid[1] == 1000020040 assert_allclose(event.en[0], 1e3) @@ -115,7 +113,7 @@ def test_nuclear_collision(): def test_photo_hadron_collision(): - event = run_in_separate_process(run_collision, 100 * GeV, "gamma", "p") + event = run_collision(100 * GeV, "gamma", "p") event.pid[0] == 22 event.pid[1] == 2212 apid = np.abs(event.final_state_charged().pid) @@ -123,14 +121,14 @@ def test_photo_hadron_collision(): def run_pythia_change_energy(): - evt_kin = CenterOfMass(10 * GeV, "p", "p") - m = Pythia8(evt_kin, seed=1) - for event in m(1): + m = Pythia8(seed=1) + kin = CenterOfMass(10 * GeV, "p", "p") + for event in m(kin, 1): assert_allclose(event.en[:2], 5 * GeV) - m.kinematics = CenterOfMass(100 * GeV, "p", "p") - for event in m(1): + kin = CenterOfMass(100 * GeV, "p", "p") + for event in m(kin, 1): assert_allclose(event.en[:2], 50 * GeV) def test_changing_beams_proton(): - run_in_separate_process(run_pythia_change_energy) + run_pythia_change_energy() From b9b6921ddad28f62915226e712c17c2764725d35 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 17:09:19 +0100 Subject: [PATCH 13/35] move CompositeTarget to util, use process_particle everywhere --- src/impy/common.py | 4 ++-- src/impy/models/qgsjet.py | 20 ++++++-------------- tests/test_generators.py | 3 +-- 3 files changed, 9 insertions(+), 18 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index ecf24e64..3cc680cf 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -14,8 +14,8 @@ classproperty, select_parents, naneq, - name2pdg, pdg2name, + process_particle, Nuclei, ) from impy.constants import ( @@ -737,7 +737,7 @@ def stable(self, particle, stable=True): a full particle history and do not allow one to set certain resonances as stable. """ - pid = name2pdg(particle) if isinstance(particle, str) else particle + pid = process_particle(particle) p = Particle.from_pdgid(pid) if p.ctau is None or p.ctau == np.inf: raise ValueError(f"{pdg2name(pid)} cannot decay") diff --git a/src/impy/models/qgsjet.py b/src/impy/models/qgsjet.py index 3519b244..f33e1048 100644 --- a/src/impy/models/qgsjet.py +++ b/src/impy/models/qgsjet.py @@ -35,22 +35,18 @@ class QGSJetRun(Model): + "/releases/download/zipped_data_v1.0/qgsjet_v001.zip" ) - # needed to skip set_final_state_particles() in MCRun - _set_final_state_particles_called = True + def __init__(self, seed=None): + super().__init__(seed) - def __init__(self, evt_kin, *, seed=None): + def _once(self): import impy - super().__init__(seed) - # logging lun = 6 # stdout datdir = _cached_data_dir(self._data_url) self._lib.cqgsini(self._seed, datdir, lun, impy.debug_level) - self.kinematics = evt_kin - - def _set_stable(self, pdgid, stable): + def _set_stable(self, pid, stable): import warnings # TODO use Pythia8 instance to decay particles which QGSJet does not decay @@ -66,12 +62,10 @@ class QGSJet1Run(QGSJetRun): _event_class = QGSJET1Event - def _cross_section(self, kin=None): + def _cross_section(self, kin): # Interpolation routine for QGSJET01D cross sections from CORSIKA. from scipy.interpolate import UnivariateSpline - kin = self.kinematics if kin is None else kin - A_target = kin.p2.A # Projectile ID-1 to access fortran indices directly icz = self._projectile_id - 1 @@ -125,9 +119,7 @@ class QGSJet2Run(QGSJetRun): _event_class = QGSJET2Event - def _cross_section(self, kin=None): - kin = self.kinematics if kin is None else kin - + def _cross_section(self, kin): inel = self._lib.qgsect( kin.elab, self._projectile_id, diff --git a/tests/test_generators.py b/tests/test_generators.py index 2048fb69..5e204099 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -9,7 +9,6 @@ ) import impy.models as im import pytest -from .util import run_in_separate_process from impy.util import get_all_models, pdg2name import impy import boost_histogram as bh @@ -176,7 +175,7 @@ def test_generator(projectile, target, frame, Model): else: assert False # we should never arrive here - h = run_in_separate_process(run_model, Model, kin) + h = run_model(Model, kin) if h is None: assert abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets return From d41a49777f04ad3257523bf16e33fe828ab02c1e Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 17:13:04 +0100 Subject: [PATCH 14/35] port sophia --- src/impy/models/sophia.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/impy/models/sophia.py b/src/impy/models/sophia.py index 65775720..59aad9f0 100644 --- a/src/impy/models/sophia.py +++ b/src/impy/models/sophia.py @@ -51,7 +51,7 @@ class Sophia20(Model): _targets = {lp.p.pdgid, lp.n.pdgid} _ecm_min = 0 - def __init__(self, kinematics, *, seed=None, keep_decayed_particles=True): + def __init__(self, seed=None, *, keep_decayed_particles=True): import impy super().__init__(seed) @@ -60,10 +60,10 @@ def __init__(self, kinematics, *, seed=None, keep_decayed_particles=True): # Keep decayed particles in the history: self._lib.eg_io.keepdc = keep_decayed_particles - self.kinematics = kinematics - self._set_final_state_particles() + def _once(self): + pass - def _cross_section(self, kin=None): + def _cross_section(self, kin): # code=3 for inelastic cross-section # TODO fill more cross-sections inel = ( @@ -72,16 +72,16 @@ def _cross_section(self, kin=None): ) return CrossSectionData(inelastic=inel) - def _set_kinematics(self, evt_kin): + def _set_kinematics(self, kin): # Here we consider laboratory frame where photon moves along z axis # and nucleon is at rest. The angle is counted from z axis. # However, because of the definitions in "eventgen" subroutine of # SOPHIA code (line "P_gam(3) = -EPS*COS(theta*pi/180.D0)") # this angle should be 180 for photon moving along z # (and 0 for photon moving in direction opposite to z) - self._nucleon_code = self._lib.icon_pdg_sib(evt_kin.p2) + self._nucleon_code = self._lib.icon_pdg_sib(kin.p2) self._angle_between_nucleon_and_photon = 180 - self._energy_of_photon = evt_kin.elab + self._energy_of_photon = kin.elab self._energy_of_nucleon = np.float32(nucleon_mass) # setting parameters for cross-section self._lib.initial(self._nucleon_code) From 6372c9384132860f0f35aaaa5a5e2c05df7dc7f6 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sat, 31 Dec 2022 17:16:16 +0100 Subject: [PATCH 15/35] port urqmd --- src/impy/models/urqmd.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/src/impy/models/urqmd.py b/src/impy/models/urqmd.py index d789b7cf..0a5378de 100644 --- a/src/impy/models/urqmd.py +++ b/src/impy/models/urqmd.py @@ -41,14 +41,16 @@ class UrQMD34(Model): def __init__( self, - evt_kin, - *, seed=None, + *, caltim=200, outtim=200, ct_params=None, ct_options=None, ): + super().__init__(seed, caltim, outtim, ct_params, ct_options) + + def _once(self, caltim, outtim, ct_params, ct_options): import impy self._pdg2modid = { @@ -122,8 +124,6 @@ def __init__( -3334: (-55, 0), } - super().__init__(seed) - # logging lun = 6 # stdout self._lib.urqini(lun, impy.debug_level) @@ -151,11 +151,9 @@ def __init__( self._lib.pots.dtimestep = outtim self._lib.sys.nsteps = int(0.01 + caltim / self._lib.pots.dtimestep) self._lib.inputs.outsteps = int(0.01 + caltim / self._lib.pots.dtimestep) - self.kinematics = evt_kin - - self._set_final_state_particles() - def _cross_section(self, kin=None): + def _cross_section(self, kin): + self._set_kinematics(kin) tot = self._lib.ptsigtot() return CrossSectionData(total=tot) From eb882900987b3d5ee0ebb067d799fcccf2c2d8f2 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sun, 1 Jan 2023 16:04:28 +0100 Subject: [PATCH 16/35] ported phojet --- src/impy/common.py | 18 +++-- src/impy/models/dpmjetIII.py | 49 +++++++------ src/impy/models/phojet.py | 45 +++++++----- src/impy/remote_control.py | 133 +++++++++++++++++++++++++++++++++++ tests/test_remote_control.py | 67 ++++++++++++++++++ 5 files changed, 262 insertions(+), 50 deletions(-) create mode 100644 src/impy/remote_control.py create mode 100644 tests/test_remote_control.py diff --git a/src/impy/common.py b/src/impy/common.py index 3cc680cf..c76217b5 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -715,11 +715,15 @@ def _composite_plan(self, kin, nevents): @property def random_state(self): - return RMMARDState()._record_state(self) + rmmard_state = RMMARDState()._record_state(self) + numpy_state = self._composite_target_rng.__getstate__() + return rmmard_state, numpy_state @random_state.setter - def random_state(self, rng_state): - rng_state._restore_state(self) + def _(self, rng_state): + rmmard_state, numpy_state = rng_state + rmmard_state._restore_state(self) + self._composite_target_rng.__setstate__(numpy_state) def stable(self, particle, stable=True): """Prevent decay of an unstable particle. @@ -760,13 +764,15 @@ def maydecay(self, particle): """ self.stable(particle, False) - def cross_section(self, kin): + def cross_section(self, kin, **kwargs): """Cross sections according to current setup. Parameters ---------- kin : EventKinematics Calculate cross-section for EventKinematics. + kwargs : + Further arguments passed to the model implementation. """ self._validate_kinematics(kin) if isinstance(kin.p2, CompositeTarget): @@ -775,12 +781,12 @@ def cross_section(self, kin): fractions = kin.p2.fractions for component, fraction in zip(components, fractions): kin.p2 = component - cs = self._cross_section(kin) + cs = self._cross_section(kin, **kwargs) for i, val in enumerate(dataclasses.astuple(cs)): cross_section[i] += fraction * val return cross_section else: - return self._cross_section(kin) + return self._cross_section(kin, **kwargs) @abstractmethod def _once(self, *args): diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index b0c39f77..c3690214 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -62,9 +62,7 @@ class DpmjetIIIRun(Model): _max_A1 = 0 _max_A2 = 0 - def __init__(self, evt_kin, *, seed=None): - super().__init__(seed) - + def _once(self): data_dir = _cached_data_dir(self._data_url) # Set the dpmjpar.dat file if hasattr(self._lib, "pomdls") and hasattr(self._lib.pomdls, "parfn"): @@ -95,34 +93,24 @@ def __init__(self, evt_kin, *, seed=None): assert False, "Unknown DPMJET version, IO common block not detected" self._lib.pydat1.mstu[10] = lun - self.kinematics = evt_kin - - # Relax momentum and energy conservation checks at very high energies - if evt_kin.ecm > 5e4: - # Relative allowed deviation - self._lib.pomdls.parmdl[74] = 0.05 - # Absolute allowed deviation - self._lib.pomdls.parmdl[75] = 0.05 - # Prevent DPMJET from overwriting decay settings # self._lib.dtfrpa.ovwtdc = False # Set PYTHIA decay flags to follow all changes to MDCY self._lib.pydat1.mstj[21 - 1] = 1 self._lib.pydat1.mstj[22 - 1] = 2 - self._set_final_state_particles() - def _cross_section(self, kin=None, precision=None): - kin = self.kinematics if kin is None else kin - # we override to set precision - if (kin.p1.A and kin.p1.A > 1) or kin.p2.A > 1: - assert kin.p2.A >= 1, "DPMJET requires nucleons or nuclei on side 2." + def _cross_section(self, kin, precision=None): + self._set_kinematics(kin) + assert kin.p2.A >= 1 # should be guaranteed _set_kinematics + + if kin.p1.A and kin.p1.A > 1: # nuclear projectile if precision is not None: saved = self._lib.dtglgp.jstatb # Set number of trials for Glauber model integration self._lib.dtglgp.jstatb = precision self._lib.dt_xsglau( kin.p1.A or 1, - kin.p2.A or 1, + kin.p2.A, self._lib.idt_icihad(2212) if (kin.p1.A and kin.p1.A > 1) else self._lib.idt_icihad(kin.p1), @@ -136,17 +124,26 @@ def _cross_section(self, kin=None, precision=None): if precision is not None: self._lib.dtglgp.jstatb = saved return CrossSectionData(inelastic=self._lib.dtglxs.xspro[0, 0, 0]) - else: - stot, sela = self._lib.dt_xshn( - self._lib.idt_icihad(kin.p1), self._lib.idt_icihad(kin.p2), 0.0, kin.ecm - ) - return CrossSectionData(total=stot, elastic=sela, inelastic=stot - sela) + + # other projectile + stot, sela = self._lib.dt_xshn( + self._lib.idt_icihad(kin.p1), self._lib.idt_icihad(kin.p2), 0.0, kin.ecm + ) + return CrossSectionData(total=stot, elastic=sela, inelastic=stot - sela) # TODO set more cross-sections def _set_kinematics(self, kin): # Save maximal mass that has been initialized # (DPMJET sometimes crashes if higher mass requested than initialized) + + # Relax momentum and energy conservation checks at very high energies + if kin.ecm > 5e4: + # Relative allowed deviation + self._lib.pomdls.parmdl[74] = 0.05 + # Absolute allowed deviation + self._lib.pomdls.parmdl[75] = 0.05 + if not self._max_A1: # only do this once if kin.frame == EventFrame.FIXED_TARGET: @@ -179,12 +176,14 @@ def _set_kinematics(self, kin): # self._lib.dt_setbm(k.A1, k.Z1, k.A2, k.Z2, k.beam[0], k.beam[1]) # print 'OK' + self._kin = kin + def _set_stable(self, pdgid, stable): kc = self._lib.pycomp(pdgid) self._lib.pydat3.mdcy[kc - 1, 0] = not stable def _generate(self): - k = self.kinematics + k = self._kin reject = self._lib.dt_kkinc( k.p1.A or 1, k.p1.Z or 0, diff --git a/src/impy/models/phojet.py b/src/impy/models/phojet.py index 8337e8a2..fe92f1b1 100644 --- a/src/impy/models/phojet.py +++ b/src/impy/models/phojet.py @@ -2,6 +2,7 @@ from impy.util import fortran_chars, _cached_data_dir from impy.kinematics import EventFrame from impy.constants import standard_projectiles +from impy.remote_control import make_remote_controlled_model from particle import literals as lp @@ -70,7 +71,7 @@ class PHOJETRun(Model): _name = "PhoJet" _event_class = PhojetEvent _frame = None - _projectiles = standard_projectiles # FIXME: should allow photons and hadrons + _projectiles = standard_projectiles _targets = {lp.proton.pdgid, lp.neutron.pdgid} _param_file_name = "dpmjpar.dat" _data_url = ( @@ -78,9 +79,10 @@ class PHOJETRun(Model): + "/releases/download/zipped_data_v1.0/dpm3191_v001.zip" ) - def __init__(self, evt_kin, *, seed=None): + def __init__(self, seed=None): super().__init__(seed) + def _once(self): data_dir = _cached_data_dir(self._data_url) # Set the dpmjpar.dat file if hasattr(self._lib, "pomdls") and hasattr(self._lib.pomdls, "parfn"): @@ -143,20 +145,10 @@ def __init__(self, evt_kin, *, seed=None): # Set PYTHIA decay flags to follow all changes to MDCY self._lib.pydat1.mstj[21 - 1] = 1 self._lib.pydat1.mstj[22 - 1] = 2 - self._set_final_state_particles() - self.kinematics = evt_kin + def _cross_section(self, kin): + self._set_kinematics(kin) - # Initialize kinematics and tables (only once needed) - if self._lib.pho_event(-1, self.p1, self.p2)[1]: - raise RuntimeError( - "initialization failed with the current event kinematics" - ) - - def _cross_section(self, kin=None): - kin = self.kinematics if kin is None else kin - self._lib.pho_setpar(1, kin.p1, 0, 0.0) - self._lib.pho_setpar(2, kin.p2, 0, 0.0) if hasattr(self._lib, "pho_setpcomb"): # The old 1.12 version of phojet doesn't have this function self._lib.pho_setpcomb() @@ -190,13 +182,22 @@ def _set_kinematics(self, k): self._frame = EventFrame.CENTER_OF_MASS self._lib.pho_setpar(1, k.p1, 0, 0.0) self._lib.pho_setpar(2, k.p2, 0, 0.0) - self.p1, self.p2 = k.beams + self._beams = k.beams + + # Initialize kinematics and tables (only once needed) + # LIMITATION: Once this has been called for a given set of particles, + # it cannot be called again for different values of k.p1, k.p2. This + # means we cannot change particles in Phojet. + if self._lib.pho_event(-1, *self._beams)[1]: + raise RuntimeError( + "initialization failed with the current event kinematics" + ) def _generate(self): - return not self._lib.pho_event(1, self.p1, self.p2)[1] + return not self._lib.pho_event(1, *self._beams)[1] -class Phojet112(PHOJETRun): +class Phojet112Base(PHOJETRun): _version = "1.12-35" _library_name = "_phojet112" _param_file_name = "fitpar.dat" @@ -210,11 +211,17 @@ class Phojet112(PHOJETRun): ) -class Phojet191(PHOJETRun): +class Phojet191Base(PHOJETRun): _version = "19.1" _library_name = "_phojet191" -class Phojet193(PHOJETRun): +class Phojet193Base(PHOJETRun): _version = "19.3" _library_name = "_phojet193" + + +# Use remote control as a workaround for Phojet limitation +Phojet112 = make_remote_controlled_model("Phojet112", Phojet112Base) +Phojet191 = make_remote_controlled_model("Phojet191", Phojet191Base) +Phojet193 = make_remote_controlled_model("Phojet193", Phojet193Base) diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py new file mode 100644 index 00000000..4bcc89d1 --- /dev/null +++ b/src/impy/remote_control.py @@ -0,0 +1,133 @@ +import multiprocessing as mp + +__all__ = ["make_remote_controlled_model"] + + +def run(output, Model, init_args, stable_changes, random_state, method, args): + seed, kwargs = init_args + model = Model(seed, **kwargs) + if random_state is not None: + model.random_state = random_state + try: + if method == "call": + for k, v in stable_changes: + model.set_stable(k, v) + for x in model(*args): + # It is weird that we need to return a copy here, + # output.put should pickle the event anyway + output.put(x.copy()) + else: + x = getattr(model, method)(*args) + output.put(x) + except Exception as exc: + output.put(exc) + output.put(model.random_state) + + +def get(timeout, process, output): + from queue import Empty + + for _ in range(timeout): + if not process.is_alive(): + raise ValueError("process died") + try: + x = output.get(timeout=1) + break + except Empty: + pass + else: + raise TimeoutError("process send no data") + + if isinstance(x, Exception): + raise x + return x + + +def make_remote_controlled_model(name, Model): + """ + Dynamically create a Model class which executes calls to Model.cross_section and + Model.__call__ to the wrapped model in a separate process to work around + initialization issues. + + Parameters + ---------- + name : str + Name of the generated class, must be unique. + Model : class + The wrapped class. + """ + # This is a big hack, but required until a proper solution is available. A new process + # is created whenever the cross_section or __call__ methods are used. + # + # Generating events is optimized, the process is kept alive during generation and + # sends its events via a queue to the main process. In an exception occurs in the + # child process, it is passed to the main process and raised there. + # + # Sending of data currently uses pickle, which has some overhead. This penalty only + # matters for event generation, where the performance drop could be noticable. We + # could avoid this by allocating EventData in shared memory, but putting more time and + # effort into this for a model that is barely used (Phojet) does not seem effective. + # + # To maintain the illusion that we are pulling events from a model instance instead of + # restarting it repeatedly, we roundtrip the state of the random number generators + # everytime we create a new process. + + def __init__(self, seed=None, timeout=100, **kwargs): + self._timeout = timeout + self._init_args = (seed, kwargs) + self._stable_changes = {} + self._random_state = None + + def __call__(self, kin, nevents): + process, output = self._remote_init("call", (kin, nevents)) + for _ in range(nevents): + yield get(self._timeout, process, output) + self._random_state = get(self._timeout, process, output) + process.join() + + def _cross_section(self, kin): + return self._remote_method("_cross_section", kin) + + def _set_stable(self, pdgid, stable): + self._stable_changes[pdgid] = stable + + def _remote_init(self, method, args): + ctx = mp.get_context("spawn") + output = ctx.Queue() + process = ctx.Process( + target=run, + args=( + output, + Model, + self._init_args, + self._stable_changes, + self._random_state, + method, + args, + ), + daemon=True, + ) + process.start() + return process, output + + def _remote_method(self, method, *args): + process, output = self._remote_init(method, args) + x = get(self._timeout, process, output) + self._random_state = get(self._timeout, process, output) + process.join() + return x + + # dynamically create a new class which inherits from Model and + # overrides the methods which need to do remote calls + return type( + name, + (Model,), + { + "__init__": __init__, + "__call__": __call__, + "_cross_section": _cross_section, + "_set_stable": _set_stable, + "_remote_init": _remote_init, + "_remote_method": _remote_method, + }, + ) diff --git a/tests/test_remote_control.py b/tests/test_remote_control.py new file mode 100644 index 00000000..8a87d4ee --- /dev/null +++ b/tests/test_remote_control.py @@ -0,0 +1,67 @@ +from impy.models import Phojet193 +from impy.kinematics import CenterOfMass + + +def test_phojet_1(): + model = Phojet193(seed=1) + + kin = CenterOfMass(100, "p", "p") + + events1 = [] + for event in model(kin, 10): + events1.append(event) + + assert len(events1) == 10 + + for event in events1: + assert len(event) > 0 + + events2 = [] + for event in model(kin, 5): + events2.append(event) + assert len(events2) == 5 + + assert events2 != events1[:5] + + +def test_phojet_2(): + model = Phojet193(seed=1) + + kin = CenterOfMass(100, "p", "p") + + events1 = [] + for event in model(kin, 10): + events1.append(event) + + assert len(events1) == 10 + + for event in events1: + assert len(event) > 0 + + kin = CenterOfMass(100, "pi-", "p") + + events2 = [] + for event in model(kin, 5): + events2.append(event) + assert len(events2) == 5 + + assert events2 != events1[:5] + + +def test_phojet_3(): + model = Phojet193(seed=1) + + prev = 0 + for en in (10, 100, 1000): + kin = CenterOfMass(en, "p", "p") + c = model.cross_section(kin) + assert c.inelastic > 10 + assert c.inelastic > prev + prev = c.inelastic + + +def test_phojet_4(): + model = Phojet193(seed=1) + + # this calls an attribute of the original class + assert len(model.projectiles) > 0 From ac6fb7db41f18104598deece83ddc4dccf6c9247 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Sun, 1 Jan 2023 17:18:42 +0100 Subject: [PATCH 17/35] fixes --- src/impy/common.py | 38 +++++++++++++++----- src/impy/models/sibyll.py | 4 +-- src/impy/remote_control.py | 4 +-- tests/test_epos.py | 40 ++++++++++++--------- tests/test_pythia6.py | 71 ++++++++++++++++++++++++++++++-------- 5 files changed, 112 insertions(+), 45 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index c76217b5..ba84af17 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -500,8 +500,10 @@ def _get_n_wounded(self): # MCEvent is not pickleable, but EventData is. For convenience, we # make it so that MCEvent can be saved and is restored as EventData. def __getstate__(self): - # save only EventData sub-state - return {k: getattr(self, k) for k in self.__dataclass_fields__} + # Save only EventData sub-state. We actually need to make a copy here, + # a view is not sufficient. + copied_event = super().copy() + return dataclasses.asdict(copied_event) def __getnewargs__(self): # upon unpickling, create EventData object instead of MCEvent object @@ -583,6 +585,7 @@ def seed(self): # ========================================================================= class Model(ABC): _once_called = False + _alive_instances = set() _projectiles = standard_projectiles _targets = Nuclei() _ecm_min = 10 * GeV # default for many models @@ -592,6 +595,19 @@ def __init__(self, seed, *args): import importlib from random import randint + if self.pyname in self._alive_instances: + warnings.warn( + f"A previous instance of {self.pyname} is still alive. " + "You cannot use two instances in parallel. " + "Please delete the old one first before creating a new one. " + "You can ignore this warning if the previous instance is already " + "out of scope, Python does not always destroy old instances immediately.", + RuntimeWarning, + stacklevel=3, + ) + + self._alive_instances.add(self.pyname) + if seed is None: self._seed = randint(1, 10000000) elif isinstance(seed, int): @@ -614,12 +630,16 @@ def __init__(self, seed, *args): self._lib.init_rmmard(self._seed) # Run internal model initialization code self._once(*args) - # Set standard long lived particles as stable - for pid in long_lived: - self._set_stable(pid, True) else: if hasattr(self._lib, "init_rmmard"): self._lib.init_rmmard(self._seed) + # Set standard long lived particles as stable + for pid in long_lived: + self._set_stable(pid, True) + + def __del__(self): + if self.pyname in self._alive_instances: + self._alive_instances.remove(self.pyname) def __call__(self, kin, nevents): """Generator function (in python sence) @@ -720,12 +740,12 @@ def random_state(self): return rmmard_state, numpy_state @random_state.setter - def _(self, rng_state): + def random_state(self, rng_state): rmmard_state, numpy_state = rng_state rmmard_state._restore_state(self) self._composite_target_rng.__setstate__(numpy_state) - def stable(self, particle, stable=True): + def set_stable(self, particle, stable=True): """Prevent decay of an unstable particle. Parameters @@ -755,14 +775,14 @@ def stable(self, particle, stable=True): def maydecay(self, particle): """Decay particle in event record. - Equivalent to `self.stable(particle, stable=False)` + Equivalent to `self.set_stable(particle, stable=False)` Parameters ---------- particle : str or int Name or PDG ID of the particle. """ - self.stable(particle, False) + self.set_stable(particle, False) def cross_section(self, kin, **kwargs): """Cross sections according to current setup. diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index dde66508..ba2a406e 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -125,8 +125,8 @@ def _set_stable(self, pdgid, stable): sid = abs(self._lib.isib_pdg2pid(pdgid)) if abs(pdgid) == 311: info(1, "Ignores K0. Using K0L/S instead") - self.stable(130, stable) - self.stable(310, stable) + self.set_stable(130, stable) + self.set_stable(310, stable) return idb = self._lib.s_csydec.idb if sid == 0 or sid > idb.size - 1: diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 4bcc89d1..bd0541f8 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -13,9 +13,7 @@ def run(output, Model, init_args, stable_changes, random_state, method, args): for k, v in stable_changes: model.set_stable(k, v) for x in model(*args): - # It is weird that we need to return a copy here, - # output.put should pickle the event anyway - output.put(x.copy()) + output.put(x) else: x = getattr(model, method)(*args) output.put(x) diff --git a/tests/test_epos.py b/tests/test_epos.py index e77369fa..834a0a2f 100644 --- a/tests/test_epos.py +++ b/tests/test_epos.py @@ -11,7 +11,7 @@ def run_pp_collision(): m = EposLHC(seed=4) - m.stable("pi0", True) + m.set_stable("pi0", True) kin = CenterOfMass(10 * GeV, "proton", "proton") for event in m(kin, 1): pass @@ -29,13 +29,15 @@ def run_ab_collision(): @pytest.fixture @lru_cache(maxsize=1) def event(): - return run_pp_collision() + # must cache a copy and not a view + return run_pp_collision().copy() @pytest.fixture @lru_cache(maxsize=1) def event_ion(): - return run_ab_collision() + # must cache a copy and not a view + return run_ab_collision().copy() def test_impact_parameter(event, event_ion): @@ -117,24 +119,30 @@ def test_parents(event): assert sum(x[0] > 0 and x[1] > 0 for x in event.parents) > 0 -def run_set_stable(stable): +@pytest.mark.parametrize("stable", (False, True)) +def test_set_stable(stable): m = EposLHC(seed=4) - for pid, s in stable.items(): - m.stable(pid, s) - print("stable", m._get_stable()) + pid = lp.pi_0.pdgid + + m.set_stable("pi_0", stable) + kin = CenterOfMass(10 * GeV, "proton", "proton") for event in m(kin, 1): pass - return event + final = event.final_state() + if stable: + assert np.sum(final.pid == pid) > 0 + else: + assert np.sum(final.pid == pid) == 0 -def test_set_stable(): - pid = lp.pi_0.pdgid - ev1 = run_set_stable({"pi_0": True}) - ev2 = run_set_stable({pid: False}) + del m - # ev1 contains final state pi0 - assert np.any(ev1.pid[ev1.status == 1] == pid) + # check that defaults are restored for new instance + m = EposLHC(seed=4) + kin = CenterOfMass(10 * GeV, "proton", "proton") + for event in m(kin, 1): + pass - # ev2 does not contains final state pi0 - assert np.all(ev2.pid[ev2.status == 1] != pid) + final = event.final_state() + assert np.sum(final.pid == pid) == 0 diff --git a/tests/test_pythia6.py b/tests/test_pythia6.py index 17eda80b..b912e5d0 100644 --- a/tests/test_pythia6.py +++ b/tests/test_pythia6.py @@ -5,7 +5,7 @@ from impy.constants import GeV, TeV import numpy as np from numpy.testing import assert_allclose, assert_equal -from .util import reference_charge, run_in_separate_process +from .util import reference_charge import pytest import pickle from functools import lru_cache @@ -25,20 +25,17 @@ def run_collision(p1, p2): return event # MCEvent is restored as EventData -def run_cross_section(p1, p2): - kin = CenterOfMass(10 * GeV, p1, p2) - m = Pythia6(seed=1) - return m.cross_section(kin) - - @pytest.fixture @lru_cache(maxsize=1) def event(): - return run_collision("p", "p") + return run_collision("p", "p").copy() def test_cross_section(): - c = run_cross_section("p", "p") + kin = CenterOfMass(10 * GeV, "p", "p") + m = Pythia6(seed=1) + c = m.cross_section(kin) + assert_allclose(c.total, 38.4, atol=0.1) assert_allclose(c.inelastic, 31.4, atol=0.1) assert_allclose(c.elastic, 7.0, atol=0.1) @@ -119,7 +116,7 @@ def test_final_state_charged(event): assert_equal(ev1, ev3) -def test_pickle(event): +def test_pickle(): kin = CenterOfMass(10 * GeV, 2212, 2212) m = Pythia6(seed=1) @@ -127,17 +124,52 @@ def test_pickle(event): pass s = pickle.dumps(event) + + event1 = event.copy() + + # draw another event so that MCEvent + # get overridden + for _ in m(kin, 1): + pass + event2 = pickle.loads(s) - assert event == event2 + assert event1 == event2 + + +def test_event_getstate_setstate(): + from impy.models.pythia6 import PYTHIA6Event + from impy.common import EventData + + kin = CenterOfMass(10 * GeV, 2212, 2212) + + m = Pythia6(seed=1) + for event in m(kin, 1): + pass + + assert type(event) is PYTHIA6Event + + # this must create a copy + state = event.__getstate__() + + event1 = event.copy() + assert type(event1) is not PYTHIA6Event + + # draw another event so that MCEvent gets overridden + for _ in m(kin, 1): + pass + + event2 = EventData(**state) + assert event1 == event2 -def run_pp_collision_copy(): + +def test_event_copy(): from impy.models.pythia6 import PYTHIA6Event from impy.common import EventData m = Pythia6(seed=4) - m.stable("pi_0", False) # needed to get nonzero vertices + m.set_stable("pi_0", False) # needed to get nonzero vertices kin = CenterOfMass(1 * TeV, 2212, 2212) for event in m(kin, 1): pass @@ -157,5 +189,14 @@ def run_pp_collision_copy(): list(m(kin, 1)) -def test_event_copy(): - run_in_separate_process(run_pp_collision_copy) +@pytest.mark.filterwarnings("error") +def test_warning_when_creating_two_instances(): + m1 = Pythia6() + + with pytest.raises(RuntimeWarning): + Pythia6() + + del m1 + + # no warning + Pythia6() From c9adb18d2c73b363db558ded7ea95d629d662755 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 12:43:52 +0100 Subject: [PATCH 18/35] fix tests, add MCRun.get_stable, add numpy state to rng state --- src/impy/cli.py | 10 ++-- src/impy/common.py | 108 ++++++++++++++++++++--------------- src/impy/models/dpmjetIII.py | 21 +++++-- src/impy/models/epos.py | 4 +- src/impy/models/phojet.py | 4 +- src/impy/models/pythia6.py | 4 +- src/impy/models/pythia8.py | 4 +- src/impy/models/qgsjet.py | 4 +- src/impy/models/sibyll.py | 34 +---------- src/impy/models/sophia.py | 4 +- src/impy/models/urqmd.py | 4 +- src/impy/remote_control.py | 38 ++++++------ src/impy/util.py | 4 +- src/impy/writer.py | 23 +++++--- tests/test_hepmc_io.py | 18 +++--- tests/test_rng_state.py | 91 ++++++++++++++++++++++++----- tests/test_setstable.py | 18 +++--- tests/test_to_hepmc3.py | 23 +++----- tests/test_writer.py | 5 +- 19 files changed, 239 insertions(+), 182 deletions(-) diff --git a/src/impy/cli.py b/src/impy/cli.py index 1218a969..83342d49 100644 --- a/src/impy/cli.py +++ b/src/impy/cli.py @@ -324,14 +324,14 @@ def main(): ) if pr > 0 and ta == 0: # fixed target mode - evt_kin = FixedTarget(Momentum(pr), args.projectile_id, args.target_id) + kin = FixedTarget(Momentum(pr), args.projectile_id, args.target_id) else: # cms mode - evt_kin = CenterOfMass(args.sqrts, args.projectile_id, args.target_id) + kin = CenterOfMass(args.sqrts, args.projectile_id, args.target_id) task_id = None try: - model = args.model(evt_kin) - ofile = FORMATS[args.output](args.out, model) + model = args.model(args.seed) + ofile = FORMATS[args.output](args.out, model, kin) with ofile: # workaround: several models generate extra print when first # event is generated, this interferes with progress bar so we @@ -344,7 +344,7 @@ def main(): TimeRemainingColumn(elapsed_when_finished=True), SpeedColumn(), ) as bar: - for event in model(args.number): + for event in model(kin, args.number): ofile.write(event) if task_id is None: task_id = bar.add_task("", total=args.number) diff --git a/src/impy/common.py b/src/impy/common.py index ba84af17..80459c9b 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -228,7 +228,7 @@ def copy(self): """ Return event copy. """ - # this should be implemented with the help of copy + # Cannot be implemented with copy.deepcopy, leads to recursion copies = [] for obj in dataclasses.astuple(self): copies.append(obj.copy() if hasattr(obj, "copy") else obj) @@ -445,7 +445,7 @@ class MCEvent(EventData, ABC): _jmohep = "jmohep" _jdahep = "jdahep" - def __init__(self, generator: Model, kinematics: EventKinematics): + def __init__(self, generator: MCRun, kinematics: EventKinematics): """ Parameters ---------- @@ -512,17 +512,21 @@ def __getnewargs__(self): @dataclasses.dataclass class RMMARDState: - _c_number: np.ndarray = None - _u_array: np.ndarray = None - _u_i: np.ndarray = None - _u_j: np.ndarray = None - _seed: np.ndarray = None - _counter: np.ndarray = None - _big_counter: np.ndarray = None - _sequence_number: np.ndarray = None - - def _record_state(self, generator): + _c_number: np.ndarray + _u_array: np.ndarray + _u_i: np.ndarray + _u_j: np.ndarray + _seed: np.ndarray + _counter: np.ndarray + _big_counter: np.ndarray + _sequence_number: np.ndarray + _composite_target_state = None + # only needed for Sibyll + _gasdev_iset: np.ndarray = None + + def __init__(self, generator): data = generator._lib.crranma4 + self._c_number = data.c self._u_array = data.u self._u_i = data.i97 @@ -531,9 +535,13 @@ def _record_state(self, generator): self._counter = data.ntot self._big_counter = data.ntot2 self._sequence_number = data.jseq - return self - def _restore_state(self, generator): + self._composite_target_state = generator._composite_target_rng.__getstate__() + + if generator.name == "SIBYLL": + self._gasdev_iset = generator._lib.rndmgas.iset + + def _restore(self, generator): data = generator._lib.crranma4 data.c = self._c_number @@ -544,22 +552,16 @@ def _restore_state(self, generator): data.ntot = self._counter data.ntot2 = self._big_counter data.jseq = self._sequence_number - return self - def __eq__(self, other: object) -> bool: - if other.__class__ is not self.__class__: - return NotImplemented + generator._composite_target_rng.__setstate__(self._composite_target_state) - return ( - np.array_equal(self._c_number, other._c_number) - and np.array_equal(self._u_array, other._u_array) - and np.array_equal(self._u_i, other._u_i) - and np.array_equal(self._u_j, other._u_j) - and np.array_equal(self._seed, other._seed) - and np.array_equal(self._counter, other._counter) - and np.array_equal(self._big_counter, other._big_counter) - and np.array_equal(self._sequence_number, other._sequence_number) - ) + if generator.name == "SIBYLL": + generator._lib.rndmgas.iset = self._gasdev_iset + + def __eq__(self, other): + a = dataclasses.astuple(self) + b = dataclasses.astuple(other) + return all(np.all(ai == bi) for (ai, bi) in zip(a, b)) def copy(self): """ @@ -567,6 +569,10 @@ def copy(self): """ return copy.deepcopy(self) # this uses setstate, getstate + # HD: These should not be public, since the random number state + # is an implementation detail. I am going to leave it, since this + # whole class will become obsolete when we switch to the numpy PRNG. + @property def sequence(self): return self._sequence_number @@ -581,14 +587,18 @@ def seed(self): # ========================================================================= -# Model +# MCRun # ========================================================================= -class Model(ABC): +class MCRun(ABC): _once_called = False _alive_instances = set() + _stable = set() + + # defaults for many models (override in Derived if needed) _projectiles = standard_projectiles _targets = Nuclei() - _ecm_min = 10 * GeV # default for many models + _ecm_min = 10 * GeV + nevents = 0 # number of generated events so far def __init__(self, seed, *args): @@ -615,7 +625,7 @@ def __init__(self, seed, *args): else: raise ValueError(f"Invalid seed {seed}") - # TODO use rmmard for this, too, instead of numpy PRNG + # TODO use single PRNG for everything self._composite_target_rng = np.random.default_rng(self._seed) if not self._once_called: @@ -633,9 +643,10 @@ def __init__(self, seed, *args): else: if hasattr(self._lib, "init_rmmard"): self._lib.init_rmmard(self._seed) + # Set standard long lived particles as stable for pid in long_lived: - self._set_stable(pid, True) + self.set_stable(pid) def __del__(self): if self.pyname in self._alive_instances: @@ -725,7 +736,9 @@ def _composite_plan(self, kin, nevents): if isinstance(kin.p2, CompositeTarget): nevents = self._composite_target_rng.multinomial(nevents, kin.p2.fractions) components = kin.p2.components - for c, nev in zip(components, nevents): + # as a workaround for DPMJet, we generate heaviest elements first + pairs = sorted(zip(components, nevents), key=lambda p: p[0].A, reverse=True) + for c, nev in pairs: kin.p2 = c self._set_kinematics(kin) yield nev @@ -735,15 +748,16 @@ def _composite_plan(self, kin, nevents): @property def random_state(self): - rmmard_state = RMMARDState()._record_state(self) - numpy_state = self._composite_target_rng.__getstate__() - return rmmard_state, numpy_state + return RMMARDState(self) @random_state.setter def random_state(self, rng_state): - rmmard_state, numpy_state = rng_state - rmmard_state._restore_state(self) - self._composite_target_rng.__setstate__(numpy_state) + rng_state._restore(self) + + def get_stable(self): + """Return set of stable particles.""" + # make a copy to prevent modification of internal state + return set(self._stable) def set_stable(self, particle, stable=True): """Prevent decay of an unstable particle. @@ -766,11 +780,15 @@ def set_stable(self, particle, stable=True): if p.ctau is None or p.ctau == np.inf: raise ValueError(f"{pdg2name(pid)} cannot decay") if abs(pid) == 311: - self._set_stable(311, stable) - self._set_stable(130, stable) - self._set_stable(310, stable) - else: - self._set_stable(pid, stable) + self.set_stable(130, stable) + self.set_stable(310, stable) + return + + if stable: + self._stable.add(pid) + elif pid in self._stable: + self._stable.remove(pid) + self._set_stable(pid, stable) def maydecay(self, particle): """Decay particle in event record. diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index c3690214..11605940 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -1,6 +1,7 @@ -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.kinematics import EventFrame from impy.util import info, _cached_data_dir, fortran_chars, Nuclei +from impy.remote_control import make_remote_controlled_model from impy.constants import standard_projectiles, GeV @@ -37,7 +38,7 @@ def _get_n_wounded(self): # ========================================================================= # DpmjetIIIMCRun # ========================================================================= -class DpmjetIIIRun(Model): +class DpmjetIIIRun(MCRun): """Implements all abstract attributes of MCRun for the DPMJET-III series of event generators. @@ -199,17 +200,17 @@ def _generate(self): return not reject -class DpmjetIII191(DpmjetIIIRun): +class DpmjetIII191Base(DpmjetIIIRun): _version = "19.1" _library_name = "_dpmjetIII191" -class DpmjetIII193(DpmjetIIIRun): +class DpmjetIII193Base(DpmjetIIIRun): _version = "19.3" _library_name = "_dpmjetIII193" -class DpmjetIII306(DpmjetIIIRun): +class DpmjetIII306Base(DpmjetIIIRun): _version = "3.0-6" _library_name = "_dpmjet306" _param_file_name = "fitpar.dat" @@ -219,6 +220,14 @@ class DpmjetIII306(DpmjetIIIRun): ) -class DpmjetIII193_DEV(DpmjetIIIRun): +class DpmjetIII193Base_DEV(DpmjetIIIRun): _version = "19.3-dev" _library_name = "_dev_dpmjetIII193" + + +DpmjetIII191 = make_remote_controlled_model("DpmjetIII191", DpmjetIII191Base) +DpmjetIII193 = make_remote_controlled_model("DpmjetIII193", DpmjetIII193Base) +DpmjetIII306 = make_remote_controlled_model("DpmjetIII306", DpmjetIII306Base) +DpmjetIII193_DEV = make_remote_controlled_model( + "DpmjetIII193_DEV", DpmjetIII193Base_DEV +) diff --git a/src/impy/models/epos.py b/src/impy/models/epos.py index 10cdce3a..fcfebc69 100644 --- a/src/impy/models/epos.py +++ b/src/impy/models/epos.py @@ -1,7 +1,7 @@ import numpy as np from impy.kinematics import EventFrame from impy.constants import TeV -from impy.common import MCEvent, Model, CrossSectionData +from impy.common import MCEvent, MCRun, CrossSectionData from impy.util import ( _cached_data_dir, fortran_array_insert, @@ -31,7 +31,7 @@ def _get_n_wounded(self): return int(self._lib.cevt.npjevt), int(self._lib.cevt.ntgevt) -class EposLHC(Model): +class EposLHC(MCRun): """Implements all abstract attributes of MCRun for the EPOS-LHC series of event generators.""" diff --git a/src/impy/models/phojet.py b/src/impy/models/phojet.py index fe92f1b1..d57dee24 100644 --- a/src/impy/models/phojet.py +++ b/src/impy/models/phojet.py @@ -1,4 +1,4 @@ -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.util import fortran_chars, _cached_data_dir from impy.kinematics import EventFrame from impy.constants import standard_projectiles @@ -60,7 +60,7 @@ def khard(self): # self._lib.poevt1.phep[0:4, 5])) -class PHOJETRun(Model): +class PHOJETRun(MCRun): """Implements all abstract attributes of MCRun for the PHOJET series of event generators. diff --git a/src/impy/models/pythia6.py b/src/impy/models/pythia6.py index fd97d067..18f012ab 100644 --- a/src/impy/models/pythia6.py +++ b/src/impy/models/pythia6.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from particle import literals as lp from impy.kinematics import EventFrame from impy.constants import standard_projectiles @@ -14,7 +14,7 @@ def _charge_init(self, npart): return np.fromiter((self._lib.pychge(ki) / 3 for ki in k), np.double) -class Pythia6(Model): +class Pythia6(MCRun): """Implements all abstract attributes of MCRun for the EPOS-LHC series of event generators.""" diff --git a/src/impy/models/pythia8.py b/src/impy/models/pythia8.py index f24b4ca8..661fd570 100644 --- a/src/impy/models/pythia8.py +++ b/src/impy/models/pythia8.py @@ -1,4 +1,4 @@ -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.util import _cached_data_dir, name2pdg from os import environ import numpy as np @@ -30,7 +30,7 @@ def _get_n_wounded(self): return hi.nPartProj, hi.nPartTarg -class Pythia8(Model): +class Pythia8(MCRun): _name = "Pythia" _version = "8.308" _library_name = "_pythia8" diff --git a/src/impy/models/qgsjet.py b/src/impy/models/qgsjet.py index f33e1048..62bdac56 100644 --- a/src/impy/models/qgsjet.py +++ b/src/impy/models/qgsjet.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.kinematics import EventFrame from impy.constants import standard_projectiles from impy.util import _cached_data_dir, Nuclei @@ -26,7 +26,7 @@ def _get_n_wounded(self): return self._lib.qgarr55.nwp, self._lib.qgarr55.nwt -class QGSJetRun(Model): +class QGSJetRun(MCRun): _name = "QGSJet" _frame = EventFrame.FIXED_TARGET _projectiles = standard_projectiles | Nuclei() diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index ba2a406e..542ade4c 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -1,8 +1,6 @@ -import numpy as np -from impy.common import Model, MCEvent, RMMARDState, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.util import info, Nuclei from impy.kinematics import EventFrame -import dataclasses from particle import literals as lp import warnings @@ -27,27 +25,7 @@ def n_NN_interactions(self): return self._lib.cnucms.ni -@dataclasses.dataclass -class RMMARDSib(RMMARDState): - _gasdev_iset: np.ndarray = None - - def _record_state(self, generator): - super()._record_state(generator) - self._gasdev_iset = generator._lib.rndmgas.iset - return self - - def _restore_state(self, generator): - super()._restore_state(generator) - generator._lib.rndmgas.iset = self._gasdev_iset - return self - - def __eq__(self, other: object) -> bool: - return super().__eq__(other) and np.array_equal( - self._gasdev_iset, other._gasdev_iset - ) - - -class SIBYLLRun(Model): +class SIBYLLRun(MCRun): """Implements all abstract attributes of MCRun for the SIBYLL 2.1, 2.3 and 2.3c event generators.""" @@ -143,14 +121,6 @@ def _generate(self): self._lib.sibhep() return True - @property - def random_state(self): - return RMMARDSib()._record_state(self) - - @random_state.setter - def random_state(self, rng_state): - rng_state._restore_state(self) - class Sibyll21(SIBYLLRun): _version = "2.1" diff --git a/src/impy/models/sophia.py b/src/impy/models/sophia.py index 59aad9f0..3a7cd131 100644 --- a/src/impy/models/sophia.py +++ b/src/impy/models/sophia.py @@ -1,5 +1,5 @@ import numpy as np -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.constants import nucleon_mass from impy.constants import microbarn from impy.kinematics import EventFrame @@ -37,7 +37,7 @@ def decayed_parent(self): return self._lib.schg.iparnt[: self.npart] -class Sophia20(Model): +class Sophia20(MCRun): """Implements all abstract attributes of MCRun for the Sophia event generator. """ diff --git a/src/impy/models/urqmd.py b/src/impy/models/urqmd.py index 0a5378de..eafb8300 100644 --- a/src/impy/models/urqmd.py +++ b/src/impy/models/urqmd.py @@ -6,7 +6,7 @@ # The current settings are taken from CORSIKA and they are optimized for speed aparently. # The license of UrQMD is quite restrictive, they won't probably permit distributing it. -from impy.common import Model, MCEvent, CrossSectionData +from impy.common import MCRun, MCEvent, CrossSectionData from impy.util import info, fortran_array_insert, fortran_array_remove, Nuclei from impy.kinematics import EventFrame from impy.constants import standard_projectiles, GeV @@ -23,7 +23,7 @@ def _get_impact_parameter(self): return self._lib.rsys.bimp -class UrQMD34(Model): +class UrQMD34(MCRun): """Implements all abstract attributes of MCRun for the UrQMD series of event generators. diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index bd0541f8..3a8f9b9e 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -1,24 +1,32 @@ import multiprocessing as mp +import traceback +import sys __all__ = ["make_remote_controlled_model"] -def run(output, Model, init_args, stable_changes, random_state, method, args): - seed, kwargs = init_args - model = Model(seed, **kwargs) +def run(output, Model, seed, init_kwargs, stables, random_state, method, args): + model = Model(seed, **init_kwargs) if random_state is not None: model.random_state = random_state try: if method == "call": - for k, v in stable_changes: - model.set_stable(k, v) + for k in stables - model.get_stable(): + model.set_stable(k, True) + for k in model.get_stable() - stables: + model.set_stable(k, False) for x in model(*args): output.put(x) else: x = getattr(model, method)(*args) output.put(x) except Exception as exc: + (msg,) = exc.args + tb = sys.exc_info()[2] + s = "".join(traceback.format_tb(tb)) + exc.args = (f"{msg}\n\nBacktrace from child process:\n{s}",) output.put(exc) + return output.put(model.random_state) @@ -71,24 +79,20 @@ def make_remote_controlled_model(name, Model): # everytime we create a new process. def __init__(self, seed=None, timeout=100, **kwargs): + Model.__init__(self, seed, **kwargs) self._timeout = timeout - self._init_args = (seed, kwargs) - self._stable_changes = {} - self._random_state = None + self._init_kwargs = kwargs def __call__(self, kin, nevents): process, output = self._remote_init("call", (kin, nevents)) for _ in range(nevents): yield get(self._timeout, process, output) - self._random_state = get(self._timeout, process, output) + self.random_state = get(self._timeout, process, output) process.join() def _cross_section(self, kin): return self._remote_method("_cross_section", kin) - def _set_stable(self, pdgid, stable): - self._stable_changes[pdgid] = stable - def _remote_init(self, method, args): ctx = mp.get_context("spawn") output = ctx.Queue() @@ -97,9 +101,10 @@ def _remote_init(self, method, args): args=( output, Model, - self._init_args, - self._stable_changes, - self._random_state, + self.seed, + self._init_kwargs, + self.get_stable(), + self.random_state, method, args, ), @@ -111,7 +116,7 @@ def _remote_init(self, method, args): def _remote_method(self, method, *args): process, output = self._remote_init(method, args) x = get(self._timeout, process, output) - self._random_state = get(self._timeout, process, output) + self.random_state = get(self._timeout, process, output) process.join() return x @@ -124,7 +129,6 @@ def _remote_method(self, method, *args): "__init__": __init__, "__call__": __call__, "_cross_section": _cross_section, - "_set_stable": _set_stable, "_remote_init": _remote_init, "_remote_method": _remote_method, }, diff --git a/src/impy/util.py b/src/impy/util.py index da9336f2..f2a8dc23 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -582,7 +582,7 @@ def tolerant_string_match(a, b): def get_all_models(skip=None): from impy import models - from impy.common import Model + from impy.common import MCRun if skip is None: skip = [] @@ -595,7 +595,7 @@ def get_all_models(skip=None): if skip and obj in skip: continue try: - if issubclass(obj, Model): # fails if obj is not a class + if issubclass(obj, MCRun): # fails if obj is not a class result.append(obj) except TypeError: pass diff --git a/src/impy/writer.py b/src/impy/writer.py index a62da125..e8c1436c 100644 --- a/src/impy/writer.py +++ b/src/impy/writer.py @@ -37,7 +37,9 @@ def _raise_import_error(name, task): # so we don't write them. Long-lived particles are final state, and there is no # interesting information in the vertices of very short-lived particles. class Root: - def __init__(self, file, model, write_vertices=False, buffer_size=100000): + def __init__( + self, file, model, kinematics, write_vertices=False, buffer_size=100000 + ): try: import uproot except ModuleNotFoundError: @@ -46,21 +48,24 @@ def __init__(self, file, model, write_vertices=False, buffer_size=100000): assert GeV == 1 assert millibarn == 1 - kin = model.kinematics header = { "model": model.label, "seed": model.seed, - "projectile_id": int(kin.p1), - "projectile_momentum": kin.beams[0][2], + "projectile_id": int(kinematics.p1), + "projectile_momentum": kinematics.beams[0][2], "target_id": ( - repr(kin.p2) if isinstance(kin.p2, CompositeTarget) else int(kin.p2) + repr(kinematics.p2) + if isinstance(kinematics.p2, CompositeTarget) + else int(kinematics.p2) ), - "target_momentum": kin.beams[1][2], + "target_momentum": kinematics.beams[1][2], } header.update( { f"sigma_{k}": v - for (k, v) in dataclasses.asdict(model.cross_section()).items() + for (k, v) in dataclasses.asdict( + model.cross_section(kinematics) + ).items() if not np.isnan(v) } ) @@ -181,7 +186,7 @@ def write(self, event): class Svg: - def __init__(self, file, model): + def __init__(self, file, model, kinematics): self._idx = 0 self._template = (file.parent, file.stem, file.suffix) @@ -207,7 +212,7 @@ def write(self, event): class Hepmc: - def __init__(self, file, model): + def __init__(self, file, model, kinematics): try: from pyhepmc._core import pyiostream from pyhepmc.io import _WrappedWriter, WriterAscii diff --git a/tests/test_hepmc_io.py b/tests/test_hepmc_io.py index 7b2d32b1..9928ce21 100644 --- a/tests/test_hepmc_io.py +++ b/tests/test_hepmc_io.py @@ -4,22 +4,12 @@ import impy.models as im import pytest import pyhepmc -from .util import run_in_separate_process from impy.util import get_all_models # generate list of all models in impy.models models = get_all_models() -def run(Model): - evt_kin = CenterOfMass(100 * GeV, "proton", "proton") - - if Model == im.Sophia20: - evt_kin = FixedTarget(100 * GeV, "photon", "proton") - gen = Model(evt_kin, seed=1) - return list(gen(3)) - - @pytest.mark.parametrize( "Model", models, @@ -32,7 +22,13 @@ def test_hepmc_io(Model): test_file = Path(f"{Path(__file__).stem}_{Model.pyname}.dat") - events = run_in_separate_process(run, Model) + kin = CenterOfMass(100 * GeV, "proton", "proton") + + if Model == im.Sophia20: + kin = FixedTarget(100 * GeV, "photon", "proton") + gen = Model(seed=1) + events = [x.copy() for x in gen(kin, 3)] + expected = [] genevent = None for ev in events: diff --git a/tests/test_rng_state.py b/tests/test_rng_state.py index 6d51c918..c4dfd99e 100644 --- a/tests/test_rng_state.py +++ b/tests/test_rng_state.py @@ -1,21 +1,36 @@ -from impy.kinematics import FixedTarget, CenterOfMass -from impy.constants import TeV, GeV +from impy.kinematics import FixedTarget, CenterOfMass, CompositeTarget +from impy.constants import TeV, GeV, PeV +from impy.util import name2pdg import impy.models as im import pickle import pytest -from .util import run_in_separate_process from impy.util import get_all_models +import numpy as np -def run_rng_state(Model): - if Model is im.Sophia20: - evt_kin = FixedTarget(13 * TeV, "photon", "proton") - elif Model is im.UrQMD34: - evt_kin = CenterOfMass(50 * GeV, "proton", "proton") - else: - evt_kin = CenterOfMass(13 * TeV, "proton", "proton") +proton = name2pdg("proton") +photon = name2pdg("photon") +composite = CompositeTarget([("p", 0.5), ("N", 0.5)]) - generator = Model(evt_kin, seed=1) + +@pytest.mark.parametrize("Model", get_all_models()) +@pytest.mark.parametrize("target", ("proton", "composite")) +def test_rng_state_1(Model, target): + if Model is im.Pythia8: + pytest.skip("Pythia8 currently does not support rng_state serialization") + + if Model is im.UrQMD34: + pytest.xfail("UrQMD fails this test for most seeds, needs investigation") + + p = proton if proton in Model.projectiles else photon + t = globals()[target] + + if t not in Model.targets: + pytest.skip(f"{Model.pyname} does not support target {target}") + + kin = FixedTarget(1 * PeV, p, t) + + generator = Model(seed=1) nevents = 10 # Save a initial state to a variable: @@ -23,7 +38,7 @@ def run_rng_state(Model): # Generate nevents events counters = [] - for _ in generator(nevents): + for _ in generator(kin, nevents): counters.append(generator.random_state.counter) # Pickle generator state after nevents @@ -33,7 +48,7 @@ def run_rng_state(Model): generator.random_state = state_0 # Compare counters after each generated event - for i, _ in enumerate(generator(nevents)): + for i, _ in enumerate(generator(kin, nevents)): counter = generator.random_state.counter assert counters[i] == counter, ( f"Counters for seed {generator.random_state.seed} " @@ -49,11 +64,57 @@ def run_rng_state(Model): @pytest.mark.parametrize("Model", get_all_models()) -def test_rng_state(Model): +@pytest.mark.parametrize("new_model", (False, True)) +def test_rng_state_2(Model, new_model): if Model is im.Pythia8: pytest.skip("Pythia8 currently does not support rng_state serialization") if Model is im.UrQMD34: pytest.xfail("UrQMD fails this test for most seeds, needs investigation") - run_in_separate_process(run_rng_state, Model) + if Model is im.Sophia20: + kin = FixedTarget(13 * TeV, "photon", "proton") + elif Model is im.UrQMD34: + kin = CenterOfMass(50 * GeV, "proton", "proton") + else: + kin = CenterOfMass(13 * TeV, "proton", "proton") + + model = Model(seed=1) + + # Save a initial state to a variable: + state_0 = model.random_state.copy() + + # Generate some events + events1 = [] + nevents = 10 + for event in model(kin, nevents): + events1.append(event.copy()) + + # Pickle generator state after nevents + pickled_state_1 = pickle.dumps(model.random_state) + + if new_model: + del model + # seed should be irrelevant when we restore state + model = Model(seed=12345) + + # Restore initial state from variable + model.random_state = state_0 + + # Generate some more events + events2 = [] + for event in model(kin, nevents): + events2.append(event.copy()) + + state_2 = model.random_state + state_1 = pickle.loads(pickled_state_1) + + # check that pickled state_1 and reproduced state_2 are equal + assert state_2 == state_1 + + # check that sequences of generated events are equal + # comparing pid arrays is enough + events1_pid = np.concatenate([x.pid for x in events1]) + events2_pid = np.concatenate([x.pid for x in events2]) + + np.testing.assert_equal(events1_pid, events2_pid) diff --git a/tests/test_setstable.py b/tests/test_setstable.py index aea80f38..27d804f4 100644 --- a/tests/test_setstable.py +++ b/tests/test_setstable.py @@ -1,14 +1,13 @@ import numpy as np from impy.constants import GeV from impy.kinematics import CenterOfMass -from .util import run_in_separate_process from impy.util import get_all_models import pytest from collections import Counter from particle import literals as lp -decay_list = [ +DECAY_LIST = [ lp.pi_plus.pdgid, lp.K_plus.pdgid, lp.pi_0.pdgid, @@ -22,14 +21,15 @@ def run_model(Model, stable): if Model.name == "Sophia": p1 = "gamma" p2 = "p" - kin = CenterOfMass(200 * GeV, p1, p2) - model = Model(kin, seed=1) - for pid in decay_list: + + model = Model(seed=1) + for pid in DECAY_LIST: model.set_stable(pid, stable) + c = Counter() - for event in model(100): + kin = CenterOfMass(200 * GeV, p1, p2) + for event in model(kin, 100): ev = event.final_state() - c.update(ev.pid) return c @@ -47,9 +47,9 @@ def test_setstable(Model, stable): if "UrQMD" in Model.name: pytest.xfail(f"{Model.pyname} does not support changing decays") - c = run_in_separate_process(run_model, Model, stable) + c = run_model(Model, stable) - csel = np.array([c[pid] for pid in decay_list]) + csel = np.array([c[pid] for pid in DECAY_LIST]) if stable: assert np.all(csel > 0), f"stable={stable} counters must be > 0, got {csel}" else: diff --git a/tests/test_to_hepmc3.py b/tests/test_to_hepmc3.py index 346d231d..a13caa19 100644 --- a/tests/test_to_hepmc3.py +++ b/tests/test_to_hepmc3.py @@ -2,7 +2,6 @@ from impy import models as im from impy.constants import GeV from impy.models.sophia import Sophia20 -from .util import run_in_separate_process from impy.util import get_all_models import numpy as np import pytest @@ -11,24 +10,20 @@ models = get_all_models() -def run(Model): - evt_kin = CenterOfMass(10 * GeV, "proton", "proton") - if Model is Sophia20: - evt_kin = CenterOfMass(10 * GeV, "photon", "proton") - m = Model(evt_kin, seed=1) - for event in m(100): - if len(event) > 10: # to skip small events - break - return event # MCEvent is pickeable, but restored as EventData - - @pytest.mark.parametrize("Model", models) def test_to_hepmc3(Model): if Model == im.UrQMD34: pytest.xfail("UrQMD34 FAILS, should be FIXED!!!") - event = run_in_separate_process(run, Model) + kin = CenterOfMass(10 * GeV, "proton", "proton") + if Model is Sophia20: + kin = CenterOfMass(10 * GeV, "photon", "proton") + + m = Model(seed=1) + for event in m(kin, 100): + if len(event) > 10: # to skip small events + break # special case for models that only have final-state particles if Model is im.UrQMD34 or Model.name in ("PhoJet", "DPMJET-III"): @@ -41,7 +36,7 @@ def test_to_hepmc3(Model): assert p.status == fs.status[i] assert len(hev.vertices) == 0 return # test ends here - # special case for Pythia8, which does not contain the parton show + # special case for Pythia8, which does not contain parton shower elif Model is im.Pythia8: # parton shower is skipped from impy.constants import quarks_and_diquarks_and_gluons diff --git a/tests/test_writer.py b/tests/test_writer.py index 09b70153..a829d5b6 100644 --- a/tests/test_writer.py +++ b/tests/test_writer.py @@ -54,9 +54,8 @@ def test_Root(write_vertices, overflow, target): class Model: label: str = "foo" seed = 1 - kinematics = kin - def cross_section(self): + def cross_section(self, kin): return CrossSectionData(total=6.6) events = [ @@ -69,7 +68,7 @@ def cross_section(self): # name must contain all parameters to not cause collisions when test is run parallel p = Path(f"test_writer_{write_vertices}_{overflow}_{target}.root") - writer = Root(p, model, write_vertices=write_vertices, buffer_size=5) + writer = Root(p, model, kin, write_vertices=write_vertices, buffer_size=5) if overflow: with pytest.raises(RuntimeError): with writer: From 40d062eff1e0b1729c9bc60c3a8157116b8a2769 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 12:53:44 +0100 Subject: [PATCH 19/35] revert name change to test references --- ...ms.pkl.gz => DpmjetIII191_He_air_cms.pkl.gz} | Bin ...pkl.gz => DpmjetIII191_He_air_cms2ft.pkl.gz} | Bin ...-ft.pkl.gz => DpmjetIII191_He_air_ft.pkl.gz} | Bin ...pkl.gz => DpmjetIII191_He_air_ft2cms.pkl.gz} | Bin ...-cms.pkl.gz => DpmjetIII191_He_p_cms.pkl.gz} | Bin ...t.pkl.gz => DpmjetIII191_He_p_cms2ft.pkl.gz} | Bin 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Sibyll23_pi-_p_cms2ft.pkl.gz} | Bin ...pi--p-ft.pkl.gz => Sibyll23_pi-_p_ft.pkl.gz} | Bin ...2cms.pkl.gz => Sibyll23_pi-_p_ft2cms.pkl.gz} | Bin ...ir-cms.pkl.gz => Sibyll23c_p_air_cms.pkl.gz} | Bin ...2ft.pkl.gz => Sibyll23c_p_air_cms2ft.pkl.gz} | Bin ...-air-ft.pkl.gz => Sibyll23c_p_air_ft.pkl.gz} | Bin ...cms.pkl.gz => Sibyll23c_p_air_ft2cms.pkl.gz} | Bin ...-p-p-cms.pkl.gz => Sibyll23c_p_p_cms.pkl.gz} | Bin ...ms2ft.pkl.gz => Sibyll23c_p_p_cms2ft.pkl.gz} | Bin ...3c-p-p-ft.pkl.gz => Sibyll23c_p_p_ft.pkl.gz} | Bin ...t2cms.pkl.gz => Sibyll23c_p_p_ft2cms.pkl.gz} | Bin ...-cms.pkl.gz => Sibyll23c_pi-_air_cms.pkl.gz} | Bin ...t.pkl.gz => Sibyll23c_pi-_air_cms2ft.pkl.gz} | Bin ...ir-ft.pkl.gz => Sibyll23c_pi-_air_ft.pkl.gz} | Bin ...s.pkl.gz => Sibyll23c_pi-_air_ft2cms.pkl.gz} | Bin ...-p-cms.pkl.gz => Sibyll23c_pi-_p_cms.pkl.gz} | Bin ...2ft.pkl.gz => Sibyll23c_pi-_p_cms2ft.pkl.gz} | Bin ...i--p-ft.pkl.gz => Sibyll23c_pi-_p_ft.pkl.gz} | Bin ...cms.pkl.gz => Sibyll23c_pi-_p_ft2cms.pkl.gz} | Bin ...ir-cms.pkl.gz => Sibyll23d_p_air_cms.pkl.gz} | Bin ...2ft.pkl.gz => Sibyll23d_p_air_cms2ft.pkl.gz} | Bin ...-air-ft.pkl.gz => Sibyll23d_p_air_ft.pkl.gz} | Bin ...cms.pkl.gz => Sibyll23d_p_air_ft2cms.pkl.gz} | Bin ...-p-p-cms.pkl.gz => Sibyll23d_p_p_cms.pkl.gz} | Bin ...ms2ft.pkl.gz => Sibyll23d_p_p_cms2ft.pkl.gz} | Bin ...3d-p-p-ft.pkl.gz => Sibyll23d_p_p_ft.pkl.gz} | Bin ...t2cms.pkl.gz => Sibyll23d_p_p_ft2cms.pkl.gz} | Bin ...-cms.pkl.gz => Sibyll23d_pi-_air_cms.pkl.gz} | Bin ...t.pkl.gz => Sibyll23d_pi-_air_cms2ft.pkl.gz} | Bin ...ir-ft.pkl.gz => Sibyll23d_pi-_air_ft.pkl.gz} | Bin ...s.pkl.gz => Sibyll23d_pi-_air_ft2cms.pkl.gz} | Bin ...-p-cms.pkl.gz => Sibyll23d_pi-_p_cms.pkl.gz} | Bin ...2ft.pkl.gz => Sibyll23d_pi-_p_cms2ft.pkl.gz} | Bin ...i--p-ft.pkl.gz => Sibyll23d_pi-_p_ft.pkl.gz} | Bin ...cms.pkl.gz => Sibyll23d_pi-_p_ft2cms.pkl.gz} | Bin ...p-cms.pkl.gz => Sophia20_gamma_p_cms.pkl.gz} | Bin ...ft.pkl.gz => Sophia20_gamma_p_cms2ft.pkl.gz} | Bin ...a-p-ft.pkl.gz => Sophia20_gamma_p_ft.pkl.gz} | Bin ...ms.pkl.gz => Sophia20_gamma_p_ft2cms.pkl.gz} | Bin ...air-cms.pkl.gz => UrQMD34_He_air_cms.pkl.gz} | Bin ...s2ft.pkl.gz => UrQMD34_He_air_cms2ft.pkl.gz} | Bin ...e-air-ft.pkl.gz => UrQMD34_He_air_ft.pkl.gz} | Bin ...2cms.pkl.gz => UrQMD34_He_air_ft2cms.pkl.gz} | Bin ...-He-p-cms.pkl.gz => UrQMD34_He_p_cms.pkl.gz} | Bin ...cms2ft.pkl.gz => UrQMD34_He_p_cms2ft.pkl.gz} | Bin ...34-He-p-ft.pkl.gz => UrQMD34_He_p_ft.pkl.gz} | Bin ...ft2cms.pkl.gz => UrQMD34_He_p_ft2cms.pkl.gz} | Bin ...-air-cms.pkl.gz => UrQMD34_p_air_cms.pkl.gz} | Bin ...ms2ft.pkl.gz => UrQMD34_p_air_cms2ft.pkl.gz} | Bin ...-p-air-ft.pkl.gz => UrQMD34_p_air_ft.pkl.gz} | Bin ...t2cms.pkl.gz => UrQMD34_p_air_ft2cms.pkl.gz} | Bin ...34-p-p-cms.pkl.gz => UrQMD34_p_p_cms.pkl.gz} | Bin ...-cms2ft.pkl.gz => UrQMD34_p_p_cms2ft.pkl.gz} | Bin ...MD34-p-p-ft.pkl.gz => UrQMD34_p_p_ft.pkl.gz} | Bin ...-ft2cms.pkl.gz => UrQMD34_p_p_ft2cms.pkl.gz} | Bin ...ir-cms.pkl.gz => UrQMD34_pi-_air_cms.pkl.gz} | Bin ...2ft.pkl.gz => UrQMD34_pi-_air_cms2ft.pkl.gz} | Bin ...-air-ft.pkl.gz => UrQMD34_pi-_air_ft.pkl.gz} | Bin ...cms.pkl.gz => UrQMD34_pi-_air_ft2cms.pkl.gz} | Bin ...i--p-cms.pkl.gz => UrQMD34_pi-_p_cms.pkl.gz} | Bin ...ms2ft.pkl.gz => UrQMD34_pi-_p_cms2ft.pkl.gz} | Bin ...-pi--p-ft.pkl.gz => UrQMD34_pi-_p_ft.pkl.gz} | Bin ...t2cms.pkl.gz => UrQMD34_pi-_p_ft2cms.pkl.gz} | Bin tests/test_generators.py | 4 ++-- 306 files changed, 2 insertions(+), 2 deletions(-) rename tests/data/test_generators/{DpmjetIII191-He-air-cms.pkl.gz => DpmjetIII191_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-air-cms2ft.pkl.gz => DpmjetIII191_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-air-ft.pkl.gz => DpmjetIII191_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-air-ft2cms.pkl.gz => DpmjetIII191_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-p-cms.pkl.gz => DpmjetIII191_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-p-cms2ft.pkl.gz => DpmjetIII191_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-p-ft.pkl.gz => DpmjetIII191_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-He-p-ft2cms.pkl.gz => DpmjetIII191_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-air-cms.pkl.gz => DpmjetIII191_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-air-cms2ft.pkl.gz => DpmjetIII191_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-air-ft.pkl.gz => DpmjetIII191_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-air-ft2cms.pkl.gz => DpmjetIII191_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-p-cms.pkl.gz => DpmjetIII191_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-p-cms2ft.pkl.gz => DpmjetIII191_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-p-ft.pkl.gz => DpmjetIII191_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-p-p-ft2cms.pkl.gz => DpmjetIII191_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--air-cms.pkl.gz => DpmjetIII191_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--air-cms2ft.pkl.gz => DpmjetIII191_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--air-ft.pkl.gz => DpmjetIII191_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--air-ft2cms.pkl.gz => DpmjetIII191_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--p-cms.pkl.gz => DpmjetIII191_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--p-cms2ft.pkl.gz => DpmjetIII191_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--p-ft.pkl.gz => DpmjetIII191_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII191-pi--p-ft2cms.pkl.gz => DpmjetIII191_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-air-cms.pkl.gz => DpmjetIII193_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-air-cms2ft.pkl.gz => DpmjetIII193_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-air-ft.pkl.gz => DpmjetIII193_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-air-ft2cms.pkl.gz => DpmjetIII193_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-p-cms.pkl.gz => DpmjetIII193_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-p-cms2ft.pkl.gz => DpmjetIII193_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-p-ft.pkl.gz => DpmjetIII193_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-He-p-ft2cms.pkl.gz => DpmjetIII193_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-air-cms.pkl.gz => DpmjetIII193_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-air-cms2ft.pkl.gz => DpmjetIII193_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-air-ft.pkl.gz => DpmjetIII193_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-air-ft2cms.pkl.gz => DpmjetIII193_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-p-cms.pkl.gz => DpmjetIII193_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-p-cms2ft.pkl.gz => DpmjetIII193_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-p-ft.pkl.gz => DpmjetIII193_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-p-p-ft2cms.pkl.gz => DpmjetIII193_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--air-cms.pkl.gz => DpmjetIII193_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--air-cms2ft.pkl.gz => DpmjetIII193_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--air-ft.pkl.gz => DpmjetIII193_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--air-ft2cms.pkl.gz => DpmjetIII193_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--p-cms.pkl.gz => DpmjetIII193_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--p-cms2ft.pkl.gz => DpmjetIII193_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--p-ft.pkl.gz => DpmjetIII193_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII193-pi--p-ft2cms.pkl.gz => DpmjetIII193_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-air-cms.pkl.gz => DpmjetIII306_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-air-cms2ft.pkl.gz => DpmjetIII306_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-air-ft.pkl.gz => DpmjetIII306_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-air-ft2cms.pkl.gz => DpmjetIII306_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-p-cms.pkl.gz => DpmjetIII306_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-p-cms2ft.pkl.gz => DpmjetIII306_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-p-ft.pkl.gz => DpmjetIII306_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-He-p-ft2cms.pkl.gz => DpmjetIII306_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-air-cms.pkl.gz => DpmjetIII306_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-air-cms2ft.pkl.gz => DpmjetIII306_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-air-ft.pkl.gz => DpmjetIII306_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-air-ft2cms.pkl.gz => DpmjetIII306_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-p-cms.pkl.gz => DpmjetIII306_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-p-cms2ft.pkl.gz => DpmjetIII306_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-p-ft.pkl.gz => DpmjetIII306_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-p-p-ft2cms.pkl.gz => DpmjetIII306_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--air-cms.pkl.gz => DpmjetIII306_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--air-cms2ft.pkl.gz => DpmjetIII306_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--air-ft.pkl.gz => DpmjetIII306_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--air-ft2cms.pkl.gz => DpmjetIII306_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--p-cms.pkl.gz => DpmjetIII306_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--p-cms2ft.pkl.gz => DpmjetIII306_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--p-ft.pkl.gz => DpmjetIII306_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{DpmjetIII306-pi--p-ft2cms.pkl.gz => DpmjetIII306_pi-_p_ft2cms.pkl.gz} (100%) delete mode 100644 tests/data/test_generators/EposLHC-He-air-ft.pkl.gz rename tests/data/test_generators/{EposLHC-He-air-cms.pkl.gz => EposLHC_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-He-air-cms2ft.pkl.gz => EposLHC_He_air_cms2ft.pkl.gz} (100%) create mode 100644 tests/data/test_generators/EposLHC_He_air_ft.pkl.gz rename tests/data/test_generators/{EposLHC-He-air-ft2cms.pkl.gz => EposLHC_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-He-p-cms.pkl.gz => EposLHC_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-He-p-cms2ft.pkl.gz => EposLHC_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-He-p-ft.pkl.gz => EposLHC_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-He-p-ft2cms.pkl.gz => EposLHC_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-air-cms.pkl.gz => EposLHC_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-air-cms2ft.pkl.gz => EposLHC_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-air-ft.pkl.gz => EposLHC_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-air-ft2cms.pkl.gz => EposLHC_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-p-cms.pkl.gz => EposLHC_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-p-cms2ft.pkl.gz => EposLHC_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-p-ft.pkl.gz => EposLHC_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-p-p-ft2cms.pkl.gz => EposLHC_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--air-cms.pkl.gz => EposLHC_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--air-cms2ft.pkl.gz => EposLHC_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--air-ft.pkl.gz => EposLHC_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--air-ft2cms.pkl.gz => EposLHC_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--p-cms.pkl.gz => EposLHC_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--p-cms2ft.pkl.gz => EposLHC_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--p-ft.pkl.gz => EposLHC_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{EposLHC-pi--p-ft2cms.pkl.gz => EposLHC_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-gamma-p-cms.pkl.gz => Phojet112_gamma_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-gamma-p-cms2ft.pkl.gz => Phojet112_gamma_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-gamma-p-ft.pkl.gz => Phojet112_gamma_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-gamma-p-ft2cms.pkl.gz => Phojet112_gamma_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-p-p-cms.pkl.gz => Phojet112_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-p-p-cms2ft.pkl.gz => Phojet112_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-p-p-ft.pkl.gz => Phojet112_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet112-p-p-ft2cms.pkl.gz => Phojet112_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-p-p-cms.pkl.gz => Phojet191_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-p-p-cms2ft.pkl.gz => Phojet191_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-p-p-ft.pkl.gz => Phojet191_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-p-p-ft2cms.pkl.gz => Phojet191_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-pi--p-cms.pkl.gz => Phojet191_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-pi--p-cms2ft.pkl.gz => Phojet191_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-pi--p-ft.pkl.gz => Phojet191_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet191-pi--p-ft2cms.pkl.gz => Phojet191_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-p-p-cms.pkl.gz => Phojet193_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-p-p-cms2ft.pkl.gz => Phojet193_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-p-p-ft.pkl.gz => Phojet193_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-p-p-ft2cms.pkl.gz => Phojet193_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-pi--p-cms.pkl.gz => Phojet193_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-pi--p-cms2ft.pkl.gz => Phojet193_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-pi--p-ft.pkl.gz => Phojet193_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Phojet193-pi--p-ft2cms.pkl.gz => Phojet193_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-p-p-cms.pkl.gz => Pythia6_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-p-p-cms2ft.pkl.gz => Pythia6_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-p-p-ft.pkl.gz => Pythia6_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-p-p-ft2cms.pkl.gz => Pythia6_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-pi--p-cms.pkl.gz => Pythia6_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-pi--p-cms2ft.pkl.gz => Pythia6_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-pi--p-ft.pkl.gz => Pythia6_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia6-pi--p-ft2cms.pkl.gz => Pythia6_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-gamma-p-cms.pkl.gz => Pythia8_gamma_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-gamma-p-cms2ft.pkl.gz => Pythia8_gamma_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-gamma-p-ft.pkl.gz => Pythia8_gamma_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-gamma-p-ft2cms.pkl.gz => Pythia8_gamma_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-p-p-cms.pkl.gz => Pythia8_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-p-p-cms2ft.pkl.gz => Pythia8_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-p-p-ft.pkl.gz => Pythia8_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-p-p-ft2cms.pkl.gz => Pythia8_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-pi--p-cms.pkl.gz => Pythia8_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-pi--p-cms2ft.pkl.gz => Pythia8_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-pi--p-ft.pkl.gz => Pythia8_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Pythia8-pi--p-ft2cms.pkl.gz => Pythia8_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-air-cms.pkl.gz => QGSJet01d_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-air-cms2ft.pkl.gz => QGSJet01d_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-air-ft.pkl.gz => QGSJet01d_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-air-ft2cms.pkl.gz => QGSJet01d_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-p-cms.pkl.gz => QGSJet01d_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-p-cms2ft.pkl.gz => QGSJet01d_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-p-ft.pkl.gz => QGSJet01d_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-He-p-ft2cms.pkl.gz => QGSJet01d_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-air-cms.pkl.gz => QGSJet01d_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-air-cms2ft.pkl.gz => QGSJet01d_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-air-ft.pkl.gz => QGSJet01d_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-air-ft2cms.pkl.gz => QGSJet01d_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-p-cms.pkl.gz => QGSJet01d_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-p-cms2ft.pkl.gz => QGSJet01d_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-p-ft.pkl.gz => QGSJet01d_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-p-p-ft2cms.pkl.gz => QGSJet01d_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--air-cms.pkl.gz => QGSJet01d_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--air-cms2ft.pkl.gz => QGSJet01d_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--air-ft.pkl.gz => QGSJet01d_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--air-ft2cms.pkl.gz => QGSJet01d_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--p-cms.pkl.gz => QGSJet01d_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--p-cms2ft.pkl.gz => QGSJet01d_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--p-ft.pkl.gz => QGSJet01d_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJet01d-pi--p-ft2cms.pkl.gz => QGSJet01d_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-air-cms.pkl.gz => QGSJetII03_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-air-cms2ft.pkl.gz => QGSJetII03_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-air-ft.pkl.gz => QGSJetII03_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-air-ft2cms.pkl.gz => QGSJetII03_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-p-cms.pkl.gz => QGSJetII03_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-p-cms2ft.pkl.gz => QGSJetII03_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-p-ft.pkl.gz => QGSJetII03_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-He-p-ft2cms.pkl.gz => QGSJetII03_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-air-cms.pkl.gz => QGSJetII03_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-air-cms2ft.pkl.gz => QGSJetII03_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-air-ft.pkl.gz => QGSJetII03_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-air-ft2cms.pkl.gz => QGSJetII03_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-p-cms.pkl.gz => QGSJetII03_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-p-cms2ft.pkl.gz => QGSJetII03_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-p-ft.pkl.gz => QGSJetII03_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-p-p-ft2cms.pkl.gz => QGSJetII03_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--air-cms.pkl.gz => QGSJetII03_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--air-cms2ft.pkl.gz => QGSJetII03_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--air-ft.pkl.gz => QGSJetII03_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--air-ft2cms.pkl.gz => QGSJetII03_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--p-cms.pkl.gz => QGSJetII03_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--p-cms2ft.pkl.gz => QGSJetII03_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--p-ft.pkl.gz => QGSJetII03_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII03-pi--p-ft2cms.pkl.gz => QGSJetII03_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-air-cms.pkl.gz => QGSJetII04_He_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-air-cms2ft.pkl.gz => QGSJetII04_He_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-air-ft.pkl.gz => QGSJetII04_He_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-air-ft2cms.pkl.gz => QGSJetII04_He_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-p-cms.pkl.gz => QGSJetII04_He_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-p-cms2ft.pkl.gz => QGSJetII04_He_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-p-ft.pkl.gz => QGSJetII04_He_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-He-p-ft2cms.pkl.gz => QGSJetII04_He_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-air-cms.pkl.gz => QGSJetII04_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-air-cms2ft.pkl.gz => QGSJetII04_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-air-ft.pkl.gz => QGSJetII04_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-air-ft2cms.pkl.gz => QGSJetII04_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-p-cms.pkl.gz => QGSJetII04_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-p-cms2ft.pkl.gz => QGSJetII04_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-p-ft.pkl.gz => QGSJetII04_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-p-p-ft2cms.pkl.gz => QGSJetII04_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--air-cms.pkl.gz => QGSJetII04_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--air-cms2ft.pkl.gz => QGSJetII04_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--air-ft.pkl.gz => QGSJetII04_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--air-ft2cms.pkl.gz => QGSJetII04_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--p-cms.pkl.gz => QGSJetII04_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--p-cms2ft.pkl.gz => QGSJetII04_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--p-ft.pkl.gz => QGSJetII04_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{QGSJetII04-pi--p-ft2cms.pkl.gz => QGSJetII04_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-air-cms.pkl.gz => Sibyll21_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-air-cms2ft.pkl.gz => Sibyll21_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-air-ft.pkl.gz => Sibyll21_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-air-ft2cms.pkl.gz => Sibyll21_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-p-cms.pkl.gz => Sibyll21_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-p-cms2ft.pkl.gz => Sibyll21_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-p-ft.pkl.gz => Sibyll21_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-p-p-ft2cms.pkl.gz => Sibyll21_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--air-cms.pkl.gz => Sibyll21_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--air-cms2ft.pkl.gz => Sibyll21_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--air-ft.pkl.gz => Sibyll21_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--air-ft2cms.pkl.gz => Sibyll21_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--p-cms.pkl.gz => Sibyll21_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--p-cms2ft.pkl.gz => Sibyll21_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--p-ft.pkl.gz => Sibyll21_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll21-pi--p-ft2cms.pkl.gz => Sibyll21_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-air-cms.pkl.gz => Sibyll23_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-air-cms2ft.pkl.gz => Sibyll23_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-air-ft.pkl.gz => Sibyll23_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-air-ft2cms.pkl.gz => Sibyll23_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-p-cms.pkl.gz => Sibyll23_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-p-cms2ft.pkl.gz => Sibyll23_p_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-p-ft.pkl.gz => Sibyll23_p_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-p-p-ft2cms.pkl.gz => Sibyll23_p_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--air-cms.pkl.gz => Sibyll23_pi-_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--air-cms2ft.pkl.gz => Sibyll23_pi-_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--air-ft.pkl.gz => Sibyll23_pi-_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--air-ft2cms.pkl.gz => Sibyll23_pi-_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--p-cms.pkl.gz => Sibyll23_pi-_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--p-cms2ft.pkl.gz => Sibyll23_pi-_p_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--p-ft.pkl.gz => Sibyll23_pi-_p_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23-pi--p-ft2cms.pkl.gz => Sibyll23_pi-_p_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-air-cms.pkl.gz => Sibyll23c_p_air_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-air-cms2ft.pkl.gz => Sibyll23c_p_air_cms2ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-air-ft.pkl.gz => Sibyll23c_p_air_ft.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-air-ft2cms.pkl.gz => Sibyll23c_p_air_ft2cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-p-cms.pkl.gz => Sibyll23c_p_p_cms.pkl.gz} (100%) rename tests/data/test_generators/{Sibyll23c-p-p-cms2ft.pkl.gz => Sibyll23c_p_p_cms2ft.pkl.gz} 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rename to tests/data/test_generators/Sibyll23d_pi-_p_cms2ft.pkl.gz diff --git a/tests/data/test_generators/Sibyll23d-pi--p-ft.pkl.gz b/tests/data/test_generators/Sibyll23d_pi-_p_ft.pkl.gz similarity index 100% rename from tests/data/test_generators/Sibyll23d-pi--p-ft.pkl.gz rename to tests/data/test_generators/Sibyll23d_pi-_p_ft.pkl.gz diff --git a/tests/data/test_generators/Sibyll23d-pi--p-ft2cms.pkl.gz b/tests/data/test_generators/Sibyll23d_pi-_p_ft2cms.pkl.gz similarity index 100% rename from tests/data/test_generators/Sibyll23d-pi--p-ft2cms.pkl.gz rename to tests/data/test_generators/Sibyll23d_pi-_p_ft2cms.pkl.gz diff --git a/tests/data/test_generators/Sophia20-gamma-p-cms.pkl.gz b/tests/data/test_generators/Sophia20_gamma_p_cms.pkl.gz similarity index 100% rename from tests/data/test_generators/Sophia20-gamma-p-cms.pkl.gz rename to tests/data/test_generators/Sophia20_gamma_p_cms.pkl.gz diff --git a/tests/data/test_generators/Sophia20-gamma-p-cms2ft.pkl.gz 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to tests/data/test_generators/UrQMD34_He_air_cms.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-He-air-cms2ft.pkl.gz b/tests/data/test_generators/UrQMD34_He_air_cms2ft.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-He-air-cms2ft.pkl.gz rename to tests/data/test_generators/UrQMD34_He_air_cms2ft.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-He-air-ft.pkl.gz b/tests/data/test_generators/UrQMD34_He_air_ft.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-He-air-ft.pkl.gz rename to tests/data/test_generators/UrQMD34_He_air_ft.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-He-air-ft2cms.pkl.gz b/tests/data/test_generators/UrQMD34_He_air_ft2cms.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-He-air-ft2cms.pkl.gz rename to tests/data/test_generators/UrQMD34_He_air_ft2cms.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-He-p-cms.pkl.gz 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tests/data/test_generators/UrQMD34_pi-_air_ft.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-pi--air-ft2cms.pkl.gz b/tests/data/test_generators/UrQMD34_pi-_air_ft2cms.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-pi--air-ft2cms.pkl.gz rename to tests/data/test_generators/UrQMD34_pi-_air_ft2cms.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-pi--p-cms.pkl.gz b/tests/data/test_generators/UrQMD34_pi-_p_cms.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-pi--p-cms.pkl.gz rename to tests/data/test_generators/UrQMD34_pi-_p_cms.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-pi--p-cms2ft.pkl.gz b/tests/data/test_generators/UrQMD34_pi-_p_cms2ft.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-pi--p-cms2ft.pkl.gz rename to tests/data/test_generators/UrQMD34_pi-_p_cms2ft.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-pi--p-ft.pkl.gz b/tests/data/test_generators/UrQMD34_pi-_p_ft.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-pi--p-ft.pkl.gz rename to tests/data/test_generators/UrQMD34_pi-_p_ft.pkl.gz diff --git a/tests/data/test_generators/UrQMD34-pi--p-ft2cms.pkl.gz b/tests/data/test_generators/UrQMD34_pi-_p_ft2cms.pkl.gz similarity index 100% rename from tests/data/test_generators/UrQMD34-pi--p-ft2cms.pkl.gz rename to tests/data/test_generators/UrQMD34_pi-_p_ft2cms.pkl.gz diff --git a/tests/test_generators.py b/tests/test_generators.py index 5e204099..b9d9491c 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -86,7 +86,7 @@ def compute_p_value(got, expected, cov): def draw_comparison(fn, p_value, axes, values, val_ref, cov_ref): import re - m = re.search(r"(.+)-(.+)-(.+)-(.+)", str(fn)) + m = re.search(r"(.+)_(.+)_(.+)_(.+)", str(fn)) mname = m.group(0) projectile = m.group(1) target = m.group(2) @@ -180,7 +180,7 @@ def test_generator(projectile, target, frame, Model): assert abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets return - fn = Path(f"{Model.pyname}-{projectile}-{target}-{frame}") + fn = Path(f"{Model.pyname}_{projectile}_{target}_{frame}") path_ref = REFERENCE_PATH / fn.with_suffix(".pkl.gz") if not path_ref.exists(): print(f"{fn}: reference does not exist; generating...") From 0f02a8f89edca2cc2d41c727cbc5ee582eb26cbc Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 14:23:19 +0100 Subject: [PATCH 20/35] remove obsolete inits --- src/impy/common.py | 17 ++++++++++------- src/impy/models/dpmjetIII.py | 2 +- src/impy/models/epos.py | 5 +---- src/impy/models/phojet.py | 3 --- src/impy/models/pythia6.py | 5 +---- src/impy/models/pythia8.py | 3 --- src/impy/models/qgsjet.py | 7 ++----- src/impy/models/sibyll.py | 8 +++----- src/impy/models/sophia.py | 11 +++-------- src/impy/models/urqmd.py | 20 ++++++-------------- tests/test_generators.py | 8 +++++--- 11 files changed, 32 insertions(+), 57 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index 80459c9b..366021e1 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -601,7 +601,10 @@ class MCRun(ABC): nevents = 0 # number of generated events so far - def __init__(self, seed, *args): + # Don't override `__init__` in derived class, use `_once` instead. + # You can pass custom initialization parameters via **kwargs, which + # are forwarded to `_once`. + def __init__(self, seed, **kwargs): import importlib from random import randint @@ -628,6 +631,10 @@ def __init__(self, seed, *args): # TODO use single PRNG for everything self._composite_target_rng = np.random.default_rng(self._seed) + self._lib = importlib.import_module(f"impy.models.{self._library_name}") + if hasattr(self._lib, "init_rmmard"): + self._lib.init_rmmard(self._seed) + if not self._once_called: self._once_called = True assert hasattr(self, "_name") @@ -635,14 +642,10 @@ def __init__(self, seed, *args): assert hasattr(self, "_library_name") assert hasattr(self, "_event_class") assert hasattr(self, "_frame") - self._lib = importlib.import_module(f"impy.models.{self._library_name}") - if hasattr(self._lib, "init_rmmard"): - self._lib.init_rmmard(self._seed) - # Run internal model initialization code - self._once(*args) - else: if hasattr(self._lib, "init_rmmard"): self._lib.init_rmmard(self._seed) + # Run internal model initialization code exactly once + self._once(**kwargs) # Set standard long lived particles as stable for pid in long_lived: diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index 11605940..119493d2 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -111,7 +111,7 @@ def _cross_section(self, kin, precision=None): self._lib.dtglgp.jstatb = precision self._lib.dt_xsglau( kin.p1.A or 1, - kin.p2.A, + kin.p2.A or 1, self._lib.idt_icihad(2212) if (kin.p1.A and kin.p1.A > 1) else self._lib.idt_icihad(kin.p1), diff --git a/src/impy/models/epos.py b/src/impy/models/epos.py index fcfebc69..cb52ecaf 100644 --- a/src/impy/models/epos.py +++ b/src/impy/models/epos.py @@ -46,10 +46,7 @@ class EposLHC(MCRun): + "/releases/download/zipped_data_v1.0/epos_v001.zip" ) - def __init__(self, seed=None, *, ecm_max=1000 * TeV): - super().__init__(seed, ecm_max) - - def _once(self, ecm_max): + def _once(self, ecm_max=1000 * TeV): from impy import debug_level self._lib.aaset(0) diff --git a/src/impy/models/phojet.py b/src/impy/models/phojet.py index d57dee24..c558d3b7 100644 --- a/src/impy/models/phojet.py +++ b/src/impy/models/phojet.py @@ -79,9 +79,6 @@ class PHOJETRun(MCRun): + "/releases/download/zipped_data_v1.0/dpm3191_v001.zip" ) - def __init__(self, seed=None): - super().__init__(seed) - def _once(self): data_dir = _cached_data_dir(self._data_url) # Set the dpmjpar.dat file diff --git a/src/impy/models/pythia6.py b/src/impy/models/pythia6.py index 18f012ab..8421ed7d 100644 --- a/src/impy/models/pythia6.py +++ b/src/impy/models/pythia6.py @@ -26,10 +26,7 @@ class Pythia6(MCRun): _projectiles = standard_projectiles _targets = standard_projectiles - def __init__(self, seed=None, *, new_mpi=False): - super().__init__(seed, new_mpi) - - def _once(self, new_mpi): + def _once(self, new_mpi=False): # setup logging lun = 6 # stdout self._lib.pydat1.mstu[10] = lun diff --git a/src/impy/models/pythia8.py b/src/impy/models/pythia8.py index 661fd570..5411c403 100644 --- a/src/impy/models/pythia8.py +++ b/src/impy/models/pythia8.py @@ -48,9 +48,6 @@ class Pythia8(MCRun): + "/releases/download/zipped_data_v1.0/Pythia8_v002.zip" ) - def __init__(self, seed=None): - super().__init__(seed) - def _once(self): self._lib.hepevt = self._lib.Hepevt() diff --git a/src/impy/models/qgsjet.py b/src/impy/models/qgsjet.py index 62bdac56..a8af58cd 100644 --- a/src/impy/models/qgsjet.py +++ b/src/impy/models/qgsjet.py @@ -35,16 +35,13 @@ class QGSJetRun(MCRun): + "/releases/download/zipped_data_v1.0/qgsjet_v001.zip" ) - def __init__(self, seed=None): - super().__init__(seed) - def _once(self): - import impy + from impy import debug_level # logging lun = 6 # stdout datdir = _cached_data_dir(self._data_url) - self._lib.cqgsini(self._seed, datdir, lun, impy.debug_level) + self._lib.cqgsini(self._seed, datdir, lun, debug_level) def _set_stable(self, pid, stable): import warnings diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index 542ade4c..bd61683c 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -45,16 +45,14 @@ class SIBYLLRun(MCRun): ) } - def __init__(self, seed=None): - super().__init__(seed) - def _once(self): + from impy import debug_level + # setup logging - import impy lun = 6 # stdout self._lib.s_debug.lun = lun - self._lib.s_debug.ndebug = impy.debug_level + self._lib.s_debug.ndebug = debug_level self._lib.sibini(self._seed) # Set the internal state of GASDEV function (rng) to 0 diff --git a/src/impy/models/sophia.py b/src/impy/models/sophia.py index 3a7cd131..db4261d1 100644 --- a/src/impy/models/sophia.py +++ b/src/impy/models/sophia.py @@ -51,18 +51,13 @@ class Sophia20(MCRun): _targets = {lp.p.pdgid, lp.n.pdgid} _ecm_min = 0 - def __init__(self, seed=None, *, keep_decayed_particles=True): - import impy + def _once(self, keep_decayed_particles=True): + from impy import debug_level - super().__init__(seed) - - self._lib.s_plist.ideb = impy.debug_level + self._lib.s_plist.ideb = debug_level # Keep decayed particles in the history: self._lib.eg_io.keepdc = keep_decayed_particles - def _once(self): - pass - def _cross_section(self, kin): # code=3 for inelastic cross-section # TODO fill more cross-sections diff --git a/src/impy/models/urqmd.py b/src/impy/models/urqmd.py index eafb8300..26cecd5a 100644 --- a/src/impy/models/urqmd.py +++ b/src/impy/models/urqmd.py @@ -39,20 +39,12 @@ class UrQMD34(MCRun): _projectiles = standard_projectiles | Nuclei() _ecm_min = 2 * GeV - def __init__( - self, - seed=None, - *, - caltim=200, - outtim=200, - ct_params=None, - ct_options=None, - ): - super().__init__(seed, caltim, outtim, ct_params, ct_options) - - def _once(self, caltim, outtim, ct_params, ct_options): - import impy + def _once(self, caltim=200, outtim=200, ct_params=None, ct_options=None): + from impy import debug_level + # The reason to have this here is to reduce import time for the module. + # It is probably premature optimisation, but the idea is to only generate + # this table when someone actually runs UrQMD. self._pdg2modid = { 22: (100, 0), 111: (101, 0), @@ -126,7 +118,7 @@ def _once(self, caltim, outtim, ct_params, ct_options): # logging lun = 6 # stdout - self._lib.urqini(lun, impy.debug_level) + self._lib.urqini(lun, debug_level) self._lib.inputs.nevents = 1 self._lib.rsys.bmin = 0 diff --git a/tests/test_generators.py b/tests/test_generators.py index b9d9491c..493f9f7c 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -21,6 +21,7 @@ import numpy as np from scipy.stats import chi2 import sys +import impy matplotlib.use("svg") # need non-interactive backend for CI Windows @@ -175,10 +176,11 @@ def test_generator(projectile, target, frame, Model): else: assert False # we should never arrive here + if abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets: + pytest.skip(reason=f"Projectile or target not supported by {Model.pyname}") + h = run_model(Model, kin) - if h is None: - assert abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets - return + assert h is not None fn = Path(f"{Model.pyname}_{projectile}_{target}_{frame}") path_ref = REFERENCE_PATH / fn.with_suffix(".pkl.gz") From fe45cfecbc5e0deb8eb55b31c837b713cecf5d7d Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Mon, 2 Jan 2023 18:14:35 +0100 Subject: [PATCH 21/35] IT WORKS --- src/impy/common.py | 2 - src/impy/models/dpmjetIII.py | 20 +++------- src/impy/models/phojet.py | 16 +++----- src/impy/models/sibyll.py | 20 +++++----- src/impy/remote_control.py | 75 +++++++++++++++--------------------- tests/test_generators.py | 1 - 6 files changed, 53 insertions(+), 81 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index 366021e1..56eee9d3 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -642,8 +642,6 @@ def __init__(self, seed, **kwargs): assert hasattr(self, "_library_name") assert hasattr(self, "_event_class") assert hasattr(self, "_frame") - if hasattr(self._lib, "init_rmmard"): - self._lib.init_rmmard(self._seed) # Run internal model initialization code exactly once self._once(**kwargs) diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index 119493d2..f860c935 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -1,7 +1,7 @@ -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import MCEvent, CrossSectionData +from impy.remote_control import MCRunRemote as MCRun from impy.kinematics import EventFrame from impy.util import info, _cached_data_dir, fortran_chars, Nuclei -from impy.remote_control import make_remote_controlled_model from impy.constants import standard_projectiles, GeV @@ -200,17 +200,17 @@ def _generate(self): return not reject -class DpmjetIII191Base(DpmjetIIIRun): +class DpmjetIII191(DpmjetIIIRun): _version = "19.1" _library_name = "_dpmjetIII191" -class DpmjetIII193Base(DpmjetIIIRun): +class DpmjetIII193(DpmjetIIIRun): _version = "19.3" _library_name = "_dpmjetIII193" -class DpmjetIII306Base(DpmjetIIIRun): +class DpmjetIII306(DpmjetIIIRun): _version = "3.0-6" _library_name = "_dpmjet306" _param_file_name = "fitpar.dat" @@ -220,14 +220,6 @@ class DpmjetIII306Base(DpmjetIIIRun): ) -class DpmjetIII193Base_DEV(DpmjetIIIRun): +class DpmjetIII193_DEV(DpmjetIIIRun): _version = "19.3-dev" _library_name = "_dev_dpmjetIII193" - - -DpmjetIII191 = make_remote_controlled_model("DpmjetIII191", DpmjetIII191Base) -DpmjetIII193 = make_remote_controlled_model("DpmjetIII193", DpmjetIII193Base) -DpmjetIII306 = make_remote_controlled_model("DpmjetIII306", DpmjetIII306Base) -DpmjetIII193_DEV = make_remote_controlled_model( - "DpmjetIII193_DEV", DpmjetIII193Base_DEV -) diff --git a/src/impy/models/phojet.py b/src/impy/models/phojet.py index c558d3b7..90428334 100644 --- a/src/impy/models/phojet.py +++ b/src/impy/models/phojet.py @@ -1,8 +1,8 @@ -from impy.common import MCRun, MCEvent, CrossSectionData +from impy.common import MCEvent, CrossSectionData from impy.util import fortran_chars, _cached_data_dir from impy.kinematics import EventFrame from impy.constants import standard_projectiles -from impy.remote_control import make_remote_controlled_model +from impy.remote_control import MCRunRemote as MCRun from particle import literals as lp @@ -194,7 +194,7 @@ def _generate(self): return not self._lib.pho_event(1, *self._beams)[1] -class Phojet112Base(PHOJETRun): +class Phojet112(PHOJETRun): _version = "1.12-35" _library_name = "_phojet112" _param_file_name = "fitpar.dat" @@ -208,17 +208,11 @@ class Phojet112Base(PHOJETRun): ) -class Phojet191Base(PHOJETRun): +class Phojet191(PHOJETRun): _version = "19.1" _library_name = "_phojet191" -class Phojet193Base(PHOJETRun): +class Phojet193(PHOJETRun): _version = "19.3" _library_name = "_phojet193" - - -# Use remote control as a workaround for Phojet limitation -Phojet112 = make_remote_controlled_model("Phojet112", Phojet112Base) -Phojet191 = make_remote_controlled_model("Phojet191", Phojet191Base) -Phojet193 = make_remote_controlled_model("Phojet193", Phojet193Base) diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index bd61683c..1f8723ee 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -1,6 +1,7 @@ from impy.common import MCRun, MCEvent, CrossSectionData from impy.util import info, Nuclei from impy.kinematics import EventFrame +from impy.remote_control import MCRunRemote from particle import literals as lp import warnings @@ -25,7 +26,7 @@ def n_NN_interactions(self): return self._lib.cnucms.ni -class SIBYLLRun(MCRun): +class SIBYLLRun: """Implements all abstract attributes of MCRun for the SIBYLL 2.1, 2.3 and 2.3c event generators.""" @@ -120,46 +121,47 @@ def _generate(self): return True -class Sibyll21(SIBYLLRun): +class Sibyll21(SIBYLLRun, MCRun): _version = "2.1" _library_name = "_sib21" -class Sibyll23(SIBYLLRun): +# For some reason, Sibyll23 requires MCRunRemote, but the others don't +class Sibyll23(SIBYLLRun, MCRunRemote): _version = "2.3" _library_name = "_sib23" -class Sibyll23c(SIBYLLRun): +class Sibyll23c(SIBYLLRun, MCRun): _version = "2.3c" _library_name = "_sib23c01" # undocumented patch version -class Sibyll23c00(SIBYLLRun): +class Sibyll23c00(SIBYLLRun, MCRun): _version = "2.3c00" _library_name = "_sib23c00" # identical to 2.3c -class Sibyll23c01(SIBYLLRun): +class Sibyll23c01(SIBYLLRun, MCRun): _version = "2.3c01" _library_name = "_sib23c01" # undocumented patch version -class Sibyll23c02(SIBYLLRun): +class Sibyll23c02(SIBYLLRun, MCRun): _version = "2.3c02" _library_name = "_sib23c02" # The c03 version was also in CORSIKA until 2020 -class Sibyll23c03(SIBYLLRun): +class Sibyll23c03(SIBYLLRun, MCRun): _version = "2.3c03" _library_name = "_sib23c03" # The latest patch c04 was renamed to d, to generate less confusion -class Sibyll23d(SIBYLLRun): +class Sibyll23d(SIBYLLRun, MCRun): _version = "2.3d" _library_name = "_sib23d" diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 3a8f9b9e..272525aa 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -1,25 +1,30 @@ import multiprocessing as mp import traceback import sys +from impy.common import MCRun -__all__ = ["make_remote_controlled_model"] +__all__ = ["MCRunRemote"] def run(output, Model, seed, init_kwargs, stables, random_state, method, args): model = Model(seed, **init_kwargs) - if random_state is not None: - model.random_state = random_state + model.random_state = random_state try: if method == "call": for k in stables - model.get_stable(): model.set_stable(k, True) for k in model.get_stable() - stables: model.set_stable(k, False) - for x in model(*args): - output.put(x) - else: - x = getattr(model, method)(*args) + # we must call MCRun.__call__ here, + # not MCRunRemote.__call__ + for x in MCRun.__call__(model, *args): + output.put(x.copy()) + elif method == "cross_section": + # same as above + x = MCRun.cross_section(model, *args) output.put(x) + else: + assert False # never arrive here except Exception as exc: (msg,) = exc.args tb = sys.exc_info()[2] @@ -27,6 +32,7 @@ def run(output, Model, seed, init_kwargs, stables, random_state, method, args): exc.args = (f"{msg}\n\nBacktrace from child process:\n{s}",) output.put(exc) return + # also return random state output.put(model.random_state) @@ -49,37 +55,32 @@ def get(timeout, process, output): return x -def make_remote_controlled_model(name, Model): +class MCRunRemote(MCRun): """ - Dynamically create a Model class which executes calls to Model.cross_section and - Model.__call__ to the wrapped model in a separate process to work around - initialization issues. - - Parameters - ---------- - name : str - Name of the generated class, must be unique. - Model : class - The wrapped class. + Base class for models which cannot switch beam particles arbitrarily. + + Calls to Model.cross_section and Model.__call__ are executed + in a separate process with a fresh instance of the underlying generator + to work around initialization issues. """ - # This is a big hack, but required until a proper solution is available. A new process - # is created whenever the cross_section or __call__ methods are used. + + # This is a big hack, but required until a proper solution is available. # # Generating events is optimized, the process is kept alive during generation and # sends its events via a queue to the main process. In an exception occurs in the # child process, it is passed to the main process and raised there. # - # Sending of data currently uses pickle, which has some overhead. This penalty only - # matters for event generation, where the performance drop could be noticable. We - # could avoid this by allocating EventData in shared memory, but putting more time and - # effort into this for a model that is barely used (Phojet) does not seem effective. + # Sending of data may use pickle (seems to be platform dependent) and should have some + # overhead. This penalty only matters for event generation, where the performance drop + # could be noticable. We could avoid this by allocating EventData in shared memory. # # To maintain the illusion that we are pulling events from a model instance instead of - # restarting it repeatedly, we roundtrip the state of the random number generators - # everytime we create a new process. + # restarting it repeatedly, we must roundtrip the state of the random number generator + # everytime we create a new process. So this functionality relies strongly on correct + # RPNG state persistence. def __init__(self, seed=None, timeout=100, **kwargs): - Model.__init__(self, seed, **kwargs) + super().__init__(seed, **kwargs) self._timeout = timeout self._init_kwargs = kwargs @@ -90,8 +91,8 @@ def __call__(self, kin, nevents): self.random_state = get(self._timeout, process, output) process.join() - def _cross_section(self, kin): - return self._remote_method("_cross_section", kin) + def cross_section(self, kin, **kwargs): + return self._remote_method("cross_section", kin) def _remote_init(self, method, args): ctx = mp.get_context("spawn") @@ -100,7 +101,7 @@ def _remote_init(self, method, args): target=run, args=( output, - Model, + self.__class__, self.seed, self._init_kwargs, self.get_stable(), @@ -119,17 +120,3 @@ def _remote_method(self, method, *args): self.random_state = get(self._timeout, process, output) process.join() return x - - # dynamically create a new class which inherits from Model and - # overrides the methods which need to do remote calls - return type( - name, - (Model,), - { - "__init__": __init__, - "__call__": __call__, - "_cross_section": _cross_section, - "_remote_init": _remote_init, - "_remote_method": _remote_method, - }, - ) diff --git a/tests/test_generators.py b/tests/test_generators.py index 493f9f7c..e03db14a 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -21,7 +21,6 @@ import numpy as np from scipy.stats import chi2 import sys -import impy matplotlib.use("svg") # need non-interactive backend for CI Windows From 632da2da586bf62aa533165ab0e0151223c24859 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 00:26:40 +0100 Subject: [PATCH 22/35] massive fixes and new tests for cross sections --- pyproject.toml | 2 + src/impy/__init__.py | 11 +- src/impy/common.py | 23 ++- src/impy/models/dpmjetIII.py | 12 +- src/impy/models/pythia8.py | 4 + src/impy/models/qgsjet.py | 67 ++++---- src/impy/models/sibyll.py | 4 +- src/impy/models/sophia.py | 1 + src/impy/models/urqmd.py | 14 +- src/impy/remote_control.py | 159 +++++++++--------- src/impy/util.py | 9 + .../test_generators/EposLHC_He_air_ft.pkl.gz | Bin 24163 -> 21348 bytes tests/test_cross_section.py | 67 ++++++++ tests/test_sibyll21.py | 10 +- 14 files changed, 254 insertions(+), 129 deletions(-) create mode 100644 tests/test_cross_section.py diff --git a/pyproject.toml b/pyproject.toml index a11575f7..309abef2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,8 @@ testpaths = ["tests"] filterwarnings = [ "error::numpy.VisibleDeprecationWarning", "error::DeprecationWarning", + "ignore::impy.AliveInstanceWarning", + "ignore::impy.FixedStableListWarning", ] [tool.cibuildwheel] diff --git a/src/impy/__init__.py b/src/impy/__init__.py index 293773ec..be699865 100644 --- a/src/impy/__init__.py +++ b/src/impy/__init__.py @@ -3,7 +3,16 @@ from impy import kinematics from impy import constants import os +from impy.util import AliveInstanceWarning, FixedStableListWarning debug_level = int(os.environ.get("DEBUG", "0")) -__all__ = ["models", "kinematics", "constants", "debug_level", "__version__"] +__all__ = [ + "models", + "kinematics", + "constants", + "debug_level", + "AliveInstanceWarning", + "FixedStableListWarning", + "__version__", +] diff --git a/src/impy/common.py b/src/impy/common.py index 56eee9d3..9d371c03 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -17,6 +17,7 @@ pdg2name, process_particle, Nuclei, + AliveInstanceWarning, ) from impy.constants import ( quarks_and_diquarks_and_gluons, @@ -32,9 +33,6 @@ from particle import Particle -# Do we need EventData.n_spectators in addition to EventData.n_wounded? -# n_spectators can be computed from n_wounded as number_of_nucleons - n_wounded -# If we want this, it should be computed dynamically via a property. @dataclasses.dataclass class CrossSectionData: """Information of cross-sections returned by the generator. @@ -604,10 +602,12 @@ class MCRun(ABC): # Don't override `__init__` in derived class, use `_once` instead. # You can pass custom initialization parameters via **kwargs, which # are forwarded to `_once`. - def __init__(self, seed, **kwargs): + def __init__(self, seed=None, **kwargs): import importlib from random import randint + self._init_kwargs = kwargs + if self.pyname in self._alive_instances: warnings.warn( f"A previous instance of {self.pyname} is still alive. " @@ -615,7 +615,7 @@ def __init__(self, seed, **kwargs): "Please delete the old one first before creating a new one. " "You can ignore this warning if the previous instance is already " "out of scope, Python does not always destroy old instances immediately.", - RuntimeWarning, + AliveInstanceWarning, stacklevel=3, ) @@ -780,6 +780,7 @@ def set_stable(self, particle, stable=True): p = Particle.from_pdgid(pid) if p.ctau is None or p.ctau == np.inf: raise ValueError(f"{pdg2name(pid)} cannot decay") + if abs(pid) == 311: self.set_stable(130, stable) self.set_stable(310, stable) @@ -789,6 +790,7 @@ def set_stable(self, particle, stable=True): self._stable.add(pid) elif pid in self._stable: self._stable.remove(pid) + self._set_stable(pid, stable) def maydecay(self, particle): @@ -818,11 +820,16 @@ def cross_section(self, kin, **kwargs): cross_section = CrossSectionData(0, 0, 0, 0, 0, 0, 0) components = kin.p2.components fractions = kin.p2.fractions - for component, fraction in zip(components, fractions): + # as a workaround for DPMJet, we use heaviest elements first + pairs = sorted( + zip(components, fractions), key=lambda p: p[0].A, reverse=True + ) + for component, fraction in pairs: kin.p2 = component cs = self._cross_section(kin, **kwargs) - for i, val in enumerate(dataclasses.astuple(cs)): - cross_section[i] += fraction * val + for key, val in dataclasses.asdict(cs).items(): + v = getattr(cross_section, key) + setattr(cross_section, key, v + fraction * val) return cross_section else: return self._cross_section(kin, **kwargs) diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index f860c935..80c94d69 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -102,9 +102,9 @@ def _once(self): def _cross_section(self, kin, precision=None): self._set_kinematics(kin) - assert kin.p2.A >= 1 # should be guaranteed _set_kinematics + assert kin.p2.A >= 1 # should be guaranteed by MCRun._validate_kinematics - if kin.p1.A and kin.p1.A > 1: # nuclear projectile + if kin.p1.A: # nuclear projectile if precision is not None: saved = self._lib.dtglgp.jstatb # Set number of trials for Glauber model integration @@ -127,6 +127,10 @@ def _cross_section(self, kin, precision=None): return CrossSectionData(inelastic=self._lib.dtglxs.xspro[0, 0, 0]) # other projectile + if kin.p2.A and kin.p2.A > 1: + # DT_XSHN: cross sections not implemented for proj/target 14 0 + return CrossSectionData() + stot, sela = self._lib.dt_xshn( self._lib.idt_icihad(kin.p1), self._lib.idt_icihad(kin.p2), 0.0, kin.ecm ) @@ -158,9 +162,9 @@ def _set_kinematics(self, kin): self._lib.dt_init( -1, max(kin.plab, 100.0), - kin.p1.A or 1, + self._max_A1, kin.p1.Z or 0, - kin.p2.A or 1, + self._max_A2, kin.p2.Z or 0, kin.p1, iglau=0, diff --git a/src/impy/models/pythia8.py b/src/impy/models/pythia8.py index 5411c403..7798cddf 100644 --- a/src/impy/models/pythia8.py +++ b/src/impy/models/pythia8.py @@ -62,6 +62,10 @@ def _once(self): self._lib.pythia = self._lib.Pythia(datdir, True) def _cross_section(self, kin): + if (kin.p2.A or 1) > 1: + # Trying to access info.sigmaTot crashes Pythia-8 + # if target is a nucleus + return CrossSectionData() self._set_kinematics(kin) st = self._lib.pythia.info.sigmaTot return CrossSectionData( diff --git a/src/impy/models/qgsjet.py b/src/impy/models/qgsjet.py index a8af58cd..9ddb37bf 100644 --- a/src/impy/models/qgsjet.py +++ b/src/impy/models/qgsjet.py @@ -2,8 +2,9 @@ from impy.common import MCRun, MCEvent, CrossSectionData from impy.kinematics import EventFrame from impy.constants import standard_projectiles -from impy.util import _cached_data_dir, Nuclei +from impy.util import _cached_data_dir, Nuclei, FixedStableListWarning from particle import literals as lp +import warnings class QGSJET1Event(MCEvent): @@ -44,12 +45,9 @@ def _once(self): self._lib.cqgsini(self._seed, datdir, lun, debug_level) def _set_stable(self, pid, stable): - import warnings - # TODO use Pythia8 instance to decay particles which QGSJet does not decay - warnings.warn( - f"stable particles cannot be changed in {self.pyname}", RuntimeWarning + f"cannot change stable particles in {self.pyname}", FixedStableListWarning ) @@ -59,13 +57,26 @@ class QGSJet1Run(QGSJetRun): _event_class = QGSJET1Event + def _projectile_id(self, pid): + # 2 is correct for nucleons and nuclei + return { + lp.pi_plus.pdgid: 1, + lp.K_plus.pdgid: 3, + lp.K_S_0.pdgid: 3, + lp.K_L_0.pdgid: 3, + }.get(abs(pid), 2) + def _cross_section(self, kin): # Interpolation routine for QGSJET01D cross sections from CORSIKA. from scipy.interpolate import UnivariateSpline + if (kin.p1.A or 0) > 1: + # nuclear projectiles are not supported + return CrossSectionData() + A_target = kin.p2.A # Projectile ID-1 to access fortran indices directly - icz = self._projectile_id - 1 + icz = self._projectile_id(kin.p1) - 1 qgsgrid = 10 ** np.arange(1, 11) cross_section = np.zeros(10) wa = np.zeros(3) @@ -93,15 +104,7 @@ def _cross_section(self, kin): return CrossSectionData(inelastic=inel) def _set_kinematics(self, kin): - self._projectile_id = { - lp.pi_plus.pdgid: 1, - lp.K_plus.pdgid: 3, - lp.K_S_0.pdgid: 3, - lp.K_L_0.pdgid: 3, - }.get( - abs(kin.p1), 2 - ) # 2 is correct for nucleons and nuclei - self._lib.xxaini(kin.elab, self._projectile_id, kin.p1.A or 1, kin.p2.A) + self._lib.xxaini(kin.elab, self._projectile_id(kin.p1), kin.p1.A or 1, kin.p2.A) def _generate(self): self._lib.psconf() @@ -116,17 +119,10 @@ class QGSJet2Run(QGSJetRun): _event_class = QGSJET2Event - def _cross_section(self, kin): - inel = self._lib.qgsect( - kin.elab, - self._projectile_id, - kin.p1.A, - kin.p2.A, - ) - return CrossSectionData(inelastic=inel) - - def _set_kinematics(self, kin): - self._projectile_id = { + @staticmethod + def _projectile_id(pid: int) -> int: + # 2 is correct for nuclei + return { lp.pi_plus.pdgid: 1, lp.pi_minus.pdgid: -1, lp.proton.pdgid: 2, @@ -137,10 +133,21 @@ def _set_kinematics(self, kin): lp.K_minus.pdgid: -4, lp.K_S_0.pdgid: -5, lp.K_L_0.pdgid: 5, - }.get( - kin.p1, 2 - ) # 2 is correct for nuclei - self._lib.qgini(kin.elab, self._projectile_id, kin.p1.A or 1, kin.p2.A) + }.get(pid, 2) + + def _cross_section(self, kin): + # qgini not needed for cross-section + inel = self._lib.qgsect( + kin.elab, + # need to use classes 1-5 here + abs(self._projectile_id(kin.p1)), + kin.p1.A or 1, + kin.p2.A, + ) + return CrossSectionData(inelastic=inel) + + def _set_kinematics(self, kin): + self._lib.qgini(kin.elab, self._projectile_id(kin.p1), kin.p1.A or 1, kin.p2.A) def _generate(self): self._lib.qgconf() diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index 1f8723ee..74f26f7c 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -114,8 +114,8 @@ def _set_stable(self, pdgid, stable): idb[sid - 1] = abs(idb[sid - 1]) def _generate(self): - kin = self._kin - self._lib.sibyll(self._production_id, kin.p2.A, kin.ecm) + k = self._kin + self._lib.sibyll(self._production_id, k.p2.A, k.ecm) self._lib.decsib() self._lib.sibhep() return True diff --git a/src/impy/models/sophia.py b/src/impy/models/sophia.py index db4261d1..5acb5d65 100644 --- a/src/impy/models/sophia.py +++ b/src/impy/models/sophia.py @@ -59,6 +59,7 @@ def _once(self, keep_decayed_particles=True): self._lib.eg_io.keepdc = keep_decayed_particles def _cross_section(self, kin): + self._set_kinematics(kin) # code=3 for inelastic cross-section # TODO fill more cross-sections inel = ( diff --git a/src/impy/models/urqmd.py b/src/impy/models/urqmd.py index 26cecd5a..5455d153 100644 --- a/src/impy/models/urqmd.py +++ b/src/impy/models/urqmd.py @@ -7,7 +7,7 @@ # The license of UrQMD is quite restrictive, they won't probably permit distributing it. from impy.common import MCRun, MCEvent, CrossSectionData -from impy.util import info, fortran_array_insert, fortran_array_remove, Nuclei +from impy.util import info, fortran_array_insert, fortran_array_remove, Nuclei, pdg2name from impy.kinematics import EventFrame from impy.constants import standard_projectiles, GeV import warnings @@ -187,10 +187,20 @@ def _set_kinematics(self, kin): self._lib.options.ctoption[24 - 1] = 0 def _set_stable(self, pdgid, stable): + # URQMD does not generate muons + if abs(pdgid) == 13: + return + + # URQMD uses K0, K0~ states instead of KS0, KL0 + if abs(pdgid) in (130, 310): # KS0 or KL0 + pdgid = 311 + try: uid = self._pdg2modid[pdgid][0] except KeyError: - warnings.warn(f"{pdgid} unknown to UrQMD", RuntimeWarning) + warnings.warn( + f"{pdg2name(pdgid)} [{pdgid}] unknown to UrQMD", RuntimeWarning + ) return # FIXME changing stabvec has no effect diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 272525aa..5ca70944 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -2,57 +2,10 @@ import traceback import sys from impy.common import MCRun - -__all__ = ["MCRunRemote"] +from queue import Empty -def run(output, Model, seed, init_kwargs, stables, random_state, method, args): - model = Model(seed, **init_kwargs) - model.random_state = random_state - try: - if method == "call": - for k in stables - model.get_stable(): - model.set_stable(k, True) - for k in model.get_stable() - stables: - model.set_stable(k, False) - # we must call MCRun.__call__ here, - # not MCRunRemote.__call__ - for x in MCRun.__call__(model, *args): - output.put(x.copy()) - elif method == "cross_section": - # same as above - x = MCRun.cross_section(model, *args) - output.put(x) - else: - assert False # never arrive here - except Exception as exc: - (msg,) = exc.args - tb = sys.exc_info()[2] - s = "".join(traceback.format_tb(tb)) - exc.args = (f"{msg}\n\nBacktrace from child process:\n{s}",) - output.put(exc) - return - # also return random state - output.put(model.random_state) - - -def get(timeout, process, output): - from queue import Empty - - for _ in range(timeout): - if not process.is_alive(): - raise ValueError("process died") - try: - x = output.get(timeout=1) - break - except Empty: - pass - else: - raise TimeoutError("process send no data") - - if isinstance(x, Exception): - raise x - return x +__all__ = ["MCRunRemote"] class MCRunRemote(MCRun): @@ -81,42 +34,98 @@ class MCRunRemote(MCRun): def __init__(self, seed=None, timeout=100, **kwargs): super().__init__(seed, **kwargs) - self._timeout = timeout - self._init_kwargs = kwargs + self._remote = _RemoteCall(self, timeout) def __call__(self, kin, nevents): - process, output = self._remote_init("call", (kin, nevents)) - for _ in range(nevents): - yield get(self._timeout, process, output) - self.random_state = get(self._timeout, process, output) - process.join() + with self._remote("call", kin, nevents) as rc: + for _ in range(nevents): + yield rc.get() def cross_section(self, kin, **kwargs): - return self._remote_method("cross_section", kin) + with self._remote("cross_section", kin, **kwargs) as rc: + return rc.get() + - def _remote_init(self, method, args): +class _RemoteCall: + def __init__(self, parent, timeout): + self.parent = parent + self.timeout = timeout + + def __enter__(self): + return self + + def __call__(self, method, *args, **kwargs): ctx = mp.get_context("spawn") - output = ctx.Queue() - process = ctx.Process( - target=run, + self.output = ctx.Queue() + p = self.parent + state = (p.seed, p._init_kwargs, p.get_stable(), p.random_state) + self.process = ctx.Process( + target=_run, args=( - output, - self.__class__, - self.seed, - self._init_kwargs, - self.get_stable(), - self.random_state, + self.output, + self.parent.__class__, + state, method, args, + kwargs, ), daemon=True, ) - process.start() - return process, output - - def _remote_method(self, method, *args): - process, output = self._remote_init(method, args) - x = get(self._timeout, process, output) - self.random_state = get(self._timeout, process, output) - process.join() + self.process.start() + return self + + def __exit__(self, exc_type, exc_val, tb): + if exc_type is None: + self.parent.random_state = self.get() + self.process.join() + + def get(self): + for _ in range(self.timeout): + if not self.process.is_alive(): + raise RuntimeError("process died") + try: + x = self.output.get(timeout=1) + break + except Empty: + pass + else: + raise TimeoutError("process send no data") + + if isinstance(x, RemoteException): + exc, stb = x.args + (msg,) = exc.args + exc.args = (f"{msg}\n\nBacktrace from child process:\n{stb}",) + raise exc return x + + +class RemoteException(Exception): + pass + + +def _run(output, Model, state, method, args, kwargs): + seed, init_kwargs, stable_state, random_state = state + model = Model(seed, **init_kwargs) + model.random_state = random_state + try: + if method == "call": + for k in stable_state - model.get_stable(): + model.set_stable(k, True) + for k in model.get_stable() - stable_state: + model.set_stable(k, False) + # we must explicitly call MCRun.__call__ here, + # to get MCRunRemote.__call__ + for x in MCRun.__call__(model, *args, **kwargs): + output.put(x.copy()) + else: + # same as above + meth = getattr(MCRun, method) + x = meth(model, *args, **kwargs) + output.put(x) + except Exception as exc: + tb = sys.exc_info()[2] + s = "".join(traceback.format_tb(tb)) + exc2 = RemoteException(exc, s) + output.put(exc2) + # also return random state + output.put(model.random_state) diff --git a/src/impy/util.py b/src/impy/util.py index f2a8dc23..532b7633 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -14,6 +14,15 @@ from enum import Enum import dataclasses + +class AliveInstanceWarning(RuntimeWarning): + pass + + +class FixedStableListWarning(RuntimeWarning): + pass + + EventFrame = Enum("EventFrame", ["CENTER_OF_MASS", "FIXED_TARGET", "GENERIC"]) diff --git a/tests/data/test_generators/EposLHC_He_air_ft.pkl.gz b/tests/data/test_generators/EposLHC_He_air_ft.pkl.gz index ad5d4419055f25db4cdd45fc18d42e05a7fb425f..969000ec02273b3537de7d39a86c6178e08686c7 100644 GIT binary patch literal 21348 zcmd43XIPVMn=Nd|2G|g!K8k{ffS~je6{Sd1s?> zUPF)6&;!JfkPy<7&Ac=FnAvme`HtCpzdyb|ck=tZ*SXHM)_GmGRQ%DS@EbL*2YC1W 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z*W~`tWm5UxW#Wg9GroJ5;(co3esSP9+hBxo@E~B6s0;(Er?BFV6lRj8&8aA~O*;yG z<@Olz6s>fmlECDSYx|7Iun>&$^eun0x`a+9zMI`_ z)bsF{?3|vI9g-e}yx2{n?%G(gqPFAK6CO0yip<)HVuh?Gm#~H46#&R={+KK{_!L6r zsh=}%c*}eInJ*N}#wng+`j8_aokWY<^!@t(?(n-o7y9dgP77&_MFmg2wO_&S97I}p zVt)Et5<9?KnZVWU1-To(r*&BVob82C2+C%%mrshOi5DS%S{X;&^~QkXh@`5%xeA1{ z3`ST~`=+1z_ZjSlJsbLPx)c*qksu>V8DBioV#vM>=ZXgm`tLUAP#WdG@O~x*9KHjV zNRkVxT`nK3tyIPLUmS;P(3gOjpvICIEX6$U?J#?FnnNAerkG;%sYLXE*ZSqL^ZAz{ zTOe1uTbn<(Y3V0zC$zHINJZkHpB${5apAJpa7i-rI%z`Da^=#tH`Q&KXF@Wnr!J`$ zH$B$Qd11(5>TonSC+ZqOecf}TamnOj|HiyaUGv>qz*a-MX#Ua7FClMd&;0rq{oEM- z>fa`LDraXg-z@EcCM({Tjs{Wfle+M*87(xV0`Y4=;rqUgXD-&MYhkt@#GcjdUdV=me>jzHm^_X+fzKhWQi$D~6r0XMf7h z8=Bj{3Yt7$Y}uBap2oN4`ozx<%--ZklPg05V;o@(tMUNEOqiX!=^${gNMdU07Iyx APyhe` diff --git a/tests/test_cross_section.py b/tests/test_cross_section.py new file mode 100644 index 00000000..f260920c --- /dev/null +++ b/tests/test_cross_section.py @@ -0,0 +1,67 @@ +import pytest +from impy.util import get_all_models +from impy.kinematics import CompositeTarget, CenterOfMass, TeV +from impy import models as im + + +@pytest.mark.parametrize("Model", get_all_models()) +@pytest.mark.parametrize("target", ("p", "O", "air")) +@pytest.mark.parametrize("projectile", ("gamma", "pi-", "p", "He")) +def test_cross_section(Model, projectile, target): + p1 = projectile + p2 = target + if p2 == "air": + if Model is im.Pythia8: + pytest.skip("Simulating nuclei in Pythia8 is very time-consuming") + + # cannot use Argon in SIBYLL, so make air from N, O only + p2 = CompositeTarget((("N", 0.78), ("O", 0.22))) + + kin = CenterOfMass(1 * TeV, p1, p2) + + if abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets: + pytest.skip("Model does not accept projectile or target") + + # known issues + if Model.name == "DPMJET-III" and not kin.p1.is_nucleus and kin.p2.is_nucleus: + pytest.xfail("DPMJet rejects h-A, although it can do h-p, p-A, and A-A") + if Model.name == "SIBYLL" and (kin.p2.A or 1) > 1: + pytest.xfail("h-A should work, but need to be fixed in Sibyll") + if Model.pyname == "QGSJet01d" and (kin.p1.A or 1) > 1: + pytest.xfail("no support for nuclear projectiles in QGSJet01d") + if Model.pyname == "Pythia8" and (kin.p1.A or 1) > 1 or (kin.p2.A or 1) > 1: + pytest.xfail("A-A and h-A cross-sections are not yet supported for Pythia8") + + model = Model(seed=1) + c = model.cross_section(kin) + + if Model is im.UrQMD34: + # only provides total cross-section + expected = { + ("pi-", "p"): 0, + ("pi-", "air"): 1200, + ("p", "p"): 0, + ("p", "air"): 1200, + ("He", "p"): 900, + ("He", "air"): 1400, + }[(projectile, target)] + + assert c.total == pytest.approx(expected, rel=0.1) + return + + # p-A should be roughly A^(2/3) * p-p + # pi-p should be roughly (2/3)^(2/3) * p-p + expected = { + ("gamma", "p"): 0.21, + ("pi-", "p"): 40, + ("pi-", "O"): 180, + ("pi-", "air"): 150, + ("p", "p"): 53, + ("p", "O"): 240, + ("p", "air"): 220, + ("He", "p"): 130, + ("He", "air"): 350, + ("He", "O"): 380, + }[(projectile, target)] + + assert c.inelastic == pytest.approx(expected, rel=0.3) diff --git a/tests/test_sibyll21.py b/tests/test_sibyll21.py index 13bf64c8..82490f12 100644 --- a/tests/test_sibyll21.py +++ b/tests/test_sibyll21.py @@ -61,14 +61,10 @@ def test_vertex(event): assert_equal(event.vt, 0) -def run_cross_section(p1, p2): - m = Sibyll21() - kin = CenterOfMass(10 * GeV, p1, p2) - return m.cross_section(kin) - - def test_cross_section(): - c = run_cross_section("p", "p") + m = Sibyll21() + kin = CenterOfMass(10 * GeV, "p", "p") + c = m.cross_section(kin) assert_allclose(c.total, 38.4, atol=0.1) assert_allclose(c.inelastic, 30.9, atol=0.1) assert_allclose(c.elastic, 7.4, atol=0.1) From e8b696309cb3aa861294add795309495d076fd88 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 00:50:31 +0100 Subject: [PATCH 23/35] terminate process if it is still alive --- src/impy/remote_control.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 5ca70944..53f9f7f3 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -89,6 +89,8 @@ def get(self): except Empty: pass else: + if self.process.is_alive(): + self.process.terminate() raise TimeoutError("process send no data") if isinstance(x, RemoteException): From 34fd2777c138f6f945b9bf694fc05c27985a164e Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 01:06:43 +0100 Subject: [PATCH 24/35] kill --- src/impy/remote_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 53f9f7f3..a62f3cae 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -90,7 +90,7 @@ def get(self): pass else: if self.process.is_alive(): - self.process.terminate() + self.process.kill() raise TimeoutError("process send no data") if isinstance(x, RemoteException): From b0555d5ab6ab2f3e07fdd3556d92ff2e7a1ed60d Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 13:04:11 +0100 Subject: [PATCH 25/35] fix bug in remote control, add reprs --- pyproject.toml | 1 + src/impy/__init__.py | 3 +- src/impy/common.py | 8 +- src/impy/kinematics.py | 10 +++ src/impy/remote_control.py | 52 ++++++++++--- src/impy/util.py | 4 + tests/test_cross_section.py | 7 +- tests/test_generators.py | 5 +- tests/test_remote_control.py | 141 +++++++++++++++++++++++++---------- tests/test_to_hepmc3.py | 5 +- 10 files changed, 177 insertions(+), 59 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 309abef2..f52602d6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ filterwarnings = [ "error::DeprecationWarning", "ignore::impy.AliveInstanceWarning", "ignore::impy.FixedStableListWarning", + "ignore::impy.OutputWarning", ] [tool.cibuildwheel] diff --git a/src/impy/__init__.py b/src/impy/__init__.py index be699865..b7fab59a 100644 --- a/src/impy/__init__.py +++ b/src/impy/__init__.py @@ -3,7 +3,7 @@ from impy import kinematics from impy import constants import os -from impy.util import AliveInstanceWarning, FixedStableListWarning +from impy.util import AliveInstanceWarning, FixedStableListWarning, OutputWarning debug_level = int(os.environ.get("DEBUG", "0")) @@ -14,5 +14,6 @@ "debug_level", "AliveInstanceWarning", "FixedStableListWarning", + "OutputWarning", "__version__", ] diff --git a/src/impy/common.py b/src/impy/common.py index 9d371c03..25583f47 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -18,6 +18,7 @@ process_particle, Nuclei, AliveInstanceWarning, + OutputWarning, ) from impy.constants import ( quarks_and_diquarks_and_gluons, @@ -382,7 +383,7 @@ def to_hepmc3(self, genevent=None): warnings.warn( f"{model}-{version}: only final state particles " "available in HepMC3 event", - RuntimeWarning, + OutputWarning, ) ev = self.final_state() else: @@ -567,6 +568,11 @@ def copy(self): """ return copy.deepcopy(self) # this uses setstate, getstate + def __repr__(self): + return ( + f"" + ) + # HD: These should not be public, since the random number state # is an implementation detail. I am going to leave it, since this # whole class will become obsolete when we switch to the numpy PRNG. diff --git a/src/impy/kinematics.py b/src/impy/kinematics.py index 9003892c..a6fd1267 100644 --- a/src/impy/kinematics.py +++ b/src/impy/kinematics.py @@ -211,6 +211,16 @@ def eq(a, b): return all(eq(a, b) for (a, b) in zip(at, bt)) + def __repr__(self): + p = f"{self.p1:d}, {self.p2:d}" + if self.frame == EventFrame.CENTER_OF_MASS: + s = f"ecm={self.ecm}" + return f"CenterOfMass({self.ecm}, {p})" + elif self.frame == EventFrame.FIXED_TARGET: + return f"FixedTarget({self.elab}, {p})" + s = f"beam=({self.beams[0][2]}, {self.beams[1][2]}), frame={self.frame}" + return f"EventKinematics({p}, {s})" + class CenterOfMass(EventKinematics): def __init__(self, ecm, particle1, particle2): diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index a62f3cae..d19c28ba 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -1,7 +1,7 @@ import multiprocessing as mp import traceback import sys -from impy.common import MCRun +from impy.common import MCRun, RMMARDState from queue import Empty @@ -56,13 +56,16 @@ def __enter__(self): def __call__(self, method, *args, **kwargs): ctx = mp.get_context("spawn") - self.output = ctx.Queue() + # Queue should only hold one item at a time + self.output = ctx.Queue(maxsize=1) + self.stop = ctx.Event() p = self.parent state = (p.seed, p._init_kwargs, p.get_stable(), p.random_state) self.process = ctx.Process( target=_run, args=( self.output, + self.stop, self.parent.__class__, state, method, @@ -75,9 +78,21 @@ def __call__(self, method, *args, **kwargs): return self def __exit__(self, exc_type, exc_val, tb): - if exc_type is None: - self.parent.random_state = self.get() + # maybe stop iteration in remote process + self.stop.set() + # empty queue until we get RMMARDState + while self.output.full(): + x = self.output.get() + if isinstance(x, RMMARDState): + self.parent.random_state = x + break + assert self.output.empty() + self.output.close() + self.process.join(timeout=1) + if self.process.is_alive(): + self.process.kill() self.process.join() + self.process.close() def get(self): for _ in range(self.timeout): @@ -89,8 +104,6 @@ def get(self): except Empty: pass else: - if self.process.is_alive(): - self.process.kill() raise TimeoutError("process send no data") if isinstance(x, RemoteException): @@ -98,6 +111,7 @@ def get(self): (msg,) = exc.args exc.args = (f"{msg}\n\nBacktrace from child process:\n{stb}",) raise exc + return x @@ -105,7 +119,7 @@ class RemoteException(Exception): pass -def _run(output, Model, state, method, args, kwargs): +def _run(output, stop, Model, state, method, args, kwargs): seed, init_kwargs, stable_state, random_state = state model = Model(seed, **init_kwargs) model.random_state = random_state @@ -116,18 +130,38 @@ def _run(output, Model, state, method, args, kwargs): for k in model.get_stable() - stable_state: model.set_stable(k, False) # we must explicitly call MCRun.__call__ here, - # to get MCRunRemote.__call__ + # otherwise we would get MCRunRemote.__call__ for x in MCRun.__call__(model, *args, **kwargs): + # If iteration is stopped in the main thread, + # we must stop it also here, to get our + # random_state below + if stop.is_set(): + break + # The copy is necessary, but shouldn't be. This + # smells like a bug in the pickling of MCEvent, or + # maybe the process.Queue does not use pickling. output.put(x.copy()) else: # same as above meth = getattr(MCRun, method) x = meth(model, *args, **kwargs) output.put(x) + except Exception as exc: + # To aid debugging, we catch the traceback in the + # Remote process and pass it along with the exception + # to the main process. Unfortunately, one cannot pickle + # traceback objects (without external library support), + # so we convert the traceback into a string here, and + # append that string to the exception message later. tb = sys.exc_info()[2] s = "".join(traceback.format_tb(tb)) exc2 = RemoteException(exc, s) output.put(exc2) - # also return random state + + # Finally also return random state, whether there + # was an exception or not. If we switch to the Numpy + # generator, we can probably allocate the random state + # in shared memory, so it is shared at no additional cost, + # and this manual synchronisation is no longer necessary. output.put(model.random_state) diff --git a/src/impy/util.py b/src/impy/util.py index 532b7633..8edcc1db 100644 --- a/src/impy/util.py +++ b/src/impy/util.py @@ -23,6 +23,10 @@ class FixedStableListWarning(RuntimeWarning): pass +class OutputWarning(RuntimeWarning): + pass + + EventFrame = Enum("EventFrame", ["CENTER_OF_MASS", "FIXED_TARGET", "GENERIC"]) diff --git a/tests/test_cross_section.py b/tests/test_cross_section.py index f260920c..2613a5e7 100644 --- a/tests/test_cross_section.py +++ b/tests/test_cross_section.py @@ -19,8 +19,12 @@ def test_cross_section(Model, projectile, target): kin = CenterOfMass(1 * TeV, p1, p2) + model = Model(seed=1) + if abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets: - pytest.skip("Model does not accept projectile or target") + with pytest.raises(ValueError): + model.cross_section(kin) + return # known issues if Model.name == "DPMJET-III" and not kin.p1.is_nucleus and kin.p2.is_nucleus: @@ -32,7 +36,6 @@ def test_cross_section(Model, projectile, target): if Model.pyname == "Pythia8" and (kin.p1.A or 1) > 1 or (kin.p2.A or 1) > 1: pytest.xfail("A-A and h-A cross-sections are not yet supported for Pythia8") - model = Model(seed=1) c = model.cross_section(kin) if Model is im.UrQMD34: diff --git a/tests/test_generators.py b/tests/test_generators.py index e03db14a..e9632e6c 100644 --- a/tests/test_generators.py +++ b/tests/test_generators.py @@ -175,10 +175,11 @@ def test_generator(projectile, target, frame, Model): else: assert False # we should never arrive here + h = run_model(Model, kin) if abs(kin.p1) not in Model.projectiles or abs(kin.p2) not in Model.targets: - pytest.skip(reason=f"Projectile or target not supported by {Model.pyname}") + assert h is None + return - h = run_model(Model, kin) assert h is not None fn = Path(f"{Model.pyname}_{projectile}_{target}_{frame}") diff --git a/tests/test_remote_control.py b/tests/test_remote_control.py index 8a87d4ee..eb1e55a2 100644 --- a/tests/test_remote_control.py +++ b/tests/test_remote_control.py @@ -1,67 +1,126 @@ -from impy.models import Phojet193 from impy.kinematics import CenterOfMass +from impy.remote_control import MCRunRemote as MCRun +from impy.common import CrossSectionData +from impy.util import EventFrame +import pytest -def test_phojet_1(): - model = Phojet193(seed=1) +class DummyEvent: + def __init__(self, generator, kinematics, counter=0): + self.kinematics = kinematics + if generator is None: + self.counter = counter + else: + self.counter = generator.event.counter - kin = CenterOfMass(100, "p", "p") + def copy(self): + return DummyEvent(None, self.kinematics, self.counter) + + def __repr__(self): + return f"DummyEvent(None, {self.kinematics}, {self.counter})" + + def __eq__(self, other): + return self.kinematics == other.kinematics and self.counter == other.counter + + +class Dummy(MCRun): + _name = "Dummy" + _version = "1.0" + _event_class = DummyEvent + _library_name = "_eposlhc" + _frame = EventFrame.CENTER_OF_MASS + + def _once(self, raise_at=None): + self.event = DummyEvent(None, None) + self.raise_at = raise_at + + def _generate(self): + if self.raise_at is not None: + if self.raise_at == 0: + raise ValueError("from raise_at") + self.raise_at -= 1 + self.event.counter += 1 + # fake drawing a random number + self._lib.crranma4.ntot += 1 + return True + + def _cross_section(self, kin): + return CrossSectionData() + + def _set_kinematics(self, kin): + self._kin = kin - events1 = [] - for event in model(kin, 10): - events1.append(event) + def _set_stable(self, pid, stable): + pass - assert len(events1) == 10 + def __repr__(self): + return f"" - for event in events1: - assert len(event) > 0 - events2 = [] +def test_dummy_1(): + model = Dummy(seed=1) + assert model.random_state.counter == 0 + + kin = CenterOfMass(100, "p", "p") + events = [] for event in model(kin, 5): - events2.append(event) - assert len(events2) == 5 + assert model.random_state.counter == 0 + events.append(event) - assert events2 != events1[:5] + expected = [DummyEvent(None, kin, i + 1) for i in range(5)] + assert events == expected + assert model.random_state.counter == 5 -def test_phojet_2(): - model = Phojet193(seed=1) + +def test_dummy_2(): + model = Dummy(seed=1) kin = CenterOfMass(100, "p", "p") + events = [] + for i, event in enumerate(model(kin, 10)): + assert model.random_state.counter == 0 + if i == 3: + break + events.append(event) - events1 = [] - for event in model(kin, 10): - events1.append(event) + expected = [DummyEvent(None, kin, i + 1) for i in range(3)] + assert events == expected - assert len(events1) == 10 + assert model.random_state.counter >= 3 - for event in events1: - assert len(event) > 0 - kin = CenterOfMass(100, "pi-", "p") +def test_dummy_3(): + model = Dummy(seed=1, raise_at=3) - events2 = [] - for event in model(kin, 5): - events2.append(event) - assert len(events2) == 5 + kin = CenterOfMass(100, "p", "p") + events = [] + + with pytest.raises(ValueError): + assert model.random_state.counter == 0 + for event in model(kin, 10): + events.append(event) - assert events2 != events1[:5] + expected = [DummyEvent(None, kin, i + 1) for i in range(3)] + assert events == expected + assert model.random_state.counter >= 3 -def test_phojet_3(): - model = Phojet193(seed=1) - prev = 0 - for en in (10, 100, 1000): - kin = CenterOfMass(en, "p", "p") - c = model.cross_section(kin) - assert c.inelastic > 10 - assert c.inelastic > prev - prev = c.inelastic +def test_dummy_4(): + model = Dummy(seed=1) + + kin = CenterOfMass(100, "p", "p") + events = [] + with pytest.raises(ValueError): + assert model.random_state.counter == 0 + for i, event in enumerate(model(kin, 10)): + if i == 3: + raise ValueError() + events.append(event) -def test_phojet_4(): - model = Phojet193(seed=1) + expected = [DummyEvent(None, kin, i + 1) for i in range(3)] + assert events == expected - # this calls an attribute of the original class - assert len(model.projectiles) > 0 + assert model.random_state.counter >= 3 diff --git a/tests/test_to_hepmc3.py b/tests/test_to_hepmc3.py index a13caa19..6f308197 100644 --- a/tests/test_to_hepmc3.py +++ b/tests/test_to_hepmc3.py @@ -16,9 +16,8 @@ def test_to_hepmc3(Model): if Model == im.UrQMD34: pytest.xfail("UrQMD34 FAILS, should be FIXED!!!") - kin = CenterOfMass(10 * GeV, "proton", "proton") - if Model is Sophia20: - kin = CenterOfMass(10 * GeV, "photon", "proton") + projectile = "photon" if Model is Sophia20 else "proton" + kin = CenterOfMass(10 * GeV, projectile, "proton") m = Model(seed=1) for event in m(kin, 100): From 698e31cf912e5baa31aca501e363ada8774328c2 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 16:48:27 +0100 Subject: [PATCH 26/35] update notebook --- examples/cross_section.ipynb | 3181 +--------------------------------- 1 file changed, 28 insertions(+), 3153 deletions(-) diff --git a/examples/cross_section.ipynb b/examples/cross_section.ipynb index db79e3bd..da54c0a1 100644 --- a/examples/cross_section.ipynb +++ b/examples/cross_section.ipynb @@ -2,3144 +2,34 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from impy import models as im\n", - "from impy.kinematics import CenterOfMass\n", + "from impy.kinematics import CenterOfMass, GeV, TeV\n", + "from impy.remote_control import MCRunRemote\n", "from impy.constants import GeV\n", "from impy.util import get_all_models\n", - "\n", - "# from util import run_in_separate_process\n", "from particle import literals as lp\n", "import numpy as np\n", - "import joblib\n" + "import joblib" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " +-----------------------------------------------------------------------+\n", - " | |\n", - " | DPMJET-III version 19.3.4 |\n", - " | |\n", - " | (Last change: 30 Dec 2022) |\n", - " | |\n", - " | Authors: |\n", - " | Stefan Roesler (CERN) |\n", - " | Anatoli Fedynitch (ASIoP) |\n", - " | Ralph Engel (KIT) |\n", - " | Johannes Ranft (Siegen Univ.) |\n", - " | |\n", - " | https://github.com/afedynitch/dpmjet |\n", - " | |\n", - " +-----------------------------------------------------------------------+\n", - " | Contact: @github \n", - " +-----------------------------------------------------------------------+\n", - "\n", - " DT_JSPARA: new value (default value)\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET 19.3.4 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (KIT)\n", - " Anatoli Fedynitch (ASIoP)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " bug reports, support and updates on:\n", - " https://github.com/afedynitch/dpmjet\n", - " ===================================================\n", - " Date: 2022/12/30\n", - " Revision: 19.3.4\n", - " Code with interface to PYTHIA 6.4.27\n", - " ===================================================\n", - "\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF 3122 1 2 1 0\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF -3122 1 2 1 0\n", - "\n", - "\n", - " NCLPOT: quantities for inclusion of nuclear effects\n", - " -------------------------------------------\n", - "\n", - " projectile target\n", - "\n", - " Mass number / charge 1 / 1 1 / 1\n", - " Binding energy - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - " Fermi-potential - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - "\n", - " Scale factor for Fermi-momentum 0.55\n", - "\n", - " Coulomb-energy 0.0000E+00 GeV 0.0000E+00 GeV \n", - "\n", - "\n", - " DT_PHOINI: PHOJET initialized for projectile A,Z = 1, 1\n", - " 1.5xp_F(max) = 0.000E+00 p(max) = 0.000E+00 0.000E+00 0.500E+05 0.500E+05\n", - " DT_PHOINI: PHOJET initialized for target A,Z = 1, 1\n", - " 1.5xp_F(max) = -0.000E+00 p(max) = 0.000E+00 0.000E+00-0.500E+05 0.500E+05\n", - " E_cm = 0.100E+06\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - "\n", - "\n", - " SHMAKI: Glauber formalism (Shmakov et. al) - initialization\n", - " ---------------------------------------------------\n", - "\n", - " variable energy run: projectile-id: 1 target A/Z: 1 / 1\n", - "\n", - " E_cm (GeV) E_Lab (GeV) sig_tot^pp (mb) Sigma_tot (mb) Sigma_prod (mb)\n", - " ---------------------------------------------------------------------------\n", - " +-----------------------------------------------------------------------+\n", - " | |\n", - " | DPMJET-III version 19.1.4 |\n", - " | |\n", - " | (Last change: 30 Dec 2022) |\n", - " | |\n", - " | Authors: |\n", - " | Stefan Roesler (CERN) |\n", - " | Anatoli Fedynitch (ICRR) |\n", - " | Ralph Engel (KIT) |\n", - " | Johannes Ranft (Siegen Univ.) |\n", - " | |\n", - " | https://github.com/afedynitch/dpmjet |\n", - " | |\n", - " +-----------------------------------------------------------------------+\n", - " | Contact: @github \n", - " +-----------------------------------------------------------------------+\n", - "\n", - " DT_JSPARA: new value (default value)\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET 19.1.4 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (KIT)\n", - " Anatoli Fedynitch (ICRR)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " bug reports, support and updates on:\n", - " https://github.com/afedynitch/dpmjet\n", - " ===================================================\n", - " Date: 2022/12/30\n", - " Revision: 19.1.4\n", - " Code with interface to PYTHIA 6.4.27\n", - " ===================================================\n", - "\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF 3122 1 2 1 0\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF -3122 1 2 1 0\n", - "\n", - "\n", - " NCLPOT: quantities for inclusion of nuclear effects\n", - " -------------------------------------------\n", - "\n", - " projectile target\n", - "\n", - " Mass number / charge 1 / 1 1 / 1\n", - " Binding energy - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - " Fermi-potential - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - "\n", - " Scale factor for Fermi-momentum 0.55\n", - "\n", - " Coulomb-energy 0.0000E+00 GeV 0.0000E+00 GeV \n", - "\n", - "\n", - " DT_PHOINI: PHOJET initialized for projectile A,Z = 1, 1\n", - " 1.5xp_F(max) = 0.000E+00 p(max) = 0.000E+00 0.000E+00 0.500E+05 0.500E+05\n", - " DT_PHOINI: PHOJET initialized for target A,Z = 1, 1\n", - " 1.5xp_F(max) = -0.000E+00 p(max) = 0.000E+00 0.000E+00-0.500E+05 0.500E+05\n", - " E_cm = 0.100E+06\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - "\n", - "\n", - " SHMAKI: Glauber formalism (Shmakov et. al) - initialization\n", - " ---------------------------------------------------\n", - "\n", - " variable energy run: projectile-id: 1 target A/Z: 1 / 1\n", - "\n", - " E_cm (GeV) E_Lab (GeV) sig_tot^pp (mb) Sigma_tot (mb) Sigma_prod (mb)\n", - " ---------------------------------------------------------------------------\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - "\n", - " PHO_FITPAR: parameter set not found in internal table\n", - "\n", - " PHO_FITPAR: loading parameter set from file /Users/hdembinski/Extern/impy/src/impy/iamdata/dpm3191/dpmjpar.dat\n", - "\n", - " PHO_FITPAR: parameter set found\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.113 ALPOMP: 0.250 GP: 5.636 5.636 B0POM: 1.279 1.279\n", - " ALREG: 0.572 ALREGP: 0.967 GR: 10.697 10.697 B0REG: 0.370 0.370\n", - " GPPP : 0.150 B0PPP: 0.372 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 1.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.220\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 1.000\n", - " PARJ(21) sigma in pt distribution: 0.360 0.420\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 2.5 119958.5\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - "\n", - " PHO_FITPAR: parameter set not found in internal table\n", - "\n", - " PHO_FITPAR: loading parameter set from file /Users/hdembinski/Extern/impy/src/impy/iamdata/dpm3191/dpmjpar.dat\n", - "\n", - " PHO_FITPAR: parameter set found\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.113 ALPOMP: 0.250 GP: 5.636 5.636 B0POM: 1.279 1.279\n", - " ALREG: 0.572 ALREGP: 0.967 GR: 10.697 10.697 B0REG: 0.370 0.370\n", - " GPPP : 0.150 B0PPP: 0.372 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 1.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.300\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 0.580\n", - " PARJ(21) sigma in pt distribution: 0.360 0.360\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 2.5 119958.5\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "1 \n", - " ******************************************************************************\n", - " ******************************************************************************\n", - " ** **\n", - " ** **\n", - " ** *......* Welcome to the Lund Monte Carlo! **\n", - " ** *:::!!:::::::::::* **\n", - " ** *::::::!!::::::::::::::* PPP Y Y TTTTT H H III A **\n", - " ** *::::::::!!::::::::::::::::* P P Y Y T H H I A A **\n", - " ** *:::::::::!!:::::::::::::::::* PPP Y T HHHHH I AAAAA **\n", - " ** *:::::::::!!:::::::::::::::::* P Y T H H I A A **\n", - " ** *::::::::!!::::::::::::::::*! P Y T H H III A A **\n", - " ** *::::::!!::::::::::::::* !! **\n", - " ** !! *:::!!:::::::::::* !! This is PYTHIA version 6.428 **\n", - " ** !! !* -><- * !! Last date of change: 5 Sep 2013 **\n", - " ** !! !! !! **\n", - " ** !! !! !! Now is 0 Jan 2000 at 0:00:00 **\n", - " ** !! !! **\n", - " ** !! lh !! Disclaimer: this program comes **\n", - " ** !! !! without any guarantees. Beware **\n", - " ** !! hh !! of errors and use common sense **\n", - " ** !! ll !! when interpreting results. **\n", - " ** !! !! **\n", - " ** !! Copyright T. Sjostrand (2011) **\n", - " ** **\n", - " ** An archive of program versions and documentation is found on the web: **\n", - " ** http://www.thep.lu.se/~torbjorn/Pythia.html **\n", - " ** **\n", - " ** When you cite this program, the official reference is to the 6.4 manual: **\n", - " ** T. Sjostrand, S. Mrenna and P. Skands, JHEP05 (2006) 026 **\n", - " ** (LU TP 06-13, FERMILAB-PUB-06-052-CD-T) [hep-ph/0603175]. **\n", - " ** **\n", - " ** Also remember that the program, to a large extent, represents original **\n", - " ** physics research. Other publications of special relevance to your **\n", - " ** studies may therefore deserve separate mention. **\n", - " ** **\n", - " ** Main author: Torbjorn Sjostrand; Department of Theoretical Physics, **\n", - " ** Lund University, Solvegatan 14A, S-223 62 Lund, Sweden; **\n", - " ** phone: + 46 - 46 - 222 48 16; e-mail: torbjorn@thep.lu.se **\n", - " ** Author: Stephen Mrenna; Computing Division, GDS Group, **\n", - " ** Fermi National Accelerator Laboratory, MS 234, Batavia, IL 60510, USA; **\n", - " ** phone: + 1 - 630 - 840 - 2556; e-mail: mrenna@fnal.gov **\n", - " ** Author: Peter Skands; CERN/PH-TH, CH-1211 Geneva, Switzerland **\n", - " ** phone: + 41 - 22 - 767 24 47; e-mail: peter.skands@cern.ch **\n", - " ** **\n", - " ** **\n", - " ******************************************************************************\n", - " ******************************************************************************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "###################################################################\n", - "# EPOS LHC K. WERNER, T. PIEROG #\n", - "# Contact: tanguy.pierog@kit.edu #\n", - "###################################################################\n", - "# WARNING: This is a special retuned version !!! #\n", - "# Do not publish results without contacting the authors. #\n", - "###################################################################\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.29D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.39D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 10.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 2.6904D+00 I\n", - " I 93 Single diffractive (AX) I 2.6904D+00 I\n", - " I 94 Double diffractive I 8.7826D-01 I\n", - " I 95 Low-pT scattering I 2.5205D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.7745D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 0.55 GeV gives sigma(parton-parton) = 6.75D+01 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "read from /Users/hdembinski/Extern/impy/src/impy/iamdata/epos/epos.iniev ...\n", - " pT0 = 0.55 GeV gives sigma(parton-parton) = 5.85D+01 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.25D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.35D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 16.238 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 3.2397D+00 I\n", - " I 93 Single diffractive (AX) I 3.2397D+00 I\n", - " I 94 Double diffractive I 1.4819D+00 I\n", - " I 95 Low-pT scattering I 2.3853D+01 I\n", - "1\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET version 1.12 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (FZ Karlsruhe)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " Manual, updates, and further information:\n", - " http://www-ik.fzk.de/~engel/phojet.html\n", - " ---------------------------------------------------\n", - " please send suggestions / bug reports etc. to:\n", - " ralph.engel@fzk.de\n", - " ===================================================\n", - " $Date: 2000/06/25 21:59:19 $\n", - " $Revision: 1.12.1.35 $\n", - " (code version with interface to PYTHIA 6.x)\n", - " (code version for usage in DPMJET 3.x)\n", - " ===================================================\n", - "\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 GRV94 LO 5 6 0\n", - " PHO_FITPAR: looking for PDF 2212 GRV94 LO 5 6 0\n", - "\n", - " PHO_FITPAR: parameter set found in internal table\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.100 ALPOMP: 0.250 GP: 6.387 6.387 B0POM: 1.161 1.161\n", - " ALREG: 0.450 ALREGP: 0.900 GR: 10.263 10.263 B0REG: 1.171 1.171\n", - " GPPP : 0.156 B0PPP: 0.500 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 3.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.300\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 1.000\n", - " PARJ(21) sigma in pt distribution: 0.360 0.360\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 5.0 100000.0\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.939 0.0000E+00\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " I 96 Semihard QCD 2 -> 2 I 3.0885D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " ====================================================\n", - " | |\n", - " | S I B Y L L 2.1 |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | Ralph ENGEL |\n", - " | R.S. FLETCHER, T.K. GAISSER |\n", - " | P. LIPARI, T. STANEV |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | R. Engel et al., Proc. 26th ICRC, 1 (1999) 415 |\n", - " | |\n", - " | last modified: 28. Sept. 2001 by R. Engel |\n", - " ====================================================\n", - "\n", - "\n", - " Table: J, sqs, PT_cut, SIG_tot, SIG_inel, B_el, rho, , \n", - " pT0 = 0.62 GeV gives sigma(parton-parton) = 7.33D+01 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " ------------------------------------------------------------------------\n", - " 1 1.000E+01 1.45 38.33 30.88 10.83 -0.185 1.964 0.003\n", - " 1 1.259E+01 1.49 38.27 31.16 11.10 -0.127 1.949 0.006\n", - " 1 1.585E+01 1.54 38.44 31.54 11.36 -0.078 1.934 0.012\n", - " 1 1.995E+01 1.59 38.84 32.05 11.63 -0.037 1.918 0.019\n", - " 1 2.512E+01 1.64 39.46 32.67 11.89 -0.004 1.901 0.029\n", - " pT0 = 0.62 GeV gives sigma(parton-parton) = 7.47D+01 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - " 1 3.162E+01 1.69 40.29 33.41 12.16 0.022 1.884 0.043\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 3.981E+01 1.75 41.28 34.24 12.42 0.044 1.864 0.060\n", - " 1 5.012E+01 1.81 42.39 35.13 12.69 0.061 1.844 0.082\n", - " 1 6.310E+01 1.87 43.59 36.05 12.95 0.075 1.821 0.109\n", - " 1 7.943E+01 1.93 44.92 37.06 13.22 0.085 1.797 0.141\n", - " 1 1.000E+02 2.00 46.35 38.14 13.48 0.094 1.771 0.180\n", - " 1 1.259E+02 2.07 47.84 39.28 13.73 0.101 1.742 0.226\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 1.00D+03 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 1.585E+02 2.14 49.39 40.48 13.98 0.106 1.711 0.279\n", - " 1 1.995E+02 2.22 51.05 41.74 14.24 0.110 1.677 0.342\n", - " 1 2.512E+02 2.30 52.84 43.09 14.50 0.113 1.641 0.413\n", - " 1 3.162E+02 2.38 54.79 44.56 14.77 0.116 1.603 0.495\n", - " +-----------------------------------------------------------------------+\n", - " | |\n", - " | DPMJET version 3.0-6 |\n", - " | |\n", - " | (Last change: 5 May 2012) |\n", - " | |\n", - " | Authors: Stefan Roesler (CERN) |\n", - " | Ralph Engel (FZ Karlsruhe) |\n", - " | Johannes Ranft (Siegen Univ.) |\n", - " | |\n", - " +-----------------------------------------------------------------------+\n", - " | Please send suggestions, bug reports, etc. to: Stefan.Roesler@cern.ch |\n", - " +-----------------------------------------------------------------------+\n", - "\n", - "\n", - "Warning! No evaporation performed since evaporation modules not available with this version.\n", - "read from /Users/hdembinski/Extern/impy/src/impy/iamdata/epos/epos.initl ...\n", - " ====================================================\n", - " | |\n", - " | S I B Y L L 2.3c |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | Eun-Joo AHN, Felix RIEHN |\n", - " | R. ENGEL, R.S. FLETCHER, T.K. GAISSER |\n", - " | P. LIPARI, T. STANEV |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | Eun-Joo AHN et al., Phys.Rev. D80 (2009) 094003 |\n", - " | F. RIEHN et al., Proc. 34th Int. Cosmic Ray Conf.|\n", - " | The Hague, The Netherlands, cont. 1313 (2015) |\n", - " | |\n", - " | last modifications: F. Riehn (09/05/2017) |\n", - " ====================================================\n", - "\n", - " DT_JSPARA: new value (default value)\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET version 1.12 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (FZ Karlsruhe)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " Manual, updates, and further information:\n", - " http://www-ik.fzk.de/~engel/phojet.html\n", - " ---------------------------------------------------\n", - " please send suggestions / bug reports etc. to:\n", - " ralph.engel@fzk.de\n", - " ===================================================\n", - " $Date: 2000/06/25 21:59:19 $\n", - " $Revision: 1.12.1.35 $\n", - " (code version with interface to PYTHIA 6.x)\n", - " (code version for usage in DPMJET 3.x)\n", - " ===================================================\n", - "\n", - "\n", - "\n", - " NCLPOT: quantities for inclusion of nuclear effects\n", - " -------------------------------------------\n", - "\n", - " projectile target\n", - "\n", - " Mass number / charge 1 / 1 1 / 1\n", - " Binding energy - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - " Fermi-potential - proton (GeV) 0.0000E+00 0.0000E+00\n", - " - neutron (GeV) 0.0000E+00 0.0000E+00\n", - "\n", - " Scale factor for Fermi-momentum 0.55\n", - "\n", - " Coulomb-energy 0.0000E+00 GeV 0.0000E+00 GeV \n", - "\n", - "\n", - " DT_PHOINI: PHOJET initialized for projectile A,Z = 1, 1\n", - " 1.5xp_F(max) = 0.000E+00 p(max) = 0.000E+00 0.000E+00 0.500E+05 0.500E+05\n", - " DT_PHOINI: PHOJET initialized for target A,Z = 1, 1\n", - " 1.5xp_F(max) = -0.000E+00 p(max) = 0.000E+00 0.000E+00-0.500E+05 0.500E+05\n", - " E_cm = 0.100E+06\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 GRV94 LO 5 6 0\n", - " PHO_FITPAR: looking for PDF 2212 GRV94 LO 5 6 0\n", - "\n", - " PHO_FITPAR: parameter set found in internal table\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.100 ALPOMP: 0.250 GP: 6.387 6.387 B0POM: 1.161 1.161\n", - " ALREG: 0.450 ALREGP: 0.900 GR: 10.263 10.263 B0REG: 1.171 1.171\n", - " GPPP : 0.156 B0PPP: 0.500 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 3.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.300\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 1.000\n", - " PARJ(21) sigma in pt distribution: 0.360 0.360\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 5.0 99965.4\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "1\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET 19.1.4 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (KIT)\n", - " Anatoli Fedynitch (ICRR)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " bug reports, support and updates on:\n", - " https://github.com/afedynitch/dpmjet\n", - " ===================================================\n", - " Date: 2022/12/30\n", - " Revision: 19.1.4\n", - " Code with interface to PYTHIA 6.4.27\n", - " ===================================================\n", - "\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF 3122 1 2 1 0\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF -3122 1 2 1 0\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - " ====================================================\n", - " | |\n", - " | QUARK GLUON STRING JET -II MODEL |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | S. OSTAPCHENKO |\n", - " | |\n", - " 1 3.981E+02 2.46 56.96 46.16 15.05 0.118 1.563 0.587\n", - " 1 5.012E+02 2.55 59.39 47.94 15.33 0.119 1.522 0.691\n", - " 1 6.310E+02 2.65 62.15 49.92 15.63 0.120 1.479 0.808\n", - " 1 7.943E+02 2.74 65.29 52.14 15.95 0.121 1.435 0.938\n", - " | e-mail: serguei@ik.fzk.de |\n", - " | |\n", - " | Version II-03 |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | S.Ostapchenko, Phys.Lett. B636 (2006) 40 |\n", - " | S. Ostapchenko, Phys.Rev. D 74 (2006) 014026 |\n", - " | |\n", - " | last modification: 26.04.2006 |\n", - " | |\n", - " | Any modification has to be approved by the author|\n", - " 1 1.000E+03 2.84 68.88 54.63 16.28 0.122 1.391 1.081\n", - " 1 1.259E+03 2.95 72.53 57.06 16.61 0.123 1.347 1.240\n", - " 1 1.585E+03 3.06 76.46 59.64 16.96 0.123 1.303 1.416\n", - " 1 1.995E+03 3.17 80.67 62.36 17.32 0.123 1.259 1.609\n", - " ====================================================\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.49D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 26.367 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 3.6597D+00 I\n", - " I 93 Single diffractive (AX) I 3.6597D+00 I\n", - " I 94 Double diffractive I 2.0911D+00 I\n", - " I 95 Low-pT scattering I 2.3473D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.9761D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 2.512E+03 3.29 85.17 65.23 17.70 0.124 1.216 1.822\n", - " 1 3.162E+03 3.41 89.95 68.24 18.10 0.124 1.175 2.055\n", - "\n", - " 1 3.981E+03 3.54 95.02 71.38 18.51 0.124 1.135 2.312\n", - " 1 5.012E+03 3.67 100.35 74.64 18.95 0.124 1.096 2.594\n", - " 1 6.310E+03 3.81 105.95 78.03 19.40 0.124 1.059 2.903\n", - " 1 7.943E+03 3.95 111.81 81.53 19.88 0.124 1.024 3.244\n", - " pT0 = 0.70 GeV gives sigma(parton-parton) = 8.74D+01 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 1.000E+04 4.09 117.90 85.14 20.38 0.125 0.990 3.618\n", - " 1 1.259E+04 4.25 124.23 88.85 20.90 0.125 0.959 4.030\n", - " 1 1.585E+04 4.40 130.77 92.65 21.44 0.125 0.928 4.481\n", - " pT0 = 0.70 GeV gives sigma(parton-parton) = 7.96D+01 mb: accepted\n", - " 1 1.995E+04 4.57 137.51 96.55 22.01 0.125 0.898 4.957\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 2.512E+04 4.74 144.44 100.54 22.60 0.125 0.865 5.394\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.34D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 3.162E+04 4.91 151.56 104.61 23.21 0.125 0.825 5.707\n", - " 1 3.981E+04 5.09 158.85 108.75 23.85 0.125 0.779 5.909\n", - " 1 5.012E+04 5.28 166.31 112.98 24.50 0.125 0.733 6.076\n", - " 1 6.310E+04 5.47 173.92 117.27 25.17 0.125 0.690 6.243\n", - " 1 7.943E+04 5.67 181.69 121.64 25.87 0.125 0.649 6.417\n", - " 1 1.000E+05 5.88 189.60 126.07 26.58 0.125 0.611 6.601\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - "\n", - " PHO_FITPAR: parameter set not found in internal table\n", - "\n", - " PHO_FITPAR: loading parameter set from file /Users/hdembinski/Extern/impy/src/impy/iamdata/dpm3191/dpmjpar.dat\n", - "\n", - " PHO_FITPAR: parameter set found\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.113 ALPOMP: 0.250 GP: 5.636 5.636 B0POM: 1.279 1.279\n", - " ALREG: 0.572 ALREGP: 0.967 GR: 10.697 10.697 B0REG: 0.370 0.370\n", - " GPPP : 0.150 B0PPP: 0.372 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 1.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.300\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 0.580\n", - " PARJ(21) sigma in pt distribution: 0.360 0.360\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 2.5 120000.0\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " 1 1.259E+05 6.10 197.66 130.57 27.31 0.125 0.576 6.799\n", - " 1 1.585E+05 6.32 205.84 135.12 28.06 0.125 0.543 7.013\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " 1 1.995E+05 6.55 214.16 139.74 28.82 0.125 0.512 7.247\n", - " ====================================================\n", - " | |\n", - " | QUARK GLUON STRING JET -II MODEL |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | S. OSTAPCHENKO |\n", - " | |\n", - " | e-mail: sergei@tf.phys.ntnu.no |\n", - " | |\n", - " | Version II-04 |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | S.Ostapchenko, PRD 83 (2011) 014018 |\n", - " | |\n", - " | last modification: 09.04.2013 |\n", - " | |\n", - " | Any modification has to be approved by the author|\n", - " ====================================================\n", - "\n", - " DATDIR/Users/hdembinski/Extern/impy/src/impy/iamdata/qgsjet/ \n", - " qgaini: cross sections readout from the file: qgsdat-II-04\n", - "read from /Users/hdembinski/Extern/impy/src/impy/iamdata/epos/epos.inirj.lhc ...\n", - "1\n", - "\n", - " ===================================================\n", - " \n", - " ---- PHOJET 19.3.4 ---- \n", - " \n", - " ===================================================\n", - " Authors: Ralph Engel (KIT)\n", - " Anatoli Fedynitch (ASIoP)\n", - " Johannes Ranft (Siegen Univ.)\n", - " Stefan Roesler (CERN)\n", - " ---------------------------------------------------\n", - " bug reports, support and updates on:\n", - " https://github.com/afedynitch/dpmjet\n", - " ===================================================\n", - " Date: 2022/12/30\n", - " Revision: 19.3.4\n", - " Code with interface to PYTHIA 6.4.27\n", - " ===================================================\n", - "\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF 3122 1 2 1 0\n", - "\n", - " PHO_SETPDF: overwrite old particle PDF -3122 1 2 1 0\n", - "\n", - "\n", - " =======================================================\n", - " ------- initialization of event generation --------\n", - " =======================================================\n", - "\n", - " PHO_SETMDL: current settings\n", - " ----------------------------\n", - "\n", - " 1:AMPL MOD 3 2:MIN-BIAS 1 3:PTS DISH 1\n", - " 4:PTS DISP 1 5:PTS ASSI 2 6:HADRONIZ 3\n", - " 7:MASS COR 2 8:PAR SHOW 3 9:GLU SPLI 0\n", - " 10:VIRT PHO 2 11:LARGE NC 0 12:LIPA POM 0\n", - " 13:QELAS VM 1 14:ENHA GRA 2 15:MULT SCA 4\n", - " 16:MULT DIF 4 17:MULT CDF 4 18:BALAN PT 0\n", - " 19:POMV FLA 1 20:SEA FLA 0 21:SPIN DEC 2\n", - " 22:DIF.MASS 1 23:DIFF RES 1 24:PTS HPOM 0\n", - " 25:POM CORR 0 26:OVERLAP 1 27:MUL R/AN 0\n", - " 28:SUR PROB 1 29:PRIMO KT 1 30:DIFF. CS 0\n", - " 1 2.512E+05 6.78 222.60 144.41 29.60 0.125 0.484 7.501\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.52D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 42.813 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 4.0180D+00 I\n", - " I 93 Single diffractive (AX) I 4.0180D+00 I\n", - " I 94 Double diffractive I 2.6753D+00 I\n", - " I 95 Low-pT scattering I 2.3784D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.9906D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 0.79 GeV gives sigma(parton-parton) = 9.49D+01 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 3.162E+05 7.03 231.15 149.14 30.39 0.125 0.457 7.763\n", - "read from /Users/hdembinski/Extern/impy/src/impy/iamdata/epos/epos.inics.lhc ...\n", - " Compute Cross-section (can take a while...)\n", - " 1 3.981E+05 7.28 239.82 153.93 31.21 0.125 0.432 7.999\n", - " pT0 = 0.79 GeV gives sigma(parton-parton) = 8.83D+01 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " ====================================================\n", - " | |\n", - " | S I B Y L L 2.3.2 |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | Eun-Joo AHN, Felix RIEHN |\n", - " | R. ENGEL, R.S. FLETCHER, T.K. GAISSER |\n", - " | P. LIPARI, T. STANEV |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | Eun-Joo AHN et al., Phys.Rev. D80 (2009) 094003 |\n", - " | F. RIEHN et al., Proc. 34th Int. Cosmic Ray Conf.|\n", - " | The Hague, The Netherlands, cont. 1313 (2015) |\n", - " | |\n", - " | last modifications: F. Riehn (05/24/2016) |\n", - " | --> rejection of failed low en. charm int. <-- |\n", - " ====================================================\n", - "\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.53D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.39D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 69.519 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 4.3456D+00 I\n", - " I 93 Single diffractive (AX) I 4.3456D+00 I\n", - " I 94 Double diffractive I 3.2423D+00 I\n", - " I 95 Low-pT scattering I 2.4599D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 3.1063D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 1 5.012E+05 7.54 248.60 158.77 32.03 0.125 0.408 8.168\n", - " 1 6.310E+05 7.81 257.49 163.65 32.87 0.125 0.385 8.278\n", - " 1 7.943E+05 8.08 266.48 168.59 33.72 0.125 0.364 8.364\n", - " 1 1.000E+06 8.37 275.56 173.57 34.59 0.125 0.343 8.442\n", - " 1 1.259E+06 8.67 284.75 178.60 35.47 0.125 0.324 8.517\n", - " 1 1.585E+06 8.97 294.03 183.68 36.36 0.125 0.307 8.590\n", - " 1 1.995E+06 9.28 303.40 188.80 37.27 0.125 0.290 8.663\n", - " ====================================================\n", - " | |\n", - " | QUARK GLUON STRING JET MODEL |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | N.N. KALMYKOV AND S.S. OSTAPCHENKO |\n", - " | |\n", - " | e-mail: serg@eas.npi.msu.su |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | N.N. Kalmykov & S.S. Ostapchenko, A.I. Pavlov |\n", - " | Nucl. Phys. B (Proc. Suppl.) 52B (1997) 17 |\n", - " | |\n", - " | last modification: Jan. 30, 2013 by T. Pierog |\n", - " | (version qgsjet01d.f) |\n", - " ====================================================\n", - "\n", - " 1 2.512E+06 9.61 312.87 193.97 38.18 0.125 0.274 8.735\n", - " 1 3.162E+06 9.94 322.42 199.18 39.11 0.125 0.259 8.808\n", - " 1 3.981E+06 10.29 332.06 204.43 40.05 0.125 0.246 8.882\n", - " 1 5.012E+06 10.64 341.79 209.72 41.00 0.125 0.233 8.957\n", - "\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - " PHO_FITPAR: looking for PDF 2212 CT14-LO 2 1 0\n", - "\n", - " PHO_FITPAR: parameter set not found in internal table\n", - "\n", - " PHO_FITPAR: loading parameter set from file /Users/hdembinski/Extern/impy/src/impy/iamdata/dpm3191/dpmjpar.dat\n", - "\n", - " PHO_FITPAR: parameter set found\n", - "\n", - " PHO_FITPAR: parameter set\n", - " -------------------------\n", - " ALPOM: 1.113 ALPOMP: 0.250 GP: 5.636 5.636 B0POM: 1.279 1.279\n", - " ALREG: 0.572 ALREGP: 0.967 GR: 10.697 10.697 B0REG: 0.370 0.370\n", - " GPPP : 0.150 B0PPP: 0.372 GPPR : 0.612 B0PPR: 0.300\n", - " VDMFAC: 1.00000 0.00000 1.00000 0.00000\n", - " VDMQ2F: 1.00000 0.00000 1.00000 0.00000\n", - " B0HAR: 1.500\n", - " AKFAC: 2.000\n", - " PHISUP: 0.600 0.600\n", - " RMASS: 1.100 1.100 3.000\n", - "\n", - " PHO_FRAINI: fragmentation initialization ISWMDL(6) 3\n", - " --------------------------------------------------\n", - " parameter description default / current\n", - " PARJ( 2) strangeness suppression : 0.300 0.220\n", - " MSTJ(12) popcorn : 2 2\n", - " PARJ(19) popcorn : 1.000 1.000\n", - " PARJ(41) Lund a : 0.300 0.300\n", - " PARJ(42) Lund b : 0.580 1.000\n", - " PARJ(21) sigma in pt distribution: 0.360 0.420\n", - "\n", - "\n", - " PHO_MCINI: selected energy range (SQRT(S)) 2.5 120000.0\n", - " particle 1 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - " particle 2 (name,mass,virtuality): p+ 0.938 0.0000E+00\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 4\n", - " particle 1 / particle 2: 990 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.230 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " 1 6.310E+06 11.00 351.59 215.05 41.95 0.125 0.220 9.032\n", - " 1 7.943E+06 11.38 361.49 220.42 42.92 0.125 0.209 9.101\n", - " 1 1.000E+07 11.77 371.46 225.83 43.90 0.125 0.198 9.158\n", - "\n", - " Table: J, sqs, PT_cut, SIG_tot, SIG_inel, B_el, rho, , \n", - " ------------------------------------------------------------------------\n", - " 2 1.000E+01 1.45 23.12 19.94 10.12 -0.067 1.845 0.009\n", - " 2 1.259E+01 1.49 23.22 20.21 10.27 -0.031 1.827 0.016\n", - " 2 1.585E+01 1.54 23.49 20.57 10.42 -0.001 1.807 0.026\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hdembinski/Extern/impy/src/impy/models/urqmd.py:203: RuntimeWarning: 13 unknown to UrQMD\n", - " warnings.warn(f\"{pdgid} unknown to UrQMD\", RuntimeWarning)\n", - "/Users/hdembinski/Extern/impy/src/impy/models/urqmd.py:203: RuntimeWarning: 130 unknown to UrQMD\n", - " warnings.warn(f\"{pdgid} unknown to UrQMD\", RuntimeWarning)\n", - "/Users/hdembinski/Extern/impy/src/impy/models/urqmd.py:203: RuntimeWarning: 310 unknown to UrQMD\n", - " warnings.warn(f\"{pdgid} unknown to UrQMD\", RuntimeWarning)\n", - "/Users/hdembinski/Extern/impy/src/impy/models/urqmd.py:203: RuntimeWarning: -13 unknown to UrQMD\n", - " warnings.warn(f\"{pdgid} unknown to UrQMD\", RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2 1.995E+01 1.59 23.87 21.00 10.56 0.024 1.783 0.040\n", - " 2 2.512E+01 1.64 24.33 21.49 10.70 0.045 1.756 0.058\n", - " ====================================================\n", - " | |\n", - " | S I B Y L L 2.3d |\n", - " | |\n", - " | HADRONIC INTERACTION MONTE CARLO |\n", - " | BY |\n", - " | Eun-Joo AHN, Felix RIEHN |\n", - " | R. ENGEL, A. FEDYNITCH, R.S. FLETCHER, |\n", - " | T.K. GAISSER, P. LIPARI, T. STANEV |\n", - " | |\n", - " | Publication to be cited when using this program: |\n", - " | Eun-Joo AHN et al., Phys.Rev. D80 (2009) 094003 |\n", - " | F. RIEHN et al., hep-ph: 1912.03300 |\n", - " pT0 = 0.89 GeV gives sigma(parton-parton) = 1.08D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " | last modifications: F. Riehn (05/20/2020) |\n", - " ====================================================\n", - "\n", - " 2 3.162E+01 1.69 24.89 21.95 10.84 0.061 1.727 0.081\n", - " 2 3.981E+01 1.75 25.53 22.48 10.97 0.074 1.695 0.110\n", - " 2 5.012E+01 1.81 26.25 23.06 11.11 0.085 1.659 0.144\n", - " 2 6.310E+01 1.87 27.04 23.69 11.24 0.093 1.621 0.185\n", - " 2 7.943E+01 1.93 27.89 24.36 11.37 0.100 1.580 0.233\n", - " 2 1.000E+02 2.00 28.80 25.08 11.50 0.105 1.536 0.288\n", - " 2 1.259E+02 2.07 29.72 25.72 11.44 0.109 1.490 0.351\n", - " pT0 = 0.89 GeV gives sigma(parton-parton) = 9.55D+01 mb: accepted\n", - " 2 1.585E+02 2.14 30.77 26.45 11.39 0.113 1.442 0.423\n", - " 2 1.995E+02 2.22 31.96 27.30 11.38 0.115 1.392 0.503\n", - " 2 2.512E+02 2.30 33.35 28.30 11.40 0.117 1.340 0.592\n", - " 2 3.162E+02 2.38 34.99 29.47 11.45 0.119 1.288 0.690\n", - " 2 3.981E+02 2.46 36.93 30.84 11.55 0.120 1.236 0.798\n", - " 2 5.012E+02 2.55 39.22 32.47 11.69 0.121 1.184 0.916\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - " 2 6.310E+02 2.65 41.93 34.36 11.88 0.122 1.132 1.044\n", - " 2 7.943E+02 2.74 45.10 36.57 12.12 0.123 1.082 1.183\n", - " 2 1.000E+03 2.84 48.81 39.13 12.41 0.123 1.033 1.333\n", - " 2 1.259E+03 2.95 52.13 41.43 12.78 0.123 0.987 1.497\n", - " 2 1.585E+03 3.06 55.69 43.86 13.16 0.124 0.942 1.673\n", - " 2 1.995E+03 3.17 59.50 46.43 13.56 0.124 0.899 1.865\n", - " 2 2.512E+03 3.29 63.55 49.12 13.97 0.124 0.859 2.072\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - " 2 3.162E+03 3.41 67.83 51.93 14.39 0.124 0.821 2.298\n", - " 2 3.981E+03 3.54 72.35 54.85 14.83 0.124 0.785 2.543\n", - "\n", - " *------------------------------------------------------------------------------------* \n", - " | | \n", - " | *------------------------------------------------------------------------------* | \n", - " | | | | \n", - " | | | | \n", - " | | PPP Y Y TTTTT H H III A Welcome to the Lund Monte Carlo! | | \n", - " | | P P Y Y T H H I A A This is PYTHIA version 8.308 | | \n", - " | | PPP Y T HHHHH I AAAAA Last date of change: 16 Nov 2022 | | \n", - " | | P Y T H H I A A | | \n", - " | | P Y T H H III A A Now is 30 Dec 2022 at 17:13:40 | | \n", - " | | | | \n", - " | | Program documentation and an archive of historic versions is found on: | | \n", - " | | | | \n", - " | | https://pythia.org/ | | \n", - " | | | | \n", - " | | PYTHIA is authored by a collaboration consisting of: | | \n", - " | | | | \n", - " | | Christian Bierlich, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip | | \n", - " | | Ilten, Leif Lonnblad, Stephen Mrenna, Stefan Prestel, Christian Preuss, | | \n", - " | | Torbjorn Sjostrand, Peter Skands, Marius Utheim and Rob Verheyen. | | \n", - " | | | | \n", - " | | The complete list of authors, including contact information and | | \n", - " | | affiliations, can be found on https://pythia.org/. | | \n", - " | | Problems or bugs should be reported on email at authors@pythia.org. | | \n", - " | | | | \n", - " | | The main program reference is C. Bierlich et al, | | \n", - " | | 'A comprehensive guide to the physics and usage of Pythia 8.3', | | \n", - " | | SciPost Phys. Codebases 8-r8.3 (2022) [arXiv:2203.11601 [hep-ph]] | | \n", - " | | | | \n", - " | | PYTHIA is released under the GNU General Public Licence version 2 or later.| | \n", - " | | Please respect the MCnet Guidelines for Event Generator Authors and Users. | | \n", - " | | | | \n", - " | | Disclaimer: this program comes without any guarantees. | | \n", - " | | Beware of errors and use common sense when interpreting results. | | \n", - " | | | | \n", - " | | Copyright (C) 2022 Torbjorn Sjostrand | | \n", - " | | | | \n", - " | | | | \n", - " | *------------------------------------------------------------------------------* | \n", - " | | \n", - " *------------------------------------------------------------------------------------* \n", - "\n", - " 2 5.012E+03 3.67 77.09 57.87 15.28 0.124 0.752 2.809\n", - " 2 6.310E+03 3.81 82.06 61.01 15.75 0.124 0.720 3.099\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " 2 7.943E+03 3.95 87.24 64.23 16.24 0.125 0.691 3.416\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.000E+04 4.09 92.61 67.55 16.74 0.125 0.664 3.761\n", - " 2 1.259E+04 4.25 98.18 70.96 17.26 0.125 0.638 4.138\n", - " 2 1.585E+04 4.40 103.94 74.44 17.79 0.125 0.614 4.543\n", - " 2 1.995E+04 4.57 109.87 78.01 18.34 0.125 0.591 4.953\n", - " 2 2.512E+04 4.74 115.96 81.65 18.91 0.125 0.565 5.303\n", - " 2 3.162E+04 4.91 122.21 85.36 19.50 0.125 0.535 5.552\n", - " 2 3.981E+04 5.09 128.62 89.15 20.10 0.125 0.504 5.737\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.61D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 5.012E+04 5.28 135.18 92.99 20.72 0.125 0.474 5.904\n", - " 2 6.310E+04 5.47 141.88 96.91 21.35 0.125 0.446 6.067\n", - " 2 7.943E+04 5.67 148.71 100.88 22.00 0.125 0.419 6.233\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.41D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 112.884 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 4.6546D+00 I\n", - " I 93 Single diffractive (AX) I 4.6546D+00 I\n", - " I 94 Double diffractive I 3.8087D+00 I\n", - " I 95 Low-pT scattering I 2.5802D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.9524D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.000E+05 5.88 155.68 104.91 22.66 0.125 0.394 6.404\n", - " 2 1.259E+05 6.10 162.78 109.01 23.34 0.125 0.371 6.584\n", - " pT0 = 1.00 GeV gives sigma(parton-parton) = 1.19D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.585E+05 6.32 170.01 113.15 24.03 0.125 0.349 6.776\n", - " 2 1.995E+05 6.55 177.35 117.36 24.73 0.125 0.329 6.981\n", - " 2 2.512E+05 6.78 184.81 121.61 25.45 0.125 0.311 7.200\n", - " pT0 = 1.00 GeV gives sigma(parton-parton) = 1.04D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " 2 3.162E+05 7.03 192.39 125.92 26.18 0.125 0.293 7.426\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " 2 3.981E+05 7.28 200.08 130.28 26.93 0.125 0.277 7.632\n", - " 2 5.012E+05 7.54 207.88 134.69 27.68 0.125 0.261 7.792\n", - " 2 6.310E+05 7.81 215.78 139.15 28.45 0.125 0.246 7.909\n", - " 2 7.943E+05 8.08 223.78 143.66 29.23 0.125 0.232 8.004\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.000E+06 8.37 231.89 148.21 30.02 0.125 0.219 8.090\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.02D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.259E+06 8.67 240.09 152.81 30.83 0.125 0.207 8.172\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.57D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 183.298 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 4.9494D+00 I\n", - " I 93 Single diffractive (AX) I 4.9494D+00 I\n", - " I 94 Double diffractive I 4.3860D+00 I\n", - " I 95 Low-pT scattering I 2.7331D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 3.4146D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " 2 1.585E+06 8.97 248.39 157.46 31.64 0.125 0.196 8.249\n", - " 2 1.995E+06 9.28 256.79 162.15 32.47 0.125 0.185 8.324\n", - " 2 2.512E+06 9.61 265.28 166.88 33.30 0.125 0.175 8.398\n", - " 2 3.162E+06 9.94 273.86 171.66 34.15 0.125 0.165 8.470\n", - " 2 3.981E+06 10.29 282.53 176.48 35.01 0.125 0.156 8.542\n", - " 2 5.012E+06 10.64 291.29 181.34 35.88 0.125 0.148 8.615\n", - " 2 6.310E+06 11.00 300.14 186.25 36.76 0.125 0.140 8.687\n", - " 2 7.943E+06 11.38 309.08 191.19 37.64 0.125 0.133 8.756\n", - " 2 1.000E+07 11.77 318.10 196.18 38.54 0.125 0.126 8.819\n", - "\n", - " Table: J, sqs, PT_cut, SIG_tot, SIG_inel, B_el, rho, , \n", - " ------------------------------------------------------------------------\n", - " 3 1.000E+01 1.45 18.30 16.13 8.84 -0.025 1.845 0.009\n", - " 3 1.259E+01 1.49 18.83 16.69 9.14 0.009 1.827 0.016\n", - " 3 1.585E+01 1.54 19.41 17.26 9.40 0.036 1.807 0.026\n", - " 3 1.995E+01 1.59 20.02 17.83 9.64 0.056 1.783 0.040\n", - " 3 2.512E+01 1.64 20.63 18.41 9.85 0.072 1.756 0.058\n", - " 3 3.162E+01 1.69 21.32 18.99 10.05 0.084 1.727 0.081\n", - " 3 3.981E+01 1.75 22.04 19.60 10.23 0.093 1.695 0.110\n", - " 3 5.012E+01 1.81 22.81 20.23 10.40 0.101 1.659 0.144\n", - " 3 6.310E+01 1.87 23.61 20.89 10.56 0.106 1.621 0.185\n", - " 3 7.943E+01 1.93 24.46 21.57 10.72 0.111 1.580 0.233\n", - " 3 1.000E+02 2.00 25.34 22.28 10.87 0.114 1.536 0.288\n", - " 3 1.259E+02 2.07 26.25 23.01 11.06 0.116 1.490 0.351\n", - " 3 1.585E+02 2.14 27.21 23.75 11.21 0.118 1.442 0.423\n", - " 3 1.995E+02 2.22 28.23 24.53 11.32 0.120 1.392 0.503\n", - " 3 2.512E+02 2.30 29.37 25.37 11.42 0.121 1.340 0.592\n", - " 3 3.162E+02 2.38 30.68 26.34 11.51 0.122 1.288 0.690\n", - " 3 3.981E+02 2.46 32.25 27.48 11.61 0.123 1.236 0.798\n", - " 3 5.012E+02 2.55 34.17 28.86 11.73 0.123 1.184 0.916\n", - " 3 6.310E+02 2.65 36.58 30.57 11.90 0.123 1.132 1.044\n", - " 3 7.943E+02 2.74 39.60 32.72 12.12 0.124 1.082 1.183\n", - " 3 1.000E+03 2.84 43.42 35.40 12.41 0.124 1.033 1.333\n", - " 3 1.259E+03 2.95 46.38 37.52 12.78 0.124 0.987 1.497\n", - " 3 1.585E+03 3.06 49.56 39.77 13.16 0.124 0.942 1.673\n", - " 3 1.995E+03 3.17 52.95 42.14 13.56 0.124 0.899 1.865\n", - " 3 2.512E+03 3.29 56.56 44.63 13.97 0.124 0.859 2.072\n", - " 3 3.162E+03 3.41 60.38 47.23 14.39 0.125 0.821 2.298\n", - " 3 3.981E+03 3.54 64.41 49.94 14.83 0.125 0.785 2.543\n", - " 3 5.012E+03 3.67 68.64 52.75 15.28 0.125 0.752 2.809\n", - " 3 6.310E+03 3.81 73.06 55.67 15.75 0.125 0.720 3.099\n", - " 3 7.943E+03 3.95 77.67 58.67 16.24 0.125 0.691 3.416\n", - " 3 1.000E+04 4.09 82.46 61.77 16.74 0.125 0.664 3.761\n", - " 3 1.259E+04 4.25 87.43 64.95 17.26 0.125 0.638 4.138\n", - " 3 1.585E+04 4.40 92.55 68.21 17.79 0.125 0.614 4.543\n", - " 3 1.995E+04 4.57 97.83 71.55 18.34 0.125 0.591 4.953\n", - " 3 2.512E+04 4.74 103.26 74.97 18.91 0.125 0.565 5.303\n", - " 3 3.162E+04 4.91 108.83 78.45 19.50 0.125 0.535 5.552\n", - " 3 3.981E+04 5.09 114.54 82.01 20.10 0.125 0.504 5.737\n", - " 3 5.012E+04 5.28 120.37 85.63 20.72 0.125 0.474 5.904\n", - " 3 6.310E+04 5.47 126.34 89.31 21.35 0.125 0.446 6.067\n", - " 3 7.943E+04 5.67 132.43 93.05 22.00 0.125 0.419 6.233\n", - " 3 1.000E+05 5.88 138.64 96.85 22.66 0.125 0.394 6.404\n", - " 3 1.259E+05 6.10 144.96 100.72 23.34 0.125 0.371 6.584\n", - " 3 1.585E+05 6.32 151.39 104.63 24.03 0.125 0.349 6.776\n", - " 3 1.995E+05 6.55 157.93 108.61 24.73 0.125 0.329 6.981\n", - " 3 2.512E+05 6.78 164.58 112.63 25.45 0.125 0.311 7.200\n", - " 3 3.162E+05 7.03 171.33 116.71 26.18 0.125 0.293 7.426\n", - " 3 3.981E+05 7.28 178.17 120.84 26.93 0.125 0.277 7.632\n", - " 3 5.012E+05 7.54 185.12 125.01 27.68 0.125 0.261 7.792\n", - " 3 6.310E+05 7.81 192.15 129.24 28.45 0.125 0.246 7.909\n", - " 3 7.943E+05 8.08 199.28 133.52 29.23 0.125 0.232 8.004\n", - " 3 1.000E+06 8.37 206.50 137.84 30.02 0.125 0.219 8.090\n", - " 3 1.259E+06 8.67 213.80 142.20 30.83 0.125 0.207 8.172\n", - " 3 1.585E+06 8.97 221.19 146.62 31.64 0.125 0.196 8.249\n", - " 3 1.995E+06 9.28 228.67 151.07 32.47 0.125 0.185 8.324\n", - " 3 2.512E+06 9.61 236.23 155.58 33.30 0.125 0.175 8.398\n", - " 3 3.162E+06 9.94 243.87 160.12 34.15 0.125 0.165 8.470\n", - " 3 3.981E+06 10.29 251.60 164.71 35.01 0.125 0.156 8.542\n", - " 3 5.012E+06 10.64 259.40 169.34 35.88 0.125 0.148 8.615\n", - " 3 6.310E+06 11.00 267.28 174.01 36.76 0.125 0.140 8.687\n", - " 3 7.943E+06 11.38 275.24 178.72 37.64 0.125 0.133 8.756\n", - " 3 1.000E+07 11.77 283.28 183.48 38.54 0.125 0.126 8.819\n", - " pT0 = 1.13 GeV gives sigma(parton-parton) = 1.33D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.13 GeV gives sigma(parton-parton) = 1.01D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.28D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.38D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 297.635 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 5.2323D+00 I\n", - " I 93 Single diffractive (AX) I 5.2323D+00 I\n", - " I 94 Double diffractive I 4.9793D+00 I\n", - " I 95 Low-pT scattering I 2.9154D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 3.9467D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.28 GeV gives sigma(parton-parton) = 1.74D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.28 GeV gives sigma(parton-parton) = 1.05D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.69D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.41D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 483.293 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 5.5044D+00 I\n", - " I 93 Single diffractive (AX) I 5.5044D+00 I\n", - " I 94 Double diffractive I 5.5901D+00 I\n", - " I 95 Low-pT scattering I 3.1262D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 4.5341D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.44 GeV gives sigma(parton-parton) = 1.94D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.44 GeV gives sigma(parton-parton) = 1.09D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.83D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.53D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 784.760 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 5.7665D+00 I\n", - " I 93 Single diffractive (AX) I 5.7665D+00 I\n", - " I 94 Double diffractive I 6.2186D+00 I\n", - " I 95 Low-pT scattering I 3.3658D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 5.2924D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.63 GeV gives sigma(parton-parton) = 2.35D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.63 GeV gives sigma(parton-parton) = 1.15D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.86D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.41D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 1274.275 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 6.0193D+00 I\n", - " I 93 Single diffractive (AX) I 6.0193D+00 I\n", - " I 94 Double diffractive I 6.8643D+00 I\n", - " I 95 Low-pT scattering I 3.6351D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 6.1267D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.83 GeV gives sigma(parton-parton) = 2.75D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 1.83 GeV gives sigma(parton-parton) = 1.30D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.55D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.26D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 2069.138 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 6.2634D+00 I\n", - " I 93 Single diffractive (AX) I 6.2634D+00 I\n", - " I 94 Double diffractive I 7.5265D+00 I\n", - " I 95 Low-pT scattering I 3.9357D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 7.1147D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.07 GeV gives sigma(parton-parton) = 3.22D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.07 GeV gives sigma(parton-parton) = 1.42D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.50D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.38D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 3359.818 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 6.4993D+00 I\n", - " I 93 Single diffractive (AX) I 6.4993D+00 I\n", - " I 94 Double diffractive I 8.2046D+00 I\n", - " I 95 Low-pT scattering I 4.2697D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 8.2349D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 2.34 GeV gives sigma(parton-parton) = 3.82D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.34 GeV gives sigma(parton-parton) = 1.60D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.83D+02 mb: accepted\n", - "\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.42D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 5455.595 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 6.7276D+00 I\n", - " I 93 Single diffractive (AX) I 6.7276D+00 I\n", - " I 94 Double diffractive I 8.8978D+00 I\n", - " I 95 Low-pT scattering I 4.6395D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 9.5330D+03 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.64 GeV gives sigma(parton-parton) = 4.20D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.64 GeV gives sigma(parton-parton) = 1.90D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.43D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.40D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 8858.668 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 6.9488D+00 I\n", - " I 93 Single diffractive (AX) I 6.9488D+00 I\n", - " I 94 Double diffractive I 9.6057D+00 I\n", - " I 95 Low-pT scattering I 5.0478D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 1.1009D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.98 GeV gives sigma(parton-parton) = 5.14D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 2.98 GeV gives sigma(parton-parton) = 2.11D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " SIG_AIR_INI: initializing target: (i,A) 1 0 air..\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.83D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.35D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 14384.499 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.1631D+00 I\n", - " I 93 Single diffractive (AX) I 7.1631D+00 I\n", - " I 94 Double diffractive I 1.0328D+01 I\n", - " I 95 Low-pT scattering I 5.4977D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 1.2720D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " SIG_AIR_INI: initializing target: (i,A) 2 14 nit..\n", - " pT0 = 3.36 GeV gives sigma(parton-parton) = 5.95D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 3.36 GeV gives sigma(parton-parton) = 2.52D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.58D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.40D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 23357.215 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.3711D+00 I\n", - " I 93 Single diffractive (AX) I 7.3711D+00 I\n", - " I 94 Double diffractive I 1.1063D+01 I\n", - " I 95 Low-pT scattering I 5.9923D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 1.4728D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " SIG_AIR_INI: initializing target: (i,A) 3 16 oxy..\n", - " pT0 = 3.80 GeV gives sigma(parton-parton) = 6.58D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 3.80 GeV gives sigma(parton-parton) = 2.91D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.40D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.37D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 37926.902 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.5730D+00 I\n", - " I 93 Single diffractive (AX) I 7.5730D+00 I\n", - " I 94 Double diffractive I 1.1811D+01 I\n", - " I 95 Low-pT scattering I 6.5355D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 1.6998D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 3\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 4.28 GeV gives sigma(parton-parton) = 7.60D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 4.28 GeV gives sigma(parton-parton) = 3.25D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.31D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.50D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 61584.821 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.7693D+00 I\n", - " I 93 Single diffractive (AX) I 7.7693D+00 I\n", - " I 94 Double diffractive I 1.2572D+01 I\n", - " I 95 Low-pT scattering I 7.1310D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 1.9589D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 4.84 GeV gives sigma(parton-parton) = 8.57D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 4.84 GeV gives sigma(parton-parton) = 3.83D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.54D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.29D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.48D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.51D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "1****************** PYINIT: initialization of PYTHIA routines *****************\n", - "\n", - " ==============================================================================\n", - " I I\n", - " I PYTHIA will be initialized for a p on p collider I\n", - " I at 100000.000 GeV center-of-mass energy I\n", - " I I\n", - " ==============================================================================\n", - "\n", - " ******** PYMAXI: summary of differential cross-section maximum search ********\n", - "\n", - " ==========================================================\n", - " I I I\n", - " I ISUB Subprocess name I Maximum value I\n", - " I I I\n", - " ==========================================================\n", - " I I I\n", - " I 92 Single diffractive (XB) I 7.9601D+00 I\n", - " I 93 Single diffractive (AX) I 7.9601D+00 I\n", - " I 94 Double diffractive I 1.3345D+01 I\n", - " I 95 Low-pT scattering I 7.7832D+01 I\n", - " I 96 Semihard QCD 2 -> 2 I 2.2825D+04 I\n", - " I I I\n", - " ==========================================================\n", - "\n", - " ****** PYMULT: initialization of multiple interactions for MSTP(82) = 4 ******\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 9.69D+02 mb: accepted\n", - "\n", - " ****** PYMIGN: initialization of multiple interactions for MSTP(82) = 4 ******\n", - " pT0 = 5.46 GeV gives sigma(parton-parton) = 4.47D+02 mb: accepted\n", - "\n", - " ********************** PYINIT: initialization completed **********************\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.200\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 2\n", - " particle 1 / particle 2: 2212 990\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CKMT-POM IGRP/ISET/IEXT 4 0 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.230\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.281\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " PDF side 2: GRV94 LO IGRP/ISET/IEXT 5 6 0\n", - " LAMBDA1,2 (4 active flavours): 0.200 0.200\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.281\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 5.00E+00 3.98E+01 7.77E+00 3.20E+01 0.00E+00 1.35E+00 1.35E+00 1.80E-01\n", - " 8.42E+00 3.86E+01 7.10E+00 3.15E+01 0.00E+00 1.64E+00 1.64E+00 3.28E-01\n", - " 1.42E+01 3.88E+01 6.84E+00 3.20E+01 0.00E+00 2.00E+00 2.00E+00 5.97E-01\n", - " 2.39E+01 3.99E+01 6.82E+00 3.30E+01 0.00E+00 2.42E+00 2.42E+00 9.32E-01\n", - " 4.02E+01 4.15E+01 6.96E+00 3.46E+01 0.00E+00 2.89E+00 2.89E+00 1.32E+00\n", - " 6.78E+01 4.37E+01 7.23E+00 3.65E+01 0.00E+00 3.39E+00 3.39E+00 1.76E+00\n", - " 1.14E+02 4.65E+01 7.67E+00 3.89E+01 0.00E+00 3.91E+00 3.90E+00 2.22E+00\n", - " 1.92E+02 5.02E+01 8.41E+00 4.18E+01 0.00E+00 4.41E+00 4.40E+00 2.69E+00\n", - " 3.24E+02 5.51E+01 9.61E+00 4.55E+01 0.00E+00 4.84E+00 4.82E+00 3.08E+00\n", - " 5.45E+02 6.15E+01 1.15E+01 5.00E+01 0.00E+00 5.13E+00 5.11E+00 3.36E+00\n", - " 9.18E+02 6.95E+01 1.42E+01 5.53E+01 0.00E+00 5.28E+00 5.26E+00 3.53E+00\n", - " 1.55E+03 7.87E+01 1.76E+01 6.11E+01 0.00E+00 5.32E+00 5.30E+00 3.65E+00\n", - " 2.60E+03 8.84E+01 2.15E+01 6.69E+01 0.00E+00 5.32E+00 5.30E+00 3.75E+00\n", - " 4.39E+03 9.82E+01 2.56E+01 7.26E+01 0.00E+00 5.32E+00 5.30E+00 3.84E+00\n", - " 7.39E+03 1.08E+02 2.97E+01 7.81E+01 0.00E+00 5.37E+00 5.35E+00 3.93E+00\n", - " 1.24E+04 1.17E+02 3.35E+01 8.33E+01 0.00E+00 5.48E+00 5.47E+00 4.04E+00\n", - " 2.10E+04 1.26E+02 3.72E+01 8.83E+01 0.00E+00 5.67E+00 5.66E+00 4.16E+00\n", - " 3.53E+04 1.34E+02 4.07E+01 9.33E+01 0.00E+00 5.94E+00 5.93E+00 4.32E+00\n", - " 5.94E+04 1.42E+02 4.40E+01 9.82E+01 0.00E+00 6.29E+00 6.28E+00 4.49E+00\n", - " 1.00E+05 1.50E+02 4.72E+01 1.03E+02 0.00E+00 6.71E+00 6.70E+00 4.69E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 5.0000E+00 1.0010E+05 1.0010E+05\n", - " max. cross section (mb) 1.0306E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.281\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " PHO_HARINI: hard scattering parameters for IP: 1\n", - " particle 1 / particle 2: 2212 2212\n", - " min. PT : 2.5 (energy-dependent) \n", - " PDF side 1: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " PDF side 2: CT14-LO IGRP/ISET/IEXT 2 1 0\n", - " LAMBDA1,2 (4 active flavours): 0.281 0.281\n", - " max. number of active flavours NF : 4\n", - " NQQAL/AQQAL/NQQPD/AQQPD: 1 1.000 1 1.000\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 5.00E+00 3.98E+01 7.77E+00 3.20E+01 0.00E+00 1.35E+00 1.35E+00 1.80E-01\n", - " 8.42E+00 3.86E+01 7.10E+00 3.15E+01 0.00E+00 1.64E+00 1.64E+00 3.28E-01\n", - " 1.42E+01 3.88E+01 6.84E+00 3.20E+01 0.00E+00 2.00E+00 2.00E+00 5.97E-01\n", - " 2.39E+01 3.99E+01 6.82E+00 3.30E+01 0.00E+00 2.42E+00 2.42E+00 9.32E-01\n", - " 4.02E+01 4.15E+01 6.96E+00 3.46E+01 0.00E+00 2.89E+00 2.89E+00 1.32E+00\n", - " 6.77E+01 4.37E+01 7.23E+00 3.65E+01 0.00E+00 3.39E+00 3.39E+00 1.76E+00\n", - " 1.14E+02 4.65E+01 7.67E+00 3.89E+01 0.00E+00 3.91E+00 3.90E+00 2.22E+00\n", - " 1.92E+02 5.02E+01 8.41E+00 4.18E+01 0.00E+00 4.41E+00 4.40E+00 2.69E+00\n", - " 3.24E+02 5.51E+01 9.61E+00 4.55E+01 0.00E+00 4.84E+00 4.82E+00 3.08E+00\n", - " 5.45E+02 6.15E+01 1.15E+01 5.00E+01 0.00E+00 5.13E+00 5.11E+00 3.36E+00\n", - " 9.18E+02 6.95E+01 1.42E+01 5.53E+01 0.00E+00 5.28E+00 5.26E+00 3.53E+00\n", - " 1.55E+03 7.87E+01 1.76E+01 6.11E+01 0.00E+00 5.32E+00 5.30E+00 3.65E+00\n", - " 2.60E+03 8.84E+01 2.15E+01 6.69E+01 0.00E+00 5.32E+00 5.30E+00 3.75E+00\n", - " 4.39E+03 9.82E+01 2.56E+01 7.26E+01 0.00E+00 5.32E+00 5.30E+00 3.84E+00\n", - " 7.39E+03 1.08E+02 2.97E+01 7.81E+01 0.00E+00 5.37E+00 5.35E+00 3.93E+00\n", - " 1.24E+04 1.17E+02 3.35E+01 8.33E+01 0.00E+00 5.48E+00 5.47E+00 4.04E+00\n", - " 2.09E+04 1.26E+02 3.72E+01 8.83E+01 0.00E+00 5.67E+00 5.66E+00 4.16E+00\n", - " 3.53E+04 1.34E+02 4.07E+01 9.33E+01 0.00E+00 5.94E+00 5.93E+00 4.32E+00\n", - " 5.94E+04 1.42E+02 4.40E+01 9.82E+01 0.00E+00 6.29E+00 6.27E+00 4.49E+00\n", - " 1.00E+05 1.50E+02 4.72E+01 1.03E+02 0.00E+00 6.71E+00 6.69E+00 4.69E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 5.0000E+00 1.0007E+05 1.0007E+05\n", - " max. cross section (mb) 1.0306E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - "\n", - "\n", - " SHMAKI: Glauber formalism (Shmakov et. al) - initialization\n", - " ---------------------------------------------------\n", - "\n", - " variable energy run: projectile-id: 1 target A/Z: 1 / 1\n", - "\n", - " E_cm (GeV) E_Lab (GeV) sig_tot^pp (mb) Sigma_tot (mb) Sigma_prod (mb)\n", - " ---------------------------------------------------------------------------\n", - " 4.5 0.996E+01 3.98 0.000 30.521\n", - " 5.6 0.156E+02 3.93 0.000 30.475\n", - " 6.9 0.242E+02 3.89 0.000 30.563\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " 8.5 0.371E+02 3.85 0.000 30.547\n", - " 10.4 0.568E+02 3.81 0.000 30.604\n", - " 12.8 0.866E+02 3.83 0.000 30.906\n", - " 15.8 0.132E+03 3.86 0.000 31.315\n", - " 19.4 0.200E+03 3.90 0.000 31.797\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 2.50E+00 4.17E+01 1.03E+01 3.14E+01 0.00E+00 1.09E+00 1.09E+00 1.12E-01\n", - " 4.41E+00 4.03E+01 8.32E+00 3.19E+01 0.00E+00 1.27E+00 1.27E+00 1.31E-01\n", - " 7.78E+00 3.91E+01 7.17E+00 3.20E+01 0.00E+00 1.49E+00 1.49E+00 2.46E-01\n", - " 1.37E+01 3.89E+01 6.63E+00 3.23E+01 0.00E+00 1.76E+00 1.75E+00 4.53E-01\n", - " 2.42E+01 3.98E+01 6.53E+00 3.33E+01 0.00E+00 2.06E+00 2.06E+00 7.13E-01\n", - " 4.27E+01 4.17E+01 6.76E+00 3.50E+01 0.00E+00 2.40E+00 2.40E+00 1.02E+00\n", - " 7.52E+01 4.45E+01 7.28E+00 3.73E+01 0.00E+00 2.78E+00 2.77E+00 1.34E+00\n", - " 1.33E+02 4.83E+01 8.13E+00 4.02E+01 0.00E+00 3.15E+00 3.14E+00 1.66E+00\n", - " 2.34E+02 5.31E+01 9.37E+00 4.37E+01 0.00E+00 3.49E+00 3.48E+00 1.97E+00\n", - " 4.13E+02 5.88E+01 1.10E+01 4.78E+01 0.00E+00 3.78E+00 3.77E+00 2.25E+00\n", - " 7.28E+02 6.53E+01 1.31E+01 5.23E+01 0.00E+00 4.05E+00 4.04E+00 2.53E+00\n", - " 1.28E+03 7.24E+01 1.54E+01 5.70E+01 0.00E+00 4.31E+00 4.30E+00 2.81E+00\n", - " 2.26E+03 8.00E+01 1.79E+01 6.20E+01 0.00E+00 4.57E+00 4.56E+00 3.08E+00\n", - " 3.99E+03 8.79E+01 2.07E+01 6.72E+01 0.00E+00 4.85E+00 4.83E+00 3.33E+00\n", - " 7.04E+03 9.62E+01 2.36E+01 7.27E+01 0.00E+00 5.15E+00 5.13E+00 3.56E+00\n", - " 1.24E+04 1.05E+02 2.66E+01 7.84E+01 0.00E+00 5.47E+00 5.46E+00 3.75E+00\n", - " 2.19E+04 1.14E+02 2.99E+01 8.43E+01 0.00E+00 5.83E+00 5.81E+00 3.92E+00\n", - " 3.86E+04 1.24E+02 3.33E+01 9.06E+01 0.00E+00 6.22E+00 6.20E+00 4.05E+00\n", - " 6.81E+04 1.34E+02 3.69E+01 9.71E+01 0.00E+00 6.64E+00 6.62E+00 4.15E+00\n", - " 1.20E+05 1.45E+02 4.07E+01 1.04E+02 0.00E+00 7.09E+00 7.07E+00 4.21E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 2.5000E+00 1.2008E+05 1.2008E+05\n", - " max. cross section (mb) 1.0386E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - " 4.5 0.996E+01 4.02 0.000 30.110\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 2.50E+00 4.17E+01 1.03E+01 3.14E+01 0.00E+00 1.09E+00 1.09E+00 1.12E-01\n", - " 4.41E+00 4.03E+01 8.32E+00 3.19E+01 0.00E+00 1.27E+00 1.27E+00 1.31E-01\n", - " 7.78E+00 3.91E+01 7.17E+00 3.20E+01 0.00E+00 1.49E+00 1.49E+00 2.46E-01\n", - " 1.37E+01 3.89E+01 6.63E+00 3.23E+01 0.00E+00 1.76E+00 1.75E+00 4.53E-01\n", - " 2.42E+01 3.98E+01 6.53E+00 3.33E+01 0.00E+00 2.06E+00 2.06E+00 7.13E-01\n", - " 4.27E+01 4.17E+01 6.76E+00 3.50E+01 0.00E+00 2.40E+00 2.40E+00 1.02E+00\n", - " 7.52E+01 4.45E+01 7.28E+00 3.73E+01 0.00E+00 2.78E+00 2.77E+00 1.34E+00\n", - " 1.33E+02 4.83E+01 8.13E+00 4.02E+01 0.00E+00 3.15E+00 3.14E+00 1.66E+00\n", - " 2.34E+02 5.31E+01 9.37E+00 4.37E+01 0.00E+00 3.49E+00 3.48E+00 1.97E+00\n", - " 4.13E+02 5.88E+01 1.10E+01 4.78E+01 0.00E+00 3.78E+00 3.77E+00 2.25E+00\n", - " 7.28E+02 6.53E+01 1.31E+01 5.23E+01 0.00E+00 4.05E+00 4.04E+00 2.53E+00\n", - " 1.28E+03 7.24E+01 1.54E+01 5.70E+01 0.00E+00 4.31E+00 4.30E+00 2.81E+00\n", - " 2.26E+03 8.00E+01 1.79E+01 6.20E+01 0.00E+00 4.57E+00 4.56E+00 3.08E+00\n", - " 3.99E+03 8.79E+01 2.07E+01 6.72E+01 0.00E+00 4.85E+00 4.83E+00 3.33E+00\n", - " 7.04E+03 9.62E+01 2.36E+01 7.27E+01 0.00E+00 5.15E+00 5.13E+00 3.56E+00\n", - " 1.24E+04 1.05E+02 2.66E+01 7.84E+01 0.00E+00 5.47E+00 5.46E+00 3.75E+00\n", - " 2.19E+04 1.14E+02 2.99E+01 8.43E+01 0.00E+00 5.83E+00 5.81E+00 3.92E+00\n", - " 3.86E+04 1.24E+02 3.33E+01 9.06E+01 0.00E+00 6.22E+00 6.20E+00 4.05E+00\n", - " 6.81E+04 1.34E+02 3.69E+01 9.71E+01 0.00E+00 6.64E+00 6.62E+00 4.15E+00\n", - " 1.20E+05 1.45E+02 4.07E+01 1.04E+02 0.00E+00 7.09E+00 7.07E+00 4.21E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 2.5000E+00 1.2008E+05 1.2008E+05\n", - " max. cross section (mb) 1.0386E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - " 5.6 0.156E+02 3.98 0.000 30.482\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - " 4.5 0.996E+01 4.02 0.000 30.110\n", - " 6.9 0.242E+02 3.94 0.000 30.766\n", - " 5.6 0.156E+02 3.98 0.000 30.482\n", - " 8.5 0.371E+02 3.91 0.000 31.031\n", - " 6.9 0.242E+02 3.94 0.000 30.766\n", - " 10.4 0.568E+02 3.90 0.000 31.333\n", - " 8.5 0.371E+02 3.91 0.000 31.031\n", - " 12.8 0.866E+02 3.90 0.000 31.596\n", - " 10.4 0.568E+02 3.90 0.000 31.333\n", - " 15.8 0.132E+03 3.92 0.000 31.980\n", - " 12.8 0.866E+02 3.90 0.000 31.596\n", - " 19.4 0.200E+03 3.95 0.000 32.417\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - " 15.8 0.132E+03 3.92 0.000 31.980\n", - " 19.4 0.200E+03 3.95 0.000 32.417\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "Hey\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 2.50E+00 4.17E+01 1.03E+01 3.14E+01 0.00E+00 1.09E+00 1.09E+00 1.12E-01\n", - " 4.41E+00 4.03E+01 8.32E+00 3.19E+01 0.00E+00 1.27E+00 1.27E+00 1.31E-01\n", - " 7.78E+00 3.91E+01 7.17E+00 3.20E+01 0.00E+00 1.49E+00 1.49E+00 2.46E-01\n", - " 1.37E+01 3.89E+01 6.63E+00 3.23E+01 0.00E+00 1.76E+00 1.75E+00 4.53E-01\n", - " 2.42E+01 3.98E+01 6.53E+00 3.33E+01 0.00E+00 2.06E+00 2.06E+00 7.13E-01\n", - " 4.27E+01 4.17E+01 6.76E+00 3.50E+01 0.00E+00 2.41E+00 2.40E+00 1.02E+00\n", - " 7.52E+01 4.45E+01 7.28E+00 3.73E+01 0.00E+00 2.78E+00 2.77E+00 1.34E+00\n", - " 1.33E+02 4.83E+01 8.13E+00 4.02E+01 0.00E+00 3.15E+00 3.14E+00 1.66E+00\n", - " 2.34E+02 5.31E+01 9.37E+00 4.37E+01 0.00E+00 3.49E+00 3.48E+00 1.97E+00\n", - " 4.13E+02 5.88E+01 1.10E+01 4.78E+01 0.00E+00 3.78E+00 3.77E+00 2.25E+00\n", - " 7.28E+02 6.53E+01 1.31E+01 5.23E+01 0.00E+00 4.05E+00 4.04E+00 2.53E+00\n", - " 1.28E+03 7.24E+01 1.54E+01 5.70E+01 0.00E+00 4.31E+00 4.30E+00 2.81E+00\n", - " 2.26E+03 8.00E+01 1.79E+01 6.20E+01 0.00E+00 4.57E+00 4.56E+00 3.08E+00\n", - " 3.99E+03 8.79E+01 2.07E+01 6.72E+01 0.00E+00 4.85E+00 4.84E+00 3.33E+00\n", - " 7.04E+03 9.62E+01 2.36E+01 7.27E+01 0.00E+00 5.15E+00 5.13E+00 3.56E+00\n", - " 1.24E+04 1.05E+02 2.66E+01 7.84E+01 0.00E+00 5.47E+00 5.46E+00 3.75E+00\n", - " 2.19E+04 1.14E+02 2.99E+01 8.43E+01 0.00E+00 5.83E+00 5.81E+00 3.92E+00\n", - " 3.86E+04 1.24E+02 3.33E+01 9.06E+01 0.00E+00 6.22E+00 6.20E+00 4.05E+00\n", - " 6.81E+04 1.34E+02 3.69E+01 9.71E+01 0.00E+00 6.64E+00 6.62E+00 4.15E+00\n", - " 1.20E+05 1.45E+02 4.07E+01 1.04E+02 0.00E+00 7.09E+00 7.07E+00 4.21E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 2.5000E+00 1.2012E+05 1.2012E+05\n", - " max. cross section (mb) 1.0386E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - "\n", - " Table of total cross sections (mb) for particle combination 1\n", - " Ecm SIGtot SIGela SIGine SIGqel SIGsd1 SIGsd2 SIGdd\n", - " -------------------------------------------------------------\n", - " 2.50E+00 4.17E+01 1.03E+01 3.14E+01 0.00E+00 1.09E+00 1.09E+00 1.12E-01\n", - " 4.41E+00 4.03E+01 8.32E+00 3.19E+01 0.00E+00 1.27E+00 1.27E+00 1.31E-01\n", - " 7.78E+00 3.91E+01 7.17E+00 3.20E+01 0.00E+00 1.49E+00 1.49E+00 2.46E-01\n", - " 1.37E+01 3.89E+01 6.63E+00 3.23E+01 0.00E+00 1.76E+00 1.75E+00 4.53E-01\n", - " 2.42E+01 3.98E+01 6.53E+00 3.33E+01 0.00E+00 2.06E+00 2.06E+00 7.13E-01\n", - " 4.27E+01 4.17E+01 6.76E+00 3.50E+01 0.00E+00 2.41E+00 2.40E+00 1.02E+00\n", - " 7.52E+01 4.45E+01 7.28E+00 3.73E+01 0.00E+00 2.78E+00 2.77E+00 1.34E+00\n", - " 1.33E+02 4.83E+01 8.13E+00 4.02E+01 0.00E+00 3.15E+00 3.14E+00 1.66E+00\n", - " 2.34E+02 5.31E+01 9.37E+00 4.37E+01 0.00E+00 3.49E+00 3.48E+00 1.97E+00\n", - " 4.13E+02 5.88E+01 1.10E+01 4.78E+01 0.00E+00 3.78E+00 3.77E+00 2.25E+00\n", - " 7.28E+02 6.53E+01 1.31E+01 5.23E+01 0.00E+00 4.05E+00 4.04E+00 2.53E+00\n", - " 1.28E+03 7.24E+01 1.54E+01 5.70E+01 0.00E+00 4.31E+00 4.30E+00 2.81E+00\n", - " 2.26E+03 8.00E+01 1.79E+01 6.20E+01 0.00E+00 4.57E+00 4.56E+00 3.08E+00\n", - " 3.99E+03 8.79E+01 2.07E+01 6.72E+01 0.00E+00 4.85E+00 4.84E+00 3.33E+00\n", - " 7.04E+03 9.62E+01 2.36E+01 7.27E+01 0.00E+00 5.15E+00 5.13E+00 3.56E+00\n", - " 1.24E+04 1.05E+02 2.66E+01 7.84E+01 0.00E+00 5.47E+00 5.46E+00 3.75E+00\n", - " 2.19E+04 1.14E+02 2.99E+01 8.43E+01 0.00E+00 5.83E+00 5.81E+00 3.92E+00\n", - " 3.86E+04 1.24E+02 3.33E+01 9.06E+01 0.00E+00 6.22E+00 6.20E+00 4.05E+00\n", - " 6.81E+04 1.34E+02 3.69E+01 9.71E+01 0.00E+00 6.64E+00 6.62E+00 4.15E+00\n", - " 1.20E+05 1.45E+02 4.07E+01 1.04E+02 0.00E+00 7.09E+00 7.07E+00 4.21E+00\n", - "\n", - " activated processes, cross section\n", - " ----------------------------------\n", - " nondiffr. resolved processes 1 1 1 1\n", - " elastic scattering 0 0 0 0\n", - " qelast. vectormeson production 1 0 0 0\n", - " double pomeron processes 1 0 0 0\n", - " single diffract. particle (1) 1 0 0 0\n", - " single diffract. particle (2) 1 0 0 0\n", - " double diffract. processes 1 0 0 0\n", - " direct photon processes 1 1 1 1\n", - " maximum search (Elow/Eup/Epeak) 2.5000E+00 1.2012E+05 1.2012E+05\n", - " max. cross section (mb) 1.0386E+02\n", - "\n", - "\n", - " PHO_HARSCA: activated hard processes\n", - " ------------------------------------\n", - " PROCESS, IP= 1 ... 4 (on/off)\n", - " 1 G +G --> G +G 1 1 1 1\n", - " 2 Q +QB --> G +G 1 1 1 1\n", - " 3 G +Q --> G +Q 1 1 1 1\n", - " 4 G +G --> Q +QB 1 1 1 1\n", - " 5 Q +QB --> Q +QB 1 1 1 1\n", - " 6 Q +QB --> QP +QBP 1 1 1 1\n", - " 7 Q +Q --> Q +Q 1 1 1 1\n", - " 8 Q +QP --> Q +QP 1 1 1 1\n", - " 9 resolved processes 1 1 1 1\n", - " 10 gam+Q --> G +Q 1 1 1 1\n", - " 11 gam+G --> Q +QB 1 1 1 1\n", - " 12 Q +gam--> G +Q 1 1 1 1\n", - " 13 G +gam--> Q +QB 1 1 1 1\n", - " 14 gam+gam--> Q +QB 1 1 1 1\n", - " 15 direct processes 1 1 1 1\n", - " 16 gam+gam--> l+ +l- 0 0 0 0\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " Compute Cross-section (can take a while...)\n", - "seedj: 1114167 0.2000000000000000D+01\n", - " done\n", - " qgaini: nuclear cross sections readout from the file sectnu-II-04\n" - ] - } - ], + "outputs": [], "source": [ - "energies = np.geomspace(10, 100000, 20) * GeV\n", + "energies = np.geomspace(10 * GeV, 100 * TeV, 20)\n", "\n", "\n", "@joblib.delayed\n", "def run(Model):\n", " values = []\n", - " m = Model(CenterOfMass(energies[-1], \"p\", \"p\"))\n", + " # disable remote control, since this breaks joblib\n", + " m = Model(seed=1, timeout=0)\n", " for en in energies:\n", " kin = CenterOfMass(en, \"p\", \"p\")\n", " c = m.cross_section(kin)\n", @@ -3148,10 +38,12 @@ "\n", "\n", "Models = [M for M in get_all_models() if lp.proton.pdgid in M.projectiles]\n", + "# Comment this to also run Pythia8, which takes several minutes\n", + "Models.remove(im.Pythia8)\n", "cross_sections = {}\n", "out = joblib.Parallel(len(Models), batch_size=1)(run(Model) for Model in Models)\n", "for Model, o in zip(Models, out):\n", - " cross_sections[Model.pyname] = o\n" + " cross_sections[Model.pyname] = o" ] }, { @@ -3161,7 +53,7 @@ "outputs": [ { "data": { - "image/png": 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uvInzvKbH78UcOXIYlr3o/QqQO3duw8+WlpbPzSf32gYGBnL16tUkf1gTEhIyvKDUqFGjCAkJoWzZspiZmdG7d+90e+89+3vwuOPxrVu3yJMnz3Pb37x5k/79++Pv7090dDRxcXG0b98+XWIR2dOmTZto1aoVsbGx1K9fnzVr1qT48+htkK0TFJ1Ol+ZbL29KUFBQkp8f/wFzdXUlMDDQsC4wMBALCwscHR3R6XRMmzaNadOmcejQIWrXrk2tWrUwMnr9hrFatWrRr18/Tpw4QdmyZV+5vU6ne27Z45gLFixoiNnV1TXZ7WNjY+nQoQNr1qyhXr16GBsbkydPnjT363lr6HSvfevlTdm4cSNOTk4ULVrU8D4NCgrC09PT8HOJEiXSdI68efNSvHhxTp06lez65N5n6cHS0pIZM2YwY8YMAH755RcqVqz4Wsd6NkZXV1cuXbpkmI+MjOTBgweG34VnjR49mhw5cnDhwgXs7OwYMWLEC1tbhFizZg3t27cnPj6e5s2bs3z58mx3W1xu8WQRvr6+XLx4kdDQUCZMmEDbtm0BaNeuHb/++isBAQFERkYyatQo2rZti06nw8/PjytXrqCUwt7eHp1Oh7GxseGYMTExSaaUKFasGD169KBDhw74+/uTkJBAaGgokydPZtOmTc9tnzNnTvR6fZLHJdu1a8fYsWN59OgRQUFBTJkyxfBNMHfu3Ny4cYOEhARAS1Di4uIM37KnTZvGvXv3Xu8/UaSrBw8eMG/ePHx8fPjmm2+SvLfGjh1LTEwMhw4dYv369bRu3TpN56pUqRJ6vZ7Zs2cTFxdHXFwc+/btMyTnuXPnfu1+J/Hx8cTExKDX65P8DHDjxg2Cg4PR6/X4+/szYcIERo0alaJ9n/VsjG3atGHdunUcPHiQuLg4Ro8eTdWqVQ1fPp7dPjw8HFtbW2xsbPj3339T9ciyeLcsWbKEtm3bEh8fT/v27Vm5cmW2S05AEpQ3ql69eknqoDz9h7Bz5860bdsWDw8P8ubNa+jBX69ePUaMGEGjRo3w8PDA1NSUqVOnAnDhwgVq1aqFra0tjRs3ZurUqXh4eABaLRNLS8skU0prLsydOxdvb2+8vb2xt7enbNmyBAUFJfvN0tramuHDh1O2bFkcHBwIDAzEx8eHokWLUqxYMapUqUL79u3p2rUrALVr18bT05NcuXJRpkwZ7OzsmDx5MvXr18fFxYUHDx5IQapM9vh9WrhwYZYuXcqCBQvo16+fYb2xsTEVKlTA3d2djz/+mOnTp6e5CJSJiQl+fn5s3bqVvHnz4urqyvjx4w3JwIABA/D19cXBwYHvvvsuVcfu3bs3lpaWbN++nT59+mBpacnevXsBuHjxIpUqVcLGxoY+ffowc+bMJKXAX7bvs56NsXjx4vz22290794dZ2dnzp8/nyTp+Oqrr2jdujUODg7s37+fr776il27dmFnZ8fAgQPTnPSJ7OmXX37B29ubxMREunXrxpIlSzA1Nc3ssDKETr2FbelhYWHY29sTGhqKnZ1dZoeTZjVr1kzS+1+IrOratWsUKlTI0AKWFjt37qRfv34EBASkQ2RCZH8//fQTQ4YMAaBfv378/PPPabqlnxlS8/n9dl2ZECLbOHPmjKEfixDixZRSjBs3zpCcfPnll0yfPv2tS05SK1t3khVCZE1ffvklK1as4Pfff8/sUITI0pRSjBw50nBr89tvv2X06NEZ1nHc4J/foUh9sM6Zsed5iVSnX3v37qVp06a4urqi0+lYt27dC7f93//+h06nM/SZeOzhw4d06tQJOzs7HBwc6Nmz5xt5HDir2r17t9zeEW8FT0/PdLm98/3333Pt2jWqVauWDlEJkT3p9Xo+++wzQ3Ly448/4uPjk7HJSWI8bBwCf34Kyztr85kk1QlKZGQk7733HjNnznzpdmvXruXQoUPJPlLXqVMnzpw5w/bt29m4cSN79+6lT58+qQ1FCCGEyJYSExPp1asX06dPR6fTMWfOHMMtngwT9RAWt4S/5wE6KFwPjDLvRkuqz9ywYUMaNmz40m1u3rzJgAED2Lp1K40bN06yLiAggC1btnD06FEqVKgAwPTp02nUqBE//PBDsglNbGysYfwO0DrZCCGEENlRfHw8Xbp0Yfny5YaRtLt06ZKxJ717Dpa1g0fXwMwGWv8GRV/+WZ/R0r2HjV6vp0uXLgwdOtQwGNzT/P39cXBwMCQnAHXr1sXIyIjDhw8ne8yJEydib29vmN7m0RmFEEKIF4mJiaF169YsX74cU1NTVqxYkfHJyYWt8FtdLTlxcIee2zM9OYEMSFAmTZqEiYkJAwcOTHb97du3k5S+Bq0GgqOj4wtLrI8YMYLQ0FDD9HTVVSGEECI7iIyMpFmzZmzYsAFzc3PWrVuXsfVwlIID02BpO4gLB4/q0Hs3OKetMnR6SdebS8eOHWPatGkcP348XTvxmJubZ8sqeUIIIQRoXRcaN27M/v37sba2Zv369dSuXTvjThgfAxs+g1N/aPPvd4OGk8HELOPOmUrp2oKyb98+7t69i7u7OyYmJpiYmHD9+nU+//xzQ70DFxcX7t69m2S/hIQEHj58mOZRdMWbFxgYmC4jZ+7evTvdK8imV2wiY+zbt4/33nsvs8NINw0bNjSMQp4Wnp6e6T6KcXrFJjLGw4cPqVOnDvv378fe3p5t27ZlbHISfgcWNtGSE52xlpg0mZqlkhNI5wSlS5cunDp1ihMnThgmV1dXhg4dytatWwGoUqUKISEhHDt2zLDfzp070ev1VKpUKT3DyVI8PT2xsrJKUup+1qxZGXKua9euYWKSfONYzZo1nxvjw9fXl7p16xrmExMTmThxIkWKFMHa2pr8+fMzYMAA7t+//9zx3N3dCQkJSdf4X9ez15aVYntbPP0+dXJyom7duqxfvz5DzvXhhx9y8uTJFG377Gv77Hv86fXPvp+//vprSpYsaehs+LSYmBgGDBiAi4sLOXPmNAwxkZJ9n7V582batWuXouvJSM9eP2Sd2MTz7ty5Q82aNfn7779xcnJi586dSUawT3e3TsCvteDGUbCwh86roVIfbeDSLCbVCUpERIQh+QC4evUqJ06cIDAwECcnJ0qVKpVkMjU1xcXFxTBWR/HixWnQoAG9e/fmyJEjHDhwgP79+9O+ffsXjvKZXWzbto2IiAjD9Omnn2Z2SMnq27cvvr6+LFiwgJCQEI4fP07evHk5cuRIZocm3oDH79Nz587Rrl07unTpwpw5czI7rNdWqFAhpkyZQvXq1Z9bN3HiRE6fPk1AQABnz57lr7/+4rfffkvRvkKk1Y0bN6hRowanT5/GxcWFPXv2UL58+Yw74Zm1ML8BhN0Ep8LQexcUrJVx50srlUq7du1SwHNT165dk93ew8ND/fTTT0mWPXjwQHXo0EHZ2NgoOzs71b17dxUeHp7iGEJDQxWgQkNDU7R9RETEC6fo6OgUbxsVFfXctinl4eGh9u3bl+w6Ly8vNXr0aFW2bFnl4OCgvL29k5xr5syZKn/+/CpnzpyqU6dOKiQkRCml1N27d1WDBg2Uvb29cnJyUu3bt1dKKXX16lVlbGz8wnMtXrw4ybIFCxaoOnXqKKWUCggIUDqdTh07dixF1/XsuTw8PNQPP/ygihUrpuzt7VW/fv2SbD9jxgxVqFAh5eTkpLy9vQ3/h7t27VIFCxY0bDd+/Hjl7u6ubG1tVeXKldXJkycN68aNG6dcXFyUra2tKlWqlDpz5oz69ttvlZGRkTI3N1fW1tZq/Pjxz8V29+5d1aFDB5UrVy7l5OSkhg0blqJrfCNiI148xUWnYtuo57dNheTep9OmTVNOTk4qISHB8H86a9YslTt3buXm5pbk/eTl5aV8fHxU+fLllbW1terRo4e6deuWqlWrlrK1tVWtWrVSMTExSqnnX/Pr16+rRo0aKUdHR1WsWDG1efNmpZRK0Wv79Pv66ffz0+rXr68WLFiQZNn777+v/vzzT8P8kiVLVLVq1VK077OejmHMmDGqY8eOqk2bNsrGxkZVrFhRXblyxbDtqVOn1IcffqgcHBxU+fLl1dGjRw3rnn4NDh06pCpUqKBsbW2Vu7u7+vnnnw3b+fv7q7JlyypbW1vl6uqqpkyZoi5fvqzMzc2VsbGxsra2ViVKlHguNqW017RQoULKxsZGffDBB+r+/fsvvTaR/i5fvqw8PT0VoNzd3dXFixcz7mSJiUrtHK/UGDttWtxKqahHGXe+l0jN53eqE5SsILUJSnIJ1eOpUaNGSba1srJ64bZeXl5Jts2ZM2eKY35VguLu7q7Onz+vHj16pGrWrKlGjx6tlFJq27ZtysXFRZ05c0ZFRESoVq1aGZLBYcOGqU8++UTFx8ermJgYdeDAAaVU2hKUWbNmKQ8PjxRfV3IJSvXq1dW9e/dUUFCQypUrl9q5c6dSSqkVK1aoUqVKqWvXrqmoqCjVoUMH9fnnnyulnv+wWr16tbp7966Ki4tTPj4+6r333lNKaQlUvnz5VHBwsNLr9SogIEAFBwcne23PxvbRRx+pnj17qrCwMBUVFaUOHjyY4uvMcI//cCQ3LWmTdNtxLi/edn7S97OalD9VYST3Pr169aoC1JkzZww/d+vWTUVHR6sDBw4oW1tbdf78eaWU9hqUKFFCBQYGquDgYOXs7KwqVKig/v33XxUWFqZKlSql5s+fr5RK+ponJiaqMmXKqGnTpqn4+Hh18OBBlTNnTnX79m3DcV/22qYlQVm3bp1hfvHixcrBwSFF+z7r2QTF0tJS7dy5U8XHx6suXboob29vpZRS4eHhytXVVa1atUolJCSotWvXKjc3N8OXpadfg2PHjqljx46pxMREdfToUWVnZ6eOHz+ulFKqUqVKasmSJUoppR4+fGhYntz1Px3bkiVLVIECBdTp06dVYmKiOnbsmAoLC3vptYn0FRAQoFxdXRWgChUqpK5fv55xJ4uNUOqPzk/+RmwZqVRiQsad7xVS8/mdvUcaymIaNmyIg4ODYdqzZ49hXffu3SlSpAgODg6MGjXK0KHtjz/+oE+fPpQoUQJra2smTJjA8uXLUUphampKcHAwQUFBmJubp/i+Zd++fZPE8fStpgcPHqS5s/KgQYPImTMn+fLlo2bNmoZ+BvPmzWPEiBF4eHhgaWnJyJEjWbVqVbLHaNWqFbly5cLU1JSRI0dy6tQpIiIiMDExITY2loCAABITEylWrFiK4r158ya7d+9m2rRp2NraYmlpSZUqVdJ0ne+Kx/+/jx49MiwbM2YMFhYWVK1alWbNmiV5HXv27ImbmxsuLi54eXlRpUoVSpYsia2tLY0aNUq238mRI0eIjo5m4MCBmJiYUKVKFby8vNi8eXOGXluDBg348ccfuX//PsHBwUybNo3IyMh0OXadOnWoVasWJiYmtG/f3nDdGzdupGTJkrRu3RpjY2NatGhB7ty5OXTo0HPHKF++POXLl8fIyIgKFSrQqFEjDhw4AICpqSmXLl3i4cOH5MiRg3LlyqUoLl9fX4YPH06pUqUwMjKifPny2Nrapss1i1c7ceIENWrU4NatW5QsWZK9e/fi7u6eMScLCYL59SFgPRiZQvOZUH88GBlnzPnS2TsxWODLxvkxNk76Qj37hNHTnh058tq1a6mKY/PmzS+8l/108Tk3NzeCg4MBuHXrVpLEw8PDg5iYGB4+fMjQoUPx8fHBy8sLKysrhg4dSs+ePV8Zx9y5c5OM/ePr62voYOjk5PTCejQp5ezsbPjZysrK8P8fGBhI3759kyRE8fHJj/Pw66+/MnXqVG7cuIFOp0MpxYMHDyhUqBA//vgjI0eO5MKFC7Rs2ZIpU6a8ctjuGzdukDt3bqytrdN0bRlm5K0Xr9M988dk6KWXbPvMd45Bp18/pv88fi/myJHDsOxF71cgSZ0jS0vL5+YfPHjw3DkCAwO5evVqkqeuEhISeP/999Mc/8uMGjWKkJAQypYti5mZGb179042vtfxst+DPXv2JLnW+Ph4bt16/j1w5swZBg0axIkTJ4iLiyMmJoZixYoB8Ntvv+Hj40OhQoUoUaIEkydPTlHSfePGDfLnz5/GqxOv4/DhwzRo0ICQkBDKly/P1q1byZkzgwbjCzykjaUTeQ+sc0G7JeBeOWPOlUHeiRYUa2vrF04WFhYp3tbS0vK5bdPL08XngoKCyJMnDwCurq4EBgYa1gUGBmJhYYGjoyN2dnZMmzaNwMBAfH19GTBgAFeuXElTHLVq1SIwMNDQCTo95c2bl4ULFxISEmKYkvu2eu3aNQYNGsTChQt59OgRwcHBhiQFtKfF/P39OX/+PNeuXWPKlCkAL6294+bmxr1794iKikr360oXZtYvnkwtUrGt5fPbptHGjRtxcnIydHSHF79fX1fevHkpXrx4kvdGREQEI0aMAF7+2qaFpaUlM2bM4MaNG1y5cgUnJycqVqyYIed6LG/evNSvX/+534OOHTs+t23//v2pUqUKgYGBhIaG0qpVK8PvQdGiRVmxYgV3796lffv2dOjQAXj1/5Wbm1uqv1yJtNuzZw9169YlJCSEqlWrsnPnzoxLTv5ZAr5NtOTEuTT03vnWJSfwjiQobwNfX18uXrxIaGgoEyZMoG3btgC0a9eOX3/9lYCAACIjIxk1ahRt27ZFp9Ph5+fHlStXUEphb2+PTqdL0iIUExOTZEqJYsWK0aNHDzp06IC/vz8JCQmEhoYyefJkNm3alKZr7NGjBxMmTODy5cuA9s18y5Ytz20XERGBkZERuXLlIiEhgTFjxhjWnT9/nt27dxMXF4eVlRXm5uaGa86dO/cL//C6urri5eXF4MGDiYiIIDo6OtkmdfHEgwcPmDdvHj4+PnzzzTdJ3ltjx44lJiaGQ4cOsX79+jRXu6xUqRJ6vZ7Zs2cTFxdHXFwc+/btMyTnL3ttXyU+Pp6YmBj0en2Sn0FrTQgODkav1+Pv78+ECRMYNWpUivZ9XU2aNOGff/5h3bp1JCQkEB0dzZYtWwgNDX1u2/DwcBwcHLCwsGDfvn34+fkZ1v3+++88ePAAExMTbG1tk/we3Lhx44WjTnfr1o1JkyZx9uxZlFIcP36c8PDwNF2TeLktW7bQoEEDIiIiqF27Nlu3bsXe3j79T6RPhK2j4M9+oI+H4k2hxxatfP1bSBKUN6hevXpJ6qA8/Yewc+fOtG3bFg8PD/LmzWuox1CvXj1GjBhBo0aN8PDwwNTUlKlTpwJw4cIFatWqha2tLY0bN2bq1Kl4eHgAWi0TS0vLJNONGzdSFOfcuXPx9vbG29sbe3t7ypYtS1BQUJq/WXbo0IGePXvSuHFj7Ozs8PLy4uzZs89tV6pUKfr27UuZMmXw9PQkf/78mJlpBYRiY2MZOnQoTk5OuLu7Y29vz+DBgwEYMGAAvr6+ODg4GIYnf9rvv/9OSEgInp6euLu7Z1h9j7fd4/dp4cKFWbp0KQsWLKBfv36G9cbGxlSoUAF3d3c+/vhjpk+fnqR15XWYmJjg5+fH1q1byZs3L66urowfP96QDLzqtX2Z3r17Y2lpyfbt2+nTpw+Wlpbs3bsXgIsXL1KpUiVsbGzo06cPM2fOpHTp0ina93XZ29vj5+fH9OnTyZ07N56envzyyy/Jbjtp0iRmzpyJnZ0dU6dOpVmzZoZ1mzZtomjRotja2vLzzz+zaNEiAGrXro2npye5cuWiTJkyzx2zY8eO9OvXjyZNmmBnZ8enn376wlutIu3Wrl1Ls2bNiImJoXHjxmzcuBEbG5v0P1FMKCxtC/4ztHmvYfDxIjDPgHO9ITr1uL3wLRIWFoa9vT2hoaGv7HvwNqhZsya9evVK0i9EiKzo2rVrFCpU6IXfzlNj586d9OvXj4CAgHSITIisZ+nSpXh7e5OYmEibNm34/fffDV+20tWDy7CsPdy/ACaW0GIWlGqV/udJB6n5/H4nOskKIbKeM2fOGIbAECK7+e233+jTpw9KKby9vZk3b94LK3ynyeVdsLIbxISArSt0WAquKXuiK6uTBEUI8cZ9+eWXrFixgt9//z2zQxEi3f3444988cUXAPzvf/9j5syZzz0FmmZKwZFfYctwUImQtwK0/x1ss8+YdnKLRwghhEgHSil8fHwYP348AEOHDmXSpEnp/xRaQhxsHgrHfLX5Mu2h6bTnn/jLguQWjxBCCPEG6fV6Bg4cyMyZMwFtnKfhw4en/4nCgmFlVwg6DOig7tdQ7bMsOdhfWkmCIoQQQqRBfHw8PXr0MBS8nDlzZsYMBht4CFZ4Q8QdMLeH1r9Ckfrpf54sQhIUIYQQ4jXFxMTQrl071q9fj7GxMQsXLqRTp07pexKl4O95sHm4Vt8kV3Gtv4lTwfQ9TxYjCYoQQgjxGsLDw2nRogU7d+7E3NyclStX0rRp0/Q9SXwMbPpcqw4LUKI5NJ/1Vtc3SSlJUIQQQohUevjwIQ0bNuTIkSPY2Niwfv16atWqlb4nCb0By7vArePaOFt1xmTb/ibJkUqyIt3pdDpD1dqGDRsaRmYGGDZsGI6OjoZB4B5X08ywMSn+s2/fPt57770MPYdInez+mnTr1o1x48YBWhXjp79Z79u3j4IFC2JjY8PRo0cJCAigdOnS2Nrasnr16gyNq2TJkvj7+2foObK74OBgvLy8OHLkCI6OjuzcuTP9k5Nr+2Gul5acWOaAzquh+qB3JjkBQL2FQkNDFaBCQ0MzO5QU8/DwUJaWlsra2towzZw5M0POdfXqVWVsbJzsOi8vL7V48eIkyxYsWKDq1KljmE9ISFATJkxQhQsXVlZWVsrT01P1799f3bt3L0XnB1RQUNBzy69fv65sbGzUgwcPlFJKxcbGKgsLC3X+/PmUXlqKeXh4qH379qX7cbO7p9+njo6Oqk6dOurPP//M7LCee98++x5/ev2z7+cxY8aoEiVKKJ1OpxYsWJDkuNHR0ap///7K2dlZOTk5qREjRiRZ/7J9X6Vr165q7Nixya6rVauWmjt3rmG+e/fuz507PYwZM0b17Nkz3Y/7Lrty5YoqUKCAAlSePHnUv//+m74n0OuV8p+l1Nc5lBpjp9Ssako9vJq+58hEqfn8lhaUN2jbtm1EREQYpgzp5Z0O+vbti6+vLwsWLCAkJITjx4+TN29ejhw5kqbjBgYG4uzsjKOjIwB3794lPj6eIkWKJLt9epRTF6n3+H167tw52rVrR5cuXZgzZ05mh/XaChUqxJQpU6hevfpz6yZOnMjp06cJCAjg7Nmz/PXXX/z2228p2jctAgMDKVGixAvnnya/B1nH2bNnqV69OleuXCF//vzs37+fkiVLpt8J4qJgTZ8nxddKt4We2yCHZ/qd423yBhKmdJfaFpSIiAgVERGh9Hq9YVlsbKyKiIhQMTExyW6bmJhoWBYXF6ciIiJUdHT0c9um1Mu+0Xt5eanRo0ersmXLKgcHB+Xt7a2ioqIM62fOnKny58+vcubMqTp16qRCQkKUUkrdvXtXNWjQQNnb2ysnJyfVvn17pVTaWlACAgKUTqdTx44dS/G1zZs3T+XLl085OzuruXPnJmlBeXy+vXv3KgsLC6XT6ZS1tbXq1q2bsrKyUoCytrZWbdq0Ubt27VIFCxZUX331lXJyclIjR45Uly5dUh9++KGyt7dXefLkee5b5h9//KFKliypbGxsVKlSpdS5c+dUz549lU6nM7QELFmyxHBspZQaO3as6tatW5Lj1KxZ0/D/curUKfXhhx8qBwcHVb58eXX06NEU/1+8ttgIbXrqPariY7Vl8THJb/vUe1QlxGnL4qKf3zYVknufTps2TTk5OamEhATDe2vWrFkqd+7cys3NLcn7ycvLS/n4+Kjy5csra2tr1aNHD3Xr1i1Vq1YtZWtrq1q1amX4nXv6NVFKa2Fr1KiRcnR0VMWKFVObN29WSin17bffKiMjI2Vubq6sra3V+PHjU9WC8lj9+vWfawV5//33k7QQLVmyRFWrVi1F+z7r0qVLqmrVqsrGxka1atVKtW3b1tCC8nRMj1tkLC0tlbOzs2rQoEGS67t//77y8PBQkyZNUsWKFVMeHh5KKaX69++v8uTJo+zt7dVHH32krl+/bjj3lStXDP93Li4uatq0aWrXrl3K1NRUmZiYKGtra9WgQQOl1JPXODAwUNnY2KjIyEjDcXx9fQ1xRkVFqX79+qk8efKovHnzqokTJ770+rO7o0ePKicnJwWokiVLqps3b6bvCR5eU2p2Na3V5OscSh2cmfTvQTaRms/vdyJBARSg7t69a1g2btw4BahevXol2fbxh+bVq1cNy3766ScFqI4dOybZNmfOnCmO+VUJiru7uzp//rx69OiRqlmzpho9erRSSqlt27YpFxcXdebMGRUREaFatWqlunbtqpRSatiwYeqTTz5R8fHxKiYmRh04cEAplbYEZdasWYY/iClx+vRpZWtrqw4dOqSioqJUly5dkk1QlHr+A+nZOHft2qWMjY3VN998o+Li4lRUVJS6dOmS2r17t4qPj1cXLlxQbm5uau3atUoppfbv36+cnJzU/v37VWJiogoICFC3bt1SSj3///30uS9cuKBy5Mih4uLilFJKBQcHKysrKxUWFqbCw8OVq6urWrVqlUpISFBr165Vbm5uzyWn6W6MnTZFPHUbbc/32rI/+yfddpyLtvzhtSfLDs7Ulq16pjl/Uv5UhZHc+/Tq1asKUGfOnDH83K1bNxUdHa0OHDigbG1tDbfpvLy8VIkSJVRgYKAKDg5Wzs7OqkKFCurff/9VYWFhqlSpUmr+/PlKqaSvSWJioipTpoyaNm2aio+PVwcPHlQ5c+ZUt2/fNhz3dW/xPPaiBGXdunWG+cWLFysHB4cU7fusDz74QI0YMULFxsaqtWvXKhMTk2QTlOT+n5+9Pg8PD1WpUiV1+/Ztw5eVZcuWqZCQEBUVFaW6d++umjdvrpRSKj4+XhUvXlyNGTNGRUdHq9DQUPX3338rpZK/xfP0uatWrapWrFhhWNewYUP1yy+/KKWU+vTTT1WHDh1UeHi4unnzpipRooTasGHDS/8Psqtdu3YpW1tbBagPPvhA3b9/P31PcGmHUt95aL/DkwoodWVv+h4/C5FbPFlUw4YNcXBwMEx79uwxrOvevTtFihTBwcGBUaNGGTqW/vHHH/Tp04cSJUpgbW3NhAkTWL58OUopTE1NCQ4OJigoCHNzc6pWrZqiOPr27ZskjqdvNT148AAXl5SP5bB69WpatWpFpUqVsLS05KuvvkrxvskxNzdn5MiRmJqaYmlpScGCBfHy8sLExITChQvTqVMn9u/fD4Cvry99+/alWrVqGBkZUaxYMfLkyfPKcxQuXBhPT0+2bdsGwKpVq6hfvz62trZs3LiRkiVL0rp1a4yNjWnRogW5c+fm0KFDabqut9nj98OjR48My8aMGYOFhQVVq1alWbNmrFq1yrCuZ8+euLm54eLigpeXF1WqVKFkyZLY2trSqFEjTp48+dw5jhw5QnR0NAMHDsTExIQqVarg5eXF5s2bM/TaGjRowI8//sj9+/cJDg5m2rRpREZGpvo4169f59SpU3z11VeYmZnRokULKlWqlKbYPvvsM5ydnbG0tASgffv22NvbY2lpybBhwwy/B4cPHyY8PJyvvvoKCwsL7OzsDJ3QX6Vdu3asWLEC0F7fPXv20KpVK5RSLFiwgB9++AEbGxtcXV355JNPkrzO74qNGzfSoEEDwsPDqVWrFjt27MDJySl9Dq4U7J8KS1pD9CNtkL++eyD/h+lz/LfcO/GYcUREBABWVlaGZUOHDmXQoEHPjS559+5dAMMfBYB+/frRu3dvjI2Nk2x77dq1VMWxefPmF97LdnNzS/JzcHAwALdu3UqSeHh4eBATE8PDhw8ZOnQoPj4+eHl5YWVlxdChQ+nZs+cr45g7dy6dO3c2zPv6+hoqIDo5OXH79u0UX1NwcPBzsaeFi4tLktfk5s2b9O/fH39/f6Kjo4mLi6N9+/YA3Lhx47U/BNq3b8/y5ctp3Lgxy5cvp3///oDWF2DPnj04ODgYto2Pj+fWrVuvf1EpMfK/45s+eY9S9TOo/CkYPfNrOvSS9q/Jk/coFXvD+11Bl/Q9yqDTaQ7t8XsxR44chmUver8C5M6d2/CzpaXlc/MPHjx47hyBgYFcvXo1yf97QkJCij9oX9eoUaMICQmhbNmymJmZ0bt372Tje5Xg4GBy5cqFhcWTsVDS+ruQL1++JPPjx49nwYIF3L17F51OR1hYGKD9Hnh4eLzWYHQff/wxo0aNIjIykjVr1lCjRg2cnJy4e/cu0dHRSfrF6PV6qlWrlqZretssW7YMb29vEhISaNq0KStWrEjyGqdJbASs7w9n1mrzZTtD4x/fivF03pR3ogXF2toaa2vrJAM2mZmZYW1tjbm5ebLbPv3LbmpqirW19XNvTGtr63SLMSgoKMnPj1sCXF1dCQwMNKwLDAzEwsICR0dH7OzsmDZtGoGBgfj6+jJgwACuXLmSpjhq1apFYGAgJ06cSNH2efLkeS72tHh2UK3Ro0eTI0cOLly4QGhoKIMGDUL9N76lm5vbC5PEVw3O1bZtW9avX8+VK1c4ceIETZo0ASBv3rzUr1+fkJAQwxQZGUnHjh3TdF2vZGatTU/HbWKmLTMxT37bpz+QjE21Zc/+cTNL+3t048aNODk5UbRoUcOyF71fX1fevHkpXrx4kv/3iIgIRowYAbz69XxdlpaWzJgxgxs3bnDlyhWcnJyoWLFiqo+TJ08e7t+/T0xMjGFZev4u7Nmzh1mzZrFp0yZCQ0OTdFh3c3Pj+vXrht+LFx3jRXFXqFCBDRs2sGLFCtq1awdAzpw5MTc358qVK4bXIywsLMNbtLKSOXPm0KlTJxISEujUqROrV69Ov+TkwWWY95GWnBiZaolJ8xmSnDzjnUhQ3ga+vr5cvHiR0NBQJkyYQNu2bQGtCfbXX38lICCAyMhIRo0aRdu2bdHpdPj5+XHlyhWUUtjb26PT6ZK08sTExCSZUqJYsWL06NGDDh064O/vT0JCAqGhoUyePJlNmzY9t33r1q1Zs2YNR48eJTo62lD3Ib2Eh4dja2uLjY0N//77r6GlB6Br167MnTsXf39/lFKcP3/e8E0+d+7cL23h8vT0pHjx4vTu3ZtGjRoZks0mTZrwzz//sG7dOhISEoiOjmbLli2Ehoam63W9DR48eMC8efPw8fHhm2++SfLeGjt2LDExMRw6dIj169fTunXrNJ2rUqVK6PV6Zs+eTVxcHHFxcezbt8+QnL/q9XyZ+Ph4YmJi0Ov1SX4GrfUhODgYvV6Pv78/EyZMYNSoUSna92keHh6UKlWKcePGER8fz/r169P81NvTwsPDMTU1JWfOnERGRib5PatYsSK2traG1yQsLIxjx44B2v/bi5KXx9q3b8+sWbPYv38/LVq0AMDIyIiuXbvy+eefExISgl6vJyAgIF2vKSv77rvv+OSTT1BK8emnn7Jo0SJMTU3T5+AXtsGvteDuWbBxhm4b4YNe71Z9kxSSBOUNqlevHjY2Nobp6T+EnTt3pm3btnh4eJA3b15Gjhxp2GfEiBE0atQIDw8PTE1NmTp1KgAXLlygVq1a2Nra0rhxY6ZOnYqHhwcAiYmJWFpaJpkeF097lblz5+Lt7Y23tzf29vaULVuWoKCgZL9ZlipViilTptCyZUs8PT1T3A8mpb766it27dqFnZ0dAwcOTPJBWK1aNaZNm0aPHj2ws7Pj448/NjR7Dxs2jOHDh+Pg4MDSpUuTPXa7du3YuXOnIRkEsLe3x8/Pz1BAztPTk19++SVdrymre/w+LVy4MEuXLmXBggX069fPsN7Y2JgKFSrg7u7Oxx9/zPTp05O0rrwOExMT/Pz82Lp1K3nz5sXV1ZXx48cbkoEBAwbg6+uLg4MD3333XaqO3bt3bywtLdm+fTt9+vTB0tKSvXv3AnDx4kUqVaqEjY0Nffr0YebMmZQuXTpF+z5r6dKl7NixA0dHR3x9fWnZsuVr/m88r0GDBlSrVg0PDw9Kly6d5PfMxMSEjRs3cvDgQfLkyUPRokUNhdjatGlDREQEOXLkMLQSPqt169b4+/tTu3btJLfYfvrpJ+zt7SldujSOjo54e3sn6YeUHSmlGD58uKHlbuTIkcyYMeO1bp89R6+HPZNhaVuICYV8FaHPHnCvnPZjZ1M69bLUOosKCwvD3t6e0NBQ7OzsMjucNKtZsya9evVK0i9EiKzo2rVrFCpUKF1qc+zcuZN+/foREBCQDpEJkTaJiYn079/fUPPn+++/Z+jQoelz8JgwWPcJnNuozVfoAQ0mabdx3zGp+fx+JzrJCiGynjNnzuDp6ZnZYQhBfHw8Xbt2ZdmyZeh0OubOnUvv3r3T5+D3LsDyTnD/Ahibaf1Nynunz7GzOUlQhBBv3JdffsmKFSv4/fffMzsU8Y6Ljo7m448/xs/PDxMTE5YsWWLoLJxm5/xgTV+ICwdbV2i3BPJl7JNp2Ync4hFCCPFOCgsLo2nTpuzduxcLCwtWr15No0aN0n5gfSLsngh7J2vzHtXgY1+wyf3S3d4FcotHCCGEeIn79+/ToEEDjh07hp2dHRs3buTDD9OhQFr4HVjdE67t0+YrfQL1xmqlAESqSIIihBDinXLjxg0++ugjzp07R86cOdm6dSvly5dP+4Gv7YdVPSDiDphaQ9NpUObjtB/3HSUJihBCiHfGhQsX+OijjwgMDCRfvnxs27aN4sWLp+2gej0c+Al2jgOlh1zFoe0iyJX8SO0iZSRBEUII8U44ceIE9evX5+7duxQpUoTt27fj7u6etoNGPYQ1feDSdm3+vQ7akzrpUMX5XZfq6jN79+6ladOmuLq6otPpWLdunWFdfHw8w4YNo3Tp0lhbW+Pq6oq3t/dz45g8fPiQTp06YWdnh4ODAz179jSMlyOEEEKkt3379uHl5cXdu3cpV64c+/btS3tyEnQU5nyoJScmFtBsBrSYLclJOkl1ghIZGcl7773HzJkzn1sXFRXF8ePH8fHx4fjx46xZs4bz58/TrFmzJNt16tSJM2fOsH37djZu3MjevXvp06fP61+FEEII8QJ+fn7Uq1ePsLAwPvzwQ3bt2pVkEMtUUwr8Z8GCBhB2AxwLQq+/oHwXKVmfjtL0mLFOp2Pt2rWG8RuSc/ToUSpWrMj169dxd3cnICCAEiVKcPToUSpUqADAli1baNSoETdu3MDV1fWV55XHjIUQQqTE0yMSN27cmJUrVyYZrT7VYkLhz34QsEGbL9ECmk0HC/ksSonUfH5n+Fg8oaGh6HQ6wxgP/v7+ODg4GJITgLp162JkZMThw4eTPUZsbCxhYWFJJiGEEOJlZs2aZRiRuGPHjqxduzZtyUnwSZjrpSUnRqbQcLJW30SSkwyRoQlKTEwMw4YNo0OHDoZM6fbt2881rZmYmODo6Mjt27eTPc7EiROxt7c3TG5ubhkZthBCiLeYUopx48bRr18/lFL079+fxYsXv/6IxErB3wvgt4/g0VWwd4eeW6FSH7mlk4EyLEGJj4+nbdu2KKWYPXt2mo41YsQIQkNDDVNQUFA6RSmEECI70ev1fP755/j4+ADaiOg///zz649IHBsBa/vCxkGQGAtFGkDfPZBXStZntAx5zPhxcnL9+nV27tyZ5D6Ti4sLd+/eTbJ9QkICDx8+xMXFJdnjmZubY25unhGhCiGEyCYSEhLo3bs3vr6+APz0008MGjTo9Q949xys8Ib750FnDHXHQJUB8LrJjkiVdE9QHicnFy9eZNeuXTg5OSVZX6VKFUJCQjh27Bjvv69loDt37kSv11OpUqX0DkcIIcQ7ICYmhg4dOrBu3TqMjY2ZN28eXbt2ff0DnvwDNg6G+CiwzQNt5oNH1fQLWLxSqhOUiIgILl26ZJi/evUqJ06cwNHRkTx58tCmTRuOHz/Oxo0bSUxMNPQrcXR0xMzMjOLFi9OgQQN69+7NnDlziI+Pp3///rRv3z5FT/AIIYQQTwsPD6dFixbs3LkTc3Nzli9fTvPmzV/vYPHRsHkYHF+ozReoCa1+A5tc6RavSJlUP2a8e/duatWq9dzyrl278vXXX5M/f/5k99u1axc1a9YEtEJt/fv3Z8OGDRgZGdG6dWt+/vlnbGxsUhSDPGYshBAC4MGDBzRs2JCjR49iY2PD+vXrk/2MStnBLsPKrnD7NKCDmsOhxlAwMk7XmN9lqfn8TlMdlMwiCYoQQogbN25Qr149AgICcHJyYvPmzXzwwQevd7Czf8K6fhAXDlY5ofWvULB2+gYsUvX5LWPxCCGEeOtcvHiRjz76iOvXr5M3b162b9/+eoP+JcTB9q/g8H9Pm7pX0fqb2EmXg8wmCYoQQoi3ytOD/hUuXJjt27fj4eGR+gOFBMHKbnDzb22+2iCo7QPG8tGYFcirIIQQ4q2xf/9+mjRpQmhoKGXLlmXLli04Ozun/kDnN8O6TyD6EVg4QMs5ULRhuscrXp8kKEIIId4KmzZtok2bNkRHR1O9enU2btyIvb196g4SH6Pd0jkyV5t3La+Vq8/xGi0wIkNJgiKEECLLe3rQv0aNGrFy5UqsrKxSd5B7F2BVD7hzWpuv0h/qfAUmUgg0K5IERQghRJY2e/Zsw7g6HTt2xNfXN3Xj6igF/yzW6pvER2lP6bSYDUXqZVzQIs0kQRFCCJElKaWYMGECo0ePBuDTTz9l+vTpqRtXJyYUNgyCM2u0+QI1oeVcsE1+aBWRdUiCIoQQIsvR6/V8+eWX/PjjjwCMHj2ab7/9Fl1qRg8OOgqre0BIIBiZQO3RUPUzGUvnLSEJihBCiCwlPj6eXr16sWjRIgCmTJnC4MGDU34AfSIcmAo7x4NKBAcPrbZJvgoZE7DIEJKgCCGEyDIiIyNp27YtmzZtwtjYmN9++41u3bql/ABhwbC2D1zdq82XagNNpoBFKp/2EZlOEhQhhBBZwoMHD2jSpAmHDh3C0tKSFStW0KRJk5Qf4PyW/2qbPARTa2g0Gcp2hNTcFhJZhiQoQgghMl1QUBD169cnICCAHDlysHHjRqpWrZqynRNiYfuYJ+XqXcpot3RyFs64gEWGkwRFCCFEpjp79iz169fnxo0b5M2bl61bt1KyZMmU7Xz/Iqzq/t8IxEDlT6Hu11LbJBuQBEUIIUSm8ff3p3Hjxjx69IhixYqxdetW3N3dX72jUnDid9g09L/aJk7/1Tapn/FBizdCEhQhhBCZws/Pj48//pjo6GgqVaqEn58fTk5Or94xJhQ2DoF/V2nz+WtAy1/ALk/GBizeKElQhBBCvHELFy6kZ8+eJCYm0rBhQ1auXIm1tfWrd7zxt1auPuQ66Iy12ibVPgMj44wPWrxRkqAIIYR4oyZPnsyXX34JQJcuXZg3b96rS9fr9Vptk13jQZ8ADu7Qej64fZDxAYtMIQmKEEKIN+LZ6rBffPEFkyZNenXp+vDbsLYvXNmtzZdsBU2nSm2TbE4SFCGEEBkuPj6eHj16sGTJEkBrRfniiy9eveOFbbDufxD1AEytoOH3UK6z1DZ5B0iCIoQQIkNFRkbSpk0btmzZgomJCfPnz6dLly4v3ykhFv76Bg7N1OadS2u1TXIVyfiARZYgCYoQQogMc//+fRo3bsyRI0ewsrJi1apVNGzY8OU73TkDq3vD3TPafKVPtNomphYZHq/IOiRBEUIIkSGuX79O/fr1OX/+PI6Ojvj5+VG5cuUX76DXay0mO76FxDiwygnNZ0LRBm8uaJFlSIIihBAi3Z05c4b69etz8+ZN3Nzc2Lp1K8WLF3/xDiFB2jg61/Zp80UaQrPpYJPrzQQsshxJUIQQQqSrAwcO0KRJE0JCQihRogRbt24lX758yW+sFJxeCX5fQGyoNshfgwlQvqt0hH3HSYIihBAi3WzYsIG2bdsSExND1apV2bBhA46OjslvHP1Iqwh7Zo02n+8DaDkXnAq+uYBFlvWKh8+FEEKIlFmwYAEtW7YkJiaGJk2asH379hcnJ5d3wayqWnKiM4Zao6D7FklOMklsbCzr1q3L7DCSkARFCCFEmiil+O677+jRoweJiYl069aNNWvWYGVl9fzG8dGweTgsbgHht8CpEPTaDl5fgrE06mcGPz8/SpUqRcuWLdm9e3dmh2Mg7wYhhBCvTa/X8/nnnzN16lQAhg0bxsSJE9El138k+BSs6Q33zmnzFXpCvbFgloIxeES6u3DhAoMHD2bTpk0AODs7Ex4enslRPSEJihBCiNcSFxdH9+7dWbp0KQBTpkxh8ODBz2+oT4SDP8PO8aCPBxtn7fHhwh+94YgFQFhYGOPGjWPq1KnEx8djamrKoEGDGD16NHZ2dpkdnoEkKEIIIVItPDycNm3asG3bNkxMTPD19aVTp07Pb/joOqz9HwQe1OaLNYGmP4O105sNWADa7bi6dety9OhRABo1asRPP/1EkSJZr0Kv9EERQgiRKjdv3uTDDz9k27ZtWFlZsXHjxueTE6XgxFKYXU1LTsxstFaTdkskOclEOp2OwYMHU7hwYTZu3Iifn1+WTE4AdEopldlBpFZYWBj29vaEhoZmqeYoIYTI7k6dOkXjxo25ceMGzs7ObNy4kQoVKiTdKOohbPgMAtZr826VoeUccMz/5gN+x92+fZuRI0dSo0YNunXrBmitKPHx8ZiZmb3xeFLz+S0tKEIIIVJk27ZtVK9enRs3blC8eHEOHTr0fHJy6S+YVUVLToxMoLYPdN8kyckbFhcXxw8//ECRIkVYsGABI0aMICYmBtBaUV6YnEQ9hG0+8M+SNxht8lKdoOzdu5emTZvi6uqKTqd77rlppRRfffUVefLkwdLSkrp163Lx4sUk2zx8+JBOnTphZ2eHg4MDPXv2JCIiIk0XIoQQIuPMnz+fxo0bEx4eTs2aNTlw4ACenp5PNoiLgk1DYUlriLgNOYtCrx1Q4wswMs60uN9FmzdvpnTp0gwdOpTw8HAqVKjA2rVrsbB4yWCLsRGwZzJMe0/r0LxjrPZIeCZKdYISGRnJe++9x8yZM5Nd//333/Pzzz8zZ84cDh8+jLW1NfXr1zdkbgCdOnXizJkzbN++nY0bN7J371769Onz+lchhBAiQyil8PHxoWfPniQkJNC5c2e2bNlCjhw5nmx06x/4xQuO/KLNV+wLffeAa9lMiflddeXKFZo2bUqjRo24cOECuXPnZv78+Rw+fPjlgzTGRcGMD2DXOIgNA+fS0HwGmGTy6NEqDQC1du1aw7xer1cuLi5q8uTJhmUhISHK3NxcLVu2TCml1NmzZxWgjh49athm8+bNSqfTqZs3b6bovKGhoQpQoaGhaQlfCCHES8TGxqrOnTsrQAFq9OjRSq/XP9kgMUGpPd8r9Y2jUmPslJpcRKmL2zMv4Hecv7+/ApSJiYkaMmSICgkJefHGT7+OSim1fqBSU99T6tRKpRITMyzG1Hx+p2sflKtXr3L79m3q1q1rWGZvb0+lSpXw9/cHwN/fHwcHhyT3LevWrYuRkRGHDx9O9rixsbGEhYUlmYQQQmScR48eUb9+fZYsWYKJiQnz5s1j7NixTwqw3b8ECxrCznGgT4ASzeFTfyhU9+UHFulGKcWpU6cM85UrV2bKlCmcPn2aH3/8EXt7++R2goCN2tNVd84+Wf7Rt9D/KJRuA0ZZo3tqukZx+/ZtQKtG9zRnZ2fDutu3b5M7d+4k601MTHB0dDRs86yJEydib29vmNzc3NIzbCGEEE+5du0a1apVY/fu3dja2uLn50ePHj20lXo9+M+COdUg6DCY2UKLOfDxQrB6wbg7It0dO3aMatWqUbFiRa5evWpYPnjwYIoVK5b8Tlf3wm91YXknuHsG9k95ss7CHoxNMzjq1MkaadIrjBgxgtDQUMMUFBSU2SEJIUS29Pfff1O5cmUCAgLImzcv+/fvp169etrKB5fBtzFsHQEJMVCgptZqUrYDJFfaXqS7u3fv0qtXLz744AP8/f0xMTHh5MmTL9/p1j+wuCUsbAo3/wZTK/jwc2j0w5sJ+jWlayVZFxcXAO7cuUOePHkMy+/cuUPZsmUN29y9ezfJfgkJCTx8+NCw/7PMzc0xNzdPz1CFEEI8Y+PGjbRr146oqCjKlCmDn58f+fLl01pNjv4Kf30N8VFa0bV6Y+H97pKYvCHx8fHMnDmTr7/+mtDQUAA6d+7MpEmTcHV1ffGOGwfD3/O1n41M4f1uUGMo2Dq/eJ8sIl0TlPz58+Pi4sKOHTsMCUlYWBiHDx/mk08+AaBKlSqEhIRw7Ngx3n//fQB27tyJXq+nUqVK6RmOEEKIFJo1axYDBgxAr9dTr149Vq5cqRXSengV/uwP1/drG+avAc1mQA6PzA34HZKYmEiVKlU4duwYAOXLl2f69OlUrVr11Ts7FgR0UKYt1BzxVtWjSXWCEhERwaVLlwzzV69e5cSJEzg6OuLu7s6gQYMYN24chQsXJn/+/Pj4+ODq6kqLFi0AKF68OA0aNKB3797MmTOH+Ph4+vfvT/v27V+eBQohhEh3er2eYcOG8cMPWnN/z549mT17NqbGxnDkV9g+BuIjtdsCH32rjUCcRTpRviuMjY1p2rQp169fZ8KECfTo0QNj42Rqy0Q+0PqVeFaHog21ZR/0goK1wLnkmw06PaT2EaFdu3YZHjl7euratatSSnvU2MfHRzk7Oytzc3NVp04ddf78+STHePDggerQoYOysbFRdnZ2qnv37io8PDzFMchjxkIIkXZRUVGqTZs2hr/j48aN0x4jfnhNKd8m2qPDY+yUmt9IqQdXMjvcd0ZERIQaPXq02r9/v2FZVFSUevjwYfI7xIQptes7pcbn1V6v6R9oj4BnQan5/JaxeIQQ4h10//59mjdvzsGDBzE1NWXBggV06tgRji3QSp3HRYCJJXz0DXzQW1pN3gClFCtWrOCLL77gxo0blCtXjqNHjybfWgKQEKv1L9n7A0Td15a5lIE6Y6BQnSzZPyg1n9/p2gdFCCFE1nfp0iUaNmzIpUuXcHBwYO3atdQsW1B70uPKLm0j9yra6MNOBTM32HfEyZMnGThwIHv37gXAw8OD0aNHY/SixPDCVvD7HEL/e6rVsSDUHgUlWmabZFISFCGEeIccPHiQZs2a8eDBAzw9Pdnk50fxmL9hVmeIC9fKm9cZA5X6yhg6b8CDBw/w8fFh7ty56PV6LC0tGTFiBF988QWWlpYv2VOnJSe2ecBrGJTrnOXqmKSVJChCCPGOWLVqFZ07dyY2NpYKFSrgt+xXch/y0UYgBnCrBM1nQc5CmRvoO2Tz5s3Mnj0bgLZt2zJ58mTc3d2TbqTXQ8Cf2oB+5btoywp/BC1mQ8mWYPqyRObtJQmKEEJkc0oppkyZwtChQ1FK0bRpE1aObIH5ymYQGwrG5lDHByp/Kq0mb8DDhw9xdNSq7nbs2JFdu3bh7e2Nl5dX0g2VgnN+sHsi3PlXq/ZavClYOmj9S8p2fPPBv0GSoAghRDaWmJjIZ599ZhiBftTAHoz9IBTdliHaBnkraN/EcxXJxCjfDUFBQQwdOpS9e/dy/vx5bG1tMTIyYt68eUk3VAoubodd4yH4hLbM3A4qffJOJZCSoAghRDYVGRlJhw4d2LBhAwCbJnrTgO3oLoWCsRnUGgVV+oOxfBRkpOjoaH744QcmTpxIdHQ0RkZG/PXXX7Rs2fL5jW+f1qq/3jiqzZtaa/2Bqg5458Y6knelEEJkQ0FBQbRo0YLjx4/j4WjOwZEVcQ1fp610LacN8Jf7BYPKiXShlGLdunUMGTKEa9euAVCjRg1+/vln3nvvveR3MrGEm8e0fyv2gmqDwDrnG4s5K5EERQghsplDhw7RokUL7ty5Q9+qOZje0BLT8JPaWCw1h2sfetJqkqHi4uJo0qQJ27dvByBfvnz88MMPtG3bFt3T9UmCjkDgIag2UJvPWQha/gL5PwTb5Mene1fIO1QIIbKRRYsW0bt3b3Kax7OjtzO1XaMhIQLyvKf1NXkbS56/hczMzMiVKxfm5uYMHTqU4cOHY21t/WSDm8dh1wS4tB3QQZH6kKuotq7Mx5kSc1YjlWSFECIbSExMZMSIEfwweTK93zdlSkMbrI0TwchEq5NRfXC2q5ORlcTFxTFr1ixatGiBp6cnAMHBwURHR1OgQIEnG94+rSUm5zdp8zpj7WmcWiPBLvuPRyeVZIUQ4h0SFhZGhw4duHhoCzu7WlHT0wRIhLzvayMPO5fI7BCzLaUUmzZtYsiQIVy4cAF/f3+WL18OQJ48eZ5sGBYMW4bB2T+1eZ0RlGkHNYZKtd4XkARFCCHeYpcuXaJV86Y0ynGF1Z9YY2Gi00Yeru0j1WAz2JkzZxgyZAjbtm0DIFeuXNStWxelVNJ+JgBm1nB1L6CDUq3Aa7g82v0KkqAIIcRbaufOnXz7SWsW1oynXB4LbWGBWtB0KuTwzMzQsrX79+8zZswY5syZg16vx8zMjEGDBjFy5Ejs7e21jR5egZPLtU7JOh1Y2GmtWY4FpEUrhSRBEUKIt9AvM6cS9ucI/mpniomRMXpze4waToL32mfJUWyzk9mzZzNr1iwAWrVqxffff0/Bgv/dprl/URtd+PRKUIngWhaKNtTWFW+SOQG/pSRBEUKIt0h8fDwzPm9HU7ZTqKoZAAnFmmPS5AewyZ3J0WVPSilCQ0NxcHAAYMiQIfj7+zN06FBq1aqlbXQ3QEtM/l0N/PfsSaG6YJc3U2LODiRBEUKIt8SDm1fw/7Yug/M8AIwIxxab9r9gUqxRZoeWbZ05c4bBgwfz6NEjDh8+jJGREdbW1mza9N9TODGhsH7Ak86vAEUbQY0vtE7K4rVJgiKEEG+BwK0zsdgxiiZ5tG/n13LVwbPnAm0AOZHukutncuLECcqXL590Q3M7eHBZ+7l4M+2pnDxl3nzA2ZAkKEIIkZWF3yZ4XmfcQ46CFVwJNUbXfDr5a3bK7Miypbi4OGbOnMk333xDaGgoAK1bt+b777/X6pkEHobDc6D5DO3JHJ0OmvwEZjbS+TWdSYIihBBZkVKo44uI3fAleYghPlGx/IYLDSbuIqeL9GvICDdu3KB27dpcvHgRgLJly/LTTz9Rs2ZNuLYfFg6Cq3u0jfOW1wbwA3CrmCnxZneSoAghRFbz4DKJfw7AOPAAFsDftxLZYt6EYb8sxtRUqsFmFFdXVxwcHHB2dmb8+PF069oV4+v7YEEjuH5A28jIBN7rAMUaZ26w7wBJUIQQIqtITIBDM1G7JmCcEENUvGLM7jgKdJzM6H4DMju6bOfevXtMnjyZr776ChsbG4yMjFi2bBm5cuXCzsoCFjaGoEPaxsZmUK6zNmSAg3vmBv6OkARFCCGyguBT2tMgwSfQATuuJDB0nxmTf/OjTp06mR1dthIXF8eMGTP49ttvCQ0NxdzcnLFjxwI8qWcC2mjCJhbwfjeoOhDs5dbamyQJihBCZKb4GNgzCQ5MA5XIoxjF51tjOBRbkPXbN1CoUKHMjjDbUErx559/MnToUC5dugRAuXLl+KhuHe0x4f0/QZsF4Jhf26HeWGg4SUtUxBsnCYoQQmSWq/tg4yB4oH1Yrjobz4DNMZSv0RD/pUuflE0XaXbixAkGDx7M7t27AXBxcWHCuLF0rWCH0f7hcC9A2/DgdGgyRftZbuVkKklQhBDiTYu4B9t94OQyAB7Gm9FzTQjrziUwdOhQJk6ciLGxDPKXnr7//nt2796Nubk5w74YzPDGhbD8ezas1Z7YwdwOKv0PKn+SuYEKA0lQhBDiTdHr4Z9FsH0MxISg0LHiig19V9wkWpmxcOFCvL29MzvKbCE6OprIyEhy5swJYEj6xo0di8fmTrBVG0sHCweo0g8q9gFLh0yLVzxPEhQhhHgT7pyBjYMh6DAAIZbutF4QyM7zN3F2dmbz2rVUqVIlk4N8+ymlWL58OcOHD6dKlSosW7YMoh/h4e7O4sWLtY2KNIDIe1D5U6jQQxtpWGQ5kqAIIURGiouE3d+B/0xQiSgza9aHl6b1t9tIVFC5cmVWrFiBm5tbZkf61jty5AiDBw/m4MGDAOSySCBm/edYnF4G7RZDof+ehqo+WCtJb2qRidGKVzHK7ACEECLbOr8ZZlaCgz+DSiTKsy4t/8pHi++05GTw4MHs2bNHkpM0CgoKonPnzlSqVImDBw9SJq8Vx7+uxpGOMVgc/w3iI5MO5mduI8nJW0BaUIQQIr2F3oDNw+DcRm3e3p3jeTpSf8AU7t+/j52dHb6+vrRs2TJz48wGdu7cSZMmTYiOjuZ9V2NmdyxCBetb6NRpUIB7VfhwCBSqm9mhilSSBEUIIdJLYoI2kNyuCdq3diMT9JX7MX5/PGM+H4VSinLlyrFy5cqkBcHEa6tYsSIODg588EEFtjQLxTL8mraiSAPtVo575UyNT7w+SVCEECI9BB3VOsHeOa3Nu1XmfpXRtBvwNTt37gSgb9++TJ06FQsLub3wuvbt28fChQv5Ze4cjC5uw6ZATQ4fPky+fPnQnV4FF7dqiYlzycwOVaSRJChCCJEW0Y/gr2/gmC+gwDIHfDSWveFutK/bjuDgYKysrJg7dy6dO3fO7GjfWlevXuXLL79k3ZpVdChlStikfTjE3YaG3+NWqa+2UZmPtUlkC+neSTYxMREfHx/y58+PpaUlBQsWZOzYsSilDNsopfjqq6/IkycPlpaW1K1b1zC8tRBCvBWUgpPLYcYHcGwBoKBsJ/T9jjJp+y1q16lLcHAwJUqU4OjRo5KcvKawsDCGDRtGuVLFcAn8k0sDbFjU0lJLTsztQJ+Q2SGKDJLuLSiTJk1i9uzZLFy4kJIlS/L333/TvXt37O3tGThwIKBV9Pv5559ZuHAh+fPnx8fHh/r163P27Flp+hRCZH33L4LfELi6V5vPWRSaTOGhXQm6dujKxo1a59jOnTszZ84crK2tMzHYt1NiYiLz5s3Dx2c0PYuEcuFTM3Jb//ed2jqXVsPkg55gIcMBZFfpnqAcPHiQ5s2b07hxYwA8PT1ZtmwZR44cAbTWk6lTpzJ69GiaN28OwKJFi3B2dmbdunW0b9/+uWPGxsYSGxtrmA8LC0vvsIUQ4tXiY2D/FG1QucQ4baRbry+hygCOHD/Bx17lCAwMxNzcnOnTp9OrVy90Ol1mR/1W0ul0/Prrr9y9e4/aTXOR2zoW5eCBrtpAKNsJTC0zO0SRwdL9Fk/VqlXZsWMHFy5cAODkyZPs37+fhg0bAtp9xNu3b1O37pNHvuzt7alUqRL+/v7JHnPixInY29sbJqkZIIR44y7vhNlVtJGHE+O0x1Y/PYSqPoTps+dSvXp1AgMDKViwIP7+/vTu3VuSk1Q6deoUUVePwNr/YRQayE8//cTUqVPxGrMZWv2KbsBx+KCXJCfviHRvQRk+fDhhYWEUK1YMY2NjEhMTGT9+PJ06dQLg9u3bADg7OyfZz9nZ2bDuWSNGjGDIkCGG+bCwMElShBBvRvgd2DoS/l2lzdvmgQbfQYnmhIWH06tdO1auXAlAq1atmD9/voxCnEq3bt5k6fg+lA7fTZmC/30sWdhTveEkqlevrs27vZ9p8YnMke4JyooVK/j9999ZunQpJUuW5MSJEwwaNAhXV1e6du36Wsc0NzfH3Nw8nSMVQoiXSEyAo79pNU1iQ0FnpA0oV2sUWNhx8uRJ2rRpw6VLlzAxMeGHH35g4MCB0mqSChGhj9g6pS+F723hi9w6yG2CXoFRqZZQpm1mhycyWbonKEOHDmX48OGGviSlS5fm+vXrTJw4ka5du+Li4gLAnTt3yJMnj2G/O3fuULZs2fQORwghUu/qPtj8Jdw9q827loMmP4FrOZRSzJ83j/79+xMTE4ObmxsrVqygcmUpCJZSiYmJLJg/j2qnvqS1k4LcOqITdYTkb0qeFt+CY/7MDlFkAeneByUqKgojo6SHNTY2Rq/XA5A/f35cXFzYsWOHYX1YWBiHDx+WkTyFEJkr9Cas7A4Lm2jJiaUjNJkKvXaAazkiIyPp3r07vXr1IiYmhoYNG/LPP/9IcpJS4bdBKUaMGEHvPn3ZcC6Ge9E6/s3dCosRV8jTY7EkJ8Ig3VtQmjZtyvjx43F3d6dkyZL8888/TJkyhR49egBaz+xBgwYxbtw4ChcubHjM2NXVlRYtWqR3OEII8WoJsdpow3snQ3yUdjunQg/tdo6VIwDnzp2jTZs2nDlzBiMjI8aOHcvw4cOf+0ImknH7NOrAz+jOrIHOa/jkk09YvHgx1g0HYtf3U3JZS58d8bx0T1CmT5+Oj48Pn376KXfv3sXV1ZW+ffvy1VdfGbb58ssviYyMpE+fPoSEhFC9enW2bNkiNVCEEG/exe3awH4PL2vzbpWh0WTIU8awybJly+jduzeRkZE4OzuzbNkyatWqlUkBvyWUgss7id39A+Y3DmLomXNlF/nrfs3169cxMzPLzAhFFqdTT5d4fUuEhYVhb29PaGgodnZ2mR2OEOJt9PAKbBkJFzZr8zbO8NFYrXPmfx1do6Oj+fzzz5k9ezYANWvWZNmyZYa+dCIZ+kQ4tQL9/mkY3Q8AIEGvWHkmgWpDl+FeuXkmBygyU2o+v2UsHiHEuyUuSiu0dmAaJMaCkQlU/gRqfAkWT/5g/v3333Tp0oVz584BMGrUKL7++mtMTOTP5ssk6vWEb/wKh4S7RMQpfjsex57YkgwdPx33ylUzOzzxFpHfNCHEu0EpCFgPW0dBaJC2rEBNaPg95Cpq2CwhIYGJEyfy7bffkpCQgIuLC76+vtSvXz9z4s7qQoLg7/ng9SUXrgbx8ccfUyzxGvkdjNhyPw8jv/2eNR9/LI9fi1STBEUIkf3dO689NnxltzZv7wb1J0DxpobbOQAXLlzA29ubw4cPA9CmTRvmzJmDk5NTJgSdxd04BodmwZm1oBLBwQ3XYm25c+cOgbE2+PTw4XC/flLDSrw2SVCEENlXTJhWmv7wHG3UW2NzqD4Iqg0CMyvDZkop5syZwxdffEFUVBT29vbMmDGDTp06yTf/pyUmwLkNcGg2BB02LFaeNdDlLIqNjQ1r1qyhaNGiktSJNJMERQiR/SgFp5bD9q8g4o62rGgjrdXkmTobt27domfPnmzZsgWA2rVr4+vrK8NpPCs2AmZVgdBAABKUESvOJvDD/iiGTulIB89qgDYemxDpQRIUIUT2EnwSNn0JQYe0eceC0HASFP7ouU1XrlzJ//73Px4+fIi5uTmTJk1iwIABUtvksfA7YPvfuGnmNiTmLEJs2H1mHo5hyr5QbkcoKlWqRIECBTI3TpEtSYIihMgeoh7CznFwbAEoPZhag9dQqPwpmCTtBxESEkL//v35/fffAShfvjyLFy+mRIkSmRF51vJf/RIOzdb67Hx2Ar2tKwsXLmT2pMOcunSb2EQoWrQoMydMoGXLlnIbTGQISVCEEG83fSIcXwg7xkL0Q21ZqdZaTRP7vM9tvmPHDrp168aNGzcwMjJi5MiR+Pj4SNGwuCjtttjhOXDv3H8LdXBlD7qyHZk1axZ/n79J3rx5+eabb+jatas8ci0ylLy7hBBvr6t7tceGb5/S5nOX0B4bzv/hc5tGR0czYsQIpk2bBkChQoVYtGiRjAEW/QgO/Ky1PEU/0paZ2XDTuTb29b7Exq00OuCHH37gyJEj9O/fH0tLy0wNWbwbJEERQrx97l2A7T5wQevYirk91BoJH/QC4+f/rB07dowuXboQEKBVNv3f//7H5MmTsbGxeZNRZ006IzjyC8RFgIMHt9yb8ZnvUVZtWMKYMQX5+uvSAHh5eeHl5ZXJwYp3iSQoQoi3R+R92D0R/l6g1d7QGcMHPcFrGFjnfG7zhIQEvvvuO7755htD0bV58+bRqFGjTAg+C0iM14rVXdkDTadpNWAs7KHu19yJNmLob7tYMmQCSimMjY2JiIjI7IjFO0wSFCFE1hcfoxUF2zcF4sK1ZUUbwUffQs7Cye5y8eJFvL29OXRIe5qndevWzJkzh5w5n09ksr2oh1o/nSO/QthNbVnZTuBeibt37zJuyWnmzJlDfHw8AG3btjWMOC9EZpEERQiRden18O9q2PHNk/L0ed6DeuOT7WcCWtG1uXPn8vnnnxMVFYWdnR0zZsygc+fO797TJvfOa51eTyyDhGhtmXUuqNATHLVHg4cPH86CBQsA+Oijj5g4cSLvv/9+ZkUshIEkKEKIrOn6Qa0D7K3j2rxdXqjzFZRuCy+oUxIcHEzPnj3ZvFkbobhWrVr4+vri7u7+pqLOOgIPwfynxg9yLg2VPyG2SBMiYhJwstEqvY4ePZrz58/z7bffUqdOnUwKVojnSYIihMhaHlzWKsCe26jNm9lo5ekr90tSnv5Zq1atom/fvoaiaxMnTuSzzz57d4quRd6H+xfA479Krvk+0FpJcpeAyp+QmK8yS5ct46tWZalcuTLLli0DoECBAhw4cCATAxcieZKgCCGyhqiHsOd7OPqrNm6OzgjKd9WezrHJ/cLdQkJCGDBgAEuWLAGgXLlyLF68mJIlS76pyDPXzWNa35J/V4OFAww+AyZmYGQMn/ijTMzx8/NjZLPynD59GoC4uDhCQ0Oxt7fP3NiFeAlJUIQQmSshVnvMde9kiAnVlhX6COqNhdzFX7ibUoq1a9cycOBAbt68iZGRESNGjOCrr77K/kXXEmK1UYSP/KIlKI/Z54XwW5DDE4B9h44ycuRI9u/fr622t2f48OEMHDgQK6sXt0YJkRVIgiKEyBxKaR+yf30NIde1Zc6ltMSkYO2X7nr9+nX69+/Pxo3abaBChQqxcOHCd2OgunN+sH4gRN3X5o3NoGQrqNgH8j3p3Lp06VI6deoEgIWFBQMHDmTYsGE4OjpmRtRCpJokKEKINy/oiNYB9sYRbd7GBWqPhrIdtVsTLxAfH8/UqVP5+uuviYqKwtTUlGHDhjFy5MjsW91UKa2ImrmtNu/goSUndnmhQg/tNphNLgDCw8OxtdW2a9asGa6urjRp0gQfHx/y5cuXWVcgxGuRBEUI8eY8vKo9MnxmrTZvagVVB0LVAWD+8qqu/v7+9O3b19CPokaNGsyZM4fixV98G+itFhsOJ/+Ao79BnrLQaq623KUUdN0A7lUNVXMPHTrE119/zZ07dzh+/Dg6nQ4bGxsuXbqUfRM3ke1JgiKEyHjRj2DvD1qficQ4QAflOkGt0WCX56W7Pnr0iBEjRvDLL7+glMLJyYnJkyfTrVu37FnX5N4FLSk5sfRJUbqIuxAfDab/JRv5awBw+PBhvv76a7Zs0Ur+m5iYcOLECcqVKwcgyYl4q0mCIoTIOPHRcHQe7PvhyUB0BWpCvXHgUvqluyql+OOPPxg0aBB3794FoFu3bkyePDl7VoO9vAsOTIUru58scyqk9S15r/2T5AQ4cuQIX3/9taHei7GxMV27dmXUqFEUKFDgzcYtRAaRBEUIkf4S4uCfxdqTOeHB2rJcxeCjsVD4I20MmJe4dOkSn376Kdu3bwegWLFizJkzJ3sPVnfr+H/JiQ6KNoSKvSF/zeeK0h08eJBq1aoBWmLi7e3NqFGjKFiw4JuOWIgMJQmKECL96BPh1HLY/d2TJ3Ps8oHXl9rYL8mMNPy02NhYJk+ezLhx44iNjcXc3JzRo0czdOhQzM3N38AFvAFKQaA//D0fijWBki205eW7ao9ZV+gJOTyS7PLgwQOcnLTKr1WqVKFChQqULFmS0aNHU6hQoTd8AUK8GZKgCCHSTq+HgD9h1wStmimAdW6o8QW83w1MXp1c7Nmzh//973+cO3cO0MaFmTVrVvb5AI4O0ZK3v+fDPe0aCQt+kqBY59QGP3zK8ePH+eabbzh48CBXr17FxsYGnU7HwYMHMTU1faPhC/GmSYIihHh9SsHFbbBzLNzWnq7BMgdUG6T1nXhJafrH7t+/z9ChQ/H19QUgd+7cTJ06lfbt22ePTrA3j2lJyenVTwbsM7WC0h9Dhe7J7nLixAm+/vpr/vzzTwCMjIzYuXMnzZo103aX5ES8AyRBEUK8nit7YOe4J7VMzGyhSj+o8ilYvLqEulKKhQsX8sUXX/DgwQMA+vbty8SJE8mRI0dGRv5mbR0NgQe1n3OX0GqXlGmb7P/RyZMn+frrr1m3bh0AOp2Ojh074uPjQ9GiRd9g0EJkPklQhBCpE3REazG5ulebN7GESn20VhOrlFUpPXfuHP/73//Ys2cPAKVLl2bu3LlUqVIlg4J+Q27/C8d8oeZw7ZYNaP83Du5aYuJW8YUdhG/cuEH58uXR6/XodDrat2/PV199RbFixd5c/EJkIZKgCCFSJviU1mJycas2b2Sq3aL48HOwdUnRIaKjo5kwYQKTJk0iPj4eKysrvv76awYNGvT23raIj4azf2q3cYIOa8sc3KDaZ9rPJVtqUzKuXr1K/vz5AciXLx/t27dHr9fj4+NDiRIl3kT0QmRZkqAIIV7u3gXYNR7OrtPmdcZaSXqvL7WWgRTavn07n3zyCZcvXwagcePGzJgxA09Pz/SP+U24fwmOLYATvz+p8WJkAsUag1vlF+6mlGL79u18//337Nq1i/Pnzxs6Ai9atAhj4xeX+hfiXSIJihAieQ+vwp7v4dQfoPSADkq1hpojIGfKn6y5desWQ4cOZenSpQC4urry888/06pVq7e3E2xMKMyuComx2ry9G7zfFcp1eWFrUnx8PCtXruT777/n5MmTgFbHZO/evYYERZITIZ6QBEUIkVTYLa3A2vFFoE/QlhVrArVGgnPJFB/m+vXrTJo0iXnz5hEXF4eRkRH9+/dn7Nix2NnZZVDwGSQkEC79pfUjAa2Da8kW2qPDFXpoxedeMMhhdHQ0v/zyCz/99BPXr2u1YaysrOjduzeDBw/Gw8Mj2f2EeNfplFIqvQ968+ZNhg0bxubNm4mKiqJQoUIsWLCAChUqAFoT55gxY/j1118JCQmhWrVqzJ49m8KFC6fo+GFhYdjb2xMaGvr2/aETIquKvA/7f9LGgUmI0ZYVrK2NMpz3/RQf5vLly0ycOJGFCxeSkKAlONWrV+enn34y/A14KyTGw4WtcHwhXNwOKPj0MOT+r9OqPvGlIy8/FhERgZubGyEhIeTKlYuBAwfyySefGAqvCfEuSc3nd7q3oDx69Ihq1apRq1YtNm/eTK5cubh48WKSxwa///57fv75ZxYuXEj+/Pnx8fGhfv36nD17FgsLi/QOSQjxMlEPwX8mHJoN8ZHaMvcqUNsHPKul+DDnzp1jwoQJLF26lMTERADq1KmDj4/P21Wi/sFlrUz/iaUQcefJ8vxeTxI3eGFycunSJZYuXYqPj49hVOGxY8diamqKt7e3DOAnRAqlewvK8OHDOXDgAPv27Ut2vVIKV1dXPv/8c7744gsAQkNDcXZ2xtfXl/bt27/yHNKCIkQ6CL0J/jO0x2Ljo7RlecpCHR8oWOeV4+U8dvr0acaNG8fKlSt5/OekYcOG+Pj4vH2PDV/ZDYuaP5m3zqV1CC7n/cp+N0eOHGHy5MmsXr0apRRbt26lXr16GRuvEG+ZTG1BWb9+PfXr1+fjjz9mz5495M2bl08//ZTevXsD2mN1t2/fpm7duoZ97O3tqVSpEv7+/skmKLGxscTGxhrmw8LC0jtsId4d9y7AgWla2XV9vLbMpYz2VE6xJilOTI4dO8a4ceMMRcUAWrRowahRo96eWzl3zmitJAVra/PuVbSkJE9ZKO8NRRqAidkLd1dKsXnzZr7//ntDTReARo0aZc8Rl4V4g9I9Qbly5QqzZ89myJAhjBw5kqNHjzJw4EDMzMzo2rUrt2/fBsDZ2TnJfs7OzoZ1z5o4cSLffPNNeocqxLvl5jGtj0nARuC/hlPPD6H6YO0DOoWJib+/P2PHjmXz5s2AVu30448/ZtSoUZQpUyaDgk9HseHw7xqtE/DNvyGHJwz4Rxs12MQcBp4Ac5tXHiY4OJh69erx77//AmBiYkKnTp344osvKFWqVMZegxDvgHRPUPR6PRUqVGDChAkAlCtXjn///Zc5c+bQtWvX1zrmiBEjGDJkiGE+LCwMNze3dIlXiGxNKbiyS0tMHld+Ba2lpNogcPsghYdR7Nmzh3HjxrFjxw5AeyS2Y8eOjBw5MutXO1VKS9COL9SSk7gIbbmRCeR5D2JDtTGE4KXJiV6vx8jICAAXFxeUUtjY2NC3b18+++wz+bskRDpK9wQlT548z1VALF68OKtXrwa0X2qAO3fukCdPHsM2d+7coWzZsske09zcPPsMtS7Em6BPhIANWmISfEJbZmQCpdtqFU5zpyyheFxUbOzYsezfvx/QWgq6du3KiBEjKFiwYAZdQDrb8Y32f/GYU2HtFs57HcAm1yt3Dw4O5ueff2bVqlWcPHkSKysrdDodS5cuxd3dHQcHh4yLXYh3VLonKNWqVeP8+fNJll24cMHwrH/+/PlxcXFhx44dhoQkLCyMw4cP88knn6R3OEK8WxJitb4lB6bBg0vaMlMrKN9VG8jPIWXf8JVS+Pn5MXbsWI4c0QYDNDMzo2fPngwbNixr1+7Q6+H6frDLC07/JVCF62lPKZVsqSUm7lVSdEvr9OnTTJs2jcWLFxMXFwfA8uXL6d5dG4X4rbilJcTbSqWzI0eOKBMTEzV+/Hh18eJF9fvvvysrKyu1ZMkSwzbfffedcnBwUH/++ac6deqUat68ucqfP7+Kjo5O0TlCQ0MVoEJDQ9M7fCHeTjFhSh34Wakfiio1xk6bJrortXO8UhH3U3yYxMREtWrVKlW2bFmF1lFFWVpaqkGDBqmbN29m4AWkg7Bgpfb+oNTU97Tr3/j5k3V6vVJRj1J0mISEBLV27VpVq1Ytw/8BoKpVq6bWrVunEhMTMyR8Id4Fqfn8TvcWlA8++IC1a9cyYsQIvv32W/Lnz8/UqVPp1KmTYZsvv/ySyMhI+vTpQ0hICNWrV2fLli1SA0WI1Iq8D4fnwpFfICZEW2abB6r0h/e7paizJ0BiYiIrVqxg/PjxnDlzBgAbGxs+/fRThgwZ8lyn9iwjMR4u7dA6vF7YAkqrv4KZLZhZPdlOpwNLhxQd8tq1a7Rq1QqlFMbGxrRq1YrPPvuMatVSXhNGCJF2GVJJNqNJHRTxzgsJhIMztA/mhGhtmVMhreNrmbba0ygpcOvWLX7//Xd+/fVXLl68CGiP/Q8cOJDPPvss61c7nVsDgk8+mXerpN3CKdkSzKxTdIizZ8+yb98++vbta1jWrVs38uTJwyeffIK7e8oHRBRCvFym1kERQmSguwFa/5LTK5+Mk5OnLHw4RHsyJwWl1yMjI1m3bh2LFi3ir7/+Qq/XA+Do6MjgwYPp379/1uz0GfkAzq6F8t3A+L8/XQVqaQXnyrTTEpMUdv7V6/Vs2rSJn3/+me3bt2NkZESDBg0MfWt8fX0z5hqEECkmCYoQWZ1SEHRES0zO+z1Znt9LS0zye72yw6der2fv3r0sWrSIlStXEhERYVhXvXp1unTpQocOHbC1tc2oq3g9ifFwcZtWdv7CVq2wnIOHNjgfwIefa2MFGZum6HChoaH4+voyffp0Ll++DICRkRHNmzdPUgxSCJH5JEERIquKi9RaSo7Og9un/luog+JNofqgFA3gd/78eRYvXszixYsJDAw0LM+fPz/e3t506dIlaz4qfPu0lpScWgFR958sz/Me8FQyZpHyW7y7d++madOmhuTMwcGBXr160a9fPzw9PdMnbiFEupEERYis5t55+Hs+nFimFRADMLGA0m2g6meQq8hLd3/48CHLly9n4cKFHD582LDczs6Odu3a4e3tTbVq1dClsHLsG3fnLMyp/mTeOrfWr6ZsR3AumeLD6PX6JPWWypcvD0CJEiUYOHAgnTt3xto6Zf1UhBBvniQoQmQFifFwbqPWWnLtqYE2HQtAhR5QthNYOb5w97i4ODZv3syiRYvYsGED8fHaGDvGxsbUr18fb29vmjVrlvVG0k2Ig4tbISwYKvXRluUurvWryeGhXXfBOk/6nKRAeHi44TaOjY0Nx44dQ6fTYWdnx/HjxylUqFDWTc6EEAaSoAiRmUJvaqMJH18EEf+NRaUzgqKNtMSkQC1tjJhkKKU4duwYCxcuZNmyZTx48MCwrmzZsnh7e9OhQwdD9eYsQyntltXjWzjRD8HUWmshMbfR+tP03pmiDr9Pu3TpEjNmzGD+/PmEh4cDWqtRYGCgofNr4cKF0/1yhBAZQxIUId40vR6u7tZaS85vflK7w8ZZq/j6flewz/fC3W/cuMGSJUtYtGgRAQEBhuUuLi506tQJb2/vrFnhNOIenF6hJSZ3/n2y3MZFu4WTGPdkWSqSk2PHjjFmzBg2bdrE46oJRYsWZcCAAXTt2hUbm5TVghFCZC2SoAjxpkQ91D6c/54HD688We75IXzQU3tM+AVPo0RERLB27VoWLVrEjh07DB/EFhYWtGjRgq5du1K3bl1MTLLwr/Tf82G3NogoxuZQrJF2C6dArVTdwgGt9ejxbZo7d+7g56c93dSoUSMGDhzIRx99ZBjUTwjxdsrCf82EyCZuHtNaS/5dDQkx2jJzO22gugo9nqvdce/ePU6ePMmJEycM07lz50hMTDRsU6NGDby9vWnTpg329vZv8mpeTZ8IV/fAqZVQvAkUa6wtf689XNquXXepVk9GD06huLg41q9fz2+//UaFChUYN24cAPXr12fkyJF07dqVIkVe3oFYCPH2kEqyQmSEuCgtITn625PRhAFcSkOFnlD6Y/SmVly5ciVJInLixAlu3ryZ7CELFiyIt7c3nTt3pkCBAm/mOlJKKbj1j/ZY9L+rIeKOtrxIQ+j4R5oOffbsWebNm8eiRYu4f1975NjV1ZXAwECMjVPXT0UIkbmkkqwQmeX+xf8eEf4dYv57RNjYjIRizbjoUIMD1+I44XuMEyfmcfLkySQF055WqFAhypYtm2RydXXNek+f6PWw7wets+uDi0+WW+aAEi20VpPXtHz5cn7++WcOHjxoWObq6kr37t3p3r27JCdCZHOSoAiRVnFRcGEzHFuo3dr4T4jOgc338jDzYCiHTi0kMXH+c7uam5tTunTpJIlImTJlsl5F16fFhoP5f/EZGWmVXh9cBBNLKNpQ6/BasA6YmKXqsI8bcx8nYXv37uXgwYMYGxvTpEkTevXqRYMGDbJ2PxshRLqR33QhXkdiPFzeCadXoQ/YgNF/A/Yl6sHvYjyzjsax7XIYiifVW52cnChXrlySZKRo0aJvxwdubDic89NaSq4fhCFnn9Rl+fBziA7R+puYpz6xevDgAUuWLOG3335j9uzZVK+uFWn73//+h5ubG127djUUWxNCvDvegr+MQmQRej0EHkSdXknCqdWYxmu1NoyAK4/0/H46nt+OxxEYqihUqBCt27wFt2heJjEeLu3QHg0+t+nJqMkAV/dCyRbaz0UbpvrQer2enTt3Mm/ePNasWUNcnPaI8YIFCwwJSunSpSldunRar0II8ZaSBEWIl1EKgk+QcGI58f8sxzL+ITrAFLgdoWf5mXhWBChsi9WkYfNGLB1bIevfokmJq3thRVetiNpjjgW12zelPwan1xu/JyYmhh9++IF58+Zx7do1w/Jy5crRq1cvOnbsmMbAhRDZhSQoQiTn/kWijy4m/vgy7OLvYoL2yxISo1gdEM+fl82wKd2Y5t1asqlBg6z3qG9q3Q2A+KgnAxDmKgYxIdo4OKVaQ5mPwbX8K0dNTo5erzfUJDEzM2P+/Plcu3YNe3t7OnXqRM+ePQ3j5AghxGOSoAjxWOgNHu2fT/zxZeROvIUlYAlExSs2nE9g6y1bbMu1pHG/VqyqWRMzs9R1As1SlIK7Z+Hsn3BmHdw/rxWM67ZRW2+TG3rtAJcyqS6iBhAdHc3WrVtZvXo1e/bs4eLFi5ibm2NkZMTYsWPR6/W0bt0aKyur9L0uIUS2IQmKeKepyPvc3D6LhBN/4MlNHpcOi09UbLucwP4QFyzLt6HR8LbMe//9t6sPSXJu/wtn1mqJydOPBRuZgoU9JCY8SUjypq5VIzw8HD8/P1avXs2mTZuIiooyrNuxYweNGjUCoFOnTmm+DCFE9icJinjnJEQ+4sKGqajTqyhifIN8T1VE33M9kaPR7lhV6Ej9sR1oXPD1+lpkGUolvS3z1xi49Jf2s7GZ9jhwyRZQpAFYOrz2aZYvX07Xrl2JjY01LPPw8KBVq1a0bt2aKlWqvPaxhRDvJklQxDsh9MEdzq6fjtGZ1ZQ2v0UJU7SersDx23pOJhbCumIXavXrgleuXJkaa5o9rup69k8IWA9dNzwZfLBMOzCxgBLNtaTEIvWVmG/fvs26desoXrw4Xl5e2mHLlCE2NpYiRYrQunVrWrduTfny5d/+FichRKaRUvciW4qMiOD4XysJObqSnCH/UNYhEkvTJx+Wlx7Bv7pi2FTuRpWm3lhbW2ditOlAKbh5HM7+d/sm5En9FepPhCqfpunwgYGBrF27ltWrV7N//36UUnz88cesWLHCsM25c+coWrSoJCVCiBeSUvfinRMTE8Ph/bsI2vs7VjcP8J7NfT7MYaS1kuQC0HEz0ogLJiWwr96TMvW6UMg0+ZGD3zq3TsDyzhAa9GSZqRUUrqfdvilc77UOq5Ri8uTJrFq1iqNHjyZZV7FiRT788MMky4oVSzrooRBCpIUkKOKtFB8fz5HDhzm1azXqwjaKGAXyoZsOLxMduAEYEZsIl+JyEZ2vOvlqdiNvKS/yvu3f7vV6uHEE4qOhYC1tmWMBiLgLptZQtIF2+6bQR2CWuidklFIEBgbi4eEBaCXnV6xYwbFjx9DpdHz44Ye0bt2ali1b4ubmlt5XJoQQSUiCIt4KiYmJ/PPPP+zbsYXQE+vJFx1AXU+o5mAEBUGr5wp34y2571AOx4ptca7chpKvUXo9y0lMgKBD2q2bs+sh4jbkee9JgmJhpz0e7FIaTC1TdejIyEj8/f3566+/WLNmDdevX+fevXuGptcvv/ySR48e0aJFC5ydndP7yoQQ4oUkQRFZkl6v599//2Xnjh1cPrQRh/t/UzNvAv08jDErquNxQhKnN+K2RUHMSzYmd9WO5M5ZhNxveyvJYwEbtUeCL21/MjIygLkd5CoOCXFPBuRzq5jiw548eZLly5ezZ88ejhw5QkJCwpNDm5tz/PhxatasCUDbtm3T40qEECLVJEERWYJer+f8+fPs3r0b/93bMLq2l6q5o2hVyAT34o+fA9beriFGOUjwrIVjxbaYFaiBu9lb3sH1sQeXtds1jxOsM2vg39Xaz5aO2lM3JVtAgZpgYp6iQ0ZERHDgwAFKlSpF3rx5ATh8+DATJ040bOPm5kbNmjVp3LgxjRo1evvL9AshsgVJUESmCAkJ4fDhwxw6dIh/jhwg/vpRyuSIon5BE3oVMca0uA7QWgfiMSEqVzlsy7fCqEh9HJ7+EH+bJcZrIwNf2AoXtsDDy/CJPziX0Na/1wHs3bTB+PJ9AEbGrzxkWFgYBw4cYPfu3ezZs4e///6bxMREZsyYQb9+/QCoU6cOXbt2pWbNmnh5eeHp6SlP3gghshxJUESGS0xM5MyZMxw6dIhDh/y59e8BcsZco3I+Y5rkM2bU+0aYfKADLAz7RFu5YlaiMcZFG2DqWQ37VPatyLKiH8GFbXBhszZScGzYk3VGplr5+ccJSuGPtCkFLl68SKdOnTh27Bh6vT7Juvz58xvGwgEoWLAgvr6+ab0SIYTIUJKgiHR37969/5KRQ5w8egB14xjvOcVRJZ8xk/IZk6uREdooN0/EmTth4lkZowI1oXBdLB0LZErs6U4pSIx7ckvm5jFY2+fJequcUKS+dvumYC14RafekJAQ9u3bx549e8ifP7+hVcTFxYXjx4+j1+spWLAgXl5ehhYSd3f3jLo6IYTIMFKoTaRJfHw8J0+e5NChQxw+5M+dswdw1d+icj5jquQzplRuI4yNkt4+SNSZoM9dCtP8VbVbF/k+0CqdZpfbDAlxcP2AdtvmwhYo3hTqjftvXSz4Nob8NaBIQ2304KdaN571559/cvHiRa5cucKhQ4c4ceIEj39lK1asyOHDhw3b+vn5UaZMGXkEWAiRZaXm81sSFJEqN2/eNLSOnD56AJM7JyifO5Eq+UyolM8YR8vnk4w4K2dMPKtg5FZJS0bylElxJ8+3RsRdbYyb85vh8i6IC3+yzrkUfHLAMKuUIiQkhGvXrnH16lWuXbtm+NnOzo7ff//dsG2hQoW4fPlyklMVLlyYmjVrUqdOHdq1a5fhlyaEEOlFKsmKNFNKcfXqVf755x9OnDjB2ZN/E37tH9xNHlI5nzHd8hlT3MsII91Tg9oAiUamKJeymHhWhnwVId8HmNnlybwLyShPP+IL8GvtJJVc9Va5eJSrIpd0BQk0LcDHT+1avnx5Tpw4kexhc+fOnWS+cePG3Lt3D09PT0qXLo2Xlxeurq7peCFCCJE1SYIiiIuLIyAggBPH/+bG6f2EX/sHo0dXcbeOo4iTEX2djMhX3gjKw3N9R2zyYupZBZ1bJchXAWPnUkk/uLOLhFgIOgJX98CV3fDoGnx+3vBkzamIHFhHPWJHkAl//BPK7vOXUWgtH87OznzcqbvhUE5OToblnp6ez01PmzZt2pu4OiGEyHIyPEH57rvvGDFiBJ999hlTp04FtHFTPv/8c/744w9iY2OpX78+s2bNkkqVb0B4WBgBR3Zy49QeIq6dQPfoMo76hxR2hE45jDCx00GZx1snTTTiTe0wci6JsaF1pAJmNrmfO0e2cfec9rTNlT0QeAgSop9Zf1ar3goM3mnMzt23kqzOlSsX+fPnx9PTk8TERIyNtWRmyZIl2NnZYWWVulL0QgjxLsnQBOXo0aPMnTuXMmXKJFk+ePBg/Pz8WLlyJfb29vTv359WrVpx4MCBFxxJpFr0Ix5cOMKNk7sNLSIOifdxt4mnopmOigCO/008qa8Rp4yJMHfBKFcRbD3LYZy7KDgVAscCmFo5Zs61vAlKwf2LYJ8X/iv8pk6vRLfvB8Mm96J1bLsYx46rCey4msDxAa44/beu34CBtGjVmoIFC+Lp6YmHh8cLR0h2cXHJ6KsRQoi3XoYlKBEREXTq1Ilff/2VcePGGZaHhoYyb948li5dSu3atQFYsGABxYsX59ChQ1SuXDmjQnr7KQUxIRB5/7/pHvGhwYQFXyHqfiAJIcHoIoLJkXgfe9MEnMDwAYr94x90JOjhTpwlEebOGOUqQo5CH+BUpCI6p8KY2ebB8SVPlWQroTf/u2WzR/s3PBh9u98xKt4EgB//PEXhu/HsuJrIjqsJnL2nx8jIiPfee4/m3tWJjY01HKpVq1aZdRVCCJEtZViC0q9fPxo3bkzdunWTJCjHjh0jPj6eunXrGpYVK1YMd3d3/P39k01QYmNjk3wYhIWFPbfNW0kpiIuAyHtJkg4i70HUA1TkPeIf3SQh7A66qAeYJYRhTNIiXKaQNBEx4vEwNdwM03MjxoIIMxeMcmuJiHu52jgWKEdeY1PeSQ+vwsHpWkLy4FKSVTEJintnDuH2X4LiWLYhHQdsonLl6rT5pDrVq1enUqVK8uSYEEK8ARmSoPzxxx8cP36co0ePPrfu9u3bmJmZ4eDgkGS5s7Mzt2/fTvZ4EydO5JtvvsmIUJMKv63Vr9Angj7hvynxmX+fXZ7S+f+WJcZC1EOIvI+KvIcuMfaF4Twu9v5sl9PQGMXdSD33ohR3IxUPY42JN7VDb5UTEwdXHApWwLN8HUqWq0jed7Wfg14PDy5qHVvt8kAhLSE+dy6AYn/PAyBRr/j7lt5wy+ZgUCJTfs7LJ/8domPHjnTp0gVT03c0mRNCiEyU7glKUFAQn332Gdu3b8fCwuLVO6TAiBEjGDJkiGE+LCwsY4pR3fkXVvVI/+O+wOOKIZFxWqJxN1JxL0r/1M+Ke5GKBIscmNrnwSqXB/auBcnrnh93d3fc3d2p4eFBjhw5ZCyVmFC48TfcOIoKOoIKOoLRf7VI7uWsQq7+WoISHGPB6n2x/L+9O4+Lqmz7AP4bdlkVUFRWBUFNgQTBhV5FQXoyl1xSzESzzFKy1KyeSs1K8tXSUiv1VZ8sK5ce07TcUJFMRUFQUFFUXBAG2WZYZICZ8/4xevAIKijDDPr7fj58HK65zjn36Ybh6j7n3PexLDUOZFahtYcPQkJCMHZsb3wfEgIvLy9xlw3180tERPXX4AVKYmIicnNz0a1bNzGmVqtx8OBBLFu2DLt27UJFRQWKiookoyhyufyeNw+am5vD3Fz3E3udv14ITUUrVFZpUFEloKJKDVWVGqpK7Vd5RRVUFVUor6hEpQao0gBVGuHWv7V/r74rVqkB8spujYCUCiiTWcKxjZtYcLg/5Q43Nzf0vPW9s7MzzMwew8d2H4VGA5TlA9YtAQAlikI0W9IBxkIlAG3hJwNQVing+HU1lKoqPH9r06DgYOx4eirGR4dgZa9eNeYdISIiw9DgBUr//v1x6tQpSWzChAno2LEj3nvvPbi6usLU1BSxsbEYPnw4ACA9PR1XrlxBz549G7o59XLxpi2ejcl4cOIdLC0tYW1tDSsrq/v+a3/rX2tra7i4uIgFCUc/6uBmEZB1HMLVBJRnxMMkOxnlzVrB5t0UAEChsgSnrtxEKysjHLmmxuFrVTiWDagdO8GvWyAG9xgs7srKygqLFi26x4GIiMhQNHiBYmNjgy5dukhiVlZWcHBwEOMTJ07E9OnTYW9vD1tbW0RHR6Nnz556f4LH29sbc+fOfWCxcftfS0tLySqx1HAEQYDy91mQnd0OW5V2fhEZqqeJq1ReBSrLAVMLuLi44LXL3eHs7omAsACMCwzEIl9fXqIhImrC9DKT7OLFi2FkZIThw4dLJmrTt3bt2mHOnDn6bsaTo0oF5J2DJucUlOf+AfIvoPnrOwAjI2g0Gvz1y/cY3VlbAGYUaHD4ahUSrgvINfdA+57PI8ZUW4DIZDLs3HtAjydCREQNjYsFUqMqOvwjypN+hUn+OTRX34CJ7K4fvzePAq06al8OCoRJhQJGbkHwfro3AgIC4Ovri2bNmtWyZyIiMnRcLJD0p6IUyD2L8iuJKEr/G8Z5Z9Fy0hbtDK0AflnyId7opNDmyoDCmwJOytVIy5eh0LQN3jNvLv5QLtuawEtoRERPKBYo9MiuHfwJwrHVaKa8CHsUwUgGWAC4/UyWOvskjG8VKLm2/liUnAhVcy9YuAfAPbAnuvr6orenJ0xMpD+OLE6IiJ5cLFDo/ipKgcLL0BRcQtGlZJRcSoRJ/lm0GbcKMo/eAIDffvgW01zTxIldcko0OCnX4EKJBYot3fCqpQdur+Ize+VWPrVEREQPxALlSaeuApTXgMLLgIMnYOcCADi1ZTHaJS+AtUy7gq8R7lhbEEDB6f2wv1WgmHcIxdfJedA4doRNh57w6tsT3bp0wQBHxxqHY3FCRER1wQLlSVJ4GTi1CZrCTJRnnwUKL8NCdQNG0N6oqgyZDduwGQCA+IQUdDXXFicFNwVcKtTgshLIR3OU2XpiiEuoWKxMfv9zAJ/r4YSIiOhxxQKlqauqAIqu3FpkMBdQZgNFl4HCTFTlXQR6TIZJ9wkAgD83rsFz2UtgBODOFXpUVQIyizQQcvNw+55qr5AX8NmfZWjR3h8e3f3g4+ODwR4eNe4TISIi0gX+tTFEleXalXZvr2xcegMoya1+7TsK6DoCAFB68TCsfh5c625MAFw7dRAutwqUfKE5fkiuwMUiDa6VmEBt4wzz1j5o5ekL7+COCO8XLm47YOAQDBg4ROenSkREVBsWKLpSVQFAAExurSGkKgauJwMVJXcUHTeqRz78xgD+kdrc3DRgVb977vqGUSu0vFWg/L7nHwxSCZCXaBcZzC4RcKlIg0uFGlws1GDSR93gcmu70OdHIq1dZ/Tx8YGbmxufkiEiIoPFAuVuN4uAnFNAZZm2mKgolX55hQG3bg6F/DTw16za8zSVQP/ZwDPaezpQcAn44fl7Hlbl0BnmtwqUv5PP46kKY+SUaHC9qBLyUu3igvJbqxwPmdwWt8dMnHy6o+Pn1mjXrh3at2+P9p3aw8fHB/18fODt7Q0bGxvxGC4uLnBxcanl6ERERFo3btxAVlYW/P399doOFih3yzl130IC5jbVBYpaBWTG3zu3olR8KVeqYG7aGmVVMuTflCFLUYnLeaU4f70I2cVVeNHFHrcvqJSZ2sM+plDc1tbWVixA2gW1Q2v/6ksxYWFhuH79+sOcKRERPeF+/fVXxMbGYsyYMQgNDQUAZGVlYcGCBfjll1/02jYWKHdr1gJw9AbMrAAza8DU8tbrW19t/cXUnApLnG89ETcUpcgtLMX1PCWu3yjC5Zx8XMnOwwdubTH+Vu6pqwqEf3Su1kOamJggqLR6YbuAgABs3LhRLEq44jEREdWXRqMRL+Vfv34d0dHRyM/Px4EDB8Sc3bt3Y+3atXBzcxMLFG9vb7Ro0UIfTZZggXIXuawl9jvOQE5ODuSX5MjJydG+lmtfz5/fCeM9tblpF64h7I3F99xXVrZcfO3u7o7w8HC0bt0aHh4e1SMi7drB2dkZxsbGYq6DgwNGjhyps3MkIqLHg0ajwbVr1+Dm5ibG5s+fj2XLliE6OhoffPABAMDa2hr//e9/AQAKhQJ2dnYAgKFDh8LV1RX9+/cXt7e0tDSIBXxZoNwlNTUVkZGR93w/KytLfO3u7o5+/fqhdevWcHJyEv+9/drDw0PM7dChA3bv3q3LphMR0WOqpKQE6enpsLS0RKdOnQBoF95zcnJCeXk5iouLYW1tDUBbtGRnZyM9PV3c3tbWFt9//z3c3d1hbm4uxgcPHozBg2t/ElTfuJrxXc6fP4/XXntNUnTc+drDwwP29vYP3hEREVE9aTQa7NmzB+np6Zg0aRIsLLSX/+fMmYN58+bh1VdfxapVq8T8Vq1aQaFQICkpCU899RQA4PLly5DL5fDx8RFHSgwFVzN+BB06dJBcnyMiItKF5ORk/Pbbb3B3d8err74KQLscyKhRo6BQKBAaGoquXbsCAHx8fNCqVSuxYLntxIkTcHJykkyi6e7uDnd398Y7ER3hRBhEREQ69u6776Jfv344f/68GDt58iQ+++wzrF+/XozJZDI899xzeOGFFyQPR0RGRkIul2Pp0qWS/To7Oz+2M3w/nmdFRETUCMrLy2FkZAQzMzMAwP79+/HOO+/Aw8MDv//+u5i3b98+JCUlIS0tDR06dAAABAYGYtKkSejevbtknz///HON4zyJT3KyQCEiInqAgoICyOVy8QZVAHj++efx119/Ydu2bRg4cCAAwMzMDCkpKSgoKJBsP2vWLKhUKgQGBoqxzp07Y8WKFY1zAk0QCxQiIqJbsrKykJqaCh8fH/FJzPj4ePzP//wPPD09kZGRIeZaWVlBo9FILtv4+fnhjz/+QMeOHSX7HTVqVKO0/3HCe1CIiOiJU1JSgt9++63GfB9Tp07Fs88+i23btomx25dk1Go1qqqqxPiCBQuQnZ2NadOmiTFra2s8//zz8PLy0vEZPP44gkJERI+1Xbt2Yffu3ejXr594KaawsBAjRoyAiYkJXnvtNZiamgLQjoCkp6dL5gpxcnKCUqmUrG0GQDLXFTU8jqAQEVGTdedUXsXFxYiKikJISAjUarUY37t3L7766ivs2rVLjDk7O+OZZ57BmDFjUFJSIsbnzp2L06dP4/XXXxdjMpmsRnFCuscRFCIiMmiCICA3NxctW7YU15ZZtWoVFixYgOHDh2PBggUAtFO0b9y4EeXl5cjMzISnp3ZdkvDwcFRVVSE8vHqhVSMjIxw8eLDxT4bqjAUKEREZhJs3b+L8+fOQyWTiBGUajQZt2rRBbm4uLl++LK45o1arceHCBaSlpYnbGxsbY/HixXB0dETLli3F+IABAzBgwIDGPRl6ZCxQiIio0e3evRunT5/Gyy+/DAcHBwDA6tWrER0djSFDhohziBgZGcHe3h43btzApUuXxAJl0KBB8PHxkTz2CwCTJ09u1PMg3WGBQkREOpOamop169bB0dERs2bNEuNTpkxBRkYG/Pz8EBoaCgDw9vZGixYtYGlpKdnHrl270LJlSzRr1kyMOTs7w9nZuXFOgvSCN8kSEdFDUalUku9nzJiBwMBAHDlyRIxdvXoVCxcuxE8//STJjYiIwLBhwyTFSHh4OAoKCmrMpOrm5iYpTujJwBEUIiK6p+LiYmRkZMDFxUW8r+PQoUMYNmwY2rZtixMnToi5aWlpSExMRFpaGnr06AEA6Nq1K9566y106dJFst9ly5bVONaTOJ073ZtMuPMZrSaiPss1ExHRgxUVFWHv3r1QKpV45ZVXxHh4eDj27t2LtWvXYvz48QCAs2fPolOnTrCyskJxcbFYWOzduxclJSUIDg5GmzZt9HEaZODq8/ebIyhERE+YDRs2YM+ePXjxxRfFp1uysrIwcuRI2NraYsKECWLR4eXlheTkZJSXl4vbe3p64vjx4/Dy8pKMeoSFhTXuidBjjfegEBE9BgRBQH5+vmSRuuzsbPTt27fG5ZXY2FisXr0af//9txjz9PREjx49MHjwYMm9JUuXLsWNGzckT8eYmpoiICAAdnZ2OjwjetKxQCEiakIqKiqwd+9erFmzRhKPjo6Go6MjvvnmGzFmZ2eHuLg4pKWlIT8/X4wPGTIEc+bMkcwNYmFhgcOHD+PHH3+EhYWFGDcx4UA76Qd/8oiIDNTOnTuxZcsWPPPMMxg7diwAoLKyUpwRddiwYWjevDkAiI/c5uXlidtbWlpiw4YNcHd3l0zVPnDgQHFNGiJD1eAjKDExMejevTtsbGzQqlUrDB06FOnp6ZKc8vJyTJkyBQ4ODrC2tsbw4cMhl8sbuilERAanvLwc6enpuHDhghirqKhAUFAQHBwcoFAoxHhSUhJWrlyJPXv2iDErKyuEhIRg4MCBkjVkpk6dirKyshpPx7z44osIDg6GmZmZDs+KqOE1eIESFxeHKVOm4MiRI9izZw8qKysxYMAAlJaWijnvvPMO/vjjD2zatAlxcXG4fv06hg0b1tBNISLSG0EQsGzZMsycORNKpVKMf/XVV+jYsSM+/fRTMWZmZobMzEwUFBTg4sWLYjw0NBQff/wxRo8eLdl3fHw8tm/fDhcXFzFmY2PDuULo8SLoWG5urgBAiIuLEwRBEIqKigRTU1Nh06ZNYs6ZM2cEAMLhw4dr3Ud5ebmgUCjEr6tXrwoABIVCoevmExFJ5ObmCseOHRNu3rwpxjZu3Cj4+fkJU6dOleQ6OjoKAITk5GQx9tNPPwnW1tbC+PHjJbn79+8XUlJShPLyct2eAJEeKRSKOv/91vk9KLeHK+3t7QEAiYmJqKyslDyO1rFjR7i5ueHw4cPi5D53iomJwSeffKLrphIRiU6dOoWtW7fCxcVFnP8DADp37oy8vDwkJyfDz88PgPYSTUpKClq0aCHZx/jx41FVVSW5/yMyMhJjxoypMSlZ3759dXYuRE2RTp/i0Wg0ePvtt9G7d2/xMbecnByYmZmJN3bd5uTkhJycnFr388EHH0ChUIhfV69e1WWziegxlZeXh9jYWMTHx0vi/fv3h729PZKTk8VYSkoKPv74Y/z444+S3Hbt2qFNmzaSe0VCQ0OxY8cOrFixQpK7cOFCLF68GO3btxdjRkZGnDGVqA50OoIyZcoUpKamSp61fxjm5uYwNzdvoFYR0eOmrKwMxcXFcHJyEmPvv/8+Tp06hS+//BIdO3YEoF10buzYsQgNDcW+ffvE3KKiIhQWFuLatWvw9/cHAPj6+uKVV17B008/LTnW4cOHYWxsLIm1bdsWbdu21dHZET2ZdDaCMnXqVGzfvh379++X3MjVunVrVFRUoKioSJIvl8vRunVrXTWHiJoYQRBw8+ZNSWzVqlWYMWOG5AmYTZs2wcrKqsaNpHv27MGff/6JjIwMMdauXTt06tQJ7dq1q7Hf1NRU9O/fX4z5+vpi9erVmDp1qiT37uKEiHSjwUdQBEFAdHQ0tmzZggMHDtT4IAgICICpqSliY2MxfPhwAEB6ejquXLmCnj17NnRziMjACIIgucSxYcMGnDt3Di+//DI8PDwAANu2bRMfj42LixNzV65ciePHj6Nv377w9PQEAHEBuztnUAWAmTNnorS0FL6+vmKsV69eOH36dI02devWrcHOj4gaRoMXKFOmTMHPP/+MrVu3wsbGRryvxM7ODs2aNYOdnR0mTpyI6dOnw97eHra2toiOjkbPnj1rvUGWiAyfWq2WjCxs2bIFGRkZGDlypFh07Ny5E1FRUejcuTP2798v5i5atAjHjx+Hn5+fmGtnZweVSoXs7GzJcUaPHo0+ffrA3d1djPXq1QuFhYU1pl2PjIxs4LMkosbU4AXKd999B6DmHel3roS5ePFiGBkZYfjw4VCpVIiIiMC3337b0E0hoockCALKyspgZWUlxtavX49z585h3Lhx4ujFzp07MXr0aHTt2lVy4+kXX3yBhIQE+Pj4iEWHpaUlcnNzaxQSgwcPhr+/v+QejqCgIFy6dElyTwkAzJgxo0ZbzczMOAkZ0WNIJ5d4HsTCwgLLly/H8uXLG/rwRFSL8vJyZGVlQa1Ww9vbW4x/++23OHfuHCZPnizeSLpjxw4MHToUQUFBOHTokJi7dOlSHD16FN26dRMLFCsrKygUihozQUdERKBDhw5o1aqVGOvWrRuSk5PRpk0bSe7HH39co73NmjUTCxsiejJxLR6iJkKj0QDQPqYKANeuXUN8fDysra0xaNAgMW/y5MlISUnBN998g+7duwPQjnS88MIL6NGjBw4fPizm/vjjjzhy5Aj69u0rFih2dnaoqqpCbm6u5PhDhgyBv7+/uOYLoC06Tp8+XWOkY968eTXab21tLc4bQkT0ICxQiHRMrVZDqVRCrVbD0dFRjG/fvh05OTl4/vnnxSfYjhw5gsWLF8PLywuff/65mNurVy8cPXoU+/btQ58+fQBoJz0cM2YMgoODJQXKyZMnceTIEVy7dk0sUOzt7WFlZVXjcf0xY8ZIbjgFgMDAQFy5ckUy+gFo5yO6m5WVFTp16vSw/2mIiO6JBQrRA6hUKhQWFsLBwQGmpqYAgDNnzmDPnj1wdnYWn0YDtAuzXbx4EevWrUPnzp0BaO/diIqKQkREBHbu3Cnmzpo1C2fOnMH+/fvFAkUul2Pjxo0IDg6WFCgajQYajUbypIqrqytCQ0PFSRBvmzt3LsrKyhAcHCzGnnnmGcnCcrdFR0fXiFlYWMDV1bVe/42IiBoaCxR6Itz9lMmuXbuQl5eHQYMGwdbWFgCwe/duLF++HAEBAZg9e7aY6+7uDrlcjhMnToiTeCUkJGDatGmIiIiQFCgnT55Eeno6bty4IcZu77+srEzSpj59+sDT01MyDbqfnx++/vpruLm5SXI3btwIU1NTyQhMt27dJJON3TZgwIAaMc5cSkRNDQsUMngajQYymUz8I5uZmYmTJ0/CyclJMkoQHR0NuVyOb775RhyRWLlyJd566y0MHToUv/76q5gbFRUFuVwuWU8lOzsb27Ztg0qlkhy/RYsWyM3NlaxI6+3tjZEjR9aYP+Prr79GVVWVZFRj4MCBKC8vr3F55fYTb3fy8PDAW2+9VSN+d8FCRPS4Y4FCOnN7JlCNRgNra2sxtnXrViiVSowcOVJcHv6vv/7C+vXr0atXL7z55pviPpycnJCbm4vMzExx7outW7fi7bffxujRo/HLL7+IuZs3b0ZOTg4+/PBDsUAxMzODSqWSrJsCACEhISgqKoKJSfWvQK9evbBixQp4eXlJchMSEmBlZSXenAoAPXv2rHViwYiIiBqx25eFiIio7nS6WGBTlJqairCwMMnqpYD2qYShQ4dKhtQzMzMRGRlZYyrs7777Dq+++qpkMqrc3Fy89dZbmDVrliT3wIEDWL16NVJTU8VYZWUljh07htTUVMlj22q1uk6PcddXaWkpsrOzJcsPaDQaHDp0CPv27UNVVZUYT0hIwJIlS7B3715Ju5577jmEhIRICoFPP/0UVlZWmDlzphiTyWSIjIxEVFSU5CmRc+fOYf369TUWcbs9anJn29zd3REUFCRZgA0AZs+ejaVLl0qWTBg2bBgyMzOxYcMGSe7mzZuxd+9ePPXUU2KsQ4cOmDRpEvr16yfJtbGxkRQnRETUCIQmSKFQCAAEhULR4Ps+ePCgAEDw9vaWxJ999lkBgPDDDz+IscTERAGA4OLiIskdNmyYAED49ttvxdjZs2cFAELz5s0luePGjRMACP/7v/8rxq5evSoAEExMTCS5b775piCTyYR58+aJMaVSKfj5+Qk9evQQKioqxPjChQuFbt26CcuXLxdjCoVCsLOzE5o1ayaoVCox/u677woAhBkzZoixiooKAYAAQMjPzxfjc+fOFQAIb7zxhqRt5ubmAgAhMzNTjH311VcCACEyMlKS+9xzzwkRERHC5cuXxVhSUpKwaNEiYefOnZLcixcvCjk5OUJVVZVARERNW33+fvMSz118fHywfv168ZLEbdOmTcOwYcMk0/G7uLhgyZIlNXJfeuklBAQEICgoSIzZ29vjo48+gqWlpSTX398f+fn56NChgxhTq9VwdXWVXH4AgJs3b0IQBEm8tLQUKSkpkMlkknhWVhaSkpIkN0yampqKIxwqlUqcfdPc3BxGRkbiPBsAYGJiAm9vb5iamkrifn5+GD16NAICAiRtW7NmDczNzeHg4CDGXn/9dbz66quS2UgB7URgd3v66adrrBoLoMZaTkRE9GSQCYIOrhnomFKphJ2dHRQKhfiExJOgtLQUSqUSlpaW4nTh5eXliIuLg0qlwuDBg8XctLQ0XLlyBV5eXmLxIwgCzp8/DzMzM7i5uYmXLYS7Fm8jIiLShfr8/WaBQkRERI2iPn+/eecfERERGRwWKERERGRwWKAQERGRwWGBQkRERAaHBQoREREZHBYoREREZHBYoBAREZHBYYFCREREBocFChERERkcFihERERkcFigEBERkcFhgUJEREQGhwUKERERGRwWKERERGRwWKAQERGRwWGBQkRERAaHBQoREREZHBYoREREZHBYoBAREZHBYYFCREREBocFChERERkcvRYoy5cvh4eHBywsLBAcHIyEhAR9NoeIiIgMhN4KlA0bNmD69OmYM2cOkpKS4Ofnh4iICOTm5uqrSURERGQgZIIgCPo4cHBwMLp3745ly5YBADQaDVxdXREdHY3333//vtsqlUrY2dlBoVDA1ta24RtXUar919QSkMm0r6sqAE0lYGQCmJjXzDVpBhjdqvfUlYC6ApAZA6YWD5lbBkAATCwAI+NbuVWAWgXIjADTZg+XW3kTEDSAsTlgbKKNadRAVXn9ciEDzCzvyC0HBDVgbAYYmz5Ergaouql9bWZVnVulAjRVgJEpYGJW/1xBACrLtK9r7c/65Nah7xvk56S2/myAn5Pb/fmoPyc1+vNRf07u0Z+P+nNyZ38+8s/JPfqTnxH8jJDkPoafEQ2sPn+/9TKCUlFRgcTERISFhVU3xMgIYWFhOHz4cI18lUoFpVIp+dKp+W21X2X51bF/vtbG/pwpzV3opY0rrlbHElZpY9umSnOXdNXG89KrY8nrtbHNr0hzlwdr49nJ1bG0/2pjv4yW5q4K1cYv/1MdO7dTG1s3RJq79l/a+IXY6tilOG3s/8KluT+N0MbP/lEdu3ZMG/u+tzR348va+MmN1TF5mja2tJs0d8skbTzxP9Wxwkva2JedpLl/vK2NH/2uOlaSo4194SbN3fVvbTz+y+pYuaK6PzVV1fF987SxffOqY5qq6txyRXU8/kttbNe/pcf7wk0bL8mpjh39Thv7421p7pedtPHCS9WxxP9oY1smSXOXdtPG5WnVsZMbtbGNL0tzv++tjV87Vh07+4c29tMIae7/hWvjl+KqYxditbG1/5LmrhuijZ/bWR27/I82tipUmvvLaG087b/VsexkbWx5sDR38yvaePL66lheuja2pKs0d9tUbTxhVXVMcVUbW+glzf1zpjb+z9fVsbL86v6805452ljcF9WxyrLq3Nt/gABtzvy22m3uxM8ILX5GaD3OnxF6pJcCJS8vD2q1Gk5OTpK4k5MTcnJyauTHxMTAzs5O/HJ1dW2sphIREZEe6OUSz/Xr1+Hs7Ix//vkHPXv2FOOzZs1CXFwcjh49KslXqVRQqVTi90qlEq6urrzE8zC5HL69lcvh2zrl8hIPL/EA/Ix4kj8jGlh9LvHopUCpqKiApaUlNm/ejKFDh4rxqKgoFBUVYevWrffdXuf3oBAREVGDM/h7UMzMzBAQEIDY2OprnBqNBrGxsZIRFSIiInoymejrwNOnT0dUVBQCAwMRFBSEJUuWoLS0FBMmTNBXk4iIiMhA6K1AGTVqFG7cuIHZs2cjJycH/v7+2LlzZ40bZ4mIiOjJo7d5UB4F70EhIiJqegz+HhQiIiKi+2GBQkRERAaHBQoREREZHBYoREREZHBYoBAREZHBYYFCREREBocFChERERkcFihERERkcFigEBERkcHR21T3j+L25LdKpVLPLSEiIqK6uv13uy6T2DfJAqW4uBgA4OrqqueWEBERUX0VFxfDzs7uvjlNci0ejUaD69evw8bGBjKZTIx3794dx44dq3Wbe713d1ypVMLV1RVXr17V+zo/9zufxtxffbZ7UO6jvF/be+y/ht2uLrkP24f8HWwafcjPUd3sj32oJQgCiouL0bZtWxgZ3f8ukyY5gmJkZAQXF5cacWNj43v+h7zXe/eK29ra6v0X637n05j7q892D8p9lPdre4/917Db1SX3YfuQv4NNow/5Oaqb/bEPqz1o5OS2x+om2SlTptT7vftto28N3baH3V99tntQ7qO8X9t77L+G3a4uuQ/bh/wdbBp9yM9R3eyPfVh/TfISjy7VZyloMjzsv6aPfdj0sQ+bPkPow8dqBKUhmJubY86cOTA3N9d3U+ghsP+aPvZh08c+bPoMoQ85gkJEREQGhyMoREREZHBYoBAREZHBYYFCREREBocFChERERkcFihERERkcFig1MMLL7yAFi1aYMSIEfpuCj2Eq1evom/fvujcuTN8fX2xadMmfTeJ6qmoqAiBgYHw9/dHly5dsGrVKn03iR5CWVkZ3N3dMXPmTH03hR6Ch4cHfH194e/vj9DQUJ0dh48Z18OBAwdQXFyMH374AZs3b9Z3c6iesrOzIZfL4e/vj5ycHAQEBODcuXOwsrLSd9OojtRqNVQqFSwtLVFaWoouXbrg+PHjcHBw0HfTqB4+/PBDZGRkwNXVFYsWLdJ3c6iePDw8kJqaCmtra50ehyMo9dC3b1/Y2Njouxn0kNq0aQN/f38AQOvWreHo6IiCggL9NorqxdjYGJaWlgAAlUoFQRDqtGw7GY7z58/j7Nmz+Ne//qXvppCBe2IKlIMHD2LQoEFo27YtZDIZfv/99xo5y5cvh4eHBywsLBAcHIyEhITGbyjdU0P2YWJiItRqNVxdXXXcarpTQ/RhUVER/Pz84OLignfffReOjo6N1HpqiP6bOXMmYmJiGqnFdLeG6EOZTIY+ffqge/fuWL9+vc7a+sQUKKWlpfDz88Py5ctrfX/Dhg2YPn065syZg6SkJPj5+SEiIgK5ubmN3FK6l4bqw4KCAowbNw4rV65sjGbTHRqiD5s3b46UlBRcunQJP//8M+RyeWM1/4n3qP23detWeHt7w9vbuzGbTXdoiN/Bv//+G4mJidi2bRvmz5+PkydP6qaxwhMIgLBlyxZJLCgoSJgyZYr4vVqtFtq2bSvExMRI8vbv3y8MHz68MZpJ9/GwfVheXi4888wzwrp16xqrqXQPj/J7eNsbb7whbNq0SZfNpHt4mP57//33BRcXF8Hd3V1wcHAQbG1thU8++aQxm013aIjfwZkzZwpr167VSfuemBGU+6moqEBiYiLCwsLEmJGREcLCwnD48GE9tozqqi59KAgCxo8fj379+uHll1/WV1PpHurSh3K5HMXFxQAAhUKBgwcPwsfHRy/tJam69F9MTAyuXr2KzMxMLFq0CK+99hpmz56trybTXerSh6WlpeLvYElJCfbt24ennnpKJ+0x0clem5i8vDyo1Wo4OTlJ4k5OTjh79qz4fVhYGFJSUlBaWgoXFxds2rQJPXv2bOzmUi3q0oeHDh3Chg0b4OvrK153/fHHH9G1a9fGbi7Voi59ePnyZUyaNEm8OTY6Opr9ZyDq+jlKhqsufSiXy/HCCy8A0D5V99prr6F79+46aQ8LlHrYu3evvptAjyAkJAQajUbfzaBHEBQUhOTkZH03gxrA+PHj9d0Eegjt27dHSkpKoxyLl3gAODo6wtjYuMbNdnK5HK1bt9ZTq6g+2IdNH/uwaWP/NX2G1ocsUACYmZkhICAAsbGxYkyj0SA2NpaXcJoI9mHTxz5s2th/TZ+h9eETc4mnpKQEGRkZ4veXLl1CcnIy7O3t4ebmhunTpyMqKgqBgYEICgrCkiVLUFpaigkTJuix1XQn9mHTxz5s2th/TV+T6kOdPBtkgPbv3y8AqPEVFRUl5ixdulRwc3MTzMzMhKCgIOHIkSP6azDVwD5s+tiHTRv7r+lrSn3ItXiIiIjI4PAeFCIiIjI4LFCIiIjI4LBAISIiIoPDAoWIiIgMDgsUIiIiMjgsUIiIiMjgsEAhIiIig8MChYiIiAwOCxQiIiIyOCxQiIiIyOCwQCGiWvXp0wcymazG17hx43R2zAkTJuCjjz665/s5OTmYNm0avLy8YGFhAScnJ/Tu3RvfffcdysrK6nSMQYMG4dlnn631vfj4eMhkMpw8efKh2k9EDeeJWc2YiOpOEAScOHECixYtwksvvSR5z9raWifHVKvV2L59O3bs2FHr+xcvXkTv3r3RvHlzzJ8/H127doW5uTlOnTqFlStXwtnZGYMHD37gcSZOnIjhw4fj2rVrcHFxkby3du1aBAYGwtfXt0HOiYgeHhcLJKIazp07Bx8fHyQkJKB79+6Ncsz4+HiMGjUKWVlZkMlkNd5/9tlnkZaWhrNnz8LKyqrG+4IgiNtpNBosWLAAK1euRE5ODry9vfHxxx9jxIgRqKqqgouLC6ZOnSoZrSkpKUGbNm2wcOFCTJ48WXcnSkR1wks8RFRDYmIiTExMGnUkYdu2bRg0aFCtxUl+fj52796NKVOm1FqcAJBsFxMTg3Xr1uH7779HWloa3nnnHYwdOxZxcXEwMTHBuHHj8J///Ad3/v/Zpk2boFarERkZ2fAnR0T1xgKFiGpISkqCWq2Gg4MDrK2txa/XX39dZ8fcunXrPS/RZGRkQBAE+Pj4SOKOjo5i29577z0AgEqlwvz587FmzRpERESgffv2GD9+PMaOHYsVK1YAAF555RVcuHABcXFx4r7Wrl2L4cOHw87OTkdnSET1wXtQiKiGpKQkREZG4pNPPpHE7e3tdXK8M2fO4Pr16+jfv3+9tktISIBGo8FLL70ElUoFQFvMlJWVITw8XJJbUVGBp59+GgDQsWNH9OrVC2vWrEHfvn2RkZGB+Ph4zJs3r2FOiIgeGQsUIqohKSkJn3/+Oby8vGp9f82aNViyZAlkMhnCw8OxaNEiZGZmYsiQIejSpQsSEhIQFhaGiIgIxMTEoLS0FFu2bEGHDh1q3d+2bdsQHh4OCwuLWt/38vKCTCZDenq6JN6+fXsAQLNmzcRYSUkJAGDHjh1wdnaW5Jubm4uvJ06ciOjoaCxfvhxr166Fp6cn+vTp84D/MkTUWFigEJHExYsXUVRUBD8/v1rfP3XqFBYvXoz4+Hg0b94cBQUF4ntnzpzBxo0b4eXlhS5dusDa2hpHjx7FihUrsGzZMnz99de17nPr1q2YNGnSPdvk4OCA8PBwLFu2DNHR0fe8DwUAOnfuDHNzc1y5cuW+BceLL76IadOm4eeff8a6devwxhtv1Hr/CxHpBwsUIpJITEwEADg5OSEnJ0fyXqtWrbB//36MGjUKzZs3ByC97OPj4yPeJ9KpUyeEhYUBALp27Yo///yz1uPl5ubi+PHj2LZt233b9e2336J3794IDAzE3Llz4evrCyMjIxw7dgxnz55FQEAAAMDGxgYzZ87EO++8A41Gg5CQECgUChw6dAi2traIiooCoH1cetSoUfjggw+gVCoxfvz4+v2HIiKdYoFCRBJJSUkAUONyjLm5OZRK5X23vfMSipGRkfi9kZER1Gp1rdv88ccfCAoKgqOj43337enpiRMnTmD+/Pn44IMPcO3aNZibm6Nz586YOXMm3nzzTTH3008/RcuWLRETE4OLFy+iefPm6NatG/79739L9jlx4kSsXr0azz33HNq2bXvf4xNR4+I8KERUL6mpqYiMjMTff/8NOzs7FBQUwN7eHpmZmRgxYgSOHz8OABgxYgSmTp2Kvn374siRI/jss8+wffv2GvsbPHgwQkJCMGvWrMY+FSIyYHzMmIjqpUuXLpg2bRp69+4Nf39/fPHFF4+0v5CQEM49QkQ1cASFiIiIDA5HUIiIiMjgsEAhIiIig8MChYiIiAwOCxQiIiIyOCxQiIiIyOCwQCEiIiKDwwKFiIiIDA4LFCIiIjI4LFCIiIjI4LBAISIiIoPDAoWIiIgMzv8D5fUmaNn8KqsAAAAASUVORK5CYII=", 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BwsKCPn36ZNq19/jvwaOOx7dv36ZQoUJp6t+6dYsBAwYQGBhIbGwsCQkJdOjQIVNiEbnTxo0b8fPzIz4+nsWLF9O2bVt0Op2xw8o0eaIFxdbW9omblZVVhutaW1unqZtZbty4YfD40R8wd3d3goODtX3BwcFYWVnh7OyMg4MDM2fOJDg4mICAAAYOHKh9yb+o+vXrExwczIkTJzJUP71fhvRidnd3T7d+fHw8HTt2ZPz48YSFhREeHk7BggXJgSswvBwL2ydv5lbPUdc6bd2XtH79elxcXAyGKT7pen1RHh4elClThvDwcG2Ljo5mxIgRQPrXWWawtrbm+++/5+bNm1y5cgUXFxeqVav2Qsd6PMbHfw9iYmIICwvTfhceN2rUKPLly8fFixeJiIhgyJAhee/3QGTYn3/+SatWrYiPj6dly5asXLkyVyUnkEcSlJwgICCAS5cuERERwYQJE2jXrh0A7du356effuL8+fPExMQwcuRI2rVrh06nY8OGDVy5cgWlFI6Ojuh0OoMWobi4OIMtI0qXLk3Pnj3p2LEjgYGBJCUlERERwZQpU9i4cWOa+vnz50ev1xsMl2zfvj3jxo3jwYMH3Lhxg2nTpmn/EyxYsCA3b94kKSkJSElQEhIStP9lz5w5k3///ffFfogiU4WFhfHzzz/j7+/Pl19+aXBtjRs3jri4OA4dOsTatWvT9OV6XtWrV0ev1zN37lwSEhJISEhg37592hd8wYIFn7vF8pHExETi4uLQ6/UGjwFu3rxJSEgIer2ewMBAJkyYwMiRIzP02sc9HuN7773H6tWrOXjwIAkJCYwaNYpatWppydzj9aOiorC3t8fOzo4zZ84815BlkbcsXryYdu3akZiYSIcOHVi5ciWWlpbGDivTSYLyCjVq1Mig023qP4RdunShXbt2eHl54eHhoXUEbNSoESNGjKBp06Z4eXlhbm7OjBkzALh48SL169fH3t6eZs2aMWPGDLy8vICUjozW1tYGW0bnXPjhhx/o1q0b3bp1w9HRER8fH27cuJHu/yxtbW35/PPP8fHxwcnJieDgYPz9/SlVqhSlS5emZs2adOjQge7duwPQoEEDvL29KVCgABUrVsTBwYEpU6bQuHFj3NzcCAsLkwmpjOzRdVqiRAmWLl3KggUL6N+/v7bf1NSUqlWr4unpSdu2bZk1a9ZLTwJlZmbGhg0b2LJlCx4eHri7uzN+/HgtGRg4cCABAQE4OTnxzTffPNex+/Tpg7W1Ndu2baNv375YW1uzd+9eAC5dukT16tWxs7Ojb9++zJ49mwoVKmTotY97PMYyZcowf/58evTogaurK0FBQQZJx+jRo2nTpg1OTk7s37+f0aNHs2vXLhwcHBg0aNBLJ30id/rxxx/p1q0bycnJvP/++yxevBhzc3Njh5UlZDXjbMDX15fevXsb9AsRIju6du0axYsX11rAXsbOnTvp378/58+fz4TIhMgbhgwZwsyZM+nfvz/fffddmr6R2d3zfH/niU6yQojs5+zZs5k6VF+IvGDatGnUrVsXPz+/XNfn5HE5K/USQuQKn376KVOnTmXUqFHGDkWIbE0pxYIFC7S5oUxMTGjTpk2uT07gBRKUvXv30qJFC9zd3dHpdE+dSOjDDz9Ep9NpfSYeuX//Pp07d8bBwQEnJyd69er11KHAud3u3bvl9o7IEby9vTPl9s7kyZO5du0atWvXzoSohMid9Ho9gwcPpmfPnnTq1CnPjep67gQlJiaGSpUqMXv27KfWW7VqFYcOHUp3SF3nzp05e/Ys27ZtY/369ezdu5e+ffs+byhCCCFErpScnEzv3r2ZNWsWOp2ORo0a5YlWk9Seuw/KO++8k2bK98c9mnZ9y5YtNGvWzGDf+fPn2bx5M0ePHqVq1aoAzJo1i6ZNm/Ltt9+mm9DEx8cbTH0eGRn5vGELIYQQOUJiYiJdu3Zl+fLl2kraXbt2ffYLc5lM74Oi1+vp2rUrw4cP1xaDSy0wMBAnJyctOQFo2LAhJiYmHD58ON1jTpw4EUdHR23LyaszCiGEEE8SFxdHmzZtWL58Oebm5qxYsSJPJieQBQnKpEmTMDMzY9CgQenuDw0NNZj6GlLmQHB2dn7iFOsjRowgIiJC21LPYimEEELkFl26dGHdunVYWlqyevXqPD0fTqYOMz527BgzZ87k+PHjmXqvzNLSMlfOkieEEEKkNnjwYPbt28eyZcto0KCBscMxqkxtQdm3bx93797F09MTMzMzzMzMuH79OkOHDtXmO3Bzc0uzIF9SUhL3799/6VV0xasXHBxssMDbi9q9e3emzyCbWbGJrLFv3z4qVapk7DAyzTvvvKOtQv4yvL29M30V48yKTWSN1KNz6taty5UrV/J8cgKZnKB07dqVU6dOceLECW1zd3dn+PDhbNmyBYCaNWsSHh7OsWPHtNft3LkTvV5P9erVMzOcbMXb2xsbGxuDqe7nzJmTJe917do1zMzSbxzz9fVNs8ZHQEAADRs21J4nJyczceJESpYsia2tLUWLFmXgwIHcu3cvzfE8PT0JDw/P1Phf1OPnlp1iyylSX6cuLi40bNiQtWvXZsl71a1bl5MnT2ao7uOf7ePXeOr9j1/PY8eOpVy5clpnw9Ti4uIYOHAgbm5u5M+fX1tiIiOvfdymTZto3759hs4nKz1+/pB9YhNp3blzh/r163Pq1CmtLDMXos3JnjtBiY6O1pIPgKtXr3LixAmCg4NxcXGhfPnyBpu5uTlubm7aWh1lypShSZMm9OnThyNHjnDgwAEGDBhAhw4dnrjKZ26xdetWoqOjta1fv37GDildH3zwAQEBASxYsIDw8HCOHz+Oh4cHR44cMXZo4hV4dJ1euHCB9u3b07VrV+bNm2fssF5Y8eLFmTZtGnXq1Emzb+LEiZw+fZrz589z7tw5tm/fzvz58zP0WiFe1s2bN3nzzTfZs2cP3bp1e+JClHmWek67du1SQJqte/fu6db38vJS06dPNygLCwtTHTt2VHZ2dsrBwUH16NFDRUVFZTiGiIgIBaiIiIin1tPr9So6OjrLN71e/8yYvby81L59+9LdV69ePTVq1Cjl4+OjnJycVLdu3dTDhw+1/bNnz1ZFixZV+fPnV507d1bh4eFKKaXu3r2rmjRpohwdHZWLi4vq0KGDUkqpq1evKlNT0ye+16JFiwzKFixYoN566y2llFLnz59XOp1OHTt27JnnlN57eXl5qW+//VaVLl1aOTo6qv79+xvU//7771Xx4sWVi4uL6tatm4qOjlZKpVxXxYoV0+qNHz9eeXp6Knt7e1WjRg118uRJbd/XX3+t3NzclL29vSpfvrw6e/as+uqrr5SJiYmytLRUtra2avz48Wliu3v3rurYsaMqUKCAcnFxUZ999lmGzjFL6fVKxUdn/ZaBa1Sp9K/TmTNnKhcXF5WUlKT9TOfMmaMKFiyoihQpYnA91atXT/n7+6vKlSsrW1tb1bNnT3X79m1Vv359ZW9vr/z8/FRcXJxSKu1nfv36ddW0aVPl7OysSpcurTZt2qSUUhn6bFNf16mv59QaN26sFixYYFBWpUoVtWbNGu354sWLVe3atTP02seljmHMmDGqU6dO6r333lN2dnaqWrVq6sqVK1rdU6dOqbp16yonJydVuXJldfToUW1f6s/g0KFDqmrVqsre3l55enqq7777TqsXGBiofHx8lL29vXJ3d1fTpk1T//zzj7K0tFSmpqbK1tZWlS1bNk1sSqV8psWLF1d2dnbqjTfeUPfu3XvquYnM988//yhvb28FKE9PT3Xp0iVjh/RKZPT7WymlnruTrK+v73PNZpfeEunOzs4sXbr0ed/6uT18+BA7O7ssf5/o6OiXbpJbuHAh27Zto2DBgrRu3ZoJEyYwbtw4tm3bxrhx49ixYwdeXl5069aNwYMHExAQwNSpUylatCjr1q0jOTnZ4LbZi9q1axeenp5Urlz5hY+xevVq9u3bR1xcHJUrV6ZNmzbUr1+flStXMm/ePLZv307BggXp1asXY8aM4dtvv01zjNKlS/PXX3/h5OTEuHHj6NatGydOnODChQvMmzePv//+W1sh1snJCX9/f3bs2GGw6OLj117nzp3x9PTkn3/+wczMTGsFNKrEhzDhFbQcfnEbLF7sGn333XcZPHgwQUFB2NjYkJyczJEjR7h+/TrHjx+nSZMmVKtWjZIlSwLwxx9/sHnzZszNzfHx8eHUqVMEBATg6elJrVq1WLp0KT169DB4D71eT4sWLejVqxdr1qzh6NGjvPvuu5w5cyZDn+3LSP33TCnF2bNnM+W4q1atYsOGDSxbtoyePXsyduxYfv31V6Kjo2nSpAnfffcdrVq1Yt26dfj5+XHx4kWsrKwMjmFubs4PP/yAj48Px48f56233qJOnTq8/vrrDBkyhGHDhtG5c2cePHjAtWvXeO2115g3bx6LFy9m+/bt6ca1ZMkSZs6cyZo1ayhbtiwnTpzAwsIiU85ZZMyFCxd46623uH37NsWLF2fHjh14enoaO6xsR9bieYXeeecdnJyctG3Pnj3avh49elCyZEmcnJwYOXKk1qHtt99+o2/fvpQtWxZbW1smTJjA8uXLUUphbm5OSEgIN27cwNLSklq1amUojg8++MAgjtS3msLCwl66s/KQIUPInz8/hQsXxtfXV+tn8PPPPzNixAi8vLywtrbmiy++4Pfff0/3GH5+fhQoUABzc3O++OILTp06RXR0NGZmZsTHx3P+/HmSk5MpXbp0huK9desWu3fvZubMmdjb22NtbU3NmjVf6jzzikc/3wcPHmhlY8aMwcrKilq1avHuu+8afI69evWiSJEiuLm5Ua9ePWrWrEm5cuWwt7enadOm6fY7OXLkCLGxsQwaNAgzMzNq1qxJvXr12LRpU5aeW5MmTZg6dSr37t0jJCSEmTNnEhMTkynHfuutt6hfvz5mZmZ06NBBO+/169dTrlw52rRpg6mpKa1ataJgwYIcOnQozTEqV65M5cqVMTExoWrVqjRt2pQDBw4AKcnL5cuXuX//Pvny5eP111/PUFwBAQF8/vnnlC9fHhMTEypXroy9vX2mnLN4thMnTvDmm29y+/ZtypUrx969eyU5eYJcvZqxjY3NK1njx8bGJkP1Nm3a9MR72aknnytSpAghISEA3L592yDx8PLyIi4ujvv37zN8+HD8/f2pV68eNjY2DB8+nF69ej0zjh9++MFg7Z+AgACtg6GLi8sT56PJKFdXV+1x6s8gODiYDz74wCAhSkxMTPcYP/30EzNmzODmzZvodDqUUoSFhVG8eHGmTp3KF198wcWLF2ndujXTpk175rLdN2/epGDBgtmv85m5TUrrxqt4nxf06FrMly+fVvak6xUwmOfI2to6zfOwsLA07xEcHMzVq1cNRl0lJSVRpUqVF447I0aOHEl4eDg+Pj5YWFjQp0+fdON7EU/7PdizZ4/BuSYmJnL7dtrr4OzZswwZMoQTJ06QkJBAXFwcpUuXBmD+/Pn4+/tTvHhxypYty5QpUzKUdN+8eZOiRYu+5NmJFzVu3Dj+/fdfKleuzJYtW8ifP7+xQ8q2cnULik6nw9bWNsu3zJjzJfXkczdu3KBQoUIAuLu7ExwcrO0LDg7GysoKZ2dnHBwcmDlzJsHBwQQEBDBw4ECuXLnyUnHUr1+f4ODgLLn94eHhwa+//kp4eLi2pfe/1WvXrjFkyBB+/fVXHjx4QEhIiJakQMposcDAQIKCgrh27RrTpk0DeOrnUKRIEf79918ePnyY6ef1UnS6lFsvWb29xDW6fv16XFxctI7u8OTr9UV5eHhQpkwZg2sjOjqaESNGAE//bF+GtbU133//PTdv3uTKlSu4uLhQrVq1LHmvRzw8PGjcuHGa34NOnTqlqTtgwABq1qxJcHAwERER+Pn5ab8HpUqVYsWKFdy9e5cOHTrQsWNH4Nk/qyJFimTqLTLxfAICAhgwYAA7d+6U5OQZcnWCkpMEBARw6dIlIiIimDBhAu3atQOgffv2/PTTT5w/f56YmBhGjhxJu3bt0Ol0bNiwgStXrqCUwtHREZ1Oh6mpqXbMuLg4gy0jSpcuTc+ePenYsSOBgYEkJSURERHBlClT2Lhx40udY8+ePZkwYQL//PMPkPI/882bN6epFx0djYmJCQUKFCApKYkxY8Zo+4KCgti9ezcJCQnY2NhgaWmpnXPBggWf+IfX3d2devXq8fHHHxMdHU1sbGy6Teri/8LCwvj555/x9/fnyy+/NLi2xo0bR1xcHIcOHWLt2rUvPdtl9erV0ev1zJ07l4SEBBISEti3b5+WnD/ts32WxMRE4uLi0Ov1Bo8hpTUhJCQEvV5PYGAgEyZMYOTIkRl67Ytq3rw5f//9N6tXryYpKYnY2Fg2b95MREREmrpRUVE4OTlhZWXFvn372LBhg7ZvyZIlhIWFYWZmhr29vcHvwc2bN5+46vT777/PpEmTOHfuHEopjh8/TlRU1Eudk3i6S5cuaY/t7e2ZNWsWjo6ORowoZ5AE5RVq1KiRwTwoqf8QdunShXbt2uHl5YWHh4c2H0OjRo0YMWIETZs2xcvLC3Nzc2bMmAHAxYsXqV+/Pvb29jRr1owZM2bg5eUFpMxlYm1tbbDdvHkzQ3H+8MMPdOvWjW7duuHo6IiPjw83btx46f9ZduzYkV69etGsWTMcHByoV68e586dS1OvfPnyfPDBB1SsWBFvb2+KFi2qdeKLj49n+PDhuLi44OnpiaOjIx9//DEAAwcOJCAgACcnJ7755ps0x12yZAnh4eF4e3vj6emZZfN75HSPrtMSJUqwdOlSFixYQP/+/bX9pqamVK1aFU9PT9q2bcusWbMMWldehJmZGRs2bGDLli14eHjg7u7O+PHjtWTgWZ/t0/Tp0wdra2u2bdtG3759sba2Zu/evUDKF0f16tWxs7Ojb9++zJ49mwoVKmTotS/K0dGRDRs2MGvWLAoWLIi3tzc//vhjunUnTZrE7NmzcXBwYMaMGbz77rvavo0bN1KqVCns7e357rvvWLhwIQANGjTA29ubAgUKULFixTTH7NSpE/3796d58+Y4ODjQr1+/J95qFS9v9erVlC9fnvHjxxs7lBxHp55nSE42ERkZiaOjIxEREc/se5AT+Pr6GoxQECK7unbtGsWLF3/i/86fx86dO+nfvz/nz5/PhMiEyH6WLl1Kt27dSE5Opm3btvz222+YmOTtdoHn+f7O2z8pIYTRnD17VlsCQ4jcZv78+XTp0oXk5GS6devG0qVL83xy8rxy9SgeIUT29Omnn7JixQqWLFli7FCEyHRTp05l2LBhAHz44YfMnj1bkpMXILd4hBBCiEwyZswYvvrqKwCGDx/OpEmTsmwUWk4kt3iEEEIII3g05H7ixIlMnjxZkpOXILd4hBBCiEzy4YcfUr169QzP7CueTFpQhBBCiBcUFxfHJ598wr1797QySU4yh7SgCCGEEC8gKiqKVq1asXPnTo4ePcrevXvllk4mkgRFCCGEeE7379+nadOmHD58GDs7O8aNGyfJSSaTWzwi0+l0Om3W2nfeeUdbmRngs88+w9nZWVsE7tFsmlm9JsW+ffuoVKlSlr6HeD65/TN5//33+frrr4GUWYxbtGih7du3bx/FihXDzs6Oo0ePcv78eSpUqIC9vT1//PFHlsZVrlw5AgMDs/Q9cruQkBDq1avH4cOHcXZ2ZufOnfj6+ho7rNxH5UAREREKUBEREcYOJcO8vLyUtbW1srW11bbZs2dnyXtdvXpVmZqapruvXr16atGiRQZlCxYsUG+99Zb2PCkpSU2YMEGVKFFC2djYKG9vbzVgwAD177//Zuj9AXXjxo005devX1d2dnYqLCxMKaVUfHy8srKyUkFBQRk9tQzz8vJS+/bty/Tj5napr1NnZ2f11ltvqTVr1hg7rDTX7ePXeOr9j1/PY8aMUWXLllU6nU4tWLDA4LixsbFqwIABytXVVbm4uKgRI0YY7H/aa5+le/fuaty4cenuq1+/vvrhhx+05z169Ejz3plhzJgxqlevXpl+3LzsypUrqlixYgpQhQoVUmfOnDF2SDnK83x/SwvKK7R161aio6O1rV+/fsYOKV0ffPABAQEBLFiwgPDwcI4fP46HhwdHjhx5qeMGBwfj6uqKs7MzAHfv3iUxMZGSJUumWz8zplMXz+/RdXrhwgXat29P165dmTdvnrHDemHFixdn2rRp1KlTJ82+iRMncvr0ac6fP8+5c+fYvn078+fPz9BrX0ZwcDBly5Z94vPU5Pcg+1BK0a1bN/755x9ee+019u/fT7ly5YwdVu6V9flS5nveFpTo6GgVHR2t9Hq9VhYfH6+io6NVXFxcunWTk5O1soSEBBUdHa1iY2PT1M2op/2Pvl69emrUqFHKx8dHOTk5qW7duqmHDx9q+2fPnq2KFi2q8ufPrzp37qzCw8OVUkrdvXtXNWnSRDk6OioXFxfVoUMHpdTLtaCcP39e6XQ6dezYsQyf288//6wKFy6sXF1d1Q8//GDQgvLo/fbu3ausrKyUTqdTtra26v3331c2NjYKULa2tuq9995Tu3btUsWKFVOjR49WLi4u6osvvlCXL19WdevWVY6OjqpQoUJp/pf522+/qXLlyik7OztVvnx5deHCBdWrVy+l0+m0loDFixdrx1ZKqXHjxqn333/f4Di+vr7az+XUqVOqbt26ysnJSVWuXFkdPXo0wz+LFxYfnbKlukZVYnxKWWJc+nVTXaMqKSGlLCE2bd3nkN51OnPmTOXi4qKSkpK0a2vOnDmqYMGCqkiRIgbXU7169ZS/v7+qXLmysrW1VT179lS3b99W9evXV/b29srPz0/7nUv9mSiV0sLWtGlT5ezsrEqXLq02bdqklFLqq6++UiYmJsrS0lLZ2tqq8ePHP1cLyiONGzdO0wpSpUoVgxaixYsXq9q1a2fotY+7fPmyqlWrlrKzs1N+fn6qXbt2WgtK6pgetchYW1srV1dX1aRJE4Pzu3fvnvLy8lKTJk1SpUuXVl5eXkoppQYMGKAKFSqkHB0d1dtvv62uX7+uvfeVK1e0n52bm5uaOXOm2rVrlzI3N1dmZmbK1tZWNWnSRCn1/884ODhY2dnZqZiYGO04AQEBWpwPHz5U/fv3V4UKFVIeHh5q4sSJTz3/vOLy5cuqYcOG6tatW8YOJUd6nu/vPJGgAApQd+/e1cq+/vprBajevXsb1H30pXn16lWtbPr06QpQnTp1MqibP3/+DMf8rATF09NTBQUFqQcPHihfX181atQopZRSW7duVW5uburs2bMqOjpa+fn5qe7duyullPrss8/URx99pBITE1VcXJw6cOCAUurlEpQ5c+ZofxAz4vTp08re3l4dOnRIPXz4UHXt2jXdBEWptF9Ij8e5a9cuZWpqqr788kuVkJCgHj58qC5fvqx2796tEhMT1cWLF1WRIkXUqlWrlFJK7d+/X7m4uKj9+/er5ORkdf78eXX79m2lVNqfd+r3vnjxosqXL59KSEhQSikVEhKibGxsVGRkpIqKilLu7u7q999/V0lJSWrVqlWqSJEiaZLTTDfGIWWLTnUbbc/klLI1Awzrfu2WUn7/2v/LDs5OKfv9seb8SUWfK4z0rtOrV68qQJ09e1Z7/P7776vY2Fh14MABZW9vr92mq1evnipbtqwKDg5WISEhytXVVVWtWlWdOXNGRUZGqvLly6tffvlFKWX4mSQnJ6uKFSuqmTNnqsTERHXw4EGVP39+FRoaqh33RW/xPPKkBGX16tXa80WLFiknJ6cMvfZxb7zxhhoxYoSKj49Xq1atUmZmZukmKOn9nB8/Py8vL1W9enUVGhqq/Wdl2bJlKjw8XD18+FD16NFDtWzZUimlVGJioipTpowaM2aMio2NVREREeqvv/5SSqV/iyf1e9eqVUutWLFC2/fOO++oH3/8USmlVL9+/VTHjh1VVFSUunXrlipbtqxat27dU38GudWj29Li5cktnmzqnXfewcnJSdv27Nmj7evRowclS5bEycmJkSNHah1Lf/vtN/r27UvZsmWxtbVlwoQJLF++HKUU5ubmhISEcOPGDSwtLalVq1aG4vjggw8M4kh9qyksLAw3N7cMn9Mff/yBn58f1atXx9ramtGjR2f4temxtLTkiy++wNzcHGtra4oVK0a9evUwMzOjRIkSdO7cmf379wMQEBDABx98QO3atTExMaF06dLaLI5PU6JECby9vdm6dSsAv//+O40bN8be3p7169dTrlw52rRpg6mpKa1ataJgwYIcOnTopc4rJ3t0PTx48EArGzNmDFZWVtSqVYt3332X33//XdvXq1cvihQpgpubG/Xq1aNmzZqUK1cOe3t7mjZtysmTJ9O8x5EjR4iNjWXQoEGYmZlRs2ZN6tWrx6ZNm7L03Jo0acLUqVO5d+8eISEhzJw5k5iYmOc+zvXr1zl16hSjR4/GwsKCVq1aUb169ZeKbfDgwbi6umJtbQ1Ahw4dcHR0xNrams8++0z7PTh8+DBRUVGMHj0aKysrHBwctE7oz9K+fXtWrFgBpHy+e/bswc/PD6UUCxYs4Ntvv8XOzg53d3c++ugjg885r1i/fj3e3t5s2LDB2KHkOXlimHF0dDQANjY2Wtnw4cMZMmQIZmaGP4K7d+8CaH8UAPr370+fPn0wNTU1qHvt2rXnimPTpk1PvJddpEgRg8chISEA3L592yDx8PLyIi4ujvv37zN8+HD8/f2pV68eNjY2DB8+nF69ej0zjh9++IEuXbpozwMCAli8eDEALi4uhIaGZvicQkJC0sT+Mtzc3Aw+k1u3bjFgwAACAwOJjY0lISGBDh06AHDz5s0X/hLo0KEDy5cvp1mzZixfvpwBAwYAKX0B9uzZg5OTk1Y3MTGR27dvv/hJZcQX/x3f/P/XKLUGQ41+YPLYr+nwyyn/mv3/GqVaH6jSHXSG1yhDTr90aI+uxXz58mllT7peAQoWLKg9tra2TvM8LCwszXsEBwdz9epVg597UlJShr9oX9TIkSMJDw/Hx8cHCwsL+vTpk258zxISEkKBAgWwsrLSyl72d6Fw4cIGz8ePH8+CBQu4e/cuOp2OyMhIIOX3wMvL64UWo2vbti0jR44kJiaGP//8kzfffBMXFxfu3r1LbGysQb8YvV5P7dq1X+qccpply5bRrVs3kpKSWLRoEc2aNTN2SHlKnmhBsbW1xdbW1mCMuoWFBba2tlhaWqZbN/Uvu7m5Oba2tgZ/fB7VzSw3btwwePyoJcDd3Z3g4GBtX3BwMFZWVjg7O+Pg4MDMmTMJDg4mICCAgQMHcuXKlZeKo379+gQHB3PixIkM1S9UqFCa2F/G4/MIjBo1inz58nHx4kUiIiIYMmQI6r/1LYsUKfLEJPFZ8xG0a9eOtWvXcuXKFU6cOEHz5s0B8PDwoHHjxoSHh2tbTEwMnTp1eqnzeiYL25QtddxmFillZpbp1039hWRqnlJmbpW27ktav349Li4ulCpVSit70vX6ojw8PChTpozBzz06OpoRI0YAz/48X5S1tTXff/89N2/e5MqVK7i4uFCtWrXnPk6hQoW4d+8ecXFxWllm/i7s2bOHOXPmsHHjRiIiIgw6rBcpUoTr169rvxdPOsaT4q5atSrr1q1jxYoVtG/fHoD8+fNjaWnJlStXtM8jMjIyy1u0spN58+bRuXNnkpKS6NKlC4sWLTJ2SHlOnkhQcoKAgAAuXbpEREQEEyZMoF27dkBKE+xPP/3E+fPniYmJYeTIkbRr1w6dTseGDRu4cuUKSikcHR3R6XQGrTxxcXEGW0aULl2anj170rFjRwIDA0lKSiIiIoIpU6awcePGNPXbtGnDn3/+ydGjR4mNjdXmfcgsUVFR2NvbY2dnx5kzZ7SWHoDu3bvzww8/EBgYiFKKoKAg7X/yBQsWfGoLl7e3N2XKlKFPnz40bdpUSzabN2/O33//zerVq0lKSiI2NpbNmzcTERGRqeeVE4SFhfHzzz/j7+/Pl19+aXBtjRs3jri4OA4dOsTatWtp06bNS71X9erV0ev1zJ07l4SEBBISEti3b5+WnD/r83yaxMRE4uLi0Ov1Bo8hpfUhJCQEvV5PYGAgEyZMYOTIkRl6bWpeXl6UL1+er7/+msTERNauXfvSo95Si4qKwtzcnPz58xMTE2Pwe1atWjXs7e21zyQyMpJjx44BKT+3JyUvj3To0IE5c+awf/9+WrVqBYCJiQndu3dn6NChhIeHo9frOX/+fKaeU3b2zTff8NFHH6GUon///vz666+Ym5sbO6w8RxKUV6hRo0bY2dlpW+o/hF26dKFdu3Z4eXnh4eHBF198ob1mxIgRNG3aFC8vL8zNzZkxYwYAFy9epH79+tjb29OsWTNmzJiBl5cXAMnJyVhbWxtsjyZPe5YffviBbt260a1bNxwdHfHx8eHGjRvp/s+yfPnyTJs2jdatW+Pt7Z3hfjAZNXr0aHbt2oWDgwODBg0y+CKsXbs2M2fOpGfPnjg4ONC2bVut2fuzzz7j888/x8nJiaVLl6Z77Pbt27Nz504tGQRwdHRkw4YN2gRy3t7e/Pjjj5l6Ttndo+u0RIkSLF26lAULFtC/f39tv6mpKVWrVsXT05O2bdsya9Ysg9aVF2FmZsaGDRvYsmULHh4euLu7M378eC0ZGDhwIAEBATg5OfHNN98817H79OmDtbU127Zto2/fvlhbW7N3714ALl26RPXq1bGzs6Nv377Mnj2bChUqZOi1j1u6dCk7duzA2dmZgIAAWrdu/YI/jbSaNGlC7dq18fLyokKFCga/Z2ZmZqxfv56DBw9SqFAhSpUqpU3E9t577xEdHU2+fPm0VsLHtWnThsDAQBo0aGBwi2369Ok4OjpSoUIFnJ2d6datm0E/pNxIKcXnn3+utdyNHDmSWbNmvdDtM/HydOppqXU2FRkZiaOjIxERETg4OBg7nJfm6+tL7969DfqFCJEdXbt2jeLFi2fK3Bw7d+6kf//+nD9/PhMiE+LlKaXo06cPP//8M5MnT2b48OHGDinXeZ7v7zzRSVYIkf2cPXsWb29vY4chhEan0/HDDz/Qvn173n77bWOHk+dJu5UQ4pX79NNPmTp1KqNGjTJ2KCKPi42N5ZtvvtFaBU1NTSU5ySbkFo8QQog8KTIyknfffZc9e/bQp0+fPNffzBjkFo8QQgjxFPfu3aNJkyYcO3YMBwcHunbtauyQxGMkQRFCCJGn3Lx5k7fffpsLFy6QP39+tmzZQuXKlY0dlniMJChCCCHyjIsXL/L2228THBxM4cKF2bp1K2XKlDF2WCIdkqAIIYTIExITE2nSpAnBwcGULFmSbdu24enpaeywxBM89yievXv30qJFC9zd3dHpdKxevVrbl5iYyGeffUaFChWwtbXF3d2dbt26pVnH5P79+3Tu3BkHBwecnJzo1auXtl6OEEIIkRXMzc2ZN28e1atXZ9++fZKcZHPPnaDExMRQqVIlZs+enWbfw4cPOX78OP7+/hw/fpw///yToKAg3n33XYN6nTt35uzZs2zbto3169ezd+9e+vbt++JnIYQQQjzBw4cPtceNGjXi4MGDBotYiuzppYYZ63Q6Vq1apa3fkJ6jR49SrVo1rl+/jqenJ+fPn6ds2bIcPXqUqlWrArB582aaNm3KzZs3cXd3f+b7yjBjIYQQGbFs2TKGDRvGzp07X3pJBvHynuf7O8snaouIiECn02lrPAQGBuLk5KQlJwANGzbExMSEw4cPp3uM+Ph4IiMjDTYhhBDiaebMmUPnzp25ffu2zHGSA2VpghIXF8dnn31Gx44dtUwpNDQ0TdOamZkZzs7OhIaGpnuciRMn4ujoqG1FihTJyrCFEELkYEopvv76a/r376+tSDxlyhRjhyWeU5YlKImJibRr1w6lFHPnzn2pY40YMYKIiAhtu3HjRiZFKYQQIjfR6/UMHToUf39/APz9/WVF4hwqS4YZP0pOrl+/zs6dOw3uM7m5uXH37l2D+klJSdy/fx83N7d0j2dpaYmlpWVWhCqEECKXSEpKok+fPgQEBAAwffp0hgwZYtSYxIvL9JTyUXJy6dIltm/fjouLi8H+mjVrEh4ezrFjx7SynTt3otfrqV69emaHI4QQIo9ISEggKCgIU1NTAgICJDnJ4Z67BSU6OprLly9rz69evcqJEydwdnamUKFCvPfeexw/fpz169eTnJys9StxdnbGwsKCMmXK0KRJE/r06cO8efNITExkwIABdOjQIUMjeIQQQoj02NjYsGHDBo4cOULjxo2NHY54Sc89zHj37t3Ur18/TXn37t0ZO3YsRYsWTfd1u3btwtfXF0iZqG3AgAGsW7cOExMT2rRpw3fffYednV2GYpBhxkIIIQDCwsJYvXo1vXr1MnYoIgOydDVjX19fnpbTZCTfcXZ2ZunSpc/71kIIIYTm1q1bNGrUiHPnzpGYmMiHH35o7JBEJpK1eIQQQuQ4ly5d4u233+b69et4eHhQr149Y4ckMpmMuxJCCJGjnDhxgjp16nD9+nVKlCjBgQMHZEXiXEgSFCGEEDnG/v378fX15e7du/j4+LB//368vLyMHZbIApKgCCGEyBFu375N48aNiYiIoG7duuzevVsW/cvFpA+KEEKIHMHd3Z0vv/yS3bt3s2LFCmxsbIwdkshCL7WasbHIMGMhhMg74uPjDWYTT05OxtTU1IgRiReVrVYzFkIIIV6EUorx48dTq1YtIiIitHJJTvIGSVCEEEJkO3q9nuHDhzNq1CiOHz/OH3/8YeyQxCsmfVCEEEJkK4mJifTu3ZuFCxcCMG3aNHr27GnkqMSrJgmKEEKIbCMmJoZ27dqxceNGTE1NmT9/Pu+//76xwxJGIAmKEEKIbCEsLIzmzZtz6NAhrK2tWbFiBc2bNzd2WMJIJEERQgiRLURHRxMcHEy+fPlYv349tWrVMnZIwogkQRFCCJEteHl5sXnzZkxNTSlbtqyxwxFGJgmKEEIIowkMDOTff//l3XffBaBChQpGjkhkF5KgCCGEMIoNGzbQtm1b9Ho9e/fupVq1asYOSWQjMg+KEEKIV+7XX3+lZcuWxMbG0qBBA8qVK2fskEQ2IwmKEEKIV0YpxeTJk3n//fdJTk6ma9eurFmzBltbW2OHJrIZSVCEEEK8Enq9nmHDhvHZZ58BMGzYMAICAjA3NzdyZCI7kj4oQgghXoklS5Ywbdo0AKZMmcKwYcOMHJHIziRBEUII8Up06tSJLVu20KhRI7p162bscEQ2JwmKEEKILHP//n3s7OywsLDA1NSURYsWodPpjB2WyAGkD4oQQogscf36dWrVqkWPHj3Q6/UAkpyIDJMWFCGEEJnuzJkzNGnShFu3bvHw4UNCQ0Nxd3c3dlgiB5EWFCGEEJlq//791K1bl1u3blG2bFkOHjwoyUkOc+jQIW7evGnUGCRBEUIIkWnWrVvH22+/TXh4OLVq1WLfvn0ULlzY2GGJDAoJCaF79+7UrFmT4cOHGzUWSVCEEEJkioULF9K6dWvi4uJo1qwZ27Ztw9nZ2dhhiQyIj49n8uTJlCxZkoULFwJgbW1NcnKy0WKSPihCCCEyRZEiRTA1NaVr1678+OOPMgFbDrFhwwaGDBnC5cuXAahevTrfffed0ddGkgRFCCFEpqhfvz5Hjx6lQoUKMlonB7h48SIff/wxGzduBMDV1ZVJkybRtWtXTEyMf4PF+BEIIYTIkRISEujXrx/nzp3TyipWrCjJSTYXGRnJp59+Svny5dm4cSPm5uYMHz6cixcv0r1792yRnIC0oAghhHgBkZGRtG3blq1bt7J582YuXLiAhYWFscMST6HX61m0aBGff/45oaGhADRt2pTp06dTsmRJI0eXliQoQgghnsvNmzdp1qwZp06dwsbGhjlz5khyks0dOXKEgQMHcuTIEQBKlCjB9OnTadasmZEje7Ls0Y4jhBAiRzh16hQ1atTg1KlTuLq6smfPHpo0aWLssMQThIaG0rNnT6pXr86RI0ews7Nj0qRJnD59OlsnJyAtKEIIITJo69atvPfee0RFRVGmTBk2btyIt7e3scMS6UhISGDWrFl8+eWXREVFAdC9e3cmTpxIoUKFjBxdxjx3C8revXtp0aIF7u7u6HQ6Vq9ebbBfKcXo0aMpVKgQ1tbWNGzYkEuXLhnUuX//Pp07d8bBwQEnJyd69epFdHT0S52IEEKIrKOUYurUqURFReHr68uBAwckOcmmNm/eTMWKFRk2bBhRUVFUrVqVwMBAAgICckxyAi+QoMTExFCpUiVmz56d7v7Jkyfz3XffMW/ePA4fPoytrS2NGzcmLi5Oq9O5c2fOnj3Ltm3bWL9+PXv37qVv374vfhZCCCGylE6nY/ny5XzxxRds3ryZfPnyGTsk8ZjLly/TokUL3nnnHYKCgihYsCC//PILhw8fpkaNGsYO7/mplwCoVatWac/1er1yc3NTU6ZM0crCw8OVpaWlWrZsmVJKqXPnzilAHT16VKuzadMmpdPp1K1btzL0vhEREQpQERERLxO+EEKIp4iLi1NLliwxdhjiGSIjI9Vnn32mLCwsFKDMzMzU0KFDVXh4uLFDS+N5vr8ztZPs1atXCQ0NpWHDhlqZo6Mj1atXJzAwEIDAwECcnJyoWrWqVqdhw4aYmJhw+PDhdI8bHx9PZGSkwSaEECLrPHjwgCZNmtC5c2dmzpxp7HBEOpRSLF68mFKlSjFp0iQSEhJo3Lgxp0+f5ttvv8XR0dHYIb6UTE1QHo2rdnV1NSh3dXXV9oWGhlKwYEGD/WZmZjg7O2t1Hjdx4kQcHR21rUiRIpkZthBCiFSuXbtG7dq12b17N/b29pQpU8bYIYnHHDt2jNq1a9O1a1dCQkIoVqwYa9euZdOmTZQuXdrY4WWKHDHMeMSIEURERGjbjRs3jB2SEELkSn/99Rc1atTg/PnzeHh4sG/fPho1amTssMR/7t69S58+fXjjjTcIDAzE1taWiRMncvbsWVq0aJGrZvHN1GHGbm5uANy5c8egp/CdO3fw8fHR6ty9e9fgdUlJSdy/f197/eMsLS2xtLTMzFCFEEI8Zv369bRv356HDx9SsWJFNmzYQOHChY0dlgASExOZPXs2Y8eOJSIiAoAuXbowadIk3N3djRxd1sjUFpSiRYvi5ubGjh07tLLIyEgOHz5MzZo1AahZsybh4eEcO3ZMq7Nz5070ej3Vq1fPzHCEEEJk0PXr12ndujUPHz6kUaNG7Nu3T5KTbGL79u1UqlSJjz/+mIiICCpXrsyBAwdYtGhRrk1O4AVaUKKjo7UlmSGlY+yJEydwdnbG09OTIUOG8PXXX1OiRAmKFi2Kv78/7u7utGrVCoAyZcrQpEkT+vTpw7x580hMTGTAgAF06NAhV/+ghRAiO/Py8mLKlCmcOXOGuXPnYm5ubuyQ8ryrV68ydOhQVq1aBUD+/PmZOHEiPXr0wNTU1MjRvQLPO0Ro165dCkizde/eXSmVMtTY399fubq6KktLS/XWW2+poKAgg2OEhYWpjh07Kjs7O+Xg4KB69OihoqKiMhyDDDMWQoiX9/DhQ3X79m3tuV6vV3q93ogRCaWUio6OVqNGjVKWlpYKUKampmrw4MHq/v37xg7tpT3P97dOKaWMlx69mMjISBwdHYmIiMDBwcHY4QghRI5z7949WrZsSUREBPv378fJycnYIeV5SilWrFjBsGHDuHnzJgBvvfUWM2fOpFy5ckaOLnM8z/d3jhjFI4QQIvNcvnyZmjVrcvDgQW7dumVw214Yx8mTJ/H19aVDhw7cvHkTb29v/vjjD7Zt25ZrkpPnJQmKEELkIQcPHqRGjRpcvnwZb29vDh48aDBxpni1wsLC6N+/P5UrV2bv3r1YW1vz1Vdfce7cOfz8/HLVsOHnJasZCyFEHvH777/TpUsX4uPjqVq1KuvWrXvi9A4iayUlJfHjjz/i7+/P/fv3AWjXrh1TpkzB09PTyNFlD5KgCCFEHrB48WK6deuGUooWLVqwbNkybG1tjR1WnrRnzx4GDRrEqVOnAKhQoQLfffcdvr6+xg0sm5FbPEIIkQc0aNCAwoULM2DAAFatWiXJiRHcuHGDDh064Ovry6lTp8iXLx/ff/89x48fl+QkHdKCIoQQuVRycrI2X4a7uzvHjx/HxcUlT/drMIbY2Fi+/fZbJk6cSGxsLCYmJnzwwQd89dVX5M+f39jhZVvSgiKEELnQjRs3qF69OsuWLdPK8ufPL8nJK6SUYtWqVZQtW5bRo0cTGxtL3bp1OXbsGHPmzJHk5BkkQRFCiFzm0KFDvPHGGxw7doxPP/2U2NhYY4eU55w7d45GjRrh5+fHtWvX8PDwYNmyZezZs0dbm048nSQoQgiRiyxcuJB69epx584dKlSowL59+7C2tjZ2WHnGvXv3GDBgABUrVmT79u1YWloycuRIgoKC6NChg7RgPQfpgyKEELlAcnIyI0aMYMqUKQC0bNmSxYsXY2dnZ+TI8obExETmzJnD2LFjCQ8PB6BVq1ZMnTqV1157zbjB5VCSoAghRA6XnJxMy5Yt2bBhAwAjR47kq6++wsREGsmzmlKKjRs3MnToUIKCggCoWLEi06dPp0GDBkaOLmeTq1cIIXI4U1NTKlWqhJWVFcuWLePrr7+W5OQVOHv2LE2aNKF58+YEBQVRoEABfvzxR44fPy7JSSaQxQKFECKHSj2MWK/Xc+nSJUqVKmXkqHK/e/fuMWbMGH744QeSk5OxsLBgyJAhfPHFFzg6Oho7vGxNFgsUQohcbs6cOfj6+hIXFweAiYmJJCdZLCEhgRkzZlCiRAnmzJlDcnIyfn5+nDt3jkmTJklykskkQRFCiBwkMTGRjz76iP79+7N//34WLlxo7JByPaUU69evp0KFCnz88ceEh4dTqVIldu7cyR9//EGxYsWMHWKuJJ1khRAihwgLC+O9995j9+7d6HQ6vvnmG/r06WPssHK1s2fP8vHHH7Nt2zYAChYsyPjx4+nRo4d2e01kDUlQhBAiBzh79izvvvsuV65cwc7OjmXLltG8eXNjh5VrPepnMm/ePPR6PRYWFnz88cd88cUX0vfxFZEERQghsrkdO3bQunVroqKieO2111i7di3lypUzdli5UkJCArNnz+bLL78kIiICgDZt2jB58mSZz+QVkwRFCCGyOW9vb8zNzfH19eX333/HxcXF2CHlOo/6mQwdOpRLly4B4OPjw/Tp02WlYSORBEUIIbIhvV6vzWVSrFgx9u3bR4kSJTA3NzdyZLnPmTNn+OSTT7R+Jq6urowfP573339f+pkYkYziEUKIbCYkJIS6deuyadMmraxs2bKSnGSyf//9l379+lGpUiW2bduGhYUFn3/+ORcvXqRXr16SnBiZJChCCJGN/PXXX7zxxhscPHiQ/v37k5iYaOyQcp2EhASmTp1KiRIlmDt3Lnq9njZt2nD+/HkmTpwonWCzCbnFI4QQ2cTy5ct5//33iYuLo0yZMqxdu1ZaTTKRUoo1a9YwbNgw/vnnHwBef/11pk+fTr169YwcnXictKAIIYSR6fV6Ro0aRYcOHYiLi6Np06YEBgZSvHhxY4eWa5w4cYIGDRrQunVr/vnnH9zc3Pj55585evSoJCfZlLSgCCGEESUkJNC+fXtWr14NwPDhw5k4caL0f8gkoaGhjBo1il9++QWlFFZWVgwdOpTPPvsMe3t7Y4cnnkISFCGEMCJzc3MKFCiAhYUFP/30E926dTN2SLlCbGws06dPZ+LEiURHRwPQoUMHvvnmG7y8vIwcncgIWc1YCCGMIDExUetfkpCQwLlz5/Dx8TFuULmAUorly5fz+eefc/36dQCqV6/O9OnTqVmzppGjE7KasRBCZFOJiYkMGzaMd955h+TkZAAsLCwkOckER44coU6dOnTs2JHr169TuHBhFi9ezMGDByU5yYHkFo8QQrwiN2/epEOHDhw4cACAbdu20aRJEyNHlfPduHGDESNGsGTJEgBsbGz4/PPPGTp0KDY2NkaOTrwoSVCEEOIV2Lp1K507d+bevXs4ODgQEBAgyclLiomJYfLkyUyZMoXY2FgA3n//fcaPH4+7u7uRoxMvSxIUIYTIQsnJyXz11VeMGzcOpRSvv/46K1eupFixYsYOLcfS6/UsWrSIL774gtu3bwNQt25dpk+fTpUqVYwcncgskqAIIUQW6tevHz/++CMAH3zwATNmzMDKysrIUeVc+/bt4+OPP+bYsWMAFC1alClTpuDn54dOpzNydCIzSSdZIYTIQv3796dAgQIsXryYefPmSXLygq5evUrbtm158803OXbsGPb29kyaNIlz587Rpk0bSU5yoUxPUJKTk/H396do0aJYW1tTrFgxrWnzEaUUo0ePplChQlhbW9OwYUNteWshhMjJ9Ho9f/31l/a8YsWKXL16lc6dOxsxqpwrMjKSzz77jNKlS/P7779jYmLCBx98wOXLl/n0008l4cvFMj1BmTRpEnPnzuX777/n/PnzTJo0icmTJzNr1iytzuTJk/nuu++YN28ehw8fxtbWlsaNGxMXF5fZ4QghxCtz//59WrZsSc2aNQkMDNTKbW1tjRhVzpSUlMSPP/5IiRIlmDx5MgkJCTRs2JATJ04wb948ChYsaOwQRRbL9D4oBw8epGXLljRr1gwAb29vli1bxpEjR4CU1pMZM2YwatQoWrZsCcDChQtxdXVl9erVdOjQIc0x4+PjiY+P155HRkZmdthCCPFSjhw5Qrt27bh+/TqWlpZcvXpV5t54AUopNm/ezPDhwzl79iwAJUuWZOrUqTRr1kxu5eQhmd6CUqtWLXbs2MHFixcBOHnyJPv37+edd94BUu4jhoaG0rBhQ+01jo6OVK9e3eB/HKlNnDgRR0dHbStSpEhmhy2EEC9EKcWsWbOoU6cO169fp1ixYgQGBtKpUydjh5bjnDx5ksaNG9O0aVPOnj2Ls7MzM2bM4MyZMzRv3lySkzwm01tQPv/8cyIjIyldujSmpqYkJyczfvx47f5raGgoAK6urgavc3V11fY9bsSIEXzyySfa88jISElShBBGFxkZSe/evVm5ciUAfn5+/PLLLzg6Oho5spzl9u3b+Pv7s2DBApRSWFhYMHDgQEaOHEm+fPmMHZ4wkkxPUFasWMGSJUtYunQp5cqV48SJEwwZMgR3d3e6d+/+Qse0tLTE0tIykyMVQoiX89tvv7Fy5UrMzMz49ttvGTRokPwv/zlER0fz7bffMmXKFB4+fAhA+/btmTBhAq+99pqRoxPGlukJyvDhw/n888+1viQVKlTg+vXrTJw4ke7du+Pm5gbAnTt3KFSokPa6O3fuyFoUQogcpU+fPpw8eZKuXbtSo0YNY4eTYyQnJxMQEIC/vz8hISFASveAqVOnys9RaDK9D8rDhw8xMTE8rKmpKXq9HkiZVMfNzY0dO3Zo+yMjIzl8+LB0KBNCZGsxMTF88cUXREVFAaDT6Zg9e7Z8qT6HLVu24OPjQ+/evQkJCeG1115j5cqV7N+/X36OwkCmt6C0aNGC8ePH4+npSbly5fj777+ZNm0aPXv2BFJ+oYcMGcLXX39NiRIlKFq0KP7+/ri7u9OqVavMDkcIITLFhQsXeO+99zh79iw3btxg0aJFxg4pRzl9+jTDhw9ny5YtAOTLlw9/f3/69esnt/BF+lQmi4yMVIMHD1aenp7KyspKvfbaa2rkyJEqPj5eq6PX65W/v79ydXVVlpaW6q233lJBQUEZfo+IiAgFqIiIiMwOXwgh0li6dKmytbVVgHJzc1O7du0ydkg5xu3bt1Xv3r2ViYmJApS5ubn65JNPVFhYmLFDE0bwPN/fOqVSTfGaQ0RGRuLo6EhERAQODg7GDkcIkUvFxsYydOhQ5s6dC4Cvry/Lli3T+tKJJ4uJidE6wMbExADQtm1bJk6cKAsl5mHP8/0tiwUKIUQ6Lly4QOvWrblw4QIAI0eOZOzYsZiZyZ/Np0lOTubXX39l1KhRWgfYGjVqMHXqVGrVqmXk6EROIr9pQgiRjvz58/PgwQPc3NwICAigcePGxg4p29u2bRvDhg3j1KlTQMqgiEmTJvHee+/J8Gvx3CRBEUKI/4SEhODm5oZOpyN//vysW7eO1157DRcXF2OHlq2dOXOG4cOHs3nzZgCcnJzw9/enf//+0gFWvLBMH2YshBA5jVKKuXPnUrx4cZYtW6aVv/HGG5KcPEVoaCh9+/alUqVKbN68GXNzc4YMGcLly5f55JNPJDkRL0USFCFEnnb79m2aNm1Kv379ePjwIX/++aexQ8r2IiMj8ff3p3jx4vz000/o9XratGnDuXPnmD59uiR1IlPILR4hRJ61cuVKPvzwQ+7fv4+VlRXffPMNAwcONHZY2VZcXBxz585l/PjxhIWFAVC9enWmTp1K7dq1jRydyG0kQRFC5Dnh4eEMGDCAJUuWAFC5cmUWLVpE2bJljRxZ9pScnMyiRYsYM2YMwcHBAJQqVYoJEybQunVr6QArsoTc4hFC5Dl///03S5YswcTEhFGjRhEYGCjJSTqUUqxdu5ZKlSrRo0cPgoOD8fDwYP78+Zw5cwY/Pz9JTkSWkRYUIUSeoJTSvkzr16/PN998w5tvvilrgD3Bvn37+Pzzzzl48CCQMjX9F198Qf/+/bG2tjZydCIvkBYUIUSud/z4cWrWrMnVq1e1ss8++0ySk3ScOnWK5s2b8+abb3Lw4EGsra0ZMWIEV65cYdiwYZKciFdGEhQhRK6VlJTE+PHjqV69OocPH2b48OHGDinbunr1Kl27dsXHx4cNGzZgamrKBx98wOXLl5kwYQJOTk7GDlHkMXKLRwiRK12+fJmuXbty6NAhAN577z1tTR3xf3fv3uXrr79m3rx5JCYmAtCuXTvGjRtHyZIljRydyMskQRFC5CpKKX788Uc++eQTHj58iIODA7Nnz6Zz587SoTOVqKgopk6dytSpU4mOjgbg7bffZsKECVStWtXI0QkhCYoQIpdZsGABH374IZDSGTYgIABPT08jR5V9xMfHM2/ePL7++mvu3bsHQNWqVfnmm2946623jBydEP8nfVCEELlK586dqVatGtOnT2f79u2SnPzn0VwmpUuXZsiQIdy7d48SJUqwYsUKjhw5IsmJyHakBUUIkaOFh4czc+ZMRo4ciZmZGZaWlhw8eBBTU1Njh5YtKKXYsGEDX3zxBadPnwagUKFCjBkzhp49e2Jubm7kCIVInyQoQogcSSnFqlWrGDRoELdu3UKn0zF69GgASU7+s2/fPr744gv2798PgKOjI59//jmDBg3CxsbGyNEJ8XSSoAghcpzr168zYMAA1q9fD0Dx4sVp2LChkaPKPg4cOMCYMWPYsWMHAFZWVgwaNIjPPvsMZ2dnI0cnRMZIHxQhRI6RmJjIlClTKFu2LOvXr8fc3JxRo0Zx6tQpatWqZezwjO7gwYM0atSIOnXqsGPHDszMzOjbty+XLl1i0qRJkpyIHEVaUIQQOcbAgQP54YcfAHjzzTeZN28eZcqUMXJUxnfo0CHGjBnD1q1bATAzM6NHjx588cUXeHt7Gzc4IV6QtKAIIXKMjz/+GHd3d3755Rd2796d55OTw4cP884771CzZk22bt2KqakpvXr14uLFi/z444+SnIgcTVpQhBDZklKKZcuWcfHiRcaOHQtAqVKluHr1KhYWFsYNzsiOHDnC2LFj2bRpE5DSKbh79+6MHDmS1157zcjRCZE5JEERQmQ7ly9fpl+/fmzbtg2dTkeLFi2oUqUKQJ5OTv766y/Gjh3Lhg0bgJTEpGvXrowaNYpixYoZOTohMpckKEKIbCM+Pp7Jkyczfvx44uPjsbS0ZNSoUZQvX97YoRnVsWPHGDt2rDZqycTEREtMihcvbuTohMgakqAIIbKFPXv28OGHH3LhwgUgZV2YOXPm5Okv4OPHj/Pll1+ydu1aICUx6dy5M/7+/pQoUcLI0QmRtSRBEUIYXXR0NK1bt+bBgwcULFiQGTNm0KFDhzy7uN+JEycYO3Ysa9asAVISk06dOjFq1ChKlSpl5OiEeDUkQRFCGIVSSktA7OzsmDx5Mn/99RcTJ04kX758Ro7OOE6ePMmXX37JqlWrANDpdHTs2BF/f39Kly5t5OiEeLVkmLEQ4pW7cOEC9evX125dAPTu3Zt58+blyeTk1KlTtGnTBh8fH1atWqUlJmfPnmXJkiWSnIg8SVpQhBCvTGxsLBMmTGDSpEkkJiZy584dmjdvjolJ3vy/0vHjx5k4cSK///47kNJi0r59e/z9/SlbtqyRoxPCuCRBEUK8Etu2beOjjz7in3/+AaBZs2Z8//33eS45UUqxfft2Jk+ezPbt24GUxKRt27aMHj2acuXKGTlCIbIHSVCEEFkqJCSEYcOGsXTpUgDc3d357rvv8PPzy1OdYJOSkli5ciWTJ0/mxIkTQMo8Ju3bt2fEiBF5fii1EI+TBEUIkaUuX77M0qVLMTExYcCAAYwbNw4HBwdjh/XKxMTE8PPPPzNt2jSuX78OgI2NDX369GHIkCEyHb0QT5Albau3bt2iS5cuuLi4YG1tTYUKFfjrr7+0/UopRo8eTaFChbC2tqZhw4ZcunQpK0IRQrxi//zzj9anAqBu3bp8/PHHHD58mJkzZ+aZ5OTu3buMHj0aT09PBg8ezPXr1ylQoADjxo0jODiYGTNmSHIixFNkegvKgwcPqF27NvXr12fTpk0UKFCAS5cuGfTMnzx5Mt999x2//vorRYsWxd/fn8aNG3Pu3DmsrKwyOyQhxCtw4cIFJkyYwNKlS7G0tKRevXoUKFAAgGnTphk5ulfn8uXLTJ06lYCAAOLi4gAoXrw4Q4cOpXv37lhbWxs5QiFyCJXJPvvsM1WnTp0n7tfr9crNzU1NmTJFKwsPD1eWlpZq2bJlGXqPiIgIBaiIiIiXjlcI8XJOnTql2rVrp3Q6nQIUoN555x11+fJlY4f2Sh05ckS99957Bj+HN954Q/3+++8qKSnJ2OEJkS08z/d3pt/iWbt2LVWrVqVt27YULFiQ119/nZ9++knbf/XqVUJDQ2nYsKFW5ujoSPXq1QkMDEz3mPHx8URGRhpsQgjjunLlCq1bt6ZixYqsWLECpRStWrXi6NGjbNy4MU8sXqeUYtOmTdSvX59q1arx+++/o5SiadOm7N69m8OHD9OmTRtMTU2NHaoQOU6mJyhXrlxh7ty5lChRgi1btvDRRx8xaNAgfv31VwBCQ0MBcHV1NXidq6urtu9xEydOxNHRUduKFCmS2WELIZ6ThYUFGzduRKfT0a5dO06ePMmqVauoWrWqsUPLcgkJCSxcuJCKFStqyYiZmRndu3fn9OnTbNiwgXr16uWpUUpCZLZM74Oi1+upWrUqEyZMAOD111/nzJkzzJs3j+7du7/QMUeMGMEnn3yiPY+MjJQkRYhXSCnF3r172bVrF2PHjgWgcOHC/Pjjj1SrVo0yZcoYN8BXJCoqip9++onp06dz8+ZNAOzt7enbty9DhgyhcOHCRo5QiNwj0xOUQoUKpZkBsUyZMvzxxx8AuLm5AXDnzh0KFSqk1blz5w4+Pj7pHtPS0hJLS8vMDlUI8QxKKbZt28a4cePYv38/AC1btuT1118HeOH/dOQ0ISEhfPfdd8ydO5eIiAgg5W/ZkCFD+OCDD3BycjJugELkQpmeoNSuXZugoCCDsosXL+Ll5QVA0aJFcXNzY8eOHVpCEhkZyeHDh/noo48yOxwhxAtQSrF+/Xq+/vprjhw5AqTc0unVqxcFCxY0cnSvzunTp5k5cyaLFi0iISEBgFKlSjF8+HC6dOki/3ESIitldg/dI0eOKDMzMzV+/Hh16dIltWTJEmVjY6MWL16s1fnmm2+Uk5OTWrNmjTp16pRq2bKlKlq0qIqNjc3Qe8goHiGyzuXLl5WPj482EsXa2loNGTJE3bp1y9ihvRJJSUlq9erVqkGDBtrPAFC1a9dWa9asUcnJycYOUYgc63m+vzM9QVFKqXXr1qny5csrS0tLVbp0afXjjz8a7Nfr9crf31+5uroqS0tL9dZbb6mgoKAMH18SFCGyTmxsrCpUqJCytbVVn376qQoNDTV2SK/E/fv31bfffqu8vb21pMTU1FS1bdtW7d+/39jhCZErPM/3t04ppYzUePPCIiMjcXR0JCIiIs/MSilEVrh9+zZLlixhz549rFmzRhsOe/DgQUqVKoWLi4uRI8x6586dY9asWSxcuJCHDx8C4OzsTN++ffnoo4/w9PQ0coRC5B7P8/0ta/EIkcfExMSwevVqFi5cyPbt29Hr9QD8/vvvtG/fHoBatWoZM8Qsp9fr2bhxI9999x3btm3TyitUqMDgwYPp1KmTzPgqhJFJgiJEHhEUFMSkSZNYuXIl0dHRWnmdOnXo2rUrTZs2NWJ0r0ZERAQLFizg+++/559//gHAxMSEli1bMmjQIJm7RIhsRBIUIXKxxMREzM3NgZSWkwULFgDw2muv0a1bN7p06ZInZnwNCgpi1qxZBAQEEBMTA4CTkxN9+vShX79+smifENmQJChC5DJhYWEsX76chQsXUqZMGS0pef311xk1ahSNGzemdu3aub6lQK/Xs2XLFr777js2b96slZctW5ZBgwbRpUsXbG1tjRihEOJppJOsELlAQkICmzZtYuHChaxbt47ExEQgpZXgzp07WFhYGDnCVycqKoqAgABmzZrFpUuXANDpdDRv3pzBgwfToEGDXJ+cCZFdSSdZIfKQSZMm8e2333Lv3j2tzMfHh27dutGxY8c8k5xcvnyZ77//nl9++YWoqCgAHBwc6NWrF/37988Tt7KEyE0kQREih7l58yYFChTQZjFNSEjg3r17uLm50blzZ7p160bFihWNHOWrkZyczLZt2/j+++/ZuHEjjxqES5UqxcCBA+nevTt2dnZGjlII8SIkQREiB4iOjmbVqlUsXLiQHTt2sGLFCt577z0AevbsSdWqVXn77bcxM8sbv9LXr19nwYIF/PLLL9y4cUMrb9q0KYMGDeLtt9/GxCTTF2sXQrxCeeOvmRA5THh4OPPnz+fEiROcOHGCCxcukJycrO0/duyYlqB4eHjg4eFhrFBfmfj4eNauXcv8+fPZtm2b1lqSL18+unbtSv/+/SlZsqSRoxRCZBZJUIQwEr1ez5UrV7QkpGjRovTq1QtI6dQ5fPhwg/rFihWjW7dudO3alaJFixojZKM4d+4cP//8MwsXLjToZ9OgQQN69+5N69atsbKyMmKEQoisIAmKEK9IcnIyAQEBnDhxgr///puTJ08aTJhWv359LUFxdHTkww8/pHDhwvj4+ODj44O7u3ueGX0SHR3NihUrmD9/PoGBgVq5u7s7PXr0oEePHtLpVYhcThIUITLZvXv3tFYRExMTPvnkEyBlxtIRI0bw77//anUtLS2pUKECPj4+1K5d2+A4c+fOfaVxG5tSiiNHjjB//nx+++03LXkzNTWlefPm9O7dmyZNmuSZfjZC5HXymy7ESwoKCmL16tXs27ePEydOcOvWLW1fkSJFtARFp9PRs2dPkpOTtVaRUqVK5fkv3LCwMBYvXsz8+fM5c+aMVl68eHF69epF9+7dKVSokBEjFEIYQ97+yyjEC1BKGdxqadu2LadPnzaoU7x4cS0J0ev12oiSb7755pXGml3p9Xp27tzJ/PnzWbVqFQkJCQBYWVnx3nvv0bt3b9588808c0tLCJGWJChCZEBcXBw7d+5k9erVbN++ndOnT2vTpLdr1w53d3feeecdqlatSsWKFbG3tzdyxNnTzZs3CQgI4Oeff+batWtauY+PD3369KFTp044OTkZLT4hRPYhCYoQT/DgwQM2bNjAmjVr2LRpk7bIHMC2bdto1aoVAKNGjTJShDlDQkICGzZsYP78+WzevBm9Xg+kdATu1KkTvXv3pnLlykaOUgiR3UiCIkQ6li9fTpcuXUhKStLKPDw8aNmyJS1btsTX19d4weUADx8+ZMuWLfz555+sW7eOiIgIbd+bb75J7969adOmDTY2NkaMUgiRnUmCIvI0pRSnTp1izZo1VKlShWbNmgFQuXJlkpKSKF++PC1btqRVq1ZUqVJF+kQ8RWRkJBs3buSPP/5g48aNPHz4UNvn5uZG9+7d6dmzp0ymJoTIEElQRJ6TlJTEvn37WLNmDWvWrNH6QrRq1UpLUEqUKMG1a9fw8vIyYqTZ3/3791m7di1//PEHW7du1Tq7Anh6etKmTRv8/PyoWbMmpqamRoxUCJHTSIIi8gy9Xk+vXr1Yu3Yt9+/f18qtrKxo1KgRbdu2NagvyUn6QkNDWb16NX/88Qe7du0ymIK/ZMmSWlIiLU5CiJchCYrIlWJiYti/fz8XLlxg8ODBQMpEaRcvXuT+/fu4uLjQokULWrZsydtvv62NyBHpCw4O5s8//+TPP/9k//792jo4ABUrVtSSknLlyklSIoTIFDqV+i9NDhEZGYmjoyMRERE4ODgYOxyRDcTFxREYGMiuXbvYuXMnR44cITExERMTE8LCwrShqzt37sTMzIxatWrl+QnSnuXSpUv88ccf/Pnnnxw9etRgX7Vq1fDz86NNmzYUL17cSBEKIXKa5/n+lr/QIsf7+uuv+frrr4mPjzco9/LyokGDBkRHR2sJSoMGDYwQYc6glOLMmTNaUpJ68jmdTkfdunXx8/PDz8+PIkWKGDFSIUReIAmKyBGSk5P5+++/2blzJ7t27WLatGmUKVMGAFdXV+Lj4ylUqBD169enQYMGNGjQIE+t+PuiYmJiCAwMZPv27fz5559cunRJ22dmZkaDBg3w8/OjVatWuLq6GjFSIUReIwmKyJb0ej2nT59m165d7Nq1iz179hjMpdGsWTMtQWnTpg1169alVKlS0v/hGaKjozl48CC7d+9mz549HDlyxGCuF0tLSxo3boyfnx8tWrTA2dnZiNEKIfIySVBEtqDX64mLi9Mm7tq2bRtNmjQxqOPg4EC9evVo0KAB77zzjlbu7OwsX6RPEBUVxYEDB9izZw+7d+/mr7/+MkhIIGVBQ19fX5o1a0bTpk1lmn4hRLYgCYowigcPHnDkyBEOHTrEoUOHOHz4MD169GDq1KkA1K5dGycnJ2rUqKHdtnn99ddlLo1niIyM5MCBA+zevZvdu3dz7Ngxg2HAkNI3x9fXl3r16uHr64u3t7e0PAkhsh1JUMQrExsbS//+/QkMDOTChQtp9h87dkx7bGdnx7179yQheYaIiAj279+v3bI5duyYttbNI0WLFtWSkXr16uHt7W2cYIUQ4jlIgiIy3b///svhw4c5dOgQZmZmjB07FkiZEG3jxo3cuXMHgOLFi1OjRg1tq1ixosFxJDlJKzw8nH379mkJyd9//50mIXnttdfw9fXVEhJPT08jRSuEEC9O5kERL+3EiRMcOHCAwMBADh06xD///KPtc3Nz4/bt29othMWLF+Pk5ET16tUpUKCAsULO1pRS3L9/n2vXrnH16lWuXbvGlStXOHToECdOnODxX9nixYsbtJDIEGAhRHYl86CILHP79m3OnDlDo0aNtLL+/ftz8OBBg3ply5bVWkaSk5O1SdG6dOnySuPNjpRShIeHa8nHoy318+jo6Ce+vkSJEgYtJB4eHq8weiGEeDUkQRHpUkpx9epV/v77b06cOMHff//N33//ze3btzExMSE8PFwb7dGoUSMcHByoWbMmNWrUoFq1atrEaHlVRETEUxOQyMjIZx7Dzc0Nb29vvL29KVq0KBUqVKBevXq4u7u/gjMQQuRYyYmQEA0JDyEhJuVxYqrHCTH/7fvvcWKqx6n3FakGTacY7TQkQREkJiZy7tw5ypYti7m5OQD9+vVj3rx5aeqamJhQoUIFQkJCtARlzJgxrzTe7CQyMpJDhw5x4MABTp06pSUg4eHhz3ytq6urloCkTkS8vb3x9PTE2to6609ACGE8+uRUSUEGEomEGEiMeew16bw+OeHZ750R1k6Zc5wXlOUJyjfffMOIESMYPHgwM2bMAFLWTRk6dCi//fYb8fHxNG7cmDlz5shMla9AVFQUJ0+eNGgZOXv2LAkJCZw8eVLrqFq2bFksLCwoX748Pj4+vP7667z++utUqlQJOzs7I5+F8dy+fZv9+/dr28mTJ9N0Un2kQIECBklH6s3Ly0ub80UIkc0p9V/i8HhLw9MShwwkGElxWRu3iRlY2IGF7f83c1vD50/cZwf2xv1OztIE5ejRo/zwww9pRmd8/PHHbNiwgZUrV+Lo6MiAAQPw8/PjwIEDWRlOnhMaGoq9vb22Uu/s2bMZMGBAunUdHR25deuW9ln16tWLDz74AAsLi1cWb3aj1+s5f/48+/fv58CBA+zfv5+rV6+mqVe0aFHq1KnDG2+8QbFixbQERFZIFsIItNsbz0gUHk8wntVKQRaOJ9GZgIU9WNgYJgjmNv9/bGGbav+T9tkZJhtmOfvvd5YlKNHR0XTu3JmffvqJr7/+WiuPiIjg559/ZunSpdrCbQsWLKBMmTIcOnSIGjVqZFVIuVJCQgLBwcHaFhQUpLWM3Llzh99//502bdoAKcNPATw8PLQWkUetI49P1pUX/3cfHx/PX3/9pbWOHDhwgAcPHhjUMTExoVKlStSpU4c6depQu3Zt6aQqxItQCpLi/0sAop6dNGR0X2bd3niSx1sZHk8qDFojHiUNdobJhYXtfwnGf4/NLEEmS0wjyxKU/v3706xZMxo2bGiQoBw7dozExEQaNmyolZUuXRpPT08CAwPTTVDi4+MNVqrNSAfD3EApxb///muQgAQHB9OqVSvefPNNALZv306zZs3Sfb2JiQnBwcHac19fX+7evSvDe/9z//59Dh48qLWOHD16NM2KyDY2NtSoUUNLRmrUqCFD20XeoxQkxqZKBKKfnUDERz0lufjvuUp+9nu/KBPztElDhp6nV/5fi4W5DZiYZF3MwkCWJCi//fYbx48f5+jRo2n2hYaGYmFhkWaUh6urK6Ghoekeb+LEiXz55ZdZEapRxcXFcePGDYKDg/H29qZYsWIAHD58mK5duxIcHJzmCxNS+jY8SlA8PT2xsbHBy8sLT09PvL29tVaRChUqGLSEWFtb59mOl0oprl+/btB/5OzZs2nqFSxYUGsdqVOnDj4+PlrHYSFyBINkIp2Wifj0kovodJ7HpKofTZbe4jB/UiuEDVjaPzlpeFJCkQtub4gsSFBu3LjB4MGD2bZtG1ZWVplyzBEjRvDJJ59ozyMjI7PFZFTx8fHcvn2b6OhoYmJiiI6ONnhcpUoVqlatCsC1a9cYO3YsERER3Lx5k+DgYO7evasda8KECYwYMQIAW1tbbdl7nU5HoUKF8PT01LbUrUzlypUjOjpa1lJJRSnFjRs3OHbsmLb99ddf3Lt3L03dkiVLGiQkxYsXl5+leHUMbnOk0zIRn15rRXSqJOMJyYVKv+N2pkg3MbB7dtLwtOTCRGaNFmlleoJy7Ngx7t69S+XKlbWy5ORk9u7dy/fff8+WLVtISEggPDzcoBXlzp07uLm5pXtMS0tLLC0tMzvUdJ07d46JEydqScbjicfo0aPp168fkHKutWvXfuKxxo4dqyUo0dHR/Prrr2nqPGr9SN2hsnjx4uzevRtPT088PDye2lE1r3+ZKqW4efMmf/31l0FC8u+//6apa2ZmRpUqVahdu7Z2y6ZgwYJGiFrkWNqw0EcJRNRTnj/eYhGV/nN90rPf90U96i9h+XgCYZc2SUjTUpFOQiG3OMQrlOkJyltvvcXp06cNynr06EHp0qX57LPPKFKkCObm5uzYsUPrvBkUFERwcDA1a9bM7HCe24MHD1i8ePET99+/f197bGdnh42NDba2ttjZ2WFnZ6c9trW1pWTJklpdd3d3Jk2ahJ2dHYULF9ZaQ/Lly5cmybCysqJevXqZf3I53KNk5FGLyNOSEVNTU8qXL0+VKlW0rWLFinn2FleelZSQTrKQOlGITtUiEf1Y68Rj5Y8mtMoqZtYZSxYsH2+xsEun/L/bHJJMiBzslazF4+vri4+PjzYPykcffcTGjRsJCAjAwcGBgQMHAqSZLv1JsnItntDQUBYvXpwm2XiUgLi7u5M/f/5MfU+RVupkJHVCIslILvcooYiPeqxVIlWykLqV4lHC8aSkIqtGdOhMU5KBx1smtMQiveeP1//v+aPRHKYyb6bI/bL9WjzTp0/HxMSENm3aGEzUlh24ubkxbNgwY4eRp+j1eq5evcrp06cNEhJJRnKA5KS0rRCpE4r4qLQtFo8nHKkfZ1VCYWqZKjmwf6zFwc6w9cHSPp1WCTvDxzIsVIgsJ6sZi1fqzp07nDlzhtOnT3P69GnOnDnD2bNniYmJSVPX1NSUcuXKUbVqVUlGMsujGTFTJwsGCUNUqn0ZeJ4UmzVxmlmlTQos00kU0t2fqtXiUZmpjMQSIjvI9i0oIveLjo7m7NmzWhLyKCFJr1UEUjpCly1bltdff12Skcfpk1MlE09LLKLSaaVIJ8HIihEephapEgL7x5KJx5+nl1A8dvtDEgoh8jxJUMRLSUxM5NKlSwYtIqdPn+bKlSvp1tfpdBQrVowKFSpoW/ny5SlevDhmZrnockw3qYhMddsjvedRjyUe/702MW3r0svTpUoM7J+QLGQk4fivvsw5IYTIZLnoG0FkJb1ez40bN7RWkUfbhQsXSEhIv9+Am5ubloA8SkbKli2bfafRfzTBlUECEfXsLeHxsixKKkzM/ksI/ksQDJIJ+7SJRur9qV/3qH+F9KEQQmRjkqAIA5GRkQQFBaXZLl26RGxs+v0N7OzsDJKQR49f2WgnvT5Vi0PkY/8+vj1e/tjrMnvq7UdJRbqJhf1jiUR6z+3A0kE6Zgoh8hxJUPKg5ORkrl27lm4iEhIS8sTXmZubU7JkSYPbMxUqVMDT0xOTF5lvISk+nY6XT0ouIiHuCUlHQtRL/DTSozNMFgwSBodUiYO9YZkkFUIIkWkkQcnFHjx4kG4Scvny5XTX+HnE1dWV0qVLU6pUKYPN28sTs+S4xzpmXoeLZ5/QUfNJo0D+K9MnZu4Jm5iDlUPaxCHN9li5xWP7ZbZMIYQwOklQcjClFPfv3+fq1atcuXKFq1evcvnyZS0RSb3WD4CNOTha6njNXkcBdyvKvOZBKS83XnPPT+ECDhTKZ4OLnTmW+rj/WiyuQ9xpuBgJpyJTyrJiwTAz68f6TvyXQFg9nmA8Kfn477G0VgghRK4hCUo2Fxsby7Vr11ISkCtXCLl2kXs3LxF15zoPw25hpWJxttbhbK3DxUZHbSsdTQvpcCqqw9HKFmdbM/JZm2Jnloyp7vHk4t//NuD+f1tG6EwNb2NkeMTHE0aAyAyaQgghHiPfDMag16e0RsTeJzkmjLAbl7l38xIRodeIvXeLhMg7qIcPMEuMwtYkHhdrHdWsdTS21mFmr4MypGzogGeNiFFAqsXIdKb/tUw4gJVjymbpkFL21MeO/2/RMLOSlgohhMil/v33X27duoWPj49R45AE5WUkJ0JsOMQ+gLj//k216R/eJz48lMTIu+hj7mMSH45ZYjRWKg6T/1ozTIGC/20AmABO/21Aeh9RIuYkWdijs3bG3LEgprYuYO0M1vnA2ilVcpHq30dJiQwvFUII8Z/ffvuNHTt20KlTJ+rXrw/ArVu3mDRpEsuWLTNqbJKgpPbwPtw5m26y8WhTseHoY8Ig9gGmSU9f2dQEsP5vM/BffhCdoLgfq3gQBw+VJYlm9mCTD3OHgti4eODo6oVz4RLYFyyCTktA8mFuboXMsymEEOJp9Hq9NsLy9u3bDBw4kLCwMHbv3q3V2bp1KwsWLMDT01NLUEqWLEm+fPmMEbIBSVBSiTq/A/t1vZ9aR0dKq0dqD2JTEo2UZOP/jx89TzSzQ2fjjKmtC5ZObuTzKIabdym8ipWiaNGilPfwwNT08aMKIYQQT6fX67l58yaenp5a2YQJE/j+++8ZOHAgI0aMAFLmq/rzzz8BiIiIwNHREYBWrVpRpEgR3nrrLe31NjY22WIBX0lQUvn74i0K/Jv8xGTj/+WgLB0xcyiArbM7BVzdcHNzw62EG66urri5uVH6v3/z58+fu6ZwF0II8cpFR0cTFBSEjY0NZcqUAVIm1nR1dSUuLo6oqCjs7OyAlKQlJCSEoKAg7fUODg7MmzcPLy8vLC0ttfJ3332Xd99999WeTAbJasapHD9+nE6dOuHm9v9E49G/qR8XKFAACwtZe0QIIUTm0uv1bNu2jaCgIPr27YuVlRUAY8aM4auvvqJ379789NNPWv2CBQsSERHB8ePHKVeuHADXr1/nzp07lCpVSmspyS5kNeMXVLlyZS5cuGDsMIQQQuQBJ06c4I8//sDLy4vevVO6F+h0Otq3b09ERAT169enQoUKAJQqVYqCBQtqCcsjf//9N66urgYt9V5eXnh5eb26E8kiMl2mEEIIkcWGDx9OgwYNuHTpklZ26tQpvv76a5YsWaKV6XQ6mjZtSuvWrdGlGnHZsWNH7ty5w6xZswyO6+HhkWu7EeTOsxJCCCFegbi4OExMTLTb/rt27eLjjz/G29ub1atXa/V27tzJ8ePHOXv2LCVKlACgatWq9O3blzfeeMPgmEuXLk3zPro8OD2EJChCCCHEM9y/f587d+5oHVQBmjdvzqZNm1i7di3NmjUDwMLCgpMnT3L/vuHU3J9++inx8fFUrVpVKytbtiw//PDDqzmBHEgSFCGEEOI/t27d4syZMykLpHp7A7Bv3z7efPNNihUrxuXLl7W6tra26PV6g9s2lSpVYt26dZQuXdrguO3bt38l8ecm0gdFCCFEnhMdHc0ff/yRZr6PAQMG0KRJE9auXauVPbolk5ycTFLS/5cOmTRpEiEhIQwePFgrs7Ozo3nz5hQvXjyLzyD3kxYUIYQQudqWLVvYunUrDRo00G7FPHjwgPfeew8zMzP69OmDuXnK/NyVKlUiKCjIYK4QV1dXIiMjsbe3NzjuoxYWkTWkBUUIIUSOlXoqr6ioKLp3706dOnVITk7Wyrdv3860adPYsmWLVubh4UHdunXp1KkT0dHRWvnYsWM5d+4cH3zwgVam0+nSJCci60kLihBCiGxNKcXdu3cpUKCAtrbMTz/9xKRJk2jTpg2TJk0CUqZoX7FiBXFxcVy7do1ixYoB8Pbbb5OUlMTbb7+tHdPExIS9e/e++pMRGSYJihBCiGwhNjaWS5cuodPptAnK9Ho9hQoV4u7du1y/fl1bcyY5OZl//vmHs2fPaq83NTVl+vTp5M+fnwIFCmjljRo1olGjRq/2ZMRLkwRFCCHEK7d161bOnTtH165dcXFxAeDnn39m4MCBtGzZUptDxMTEBGdnZ/7991+uXr2qJSgtWrSgVKlSBsN+AT788MNXeh4i60iCIoQQIsucOXOGhQsXkj9/fj799FOtvH///ly+fJlKlSpRv359AEqWLEm+fPmwsbExOMaWLVsoUKAA1tbWWpmHhwceHh6v5iSEUUgnWSGEEC8kPj7e4PnQoUOpWrUqhw4d0spu3LjBlClTWLx4sUHdxo0b4+fnZ5CMvP3229y/fz/NTKqenp4GyYnIG6QFRQghxBNFRUVx+fJlChcurPXrOHDgAH5+fri7u/P3339rdc+ePcuxY8c4e/YsNWrUAKBChQoMGjSI8uXLGxz3+++/T/NeeXE6d/FkOpV6jFYO8TzLNQshhHi28PBwtm/fTmRkJD179tTK3377bbZv386CBQt4//33Abhw4QJlypTB1taWqKgoLbHYvn070dHRVK9enUKFChnjNEQ29zzf39KCIoQQeczy5cvZtm0b7dq100a33Lp1i7Zt2+Lg4ECPHj20pKN48eKcOHGCuLg47fXFihXjr7/+onjx4gatHg0bNny1JyJyNemDIoQQuYBSirCwMINF6kJCQvD19U1ze2XHjh38/PPP7N+/XysrVqwYNWrU4N133zXoWzJr1iz+/fdfg9Ex5ubmVKlSBUdHxyw8I5HXSYIihBA5SEJCAtu3b+eXX34xKB84cCD58+fnu+++08ocHR3Zs2cPZ8+eJSwsTCtv2bIlY8aMMZgbxMrKisDAQBYtWoSVlZVWbmYmDe3COOTKE0KIbGrz5s2sWrWKunXr0qVLFwASExO1GVH9/PxwcnIC0Ibc3rt3T3u9jY0Ny5cvx8vLy2Cq9mbNmmlr0giRXWV6C8rEiRN54403sLe3p2DBgrRq1YqgoCCDOnFxcfTv3x8XFxfs7Oxo06YNd+7cyexQhBAi24mLiyMoKIh//vlHK0tISKBatWq4uLgQERGhlR8/fpwff/yRbdu2aWW2trbUqVOHZs2aGawhM2DAAB4+fJhmdEy7du2oXr06FhYWWXhWQmS+TE9Q9uzZQ//+/Tl06BDbtm0jMTGRRo0aERMTo9X5+OOPWbduHStXrmTPnj3cvn0bPz+/zA5FCCGMRinF999/z7Bhw4iMjNTKp02bRunSpRk3bpxWZmFhwbVr17h//z5XrlzRyuvXr4+/vz8dOnQwOPa+fftYv349hQsX1srs7e1lrhCRu6gsdvfuXQWoPXv2KKWUCg8PV+bm5mrlypVanfPnzytABQYGpnuMuLg4FRERoW03btxQgIqIiMjq8IUQwsDdu3fV0aNHVWxsrFa2YsUKValSJTVgwACDuvnz51eAOnHihFa2ePFiZWdnp95//32Durt27VInT55UcXFxWXsCQhhRREREhr+/s7wPyqPmSmdnZwCOHTtGYmKiwXC00qVL4+npSWBgoDa5T2oTJ07kyy+/zOpQhRBCc/r0adasWUPhwoW1+T8AypYty7179zhx4gSVKlUCUm7RnDx5knz58hkc4/333ycpKcmg/0fHjh3p1KlTmknJfH19s+xchMiJsnQUj16vZ8iQIdSuXVsb5hYaGoqFhYXWsesRV1dXQkND0z3OiBEjiIiI0LYbN25kZdhCiFzq3r177Nixg3379hmUv/XWWzg7O3PixAmt7OTJk/j7+7No0SKDukWLFqVQoUIGfUXq16/Phg0b+OGHHwzqTpkyhenTp/Paa69pZSYmJjJjqhAZkKUtKP379+fMmTMGY+1fhKWlJZaWlpkUlRAit3n48CFRUVG4urpqZZ9//jmnT59m6tSplC5dGkhZdK5Lly7Ur1+fnTt3anXDw8N58OABN2/exMfHB4CKFSvSs2dPXn/9dYP3CgwMxNTU1KDM3d0dd3f3LDo7IfKmLGtBGTBgAOvXr2fXrl0GHbnc3NxISEggPDzcoP6dO3dwc3PLqnCEEDmMUorY2FiDsp9++omhQ4cajIBZuXIltra2aTqSbtu2jY0bN3L58mWtrGjRopQpU4aiRYumOe6ZM2d46623tLKKFSvy888/M2DAAIO6jycnQoiskektKEopBg4cyKpVq9i9e3eaPwRVqlTB3NycHTt20KZNGwCCgoIIDg6mZs2amR2OECKbUUoZ3OJYvnw5Fy9epGvXrnh7ewOwdu1abXjsnj17tLo//vgjf/31F76+vhQrVgxAW8Au9QyqAMOGDSMmJoaKFStqZbVq1eLcuXNpYqpcuXKmnZ8QInNkeoLSv39/li5dypo1a7C3t9f6lTg6OmJtbY2joyO9evXik08+wdnZGQcHBwYOHEjNmjXT7SArhMj+kpOTDVoWVq1axeXLl2nbtq2WdGzevJnu3btTtmxZdu3apdX99ttv+euvv6hUqZJW19HRkfj4eEJCQgzep0OHDtSrVw8vLy+trFatWjx48CDNtOsdO3bM5LMUQrxKmZ6gzJ07F0jbIz31SpjTp0/HxMSENm3aEB8fT+PGjZkzZ05mhyKEeEFKKR4+fIitra1WtmTJEi5evEi3bt201ovNmzfToUMHKlSoYNDx9JtvvuHIkSOUKlVKSzpsbGy4e/dumkTi3XffxcfHx6APR7Vq1bh69apBnxKAoUOHponVwsJCJiETIhfKkls8z2JlZcXs2bOZPXt2Zr+9ECIdcXFx3Lp1i+TkZEqWLKmVz5kzh4sXL/Lhhx9qHUk3bNhAq1atqFatGgcOHNDqzpo1i8OHD1O5cmUtQbG1tSUiIiLNTNCNGzemRIkSFCxYUCurXLkyJ06coFChQgZ1/f3908RrbW2tJTZCiLxJ1uIRIofQ6/VAyjBVgJs3b7Jv3z7s7Oxo0aKFVu/DDz/k5MmTfPfdd7zxxhtASktH69atqVGjBoGBgVrdRYsWcejQIXx9fbUExdHRkaSkJO7evWvw/i1btsTHx0db8wVSko5z586laen46quv0sRvZ2enzRsihBDPIgmKEFksOTmZyMhIkpOTyZ8/v1a+fv16QkNDad68uTaC7dChQ0yfPp3ixYszfvx4rW6tWrU4fPgwO3fupF69ekDKpIedOnWievXqBgnKqVOnOHToEDdv3tQSFGdnZ2xtbdMM1+/UqZNBh1OAqlWrEhwcbND6ASnzET3O1taWMmXKvOiPRgghnkgSFCGeIT4+ngcPHuDi4oK5uTkA58+fZ9u2bXh4eGij0SBlYbYrV66wcOFCypYtC6T03ejevTuNGzdm8+bNWt1PP/2U8+fPs2vXLi1BuXPnDitWrKB69eoGCYper0ev1xuMVClSpAj169fXJkF8ZOzYsTx8+JDq1atrZXXr1jVYWO6RgQMHpimzsrKiSJEiz/UzEkKIzCYJisgTHh9lsmXLFu7du0eLFi1wcHAAYOvWrcyePZsqVaowevRora6Xlxd37tzh77//1ibxOnLkCIMHD6Zx48YGCcqpU6cICgri33//1coeHf/hw4cGMdWrV49ixYoZTINeqVIlZs6ciaenp0HdFStWYG5ubtACU7lyZYPJxh5p1KhRmjKZuVQIkdNIgiKyPb1ej06n075kr127xqlTp3B1dTVoJRg4cCB37tzhu+++01okfvzxRwYNGkSrVq347bfftLrdu3fnzp07BuuphISEsHbtWuLj4w3eP1++fNy9e9dgRdqSJUvStm3bNPNnzJw5k6SkJINWjWbNmhEXF5fm9sqjEW+peXt7M2jQoDTljycsQgiR20mCIrLMo5lA9Xo9dnZ2WtmaNWuIjIykbdu22vLwmzZtYsmSJdSqVYt+/fppx3B1deXu3btcu3ZNm/tizZo1DBkyhA4dOrBs2TKt7u+//05oaCgjR47UEhQLCwvi4+MN1k0BqFOnDuHh4ZiZ/f9XoFatWvzwww8UL17coO6RI0ewtbXVOqcC1KxZM92JBRs3bpym7NFtISGEEBmXpYsF5kRnzpyhYcOGBquXQsqohFatWhk0qV+7do2OHTummQp77ty59O7d22Ayqrt37zJo0CA+/fRTg7q7d+/m559/5syZM1pZYmIiR48e5cyZMwbDtpOTkzM0jPt5xcTEEBISYrD8gF6v58CBA+zcuZOkpCSt/MiRI8yYMYPt27cbxNW0aVPq1KljkAiMGzcOW1tbhg0bppXpdDo6duxI9+7dDUaJXLx4kSVLlqRZxO1Rq0nq2Ly8vKhWrZrBAmwAo0ePZtasWQZLJvj5+XHt2jWWL19uUPf3339n+/btlCtXTisrUaIEffv2pUGDBgZ17e3tDZITIYQQr4DKgSIiIhSgIiIiMv3Ye/fuVYAqWbKkQXmTJk0UoH799Vet7NixYwpQhQsXNqjr5+enADVnzhyt7MKFCwpQTk5OBnW7deumADV58mSt7MaNGwpQZmZmBnX79eundDqd+uqrr7SyyMhIValSJVWjRg2VkJCglU+ZMkVVrlxZzZ49WyuLiIhQjo6OytraWsXHx2vlw4cPV4AaOnSoVpaQkKAABaiwsDCtfOzYsQpQH330kUFslpaWClDXrl3TyqZNm6YA1bFjR4O6TZs2VY0bN1bXr1/Xyo4fP66+/fZbtXnzZoO6V65cUaGhoSopKUkJIYTI2Z7n+1tu8TymVKlSLFmyRLsl8cjgwYPx8/MzmI6/cOHCzJgxI03dzp07U6VKFapVq6aVOTs7M2rUKGxsbAzq+vj4EBYWRokSJbSy5ORkihQpYnD7ASA2NhallEF5TEwMJ0+eRKfTGZTfunWL48ePG3SYNDc311o44uPjtdk3LS0tMTEx0ebZADAzM6NkyZKYm5sblFeqVIkOHTpQpUoVg9h++eUXLC0tcXFx0co++OADevfubTAbKaRMBPa4119/Pc2qsUCatZyEEELkDTqlsuCeQRaLjIzE0dGRiIgIbYREXhATE0NkZCQ2NjbadOFxcXHs2bOH+Ph43n33Xa3u2bNnCQ4Opnjx4lryo5Ti0qVLWFhY4Onpqd22UI8t3iaEEEJkhef5/pYERQghhBCvxPN8f0vPPyGEEEJkO5KgCCGEECLbkQRFCCGEENmOJChCCCGEyHYkQRFCCCFEtiMJihBCCCGyHUlQhBBCCJHtSIIihBBCiGxHEhQhhBBCZDuSoAghhBAi25EERQghhBDZjiQoQgghhMh2JEERQgghRLYjCYoQQgghsh1JUIQQQgiR7UiCIoQQQohsRxIUIYQQQmQ7kqAIIYQQItuRBEUIIYQQ2Y4kKEIIIYTIdiRBEUIIIUS2Y9QEZfbs2Xh7e2NlZUX16tU5cuSIMcMRQgghRDZhtARl+fLlfPLJJ4wZM4bjx49TqVIlGjduzN27d40VkhBCCCGyCZ1SShnjjatXr84bb7zB999/D4Ber6dIkSIMHDiQzz///KmvjYyMxNHRkYiICBwcHDI/uISYlH/NbUCnS3mclAD6RDAxAzPLtHXNrMHkv3wvORGSE0BnCuZWL1j3IaDAzApMTP+rmwTJ8aAzAXPrF6ubGAtKD6aWYGqWUqZPhqS456uLDixsUtWNA5UMphZgav4CdfWQFJvy2ML2/3WT4kGfBCbmYGbx/HWVgsSHKY/T/Tyfp24GPvtMuU7S+zwz4Tp59Hm+7HWS5vN82evkCZ/ny14nqT/Pl75OnvB5yt8I+RthUDcX/o3IZM/z/W2UFpSEhASOHTtGw4YN/x+IiQkNGzYkMDAwTf34+HgiIyMNtiw1wT1lexj2/7KDM1PKNg4zrDuleEp5xI3/lx35KaVs7QDDujMqpJTfC/p/2YklKWW/9zSsO7t6SnnIif+Xnf0zpWxZB8O6P9VPKb9+8P9lFzenlC1saVh3wTsp5f/s+H/Z1T0pZfPfNqy7+L2U8gvr/l9282hK2bzahnVXdE0pP7Xi/2V3zqaUzapsWHdV35TyYwH/L3twNaVsahnDuuuGpJQfnvv/sujQlLJvPA3rbvnif+3dfVBUdbgH8O+Cw5LyllAo7iqjJEW8bAHbGDRQwWjNaDkwcRlNMEfLkPHKkGUvmlasTTTRJJV4k6KbdxTnGqhNtxsaoqYo+D6SEVIYLWZc3pZxve6e+8ded9l2wV3Y3XM2vp8ZZtzn9zvnPIdnzuHxnLO7pnjD+5bY9V5LPY03LfEDm0yxA5ssMeNNy9zrvZZ4w/um2H+9ar29zdNN8QGtJXb8E1Ns779az33/PlP8fy5bYk2fm2J7VljP/ehBU7zrgiV2dpcptutZ67mfppjiV05YYi17TbF/z7ae+2+ZpvjlekvslzpTrPIJ67lVT5nil761xH49aopte9R67n/8iyl+4T8tsT9Om2LlD1nP3f2cKX76K0vs2k+mWFmc9dzaVaZ44zZLrLfDFHsvynruN8Wm+NEPLbHBvyz1HOq/N5hi9Zstsf8dtMy99QcIMM0piTAtMxTPESY8R5j8k88RIhKlQbl27RoMBgPCw8Ot4uHh4dBqtTbzNRoNgoODzT9KpdJTqRIREZEIRLnF09nZiWnTpuHo0aOYM2eOOb527VrU19fj+PHjVvP1ej30er35dV9fH5RKJW/xjGYuL9/+/1xevnVoLm/x8BYPwHPEeD5HuJgzt3hEaVBu3LiBiRMnYvfu3Xj66afN8by8PPT09KCmpmbE5d3+DAoRERG5nOSfQfHz80NiYiLq6iz3OI1GI+rq6qyuqBAREdH4NEGsDRcVFSEvLw9JSUlQq9UoKyuDTqfD0qVLxUqJiIiIJEK0BiUnJwd//vkn1q9fD61WC5VKhW+//dbmwVkiIiIaf0T7HJSx4DMoRERE3kfyz6AQERERjYQNChEREUkOGxQiIiKSHDYoREREJDlsUIiIiEhy2KAQERGR5LBBISIiIslhg0JERESSwwaFiIiIJEe0j7ofi1sfftvX1ydyJkREROSoW3+3HfkQe69sUPr7+wEASqVS5EyIiIjIWf39/QgODh5xjld+F4/RaERnZycCAwMhk8nM8eTkZJw4ccLuMsON/T3e19cHpVKJjo4O0b/nZ6T98eT6nFnudnPHMm5vjPVz7XKOzB1tDXkMekcNeR51z/pYQxNBENDf34+IiAj4+Iz8lIlXXkHx8fGBQqGwifv6+g77ixxubLh4UFCQ6AfWSPvjyfU5s9zt5o5l3N4Y6+fa5RyZO9oa8hj0jhryPOqe9bGGFre7cnLLP+oh2YKCAqfHRlpGbK7ObbTrc2a5280dy7i9MdbPtcs5Mne0NeQx6B015HnUPetjDZ3nlbd43MmZr4Im6WH9vB9r6P1YQ+8nhRr+o66guIJcLseGDRsgl8vFToVGgfXzfqyh92MNvZ8UasgrKERERCQ5vIJCREREksMGhYiIiCSHDQoRERFJDhsUIiIikhw2KERERCQ5bFCcsHDhQtx5553Izs4WOxUahY6ODqSnpyMmJgbx8fGorq4WOyVyUk9PD5KSkqBSqRAbG4tt27aJnRKNwuDgIGbMmIHi4mKxU6FRiIyMRHx8PFQqFR599FG3bYdvM3bCDz/8gP7+fnzxxRfYvXu32OmQk/744w90dXVBpVJBq9UiMTERly5dwqRJk8ROjRxkMBig1+sxceJE6HQ6xMbG4uTJkwgNDRU7NXLCa6+9htbWViiVSpSWloqdDjkpMjIS58+fR0BAgFu3wysoTkhPT0dgYKDYadAoTZ06FSqVCgAwZcoUhIWFobu7W9ykyCm+vr6YOHEiAECv10MQBIe+tp2k4+eff0ZLSwueeOIJsVMhiRs3DcqhQ4cwf/58REREQCaT4euvv7aZU15ejsjISPj7++Ohhx5CY2Oj5xOlYbmyhk1NTTAYDFAqlW7OmoZyRQ17enqQkJAAhUKBl156CWFhYR7KnlxRv+LiYmg0Gg9lTH/nihrKZDKkpaUhOTkZX331ldtyHTcNik6nQ0JCAsrLy+2O79y5E0VFRdiwYQOam5uRkJCAuXPn4urVqx7OlIbjqhp2d3djyZIlqKio8ETaNIQrahgSEoIzZ87g8uXL2LFjB7q6ujyV/rg31vrV1NRg9uzZmD17tifTpiFccQwePnwYTU1NqK2tRUlJCc6ePeueZIVxCICwZ88eq5harRYKCgrMrw0GgxARESFoNBqreQcPHhSysrI8kSaNYLQ1vH79uvDII48IVVVVnkqVhjGW4/CWlStXCtXV1e5Mk4Yxmvq98sorgkKhEGbMmCGEhoYKQUFBwsaNGz2ZNg3himOwuLhYqKysdEt+4+YKykhu3LiBpqYmZGRkmGM+Pj7IyMjAjz/+KGJm5ChHaigIAvLz8/HYY4/h2WefFStVGoYjNezq6kJ/fz8AoLe3F4cOHUJ0dLQo+ZI1R+qn0WjQ0dGB9vZ2lJaWYvny5Vi/fr1YKdPfOFJDnU5nPgYHBgZw4MAB3H///W7JZ4Jb1uplrl27BoPBgPDwcKt4eHg4WlpazK8zMjJw5swZ6HQ6KBQKVFdXY86cOZ5Ol+xwpIZHjhzBzp07ER8fb77v+uWXXyIuLs7T6ZIdjtTw119/xYoVK8wPxxYWFrJ+EuHoeZSky5EadnV1YeHChQBM76pbvnw5kpOT3ZIPGxQnfP/992KnQGOQmpoKo9Eodho0Bmq1GqdPnxY7DXKB/Px8sVOgUZg5cybOnDnjkW3xFg+AsLAw+Pr62jxs19XVhSlTpoiUFTmDNfR+rKF3Y/28n9RqyAYFgJ+fHxITE1FXV2eOGY1G1NXV8RaOl2ANvR9r6N1YP+8ntRqOm1s8AwMDaG1tNb++fPkyTp8+jcmTJ2P69OkoKipCXl4ekpKSoFarUVZWBp1Oh6VLl4qYNQ3FGno/1tC7sX7ez6tq6Jb3BknQwYMHBQA2P3l5eeY5H330kTB9+nTBz89PUKvVwrFjx8RLmGywht6PNfRurJ/386Ya8rt4iIiISHL4DAoRERFJDhsUIiIikhw2KERERCQ5bFCIiIhIctigEBERkeSwQSEiIiLJYYNCREREksMGhYiIiCSHDQoRERFJDhsUIiIikhw2KERkV1paGmQymc3PkiVL3LbNpUuX4vXXXx92XKvVYvXq1YiKioK/vz/Cw8ORkpKCTz75BIODgw5tY/78+Zg3b57dsYaGBshkMpw9e3ZU+ROR64ybbzMmIscJgoBTp06htLQUixYtshoLCAhwyzYNBgP27duH/fv32x1va2tDSkoKQkJCUFJSgri4OMjlcpw7dw4VFRWYNm0aFixYcNvtLFu2DFlZWbhy5QoUCoXVWGVlJZKSkhAfH++SfSKi0eOXBRKRjUuXLiE6OhqNjY1ITk72yDYbGhqQk5OD33//HTKZzGZ83rx5uHDhAlpaWjBp0iSbcUEQzMsZjUa8++67qKiogFarxezZs/HGG28gOzsbN2/ehEKhwKpVq6yu1gwMDGDq1Kl477338MILL7hvR4nIIbzFQ0Q2mpqaMGHCBI9eSaitrcX8+fPtNid//fUXvvvuOxQUFNhtTgBYLafRaFBVVYVPP/0UFy5cwJo1a7B48WLU19djwoQJWLJkCT7//HMM/f9ZdXU1DAYDcnNzXb9zROQ0NihEZKO5uRkGgwGhoaEICAgw/zz//PNu22ZNTc2wt2haW1shCAKio6Ot4mFhYebcXn75ZQCAXq9HSUkJtm/fjrlz52LmzJnIz8/H4sWLsXXrVgDAc889h19++QX19fXmdVVWViIrKwvBwcFu2kMicgafQSEiG83NzcjNzcXGjRut4pMnT3bL9i5evIjOzk48/vjjTi3X2NgIo9GIRYsWQa/XAzA1M4ODg8jMzLSae+PGDTzwwAMAgHvvvRcPP/wwtm/fjvT0dLS2tqKhoQGbNm1yzQ4R0ZixQSEiG83NzXjnnXcQFRVld3z79u0oKyuDTCZDZmYmSktL0d7ejqeeegqxsbFobGxERkYG5s6dC41GA51Ohz179uCee+6xu77a2lpkZmbC39/f7nhUVBRkMhl++uknq/jMmTMBAHfccYc5NjAwAADYv38/pk2bZjVfLpeb/71s2TIUFhaivLwclZWVmDVrFtLS0m7zmyEiT2GDQkRW2tra0NPTg4SEBLvj586dwwcffICGhgaEhISgu7vbPHbx4kXs2rULUVFRiI2NRUBAAI4fP46tW7diy5Yt+PDDD+2us6amBitWrBg2p9DQUGRmZmLLli0oLCwc9jkUAIiJiYFcLsdvv/02YsPxzDPPYPXq1dixYweqqqqwcuVKu8+/EJE42KAQkZWmpiYAQHh4OLRardXY3XffjYMHDyInJwchISEArG/7REdHm58Tue+++5CRkQEAiIuLwzfffGN3e1evXsXJkydRW1s7Yl4ff/wxUlJSkJSUhDfffBPx8fHw8fHBiRMn0NLSgsTERABAYGAgiouLsWbNGhiNRqSmpqK3txdHjhxBUFAQ8vLyAJjeLp2Tk4N169ahr68P+fn5zv2iiMit2KAQkZXm5mYAsLkdI5fL0dfXN+KyQ2+h+Pj4mF/7+PjAYDDYXWbv3r1Qq9UICwsbcd2zZs3CqVOnUFJSgnXr1uHKlSuQy+WIiYlBcXExXnzxRfPct956C3fddRc0Gg3a2toQEhKCBx98EK+++qrVOpctW4bPPvsMTz75JCIiIkbcPhF5Fj8HhYiccv78eeTm5uLw4cMIDg5Gd3c3Jk+ejPb2dmRnZ+PkyZMAgOzsbKxatQrp6ek4duwY3n77bezbt89mfQsWLEBqairWrl3r6V0hIgnj24yJyCmxsbFYvXo1UlJSoFKpsHnz5jGtLzU1lZ89QkQ2eAWFiIiIJIdXUIiIiEhy2KAQERGR5LBBISIiIslhg0JERESSwwaFiIiIJIcNChEREUkOGxQiIiKSHDYoREREJDlsUIiIiEhy2KAQERGR5LBBISIiIsn5PwNMGgmTQv4vAAAAAElFTkSuQmCC", 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", + "image/png": 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", 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p+eTk5AqYWVVLTkwtoN5Y6BqUZZMTyOE1KDY2Nkn+oknP10mJ9evXU7NmzWT3Pf2c0NPTkxs3bgBw/fr1JL9Qvb29iY2N5d69ewwdOpSAgAB8fX2xsbFh6NCh9OjR46VxzJkzx/iXKGgN9h5X07u4uBAeHp6i63me69ev4+XllSTm69evP7f8qlWr+PLLL7l8+TKgDcl89+7d14oh2zC30Wo3MuJ1XtHjezFXrlzGbc+7XwFcXV2NP1tbWz+zntz/bUhICMHBwUl+sSYmJlKxYsVXjvu/LCwsaNWqFWXKlKFChQqULFkSOzs74+Me0OYJeVzram1tTc2aNY1zlQwZMoRx48Zx8eLFF/eI+Jd8DkR6W7duHa1atSIuLo769euzYsWKZ7+PHt2DdUPg5HJtPW9ZaDkHXJ+tCcxqcnSCotPpXvvRS0YJDQ1N8nPevHkB8PDwICQkxLgvJCQEKysrnJ2d0el0TJ8+nenTp7N//35q166Nn58fJiavXjHm5+dH3759OXbsGOXKlXtpeV0yjakex1ywYEFjzB4eHsmWj4uLo0OHDqxYsYJ3330XU1NT8ubN+9rterINne6VH71klLVr1+Li4kLRokWN92loaCg+Pj7Gn0uUKPFar5EvXz6KFy/OiRMnkt2f3H32qhISErh06RIlS5akRIkS/PPPP8bHPCdPnjS2CSlVqhT//PNPis4pnwOR0VasWEH79u1JSEigefPmLFmy5NnHgRc2w+p+EBUOOlN4Zwi8MzRTxzZJDXnEk0UEBgZy4cIFIiIimDBhAu3atQPgvffe46effuLMmTNER0czYsQI2rVrh06nIygoiMuXL6OUwtHREZ1Oh6mpqfGcsbGxSZaUKFasGN27d6dDhw7s27ePxMREIiIimDJlCuvWrXumfO7cuTEYDISFhRm3vffee4wdO5b79+8TGhrKN998Q/v27QHtr+uwsDASExMB7RdzfHy88a/s6dOnc/v27Vd7E0Waunv3LnPnziUgIIAxY8YkubfGjh1LbGws+/fvZ82aNbRu3fq1XqtKlSoYDAZmzZpFfHw88fHx7Nq1y5icu7q6prrdCUBwcDBBQUHExsYSFxfH9OnTCQsLo3LlygB06tSJqVOncvv2bS5evMhPP/2Ev78/AK1ateL48eP89ddf6PV6pk2bhru7e5KGvY/J50BkpIULF9KuXTsSEhJo3749f/zxR9LkJO6h1tbktzZacpK7CPTcDH7Ds01yApKgZKh33303yTgoI0aMMO7r1KkT7dq1w9vbm3z58jF8+HDjMcOGDaNRo0Z4e3tjbm7OtGnTADh//jx+fn7Y29vTuHFjpk2bhre3N6CNZWJtbZ1kefqX54vMmTMHf39//P39cXR0pFy5coSGhhp/qT/N1taWzz//nHLlyuHk5ERISAgBAQEULVqUYsWKUa1aNdq3b0+XLl0AqF27Nj4+PuTJk4cyZcrg4ODAlClTqF+/Pu7u7ty9ezfZLwCRcR7fp4ULF2bRokXMmzePvn37GvebmppSqVIlvLy8aNu2Ld99912KHnm8iJmZGUFBQWzcuJF8+fLh4eHB+PHjMRgMAPTv35/AwECcnJyYNGlSis+rlGLcuHG4urri7u7OsmXLWLt2rbFdzYcffoivry+FCxemevXqDB48mDp16gDa485ly5bRv39/nJycWLlyJStXrkySqD0mnwORUX788Uf8/f3R6/V07dqVhQsXYm7+VNJxZY/WQ+fofEAHVftC752QL+0el2YUncqGdYiRkZE4OjoSERGRpIFbdlWrVq0kPRSEyKquXLlCoUKFjH/5v46tW7fSt29fzpw5kwaRCZHzffvttwwePBiAvn37MmPGjCeP9BNiYetY2PcDoMDRS+uhU+DtzAs4Gan5/s7RbVCEEFnXqVOnjO1YhBDPp5Ri/PjxBAQEAPDpp58yadKkJ22Zrh3VJvi7c05bL98Z6k8Aq+z9B7wkKEKIDPfpp5+ydOlSfvvtt8wORYgsTSnF8OHDjY82v/zyS0aOHKklJ/oE2DkVdk4BpQc7N2j2HRSpn8lRp41Ut0HZuXMnTZs2xcPDA51Ox6pVq55btk+fPuh0OmObicfu3btHx44dcXBwwMnJiR49emRId+Csavv27fJ4R2QLPj4+afJ4Z/LkyVy5coUaNWqkQVRC5EwGg4GBAwcak5Ovv/6agIAALTm5dRZ+rgs7JmnJScmW2lD1OSQ5gVdIUKKjoylbtiw//PDDC8utXLmS/fv3G7vVPa1jx46cOnWKzZs3s3btWnbu3MkHH3yQ2lCEEEKIHEmv19OzZ0++++47dDods2fP1tqfGAxaO5M578CNY2DlBK3nQttAsHHO5KjTVqof8TRs2NA4cNHzXLt2jf79+7Nx40YaN26cZN+ZM2fYsGEDhw4dolKlSgB89913NGrUiKlTpyab0MTFxRnn7wCSDKwkhBBC5CQJCQl07tyZJUuWYGJiQmBgIJ07d4YHobDqQ20OHYBC9bRHOg55MzfgdJLm3YwNBgOdO3dm6NChxgGPnrZv3z6cnJyMyQlA3bp1MTEx4cCBA8mec+LEiTg6OhqX587OKIQQQmRjsbGxtG7dmiVLlmBubs7SpUvp3KkTHFsMs6r/O8GfjTbBX8c/cmxyAumQoHz11VeYmZkxYMCAZPeHh4cnGfoatDEQnJ2dnzvE+rBhw4iIiDAuT4+6KoQQQuQE0dHRNGvWjD///BNLS0tWrVpF6wa+sLQzrOoDcZGQvzL02Q2VumsjUedgadqL58iRI0yfPp2jR4+m6dDUlpaWMqOnEEKIHCsyMpLGjRuze/dubG1tWbNmDbXzxcPMahB9C0zMwW8YVB8Ipm9GB9w0rUHZtWsXt27dwsvLCzMzM8zMzLh69SqffPKJcbwDd3d3bt26leS4xMRE7t2799qz6IqMFxISkvzMmam0ffv2NB85M61iE+lj165dlC1bNrPDSBPyORCv4969e9SpU4fdu3fj6OjIX+tWUTtqJSx+T0tO8hSHXlvh7U/emOQE0jhB6dy5MydOnODYsWPGxcPDg6FDh7Jx40YAqlWrxoMHDzhy5IjxuK1bt2IwGKhSpUpahpOl+Pj4YGNjk2So+5kzZ6bLa125cgUzs+Rv4lq1ahlnLn4sMDCQunXrGtf1ej0TJ06kSJEi2NraUqBAAfr378+dO3eeOZ+XlxcPHjxI0/hf1X+vLSvFll08fZ+6uLhQt25d1qxZky6v9fbbb3P8+PEUlf3v/+1/7/Gn9//3fm7cuDGurq44OjpSpUoV9u3bZ9xnMBj4+OOPcXJyws3NjW+//TbJ64aHh9OmTRscHR1xcXFh2LBhycaXle41+RxkLzdv3qRWrVocPnwYFxcXDvwxnapHB8PRBYAOqvWDD7ZD3jKZHWqGS3UqFhUVxcWLF43rwcHBHDt2DGdnZ7y8vHBxcUlS3tzcHHd3d+NcHcWLF6dBgwb06tWL2bNnk5CQQL9+/Wjfvn2yPXhykk2bNlGzZs3MDuOlevfuza5du5g3bx6VK1cmKiqKOXPmcPDgQRo1apTZ4Yl09vg+vX37NqtWraJz58589dVX9OnTJ7NDeyWTJ0+maNGimJmZsWbNGpo3b87NmzeNXTe3b9/O+fPniYiIoFatWpQpU8Y4H0+zZs1o3749CxYsQKfTJfndJ8TrCgsLo06dOpw/fx4vDzcOT21Nnj0DMQ5V33IW+GT974x0o1Jp27ZtCnhm6dKlS7Llvb291bfffptk2927d1WHDh2UnZ2dcnBwUN26dVMPHz5McQwREREKUBERESkqHxUV9dwlJiYmxWUfPXr0TNmU8vb2Vrt27Up2n6+vrxo5cqQqV66ccnJyUv7+/kle64cfflAFChRQuXPnVh07dlQPHjxQSil169Yt1aBBA+Xo6KhcXFxU+/btlVJKBQcHK1NT0+e+1q+//ppk27x581SdOnWUUkqdOXNG6XQ6deTIkRRd139fy9vbW02dOlUVK1ZMOTo6qr59+yYp//3336tChQopFxcX5e/vb3wPt23bpgoWLGgsN378eOXl5aXs7e1V1apV1fHjx437xo0bp9zd3ZW9vb0qVaqUOnXqlPryyy+ViYmJsrS0VLa2tmr8+PHPxHbr1i3VoUMHlSdPHuXi4qI+++yzFF1jhoiLev4SH5OKso+eLZsKyd2n06dPVy4uLioxMdH4ns6cOVO5uroqT0/PJPeTr6+vCggIUBUqVFC2traqe/fu6vr168rPz0/Z29urVq1aqdjYWKXUs//nV69eVY0aNVLOzs6qWLFiav369UoplaL/26fv66fv56cZDAa1du1aBRg/Q1WrVk0S/+jRo5W/v79SSql169apmjVrpuh9k8+BSK1Lly4pHx8fBah6ZfOp2G8rKjXaQVtWfqhUTMq+37Kb1Hx/pzpByQpSm6Akl1A9Xho1apSkrI2NzXPL+vr6JimbO3fuFMf8sgTFy8tLnTt3Tt2/f1/VqlVLjRw5Uiml1KZNm5S7u7s6deqUioqKUq1atTImg5999pn68MMPVUJCgoqNjVV79uxRSr1egjJz5kzl7e2d4utK7hdzzZo11e3bt1VoaKjKkyeP2rp1q1JKqaVLl6pSpUqpK1euqEePHqkOHTqoTz75RCn17C/m5cuXq1u3bqn4+HgVEBCgypYtq5TSEqj8+fOrGzduKIPBoM6cOaNu3LiR7LX9N7Z69eqpHj16qMjISPXo0SO1d+/eFF9nunv8iym5ZWGbpGXHuT+/7C9J72f1VYFUhZHcfRocHKwAderUKePPXbt2VTExMWrPnj3K3t5enTt3Timl/R+UKFFChYSEqBs3big3NzdVqVIldfLkSRUZGalKlSqlfvnlF6VU0v9zvV6vypQpo6ZPn64SEhLU3r17Ve7cuVV4eLjxvC/6v31ZgtK4cWNlYWGhANWnTx/jdgcHhyRf+suWLVMVK1ZUSin1xRdfqE6dOqkGDRooFxcXVbt2bXX69Olk3zf5HIjUOHPmjPLw8FAmOtSkpnmVYYyL9vn9qoBSp//M7PDSVWq+v9O8m7F4voYNG+Lk5GRcduzYYdzXrVs3ihQpgpOTEyNGjGDJkiUA/P7773zwwQeUKFECW1tbJkyYwJIlS1BKYW5uzo0bNwgNDcXS0pLq1aunKI7evXsnieOjjz4y7rt79+5rN1b++OOPyZ07N/nz56dWrVrGdgZz585l2LBheHt7Y21tzfDhw1m2bFmy52jVqhV58uTB3Nyc4cOHc+LECaKiojAzMyMuLo4zZ86g1+spVqxYiuK9du0a27dvZ/r06djb22NtbU21atVe6zrfFI/f3/v37xu3jR49GisrK6pXr06zZs2S/D/26NEDT09P3N3d8fX1pVq1apQsWRJ7e3saNWqUbLuTgwcPEhMTw4ABAzAzM6NatWr4+vqyfv36NLmGtWvX8vDhQ5YvX57kcxIVFZVkRlUHBwfjtBvXrl3j999/Z8CAAVy/fp0GDRrQokUL9Hp9il5TPgciOceOHeOdd97B4tENDnyYh88qRKMzJECRhtpQ9cWbZHaIWcYb0Rz4RfP8mJqaJln/bw+jpxmntf7XlStXUhXH+vXrn9sG5enB5zw9Pblx4wYA169fT/IL1dvbm9jYWO7du8fQoUMJCAjA19cXGxsbhg4dSo8ePV4ax5w5c5LM/RMYGGhsVOfi4vLc8WhSys3NzfizjY2N8f0PCQmhd+/eSRKihISEZM/x008/MW3aNMLCwtDpdCiluHv3LoUKFeLrr79m+PDhnD9/npYtW/LNN9+8dNrusLAwXF1dsbW1fa1rSzfDrz9/ny7pPcrQF7SD0P3nb46P/3n1mP71+F7MlSuXcdvz7lcgyThH1tbWz6zfvXv3mdcICQkhODg4SW+TxMREKlas+NrxP2ZhYUGrVq0oU6YMFSpUoGTJktjZ2SUZmToyMhI7OztjrDVr1jSOnD1kyBDGjRvHxYsXjW3qXkQ+B+K/Dhw4QIMG9WlV4BEzGjlgaxYHFnbQYBKU75TjxzVJrTeiBsXW1va5i5WVVYrLWltbP1M2rTw9+FxoaCh582qjA3p4eBASEmLcFxISgpWVFc7Ozjg4ODB9+nRCQkIIDAykf//+XL58+bXi8PPzIyQkhGPHjr3WeZKTL18+5s+fz4MHD4xLdHT0M+WuXLnCxx9/zPz587l//z43btww/nIGrbfYvn37OHfuHFeuXOGbb74BeOHYO56enty+fZtHjx6l+XWlCQvb5y/mVqkoa/1s2de0du1aXFxcknwpP+9+fVX58uWjePHiSe6NqKgoY6+ZtBxXKSEhgUuXLgFQokQJ/vnnSRJ38uRJ4wjYpUqVStPXfUw+B2+mHTt20KFZXeY3iGduM2tszRR4VYcP90CFzpKcJOONSFCyg8DAQC5cuEBERAQTJkygXbt2ALz33nv89NNPnDlzhujoaEaMGEG7du3Q6XQEBQVx+fJllFI4Ojqi0+mS1AjFxsYmWVKiWLFidO/enQ4dOrBv3z4SExOJiIhgypQprFu37rWusXv37kyYMMH45XDjxg02bNjwTLmoqChMTEzIkycPiYmJjB492rjv3LlzbN++nfj4eGxsbLC0tDRes6ur63NrtTw8PPD19WXQoEFERUURExPD/v37X+t6crq7d+8yd+5cAgICGDNmTJJ7a+zYscTGxrJ//37WrFlD69atX+u1qlSpgsFgYNasWcTHxxMfH8+uXbuMyfmL/m9fJDg4mKCgIGJjY4mLi2P69OmEhYVRuXJlADp16sTUqVO5ffs2Fy9e5KeffsLf3x/QHq8cP36cv/76C71ez7Rp03B3d3/tcUrkc/Dm2bBhAz/0b8h+f2hW1BxlagH1voSuayGXT2aHl2VJgpKB3n333STjoIwYMcK4r1OnTrRr1w5vb2/y5cvH8OHDjccMGzaMRo0a4e3tjbm5OdOmTQPg/Pnz+Pn5YW9vT+PGjZk2bRre3t6ANpaJtbV1kiUsLCxFcc6ZMwd/f3/8/f1xdHSkXLlyhIaGGn+pv6oOHTrQo0cPGjdujIODA76+vpw+ffqZcqVKlaJ3796UKVMGHx8fChQogIWFBaBNHDl06FBcXFzw8vLC0dGRQYMGAdC/f38CAwNxcnIyTk/+tN9++40HDx7g4+ODl5dXuo3vkd09vk8LFy7MokWLmDdvHn379jXuNzU1pVKlSnh5edG2bVu+++67FD3yeBEzMzOCgoLYuHEj+fLlw8PDg/Hjx2MwGICX/98+j1KKcePG4erqiru7O8uWLWPt2rXG9hoffvghvr6+FC5cmOrVqzN48GBjF2MXFxeWLVtG//79cXJyYuXKlaxcufKZx8KpJZ+DN8ufyxZxc05rlrYyx9XWBEOeEuh6bYMaA8Hk9e6lnE6nHtcXZiORkZE4OjoSERHx0meu2UGtWrXo2bNnknYhQmRFV65coVChQiQmJr72ubZu3Urfvn05c+ZMGkQmRNbz189fUOjk1/g4mWBQoKr3x7ROAJi9uVO3pOb7+41oJCuEyHpOnTplnAJDiBwlMY4T09pR++E2TJxMuJVgg3OPPzB76w0edO0VSIIihMhwn376KUuXLuW3337L7FCESFu3znBrdnPKGG6CTseuRwWpMWYbJtaOmR1ZtiOPeIQQQojXpRTqwBwS1w/HXKfndrSBILOGdJmwJF16g2VX8ohHCCGEyCgPw1GrPkJ3aQvmOlh3IYFLpQbTf9i4zI4sW5MERQghhHhVZ4NQa/qje3SXmATFkM2xlOzyLf2f6vkmXo0kKEIIIURqxUfDhmFwdD464O8bevxXx/H514F07Ngxs6PLESRBEUIIIVLj2hFY3gvuXcKgYOreOMbthd9+X0HTpk0zO7ocQxIUIYQQIiUMetj9DWyfBIZEbsaY0X5pBIfvWLNm7Rr8/PwyO8IcRUaSFWlOp9MZR61t2LChcWZmgM8++wxnZ2fjJHDfffcdrq6u5M6dO11j2rVrF2XLlk3X1xCpk9P/T+RzkMPcvwLzGsHWcWBIZH2oDcVm3ONEpCNbt26V5CQ9qGwoIiJCASoiIiKzQ0kxb29vZW1trWxtbY3LDz/8kC6vFRwcrExNTZPd5+vrq3799dck2+bNm6fq1KljXE9MTFQTJkxQhQsXVjY2NsrHx0f169dP3b59O0WvD6jQ0NBntl+9elXZ2dmpu3fvKqWUiouLU1ZWVurcuXMpvbQU8/b2Vrt27Urz8+Z0T9+nzs7Oqk6dOmr16tWZHdYz9+1/7/Gn9//3fm7UqJHKkyePcnBwUJUrV1Z79+417tPr9WrgwIHK0dFRubq6qm+++SbJ6964cUO1bt1aOTg4KGdnZ/X555+nOGb5HOQQBoNSxxYrNT6fUqMdlH5cXjW4jocCVN68edXJkyczO8JsJTXf31KDkoE2bdpEVFSUcXl6uvWspHfv3gQGBjJv3jwePHjA0aNHyZcvHwcPHnyt84aEhODm5oazszMAt27dIiEhgSJFiiRbPi2GUxep9/g+PXv2LO+99x6dO3dm9uzZmR3WK5s8eTLXr18nIiKCESNG0Lx5c+OMwLNnz2b79u2cP3+e3bt3M3XqVLZs2WI8tlmzZlSvXp0bN24QFhbG+++//9rxyOcgG4m5D8u6w8reEP+QR7nLUP1XxTdbrlOgQAF2795tnP1apIP0z5fSXmprUKKiolRUVJQyGAzGbXFxcSoqKkrFxsYmW1av1xu3xcfHq6ioKBUTE/NM2ZR60V8yvr6+auTIkapcuXLKyclJ+fv7q0ePHhn3//DDD6pAgQIqd+7cqmPHjurBgwdKKaVu3bqlGjRooBwdHZWLi4tq3769Uur1alDOnDmjdDqdOnLkSIqvbe7cuSp//vzKzc1NzZkzJ8lfjo9fb+fOncrKykrpdDpla2urunbtqmxsbBSgbG1tVZs2bdS2bdtUwYIF1ahRo5SLi4saPny4unjxonr77beVo6Ojyps3rxo2bFiS1/79999VyZIllZ2dnSpVqpQ6e/as6tGjh9LpdMaagIULFxrPrZRSY8eOVV27dk1ynlq1ahnflxMnTqi3335bOTk5qQoVKqhDhw6l+L14ZXFR2vLUPaoS4rRtCbHJl33qHlWJ8dq2+Jhny6ZCcvfp9OnTlYuLi0pMTDTeWzNnzlSurq7K09Mzyf3k6+urAgICVIUKFZStra3q3r27un79uvLz81P29vaqVatWxs/c0/8nSmk1C40aNVLOzs6qWLFiav369Uoppb788ktlYmKiLC0tla2trRo/fnyqalAeMxgMau3atQowfoaqVq2aJP7Ro0crf39/pZRS69atUzVr1kzxeyefgxzm8g6lvi6u1GgHpb7IpcIWDVSuuZ0VoEqWLKmuXbuW2RFmS6n5/n4jEhRAAerWrVvGbePGjVOA6tmzZ5Kyj39ZBAcHG7d9++23ClDvv/9+krK5c+dOccwvS1C8vLzUuXPn1P3791WtWrXUyJEjlVJKbdq0Sbm7u6tTp06pqKgo1apVK9WlSxellFKfffaZ+vDDD1VCQoKKjY1Ve/bsUUq9XoIyc+ZM5e3tneLr+ueff5S9vb3av3+/evTokercuXOyv5iVevYL6b9xbtu2TZmamqoxY8ao+Ph49ejRI3Xx4kW1fft2lZCQoM6fP688PT3VypUrlVJK7d69W7m4uKjdu3crvV6vzpw5o65fv66Uevb9fvq1z58/r3LlyqXi4+OVUlo1vo2NjYqMjFQPHz5UHh4eatmyZSoxMVGtXLlSeXp6PpOcprnRDtoS9dRjtB2TtW2r+yUtO85d237vypNte3/Qti3rkbTsVwVSFUZy92lwcLAC1KlTp4w/d+3aVcXExKg9e/Yoe3t74+MJX19fVaJECRUSEqJu3Lih3NzcVKVKldTJkydVZGSkKlWqlPrll1+UUkn/T/R6vSpTpoyaPn26SkhIUHv37lW5c+dW4eHhxvO+6iMepZRq3LixsrCwUIDq06ePcbuDg4M6fvy4cX3ZsmWqYsWKSimlvvjiC9WpUyfVoEED5eLiomrXrq1Onz6d7Psmn4McJCFOqU0BSo121D5T08upw6vnKHt7ewWo//3vf+rOnTuZHWW2JY94sqiGDRvi5ORkXHbs2GHc161bN4oUKYKTkxMjRowwNqj7/fff+eCDDyhRogS2trZMmDCBJUuWoJTC3NycGzduEBoaiqWlJdWrV09RHL17904Sx9OPmu7evWucij4lli9fTqtWrahSpQrW1taMGjUqxccmx9LSkuHDh2Nubo61tTUFCxbE19cXMzMzChcuTMeOHdm9ezcAgYGB9O7dmxo1amBiYkKxYsXImzfvS1+jcOHC+Pj4sGnTJgCWLVtG/fr1sbe3Z+3atZQsWZLWrVtjampKixYtcHV1Zf/+/a91XdnZ4/vh/v37xm2jR4/GysqK6tWr06xZM5YtW2bc16NHDzw9PXF3d8fX15dq1apRsmRJ7O3tadSoEcePH3/mNQ4ePEhMTAwDBgzAzMyMatWq4evry/r169PkGtauXcvDhw9Zvnx5ks9JVFRUkuG2HRwciIqKAuDatWv8/vvvDBgwgOvXr9OgQQNatGiBXq9/5vzyOcghbp+Dn2vDnumAggpdWO89ghrtBvDw4UP8/PzYsmULLi4umR3pG+GNSFAet/l4uoX80KFDiYqK4vvvv09S9tatW0RFReHl5WXc1rdvX6Kiopg7d26SsleuXElVHOvXr+fBgwfGxdfX17jP09Mzyc83btwA4Pr160li8fb2JjY2lnv37jF06FC8vLzw9fWlWLFiz8T3PHPmzEkSx8yZM437XFxcCA8PT/E13bhx45nYX4e7uztmZk96v1+7do2WLVvi7u6Oo6Mj06ZN4+7duwCEhYVRoECBV3qd9u3bG5PAJUuW8N577wFa+4AdO3YkSeDOnDnD9evXX+u6Xmr4dW2xeeoXX/WB2rZGU5OWHXpR2+741HtduZe2rVnS+5mP/3nt0B7fi7ly5TJue979CuDq6mr82dra+pn1xwnA00JCQggODk7yvm/YsCHJeV+XhYUFrVq1YsqUKZw6dQoAOzs7IiMjjWUiIyOxs7MzxlqzZk0aNmyIhYUFQ4YMITw8nIsXLz5zbvkcZHNKwcGfYM47EP4PWDvDe7+xOLoazdp0IC4ujqZNm7Ju3Trs7e0zO9o3xhuRoNja2mJra5tkwiYLCwtsbW2xtLRMtqyJyZO3xtzcHFtbW6ysrJ4pm1ZCQ0OT/Pz4LyAPDw9CQkKM+0JCQrCyssLZ2RkHBwemT59OSEgIgYGB9O/fn8uXL79WHH5+foSEhHDs2LEUlc+bN+8zsb+O/06qNXLkSHLlysX58+eJiIjg448/NjZw9PT0fG6S+LLJudq1a8eaNWu4fPkyx44do0mTJgDky5eP+vXrJ0ngoqOj06Rx5AtZ2GrL03GbWWjbzCyTL/vUPYqpubbN3OrZsq9p7dq1uLi4ULRoUeO2592vrypfvnwUL148yfseFRXFsGHDgJf/f6ZGQkICly5dAqBEiRL888+TJO7kyZPGRo+lSpVK8evK5yAbi7oFi9rBuiGQGAsF68BH+5i9I4yOHTuSmJhIx44dWb58+TPfASJ9vREJSnYQGBjIhQsXiIiIYMKECbRr1w6A9957j59++okzZ84QHR3NiBEjaNeuHTqdjqCgIC5fvoxSCkdHR3Q6HaampsZzxsbGJllSolixYnTv3p0OHTqwb98+EhMTiYiIYMqUKaxbt+6Z8q1bt2bFihUcOnSImJgYxo1L28mxHj58iL29PXZ2dpw8eZKFCxca93Xp0oU5c+awb98+lFKcO3fO+Be3q6vrC2u4fHx8KF68OL169aJRo0bGZLNJkyb8/fffrFq1isTERGJiYtiwYQMRERFpel3Zwd27d5k7dy4BAQGMGTMmyb01duxYYmNj2b9/P2vWrKF169av9VpVqlTBYDAwa9Ys4uPjiY+PZ9euXcbk/GX/n88THBxMUFAQsbGxxMXFMX36dMLCwqhcuTIAnTp1YurUqdy+fZuLFy/y008/4e/vD0CrVq04fvw4f/31F3q9nmnTpuHu7k6hQoWeeR35HGRT59bDzGpwYROYWkKDr6DjMib9EMiHH36IUoqPPvqIBQsWYG5untnRvnEkQclA7777LnZ2dsZlxIgRxn2dOnWiXbt2eHt7ky9fPoYPH248ZtiwYTRq1Ahvb2/Mzc2ZNm0aAOfPn8fPzw97e3saN27MtGnT8Pb2BkCv12NtbZ1keTxo1MvMmTMHf39//P39cXR0pFy5coSGhhp/qT+tVKlSfPPNN7Rs2RIfH58Ut4NJqVGjRrFt2zYcHBwYMGBAki/CGjVqMH36dLp3746DgwNt27Y1Vtd/9tlnfP755zg5ObFo0aJkz/3ee++xdetWYzII4OjoSFBQkHHgLB8fH3788cc0vaas7vF9WrhwYRYtWsS8efPo+9TEZ6amplSqVAkvLy/atm3Ld999l6R25VWYmZkRFBTExo0byZcvHx4eHowfPx6DwQBA//79CQwMxMnJiUmTJqX4vEopxo0bh6urK+7u7ixbtoy1a9ca29V8+OGH+Pr6UrhwYapXr87gwYOpU6cOoD3uXLZsGf3798fJyYmVK1eycuXKJInaY/I5yGYSYiDoE1jcHh7dAbdS8MF2VJXefD58uLHmbvjw4Xz//fdJatRFxtGpx/WE2UhkZCSOjo5EREQkaeCWXdWqVYuePXvSqVOnzA5FiBe6cuUKhQoVSpOxObZu3Urfvn05c+ZMGkQmRArdOquNbXJLa4dEtX5QZxR6nRn9+vUzjvkzefJkhg4dmomB5kyp+f6WuXiEEJni1KlT+Pj4ZHYY4k2hFBwJ1GYgTowB2zzQcg4UqkNCQgJd/DuzePFidDodc+bMoVevXpkd8RtPEhQhRIb79NNPWbp0Kb/99ltmhyLeBDEP4M+BcHqVtl6wtpac2LkSExND27ZtCQoKwszMjIULFxp7M4nMJY94hBBC5FwhB2B5T4gIARMzqDMKqvUHExMiIyNp2rQpO3fuxMrKiuXLl9OoUaPMjjhHk0c8Qggh3mwGPez+BrZNBKWHXD7Q5hfIp80gfefOHRo0aMCRI0dwcHBg7dq1vP3225kbs0hCEhQhhBA5S+QNWNELruzS1ku3hcbfgJX2F3tYWBj16tXj7Nmz5M6dm40bN1KhQoVMDFgkRxIUIYQQOce5DbDqQ4i5B+a20HgqlO1gHATx/Pnz1KtXj5CQEPLnz8+mTZsoXrx4JgctkiMJihBCiOwvMQ42j4YDs7R19zLaI53chY1Fjh07Rv369bl16xZFihRh8+bNSaYSEVlLqkef2blzJ02bNsXDwwOdTseqVauM+xISEvjss88oXbo0tra2eHh44O/v/8z8Dffu3aNjx444ODjg5OREjx49kp2fQwghhHipOxfg5zpPkpOqH0HPv5IkJ7t27cLX15dbt25Rvnx5du3aJclJFpfqBCU6OpqyZcvyww8/PLPv0aNHHD16lICAAI4ePcqKFSs4d+4czZo1S1KuY8eOnDp1is2bN7N27Vp27tzJBx988OpXIYQQ4s2jFPz9G8zx1Sb5s3GB95dCg4lJ5rAKCgri3XffJTIykrfffptt27YlmcRSZE2v1c1Yp9OxcuVKWrRo8dwyhw4donLlyly9ehUvLy/OnDlDiRIlOHToEJUqVQJgw4YNNGrUiLCwMDw8PF76utLNWAgh3nCxkbB2EJxcpq0XeAda/ggOSSeuXLx4Mf7+/iQmJtK4cWP++OMPrK2tMyFgAan7/k73CQYiIiLQ6XQ4OTkBsG/fPpycnIzJCUDdunUxMTHhwIEDyZ4jLi6OyMjIJIsQQog3VNgRmPO2lpzoTKF2AHRe9UxyMnPmTOOMxO+//z4rV66U5CQbSdcEJTY2ls8++4wOHToYM6Xw8PBnqtbMzMxwdnYmPDw82fNMnDgRR0dH4+Lp6ZmeYQshhMiKDAbYPQ1+eRfuXwFHL+i+Ad4ZAiZPJnF8PElk3759UUrRr18/fv31V5mROJtJtwQlISGBdu3aoZRi1qxZr3WuYcOGERERYVxCQ0PTKEohhBDZwsOb8Ftr+Gs0GBKhRAvosws8k86ybjAY+OSTTwgICAC0maBnzJghMxJnQ+nSzfhxcnL16lW2bt2a5DmTu7s7t27dSlI+MTGRe/fuGadA/y9LS0ssLS2T3SeEECKHu/gXrOwD0bfBzBoaToIKXYxjmzyWmJhIr169CAwMBODbb7/l448/zvh4RZpI8wTlcXJy4cIFtm3bhouLS5L91apV48GDBxw5coSKFbUhh7du3YrBYKBKlSppHY4QQojsKjEetn4Je7/T1l1LamObuBZ7pmhsbCwdOnRg1apVmJqaMnfuXLp06ZLBAYu0lOoEJSoqiosXLxrXg4ODOXbsGM7OzuTNm5c2bdpw9OhR1q5di16vN7YrcXZ2xsLCguLFi9OgQQN69erF7NmzSUhIoF+/frRv3z5FPXiEEEK8Ae4Fw7JucP1vbf1/veDdcWBu9UzRhw8f0qJFC7Zu3YqlpSVLliyhefPmGRywSGup7ma8fft2/Pz8ntnepUsXvvjiCwoUKJDscdu2baNWrVqANlBbv379+PPPPzExMaF169bMmDEDOzu7FMUg3YyFECIHO7MWVn0EcRFg5QTNf4DiTZItevfuXRo2bMihQ4ews7NjzZo1yX5HiawhNd/frzUOSmaRBEUIIXIgfQL89QXs+15bz18Z2s4Dx/zJFg8LC+Pdd9/lzJkzuLi4sH79ev73v/9lXLwi1VLz/S1z8QghhMh8D0K1Rzphh7T1av2g7hdgmnzX4AsXLlCvXj2uXr1Kvnz52Lx5s0z6l8NIgiKEECJznd8EKz+AmPtg5QgtZkGxxs8t/vSkf4ULF2bz5s14e3tnYMAiI0iCIoQQInPoE2HbONj9rbbuUR7aBkIun+cesnv3bpo0aUJERATlypVjw4YNuLm5ZUi4ImNJgiKEECLjRd6A5T3g6h5tvXJveHdskkn+/mvdunW0adOGmJgYatasydq1a3F0dMyggEVGkwRFCCFExrq0FZb3gkd3wMIemn8HJVu+8JCnJ/1r1KgRf/zxBzY2NhkUsMgMkqAIIYTIGAY97JgMO74CFLiVhnbzwaXgCw+bNWuWcV6d999/n8DAQJlX5w0gCYoQQoj0F3ULlveE4B3aesWu0GASmD9/dmGlFBMmTGDkyJEAfPTRR3z33Xcyr84bQhIUIYQQ6evKbljWA6LCwdwGmkyDsu+98BCDwcCnn37K119/DcDIkSP58ssv0f1n/h2Rc0mCIoQQIn0YDLDnW9g6DpQB8hSDtvOTnUvnaQkJCfTs2ZMFCxYA8M033zBo0KCMiFhkIZKgCCGESHuP7sGKD+DiZm29bAdo/DVY2L7wsOjoaNq1a8e6deswNTXl559/pmvXrukfr8hyJEERQgiRtkIOaKPCRl4DMytoNBXKd4KXPJ65e/cuTZo0Yf/+/VhbW7N06VKaNEl+Dh6R80mCIoQQIm0oBft+gL9GgyERnAtCuwXgXuqlh4aGhlK/fn3OnDlDrly5WLt2LdWrV8+AoEVWJQmKEEKI1xdzH1b1hXNB2nqp1tB0Oljav/TQ06dPU79+fcLCwsiXLx8bN26kZMmS6RywyOokQRFCCPF6rh2FP7rAgxAwtYAGE6FSj5c+0gHYt28fjRs35v79+xQrVoyNGzfi5eWVAUGLrE4SFCGEEK9GKTj0M2wcDvp4cPLWBl7zKJ+iw4OCgmjbti0xMTFUqVKFoKAgXFxc0jlokV1IgiKEECL14qJgTX84tUJbL9YEmv8A1k4pOnz+/Pn06NEDvV5Pw4YN+eOPP7C1fXEPH/FmkeH4hBBCpM6di/BzHS05MTGD+hPhvYUpTk6mTJlC165d0ev1dO7cmdWrV0tyIp4hNShCCCFS7mwQrOwDcZFg56710vGqkqJD/zs67JAhQ/jqq69k6HqRLElQhBBCvJxBD9snws4p2rpXdWgbCPZuKTo8ISGB7t27s3DhQkCrRRkyZEg6BStyAklQhBBCvNije9pEf5e2aOtVPoR3x4JpymYUjo6Opk2bNmzYsAEzMzN++eUXOnfunI4Bi5xAEhQhhBDPd+MELOkED66CmTU0mwFl2qX48Dt37tC4cWMOHjyIjY0Ny5Yto2HDhukYsMgpJEERQgiRvOO/w58DITEWcvloDWHdS6f48KtXr1K/fn3OnTuHs7MzQUFBVK1aNf3iFTmKJChCCCGSSoyHTSPg4I/aeqF60PonsM6V4lOcOnWK+vXrc+3aNTw9Pdm4cSPFixdPp4BFTiQJihBCiCcib2ijwoYe0NZ9PwPfzyEVPW327NlDkyZNePDgASVKlGDjxo3kz58/nQIWOZUkKEIIITRX92nJSdRNsHSEVnOgaOrai/z555+0a9eO2NhYqlevzp9//omzs3M6BSxyMul8LoQQbzql4MCPML+Jlpy4loAPtqU6OZk3bx4tW7YkNjaWJk2asHnzZklOsom4uDhWrVqV2WEkIQmKEEK8yeIfaQOvrR8KhkRtFuKef4FLwRSfQinFpEmT6N69O3q9nq5du7JixQpsbGzSMXCRVoKCgihVqhQtW7Zk+/btmR2OkTziEUKIN9W9YFjSGW7+AzpTbWyTqh+laBbixwwGA5988gnTpk0D4LPPPmPixInoUnEOkTnOnz/PoEGDWLduHQBubm48fPgwk6N6QhIUIYR4E134C5b3gNgHYJNbGxW2wNupOkV8fDzdunVj0aJFAHzzzTcMGjQo7WMVaSoyMpJx48Yxbdo0EhISMDc35+OPP2bkyJE4ODhkdnhGkqAIIcSbxGCAXV/DtvGAgnwVod2v4JgvVad5+PAhbdq0YdOmTZiZmREYGEjHjh3TJ2aRZpRS1K1bl0OHDgHQqFEjvv32W4oUKZLJkT1L2qAIIcSbIjYClnSEbeMABRW7Qbf1qU5Orl27xttvv82mTZuwsbFh7dq1kpxkEzqdjkGDBlG4cGHWrl1LUFBQlkxOAHRKKZXZQaRWZGQkjo6OREREZKnqKCGEyLJunYHfO8K9S2BqCY2nQgX/VJ/mxIkTNG7cmLCwMNzc3Fi7di2VKlVKh4BFWggPD2f48OG88847dO3aFdBqURISErCwsMjweFLz/S01KEIIkdOdXAE/1dGSE4f80H3DKyUnmzZtombNmoSFhVG8eHH2798vyUkWFR8fz9SpUylSpAjz5s1j2LBhxMbGAlotSmYkJ6mV6gRl586dNG3aFA8PD3Q63TP9ppVSjBo1irx582JtbU3dunW5cOFCkjL37t2jY8eOODg44OTkRI8ePYiKinqtCxFCCPEf+kTYNBKWdYOEaCjgC713QL4KqT7VL7/8QuPGjXn48CG1atViz549+Pj4pH3M4rWtX7+e0qVLM3ToUB4+fEilSpVYuXIlVlZWmR1aqqQ6QYmOjqZs2bL88MMPye6fPHkyM2bMYPbs2Rw4cABbW1vq169vzNwAOnbsyKlTp9i8eTNr165l586dfPDBB69+FUIIIZJ6dA8WtoK932nrNQZCpxVgmztVp1FKERAQQI8ePUhMTKRTp05s2LCBXLlSPi+PyBiXL1+madOmNGrUiPPnz+Pq6sovv/zCgQMHsuckjeo1AGrlypXGdYPBoNzd3dWUKVOM2x48eKAsLS3V4sWLlVJKnT59WgHq0KFDxjLr169XOp1OXbt2LUWvGxERoQAVERHxOuELIUTOFH5KqWlllBrtoNS4vEqdXPlKp4mLi1OdOnVSgALUyJEjlcFgSNtYRZrZt2+fApSZmZkaPHiwevDgQWaH9IzUfH+naRuU4OBgwsPDqVu3rnGbo6MjVapUYd++fQDs27cPJyenJM8t69ati4mJCQcOHEj2vHFxcURGRiZZhBBCJOPMWphbD+5fASdv6LkZSrZI9Wnu379P/fr1WbhwIWZmZsydO5exY8fKAGxZiFKKEydOGNerVq3KN998wz///MPXX3+No6NjJkb3+tI0QQkPDwe00eie5ubmZtwXHh6Oq6trkv1mZmY4Ozsby/zXxIkTcXR0NC6enp5pGbYQQmR/BgNs/0rrRhwfBQXegQ+2g1vJVJ/qypUr1KhRg+3bt2Nvb09QUBDdu3dP+5jFKzty5Ag1atSgcuXKBAcHG7cPGjSIYsWKZWJkaSdb9OIZNmwYERERxiU0NDSzQxJCiKwjLkqbhXj7BG29cm+tvYlN6ifqO3z4MFWrVuXMmTPky5eP3bt38+6776ZxwOJV3bp1i549e/K///2Pffv2YWZmxvHjxzM7rHSRpgmKu7s7ADdv3kyy/ebNm8Z97u7u3Lp1K8n+xMRE7t27ZyzzX5aWljg4OCRZhBBCoD3K+aU+nFkDJubQ7DtoNBlMzVN9qrVr1+Lr68vNmzcpU6YM+/fvp0yZMmkfs0i1hIQEpk2bRpEiRZg7dy5KKTp16sT58+dp0aJFZoeXLtI0QSlQoADu7u5s2bLFuC0yMpIDBw5QrVo1AKpVq8aDBw84cuSIsczWrVsxGAxUqVIlLcMRQoicLXgn/OgHN0+CrSt0DXql8U0AZs6cSfPmzXn06BHvvvsuu3btIn/+/GkcsHgVer2eatWqMWjQICIiIqhQoQJ79uzh119/xcPDI31eNAuM4ZrquXiioqK4ePGicT04OJhjx47h7OyMl5cXH3/8MePGjaNw4cIUKFCAgIAAPDw8jBle8eLFadCgAb169WL27NkkJCTQr18/2rdvn35vtBBC5CRKwaGfYf1noPSQtxy0/w0cU59QGAwGPvvsM6ZOnQpAjx49mDVrFubmqa+BEenD1NSUpk2bcvXqVSZMmED37t0xNTVN+xcyGODkcvh7AXhVA7/haf8aqZHaLkLbtm0zdjl7eunSpYtSSutqHBAQoNzc3JSlpaWqU6eOOnfuXJJz3L17V3Xo0EHZ2dkpBwcH1a1bN/Xw4cMUxyDdjIUQb6yEOKVW99e6EI92UGpZD6XiH73SqR49eqTatGlj/D0+btw46UacBURFRamRI0eq3bt3G7c9evRI3bt3L/1ffFYN7b76pqRSen2anz41398yF48QQmQXUbdgSWcI3Q/ooN4YqD4AXqHr7507d2jevDl79+7F3NycefPmyYR/mUwpxdKlSxkyZAhhYWGUL1+eQ4cOpU9tSWwE/LNMW95fAlb/fpee+APuXoTyHcHJK81fNjXf36l+xCOEECITXD+mTfYXGQaWjtBmLhSu90qnunjxIg0bNuTixYs4OTmxcuVKatWqlabhitQ5fvw4AwYMYOfOnQB4e3szcuRITEzSsKmoUnB1L/z9K5xaBYkx2vZTK6BiV+3nMm3T7vVekyQoQgiR1f2zDFb3075QXApBh98hd+FXOtXevXtp1qwZd+/excfHh3Xr1lG8ePE0Dlik1N27dwkICGDOnDkYDAasra0ZNmwYQ4YMwdraOm1eJOY+HAmEvxdqtSOP5SkG5TtDsSZp8zppTBIUIYTIqgx62DoWdn+rrReqB61/BmunVzrdsmXL6NSpE3FxcVSqVIk///zzucM7iIyxfv16Zs2aBUC7du2YMmUKXl5p/GglPhr+GgMoMLeFUq203l75//dKjwcziiQoQgiRFcVGwPJecGGjtl5jINQZDSapb4+glOKbb75h6NChKKVo2rQpixcvxtbWNo2DFilx7949nJ21QfTef/99tm3bhr+/P76+vmlw8mCtpuRhOLT4d1Jfx/xQvb9W61ayJVjav/7rZABpJCuEEFnN3UuwuD3cOQ9mVtrga2XavdKp9Ho9AwcONM5A369fP6ZNm5Y+DS/FC4WGhjJ06FB27tzJuXPnsLdPo0QhIUabg+nofLiy69+NOhh08pW6nqcnaSQrhBDZ1cW/YFl3rQbF3kMb3yRfhVc6VXR0NB06dODPP/8E4Ouvv2bQoEEy4V8Gi4mJYerUqUycOJGYmBhMTEz466+/aNmy5eud+PY5bTycE0u0+wUAHRSsDRU6g22e1449M0mCIoQQWYFSsO972DwKlAHyV4b3FoK928uPTUZoaCgtWrTg6NGjWFpasnDhQtq0aZPGQYsXUUqxatUqBg8ezJUrVwB45513mDFjBmXLln39FwjZDwd/1H529ITynaDc++nSPTgzSIIihBCZLSEG/hyo/SUM2hdN42/AzPKVTrd//35atGjBzZs3yZ07N6tXr6Z69eppGLB4mfj4eJo0acLmzZsByJ8/P1OnTqVdu3apr8FSCq7ugaMLwLv6ky7BpVppj3TKdoC3ar1S+6SsTBIUIYTITJHXtfFNrh8FnSk0mAiVP3jl3hULFiygV69exMfHU7p0adasWYOPj0/axixeysLCgjx58mBpacnQoUP5/PPPU98o+WE4HFukjVty77K27eZpqNBFuz8s7bVeXTmUNJIVQojMEnYYfn8fom6CdS5oG6j9JfwK9Ho9w4YNY8qUKQA0b96chQsXYmdnl3bxiueKj49n5syZtGjRwpgQ3rhxg5iYGN56663Unez8Rjg8Dy5s0uZaArCwg1Ktte7B+Spm6e7BLyKNZIUQIqs7/jusGQD6OMhTHDosBucCr3SqyMhIOnTowLp16wAYMWIEX375ZdqOQiqSpZRi3bp1DB48mPPnz7Nv3z6WLNEe1eXNm/fVTnp0AZxfr/3sWVVr8FqiBVi+WcmmJChCCJGRDHrYMgb2TNfWizaGVnNeeWyKixcv0qxZM86cOYOVlRXz5s2jffv2aRiweJ5Tp04xePBgNm3aBECePHmoW7cuSqmUtTOJfwSnV2uPcJp9By4Fte2Ve2nJanl/yFMkHa8ga5MERQghMkpsJCzv+WTwtbc/Ab+R8Io1HVu3bqVNmzbcv38fDw8PVq9eTaVKldIwYJGcO3fuMHr0aGbPno3BYMDCwoKPP/6Y4cOH4+jo+OKDlYLrf2u1JCeXQ1yktv3Yb1BnlPbzW7Ve+VFfTiIJihBCZIR7wdrga7fP/jv42vevNTHbzJkzGTBgAHq9nsqVK7Ny5Uo8PDzSMGDxPLNmzWLmzJkAtGrVismTJ1OwYMEXHxT/SEtK/v4Vbp58st3JW5sPp9z76Rhx9iQJihBCpLfgnbDUX5u0zc4dOizSGjq+goSEBAYMGMDs2bMB6NixIz/99FPaTSwnnqGUIiIiAicnJwAGDx7Mvn37GDp0KH5+fik/0bbxWo2JqSWUaKYlJj5vv3INWk4nCYoQQqSnQ3Nh/adgSASPCtB+ETi8WuPJO3fu0LZtW7Zv345Op2PixIl8+umnMjJsOjp16hSDBg3i/v37HDhwABMTE2xtbY0NkpMVcU3rHhy6Hzou03rcWNjAO0PAzFqrObPOlXEXkU1JgiKEEOlBnwAbPteGIgco3VZrCGn+ajUdp06domnTpgQHB2NnZ8eiRYto2rRpGgYsnpZcO5Njx45RocJzph3QJ2jdg48ugIubtdGAAUIPgFdV7ecaAzMm+BxCEhQhhEhrj+7BH120RzvotMaPNQe98tgVf/75J++//z5RUVG89dZbrFmzhpIlS6ZtzALQxjP54YcfGDNmDBER2vw2rVu3ZvLkycmPZ/IgVEtCjy2C6FtPtnvX0MYscS+TQZHnPJKgCCFEWrp9Dha9B/eDtcG1Wv0ExRq90qmUUkyePJlhw4ahlKJWrVr88ccf5M6dO42DFgBhYWHUrl2bCxcuAFCuXDm+/fZbatWq9fyD7l6EPdO0n23zaI1dy/tD7kLpHm9OJwmKEEKklfObYHkPrSGkkxd0+B3cXq2mIzY2ll69erFw4UIA+vTpw4wZMzA3N0/LiMVTPDw8cHJyws3NjfHjx9O1a1dMTZ+a3yb8H+0Rjm0e8P1U21bAF8p1hKINoUgDMJX/n7QiQ90LIcTrUgr2fqfNRIzSqvfbLQDbV6vpuHHjBi1atODgwYOYmpoyY8YMPvroo7SNWXD79m2mTJnCqFGjjFMCXLp0iTx58jz5bomNhJPLtMTk+t/aNpvcMPgMmFlkUuTZlwx1L4QQGSUxDv78GI4v0tYrdIFGU1/5y+vw4cO0aNGCa9eukStXLv744w/q1KmTdvEK4uPj+f777/nyyy+JiIjA0tKSsWPHAjwZzyTsMBz+BU6thIRH2jYTcyjeRGtbYiJfn+lN3mEhhHhVD2/Ckk4QdjBNZiL+/fff6datG7GxsRQvXpw1a9ZQqJC0ZUgrSilWr17N0KFDuXjxIgDly5enXr16zxY+uVwb3RUgTzEtKSnTHmxdMjDiN5skKEII8SpuHIfFHSDyGlg5Qtv5UDAVg3Y9xWAwMGrUKMaPHw9Ao0aNWLRo0cuHTRcpduzYMQYNGsT27dsBcHd3Z/z48XTp3AnTq/8OpFf5A/CpqR1QwV97vFOxC+T/X7adPTg7kwRFCCFS69RKWPkhJMaAS2F4f8mTid5SKSoqis6dO7Nq1SoAhg4dysSJE5M2zhSvbfLkyWzfvh1LS0uGDBnC5x92xu7CSvi+AkSEaoVMzJ8kKK7FocUPmRewkARFCCFSzGCAHV/BjknaeqG60HouWDu90ukuX75My5YtOXHiBBYWFvz000/4+/unXbxvsJiYGKKjo41dsidOnIiZqQlTP6iLa8g6+Lnqk8HUrJygbHtt6HmRZUgvHiGESIn4aFjZB86s0dar9YN6X4LJq9V0rF69mi5duhAREYGbmxsrV66kWrVqaRjwm0kpxZIlS/j888+pVq0aixcvfnonfFcR7l3S1n3e1ho1F28K5laZE/AbRnrxCCFEWnoQCr930MbBMDGHptOgfKdXOlVCQgLDhg3j66+/BqBq1aosXboUT0/PNAz4zXTw4EEGDRrE3r17MTMBO8844n/rgEW7eVoCotNB9f5w77KWmMhgalmaJChCCPEiIQdgSUeIvq0N0PXewidzq6RSWFgY7733Hnv37gVg0KBBTJo0CQsLGU/jdYSGhjJs2DB+++03fJx0fPWuHR9Ws8eeaLiwTqv1KtNOK1ypW+YGK1JMEhQhhHieowsg6BPQx4NbaeiwGJxeraZj06ZNdOzYkTt37uDg4EBgYCAtW7ZM44DfPFu3bqVFsybU80pgQycb6r1lhokOIPrfoec7gmflzA5TvAJJUIQQ4r8S42HjsCczERdvBi1ng4Vtqk+l1+sZM2YM48aNQylF+fLl+eOPP54MCCZeS+XKlSnj6cDydjFPNhasDRW7QpGGMtprNiYJihBCPC3qtjYmRsheQAd+I+DtT8DEJNWnunnzJu+//z5bt24FoHfv3kybNg0rK2mQ+ap279jKyWVf8UGbOpj4foqdnR2LNx1CHRiNLpeP1hPHuUBmhynSgPTiEUKIx64d1UaGjbwGlg7aTMRFG7zSqXbu3En79u25ceMGNjY2zJkzh06dXq1hrYDQY9s5+vNAqllfxtXWBAMmmAw5B3aumR2aSIXUfH+n/k+Cl9Dr9QQEBFCgQAGsra0pWLAgY8eO5ek8SCnFqFGjyJs3L9bW1tStW9c4vbUQQmSKY4vhlwZacuJSGHptfaXkxGAw8NVXX1G7dm1u3LhBiRIlOHTokCQnryIxjkcHF3AxoDieq5rTPPcVXG1NuK+35lHFPtr0AiLHSvNHPF999RWzZs1i/vz5lCxZksOHD9OtWzccHR0ZMGAAoI3oN2PGDObPn0+BAgUICAigfv36nD59Wqo+hRAZS58ImwNg/0xtvUhDaDVHG74+le7du0eXLl1Yu3YtAJ06dWL27NnY2qa+7cqbTq/Xs39GL2pErqaQKegNikMPnHBvPAyfur3AVFoo5HRp/oinSZMmuLm5MXfuXOO21q1bY21tzcKFC1FK4eHhwSeffMKQIUMAjAMVBQYG0r59+2fOGRcXR1xcnHE9MjIST09PecQjhHg90XdhWVcI3qmt+34Gvp+/UnuTgwcP0rZtW0JCQrC0tOS7776jZ8+e6GQOl5TRJ8L5DVpD5IJ+GAwG6tWsxKyK59l43YHi70+gTqsu8n5mc5n6iKd69eps2bKF8+fPA3D8+HF2795Nw4YNAQgODiY8PJy6desaj3F0dKRKlSrs27cv2XNOnDgRR0dH4yIDGgkhXtuNE/BjLS05sbDTxjfxG57q5EQpxXfffUfNmjUJCQmhYMGC7Nu3j169esmXaUpEXoftk2BaaVjSEf1fXwJgYmLCmMkzWF9wHH1+v0rd1l3l/XzDpHkd2eeff05kZCTFihXD1NQUvV7P+PHj6dixIwDh4eEAuLm5JTnOzc3NuO+/hg0bxuDBg43rj2tQhBDilfyzDFb30yb7c34L2i/SJodLpcjISHr27Mkff/wBQKtWrfjll19kFuKXMRggeDsc/gXOrgOlB+BWtIFz1014W58IpmbUrFmTmjVrZm6sItOkeYKydOlSfvvtNxYtWkTJkiU5duwYH3/8MR4eHnTp0uWVzmlpaYmlpWUaRyqEeOMY9PDXF7B3hrZeqC60/hmsc6X6VMePH6dNmzZcvHgRMzMzpk6dyoABA+Sv/JRY3ReOLzKu7g5V/HAwhhVnEnm/sxdvS/sSQTokKEOHDuXzzz83tiUpXbo0V69eZeLEiXTp0gV3d3dAGx8gb968xuNu3rxJuXLl0jocIYTQPLoHy3vAJW1MEmoOgtoBqZ7sTynFL7/8Qr9+/YiNjcXT05OlS5dSteqrDX+f4ykFYYcgVwGwywOAvlA99CeWs/AfPd/sfMCp2waqVavG9l1fy4SJwijN26A8evQIk/88wzU1NcVg0Ka1LlCgAO7u7mzZssW4PzIykgMHDsiNKYRIHzdPwU9+WnJibgNt5kHdL1KdnERHR9OtWzd69uxJbGwsDRs25O+//5bkJDlxD+HQXJj9NsytB0cDjbtG/LoH5/G36bH8HjH2Pvzxxx/s2bNHvgNEEmleg9K0aVPGjx+Pl5cXJUuW5O+//+abb76he/fuAOh0Oj7++GPGjRtH4cKFjd2MPTw8aNGiRVqHI4R4051aBas+goRocPLW2pu4l0r1ac6ePUubNm04deoUJiYmjB07ls8///yZP8jeeOEn4fBcOLEU4qO0bWZWqLhoHj/86v1hP+b/uoixn37KRx99JI/wRbLSPEH57rvvCAgI4KOPPuLWrVt4eHjQu3dvRo0aZSzz6aefEh0dzQcffMCDBw+oWbMmGzZskDFQhBBpx6CHbeNh19faegFfaBsINs6pPtXixYvp1asX0dHRuLm5sXjxYvz8/NI23uxOKVjQHIJ3PNnmUojIIm0IWH6Ge+cu8Ws9bXOBAgW4evWqzOIsXkiGuhdC5DwxD2BFL7iwSVuv1g/qjkn14F4xMTF88sknzJo1C4BatWqxePFiY1u6N15EGDjmf7K+8kP4ZykUa0xMqY5MXrqPKVOnEh0dDcD58+cpXLhwJgUrsoJMHQdFCCEy1e1z8FNtLTkxs4KWP0L98alOTg4fPkyFChWMycmIESPYvHmzJCcGA1z4C35rC9+WghvHn+zzG45+wAl+iapFwbrd+GLMGKKjo6latSp79uyR5ESkivTlEkLkHGeDYEVviH8IDvmh/W/gUS5Vp0hMTGTixIl8+eWXJCYm4u7uTmBgIPXr10+fmLOL2Ag4tggO/gT3Lj3ZfnUv5C0LwPlbMbRt25YTJ04A2qOcSZMm0bZtW+l+LVJNEhQhRPZnMMCOr2DHJG3du6bW3uTfbq0pdf78efz9/Tlw4AAAbdq0Yfbs2bi4uKRxwNlIzAPYOlabTDFBe1SDpQOU7wT/6wkuBY1FPTw8uHnzJk5OTgQEBNC3b19pACtemSQoQojsLTYSVvaGc+u09cq9/32kY57iUyilmD17NkOGDOHRo0c4Ojry/fff07FjR/nL38IWzqzVkpM8xaByLyjTHiztCA8PZ+748QwbNgwTExPs7OxYsWIFRYsWfbOTOpEmJEERQmRf4Sfhj65w9wKYWkCTb7W/7FPh+vXr9OjRgw0bNgBQu3ZtAgMD38zpNB7dg6Pz4cJm6PKnNk6MqTk0nATWzlDgHdDpiIiIYOq4AL799luio6N566236NChA6DNxyZEWpAERQiR/SilzeOyYRjo48DeA977FfJXStVp/vjjD/r06cO9e/ewtLTkq6++on///m/e2CY3jsPBH7U5ihJjtW3nN0CxxtrPJVsCEBsby8yZM5kwYQJ3794FoEqVKrz11luZEbXI4SRBEUJkL7ERsGYAnF6lrReqBy1ng23uFJ/iwYMH9OvXj99++w2AChUq8Ouvv1KiRIl0CDiL0ifAmTVw4EcI3f9ku3sZqNIbCtY2bjIYDMyfP5/Ro0cTGhoKQNGiRZkwYQItW7aUx2AiXUiCIoTIPq4dgWXd4f4VMDGDOqO1MU5SUeOxZcsWunbtSlhYGCYmJgwfPpyAgIA3b9CwG8e19xK097JEc639jmdl+E/CodPpmDlzJqGhoeTLl48xY8bQpUsXzMzkK0SkH7m7hBBZn1KwfyZsHg2GBHD0grbzUvVIJyYmhmHDhjF9+nQAChUqxIIFC96M+V+UgrDDcOc8lO+obctXEYo01LoIV+oG9knHd9m1axflypXD3t4enU7H1KlTOXjwIP369cPa2joTLkK8aWQkWSFE1vbonjaXzvn12nrxptDsO7DOleJTHDlyhM6dO3PmzBkA+vTpw5QpU7Czs0uPiLOOhFg4tQIOzIEbx7SJEgefAWun5x5y4sQJhg0bxrp16xg9ejRffPFFRkUr3gCp+f6WGhQhRNYVsl97DBF5TeulU3+CNvZGCts8JCYmMmnSJMaMGWMcdG3u3Lk0atQonQPPZFG3tJmED8+F6NvaNlNLrbFrQkyyCUpwcDCjRo3it99+QymFqakpUVFRGRu3EE+RBEUIkfUYDLDnW9g6HpQenAtqj3T+HbE0JS5cuIC/vz/792sNQFu3bs3s2bPJnTvljWmzpVOrtHmI9PHaukN++F8PqNAFbJ8dm+TWrVuMGzeO2bNnk5CQAEC7du2MM84LkVkkQRFCZC1Rt2DFB3B5m7Zeuq02vomlfYoOV0oxZ84cPvnkEx49eoSDgwPff/89nTp1ypm9TQwGiH3wZJZmz8qgDJCvElT7CIo3f+E8RJ9//jnz5s0DoF69ekycOJGKFStmQOBCvJgkKEKIrOPydi05iboJZtbQaIo28FoKE4sbN27Qo0cP1q/X2qv4+fkRGBiIl5dXOgadSeIfwfHFsH8W5PKGTsu17Q4e0PdgkiHonxYXF0dUVJRxpNeRI0dy7tw5vvzyS+rUqZNR0QvxUpKgCCEynz5Rm0tn5xRAQZ7i2iMd1+IpPsWyZcvo3bu3cdC1iRMnMnDgwJw36FrkdW3CviPzIOa+ti3qltaY+HEtSjLJiV6vZ9GiRYwaNYqqVauyePFiAN566y327NmTUdELkWKSoAghMlfkdVjeE67++yVZwR8afAUWNik6/MGDB/Tv35+FCxcCUL58eX799VdKliyZXhFnjvCTsGe61ivHkKhty+UDVT7Uug4/5xGYUoqgoCCGDx/OP//8A0B8fDwRERE4OjpmUPBCpJ4kKEKIzHN+I6zsAzH3wMIOmkyDMm1TdKhSipUrVzJgwACuXbuGiYkJw4YNY9SoUTlz0LXrR+GfpdrP3jWg6kdQtKE2X85z7Nq1i+HDh7N7924AHB0d+fzzzxkwYAA2NilLAIXILJKgCCEyXmI8bP0S9n6nrbuXgbaBz2038V9Xr16lX79+rF27FtAGXZs/f37Omagu7iH8vRBs80DpNtq20m21kXQrdgWP8i89xaJFi+jYURuUzcrKigEDBvDZZ5/h7OycjoELkXYkQRFCZKz7V7WxTa4d1tYr94Z3x4KZ5UsPTUhIYNq0aXzxxRc8evQIc3NzPvvsM4YPH54zRje9f1WbtO/oAoiL1LpXl2ylDeVvbg1Np7/w8IcPH2Jvrz3qadasGR4eHjRp0oSAgADy58+fEVcgRJqRBEUIkXFOr4bV/SEuAqwcofkP2siwKbBv3z569+5tbEfxzjvvMHv2bIoXT3lD2ixJKQg9CPt/gDN/al2EAVwKQ9UPtXFgeHFD3/379/PFF19w8+ZNjh49ik6nw87OjosXL+aMxE28kSRBEUKkv4RY2DQSDv2kreerBG1+0brHvsT9+/cZNmwYP/74I0opXFxcmDJlCl27ds0Z45psHgV7ZzxZf6sWVO0Lheq+dBLEAwcO8MUXX7BhwwYAzMzMOHbsGOXLa4+AJDkR2ZkkKEKI9HX7PCzvDuFazQc1BkLtADA1f+FhSil+//13Pv74Y27dugVA165dmTJlSvYeDTb+kTbK6+Ph5gvX0+bKKdNWa/jq9vLeRwcPHuSLL74wjvdiampKly5dGDFiBG+99VY6Bi9ExpEERQiRPhLjYe902DFZ+0K2cYGWc7Qv5Je4ePEiH330EZs3bwagWLFizJ49G19f3/SOOv1E39Halxz8Ccq9D/XHa9t93obBp8E2ZUnX3r17qVGjBqAlJv7+/owYMYKCBVPWwFiI7EISFCFE2gs7DGv6w63T2nrBOtD8e22U0xeIi4tjypQpjBs3jri4OCwtLRk5ciRDhw7F0vLljWizpLuXYN/3cGwRJMZq24J3aEPUm5hoo+S+JDm5e/euceTXatWqUalSJUqWLMnIkSMpVKhQel+BEJlCEhQhRNqJi4Kt4+DAbEBptSYNvtK6yr6kvciOHTvo06cPZ8+eBbR5YWbOnJl9v4DDjmgTHp5ZCyhtm0cFqDEAijd7afsSgKNHjzJmzBj27t1LcHAwdnZ26HQ69u7di7n5ix+RCZHdSYIihEgbFzbD2kEQEaqtl2kP9SckO4Pu0+7cucPQoUMJDAwEwNXVlWnTptG+ffvs3Qj25DKtVw5A4fpaYuJdI0XzCh07dowvvviC1atXA2BiYsLWrVtp1qwZgCQn4o0gCYoQ4vVE34ENw56McuroBU2/1XqhvIBSivnz5zNkyBDu3r0LQO/evZk4cSK5cuVK76jTVkIsnFgC7qUg378zAVf9UBvLpFp/cC2WotMcP36cL774glWrVgGg0+l4//33CQgIoGjRoukUvBBZkyQoQohXo5T2pbxhmDZUvc5EmxfGbzhY2r3w0LNnz9KnTx927NgBQOnSpZkzZw7VqlXLiMjTTsx9ODRX64UTfQuKNoIO2iR8OHlp47ykUFhYGBUqVMBgMKDT6Wjfvj2jRo2iWLGUJTdC5DSSoAghUu/+VVj7MVzaqq27lYJmM57UHjxHTEwMEyZM4KuvviIhIQEbGxu++OILPv744+z12OJBCOybqY34mhCtbXPIr/XIUSpFj3EAgoODKVCgAAD58+enffv2GAwGAgICKFGiRHpFL0S2IAmKECLlDHqtAezWcZDwCEwtodZnUH3AS8c12bx5Mx9++CGXLl0CoHHjxnz//ff4+PhkQOBpaPNobQ4hpdfW3Upp11+q1UvfA9AebW3evJnJkyezbds2zp07Z2wIvGDBAkxNnz/5nxBvEklQhBApE35S6zp8/ai27l1Tmxsm94t72Vy/fp2hQ4eyaNEiADw8PJgxYwatWrXKHo1gldKGn388a7Bjfi05eauWlpgUrJ2iGpOEhAT++OMPJk+ezPHjxwFtHJOdO3caExRJToR4QhIUIcSLJcTCzsmwZzoYEsHSEd79Esr7v7Cr7NWrV/nqq6+YO3cu8fHxmJiY0K9fP8aOHYuDg0MGXsAr0ifAyRXaMPRVP4TynbTt5TqCZ2XIWzZFp4mJieHHH3/k22+/5erVqwDY2NjQq1cvBg0ahLf3y4f7F+JN9PKO+K/g2rVrdOrUCRcXF6ytrSldujSHDx827ldKMWrUKPLmzYu1tTV169blwoUL6RGKEOJ1XNkNs2vArq+15KR4M+h3ECp2fW5ycunSJXr27EmhQoWYNWsW8fHx1KxZkwMHDjB9+vSsn5zERWntS6aXg5UfwM2TcHjek/0WNilOTgD0ej1ffPEFV69eJU+ePIwdO5aQkBCmTZsmyYkQL5DmNSj379+nRo0a+Pn5sX79evLkycOFCxeSdBucPHkyM2bMYP78+RQoUICAgADq16/P6dOnsbKySuuQhBCpFfNAm8Tu6Hxt3c4dGk994czDZ8+eZcKECSxatAi9XmufUadOHQICArLHEPVRt+HgHG0o+tgH2jZbV6jaByp1T/FpLl68yKJFiwgICDDOKjx27FjMzc3x9/eXCfyESCmVxj777DNVs2bN5+43GAzK3d1dTZkyxbjtwYMHytLSUi1evDhFrxEREaEAFRER8drxCiH+49RqpaYUUWq0g7asGajUo/vPLX7ixAnVrl07pdPpFNqQqaphw4Zq7969GRZymljY5sk1Ty+v1OF5SsXHpPjwAwcOqDZt2hjfh40bN6ZfrEJkU6n5/k7zRzxr1qyhUqVKtG3bFldXV8qXL89PP/1k3B8cHEx4eDh16z4ZxMnR0ZEqVaqwb9++ZM8ZFxdHZGRkkkUIkcYib8DvHWFpZ4gKB5dC0HUdNJ32ZObdpxw5coSWLVtSpkwZli5dilKKFi1acOjQIdatW5f1xzS5dlSrNXms6odaN+l2v0K/Q9pjLPMX1+gqpVi3bh21atWiSpUqLFu2DKUUjRo1yt4zLguRBaT5I57Lly8za9YsBg8ezPDhwzl06BADBgzAwsKCLl26EB4eDoCbm1uS49zc3Iz7/mvixImMGTMmrUMVQoA2ad3R+dojnbhIMDGDmoPg7SHJfkHv27ePsWPHsn79ekAb7bRt27aMGDGCMmXKZHT0qaMUXNqiNfgN3gk1B0Pd0dq+t/y0JYU9i27cuMG7777LyZMnATAzM6Njx44MGTKEUqVKpdcVCPHGSPMExWAwUKlSJSZMmABA+fLlOXnyJLNnz6ZLly6vdM5hw4YxePBg43pkZCSenp5pEq8Qbyyl4Oxa2DbhyazD+SpCs+/AreR/iip27NjBuHHj2LJlC6B1iX3//fcZPnx41h/tVJ8Ap1ZqiclNLaHAxAzio56USUFiYjAYMPm3cbC7uztKKezs7OjduzcDBw6U30tCpKE0T1Dy5s37zAiIxYsXZ/ny5YD2oQa4efMmefPmNZa5efMm5cqVS/aclpaW2XeqdSGymse1CFvHwfW/tW2WjlDrc6jS+8l4HzwZVGzs2LHs3r0b0GoKunTpwrBhwyhYsGBmXEHqHJ4Hu76BiBBt3dwWKnaBqh+BU8oSihs3bjBjxgyWLVvG8ePHsbGxQafTsWjRIry8vHByckq/+IV4Q6V5glKjRg3OnTuXZNv58+eN3ekKFCiAu7s7W7ZsMSYkkZGRHDhwgA8//DCtwxFCPO3KHi0xCdmrrZvbam0vqvcD6yc97ZRSBAUFMXbsWA4ePAiAhYUFPXr04LPPPste3WNvntSSE5vc//bI6QE2zik69J9//mH69On8+uuvxMfHA7BkyRK6desGkPUfaQmRnaV1C92DBw8qMzMzNX78eHXhwgX122+/KRsbG7Vw4UJjmUmTJiknJye1evVqdeLECdW8eXNVoEABFROTshbz0otHiFQKO6zUghZPeql8mUep9cOUengrSTG9Xq+WLVumypUrZ+yRY21trT7++GN17dq1TAo+Fe5eVmrtYO16n9528Gel4h+l6BSJiYlq5cqVys/Pz/geAKpGjRpq1apVSq/Xp1PwQuR8qfn+TvMERSml/vzzT1WqVCllaWmpihUrpn788cck+w0GgwoICFBubm7K0tJS1alTR507dy7F55cERYgUCj+p1OL3nyQmY5y1bsMPwpIUS0xMVIsWLVIlS5Y0fiHb2dmpTz/9VIWHh2dO7Klx7W+llnZV6gsn7Tp/7/TKp7p48aKxq7Cpqalq27at2r17d9rFKsQbLDXf3zqllMqcuptXFxkZiaOjIxEREVl/VEohMsPdS1rj15PLAQU6EyjzHvh+Bs4FjMWuX7/Ob7/9xk8//WQczdnR0ZEBAwYwcOBAXFxcMukCUkApuPiXNnFf8I4n2wvVhRoDocA7KTrN6dOn2bVrF7179zZu69q1K3nz5uXDDz/Ey8srrSMX4o2Vmu9vSVCEyEkehMCOr+DY4iez7ZZoAX7DIU9RAKKjo1m1ahULFizgr7/+wmAwAODs7MygQYPo169f9mj0+VtbuLBJ+1lnCqVaQ40B4F76pYcaDAbWrVvHjBkz2Lx5MyYmJly+fDl7ta0RIhtKzfe3TBYoRE7wMFybL+fwPDAkaNsK14faIyBvWQwGAzu3b2fBggX88ccfREU96V5bs2ZNOnfuTIcOHbC3t8+kC0iB6LtgaQ9mFtr6W35wdZ/WI6dKb3B6eU1HREQEgYGBfPfdd1y6dAkAExMTmjdvTlxcXHpGL4RIJUlQhMjOHt2D3d9q88ckxmjbfN6GOqPAszLnzp3j1x9G8uuvvxISEmI8rECBAvj7+9O5c+es31X4zgXY9wMcXwxNZ0DZ97TtFbtC+Y5g5Zii02zfvp2mTZsakzMnJyd69uxJ37598fHxSZ/YhRCvTBIUIbKj2Ahtxt19P0D8Q21b/v9B7QDuOZVmyZIlzJ8/gAMHDhgPcXBw4L333sPf358aNWqgS+GIqZlCKbi6B/Z+D+fXP9l+eduTBMXC5oWnMBgMScZbqlChAgAlSpRgwIABdOrUCVtb23QJXwjx+iRBESI7iY+Ggz9qI6LG3Ne2uZcm4Z3PWXc+kQVDZ/Dnn3+SkKA95jE1NaV+/fr4+/vTrFmzrD+TrlJaw9593z8ZRA6gSENtrBbvGi89xcOHD42Pcezs7Dhy5Ag6nQ4HBweOHj1KoUKFsnZyJoQAJEERIntIjPt3RNSvIfoWACp3ES57tWf65mAWjezK3bt3jcXLlSuHv78/HTp0MI7enC3odHAkUEtOzKygbAeo1hdyF37poRcvXuT777/nl19+4eFDrVbJwcGBkJAQY+PXwoVffh4hRNYgCYoQWVlEGBxdoC0PbwCQaJ+fDbHl+HzWUU6dHmIs6u7uTseOHfH3988+I5w+CIUDs6HGx2CXR9v29idaO5r/9QDbl88IfOTIEUaPHs26det43CmxaNGi9O/fny5dumBnZ5eOFyCESC+SoAiR1Rj0cGEzHJmndaNVWjfgR6aO/HQ+F58u/od4vTa5n5WVFS1atKBLly7UrVsXM7Ns8pG+dkRrX3J6tdYd2sIO/IZp+wr6acsLKKWMj2lu3rxJUFAQAI0aNWLAgAHUq1fPOKmfECJ7yia/zYR4A0Reh6O/oj88D9OoG8bNh25bMm13BMtORRKvDwXgnXfewd/fnzZt2uDomLJeLJnOYNAavO79/slcQAAFfMG7+ksPj4+PZ82aNfz8889UqlSJcePGAVC/fn2GDx9Oly5dKFKkSHpFL4TIYDJQmxCZxGAwcPniBcL3/IZL8BqK6IIx/bft5t1HBgKPJ/DTkQTO3dVqUAoWLIi/vz+dOnXirbfeysTIX4FBD3Pe0SbuAzAxg1JttPYleV/8OOr06dPMnTuXBQsWcOfOHQA8PDwICQnB1NT0hccKIbIWGahNiCwmJiaGU6dOcezYMY4dO0bo6UNUNDmNfymo6WQC/z6N2Hk1kTlH4jkW502JMuXpPKgc5cppi4eHR/bqfXL/CuTy0X42MYV8FSAiFCp20wZWc/B44eFLlixhxowZ7N37pLbFw8ODbt260a1bN0lOhMjhJEERIo3dvn2b48ePG5ORY8eOcfbsWQx6Pe8WNKV3RQua1jTD7N82EhHxJhyML8htz0Z416vP7BllsvaIri+iT4Bz6+DQzxC8E3pt0xITgNqjoP5EsEy+0erjytzHSdjOnTvZu3cvpqamNGnShJ49e9KgQYPs085GCPFa5JMuxGs6e/Ysq1evZteuXRw7doxr164l2e9mq+PTaub0/p8t3k/VaEa7lMGqRh8cS7einnkWH5/kZSKvw5H5WhfhqHBtm84Ewg49SVAe99L5j7t377Jw4UJ+/vlnZs2aRc2aNQHo06cPnp6edOnSxTjYmhDizSEJihCpZDAYOHDgAKtXr2bVqlWcO3fumTKFCxWkcw0v2hR4SFEuYfJ44j4rR21sj4pdsXUtnsGRp4PoO7B2EJwNejI5oW0eqNBFG4reyTPZwwwGA1u3bmXu3LmsWLGC+Ph4AObNm2dMUEqXLk3p0i+f+E8IkTNJgiJECsTGxrJ161ZWrVrFmjVruHnzpnGfubk5tWvXpmHDhlQrXZCy6iSWJxfD/SPwuAl6/spQqZs2s/BLhmjP8gx6rU0JgJUThB3WkhPvGlCpOxRv9mRCv/+IjY1l6tSpzJ07lytXrhi3ly9fnp49e/L++++nf/xCiGxBevEI8Rz3798nKCiI1atXs379eqKjo437HBwcaNSoES1atKBB3Vo43j0GxxZpNQmPZxO2dIAy72mJiVvJzLmItHT9mNa25Ope6HsQTP/9++bCZnDIB24lkj3MYDAYxyQxGAwUKlSI4OBgHB0d6dixIz169DDOkyOEyNmkF48QrygkJMT46GbHjh3o9Xrjvnz58tG8eXOaN29OrcplsLiyDc79CbMHQcKT5IV8lbSkpGRLsMjmk9ElxMCplXBoLlw7/GT75e1QuK72c+F6zxwWExPDxo0bWb58OTt27ODChQtYWlpiYmLC2LFjMRgMtG7dGhubbF6bJIRIN1KDIt5oSilOnDjBqlWrWL16NX///XeS/aVKlaJ58+a0aNGCigVyoTu3XuulErLPOMIroNUgFGsMFfzBPQe0m4i4Bgdmwd8Ln0xKaGIOJVtApR7gVVWbN+cpDx8+JCgoiOXLl7Nu3ToePXpk3BcUFESjRo0y8AKEEFmR1KAI8QKJiYns2rXLWFNy9epV4z4TExNq1KhBixYtaN6sKQUtH8C5IDjQF9aeTXoit9JQrBEUbQh5yz3zhZ2tRd+Cvd9pPzt6QaWuUN7/uT1xlixZQpcuXYiLizNu8/b2plWrVrRu3Zpq1aplQNBCiJxEEhTxRoiIiGDLli2sWrWKoKAg7t27Z9xnZWXFu+++S4sWLWjSoC55Ik9ptSSrGkPUk8awmJhpDUGLNYYiDSCXdyZcSTp4EArHf4fEWKgToG3zKA9VP9KGoS9c70mjWCA8PJxVq1ZRvHhxfH19AShTpgxxcXEUKVKE1q1b07p1aypUqJC9BpYTQmQp8ohH5EjR0dHs3r2bbdu2sXXrVo4cOYLB8OSRjIuLC02bNqV58+bUq1EB27BdWlJyaSskPHk0gaUDFKoLRRtpbS6sc2XC1aSDmPvaRH0nlsLVPdo2M2v45Eyy1xgSEsLKlStZvnw5u3fvRilF27ZtWbp0qbHM2bNnKVq0qCQlQojnkkc84o0TGxvLvn37jAnJwYMHSUhISFKmcOHCNGnShBYtWlC9mBtmFzbCuTkwc/+z7UmK/vvoxuft53aZzZYub4eDP2mzJOvjn2z3rgkVOoP5k0arSimmTJnCsmXLOHToUJLTVK5cmbfffjvJtmLFiqVn5EKIN4wkKCJbSkhI4ODBg8aEZO/evUnaPwB4eXlRu3ZtateujZ/v2+TX3dK6AZ8YAlv/M7iae2ko2vjf9iRlc057EoNBS74edwm+dhTOrtV+disFpdtC6TbgmB+lFCEhIXh7a4+udDodS5cu5ciRI+h0Ot5++21at25Ny5Yt8fRMfgA2IYRIK5KgiGxBr9fz999/s3XrVrZu3cru3buTjEsC4O7uriUjfn7Ueac6PuZ30IXsh5AVsPBziI96UtjEDHxq/puUNAAnrwy+onQWfhJOLIGTy6Hel1oSAlpCEhsBZdqBW0mio6PZt28ff/31PStWrODq1avcvn3bWPX66aefcv/+fVq0aIGbm1smXpAQ4k0jbVBElmQwGDh58iRbt25l27Zt7Nixg4iIiCRlXFxc8PPzw8/Pj7o1KlLY8i66kH1aF+Drx54MmPaYpaPWjqRoI61dibVThl1PhngQCieXae1Kbp1+sr14M3jvV+Pq8ePHWbJkCTt27ODgwYMkJiYa91laWrJhwwZq1aqVgYELId4U0gZFZDsGg4Fz586xfft2tm7dyvbt27lz506SMg4ODtSqVQs/Pz/qVy1JUet7mITuh6sLYOWwZ09qnxe8qoF3dW3cDtcSSXqj5BiJcbCwNVzZ9WSbqQUUqU9M4WbsvmlNiWvXyJcvHwAHDhxg4sSJxqKenp7UqlWLxo0b06hRo+w7k7IQIkeRBEVkigcPHnDgwAH279/P/v37OXDgAPfv309SxtbWlpo1a1Lbz49GlQtSwvYBJqEHIORn2BD67EldCoN3NfD6NyHJ5ZNz2pI8LSEWbhzTrhHAzBL0Wm1RYv5qnDEvzfLTCWz4dj+HD7+PXq/n+++/p2/fvgDUqVOHLl26UKtWLXx9ffHx8ZGeN0KILEcSFJHu9Ho9p06dMiYj+/fv58yZM8+Us7Kyolq1atT186VxhfyUtI/ALOwghM6CHUmTF3SmkLeMlox4V9NqSmxzZ9AVZQKDQesOfGIJnF6jDa3/yXmwdQEgpFQ/Ppp/i/V7NmMwbExyaIECBYxz4QAULFiQwMDAjIxeCCFSTdqgiDR3+/btJMnIwYMHiYqKeqZcwYIFqVq1CvX+V5SaBR3wsYzE9NpBbXbcxJikhc1tIH8lLRHxqgb5/weWdhl0RZkkPhou74ALG+H8Rnh4w7jrgcGWv5w70+bjrwBtmPlcuXKh1+spWLAgvr6+xhoSL68c1gBYCJFtSRsUkWESEhI4fvx4koTk0qVLz5Szs7OjepVKNK1SmJqFHSniEIdNxEUI3wX318Lh/xxg7fxv+5F/E5K8ZcHUPGMuKjMZDPC4tuPkCljTz7jrYYIJS/+J5dcTCey8Gsn/Km+nzcfaPnt7e1avXk2ZMmWkC7AQIkeQBEWkyrVr15IkI4cPHyY2NvaZcpXLFKVl9cK8UyQXxZwSyBUbgu72P2A4Clf+U9jEHFyLg3uZJ7UkuYs8+aLOYZRSPHjwgCtXrhB8+RLxl3bjcvcQhbnMrigfOv+wTytY+F1CHpqw+kwsQecT2H5FT5xeG3CuZ69a1KlTJ8l5GzdunAlXI4QQ6UMSFJEspRTBwcH8/fffHDt2jL///pu///6b69evP1O2lKcjrWsUxbdoLko468mdeB3TyDDgBkSgLY9ZOWqJiHvpJ//mLpKzRmsFYwJy5coVYmJi6NChg3Gfb5WyeDw6Q5MiZjQsZIaLjQlYa/uu3nlqQkJ7N75O9Oe2020qtPOhS+nS+Pr64uHhkcFXI4QQGU/aoAji4+M5c+ZMkmTk2LFjREZGJilnZgIlXM1oUsmLWsVcKOWicDWEYxofmfyJHb3+TURKaw1a3UuDo2eO7FkzadIkDhw4YExKHjx4YNzn5uZGeHi4tpIYR8wYd6xNnwytH6U35wI+3HWuhK5wXeo0aZPB0QshRMbIUm1QJk2axLBhwxg4cCDTpk0DtHlTPvnkE37//Xfi4uKoX78+M2fOlJEqM8DDhw85fvx4kmTk1KlTxMdr87I4WcFbuUx4N78JhXNbU+ktF4q5WZHPJgEHFYFOGYA72vL4yY6JGeQp9iQZebzklIn1nnL9+nV2797N+fPnGTlypHH7xo0b2b59u3HdwhSal3WhdWk73nIxR6/XY2pqCmaWmPjUwBBzC5OiDaFIA+zy/4/yplKZKYQQT0vXGpRDhw7Rrl07HBwc8PPzMyYoH374IUFBQQQGBuLo6Ei/fv0wMTFhz549KTqv1KCkTHh4uPHRzONkJPjSRTwddbyVy8S4FMxlQuHc5hR0NsXBXP/ik1rYP5uIuBbXxuLIYQwGA2fOnGH37t3s2bOH3bt3ExwcbNx/584dXFy0br4rVqzgQegZKjnexyfhHPa3DqN7elbkQafBURsojYQYMLfOyEsRQogsIUvUoERFRdGxY0d++uknxo0bZ9weERHB3LlzWbRoEbVr1wZg3rx5FC9enP3791O1atX0CilHio2NJSwsjJCQEEJCQjh37hzn/zlMRPBxHNV9YwLSM5cJbzU0wdvRHnPT5z1i+Tc5sXUF5wLaQGfG5d91O7cc23g1Li4Oc3Nz45gh/fr1Y9asWUnKmJiYULZsWWrWrJlkcsJWzufhxGR4ergWO3coUh+KNAAb5yfbJTkRQoiXSrcEpW/fvjRu3Ji6desmSVCOHDlCQkICdevWNW4rVqwYXl5e7Nu3L9kEJS4uLsmXwX/bRuRUSilu375tTD5Crl7hXtgFYm5eIvF+GCaPbmGvovCw1+Fhr6OsvQlNHHXkrmQClQBskj+xqSXk8k4+AcnlDRa2GXWJmer+/fvs3buX3bt3s3v3bg4dOsTevXupUKECAJUqVcLGxoaqVatSs2YN6lUqQsXcsVjf+huu7gKDP/Bvg1Xnt7R/81WEwvW1xCQnzYoshBAZLF0SlN9//52jR49y6NChZ/aFh4djYWGBk5NTku1JGhL+x8SJExkzZkx6hJqpYmNjCQ0NJeTqVW5cPc+D0DPE3ryMPuIapo9uY6uPwNXagIe9jkoOJjS102Fur4MkU6Uk/2glwcIJk9wFMXV566nk49/FPm+OrQV5mRMnTjBz5kx2797NqVOnntl/4MABY4LyfrO6dCnxDaZhB+DqH3DkPz2Yru7RukUDFG0IQy6CXZ70vgQhhHgjpHmCEhoaysCBA9m8eTNWVlZpcs5hw4YxePBg43pkZGSmDkallCImJoaoqCiio6OJiooiKiqKR1GRxEbcIS7qPgnR90l8FEHiowgMsQ9RcQ8hPhpd3EPMYu9io4/AxSIeD3sdVexNsLP49y9tt38XAEz/XZ4wKIgxsSPBKjc6h7xY5imAZW5vdPZ5wcFDSz5y+WCe00dZfQGlFKGhoRw5coQjR45Qt25d4+y8d+/eZc6cOcayRYsWpWbNmtSsXpXaJfLgWbCYcZ/Vo+uw4dMnJzYxh3wV/p18sDp4VXmyz9JeW4QQQqSJNE9Qjhw5wq1bt4x/hYI2F8vOnTv5/vvv2bhxI/Hx8Tx48CBJLcrNmzdxd3dP9pyWlpZYWqZ/I8wzh3aw47cpqLgodAmPMEmMwVQfg5khDnMVjwUJWJkkYmWix84C7Cx02FnoyPvvz1Zmz6nOt+Q5FR1J3/5ogzmPTBxIsM6DiaMHVnl8sMtbBLNc+cHeA+zdMbFzxfZNGFE1FaKioti8ebMxITly5Ai3b9827o+JiTEmKJUrV+aTTz7hneqVefstG3JFnoGreyF0NIREwf96QuOvtQM9ykOhupC/spaU5KsIFs95bCaEECJNpXmCUqdOHf75558k27p160axYsX47LPP8PT0xNzcnC1bttC6dWsAzp07R0hICNWqVUvrcFLl0aV99HHa9ZJSOl72tiUadMQYTIlTZsRjQaKJJYkmlhjMrDGY2WDq6IFVngI45i+Kbd5CWu2HfV5sLWx4M1p/vBqlFGFhYRw5cgRHR0f8/PwArS1Jq1atkpQ1MzOjZMmSVKxYkXfeece43dZcx9QSp+DUfDgRn/QFrBy1LtPGk1hCp+Xpdj1CCCGeL80TFHt7e0qVKpVkm62tLS4uLsbtPXr0YPDgwTg7O+Pg4ED//v2pVq1apvfgcS9UhtCzedGbWmMws0aZ26KztENnaYeptQOm1o6Y2zphYZcLS3sXLO2dMbG01xqVGhc7zMwssOc/TUVEqjydjDxeDh8+bKwZadasmTFByZ8/P35+fhQoUICKFStStXRBSjknYnH3NFw/Bvp1QHPtxBY28PA66OO1Hkne1cG7xv/bu/e4qKq1D+C/GS7DHQQU5WqKoChCgpCKr1ogHcu7pliJZZmlZCn51jmlZh3Jo6WlVuqrnizteDnHI1l5Q0IzFQVBQEUJUUQuKjLcZJCZ/f4xucftoIECM8jv+/nMJ3jm2Xuv7YLhae+119JOr9/Br82OzSEiMjYGmR1q2bJlkMvlGDt2rGSiNkNzCx4GBA8zdDPaHI1GgwsXLqCsrAxBQUFizNfXFzdvSlc1NjExQc+ePdG9u26siEwmw4GPxwB5vwKFXwAJhZJtYOEACILuiZqRXwK2HbVP3vApGyIio8Sp7qlFFRcXIyMjA5mZmcjIyEBGRgaysrJQXV0NPz8/yZM1gwYNQllZGYKDgxHUpw/6+bnDr90tKK6fAcoLgNFf63a8cRSQm/jHNzLAuZv2Md/bL68wXh0hIjIwo5iojdq2yspKZGVloaCgQDI+ZOjQoTh16pRevkKhgKWlJerq6mBqqv2xTFy3APILiUBhOlC4BEi6Id0ocpFuArSgaO2EaJ0CgI69+EQNEVErxwKFHtr58+eRmpoqXhHJyMgQp4S3sLBAZWWldh0aAIGBgbh58yb8/f3Rq1cv9PHrgsfdLOGmqILJ9XOAcAu3fyzlZ+OBE+t1B5KbAS5+uqsi8jsewe45uqVOl4iIWgALFGoQjUaD/Px8ZGVl4fTp05gzZw5kf4zfePfdd/Gf//xHb5uOHTvC398fZWVl4po1/1wwFbLT7YGrZ4Fr3wNnioEzd2zUaxzgrh2HAu8IADJdQfKIrvlDRET6WKBQvQ4ePIh9+/YhOzsb2dnZOH/+vGTA6vjx4+Hl5QUACA0NxZUrV+Dv3wtP+HkhyNMaXe3rYFN9GbiaDahLAGgLFFlRBnB8rfRgdu5Ae1/t685bM92HaV9ERNTmsEBpg9RqNfLy8sTi4/br+++/FyfL++mnn7B48WLJdmZmZvDx8UFv/16oq9UVK3NHBWJuu93A1Z+Bsgqg7K4DFmdpr34A2sd6B8wC2ncHnH21g1ktONCZiIikWKA8wm7cuAFra2uYm5sDAL755hv84x//QE5ODmpra/Xyz5w5IxYoT4WFoF3lGPh5OKCrswU62Qiwl1VBXpoLXPsFuJUN4I9HfQU1UJCi/Vpuqn18t72vrgjx6q87iOvj2hcREdF9sEBp5SoqKpCdnY3c3FxcuHABOTk5yM7OxtmzZ3H16lUcPHgQAwcOBADU1tbi9+zTcLeToYuXJYK8O6GnZzt4d7CEm60MNjbXxP1GdLNCxIn9wE0A+fUc+Oo53dfuIcD4b7QFiWMXwNS8eU+aiIgeeSxQjNzNmzeRl5cnFiC5ubmYOnUqevbsCQDYvHkzpk+fDgCwVwAdbeRwt5PhL65yuHc3R2V2EvBHgTLK3xavvn/n7ZRrf7wAVAFQFejesncHHDwBOzftIoR2rtqvHTy1hYiDly7X2gnoOarZ/g2IiKjtYYFiYGq1GgUFBbC3t4e9vT0AICEhAfPmfYBrly9AXVGM9tZydLCWob2VDB2sZcjztRcLlD7t63Al1h7OloCZvJ4597rrVjVu31m7DUwtpUWHXSftfz3vWAupYy/grQwQEVHbcvXqVRQUFCAwMNCg7WCB0kKuFBQgad8ulBdeQNmVHFSVXMQtZSHkN0vhZKFBzzHvIPK1jwAAdqUZODAkEwpTGepb0SffTil+3bdfGHDqjsJEYXdH8eGqG5wKaK98zL0AWLbjFO9ERIR//etfSEhIwKRJk8T1zQoKCrB48WJ8//33Bm0bC5SHUHTpd6Ts347qq5dQW1YIdUUJZDdLYXarHJaaalj1n4qIN5YAAAqPbEFU3ofaDZ1w+6lbAGYAgMM3Tov77eYfBEWWtoAQzKwBa2fIrNsD1s6AtTM8ekTqGuHsA0xL0r5n5QyYWdy7wSZmuplXiYjokabRaCD/Y4mPK1euICYmBtevX8cvv/wi5uzduxcbNmyAp6enWKD4+PigXbt2hmiyBAuUu5TknUH2vm+gKr2MuooSoPo6TFVKWGiqYC2rQbnfixg08wsAwJXkeDxzaZF2Q8UfrzskXjsrfu3s6QtkAiqNCapl1qhTOEBu2wEKRzdYOXthgE+EmOvgHQq8laktTMws799gM0vANbAJzpyIiFobjUaDy5cvw9PTU4wtWrQIK1euRExMDN577z0AgI2NjTihplKpFIcUjBo1Ch4eHnjqqafE7a2srIxiAV8WKHe5nLIHAwu+0n5jCuCuKToSS3PFr529uqM01RRVgiVUJjaoU9gDVs4wtXOBhaMrgv11Vzq8gp8GHi+Cwszy7jpGn6k54ODRJOdDREStX2VlJbKzs2FlZYUePbS37svLy+Hi4oKamhpUVFTAxkY75lCj0aCwsBDZ2dni9nZ2dvj666/h5eUFhUL3V2jEiBEYMWJEy55MA3E147vkpexD1XeTcVNugzpzO8DKCSa2HaBo1wnW7b3g1GMgHB4LaNJjEhERAdri4vYs3tOmTYOFhfa2/fz587Fw4UK88sorWLtWNxt3hw4doFQqkZqaKj48cfHiRRQXF8PX11e8UmIsuJrxQ+gcFAEEFRq6GURE9IhLS0vDv//9b3h5eeGVV14BAMhkMkyYMAFKpRJDhgyBv78/AMDX1xcdOnQQC5bbTp48CRcXF3EVeADw8vISlyJpzeSGbgAREdGj7p133sGTTz6J8+fPi7FTp07h448/xqZNm8SYTCbDsGHDMHr0aHFBVgCIiopCcXExVqxYIdmvm5ubpDh5lDyaZ0VERNQCampqIJfLxSVFEhMT8fbbb6Nz587473//K+YdOHAAqampyMrKQrdu3QAAwcHBmDZtGvr27SvZ5+bNm/WOI2uDU0OwQCEiIvoTpaWlKC4uFgeoAsCzzz6Ln3/+GfHx8XjmmWcAAObm5khPT0dpaalk+7lz50KlUiE4OFiM+fn5YfXq1S1zAq0QCxQiIqI/FBQUIDMzE76+vujcuTMA4NChQ/if//kfdO3aFTk5OWKutbU1NBqN5LZNQEAAfvjhB3Tv3l2y3wkTJrRI+x8lHINCRERtTmVlJf7973/rzfcxc+ZMPP3004iPjxdjt2/JqNVq1NXVifHFixejsLAQs2bNEmM2NjZ49tln4e3t3cxn8OjjFRQiInqk7dmzB3v37sWTTz4p3oq5ceMGxo0bB1NTU7z66qswM9PO6h0QEIDs7GzJXCEuLi4oLy+Hra106ZHbV1ioefAKChERtVp3TuVVUVGB6OhohIWFQa1Wi/H9+/fjs88+w549e8SYm5sbBg4ciEmTJqGyslKML1iwAKdPn8Zrr70mxmQymV5xQs2PV1CIiMioCYKAkpIStG/fXlxbZu3atVi8eDHGjh2LxYsXA9BO0b5161bU1NQgLy8PXbt2BQBERESgrq4OERG6JUXkcjkOHjzY8idDDcYChYiIjMLNmzdx/vx5yGQycYIyjUaDTp06oaSkBBcvXhTXnFGr1fj999+RlZUlbm9iYoJly5bB2dkZ7du3F+NDhw7F0KFDW/Zk6KGxQCEioha3d+9enD59Gi+++CKcnLTLu69btw4xMTEYOXKkOIeIXC6Ho6Mjrl69igsXLogFyvDhw+Hr6yt57BcApk+f3qLnQc2HBQoRETWbzMxMbNy4Ec7Ozpg7d64YnzFjBnJychAQEIAhQ4YAAHx8fNCuXTtYWVlJ9rFnzx60b98elpa61d3d3Nzg5ubWMidBBsFBskRE9EBUKpXk+zlz5iA4OBhHjx4VY/n5+ViyZAm+++47SW5kZCTGjBkjKUYiIiJQWlqqN5Oqp6enpDihtoFXUIiI6J4qKiqQk5MDd3d3cVzH4cOHMWbMGLi6uuLkyZNiblZWFlJSUpCVlYUnnngCAODv748333wTvXr1kux35cqVesdqi9O5073JhDuf0WolGrNcMxER/bmysjLs378f5eXlePnll8V4REQE9u/fjw0bNmDKlCkAgLNnz6JHjx6wtrZGRUWFWFjs378flZWVCA0NRadOnQxxGmTkGvP3m1dQiIjamC1btmDfvn147rnnxKdbCgoKMH78eNjZ2eGll14Siw5vb2+kpaWhpqZG3L5r1644ceIEvL29JVc9wsPDW/ZE6JHGMShERI8AQRBw/fp1ySJ1hYWFGDx4sN7tlYSEBKxbtw6//vqrGOvatSueeOIJjBgxQjK2ZMWKFbh69ark6RgzMzMEBQXB3t6+Gc+I2joWKERErUhtbS3279+P9evXS+IxMTFwdnbGF198Icbs7e2RlJSErKwsXL9+XYyPHDkS8+fPl8wNYmFhgSNHjuDbb7+FhYWFGDc15YV2Mgz+5BERGandu3djx44dGDhwIF544QUAwK1bt8QZUceMGQMHBwcAEB+5vXbtmri9lZUVtmzZAi8vL8lU7c8884y4Jg2RsWryKyhxcXHo27cvbG1t0aFDB4waNQrZ2dmSnJqaGsyYMQNOTk6wsbHB2LFjUVxc3NRNISIyOjU1NcjOzsbvv/8uxmpraxESEgInJycolUoxnpqaijVr1mDfvn1izNraGmFhYXjmmWcka8jMnDkT1dXVek/HPPfccwgNDYW5uXkznhVR02vyAiUpKQkzZszA0aNHsW/fPty6dQtDhw5FVVWVmPP222/jhx9+wLZt25CUlIQrV65gzJgxTd0UIiKDEQQBK1euRGxsLMrLy8X4Z599hu7du+Ojjz4SY+bm5sjLy0NpaSlyc3PF+JAhQ/DBBx9g4sSJkn0fOnQIu3btgru7uxiztbXlXCH0aBGaWUlJiQBASEpKEgRBEMrKygQzMzNh27ZtYs6ZM2cEAMKRI0fq3UdNTY2gVCrFV35+vgBAUCqVzd18IiKJkpIS4fjx48LNmzfF2NatW4WAgABh5syZklxnZ2cBgJCWlibGvvvuO8HGxkaYMmWKJDcxMVFIT08XampqmvcEiAxIqVQ2+O93s49BuX250tHREQCQkpKCW7duSR5H6969Ozw9PXHkyBFxcp87xcXF4cMPP2zuphIRiTIyMrBz5064u7uL838AgJ+fH65du4a0tDQEBAQA0N6iSU9PR7t27ST7mDJlCurq6iTjP6KiojBp0iS9SckGDx7cbOdC1Bo161M8Go0Gb731FgYMGCA+5lZUVARzc3NxYNdtLi4uKCoqqnc/7733HpRKpfjKz89vzmYT0SPq2rVrSEhIwKFDhyTxp556Co6OjkhLSxNj6enp+OCDD/Dtt99Kch977DF06tRJMlZkyJAh+PHHH7F69WpJ7pIlS7Bs2TJ06dJFjMnlcs6YStQAzXoFZcaMGcjMzJQ8a/8gFAoFFApFE7WKiB411dXVqKiogIuLixh79913kZGRgU8//RTdu3cHoF107oUXXsCQIUNw4MABMbesrAw3btzA5cuXERgYCADo3bs3Xn75ZTz++OOSYx05cgQmJiaSmKurK1xdXZvp7Ijapma7gjJz5kzs2rULiYmJkoFcHTt2RG1tLcrKyiT5xcXF6NixY3M1h4haGUEQcPPmTUls7dq1mDNnjuQJmG3btsHa2lpvIOm+ffvw008/IScnR4w99thj6NGjBx577DG9/WZmZuKpp54SY71798a6deswc+ZMSe7dxQkRNY8mv4IiCAJiYmKwY8cO/PLLL3ofBEFBQTAzM0NCQgLGjh0LAMjOzsalS5fQr1+/pm4OERkZQRAktzi2bNmCc+fO4cUXX0Tnzp0BAPHx8eLjsUlJSWLumjVrcOLECQwePBhdu3YFAHEBuztnUAWA2NhYVFVVoXfv3mKsf//+OH36tF6b+vTp02TnR0RNo8kLlBkzZmDz5s3YuXMnbG1txXEl9vb2sLS0hL29PaZOnYrZs2fD0dERdnZ2iImJQb9+/eodIEtExk+tVkuuLOzYsQM5OTkYP368WHTs3r0b0dHR8PPzQ2Jiopi7dOlSnDhxAgEBAWKuvb09VCoVCgsLJceZOHEiBg0aBC8vLzHWv39/3LhxQ2/a9aioqCY+SyJqSU1eoHz11VcA9Eek37kS5rJlyyCXyzF27FioVCpERkbiyy+/bOqmENEDEgQB1dXVsLa2FmObNm3CuXPnMHnyZPHqxe7duzFx4kT4+/tLBp5+8sknSE5Ohq+vr1h0WFlZoaSkRK+QGDFiBAIDAyVjOEJCQnDhwgXJmBIAmDNnjl5bzc3NOQkZ0SOoWW7x/BkLCwusWrUKq1ataurDE1E9ampqUFBQALVaDR8fHzH+5Zdf4ty5c5g+fbo4kPTHH3/EqFGjEBISgsOHD4u5K1aswLFjx9CnTx+xQLG2toZSqdSbCToyMhLdunVDhw4dxFifPn2QlpaGTp06SXI/+OADvfZaWlqKhQ0RtU1ci4eoldBoNAC0j6kCwOXLl3Ho0CHY2Nhg+PDhYt706dORnp6OL774An379gWgvdIxevRoPPHEEzhy5IiY++233+Lo0aMYPHiwWKDY29ujrq4OJSUlkuOPHDkSgYGB4povgLboOH36tN6VjoULF+q138bGRpw3hIjoz7BAIWpmarUa5eXlUKvVcHZ2FuO7du1CUVERnn32WfEJtqNHj2LZsmXw9vbG3//+dzG3f//+OHbsGA4cOIBBgwYB0E56OGnSJISGhkoKlFOnTuHo0aO4fPmyWKA4OjrC2tpa73H9SZMmSQacAkBwcDAuXbokufoBaOcjupu1tTV69OjxoP80RET3xAKF6E+oVCrcuHEDTk5OMDMzAwCcOXMG+/btg5ubm/g0GqBdmC03NxcbN26En58fAO3YjejoaERGRmL37t1i7ty5c3HmzBkkJiaKBUpxcTG2bt2K0NBQSYGi0Wig0WgkT6p4eHhgyJAh4iSIty1YsADV1dUIDQ0VYwMHDpQsLHdbTEyMXszCwgIeHh6N+jciImpqLFCoTbj7KZM9e/bg2rVrGD58OOzs7AAAe/fuxapVqxAUFIR58+aJuV5eXiguLsbJkyfFSbySk5Mxa9YsREZGSgqUU6dOITs7G1evXhVjt/dfXV0tadOgQYPQtWtXyTToAQEB+Pzzz+Hp6SnJ3bp1K8zMzCRXYPr06SOZbOy2oUOH6sU4cykRtTYsUMjoaTQayGQy8Y9sXl4eTp06BRcXF8lVgpiYGBQXF+OLL74Qr0isWbMGb775JkaNGoV//etfYm50dDSKi4sl66kUFhYiPj4eKpVKcvx27dqhpKREsiKtj48Pxo8frzd/xueff466ujrJVY1nnnkGNTU1erdXbj/xdqfOnTvjzTff1IvfXbAQET3qWKBQs7k9E6hGo4GNjY0Y27lzJ8rLyzF+/Hhxefiff/4ZmzZtQv/+/fHGG2+I+3BxcUFJSQny8vLEuS927tyJt956CxMnTsT3338v5m7fvh1FRUX429/+JhYo5ubmUKlUknVTACAsLAxlZWUwNdX9CvTv3x+rV6+Gt7e3JDc5ORnW1tbi4FQA6NevX70TC0ZGRurFbt8WIiKihmvWxQJbo8zMTISHh0tWLwW0TyWMGjVKckk9Ly8PUVFRelNhf/XVV3jllVckk1GVlJTgzTffxNy5cyW5v/zyC9atW4fMzEwxduvWLRw/fhyZmZmSx7bVanWDHuNurKqqKhQWFkqWH9BoNDh8+DAOHDiAuro6MZ6cnIzly5dj//79knYNGzYMYWFhkkLgo48+grW1NWJjY8WYTCZDVFQUoqOjJU+JnDt3Dps2bdJbxO32VZM72+bl5YWQkBDJAmwAMG/ePKxYsUKyZMKYMWOQl5eHLVu2SHK3b9+O/fv3o2fPnmKsW7dumDZtGp588klJrq2traQ4ISKiFiC0QkqlUgAgKJXKJt/3wYMHBQCCj4+PJP70008LAIRvvvlGjKWkpAgABHd3d0numDFjBADCl19+KcbOnj0rABAcHBwkuZMnTxYACP/4xz/EWH5+vgBAMDU1leS+8cYbgkwmExYuXCjGysvLhYCAAOGJJ54QamtrxfiSJUuEPn36CKtWrRJjSqVSsLe3FywtLQWVSiXG33nnHQGAMGfOHDFWW1srABAACNevXxfjCxYsEAAIr7/+uqRtCoVCACDk5eWJsc8++0wAIERFRUlyhw0bJkRGRgoXL14UY6mpqcLSpUuF3bt3S3Jzc3OFoqIioa6uTiAiotatMX+/eYvnLr6+vti0aZN4S+K2WbNmYcyYMZLp+N3d3bF8+XK93Oeffx5BQUEICQkRY46Ojnj//fdhZWUlyQ0MDMT169fRrVs3MaZWq+Hh4SG5/QAAN2/ehCAIknhVVRXS09Mhk8kk8YKCAqSmpkoGTJqZmYlXOFQqlTj7pkKhgFwuF+fZAABTU1P4+PjAzMxMEg8ICMDEiRMRFBQkadv69euhUCjg5OQkxl577TW88sorktlIAe1EYHd7/PHH9VaNBaC3lhMREbUNMkFohnsGzay8vBz29vZQKpXiExJtQVVVFcrLy2FlZSVOF15TU4OkpCSoVCqMGDFCzM3KysKlS5fg7e0tFj+CIOD8+fMwNzeHp6eneNtCuGvxNiIioubQmL/fLFCIiIioRTTm7zdH/hEREZHRYYFCRERERocFChERERkdFihERERkdFigEBERkdFhgUJERERGhwUKERERGR0WKERERGR0WKAQERGR0WGBQkREREaHBQoREREZHRYoREREZHRYoBAREZHRYYFCRERERocFChERERkdFihERERkdFigEBERkdFhgUJERERGhwUKERERGR0WKERERGR0WKAQERGR0TFogbJq1Sp07twZFhYWCA0NRXJysiGbQ0REREbCYAXKli1bMHv2bMyfPx+pqakICAhAZGQkSkpKDNUkIiIiMhIyQRAEQxw4NDQUffv2xcqVKwEAGo0GHh4eiImJwbvvvnvfbcvLy2Fvbw+lUgk7O7umb1xtlfa/ZlaATKb9uq4W0NwC5KaAqUI/19QSkP9R76lvAepaQGYCmFk8YG41AAEwtQDkJn/k1gFqFSCTA2aWD5Z76yYgaAATBWBiqo1p1EBdTeNyIQPMre7IrQEENWBiDpiYPUCuBqi7qf3a3FqXW6cCNHWA3AwwNW98riAAt6q1X9fbn43JbUDfN8nPSX392QQ/J7f782F/TvT682F/Tu7Rnw/7c3Jnfz70z8k9+pOfEfyMkOQ+gp8RTawxf78NcgWltrYWKSkpCA8P1zVELkd4eDiOHDmil69SqVBeXi55NatFrtpX9XVd7LfPtbGfYqW5S7y1cWW+Lpa8VhuLnynNXe6vjV/L1sXSNmlj21+W5q4K1cYL03SxrP9oY99PlOauHaKNX/xNFzu3WxvbOFKau+Ev2vjvCbrYhSRt7P8ipLnfjdPGz/6gi10+ro19PUCau/VFbfzUVl2sOEsbW9FHmrtjmjae8k9d7MYFbezTHtLcH97Sxo99pYtVFmljn3hKc/f8VRs/9KkuVqPU9aemThc/sFAbO7BQF9PU6XJrlLr4oU+1sT1/lR7vE09tvLJIFzv2lTb2w1vS3E97aOM3LuhiKf/UxnZMk+au6KONF2fpYqe2amNbX5Tmfj1AG798XBc7+4M29t04ae7/RWjjF5J0sd8TtLENf5HmbhypjZ/brYtd/E0bWztEmvv9RG086z+6WGGaNrYqVJq7/WVtPG2TLnYtWxtb7i/NjZ+pjSev1cWU+drYEm9p7k+x2vhvn+ti1dd1/XmnffO1saRPdLFb1brc23+AAG3OIlftNnfiZ4QWPyO0HuXPCAMySIFy7do1qNVquLi4SOIuLi4oKirSy4+Li4O9vb348vDwaKmmEhERkQEY5BbPlStX4Obmht9++w39+vUT43PnzkVSUhKOHTsmyVepVFCpVOL35eXl8PDw4C2eB8nl5ds/cnn5tkG5vMXDWzwAPyPa8mdEE2vMLR6DFCi1tbWwsrLC9u3bMWrUKDEeHR2NsrIy7Ny5877bN/sYFCIiImpyRj8GxdzcHEFBQUhI0N3j1Gg0SEhIkFxRISIiorbJ1FAHnj17NqKjoxEcHIyQkBAsX74cVVVVeOmllwzVJCIiIjISBitQJkyYgKtXr2LevHkoKipCYGAgdu/erTdwloiIiNoeg82D8jA4BoWIiKj1MfoxKERERET3wwKFiIiIjA4LFCIiIjI6LFCIiIjI6LBAISIiIqPDAoWIiIiMDgsUIiIiMjosUIiIiMjosEAhIiIio2Owqe4fxu3Jb8vLyw3cEiIiImqo23+3GzKJfassUCoqKgAAHh4eBm4JERERNVZFRQXs7e3vm9Mq1+LRaDS4cuUKbG1tIZPJxHjfvn1x/Pjxere513t3x8vLy+Hh4YH8/HyDr/Nzv/Npyf01Zrs/y32Y9+t7j/3XtNs1JPdB+5C/g62jD/k52jz7Yx9qCYKAiooKuLq6Qi6//yiTVnkFRS6Xw93dXS9uYmJyz3/Ie713r7idnZ3Bf7Hudz4tub/GbPdnuQ/zfn3vsf+adruG5D5oH/J3sHX0IT9Hm2d/7EOdP7tyctsjNUh2xowZjX7vftsYWlO37UH315jt/iz3Yd6v7z32X9Nu15DcB+1D/g62jj7k52jz7I992Hit8hZPc2rMUtBkfNh/rR/7sPVjH7Z+xtCHj9QVlKagUCgwf/58KBQKQzeFHgD7r/VjH7Z+7MPWzxj6kFdQiIiIyOjwCgoREREZHRYoREREZHRYoBAREZHRYYFCRERERocFChERERkdFiiNMHr0aLRr1w7jxo0zdFPoAeTn52Pw4MHw8/ND7969sW3bNkM3iRqprKwMwcHBCAwMRK9evbB27VpDN4keQHV1Nby8vBAbG2voptAD6Ny5M3r37o3AwEAMGTKk2Y7Dx4wb4ZdffkFFRQW++eYbbN++3dDNoUYqLCxEcXExAgMDUVRUhKCgIJw7dw7W1taGbho1kFqthkqlgpWVFaqqqtCrVy+cOHECTk5Ohm4aNcLf/vY35OTkwMPDA0uXLjV0c6iROnfujMzMTNjY2DTrcXgFpREGDx4MW1tbQzeDHlCnTp0QGBgIAOjYsSOcnZ1RWlpq2EZRo5iYmMDKygoAoFKpIAhCg5ZtJ+Nx/vx5nD17Fn/5y18M3RQycm2mQDl48CCGDx8OV1dXyGQy/Pe//9XLWbVqFTp37gwLCwuEhoYiOTm55RtK99SUfZiSkgK1Wg0PD49mbjXdqSn6sKysDAEBAXB3d8c777wDZ2fnFmo9NUX/xcbGIi4uroVaTHdrij6UyWQYNGgQ+vbti02bNjVbW9tMgVJVVYWAgACsWrWq3ve3bNmC2bNnY/78+UhNTUVAQAAiIyNRUlLSwi2le2mqPiwtLcXkyZOxZs2almg23aEp+tDBwQHp6em4cOECNm/ejOLi4pZqfpv3sP23c+dO+Pj4wMfHpyWbTXdoit/BX3/9FSkpKYiPj8eiRYtw6tSp5mms0AYBEHbs2CGJhYSECDNmzBC/V6vVgqurqxAXFyfJS0xMFMaOHdsSzaT7eNA+rKmpEQYOHChs3LixpZpK9/Awv4e3vf7668K2bduas5l0Dw/Sf++++67g7u4ueHl5CU5OToKdnZ3w4YcftmSz6Q5N8TsYGxsrbNiwoVna12auoNxPbW0tUlJSEB4eLsbkcjnCw8Nx5MgRA7aMGqohfSgIAqZMmYInn3wSL774oqGaSvfQkD4sLi5GRUUFAECpVOLgwYPw9fU1SHtJqiH9FxcXh/z8fOTl5WHp0qV49dVXMW/ePEM1me7SkD6sqqoSfwcrKytx4MAB9OzZs1naY9ose21lrl27BrVaDRcXF0ncxcUFZ8+eFb8PDw9Heno6qqqq4O7ujm3btqFfv34t3VyqR0P68PDhw9iyZQt69+4t3nf99ttv4e/v39LNpXo0pA8vXryIadOmiYNjY2Ji2H9GoqGfo2S8GtKHxcXFGD16NADtU3Wvvvoq+vbt2yztYYHSCPv37zd0E+ghhIWFQaPRGLoZ9BBCQkKQlpZm6GZQE5gyZYqhm0APoEuXLkhPT2+RY/EWDwBnZ2eYmJjoDbYrLi5Gx44dDdQqagz2YevHPmzd2H+tn7H1IQsUAObm5ggKCkJCQoIY02g0SEhI4C2cVoJ92PqxD1s39l/rZ2x92GZu8VRWViInJ0f8/sKFC0hLS4OjoyM8PT0xe/ZsREdHIzg4GCEhIVi+fDmqqqrw0ksvGbDVdCf2YevHPmzd2H+tX6vqw2Z5NsgIJSYmCgD0XtHR0WLOihUrBE9PT8Hc3FwICQkRjh49argGkx72YevHPmzd2H+tX2vqQ67FQ0REREaHY1CIiIjI6LBAISIiIqPDAoWIiIiMDgsUIiIiMjosUIiIiMjosEAhIiIio8MChYiIiIwOCxQiIiIyOixQiIiIyOiwQCEiIiKjwwKFiOo1aNAgyGQyvdfkyZOb7ZgvvfQS3n///Xu+X1RUhFmzZsHb2xsWFhZwcXHBgAED8NVXX6G6urpBxxg+fDiefvrpet87dOgQZDIZTp069UDtJ6Km02ZWMyaihhMEASdPnsTSpUvx/PPPS96zsbFplmOq1Wrs2rULP/74Y73v5+bmYsCAAXBwcMCiRYvg7+8PhUKBjIwMrFmzBm5ubhgxYsSfHmfq1KkYO3YsLl++DHd3d8l7GzZsQHBwMHr37t0k50RED46LBRKRnnPnzsHX1xfJycno27dvixzz0KFDmDBhAgoKCiCTyfTef/rpp5GVlYWzZ8/C2tpa731BEMTtNBoNFi9ejDVr1qCoqAg+Pj744IMPMG7cONTV1cHd3R0zZ86UXK2prKxEp06dsGTJEkyfPr35TpSIGoS3eIhIT0pKCkxNTVv0SkJ8fDyGDx9eb3Fy/fp17N27FzNmzKi3OAEg2S4uLg4bN27E119/jaysLLz99tt44YUXkJSUBFNTU0yePBn//Oc/cef/n23btg1qtRpRUVFNf3JE1GgsUIhIT2pqKtRqNZycnGBjYyO+XnvttWY75s6dO+95iyYnJweCIMDX11cSd3Z2Ftv2v//7vwAAlUqFRYsWYf369YiMjESXLl0wZcoUvPDCC1i9ejUA4OWXX8bvv/+OpKQkcV8bNmzA2LFjYW9v30xnSESNwTEoRKQnNTUVUVFR+PDDDyVxR0fHZjnemTNncOXKFTz11FON2i45ORkajQbPP/88VCoVAG0xU11djYiICElubW0tHn/8cQBA9+7d0b9/f6xfvx6DBw9GTk4ODh06hIULFzbNCRHRQ2OBQkR6UlNT8fe//x3e3t71vr9+/XosX74cMpkMERERWLp0KfLy8jBy5Ej06tULycnJCA8PR2RkJOLi4lBVVYUdO3agW7du9e4vPj4eERERsLCwqPd9b29vyGQyZGdnS+JdunQBAFhaWoqxyspKAMCPP/4INzc3Sb5CoRC/njp1KmJiYrBq1Sps2LABXbt2xaBBg/7kX4aIWgoLFCKSyM3NRVlZGQICAup9PyMjA8uWLcOhQ4fg4OCA0tJS8b0zZ85g69at8Pb2Rq9evWBjY4Njx45h9erVWLlyJT7//PN697lz505Mmzbtnm1ycnJCREQEVq5ciZiYmHuOQwEAPz8/KBQKXLp06b4Fx3PPPYdZs2Zh8+bN2LhxI15//fV6x78QkWGwQCEiiZSUFACAi4sLioqKJO916NABiYmJmDBhAhwcHABIb/v4+vqK40R69OiB8PBwAIC/vz9++umneo9XUlKCEydOID4+/r7t+vLLLzFgwAAEBwdjwYIF6N27N+RyOY4fP46zZ88iKCgIAGBra4vY2Fi8/fbb0Gg0CAsLg1KpxOHDh2FnZ4fo6GgA2selJ0yYgPfeew/l5eWYMmVK4/6hiKhZsUAhIonU1FQA0Lsdo1AoUF5eft9t77yFIpfLxe/lcjnUanW92/zwww8ICQmBs7PzfffdtWtXnDx5EosWLcJ7772Hy5cvQ6FQwM/PD7GxsXjjjTfE3I8++gjt27dHXFwccnNz4eDggD59+uCvf/2rZJ9Tp07FunXrMGzYMLi6ut73+ETUsjgPChE1SmZmJqKiovDrr7/C3t4epaWlcHR0RF5eHsaNG4cTJ04AAMaNG4eZM2di8ODBOHr0KD7++GPs2rVLb38jRoxAWFgY5s6d29KnQkRGjI8ZE1Gj9OrVC7NmzcKAAQMQGBiITz755KH2FxYWxrlHiEgPr6AQERGR0eEVFCIiIjI6LFCIiIjI6LBAISIiIqPDAoWIiIiMDgsUIiIiMjosUIiIiMjosEAhIiIio8MChYiIiIwOCxQiIiIyOixQiIiIyOiwQCEiIiKj8/9bHvOUKLsi4AAAAABJRU5ErkJggg==", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -3351,7 +233,7 @@ " energies / GeV,\n", " val,\n", " label=model2 + \" \" + kind,\n", - " ls=[\"-\", \"--\", \":\"][i],\n", + " ls=[\"--\", \"-\", \":\"][i],\n", " color=[\"k\", \"C1\"][k],\n", " zorder=1 + k,\n", " )\n", @@ -3360,13 +242,6 @@ " plt.xlabel(\"$E_\\\\mathrm{cm}$ / GeV\")\n", " " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 2029384c270391f4f4ade0412209bc5a41fe8203 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 16:50:47 +0100 Subject: [PATCH 27/35] mark bad combinations for cross_section test, allow MCRunRemote to run without remote --- src/impy/common.py | 12 +++--- src/impy/models/dpmjetIII.py | 20 ++++------ src/impy/remote_control.py | 53 +++++++++++++------------- tests/test_cross_section.py | 72 +++++++++++++++++++++++++----------- 4 files changed, 94 insertions(+), 63 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index 25583f47..d7957a3b 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -596,7 +596,6 @@ def seed(self): class MCRun(ABC): _once_called = False _alive_instances = set() - _stable = set() # defaults for many models (override in Derived if needed) _projectiles = standard_projectiles @@ -608,11 +607,14 @@ class MCRun(ABC): # Don't override `__init__` in derived class, use `_once` instead. # You can pass custom initialization parameters via **kwargs, which # are forwarded to `_once`. - def __init__(self, seed=None, **kwargs): + # + # timeout parameter is used MCRunRemote and ignored here + def __init__(self, seed=None, timeout: int = -1, **kwargs): import importlib from random import randint self._init_kwargs = kwargs + self._timeout = timeout if self.pyname in self._alive_instances: warnings.warn( @@ -620,7 +622,7 @@ def __init__(self, seed=None, **kwargs): "You cannot use two instances in parallel. " "Please delete the old one first before creating a new one. " "You can ignore this warning if the previous instance is already " - "out of scope, Python does not always destroy old instances immediately.", + "out of scope; Python does not always destroy old instances immediately.", AliveInstanceWarning, stacklevel=3, ) @@ -652,12 +654,12 @@ def __init__(self, seed=None, **kwargs): self._once(**kwargs) # Set standard long lived particles as stable + self._stable = set() for pid in long_lived: self.set_stable(pid) def __del__(self): - if self.pyname in self._alive_instances: - self._alive_instances.remove(self.pyname) + self._alive_instances -= {self.pyname} def __call__(self, kin, nevents): """Generator function (in python sence) diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index 80c94d69..8756a49b 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -104,7 +104,9 @@ def _cross_section(self, kin, precision=None): self._set_kinematics(kin) assert kin.p2.A >= 1 # should be guaranteed by MCRun._validate_kinematics - if kin.p1.A: # nuclear projectile + projectile_id = 2212 if kin.p1.is_nucleus else kin.p1 + + if (kin.p2.is_nucleus and kin.p2.A > 1) or (kin.p1.is_nucleus and kin.p1.A > 1): if precision is not None: saved = self._lib.dtglgp.jstatb # Set number of trials for Glauber model integration @@ -112,9 +114,7 @@ def _cross_section(self, kin, precision=None): self._lib.dt_xsglau( kin.p1.A or 1, kin.p2.A or 1, - self._lib.idt_icihad(2212) - if (kin.p1.A and kin.p1.A > 1) - else self._lib.idt_icihad(kin.p1), + projectile_id, 0, 0, kin.ecm, @@ -126,14 +126,10 @@ def _cross_section(self, kin, precision=None): self._lib.dtglgp.jstatb = saved return CrossSectionData(inelastic=self._lib.dtglxs.xspro[0, 0, 0]) - # other projectile - if kin.p2.A and kin.p2.A > 1: - # DT_XSHN: cross sections not implemented for proj/target 14 0 - return CrossSectionData() - - stot, sela = self._lib.dt_xshn( - self._lib.idt_icihad(kin.p1), self._lib.idt_icihad(kin.p2), 0.0, kin.ecm - ) + assert kin.p1.is_hadron + assert kin.p2.is_hadron + target_id = self._lib.idt_icihad(kin.p2) + stot, sela = self._lib.dt_xshn(projectile_id, target_id, 0.0, kin.ecm) return CrossSectionData(total=stot, elastic=sela, inelastic=stot - sela) # TODO set more cross-sections diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index d19c28ba..a2bc925d 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -15,6 +15,10 @@ class MCRunRemote(MCRun): Calls to Model.cross_section and Model.__call__ are executed in a separate process with a fresh instance of the underlying generator to work around initialization issues. + + If the parameter timeout is set to zero or negative values, the remote + control feature is disabled. This may be required when you want to run + the models in parallel in a Notebook with joblib, and possibly elsewhere. """ # This is a big hack, but required until a proper solution is available. @@ -32,35 +36,35 @@ class MCRunRemote(MCRun): # everytime we create a new process. So this functionality relies strongly on correct # RPNG state persistence. - def __init__(self, seed=None, timeout=100, **kwargs): - super().__init__(seed, **kwargs) - self._remote = _RemoteCall(self, timeout) - def __call__(self, kin, nevents): - with self._remote("call", kin, nevents) as rc: - for _ in range(nevents): - yield rc.get() + if self._timeout > 0: + with _RemoteCall(self, self._timeout, "call", kin, nevents) as rc: + for _ in range(nevents): + yield rc.get() + else: + return super().__call__(kin, nevents) def cross_section(self, kin, **kwargs): - with self._remote("cross_section", kin, **kwargs) as rc: - return rc.get() + if self._timeout > 0: + with _RemoteCall(self, self._timeout, "cross_section", kin, **kwargs) as rc: + return rc.get() + else: + return super().cross_section(kin, **kwargs) class _RemoteCall: - def __init__(self, parent, timeout): + def __init__(self, parent, timeout, method, *args, **kwargs): self.parent = parent - self.timeout = timeout - - def __enter__(self): - return self - - def __call__(self, method, *args, **kwargs): ctx = mp.get_context("spawn") # Queue should only hold one item at a time self.output = ctx.Queue(maxsize=1) self.stop = ctx.Event() - p = self.parent - state = (p.seed, p._init_kwargs, p.get_stable(), p.random_state) + state = ( + parent.seed, + parent._init_kwargs, + parent.get_stable(), + parent.random_state, + ) self.process = ctx.Process( target=_run, args=( @@ -75,6 +79,8 @@ def __call__(self, method, *args, **kwargs): daemon=True, ) self.process.start() + + def __enter__(self): return self def __exit__(self, exc_type, exc_val, tb): @@ -121,7 +127,8 @@ class RemoteException(Exception): def _run(output, stop, Model, state, method, args, kwargs): seed, init_kwargs, stable_state, random_state = state - model = Model(seed, **init_kwargs) + # timeout = 0 disables remote call, we don't want this to be recursive + model = Model(seed, timeout=0, **init_kwargs) model.random_state = random_state try: if method == "call": @@ -129,9 +136,7 @@ def _run(output, stop, Model, state, method, args, kwargs): model.set_stable(k, True) for k in model.get_stable() - stable_state: model.set_stable(k, False) - # we must explicitly call MCRun.__call__ here, - # otherwise we would get MCRunRemote.__call__ - for x in MCRun.__call__(model, *args, **kwargs): + for x in model(*args, **kwargs): # If iteration is stopped in the main thread, # we must stop it also here, to get our # random_state below @@ -142,9 +147,7 @@ def _run(output, stop, Model, state, method, args, kwargs): # maybe the process.Queue does not use pickling. output.put(x.copy()) else: - # same as above - meth = getattr(MCRun, method) - x = meth(model, *args, **kwargs) + x = getattr(model, method)(*args, **kwargs) output.put(x) except Exception as exc: diff --git a/tests/test_cross_section.py b/tests/test_cross_section.py index 2613a5e7..db12b9ab 100644 --- a/tests/test_cross_section.py +++ b/tests/test_cross_section.py @@ -15,7 +15,7 @@ def test_cross_section(Model, projectile, target): pytest.skip("Simulating nuclei in Pythia8 is very time-consuming") # cannot use Argon in SIBYLL, so make air from N, O only - p2 = CompositeTarget((("N", 0.78), ("O", 0.22))) + p2 = CompositeTarget((("N", 0.8), ("O", 0.2))) kin = CenterOfMass(1 * TeV, p1, p2) @@ -27,44 +27,74 @@ def test_cross_section(Model, projectile, target): return # known issues - if Model.name == "DPMJET-III" and not kin.p1.is_nucleus and kin.p2.is_nucleus: - pytest.xfail("DPMJet rejects h-A, although it can do h-p, p-A, and A-A") - if Model.name == "SIBYLL" and (kin.p2.A or 1) > 1: + pyname = Model.pyname + if pyname.startswith("Sibyll") and (kin.p2.A or 1) > 1: pytest.xfail("h-A should work, but need to be fixed in Sibyll") - if Model.pyname == "QGSJet01d" and (kin.p1.A or 1) > 1: + if pyname == "QGSJet01d" and (kin.p1.A or 1) > 1: pytest.xfail("no support for nuclear projectiles in QGSJet01d") - if Model.pyname == "Pythia8" and (kin.p1.A or 1) > 1 or (kin.p2.A or 1) > 1: + if pyname == "Pythia8" and ((kin.p1.A or 1) > 1 or (kin.p2.A or 1) > 1): pytest.xfail("A-A and h-A cross-sections are not yet supported for Pythia8") + if pyname == "DpmjetIII191" and projectile == "He" and target == "air": + pytest.xfail("DpmjetIII191 returns NaN for (He, air)") + if any( + [ + (pyname == "DpmjetIII193" and kin.p1.is_nucleus and kin.p2.is_nucleus), + (pyname.startswith("Dpmjet") and abs(kin.p1) == 211), + ] + ): + pytest.xfail(f"{Model.pyname} aborts on ({projectile}, {target})") c = model.cross_section(kin) if Model is im.UrQMD34: # only provides total cross-section + # HD: these values are "accepted, but make no sense expected = { - ("pi-", "p"): 0, + ("pi-", "p"): None, ("pi-", "air"): 1200, - ("p", "p"): 0, + ("p", "p"): None, ("p", "air"): 1200, ("He", "p"): 900, ("He", "air"): 1400, + ("pi-", "O"): 1266, + ("He", "O"): 1500, + ("p", "O"): 1200, }[(projectile, target)] + if expected is None: + pytest.xfail("UrQMD34 returns 0 for p p and pi- p") + assert c.total == pytest.approx(expected, rel=0.1) return - # p-A should be roughly A^(2/3) * p-p - # pi-p should be roughly (2/3)^(2/3) * p-p + # These are "average" values derived from the output + # of several models. They just indicate that the + # model gives an answer in the expected ballpark. + # + # Models seem to agree on p-p, so we base other + # reference values on that. + # - p-A should be roughly A^(2/3) * p-p + # - pi-p should be roughly (2/3)^(2/3) * p-p + # - gamma-p should be 1/137 * (1/3)^(2/3), assuming alpha=1 + pp = 53 + power = 2 / 3 # volume to area + f_pi = (2 / 3) ** power + f_O = 16**power + f_He = 4**power + f_air = 0.8 * 14**power + 0.2 * f_O + f_gamma = (1 / 3) ** power / 137 expected = { - ("gamma", "p"): 0.21, - ("pi-", "p"): 40, - ("pi-", "O"): 180, - ("pi-", "air"): 150, - ("p", "p"): 53, - ("p", "O"): 240, - ("p", "air"): 220, - ("He", "p"): 130, - ("He", "air"): 350, - ("He", "O"): 380, + ("gamma", "p"): pp * f_gamma, + ("pi-", "p"): pp * f_pi, + ("pi-", "O"): pp * f_pi * f_O, + ("pi-", "air"): pp * f_pi * f_air, + ("p", "p"): pp, + ("p", "O"): pp * f_O, + ("p", "air"): pp * f_air, + ("He", "p"): pp * f_He, + ("He", "air"): pp * f_He * f_air, + ("He", "O"): pp * f_He * f_O, }[(projectile, target)] - assert c.inelastic == pytest.approx(expected, rel=0.3) + # large tolerance to accommodate substantial differences between models + assert c.inelastic == pytest.approx(expected, rel=0.6) From fd69c9b678f22907a61d36dd78ce109ae0a72337 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 17:14:03 +0100 Subject: [PATCH 28/35] fix remotecall --- src/impy/common.py | 2 +- src/impy/remote_control.py | 18 +++++++++++------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/src/impy/common.py b/src/impy/common.py index d7957a3b..c46d8379 100644 --- a/src/impy/common.py +++ b/src/impy/common.py @@ -609,7 +609,7 @@ class MCRun(ABC): # are forwarded to `_once`. # # timeout parameter is used MCRunRemote and ignored here - def __init__(self, seed=None, timeout: int = -1, **kwargs): + def __init__(self, seed=None, *, timeout: int = -1, **kwargs): import importlib from random import randint diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index a2bc925d..d95cc2c0 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -36,25 +36,29 @@ class MCRunRemote(MCRun): # everytime we create a new process. So this functionality relies strongly on correct # RPNG state persistence. + def __init__(self, seed=None, *, timeout=100, **kwargs): + super().__init__(seed, timeout=timeout, **kwargs) + def __call__(self, kin, nevents): - if self._timeout > 0: - with _RemoteCall(self, self._timeout, "call", kin, nevents) as rc: + if self._timeout: + with _RemoteCall(self, "call", kin, nevents) as rc: for _ in range(nevents): yield rc.get() else: - return super().__call__(kin, nevents) + super().__call__(kin, nevents) def cross_section(self, kin, **kwargs): - if self._timeout > 0: - with _RemoteCall(self, self._timeout, "cross_section", kin, **kwargs) as rc: + if self._timeout: + with _RemoteCall(self, "cross_section", kin, **kwargs) as rc: return rc.get() else: - return super().cross_section(kin, **kwargs) + super().cross_section(kin, **kwargs) class _RemoteCall: - def __init__(self, parent, timeout, method, *args, **kwargs): + def __init__(self, parent, method, *args, **kwargs): self.parent = parent + self.timeout = parent._timeout ctx = mp.get_context("spawn") # Queue should only hold one item at a time self.output = ctx.Queue(maxsize=1) From 74cc7150a6f64db28ce41353331bc8d585ac8022 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 17:24:08 +0100 Subject: [PATCH 29/35] fix --- src/impy/remote_control.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index d95cc2c0..1a9af308 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -40,19 +40,19 @@ def __init__(self, seed=None, *, timeout=100, **kwargs): super().__init__(seed, timeout=timeout, **kwargs) def __call__(self, kin, nevents): - if self._timeout: + if self._timeout > 0: with _RemoteCall(self, "call", kin, nevents) as rc: for _ in range(nevents): yield rc.get() else: - super().__call__(kin, nevents) + return super().__call__(kin, nevents) def cross_section(self, kin, **kwargs): - if self._timeout: + if self._timeout > 0: with _RemoteCall(self, "cross_section", kin, **kwargs) as rc: return rc.get() else: - super().cross_section(kin, **kwargs) + return super().cross_section(kin, **kwargs) class _RemoteCall: From fda357401425d8182cbffebbe49c9fd1974047b6 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 17:32:32 +0100 Subject: [PATCH 30/35] fix --- src/impy/remote_control.py | 14 ++++++++----- tests/test_remote_control.py | 40 +++++++++++++++++++++++++----------- 2 files changed, 37 insertions(+), 17 deletions(-) diff --git a/src/impy/remote_control.py b/src/impy/remote_control.py index 1a9af308..255d00b2 100644 --- a/src/impy/remote_control.py +++ b/src/impy/remote_control.py @@ -45,7 +45,8 @@ def __call__(self, kin, nevents): for _ in range(nevents): yield rc.get() else: - return super().__call__(kin, nevents) + for x in super().__call__(kin, nevents): + yield x def cross_section(self, kin, **kwargs): if self._timeout > 0: @@ -131,8 +132,8 @@ class RemoteException(Exception): def _run(output, stop, Model, state, method, args, kwargs): seed, init_kwargs, stable_state, random_state = state - # timeout = 0 disables remote call, we don't want this to be recursive - model = Model(seed, timeout=0, **init_kwargs) + # setting timeout=0 is not enough to disable remote call + model = Model(seed, **init_kwargs) model.random_state = random_state try: if method == "call": @@ -140,7 +141,8 @@ def _run(output, stop, Model, state, method, args, kwargs): model.set_stable(k, True) for k in model.get_stable() - stable_state: model.set_stable(k, False) - for x in model(*args, **kwargs): + # We must call MCRun.__call__ here + for x in MCRun.__call__(model, *args, **kwargs): # If iteration is stopped in the main thread, # we must stop it also here, to get our # random_state below @@ -151,7 +153,9 @@ def _run(output, stop, Model, state, method, args, kwargs): # maybe the process.Queue does not use pickling. output.put(x.copy()) else: - x = getattr(model, method)(*args, **kwargs) + # We must call MCRun. here + meth = getattr(MCRun, method) + x = meth(model, *args, **kwargs) output.put(x) except Exception as exc: diff --git a/tests/test_remote_control.py b/tests/test_remote_control.py index eb1e55a2..c5610748 100644 --- a/tests/test_remote_control.py +++ b/tests/test_remote_control.py @@ -57,14 +57,18 @@ def __repr__(self): return f"" -def test_dummy_1(): - model = Dummy(seed=1) +@pytest.mark.parametrize("timeout", (100, 0)) +def test_dummy_1(timeout): + model = Dummy(seed=1, timeout=timeout) assert model.random_state.counter == 0 kin = CenterOfMass(100, "p", "p") events = [] for event in model(kin, 5): - assert model.random_state.counter == 0 + if timeout: + assert model.random_state.counter == 0 + else: + assert model.random_state.counter > 0 events.append(event) expected = [DummyEvent(None, kin, i + 1) for i in range(5)] @@ -73,13 +77,17 @@ def test_dummy_1(): assert model.random_state.counter == 5 -def test_dummy_2(): - model = Dummy(seed=1) +@pytest.mark.parametrize("timeout", (100, 0)) +def test_dummy_2(timeout): + model = Dummy(seed=1, timeout=timeout) kin = CenterOfMass(100, "p", "p") events = [] for i, event in enumerate(model(kin, 10)): - assert model.random_state.counter == 0 + if timeout: + assert model.random_state.counter == 0 + else: + assert model.random_state.counter > 0 if i == 3: break events.append(event) @@ -90,15 +98,19 @@ def test_dummy_2(): assert model.random_state.counter >= 3 -def test_dummy_3(): - model = Dummy(seed=1, raise_at=3) +@pytest.mark.parametrize("timeout", (100, 0)) +def test_dummy_3(timeout): + model = Dummy(seed=1, timeout=timeout, raise_at=3) kin = CenterOfMass(100, "p", "p") events = [] with pytest.raises(ValueError): - assert model.random_state.counter == 0 for event in model(kin, 10): + if timeout: + assert model.random_state.counter == 0 + else: + assert model.random_state.counter > 0 events.append(event) expected = [DummyEvent(None, kin, i + 1) for i in range(3)] @@ -107,15 +119,19 @@ def test_dummy_3(): assert model.random_state.counter >= 3 -def test_dummy_4(): - model = Dummy(seed=1) +@pytest.mark.parametrize("timeout", (100, 0)) +def test_dummy_4(timeout): + model = Dummy(seed=1, timeout=timeout) kin = CenterOfMass(100, "p", "p") events = [] with pytest.raises(ValueError): - assert model.random_state.counter == 0 for i, event in enumerate(model(kin, 10)): + if timeout: + assert model.random_state.counter == 0 + else: + assert model.random_state.counter > 0 if i == 3: raise ValueError() events.append(event) From 4f662f10f22221a1dcdf5fa34c0791e15fad3e5c Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 17:34:01 +0100 Subject: [PATCH 31/35] fix --- tests/test_remote_control.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/test_remote_control.py b/tests/test_remote_control.py index c5610748..5b82abc4 100644 --- a/tests/test_remote_control.py +++ b/tests/test_remote_control.py @@ -45,7 +45,8 @@ def _generate(self): return True def _cross_section(self, kin): - return CrossSectionData() + self._lib.crranma4.ntot += 1 + return CrossSectionData(inelastic=float(self._lib.crranma4.ntot)) def _set_kinematics(self, kin): self._kin = kin @@ -76,6 +77,9 @@ def test_dummy_1(timeout): assert model.random_state.counter == 5 + c = model.cross_section(kin) + assert c.inelastic == 5 + @pytest.mark.parametrize("timeout", (100, 0)) def test_dummy_2(timeout): From 367d90c7d37df6b44e7912511cd4333d27559200 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Tue, 3 Jan 2023 17:39:29 +0100 Subject: [PATCH 32/35] check more stuff --- tests/test_remote_control.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/tests/test_remote_control.py b/tests/test_remote_control.py index 5b82abc4..b8bda99a 100644 --- a/tests/test_remote_control.py +++ b/tests/test_remote_control.py @@ -46,7 +46,9 @@ def _generate(self): def _cross_section(self, kin): self._lib.crranma4.ntot += 1 - return CrossSectionData(inelastic=float(self._lib.crranma4.ntot)) + if self.raise_at is not None: + raise ValueError("from raise_at") + return CrossSectionData(inelastic=float(self._lib.crranma4.ntot[0])) def _set_kinematics(self, kin): self._kin = kin @@ -78,7 +80,8 @@ def test_dummy_1(timeout): assert model.random_state.counter == 5 c = model.cross_section(kin) - assert c.inelastic == 5 + assert c.inelastic == 6 + assert model.random_state.counter == 6 @pytest.mark.parametrize("timeout", (100, 0)) @@ -121,6 +124,12 @@ def test_dummy_3(timeout): assert events == expected assert model.random_state.counter >= 3 + prev = int(model.random_state.counter) + + with pytest.raises(ValueError): + model.cross_section(kin) + + assert model.random_state.counter == prev + 1 @pytest.mark.parametrize("timeout", (100, 0)) From 9b401fea682045dc6c42046b652a428784a44fcd Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 4 Jan 2023 17:21:21 +0100 Subject: [PATCH 33/35] make naming in sibyll more consistent --- src/impy/models/sibyll.py | 50 ++++++++++++++++----------------------- 1 file changed, 21 insertions(+), 29 deletions(-) diff --git a/src/impy/models/sibyll.py b/src/impy/models/sibyll.py index 74f26f7c..6aa3a5a1 100644 --- a/src/impy/models/sibyll.py +++ b/src/impy/models/sibyll.py @@ -1,5 +1,5 @@ from impy.common import MCRun, MCEvent, CrossSectionData -from impy.util import info, Nuclei +from impy.util import Nuclei from impy.kinematics import EventFrame from impy.remote_control import MCRunRemote from particle import literals as lp @@ -34,17 +34,6 @@ class SIBYLLRun: _event_class = SibyllEvent _frame = EventFrame.CENTER_OF_MASS _targets = Nuclei(a_max=20) - _cross_section_projectiles = { - p.pdgid: sib_id - for p, sib_id in ( - (lp.p, 1), - (lp.n, 1), - (lp.pi_plus, 2), - (lp.K_plus, 3), - (lp.K_S_0, 3), - (lp.K_L_0, 3), - ) - } def _once(self): from impy import debug_level @@ -61,7 +50,8 @@ def _once(self): self._lib.pdg_ini() def _cross_section(self, kin): - if kin.p2.A > 1: + self._set_kinematics(kin) + if self._target_a > 1: # TODO figure out what this returns exactly: # self._lib.sib_sigma_hnuc warnings.warn( @@ -70,9 +60,9 @@ def _cross_section(self, kin): ) return CrossSectionData() - sib_id = self._cross_section_projectiles[abs(kin.p1)] - - tot, el, inel, diff, _, _ = self._lib.sib_sigma_hp(sib_id, kin.ecm) + tot, el, inel, diff, _, _ = self._lib.sib_sigma_hp( + self._projectile_class, self._ecm + ) return CrossSectionData( total=tot, elastic=el, @@ -86,25 +76,28 @@ def _cross_section(self, kin): def sigma_inel_air(self): """Inelastic cross section according to current event setup (energy, projectile, target)""" - kin = self.kinematics - sib_id = self._cross_section_projectiles[abs(kin.p1)] - sigma = self._lib.sib_sigma_hair(sib_id, self._ecm) + sigma = self._lib.sib_sigma_hair(self._projectile_class, self._ecm) if isinstance(sigma, tuple): return sigma[0] return sigma def _set_kinematics(self, kin): - self._production_id = self._lib.isib_pdg2pid(kin.p1) - assert self._production_id != 0 - self._kin = kin + self._projectile_id = self._lib.isib_pdg2pid(kin.p1) + self._projectile_class = { + lp.p.pdgid: 1, + lp.n.pdgid: 1, + lp.pi_plus.pdgid: 2, + lp.K_plus.pdgid: 3, + lp.K_S_0.pdgid: 3, + lp.K_L_0.pdgid: 3, + }[abs(kin.p1)] + self._target_a = kin.p2.A + assert self._projectile_id != 0 + assert self._target_a >= 1 + self._ecm = kin.ecm def _set_stable(self, pdgid, stable): sid = abs(self._lib.isib_pdg2pid(pdgid)) - if abs(pdgid) == 311: - info(1, "Ignores K0. Using K0L/S instead") - self.set_stable(130, stable) - self.set_stable(310, stable) - return idb = self._lib.s_csydec.idb if sid == 0 or sid > idb.size - 1: return @@ -114,8 +107,7 @@ def _set_stable(self, pdgid, stable): idb[sid - 1] = abs(idb[sid - 1]) def _generate(self): - k = self._kin - self._lib.sibyll(self._production_id, k.p2.A, k.ecm) + self._lib.sibyll(self._projectile_id, self._target_a, self._ecm) self._lib.decsib() self._lib.sibhep() return True From 12ede78e660eda87de127205b16f3879d950089d Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 4 Jan 2023 17:23:36 +0100 Subject: [PATCH 34/35] fix my bug introduced to dpmjet --- src/impy/models/dpmjetIII.py | 72 +++++++++++++++++++----------------- tests/test_cross_section.py | 11 +----- 2 files changed, 40 insertions(+), 43 deletions(-) diff --git a/src/impy/models/dpmjetIII.py b/src/impy/models/dpmjetIII.py index 8756a49b..85bfb298 100644 --- a/src/impy/models/dpmjetIII.py +++ b/src/impy/models/dpmjetIII.py @@ -1,7 +1,7 @@ from impy.common import MCEvent, CrossSectionData from impy.remote_control import MCRunRemote as MCRun from impy.kinematics import EventFrame -from impy.util import info, _cached_data_dir, fortran_chars, Nuclei +from impy.util import info, _cached_data_dir, fortran_chars, Nuclei, is_real_nucleus from impy.constants import standard_projectiles, GeV @@ -60,8 +60,6 @@ class DpmjetIIIRun(MCRun): + "/releases/download/zipped_data_v1.0/dpm3191_v001.zip" ) _ecm_min = 1 * GeV - _max_A1 = 0 - _max_A2 = 0 def _once(self): data_dir = _cached_data_dir(self._data_url) @@ -104,17 +102,15 @@ def _cross_section(self, kin, precision=None): self._set_kinematics(kin) assert kin.p2.A >= 1 # should be guaranteed by MCRun._validate_kinematics - projectile_id = 2212 if kin.p1.is_nucleus else kin.p1 - - if (kin.p2.is_nucleus and kin.p2.A > 1) or (kin.p1.is_nucleus and kin.p1.A > 1): + if is_real_nucleus(kin.p2) or is_real_nucleus(kin.p1): if precision is not None: saved = self._lib.dtglgp.jstatb # Set number of trials for Glauber model integration self._lib.dtglgp.jstatb = precision self._lib.dt_xsglau( - kin.p1.A or 1, - kin.p2.A or 1, - projectile_id, + self._a1, + self._a2, + self._projectile_id, 0, 0, kin.ecm, @@ -129,47 +125,53 @@ def _cross_section(self, kin, precision=None): assert kin.p1.is_hadron assert kin.p2.is_hadron target_id = self._lib.idt_icihad(kin.p2) - stot, sela = self._lib.dt_xshn(projectile_id, target_id, 0.0, kin.ecm) + stot, sela = self._lib.dt_xshn(self._projectile_id, target_id, 0.0, kin.ecm) return CrossSectionData(total=stot, elastic=sela, inelastic=stot - sela) # TODO set more cross-sections def _set_kinematics(self, kin): + self._a1 = kin.p1.A or 1 + self._z1 = kin.p1.Z or 0 + self._a2 = kin.p2.A or 1 + self._z2 = kin.p2.Z or 0 + # Save maximal mass that has been initialized # (DPMJET sometimes crashes if higher mass requested than initialized) - # Relax momentum and energy conservation checks at very high energies - if kin.ecm > 5e4: - # Relative allowed deviation - self._lib.pomdls.parmdl[74] = 0.05 - # Absolute allowed deviation - self._lib.pomdls.parmdl[75] = 0.05 - - if not self._max_A1: - # only do this once + if not hasattr(self, "_a1_max"): # only do this once if kin.frame == EventFrame.FIXED_TARGET: self._lib.dtflg1.iframe = 1 self._frame = EventFrame.FIXED_TARGET else: self._lib.dtflg1.iframe = 2 self._frame = EventFrame.CENTER_OF_MASS - self._max_A1 = kin.p1.A or 1 - self._max_A2 = kin.p2.A or 1 + self._a1_max = self._a1 + self._a2_max = self._a2 + self._lib.dt_init( -1, max(kin.plab, 100.0), - self._max_A1, - kin.p1.Z or 0, - self._max_A2, - kin.p2.Z or 0, + self._a1_max, + self._z1, + self._a2_max, + self._z2, kin.p1, iglau=0, ) - if (kin.p1.A or 1) > self._max_A1 or (kin.p2.A or 1) > self._max_A2: + # Relax momentum and energy conservation checks at very high energies. + # Must be called after dt_init, which sets pomdls.parmdl. + if kin.ecm > 5e4: + # Relative allowed deviation + self._lib.pomdls.parmdl[74] = 0.05 + # Absolute allowed deviation + self._lib.pomdls.parmdl[75] = 0.05 + + if self._a1 > self._a1_max or self._a2 > self._a2_max: raise ValueError( "Maximal initialization mass exceeded " - f"{kin.p1.A}/{self._max_A1}, {kin.p2.A}/{self._max_A2}" + f"{self._a1}/{self._a1_max}, {self._a2}/{self._a2_max}" ) # AF: No idea yet, but apparently this functionality was around?! @@ -178,6 +180,10 @@ def _set_kinematics(self, kin): # print 'OK' self._kin = kin + # projectile id for _generate and _cross_section + self._projectile_id = self._lib.idt_icihad( + 2212 if is_real_nucleus(kin.p1) else kin.p1 + ) def _set_stable(self, pdgid, stable): kc = self._lib.pycomp(pdgid) @@ -186,13 +192,11 @@ def _set_stable(self, pdgid, stable): def _generate(self): k = self._kin reject = self._lib.dt_kkinc( - k.p1.A or 1, - k.p1.Z or 0, - k.p2.A or 1, - k.p2.Z or 0, - self._lib.idt_icihad(2212) - if (k.p1.A and k.p1.A > 1) - else self._lib.idt_icihad(k.p1), + self._a1, + self._z1, + self._a2, + self._z2, + self._projectile_id, k.elab, kkmat=-1, ) diff --git a/tests/test_cross_section.py b/tests/test_cross_section.py index db12b9ab..9be24ffa 100644 --- a/tests/test_cross_section.py +++ b/tests/test_cross_section.py @@ -34,15 +34,8 @@ def test_cross_section(Model, projectile, target): pytest.xfail("no support for nuclear projectiles in QGSJet01d") if pyname == "Pythia8" and ((kin.p1.A or 1) > 1 or (kin.p2.A or 1) > 1): pytest.xfail("A-A and h-A cross-sections are not yet supported for Pythia8") - if pyname == "DpmjetIII191" and projectile == "He" and target == "air": - pytest.xfail("DpmjetIII191 returns NaN for (He, air)") - if any( - [ - (pyname == "DpmjetIII193" and kin.p1.is_nucleus and kin.p2.is_nucleus), - (pyname.startswith("Dpmjet") and abs(kin.p1) == 211), - ] - ): - pytest.xfail(f"{Model.pyname} aborts on ({projectile}, {target})") + if pyname.startswith("DpmjetIII19") and target == "air": + pytest.xfail(f"{pyname} returns NaN for (any, air)") c = model.cross_section(kin) From 7a2da7524e60f0067918609490a1dccecd8ed8c0 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 4 Jan 2023 17:34:51 +0100 Subject: [PATCH 35/35] examples --- examples/cross_section.ipynb | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/examples/cross_section.ipynb b/examples/cross_section.ipynb index da54c0a1..b8ed394b 100644 --- a/examples/cross_section.ipynb +++ b/examples/cross_section.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -48,12 +48,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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Kyb0Ww4z//Ufqcf/+Y3zv3r2n1rWwMM7ngoODXyiOTZs2Ub169Scee/w5YYECBQyT6ty5c8co8fDz8yMpKYno6GgGDRrEiBEjqFWrFvb29gwaNIiuXbs+M4558+bRvn17w/6iRYsMHWfd3d0z/E3vzp07+Pr6GsV8586dp9ZfvXo1Y8eO5fr16wDEx8f/57fuHGno018fNP9KGAZd/Y+6//rO0f/Mc4fg7u5OZGQkWq023e/F3bt38fDwwM3NDYDw8HD8/PwADH2TcuXKZeiU6uvry9y5cwG4fPkybdu2pX///ixbtszovKGhoezatQsXFxdDWWpq6n++X57G0dGRuLg4w35cXByOjo7p6tnZ2dGlSxfy5s3LuXPncHd3f+a5X/Q9vXfvXgYPHsyFCxfQ6XQkJia+fu9pkek2btxIixYtSE5O5pdffuHDDz9Eo9GYOyyTeS1aUBwcHJ665cqV67nr2tnZpatrKjdv3jT6d968eQHw8fEhNDTUcCw0NJRcuXLh5uaGk5MTM2fOJDQ0lEWLFtG3b1/Dh/zLql27NqGhoZw8efK56j/pl+FJMfv4+DyxfnJyMm3atGHChAlERUURExODp6en4YPutWHj8PTNOtcL1LVLX/c5ValSBSsrKzZs2GBUfvLkSUJCQqhVqxbFixfH29ubtWvXPvd5ixYtSufOnTl79my6Y/ny5aN+/frExMQYtsTERNq2bQs8+f31NCVLluTMmX8SsrNnz1KqVKkn1lVKER8fz+3bt5/r3C/yngbo0KEDnTp1Ijw8nJiYGCpWrPj6vadFpvrjjz9o1qwZycnJNG3alN9//z1HJSfwmiQo2cGiRYu4cuUKsbGxTJw4kVatWgHw0UcfsWDBAi5cuEBiYiLDhg2jVatWaDQaNmzYwPXr11FK4ezsjEajMfrmm5SUZLQ9j+LFi9OlSxfatGnDgQMHSEtLIzY2lilTprBx48Z09T08PNDpdEZDMj/66CPGjRvH/fv3uXnzJtOmTaN169aAvo/PrVu3SEtLA/QJSkpKCp6engDMnDmTiIiIl3sRRYa4urry1Vdf0atXL3bv3k1aWhqnT5+mffv2tGvXzvDIIigoiJEjR/Lrr78SHx+PTqfjyJEjaLVaAO7fv8+YMWMIDg5GKcXt27dZtmwZlSpVSnfNxo0bc+LECVavXk1aWhoPHz5k8+bNxMbGAvr3y/O2VLZv35558+Zx/fp17t69y7Rp0+jYsSMAx48fZ/fu3aSkpJCYmMjgwYNxcXGhePHiTzzXv6/70UcfMXXqVG7fvk10dDTjxo0zek9HRESQmJhoqB8fH4+7uzvW1tasXLmSY8eOPdc9CPE8fvnlF1q1akVqaiqtW7fm999/z/RRcmaRmb11M8uLjuLJCvz8/AyjER5tQ4cOVUrpR9YMGzZMlS9fXjk7O6v27durxMREw89+++23yt/fX7m7u6s2bdqo6OhopZRS06ZNU76+vsrBwUEFBASo+fPnK6X0o3iAdNvNmzefOYpHKf0oh4kTJ6rChQsre3t75e/vr/r27fvUXuFDhgxR7u7uytnZWYWEhKgHDx6oTz75RHl6eqp8+fKpUaNGKa1Wq5RSKikpSdWvX1+5uLioMmXKKKWUmjp1qnJ3d1ceHh5qxIgRqlixYoaRPK/NKJ4sZObMmapIkSLK2tpaAap79+7pRrutXLlSVa5cWdnZ2SlPT09VrVo1tWTJEqXValVCQoLq0KGDKlCggHJwcFB58+ZVXbp0MbxvFy5cqOrWrWs416lTp1SdOnWUq6ur8vT0VM2bNzeMVFu5cqXKly+fcnZ2Vr/++ut/juJRSqmJEycqd3d35eLiogYNGqR0Op1SSqmDBw+q8uXLK0dHR+Xm5qbq16+vTp069dTX4LvvvlOenp7K2dlZ7dmzR6Wlpanhw4crHx8f5eXlpXr37m30mnTo0EG5uroqZ2dnpZRSS5YsUT4+PsrZ2Vl1795d1a1bVy1cuFApJaN4RMbMmzdPaTQaBajOnTurtLQ0c4f0Ql7k81tWM84CAgMD6datm1G/ECGygkGDBnHo0CG2bt1qsm9o3333HXv37k3XH0UI8Wz9+/dn5syZ9O7dm2+//TZd38isTlYzFkKYxKRJk2jatCnHjx83yfmSk5NZu3btcw9jF0IYmzZtGitWrGDWrFnZLjl5UTn77oQQGWJhYcHAgQOpWrVqhs8VGhqKp6cn9vb2fPLJJyaIToicTynFwoULDXNDWVhY0LJlyxzXIfZJXjhB2b17N02aNMHHxweNRsPq1aufWveTTz5Bo9EwY8YMo/Lo6GjatWuHk5MTLi4udO3a9T+HAud0O3fulMc7Isfz9fUlNjaW1atX54hHs0JkNp1OR79+/ejSpQtt27Z97UaCvXCCkpiYSLly5Zg9e/Z/1lu1ahUHDx40DMV7XLt27Th37hxbt25l/fr17N69mx49erxoKEIIIUSOpNVq6datG7NmzUKj0VCvXr3XotXkcS88Udt7772Xbsr3f3s07fqff/6ZbtrsCxcusHnzZo4cOULFihUBmDVrFg0bNuSbb755YkKTnJxsNPX545MxCSGEEDlJamoqHTp0YPny5VhYWLBo0aLXcnFLk/dB0el0dOjQgUGDBj1xkqQDBw7g4uJiSE4A6tati4WFBYcOHXriOYOCgnB2djZs2Xp1RiGEEOIpkpKSaNmyJcuXL8fa2prffvvttUxOIBMSlEmTJmFlZcVnn332xOPh4eGGSbkesbKyws3N7alTrA8ZMoTY2FjD9visq0IIIURO0b59e9atW4etrS2rV69Ot6Dt68Ska/EcO3aMmTNncvz4cZM+K7O1tc2Zs+QJIYQQj+nXrx979uxh6dKl1KlTx9zhmJVJW1D27NnDvXv38PX1xcrKCisrK0JCQhg4cCD+/v6AfhXUfy/Il5aWRnR0dIZX0RWvXmhoqNFCby9r586dFC5cOOMBPcZUsYnXj6Oj40stWPi44OBgrKxMvx6rKWITWcvjo3Nq1KjB9evXX/vkBEycoHTo0IHTp09z8uRJw+bj48OgQYP4888/AahatSoxMTFGa1Ns374dnU5H5cqVTRlOluLv74+9vT2Ojo6Gbc6cOZlyrf/6wxgYGGhYufiRRYsWUbduXcO+VqslKCiIokWL4uDgQEBAAH379iUyMjLd+Xx9fYmJiTFp/C/r3/eWlWLLTubMmUPx4sWxs7PD39+fcePGodPpjOqsWbOGqlWr4uDggJeXFzVq1GDp0qWG42fOnOGdd97B1dUVV1dXqlWrxpEjR4D077f/4u/vz969ew37/05k/33866+/Jk+ePLi5ufHll18a/vAnJCRQvXp13N3dcXFx4Z133uHixYtPvW5CQsITO+y/ap07d2b8+PFGZVklNmEad+/epXbt2pw+fdpQZsqFaLOzF05QEhISDMkHwI0bNzh58iShoaG4u7tTunRpo83a2hpvb2+KFSsGQIkSJWjQoAHdu3fn8OHD7Nu3jz59+tC6desc/0u3ZcsWEhISDFuvXr3MHdIT9ezZk0WLFrFw4UJiYmI4fvw4+fLl4/Dhw+YOTWSyoKAgJk6cyPz584mPj2fNmjUsX76cvn37GuosXryYTp060atXL8LDwwkLC2PKlCn89ddfhjrvv/8+TZs2JSIigrCwMMaOHZvpj2k3btzI7NmzOXjwIOfPn2fTpk389NNPgP4x8YIFC4iIiCA6OprmzZu/th0PRdZx69Ytatasya5du+jYsWO6LwKvvRdd6GfHjh1PXIiuU6dOT6zv5+enpk+fblQWFRWl2rRpoxwdHZWTk5P6+OOPVXx8/HPH8LyLDel0OpWQkJDp26MFyf7Lvxc1e1ytWrXU8OHDVfny5ZWLi4vq2LGjevDggeH47NmzVUBAgPLw8FDt2rUzLKR279491aBBA+Xs7Kzc3d1V69atlVL6xQItLS2feq3/WizwwoULSqPRqGPHjj3znp50LT8/P/XNN9+o4sWLK2dnZ9W7d2+j+t99950qXLiwcnd3Vx07dlQJCQlKKZVuEbgJEyYoX19flTt3blWlShWjhd3Gjx+vvL29Ve7cuVXp0qXVuXPn1NixY5WFhYWytbVVDg4OasKECeliu3fvnmrTpo3KkyePcnd3V4MHD36ue8xUOp1SyQmZvz3HezQmJkbZ2dmpNWvWGJWfOHFCWVhYqKtXryqtVqvy5s2rZs6c+dTz3Lt3TwFG7+HH/XtxytOnT6saNWooFxcX9eabb6ojR44opZTq2rWr0mg0hkU2f/nll/9cLLB169ZGC0suXLhQ1axZM93109LS1Jw5c5Srq+tT74G/F9dUSv87M3LkSFWhQgWVO3du1apVK5WUlGSo+/vvv6uSJUsqV1dX1aRJE3X37l2lVPrfjR9//FEVKVJEOTo6qjJlyhgWxHx0zNfXVzk6OqqiRYuqHTt2qEWLFikrKytlY2OjHBwcVM+ePdPFlpCQoD799FOVN29e5eLiotq3b//UexJZy7Vr15S/v78ClK+vr7py5Yq5Q3olXmSxwBy9mnFCQsITkylTb48+ZP/LsxIUX19fdenSJXX//n0VGBiohg8frpRSasuWLcrb21udO3dOJSQkqBYtWhiSwcGDB6tPP/1UpaamqqSkJLVv3z6lVMYSlDlz5ig/P79n3s8jT0pQqlevriIiItTNmzdVnjx51Pbt25VSSv3222+qdOnSKjg4WD148EC1adNGDRw4UCmVPkFZuXKlunfvnkpJSVEjRoxQ5cqVU0rpE6j8+fOrsLAwpdPp1IULF1RYWNgT7+3fsb377ruqa9euKi4uTj148EDt37//ue8z0yQnKDXKKfO35Ge/Rzdt2qSsrKyeuDqqv7+/mj9/vjp//rwCVHBw8FPPo9PpVKFChVTz5s3V2rVrVWRkpNHxx99v8fHxysfHR61YsUKlpaWpVatWqQIFChhWCv73781/JShly5Y1Sq6OHj2q3N3dja5dpkwZZWVlpTQajQoKCnrqPfw7QSlevLgKDg5W9+/fVyVLllQ//fSTUkqpQ4cOqXz58qnTp0+rlJQUNWjQINWyZUulVPr334YNG1RoaKhKS0tT8+fPV15eXiopKUklJCSo3Llzq8uXLyullAoODlbXr19XSj15Ne/HY+vWrZtq1KiRioiIUCkpKU/9GyOylgsXLigfHx8FqMKFC6uQkBBzh/TKvEiCImvxvELvvfceLi4uhm3Xrl2GYx9//DFFixbFxcWFYcOGsXz5cgCWLVtGjx49KFmyJA4ODkycOJHly5ejlMLa2pqwsDBu3ryJra0t1apVe644evbsaRTH44+aoqKiMtxZuX///nh4eJA/f34CAwM5deoUAD/++CNDhgzBz88POzs7hg4dyooVK554jhYtWpAnTx6sra0ZOnQop0+fJiEhASsrK5KTk7lw4QJarZbixYs/V7y3b99m586dzJw5k9y5c2NnZ2eS9WVyksjISDw8PLC0tEx3zMvLi4iICKKiogCMXvNKlSrh4uKCnZ0dISEhaDQatm/fjqenJ3369MHT05NGjRo9cRqB9evXU6pUKVq2bImlpSXNmjXD09OTgwcPvnD8CQkJRlPoOzk5pVtC4/Tp08TFxbFgwQLKlSv33Ofu1q0bfn5+uLi40KhRI8N7+qeffqJXr16UKVMGa2trRowYwZo1a0hLS0t3joYNG1KgQAEsLS3p3r07Go2GK1euAKDRaDh37hzJycn4+fkREBDwzJh0Oh2LFy9m+vTpeHh4YG1tTfXq1Z/7noR5nDx5kpo1a3Lnzh1KlSrF7t278fX1NXdYWZLpu5hnIfb29q9kjR97e/vnqrdp06an/gF5fPK5AgUKEBYWBsCdO3eMEg8/Pz+SkpKIjo5m0KBBjBgxglq1amFvb8+gQYPo2rXrM+OYN2+e0do/ixYtMnQudXd3f+p8NM/Ly8vL8O/H/x+EhobSs2dPo4QoNTX1iedYsGABM2bM4NatW2g0GpRSREVFUbhwYaZOncrQoUO5fPkyzZs3Z9q0ac9c2+XWrVt4enpmvc5n1vYw9BWMyLB+9nvU3d2dyMhItFptuiTl7t27eHh44ObmBujnM/Lz8wMw9E3KlSuXoVOqr68vc+fOBeDy5cu0bduW/v37s2zZMqPzhoaGsmvXLqPRVqmpqS81SsXR0dFolum4uDgcHR3T1bOzs6NLly7kzZuXc+fO4e7u/sxz//s9HR0dbYh/8eLFTJ482XDcysrqib9Dq1evZuzYsVy/fh2A+Ph4oqKiKF26NEuXLuWbb76hc+fOvPvuu8ycOfOZffIiIiJITk5+rmRGZB3jxo0jIiKCN998kz///BMPDw9zh5Rl5egWFI1Gg4ODQ6Zvppjz5fHJ527evEnevHkB8PHxITQ01HAsNDSUXLly4ebmhpOTEzNnziQ0NJRFixbRt29fwx+/l1W7dm1CQ0MNnaBNKV++fPzvf/8jJibGsCUmJqarFxwcTP/+/fnf//7H/fv3CQsLMyQpoB8tduDAAS5dukRwcDDTpk0D+M//DwUKFCAiIoIHDx6Y/L4yRKMBG4fM357jPVqlShWsrKzYsGGDUfnJkycJCQmhVq1ahhartWvXPvctFi1alM6dO3P27Nl0x/Lly0f9+vXTvSfatm3798vz/L9bJUuW5MyZM4b9s2fPPnE2a9AP64yPj+f27dvPff4nyZcvH+PGjTOK/+HDh+TPn9+oXnJyMm3atGHChAlERUURExODp6en4T3dsGFDtm/fzq1bt7C1tWXo0KHAf99/njx5sLW1JTg4OEP3IF6tRYsW0adPH7Zv3y7JyTPk6AQlO1m0aBFXrlwhNjaWiRMn0qpVKwA++ugjFixYwIULF0hMTGTYsGG0atUKjUbDhg0buH79OkopnJ2d0Wg0Rt98k5KSjLbnUbx4cbp06UKbNm04cOAAaWlpxMbGMmXKFDZu3Jihe+zSpQsTJ07k2rVrAISFhbF58+Z09RISErCwsCBPnjykpaUxatQow7FLly6xc+dOUlJSsLe3x9bW1nDPnp6eT/1j7ePjQ61atfj8889JSEjg4cOHL/UYISdzdXXlq6++olevXuzevZu0tDROnz5N+/btadeuHcWKFcPCwoKgoCBGjhzJr7/+Snx8PDqdjiNHjqDVagG4f/8+Y8aMITg4GKUUt2/fZtmyZVSqVCndNRs3bsyJEydYvXo1aWlpPHz4kM2bNxMbGwv89//Tf2vfvj3z5s3j+vXr3L17l2nTptGxY0cAjh8/zu7du0lJSSExMZHBgwfj4uJC8eLFM/Saffzxx3z33XeGRz7R0dGsWbMmXb3k5GRSUlIMs2jPnDmTiIgIQN86tX79eh4+fIitrS329vbP9Z62sLCgY8eODBgwgKioKFJTU9m3b1+G7kdkjkeP8gBy587NrFmzcHZ2NmNE2YMkKK9QvXr1jOZBGTZsmOFY+/btadWqFX5+fuTLl8/wDapevXoMGTKEhg0b4ufnh7W1NTNmzAD0Tee1a9cmd+7cNGrUiBkzZhia3bVaLXZ2dkbbrVu3nivOefPm0bFjRzp27IizszPly5fn5s2bT/yAeRFt2rSha9euNGrUCCcnJ2rVqsX58+fT1StdujQ9e/akbNmy+Pv7ExAQgI2NDaD/Qz9o0CDc3d3x9fXF2dmZzz//HIC+ffuyaNEiXFxc+Prrr9Od99dffyUmJgZ/f398fX1fqBXgdTFq1Ci+/PJLunXrhr29PeXKlaNatWosWLDAUKdz5878+OOPzJo1Cy8vL/LmzUv//v35+eef8fX1xcbGhmvXrlGzZk1y587NW2+9RbFixZg6darhHI9aBpydndmwYQOzZs3C09MTf39/5s+fb6g3ePBgvvrqK1xcXFiyZMl/xt6oUSM+/fRTKlWqRPHixalfvz5dunQB9I+N+vXrZ3jfnDlzhk2bNhneVy+rWrVqfPPNN3Ts2BEnJyfefPPNJyYJTk5OTJkyhfr16+Pt7W14XAn6viSTJ0/Gy8sLT09Pbt++bZj7pEuXLhw6dChdX7FHpk2bho+PD6VKlcLLy8votRNZw+rVqyldujQTJkwwdyjZjkapx6awyybi4uJwdnYmNjb2mX0PsoPAwEC6detm1C9EiKxg0KBBHDp0iK1bt5psHpPvvvuOvXv3puuPIkROs2TJEjp27IhWq+XDDz9k2bJlWFi83u0CL/L5/Xq/UkKI/zRp0iSaNm3K8ePHTXK+5ORk1q5dy5tvvmmS8wmRVf3www+0b98erVZLx44dWbJkyWufnLwoebWEEE9lYWHBwIEDTTIkOzQ0FE9PT+zt7fnkk09MEJ0QWdPUqVPp3r07Sik++eQTFi5cmCnrMuV08oplATt37jR3CEJkOl9fX0PnVyFyqlGjRjF27FhA/4h00qRJJhnp+TqSFhQhhBDCRB5NEREUFMTkyZMlOckAaUERQgghTOSTTz6hcuXKvPHGG+YOJduTFhQhhBDiJSUlJTFgwAAiIyMNZZKcmIa0oAghhBAvIT4+nmbNmrF9+3aOHDnC7t275ZGOCUmCIoQQQryg6OhoGjZsyKFDh3B0dGTcuHGSnJiYPOIRJqfRaAyz1r733nuGlZlBPzOom5sbFSpUADDMIJrZa1Ls2bPnhVavFeJx/v7+7N27F9D3MXh8ccB/v4dXrFhBvnz5cHR05N69e5kWU2hoqNEii+LVCQsLo1atWhw6dAg3Nze2b99OYGCgucPKeVQ2FBsbqwAVGxtr7lCem5+fn7Kzs1MODg6Gbfbs2ZlyrRs3bihLS8snHqtVq5ZavHixUdnChQvVO++8Y9hPS0tTEydOVEWKFFH29vbK399f9enTR0VERDzX9QF18+bNdOUhISHK0dFRRUVFKaWUSk5OVrly5VKXLl163lt7bn5+fmrPnj0mP+/rYPbs2apYsWIqV65cys/PT40dO1ZptVqjOqtXr1ZVqlRR9vb2ytPTU1WvXl0tWbLEcPz06dOqTp06ysXFRbm4uKiqVauqw4cPK6XSv9/+y7//P+7YsUMVKlToqceDgoKUh4eHcnV1VYMGDVI6nU4ppVR8fLx6++23lZubm3J2dlZ16tRRFy5ceO7X5Gnvpye9hwMCAtSff/753Od+Xk/63RWv3vXr11WhQoUUoPLmzavOnj1r7pCylRf5/JYWlFdoy5YtJCQkGLYnra2RFfTs2ZNFixaxcOFCYmJiOH78OPny5ePw4cMZOm9oaCheXl64ubkBcO/ePVJTUylatOgT66elpWXoeuLFBQUFMXHiRObPn098fDxr1qxh+fLl9O3b11Bn8eLFdOrUiV69ehEeHk5YWBhTpkzhr7/+MtR5//33adq0KREREYSFhTF27FiTTZX/NBs3bmT27NkcPHiQ8+fPs2nTJn766ScAbG1tWbBgAREREURHR9O8eXM6dOiQ4Ws+6T0cGhpKyZIln1hf3tPZm1KKjh07cu3aNQoWLMjevXufumK2MIHMz5dM70VbUBISElRCQoLh25RS+m8+CQkJKikp6Yl1H//GmJKSohISEtTDhw/T1X1e//WNvlatWmr48OGqfPnyysXFRXXs2FE9ePDAcHz27NkqICBAeXh4qHbt2qmYmBillFL37t1TDRo0UM7Ozsrd3V21bt1aKZWxFpQLFy4ojUajjh079tz39uOPP6r8+fMrLy8vNW/ePKMWlEfX2717t8qVK5fSaDTKwcFBde7cWdnb2ytAOTg4qA8++MDw7XjkyJHK3d1dDR06VF29elXVqFFDOTs7q7x586ohQ4YYXXvZsmWqVKlSytHRUZUuXVpdvHhRde3aVWk0GkOL1S+//GL0zXvcuHGqc+fORucJDAw0vC6nT59WNWrUUC4uLurNN99UR44cee7X4qUlJ+i3x96jKjVZX5aa9OS6j7dqpKXoy1Iepq/7nGJiYpSdnZ1as2aNUfmJEyeUhYWFunr1qtJqtSpv3rxq5syZTz3PvXv3FGD0Hn7cv1tQnvZ6P+v/o1LGv1etW7dW48aNM7pOzZo1010/LS1NzZkzR7m6uj71HjZu3KgKFSqkXF1d1ejRo42u06lTJzVu3DgVEhKS7j3s4OCgAGVvb6/eeustw+/i999/r3x8fFS7du1UdHS0ql+/vnJ3d1ceHh6qe/fuRn+Htm3bpipUqKBy586tChcurHbv3q3Gjh2rLCwslK2trXJwcFATJkww+j1fvHixCgwMNLqHzp07G16PkJAQ1bBhQ+Xm5qaKFy+uNm3a9NR7F//t6tWrqm7duur27dvmDiVbepHP79ciQQEUoO7du2coGz9+vAJUt27djOo++oNz48YNQ9n06dMVoNq2bWtU18PD47ljflaC4uvrqy5duqTu37+vAgMD1fDhw5VSSm3ZskV5e3urc+fOqYSEBNWiRQvVqVMnpZRSgwcPVp9++qlKTU1VSUlJat++fUqpjCUoc+bMUX5+fs99X2fOnFG5c+dWBw8eVA8ePFAdOnR4YoKiVPrm+X/HuWPHDmVpaanGjBmjUlJS1IMHD9TVq1fVzp07VWpqqrp8+bIqUKCAWrVqlVJKqb179yp3d3e1d+9epdVq1YULF9SdO3eUUv/9aODy5cvK1dVVpaSkKKWUCgsLU/b29iouLk7Fx8crHx8ftWLFCpWWlqZWrVqlChQokC45NblRTvot4bHHaLsm68vW9DGuO95bXx4d/E/Z/tn6shVdjetOCnjuEDZt2qSsrKxUWlpaumP+/v5q/vz56vz58wpQwcHBTziDnk6nU4UKFVLNmzdXa9euVZGRkUbHH3+/Pev1fpFHPGXLljVKro4eParc3d2Nrl2mTBllZWWlNBqNCgoKemL89+7dU46OjmrdunUqOTlZDRo0SFlaWqZLUJR68u/a4+//GzduKED17NlTPXz4UD148EBFRkaqtWvXqqSkJHXnzh31xhtvqOnTpyullLp27ZrKnTu3WrdunUpLS1MhISHqypUrSqn0v7uPXzsuLk45ODio8PBwpZT+C5iLi4u6fPmy0mq1qmzZsmrmzJkqNTVV7d+/X3l4eBjqimd79FhaZJw84smi3nvvPVxcXAzbrl27DMc+/vhjihYtiouLC8OGDTN0LF22bBk9evSgZMmSODg4MHHiRJYvX45SCmtra8LCwrh58ya2trZUq1btueLo2bOnURyPP2qKiorC29v7ue9p5cqVtGjRgsqVK2NnZ8fIkSOf+2efxNbWlqFDh2JtbY2dnR2FChWiVq1aWFlZUaRIEdq1a2forLho0SJ69uzJ22+/jYWFBcWLFzfM4vhfihQpgr+/P1u2bAH0nRrr169P7ty5Wb9+PaVKlaJly5ZYWlrSrFkzPD09OXjwYIbuKzuIjIzEw8MDS0vLdMe8vLyIiIggKioKwOg9UqlSJVxcXLCzsyMkJASNRsP27dvx9PSkT58+eHp60qhRI8LDw9Od15Svd0JCgtHqqE5OTiQkJBjVOX36NHFxcSxYsOCpnaY3btxIhQoVaNy4MTY2NowePTrDi7yNGjWKXLlyYWdnh7u7O02aNMHW1pa8efPSs2dPw3t66dKlNGnShMaNG2NpaYmvry+FCxd+5vlz585NvXr1WLFiBaB/nBwQEECRIkU4fPgwDx8+5LPPPsPKyoqqVatSq1YtNm3alKF7el2sX78ef39/NmzYYO5QXjuvRYLyqM/H4yNFBg0aREJCAt99951R3Xv37pGQkICvr6+hrHfv3iQkJPDjjz8a1Q0ODn6hODZt2kRMTIxhq1WrluFYgQIFjP4dFhYGwJ07d4xi8fPzIykpiejoaAYNGoSvry+1atWiePHi6eJ7mnnz5hnFMWfOHMMxd3f3J36QPE1YWFi62DPC29vbaFGt27dv07x5c7y9vXF2dmbGjBmGD8lbt24REBDwUtdp3bq1IQlcvnw5H330EaDvP7Br1y6jBO7ChQvcuXMnQ/f1TEPv6Dd793/KqvXTlzX8xrjuoKv6cufHXutK3fVl7xu/n+l/5rlDcHd3JzIyEq1Wm+7Y3bt38fDwMPQfevw9cvjwYWJiYlD6FllAv+7O3LlzCQkJ4cKFC9y9e5f+/funO68pX29HR0fi4uIM+3FxcTg6OqarZ2dnR5cuXfj4448N76XH/fs9bW9vj7u7e7p6z8vCwsIocY6Pj6djx47kz58fJycnBgwYkOnv6Rs3bhi9xps3bzb8jRFPt3TpUpo3b058fDyLFy82dzivndciQXFwcMDBwcFojLqNjQ0ODg7pOu49qvv4NyZra2scHBzIlStXurqmcvPmTaN/P/qD5uPjQ2hoqOFYaGgouXLlws3NDScnJ2bOnEloaCiLFi2ib9++XL9+PUNx1K5dm9DQUE6ePPlc9fPmzZsu9oz49zwCw4cPx9XVlcuXLxMbG0v//v0NH4IFChR4apL4rPkIWrVqxdq1a7l+/TonT56kcePGAOTLl4/69esbJXCJiYm0bds2Q/f1TDYO+u3xuK1s9GVWtk+u+/i3ektrfZl1rvR1n1OVKlWwsrJK903x5MmThISEGBJhb29v1q5d+9znLVq0KJ07d+bs2bPpjj3r9X6ReSVKlizJmTP/JGRnz559agdGpRTx8fHcvn073bF/v6cfPnz4xETmef37HqZNm0ZERAQnT54kLi6OadOmmeQ93bhxY06ePMn169dZt24drVq1AvSvcYkSJYxe44SEBIYMGfLS9/Q6mDt3Lu3atSMtLY327dtLgmIGr0WCkh0sWrSIK1euEBsby8SJEw1/XD766CMWLFjAhQsXSExMZNiwYbRq1QqNRsOGDRu4fv06SimcnZ3RaDRGzfNJSUlG2/MoXrw4Xbp0oU2bNhw4cIC0tDRiY2OZMmUKGzduTFe/ZcuW/PHHHxw5coSHDx8yfvx407wgf4uPjyd37tw4Ojpy9uxZfvnlF8OxTp06MW/ePA4cOIBSikuXLhm+FXp6ev5nC5e/vz8lSpSge/fuNGzY0JBsNm7cmBMnTrB69WrS0tJ4+PAhmzdvfi1W4XV1deWrr76iV69e7N69m7S0NE6fPk379u1p164dxYoVw8LCgqCgIEaOHMmvv/5KfHw8Op2OI0eOGFpe7t+/z5gxYwgODkYpxe3bt1m2bBmVKlVKd81nvd7P+v/4uPbt2zNv3jyuX7/O3bt3mTZtGh07dgTg+PHj7N69m5SUFBITExk8eDAuLi4UL1483XkaNmzIsWPH2LhxIykpKYwZMwadTveSr2p68fHx2Nvb4+zsTEhIiFELZps2bVi3bh0bN25Ep9Nx8+ZNrl27Bjz7tbC3t6dRo0Z0796dYsWKGVpiKleujE6n4/vvvyclJYWUlBT27Nlj9MVHGPv666/59NNPUUrRu3dv/ve//2FtbW3usF4/mdYTJhPllHlQhg4dqpTSd34bNmyYKl++vHJ2dlbt27dXiYmJhp/99ttvlb+/v3J3d1dt2rRR0dHRSimlpk2bpnx9fZWDg4MKCAhQ8+fPV0r90zHv39vNmzdfaB6UwoULG+ZB6du371PnQVmwYIHKly+f8vT0fOooHqWer5Ps48eVUurUqVOqTJkyysHBQdWuXVv169fP0ElYKaV+/fVXVbx4ceXo6KjKlCmjLl68qJRSauXKlSpfvnzK2dlZ/frrr08896POzytWrEh3zTp16ihXV1fl6empmjdvbhg59TqYOXOmKlKkiLK2tlaA6t69e7pOwitXrlSVK1dWdnZ2ytPTU1WrVk0tWbJEabValZCQoDp06KAKFCigHBwcVN68eVWXLl0M79uFCxequnXrGs71X6/3s/4//rsT7cSJE5W7u7tycXExmgfl4MGDqnz58srR0VG5ubmp+vXrq1OnTj31NVi3bp0qWLCgcnFxeeooHqWer5Psv4+HhoaqqlWrKgcHB1WhQgU1atQoVatWLcPxrVu3GmItUqSI4bp79uxRhQoVUs7OziooKOiJ5161apUC1DfffGNUHhwcrJo2bao8PDyUu7u7ql+/vtFAAKGn0+nU4MGDDX8zhw0bZjT6U2Tci3x+a5T6u20xG4mLi8PZ2ZnY2FijTnHZVWBgIN26daN9+/bmDkUII4MGDeLQoUNs3brVZPOYfPfdd+zdu5dly5aZ5HxCmIpSiu7du/Pjjz8yefJkBg0aZO6QcpwX+fyWRzxCiKeaNGkSTZs25fjx4yY5X3JyMmvXruXNN980yfmEMCWNRsO8efPYsmWLJCdZgCQoQoinsrCwYODAgVStWjXD5woNDcXT0xN7e3s++eQTE0QnRMY9fPiQr7/+2jDLr6WlJe+++66ZoxIgqxlnCTt37jR3CEJkOl9f39eis7HIPuLi4nj//ffZtWsX169fZ/78+eYOSTxGEhQhhBCvncjISBo0aMCxY8dwcnIyydpMwrQkQRFCCPFauXXrFu+++y4XL17Ew8ODP//8U/pFZUGSoAghhHhtXL58mXfffZfQ0FDy58/Pli1bKFGihLnDEk8gCYoQQojXQmpqKg0aNCA0NJSiRYuydetWo6VERNbywqN4du/eTZMmTfDx8UGj0bB69WrDsdTUVAYPHkyZMmVwcHDAx8eHjh07pltXIzo6mnbt2uHk5ISLiwtdu3ZNt6iXEEIIYUrW1tbMnTuXypUrs2fPHklOsrgXTlASExMpV64cs2fPTnfswYMHHD9+nBEjRnD8+HH++OMPLl26xPvvv29Ur127dpw7d46tW7eyfv16du/eTY8ePV7+LoQQQoinePDggeHf9erVY//+/Xh6epoxIvE8MjSTrEajYdWqVTRr1uypdY4cOUKlSpUICQnB19eXCxcuULJkSY4cOULFihUB2Lx5Mw0bNuTWrVv4+Pg887o5bSZZIYQQmWPp0qV88cUXbN++nWLFipk7nNdelppJNjY2Fo1Gg4uLCwAHDhzAxcXFkJwA1K1bFwsLCw4dOvTEcyQnJxMXF2e0CSGEEP9lzpw5tGvXjjt37sgcJ9lQpiYoSUlJDB48mDZt2hgypfDw8HRNa1ZWVri5uREeHv7E8wQFBeHs7GzYChQokJlhCyGEyMaUUowfP57evXsbViSeMmWKucMSLyjTEpTU1FRatWqFUorvv/8+Q+caMmQIsbGxhu3mzZsmilIIIUROotPpGDhwICNGjABgxIgRzJo1CwsLWdklu8mUYcaPkpOQkBC2b99u9JzJ29ube/fuGdVPS0sjOjoab2/vJ57P1tbWZCupCiGEyJnS0tLo3r07ixYtAmD69On079/frDGJl2fylPJRcnLlyhX++usv3N3djY5XrVqVmJgYjh07Zijbvn07Op2OypUrmzocIYQQr4mUlBQuXbqEpaUlixYtkuQkm3vhFpSEhASuXr1q2L9x4wYnT57Ezc2NvHnz8sEHH3D8+HHWr1+PVqs19Ctxc3PDxsaGEiVK0KBBA7p3787cuXNJTU2lT58+tG7d+rlG8AghhBBPYm9vz4YNGzh8+DD169c3dzgig154mPHOnTupXbt2uvJOnToxevRoAgICnvhzO3bsIDAwENBP1NanTx/WrVuHhYUFLVu25Ntvv8XR0fG5YpBhxkIIIQCioqJYvXo1Xbt2NXco4jm8yOf3C7egBAYG8l85zfPkO25ubixZsuRFLy2EEEIY3L59m3r16nH+/HlSU1P55JNPzB2SMCFZi0cIIUS2c+XKFd59911CQkLIly8ftWrVMndIwsRk3JUQQohs5eTJk1SvXp2QkBCKFCnCvn37ZEViU0pNgi0j4PaxZ9fNRJKgCCGEyDb27t1LYGAg9+7do3z58uzduxc/Pz9zh5Vz3D4G82rC/m9hdS/QppktFHnEI4QQIlu4c+cO9evX58GDB9SoUYN169bh7Oxs7rByhrRk2DUJ9s4ApQVHL3hnFFiaL02QBEUIIUS24OPjw5gxY9i5cye//fYb9vb25g4pZwg7Bas+hXvn9PtlPoT3JoO9m1nDytBqxuYiw4yFEOL1kZycbDSbuFarxdLS0owR5RDaVNgzFXZPAV0a2HtA42lQsmmmXTJLrWYshBBCvAylFBMmTKBatWrExsYayiU5MYG752BBHdgZpE9OSjaF3ocyNTl5UfKIRwghRJaj0+n48ssvmTp1KgArV66kS5cuZo4qB9Cmwb4ZsPNr0KWCnSs0mgqlWoBGY+7ojEiCIoQQIktJTU2lW7du/PzzzwBMmzZNkhNTiLgEqz6BO8f1+8UaQuMZkNvLrGE9jSQoQgghsozExERatWrFxo0bsbS05IcffqBz587mDit702nhwHewfQJokyGXs74TbNmPslyryeMkQRFCCJElREVF0bhxYw4ePIidnR2//fYbjRs3NndY2VvkVVj9Kdw6rN8v/C68/y04Zf3FeSVBEUIIkSUkJCQQGhqKq6sr69evp1q1auYOKfvS6eDwPPhrDKQ9BJvc0GAivNEhS7eaPE4SFCGEEFmCn58fmzdvxtLSkpIlS5o7nOwr+gas6Q0h+/T7BQPh/e/ApYBZw3pRkqAIIYQwmwMHDhAREcH7778PQJkyZcwcUTam08HRH2HrKEhNBGsHqDcOKnbJNq0mj5MERQghhFls2LCBDz/8EJ1Ox+7du6lUqZK5Q8q+YkJhTR+4sUu/71cdmn4HbgHmjSsDJEERQgjxyv3vf/+ja9euaLVa3nvvPUqVKmXukLInpeD4z/DnMEiJBys7qDsaKvUAi+w9F6skKEIIIV4ZpRRTpkxh8ODBAHTo0IEff/wRa2trM0eWDcXehrV94do2/X6BytDse3AvZN64TEQSFCGEEK+ETqdj0KBBTJs2DYAvvviCSZMmYZHNv+m/ckrBqaWw6StIjgVLW3hnBFTpBRY5ZxkASVCEEEK8Er/++qshOZkyZQpffPGFmSPKhmJvw/r+cGWLfj9fBWg2F/IUNWtYmUESFCGEEK9E27Zt+fPPP6lXrx4dO3Y0dzjZi1JwYrG+r0lyHFjaQOBXUK0fWObMj/KceVdCCCGyhOjoaBwdHbGxscHS0pLFixejyYZDXs0q5ias+wyubdfv56sITWeDZ3HzxpXJ5MGfEEKITBESEkK1atX4+OOP0el0AJKcvAil4OhPMKeqPjmxtIV3x0HXLTk+OQFpQRFCCJEJzp49S4MGDbh9+zYPHjwgPDwcH5+sv/5LlnE/RD9C59G8JgUq61tNPIqYN65XSFpQhBBCmNTevXupUaMGt2/fpmTJkuzfv1+Sk+el08HhBfpWkxu79POa1A+Cjze90uTk4MGD3Lp165Vd70kkQRFCCGEy69at49133yUmJoZq1aqxZ88e8ufPb+6wsofo6/C/JrDxC/1U9b7V4NN9UPXVDR8OCwujU6dOVK1alUGDBr2Saz6NPOIRQghhEj///DNdunRBq9XSqFEjfvvtN+zt7c0dVtb3aOXhbWMh9YF+DZ26o+Gtbq9sNtjk5GRmzpzJuHHjSEhIAMDOzg6tVoulpXnmVpEERQghhEkUKFAAS0tLOnTowPz582V22OcRdU2/8nDoAf2+fw39Gjqu/q8shA0bNtC/f3+uXr0KQOXKlfn222/NvjaSJChCCCFMonbt2hw5coQyZcrIaJ1n0Wnh4BzYPh7SksDGEd4dCxU+fmWtJpcvX+bzzz9n48aNAHh5eTFp0iQ6dOiQJWb3NX8EQgghsqWUlBR69erF+fPnDWVly5aV5ORZIi7DT/Vhy3B9clKwNvQ6AG91fSXJSVxcHF9++SWlS5dm48aNWFtbM2jQIC5fvkynTp2yRHIC0oIihBDiJcTFxfHhhx+yZcsWNm/ezMWLF7GxsTF3WFmbNg0OzIIdQaBNBlsnqDce3uwIryCp0+l0LF68mK+++orw8HAAGjZsyPTp0ylaNOtNlS8JihBCiBdy69YtGjVqxOnTp7G3t2fOnDmSnDzLvQuwuhfcOa7fL/wuNJkBzq9mhNPhw4fp27cvhw8fBqBIkSJMnz6dRo0avZLrvwxJUIQQQjy306dP07BhQ27fvo2Xlxfr16+nYsWK5g4r69Kmwr4ZsGsyaFMgl7N+XpPybV9Jq0l4eDhDhw5l4cKFADg6OjJixAj69euHra1tpl8/IyRBEUII8Vy2bNnCBx98QHx8PCVKlGDjxo34+/ubO6ysK/wsrOkFYaf0+0Xfg8bTwSlvpl86JSWFWbNmMWbMGOLj4wHo1KkTQUFB5M2b+dc3hRfuCbN7926aNGmCj48PGo2G1atXGx1XSjFy5Ejy5s2LnZ0ddevW5cqVK0Z1oqOjadeuHU5OTri4uNC1a1fDuGshhBBZj1KKqVOnEh8fT2BgIPv27ZPk5GnSUmDn1zA/UJ+c2LlCiwXQZukrSU42b95M2bJl+eKLL4iPj6dixYocOHCARYsWZZvkBF4iQUlMTKRcuXLMnj37iccnT57Mt99+y9y5czl06BAODg7Ur1+fpKQkQ5127dpx7tw5tm7dyvr169m9ezc9evR4+bsQQgiRqTQaDcuXL2fo0KFs3rwZV1dXc4eUNd06CvNqws4g0KVC8cbQ6xCUbZXpj3SuXr1KkyZNeO+997h06RKenp789NNPHDp0iCpVqmTqtTOFygBArVq1yrCv0+mUt7e3mjJliqEsJiZG2draqqVLlyqllDp//rwC1JEjRwx1Nm3apDQajbp9+/ZzXTc2NlYBKjY2NiPhCyGE+A9JSUnq119/NXcY2UNyglKbvlJqlLNSo5yUmlxIqTMrldLpMv3ScXFxavDgwcrGxkYBysrKSg0cOFDFxMRk+rVf1It8fpt0sPONGzcIDw+nbt26hjJnZ2cqV67MgQP6WfIOHDiAi4uLUaequnXrYmFhwaFDh5543uTkZOLi4ow2IYQQmef+/fs0aNCAdu3aMXPmTHOHk7Vd3QZzqugnXkNBuTbQ+zCUbpGprSZKKX755ReKFSvGpEmTSElJoX79+pw5c4ZvvvkGZ2fnTLv2q2DSTrKPxlV7eXkZlXt5eRmOhYeH4+npaRyElRVubm6GOv8WFBTEmDFjTBmqEEKIpwgODqZhw4ZcuHCB3LlzU6JECXOHlDU9iIY/h8GpJfp9Z19oMh0K1/3vnzOBY8eO0bdvX8OX/0KFCjF9+nQaN26cYybKyxrTxT3DkCFDiI2NNWw3b940d0hCCJEjHT16lCpVqnDhwgXy5cvHnj17qFevnrnDylqUgnOrYHalv5MTDVT+VD8bbCYnJ/fu3aN79+689dZbHDhwAAcHB4KCgjh37hxNmjTJMckJmLgFxdvbG4C7d+8a9RS+e/cu5cuXN9S5d++e0c+lpaURHR1t+Pl/s7W1zfLjtYUQIrtbv349H330EQ8ePKBs2bJs2LCB/PlfzURi2UbcHdjwBVzaoN/PUxzenwUFMndhvdTUVGbPns3o0aOJjY0FoH379kyaNAkfH59Mvba5mLQFJSAgAG9vb7Zt22Yoi4uL49ChQ1StWhWAqlWrEhMTw7Fjxwx1tm/fjk6no3LlyqYMRwghxHMKCQmhefPmPHjwgHr16rFnzx5JTh6n08HRhTC7sj45sbCGWl9Bz92Znpz89ddflCtXjs8//5zY2FjefPNN9u3bx+LFi3NscgIv0YKSkJBgWJIZ9B1jT548iZubG76+vvTv35/x48dTpEgRAgICGDFiBD4+PjRr1gyAEiVK0KBBA7p3787cuXNJTU2lT58+tG7dOke/0EIIkZX5+fkxZcoUzp49y/fff4+1tbW5Q8o6oq7Bun4QvEe/n68CvP8deJXM1MveuHGDgQMHsmrVKgA8PDwICgri448/xtLSMlOvnSW86BChHTt2KCDd1qlTJ6WUfqjxiBEjlJeXl7K1tVXvvPOOunTpktE5oqKiVJs2bZSjo6NycnJSH3/8sYqPj3/uGGSYsRBCZNyDBw/UnTt3DPs6nU7pXsGw2GwjLVWpPdOVGuepHzo83lup/bOV0qZl6mUTEhLU8OHDla2trQKUpaWl6tevn4qOjs7U674KL/L5rVFKKfOlRy8nLi4OZ2dnYmNjcXJyMnc4QgiR7URGRtK0aVNiY2PZu3cvLi4u5g4pawk7DWv7/DNNfcFAaDITXP0z7ZJKKX777Te++OILbt26BcA777zDzJkzKVWqVKZd91V6kc9vWYtHCCFeM1evXuW9997j6tWruLi4cPXqVVnw75HUJNg1CfbNBKWFXC5Qf2KmL+536tQpPvvsM3bv3g2Av78/U6dOpXnz5jlqZM6LkARFCCFeI/v37+f9998nKioKf39/Nm7cKPOcPBKyH9b2hai/+1mWbAbvTYbcXv/5YxkRFRXFyJEjmTt3LjqdDjs7O4YMGcIXX3yBnZ1dpl03O5AERQghXhMrVqygffv2JCcnU7FiRdatW/fU6R1eK0lx8NcoOPqTft/RGxpNhRKNM+2SaWlpzJ8/nxEjRhAdHQ1Aq1atmDJlCr6+vpl23exEEhQhhHgN/PLLL3Ts2BGlFE2aNGHp0qU4ODiYOyzzu7QZNgyAuNv6/Tc7wbtjwc4l0y65a9cuPvvsM06fPg1AmTJl+PbbbwkMDMy0a2ZHkqAIIcRroE6dOuTPn5+mTZsyY8aM12OY6n9JiIDNX8HZFfp91wB4/1sIqJlpl7x58yaDBg1i+fLl+ku6ujJu3Dh69uyJlZV8HP+bvCJCCJFDabVaQyLi4+PD8ePHcXd3f207XQL6CddO/gJbRkBSDGgsoGofCBwCNvaZcsmHDx/yzTffEBQUxMOHD7GwsKBnz56MHTsWDw+PTLlmTpAt1uIRQgjxYm7evEnlypVZunSpoczDw+P1Tk4iLsGiRvqOsEkx4F0Gum2DeuMyJTlRSrFq1SpKlizJyJEjefjwITVq1ODYsWPMmTNHkpNnkARFCCFymIMHD/LWW29x7NgxvvzySx4+fGjukMwrNQl2TITv34bQ/WBtD/XGQ/edkO/NTLnk+fPnqVevHi1atCA4OJh8+fKxdOlSdu3aZVibTvw3ecQjhBA5yM8//0z37t1JSUmhTJkyrF279vUernpjN6z//J+hw0XqQ6NvwCVzRspERkYyevRo5s6di1arxdbWli+++IIhQ4ZIp+QXJAmKEELkAFqtliFDhjBlyhQAmjZtyi+//IKjo6OZIzOTxCjYMhxOLdHvO3rDe5OgZNNMmXAtNTWVOXPmMHr0aGJiYgBo1qwZU6dOpWDBgia/3utAEhQhhMjmtFotTZs2ZcOGDQAMGzaMsWPHYmHxGj7FVwpOLYU/h8HDaEADb3WFd0ZCLudMuJxi48aNDBw4kEuXLgFQtmxZpk+fTp06dUx+vdeJJChCCJHNWVpaUq5cObZt28bChQtp3bq1uUMyj8irsL7/P6sOe5bSr59T4K1Mudy5c+cYMGAAW7ZsASBPnjxMmDCBLl26yDBuE5DFAoUQIpt6fBixTqfjypUrFCtWzMxRmUFaMuydAXu+AW0KWNlB4FdQtTdYWpv8cpGRkYwaNYp58+ah1WqxsbGhf//+DB06FGdn07fS5CSyWKAQQuRwc+bMYenSpWzdupVcuXJhYWHxeiYnwfv0rSaRl/X7hd7RT1PvFmDyS6WkpDBnzhzGjBlj6GfSokULJk+eTKFChUx+vdedJChCCJGNpKam8tlnnzF37lxAP2qnR48eZo7KDB5Ew9aRcGKxft/BExoEQemWJu8Eq5Riw4YNDBw4kMuX9YlQuXLlmD59OrVr1zbptcQ/JEERQohsIioqig8++ICdO3ei0Wj4+uuv6d69u7nDerWUgjO/w+Yh8CBSX1bhY6g7CuxcTX65c+fO8fnnn7N161YAPD09mTBhAh9//LH0M8lkkqAIIUQ2cO7cOd5//32uX7+Oo6MjS5cupXHjzFttN0uKuqZf2O/6Tv1+nuL6TrC+VUx+qUf9TObOnYtOp8PGxobPP/+coUOHSt/HV0QSFCGEyOK2bdtG8+bNiY+Pp2DBgqxdu5ZSpUqZO6xXJy0F9n8Lu6dAWhJY2kKtL6HaZ2BlY9JLpaSkMHv2bMaMGUNsbCwALVu2ZPLkyTKfySsmCYoQQmRx/v7+WFtbExgYyIoVK3B3dzd3SK9O6EFY1x8iLuj3CwZCo2ngbtpOqUop1q9fz8CBA7ly5QoA5cuXZ/r06QQGBpr0WuL5SIIihBBZkE6nM0y0VqhQIfbs2UORIkWwtjb9sNks6UE0bBsDxxbp9+099J1gy3xo8k6wZ8+eZcCAAYZ+Jl5eXkyYMIHOnTtLPxMzeg2nGRRCiKwtLCyMGjVqsGnTJkNZyZIlX4/kRKeDE7/CdxX/SU7e6AB9jkDZViZNTiIiIujVqxflypVj69at2NjY8NVXX3H58mW6du0qyYmZSQuKEEJkIUePHqVZs2bcvn2b3r17c+nSpdcjMQEIPwsbBsLNg/r9PMX1c5r4VzfpZVJSUpg1axbjxo2TfiZZmCQoQgiRRSxfvpzOnTuTlJREiRIlWLt27euRnCTHw44gODQXlBasHSBwMFTpZdKZYJVSrFmzhi+++IJr164B8MYbbzB9+nRq1aplsusI05AERQghzEyn0zFy5EgmTJgAQMOGDVmyZEnOnzZdKTj3h35hv/gwfVmJ9/V9TZzzm/RSJ0+e5PPPP2fnzp0AeHt7M2HCBDp16iSPcrIoSVCEEMKMUlJS+Oijj1i9ejUAgwYNIigoKOd/aEZegY1f/DOniWsANPwGitQ16WXCw8MZPnw4P/30E0opcuXKxcCBAxk8eDC5c+c26bWEaUmCIoQQZmRtbU2ePHmwsbFhwYIFdOzY0dwhZa6UB7BnKuybCbpU/ZwmNQbC2/3AOpfJLvPw4UOmT59OUFAQCQkJALRu3Zqvv/4aPz8/k11HZB5ZzVgIIcwgNTXV0L8kJSWF8+fPU758efMGldkubYKNX0JsqH6/SD14bxK4ma5jqlKK5cuX89VXXxESEgJA5cqVmT59OlWrVjXZdcTLkdWMhRAii0pNTWXIkCGcPHmSP//8E0tLS2xsbHJ2cnI/GDZ9BZf/HjbtlB/e+xqKNzbpsOHDhw/z+eefs3//fgDy58/P119/TZs2bQxzyojsQxIUIYR4RW7dukXr1q3Zt28fAFu3bqVBgwZmjioTpSX/PUX9N/op6i2soGof/TT1Ng4mu8zNmzcZMmQIv/76KwD29vZ89dVXDBw4EHt7e5NdR7xakqAIIcQrsGXLFtq1a0dkZCROTk4sWrQoZycn13boO8FGXdXv+9fQd4L1LG6ySyQmJjJ58mSmTJnCw4cPAejcuTMTJkzAx8fHZNcR5iEJihBCZCKtVsvYsWMZN24cSineeOMNfv/9dwoVMu1aMllG3B39sOFzf+j3HTyh/kQo84HJHufodDoWL17M0KFDuXPnDgA1atRg+vTpVKhQwSTXEOYnCYoQQmSiXr16MX/+fAB69uzJjBkzyJXLdKNVsgxtKhyaBzuDICUBNBZQqQfUHgq5TDefy549e/j88885duwYAAEBAUyZMoUWLVqgMfEaPcK8JEERQohM1Lt3b1atWsX06dNp166ducPJHCEH9FPU3zun38//ln6K+rzlTHaJGzdu8OWXX7JixQoAcufOzfDhw/nss89yZsInTL9YoFarZcSIEQQEBGBnZ0ehQoUMTZuPKKUYOXIkefPmxc7Ojrp16xqWtxZCiOxMp9Nx9OhRw37ZsmW5ceNGzkxOEiNhdS9Y2ECfnNi5wvuzoMsWkyUncXFxDB48mOLFi7NixQosLCzo2bMnV69e5csvv5TkJAczeYIyadIkvv/+e7777jsuXLjApEmTmDx5MrNmzTLUmTx5Mt9++y1z587l0KFDODg4UL9+fZKSkkwdjhBCvDLR0dE0bdqUqlWrcuDAAUO5g4PpRqxkCdo0/eOcWW/CSf3IGd7sCH2O6f9rgiG9aWlpzJ8/nyJFijB58mRSUlKoW7cuJ0+eZO7cuXh6emb4GiJrM/kjnv3799O0aVMaNWoEgL+/P0uXLuXw4cOAvvVkxowZDB8+nKZNmwLw888/4+XlxerVq2ndunW6cyYnJ5OcnGzYj4uLM3XYQgiRIYcPH6ZVq1aEhIRga2vLjRs3cubEYNd36uc0ibig3/cuA42mQ4G3THJ6pRSbN29m0KBBnDunf2RUtGhRpk6dSqNGjaSfyWvE5C0o1apVY9u2bVy+fBmAU6dOsXfvXt577z1A/xwxPDycunX/WW/B2dmZypUrG33jeFxQUBDOzs6GrUCBAqYOWwghXopSilmzZlG9enVCQkIoVKgQBw4coG3btuYOzbTuh8Dy9vBzU31yYucGjadDj10mS05OnTpF/fr1adiwIefOncPNzY0ZM2Zw9uxZGjduLMnJa8bkLShfffUVcXFxFC9eHEtLS7RaLRMmTDA8fw0PDwfAy8vL6Oe8vLwMx/5tyJAhDBgwwLAfFxcnSYoQwuzi4uLo1q0bv//+OwAtWrTgp59+ylmrEKc8gH0z9GvnpCWBxhLe6gaBX4G9m0kucefOHUaMGMHChQtRSmFjY0Pfvn0ZNmwYrq6uJrmGyH5MnqD89ttv/PrrryxZsoRSpUpx8uRJ+vfvj4+PD506dXqpc9ra2mJra2viSIUQImOWLVvG77//jpWVFd988w2fffZZzvmWrxScXw1/Doe4W/oy/xr6tXO8SpnkEgkJCXzzzTdMmTKFBw8eAPDRRx8xceJEChY03fo8InsyeYIyaNAgvvrqK0NfkjJlyhASEkJQUBCdOnXC29sbgLt375I3b17Dz929ezdnr0UhhMhxunfvzqlTp+jQoQNVqlQxdzimE34WNg2GkL36fecCUH8ClHjfJJOtabVaFi1axIgRIwgLCwP03QOmTp2as15HkSEm74Py4MGDdIsyWVpaotPpAP2kOt7e3mzbts1wPC4ujkOHDuXMDmVCiBwjMTGRoUOHEh8fD4BGo2H27Nk550P1QTRs+ALm1dAnJ1a5IHAI9D4MJZuaJDn5888/KV++PN26dSMsLIyCBQvy+++/s3fv3pzzOgqTMHkLSpMmTZgwYQK+vr6UKlWKEydOMG3aNLp06QLof6H79+/P+PHjKVKkCAEBAYwYMQIfHx+aNWtm6nCEEMIkLl68yAcffMC5c+e4efMmixcvNndIpqPTwrGFsH08PLyvLyvZFOqNBxdfk1zizJkzDBo0iD///BMAV1dXRowYQa9eveQRvngikycos2bNMrzp7t27h4+PDz179mTkyJGGOl9++SWJiYn06NGDmJgYqlevzubNm2XCHSFElrR06VK6d+9OYmIi3t7edO3a1dwhmU7wPv3jnLtn9PueJfX9TAJqmuT0YWFhjBw5kp9++gmdToe1tbWhA6ybm2k62YqcSaMen+I1m4iLi8PZ2ZnY2FicnJzMHY4QIod6+PAhAwcO5PvvvwcgMDCQpUuXGvrSZWuxt2DrSDi7Ur+fyxlqD4eKXcAy499dExMTDR1gExMTAfjwww8JCgrKuQslimd6kc9vWYtHCCGe4OLFizRv3pyLFy8CMGzYMEaPHo2VVTb/s5maBPtnwd5pkPoA0ECFzlBnBDi4Z/j0Wq2W//3vfwwfPtzQAbZKlSpMnTqVatWqZfj84vWRzX/ThBAic3h4eHD//n28vb1ZtGgR9evXN3dIGaMUXFwPfw6DmBB9mW9V/eMcE62bs3XrVr744gtOnz4N6AdFTJo0iQ8++CDnDL8Wr4wkKEII8bewsDC8vb3RaDR4eHiwbt06ChYsiLt7xlsWzOreRdg8WD9NPUBuH6g3Dkq3NMnInLNnzzJo0CA2b94MgIuLCyNGjKB3797SAVa8NJMPMxZCiOxGKcX3339P4cKFWbp0qaH8rbfeyt7JycMY2DwEvq+mT04sbaDGF9DnCJT5IMPJSXh4OD169KBcuXJs3rwZa2tr+vfvz9WrVxkwYIAkJyJDpAVFCPFau3PnDl27djV8+//jjz+y/zo6aSlw9EfYNRkeRuvLijWC+uPBLeMztMbFxTFlyhSmT59u6ADbsmVLvv76awoXLpzh8wsBkqAIIV5jv//+O5988gnR0dHkypWLr7/+mr59+5o7rJf3aHr6v8bA/Rv6Mo9i0GAiFK77nz/6PJKSkvj++++ZMGECUVFRAFSuXJmpU6fy9ttvZ/j8QjxOEhQhxGsnJiaGPn368OuvvwLw5ptvsnjxYkqWLGnmyDIg9BBsGQ63Duv3HTyh9lB4o0OGhw1rtVoWL17MqFGjCA0NBaBYsWJMnDiR5s2bSwdYkSkkQRFCvHZOnDjBr7/+ioWFBUOHDmXEiBHY2NiYO6yXE3UN/hoFF9bp963todpnUK0v2Dpm6NRKKdatW8fQoUM5d+4cAPny5WPMmDF06tQp+w+5FlmavLuEEK8FpZThm37t2rX5+uuvqVmzZvZdAywxEnZNgqM/gS4NNBbwRnsIHApOeZ/988+wZ88evvrqK/bv3w/op6YfOnQovXv3xs7OLsPnF+JZZCZZIUSOd/z4cXr16sXSpUsJCAgwdzgZk/oQDn4Pe6dDcpy+rEg9qDsGvDL+iOr06dMMHTqUDRs2AGBnZ0f//v358ssvcXFxyfD5xetNZpIVQgggLS2NSZMmMXr0aNLS0hg0aBArVqwwd1gvR6eD08v1C/rF3dKXeZfRL+hXMDDDp79x4wYjR47k119/RSmFpaUl3bp1Y+TIkfj4+GT4/EK8KElQhBA50tWrV+nQoQMHDx4E4IMPPjCsqZPtXNsBW0dA+N8L+jnlh3dGQJlWYJGx6azu3bvH+PHjmTt3LqmpqQC0atWKcePGUbRo0YxGLsRLkwRFCJGjKKWYP38+AwYM4MGDBzg5OTF79mzatWuX/Uab3D2vX9Dv6lb9vq0T1BgAlT8B64z1A4mPj2fq1KlMnTqVhIQEAN59910mTpxIxYoVMxq5EBkmCYoQIkdZuHAhn3zyCaDvDLto0SJ8fX3NHNULiguDHRPg5K+gdGBhBRW7Qq3BGV7QLzk5mblz5zJ+/HgiIyMBqFixIl9//TXvvPOOKaIXwiSkk6wQIkdJTk6mZs2atGnThs8++wyLDD4CeaWSE2D/t/rVhlMf6MtKvA91R4N7oQydWqvVsmTJEkaOHElwcDAARYoUYcKECbKYn3hlpJOsEOK1ERMTw8yZMxk2bBhWVlbY2tqyf/9+LC0tzR3a89OmwYmfYUcQJN7Tl+WvpO8A61s5Q6dWSrFhwwaGDh3KmTP6Pix58+Zl1KhRdOnSBWtr64xGL0SmkARFCJEtKaVYtWoVn332Gbdv30aj0TBy5EiA7JOcKAWXN8PWURB5SV/mVlDfYlLi/Qwv5rdnzx6GDh3K3r17AXB2duarr77is88+w97ePoPBC5G5JEERQmQ7ISEh9OnTh/Xr1wNQuHBh6tbN+Fozr4xScGMX7JgINw/py+zc9H1MKnYBq4zNartv3z5GjRrFtm3bAMiVKxefffYZgwcPxs3NLaPRC/FKSIIihMg2UlNTmTFjBqNHj+bBgwdYW1szePBghg4dmn1mNw3ZD9snQIi+VQOrXFC5J1QfAHYuGTr1/v37GT16NFu36kf9WFlZ0aVLF0aMGEH+/PkzGLgQr5YkKEKIbKNv377MmzcPgJo1azJ37lxKlChh5qie062j+knWru/Q71vaQIXO+sQkg1PTHzx4kFGjRrFlyxZAn5h8/PHHDB06FH9//4zFLYSZSIIihMg2Pv/8c9atW8f48ePp3Llz9hh5cuek/lHOlT/1+xZW+hWGa34Bzhlr1Th06BCjR49m8+bNgL7vTefOnRk2bFj2n9JfvPYkQRFCZElKKZYuXcrly5cZPXo0AMWKFePGjRvZY+Xh8LOwMwgu6vvJoLGEcm2g1iBw9c/QqQ8fPszo0aPZtGkToE9MOnXqxLBhwyhYsGAGAxcia5AERQiR5Vy9epVevXqxdetWNBoNTZo0oUKFCgBZPzmJuKRPTM6t+rtAA2U+hMCvMjyXydGjRxk9erRhIT9LS0s6dOjA8OHDKVQoY+cWIquRBEUIkWUkJyczefJkJkyYQHJyMra2tgwfPpzSpUubO7Rni7oGuybBmd/1s78ClGwGgUPAs3iGTn3s2DFGjx5tGLVkYWFhSEwKFy6cwcCFyJokQRFCZAm7du3ik08+4eLFi4B+XZg5c+Zk/Q/g+yGwezKcXApKqy8r1ghqD9GvNpwBx48fZ8yYMaxduxbQJybt2rVjxIgRFClSJKORC5GlSYIihDC7hIQEmjdvzv379/H09GTGjBm0bt06a3eCjb0Fu7+BE4tBl6YvK1IPag8FnzcydOqTJ08yevRo1qxZA+gTk7Zt2zJ8+HCKFSuW0ciFyBYkQRFCmIVSypCAODo6MnnyZI4ePUpQUBCurq5mju4/xIfDnmlwbCFoU/RlBQOh9jAoUClDpz516hRjxoxh1Sp9/xWNRkObNm0YMWIExYtn7DGRENmNLBYohHjlLl68yCeffMKAAQN4//33zR3O80mMhL3T4cgPkJakL/N7W5+Y+L+doVOfPn2aMWPG8McffwD6xKR169aMGDEi+8zzIsRzkMUChRBZ0sOHD5k4cSKTJk0iNTWVu3fv0rhx46y94vCDaP3qwofmQWqiviz/W/rEpGBghtbLOX78OEFBQaxYsQLQJyYfffQRI0aMoGTJkiYIXojsSxIUIcQrsXXrVj799FOuXbsGQKNGjfjuu++ybnISfxcOzoYjP0FKvL4sb3moMxwK133pxEQpxV9//cXkyZP566+/AH1i8uGHHzJy5EhKlSplohsQInuTBEUIkanCwsL44osvWLJkCQA+Pj58++23tGjRImt2gr0fDPu+hRO/gDZZX+ZVWt/5tVjDl05M0tLS+P3335k8eTInT54E9POYfPTRRwwZMiR7DKUW4hWSBEUIkamuXr3KkiVLsLCwoE+fPowbNy5r9h27d0Hfx+TMin+GC+d/S79WTtEG8JItPYmJifz4449MmzaNkJAQAOzt7enevTv9+/eXtXKEeIpMaVu9ffs27du3x93dHTs7O8qUKcPRo0cNx5VSjBw5krx582JnZ0fdunW5cuVKZoQihHjFrl27ZuhTAVCjRg0+//xzDh06xMyZM7NecnLrKCxtC3OqwOnl+uSkYG3otB66boXiDV8qObl37x4jR47E19eXfv36ERISQp48eRg3bhyhoaHMmDFDkhMh/oPJW1Du37/P22+/Te3atdm0aRN58uThypUrRsMGJ0+ezLfffsv//vc/AgICGDFiBPXr1+f8+fPkypXL1CEJIV6BixcvMnHiRJYsWYKtrS21atUiT548AEybNs3M0f2LUnB9J+ydBjd2/12ogRJNoPrnkO/Nlz711atXmTp1KosWLSIpST/ap3DhwgwcOJBOnTphZ2eX8fiFeA2YPEGZNGkSBQoUYOHChYayx1fVVEoxY8YMhg8fTtOmTQH4+eef8fLyYvXq1bRu3drUIQkhMtGZM2cYP348v//+O49mLahVqxZxcXGGBCXL0Ong0gbYMxXunNCXWVhB2Y/g7f6Qp+hLn/rIkSNMnjyZlStXGl6Ht956i8GDB9OsWTMsLS1NcANCvD5M/ohn7dq1VKxYkQ8//BBPT0/eeOMNFixYYDh+48YNwsPDqVu3rqHM2dmZypUrc+DAgSeeMzk5mbi4OKNNCGFe169fp3nz5pQtW5bffvsNpRTNmjXjyJEjbNy4MWstXqdN1U9FP6cKLG+vT06sckGlnvDZCWg256WSE6UUmzZtonbt2lSqVIkVK1aglKJhw4bs3LmTQ4cO0bJlS0lOhHgJJm9BuX79Ot9//z0DBgxg6NChHDlyhM8++wwbGxs6depEeHg4AF5eXkY/5+XlZTj2b0FBQYwZM8bUoQohMsDGxoaNGzcahsgOGzaMsmXLmjssY6kP9aNx9n0LsaH6MltnqNQNKn8Kji/XwpOSksKyZcuYMmUKZ8+eBcDKyop27drxxRdfyIgcIUzA5AmKTqejYsWKTJw4EYA33niDs2fPMnfuXDp16vRS5xwyZAgDBgww7MfFxVGgQAGTxCuEeDalFLt372bHjh2MHj0agPz58zN//nwqVaqU9WY7TYrVz/h68HtIjNCXOeSBKr3gra6Qy/mlThsfH8+CBQuYPn06t27dAiB37tz06NGD/v37kz9/flPdgRCvPZMnKHnz5k03A2KJEiVYuXIlAN7e3gDcvXuXvHnzGurcvXuX8uXLP/Gctra22NramjpUIcQzKKXYunUr48aNY+/evQA0bdqUN97QL4b3sl86Mk1CBByco09Okv9+FOzsC29/Bm+0B+uX66AaFhbGt99+y/fff09sbCyg/1vWv39/evbsiYuLi4luQAjxiMkTlLfffptLly4ZlV2+fBk/Pz9A32HW29ubbdu2GRKSuLg4Dh06xKeffmrqcIQQL0Epxfr16xk/fjyHDx8G9I90unbtiqenp5mje4KYUP109Md//medHI9i+hE5ZT4AS+uXOu2ZM2eYOXMmixcvJiVFvzBgsWLFGDRoEO3bt5cvTkJkIpMnKJ9//jnVqlVj4sSJtGrVisOHDzN//nzmz58P6Kd07t+/P+PHj6dIkSKGYcY+Pj40a9bM1OEIIV7QtWvX+OCDDwyzndrZ2dGzZ08GDRqEj4+PeYN7nFJw64i+teTsStCl6ct93oQaA/Wzvr7E/CVarZb169fz7bffsn37dkP522+/zZdffpn11w4SIocweYLy1ltvsWrVKoYMGcLYsWMJCAhgxowZtGvXzlDnyy+/JDExkR49ehATE0P16tXZvHmzzIEiRBaQL18+7t69i4ODA71792bAgAHpOrWbVUoinPldn5iEn/mnPKCmPjEJqPVS09Hfv3+fn376ie+++47g4GBAPxV9ixYt6NevH2+/nbEVi4UQL0ajHg3Yz0ZeZLlmIcTT3blzh19//ZVdu3axZs0aw3DY/fv3U6xYMdzd3c0c4WMiLsGRH+HU0n/6l1jlglIt9KNy8lV4qdOeP3+eWbNm8fPPP/PgwQMA3Nzc6NGjB59++im+vr6mugMhXnsv8vkta/EI8ZpJTExk9erV/Pzzz/z111/odDoAVqxYwUcffQRAtWrVzBniP7SpcHGDvrUkeM8/5a4B+tE45duBvdsLn1an07Fx40a+/fZbtm7daigvU6YM/fr1o23btjLjqxBmJgmKEK+JS5cuMWnSJH7//XcSEhIM5dWrV6dDhw40bNjQjNH9S+xtOP4/OPY/SPh7fiSNBRR9T5+YFKz9Uv1LYmNjWbhwId999x3Xrl0DwMLCgqZNm/LZZ59Rq1atrLnCshCvIUlQhMjBUlNTsbbWj2BJTEw0LEFRsGBBOnbsSPv27bPOjK+P1sc5+iNc3PjPisIOnvBmR6jQGVxebv6jS5cuMWvWLBYtWkRiYiIALi4udO/enV69esmifUJkQZKgCJHDREVFsXz5cn7++WdKlChhSEreeOMNhg8fTv369Xn77bezTkvBw/v6aeiP/ghRV/8p93tb31pSvAlY2bzwaXU6HX/++SfffvstmzdvNpSXLFmSzz77jPbt2+Pg4GCKOxBCZALpJCtEDpCSksKmTZv4+eefWbduHampqYC+leDu3bvY2Lz4B3ymu3NC37fkzEpIe6gvs8kN5VpDxS7gVfK/f/4p4uPjWbRoEbNmzeLKlSuAfnqDxo0b069fP+rUqZN1kjMhXjPSSVaI18ikSZP45ptviIyMNJSVL1+ejh070qZNm6yVnKQ+hLN/6FtLbh/7p9yzlL61pGwrsM39Uqe+evUq3333HT/99BPx8fEAODk50bVrV3r37p11HmUJIZ6LJChCZDO3bt0iT548hllMU1JSiIyMxNvbm3bt2tGxY8est2hf1DU4+pN+4b6kGH2ZhTWUagYVu4JvlZeau0Sr1bJ161a+++47Nm7cyKMG4WLFitG3b186deqEo6Oj6e5DCPHKSIIiRDaQkJDAqlWr+Pnnn9m2bRu//fYbH3zwAQBdunShYsWKvPvuu1hZZaFf6eQEuLAWTi4xHiLs7AsVO8MbHV96NeGQkBAWLlzITz/9xM2bNw3lDRs25LPPPuPdd9+V2V6FyOay0F8zIcQjMTEx/PDDD5w8eZKTJ09y8eJFtFqt4fixY8cMCUq+fPnIly+fuUI1ptPpk5FTS+H8WkhN/PuABgrXhbe6QZF3wcLyhU+dnJzM2rVr+eGHH9i6dauhtcTV1ZUOHTrQu3dvihYtasKbEUKYkyQoQpiJTqfj+vXrhiQkICCArl27AvpOnYMGDTKqX6hQITp27EiHDh0ICAgwR8hPF3VN31JyejnE/tOigVtBKNcGyn4Ern4vderz58/z448/8vPPPxv1s6lTpw7dunWjefPmskyGEDmQJChCvCJarZZFixZx8uRJTpw4walTp4wmTKtdu7YhQXF2duaTTz4hf/78lC9fnvLly+Pj45O1Rp88jIFzf+iHCN86/E+5rROUag7l20KByi/VtyQhIYHffvuNH374gQMHDhjKfXx8+Pjjj/n444+l06sQOZwkKEKYWGRkpKFVxMLCggEDBgD6GUuHDBlCRESEoa6trS1lypShfPny6Raj+/77719p3M9FmwbXtsOpJfrJ1LTJ+nKNBRSqo28tKd4IrF98mnilFIcPH+aHH35g2bJlhuTN0tKSxo0b061bNxo0aJC1+tkIITKN/KYLkUGXLl1i9erV7Nmzh5MnT3L79m3DsQIFChgSFI1GQ5cuXdBqtYZWkWLFimWPD9y75/SPcM78Dgl3/ynPUwLKt4EyrcAp70udOioqil9++YUffviBs2fPGsoLFy5M165d6dSpE3nzvty5hRDZVzb4yyhE1qKUMnrU8uGHH3LmzBmjOoULFzYkITqdzjCi5Ouvv36lsWZIYqQ+ITm5BMJP/1Nu5wZlPtQnJnnLv9QjHJ1Ox/bt2/nhhx9YtWoVKSkpAOTKlYsPPviAbt26UbNmzaz1SEsI8UpJgiLEc0hKSmL79u2sXr2av/76izNnzhimSW/VqhU+Pj689957VKxYkbJly5I798tNNmZ2aSlwebN+FM6VLaBL05dbWEPR+vpHOEXqvdTU86Cfw2XRokX8+OOPBAcHG8rLly9P9+7dadu2LS4uLhm/DyFEticJihBPcf/+fTZs2MCaNWvYtGmTYZE5gK1bt9KsWTMAhg8fbqYITUQpuHMcTi2DMyvgYfQ/x/KW13d2Lf0BOLi/1OlTUlLYsGEDP/zwA5s3b0an0wH6jsBt27alW7duvPnmmya4ESFETiIJihBPsHz5ctq3b09aWpqhLF++fDRt2pSmTZsSGBhovuBMQafTj7y5sE4/mVpM6D/HHL31U86XbwueJV7q9A8ePODPP//kjz/+YN26dcTGxhqO1axZk27dutGyZUvs7e0zeidCiBxKEhTxWlNKcfr0adasWUOFChVo1KgRAG+++SZpaWmULl2apk2b0qxZMypUqJC9+0RoU/WTqF1YBxc3GHd2tbKD4g2hXFsoGAiWL/6nIS4ujo0bN7Jy5Uo2btzIgwcPDMe8vb3p1KkTXbp0kcnUhBDPRRIU8dpJS0tjz549rFmzhjVr1hj6QjRr1syQoBQpUoTg4GD8/F5ucrEsI/WhfljwhXVwaSMk/dOSga0TFG0AJZpA4XfAxuGFTx8dHc3atWtZuXIlW7ZsMXR2BfD19aVly5a0aNGCqlWrYmn54rPHCiFeX5KgiNeGTqeja9eurF27lujof/pZ5MqVi3r16vHhhx8a1c+2yUlSnL6D64W1cOWvx6abB+w99POUlHgfAmq+VGfX8PBwVq9ezcqVK9mxY4fRFPxFixY1JCXZvsVJCGFWkqCIHCkxMZG9e/dy8eJF+vXrB+gnSrt8+TLR0dG4u7vTpEkTmjZtyrvvvmsYkZNtJUbqW0gurIPrO0H7T0sGTvn1rSQlmuhXDX6JdXBCQ0P5448/+OOPP9i7d69hHRyAsmXLGpKSUqVKSVIihDAJjXr8L002ERcXh7OzM7GxsTg5OZk7HJEFJCUlceDAAXbs2MH27ds5fPgwqampWFhYEBUVZRi6un37dqysrKhWrVr2mCDtv8Te1vclubAWQvaB0v1zzL2wvpWkRBPweeOl5iq5cuUKK1eu5I8//uDIkSNGxypVqkSLFi1o2bIlhQsXzuidCCFeEy/y+Z3N/0ILAePHj2f8+PEkJycblfv5+VGnTh0SEhIMCUqdOnXMEKEJRV37e+TNOrh91PiYd9l/kpI8xV44KVFKcfbsWUNS8vjkcxqNhho1atCiRQtatGhBgQIFTHE3QgjxVJKgiGxBq9Vy4sQJtm/fzo4dO5g2bRolSuiHwHp5eZGcnEzevHmpXbs2derUoU6dOllvxd+XoU3TJyJXt+lbS+6de+ygRr8YX4kmUKIxuPq/8OkTExM5cOAAf/31F3/88QdXrlwxHLOysqJOnTq0aNGCZs2a4eXllfH7EUKI5yQJisiSdDodZ86cYceOHezYsYNdu3YZzaXRqFEjQ4LSsmVLatSoQbFixXJG/4f7IfqRN9e2wfXdkPzYyBsLK/CvoU9KijeC3N4vdOqEhAT279/Pzp072bVrF4cPHzaa68XW1pb69evTokULmjRpgpubm6nuSgghXogkKCJL0Ol0JCUlGSbu2rp1Kw0aNDCq4+TkRK1atahTpw7vvfeeodzNzS17f5CmJELwXn0rybVtEHXV+Lidq35ukiL19MOC7Z//XuPj49m3bx+7du1i586dHD161CghAf2ChoGBgTRq1IiGDRtm32n6hRA5iiQowizu37/P4cOHOXjwIAcPHuTQoUN8/PHHTJ06FYC3334bFxcXqlSpYnhs88Ybb+SMuTR0Orh7Vp+MXN0GoQdBl/rPcY0l5H9LPzdJoXfAp/xzj7yJi4tj37597Ny5k507d3Ls2DGjYcCg75sTGBhIrVq1CAwMxN/fP2e0PAkhchRJUMQr8/DhQ3r37s2BAwe4ePFiuuPHjh0z/NvR0ZHIyMickZAAJET889jm2g5IvGd83MVXn4wUfkc/P0ku5+c6bWxsLHv37jU8sjl27JhhrZtHAgICDMlIrVq18Pf3N9FNCSFE5pEERZhcREQEhw4d4uDBg1hZWTF69GhAPyHaxo0buXtXP8V64cKFqVKlimErW7as0XmydXKSlgI3D/792GY7hJ82Pm7tAAE1/klK3Ao+16ibmJgY9uzZY0hITpw4kS4hKViwIIGBgYaExNfX15R3JoQQr4TMgyIy7OTJk+zbt48DBw5w8OBBrl27Zjjm7e3NnTt3DI8QfvnlF1xcXKhcuTJ58uQxV8imp5R+CPCjVpIbe4xncAX9MODC70ChOvrRN1a2TzmVIjo6muDgYG7cuEFwcDDXr1/n4MGDnDx5kn//yhYuXNiohUSGAAshsiqZB0Vkmjt37nD27Fnq1atnKOvduzf79+83qleyZElDy4hWqzVMita+fftXGm+mUQoir+gnSAvZr9/ibhnXcfDUJyOF6kCh2uDo+fePKmJiYrhx4xzBwcGG7VEyEhwcTEJCwlMvXaRIEaMWknz58mXmnQohhFlIgiKeSCnFjRs3OHHiBCdPnuTEiROcOHGCO3fuYGFhQUxMjGG0R7169XBycqJq1apUqVKFSpUqGSZGyzF0Ov0cJCH7/0lKEiOM61hY66eSL/wO8V6VuZaQi+CQUIJ3BBO8cKJRAhIXF/fMS3p7e+Pv74+/vz8BAQGUKVOGWrVq4ePjk0k3KYQQWYckKILU1FTOnz9PyZIlsba2BqBXr17MnTs3XV0LCwvKlClDWFiYIUEZNWrUK433ldCmQtipf5KR0APGKwEDWOUizfsNQsjHnps6Np+K4tKqmwQHjyUmJuaZl/Dy8jIkII8nIv7+/vj6+mJnZ5c59yaEENlApicoX3/9NUOGDKFfv37MmDED0K+bMnDgQJYtW0ZycjL169dnzpw5MlPlKxAfH8+pU6eMWkbOnTtHSkoKp06dMnRULVmyJDY2NpQuXZry5cvzxhtv8MYbb1CuXDkcHR3NfBeZIDUJbh/7OyHZBzcPQ+oD4zo2jiR5vcHlZA+2XU1i2e5LHD2xNV0n1Ufy5MljlHQ8vvn5+RnmfBFCCJFepiYoR44cYd68eelGZ3z++eds2LCB33//HWdnZ/r06UOLFi3Yt29fZobz2gkPDyd37tyGlXpnz55Nnz59nljX2dmZ27dvG/5fde3alZ49e2JjY/PK4n2lkuP1Scij/iO3jxqvAAwoO1fiXUtzNt6ZPy/EsWTHWa5e35DuVAEBAVSvXp233nqLQoUKGRKQbL9CshBCmFGmJSgJCQm0a9eOBQsWMH78eEN5bGwsP/74I0uWLDEs3LZw4UJKlCjBwYMHqVKlSmaFlCOlpKQQGhpq2C5dumRoGbl79y4rVqygZcuWgH74KUC+fPkMLSKPWkf+PVlXjvt2nxj5d0Ly9yObsFOgjCcwUw6eRDoW50S0HWtORrJs2wmi7xsnJBYWFpQrV47q1atTvXp13n77bemkKoQQmSDTEpTevXvTqFEj6tata5SgHDt2jNTUVOrWrWsoK168OL6+vhw4cOCJCUpycrLRSrXP08EwJ1BKERERYZSAhIaG0qxZM2rWrAnAX3/9RaNGjZ748xYWFoSGhhr2AwMDuXfvXs4a3vskackQfgZuHYVbR/StI/eD01XTOuXnjnUAh8MtWXEkjFW7T5OcbDzNvL29PVWqVDEkI1WqVJGh7UII8QpkSoKybNkyjh8/zpEjR9IdCw8Px8bGJt0oDy8vL8LDw594vqCgIMaMGZMZoZpVUlISN2/eJDQ0FH9/fwoVKgTAoUOH6NChA6GhoUaJ2SN58uQxJCi+vr7Y29vj5+eHr68v/v7+hlaRMmXKGLWE2NnZ5byOl0rpk49bR/WJyK2j+knR/vW4BiDFOYAQ5cOeUB1L9gWz7eh54LxRHU9PT0PrSPXq1Slfvryh47AQQohXx+QJys2bN+nXrx9bt24lV65cJjnnkCFDGDBggGE/Li4uS0xGlZyczJ07d0hISCAxMZGEhASjf1eoUIGKFSsCEBwczOjRo4mNjeXWrVuEhoZy794/051PnDiRIUOGAODg4GBY9l6j0ZA3b158fX0N2+OtTKVKlSIhIeH1WUslKVbfmfXWsX9aRx5EpaumzeVKhLUvZ2Ps2H45juV7r3D9zinglFG9okWLGiUkhQsXfn1eSyGEyMJMnqAcO3aMe/fu8eabbxrKtFotu3fv5rvvvuPPP/8kJSWFmJgYo1aUu3fv4u395KXjbW1tsbV98qybpnb+/HmCgoIMSca/E4+RI0fSq1cvQH+vb7/99lPPNXr0aEOCkpCQwP/+9790dR61fjzeobJw4cLs3LkTX19f8uXL958dVXP0h6k2De6d/zsROaZvHYm8lK6asrDmfq4CXEzIzZ7rD1hxMISj10KAEKN6VlZWVKhQgbffftvwyMbT0/MV3YwQQogXYfIE5Z133uHMmTNGZR9//DHFixdn8ODBFChQAGtra7Zt22bovHnp0iVCQ0OpWrWqqcN5Yffv3+eXX3556vHo6GjDvx0dHbG3t8fBwQFHR0ccHR0N/3ZwcKBo0aKGuj4+PkyaNAlHR0fy589vaA1xdXVNl2TkypWLWrVqmf7msjKlIO7OP49pbh2FsJPph/oCiTZ5uJrkyv7QVNYcvcWOi1GkaI1bUSwtLSldujQVKlQwbGXLls15j7iEECKHeiVr8QQGBlK+fHnDPCiffvopGzduZNGiRTg5OdG3b1+AdNOlP01mrsUTHh7OL7/8ki7ZeJSA+Pj44OHhYdJrvnZ0Ooi+ph9JE34awk7r//uERzWpFvaEaN05fEex/mQ4W8/fJ/KB8VtWkhEhhMgesvxaPNOnT8fCwoKWLVsaTdSWFXh7e/PFF1+YO4ycIy0Z7l0wTkTCz6ZfSA/QYcFd5cbxe1ZsPhvJXxdjuBQZh+KfztOWlpaUKyfJiBBC5HSymrEwnaQ4/fDex5ORiIugS0tXNRVrQpNzcyJcx86L0RwMecC5CB1Jj1W1tLSkVKlSVKxYUZIRIYTIAbJ8C4rIAeLv/p2IPPaY5v6NJ1ZN1NlyMdaGg8EP2Hc9nhPhOi5H6dCpfx7p2NraUrJMSd544w1JRoQQQkiCIp5BmwqRV/Sjae6e1beQhJ2GxHtPrH4vxZZTdxX7rsVzIlzLiTAtNx+bWE+j0VCoUCGa1ihDmTL6rXTp0hQuXBgrK3k7CiGE0JNPBKH3aBTN3XNw7xzcPa9PSiIugS41XXWdghvx1hy+mcSx26mcCNdyMlxH9MN/khFvb2/KVC7DB6VLG5KRkiVL5rxp9IUQQpicJCivo6Q4fcfVR4nIo6QkKfaJ1RNSNZy5q+VkeBqn72o5Eabj9F0tD//uL+Lo6Ejp0qVpWfefFpEyZcrIaCchhBAvTRKUnEybBlFX/05EHrWKnIOY0CdWT9PBxUgtZ+7qOHNPy+m//xsaq+9HbW1tTdGiRSlTowzvl/nnEY2vry8WFhav8s6EEELkcJKg5AQ6HcSGQsRliLhgSERUxCU0T1iTBuBWnC5dInIxUkeKVr8uUvHixSlWuxh1ihWj2N+bv7+/9BMRQgjxSsinTXaiTYXo6/qhuxGXUZGX0IafRxN1FUtd+kUFNUB8suLsPS1n7ukfy5y5p+PMXS0PlC1FihTRJyKVi1H/sUTE2dn51d+bEEII8RhJULKilET9yJnIy6SGneNh6Aksoq5glxSOJTpDNQ3//A9MTlNcjtJxIdI4EUmx96ZY8RL65KNuMRr/nYT4+vpiaWlpltsTQgghnkUSFHN6EI323gVirhzmQegJNBGXcXh4C1dNvKGK9d/bI/HJiguROi5EaPX/jdQRpXHHwr0gfgGFKFiyICWbF6N5sWIULVqU3Llzv/LbEkIIkX1FRERw+/Ztypcvb9Y4JEHJRFqtlsiICCKDz/Ew9CS6u+exirmOw4Nb5NHcx80mFUvA/e8N0DeLABGJOs5H6BOQGwk2xFl7keZWGKf8xQkoV5CCBQtSKSAAf39/mcxMCCHES1m2bBnbtm2jbdu21K5dG4Dbt28zadIkli5datbYJEF5QTqdjqioKO7evUt4eLjhv/fuhqGNvIF94k1ctPfwsYrD3zGFEh4WlMplvFoxtv/8MzRWx8Uoxe1kR2JtvElzKYhNvjLkLVWagIAAPgwIwM3NLd2Kx0IIIcR/0el0hhGWd+7coW/fvkRFRbFz505DnS1btrBw4UJ8fX0NCUrRokVxdXU1R8hGZC2ex0RHR3Pq1CmjxOPf/42JukdBZ0WJPBaU8LCghIclJfNYUNTdAjvrJycRaTq4lWjFnZTcRFl4kOIcgLVPaVyLVMavSEny5csn/UGEEEK8MJ1Ox61bt/D19TWUTZw4ke+++46+ffsyZMgQ4J/PTYCYmBjDv9euXcuxY8eoX78+1apVy/R4ZS2el7R3716aNm0KgKMNFP87AamZx4ISRS0oUc2CQm72WFk8JRHBkjgbL5Jz+0OeYuQqUI7cARWx8iyKv5Ut/q/uVoQQQuQgCQkJXLp0CXt7e0qUKAHoP+y9vLxISkoiPj4eR0dHQJ+0hIWFcenSJcPPOzk5MXfuXPz8/LC1/acZ//333+f9999/tTfznCRBeUwxu/vs6eFBEVeFV67007s/omxzo/EoBnn+3jyKQZ6iWLn44WYhLSFCCCFejk6nY+vWrVy6dIkePXqQK1cuAKZMmcLYsWPp1q0bCxYsAPRJR+7cudHpdISEhFCqVCkAOnToQL169ShWrJjRuXv27PlqbyaDJEF5TLFC/hTL+9jEZg55IE9x8Cj6dyJSFPIUR5PbG6RPiBBCiAw4efIkK1euxM/Pj27dugH6BVU/+ugjYmNjqV27NmXKlAGgWLFieHp6GhKWR06cOIGXl5fRJJp+fn74+fm9uhvJJJKgPC5vWWjy7T/JiL2buSMSQgiRAwwaNIhjx44xb948ihQpAsDp06cZP348gYGBRglKw4YNSUpKMhoc0aZNG9q2bZvuvPny5Xs1N2AGkqA8zs4VKnQydxRCCCGyiaSkJCwsLLCxsQFgx44dfP755/j7+7N69WpDve3bt3P8+HHOnTtnSFAqVqxIjx49eOutt4zOuWTJknTXeR1HckqCIoQQQjxDdHQ0d+/eNXRQBWjcuDGbNm1i7dq1NGrUCAAbGxtOnTpFdHS00c9/+eWXJCcnU7FiRUNZyZIlmTdv3qu5gWxIEhQhhBDib7dv3+bs2bOGBVIB9uzZQ82aNSlUqBBXr1411HVwcECn03HlyhVDWbly5Vi3bh3Fixc3Ou9HH330SuLPSSzMHYAQQgjxqiUkJLBy5UrmzJljVN6nTx8aNGjA2rVrDWWPHslotVrS0tIM5ZMmTSIsLIx+/foZyhwdHWncuDGFCxfO5DvI+aQFRQghRI72559/smXLFurUqWN4FHP//n0++OADrKys6N69O9bW+lXPypUrx6VLl4zmCvHy8iIuLi7d2maPWlhE5pAWFCGEENnW45Ohx8fH06lTJ6pXr45WqzWU//XXX0ybNo0///zTUJYvXz5q1KhB27ZtSUhIMJSPHj2a8+fPG80ZotFoZOFVM5AWFCGEEFmaUop79+6RJ08ew9oyCxYsYNKkSbRs2ZJJkyYBYG9vz2+//UZSUhLBwcEUKlQIgHfffZe0tDTeffddwzktLCzYvXv3q78Z8dwkQRFCCJEl/L+9ew+Lqs7/AP4e7nIbBApFUFMENQWSmxd6xELpsl5CTSkV1DIL0VJya0st26RdLTVFU1ctzVrE5+fipbUEichAFASFvISK4gXwxgyCDDJzfn/MevA4aIAzzKDv1/PwJJ/zOed8hi8zfDqX77l58yb++OMPyGQycYIyjUaDjh07oqKiAmfPnhWfOaNWq3Hq1CkUFRWJ65ubm2Pp0qVwdXXFY489JsaHDRuGYcOGte6LoQfGBoWIiFrdTz/9hN9//x0TJ06Ei4sLAGD9+vWIi4vDyJEjxTlEzMzM4OzsjMuXL+PMmTNigzJ8+HD4+PhIbvsFgOnTp7fq6yDDYYNCREQGU1hYiE2bNsHV1RVz584V47GxsSguLoafnx+GDBkCAPD29kb79u1ha2sr2caPP/6Ixx57DO3atRNjnTp1eqhnUSVeJEtERC2kUqkk38+ZMweBgYHIzs4WY6WlpVi8eDG+/fZbSW5ERAQiIyMlzcjQoUNx7do1nZlUO3fuLGlO6NHAIyhERHRPVVVVKC4uhoeHh3hdx/79+xEZGQl3d3ccPnxYzC0qKkJubi6KiorQv39/AEDfvn0xc+ZM9OnTR7LdlStX6uzrUZzOne5NJtx5j1YboVQqIZfLoVAo4OjoaOxyiIjavMrKSqSmpkKpVGLKlClifOjQoUhNTcXGjRsRExMDADh+/Dh69eoFOzs7VFVViY1Famoqbty4gZCQEHTs2NEYL4NMXHP+fvMIChHRIyYpKQl79+7Fyy+/LN7dcuHCBYwdOxaOjo6YPHmy2HR4eXkhPz8ftbW14vrdu3fHoUOH4OXlJTnqER4e3rovhB5qvAaFiOghIAgCrl69KnlI3aVLlxAWFqZzeiUtLQ3r16/Hr7/+Ksa6d++O/v37Y8SIEZJrS1asWIHLly9L7o6xtLREQEAA5HK5AV8RPerYoBARtSF1dXVITU3Fhg0bJPG4uDi4urriyy+/FGNyuRwZGRkoKirC1atXxfjIkSOxYMECydwgNjY2yMrKwubNm2FjYyPGLSx4oJ2Mg795REQmas+ePdi+fTuefvppTJgwAQBw69YtcUbUyMhIODk5AYB4y+2VK1fE9W1tbZGUlIQuXbpIpmp/8cUXxWfSEJkqvR9BSUhIQFBQEBwcHPD4449j1KhROHHihCSntrYWsbGxcHFxgb29PUaPHo3y8nJ9l0JEZHJqa2tx4sQJnDp1SozV1dUhODgYLi4uUCgUYjwvLw9r167F3r17xZidnR1CQ0Px4osvSp4hM2PGDNTU1OjcHfPyyy8jJCQEVlZWBnxVRPqn9wYlIyMDsbGxyM7Oxt69e3Hr1i0MGzYM1dXVYs4777yDnTt3Ijk5GRkZGbh48SIiIyP1XQoRkdEIgoCVK1ciPj4eSqVSjH/xxRfo2bMnPvnkEzFmZWWFkpISXLt2DadPnxbjQ4YMwbx58zB+/HjJtjMzM7Fr1y54eHiIMQcHB84VQg8XwcAqKioEAEJGRoYgCIJQWVkpWFpaCsnJyWLOsWPHBABCVlZWo9uora0VFAqF+FVaWioAEBQKhaHLJyKSqKioEA4ePCjcvHlTjG3dulXw8/MTZsyYIcl1dXUVAAj5+fli7NtvvxXs7e2FmJgYSW56erpQUFAg1NbWGvYFEBmRQqFo8t9vg1+DcvtwpbOzMwAgNzcXt27dktyO1rNnT3Tu3BlZWVni5D53SkhIwMcff2zoUomIREePHkVKSgo8PDzE+T8AoHfv3rhy5Qry8/Ph5+cHQHuKpqCgAO3bt5dsIyYmBvX19ZLrP6KiovDKK6/oTEoWFhZmsNdC1BYZ9C4ejUaDt99+G4MGDRJvcysrK4OVlZV4Yddtbm5uKCsra3Q777//PhQKhfhVWlpqyLKJ6CF15coVpKWlITMzUxJ/9tln4ezsjPz8fDFWUFCAefPmYfPmzZLcJ554Ah07dpRcKzJkyBDs3r0ba9askeQuXrwYS5cuRbdu3cSYmZkZZ0wlagKDHkGJjY1FYWGh5F77lrC2toa1tbWeqiKih01NTQ2qqqrg5uYmxt577z0cPXoUn3/+OXr27AlA+9C5CRMmYMiQIdi3b5+YW1lZievXr+P8+fPw9/cHAPj6+mLKlCl46qmnJPvKysqCubm5JObu7g53d3cDvTqiR5PBjqDMmDEDu3btQnp6uuRCrg4dOqCurg6VlZWS/PLycnTo0MFQ5RBRGyMIAm7evCmJrVu3DnPmzJHcAZOcnAw7OzudC0n37t2LH374AcXFxWLsiSeeQK9evfDEE0/obLewsBDPPvusGPP19cX69esxY8YMSe7dzQkRGYbej6AIgoC4uDhs374dP//8s84HQUBAACwtLZGWlobRo0cDAE6cOIFz585hwIAB+i6HiEyMIAiSUxxJSUk4efIkJk6ciK5duwIAduzYId4em5GRIeauXbsWhw4dQlhYGLp37w4A4gPs7pxBFQDi4+NRXV0NX19fMTZw4ED8/vvvOjX169dPb6+PiPRD7w1KbGwsvvvuO6SkpMDBwUG8rkQul6Ndu3aQy+WYOnUqZs+eDWdnZzg6OiIuLg4DBgxo9AJZIjJ9arVacmRh+/btKC4uxtixY8WmY8+ePYiOjkbv3r2Rnp4u5i5ZsgSHDh2Cn5+fmCuXy6FSqXDp0iXJfsaPH4/BgwejS5cuYmzgwIG4fv26zrTrUVFRen6VRNSa9N6grF69GoDuFel3Pglz6dKlMDMzw+jRo6FSqRAREYFVq1bpuxQiaiFBEFBTUwM7OzsxtmXLFpw8eRKTJk0Sj17s2bMH48ePR9++fSUXnn722WfIycmBj4+P2HTY2tqioqJCp5EYMWIE/P39JddwBAcH48yZM5JrSgBgzpw5OrVaWVlxEjKih5BBTvH8GRsbGyQmJiIxMVHfuyeiRtTW1uLChQtQq9Xw9vYW46tWrcLJkycxffp08ULS3bt3Y9SoUQgODsb+/fvF3BUrVuDAgQPo16+f2KDY2dlBoVDozAQdERGBHj164PHHHxdj/fr1Q35+Pjp27CjJnTdvnk697dq1ExsbIno08Vk8RG2ERqMBoL1NFQDOnz+PzMxM2NvbY/jw4WLe9OnTUVBQgC+//BJBQUEAtEc6XnrpJfTv3x9ZWVli7ubNm5GdnY2wsDCxQZHL5aivr0dFRYVk/yNHjoS/v7/4zBdA23T8/vvvOkc6Fi5cqFO/vb29OG8IEdGfYYNCZGBqtRpKpRJqtRqurq5ifNeuXSgrK8Nf/vIX8Q627OxsLF26FF5eXvj000/F3IEDB+LAgQPYt28fBg8eDEA76eErr7yCkJAQSYNy5MgRZGdn4/z582KD4uzsDDs7O53b9V955RXJBacAEBgYiHPnzkmOfgDa+YjuZmdnh169erX0R0NEdE9sUIj+hEqlwvXr1+Hi4gJLS0sAwLFjx7B371506tRJvBsN0D6Y7fTp09i0aRN69+4NQHvtRnR0NCIiIrBnzx4xd+7cuTh27BjS09PFBqW8vBxbt25FSEiIpEHRaDTQaDSSO1U8PT0xZMgQcRLE2z766CPU1NQgJCREjD399NOSB8vdFhcXpxOzsbGBp6dns35GRET6xgaFHgl332Xy448/4sqVKxg+fDgcHR0BAD/99BMSExMREBCA+fPni7ldunRBeXk5Dh8+LE7ilZOTg1mzZiEiIkLSoBw5cgQnTpzA5cuXxdjt7dfU1EhqGjx4MLp37y6ZBt3Pzw/Lly9H586dJblbt26FpaWl5AhMv379JJON3TZs2DCdGGcuJaK2hg0KmTyNRgOZTCb+kS0pKcGRI0fg5uYmOUoQFxeH8vJyfPnll+IRibVr12LmzJkYNWoU/v3vf4u50dHRKC8vlzxP5dKlS9ixYwdUKpVk/+3bt0dFRYXkibTe3t4YO3aszvwZy5cvR319veSoxosvvoja2lqd0yu373i7U9euXTFz5kyd+N0NCxHRw44NChnM7ZlANRoN7O3txVhKSgqUSiXGjh0rPh7+v//9L7Zs2YKBAwfirbfeErfh5uaGiooKlJSUiHNfpKSk4O2338b48ePx/fffi7nbtm1DWVkZPvjgA7FBsbKygkqlkjw3BQBCQ0NRWVkJC4uGt8DAgQOxZs0aeHl5SXJzcnJgZ2cnXpwKAAMGDGh0YsGIiAid2O3TQkRE1HQGfVhgW1RYWIjw8HDJ00sB7V0Jo0aNkhxSLykpQVRUlM5U2KtXr8Zrr70mmYyqoqICM2fOxNy5cyW5P//8M9avX4/CwkIxduvWLRw8eBCFhYWS27bVanWTbuNururqaly6dEny+AGNRoP9+/dj3759qK+vF+M5OTlYtmwZUlNTJXW98MILCA0NlTQCn3zyCezs7BAfHy/GZDIZoqKiEB0dLblL5OTJk9iyZYvOQ9xuHzW5s7YuXbogODhY8gA2AJg/fz5WrFgheWRCZGQkSkpKkJSUJMndtm0bUlNT8eSTT4qxHj16YNq0aXjmmWckuQ4ODpLmhIiIWoHQBikUCgGAoFAo9L7tX375RQAgeHt7S+LPPfecAED45ptvxFhubq4AQPDw8JDkRkZGCgCEVatWibHjx48LAAQnJydJ7qRJkwQAwj//+U8xVlpaKgAQLCwsJLlvvfWWIJPJhIULF4oxpVIp+Pn5Cf379xfq6urE+OLFi4V+/foJiYmJYkyhUAhyuVxo166doFKpxPi7774rABDmzJkjxurq6gQAAgDh6tWrYvyjjz4SAAhvvvmmpDZra2sBgFBSUiLGvvjiCwGAEBUVJcl94YUXhIiICOHs2bNiLC8vT1iyZImwZ88eSe7p06eFsrIyob6+XiAioratOX+/eYrnLj4+PtiyZYt4SuK2WbNmITIyUjIdv4eHB5YtW6aT++qrryIgIADBwcFizNnZGR9++CFsbW0luf7+/rh69Sp69OghxtRqNTw9PSWnHwDg5s2bEARBEq+urkZBQQFkMpkkfuHCBeTl5UkumLS0tBSPcKhUKnH2TWtra5iZmYnzbACAhYUFvL29YWlpKYn7+flh/PjxCAgIkNS2YcMGWFtbw8XFRYy98cYbeO211ySzkQLaicDu9tRTT+k8NRaAzrOciIjo0SATBAOcMzAwpVIJuVwOhUIh3iHxKKiuroZSqYStra04XXhtbS0yMjKgUqkwYsQIMbeoqAjnzp2Dl5eX2PwIgoA//vgDVlZW6Ny5s3jaQrjr4W1ERESG0Jy/32xQiIiIqFU05+83r/wjIiIik8MGhYiIiEwOGxQiIiIyOWxQiIiIyOSwQSEiIiKTwwaFiIiITA4bFCIiIjI5bFCIiIjI5LBBISIiIpPDBoWIiIhMDhsUIiIiMjlsUIiIiMjksEEhIiIik8MGhYiIiEwOGxQiIiIyOWxQiIiIyOSwQSEiIiKTwwaFiIiITA4bFCIiIjI5bFCIiIjI5LBBISIiIpNj1AYlMTERXbt2hY2NDUJCQpCTk2PMcoiIiMhEGK1BSUpKwuzZs7FgwQLk5eXBz88PERERqKioMFZJREREZCJkgiAIxthxSEgIgoKCsHLlSgCARqOBp6cn4uLi8N577913XaVSCblcDoVCAUdHR/0XV1et/a+lLSCTaf9dXwdobgFmFoCFtW6uRTvA7H/9nvoWoK4DZOaApU0Lc2sACICFDWBm/r/cekCtAmRmgGW7luXeugkIGsDcGjC30MY0aqC+tnm5kAFWtnfk1gKCGjC3AswtW5CrAepvav9tZdeQW68CNPWAmSVgYdX8XEEAbtVo/93oeDYntwljr5ffk8bGUw+/J7fH80F/T3TG80F/T+4xng/6e3LneD7w78k9xpOfEfyMkOQ+hJ8Retacv99GOYJSV1eH3NxchIeHNxRiZobw8HBkZWXp5KtUKiiVSsmXQS1y137VXG2I/bZcG/shXpq72EsbV5Q2xHLWaWM7Zkhzl/XVxq+caIjlb9HGtk2R5iaGaOOX8htiRf+njX0/Xpq7bog2fva3htjJPdrYppHS3I3Pa+On0hpiZzK0sX8NleZ+O0YbP76zIXb+oDb21SBp7taJ2viRrQ2x8iJtbEU/ae72adp47tcNsetntLHPe0lzd76tjR9Y3RC7UaaNfdZZmvvj37TxzM8bYrWKhvHU1DfE9y3UxvYtbIhp6htyaxUN8czPtbEf/ybd32edtfEbZQ2xA6u1sZ1vS3M/76WNXz/TEMv9WhvbPk2au6KfNl5e1BA7slUb2zpRmvvVIG38/MGG2PGd2ti3Y6S5/xqqjZ/JaIidStPGNj4vzd00Uhs/uachdvY3bWzdEGnu9+O18aL/a4hdytfGEkOkudumaOP5WxpiV05oY8v6SnN3zNDGc9Y1xBSl2thiL2nuD/Ha+G/LG2I1VxvG8057F2hjGZ81xG7VNOTe/gMEaHMWuWvXuRM/I7T4GaH1MH9GGJFRGpQrV65ArVbDzc1NEndzc0NZWZlOfkJCAuRyufjl6enZWqUSERGRERjlFM/FixfRqVMn/PbbbxgwYIAYnzt3LjIyMnDgwAFJvkqlgkqlEr9XKpXw9PTkKZ6W5PLw7f9yefi2Sbk8xcNTPAA/Ix7lzwg9a84pHqM0KHV1dbC1tcW2bdswatQoMR4dHY3KykqkpKTcd32DX4NCREREemfy16BYWVkhICAAaWkN5zg1Gg3S0tIkR1SIiIjo0WRhrB3Pnj0b0dHRCAwMRHBwMJYtW4bq6mpMnjzZWCURERGRiTBagzJu3DhcvnwZ8+fPR1lZGfz9/bFnzx6dC2eJiIjo0WO0eVAeBK9BISIiantM/hoUIiIiovthg0JEREQmhw0KERERmRw2KERERGRy2KAQERGRyWGDQkRERCaHDQoRERGZHDYoREREZHLYoBAREZHJMdpU9w/i9uS3SqXSyJUQERFRU93+u92USezbZINSVVUFAPD09DRyJURERNRcVVVVkMvl981pk8/i0Wg0uHjxIhwcHCCTycR4UFAQDh482Og691p2d1ypVMLT0xOlpaVGf87P/V5Pa26vOev9We6DLG9sGcdPv+s1JbelY8j3YNsYQ36OGmZ7HEMtQRBQVVUFd3d3mJnd/yqTNnkExczMDB4eHjpxc3Pze/4g77XsXnFHR0ejv7Hu93pac3vNWe/Pch9keWPLOH76Xa8puS0dQ74H28YY8nPUMNvjGDb4syMntz1UF8nGxsY2e9n91jE2fdfW0u01Z70/y32Q5Y0t4/jpd72m5LZ0DPkebBtjyM9Rw2yPY9h8bfIUjyE151HQZHo4fm0fx7Dt4xi2faYwhg/VERR9sLa2xoIFC2BtbW3sUqgFOH5tH8ew7eMYtn2mMIY8gkJEREQmh0dQiIiIyOSwQSEiIiKTwwaFiIiITA4bFCIiIjI5bFCIiIjI5LBBaYaXXnoJ7du3x5gxY4xdCrVAaWkpwsLC0Lt3b/j6+iI5OdnYJVEzVVZWIjAwEP7+/ujTpw/WrVtn7JKoBWpqatClSxfEx8cbuxRqga5du8LX1xf+/v4YMmSIwfbD24yb4eeff0ZVVRW++eYbbNu2zdjlUDNdunQJ5eXl8Pf3R1lZGQICAnDy5EnY2dkZuzRqIrVaDZVKBVtbW1RXV6NPnz44dOgQXFxcjF0aNcMHH3yA4uJieHp6YsmSJcYuh5qpa9euKCwshL29vUH3wyMozRAWFgYHBwdjl0Et1LFjR/j7+wMAOnToAFdXV1y7ds24RVGzmJubw9bWFgCgUqkgCEKTHttOpuOPP/7A8ePH8fzzzxu7FDJxj0yD8ssvv2D48OFwd3eHTCbDf/7zH52cxMREdO3aFTY2NggJCUFOTk7rF0r3pM8xzM3NhVqthqenp4GrpjvpYwwrKyvh5+cHDw8PvPvuu3B1dW2l6kkf4xcfH4+EhIRWqpjupo8xlMlkGDx4MIKCgrBlyxaD1frINCjV1dXw8/NDYmJio8uTkpIwe/ZsLFiwAHl5efDz80NERAQqKipauVK6F32N4bVr1zBp0iSsXbu2NcqmO+hjDJ2cnFBQUIAzZ87gu+++Q3l5eWuV/8h70PFLSUmBt7c3vL29W7NsuoM+3oO//vorcnNzsWPHDixatAhHjhwxTLHCIwiAsH37dkksODhYiI2NFb9Xq9WCu7u7kJCQIMlLT08XRo8e3Rpl0n20dAxra2uFp59+Wti0aVNrlUr38CDvw9vefPNNITk52ZBl0j20ZPzee+89wcPDQ+jSpYvg4uIiODo6Ch9//HFrlk130Md7MD4+Xti4caNB6ntkjqDcT11dHXJzcxEeHi7GzMzMEB4ejqysLCNWRk3VlDEUBAExMTF45plnMHHiRGOVSvfQlDEsLy9HVVUVAEChUOCXX36Bj4+PUeolqaaMX0JCAkpLS1FSUoIlS5bg9ddfx/z5841VMt2lKWNYXV0tvgdv3LiBffv24cknnzRIPRYG2Wobc+XKFajVari5uUnibm5uOH78uPh9eHg4CgoKUF1dDQ8PDyQnJ2PAgAGtXS41oiljuH//fiQlJcHX11c877p582b07du3tculRjRlDM+ePYtp06aJF8fGxcVx/ExEUz9HyXQ1ZQzLy8vx0ksvAdDeVff6668jKCjIIPWwQWmG1NRUY5dADyA0NBQajcbYZdADCA4ORn5+vrHLID2IiYkxdgnUAt26dUNBQUGr7IuneAC4urrC3Nxc52K78vJydOjQwUhVUXNwDNs+jmHbxvFr+0xtDNmgALCyskJAQADS0tLEmEajQVpaGk/htBEcw7aPY9i2cfzaPlMbw0fmFM+NGzdQXFwsfn/mzBnk5+fD2dkZnTt3xuzZsxEdHY3AwEAEBwdj2bJlqK6uxuTJk41YNd2JY9j2cQzbNo5f29emxtAg9waZoPT0dAGAzld0dLSYs2LFCqFz586ClZWVEBwcLGRnZxuvYNLBMWz7OIZtG8ev7WtLY8hn8RAREZHJ4TUoREREZHLYoBAREZHJYYNCREREJocNChEREZkcNihERERkctigEBERkclhg0JEREQmhw0KERERmRw2KERERGRy2KAQERGRyWGDQkSNGjx4MGQymc7XpEmTDLbPyZMn48MPP7zn8rKyMsyaNQteXl6wsbGBm5sbBg0ahNWrV6OmpqZJ+xg+fDiee+65RpdlZmZCJpPhyJEjLaqfiPTnkXmaMRE1nSAIOHz4MJYsWYJXX31Vssze3t4g+1Sr1di1axd2797d6PLTp09j0KBBcHJywqJFi9C3b19YW1vj6NGjWLt2LTp16oQRI0b86X6mTp2K0aNH4/z58/Dw8JAs27hxIwIDA+Hr66uX10RELceHBRKRjpMnT8LHxwc5OTkICgpqlX1mZmZi3LhxuHDhAmQymc7y5557DkVFRTh+/Djs7Ox0lguCIK6n0Wjwj3/8A2vXrkVZWRm8vb0xb948jBkzBvX19fDw8MCMGTMkR2tu3LiBjh07YvHixZg+fbrhXigRNQlP8RCRjtzcXFhYWLTqkYQdO3Zg+PDhjTYnV69exU8//YTY2NhGmxMAkvUSEhKwadMmfPXVVygqKsI777yDCRMmICMjAxYWFpg0aRK+/vpr3Pn/Z8nJyVCr1YiKitL/iyOiZmODQkQ68vLyoFar4eLiAnt7e/HrjTfeMNg+U1JS7nmKpri4GIIgwMfHRxJ3dXUVa/vrX/8KAFCpVFi0aBE2bNiAiIgIdOvWDTExMZgwYQLWrFkDAJgyZQpOnTqFjIwMcVsbN27E6NGjIZfLDfQKiag5eA0KEenIy8tDVFQUPv74Y0nc2dnZIPs7duwYLl68iGeffbZZ6+Xk5ECj0eDVV1+FSqUCoG1mampqMHToUEluXV0dnnrqKQBAz549MXDgQGzYsAFhYWEoLi5GZmYmFi5cqJ8XREQPjA0KEenIy8vDp59+Ci8vr0aXb9iwAcuWLYNMJsPQoUOxZMkSlJSUYOTIkejTpw9ycnIQHh6OiIgIJCQkoLq6Gtu3b0ePHj0a3d6OHTswdOhQ2NjYNLrcy8sLMpkMJ06ckMS7desGAGjXrp0Yu3HjBgBg9+7d6NSpkyTf2tpa/PfUqVMRFxeHxMREbNy4Ed27d8fgwYP/5CdDRK2FDQoRSZw+fRqVlZXw8/NrdPnRo0exdOlSZGZmwsnJCdeuXROXHTt2DFu3boWXlxf69OkDe3t7HDhwAGvWrMHKlSuxfPnyRreZkpKCadOm3bMmFxcXDB06FCtXrkRcXNw9r0MBgN69e8Pa2hrnzp27b8Px8ssvY9asWfjuu++wadMmvPnmm41e/0JExsEGhYgkcnNzAQBubm4oKyuTLHv88ceRnp6OcePGwcnJCYD0tI+Pj494nUivXr0QHh4OAOjbty9++OGHRvdXUVGBQ4cOYceOHfeta9WqVRg0aBACAwPx0UcfwdfXF2ZmZjh48CCOHz+OgIAAAICDgwPi4+PxzjvvQKPRIDQ0FAqFAvv374ejoyOio6MBaG+XHjduHN5//30olUrExMQ07wdFRAbFBoWIJPLy8gBA53SMtbU1lErlfde98xSKmZmZ+L2ZmRnUanWj6+zcuRPBwcFwdXW977a7d++Ow4cPY9GiRXj//fdx/vx5WFtbo3fv3oiPj8dbb70l5n7yySd47LHHkJCQgNOnT8PJyQn9+vXD3/72N8k2p06divXr1+OFF16Au7v7ffdPRK2L86AQUbMUFhYiKioKv/76K+RyOa5duwZnZ2eUlJRgzJgxOHToEABgzJgxmDFjBsLCwpCdnY2///3v2LVrl872RowYgdDQUMydO7e1XwoRmTDeZkxEzdKnTx/MmjULgwYNgr+/Pz777LMH2l5oaCjnHiEiHTyCQkRERCaHR1CIiIjI5LBBISIiIpPDBoWIiIhMDhsUIiIiMjlsUIiIiMjksEEhIiIik8MGhYiIiEwOGxQiIiIyOWxQiIiIyOSwQSEiIiKTwwaFiIiITM7/Ay6FrjH04cCxAAAAAElFTkSuQmCC", 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", + "image/png": 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", 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" ] @@ -260,7 +260,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]" }, "orig_nbformat": 4, "vscode": {