From 9cba4f36d68474da4f16160587e82eeca36f5b67 Mon Sep 17 00:00:00 2001 From: Leander Schlegel Date: Thu, 3 Nov 2022 14:51:14 +0100 Subject: [PATCH 01/87] fixed minor typo at the end of two code lines --- doc/pages/Installation.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index e6540a2e6..91cce9c23 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -80,12 +80,12 @@ worthwhile effort afterwards. ``` and make the directory ```sh - mkdir -p $CRPROPA_DIR` + mkdir -p $CRPROPA_DIR ``` 2. Initialize the Python virtual environment with the virtualenv command. ```sh - virtualenv $CRPROPA_DIR` + virtualenv $CRPROPA_DIR ``` if there is virtualenv available on the system. If the virtualenv is not installed on a system, try to use your operating From eb32c89e7daa405c5f9c4b64dc1a7fc98bb88194 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 3 Nov 2022 15:49:25 +0100 Subject: [PATCH 02/87] removed deprecated ObserverLargeSphere --- include/crpropa/module/Observer.h | 25 +++------------------ src/module/Observer.cpp | 36 ------------------------------- 2 files changed, 3 insertions(+), 58 deletions(-) diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index f87355468..ea5abf8c7 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -59,7 +59,7 @@ class Observer: public Module { void add(ObserverFeature *feature); /** Perform some specific actions upon detection of candidate @param action module that performs a given action when candidate is detected - @param clone if true, clone candidate + @param clone if true, clone candidate */ void onDetection(Module *action, bool clone = false); void process(Candidate *candidate) const; @@ -142,25 +142,6 @@ class ObserverTracking: public ObserverFeature { }; -/** - @class ObserverLargeSphere - @brief Detects particles that exit a sphere from the inside to the outside - */ -class ObserverLargeSphere: public ObserverFeature { -private: - Vector3d center; - double radius; -public: - /** Constructor - @param center vector containing the coordinates of the center of the sphere - @param radius radius of the sphere - */ - ObserverLargeSphere(Vector3d center = Vector3d(0.), double radius = 0); - DetectionState checkDetection(Candidate *candidate) const; - std::string getDescription() const; -}; - - /** @class ObserverPoint @brief Detects particles when reaching x = 0 @@ -180,7 +161,7 @@ class ObserverPoint: public ObserverFeature { @brief Detects particles in a given redshift window When added to an observer, this feature generalizes it to four dimensions. - The fourth dimension is the redshift, a proxy for time. This is particularly + The fourth dimension is the redshift, a proxy for time. This is particularly useful in "4D" studies, including either time-dependence (e.g. flaring objects), or in 3D studies including cosmological evolution. Note that redshifts should be assigned to sources when using this feature. @@ -299,7 +280,7 @@ class ObserverTimeEvolution: public ObserverFeature { @param max maximum time @param numb number of time intervals @param log log (input: true) or lin (input: false) scaling between min and max with numb steps - @param + @param */ ObserverTimeEvolution(double min, double max, double numb, bool log); // add a new time step to the detection time list of the observer diff --git a/src/module/Observer.cpp b/src/module/Observer.cpp index 51d18bb0f..53efc89f9 100644 --- a/src/module/Observer.cpp +++ b/src/module/Observer.cpp @@ -177,42 +177,6 @@ std::string ObserverTracking::getDescription() const { return ss.str(); } -// ObserverLargeSphere -------------------------------------------------------- -ObserverLargeSphere::ObserverLargeSphere(Vector3d center, double radius) : - center(center), radius(radius) { - KISS_LOG_WARNING << "ObserverLargeSphere deprecated and will be removed in the future. Replace with ObserverSurface( Sphere(center, radius) )."; -} - -DetectionState ObserverLargeSphere::checkDetection(Candidate *candidate) const { - // current distance to observer sphere center - double d = (candidate->current.getPosition() - center).getR(); - - // conservatively limit next step size to prevent overshooting - candidate->limitNextStep(fabs(radius - d)); - - // no detection if inside observer sphere - if (d < radius) - return NOTHING; - - // previous distance to observer sphere center - double dprev = (candidate->previous.getPosition() - center).getR(); - - // if particle was outside of sphere in previous step it has already been detected - if (dprev >= radius) - return NOTHING; - - // else: detection - return DETECTED; -} - -std::string ObserverLargeSphere::getDescription() const { - std::stringstream ss; - ss << "ObserverLargeSphere: "; - ss << "center = " << center / Mpc << " Mpc, "; - ss << "radius = " << radius / Mpc << " Mpc"; - return ss.str(); -} - // ObserverPoint -------------------------------------------------------------- DetectionState ObserverPoint::checkDetection(Candidate *candidate) const { double x = candidate->current.getPosition().x; From a7bacb9513e48b894544b385e5f8cf8f995ab60d Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 3 Nov 2022 15:55:22 +0100 Subject: [PATCH 03/87] removed deprecated ObserverSmallSphere and updated example notebook --- .../extragalactic_fields/MHD_models.v4.ipynb | 2 +- include/crpropa/module/Observer.h | 21 --------- src/module/Observer.cpp | 44 ------------------- 3 files changed, 1 insertion(+), 66 deletions(-) diff --git a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb index ce1900c09..1d1017d11 100644 --- a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb +++ b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb @@ -285,7 +285,7 @@ "\n", "## initialize observer that registers particles that enter into sphere of given size around its position\n", "obs = Observer()\n", - "obs.add( ObserverSmallSphere( obsPosition, obsSize ) )\n", + "obs.add( ObserverSurface( Sphere( obsPosition, obsSize ) ) )\n", "## write registered particles to output file\n", "obs.onDetection( TextOutput( filename_output ) )\n", "## choose to not further follow particles paths once detected\n", diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index ea5abf8c7..2e0fcbea0 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -100,27 +100,6 @@ class ObserverSurface: public ObserverFeature { }; -/** - @class ObserverSmallSphere - @brief Detects particles that enter a sphere from the outside to the inside - */ -class ObserverSmallSphere: public ObserverFeature { -private: - Vector3d center; - double radius; -public: - /** Constructor - @param center vector containing the coordinates of the center of the sphere - @param radius radius of the sphere - */ - ObserverSmallSphere(Vector3d center = Vector3d(0.), double radius = 0); - DetectionState checkDetection(Candidate *candidate) const; - void setCenter(const Vector3d ¢er); - void setRadius(float radius); - std::string getDescription() const; -}; - - /** @class ObserverTracking @brief Tracks particles inside a sphere diff --git a/src/module/Observer.cpp b/src/module/Observer.cpp index 53efc89f9..9f13333cb 100644 --- a/src/module/Observer.cpp +++ b/src/module/Observer.cpp @@ -98,50 +98,6 @@ std::string ObserverDetectAll::getDescription() const { return description; } -// ObserverSmallSphere -------------------------------------------------------- -ObserverSmallSphere::ObserverSmallSphere(Vector3d center, double radius) : - center(center), radius(radius) { - KISS_LOG_WARNING << "ObserverSmallSphere deprecated and will be removed in the future. Replace with ObserverSurface( Sphere(center, radius))."; -} - -DetectionState ObserverSmallSphere::checkDetection(Candidate *candidate) const { - // current distance to observer sphere center - double d = (candidate->current.getPosition() - center).getR(); - - // conservatively limit next step to prevent overshooting - candidate->limitNextStep(sqrt(fabs(d*d - radius*radius))); - - // no detection if outside of observer sphere - if (d > radius) - return NOTHING; - - // previous distance to observer sphere center - double dprev = (candidate->previous.getPosition() - center).getR(); - - // if particle was inside of sphere in previous step it has already been detected - if (dprev <= radius) - return NOTHING; - - // else detection - return DETECTED; -} - -void ObserverSmallSphere::setCenter(const Vector3d ¢er) { - this->center = center; -} - -void ObserverSmallSphere::setRadius(float radius) { - this->radius = radius; -} - -std::string ObserverSmallSphere::getDescription() const { - std::stringstream ss; - ss << "ObserverSmallSphere: "; - ss << "center = " << center / Mpc << " Mpc, "; - ss << "radius = " << radius / Mpc << " Mpc"; - return ss.str(); -} - // ObserverTracking -------------------------------------------------------- ObserverTracking::ObserverTracking(Vector3d center, double radius, double stepSize) : center(center), radius(radius), stepSize(stepSize) { From cec4083c19c3d06ae05f25fbb9e194437a7f484e Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 3 Nov 2022 15:58:21 +0100 Subject: [PATCH 04/87] Added Observer1D and declared ObserverPoint for deprecated --- include/crpropa/module/Observer.h | 16 ++++++++++++++-- src/module/Observer.cpp | 19 +++++++++++++++++++ 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index 2e0fcbea0..6262fc34d 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -125,8 +125,7 @@ class ObserverTracking: public ObserverFeature { @class ObserverPoint @brief Detects particles when reaching x = 0 - This module limits the next step size to prevent candidates from overshooting. - Should be renamed to Observer1D, once old observer-scheme is removed. +Should be removed and replaced by Observer1D */ class ObserverPoint: public ObserverFeature { public: @@ -135,6 +134,19 @@ class ObserverPoint: public ObserverFeature { }; +/** + @class Observer1D + @brief Detects particles when reaching x = 0 + + This module detects particles when reaching x = 0 and also limits the next step size to prevent candidates from overshooting. + */ +class Observer1D: public ObserverFeature { +public: + DetectionState checkDetection(Candidate *candidate) const; + std::string getDescription() const; +}; + + /** @class ObserverRedshiftWindow @brief Detects particles in a given redshift window diff --git a/src/module/Observer.cpp b/src/module/Observer.cpp index 9f13333cb..bdae81e8f 100644 --- a/src/module/Observer.cpp +++ b/src/module/Observer.cpp @@ -135,11 +135,14 @@ std::string ObserverTracking::getDescription() const { // ObserverPoint -------------------------------------------------------------- DetectionState ObserverPoint::checkDetection(Candidate *candidate) const { + KISS_LOG_WARNING << "ObserverPoint is deprecated and is no longer supported. Please use Observer1D instead.\n"; double x = candidate->current.getPosition().x; if (x > 0) { + // Limits the next step size to prevent candidates from overshooting in case of non-detection candidate->limitNextStep(x); return NOTHING; } + // Detects particles when reaching x = 0 return DETECTED; } @@ -147,6 +150,22 @@ std::string ObserverPoint::getDescription() const { return "ObserverPoint: observer at x = 0"; } +// Observer1D -------------------------------------------------------------- +DetectionState Observer1D::checkDetection(Candidate *candidate) const { + double x = candidate->current.getPosition().x; + if (x > 0) { + // Limits the next step size to prevent candidates from overshooting in case of non-detection + candidate->limitNextStep(x); + return NOTHING; + } + // Detects particles when reaching x = 0 + return DETECTED; +} + +std::string Observer1D::getDescription() const { + return "Observer1D: observer at x = 0"; +} + // ObserverRedshiftWindow ----------------------------------------------------- ObserverRedshiftWindow::ObserverRedshiftWindow(double zmin, double zmax) : zmin(zmin), zmax(zmax) { From 198ad5aecae7d7c234c613b5ffda26b90b65af57 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 3 Nov 2022 16:01:23 +0100 Subject: [PATCH 05/87] synchronized web documentation with observer.h documentation --- doc/pages/Simulation-Modules.md | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/doc/pages/Simulation-Modules.md b/doc/pages/Simulation-Modules.md index 1c55ebeec..cf2b9904d 100644 --- a/doc/pages/Simulation-Modules.md +++ b/doc/pages/Simulation-Modules.md @@ -53,24 +53,22 @@ Periodic- and ReflectiveBox implement boundary conditions for the particles. The * **CylindricalBoundary** - Cylindric simulation volume * **PeriodicBox** - Periodic boundary conditions for the particle: If a particle leaves the box it will enter from the opposite side and the initial position will be changed as if it had come from that side. * **ReflectiveBox** - Reflective boundary conditions for the particle: If a particle leaves the box it will be reflected (mirrored) and the initial position will be changed as if it had come from that side. -* **DetectionLength** - Detects the candidate at a given trajectory length. +* **DetectionLength** - Detects the candidate at a given trajectory length. ### Observers Observers can be defined using a collection of ObserverFeatures. The names of ObserverFeatures all start with "Observer" so you can discover the available options from an interactive python session by typing "Observer" and pressing "tab". The list includes * **ObserverSurface** - Detects particles crossing the boundaries of a surface defined (see, e.g., `Geometry` module) -* **ObserverSmallSphere** - Detects particle when they enter the sphere -* **ObserverLargeSphere** - Detects particles when they leave the sphere -* **ObserverTracking** - For recording the tracks of particles inside a small observer sphere -* **ObserverPoint** - Observer for 1D simulations +* **ObserverTracking** - For recording the tracks of particles inside an observer sphere +* **Observer1D** - Observer for 1D simulations that detects particles when reaching x = 0 * **ObserverDetectAll** - Detects all particles -* **ObserverRedshiftWindow** - Detect particles within a given redshift interval around z=0 +* **ObserverRedshiftWindow** - Detect particles within a given redshift interval * **ObserverInactiveVeto** - Veto for inactive particles * **ObserverPhotonVeto** - Veto for photons * **ObserverElectronVeto** - Veto for electrons/positrons * **ObserverNeutrinoVeto** - Veto for neutrinos * **ObserverNucleusVeto** - Veto for protons/neutrons and nuclei -* **ObserverTimeEvolution** - Records all candidates at a series of equidistant trajectory length intervals. +* **ObserverTimeEvolution** - Records all candidates along their trajectory using linear or logarithmic steps ### Output modules Main output modules From 72e2ea635695941ab01c1f7df0123980fc403432 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 3 Nov 2022 16:24:42 +0100 Subject: [PATCH 06/87] Add link to saga repository. --- include/crpropa/magneticField/AMRMagneticField.h | 1 + 1 file changed, 1 insertion(+) diff --git a/include/crpropa/magneticField/AMRMagneticField.h b/include/crpropa/magneticField/AMRMagneticField.h index a15d535db..b601b12b2 100644 --- a/include/crpropa/magneticField/AMRMagneticField.h +++ b/include/crpropa/magneticField/AMRMagneticField.h @@ -31,6 +31,7 @@ namespace crpropa { /** @class AMRMagneticField @brief Wrapper for saga::MagneticField + Can be found in github.com/rafaelab/saga */ class AMRMagneticField: public MagneticField { From 643791eff54a74740801a4e03ed3f1502b213fe2 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 3 Nov 2022 17:42:54 +0100 Subject: [PATCH 07/87] implemented style convention in observer class --- include/crpropa/module/Observer.h | 16 ++++++++-------- src/module/Observer.cpp | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index 6262fc34d..5f49bbeff 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -50,11 +50,11 @@ class Observer: public Module { bool clone; bool makeInactive; public: - /** Default observer constructor. + /** Default observer constructor */ Observer(); - /** Add a feature to the observer. - @param feature observer feature to be add to the Observer object + /** Add a feature to the observer + @param feature observer feature to be added to the Observer object */ void add(ObserverFeature *feature); /** Perform some specific actions upon detection of candidate @@ -65,7 +65,7 @@ class Observer: public Module { void process(Candidate *candidate) const; std::string getDescription() const; void setFlag(std::string key, std::string value); - /** Determine whether candidate should be deactivated on detection. + /** Determine whether candidate should be deactivated on detection @param deactivate if true, deactivate detected particles; if false, continue tracking them */ void setDeactivateOnDetection(bool deactivate); @@ -85,7 +85,7 @@ class ObserverDetectAll: public ObserverFeature { /** @class ObserverSurface - @brief Detects particles crossing a given surface + @brief Detects particles crossing the boundaries of a surface defined (see, e.g., `Geometry` module) */ class ObserverSurface: public ObserverFeature { private: @@ -230,7 +230,7 @@ class ObserverElectronVeto: public ObserverFeature { /** @class ObserverParticleIdVeto - @brief Custom veto for user-defined particle types. + @brief Custom veto for user-defined particle types Vetoes for more than one type of particle can be added by calling this feature multiple times. */ @@ -274,9 +274,9 @@ class ObserverTimeEvolution: public ObserverFeature { @param */ ObserverTimeEvolution(double min, double max, double numb, bool log); - // add a new time step to the detection time list of the observer + // Add a new time step to the detection time list of the observer void addTime(const double &position); - // using log or lin spacing of times in the range between min and + // Using log or lin spacing of times in the range between min and // max for observing particles void addTimeRange(double min, double max, double numb, bool log = false); const std::vector& getTimes() const; diff --git a/src/module/Observer.cpp b/src/module/Observer.cpp index bdae81e8f..c600dce50 100644 --- a/src/module/Observer.cpp +++ b/src/module/Observer.cpp @@ -297,7 +297,7 @@ DetectionState ObserverTimeEvolution::checkDetection(Candidate *c) const { // Calculate the distance to next detection double distance = length - detList[index]; - // Limit next Step and detect candidate + // Limit next step and detect candidate. // Increase the index by one in case of detection if (distance < 0.) { c->limitNextStep(-distance); From 94474cd1b43b3dd4463ffb3f78281cf28293f890 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 4 Nov 2022 16:18:17 +0100 Subject: [PATCH 08/87] Additional documentation of magnetic fields. Including them into MagneticField group. --- include/crpropa/magneticField/AMRMagneticField.h | 15 +++++++++++++++ .../magneticField/ArchimedeanSpiralField.h | 9 +++++++-- include/crpropa/magneticField/CMZField.h | 13 ++++++++++--- .../crpropa/magneticField/GalacticMagneticField.h | 11 +++++++++++ 4 files changed, 43 insertions(+), 5 deletions(-) diff --git a/include/crpropa/magneticField/AMRMagneticField.h b/include/crpropa/magneticField/AMRMagneticField.h index b601b12b2..df95f742c 100644 --- a/include/crpropa/magneticField/AMRMagneticField.h +++ b/include/crpropa/magneticField/AMRMagneticField.h @@ -15,6 +15,8 @@ #include "crpropa/magneticField/MagneticField.h" #include "crpropa/Vector3.h" +#include "kiss/logger.h" + #include "saga/LocalProperties.h" #include "saga/AMRgrid.h" #include "saga/MagneticField.h" @@ -32,6 +34,10 @@ namespace crpropa { @class AMRMagneticField @brief Wrapper for saga::MagneticField Can be found in github.com/rafaelab/saga + + Deprecation Warning: + As SAGA (SQLite AMR Grid Application) is no longer supported, the AMRMagenticField + class will be removed in the future. */ class AMRMagneticField: public MagneticField { @@ -42,12 +48,21 @@ class AMRMagneticField: public MagneticField { double cfMagneticField; public: + /** Constructor + @param field_ saga magnetic field + @param convLength rescaling length + @param convDensity rescaling density + @param convMagneticField rescaling magnetic field strength +*/ AMRMagneticField(saga::ref_ptr field_, double convLength, double convDensity, double convMagneticField) { field = field_; cfLength = convLength; cfDensity = convDensity; cfMagneticField = convMagneticField; + + KISS_LOG_WARNING << "DEPRECATION WARNING: AMRMagneticField class will be removed in the future, + as the underlying library (saga) is no longer supported." } Vector3d getField(const Vector3d &position) const { diff --git a/include/crpropa/magneticField/ArchimedeanSpiralField.h b/include/crpropa/magneticField/ArchimedeanSpiralField.h index c8a8966b8..3ac15f2fa 100644 --- a/include/crpropa/magneticField/ArchimedeanSpiralField.h +++ b/include/crpropa/magneticField/ArchimedeanSpiralField.h @@ -16,10 +16,15 @@ namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ + /** @class ArchimedeanSpiralField -@brief Magnetic field model following a Archimedean spiral +@brief Magnetic field model following a Archimedean spiral. See e.g. Jokipii, Levy & Hubbard 1977 */ @@ -52,7 +57,7 @@ class ArchimedeanSpiralField: public MagneticField { double getOmega() const; double getVw() const; }; - +/** @} */ } // end namespace crpropa diff --git a/include/crpropa/magneticField/CMZField.h b/include/crpropa/magneticField/CMZField.h index db1d3f023..f786b4313 100644 --- a/include/crpropa/magneticField/CMZField.h +++ b/include/crpropa/magneticField/CMZField.h @@ -8,6 +8,12 @@ namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ + + /** * @class CMZField * @brief Magnetic Field Model in the Galactic Center from M. Guenduez et al. @@ -41,8 +47,8 @@ class CMZField: public MagneticField { void setUseNTFField(bool use); void setUseRadioArc(bool use); - /** Magnetic field in the poloidal model. Used für inter-cloud(IC), non-thermal-filaments(NTF) and for the RadioArc. - @param position position in galactic coordinates with Eart at (-8.5kpc, 0,0) + /** Magnetic field in the poloidal model. Used for inter-cloud(IC), non-thermal-filaments(NTF) and for the RadioArc. + @param position position in galactic coordinates with Earth at (-8.5kpc, 0,0) @param mid midpoint of the object @param B1 normalized magnetic field strength @param a fitting parameter for the radial scale height @@ -51,7 +57,7 @@ class CMZField: public MagneticField { Vector3d BPol(const Vector3d& position,const Vector3d& mid, double B1, double a, double L) const; /** Magnetic field in the azimuthal model. Used for molecular clouds (MC) - @param position position in galactic coordinates with Eart at (-8.5kpc, 0,0) + @param position position in galactic coordinates with Earth at (-8.5kpc, 0,0) @param mid midpoint of the object @param B1 normalized magnetic field strength @param eta ratio between radial and azimuthal component @@ -66,6 +72,7 @@ class CMZField: public MagneticField { Vector3d getField(const Vector3d& pos) const; }; +/** @} */ } // namespace crpropa diff --git a/include/crpropa/magneticField/GalacticMagneticField.h b/include/crpropa/magneticField/GalacticMagneticField.h index 305f4c236..420fc25a3 100644 --- a/include/crpropa/magneticField/GalacticMagneticField.h +++ b/include/crpropa/magneticField/GalacticMagneticField.h @@ -5,6 +5,10 @@ #include namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ /** @class TorroidalHaloField @@ -31,6 +35,12 @@ class TorroidalHaloField: public MagneticField { return Vector3d(cos(phi), sin(phi), 0) * b; } }; +/** @} */ + +/** + * \addtogroup MagneticFields + * @{ + */ /** @class LogarithmicSpiralField @@ -90,6 +100,7 @@ class LogarithmicSpiralField: public MagneticField { return Vector3d(cosPitch * cos(phi), sinPitch * sin(phi), 0) * b; } }; +/** @} */ }// namespace crpropa From 437ad1f276b94af515b7f16d99c43483396ac8f1 Mon Sep 17 00:00:00 2001 From: mertelx Date: Mon, 7 Nov 2022 17:50:44 +0100 Subject: [PATCH 09/87] Add more documentation of parameters. --- include/crpropa/module/Acceleration.h | 42 ++++++++++++++++++++++----- 1 file changed, 34 insertions(+), 8 deletions(-) diff --git a/include/crpropa/module/Acceleration.h b/include/crpropa/module/Acceleration.h index 6551f33fb..0a4100540 100644 --- a/include/crpropa/module/Acceleration.h +++ b/include/crpropa/module/Acceleration.h @@ -19,6 +19,11 @@ namespace crpropa { class StepLengthModifier : public Referenced { public: /// Returns an update of the steplength + /// @param stepLength Modifies step length, e.g., based on scattering + /// model. + /// @param candidate Additional candidate properties are usually + /// included in the calculation of the updated + /// step length. virtual double modify(double steplength, Candidate *candidate) = 0; }; @@ -62,6 +67,11 @@ class SecondOrderFermi : public AbstractAccelerationModule { std::vector angleCDF; public: + /** Constructor + @param scatterVelocity velocity of scattering centers + @param stepLength average mean free path + @param sizeOfPitchangleTable number of precalculated pitch angles + */ SecondOrderFermi(double scatterVelocity = .1 * crpropa::c_light, double stepLength = 1. * crpropa::parsec, unsigned int sizeOfPitchangleTable = 10000); @@ -81,6 +91,10 @@ class DirectedFlowScattering : public AbstractAccelerationModule { crpropa::Vector3d __scatterVelocity; public: + /** Constructor + * @param scatterCenterVelocity velocity of scattering centers + * @param stepLength average mean free path + */ DirectedFlowScattering(crpropa::Vector3d scatterCenterVelocity, double stepLength = 1. * parsec); virtual crpropa::Vector3d @@ -97,6 +111,9 @@ class DirectedFlowOfScatterCenters : public StepLengthModifier { Vector3d __scatterVelocity; public: + /** Constructor + * @param scatterCenterVelocity velocity of scattering centers + */ DirectedFlowOfScatterCenters(const Vector3d &scatterCenterVelocity); double modify(double steplength, Candidate *candidate); }; @@ -127,6 +144,13 @@ class QuasiLinearTheory : public StepLengthModifier { double __minimumRigidity; public: + /** Constructor + * @param referenecEnergy reference energy - break of power spectrum + * @param turbulenceIndex power law index of the isotropic magnetic + * turbulence power spectrum; default is set + * to Kolmogorov turbulence. + * @param minimumRigidity minimal rigidity + */ QuasiLinearTheory(double referenecEnergy = 1. * EeV, double turbulenceIndex = 5. / 3, double minimumRigidity = 0); @@ -151,14 +175,16 @@ class ParticleSplitting : public Module { std::string counterid; public: - /// @params surface The surface to monitor - /// @params crossing_threshold Number of crossings after which a particle is split - /// @params num_splits Number of particles the candidate is split into - /// @params min_weight Minimum weight to consider. Particles with - /// a lower weight are not split again. - /// @params counterid An unique string to identify the particle - /// property used for counting. Useful if - /// multiple splitting modules are present. + /** Constructor + @param surface The surface to monitor + @param crossing_threshold Number of crossings after which a particle is split + @param num_splits Number of particles the candidate is split into + @param min_weight Minimum weight to consider. Particles with + a lower weight are not split again. + @param counterid An unique string to identify the particle + property used for counting. Useful if + multiple splitting modules are present. + */ ParticleSplitting(Surface *surface, int crossingThreshold = 50, int numSplits = 5, double minWeight = 0.01, std::string counterid = "ParticleSplittingCounter"); From f651bbad12f93136046375b45f9b70f18957951d Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 10 Nov 2022 14:56:24 +0100 Subject: [PATCH 10/87] replaced ObserverPoint by Observer1D in all notebooks and tests --- doc/pages/Cpp-projects.md | 2 +- .../example_notebooks/basics/basics.v4.ipynb | 199 ++++++++--------- .../photon_propagation/cascade_1d.ipynb | 74 +++---- .../secondaries/neutrinos.v4.ipynb | 76 +++---- .../secondaries/photons.v4.ipynb | 202 +++++++++--------- .../secondaries/secondary_photons.ipynb | 112 +++++----- .../example_notebooks/sim1D/sim1D.v4.ipynb | 22 +- test/testBreakCondition.cpp | 2 +- test/testOutput.cpp | 2 +- test/testSimulationExecution.py | 2 +- 10 files changed, 324 insertions(+), 369 deletions(-) diff --git a/doc/pages/Cpp-projects.md b/doc/pages/Cpp-projects.md index 78d8e3b75..adcd8d619 100644 --- a/doc/pages/Cpp-projects.md +++ b/doc/pages/Cpp-projects.md @@ -23,7 +23,7 @@ int main(void) { sim.add(new MinimumEnergy(1*EeV)); ref_ptr obs = new Observer(); - obs->add(new ObserverPoint()); + obs->add(new Observer1D()); obs->onDetection(new TextOutput("events.txt", Output::Event1D)); obs->onDetection(new TextOutput()); sim.add(obs); diff --git a/doc/pages/example_notebooks/basics/basics.v4.ipynb b/doc/pages/example_notebooks/basics/basics.v4.ipynb index ae2f187f8..e64fc8ff6 100644 --- a/doc/pages/example_notebooks/basics/basics.v4.ipynb +++ b/doc/pages/example_notebooks/basics/basics.v4.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction to Python Steering\n", "The following is a tour of the basic layout of CRPropa 3, showing how to setup and run a 1D simulation of the extragalactic propagation of UHECR protons from a Python shell.\n", @@ -15,12 +16,13 @@ "In general the order of modules doesn't matter much for sufficiently small propagation steps. For good practice, we recommend the order: Propagator --> Interactions -> Break conditions -> Observer / Output.\n", "\n", "**Please note** that all input, output and internal calculations are done using SI-units to enforce expressive statements such as ```E = 1 * EeV``` or ```D = 100 * Mpc```." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "from crpropa import *\n", "\n", @@ -35,38 +37,30 @@ "sim.add(ElectronPairProduction(CMB()))\n", "sim.add(NuclearDecay())\n", "sim.add(MinimumEnergy(1 * EeV))" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Propagating a single particle" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The simulation can now be used to propagate a cosmic ray, which is called candidate. We create a 100 EeV proton and propagate it using the simulation. The propagation stops when the energy drops below the minimum energy requirement that was specified. The possible propagation distances are rather long since we are neglecting cosmology in this example." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 5, - "source": [ - "cosmicray = Candidate(nucleusId(1, 1), 200 * EeV, Vector3d(100 * Mpc, 0, 0))\n", - "\n", - "sim.run(cosmicray)\n", - "print(cosmicray)\n", - "print('Propagated distance', cosmicray.getTrajectoryLength() / Mpc, 'Mpc')" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "CosmicRay at z = 0\n", " source: Particle 1000010010, E = 200 EeV, x = 100 0 0 Mpc, p = -1 0 0\n", @@ -75,32 +69,32 @@ ] } ], - "metadata": {} + "source": [ + "cosmicray = Candidate(nucleusId(1, 1), 200 * EeV, Vector3d(100 * Mpc, 0, 0))\n", + "\n", + "sim.run(cosmicray)\n", + "print(cosmicray)\n", + "print('Propagated distance', cosmicray.getTrajectoryLength() / Mpc, 'Mpc')" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Defining an observer\n", "\n", "To define an observer within the simulation we create a ```Observer``` object.\n", "The convention of 1D simulations is that cosmic rays, starting from positive coordinates, propagate in the negative direction until the reach the observer at 0. Only the x-coordinate is used in the three-vectors that represent position and momentum." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 6, - "source": [ - "# add an observer\n", - "obs = Observer()\n", - "obs.add(ObserverPoint()) # observer at x = 0\n", - "sim.add(obs)\n", - "print(obs)" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Observer\n", " ObserverPoint: observer at x = 0\n", @@ -110,22 +104,30 @@ ] } ], - "metadata": {} + "source": [ + "# add an observer\n", + "obs = Observer()\n", + "obs.add(Observer1D()) # observer at x = 0\n", + "sim.add(obs)\n", + "print(obs)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Defining the output file \n", "We want to save the propagated cosmic rays to an output file.\n", "Plain text output is provided by the TextOutput module. \n", "For the type of information being stored we can use one of five presets: Event1D, Event3D, Trajectory1D, Trajectory3D and Everything. \n", "We can also fine tune with ```enable(XXXColumn)``` and ```disable(XXXColumn)```" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": {}, + "outputs": [], "source": [ "# trajectory output\n", "output1 = TextOutput('trajectories.txt', Output.Trajectory1D)\n", @@ -136,22 +138,22 @@ "#output1.enable(Output.CurrentEnergyColumn) # current energy\n", "#output1.enable(Output.CurrentIdColumn) # current particle type\n", "# ...\n" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "If in the example above ```output1``` is added to the module list, it is called on every propagation step to write out the cosmic ray information. \n", "To save only cosmic rays that reach our observer, we add an output to the observer that we previously defined.\n", "This time we are satisfied with the output type Event1D." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": {}, + "outputs": [], "source": [ "# event output\n", "output2 = TextOutput('events.txt', Output.Event1D)\n", @@ -159,42 +161,33 @@ "\n", "#sim.run(cosmicray)\n", "#output2.close()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Similary, the output could be linked to the ```MinimumEnergy``` module to save those cosmic rays that fall below the minimum energy, and so on. \n", "**Note:** If we want to use the CRPropa output file from within the same script that runs the simulation, the output module should be explicitly closed after the simulation run in order to get all events flushed to the file." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Defining the source\n", "To avoid setting each individual cosmic ray by hand we defince a cosmic ray source.\n", "The source is located at a distance of 100 Mpc and accelerates protons with a power law spectrum and energies between 1 - 200 EeV." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 9, - "source": [ - "# cosmic ray source\n", - "source = Source()\n", - "source.add(SourcePosition(100 * Mpc))\n", - "source.add(SourceParticleType(nucleusId(1, 1)))\n", - "source.add(SourcePowerLawSpectrum(1 * EeV, 200 * EeV, -1))\n", - "print(source)" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cosmic ray source\n", " SourcePosition: 100 0 0 Mpc\n", @@ -204,116 +197,114 @@ ] } ], - "metadata": {} + "source": [ + "# cosmic ray source\n", + "source = Source()\n", + "source.add(SourcePosition(100 * Mpc))\n", + "source.add(SourceParticleType(nucleusId(1, 1)))\n", + "source.add(SourcePowerLawSpectrum(1 * EeV, 200 * EeV, -1))\n", + "print(source)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Running the simulation\n", "\n", "Finally we run the simulation to inject and propagate 10000 cosmic rays. An optional progress bar can show the progress of the simulation." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 10, + "metadata": {}, + "outputs": [], "source": [ "sim.setShowProgress(True) # switch on the progress bar\n", "sim.run(source, 10000)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (Optional) Plotting\n", "\n", "This is not part of CRPropa, but since we're at it we can plot the energy spectrum of detected particles to observe the GZK suppression.\n", "The plotting is done here using matplotlib, but of course you can use whatever plotting tool you prefer. \n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 11, - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "output2.close() # close output file before loading\n", - "data = np.genfromtxt('events.txt', names=True)\n", - "print('Number of events', len(data))\n", - "\n", - "logE0 = np.log10(data['E0']) + 18\n", - "logE = np.log10(data['E']) + 18\n", - "\n", - "plt.figure(figsize=(10, 7))\n", - "h1 = plt.hist(logE0, bins=25, range=(18, 20.5), histtype='stepfilled', alpha=0.5, label='At source')\n", - "h2 = plt.hist(logE, bins=25, range=(18, 20.5), histtype='stepfilled', alpha=0.5, label='Observed')\n", - "plt.xlabel('log(E/eV)')\n", - "plt.ylabel('N(E)')\n", - "plt.legend(loc = 'upper left', fontsize=20)\n" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Number of events 10000\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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aa6/lZz/7WVn7Z599xkknncTVV1/NqaeeSq9evQCYNm0a06dP57DDDmP27Nk0bdoUgMsvv5y+fftu9/srSVJV3GWZJ1auXAnA3nvv/bl9S/u89957FdoHDhxYIYwB/OhHP2LfffflySefZPny5RXm7bHHHpXW3bhxY5o3b172/MYbbwTg1ltvrRDGAIYPH06vXr24++67q6zz17/+daUwBpnRr+LiYu65554K7Q8//DDr1q3jzDPPpGHDzP8iH3zwAXfddRd9+vSpEMYAmjZtyrXXXkuMkalTp5a1T548GYCrr766LIwBFBYWctlll1VZqyRJ2+MIWZ6IMQKZa2TtbN8BAwZU6ltQUED//v1ZunQpCxcu5Itf/CIDBgygU6dOTJgwgQULFjB48GD69etHr169KCgoqLD8s88+S6NGjbjvvvu47777Kq2/uLiY1atXVxrtatq0KV/+8perrP973/sel112GVOmTOGHP/xhWfuUKVOAirsrn3/+eUpKSgghMG7cuErr2rRpEwBLliwpa1uwYAENGjSgf//+lfofeeSRVdYkSdL2GMjyRMeOHfn73//O22+//bl9V6xYUbZMeR06dKiy/xe+8AUA1q9fD0CLFi2YN28eY8eOZcaMGTz22GMAtG3blvPOO49LL72URo0aAZkRqs2bN3P55Zdvt6aPP/64QiBr3779NsNl586dGTRoEDNnzmTJkiUccMABrFq1ir/85S/06tWLgw8+uKzvBx98AGSC2fPPP7/d1y+1fv16CgsLy95DVd8LSZJ2hLss80TpaM4TTzyx3X4lJSVlV5jv169fhXnvv/9+lcv885//BKBly5ZlbZ07d+b2229n1apVLFq0iBtvvJE2bdpwxRVXcMUVV5T1a9myJa1btybGuN3HF7/4xQqv+XkjfaWjYKWjYnfffTebN2+uMDpWvuYLLrhgu68/e/bsCsusXbu2bPSsqu+FJEk7wkCWJ4YPH05BQQEPPvggixcv3ma/SZMm8d5779GjR49KuyiffvrpSv1LSkrKThg45JBDKs0PIdCzZ09+/OMfM3PmTAAeeuihsvmHHXYY69at225NO+OUU06hRYsW3HXXXWzZsoUpU6bQsGFDhg4dWqHfoYceSoMGDfi///u/aq+7d+/ebNmypex9l+ftkiRJO8NAlif22Wcf/vM//5NNmzZxwgkn8Oqrr1bq89BDD3H++edTUFDAzTffTIMGFX88nnzySR555JEKbb/73e9YunQpRx11VNko1qJFi1i2bFml9ZeOsO25555lbRdccAEA3//+9yudRADwySefMG/evB17s2ROKDj99NN59913ueGGG3jppZcYPHgw7du3r9Cvffv2nHnmmcyfP5/x48ezefPmSutaunQpb731VtnzESNGAPCLX/yCzz77rKx97dq1FS6PIUlSdXkMWR4ZN24cn3zyCddffz0HH3wwxx57LD179mTTpk389a9/5bnnnmOPPfZg2rRpDBw4sNLyQ4YM4eSTT+bkk09mv/3246WXXuLRRx+lsLCQm2++uazfE088wZgxYzjiiCPYf//9ad++PStWrGD69Ok0aNCAiy66qKzvoEGDmDBhApdccgndunVj8ODBdO3alY8//pjly5fz9NNP079/f/7yl7/s8PsdNmwYt912G5dccknZ86r87ne/4/XXX+eXv/wl//M//0P//v3p0KED7733HkuWLOH5559n2rRpdO3aFYAzzjiDP/7xj8yYMYMDDzyQE088kU2bNnH//ffTt29fli5dusO1SpLyWyg9oy4nKw/hAuBsIAKvACOAjsA9QCGwADgrxlgcQmgC3Al8BfgA+HaMcdn21t+nT584f/78z62j9MBuZfztb3/jpptuYs6cOfzzn/+koKCAoqIijjvuOEaPHk3nzp0r9C+9HtjkyZNp27YtV111FS+//DKNGjVi0KBBXHPNNXTv3r2s/5IlS7j11luZM2cOy5cvZ8OGDXTs2JE+ffqUBbWtzZ07lxtvvJG5c+eyZs0aWrZsSadOnRg4cCBDhw6lT58+ZX2LiooAqhyF21q3bt144403KCwsZOXKlTRu3LjKfsXFxUycOJGpU6eyePFiPvvsMzp06EC3bt0YMmQIZ511VoWTCoqLi5kwYQJ33HEH7777Lh07duTMM8/kl7/8JU2bNmXAgAE7tPvSn1HlrdnXpK7g8x11SeoKVE+EEF6IMfapcl6uAlkIoRMwF/hSjPFfIYR7gUeBwcCfYoz3hBBuAV6KMf4+hHAe8OUY4/8fQvgOcHKM8dvbew0DmeoLf0aVtwxkyiPbC2S5PoasIbBHCKEhsCewEhgI3J+dPwU4KTt9YvY52fmDQnUumiVJkrSby1kgizG+C1wHvE0miK0HXgA+jDGWHjm9AuiUne4EvJNddnO2fxu2EkI4J4QwP4Qwf/Xq1bkqX5IkqdbkLJCFEFqTGfXqCvwH0Aw4voqupftMqxoNq7Q/NcY4McbYJ8bYp127djVVriRJUjK53GX5deCtGOPqGOMm4E/AEUCr7C5MgM5A6bUOVgB7A2TntwTW5rA+SZKkOiGXgext4LAQwp7ZY8EGAa8Cs4FTs32GAdOz0zOyz8nOfzLm8hRQSZKkOiKXx5A9R+bg/AVkLnnRAJgI/BwYE0J4g8wxYrdnF7kdaJNtHwNcnKvaJEmS6pKcXhg2xjgWGLtV85vAoVX0/Qw4LYe1fO79D6UUHAiWJOXFrZMKCgqqvBG0VBds2rSJgoKC1GVIkhLKi0DWvHlzNmzYkLoMqUobNmygefPmqcuQJCWUF4GssLCQdevWsWbNGoqLi91FpORijBQXF7NmzRrWrVtHYWFh6pIkSQnlxc3FmzRpQpcuXVi7di3Lli2jpKQkdUkSBQUFNG/enC5dutCkSZPU5UiSEsqLQAaZUNaxY0c6duyYuhRJkqQK8mKXpSRJUl1mIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJZbTQBZCaBVCuD+E8PcQwpIQwuEhhMIQwswQwuvZr62zfUMI4cYQwhshhJdDCL1zWZskSVJdkesRst8Cf4kx7g8cDCwBLgZmxRi7AbOyzwGOB7plH+cAv89xbZIkSXVCzgJZCKEF8DXgdoAYY3GM8UPgRGBKttsU4KTs9InAnTFjHtAqhNAxV/VJkiTVFbkcIdsHWA1MDiEsDCHcFkJoBnSIMa4EyH5tn+3fCXin3PIrsm0VhBDOCSHMDyHMX716dQ7LlyRJqh25DGQNgd7A72OMhwCf8O/dk1UJVbTFSg0xTowx9okx9mnXrl3NVCpJkpRQLgPZCmBFjPG57PP7yQS090t3RWa/rirXf+9yy3cG3sthfZIkSXVCzgJZjPGfwDshhB7ZpkHAq8AMYFi2bRgwPTs9A/he9mzLw4D1pbs2JUmS6rOGOV7/j4G7QwiNgTeBEWRC4L0hhFHA28Bp2b6PAoOBN4BPs30lSZLqvZwGshjji0CfKmYNqqJvBH6Yy3okSZLqIq/UL0mSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCWW6wvDSpLqk9nXpK5AqpccIZMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUqsYXU6hRCaAt8Evgr8B/AvYBHw5xjj4tyVJ0mSVP99biALIYwDhgBPAc8Bq4CmQHdgQjas/TTG+HLuypQkSaq/qjNC9nyMcdw25l0fQmgPdKm5kiRJkvLL5wayGOOftzUvhNAwxriKzKiZJEmSdsLnHtQfQphbbvp/tpr9txqvSJIkKc9U5yzLZuWme241L9RgLZIkSXmpOoEs7uQ8SZIkVUN1DupvFUI4mUx4axVCOCXbHoCWOatMkiQpT1QnkD0NnFBueki5eXNqvCJJkqQ8U52zLEfURiGSPsfsa2p2fUddUrPrkyTttOpcGPa7wNQY45ZtzN8X6BhjnFvVfO2GavqDH/zwzwf+3EjSTqvOLss2wMIQwgvAC8BqMlfq3w8YAKwBLs5ZhVJVHC2SJNUj1dll+dsQwu+AgUA/4Mtk7mW5BDgrxvh2bkuUJEmq36p1c/EYYwkwM/uQJElSDarOMWS/3M7sGGMcX4P1qL7KxfFFqlOeffODGl/n4UfV+ColqU6qzgjZJ1W0NQNGkTm+zEAmKSdumPmPGl3fBUd3r9H1SVJNqc4xZL8unQ4hNAfOB0YA9wC/3tZykrSrDnt7Yg2v8boaXp8k1YxqHUMWQigExgBnAlOA3jHGdbksTPVLTe/OOnyfNjW6PkmSUqrOMWT/BZwCTAQOijF+nPOq6pLd4dpKNVxjLo4FyjseMydJ2gHVGSH7KbARuBT4RQihtD2QOai/RY5qkySp3qnpYyPB4yPrg+ocQ9agNgqpqzxzTHVFje/2xVE8SaorqnUMmVTX1Hg4MSRLkhLK69EvSZKkusBAJkmSlJi7LCWpPvOM37zgRZR3f46QSZIkJWYgkyRJSsxdlvWAF3LddTU+3L8b/Gb5c1NHuYtRykuOkEmSJCW2G/wfL0n5w/u+SvnJQCYBh709sUbX92yNrk111m6we9GAJ+0eDGSS8kY+Hisoaffgn5MUdoP/qiVpd+Wt1Xad1zWrfQayBDy7TZIy/HsoZXiWpSRJUmKOkEmStB01vftOqoqBTJJ2Uj7ubsvH9yzVhpzvsgwhFIQQFoYQHsk+7xpCeC6E8HoI4Y8hhMbZ9ibZ529k5xflujZJkqS6oDaOITsfWFLu+bXADTHGbsA6YFS2fRSwLsa4H3BDtp8kSVK9l9NAFkLoDHwDuC37PAADgfuzXaYAJ2WnT8w+Jzt/ULa/JElSvZbrEbLfAD8DtmSftwE+jDFuzj5fAXTKTncC3gHIzl+f7V9BCOGcEML8EML81atX57J2SZKkWpGzQBZC+CawKsb4QvnmKrrGasz7d0OME2OMfWKMfdq1a1cDlUqSJKWVy7Ms+wEnhBAGA02BFmRGzFqFEBpmR8E6A+9l+68A9gZWhBAaAi2BtTmsT5IkqU7IWSCLMV4CXAIQQjgSuDDGeGYI4T7gVOAeYBgwPbvIjOzzZ7Pzn4wxVhohk6SdVdM3kZekmpLiSv0/B8aEEN4gc4zY7dn224E22fYxwMUJapMkSap1tXJh2BjjU8BT2ek3gUOr6PMZcFpt1CNJklSXeC9LSZKkxAxkkiRJiRnIJEmSEvPm4pIkbUdNn507r8s5Nbo+1Q+OkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDFdkvPsAAAvhSURBVGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSixngSyEsHcIYXYIYUkIYXEI4fxse2EIYWYI4fXs19bZ9hBCuDGE8EYI4eUQQu9c1SZJklSX5HKEbDPw0xjjAcBhwA9DCF8CLgZmxRi7AbOyzwGOB7plH+cAv89hbZIkSXVGzgJZjHFljHFBdvojYAnQCTgRmJLtNgU4KTt9InBnzJgHtAohdMxVfZIkSXVFrRxDFkIoAg4BngM6xBhXQia0Ae2z3ToB75RbbEW2bet1nRNCmB9CmL969epcli1JklQrch7IQgh7AQ8Ao2OMG7bXtYq2WKkhxokxxj4xxj7t2rWrqTIlSZKSyWkgCyE0IhPG7o4x/inb/H7prsjs11XZ9hXA3uUW7wy8l8v6JEmS6oJcnmUZgNuBJTHG68vNmgEMy04PA6aXa/9e9mzLw4D1pbs2JUmS6rOGOVx3P+As4JUQwovZtv8EJgD3hhBGAW8Dp2XnPQoMBt4APgVG5LA2SZKkOiNngSzGOJeqjwsDGFRF/wj8MFf1SJIk1VVeqV+SJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYgYySZKkxAxkkiRJiRnIJEmSEjOQSZIkJWYgkyRJSsxAJkmSlJiBTJIkKTEDmSRJUmIGMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISa5i6AEmS8slhb0+s8XXO63JOja9TtcsRMkmSpMQMZJIkSYkZyCRJkhIzkEmSJCVmIJMkSUrMQCZJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgkSZISM5BJkiQlZiCTJElKzEAmSZKUmIFMkiQpMQOZJElSYnUqkIUQjgshvBZCeCOEcHHqeiRJkmpDw9QFlAohFAA3AUcDK4DnQwgzYoyvpq1MkiTtihtm/qNG13fB0d1rdH11QV0aITsUeCPG+GaMsRi4BzgxcU2SJEk5V2dGyIBOwDvlnq8A/r+tO4UQzgHOyT79OITwWo7ragusyfFraMe5Xeoet0nd5Hape3KwTX5ds6ur48bkZrW18bvyxW3NqEuBLFTRFis1xDgRmJj7cjJCCPNjjH1q6/VUPW6XusdtUje5Xeoet0ndlHq71KVdliuAvcs97wy8l6gWSZKkWlOXAtnzQLcQQtcQQmPgO8CMxDVJkiTlXJ3ZZRlj3BxC+BHwGFAATIoxLk5cFtTi7lHtELdL3eM2qZvcLnWP26RuSrpdQoyVDtOSJElSLapLuywlSZLykoFMkiQpsbwOZCGESSGEVSGEReXaeoUQ5oUQXgwhzA8hHLqNZYeFEF7PPobVXtX12y5uk5JsnxdDCJ4QUkO2sU0ODiE8G0J4JYTwcAihxTaW9XZoObKL22VZts+LIYT5tVd1/RZC2DuEMDuEsCSEsDiEcH62vTCEMDP7eTEzhNB6G8v7uVLDamCb1N7nSowxbx/A14DewKJybY8Dx2enBwNPVbFcIfBm9mvr7HTr1O+nPjx2dptk532cuv76+NjGNnkeGJCdHgmMr2K5AmApsA/QGHgJ+FLq91NfHju7XbLzlgFtU7+H+vYAOgK9s9PNgX8AXwJ+BVycbb8YuLaKZf1cqWPbJDuv1j5X8nqELMY4B1i7dTNQ+l9lS6q+FtqxwMwY49oY4zpgJnBczgrNI7uwTZQj29gmPYA52emZwLeqWNTboeXQLmwX5UiMcWWMcUF2+iNgCZm70JwITMl2mwKcVMXifq7kwC5uk1qV14FsG0YD/xVCeAe4Drikij5V3eapUy3Ulq+qs00AmmZ3ac4LIST/5arnFgEnZKdPo+JFnUv5e1L7qrNdIPNPzuMhhBeyt6NTDQshFAGHAM8BHWKMKyETEID2VSzi70uO7cQ2gVr8XDGQVfYD4IIY497ABcDtVfSp1m2eVGOqs00AusTMbS+GAr8JIexbWwXmoZHAD0MIL5DZDVBcRR9/T2pfdbYLQL8YY2/g+Gz/r9VWgfkghLAX8AAwOsa4obqLVdHm70sN2cltArX4uWIgq2wY8Kfs9H1kdrtszds81a7qbBNijO9lv74JPEXmPyHlQIzx7zHGY2KMXwGmkTlWbGv+ntSyam6X8r8rq4AH2cbvlHZcCKERmQ/+u2OMpX+33g8hdMzO7wisqmJRf19yZBe2Sa1+rhjIKnsPGJCdHgi8XkWfx4BjQgits2dmHJNtU2587jbJbosm2em2QD/g1VqrMM+EENpnvzYALgVuqaKbt0OrZdXZLiGEZiGE5qXTZP5+Ldq6n3ZcCCGQGcFfEmO8vtysGWT+sST7dXoVi/u5kgO7sk1q/XMl9RkQKR9k/oNcCWwi89/JKKA/8AKZM8KeA76S7dsHuK3csiOBN7KPEanfS3157Ow2AY4AXsn2eQUYlfq91JfHNrbJ+WTOVvoHMIF/3/XjP4BHyy07ONtnKfCL1O+lPj12druQOev1pexjsdulRrdJfzK7GV8GXsw+BgNtgFlk/pmcBRRm+/u5Uoe3SW1/rnjrJEmSpMTcZSlJkpSYgUySJCkxA5kkSVJiBjJJkqTEDGSSJEmJGcgk7bZCCB/v4vL3hxD2yU4vCyG8EkJ4Mfu4sVy/w0MIt25nPW+FEHps1fabEMLPQggHhRDu2JU6JdV/DVMXIEkphBB6AgUxcwXuUkfFGNdU0f044C/bWd09ZC58e3l23Q2AU8ncomh5CKFzCKFLjPHtGipfUj3jCJmk3V7I+K8QwqLsKNe3s+0NQgg3hxAWhxAeCSE8GkI4NbvYmVR9xfSqDAKeCCEUZF/n+RDCyyGEc7Pzp5EJZKW+BiyLMS7PPn94q/mSVIGBTFJ9cArQCzgY+DrwX9n7050CFAEHAWcDh5dbph+ZO0CUN7vcLssLoOyWKZtijOvJXA1/fYyxL9AX+H4IoWuM8WVgSwjh4Ox6vkMmpJWaD3y1xt6tpHrHXZaS6oP+wLQYYwmZmwY/TSYw9QfuizFuAf4ZQphdbpmOwOqt1lPVLstjgMfLTX+53ChbS6Ab8BbZUbIQwmLgROCX5daxisztiySpSgYySfVB2MF2gH8BTaux7uOB0psSB+DHMcaqbvo8jUxwexp4Oca4qty8ptnXk6QquctSUn0wB/h29hivdmSO4fobMBf4VvZYsg7AkeWWWQLst72VhhAC8GUyNyQGeAz4QQihUXZ+9xBCM4AY41LgAzI39Z621aq6A4t2/u1Jqu8cIZNUHzxI5viwl4AI/CzG+M8QwgNkDshfBPwDeA5Yn13mz2QC2hPl1jM7hFCSnX4ZuBFYGGOM2bbbyByTtiAb1lYDJ5VbfhpwTbae8o7Kvp4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" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "output2.close() # close output file before loading\n", + "data = np.genfromtxt('events.txt', names=True)\n", + "print('Number of events', len(data))\n", + "\n", + "logE0 = np.log10(data['E0']) + 18\n", + "logE = np.log10(data['E']) + 18\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "h1 = plt.hist(logE0, bins=25, range=(18, 20.5), histtype='stepfilled', alpha=0.5, label='At source')\n", + "h2 = plt.hist(logE, bins=25, range=(18, 20.5), histtype='stepfilled', alpha=0.5, label='Observed')\n", + "plt.xlabel('log(E/eV)')\n", + "plt.ylabel('N(E)')\n", + "plt.legend(loc = 'upper left', fontsize=20)\n" + ] } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.3 64-bit" + "display_name": "Python 3.9.5 64-bit", + "name": "python3" }, "language_info": { "name": "python", - "version": "3.8.3", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "interpreter": { - "hash": "d7f94b8b1e41b02170d45ac71ce2d6b011e7cd56207b4c480f5292088bcfab93" + "version": "" } }, "nbformat": 4, "nbformat_minor": 1 -} - +} \ No newline at end of file diff --git a/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb b/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb index aaabcbd5f..53df6bc70 100644 --- a/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb +++ b/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb @@ -2,18 +2,20 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Electromagnetic cascade example\n", "\n", "This is a simple 1D example of gamma-ray propagation over cosmological distances.\n", "Note that only pair production and inverse Compton scattering are relevant for the energy range of this example.\n", "Moreover, the radio background is negligible for the energy range below PeV.\n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 34, + "metadata": {}, + "outputs": [], "source": [ "from crpropa import *\n", "\n", @@ -46,7 +48,7 @@ "sim.add(MinimumEnergy(10 * GeV))\n", "\n", "obs = Observer()\n", - "obs.add(ObserverPoint())\n", + "obs.add(Observer1D())\n", "obs.add(ObserverElectronVeto()) # we are only interested in photons\n", "output = TextOutput('cascade_1d.txt', Output.Event1D)\n", "output.setEnergyScale(eV)\n", @@ -64,23 +66,36 @@ "sim.run(source, 10000, True)\n", "\n", "output.close()\n" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Plotting\n", "\n", "We will now plot the spectrum of photons arriving at Earth.\n", "Note that whenever thinning is used, the weight column has to be enabled and the weights must be accounted for in the analysis." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} + ] } ], "metadata": { - "orig_nbformat": 4, - "language_info": { - "name": "python", - "version": "3.8.3", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.3 64-bit" + "display_name": "Python 3.9.5 64-bit", + "name": "python3" }, - "interpreter": { - "hash": "d7f94b8b1e41b02170d45ac71ce2d6b011e7cd56207b4c480f5292088bcfab93" - } + "language_info": { + "name": "python", + "version": "" + }, + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb b/doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb index 692cc0c16..5d2b570d5 100644 --- a/doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb +++ b/doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "## Example with secondary neutrinos\n", "\n", @@ -9,12 +10,15 @@ "Hadrons and Neutrinos are stored separately using two observers.\n", "\n", "**Note: the simulation might take a minute**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ "from crpropa import *\n", "\n", @@ -40,7 +44,7 @@ "\n", "# observer for hadrons\n", "obs1 = Observer()\n", - "obs1.add(ObserverPoint())\n", + "obs1.add(Observer1D())\n", "obs1.add(ObserverNeutrinoVeto()) # we don't want neutrinos here\n", "output1 = TextOutput('out-nucleons.txt', Output.Event1D)\n", "output1.setEnergyScale(eV)\n", @@ -48,7 +52,7 @@ "m.add(obs1)\n", "# observer for neutrinos\n", "obs2 = Observer()\n", - "obs2.add(ObserverPoint())\n", + "obs2.add(Observer1D())\n", "obs2.add(ObserverNucleusVeto()) # we don't want hadrons here\n", "output2 = TextOutput('out-neutrinos.txt', Output.Event1D)\n", "output2.setEnergyScale(eV)\n", @@ -68,22 +72,33 @@ "\n", "output1.close()\n", "output2.close()" - ], - "outputs": [], - "metadata": { - "collapsed": true - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Plotting the neutrino energy distribution" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" - ], - "image/png": 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n+fMktyT5dJK/Pc4aRynJxiSfT7IvyW1JLmj2Pz3J9UnubP77tHHXOirzXPMbmu0fJpmoR1PnuebfSHJHkq8l+USSY5b9WdPSp5Dkp4CHgD+qqucNeP+3gO9W1SWdF7dCBl1zks8Av1NVf5zknwD/pqpeOsYyR2rINe8G3lVVX0xyHnBCVf3KOOsclSTHAcdV1c1JjgZuAl4LvAX4dlVd2swz9rSqevcYSx2Zea65gB8Cl9P7994zxjJHap5rPp7eU5oHk7wfYLn/zlNzp1BVNzBkzEOSAP8MuLrTolbYkGsuYPYv5ady6FiRNW3INT8buKF5fT3w+k6LWkFV9fWqurl5/f+AffRmBjgbuKo57Cp6XyATYdg1V9W+qjrc2Q1WtXmu+TNVdbA57EZ6IbEsTp3d84+Bb1TVneMupANvB65L8pv0/ij4R2Oupwu3AluBTwFv4PGDKCdGM6vwKcCXgR+tqq9D7wslyd8ZY2krZs41T4V5rvk84Nrlnn9q7hQW8CYm7C5hHr8EvKOqNgLvoDc2ZNKdB7wtyU3A0cAjY65n5JI8BfjvwNur6q/GXU8XvObHrjnJe4CDwIeW+xlTf6fQTKfxOuCF466lI+cCFzSvPwp8cIy1dKKq7gB+GiDJjwOvGW9Fo5Xkb9H7ovhQVX282f2NJMc1dwnHAd8cX4WjN+SaJ9qwa05yLnAW8IpRzADhnQK8ErijqmbGXUhH7gde0rx+OTDxTWazTSdJngC8F/jD8VY0Ok1/2BXAvqr67b63+qeMOZde09lEmOeaJ9awa05yBvBuYGtVfW8knzVFTx9dDbyU3nSz3wB+taquSPJfgBuramK+KGYNumZgP/Af6N0lfh/4V1V107hqHLUh1/wU4G3NIR8HLpqUObWS/CTwZ8At9J68Afi39NqbPwJsAv4CeENVTcTkkvNc85HAfwTWAw8Ce6vq1WMpcsTmuebfpXfd32r23VhVv7isz5qQ/zckSSNg85EkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoaOokeWiFz39Uki8mWdds/8skX+9bu2Nvkuc3712e5PR5zvWFJK+es+/tSX4/yRFJbmimapFGwlCQRu884ONV9WizfTLw3qp6Qd/PLc17p9Gb8niYq4Ftc/ZtA66uqkeAzwJvHGHtmnKGgqZWkncmubX5eXvf/l9pVrO6PsnVSd61xFO/mcfPNfR84JBV/ZI8B/g/s+GR5OeSfKW5k7i8udP4GHBWkiObYzYDfw/4UnOaTzafJ42EoaCplOSFwL+g95f6i4FfSHJKs4zj6+nNV/86YEnLOiY5AnhWVd3dt/u5wH/uazra3uw/E/iT5veeQ+8v/tOr6gXAo8Cbq+pbwFeAM5rf2QZc2zd3063Ai5ZSozQf2yI1rX4S+ERV/TVAko/TW2zpCcCnqurhZv+nZ38hybOA9wBPrapzkjwZ+H166zN8oao+RG8ivgf7fmcj8M2qOnlADa+mF0wAr6A3ffvu3oSYHMVj013PNiF9qvnvebMnqKpHkzyS5OhmRS5pWbxT0LTKEvdTVXdV1Vv7dr0O+FhV/QK9ld0AHgae2HfMycAdh3xI8iTgmKqaXQ41wFV9fQ7Prqr3Ne99EnhFkn8AHDW7LGOfI+nNeCstm6GgaXUD8NokT2r+4v8ZelMTfwn4p0me2KxyNd+CPMcD9zavHwWoqu8A65LMBsPzGRAKwMuAz/dtfxY4p2/th6cneWZzzoeALwBXMmeFwCTPAB6oqh8s6qqlBdh8pKlUVTc3a2l8pdn1war6KkCSncD/Bu4B9gDfHXKaGXrBsJfH/4H1GXrNU39KLxRekuTM2Y+m10x1Jr1O5Nl6bk/yXuAzzWJAP6C3BsQ9zSFX01sLYu6TSC8Ddi36wqUFuJ6CNEeSp1TVQ00Tzw3A9iZEngH8O+BV9JYx/V3g9+g13Xyp6VMgySnAO6vqn8/zGTcDpy33L/ymL+Siqtq/nPNIswwFaY4kHwZOotc3cFVV/fvDOMd5ze8+uuDBh6l50mlbVf3RSn2Gpo+hIElq2dEsSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWr9f1yGQv5w/5ZYAAAAAElFTkSuQmCC" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} + ] } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.3 64-bit" + "display_name": "Python 3.9.5 64-bit", + "name": "python3" }, "language_info": { "name": "python", - "version": "3.8.3", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "interpreter": { - "hash": "d7f94b8b1e41b02170d45ac71ce2d6b011e7cd56207b4c480f5292088bcfab93" + "version": "" } }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/doc/pages/example_notebooks/secondaries/photons.v4.ipynb b/doc/pages/example_notebooks/secondaries/photons.v4.ipynb index e49ea5812..2ab01c312 100644 --- a/doc/pages/example_notebooks/secondaries/photons.v4.ipynb +++ b/doc/pages/example_notebooks/secondaries/photons.v4.ipynb @@ -2,19 +2,26 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Photon Propagation\n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": { + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true + }, + "outputs": [], "source": [ "from crpropa import *\n", "\n", "obs = Observer()\n", - "obs.add(ObserverPoint())\n", + "obs.add(Observer1D())\n", "obs.add(ObserverInactiveVeto())\n", "t = TextOutput(\"photon_electron_output.txt\", Output.Event1D)\n", "obs.onDetection(t)\n", @@ -45,25 +52,37 @@ "sim.add(obs)\n", "sim.run(source,1000,True)\n", "t.close()" - ], - "outputs": [], - "metadata": { - "jupyter": { - "outputs_hidden": true - }, - "scrolled": true - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (Optional) plotting of the results" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
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" - ], - "image/png": 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "jupyter": { - "outputs_hidden": false - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "# Photon Propagation outside of CRPropa with EleCa and DINT\n", "\n", @@ -133,21 +134,22 @@ "\n", "Note that the differing results in EleCa (and correspondingly the high energy part of the combined option) are due to an incorrect sampling of the background photon energies in EleCa. The EleCa support will be removed in the near future.\n", "\n" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Photons from Proton Propagation\n", "\n", "The generation of photons has to be enabled for the individual energy-loss processes in the module chain. Also, separate photon output can be added:" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": {}, + "outputs": [], "source": [ "from crpropa import *\n", "\n", @@ -164,7 +166,7 @@ "\n", "# observer\n", "obs1 = Observer() # proton output\n", - "obs1.add( ObserverPoint() )\n", + "obs1.add( Observer1D() )\n", "obs1.add( ObserverPhotonVeto() ) # we don't want photons here\n", "obs1.onDetection( TextOutput('proton_output.txt', Output.Event1D) )\n", "m.add(obs1)\n", @@ -185,96 +187,113 @@ "\n", "# run simulation\n", "m.run(source, 10000, True)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The file 'photon_output.txt' will contain approximately 300 photons and can be processed as the photon example below." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Propagation with EleCa\n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 4, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], "source": [ "import crpropa\n", "\n", "# Signature: ElecaPropagation(inputfile, outputfile, showProgress=True, lowerEnergyThreshold=5*EeV, magneticFieldStrength=1*nG, background=\"ALL\")\n", "crpropa.ElecaPropagation(\"photon_output.txt\", \"photons_eleca.dat\", True, 0.1*crpropa.EeV, 0.1*crpropa.nG)" - ], - "outputs": [], - "metadata": { - "jupyter": { - "outputs_hidden": true - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Propagation with DINT\n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], "source": [ "import crpropa\n", "\n", "# Signature: DintPropagation(inputfile, outputfile, IRFlag=4, RadioFlag=4, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", "crpropa.DintPropagation(\"photon_output.txt\", \"spectrum_dint.dat\", 4, 4, 0.1*crpropa.nG)" - ], - "outputs": [], - "metadata": { - "jupyter": { - "outputs_hidden": true - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Combined Propagation" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 6, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], "source": [ "import crpropa\n", "\n", "# Signature: DintElecaPropagation(inputfile, outputfile, showProgress=True, crossOverEnergy=0.5*EeV, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", "crpropa.DintElecaPropagation(\"photon_output.txt\", \"spectrum_dint_eleca.dat\", True, 0.5*crpropa.EeV, 0.1*crpropa.nG)" - ], - "outputs": [], - "metadata": { - "jupyter": { - "outputs_hidden": true - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (Optional) Plotting of Results" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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s5+w+Y4H5jlwlGhFBizOjUreJZsmYDpaM6dh3K++e1Y2IiJRNs85ERCRWdVvRiEi6FK6XKaT1Msmr60ST6TEXYH4me45E/5uFi0i1ya+X6ZlUtF4meXWbaIL+4uXP+VaiEalN+fUyUl3qNtF0dPSeXJYZkUwsIiJppskAIiISKyUaERGJVaoTjZnNMbPO7du3Jx2KiEjdSnWicffF7t7R2NiYdCgiInUr1YlGRESSp0QjIiKxUqIREZFYKdGIiEislGhERCRWdbszgIjUudwunM2Z3N5TbQ3agTMmqmhEpD7ld+EspB04Y6GKRkRqTtCRAAMqRlpa2MB3AXSSZoxU0YhIzVExUltU0YhITUrLkQA9z8WC9J2NpUQjIpKQYhVY2s7GUqIREUlI0LlYkL6zsTRGIyIisUp1otExASIiyUt1otExASIiyUt1ohERkeRpMoCIVK2ghZmgnWJqjSoaEalaQQszQYsza40qGhGpamlZmFnPVNGIiEislGhERCRWSjQiIhKrUGM0ZnZgiLe97u7/U2Y8IlKnItv6X6pO2MkAz+e+rI/3DAbGlR1Rwpq7A7ZShewUl6BNiUQkEvkZZoWJpaTZZcXmQoP+/SYsbKJZ5+59ngpkZo9FEE+iljZl/0b3+g9Ufn6l/qKKxKqsGWZBmQr077cKhE00MyN6T1VbMqaDJWM6ev9FD6pwRKT6BGUq/ftNXKjJAO6+s9hrZnZwf+8REZH6FcWssx9GcA8REUmpshONu38wikBERCSdStqCxsz+b9B1d/9qNOGIiEjalLrX2csFj4cBs4F10YUjImmnHZnrT0mJxt2/XfjczL4F/DLSiEQk1YrNQtaOzOlV7u7N+wNjowhEROqHdmSuL6WO0TwBeO7pYGAUoPEZEREpqtSKZnbB493AZnffHWE8IiKSMqWO0fyp5zUzO9jd/xJdSMnKBGx1Nj8DTaNhTCIRiYjUtihO2PwhkIq1NMUGIru7s78q0YiEp9llkld2ool7waaZTQC+BDS6+2m5a5OAK4FtwG/cfVEUn9XREbzvXmZEFHcXqS+aXSZ5UVQ0JTOzW8iO92xx98kF108BriM70eBmd7/G3TcC55lZYTJ5P3CDu//OzO4FIkk0IhItzS4TGMAWNGb2VjObYWbvyX8N4HMXAKf0uO9g4CaySWQScFaucgnyU+BMM/smMHIAny8iIhVS6vTmTwKXkl07kwGOB34PvLeU+7j7g2Y2vsflGcCGXAWDmd0BnAqsDfj+LcCnc8np7lI+W0REKqvUiuZS4FjgT+5+AjANiOr45kOA5wqedwGHmNlIM/s+MM3MLgcws/Fm1gn8BPhm0M3MrMPMVpnZqq1bt0YUooiIlKrUMZqd7r7TzDCzoe6+3swmxhJZjrtvAy7ocW0T0Odxee7eCXQCtLa2el/vFRGR+JSaaLrMbARwD/BrM/sr0GttzQD9GXh7wfOxuWsiIlLDSl2w+eHcwyvNbBnQCPwiolhWAoeb2aFkE8yZgCZBiojUuFCJxsz+4O7TC6+5+wP9vaeP+90OtAEHmVkXcIW7/9DMLia7G/Rg4BZ3fyrM/fr4nDnAnObm5nJuIyL9CFqcqYWZkhe2onmnma3p43UjW92E4u5nFbl+H3Bf2PuE+JzFwOLW1tbzo7qniPQWtDhTCzMlL2yiOTLEe/aUE4iI1DYtzpRiQiWaoM00RUREwih5Z4BaYmZzzKxz+/btSYciIlK3Up1o3H2xu3c0NoYePhIRkYilOtGIiEjyEtm9WURqk86YkYFQRSMioeWnMfekqczSl7ALNo8qd/GkiKSDpjFLqcJWND/NP8gdFUDB8/0jjShCmnUmIpK8sInGCh5f1OO130UUS+Q060xEJHlhE03hNvvW4zWN84iISFFhZ50dbGZzgcfpnWh01ouIiBQVNtFcCRwDnAOMNbO1wDpgPXBQPKGJiEgahE00XcASd38ewMzGAlOAo4EHY4pNRBKkrf8lKmHHVz4M3Gtmz5nZr4HLgJFkt/Q/O67gyqVZZyIDF7RmRutlZCDC7t58PoCZ/TNwCLAROAHoBF4ie+xy1dF5NCLl0ZoZiUKpW9Cc4e5T80/M7N+AL0QbUnVq7s5AW1vvF9rboaOj4vGIiNSKUqcm7zCzY/JP3H01cES0IVWfpU3tbGgI6JjOZII3fhIRkb1KrWjOA+42s5XAarITAnZFHlWVWTKmgyVjOnp3IQRVOCIiso+SKhp3fxqYDtwPjCY7xfkDMcQlIiIpEXZTzZnAw571GnBn7ktEapy2/pe4ha1o/glYbWZ3mNlcMzs4zqCiounNIv3T1v8St7DTmy8EMLMjgfcDC8ysEVgG/AJY4e57YotygDS9WSQcTWOWOIWqaMzsHQDuvt7dv+vupwDvBR4CPgY8El+IIiJSy8LOOrvfzJrI7m22Bngi9+tD7n5fXMGJiEjtC9t1NsnMhgKTeGOPs1OBo83sVXc/NMYYRSQJxWYJaJGylCj09GZ3f9XdHwN+Trar7C/ATrJHB4hI2gTNEtAiZRmAsNObJwIfBGYDo4BfA7cCHbnpziJSA0rekbnnLAEtUpYBCDtGsw54DLgW+A93fzW+kEQkLvkipTCxaBqzxC1sorkQmAx8GrjRzLaRnRDwBPCEu98TU3xlMbM5wJzm5uakQxGpGprKLJUWaozG3f/d3T/j7rPcvQk4CfgR8Brw0TgDLIe7L3b3jsbGxqRDERGpW2HHaD4AZPInbLp7F9lTN++PMTYRqQTtQSMxC9t19mHgq2Y2muxamseBTO7XtdW4K0DUMgHH0czPQNNoGJNIRCIRCRq4AQ3eSGRSfcJmVIr9W+vuzv6qRCM1TwM3EiOdsBlCR0fw+rTMiMrHIiJSa0pNNDvM7JjcyZq4+2ozS/0JmyK1RsMuUk10wqZICmnYRapJSYnG3Z82s+nAh8gmmXXAP8cRmIiUR8MuUi36TTRmdhJwOnCTu2eAue7eiU7YFBGREMJUNOeS3RngX8zsQEA9vCIiElqYnQH+5u7/4+6fB04Gjo05JhERSZEwieb/5R+4+zzgJ/GFEy0zm2Nmndu3b086FBGRutVv15m7/4eZHeXuT+We3xB/WNFw98XA4tbW1vOTjkUkDprGLLUg7MFnP80/MLNPFr5gZvtHGpGIhBZ0NhloGrNUl7DTm63g8UXAzQXPfwccE1lEIlKSkqYxl3zymUj5wlY0XvDYerwW+jhoEUlYUAmk8kdiFraiOdjM5pLdrblnovHebxeRqqWVnFJhYRPNlWS7x84BxprZWrK7AqwHDoonNBERSYOwxwR0Fj43s7Fkt6A5GngwhrhERCQlSt1UE9AJmyJJ0Di+1CoN5IvUCI3jS60aUEUjIsnQOL7UIiWaMjV3Z6Ctbd+L7e3BR3KKiNQhJZoyLG3K9lns00We79tQohERAZRoyrJkTAdLxnTs25XRs7oREalzmgwgIiKxUqIREZFYpbrrzMzmAHOam5uTDkUkNG39L2mT6orG3Re7e0djY2PSoYiEpq3/JW1SXdGI1Kqy18uoLJIqkuqKRqRuqSySKqKKRiSttI2AVAlVNCIiEislGhERiZW6zkQSpK3/pR6oohFJkLb+l3qgikYkYXGP2T/f+Tybb9vc6/ro9tGM6RgT3weL5KiiEUm5zbdtpjvTvc+17kx3YPIRiYMqGpE60NDSwLTl0/Y+f6ztsQSjkXqjRFOmTI9zz+ZnoGk0qENCRCRLiaYMQQO23bkeCiUaEZEsJZoydHT0PkgzMyKZWKROaU8zqQFKNCIVEFs+yM+P7nkTzZGWKqJEI1IBseYD7WkmVU6JRqRClA+kXmkdjYiIxEqJRkREYqWuM5EkFZsl0N7ee0qjSI1SRSOSpKBdNTOZ4OQjUqNU0YhEaEDTmHvOEijcakIkBao+0ZjZBOBLQKO7n5a7Ng64HngJeNrdr0kwRJG9IpvG3HNvo77ep4WZUuUSSTRmdgswG9ji7pMLrp8CXAcMBm5292vcfSNwnpktKrjFFGCRu//MzBZWMvYwmruL/JBQv3tdKHsacykZSQszpQYkVdEsAG4EfpK/YGaDgZuAk4AuYKWZ3evuawO+/2FgkZmdC/w0/nDDW9qU/Uff6/+Y+X54JZr6VEqfWtDeRiI1LJFE4+4Pmtn4HpdnABtyFQxmdgdwKhCUaM4BrsjdZxHwoxjDLcmSMR0sGdPR+3+06nevb9oqRupYNY3RHAI8V/C8CzjOzEYCVwPTzOxyd/868AvgSjNrBzYF3czMOoAOgHHjxsUZt0g42hpA6lQ1JZpA7r4NuKDHtSeB0/r5vk6gE6C1tdVjC1DSr1i3V4D5+ZnKbT1e0KC91LFqSjR/Bt5e8Hxs7ppIsop0ez3/AmzpcRpydzc0NATcQ11kUseqKdGsBA43s0PJJpgzAf3LlOoQ0O3V3gaZzb0LlfZ2aNFYvsheSU1vvp1s58JBZtZFdmD/h2Z2MfBLstObb3H3p8r8nDnAnObm5nJDFgmkYReR/iU16+ysItfvA+6L8HMWA4tbW1vPj+qeIiJSmmrqOhNJXtDAvwbyRcqiTTVFCgVtcqmBfJGyqKIR6UkDLyKRSnWi0WQAEalV3d37bigyPwNNo2FMYhENXKoTjSYDSBQGtPW/SBmaRve+1t2d/VWJRqRWlJA9tE2ZVNqYt2W/CntwMyMSC6dsSjRSn0rMHhq2ERk4JRqpX8oeIhWR6kST1GSAoMMRa3kgT0SkHKlONElMBijWZ1/LA3k1T4swRRKV6kSThGKHI9byQF7NCxqP0Ui+SMUo0Uh9CDkeo+JHJHragkakgHagEYmeKpoKau4OmCXQ3h7c1yali2hlpSajiUQr1RWNmc0xs87t27cnHQpLm9rZ0NDjh10mE/qIYAkhqBwBlSQiCUt1RVNNW9AsGdPBkjEd+/5PuWd1I+VTOSJSdVJd0YiISPKUaEREJFap7joTKUY7MotUjioaqUuaNyBSOapopPZoGrNITUl1RVNN05slQipHRGpKqiuaapreLBFTOSJSM1Jd0YiISPKUaEREJFap7jqTFIhgO2XtyCySLFU0Ut0i2E5ZOzKLJEsVjVS/CAb+NXdAJDmqaEREJFaqaJKWCTijBnROjYikRqoTjZnNAeY0NzcnHUqwYoME+QEFJRoRSYFUJ5pqW7DZu3jpoL29o3c+qcdzarTLpUhqpTrRVJOg4kWFS4H81LCeSaWE6WHKVSLVSYmmQjo6eieUeixc+lTm1LAIcpWIxECJRlJF05hFqo+mN4uISKyUaEREJFbqOpPK0+ZjInVFFY1UnjYfE6krqmgkGWWM2msas0htUUUjNUcnOYvUllRXNFW/BY0MmKYxi9SOVCeaatuCpiRBm21qo00RqUGpTjQ1Ky371WgwRURQoqlOadmvRnvCiAhKNBI3DaaI1D0lGqlqWtspUvuUaBKmAzb7FtT7pp43kdqiRJOgVB2wGWPpod43kdqmRJOgoDF/qM1xf5UeIlKMEk0tqfZ+NpUeIhJAiaZWpKqfTUTqiRJNrUhVP1tvWtspkl5KNFKamDKC1naKpJcSjZQmxoygIR6RdFKikdIpI4hICZRo0qDc2WjFusOKfZYGTUSkBKk++MzM5phZ5/bt25MOJT7t7cE/+DOZ8Mmj2EliQTRoIiIlSnVFU9Pn0YQV1Wy0CnaHaf8ykfqS6kRT94p1qQW9r4I/5bWJgEh9UaKpUmUfsFnKT+0EfsprPoFI/VCiqUKRHLBZrEtNRKTClGiqUFoO2BQRASUaiZG2lRERSPn0ZklWsVnTGvgXqS+qaCRWGvQXEVU0IiISKyUaERGJlbrOakg1H7Cp1f4iUowqmhoRxZZmcQoa+Negv4iAKpqaUQsHbGrgX0SCqKIREZFYqaKRkmgRpoiUShWNlESLMEWkVKpoUqDSs9E0FiMipVCiqXHFqoiSd3vuQV1kIhIVJZoaF9dstKDDyUBdZCJSOiWaFAt7eFpfiy3VRSYi5VKiSamgquOBB7JfPZPKAw9kf501641rqlxEJCpKNCkV1KVWbNxl1qzq2MZGRNKp6hONmU0AvgQ0uvtpuWv/AHycbPyT3P1/JRhizdDpziKShETW0ZjZLWa2xcye7HH9FDP7o5ltMOQJtiAAAAfESURBVLN5AO6+0d3PK3yfu//O3S8AlgA/rlzkIiJSqqQWbC4ATim8YGaDgZuA9wOTgLPMbFI/92kHqmBLSRERKSaRROPuDwIv9bg8A9iQq2BeA+4ATi12DzMbB2x397/FF6mIiJSrmsZoDgGeK3jeBRxnZiOBq4FpZna5u3899/p5wI+K3czMOoD8iMSrPbvpBqAR2F7m+4Je6+9amMcHAS+GiK0v5bYv7PW+nsfVvrj+7IKul9q+yv3ZWcD7rM97FL9mFvSeZNvXO7Z9XzN74x5WeN/pAdcK7xcYR//tC46j4PXA+/Z1j77bF8/PlonBwZfI3RP5AsYDTxY8Pw24ueD5J4AbI/qsVRHco7Pc9wW91t+1kI8Tb1/Y6309j6t9cf3ZRdG+avizK/ZamGtpbl+YtqalfXH+bHH3qtpU88/A2wuej81dqxaLI3hf0Gv9XQvzOArlti/s9b6ex9W+uP7sgq6nqX1hrqW5fWHbWq5qaF+cP1uwXNaqODMbDyxx98m55/sBTwMnkk0wK4F2d38qgs9a5e6t5d6nWql9tSvNbQO1r9ZF1b6kpjffDvwemGhmXWZ2nrvvBi4GfgmsA+6MIsnkdEZ0n2ql9tWuNLcN1L5aF0n7EqtoRESkPlTTGI2IiKSQEo2IiMRKiUZERGJVN4nGzCaY2Q/NbFGP68PNbJWZzU4qtigEtc/M2szsd2b2fTNrSzC8shVp3yAzu9rMbjCzs5OMr1xF2vcPuT+7m83sP5OMr1xF2jfOzO7J7X04L8n4ylGkbZPM7E4z+56ZnZZkfOUysw+Z2Q/MbKGZnZy7NtzMfpy7/vH+7lHTiabczTlz/g9wZyXiLVUE7XOgGxhGdqeFqhJB+04lu95qFylsn1f55rER/PlNARa5+7nAtAqFHUoEbXs/cIO7Xwj8U4XCDq3E9t3j7ucDFwBn5N76EbJ/ducD/9jvB0ax6jOpL+A9ZPePKNxhYDDwDDABeBPwONmjBPKvLyp4fBJwJjAXmJ10e2Jo36Dcr6OBW5NuTwztmwd8quf1avkqt30F1+4E3pJ0e2L48xsJLAN+C5yTdHsiblsT2U2CvwmsSLo9EbXv28D03OPLgZbc49v6+7yarmi8/M0524Djye4Cfb6ZVdXvR7ntc/fXcw//CgyNLdABiuDPr4ts2wD2xBPlwEXQvqrePDaC9p0DXOHu7wU+GF+kpYvg394Wd/802f8MlbvXW+RKaZ9lXQvc7+5/yL23i2xvAoToGauqH6wRCdqc8xAzG2lm3ye3OSeAu3/J3S8je9TADwp+MFez0O0zs4+Y2b8DPwVurHyoAxK6fcDdwP82sxuABysc50CV0j7oZ/PYKlRK+34BXJK7vqmyYQ5IKf/2xptZJ/ATslVNLQhsH/AZ4H3AaWZ2Qe61u4GPmtn3CLFlTTXt3hwrd99Gto8x6LUFlY0mekHtc/e7yf6FqHlF2vcK2R/ENa/Y3093vyKBcCJX5M/vSbKb6da0Im3bxBu7x9c0d78euL7HtZfJVqShpLGiqfbNOcul9tU2ta92pbltEGP70phoVgKHm9mhZvYmsoP99yYcU5TUvtqm9tWuNLcN4mxf0rMfypw5cTvwAm9Mbz0vd/0DZHeCfgb4UtJxqn1qn9pXW19pblsS7dOmmiIiEqs0dp2JiEgVUaIREZFYKdGIiEislGhERCRWSjQiIhIrJRoREYmVEo1IATPbY2aZgq+qOCelIK4xfbznCjP7eo9rLWa2Lvd4mZl1m1lr3PGKFNI6GpECZtbt7g0R33M/d99d5j36jcvMjgB+4e4TCq5dA7zi7l/NPV8OfN7dV5UTj0gpVNGIhGBmm8zsK2b2BzN7wsyOzF0fnjtE6lEze8zMTs1dn2tm95rZb4HfmNn+lj1xca2Z/dzMHjGzVjM718zmF3zO+Wb23RDxnGxmv8/Fc5eZNbj708Bfzey4greeTnYVuEhilGhE9vXmHl1nZxS89qK7Twe+B3w+d+1LwG/dfQZwAvBNMxuee206cJq7zwIuAv7q7pOALwPH5N5zJzDHzIbknp8D3NJXgGZ2EPAvwPty8awCPpt7+Xaye1RhZscDL7n7f5X+2yASnbo5JkAkpL+7e0uR1/JHLqwme5QtwMnAP5pZPvEMA8blHv/a3fOHS70buA6y2+Ob2Zrc4+5c1TM7N5YyxN2f6CfG44FJwAozg+xpiL/PvbYQ+E8z+xzZhKNqRhKnRCMS3qu5X/fwxr8dAz7q7n8sfGOu++rlkPe9GfhnYD3hDjkzsknsrJ4vuPtzZvYsMAv4KDAzZAwisVHXmUh5fgl8xnKlhZlNK/K+FWTHSzCzScCU/Avu/gjZc0DaCVeBPAy8y8yac/cbnpsIkHc78F1go7t3ldYckegp0Yjsq+cYzTX9vP9fgSHAGjN7Kvc8yL8Bo8xsLXAV8BSwveD1O4EV7v7X/gJ0963AXOD2XBfc74EjC95yF3AU6jaTKqHpzSIVYGaDyY6/7DSzw4ClwER3fy33+hLgu+7+myLfH8m0a01vliSoohGpjP2Bh8zsceDnwEXu/pqZjTCzp8lOQghMMjk7+luw2R8zWwZMIHvYlUjFqKIREZFYqaIREZFYKdGIiEislGhERCRWSjQiIhIrJRoREYmVEo2IiMTq/wMOzyt/JaEwQAAAAABJRU5ErkJggg==", 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "jupyter": { - "outputs_hidden": false - } - } + ] } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "display_name": "Python 3", - "language": "python", + "display_name": "Python 3.9.5 64-bit", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb b/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb index 5e2d24d93..034e84ae2 100644 --- a/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb +++ b/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb @@ -2,16 +2,17 @@ "cells": [ { "cell_type": "markdown", - "source": [ - "# Photon Propagation" - ], "metadata": { "deletable": true, "editable": true - } + }, + "source": [ + "# Photon Propagation" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "This examples shows how to propagate electromagnetic cascades at ultra-high energies.\n", "Note that the `EM*` modules act on photons and electrons only, such that these modules can be used concomitantly with the modules to propagate cosmic-ray nuclei to treat secondary photons produced by cosmic rays.\n", @@ -19,19 +20,25 @@ "These simulations can be very time consuming. This particular example shown below can take several minutes to run.\n", "\n", "Here we simulate the propagation of UHE protons. We track the electromagnetic cascades initiated by the photons and electrons produced via photopion production. We ignore the electrons produce via Bether-Heitler pair production to make it possible to run the example within a reasonable time." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Setting up the simulation" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 18, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true, + "scrolled": true + }, + "outputs": [], "source": [ "from crpropa import *\n", "\n", @@ -63,7 +70,7 @@ "\n", "# observer\n", "obs1 = Observer() # proton output\n", - "obs1.add(ObserverPoint())\n", + "obs1.add(Observer1D())\n", "obs1.add(ObserverPhotonVeto()) # we don't want photons here\n", "obs1.add(ObserverElectronVeto()) # we don't want electrons\n", "out1 = TextOutput(filename1, Output.Event1D)\n", @@ -72,7 +79,7 @@ "obs1.onDetection(out1)\n", "\n", "obs2 = Observer() # photon output\n", - "obs2.add(ObserverPoint())\n", + "obs2.add(Observer1D())\n", "# obs2.add(ObserverDetectAll()) # stores the photons at creation without propagating them\n", "obs2.add(ObserverElectronVeto())\n", "obs2.add(ObserverNucleusVeto()) # we don't want nuclei here\n", @@ -86,7 +93,7 @@ "obs2.onDetection(out2)\n", "\n", "obs3 = Observer() # electron output\n", - "obs3.add(ObserverPoint())\n", + "obs3.add(Observer1D())\n", "# obs3.add(ObserverDetectAll()) # stores the photons at creation without propagating them\n", "obs3.add(ObserverPhotonVeto()) # we don't want photons\n", "obs3.add(ObserverNucleusVeto()) # we don't want nuclei here\n", @@ -123,28 +130,41 @@ "out1.close()\n", "out2.close()\n", "out3.close()" - ], - "outputs": [], - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "scrolled": true - } + ] }, { "cell_type": "markdown", - "source": [ - "## Plotting results (optional)" - ], "metadata": { "deletable": true, "editable": true - } + }, + "source": [ + "## Plotting results (optional)" + ] }, { "cell_type": "code", "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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8atWqKrcff/zxvPvuu6xcuXJPO7BPCfzSmjdvTsuWLffMlzzzzDN7Ri0VWbFiBZ06dWLcuHEMHjx4n6KVkvzmrcylV8dW1KtD8ykSjJiPVNz9kgraZwIzo3SOyBbpCki3bt244447OOOMMygqKiIjI4OHH364yu19+vRhwoQJDBkyhKKiItq2bcu///1vBg0axNChQ3n11Vd58MEH9znfU089xVVXXUV+fj6dOnXiySefrDS+559/nmeffZaMjAwOPPBA/vCHP0S9DyQ4a7bsYFVuPiOO7xB0KFIHWCrdv52VleXz58/fq23JkiV07do1oIhSh/oxeb28MIfrX/iMmeNOrnO3E7/66bdcN+VT3vlVPzq1aRp0OAnLzBa4e1Y0jpUStRoS/fKXSJDmfp1Li8YZHHlgs6BDkTogJZKKu89w97HNm2vRIZGy5q3MpbfmUyROUiKpaKQiUr6czfms3rRD9b4kblIiqVQ1UkmleaMgqP+S17zi9egPU1KR+EiJpFKZhg0bkpubqz+MNeTu5Obm0rBhw6BDkRqY+3UurZrUp0vbuj2fov/74yeohx/jpn379uTk5JDIdcESXcOGDWnfvn3QYUgNzFuh+RSJr5RIKpWVacnIyNhT5kSkLlm9KZ9vt+zgyn6dgg5F6pCUuPylu79E9jX3a9X7kvhLiaQiIvuatyKX1k3qc3hbPfQn8aOkIpKC3J25K3Lp06l1xEVJRaIhJZKKnlMR2ds3m/L5Lq9AtxJL3KVEUtGcisjeiudTju9U8Ro8IrGQEklFRPY2b0UubZo14DAVUZQ4qzKpmNmJZtYk/P4yM7vXzH4U+9BEpCY0nyJBimSk8iiQb2bHAr8BVgFPxzQqEamx7Nx81m39gT669CUBiCSp7PZQjZNzgPvd/X4goWo+aKJepMSXa0Orgma2axFwJFIXRZJUtpnZb4HhwGtmlgZkxDas6tFEvUiJ4jp3Gem69CXxF0lSuQj4ARjl7muBdsA9MY1KRCSKVE82fqpMKuFE8hLQINy0EXgllkGJiESDblSIv0ju/hoDTAX+L9zUDpgWy6BERCQ5RXL562fAicBWAHdfBrSNZVAiIpKcIkkqP7j7zuIPZpaO1rwREZFyRJJU3jWz3wGNzOwnwIvAjNiGJSIiySiSpDIe2AB8AVwJzARujmVQ1aXnVEREEkOVKz+6exHw9/ArIbn7DGBGVlbWmKBjERGpyypMKmb2BZXMnbh7ZkwiEhGRpFXZSOXsuEUhIiIpocKk4u6rAMysI/CduxeEPzcCDohPeCIikkwimah/ESgq9bkw3CYiCUj3+0uQIkkq6aWfUwm/rx+7kEQkGgyVKCmhVBsvkSSVDWY2uPiDmZ1DqP6XiIjIXqq8pRi4CnjOzB4CDFgNXB7TqEREokBjtfiL5DmVr4E+ZtYUMHffFotAzOxc4CxCdcUedvc3zawTcBPQ3N2HxuK8IiISPZU9p3KZuz9rZteXaQfA3e+t6uBmNpHQrcnr3f3oUu0DgPuBNOBxd/+Tu08DpplZS+AvwJvuvgIYbWZTq/+jiYhIvFU2p9Ik/LVZOa+mER5/EjCgdEN45ciHgYFAN+ASM+tWapebw9tFRCTJVPacSvH6KW+5+/ult5nZiZEc3N1nm1mHMs29gOXhUQhmNgU4x8yWAH8CXnf3hZGFLyIiiSSSu78ejLAtUu0ITfYXywm3/Rw4HRhqZlcBmFlrM3sM6GFmvy3vYGY21szmm9n8DRs21CIsERGprcrmVI4HTgDalJlX2Y/QXEhNlXdDhrv7A8ADZRpzCd19ViF3nwBMAMjKytLN6CIiAapspFKf0NxJOnvPp2wFanMnVg5wSKnP7YE1tTieSt+LiCSIyuZU3jWzOcAx7n5bFM/5MXB4uKbYt8DFwKW1OaBK34uIJIZK51TcvRBoVdODm9lkYC5whJnlmNlod98NXAu8ASwBXnD3xTU9R/g8GqmIhLkuAkuAInmi/hMzm06oiOT24kZ3f7mqb3T3Sypon0loBcmo0EhFZF+mx8klAJEklVZALnBaqTYHqkwq8WJmg4BBnTt3DjoUEUlAGr3FTyRlWq6IRyC1oZGKiJRHo7X4qzKpmFlDYDRwFNCwuN3dR8UwLhERSUKRPPz4DHAgcCbwLqFbgGNSVLKmNFEvIpIYIkkqnd3998B2d3+KUCXhY2IbVvW4+wx3H9u8efOgQxERqdMiSSq7wl+3mNnRQHOgQ8wiEhGRpBXJ3V8TwuXofw9MJ/SU/e9jGpWIiCSlSpNKeOGsFkAvd38D6BSXqKpJtxSLiCSGCi9/mdkjwC+B1sDtZpawoxPNqYiIJIbKRip9gWPdvdDMGgPvAbfHJywREUlGlU3U7wzX/sLd8ym/ZL2IiMgelY1UjjSzz8PvDTgs/NkIrX+SGfPoIqQ5FZESjmqSSHAqSypd4xZFLalMi8i+dGlBglDZeiqr4hmIiEisaOwWP5E8/CgikpRM47W4U1IREZGoqew5lbfDX++OXzg1o4KSIiKJobKRykFm1g8YbGY9zKxn6Ve8AoyEHn4UEUkMld399QdgPKFS9/eW2ebsvRKkiIhIpXd/TQWmmtnv3V1P0ouISJUiWU74djMbTKhsC8Asd/9nbMMSEZFkVOXdX2Z2F3Ad8N/w67pwm4iIyF4iWU/lLKC7uxcBmNlTwCfAb2MZmIiIJJ9In1NpUeq9brESEZFyRTJSuQv4xMz+Q6icUF8SbJSigpIiJVw1SSRAVY5U3H0y0Ad4Ofw63t2nxDqw6tBzKiL7MlUo2UOJNn4iGang7t8RWp9eRCRpKLHGn2p/iYhI1CipiIhI1FSaVMysnpktilcwIiKS3CpNKuFnUz4zs0PjFI+IiCSxSCbqDwIWm9lHwPbiRncfHLOoREQkKUWSVG6LeRQiIpISIiko+a6Z/Qg43N3fMrPGQFq0AzGzcwmVhGkLPOzub5pZE+ARYCehQpbPRfu8IiISPZEUlBwDTAX+L9zUDpgWycHNbKKZrS872W9mA8zsSzNbbmbjAdx9mruPAUYCF4V3HQJMDbfrcpuISIKL5JbinwEnAlsB3H0ZodFEJCYBA0o3mFka8DAwEOgGXGJm3UrtcnN4O4QWCFsdfl8Y4TlFRCQgkSSVH9x9Z/EHM0sntPJjldx9NrCpTHMvYLm7rwgfdwpwjoXcDbzu7gvD++YQSiwVxmpmY81svpnN37BhQyRhiYhIjESSVN41s98BjczsJ8CLwIxanLMdJaMPCCWOdsDPgdOBoWZ2VXjby8D5ZvZoRed09wnunuXuWW3atKlFWCKpQWWuJEiR3P01HhgNfAFcCcwEHq/FOcurxuPu/gDwQJnG7cAVVR5QVYpFyqHCV8VcqTZuIrn7qyi8MNeHhP4R9KV7rWp+5gCHlPrcHlhTi+Ph7jOAGVlZWWNqcxwRSS1Kq/EXyd1fZwFfExpFPAQsN7OBtTjnx8DhZtbRzOoDF6MKyCIiKSGSOZW/Aqe6+ynu3g84FbgvkoOb2WRgLnCEmeWY2Wh33w1cC7wBLAFecPfFNQt/z3kGmdmEvLy82hxGRERqKZI5lfXuvrzU5xXA+kgO7u6XVNA+k9DcTFTo8peISGKoMKmY2ZDw28VmNhN4gdCcygWELmElDE3UiySZNZ/CAUdBWkbQkUiUVXb5a1D41RBYB/QDTgE2AC1jHlk1aDlhkSTy3+kwoR989UbQkUgMVDhScfcqb+UVEamWgq3w+m9C73flBxuLxESVcypm1pHQg4kdSu+fSKXvdflLJEm8cwds+y7oKCSGIpmonwY8QeiJ9qLYhlMzmqgXSQLfLoCPJkDn02H5W0FHIzESSVIpCD/tLiJSM4W7YcZ10OxAOO33SiopLJKkcr+Z3QK8CfxQ3Fiq6GPgdPlLJMF9+Cis/QIufAYaNAs6GomhSJLKMcBw4DRKLn95+HNC0OUvkRK1q6IUA1u+gf/cCV0GQtdBsGlF0BFJDEWSVM4DOpUufy8iic8SofCVO7x2A2DwP/cEFlSi5dlUFklS+QxoQYRP0YtIsBrlr+H+jIdo8850aLE/NGpZzqtF6GuD/WL7h37JdFj2BpzxR2hxSNX7R1lCJNY6JpKkcgCw1Mw+Zu85Fd1SLJKAuv73Pg6o9yH1vlkBy/Jg946Kd7a0kgTTqCX0HAE9h0cnkII8mPkbODATel9V9f6SEiJJKrfEPIpa0pyKSNjaRbTPmckjhYMYMPJRDmvTFHbtgB1boGAL7NhcwWsLbFwG06+FzStDd2jV9p/579wB29fDJZMhLZI/NZIKIllP5d14BCIiUfDOHexOb8r/FZzNgOK2jEah134HVf69hbvhtevhvb9C3rcw+EFIr1+zOHLmw0d/h95XQrueNTuGJKVInqjfRskKpfWBDGC7u+8Xy8BEpJpWfwRfvc6yrtex9ZOm1f/+tHQYdD80PwT+cwd8vzZ0C3DDav6vXrgr/EzKQXDqTRXvp9nzlFTleiru3szd9wu/GgLnE1qsS0QShTu8/b/QpA0rDrus5scxg36/hnMegew58ORA2FrNhVnnPQrrFsH//Ln6CUmSXiSLdO3F3aeRQM+oiAiwYhZkvwd9f01hepPaH6/HMLj0edicDY//BNYviez7Nq+CWXfBEf8DR55d+zgk6USynPCQUq+hZvYnSi6HJQSt/Ch1WvEopfkh8OOR0Ttu59PhiplQtAsmnhkauVQVx8zwMykD/6z7eeuoSEYqg0q9zgS2AefEMqjq0noqUqct/SesWQinjIf0BtE99kHHwuh/Q9MD4JnzYNFLFe/731dh2Ztw2k2BPJMiiSGSu7+0ropIoioqDN262/pwyLw4Nudo+SMY9QZMuRSmjgrNsRx/7d4jkYI8eP3GUBLqdWXlx9MIJqVVtpzwHyr5Pnf322MQj4hUxxcvwoalcMGk2D4L0rgVDJ8Gr4yFN28O3XJ85h+hXlpo+9v/G3om5dIpeialjqvs8tf2cl4Ao4EbYxyXiFRl985QocYDM6Hrvlekoz4eyGgIQydBn2tCVYdfHBl6sHL1x/DxE6ERysE9on3WqNDdy/FT2XLCfy1+b2bNgOuAK4ApwF8r+j4RiZNPnoYtq2DYS1Cv2jdy1ky9ejDgLmjeHt74HTy9Hn7YFnom5bRKnkkJjC61xVul41QzawVcDwwDngJ6uvvmeAQmIpXYmQ/v3gOHngCd+8f//Mf/LJRIXrkSCnfCRc9pnRQBKp9TuQcYAkwAjnH37+MWVTWpoKTUOR9NCD3xfsGTwU18Hz0kdBvz2s+gq55JkZDKxsy/Ag4GbgbWmNnW8GubmW2NT3iR0S3FUqcU5MGc+6DzT+BHJwQbyyHHwXE/DTYGSSiVzanE6SKtiFTLBw+FKg73/33QkdSSZs9TkRKHSDL5fgPMfRiOOi/0TIhIglFSEUkmc+4LLbpVWfVfkQApqYgki7wc+Phx6H4p7H940NGIlEtJRSRZvHs34NBPzx5L4lJSEUkGG5fDJ89B1ihocWjQ0YhUSElFJBnMuhPSG8LJvwo6kijQU+6pTElFJNGt/SJUcr7P1dC0bZW7q86VBClhkoqZdTKzJ8xsaqm2bmb2gpk9amZDg4xPJDDv3AENm8MJP6/Wt5lKzO/heiYmbmKaVMxsopmtN7NFZdoHmNmXZrbczMYDuPsKdx9d5hADgQfd/Wrg8ljGKpKQvvkQvvoXnPgLaNQi6GiSjvJq/MV6pDIJGFC6wczSgIcJJYxuwCVm1q2C738GuDhch6x1DOMUSTxf/gumXgFN2kLvKha+EkkQMU0q7j4b2FSmuRewPDwy2UmolH65yxO7+3p3/xkwHthY3j5mNtbM5pvZ/A0bNkQxepGAfL8+tFbJ5ItCl72GvQD1mwQdVfRp8iclBTGn0g5YXepzDtDOzFqb2WNADzP7LYCZdTCzCcDTwD3lHczdJ7h7lrtntWnTJtaxi8SOOyx8Bh46Dpa+BqfeDGPfTdiFr0TKE8S6n+Vd5XR3zwWuKtOYDYyt8oAqfS/JLvdr+OcvYOVs+NGJMOh+PTUvSSmIkUoOcEipz+2BNbU5oErfS9Iq3AXv3QuPngBrPoOz/wYj/qmEIkkriJHKx8DhZtYR+Ba4GLi0NgfUSEWS0rcLYVE/cLAAAAsFSURBVPo4WPcFdB0EA++B/Q4KOiqRWon1LcWTgbnAEWaWY2aj3X03cC3wBrAEeMHdF9fmPBqpSFLZuR3euAke7w/bN8BFz4ZeSiiSAmI6UnH3SyponwnMjOW5RRLS8rfgn7+ELd/Aj6+A02/V8yeSUoK4/BV1uvwlCc8dZlwHC5+C1ofDFa8HvxSwSAwkTJmW2tDlL4mn1VtX8/Kyl6v3Td+vCyWUYy+Bq+YooUjKSomkIhJPd3x4B7d8cAtenYf3vCj09dA+kNEwNoEVnyrR61ypdkpKS4mkYmaDzGxCXl5e0KFIilu6aSkfrPkg6DAioj/dJfTwfvykRFLR5S+Jl4mLJsb1fHk/5LGpoGylI4mUEmv8pcREvUg85GzL4c3sN2mY1pCCwoK4nPOkKScB8MWIL+JyvvjS8CEVpcRIRZe/JB6e/u/TmBlndTor6FBEElZKJBVd/pJY21ywmVeWvcJZHc/igCYHBB2OSMJKiaQiEmuTl06moLCAK46+IuhQRBKakopIFfJ35TN56WROaX8Kh7U4LOhwRBJaSiQVzalILL2y/BW2/LCFUceMCjoUkYSXEklFcyoSK7uLdvP04qfp3qY7PdpqsSyRqqREUhGJlTez32TN9jWMOlqjFJFIKKmIVMDdmbhoIp2ad6LfIf2CDkckKSipiFRg7pq5fLn5S0YeNZJ6Vsv/VVQnROqIlEgqmqiXWJi4aCJtG7Wt8GHHmhVujH3hkMTPXyqekspSIqlool6ibfHGxXy49kOGdxtO/bT6QYdTIyoGDKZOiLuUSCoi0TZx0USaZTRjaJehQYeSuhJ/SCU1oKQiUsY3W7/hrW/e4sIjLqRp/aZBhyOSVJRURMp4avFTpFkaw7oOCzoUkaSjpCJSysYdG5m2fBqDDxtMm8Ztgg5HJOkoqYiU8o8l/2BX0S5GHjUy6FBEkpKSikhY/q58pnw5hf6H9qdD8w5BhyOSlFIiqeg5FYmGqV9NZdvObSpvL1ILKZFU9JyK1Nauol08/d+nyTogi8w2mZXua3p4T6RCKZFURGrr9ZWvsy5/nUYpIrWkpCJ1nrvz5KIn6dyiMye3OznocESSmpKK1Hnvffsey7csZ9TRo2JY1kNPj0vdoKQidd7ERRM5sMmBDOg4oFrf5zUpMxKHWlQJX/0kgHpcCd8nKURJReq0zzZ8xoJ1C7i82+Vk1MsIOpyoSvwbCmL/lz7ReyAVKalInTbp/TvYL70J5x9+ftChiKSE9KADEAnS57mL6LfjBxpnNA46FJGUkDAjFTPrZGZPmNnUUm2Hmtl0M5toZuODjE9SV4Ym0UWiJqZJJZwM1pvZojLtA8zsSzNbXpws3H2Fu48uc4guwGvuPgroFstYRUSk9mI9UpkE7HVLjZmlAQ8DAwklikvMrKKE8QlwsZm9A/wnhnGKiEgUxDSpuPtsYFOZ5l7A8vDIZCcwBTingkNcAdzi7qcB5S8ULiIiCSOIOZV2wOpSn3OAdmbW2sweA3qY2W/D2/4FjAu3Z5d3MDMba2bzzWz+hg0bYhm3iIhUIYi7v8q7ddzdPRe4qkzjIqDSRcLdfQIwASArK0szriIiAQpipJIDHFLqc3tgTW0OqNL3IiKJIYik8jFwuJl1NLP6wMXA9NocUKXvRUQSQ6xvKZ4MzAWOMLMcMxvt7ruBa4E3gCXAC+6+uJbn0UhF4s71fEvtxLEgl/5bxY/VqChegjKzbcCXUT5scyDSbBXJvhXtE2l7ZZ9Lv98f2FhFLNWlvqg6xpruq76oep/y2iNpi1dfVKcfIt0/Xn1xhLs3qyrgiLh7yryA+TE45oRo7lvRPpG2V/a5zHv1hfoi5fsikrZ49UV1+iGV+yJhyrQksBlR3reifSJtr+xzdWKtCfVFzY6vvqjevtXpi0ja4tUX1T12SvZFql3+mu/uWUHHkQjUFyXUFyXUFyXUFyWi2RepNlKZEHQACUR9UUJ9UUJ9UUJ9USJqfZFSIxUREQlWqo1UREQkQEoqIiISNUoqIiISNSmZVCpYRbKemf3RzB40sxFBxhdPFfTFyWb2mJk9bmYfBBlfPGl10RIV9EU3M3vBzB41s0oLuaYSMzvXzP5uZq+a2RnhtiZm9lS4fVjQMcZLBX2xz+9KpaL58E8sX8BEYD2wqEz7AEJP0S8HxpfZNrXU+/MILRp2L9A/6J8nyL4o1XYucGXQP0/AvxenF/cB8HTQP0/AffEr4OTw++lB/zwB9EVL4Inw++HAoPD754P+eYLsi/J+Vyp7JdNIZRK1W0XyCGCuu18PXB3DOONhErXri2KXApNjEWAcTUKrixabRO364hlCfXEP0DqGccbDJKrfFzeHt0Ooenrxuk+FMY009iZRu76olqRJKl77VSRzgM3h90n9SxKFvsDMDgXy3H1r7CKNvSj0RcqsLlrbvnD39e7+M2A80a8PFlfV6QsLuRt43d0XhvfNIZRYIIn+TpYnCn1RLUndWVRvFcmXgTPN7EFgdpzjjIfq9AXAaODJeAYYR1FdXTTJRdwXZtbBzCYATwP3xD/UmCu3L4CfE7oMOtTMihcKfBk438weJfZlboIQcV9U8jekXEGs/BhN1VlFMp/QH9JUFXFfhDfcEvuQAhPV1UWTXHX6IhsYG4+gAlJRXzwAPFCmcTuhUWyqqk5flPs3pCLJPlKJ+iqSSUx9UUJ9UUJ9UUJ9USJmfZHsSSXqq0gmMfVFCfVFCfVFCfVFiZj1RdIklXitIpkM1Bcl1Bcl1Bcl1Bcl4t0XKigpIiJRkzQjFRERSXxKKiIiEjVKKiIiEjVKKiIiEjVKKiIiEjVKKiIiEjVKKlInmVmhmX1a6pUQa6mUiuvgSva51czuKtPW3cyWhN//x8y+N7OsWMcrUpaeU5E6ycy+d/emUT5mevihstoco8q4zOwIQlVkO5Vq+xOw3d1vD3+eBdzg7vNrE49IdWmkIlKKmWWb2W1mttDMvjCzI8PtTSy0OuTHZvaJmZ0Tbh9pZi+a2QzgTTNrbKHVEz83s+fN7EMzyzKz0WZ2X6nzjDGzeyOI5wwzmxuO50Uza+ruXwJbzKx3qV0vJFS+XCRQSipSVzUqc/nrolLbNrp7T+BR4IZw203AO+5+HHAqcI+ZNQlvOx4YEV6T5Rpgs7tnArcDPw7vMwUYbGYZ4c9XUMXSA2a2P6HFkk4PxzMfuD68eTKhek2YWR8g192XVb8bRKIr2Uvfi9TUDnfvXsG2l8NfFwBDwu/PIJQUipNMQ+DQ8Pt/u3vxIkgnAfdDqKy+mX0efr/dQqtLnh2e+8hw9y+qiLEPoVX53jczgPqEajhBKEl9YGa/IpRckn0FT0kRSioi+/oh/LWQkv9HDDg/fOlpj/AlqO2lmyo57uPA74ClRLZAmhFKWJeU3eDuq80sG+gHnE9otCQSOF3+EonMG8DPLTxkMLMeFew3h9D8BhZa8/uY4g3u/iGhNSwuJbKRxTzgRDPrHD5eYzPrUmr7ZOA+4Gt3z6nejyMSG0oqUleVnVP5UxX73w5kAJ+b2aLw5/I8ArQJX/a6EfgcyCu1/QXgfXffXFWA7r4BGAlMDh9vHnBkqV1eBI5CE/SSQHRLsUgUmVkaofmSAjM7DHgb6OLuO8Pb/wnc5+5vV/D9UbnVWbcUS1A0UhGJrsbAHDP7DHgFuNrdd5pZCzP7itANAuUmlLCtVT38WBUz+w/QCdhV02OI1JRGKiIiEjUaqYiISNQoqYiISNQoqYiISNQoqYiISNQoqYiISNQoqYiISNT8P/NACKnY0UqzAAAAAElFTkSuQmCC", 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" - ], - "image/png": 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": true - } + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.3 64-bit" + "display_name": "Python 3.9.5 64-bit", + "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - }, - "interpreter": { - "hash": "d7f94b8b1e41b02170d45ac71ce2d6b011e7cd56207b4c480f5292088bcfab93" + "version": "" } }, "nbformat": 4, diff --git a/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb b/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb index 7534b4c93..412741de0 100644 --- a/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb +++ b/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb @@ -48,7 +48,7 @@ "\n", "# observer and output\n", "obs = Observer()\n", - "obs.add( ObserverPoint() )\n", + "obs.add( Observer1D() )\n", "output = TextOutput('events.txt', Output.Event1D)\n", "obs.onDetection( output )\n", "sim.add( obs )\n", @@ -87,7 +87,7 @@ "outputs": [ { "data": { - "image/png": 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z3WY412mKdRVHHe2pEEIIfSfBTTyTNJr7d78OH65dduECbNt2P8j9+COMH69d5+1tQdOmLbRtCLzokcWlU3GcjdvBmUPbObFtDXHr/gS088k512mCc52mOPs3lzNyQgghio1eBze5OUE8CXt77UTDuZMN37kDu3ffD3KLF8M07Uk2Klc2pEkTf5o29adJm1dpMyqH2xePcPrgVk7v/5tjf6/k0FrtDOW2Du44122KS91mONdtimVFCXJCCCGejl4HN7k5QRSFmRm0aKFtoB1ePXo0//DqsmXadQYGGurU8aVhQ1+Cgl6hVY9sKpke4czBvzl9cCvH/lrOoTWzAajg6KEdVq3bFOc6TbGsaK+jPRRCCFHe6HVwE6I4aTTg46Ntw+79KXDxonbS4J07Ydcu+PNP+O03AAOsrPxo0MCPhg3/R4MB2VSvcpSM85s5fXArCZuXcnD1LADsnGrkhTjnuk2xsKuqs30UQghRtklwE6IIqlaFLl20DbRn5RIStCEuN9B99x1kZRkAtXF2rk3Dhq/RoFEONZ1OUlHdxOWEzcRHL+bAqpkA2DnXxLV+MLWf703VmnVRFEV3OyiEEKJMkeAmRDHSaKBWLW0bOFC77M4d2Lfv/lm5nTth4UINUBONpia+vq8QFJRDLbfTVDPdhsGV5Rxa8wf7lk2lsocvfu37Ueu5HphZV9DpvgkhhNA9ebaPECXMzAyaNIGRI2HOHDh1SjvEunIlfPyx9qaIhQs1jPrElZDRfRn8yxxW3U3kTu1FZGabs2nSh/zW15eV/zeM5L2bUXNydL1LQgg9MW7cOBRFQVEUEhISdF1OPrdv3yYyMpKQkBC8vb2xsLDAysqKwMBAfvjhBzIefKh1IcTExOTt63+106dPl9AeFQ854yaEDlSpAh07ahtoh1hPnLh/Vm71aiOiNrbE0rIlndpfJchpAYl7vuNozBKsq7rg264vvm1DZM44IcRTU1WV8PBwFEVBVVWmTp3K999/r+uy8mzZsoX+/ftjZ2dHq1at6NatG9euXWP58uWMHj2axYsXs3HjRkxNTQvVn5ubG59//nmB6w4dOsTixYvx9fXF2dm5OHej2Ol1cJPpQER5odGAp6e2vfwy/PIL/P03zJgB8+fbMffWK3i4D+OFFkfxNvmFbbPGs+2Pb3ELeA6/9v2o0bg9BkbGut4NIUQ5sn79epKSkggNDWXt2rXMnDmT//u//8PYuGz8LLG3t2f27Nn07NkzX03ff/89wcHBbNu2jYkTJzJq1KhC9efm5sYXX3xR4Lq+ffsCMGxY2Z+EQq+HSlVVXaGq6nAbGxtdlyLEE1EUaN5cO2/chQswaxa4e2iYNMuHN6f8xrLbZ7jqHMHZ40msGDuY30L8iP7tEy4nHdV16UKIcmLq1KmANqz069ePy5cvs2TJEh1XdZ+/vz/9+vV7KEhaWVnlhbWYmJgiv0/ufpuZmTFgwIAi91fS9Dq4CaEPLCy0Z+GioiAxEb76Ci78Y8rX0zrx8Zod7DA+yDXLIcQum86M4c2IfKsdB1fPIv12qq5LF0KUURcvXmT58uV4enrSpEkTQkNDAZgyZYpuCyskIyMjAAwNiz5wOHPmTNLT0+nZsye2trZF7q+k6fVQqRD6xtUVPvlEe1PDtm0wY4bCvHkOLEp9DxfnUbRttI/si99yPuwdNk3+BK+WXfFr3w/H2g1lWhEh/sPatWu5cOGCrsv4T/b29rRv375Y+oqIiCAzMzMvsPn6+hIQEEB0dDQnTpzgSS4xCgsL4/r164Xe3t/fn27duj1pyflMnz4doFi+HrlnHl955ZUi91UaJLgJUQ4pCjRtqm0//wxLl8KMGQZMWxiIqs6nYUAqTWqsJD36Kw6vn4OdUw1qtwuhVqsXsa7ipOvyhRA6lHtTgkajyTc0GBoaSmxsLFOnTmV87oOaCyEsLIzk5ORCbz9w4MAiBbcJEyawdu1a/P39GTx48FP3A7B582YSEhLw9fWlSZMmReqrtEhwE6KcMzeHkBBtO30aZs+GGTOs+GleX8zN+/BcoyTq3JjK5fCv2DLtSxx8gvAO7o5Xiy7ylAYh7imuM1nlwaZNmzh58iTt2rXD0fH+nekhISGMGjWKGTNmMHbs2LzhyMdJSkoqoUoftnjxYkaMGIG9vT2LFi3KV+P169cJCwt76DUjRox45BBo7tDw8OHDS6bgEiDBTQg94uwMH34IH3wAO3bkDqW6s/LG/+Ho8CXN6x7getI0zkz8iOjfPsbJrwnewd2p2awT5jYVdV2+EKIU5IaV3GHSXHZ2dnTu3JlFixaxbNkyevTooYPqHm3p0qX06dOHKlWqEB0djYeHR771169fZ8yYMQ+9LjQ0tMDgdvXqVRYtWoSZmRkvv/xyidVd3CS4CaGHFAUaN9a2sDBYtgwiIw1ZvD6AjIwAqtn/TFO/A3gcnkry/nfZOOF9XOq11Ia4ph0wsbDW9S4IIUrApUuXWLp0KaCdAiN3Gox/mzJlSqGDW2lc47ZgwQJCQkKwt7dn06ZN1KxZ86Ft3NzcUFW10H3m3pQwcODAcnFTQi4JbkLoOTMz6NNH227c0D6xYeFCI1asCSQ9PZAqlSfQxHc/HruncHLX2xibvIN7gzZ4t+yGR6N2GJtZ6HoXhBDFZObMmWRkZBAQEIC/v3+B2yxfvpyoqCgSExNxd3d/bJ8lfY1bZGQkAwcOxNHRscAzbU8r96aE8jRMCnoe3GQCXiHys7GBfv20LTUVVq/WhrhVqxpw504DKtpNorHPPjw2TyXh79cwMTOmeqO2eAe/iHuD5zA0LtwM5UKIsik3rEyaNImgoKACt/n0008ZO3Ys4eHhjBs37rF9luQ1bjNnzmTw4MG4uroSHR2Nq6trsfS7ZcsW4uPjy9VNCbn0OripqroCWBEYGFj2p0IWopRZWUHv3tp2+zasXXvvTNyKIFbeDsLWJpOG3vvwWDOVw9FDMLc0pUaTDngHd8O1fjAGhoW7cFkIUTbExMRw7Ngx/Pz8HhnaAIYMGcK4ceOIiIhgzJgxxTJX2tOIjo5m8ODB5OTk0KpVKyIiIh7axtbWlhEjRjxx3+XxpoRceh3chBCFY2EBL72kbXfuwLp12hC3fHkQ61KDsLbKJMhrLx5LpnJw/UAsbSzwbNYJ7+DuONVpisbAQNe7IIR4jNyzbUOHDv3P7dzc3GjTpg0bNmxgxYoVdO/evTTKe0hycjI5OTnA/Xnb/s3V1fWJg9u1a9dYuHBhubspIZcENyFEPmZm0K2btqWnw4YN2hC3bFlDoq43xNIik8Cae/E4HU6NlSFUqGKLd6uX8Gndg8oevjLRrxBlVGRkJJGRkYXadv369SVczeOFhoY+dOdrcahQoQJ37twp9n5LiwQ3IcQjmZhAp07alpEBmzZpQ9ySJQ2JudoQc7MsGtTYgeeJSVSf34Yq7jXxad2TWs+9JBP9CiFECZDgJoQoFGNjaN9e2yZPhs2bYd48QxYubMbm682oYHOHBpej8DwwAZdpX+Fctwk+rXvi2bwLppY2ui5fCCH0ggQ3IcQTMzKCNm20bcIE7Y0NkZFmrFjRmfV3O+NQ5Tr1Ly2m1vaJVLN9H4+G7cipWousJo0xNDbRdflCCFFuSXATQhSJiQl07aptN2/CkiUQGWnL6o2DWJkzmJrOZ/E9M5vaNrM4u34KXi26Uuu5HtoH32s0ui5fCCHKFQluQohiY20NAwdq2/nzCvPnQ2SkI0t2v89S5T383BOodWIatZf2o6qTDbWe64FP655UdPHUdelCCFEuSHATQpSIatXg7be17dgx+PrrJLZu9Wbewe8wMvqGeu6xeB/6De/ZrXDy8qJW6554B3fHsqK9rksXQogyS4KbEKLEeXrCwIHJTJ/uzp498OefBsydG8SuY0FYmKdT/1w0tXb+To0pY3Cv35z63Yfj3qCNTC0ihBD/IsFNCFFqFAUaNNC2776D6GiIjDRh8eL2bEltT0XbVPyTl9Jgywf4+Fegcb9RVG/UXgKcEELcI1cGCyF0wtAQnn8eZsyAixdh/nxoHmzFX6f68/2O3UxZ9wbT3/uYP157jmN/r0S9N4O6EEI8y/Q6uCmK0llRlCk3btzQdSlCiP9gZgY9e2rvSE1KUnj9DQ27T3flu+17CN/4NjM+/IyZr7bkaMwScrKzdV2uEELojF4HN1VVV6iqOtzGRib/FKK8cHCAsDA4dUrhtdc17ErRBrjpm99l1qdfMmN4M45sXEBOdpauSxVCiFKn18FNCFF+OTjAzz/DyZMK/3tNw67kzny3PZZZ2z4k8otxRAxtQtz6uRLghBDPFAluQogyzdERfvlFG+Be/Z+G7ac6892Ovcze+SlzvvqGaYMbcWjNbLIzM3RdqhBClDgJbkKIcsHREX79VRvghr+iYdvJTny3Yy9zYj9n7v99R/igIPavjCArI13XpQohRImR4CaEKFecnLTPRz15UmHYcA1bj3fiux37WHBwLAvGhxEeGsjeZeFkZdzVdalClHnjxo1DURQURSEhIUHX5RTo77//pmvXrri5uWFqaoqLiwsdOnRg7dq1T9TP7du3iYyMJCQkBG9vbywsLLCysiIwMJAffviBjIyCz9pnZ2cTGRlJ8+bNsbe3x9zcHE9PTwYNGsThw4eLYxefiAQ3IUS55OQEEyfCiRPaALcloSPf7djHwsPjWfzDL0wdEEDs4t/IvJum61KFKJNUVSU8PDxvnsSpU6fquKKHTZ48mebNm7Nx40aaN2/OyJEjadmyJZs3b+aFF15g3Lhxhe5ry5Yt9O/fn3Xr1uHr68ubb75JSEgIZ8+eZfTo0bRq1Yq7dx/+gy8kJIT+/fuTlJTEiy++yJtvvkmNGjWYOXMm9evXZ9OmTcW5y4+nqqret4CAAPVpRUdHP/VrRemQY1Q+lPRxSklR1f/9T1WNjHJUI8Ns9fm6q9WPm/upE3t6qzvn/aKmp90q0ffXF/r8/+nIkSO6LqFY3Lx5s1j6Wbt2rQqooaGhqr29vVqpUiU1PT29WPouDhkZGaqNjY1qamqqHj16NN+6I0eOqCYmJqqZmZl69+7dQvW3b98+dfbs2Q/t482bN9X69eurgPr999/nW7dr1y4VUGvXrq3evn0737rp06ergNqqVasC36+g41TY70Fgj/qITCNn3IQQesHZGSZN0p6BGzJUQ8yRF/hux36Wn/yJ5ROmMG1QEIfWRso8cELck3uGbdiwYfTr14/Lly+zZMkSHVd139WrV7lx4waenp54eXnlW1erVi08PT25c+cOt27dKlR//v7+9OvXD2Nj43zLraysGDVqFAAxMTH51p06dQqA1q1bY25unm9d165dAbh06VKh96k4SHATQugVFxeYPBmOH4dBgzVsOtSe73YcYHnCVywZ/xmzXmtF0p5oXZcphE5dvHiR5cuX4+npSZMmTQgNDQVgypQpui3sAVWqVKFy5cocO3aM48eP51uXu8zf35+KFSsW+b2MjIwAMDTM/yTQ2rVrA7Bp0ybu3LmTb93KlSsBaNOmTZHf/0nIs0qFEHrJ1RV++w0+/BC++kpDRER3tlt2ov2tn7n4QV+qB7Wg5bAxVHavpetSRRkwYu0I9l/Yr+sy/pO/vT9h7cOKpa+IiAgyMzPzApuvry8BAQFER0dz4sQJatSoUei+wsLCuH79eqG39/f3p1u3bo/dTlEUJk6cSP/+/QkICKB79+44ODhw9uxZlixZQu3atZk7d26h3/e/TJ8+HYD27dvnW+7r68vIkSP56aef8Pb2plOnTlhZWXH48GHWrl1Lnz59GDt2bLHUUFgS3IQQes3VFcLD4a23FEaNMmZe1LtsdxhG+9vvkxTbEr/2ITQd8AGWFe11XaoQpUK9d1OCRqNhwIABectDQ0OJjY1l6tSpjB8/vtD9hYWFkZycXOjtBw4cWKjgBtCzZ08cHBzo27cvs2bNyltetWpVBg0ahIeHR6Hf91EmTJjA2rVr8ff3Z/DgwQ+t//HHH/Hy8mLkyJFMmjQpb3lAQAADBw7EwsKiyDU8kUdd/KZPTW5O0G9yjMqHsnCccnJUddUqVfX2VlVQVX/Pk+rIJm3UsM4u6tZZ4+UGBrVsHKeSIjcnaEVFRamA2q5duyrFTwMAACAASURBVHzLr1y5ohobG6tVqlRRMzIyivQexeWPP/5QTU1N1ZCQEDU+Pl5NS0tT4+Pj1ZCQEBVQe/bsmbfttWvX1M8///yhdu3atUf2v2jRItXAwEC1t7dXT548+dD6nJwc9c0331QNDAzUr7/+Wj19+rSampqqbtmyRQ0MDFQBdcKECQX2XVI3J+g8VJVGk+Cm3+QYlQ9l6ThlZKjqxImqWrGiqipKjtq6/hb1k+a11Um9fdSDa2ar2VlZui5RZ8rScSpuEty0evXqpQLqnDlzHlr30ksvqYC6YMGCIr1HcUhISFCNjIzUgIAANTs7O9+67OxsNSAgQAXyvmcTExNV4KGWmJhYYP9LlixRjYyM1GrVqj1012quiIgIFVBHjhz50Lrz58+rZmZmqqWlpZqamvrQ+pIKbjJUKoR45hgZwWuvQUgI/N//Kfz8czO2GRygQ51Irn33IbFLfid42BjcAlvpulQhitWlS5dYunQpAH379qVv374FbjdlyhR69OhRqD5L6hq39evXk5mZScuWLdFo8t9LqdFoaNGiBbGxscTGxhIcHIybm5v2jFQhLFiwgJCQEOzt7dm0aRM1a9YscLvcGxBatXr4Z4G9vT3e3t7s27ePhIQEAgICCvXeRSXBTQjxzLK1hW+/hVdegQ8+MGDhwgH8XbknHe5+zT8f9sKjQStaDvuCyu4+ui5ViGIxc+ZMMjIyCAgIwN/fv8Btli9fTlRUFImJibi7uz+2z5K6xi09Xfv4ukdNt5G7/N/TezxOZGQkAwcOxNHRkejo6P+8Tq6kaiiSR52K06cmQ6X6TY5R+VAejtOWLaoaGKi9iMTb4x/17RZ91O/bVVbX/vCWmnr5vK7LKxXl4Tg9LRkqVVVPT08VUHfu3PnIbT755BMVUD/66KOnfp/isHPnThVQzczM1AMHDuRbt2/fPtXU1FRVFEWNi4srdJ8zZsxQNRqN6u7uriYlJT12+/Hjx+dNwHv9+vV86yZPnqwCqr29vZpVwOUVMlQqhBAlrFkz2LkT5syBDz6ozM9/zaG5/2GuLBtKfHQQQb3eILDH6xiblfJdZEIUg5iYGI4dO4afnx9BQUGP3G7IkCGMGzeOiIgIxowZ89DcZqUlKCiIQYMGERERQYMGDejevTuurq4kJSWxdOlSMjIyGDFiRN5ca48THR3N4MGDycnJoVWrVkRERDy0ja2tLSNGjMj7/LXXXiMyMpKDBw/i6elJly5dsLW1Ze/evWzatAkDAwMmTpyIgYFBse3345S74KYoigfwMWCjqmrhBuCFEKKQNBro1w+6d4effoKvv67NjoxtdGiwnpvTX+PAqpk0C/2I2s/3QVOKP6yFKKrcJyUMHTr0P7dzc3OjTZs2bNiwgRUrVtC9e/fSKK9A06ZNo0WLFsyYMYN169aRmpqKtbU1zZo1Y9iwYfTp06fQfSUnJ5OTkwPcn7ft31xdXfMFN0tLS7Zu3cqPP/7I4sWL+fPPP8nIyKBy5cr07NmT0aNH/2cILgmKWsgL+YrlzRRlOtAJ+EdVVd8HlrcHfgYMgHBVVb8pRF8LCxvcAgMD1T179jxVzTExMQQHBz/Va0XpkGNUPpTX43T+PHz2GUybBjbWWXTxn4KP4ZdUre5J69e/wblOE12XWKzK63EqjPj4eGrVKv8TLqempmJlZaXrMsRjFHScCvs9qChKrKqqgQWtK+1HXs0A8k1LrCiKATAReAHwAfoqiuKjKIqfoigr/9WqlHK9QohnXLVqMHUq7NsHAYGGzNr8Gr8dO8GeY/WZ9243tv3xrTz/VAhRakr1jBuAoihuwMrcM26KojQGvlBVtd29zz8EUFX168f0859n3BRFGQ4MB6hatWrA0z4W49atW1haWj7Va0XpkGNUPujDcVJV2LHDjsmTa3D6tDl1PfbQ3TkE+xrVcO/6NsZWdrouscj04Tg9io2NzRM9yqmsys7OLtVrqsTTKeg4nThxghs3bjz2ta1atXrkGbeycI2bI3D6gc/PAA0ftbGiKBWBcUA9RVE+fFTAU1V1CjAFtEOlT3vqX5+HDfSFHKPyQV+OU6tWMHo0/PorfPhhIOdv7qdXxgCybnxIh/cm4d6gta5LLBJ9OU4FiY+P14shRhkqLR8KOk6mpqbUq1evSP2W9lBpkamqekVV1VdVVa3+uLNyQghREoyM4J13YPt2sLY1Z9LOBWxKfpcFH/Vl89QvyM7K1HWJQgg9VRaC21nA+YHPne4tE0KIMq1+fYiNhd69FRbvHsrclL/ZOHs+c0d15saFFF2XJ4TQQ2UhuO0GaiqK4q4oijHQB1heHB0ritJZUZQphRlPFkKIp2FtDZGREB4OR097MunQXrbursys11px7O+Vui5PCKFnSjW4KYoyB9gOeCmKckZRlCGqqmYBbwDrgHhgvqqqh4vj/VRVXaGq6nAbG5vi6E4IIQqkKDBkCOzaBVWrmfLbjllEpXzFki+GEDXhfbIy7uq6RCGEnijVmxNUVS3wabaqqq4GVpdmLUIIUdx8fbXh7a23FKZPDyGxRhNS53fh3OFddPp4KnZO5f+ORiGEbpWFodISI0OlQojSZmGhnax39mxIvODGxAN72HrAkz9ea82RqPm6Lk8IUc7pdXCToVIhhK7066e9ccHNw5gp239n/dmfWf7N26z94S0y7tzWdXlCiHJKr4ObEELokqendsqQ11+H1Xu7MetULJuXbmP2m89zKfGIrssTQpRDEtyEEKIEmZrChAmwaBGcu+rAxP072HakIZFvtuXAqpmU9tNrhBDlm14HN7nGTQhRVrz4ovZ5p7V8DJm2/SfWnAtn9Y8fsfL/hpJ++6auyxNClBN6HdzkGjchRFni7g5btmgfmbV+X3umn9jP32vjmfXac1w4tk/X5QkhygG9Dm5CCFHWGBvDd9/BypVw7XZlJsRuYdvxdvw5ogN7Fk2WoVNR6saNG4eiKCiKQkJCgq7LKbQrV64QHh5O9+7dqVGjBmZmZtjY2NCsWTOmTZtGTk7OI1+7atUq2rZti5OTE2ZmZnh4eNCzZ0+2b99einvwdCS4CSGEDnTsCPv3Q0CgATO2j2PluXmsn/Q1y74YQPrtVF2XJ54RqqoSHh6OoigATJ06VccVFd6CBQsYNmwYO3fupGHDhowYMYKXXnqJuLg4hg4dSq9evQr8Q+j999+nU6dO7N27l/bt2/P2229Tv359li1bRtOmTZk9e7YO9qbwJLgJIYSOODnBpk3w6acQc7A5UxMO8HfUaSLfbsfVMyd0XZ54Bqxfv56kpCQGDhyIvb09M2fOJCMjo0h97ty5k+zs7GKq8NE8PT1Zvnw5Z86cITIykq+//prp06dz9OhRnJ2dWbRoEYsXL873mgsXLvD9999TtWpVjhw5Qnh4ON988w0LFy5k3bp1qKrKZ599VuK1F4VeBze5OUEIUdYZGsKXX8KGDQp3siowae9GtsY1IPLNtiTu3qjr8oSeyz3DNmzYMPr168fly5dZsmRJkfrs3bs3zs7OvPPOO8TGxhZHmQV67rnn6Ny5MxpN/ihjb2/Pq6++CkBMTEy+dcnJyeTk5NCwYUOqVKmSb12rVq2wsrLi0qVLJVZzcdDr4CY3JwghyovWrbV3nfrXM2D6zp9Zn/QlCz4OYdf8X+S6N1EiLl68yPLly/H09KRJkyaEhoYCMGXKlCL1O3r0aKpUqcJPP/1EYGAg3t7efPXVV5w8ebIYqi4cIyMjAAwN8z/Zs2bNmhgbG7Nr1y4uX76cb91ff/1Famoqbdq0KbU6n0apPqtUCCHEo1WrBtHR8MYbEB7enwuefqRP7sY/J+NoNzIMI1NzXZeov0aM0F50WJb5+0NYWLF1FxERQWZmZl5g8/X1JSAggOjoaE6cOEGNGk/3bN033niDN954g/j4eCIjI5kzZw6fffYZn332GY0bN6Zfv3706tWLypUrF9u+PCgrK4tZs2YB0L59+3zr7OzsGD9+PO+88w4+Pj5069aNihUrcvLkSZYvX87zzz/P77//XiJ1FRe9PuMmhBDljYkJTJkCEyfC/lN1mJYQy1+rDjLnnY7cuHha1+UJPZF7U4JGo2HAgAF5y0NDQ1FVtVhuUqhVqxZjx47l5MmTbN++nTfffJNTp07xxhtv4ODgQMeOHfnzzz9JS0sr8ns96IMPPiAuLo4OHTrQrl27h9aPGDGCxYsXk5WVxdSpU/nmm29YsGABzs7OhIaGPjSEWuaoqqr3LSAgQH1a0dHRT/1aUTrkGJUPcpyeXEyMqlaqpKqWFhnq8Eah6oQenmrKgb9L9D31+TgdOXJE1yUUi5s3bxa5j6ioKBVQ27Vrl2/5lStXVGNjY7VKlSpqRkZGkd/n37KystR169apL7/8sqrRaFRADQ0NLbb+f/75ZxVQvb291StXrhS4zfjx41UDAwN15MiR6smTJ9Xbt2+rsbGxatu2bVVAfffdd4ulloKOU2G/B4E96iMyjZxxE0KIMqplS9izB2rUNGLqzulsSnyb+e+9xL7l0+S6N1Ekudex5Q6T5rKzs6Nz5878888/LFu2rNjfd+/evaxdu5aNGzeSk5ODsbExXl5exdL3hAkTePvtt/Hx8SE6Oho7O7uHtomJieH999+nS5cu/Pjjj3h4eGBubk79+vVZsmQJjo6O/PDDD5w6dapYaioJeh3c5K5SIUR55+oKW7dCr14Ki2NfY/HpJaz5eQzrfxpBVka6rssT5dClS5dYunQpAH379s2bfDe3LVq0CCj6TQq5jhw5wqeffkqNGjUICgoiLCyMGjVq8Pvvv3PhwgU++OCDIr9HWFgYb775Jr6+vkRHR2Nvb1/gditXrgS0d5D+m7m5OUFBQeTk5LBvX9l9kole35ygquoKYEVgYOAwXdcihBBPy9wc5syBevXgww8b8U/qbm4tac+VlG50+TQCy4oF/5ISoiC5c7UFBATg7+9f4DbLly8nKiqKxMRE3N3dn/g9kpOTmTt3LnPmzOHAgQMA+Pn58c033xASEoKzs3OR9uFB48eP54MPPsDf358NGzZQqVKlR26bnq79Y+dRU37kLjc2Ni62+ordo8ZQ9anJNW76TY5R+SDHqXisXq2qNjaqamtzV329UU91cp/a6rn42GLrX5+Pk1zjpuXp6akC6s6dOx+5zSeffKIC6kcfffTE/Xfp0kVVFEUFVGdnZ/W9995TDxw4UJSSH+nLL79UATUgIOCR17Q9aN68eSqgVq1aVT1z5ky+datXr1YVRVFNTU3Vy5cvF7m2krrGTa/PuAkhhL554QXYtQu6djXht93z6JH1LWnvdKbtiB/wbdtH1+WJMi4mJoZjx47h5+dHUFDQI7cbMmQI48aNIyIigjFjxjw0H9p/SUlJYciQIfTv358WLVrkPU6ruM2cOZPPPvsMAwMDmjdvzi+//PLQNm5ubvmu4+vRowdt2rQhKiqKWrVq0b17d+zt7YmPj2flypWoqso333xDxYoVS6Tm4iDBTQghyhlPT9i5E/r3V5i34n0u+gaQ8e0A/jl5iODhY9AYyI92UbDcaT6GDh36n9u5ubnRpk0bNmzYwIoVK+jevXuh32PPnj0YGBgUqc7CSExMBCA7O5uwR8xv17Jly3zBTaPRsHr1aiZOnMjcuXNZsmQJaWlp2NnZ0aFDB9566y3atm1b4rUXhfzvFkKIcsjaGpYuhc8/h7Fj23Dx9g7S5r7A5cQjdP5kGmbWD99RJ0RkZCSRkZGF2nb9+vVP9R6lEdoAvvjiC7744osnfp2RkREjRoxgxIgRxV9UKdDru0qFEEKfaTTw1VewYAEkX3Th98O72bY1g9lvPM+lU4d1XZ4QogTodXCT6UCEEM+CHj1g+3YwtzJn8u4VbEloT+TbL5Dw13JdlyaEKGZ6PVSqltJ0IHfv3kVVVczMzErybUQhqKpKWloaN2/ezNdSU1O5efMmVlZW+Pr64uHhUWqn84UoDXXqaCfr7dVLwx+bxnGxbgPSvxxO035xNB34AYpGr/9OF+KZodfBrbTExsayadMmatSoQe3atfHy8sLExETXZemdnJwcbt269VAoO3HiBImJiXkBLTs7O9/rFEXBysoKKysrjh07xsGDBzEzM8PHxwc/Pz9cXFxK7K4nIUpTxYqwbh28+y6EhXXjYg0f7s7sxJXTx+jyyXQJb0LoAQluxaBmzZrcvn2bw4cPc+zYMQwNDfH09KR27drUrFkTIyMjXZdYbuTk5HDx4kVOnz7N1atX850xS01NfegxPwYGBhgZGWFubo6zszNWVlZYW1vnaxYWFmju/cLKysri5MmTxMXFcfDgQWJjY7G2tqZ27dr4+vpSrVo1CXGiXDM0hJ9+An9/eOWVmvx+Yzd90jvj4DOZBj1e13V5QogikuBWDKpUqULbtm15/vnnOX36NHFxcRw5coQjR45gbGyMt7c3tWvXpnr16jI89y+ZmZmcPXuWlJQUUlJSOH36NBkZGYB25urc8FW5cuUCQ5mZmRmbN28mODi4UO9naGiIl5cXXl5eZGRkkJCQQFxcHDt37mT79u1UrFgRX19ffH19/3P2bSHKuoEDoVYthe7drZmyfw1Wk1rgWq8lVar76ro0IUQRSHArRoqi4OLigouLC+3btycpKYm4uDji4+Pzhudq1aqFr68vrq6ueWeBniV37tzh9OnTJCcnk5KSwrlz58jJyQG0AbhOnTp5X0MbG5sSrcXY2Bg/Pz/8/Py4c+cOR44cIS4ujs2bN7N582bs7e3zQlxJ1yJESQgKgpgYBX9/cxbE/4zr168yYFIUhsamui5NZ1RVlbPqQif+PWL0tCS4lRCNRoOHhwceHh507Ngxb3guLi6OvXv3YmFhkTc85+TkpLc/SG7cuEFKSgrJycmcPn2af/75B9B+fRwdHWnUqBGurq44Ozvr9OYOMzMzAgICCAgI4ObNmxw+fJi4uDiioqKIiorCxcUFX19fateujbm5uc7qFOJJ1awJ332n8PrrzVi5tTHu076i1f/G6bosnTAwMCAzM7NsP4dS6K3MzMxiGXWT4FYKDAwM8PT0xNPTk8zMTI4fP05cXByxsbHs2rULGxubvBBnb29fLCEuKyuLtLS0vHb79m3S0tK4e/cuhoaGGBsb5zUTE5N8n+cue9JvMFVVuXTpUt6wZ0pKCrlTsRgbG+Ps7Ezt2rVxcXHB0dGxzF77Z21tTePGjWncuDFXr14lLi6OQ4cOsXr1atasWUP16tXx9fXF29tbbkIR5cL//qedrHfN5nF4RjbFvUEb3AJb6bqsUmdlZcXNmzflMgihE7kzGxSVBLdSZmRkhI+PDz4+PqSnp+ddY7Vjxw62bdtGxYoV80Jc5cqVAW0gSk9PfyiEFRTMcv+de51YUWg0modCXUEhz8DAIO+Ggjt37gBgaWmJi4sLjRs3xsXFhapVq5bLoWE7OztatGhB8+bNuXjxYt5Z06VLl+bdhNKgQQPc3Nx0XaoQj6QoMH06+Poasej4NJy/7cfg8M3P3NMV7OzsSElJAbR/oBkZGentaIcoG1RVJTMzk5s3b3Lt2jVcXFyK3KdeBzdFUToDnWvUqKHrUgpkYmJCnTp1qFOnDmlpacTHx3P48GH++usv/vrrL2xtbcnOziYtLe2hKS5yGRoaYm5ujrm5ORYWFtjZ2eV9nrvswc/NzMzIysoiIyMjX0tPT//Pzx9cfuvWrXzLsrKysLOzw8vLC1dXV1xcXKhQoYJe/UBUFAV7e3vs7e1p3bo1Z86c4dChQxw+fJj4+Hg6dOhAYGCgrssU4pGcnGDiRIX+/euw5kBvnMLeocunEXr1//RxTExMcHFx4erVqyQlJT3y52pZd/fuXUxNn93rFMuL3ONkYGCAlZUVLi4uxTJKo9fBrbQm4C0O5ubmeddYpaamcuTIEVJSUjAxMSkwgOV+/jR/MeaeKSsuOTk55fJs2tNSFAVnZ2ecnZ15/vnnWbhwIatWrSI1NZXg4OBn6hehKF9CQmDJEli+7EO81gRTvdFcfNv21XVZpcrExIRq1apRrVo1XZfy1GJiYqhXr56uyxCPUVLHSa+DW3llZWVFw4YNadiwoa5LKZRnKbT9m5GREb1792bFihX89ddfpKam0qlTp2f6ayLKLkWByZNhyxYNi07OxOHXtjj5NsLWwV3XpQkhCkl+uwhRRBqNhi5dutC8eXP27dvH/PnzyczM1HVZQhSocmWYOlUh+ZIH64+NZPW3r5GTnaXrsoQQhSTBrRjcyrhFanqqrssQOqQoCs899xwdOnQgISGBP/74I+9GDSHKmi5dYNAgiDrxGtu35bBzTpiuSxJCFJIEt2Lw+57fcfzRkTdWv0H8pXhdlyN0qEGDBvTs2ZNz584xffr0vOlQhChrwsLAyUlh8alZxMycwPn4WF2XJIQoBAluxaDTP7ZMi6vO7em/8ep7PoT80IQlB+eTlSPDD88iHx8f+vfvT2pqKtOmTePixYu6LkmIh1hbw4wZcO5KVdanfM2q8a+SceeWrssSQjyG3JxQDLwOX8Drz/30zFuynRy2c9HGgGwnByp518fUwxNcXcHF5X6ztdVeLSz0jpubG4MGDSIyMpKIiAj69u2Lq6urrssSIp9WreDtt+Hnn/tS02ohLr99inG9rrouSwjxHyS4FYePP4aRI+H0aUhJITspkeP7N5J8aAuGZ05zN/o0rss0GGXl5H+dpaU2wP070OU2R0coo08XEI9XtWpVBg8eTGRkJH/88QcvvvgiPj4+ui5LiHy+/hrWrYOlp6bjvLwevhbVIDhY12UJIR5BgltxMTcHLy/w8sIA8GY43sDRy0f5ZfckZu2bgenVVJ439GRQxdY0wwXjM+chJUXbdu+Gy5fz92lmBkOHwrvvgrOzLvZKFJGtrS2DBg1izpw5LFiwgA4dOtCgQQNdlyVEHjMzmDULGje2Zt25yVit+h+3ewzEwq6qrksTQhRArnErYd6VvPnlhV84M/ocX/SZzH5XY1qnTcY+51tGdzTi5LTvITYWLl2C27fh6FFYvx7Cw6FXL+2kS9WrawPciRO63h3xFMzNzRkwYACenp6sXr2aTZs2oaqqrssSIk+DBvDRRwpbj7Vj/+lg1nz/lnyPClFGSXArJZbGlrwa+CoHXz1IzMAY2ni0IWxHGDV/rUnHPzuy+vhqcsxMtWftnn8ehgzRXjl84gQMHw6zZ2vX9e0Lhw7penfEE8qdqLdevXps2bKFFStWkJOT8/gXClFKPvkE6teHJcd+IW7bAfavmK7rkoQQBdDr4KYoSmdFUaaUpSkZFEWhpVtL5vecT/KIZD5t8Sl7z++l458d8fzVkx+2/cDVO1fvv8DVFSZMgKQkGD0aVq6EOnWga1fYtUtn+yGenEajoXPnzrRo0YJ9+/Yxb948mahXlBnGxtoh0zsZZqw+P4uY3z/nSsoxXZclhPgXvQ5uqqquUFV1uI2Nja5LKZCjtSNjWo0heUQyc1+aSzWraozeMBqnH50Yunwosedi7w9X2NvD+PGQnAxffAFbtkDDhtqzc9HRIMMa5YKiKLRq1YoOHTpw7NgxZs2aRVpamq7LEgKA2rVhyJAkdh8LYt+lEFZ98wrZmRm6LksI8QC9Dm7lhbGBMb19e7Nl0Bb2v7Kfl+u8zJy4OQRODcThRwdeXvIysw7M4uzNs2BnB59/rg1w336rHTZ97jlo2hRWrZIAV040aNCAXr16cf78eaZPn87169d1XZIQAPTocZrmzWHJkXEkHLrK1lnf6LokIcQDJLiVMXXt6/J75985+85ZwjuHE+wWzLoT6xi4dCBOPznhM9GHt9a8xfJz0dx86xVITISJE+HsWejUCerVg/nzITtb17siHqNWrVq8/PLL3Lp1i+nTp8tEvaJMMDDQXl6rYsTqiwvYMW8Cpw9u1XVZQoh7JLiVUbamtgypP4Q5L83hwugL7H9lP98//z2utq6E7w2n69yu2I23o+mcNnxe6yJ/b5xB1rSpcOcO9O6tHfOYMQPkGqoyzdXVlUGDBgEQERFBUlKSbgsSAvDwgB9/hP3HPdl/YzSrx7/G3Vtl51phIZ5lEtzKAY2ioa59XUY1GcWafmu49v41ogdG80GzD8jOyWbslrE0j3wO2wsj6PxpdVaOHcAdQ7RPka5ZEyZN0gY6USZVrVqVIUOGYGVlxezZszly5IiuSxKCYcPghRdgycHRJKaYs/HX93RdkhACCW7lkomhCcFuwYx9biw7hu7gyntXWNJ7CaH+oRy/forOWbMw75FA/8G2HDO9Da+/TrabK3z3HaSm6rp8UQAbGxsGDRpEtWrVWLBgAbvkjmGhY4qinU7SzEzDivOLiNu4lPhNC3VdlhDPPAluesDW1JZu3t2Y0GECR984SvKIZKZ3nY7asQPNhyi0GgjRFpfgvfdIq1aZ6x1bww8/wPbtkJ6u6/LFPbkT9Xp5ebFmzRqWLVsm04UInXJw0M4BfuSkA7Fp3xL163vc/OeMrssS4pkmj7zSQy42LgyqN4hB9Qahqipx/8QRdSqK1RsWUn/JThpv34Tt6k0AqMbGKIGB0KTJ/VZVHnWjK0ZGRvTq1YuYmBi2bNnChQsX6NWrFxUqVNB1aeIZ1bs3LFkCixcPwLXJXFZ/+xq9xi9BY2Cg69KEeCbJGTc9pygKflX9GNl4JD9+tpVO2y+zbNUPNPjSie69YWpTU86mnkP95Rd48UXtfHE1asCAAfDbb3DwoNyhWso0Gg3PPfccffr04dq1a0yZMoXjx4/ruizxDJs4ESpWVFiaPIek/XvYs2iirksS4pklwe0ZY2tqyzuN32HHx0kM+HwxkQP8cXopiUqfGPHjDz24NOZ9qFtX+7zU//1P++8KFaBtW+38cevWQRl6EoU+8/LyYvjw4djY2PDnn38SHR0tj8kSOlGxIkybBieSbNl1dyp/z/iaiycO6rosIZ5JEtyeUQYaOPNaMQAAIABJREFUA7rX6s7m0M3EDo+lk99LfJi2nKrqt3TukcHGrbNRT57UPiP15Zfh0iUYOxbat9cGOT8/eOUVmDkTjh+XiX9LiJ2dHUOGDKFu3br89ddfzJkzhztyh7DQgQ4dtHeaLt3WgXPprVn1zavyVAUhdECCm6B+tfrM7DaT5BHJfNbyM3ad3UWb2c9TZ11Xwmvd4U7Y97BvH1y/DlFRMGYMODnBvHkQGgqentCmjXYbUeyMjIzo2rUrHTt2JDExkSlTpnD+/HldlyWeQT/8AG5uCgsSfuf8qbPERy/SdUlCPHMkuIk89pb2fBH8xf+zd99xUVxbAMd/d6kqgh0VG3axK6JiQRDsNdg7xqCJJWrUGM1LTHxqjCa2JMYWNcbesQUr9oK9Ye+9F+zAvD9GXtRYKAuzwPl+PvvBnd25c9Z9vBzn3nsOF3peYFrDaVibrPlk2SfkHJWTgesGcll7ANWrw3/+A6tWwZ07cPiwXmbkwAEoW1ZP5C7JrjNzU0rh7u5OQEAAUVFRTJkyhb179xodlkhh0qbV63pfvJKaddfHsmvOGKJkDawQiUoSN/EvdtZ2tC/Vnr2BewlpH0KV3FUYtmUYecbkofWi1uy6/LLGmMmkd2jo0wdOnYK+fWHOHP0O3NdfS824BODi4kLnzp3JnTs3y5YtIygoiIiICKPDEilI1arQq5diw9GGHAmz4dS2lUaHJESKIombeCelFF55vFjcfDGnepyiu0d3lh1fRvnJ5fGc4sm8I/OIiHqZNKRLB8OHw7Fj0KgRDBmi706dMAEksTCr1KlT07p1a6pUqcK+ffukSb1IdAMHQqpUGjtv9WHn7FFossZViEQjiZuIkbzp8/JzzZ+51PsSY2qN4cajGzRf0BzXMa4M3zKc6+EvG6TnyQOzZsHOnVCoEHTpou9MXblSNjCY0aslQ+7cucPEiRM5deqU0WGJFCJDBujQQbHrXD1OHbnK+T0hRockRIohiZuIFUc7R3qU78HxbscJahFEwYwF6b+uPy4/u1Drr1rMODCD8Ofh4OEBGzfqlTtfvIC6dcHPD/bvN/ojJCvRJUMcHR2ZOXMmISEhcvdDJIqePeFFhBW77/Rgx5xRRocjRIohiZuIEyuTFfUL1Wddu3Uc+ewI/Sr149itY7Rb0o4sI7LQamErVpxcyYv6dfUNDGPH6klbmTIQEACXLxv9EZKN6JIhJUqUYOPGjcyaNUtKhogEV7Ag1K8P2y504Oy+vVw+Iv11hUgMSTJxU0o1UkpNUkrNVUrVMDqelM4tsxtDqw/lzOdn2BywmfYl2xN8Oph6s+uR/efsdF3Ti22NyqKdPKlvZJg1CwoUgG++kQ0MZmJjY0OjRo2oU6cOZ86ckZIhIlH07g33HqTi8L327Jwz2uhwhEgREj1xU0r9oZS6oZQ6/MbxWkqp40qpU0qp/u8bQ9O0JZqmfQJ0AZonZLwi5kzKROVclRlfbzxXv7hKUIsgqrtW54/9f1Dpj0rk+7MsX9ey5dS2FdCwIQwerCdwkybJBgYzUEpRrly510qG7JPaeiIBeXlB6dKw7VpvTu9Yzc0zR4wOSYhkz4g7btOAWq8eUEpZAb8CtQE3oKVSyk0pVVwptfyNR5ZXTv365XnCwtha2VK/UH3mNJnDjT43mN5oOgUyFmDYlmEUWO5HmWrHmTmxO8/y5obAQChVSq8NJ+uz4i1HjhwEBgaSK1cugoKCWLZsmZQMEQlCKejVC85dycjp8DrsnDvG6JCESPaUEQuZlVJ5gOWaphV7+bwiMEjTtJovn38FoGnasHecr4AfgDWapq19x3sCgUAAZ2fnsnPmzIlTrOHh4Tg4OMTpXPFvd57fYf2N9ay7sY5jD4+hNOh9wZX+K++S6fo97pQty+kuXXiUP3+Mx5Tv6O00TePs2bNcvHgRBwcHihYtir29vWHxyPeUNMT2e3rxQtGyZQWyOZ2ifX5vinUZi336rAkYoZDfpaQhPt+Tt7f3Hk3T3N/2mqUkbk2AWpqmdXr5vC1QXtO0bu84vwfQHggF9mua9vv7rufu7q7t3r07TrGGhIRQrVq1OJ0r3u/E7RPMOjSLmYdmcv7GKbrtteK7TVY4PHpBVLu2WH3ZHwoX1v9Z/x7yHb3fsWPHWLJkCSaTCX9/f/Lly2dIHPI9JQ1x+Z6GDYMBA6BP5er4+ZekRs+fEyY4AcjvUlIRn+9JKfXOxO2DU6VKqQwxeKSLU2RxpGnaWE3Tymqa1uVDSZuwXAUzFmRQtUGc6HaCLV12EtmtK2X6OTKyokbEX3+Cmxvhrjl40bsnbNok6+DiqHDhwgQGBpI2bVpmzpzJ1q1bpWSIMKvOnSF1ajj0YhhH1swh/LZsjBEiocRkjdsVYDew5z2Pg/GM4zKQ85XnOV4eixelVH2l1MT79+/HdyiRgJRSeLh4MKb2GMIGXqXE9L/pO8GfLxqlYrPNFaLGjgEvL55mTs+zNi1g4ULZjRpL0SVD3NzcWLt2LQsWLOD58+dGhyWSCb0gL2zYV477j9Oze+F4o0MSItmKSeIWpmlaXk3TXN/1AG7HM45QoIBSylUpZQu0AILiOSaapi3TNC3QyckpvkOJRGJtsqZm/pqM/XgBPyy4j03wGgbO+YTO7TIwL1c44YvmQpMmRGZMz5Ma3vD779jevGl02EmCra0t/v7++Pn5ERYWxuTJk7l9O76/ukLoPv9cX+92wjScA8un8+TBHaNDEiJZikniVtFM7wFAKTUb2A4UUkpdUkp9rGlaBNANCAbCgHmapsm+8hTOxsoG37y+jGwykd+n3aLIyl2MXtKf9j1zM9o9ksu7Q+DTT/Fs1ozHpYuhff89HDggO1PfQymFp6cnbdq0ITw8nEmTJnHixAmjwxLJQHRB3tX7a/H4UST7lk42OiQhkqUPJm6apj1912tKqawfes9bxmupaVo2TdNsNE3LoWnalJfHV2qaVlDTtHyapg2J6XgiZVBKUc6lHINrDGP6qHPUX3acRUE/0PL7kvSvDvtvHUEb9C2UKsXTnNmI6tYN1q4FmQ58q7x58xIYGEj69OmZPXs2GzdulHVvIt569YLbd6y5mHoQe5dM5PmTcKNDEiLZiW8dtylmiSKByBq35KtgxoL0q/wls/+zn3J9F3Bo8e+0/sWbTxpZEexwnacTfwU/P15kTE9k86Ywezbcu2d02BYlXbp0dOzYkZIlSxISEsLcuXN5+jTG/wYT4l+iC/KuPdqaxw/uc3DFn0aHJESyYx2fkzVNq2uuQBKCpmnLgGXu7u6fGB2LSDgZ7TLi7+5PZ/fOPPj4AStPrqTLgQU8DV6J35HHNFi5EOd5C4iytkJzdcXK0QnSpn37w8Hh3a9Fv5469QdLlCQVNjY2NGzYkGzZsrF69WomT55M8+bNyZw5s9GhiSRIKb0NVtu2qbhTqju7F/5GqQYfY21rZ3RoQiQbMU7clFLfvO24pmnfmy8cIeLH0c6RFsVa0KJYC541f8aGcxv49sgiLqxdQOX9d8l37zS5XqTD5Y4jmW7akeppJOrhQ32Xakwbs5tMryd4pUvDiBHg4pKwHy6BKKUoX748WbNmZf78+UyePJmGDRvi5uZmdGgiCWrWDL78ErZd7kYmx7EcXTuXEnXaGR2WEMlGbO64PXrlz/ZAPfSNBEJYJDtrO2rlr0Wt/LWIavA7Oy7tYNXJVYw8/Te7r+gFmTOnzkzN/DWpla8WNXL7kFlLpSdx4eH6z/c9wsPh/n1YvBhWrIAff4RPPtETuyQod+7cBAYGMm/ePObPn0/lypXx9vbGlEQ/jzCGrS107w5ffZWBWi0as2veOIrVbIXJKl4TPEKIl2L8m6Rp2k+vPldKjUTfBWqxlFL1gfr5Y9E+SSRPJmXCM6cnnjk9GewzmBuPbrDm9Br+Pv03waeC+evgXygUZbOXpVY+PdkrX6g81qYY/IqcOqX3W+3SRV9LN3GivsUuCXJ0dKRDhw6sWrWKLVu2cPXqVfz9/UmVKpXRoYkkJDAQBg+GvQ++ocKL0pzYFERh74+MDkuIZCE+/5ROjV4o12JJHTfxLlnSZKF1idbMaDyDa32uEfpJKN97f4+dlR1Dtwyl8tTKZPoxE03nN2XK3ilcenDp3YPlzw/r1sHkybB/P5QoAT/8AC9eJN4HMiNra2vq169PvXr1OHfuHBMnTuTatWtGhyWSkOiCvMvW5sAqUwV2zBktu5aFMJMYJ25KqUNKqYMvH0eA48DohAtNiMRhUibcs7vzddWv2dJxC7f63mJ+0/k0cWvC9ovb6bSsEzlH5aT4+OL0Xd2XdWfW8Szi2euDKAUffwxhYVCvHnz1FXh4wJ49xnwoMyhbtiwdOnQgMjKSKVOmcOjQIaNDEknIqwV5b509ypmdq40OSYhkITZ33OoB9V8+agDZNU37JUGiEsJA6VOlp4lbEyY3mMzFXhc59OkhRviNwDmNM2N3jcV3hi8ZfsxA/dn1+XXXr5y7d+6fk7NlgwULYNEiuHYNypeHfv3g8WPDPk985MiRg8DAQLJnz86iRYsIDg4mKirK6LBEEhBdkHdBsBv2GfOzU+66CWEWMU7cNE07/8rj8stuB0Ika0opimUpRh/PPqxtt5bb/W6zrOUyAkoFcPTmUbqt6obrGFc8Jnnw07afuHj/on5i48b63beAAH3HaYkSsGGDsR8mjhwcHGjXrh0eHh7s2LGDGTNm8OjRow+fKFK83r3h1i3F9YwjuXI0lEuHthkdkhBJXry2i0V3TrBUUoBXmJuDrQP1Ctbjlzq/cLrHaU50O8Fw3+FEaVH0WdOHXKNz4TnFkzE7xnDF6jFMmgTr1+sn+/jou06TYCFgKysrateuTaNGjbh06RITJ07k8uXLRoclLFzVqlCmDCxY54m9UxZ2zhljdEhCJHnJunOCbE4QCa1AxgL0q9SP3YG7Odn9JEN8hvD4xWN6Bvckx8858JrmxW8OYVzfvlafMp06FYoU0adSk6CSJUvSsWNHlFJMnTqVffv2GR2SsGDRBXmPHTPxIt9Qzu1ez7UT+40OS4gkLV6Jm6V3ThAiMeXPkJ8BVQawv8t+wrqGMajaIG49vkXXlV3JPj4f1YvuZuH0/kRkyQT+/vrj6lWjw461bNmyERgYSK5cuQgKCiIoKIgXSXQHrUh4TZtC9uywfFc97NI4smvuWKNDEiJJi1XippRKr5TyUEpVjX4kVGBCJGWFMxXmG69vOPLZEQ5/epiBVQZy6cElmpwagkOjo0xpXoCIFcuIcisCU6ZAElu0nTp1atq0aUPlypXZt28fkydP5tatW0aHJSxQdEHe9RusSVv6K05sWcbtCyeNDkuIJCs25UA6AZvQi+5+9/LnoIQJS4jko2iWonzv/T3Huh5jf+f99K76JUM9IykS+ILN6R5Ap05cLV+Uh0eS1rSjyWSievXqtG7dmvDwcCZOnCglQ8RbBQbqLX7XHWuLta09ofPHGR2SEElWbO64fQ6UA85rmuYNlAaS3iprIQyilKJk1pIMrT6UU91PMaf/blb+/gX9m2Ug9cEwrEqXYVrroszeN4OHzx4aHW6M5c+fn86dO5MtWzYWLVrEsmXLZOpUvCZDBn2D9byF9rh4duHo2nk8uPGeotZCiHeKTeL2VNO0pwBKKTtN044BhRImLPOQXaXCUimlt9caXnMEw+bc4tSWIE6Xy0eHWUcpULcdDXtlZXDI90kmgXN0dKR9+/ZUqlSJvXv3MmXKFG7fvm10WMKC6AV5Yfet7gDsXvCbwREJkTTFJnG7pJRKBywB1iillgLnEyYs85BdpSIpUEpR1r0+xbecJGreXEpEZGD9+Md0rvstIeUyEdL7I57t3gmRkUaH+l4mkwlfX19atWrFgwcPZOpUvKZAAWjQAKZMdyRv1VYcXDWDx/dkXaQQsRWbAryNNU27p2naIOA/6KVAGiVUYEKkOEphatoM2+OnYNIkTLVrU+6qiWqjFmNXrgLP0jsSVa+eXtB3506L7YVaoEABOnfujLOzM4sWLWL58uVEREi9bgG9esHt23BGfUnE86fsXTzR6JCESHKs43KSpmkbzR2IEOKl9OmhUycydeoEwNbNswie9jU59p/Fd/ca8q5Yob8vdWrw9NSrnHp56b1R7e0NDPwfTk5OtG/fng0bNrB161YuXbpErly5jA5LGCy6IO+k6c4MbV6ffUGTKdesG3ZpHI0OTYgk44N33JRSe83xHiFE3FSq0orvJp8m81+LqP9Nfpz7wJeBebng74t24wZ8+62euDk56f9l/PprWL0awsMNjdvKygpfX19atmzJgwcP2Lt3L4cPHzY0JmGsfwrywmOXgTx79ID9y6YaHZYQSUpMpkqLKKUOvudxCMiU0IEKkZIppWhcpDEHuxzkx9bTmFskktz5gvDpnYHQA6sgKAh69IBnz+CHH6BmTUiXTm9y37cvLFsGd+8aEnvBggXp3LkzadKkYeHChaxYsUKmTlOwpk3BxQWmzctHHncf9iz6nRfPnhgdlhBJRkwSt8JA/fc86gGeCRVgfMiuUpHcWJmsaF+qPce7HWdsrbEcvXkUj0W1aPhoMof7ttfXvt29C8HB0L8/2NnB2LH6qvAsWeCjj2DVqkTf6ODk5ETJkiXx9PRk9+7dTJkyhTt37iRqDMIyRBfkXbsWHMt8xeN7NzkcPMvosIRIMj6YuGmadj4GD4ssyCO7SkVyZWdtR/fy3Tnd4zT/9f4vIedCKDG+BG0Xt+VMxE2oUQP++1/YtAnu34eNG6FnT9iyBerUgbx54bvv4MKFRIvZZDLh5+dHixYtuHfvHhMmTODIkSOJdn1hOaIL8s5eUYrsbh6Ezv+FyAjL3GwjhKWJb5N5IYSBHGwdGFh1IGc/P0tfz74sOLqAQr8UouuKrlx9+LIPqr29vvZtxAi4dAnmzYPChfXELU8ePZFbvDjRdqkWKlSILl26kCVLFhYsWMDKlStl6jSFSZ9eL8g7c6bC1bcvD65f5NiGRUaHJUSSIImbEMlAhlQZGO43nNM9TtOpdCcm7p1IvrH56L+2P3efvLK2zdZWX2QUHAynT8PAgXDggD6FmisXfPWVfjyBOTk50aFDBypWrEhoaCh//PGHTJ2mMJ9/DhERsGJHNTLnLcrOuWPQoqKMDksIixfrxE0plUYpZZUQwQgh4id72uyMrzeesK5hNC7SmB+3/ojrGFeGbh7Ko+ePXn+zqysMHgznz+ubG8qVgx9/hPz5oXp1mDNH3+yQQKysrKhRowYtWrTg7t27TJw4kaNHjybY9YRliS7I+/vvihKNenLnwglObVtldFhCWLyYlAMxKaVaKaVWKKVuAMeAq0qpo0qpEUqp/AkfphAiNvJnyM/Mj2ayv8t+quSuwsD1A8k/Lj/T9k8jSnvjroa1NdSvrydvFy7oa+POnIGWLSF7dr1qagImVIUKFaJz585kypSJ+fPns3LlSul1mkL07q0X5N15rgHpsruyc+5oNE0zOiwhLFpM7rhtAPIBXwFZNU3LqWlaFqAysAMYrpRqk4AxCiHiqIRzCZa1XMaWgC3kSZeHgKUBeE7xJPRy6NtPcHHRp09Pn9ZrwVWvDr/+CkWLQqVKMG0aPHr09nPjIV26dAQEBFChQgVCQ0MZPXo0Gzdu5MkTKRORnFWpAmXLwpgxVpT178a14/u4fGSn0WEJYdFikrh11zRtsKZpBzXtn3+qa5p2R9O0hZqm+QNzEy5EIUR8VcpVia0dtzK90XTO3TuHx2QPPl76MdfDr7/9BJMJ/Pz0jQyXLukbG27f1leUZ88On30Ge81bd9vKyoqaNWvSsWNHcuTIQUhICKNGjSI4OJgHDx6Y9VrCMrxakPdCRDOs7VITtm6+0WEJYdFikrhNi/6DUqrTqy8opVIDaJom8xpCWDiTMtGuZDtOdD9Bn4p9+PPgnxT8pSCjd4zmReR7foWzZIE+fSAsTC8v0rAhTJ2q3yopXVovyjVpEuzYYZZuDTlz5qRly5Z06dKFIkWKsHPnTsaMGcPSpUu5dUuakic30QV5x/2aivyetTi+KYjIF8+NDksIixWTxE298ufP3nhtsxljMTspwCvEvznaOTKixggOfXqIijkq0iu4FyV/L8naM2vff6JS+tzWn3/ClSswbhw4OOjTp4GBULEipE2rb2746CMYNAgWLYKTJyEOuwWdnZ1p3LgxPXr0wN3dncOHD/Prr78yd+5cLl2yyNKRIg5sbPTcf906IGcHnj68y9nd64wOSwiLFZPE7dWVouqN1yy6nIgU4BXi3QpnKsyq1qtY2mIpzyKf4TfDD/95/py7d+7DJ6dPD926webNeoHfM2dgyRL4/nv9LtzRo/qOVX9/KFgQ0qalzKefwief6Anfxo0Qw/If6dKlo3bt2vTs2ZOqVaty7tw5pkyZwvTp0zl16pQsZk8Gogvyzl9dgVROGQlbv8DokISwWNYxeE9WpVQH4AD/Ttzk/zGFSMKUUjQo1IAa+Wrw8/afGbJ5CCtPrqSfZz++rPwlqW1Sf3gQk0kvLeLqqk+jRnv8WE/gDh6EgweJ3LRJL/Q7efI/73FxgRIlXn8UKqTfhokWFQWRkaSxssK7XDkqFSvGgb172b1zJ0sOHcI5UyY8ypShQN68mDRNb+cVGakXCYv+c1QUZMqk16p7dWxhEdKnh44dYcIEE/UGteH0tgk8e/QQuzRpjQ5NCIsTk8RtEFAWCAByKKWOAmHoZUGkubwQyYC9tT0DqgygbYm29Fvbj+83fc+0A9P4qcZP+BfxR6k3/80WA6lTg7u7/gAOhIRQzcsLrl37fzLHoUP6z7Vr/+ncYGWllyiJTrzeYAuUe/mINZNJTxZdXfWuEdEJZ/SfXVz064tE9/HH8MsvcEW1JuL5GE5uXU6xGi2NDksIi/PBxE3TtImvPldK5QCKAyWATQkUlxDCADmdcjLbfzafun9K91XdaTq/KT6uPoypNYZiWYrF/wJKQbZs+qNmzX+Ov3gBx4/rSVxYmP7cyuqfh7X1689fOa6ZTFy9cYOTZ85w+949bFOlIl/hwuQrUADbVKn095lMcP06nD0L587pP9et09fqvTrVam2t35V7M6GL/nPWrPpnEGZXogRkzAj7T7jimS0PYesWSOImxFt8MHFTSintlUUkLxvKXwJWves9QoikrWruquwJ3MPEPRP5ev3XlPq9FF3LdWVQtUGkT5Xe/Be0sYFixfRHLCkgO5BN0zh//jxbtmxh3unT2J49i7u7OxUqVCBt2ndMuT17phcdjk7mXk3sgoLgxo3X329vD7lz/5PMVamib8Sws4t13OJ1JhN4ecH69YqOg/zZNXcU4bev4ZAxq9GhCWFRYjJVukEptRBYqmnaheiDSilb9CK87dGL9E5LkAiFEIawNlnzWbnPaF60Of/Z8B9+Cf2FWYdnMdRnKB1Ld8TKZFlTikop8uTJQ548ebh69Srbtm1j+/bt7Ny5k5IlS+Ll5YWjo+PrJ9nZ6b2XChR4+6CPH/+TyL35c8cOGD9eXzvXoYO+wv5d44gY8fHRNyKnLtACLeonjoUsxt3/U6PDEsKixGRXaC0gEpitlLrystXVWeAk0BIYrWnatASMUQhhoIypM/Jb3d/YE7iHIpmKELg8kPKTy7Pt4jajQ3unbNmy4e/vT7du3ShVqhQHDhxg8uTJXLt2LXYDpU4Nbm5Qty507QojR8KCBbBnj16QODgYqlaFUaP03bO+vvrr0rIrTry99Z/7j7vinL+E7C4V4i0+mLhpmvZU07TfNE2rBOQGqgOlNU3LrWnaJ5qm7UvwKIUQhiuVtRQbO2xk1kezuBZ+jUp/VKLt4rbsuLTDYktyZMiQgXr16vHJJ58AMHXqVE6fPm2ewU0mqFEDFi7Up1sHD4YTJ/SKsjlz6q3Dzp0zz7VSiCJFwNkZNmyAIj5NuH7yAHcunjQ6LCEsSkyazP+slOqglCoDmDRNu6pp2r1EiE0IYWGUUrQs3pJj3Y4xoPIA5h+ZT8UpFXEd40q/Nf3Yc2WPRSZxzs7OdOrUifTp0zNz5kz27TPzvzezZ4evv9anUJcvBw8P+OEHyJsX6tTR18tFRJj3msmQUlCtmp64FarWGJTiqNx1E+I1MZkqPQVUAMYBV19Olc5RSg1QSvkppWRVrhApjIOtA0OqD+Fan2tMazgNt8xujNoxCvdJ7hQYV4CB6wZy4NoBi0riHB0dCQgIwNXVlaCgIDZs2GD++Kys9GnVoCA9ifvPf+DAAb2+XZ488N13eu9X8U4+Pvpm3yu3s5GrVBXC1i+0qP8dCWG0mEyV/qZpWhdN0yppmpYBqAvMennup0CYUqrmewcRQiRL6ezT0b5Ue1a2Xsm1L64xuf5k8qbPy/Ctwyk1oRRFfi3Ctxu+5ejNo0aHCoCdnR2tWrWiVKlSbNq0iaVLlxL5llpxZpErl56onT+vFx4uVkx/njs3NGoEq1a9tU5dShe9zi16uvT+1XNcPbbH0JiEsCSxblmladpZTdOCNE37r6ZpHwGVgKHmD00IkZRkTJ2Rj8t8zOq2q7n6xVXG1x1P9rTZGbxpMEV/K0pAaACDNw7mxO0ThsZpZWVFgwYNqFatGgcOHGDWrFk8ffo04S5oba0nan//DadOQb9+sH27PoWaPz8MHaoXJRaA/lfi4qInbgUr18PKxk42KQjxinj3GtU07Sr6HTiLI03mhTBG5jSZ6eLehfXt13PliyuMqz2OtNZp+SbkGwr9UojSE0ozbPMwztw9Y0h8Sim8vLxo2LAh586dY+rUqTx48CDhL5w3LwwbBhcvwty5+vOBA/XNDE2b6kWBo6ISPg4LppR+123DBrBN7Ui+CjU5FrKEyAjZqSsEmKlJvKZpP5ljHHOTJvNCGC+rQ1a6eXRjbOmxXOp1iVE1R+ktttYPIN/YfJSbVI6R20Zy4f6FDw9mZqVKlaJ169bcu3cvbuVC4srkG8FlAAAgAElEQVTWFpo10xO148fh889h/Xq9nEiZMrBzZ+LEYaF8fODmTThyRJ8ufXL/Fuf3bjQ6LCEsglkSNyGEiAkXRxd6VujJ9o+3c+7zc4zwGwFA3zV9yT06NxWnVGT0jtGJmsTlzZuXjh07AmYuFxJTBQvq9eEuX4bp0+HWLahYEbp1gxQ6W/DqOjfXctWxc3CS6VIhXpLETQhhiNzpctPHsw+hn4RyusdphlUfxtOIp/QK7kXu0bkpN6kcQzcPJexmWILHEl0uJF26dMyaNcv85UJiwt4e2rXTe7V27w6//aYX/1248PV+qilAnjz6Y8MGsLa1o1CVBpzatornTx4ZHZoQhpPETQhhuLzp89K/cn/2dd7HiW4nGO47HGuTNQPXD8TtNzcK/1KYAesGEHo5NMFKQ0SXC8mTJw9BQUGEhIQYU4YibVoYM0afLs2SBZo00cuJXEj8qWQjeXtDSIi+5K9I9aa8ePqI09tXffA8IZI7SdyEEBalQMYC9KvUj+0fb+dSr0v8WudXcjrl5MetP+Ix2YNco3PRfWV31p9dT0SUeYva2tvb/79cyMaNGwkKCkq4ciEfUq4chIbq06jr1ul330aNSjGFfH184O5dvQxejmIVSJvZRYrxCoEkbkIIC+bi6MJn5T5jTds13Oh7g+mNpuOe3Z0p+6ZQ/c/qZB2ZlYClAQQdD+LJiydmuWZ0uRAvLy/279/PrFmzePbsmVnGjjVra/jiC32VvpcX9O4N5cvrvVKTuVfXuSmTiSI+/pzbvYHH924ZG5gQBpPETQiRJGRIlYF2JduxuPlibva9yaJmi6hdoDZLji2h4ZyGZB6RmSbzmjDr0CzuP43fon6lFNWqVaNBgwaJWy7kXfLk0VtpzZuntxXw8IBevSA83LiYEpiLCxQooCduoO8u1aIiOb5pqbGBCWEwSdyEEElOGts0NC7SmBmNZ3Cjzw1Wt1lNu5Lt2HpxK60XtSbziMzU+qsWE3ZP4Fp43Et8lC5dmlatWnH37l0mT57M9evXzfgpYkkpvdZbWBgEBsLo0fr06bJlxsWUwHx8YNMmfXY4s6sbmVzdZHepSPEkcRNCJGk2Vjb45fPjt7q/cbn3ZbZ13EbPCj05ffc0XVZ0IftP2an8R2V+2vZTnAr+5suXj4CAAEAvF3LmjDFFg/8vXToYPx62bgVHR2jQAPz99XIiyYy3Nzx4AHv36s+L+DThytFQ7l05a2xgQhhIEjchRLJhUiYq5qzIj34/cqLbCQ52OcigaoMIfx5OnzV9yDc2H6V+L8V3Id9x8PrBGO8azZo1K506dcLJyYmZM2eyf//+BP4kMeDpqWc0Q4fCypVQpAj8+muy6n9arZr+8//Tpd4fARC2fqExAQlhASRxE0IkS0opijsX5xuvb9jfZT9nepzhpxo/kdYuLd9t/I6Sv5ekwLgC9F3dl20XtxGlvb/VVHS5kNy5c7N06VI2btxoTLmQV9nawldfweHDUKGCXrS3UiU4eNDYuMzE2VmfDY5O3Byz5CBH8YqErV9g/N+9EAaRxE0IkSK4pneld8XebA7YzNUvrjKh3gQKZCzAmJ1jqPRHJXL8nINPl3/K6tOreR75/K1j2Nvb07p1a0qWLElISAhr1qxJ5E/xDvnyQXAw/PUXnDmjt8368kt4/NjoyOLNxwe2bIHnL7+SIj5NuHPpFNdPHjA2MCEMIombECLFcXZwJrBsIKtar+Jm35vM/GgmlXJVYsbBGdT8qybOI51pu7gti8IW8ej569X6raysaNiwIe7u7mzfvp1jx44Z9CneoBS0bq1vXmjfHn78EYoWhb//NjqyePH2hkeP9JJ2AAWrNMBkbSObFESKJYmbECJFc7J3olXxVsxvOp+bfW+ytMVSGhVuxMqTK/Gf50/mEZlpPLcxfx74kztP7gD6NGzNmjXJli0bQUFBxpYKeVPGjDBlit52wM4OatfWE7ok2vfUy0vPSaOnS1M5pidvOV+OhSwmKhmt5xMipiRxE0KIl1LZpKJBoQZMbTiV632us67dOj4u/TGhl0Npv6Q9WUZkwW+GH7+F/saNJzfw9/cnMjKSRYsWERX1/jVyic7LS2878O23MHcuuLuDJWyqiKWMGaFEiX8SN4Ai1Zvw6M51Lh7YYlxgQhhEEjchhHgLa5M1Pq4+jKszjgu9LrCz0076evblwv0LdF3ZFZefXei5qSfVa1bn/PnzbNq0yeiQ/83ODgYN0u++PX6sb2CYODHJNa338YFt2+DpU/153vI1sE3tIC2wRIokiZsQQnyASZnwcPFgmO8wjnU9xpHPjtCnYh/+OvgXvfb3Io9bHjZt2sT58+eNDvXtKlfW77Z5eUHnztCmTZLquuDtrSdtO3boz23sUlGgcn1OblnGi2fmaXUmRFKR5BI3pVQRpdTvSqkFSqlPjY5HCJGyKKVwy+zGiBojmNtkLnuu7GHw1cG8cHzBokWLeGypOzkzZ4ZVq2DwYJgzR29if/iw0VHFSNWqYDK9Pl3qVr0pzx+Hc2bnauMCE8IAiZq4KaX+UErdUEodfuN4LaXUcaXUKaVU//eNoWlamKZpXYBmQKWEjFcIId6nWdFmrG+/nvvP7vPL0184/PAwQUFBlltjzGSCr7+GtWvh7l295+m0aUZH9UFOTnqFk1cTt5wlKpEmgzNh62S6VKQsiX3HbRpQ69UDSikr4FegNuAGtFRKuSmliiullr/xyPLynAbACmBl4oYvhBCv88zpyY6Pd5DFIQszmMH84/MJja5dYam8vfWp0woVICBAf1jqncKXfHz0qdLoME1WVhT2/ogzoWt58uCuscEJkYgSNXHTNG0TcOeNwx7AKU3Tzmia9hyYAzTUNO2Qpmn13njceDlOkKZptYHWiRm/EEK8Tb4M+djWcRsVclZgEYsY8PcArl69anRY75c1K6xZA//5D0yfDuXLg6XUpHsLb2948UJv0RrNzacJUREvOLE5yLjAhEhk1kYHALgAF195fgko/643K6WqAR8BdrznjptSKhAIBHB2diYkJCROwYWHh8f5XJE45DtKGlLC9/R17q/54fEPrLu1jgZTGvDf8v/FzsbO6LDez8eH9GnTUmTIEKxKl8axa1dCjI7pLTTNCiurSkybdhEbm7Mvj2nYZ3Rh+6Ip3E2T2+AIE09K+F1KDhLse9I0LVEfQB7g8CvPmwCTX3neFvjFnNcsW7asFlcbNmyI87kicch3lDSklO8pKipK676ou8YgtBIjS2h3n9w1OqSYuXRJ0ypX1jTQtMBATXvyxOiI/qViRU2rUOH1Y9v+GqmN8Muo3b9+0ZigDJBSfpeSuvh8T8Bu7R05jSXsKr0M5HzleY6Xx4QQIslRSjG28Vj6FuzL4fDDuP/mzvl7Flom5FUuLrBhAxdattRrvVWsCKdOGR3Va3x89NZXDx/+c6yIjz8AYRsWGRSVEInLEhK3UKCAUspVKWULtADMsmBBKVVfKTXxfhJt9SKESLp+aP4DvTP35vLDy3hM8mD3ld1Gh/Rh1tacCQyE5cvh/Hl9K+f8+UZH9X/e3hAZCZs3/3MsXbY8ZHcrJ71LRYqR2OVAZgPbgUJKqUtKqY81TYsAugHBQBgwT9O0I+a4nqZpyzRNC3RycjLHcEIIEWMmk4mBrQbyqe2nRD6LpOrUqiw9ttTosGKmbl1912nRotCsGXTvDs+eGR0Vnp5ga/t6WRCAIj5NuHX2KDfPmOU/HUJYtMTeVdpS07RsmqbZaJqWQ9O0KS+Pr9Q0raCmafk0TRuSmDEJIURCSZcuHZ0adiIgMoBc9rloPLcxY3aMMTqsmMmVCzZuhF694Jdf9O4LZ88aGlKqVPoM7puJW6GqDVEmK2mBJVIES5gqFUKIZMvNzQ2vsl74h/vjm9OXnsE96bGqB5FRkUaH9mG2tvDzz7B4MZw8qU+dLjX2rqG3N+zbp9cPjpY6XSbyuHtzbMMitKgo44ITIhEk68RN1rgJISxBzZo1yZElB763felWthvjdo2j0dxGhD9PIv1CGzWCvXshXz79z198oRdVM4C3N0RFwaZNrx9382nCw5uXuXR4uyFxCZFYknXiJmvchBCWwMbGBn9/f148f4HHXQ/G1R7HypMr8ZrmxdWHFl6oN1revHr1265d9btw1aq9ftsrkZQvD/b2/54uzVexNjb2aaQFlkj2knXiJoQQliJLlizUqlWLM2fOUPpFaZa2WMrxW8cpP7k8h64fMjq8mLGz09e7zZkDu3dDzZqQyDMadnb6crs3EzfbVGnI71mb45uDiHhu/EYKIRKKJG5CCJFIypQpg5ubG+vXr6dU6lJsCthERFQElf6oxOrTq40OL+aaN4cFC/TFZnXqvF5YLRF4e8PBg3Dz5uvH3ao35Vn4fc6GrkvUeIRITMk6cZM1bkIIS6KUon79+qRNm5aFCxfilt6NnZ12kiddHurMrMPkvZONDjHm6tfX77zt3An16sGjR4l2aW9v/efGja8fz13Gi9TpMktNN5GsJevETda4CSEsjb29Pf7+/ty/f5/ly5eTwzEHWzpuwTevL58s+4R+a/oRERVhdJgx4+8Pf/0FW7ZAw4bw5EmiXNbdHdKk+fd0qcnKmkJejTi9I5hnjx4kSixCJLZknbgJIYQlypkzJ97e3hw5coR9+/bhaOfIspbL+NT9U0ZsG4HPdB8uP0ginf9atIBp02D9emjcGJ4+TfBL2thA1ar/TtxA310a+eIZJ7YsT/A4hDCCJG5CCGGASpUq4erqyqpVq7h58yY2Vjb8Vvc3ZjSewd6reyk1oRTBp4KNDjNm2raFSZMgOBiaNoXnzxP8kt7eEBYGV9/YlJu1cBnSZXeV6VKRbCXrxE3WuAkhLJXJZKJx48bY2tqycOFCIiL06dE2JdqwO3A3WR2yUntmbb5e/3XSmDr9+GMYP17vc9qiRYLXeYte5xYS8vpxpRRFfPy5sH8z4beTSKkVIWIhWSdussZNCGHJ0qZNS6NGjbh+/TqrV/+zq7RwpsLs7LSTgFIBDNk8BN8/fbny8IqBkcZQly4wZozeaaFtW4hIuISzdGlwcnr7dGkRnyagaRzbsDjBri+EUZJ14iaEEJauQIECVKhQgdDQUI4dO/b/46ltUjOl4RSmN5pO6JVQSv1eijWn1xgYaQz16AEjRsDcuRAQAJEJ09rLygq8vN6euGXIkR/ngqVkulQkS5K4CSGEwXx9fcmWLRtLly7lzaUd7Uq2I/STUDKnyUzNv2ryzYZvLL/PaZ8+MGSIvuM0MFDvUZUAvL3h1Cm4ePHfr7n5NOH6qYPcvnAyQa4thFEkcRNCCINZWVnRpEkToqKiWLJkCZqmvfa6W2Y3dnXaRbuS7Ri8aTB+M/wsv1XWgAHwzTfwxx/w2Wfwxmcyh+h1bm+761a4WmOUySR33USyI4mbEEJYgAwZMuDr68u5c+demzKNlsY2DdMaTWNqw6nsuLSD0hNKs+6MhXcIGDQI+veHCRPg88/NnrwVLw4ZM749cUuTwZlcpaoStmHhvxJhIZKyZJ24ya5SIURSUrZsWTJnzsyaNWv+v8v0TR1KdSD0k1AypMqA3ww/vgv5znKnTpWCoUOhd28YNw769jVr8mYy6b3u169/+7BFfJpw/+o5Lh7YYrZrCmG0ZJ24ya5SIURSYjKZqFGjBnfv3mXXrl3vfF/RLEXZ9cku2pRow6CNg6j5V02uh19PxEhjQSkYORK6dYOffoKBA82avHl7w4ULcPbsv18rWKUeabPkYPXo3jx/nLj9VIVIKMk6cRNCiKQmf/78FChQgE2bNvHoPf0/HWwdmN5oOlMaTGHrxa2UmlCKDWffMmdoCZTSy4QEBsKwYfD992Yb+n3r3GxTOVC3/+/cv3aetb98abZrCmEkSdyEEMLC+Pn58fz5c0LerC77BqUUHUt3ZFenXTjZOeE7w5fBGwdb5tSpyaQX6O3QQV/7NmyYWYYtUgScnd+euAHkKFaBCq2+4OjaeRxdN98s1xTCSJK4CSGEhcmcOTPu7u7s2bOHGzdufPD9xZ2LsztwNy2LteSbkG+oNbMWNx59+LxEZzLB5MnQurW+6/Snn+I9pFL6XbcNG949A1ux9Re4FC3P2nF9uXf1XLyvKYSRJHETQggLVK1aNezs7Fi9enWMdkU62Dowo/EMJtWfxJYLWyj1eyk2ntuYCJHGkpWV3pS+aVO93tu4cfEe0tsbrlyBEyfe/rrJypo6/X9HmaxYPjSQyIiEbcclREKSxE0IISxQ6tSpqVq1KqdPn+bUqVMxOkcpRacyndjZaSdp7dLi86cPQzYNIUpLmAK4cWZtDTNnQqNGeqeFCRPiNdz71rlFc3LOSY2eo7h2fC/b/hwer+sJYaRknbhJORAhRFLm4eFBhgwZWL16NZGxaB1VwrkEuz/ZTfOizfl6w9fUnVWXB88eJGCkcWBjo7fFqltX73H6xx9xHip/fsiR4/2JG0Chqg0oXrsNO+eO4cK+TXG+nhBGStaJm5QDEUIkZVZWVtSoUYNbt26xZ8+eWJ2b1i4tMz+ayfi641lzeg1e07wsr1G9rS0sWAA1akCnTnqLrDiIyTq3aN5dhpAhR35WDP+Ux/dvx+l6QhgpWSduQgiR1BUsWBBXV1dCQkJ48uRJrM5VStHFvQsrWq3g5O2TVJxSkbCbYQkUaRzZ28OSJXrm1b69XirkHcWH38fbG27ehCNH3v8+21RpqDdgIk8f3iX4px7SVUEkOZK4CSGEBVNKUaNGDZ48ecKmTXGb3quZvyabAjbxLOIZlf6oxJYLFtZJIFUqCAqCVq3g22/1dgjnzsVqiJisc4uWJV9xqn78Dad3BLN/WdynaIUwgiRuQghh4bJmzUrp0qXZtWsXt2/HbXqvTLYybP94O1nSZMH3T18WHl1o5ijjKU0amDFD37Rw6BCULAmzZsX49Dx5wNU1ZokbQJnGnXH18CVkwjfcPHs0bjELYQBJ3IQQIgnw8fHB2tqaNWvWxHkM1/SubO24lTLZytB0flPG7hxrxgjNpFUr2L8fihXT6721bQsPYraxwtsbQkIgKgabaJVS1PpiHHYOTiwfGsiLZ7GbhhbCKJK4CSFEEuDg4ECVKlU4fvw4Z9/WmDOGMqbOyLp262hYuCGf//05fVf3tbxyIa6usHGj3mFh1iwoVQq2b//gad7ecPcuHDgQs8ukSZ+ZOn1/5fb5Y4RM+CZ+MQuRSCRxE0KIJKJChQqkS5eO4OBgomJyW+kdUtmkYkHTBXQt15WR20fSelFrnkU8M2OkZmBtra9327xZf16lygc3LsRmnVu0PO7euDfpyoHlUzm5ZUU8AhYicUjiJoQQSYS1tTW+vr5cv36d/fv3x2ssK5MV42qP44fqPzDn8BxqzazFvaf3zBSpGXl66lOnLVt+cOOCiwsULBi7xA2gSsBAnAuUJHhUTx7etLCSKUK8IVknblKAVwiR3Li5uZEzZ07Wr1/Ps2fxu0umlOLLyl/yV+O/2HphK1WmVuHGUwvsceroGOONC97esGlT7CqKWNnYUm/ARCJfPGfF8C5ExaLYsRCJLVknblKAVwiR3CilqFmzJo8ePWJz9DRiPLUu0ZpVrVdx/t55uu7ryqHrh8wyrtnFYOOCt7d+aO/e2A2d3iUf1bsN59LBbeyaO8aMQQthXsk6cRNCiOTIxcWFEiVKsGPHDu7dM8/0ZvW81dkcoCeCVaZWYcPZWM43Jpa3bVzYtu3/L1erpv+M7XQpQFG/5hT2/oitfw7nytFQs4QrhLlJ4iaEEElQ9erVUUqxdu1as41ZMmtJfin9Cy6OLtSaWYs5h+eYbWyzenPjQtWq8N13EBGBszMULRq3xE0phV+PkaTN7MLyYZ159sjC+rsKgSRuQgiRJDk6OlKpUiWOHDnChQsXzDaus70zWwK2UCFHBVoubMnIbSMtty3UqxsXBg36/8YFb2/YsgWeP4/9kHZpHKk3YCIPb15mzZgvLPezixRLEjchhEiiPD09SZs2LcHBwWZNMNKnSk9wm2CaFW1G3zV96fl3TyKjLHTB/ls2LrS1nsWjRxAax9nO7EXcqdTuS46FLObIGgu96yhSLEnchBAiibK1taV69epcuXKFQ4fMu6HA3tqe2f6z6VWhF2N3jaX5guY8jXhq1muY1SsbFzxGt+ZP2rJtVdwrCng0/5ycJSux7pf+3Ll0yoyBChE/krgJIUQSVqJECbJnz87atWt5Hpe5wfcwKRM/1/yZn2v8zMKwhfjN8OPOkztmvYZZvbJxoRWzaPPT6xsXYsNkZUWdfuOxsrFlxbDORL4w79+tEHEliZsQQiRh0eVBHj58yLY4Jikf0qtiL+Y2mcuuy7uo/Edlzt87nyDXMYuXGxd+bb6Zp88UWtWqMG1anIZKmzk7tb4Yw/WTB9g8dYh54xQijiRxE0KIJC5Xrly4ubmxbds2HsSwIXtsNSvajNVtVnM1/CoVplRg/7X4dW5IaK6tPSmp7eduaR/o2FFfAxcH+T3rULJeALsX/Mq53RZaIkWkKJK4CSFEMuDr60tUVBTr169PsGt45fFiS8AWbEw2VP6jMuNDx1teg/qXqlaFRyZHfvNbou82bdcO5s+P01jVOn9PxtyFWTmiK4/u3jRvoELEkiRuQgiRDKRPn54KFSpw4MABrlxJuH6bRbMUZUenHXjm9OSzlZ9R/c/qnLl7JsGuF1dOTlC2LKzekhqWLYNKlfSyIYsXx3osG7tU1BswkWfh9/l7ZHe0KMtMVkXKIImbEEIkE1WqVCFNmjRmLw/ypuxpsxPcJphJ9Sex9+peio8vztidYy3u7pu3N+zYAY9VGlixAjw8oHlzWL481mNldnWjWufvORu6lr1LJiZAtELETLJO3KTJvBAiJbGzs8Pb25sLFy5w9OjRBL2WUopOZTpx5LMjVMtTjc///pyqU6ty4vaJBL1ubHh7w4sXsH49kDYtrFqlN6j394e//471eKXqdyRfxVpsmvI9lw7vMH/AQsRAsk7cpMm8ECKlKV26NM7Ozqxdu5aIiIgEv14Oxxwsb7mc6Y2mc+TmEUr+XpKR20ZaRMHeatUgZ0745huIjESfP129GtzcoFEjiGW7MKUUNXuPwSFjNub2acj2v0YSFZnwf8dCvCpZJ25CCJHSmEwmatSowb1799ixI3HuCimlaFeyHUc/O0rNfDXpu6Yvlf6oxNGbCXvX70Ps7WH4cNi3D6ZPf3kwfXpYswYKFoQGDfS6b7GQ2ikjbX9bT2GvRmz98wfmfFGfe1fPmTt0Id5JEjchhEhm8ubNS8GCBdm8eTPh4eGJdt1sabOxuPliZn00i1N3TlF6QmmGbR5GRJRxd6VatICKFWHAAHj48OXBTJn0u22urlC3LmzdGqsx7R2cqPvVBOr2/53b548zvYsXh1fPlr6mIlFI4iaEEMlQjRo1iIiIYMOGxK09ppSiZfGWHPnsCA0KNWDA+gFUmFyBQ9fN25Ir5vHAmDFw/ToMHfrKC1mywLp14OICtWvDzp2xHruITxPaT9iEc4GS/D2yO0GDO/LkgQV3lhDJgiRuQgiRDGXMmJFy5cqxb98+rl+/nujXd3ZwZn7T+cxvOp8L9y9QdmJZvt/4PS8iXyR6LOXK6WXcfv4Zzp595YWsWfWdC1myQM2asGdPrMd2zJKDZsMXU7XTN5ze8TfTAqtwbk+I2WIX4k2SuAkhRDLl5eWFvb09q1atIsqg2mNN3JpwtOtRmrg14duQbyk3qRz7ru5L9DiGDtW7YfXr98YLLi568pY+Pfj5wYEDsR7bZGWFR7MetB4bjJ2DEwu+asKG8QOJeP7UPMEL8QpJ3IQQIplKlSoVfn5+nD9/nuXLlxu2BitT6kzM8p/FkuZLuP7oOuUmlePr9V/zLOJZosXg4gL9+8OCBW/Zj5Arl568OTiAry8cPhynazjnL0HbX9dRuuEn7Fk8gRldfblxOm5jCfEukrgJIUQyVrp0aapWrcq+fftYt26dobE0LNyQI58doXWJ1gzZPISyE8sSejk00a7fp49eHqRXr5flQV7l6qonb7a2UL06hIXF6Ro2dqmo3nUYH/13Dk8f3mFmjxqELvhVui0Is5HETQghkrlq1apRtmxZtm7dyrZt2wyNJUOqDExvNJ3lLZdz7+k9KkypQP+1/XkakfDTiqlSwY8/6uVBpk17yxvy59eTN6XAxwdOxL2YcF4PX9r/vglX9+psnPgt8/v78/BmwrUiEymHJG5CCJHMKaWoU6cObm5urFmzhgNxWMdlbnUL1uXIZ0cIKBXA8K3DKT2hNNsvbk/w6zZvDp6eMHAgPHjwljcUKqQnb5GRevJ2+nScr5U6XSYaDvqTmr1Gc/XYXqZ1rsKxjUviHrwQSOImhBApgslkonHjxuTNm5elS5dy/Phxo0PCyd6JyQ0mE9wmmMcvHlN5amVWn16doNdUCkaP1suDDBv2jje5uel13p480ZO38+fjcT1F8dptaDd+Axly5Gf5kE6s/PEznj16W9YoxIdJ4iaEECmEtbU1zZo1I1u2bCxYsIDz8UhIzKlGvhoc+vQQhTMVpv2S9tx8dDNBr/dqeZAzZ97xphIl9A4LDx7oTU8vXYrXNdO75KXFz8up2KYvYesXML2LF5cOJfwdRpH8SOImhBApiJ2dHa1bt8bJyYnZs2dz7do1o0MCwNHOkdn+s7n75C4dgzom+A7YYcPeUR7kVWXK6L1Nb9/W77xdid8aNStrGyq1+5KWP69AmayY27chm6cOIfLF83iNK1IWSdyEECKFSZ06NW3btsXW1paZM2dy9+5do0MCoIRzCYb7Dmf5ieX8Fvpbgl4re3b46itYuPAD7UrLlYO//4arV/XdpmYoZpzdrRztx2+gWI2W7Jw9ilk9a3P7wsl4jytSBknchBAiBXJycqJt27ZERkYyY8aMRO1p+j49yvegdv7a9FnTh8M3Eo2LCnYAACAASURBVLYG2hdf6CXcevZ8S3mQV1WsCCtXwoULevJ2M/5Tubap01Kz9xgafDON+9cuMKOrDyc2L4v3uCL5k8RNCCFSqMyZM9OqVSvCw8P566+/ePrU+Er/SimmNpyKo50jLRe2TNAyIdHlQfbvf0d5kFdVqQLLl+u7TP384MYNs8RQsHI9OkzcRGZXN1aN7Mb9axfMMq5IviRxE0KIFCxHjhw0a9aMmzdvMmfOHCLfe+spcTg7ODO14VQO3zjMl2u+TNBrNWsGlSrBgAHvKA/yKm9vCAqCY8f0siFjxsCL+PdedciYjXoDJqGUYtXIblKsV7xXkkzclFJplFK7lVL1jI5FCCGSuvz589O4cWPOnz9PWFiYYX1NX1WnQB16ePRg7K6xrDixIsGuE10e5MYNvZ/pB/n5wd694OGhz7GWKAGrVsU7DqesufDu/F8uHdzG3qWT4j2eSL4SNXFTSv2hlLqhlDr8xvFaSqnjSqlTSqn+MRjqS2BewkQphBApT7Fixahduza3b99m2bJlhvU1fdVwv+EUz1KcgKUBXAtPuN2v7u7Qvj2MGvWe8iCvcnPTNywsW6YvjqtTR38cOxavOIrVak3e8n5snjJYNiuId0rsO27TgFqvHlBKWQG/ArUBN6ClUspNKVVcKbX8jUcWpZQfcBQwzwIDIYQQAHh4eJA7d27279/P2rVrjQ4He2t7ZvvP5uHzhwQsDSBKS7g7gUOHgo0N9O0bwxOUgnr19Ib0P/0E27ZB8eL6Xbg47tJVSlGj5yis7VKxakRXoiIj4jSOSN5UYv+rSimVB1iuaVqxl88rAoM0Tav58vlXAJqmvbWmtVJqCJAGPcl7AjTWtH//NiulAoFAAGdn57Jz5syJU7zh4eE4ODjE6VyROOQ7Shrke0oaHj58yLVr17hy5Qr/a+/e43Os/weOv96bmQ07yGma2KhhhUw1Mcw0pqgcviI68I1fIXQkfTvJt1Jfh6iUQ/l+FZVD1sG5iUIhpxqWw+QUEeUwZtvn98d1L8Mw233f1+577+fjcT923/d1XZ/rffex7veuz/X5vCMjI6levbrdIfHZ3s8Yu20s/Wr1o3N4Z5edZ9q0a5g8OZLRo9fTsOHRKzrW7+hRIqZMIezLL8kqV46dvXqx/447ML6+VxzHH6nfseOz0VRr0Y1qTTtdsF1/lzxDUfopPj5+rTGmcb4bjTFufQA1gZ/yvO4MTMrzuicwvgDtPADcUZBzxsTEmMJKSUkp9LHKPbSPPIP2k2dISUkx2dnZ5tNPPzUvvPCCWbdund0hmZycHNP+o/am9PDSZv3+9S47z8mTxtSoYUyDBsZkZRWykfXrjWnZ0hgw5vrrjVm8uFDNJL/c2/wnqao5sG3jBdv0d8kzFKWfgDXmIjmNR05OADDGfGCM+cLuOJRSytvkrWuanJxse11TEWFyh8lUCKhAt1ndOHnmpEvOk7s8yIYN8P77hWykQQOrSP2sWXDiBLRuDXfdBdu2XVEzrQeMJCAolK9G9iMr83Qhg1HeqDgkbnuBvNfiwx3vFZmItBeR9/78809nNKeUUiWGr68vXbt2LTZ1TSuVrcR/7/ovmw9t5omFT7jsPF26QLNmMGxYAZYHuRgR6NgRUlOt2lpLllgTGp56qsCNBgRVIHHwGA7tTGXF/0YWMhDljYpD4rYauFZEIkSkNHAPkOyMho0xnxtj+gQHBzujOaWUKlFKly7NvffeS0hISLGoa3pbrdt4vMnjvLPmHeZumeuSc+QuD/L77zBiRBEbK1MGhgyBtDTo0QPeeAOuvRYmTbpMqQZLrVsSuaHtvaz+dBz7UlcXMRjlLdy9HMh0YCUQJSJ7RKS3MSYL6A8sADYDnxhjfnZnXEoppfIXGBhIjx498Pf3Z9q0abbXNR3RagQ3Vr2R3sm92XesaEXfLyYmxloeZMwYq1BCkYWFwZQpsHq1lbg99JBVA3XZssse2rLvy5SvdDXzXu9HZsYJJwSjPJ1bEzdjTDdjTJgxxs8YE26Mmex4/ytjzHXGmFrGmKL+jaOUUsqJgoOD6dGjBzk5ObbXNfUv5c9HnT7i5JmT3DfnPpctETJixBUuD1IQMTGwfDnMmAGHDkGLFlbphvT0ix7iX7Y8bR9/kyN7d7B8ynAnBqM8VXEYKnUZvcdNKaWcozjVNa1TsQ5j245lyc4ljFo5yiXnqFbNKoM1Zw6kpDixYRHo2tVarPfFF636p3XqwLPPWpMZ8nFNwzga3d2XdXMnsWvd5a/SKe/m1Ymb3uOmlFLOEx4eTteuXf+ua2pnaax/Nvond9e5m2eWPMOP+390yTkGD4YaNayfTi/hGhgIzz1n3f/WubN1ia9tW8jMzHf3uAeHERpei/lvDCDrlA6ZlmRenbgppZRyrlq1atGhQwd27drFmjVrbItDRJjYfiKVy1am26xunMh0fjITEACvv24tDzJlitObt4SHw7Rp8OGH8O23MHBgvrv5lQmk3ZNvc/zwfnYv/sBFwShPoImbUkqpK1K/fn0iIiJISUnh5EnXrKlWEFcFXsX/7v4fvxz+hUHzB7nkHJ07n10exKV33XTvDk8/DRMmwHvv5btLWN0Ybuk6iMMbU9i2suiF7ZVn8urETe9xU0op5xMR2rRpw+nTp1m6dKmtscRHxPN006eZtG4Ss1JnOb393OVBDh1ywvIglzNiBCQlQf/+8N13+e7SpMcTBFSuycIxj3Pyz8MuDkgVR16duOk9bkop5RpVqlQhJiaGNWvWcPDgQVtjeSn+JW6qdhMPff4Qu//c7fT2Y2LggQesBO4KCyBcGV9f+OgjqFkTOnWC3Rd+Fl+/0kR06M+pY0dY/OaTuSUgVQni1YmbUkop14mPj8ff35/58+fbmkD4+frxUaePyMzO5L7P7iM7x9kzCayLYf7+VvEDlwoJgblz4eRJuPtuyMi4YJfAyjVpet8Q0pYns2XpbBcHpIobTdyUUkoVSmBgIPHx8ezcudP2eqa1K9RmfLvxLE1fysjvnF8iKizMRcuD5KduXWvCwtq10KcP5JMU39SlH2F1G7Nk/NMcP7zfxQGp4kQTN6WUUoXWuHFjKlWqxMKFC8nKyrI1lvsb3M8/ov/Bc0uf44e9Pzi9/dzlQQYNcsHyIOfr0AFeeslK4EaPvmCzj28p2j31FlmZp1kwarAOmZYgXp246eQEpZRyLR8fH9q2bcuRI0dYtWqVrbGICBNun0C18tXoPqs7x04fc2r7ZcpYy4Ns3AgTJzq16fwNG2YVq3/ySVi06ILNoVfXovk/n2fn6sVsmvc/NwSkigOvTtx0coJSSrleZGQkUVFRLFu2jGPHnJssXanQgFCm3T2NnUd38uj8R53efufO0KqVVTt+v6tHKH18YOpUqFfPqraQT+HUG9v34pqGcaS8+y+O7t/l4oBUceDViZtSSin3SExMJCcnhyVLltgdCnE14hgWN4wP1n/Axz997NS2Rayl1k6dsoZMXa5cOfjsM+v5XXfBeXVixceHtk+MQ0SY/0Z/jI3VLJR7aOKmlFKqyCpUqEBsbCwbNmxg7969dofDcy2eIzY8lr5f9OXXP391atvXXgv/+hd88gl8+aVTm85frVrWyVJT4f774bzkLKhyOPEP/5s9m1ayds67bghI2UkTN6WUUk4RFxdHuXLlmDdvnu03y5fyKcWHHT/kTM4ZBi8Y7PT2n3wSoqPhkUcuuAjmGq1bwxtvwOzZ1Jg27YLN1yd2o1ZsG5ZPeZnDv6a5ISBlF03clFJKOYW/vz8JCQns3buXjRs32h0OkaGRDIsbxuzNs1m4faFT2y5d2qpM9euvVq14txg0CHr2JOL996213vIQERIHjaJ0QFnmvd6PnGx7Z/gq1/HqxE1nlSqllHs1aNCAatWqsXjxYjIzM+0Oh8ebPE7tCrV5dN6jZGY7N55bb4X/+z8YO9Zacs3lRODdd/krKgp69LCGTvMoW6EKrR99nd+2ruP76WPcEJCyg1cnbjqrVCml3EtEaNu2LcePH2f58uV2h4N/KX/Gth3L1sNbGbtqrNPbf+UVqFIFHnoI3LKMXUAAPw8fDmXLwp13wpEj52yOan4ndeI7svLDNziwzf6rnsr5vDpxU0op5X7Vq1enfv36rFy5kiPnJRZ2aHdtO9pf156Xlr3EvmP7nNp2SAi8+SasW2f9dIfTlSrBrFmwaxd0737BasAJ/V4jIPgq5o3sR1bmafcEpdxGEzellFJOl5CQgI+PD4vyWTjWDqPbjOZM9hmeWuT8YqOdOkH79tZM0/R0pzefv6ZNYfx4mD/fqsWVR0BQKG0eG8Oh9M18M/F52yeKKOfSxE0ppZTTBQUF0axZMzZv3szOnTvtDodaFWrx5K1P8uGmD1m2a5lT2xaxcigRa5ap2/KkPn2sm+xGjoTp08/ZFHnzbcTc3Zd1cyfx9dtDdX03L6KJm1JKKZdo0qQJISEhzJ8/n5xikDgMjRvKNcHXMGDeALJynHtD2jXXwMsvw7x51pJrbjN2LDRrBr17W+O1ebT8v5dp3PkR1s2dxLzX+5GddcaNgSlX0cRNKaWUS/j5+ZGYmMjBgwdZ65Zpl5cW6BfIqMRRbDywkXfXOH+h2gEDoHFjGDjwgjkDrlO6NMycCRUrWpUVDh78e5OI0OKhF2n2wDOkLvmU5OEPkpV5yk2BKVfx6sRNlwNRSil71alTh5o1a5KSkkJGRobd4dCxbkcSIhJ4NuVZfj/xu1Pb9vW11nY7dAieftqpTV9alSowZ46VtHXpAmfOXlkTEWK7P0ZC/9fYvnI+s5/tRuZJe+vJqqLx6sRNlwNRSil75S4PcurUKZYuXWp3OIgIbya9yfHM4zyz5JnLH3CFbrwRBg+GiRPBrauhxMTA5MmwbFm+RVRv7NCbdk+9ze6NK/jk6U5k/PWHG4NTzuTViZtSSin7ValShZiYGFavXs3BPEN5dqlXqR4DbxnI5HWTWb13tdPbf+EFqFHDmjtw2p2rcXTvDk88AW+/DZMmXbC5Xut/cOdz7/P7jp+Z8XgHjh/e78bglLNo4qaUUsrl4uPj8ff3Z8GCBcVieYrnWjxHlXJV6D+vPznGuRMnypaFd96BLVvgtdec2vTlvfoqJCZa01tXrLhgc+1b29FpxAz+Orib6Y+15+j+dDcHqIpKEzellFIuFxgYSMuWLdmxYwdpafYXQQ/yD2Jk65H8sPcHPlj/gdPbT0qCe+6BESOsBM5tfH1hxgxrmmunTrB37wW7XNMwjn+8NofTx/9k+uDbOZTuzgBVUWnippRSyi0aN25MxYoVWbBgAVluqQ91aT3q96Bp9aYMWTyEo6eOOr39MWMgMNBaas2tFxlDQ60i9MePw+23w+HDF+wSVqcR9/zncwBmPN6e/Vt+dGOAqig0cVNKKeUWvr6+tG3bliNHjvD999/bHQ4iwvh24zmccZjnU553evtVqsDrr8M338D77zu9+UuLjrbKYm3ZAgkJ1lTX81SsWYduo7/Ev2wQnzx9N7+ut7+2rLo8TdyUUkq5Ta1atYiKimLZsmUcP37c7nBoWLUhfWP6Mn71eDYecH5R9l69IC7OmjPg9nkZiYmQnAxbt140eQsJq0m30V8QVLk6s4bdw7aV89wcpLpSmrgppZRyq8TERLKysliyZIndoQDwcquXCS0TyoB5A5w+ccLHB959F06csJYJcbvc5C0t7aLJW7mrwrjnP8lUioxm7osPkLrYnaUf1JXy6sRNF+BVSqnip0KFCsTGxrJ+/Xr25nPzvNvjCajAvxP+zbJdy5jx0wynt1+3LgwdCh99ZNWEd7vbboPPP7eSt1at4PcLFx4OCKrAP16bRfgNTfhq5COsS55sQ6CqILw6cdMFeJVSqnhq3rw5ZcuWZf78+cVieZDeN/YmJiyGJxY9wfFM5w/hDh0KUVHw8MPW1Te3a93aSt5++cW68pZP8lY6sDydRsygdpMklox/mlXTRxeLvlHn8urETSmlVPHk7+9PQkICe/bsYdOmTXaHg6+PL+PbjWffsX28vOxlp7fv72+Vw0pPhxdfdHrzBdO6NXzxBWzbZl15y+emu1Kly9D+X1Ool9CFb98fwTcTX9DkrZjRxE0ppZQtGjZsSFhYGIsXLyYzM9PucIgNj+WBhg8wauUoth7a6vT2mzeHf/4TRo2C9eud3nzBJCRYydv27RdN3nxL+ZH05Fs07NCbNTPfYuGYweRkZ9sQrMqPJm5KKaVsISIkJSVx7Ngxvv32W7vDAeDVhFcJ8Atg4PyBLrnSNHIkXHUVPPQQ2JYLtWplJW87dkB8PBw4cMEu4uNDQr9Xie3+OJvmTeOLV/qQfcb+5Fpp4qaUUspG1atX54YbbmDFihUcPer8RXCvVJVyVXix5Yss2L6A5K3JTm8/NNRamHfNGnjrLac3X3CtWsFXX1ljt61a5Z+8idDsgaG07PMSacvmMuf5npw5ddL9sapzaOKmlFLKVq1bt8bHx4dFixbZHQoA/W7qR3SlaAYtGETGmQynt3/PPdC2LQwbBrt3O735gmvZ8mzyFh8Pv/2W726NOz9Cm8Fj2PVjCjOHduHUcV2pwU6auCmllLJVUFAQzZo1IzU1ldmzZ2P3Ek5+vn6MSxpH+tF0Rn430unti8Dbb1tDpf36ubkc1vlatLCSt127rORt//58d7shqQd3PDOJ/Vt/ZM5z9+qEBRtp4qaUUsp2TZs2JS4ujs2bNzN+/Hi+/vprWycsxEfE0zW6K69+9yrpR9Od3n5EBLz0krVCx+zZTm/+yrRoAfPmWZf/LpG8RTXvQMu+w9n70yp+09qmttHETSmllO18fX1p1aoV/fv3p27duixfvpxx48axbt06cnJybInpjcQ38BEfHlvwmEvaHzQIGjaEAQPA9nXimze3krc9ey6ZvEW37opfmbJs+Oq/bg5Q5dLETSmlVLERHBxMx44d6d27NyEhISQnJzNx4kTS09PdHkt4UDjPxj3LnC1zWLBtgdPbL1XKWtvtwAFrgV7bxcWdTd5atoR9+y7Yxb9seerE382WpXM4feKY+2NUmrgppZQqfsLDw+nVqxedOnUiIyODqVOn8vHHH3P48GG3xvFYk8eoXaE2j85/lMxs5w/d3nSTdcVtwgRYscLpzV+5uDirLte+fdaVt3ySt/pJ95F1+iSbU2baEKDy6sRNa5UqpZTnEhGuv/56+vXrR6tWrdixYwdvv/02CxYsICPD+bM98+Nfyp83275J2uE0xqwa45JzDB8O5cvD1Kkuaf7KNWt2Nnlr2RLOqydbNepGKkVez6av/mdPfCWcVyduWqtUKaU8n5+fH3FxcQwYMIAGDRqwatUqxo0bxw8//EC2G1axTbo2ifbXtWf4suHs/Wvv5Q+4QuXLQ1AQZGU5venCa9oUFiywlghp2dIaPnUQEeq3u48D2zbyW5pdJSBKLq9O3JRSSnmPcuXK0aFDB/r27UvVqlWZN28eEyZMIC0tzeXLU4xuM5oz2Wd4avFTRW7rTPYZtv+xnUXbF/He2vcYungoh1t2Z3eZL50QqRPdequVvB04cEHyVi+hM6X8A9iokxTcrpTdASillFJXomrVqvTs2ZO0tDQWLlzI9OnTiYyMpE2bNlSuXNkl56xVoRZPNX2K4cuG0zemL81rNL/ovjkmh9+O/8bOIzvZeXQnO4/sZMfRHX+/3vPXHnLM2ZmypXxKkV2jNCtlJVk5bSjlU4y+mps0sZK3Nm2s5C0lBapXx79sEFEt7mJzyixa9n2J0gHl7I60xChG/zqUUkqpghERoqKiqF27NqtXr+abb75hwoQJNGrUiPj4eMqWLev0cw5pNoSpG6bS/6v+fH3/1/z656/nJGc7j+5kx5EdpB9N53T26XOODSsXRmRoJM1rNCciJIKIkAgiQyOJCI3g6vJXU6V5Modv68hnWz6jc73OTo+9SJo0gYULzyZvS5dC9eo0aHcfPy+czpalc6if1NPuKEsMTdyUUkp5LF9fX2JjY2nQoAFLly5lzZo1bNq0ibi4OGJjYylVynlfc4F+gYxKHEXnTztT6fVK52wL9g8mMjSS6MrR3HHdHVZSFhJBRGgENYJrEOAXcMm2y/zagfJnIhm9anTxS9wAYmOt5C0xEdq1gw0bCKvbmKtq1GHjV//VxM2NNHFTSinl8QICAkhKSuKmm25i0aJFLFmyhLVr19K6dWvq1auHiDjlPB3rduTNtm9yOvv034lZREgEoQGhRWpXjC/1jg1kxe6BrNqzitjwWKfE61S33AJvvQU9e8LixUhiIg3a3cfX7zzDwe2bqFzrBrsjLBF0coJSSimvUbFiRbp160bPnj3x9/dn5syZbN682WntiwgDbhnAE7c+Qad6nWgU1qjISVuu6048SLB/MKNXjXZKey7RpQtUrgzjxwNQN6ELvn7+bNSlQdxGEzellFJeJzIykj59+hAUFMTGjRvtDqdA/Ex5+sT0YWbqTJfUR3UKf3/o0we++AJ27iQgKJSo5h1IXfIpmRkn7I6uRNDETSmllFfy8fGhXr16bNu2jVOnTtkdToEMuHkAgjDu+3F2h3JxffuCj49V7gGo3+4+Mk8eI215ss2BlQyauCmllPJa0dHRZGdns3XrVrtDKZDqwdXpEt2FSesm8dfpv+wOJ3/h4XDXXTBpEmRkcPX1sVSofq2u6eYmmrgppZTyWldffTXBwcGkpqbaHcpl5a4hPDh2MH+d/osp66bYG9Cl9O8Pf/wBM2ZYlRSSerIvdTW/73Te/YQqf5q4KaWU8loi4hHDpXknvd589c00u6YZY78fS1ZOcaqDlUeLFhAdbU1SMIZ6t3XF1680m+bpJAVX08RNKaWUV4uOjiYnJ4ctW7bYHUqBPRb7GOlH0/lsy2d2h5I/EejXD378Eb7/nsDgq7i26e38vPhjzpzOsDs6r6aJm1JKKa9WrVo1jxkuzdUhqgORoZHFe2mQnj0hKMha2w1rksLp43/yy/LPbQ7Mu2nippRSyquJCNHR0Wzfvp2MDM+4GuTr48vAWwayYvcKVu1ZZXc4+StXDh54AD75BA4coHqDZoRUi9BJCi6miZtSSimv54nDpQ829IAFeR95BDIzYdIka5JCu57s+WkVh39Nszsyr6WJm1JKKa8XFhZGSEiIRw2Xlvc/uyDvrqO77A4nf1FRcNtt1ppuWVlE39YNn1J+bNRJCi6jiZtSSimvlztcumPHDk6ePGl3OAX294K8PxTjBXn794c9eyA5mbKhlah9aztSF31MVmbxncXryTwucRORliKyXEQmiEhLu+NRSinlGTxxuDR3Qd6JP04svgvy3n471Kjxd/3S+kk9yfjrD3757kubA/NObk3cRGSKiBwUkZ/Oe7+tiGwVkW0iMuQyzRjgOFAG2OOqWJVSSnmXqlWrEhoa6lHDpeABC/L6+sLDD0NKCqSmUuPG5gRXraGF513E3VfcPgDa5n1DRHyBt4AkoB7QTUTqicgNIvLFeY/KwHJjTBLwNPCim+NXSinlofIOl544UfwKoudWTjifRyzI27u3VYD+rbcQHx/qJ/Vk94Zv+WPPNrsj8zpiLvYvxVUnFKkJfGGMud7xugnwgjGmjeP1UABjzCuXaac08JExpvNFtvcB+jheRgGFLVRXEThUyGOVe2gfeQbtJ8+g/VT8aR95hqL0Uw1jTKX8NpQqfDxOczWwO8/rPcAtF9tZRDoCbYAQYPzF9jPGvAe8V9TgRGSNMaZxUdtRrqN95Bm0nzyD9lPxp33kGVzVT8UhcbsixpjZwGy741BKKaWUcrfiMKt0L1A9z+twx3tKKaWUUiqP4pC4rQauFZEIx31r9wDJNseUV5GHW5XLaR95Bu0nz6D9VPxpH3kGl/STWycniMh0oCXWDXsHgOeNMZNFpB0wBvAFphhjRrgtKKWUUkopD+H2WaVKKaWUUqpwisNQqVJKKaWUKoASm7jlV8VBRBqKyCoRWS8ia0Tk5osce7+I/OJ43O++qEuWIvZRtmOf9SJSnO6Z9DoX6acGIrJSRDaJyOciEnSRY6+kaooqgiL2U7pjn/UissZ9UZcsIlJdRFJEJFVEfhaRgY73K4jIIsd3ziIRCb3I8frd5AZO6KeifT8ZY0rkA2gONAJ+yvPeQiDJ8bwdsDSf4yoAOxw/Qx3PQ+3+PN74KGwfObYdtzv+kvK4SD+tBlo4nvcChudznC+wHYgESgMbgHp2fx5vfRS2nxzb0oGKdn8Gb38AYUAjx/PyQBpWRaGRwBDH+0OA1/I5Vr+bPKCfHNuK9P1UYq+4GWOWAX+c/zaQ+xdnMLAvn0PbAIuMMX8YY44AizivjJdyjiL0kXKji/TTdcAyx/NFQKd8Dr0Z2GaM2WGMyQRmAHe6LNASrgj9pNzEGLPfGPOj4/kxYDPWIvV3AlMdu00F7srncP1ucpMi9lORldjE7SIGAa+LyG7gDWBoPvvkV+nhajfEpiwF6SOAMo6h1FUi4pJfHnVJP3M2CevCuWs15tLfJfsVpJ/A+oNpoYisdZQTVC7mKA95I/A9UMUYs9+x6TegSj6H6O+TDQrRT1DE7ydN3M71MDDYGFMdGAxMtjkedaGC9lENY5Ua6Q6MEZFa7gpQAdaw2yMishZrKCHT5nhU/graT82MMY2AJKCfiDR3V4AlkYiUA2YBg4wxf+XdZqyxNl0OohgoQj8V6ftJE7dz3c/ZclqfYg3lnE8rPdirIH2EMWav4+cOYCnWX0TKTYwxW4wxicaYGGA61r1s59PfJZsVsJ/y/j4dBOZwkd87VXQi4oeVDHxorBKPAAdEJMyxPQw4mM+h+vvkRkXopyJ/P2nidq59QAvH81bAL/nsswBIFJFQx4yRRMd7yj0u20eOvvF3PK8INAVS3RahQkQqO376AM8CE/LZrbhXTfF6BeknESkrIuVzn2P9P++n8/dTRScigjWKsNkYMyrPpmSsP1px/Jyb9zG1KwAABCRJREFUz+H63eQmReknp3w/2T07w64H1l+X+4EzWPcC9AaaAWuxZrd9D8Q49m0MTMpzbC9gm+PxoN2fxVsfhe0j4FZgk2OfTUBvuz+LNz8u0k8DsWZapQGvcnax72rAV3mObefYZzswzO7P4s2PwvYT1qzfDY7Hz9pPLu2jZljDaxuB9Y5HO+AqYAnWH6qLgQqO/fW7ycP6yRnfT1o5QSmllFLKQ+hQqVJKKaWUh9DETSmllFLKQ2jippRSSinlITRxU0oppZTyEJq4KaWUUkp5CE3clFJKKaU8hCZuSimllFIeQhM3pZTXEZHjLm4/QES+ERFfx+tsEVmf5zEkz74TRKTpJdpKEZE25703SETeEZHSIrJMREq57tMopTyJJm5KKXXlegGzjTHZjtcZxpiGeR6v5tk3Flh1ibamY5X7yuseYLoxJhNrJfauzgpcKeXZNHFTSnktEXlMRH5yPAblef9fIrJVRL4Vkeki8sQVNn0v+deLPP/8dYG03ARPRHqIyA+Oq3LvOq7YzQRud9RrRURqYpWcWu5o5jPH+ZRSShM3pZR3EpEY4EHgFqyrXg+JyI0ichPQCWgAJGHVEbySdksDkcaY9DxvB5w3VJp7hSwJmO84ri7WlbOmxpiGQDZwrzHmD+AHx75gXW37xJytR/gTcNOVxKiU8l5634RSyls1A+YYY04AiMhsIA7rD9a5xphTwCkR+Tz3ABGJBIYBwcaYziJSFngbyASWGmM+BCoCR887V4YjGTtfG6zkESABiAFWiwhAAHDQsS13uHSu42fv3AaMMdkikiki5Y0xxwr3n0Ip5S30iptSSjkYY3YYY3rneasjMNMY8xDQwfFeBlDmcm2JSCAQYozZl/sWMDXPfXBRxpgXHNvmAgki0ggINMasPa85f+BU4T6VUsqbaOKmlPJWy4G7RCTQceXsbsd73wHtRaSMiJQD7rhEG+HAbsfzbABjzBHAV0Qul7zFAyl5Xi8BOotIZQARqSAiNRxtHnfsOwXr6tvfROQq4JAx5szlPrBSyvvpUKlSyisZY34UkQ+w7h8DmGSMWQcgIsnARuAAsAn48yLN7MFK3tZz7h+6C7GGYhc7XgeIyPo82+cD5bAmHuTGkyoizwILRcQHOAP0A3Y5dpkOzOHCGabxwJcF+MhKqRJAzt7/qpRSJYOIlDPGHHcMZy4D+jgSvauAEcBtwCTgTWA81jDlt4573HAMaQ42xvS8xDl+BG4p6pUyx715Q4wxaUVpRynlHTRxU0qVOCLyEVAP6161qcaYVwrRRi/HsdmX3bmQHDNY7zHG/NdV51BKeRZN3JRSSimlPIROTlBKKaWU8hCauCmllFJKeQhN3JRSSimlPIQmbkoppZRSHkITN6WUUkopD6GJm1JKKaWUh9DETSmllFLKQ/w/V7gCseZUES8AAAAASUVORK5CYII=\n", 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", 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" ] @@ -151,22 +151,16 @@ } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "display_name": "Python 3", - "language": "python", + "display_name": "Python 3.9.5 64-bit", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -178,4 +172,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/test/testBreakCondition.cpp b/test/testBreakCondition.cpp index 499d083e0..468a08387 100644 --- a/test/testBreakCondition.cpp +++ b/test/testBreakCondition.cpp @@ -205,7 +205,7 @@ TEST(ObserverFeature, LargeSphere) { TEST(ObserverFeature, Point) { Observer obs; - obs.add(new ObserverPoint()); + obs.add(new Observer1D()); Candidate c; c.setNextStep(10); diff --git a/test/testOutput.cpp b/test/testOutput.cpp index 3c56c2d00..da104d697 100644 --- a/test/testOutput.cpp +++ b/test/testOutput.cpp @@ -229,7 +229,7 @@ TEST(ParticleCollector, getTrajectory) { sim->add(new SimplePropagation(1, 1)); ref_ptr obs = new Observer(); - obs->add(new ObserverPoint()); + obs->add(new Observer1D()); obs->onDetection(output); sim->add(obs); diff --git a/test/testSimulationExecution.py b/test/testSimulationExecution.py index 4b4d37723..1db919033 100644 --- a/test/testSimulationExecution.py +++ b/test/testSimulationExecution.py @@ -42,7 +42,7 @@ def runTest(self): # observer obs = crp.Observer() - obs.add(crp.ObserverPoint()) + obs.add(crp.Observer1D()) sim.add(obs) # output From eb66ba3018f152f7714a6defd85e6a7d5918f36e Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 10 Nov 2022 15:32:37 +0100 Subject: [PATCH 11/87] removed deprecated VectorGrid --- include/crpropa/Grid.h | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/include/crpropa/Grid.h b/include/crpropa/Grid.h index ceac782d6..a37a6ba8b 100644 --- a/include/crpropa/Grid.h +++ b/include/crpropa/Grid.h @@ -524,24 +524,6 @@ typedef Grid Grid3d; typedef Grid Grid1f; typedef Grid Grid1d; -// DEPRECATED: Will be removed in CRPropa v3.2 -class VectorGrid: public Grid3f { - void printDeprecation() const { - KISS_LOG_WARNING << "VectorGrid is deprecated and will be removed in the future. Replace it with Grid3f (float) or Grid3d (double)."; - } -public: - VectorGrid(Vector3d origin, size_t N, double spacing) : Grid3f(origin, N, spacing) { - printDeprecation(); - } - - VectorGrid(Vector3d origin, size_t Nx, size_t Ny, size_t Nz, double spacing) : Grid3f(origin, Nx, Ny, Nz, spacing) { - printDeprecation(); - } - - VectorGrid(Vector3d origin, size_t Nx, size_t Ny, size_t Nz, Vector3d spacing) : Grid3f(origin, Nx, Ny, Nz, spacing) { - printDeprecation(); - } -}; // DEPRECATED: Will be removed in CRPropa v3.2 class ScalarGrid: public Grid1f { From 41f1022d88c875a7ad1496ffc02f3095f6db9514 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 10 Nov 2022 15:39:33 +0100 Subject: [PATCH 12/87] removed also deprecated ScalarGrid and adapted examples --- CHANGELOG.md | 1 + doc/pages/FAQ.md | 2 +- .../extragalactic_fields/MHD_models.v4.ipynb | 20 ++++++--------- include/crpropa/Grid.h | 25 ------------------- 4 files changed, 9 insertions(+), 39 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f9b300c9c..50933dae9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,7 @@ ### Features that are deprecated and will be removed after this release +* ObserverPoint will be renamed into Observer1D. ### New plugins and resources linked on the webpages: diff --git a/doc/pages/FAQ.md b/doc/pages/FAQ.md index 19bb8380c..53d50493e 100644 --- a/doc/pages/FAQ.md +++ b/doc/pages/FAQ.md @@ -37,7 +37,7 @@ Out[20]: 0.37454011439684315 ### How to define source positions from a matter density grid? ```python -grid = ScalarGrid(Vector3d(0, 0, 0), 256, 256, 256, 100 * kpc) +grid = Grid(Vector3d(0, 0, 0), 256, 100 * kpc) loadGridFromTxt(grid, 'some_density_grid.txt') #loadGrid(grid, 'some_density_grid.raw') density = crpropa.SourceDensityGrid(grid) diff --git a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb index ce1900c09..6126fe594 100644 --- a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb +++ b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb @@ -164,7 +164,7 @@ "\n", "source = Source()\n", "## initialize grid to hold field values\n", - "mgrid = ScalarGrid( gridOrigin, gridSize, spacing )\n", + "mgrid = Grid( gridOrigin, gridSize, spacing )\n", "## load values to grid\n", "loadGrid( mgrid, filename_density )\n", "## add source module to simulation\n", @@ -321,22 +321,16 @@ } ], "metadata": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + }, "kernelspec": { - "display_name": "Python 3", - "language": "python", + "display_name": "Python 3.9.5 64-bit", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -348,4 +342,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/include/crpropa/Grid.h b/include/crpropa/Grid.h index a37a6ba8b..c8657e1a2 100644 --- a/include/crpropa/Grid.h +++ b/include/crpropa/Grid.h @@ -519,31 +519,6 @@ class Grid: public Referenced { }; // class Grid -typedef Grid Grid3f; -typedef Grid Grid3d; -typedef Grid Grid1f; -typedef Grid Grid1d; - - -// DEPRECATED: Will be removed in CRPropa v3.2 -class ScalarGrid: public Grid1f { - void printDeprecation() const { - KISS_LOG_WARNING << "ScalarGrid is deprecated and will be removed in the future. Replace with Grid1f (float) or Grid1d (double)."; - } -public: - ScalarGrid(Vector3d origin, size_t N, double spacing) : Grid1f(origin, N, spacing) { - printDeprecation(); - } - - ScalarGrid(Vector3d origin, size_t Nx, size_t Ny, size_t Nz, double spacing) : Grid1f(origin, Nx, Ny, Nz, spacing) { - printDeprecation(); - } - - ScalarGrid(Vector3d origin, size_t Nx, size_t Ny, size_t Nz, Vector3d spacing) : Grid1f(origin, Nx, Ny, Nz, spacing) { - printDeprecation(); - } -}; - /** @}*/ } // namespace crpropa From e776c24a5222b6350a098bbb802e86db7dda2b39 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 11 Nov 2022 14:37:15 +0100 Subject: [PATCH 13/87] Fix a typo. --- include/crpropa/magneticField/AMRMagneticField.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/crpropa/magneticField/AMRMagneticField.h b/include/crpropa/magneticField/AMRMagneticField.h index df95f742c..8f1e75b7c 100644 --- a/include/crpropa/magneticField/AMRMagneticField.h +++ b/include/crpropa/magneticField/AMRMagneticField.h @@ -61,8 +61,8 @@ class AMRMagneticField: public MagneticField { cfDensity = convDensity; cfMagneticField = convMagneticField; - KISS_LOG_WARNING << "DEPRECATION WARNING: AMRMagneticField class will be removed in the future, - as the underlying library (saga) is no longer supported." + KISS_LOG_WARNING << "DEPRECATION WARNING: AMRMagneticField class will be removed in the future," + <<"as the underlying library (saga) is no longer supported."; } Vector3d getField(const Vector3d &position) const { From 4da8140fb3016275aec4ba2ce2e4a5de4c6e7bad Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 11 Nov 2022 14:39:23 +0100 Subject: [PATCH 14/87] Re-add older analytic Galactic magnetic field models The file was always available but not listed in CRPropa.h Include basic tests and documentation. --- include/CRPropa.h | 1 + .../magneticField/GalacticMagneticField.h | 27 +++++++++++++++++++ python/2_headers.i | 1 + test/testMagneticField.cpp | 22 +++++++++++++++ 4 files changed, 51 insertions(+) diff --git a/include/CRPropa.h b/include/CRPropa.h index b803f7971..56cb4f291 100644 --- a/include/CRPropa.h +++ b/include/CRPropa.h @@ -60,6 +60,7 @@ #include "crpropa/magneticField/JF12Field.h" #include "crpropa/magneticField/JF12FieldSolenoidal.h" #include "crpropa/magneticField/MagneticField.h" +#include "crpropa/magneticField/GalacticMagneticField.h" #include "crpropa/magneticField/MagneticFieldGrid.h" #include "crpropa/magneticField/PolarizedSingleModeMagneticField.h" #include "crpropa/magneticField/PT11Field.h" diff --git a/include/crpropa/magneticField/GalacticMagneticField.h b/include/crpropa/magneticField/GalacticMagneticField.h index 420fc25a3..acb2ba010 100644 --- a/include/crpropa/magneticField/GalacticMagneticField.h +++ b/include/crpropa/magneticField/GalacticMagneticField.h @@ -21,6 +21,17 @@ class TorroidalHaloField: public MagneticField { double r0; // radial scale public: + /** + * Constructor + * @param b0 halo field strength + * @param z0 vertical position + * @param z1 vertical scale + * @param r0 radial scale + */ + TorroidalHaloField(double b0=1., double z0=1., double z1=1., double r0=1.) { + setParameters(b0, z0, z1, r0); + } + void setParameters(double b0, double z0, double z1, double r0) { this->b0 = b0; this->z0 = z0; @@ -74,6 +85,22 @@ class LogarithmicSpiralField: public MagneticField { } public: + /** + * Constructor + * @param isBSS switch for the magnetic field model + * true for BSS, false for ASS + * @param b0 magnetic field strength + * @param pitch pitch angle [rad] + * @param rsol distance of sun from Galactic center + * @param rc radius of central region with constant field + * @param d distance to first field reversal + * @param z0 vertical attenuation length + */ + LogarithmicSpiralField(bool isBSS=true, double b0=1., double pitch=M_1_PI/4., + double rsol=8.5*kpc, double rc=3*kpc, double d=5*kpc, double z0=3*kpc) { + setParameters(isBSS, b0, pitch, rsol, rc, d, z0); + } + void setParameters(bool isBSS, double b0, double pitch, double rsol, double rc, double d, double z0) { this->isBSS = isBSS; diff --git a/python/2_headers.i b/python/2_headers.i index 2e611efcb..6479b7c33 100644 --- a/python/2_headers.i +++ b/python/2_headers.i @@ -444,6 +444,7 @@ using namespace crpropa; // for usage of namespace in header files, necessary %template(CylindricalProjectionMapRefPtr) crpropa::ref_ptr; %include "crpropa/magneticField/MagneticFieldGrid.h" +%include "crpropa/magneticField/GalacticMagneticField.h" %feature("notabstract") QuimbyMagneticFieldAdapter; %include "crpropa/magneticField/QuimbyMagneticField.h" %include "crpropa/magneticField/AMRMagneticField.h" diff --git a/test/testMagneticField.cpp b/test/testMagneticField.cpp index 02749bf82..9c319c7c1 100644 --- a/test/testMagneticField.cpp +++ b/test/testMagneticField.cpp @@ -3,6 +3,7 @@ #include "crpropa/magneticField/MagneticFieldGrid.h" #include "crpropa/magneticField/CMZField.h" #include "crpropa/magneticField/PolarizedSingleModeMagneticField.h" +#include "crpropa/magneticField/GalacticMagneticField.h" #include "crpropa/Grid.h" #include "crpropa/Units.h" #include "crpropa/Common.h" @@ -180,6 +181,27 @@ TEST(testCMZMagneticField, TestAzimutalComponent){ EXPECT_DOUBLE_EQ(bVec.z, 0); } +TEST(testTorroidalHaloField, SimpleTest) { + ref_ptr field = new TorroidalHaloField(); + Vector3d b = field->getField(Vector3d(0.)); + EXPECT_DOUBLE_EQ(b.x, 0); + EXPECT_DOUBLE_EQ(b.y, 0); + EXPECT_DOUBLE_EQ(b.z, 0); + + b = field->getField(Vector3d(1,0,0)); + EXPECT_DOUBLE_EQ(b.x, 0.5); + EXPECT_DOUBLE_EQ(b.y, 0); + EXPECT_DOUBLE_EQ(b.z, 0); +} + +TEST(testLogarithmicSpiralField, SimpleTest) { + ref_ptr field = new LogarithmicSpiralField(); + Vector3d b = field->getField(Vector3d(8.5, 0, 0)*kpc); + EXPECT_NEAR(b.x, -1., 1E-2); + EXPECT_NEAR(b.y, 0, 1E-10); + EXPECT_NEAR(b.z, 0, 1E-10); +} + TEST(testPolarizedSingleModeMagneticField, SimpleTest) { PolarizedSingleModeMagneticField B(2, 4, 0.5, Vector3d(1,1,1), Vector3d(0,1,0), Vector3d(1,0,0), "amplitude", "polarization", "elliptical"); Vector3d b = B.getField(Vector3d(1,1,2)); From a365b3a01c1cbd676294fb6485e920437fa284b1 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 11 Nov 2022 14:42:12 +0100 Subject: [PATCH 15/87] Update changelog --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f9b300c9c..b4fc7d75c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,7 @@ -# CRPropa NEXT +# CRPropa 3.2.1 ### Bug fixes: - +Re-added TorroidalHaloField and LogarithmicSpiralField models. ### New features: @@ -10,7 +10,7 @@ ### Features that are deprecated and will be removed after this release - +AMRMagenticField - underlying library (saga) is no longer supported. ### New plugins and resources linked on the webpages: From 897087925fe6f7950d3f2d21a1103f17b5b8dc79 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 11 Nov 2022 19:06:18 +0100 Subject: [PATCH 16/87] implemented the change request from @lukasmerten --- doc/pages/Simulation-Modules.md | 2 +- include/crpropa/module/Observer.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/pages/Simulation-Modules.md b/doc/pages/Simulation-Modules.md index cf2b9904d..0afda7ab2 100644 --- a/doc/pages/Simulation-Modules.md +++ b/doc/pages/Simulation-Modules.md @@ -58,7 +58,7 @@ Periodic- and ReflectiveBox implement boundary conditions for the particles. The ### Observers Observers can be defined using a collection of ObserverFeatures. The names of ObserverFeatures all start with "Observer" so you can discover the available options from an interactive python session by typing "Observer" and pressing "tab". The list includes -* **ObserverSurface** - Detects particles crossing the boundaries of a surface defined (see, e.g., `Geometry` module) +* **ObserverSurface** - Detects particles crossing the boundaries of a defined surface (see, e.g., `Geometry` module) * **ObserverTracking** - For recording the tracks of particles inside an observer sphere * **Observer1D** - Observer for 1D simulations that detects particles when reaching x = 0 * **ObserverDetectAll** - Detects all particles diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index 5f49bbeff..49095fcee 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -85,7 +85,7 @@ class ObserverDetectAll: public ObserverFeature { /** @class ObserverSurface - @brief Detects particles crossing the boundaries of a surface defined (see, e.g., `Geometry` module) + @brief Detects particles crossing the boundaries of a defined surface (see, e.g., `Geometry` module) */ class ObserverSurface: public ObserverFeature { private: From 17dd1dad18917d6a08200e6d01076b883c943fd5 Mon Sep 17 00:00:00 2001 From: mertelx Date: Wed, 16 Nov 2022 15:00:42 +0100 Subject: [PATCH 17/87] Re-add typedef of grids that got lost in PR #2. See issue #3, which is closed with thhis commit. --- include/crpropa/Grid.h | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/include/crpropa/Grid.h b/include/crpropa/Grid.h index c8657e1a2..55b9ae41a 100644 --- a/include/crpropa/Grid.h +++ b/include/crpropa/Grid.h @@ -519,6 +519,11 @@ class Grid: public Referenced { }; // class Grid +typedef Grid Grid1d; +typedef Grid Grid1f; +typedef Grid Grid3f; +typedef Grid Grid3d; + /** @}*/ } // namespace crpropa From d6e11bbfdb3b7bb2d1f50d6279da98b6ae9f2796 Mon Sep 17 00:00:00 2001 From: mertelx Date: Wed, 16 Nov 2022 16:56:03 +0100 Subject: [PATCH 18/87] Add more documentation to simple magnetic fields. --- include/crpropa/magneticField/MagneticField.h | 45 ++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/include/crpropa/magneticField/MagneticField.h b/include/crpropa/magneticField/MagneticField.h index d666b4333..213da7296 100644 --- a/include/crpropa/magneticField/MagneticField.h +++ b/include/crpropa/magneticField/MagneticField.h @@ -34,14 +34,35 @@ class MagneticField: public Referenced { /** @class PeriodicMagneticField @brief Magnetic field decorator implementing periodic fields. - */ + + The periodic cube is defined by its origin (Vector3d) and an + extends parameter (Vector3d). All points x=(x_1, x_2, x_3) + that are described by x_i = origin_i + epsilon * extend_i, + with epsilon = 0...1 are within the base cube. Magnetic field + strengths for all positions outside of this cube are calculated + based on the values in the base cube. + This can be done periodically or reflectively. +*/ + class PeriodicMagneticField: public MagneticField { ref_ptr field; Vector3d origin, extends; bool reflective; public: + /** + * Constructor + * @param field magnetic field reference pointer + * @param extends length, width, and height of the base cube + */ PeriodicMagneticField(ref_ptr field, const Vector3d &extends); + /** + * Constructor + * @param field magnetic field reference pointer + * @param extends length, width, and height of the base cube + * @param origin defines the reference position + * @param switch for periodic or reflective behavior + */ PeriodicMagneticField(ref_ptr field, const Vector3d &extends, const Vector3d &origin, bool reflective); Vector3d &getOrigin(); @@ -72,6 +93,11 @@ class MagneticFieldEvolution: public MagneticField { ref_ptr field; double m; public: + /** + * Constructor + * @param field magnetic field reference pointer + * @param m cosmic evolution parameter + */ MagneticFieldEvolution(ref_ptr field, double m); Vector3d getField(const Vector3d &position, double z = 0) const; }; @@ -83,6 +109,10 @@ class MagneticFieldEvolution: public MagneticField { class UniformMagneticField: public MagneticField { Vector3d value; public: + /** + * Constructor + * @param value magnetic field strength + */ UniformMagneticField(const Vector3d &value) : value(value) { } @@ -100,6 +130,13 @@ class MagneticDipoleField: public MagneticField { Vector3d moment; double radius; public: + /** + * Constructor + * @param origin singularity of the dipole field + * @param moment magnetic moment of the dipole field + * @param radius inside a radius around the origin the + * magnetic field is constant: moment * 2 * mu0 / 3 + */ MagneticDipoleField(const Vector3d &origin, const Vector3d &moment, const double radius) : origin(origin), moment(moment), radius(radius) { } @@ -117,6 +154,12 @@ class RenormalizeMagneticField: public MagneticField { mu::Parser *p; double Bmag; public: + /** + * Constructor + * @param field magnetic field reference pointer + * @param expression muParser expression used to renormalize the field, + * e.g., "gauss". + */ RenormalizeMagneticField(ref_ptr field, std::string expression); ~RenormalizeMagneticField() { delete p; } Vector3d getField(const Vector3d &position); From 2698cdbde2298204d98fa5e8857e67ee376745b9 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 17 Nov 2022 13:47:45 +0100 Subject: [PATCH 19/87] Add hint to run make test before make coverage --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d66fb3da7..6088ba2fc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -118,7 +118,7 @@ if(ENABLE_COVERAGE) find_program(LCOV_PATH lcov) find_program(GENHTML_PATH genhtml) if(LCOV_PATH AND GENHTML_PATH) - message("Enabling coverage report via $make coverage") + message("Enabling coverage report via $make test && make coverage") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage") list(APPEND CRPROPA_EXTRA_LIBRARIES "-lgcov") list(APPEND CRPROPA_EXTRA_LIBRARIES "-fprofile-arcs") From d6711e9cd911ab4cf39aabeb1d0ede2a870a55fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 17 Nov 2022 14:35:12 +0100 Subject: [PATCH 20/87] added comments to HDF5Output --- include/crpropa/module/HDF5Output.h | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/include/crpropa/module/HDF5Output.h b/include/crpropa/module/HDF5Output.h index f25cf0661..be86fe33f 100644 --- a/include/crpropa/module/HDF5Output.h +++ b/include/crpropa/module/HDF5Output.h @@ -22,7 +22,7 @@ const size_t propertyBufferSize = 1024; /** @class HDF5Output @brief Output to HDF5 Format. - +In the base class is an overview of the possible columns. HDF5 structure: ``` @@ -96,8 +96,21 @@ class HDF5Output: public Output { unsigned int flushLimit; unsigned int candidatesSinceFlush; public: + /** Default constructor. + Does not run from scratch. + At least open() has to be called in addition. + Units of energy and length are, by default, EeV and Mpc. + This can be changed with setEnergyScale and setLengthScale. + */ HDF5Output(); + /** Constructor with the default OutputType (everything). + @param filename string containing name of output hdf5 file + */ HDF5Output(const std::string &filename); + /** Constructor + @param outputType type of output: Trajectory1D, Trajectory3D, Event1D, Event3D, Everything + @param filename string containing name of output hdf5 file + */ HDF5Output(const std::string &filename, OutputType outputtype); ~HDF5Output(); @@ -111,6 +124,8 @@ class HDF5Output: public Output { /// with frequent output this should be set to a high number (default) void setFlushLimit(unsigned int N); + /** Create and prepare a file as HDF5-file. + */ void open(const std::string &filename); void close(); void flush() const; From 409742f85df3497aa65fc2f9798f57339920ed30 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 17 Nov 2022 16:02:50 +0100 Subject: [PATCH 21/87] Work on documentation, e.g., including magnetic fields in the corresponding doxygen group. --- include/crpropa/magneticField/JF12Field.h | 10 ++++++++++ .../crpropa/magneticField/JF12FieldSolenoidal.h | 6 ++++++ .../crpropa/magneticField/MagneticFieldGrid.h | 10 ++++++++++ include/crpropa/magneticField/PT11Field.h | 6 +++++- .../PolarizedSingleModeMagneticField.h | 17 +++++++++++++++++ include/crpropa/magneticField/TF17Field.h | 6 +++++- 6 files changed, 53 insertions(+), 2 deletions(-) diff --git a/include/crpropa/magneticField/JF12Field.h b/include/crpropa/magneticField/JF12Field.h index 26c3d8097..2dac9adc5 100644 --- a/include/crpropa/magneticField/JF12Field.h +++ b/include/crpropa/magneticField/JF12Field.h @@ -6,6 +6,10 @@ #include "kiss/logger.h" namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ /** @class JF12Field @@ -133,8 +137,13 @@ class JF12Field: public MagneticField { // All set field components Vector3d getField(const Vector3d& pos) const; }; +/** @} */ +/** + * \addtogroup MagneticFields + * @{ + */ /** @class PlanckJF12bField @brief PlanckJF12bField: the JF12 galactic magnetic field model with corrections @@ -150,6 +159,7 @@ class PlanckJF12bField: public JF12Field { public: PlanckJF12bField(); }; +/** @} */ } // namespace crpropa diff --git a/include/crpropa/magneticField/JF12FieldSolenoidal.h b/include/crpropa/magneticField/JF12FieldSolenoidal.h index 27d1cd213..6808658d5 100644 --- a/include/crpropa/magneticField/JF12FieldSolenoidal.h +++ b/include/crpropa/magneticField/JF12FieldSolenoidal.h @@ -7,6 +7,11 @@ namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ + /** @class JF12FieldSolenoidal @brief JF12FieldSolenoidal galactic magnetic field model @@ -107,6 +112,7 @@ class JF12FieldSolenoidal: public JF12Field { */ double getSpiralFieldStrengthConstant(const double& r, const double& phi) const; }; +/** @} */ } // namespace crpropa diff --git a/include/crpropa/magneticField/MagneticFieldGrid.h b/include/crpropa/magneticField/MagneticFieldGrid.h index 7107b3970..b385d6510 100644 --- a/include/crpropa/magneticField/MagneticFieldGrid.h +++ b/include/crpropa/magneticField/MagneticFieldGrid.h @@ -19,6 +19,10 @@ namespace crpropa { class MagneticFieldGrid: public MagneticField { ref_ptr grid; public: + /** + *Constructor + @param grid Grid3f storing the magnetic field vectors + */ MagneticFieldGrid(ref_ptr grid); void setGrid(ref_ptr grid); ref_ptr getGrid(); @@ -39,6 +43,12 @@ class ModulatedMagneticFieldGrid: public MagneticField { public: ModulatedMagneticFieldGrid() { } + /** + *Constructor + @param grid Grid3f storing the magnetic field vectors + @param modGrid Grid1f used to scale the magnetic field strength + B^new_i = B^old_i * scale + */ ModulatedMagneticFieldGrid(ref_ptr grid, ref_ptr modGrid); void setGrid(ref_ptr grid); void setModulationGrid(ref_ptr modGrid); diff --git a/include/crpropa/magneticField/PT11Field.h b/include/crpropa/magneticField/PT11Field.h index 767659412..a0d211a48 100644 --- a/include/crpropa/magneticField/PT11Field.h +++ b/include/crpropa/magneticField/PT11Field.h @@ -4,6 +4,10 @@ #include "crpropa/magneticField/MagneticField.h" namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ /** @class PT11Field @@ -56,7 +60,7 @@ class PT11Field: public MagneticField { Vector3d getField(const Vector3d& pos) const; }; - +/**@}*/ } // namespace crpropa #endif // CRPROPA_PSHIRKOVFIELD_H diff --git a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h index 6825e432c..e9925ae63 100644 --- a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h +++ b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h @@ -15,6 +15,10 @@ #include "crpropa/Units.h" namespace crpropa { +/** + * \addtogroup MagneticFields + * @{ + */ /** @class PolarizedSingleModeMagneticField @@ -40,10 +44,23 @@ class PolarizedSingleModeMagneticField: public MagneticField { double B_max; // Maximal value of the magnetic field (i.e. the amplitude/semi-major value of the mode) public: + /** + * Constructor + * @param B_0 Magnetic field strength in the direction of e_1 at r_0 (for flagAmplitudeRms = "amplitude"), or the RMS value of the magnetic field (for flagAmplitudeRms = "rms") + * @param wavelength Wavelength of the single mode (corresponds to its coherence length) + * @param double Polarization parameter + * @param r_0 Reference position + * @param e_1 First vector spanning the polarization plane + * @param e_2 Second vector spanning the polarization plane + * @param flagAmplitudeRms Flag to specify whether B_0 denotes the maximum ("amplitude") or the RMS ("rms") value of the magnetic field + * @param flagPolarizationHelicity Flag to specify whether sigma denotes the standard polarization parameter ("polarization") or f_H, the fraction of maximal helicity ("helicity") + * @param flagMode Flag to specify the polarization mode; possible choices are "elliptical", "circular" or "linear" + */ PolarizedSingleModeMagneticField(const double &B_0, const double &wavelength, const double &sigma, const Vector3d &r_0, const Vector3d &e_1, const Vector3d &e_2, std::string flagAmplitudeRms, std::string flagPolarizationHelicity, std::string flagMode ); Vector3d getField(const Vector3d &position) const; }; +/** @} */ } // end namespace crpropa diff --git a/include/crpropa/magneticField/TF17Field.h b/include/crpropa/magneticField/TF17Field.h index 21da214b6..3aece7411 100644 --- a/include/crpropa/magneticField/TF17Field.h +++ b/include/crpropa/magneticField/TF17Field.h @@ -5,6 +5,10 @@ namespace crpropa { using namespace std; +/** + * \addtogroup MagneticFields + * @{ + */ /** @class TF17Field @@ -192,7 +196,7 @@ class TF17Field: public MagneticField { */ double zscale(const double& z) const; }; - +/**@}*/ } // CRPROPA NAMESPACE #endif // CRPROPA_TF17FIELD_H From 5e16b1b05a4e71aafe339bb6ae141753da9a8a03 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 17 Nov 2022 16:05:50 +0100 Subject: [PATCH 22/87] added extending-CRPropa.ipynb to fix issues in the old example, python section is done --- .../extending-CRPropa/extending-CRPropa.ipynb | 290 ++++++++++++++++++ 1 file changed, 290 insertions(+) create mode 100644 doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb diff --git a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb new file mode 100644 index 000000000..5ed12223e --- /dev/null +++ b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implementing additional Modules and Features using Python only" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Derivatives of base classes such as Module, Source, SourceProperty, etc. can be implemented in\n", + "Python and used like the built-in classes. Here is an example for a custom\n", + "Module:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.0\n" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "class MyModule(Module):\n", + " \"\"\" Reduces the cosmic ray energy by 10% in each step \"\"\"\n", + " def process(self, c):\n", + " c.current.setEnergy(c.current.getEnergy() * 0.9)\n", + "\n", + "m = ModuleList()\n", + "\n", + "mod = MyModule() # See https://github.com/CRPropa/CRPropa3/issues/165\n", + "m.add(mod)\n", + "\n", + "c = Candidate()\n", + "c.current.setEnergy(10)\n", + "m.process(c)\n", + "print(c.current.getEnergy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When redefining the constructor make sure to call the constructor of the super\n", + "classes as well, as otherwise the code will segfault." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class MyModule(Module):\n", + " def __init__(self):\n", + " Module.__init__(self)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial properties of a cosmic rays can be set with a Source, composed of several SourceProperties.\n", + "Custom SourceFeatures can be written in the following way:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0\n" + ] + } + ], + "source": [ + "class MySourceFeature(SourceFeature):\n", + "\t\"\"\" Set the initial energy to 10 EeV \"\"\"\n", + "\tdef __init__(self):\n", + "\t\tSourceFeature.__init__(self)\n", + "\n", + "\tdef prepareParticle(self, particleState):\n", + "\t\tparticleState.setEnergy(10 * EeV)\n", + "\n", + "# The source feature has to be created outside of the class attribute\n", + "# s.add(MySourceFeature()) wil NOT work! (SWIG issue)\n", + "srcFtr = MySourceFeature()\n", + "s = Source()\n", + "s.add(srcFtr)\n", + "c = s.getCandidate()\n", + "print(c.current.getEnergy() / EeV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The redshift is stored in the Candidate, not in the ParticleState. To set it with a SourceFeature, use the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6\n" + ] + } + ], + "source": [ + "class MySourceFeature(SourceFeature):\n", + " \"\"\" Set the initial redshift \"\"\"\n", + " def __init__(self):\n", + " SourceFeature.__init__(self)\n", + "\n", + " def prepareCandidate(self, candidate):\n", + " candidate.setRedshift(0.6)\n", + "\n", + "# The source feature has to be created outside of the class attribute\n", + "# s.add(MySourceFeature()) will NOT work! (SWIG issue)\n", + "srcFtr = MySourceFeature()\n", + "s = Source()\n", + "s.add(srcFtr)\n", + "c = s.getCandidate()\n", + "print(c.getRedshift())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Simulation Processing\n", + "If necessary, the simulation chain (ModuleList) and the cosmic ray source (Source, SourceFeature) can be\n", + "replaced by custom loops." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CosmicRay at z = 0\n", + " source: Particle 1, E = 10 EeV, x = 0 0 0 Mpc, p = -1 0 0\n", + " current: Particle 1, E = 4.30467 EeV, x = -7000 0 0 Mpc, p = -1 0 0\n" + ] + } + ], + "source": [ + "mycandidate = Candidate(1, 10. * EeV)\n", + "\n", + "m1 = SimplePropagation()\n", + "m2 = ElectronPairProduction(CMB())\n", + "m3 = MinimumEnergy(5 * EeV)\n", + "\n", + "while mycandidate.isActive():\n", + " m1.process(mycandidate)\n", + " m2.process(mycandidate)\n", + " m3.process(mycandidate)\n", + " # reduce the energy by 10%\n", + " E = mycandidate.current.getEnergy()\n", + " mycandidate.current.setEnergy(0.9 * E)\n", + "print(mycandidate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A source can be replaced by setting all necessary cosmic rays properties by hand.\n", + "The created Candidates can either be propagated one-by-one, or first collected\n", + "in a CandidateVector and then propagated." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(1000):\n", + " p = ParticleState()\n", + " p.setId(nucleusId(12, 6))\n", + " p.setEnergy(200 * EeV)\n", + " p.setPosition(Vector3d(100, 10, 10) * Mpc)\n", + " p.setDirection(Vector3d(-1,0,0))\n", + "\n", + " c = Candidate(p)\n", + " m.process(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plugins: Integrate Custom C++ Code to CRPropa's Python Steering\n", + "Extending CRPropa with C++ code and keep python steering is also possible using\n", + "SWIG. This allows to integrate your code seamless as e.g.\n", + "```\n", + "import crpropa\n", + "import myPlugin\n", + "\n", + "ml = crpropa.ModuleList()\n", + "ml.add(crpropa.MaximumTrajectoryLength(1000 * crpropa.parsec))\n", + "ml.add(myPlugin.MyModule())\n", + "\n", + "source = crpropa.Source()\n", + "source.add(myPlugin.AddMyProperty())\n", + "```\n", + "A template is in the [plugin-template\n", + "folder](https://github.com/CRPropa/CRPropa3/tree/master/plugin-template) of the\n", + "CRPropa source. Although the template is complete with build and SWIG wrapper\n", + "code, deeper knowledge of SWIG, C++, and CMake are likely required for complex\n", + "projects.\n", + "\n", + "To get started with your own plugin\n", + "\n", + "1. Copy the folder to a new location. We highly recommended to manage the files\n", + "in a (git) repository from the beginning.\n", + "2. Test compiling the template\n", + "```\n", + "mkdir build\n", + "cd build\n", + "cmake ..\n", + "make && python ../testPlugin.py\n", + "```\n", + "This should work if CRPropa is installed and can be found by python.\n", + "\n", + "3. Customize the template to your needs, starting with\n", + "naming your plugin by modifying the according line in `CMakeLists.txt' and\n", + "renaming the files accordingly.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('crp')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "9b0c5e5016d73719a8e2817ed012b8006f294be990373a75d6bf3c89fad1d7fd" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 3a0e2214949055a44233c5606654ebd646ea2604 Mon Sep 17 00:00:00 2001 From: JulienDoerner <32195256+JulienDoerner@users.noreply.github.com> Date: Thu, 24 Nov 2022 10:04:30 +0100 Subject: [PATCH 23/87] Documentation (#5) * update source doucmentation * add example notebook for custom photonfield * add example notebook for galactic diffusion --- .../Diffusion/GalacticDiffusion.ipynb | 342 ++++++++++++ .../custom-photon-field.ipynb | 510 ++++++++++++++++++ doc/pages/extending_crpropa.rst | 2 + doc/pages/galactic_cosmic_rays.rst | 7 + include/crpropa/Source.h | 113 +++- 5 files changed, 949 insertions(+), 25 deletions(-) create mode 100644 doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb create mode 100644 doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb diff --git a/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb b/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb new file mode 100644 index 000000000..e83299831 --- /dev/null +++ b/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# example for galactic propagation\n", + "\n", + "This is an reduced example to reproduce the example in section 3.1 of the CRPropa 3.2 paper [R. Alves Batista *et al.* JCAP**09** (2022) 035](https://iopscience.iop.org/article/10.1088/1475-7516/2022/09/035)\n", + "\n", + "In this example anisotropic diffuion in different magnetic field configurations is considerd. We use the default values for the parallel diffusion coeficient $\\kappa_\\parallel = \\kappa_0 \\left(\\frac{E}{4 \\, \\mathrm{GeV}}\\right)^\\alpha$ with $\\kappa_0 = 6.1\\cdot 10^{28} \\mathrm{m^2/s}$ and $\\alpha = \\frac{1}{3}$ as well for the anisotropy parameter $\\epsilon = \\kappa_\\perp / \\kappa_\\parallel = 0.1$. We test the JF12Solenoidal field from [Kleiman *et al.* ApJ **877** (2019) 76](https://doi.org/10.3847/1538-4357/ab1913) one time alone and once with the superposition of the inter cloud component of the CMZField from [Guenduez *et al.* A&A **644** (2020) A71](https://doi.org/10.1051/0004-6361/201936081).\n", + "\n", + "We simulate Protons with a fixed energy of $E_p = 10$ TeV where the source position follows the SourcePulsarDistribution. \n", + "\n", + "To calculate the stationary solution we follow the weighting approach presented in [Merten *et al.* JCAP **06** (2017) 046](https://doi.org/10.1088/1475-7516/2017/06/046). Therefore, we use the ObserverTimeEvolution with $n = 100$ steps and $\\Delta t = 5 \\, \\mathrm{kpc} / c$.\n", + "\n", + "The simulation volume is limited by a cylinder with the height of $z = \\pm 2 \\, \\mathrm{kpc}$ over the Galactic plane and a Galactocentric radius of $r_\\mathrm{gc} = 20 \\, \\mathrm{kpc}$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## import of packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from crpropa import * \n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## simulation\n", + "This simulation is with a reduced particle number. Here only $5 \\cdot 10^5$ (pseudo) particles are simulated. The plot for the CRPropa3.2 paper are produced with $10^8$ particles and a finer grid for the plots. The number of simulated (pseudo) particles can be changed in the second last line of the next cell. With the reduced particle number this example should take about four minutes for each simulation on a 12 core computer." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def simulation(field, name):\n", + " \"\"\" \n", + " runs the simulation with different field configuration \n", + "\n", + " field: magnetic field\n", + " name: simulation name for output naming\n", + " \"\"\"\n", + " sim = ModuleList()\n", + " sim.setShowProgress(True)\n", + "\n", + " # propagation\n", + " sim.add(DiffusionSDE(field))\n", + " \n", + " # observer and output\n", + " out = TextOutput(name + \".txt\")\n", + " out.setLengthScale(kpc)\n", + " out.disableAll()\n", + " out.enable(Output.CurrentPositionColumn)\n", + " \n", + " nStep = 100\n", + " deltaStep = 5 * kpc\n", + " obs = Observer()\n", + " time_evolution = ObserverTimeEvolution(deltaStep, deltaStep, nStep)\n", + " obs.add(time_evolution)\n", + " obs.setDeactivateOnDetection(False) # propagate candidates after detection\n", + " obs.onDetection(out)\n", + " sim.add(obs)\n", + " \n", + " # boundary\n", + " sim.add(MaximumTrajectoryLength(nStep * deltaStep)) # limit propagation time, no detection afterwards possible\n", + " rMax, zMax = 20 * kpc, 2 * kpc\n", + " outer_bound = CylindricalBoundary(Vector3d(0), 2 * zMax, rMax)\n", + " sim.add(outer_bound)\n", + "\n", + " # source\n", + " source = Source()\n", + " source.add(SourceParticleType(nucleusId(1,1))) # proton\n", + " source.add(SourceEnergy(10 * TeV))\n", + " source.add(SourceIsotropicEmission())\n", + " source.add(SourcePulsarDistribution()) # for source position\n", + "\n", + " # run simulation\n", + " Npart = int(5e5)\n", + " sim.run(source, Npart) " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 12\n", + "Run ModuleList\n", + " Started Thu Nov 3 11:33:49 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:43 - Finished at Thu Nov 3 11:37:32 2022\n", + "crpropa::ModuleList: Number of Threads: 12\n", + "Run ModuleList\n", + " Started Thu Nov 3 11:37:32 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:45 - Finished at Thu Nov 3 11:41:17 2022\n", + "\r" + ] + } + ], + "source": [ + "# only JF12Solenoidal\n", + "jf12 = JF12FieldSolenoidal()\n", + "simulation(jf12, \"jf12\")\n", + "\n", + "# combined with CMZfield \n", + "cmz = CMZField()\n", + "cmz.setUseMCField(False)\n", + "cmz.setUseICField(True) # only use largescale field\n", + "cmz.setUseNTFField(False)\n", + "cmz.setUseRadioArc(False)\n", + "field = MagneticFieldList()\n", + "field.addField(jf12)\n", + "field.addField(cmz)\n", + "simulation(field, \"combined\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# load data \n", + "names = [\"X\", \"Y\", \"Z\"]\n", + "data_jf12 = pd.read_csv(\"jf12.txt\", names = names, delimiter = \"\\t\", comment = \"#\")\n", + "data_cmz = pd.read_csv(\"combined.txt\", names = names, delimiter =\"\\t\", comment= \"#\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Face on view of the Milky-Way. \n", + "In the paper more bins are used. The reduction of bins here is due to the lower number of propagated candidates. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.linspace(-20, 20, 201) # In the paper 300 bins are used # np.linspace(-20, 20, 301)\n", + "binMid = (bins[1:] + bins[:-1])/2\n", + "\n", + "nJF12 = np.histogram2d(data_jf12[abs(data_jf12.Z) < 0.3].X, data_jf12[abs(data_jf12.Z) < 0.3].Y, bins = [bins, bins])[0].T\n", + "nCMZ = np.histogram2d(data_cmz[abs(data_cmz.Z) < 0.3].X, data_cmz[abs(data_cmz.Z) < 0.3].Y, bins = [bins, bins])[0].T\n", + "\n", + "# restrict to simulaiton volume\n", + "vmax = np.max([nJF12.max(), nCMZ.max()])\n", + "mXY, mYX = np.meshgrid(binMid, binMid)\n", + "boolR = ((mXY**2 + mYX**2) > 20**2)\n", + "nJF12[boolR] = np.nan\n", + "nCMZ[boolR] = np.nan\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1,2, sharex=True, sharey=True, dpi = 150)\n", + "\n", + "p1 = ax1.pcolormesh(binMid, binMid, nJF12, cmap = plt.cm.CMRmap, vmax=vmax)\n", + "ax1.set_title(\"only JF12 sol.\")\n", + "\n", + "p2 = ax2.pcolormesh(binMid, binMid, nCMZ, cmap = plt.cm.CMRmap, vmax=vmax)\n", + "ax2.set_title(\"with CMZ field\")\n", + "\n", + "for ax in (ax1, ax2):\n", + " ax.set_aspect(\"equal\")\n", + " ax.set_xlabel(\"x [kpc]\")\n", + " ax.set_ylabel(\"y [kpc]\")\n", + "\n", + "plt.colorbar(p1, orientation = \"horizontal\", ax = ax1)\n", + "plt.colorbar(p2, orientation = \"horizontal\", ax = ax2)\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### edge on view of the Milky-Way\n", + "Additional plot of a edge on view of the Milkyway. Here, all data are integrated over the y-axis. " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "binsZ = np.linspace(-2, 2, 101)\n", + "binMidZ = (binsZ[1:] + binsZ[:-1])/2\n", + "\n", + "nJF12 = np.histogram2d(data_jf12.X, data_jf12.Z, bins = [bins, binsZ])[0].T\n", + "nCMZ = np.histogram2d(data_cmz.X, data_cmz.Z, bins = [bins, binsZ])[0].T\n", + "\n", + "vmax = np.max([nJF12.max(), nCMZ.max()])\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1,2, sharex=True, sharey=True, dpi = 150)\n", + "\n", + "p1 = ax1.pcolormesh(bins, binsZ, nJF12, cmap = plt.cm.CMRmap, vmax=vmax)\n", + "ax1.set_title(\"only JF12 sol.\")\n", + "\n", + "p2 = ax2.pcolormesh(bins, binsZ, nCMZ, cmap = plt.cm.CMRmap, vmax=vmax)\n", + "ax2.set_title(\"with CMZ field\")\n", + "\n", + "for ax in (ax1, ax2):\n", + " ax.set_aspect(4)\n", + " ax.set_xlabel(\"x [kpc]\")\n", + " ax.set_ylabel(\"z [kpc]\")\n", + "\n", + "plt.colorbar(p1, orientation = \"horizontal\", ax = ax1)\n", + "plt.colorbar(p2, orientation = \"horizontal\", ax = ax2)\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### distribution over the z-axis\n", + "Additional plot showing the distribution over the z-axis. " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(dpi = 150)\n", + "plt.hist(data_jf12.Z, bins = 100, alpha = .8, label=\"jf12\")\n", + "plt.hist(data_cmz.Z, bins = 100, label=\"cmz\", alpha = .8)\n", + "plt.xlabel(\"z [kpc]\")\n", + "plt.ylabel(\"n\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CRPropaMaster", + "language": "python", + "name": "crpropamaster" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb new file mode 100644 index 000000000..aaf11d3fb --- /dev/null +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# custom photon fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## example to create necessary tabulated data\n", + "\n", + "For the use of custom photon fields in CRPropa it is necessary to generate some tables with precalculated interaction rates. \n", + "All scripts needed are given in the CRPropa data repository (https://github.com/CRPropa/CRPropa3-data) where also the calculation for the pre-defined photon fields is done. The easiest way is to download/colne the full repository. \n", + "\n", + " git clone https://github.com/CRPropa/CRPropa3-data.git" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1. create a python class with your custom photon field\n", + "In this example we show the production of a custom photon field for 2 different cases. \n", + "In the first case we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", + "\n", + "The second example is a tabulated data file. Here we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php\n", + "as CRPropa allows only isotropic photon fields we use the position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", + "\n", + "\n", + "\n", + "\n", + "All photon fields must have the following content\n", + "- name (string): name of the photon field, needed for the naming of the files\n", + "- info (string): information tag used for the comments at the begining of the file\n", + "- redshift (None/Array): Determines if the photon field is redshift dependen. If None no redshift dependence is given. Otherwise the (tabulated) redshift is given as a 1D array\n", + "- getDensity (function): returns the spectral number density dn/deps at given photon energy and redshift\n", + "- getEmin (function): returns the minimum effective photon energy\n", + "- getEmax (function): returns the maximum effective photon energy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd \n", + "from scipy.interpolate import interp1d\n", + "import warnings\n", + "import os\n", + "os.chdir(\"./CRPropa3-data/\")\n", + "\n", + "import matplotlib.pyplot as plt # optional for plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "eV = 1.60217657e-19 # [J]\n", + "h = 6.62607015e-34 # Js\n", + "c_light = 299792458 # m/s\n", + "ccm = 1e-6 # m^3\n", + "\n", + "class PowerlawPhotonField:\n", + " # general parameters\n", + " name = \"PowerlawPhotonField\"\n", + " info = \"custom photon field as a powerlaw\"\n", + " redshift = None\n", + "\n", + " # model parameters, will be setted by initilation\n", + " slope = -2 \n", + " norm = 1\n", + " eMin = 0 \n", + " eMax = np.inf \n", + "\n", + " def __init__(self, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", + " \"\"\"\n", + " initilize the photon field as a powerlaw with exponential cutoff at both ends. \n", + " The slope, the normalization (n(eps = 1 eV)) and the minimal and maximal energy can be modified. \n", + " \"\"\"\n", + " self.norm = norm\n", + " self.slope = slope\n", + " self.eMin = eMin\n", + " self.eMax = eMax\n", + " \n", + " def getDensity(self, eps, z = 0):\n", + " \"\"\"\n", + " Comoving spectral number density dn/deps [1/m^3/J] at given photon energy eps [J] and redshift z.\n", + " Multiply with (1+z)^3 for the physical number density.\n", + " \"\"\"\n", + " # array handling\n", + " if (type(eps) == np.ndarray):\n", + " return np.array([self.getDensity(_eps, z) for _eps in eps])\n", + " if (eps >= self.eMin) & (eps <= self.eMax):\n", + " return self.norm * (eps / eV)**self.slope\n", + " else:\n", + " return 0.\n", + " \n", + " def getEmin(self):\n", + " \"\"\"Minimum effective photon energy in [J]\"\"\"\n", + " return self.eMin\n", + " \n", + " def getEmax(self):\n", + " \"\"\"Maximum effective phton energy in [J]\"\"\"\n", + " return self.eMax\n", + "\n", + "\n", + "class ISRF:\n", + " # general parameters\n", + " name = \"ISRF\"\n", + " info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", + " redshift = None\n", + "\n", + " def __init__(self, dataPath = \"../Porter_etal_ApJ_846_67_2017_SEDonly/F98/freudenreich_DL07_PAHISMMix_0.0173648_0.0984808_0_Flux.dat\"):\n", + " names = [\"micron\", \"total\", \"direct\", \"scattered\", \"transient\", \"thermal\"]\n", + " df = pd.read_csv(dataPath, delimiter=\" \", names = names)\n", + " df[\"E\"] = h * c_light / (df.micron * 1e-6)\n", + " df[\"n\"] = df.total *(eV / ccm) / df.E**2\n", + " self.data = df[df.total>1e-25] # limit nan-values \n", + " self.eMin = self.data[\"E\"].min()\n", + " self.eMax = self.data[\"E\"].max()\n", + "\n", + " def getDensity(self, eps, z = 0):\n", + " \"\"\"\n", + " Comoving spectral number density dn/deps [1/m^3/J] at given photon energy eps [J] and redshift z.\n", + " Multiply with (1+z)^3 for the physical number density.\n", + " \"\"\"\n", + "\n", + " f = interp1d(self.data[\"E\"], self.data[\"n\"], bounds_error=False, fill_value=0)\n", + " return f(eps)\n", + " \n", + " def getEmin(self):\n", + " \"\"\"Minimum effective photon energy in [J]\"\"\"\n", + " return self.eMin\n", + " \n", + " def getEmax(self):\n", + " \"\"\"Maximum effective phton energy in [J]\"\"\"\n", + " return self.eMax\n", + " \n", + "\n", + "field = PowerlawPhotonField()\n", + "isrf = ISRF()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### plotting (Optional)\n", + "Here, both custom photon fields are plotted. Additionaly the already implemented CMB is plotted to compare." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/home1/jdo/Test_CRPropa/CRPropa_Documentation/CRPropa3-data/photonField.py:31: RuntimeWarning: overflow encountered in expm1\n", + " return 8*np.pi / c0**3 / h**3 * eps**2 / np.expm1(eps / (kB * T_CMB))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from photonField import CMB\n", + "\n", + "field_cmb = CMB()\n", + "\n", + "eps = np.logspace(-4, 2, 200) * eV\n", + "c = eps**2 / eV\n", + "plt.figure(dpi = 100)\n", + "y1 = c * field.getDensity(eps)\n", + "y2 = c * field_cmb.getDensity(eps)\n", + "y3 = c * isrf.getDensity(eps)\n", + "plt.plot(eps / eV, y1, label = \"powerlaw\")\n", + "plt.plot(eps / eV, y2, label = \"CMB\") \n", + "plt.plot(eps / eV, y3, label = \"ISRF (F98)\")\n", + "\n", + "plt.loglog()\n", + "plt.legend(ncol = 3, loc= \"upper center\", bbox_to_anchor=(0.5, 1.12))\n", + "plt.ylim([1e-10, 1e8])\n", + "\n", + "plt.xlabel(\"$\\epsilon$ [eV]\")\n", + "plt.ylabel(\"$\\epsilon^2 ~ dn/d\\epsilon$ [eV/m$^3$]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Create tables for wanted processes \n", + "\n", + "Here, all possible processes are used and the tables for the interactions are calculated. This process takes some time (ca. 45 minutes)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running ElasticScattering\n", + "running EMPairProduction\n", + "running EMDoublePairProduction\n", + "running EMTripletPairProduction\n", + "running EMInverseComptonScattering\n", + "running ElectronPairProduction\n", + "running PhotoDissintegration\n", + "running PhotoPionProduction\n", + "finished rate calculation\n" + ] + } + ], + "source": [ + "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to divde errors\n", + " warnings.simplefilter(\"ignore\")\n", + " \n", + " # elasticscattering\n", + " from calc_elasticscattering import process\n", + " print(\"running ElasticScattering\")\n", + " process(field)\n", + " process(isrf)\n", + " \n", + " # electro-magnetic\n", + " from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", + " print(\"running EMPairProduction\")\n", + " process(sigmaPP, field, \"EMPairProduction\")\n", + " process(sigmaPP, isrf, \"EMPairProduction\")\n", + " print(\"running EMDoublePairProduction\")\n", + " process(sigmaDPP, field, \"EMDoublePairProduction\")\n", + " process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", + " print(\"running EMTripletPairProduction\")\n", + " process(sigmaTPP, field, \"EMTripletPairProduction\")\n", + " process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", + " print(\"running EMInverseComptonScattering\")\n", + " process(sigmaICS, field, \"EMInverseComptonScattering\")\n", + " process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", + "\n", + " # pair production\n", + " from calc_pairproduction import process\n", + " print(\"running ElectronPairProduction\")\n", + " process(field)\n", + " process(isrf)\n", + " # currently the spectrum can not be provided. Only as energy loss for primary\n", + "\n", + " # photo disintegration\n", + " from calc_photodisintegration import processRate, processEmission\n", + " print(\"running PhotoDissintegration\")\n", + " processRate(field)\n", + " processRate(isrf)\n", + " processEmission(field)\n", + " processEmission(isrf)\n", + "\n", + " # photo pion production\n", + " from calc_photopionproduction import process\n", + " print(\"running PhotoPionProduction\")\n", + " process(field)\n", + " process(isrf)\n", + " print(\"finished rate calculation\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. copy files to the share folder\n", + "\n", + "The files stored in \"CRPropa3-data/data\" must been copied in the share folder of crpropa. \n", + "\n", + " cp -r CRPropa3-data/data/* /share/crpropa/ \n", + "\n", + "\n", + "For ElectronPairProduction a calculation of the spectrum is not possible. As the spectrum is not needed for the energy loss of the primary the following work around is possible:\n", + "- go to the share folder and in the subfolder ElectronPairProduction \n", + "\n", + " cd /share/crpropa/ElectronPairProduction\n", + "- copy one spectrum file and rename CMB or IRB with the first 3 letters of your photon field (here *Pow* or *ISR*)\n", + "\n", + " cp spectrum_CMB.txt spectrum_.txt\n", + "\n", + "***Using this workaround and including the production of secondaries from the ElectrtonPairProduction module is not recomended!***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementing custom PhotonField in CRPropa\n", + "for implemting your custom model in the final CRPropa simulation a PhotonField class is needed. Here are two different implementations possible. On the one hand a seperate python-based class with the analytical description of the photon density can be provided" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from crpropa import * " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class CustomPhotonField(PhotonField):\n", + " \"\"\" analogue implementation like above but inheriting from the CRPropa module for compatibility\"\"\"\n", + "\n", + " def __init__(self, name, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", + " PhotonField.__init__(self)\n", + " self.setFieldName(name)\n", + " self.norm = norm\n", + " self.slope = slope\n", + " self.eMin = eMin\n", + " self.eMax = eMax\n", + "\n", + " def getPhotnDensity(self, eps, z = 0.):\n", + " return self.norm * (eps / eV)**self.slope * np.exp(- eps / self.eMax - self.eMin / eps)\n", + " \n", + " \n", + " def getMinimumPhotonEnergy(self, z = 0):\n", + " return self.eMin\n", + " \n", + " def getMaximumPhotonEnergy(self, z = 0):\n", + " return self.eMax \n", + "\n", + "powerlowField = CustomPhotonField(\"PowerlawPhotonField\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the other hand the Baseclass `TabularPhotonField` can be used to create a field on tabulated data. In the following the needed Scaling files will be created. In the end this files have to be copied to the sharefolder as described above" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def createScaling(field, nBin = 100):\n", + " \"\"\"\n", + " create the scaling files needed for the TabularPhotonField class. \n", + "\n", + " field: class instance for the description of the photon field as used for the generation of data files\n", + " nBin: number of log-bins between eMin and eMax. Should be increesed for large energy ranges \n", + " \"\"\"\n", + "\n", + " eMin = field.getEmin()\n", + " eMax = field.getEmax()\n", + " \n", + " eps = np.logspace(np.log10(eMin), np.log10(eMax), nBin)\n", + "\n", + " folder = \"data/Scaling/\"\n", + " if not os.path.isdir(folder):\n", + " os.makedirs(folder)\n", + " \n", + " header = \"# Custom Photon Field: \" + field.name + \"\\n\"\n", + " header = \"# \" + field.info + \"\\n\"\n", + " \n", + " file_photonEnergy = open(folder + field.name + \"_photonEnergy.txt\", \"w\")\n", + " file_photonEnergy.writelines(header + \"# photon energies in [J] \\n\")\n", + " \n", + " file_photonDensity = open(folder + field.name + \"_photonDensity.txt\", \"w\")\n", + " file_photonDensity.writelines(header + \"# Comiving photon number density in [m^-3] \\n\") \n", + " \n", + " for e in eps:\n", + " file_photonEnergy.write(\"{:.8e} \\n\".format(e))\n", + " file_photonDensity.write(\"{:.8e} \\n\".format(field.getDensity(e)))\n", + "\n", + " file_photonDensity.close()\n", + " file_photonEnergy.close()\n", + "createScaling(isrf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "now the files must be copied!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "isrf_field = TabularPhotonField(\"ISRF\", False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## check implementation\n", + "To check the new photon fields all interactions on a photon fields are added to a ModuleList. If the instalation is done correctly this should run without any error and all interactions can be used. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Everything works fine\n" + ] + } + ], + "source": [ + "sim = ModuleList()\n", + "\n", + "sim.add(ElasticScattering(powerlowField))\n", + "sim.add(ElasticScattering(isrf_field))\n", + "\n", + "\n", + "sim.add(EMPairProduction(powerlowField))\n", + "sim.add(EMPairProduction(isrf_field))\n", + "\n", + "sim.add(EMDoublePairProduction(powerlowField))\n", + "sim.add(EMDoublePairProduction(isrf_field))\n", + "\n", + "sim.add(EMTripletPairProduction(powerlowField))\n", + "sim.add(EMTripletPairProduction(isrf_field))\n", + "\n", + "sim.add(EMInverseComptonScattering(powerlowField))\n", + "sim.add(EMInverseComptonScattering(isrf_field))\n", + "\n", + "sim.add(ElectronPairProduction(powerlowField))\n", + "sim.add(ElectronPairProduction(isrf_field))\n", + "\n", + "sim.add(PhotoDisintegration(powerlowField))\n", + "sim.add(PhotoDisintegration(isrf_field))\n", + "\n", + "sim.add(PhotoPionProduction(powerlowField))\n", + "sim.add(PhotoPionProduction(isrf_field))\n", + "\n", + "print(\"Everything works fine\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.2 ('documentation')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "fb0d053f2fc7a51fab1b12c61652c7afb98f5cf72d66d469d3887d807bdc2ded" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/pages/extending_crpropa.rst b/doc/pages/extending_crpropa.rst index e562ef210..299792666 100644 --- a/doc/pages/extending_crpropa.rst +++ b/doc/pages/extending_crpropa.rst @@ -7,5 +7,7 @@ needs in in several ways: .. toctree:: Extending-CRPropa.md example_notebooks/advanced/CustomObserver.v4.ipynb + example_notebooks/custom_photonfield/custom-photonfield.ipynb Cpp-projects.md + diff --git a/doc/pages/galactic_cosmic_rays.rst b/doc/pages/galactic_cosmic_rays.rst index 7441c9515..6b19c001e 100644 --- a/doc/pages/galactic_cosmic_rays.rst +++ b/doc/pages/galactic_cosmic_rays.rst @@ -23,6 +23,13 @@ changes, which can be modeled following the next examples. .. toctree:: example_notebooks/Diffusion/AdiabaticCooling.ipynb + +Example of diffusion in the Milky way +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + example_notebooks/Diffusion/GalacticDiffusion.ipynb + Gas Densities ^^^^^^^^^^^^^ diff --git a/include/crpropa/Source.h b/include/crpropa/Source.h index 7162f5757..bc388ff3f 100644 --- a/include/crpropa/Source.h +++ b/include/crpropa/Source.h @@ -146,7 +146,7 @@ class SourceEnergy: public SourceFeature { @class SourcePowerLawSpectrum @brief Particle energy following a power-law spectrum - The power law is of the form: E^index, for energies in the interval [Emin, Emax]. + The power law is of the form: dN/dE ~ E^index, for energies in the interval [Emin, Emax]. */ class SourcePowerLawSpectrum: public SourceFeature { double Emin; @@ -166,7 +166,7 @@ class SourcePowerLawSpectrum: public SourceFeature { /** @class SourceComposition - @brief Multiple nuclei with a rigidity-dependent power-law spectrum + @brief Multiple nuclei species with a rigidity-dependent power-law spectrum The power law is of the form: E^index, for energies in the interval [Emin, Z * Rmax]. */ @@ -211,7 +211,7 @@ class SourcePosition: public SourceFeature { */ SourcePosition(Vector3d position); /** Constructor for a source in 1D - @param d distance of the point source to Earth [in meters]; + @param d distance of the point source to the observer at x = 0 [in meters]; internally this will be converted to a vector with x-coordinate equal to d */ SourcePosition(double d); @@ -232,9 +232,9 @@ class SourceMultiplePositions: public SourceFeature { The sources must be added individually to the object. */ SourceMultiplePositions(); - /** Add an individual source with a given luminosity. + /** Add an individual source with a given luminosity/contribution. @param position vector containing the coordinates of the point source [in meters] - @param weight luminosity of the individual source + @param weight luminosity/contribution of the individual source */ void add(Vector3d position, double weight = 1); void prepareParticle(ParticleState &particle) const; @@ -301,11 +301,11 @@ class SourceUniformShell: public SourceFeature { /** @class SourceUniformBox - @brief Uniform random source positions inside a box + @brief Uniform random source positions inside a box. The box is aligned with the coordinate axes. */ class SourceUniformBox: public SourceFeature { - Vector3d origin; - Vector3d size; + Vector3d origin; // lower box corner + Vector3d size; // sizes along each coordinate axes. public: /** Constructor @param origin vector corresponding to the lower box corner @@ -319,16 +319,18 @@ class SourceUniformBox: public SourceFeature { /** @class SourceUniformCylinder - @brief Uniform distribution of source positions inside the volume of a cylinder + @brief Uniform distribution of source positions inside the volume of a cylinder. + + The circle of the cylinder lays in the xy-plane and the height is along the z-axis. */ class SourceUniformCylinder: public SourceFeature { - Vector3d origin; - double height; - double radius; + Vector3d origin; // central point of cylinder + double height; // total height of the cylinder along z-axis. Half over/under the center. + double radius; // radius of the cylinder in the xy-plane public: /** Constructor - @param origin vector corresponding to the lower part of the cylinder axis - @param height height of the cylinder + @param origin vector corresponding to the center of the cylinder axis + @param height height of the cylinder, half lays over the origin, half is lower @param radius radius of the cylinder */ SourceUniformCylinder(Vector3d origin, double height, double radius); @@ -339,7 +341,7 @@ class SourceUniformCylinder: public SourceFeature { /** @class SourceSNRDistribution - @brief Source distribution that follows the Galactic SNR distribution + @brief Source distribution that follows the Galactic SNR distribution in 2D The origin of the distribution is the Galactic center. The default maximum radius is set to rMax=20 kpc and the default maximum height is zMax = 5 kpc. @@ -355,6 +357,8 @@ class SourceSNRDistribution: public SourceFeature { double rMax; // maximum radial distance - default 20 kpc // (due to the extension of the JF12 field) double zMax; // maximum distance from galactic plane - default 5 kpc + void setFrMax(); // calculate frMax with the current parameter. + public: /** Default constructor. Default parameters are: @@ -375,13 +379,36 @@ class SourceSNRDistribution: public SourceFeature { SourceSNRDistribution(double rEarth,double alpha, double beta, double zg); void prepareParticle(ParticleState &particle) const; + /** + radial distribution of the SNR density. + @param r galactocentric radius in [meter] + */ double fr(double r) const; + /** + height distribution of the SNR density. + @param z height over/under the galactic plane in [meter] + */ double fz(double z) const; - void setFrMax(); + + /** + Set the exponential cut-off parameter in the z-direction. + @param Zg cut-off parameter + */ void setFzMax(double Zg); + + /** + @param rMax maximal radius up to which sources are possible + */ void setRMax(double rMax); + + /** + @param zMax maximal height up to which sources are possible + */ void setZMax(double zMax); + + // parameter for the raidal distribution void setAlpha(double a); + // parameter for the exponential cut-off in the radial distribution void setBeta(double b); double getFrMax() const; double getFzMax() const; @@ -434,7 +461,16 @@ class SourcePulsarDistribution: public SourceFeature { */ SourcePulsarDistribution(double rEarth, double beta, double zg, double rBlur, double thetaBlur); void prepareParticle(ParticleState &particle) const; + + /** + radial distribution of pulsars + @param r galactocentric radius + */ double fr(double r) const; + /** + z distribution of pulsars + @param z height over/under the galactic plane + */ double fz(double z) const; double ftheta(int i, double r) const; double blurR(double r_tilde) const; @@ -471,7 +507,7 @@ class SourcePulsarDistribution: public SourceFeature { class SourceUniform1D: public SourceFeature { double minD; // minimum light-travel distance double maxD; // maximum light-travel distance - bool withCosmology; + bool withCosmology; // whether to account for cosmological effects (expansion of the Universe) public: /** Constructor @param minD minimum distance; comoving if withCosmology is True @@ -505,10 +541,10 @@ class SourceDensityGrid: public SourceFeature { @brief Random source positions from a 1D density grid */ class SourceDensityGrid1D: public SourceFeature { - ref_ptr grid; + ref_ptr grid; // 1D grid with Ny = Nz = 1 public: /** Constructor - @param densityGrid 1D grid containing the density of sources in each cell + @param densityGrid 1D grid containing the density of sources in each cell, Ny and Nz must be 1 */ SourceDensityGrid1D(ref_ptr densityGrid); void prepareParticle(ParticleState &particle) const; @@ -550,11 +586,31 @@ class SourceDirectedEmission: public SourceFeature { double cd; double sd; public: + /** Constructor + @param mu mean direction of the emission, mu should be normelized + @param kappa concentration parameter + */ SourceDirectedEmission(Vector3d mu, double kappa); void prepareCandidate(Candidate &candidate) const; + /** + set sampling parameter Ca + @param alpha angle between x and y component of direction. alpha = arctan(mu.y / mu.x) + */ void setCa(double alpha); + /** + set sampling parameter Sa + @param alpha angle between x and y component of direction. alpha = arctan(mu.y / mu.x) + */ void setSa(double alpha); + /** + set sampling parameter Cd + @param delta angle between mu vector and z-axis. delta = arcsin(mu.z) + */ void setCd(double delta); + /** + set sampling parameter Sd + @param delta angle between mu vector and z-axis. delta = arcsin(mu.z) + */ void setSd(double delta); double getCa() const; double getSa() const; @@ -573,9 +629,9 @@ class SourceDirectedEmission: public SourceFeature { technique: see e.g. http://physik.rwth-aachen.de/parsec */ class SourceLambertDistributionOnSphere: public SourceFeature { - Vector3d center; - double radius; - bool inward; + Vector3d center; // center of the sphere + double radius; // radius of the sphere + bool inward; // if true, direction point inwards public: /** Constructor @param center vector containing the coordinates of the center of the sphere @@ -596,7 +652,7 @@ class SourceDirection: public SourceFeature { Vector3d direction; public: /** Constructor - @param direction vector corresponding to the direction of emission + @param direction Vector3d corresponding to the direction of emission */ SourceDirection(Vector3d direction = Vector3d(-1, 0, 0)); void prepareParticle(ParticleState &particle) const; @@ -606,7 +662,10 @@ class SourceDirection: public SourceFeature { /** @class SourceEmissionMap - @brief Deactivate Candidate if it has zero probability in provided EmissionMap + @brief Deactivate Candidate if it has zero probability in provided EmissionMap. + + This feature does not change the direction of the candidate. Therefore a usefull direction feature (isotropic or directed emission) + must be added to the sources before. The propability of the emission map is not taken into account. */ class SourceEmissionMap: public SourceFeature { ref_ptr emissionMap; @@ -630,11 +689,15 @@ class SourceEmissionCone: public SourceFeature { double aperture; public: /** Constructor - @param direction vector corresponding to the cone axis + @param direction Vector3d corresponding to the cone axis @param aperture opening angle of the cone */ SourceEmissionCone(Vector3d direction, double aperture); void prepareParticle(ParticleState &particle) const; + + /** + @param direction Vector3d corresponding to the cone axis + */ void setDirection(Vector3d direction); void setDescription(); }; From 4ebb47c9166ff1d616b291d0c4ba77a57c486374 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 24 Nov 2022 14:19:30 +0100 Subject: [PATCH 24/87] Fix typos --- .../Diffusion/GalacticDiffusion.ipynb | 94 +++++++++---------- 1 file changed, 43 insertions(+), 51 deletions(-) diff --git a/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb b/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb index e83299831..d68d40d30 100644 --- a/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb +++ b/doc/pages/example_notebooks/Diffusion/GalacticDiffusion.ipynb @@ -4,13 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# example for galactic propagation\n", + "# Example for Galactic Propagation\n", "\n", - "This is an reduced example to reproduce the example in section 3.1 of the CRPropa 3.2 paper [R. Alves Batista *et al.* JCAP**09** (2022) 035](https://iopscience.iop.org/article/10.1088/1475-7516/2022/09/035)\n", + "This is a reduced version of the example presented in section 3.1 of the CRPropa 3.2 paper [R. Alves Batista *et al.* JCAP**09** (2022) 035](https://iopscience.iop.org/article/10.1088/1475-7516/2022/09/035).\n", "\n", - "In this example anisotropic diffuion in different magnetic field configurations is considerd. We use the default values for the parallel diffusion coeficient $\\kappa_\\parallel = \\kappa_0 \\left(\\frac{E}{4 \\, \\mathrm{GeV}}\\right)^\\alpha$ with $\\kappa_0 = 6.1\\cdot 10^{28} \\mathrm{m^2/s}$ and $\\alpha = \\frac{1}{3}$ as well for the anisotropy parameter $\\epsilon = \\kappa_\\perp / \\kappa_\\parallel = 0.1$. We test the JF12Solenoidal field from [Kleiman *et al.* ApJ **877** (2019) 76](https://doi.org/10.3847/1538-4357/ab1913) one time alone and once with the superposition of the inter cloud component of the CMZField from [Guenduez *et al.* A&A **644** (2020) A71](https://doi.org/10.1051/0004-6361/201936081).\n", + "In this example anisotropic diffuion in different magnetic field configurations is considerd. We use the default values for the parallel diffusion coeficient $\\kappa_\\parallel = \\kappa_0 \\left(\\frac{E}{4 \\, \\mathrm{GeV}}\\right)^\\alpha$ with $\\kappa_0 = 6.1\\times 10^{28} \\mathrm{m^2/s}$ and $\\alpha = \\frac{1}{3}$ as well for the anisotropy parameter $\\epsilon = \\kappa_\\perp / \\kappa_\\parallel = 0.1$. We test the JF12Solenoidal field by [Kleimann *et al.* ApJ **877** (2019) 76](https://doi.org/10.3847/1538-4357/ab1913) one time alone and once with the superposition of the inter cloud component of the CMZField by [Guenduez *et al.* A&A **644** (2020) A71](https://doi.org/10.1051/0004-6361/201936081).\n", "\n", - "We simulate Protons with a fixed energy of $E_p = 10$ TeV where the source position follows the SourcePulsarDistribution. \n", + "We simulate Protons with a fixed energy of $E_p = 10$ TeV where the source positions follow the SourcePulsarDistribution. \n", "\n", "To calculate the stationary solution we follow the weighting approach presented in [Merten *et al.* JCAP **06** (2017) 046](https://doi.org/10.1088/1475-7516/2017/06/046). Therefore, we use the ObserverTimeEvolution with $n = 100$ steps and $\\Delta t = 5 \\, \\mathrm{kpc} / c$.\n", "\n", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,19 +41,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## simulation\n", - "This simulation is with a reduced particle number. Here only $5 \\cdot 10^5$ (pseudo) particles are simulated. The plot for the CRPropa3.2 paper are produced with $10^8$ particles and a finer grid for the plots. The number of simulated (pseudo) particles can be changed in the second last line of the next cell. With the reduced particle number this example should take about four minutes for each simulation on a 12 core computer." + "## Simulation\n", + "This simulation uses a reduced number of candidates compared to the one presented in the paper. Here, only $5 \\times 10^5$ pseudo-particles are simulated. The plots for the CRPropa3.2 paper are produced with $10^8$ particles and a finer grid for the plots. The number of simulated pseudo-particles can be changed in cell below. With the reduced particle number this example should take about four minutes for each simulation on a 12 core computer." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def simulation(field, name):\n", - " \"\"\" \n", - " runs the simulation with different field configuration \n", + " \"\"\" Runs the simulation with different field configuration \n", "\n", " field: magnetic field\n", " name: simulation name for output naming\n", @@ -61,10 +60,10 @@ " sim = ModuleList()\n", " sim.setShowProgress(True)\n", "\n", - " # propagation\n", + " # Propagation\n", " sim.add(DiffusionSDE(field))\n", " \n", - " # observer and output\n", + " # Observer and Output\n", " out = TextOutput(name + \".txt\")\n", " out.setLengthScale(kpc)\n", " out.disableAll()\n", @@ -79,39 +78,41 @@ " obs.onDetection(out)\n", " sim.add(obs)\n", " \n", - " # boundary\n", + " # Boundary\n", " sim.add(MaximumTrajectoryLength(nStep * deltaStep)) # limit propagation time, no detection afterwards possible\n", " rMax, zMax = 20 * kpc, 2 * kpc\n", " outer_bound = CylindricalBoundary(Vector3d(0), 2 * zMax, rMax)\n", " sim.add(outer_bound)\n", "\n", - " # source\n", + " # Source\n", " source = Source()\n", " source.add(SourceParticleType(nucleusId(1,1))) # proton\n", " source.add(SourceEnergy(10 * TeV))\n", " source.add(SourceIsotropicEmission())\n", " source.add(SourcePulsarDistribution()) # for source position\n", "\n", - " # run simulation\n", + " # Number of simulated candidates\n", " Npart = int(5e5)\n", + "\n", + " # run simulation\n", " sim.run(source, Npart) " ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "crpropa::ModuleList: Number of Threads: 12\n", + "crpropa::ModuleList: Number of Threads: 8\n", "Run ModuleList\n", - " Started Thu Nov 3 11:33:49 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:43 - Finished at Thu Nov 3 11:37:32 2022\n", - "crpropa::ModuleList: Number of Threads: 12\n", + " Started Thu Nov 24 13:41:06 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:05:03 - Finished at Thu Nov 24 13:46:09 2022\n", + "crpropa::ModuleList: Number of Threads: 8\n", "Run ModuleList\n", - " Started Thu Nov 3 11:37:32 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:45 - Finished at Thu Nov 3 11:41:17 2022\n", + " Started Thu Nov 24 13:46:09 2022 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:05:39 - Finished at Thu Nov 24 13:51:48 2022\n", "\r" ] } @@ -137,16 +138,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## analysis" + "## Analysis" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# load data \n", + "# Load data \n", "names = [\"X\", \"Y\", \"Z\"]\n", "data_jf12 = pd.read_csv(\"jf12.txt\", names = names, delimiter = \"\\t\", comment = \"#\")\n", "data_cmz = pd.read_csv(\"combined.txt\", names = names, delimiter =\"\\t\", comment= \"#\")" @@ -156,25 +157,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Face on view of the Milky-Way. \n", + "### Face-on view of the Milky Way. \n", "In the paper more bins are used. The reduction of bins here is due to the lower number of propagated candidates. \n" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - }, { "data": { - "image/png": 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LoFoyABU8vVF0Ry5bwN7Qm1YgxRizKv/EolG0bBhtNwQpVH4JaNL6e68V5dLhd4wx/7cx5m2S7mKMOWGM+ccgig5Ak0oAvl7gj7p//7ox5v90kTkYY1aMMd8P4K+65T+8l3wP1w/r/r75yg9LoVDcJHg3yDk0ABX8fgnAx6J1eJL3Zvd5Jfl0jyM4of70DYzWrYHKvmyAHJ+/Y4z5u8aY13CfDOFhY8x3g9Sc/xxIsGQ37c+ttd9trf0ua+2/222/jDGvRaBqvhvAX9z9Ub2s+IsgW/cQgPcaY77OGDPghcaYu40xf9wY86sA/sEe2v2nICGUPoBfMMZ8ubgebwbwK9A5wC0HvaAKBQBr7T8B8I/cv98G4AVjzAVQvsCPgigyv46mAuXLiX8NilwxrpXX8XoUIH0p+mP84+j37462+0YAb3PfcwD/cUHh87PGmLdib1gBGdFfB3DZGHPRGDMGKYR9u1vnH1lrfzHa7m8A+A+gF5HvBXDe3R/nEQrCvhPA9+yxPwqF4haCK5z9O+Knd3HRZ4F4ErfnSZ3bz79x//4ggLEx5mljzGljzKKaZ9ccjr7+BQA+BJLd/99BE9mZMeY8aPL5SdCk5C4Avwjg2evcrR90fQGA1wB4dgdbIv/uuc5987DWfhxEDz0L4GFQbvfYGLNmjNkCTcz+NfZIlXTld74eJAR2CjSJGxtjLoMUWE+B3nUUtxD2Kl+rUNyysNb+FWPMzwD4C6D6MUdBMtAfBhnPfy2S3W9E/zaMMb8EGqivhUBKz31uX2U71xLS0TRwf4vQ22F5jD8C4CtBkcBXADgOGgefBhm6n7DWNgQLHGXqDxtj/gOAPwXKnzsMmtR9AMA/t9b+lz32RaFQ3Jr4dQBf7L6nBFA+AIpsLbvPD13hfv4CaHL0BwE8AOBe9/tOtTevOVyB6zcYY74aZKO+EDS+8jE+CeC3APxba+2VHu9eIG3HIfe3W+y2wPc1gbX23S6f/1sB/H4ArwY5ILdBk+EPgpSa/1tbGy3tfsBFLL8XRAW+A+RM/c8AfgChlIXiFoFpOpAUCsXNCJcP9hxosvltV5sf4OgcbwfwG9bat119DxUKhUKhUCgUNwJKv1Qo9g/+KGhCt4GrFEhxuWtc/PbDV9cthUKhUCgUCsWNhE7qFIp9AGPMAwh1en78agRSjDF3Afi3AA66n152NU+FQqFQKBQKxbWD0i8VipsYxpjfQsj9ykBJ049aa9evoK0/DODHUZe//ifW2ptVFUyhUCgUCoVCsQvctpE6J1X/B4wx/9IY82ljzMQYs2mM+Ygx5nuNMaMF236zMeZ9xpixMeaCMebnrkCFT6HYDU6CZKIvAvgvAL7sSiZ0DkugZPF1AL8B4E/qhE6hUCgUCoVi/+O2jdQZY/40gH/u/v0kqLDzMoC3gmhpnwLwpdbac9F2PwLgO0CqRL8EUlj6cpDU+Tdaa//ry9B9hUKhUCgUCoVCoQBwe0/q/iRoAvcjToqXf78LwM8C+DwA77TW/jGx7PcA+GWQjPlbrLWfcb+/BcC7AGwBeMVVRFIUCoVCoVAoFAqFYk+4bSd1i+Amae8BMAWw7GpUwRjzc6BaH3/ZWvsj0TY/CuAvAfgua+0/fHl7rFAoFAqFQqFQKG5X3LY5dTvgI+6zD5KQhzFmCVTTCwD+U2Ib/u1rr2/XFAqFQqFQKBQKhSJAJ3Vp3O8+5wAuuO+vBE3yXrLWnkls8yH3+drr3DeFQqFQKBQKhUKh8NBJXRrf4T5/wVo7dd/vdZ+pCR2stZsgVcHDxpiDqXUUCoVCoVAoFAqF4lojv9EduNlgjPl9AP4UKEr3PWIRlzjYWrD5JqgG2EEAl3exr0+0LHoIpK757E5tKBQKhWLPuAfAlrX2+I3uyPWE2hiFQqG4YXjZ7YxO6gSMMQ8D+Leg8gR/1Vr7kR02uV7I+v3+wQceeOCRG7R/xW2Exx577EZ3weORR/SWV1x/PPnkk5hOpzuveOtCbYziZcXNYmfUxiheLtwIO6OTOgdjzN0AfgHAYQA/bK390WiVsfscLmjmgPvcMUoHANbaV7f05RMPPPDAI5/4RJuTVaG4djDG3OgueOg9r3g58OpXvxqPPfbYLR+lUhujuFlws9gZvecVLxduhJ3RSR0AY8wRUCHx+wD8JIDvSqz2jPs82dLGARD18qK1dleTOoXieuBmMZ5Xgr32XUuyKBQKxcuP/Wpn1MYobmXc9kIpxpgRgJ8H8AiAnwbwZ2z6Kf40qG7dHS6qF+P17vOj16WjCoVCoVAoFAqFQpHAbT2pM8b0Afw3AG8C8IsA/qi1tkyta63dBvBr7t9vSqzyje7zZ651PxUKCWPMwr/9BGNyGJO3/r9oHfpbfC722/lQKBSKG41bZVxN2ZO9t3FrnAvF7YHbdlJnjOkAeCeooPj/BPAN1trZDpv9sPv8m8aYzxFtvQXAt4FKGvzLa99bxe2G282AZNkAWTbw/8uJW/wbAFhbwNoimuDlLdvdXudSoVAodsLtMC6ynbjeuB3OpWJ/4HbOqft2AF/vvq8B+LGWB/C7rLVrAGCt/RVjzI+C6th92BjzywB6AL4CpJj5Ldba9evdcYVCoVAoFAqFQqFg3M6TusPi+9e3rgV8H2jSBwCw1n6nMebDoEnhVwCYAfgVAN9vrX3Pte+m4laFevEC9uJNlevyd2Py2neJFP0mrGsa65F3V5PjFQrF/obamJcHsY2RNmrRNVA7o7jWuG0nddba7wNN2K5k23cAeMe1641CoVAoFAqFQqFQXBlu20mdQvFyQj2maVyrfAfOr5PYbYK8jNDx/23XSz2rCoXiZoTamJcXkh2SsmOxXUm30bxmamMUV4PbVihFoVAoFAqFQqFQKG4FaKROobgOUK/py4tYGTPGbvLt2r7XtzfiN/WoKhSKGwe1M9cfbXZFskPa7Evbb/H6su0s69Z+Uzuj2At0UqdQXCX2o2FdZGj2I6R09aKkdcZuj5/XSxtrpc4oFIrrj/1oY251ZNlgTzYntd5Ov9N+uo121c4o2qD0S4VCoVAoFAqFQqHYx9BInUJxBdhPntOd6B/7OUKXQnw8u6G/xN93c05S68f3hXpUFQrFlWA/2ZjbAbuNxqXW3Qs6nQEAoKrSpXsAit5p1E6RgkbqFAqFQqFQKBQKhWIfQyN1CsUC3Are0kUJ3fs9Sreb3LhFx5g6N23t8u+LkuJjaD6EQqHYCfvdziwaQ6+3jbkZbNlubIK0NYsEVThCt1ObMmon16fvamNuV2ikTqFQKBQKhUKhUCj2MTRSp1BE2O9eU8ZOXsNbAbvxkO4UzdtNDp5c92r2RetoWQSF4nbG9bAx1zNitVO+8W5/i9vbD7ndu1G43G3edht2YoDs9XxpbvftC53UKW57XEsDu5uBf7fGazeUyb0mZO9morfbNvciJvJyGOy90SIHtW3k950mbvGxdDoDVBW3W6fQyP6k7g2tSaRQ3B64HnZmkXNqr22l2thpMncl4/tOtUH3ehwv12Qwti87T6zaa6KmftvN+rtdro7E2xdKv1QoFAqFQqFQKBSKfQyN1Clua1xrGsxei462rSeLaafWafNq7tV7KNdNRa/2Eh1MiY7crEnyO3md93IdZVSOwRE7uTyO4qUoN8YY9aQqFLcQrgfVcrfUxr3SAheNp23Mkba2FkX9drOfVDtXOt5fS8bIlbZBzIymjeX/g/BJndlhbVGzGTGCraHPspz4Zal7ge9HtTO3JjRSp1AoFAqFQqFQKBT7GBqpU9xWuJ75c4uQ8pru1nvI63FR0pQn7mr7t9P2siDqXgtzA7s/xqvJN9xNdHA3HuKdktZ5GXtdJVJRu3hZPYrX7IPm2SkU+xvXws6koix7yWvLskFjTJQsj7Z2JKTNSeUG71Xco207yY6I95Nin1zLvG/Z5tVE4dr6tRsb1ekMvF03Jh3Nk8iyvGFrZDRvN1FVzbO7NaGTOsUtj+ulNLaXiYhcP95OGl+5fgwetPN81LrPqlos8pHqVxuk4ZAGRE7w2trbKy10r/XzdkMBWkTb2YkutJvJ8m6oQ0CaThMvY8hzypNGOclT46tQ3HxI2Zi9Orbi7VIiKHtRYExR61P9SFP0mlTx0JbcT1MYitdZRBmM+1Kf1Fz9BDHeTv7fdh2udPKcOp42qmmbaJY8f0Uxbt1Orh/bjhRSzth0H1Ut81aB0i8VCoVCoVAoFAqFYh9DI3WKWxbXkgKzm9/3IjG80/rSo7jXyBivv6imTYoKGJbV15GQ68fCH3k+2pO3NXVOFvU5bqdt/VRENJWY3rafK6H7xMeWumZ8blIiKgxaVk94T3lU1ZOqUNwcaLMze6Gp7zQe7bQ9EMYciZRIUyzIkWJhSIo/Y9H6WRQeWERDT/V1ES00FRmUY+NeqfuLxEoWRf32asNT67b1da/RRWOa9EuJ2K63iea09YvZIVU1b92H4uaERuoUCoVCoVAoFAqFYh9DI3WKWwrXOjq3Gy/gbrx1JICRzj+QbUgPGydMMzodXmeAqppE2w3c/2kRlTh6xW1RfybROotz0ZoJ+fJ7e/5YnGOxFy+nbGu3+Xa7bTMVQbzS3IpFUVVGytPNKMtJ4xzG+6XtllxbE9cXjdwpFC8HdmtjriQHq01Qqy2nbueofx2dzqDRVhi/Q8Qmz0e1/qWYIIvy2jqdZvRnUT4cyf2n7UFVFUn2SYzdjtm7iYTuVmxrN7ngqeNdhN3ki6cQn6O2Puwk5iL/z7KuuEZqY/YDNFKnUCgUCoVCoVAoFPsYGqlT7Gss8pruNhLU5v3bq3cLSHtHY8TRGemdlXlXTa8s5zuMGxG6lMx+SsY6jsrJffO2vE6qfbntohy5tApk46eFim1tymqp9XeKru0Ve5XCZsR5cKkcxNR+5PrhGlJbMi+G8x25LWt5O/WoKhTXC3uNzu30W9s6nc5gYZ5t+C2My2Hsb9ovycho60uqNEs8pst12nLS4vbj5Xm+0rqsqiatNirLUrZWjonx8bfnrskSMim7lcrLlvawDXuJwEnsNUoot0vlzQF0fVhBU+Z4x/eVzJ+M30mknQnnV1WY9wN0UqfYl4iNbGqSsoiqkdo2bfDqEwv5f0y1W1RqQCJlKFO0l7ZJU6czarSVpm82Jxvd7kqtL5LaE/oejHZs0LJskKB5tFOA6vSa+qQxZTDlMcfnnOmobLDiY0y10fbbIjpOqq3UMcn/5UuZREp6Wk78UpSZ+KWGX8wWUVnrx6ZiKgrFtcDVOA0XO6wIMc1RQtKw22zBoolC2wRrLykFbGvkusEGpm1Z3Gce46RUf9wHadMWUf55Gdsx+r056eL1YxuS5yM/vvLEZZEzUo7ti94r9iqwsmjZoj7I3+L+h0nxuDbBZfBvsR1OORvTgmiyj2pjblYo/VKhUCgUCoVCoVAo9jE0UqfYd0h5TxclFy9KQgfSUs3y99Q6kh6ZiqSk+tDm1ZS/Sa9mHAnkPki56TaKJm0naZRpL52MoIX+83by+MN28fr1JPr6uShL+ux2VwS1NCTaL/Iap+ia8THLqNai85uKru0Ge6fMpCO78reUh7dedDztEZZ0qnT/YzEBo55UheIKsBfBrbaIXRwBkZEOXsbRK47YSaGkRftKUQJT0Zx4LJQ2sNdbrS1LiTSFMXWEGJIWmRIfabJIBrVjldtJZkZ8PLItabe4TY7yhchTEBKLx8t6ugHvRzJT2iOguxFF2a1d2QubqE0oZ5FNi2n6qfWkHWfbnCqLEe+nXiheI3Y3GzRSp1AoFAqFQqFQKBT7GBqpU9z02I3XdKdoHBB7mJo5aHF+Uyp6ksqB2q1ARxzhivMDaJ+hf7H3jPvT6QTvZFmOa23JY5f5ECkRFNknuS0ntJfluOFdLctJI+cr3r6t/ZRMtvRey2MtinHS88ztBE8s93XSWG8RFuVDpNpI5eCl7rlFEbpF0V72GsviwAx5H8TXOeSHpCOK8bOjHlWFIo0rFUNZlJMlIcc/KWAB1J/x1HgUj9ty/I/z32Q0Kx4vUpGbOJ87dRxSmIPHmqqaNPYpEY6pbh8l20PapTyvl0pI5aUVRb1/qRzytghl/H2n6Fi8HUPmq8Xsi73m0u1GRGe366X2l4oOh2XBdsQsIFm2IkT2moymcB1VROVmwW0dqTPGvMEY89eNMT9tjDljjLHGmNY70hjzfbxOy9/ffzn7r1AoFAqFQqFQKBS3e6TuewB83RVs924ATyR+/+DVdUcRY6/FxLNs0KpYCaQLp4YoTzOnLpWfxdvHy1LyyjKfoM07V5Zj4fEMHtxYGYwLi6fyx2S5A3ku5CdQj7jxfnh5UwY5eG7n83W/77Y8vjwf+TaCF5D3M2pE+CgfArV9yhyTtlIG0tPLkFHYRfmVu82ta/PGp9RC0zlyYdvYQ7rIW56S705Fh5v5KqEtmU8hI5i0vuZAKBQSe2GCpH6r52m1jzlStTeGzKNNsS7a1C87nVGjb8z8kHnZMp85ZQN4H7ENkG2zDZA50SllRPp/4vcTGCnN44n7LJdxXzqdINHf7x+v9a/e12Y+XDz+LWLy0PHEbTbPSTjG3eVb72bfV4NUVDXuq7wvU0ykGCEnPlxHeT3k8rgPamNuLG73Sd1vA/gogPe7v9MA+rvY7l9Ya99x/bqlkIY29UK+G+pEXKOnnowd2kolB3M7MTVFGglJVwG4Psy6/x73vWkUQls8GUsZt9QkK+5zGHBHyWT3eACP+0595heTOk1S9ms+X0/W2YvXjyeUWdY0NJLeGe+H+t9Miqf/B8kXpDb6rJxsxy9fO9E2F73MpZBKgG8zntI4MmSSf3wc8rzLe4HWCXWe5MtN/FIX1lERFcXtjVRZnEWUuZgGL3+PxzEan5pOxdT/sk1JsZbUxNREr63P/KzLEjvc9ywbYDZbc8tXam3K8SJVmoaFVWJxF9nXNlsa9zn1W2xj5Xo8IUwJy8SQk6eU2FhqotcGciCmJ64p7PYdZdE+r3TiJyn58f0oSzCl7MRuJnipfcXnV/adKZlVNd/7wSiuGLf1pM5a+w/k/3uNCikUCoVCoVAoFArFjcZtPalT3HzYTbHXvYpcSM9f7CVNUVxSnssUtS8VeYuLtUrvWRxdYW9ar7faEDyZz9cbtAdJKw10mnoksSjGjUgbUVJru671j/cZU1vKcoI48pTno8Z5iiNLQDNqRn1opw6l1mcw7UdGNmOvY1lOElGvsL+28gidzmChl5Ihj3kR/TJeX64X911KiAdPZ7PofFzigLYNkdl4f4uej/r1UE+q4vZEGxNkkfiTjIBJWFskaYttETo5toVxtZkWwEixKuosjPqyVES/bh/SolkpyHGfkWKTxMJd3e6KsFtNZkdKiEoWJ5f7lBTA+PykyiNIsO3o9Wg9EgWp2ys5JqaoluF7LDIW+hKzKdpsSpvtXISdIsiptIaUbYrfGeR1icVsJNsptkMSoY2J30eTraJ0zJcTOqm7MrzdGPM6UGGoMwB+3lqr+XQKhUKhUCgUCoXiZYdO6q4Mfzz6//uNMf8ZwDdba9uzoSMYYz7RsuiBK+7ZPsRuonPyu8yRSnH4FxUQTUXj4vy3Nsn++Ldm0e52z1vKu8mYzdYa0RiZHB4iZ+3HzOv0eqvJpP228ghS3ERGvfj/sO+wTtt5ouhP/ZzIddmrmsq/YI9qCrzdbBaS/eOInvT0xsiyQSMSKr20Ib9ud3kFbSIqqd+ybNBYX57flEc3PncyQhDfXzvlZsQ5NfVoH/exW1umHtVbB2pjCKn8OUbqGYrH2PpYOGms0yZuBKTtRNjPqLa+zG8K6zftkMy3jcdVzuuWkHlkcX+knYijans9T0DoC+fBNe1FU9RK5v+l9hdH76S4V3zuF+WkZVkog8Oo58nX9y2vRyq6Fo+vAc3SNymWxyLsZh3Zfmq7vdqt9DtNkzESn4tUTl28vyzrqo15GaCTur3hCQDfBeDnATwN4DCALwHwgwD+IIAOgK+/Yb3bZ7jSukBAc6KziHIWC4fI75IKEieoyzpiTI+REweeKMiBMDVpJLRPsNpqE4XaZfzZnNQ0RUSKmqgJt9+WwC4NFCfQSwMa+tqkv8R15zqdUevkLKaeAnQeuF+chM/9kfXwGLyOpHgwjMn9ccfnpijGftv4GOVkMJV8L885I0UdSv0Wr5u6N+J7NGVouV8yAT4+RqIKN2lhuxF1iY9LRVQUtwoWTeZSlOR42aIXeTlOLKLdpcboNkePXF+OR7FThrfvdkeNPrYpNgM0hvIYHdP7iTJaXz8lviGp320OVGkHUhNe7qsc22LI9WPqIKNNbKptgtjpDGpKzvF5SE1q4rQBnrhKyn+YZKeFW3h/7e8HV49Um4smdfI9JBaPkWkBqXsvXj/lNEzV8AvHr5TM6wWd1O0B1tp/G/20CeCnjDG/DuBjAP6AMeYLrLXv3WV7r0797ryrj1xVZxUKhUJxW0NtjEKhUNw+0EndNYC19gVjzE+ConhfBWBXk7rbFXsVQ4kjbalk3JRIx6I2g6dzpZXKKSMwwQsaonkpGmJMSUxFuFjqWnq7OMokaYKxN0x6NeOk/XBuxt4TyX2fTM421gve4FWwtHHsBS2Kca3OHIPXiz2e0ksde6dl1DPldeVIGrc5GJzEdHq21pask5PyFDZl+/kahGhsOM9MG2l6wet0JD7n7R7P3SL24qaS2KXAwiJBlqb4SpO+tIg+TJE9+h7Tb+m7elIV+xs72ZlUhC4eAyW9rBkRCrYgRIKa9iEWQ5JUw0XpAKm+chkCRp6PGtElKVzVlLZvRpJ4TKyqptiUjKgFGl4Y02MaZWqsiiHHUv4uyzakIkMcQVxEDw12JSxfWjoJADWbGGrCNm1IbB+LYtxg8Mh+xW2UJfdnjPj1uv4+sXOEbhGVUfZjJwp+vH4srJMS7IqXAzGleHfXt60/ygq5fmjRhFNcAT7jPu+6ob1QKBQKhUKhUCgUtxU0UnftcNh9bt7QXtzE2GuEDqhHeHbjkZLepJ2EToB01E+uG3vpGHWev5QGrnsBZf5FM4+gyfOXuWXsQeU+cD5YpzNoLONPKo9A+0kViY2PdTo924heySgmg/tO0Tskz4kU8uh2j/u+AuQpjdcnqepmriKvn+oHr8vnaZG3ks+vPCfN89BM2pd5lqkivDGMyRv5KTttw7+n7tE4MT2V/9k8jmYphNRvDFnkPM6NlN5pjdgp9htSdmY3+awppkEq6pDKT47blxH3ONIhIyPNyEWI/sjyOG15c8ToGNd+4+2KYr2W70z7Hjfy2FKRmNRxhWhZ2D7OVV6Uz1dnQtTX63ZXdsU0kOsDZCd4O2k7pZCKbKvbXREME2qDGSHG5A3GSK83aNhyfm2W0dtUjne8XYpN1JYryO3H26WQuofiZSnI+z6s17zvF0Xlmu9rA3B5g7j/bf1SG3NtoZG6awBDdyULpHzoRvZFoVAoFAqFQqFQ3F7QSN0uYYy5A8AfAvCvrbWXxe8jAD8E4M0AzgL46RvTw5sbi7ynxHOvq4fVvaftnqq4CLXkrae8ZykJ4kWerrZ1ZrO1Rn6D5NfHsv1SKSuoUo78/7w+ew3rypP1tup5BPXIkCwrwF5HuW1K6ZF/i/MBpYeY1+9282SB1nj9WCFO5pjINmUuILW/4vsZR7+C/PfIr18UIdpUFKEf9FtTkUueJz7moKpJbcooYawGF+cjMOJI4KJzkspzSD0LqXXiY5Ge3lTuxyKV1DY1tNQzpLkPipsdu1VTpnXzpGIfIxXZ5/VT0ahUfp7MoeM26PcwZqee2ZQCY4qJQm2Ffsa2QI6Tcr14nOdxUv4WRzHlb3JsSDFMeFksdy+ja7HSsGQ4hGPN/bI2ZkZKlVO2ESt9pgqZ5/mDAMimS8VoXkeW0pHHKI+bIXO1q6oeqY23pTZ3ZmoAeTLq1RaF28nmtO+nGbWW5aP4PSxVykNGleOC5Gwz6+qXzXcMjdhdG9zWkzpjzFcD+B7xU8/9LoVOvt9a+7MADgD4JwD+vjHm/QBeAHAHgNcDOApgHcA3Wmu3Xoau7xvshgqzW8gJWVvtn0XS86k6X3K9xcndcVL1qGEceR8ScmIWT6wWJ1xPWmkPKSnt8FuYpEnaYkwLTFGN4kmqtYXv13wuk9ubFCBevzkRDVSdQPMMRj5M6oIBp76MBGWoTmPJ85GoV7SePD7ZP5oo1a+xXD++/nICGtd8WmSEJVKUyfj+WkSPlO2m6gMFNNteVGdRvnymhAJ4ndQkVY2u4mbEorIFbaVc6s9L0fieEs2KHXXyRVuKh/CyeKxI2a/UmBD3fZHwhJwgxWNJ20SuzXbKvqTKADVFVML/YfJIvy0tnRQTpLpjUI6JcvLUZsvzvHmepGOUv0t7x20NBscbfU3ZR24rnrBbW6DfP177LTheg0BOTPNso6HGNNr4f+pHOzVzkUOQkSp5lFqvLpzSnODRsuZ9LNtKOc6bvzX7IssdpByItL7amSvBbT2pA03K3pz4/c3ROgBwHsA/APAFAB4C8FYAJYDPAngHgH9krX3uuvVUoVAoFAqFQqFQKBK4rSd11tp3gCZku1n3MoC/fj37cythLxG6nagBcRHLlFcvJbnPESHpyUwJmbT1pyzHC/uaknFmj12go9RpiLKNVERFeoNjuqZMoI+9eeF4Qlvb26d9+0G2Oj5vQSiEP6UIS0rwJaaBhmVNT1xVrfv9xLRTKmxaPxcy0hlEV6g/fG6zbNCg1UgqECNFVYlLQZRlEBqQyfQpsYK4X3I/bV7QRdHftmVtpRlSQimSEhbTr1L9CfsYN+5D2a9F1GX1pCpuFkg7kxarSD8vQDtlGmhS7dKRJH5+0rS1+PlNlYeJWQ/d7oof5+R4nGKY8LI2sag2uxLbjNQYF49Bbe3Lsg6EYEti1kqgVY4a1PpFNE/Z5zgVQdL0JWKhlBStn/fDIi/yXMqIbWwXJdWUt40ZIJKZI883R624D9ImxtdRRjbZTi4S8EmxPhZF9lIUyHD+2tlUqWdB3sfxO4NsI3wP1Nw2O6iU/yuDCqUoFAqFQqFQKBQKxT7GbR2pU1wfpKJ0kuedQpt8rxSmkDk/IYehzl2XntiYMy+TqhdFP8JxyNwMjuyEaEhKDCXkQdS9oalIisxtaObUjRseNenhTRWM5rZiiXrpUYz3XVXBoxqiiiE/IhYHWCSvLQvIMlLXjPPU8jx4RuOyClm2yDPejBbKPLu4+Dp5Oldr7XMfUknfdD3S94TsU11WvBm9k/3bCYu8pqmIQkpMJVUcdzf9iJ+JuuBPPQIh19OIneJGQdqY1H0fltXHUHmPL4o8yd/kPuL2aZlcL6yzyN6lBJIA1MZpHu9ns7XGMdYLQaefXym2JXO/YlEXXr/XW230K2ZExIhtYJatNo4/NY7H434qr1zmV8dMC96fLFEgIUVZ5OdgcNJf45hVk+ejhp2U/W9egxEAWp/z7rpdti9N+23Mim9/N6JZ8e8SxGBavF0b4vU7nYFg1qRz6+T6Mhq3m5y6mAlTb6s9Z5TaUhuzV+ikTnHNkKLCpB/kJlUlfrBTye4pIYsY8qU4pnZI2p5ErIKWUjKLJ2TyJVq+dMeCHXKiwetzHxYpEaYmm/K4FqmFxkIsO1GOimhMZRplpzMSk0dJIYkpjM1BO75mUlgmJMAHykmcFJ5lA0Gdqb9QVFVTDEa+wMS1k0h0BrX1JcJ5bt6z8bVKUXJlu/F9IusgxvtL04gHSeoTQ4qmAGm616L2U0jtpynA0/7SoDQZxcuFlChKu2BEsCtSSRGoj7ly4pd+cV8saiTthNw+UL2bky5ul/ctnYbxmN7pBOcXq/2Gl/Am/ZAFr9KKkuEFnsdJdnBRDdL6GJ06Zjk+sWhI7Fzt9VYbzjVJMW06LAeYzai/Bw48WFs/z0cNu51SoEyNezyOyXUmkzXfrtwPOTFHbjv49QeDlVr7Mi1gaelUbRmfD6qNyu8fgWoqj1eifn7Hjd/iSe0iuupOSL2Hxe9ki5yM0iHS5pyQx2ht0yEir1+sUh07Img7ndztFkq/VCgUCoVCoVAoFIp9DI3UKa4a7EVJJ8QSZCSBwd7TVDRAek9jcRNgd5SDONrAScrx9rH3K0QNJw2PotwuVdct9nROJmfFdvWIlvQ+LkpyD5D7jqktTc9aShI7CKY0E5vjYyzLcSOJvijGjWgRQ3pr43o/Mjk+RdeMl5XluEEjlecm7qv0SsfXYGlppSE2I73b3Edex5jce3bj6yDLYqT2GXtUU/1PeVSl2Exb0npVtdNCZRspOe2U8MMimk/s6a4LxaSuh3pSFdcXbaIobc+L/J6KiMdju3xu4kgKR1skUs9xPQIxqrWfEkppUvRyWMtthLEkHtMkfTF+7iVbIK6jJm3UePypWltyPI7HV6Kwp8dj7mN8rLGNCuImgbWSqv0Wl5GRY1VcOkJS+CW9v42SakyeZGsweJnsV9dd+1lJ/eLyPmU5aTAzmIYpo1mhDwW63Tjq1W4L5GcsnkKpJO3RtVSbTfsu19vZFiyK3i2KKsp1FtFOw3Xk7VP19tTO7ASN1CkUCoVCoVAoFArFPoZG6hRXjFSELvU//9b0FAVPThwRSAmhyO1jD18qkTvmZkuPpxQHkd4v2mczxyIlYx97xCQ3Pc5rk32OPWzSq1cvmN2MtHHfY++WlGBOlVFolxlu5thJxF5Tua+4qGjKu2dtyCeoqpXGMcbebJl4H98v8pr1es3rH5Lo+V4IeXexB1p6WBd5S+OoXCqfs6oKcY/xenyNdxuFle3VvfK7yWuTx8HXRfY1dX+19UvuI+X9l8cm26Zt1ZOquLZICW+FZU1xE0aqcLLM2U2JbMURp3qEuj5WStsgo2+h/WaUn5fFeVOc/yu3kUIhQN12cORQ2lXunxQT4RwvOX7J45XrLxpb5PHICE889qfKNcQ57r3equhPWCewLmj7VFFxRmB4rPvlvG/OKaT268dETJNgY2S/ZFF4xiA7gqndcMdNfZXRuJBXPvG/0X4HPiony+gEAbUQaeXtgp1Dsk0gnTfYLBi+N5sjmT+Lio/LSHNsH1PPS8wYSuXzp/q1qNRI2E7zuNugkTqFQqFQKBQKhUKh2MfQSJ3iipHy7DNSxStT0a74O3vbZG5dzM2ve6nqnjVZViCFOPohi72m5KybUZM6hx6IFS/jaJzcd55YP+wr/j/2WKXyFeI+p6I6KQ48Q5YtaHLhmxx4eT3iXES5n9C/cI1j72xVFf58xp7uPB8Jzyt9Ss9tHHEry0kjosde0LpMeN1LW5YTH/WTapkhArjSer6C5zIX3t446lu/B+rL0mU+2lUmdzdcp3JkUvmPbV5WGeFLSVfvJsdRobjWSOXRpYp8M6pqnNw2hmSJxCyS+Fmn73yPh2cjNYZK9oQEKSHXx1o5bsZlZGh53d5JBkH8jPLnbLaWzJOOy8fweDmbtecBp/qaQrCPo0b+n8wlDzmBx/22bWwSqSQa5+elyvXIvDnOKeTo2nBwBBWqWlv9Pkfbxp6Z4vPzsgy5cdc2UgslxshqbX15HmQEkPcXFEfr7wB128n2IRxzKEjffHdg7JRvvRv7EbeRioLLttpYWRLyvknZk6b94WVNVXS1LztDJ3WKK4IxZseQPpAWNOEHVVIm41C9XDdltJqSusGoxsY0HiyBQF+RtAd++Y5pGanjSJVGkP2K25rP1xvHvYiOIPu6G8qdXDdFx2ijcspjS/3eFCRpJtOnrmfzOobtmL5D576+Hq8znQaRltmsfm9I4yhfHNhwb2+fdusHB4EUbJHHIxPtl5akcR+7fpx16510fVnz68vSCfwSEdcPJNpt/TylJtuLxFPk//Jldie0JabHbbWJMMTbNSeGzWc63BtKw1RcHfgeSpV+WXT/S9vAdiG0Gcau2MkmJwPxBEM6C2PKuLQ5LHJSluPG8ySdJrGNklT8PK+PbbPZml+Px6MURTruszw2WQ8uHqMC9XOA2PEqz1tKPIntXOq6pMSZuE8pp1Hs2JX9bKOKygkfj8dVNcHW1ulaG5ubT/g+ccmEuPRNr7cq6JH0ORf3XJ4Na+ek211Bx71CF6iPjdJJKZ2R8eRP1oaN+yNt9W5KIMhztOj9K4as37vouUq9f6VsRps9SU0GF6UBENLLqC21MSko/VKhUCgUCoVCoVAo9jE0UqfYE6Q4Suz92cl7GlMG657OegJxSmBEhuc52hMnYctk9xT9IyQfj91n00uVipoEr1kzipGKbMReYEqSjr15wUubosfFnlF5PFxMu002Wq6fZTISyts1KREMWUqgSd+ZNK6fpPu1Fa3N85XGfuqRzeb5jfvKtJeybBYf73RGQqSk6SFuUrOaXm0GnXva18GDrwEATCZnGutLD29d1AAARn6dsE0zUrdXOkkcCZa/x5FToBmtrh9ju7c15emN16t74JvRCFqmCe2KvSGO0EnEY4+MLsU2RI6FsbQ/0Rab67WxDyhaNEIKqTSClMBX2Pe6KDZe3w/R2+k4ZFRuN6kLKRp1qq+8Ho+dgYURmClSlIrXiaNFqVQHSVePI28yohTb7W53pUHZ5z7k+WhhikSIUI1rvwNhjJbRRWZy8JgeInzh+vJxZMgwmZ2jNp3wSSjuvo4yKlMUH7tsX9L6GYGGi8b6si0uoyCPI6ZwSrZTKsUj7lsq8hZPCerL4Po6bhzvov3spg/xPnk/kopJvzUjuxqxq0MjdQqFQqFQKBQKhUKxj6GROsWusFfvKXvNZHQujhqk8gJS+QehD3nyO1CPSMRFq1N5AvX8Bo7apCNQqe1SHqx65IVzuAaNbetFP+veRnm+4nwQuT6flzxv5uClvGDx+Yq9gXI7GemJI47Syxq3LfMTm21OEOdWdDoj70mNi7JSiYL6PSTb5DwFKXwT5801i8mHfbO3taomPleitDPfPufnBU9v2kvP68Q5dTLhPi5zEIrkBm9qKrck5cVs837KUhYMimhP/HKJlLdVPmvxtZJR2Hj9eiHcer8oSqqeVMXuYIxJRo6BtOR8SjhC5vamhLSa+2wyRmKBFBnZXmQXUtHuWAxKSsIzFuVvy2cvZgRIFkKcd7YomiePTUbu4lxxyRLhZXkezqkcR6l/6/733bAQ2A7JSGAzMjRBin3BiKN+8l0jVQCbEcZq+tzaOo3h8FStX93OqhA3WW/sO+5XLz8CAJiXG41r1elI9s2otp18b4nLT1TVpCFuU5ZjISDWjLx1OnVbvluhlNheSGZO6n6K/190f6Vsm8y1j9uSz+5ubKKyQggaqVMoFAqFQqFQKBSKfQyN1CkWoq3AuJS2D7kJ0ksTIk7NNpuRukUROoYsEh574sL+0mqMch0Ja4uGGmVKyWlR/pyMksURndQ+46iJ9GDJPqe477x98AI3vWFxhIryFep5hqk8klActhlJ4khaSsGqvt5K7bdFnjXJmY/P83R6trGf2PMN1JUu4witjAjG91fqPusaithVpoBx/q4qn/g2AGA2a27b661ie5vyM+JC9kBQ+GzuW56b5vVIebrb8mZShZZT996iZ4+RKgmSii7INuNjSsmfa8RO0QZpZ3aTbyPH3FianyH/j8d22baMTsTP16Ji2jKHL857Looxut326H4sdy+fy0URlZTtSBXRpn6l88oXqQ3GYyjD2kLkhtUjUHH73IdF1zGV08zfQ45bkzHCDAppc2ImhMydT4GXxcXN5/N1r5LJ43lx4EEc6N9L7bsoXO7G6gITz+7wSqXFBVpH5BbKeypWTg3Rz1Fj/cHguF+H1wsKmfVi4Nw+I9iFYIf3kr8t801T92rbPbQTu4kh79mUAiwfQ5vNrKpm5FEVMQk6qVPsCXXJ4nYaXvwbrR/Tt8KkIDWJA9ppGfGkIdWvRYOY7FdMOUkNUPHgkhrYZLJ3jBR1SL6Y83c5KYrpeql+xUZCGg653SJqTmxY5AQzTnKXbcT14GStOK79w/RFa4PgDbfF28n1JD0yprtw2/P5ujdusn9tk+CynDQm7uE8r4fJWbYc+lOuN84hUJe9DucwGGQ+jjC5GwBYd9/DZJbbXkQNTomVMFLLYkeKrOHVtg+JlCQ29zW173pCe/r5lW0pFG1YRBWXL+spsaV4DJSODkbs1ALSE6pYbElSwFMlEAB61uPnKfWSLgVdYtu5iL6WZYNaaZz43LRNnqwN5YAW0e0XOVLjY5Df6y/psTOreX7rx8zrhxf52CEqHUScihAcafQ5nZ6tlX4A6NzHpSmkzZLXTa5Td5bSNd7ePt0454dGr6HtMfA17Kwh3nnlRF7YfkjQu0x6op8SRmP0eqsNByqJx8B9D+3zsabaapvoLnpPSonhLHI2yuWxjaq/A4TrEe47tqtNBzJDrpsq57SXieutCqVfKhQKhUKhUCgUCsU+hrpQFa3Ism6DrpfyhMTJwtI7v4julaJoxZB0lEC1bIo2SMTy+IEa04yW7FaMIo5OpfafosDJSE+c0L6InrK0dLKRaC0pQXFkT0a6ZPJ5vO/UvmRZADqO4CGOo2rS28iRNCkbHXul5XaLzmuzKGu4xjEVytqi4XWU54Slqnn7fv94wwMrk9K917e84Lfz3lwnolKhahx/rwd//HExdJYjL4o14RluP0b2UkpaSVsyuoRcN5z7tMc3biM+r3GCfvw93i5Fb00JVzT7rAntCkKqwHgbZNRg0diWEkhaREOUUbmYmSDbaBMdkm3IcUUyCyTqZXfQ2i85DqcEUhgp6j4jpkx2OiPfrpftFzYrVdw73p/cb2yHZHStGQkL5zbYxRDFiW2rtCHh3YKOg2mYwHF/fgeDk367cB3Q2v9UFChVKD0+NxdEHw4MTtH6Lj6SuVfqfucI5nar0b6/Vs6uLPVPUJ9R+WWcUpCyA0EMLNyDVVV/P6LrUj+2tqLyqfMQI/WeFrOh5DPYxphJ7VNG3+O0GRlxTNOi+do0r9XtTMPUSJ1CoVAoFAqFQqFQ7GNopE7RgPSetkWeUvltkscd5yKlClPHCbJymfS2xnlgsl+xB0cmrcdRI+k9XRShSx2jLITK26faiHO32FMqufxxkrSMcMmIksw5k21JLyh7Pw8cWHH7CVLSS4Z+m2CMgcthKEVxbwCoxP+FdR5V0xPLXe6aGCrYG1n2jtFxeK8pGt5p/pReXZZbLstxI6rImM/XG3kwcb6L3E4Kq8RexKJYT+Ys8jpxTqG8tkVF3tZuRstmxRgHetRGZUL/4jaqaqVx3HHCeeqYjGkWlWUsumdTXlRqf1L7bVHuUqqNuPyG7KvMlWjmM7V7hmlft68nVVEXRqn/3hR6kvYhzmfrdNpZCLJodSrHiNeXTAN+HuOonIygp/bD4LYkkyCVPxfnOMsIpMwr5u05pyzOg5PrxX2Q/eJxdj5fb9jf+BzJY0yNOTI/j593Pka2R93uSqNgOI+RS0snG4yZXm81IfQSzqFkBcR9XVo6WTvuXm+11i5QZ2/EY5s8p6mi6/E1kraN+8x9ONAJtrAws9p5k1EsFljxx4Ucs4rbOlXbT1GsN6KYzASRfWVI+xjO6QjG1Mfk2Dak2pJ9TK0n37toP03bvCiKJ0smpPaXsj+ybbk+lUGqvyvejnZGJ3UKj9jQyoctpkzWawHxeuHhjSdbktIWDwTSyDHqofR4YtmsvyVFImLDx4NjaiJWpzbU6St5PqopSMr1ZVvSoAUqzEp0buqDj2xrODzVeFmxdqVBtel2SMijj5GfnMWTtSFWfNJ2D0TxmGELHdBELdBE6HOCMXK3bGqcUQH1vYMcc0cL7DrFL4sKBerGatahic8UY0xRn6TIcxpTXoti7Cmc8USm0xm01u2h782hK34ZZGpPUYwbxir1IiavCythTi3te+4Mbp6PMHO/MYWm31/Bdudsbd+dDk/q0/c7f2bZSu0YjcmTE09GPPlN0bBCrbh2A51KQpcvvuEctq+fMtbxMkk1k8tuZ6N7u8MY0zppoPsi/Voi78HYcRe2DS/rUoArRZlMjccp8QVCU+GSIemVKdXfmFaWqufJkBS91G+pl+dULVE+npjKKccXBlMZsyxvCKpw3TWDzDv9WByEbQoQbA07CS0qDHoP1g9OXFa5XhuY8j7DFibVBX9MQJPSSv1vioLI2oC8fTy2SWdpsAUroR+J2njcFtuvixdp0mhXPh8AcCi/HwdwxPefDr/nbWwVHfesXEfznYn+7/eP1+rTAUQ1TdGHgbotTVFy4/bbxN14nbDvnSf9UgwlXlc+v/Ie53ZTlNfY+SGPNd53Kjgg7cztYmOUfqlQKBQKhUKhUCgU+xgaqVM0kKJcsKdLhvBjilVKFCTVZhyBkbVQUh6g2CsrE2jj6EdRNCXUU3LLcZ/bjjsVoZPHIH+rqklN3p/2veLXjyOB/P9SfhxlTtGvvou89TD0UbKhi5wx7dGi8pEzjprxuhly7/1kT2ofmf/NJ3KL/eQiCldvK/ORPd5fBz100AEATLFZ659B5vszzeh6jntr/njZY8le2nm5gdmMvd/rjXMZJ2hLOqaMvgJ0nSSdFYgpwuu1Npge2e2uNGhYWTZAxyWwx6IwMpm+2xn69gc5UVE3p8/4dhncj1BjiOleo6SwSEwVS0Xsdou2CHiKhiZpyk2RmkEtGi6xiLIjv6coM1dyTIr9jxT7AqAoc3w/ybE3tgWSYhlT33cS05IiHfxbmzCDLA8SIgrrfn+LxCYWRQnjWnayvhufh6IYN+j/cTvU1kqtz8RQqIui5PkIBw++BgDQN8T8kIwOZnDEzI4uBj7KxBEoIDBEmO1R+f8H/jt/yjZ5P9L2lM52ZNFraRcDlK5UwAQbAIC5Y0Js4oLfjtkUs9lag/IZyryMGmNgvczF8do5lLX+UuJqoX1ah+vczbprODp8fe18TbDhz0Hujp9tYorJJO+3ELEK9iKOqoYI19la7T3+bGu/02nW9ZNR772M1fK5iqn4bdT/+H1QRrRTjBH+P+6PfI+8naGROoVCoVAoFAqFQqHYx9BInQJAPc+BUVVFI/8r5bWJI1ApCV5j8kYSKyPl6ZT7aebPtUcLU+2nvKipfKCUKExcvFVGj2KvqfS2hchmiCRxfgJ7RvlTgn8bYNl78QYgj2opctnYg7rsvIAcPRtlW7hcLQEIUTwjfDfcRo4uAGDJGGzx8bvhYGQO0DqmwCibUx/MZd/GuKI+TmzPLZu53/soLe2bI3v8aZD5CCB/bnTOYm7WaV9RIri1Bba2TtfOoZTEjnNrsmzQyEWT8trxtQ3XMXj2pJDL5vZTAIADS/e7/q34fXsPJMJ5m1XkQWaJahZYAUJkLs5lIU9kPUe1qoqGZ5OLy7YVj2Wk8xXqz46MYrZFjikvgs+XfE4mtd8YMqE9JZQiozCM5jOquXW3OmTOdpvsuSw7E7bjZc1xXNqo2KufZXlDbCWsO25ExKQUPkNGcWTxcLmdFAVhyDzxJstjJRm9iPfJEY/UelLuvy3yNBqdxGhA4xeP7cyqAAIzg8foAUZ+bOYI3JKzPQDQB0WxWDSLcrXzWhvcpsz75nWMj871sNIhlsd6ybbGou/syFblbAdHfEyFTSfbn/tIV88fD9tMZodgAKzhNAD4XDy2JXUxlPZ3hzA2jj3bJo7USXEuRnjHWcN5fAgAMBo+5Po88P3edhFHjlhWJvff4YTKOh36nMzO+XufRVQyZNiaPFPrTziG1ca4b8yqP975vB6hTtmVulhJu91JRe9SeZz8e8pGxe9fkjGzG2G7ndoPx3R72JjbOlJnjHmDMeavG2N+2hhzxhhjjTE7XnFjzDcbY95njBkbYy4YY37OGPPWl6PPCoVCoVAoFAqFQiFxu0fqvgfA1+1lA2PMjwD4DgDbAH4JwADAVwD4SmPMN1pr/+s17uPLglQOGylFNb0tAHkpY063zHlKyTg31fnCsjjyIr21sSS8/C3uf4prnfL2yIKobdFIGY3jZYMBK4UNWvPtgOBR42V9s+w9luxZ5EhaD0Pv6ZS5CXG0i9FB7r2gHHmrnH9m2w5gYNx2fQDA4c4GtiznxHVcK87TawocdXmS27a+n+VsjLkrYrqchXN/IHO5cZb2OXL/b1Qjvz5/cpu5KTCujrp+Ud9frFYwG9wLALiMc7Vzsl2cxXBI5zDOW5jN1hrewLIcN667vIfC9/r9PJmcqclw87qdjvMaFuTp5XWybOA9iawaZ1CFvDTnqQ7316BxT8t8hzhnjxRXQ4Rxt2jLd4i9sfVyBxzRrOcpyZxYqUgWcu/qnt5udyUhJd30mMrIKUNz624ftCnmSaTy2lIKwjIinGWj2nrBTgwQK/wFG5U37mOZ99wcX5qFkCVzJI7wSVuVyu1LFRHndWNVQ2nTeOzgMcjawo+TvIzzewdY9urIHKnLkHubwXnVnKfWw9DbpqFjgPC6BWbeDmXCjnH0TebSAaSEfDhbqh3btHIRKFPhUkn7Xum4MQiVj8zx50qH+jU0E88O4c+Nipgmx80Sppa+r5iDACjSx8fmc7tHZGe2sYGL4w/7cwfUWQxxrrbM347vCSqd0Jb3PcJkchYAavZidYny7Pi6FN5+z3yZg5CzTVHSPB95pWW2L0W15Yutj8ef8usBdB9Yu+Z+W/F9j/O3pY5BzG5h7JQvGsOY8FxlLmSUKkHEyLKmOrLUJVi07zjvm9ZVO3K7T+p+G8BHAbzf/Z0G3JtwAsaY3wOa0J0H8BZr7Wfc728B8C4AP2mMeZe1dv269voaIsu6/ntKYEQatfp2zcmTXNaWlAuEQSuV9BpTb6Qkutwu/UCnJXUZcvIopa5j+kqQQR40Xvjrx1l/Ge52V7wUPlMm3fwKADByNd8KN7HgdeaYeCPExtEg8xO8VFmBJZAB4wnVZWcwCwtUKAEAPUc32LIDFG6S1XEUl8POYPLkS6J0k7VxNcSyM7pTR7VcyiYYGRY84fIIZGiOdta90fXbVXwMuf/O63TMAKU97H67m/qKSwCAjXwFs5z2s71EfeVaQ9JASTERSc8E0hMGvtaSjhU7DbrdvCGZHZatCEEG15a4f/udI65derGaFOe88W3SScNLZ6r+UpuYBO27nb6VQnhOwrMRP0PSgRHTd+T3WDo+lYSfEquRaBcFuH2kp28XpGrSpZx+MeLailk2QCyUIu/VWExE1jiNxUrKciLG+xW3fpPWL50VTapo2s4A7Oioj0MpwZS471KQaJHABE/ker1VDHOifGd+PA6USJ50MS0yR0+IlLCQ1sivz5R/Lk3D2/cxEmJb9Fsp2sgNvUdM7Nyvv841Pp3d6rtSO6XNcEhM5gBgmE08/TJzXt9DWVindDMEtltsQ0pkfr2LJdnTo51gr0qXZrBWPEz9wxiHRzQer+N5+s1RNCeTM7V6fkDd7rMN4PF8Pl9HLM4jxdzC+wod//b2GZx3y1cGD0MiQ4aCHccd6gOf7162jLl1NldM+Oalm/S6e4Enj0BwOPJztb19Jllagz6b43WqdIZ0gixKl2nSQVNUyLB+7DiXDsj4vMq2U877FNU/Xv9Wp2He1pM6a+0/kP/zxV6Av+I+f4AndK6d3zbG/DiAvwTgTwH4h9eynwqFQqFQKBQKhULRhtt6UrcXGGOWALzd/fufEqv8J9Ck7muxDyZ1sfd0JwlyhvTcxDQRuSwlLZsKvce/x97Jsiwa2y1KtJdYlKgr6S/snYspNN3uSqOP/P9weKqRVL2EZe+VrIRn1PdHROEACDrmEe8ZldRMpmZwm0HkZIDSRePYY5k7L2hhjadYbttt2rEFltxxr1fknbrsqJCZEDBhueUjHdpubnOcmR937VPfVzvrmHN0yR3jiS5RJ18qjqB0x7bh6DXDjNoemrH3tg7ddVnKJn69mF5zF+7GudJFGrEOANgcOErQYIbNsk5t6XTGIXJW1e+JlDefUVUTf6/KkgNLSyf9toCM8E28F5Sv+2y2hmHvhDvVdRdhLz/iRVNGI/LObm09QcdRyiL1IRrNiexMAU31da9YnEBeX1ZVweO5aP1FRZsDBWcgPMI7RxeJ+nlre1JvJywqNA6E8TRVcDgW8gEkk6NorB9HGawNKQLxvSfTB2Q0QNIaY7RRJiVdWdI9U6JBAI0z4bd6VG42C+sGyneOpaUHAYTIEdPxZHkAth0dISJSCHEtBlP3eRlH54AQtSsjGtsMW95+HcBht78ldJzd4QidLEcQC4HN3PN8MJt4e1IJVsiWs3PMPmEhrqGZ4KD7jbfjCF+FrMYUAYgVwu0yE2XQDSIsW/YOAMBKRWP2ZkaRuvHwFNZtYIMAdG/ENkOKj4RSN3yPD/x2cSklY3Jf8oB/u2P4JtofKsyz8K4ARO8Ohm1C+C2mfPrt8sAmmU7P+j4bw/d73YbI+7d5Xzbp0PG28f9tUXgpzrUo4raI5REo/EXr8bf173YR57qthVL2iFeCqJkvWeue/Do+5D5f+/J1SaFQKBQKhUKhUNzu0Ejd7nGv+0xN6GCt3TTGrAM4bIw5aK29nFrvZkHKe5ryzscCC3HESkJGQ1KCJ7FnJeUBitepqkmjr4t42zInIV4nz0eNfstIHbfB/0sPFnu+OErTMyPvSWPPaBcDH03jRGhZToA9nf1o2RyThngKUPfUyWUVqlqOBAAYG/bDkTcpuX/JcfJ5GRdEnWJWk6gGgJdKjiAuwYK8WKXzmj5XHfPRN05oZxGWQ9nY5zycL1YABG/rgWzLt7Hktj/WOe9FUzgHj7frmgJ9Q9+n9pDrF/V5ExeQuSjWZInyKGbFBX//sbddSiO3ec2zbOBLBshlcU4dI89HidzTAaYleXt7Lj+HPdYGGTpZPTeSo3PGhJyBUMh4sYR03NdFz1c6es3PbzPaHdaZ+Dwj+VzFUWt5vkJx3HrfU2JLWdbMQVGhlFsTqShWPYe6maud8ubzslRh8vg3BuVj16MSHBHs9483ot5039fvY5lvuhtxLs4zNSZvCGktKvchj5W3Gw4pOtfrrQbbZOpRoyUse3GSmNExxdgzRniMLzCr2R1aFmT24xw8meud+Rzvqd8+t+m8b4MMA5dnN7XEKvFldLLLnpHB5XAA+Jw6tg+8bKMa+d9GHLFyEcK5zf123h5VA/Q77hy4PO5pSZ9HO+u4x7X19Jwidb2K7MvIHsXIRbMuDunzcnkG29vJV76F+fupe1W+y/C9t7ZNsYCDSw/5c87XoPDMnJ5/P5AlJ+Ji3YPBCi0rx417W5bRiAvSA+uNvspoWyo/LV5Pbh+P7Xlez4MF0s9VyPlrntNYZE9qLaSZH+0Mk7jNWw06qds9eDTdWrDOJoAVAAcB7DipM8Z8omXRA3vqmUKhUCgUEdTGKBQKxe0DndTdZkgpkQHpwq717Zoe9bbIgJQ/l+3LPBsJWUw6FcVLeTjbPPzkNU17cGRb0kPaJjkPiHy5HkXoOAJzAEe8Z01GzWLZZ0aBiVe75Bwxr26FoZeVZs+qLGPAETTpDeVtJ9H6FSpRtNzlqyGoYrEXkNcpUWDbtcvewG3hweXffJFUVChccVjO0+No3DTr4W6XX9fPXMSSva3lyHtXuTzCpeqgV9BkBbNDPfocV0NsVNR/lrY+6qJzzxXHcMF5ejdBEbKNPMMUnF9H/ZO5EIuiX3G+ncytkUqoDF7GKmj9zhEfFe2IkhSA82ZHBXM77l6aFmsoS855CH2NFdhkVIP7KHNvGKmIQLvKZDNSJ89Dav223AUqAF1/rmS0PFb2XFQktt6vWzPn4XYAX7t68W4eX0eNSJ0cjxcpJ6fur9B+HPUbLVDGnPi+hVzcomEHg70YNO7VlC1cxHJJ2S9+NviZ7/ePY9A7VtuPQQYYZk/QuOcVJYUqJUNGdQbuO4/3BSY+sueZA4IRwraCi5TzOFaJfVQugmRR+dw7tndLhsTDJ3buVZQPZC6Hz7o8OJt5hgbbjoGZ+Vy6bWdfOhmrbVaewTF1rAfO486qUArBR/OyLd/uoZyu7bH8gt/f2YLO9Ymc2uA88PVyGYcs9f9ASVk0484JrDu1zPPbH6Z+eZXls7XxEZDMhhEyPm53S8RRY1pGv21sPYZDw9fQtlGxdovKf/clJKoJRr17a235+6DT1CuQpRniCHJVDcCF7uNnSCpKMjgaTes3o13x+1SK3SVtTrCt8ZgwaKiLyud/N4qYqUhi3IdbTXFZJ3W7Bz+RwwXrHHCfu6JeWmtfnfrdeVcf2X3Xdo9FSevxOpKOEi9LSTzLdVIGmR9QHshCkvGgMZikXiYX9TllYEPdsUntfwA1ig/TEOL1Dy495I1VXGOuQuFf4P1+kQkKTOHXA0jcJKa2SNpjPCkAQv2gWFhlG+uNffNEcQnL3oBLOgdPyibuNuZ1OmIIyARFJ96O25d1jmYuCZ0pOB2MMJ05KqozEpIaI+vZAZT0fk9Okxqm4zCVc5Rt4eH+U7U2WLTlnvwshuUKAGCtPAWAJtkXc6LJXOLjEXXtGDGdlmsJAfW6PW3UjNlszb+AcRJ61j/hX3q2Szep5dIG4jrxvcPnspcfQdmtOzGqapJ4OeUXy6JBOeFaQDJxfDdlEaQxTU2sghR802A2XxiaEz5ZakJO5rh97neK2qOUzGuHG2FjYsQUw9TLbbifm5MnhnwxS0mXx4IWQLvoSlmOBT070EPjySJvP5+ve/sQ35+yv1I8KHYIyRpoPIbwJ7e91DnWENTK0fPfuSwOQ9L7Y4qeQebHcmmP+Dce73l/0pEY6qDS57Gsi3VXG47H/46waQwWLTmclRi7a8MTuEEWqJbs4OsbtkOVL0OQuWXnHN3+UDb2toM/Xypo2TCb+EkZ25CuoN7N3TGyzbmn+4LfD08eh85WHe5s+PY/O6OJ3LniCLKKJk/9JWrjkiuFIOul+uNKjHuMXm+1Qb+Uk5aX1t8FADi28nZ3LgP4PPtrlAVHa0yjHWCEmTuv8p0rpNDUnwlZfiHlUNlLvVRJi2RI25Z6h4snbKGEVSh3EE/O2uihi0rq3C52RYVSdo9n3OfJ1EJjzAEQ9fLizZ5Pp1AoFAqFQqFQKG4daKRu9/g0gCmAO4wxd1trn4uWv959fvTl7dbuIQuNL4IUYWBPfezlSCUIp4qJ14tX1qMSKZpYygu6SEo3FREcDI7X1pUFa1ORC/aS8mc/o88Ocu/9shHNsRACI+zxtKh8FIZLFOSejlc1JZ5FeiZHevi3GXpBBMVH2Tb8/ph2yG2yR9UKERUZjSt9xLBOD6yQN6St+XjmmPj1GEaUQAh9p3NysRqg54qi56ZOZ1jOxthyntGjOZ2jISo8W9C1OpzRsd3ffRYAcK48ir4r28Be3Yd6pwEAT8zu9ZFA9qzObY7nCgo89HI6Jxc6tH6WDXxUmD2kPsqW5Z5OIkUS4vtLCpnIqAKvMxrcT8ftInTSC87f+Z4IIjXjxn3f7x8XnsT1Wh+63RXv7Y8pySkPaSpivkhYJZZ4l+dLCqUwUiVJ4hIQ8je5vziJntdPl1BQGuZ+QYp2mUJMh2I7s5P4BN+bkgLWFPppRs4YsmRB0xYMUFX1KCKvw88G9TVOH5DCJyEq16HKMjVxFl7nwIEHqV0nfFKJ8Zl7v+To+nNM/JiRYmGwTZoi6jsybLsxRNL3ci94wrT7Fd8HThGIKYCXqhy5K5XDfV0yfSyZQHmU6JgKx3M619suMsS0x9wUnpbPpQe6pvBUfP6Ntx+aiS8szpE3KYoSo2sKT8Ucl8NaWzPbxZD7nG81+nDJRfTu61I0LkOFwy6yd7a4DwBwwNK16I9GeGn8Plovoih2uys1G8PgeyFefzZb8+8f5y9Tm4cOEgW0j/o9TP3KaxFZoM4K8eweXxZjgO3twEThPgL0LPBzkXrXivuaiozFFH4J/q3bXWmsL8f7oohZKE0GSNh+UGPWUL8mnnUVv2um+ip/v5VsjEbqdglr7TaAX3P/flNilW90nz/z8vRIoVAoFAqFQqFQKDRSt1f8MIDfC+BvGmN+1lr7GQAwxrwFwLeBXOv/8sZ1L402cRSJVCJplqUiW4sjdPx/SEhvekpS27UtS+0vz0cN0RX2pMqIhYzQ8bpxAdlOJ/w2yOp5ULHYCRAnjDtZZechNchq4ieALEZdNbymIYcteKPYE9vD0HtE2QPLbZV2ho4JMtSAkCnOhpgVF9xvzRwUnz/ovLsyWji1Lv/C5bws5cex5SKCjB6GDc8h9ytD7iOViBxeuT3gPbGcy1CZzJcyYC+uzLc70qHsuDsz8iI+W94FgCJ9nGfHuRVT2/Pe4ueLu6n/FXl3L/SewcQJsEwmZ2r9kv/LfBo+n+w9ZI9nno98tEx67wufZxKitnxO4t/ksgM9YnNPK2pzOh03JN1lkfM475MLlbdFtGXemzzG+rJB67J6hI+jJPXInoyu8L0jo55xRK/TGTXyqmSOVFvOw62W0H4rgW0MYyfhLUYc9ZJoCvM015UlbOJ8G2lXONKe2m6vOeSxAIQsXyDvfynbLj+Hwwe9jYkZIDLyxsjRa0To2HaUmDWk8BlTjH1bcuzZcm1x9K8UeXeFL4fjmANurJ9hC0uOhcFj3bodY83ZDDZzPP7n6Hm7FVgiL7jzVvmIoBQZ4/7zssMl5bIdzja8nWBBLS4qnqHy0buOsM1sYziid9HZgnE19FHCV3Rp7F8y2367YUbfL1ckj/CGwSfw6dkr3CFS//olnZNu9TD6I/p+vnyczpPI345zKlOy/xwZtrZZomC8RW2a4cP+nMh3krjkEf9fYNYokTTD2EeKeT+pXPNULlqbqBHvVSIlNsTryLw+fk5kDu2iiHv8LicF7mR0vE2bIcua+du3Kivktp7UGWO+GsD3iJ967vf3it++31r7swBgrf0VY8yPAvgOAB82xvyy2+YrABgA32KtXX85+n6lWKR6KcPkQPuLYlub0gDyi29Ixm0qYjLSD1ee/B5vE9eYo5fI+stwoJWFPhw8+BoA9YExpiZmyL2CZGxUrVCZ5ITlGbYayeopCiSDX/YrVL4NWUNITuKAMKhOp2cbif9+EiJemPl6DgbHGwqEc0dBycSLQG5ocOzny77vmad20LJtu47K0SHjSWeJokYjAoKBPlu9hGUcd8cT6C48iWMaj6fXoPBGemaJNsw0mYPZJg4a2mfVcUIuduApN6y8mc2oDtGgeg0uuvKSlwfUr03xIsfniSd43e6KNwChlg+hKMbodh091d/jQejHMl3L0apyQaONkaMXzlfmnACiJlVcV4ju6zptpf7s7S0BvI0KIydpcjLIRja8pAQDKo00UKdf7kbAJfx/ayex3+rg679IDTJlV+R4tliQB/67bJPaaDrx4nqTUv0yfoFN0fPly25M95IINOhwHLyvWBSFjj9M4gApwBVsATvsuhh4W8E2pCNe4G1Eh5f1zeJ6c1tYb0wQOmJSFGqhkt1aZ1EQbPnJXzzpBOr103idWPVXgh2c3Bcg2BG2meOK+vVsBeQF14K9u7bdamfuVSwPuYlf38z8pI9tAU/yjudr3q7wRG/DnZvlbIy+O8Z7HP3yYLbp7crZ4g5a39msJ2b3old8DvXL3UsXl8iGbM2eb4hadTo5JF1eLptM1htCcnxfbk5OozN4CEBd/TQWupkLFesgdubuF9OD6dTtVlBSDs5Cfq4k7T44TUJaDqOpFpt6Ntrf71J0a/nMxXROqSQbpw/UHS9N4Zc2JcxFwmj7Ebf1pA7AHQDenPj9zdE6Htba7zTGfBjAt4MmczMAvwKa/L3nOvVToVAoFAqFQqFQKJK4rSd11tp3AHjHy7Xdy40U7TL2VqSEEyRiComkuISk36anJfaYyO9pWVreT9MLmArLt3lU83zUGhEcDE56CgJ7D7uJUgPSa1p5ekidrpgh8x5O9pBZVP67FDXh7Xg/QdSkHhkEgHlJ2xXFuJHsKz3d7G2Tyfe8jL3FsoQDt5WS2o9LGXR8tKx5bobmiN8ulG+Y+WOO6+nweSgwwwUnIDupVvx+RxV5r5k6+ZATN9myAy89veQpQdTm5eoAVvOLAIDj+Uu+P+yNvVgdqv1/2OZ4Zn5/7diqvvNuzteRZfWImKQMypo5AN1nIQoVolHshffXwTA1d9JIaOfo7yYuYMmJFPgahJ1lFJ06NZFRFGMfEUvRZZoJ7c0oyaJnnCEpMTIywu2l6vrFbQWPb7sIkvxNRiNZBCO9/v6nx9xqMMbUxA2ANlGCEOmNxYYY8vqH9kNULqayUXmM+v0l6cqxvZPR9XQdvPpzHz8H8hjjqKFsazA4LlIC6s+JHFclW4M+C2SJCFeIoFF/JsJmxOVzOCony9Xw9j0MfSSQpfkZW1Wg48WRR2YeAHXK/tBROCvBYPHnIhLw4O0MslqkkX9r2iFOgwhpDXysbEvG5RCfLYnJcYchYZElM8GrXDmcaUltsPDJs8Vxb2uGjnbJQlyn8jPoOHGu0onCnCtXse4ieoNsCiDYqmfnx/Fgj/rxkiu/kJXu/u9Vnt4YqLmTRjROnmcZ0ZOYz9cx7ZMdXTbH/LmMyyDJMhTh2ofni89/XM6n11v1oi4yzQDgkgbr/jsQyuikkKJfyuh4bAvkOYhtGu0rjgSG5zM8m+EdVb4jXQ32M9VfhVIUCoVCoVAoFAqFYh/jto7U3epoz1upCxkwUl7WIIBQ5zZLb2qqCOsiT0mKdx0nqC8SSsmyvPE7e4NkdKpR2DU/LqJl5A2UuXGyXAEADLHil7EnkhPJpaSwTFCeOW9nN6vnvBlkmBa0zBcgFZ4mjsCEgriDRpRI5hHGeVdSejvOa+li4D2dnBwvyyu0FUwvMGt4AysUflvpeWVUPgrpinGzkAsKcS7DfqZ2BQAwLelafXjyMADgRPecT2jfcInpQxdR66LAGVcKQUbqjnXo3HWM87b22BsevMd5cQ+dU5dzgVFISIfL+ZvN1oSnniNO67R9vtLIHyvLMTY3n6DmRg/XjnEJKz5ayeeNz1cfI39O2Ls+xdjft1VVj46Tp3fF9XXd7xtYLILUtjxEzOGOtZlbJJ/VOAcpjthJhIKy4TndTX5uWU5uqfyG2wVt11aKDkk58nj8ls9bqlh9aKu5LGZ5cNTB2sJH0+L+ybIlvJ21RavdoeegHiWsRxTo+6FDr/P762UU4WF74sdsUzSiWEUiH0rmb4f8N47KzHzbPH5LsRX+jAWxZtjCxuy0OKb0tevl9QhciZnvg+y7FPai/oW2YrvCfTfIfP8ZFlVD6CUUOR96O3KI87KFPWIWzfP2SeqLHeL89itq/eLC5Ce653An2wkXlZs5RsiL5VEftcvdsiWzjVP5JgDgTHFXbbtX9Z/yJRAYE0v961cjHBxRVO1CRVFDY/JGPltZhig0235m38jn4PLlj9M5XabjOIJ7GwJq8j3EuOuQi/uFS2RsOjsqtQYqx57hcZv7MJutJaNrccFwyb6KmVjxO4o8NhI8GTV+AyiaGb+nyghiaC8wbMIzveLaWPfrxyySRWVR9rMN0kidQqFQKBQKhUKhUOxjaKTuFkScS1cv5lj3zjNS0S+ZrxDnOZTluOFhSfelmVsXL0vl2KSkpKUsf6xwybC28F4mnw+QhzywWJFLejqb+WBjH3GReQS0jsiDEzl1Xmo+OtaUdL5XopyvJyV7U+Ua5DoAMOi4aKQ7hjkmGLo8LRkZ4uNlbykvWzLG56qxAuXFqlM7D/IYexii5+6vLbtdOzcZch+VmjgvIvcrQ96IhHaQexU0H+Gz5N18fn7Me1e7bhkXkD2WX8Bqh0otsDLmSrbhvazsSX1Ffsb1c4BX9ek4uBRCOSO57Bw9vDh0uXpbn6K+VBNfKiB4VEOeWyxVDTSLmneH5BXdxnqjBAQf/wDLGIOOqZbP6dTh56Z+n8gSI1XVvBe4XyHCG/J+4mc0lUckPcOxfDu3J3+T928crUs9v23S1fW+N9e/VaWn9zuknYmjPqlxP5YgB5rXWHrg43GfFFjrdsSYvJGnFC+XkGNoqlByKLtRf74WrTscPujHaM+OyEY1FUogKOOWKPxvcXSKcqUq/x2gMaFNOVmWAojHmSnGuFASg0CeGz62+Hzl+chfm5DPFsolcPsyqigjeYAsk1C0lr7hbYGQc837k8ctVZVj9U953sr4PKPCpivFcwk0Hn94Su8EH5/eh3u75wEAj/aJoZE5u/FAZw19U7fcW3aAFUN9HGWbbj9kX3pm5u0Qs0FYbfN8sYLnCsrjNm7gPF894e/V1H3G91B8XeT4tz7+KADg4OhYrdwEEHLju8i8PeXzlSH355NZRHMn1N7pjNDppHNI5bMdItUTNHPd8uR3biM+Fmlz4kheXJJKQuoyxDndeT5q5NnJd9R434ueaWp3f9oYndTdBpAGN34xY8jwtHypShlKIC1BnXqRk0a4TTAhRbGUD2r8W1mOked1ioKkWnrDFA0KMkFdlhpgw9SNpJe3cKFWlw6oG45ZtVFbNp+vi0Flzf/G/ZT1VOSxZVmgIMjJavwSxINxHyN/HDyBY4nnEjOfvM6Tr/u6z2OUkbAIT2oYmal84vdBZ7Q4gXytPIK+oeTwsavbs2UHvrTAufJIra0Opnh2TpLTm5aMIxuSDnre+Er56yKS42ZjPLU9bBd0THd0tl0f6EWgX80wcZQZ7jsdL+3znvwF19cl2ndV4kSPZK/ZCLPxfWJ2L2bVSQCAHdK13kjI90uZ6ZiGKJ0MfG23p5SY3+8fF3Wa6i9yBSYN2pIU1OH2mT6c56NG8r0Ud1k8CWoO9TE9Jvw+aLx0VFUhnnn3siUETdqkp6UzKEX5ZtRl6/cv9eV2hKRAssiDfGmL62/x73Eb/HvsZOB7SNoVOeHjZyLlPIiFVaRTM36Os2zQuEeleEP8wshlcSQdnkuTZMg93T5zTsVQnzTzzz2PiVJohH+TY0M8qYsFrIAgHsKUs6oq/Lmpl1PIG7/x8TM60athhtzT9zqg7UZYxTCjCQ7Xj2P70jcGx9w4nEU2dJhNsOWcUjwOPz8/hpKFpNw4Pq4OAwAulsu+jABjbMlWbeCsn9zMfWoEGmMu/z/BGj41p/2cnr8RAPCq3osAyDF40FC7XMYACPRMLnewZcN5GjpH6JsGHwEAL6rysekrfW29z87uA0CTu+edE4/B9qLbXWncx3wvTqdnPZ2QnYsvzj6MU70vqR0jv9MUmHlKaibKHXSi9xt2fpflGAcOPAgAuHTpA7U+dLsrjXe5+bzpeJMCJjH9Uk7SYsdj6r2TQTaH2mWKtLRVqffONkGw1LiSel+N296PUPqlQqFQKBQKhUKhUOxjaKTuFkSbQAqFuuseCCmOkioc21YIVibAp+hSKW9Im0y8XD8lkx5TLDudQSMaF4pnDoJoSqdeZLVC4b1VMRUQCJ4uLxRSbTU8w1uz58W5rEccZAQiFpPodleSRZsB8pjG++n1Vn0xcO4rR+MO46Q/jtUOeRaZmtg3MxzPPw0gFPIemm0c7awDgC/afd55QU/lZ3yEiz2R7KXcrgZ4oPs0rV/S+lt24GmO7JV8fE5J6TPbw2sHtO8XXKHWswV5ddfLJYyrowCAy/ay69+k4WXk/+eY+Gszd0IpF0oSORlXQxzLKeLGkbqemfui5sc6RK9ZddHJJ4r7fF952ev7nwBAtJnDBXnSP8l0mVGGzclpOt4t+gw0zGaUia4n34e03vZ22K7shCgcEChKhaBhWUFz8vdmXDB+vu6fBaZ5MvJ85L2+9WeuTqNMCUzwOotETSiC1jx2uVzuJyU5Lz228TjEz8Z8Xng2QVzaQPZfceMh75dmOY1wr0jxKkYcVU7RseJ15XpthYQl5H3WFFxYadioVLFj2QfeN0cNUlR5GQ3pRRE6fq4lrZ+jS1KIKi53MBcR/VBuh7bfwhYub3+80VfGYHC8tizLRjX2CBCuT8+M/NjEtob/v6970UfhRtklAMAd+Wc9NZ7HYWZTAEHEisfhqaPKH8w2fUSMRa1mg56PgDHDgsfzEh3/2/PFnQCAYUbsjXPFUTw7PwgAeNbZoUt4Eb2IkRMiogVKN9byMX5kRtt/ZvbV+P+MfhsA8EjvM74vbB977viZ0SIjdivZRq3Pj/Q+g8/OyV5VXfptuTqMzoCia8/N3gcAMGad1qkm/jrExceJCUG/BRGtCdZwGgCJpkgYUW4pExFdtjFFFAmedYI9GQyItcLpInX2UXjfa9KSmzTr8L98/6zTPFMRNH8c4rmSpUi4zRR1u81OS+G5lGDKrWRXNFKnUCgUCoVCoVAoFPsYGqm7xWCMaXgzU4IkMWQeTUpEIS5GKoUjGLHMrdynbD8VsYuLm7N3R3pfpEeV12PPlf/srPh9s/dTRn9CPpP03NU9Uf0stCGjJPL/7e3TjVxAKRgRnwtr2wuGSy/dICehkAGWsQz6zgWqObdslF3CK3rkSWPZ/5CwXfqoHHsNT+VnPPf/aIe8pux9rA5MkG26yNAhlwh+6YDf3ozI03l4mXLSDuUVssK1u0bH+ujsU3RO7JKPiLFXliOCT87vw3POy/qJKfH3t+xhXK7IU8vR1JAjMvNe1rige1kcC+ecSw50cxx2HuQPzx7xx83HcWdGkcypuxfOlRQ1PJ6vYdnlg7xQfJ7b8xbswHk1vQBKeHZSz1F8n7BHcTZbQ29ppbZuJZL9+djYazwXHk++byvOqeyugEsZ8P0S+jJuyGRzbg3QLJgsf0sdSyyUInMJZRSO21kUMUnJycfLQtRw0CjTsChCuJ+LxO5XsIBAW+4ysHdhrJSdiNdJQa7btC9FI0oshSpSZTu437E9yrKBj2KEPJ9RWBaJVpR2JvrN+XK0bICRf87jEjsy0sd2qYehHws453hzdqZxHCnBidhOAsH+MNjOHMNDXkTkgIu8Het8zK93uEPjK4uJHOus4YHcMTncOM92Zsls44WSxnsWGHlVh1gud+QXMKn6rl+0H5uFZ7gzo/7zOvnoMvItOv6NkqJqHCVbr5bxvNsPi2Y9X96Jj05eCQD4+GzFnUuX64dRI2LF12CMNfzS+K0AgI/mDwEAvuHgL/koXIy7zIv+GDkP/WA+dn3p+YhePyMGyLCYoOtZGq8DADw7q+fYAUDubpvUfS+jU9sl2eRpp16miO6tev7cARzBBmj9uNUKBay7P+Ii5DKXmpdV1Xoj728R6hHHwNZog3yHCu+ITRuSKskRjx3SZi16D07l44bv+0swRSN1CoVCoVAoFAqFQrGPoZG6WxBt+Q3Sm56Sx99NRG9RIcn0+kFtLF5fenpDdBGNZTGk1zGs57w7KETR0yCdDzTlowFgXo2Fx9bxt50XtSjGvq/spapHFsJ6vH3siUopq2W1yAvQy5a9iuUdoCjWclb6CNLdXeL3r3TIY9g3M+8hvdPliLE39KAZew8ho4MSee4K2roozuwOaiubdYAlUrhkbyijOrLho3JVXvl15svkldw+RrlxxZD6UA7mXsT62NlDAIBT5+i43tD7mPfmrh/gXLz78diMjpejd+slRQknGHvlzCB7TX3fxjo+W9A5PF9SPkGJDKXLXRi6UgucI/hA92nvQS5duYbX9R4DQDkTT86pje0lavOJ2b142uXxVSNXJsFdu62t0zXFSQCoqrHPXeHfZH7brLfu2qirtB3AEf+dj7UvvPh8H1eZiyRnIUId58/JnAGGjHqklTHr44RUKYzzXmW+UeylLYpxI+ohI3wpRbK4ryHnqd2LKnEr5UDsN6SU4lLKdUB9/JZe8LY8mp3YJHHkLbW+tH9BRbm+v0VsFAmOGvT7x/0zHuc/A/UIHS/zCrKmPq5OMK7lOgF19cugehm223IR+vHkqVpfy3Li7WGwoS4He3BSrEfP9nLnXl/A+86Mol4nu2TbjnU+gtWcmBz3uKhaCRovh2bimQ8cJTvWOY8XHGviRIeiQMzUGJoJvvDQu6mvE2fv3DhWDGcwLkI37YXrx/anHFAk0A5o3C+qDDNnc6plp1TpWCJHigyvHJN9nBaUd9fPt7G2ROfkk/PPAQD8+vYXAOCxvZ47H0oBDDAG2bIXCmJy/LP1P4IvP/BeAMC3jP4jAOCDs0cBAAc7Y59nV1o6T8fy8/54mBXDtud0dhJdV6amctdgNqLI4Ivj9zTedeR7W7jv6Tp2OgNsb9P1eGlAbd6dvwkAsIzjuOyichyNnGHLvw95uyJyC+dRySaGVOWsKxTDfa8/O1K3IcUEiUvfpJ5ftkPyfDQj+s3ntyzHYj1+lnd+p5V533xci9ghNzt0UneLQFJi4pcoSQGME1ybNK66sYqpUBLNZPfmOnJy00ajodB4M2md+zkaPVxrn4x7nQLjhVbMwBvKUFcnTO4yn3weKBUxZU6+MLDgBUMOLvN5nYJqbVGj5AB1OXqWJWYqzUFHezmIYzjRodD+Pd3PAgCWszGGTrDkwW69Dz0z90aXJ3WFM745Sm9gLlumUXZwFGRgLrvSBEdfCu0xTdO4iV95IAxoxZB+K0Y08Zsvb2P0DNE9pkfq1Jms6HjDvHWcqDr2xDq1OZjj0GkylCsF9e+hjcfxtRXdt7+z9Qb6nL4OAPD49BTOOrGVdfcSwS9OW6L224ZjRGyUIzzrDOXAy2rTy0E57+CuDr0MvNKd3yVHl3m2PIEHus/Uzu9WNcBaSaUZ+AUoW3LnyOQ+iTzLwn3C91As3z6frzeS0N08GdvY8AZWUoJYOnzb3aNMv71cPe/vJza04Z7NYW37M5oqJ7Ib0SQ5SWsTYJKS7oGmHZ6l+LnKstDXRdS6eD9ttL39Ro/Zr4jrn4bfc3H9m7YgJaQVIzWxSov1NEUb4jZS/zfTCMKy4JRr1sgbDk+59UfieakfozE5iqikAU/uABLckjAmx7yKbbRznqDn5eh5vNuYPO7PWfzCK48/tuVlOUY/p8nNKogCeF/noHcWHu7QuHcipwnAyfwsjmZMz6f+82Rtip5ftuTG4xxlEAhx9ofXmaLnJ3OdHtmOak70yLJXeGehf+nOK8yW6ZzMVpxolrMls+UJehuD2m8bD1K/BmsjzFbchM9NqMpJF4fGtM8vnNB6r+mRgNfHZ6/E7zp6/ge2XwMAeK6c+vPNE2qmuS7ZFfzsmGzTUzMav7/78E/4c842hu0J0zDXq2XcKUolAMFGAyFt4K6C+jIdjbE5pevB17Rwt1ueo1HKBmg+A1zzVYptsV3JxKt+XOuvwMSLsqFfd1yU5djvk38jezZ2y4to/SYtMiW8l0KcZrPonVG2w++o8nmP02DkGMWQY1VKSKxJA98fdkbplwqFQqFQKBQKhUKxj6GRulsEKRrlosgZQ4bN0wWK6+vvhvaUomZKGdzYY9vpDPwy9goxVU2WM5C0EqavhCKb4X+mVfCnLNDKVD4pbMH7jmkGcv8ciZnNztb6CaTpoEEMxUUz8iO1kgQAcDwjusg93bM40XXFsZ2ns0THez0ZLMefCzEU9v4x1fDFahVnirsABA/h0ewiPjglygiXKGChkJnt4ViHjvvc9AQd84zES9arZd+Hw2NHPy0yT4+Bi7KxF3U+mmKySl6zKqc2OhPyzla9ElP2qDrKzfhU6T2vr3/+PQCAz3dSz79z8S34gKO5fGxCSesvFI6ygi1sOU8qFy9/rjiC8+UKACq2DgCfnJF3+lW9p/x54nPCBWVf3X/cRwS53MNlO/KFcD/paKFe0KA/8/dJilYYR447nYGIBAcqFxBoMEC9MPnEeUFjunA/W/ECCXz/cRSw11vFZHLW75P70ObpTNEp5WdMP6lTVJpCFjFdU54Pji7E23AfAaAomhGaUNqguUzply8/4vtERnYZ4XsYz/n6L7p2qbIF8Xp14YQQeWsTBquqiWdHxG2nRBukjQrskMC4SNGbGcOMbMBW1RS+aEYIQr94mSw0vl48ASDYnHo/6sfY6TTHnGFO4/gqTuGIIcr7g71nXD8v4pU9onDGY+HJ/AUfaeJlLKi1gg0s9YluX05pLD1dnPRUTF6PUdoMY8cK6RVkm5iaefDFTV/KgO3LwWwTJRf5rmiMY8o/EBgjpaMasl2Zj6b+O69jRlOM7yX7MHA0za4TjXrr2Zfwuk2i3n/t8FcBAP9t8ysAAL+y+YbGu8MUYx8x/diMzuX3X/h2AMCfP/Tv8PBBauvs5knXUy53sIQXZySiwhG719nHsO2Krt+R07n4PHcLzSavwdOOkjpLiqfUo1gUERvU1r80cO9HZrm5PXoN+iXblx6G3p4aw+kW8p4ai+91yIgeQM9SG+VfRr/kcxhH+eNIuESKPi33wzaWRcIWlT5JtX8r2BeN1CkUCoVCoVAoFArFPoZG6m4xSO98nD8nvSgxUhxi2UYqcTT2fgQZ6EnNE8O/xZ4bGZWLcwsY3e6Kb3c4CEU2TZRoXvpo3EQUEaff2DM1xdjnN7AXtNtd8dGO2GNkbdHwmi0tnfTb9yP+eUomm0sUHMUpn5/1QO85AMBhl+d1T/cF7xllT+nR7KLn68d4VfczPlmbPaScF3fZjryHlHn+H5w96ksMPF9Sf15ykbpRtoUOyGvKfeB1jnbWkbtlxy64HC6UWBnTegcvsDgLfY6WNnw+BEfsslkYYlhghVEcKVGtl/XfXBTwzZ/6be9RfXJwHwDgPdPXA6BciDMuQZ8FRrax7j3On53TNWJhmS4KX5ydj/ERJz4DAKvLdD2OudIOJTo+8lm43IfJjDzxY7Pmc+NkjkGbsE9RhFIDcV7ELAs5HJzn0BFDci4KEgN0zw96dNyzgjyrfA/OZmsimT7sv1k+JIwJcYmCVFRdIs6XkmNC/NwyrC388coyKHFiesqbG5dOkOspbjzqudfpqK8Um0p553cjgpMqeryo4LAU2+Jt43yzohg3nlXZVpxHVBRj/+xxvpy8/ydm52i0zNVecsItMK6vzp5dmj3lc3YZvd5qI0LJ9ivPR77dO3uvAxCKUT/cewHH82cBhHIEK9mGF9LicbvnomWPdD/jywMw2JYMO9tYn5LNYEZEgY5fnyNQnIv32fk9eEWX9v10QfnJlbNRmalwzJXg4UiftHdHP0v9YWGWo89cRGeZ+s953J2JyyM7dtnn53VmFEGcrI49A2S+HEVXZzkyZ5tOTF4AAHz75HEAwLdeOoIf2fgWAMCHtinXbWzDmMzj8PPO9vzo+p/En8U7AQRhmXNlYO1wbvvWnO6plWwDD3UpSsqRysPOHl0sl1HMXwsAeLL6OVqnUx+7gbrwWhBSqT97a4MejoMizWwfC8x8Xh3no7Mgl0XlGU9epKtD0T6yK/V3OSl0xFoIcHnfZTleGGmPhVKkuAkjlS/KCBHqUSOXTjJGYoZVKle3GSFs7id1HDc7dFK3z5Fl9LIub9SYFiNpKW3CCXIiJtFWM2gRhSZNBR00XnzlwxjX/pEvCQcGp6h9N0mTicB+P84ozoSKExvKmaWHX75884BQVZMG7TKoGk4ax8TJy/IFQ74IMK3mgKtHcxTU91fkU4wyMnJMtWTD1jMzX0eNE877Zu4N3iNdouN8ev4KAMDPbP8eb0R5Hd6ugxKXKlI1O+Poilt2gOfnZIhYfOVsQcZnlG35xPk5q0u6tpeyCbrOkC13nHGwOfrOcGdGFD0D8IrJGZy8TIZy9TkyzHfc/Uk6ngsHPG1z7kRXTDXFfJV+m+T0gmGHlIS8/vBZdMfUxqvO0nl6zWVKdj+VfzF+Y5uUvh6f0vmdWOOptWNLbc0KOg9yUscUIH4ZWck2kOU02TR9Oq6HzZN++UO903QOKzKSRXE/1pwonawDJ+vSAXXaFtPPYhoahqeQZ9QYU3MtKnTcfcsvEYwuBp5Cw/dZx9FwJT0sPFeT1ueXDGCa5lJVkyTVLtAg6w6YRbXldq471v5yHysdSqQdUPsjkX0/QtY/Db81HXYpteLd2Iw2p0BqXYmU4rDsV6irWndqpFRZB4OT3knCzwarZsqXUa9mmYX/2YEojzV+6ZR2z79Qu2Majz8FgF5CuY/8jEtbEwu9LA8ewknQZGCU0f1/d07OwuP5GvqZEwlzY/XBbNOLn7DtYEfXY/PP8c6u90zfUDvPp4uTeGxOVPSNUqRERDaAJ3wdVPjYlGrFTZ2zjSntAzPz9uiQsysbZuRt0xnn/Px0QfT5R7ufQrnmxLUukdAVpxgce34NQ9f/fOjGoKKD7ZPOobdOte7mx2kSZbN1dMf1e+3yKRqzl85t4X/b+gcAgJ97+hsBAD8z/jI8U9C552u25Y7nheIofmz9/wsA+GuH/xkA4J6c7N96tRwcrs42VwcmODWuT9h54neiew7nSrIxl5bIeXlhSk7NLBt429HthnMfT5B4nXm5gVmH3oOMsCU8mWM7ye9QE2z5iWs/qrfY7x9v1OidzdYa70qMlENQChHFz7C0D22KyHLfDPlspyZsqfSBmCqaQppKXsfNXhtV6ZcKhUKhUCgUCoVCsY+hkbp9jtibIL03sRclWzCFl54M6e2If5P7lSUG4mWLkmRDCL3pneVIhxeT6K5gVq7Td0cJyJB7TxLT16ZCXIJ/Y8+apFBydIW9R1U1adDPQhSv8CIocd97vVVPxWSPURcDT305kVFSNdcAOpht4Q4XmWPvJntIR9kmpo5qx7Q/Lj0AAL+4/cUA4Ou8vVis+mgZyyxzJK2DChdLOk+V89lkqPBSeUSeZowyR0Mtczw9P1H7jamcU9vzdXUYS9kEHeeVZnrjZkXn++nZCTzUPw0giLqsPE3UyZnt4k39j9C5YKrS8hibY6LVbJ9y/V8nz2UxnHm6JlNoBuco8vals5/BiWfJ2/wL2ZcAAD40eQRF5SSwPf2W+n6xWsZFF6EbVBQl5PpK93Sex5HJZm0/RZHh7XiPX87nAgCeLl7t7y++R8ty4r2ZMfVkPl9vRCOkkAmX25BiPtzvUG8xXAOOALPktozK8Xe+L6lsQT0isFuho/iZlnTN4DUOY0LsNZUCR/E6i2je8XfaLtBE4+dwv1Fj9ivqpQOa3vCU2E7bMhnFiu8TaSfi6G+WDZJ2KPWd0RQ3WQFA43csztXvH29EP7qZi1yU6z7q4QWMDPz/haNDS1p/iioK1MvurF/+eG2dbnelwbApy3FDZOnOjOiBd5q7vGjW0Zw+mdK3ml/0YznX5TxT3OXZCmxj+PN8ddiPi0zBZxGprimQsTy+s1+b1RCXCuoXj49sQ9arZd8vjsCxXXquXPE2hNvawgAbzg4vufVPZUSLf//0c/16j88pescpAw90B/779pSExx64/DROPEHj9sVHKHKWXXDXoOpg+xidH7NK56Z/gcbz8b0XPK3z92b/AQDwthfeix+48BfonLhzc8kJhFWo8FxB547X+QerPwiABchczbouReyKvERnQMf2QPUkAOCgsz09M/PnZGuLUis2+3RPzWZrNWE2gJ+N+njK4+R8vo6LHYoIcikLQETfolJPyzjm6yAymPo/Ec+PZFPxe1Rcj7eqmiUG4nUkZMrKbphl8bbxsjYxp6pq0lTl/mKqf5blSYGv/QCN1CkUCoVCoVAoFArFPoZG6vYxZJ6DzD9JSbUC5OmOo2PSe5LyZMQekpRgSsxjTom15PmoUTCcvSOyT6EUgMy3c54lJ3JSmlDYtXT5DZlIZt4sKTrGnpZUAXUpF82/y9+oD7n3kMURj05n4PvFkZsTeAQncvLKvbJHORLsucxNgYdc4WuO0HESeoGOFyRhLyoQvKWcI8e5DFt2gKeKk+54yS9zcR62G1fWtUvnaYBR+G4oB/N82ffr566Ns6V1/1O0zGKOqctHHGHV9cV4j9+guKd2vgbG+v6cyeg6srf2UOcy/sfWlwMg6WwAuPP8Gh7ZovM0uEDnYv0hygfpjvuYrbpI3YorfD5ySdwbS3h48iEAwF0XaP1R9tX47e3XAQCeK+se9XPVRbzXeT8/b0B5eXmPzvcHZ4/iq/LfoHPpvLTloIDNQg4KAB8ZfePgOfzOhLyfnLdQ9IIYRJw3Jz1+8bM3n6+j3zniznUoPh4nsvO162Pkpaf5fl/qn/Dtyeig7AP1o55LKseIOLdORtrlcxzyktpzb1PRiVT0LkTrmyISbaC8vp0jjppbd+0gC47vRR580fVZVGqjnp/XjPYu8tyHPjcje6lyB3H5GbpP09HkXmfFP6Myqg4Am5PT/nuqBI9vw7icpnINl12ELhahkKVGOKcuy4INvNtQ/txdGY2X9/ee8jbmaIdykO90EaJRtulztWUJAc6lu2ypjTPzBwCEaBsQBKI49zo3RRCNElG5sWNpDA31mXPqtqpw7Gvut0Mud3toJr4PzPLIUHlWCB/PE3NiefTNzPeN2SFc+ud3pq/zY/QJJzz22PxBfNgVGF95P63/lgHZi/nhMS6+hqJ4dkT2YT4j+5LNOihGZL8vn6Jz2FvZxg8+830AgP/nxW8FAHx4QrlvG+UI2y4SylHI7z3/nQCAP7vyTiyVrjyEdeUhio4v9cNCYsfPvuTO22F8xB3/q/qUe39pSnZmOz/dyBvd3l5fyFbYmlKkbubu7SUse3vIUThGgZlfxnaFbfxSfhzboPcpth3d7oq3MZLVBKRZHhKLcq5jYT9eLo8xJbaUEo9p5upOYG36fKXGs6pqRhDrx3Hz2hiN1CkUCoVCoVAoFArFPoZG6m4RSG9Dm6qd9GQskouVSKkVARSRiHMrUpLo8nORdzX2WA4Gx/3/zN9OtTUp13x/AIpcxBE66fnh36TiUuyR4eic5LGzp5SjjL3eqs9v4lIFD3Yv+9w2VpRkr+Mo2/QROs5zkODi2J8UXlP2gp4vqB/PFZRTt9rZxEsl5Q+watVmFMGR2MK6/33deQ0ZBlkjV4TzEzPkfrsLoOK1S1gRZSQogrQNLmI7xMdnFFXsgdQjl5xH61h+ASdyyoNj728/m/nI5EMzkno+6qJll0+dh5m4shXH3DEVtN/5cI71HnkPWSHzz31qDYcyKo77nu3Pc/s5RP2zQxiXAPOsUwTl67KSbfi8kZUNOo4sW/ee1N4LdI1f33eedfMIzhX026Sg/MlpfgFVtVI7r6FMRlB9jctjdLsrPn+Oo3I5el61laOeHAkG6vl1QLhmeT5qqJTJ31LlDhhxFCPlPU1FVxbl6UlvbaxuJp/pmGkAhIhgihXgj1tz6l5WyGvPYyCPs3QN8uT6Muor0ebNlx74toL29FvIlQle+VHrvjudFQDBFsic6FTUwNs5G+7ZWEI+dbzBFgYbxbngbKvG408l8nrCMcY5SMP8XtyN1wAAjmQUGXhV342XnXWczM/W+sJMCGZ/AEFl8cVqFc8XNDafdrnUrHY8yrZ8BI1tD4+T29XA59QNBFOG88A4yjZ30RDOowOAVcfWYLs3t7mP+m253/pm5m0nR/nYTix3xn4/udvPesn59VVQgq7IJt7ZeclHDtm+vGvyBQCAB196Gic+5Gzhw3Te5ssUqcMwB0aO0VDQed4ehTI9f+5TPwYAeO+LbwMA/PvLv88fL/eVP//5pT+MP3rwfwAA3tD7GJ0rW6HqOfViV3KByzycys/gSwe/AwD4rckbAQAPlw9S/5beirOT96EN8ThclhMffeb3AiDY69LlarNtp5IG1B+2J7zuHOH9SJYJiG1B6nmMIZ+hlF2JtRboPba+fkrdMi7bI3+TDJpmG4VYP2akhWX7zdbopG4fI528XjRu0JTwSaqNFOUqHjBkculuwuzSQMUPU1wTSH7ndbrdUUOCVhrrmDK5uf1UUmYXIJEUXpaS2Y2TkTudke8jr8PlFXoY4hho0H2gS2IfD/VPe0ljBhvWo9lFnHO14ToR1bJv5vjIlCgdTDO5WC57A3vOSfP33ARpoxrBwiVfu8FXlnTgiZ6sfcYTsU5i8I4HdCNq1vCEgg1AhsxTM7hN3r5AOO/brl8TpqfMOxhXdO74ReGOzgW8q3ozAHrZAIATa0ShecPGxzA9RZPA4oKbPL7W1e1b7qHMHRV3SNSbC/nz+INn/h0A4K7naLt3bVHbZ4tVLxTDLytsfOfI8ZoeUTKPZy+5c0gUTwDoHKbr+RDNHTE0E08x2qgeok8cxwz0wiZLGQBkYNsoY0UxxvaUqECdfqDEhHPtniExTMciQPMqOC5SwkPxC3ia4lI3WqnyJin6nRwneNki6me9Xyu1faZoniljut8M7H5HSoQgJYLVNuFPyfHLdeNaifHyuK0Y8p5PvRTG9wkvGwyON4RSUsfqy+IUF/y9zS+30s7G++l2V5Bn9Kxy2sDW1mm/fXxM8tlica4jPbIJx/Ew7s5pAOLSLJ/jSq3c03nBj51MteRap0/O7/O0S7Y1a+URL67F4iP8yRRCIEzcwoRp4G0TT+AOdzb8d57wsSNqORv7sbbytoY+uyj85IwxzCZ+X+fdJ29X2cwLiW24mm++rA4q3y+eFMrjiCmd58sVPHKRaKev+iDtZ+OVNMbPl7dRrjvhquOUpoDlHsoRTfAuT4iS+fnDXwIArDy9gX+0/s219hkXy2V8cEoT8S889G76cdoDWGTF0f+LQ2S/8myMR5yuCovBbPWofy8Ur8b6gOwE1zCU9y+P7XzfzGZrgh5Jz9xSb9nbfoYsEcX2hKn/XHfOYAOVo8jLcj18v8b14Oh9qjnxAtoDCTE9XwrXcR1TLuWQGkOC/cobz5NMGYi3lXWVY+yW6n8zQumXCoVCoVAoFAqFQrGPoZG6fQhO0mwTLGDvhqRhhW3TXtB6+036SpxMmhJjkVSSZng+FChmj9Kifkiv0059BOqRh5iuyaiqohHZS7UpKZbsxeH+LDuq5QpO4BVdovvd48oWVDYLcv0OnMR9vjzsPaosk88FVMfV0MslM7VvbKeiFerDzDrPKjIfhWOqpIzE5Z5eEc4bLx+CKIlcjLawIxzI5L6AueUC6F0cdsn3jI1qhPMlHccBRwXadN5HGVHykSRBK1wrc3e8gaqy5Ggy7KV9unM3AJLZXnmc6JCfe4xoKcWAPIXz15TAyHlSj1FEbbYyxXxE1+GLZ78GADi4RufmnZe/1nt9mdojSzxwUv2pnLyg3XEfxbBOk2HqLHLgrpwieuMeeaKfnaxie0De7+3t0wAkJaSoRcCAumeVf2O6i0xo5/PJ/88x8VFRXn+Q0XFMu8HjydSb6fRsI9LM6HSK1oh2m5R0SlCFj4u9s/GYJKN+MsITIobj2nYpCk1d6Gj3XtObvUjszQwpkALUx/sUXSpFI2SkaJRxG9YGCv8i2qUs4M3bx3ZOFho3pk7rT0XlGL1s2dOhS+voZzVJ93qEjm3JfL7eaDfPhpi5MgccoUtHrOuR9E5n4CN0d4KYAIeyKV47eBwAcDgjVshB4+xKdTjQ+h2F//mS6JVbdoDPzkjMiu3L3OaeMsiRNBmB44iTl9cXUTNZNicGt8/lCF4qj/hoXLz+0XwdXXeeuTh6ZUP7nG7A/bojv+Dtw6VqVGtrsxrigOuzT1eoVhqRszVDbZ4vV3xpII5eHvwYnctHDn8kUDKH7v7IDLDm7O8pR+UbUkjtzlM/i7/4G3TOf3bzywAEBsi4GuKjU7p+/+3i1wAAfv/Rn/H9yWYd90n7mRy7jMFsHQDwhpLomhcrstWnui/hwpyo/udc2Yr5PIyDfA9xJE2K0vGzMcUYR3HKfwfqTJABQnQTCEIpOXqeHTIRIkBsY3ifqXSWGCkBLqCdii0Rjwl1G9KM3sXvq1SGIfRDYqe0o/0mmKKRuiuAMeZdxhi74O+rbnQfFQqFQqFQKBQKxe0BjdRdHf4zgHHi9+eu505TSabSWxEXo1yUtJ7yUkiZ2uAZbfYh5ihLOWdZjJLWKRoCJEEkYeB/i71OVTUJkbOcohIVKhH1WK/tLz5eoJ7nE+eD5Plqo9RC6F+OPCfP20pGnjL2dt2dX/TCH/x5rLOGvuFcN/LEzZy38cVqFS84QY4LJXngONn7fLniZZ+3ncdnjkmN8w6EHDkpSdyNkp7ZwyZxJAuFzF/rBD98noPNfJ4GF+hmL+owm2CYbbvjoMhYZTPvQeVP9sieLVb9b2fm5MlbApVO2LLb3gu+xfcnehhbyi2Yz07V+rxlB7546ysvUimI0TPOe1ysY3pqXj/I5R5sTsd0aULX49Wj/wkA+EOnO/i5zbfVVmfP8sVq2ZeWeHz+CgDAKXsGGNB5NJW78XMX6dza9pHWNVdg/gg+D5uGvPJlrz4czOfrjYLG/HzNZuGek9E4zmtgj6ovdo6Rj9Dxde5wHqQJ5Tfks9OGeikTHi+aY0EqahLnxFpbNCJtO4kiheeP+izHnFQyfNxnxY2DzEdmxGPuIk+8zMFMyZLHbabaSgm3MMJ90ow48zMii497W4UC89LJzzthlcvT0wDItvmoelk/D4PBSX88fD9PZucwHn+q1j+2VXm+4pkfcT73nf3X4zAo6v9Qj/KLl7MxTuT0vY8gUgJQ0WrO8eWI3ZMFlQJ4fHoK25HwyVY18FEszjfjdbqm8HlvU7c+ryOjZby/rik804IjdCxQMups+W25zXucoMso28KSy/vj6GIHpd/2lV0SgdmyS67NbX+858rA8gCAbn4OT8zurR3jMJv473HOX6eqfGmgj9hXAQDe2P8IAOCxi5+LV32AtrvQo8ho9doRcJz6ARe9qxz7xpwrcN8bfwEA8BXvI/v4sdkrXf/u8H34ufGXAgC+uP8+LN3/BPXDCYJ1JrSdKTLPCuHz8EjvMwCohMTpOYl/XcrJ9hiTN4SxUvnSvGyGrUb+/cQJnPUw9OweZocMogLlQLqIeCxqJZkpKYYGQ9qLNlvR6Qwaz6hke6XYY3GZLD5H0pYsYqul3iPbipbfrNBJ3dXhu6y1p290JxQKhUKhUCgUCsXtC53U7XPEXgMZvYu959LbuEiRLGy/WJ429Z3/j5X4ZJ4de1Oqir0ug1bPilTDKzszv06cnySjcnGfeR2Z3yNzIOI8DfaidjvLvuj2ivOe3pVTTsMo2/LeyaOuEOrQTLy3lJUr37X9BQAomvWSy5djTyfnNlyqDCagdkM+Vc+rSTLfPQd59abY9McWFw1dzfq+nALnzZ3IH/d9PdahiJLPEUPIJeM+szrnnZ3z/jc+RgA+J4Hb4Gjks8VdvmD6ep/6fNFFJTeqEZ510bsNlxcxtaWPRq5Vl9wx0v7eu/UoHuhRwPvHyz8GAHjtlDzfby9+AVlB63PpAXtyACzT+ZmfIM/udkE5j49mv47zn6E+f3hG3ln2VpfIfDHZ1/UeAwD0elsonSd1coTONefYrTze9edr23mSzxar2HTe4ksVeZljtUkJ+Vw2ckkx9N5RVivjc1Kh8NFaEzHnKc+SwPe2lJ6Ooyv0PDbzUYH0My8j5qn14hIF8RgkfyvLsX/GUuepTe2QntW6N/pm95ruV8TnXqqrptSV42u2qEyA3J6vv0Qzsj1p/F63aRwJqN/HkmnCOUDyfuy6EgjTgiLbpZ35vnLxZpnXGXvq+TmjSDW1xTm10+nZxnkK6n6TxjnhPLplHPe52jxGPdh7xkfoWAWSc7dPFyd9+x+bUpSIGRfjaugl87lg+NzmfkxnRgZH2/pmhlf06LhZQZKLgz8/P+bz3zj6dbSz7m0N/3Z/91m37KK3GZxXznmAJ/rP+z7PR5TPnW/1YFxuNmNrHq53z6ldsu3hvMHz5Qoe6hKT47PFPbW+AyEH77NO8fNoZ91HDvk8zZ3t7JsZjnXoXrjzfacAAOdwGtWbyIZxSR0cIfaJPdLH7Cm6Vg99/v8AAMze33NtzfGhCeVqs+39ofU/g79z7gdoPVcmoeq5MXgwx/hess2rM7JtxbMUxTxdnPTqpy+5COUFrNfyMOVnUYwb70XjrcdxZEg2iu3K0JVkkuwetsf8PpEjvH/MO3Qc83lQEQ/RO763m+9fvm1b+Gcgpb8gldUBjug3c62B+ngkbVycQy7f95jBEp83+WwzZMRxEevkZsT+6q2igRTtJa4flyppENOq6nXnAr2qjcIlH1DGTnQcXj/ISp/0/8cCJpKayeuH2l9Fg3ogX/J4GRtyY5rHIOmq/IIp9wkQ3e0u0CTgqEtQPupq7jzYexpvcFRGFjyZ2q5PvubJDRuQ5+YnPGXkcuUoh3DURjGoMvWuQoElJyvMA+1lkEBHD8Na7TIAfgJ0uLOBB7tP15bdlZ/DqjOwDzlqC9ct6tzxkjcwS+eodALTQLaOb2B4jIxWVjh56bzCHc748m8H3PpHio/jC8+Q0Z1XdP1ZhORcuYqTXTpPbGDPlUcwrZieGkRgAKp99+kZ0VW3Ionr3otzfMnsN+g4nFGcTUqgR8eElb47r/TCMDvyEr5y4+doGbGY8DHQC9ClauTlux+bPwgAOLp9EXcM6UWh4xLap0eI7jm+9wJesebq/aw7YYL+MXxyRuUTDvTo2PhZ2N4+00ojlC+YXJpgO+t5R0KoV+deIjHw9wdP+PneyZA3JlQSi5LBY/ln6ZBZRLvkNiXFtEFpqyYNgYlFk4BOZ9B40Q8CLXW567gtxfVDpzNoFctK0ZfkfRC/yKVeOyRFi6XE45dVCVl+JnZcSKpWr/dgrQ3vsMtGfjLHSNUslXYmnpSm7sHNTaLX5fmolZIqyzzwZO4OkIz9XfnUy/Xf16UxfWq7uGyJIs6UxPOWjr9EB0+7enM8lvJEroPK26OJpfGrwiZGCLVDgVAeYJhNfBtMUeQx93Bnw5c+eKUrpwAAD3bpO0/Y7nKpCKfyM6gOOJuc0XY8aTl7agpM3L2w7FIJNmbweionnG076yjwk8xP+EbP03E/eoEmj1VeYWONxtw39Elg5NniLn/cz5Z0brhW3lq5EmikbnLL/5/Iz+HJOVFX2T4uvfsIpkd+t94vFulanwEP0YRv6krrvH7j5wEAnU9/Jc51j9TOZdcU+O/nvxYA8DW9/wIgTGrlhLY7Jvt1f5dqw65Xy/j0lFIDVgu6T9azMzWnAhDufzmBkYJdl0DvTyzAI2vaMq0/i9I5MuRNJ2M+8jUe+X7n8ZtqozbHaP6M30kXBQdSbTDqNVjDOCFFkmS/ZBthMhfegRfVntxv9kUndVeHP2WMOQoajh4H8F+ttc/c4D4pFAqFQqFQKBSK2wg6qbs6/M3o/x8yxny/tfb7r8fOsswl1SaET2JxFFq/7uGUtI9mxK55K0jvRSxnLmk1KZpUvO9ud6UhRCI9/Qwpxw4Qrauq6m1Jzyh7p7iNPB+Jws91z22WDRqy1/3+cb982CfvE5ctOIhjOORklpm2eEce6IvsDeTo12Pzz8Fn5hRdYu8pUy1fKA75/nPhcE5UZnodEBKVM1EwvPBeNFp2NDMYZSQiwjLQr+xRZOme/AW8tf8ht4yibJMjmygHtM8Zf65QlHB+rABmFDncPEXn0HsitwpfMgBbRVg2cUS/LVcKIXfJ1LMKExfZ42jfq9epnyeefSWOlhQtfKRL3r2Pzx7CswWd646jZnI5hc0q0BCfKchLvW3J675VDbxwy6MfpMLh2er/BHqOkugidb6fgxyXHqQo51fNqHDs5QvU5pniuPfUvuDEU57s3IfjrsgrnzeOYs6WJ56Kec9lKlp7Kj+DB11EYH3mIrTunEzMWRFBiykk4waNUBaDD4nt4T6Yu3uGI3ZMx5yj6WmU3kxJNQHSUYlUpF3S3FJjBEDPc5u4SX3fMqk+TauR+w7lDuoMgtS68jjqy28+yembHVnWTZbDYaQEE3ZDUZKUXI4khH02o3HxdtKuSMpwmwe+3z/ubQGzQhjzatyIPKdo/fz8dLsrIgJYt3u93qoXRUlF4+KyBf3+cRzq03hxDPR5PKfx+J78LIaOrsciIic657ytKV0UjgW5Hp/d7SNBDKZXnisOonK2ZmichL4xmNo6dVuWEmAqIveBo3hHO+s43CFaINPU7+ycx8kl8mFnhRMGW6bj2Dyy6SNzflzmqNypFWDZRb1W30Cf8zHgJP1x/jR9Hqd17PoU1m17+Ry1fzl3tuf5TXTXyMYMN+i43/DMWaxPjwIAHqiofx93AiafLU56xsxWQeuzoMlGNcLvzl4NAHi2JFbJ2wbvxZ2/RLTFF7/K+etPOuGx1UGIOJ6ia3C5omN4XfErePEztJ/nDVFFJ7aHT84eAAB8/nm67qtDOpfloPCsE7Yv1ScoiruaXcR93ed9HwFgbX4S6+50hohVuHdTIiWTkp4526F9SyYIR+H4s+fug22ENA1fYsfktWcZCNTishwno/Xyk9ajvna7eWPskMyMOMovP5vvt4WI7odnOl7/SgW4Fq13M9kZndRdGX4TwL8A8B4ALwC4B8A3giZ5f9sYs2Gt/dGdGjHGfKJl0QPXqqMKhUKhuD2hNkahUChuH+ik7gpgrf3e6KfHAfxdY8wHAPwigO8zxvyEtXa7ufXV7LfuKWjzjjY9H6mIW/x/uq1YIlb2JfaQMuqS5SHXJpX/RtsHTyN7VmR+RLfbzJlo85qkJHI5j0Keh8GAIkMygngIJ9wnLbs33/S5dOwpeyCnfLUSHS+F/zzIE/dscZcXA2FwpK7ANioXcZFSwgCJXnRRP4c5esgNeX2WDEWcDndYIGWKE13KXeAkcc71e/vSe7B1nDyqF53IRzbLsfVIVOqAo3Enl4GZS2bgRPCcZfxNyFNz+Q3YmKP7lCu7cMRFwnKXaH6m75tnyWbG0buewJ1b5OncuEzRrGOdNZ9zd8gl3HNph6ntiRxEauNCSVHDib3fJ+azSMvX/Oo5rL35SQBAecrt9Jg7p+szzF3h2Mtb5NV99DJF+F4sV7HhBGt4f6c7J/GmyQF3HOTp3jpO18zmJaqcjjs/Sp7PlbMbeLBHx/bkjDzPnOs2XzqJy5c/XjsX0qvJ0Si+73udFSF+Q58csTuIFX+fcPu8rkHWeCZS0Y9UJIEhI9qpiH5cYFxG8drEMMpy0njeU3L0UswoHhdCxC70laMlimsPObbL6Gg8fkubscg28X3CUbNFpTbqOeF1W5VlA7EsbNMWye10Rg2BFC/xLiKFeUbj8BzrjVxttm0yNzTOz6u1FeWWAvVcJ4AYISy8NcrIw3+3y0WThblZ2v6Z4oQXJLlUEWOAx6rPzk56QRXOeePi3UCJrisp0ze0zrH8Ag53aCxj8ZTn58f89jwOr7h1mKHyQP60j9DhniB0Uq27UgknyOZsH6PtilMd4ARFy3DcRUk7zj4cehVw4B7XgGN5bL8IbLl2XTQO7txgVgEfcufY2S2zwbJQwHzFRahcflrVK1ENKR/x2Bpdj695mpgXH549gnMl9es3tt8EIAjGbFQjPOXyvSddOjfny8M4NiebcfQ36V46//nUFh45DBxxYyyPTRtkLzZedxFfe+ZXAAD/ZvMb6BwVAx/5/MXtLwYA/MmtJ/z544LkhWOHHDpEbCVcOo7P7X2SjtW9sj89fwMumaAxAMjxPjxfLOAjxVNcANi/jwyw7FlBnFtXilIGsc2RdoWfBcmOitlQUr9B5t7JZXG7hGYOnhSz42c1NR7xM8nPaFk2RckYqbxsmZ+336CTumsIa+0vuYnd5wN4M4B37bD+q1O/O+/qI83f2y9XqpYPvwzVt6ObN0VlilFVRTIpnD+b9K2QsJqqXRfXg5PiCPGLn1Qxig0tJdPT+uHB5glfGBhSL5HcH6bjdLsrONih7wecGtSS4cnJphfRYCWvy07lKUfpaS5bJX0+Pz/ma8+dKWgCUsDREdFDGVHkmE5pqfKe+y3UKxs4ygzXkZNiLY92aVJyT04UwLsP0GTz8ol1jF/rBm+enK1kwBFH/xy4czEiegmqMhjbi05g5SlHuVjpA+fc91V3Xi9MYNyxDU87SpAzQp1JF52ZG3ydgapcfbes6KAY0wsJ1x06lZ/xk2Se3DHN6IPTR33S/qQ6Wjtv46rwtYmYOvSr22/FFz5D1+jS8kv1Y13uegoQi8I8fPAxv7//Wb0RQKgBdb5cwfumrwMAvGX9gwAA42il8+HMH9PoGbpfjmYXcadTTTvp1FEvFm7CLoR4JhMywtJQ8XPIdOPh4F5PpeRJP9+9FpWfxDFFkw3uFGP0sxVarytpLnR+ZrPo3hMGtlnnaNwwsPJ5j8ch+fymqHmLVMTY+MaiS3EbvH0sorLfkthfbuzVxtCypnqxpDal1o+vsZz4xxTOlOLlogl/vE7b+p5GPzwFgF7oAk1/vbFv7uOsoIkLP4Nxu4xYJZnb2t4+49fhl9vp9KyfUMaiZHfgQdzbIafRihs3+GX/YLaJy85ZyIqPW9XAT8C4/idP6uY2x4XyYK2fldPBHRiLoZuk3elsyKFsjKfmZO/YrrCDbCmb+PSCz++R6MgDTnTraHYRmw/TRMkrDg8qX7vNOwlXHK3y5F3AComMYelE/UROzwOn/wN9v+hUldenJDwCBGcc0+dnJXDKHaOb8NlVt7+NmbdN1qUIFOM55stOgMSJeXUm6wCA1134CF7Yvpuadef0s05BdGs+QGF50kR9/g3zZi969mhJNvfwh2n8v3h8XRy/u1eHbkJaVFh7O03EHvkFqjc3y3v44PQ1tC8neDO+SHY437rkJ3PWOQ0vPUQT/dEzBR44S8f7EafefDK/hBfdPVA4hy6jLHPxna5Vno/8/bsFWv8g6LgKTLwoG0/l2L5QfTsC26PCLGNiwz0PoGZL4slQSgFZ/t9mV6hmadp2pJySUik9VsaUfWSkaKHhvbgQjsmdbczNpJCZ7byKYo/4jPu864b2QqFQKBQKhUKhUNwWuHmml7cODrvPzYVrXQHaElDlb1I2NpUY2kazTGHRutJrG3spimIsygSEfsVefPaKbG+faSS4SkpLLIoijztFCw3eqZXadv3+8SBpzQn0nVUMnZeKPVFMPXmw90wjaZ2ljs9XKw0qzEvFEV8jp3QROk4u3sQF7/3iui+ZoM5xdCZz6xwwXYycl/W+HtFS2Hv66eJ+70FdPkn0jZecEIgdGuBIJL18pA9kLqt6xTnnLxNVEU++ABQuufd5umW76xS5m5/YRO8ZamO2Tueku9H30TiO0LEs8/T4tqdyHjxN54RpmMVwhnyVvMAs39yZdJFPqH2mGh3MqQ/bdsnX9zk8p31/bEqUnQIzvFjSdp+aksTzuBrikWfpXBxykbTLy3QNfJQRQHmc9n3xEYpwvuFDH/Ny4Vz7aK04jI/OKEmdKUcs/GIqg20XtWPct/pJnD9Hj/1D/dMAgDPF51IfsnPYLKlfsecvRfuYFRdQ5O7cCdEcgCgxcX06ps30McIW6PymPJCLkIrKxUhJ2teT1tMRfRm150iIpGvGbaXOSV0en8e09mPcr1LUNxNSpSNiKr68/m3nXN5bi+hMcvtYiCQVxZVMDh7fU/XtYhppnU5Mv00ma4m+1m2NvC/5+DlCl2XNqIGk9fd6pwAAqx0aUw7hTmSOgs90R46Q5Sh9SQIWbhpXQ0/jZ3q/BY3ZPVNg24kz8NgwzCjSM8q2PH2S7diz8+N+n8xCGbr6Y3fkF3BPh8ZFFv86OiC7snnyIrZPOtYG0yLvPRhszB2OTbFMcvlYfTMwdTb8zM/Q51OOQTHqhu0qZ3u2iiCowhE6jtz1spAacNwJdzmaIwa5YJ+4OqWDOTCgMd1uuHcNZxOK4QwrUzqOLxq8HwBwqqDr+Kv2rd6WMy5Wy3jflMZyrkv6NeeJjnnwt6a4/DWuH64WH3ITztEK2YzXPPhrAIDfevwP4HP7FL1734Ta/PmttwEAvm7rKR+pY3CU0WaVrx37FUv/EwClJzzmSiltO3vBtmCGC57BFEfOAWJ1AMARENulhyG2XfSO3014nS4GnnbJ7XcxaAikdDr1d05gcS3RxfWR22ndsp5xnP4j15XCefwZRMLqzDJrC/HO3Ohqo3+p47mZbI1G6q4hjDF3APhi9++HbmRfFAqFQqFQKBQKxe0BjdTtEcaYtwI4BuBnrLWl+P0UgH8L4ACA/25tRDq+un26z/bLtZcIHNDMqUttn04gHTWWcb9kQdg4Gtfp5A2Pa5C1DdvFBSWrauI9sdJTwpE86bHl9TlCF8ovjPz/vD73ZQUnfC7diZwiMBypG2VbOOL46sy/P1fSuuvlsi+KzZLIL5VLqFxBcS4YPXUROComzgXJndy98IYtG8p5u8N5VkfZeV8c9tEecfkPOm/gHz/w0zAj8p6dP0VeQ59jcGzJewh9Tl3/aEhIf+YDtM9fo30XoyngImfLT1H/OJJWna1gc6FIAIqucUI6b2cz523NjPfiXn6Q+jU4Sx7Tslei6ySbuc2yV+CAKyb7yIVPAQA+PfkcAMCS2caJDlUK7zgPIecwni9WcLGiY3tqTu3P7Un86uQLAQB/yOXWGSepbU9WIRp5L133eUXXejp+Fic+QdG+J+an3GFUyNw+OZ/ilMuH2zqxjr4XB1gHAAyfX/GRRhYtuNNFHM8WQxw48CAAYDKpDwnSqymjDJxfyZ53jtgViUgdR/FmWPdy1Iy6mFFImG+DjLwsynGLvaxlGfKm+JjCcxzGiTRzoO5lrUdXmpGdK43Q3UyS0zcrFtkZeS+F9fPGsvgay7y2qmoyLeLrL5+D+H4j8PohChDfX7JkjszzBtAoyiyXbW+f8QJanIMq8+jYrmxtnQYQniUZjZY2kZcfWXotAGAEiqgcz9d8Pls/c/L1Ljq30tnAZ13OG+fZvVQewSVXtiBePzMVOq68y8GMbA+X4TmQbWHucsSec2IoR/N1HM7IvnGJggdckeuVbAOPdmkcxrLLF1ym8zA9sgWccFL+HC3LM+AuykdG30XqenSO8Ox/AT7lMlGecWMOR+fOTXwJAI8i8VxyRDDPgpgXtzFw/8/KUG7n/npuIeBK9gCYr7ro2qcOozNwuZMvUKSKo2Cf2/8kHujSMX5qRjZhanu+ADvbgl/dfisA4EvXfxl4iiKbeN3R0FcAeHDZH/fl19N5ftPTH8EnZ2QLjuXUH2b75OO+l0q7fD8tWzpLv0xWxzi8Qbbw3KWQS8nCbudxmtabnfPHLQVCAHqu+H7knLol97xv2K2GXTngCGebuOjztplNZJBhqU/7jktiZdmgoYHAkGUFUmN0UyBJsgPQOB5ZCovbjMcCXr+eS5vXli0SUNktAlPO3HAbo5O6veMhAD8J4Kwx5kMA1gHcB+ANIIvzCQB/5ob1TqFQKBQKhUKhUNxW0End3vE7AP4pSN3yjaAcuk0AHwbwHwH802tdymAR4pyBTqepSin/b1O9lL/L/JhF5RGkd4b2HaJmvG/2mrLnSLbB60h+NLfJHlUZEWTPTFVNGgpkKQ+QlL/lvgwH5J1j5adDOIGDGXHZ2XvK8tEAcMHlMjA2XVTuYrmMKUfvCorezXDJe7zYq8VRuaGLBgJoKBgeNAdr0UGA8ugedMqQ7D3lvD4sj7H2WlIis/c7r+mJYegkez1XKEcMG2eB9zkvnnNw9tfJEzdYG/m8Nub0Vy6iNnx+xefNsZplVnRQuPVtRo11eNm5HFNXRqG3Qf2aj9x56JVe1avvCn9nsw427qc8iwM96s+D65RzMLvY80qjpTtfnB9S2BwXq0PuHNI1PlNk+K2t17v16Dp+0TPkkb3cWwsFYzmK6YrZbh+7jIfPEVN6/UXKJ/j47CEMMopGshIdnyNTdLB9gqJ8ZpL5c3jSqZAeK8jLyhLljxfLmDjP+DRS+ZK5p4z5fB1F7pRdhWeUjrXnv3OeA0fxehiiiNRVe73V2r5itEVeUlF4+Z2f8/g5lpDe0DiaLpXL4jyHFOR2KUn7mymf4VbBomu6SHkyLrgt1Yv5fpFKmnG5A6lgJ/OxgXpeZ0oxTzI+aPuB70dc7FzaMZl/w99j9bylpZN+WXw81hZe6VKeD47QL7sSOUsuX2mOS15xkqNmzAg5UxzHiZyiMk/M7gNAUbY5nJojKLrSNy6ykggK5Nx2Z8PniLFK8Cjb8hG6V7pyOJw/10GJ6Skat9gW8HhePTgIY+hh6heWHwqRufOkEoynaPzG+gw4S69BpnBRclE6x3zYlYg5Ruf3wEeH2HyNy0ub1XPrsucLz+7wh8uBpZU+RevE+hjmIeeO++xUM8cnL2L5KTonrMK84q7BZXsAww79VrhSPo/PXuHt/GdduQP+v//iHF/4a++m5o+5fEG2w/2jwDHXH9dW9qpP4+DHKA+Obftjc7pHPvz8F+F1J/9n7Xyxnck3Ajvm6JhYKP1shkPORvVdQXImamxNnmmwKba2TvvnY3v7NABgY4nsZReDGhsEAGaOcTTECtZBOf3MPrKoUFpab1EOdQrN8gtolEBgEMMqnYebemcsy6LxW1GE8xAzP2R+Xjze0ThTz6FdxBK5mWyQTur2CGvtJwH8+Ruz76YxTdXoaRMhkEiFuhn1CR4a++T1mzWpHJ2wu+LbCA9seIFN9ScuScDrbm+faTyMUo49/pSDRfzCPBicRB/UHza0QKjxxoaWKS5dM8fF8lBt2WU3qTsvJnvbomzBGI7e4x4tnsx1kPtBkZcxxeG+7hlfO+hBJ4DyUPcpPNJ9gtro0rnZdLWAxsvbsMxeYeoJJ5wPcmDoRveP0/bZU1P014nmwROxnqMQVr3Ci4D01mkilhVkhOZVjq5L1mZKZr7VQz7u+20BoOsmcL3zBzE8SJMNFhPhtovlTRSrrg7PKTfQnp2if4H6se3qwM0d3ec1m5/GkjO6bHzfXxKNaclMcH/XyUo7YZpz1dzX8PnolAQJ7nnyzQCAk4Pfwta9LI/tXiyW3cT0yAyXHYX1iy69Hwym2pyv6Bqx4Mvw7DLG7rhLd3tNj2yh/wLdM0wZfSKjF58VPODpLlNfyyfcq3zfT6f02eut+gkbT1g7ovQFi/rwfcxtdzHwiewpsSE26LJ+D39PjRcpeekwZtRfGCRFmmtKyv2m5O5j487S1SR8UX+WdxJ+UWGUa4/UOW0rW5Flg8YkK4z/zfI2xkwQ0yJTUuJxX6RdkvUNeZyPKaDyRS4lzBDfV7KWFbcp6ZfxxFC+yMblg7JsgCMZUfjYgbjqyp0czja8PeEx60U3mFQ2wyem9KL/zJyccgZNTOzcLctwR4fG2nu65MRiJ9gTs3s9Lfx4l/p+V+cl3OUcTne5sYrL9Ww+/By2l92kZuQmRewEKyrg4B30/SjV4sTW88Anf56+cxkcFi1Zm8C4yVmX7cXEvSRP+t7G9J4i21HlFQ58nCZgbGvKARdUM35yaTeEeApADswjriQPT+oyhEkfUzhXaB2bzTA/R5Oh7gk6D3efnfrz2nE1YbnkwCO9J/C4o+VzusWGo8I+Xx7DZJvsD54/XT9fB2YA998JdW2/fgMnP0nOv+mc7A9TPy9XB1A6u8L2dX7E3bNFB8WQjvtYh2zV0Gx7J/STM2cLrBPbyUf+PpQ0+FDvs25X5phgOeu6ftAnv6uwLZKYYavxTPPYIOvHScc8UKdfyudxEa07FvEKjp5mLVXpjIzFUOq/Nac9TSdReOdMiUbdzLbmiid1xpgvuVadsNb+5rVqS6FQKBSK/QS1pwqFQqG4WlxNpO5dSAb/9wx7lf24bZAKa8cy0xK7idAtgpRqTtFd2jwsgPR0BhoOe/Nl0XGAvEkcSeB12MtTlmPf15TAQixnbUzeKG4+HD7ol7Fk7xAUgbune9YLknBSORe9ngrhCRbp2Hbeuq1qgALkzZyBPLA5ej4Kx14wpljm6Hk6XQ7yhvF+c1P44rAsivLo0id8BOlSj7x5XPQ1m3UCzeMBVw6x60J3688BHyBq5uAZJxywcRh9F4WbrpAn1hcJv7DsH+JtRyt5saJr18cMW1vURn+bjvWx+YP4SierzJTEDVf8tt+9hNklRzO9RF7d7n2UhF8OCpQuqliR4xp2aDBxgiLsWWXaT++ui7hvi5YNz7v7xXk1f2P7zdgo6Hg5wrltj+HFgovuEk3m1ydvAQD8hac+he1jRDWyj7gdLQca5ux+55V83lFgn9vEUetoLob688SEvO535b+Lcug8fheCJ3mp77zlFXli2RM/MBbWUgSNhRaq6rT7nAjBH1pmTI556bzeHTqXHJWbYuzFdXxUzt1nBln4LiIIcTSCnw1JX4nXib2jMZqUm1Ht+ZOfRTFu7FuKSMT7rqqiQcfZySu61xIONynehRtsT3cSBmgrPl7beXRvzGZrQrSgSQGOKVfd7kqSFcJgWxDTJIF69A4ABoNRg67Jdmk6PZsscxDfxzLqHYt4peTiefvl4SM+qs70fi4hMMq2vAgKR324fMuZ+XEvvMWwsN6ucBkcFlMaZiXu750BEIRVPjwhpsIremd8+YK7OkQPvCd/wTMgHhwQ7XJ6hKIxpjIoB+4Z4kgYR8He8FXAwA3cG4/T50d+Gzjjxm8uwu1ojkvnlj0Lw58bx9ooeyV658lmVEsUJcsn3UaqA9Miy4NbGKy5a+toiNvHaFmxMkPnDI37jlxB0cJV1+9Vl54wdpHH3GDrETre3mknWObs6vHHC8wnFC3sOxrimSqUG+bze8nRHc8Ux/FsScs/5zepP+eOUfQTp04CmevDlOw47j2N/HNIPOYBpyGzXtH7yMz20DlDkdl+L2JO5CUmLlLJUbxHp5/G03Mqoj50jJ+xcZHkTqBYSmGReGzedCVwlnEcmxWXw6DzNHaEFoPMC6UwK0S+y/Dzl3q2U++MccQ8ZQtSAkxNCuQkWVqF+xELfXU6g8Z7YXh3luUO0nTP/YSrnUw9CeDdV7H9FwG4/yr7oFAoFArFfofaU4VCoVBcMa52Uvdb1tr/9Uo3Nsb8JNQIXRNIb2Ocg7Cb6JzMmUlxjlNe8DiZPOTYDWq5dHIZbeciD64YcZYNGt4a6cFNFRiPpXSldycuW8D/D7HiyxewIMlyZ+wjdOwtzYwrsmkKvOREUDhP4fScIlAWc1wGcfLZkzXFuFagUyI3Fj0Xoesb2vcJl9twLL+AB1zi9GtcpG4+miJz0bSZyzPjEgLlQ33guPOCLjlP4vNUJLvzW+tYfoI8fhxJy7d6eP+E8tEeuESRs0su4vjp4n7k8JU5qD9OaORMeZePUHKOQWkzvGvyBdSu267nPJiXpwd88j1v9/CZkFvHQikbLu+wGlTor9FxcI4Fe023TlzC4Bx5c4+t0/qnC4rAHe2s+2vFyevH8zWcnlP0lT/7hkRxfmX8JXjjGnmqtzkng6WxH77TF8Vl7+/rXnoMj89fAQB4bE4lFlgw5d7xY8jHTlhmxUUNim3A5Uiwd5kjdcudMbKimXMKhPtfQj6/fC9tu0LFA4y8UArfXxy5y9HzeRAcHa6yAaxdB5COqofnvD13Lc59kr8xynLc8JqmooUh/2LciAry/0Wx5kVQ9iIpLdffxxG7m9ae1r3tLpfW54iOa2M/ffI9X6Ao1mttyeLzzesfPP7cPt83UsJc2hxeL7Z3MkrM9oTLEcg8OJlHF+fgMTY3nxB9XG+cE44AcsT9II5hNaNx6LDL2T7gbE7XFF5sg23PRVdy4FI1wrmSojEDQ4HbwhpM3BjA0b+OE816sPdZnxP+6dkpAMGudFHgoR6NxwcNHeub+h/2IlBjl6PN+Vo89lLDLlfskS+jz/5R4KX30Pf3foI+R92Qs+ZyuoeuRM3w7LK3X2bqxqMD7pyuHcJ5N56ub9B+LtsR7sxonOd8Zi72fao4g3u7xD6BE9niHOfsidzn7G2547n8+WMqmwB4YTBfvBzwZQdmxya1/y/mL+DI71Iu9AMut/10cdJfKy7gfn5Ox7hRjvCx2SsBAA9VFL3MH6dIV/HQk8Cqyz10uYQYjbD5ILVrHid7waWSOqbEs66Y+LH8WWqL89FHU3+NsoLL3JT+ug/xAK3vxv05thpjdFGMa4W4gVBgvGcMtl2JkLLie9q6/fRgXFSOS3Js4YLfNn7uZ7O1ZIQuRiq3NWVr2qP2UmwrMDr4+YvbjhkBsq2UiNgijQrZr5vR1tzo4uMG6TxghUKhUCgUu4faU4VCobiNcTWRusOAIx5fOf4cgO+4yjZuaRhjdlAHai67Vt4DqUyUyreRCmcSeT5qeGyLYtz4jTGbrTVy6tiLOp+v++1krk3sSZX94zYa6pdY9p7OoVP8AkIUruPzlMInF6F+YnZvra1trPuoCXutShQNdUIuaVDaPvKM+sh5YOz5u7PzEl7Xo0gb5xjMlieYrZBnl/MI7INOnvm+Q6Ej6y6/4aPE219+4i6fP8e4UK54rx6rdj5ZkEeyZ+ZYcwqPnEfwhFs2NNvek8h5GADw5JzOxdT2ff8BilJ9YPYogFCG4b2TzwMA3PPiCzh655O0Hy5pUGS+ZAIrf9keeQjnx4BiSMfE67zhcfJOX7YHfKFwLgXxUnEEh1wR9IuVUzBz0cjTxUk88onPpX2eoHxA3O880Z2Q9zE5TnkLo49nPjLJapbPlxT97MzykHfi1M3KQYHZUVfM/CVqd9WVX+iKe2IDFCWVz1DsnZTL+N6pqZQ51Vb+jXNrxljzctTssZ2I0h9B2j1E9NskmlOKZGU5SUbFqe+jRhtSKTHlJV2UzxeXSkh5ReMCsrsFF9h+5JFH9rTdy4AbZk/5nIT/myUt6hHXeh6NvP4hmtVUm5TKdymvPG8fl0WQOdhxXlu3u+L7wV56XiZzteNSGGU5Sdw7k0bkgaPp1haNPB15zPx9pUf5232MfD5b6QqFc/R+antetZdtAZce6Joi2BVL45hF5e0JlzTgfOyjnXVfUmfVjfE8Jq5mF7Hq7BwzQErbweYJWo/Vi7dPuBze40tBvfE1X0WfXFR87X3AR6kNs+ZyBD/VR3eDlnPJge6Guzd6JbBBfX62pELV6xfpmNerZT+usjrnC+UBb5uYkcE28YniPs+UONqh49l6jmwQMyMAYOVpin5tHb+EcuTGClbE5NzAQSdEF30hc1Z7nmJ+mK7Z0Qt0DR7oPu0VMV8oKIL4UP80ALI5L5V0/L99mZSW3/AUHc/FC2PgkIsAcW7d8B7g88mmH/vF87X+r1fLXmn5HqdMvXWcIo/zkxV6p6kNZu08uv5pPD6noPxqh+zWZ5PRqDqbCgjvbZxTN7FzdJzNCMrLLoKITrL4eAx+15LlAeL3vKpKv0+2lcaRjKxU1K/TiZllkg1WZwDIoujxeUiVO5CFz2PI9VP94vH0RhUhv+JJnbX20tXu3NVze9lqut0qkDd/uPFSRrQZuk7Js6bWjfcVP4x5vuofHJ5ELS2tuLYGoo+BJsO0lZQQAwtGhKT6if8/DrNLilo8GcyyvEmFycio9DBEzw3QbGBH2ZafxDF4knd2ftwbGpb63XbJwly6gNsFyJDHtMuhoclBbmbeyL/CJbbf3yWaxdsG78WBo1QLZjIK8spMubAnXZtOlhkH7vH1gDofcmIiz5Nx6Z0/iE876mBpyVi9WK36CdtlS8bx47OH6HiqAY7nNCm708klswE9Vx31SeEsn/xg/jRmJgjIAIFC8t7J53laKyeAM6VmvVrGoy6HfGXwKX98PEFigRSWf8asgj3ljNuQJkyDNTLor1t7zPdx4va9UY48bZYHd752T8zuw3qf+nOESz+wYb/4YjivrrbR5P6zeJQ0BLzhZKrpxuVjyMd0IPOBm2yPMmy5F6XDbkL9SPEEAOADndfAFHTvHOjQhGxsz/hzF0tP93qr/tlhUZ8S/P9IGFY6N1ybrouBnwTy8ef5Cqpqrda+RPwinkpQly/ybRMxci7VX3hlTbO4Tl1ZTsTEoP1FJKbCyYR2xs0uL71b3Ez2NHVO5TVL3S+7KaOzqKxALEwCQJTJWPHbx5RMY/IG/TIlPR6LdKXETapqgviFdDIJ43wMKaqytEQTEXbgHMKdGLt9nezWadZ9M8Mop3Hy6TnZJqb4vVSVjqQfXqILTPxYwGULTnSJYrlRjnxZhOMdWsb0yrvyc55Kz/TL6sgG+MpsHQ8TIgA0yXnd19H32Tp9Fk7S/vxpqj2HINYBANY50kbP0MRyUtGynpnjceccZFsQJmbr/vt5SxOZZ4u7/ISNHZBcp1RO3Lgtngw+Pn8FHnKUf6Z3Ds8ewtQ5RLl/XOcPGYKN4XquojzCxUdI6CoraIx//acuY+vFLwcQhLrOOJrkUjbBhrOPTBl9k6vZitOXgVcdq5/LyTlgma732ps/BgC4691031yeH8A5rpHjUOXhvWR2nPp/wNFbS9tppIvwe8j2PNxv0jkR13GcVRv+nGRR7dwu6Dp2TYFtHreFz4nL7fD8bjNhXyTdHkhPhlK2pi6alU4NSAm/VFWwDfFz3+02x7RUfbu4JIKE3L6NfrnXlIHrgRtNv1QoFAqFQqFQKBQKxVXgmk0rjTGvB/C/AHintfb9Leu8CcAfAfCvrbUfvlb7vtXR5hXodAYNj4L0fO+lfEHbfmJviKTLsCcmTnZPlSjY6Ti435LuApBHhqMMUgwmLmkQaGZBEnvQI0/ZIUdZG+IIRhlFttgb2EHlvaRMtWTa3tzmPlLFFEsWR8kx8FESFqs4gJ6nMfRNp7afUbblo1hHOuSUf2v/gwDIi2ocLe7yKkXL+utLKI+7aNIxR6c8RhLGePwx4Anysh16nH6zF8jjebq801MFWeyjgxJj5xnlYy09xafnl5Uu+V4Kx3DCOEfjpuj54rVMk5EeQ/YaPucK5+bd0h2/k75G8DZuH9vwVEyOSo4dJcSudoCzTlhgzVFPnPf14PlNnOicg8RGOcInZxRVqxzdacNS0GKjGuFjc0po/9on3ke/vc5FBntZKODuCpJPVsc48Fk6DvZ087kpbYaBi8bNj7s2jvRhnuGCuc5DuE3r39M9i49Pifq5DorGyqhEHKmbz9f9c1V1qK1A5S2w5Dyk7DXl4vbbWPfr8T3aQS5EWZrR8ThaJp/LmGqX8kBKOrWUzpZIRXGAZnJ6SqK+qgfQkwyFVJ9vBdwIeyqjVymBAlmmAkAjAiu3k3SmRaVvUl762J5IG9SUOJ+gLOu/yXsxjvYGexH6LOn6i8QT2qKRxuQ4mj8MIBQaH2YTT60s3HjK5XAG+czbGi5fwGOVReWfX4685Bhg6OwJR+jYlpwtVnF/lyL/LE51f+cZt12JezoUeWLRkc5wzdPZ7cA9YKdIkAqvfnOIKpUu4PsZd+s9sYHBGWqDBVBGzxzxJQAuuPY5kna+XEHH2QJmjHB5mNJmgWro+ve63mN+20e7RPNkAa6Z7YWSDywm4lglK3bD0yIPFTRWdzcG6LkxeuZK+FQ9srnlKoC1Sf24+f8sCJFwlGx6ZBNvXSc7/VObLorpcL5Y8df4s/N7AADPXiQGzNHHNrD15g/TiksuYje/DGyQDSjvpXO48tsb/niYTYP/P3v/HmTHleaHgb+TmTfvo25dXBQKhUIRJEGQTbLZzW5Ov9XTI/VMSzPSPFYaSWFbCr1Wsh2xlnfXdoS94bW8ivV67fDaVmxY8koOjWWt9bb1tGal0Whm1D3d0z3T093Dab5BEAQBEI9CPW7dunUf+dw/vkeePJlVAJsgAZD5RSBu4WbekyczT54vz/f7fb9vk1g32Tli0CDwAZ7vBIEc7EywEZcprPLu0QnXkGS033xOY8N+j5LnVhhNHdOCCKMIeBkx4O+jjS7oOsk7UIZMkT05jo3iV1NpCv/iPtt1SJ2YzQ4Rq0P5bV9lM7xo/+K5P4wyWTcf+b5dyByH2v0omHI3kbp/F8C/A+DSEfu8yfv82bt43MYaa6yxxhr7IFnjTxtrrLHGGntHdjcJoD8C4Ht5nt86bIc8z28ZY74H4HfdxeN+4O2wvLY7ReDuppWEHJxCj3byu+TItduEktlSt2J2Hp1bkFwiLjbyWFcY0i0Wa7fRVflnimAd84p8NYmQxnmgRUX383Kx1FvpCrYzyucSWXmxBSaaMGyLowhCJ3kUghod98fYCCiy9hOdXwVQRBvRinFpjxLs17YI0dp/cgt4hKKZWtj1AuUOmPMTLF+k0grjLULLJMoJAJucvC05ZeO0r5FdieZKjtwJf6SRvoTRqE+1SbI6QKrR0u2U+uKbVPMzJNdB/v+V7je1rQtOPsUs6+C1hJA0KW7+8flvqmyzlF8QWe38WgqTFLkbQIGCtU5t4vEbFCF+OabrlsFDi7NFpGj8hk9hx610iK2UC3kzMogJX/tBWCChXDg3GSaan7HKOTURy3JfSTfw9PzNUr+ws0DaoXskkdTVPcoPaSGpJJZLxNQez3a+gzzfggDLOA4QaiK7tOlZU7j8LdtSJBWUW57RMFzFbFbk9tn7uH2TbW5OlViSFLlO7txkC2wcNV/ZCJ1b3kTmFzuq63nSzzuLkt6PEdXb2PvuT48qW2Nvr0Nvi3tcHTd3Ehm323HbkDHcag1rovMF2i2+4k7QQnssidl55e5zY/9dlP6h67C09IQ+e1KGJM7b6leUyZDRHLKX9nElI7+4k4qo1VR/L6VJEkXjeyqMIqiflNpZ83eUHSHonyJe7de07+EpQsQOVieK1OHpIX0+9cP0GfSAmywkdZH8nn+ei4NPO1quoM1CHpfjh3CTfY3M+zK3z7IOPhpSXvFTASUoi+9ZtlgbYu1ghpwR17xdlu+/GZ3CsL2t7dLxaK5+PHhLUb+DGX125zPMzzHjx3PEKuZJ4U+lcLrk1HkG6ZCv5djo7yWX7jMh5cH96vxzAAgtFVaLIHbCXtnYeQXTeJ9PjsVm+mcBj/PRN+gerDCTZ9lM8AqXz2l1Dvj8mR2yNYfhv0XcJg1ThDNqQ9hAYcw5dZz3DxS6Amk6qX1XAoBJfoAhi7l4fD5SMgPIsMABf8esEhQ+xi2fBRTzt8wTrvYC9atgdLjPrfy/1RpWcmDt47VaRS6stO+K45WZH/XzQ928R/NQlQ1St9/9ZndzUfcQgFqaiGNvAfjEXTzuB97uJPnSfokqBEWOTva8ndmCJ64iGalZlvslL3ZxPCpqoVi16OQBk8Vc3XlJn+sWcPb5yEudPQHIZ69HL/o962UYABZ5S+krIo7SNpEmmovDECd5K/X1JVqolqI2mCFRJUKhvXnwkOTkDMYpfSeT/VPhRaUMymJIaI/9+ABnu7RQGPGiAE8PC3Uudj7h9+nY3c2T6N0gBcwr7NCEcjnLOkqVlEXORmtTnU+L76csap8I31KKodjZgF72U/g4weppQcCiLV6O0YKc1MMBvSjI8eQaA1DVtYj/v2wOVDRFFl2v3voUTrMK2smEXlqWrtF5HWzswXBdPnHuuUfnn3RirHVoXD0eX+bjtpU2ayvJAUCWeUqjfXNMjvPEt+leLL5scSvkeq91EffZASR0nTaYOtmN5/B5AYqIldV6AeIhvfz0rpWVBImows8On7ftoNyE7nZ7vXhx5bEmv1vCigr9yBg94O630EGEHf0bICfsqguK2c+oPI/2s+7Srm36nTyb8sKQ50nlOQzDjh6noEoXQRz35VyvV3Y4/c4+jthRyev29b0fne9t7H3zp0cteOr8Sp1i5WFt1vkQu333BbBuEWjfw0LNsvzCCJDggW12nTqXpk+1FWn8dzrreq62wI9tdarP4oOW/HWlSsrnCX8bAc8/MvfKM7vIQ6XByzOui0HMlVKdWmq2XV40vc317EQwZZp3cNLQcy9+RYRDTgebiE7Sd1LXLRx3MHmaFzof+Sx9Cj1w9IrWdTMXadEUjuga+ZGv9eDGKdHxQhMpxVIWkjLvD4OxUu5lUeufpDk36UXwWFBEasvlV4d6fbubHAjkuqHHozkipkOu9i9qGwDQuTZEmNPipt8e0XVOfFX5laDhbI0WWEuXhvrbmEXJsnM83rJclTC9He0OvA71v5vQNZHg7GayooFT8TVSt+6LW98FvsH1/L7AtMpjj9PCGVDfMXmE/d7VIc7k5E/F74mSaNyfIx8y3TFiNe1OjOE+XR8J2C6BqKlT7CgHrxUyJX/6am2wA+Aap7kogNP5y31NcqNibzHXsgvR07qJrmDdYnGj8myL2fPxnQT47PnepV17XqcimlUX9LGpn26/buc7gAfOZwC4u/TLBcBv0UfbAHCqHTfWWGONNdZYY2KNP22sscYaa+wd2d1E6l4C8CVjzEqe5zt1OxhjVgD8TgAv3sXjfqCtjhJVV+9NIh9H1W2qo5y42+1j2vu7stGt1tCiXZYjIESnLLeVppNKFOVOKFE2DF5nbv+AgiYgiIV8Dvw9jUjNmArS9iP9TqKn24m0tVA5eaHCFJSYoUZSbWRkNaDIo1AizrYI4emZudZ6k7ICQr8cZQP0hoSOLZ7mUGknACL+e0QRxfaIUKzO9eP4zcXHuS26B1I7rmfmKpMt0cNROtDooqCDcmzfpBqVE4ROIr6zrKNRytkKRV1bkzYGXALh8v7jfMwZ9+FRFRQR2uYzLaLg9L0DvBIRgir0m2vpmkZ430ho/2evE2Won3lKxQRHLsFUmtakjQX355n56wCAV6PHFRUVmWlB5+a5UbrStxafBgD861epBtIimZQQNwDAoIWDMyM+Fgsy0Ba040jpSyZiis5q8UxJHSGh7Ay8CUKuW5NZ4j9Aedy79bSAAqGTKH6EKWIeqyELE3T5Ho/zTPeTMWrgHSoXfVj9MDGXTud5fStaWlBntK9OTTH7OPKdTdGUEgh1UdPD5rksq5ZeSdOqEMv9ICt9F+y+8Kf29T6MfnuUj7JFrWykoK7MhZgrtnNUfbs6f2fX0TuMkmlMUKGj+X6glOSjBMKkD1LGYBlrWGJ0TZD0GIH6lUyp0vR8zrIONlMWVmImiJQv6KKg0Ye8pl8N9tD26JleWKwIAHg4uKHHkXlb5uNZ1kFvhynpK3Scybld4Bwjc0IL5Lqe2LmorBChwwtq1N0cYLKg/gjFMIFfotfT+VM/+94Blo8RmjjjOVGRsTBBFnINPk8ESaaKJkYDFjdhdM5kmaJril5xm9FkhuE0Le2PuIXu5nLpu5UXySdO18fKtJA2ETC2ERii4wNIVqjN9PwYnS0aJ8tTujYiaNL3ptjl8xeq7WPsQ68kp3F8i3xfLGV0jE//AGCdfPlsjdC5/uUVpWLG7F8EGfXnLeQRtZF2uF+dWO+zjAmhRMaY698HC2KyJElBv9T3NgvTEd8pbJc9Lk3hwei5CTUzsUob+M4SgsT76pkW9t+3Q/cBelbdOccu4VUtdTWpzCs29fOd0PRprin8zu36fD8xQe4mUvc3AfQB/H1jzBl3ozHmIQD/C4AegL91F4/bWGONNdZYYx8ka/xpY4011lhj78juZkjz5wD8EQBfBnDeGPMLAN7gbY8D+AkAXQC/BuCv3MXjfmjMTdQ2JqiVIz8sJ6HO6tA+KfjoeZ3Ssdz9i2jIsPR/+3h2fozk7ogVstSHJ6/WoXS2+IIbfep01lVYQnKZOh5FvnxkmnD+Nssgt70I1yL6W/LMxBaYaC5dkRfF1woJuixfnVrIiCSMP8Sy/8c9Qu7W/C2VdJY8NcOS9Z3Tb2H3Sa7MvUJ9Rz8ALjNSxYhdh6OPvzz7oqJrIvEsCNmF+FHtv0TdMni6/6OttwEAnw+fp33yIgdPIoXTdZZ/7kXwWZBFkrZzL4dkjQmyJ7kia/625tAJUnWdc/22suNaXFaiu9O8q+Isfb42V1I6n8eSLc27EFSuNSZ0avLIjkZiBXH8Uuc7+IXp76S2OMdAIpiZ6WgJhysJ5c94OxwR39kuCpF3nKR6y3KO+IYmwoKjzBI1joax3iOJcEseydAba56lmDwT7fa6PhMy7qNorrlqLS1fwGMdHfQ8OvaUo+vH+DjTNNTcThFY8RDUiI4UqInkJNQh5y66YvfRPQ+7DXebm/cAUC5ekozoHBm9s0Vb7HaBAs2z861cBKbOjoqsPgB23/lTFxXz/eq1rEOG68aGO7+LX7FRYNcntFpFvqnNCnHHrz1exFy/4vt9/dvOKS38W/nckmSiY1XaPRaQ8JM8pwCVMgDI1whjwM2pu5ksAewzBKGTNiJM9TmW/KZzrauauyUMkIFP53jM29fcZpmPhRGy5E+RCtrFeck4uwyc5ly6gyv0OSM2CV7eBcbsK7lUQchz3GJ/qPliUmj7VnoC51qEBEle9uP8/2PdLcQsbCUIXR4UKJOURchDRuoklxyFuIkgammYaP+lLRXYmoZFEXX+XWcTmB4wcjq8Xtq/s9VXf6KWMBSz0i1YGzzU8yDV/U9NyEcvmO3R96aKYgkae5X9i48Uv/dlQuo2f4wFUxbbBTraIv+VDC7rOQpyeLBNqOIgIUZM9sQt9YXioaLBXN81ZEx4eIz3yRClo9IpdjpndC7WMh+MHHdMS9999rOWtgEAHS/V71rs+eeWaJyI0Iml6by2HIgc10Xj8jyx3gPL74/2s+36BGMK32YzQNz9RfCo/E4qPqQ4ns0oK/pb/95ss09cRP9+sLvWkzzPE2PM7wPw3wH4kwD+gLNLCuB/AvB/zu8nrLKxxhprrLHG7iNr/GljjTXWWGPv1O7q8jLP8ymAf9MY85+CIowP86YrAL6a5yzv09ht7eWXX4Yx5o4jAHXy0kf91s1NAepVzST3xT2OrWQk0Q07ClN37CKSOqr087D3Elt173b5ggChABL1FCUyUbo8GewoQifS0K8uzmmuwwHzyAXpSJEoSiKRK0Hs2uir9LQcb9mLlZsuBbpDRqCiPCxUJdfo/D2Ogk439oBzXAhV5JXnqRZFXX6Zoo7JlO7FZroKn3M3rvH5SDR4yZtqjkXPn+s2QeaebJF6mOlTZDSYhholnXOkU/IQupvLSFlCefM6RWlPeLuaN3EppYhiysc7G1zVQrNTcLFbRuzOBldLxdAB4GH/GgJH40GuV2vS1nwOyVOTSGbSixQ5ExXQRd7S6LXkFEq/5rnBKCVkTlC8F+KnAQCnszeBIZdOEMQuShEPKCKsKpg3TnKbvkZ9989xWYTE13w8yQPsvhnx+URY9riQK0ua26hB3dju9ynaW+TZFDbh/IxjjD5LhDXDTPeXcZ9gXlILs4/teQGy7PAo4518Z0dFD8vda7WGlchtls2tqGxVUdF9zm3UrzimoDjFvFGX63XYXPjyyy9Xzu9+s/fan4qPqT/27dXp7iSfxB4bbkSetpfvo10UvBgTRR6dez9pLJWj5q4Sq/RD9pc+y3ey33x+9UgE2M0XlJzXAB0u4Ay0DeddmaiS/yZz0Azb6keE5SH5TT5ChPzEnwhGAIhNIUwDKUUjrIoT3q4qDgsDYhgUSMpsvfgbAPDsF4q/U0bH5vyZ5Fi+sErnz3Pv5NpZAMS4kNIBYh1voSwPYaEcbxETJg0yzY0TpGuJSyJkQabzt6BxvZ2l0vxuWzjqFYqQnGcm6Nx0fQyfUawDVtIEgHBSzsGT4+Verm0I6pdKzttoAUx4/K3SPsE01P5cW5BfeSJ4i84ZkZZpOOB5WO55aCL4jEaqT59tA8uUh46Mz5HL6UhfgCI3csHXD4DmIMr5tEddGPbDHS7V1MaS7u8+m/QslTUQ5L2la+aaf596h2dkyTtQCx0d++JzCsZUp5Lv6qJzrrmqurZOQtUXHJ6fV4egFb/rVJgm9nzk/s6e09z5rU6bop4VUj+3vtf2nmCG7Gz+znvR9ofRqo5MHGB1X88LKtLOYkfVmrLbtz/rKJ9AOZndFU6wHy55oQuCPrJsUtpf+iAQuW31jrnogxxf6pJIPbwuBuhjtdSWTLTX4jWdvORzkvX05T8BTY4LXtTJhAUUizmZCA08fXnWhZ45UNqdOFgRChl6Y1xhquTaiBLIZbECoJDTl6TtSYz2JX5puEzn9k2mfSwQYjM+wceM9DwAABnwUItoInYtH1lkymJOFnKztTFMVi4ZIOIgcX+Bty/T4kfKMeznSxgxRVJM6KSvxB/BGaYCCV1IhFyGFmXjFFMm97MlXZxKbT1xaGnURosduS7u1vilauIVCfZ8HidujfS3QnOSe7zsebpwP+ZN+HqRA/Suxsi4ZpAkySOUGj3Q+kBBnyk0k2UVbjEJC6UMWrqo88/TC0J4jF9ubvoqcqBqK2zk5NyASFAseJwfpFaC+j4vEH2IGENR3yqyxBeKkiLUvv2sHSb3X0djybJE6XZ2WRO9To7jlsCN/Z09j/l+UmlD9nHrGpVpdOVF3e3sfkxkf6d2L/ypS789rJaTvY9tdUFG+yXPpTa5/sXe/6gFv+93DqVaRdFWZYzX0Ttt2mb9yzAQBKs6ppeWngBQlLLpoI+AFzUDnl8yeBpoE2refr7P/88Q8XyY6Ysy9wkejnFw6uHghv5eglHyAi+08wCpUh9lHl7q0meKgrJ/68eJdo9kCswu8MXk+YTLuyxdPIbsLRLc2uIFnAhwRXmoVHmh+p/ytjTA9zgvdBZDrvXZX+DgLF0vCZYdbND//XlLF0rdMfnJuL9Q6nrEc7oIlJjMFOJUPPf2bhSCMiqGwjVIszAt5miuL/r6hAKKD5/9rtI7ZaHXOk/txBsx8AQFCXGDSxn1Y/WHJ04R+3n/BlUQeSt5SIPDUr5CFt++yTQgiDkHLtfWgZQXakscn+lTcMmPfD0PeXc4NuIyQh3ATI1eJ4CCh/l1ujc76TE+Npe7QXVxk6ZzKygx5P0LfyK04QmX8BEf6nMFWACYsV/xEWqKy5Rr4tl1HetSguT/9aUDwP0q+4k0nVeAhqLteaWtd0q3l7kgSW5UttnzU10bhx2zbt57v+09I4IaY04B2OD/Xsvz/OZ7dazGGmusscYa+6Ba408ba6yxxhq7nd3VRZ0hvPH/CODfBSVz29suAvhLAP5inudZzc8bO8SqUPLhty3Ljo6G3AmcLWbTXSSqYUPY7nc2uuZSbpJkUkHvCvi7aNNF7ewi5zZ8L8cWJE/QgxY6FYRD0KyWSRRJk+LggUmQMH3FluoFqIinsWgxABQNCRDqtlWf6CXrwZYmLwuyJfSMNbOlKFYaMf1jQBHV+GnfktPnROpfPY/+ZXqHm7B8vchGz7O2Ik9XYqHq0e8fCjaxbOj6CkoYmghLJygZfsK0FaW4ZAaDi4RsCnIo9MLv7HxeI7CCqJ0wu3g5pgLegoxp8VfvQBE6EWZJGQXcTodYZmqS/C6Bj+tMH5WIr6CmV9INnMq3Sn31xxwp7sTwI6YVcgJ9mntK/TzO7cs9BooyB7tMgZKoc2erj6lEUh/i6On0Tf2dUHsk6to+6ABMgZHk9TTKAI7sJT1qSxC+0ER638TqksTTtBjjKhrhJKEDUGqWoH8iwtLKO4oY63FQRUTkuZnPr1bavl3Rbjfy6FI7687RmKAiDV1nMnfUSejLfJGm8yPnvvspWf1u2L30p3dCz7ftsDI1dZFrux23FE8ddV/GrF0Q2C6h4foTaSuORyWmh+zv9kvSB2z2ifvcpOkEnc4ZAMCyoTm3YG0YnfcFldvL+oriCGIn5QRTRBVavyB2AUKc9Gm9LujcbjbQv+1yM2JC6xe7NSUmxerg7aIszCfYr+y/UYh1XCU2AXcdvRvHIHIlIngl9MpRNsBeRqjf0+Eb3NcUnw5foDYeIt8mpW+MRUlHwrT+AbMYxj4wLc9tna2+FuLuMqVekLqrs0e0kLmUSRDhrixINS3BG44AAHs7D2nZnKBH5/+kwHGby3pNpH29RoEHXGRGibA2Or6yQsQvih875u1rSQO5Py2+r5vpCaRjRv12yM/jsYeBE8S2wbVfpM9VTsFYHyuCuMzIkWHkkkS4mD3C+8zXDnBKS1jw+xe/v+TILMo6j8tsXhUsQoEk77GvjNiHnmZ67610Rd+LZP85xjp+xeznXp7D+bzMEiG2VtmvELNs7nx3ON3aFjJy5xryndU0A0DeIwsav21B0C9dp3djtysb9n7YXTu6MaYN4J8C+AoAA2AXwFu8+RGQU/oLAH7aGPPTeZ4vahtqrLHGGmussQ+xNf60scYaa6yxd2p3c0n5fwXwu0GFUP/DPM//hb3RGPPjAP5rAD/G+/75u3jsD6zVrfwl2kH5BPRdXYK3m5heV77ARg3qygq4kQ87ry8IAue7Itrh5mRQX4uoibRf9MflgNM+rdawkkNYFwkJPOKCt9HXJF5JXj/OOQddM9dIqnwmeYDAUPQrysuFYH2EGv0ShK6jctOhRq76nMvwWHhVRUAECRoaaStVFMrnyJ9KMa8fLwqNX6aIZ/fyErLREECRn7bg85pkPZXmF1EUQSAXeaiRO8m1GHpjjaAmnJsgQiDdawPNU5DIpUQ+P9/7Li7MSbb7jeRRvkahiptIEVrJAVg2E5W0vh4TAie5fHLuQCGJncLXvz/dfhEAicAAwMPBdT1fKV9wcI4S4c3UVwEXQfGGo7GiiXJvxUZZDp/FoAU51XsRBYAUDw/pOiPJtIKnFrRl80yGmJFWiRanHlQKW6W2RQDAzFU8J2eoSvLcgqCPg4Myn9+OZso4ltxNAw8x533mmSTkp7pNkOMi+l9ItNclo7v5bHUlDWx05bAcqqNyqkrF1K1cPPfYtqS9i9bbc0hVHr/IIT4qef0BzK27Z/60nPNmX68y0majuYchdHE8qiC6aVoUHy/mdBu9m5f2t++Zi97aQlrSZ/m971cFfOyccGlLymnYEfyi8H1fz6PfHwIofECHC4XnyBWNkzIyVNKA/j5gJkPqlDEACrQvs66zsC7k84Q/whnOrxMWhjAhemamJQwk103QvCxINY8NHgk9YZIAGQs88bUX8Y00TNRvfXfxLADKoabjzLU/Yl9a/qYKeAjHRdge2YrRXDJBAvuXSfAr7s8RjohxEHKZGpMZ3S5z5ysHHwUA3MxW8VxIuWezPToPn5G43EswZSZLe5f6vuwdIAynfF3B14LnxDDRcgo6f7MvwaBbVG6W3Pa1LuYR5/Yy6+TEy3T/u94cZwPKVbyeUr+EEXLS38bz0TMAgEfHjBJmEZU1AIAOF4APLvF55Fi+RAiq5BRKfqI/byFdIf9lJvTZGnU0703uiz3/u3NhGK5q6Q5lWPGYa5tIfaaIekmOfpTn+p4j7duMEBm/Nlp2WN5zmhZ5cHYOtquxYOfU1bG0ZJ86dkBdCR0xd15xGWblflVZbfZx7mc/cjeLj/8xACMAP+o6IADI8/wXQVHHPQB//C4et7HGGmusscY+SNb408Yaa6yxxt6R3U2kbgPAz+d5vn3YDnmebxljfgXAT93F436grU7uv04GvS5PwY2Y2G3V5UxI5EKiqLb6pZhE0bvdM5WCrjY6d5jMtG1131WjKQlcqdva8g0cn1hggmUuCi5KZBJF7ZoC0ZonBadf8pJENdCOqGoRTo7KynFSRFhmWXlpE4By+QUJklyAoTfW/ATJV8iG/KOrk0JOf0rns3zxDF6KCSW7ycqQoth4JVnHWxHl24nsdZejvwNvov2RaG6yvlNBnJYvUpuCzgHA4oByAAQ9exjXFTkTu5mewDUuKC4S3V0Oxb6dn9JzFERM7GxwFY+3iD0mOXmjdKDInlwTya0bZQM83KbiuHOWoNacun4CySKS8zoZ7Gh+x2UuaSCqp8ueh+OMprat9gHOi7vGxWiPc0bJSh9Yp3OSqKnkeWS5hxbLrgvqiSBUtTUxif4uZiG2GGkNQFOjKLXOZpc050eiqLayqxshtf8WdFnGdoIDHauC8KVINArq5i3UIfq22mRd/pONvgDlHCQ5D0Eh5f/l3KTiORHVWmlL+nk7dc7DCsLaVpcH6M4d93Okle2e+dOyIl2BgsnfhxUOr2vD84LawsGu2b7DHY9ii8UNHTeF76mWrbA/XVSxyJkpxq5E9/M8qeT8iBGyV/9crvj7Ot/N+LNr5pqftMBuqa0+VnGAnVIbNgoiZQuEcXHM3y9UgRlJ6ymbJC2+4/1FITPODLIvcRkCmf5XVoGYlXwjUTum+XWxfQq/OPsRAND8OWGJHPfHWOU5dINz/hC3MDtD3wlCJ4qSZmSQDzlv+SoXZeE50i5RkHFB8vzmmvodVUyWc049VeGUeVsLrudXcZVVpQWxPOGP0M7o74zn4XiX/F2++rYWVJdcuqWrdI32O1tFaaEVLnOzNS9K3lzl9wT2K2eDq/jmnHLkJH9bcus2/E29H5CSCckUiEb0t6B3FqwSOznt/px+N1s3mpCcD1iNeauluX7i0wQBzixNAXv+rqiUy5gzGRaZlKaISufRMj1cTQtVTQDoYajjV3xOmeVRzyyzrZhXquidjabXlSIpjuOySfqlZxko/IldyNzNwbX3P6rPtt3PfuRuLureBmoy+6vWAnDtLh73Q2NH0YtsEQK39o+9sHIfONsh2gspAPD9AG7CqZ2M7j5UdlK6m3DqeYG+uBaUsKL/8tIpL4ryMIbhauFMLYqWHEuS10VUoocVHONkYpHpXeIJapGH8Hg1IJPYDPuYsSyvTFAyOXrw4HG7RV0hXnRhgBbLWMvkHZpYqSDi+MQJAcBTAdWIa3V4ERHwQqATAJvkhMyE+vD65CktgfBW8hAAaFI2UCzmTnBC87kWUYjOBDeUoiO1jA6CTAU//Ij6J44j9zL4+0S1kDpEMz6H7bSoS3Qzo5epN+OH9bt9KaPAjqltIlzivopzkPpFaVKUCRBrm1hluGUxLAvYM8F1pJ0yxbLNktXJPFLHLC8RB2lP+y8O6dkOOc7n50/ry1ZP6qLxgjcLMuAs0X70ZSfoAQMRGSeTl4M8SGHE6fI1TUJf6TqyyBQBlyhvqWBC4tSkAqrPKFBQiWVxJsEGkk6nsS2BCDCt1EOgY1RKcnjwal5m+RhBv1R2AChTXOrEj+SZK6hv1QWfu4iso8D5fkcpb3WJ6YfRPOsoN3W/s/9/r5PW34XdM396mF9xF3G2LzlMKIWEu8qBOs+rLsTqBEzcMhn2eJOxWEedstty0xJkfPp+XwMQ9suemzZgn1+PZdzlZVjoa57J0JLAJvuQSdZTX+HS1g6wU6FiSttrwb76JpkmTmCktH5ZwEnwq2fmuvgRE0q+0PcAADt8nU7uA28Rjb3/VZrjJGB1M1vSUgmycDnm0ZzY9eZKuxfxrOTYgc7Rep0CXsD0fGBCc38woXOTxWNrt4+syzTyKfma69lx9RUjpjlKmRtdHMGqxZfROX99/ln1V+JLZnkXbb6+PtMwgxP87nH5ZBGUc+rihTtdRM+wn+LgKm7MYMZCracFX689oj7PP4u2ofMQ+uJxf0/7+YVjv07nIQHb1jIweIovFItzbX+Xfh+OdYEb8P1frNA8mwUZMOQxIWkamcFWRD7ZtwR46P8BFin1sSgxM0EvpGCnG6huIYHPPlOChLKYT+EhtHwMQEIpbtkne7F1VD1hsboAfV3wR3yUW1PSLj8iVkcDt8X83HnErq/sll2pa+tOSibcD4u8u0m//FsAvmKMefSwHXjbVwD87bt43PfdjDFdY8x/Zow5b4yZG2OuGWP+mjHmoXvdt8Yaa6yxxh54+9D408Yaa6yxxu6O3c0w5n8O4IcA/Kox5v8O4O/leX4AAMaYJQD/GiiZ+5cB/Gd38bjvqxnK6P4VAF8AcB3APwFwFsD/HqRE9oU8zy/ereMdlYR+uyi0i8r5fqcimFAvR14ge64ktC03WwijSFSk2mcxKn5ZjnjYEdI6uFx+V/SxuA4uHC9iEsdwClMOZglaZNuNhKJbUpga+TLmXAi2oC9wvxAh5GMKuiKIYAttlZ7W4tIoKCP7TJUUBGqSLeEKF+1eP8fDY8J9WDNAyInKFwk1mppYhUIkEVr6nuSByvYLVUdQw+fCl3Hq5OvUPNM5osFMESSNljLStTcr6JVCIZLCs1vZcaW9iM3yjvZDkqkTRgQ9ZIogiuT2WxlFB7vhTIVMepjpdZNjTlO+3xyJ/ur8C/hiTlHM3jpFPxcs9pIOUkQr1Eb3BkVPl6x7LQnkEvHumMhCDiXCbdFzRoycrvAADpaVDpuyEIAIAoSjrl7LRKqGbc1JXAVAMKXo535M0W8bqZUIaZqz9LSV2G2j6VGyw/04CwBYNSxBjhRzRuh6HG2Wc/QQaPuFVHVWeb7l+acSI+Wooj1fiECEvY+Lekjfw3C1Igsvz3GaFlLaxdwRWMcqR019v1Oh3dXZUYid2AOM0gHvoz+9EyqRLT0uoiZ1QjR1qKztM6itTsUH2NQwic67DI0sS+AKuNgReNfs/sn4Eupz3bnapXhc63bPKnLuInXbyVB9gG9R3wTNSC1/ArCQBbcR8vgXpK6FAl0ThErES4CifIFsm+YdvBGX1/1CTZyduQAwuqS2OwNuyDEIqRPGwTTv6Hy1yEXoiea9NiJ8us3lC/qE6sxWJ5jznJyvUhutyzw2Jim8iP0DF8wWOf7F9iksR3RNXksOjVkoU+S58GUV6irk++l4UR4qu0OEwRL46juP71Afci4Z8+3oWS31E4D8ipQS8KMAuMxzzxpTRnsBght+6Tyuzyh2H5pImTlDFsMStPF0sEntAeobYHxgxqC6lJWY0e+i4axIhWBqqj/ncd9LgR1uo1OMBTmWsk6s+d+lLQZBHzELwbUN3eM2U5AlTQEo3osEeYyzDqQUh+6Djo7fOKPrJSwsO13GntPp/9WyCkD9nCH/L5czKfsg13fWo36CGnYqTDH7ffQoxsidmO2P7jU18256vddA0stnAPxVAH/VGCNk8uPWfjmA16gET/FdnuelOjz3sf050ILuWwB+PM/zCQAYY/4DAP8tgL8G4Mv3rHeNNdZYY4096PZh8aeNNdZYY43dJbubi7qzNd+t1Hx3eGjmPjdjTAgqBAsAf1YWdACQ5/lfMMb8SQC/yxjz6TxnmOFdH7OaY2DbnUSsj4oYuMIkdlvebci5LuonZhcat9t1c+mKbdXCkLfjL0vCfNtnGWRB0EyiCerCD5/ngq4lmm8g+4xxs5RDBxQRLwOvkhQvny2TKIImbe6kx/CwT5G41Ej0kyJ5z7QuqGhKNOBI6SMcmfMMENL+kos1ygYaNZUC44LK7GYDfKJN+WISGZYI7pngOhac/yViHdFwhqWrQwCFMIpZsJhG7mkumgigSO6EXXBW8tTmeagFvCWqJ/z7SdZTFO+YT/fx0RZdj5vpSQwTikoKemkn+YtJTscJb1dRzzkjY+k6v7hOcixdpmNKnt12dlyv9TUupyARzLaJtM8ngx3uM53zfHUC9DhqGnBUOxwCYyqwmzuPVRq1MXuCIuGaAD8MgWt0fSTqu8f37nK8gUnO0Wy+V75hdM3rVPLUbGRDxp4gj1nuwefrleV0HEEIJlmu4zjSEsKFuSInhHCUI5ZHJbRL34A6efihoituxNNG3mw0T47lojJRtHVopNNmALilGuz+POClDMTO1nz3nvrTo/xLlh1+LeuK1df5EzF7jBRjrkBnXWRPjPJAJ5VtrsBXlhXHkzHnFmMOw9UKK8QWbZDjiJ/x/Y6yNFxEvGUSLTotTIBpXqAZCcrPhIGnbYnPCRix63vbyl7wOaHx0dbb2PA3uV0uBcAoDQBlQNiFwgGgNcgLAS4R+5gmOm/J3Cm5WVfSDc1jk/w0afOEv6tFtxcsHjVdHyN/olc6t3iDc7e/d6DCHyH7njajZn57hP0FfSdz/Ga6quctxykEv1J8tPV66Tjis7bT4yXUDiAfIkyRgBkT0vbZ4KrmI4KRtFzKAQ2nQMD+aMzXdxLD4xI2UoYh4/1P+CMcT8el9qXtWdYpSiV0eDy2TxS5dCKYwnmHNgNE8reN5KGvdoBNHkNcyH32yD6yl+j+TTPqlzCOMtjzJOeieX2LwcEaCDrmpurDZfzKO5NnMvT4+s5YjyDCVFFoyf+2Bb/cnDo7T02e0br5WJ61gqlRoPCF8NZcj+MidLbvnM+vlo4t28vtH251Ynz2tqP8yb32NXdtUZfn+W2WAB8I+2EAxwC8kef5b9Vs//sAPgHgZwDclUXdUXbY4HFfzspwdtm52Q9ZHc3TTTC3Fe9cFTRxzK3WsOJM43hU6Zft3O+kBpYtzOJOHEK/bJmkUqfsmKWCKZPWOC8oBb4jMCFtRZjqNjGZCNeDLTzENdiEenHC29WkbZnkhS5yLV3DF1o0ZK7L4oQpl7h6oDV92jvE6buerGEzEdodmUy8fW+q1EdbeVNMEuRlgdgad9RBKA2TFwrb2XF1ouJg5ThdM9P+y3dZ7uk13EzJ2algAGIkfP5yD0QZ0wsyXOdF4yaraU3zjqpfCh1SFD773oEmgHf45WO+s8/XaKk4j10aj/vZkvZRTFQnF3mIh1s3Stukfl5nq48DrjGHlBfbwYbej5wjGyIs45lMF24YMG0l8HS7mLxYTbKevtSJUxV6pe1cirpdFl2N9+/w9Z7kPSQQcR7q3zij+3qA7VKtK6BM73TFjKJo60j65VEiJfK7kOv62bWBXJVNW3zCrmPpHqf8ol9P86T/lxPaj1IHvt/rCR1l76c/vZPA4O3MFc2qa9MWbXCVNIXua//GpV4BVRU8epErU4XrhFJkW6fT1z5IQMGuc5eywETR56IvIkTRBQXZJJDY8w7UB0hAyTMZ5qx2K0EWX+n0c31Wxdfo+cFTsRVRb142haLxEGUVzLaJVDVSglinOS1ga6Vd0PUkABUY+CMWbuFFjcxVV5PT6gNu8eLuJAfZRtlAF49zpiGazCCfOs/eBVaQ7qRocQ06OY5Q2XMvxxIvGvcPzgCgxZZQPqU/X+78Ol2v4xPsn6V+SA27tZeeBkCLQZ/9idAwp3kHz7ZeAwC8Fj9G15ADiWv+NswKHVvUi1P+zDopELFvFkGSwKivkf1EqTnKW0qNFQqj+CzfpAX9Umy+Caw8R3/3WHDM+5e0/7yl9FTxL6LOiSQHnmAa/5jG4PFfWsGUx4QEfeessuojRM5jYc6BC9MK0DJFvVMAWOIYUcvc0vEl7xWZqndPcYsXjfJ+NMVIAxvziOvQ8nPW6ZypiHMFQfHcuz6k1epX5gr7mTtsTvL9fq1au+s7bP9ymAJ0HfWybj/7vNz57n6i+n8YFmJ30z7Jn987ZLt8/4n3oS+NNdZYY4011lhjjTXWWGN3D6kzxvyRPM//zh3sZwD8XJ7nf+ZuHft9NFGNuHrIdvn+rlFi6hA0sboodZ1IyZ1Eq6mkQXU4uJFRoazYERm7DfmNGyGl7ya1+3tegDguI3t2391j+35BWysoLlJ/LMeyV072nTIV41a6olLwKddZAYpIqkSiupy0bqN0gtAtG6LoeeZAI6lio2ygNBmhEQryJN8DgNnhaPYqR083eiS2AaL3iaUW1RMABkxp7HtTRQI7jhiM8ZNCap+lmrubywg5airJ1+OUzuNscBXfXFCtnVMeJRJLbbrTwaZeQ6F+LvJQv5NoXS3dLzsJoCjD0M+mGtWUaKDUUwJglYKQ67dV1NnjejxCIV0MZ2gzMpYt0XV7/OAyvsXnMc4ocicUnL43VSruCUYOJSI9XR8DfYoWI+X+JFNgle5D3J/wMekcQy/TcgpTHmeIUiQDrlnI8uByrm/FG4g4gir0GLGy4E8x7kU0wXOm56E/1npIMjbsMSr3QZ4J+/kUVNCOSroURlu+Hg7qZwsmuRSaJJlUqJm2fL38HUV0nDge1c5bRb/KyJ6YnWhvz4mHzXcPckmD98uf1tb8rKlhaKO6VYS3vj27TcAuhRFY4lrV+f4w1M+O9NuR+MPoVHXUq6KEyLyyn91XEVQRa5mePo9ahsAw7S0Pda45yGke8PJCuEjo/EI0TxGpSIV8nm3dorbzQJ/trkXzE0q8CICIX7FNEK54wyptKIgT0/YwTxQJGh/QHC0iJL4liPF0SPTzSwnNjU/5byrlcyrlXbxc/ZawTqT+XP/SKgwzRXxG15TtkBQDRtC/m+kJRR/F/5xdeRUAsH9uC/FzjHJeZWpmRkjc2osntPSPlPfpmRmej56h7SyeIufWMzPEFtJo9w/9WNMghBbpjY321zhleUbZAPOMa/yxf9w2RAt9xlzAlAW+0OdnIlugYjeYth8EyDIW42L/IsJgAJQ5Ip8mMypiJvR8nfeRqBiXWJ4nlKFr7ddnam6cB+qTxSTdIoOnPkbFUTCvlDIQXzKfF4yYuvdWtyyK/byL2f93KZN2Ws9RZU3cuaPOv9T5CZnL7DnuKIHCOnbbvWaH3E2P99eNMTt5nv+L2+z3PwH44wAexEWdcESqb7Fk8hQuH7K9ZMaYlw7Z1CS5N9ZYY419eO2u+NPGxzTWWGONfXjsbi7qtgD8A2PM78nz/FvuRo4o/n8B/DEAv3YXj/uBNnvlfyfFFm2TaMid5JiUi48XkQ87h47alKj7ViU6LxGTuqK0dX21Cxy7x7blrF0xiTBc1f2EFy65CV3TxiKTQqCEjAjfPcmNRphEZjrCVCOpElkVxM63ZOIlH2KWL/j/mSJCgtgN/XFJwh4oEs5fiJ7C4GPEzs0HTiHuwFP0zutQ//anSxr9FXQms9JsJE/wM+3vl44zXR8rQic5DHF/oVFJkdyX/AMAWOXfSoFxQQHPx49V0MK2iTTno4Wl0rWZYkejeRFLc/sarfa0KHrPp23L5kC/kyitnEdoYqBF0VLJEYxZYMaLAo2aCvJ4Mz2h6NsG58tpfoDFMhfhFjnnfGCAKefSPfpl+lxsabkJiZpKfoQXBZg8zdHIDsteTzKIkrlcZ4lwx3lQQTIFcbZzVmWMh+GqosLyOWdRn5aVzyg2Ao3HOrQ0jke1OW72/6UfttnoSYHiFSISvl/NV3DbtXN3JU/J3v+wfAU7L8JF6gjhQe1xaP/DI6r3Onr6A9j74k8PGweuhHhdOZyj0FK7ffde2bmRdf0Q/yG+xx7DblkNu/26eyzHFH8iz5n9bNQhk26uX4BQc+jE1wQ8J3ZMZOUhUzuLPFUfE1i5dGLCLBFehvz+uD/GCW8EwJLvzz31KyLEIXl0oYmRsl+QfG7N+QWAPqNQIpiyNUd3c5n7yEwW/t319CQSZjRcjim3+7HWFWrGO1BWhMxx+aoP9CwBFgD+FjNnehGWL5I/EVEuQZTi/lxRO5nvrySntRSDzOMyx6dhAmR8Hit0vPxqqvt+NLwAoEAq97MlFQ5T8RXOqcPatubGyXXKuei3vwmkj5Rz6vLA6P69G9R+zy+QULscAFCIvCwQFnnWISN00+uFQMqckEYEnl4Tw+8tBxuE8CmCON3X88ZOUX5BctTFzxkUyJ6IcWXWMydjbomxCXmHaJtIx5qcjzBbJlkPKedx5+r3O0jERylaNtFP9/1Onr06VE6+P+z/bp6dPI/kV8rziud11LfWmasnYR/ndsJ89j73u1+5m4u6nwDwqwD+qTHmy3mevygb2AH9DQB/FMA3APzkXTzu+2nC8+gdsl04Eft30lie5x+r+56jq8+8s6411lhjjTX2AbG74k8bH9NYY4019uGxu7aoy/P8RWPMTwH4RQD/whjzpTzP3zTG+AD+JoB/HeSkfkqKqD6Adpk/zxyyXb5/624d8KhIwO3yReq213Ga5dMtQ1DmQpejmr5fFJCVKL2dd1OnLOTm29iqdocp3gH9SqQ3TefodotjAUU0NDAJ+izzLtEmQZlCk+KAC3AKCjLBlqqaCeIkZuc0SZTqpJ/ytkxz9SS61TIxFjlF1AR5kryI08GmRvwUOBL1xCxXxGk+o2jg0Btr+xJtlPMYpQONskl08rnwZQBAEmSaMyCqY9FwqnkBtxJCNiVyeS09VcnP8LV4dxunNCeB1TLToeb2Sb/2ORegi6FGo0/5cen8nwwvqeS0yj/7sX4nUdpeiyN+HStyryqeFFHuXz0OTwrmxhS5C02s6KPcj1spnWvbRIrQSaRbIsTjjl/kUcxINQ5br2ruQsS5cnItw1FXI6gJ50OgEwA9Rhc4OnsxJnWzWd5RJNguOg7IM1hEGQGSnhYkYMnQcY5z5L5lklIEFbBKeaCDfRBCqbk82bySE1tndc+Xa3aEtS6y6spRS8S03V7Xv4uyB0nlOXeVC+1jFshdcdy6XK87iaTej5HVOrsX/vQoJbc0nVcQ0DuJbttodOF7qohYoZ63XlLJdI/nMkfsseTm1pFPo7/rcvfc8e77nYrfEp/mIVAWwlBQL1EAzENF0PcYxcqQVBA6W51WnlExmduXvKnmM0k+2LJ3gJTRevmUedMuvv1sSOjPbJ1zd3uhImgQhd+OjyxMnTZa2g9hZqiiM+epLZsJsqBQr6T2g6J9viayTzBpF7lrzBzxdshX+fs9CJa4n9P9/HT7RZxnpUplu/DtXHk5wSgjdoTkhh87T0jla9kAr0RPlK7lR8MLypT4XPt5vYYAkN5cg995m857WC5Dk4UpMOR4PecI5tMErZd57MR0neR407yr10dYO6HFpJitcWz/LEssHHuqyKs7ICRRcuqAAMGE3h0ESd39OBcq77c0d7F7kdHYSQ/77LcvRoTYxSBUNUWEJONzs55ReeeRcbke0PjaTQeKonq5qGPT7475E2ymdG8lJzzCVFG/Ym6XfNkOFosb+jdQLStim43y1807dh6ue5zqPlWF5sJPVN9Dj8r1q7M7KSl2P/iXu5pFnuf5t4wxfxjA/wbgF40xPwYqyP2HAXwVwE/neX5YPtqDYL/Nn586ZLt8//33shN1zreOjuLSIT2vgywrO8q6ekJlCen6hPY6J287Vfc7G5Yv6gNV++zKWdf1wfMCdEKa1NsoO/kkDzDNy5TBniXXq5Q2i2IpdEtxvl2QU8mRaf0gceie5YxPBCNug2kleQurPgmwXE+ofzLZn/JvYYOdHM6SmIZSY64dKPXCrjkkzl3ERva4rZ6Z62LJrVvk7/Qw5wRrqXeTBRnCETmr2AidlD6XzUQXmUL3FGpi6N/Ul4iboJf2FJ7uJ3TQj4Rbpd8DxUuBCLlcS9aUrnSOKT3PtF7X2nhSw08crBf5JGKCYkHFa3WYxFOZ6W5M1+FSdEYpJOKghJYUeAmO+eRgn2y9Scd59G3uaFjVAM5yIMlK/Vm6NuRrmmKxylPYkBfD0wRmxPvvHC9dm538ljpDGfc2/aMS4LBqWMmLotRDjPNAx4Sb2J4jQ4fH7X5edqqlU7MCN0c5U9e5GROUaC6AncRezEkuZY5obuWFmyuYVD5uxylhUHa4bk29cj/L+x+16LgfnO/t7F760/qSBNUxcdT+AM3jdWOj1ZK/eR62Funt9hBANUBgBxLt4xYvjXd+/20xL3ucifx63TlKsGWXg1h9w2JKeaCBsJzp0DHmSr+UxZz4lzb6Kj6hL9o8Nx5kPbS5/IrM7UCxKBFREKnb9kL8tNIVxeIB+5mwY9XSZPrejakGxGTxJHO8b/k2mUtlwXeicwtzXpwtzvJEnATAGZ4DL7Gf7/NzmXhafkBKB2Qr1M88MzAjWrgEljiL1A7dz6nNN2JaDI22Bnjye1yDb0EL2O+wSMh+3tfyNLKQWzYTLHNwUM4j6NP8n/fnesTFI3QeZsyL1TCHeZX3Y3Eu9FsqPCb+TWrk9cwMN1gQ7JGAFmBnODj53NJvY1MWdQuma/rdgn55k/yQ0DzDUU8p/lLKIF7h6xx0gAmX9+E6s5eSM7iUPASAAtO2xelYx34YSB3fjo65YyBqrQfygRk89dMJj3cJ3F6MTum4MBVHWb9AcrfVLcDs98nDSgvYwX7fH9L5cPAwjkeWP+rz76tUb7FWa4goKl+numCR/Q7rznd3Kp5yr33LXS9pwIndfwLAYwBeBTmgfwWKKD7ICzqAchf2ADxujHmuZvsf5s9/+r71qLHGGmussQ+kfcD9aWONNdZYY3fR7ipSJ5bn+d8zxgwB/GUAvwTgZ/I8r9FzfbAsz/PIGPOXAPwnAP57Y8yPC/XFGPMfgOrTfS3P8/e08PjtogI2RdLeh5CB8i0vIqX9Cu3qKBTPbkvKEdTTPetQiRF/FtEO2eb23e5/HbooKEifkaQkN1hiqXkPZcQmg6fR0sQSTJFIqvQ14fbb6GskVc9HJP6tBGkbvZPoqqBFbUPD/oQ/0silyDIr/RKcDI4i2vhqdA6u2UiUoDjPtl4FYFFWzl5BvErHbO1wQdR5SxEniYxKon2Uh9hk+o5EfEX+OTQxUv5baJhv4owKkQj1RK5F35uqaIxE+o55+7xPG8c9SgCXJHYqAXGz1P+Ir9HKymVFGjUx3yrKKhQViZ6+ED+F6+lJ7j9dexHK6Xlz7KSEjkrZhkIu2gcCjohnHBnNCqEBSY4H92GxckAlKAClXGKeYvkSXUOJNm8y9TNFpOPLjWraz4vQvNroq/hPj6+dXNMbyaoigILU5SxIkyJRxNluX6gwLpUtjkeHRiJtRCQIhtpmFaEr2pS5QyKedQXG6xLT61AVF+W3+1kn/uQKeLjt2Havo6g/iN0Lf3oUdb9OKMedv+vasmnHRUS9f+g2F+GzxXrKaQPl/ZKkHJGn7wo/d9g52vRL+RRkIEemyNs6Uw33M0JwQpMgEnqkg8oBhShK8f85Wly2pKBP03PcNXNF5WQ+TnMfN7PyHH0lJbRllnV03mszW+PRATNAJglUkGaFz/vGNryE5kKhdwrrw8sytOS+8Lwv81kU9RD3GXESymXgAWNOKVjrWMck6mGL6YQyjwvKFA1nCHp8vhyWMH6ifuG1pOz7RtkA/2D0MwCAx1uUASMiIT5SLfPwVHBRr4P0X6n+jLyZGyeRPE1tCJVxdobGRmunjbg/L/U138n171e49IO0eSU5jRZfcylho/csaiNf4/G1zEKzs+uE1gFKhxUWSu7laO/QnC5CZ2bKfm9nge7lJd6fjnM+PqeF7oV2L2kk9vuU0DCN5xW0S599GfvtNPfUV875u1EqwjwG+xw7SjRNJaiMaZuFdRhNu67Id5kVRtvlmbMF9FyfGQT9iiiKLSroll2xaZ62QB8gDJB5ZX+hbrt+0hbsqkMX77X9wD0wxly8g90SUDL2K5TbrZbnef6gSir/5wB+N4AvAnjdGPN1UF26zwO4BeBP38O+NdZYY4019oDZh9ifNtZYY401dpfs3Swrz97hfhvv4hj3neV5PjfG/CiA/xikPvYHAOwA+OsA/tM8z6++9314Z7kidrSjLpFdttUhe26iqXK1ACMuPQAA62pJREFUw1WNdNgy0Ycd245ouJH+uij9UXKzeZ5YBV0psiSJ56FJVWBEEoFnHFlLc08jTCIBnyIqpHqZmy3c8cASThE+esrFyyWyBQCfar8IgKJnEsXrGUFQKLImeWp0cA7zbM6K9hmhkuRnz2S4xnl5dlkAgBCxNc7dk4jtEx16JxxlBv6YjilRx+VLq4oSTp0Cr2eC61iDVawWRf7GNO9oMXCJAn+h+zyuJusAinw5yQcZp/2isLjkg+Ui9rGn7Yt89wl/hMeDsqaQz/LPi04MjyWtIy5lIHmB4dVVbDOyJ0Vml82BRh63k6FeJ+nfJ8NXAABrxy8BALZE968XAGPOfUj4fiQ5sEYRVYma5lNOUF/bB8YcpZScyM0ZZuuE7t18k66rCNIs8HYlad0e41LkWJEB9BR93su40L2MT5NofqgIptzIqM+2CIMdkZT20/QSfxboV1EgnE/byidwEbQg6BeohTUHyHHcZ7Quomqf/1ECJkUeRTVK66I4tlCGm2dn51jcDxHU29jZO9zvnvnTOnEtoD7abrMp5HeCktklMMR3HJXfWbRd5LzVFzY+GgkAivFfEpDgPmTZXMeqjO3AoznHR6h+Qv2J5sz5WA1ofruW0JyQI1MfI/5EWB8heupbxB8p8yMDphnNNeIzBMECivmuLaiJSdUXaPFwQc+G7SLnzS9KD4iYkxb5tpgTwgAR9oYgYz7SQmKf50YtiA0oQicIVNIrBLvkeDKXhqOu5kQbRuze3H9cBbuETSG+9Hp6SoW0xHeI3whNpKUcRPyq682VwSG+TBCxbKXww5K7hg77y8EC4Ard0vfBhTU9728uSDJByj6kVvbSKceHpmujgskh1j0N7L3C14vzDNeojXQaY3P3LLWV0TWXUgtIjDJFRCgmhY83Y7pHOYjtIu9CmZdV8pYNvJLwG1D4kOP+WK+1+Ew5x1G6pOPXLj4u+WnyHMtnEBQ5tG7pABc9A0TnoZw356Lk9Nvys50khUaDzCtpOqmU3bHZJK6Qkp1HVyeQ4gop2SyRPK/ONfa53kv7gT1dnltFsz5kluf5DMD/jf811lhjjTXW2A9sH2Z/2lhjjTXW2N2x+z582dg7k5C293M/Pa+qHnZUO7Z8tMttTtMiOj+fl3MYWq2hFT3f4s8EEGnnmoiNWBHpreY+2MqYknckPHKJeGZYQptlEiWiOkpFsj8voW/0+0QVLlOLMw5Qvp6oa6pymUdRvbaJ8BhHOAv1sFTz3iRqKkpe07yjkUoEfK0FsVtpI16jiOJnOySc+s3Fp1W9U5CwKSNQbRMpIiY5aRr5THzNQRNFzfnKAQyXBRhGFFE8kVDEMspbGiEV5S6Rlu6ZuUbw5HPD38QOR0slutzzKNraMonm0gm61PG4WLtJ8eXOr/MxQz2e5DsKorlgxUtBFgGgvbPE52N0X0EoJRfvzfhhXI3XS8fu8T17vHUZz7QuUPtcyFzLGAQe0OccuTEneIwWwDX620vo2EFA925nYwQMO8V+AOAZHH+R+vO/zH8IAHAppePMMNYx7eabheFqURTZUB+6GGhE37cQOvuTzpuuzwK3AND4dHND43h0ZDHWujwFoF4ZsxxRLedBAagoCdpKh+5x6uSl6/5fi9DXRnvLSJ29z4Okdnk/2WEqcnVm+45ijNu+5/AoeF0ZncOKyPt+UXbHLY4OVPNGbRPkouzTqqyVIno/AkBKgnTsNX0uE2ZrBGB1xzzXHCTD6pcJIkXqxOcIUpIgwjKIhXHM0PzSMnScthfp/CXy+AuEijw93iozG25mqzoHDnkObY1o33glLkoZ8PmYyFPp/JsZ5y7xHLyfLVXKAGmus5VD3nqVWAnxmUxVgjHne7bDc++NY4gGfG+ZcSHHzYIM0wPy3+Inz7Uu40Zyks+bUCw5Z99khUqmE/pom1h9h1yvoTdG72S5bEHMviPpRZivOzntXL7A34SWHZJ8wHDcwXcnNKc/7JN/fDOhcjUtJJozLiWFTj5ESNzeuS1guMwXjD/TWaF+mXDeNqOdrckSLnKee3+DxdOtnG3pj/je8/FZHIAQWnn3kfcXDx6yGv2BDr/LnGRkV1gfbRNhnNA2yakT3xNhqgi16BAsUKBk8pzU5bjKNlsnoS6PVZ45933V9zuVucBGBBOe0u25XfydtGkfz2V+FHNPUNGTsPPK3TxuW/m9zu61r2kWdQ+AHTVIXCdnCxoc1U5dmy6d0h68UgpBBngZ6mYnYr1AuvC/73csKuaotK2OClNHx7GPJxOYlB/wrJIDc37hFQcgC7IcGaagY2dKaUx00oql3INXeA5XxldoCZ7JtD6QyPHvZcuaOL3g+ju2iMrwVVp07H6K66Gt8n3Kck2EPkjp5b6FRCffGwk532O8YEryQBd1QteU+nbBfIzZOXZaclmnuVJgZGFky0Cfcagty4Z+/3BwHRc4OXzZ0LFPeLt4NCCHKQnq4nw9k+GjvHgSwRcxWiB2S9+luVd6WQCKGj17T95UCoyIkMjC9SBb0r7KS8eF6BFdZAo19gxLgw+9sYoBTB7Z4Q6xo109BkS80JMXoFEEMyWn22WhFKVhBtaLDAsGdK8N8NYBJfefj87SuQnNNy+ek9mszMy2HUk/IIceooc2JAhRplq2TKLnLeNSZNanGOlLo72IOqxOHTnLglop+wP1Dq3VGtY4QxYUaq/DmK1SW0LPsek4xtRTVmyzKZbVRPuksoBz/7b3r6ObN3Zn5l43+9ofZXV1pMTq6lXV0fPdl0E7sFBQeYtxXbwUVhfwbhtu8KF8joUwgwQu5NPAUz/R8Zg6x4uh4/4YNxMSFBFf4yPQRaAKWPAz28VAF3wT7nOfP2GVhZFyLz0zV2qhLXBF22baDxHrEAn+eGC0vpmUNMj7Rd1PoXC+ltLctchCFfhq8dzwZIto/aGJIMo88ZoVnFxhWif7spxFndKdBH2et+ci7c8W3hqoz5SSCUknxsmM/JBZFAIeANExRYBE/Ios4PreAR7xWZqf6Ypxf1HUhGWT8jh5YNUHnJT3yQKD4y9SmQCwKNdssawBxPMRBTvFp/e9qdJCT3NwNeL2s7Mh8BC3tfIcfY7PAwechsC+g90q+pdX8END0tfb4utlNMPD08Dm81wj70ayih0um6yBbab5Z9m8GL8oaP1rhhajMW5o/wHgYnRGy+bIWJKAeIKZpgPIO1OcjvWdz32uyHeU/UOxz6T2vU5M6JY2zd99v7UXgHVlBeT90fV3STI6FEwwJqjtTzVwVPjEulqu8pt7vaj7gSkfxpgVY0zv9nse2UbPGLPybtporLHGGmussQfZGn/aWGONNdbYu7V3E8a8BRIH+TPvoo3/HsAff5f9+MDbUdHmOuGTIrJN39lRFbcgpBsJsb/z/X6tWIHYYdLTdkTGPo60exS6WBSaFSpYoLLqdSZIhSStz/IQQ6YDFgWaLUESHmoLibogKBLaJRpk0WWEctADoWxtRrFO+COlfbzBhVA/Hp7XSKIIhEjx8UvJGfwQR0iVdin0vY6vBVqFdHjM28euRyiRCKRMGMXreXOlTrwSfwQANGLYzwzMRApnM22iFylCdfy3qa8SBY3ylhZKFzTrhE9RYRJKoQikFL31TaZRU0H7xIbeWK/Fk10qrirI0tAb47H2JQDAIiHEruUlGhEWWzAiNrh4EgumzkiS++4VEvi7lq5pAVhJ6B/4E0VRJQIp9unwBSwfo+jk1gYjgyscEQ+HQCrFdPm+DEIEl1n2eZui5cFDhLLlA7+4f1Kf9upx/Gr0FF0npoBGeAPA0c+uPe57LHHuIUAOihJLyQiJnk+ynqJ2ii7z+AzR05IGdlTzMGEkC4yujYJKxNMWkbDl4KkN+p1d1FWeX/vZrotcuknrYkeVPbD/rkPsXKubq+5j5O6+8afl+2WxIw4pHQFUi86LHUbfrCv2K1bcY5Ta9P1ORWTLplPWibTURexlH9cf2b+V8eXVxL2FCTITBCMdqg+Rz8QqZaL94//PMSk97wAQM6Wz583VZ6RWmmV4jMVQdgnFEBGtS8kZnR/0OIJG7SQFkiZlDjo+ZowEnZjSPB8kLK9vMqWRJsLI4dSC7ew4+h7N996Y+pp5KbDDPizgvjL6ZZIekn656obI8WdLc+STcvzCSzxF1+SsTyxGAIDryRqWfLquyxn5XynHgNUdLLKyuEnSixCOO6XvxPqXV1RATNgXaYfpfpGvfTZvkh9/PnpGqZ9PhuTTJF1h6I3x6fAF2p8FX1R85cwSMGQ1LqFcRiOlW5pNFhmb0v2ZLZYRdna0HwCQDKlf/QtDbE0ILbySkh9+MzoDj8VZEpTvP2CxLvhdJkQPj7XIh8m7gwi9rAU76rfEv8ingadt2CUT5JmUub9gcB3OxvC8TuX5ku+BwnfI/4Ogb5XBKvu2OgqkzUw5iopt7w/Ul/qS7+2+3qmvudeU/3eTnG0gUkHvzu5GG4011lhjjTX2oFrjTxtrrLHGGntX9m7Dll8yxvy1d/P7d3n8D4UdJhde911dpLsuilD3Ozdh1f7bFUqhcgfl/e1iwRKxsSMshTRsFSW0i6DbZqOFdecgOXIdzq3zEajEsViqEdIx5oxmiADKFEWek54H8/WXMdQkZEFGTgYUTRulA0W0RO5/KzuuRacPGCUUkZM095XP70852iRFXIdtjRq2l4hz353PscgoQrbHkVuJEMcI8NKCuPWf6rxE/eHcsvWrxzE5SxE8EUrJhz7alzn36mHaFr1Jkd5nWhewxaiXoGyCEAVIdZsIwJzytjSfQco2iA3b23jGktoGily8KA+Rp+XobxomWlA8dsoWzNb2tfD3G1NC6AQZvZJuaF81mR4ZepzPIZFIkb3ezo4j+eJvUidXOJeuy0XFg2Vgl8UHpETBq3tY+T7lmUR8rin304xT5Bz1lmjrb+18Br+9eBoAMM7pPCRibz9LLurd6ZzR5HWJhvaMj4FPY0DECiRXMMkDjcpHefnazzHWMerKTQPVkiS2uXOHXfS16Hu/8lu76HNdIVj5dFEZO9ft6NIGR+USF1FUF71L08PP8T63e+pP7XtWRUmP9ituZLy4j/W+6rDctiwryugUOTnFvu5x7Ii9e2wbjXPzewSxs7fZOTySnyd5Snb4e5mZICajIt5RXkj75xZqIiicIByCqhsUpXWKkgYsVZ97ypg45dNc3TUz+Ps93Q5AfdyyOdD5WLZJ/nTUgyUGMqTPtSliFjARxoV8TvOOCmQc8/f5XAkZOxXexC7P0SLxb3YSpOssOCXCHyzuMT07hrnI6OMNuk5bY8ox85HC53lM8voQt7A5o+2nlq/QVyepX6emc2QHdI82jhFaJmVuTGaY12AjbzFmjPpJLpoUQI8GM0UyxdeI+VGA9iUuI6Rolq/bxZc9EZK/+GjrdfW7ZkhMmegcj4X1Z4DBk/S34TZe+zbAhevzgHotAijTvINstZivASAYMVocBVp+4rWI/NKN/G0dT26pAsBmG0n+ZB8nAhLVkvcK8Z0pPGW3CLtJPlNL8EfHbDLBbHaJ2nf8hK2X4G6rm9s7ndVKeRPXTwJVkS37fVUsSao5ezYKX1fyRPZ1969jebh9r9t2P9i79XRP8L93Y/ntd2kMqCZ9HyZ88oMo89iOto4yU1Ut6mOxIEqbm5yaZWUqJn03r0D09oN01Eueq8QWBP3KYksmMarHQt/JC/CMR5itfDnjxWALheiK75UniQUmOmEuGXJkMvkNvAmeapGDkQT1UTbQF/El3m+cUpvX05NKP5GFjNL4AORrfG3EGSHS5GU55iIbUj+tF4dFTk5BFl3RYIYOi41IW/k0w/4T9IKwdInaWD1JCfBe4uHsnK6FUCWFXnLav4meoWu+zCIty96BLvSWQQ5fXgoAiybELyE9SxzD+HRtuuyMZrMB/FNE6ZEXBVn4tkddXJ09Umrj64vPAiDHJrUIbSqJXCepTygvDo+e+S2MuBYRzpFThM8Ofec1VT/DeV5QXxton4Menffuk+QQsdoBRjReTn77LADgq8mjuBAxrdV5WUvTiS6yVOmSnxcPnqrgieBP20RKSd1lKpQEEvbSvjpkoRQXqq9Z8QLKlmVzfUZdK1Oxy/QSW6XMflZdJy2/s2sGubTrJCkUOIu5oDoH1c9f5f3sBVxxjsX/3XnlfnS4t7H7wp8eJjBzmFCK/VJUpxTn/q6ufblXtnKy/R0AzOc3KrWjyv2qiq64tRVlW1kEbKT7u4FNqV2aI1NfsJvRXOIxLc+HjxyiGljUnxO6ZSGeUvgoSRuQdICIA12zvKM+Q+bZoTfG+Zj+FmEspR8CeCGixYPMk+pfpklBj+xwHbVBiNk6/TZ7oViwACSSIbS7nZQWYiLM8nj8FgYXSZ1y7wmas9NhBmzytRbRrw0Webqwp9TH2RovHifLfK6h+sxei+aErWgVJ05foH6wn/Q3h3SclTHSkPaTBalQ81uTth5HKJdJL1L15MUK+ag0ZJXJcRfdTVoQS9Az5QVfOO5oHUBZRF1JT6sa5ymP2hDRGgwmaJ2hoPDeOQ6yfYrESLD0MBCzQMzO8/QZ+iqQ0hqT35688glq8/QFHPC5JX1OxWAVU1zZUMGy35hRusUevleoUrIwidRUjPOkovL9yTBXERQJEopS9SILdRGnQUNI/dNMx/GCn5MkGenz5wYLaY5G6Tsx+/+2OJe7OLOff1cl194mf9t1UN3aeLJPFG0duuCrU4WvW4Da76ESOKwTfrnXfufdLOp+9K71orHGGmusscY+vNb408Yaa6yxxt6V/cCLujzPv3Y3O9JY2Z555hm89NJLMKZIkbiTOnV1+9QlkNdFs12E7qi26mhVYp4X1NbHOiyKYvetrj5SQeW0ju1EoiQa2kIHs5ypckw9kIhWikRlpu2kdfc8e5aAnNDjpM1bCW3rtQqZaUGqvjr/QoGiMWInaFGSB5jvUtSzPSJEaBYywvX0UI833RgBANZubOE4t3stp98loAhe35sqEtg2FKWU8gqnvNfR2WK6JtM5kl6kCJ1QTyS62Zq0lfayz2UY5Hz2876e43WW176ZnlB6pmsmMypqIlLVwQmKCg93BiqQEvQpgrnYH2B4i9CoggrCFMvktFJb3uAopVhgEkXl2kw96pq5InRnuX6glGoYPX0D+Pg6d5LH3BJHVPfeAK5yCQhOYl++dAKTBV0vc4bQWKVfjSP0z1MU++KEQJU34kdwM6Xz3QfLa2u5gKFVP41FTVgAZwkr6LJgwpBpOYu8hXWPzqPF41KovBk8HU8xR2dl3ANl+iRAJRTc+cGWZxYERMymvbjiKeU2BHEr2hYxIxu9o32qc4gtuuEmwJN0fvl87G1idcnr7px2VHmEZ555Bi+//HKlb/fK7oU/dX3M7cRkbCQXqFIa7X0EZbXLYxTiJrYgSTUS70blpRSI73cqvsaui1UXza8TVCn6Oi9ts+mdivDxfO4jLEoaMGtjobRLX32NPPcRporsaakR9UMFpW3ASL1QOrtmbgkjLfFxWlhjKqagcV2mvl9JTqvPEQRGytYgy4E1TkXguQ2DUJkJMtdKW9O8o/PqLU4jEGTwjeRRPB0wHZSZINH6omA5CBMiy/U40SpTDL+T8zVlJsFwG11G4XbjFT7/g6KOnVAlz9A8GI47WKzwb/ncpA/z1YmyO8Sn+ZGvbXXmji/sL/T6iJ/s3qI+PB89oz5H6rL6SPGwfw1AkZYgaReztX3MNhiNW+Pxtfp56vPqVxDNztN3VylFAjsLvQ/Dl0nwJDvB4ivre4g2uM7ciOv6MY32hfgpXIzJX10HzVkBQkXOdNzzHBz6Q/Urx3EGAPBo6wVl4sj4krE0zvsVMTbwu4Zd8kmQwCyril/ZFPgCOS/P0XVpQMYElZQbG4E7rDyXXSpHzGaMHPWu7JbbspliR1m1xEHVbGZZnuf42Mc+9r77mXcjlNJYY4011lhjjTXWWGONNdbYPbYHInu8scPtnUh134lIgv237/etKEs1D06siM4X+7jtHhVRryskactaS2TYzqeIfUY9UCT00mdiRUgpZmFHUVVMAuX8I7s/kZEo6joCB9mTaGjXm2M/pyjmTS4O/njrLZX+l8KxgiRtpUP4nINh5nyvJIq6M9cEc0n23vA3NaImUcMlzpWQXCvaRm0IkvbM9iryRwktEnnmeC3RYqqS17eQEgqTtkYeT2VbfA19Pe4M1L4k4/smU4lnifRJH6ZxF70hI1WcyyCJ4BhMgDEhXB4nvbcR4Xz8WKkNSfbfz/uK3l1J1mFb35tqDkDbi/Q6S57Jjyx9i85xSBHo3U+tAms/RD+WPIdrX6XPG9Miz2GLIvDz2QC9ZYpK73GeQ2vExXu9DEtXj5eu+ZvRGYxA0VwR4pkuCF3IsnlJGAUoIvcdDDD0qN0F57I8Hl7WiKpE4D0rEj/NJZeOcxp4XE6Ta5q7VyDzgQo+uKiELSLhohl10VGSo5bnvJzrZBeFns+3Sr9L0yoSnqbzCqpimxvZtXOyjhJPOUoo5V7nOTxIZueyHLbd/rSR1zpktJjLBZVLYEwZXdMi39Z4cMtjUFS/3K80ndegBoVvKkrjiO+YaJ/rhL7cHJ44JdZC4s/VjwhCF3ABmhy5+h+7jIGi9cwqkZIjPoJKvp3kBve8eSkPGaA5VwRFeqdpXtm+/kSxjefmG+yHZH6antkDNjnXS3LeglDRtPUTlMP2nevPAqAyOtspF1HneUMEWbay44h3iQ3is+BG52oX8ye5gPc6+7LLjIKsdYGLdO1ma7SPCJOEoy4E4xRWSLo8Rcy+T/KrfWZ9LIYzhCNqX/IFBW0zmUHLyqUDgCxMEUzlCNSW+L3h5XXc3KP85z32c+LborylRd3FzgZXlR3xQ93vU1+5BEI2tfb92Cfpc5lEvcLwLKLX/9/0nQiiRSn8yzwm+Bz31/e4zVhL6ixdI+RwdpHu8ZXkNH5jRrl3OQj1m+Y7emh3HLfNULf9rt4rdG3yAMdYSEz8ifiZFB7mfN5zLq2RWojzAehYUnzcFhQ6CrUSs4WL3Hc5+r78zmfn0rrt183jdk6dm+NmM8ZcoRR7jipE/Kr+yGWPZVl1nrN9z70W5WqQusYaa6yxxhprrLHGGmussQfYGqTuATY7Z8Q2N9pQSI93KihZnWqP7J+mE43GV6Wn5xW1uULxruAsF1H/uUbQ6yImbr8ENSC+M/VBkIhO54wqiUn0SKKoCeYaGc2sHAaAchokMmpLT/tGJOM5QmShc/JbMYlgTrKeKmSJ9fyZRh5vpqQUJqjasjfFNxefAgB8aUqKhOYaRenyM0uqhJlsUFRzzd8qcqkYvRLkbp4bbVcQqwXnsD0fPYPPXWaZbEbLkGSE1gFAxGponMM1TUZY5qjhk1NSxJTC3nbUcpTQ8U54u3qOW7of/T40MXpbnBsS0KeojuVBqipiYlKoHSjKKUheX4BUz03U4ERZdJL1CoVLvo9Db4zPtX+brg/nT4w+wcXR158rcuhu/QafPx+43wIu0PmsfJcKiJvWRCOokq8hORrhuIPzU1Kb+6XpFwEAryexjqtZWi7IDlSfjz5o3PQwVKXL1WCk5yh5LVquQfMyjapdCtIs0f84HulzJ8+JHeWsU86tqlmW85xkP7Eip6qcS+t5SQkdtPe1UTlbMe0ohcrqd4ejeTaqdFTUuI5h0Fi9UV5b+R54XnWOFovjUQUtsCPxbk73UVLlde2L2fe3Lp+7Tt1OxqOLLmZZgjTdKu1v5+lUFJ0t33HcI9ToIKP5zLfGp6DwLXT0WZ1zkXJhfSSILH9F+xQS8p7Oc74yImaqEpmNy8/sspkoe0QQu9f2PgYAWN+5iWjDEUONFqTCCCBiJsMzW68DAH4l+aKWU5AyOoL+9b0Dna+fYrXkYBrCu8HPdIfmS82ti1JVwsxu0Fwl+eLB/CRSUbiUfK1pqCicqndmUhJhoN8JeiclDbo3jinqJSqb0WCu+8u8vXmd5nZ4Yy3gLYio5KPP8q5ec/Fxp/xtfGyZCoxP2a+oXwWKfPhTPwIAaLUp/3ty9S8DU/YF13h/z2D4KrFOJo/scFt0bdJOgnCT3zsC6oMocf7K9HfgrYxy7+wc6ooiuSn8teTSDTwq5bPIQx0n8imI3UHWU58z5rJJI6vUk6B2oqScppND5wJb+dwtP2LPIXZ5K5kXfL+c82bn19bl3orJs5plc/V9dfNQ0ceyP6pjJtSVXbmTEjv2se+VNYu6D6BVHxybvlQVTQFccYRicWaMJI5XHw4XerbrVtk1qWSbW39E9rGT6d2Fob1NXhzSdKKLMqFYykLMR6gTnyzuZFIyFjAti8I4m2gpA0kEFkcbWFSa1KFftkyiCympU3ctPYUN/yZ/R4snLUeQh7oIFDERcUbxOCoS2ZkaMz93Az/56r+i7byQFAfbQkfpOvIycJVpn0NvTFRHWOUBbnSxWGO6KdM8zQ4vEM/M4LOT7l6glxVZzG1lx3GKRTtkAXcpOaMOT+iOsr/QWABgErP0dlTsYy/iACoFUVBAyMlLYv6tZAUDpouIpL+8AEzzji6GPssLuWXvAMvHyOlsPUP3AD9GlBWsfh7Yf4P+Dsq1iTCOcPqrJBN9kft3+tSLyFQCm8aVyGXvvP2kOttvz8mBT3GptLiyP3u9szp+e5y8LuI7J/1UHask4cd5oOcGvsfbyZCvV6QviFrfKimcWJ2IRJ3ksvt/Nwhkv9y22+vaVhEIqr6cp6m0Jc9vQcO8k0BSXRK9mO0wZQ6x54fDjuN5QYkS3tidWV2wkGTKi+vq7u/6lbo6dIWfqK8pZ7dXZ/a9rgscugu3o6i9tOArUzLtY1TKYyDTxduYF3M9Q/PlwrpW4oemGGlAUPyQBGJ8hEq/lHqpJ3yiNE7zDlL2U7LAupaewlrKJQkCalPm2mXvABsBLR6enz8NALpoeXTUQ3ae54sVpsF3esAT1P/JiGj3vTf43pkMfi5iMFJbT3zPSaXdL3bpfD7a/6bOi5JRoFTDQVj4tDM0p8djXjicu4VwRAsd/zIFP/fjFQS7tH/3GJePGff5uvloc5BQhMqORzTHtyY+9mOi9Q8sOmTc53NKvNL1upKcVqr/G/GjpW1Db6y+XATCPtY+jzmnMUiJIFkM530PeIjSB9o98jWL6ff5OlwjqisArJPPWfn7y5qOcHCG2o/PsBDPDR9tpqdev0DB35dj8ksX4i6meBO21QVGpFZvBwN8sUsL9ZMssHMtPYWYha1k8S/vFfM8xIiDxBFof3k/mmBLjyWLuiSZHFqX0p4b6qiMxQKuGjis81WHLRDTdF6hSttCKe7za89pbl9tyqS9z2G00PL5lM+xoV821lhjjTXWWGONNdZYY4019q6sCV8+wFYXObCT1qs0TLvgahWxOypq7srB0v/rYWYbIbAjt3WUHtfcfpXh+eI7iXS6NEwgUjlfQU9scRTjxDE8r6PFO+Xc+sEZ3V9Mkb2cooKjdIBVjuY93roMgGgyEv2TiJ9QafayvoqoSCK4FD3FCEUi+4R+F/cXOMVRNqEaCiVmNx1ATvdaTJLYQtHsenM8s3sWALB6kaWeB3N4CRe3XSehkDxgespOqNFD06fzPbG/y+ecqsSzyDqfz84pmiYCLg/7XDogG6hAjJigmbO8qwVU5xkdzzOZ/raQkC7EQUQEpceS28seRXdDE+t3co3WTr+GA6bH4CsU4cRDP0Gfs5vALlFoEHBCPwsIdL8JvB7R9dKk/U6ictdiIZde+G70LL42/RwAimICFJWfJ2XapSBcxgRo+xRdlkiqiPsEJlYkVz4nWQ9jvs9bjGIeMA0pwkQpXFOMqK9pWewEKFDCIOiXiq9Kf+TTRd7KlOeg1K49r7gIvR0hFbP3iaKRtiF9PgyZry9CDv19NWm9iKjWyWwfJond2OFmz9/losJJZT8xlx5lX/diXBVtufeljhZbF/GuIrT1VGExl5Irx7HHeOFrgpoxx+wQRKWyOQBUtChEr0TnB4hBkjkUS2OxSrRwNLcpDJCemStqIiUNAGCTKYI+C4CIfwlNrPOWzCGvRE8AAL48aSPoc2oB+xUMpupj8h75AGFchCZW5ocIZyyY+p74AVaYTSA+wZ+3lHWCTRagOkulBpDlgKKD5Huyp+l4i8sTLF+i80nXRtytKXbHNGcuM+KWh1y8fDHE9oKYIjczYqu0D2Lti5RkEBTvSrqBZzJCqs6zP7bFv0R0Rq6bCIWdDa7qd5IqEPfnZbqlbT/2WSw//f8CAOyPf4m+u/l1/nxNRWPaL9N996OAyuugEHUxOzRmw3Ef2ZvU11cEoYsoJWELF/WQuSVWIuO2beh+COr7hH8Sj7bo/CWdoYVE0zcKSi2Nlyz3MM9jvk7Un9gS4prNLtHfR1Cky2VNys+cPIN2CR3xUUtLT+h3dUwu+U7alzbEn9n719HG6/pUR+V0333tecNmGBxm9xP98q4hdcaYP2+MOXP7PRtrrLHGGmusscOs8aeNNdZYY429U7ubSN2fB/DnjDG/AOCvAvj5PM+PWNs29m6tLvchTauFFO2oqxshr0PnCgGE4juJrEikJE0nWqA1SWhbEQUtjn0nUQubH+323UYgpP1e7wmNerrR0xxepVyB/D9HZu1HnZylmxVkU/bvoShC7jl5DiK9D1CyulhRuJuirMJfz3JPo6BvjikSt36Dkssnq/uUWG6fdy/CxvIlamO2KLXVNpFGdmeMcMUx9X2jtanRzLUdimb1Jm1M16lfJqKoIwfwkHRSjPqUn3Ds1TU+CTr/7pVQ8w0kOvvR8IKeu8hdy7lGeaj7STT0hYzyPJ4I3lJ0bTOjKO3Am2hfxSRCPPAneJiLh4tgiBTgfdi/hsd7lCMXccmB+coBZp/gaPRDv48+ZewttoFwSH/foHP1nqd71nn5o5rP1nnoLdo2DTUno3ORosfPx3QeL0dP4NWYkLMdEEKb5NVnzhZaWOYCw5K8PvSonx0TabK+GyGn86Vcwjf5euXIMAZFem3EQY4jSLMgI4SOlxGwIqk8KRVddj/lbxsdb7XK+0mOhR0FdXMf6iKnR+Um2HOHfFcnjy9mI4hidQjPvY6e/gB2z/xpXU7LUdevLqfONveekQhBeZyIJcmohJzZv7fHpZjnBeqb3GLotqCDW1bB9oXlfjpMES2HkyniJp/iS2wGiHwXolcStQAKtkeCSAVVCl/FaBY8BMzuEEZEaCLNWz7TpTlnshgCADb8m5rjK3PIrszBoy5iFhTxXyYkLfWMimRhwGjck1cAAM+99DK+vSBpfhGnGuUskGVGeC06R/1p0/w1311DxrlhrU1mskCKcXc1lww+i6f0eH5+8hgmN1gmn/O+j50/hdWQ/JWIay0OiBWz1N2FP+Pv+BpeS9f4qqUqdCJonC1gJmya17h0ztngqvqVby+eAwA8HtC8v7x+WYVVZozOBdNQBV4Elcy/skGNb/w4JpNv0N8jZoL4PH8fOw5zjQvGf5+Ea7LhSM83GdC1WD5Pfe1s9fGdmFCr316Qr/lexGgh5ohynttNMcYVaeaxJ0jds+3zynxJcuu9jv8WBkymY9tT5pP7OYtu6JxeJ3QlZs/VLstDfI6N1BXvkXOdYwR9s+cQF5m3ETqXfZKmI+1HHE9KbdW9KxfnUPgVEWupy/mz56NCKwL3nd3NnLo/B+AygJ8C8I8AXDHG/D+MMWfv4jEaa6yxxhpr7INujT9trLHGGmvsHdldQ+ryPP8vAPwXxpjfDeDfBPAHAPwnAP5jY8wvgaKN/yR/AEOm94vVyZKL3ellrUZYCn6xRGLsIsQugmYjfRJtdX8Xx6PK/rbamGu+3zn03OwIi+Q3ZFlRCFYUySR6uoQVVRuT7ySCNcWOKpLJ70J/iIjzkzSqyyhehkTzoDRKy+jUOOvj7YTyx857FMF8snURvqGIokRZN1MuJJp34HGgXSKJZzlva7a5j7TPSB3nIaRhgv0JqYA9vkvR2Ssx5eS1vUhz6gSxE5Wy7WSI7y4+Tu0ywvXD3neKIuByzUd0/dJeAn/KUWNW9WrvUIR16cQ1VTeTyg5PhBdxM6LzfiF6qtTmIm8pUikW5iyzHT+mqNTZ1lXdLhHoPufLiXooADzbeg1AgQQKUrfij5CyZHXGeYmzLxvgE7+vdGyMWfFy/w1gShFbnCd09Ng3Kbq9nR5XNDIccxH19T3gdbqnz3OE95dnPwwA+Nr0o9jDeQCFfLkduRT0WuSl+2YDJ3CW+h9QFFuKwLZNpApkeh/zQNG6zYTyUyLODT3AjkZQXZUvV/lSTJ45iYzaSJr8ttNZL/3GmKCC4tnPr1vknP6uz7eyr42dk1cnMV/0uTxH2fvUK6pJ+1WVsnutRPaD2r30p3ZOXV2ZiKOuaR3zo+6eHdZGEAytAuEy3490u6t+akxQyZG5E79YV0an1RpazxVH+vkZ9xFo/ls1nzupsEOAwmdIDq3keNsmaN4kIxXI4/4Yi4yff0aght5Y58eYlRjbzK7A9imcDWg+vR5QG5Iz9cLsY3g0+BqdT4+vwzgGBpTnjDXODTxDc+Iz51/Hr89/CICVb8V+djsZKsIjuX5XktP4yFXKJZ5uUBvJhMsHdaKi8PmQ0StGBrE5K4qWc37fbLyH9g61Kzlsbc43i8MUbUa4zh7I9aJ9b6YnKgXDu2ambBJB8STvPc09BD26D18yJPefHKPjedMQCV9fKW4+Wx8DAf2dfZHaWv7036F9TIDxzf+Zz+MSfxIKiFdHWr5AkFA/CjSXLhjTPejdoPeL5w8+iV+dUa72N2bkcyJmghh4BXOCxxy8vqLCoiHwVEBI5Rc6zyuSKe8hKXx02f9ucu6hlEUaZS19ByrGoyhdjmp9i8uiELRccsmBKsrveZ3Kc2/nfReIfDGPH/b8xvGolqEh/XC1I+p0IsoKutV36gK9K7Pa7PnR/b34oHtpd93j5Xn+SwB+yRhzAsCfBPBnAPw4gN8D4JYx5q8D+B/zPH/9bh/7g2h5nsMYc9v9Dqsbcrvv7G2unLPv9y051/LCDSgeCteZ2nQcmzpzVP2hw+iXdZLlUbSFrHOWfisy2zyUA4SWI+akYktMxV0EAkDLp8ktTsel88m8RPcXuuatlCbOoV8kwguF8DnzMnxeeGz45OzE+fTMXPd/IabF0JMjSoDuXZthv8+0gkHhAOdnyLF+/oXnARS13HazgS7YpE1waZ8L0SM47tN5vMx0jq43x/Im3as1FhOZre3r+acrXEYhIOcmdXLCUU+dUF+ELHaWsGxov89zOQFZFF1KzqiDleR1SdS26ap2CQShcJ7mxZzUyNvwN5VC8sTgVWqDJaWTKMDeE3R9o08xtedzfwyI+ZwS/tx7hT69ELjISfG/vsHXjezU8hV9ifDZwfYvr+CXmYb0Ii9cf2NGktUjvIUDEHVIaMf2i6VQYUTQpIMBeh5dT3lREhrtcX+MtxNyvrLgTeEhYelpCUDIC+McY8zyER9bAioFvdJ9vmxqmkuPswMpxf7VZ7DumXUT4el39J3QXo5aWB3lrIliCe2j3RZ93p6SeVTQK89prH/sYx+rPf79Zu+HP5VrYvuZoxZn70RQKwxXKwt33z+8/lSSVAOCdp8K31T4hcNepGw6ZRAMnW1VsR47AGGXMpBPCfa51EnPoq+Vy+YEpU/ZZqz9XYEVedEGoPP4CX9XywmI7H3u0e9a3oHWCZX5ReaS1+LH8NTWd0vtT8P9YlEnh2aRruSRG/jk6zRnSvmZjEsCeMjU18ii8Q3vUZzdpQVlwDT4FgfZkixClh2U2pcFHPotomcCuvCb9CaIL5YXVLKAzcIUnU0KcAXsA/pj/owP1J9IQLHrzdVXPhe+TG326DjerI2I2/W4r0Lh97l2HgDsfYrmeIQe8By1dfxzv0C/4/Gyvf03qXQBAMxYKOs7FIjtvzpQCr+cx6y3rwJcS9eIWro5p0XQtxefxFenz9L1BZVFkGAAUKb6AkAXAyxxesgTPvnMP7j88wDo3olAmdh2OlQxNaHpjrgmXYoIcw44iPiX+I26oI6d4iP0fjvdwJ3v7bI4de95EiSsm+9dIMCmgFZFsypdrRUCdGuq0ndJ5Tt3vrPfZd197Jp3Mp/eK3vPShrkeb6d5/lfyPP8YwC+BODvAFgD8B8CeNUY88vGmJ99r47fWGONNdZYYx8Ea/xpY4011lhjt7P3nJtijHkcwM8A+Ir19VUAPwrgy8aY7wL4Q3meX3mv+/Kg21FFGu8E/rUpJ3ViKm60olyQnBEXK1J6mCBLXZ/rouZCr0ENLcWG7oXSJv1qtYZadNnnAp9S0DnCFH1I4rQkBNPxAoRavNmOgs2jzVK/JLIUYaqUmQHWuU3aZ5GFSpkTyuB+XqBYkowtFMU4D3CLqZiC1PgsMRyOO/AnfP9YRAOhp0Vbs8eJhvGTr3+Vjp23Ner2ZkznKujRIF/DSwtC6IYc6U3hK3XxZ85TZDX33qZzHE6RMjKXDzjh+hIdd7460Sij0De9KFAqjFg+pajrU53XYTJqQ8oWSMR32TtQGuXQo2uzHtzScgKCbMp1a3UOtHj6hJPxxZJehPgzjGh+7g/RZzQCEqZAHYx4R/7/vzqP4y8TQjfZJQqrRL4DUDI8AExjGkPfXHwKV1La/xtTKgR7OacIdoK5InQyfutQJ4miHsdD6LCQznFHejyDp+UqpMC4jwxTRmFTUHRZxFFizDWCKtFNO3JpF1YG6DmRZ8dFJYwJNLrqClMkyaTyO/uYbtHmJJlU0Po6gaMiClqUJrCRRunXURFStwSC5x3OAKhDkh50e7/9qR0Nr4tmH2a2EILsX46Cl6PyNgXL88r7iNWJKtj0reo9TqzflhGFJKkiCva4l/ZtJE38iPgEQU8MPEXmZVuCSL+zETraNtffCt3tVkbP+hnfV/q1ULM301WlWIZZJJ0BAHS8hc6dr8VEGRck5qvTz+Mr298EAHRD8iGL0QHSDqMvUn6Abba+hy9e+za1tUdtia/yvAzbXGJFhFiupWs6v59gnzG8THPc9BMXsejw/HuDP3t8vTs+EPEJ9AU1zLGY035mSkiHx8hZ2ksUmVy6SqhUzvT7vLvAk8uEbEmpoCxM8Zk5+UMRXRHGSbI6Afi3yQr7Wv7/vBcpugZwv37s92LlI/8N9YtFSrY3/z+0LU8LgZRfI8Tu5Dcf0b6L75Rjh+OBopDRHvmH7y4Infu16aewjUv0W34tl7Eh6SQA0GOqpYdA32E+36XzT3O6Xie8XRUsE2GZtonwViIsFWGTcLkhjPS9KM7ZN/GcHUVbFWZGXSkTMZu6fxR12xans9N2gDIbTEy21THLXL93p1Z+fy5/V8cAEbPfiw/b517ae4LUGWNaxph/wxjzywDOA/i/gN6f/gKAp/M8fxTADwP45wA+A+AvvRf9aKyxxhprrLEH2Rp/2lhjjTXW2J3YXQ1fGmM+CuDfAvDHAawAMAC+CeCvAPhf8zxfyL55nn8LwE8bY34dwO+6m/34oFpdrsE7jRgcFtW2k8RdOXMAlVyGo/L0gqCvkRW7n9Wo7Ly03bY6QQc5xyjaKlASDlkKJzxDpmha4BQmb6FTyX3wEFSis4p0eMPKORZtxoqoSMHts8FVLeQq8spSAqFtIkXqpKj0z08p2P5Hl/6uRvAQcJwl9IEp9XXyCWrj1A0Kvm9ENzUX6yRHZQ2LrySIkKqIDCNdWU+jt9/yPg0A+PgrdP0Gp65iwSUMIv6UHDMv8ZAPqa18nuk2yW0TieelaxTpTDMDMALW4nt1dk4RZqzuYGNEiN4i4c/hFGcTypeTyKsgaeHajSK6KsnrnN8R//4TwKOE0Hldzv249Y2ibIFET3+b8htO/+qTENtnhHMwoOh2GibwdiiqKVHn344+qmjnjZwQTYnAL7KRhRzxOPEHOsZWQJHaIZcvOO6P8WiLorgDn6P4HIHfy/oqiiD3J0aAUU45gZLfYOf1uHkBgkDYMst2IriMbdeCoF+JiMozF4arFmpXCLEchnbZ+Q2udLz0z95GqGL5mbbnGncusxEeV17aPt86q2MKPCh2r/xpXV7M7fZ3I/YyluqQNFuUy0X9PC+wtnUq+8i4sYV8JN9b7CiEoMgDTxAEncp+bvuzBedMtTfQBz1LxomF58gqQikeAn25kvxtYY54CHQ+8Rl5EVvkoeYfCyK2lR3HeUbhvtQhcQ/j87OUBpqPLIyRtwwhMrfSFXxrTkyDn9ocAQAONvaQymmPGPXj4uDJWqLFsZe/zffPp/n8QvSIIofCEhmlA83b7vE88dHwAgDgsZdn2HuC5t/ZCg9TQeo258BGTy5U0QdG7aQoesr+D2tLSBKaA8cdajMcMVIfBcq0WKwwkyDx9LsWi1/lzEaBl2MuxcTZ56ScW5c92QVWGb188icBACuP/jnEMV2T2exF2jZnkZq982j9YxofyxfpmicT+n07mBWiKNyXzlZfEbq/N/1pAMD358TkOZ9e13cTt0zTHGMVbBN0roUOfqxL/fhC57cAANf5ncA3KbY4N11KU+xlfbSkfb6PB9jl9id67Dn76+n0El0bqyyILSjkPmN1pQPcd8VWa2ixs6DbXHETeUaTZIJud6j9sM2YoKLpYLdRJ5RSl6NL+yQllord97r973fWx13rnTHmGwB+B8jxjAH8ZQB/Jc/zF2/z05cAfPZu9ePDYLcbVG59H9sp2gqSdlu2I7cpVK56Xvl35Rc4MVp0lUVXyqpC5Qmh7oGzXzjdF9kg6Ot3MskJ/dLA05dgUb3MmVqQIdEFn+yTIqrQw/SF2Ysw499KWzK57qYDPNyiyV4S1ad5F9d5gSfiIc+GpOD4cvQRPM4UmPOLswCA35x/HADwpf3H8ej3aVF363OXqH/eQlW3kJHz2fkETbhnv34Vz+Oj1GdTvPADQAtt3OTFjywexllfaX4vRrTAETW1h294ODFi57smzpG2mcxDeJXOLTrDVBXMkPCirsNqZUKPDMcdXfCJtSZ8/70McxZpaU14fAWZqljK78LBm9TmIzvIO0zRkUT756i+H878pIqiZLe+URzswj8DALR/gV6whi+QititdKiCLUJjyvkatac9fI9r0H19TtPQS4sncCGhazAGLf5mCd1rW9RHBHYMPKzwIk5e/J4KaTF4zJ8ohanDn9Ncalol6AfU11lMbV6Nl3DAqmfyopgojTjT2nDyWZcIXheUOcohSRu9HvV9Pr9RUdesqwdX51RdWqVt9bSdcptHqWDWnUeazh8YZ/tO7F7703papOtXoPu49N6jBHbqVEwLsZJCTMH1K3WKy/aCzl3A2ykFUn+qaCuo8SvDSvBSzMCz6JNlVUu7Hp2v1LkiEGQHZeTTcwS7JFg4znyc8GkRJIGevXQZ1wy9sEvgKUhokfJI6209tgiGiF+KEeBXZzQUPtd+HgDQ2xwhD0Z07CecQOKghfkGzTl/YP0fAgD+6tv/Fv3Omyv9ssXqhDeSVe3jJ9tET5eaeW1EOHVZqI/cny0WH3nyGPAq9UFSDBB4qsaJFf7c4us6iZWuma/QdVucYYrmpQP1K7JIy3sG3pjH2lmm7rMP7W4O1NdkZ5hi6fHn2T66zxG1stf7DACqfTafU+Ax2f112u/yv6C2/uEUw1cpiCf00GRAKqAHwxk89jGysHx+63N6/y5FDwEAvhtVRfC6/I4hgcIeVvQ7Weh9rr2PH+t+iy4Pi7EJ5XIvWy7q4/KY7ZgIb7Mi5lZG40vGbYpIBbjqarm5aQZ5nlSULe33PaGpus9vliW1CrV1AUCAgotKg3aebdsPi7lAgm3vVB3e9kN1v3Xnr/tB9VLsbnrBLwL4HiiK+LfzPK9q/NbbzwH41bvYj/fMjDFfBvCvjtjlN/I8/8L705vGGmusscY+oPaB96eNNdZYY43dXbubi7rP5nn+3dvvVjamjXzrLvbj/bA3AHzjkO/fMztKZrqOflkXWT2KjuRGW22TKIodIa+rGSSfdVD3UTLmLkpoowLSH4nEEIxfUBOAghIjiB1QjZSG6Gl0SlCQNvpITVHeACiuV5JNYbyCpgkUFJqel2pCu9BRNtMTaGupBK4Pw9t63gzThBERpmSKfPU/m/4o/iiLaAwuEEK098xmgVAxbSXtUL+ePfEdfJsl98cZne/YMAKLDHFeLr+wl65pjZolFum4mhDNcZQN8EVG7dojiqwKDTPuzzXRvHWNzzUs7qFESCVKuVg50Oik9FUknINpqKUShOITTEOlcApFRdt8sgd8lNBIPMT153Km0MyuFTXotiiKipd30bnA0tav0rldYzpK15tjwyOqSrrMyNge1XS6lJzRun7PzwmxeyOZqTjJPKPosj22RR49yaitFe+cInQbAUWBjzHV8qS/o2hqaGib1Ala5CGuROsAiqj8HGMdmzJWFyn1IY5HFj2kjLjX1cqro2nbdMyC3lZOOO901jGf8z3iiKwthuKKJyVJNUpah6S5yIh9zKJPQQVxq4vq2kIssr8bPb0fk9jfgd1X/rSu1IS9TUzru1ljShA9sTopcZFGDwLA94eHtDWvoMU2PaxO1Mutp1VH5axLB5BzVGpY+xF9LpcckZMYc0VSEqsMiVuuQNu2hC8EoRNGSB+rSo8T206HOOYTM0Hm7Y8zA+Ry/JBS7YQV8p2IxDeOp2OdVwRB+/SVDGadhDXMVUZNPkHy+ticA0MSuNr5ONEKP36LanK+Fj+m/kpKzYzSgVL934gfBQB8rkNlbq6la1js07k9cp5QstGTNAfHF8dFOQWpYZfkiqZhh+maZ9iXJ1lRAuEqUydD8m3ZuiW6IpTOi2Nkj/AYTYLScWb9GdDh75h2io8Te2Xt07+sY286fR4AcHDw60hu/P9ov9+geL5/gZkXWyfV94mJ3zOZUeGW8Rahc5vpKr46/TwA4JWYRV0skTh5h5FxI2PDLtP08ZB8yGc6L2iKh9x/qZHbMjH2MqKBylia5yG2ErrPMbNPRFwtwlSftaPSZmyUrXieyvNxEPTVN9m+g/atCvXZz7Pbpk3rd0ss0FxQ9R3yXZ24i/uc23VT63zFYe+rnle///1id21R94M4oAfYvpHn+Z+6151orLHGGmvsg2cfMn/aWGONNdbYXbAPThLCB9ikmKHH3O9yfhp4m0Q+CilWl+9M+1fz2eh7O/JQ5D6EYT1v2fM6h6J+STKx8iKk/Wqh2eL8qiiejRq6ERYbsdCcP0UX+xrhSjQxnSJglFNH39lywb5hFErRQit6xL8V1E+k6uPclMQtAMBHqlHMNqMyy4yCTeOutjnwqC35/dvJmqJFX7lOEdvZ2j6igCOWEqXkRPKt33sLf+qr/wMA4Ofe+LPcH+rDbjrAPC+KqAJAihRXEkKEBDUSdPEznReU5//wPpUT6L9I/Vqc3dQkcpFnBooirSohzaUHTGYUyfPnAZ8HnU9r3NXEcSl7kKznwISkvDmQDDxDOQroPQwM+cvJJfoMOBJ769vAS5R713qZjj24eAo7O/Tb3+K8xieCt6gvSNHxqP83Rg/TNeFr9MuzL+I3Z3Ttr2SUfzHFqIjC83gvymp0dDx1PUICW+jgyRbd5yfb1NceF18HitIVknspeQ7jrK9F5PczirLa+TlRXs4fkvwC+q68zS5XIs+J7xe5p5ITK/u0WsNK7p3sa+fEyvmTpD1K39kRXPf5teWp6wqSHxXprBarrs5fRzEU7Fxiidze64KwD4LVFSEvb6/mrAH1zI6j7ChWCVCWO6f9C4TAHWe2H7IRAdlf8nvE7GfkqLakD3aejyBsLqISoqffCZqXI6vk4AnakiPT38rz3kb1mowYGWubSBkg1zgvyo9ovjgdbOpcIzlVz7SoDv2V+DTaHv3uazNCiD7dfhHDV8kXbD3HVS9swRRGrzJGzT757D8FAKy9/DsUGbrJxceTPFA/4vH5/NqMhLg+2/m++r6bezQvp79JOc7tH/k6sh6zLi4SAqkoGwAMBL3jCSfwgDlfH8nBk0Lmg7Bc1BygnDwB0CRPz+MxPWgBv/P30+4P/dsAijExm72I6fQ71NcF+UJ8/X8ALpIPO/48iaGEY2pzshjCnCD0UYS+pCTC0tXj+OUx6RW9kRCK+S8nX1ThrSnod/KuEqKn+f7yjmL0vSXDEz6d9+/t/XMAVGhd7reMje1sCIBy6t6MzvBvqY2tdKhictK+jMHEKpUjVl82q1P5rs6/HPYuB9RrJ1TfH0d6PLdfNjPFfZ+0Ub+68l91eeL0+6SC6NsF1uvM9T/F3Hjv/cx7Vny8scYaa6yxxhprrLHGGmussffeGqTuB7OPGGP+SwAnAGyB8ut+Ic/zehL9XbK6nLoiql38343A2/lqd6ICJJGMVmtoRf2HlbYkCuoWj6wrhUB9LCN0YnXnU4cC2PtJVFYKhev3iBA4Cpdzjp76CDRa6ltRU/fYpQKU3JZEYIWHbrJVeFwwetWn8970VjV69lSLkKR9VqY65d/CLS6KLipiolA1yzqqminb+jdGSDtUMDydixwzRxtX25qX9kdv/hMAwN/FzwBgRc2MkD2J9M0xRpgTn16irAcWyihFsb/UoSjlGS4A3n4zROsh+luQOi/xNY9Ao5MDRqUCo0XUw1GvvM+TAPpLKNmZJcDjqOwqRXjhWxHbS/+YPkeMEvJ16H4jh0koKhu8dhYA8Fa6pte+x/mDgpqu+dt4O6Zx8s0FSXy/Gj1O12v2ceyAItZSQmCa7yh6KxHItqG2PXgasV8GRc0/3srxBCubrnqsBCr3Nu9ik0tZJBaaCkBLYgAo5XoKKuwWGs+yovC55PVlGW2z8xUkp8HzAnhekWNqn0+3vaHfSW6gbW4E1j0W9a+MFtq/KxTN5pX8Cd/vaJ7VneQm1ElPi9UpZNpR1AexlMH9bq6ipB01vxOVVWB+R0it+Je6POv6dstml8Mp9q0iDzb7xM3tVhQQkT6Xgq6IEiFQMDnEX9j52wsrbwqgeVlQGfFH+5zn1EYfU86RkrzcRR7ieko5wMc8QrYWvuRbpYrQSM6u2LPt1/D8nPKSX4moJMLPT7+C35dTbliXyxxMReK/Y92zASs6n6Pjnd35PoaXiTkx4rlQ+gZQyQOASrgAwPcXT+NiTN99MiRlzCdbF+kcN5dLzA8ASKKoKCjeYX8iJRACr0DvpI+Czs1TYIXyADGOit/1mK0iSppnqURBe+VHEQTkC+TeTkaEfmH0MnCdSkbg23Q/hi+eQneTfPM8o+OkXLZnubWHOTNTxN9t3qT8vN9MHsWLEZUr+MUDyn/fxGuKkrnF52PMVWFbitZLofEnWzF+eon6KOhnCh+nA+qjMG1iZoJ0TKT3Qe7LIguRgJU5+R1GxuXB7GIlZ61OrdzOx5b93PztTueM+qi6HG/3ucrzpPKMiv9K08mR84lbFud2ug02q6Xalvgt8GdQYnzY+9jzyv3oX5pF3Q9mX+R/tr1gjPlDeZ6/fi86JGY7uKPq2tXVjCsSzIsFVZ1ICVCuW1VHy7mTQV8ve11+uOqOLX0DoC/fsujKkOmEKZOkSzcACipNikj38wJ2sLPzegyZYFz65QIT9NgJSd25wCRY82khJonp8nI/9Ma4yY75sfAqAOAqLzR204GWNxDJ/d+5uaN9HW2wLPM5dqY7c+SPcP2dH3oRAPCl36QE+rfiDcwyemHf49p3LXQQsAy1iLtMkp4eWxYkf3/yewEAn+lQm7+j/V2cvEGLQS8hJzlfnWi9OKFatrm0wWJtCi6Xh1mPnIrSZVbawCnqI07wAm6+qeeIg6v8Q64B9PImvMtxqf34ZXpBGeddXcAt2JE91XoTJ3hxLXSk7ZRejp6PnlFhGak/92JEvzvAaypSkKAYU/Ky1eZahbYAzzHQ9V31yMlvtM4rrXXIC+RtfjELTaSLuRu8oBba7STrYTcnRyvjd4ItzBO6Lm7dxPICicajLSUdcp2+urpuLv0yzaNDqS22lLS9j+t868qhSLDlKGpmuQ5aeVvdy35B86vWMqoTjXLPvbEf3I4KJIrZNN86kRsJOIoYiu93lOLu3s9WK6gNAkhbxX5Dq/1yW2VhnXJ/7DqKWhbHell1nxebhimLWHcB17bmBvErNsXS9jXaLw64pU55hAPsaNkcmavHaR9jDs50rEAgQC/0UqdulpNPEJ/zsH8N5z2iPD4VXgIAfGv2nC6uPn+R5xeex+dnZoCmW3AAkemYu5+5gR+/9XUAwN9IfxYAsB5s4e14jY/Nv+P443Y61PlY0hOEhvjxVzbw1GPfAFCIcuVBpuUKMGUKG5e+0UUbUNAwRUQsyoBztGDFwz+ru4nfbrdpvp8eUMBycennsLj6ErfPC8MLLLYWeehfpXm799ZTeh6dkK7TfkJ+SM4rSUJdzL19QOcmIjXfnP0QXliQv9/CBTpH6/1DAogyNnoYqs85zuVxHm2R3//Tg/+1OC+Lyv9KROe24DakruEiC/FadBa2LXBQKZGzSKpzvBugB4praT9fxfPBgY6g+L8bZKmbG2zBE7G6EgjyndTPsymgde+YdXOA+3833aBO+MQu2eVuy/MiuHg/lTIQaxZ178z2APzXAP4BAFm8PQfg/wngCwB+0RjzXJ7zm9ptzBjz0iGbHn+X/WysscYaa+xDbo2Paayxxhr78NiHalFnjPlHAFdtvnP7E3mefxsA8jz/LQC/5Wz/FWPMl0D1634EwL8D4L98t329G3aU4ICbZFpXxNg2V7rWmKAS6bcpOHcSJT8qsip9DoK6RPiCJhMlhGh1mVLhI9CIlJvYHmGqfwv9MkNSocfY0aDi+vS0DYCKkG+yRPBDLUJWemauhUCHTGE8xcjdzfQEHglIJlokiE8G1Pd5HiqN5fvR0wAo2vqZyW8AAJa/QxG4faY14OkhcIMFRs7RtXlsTFHU/90rS/il2Q8DKJChrXSIcUZRSaF2zJmK0cuH+K05vd+dDui7X5sRRfG3Fs/gmZBiFz+SEC1liDdxsEH9kAiv0GYwCItCtpLkLlHWXgDc5OTzt4gyitFCo7HeDrWxxJHSYLqiZQ5+a0TI3isxFR8feuNKdPpmekILjEsEU2SdryVruMKo6Jsp0VcOmO60wEQFSQKme/kIdXwUlJiCcnkmoGv/RPgqAODx1luK0F1JCcWTUhbXkjXscdkJz5E4z6yCxlOMeJ9Ax7ktjCJ2WOkPibAC5efQjbIWyeFVWrRQmeN4VDmOjeRL/2xUzo56Ur+q0VO7AHRVpKJIVHcRmqPo4zbt1J3vGnv3VkdVstFhsaNoUu4+nheURArcfQoUuowa2GPQHv/FmCsLrNSVx7CFu9xt9jNhI4fy/7ZPLA1BXIRCaeCpXxDELcJUyxy4/sVHqKyAQryiQP1k/05OvmQ3G6DPpWjW2GfIfBOaCJtMj9jn/Z9k6v/19BQ2mKL3VlzMS99ZfIL2Z1/1Yxe+yj3bxLzH6NUOQ27CtBhFMM++DAD4w79N9+5vTX6/0vyylOYxYSEc8ydKM9di5Vz2ZTsd4o3zRAt8LqQ2z668iuk6tbVgOmja47ExaBViYSfO0ucSoVmIRgAzFDBnlsfuC0g7lOownfwN+u77r+l5CNp3/PvEHAkmdF+DeQuXYyoKnvH1jfIQ05jGnKBkkiLxRvIo3mCEbjejd4HfmNG1vZbGmMJiooDTIHh89JjxI+YjxJAZIE+GdB5/evD3ART3CSjorpNsScfAC0zzFEaIlPKh/civHmBHx9o0ofcQKVtjz6nic+re5TyP7l+aTirsKZtFVSeGAtAzpMJ2/FwtFjdqfY182qVrytuqVM66Y9v72wJKrtWV/zrMbieicq/t/u3Ze2OPAXjqHf6md7sd8jxPjTH/FWhR9xO4w0Vdnucfq/ueo6vP1G1rrLHGGmussTuxxsc01lhjjX147EO1qMvz/Ln3sHmhY55+rw7gSk7bMuZ1kVI30bMuQlEXcagXYinnLVCb9LcWaLXkY+skcSUCWyc3W+xTztux0QYbZXCj8hI99SykTqJi8pkgqiQoZ0iq31nopUak2uXcughTRXFE8OJGsqp8e0GQBJVb9g7gc8LB0CuXHGiZRIVLvj+nROsUHh7eI2TrRIcKbbcvUZRu4e0VkUsWDznYIJTtsztfx/ItinT+o4Mfpz6kQ0XoJEIs/P0D7Oj1uc7Io3w+FOwq2ifFZR+fv4WNKxSBlGK3cl6d7hiG8/iiIaFZycsU+e1s9TA5S6hlOOJ8QC/E4spZAEWy98M+RRGvp6dwgXMwAm4/YUntUTbAVS7RsB7c0u/Ox9TW+QV9StR4M4sxwS2+ruUyF0CRlymoWYBQ/5Z7fMqjyO+jrWsanRbkrWfmihKKCUoIFOUjJMK7k9K2CbZKBWAB4CC6WolO2hFJ99mRZyIMVy00faTfSa6D5A3JNqCK0tvlAqKojADGcTXRvEBIDs9hsJ/nOoSn6Es1QurmW9m5D3cSKaXrde8lph80y/NcfYw93g675nU5LXZZDXcbUPUZYpQbuoo6s2XJ6/yc3Yb0S8avyzTxvI4+C9LHMmOknN8TRVuKRoQeI9o8l+bISggdQHOJimrxXKLlcfJxUSjdysEDaH4qcvZonmiZROcOmY+v+PSa0TNzzcN2a52fCa7jDMiHiFjTcX+sPuaUT3PizpSQneOXMuQefRcNeM6RQuC9APOz9HcnoPy0n/1OBz8//UrpmG3D5XDyUAuTC1NhPSAUaDcdaAmetxJCxjD9PfjcLSqKfoLFpsSeOv4isoB8QNq5zJ+UFzh5pMg9F7+SBRna/LfhXHAROwGAvRldw5CRrvMx5R3uZ0taikhKRwCFb1rzqf/f5by5rXRFhUheiyh/Tu55ikjz5uwcf/lb/K+97YtdeoX8k8v/EADU/y2biZaTCHK6Dj5S7aMIrm0mnO+fhzhgP3wA8rn72EQmY8zJZ0vTeamMAFDOLS3m8sIvuQIp8jz3emdryoLMtS15/uTYQdDX51f6IP/PsnnlvdN+tuuQOteOytF+p7+rs/uplIFYU9Lg7tlx/jy4p71orLHGGmusscYaa6yxxj5U9qFC6t5j+0P8+b3364BlFTkyO5rtRsbrcursaIdISJeLMtL+ddHTw/Io6koX2EqSEpFxcyDsY9e1XRcplrZi4XSbQgpYTNQNfQQaXS0Qm0BVzCSS2ulQZC6OR9oPUYpCQNdhjrEex84dkDw5QYlEnXEjvKlqjMKFv5Gc5H5lqk4ZsOjYS/Mn8FhAEcGf3iZkLO1QFDH3JojmHDXjgqv5WbpuO7iGj1yg/X7iMn03zTraH+nrLY7qpkg0n0vyyEQdq5Oe0nwI+f0ri3N6jv/bAUVpBZ1c7IUFirVNBxAkrY0I4Q61G3FewAKhFk6VSOTVhCLQN9MTWppAckVEsnuc9RUle4VLE9xKVjRvZCenY864VMEMY0Uo6+T75VloW1LSUq5ghfMFH25d5fPZ0mvS84oC4xJJlz4L8rqX9isInV2+QK79QUTtJ8kIiwXlOtwJGiXPpb2v/ax2QjqPNKfrLMgdAIQBRXbjlO6ZjaBV85Q6GmWViG2hzlnkK7goY11Rafd727IsqeT9CdpSN6/UFbu1tzX27uwo+e+6/dySFvZ3dYV965gmLjtExnNdjqnvd2qKlVf7akxS87vysetk3AWxNiZAr3cWQDE/BqpqmVQQmByZ9XeVfaIoNLdll9gRhCfgefLxwC+VPwGASzEzG4LreI2RJsnfFmRp2Uw0D0zmpYE/UZRMCoXLto/uXcDamJGUkBCheMrXJMmAkHPT1wmxW/6xX8Kf+Rod83/c/9cAFH7iSryuCNIio/N4KyrKqEhRbPElAPC12ecAFDl44hMFGQOgudRivdfmqvYpPsRHWmFOnPLomr6WnNOyM4IIinrkZroK3xRIGEAqxlNm3fyjg98DoCgLdD1NMeP521g52tSXRNFbG42V74QB8lhA/uUnlr6qZZDk/olvHGWDyr3dSYe4yGNAfI2MkVupjz2QPxmDfEmGBNM5oZx1OdHybubOsXaOcxCU383sv/XZSCYWOl59HuuR9gI9B4A0HZWOb/er8DNVVWW3b/b+1Ia8F1fnnrrcXpeRctS8dz/Z/dej+9iMMf8egH+Q5/kV6zsD4N8G8O8DyAH85fezT+4gth1mVbxgXnlhqqPEiBNN05EKHsg2W5DhMIja/r6gflZfvur6VXd+RbJroP1zYf8usxENPE0IFqEUu7RBrPTDAqR2na9NMxDTa8qLiRhzfSEvHHqm9JhzvAgQpzq1Zfi5XyKDfzLYQRxT+2OmqmTw8B0uc9Blif7nLlNS+WB6Fbt9otXkPYb9mYaZDwz2niQH8+TazwMA/sR3D/BPmSYjznQ/O8n/T/Sa6LnyPjv5LgKWqpbzDhBikTP9iF8OYk56nmQ9FSSRxP4XFkT1OdO6gW2u6yfJ/if8gmYjLxZCV+2Zmf4tv5O+T7KeLk5vpOTIZxgjAjlFWbDLOFjkYx0vgSeOVkRRAh0nMiaOYQOPtbadvo4AAANvovdD7u3NbFUFcm7wdRUHu50O9TppojpTdOaYYJaQ0xXJ5rqkde170FfHJ8+L/fLsPkNhuIp5RGNBqGPyLOV5guSI59Gu/QOQs5ZnrY5q6Uo7172k15k7h/h+tVZYXbuHzReH/a6xH8zqXsIOW5ADVSpjXeDRbu8o6XG71qHbvk0dPmpRfxhFK0kmNYvBpDKu7BIiKiLRoX0CUwRPUmdxliLRv+WlXqiZdv9lPor5xTYxc3TNEEAxX1xLzqBtaJ57m0UwZD7qenOldwt1UBYrs7yLRUZBvzMBzTPjqK9z8xWmsH9nQYum08EmehdJcmB1620AwFaHXnXylRxg0Q0RwcoHAWY//NsAgH//16me3V8d/xt0fvB0DhTqp5RhSOHhmEfnJvTFNX8HY/bTIgbysfYFAEQ5Fb94hamvPvujjWBTz0PO64ngLaUmynV6g2vmLXsH6leucACx6821f7JN2n9lcQ6XYprT5xwcjlhkLEBYuqe2BQhLdQzpu44u6p4N6R79H479LQDk/6TkgywoxYbeWPslAdEbyaolmlJe1C2wW+nXZHpeA3pJUvY1tiCcvPuVxbZ4vFuCJ8XCrZ4qLfvZVrcYtI/t7k9+qBA6cdtw27KDi3VBI1f8pLzNBSYOPa3b+rR7bc2i7p3ZvwfgvzHGfA/AmwA6AJ4FCbBkAP5PeZ5/9951r7HGGmusscYaa6yxxhr7sFmzqHtn9t8C+HEAHwMph7UAXAfwNwH8d3me/+b73aGjREfEbElxVxShroBwkeTesZJYyxC5HcGRKL2NIhTJuPXRmbr/U1tVSmbRr6IvNmIIFKiG3w5LFBigXL5AKJNCk8iRlZLbgYKiFkVbVnFNjlIxfS/xOkrblIjqbjZQ6sgtjk4+5lFUbJZ1lCYiqM7DPqFtbyUPKWo3t6JvLb6uX5t9HkBBrzke3cDyJRLuGLNktVBj0AuQr9H5Ls5QZPVk71/gP/reKwCAv7lLNJlv5FS24EqyrJFkoakKcmfQ0+tki4hsa3kEw21QpHSetbDiU8R2kZbRyPOLsxpJFZrklXhdkTBB3iRqOsl6GKd0zae50EgpWj3FDg5YAECkwDMkej98R3wgNH1kfF9cmlQHAx0TA0MCMY+1rmpS/3GforLL3gG3XURRN1O6BwurJIWgchKd3k0HWHCardCqJOJ7kG+V5NoBGnMu7cP3h3BNxqhNURTasDwLxgRF5JXvX2wJWAjN0y7kLJ9u9JSKyjIyGzNF6w6Tye3i4fI797d16I9LEbfbErOpM03x8ffWiutZpuYaEyilyRVcOIw+X0fTFHMFfOr2rfNzbppBkkyOFHcpSh9IuwWly0UcjQn0eZHnTObEGUbo8hyS8xxksx+K0jrFM6U0Mt7HPlfXH3UxwJIh6rZQGWecKrGdDHUOFaq/zJdDb6yIlTAh1oJtTDOitQgt/BLPx99efBI/2f0qAGg5maVrNCdOOrtAh68lU/6R5cj6NMduPUeI3h+/+nPU1pWv4LsLYprInCiF03fTgYqnyPlcyk7qHCWU1GlGv+95c6VyDhjhE3TqpcUT6lcEXTsfP6bz8SK3CpcDmKbdinCVtPVWvKG0/oOcfFyMORKet93SFFOM9N1CTPxKiJ713kFtnvW7eLZD4mK/v/cvqc8W20MQVmH0iF1JN6yUjVVu01MGyyb74wWImnqAHe3rNCLhMRsFq0PAxapiWB19B7LfAcWv1FHibVqna+57qk2ptlFx+TwKoXONRFcKJopr7nxin6N7HvX+qJj37mff0izq3oHlef4XAfzFe92PxhprrLHGGmusscYaa6wxsWZR9wCaW9rgMKuTIHcjEjZi55YJ8LyqhLQdRbULkdufh0UxDkPqbHOFUmzpWrvvrsStnodV0FmQmxTFcSS/rkCjPEV0krwc5SF0ohzVkmjNwowRc+5DqAnNQJZTBEuQHpHhv5auKdojOVmC3B3z9rHwKbI4EtEVL9EI58WIIsP/bPplPY+z+4QAHp9T1FSkndNOjEwKyHIh8HTFYOtTlCT9R14mDv/jt94CQGInUqx0i9ElyRXMkWnEb4mLpe5x4jVQIKCLrCgZMUoz/RsoRGsMcuRpu7Stg2W8FlPUuKUlFzi5HhP4oIT8CLulbe7f1L6n91k+BYGMMUeHCwFLTsMxELrYM1083SZ57I+EpHE0z9qKMMo9Wzb0uZ0dVwlxkeO+kaxqJFwQ2i0uC3GAbb2GgtCNk0sAgNnsagWpM6ZA04s81uL5dZENQZCNCUoInbSpz47zjKbppPKszWZFroVbhNaOTt6JlHRZdKKKpB2W+2BHTeuO4/bZ/q5aDuX+kZl+0Mz1MXW5cW5ZDaCK0BEiVh6z5IcOO24Cz6uiZPZ2+9PzOrXHlP/X5ZzLtqMYI/V5owWaDgDztuTlFsXEBanpYajPu5yqznFWoWYpp6IMkGSCXJ5phrYXmGCaDbl9mu/bHs1/WVoUsVbhJj7gft7XeUvQnzQvEotE1OoEz/e/MfuEFrD+ofAlAMDvuPobAAi5m60RC0PK1eRDHxjwHBvS9dpdIQbF0+f+Ln7kO5Qj9rf3/iCAQtQqzT3MGE3chwiThNovmdv3Gc3by9rqoyW/rWNofHomU6SukxTS/uI7j/mMWHG+WcskyjQRk/sUsPw/UNzHGHN9n/Ct7wCgg36paDxgsUPQ0+8+1yV07meXflHLIoj4ilhgMUBS651B9pVrJ+yVG8kqrmbEIrFZR7TPZUWVC5GTvvoTeW+z5812ex2HmV3eAEBpXxcd9/2iDJTrxzwvUJR7MnlV27ZLGNBn8TwKuyNJyj7hMAaAy0Sz9zls/rLntjoxr6PLhpXH0v1gzaLuA2J1LzniOO3BKbQSgbjr6Cn2w+4O6LqXT7f+HB1jom3Itjo6lfTPVSaS/9t1Uuy+uC93MnEsWltos0KlOIeZJWgifxcv/IG+6MeGF0GWUlox0ZQpQQAw5uRzWTwOsF7U32FKi1Bh1vwdpYIc92gyFiUroKg1c9Knxdl2OlQqp5hQFL+5+JQ66U/fImcgZNRoOFWnGw+4xtBqB2lCf4uIyqc7XwUAnL1+Fd9cEBVT1NCEljLNOtjLe6U+dDEoicXYnwaeXnOXShNhqr+zaUt1SnJyTWU/24kCRC+x6xLKp00RtbctYUXpUWsevRQJZemx8AU81aJFnSShnwmvY9nQ+L2eUn0jSVQ/4e1q/SCh8WS5pw5YavxNWA1tH5sqorA7e5GuiVWrx6U5tlpDHduGhRiK8V+IiMhiTrbZipfuM26b7exkv0LFsvqs2nXtbhesOWwfl8riedUXapvudthxsixRBTb79+9ksdnY3TO7/ql7P+uEEOqolq7K6lG0qTSd1AiejA71K3J8oEoVLo/xwn+5/rB4lqo0M5lvPCuQKHOOLBRke6lNE6rirFHaqlDbgmJ+ECp60NF21z1a1IjoSGIC9RmbCdHBU784ngiFqChVWqg5nmmR/xI1xxYSPD9/GkCxQPQn5F/WZts4M6fFyWxOi7upNwJWueaq0P+FmrsS4MYGUTL/4Df/ZwDAztskmvXXJ38ItxJZjA75c6YLztRR+kwR6XwvNuF4jZ+HmDCd1GCJ91/AgF62r6cpb6PzWeSTStBXru0MY12IZTxnG3gVdWxRRo4x19/2QdfQ5wXZp7sv40e6lI3z5c6vAyC/KvfD9v3UrxaupORXbNVLALgWrynt8jrf47lFBZVrM8opKLdY3KilIruLM/vZOGzOtP2RLaJSV5tYtrlzgJ0qU6dg6+5f0C/nlYWVWJZVFXTrzN7nsHdZO2Bj97noTzUl6H72MU2dusYaa6yxxhprrLHGGmussQfYGqTuAbc6SNmlOxX/ryJ09qctXw6UaTUF6ldERevgaNq3GtWU39T1nT7LkZIigjup3d+Vv7Xhc4/pahLBksTzHJlG1AQR6mKAqRMFTPIRAEqIl8iSRKagCfBF9Cm1kuMPmCooieADlqpOfSHOFPQKoeztZcvoeISkbbQI4cngKdo3ZJqMIHWLxTmlyUhy/HObVO7gxPgWDrKiVAAAZNME6ZBoVHGfjjldJ7TwBC7gZycUUX12TJHYr86/AIAihDFPERIpnGTL2MkO9HraliPT72RLISYz1m02RUVsphSYUD9dhK74/0CpTPKdj0CjrELlOQZC2daDLTzUIonuh1tED9rgekdr/jaiXO4M9yXrKL1JTOgx07yj+4sYyjjr41pCQgb7TE+1aaSSrO7SoW1BEolm2uJEYt3uGf2dK+Agv2v5A6VwyTPT8gdan05M2u6Ea5W6RTb9xUX57L7WoRZHoWti9nxiR3bt39ehfnV0cNvq+tPYe2d1dNo68QH7e9uMCRRxrSutcxTlvw59dvc/CvWzfUiBHBZtFii5i9gV4l9KY2MaWoieIjZiHgL9zqVmdjBAYqalc7OfDbeOVoy5iiztsl857fu6v8zNJwLql9R3O+bt61wlKJhvUjzG9PGrInDFDIW2F6lwydcOPgsA2OkQ8+D3db+GyS6VADg+IcZJa9LGYot8QdKjeSbZ4JSQ0NPSB6OP01zrP0H1Q/+j71/Ct279LgDAL02/CKAo5UON0Ly6yKmtjvEx5X5lDrU+NCkmgvZbFE7xC+InbIl/YU64jBO7ffmO0g7Kx14Cl92xBFw+1qbSDn+4/88BAI8cuwBk1P90Skhi28R4MaKSEcL8kPvSM3NFXy+mDwMoGDNX43VspXQfF1wOJ0WiNeikr1IWxy7NYaPQIq5llyaQ/7vvg7Z4lvssU83hcvvyO6F9AqjQMO1nyH5/c9H0Oup+HQPANRtxq0vjOSxNyG7Lfv7vhJlyP1qD1DXWWGONNdZYY4011lhjjT3A1iB1D7DleV4rluJGPsrFVavRT/d3YnU5dcWx64vKSjtutNSO+LhInp0s60ZY6hJcbZSwSAQuti2yEf3tlaWB7cLkkufQxVAjdoIWtQzn2OXTihiAHG8+vwGtLRoU7YsAxygj5K3H0TYAeDQkxEbQNZ8LyrZMjLdjQpUk6bvvTZV/79oiD/HS4gkAlBRObdE5PJwNsPEGoX1tEBJjMg/xhPq94Hw7ybtbrE3Rv0hRwzOr3wEA/Kk5RR23rz+huWJfm1NZhVE60ALj44wiins5IYM5sorgiUQ3Q/Qq+XYegoqgil2WwDgxJ0Hi2uhbhX3peH2zpNfuyfASt0WJ78+2X0OPxWk2fLo2IlZDF4g+Zpxr4ptU8xlkvwsZ5TtcjB5WFPZiRPdsgQnG4Lw8jghL9HQaXVO01809TdN5KRdOTKKktgiKfBboSBkRAYpCxiq6kE0rea+KgOdRRaSlyB/aqn3m6vI0XDsqp64uv6FuLrid4JK7zX02G4GUu2dHiXLZ/sWNgtv+pc5P2Dlk9mccT5DwrZVnoygrM6zkwdFvUTp2ket3eA6MjTzb46cuf4b2KXK73VIgXttTFE7mJQPPKp/i6XcAzQ09Ltos84SIdNl9VR9oiXntc67u8Zzmo4PM4LEWXQtBeqTcgY9M2SARo0197wA3uRRLrH2mttf8HeyyoFSc0bYX5pQH1zERnglfBwA8kZHI1ulrNxENqP32DuWz5VxyIB3Oizw7FlMxU2I77D5zHR+b/wMAwOcv/ysAwM1bH8E/m32Zzo2ZKeLbNtMVrPg0D3dNeb4YZ330QPP2fk65fnbhbxH96vJ5tdCxBLWkYHyBxLkskjb6et9EnOVT3e8DAJ4LX1HRrI+GF0r9MvNi/GyJSA189Fl4S3Lt5b68lpzDTkqo6LWYfK6UWriWxpjilvYRoPI48rcwLgo2UbXMlO07imdTULl1fcbEB9xuDrYFWOz9SEBvVNpm5267x47jUSWXTqwuv7quX/bzUiekJX04nE1SjKm6d1SX8UZsuPtPIEWsQeoaa6yxxhprrLHGGmusscYeYGuQug+IHZWTIGYr+tTt60bNW61hJfJ6p7kPdo6A7GtHc+zf03e3RwTt31elqinKQ4Vdqf0gpEiXnbtV8Om5z0gqxaoFPaLcD0EvqlEkkYDP2tyvsFDSlNy9rZQimH1vqrkPHwkp0lknL13009NCqMKtH7A88zjtI+N4zB5LHEux1SfCt/B4iyJ3q69RNPfjSy9iscLIFheVzQJGJ9fHODgzous0oojnnLedml7GcJ9lrxk13ExPqHKXlEAQ5O5GsqqlALaTk3weFJ3NkJRUMgG63nZOHFCgcVOM9FpK/kSPkc0T/khz3tY4N+5c64qWiBDUUgq2PhVcVClp6buUKojyUH83SyXiu6RInZQtkLIPV+J13EipHyNG5zIkivJK9HSRFOicFhpOyjlitiqYmP3MCRJg57y56peCzgHQ/Lm2R79LEVVy6myZ6QKF5jHECEQcj45UJawrOu7mv8nzL7lTbhvud3W5vUcdx7Z3UqC2sXdv1Xk/gZTRdhGuLJtXot+eF9T6B4DGvDuG7H1cNI7yP6u5qvanva3oVwJg7nw3LyEbtkXRlj6P4gsU6UNYQeNyZJoHJ3OaIEQ5sgoL4bA8dIAYIy1G3+R3wgTpoI+LMTEt1nz6ThAeKasDFPN3z8ywCULqjvP8ODbFPCSKy2usqCltfXP6nCJn2226Do+3LuPUS+TTTncpZzkc0f6z9T3EfepPFhKqkYlS5iBEdpmQt/1z1MfeuS380dHz1MaY5uHoJuXw/ePpj6sPlNw78Z2DfKJKmm1Wlx5nPrqMLC8bOlfJQR94E8TMlJnly7yNc9+Q6O8eC8m3PxU+j6dabwIAngspb91nn9bpjpHNnTHalvcL4MYB+Zouq5heis/oftdYVflKQue4nQz1Wr/J+01zukZTbKp/lDGVIcE8ItS2brxX1RyHOMzq3rHqiovb722uf7BVwo9SmXQZVnX50Pa8f9Q742HnYlvd79ySOfbxjirx5e5zv1qzqHvAzaXHHCXvaj9wYvaCTF7uirarlKuCxlXUB3Khe/t39nGOkhx3Hxz35cDuq91+8Z3Qckba1yiRF2DuJ+a6cBNHO7Okp2XxIFSNSXqtknxvJxm7k8M82USLj+Ue5834BH6YRVCmmSROk9PzTYbjhoRLNtPCCckCSWiFQpNZ5KEu4mThJ6Iqm+kKdpnG8XRIi47R/gDPLcgh9ZaJkiiJ7eG4g+k6XQNxwi1OhD/YGKE1IceyFpDTPnf1OJ5NSVBFFj5X0tPcv1RLAFxONmDbdjrUvgp9cZz20TJClYv5XOk4PW+OlGmdIgDQ4peiR4O3dVEmC7KhN0aa00uDONGHfaK7trwE45Qc+Cl/m68hOfab6Qld/ElNwZfjj+A6L0ovRI8AKKhNu1mKEa7x/nRsWwRmuqCXARk3STIp0WJsKwc1bJqMvASXF3/2M9dxXhTn6RZaPgvpMP048HpaB0veISWZPk2r5RRs8RWhjNZRW9y6QrY4U9251YkyuduOSoC3r5Er4mS/KDS0y/fO8jyH57UO3X5YrUDfL+Z9uzyAG5wQs3/vyqYDxZi703pzdfWqZF8R/ypozodTIEmkpdyWjMVZcgN+UKaDt9BBm/2JvJBLkCpCEVyURZo8u0k2rdCiPa8jWh2IRGCFn/sWzin1c5frgD7Efmaeh+pDxGTOBgpq+UMtCoztZ0sqxiUmYlBZ7ulcKLU4R52B0g/F/DkteI4Fb6A1oWsxX6G5WvRosjACeK7NPepfMA3V/yx4f3OG5v+fyl6Bl/B14jY7mzSfTw9WdGH0G9Fzeh5SbkZ8gfzfMxke9q+XtrWtmqeycGsdJz8R7C0hOXbA50bjP+egZxqk8Dyac+Qq5yyKMsu7WMj9z+majLIBrqTkFyXdQq73rXRFa+nNc/KFkioSYaq0/mnCvscqUVAXCHep+3aApG7h45Y5sJ8lVwzFfqbcYH9dyo4sEJNkUhscP6xfR6X4HNbGUb7GFQmsr59avU4u/fJ+9zMN/bKxxhprrLHGGmusscYaa+wBtgap+wBbgSQV0ZrDIuJ1RWKPEkqh7eXIRx2dsq4EQl2EyY2G2P0Sq0M2XDifqGOd0j5RwOIjWFV5aVuO3y0uKp+hP9R9bIlf+XSRyjSd4EAiPZK8bxXEfnVxDkBRokDoMY+3LquAh9iV5DQ6jEJJW5L0ngYeLkUUnUwhUSNOPE77+MaUionvclS2701V8OTEgqKfEpHsb1xCl6OeEomMOfl9vnKAaMjlIDgiuVg50KT41Qn1fyU8r79/bsRtcYT4KkdRQxPhhEfHfiF+GgChkk8yteWNmKLAgpq1TayR5P2coo6r/PuumSlSJ5HnNPfhe2mpDRE+maXVYq9io2yAby6o6LrIeV9L1vB2IsnqdN4aKcWoIoZysLhcKphq22Jxo5YGDZSTt+3IqoteCMLhwVM6ZewIBnT8VY3+CyUzQ1ZB44qI6qT0t202ildHTTlKDMVOJpfPurIFdbRL2eZeQ5ua7SbCH1U4t7G7a7eTEweq834dkmbfL1dsx/MCuHRa+3guim3bUVF9tw9B0K+lfhVMlGHpdzbTRL7b338RACER4mMCq9SKW3bFleMHCqTd7qeLbFJfGU33aNvMozb2sYkhCP2JuMD22yy00femyu7IAnIij7beVvrgmzEJXQnlcjcdFCIljMapiEqwo98JuvTLB78Dz3VeBVDMr+JXNq+s4skWMUV6TBM0LPGf9CL1J2mHUKnFcIY84ELhCaN4fdrWGreRMjrmRdTX/XMsHBLcxMMTOs56/1fod0GmCKCkGcixTWbgRXT/xO/JtizIFBE0nKaQtyN43B9BEL1rdH1bnQP97eZ8na8bo1LwMcnIT+7z50vRRyAmqRhFWZwYExBLJeNrLlTLRT5WarwtTOLOq/YYt8sIyDYXVWtzSY7AK0pyyBjNBOEzIUyrPEcHXg9xOi4d26Zrus9aUWqheN5sdoiLpteVMDmKpm+bS9esM3euqttWRvvLzJT73RqkrrHGGmusscYaa6yxxhpr7AG2Bqn7gNlRyeH2d3VRjmoSerV9O7pZTYCvysHWyZ7XlVMorMqrrpO1le+SxOV0B5qTIOia5AfNw1DzGlJLOt8tHGtHTwVpc3NGgqCv52b3Qc4t4jw4W+L/airtDvjYIiW9jZucvC4oUxr4midnR1IBoG0inA6I879tlUwAgFmew3Ak9vvzxwAAx7yFJmFLArygWGevnFIE7XRAEVXNXxh3KhHV1qStEUtB9CQ/Lw0TmGxH9wOAQYdyAJAZpIzwfWL6PQBAPulpIXYxKQoe5SEGPklUi7z2NhdsPeGPNCF9PaI8jIO0pzkMUsh9mwUEtrPjGkkW9E9yG/bSZUXlbCnpGejYErkUKfEFJjhIC0ERgMZXXYFWoH6MF1Ltcx2jYoRGFdHYcls9hJaogd0/2ySK2vIHWHAf3bw+O1eiLpehmhvVUZlssaPkn91zKPXZEqSwjwnQNTkM9auzBqV7/+1O8h+Pui912+ycH/f+ezWhZxuxrcvfdo/jHtNmdNgIhptXXpf/KSaoyWJxQ/sTBjTP+gj12SzKHVSl86WUixSSNiZAp0NzlaAUcTyqisZIfjUmyhwQvyXIYB9tnfdFYORi/LD6gJ5Xvm62WJeIckk+97V4Tf1VnAuq4+Fr048CAD4a0rzdNoXUu7BDnrxCbIyzXfr0okAZICKs0t0cIBrQ3CzomvicaGUGE7Ef5lIBaYfHYOJpWQWIrwoTRdDa3L4IhMX9ufo0Re8Yict6Ebqb5DOk5M98dYLOVp/7SMjea+xLzs6vquCJ5OUJKjfKBriZ0Ty/x/nc46yvPl3y50ZchmGCLb2ngtBJfrY9X87nNE7c8QDAEnULKoi573dgzBBAkb8prI8MGdKckWYW5JFHLk7HFd8WWSyPupI8seNz6kTw3NxYe7vt9+5kjrGf97pSPGJ2nrv9f9+v6kPQ8etzFu93a5C6xhprrLHGGmusscYaa6yxB9gapO4DYnVFYusiqncSZbWVxYooRVmtjPLtqpESMVeW2o7I2GpIbr+qUdoqt9nz6lWXxNwolqphZmMYDvumluKVKAlKhFPkqX2EmoPnysxH0Zaeh/Dd8zypIJOGcxkyZKqqeT2lqOHAE9WyY9hOWZaaEbh51tZoaZaXYy/7WU+jrItc1NC4sG3aR4xFaf9bWYoZ5/O9aSjKKGUIHm49gjMBqYE9lVEkdbhLEdtHWm8rSpay2mboTwtkjiOpkr/Q3lnSCKxEUr3I131FBc3jiKwXHWgexcZku9TnMDMqOB6O6f6d9KifyAziOUVEDUeG9/MlTBl9lDw7+TwfP6bbLkafAFBEoK/E64qA7rMq6QS3NOo94+K1opIaYw5XeTUMVw+N5nleMbbdEgWt1rCiUma3I1HTlhYTDjWaWxRpp99FmGrkVSKxUbJjFW6m4whqnWXzSi5dXe6AnbtWlxsnfT9MerpO9bauJElxve4M/bGjqPe7GtkHxVwfI+MkCPqHliEAqurIdb7Hnr+rZXSG+n+35ECaFiqAMsYLP9GpjPE69K6u78UzXoyzKhrPyIWF1Ad96msXA6Q8J4sCsp2LKwyQOc8rgTI65oq0277EZZ9kXoHKCbIjZXRENXEn66DN/ZJyBH2vo/5E5kA9Z3jKChmnZcSy583R5/7LfBkjQIvLCLwWUekXUcj8WOcCpi06tvi2V6InAACPT9/CiS1WNuZSC2jFMEnZz83WCMXqXmshC/laMyonTBCg8A+intnZXFY/JAidWGvSQXuH0LsFo3EZ/y4cd0usEwBYunoc12cPcV+pz5Lb/vX5Z/Xv/Zz80YX4LAC63lLcXRgg46yvZQoSZoLsgtC4HJkWSpfi3fZ41PIZ/F2STOD7BbsBKMZGGK5qCQN9Rq08ONc8eMztAeKsjGIZE6iCspRQoOe9nKtdlPuYV/LghMlk52rbJRcOy6+uy8/TPtcwYI7K37XnHFfF2ZjgjgqfPyh+plnUfYgsTecVoYGjEsHraFJue7bV1TY5qnxBXWJvNWk2sagEgfUdKm24x5TFlrxM+H5HF3NChZliRxPXhSZT7BMg521JzYuIm7xsO2E5R5G9n2Gki0d5IZdkaaCQVxbZ/543R5uFUoRWOPCo/a6ZV6iZUuKgZRL4IMGTSCehSJ3JJKf2DyJKkt/NBtoPoTeKo3o8eUslq6UPp7CF8BonWndov/kqLz7DRJPKxUGLk/SiQOkxQm1JwwRL14bUBi/42lwrrz1aKn7LbR6kPe2LLNgkGf1C8qjWLor4Wkif99JlpanKC8nFiBa3+1lXnekC23K1dBG3yHkxxy9VaTrRcW+Px0LcQMYxXfm6F1hJUPe8wgl1fLoHBp4u3IS669KDZT+gGF8+Qh23kmhvH1vGqizq7HOqk4mvo8JU+mA9e/IS4T7vR/3OtvLisfy8l2Wp3Wtfaaqx98lcaXCgen/o73qavt1G8ftJZeEm47Suzmq5/fKYrVuI1R23ePmcWOVDymPPFnRwfY4tLy8v5HEQ6jO64EVWjJpnQUS5+FnPkWHu1BizaaHqV3J+Zk1SiFvwc78EWsC10MGMKYY+B976mOJKQvOPCHVJqZieN1daoPiVntQR9TPdJjTNa8maCmIludR4pc/n5w/rwlAWj/K7/XxJfYzQ4Z9K3oQ/o6WFb+hzbZfm43R5qouzgw0q/dNhKn8aJkqnlHIHrXEHPgcT3bqs8WBeBBzZR4Vj6udk9zR63J/tbQ48w1e/8vXpj8O268maXie5NlLPdC/rYzsZUruQgOWujgFZiE+yIiB82Dh2/wbKpUC63TOlbb7f13eaXKiPyND110r7ib/wECA0XG5DS2YUE6ss5sSybK7zvbzzyDNaR4e2BbbqxLaOChaK1dEq60pwuX/XCXCJj66jdRd9TkrvwQ+SNfTLxhprrLHGGmusscYaa6yxB9gapO4DZnmelyiY9J1NRylHK2xzIxNU7NiNpBZRC5t+Q9sK6N6mCch3dYIM8nkYHYu+P4qSKedTRG2kj1ocdnYJANDvP13sw1GqNI9wYDhxnJPWRUwlQaSoiUSTBPGoSxDO85EiIbK/CmGYQKNziq4wogQLsctaFGfZaG0CvP1c6wqAApW6kaxiLaA+bzECteFRNO1C9EgFvYvzLsaM1AkFSM5rM1nWY4/ndA2l5MKVZB3HPUqEbzHN8eHgukpiP55eBkDFzQHgmdbrWOL9si5RWmKmWuZejnDcLV2vpBMrMieoXLRHUebNfAnhgto6H5PgiySh98xco7kigOIj1fIJgsoJ+nkjWS2KhzNSt5tTxHeBW3pNhLZkU6AKAY8Jf85rKcXudzZq7Y6FQmxnqBFVoVEG6BSUX5SpwiF6OADdd7l/dmkO9/m1++eKoiTJpII81EUmDyspYm+z6ZQuDdOm0ByFzNv068PQFfv/BX3nwaDEfJCsjup/GJ3fntvvRGa8zk8cRcfKsqTW14gdNcbt8QuUBRMKYaG5HqeuTI+YzPuCwttUf7dUToCwIm5irPh6MU8U51NF1QtKaxAyPY7ZBTJftNDBVsbzcE7z3jTrKGKWsOAJ+DZuJitK6xdWiJggUEDhVwbexBLvArdJf0SY4tW4zc3THN8DpQBshislURaASgGs+XQNH2+xX2E2xmx3raA+XqE+yxzvm0yRtBMdKnMwWQyRzGj+DZRYyLYN+CKWJb6D0cYAKa5ndJ4icrKZnsD1pKBPAoVQzGaygllOY0GQuignhHCCLUzxOoCCmRNhilk+KnVHxk2doIftL2RcuVRL21T4A4EeU/xYx1/VMSc+p8XonIGn/RKqpViWFf6uED7pKyLtPgtxPCqhdvZnls1rWVquj61jj90J0m77lTpfJs+vyyawBVbqhVseLB/TIHWNNdZYY4011lhjjTXWWGMPsDVI3YfIjpJ8tbe5n0A9z9tNTG+1CrSsiDbRZ5JMDo3Ou8cCCr5zllULzdYVnvW8AoGwj0l9GOr/3eNk2RxZUOQzAAWfvBRR5ShSr03RvTibVCLPdYjFwcEFAMR7F2kW49PJTRl1yfKBooQS+buRrKr09BVGoERSejXYxTyjKKjk4EnEtO9N0fboSJLT4CHDSUMRxJtcYFuiuRGmuJzkfP60jxefBAA85BuNTj7Woty6K/FpFXB5MXoKAHCMSw98e/FJLBvKjftoROfd3qPjnPB3FWkTu5JuaJR1c48io4LApbmPkHMK30ooUf20T5HYvWwZt7ho6x6fo0SP7WshwidjbCLgcPQUV/hcObcQO4qMSe7AURF4oCr4EIarup/kGrjRdvt3djHxougry3gjsvIZyjlyiSXu41qaR0U5DStvbjaj++ZGT+tQj6OElexrUuT3FKiBe03+/+29f7RsWVXf+5276pxb997Tze2maRpsfmiDIhgFjRgF3iNohCeSgNEYR56AIzhiMvJCnho1z2gQfXEkL88BRH1xRAMSfw8CRgKi8UUSfj0wIbaGTpRu0mBL3+6+dJ++fe69depU1Xp/rDX3mnvtuXfVOff8qnO+nzHOqFP7x9prr9q1Zq3501pIyuvY0h9lvEJfAidyPPHkimeV9eiLV2k/e215ZL022la89jNu4wCHw3Ycdxkba7X63rOt70sPjhBugaw3LWFqIdnBvI6XFTMPAzG+SRMkBSPT+gpNT+bJqyDFzanV70HcjSck69ijIc7LZ8ONtYxRi5Na585W4zq5i86vT0gy6PGDTVQp0dfj0vz/pzu31ueqpep8pd/r9md+FdE74s7JDVhHLAXwuBQPdzWMAMTi3B/b/jIAwE2pvI+WqAGApwxiiRwthTPADDdU8d7u3Yqy+Vo4W1v5rga1oMWxPyvX6rJBeozum2FQyzstBfHILCe8UdRK+MD0fD3WmlBLPSkmuFrP1ypfpvOrLY8J+wyVXh52Ti29oqyFSxNj2djrXIopHY95nQBOPZGytXhSP3NqObbPuPbZ/t4rLYxlTKlto2mFb877ffO99XLJ38s8JqXlrTzXnmexBdn1WO93cZf32HGHEvSU4wlHL5BdF1laq8r7sluXkHa2omEra6DnTlW6hVbV0F3EeT9AFe2XZofSH7SDwUbdvv74Fhk2gs4BYJbc8CSGEMd+pCBjTZwRXVObbp7NhA5N95UrV8Y4f/4ZAICtWRRMYZB/rOsk/JmdWK/uXHWhPnc0j8JB3SKr+bwWOhqErq6GV+bnagE71UkvVHUylPUkDO0CwbqHADmJzJ/M1lGlLF13TeJCbw0j3FBda1xbX9dkmrNKpoWoLjpvGjxaC1NdpH1udqH+YfG45O5jayBVxrUQAP57yuD56DwrD65orSFU9UJV3VxVQAXMcTl9ppq0YHuaXGGnW7WA0oWYdfvQ58UmxdHPVo+3Na9G6/E5mc7jOJwZZtdaFbq1wMWwUbvKvsb96+n4ad13/TFYP0O168q4vg/t83h80bhSD1Nf2+7Q5ffL+5Ee280/KOxrX+IL+4PEtl9e27q2tZMlZRe4LncccrSUWYvtYqhPWeC5SXUlQLDn2+sNBsntrHDDrKpRI0tmeR1vcVZVG/W59tr2/1KpYReu43FUothEFjovWfdLpUyGNMW4TjZi77VcDNi+6DWnSaZNUkbJGwa314uMETRj5RhICx1d3M3nORumKg7PpflYF1Tbs/VamXfNJEzRMABN3HVWVA49vpHR03IFD9fJqa5ogpHx+Vqx+YSBKvZS9tBqUidWubQWk3np4stm8FSX0cvzjVoebiclpir9tsN6nYBMM0ZrCMM8VKiSK+ZDKVtohTkemaekK+lzu5xqls7wWCsJzmPT6DpaVaNWbdCmK2B7sZITaKlCIX/m+szptqoa1YlPdHxDrRic1gu3dfMTX/eXIRgVhvXvAdu+fR/7n78LpTzRe7ULt7Z8acsV65JZhvp4WZWb5zbnBStrPPlQulv3KZQ8N/BVge6XhBBCCCGEELLC0FJ3AvEC2eN2m6a1qR2xZQJsopRSW2EtaSE0NR9W4+m5uyyj+ShN6TFxQqnd6XYjjYG9vkXh2rV763s7d+7pAIDR8NZag7oVogZuXeL5axjV1pJp0shpIHGFISa12+mF+toiTS1QtkAOjSUltn9V4vt5ld1x1FKzHm6q69uoNlLdPm5fu1hrG28dxHTJWidnTaa1pe5pa9GaM5Q87tqGamkD5rXGTvXHmjBkgGErLfcUEzw0j/0+m9xXziJa5YZYa9U+su/1mtr3Gao6nfZ9qW6eWufWZIqHZzekMVGLpj4bO7VmUbWOc0zrz0rLEejxc8yzu+lsM17bPGejUXTb0c8xhGlD8wgA6+vt4G3dNxxuZLeQNF7nK9WiThqaeaCpIVWy9W4d89TvUsMfMMeV+YON/tvnXrWqtm6iHmcTvQB+Pbi+FNpWC1q6Z3tux33WGa/98lp6T5ZosdD7Xq3g9ZOIlTPlvL0oHXmfu2ZZ+qCstWgZDEbGM6Mph2xb5XVsOnNrNck169r9Usq5PXqTxP93dpLXg61dp/1ObQbM6/lrWFjqKwxrC1dtDTFjNZ02+zydbnZaNie4Wnst6JxzE26HJH/QzVmUGU8axr5/bnahlg8qM26dR0vcThjWc/m8xxagtdlG1U6diGWeXlWunMOFeo7W5C6Cqv5/cxaP17quMq/q0IA/2fny2EayxK3JtLbQaYKwrfm5Wo5ofzQUQWUPkOXRdoiWwSHWahfRgGt1v7YQ66NaGaiM0/iUVtzZbKv17FiLW2lVHgxGDbf8uC25hRrX+jqspdpo1NoFstV35NQztbKmtJxOw7g1b9ukJfN503o3mVyq92v/9TVa3rJnhaW7/mlTnnh4IUF97pdd59t7s59V2Ub0JllNGUNLHSGEEEIIIYSsMLTUnSI8K4MXr6ZEzWX8vyxabrflmDXVMI6MxidbFPrSXpeB6VmT0zxOr9PVb6+0gS1eq9oiTSAh54Y4M4j+8xosrNj4JtWCqbZujmlt2cnxVhc6U/1azZ32R8+vzo7wWPLTP5viCqZhgrMpDfW1EPunMXUPTW+uYwZGSfM4NolCVGOp8WlX5yM8Yz36+t+WrGwaY/Dg7Oa6SOo2YsC5jf0qtYETXMWZVERdY9YewX31GG0n651qZbeTlm8No1r7qe0PzPRzBZ8GANyAaOG6ik0Mkta0LLRdoWqlBB9giHEawxwbGNufzDY7EyZ4qaGn063GswxkLeN0uoWzZ57cOH6A9ZY1TseoMlpR1Z7q/axhVI+FPlfWcjpRi12YNPpg78MWRS+TGc1mWQNbxr9aK17Zpo1f8FLNl5YUzwJnY5m8kgZ9pVVKyjmBHD/KZ8dLbuJZb7ssdt7xs9m4FbtXVU1PibhNj2m3W8Y6N/vaLhmQLWI2yVY7Bqi0gF+5crex5D8jtm0SINm4WgC1dS5gXm9Ti8q6bOAamv3RPmtsONBOj1+dH2Gekq3YsgoX0Jy/Hklz9jQM65i4xw83AaCOxb5teKmO49ZEXGdkUlvC1Iqnx4xkgp3SIyFcSPc1qe9RrVI7GLfkQvbCyJ4WauFbT9eZYYJqpm2tpTvaqY/PXig76R4n9XUmeKDVB0WPuYKH6/36+Wlc/UDWO+OzRIatRDze7x99dtcGN5qkWclbRxOaCHB+cEs9dnqMypwZ2vOi/l7RMdzBuCV/LGX8mzfXWg8Qxcqfkhx3F4/x4tT6PEa8WO2+OaRL1pQsMxessqyhpY4QQgghhBBCVhha6k4wZWydtZaV2f2s5cvTzvuxBe3sQ+V1VKu5KJtQ1z7bVp9W32bWrFP9muyEuk8tdBpTt719EdXZ2K4W4xzPosazqkY4Jzc3rqNxW5OwZbSl2TqY/bSb2jmb8U+3qfVke/si5qptTmmpBxjWmsNrKQvY5VR64KnDjTpm4L+ljJBPXotWqnmo6rgBm+VLYyU0g9nlaeznORkDSSurmcIeTZrbGSaYFFrAIdZbqfWtZnVWaxKbGdymmNQaSFs4e15rc+M9qsXSoumirYZR27JFU7Wt2oqVsnCtDW6si7DqM5tTj+fnqy4Evr7e6v+oStnQhlX9DFgrpm7LWvZ2+YEy090c00ZMiY5JncUsWejqGJnJpZZ1zWZIy9uytcHG/QH5mbPnlN89LxW8pYxZ7ZsnbEkDmw3Oi8vT87rioFY5zuEkE0JoyBigKVdE2tr1PmuvVyqmfc3syVG27z3j2oa9nvf8W6ug9r9sy5bI0XvVDJz2WM1KWXoFnB89vbbC6Lyh3/k1jOo5wXpMqEVO27R91/ZHoxiXrBa7q1fvrec560igc9ONyStiPFeZM8MkxFI5W5NozbNeH2q9uzob1fvmSTZp6QP1Djkjk1rmaAzeSOLrLF0DyJmKNTsnkC1mKhMqVAhpLMp0/HNMW1lFp5jU46njq9k2A+a1Zav0QhHjAaJtTsJWHWM/CRq7d66+dvkc194ng1Gex+s5Ln9WZyTLWL2eyge9f71XQVX3R7fp9eN9J2tyHUM+xRWTwVvHRtvVuHL7XSh/Y+kzNJvlWG0re/Q5LK2QXnxbMw6u+X33ygn0x8/l+aUrI6zth2cJ9GjHhK+unDm1izoROQ/gmwA8P/09F3Hq+5EQwhsWnHs7gB8F8FIANwP4DIBfBvDjQfO5HkM887/9Emo6Z0/I6Zfcfvk995jYzrD+4WcFbBf+D8f2YtO6BuQfg013HFvbpJxgdnY26x+39n70/3nap3VfgGb9IMCko5YNBFE3OdRtlUH79tooXGiU7e2L2RUg7QrVvE7xrAJPJ/3PTS/gchI0ukjT2myPq7ZaKZsrzHFTct28VATC74RhnaxkLbnqDNJ9jcM6rs7j8eM6+cikFiw6FjZFck7Rn9zr0riNsVUfb4WXLtNU4EyN24znpglkV0XALAowxM4s/rAoa6bNwqQec/1sa0Eo2e1HqTCs+6Vjn92FjHuYcXHReyp/kA2xXv+I2CkD1DGphXpdfsH82CxTYtvnRvfp82ZdSTx3yK76SHab94PXS4aieAtF78e6brMu1l3B8V6a6VV2hTltlG5cQPsHVl/dOpuQpzwuJuBq/vi0Cbi8H4NdblveMxVCu6ae930pZVrXgtQq7Wwbk9lmnQSjXJDYeU+vODOudprUKf/ozn0Zjy+mPmTZqOEAO5pEZpAVY5fT63ncXF9bFWh1H5KbfjW/EYNZlAsaBrA9X28kLAGAJwzz4u6mdAWVTSqXroYRZsl98nGIC1Fb1qd0D5ybFP0qH/T9GkZ4GJ+pj9O+ey6JQHOuDka5pq87IS3qVBkmo3qO1vAMW29wWOWFl45hbMskP0vydCij1r1t4Ja6X4rem34G2o69f9tOXXYpbRtjqzVe29hqybmcgOhC6xm1yapKOTSdbjnf87YLs+Iptu2+PhfsrsWiDS2w8qurTEFVjUxZk2YfTpp8ObWLOsRKl2/f7Uki8gwAHwFwC4D/CuADAP4sgB8G8LUi8rUhhO2eJgghhBBCCCFk3zjNi7rHAPwcgN9Lfy8H8MYlznsb4oLuLSGE1wOARHXCrwF4FYC/D+AN+9/dvaOm5Kpaa+3zXGLyed0aDK8QZVf68va5TZO9dd8qtf9eghVrBWsnh+h247Go5icGwF9qtKuud/P5GOtV1DKqBq4O1Ma57BKRjtmePdyppbJJWkprXtQwZRc7AJgPxwjDeE11HVGt3hxTrIeoibs6PRuvnVwn78NtuH0tat1uSMHrs1C1Sg1oopSdMKz/V02qutDcVF3GeB6LoavFKmCOMxLTS2+FZmIV7RuQXUH0vMq4l+hYAjmhSJniO2DeSB4AZAtdZUotKIKqttCpllL7IjKqrWRlsW/r9mM1omX/9Hxreds2Li6qzS21odbttHZ/madnqMrXrt3WZB3jjuBzr/i21ZiW2/qs5NaiXRZTHgw2XItbWWpgmaBy+93OGtVhy2LiWUTaiVxW1yXmpOO5+sf3w5Y3RVU5DfS23e0eaWVBmVCnee1mW1Zz75UmaLsHt92Ic5KMaS23PEugHq/FpXd2chmCsrzJDG1Lg3W/02+HepXY8kH5+3uh7oOOuVpipoMt4Mzt6VqTxut53NxZMHw8vxGXkueIJuUaYF4X696qE5fkD/d8kjkqV1TOPKF6GFf0+OS+eXF6CwLS75QiMYl1SYfx5ACiZ8SNydpnrV3zIlmWzr2CqpY1NvGW7lNrnJdMZK0emzzXlzIty5l8vsrtGG7QlDv6/gbc2vBAsfuA7Flik4Z1XduGSGjJBZFh/Tmrt8p0Hsfo6tW762f52rV747XN7yqbeAvIv1E84neoua0vWUnfNs+t37pTqtW+r01vvzrTZWu/lWmrL2NObaKUEMI9IYTXhRB+JoTwcSClR+pBRJ4P4AUAHgTwfaatKYC/mdr4O7Lo6SKEEEIIIYSQfYKLj93x8vT67tLFMoTwgIh8AMBLALwQwPsPuW8Lmc93ak2qn/gkvjYtYX58g22j1Jp2aS5LLaiXTGEZ/+bZrB1c27xPXyMcwrS2Xqytta+nSVTOn0/xV9VG7WNfB8mnuEMga8hqP3cZ1oHpep2dnYutfXod25cylbbI7dgZNP37Nd4ByCUDRsmSdH/67G6Wm/DpnRjkfk5yvIPGyd2aAtqVB2c310ViVcu6kbScazLFE4ePAMia2ApzbIf4DF2oYsD7I/Nr6bxbWglC9P0ZbDQsbYpqF0ut4xCjRkIVyxpGtfVyzWgw1UKXzxvWfdD4xDLQ3lrnVAt6BhvZqlYEzjeLteeg9bK9HOyf77WOkUjP0GR+uWW1nYWtOg5Gn2NrUVBNpQ1kt8cA+bnyShPU9+qml06a6OlmK6C9KwZJX7sKIC+Ko1jG2jefL9S3kWNCabGLtOMmu+Z737I7qs/z4uesBTgeZ6/V/A71yZn5fFonPPEs4l7x83zfTWuZ57WiVpAQbq+TpoQUx5zqgWMd51rWpaFJmlUnVElziJwZ1u16CVxyHFG2lqo9qy6po2UY8HA9R2nhb/Wc2MRnIfM4B27PnwQgljZQr47HDzbjtmSVuzzfqGO1S0+QK6Y4uN73QOZ1EfBzchaW7TCrrUxTY1UEokyc1zIjxymW3h3ePuulAUSrn5YUsrHO+nmoDBhprBymDWsakD071nGujq/W86P3STMxTmlls9vUOreNrdryqKxh1ChvoH1V1NJaJ+mRc/XxmoArPycbraQ+NjVEThqUf+eVSYlsIfDSo0Ox874X/1p+J73YcJ1LYgKmYev4nPOh/fuwnRDMtr/6Fjrl1Frq9siXpdePd+zX7V96CH0hhBBCCCGEEFrqdslT0+t9Hft1+9OWaUxEPtGx647ddGo/6NKYLJM9TDUyubDxuFFGoIuc/n+rdZxNYdtOk93uj5f+1sYIlfemGszt7WndVy1zoOdZ+0CdPTFpz2xGsjqOzMQpaabL0oIBtDWqNruUTSlcWklnGkcxmNeaSrUQqVbvkfAoHoeb4r2l/p2rxnXWy8dVai2L2sCbBpfr7GRVim/Y0mKsYdhIUQ1EDazG52kGzXlIlqcwqPtRZsYUVK34OZuOWvHiAhQ932ZDsymhy21Do1FVDXSZudOWLljr0cDWKa5xtW7Dale9VNv6WmpU7T2XmS6n063eNNGa4a4sjjwYbLQymAH98W9eprPyfF9b2sRmLOzLZrmMdraZ7dbPZEaW5zjJGEvXs2Qtzt5zWT7Hg8HIeGZ0P8fls9fMWLn4ubT7y4LTntXbO1/n+8nkUi0DNJulltGZDW6s5y3reVBmV7SU5Q5sXLpn9dQxVllzeX5v7MvwVlPQOs5ZOp+tI1t6HtWi3fOqjhdTGaJZliuZ194dNWaq18zMj85iXzeqq0A6fpo8QSSZ8c4IUCF6hQxDM+YvZrrM8XL1mNQWsab3hqBysh3ncgE6Rw+MF0YZa11nKjafRVlCAUBt9SstffH+028G067ur4xlT+/Fyp94ftXKnJx/C6zne0yW0Ckm9ede/u6Yzbbq//W5zJ5D7SyTto16TEwJpy5ZYC1pnlwpv1cxD0Hzu+2VriploXft05RVmYu63aG/0K927L+SXm84hL7sidI9xn7JyjICVdUvDMsvo/2itwNb2wK5PN/DC7Tv+zJGN8+yjbw4KoN+7eRw9eq9jfbPnLmtnuTK+mNeHbKzw9tyWuVRcp3Ybv4It/c7TENjf8jbxfBk0k5Woe+3k2vHhsRaQ0PjxvhwOu58iAu/q/NbcaZK/U3XtK4x6n6pCz1d+O2EYZ2q2qLB8ZtJkN+Yjr8a7OcYk/JcSSURzopgq0gKWxnBWbqjrONca2GoP3ZiWupm3Z4pxvVnVJZAiA5MTbcXFb6VSSFuA+ZntXBvukKdwUZL+M4wqd1BdZ+tgbQ9bQaW2zIhpfLDCsVScNofaYoV1J5L46IkJoBfK8xzoen7nrYTt6jLTm5zbW2jPqbfFUbbOjkuMacNK2fKFOLNJFvNbcPhRqdLbpcLsMqtUp7Y58xT8O3G5d+67pfne4qLqho6YQl5js9yIW6rFYpmoWDLo+jCopyPIACSjFLUfbuqspuczjNWPqoLd70onD6I9WGUGdNiwTDDtFaq5bT90S0TyK6MZ5IM2J6t17KjLmUwH9XvH0m1UG+qUtmD+Y2NUgmWHQwxnkd58rgquvo/mJ6XNeQyAc25V5O5NOf9GSYt5aLO1Wew0VII2nPVlVPPH2BYyyYdp7WGjIr/20Va/h2BdHzs8zVcNnXnmvcf67/GfbZ2nb0ny/bsYZwdxN8FtWvmbKv+vMvFkF1sleULgLZi0EuSVyYisljZ1ud2X55rZdp02lyUNY0K+n+366dNJFa2cdLkzMou6kTkXQC+eJenvTqE8LGD6M9eCCE8x9uetKvPPuTuEEIIOUFQxhBCyOlhZRd1AD4fwBft8pxziw/pRVV1Xe2cT6+PXed1Dh3PfRHwXVniMaOW1sVqSXKa9Gw+7wqStRrVdvKGoeNes7t7s9raroQO9n/rimCDiS3rOFdbdqwVyGrGgJzGenv7otEeNe9xbe1Cfbxee3v7Yn3NwaBtBVHt6pX1OCaaMGWCq7V2UjV4I9yIK2nQNic3NI5/0vBzdQHZh2ZRS6vulRXmtbZV3TDXZFpvU83qQC18slW73KhWVp+aSQg4k74eG3KmbnOGGBxfaimtJlLdWCZFmmo9Doiaa9WglppLz2XSujPZ0gRA0w1H+5MD8y/X/cpa2klOYGBcOIGYDKVMza+f8WCQLcdeAe9Sa2qf1bLN5vc3uevOtlrfTXt8l2t00zWtbcWz80Psb9tl0vveexaNvrIL5GTgpSXX56PLddKeV6f/dyzJ0+lW7W6oballzFq2yzb6Qgw85vMpqsr3LLHvs1Vj2DjXIjKuk2WdOxePU9fJM2duwzh5lmxUUXZY90tvrpokF7swbN5jVU2dRBZb5vvYtM5U1Qiz9WYoxTzJnmvYrEsH6Fy6jnN1f3QufDClyT+LGxGS1W4SYuKTC4PoyPS52YU6sYomUdmormKe3P9VvqjnxxqmGA2a1r715Akyw6Tl0WHdJdcL9/5oLYvHbyFartTKuINxLWs0KdnAuD7apFzx2tNWyRvlLG5sud2PcGMtm0oZMsW4tuhlOZctdrngebzONrbqbQMjM4FY0uKx7U81+jOfjxvuv7otHp9/25QyZ2dnszXfAzByq+3W6MmM8jzFc6n23KbL80OYujLQlieJ93gyXSz7WNlFXQjhuUdw2c8AeB6A2zv26/ZPH053CCGEEEIIIaedlV3UHRF3AvhLAL68Y79u/4PD6c7e8VJPexYw1XiU8Qp9CVBs7JqN0ys1ojlge6OlpclJItqafGvZ60uRW2qYonYn+5EDMZ5AY+5srBMArK/fUmux7DYgat9mReHYAYaYplTAWuBzMo0lBNbWLhgtrvq2t7XTGjg/mVxqxf8p02kzyBkApimuYn14c6091DivKSa1dlW1jVpE/b7p43A+FcFWi5tqQ9dkWmtLR5KtXxrcrmUS1tL4tQLjATxusFW3qbEVGiR/Ria1VlbPHacxGUkAMGgcP0mu7zaOoox98ziPm+v7zYkApvXYlIHs1rJXaqJ3MK6Ps4lPSm3xePpgvM5sXD87ZRD6dLpZf7aqGd/Z2Wx9thavwLK+L78zIUzdeEx9LYPPFfv9zd+TjVrT6xWX7UpqYjWx1ipjY1rtdeJ3+2TFOJxmQghFeYOY/MorJgw0NetlrFzzOJuYofmsls9zebzS5Wlit/Ul7umLMfWwz7hNjGTbmkwu1TJnaxjj7jaq29zSKkBzPsrp3LPMVS+P+dzGvTfHq5Zp83FtMaxLLqzneeNydbHRB1vS5QbEGC5rnSo9LTZn6kkxxZ9OYzKvW4ePpX03msRbOVEXAGxiu56jz0qyRqUMIOer4MTgjWvrm46bjdXWtqzFUe+rjJceYr1VIkfnfTv2ii1zZNvV80pr4nZdmujG2gqncZVi5EuObZyk+1+vt23PN2N/0jPlxUFPp1ut3z7KbLbVKo3TTLbVbUkrk5vYNrzfil6JrK5Yt0XlDmzpLT3e+82n+9oy82TKGS7qdsd7APwwgFeIyBlbq05EngjgRQAeAfChI+rfrrGLu1LQNgWaCgddiOUfjHWNLVeYtn/IKfkH5rC1wPHcvbQtb0HpfXlns7bbj5cVqVyAar+2ty+2BKX++N4OW/XCzS4o1iS5+aX6dhp4PguTui11nVxfzy5EGjivnD17ez1J5x/57R8mumDQHwfTtS1Mk5BWRtXNddB9KWjWMML2PN6TCmSt6baGGxEQn4+hxNezMq4XWSpMd5zPQxd8s5CF3o4KxbTtaphhiLOpP1FIr6fzdsKgdgtV4X6DRNfR7TCrBWuZQMCi7rFjbGV3yOK8ETZqQWvRBXHphmkFswp+63qjP4r0GbLuLJqIx3Mxu3Ilf/45gUHT5ddmIivP92p/2aQrpfvwfD6uF2ddLi7ltcvvuec6qti+e0lRulzdTqqgPc34tesipYLAumYuUhzm9v3jPdd6r75d3zabyCX3S5/7TQBxPveSfpWKEf3+iAyxvn4BQP6u6/XW128xMjAeP8Zlk9gpKxABNOauclE7HG7UslPnIdu+Xju7X1pX6WljXwjTevGn7FSjul6eujLWLoC4UPdNX7dN7Ts97oHpRtpW4cY032+FpnJwQ87X/5eZMcfzNUwRf4adl7V01BqQ5BaC1lfVhdW8Xgx5NePKZDAzTOrjSzfMCsNWzVK7WFOZqwwxqmWFFwZQZly2tQnLzM5TjLEzi+2Xz5CVE5PJvbENowgv685FWZB/p+nxSl/mSE9Z0vW7UGTYcpW2x3uu0dofdfP0FnpdyVG8/sbjTnbdU9ap2wUpycqHANwK4B/rdolP1U8jziZvCSGc7KeGEEIIIYQQcmw41Za6lEHzSentk9Pr60TkZen/+0MIrypO+w4AHwHwehF5CYC7AHwlgC8A8GEAP36wvT44svYlvveTlCzWotraQVmbMmrVZ/PcZUqTvbWklQlGLJ4bWjvNetay2vZLTY8X6Hv27NMBNAPapykoXFUjFYa1hU4tduqGN59nTadq1qoqjsdwuAGR21t9rmvjqStMT/pfZXv7YsvSU50bZe1s1bTYnMfNLc3lY4h9FuQ6RNeSW+k4jLBRSbrHpM1NlrRxEJyrYuITtdCp1nWKHewkw3az/k6zLS2XAAzx6Fy1slHreiYp+ie4WlvtrqU2d0ygubq07JgyDzmBSdNl9goerseiTnKDaV0HaVC4VdpSFtem0bpmEzLos6SWV+tGa2v/KF6SBv3cSsu21baWeLWyojtK+/sExGe81LLa80vXlrJd25Y9t89lro/dHk9Wl74kPUD72fMSatlnt8sN0n4nvGP60quX7vxV1V1SJ4Rp/Z3Vuddrw16vdK3W+aKqhtjYeFbjOrMwwUCaLoY6DwmqbDlSGT3IY1O6WFqreimPp9Nxa5xszTD1NMgWuy3syGY8N21bl2SJwbSeO9eLWnFTTOr5VOffdZzDteSeuZbm+wkeRTrRrfUWjx1hLdWweyR8rj7mfKrVOoc6UyUrFiYNC5htcw2jluwA8rXHaZ9ayyoMG66Yem+x71fr89T1f4Bx/b8mKhsjfx5lWZ/KuHtq+1vX7o77TL3BMunUdLrZ8p6YTC6ZOsLt75CWm7GWWaXrmbDXtr+ZSmuvtVB7ifC6kn6JDOv7sFZIbXMZ9+ndlC05KZzqRR1i0pOyUPjnpT/ASXgSQvikiDwPwBsBvAzAqxATqPwogH9kXTIJIYQQQggh5KA51Yu6EMLT93jenyBa7E4MXkD7osQpXdoP69Pd1E6OG23YgpV9GlWPrviJPg2wd5ynNbYWRNUUWQsdAFy7dh/Ono3WNbXYVdWopVHVmDp7nGo6bTKVMp5vPL6vpQWz8VqltcSWYSh95u09TlOhcNXaXalyHIAGtCvWx1+1rVu4hHkqKJ6LqkZv4wuVGOvaTto3r9tS7ayWB9jBGFeTNjYEfRjiiy3yrW2MQ9asXg2xCK21Mtp0z7otHpOta+UxY1xuJVu5hsu1JtVa8hSNpfG0muXYW0pNv33urWbVKz+gx3sJgcrr9SUzss9NGfvQLKvQtrx1JWmJ3+duq0RfWuk8FoylO+nY2LquRFeW0pJsrXL2eS89LGxbXaUMvPg5DyuzVG6VVoOqyt9je70uz4/4fSg9YOKxV6/eWx+vckJkiGtB5+0Ljf7ZQtW1B0Q6xivhE9O+J2uUKa2i91N6z+i+yeRSa24bjW6v27p2LbWZrr22dqEeJ53v5yYWTedaWwhc4/JUHtl7Kwt/2/I2ZXITQYVH8UAcwzoWbatuR5OolGVoruFy7aGhxcdj4e9Joy1rUdN+qVVS25phUu+b1clj8n2X3jG2/9fqBGdJts/aZXFsvH9OvJU9oLw453KbfY675FaXB0iXldtLVmKTtfRRfh9VzsZ+pfEqrI3e+fZ4u++0yBjG1BFCCCGEEELICnOqLXXEpy+2wGpmuvyVPd/m2Wzc0nDazJKqWVHtjNUw9mllvP719SdrcNpxB6XlwmbpUw3WtWv3Aogxdjk2Lse+TesYhAuN89arG+tMYdl6Na/vVTWets0y/k+ZTC61tNg2XivfT9NP3vY1X2dY93VnPVohbQYw1SiqxWqAYSsts97PlXmOMVDNpY2FyBkoc5rpMpOkHrONLRN7N2rs0+32f0HV6teOiYubJO2nxjTYArV1f9K4DWVUxzqoddTGuZWpo238Y2k5nc/HnUXErTbR+y71WRLyd2mrdcyi7LD66mlZgWYsrWdJ9GKQ9HiNzejLXnua4huIT5+Frp0luft8wLcWlMd4GZr7suXla7fbKvsXCzvH/zVObWdn05mbc//KzLb2+19myZ3Px/UcrbF3tcWiGmGe5ETObJwsQ1WVvSmqPOeobPXS2JffVWvBKTMXjsf31f/XXitJfts4w62q+fmcq25plI8B4hytcsfGNgPNbJlqxVIC5q1MxRoH7h1fYVhb0BSNgxtivR6v8hq2r9mDZNyKpbNeIvp/zlhaNUpR2D5bT5M63nsSZc9sttXKpjydbrUyhVuLWLlvOt1qxap6sd1dv/fsNru9/P4OBiNHvo1b+8pzbH88a3oZq+3JO88r5DTKGi7qSE1f6mmPLjex5iKqLTjbE4b98jaFXd+PQ++4wWDkllbQY8og3hDGrX7b4Nxcm6vpSjMe31cL8KZ7UFOQ122aemj1Yg66L9+PXTzoubrN/kgoSxlkgZvvXfetrbW/5rbuni5calea9BnYBZ+mhB5iVP/fFlpDN80/0Axw90oIlIgRgHr83FnM2aD3WS2cm4tAQVWPtZ6nPwSGWG/8eALiD6CyxMS1a3GMvEXXbDau95fPe58bpk0C5C22WmPiBpqPGvvtq33eSxfLvoWf53Zt95fXDmGKwaC5yPT6ZRMd0e3y9OK5+pfzMtCuYdflJlm67Hs/CnVB1fUjNR7TLm9Tttm8brs/+qNbZNj67peyzWLncb2WTVWvfbWLRt139kzM8Tau5+M8V9dKs1rxmMdE29CQAlsjU+WJnS/sNYE4plpXtdw3n09b9Tl135VhHksNQZibtP11TboUDjDGZeNGmeYoM7eXZWeu4OFaLlhXf227dOu3SlZtY1onQ6laJQ0suhAtk3RNcNUtP1HLq/QZaEK1K7OLrd8adpFjSxMA8XMp64yGsFkfUy6srOJcWUaB6Cn/tD17vP0OZhnQ/E6vrW00+q/HlK7+WaGwVX9n+hQwWX63FfqnUc7Q/ZIQQgghhBBCVhha6kgLryB5meQEaFuv8vm+paHUBnkuMW1XsJy6t6/9Ra42ZZ+98zxtUNYU6THZEjEYqNtD3Le9fTGXESiC3WezLczTtdYr1U5mDZjnxqDarNLNczDYwJkzySJWJOuwmrWsDcvp67P17kLdLz1erU3WMjgYZG0xkLW6QCxqDjTdKW2wun21xcHtvlKjqsHy1s0zt7He2qdulFNMa22uaqxL11l7HzoO2+ZZstrp8jjbVhnwPZ1u1W5HXlB4+Uzb57m0xsZrdLuMlC5dfedZa0OZ+GA+n3a6gNk009bCUbZhKecAe4+nOWid+JReIYsSXAHxO1V6LXhhANbaVlqdPbeyLMeG9fFdoQV6XPt+vOPKBClZXtq5uaT0HJjPc+ILnX9Ho/g6m41rD4Y8z2S3eLU86bw8lTEGqewAzj09bivmOPu/Laczm7XHWT0ayuPtvZWWJ8+dcDjcQBg0x3Aqk/o+bMFv3QZEl1O9R2s1K8sWrBnvjbLIt47fEOu1hU5lSWXsHvPaojet21bLnJ63UydaWa/LGymz2VbLxVBTpXseNjYxWpnUxs7R3ncnP1dq/esOS/EScFkrW9l+3/PvhSJki6KVCXq/2SVzZ6f9O690LbX7SjnsPb+nUc7QUkcIIYQQQgghKwwtdaSTEAKqaq2xzWo+y+DVZlrbps814BX1bscReSmruxKslNeM501bAd19cRTNe9M+dLev+6bTzZZ2MiY8Gdf/A81itBr7oFo927bVysbzR50W0MFgVI+93qu12HmFR/VcDWi31j/VoOXPoKm1s0ynd+e4jlR4VvuwNrgRW+NPAbAFaiPD6lwd66bazznmddpmTcRSly/AVn3cNBU+356lOMDhzZhM4/FZ05lTiJdadht/UKZG3tnZrMdStc5eoHm+/y14Fuq+56qMT7EJebw0z+1YifzdKJ85a7mzVmE9r3yms2Uwt1VeZz73Yyu6CosPBhutOcBa58p9p1F7SnxKi539TnmWZ69MQFfCiL7Ya08meJ4ddl85h9j3GlNqv4+DOja5KdM8K6EXE21Tu5feKnrt9fVbai+Bet4bRMvVHNNWAqohRjlFv3qMVNqHCy0Z27yfpgfM2toFJ84u71PKRFF+nPEFVKncjh5vSzrUMqZINjbG5dqTI3t5rDc8Q4Asc+1Y2KLjQCw9UMbZAdkTRS2C1gq4Pb3UuG8dh7FJkGXj5Esrlz5LVkaXz7hNUtOXsMqTEx7LJNKysZSlR4fXtvUA6op/sxZq+1x1eU9V1cj5zYj6PK8/lDG01BFCCCGEEELISkNLHeml1ODY92WcXV9ZAbutzHQ2n49b7Vstlefn3Ufpy22LnC+jpbKa2LYFwp5Zap3urWMeZrN2dqidWUynX8ZHrOMcJsOslY3nj3ssZllzluMUmnEYQNaW2m3j8cXG2HhjYrOueTEfqjVWTXSZZjteJ8cDAMAETQuVXlu5VsRreRpJvccxLtb77PFl5kVtI2ZEbbal/Sv7Ya9j8carKxNmuU81tqXWMVoQm8+Jbb8stGpjIz3reGmpjOe0bgVA85nI1ue29tOzaPTFAfV97wlZhJ3vvRTsXhxol4xqWsuasqC8ph7jfX91Xyl3rJywc432Rdv1LHZty0PbS8Teo2LnZiDOLZqBUskeHRutuX3N6PHr+LFhjGeehUlD/tj7se1aa06WMW2PhnI+tnNKLquwVV+vnO/sver/5TOxvn5La36dz8f1/9q+Wv1sqQnF8/xRzpy5rf4crxXlaqxFzMuSXcaqDwYjTCY5rtBe21ozvRjRMpbaO05pzu06XmPoz33PAl5+3vY59n77eTF+ZV9Lqx8wbnw3c3/9Z9/KjlK+eDGCJMJFHemlK6DdWwyUk375f9dx3vF2cix/rFZVngjKBd9sNq4Ft26zAn2vE4F3b9Z1Aog/wqfTu+t+6DYgLrDs/0BeDOHMba32R+s3YxZS8HXhxiEyrM/VSftcCnrf3r7oCgDF+zzKHx/2vDIRi60149Vrav/gbwov+7+dtPvcb3Mg9WbjmPK+uvp17dp9LZdGe55X8698Nssgbtsvb0ztYrPsr/fcK9ZFpSuRSWy/+aPI+57odu1HpL0Q9e6nz6XaGy8vkVLJaXaJIf0sU07Hc7/0fnRasitye87I7ebFRJcbdZ/csMkksvv8sPWD1CpFyrm5DEmIfW8vrMp6qWfO3OaEOlyoj2+5ogM4O4zKRHUxrGuzSa6vur7e/EE+HG603Cjt/KTup8pkcqmzhIMnC2ISkeYiUBORWQWyt9D3ZUhzkWUX611u+p4b+Wy21XIZtM9CmWQmPweXWttsUjLFjukyMtDOs6UsK8sM2P7N5+P6d8dw2G6/fO7tdbwwkDIhXPOam4028r1mWWjHoVRe29d2iaC2srH5P2UM3S8JIYQQQgghZIWhpY4sRV8K6tKcHy1pTY2qpbQMWe2bpxXzNGSlK2ZpufP61UWXdnY+n9bua1lTmLVpbbfQ7O6j7n3qYmldTkrG4/vqtuokJMlVE8iuI57VpAyw73aZ9LWTMVBZx6lpjYqBym23pdKSZN0s5vOme5C9Z703HRtv3EWyRtUrgBvvZauh9Y7HbLa0gJ7GsrymDdTOLjttC6el3X7b4hjCYldFm3zE7vM0rtp2aU31vkOe9U7xXJhLzbU3vk2rYXOcrTuZV+SZ2lOyLLYwuWeJKL0QPKu0Z3Hu8xhRPGu//S6WrszWalAWTm7KoeWti7acik1MYj0lLNvbF1vfUZ1fB4ONuvSBdXHbnm8CyIlSNPX+QNbr+SHPM/m6XR4E2m97r969eR4a1irTDnXQ921rnL1uuW043DBzmcqTzfr8rgRknqV2Z2ez5WJpr1d6KNgx8pKClPddelzoNW2bNvmbnWfL3x92TMrnxAuXaY5D8/uU3SrbLv/2+S2f7Z2dzYYrpm3TK0+l9wfE4uT2eO9YS9vThHIGoKWOEEIIIYQQQlYaWurIrvBiH0otpbVweX7bpSYmhFHLMqB48U3NIPTSatBtzbNteD7ZnmWkDKy3vva5LWtZUY2ltn8x7bu9lTzE+pKXAdM2JXSpKZvNcvp6r/irSOzDNHXBxkO0td8bGA59zZiNkROxVsLmWNi4hfLzsFYj/b+2RjpaUJv+ubz/shyBbT/GUvraXC8Rj6f5tQkTWrEoHZY+xeu315ey/1mDrvvaKaE9DWm7sG+O/bAFg20cqr2e972yyWlUa2rxiq533WPuO7WnZHfoM9Mup5PlipcUQ/ESkpTJnYB2/LalnMe8OLD8nWrH+tjvSmm5iPNL0yvAsy7afWWZBmsZUhkwmcRt2To3bsUhT6djrK3F465sf6Zxrxhs1OOzJrH4ttSxdZstq5+VgV6ccJn0ysoxL27Mzk22zxYvyZZ3XFlqwb6WpRms7PXi2kqrrf0MbBFt2/fZzE/g4yWN0WO6SwEMW/GM9jnJv1GyVbWck63lrpRtVq54r33fgfL71Ryf9r22vbraJRNsW11x381tlDEWLurInrA17LomvXhc+/8yM5WfYAP1sd6P9LJN6wqXj2sKwC53TG+C1dd2AHSzzea+nKSl/MGwvX0RIcSFWK4xd6Huczk23o8CK6g0GLlsazbbqidJfb127b5WBrY8Rt3ZRT13kUWJZroWQ8Oh/4NHKZOhiAwbNf7sMZNJe5Fm6XPbKI+fzdptxff+AhFoLpq0DfuDDWg+a6XrjH2+NGhdse6gnoC1i9iyf31uUZ47Tru+V14wei6p3o9Nbau9WKagJdfHfL4DoLm4y+6NbWWMYn9Mdv34HAw2Wq7l9jlWrBtfuTizssBz2S5/dOsiMNbGbM4JfS6AXpvq5mznBD3vypW7AUTX/1KuWPf8cj62GSgn0pyrhsMLtczJ88SFVr9sAo18fLye507a55Jpk1uVc1tf5lErN3ScbS0/fV68OnJllk0bZpH7qgu3qXk+svJS33uJpUrFW/nZ2f5YOek9E31yJR+fr921oPR+Y1mF7TKuy953oU9ZYtvxXKnLa5S/3eLzQhnjQfdLQgghhBBCCFlhaKkje6bLimXxXRXa7ZT7bEKI0rWtT7Nk6wmVbhb2OMVP1tHtZpDvK2te+yxDts+qGZzPmxbN9fVbjHVMtY72em0LTDnWqmG0KYhtPbxSA5k1jNnqV1ohq2qjpXXzEgAoa2sXWlps69rXZwktXSxtP8qg9+Fwo9WfEKYtja2X/rm0pPluVW33Sy9hgK3pU96bWuC8oHLPVSlbAYYtLb7nTua5HXvWgnLs+hIZeIkcFHvt3VpvCbkefJertuuY4n2nfdfypveCV9+rdPO317ZzSZd7t0fTst+cj+3/dr7oTsM/MnNms1+2FqdahtbXb2mU4Cn7muVElr96rPd973Lrt66GZYIZL4mIdd3P7p157EsvAWvNslY1ew9lG3q9ssaaHe8y8VgIU+NR1L5OmXTE+8ys7LBJ1SyeJdjKv/J58a5pn72++rJeQjBvvve2K13Pd99vJ88S7rXp/Y6kjFkeWuoIIYQQQgghZIWhpY7sma6CsX0xAfG8ptbFFv3M2q2sRSq1ejbmx9eGxfNK7WG02LQtNIv613VMGaBsLTxeQe/yHnMswxY2Np7V2GatkaXf/Xw+biUWsda5lPek1kTaNMOeRrkM/LfWNW1/Zyf33dMu63VKn/kce9gfd1JiLa7ar6zJ9ePgylg3G1uo2ls9T4vl9gWvW7RtG5tg43O6zm1aL5v337R+jerjy4K53nfCi6lbJvbBssx9lxpyb18zbpZxDmR/sXLGs9p3PeN2ezlnxcRCaGzzPEDsd6TLyyOW8Glbr9vWj+7vlxffZC0xftyvHxNsk3tdvXovABvDNa4TnmRZ0+5zadVpytz2ffQVBc9zbpZHuRzOhdT+ppGZbUtll3fA2toF4wHTPdfpvun0UkO+AajlpTf2niXYer2U+3yrb9szo/TWsGWg+pKV2P6Vvwtsn0tr7HS61WkJ9L4nXqkFS1eJJD82sjvGNf6O8uWPHa+2ZxblTBe01BFCCCGEEELICkNLHbluPItdWQpg2di10pffi+HyNHc2zX6Xxs6LfbCauN36bZfxTfG6TS1YLtswbWngbDuPPvr7AHKhcZs9q85IlrRv6+u31DFbXVYgoGn1yxa3zdR+PN5akso00MNh1rJZS1Wpsc2WMXtvzWOqql1oW6S9zfOrL2MG5vNpnXK/z8qULaLNVOZ2n5fp0dOylxZYILfZFfMAxDH1NK/x/Kzp1c/KptVeJmbVxoV4xW5L7LO+TNaxvnhUWujIYWILk5cWO6BtEVkU36PY78EysqCMYYsxX83vkp3XynnSs0B4VjnvPvose12p+uO2ODeMx/e1PBlms3xvOs+XVnzrhWGzUuo1StnhYWPIr127r9HnGAvenKP7rJd2HPqybpefp20rx8/l92UJCPu/lYvLUMqCvrGpqlHreC8zpmc59rKLKqU11uvPogzj5XPoWdysN4k+Q+Vzb6/hWX1LueV7jlDOLIKLOrJv2PpCfT8APRfFLtP7fD520z+XE2Df4qavD4sCd7t+8DbdI9vXLN085/Ot1o8OvQf7wz8nwLCJVS40zvMWCtYN00v/O536rh22Dk924dys35dpn+3ioas+lP3f1rcrSxmEMG49C33uTtYVqJ0MpR0Y3vejKJd4GKKq4kI6B9BvtNqwffCSJ2idojIRi3UPK+811tZruul6z6itB9eXTKC8R9uW134eu+b1FrnJ0hWGHBV9dVLL79eiMAC//e4FmOK503np5cvvkZU57TCA7qQVVpFW9tMq//prZOax0ZIHqkC09ee0xqlIe5HqLRp0ztS2vERM5eezs7OJs2dvB9BUZlnFIdAMKehalFv3S1vmot2HrCwuyxzYcgllX73yO9alUftchkN4CVzi595csNiQAS/MRN97C/ZyTrfu+uXzaOWWt0Du+j3kPceeO7CVv13J1ZqLum5lrHc8Zczy0P2SEEIIIYQQQlYYWurIvjOf76BMnmJdNcoUx55VRvFcLeM1mlqgpqXHD+i2bfXhBSiX+7yAYI8+lxsbjK7byoQeXf1SSvfFwSD3y2rDrKXNYj8XT8NdFkIdDEYtray62nopwa3mu0xkYse2TPhik8GUriNWe2jvv3STUQ1ueS37vqpyqm5bOsMrGaDHaNIYq81tfw75PrQwvGqGvWQ4niWsDGi303V2gcpaWs+yma2CpZW0/T2ghY6sCtZil5Nktb0q+lyLvTmhz0XeS/KgtF2jp65lS9tut9GWOXY+Lq1LfVaWPsuI3a5tXb4cZc76+i0t61o+v50URKSdnMp6QHTJ2jNnbut0b7fXtm6SpXy0c/2ZM2ppVHf7rVZ/rCtj6RXiW4ba11ZU/vVZam1Yg5WdZdFxZTYbt8bS9sVLxFP21VrL+ixgXvkF75nTdspn1XpKlefZBC5liSSPRb/JKGN2Dy11hBBCCCGEELLC0FJHDoSucgf+sW3Nj+IF7HraSU9zqRbBvRaz9AN1s8asTwvsaXxLzZV3P7ZIdk7D3PTXb/rY55gEoNS25uO9gtl6vb6i2KXG1vZDLVY5SU070YCXMECx8RCK/Ty1ALunWSzjL6bTzdbnlQO22xYoGytRalSHw42WJVCtADaeTxEZupZJ3afaZe/Z7gtyV2xsiWcl0OuUz6N33/a8Mv7Pe36986g9JccFmzzF+/53eVrY4704aaUvVteTD/Y7VcZI9Xl22OQrfanm1epvLT27iSGsqmEdX13OcZPJpbrd0ej2dL1sgfLiq0urV7aktecjWwpG29I52isw7smj9fXm/dgELp5nihdzre3ZvpbH2M+jtBza/vV58lhZrtvKZ8DKnPI5seeX59lrl7F13nMcrX1+/LmVE2UZIevJYz161Mpb7rPPZSlfyv50kb+XlDN74dRa6kTkvIh8u4j8MxH5qIhsi0gQkTcsOC8s+GtLAUIIIYQQQgg5IE6zpe6ZAN6+x3OvAHhHx75Zx/ZTiWex0zggqwHrKqTpZVTss6B5cRE2hq/MOtbXlsWLAVhO29S27JXaMKvxzNdpa5RtjFmXVWp7+6LJ9JUtg+WY2BTceayb/bMWKY0jsOObyy+oxjdrA3MGrKnbhr6WMXt2vLwx7GI4vNDqf1lAF0BLQ2zvx8b1dVnerHXNZsFsZ/ZsP4+e9tRmQPWO6drnaYi9eIiuMfOKFtu2/e8FNafk+FHKGBtTW1rAy+yGff/b9953wrNAeCnavba6LOHxe9wdi1RaZ+bzccsi780T2TLUzk5p0/jrtmvX7gWQLYNVNaz/t/LIFjW3VNVGa05XzwubXdjOf3n+RaNfVnaW46YyJbab78vGxNnzQpi2YrXtvuw50f7c82fbjokuZUKUIe0+l59HllnjlseEtUAuE7dv35dzuy0+XsqqwWDUKklhfyd57S/qg/Z7EZQz+89pXtQ9BuDnAPxe+ns5gDcuee6lEMJrD6hfJxLrJpMnwLYrmBeU27fI8tww+9wJy4Dm+dyfFMvz2vfT/QN70fGKnTjt5B735TTOZY216DLZFPxa92dt7YLr5lm6e6gwtvfpl2ZopkG2fS0Xg7PZ1ATKNxcrw+GG++NDr10GiVvB4yUoKF1S4zX12vq5bdTH5B8uzWfCCi3rXtIVVG77bH9YdbkNe6mkbdtewHzpTuS5V3kuMV377LXLgHsPX5BT0JLjTdfirgvvu10u/rxaYfn7n92i+67luaEtIzOsm9983lyINNPdq6ths6+2T15Srty/3FbbFXuz7svaWmxDF1J2Ti/ru9lSC6W742DQdvcDgKtX70372wvvEisTSrly5sxtrUWKddPPSrwLjettb19suRFa2WlljWIXvfG83Gd1YfVK0pTjZcsUtcsRtOV3c3/zOS7/1zZKt1Zbb7BLsWtl2zJK7GVqBXa1RRmzP5zaRV0I4R4Ar9P3IvL1R9gdQgghhBBCCNkTp3ZRRw6ffk1q81HsSpAC+NpG7zzPRc3TKJXX8awffVqqPldOS5cFMGob4//WJbPsR58rpzKbbRkLkm6dthKLqNY0alub7h7WfclzeVULo2oNPatZ6aJp3YRUY2gTGZTa5qoa1e2V1qWqGmJtrXmdyeRSy+JoNeldRb6ty439XHJ67M3GebbUgveclJbQ+Xxcj4Gnzc7Xs+PrW/as66d1B1W856XLlavLAl5C7SlZNaycKb0PyuQVQNNNvbSS5GPGrXmidJNrHj91/9fzuxJs+PfTnnu9xB86z+gcZD0OvLbKbXZfKV+sd0TTxTD2o0xKFT1gNht9zQm2Nt0kTeU1bYmKtuWpbRG0VsNSJtmEXKVbvp2X9T48109vnPT/MqwjJqRpuznme2vKO5ucK99zfi7LfcPhRisBV58lzPOK8stcdCfd6bO42d8QpUWQFrrDhYu6vXFeRH4QwFMBXAXwXwC8M4Sw1X8aIYQQQgghhOwvXNTtjVsA/Fix7SdE5DUhhPcs24iIfKJj1x177tkK4FnsSm1mX8FKL9DcWimWiYmzAfRdx/VZBPuC0buOK7dZq4uN8Yv78vFeu2UsVtZWArNZtsLp+dYyB1hN4biVrMTTVvfFm62vZ61x1vhtprYu1NtLDd58Pnbj+GL/Rg2Lnh6vbZUWKxswX8Y+RA1pjlW0fQCy1dLGGSpZA9l8b69tKa9dVaNWwgDtgy0Ybi1vXc+2Fy9qKbWmnjVi2fTn1J6eDE6rjAGacdzl8z6fT1syxrOglftju4s9QLzvntdW7mt7rvWuZ70ogOZ8M0sp2mySsdLCFeOnumVnl7dK8x71vragSUN0vrfJs8q2rPVrZ6fp0RDjsZtJbUqPCzsWtgj7MvK3WZagHRsHNK1lnmwur2NlVPlMzGbt84B2XLkiMmx4tdjz1Apo8bxobF+6LM5AO1GK561S9qG5b+pa+7TtPstmCeXM/sNF3e55O4BfAvCHAB5FzKL53QC+HcA7ReSFIYTfO8L+rQx2cdeemJd7ND0BW04qfZO9rQe3G5bNCtVH34IvCk7fdU6vb4/3FgV9yUbsAk7rzem+8r093wrR0lVlff2WeoHkCYfSvXMw2EBVtRd6eh0vkUF8P224qeox7VpJWeiVPzC8rGueK2MOJt9qtWVde7pqANkfBV7dIs+tqCvA3LpT9j0birdgXrSYi9ehoCUng1KBaL9b2T0dZlv7R73SpxDJ84Ueu5wsWCaZhJ1z+o5XrOLKT6LhLXSac1ef+6nvThf3acIuO0alC/va2gXjPqnummNsb281jrMyrVSW2XlfFYc679u+li7v9r7L+fHatfvMIjMr3roSt8zn00Yf7TF2cWPrAHqJuvT8UjZ7iXj65HxzzJuKRE8haJ+lrt8ydgFnn0dvoVmey8Xc0bCyizoReReAL97laa8OIXzseq4bQnhNsen3AbxaRP4EwP+BaMF76ZJtPcfbnrSrz76ObhJCCDnlUMYQQsjpYWUXdQA+H8AX7fKccwfRkcQ/AfD9AF4sIushhMkBXutEYbU2qlG1rno2YDoe33Zt8JJdlMdYNwN7TGntsFrWMr18n/Z0t3iaMq/NUqNcVW2Lk2eVtJo5zwUGiNpD1YLqmI9Gt5k2mlpdG5iumlHFJivR/ljLXdZcxtft7Yutz89a5VRrWpZ0EBk3At/z9ZraSa9mUHvfsOXK5Fm4vPEt+1D2X9vyNLxd7dv/y2suqotV4j0LHtSakpNO+Yzbuqm+Z0O3y+SyHhp9cqWvjd2481u3xfZcMG1ZhgaDdtIN23aXB4jIsJVgxM7pij0/t6HeDjmRic61NhmW9nV7+2LjOrYtRWXDZHKp5QK5tnahNadby1UpCxQv0ddgkD1Gyvp2NmlW/l3R/uyszNGyO6V1zfMmsb+Bys/DfoZ9ljFv37JhIiV2nPu8obraopw5HFZ2URdCeO5R98ESQnhURB4E8CQAjwdw/xF3iRBCCCGEEHIKWNlF3XFDRCoAN6a3V46yL6tMVwyExRbj9HzTVWtWWnim03bsk9U69aW79mL3yv/7tGH9/uXtxC/e8U2LTdM/voxJK8/r8qe3wd7KeHyx/j9rOjcBNGPdPOtil7Vrbe1CIy5NKfubU2JPMZtdrM+1+7wENtPpVqOYqt6bXm8yGTfa8DSLpRWzvDd9Lcfcu1/vnvq08zZWzn425XH7EbeZ91FzSk4nIQRU1Vr6v+2ZUdLnTeElShkM2nNUnwWuvFbZr3KfPabLWm/jxW0Mchmf5pXuKfvYnN/yPNxn2Wu3ka1Tes0ygZe2a4+vqpEpXK7y6L70ftRKfjWdbrmeInofIk055I2zTXjTji/M+8qyO6UMiv9nb5RSTti5vkxg4sVB9lne7D7PA6brs7LPkJcAZZmSHOX9NLdRzhwm1eJDyJK8DMB5APeEEC4fdWcIIYQQQgghpwNa6naBiPxVxEXb7xXb/2cA/yK9/alD79gJxMuMqcznTc1j3NbOPlhqHa2Fp09r2md5261vep+2dbcWGC/OLoSm5cmzMs5mm60Ux2WcgN1nx6vMJDmfb2Ew8McuhGnL2qVtbm9fNMfn7F5lLJ1XJFU1sTZjZ1l+wSt6WvbNYrW7y1jQ7LiWFl2bBa0dB9dtwfPi7awlsNSQLvu8LBPrSe0pOe3M5zsAsldIjBvz51PPsm/nrix3slXes3B4/9s2veOt5aXL2uLfn/0/ewLoPZVx6F65A79d7cty/SrHy4uRa8qhzcbxg4G1JLXT+Of5Mlvgujx5rOWtjMXzxjLGLKo1rpQvw5ZXiC0dU1ocbTbSMrbbxue1Y7D9GO8+y5snf7qeFa+8xyLPIqVP1lDGHA2nelGXMmg+Kb19cnp9nYi8LP1/fwjhVeaUlwF4jYj8MYBPANgB8IUAnpv2/wqANx9op08ZXq2hEHJKXS/dcJeroYc9rwygjvu6BXMp0KzLSZew8/qzKFFKeW1PKGYh4Z9bpkn26rWVbc1mWy3hIDJsuYl4Y1IKOesKpPvs4kyxQqgrbbL9sWTdWdpJWjbr7Xqcrd0HRJfLnMbaurs0Bf58PqzP91xUSsHquc70nVfeg7fPc9O15y3zQ4+ClpAmVoHYldSoqvykUYrnkt1V3sAqoPKP6W4FUbOvbZnjLZq6zo/99pVy3oLOU5R1uah6/eza5im/vDEE1LW+mZzLSzBi+1UmSrHXKxWJi34nlLJS3zdlVXYVje9zDdYu93vvXpc9zqvH642vlT19Ss8ut9BFCVC4mDt+nOpFHYDnAXhase3z0h8AfLrY96uIY/YVAP48YhXLhwH8JoB/GUJ4x8F1lRBCCCGEEELanOpFXQjh6bs8/jcRF3DkECk1P1W11nKhsNqqLk2cF7ztFZz2NJ1eeuq+tL7LuHAuq6Xr27eM26al7V7kuxj29aFPQ9il9QyhbUla1HfryhL719YQq6XPCwT3+qIJU/I1xq0gfC/Q3HOJbFrjut0sy/vpd5lq34dlt25Y1JoSshz2u6JJVBTrAeEl1OqzbJQWes/N0c4ru5ELfW54i9oq3f28xCq2ULd107T35eElkaqcDA7eXOclsyqtXZ6lUq1zdkzKsjvxnprulN5vAJ33rTfJsnJL6bO4lffdFzYSXXmb8sUbX8V+Lst4h3gsstCVUM4cH5gohRBCCCGEEEJWmFNtqSOryXy+A1s8Nm5TTV63ZtKLSbKaPi8BRqnp8rSgi+Lsyn1dx5b0Wfb6NLdKGWPhYWM4vFgLT7uaY0vaY5mPaVtQveDtPitc2W+rre0rTVDGtVXVqJWIRTXQNi23jZHxitR3XSf2p7mt77Pqi41cdHzXMR7UnhKyN8okKvH/7p9LpezwYrf867S9JHIikt15cijevG/npa7YNS9hRjlflueV/bHj0B2f2I49L/tY7istaJ7VzLP2eed3za/efe3sbLq/GfSYMlGK5zHkjX1pefQsrt41rdzrSvQSQnvsu/pf0vcZeFDGHD9oqSOEEEIIIYSQFYaWOrKSlEXKLX3FWPt88r1iz11pqbusLN0xZcsVGPf2L6s102NLS5WntbOazC7tp8jQtUCVBVb1/XS65WoslfL42Wyr08IVr+1ruLsso13j05US2vvfa9e24V3HWjvLrJ+2jfJz8fZZ+rStfVB7Ssj+4JXWyZb99pzY5x1hvT76MuJ6x5fzyqK43O776Y8FL/vh9aEpC5rWSE+GltuiRbCziy3rlxfH7c3HTQ8TP0PnbLbV+Rl1eeGU4+ndW5f1s6T0TPE+K8+jpWx3Nhu3+r9sHNwy3iGLoIw5vnBRR1aacnKxwrec2K17iXW59IQVAAyHo1bK/SzkvL74Cz3bprdt0Tm7mXA9wdTnMtoXhO1dN6ZSLt1q4utgMDI/OvoWlG2Xo7JfMY310D3eczHtSzBiBf9u6vFou7Zf8/m494eVV1tuNy65e4VClpCDw36/PEWiLvD65jalqrxEGe3FXV86+r7FwzJun/bafWEAOrfbPlh5WSrqrOxV2VnW+hsMhq3kHvY4LwGLpxDL58X+2LI9fYlFvIVhiRc2sMw+O6bl7w8vPMGeV4aBWJdXuyD2+uG972KZ+/cXfpQxqwDdLwkhhBBCCCFkhaGljpwo+tJSW3dCqxXs0nRFbVtTg7psMc9FSVCWu5frs+J4FrqSPrdFrw+eu0vWIu6uf1Zb6wW+l9ig/VzY1VpSu7Te/QkAvPvpc83ZrWtLVxrrZd1v/X3UmhJyFHjeISWz2bTXFb3bda4996gVsNmHxe7dXXKpa17xLE+K9XIpSyHofktVAWtrF9x+ddHnkVHieTt4Fj4vWUm5z9JnvfTm7S5ZaEtA6LbS66fsc3n/ZSIUy/LWtXZ4Qp9sa59PObNq0FJHCCGEEEIIISsMLXXkxNKXltqLfejTWJVaQJEhhsMNAM2C3l0WukW++UfFXgKqu7S5y7ZlxygnHWhrcUsNtY23yymq8/l9bXVZEW1MpY0P6Uq2c33WtW6r57JQc0rI8SKEUMsYLzFUO6Zu2LIgKdZzYNmC0WWSJm/+W9ayU+6zc3U5H/dZC21MtN6rFv2OMqTZvlc+yMbitWMQ27HUljx23SVzvNhDT253lcPps7yGMHUtcyXeM+IlD+uzHJYwEcrphos6cuLxXDK9SdQTirpw63NpaU7y/sKiz23Ra3c/F3re4mGvSTt262q6yFVlN4ll7L7s5tIWfGUbfdeJbTTdXObz5cZiP91j+4+jgCXkOLOMS6YSs1/6Cbi8bZ6CqW8h48mXPvnl0aeUXOSSqO/L5DGK5/I+HNo6eIvnxbLOW/l/OXa2zb6syv0JaJqyfbchGHuV99497rcimDLm5ED3S0IIIYQQQghZYWipI6cKdclUbDKVMj3zfD5tpWX2XCKU6KLS1NwtY8XaixvebunTDHalyfb6tUzyFY9lE40sOneZdr06P4o3Dt49do3XYXxW1JoSstp43iF9CS+8mnd+u93ypDzGu07X/kVt2vOW6UMfTS+WtnWtTOlvXTMV66JYuuJbumqqeniyoC8ZjJcMxbbVF16xVxl4PYnX2m1SzpxEaKkjhBBCCCGEkBWGljpyqpnPd1rxD32p+fu0b7PZuKVRWxQbpm0tk1il3O+15R1zPck9dnPeIg1xn/VyGYvgbi12Xj+Wj2PbW6KU64XaU0JOFl7Crj68OK3SGmWTiZSJRVR+dc1VXUlaLLu1CO7GKtXVZpmIxVr0upJmeZY0r0yAfd9lrfNiF0Noy/TmOd1FzhfJw/a1umPw+trcLZQxJxta6gghhBBCCCFkhaGljpx6ujKXNbVqbd/8dsHV4VJWvt337/o0eIcRB9aFp+Vc1mq2G8vesv04blBrSsjpwH7XyxIIiwqUa3yZ4lmbSnnUNe/3xdQt433h4VnjduPtsCh7cSk7vWye+fx2dk1LV8bKvWZiXqaNZfcve8xuoZw5PXBRR0iBJ3w1YUpOwmGPn7a2+e0uP1l7i6Bljj8ol43rZVHgeNc5Xe+PQ42/vUIBS8jppq8Eguce2Zc6v4v5fNyqxWbrtZVzp6355inUdjPXLnvssu6dy7S321IOXuKXZcoQrILMoYw5vdD9khBCCCGEEEJWGFrqCOmhS+MlIr3JTdQ9Jqes9gue5va63W/20w3xKNnPvq7KfVNjSghZhDdPLJtYZRnXxOFwA0AzDf+idg6D3c7jpSxc1mWyrxTPItfXvfb1sKCMIRZa6gghhBBCCCFkhaGljpA94MXdWWwx2Xh8d0Fr+793vKdB3atWdZXiAlYRak0JIftBn4zpszwtKlmQk6w0yyP0Fd9eNj3/wZd8ub6SNLtp4zhDOUO6oKWOEEIIIYQQQlYYWuoIuU6WjYfoy8SV29pbQdc+ze1J0EweR6gtJYQcBn2x3cvIFbXGiQwblrl2e/tTdmfRPsqk5aGcIbuBizpCDoC+lNXL1PTZS52fcv9RuMecdChgCSHHhUVhAH2yYLn2d6csPOluj4cBZQy5Huh+SQghhBBCCCErDC11hBwCfS6aXRrPrtTL1ornpXZmMpTrh9pSQsgqsVsZk4/pLrTtX4dyZb+gnCH7zam11InIs0Tk+0Xkd0XkkojsiMhFEXmniLxowbm3i8hbReSzIjIWkT8WkR8RkdFh9Z8QQgghhBBCgNNtqfsdAJ8HYAvA/wfgYQDPBvAqAK8Uke8OIbypPElEngHgIwBuAfBfAXwAwJ8F8MMAvlZEvjaEsH0od0BWmj4tnYh0JkPpi7ezx5HFUFNKCDmpLJIx8ZjlYuMoV/YGZQw5TE6tpQ7AfwfwagBPCCH8hRDCt4YQ/gyA7wIgAP6piDzbOe9tiAu6t4QQ/kwI4VsBfBGAdwF4AYC/fyi9JyeaEELP3+EI170G1x83+saSEEJOI5wX9w+OJTkunNpFXQjh60II/yqEMC62/wyA3wYwAPAtdp+IPB9x4fYggO8z50wB/E0AOwD+jpyUX8OEEEIIIYSQY8+pXdQt4M70+uRi+8vT67tLF8sQwgOIrpg3AXjhwXaPnGb6rXj7pxlcBXebRWNBTSkhhOwOzqsZjgVZJbio8/mC9Hqx2P5l6fXjHefp9i/d9x4RQgghhBBCiAPdBAtE5A4A35je/kax+6np9b6O03X70/a7X4Qsy241h17R2qOCWk9CCDn+rKqcoYwhJxku6gwpFu5tAM4A+NUQwn8uDtlIr1c7mriSXm9Y8nqf6Nj1rHvuuQfPec5zlmmGkBMDn3lyGNxzzz0A8JSj7sdBQxlDSBM+8+SwOAo5s7KLOhF5F4Av3uVprw4hfKxn/1sQ4+E+BeBv7bVv+0C1vb09v+uuu/77EfbhJHJHer3nSHtxMtmXsb3rrrv2oSsnCj6zB8OzAJw96k4cIZQxBwe/swcDZczBwOf14Dh0ObOyizoAn49YSmA3nOvaISI/iJjB8gEALw0hPOwctrWgnfPp9bFlOhNCcFVGql3t2k/2Bsf14ODYHgwc14Ohx4J1oqCMOXw4tgcDx/Vg4LgeHEchZ1Z2URdCeO5+tSUi3wXgxwA8CuBlIYS7Ow79DIDnAbi9Y79u//R+9Y0QQgghhBBC+jj12S9F5K8C+CnEOLmXhxB+v+dwLXXw5R37dfsf7E/vCCGEEEIIIaSfU72oE5FvAPB2AFMArwohfGjBKe9Jr68QkTNFW08E8CIAjwBY1A4hhBBCCCGE7AundlEnIi8A8A4AAuBbQwi/veiclGTlQwBuBfCPTVtDAD8NYA3AW0IIOwfSaUIIIYQQQggpWNmYun3g3yJmpfkfAF4pIq90jvlgCOFni23fAeAjAF4vIi8BcBeAr0QsWP5hAD9+YD0mhBBCCCGEkAI5rYUYRWSZG//5EMJrnXOfAuCNAF4G4GbEBCq/DOAfhRDG+9lPQgghhBBCCOnj1C7qCCGEEEIIIeQkcGpj6gghhBBCCCHkJMBFHSGEEEIIIYSsMFzUEUIIIYQQQsgKw0UdIYQQQgghhKwwXNQRQgghhBBCyArDRd0xQUSeJSLfLyK/KyKXRGRHRC6KyDtF5EULzr1dRN4qIp8VkbGI/LGI/IiIjA6r/8cVETkvIt8uIv9MRD4qItsiEkTkDUucy3HtQUTOisgb07iM0zj9SxH5vKPu23FHRL5CRH4gfb/vS8/kwlTEIvJaEfmYiGyJyMMi8l4R+ZrD6PMqICLnROSVIvJzIvJH6bm8IiJ3isgPi8hGz7knfmwpZw4GypmDg3Jmb1DGHAzHXsaEEPh3DP4A3AcgAHgMwL8D8KsA/jBtmwP4ux3nPQPAQ+m4P0zn3ZPefxDAmaO+tyMe1+emsSj/3rDgPI5r//iMAHwkjcdn0/h8NL1/EMAXHHUfj/MfgF/3nssF57wpHXc1nf8+ADsApgBeedT3dBz+ALzOjOddAH4tjdPltO2/Abj1tI4t5cyBjSvlzMGMK+XM3seOMuZgxvVYy5gjHyD+1R/47wD4dgCjYvvfSA/CFMCznfM+mPa/2WwbAnjnMkLlpP8BuAPAz6Zx/HIAP7SksOW49o/Pj6Vx+DCADbP9u9P29x91H4/zH4DvB/BGAK8AcBuAcZ/ABfB1aVwvAXim2f7VALYBPALgwlHf11H/AXgNgJ8B8MXF9icB+Hgaw186rWNLOXNg40o5czDjSjmz97GjjDmYcT3WMubIB4h/Sz1Ev5UeiH9YbH9+2v4ACo0egCcCmAB4GMDwqO/huPwB+IFFwpLjunAM1wFspjF6nrP/zrTvK466r6vyt4TAfW8a07/r7Htz2vc9R30fx/kvCdCQxnqdY9u6V8qZ/RtLypnrH0PKmf0dT8qYgx/jI5cxjKlbDe5Mr08utr88vb47hLBtd4QQHgDwAQA3AXjhwXbvxMFx7ecFAB4H4J4Qwn9x9r8jvb7i8Lp0chGRswBekt6+wzmE470cOo+eAfB4gGNbQDlzuHBc+6GcOSQ4D+4bRy5juKhbDb4gvV4stn9Zev14x3m6/Uv3vUcnG45rPxyfw+WLEIXEQyGE+5z9HO/l0Hl0B9ECAnBsLZQzhwvHtR+Oz+HBeXB/OHIZw0XdMUdE7gDwjentbxS7n5pevQfFbn/afvfrhMNx7Yfjc7j0jncI4Qqim9JNInLDYXVqBXl9en2fsYxwbEE5c0RwXPvh+BwenAf3hyOXMVzUHWNEZAjgbYir/F8NIfzn4hBNnXq1o4kr6ZVfwt3Bce2H43O4LBpvgGPei4h8A4C/jqhB/SGz69SPLeXMkcFx7Yfjc3ic+nnwejkuMmZ4vQ2QiIi8C8AX7/K0V4cQPtaz/y2I/vSfAvC39tq3VeaAxpUQckoQkWcB+AUAAuDvhRDuXHDKsYVy5mCgnCGE7JXjJGO4qNs/Ph/Rd3Y3nOvaISI/COBvImbGemkI4WHnsK0F7ZxPr4/tsl/HiX0d1yU5DeN6PXB8DpdF4w1wzF1SgeL3ISac+IkQwpuLQ1ZtbClnDgbKmeMHx+fwWLV58Nhw3GQMF3X7RAjhufvVloh8F2J9lkcBvCyEcHfHoZ8B8DwAt3fs1+2f3q++HTb7Oa674MSP63XymfTK8TkcesdbRM4DuADgkRACBW5CRG4G8NuIMTdvBfC9zmErNbaUMwcD5cyxhHLm8FipefC4cBxlDGPqjhki8lcB/BSi/+3LQwi/33O4mni/vGO/bv+D/endqYHj2g/H53D5I8QCpU9IWsESjneBiGwA+E0Az0Ys5PydIRUFKjiVY0s5cyzguPbD8Tk8TuU8eD0cVxnDRd0xIgVavh3AFMCrQggfWnDKe9LrK0TkTNHWEwG8CLFS/aJ2SBOOaz8fQtTu3yEiz3X2f3N6ffeh9egEE0K4BuDfp7ff4hzC8Tak7+y/QSzu/FsAvi2EMPOOPY1jSzlzbOC49kM5c0icxnnwejjOMoaLumOCiLwAsQihAPjWEMJvLzonBWl/CMCtAP6xaWsI4KcBrAF4Swhh50A6fULhuPYTQpgA+Mn09qeS+wAAQES+G7Heyn9wsuiRvfMT6fUfiMgzdaOIfDWAv4GYEvnnjqBfxwoRGQD4ZcRirx8A8E3pee3j1Iwt5czxgePaD+XMoXNq5sHr4bjLGPGtheSwEZFHEP1q/weA/9hx2AdDCD9bnPdMAB9BrF7/hwDuAvCViEUQPwzgJaZexqkkZTZ7Unr7ZABPAfCnyHVD7g8hvKo4h+Pag4iMALwfwFcBuB9xcntaev8QgD8XQvjUkXXwmCMiL0cz7fHzEX9of9Rs+9EQwnvMOW9CrINzFcC/A7AO4C+k8745hPDrB9vr44+IvB7Am9LbdwG43HHo94YQLpnz3oRTMLaUMwcH5cz+QzmzdyhjDoZjL2NCCPw7Bn8AwhJ/b+s49ymIQZr3I/rufhLAGwGMjvq+jsMfgHsXjOu9HNc9jevZNB53p/G5P43X7Ufdt+P+B+C1S3zfX9tx3n9CrGvzCKJP/9cc9f0clz8Ab1hyLn36aRxbypkDHVvKmYMZV8qZvY0bZczBjOuxljG01BFCCCGEEELICsOYOkIIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIAEBEXiwiofi7pTgmiMi9R9TFFiJywenzi4+6X4QQQppQxhBysAyPugOEkGPHPQA+mP4fH2VHlmAC4OfT/y8EcMcR9oUQQshiKGMIOQC4qCOElHwwhPDao+7EMoQQrgJ4LQCIyNtAgUsIIccdyhhCDgC6XxJCCCGEEELICsNFHSErjIj8avLx/yfOvi8Uka3098wD7seXiMifisiOiPw1sz2IyL0isi4iPyIi94jIWEQ+JSJvFJFRR3trIvJdIvJBEdkUkWsicreIvFVEvuIg74UQQkiEMoaQ1YHul4SsNt8F4GsAfI+I/GYI4XeBKLAA/CKA8wC+M4TwyYPqgIh8NYD3ABgB+EshhPeWhwD41wC+FsD/C+D30/8/BOBrROSlIYSZae88gPcC+J8AXEGMvdgE8HQAfw3AowD+80HdDyGEkBrKGEJWBC7qCFlhQgiPiMirAfwOgLeLyJeGEB4B8AYAfxbAr4cQfvagri8iL0UUpjsAvj6E8EHnsKciegV8SQjhU+m8JwD494iC938D8CZz/JsRhe1/BPDNIYSHzPWeiCh4CSGEHDCUMYSsDnS/JGTFSZrT/xvA7QD+uYi8CMAPALgfwHce1HVF5FsBvBvAFoAXdwhb5Y0qbFOfHwLw99Lbv23afDJiUPo2gFdbYZvOeyCE8NH9uQNCCCGLoIwhZDXgoo6Qk8E/QHQ5+SsA/i2iO8p3hBAuHcTFROS7APwSgPsAvDCEcOeCU36l3BBCeB+ARwDcISJPSptfDGAA4H0hhE/vX48JIYRcB5QxhBxzuKgj5AQQQpgAeE16eyOAfx5C+K0DutztAP4fxPo9fz6EcPeC4x8JITzWsU+F6pPT61PS6z3X10VCCCH7BWUMIccfLuoIOTl8q/n/uSIyOKDrPIgYjD4C8E9FhLG5hBBy8qGMIeQYw0UdIScAEXkhgO8HcBExoP2rAfzgAV1uAuAVAH4XwDcD+MUFwv0mEbmhY99T0+tn0+ufpFcWeCWEkGMCZQwhxx8u6ghZcUTkRgD/CjFO4DsA/K8AHgLwQyLyVQdxzRDCNQDfCOA/IMZY/KsFQvevlBtE5OsB3AzgUyGE+9Pm9wOYAXipiDylPIcQQsjhQhlDyGrARR0hq89PIqZg/skQwvtCCA8AeB1iyZJfSDV59p0QwlUALwfwAQDfBuDnRaRrTvmHIvJ0fSMitwD4v9LbnzJtfhbA2xHdbn5eRB5vGxGRWw/qRwQhhBAXyhhCVgAu6ghZYUTkWwB8O4C7AHyfbg8h/AaAfwHgGYg1eQ6EEMIVAN8A4EOIRVvf6gjdzwC4E8AnROQ3RORfA/gkgC9FdK95S3H86wF8GMCfB/BpEXmviPyKiHwE0XXm2w7qfgghhGQoYwhZHbioI2RFEZHPA/AziPEHfy25q1j+d0TB9tdF5FUH1Y8QwhaA/wVRSL4awM8VQjcgxkW8CcCfQXSpeRTA/wng5SGEadHeY4jC9vUAPgHgRQD+IoAnAPhFRC0rIYSQA4QyhpDVghmFCFlRQgh/ihgv0LX/CoAv3OdrSsf2xwC8oOe8bcSg+qUC61P67LegrWElhBByCFDGELJacFFHCCl5oYi8Lf3/t5OW9FgiIucA/HR6+8Kj7AshhJCloIwh5ADgoo4QUnIHcrrn7wVwbAUugHXkgriEEEKOP5QxhBwAEkI46j4QQk4oIhIAfDqE8PSj7gshhJCTBWUMIRku6gghhBBCCCFkhWH2S0IIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIIYQQQghZYYZH3QHSRkR+A8AdR90PQgg5wdwTQviLR92Jo4AyhhBCDoVDlTNc1B1P7gDw7PxW0l96J833ul/EPz7uax+/qL24b9n2zL7GrZTvmu8b/XCO3//2mkcJQrE9v4+3GIpzQtFq+b67zTxk7TaavQxue42+6f8S/+9qq/dcZ7/e86L2utpGMWb25AAA0hyb5o0HhM6PMxTvr7e9jveNbbLcOSIL9u9Hex3ft/rY4rXn+9nqj9uebt/t8c3XvjlpN3PUMnOcP6/5/br77rsxmUxwitmDjImvWSa0X7vGvO+zWK49Z19re90iUGxp/ufv34uc6epDuWUvcmYZmXA97dk+9soHsXN9f1+WkTdWDnoydZGsWigTgB65ELf7cqEtt66vvWXfyxLHLytjrre9JWVC5z7z2iW3utrblZxrntv1O3X3MkaPc+6n8bporozvj0LOcFF3jBEZ1q9VNWy81z8A9b5yf1U13/ftu+5rJU9eQVX/6ftF+6Rjf+Ucv0x7/deKX7aBzFFhjkrm8T3i+0F6r/sGyO/1HAD1vvK9t6/rWo33xbXi+1l9/hAz8362cJ/eh+4bSNqf9g1h3y9ub5lrVTJHqOYIVRJ0VYj/1+/neVvajyogVPPG8bp/mX17vRYqic7nlZj30r+vfu8dL9mZfdnjl72WDOIfAFQDQIb5ve6r9P2webzuS9/XpfZdz7WcuSHizxsqgrx5SM/x9i/bXte+L/mSL8Ndd90FsrxM6Nt3vbJpUXv1vgOQF+XxffuWv5b0yotl9u1GXiwjm8r28r79lxdle337dnMtlTHA4cqLpduz/dizvFhS/izd1hLXOip5sUx73r5Dlhd7vdZznvOcQ5czjKkjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWG4qCOEEEIIIYSQFYaLOkIIIYQQQghZYbioI4QQQgghhJAVhos6QgghhBBCCFlhuKgjhBBCCCGEkBWGizpCCCGEEEIIWWGGR90B0k0I0/Q6w3w+qbeLCAAxR8b3Is33dr9I+/hF7cV9y7Zn9jXuonzXfN/oh3P8/rfXPEoQiu35fbzFUJwTilbL991t5iFrt9HsZXDba/RN/5f4f1dbvec6+/WeF7XX1TaKMbMnBwCQ5tg0bzwgdH6coXi/X+0V5zeOEadN770s2L8f7XV83+pji9ee72erP257un23xzdf++ak3cxRy8xx/rzm9+vuu+8GiYQwXVLGxNcsE9qvXWPe91ks156zr7W9brG4w8UyxmtlmTa7+lBu2YucWUYmXE97to+98kHsXN/fl2XkjZWDnkxdJKsWygSgRy7E7b5caMut/WuvT05J8b7cj13ImOttb0mZ0LnPvHbJra72diXnmud2/U7dvYzR45z7abyWcsZv9yjkDBd1x5OnANgGcE98G2AniOD8Du3bTk4sd6TXe460FyeWZb9Qx/6Lx+fE5zSPx55kzKJ95MTBuePAWeYLdey/dHxOujnUMeGi7njyJwAQQnjOUXeEHF9E5BMAnxPSD58T4kAZQxbCuYMsA5+T4wNj6gghhBBCCCFkheGijhBCCCGEEEJWGC7qCCGEEEIIIWSF4aKOEEIIIYQQQlYYLuoIIYQQQgghZIWRwPzEhBBCCCGEELKy0FJHCCGEEEIIISsMF3WEEEIIIYQQssJwUUcIIYQQQgghKwwXdYQQQgghhBCywnBRRwghhBBCCCErDBd1hBBCCCGEELLCcFFHCCGEEEIIISsMF3XHCBE5KyJvFJE/FpGxiHxWRP6liHzeUfeN7A0R+QoR+QEReaeI3CciQUQWFocUkdeKyMdEZEtEHhaR94rI1yw45wXpuIfTeR8TkVcvOOd2EXlretbG6dn7EREZ7fZeyd4QkXMi8koR+TkR+aP0OVwRkTtF5IdFZKPnXD4nZGkoY04elDFkEZQxp4gQAv+OwR+AEYCPAAgAPgvgVwF8NL1/EMAXHHUf+benz/XX02fY+FtwzpvScVfT+e8DsANgCuCVHef85bR/DuD9AN4B4JHUzj/tOOcZAB5Kx/xheubuSe8/CODMUY/fafgD8DrzbNwF4NfSZ345bftvAG7lc8K/63zOKGNO4B9lDP+WeEYoY07J35F3gH/pgwB+LD3AHwawYbZ/d9r+/qPuI//29Ll+P4A3AngFgNsAjPsELoCvS5/3JQDPNNu/GsB2mhwvFOfcDODRdN43me1PBPDJtP3FzrU+mPa92WwbAnhn2v6Gox6/0/AH4DUAfgbAFxfbnwTg4+mz+CU+J/y7zueMMuYE/lHG8G+JZ4Qy5pT8HXkH+BcAYB3AZnp4n+fsvzPt+4qj7iv/rvuzXiRw35s+67/r7Htz2vc9xfbvS9t/3TnnVWnfu4vtz0/bH0ChBUsT8ATAwwCGRz1mp/kvCdCQnpt1Pif82+NzRBlzSv4oY/i3y+eFMuYE/TGm7njwAgCPA3BPCOG/OPvfkV5fcXhdIoeNiJwF8JL09h3OIV3Pwct7znkP4mT9dYVvup7z7hDCtj0hhPAAgA8AuAnAC5frPTkg7kyvZwA8HuBzQvYEZQzh3EE8KGNOEFzUHQ++LL1+vGO/bv/SQ+gLOTq+CHFifSiEcJ+zv+s56Hx+QggTAP8VMZ7mC5c5Z8G1yOHyBel1B1FbCfA5IbuHnyMBOHeQNpQxJwgu6o4HT02v3pfHbn/aIfSFHB29z0EI4QqiC9VNInIDAIjIjYga+M7z4D8/fOZWg9en1/cZLSafE7Jb+DkSgHMHaUMZc4Lgou54oOlkr3bsv5JebziEvpCjY9FzALSfBZuKeDfPD5+5Y46IfAOAv46oQf0hs4vPCdkt/BwJwLmDGChjTh5c1BFCyDFDRJ4F4BcACIC/F0K4c8EphBBCyFJQxpxMuKg7Hmyl13Md+8+n18cOoS/k6Fj0HADtZ2HL7NvN88Nn7piSCkG/DzE4/CdCCG8uDuFzQnYLP0cCcO4goIw5yXBRdzz4THq9vWO/bv/0IfSFHB29z4GInAdwAcAjIYTHACCEcBmxLkznefCfHz5zxxARuRnAbyPGD7wVwPc6h/E5IbuFnyMBOHeceihjTjZc1B0P1Oz95R37dfsfHEJfyNHxR4hFPZ+QNGklXc9B5/MjImsAvgQxlfAfL3POgmuRA0JENgD8JoBnIxZd/c6QivUU8Dkhu4WfIwE4d5xqKGNOPlzUHQ8+hKjhuENEnuvs/+b0+u5D6xE5dEII1wD8+/T2W5xDup6D9xT7Ld+ImEL4d0IIY+ecV4jIGXuCiDwRwIsAPIL4bJIDJn0G/waxEOtvAfi2EMLMO5bPCdkDlDGEc8cphjLmlHDU1c/5F/8A/BiAgPjgnjfbvzttf/9R95F/+/I5j+PXrnP/16XP+xKAZ5rtX53OfQTAheKcmxF/sAUA32S23wrgk2n7i51rfTDte5PZNgTwr9P2Nxz1eJ2GPwADRK1pAPAfAZxb4hw+J/zb7XNGGXMK/ihj+Od8DpQxp+RP0uCRI0ZERgDeD+CrANwP4AOIPs9fBeAhAH8uhPCpI+sg2RMi8nI0UwU/HzHb1EfNth8NIbzHnPMmxNoxVwH8OwDrAP5COu+bQwi/7lznLwP4tXTM+wF8DnFSvoAYCP09zjnPBPARAI8H8IcA7gLwlYjFSD8M4CUh160hB4SIvB7Am9LbdwG43HHo94YQLpnz3gQ+J2RJKGNOJpQxZBGUMaeIo15V8i//ATgL4I0A7kb0Z74fMZD19qPuG//2/Jm+FlHT1Pf32o7z/hNijZZHEP3gv2bBtV6Qjnsknfd7AF6z4JynpGfs/vTMfTI9g6OjHrvT8gfgDUs8IwHA0/mc8O86nzXKmBP2RxnDvyWeEcqYU/JHSx0hhBBCCCGErDBMlEIIIYQQQgghKwwXdYQQQgghhBCywnBRRwghhBBCCCErDBd1hBBCCCGEELLCcFFHCCGEEEIIISsMF3WEEEIIIYQQssJwUUcIIYQQQgghKwwXdYQQQgghhBCywnBRRwghhBBCCCErDBd1hBBCCCGEELLCcFFHCCGEEEIIISsMF3WEEEIIIYQQssJwUUcIIYQQQgghKwwXdYQQQgghhBCywnBRRwghhBBCCCErDBd1hBBCCCGEELLCcFFHCCGEEEIIISsMF3WEEEIIIYQQssL8/9D8judAPQJoAAAAAElFTkSuQmCC", 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", 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" ] @@ -202,7 +196,7 @@ "fig, (ax1, ax2) = plt.subplots(1,2, sharex=True, sharey=True, dpi = 150)\n", "\n", "p1 = ax1.pcolormesh(binMid, binMid, nJF12, cmap = plt.cm.CMRmap, vmax=vmax)\n", - "ax1.set_title(\"only JF12 sol.\")\n", + "ax1.set_title(\"only JF12 (solenoidal)\")\n", "\n", "p2 = ax2.pcolormesh(binMid, binMid, nCMZ, cmap = plt.cm.CMRmap, vmax=vmax)\n", "ax2.set_title(\"with CMZ field\")\n", @@ -223,18 +217,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### edge on view of the Milky-Way\n", - "Additional plot of a edge on view of the Milkyway. Here, all data are integrated over the y-axis. " + "### Edge-on view of the Milky Way\n", + "Additional plot of a edge on view of the Milky Way. Here, all data are integrated over the y-axis. " ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -278,18 +272,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### distribution over the z-axis\n", + "### Distribution along the z-axis\n", "Additional plot showing the distribution over the z-axis. " ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -307,22 +301,15 @@ "plt.xlabel(\"z [kpc]\")\n", "plt.ylabel(\"n\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "CRPropaMaster", + "display_name": "Python 3.9.2 ('crpropa_master': venv)", "language": "python", - "name": "crpropamaster" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -335,6 +322,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "7dc99857f02ebf862368f83a013de1e635cf753063bd3c6272d454ddc461751e" + } } }, "nbformat": 4, From cfd7f302186441a9c3f3c880d6fe8d852950d797 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 24 Nov 2022 14:21:54 +0100 Subject: [PATCH 25/87] Fix typos, missing link and go through custom photon field example. WARNING: Not yet working because it relys on newer crpropa-data repo. --- .../custom-photon-field.ipynb | 145 +++++++++--------- doc/pages/extending_crpropa.rst | 2 +- 2 files changed, 77 insertions(+), 70 deletions(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index aaf11d3fb..edc0fcdc2 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -4,17 +4,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# custom photon fields" + "# Custom Photon Fields" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## example to create necessary tabulated data\n", + "## Example to create necessary tabulated data\n", "\n", "For the use of custom photon fields in CRPropa it is necessary to generate some tables with precalculated interaction rates. \n", - "All scripts needed are given in the CRPropa data repository (https://github.com/CRPropa/CRPropa3-data) where also the calculation for the pre-defined photon fields is done. The easiest way is to download/colne the full repository. \n", + "All scripts needed are stored in the CRPropa data repository (https://github.com/CRPropa/CRPropa3-data). This is also the preferred location to perform the calculation for the pre-defined photon fields. The easiest way to get the relvant files is to download/clone the full repository. \n", "\n", " git clone https://github.com/CRPropa/CRPropa3-data.git" ] @@ -24,21 +24,18 @@ "metadata": {}, "source": [ "\n", - "### 1. create a python class with your custom photon field\n", - "In this example we show the production of a custom photon field for 2 different cases. \n", - "In the first case we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", + "### 1. Create a python class with your custom photon field\n", + "In this example we show the production of a custom photon field for two different cases. \n", + "In the first case we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with a given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", "\n", - "The second example is a tabulated data file. Here we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php\n", - "as CRPropa allows only isotropic photon fields we use the position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", + "The second example is based on a tabulated data file. Here, we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php. As CRPropa allows only for isotropic photon fields we use the position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", "\n", "\n", - "\n", - "\n", - "All photon fields must have the following content\n", + "All photon fields must have the following mandatory parameters and functions:\n", "- name (string): name of the photon field, needed for the naming of the files\n", - "- info (string): information tag used for the comments at the begining of the file\n", - "- redshift (None/Array): Determines if the photon field is redshift dependen. If None no redshift dependence is given. Otherwise the (tabulated) redshift is given as a 1D array\n", - "- getDensity (function): returns the spectral number density dn/deps at given photon energy and redshift\n", + "- info (string): information tag used for the comments at the beginning of the file\n", + "- redshift (None/Array): Determines if the photon field is redshift dependend. If None no redshift dependence is given. Otherwise the (tabulated) redshift must be provided as a 1D array\n", + "- getDensity (function): returns the spectral number density dn/deps(eps, z) at a given photon energy (eps) and redshift (z)\n", "- getEmin (function): returns the minimum effective photon energy\n", "- getEmax (function): returns the maximum effective photon energy\n" ] @@ -54,7 +51,11 @@ "from scipy.interpolate import interp1d\n", "import warnings\n", "import os\n", - "os.chdir(\"./CRPropa3-data/\")\n", + "import sys\n", + "\n", + "#Change for path to the CRPropa data repository\n", + "crpropa_data_path = \"/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/\"\n", + "sys.path.append(crpropa_data_path)\n", "\n", "import matplotlib.pyplot as plt # optional for plotting" ] @@ -73,10 +74,10 @@ "class PowerlawPhotonField:\n", " # general parameters\n", " name = \"PowerlawPhotonField\"\n", - " info = \"custom photon field as a powerlaw\"\n", + " info = \"Single power law photon field with exponential cutoffs at both ends.\"\n", " redshift = None\n", "\n", - " # model parameters, will be setted by initilation\n", + " # model parameters, will be set by initialization\n", " slope = -2 \n", " norm = 1\n", " eMin = 0 \n", @@ -84,7 +85,7 @@ "\n", " def __init__(self, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", " \"\"\"\n", - " initilize the photon field as a powerlaw with exponential cutoff at both ends. \n", + " Initialize the photon field as a powerlaw with exponential cutoff at both ends. \n", " The slope, the normalization (n(eps = 1 eV)) and the minimal and maximal energy can be modified. \n", " \"\"\"\n", " self.norm = norm\n", @@ -120,7 +121,7 @@ " info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", " redshift = None\n", "\n", - " def __init__(self, dataPath = \"../Porter_etal_ApJ_846_67_2017_SEDonly/F98/freudenreich_DL07_PAHISMMix_0.0173648_0.0984808_0_Flux.dat\"):\n", + " def __init__(self, dataPath = \"../test_data/field.dat\"):\n", " names = [\"micron\", \"total\", \"direct\", \"scattered\", \"transient\", \"thermal\"]\n", " df = pd.read_csv(dataPath, delimiter=\" \", names = names)\n", " df[\"E\"] = h * c_light / (df.micron * 1e-6)\n", @@ -168,13 +169,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/home1/jdo/Test_CRPropa/CRPropa_Documentation/CRPropa3-data/photonField.py:31: RuntimeWarning: overflow encountered in expm1\n", + "/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/photonField.py:31: RuntimeWarning: overflow encountered in expm1\n", " return 8*np.pi / c0**3 / h**3 * eps**2 / np.expm1(eps / (kB * T_CMB))\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -220,71 +221,77 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "running ElasticScattering\n", - "running EMPairProduction\n", - "running EMDoublePairProduction\n", - "running EMTripletPairProduction\n", - "running EMInverseComptonScattering\n", - "running ElectronPairProduction\n", - "running PhotoDissintegration\n", - "running PhotoPionProduction\n", - "finished rate calculation\n" + "running ElasticScattering\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'calc_elasticscattering' has no attribute 'process'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 9\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcalc_elasticscattering\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mes\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mrunning ElasticScattering\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m es\u001b[39m.\u001b[39;49mprocess(field)\n\u001b[1;32m 11\u001b[0m es\u001b[39m.\u001b[39mprocess(isrf)\n\u001b[1;32m 13\u001b[0m \u001b[39m## electro-magnetic\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[39m#from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39m#print(\"running EMPairProduction\")\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[39m#print(\"running ElectronPairProduction\")\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[39m#process(field)\u001b[39;00m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'calc_elasticscattering' has no attribute 'process'" ] } ], "source": [ - "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to divde errors\n", + "os.chdir(crpropa_data_path)\n", + "\n", + "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to division by zero errors\n", " warnings.simplefilter(\"ignore\")\n", " \n", " # elasticscattering\n", - " from calc_elasticscattering import process\n", + " #from calc_elasticscattering import process\n", + " import calc_elasticscattering as es\n", " print(\"running ElasticScattering\")\n", - " process(field)\n", - " process(isrf)\n", + " es.process(field)\n", + " es.process(isrf)\n", " \n", - " # electro-magnetic\n", - " from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", - " print(\"running EMPairProduction\")\n", - " process(sigmaPP, field, \"EMPairProduction\")\n", - " process(sigmaPP, isrf, \"EMPairProduction\")\n", - " print(\"running EMDoublePairProduction\")\n", - " process(sigmaDPP, field, \"EMDoublePairProduction\")\n", - " process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", - " print(\"running EMTripletPairProduction\")\n", - " process(sigmaTPP, field, \"EMTripletPairProduction\")\n", - " process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", - " print(\"running EMInverseComptonScattering\")\n", - " process(sigmaICS, field, \"EMInverseComptonScattering\")\n", - " process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", - "\n", - " # pair production\n", - " from calc_pairproduction import process\n", - " print(\"running ElectronPairProduction\")\n", - " process(field)\n", + " ## electro-magnetic\n", + " #from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", + " #print(\"running EMPairProduction\")\n", + " #process(sigmaPP, field, \"EMPairProduction\")\n", + " #process(sigmaPP, isrf, \"EMPairProduction\")\n", + " #print(\"running EMDoublePairProduction\")\n", + " #process(sigmaDPP, field, \"EMDoublePairProduction\")\n", + " #process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", + " #print(\"running EMTripletPairProduction\")\n", + " #process(sigmaTPP, field, \"EMTripletPairProduction\")\n", + " #process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", + " #print(\"running EMInverseComptonScattering\")\n", + " #process(sigmaICS, field, \"EMInverseComptonScattering\")\n", + " #process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", + "#\n", + " ## pair production\n", + " #from calc_pairproduction import process\n", + " #print(\"running ElectronPairProduction\")\n", + " #process(field)\n", " process(isrf)\n", " # currently the spectrum can not be provided. Only as energy loss for primary\n", "\n", " # photo disintegration\n", - " from calc_photodisintegration import processRate, processEmission\n", - " print(\"running PhotoDissintegration\")\n", - " processRate(field)\n", - " processRate(isrf)\n", - " processEmission(field)\n", - " processEmission(isrf)\n", - "\n", - " # photo pion production\n", - " from calc_photopionproduction import process\n", - " print(\"running PhotoPionProduction\")\n", - " process(field)\n", - " process(isrf)\n", - " print(\"finished rate calculation\")" + " #from calc_photodisintegration import processRate, processEmission\n", + " #print(\"running PhotoDissintegration\")\n", + " #processRate(field)\n", + " #processRate(isrf)\n", + " #processEmission(field)\n", + " #processEmission(isrf)\n", + "#\n", + " ## photo pion production\n", + " #from calc_photopionproduction import process\n", + " #print(\"running PhotoPionProduction\")\n", + " #process(field)\n", + " #process(isrf)\n", + " #print(\"finished rate calculation\")" ] }, { @@ -482,7 +489,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.2 ('documentation')", + "display_name": "Python 3.9.2 ('pymatrix': venv)", "language": "python", "name": "python3" }, @@ -501,7 +508,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "fb0d053f2fc7a51fab1b12c61652c7afb98f5cf72d66d469d3887d807bdc2ded" + "hash": "227eec005d0ede80e71143bfc809cd4c55722d59033e1345ec33f8f193acf655" } } }, diff --git a/doc/pages/extending_crpropa.rst b/doc/pages/extending_crpropa.rst index 299792666..0afc66a06 100644 --- a/doc/pages/extending_crpropa.rst +++ b/doc/pages/extending_crpropa.rst @@ -7,7 +7,7 @@ needs in in several ways: .. toctree:: Extending-CRPropa.md example_notebooks/advanced/CustomObserver.v4.ipynb - example_notebooks/custom_photonfield/custom-photonfield.ipynb + example_notebooks/custom_photonfield/custom-photon-field.ipynb Cpp-projects.md From 46a4a215556a6b9385d211e63bfec6474375e6e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 24 Nov 2022 14:27:46 +0100 Subject: [PATCH 26/87] added version printing --- .../extending-CRPropa/extending-CRPropa.ipynb | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb index 5ed12223e..9af608661 100644 --- a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb +++ b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb @@ -25,12 +25,14 @@ "name": "stdout", "output_type": "stream", "text": [ + "3.2-2-g8c8daa09\n", "9.0\n" ] } ], "source": [ "from crpropa import *\n", + "print(crpropa.__version__)\n", "\n", "class MyModule(Module):\n", " \"\"\" Reduces the cosmic ray energy by 10% in each step \"\"\"\n", @@ -77,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -115,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -155,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -196,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ From 148e82d1b9009c2ea26525797d2dc9aa2a312dba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 24 Nov 2022 14:32:08 +0100 Subject: [PATCH 27/87] replacing the old md file with the new py-notebook --- doc/pages/extending_crpropa.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/extending_crpropa.rst b/doc/pages/extending_crpropa.rst index e562ef210..e5aa7912e 100644 --- a/doc/pages/extending_crpropa.rst +++ b/doc/pages/extending_crpropa.rst @@ -5,7 +5,7 @@ CRPropa can be interfaced to user code, respectivily adapted to specialized needs in in several ways: .. toctree:: - Extending-CRPropa.md + example_notebooks/extending-CRPropa/extending-CRPropa.ipynb example_notebooks/advanced/CustomObserver.v4.ipynb Cpp-projects.md From 596a85fb3b2e6a74f715926d2ec584104612aac7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 24 Nov 2022 15:44:32 +0100 Subject: [PATCH 28/87] removed keyword arguments --- .../advanced/CustomObserver.v4.ipynb | 33 +++++++++++++------ 1 file changed, 23 insertions(+), 10 deletions(-) diff --git a/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb b/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb index 4cd3461ae..15b17ca3d 100644 --- a/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb +++ b/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb @@ -63,18 +63,26 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": true } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 2\n" + ] + } + ], "source": [ "from crpropa import Mpc, nG, EeV\n", "import numpy as np\n", - "\n", - "turbSpectrum = crpropa.SimpleTurbulenceSpectrum(Brms=1*nG, lMin = 2*Mpc, lMax=5*Mpc, sIndex=5./3.)\n", + "Brms, lMin, lMax, sIndex=1*nG, 2*Mpc, 5*Mpc, 5./3.\n", + "turbSpectrum = crpropa.SimpleTurbulenceSpectrum(Brms, lMin, lMax, sIndex)\n", "gridprops = crpropa.GridProperties(crpropa.Vector3d(0), 128, 1 * Mpc)\n", "BField = crpropa.SimpleGridTurbulence(turbSpectrum, gridprops)\n", "\n", @@ -113,12 +121,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -158,12 +166,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -186,7 +194,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.6 ('crp')", "language": "python", "name": "python3" }, @@ -200,7 +208,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "9b0c5e5016d73719a8e2817ed012b8006f294be990373a75d6bf3c89fad1d7fd" + } }, "widgets": { "application/vnd.jupyter.widget-state+json": { From 2f98015292791c343de1d1135e774d72a945ae5d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 1 Dec 2022 16:01:04 +0100 Subject: [PATCH 29/87] fixed testPlugin.py --- plugin-template/testPlugin.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plugin-template/testPlugin.py b/plugin-template/testPlugin.py index efe9d0788..1ddce4b98 100644 --- a/plugin-template/testPlugin.py +++ b/plugin-template/testPlugin.py @@ -1,5 +1,5 @@ import crpropa -import myPlugin +import build.myPlugin as myPlugin print("My Simulation\n") From 98c294c851981d06a6f02ef1c36d1bdc60ddf78a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?JulienD=C3=B6rner?= Date: Wed, 7 Dec 2022 08:47:46 +0100 Subject: [PATCH 30/87] add comments for the functions in the interaction modules --- .../crpropa/module/EMDoublePairProduction.h | 14 +++++++ .../module/EMInverseComptonScattering.h | 14 +++++++ include/crpropa/module/EMPairProduction.h | 24 +++++++++--- .../crpropa/module/EMTripletPairProduction.h | 14 +++++++ .../crpropa/module/ElectronPairProduction.h | 20 +++++++++- include/crpropa/module/NuclearDecay.h | 13 +++++++ include/crpropa/module/PhotoDisintegration.h | 10 +++++ include/crpropa/module/PhotoPionProduction.h | 38 +++++++++++++++++++ include/crpropa/module/SynchrotronRadiation.h | 14 +++++++ 9 files changed, 155 insertions(+), 6 deletions(-) diff --git a/include/crpropa/module/EMDoublePairProduction.h b/include/crpropa/module/EMDoublePairProduction.h index fd6cff207..42b136e51 100644 --- a/include/crpropa/module/EMDoublePairProduction.h +++ b/include/crpropa/module/EMDoublePairProduction.h @@ -45,11 +45,25 @@ class EMDoublePairProduction: public Module { */ EMDoublePairProduction(ref_ptr photonField, bool haveElectrons = false, double thinning = 0, double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool haveElectrons); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + + /** Apply thinning with a given thinning factor + * @param thinning factor of thinning (0: no thinning, 1: maximum thinning) + */ void setThinning(double thinning); + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/EMInverseComptonScattering.h b/include/crpropa/module/EMInverseComptonScattering.h index 791b64508..f2a2c888e 100644 --- a/include/crpropa/module/EMInverseComptonScattering.h +++ b/include/crpropa/module/EMInverseComptonScattering.h @@ -50,11 +50,25 @@ class EMInverseComptonScattering: public Module { */ EMInverseComptonScattering(ref_ptr photonField, bool havePhotons = false, double thinning = 0, double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary photons are added to the simulation void setHavePhotons(bool havePhotons); + + /** limit the step to a fraction of the mean free path + @param limit fraction of the mean free path, should be between 0 and 1 + */ void setLimit(double limit); + + /** Apply thinning with a given thinning factor + * @param thinning factor of thinning (0: no thinning, 1: maximum thinning) + */ void setThinning(double thinning); + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/EMPairProduction.h b/include/crpropa/module/EMPairProduction.h index 065cb4ed5..f2ab8a1f7 100644 --- a/include/crpropa/module/EMPairProduction.h +++ b/include/crpropa/module/EMPairProduction.h @@ -28,10 +28,10 @@ namespace crpropa { */ class EMPairProduction: public Module { private: - ref_ptr photonField; - bool haveElectrons; - double limit; - double thinning; + ref_ptr photonField; // target photon field + bool haveElectrons; // add secondary electrons to simulation + double limit; // limit the step to a fraction of the mean free path + double thinning; // factor of the thinning (0: no thinning, 1: maximum thinning) std::string interactionTag = "EMPP"; // tabulated interaction rate 1/lambda(E) @@ -52,11 +52,25 @@ class EMPairProduction: public Module { */ EMPairProduction(ref_ptr photonField, bool haveElectrons = false, double thinning = 0,double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool haveElectrons); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + + /** Apply thinning with a given thinning factor + * @param thinning factor of thinning (0: no thinning, 1: maximum thinning) + */ void setThinning(double thinning); - + + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/EMTripletPairProduction.h b/include/crpropa/module/EMTripletPairProduction.h index a898709eb..c689fc2ba 100644 --- a/include/crpropa/module/EMTripletPairProduction.h +++ b/include/crpropa/module/EMTripletPairProduction.h @@ -50,11 +50,25 @@ class EMTripletPairProduction: public Module { */ EMTripletPairProduction(ref_ptr photonField, bool haveElectrons = false, double thinning = 0, double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool haveElectrons); + + /** limit the step to a fraction of the mean free path + @param limit fraction of the mean free path, should be between 0 and 1 + */ void setLimit(double limit); + + /** Apply thinning with a given thinning factor + * @param thinning factor of thinning (0: no thinning, 1: maximum thinning) + */ void setThinning(double thinning); + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/ElectronPairProduction.h b/include/crpropa/module/ElectronPairProduction.h index 4a5ee2a37..2ac0ac029 100644 --- a/include/crpropa/module/ElectronPairProduction.h +++ b/include/crpropa/module/ElectronPairProduction.h @@ -27,16 +27,34 @@ class ElectronPairProduction: public Module { std::vector tabLorentzFactor; /*< tabulated Lorentz factor */ std::vector > tabSpectrum; /*< electron/positron cdf(Ee|log10(gamma)) for log10(Ee/eV)=7-24 in 170 steps and log10(gamma)=6-13 in 70 steps and*/ double limit; ///< fraction of energy loss length to limit the next step - bool haveElectrons; + bool haveElectrons; /*< if true, secondary electrons will be added to the simulation */ std::string interactionTag = "EPP"; public: + /** + * @brief Constructor for the Electron Pair Production + * + * @param photonField target photon field + * @param haveElectrons If true, secondary electrons will be added to the simulation + * @param limit step size limit as fraction of mean free path + */ ElectronPairProduction(ref_ptr photonField, bool haveElectrons = false, double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool haveElectrons); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/NuclearDecay.h b/include/crpropa/module/NuclearDecay.h index 73686bddf..11f69c36b 100644 --- a/include/crpropa/module/NuclearDecay.h +++ b/include/crpropa/module/NuclearDecay.h @@ -44,11 +44,24 @@ class NuclearDecay: public Module { @param limit step size limit as fraction of mean free path */ NuclearDecay(bool electrons = false, bool photons = false, bool neutrinos = false, double limit = 0.1); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool b); + + // decide if secondary photons are added to the simulation void setHavePhotons(bool b); + + // decide if secondary neutrinos are added to the simulation void setHaveNeutrinos(bool b); + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/PhotoDisintegration.h b/include/crpropa/module/PhotoDisintegration.h index 6e496df97..7341bb334 100644 --- a/include/crpropa/module/PhotoDisintegration.h +++ b/include/crpropa/module/PhotoDisintegration.h @@ -50,10 +50,20 @@ class PhotoDisintegration: public Module { */ PhotoDisintegration(ref_ptr photonField, bool havePhotons = false, double limit = 0.1); + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary photons are added to the simulation void setHavePhotons(bool havePhotons); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); std::string getInteractionTag() const; diff --git a/include/crpropa/module/PhotoPionProduction.h b/include/crpropa/module/PhotoPionProduction.h index 57a2304fb..ae712f0fc 100644 --- a/include/crpropa/module/PhotoPionProduction.h +++ b/include/crpropa/module/PhotoPionProduction.h @@ -95,6 +95,17 @@ class PhotoPionProduction: public Module { public: + /** + * @brief pion production on a given target photon field + * + * @param photonField target photon field + * @param photons if true, secondary photons are added to the simulation + * @param neutrinos if true, secondary neutrinos are added to the simulation + * @param electrons if true, secondary electrons are added to the simulation + * @param antiNucleons if true, secondary anti nucleons are added to the simulation + * @param limit fraction of the mean free path, to which the propagation step will be limited + * @param haveRedshiftDependence use redshift dependent tabulated loss rates; if false, the redshift scaling of the photon field will be used + */ PhotoPionProduction( ref_ptr photonField, bool photons = false, @@ -103,15 +114,42 @@ class PhotoPionProduction: public Module { bool antiNucleons = false, double limit = 0.1, bool haveRedshiftDependence = false); + + // set the target photon field void setPhotonField(ref_ptr photonField); + + // decide if secondary photons are added to the simulation void setHavePhotons(bool b); + + // decide if secondary neutrinos are added to the simulation void setHaveNeutrinos(bool b); + + // decide if secondary electrons are added to the simulation void setHaveElectrons(bool b); + + // decide if secondary anti nucleons are added to the simulation void setHaveAntiNucleons(bool b); + + // decide if redshift dependent tabulated loss rates are used void setHaveRedshiftDependence(bool b); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + + /** set a custom interaction tag to trace back this interaction + * @param tag string that will be added to the candidate and output + */ void setInteractionTag(std::string tag); + void initRate(std::string filename); + + /** get the mean free path for a nucleon + * @param gamma Lorentz factor of the nucleon + * @param z redshift + * @param onProton true for protons, false for neutrons + */ double nucleonMFP(double gamma, double z, bool onProton) const; double nucleiModification(int A, int X) const; void process(Candidate *candidate) const; diff --git a/include/crpropa/module/SynchrotronRadiation.h b/include/crpropa/module/SynchrotronRadiation.h index 45c4e905a..6e9edd8a0 100644 --- a/include/crpropa/module/SynchrotronRadiation.h +++ b/include/crpropa/module/SynchrotronRadiation.h @@ -53,11 +53,25 @@ class SynchrotronRadiation: public Module { */ SynchrotronRadiation(double Brms = 0, bool havePhotons = false, double thinning = 0, int nSamples = 0, double limit = 0.1); + // set the target photon field void setField(ref_ptr field); + + // set the rms value of the magnetic field (no 3d field is used) void setBrms(double Brms); + + // decide if secondary photons are added to the simulation void setHavePhotons(bool havePhotons); + + /** Apply thinning with a given thinning factor + * @param thinning factor of thinning (0: no thinning, 1: maximum thinning) + */ void setThinning(double thinning); + + /** Limit the propagation step to a fraction of the mean free path + * @param limit fraction of the mean free path + */ void setLimit(double limit); + /** Set the maximum number of synchrotron photons that will be allowed to be added as candidates. This choice depends on the problem at hand. It must be such that all relevant physics is captured with the sample. Weights are added accordingly and the column 'weight' must be added to output. @param nmax maximum number of synchrotron photons to be sampled From 07eb971a78050c10b67905cb7eb767feb92936a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jurek=20V=C3=B6lp?= Date: Thu, 8 Dec 2022 14:32:20 +0100 Subject: [PATCH 31/87] fixed comments --- .../example_notebooks/extending-CRPropa/extending-CRPropa.ipynb | 2 +- include/crpropa/module/HDF5Output.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb index 9af608661..d4885a607 100644 --- a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb +++ b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb @@ -100,7 +100,7 @@ "\t\tparticleState.setEnergy(10 * EeV)\n", "\n", "# The source feature has to be created outside of the class attribute\n", - "# s.add(MySourceFeature()) wil NOT work! (SWIG issue)\n", + "# s.add(MySourceFeature()) will NOT work! (SWIG issue)\n", "srcFtr = MySourceFeature()\n", "s = Source()\n", "s.add(srcFtr)\n", diff --git a/include/crpropa/module/HDF5Output.h b/include/crpropa/module/HDF5Output.h index be86fe33f..a3d9854e7 100644 --- a/include/crpropa/module/HDF5Output.h +++ b/include/crpropa/module/HDF5Output.h @@ -22,7 +22,7 @@ const size_t propertyBufferSize = 1024; /** @class HDF5Output @brief Output to HDF5 Format. -In the base class is an overview of the possible columns. +The baseclass gives an overview of possible columns HDF5 structure: ``` From e4b8bc02021c24efa84254b1c025b9e5d3e3b5f8 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 8 Dec 2022 15:27:16 +0100 Subject: [PATCH 32/87] Fix typos --- .../custom-photon-field.ipynb | 384 +++++++++++++++--- 1 file changed, 331 insertions(+), 53 deletions(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index edc0fcdc2..6e99018e9 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -221,25 +221,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "running ElasticScattering\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'calc_elasticscattering' has no attribute 'process'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 9\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcalc_elasticscattering\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mes\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mrunning ElasticScattering\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m es\u001b[39m.\u001b[39;49mprocess(field)\n\u001b[1;32m 11\u001b[0m es\u001b[39m.\u001b[39mprocess(isrf)\n\u001b[1;32m 13\u001b[0m \u001b[39m## electro-magnetic\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[39m#from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39m#print(\"running EMPairProduction\")\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[39m#print(\"running ElectronPairProduction\")\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[39m#process(field)\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'calc_elasticscattering' has no attribute 'process'" + "running ElasticScattering\n", + "running EMPairProduction\n", + "running EMDoublePairProduction\n", + "running EMTripletPairProduction\n", + "running EMInverseComptonScattering\n", + "running ElectronPairProduction\n", + "running PhotoDissintegration\n", + "running PhotoPionProduction\n", + "finished rate calculation\n" ] } ], @@ -250,48 +247,47 @@ " warnings.simplefilter(\"ignore\")\n", " \n", " # elasticscattering\n", - " #from calc_elasticscattering import process\n", - " import calc_elasticscattering as es\n", + " from calc_elasticscattering import process\n", " print(\"running ElasticScattering\")\n", - " es.process(field)\n", - " es.process(isrf)\n", + " process(field)\n", + " process(isrf)\n", " \n", - " ## electro-magnetic\n", - " #from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", - " #print(\"running EMPairProduction\")\n", - " #process(sigmaPP, field, \"EMPairProduction\")\n", - " #process(sigmaPP, isrf, \"EMPairProduction\")\n", - " #print(\"running EMDoublePairProduction\")\n", - " #process(sigmaDPP, field, \"EMDoublePairProduction\")\n", - " #process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", - " #print(\"running EMTripletPairProduction\")\n", - " #process(sigmaTPP, field, \"EMTripletPairProduction\")\n", - " #process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", - " #print(\"running EMInverseComptonScattering\")\n", - " #process(sigmaICS, field, \"EMInverseComptonScattering\")\n", - " #process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", - "#\n", - " ## pair production\n", - " #from calc_pairproduction import process\n", - " #print(\"running ElectronPairProduction\")\n", - " #process(field)\n", + " # electro-magnetic\n", + " from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", + " print(\"running EMPairProduction\")\n", + " process(sigmaPP, field, \"EMPairProduction\")\n", + " process(sigmaPP, isrf, \"EMPairProduction\")\n", + " print(\"running EMDoublePairProduction\")\n", + " process(sigmaDPP, field, \"EMDoublePairProduction\")\n", + " process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", + " print(\"running EMTripletPairProduction\")\n", + " process(sigmaTPP, field, \"EMTripletPairProduction\")\n", + " process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", + " print(\"running EMInverseComptonScattering\")\n", + " process(sigmaICS, field, \"EMInverseComptonScattering\")\n", + " process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", + "\n", + " # pair production\n", + " from calc_pairproduction import process\n", + " print(\"running ElectronPairProduction\")\n", + " process(field)\n", " process(isrf)\n", " # currently the spectrum can not be provided. Only as energy loss for primary\n", "\n", " # photo disintegration\n", - " #from calc_photodisintegration import processRate, processEmission\n", - " #print(\"running PhotoDissintegration\")\n", - " #processRate(field)\n", - " #processRate(isrf)\n", - " #processEmission(field)\n", - " #processEmission(isrf)\n", - "#\n", - " ## photo pion production\n", - " #from calc_photopionproduction import process\n", - " #print(\"running PhotoPionProduction\")\n", - " #process(field)\n", - " #process(isrf)\n", - " #print(\"finished rate calculation\")" + " from calc_photodisintegration import processRate, processEmission\n", + " print(\"running PhotoDissintegration\")\n", + " processRate(field)\n", + " processRate(isrf)\n", + " processEmission(field)\n", + " processEmission(isrf)\n", + "\n", + " # photo pion production\n", + " from calc_photopionproduction import process\n", + " print(\"running PhotoPionProduction\")\n", + " process(field)\n", + " process(isrf)\n", + " print(\"finished rate calculation\")" ] }, { @@ -313,7 +309,7 @@ "\n", " cp spectrum_CMB.txt spectrum_.txt\n", "\n", - "***Using this workaround and including the production of secondaries from the ElectrtonPairProduction module is not recomended!***" + "***Using this workaround and including the production of secondaries from the ElectronPairProduction module is not recommended!***" ] }, { @@ -415,7 +411,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "now the files must be copied!" + "Now the files must be copied!" ] }, { @@ -479,6 +475,288 @@ "print(\"Everything works fine\")" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import crpropa as crp\n", + "import photonField as pf" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "crpCMB = crp.CMB()\n", + "pfCMB = pf.CMB()\n", + "crpCMB2 = crp.BlackbodyPhotonField(\"CMBTest\", 2.72548)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMB\n", + "1.6021764870000002e-20\n", + "4.783281527694803e-24\n", + "190679052.33698764\n", + "1.0\n" + ] + } + ], + "source": [ + "print( crpCMB.getFieldName())\n", + "print( crpCMB.getMaximumPhotonEnergy(0))\n", + "print( crpCMB.getMinimumPhotonEnergy(0))\n", + "print( crpCMB.getPhotonDensity(1e-3*crp.eV, 0))\n", + "print( crpCMB.getRedshiftScaling(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMBTest\n", + "1.6021764870000002e-20\n", + "4.775361955348583e-24\n", + "189320351.79205775\n", + "1.0\n" + ] + } + ], + "source": [ + "print( crpCMB2.getFieldName())\n", + "print( crpCMB2.getMaximumPhotonEnergy(0))\n", + "print( crpCMB2.getMinimumPhotonEnergy(0))\n", + "print( crpCMB2.getPhotonDensity(1e-3*crp.eV, 0))\n", + "print( crpCMB2.getRedshiftScaling(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMB\n", + "1.60217657e-20\n", + "1.60217657e-29\n", + "189320351.79205772\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'CMB' object has no attribute 'getRedshiftScaling'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 23\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39mgetEmin(\u001b[39m0\u001b[39m))\n\u001b[1;32m 4\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39mgetDensity(\u001b[39m1e-3\u001b[39m\u001b[39m*\u001b[39mcrp\u001b[39m.\u001b[39meV, \u001b[39m0\u001b[39m)\u001b[39m*\u001b[39mcrp\u001b[39m.\u001b[39meV\u001b[39m*\u001b[39m\u001b[39m1e-3\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39;49mgetRedshiftScaling(\u001b[39m1\u001b[39m))\n", + "\u001b[0;31mAttributeError\u001b[0m: 'CMB' object has no attribute 'getRedshiftScaling'" + ] + } + ], + "source": [ + "print( pfCMB.name)\n", + "print( pfCMB.getEmax(0))\n", + "print( pfCMB.getEmin(0))\n", + "print( pfCMB.getDensity(1e-3*crp.eV, 0)*crp.eV*1e-3)\n", + "print( pfCMB.getRedshiftScaling(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "crpropa_data_path = \"/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/\"\n", + "\n", + "os.chdir(crpropa_data_path)\n", + "\n", + "import numpy as np\n", + "import pandas as pd \n", + "import os\n", + "import gitHelp as gh\n", + "import photonField as pf\n", + "import crpropa as crp\n", + "\n", + "\n", + "#eV = 1.60217657e-19 # [J]\n", + "#cm3 = 1e-6 # [m^3]\n", + "#c0 = 299792458 # [m/s]\n", + "#h = 6.62606957e-34 # [m^2 kg / s]\n", + "#erg = 1e-7 # [J]\n", + "#kB = 1.3806488e-23 # [m^2 kg / s^2 / K]\n", + "\n", + "def IRB_Stecker05(fileDir, outDir):\n", + " name = 'IRB_Stecker05'\n", + " info = '# cosmic infrared and optical background radiation model of Stecker at al. 2005'\n", + " redshift = np.linspace(0., 5., 26)\n", + " filePath = fileDir + \"EBL_Stecker_2005/data2.txt\"\n", + " data = np.genfromtxt(filePath)\n", + " energy = []\n", + " photonField = []\n", + " for i, zSlice in enumerate(data):\n", + " eps = 10**zSlice[0] # [eV]\n", + " energy.append(eps)\n", + " dens = 10**zSlice[1:] / eps # [1/eVcm^3]\n", + " dens /= (redshift + 1)**3 # make comoving\n", + " photonField.append(dens)\n", + " print (\"energy\")\n", + " print(energy)\n", + " print(\"density\")\n", + " print(photonField)\n", + " print(\"redshift\")\n", + " print(redshift)\n", + " createField(name, info, energy, redshift, photonField, outDir)\n", + " return photonField\n", + "\n", + "def createField(name, info, energy, redshift, photonDensity, outDir):\n", + " try:\n", + " git_hash = gh.get_git_revision_hash()\n", + " addHash = True\n", + " except:\n", + " addHash = False\n", + "\n", + " with open(outDir + \"/\" + name + \"_photonEnergy.txt\", 'w') as f:\n", + " f.write(info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# photon energies in [J]\\n\")\n", + " for e in energy:\n", + " f.write(\"{}\\n\".format(e * crp.eV)) # [J]\n", + " if redshift is not None:\n", + " with open(outDir + \"/\" + name + \"_redshift.txt\", 'w') as f:\n", + " f.write(info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# redshift\\n\")\n", + " for z in redshift:\n", + " f.write(\"{}\\n\".format(np.round(z, 2)))\n", + " with open(outDir + \"/\" + name + \"_photonDensity.txt\", 'w') as f:\n", + " f.write(info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# Comoving photon number density in [m^-3], format: d(e1,z1), ... , d(e1,zm), d(e2,z1), ... , d(e2,zm), ... , d(en,zm)\\n\")\n", + " for i, densSlice in enumerate(photonDensity):\n", + " for d in densSlice:\n", + " f.write(\"{}\\n\".format(d * energy[i] / crp.cm**3.)) # [# / m^3], comoving\n", + " print(\"done: \" + name)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def createFieldNew(field, outDir):\n", + " name = field.name\n", + " info = field.info\n", + " redshift = field.redshift\n", + " energy = field.getEnergy() / crp.eV\n", + " photonDensity = [[field.getDensity(eps, z) * crp.eV * crp.cm**3. for z in field.redshift] for eps in field.getEnergy()]\n", + "\n", + " try:\n", + " git_hash = gh.get_git_revision_hash()\n", + " addHash = True\n", + " except:\n", + " addHash = False\n", + "\n", + " with open(outDir + \"/\" + name + \"_photonEnergy.txt\", 'w') as f:\n", + " f.write(\"# \"+info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# photon energies in [J]\\n\")\n", + " for e in energy:\n", + " f.write(\"{}\\n\".format(e * crp.eV)) # [J]\n", + " if redshift is not None:\n", + " with open(outDir + \"/\" + name + \"_redshift.txt\", 'w') as f:\n", + " f.write(\"# \"+info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# redshift\\n\")\n", + " for z in redshift:\n", + " f.write(\"{}\\n\".format(np.round(z, 2)))\n", + " with open(outDir + \"/\" + name + \"_photonDensity.txt\", 'w') as f:\n", + " f.write(\"# \"+info+'\\n')\n", + " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", + " f.write(\"# Comoving photon number density in [m^-3], format: d(e1,z1), ... , d(e1,zm), d(e2,z1), ... , d(e2,zm), ... , d(en,zm)\\n\")\n", + " for i, densSlice in enumerate(photonDensity):\n", + " for d in densSlice:\n", + " f.write(\"{}\\n\".format(d * energy[i] / crp.cm**3.)) # [# / m^3], comoving\n", + " print(\"done: \" + name)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done: IRB_Stecker05\n" + ] + } + ], + "source": [ + "os.chdir(crpropa_data_path)\n", + "\n", + "#test1 = IRB_Stecker05(\"tables/\", \"./testdata/Scaling\")\n", + "createFieldNew(stecker, \"./testdata/Scaling\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "stecker = pf.EBL_Stecker05()\n", + "stecker.getEnergy()/crp.eV\n", + "test2 = [[stecker.getDensity(eps, z) * crp.eV * crp.cm**3. for z in stecker.redshift] for eps in stecker.getEnergy()]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.18045278e-08 5.18045267e-08 5.18045276e-08 5.18045266e-08\n", + " 5.18045274e-08 5.18045270e-08 5.18045276e-08 5.18045279e-08\n", + " 5.18045262e-08 5.18045274e-08 5.18045280e-08 5.18045269e-08\n", + " 5.18045268e-08 5.18045272e-08 5.18045272e-08 5.18045269e-08\n", + " 5.18045270e-08 5.18045268e-08 5.18045276e-08 5.18045271e-08\n", + " 5.18045269e-08 5.18045268e-08 5.18045266e-08 5.18045271e-08\n", + " 5.18045271e-08]\n" + ] + } + ], + "source": [ + "relErr = (np.array(test1)-np.array(test2))/np.array(test1)\n", + "#print(relErr)\n", + "print(np.mean(relErr, axis=1))\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -489,7 +767,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.2 ('pymatrix': venv)", + "display_name": "Python 3.9.2 ('crpropa_master': venv)", "language": "python", "name": "python3" }, @@ -508,7 +786,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "227eec005d0ede80e71143bfc809cd4c55722d59033e1345ec33f8f193acf655" + "hash": "7dc99857f02ebf862368f83a013de1e635cf753063bd3c6272d454ddc461751e" } } }, From 051bd26fb55159529e70394474eee1ffeb881528 Mon Sep 17 00:00:00 2001 From: Leander Schlegel Date: Thu, 8 Dec 2022 15:37:12 +0100 Subject: [PATCH 33/87] fixed minor typos in doc-string and print-message --- include/crpropa/Grid.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/include/crpropa/Grid.h b/include/crpropa/Grid.h index ceac782d6..bd362a945 100644 --- a/include/crpropa/Grid.h +++ b/include/crpropa/Grid.h @@ -228,7 +228,7 @@ class Grid: public Referenced { } } - /** returns the positon of the lower left front corner of the volume */ + /** returns the position of the lower left front corner of the volume */ Vector3d getOrigin() const { return origin; } @@ -379,7 +379,7 @@ class Grid: public Referenced { __m128 pos2 = _mm_set1_ps (position*position); __m128 pos3 = _mm_set1_ps (position*position*position); - /** SIMDY optimized routine to calculate 'res = ((-0.5*p0+3/2.*p1-3/2.*p2+0.5*p3)*pos*pos*pos+(p0-5/2.*p1+p2*2-0.5*p3)*pos*pos+(-0.5*p0+0.5*p2)*pos+p1);' + /** SIMD optimized routine to calculate 'res = ((-0.5*p0+3/2.*p1-3/2.*p2+0.5*p3)*pos*pos*pos+(p0-5/2.*p1+p2*2-0.5*p3)*pos*pos+(-0.5*p0+0.5*p2)*pos+p1);' where terms are used as: term = (-0.5*p0+0.5*p2)*pos term2 = (p0-5/2.*p1+p2*2-0.5*p3)*pos*pos; @@ -427,7 +427,7 @@ class Grid: public Referenced { __m128 result = CubicInterpolate(interpolateVaryX[0], interpolateVaryX[1], interpolateVaryX[2], interpolateVaryX[3], fX); return convertSimdToVector3f(result); #else // HAVE_SIMD - throw std::runtime_error( "Tried to use tricubic Interpolation without SIMD_EXTENSION. SIMD Optimization is neccesary for tricubic interpolation of vector grids.\n"); + throw std::runtime_error( "Tried to use tricubic Interpolation without SIMD_EXTENSION. SIMD Optimization is necessary for tricubic interpolation of vector grids.\n"); #endif // HAVE_SIMD } From 729a767db31f1a1abceb8e4ac9b119cb3c9baef7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?JulienD=C3=B6rner?= Date: Thu, 8 Dec 2022 17:17:31 +0100 Subject: [PATCH 34/87] improve comments for MFP scaling in PPP, add more comments --- include/crpropa/module/PhotoPionProduction.h | 9 ++++++++- include/crpropa/module/SynchrotronRadiation.h | 2 +- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/include/crpropa/module/PhotoPionProduction.h b/include/crpropa/module/PhotoPionProduction.h index ae712f0fc..f736f86b7 100644 --- a/include/crpropa/module/PhotoPionProduction.h +++ b/include/crpropa/module/PhotoPionProduction.h @@ -145,12 +145,19 @@ class PhotoPionProduction: public Module { void initRate(std::string filename); - /** get the mean free path for a nucleon + /** get the mean free path (MFP) for a single nucleon. + * To get the MFP for the full nucleus the nucleonMFP has to be divided by by the nucleiModification factor * @param gamma Lorentz factor of the nucleon * @param z redshift * @param onProton true for protons, false for neutrons */ double nucleonMFP(double gamma, double z, bool onProton) const; + + /** scaling factor for mean free path of the nucleus (converting the MFP of a single nucleon) + * + * @param A mass number of the nucleus + * @param X charge number of the nucleus + */ double nucleiModification(int A, int X) const; void process(Candidate *candidate) const; void performInteraction(Candidate *candidate, bool onProton) const; diff --git a/include/crpropa/module/SynchrotronRadiation.h b/include/crpropa/module/SynchrotronRadiation.h index 6e9edd8a0..da9e833b1 100644 --- a/include/crpropa/module/SynchrotronRadiation.h +++ b/include/crpropa/module/SynchrotronRadiation.h @@ -56,7 +56,7 @@ class SynchrotronRadiation: public Module { // set the target photon field void setField(ref_ptr field); - // set the rms value of the magnetic field (no 3d field is used) + // set the root-mean square (rms) value of the magnetic field (no 3d field is used) void setBrms(double Brms); // decide if secondary photons are added to the simulation From 9f4fb5f2f285152c13b4ce97b3bfcc5900ab1d8e Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 15 Dec 2022 10:26:00 +0100 Subject: [PATCH 35/87] updated reference to be cited --- README.md | 4 ++-- doc/pages/howto_cite_crpropa.rst | 6 +++++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a8f191af2..a85e3431e 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,9 @@ a mail with subject: subscribe crpropa-user to sympa@desy.de from the address you wish to subscribe. ## How to cite CRPropa -If you use CRPropa 3 for your research, please cite +If you use CRPropa 3.2 for your research, please cite -**JCAP 1605 (2016) no. 05, 038; [arXiv:1603.07142](https://arxiv.org/abs/1603.07142)** +**JCAP (2022) no. 09, 035; [arXiv:2208.00107](https://arxiv.org/abs/2208.00107)** as well as [additional publications](https://crpropa.github.io/CRPropa3/pages/howto_cite_crpropa.html) dependent on the components you are using. diff --git a/doc/pages/howto_cite_crpropa.rst b/doc/pages/howto_cite_crpropa.rst index bf136049a..a04ac8d1e 100644 --- a/doc/pages/howto_cite_crpropa.rst +++ b/doc/pages/howto_cite_crpropa.rst @@ -1,7 +1,11 @@ How to cite CRPropa =================== -If you use CRPropa 3 for your research, please cite: +If you use CRPropa 3.2 for your research, please cite: + +**JCAP (2022) no. 09, 035;** `arXiv:2208.00107 `_. + +If you still use an older version of CRPropa for your research, please cite: **JCAP 1605 (2016) 038;** `arXiv:1603.07142 `_. From 0b1bfd4be2531fab06c1acd038d453106c9fee33 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Thu, 15 Dec 2022 10:27:43 +0100 Subject: [PATCH 36/87] added interpolation paper as a reference --- doc/pages/howto_cite_crpropa.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/pages/howto_cite_crpropa.rst b/doc/pages/howto_cite_crpropa.rst index a04ac8d1e..a6fb6ecd1 100644 --- a/doc/pages/howto_cite_crpropa.rst +++ b/doc/pages/howto_cite_crpropa.rst @@ -15,6 +15,9 @@ following papers: Core Components --------------- +PlaneWaveTurbulence module and trilinear interpolation + ApJ 889 (2020) 123; `arXiv:1907.09934 `_. + Photon production and electromagnetic cascade propagation Astropart. Phys. 102 (2018) 39; `arXiv:1710.11406 `_. From a1c21de19ca02352a213aa4a25c8d2204912639d Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 16 Dec 2022 16:48:03 +0100 Subject: [PATCH 37/87] replace old checks by novel checkGridRequirements --- .../turbulentField/SimpleGridTurbulence.cpp | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/src/magneticField/turbulentField/SimpleGridTurbulence.cpp b/src/magneticField/turbulentField/SimpleGridTurbulence.cpp index 1c7e09cb2..030662318 100644 --- a/src/magneticField/turbulentField/SimpleGridTurbulence.cpp +++ b/src/magneticField/turbulentField/SimpleGridTurbulence.cpp @@ -21,23 +21,7 @@ void SimpleGridTurbulence::initTurbulence(ref_ptr grid, double Brms, Vector3d spacing = grid->getSpacing(); - // TODO(adundovi): replace it with checkGridRequirements(grid, lMin, lMax) once - // the old interface is removed - size_t Nx = grid->getNx(); - size_t Ny = grid->getNy(); - size_t Nz = grid->getNz(); - - if ((Nx != Ny) or (Ny != Nz)) - throw std::runtime_error("turbulentField: only cubic grid supported"); - if ((spacing.x != spacing.y) or (spacing.y != spacing.z)) - throw std::runtime_error("turbulentField: only equal spacing suported"); - if (lMin < 2 * spacing.x) - throw std::runtime_error("turbulentField: lMin < 2 * spacing"); - if (lMin >= lMax) - throw std::runtime_error("turbulentField: lMin >= lMax"); - if (lMax > Nx * spacing.x) // before was (lMax > Nx * spacing.x / 2) - throw std::runtime_error("turbulentField: lMax > size"); - //--- end of check + checkGridRequirements(grid, lMin, lMax); size_t n = grid->getNx(); // size of array size_t n2 = (size_t)floor(n / 2) + From 0aa96a4a811f3fa9526abbde9afbd4f0953b260d Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sat, 17 Dec 2022 11:50:16 +0100 Subject: [PATCH 38/87] added comment about testing in installtion procedure for virt environment --- doc/pages/Installation.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index e6540a2e6..a4de252c7 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -125,7 +125,9 @@ worthwhile effort afterwards. make install ``` -5. (optional) Check the installation. +5. A set of unit tests can be run with ```make test```. + +6. (optional) Check the installation. ```python python import crpropa From be0fd18a4ca65e2adace042d00b65e48b84c58fc Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sat, 17 Dec 2022 11:56:52 +0100 Subject: [PATCH 39/87] fixed anchor linkage in markdown installation file --- doc/pages/Installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index a4de252c7..08536061c 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -104,7 +104,7 @@ worthwhile effort afterwards. source $CRPROPA_DIR"/bin/activate" ``` -3. Check the dependencies (see [dependencies](#Dependencies) for details) and install at least mandatory ones. This can be done with package managers (see the [package list](#dependencies-in-different-oses) in different OSes). If packages are installed from source, during the compilation the installation prefix should be specified: +3. Check the dependencies and install at least mandatory ones. This can be done with package managers (see the [package list](#notes-for-specific-operating-systems) in different operating systems). If packages are installed from source, during the compilation the installation prefix should be specified: ```sh ./configure --prefix=$CRPROPA_DIR make From 546761bcfa015df563053e5941f760129e2f8333 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sat, 17 Dec 2022 12:11:36 +0100 Subject: [PATCH 40/87] fixed final links --- doc/pages/Installation.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 08536061c..a12c13be6 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -64,7 +64,7 @@ However, we highly recommend to use a virtualenv setup to install CRPropa! ### Installation in python virtualenv CRPropa is typically run on clusters where superuser access is not always available to the user. Besides that, it is easier to ensure the reproducibility -of simulations in a user controlled and clean environment. Thus, the user +of simulations in a user controlled and clean environment. Thus, the user space deployment without privileged access to the system would be a preferred way. Python provides the most flexible access to CRPropa features, hence, Python and SWIG are required. To avoid clashes with the system's Python and its @@ -83,7 +83,7 @@ worthwhile effort afterwards. mkdir -p $CRPROPA_DIR` ``` -2. Initialize the Python virtual environment with the virtualenv command. +2. Initialize the Python virtual environment with the virtualenv command, ```sh virtualenv $CRPROPA_DIR` ``` @@ -92,7 +92,7 @@ worthwhile effort afterwards. system software repository to install it (usually the package is called `virtualenv`, `python-virtualenv`, `python3-virtualenv` or `python2-virtualenv`). There is also an option to manually download it, - un-zip it and run it: + un-zip it, and run it: ```sh wget https://github.com/pypa/virtualenv/archive/develop.zip unzip develop.zip @@ -104,7 +104,7 @@ worthwhile effort afterwards. source $CRPROPA_DIR"/bin/activate" ``` -3. Check the dependencies and install at least mandatory ones. This can be done with package managers (see the [package list](#notes-for-specific-operating-systems) in different operating systems). If packages are installed from source, during the compilation the installation prefix should be specified: +3. Check the dependencies and install at least mandatory ones (see [prerequisites](#prerequisites)). This can be done with package managers (see the [package list](#notes-for-specific-operating-systems) in different operating systems). If packages are installed from source, during the compilation the installation prefix should be specified: ```sh ./configure --prefix=$CRPROPA_DIR make From 5e629039b68c9a71146a72876f11c288171cf1b7 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sun, 18 Dec 2022 10:37:55 +0100 Subject: [PATCH 41/87] added installation support for planeWaveTurbulence optimization --- doc/pages/Installation.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index a12c13be6..72e213d14 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -172,6 +172,13 @@ cmake -DENABLE_PYTHON=ON .. -DCMAKE_Fortran_COMPILER=ifort ``` ++ The PlaneWaveTurbulence computation can be improved using the FAST_WAVES flag: +1. In cmake: enable the FAST_WAVES flag. +2. Also in cmake: set SIMD_EXTENSIONS to “native” (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). +3. Generate files and exit cmake, then build. + +Note: If your CPU does not support the necessary extensions, the build will fail with an error telling you so. In this case, you won’t be able to use the optimization; go back into cmake, disable FAST_WAVES, and build again. If the build runs through without errors, the code is built with the optimization. + + Quite often there are multiple Python versions installed in a system. This is likely the cause of many (if not most) of the installation problems related to Python. To prevent conflicts among them, one can explicitly refer to the Python version to be used. Example: ``` -DCMAKE_PYTHON_EXECUTABLE=/usr/bin/python From cd1278f70944f9f4575df20d001bdaefb15312cd Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sun, 18 Dec 2022 10:41:41 +0100 Subject: [PATCH 42/87] provide link to PlaneWaveTurbulence --- doc/pages/Installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 72e213d14..46bda9fe2 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -172,7 +172,7 @@ cmake -DENABLE_PYTHON=ON .. -DCMAKE_Fortran_COMPILER=ifort ``` -+ The PlaneWaveTurbulence computation can be improved using the FAST_WAVES flag: ++ The PlaneWaveTurbulence computation can be improved using the FAST_WAVES flag (see [documentation](https://crpropa.github.io/CRPropa3/buildingblocks/MagneticFields.html#classcrpropa_1_1PlaneWaveTurbulence) for details): 1. In cmake: enable the FAST_WAVES flag. 2. Also in cmake: set SIMD_EXTENSIONS to “native” (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). 3. Generate files and exit cmake, then build. From 4b92d5f78ce3ff9ea4668cb79fe9e756e15c18cf Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sun, 18 Dec 2022 11:02:41 +0100 Subject: [PATCH 43/87] Optimized parallelization usage for simulations with few particles --- doc/pages/Installation.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 46bda9fe2..ceafd07d1 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -173,11 +173,11 @@ cmake -DENABLE_PYTHON=ON .. ``` + The PlaneWaveTurbulence computation can be improved using the FAST_WAVES flag (see [documentation](https://crpropa.github.io/CRPropa3/buildingblocks/MagneticFields.html#classcrpropa_1_1PlaneWaveTurbulence) for details): -1. In cmake: enable the FAST_WAVES flag. -2. Also in cmake: set SIMD_EXTENSIONS to “native” (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). -3. Generate files and exit cmake, then build. + 1. In cmake: enable the FAST_WAVES flag. + 2. Also in cmake: set SIMD_EXTENSIONS to “native” (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). + 3. Generate files and exit cmake, then build. -Note: If your CPU does not support the necessary extensions, the build will fail with an error telling you so. In this case, you won’t be able to use the optimization; go back into cmake, disable FAST_WAVES, and build again. If the build runs through without errors, the code is built with the optimization. + Note: If your CPU does not support the necessary extensions, the build will fail with an error telling you so. In this case, you won’t be able to use the optimization; go back into cmake, disable FAST_WAVES, and build again. If the build runs through without errors, the code is built with the optimization. + Quite often there are multiple Python versions installed in a system. This is likely the cause of many (if not most) of the installation problems related to Python. To prevent conflicts among them, one can explicitly refer to the Python version to be used. Example: ``` From f58e20e45e0767b180951e11d05967a08bef1a28 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sun, 18 Dec 2022 11:04:51 +0100 Subject: [PATCH 44/87] linked discussion about OMP_SCHEDULE parameter --- doc/pages/Installation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index ceafd07d1..0d1685c1e 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -157,6 +157,7 @@ cmake -DENABLE_PYTHON=ON .. + Enable unit-tests ```-DENABLE_TESTING=ON``` + Enable Coverage (code coverage tool) ```-DENABLE_COVERAGE=ON``` + Enable Git ```-DENABLE_GIT=ON``` ++ Optimized parallelization usage for simulations with few particles ```-DOMP_SCHEDULE:STRING=dynamic``` (see [discussion](https://github.com/CRPropa/CRPropa3/issues/117)) + Enable SWIG-builtin ```-DENABLE_SWIG_BUILTIN=ON``` + Debugging symbols included: ```-DCMAKE_BUILD_TYPE:STRING=Debug``` @@ -173,9 +174,8 @@ cmake -DENABLE_PYTHON=ON .. ``` + The PlaneWaveTurbulence computation can be improved using the FAST_WAVES flag (see [documentation](https://crpropa.github.io/CRPropa3/buildingblocks/MagneticFields.html#classcrpropa_1_1PlaneWaveTurbulence) for details): - 1. In cmake: enable the FAST_WAVES flag. - 2. Also in cmake: set SIMD_EXTENSIONS to “native” (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). - 3. Generate files and exit cmake, then build. + 1. Enable FAST_WAVES flag ```-DFAST_WAVES=ON``` + 2. Enable SIMD_EXTENSIONS ```-DSIMD_EXTENSIONS:STRING=native``` (the compiler will automatically detect support for your CPU and run the build with the appropriate settings). Note: If your CPU does not support the necessary extensions, the build will fail with an error telling you so. In this case, you won’t be able to use the optimization; go back into cmake, disable FAST_WAVES, and build again. If the build runs through without errors, the code is built with the optimization. From f8dbd467a44cdda7ab35a67b59f4692a238661bc Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Sun, 18 Dec 2022 11:18:06 +0100 Subject: [PATCH 45/87] added further prerequisites --- doc/pages/Installation.md | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 0d1685c1e..0b10cb2ca 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -10,6 +10,7 @@ git clone https://github.com/CRPropa/CRPropa3.git ## Prerequisites + C++ Compiler with C++11 support (gcc, clang and icc are known to work) + Fortran Compiler: to compile SOPHIA ++ numpy: for scientific computations Optionally CRPropa can be compiled with the following dependencies to enable certain functionality. + Python and SWIG: to use CRPropa from python (tested for > Python 2.7 and > SWIG 3.0.4) From 700cefec7d51fef7b253dd002a0788a69f915c15 Mon Sep 17 00:00:00 2001 From: mertelx Date: Sun, 18 Dec 2022 14:50:17 +0100 Subject: [PATCH 46/87] Add additional documentation for plugins --- .../extending-CRPropa/extending-CRPropa.ipynb | 30 ++++++++++++------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb index d4885a607..b4eacba2d 100644 --- a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb +++ b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb @@ -18,14 +18,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3.2-2-g8c8daa09\n", + "3.2-80-gfd273fff\n", "9.0\n" ] } @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -214,12 +214,20 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Plugins: Integrate Custom C++ Code to CRPropa's Python Steering\n", "Extending CRPropa with C++ code and keep python steering is also possible using\n", - "SWIG. This allows to integrate your code seamless as e.g.\n", + "SWIG. Make sure that the directory of ~/plugin-template/build is your python PATH\n", + "or add it manually, e.g. with:\n", + "```\n", + "import sys\n", + "sys.path.append(\"path/to/plugin-template/build\")\n", + "```\n", + "\n", + "Afterwards, this allows to integrate your code seamless as e.g.\n", "```\n", "import crpropa\n", "import myPlugin\n", @@ -258,7 +266,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.6 ('crp')", + "display_name": "crpropa_master", "language": "python", "name": "python3" }, @@ -272,11 +280,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.2" }, "vscode": { "interpreter": { - "hash": "9b0c5e5016d73719a8e2817ed012b8006f294be990373a75d6bf3c89fad1d7fd" + "hash": "7dc99857f02ebf862368f83a013de1e635cf753063bd3c6272d454ddc461751e" } }, "widgets": { From 60c18e42df3adc678b9982099fdba08822ef78b7 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Mon, 19 Dec 2022 09:59:21 +0100 Subject: [PATCH 47/87] updated checkGridRequirements with checks for lMin > lMax --- src/magneticField/turbulentField/GridTurbulence.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/magneticField/turbulentField/GridTurbulence.cpp b/src/magneticField/turbulentField/GridTurbulence.cpp index 468e170f3..2e0672ac0 100644 --- a/src/magneticField/turbulentField/GridTurbulence.cpp +++ b/src/magneticField/turbulentField/GridTurbulence.cpp @@ -138,6 +138,8 @@ void GridTurbulence::checkGridRequirements(ref_ptr grid, double lMin, throw std::runtime_error("turbulentField: lMin < 2 * spacing"); if (lMax > Nx * spacing.x) // before was (lMax > Nx * spacing.x / 2), why? throw std::runtime_error("turbulentField: lMax > size"); + if (lMax < lMin) + throw std::runtime_error("lMax < lMin"); } // Execute inverse discrete FFT in-place for a 3D grid, from complex to real From f6bd485a6423373e4e61de538c616a3c1fb3e4d8 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Mon, 19 Dec 2022 12:57:39 +0100 Subject: [PATCH 48/87] fixed typo in interpolation papaer ref --- doc/pages/howto_cite_crpropa.rst | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/pages/howto_cite_crpropa.rst b/doc/pages/howto_cite_crpropa.rst index bf136049a..db43a7869 100644 --- a/doc/pages/howto_cite_crpropa.rst +++ b/doc/pages/howto_cite_crpropa.rst @@ -1,7 +1,11 @@ How to cite CRPropa =================== -If you use CRPropa 3 for your research, please cite: +If you use CRPropa 3.2 for your research, please cite: + +**JCAP (2022) no. 09, 035;** `arXiv:2208.00107 `_. + +If you still use an older version of CRPropa for your research, please cite: **JCAP 1605 (2016) 038;** `arXiv:1603.07142 `_. @@ -11,6 +15,9 @@ following papers: Core Components --------------- +PlaneWaveTurbulence module and tricubic interpolation + ApJ 889 (2020) 123; `arXiv:1907.09934 `_. + Photon production and electromagnetic cascade propagation Astropart. Phys. 102 (2018) 39; `arXiv:1710.11406 `_. From 27ef7a10343a59dec91be7508e6ce749413e95cf Mon Sep 17 00:00:00 2001 From: mertelx Date: Wed, 21 Dec 2022 11:15:57 +0100 Subject: [PATCH 49/87] Fix typos --- .../example_notebooks/extending-CRPropa/extending-CRPropa.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb index b4eacba2d..514da2ddb 100644 --- a/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb +++ b/doc/pages/example_notebooks/extending-CRPropa/extending-CRPropa.ipynb @@ -280,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "3.9.2 (default, Feb 28 2021, 17:03:44) \n[GCC 10.2.1 20210110]" }, "vscode": { "interpreter": { From d4db7db378e6224231345f3dbd80fa03749412bd Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 22 Dec 2022 16:35:07 +0100 Subject: [PATCH 50/87] Extend the units file to remove all self-defined units in crpropa-data scripts. --- include/crpropa/Units.h | 3 +++ 1 file changed, 3 insertions(+) diff --git a/include/crpropa/Units.h b/include/crpropa/Units.h index 4cffe2213..c8add3e48 100644 --- a/include/crpropa/Units.h +++ b/include/crpropa/Units.h @@ -72,6 +72,9 @@ static const double h_planck = 6.62606957e-34 * joule * second; static const double k_boltzmann = 1.3806488e-23 * joule / kelvin; static const double mu0 = 4 * M_PI * 1e-7 * newton / ampere / ampere; static const double epsilon0 = 1.0 / mu0 / c_squared * ampere * second / volt / meter; +static const double alpha_finestructure = eplus * eplus / 2. / epsilon0 / h_planck / c_light; +static const double radius_electron = eplus * eplus / 4. / M_PI / epsilon0 / mass_electron / c_squared; +static const double sigma_thomson = 8. * M_PI / 3. * radius_electron * radius_electron; // gauss static const double gauss = 1e-4 * tesla; From 55c2eee3ba97b6d3af7abcf2216ab14ef02a86dc Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 22 Dec 2022 16:37:52 +0100 Subject: [PATCH 51/87] Fix documentation bug of synchrotron threshold energy. Now consitstent with the code --- include/crpropa/module/SynchrotronRadiation.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/crpropa/module/SynchrotronRadiation.h b/include/crpropa/module/SynchrotronRadiation.h index da9e833b1..6425ef8c4 100644 --- a/include/crpropa/module/SynchrotronRadiation.h +++ b/include/crpropa/module/SynchrotronRadiation.h @@ -17,7 +17,7 @@ namespace crpropa { This module simulates the continuous energy loss of charged particles in magnetic fields, c.f. Jackson. The magnetic field is specified either by a MagneticField or by a RMS field strength value. The module limits the next step size to ensure a fractional energy loss dE/E < limit (default = 0.1). - Optionally, synchrotron photons above a threshold (default E > 10^7 eV) are created as secondary particles. + Optionally, synchrotron photons above a threshold (default E > 10^6 eV) are created as secondary particles. Note that the large number of secondary photons per propagation can cause memory problems. To mitigate this, use thinning. However, this still does not solve the problem completely. For this reason, a break-condition stops tracking secondary photons and reweights the current ones. From 809e981c773993033cdc6698fefeea1bea7e8717 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Tue, 10 Jan 2023 15:59:32 +0100 Subject: [PATCH 52/87] fixed typos in breakCondition.h --- include/crpropa/module/BreakCondition.h | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/include/crpropa/module/BreakCondition.h b/include/crpropa/module/BreakCondition.h index 216507d40..5376b2ba7 100644 --- a/include/crpropa/module/BreakCondition.h +++ b/include/crpropa/module/BreakCondition.h @@ -13,9 +13,9 @@ namespace crpropa { @class MaximumTrajectoryLength @brief Deactivates the candidate beyond a maximum trajectory length - This modules deactivates the candidate at a given maximum trajectory length. + This module deactivates the candidate at a given maximum trajectory length. In that case the property ("Deactivated", module::description) is set. - It also limits the candidates next step size to ensure the maximum trajectory length is no exceeded. + It also limits the candidates next step size to ensure the maximum trajectory length is not exceeded. */ class MaximumTrajectoryLength: public AbstractCondition { double maxLength; @@ -34,7 +34,7 @@ class MaximumTrajectoryLength: public AbstractCondition { @class MinimumEnergy @brief Deactivates the candidate below a minimum energy - This modules deactivates the candidate below a given minimum energy. + This module deactivates the candidate below a given minimum energy. In that case the property ("Deactivated", module::description) is set. */ class MinimumEnergy: public AbstractCondition { @@ -52,7 +52,7 @@ class MinimumEnergy: public AbstractCondition { @class MinimumRigidity @brief Deactivates the candidate below a minimum rigidity - This modules deactivates the candidate below a given minimum rigidity (E/Z in EeV). + This module deactivates the candidate below a given minimum rigidity (E/Z in EeV). In that case the property ("Deactivated", module::description) is set. */ class MinimumRigidity: public AbstractCondition { @@ -69,7 +69,7 @@ class MinimumRigidity: public AbstractCondition { @class MinimumRedshift @brief Deactivates the candidate below a minimum redshift - This modules deactivates the candidate below a given minimum redshift. + This module deactivates the candidate below a given minimum redshift. In that case the property ("Deactivated", module::description) is set. */ class MinimumRedshift: public AbstractCondition { @@ -86,7 +86,7 @@ class MinimumRedshift: public AbstractCondition { @class MinimumChargeNumber @brief Deactivates the candidate below a minimum number - This modules deactivates the candidate below a given minimum charge number. + This module deactivates the candidate below a given minimum charge number. A minimum charge number of 26 deactivates all (anti-) isotopes which are ranked in the periodic table before iron (Fe). In that case the property ("Deactivated", module::description) is set. @@ -105,9 +105,9 @@ class MinimumChargeNumber: public AbstractCondition { @class MinimumEnergyPerParticleId @brief Deactivates the candidate below a minimum energy for specific particle Ids. - This modules deactivates the candidate below a given minimum energy for specific particle types. + This module deactivates the candidate below a given minimum energy for specific particle types. In that case the property ("Deactivated", module::description) is set. - All particles whose minimum energies are not specified follow the more general minEnergyOthers condition. + All particles whose minimum energy is not specified follow the more general minEnergyOthers condition. */ class MinimumEnergyPerParticleId: public AbstractCondition { std::vector minEnergies; @@ -127,7 +127,7 @@ class MinimumEnergyPerParticleId: public AbstractCondition { @class DetectionLength @brief Detects the candidate at a given trajectoryLength - This break condition can be used for non-regular time observation of the particle density. See also TimeEvolutionObserver. + This break condition can be used for non-regular time observation of the particle density. See also ObserverTimeEvolution. */ class DetectionLength: public AbstractCondition { double detLength; From d2d42da642d503dfb873ac4556aa38af17c3e36b Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 17 Jan 2023 14:50:05 +0100 Subject: [PATCH 53/87] Load secondary spectra tables only if haveElectrons==true --- src/module/ElectronPairProduction.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/module/ElectronPairProduction.cpp b/src/module/ElectronPairProduction.cpp index 5a8d77563..2a72e4904 100644 --- a/src/module/ElectronPairProduction.cpp +++ b/src/module/ElectronPairProduction.cpp @@ -12,9 +12,9 @@ namespace crpropa { ElectronPairProduction::ElectronPairProduction(ref_ptr photonField, bool haveElectrons, double limit) { - setPhotonField(photonField); this->haveElectrons = haveElectrons; this->limit = limit; + setPhotonField(photonField); } void ElectronPairProduction::setPhotonField(ref_ptr photonField) { @@ -22,11 +22,17 @@ void ElectronPairProduction::setPhotonField(ref_ptr photonField) { std::string fname = photonField->getFieldName(); setDescription("ElectronPairProduction: " + fname); initRate(getDataPath("ElectronPairProduction/lossrate_" + fname + ".txt")); - initSpectrum(getDataPath("ElectronPairProduction/spectrum_" + fname.substr(0,3) + ".txt")); + if (haveElectrons) { // Load secondary spectra only if electrons should be produced + initSpectrum(getDataPath("ElectronPairProduction/spectrum_" + fname.substr(0,3) + ".txt")); + } } void ElectronPairProduction::setHaveElectrons(bool haveElectrons) { this->haveElectrons = haveElectrons; + if (haveElectrons) { // Load secondary spectra in case haveElectrons was changed to true + std::string fname = photonField->getFieldName(); + initSpectrum(getDataPath("ElectronPairProduction/spectrum_" + fname.substr(0,3) + ".txt")); + } } void ElectronPairProduction::setLimit(double limit) { From 5eab83dbf18b2f9bbccbbab3505e69a8a659e0be Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 17 Jan 2023 16:58:02 +0100 Subject: [PATCH 54/87] Restructure custom photon field example --- .../custom-photon-field.ipynb | 591 ++++++------------ 1 file changed, 198 insertions(+), 393 deletions(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index 6e99018e9..6ae794239 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -8,36 +8,45 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Example to create necessary tabulated data\n", + "## Introduction\n", "\n", - "For the use of custom photon fields in CRPropa it is necessary to generate some tables with precalculated interaction rates. \n", - "All scripts needed are stored in the CRPropa data repository (https://github.com/CRPropa/CRPropa3-data). This is also the preferred location to perform the calculation for the pre-defined photon fields. The easiest way to get the relvant files is to download/clone the full repository. \n", + "To fully integrate custom photon fields into a CRPropa simulation **two** photon field classes for each photon field model have to be generated. This comes from the separation of the generation of tabulated data, e.g. interaction rates, of the actual propagation code. \n", + "\n", + "All tabulated files are precalculated with tools in the CRPropa-data repository (https://github.com/CRPropa/CRPropa3-data). Usually, the resulting data files are downloaded and moved to the correct location during the install process of CRPropa. For custom photon fields all relevant files have to be generated and copied to the correct location manually. Here, the first photon field class---inhereting from a base class in CRPropa-data---is used.\n", + "\n", + "In addition to all precalculated data files, CRPropa needs more functionality for photon targets. This is implemented by the implementation of the second photon field class---this time inhereting from a CRPropa module.\n", + "\n", + "### Downloading CRPropa-data\n", + "The easiest way to get the relevant files is to download/clone the full repository. \n", "\n", " git clone https://github.com/CRPropa/CRPropa3-data.git" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "### 1. Create a python class with your custom photon field\n", + "### 1. Create a python class with your custom photon field (CRPropa-data)\n", "In this example we show the production of a custom photon field for two different cases. \n", - "In the first case we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with a given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", + "First we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with a given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", "\n", - "The second example is based on a tabulated data file. Here, we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php. As CRPropa allows only for isotropic photon fields we use the position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", + "The second example is based on a tabulated data file. Here, we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php and is included in the crpropa installation. As CRPropa allows only for isotropic and homogeneous photon fields we use the field at position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", "\n", "\n", "All photon fields must have the following mandatory parameters and functions:\n", "- name (string): name of the photon field, needed for the naming of the files\n", "- info (string): information tag used for the comments at the beginning of the file\n", "- redshift (None/Array): Determines if the photon field is redshift dependend. If None no redshift dependence is given. Otherwise the (tabulated) redshift must be provided as a 1D array\n", + "- energy (Array): Energies used to calculate files in data/Scaling (must be given in [$\\mathrm{eV}$]).\n", + "- photonDensity (Array): Spectral energy density used to calculate files in data/Scaling (must be given in [$\\mathrm{eV}^{-1}\\,\\mathrm{ccm}^{-1}$]).\n", "- getDensity (function): returns the spectral number density dn/deps(eps, z) at a given photon energy (eps) and redshift (z)\n", "- getEmin (function): returns the minimum effective photon energy\n", - "- getEmax (function): returns the maximum effective photon energy\n" + "- getEmax (function): returns the maximum effective photon energy" ] }, { @@ -52,11 +61,20 @@ "import warnings\n", "import os\n", "import sys\n", + "import crpropa as crp\n", + "from crpropa import eV, ccm, c_light, h_planck\n", + "import subprocess\n", + "\n", "\n", "#Change for path to the CRPropa data repository\n", - "crpropa_data_path = \"/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/\"\n", + "crpropa_data_path = \"/rest/CRPropa3-data/\"\n", "sys.path.append(crpropa_data_path)\n", "\n", + "crpropa_share_path = \"/rest/venvs/crp_docu/share/crpropa/\"\n", + "\n", + "import photonField as pf\n", + "import calc_all as ca\n", + "\n", "import matplotlib.pyplot as plt # optional for plotting" ] }, @@ -66,32 +84,23 @@ "metadata": {}, "outputs": [], "source": [ - "eV = 1.60217657e-19 # [J]\n", - "h = 6.62607015e-34 # Js\n", - "c_light = 299792458 # m/s\n", - "ccm = 1e-6 # m^3\n", - "\n", - "class PowerlawPhotonField:\n", - " # general parameters\n", - " name = \"PowerlawPhotonField\"\n", - " info = \"Single power law photon field with exponential cutoffs at both ends.\"\n", - " redshift = None\n", - "\n", - " # model parameters, will be set by initialization\n", - " slope = -2 \n", - " norm = 1\n", - " eMin = 0 \n", - " eMax = np.inf \n", + "class PowerlawPhotonField(pf.PhotonField): \n", "\n", " def __init__(self, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", " \"\"\"\n", - " Initialize the photon field as a powerlaw with exponential cutoff at both ends. \n", + " Initialize the photon field as a powerlaw with exponential cutoffs at both ends. \n", " The slope, the normalization (n(eps = 1 eV)) and the minimal and maximal energy can be modified. \n", " \"\"\"\n", + " super(PowerlawPhotonField, self).__init__()\n", + " self.name = \"PowerlawPhotonField\"\n", + " self.info = \"Single power law photon field with exponential cutoffs at both ends.\"\n", + " self.redshift = None\n", " self.norm = norm\n", " self.slope = slope\n", " self.eMin = eMin\n", " self.eMax = eMax\n", + " self.energy = np.logspace(np.log10(self.eMin),np.log10(self.eMax), 101) / eV\n", + " self.photonDensity = self.getDensity(self.energy * eV) / (eV**-1 * ccm**-1)\n", " \n", " def getDensity(self, eps, z = 0):\n", " \"\"\"\n", @@ -111,25 +120,28 @@ " return self.eMin\n", " \n", " def getEmax(self):\n", - " \"\"\"Maximum effective phton energy in [J]\"\"\"\n", + " \"\"\"Maximum effective photon energy in [J]\"\"\"\n", " return self.eMax\n", "\n", "\n", - "class ISRF:\n", - " # general parameters\n", - " name = \"ISRF\"\n", - " info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", - " redshift = None\n", + "class ISRF(pf.PhotonField): \n", "\n", " def __init__(self, dataPath = \"../test_data/field.dat\"):\n", + " super(ISRF, self).__init__()\n", + " self.name = \"ISRF\"\n", + " self.info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", + " self.redshift = None\n", " names = [\"micron\", \"total\", \"direct\", \"scattered\", \"transient\", \"thermal\"]\n", " df = pd.read_csv(dataPath, delimiter=\" \", names = names)\n", - " df[\"E\"] = h * c_light / (df.micron * 1e-6)\n", + " df[\"E\"] = h_planck * c_light / (df.micron * 1e-6)\n", " df[\"n\"] = df.total *(eV / ccm) / df.E**2\n", " self.data = df[df.total>1e-25] # limit nan-values \n", " self.eMin = self.data[\"E\"].min()\n", " self.eMax = self.data[\"E\"].max()\n", "\n", + " self.energy = df[\"E\"] / eV\n", + " self.photonDensity = df[\"n\"] / (eV**-1 * ccm**-1)\n", + "\n", " def getDensity(self, eps, z = 0):\n", " \"\"\"\n", " Comoving spectral number density dn/deps [1/m^3/J] at given photon energy eps [J] and redshift z.\n", @@ -144,7 +156,7 @@ " return self.eMin\n", " \n", " def getEmax(self):\n", - " \"\"\"Maximum effective phton energy in [J]\"\"\"\n", + " \"\"\"Maximum effective photon energy in [J]\"\"\"\n", " return self.eMax\n", " \n", "\n", @@ -153,11 +165,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "#### plotting (Optional)\n", - "Here, both custom photon fields are plotted. Additionaly the already implemented CMB is plotted to compare." + "#### Plotting (Optional)\n", + "Here, both custom photon fields are plotted. Additionaly, the already implemented cosmic microwave background is plotted for comparison." ] }, { @@ -169,13 +182,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/photonField.py:31: RuntimeWarning: overflow encountered in expm1\n", - " return 8*np.pi / c0**3 / h**3 * eps**2 / np.expm1(eps / (kB * T_CMB))\n" + "/rest/CRPropa3-data/photonField.py:79: RuntimeWarning: overflow encountered in expm1\n", + " return 8*np.pi / c_light**3 / h_planck**3 * eps**2 / np.expm1(eps / (k_boltzmann * self.T_CMB))\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -187,12 +200,10 @@ } ], "source": [ - "from photonField import CMB\n", - "\n", - "field_cmb = CMB()\n", + "field_cmb = pf.CMB()\n", "\n", "eps = np.logspace(-4, 2, 200) * eV\n", - "c = eps**2 / eV\n", + "c = eps**2 / eV**2\n", "plt.figure(dpi = 100)\n", "y1 = c * field.getDensity(eps)\n", "y2 = c * field_cmb.getDensity(eps)\n", @@ -203,7 +214,7 @@ "\n", "plt.loglog()\n", "plt.legend(ncol = 3, loc= \"upper center\", bbox_to_anchor=(0.5, 1.12))\n", - "plt.ylim([1e-10, 1e8])\n", + "plt.ylim([1e9, 1e27])\n", "\n", "plt.xlabel(\"$\\epsilon$ [eV]\")\n", "plt.ylabel(\"$\\epsilon^2 ~ dn/d\\epsilon$ [eV/m$^3$]\")\n", @@ -211,12 +222,73 @@ ] }, { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_cmb = pd.read_csv(crpropa_share_path+'/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", + "df_cmb_e = pd.read_csv(crpropa_share_path+'/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", + "\n", + "#df_cmb = pd.read_csv('/rest/venvs/crp_patrick/share/crpropa/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", + "#df_cmb_e = pd.read_csv('/rest/venvs/crp_patrick/share/crpropa/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", + "\n", + "#df_cmb = pd.read_csv('/rest/CRPropa3-data/data/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", + "#df_cmb_e = pd.read_csv('/rest/CRPropa3-data/data/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", + "\n", + "df_pwl = pd.read_csv('data/Scaling/PowerlawPhotonField_photonDensity.txt', comment='#', names=['n'])\n", + "df_pwl_e = pd.read_csv('data/Scaling/PowerlawPhotonField_photonEnergy.txt', comment='#', names=['e'])\n", + "\n", + "df_isrf = pd.read_csv('data/Scaling/ISRF_photonDensity.txt', comment='#', names=['n'])\n", + "df_isrf_e = pd.read_csv('data/Scaling/ISRF_photonEnergy.txt', comment='#', names=['e'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000000000.0, 1e+27)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(dpi = 100)\n", + "plt.plot(df_pwl_e.e/eV, df_pwl.n*df_pwl_e.e/eV**2)\n", + "plt.plot(df_cmb_e.e/eV , df_cmb.n*df_cmb_e.e/eV**2)\n", + "plt.plot(df_isrf_e.e/eV, df_isrf.n*df_isrf_e.e/eV**2)\n", + "plt.loglog()\n", + "plt.xlim(5e-5, 1e2)\n", + "plt.ylim([1e9, 1e27])" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Create tables for wanted processes \n", + "### 2. Create tables for all photon field dependent processes \n", "\n", - "Here, all possible processes are used and the tables for the interactions are calculated. This process takes some time (ca. 45 minutes)." + "Here, tables for all CRPropa interactions are calculated. This process takes some time (~15 minutes)." ] }, { @@ -241,105 +313,60 @@ } ], "source": [ - "os.chdir(crpropa_data_path)\n", - "\n", "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to division by zero errors\n", " warnings.simplefilter(\"ignore\")\n", - " \n", - " # elasticscattering\n", - " from calc_elasticscattering import process\n", - " print(\"running ElasticScattering\")\n", - " process(field)\n", - " process(isrf)\n", - " \n", - " # electro-magnetic\n", - " from calc_electromagnetic import sigmaPP, sigmaDPP, sigmaICS, sigmaTPP, process\n", - " print(\"running EMPairProduction\")\n", - " process(sigmaPP, field, \"EMPairProduction\")\n", - " process(sigmaPP, isrf, \"EMPairProduction\")\n", - " print(\"running EMDoublePairProduction\")\n", - " process(sigmaDPP, field, \"EMDoublePairProduction\")\n", - " process(sigmaDPP, isrf, \"EMDoublePairProduction\")\n", - " print(\"running EMTripletPairProduction\")\n", - " process(sigmaTPP, field, \"EMTripletPairProduction\")\n", - " process(sigmaTPP, isrf, \"EMTripletPairProduction\")\n", - " print(\"running EMInverseComptonScattering\")\n", - " process(sigmaICS, field, \"EMInverseComptonScattering\")\n", - " process(sigmaICS, isrf, \"EMInverseComptonScattering\")\n", - "\n", - " # pair production\n", - " from calc_pairproduction import process\n", - " print(\"running ElectronPairProduction\")\n", - " process(field)\n", - " process(isrf)\n", - " # currently the spectrum can not be provided. Only as energy loss for primary\n", - "\n", - " # photo disintegration\n", - " from calc_photodisintegration import processRate, processEmission\n", - " print(\"running PhotoDissintegration\")\n", - " processRate(field)\n", - " processRate(isrf)\n", - " processEmission(field)\n", - " processEmission(isrf)\n", - "\n", - " # photo pion production\n", - " from calc_photopionproduction import process\n", - " print(\"running PhotoPionProduction\")\n", - " process(field)\n", - " process(isrf)\n", - " print(\"finished rate calculation\")" + " ca.createPhotonTargetInteractions([field, isrf])" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. copy files to the share folder\n", - "\n", - "The files stored in \"CRPropa3-data/data\" must been copied in the share folder of crpropa. \n", - "\n", - " cp -r CRPropa3-data/data/* /share/crpropa/ \n", - "\n", - "\n", - "For ElectronPairProduction a calculation of the spectrum is not possible. As the spectrum is not needed for the energy loss of the primary the following work around is possible:\n", - "- go to the share folder and in the subfolder ElectronPairProduction \n", - "\n", - " cd /share/crpropa/ElectronPairProduction\n", - "- copy one spectrum file and rename CMB or IRB with the first 3 letters of your photon field (here *Pow* or *ISR*)\n", - "\n", - " cp spectrum_CMB.txt spectrum_.txt\n", - "\n", - "***Using this workaround and including the production of secondaries from the ElectronPairProduction module is not recommended!***" + "### 3. Copy files to the share folder\n", + "The files stored in \"CRPropa3-data/data\" must be copied in the share folder of the CRPropa installation. " ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 6, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CompletedProcess(args=['cp', '-a', './data/.', '/rest/venvs/crp_docu/share/crpropa/'], returncode=0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Implementing custom PhotonField in CRPropa\n", - "for implemting your custom model in the final CRPropa simulation a PhotonField class is needed. Here are two different implementations possible. On the one hand a seperate python-based class with the analytical description of the photon density can be provided" + "subprocess.run(['cp', '-a', './data/.', crpropa_share_path])" ] }, { - "cell_type": "code", - "execution_count": 5, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from crpropa import * " + "### 4. Implementing custom PhotonField in CRPropa\n", + "To fully implement a custom model in a CRPropa simulation an additional class definition---this time inhereting from CRPropa's *PhotonField* class---is needed. Two different implementations are needed for the power-law and interstellar radiation field, since the latter one must be implemented as a so called *TabularPhotonField*. The power-law photon field is realised with a simple python class:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "class CustomPhotonField(PhotonField):\n", + "class CustomPhotonField(crp.PhotonField):\n", " \"\"\" analogue implementation like above but inheriting from the CRPropa module for compatibility\"\"\"\n", "\n", " def __init__(self, name, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", - " PhotonField.__init__(self)\n", + " crp.PhotonField.__init__(self)\n", " self.setFieldName(name)\n", " self.norm = norm\n", " self.slope = slope\n", @@ -356,62 +383,15 @@ " def getMaximumPhotonEnergy(self, z = 0):\n", " return self.eMax \n", "\n", - "powerlowField = CustomPhotonField(\"PowerlawPhotonField\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On the other hand the Baseclass `TabularPhotonField` can be used to create a field on tabulated data. In the following the needed Scaling files will be created. In the end this files have to be copied to the sharefolder as described above" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def createScaling(field, nBin = 100):\n", - " \"\"\"\n", - " create the scaling files needed for the TabularPhotonField class. \n", - "\n", - " field: class instance for the description of the photon field as used for the generation of data files\n", - " nBin: number of log-bins between eMin and eMax. Should be increesed for large energy ranges \n", - " \"\"\"\n", - "\n", - " eMin = field.getEmin()\n", - " eMax = field.getEmax()\n", - " \n", - " eps = np.logspace(np.log10(eMin), np.log10(eMax), nBin)\n", - "\n", - " folder = \"data/Scaling/\"\n", - " if not os.path.isdir(folder):\n", - " os.makedirs(folder)\n", - " \n", - " header = \"# Custom Photon Field: \" + field.name + \"\\n\"\n", - " header = \"# \" + field.info + \"\\n\"\n", - " \n", - " file_photonEnergy = open(folder + field.name + \"_photonEnergy.txt\", \"w\")\n", - " file_photonEnergy.writelines(header + \"# photon energies in [J] \\n\")\n", - " \n", - " file_photonDensity = open(folder + field.name + \"_photonDensity.txt\", \"w\")\n", - " file_photonDensity.writelines(header + \"# Comiving photon number density in [m^-3] \\n\") \n", - " \n", - " for e in eps:\n", - " file_photonEnergy.write(\"{:.8e} \\n\".format(e))\n", - " file_photonDensity.write(\"{:.8e} \\n\".format(field.getDensity(e)))\n", - "\n", - " file_photonDensity.close()\n", - " file_photonEnergy.close()\n", - "createScaling(isrf)" + "powerlaw_field = CustomPhotonField(\"PowerlawPhotonField\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Now the files must be copied!" + "The implementation for the interstellar radiation field is even easier as a base class for the tabular photon field exists:" ] }, { @@ -420,15 +400,16 @@ "metadata": {}, "outputs": [], "source": [ - "isrf_field = TabularPhotonField(\"ISRF\", False)" + "isrf_field = crp.TabularPhotonField(\"ISRF\", False)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## check implementation\n", - "To check the new photon fields all interactions on a photon fields are added to a ModuleList. If the instalation is done correctly this should run without any error and all interactions can be used. " + "### 5. Check implementation\n", + "To check the new photon fields all interactions depending on photon target are added to a *ModuleList*. If the installation was done correctly this should run without any error meaning all interaction can be used. " ] }, { @@ -445,60 +426,49 @@ } ], "source": [ - "sim = ModuleList()\n", + "sim = crp.ModuleList()\n", "\n", - "sim.add(ElasticScattering(powerlowField))\n", - "sim.add(ElasticScattering(isrf_field))\n", + "sim.add(crp.ElasticScattering(powerlaw_field))\n", + "sim.add(crp.ElasticScattering(isrf_field))\n", "\n", "\n", - "sim.add(EMPairProduction(powerlowField))\n", - "sim.add(EMPairProduction(isrf_field))\n", + "sim.add(crp.EMPairProduction(powerlaw_field))\n", + "sim.add(crp.EMPairProduction(isrf_field))\n", "\n", - "sim.add(EMDoublePairProduction(powerlowField))\n", - "sim.add(EMDoublePairProduction(isrf_field))\n", + "sim.add(crp.EMDoublePairProduction(powerlaw_field))\n", + "sim.add(crp.EMDoublePairProduction(isrf_field))\n", "\n", - "sim.add(EMTripletPairProduction(powerlowField))\n", - "sim.add(EMTripletPairProduction(isrf_field))\n", + "sim.add(crp.EMTripletPairProduction(powerlaw_field))\n", + "sim.add(crp.EMTripletPairProduction(isrf_field))\n", "\n", - "sim.add(EMInverseComptonScattering(powerlowField))\n", - "sim.add(EMInverseComptonScattering(isrf_field))\n", + "sim.add(crp.EMInverseComptonScattering(powerlaw_field))\n", + "sim.add(crp.EMInverseComptonScattering(isrf_field))\n", "\n", - "sim.add(ElectronPairProduction(powerlowField))\n", - "sim.add(ElectronPairProduction(isrf_field))\n", + "sim.add(crp.PhotoDisintegration(powerlaw_field))\n", + "sim.add(crp.PhotoDisintegration(isrf_field))\n", "\n", - "sim.add(PhotoDisintegration(powerlowField))\n", - "sim.add(PhotoDisintegration(isrf_field))\n", + "sim.add(crp.PhotoPionProduction(powerlaw_field))\n", + "sim.add(crp.PhotoPionProduction(isrf_field))\n", "\n", - "sim.add(PhotoPionProduction(powerlowField))\n", - "sim.add(PhotoPionProduction(isrf_field))\n", + "sim.add(crp.ElectronPairProduction(powerlaw_field))\n", + "sim.add(crp.ElectronPairProduction(isrf_field))\n", "\n", "print(\"Everything works fine\")" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import crpropa as crp\n", - "import photonField as pf" - ] - }, - { - "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "crpCMB = crp.CMB()\n", - "pfCMB = pf.CMB()\n", + "crpCMB = crp.CMB() #T_CMB = 2.73 --> Leading to small differences \n", "crpCMB2 = crp.BlackbodyPhotonField(\"CMBTest\", 2.72548)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -517,13 +487,13 @@ "print( crpCMB.getFieldName())\n", "print( crpCMB.getMaximumPhotonEnergy(0))\n", "print( crpCMB.getMinimumPhotonEnergy(0))\n", - "print( crpCMB.getPhotonDensity(1e-3*crp.eV, 0))\n", + "print( crpCMB.getPhotonDensity(1e-3*eV, 0))\n", "print( crpCMB.getRedshiftScaling(1))" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -542,13 +512,13 @@ "print( crpCMB2.getFieldName())\n", "print( crpCMB2.getMaximumPhotonEnergy(0))\n", "print( crpCMB2.getMinimumPhotonEnergy(0))\n", - "print( crpCMB2.getPhotonDensity(1e-3*crp.eV, 0))\n", - "print( crpCMB2.getRedshiftScaling(1))" + "print( crpCMB2.getPhotonDensity(1e-3*eV, 0))\n", + "print( crpCMB2.getRedshiftScaling(1))\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -556,218 +526,53 @@ "output_type": "stream", "text": [ "CMB\n", - "1.60217657e-20\n", - "1.60217657e-29\n", + "1.6021764870000002e-20\n", + "1.602176487e-29\n", "189320351.79205772\n" ] - }, - { - "ename": "AttributeError", - "evalue": "'CMB' object has no attribute 'getRedshiftScaling'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 23\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39mgetEmin(\u001b[39m0\u001b[39m))\n\u001b[1;32m 4\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39mgetDensity(\u001b[39m1e-3\u001b[39m\u001b[39m*\u001b[39mcrp\u001b[39m.\u001b[39meV, \u001b[39m0\u001b[39m)\u001b[39m*\u001b[39mcrp\u001b[39m.\u001b[39meV\u001b[39m*\u001b[39m\u001b[39m1e-3\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mprint\u001b[39m( pfCMB\u001b[39m.\u001b[39;49mgetRedshiftScaling(\u001b[39m1\u001b[39m))\n", - "\u001b[0;31mAttributeError\u001b[0m: 'CMB' object has no attribute 'getRedshiftScaling'" - ] - } - ], - "source": [ - "print( pfCMB.name)\n", - "print( pfCMB.getEmax(0))\n", - "print( pfCMB.getEmin(0))\n", - "print( pfCMB.getDensity(1e-3*crp.eV, 0)*crp.eV*1e-3)\n", - "print( pfCMB.getRedshiftScaling(1))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "crpropa_data_path = \"/home/home1/mertelx/.py_venvs/crpropa_master/CRPropa3-data/\"\n", - "\n", - "os.chdir(crpropa_data_path)\n", - "\n", - "import numpy as np\n", - "import pandas as pd \n", - "import os\n", - "import gitHelp as gh\n", - "import photonField as pf\n", - "import crpropa as crp\n", - "\n", - "\n", - "#eV = 1.60217657e-19 # [J]\n", - "#cm3 = 1e-6 # [m^3]\n", - "#c0 = 299792458 # [m/s]\n", - "#h = 6.62606957e-34 # [m^2 kg / s]\n", - "#erg = 1e-7 # [J]\n", - "#kB = 1.3806488e-23 # [m^2 kg / s^2 / K]\n", - "\n", - "def IRB_Stecker05(fileDir, outDir):\n", - " name = 'IRB_Stecker05'\n", - " info = '# cosmic infrared and optical background radiation model of Stecker at al. 2005'\n", - " redshift = np.linspace(0., 5., 26)\n", - " filePath = fileDir + \"EBL_Stecker_2005/data2.txt\"\n", - " data = np.genfromtxt(filePath)\n", - " energy = []\n", - " photonField = []\n", - " for i, zSlice in enumerate(data):\n", - " eps = 10**zSlice[0] # [eV]\n", - " energy.append(eps)\n", - " dens = 10**zSlice[1:] / eps # [1/eVcm^3]\n", - " dens /= (redshift + 1)**3 # make comoving\n", - " photonField.append(dens)\n", - " print (\"energy\")\n", - " print(energy)\n", - " print(\"density\")\n", - " print(photonField)\n", - " print(\"redshift\")\n", - " print(redshift)\n", - " createField(name, info, energy, redshift, photonField, outDir)\n", - " return photonField\n", - "\n", - "def createField(name, info, energy, redshift, photonDensity, outDir):\n", - " try:\n", - " git_hash = gh.get_git_revision_hash()\n", - " addHash = True\n", - " except:\n", - " addHash = False\n", - "\n", - " with open(outDir + \"/\" + name + \"_photonEnergy.txt\", 'w') as f:\n", - " f.write(info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# photon energies in [J]\\n\")\n", - " for e in energy:\n", - " f.write(\"{}\\n\".format(e * crp.eV)) # [J]\n", - " if redshift is not None:\n", - " with open(outDir + \"/\" + name + \"_redshift.txt\", 'w') as f:\n", - " f.write(info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# redshift\\n\")\n", - " for z in redshift:\n", - " f.write(\"{}\\n\".format(np.round(z, 2)))\n", - " with open(outDir + \"/\" + name + \"_photonDensity.txt\", 'w') as f:\n", - " f.write(info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# Comoving photon number density in [m^-3], format: d(e1,z1), ... , d(e1,zm), d(e2,z1), ... , d(e2,zm), ... , d(en,zm)\\n\")\n", - " for i, densSlice in enumerate(photonDensity):\n", - " for d in densSlice:\n", - " f.write(\"{}\\n\".format(d * energy[i] / crp.cm**3.)) # [# / m^3], comoving\n", - " print(\"done: \" + name)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "def createFieldNew(field, outDir):\n", - " name = field.name\n", - " info = field.info\n", - " redshift = field.redshift\n", - " energy = field.getEnergy() / crp.eV\n", - " photonDensity = [[field.getDensity(eps, z) * crp.eV * crp.cm**3. for z in field.redshift] for eps in field.getEnergy()]\n", - "\n", - " try:\n", - " git_hash = gh.get_git_revision_hash()\n", - " addHash = True\n", - " except:\n", - " addHash = False\n", - "\n", - " with open(outDir + \"/\" + name + \"_photonEnergy.txt\", 'w') as f:\n", - " f.write(\"# \"+info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# photon energies in [J]\\n\")\n", - " for e in energy:\n", - " f.write(\"{}\\n\".format(e * crp.eV)) # [J]\n", - " if redshift is not None:\n", - " with open(outDir + \"/\" + name + \"_redshift.txt\", 'w') as f:\n", - " f.write(\"# \"+info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# redshift\\n\")\n", - " for z in redshift:\n", - " f.write(\"{}\\n\".format(np.round(z, 2)))\n", - " with open(outDir + \"/\" + name + \"_photonDensity.txt\", 'w') as f:\n", - " f.write(\"# \"+info+'\\n')\n", - " if addHash: f.write(\"# Produced with crpropa-data version: \"+git_hash+\"\\n\")\n", - " f.write(\"# Comoving photon number density in [m^-3], format: d(e1,z1), ... , d(e1,zm), d(e2,z1), ... , d(e2,zm), ... , d(en,zm)\\n\")\n", - " for i, densSlice in enumerate(photonDensity):\n", - " for d in densSlice:\n", - " f.write(\"{}\\n\".format(d * energy[i] / crp.cm**3.)) # [# / m^3], comoving\n", - " print(\"done: \" + name)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done: IRB_Stecker05\n" - ] } ], "source": [ - "os.chdir(crpropa_data_path)\n", - "\n", - "#test1 = IRB_Stecker05(\"tables/\", \"./testdata/Scaling\")\n", - "createFieldNew(stecker, \"./testdata/Scaling\")" + "print( field_cmb.name)\n", + "print( field_cmb.getEmax(0))\n", + "print( field_cmb.getEmin(0))\n", + "print( field_cmb.getDensity(1e-3*eV, 0)*eV*1e-3)" ] }, { - "cell_type": "code", - "execution_count": 21, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "stecker = pf.EBL_Stecker05()\n", - "stecker.getEnergy()/crp.eV\n", - "test2 = [[stecker.getDensity(eps, z) * crp.eV * crp.cm**3. for z in stecker.redshift] for eps in stecker.getEnergy()]" + "### 6. Limitation\n", + "For Bethe-Heitler pairproduction (*ElectronPairProduction*) secondary electrons cannot be injected into the simulation chain. This is due to a lack in the production of the needed tabulated data files. If you want to help to improve CRPropa take a look at the corresponding file (calc_pairproduction.py) in the CRPropa3-data repository (https://github.com/CRPropa/CRPropa3-data.git) and open a pull request." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.18045278e-08 5.18045267e-08 5.18045276e-08 5.18045266e-08\n", - " 5.18045274e-08 5.18045270e-08 5.18045276e-08 5.18045279e-08\n", - " 5.18045262e-08 5.18045274e-08 5.18045280e-08 5.18045269e-08\n", - " 5.18045268e-08 5.18045272e-08 5.18045272e-08 5.18045269e-08\n", - " 5.18045270e-08 5.18045268e-08 5.18045276e-08 5.18045271e-08\n", - " 5.18045269e-08 5.18045268e-08 5.18045266e-08 5.18045271e-08\n", - " 5.18045271e-08]\n" + "ename": "RuntimeError", + "evalue": "ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/rest/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 25\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m crp\u001b[39m.\u001b[39;49mElectronPairProduction(powerlaw_field, \u001b[39mTrue\u001b[39;49;00m)\n", + "\u001b[0;31mRuntimeError\u001b[0m: ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt" ] } ], "source": [ - "relErr = (np.array(test1)-np.array(test2))/np.array(test1)\n", - "#print(relErr)\n", - "print(np.mean(relErr, axis=1))\n" + "crp.ElectronPairProduction(powerlaw_field, True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.2 ('crpropa_master': venv)", + "display_name": "crp_docu", "language": "python", "name": "python3" }, @@ -786,7 +591,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "7dc99857f02ebf862368f83a013de1e635cf753063bd3c6272d454ddc461751e" + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" } } }, From 025f261fee163b25b1e86131877fbf14d2bf9031 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 19 Jan 2023 12:11:34 +0100 Subject: [PATCH 55/87] Update path of isrf field data. Now included in tar ball --- .../custom-photon-field.ipynb | 171 ++++-------------- 1 file changed, 33 insertions(+), 138 deletions(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index 6ae794239..60f393eca 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ "\n", "class ISRF(pf.PhotonField): \n", "\n", - " def __init__(self, dataPath = \"../test_data/field.dat\"):\n", + " def __init__(self, dataPath = crpropa_share_path+\"CustomPhotonField/isrf_example_field.dat\"):\n", " super(ISRF, self).__init__()\n", " self.name = \"ISRF\"\n", " self.info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", @@ -175,27 +175,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/rest/CRPropa3-data/photonField.py:79: RuntimeWarning: overflow encountered in expm1\n", - " return 8*np.pi / c_light**3 / h_planck**3 * eps**2 / np.expm1(eps / (k_boltzmann * self.T_CMB))\n" + "ename": "NameError", + "evalue": "name 'field' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/rest/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 7\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m c \u001b[39m=\u001b[39m eps\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m \u001b[39m/\u001b[39m eV\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m\n\u001b[1;32m 5\u001b[0m plt\u001b[39m.\u001b[39mfigure(dpi \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m y1 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m field\u001b[39m.\u001b[39mgetDensity(eps)\n\u001b[1;32m 7\u001b[0m y2 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m field_cmb\u001b[39m.\u001b[39mgetDensity(eps)\n\u001b[1;32m 8\u001b[0m y3 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m isrf\u001b[39m.\u001b[39mgetDensity(eps)\n", + "\u001b[0;31mNameError\u001b[0m: name 'field' is not defined" ] }, { "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi = 100)\n", "plt.plot(df_pwl_e.e/eV, df_pwl.n*df_pwl_e.e/eV**2)\n", @@ -293,25 +270,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running ElasticScattering\n", - "running EMPairProduction\n", - "running EMDoublePairProduction\n", - "running EMTripletPairProduction\n", - "running EMInverseComptonScattering\n", - "running ElectronPairProduction\n", - "running PhotoDissintegration\n", - "running PhotoPionProduction\n", - "finished rate calculation\n" - ] - } - ], + "outputs": [], "source": [ "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to division by zero errors\n", " warnings.simplefilter(\"ignore\")\n", @@ -329,20 +290,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CompletedProcess(args=['cp', '-a', './data/.', '/rest/venvs/crp_docu/share/crpropa/'], returncode=0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "subprocess.run(['cp', '-a', './data/.', crpropa_share_path])" ] @@ -358,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -414,17 +364,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Everything works fine\n" - ] - } - ], + "outputs": [], "source": [ "sim = crp.ModuleList()\n", "\n", @@ -458,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -468,21 +410,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMB\n", - "1.6021764870000002e-20\n", - "4.783281527694803e-24\n", - "190679052.33698764\n", - "1.0\n" - ] - } - ], + "outputs": [], "source": [ "print( crpCMB.getFieldName())\n", "print( crpCMB.getMaximumPhotonEnergy(0))\n", @@ -493,21 +423,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMBTest\n", - "1.6021764870000002e-20\n", - "4.775361955348583e-24\n", - "189320351.79205775\n", - "1.0\n" - ] - } - ], + "outputs": [], "source": [ "print( crpCMB2.getFieldName())\n", "print( crpCMB2.getMaximumPhotonEnergy(0))\n", @@ -518,20 +436,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CMB\n", - "1.6021764870000002e-20\n", - "1.602176487e-29\n", - "189320351.79205772\n" - ] - } - ], + "outputs": [], "source": [ "print( field_cmb.name)\n", "print( field_cmb.getEmax(0))\n", @@ -550,21 +457,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/rest/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 25\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m crp\u001b[39m.\u001b[39;49mElectronPairProduction(powerlaw_field, \u001b[39mTrue\u001b[39;49;00m)\n", - "\u001b[0;31mRuntimeError\u001b[0m: ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt" - ] - } - ], + "outputs": [], "source": [ "crp.ElectronPairProduction(powerlaw_field, True)" ] From c1ee7e3f7466ab7fd2a251afbc10b9e528a4e425 Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 31 Jan 2023 09:51:44 +0100 Subject: [PATCH 56/87] Update Diffusion example chisquare test due to updated scipy function --- .../Diffusion/DiffusionValidationI.ipynb | 518 ++++++++++++++++++ .../Diffusion/DiffusionValidationI.v4.ipynb | 497 ----------------- ...I.v4.ipynb => DiffusionValidationII.ipynb} | 0 3 files changed, 518 insertions(+), 497 deletions(-) create mode 100644 doc/pages/example_notebooks/Diffusion/DiffusionValidationI.ipynb delete mode 100644 doc/pages/example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb rename doc/pages/example_notebooks/Diffusion/{DiffusionValidationII.v4.ipynb => DiffusionValidationII.ipynb} (100%) diff --git a/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.ipynb b/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.ipynb new file mode 100644 index 000000000..413ec10dd --- /dev/null +++ b/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Diffusion Validation I" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This notebook simulates a diffusion process in a homogeneous background magnetic field. The diffusion tensor is anisotropic, meaning the parallel component is larger than the perpendicular component ($\\kappa_\\parallel = 10\\cdot\\kappa_\\perp$)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load modules and use jupyter inline magic to use interactive plots." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from scipy.stats import chisquare\n", + "from scipy.integrate import quad\n", + "from crpropa import *\n", + "\n", + "#figure settings\n", + "A4heigth = 29.7/2.54\n", + "A4width = 21./2.54" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Definition of the probability distribution function of the particle density in one dimension:
\n", + "$\\psi(R, t) = \\frac{2}{\\sqrt{4 \\pi D t}} \\cdot \\exp{-\\frac{R^2}{4 D t}}$
\n", + "Here, $R=||\\vec{R}||$ is the norm of the position." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def pdf(R, t, epsilon):\n", + " \"\"\"Probability distribution function of a diffusion process.\n", + " The diffusion coefficient is D=1e24m^2/s\n", + " \n", + " R - distance from injection\n", + " t - time elapsed since injection\n", + " epsilon - scaling for the perpendicular component\n", + " \"\"\"\n", + " D = 1e24*epsilon\n", + " pdf = 2 * pow(4 * np.pi * D * t, -0.5) * np.exp(- R**2. / (4 * D * t))\n", + " return pdf\n", + "\n", + "def dataCheck(df):\n", + " \"\"\"Check if all candidates are recorded 50 times.\"\"\"\n", + " cnt = df.SN.value_counts()!=50\n", + " err = cnt[cnt==True].index.to_numpy()\n", + " if len(err) != 0:\n", + " print(\"Something went wrong!\")\n", + " print(\"The following serial numbers ({}) have an incomplete set of observations.\".format(err))\n", + " print(\"Try to rerun the simulation cell or run that part of the program outside of jupyter.\")\n", + " print(\"File an issue on github if the problem persists; https://github.com/CRPropa/CRPropa3/issues\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulation set-up
\n", + "Using 10000 pseudo particles to trace the phase space." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Simulation finished\n" + ] + } + ], + "source": [ + "N = 10000\n", + "\n", + "# Number of Snapshots\n", + "# used in ObserverTimeEvolution\n", + "# candidates are recorded every deltaT=2kpc/c\n", + "n = 50.\n", + "step = 100*kpc / n\n", + "\n", + "# magnetic field\n", + "ConstMagVec = Vector3d(0*nG,0*nG,1*nG)\n", + "BField = UniformMagneticField(ConstMagVec)\n", + "\n", + "# parameters used for field line tracking\n", + "precision = 1e-4\n", + "minStep = 0.1*pc\n", + "maxStep = 1*kpc\n", + "\n", + "#ratio between parallel and perpendicular diffusion coefficient\n", + "epsilon = .1\n", + "\n", + "# source settings\n", + "# A point source at the origin is isotropically injecting 10TeV protons.\n", + "source = Source()\n", + "source.add(SourcePosition(Vector3d(0.)))\n", + "source.add(SourceParticleType(nucleusId(1, 1))) \n", + "source.add(SourceEnergy(10*TeV))\n", + "source.add(SourceIsotropicEmission())\n", + "\n", + "# Output settings\n", + "# Only serial number, trajectory length and current position are stored\n", + "# The unit of length is set to kpc\n", + "Out = TextOutput('./Test.txt')\n", + "Out.disableAll()\n", + "Out.enable(Output.TrajectoryLengthColumn)\n", + "Out.enable(Output.CurrentPositionColumn)\n", + "Out.enable(Output.SerialNumberColumn)\n", + "Out.setLengthScale(kpc)\n", + "\n", + "# Observer settings\n", + "Obs = Observer()\n", + "Obs.add(ObserverTimeEvolution(step, step, n))\n", + "Obs.setDeactivateOnDetection(False) # important line, as particles would be deactivated after first detection otherwise\n", + "Obs.onDetection(Out)\n", + "\n", + "# Difffusion Module\n", + "# D_xx=D_yy= 1e23 m^2 / s, D_zz=10*D_xx\n", + "# The normalization is adjusted and the energy dependence is deactivated (setting power law index alpha=0)\n", + "Dif = DiffusionSDE(BField, precision, minStep, maxStep, epsilon)\n", + "Dif.setScale(1./6.1)\n", + "Dif.setAlpha(0.)\n", + "\n", + "\n", + "# Boundary\n", + "# Simulation ends after t=100kpc/c\n", + "maxTra = MaximumTrajectoryLength(100.0*kpc)\n", + "\n", + "# module list\n", + "# Add modules to the list and run the simulation\n", + "sim = ModuleList()\n", + "\n", + "sim.add(Dif)\n", + "sim.add(Obs)\n", + "sim.add(maxTra)\n", + "\n", + "sim.run(source, N, True)\n", + "\n", + "# Close the Output modules to flush last chunk of data to file.\n", + "Out.close()\n", + "\n", + "\n", + "print(\"Simulation finished\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the simulation data and add a time column" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Test.txt', delimiter='\\t', names=[\"D\", \"SN\", \"X\", \"Y\", \"Z\", \"SN0\", \"SN1\"], comment='#')\n", + "df['t'] = df.D * kpc / c_light #time in seconds\n", + "dataCheck(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Distribution in x, y and z\n", + "Plot the density distribution in all three coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(1.*A4width,A4heigth/4.))\n", + "\n", + "coord = ['X', 'Y', 'Z']\n", + "\n", + "for i, x_i in enumerate(coord):\n", + " L = [10, 50, 100]\n", + " for j, l in enumerate(L):\n", + " t = l*kpc/c_light\n", + " s = '%.2e' % t\n", + " df[df.D==l][x_i].hist(bins=np.linspace(-0.5, 0.5, 20), ax=axes[i], label='t = '+s+' s', zorder=3-j)\n", + " axes[i].set_title(x_i)\n", + " axes[i].set_xlim(-0.5, 0.5)\n", + " axes[i].legend(loc='best')\n", + " axes[i].set_xlabel('Distance [kpc]')\n", + " if i>0:\n", + " axes[i].set_yticklabels([])\n", + " else:\n", + " axes[i].set_ylabel(\"number density\")\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "${\\mathrm{\\bf Fig 1:}}$ Distribution of particles in three directions. Z-axis is parallel to the mean magnetic field. $\\kappa_{\\parallel}=100*\\kappa_{\\perp}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the absolute distance from origin $|x|, |y|, |z|$ and compare to analytical expectations." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(1.*A4width,A4heigth/4.))\n", + "\n", + "coord = ['X', 'Y', 'Z']\n", + "\n", + "ChiDict = {}\n", + "\n", + "for i, x_i in enumerate(coord):\n", + " ChiDict[str(x_i)] = {}\n", + " \n", + " L = [10, 50, 100]\n", + " colors = sns.color_palette()\n", + " for j, l in enumerate(L):\n", + " ChiDict[str(x_i)][str(l)] = {}\n", + " if i <2:\n", + " abs(df[df.D==l][x_i]).hist(bins=np.linspace(0, 0.1, 11), ax=axes[i], alpha=1., zorder=3-j, color=colors[j])\n", + " hist = np.histogram(abs(df[df.D==l][x_i]), bins=np.linspace(0,0.1,11))\n", + " \n", + " PDF = np.zeros(len(hist[0]))\n", + " xP = np.zeros(len(hist[0]))\n", + " for k in range(len(PDF)):\n", + " a, b = hist[1][k]*kpc, hist[1][k+1]*kpc\n", + " xP[k] = (a+b)/2./kpc\n", + " PDF[k] = quad(pdf, a,b, args=(l*kpc/c_light, 0.1))[0]*N\n", + " t = l*kpc/c_light\n", + " s = '%.2e' % t\n", + " axes[i].plot(xP, PDF, color=colors[j], label='t = '+s+' s', zorder=10)\n", + " #axes[i].legend(loc='best')\n", + " axes[i].set_xlim(0., 0.1)\n", + " else:\n", + " abs(df[df.D==l][x_i]).hist(bins=np.linspace(0, 0.5, 11), ax=axes[i], alpha=1., zorder=3-j, color=colors[j])\n", + " hist = np.histogram(abs(df[df.D==l][x_i]), bins=np.linspace(0,0.5,11))\n", + " PDF = np.zeros(len(hist[0]))\n", + " xP = np.zeros(len(hist[0]))\n", + " for k in range(len(PDF)):\n", + " a, b = hist[1][k]*kpc, hist[1][k+1]*kpc\n", + " xP[k] = (a+b)/2./kpc\n", + " PDF[k] = quad(pdf, a,b, args=(l*kpc/c_light, 1.))[0]*N\n", + " \n", + " axes[i].plot(xP, PDF, color=colors[j], label='t = '+s+' s', zorder=10) \n", + " axes[i].set_xlim(0., 0.5)\n", + " ChiDict[str(x_i)][str(l)]['Obs'] = hist[0]\n", + " ChiDict[str(x_i)][str(l)]['Exp'] = PDF\n", + "\n", + " axes[i].set_xlabel('Distance [kpc]')\n", + " axes[i].legend(loc='best')\n", + " axes[i].set_title(x_i)\n", + " if i>0:\n", + " axes[i].set_yticklabels([])\n", + " else:\n", + " axes[i].set_ylabel(\"number density\")\n", + " \n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "${\\mathrm{\\bf Fig 2:}}$ The distance from the source position follows nicely the expected pdf." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate the pValue of the $\\chi^2$-test to prove the visual statement from above." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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statisticpvalue
CoordinateTime [s]
X1.03e+121.8162880.994051
5.15e+1210.6660910.299293
1.03e+135.5231790.786528
Y1.03e+121.1496710.999008
5.15e+126.2252690.717178
1.03e+1314.9548720.092183
Z1.03e+124.4910740.876229
5.15e+129.5994630.383873
1.03e+1311.9008510.218957
\n", + "
" + ], + "text/plain": [ + " statistic pvalue\n", + "Coordinate Time [s] \n", + "X 1.03e+12 1.816288 0.994051\n", + " 5.15e+12 10.666091 0.299293\n", + " 1.03e+13 5.523179 0.786528\n", + "Y 1.03e+12 1.149671 0.999008\n", + " 5.15e+12 6.225269 0.717178\n", + " 1.03e+13 14.954872 0.092183\n", + "Z 1.03e+12 4.491074 0.876229\n", + " 5.15e+12 9.599463 0.383873\n", + " 1.03e+13 11.900851 0.218957" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C, Time, pValue = [], [], []\n", + "for c in coord:\n", + " for l in L:\n", + " C.append(c)\n", + " t = l*kpc/c_light\n", + " s = '%.2e' % t\n", + " Time.append(s)\n", + " obs = ChiDict[c][str(l)]['Obs']\n", + " exp = ChiDict[c][str(l)]['Exp']\n", + " # additional normalization of the expected histogram is needed\n", + " # as scipy's chisquare needs two samples of exactly equal normalization.\n", + " pValue.append(chisquare(obs, exp/sum(exp)*sum(obs)))\n", + "Chi = pd.DataFrame(pValue, index = [C, Time])\n", + "Chi.rename(columns = {0:\"p-Value\"}, inplace=True)\n", + "Chi.index.names = [\"Coordinate\", \"Time [s]\"]\n", + "Chi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "${\\mathrm{\\bf Tab 1:}}$ The assumption that the two samples (observed and expected) are drawn from the same distribution cannot be rejected for any direction." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb b/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb deleted file mode 100644 index 0762798a6..000000000 --- a/doc/pages/example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb +++ /dev/null @@ -1,497 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Diffusion Validation I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### This notebook simulates a diffusion process in a homogeneous background magnetic field. The diffusion tensor is anisotropic, meaning the parallel component is larger than the perpendicular component ($\\kappa_\\parallel = 10\\cdot\\kappa_\\perp$)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load modules and use jupyter inline magic to use interactive plots." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "from scipy.stats import chisquare\n", - "from scipy.integrate import quad\n", - "from crpropa import *\n", - "\n", - "#figure settings\n", - "A4heigth = 29.7/2.54\n", - "A4width = 21./2.54" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Definition of the probability distribution function of the particle density in one dimension:
\n", - "$\\psi(R, t) = \\frac{2}{\\sqrt{4 \\pi D t}} \\cdot \\exp{-\\frac{R^2}{4 D t}}$
\n", - "Here, $R=||\\vec{R}||$ is the norm of the position." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def pdf(R, t, epsilon):\n", - " \"\"\"Probability distribution function of a diffusion process.\n", - " The diffusion coefficient is D=1e24m^2/s\n", - " \n", - " R - distance from injection\n", - " t - time elapsed since injection\n", - " epsilon - scaling for the perpendicular component\n", - " \"\"\"\n", - " D = 1e24*epsilon\n", - " pdf = 2 * pow(4 * np.pi * D * t, -0.5) * np.exp(- R**2. / (4 * D * t))\n", - " return pdf\n", - "\n", - "def dataCheck(df):\n", - " \"\"\"Check if all candidates are recorded 50 times.\"\"\"\n", - " cnt = df.SN.value_counts()!=50\n", - " err = cnt[cnt==True].index.to_numpy()\n", - " if len(err) != 0:\n", - " print(\"Something went wrong!\")\n", - " print(\"The following serial numbers ({}) have an incomplete set of observations.\".format(err))\n", - " print(\"Try to rerun the simulation cell or run that part of the program outside of jupyter.\")\n", - " print(\"File an issue on github if the problem persists; https://github.com/CRPropa/CRPropa3/issues\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simulation set-up
\n", - "Using 10000 pseudo particles to trace the phase space." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation finished\n" - ] - } - ], - "source": [ - "N = 10000\n", - "\n", - "# Number of Snapshots\n", - "# used in ObserverTimeEvolution\n", - "# candidates are recorded every deltaT=2kpc/c\n", - "n = 50.\n", - "step = 100*kpc / n\n", - "\n", - "# magnetic field\n", - "ConstMagVec = Vector3d(0*nG,0*nG,1*nG)\n", - "BField = UniformMagneticField(ConstMagVec)\n", - "\n", - "# parameters used for field line tracking\n", - "precision = 1e-4\n", - "minStep = 0.1*pc\n", - "maxStep = 1*kpc\n", - "\n", - "#ratio between parallel and perpendicular diffusion coefficient\n", - "epsilon = .1\n", - "\n", - "# source settings\n", - "# A point source at the origin is isotropically injecting 10TeV protons.\n", - "source = Source()\n", - "source.add(SourcePosition(Vector3d(0.)))\n", - "source.add(SourceParticleType(nucleusId(1, 1))) \n", - "source.add(SourceEnergy(10*TeV))\n", - "source.add(SourceIsotropicEmission())\n", - "\n", - "# Output settings\n", - "# Only serial number, trajectory length and current position are stored\n", - "# The unit of length is set to kpc\n", - "Out = TextOutput('./Test.txt')\n", - "Out.disableAll()\n", - "Out.enable(Output.TrajectoryLengthColumn)\n", - "Out.enable(Output.CurrentPositionColumn)\n", - "Out.enable(Output.SerialNumberColumn)\n", - "Out.setLengthScale(kpc)\n", - "\n", - "# Observer settings\n", - "Obs = Observer()\n", - "Obs.add(ObserverTimeEvolution(step, step, n))\n", - "Obs.setDeactivateOnDetection(False) # important line, as particles would be deactivated after first detection otherwise\n", - "Obs.onDetection(Out)\n", - "\n", - "# Difffusion Module\n", - "# D_xx=D_yy= 1e23 m^2 / s, D_zz=10*D_xx\n", - "# The normalization is adjusted and the energy dependence is deactivated (setting power law index alpha=0)\n", - "Dif = DiffusionSDE(BField, precision, minStep, maxStep, epsilon)\n", - "Dif.setScale(1./6.1)\n", - "Dif.setAlpha(0.)\n", - "\n", - "\n", - "# Boundary\n", - "# Simulation ends after t=100kpc/c\n", - "maxTra = MaximumTrajectoryLength(100.0*kpc)\n", - "\n", - "# module list\n", - "# Add modules to the list and run the simulation\n", - "sim = ModuleList()\n", - "\n", - "sim.add(Dif)\n", - "sim.add(Obs)\n", - "sim.add(maxTra)\n", - "\n", - "sim.run(source, N, True)\n", - "\n", - "# Close the Output modules to flush last chunk of data to file.\n", - "Out.close()\n", - "\n", - "\n", - "print(\"Simulation finished\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the simulation data and add a time column" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('Test.txt', delimiter='\\t', names=[\"D\", \"SN\", \"X\", \"Y\", \"Z\", \"SN0\", \"SN1\"], comment='#')\n", - "df['t'] = df.D * kpc / c_light #time in seconds\n", - "dataCheck(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Distribution in x, y and z\n", - "Plot the density distribution in all three coordinates." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(1.*A4width,A4heigth/4.))\n", - "\n", - "coord = ['X', 'Y', 'Z']\n", - "\n", - "for i, x_i in enumerate(coord):\n", - " L = [10, 50, 100]\n", - " for j, l in enumerate(L):\n", - " t = l*kpc/c_light\n", - " s = '%.2e' % t\n", - " df[df.D==l][x_i].hist(bins=np.linspace(-0.5, 0.5, 20), ax=axes[i], label='t = '+s+' s', zorder=3-j)\n", - " axes[i].set_title(x_i)\n", - " axes[i].set_xlim(-0.5, 0.5)\n", - " axes[i].legend(loc='best')\n", - " axes[i].set_xlabel('Distance [kpc]')\n", - " if i>0:\n", - " axes[i].set_yticklabels([])\n", - " else:\n", - " axes[i].set_ylabel(\"number density\")\n", - "\n", - "plt.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "${\\mathrm{\\bf Fig 1:}}$ Distribution of particles in three directions. Z-axis is parallel to the mean magnetic field. $\\kappa_{\\parallel}=100*\\kappa_{\\perp}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use the absolute distance from origin $|x|, |y|, |z|$ and compare to analytical expectations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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cuFz7PPfcc1x11VVER0fz5JNPOr1fu3btip2bKDY2liZNmgAQFRVFVlYWOTk5RbabMmUK7du3Jzo6mkceeaRcMSvvo/mu+V4buTLvW7RoQXR09Hlzi17oyJEjpKam0rVrV0SE0aNH8/HHHwMQGhp6bruMjAxE5NzzcePG0aVLF2JjY1m1apXTMZX0N1ZSWRfG2qNHD2JiYujQoYNLponRueSqwn+nwNGd1LHlgY+LfuUXd4SBs0tcPXv2bHbt2nVucsMLlXfy3RUrVrBx40batGnDiy++SLNmzcoVbnH7x8XFkZiYyObNmzHGcMMNN7Bx48Yi36IrasWKFXTu3LnIhKSnTp1i5cqV7Nu3DxEhJSXFJeVVV3a7wWIp+g+nwhz5Drgu512U7+Dc5Lua76rcCuW9y7gw7wur6OS7ycnJREZGnnsdGRlJcnLyuddTp05lyZIlhIWFsWHDBgBmzZpF7969WbhwISkpKXTp0oW+ffsSFBRU7vILW7BgAXPmzCE3N7fYW9FLly5lwIABTJ06FZvNRmZmZqXKA60wFfH76UyaNajr6TDcrjyT7w4dOpTbb7+dgIAAXn/9dcaMGVOuthIl7R8XF0dcXNy5iRjT09NJTEykR48e9OrVi7y8PNLT0zl9+vS5uYyeffZZBgwYUGaZu3fvZvLkycTFxRVZFxYWRmBgIHfddRdDhgxhyJAhTp9LTZSQlELnS+p7Ogy3Ks/ku57I96uvvpqsrCwyMzM135XLuHry3bLMmjWLWbNm8cwzzzB//nxmzJhBXFwcq1ev5vnnnwcgOzubQ4cOkZeXd+4W8tGjR/H39+ell14CYP369fj7+5da1oQJE5gwYQJLly7lqaeeKjKf3FVXXcW4ceOwWq0MHz78vPnwKkorTBf4Yu8x/tLtUtce1PENIcuL5gIqzxWm8PDwc8/vvvtuJk2aVK6yStrfGMNjjz3GfffdV2SfDRs2EBISQnx8PIsWLSr2/nZJkpKSuPHGG1myZAktW7Ysst7X15fNmzezfv16li9fzvz582t1Y9l1e465tsJU6Buxt+R8ea4weSLfv//+e9LS0ti6davmeylE5F7gXoDmkz+t8HHi4+NdEk9YWNgfV3C6T3XJMYtwHN9msxW5WpSeno7dbi/2KhLAgAEDSE9PL7L8qaeeolevXsXuY7VaycrKKvaYYWFhHDp06Ny6xMREGjVqVGTbYcOGMWLECB555BFsNhtLliyhdevWRY5XUKF7+umnad68OaNGjTq37sLzLekcBw8ezPjx44usj42N5bPPPuPzzz9n9OjRTJgwoUj7rOzs7HLlglaYLhC32w0VJg8ICQkpMcGgfFeYjhw5cu4+8urVq8s92WRJ+w8YMIBp06YxatQogoODSU5Oxs/Pj0aNGpXr+IWlpKQwePBgZs+eTbdu3YrdJj09nczMTAYNGkS3bt247LLLKlxeTbBuzzEmX3+5p8OoFGfzHcrOec1372WMeQN4Ayo3+W7Pnj1dEs/evXur7AtBcXnbuHFjMjIySozhf//7X7nL8fPzo06dOsUeMyQkhHr16rF7926uvvpqli1bxsSJEwkJCSExMfFcpWj9+vW0b9+ekJAQBg4cyMKFC5k3bx4iwvbt289dZS0QEBBAYGDgeWVeeL6Fnxcu65NPPqF169ZF4j148CAtW7Zk4sSJiEix71VgYGCRWErj9kbfIuIjIttF5FPH60tF5HsR2S8iH4iIv2N5gOP1fsf6FoWO8Zhj+U8iUvb16UrY/NtpUjJz3VlElQgPD6dbt2506NCh0o0BX375ZaKioujUqRMvv/zyed9+C1/mnDRpEpGRkWRmZhIZGcn06dNL3b9///6MHDmSa665ho4dOzJixIhSP/QKW7lyJZGRkXz77bcMHjz43G2L+fPns3//fmbOnHmuW/fx48fP2zctLY0hQ4YQHR1N9+7dmTNnTsV/OTXA/uPp/Hoyw9NhVIrmu+Z7beTKvP/hhx+IjIxk2bJl3HfffURFRZ1bVzjvX3nlFe6++25atWpFy5YtGThwIJDfAL1Dhw5ER0cTFxfH3LlzgfzhBqxWK9HR0URFRTFt2jSnYyrpb2z+/PlERUURExPDnDlzityOg/yriJ06dSI2NpYPPviABx98sCK/lvNIwRgK7iIiDwNXAqHGmCEi8iHwkTHmfRF5DfjRGPOqiNwPRBtj/ioitwE3GmNuFZH2wHtAF6AJ8AXQxhhjK6nMynzzAJhzSydu6hxZ9oal2Lt3b5Fvps5czalq3hgTeD6u4t4/EdlqjLnSQyGVqrI5/49Bl3Nvj6K3c5xV3O8LPP8+FscbYwLPx1Wbcv632YNdEkNJee8Ons6PqlYV51venC/zCpOIDBWRCl2JEpFIYDDwluO1AL2BglGxFgPDHc+HOV7jWN/Hsf0w4H1jTI4x5ldgP/mVJ7doFBLAuj3H3HV4pbxOu8ahmvNKKVUGZ9ow3Qq8JCIrgIXGmH3lOP5LwCSgoJoYDqQYY/Icr5OApo7nTYHfAYwxeSJy1rF9U+C7QscsvI/L9WsfwcrtyWRbbQT6+birGKUqzVUNYNvUzWL1ASur4zYQ6l+x4QXOa/xaSHENVT3NG2MCz8dV3gawStU2ZVaYjDF3iEgocDuwSEQM8G/gPWNMiX/dIjIEOG6M2SoiPV0VcCnlueTDIyLvGJm5Nl5buYGYRhVvE1/cB4in/yEWxxtjAs/HVR0+PFzVAPaeQVezat43ZDdoxQ1Xlm+8oQIlNX71xtsI3hgTeD6u8jaAVaq2capGYIxJFZHlQB3gIeBG4FERedkYM6+E3boBN4jIICAQCAXmAvVExNdxlSkSKBj1KhloBiSJiC8QBpwqtLxA4X0Kx+iSD4/7buzFG7u+4JhvI3r2jK7oYYr9APH0P8TieGNM4Pm4atOHR1STUJqEBbJuzzFuqWCFSSmlajpn2jANE5GVQDzgB3QxxgwEOgF/L2k/Y8xjxphIY0wL4DbgS2PMKGADMMKx2RigYJz01Y7XONZ/afJbpK8GbnP0orsUaA0UP4GTCwT4+vCntg1Zt+c4drt7G8Qr5Q1EhL7tI/g68QRZuSX2pVBKqVrNmcbcNwEvGmM6GmOeM8YcBzDGZAJ3VaDMycDDIrKf/DZKbzuWvw2EO5Y/DExxlLMb+BDYA6wFJpTWQ84V+reP4GR6Dtt/1ykEVO3Qr30E2VY73+w/6elQlFLKKzlTYTpqjNlYeIGIPAtgjFnvTCHGmHhjzBDH81+MMV2MMa2MMTcbY3Icy7Mdr1s51v9SaP9ZxpiWxpi2xpj/On12FdSzbSN8LULcnqPuLsptXDmL9caNG+ncuTO+vr4sX768xO22bt1Kx44dadWqFQ888AAFQ1ZMmzaN6OhoYmJi6N+/P4cPH650TFOnTqVZs2YEBweft/y1116jY8eOxMTE0L17d/bs2VPpsmqDqy8NJyTAl3XVNOc13zXfayPN+6rNe2cqTP2KWTbQ1YF4k7A6flzTMrxad7V25R/SJZdcwqJFi4oMK3+h8ePH8+abb5KYmEhiYiJr164F4NFHH2XHjh0kJCQwZMgQZs6c6XTZ06dPL3aaiKFDh7J5c9E7syNHjmTnzp0kJCQwadIkHn74YafLqs38fS30vLwR6/cex1YNb0Vrvmu+10aa91Wb9yVWmERkvIjsBC4XkR2FHr8CO6okOg/q1z6CX05ksP940Xl4qoMpU6Zw4MABYmJiKj0CbIsWLYiOjj5vzrkLHTlyhNTUVLp27YqIMHr0aD7++GMAQkNDz22XkZFB/vBa+c/HjRtHly5diI2NZdWqVcUeuzhdu3Y9N/1EYSWVdWGsPXr0ICYmhg4dOlT5BJXeql/7CE5l5LL90BlPh1Jumu+a77WR5n3V5n1pveSWAv8FnsHRnsghzRhzutIle7m+7SJ4YtVu1u05RqtGwWXvUIpnNz/LvtP7sNls+Pi4ZmynyxtczuQuk0tcP3v2bHbt2kVCQkKx68sz+a4zkpOTiYz8Y3T0yMhIkpP/6Mw4depUlixZQlhYGBs2bADyZ7bu3bs3CxcuJCUlhS5dutC3b1+CgoLKXX5hCxYsYM6cOeTm5hY7yejSpUsZMGAAU6dOxWazkZmZWanyaoqebRvi5yOs23OMK1s0qPBxCvIdcFnOuyrfoezJd52h+a4uVDjvXcWVeV+Y5n3FlFZhMsaY30RkwoUrRKRBTa80NalXh45Nw4jbc5TxPSs+ZYS3Ks/ku64wa9YsZs2axTPPPMP8+fOZMWMGcXFxrF69mueffx7IH/vo0KFD5OXlMWrUKCwWC0ePHsXf35+XXnoJyJ/UsfBs8MWZMGECEyZMYOnSpTz11FNF5hm66qqrGDduHFarleHDh583T1JtFhroR9fL8m9FPzaoaqZ7qCrlmXzXFcqb73feeSd2u53jx49rviuXqeqriRXJe6DY//P+/v6lluWJvC/rCtMQYCtggMLXvAxQfae8dlL/9hHM+eJnjqdm0yg0sMLHKfiG4OmxhQpz9RWmpk2bkpSUdO51UlISTZsWHZB91KhRDBo0iBkzZmCMYcWKFbRt27bIdps2bSIkJITp06fTokULxo4dW+6YbrvtNsaPH19keY8ePdi4cSNr1qxh7NixPPzww4wePbrcx6+J+rXPv7K6/3h6ha+sFv5G7C057+orTK7O94SEBNLS0njhhRc036up0q4EeYqrrzC5I++BYv/POztwcVXmfYk3Kwv1arvUGHOZ42fBo8ZXlgD6RUVgDHyx93jZG3uZkJCQUhPu66+/JiEhgU2bNpGQkHDuUZE/IoDGjRsTGhrKd999hzGGJUuWMGzYMAASExPPbbdq1Souv/xyAAYMGMC8efPO9bLYvn17hcourHBZa9asoXXr1kW2OXjwIBEREdxzzz3cfffdbNu2rdLl1hR920UAVLsOD87m+4U5r/leO9WUMfbKk/f6f77yee/MwJXdRCTI8fwOEZkjIpdUuuRqoG1ECJc0qFstu1qHh4fTrVs3OnToUOnGgD/88AORkZEsW7aM++67j6ioqHPrCl/mfOWVV7j77rtp1aoVLVu2ZODA/M6UU6ZMoUOHDkRHRxMXF8fcuXOB/G6oVquV6OhooqKimDZtmtMxTZo0icjISDIzM4mMjGT69OkAzJ8/n6ioKGJiYpgzZ06Ry7QA8fHxdOrUidjYWD744AMefPDBivxaaqQm9erQoWlotct5zXfN9/JIOpPl6RBcQvO+avNeCmp9JW4gsoP8Ub2jgUXAW8Atxpg/Vbp0N6nM1Ci/zR583ut/frqH/3x3kG3T+hEc4Pzccnv37qVdu/PbgXjL7YnCvDEm8Hxcxb1/IrLVGHOlh0IqlStzfu4Xiby0/mc2/6MvDUMCnDpGcb8v8Pz7WBxvjAk8H1dtyvm3Rl9J3/YRlY6hpLx3B0/nR1WrivMtb847Mw5TnmOKkmHAfGPMAqDWvGv920eQm2dn488nPB2KUlWiX/v8W9Hr91av23JKOeunY9434bjyfs5UmNJE5DHgDmCNiFjIn1OuVriieX3q1/Wrdm06lKqodo1DaFqvjua8qrEStcKkKsCZCtOtQA5wlzHmKBAJPOfWqLyIr4+FPu0iWL/3GFab3dPhKOV2IkK/9hF8s/8kmbl5ng5HKZf76Vj1HJBYeVaZFSZjzFFjzBxjzNeO14eMMUvcH5r36Nc+gtTsPH74tXxDT5XVPkx5J33f8m9F5+TZ2fiz85Px6u+t+qpt792B4+nkuegLcG373dUUFXnfnOkld5OIJIrIWRFJFZE0EUmtUITV1HWtLyLA10JcOW5RBAYGcurUKf1jqmaMMZw6dYrAwIqPu1UTXHVpA0IDfZ2+Laf5Xn3VxpzPtdk5eLryIz9r3ldPFc15Z7p9/QsYaozZW6HIaoC6/r5c17oh6/Yc48mh7Yudt+ZCkZGRJCUlceLEH43Fs7Ozve6fkjfGBJ6NKzAw8Lzh/2u6FlPWlLhuxbYkVmxLKnF9QQ+74vIdvDO/vDEm0Jyvaj8fTaNlw8pNe1VS3ruDt+atu7j7fCuS885UmI7V5spSgf7tI/hi7zF2H06lQ9OwMrf38/Pj0ksvPW9ZfHw8sbGx7gqxQrwxJvDeuFTximhgkXoAACAASURBVMt38M730RtjAu+NqyYSgZ+PpTOwY+WOU1Leu0Ntyw9vPF9nKkxbROQD4GPyG38DYIz5yG1ReaE+7RphkfwRkJ2pMCmllPJOlzSoy8/aU06VkzMVplAgE+hfaJkBamSFqbTbEwBz1ycyd31isesuHABQKaWU92kTEaJjMalyK7PCZIz5S1UEopRSSlWFthEhbNh3nJw8GwG+Pp4OR1UTzvSSayMi60Vkl+N1tIg87sR+gSKyWUR+FJHdIjLDsfxSEfleRPaLyAci4u9YHuB4vd+xvkWhYz3mWP6TiAyo6MkqpZRSrSOCybMbfj2Z4elQVDXizMCVbwKPAVYAY8wO4DYn9ssBehtjOgExwPUi0hV4FnjRGNMKOAPc5dj+LuCMY/mLju0QkfaO8qKA64FXRES/EiillKqQthfnz+71sw5gqcrBmQpTXWPM5guWlTn8r8lXkI1+jocBegPLHcsXA8Mdz4c5XuNY30fy++8PA943xuQYY34F9gNdnIhbKaWUKuLSi4LwsQg/H9V2TMp5zjT6PikiLcmv7CAiI4AjzhzccSVoK9AKWAAcAFKMMQUVriSgqeN5U+B3AGNMnoicBcIdy78rdNjC+yhVa4nIvcC9AM0nf+qRGOLj40tdn56eXuY2Vc0bYwLvjasmCvD14dKLgrThtyoXZypME4A3gMtFJBn4lfyJeMtkjLEBMSJSD1gJXF7RQMuiHx4V440xgffG5U2MMW+Q/7dJiylrPDLUcM+ePUtdHx8fX+Y2Vc0bYwLvjaumahsRwu7DZz0dhqpGnOkl9wvQV0SCAIsxptxVcmNMiohsAK4B6omIr+MqUySQ7NgsGWgGJImILxAGnCq0vEDhfQqXoR8eFeCNMYH3xqWUqhlaRwTz2a4jZOXaqOOvzWJV2UqsMInIwyUsB8AYM6e0A4tIQ8DqqCzVAfqR35B7AzACeB8YA6xy7LLa8fpbx/ovjTFGRFYDS0VkDtAEaA1c2KZKKaWUclrbiBCMgf3H0+kYqYMRq7KVdoUpxPGzLXAV+RUagKE4V2FpDCx2tGOyAB8aYz4VkT3A+yLyFLAdeNux/dvAf0RkP3AaR088Y8xuEfkQ2EN+Y/MJjlt9SimlVIW0OddTLk0rTMopJVaYjDEF4yZtBDoX3IoTkelA6cNhc274gSITwThu8RXp5WaMyQZuLuFYs4BZZZWplFJKOaN5g7r4+1h0ihTlNGcafUcAuYVe5zqWKaWUUlXGVZ17CjqURNSFb/ceJL7uMVeE51a1rSOMN56vMxWmJcBmEVnpeD0cWOS2iJRSSqliuKpzT0GHks5Ht7PltzPVooNJbesI443nW+bAlY7bYX8hf1TuM8BfjDHPuDswpZRSyp3aRISQnJJFWrbV06GoasCZK0wYY7YB29wci1JKKVVl2kbkN/xOPJ5O50vqezga5e2cmRpFKaWUqhb8yOMfvu/SgNQyt23jqDDpFCnKGVphUkopVWO0k4OM9onjA/9/EsHpUreNrF+HOn4+OgmvckqpFSYR8XGM0K2UUkp5vR2mJWNyp9BYTrHMfwbNpOQecBaL0DoiWIcWUE4ptcLkGCDSLiI6qpdSSqlq4XvTjlG5/yBEsljuP4PWklTitm0iQnQSXuUUZ27JpQM7ReRtEXm54OHuwJRSSqmK+tG04tbcaQjwgf9MOsovxW7XNiKEE2k5nMnILXa9UgWcqTB9BEwDNgJbCz2UUtVA/i0Jj8xHrZRH/WyacXPuE2QSyFL/WXSRvUW2aR0RnL+tXmVSZXBmHKbFwIfAd8aYxQUP94fmHnP95jPR5yOacsLlx87K1SnulHcJI50v/B/lc//J3OPzKQ1JcenxNeeVtztoLmZEzpMcM/VZ4j+bnpaE89a3LZhT7rg2/FalK7PCJCJDgQRgreN1jIisLn0v7xRALg1J4e9+y9kU+CBL/Z7iJstG6pDtkuN/+8tJlxxHKVfJxZfpeWNIpw5T/ZbybcDfeNvvOQZavsefyg/WpzmvqoOjhHNL7hPsN015w+8F2L3y3LqLQwMJCfTVoQVUmZy5JTed/MlyUwCMMQnAZW6MyW1y8Gek9XG658zlBesImspJ5vi/xg8B9/Oc72tcLXsR7BU+/pf7jrswWqUqL4tA3rP14c+5M+iT8xxv2IYQZfmNV/3n8n3ABKb7LqKD/EJFb9lpzqvq4jShjMydyo+mJSwfB9v+A4CIaMNv5RRnRvq2GmPOikjhZRWvVXiBJNOQebabmGe7kavkJ/7ss5HBPt9zs+9Gfrc3ZIX9OlbYruN3U745hr/cexwzzHDB70opr3DANOVfebfxfN4tdLfsZITPRm732cBY3zj22Zux3NaDVbZunKCe08fcsO8ExmjOq+ohlSBG505hb9Q7sPpvkJsOXcfTJiKEtbuOaC6rUjlzhWm3iIwEfESktYjMA/7n5riqiPCDuZwpefdyVc4rPJh7P7+ZCB7wWcnXAf/HB/4zudknniCynDra4bPZ7NPLusrL2bGw0d6JB6wTuSpnAVOt48gigMf93uXbgL/xpt/zDLBsxo+8Mo+VnJKlg/6paiWLQLj9fbh8CKydAvHP0qZREGcyrZxIz/F0eMqLOVNhmghEATnAe0Aq8JA7g/KEbAJYZe/OndZ/0C3nZf5lvYWLOMtzfm/wQ8D9vOD3CtdadpV5y05vUajqJJVg3rX15cbcmfTJeY43bYOJtvzC6/4v8X3A/Tzpu5gmlN5OSXNeVTu+AXDzYuh0O8Q/Tf/k+YAhUSv/qhTO9JLLNMZMBfoAvYwxU40xrmkl7aWOEM4rtuH0yX2em3Kms9LWnX6WbSz1f5rl/jNoKcnF7texaZh+eKhq64BpyrN5t3NtzjzG5E5mk70DI32+JC5gEqN8vij2y0L7xqFs0JxX1ZGPLwx7BbrcS9O9b/O071v8fMS1vUhVzeJML7mrRGQnsIP8ASx/FJEr3B+aNxC2mTZMzbuLq3Je4VHrvVwqR/jM/zEm+HyM7wW3LHpd3ojth85wWgdAU9WYDR++sndiovUB+uQ+R4K9JbP8FrLU72kuuWCaid6XN2LLwdOkZGrOq2rIYoGB/4Lr/s5I3w103vIo2Crfe1TVTM7cknsbuN8Y08IY0wKYAPy7rJ1EpJmIbBCRPSKyW0QedCxvICLrRCTR8bO+Y7k4RhHfLyI7RKRzoWONcWyfKCJjKnSmlZSDP8tsPemX8xzr7FfyqN+HrPafRpT8em6bPpc3wm7gq5/1G7eqGZJMI+6w/oPJ1nuIsvzK5/6TucvnMyyOq0292xXkvOvHNVOqSohAnyd4N+QuOp39Et4fBdYafRNFVZAzFSabMebrghfGmG/Aidag+dv83RjTHugKTBCR9sAUYL0xpjWw3vEaYCDQ2vG4F3gV8itYwJPA1eQPb/BkQSXLE04Rxt+sD3Bv7v8RLmdZ5T+NSb7vE0AuHZuGcVFwAF/u0w8PVZMIH9h60T/nX/zPHsU0v3dY7j+dlpJMp8h6NAjy19tyqtr7qdU4Zpq7IfHz/MbgSl2gxGEFCl3h+UpEXie/wbcBbgXiyzqwMeYIcMTxPE1E9gJNgWFAT8dmix3HmuxYvsQYY4DvRKSeiDR2bLvOGHPaEdc64HpHPB4TZ7+K73LaMdV3Kff7rmaA5Qcsv19Mr7YN+Xz3Uaw2O34+ztRHlaoejhLOXdZHGGbbxHS/JXzm/xg+36TSu3VP1v98Apvd4GPRLtmqGphedD75Nnl9eDzvLv4v4B1Ctv4btpZwI2X6WTcHp7xVaeMwvXDB6ycLPS/XKHci0gKIBb4HIhyVKYCjQMFgR02B3wvtluRYVtLyC8u4l/wrUzSf/Gl5wquwVIKZnHcvn9iv4RnftzD/Hsjt9a5nTfbNvL1qA5c38Dlv+/T0dOLj46skNmd5Y0zgvXF5E0/kPAir7N3ZlNORGX7/ZvCX/+SRgHfYk3UPC1f50Lq+5nxFeWtctUVbSxIAW+1t6Omzw8PRKG9UYoXJGNPLFQWISDCwAnjIGJNaeFAwY4wREZfMCmqMeQN4A6DFlDVVOtPoN/aODMh9lj1/2kzs968TF/AD3+RMo2fPsedtFx8fT8+ePasytDJ5Y0zgvXF5E0/m/EnCmGB9iMG359FozSOs8p/GDyfHcO3Q2fldth288X30xpjAe+OqLdpIfoXpJ3MJPdEKkyrKmV5y9UTkARGZ42iU/bKIvOzMwUXEj/zK0rvGmI8ci485brXh+FnQ+CEZaFZo90jHspKWe5VMAmHgs8i4tVj8Arntpwfh4/sh64ynQ1PKfdoPwzLhe/5XtxfXJi+E13tA0hZPR6VqOfEt//AAYZJBBKf52V7kBoZSgHONvj8DWgA7ga2FHqWS/EtJbwN7jTFzCq1aDRT0dBsDrCq0fLSjt1xX4Kzj1t3nQH8Rqe9o7N3fscw7XdKVuOtWMD9vGObH92HB1bCnWs5VrJRz6jZgb9d/MTb3UWxZqfB2P4h7HHIzPR2ZqoXE7yTBrWdTt/kr+NX/FnwynN63jSWJn02zsjdUtZIzFaZAY8zDxph/G2MWFzyc2K8bcCfQW0QSHI9BwGygn4gkAn0dryG/YvYLsB94E7gfwNHY+5/AD47HzIIG4N6qZ1Qzns+7lU+6vAvBjeDDO+HD0fjl6qBoqmbqfXkj4u2xrOi6DDqPhv/Ng9e6EZL6s6dDU7WNvQ45xwaCJYfAi1cR3HoWgZGL8Q3ZCVL6GEttJIlE0xS70c4LqihnJt/9j4jcA3xK/vQowLmKTIkcww+UlHV9itnekD/GU3HHWggsdCJWr9DioiAuaxjEssN1uOGeDbBpLnz1LFf6fA3tmkCzLp4OUSnXcPQ2am0gUl4i7rOt3OK/KH/d6V+44vSjsO3REvbV3kbK9YwtiNzTfyL39J+wBBzGL2w7vqEJ+IXsxdgCsaZ2ZEt2AJ2zc4pcMWgrv5NNAL+bhjQXHSpDnc+ZClMu8BwwlT96xxngMncFVW0V6qra2zqKJSf6kzGzKUGSX88MsJ3Ov11R7L764aGqLxHobUlgma0H2caPwDK+yStVFew5Tcg53oSc4wPxCTqAX+h2/MJ+5C/1I7Dn1sOaGkve2VjsuY3O2+9PuS+d9/oGyyZe9l/A/Lxh/K0qT0B5FWduyf0daOUY6ftSx6PaVpZ8Q7fjE/QT4n8C58bfrJjelu3k4sc39o5uK0OpstkJbPouAY0+w6/ed/gEJSJ+pwCby0vqZdlOFoF8Z2/n8mMrVTkWbBmtyT5yC+k/P05W8q3YcxvhHx5PUMs51G0xH7/63yA+xU++u9rejffzenK/z2o48GUVx668hTNXmPYDNaT1piGw8UeIJf/brzGCsYZht4Zjzw3HWBtgzw3HntsAuzUc7IEVLukqy0+EkMkGewwDfLTXkPIQnywsAUexBO9FLH98QTDGgrHWPy/fTW4D7LkXYbc2AONX7qKusewhkBw22GN1HBvlvYw/eamx5KXGIj5p+Ib+iF/YdgIv/hQT8Rm2jNbknOiHPTvyvN2m542hsyWRNh/dC3/dBCERJRSgaipnKkwZQIKIbOD8NkwPuC0qN8o48CgWv9OI/yksfqexOH76huzG4nt+bwp7XtAflaiCn1mRjsu3pTcK9BMbPSw7+NIWi91XsLhmuCmlyscWROYvfwfsiG/quXwX/1NY/E9j8TuFX9ghxOf8ubPs1lDs1gaY3HBsWZeQl9ESYw2ntLwPFCvdLLv50h7LdLMYKavdrN2eP/mpUh5ibCFYz3THeqY7Fv9j+IZtx6/eFuq2WID1zNXknBgA9joAZBPABOuDrMt5Ej66B+5cCRafMkpQNYkzFaaPHY8aQDB5odjyQiGrRdHVluzzKlEFHyo+dQ7iG/ojBWNs2vOCsWVchi2zJXmZl2FyL6K4D5LePttZY+/KbtOCjoUm6S3Wuieg9xPg48xbolR5WTB59bDl1cNGywvWGfDJLJT7+Xkv/qfwCf4Jv3r5o4jYrWHYMlqSl9kSW0ZLTF69IqX0smxnfV5nDpgmtJLDpYf0/u1w4+tQp+hxlKpq9twIck9cT+6pngQ0XIdf/f/hG7qLnGODyEuNBYREEwmD/gWrJ8I3c6BHCR0aVI1U5qezk0MI1Az2QOw5TbDnNClmZR7ifya/8hR0AJ+6B/ALy7/tYLeGYstsycrcIK7KziYyL799SE9LAoKdL+0xdLSUUWHaNBeSt8GIhflDEShVZQRsQdhtQdizLxyDxiD+J/GtewCfoF8cFahtANhzw5me3YAu2dl0yc7mIpudXj4JkAdf2mNpZSmjwrT/C3ijJ9z6DlzcwS1nplS52QPJOTYUa8oVBF78MXWafkhevR/IOToce24ExN4Jv26EDU9D827Q/FpPR6yqSJkVJhH5lWLmjqvODb8rxheT25C83Ibknb0SMIjfqXOVJ5+gRJ4ICwegiTXv3IdIB/sOvszrzIO+K0s//PBX4dP/yx8p+ZYlOvSA8hKCyW2INbch1pSugB1LwHFHzh8gLugEK0KDAbgs10qX7Gyap24gLqsd97Km9EOP/Qw+HA1v9YUbXoboW9x/Oko5yZ7ThMyDf8Wv3hYCGv2XupfNJffUdWTm9aLukBcheSssvwv++g0EhXs6XFUFnLn/c2Wh54HAzUAD94RTnQjGehHWlIuwplwNGNaHjOH7wEB+qBPIhrp1+DgkGBq+jz03nMeyIxiQmcq1WVn4F3e4mJEQ0SF/kMt/D4QBT0OXeym7IYhSVcmCPedi7DkXYz3Tja8DR7LP35/NdQL4PjCQVcFBZIV+zikj3JTbhH6Z6QzIyOQyazE9Ui+5Gu7bCMv/kt8mJGkL9H8KfIv9C1HKAyxYU7qQl9Ye/0ZrCbjoK4avGs6ULlPoPeLf+cPEfDweRn6g/6trgTJbXBpjThV6JBtjXgIGV0Fs1YzQ0prHyLR0Xjx+ko2HklmWfIQ7T4I9pxHrggKZeHFDel4SybSLGrCpTiBFRqppHA33xkOrvvDfSbDibsh1flh/paqaDxCVm8tfzqbx2rETbDqYxD+S/Mk90RervS6v1gtjWGQTRjS5mLfCQknyvaCRbEgEjF4F1/wNNr8Oi4dA6hGPnItSJTG2YHKOjCDzt78S5BfEgxseZOLet0nqOQkSP4dvF3g6RFUFnJl8t3Ohx5Ui8lecuzJVq1mAy3OtPJp6iLCkIVyx/wYWHD1Or8xMvgiqy18vbkSfS5oyM7w+mwMDsNkd4+LUqQ+3vQe9H4ddK+DNPnByv0fPRSln+QG35B6g7qkutD7Uly9+P8yUU6cJNIa5DeoxsFlTRjWO4D+hIRzLOJa/k48fDJgFI/4NR3fl35b+7RuPnodSxbFlteDDoR/yyJWP8P3R77nx4Ie82aoLuV88CUllTrGqqjlnKj4vFHqeB/wGaGMDJ4lAL58EVtuuZV6mlR5Zp8k5dZpv6tTh86C6fBocxLLQEC5a3pf+zftz/aXX06lhJyw9HoWmV+TfI3+jJwxfAO2Hefp0lCqTjxj+ZNnBV/ZOPJ9nZ1RqOqNS00n29eHzoLqsDQriX+H1eW55PzpHdGZgi4H0a9GPBh1ugkbt4YNRsPgG6Dsdrp2otzrUOSJyL3AvQPPJn3okhk0bN9Gc5jwW8RgfnfmIlzMTWNXkYiZ9OBJLxxfI8wt2S7np6enEx8e75djeyBvP15lecr2qIpCarI9lO+/Z+rDZ3o7uPrsIMNAnM4s+mVlkibCxTiBrgzNZnvEuS/ctJSIvjwEZmQxMzyQqNzd/wIIPR5dcgE6rorxMb5/trLJ340dzGbFyAICmeTbGnU1j3Nk0fvP1ZW1wXf6b+x1PHdvKM9/9k6uzsrk+I5M+mZmEGgPrpuU/iqM5XysZY94A3gBoMWWNRwa369mz57nnN3IjXyd9zdObnmSC3wkGHfsXk/+8kgZ1XN8IPD4+/ryyazpvPF9neskFAH8GWhTe3hgz031h1SzXWnYTSA7v23rR3WfXeevqGMOAzCwGZGaRLsKGunX4PDiIpaEhLAkLJdJqZUBGJkPTM2hZXMNZpbzQnyw/EkgOb+cNYr7/vCLrW+Tl8deUVO5LSSXRz4+1wXVZG1SXJxqGM9M0oHtmFjekZ9AzM4vyjzmulBsVmjMU4DpgpQgLw0J5q57hu/euY/rJ0/TKzCpmX63oV2fO3JJbBZwFtlJopG9VVIvspaWu/9R+DZ9mX1Psut8CRxJsDEMzMhmakclZi/Bl3fwPkUVhobxdL4xO2TnclJbf6yjI6MjhyvMqm/MCtLFaaXPmLBPPnGWPvz//Da7Lf4PqEh9UlwY2GzekZXBjenrxPe2U8gKBxnB/yln6ZWTyWMNwHohoyI1p6Uw6dYZg/V9dYzhTYYo0xlzv9kjUecLshhvTM7gxPYNTFgufBgfxUUgwTzYMZ3Z4fa7PyOSmtHQ65eSWMUmLUtWDkN/jLup0Lv93OoX/1Qnko5Bg3gkLYVG9UGKzs7kpLYP+GZnU9XSwShWjtdXKe4eP8kr9MBaGhbI5MJCnTp7iymy91lATODOR0/9EpKPbI1ElCrfbGZOaxsfJR/jP4aNcn5HJ2qC63NnkYoY3bcyiLS9xKuuUp8NUymV8gOuysnnx+EnWHUrm76fOcMbiw7SG4fS+pCkz1j/IrpO7MPrtXXkZP+DBM2dZfOQYFgzjLm7EC/XrkaPfbKs9ZypM3YGtIvKTiOwQkZ0iolORe4AAMTm5zDx5mg2Hkplx4hQhdjsv7H6bvst689CGh9iYtJE8u966UDXHRXY7Y1PTWJ18hMWHj9E3I5NPD63n9jW3M+KTEby7913O5mjbEOVdYnJyWZ58lBFp6SyqF8ptTS5m3+l9ng5LVYIzFaaBQGugPzAUGOL4WSoRWSgix0VkV6FlDURknYgkOn7WdywXEXlZRPY7KmWdC+0zxrF9ooiMKe8J1lRBxnBTegbvHDnGquxgRqWksD3payasn8CAFQN4edvL/J72u6fDVMplBOick8NTJ0/zpaUF006exu/sYWZvnk3vD3szaeMkvjvyHXZj93SoSgFQ1xieOHWGV44eJ8Xiw+1rbuetnW/9Me6eqlacGen7YHEPJ469CLiw7dMUYL0xpjWw3vEa/qiUtSZ/jI1XIb+CBTwJXA10AZ4sqGSpP1w2bgOPXHojXxzYz4u2+rQNac7bu95m0EeDuOvzu1jzyxpybHoPXdUcIXeu4pbOf+P9A3tZnhnIiGZ9+Cb5G+6Ju4dBHw3izR1vciLzhKfDVArIv728MvkIvZv1Zu62uYxdO5ZDqYc8HZYqJ2euMFWIMWYjcPqCxcOAxY7ni4HhhZYvMfm+A+qJSGNgALDOGHPaGHMGWEfRSpjyC4ShL+F301v0PbKfV3bE83nnaUyMncjh9MNM+XoKfZb14dnNz5J4JtHT0SpVeRYf6PUY3PkRbdNO89imd/iy3d+Yfd1smgY35eXtL9NveT8e+PIBNiZt1G/0ymX22ptVaL96djvP/+l5Zl83mwNnDzDikxF8+NOH2g6vGqnqKU4ijDEFE0UdBSIcz5sChe8fJTmWlbRcFdJx8R9t8i9tFMoLx0/SevldEBbK4fphIMLZnLO8s/cd3tn7znn7zmtedIwcpbxd4ZxvFO7Pv46nccXH48kICSahQX2wCDZjY8PvG9jw+4bz9tWcV5Wx0nYd7SylD6dREtm5jMHRt3BFxBVM2zSNf373Tzb8voGZ186kYd2GLo5UuZrH5oQzxhgRcVnV2huGzPeUnb8Wf2n33rOp3Hs29dzrMxYLnwQHsSIkmF/8/Qiy2/nP0f9w6PNDNPNvhnjRFBTeOCy+t6nNOT8+ePx5rzeH2ZBj67jlxFf0sobwRbNbORMYgc3Y+M32G3ute/ndlv/da/7h+fz42Y90qNMBH/Ep7vAeoTlfPayyXctk3/fwqcjH16oJEBbJxc2v5fV+r/P+vvd5ceuL3Lj6RqZ1ncaAFgNcH7BymaquMB0TkcbGmCOOW27HHcuTgcLXOSMdy5KBnhcsjy/uwN4wZL63q2+3Mzo1jTtT00gI8GdFSDCf+Wxn89HNXN7gcm5qfRODLxtMqH+op0P1ymHxvU1tzvn7dz5W4rqG2Ue4PfGlIsuTfX34KDiYDy46ylsn3qJhnYYMbzWcm1rfRGRIpDvDdYrmfPVwjAZ8a48qMmuDM3612Km/eDB3NIngoN8fY9hn27J55KtHeOSrR0rcV6+Mel5VV5hWA2OA2Y6fqwot/5uIvE9+A++zjkrV58DThRp69wdK/k9ZjZ01dQmTzCopS4DYnFxic04TfvEDLMxYyL7T+3j6+6d5+vunnT7OzjE73RekqvEyTABBUnWdEZrm2ZiYcpb2nWZgaWlhReIK3t71Nm/ufJNrGl/Dn9v8md7NeuPno5OxqJKFkMlKW7cKVZjuj2jEO4ePsuDoCe5oEkGKj/dc4VRlc1ujbxF5D/gWaCsiSSJyF/kVpX4ikgj0dbwG+Az4BdgPvAncD2CMOQ38E/jB8ZjpWFbjzMu70SPlBkiAR8pV6tW8GzxSro/40OuSXszvM5/P//w598fcz2+pv/HIV4/Qd3lfnt38LPtO79PGuKpYg3y+Z629C1nGv9z7Jvn58mBEQy625fHysRP42zXHqhO3XWEyxtxewqo+xWxrgAklHGchsNCFoXmlRbYBjPRZz2WWo54ORakq8YZtMLf5biBSTlZpuXtO7mbi4onFrjudfbrYzhEF9KqqGm75hg9svYizX8Ewn2/Lvf+PgQH8o+FFvHD8JE+dPMXkhuEYL2o/qkrmtitMqnwCyWVW3h1VXu6Ag+/QKK9iI4NrV21VGRYMz1hL+l7lPkN/W0ik1VqhfbPzsl0cjapurrbsowkn+djWvcLHiAuqy4v16zEwI5O/nXFulPotGVv0qqeHaYXJS0z0/Zj19s58ZYuu0nKbp/3EqqQjjDybhqWcf4wDVgzgWvLT4gAAGWFJREFUpa0v8UvKL26KTtVk9/l8yhr7Nfxgb1ul5TbK/J2VyUe5K+UsvuXM+V4f9mL6/6az7dg2/fCqpSxiGOaziY32aE6aineQWRgWwvKQIO49m8rwtPQyt198cjH3rLuH387+VuEyVeVohclLjPVZS3M5yj/z7sBqqq4h4PutH+LHwAAeO32Gdw4fo21OrtP7tm3QlkW7FzFs1TBu+/Q23t37Lqeza2QTM+UGf/X9hMacYqb1Tuym6m5JvNfmYTbWCeShM2f5MPkoncoxk3zvS3rz2a+fMWbtGAZ9NIhXE17VKYhqoZt8vsGGD5/Yrqn4QUSYFd6ATXUCeeLkabpmlX718pYGt7Dn5B5uWn0Trya8qrM3eIBWmLxEgOQx1fdd9ptI3rUVaeblNqkB4fw1oiGPNgynSV4e7x8+yt9PnaGOvez5uBb0WcAXN3/BpKsmYTd2Zm+eTZ8P+zDxy4msO7hOb1+oUtWRXCb7vc9OcxkrbNdVWbmZfqH8PaIhf4u4iGC7nSVHjjH15GmCncj5Wd1nEX9LPLO6z6JpcFNe/fHV/2/vzMOrqu69//ntfU6SE8hABiqjIDgExKoF20oYlMGpAgJWiiK89V5v29vXp1bq83odWi9abd9KW2uF15e2WrRFUaxUbRVldGASmcSCTCqQIGQmCSQ5Z90/9k48hCQnwxmT3+d59nP2XtP+7XW+5+zfXmvttbh22bXM/sdsXtrzEqUnS6NwBUqsOdc6zFA50OZuuR0HPjtt+/Dg54ysPokX+P+FX5wRH7yNShvF8huWM/7s8Ty57UmmL5/O+oL1kblApUliNnGlciYTrA/It3bw67rpTLbfo4eEbqYNCyL8s3s33vP5+FFJKXPKK5hYWcXDOVmsTfW1mDXHl8OsIbOYNWQWe0r28Oq+V3l1/6us/nw1Po+P/D75jOs/jtF9R5OWlBad61EShknWezwtE/ll3U1ca2+IyjQDP9hxj/MabhAzKk4woxXdIgCp3lQmDZrEpEGTKDhRwGsHXuOVva/ws/d/xrz18xh+1nDG9x/Plf2vpGdqz/BfgBIX3GC/y0N1t7Av0ItBVkHoDGEgx5fDL0f/kimDp/Dw+of59zf/nevOuY65w+eS48uJig1dGXWY4ggRuN+zmGtqHuU3ddN40PtM6EwdpKmbB0Bvv5/fH2158dLg5SmaorqumhWfrmDFpyvOiNO3jRRwxoM84F3M1Jr/ZkHdJOZ6l8bapBYJpXm/8bOhYAMbCjbw8IaHT4tTzXcuJtnv8fO6mfzNn89dVnR1e3nvy1k2eRmLdiziDzv+wNrP13LzkJuZecFMeqTo+vSRQh2mOON86xA322/xrH88N9tvcZ51ONYmRYRb/3Er4/qPY1z/cXExy7ISOy619jLFeoen/Ndxk72KflZ0pxmIFtOWT2N8//GMO3sc52aeG1dLESltp6eUMtLaycuBkfzYLCXSX+euol3NTodRG6hl4baFLNy2sMl4ddbDg45hikPu9LxEN04yr24WnfVFnKraKn61+Vdcs+wapi+fzoJtC9hTskffPOqi3O1dgoXh0broTzMQLbp7u7Ng2wKmLZ/GdS9fx/zN89l2bBsBE3rslBKfTLXXccj0ZLOJ7pueSmxQhykOyZIK7vS8yLrARbwduDTW5jTL3UUl9K5t3xxOL056kdenvs7c4XNJ9aayYOuXN5LHNj/G7urdOmi8C9Fbivme5+8xmWagLdx3vJh+7ZzD6ZlrnmHlt1fywDcfoH9afxbvWswtr9/ChKUTeGj9Q+yo2kF5TXnogpS4YaK1GR8nedk/MuLn6l+xu915NxRs0IfRMCCdsRITcSHSgykzTzuuNTbX1DyCH5s3ku4mSeJvkshanHXp3uyWyjMZ6exKbvtSAe0hVs3LIvKBMWZ4TE4egs6g+WqTxJWnHiNHyngl6X6s9qwGH2FqcMYxvJ3q4+mMdLanRGdpIdX8mcRK8411+6OaH7AqcDEbk39AsrTvAbK1/CW9O/N7ZHLKantbx9DsoXz3wu8yrv84bCv+17CL1WLULWleW5jiFK/4uc/zLAdML57xXxVrc5rkmn69WZyRxuiqap4/UsiigqOMqqpGwuCEX5tybbNxf9r5Jz46/pHONN7JiNU0A21hYr8+LMpI5+snT/JcwVGePnKUsZVVYdH8ZN/kZuMWbFvAlqNbqPW3r3VLiRw32O9QRndWBS6O6Hm2ZY9kZvkJlhw5ynltmC8P4IFvPkBFTQV3rbmLSX+bxAu7X9B5nNqBOkxxzFh7O1dYH/J43VSOdWBG2Uhx1ONhflYPJvTvw6+yMhlQW8eTR4+x7HAhUypOtGoup+Y423N2s3HzP5jPjNdmMOr5Udyx8g6e+/g5Pin5RJucOwGTrXe5RD7hl3U3UW5antIiFhR5bH6XlcmEfn34RVYmverq+N0Xx/nb4QKmVpxo1VxOzdHb7t1s3IKtC5j9z9mMXDKS7731PeehoUgfGuKBkdZOcijt0FIpreHd3t/i9q/kkhHw89cjhcwqK2+1o37jeTeyfMpyHhvzGGlJacxbP4+rXryKRTsWaTdwG9C35OKc+zzPcnXNL7j21CP80PMKM+yVEW/2bSsnLItnMtJ5Lj2NqyurmFNWzrzjxdx/vJhNvhTW+nysTU3hkNcblvOtvHElmwo3sbFwIxsKNrDq81UAZKVkMeKsEQzLGcaQ7CFckHWBzv0Uxww4+ZcW4y869Ydm4xp3i0SbKsvi2Yx0lqSnMdHV/IPHi7nveDEfpiSzJtXHWp+Pg14P4Xh9at2MdWwu3MyGwg1sLNjI/A/mA5CelM7wrwxnWO4whmQNIS87T18rjzIeCTDZfo/F/gmUmW5kSGXEzvV+qo+pfXrx4PFi7i4uJb/qJPflZnHME/pWbls2EwdMZMLZE9hUuIk/7vwjv93yW57a/hT5ffIZ03cM+X3yyfZlR8z+REfHMHUCYn3zaA+XDOhHXQs3ku93/z4LTiwI+3nX3bSOzJTMduXV8RzxQyJq/tIB/aiNgeZXfXtVuyc1VM2fSVPa2xkYwLdqfs7PPYuY6VkZkfM+OeyRL/VhDNMrKvlJcQmnRHgwJ4u3U31hcc4bE6vxc/E4hklbmJSYsPbTQ7zvS2Ftqo93Un0U2dEZhDjq+VH07tabvOw88rLyyMvOY0j2EJ0lV4k46z49xHuu5tf5fBR5oqP5K164gp6+ngzJHtKg97ysPHqm9tS5oMLEUDnIYDnEy/78iDlMpyHCi+nd2ZySzKPHivjNF8f5zONhRbdUVnTz8VFSUticp4fWP8SYvmO4rNdlJNvReckhXlGHqRPwuv8yrrY2xeVbRc3xRrdURlWfZGJVNQHgo6Qk1qb6WJvq4+Ok8HTdNcWPv/ZjdhXt4uPij3n7s7cbwnv6epKXncfsobMZcdaIiJ1fCQ8r/Jcy3toS8ckCw8lr3bsxpqqaCVXVAOxMSnK67iKs+btH3M3HRR+zq2gXaw+vbZj3KTslm7zsPO645A7ysvMidv7ORqiu5JbiO9Iy2tyqDPX0r6vjtrJybis7c0zSsIH9233e5fuW8/zu5/F5fHyj1zcY228so/qMIjc1t91lJiraJddJGCIHucuzlCutDxPiJjJsYH8whgtqahldVc3o6mqGnarBAo7ZFpWp5/GWdYR9SV4+8Xo54PVSY8X2wnbO2andE3HEV2Uvcz0vkG/tTCjNn19Ty+jqasZUfan547ZFZeq5vG0dYa/Xy96kJPZ7Pe16fTycqObDS6y6kv+a1p03u6WyJSWZQBt/LJtv2cymwk2s/nw1aw6tobCyEIALsy9kVN9RXJB1AYMyB9G3e9+wTlegXXJKRPi19/f8pm4at9X+hIvlE+Z6ljIyzm8iOw581mxcrj9AbsW/+Ldm4v9fZrp7U/Fy0OttcSyU0jl51PMUj9dNZVbtf/F12cVPvC8w3NoTa7NapCXN5/gD5FTs5rvNxKvmlY4w5UQl36k4QZFlsaqbjzdTU9nkS2mVjpLtZPL75JPfJ597zb3sKdnD2kNrWXNoDQu3LcTg+K1JVhIDMwYyKHMQgzMHc07mOQzOHBx2RyqWJIzDJCJXA78FbGCRMebRGJsUN9xgv8u3rPW85B/F43VTucW9icz1LmWE1f7ZYeOV20rLG4RbC3zm9bA3KYm9Xi/7krwctW2KbZsi26I6xk/oSmSY4VnNDfY7/NV/JU/UTWF6zc8YY21lrmcpw6wDsTYv7DTW/Keu81Sv+WO2TZFqPu6ZdGoeN9jvcL39PjkSvdf5x/TvQ371ScZXVnHNiSqmV1RSZlmsTvWxMtXHfq+XQo/NySa0E2rB6XpqAjXsLtnN7pLT7znBjlT9lqiOVEI4TCJiA78HJgCHgE0istwYsyu2lsUPXvE33ESW+K/kibrJ3FjzU0ZZ2xljbSNXSsmljFwpI1dKyaAyrlugWuKyAf0YWFvL4JpaBtXUMri2lrxTNUyorDpjYrEqEYpti6J6J8qyXGfKubnU7xfbFmWWhUnUSumCJEsdczxv8m17Dc/4J7Kw7nqur3mYidYmLrP+1aD5nlJKrpSR3ok0f25tLUNP1TCxNZq3LYqtLzVfFKT5ctV8VPFj8WDdbB6qu4Wx1jZusNcx3tpCikR2QtJqy3IHhKeSFDBcXl3N+KpqrqiqYvKJL6dBKLUsCjw2hR4Phbb76bGdMNvDMY/dptbNeSPnsa90H3tL97L1i628fuD1hjiP5aFHcg96pDhbVnIWmSmZDftHKo/QrbBbQ5rM5MyYO1gJMYZJRL4J/MwYc5V7fA+AMeaRptInYt92rLhI9jU4UbmUkVO/L2XkUEay1GATwCKAhWl5PwEGnR+zLXr4A00+KdQB9+dm82r3bk3m1fEcnYOvyt4GjTsPEaUNv4FsykmS2gZt2xhX4wE3zJy+n+CarwX+46yebPKlNJlXNR9eDqbMZHegL8v8+bziH0kh2aRRyXX2Bq633idXSrEJ4MGPRQCPBLDx46H+098QbxOI+gOAHzhu264T5eGYbVMtwklLOCVCjZz+2TisSoQqS6h2jwMIAQHDl1tzFyUIGckZZCZnkpmcSYonhRQ7hWRPMsl2csN+ip3iHHucz+D9+jRDs4eS4mla851hDFMf4POg40PA12NkS6eih1RQYLLYHhhIERkEOjj5+5c3kwDCmf9nTYedSVPpwoUQIINKsqWcbKkgmzKypIJsKWdi4ec8Ym9vJp/SGciQSg6bHLYGBlNEOqaDmrfxNzhSiaD5HCkni3KypZwsqeCOIx9zsbW/mXxKuDnfOsQ91hLu9jzP+4GhLPPns9x/OUv8V7a5LBu/uwU69F01p71UquklJfSSIs6SYs6SEnr5izirppgLpITRUkoKNWF9cKgFSt1W0RLbosS2KXF7BoqtCortAspsi0oRikU4KcIpiwYH7ZRIyIHtfxz+OCOGXtFm2xLFYQqJiNwO3O4engJ2xtCc5sgBjsfaiGD+HIc2ucSjXefH2oBgVPPtY3Ec2uQSj3ap5sPImbfx99ytVcSjPiJJxK73Mlp0Tptdl6tTdsmJyOZ4bEaOR7vi0SaIT7vi0aZ64tW2eLQrHm2C+LQrHm2qJ55tiwR6vbEnUV6n2AScKyIDRSQJmAEsj7FNiqIoiqJ0ERKiS84YUyciPwTewJlW4I/GmI9ibJaiKIqiKF2EhHCYAIwxrwOvh0zo8FQkbekA8WhXPNoE8WlXPNpUT7zaFo92xaNNEJ92xaNN9cSzbZFArzfGJMQYJkVRFEVRlFiSKGOYFEVRFEVRYkZCOEwicrWI7BaRvSLyf5qITxaR5934DSIyICjuHjd8t4hc1doyI2WTiEwQkQ9EZIf7eWVQntVumVvdrWcU7RogItVB514YlOdrrr17ReRxkbZNl9YBm24OsmeriARE5GI3Lhp1NVpEtohInYhMbxQ3W0Q+cbfZQeEdqqs22Kaa77hdXUrz8az3Dtrd7G8hEWnF9f5YRHaJyHYReVtEmn0NPhFo7f+SiEwTESMisXtzzhgT1xvOIO99wDlAErANGNIozQ+Ahe7+DOB5d3+Imz4ZGOiWY7emzAjadAnQ292/EDgclGc1MDxGdTUA2NlMuRuBb+BMI/IP4Jpo2NQozTBgX5TragBwEfBnYHpQeBaw3/3s4e736GhdqeZV85HSfDzrPRp1mShbK6/3CiDV3f9+Z79eN10asBZY35H/i45uidDCdBmw1xiz3xhTAywBJjdKMxl4xt1/ERjnPulMBpYYY04ZYw4Ae93yWlNmRGwyxnxojDnihn8E+EQkuQ3njohdzRUoIr2AdGPMeuMo98/AlBjY9B03b7gIaZcx5qAxZjsQaJT3KmCFMabYGFMCrACuDkNdtdo2VPMdtqu5Ajup5uNZ7x2ymzZ+v3FOa76nVcaYKvdwPdA3yjaGk9b+L80DfgGcjKZxjUkEh6mpZVH6NJfGGFMHlAHZLeRtTZmRsimYacAWY8ypoLA/uc3t97fjR99RuwaKyIciskZERgWlPxSizEjaVM9NwF8bhUW6rtqat6N11RbbVPPhsauraD6e9d6eczeZpoW6TBTa+j3dhtOyl6iEvF4RuRToZ4x5LZqGNUXCTCvQ2RCRoTge88Sg4JuNMYdFJA14CZiF89QWDQqA/saYIhH5GvA318aYIyJfB6qMMcHLIMSyrpR2oJpvPap5JRQicgswHBgTa1sihYhYwHxgToxNARKjhekw0C/ouK8b1mQaEfEAGUBRC3lbU2akbEJE+gIvA7caY/bVZzDGHHY/K4C/4DRXtoV22+V24RS55/8Ap1/5PDd9cJNvVOvKZQaNnrSjVFdtzdvRumqLbar5DtrVxTQfz3pvz7mbTNNMXSYSrfqeRGQ8cC8wqVFrbaIR6nrTcMY9rhaRgzjj5ZbHbOB3rAZPtXbDaQXbjzOAtX5Q2NBGaf6T0wf9veDuD+X0AbD7cQaZhSwzgjZluumnNlFmjrvvxemL/14U6yoXsN39c3BEm+UeNx7YeW00bHKPLdeWc6JdV0Fpn+bMQbAHcAbA9nD3O1xXqnnVfKQ0H896j2RdJtrWyuu9BMe5PzfW9kbjehulX00MB33HvMJaWanXAntckdzrhv03jncNkAIsxRngurHRH829br7dBL3B0VSZ0bAJuA+oBLYGbT2BbsAHwHacgbG/xf0zj5Jd09zzbgW2ANcHlTkcZ1XwfcATuBOeRun7Gwusb1RetOpqBE6feiXOE+tHQXm/69q7F/hf4aor1bxqPlKaj2e9R6ouE3FrxfW+BRwN+i0tj7XNkbzeRmlXE0OHSWf6VhRFURRFCUEijGFSFEVRFEWJKeowKYqiKIqihEAdJkVRFEVRlBCow6QoiqIoihICdZgURVEURVFCoA5TDBARv7u8wUcisk1E7nJnNEVEhovI4y3kHSAiM6Nn7RnnrhaRrUHHO0Pla0W5d4rIZyLyRMetVOIR1fwZ5armOzmq+TPKTXjN69IosaHaGHMxgIj0xJm1Nx34qTFmM7C5hbwDgJlunliwr972cGGM+bWIlODM6aJ0TlTzQajmuwSq+SA6g+a1hSnGGGO+AG4HfigOY0XkVQARGeM+oWx1FwhNAx4FRrlhd7re/zoR2eJul7t5x4rIahF5UUT+JSLP1S/WKSIjROQ996lno4ikiYgtIv9XRDaJyHYR+Y+2XIeInOPaOEJE5ojIK+75PxGRnwalu9Utf5uILA5XPSqJg2pe6Wqo5jsJsZ7lsytuwIkmwkqBr+DM8vuqG/Z3YKS73x2nRbAh3g1PBVLc/XOBze7+WJxVu/viOMbvA/k408/vB0a46dLdcm8H7nPDknGefgY2snEAsLPxMXA+8CHwVTd8Ds7CptmAz00zHGfZjj18ucRDVlBZc4AnYv3d6Kaab2Sjal431bxqHmOMdsnFOe8C80XkOWCZMeaQ+/AQjBd4QkQuBvw4i4fWs9EYcwhAnP7oATg/rgJjzCYAY0y5Gz8RuEhEprt5M3B+mAdC2JgLvIKzTtiuoPAVxl3YVESW4fyI/cBSY8xx99zFraoFpSuhmle6Gqr5BEEdpjhARM7BEdkXQF59uDHmURF5DWetnXdF5Komst+Js67QV3GeME4GxQWvYu2n5e9bgP9tjHmjjeaXAZ/h/FCCf0iN19zRNXiUBlTzSldDNZ/46BimGCMiucBCnGZK0yhukDFmhzHmF8Am4AKgAkgLSpaB8yQRAGbhrEzfEruBXiIywj1Hmoh4gDeA74uI1w0/T0S6teISaoAbgFvl9Lc6JohIloj4gCk4T1ErgRtFJNs9R1Yrylc6Gap5pauhmu8caAtTbPC5TadeoA5YDMxvIt2PROQKIICzQvk/3H2/iGwDngaeBF4SkVuBf+KsNN4sxpgaEbkJ+J0r8mpgPLAIpyl3izto8BjODyAkxphKEfkWsEJETrjBG4GXcPrWnzXOWyGIyMPAGhHx4/SHz2nNOZSERzWvmu9qqOY7mealkbOrKM0iIgNwBiJeGCLdHGC4MeaHbSi7zXkUJdKo5pWuhmq+ebRLTmkLfiDDfWoKGyJyJ3APUB7OchUlDKjmla6Gar4ZtIVJURRFURQlBNrCpCiKoiiKEgJ1mBRFURRFUUKgDpOiKIqiKEoI1GFSFEVRFEUJgTpMiqIoiqIoIVCHSVEURVEUJQT/AxYAQXwg2gAHAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(1.*A4width,A4heigth/4.))\n", - "\n", - "coord = ['X', 'Y', 'Z']\n", - "\n", - "ChiDict = {}\n", - "\n", - "for i, x_i in enumerate(coord):\n", - " ChiDict[str(x_i)] = {}\n", - " \n", - " L = [10, 50, 100]\n", - " colors = sns.color_palette()\n", - " for j, l in enumerate(L):\n", - " ChiDict[str(x_i)][str(l)] = {}\n", - " if i <2:\n", - " abs(df[df.D==l][x_i]).hist(bins=np.linspace(0, 0.1, 11), ax=axes[i], alpha=1., zorder=3-j, color=colors[j])\n", - " hist = np.histogram(abs(df[df.D==l][x_i]), bins=np.linspace(0,0.1,11))\n", - " \n", - " PDF = np.zeros(len(hist[0]))\n", - " xP = np.zeros(len(hist[0]))\n", - " for k in range(len(PDF)):\n", - " a, b = hist[1][k]*kpc, hist[1][k+1]*kpc\n", - " xP[k] = (a+b)/2./kpc\n", - " PDF[k] = quad(pdf, a,b, args=(l*kpc/c_light, 0.1))[0]*N\n", - " t = l*kpc/c_light\n", - " s = '%.2e' % t\n", - " axes[i].plot(xP, PDF, color=colors[j], label='t = '+s+' s', zorder=10)\n", - " #axes[i].legend(loc='best')\n", - " axes[i].set_xlim(0., 0.1)\n", - " else:\n", - " abs(df[df.D==l][x_i]).hist(bins=np.linspace(0, 0.5, 11), ax=axes[i], alpha=1., zorder=3-j, color=colors[j])\n", - " hist = np.histogram(abs(df[df.D==l][x_i]), bins=np.linspace(0,0.5,11))\n", - " PDF = np.zeros(len(hist[0]))\n", - " xP = np.zeros(len(hist[0]))\n", - " for k in range(len(PDF)):\n", - " a, b = hist[1][k]*kpc, hist[1][k+1]*kpc\n", - " xP[k] = (a+b)/2./kpc\n", - " PDF[k] = quad(pdf, a,b, args=(l*kpc/c_light, 1.))[0]*N\n", - " \n", - " axes[i].plot(xP, PDF, color=colors[j], label='t = '+s+' s', zorder=10) \n", - " axes[i].set_xlim(0., 0.5)\n", - " ChiDict[str(x_i)][str(l)]['Obs'] = hist[0]\n", - " ChiDict[str(x_i)][str(l)]['Exp'] = PDF\n", - "\n", - " axes[i].set_xlabel('Distance [kpc]')\n", - " axes[i].legend(loc='best')\n", - " axes[i].set_title(x_i)\n", - " if i>0:\n", - " axes[i].set_yticklabels([])\n", - " else:\n", - " axes[i].set_ylabel(\"number density\")\n", - " \n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "${\\mathrm{\\bf Fig 2:}}$ The distance from the source position follows nicely the expected pdf." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate the pValue of the $\\chi^2$-test to prove the visual statement from above." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
p-Value
CoordinateTime [s]
X1.03e+120.988314
5.15e+120.379516
1.03e+130.340236
Y1.03e+120.763136
5.15e+120.391583
1.03e+130.786104
Z1.03e+120.618256
5.15e+120.885988
1.03e+130.367668
\n", - "
" - ], - "text/plain": [ - " p-Value\n", - "Coordinate Time [s] \n", - "X 1.03e+12 0.988314\n", - " 5.15e+12 0.379516\n", - " 1.03e+13 0.340236\n", - "Y 1.03e+12 0.763136\n", - " 5.15e+12 0.391583\n", - " 1.03e+13 0.786104\n", - "Z 1.03e+12 0.618256\n", - " 5.15e+12 0.885988\n", - " 1.03e+13 0.367668" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "C, Time, pValue = [], [], []\n", - "for c in coord:\n", - " for l in L:\n", - " C.append(c)\n", - " t = l*kpc/c_light\n", - " s = '%.2e' % t\n", - " Time.append(s)\n", - " pValue.append(chisquare(ChiDict[c][str(l)]['Obs'], ChiDict[c][str(l)]['Exp'])[1])\n", - "Chi = pd.DataFrame(pValue, index = [C, Time])\n", - "Chi.rename(columns = {0:\"p-Value\"}, inplace=True)\n", - "Chi.index.names = [\"Coordinate\", \"Time [s]\"]\n", - "Chi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "${\\mathrm{\\bf Tab 1:}}$ The assumption that the two samples (observed and expected) are drawn from the same distribution cannot be rejected for any direction." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/pages/example_notebooks/Diffusion/DiffusionValidationII.v4.ipynb b/doc/pages/example_notebooks/Diffusion/DiffusionValidationII.ipynb similarity index 100% rename from doc/pages/example_notebooks/Diffusion/DiffusionValidationII.v4.ipynb rename to doc/pages/example_notebooks/Diffusion/DiffusionValidationII.ipynb From 74b051c2c69874063915b8a41b1da788ee9d159d Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 10:42:00 +0100 Subject: [PATCH 57/87] Undo changes to acceleration example as thex have been accidentally commited. --- .../acceleration/first_order_fermi_acceleration.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/example_notebooks/acceleration/first_order_fermi_acceleration.ipynb b/doc/pages/example_notebooks/acceleration/first_order_fermi_acceleration.ipynb index ace4f3be6..b859a2f98 100644 --- a/doc/pages/example_notebooks/acceleration/first_order_fermi_acceleration.ipynb +++ b/doc/pages/example_notebooks/acceleration/first_order_fermi_acceleration.ipynb @@ -123,7 +123,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] From 77c5efad4e5b714dd55e4983f7f1b4d5615c11ae Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 11:05:40 +0100 Subject: [PATCH 58/87] Update custom observer example to work with CandidateTagColumn --- .../advanced/CustomObserver.v4.ipynb | 21 ++++++++++--------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb b/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb index 15b17ca3d..d3c49b3cd 100644 --- a/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb +++ b/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb @@ -20,6 +20,7 @@ "outputs": [], "source": [ "import crpropa\n", + "import numpy as np\n", "\n", "class ObserverPlane(crpropa.ObserverFeature):\n", " \"\"\"\n", @@ -63,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": true @@ -74,13 +75,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "crpropa::ModuleList: Number of Threads: 2\n" + "crpropa::ModuleList: Number of Threads: 8\n" ] } ], "source": [ "from crpropa import Mpc, nG, EeV\n", - "import numpy as np\n", "Brms, lMin, lMax, sIndex=1*nG, 2*Mpc, 5*Mpc, 5./3.\n", "turbSpectrum = crpropa.SimpleTurbulenceSpectrum(Brms, lMin, lMax, sIndex)\n", "gridprops = crpropa.GridProperties(crpropa.Vector3d(0), 128, 1 * Mpc)\n", @@ -92,6 +92,7 @@ "\n", "# Observer\n", "out = crpropa.TextOutput(\"sheet.txt\")\n", + "out.disable(crpropa.Output.CandidateTagColumn) # not needed here, if activated loading of data needs to be updated.\n", "o = crpropa.Observer()\n", "# The Observer feature has to be created outside of the class attribute\n", "# o.add(ObserverPlane(...)) will not work for custom python modules\n", @@ -121,12 +122,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -166,12 +167,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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ZaUQsj4iOiOhoamqqoDwzMxusktDvA/oi4pH0+jZyfwRePDBsk77vSPP7gZl5y7ekNjMzq5GyQz8iXgC2SvpAapoLPA6sBhamtoXAnWl6NXBBuornJGB33jCQmZnVwOgKl/874CZJY4FngQvJ/SG5VdJFwG+Bz6S+dwNnAL3Aq6mvmZnVUEWhHxGPAR0FZs0t0DeAiyvZnpmZVcafyDUzyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGVJx6EsaJemXkv4zvW6X9IikXkn/np6qhaRx6XVvmt9W6bbNzKw01TjS/xLwRN7ra4BlEfE+YBdwUWq/CNiV2pelfmZmVkMVhb6kFuATwA/SawEfA25LXVYBZ6bpBek1af7c1N/MzGqk0iP964CvAm+m11OBVyJif3rdBzSn6WZgK0Cavzv1fxtJXZJ6JPUMDAxUWJ6ZmeUrO/QlfRLYERHrq1gPEbE8IjoioqOpqamaqzYzy7zRFSx7MvBpSWcA44E/Ab4NTJY0Oh3NtwD9qX8/MBPokzQamATsrGD7ZmZWorKP9CNicUS0REQbcB5wf0T8FfAAcHbqthC4M02vTq9J8++PiCh3+2ZmVrqRuE7/H4EvS+olN2a/IrWvAKam9i8D3SOwbTMzG0IlwztviYgHgQfT9LPACQX67AXOqcb2zMysPP5ErplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLkEoejD5T0gOSHpe0RdKXUvsUSWslPZO+H53aJek7knolbZQ0p1o/hJmZHZpKjvT3A/8QEccCJwEXSzqW3GMQ74uIWcB9/PGxiPOBWemrC7i+gm2bmVkZKnkw+vaI2JCmfw88ATQDC4BVqdsq4Mw0vQC4MXLWAZMlTS93+2ZmVrqqjOlLagM+CDwCHBMR29OsF4Bj0nQzsDVvsb7UNnhdXZJ6JPUMDAxUozwzM0sqDn1JRwG3A38fEb/LnxcRAUQp64uI5RHREREdTU1NlZZnZmZ5Kgp9SWPIBf5NEfHT1PzigWGb9H1Hau8HZuYt3pLazMysRiq5ekfACuCJiPjXvFmrgYVpeiFwZ177BekqnpOA3XnDQGZmVgOjK1j2ZOBvgE2SHkttlwNXA7dKugj4LfCZNO9u4AygF3gVuLCCbZuZWRnKDv2IeBhQkdlzC/QP4OJyt2dmZpXzJ3LNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhldx7x+wdofO2Trbt2XZQ+4wJM1hz9po6VGRWPw59O+xt27ONTQs3HdQ+e9Xs6m5o6aSD2ya1wqUHb9usXhz6VhvLZsPu5w9ur2YoFttGe2vB7jMmzCga/KX+F/DmG5OZ3V5gPfv24/8lrJE49K0mOifuZ9uUg8O3nFAsNlzDFKDINgoZKtQ7V84u+Afh3fveLNh/z6+7+c3Vnziover/TZhVyKFvNbFtzOiCQyydP/izkoNxxoQZBdfF0kmwdPfB7ctmFx56GcKaIv+BtHXfVdJ6zBqNQ9/qas3vR5c+7FMsxCcVHsapxZh68+QjC/5B+MD79hf8o+aTyFYvNQ99SfOAbwOjgB9ExNW1rsEGKTYWXobO1la2jTq4vdgQS8nBDrlwL3REX0f/3f2xgu19S75Iiw4eiupsLT704z8INpJqGvqSRgHfAz4O9AGPSlodEY/Xso7MGupkaokhWvQyyD/ApueKbKMUdb7i5eSr76f/ldcOam+efGRJ6/nw698pONa/pthQFLl9W8qQ14x9+1nTV+AcRzl8tdFhr9ZH+icAvRHxLICkW4AFwDs29IcKvzXPlzBsUcbVLZ0rZxc8qi6qyIlOAKo1rn6Y6H/ltYJhXTWTWov+J1P0GL/Ie6Hztk5mj6nir3IVTj7P+AOs+XyBWkt9z1L8P59i6yq2bctR7tG1NdqYdDYwLyL+Nr3+G+DEiLgkr08X0JVefgB4qoJNTgNeqmD5keK6SuO6SuO6SnM41vWnEdFUaEbDnciNiOXA8mqsS1JPRHRUY13V5LpK47pK47pKk7W6an3vnX5gZt7rltRmZmY1UOvQfxSYJald0ljgPGB1jWswM8usmg7vRMR+SZeQO1c1ClgZEVtGcJNVGSYaAa6rNK6rNK6rNJmqq6Yncs3MrL58P30zswxx6JuZZchhF/qSrpX0pKSNku6QNDlv3mJJvZKektRZ47rOkbRF0puSOvLa2yS9Jumx9HVDI9SV5tVtfw0maamk/rz9dEYda5mX9kmvpO561VGIpN9I2pT2UU8d61gpaYekzXltUyStlfRM+n50g9RV1/eWpJmSHpD0ePpd/FJqH5n9FRGH1RdwOjA6TV8DXJOmjwV+BYwD2oFfA6NqWNefkfuw2YNAR157G7C5jvurWF113V8F6lwKXNYA769RaV+8Bxib9tGx9a4rr77fANMaoI5TgTn5723gX4DuNN194HezAeqq63sLmA7MSdMTgafT79+I7K/D7kg/Iu6NiAN391pH7rMAkLvdwy0R8XpEPAf0krstRK3qeiIiKvl08YgYoq667q8G9tatRCLiDeDArUQsT0Q8BLw8qHkBsCpNrwLOrGVNULSuuoqI7RGxIU3/HngCaGaE9tdhF/qDfB64J003A1vz5vWltkbQLumXkn4u6ZR6F5M04v66JA3brazH0EDSiPslXwD3SlqfbmnSSI6JiO1p+gXgmHoWM0gjvLeQ1AZ8EHiEEdpfDXcbhkMh6b+A/1Vg1hURcWfqcwWwH7ipkeoqYDvQGhE7JX0I+A9Jx0XE7+pcV80NVSdwPfANcqH2DeBb5P6o29t9OCL6Jb0bWCvpyXR021AiIiQ1yvXiDfHeknQUcDvw9xHxO0lvzavm/npHhn5E/OVQ8yV9DvgkMDfSgBg1uAXEcHUVWeZ14PU0vV7Sr4H3A1U7CVdOXdThlhmHWqek7wP/OZK1DKGhbyUSEf3p+w5Jd5AbjmqU0H9R0vSI2C5pOrCj3gUBRMSLB6br9d6SNIZc4N8UET9NzSOyvw674Z30kJavAp+OiFfzZq0GzpM0TlI7MAv4RT1qzCepKT1nAEnvIVfXs/WtCmiw/ZXe9AecBWwu1neENeytRCRNkDTxwDS5ixrqtZ8KWQ0sTNMLgYb4L7Pe7y3lDulXAE9ExL/mzRqZ/VWvM9YjeCa8l9yY62Pp64a8eVeQu/LiKWB+jes6i9z47+vAi8Ca1P5/gS2p1g3ApxqhrnrvrwJ1/hjYBGxMvwzT61jLGeSusPg1uSGyuu2XQXW9h9zVRL9K76m61QbcTG7ocl96f10ETAXuA54B/guY0iB11fW9BXyY3NDSxrzcOmOk9pdvw2BmliGH3fCOmZkV59A3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWXI/wBuV9pFbF9dFQAAAABJRU5ErkJggg==", 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" ] @@ -194,7 +195,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.6 ('crp')", + "display_name": "crp_docu", "language": "python", "name": "python3" }, @@ -208,11 +209,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.2" }, "vscode": { "interpreter": { - "hash": "9b0c5e5016d73719a8e2817ed012b8006f294be990373a75d6bf3c89fad1d7fd" + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" } }, "widgets": { From 1ca6903e769bbd9dfd2a2be8f8c1b190cf6be50f Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 12:47:25 +0100 Subject: [PATCH 59/87] Remove deprecated initTurbulence from backtracking example --- .../basics/{basics.v4.ipynb => basics.ipynb} | 0 .../galactic_backtracking.ipynb | 341 ++++++++++++++++++ .../galactic_backtracking.v4.ipynb | 339 ----------------- 3 files changed, 341 insertions(+), 339 deletions(-) rename doc/pages/example_notebooks/basics/{basics.v4.ipynb => basics.ipynb} (100%) create mode 100644 doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.ipynb delete mode 100644 doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb diff --git a/doc/pages/example_notebooks/basics/basics.v4.ipynb b/doc/pages/example_notebooks/basics/basics.ipynb similarity index 100% rename from doc/pages/example_notebooks/basics/basics.v4.ipynb rename to doc/pages/example_notebooks/basics/basics.ipynb diff --git a/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.ipynb b/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.ipynb new file mode 100644 index 000000000..2058d32c1 --- /dev/null +++ b/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Galactic backtracking\n", + "The following setup shows how to use CRPropa for backtracking simulations. \n", + "In the JF12 model the Galaxy is a sphere of 20 kpc radius.\n", + "For the magnetic field we are going to consider the regular component of the JF2012 model. The large-scale (striated) and small-scale (turbulent) random components can optionally be activated with the outcommented sections and a random seed can be set for reproducability." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ModuleList\n", + " Propagation in magnetic fields using the Cash-Karp method. Target error: 0.0001, Minimum Step: 0.0001 kpc, Maximum Step: 0.1 kpc\n", + " Observer\n", + " ObserverSurface: << Sphere: \n", + " Center: 0 0 0\n", + " Radius: 6.17136e+20\n", + "\n", + " Flag: '' -> ''\n", + " MakeInactive: yes\n", + "\n", + "\n" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "# magnetic field setup\n", + "B = JF12Field()\n", + "#seed = 691342\n", + "#B.randomStriated(seed)\n", + "#B.randomTurbulent(seed)\n", + "\n", + "# simulation setup\n", + "sim = ModuleList()\n", + "sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", + "obs = Observer()\n", + "obs.add(ObserverSurface( Sphere(Vector3d(0), 20 * kpc) ))\n", + "# obs.onDetection(TextOutput('galactic_backtracking.txt', Output.Event3D))\n", + "sim.add(obs)\n", + "print(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backtracking a single cosmic ray\n", + "\n", + "Let's assume we observed a 10 EeV cosmic ray coming from the direction given by longitude and colatitude (1.95, 0.96) radian and want to investigate its direction before having traversed the Galaxy.\n", + "\n", + "Backtracking corresponds to forward-tracking a particle of the opposite charge, thus we select an anti-proton, which in the HEP ID numbering scheme is denoted by a negative sign.\n", + "Assuming the cosmic ray was a proton the backtracking turns out as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CosmicRay at z = 0\n", + " source: Particle -1000010010, E = 10 EeV, x = -0.0085 0 0 Mpc, p = -0.303249 0.760996 0.57352\n", + " current: Particle -1000010010, E = 10 EeV, x = -0.0144674 0.011531 0.00759822 Mpc, p = -0.434112 0.764527 0.476493\n", + "Galactic deflection 0.16 radian\n" + ] + } + ], + "source": [ + "pid = - nucleusId(1,1) # (anti-)proton\n", + "energy = 10 * EeV\n", + "position = Vector3d(-8.5, 0, 0) * kpc\n", + "lat = 0.96\n", + "lon = 1.95\n", + "direction = Vector3d()\n", + "direction.setRThetaPhi(1, lat, lon)\n", + "p = ParticleState(pid, energy, position, direction)\n", + "c = Candidate(p)\n", + "\n", + "sim.run(c)\n", + "print(c)\n", + "\n", + "d1 = c.current.getDirection() # direction at Galactic border\n", + "print('Galactic deflection %.2f radian' % direction.getAngleTo(d1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backtracking including uncertainties\n", + "The impact of the cosmic ray uncertainties on backtracked directions can be investigated with a MC approach. In the following, the cosmic ray energy and direction are varied within the statistical uncertainties before backtracking." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "R = Random() # CRPropa random number generator\n", + "\n", + "pid = - nucleusId(1,1)\n", + "meanEnergy = 10 * EeV\n", + "sigmaEnergy = 0.1 * meanEnergy # 10% energy uncertainty\n", + "position = Vector3d(-8.5, 0, 0) * kpc\n", + "lat0 = 0.96\n", + "lon0 = 1.95\n", + "meanDir = Vector3d()\n", + "meanDir.setRThetaPhi(1, lat0, lon0)\n", + "sigmaDir = 0.002 # 1 degree directional uncertainty\n", + "\n", + "lons, lats = [], []\n", + "for i in range(100):\n", + " energy = R.randNorm(meanEnergy, sigmaEnergy)\n", + " direction = R.randVectorAroundMean(meanDir, sigmaDir)\n", + "\n", + " c = Candidate(ParticleState(pid, energy, position, direction))\n", + " sim.run(c)\n", + "\n", + " d1 = c.current.getDirection()\n", + " lons.append(d1.getPhi())\n", + " lats.append(d1.getTheta())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Optional) Plotting\n", + "Finally we are plotting a skymap of the observed direction along with the distribution of directions at the galactic border." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Angle definitions:\n", + "# CRPropa uses\n", + "# longitude (phi) [-pi, pi] with 0 pointing in x-direction\n", + "# colatitude (theta) [0, pi] with 0 pointing in z-direction\n", + "# matplotlib expects\n", + "# longitude [-pi, pi] with 0 = 0 degrees\n", + "# latitude [pi/2, -pi/2] with pi/2 = 90 degrees (north)\n", + "lat0 = np.pi/2 - lat0\n", + "lats = np.pi/2 - np.array(lats)\n", + "\n", + "plt.figure(figsize=(12,7))\n", + "plt.subplot(111, projection = 'hammer')\n", + "plt.scatter(lon0, lat0, marker='+', c='black', s=100)\n", + "plt.scatter(lons, lats, marker='o', c='blue', linewidths=0, alpha=0.2)\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backtracking to Generate a Lens\n", + "\n", + "The following is an example for a backtracking simulation with a uniform isotropic coverage suitable to generate a magnetic lens. Here, anti-particles are emitted following the healpic scheme to achieve an uniform coverage of the starting direction. Please note that for production use, nside = 1024 should be used and as well a fine binning of rigidities extendig down to ~0.1 EeV is required, e.g. $10^{16.99}$ eV; $10^{17.01}$ eV; $10^{17.03}$ eV ... ;. The backtracking data can be post processed with the [create-lens.py](https://github.com/CRPropa/CRPropa3-data/blob/master/create_lens.py) program." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "jupyter": { + "outputs_hidden": true + }, + "scrolled": false + }, + "outputs": [], + "source": [ + "from crpropa import *\n", + "import healpy\n", + "\n", + "def backtrackingRun(logE=18., nside=16):\n", + " \"\"\"Galactic Lens: Backtracking run\n", + " \n", + " Runs the backtracking simulation for a given rigidity and\n", + " healpy pixel configuration. Creates a file with detected \n", + " candidate properties at r_gal=20*kpc.\n", + " \n", + " Input\n", + " -----\n", + " logE=18. : float\n", + " log10(R/V), rigidity of the backtracked particles\n", + " \n", + " nside=16 : int\n", + " healpix parameter, should be increased to ~1024 for production\n", + " of real lenses\n", + " \n", + " Returns\n", + " -------\n", + " \"\"\"\n", + " \n", + " # magnetic field setup\n", + " B = JF12Field()\n", + " seed = 1703202123\n", + " B.randomStriated(seed)\n", + " B.randomTurbulent(seed)\n", + " \n", + " \n", + " # simulation setup\n", + " sim = ModuleList()\n", + " sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", + " obs = Observer()\n", + " obs.add(ObserverSurface( Sphere(Vector3d(0.), 20 * kpc) ))\n", + " \n", + " ofname = 'galactic_backtracking_{:.2f}.h5'.format(logE)\n", + " print(\"Writing output to {}\".format(ofname))\n", + " out = HDF5Output(ofname, Output.Event3D)\n", + " obs.onDetection(out)\n", + " sim.add(obs)\n", + " \n", + " pid = - nucleusId(1,1) # (anti-)proton\n", + " energy = 10**logE * electronvolt\n", + " \n", + " print(\"Running at 10**{} eV = {} EeV\".format(logE, energy / EeV))\n", + " \n", + " position = Vector3d(-8.5, 0, 0) * kpc\n", + " \n", + " # submit a particle in every direction of a healpix map, 256 per pixel of the\n", + " # lens\n", + " nparts = healpy.nside2npix(nside)\n", + " print('simulating {} particles'.format(nparts))\n", + " # Use candidate vector to enable multi core processing\n", + " cv = CandidateVector()\n", + " print(\"Preparing Particles ...\")\n", + " for i in range(nparts):\n", + " v = healpy.pix2vec(nside, i)\n", + " direction = Vector3d(v[0], v[1], v[2])\n", + " p = ParticleState(pid, energy, position, direction)\n", + " c = CandidateRefPtr(Candidate(p))\n", + " cv.push_back(c)\n", + " \n", + " sim.setShowProgress()\n", + " sim.run(cv)\n", + " out.close()\n", + " print (\"Finished!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing output to galactic_backtracking_18.00.h5\n", + "Running at 10**18.0 eV = 1.0 EeV\n", + "simulating 3072 particles\n", + "Preparing Particles ...\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 2 12:43:59 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:20 - Finished at Thu Feb 2 12:44:19 2023\n", + "Finished!\n" + ] + } + ], + "source": [ + "backtrackingRun()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb b/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb deleted file mode 100644 index 48520d012..000000000 --- a/doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb +++ /dev/null @@ -1,339 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Galactic backtracking\n", - "The following setup shows how to use CRPropa for backtracking simulations. \n", - "In the JF12 model the Galaxy is a sphere of 20 kpc radius.\n", - "For the magnetic field we are going to consider the regular component of the JF2012 model. The large-scale (striated) and small-scale (turbulent) random components can optionally be activated with the outcommented sections and a random seed can be set for reproducability." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ModuleList\n", - " Propagation in magnetic fields using the Cash-Karp method. Target error: 0.0001, Minimum Step: 0.0001 kpc, Maximum Step: 0.1 kpc\n", - " Observer\n", - " ObserverSurface: << Sphere: \n", - " Center: 0 0 0\n", - " Radius: 6.17136e+20\n", - "\n", - " Flag: '' -> ''\n", - " MakeInactive: yes\n", - "\n", - "\n" - ] - } - ], - "source": [ - "from crpropa import *\n", - "\n", - "# magnetic field setup\n", - "B = JF12Field()\n", - "#seed = 691342\n", - "#B.randomStriated(seed)\n", - "#B.randomTurbulent(seed)\n", - "\n", - "# simulation setup\n", - "sim = ModuleList()\n", - "sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", - "obs = Observer()\n", - "obs.add(ObserverSurface( Sphere(Vector3d(0), 20 * kpc) ))\n", - "# obs.onDetection(TextOutput('galactic_backtracking.txt', Output.Event3D))\n", - "sim.add(obs)\n", - "print(sim)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backtracking a single cosmic ray\n", - "\n", - "Let's assume we observed a 10 EeV cosmic ray coming from the direction given by longitude and colatitude (1.95, 0.96) radian and want to investigate its direction before having traversed the Galaxy.\n", - "\n", - "Backtracking corresponds to forward-tracking a particle of the opposite charge, thus we select an anti-proton, which in the HEP ID numbering scheme is denoted by a negative sign.\n", - "Assuming the cosmic ray was a proton the backtracking turns out as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CosmicRay at z = 0\n", - " source: Particle -1000010010, E = 10 EeV, x = -0.0085 0 0 Mpc, p = -0.303249 0.760996 0.57352\n", - " current: Particle -1000010010, E = 10 EeV, x = -0.0144674 0.011531 0.00759822 Mpc, p = -0.434112 0.764527 0.476493\n", - "Galactic deflection 0.16 radian\n" - ] - } - ], - "source": [ - "pid = - nucleusId(1,1) # (anti-)proton\n", - "energy = 10 * EeV\n", - "position = Vector3d(-8.5, 0, 0) * kpc\n", - "lat = 0.96\n", - "lon = 1.95\n", - "direction = Vector3d()\n", - "direction.setRThetaPhi(1, lat, lon)\n", - "p = ParticleState(pid, energy, position, direction)\n", - "c = Candidate(p)\n", - "\n", - "sim.run(c)\n", - "print(c)\n", - "\n", - "d1 = c.current.getDirection() # direction at Galactic border\n", - "print('Galactic deflection %.2f radian' % direction.getAngleTo(d1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backtracking including uncertainties\n", - "The impact of the cosmic ray uncertainties on backtracked directions can be investigated with a MC approach. In the following, the cosmic ray energy and direction are varied within the statistical uncertainties before backtracking." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "R = Random() # CRPropa random number generator\n", - "\n", - "pid = - nucleusId(1,1)\n", - "meanEnergy = 10 * EeV\n", - "sigmaEnergy = 0.1 * meanEnergy # 10% energy uncertainty\n", - "position = Vector3d(-8.5, 0, 0) * kpc\n", - "lat0 = 0.96\n", - "lon0 = 1.95\n", - "meanDir = Vector3d()\n", - "meanDir.setRThetaPhi(1, lat0, lon0)\n", - "sigmaDir = 0.002 # 1 degree directional uncertainty\n", - "\n", - "lons, lats = [], []\n", - "for i in range(100):\n", - " energy = R.randNorm(meanEnergy, sigmaEnergy)\n", - " direction = R.randVectorAroundMean(meanDir, sigmaDir)\n", - "\n", - " c = Candidate(ParticleState(pid, energy, position, direction))\n", - " sim.run(c)\n", - "\n", - " d1 = c.current.getDirection()\n", - " lons.append(d1.getPhi())\n", - " lats.append(d1.getTheta())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## (Optional) Plotting\n", - "Finally we are plotting a skymap of the observed direction along with the distribution of directions at the galactic border." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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zlUwmYTKZyrZarVZZxXAikcChQ4fwyiuvpF9++eVCJBIpqNXqt4LB4M8FQXiTUjorm3EMhoww8ctgvEMIIe0Gg+F9LpfrNwVB6Nu+fTt54IEHbPfcc4+mr69PtuNQYFlAZLNZhEIhhMNhFAoF2O32stithhDPZrPw+/0IBALQaDSor69HfX39DVemKJVKiMVi5SFVlbDZbLBarTCbzTCbzdd+IobikBIp8/k8UqkUEolEuQqDtNmx2+03VCZPes5gMIhQKAS9Xg+v1wuv11vxjZP0uZPEcCqVQm1tLdxuN9xut6wbYGC5W92JEyfw2muv5X/xi19kpqameI1Gc8jv9/+IUvoqpTQqq4EMRpVg4pfBuE4IIU5CyH0+n+8/8zx/54YNGzQf+tCHat/3vvfpent7ZfXwAMvhBNLRcCwWg8lkKi+61RKHmUwGfr8fwWAQWq0WPp9vzUWHlNU/OTmJWCwGvV4PQghMJhNqampgtVphsVhgsVhYyISCKJVKSKfT5ZFKpVAqlSAIAnieh9frRUdHB8xm85p+ljKZDAKBAAKBQPmerK+vr4oQlYT40tISlpaWIIoiXC4X3G43HA6HrBtkYHnOOHr0KF566aXsz3/+81w0Gk1TSn8ZCoVeBHCAUlqQ1UAGo0Iw8ctgXAFCiAHAXR6P50OEkPc5nU7rgw8+aHr/+99vvuOOO2SpCboaaWGVvLtyLazpdLoseA0GA7xeL+rr6yviZUulUpicnEQqlUJraysaGxuhVqvLR+WpVKosrjKZDARBgF6vL3uHzWYzTCYTTCaT7H+/Ww1KKYrFInK5HHK5HLLZbHnwPA+dTgeLxQKr1Qqr1YqampqyAC0UCpiZmUEgEIDX60Vra2tFOgNms9myEFar1fB6vfD5fFXzyHIch0gkgqWlpQs2qB6PR5Za3ReTSqWwf/9+/OxnP0u88sorXKlU8udyuRcTicQ/AxiklIpy28hgrAVM/DIYK6wkqW212WwPmkymD+l0Ot973vMe7Qc/+EHbvn37ZKmvezGCICAcDiMYDCIej6OmpkaWI9V8Po/FxUX4/X7o9fqyN60SgpJSilAohKmpKahUKrS3t6Ouru66vIOUUpRKpQuEmCTOeJ4HAKjVahBCUFdXB6PRCIPBAIPBAL1eD51OJ7tHXwmIoohisYhisYhCoYBCoVCO49ZoNBCE5QpbOp2uvMFYvdm43vtCEAQsLi5ienoaVqsVHR0dFUsMzeVyCAQC8Pv90Gg0aGxsrNg9fDmkEImlpSWEQiGUSiXU1dWhvr7+HZdqW2v8fj9efvll8cUXX4wdPXpUVKvVI5FI5EeFQuFfWbwwYz3DxC/jtmal1u67m5qaHuU4bs/OnTvVH/7whx2/9mu/pvL5fHKbB2DZKybFL+bzedTV1cHj8VT92LRUKsHv92NxcREA0NDQUNEavaIoYmFhAdPT07DZbGhvb4fVal3T5x8dHUU0GkVHRwcIIcjn8ygUCmWRVyqVyr+v1Wqh0+nKQ6vVln+m0Wig1Wqh0WhAKYXRaCyLaqVAKQXP88jn8yCEgOd5cBwHnudRKpXAcRw4jkOpVCoPaYNACIFery9vCgwGA4xGY7mTWldXF5qamtbU1kgkgsnJSVBKsWHDBjidzoq9n9lsFgsLCwgEAjCbzWhsbITb7a5qS24pbCkUCiGRSMBms8Hj8cDtdisifIdSitHRUfzyl78s/eQnP0nMzMzkCSG/CAQC3wdwhNUYZqwnmPhl3HYQQhqNRuMHHQ7H/63VatseeOABY29vr+XBBx9ES0uL3OaBUop0Oo1AIIBQKAS1Wo36+np4PJ6qd0UTBAHBYBALCwsoFovw+XxoaGioaC1enucxOzuLubk51NfXo729fc292qlUCoODg/D5fGXhezUopZcIQ0ksrh7pdBr5fB4mk6nsDb0YtVoNlUp1wSCEgBBS/hrAZW2ilF52iKJYHoIgQBQvfzqtVquRSqXgdDphNBrLon21kF8t7jUazTXfG47jcObMGYiiiM2bN6/5ZiiVSuH8+fPI5XLo6OiA1+utmAiWaggvLCxgaWkJdrsdjY2NFRXeV7IjHo8jGAwiHA5Dp9Ohvr4eXq+3IuEgN4ooilLiHI4fPx47cOAAr1arj/r9/u+JovhvlNKU3DYyGFeDiV/GLc9KGbIBl8v1EY1G8+uNjY2Whx56yPbggw/qOjo6ACwv4CdPnoTBYMDGjRur7mmhlCKRSCAQCGBpaQkmkwk+nw8ej6fqsanSwjs/P49YLIb6+no0Njauqdf1cpRKJUxNTSEQCKCpqQktLS0VaVowOTkJv9+PrVu3rmkoSz6fx1tvvYW9e/deUQBSSsvidPW4kqgFgOHhYWzatKkskAFcIpYvFtOrRfTFRKNRjI2N4c4771xTQef3+zE+Po6NGzdWpDNhLpfD5OQkotEo2tra0NTUVNGTDymxcmFhAfF4HF6vF01NTbJUFpFCNILBICil5eoVcsQJ53I5nDp1Ci6XC11dXSCElMXwT3/608yLL76Yz2azi6lU6vuZTOZFSulU1Y1kMK4BE7+MWxJCiBnAexoaGv6rIAh37t69W/3QQw853/ve95IrCR5KKWZnZzEzM4P+/n44nc6K2kgpRSwWQyAQQDgcRk1NDbxer2zHnPl8HvPz8/D7/bBarWhubobL5aq4x6tYLGJiYgLhcBhtbW3lJLa1plAo4NSpU6ipqUFvb++aCidKKd566y10dnairq5uzZ4XAF5//XXce++9a/qcY2NjUKlU6OrqWtPnreR7LFEsFjE9PY1AIIDW1lY0NzdXPDyB53kEAgHMz8+DUoqmpib4fD5ZPqdSGJRUM1vyCFd6c0opxdzcHKanp685Py4sLODnP/859/d///eJqampvCiKLy0tLf0AwGEWHsFQAkz8Mm4ZCCFWrVb7Abfb/XGdTtf9wQ9+0PAbv/Eb1l27dt3Q4pjNZnHmzBnodDr09fWt6TGj5E1aXFxELBaDzWaDz+eDy+WqanyhhCiKCAQCmJubgyAI5UW9Gt7mQqFQblHb0dGBhoaGinnylpaWcPbs2Yp5JScnJ1EoFLBx48Y1f+5KiF9RFHHo0CFs2rQJNpttTZ9b8q4HAgFs27atYqE6HMdhamoKfr8fLS0taGlpqcpnKJfLYX5+HoFAADU1NWhpaYHD4ZAlvrtUKpWFcKFQgMfjQUNDw5oL4UQigeHhYdhsNvT09NyQ6M/lcvj3f/93/PCHP4y9/vrrPCHkDb/f/9cAXqeU8mtqKINxnTDxy1jXrBa8er2++yMf+Yjpt37rt8x9fX039byUUgSDQYyPj6O+vh4dHR3vWBBKIQ2Li4sIh8Ow2+1oaGioeBvWq5HJZDA7O4ulpSV4PB40NzdXLZ64UCjg/PnziEaj6OzsRENDQ8WEgyiKGBkZQSaTwbZt2ypSESOTyeDEiRO46667KiK+KiF+gcrbHY/Hcfr0aXR0dKxpMtzFcByH6elpLC4uorm5Ga2trVURwdLJzezsLFKpFJqamtDU1CRbB0KO4xAKhbC4uIhisQiv14uGhoabCo3I5XIYHR1FsVjEpk2bbjpMSBAEHDhwAC+88EL83/7t37gVIfxXAN5gQphRTZj4Zaw7KiV4L4coiuVQiMbGRrS2tl63CE6lUlhcXEQoFILVakVDQwPcbrdsglfy8s7OLlcoam1tRX19fdXsKZVKOH/+PMLhMDZs2ACfz1dRb1kul8OJEyfKzRMqcS1KKQ4ePFgRD6pEpcQvUFmPNbAsyIaGhqBWq9Hf319RUcpxHGZmZrCwsIDW1la0tLRU9d6en5/H/Pw8rFYrWltbZfMGS/YEAgEsLi5CFEX4fD74fL7rPsXK5XKYmJhAIpFAT0/PdZcWvBGYEGbICRO/jHXBxSENH/3oRysmeC/HxRUI2traLruQ5PN5LCwswO/3w2AwoLGxER6PR9ZSRdlsFjMzMwiFQvB4PGhtba1q0s7q42nJC1hpURAKhTAyMoLNmzdXNHb7/Pnz4Hkevb29FbtGJcUvpRSHDh1Cb28vHA5Hxa4hfXYGBgYqfu9xHIfJyUkEg0F0dHSgsbGxaiJU8gbPzMwgnU6jqakJzc3NsjZUKRQK8Pv98Pv9UKlUaGxshNfrvaxNqVQKExMTyGaz6OzsRH19fVXeO0EQ8Oabb+K73/0uE8KMqsDEL0OxEEIMWq32g263+3GdTlf28FbKS3U9SLVnZ2ZmYDab0d7eDovFgmAwWE6GaWxsrFrc7JWglCIcDmNqagqCIKC1tRVer7eqXmdBEDA9PY35+fmqeeIopRgfH0c8Hsf27dsr2vhDChu4++67K/q6Kil+geXN0fHjxysW/iCRSCQwODiIrq4uVKOGdrFYLIfXdHV1VU3ISaz2BtvtdrS1tcneKGd1vLKU1OpwOBAKhTA9PQ2NRoP29vaqJLpeidVC+Fe/+hWnUqne8Pv9zwF4k3WYY6wVTPwyFMVKl7U7fT7fE4SQez7ykY8YH3nkEYucgvdyiKKImZmZ8rGxy+VCb29vxY6+rxeO4zA/P4+5uTnYbDa0tbVVrEPWlaCUYn5+HpOTk2hsbERbW1tVPN+lUgknTpyA3W5Hd3d3RRfvanhMJSotfoFlD7YgCOjp6anodUqlEk6dOgWr1Yre3t6qCKx8Po9z584hlUqhr6+v4lVcLoZSiqWlJUxPT0MURbS1tVVdiF/OJr/fj/PnzyOdTperc1QiGfRmEAQB+/fvx1/91V/FXn/99YIoiv8QDoe/RSmdkNs2xvqGiV+GIiCEtDqdzv+u0Wge3rt3r+Hxxx933nPPPbLFx16JbDZb9pzYbDY0NTXBarViYWEBCwsLMBqN5VCHalZvyGQymJ6eRiQSQWNjI1paWmRJvFlaWsLY2BicTic2bNhQNRtSqRROnjyJ7u5ueL3eil9POtbu7++v+LWqIX5FUcSBAwfWvPbx5ZC884lEAgMDA1U7IUmn0xgZGQEhBH19fVVvGAPI/zktFovlLo1qtRrNzc2oq6tDOBzG/Pw8OI5DY2MjGhsbZT25uhy5XA7/9E//JH7jG9+Izs7ORpLJ5DdyudwPKKVJuW1jrD+Y+FUghBA1gOMAFiml/5EQ8l0A9wCQPuQfo5QOrjRv+C6ATgD/jVJ6Vg573ymEEKvRaPzPNpvtEw0NDZ7HH3/c8eEPf1gtx6J0NURRRDAYxOzsLERRRHNzM7xe72W9mVJ3qFAohJqaGvh8voq1SZXiCycmJsDzPNrb22XzKKVSKYyMjECj0aC3t7eqMcV+vx/nzp3DwMBAxWudAssxlIcPH8bdd99dFY92NcQvACSTSZw5cwZ79+6tyj0UCAQwPj6O7du3VzUcIBKJYHR0FLW1teju7q5oaMyV4DgOCwsLmJ2dhcPhQEdHR8U+M8ViEYFAAIFAADzPl7s0Xi5noVAolDfyNTU1aG1thd1uV1SbbmD5M/+9730v//zzz2eKxeLpxcXF/w3gZRYfzLhemPhVIISQTwLYAaBmlfj9F0rpTy/6vfcB6ADwUwBfopQ+WnVjb5AVYX+fz+f7pFar3fHII4+YH3nkEWNzc7Pcpl3C6nJgbrcbLS0t1+0tksqb+f1+hMNhmM1meDweeDyem15sKaUIBAKYnJyEyWRCR0eHbOEWxWIRo6OjyGQy6Ovrq3gIwGrk8iCeOHECPp+vKh5moHriFwDOnDlTrl1bDSSPfU9PD+rr66tyTWD53llcXMT58+fR2NiIjo4OWU6ZpJKKU1NT0Ol06OjoWJPPUCaTQTAYRCgUAqW03AjjegX26sS9TCaDxsZGWcu4XQlKKU6ePIlvfetbiZdeeqlIKf15KBT6BqX0jNy2MZQNE78KgxDSCOB7AL4I4JPXEL/vB9CMZfH7FUrpI9W293ohhLS53e4nVSrVb9x///36xx57zH7HHXcozqMgCELZywsALS0tN50oRilFOp1GKBQqL0Zutxt1dXWw2WzX/dw8z2Nubg6zs7Ooq6tDe3u7LO1NgWVv+PT0NObm5sqhBtX8WwqCgFOnTpXbUVfr2lIS4a5du6pyPaC64pfjOBw4cOCqLZrXmlKphOPHj8Pj8aC9vb3q95HU7rq3txcej6dq176YWCyGyclJFItFdMsaUO0AACAASURBVHR03NApDs/ziEQiCIfDiEajMBqNqK+vh9vthtFovCm7lFbG7UqUSiX84he/oN/4xjeiIyMjqWw2+810Ov03lNKE3LYxlAcTvwqDEPJTAF8CYAXw6VXidw+AIoB/B/DHlNIiIUQD4PsAWgD8rtJ2u4QQtUqler/H4/lsfX192x/90R85PvShD6nkOGa8Fvl8HtPT0+VyYC0tLRU7hiyVSgiHw1haWkIikYDZbIbL5YLL5YLVar1kUSmVSpiamkIgELjhWsOVYGlpCaOjo/B4POjs7Kx6GbdCoYDjx4+X34tqIYoi9u/fjzvuuKOqm45qil8A5WYsW7durdo1RVHE6dOnoVKp0N/fX3UvbD6fx8jICDiOw6ZNm2SJB5bIZrOYnJxELBZDW1sbmpqaLnk/RFFEPB5HJBJBJBIBz/NwuVyoq6uD0+msWJhVPB7HzMwMUqkUmpub0dTUpLjYYACIRqN44YUXCs8991y6WCy+GQgEnqWUnpDbLoZyYOJXQRBC/iOA/0ApfYwQci/eFr9eAEEAOgB/DWCSUvoFGU29KoSQeqfT+YcajeZjDz74oOnJJ5+srWQd1HeK1Gp4amoKxWIRbW1t8Pl8VV14KaXIZDKIRCKIRqNIp9Mwm81wOp2wWCwIh8OIRCJXXASrSS6Xw/DwMAgh2LhxoyxeZ+mYfOPGjairq6vqtScnJ8HzPLq7u6t63WqLX0opDh8+jL6+vqqG01BKy+2ud+zYIYuoikajOHv2LJxOJ7q7u2Wtz10sFjE1NYVgMFhOrE0kEohEIuA4Dna7HU6nEy6Xa01bsF8PpVIJs7OzWFhYgMvlQltbm6wbhitBKcVrr72GL3/5y5GhoaFYKpX6ai6X+3tKaU5u2xjywsSvgiCEfAnAwwB4AAYANQD+kVL6X1b9zr1YEcWyGHkFVkqU3evz+T5bU1Oz+VOf+pTtoYce0sh1LH81eJ4vJ5tYrVa0t7fLXqJMQhLk4+PjSKVSUKvVMJlMcDgcsNvtsNvtVV/oRFHExMQE/H6/LKJTIhKJYHh4uGqJbauRktz27dtX1SoeQPXFL7C8yRgaGqpa8ttqpBJcd9xxx00f2b8TpKYc09PT6OnpqVps9+rrZzIZJBIJxONxxGIxlEolCIIAt9uN7u5uxQhNKWZ5enoahBC0tbXB4/EoLiQCWE6w/OY3v5l94YUXcoIg/EswGPwKpXRMbrsY8sDEr0K52PNLKQ2sCMz/F0CBUvrH8lq4DCHEVltb+7sGg+H377nnHstTTz3l2LFjh9xmXZZcLoepqSmEw2E0NDSgpaVFlkzvK5HNZnHu3Dmk02l0dnaW42iLxSLi8Xh5FItFmM1m1NbWwmazwWazVex1RKNRDA8Pw+v1orOzUzbP8+LiIiYnJ3HHHXdUXfwDwKlTp+DxeKrSnOFi5BC/ADA0NAS73Y6mpqaqXzsajeLMmTNVrwSxmmKxiOHhYfA8j/7+/oqcdFBKkcvlkEgkkEwmkUgkyp9vabNrs9mg0Wgu6DLp8/nQ3t6uqJCDdDqN6elpxGIxRXS2uxI8z+Nf/uVf6Je//OXo7OzsYiQS+RLHcf9IKeXkto1RPZj4VSgXid9XAdQBIAAGAfwepTQjs33bvV7vZ/V6/d1/8Ad/YH3kkUf0drtdTpOuSDwex+TkJPL5fDmRREn1g3O5XFn0dnV1we12X9VzQilFNpstL5bJZBLFYhFGoxE1NTWoqalBbW0tzGbzO36dpVIJZ8+eRbFYRH9/f1VLl12MdPS7c+dOWRbTZDKJ4eFh3HnnnbJ4tOQSv6VSCQcPHqxaSbeLSafTOHHiBPr7+6vemGI14XAYZ8+eRWNjI9rb29/xZ4rneaTTaaRSKSSTSaRSKXAcB5PJVN7E1tbWXnNzJwgC5ubmMDMzg4aGBrS3t8sannExHMdhbm4Oc3NzcLvdaG9vl8WDfz1MTk7i61//evonP/lJThCEH4bD4a9SShfltotReZj4ZVw3hBCVSqV6n8fjebanp6fxc5/7nPPee+9V5BEXpRShUAiTk5PQarXo7Oysahmu60HqPJVMJtHV1XVTx4WUUhQKhQsW1kxmeX9ksVhgtVrL42qieHUJKKkNrVx/X0opRkdHkc/nsW3bNtlKUckR/7oaucQvIF+cs0Q+n8exY8ewYcOGqocfrEYQBExMTCAYDGLz5s242kaf53lkMhmk0+nyyOfzUKlUsFqtqK2tLW9Sb6aihiAImJmZwdzcHJqamtDa2qooESyKYrkko9lsRmdnZ9W7TV4vhUIBP/7xj4Vnn302nkql3goEAn9KKR2S2y5G5WDil3FNCCEGi8XyX81m85+8973vrf3TP/1TW1dXl9xmXRZBELCwsIDp6WnY7XZ0dHQoJj5OolAo4Pz584jFYuVFvVICUxRFZLNZpFIppNNpZDIZZLNZUEphNBphsVhgNpthsVigVqsxPj5eLh8m55ElpRRDQ0MghKC/v182AR4KhbC4uIjt27fLcn1AXvErVbjYvXu3LOEmwLIn8ciRI2hpaZElBGM16XQap0+fLnd3LBQK5c9UJpNBsViERqOBxWKBxWJBTU0NrFYrjEZjxe5hnucxMzOD+fl5NDc3o7W1tepx6VdDymOYmJiAKIro7OxEXV2dYp0mb7zxBj73uc9FJycnZwKBwJ8AeIUyoXTLwcQv44oQQpxOp/OTWq32tx999FHLE088YZYr2elacByHmZkZLCwswOfzobW1VVHxvMCyjRMTEwiFQujs7ERDQ4OsXtV8Po9MJoNMJoNAIIBEIgGdTgetVguj0QiTyQSz2Qyz2Vz+vhqeJVEUcerUKZhMJvT09Mj6HkmlzeQ8tpVT/AIoN2rZsmWLbDbwPI9jx46hvr4ebW1tFb8epRQcxyGXyyGXyyGbzZYfi8UiOI4Dx3Gor6+Hy+Uqi12dTifb/crzPKamprC4uIj29nY0NzcrTmCm02lMTk4imUyis7NT1pOlazEyMoJnnnkm/tprr8XT6fQzK62US3LbxVgbmPhlXAIhpL2+vv5pvV7/wGc+85maj33sYzqlxmytroHb0tKC5uZmRR39ARc2hGhra0Nzc7NiYo6z2SxOnz4Nq9WK3t5eaDSasjCWFvtsNlv+XhAEEEJgNBovOwwGw00tZoIg4Pjx43A6nejs7FzDV3rjzM3NIZ1OY+PGjbLaIbf4pZTiwIED2L59u6yx34Ig4MSJE7DZbLjZkydBEFAoFJDP5y8ZhUIBAKDVamEymcqbQOlREri5XA6Dg4OwWCzo6+tTzLxTKpVw/vx5RCIRdHd3K7L6Qj6fx+TkJCKRCNrb29HY2KiYOfFigsEgvva1r2W+//3vZ0ql0rdisdjXKaVJue1i3BxM/DLKEEJ2+Xy+L7nd7v6nn37a8YEPfECl1AmpWCxiYmIC4XAYra2taGpqUtRRH/B2/OzExAS8Xi86OjoUs0BSSjE3N4fp6ekbTigSRfGyoiGfz6NYLIJSCkIIDAYDjEYj9Ho9DAYDDAYD9Hp9eVx8bwmCgKNHj8Lr9Va1ecXlEAQB+/fvr2qnsysht/gFlpO+5ubmMDAwIKsd0qmA2WxGT0/PJf8uCAKKxSIKhcIlj9L9CQAqlap8f97MBm7152jz5s2KyivI5/MYGxtDLpdDb2+vomyTWF3LuLW1Fc3NzYqbxyWy2Sz+5m/+pvi1r30tVSwWfxYKhZ6hlM7JbRfjncHE720OWc5i+zW32/21LVu2eD//+c87q9m69UbJ5/OYmJhANBpFR0cHGhoaFOkxCIfDGB0dhd1uR1dXl6JCMAqFAk6fPl2O7a2EIJdEiCQ4LhYjkkgGlj1sWq0WiUQCtbW1cLvd0Ol00Ol00Ov10Gq10Ol0VV0U5U70Wo0SxC8AHDp0CBs3bqxa0hKlFIIgoFQqXTCKxSLm5ubKJxBSDVzgbVErbbikjdbqjVglvKDZbBaDg4Ow2+3o6elR1JyUSqUwMjICtVqN3t5exeVAAMshYVNTU/D7/WhubkZLS4tiHAUXIwgCXnzxRfHpp5+OxWKxo4FA4NOU0lG57WLcGEz83qYQQohGo3m/0+n86r59+9xf/OIX7Rs2bJDbrCtSzSSxmyGbzeLs2bMghKCvr0/WY+LL4ff7MT4+jr6+Png8HrnNKVepOHbsGFwuF+x2e1ngSI8cx6FUKkEUxfL/kwTzxUOj0VzytUajgUajgUqluq57hud5vPnmm7KV+LoYpYjfeDyOc+fO4Xo3x5J45XkePM+X42Slr6XHUqlU/n61kAVQ/htKGyFpU6TT6TAzMwOj0Yi+vj5F1JOllGJychJ+vx9bt26VrT7xlYhEIhgZGYHT6URXV5ci3rOLUXry3moopXj99dfx1FNPRRYXFweDweAnKaVn5LaLcX0w8XubQQghOp3u1x0Ox5ff/e531z3zzDO2aiSQvFNWhzds2LBBsQkSHMfh3LlziEaj6Ovrg8vlktukC+B5HkNDQxAEAVu2bJH9KF9CEAQcOXIEjY2NaG5uvq7/Qym9QExJo1QqXSKsVj+uFs8AQAiBRqOBWq0uP6rVaqTTaRBCUFdXV/6ZWq2GSqW65PFKgxBS9v7d7P26FuKXUloeoiheMiShKopi+XH119L7Nzs7C7vdDo1GA0EQyuJW+vri9UR6b1dvSFZ/f7mhVquv6z2jlOL06dPQ6XTo7e1VzLyQSqUwODhYrsGrFLuAt8M0pqamFJsUByzPV9PT01hYWEBraytaWloU5U2/mAMHDuCpp56Kzs7OngkEAk9SSgfltolxdZj4vU1YEb0fqqur+//27t1rf/bZZ00dHR1ym3VFVldG6OjoQGNjoyIn6fWwmCQSCQwODqK9vR1NTU2KsU8QBBw7dgxerxctLS1Vv74k7iTxJnkeT58+jd7e3rKN0rhYEEqPknC8WFhKYvNmSafTa9LOWRLk0uOVxuWEvjTy+Tz8fj82bdp0yaZBo9FU/d6ilJZjgJUQoiIhCAJGRkaQzWaxbds2RYU9AcrfrEusDoeQ5i8li+DXXnsNf/Inf5JdXFwcXlhY+D0mgpULE7+3OKvCG/73e9/73rrPf/7zNkopZmZm4Ha70dHRIVv9zsuxulyP0iojXEwsFsPw8LBijxEppeX3ctu2bWsioNYKURRx/PhxuFwutLe3y21OmYmJCRBCoKSNoVLCHiSOHDmCrq6uqzZ6qCaU0nIVCLkrhFxMMBjE6OgoNm3aBCWWiVwdprVp0ybFdmIrlUqYmJjA0tKS7GUiL0cymcT58+dRKBTQ2dmJmZkZfPKTn4xOT0+fCQaDf0ApHZbbRsaFMPF7i7KSyHZ/XV3d/7nvvvs8zz77rH11Br0oilhcXMTU1BQsFgva2tpgt9tlm1BEUSxnTSs91qtUKmFkZAT5fB79/f2KTCApFotlj1hfX5+i3kulihWpwoNSYn0llCZ+bzT2txqIoohjx47B7XZXpQ7wjZDP53Hq1CnYbDbFJcNJLC0tYWRk5KZbOFeaYrGIc+fOIR6Po7e3V9YNhSiKCAaDmJ6ehlqtxoYNGy6pmnPw4EE8+eSTkfn5+ZPBYPAJlhinHJj4vQUhhOzyeDzfvvvuuxv//M//3H41LxalFLFYDNPT08jlcmhpaUFDQ0PVFn+pDfHY2Bjcbjc2bNigOA+qxOoQB7lb/16NWCxWPrqvr6+X25wLkDq36fX6y5aqkpOpqSnwPH/TNWTXGqWJXwA4fPgwNm7cqKikLqlUXlNTExobG+U25wIopTh//jzC4TC2b9+uSA/r6hbOmzZtuqHyh9Umm81idHQUPM+jr6+vqvdhPp/H3Nwc/H4/6urq0NbWds3E5jfeeANPPvlkxO/3HwyFQo9TSheqZC7jCjDxewux0pziL7u6urZ/85vfdN5ocX7pQx0IBFBbW4vm5mY4HI6KCbx4PI6RkZFyJy8lLggSyWQSZ86cgc1mQ3d3tyIFuhTm4Pf7MTAwAJPJJLdJlzA2NoZSqSRry+LLIYoi3njjDdx1112K+9sqUfxGIhHMzs7KXvf3Yniex+HDh9Hd3Q232y23OZcQiURw5swZxYZBAMvC8syZM9Dr9ejr61NcvPJqYrEYRkdHK76GCIKAYDCIubk5CIKApqamG3YSUUrx0ksviU888UQslUr9IBwOf45SmqqIwYxrwsTvLQAhxOF2u79st9t//bnnnnO++93vvilVIXmDZ2dnkUql4PF40NDQsGa761wuh5GREXAch76+vqrVDX0nCIKAsbExxONx9Pf3K9ZWjuMwODgIvV6PjRs3KirMQWJqagrRaBQ7duxQlPAFlru5ZTIZ9PX1yW3KJShR/FJKcfDgQWzbtk1x5fyKxSIOHz6suKYTEoVCASdOnEBdXR02bNiguM8CsPz3DQQCGB8fR0dHh6ISZS+GUopgMIjx8XF4PB5s2LBhTU4uKaWIRCJYXFxEPB6Hx+NBc3PzTYe58TyP559/vviFL3whkcvlvpxMJv+CUsrdtMGMG4KJ33UMIcRgt9s/bTab//DP/uzPbA8//LB2rWO1eJ5HKBTC4uIi8vk86uvr4fP53lHyFM/z5QoOvb29ivTMrCYSiWB4eBjNzc1oa2tT7OSfSqVw8uRJdHZ2Ku64V2JxcRGzs7PYtWuX4oQ5pRT79+/Hrl27FJX8KaFE8QsAgUAA4XAYmzdvltuUS8jlcjhy5Ah27NihqERPCVEUMTo6ikwmg+3btyvutEGC4ziMjIwgl8thy5YtijxNkpBK8c3MzJTnwhudsyXHj9/vRyQSgcPhQENDA5xO55rP/5lMBl/84hczL7zwQiwejz9ZKpVepEyQVQ0mftchhBCV0Wh8qKam5s8ff/xx+6c+9SlTNUIGOI5DIBBAMBhELpdDXV0dvF7vNRPlKKXw+/04d+4cWlpa0NraqtiECmB9TfiSd2b79u2Kir9cTTQaxdmzZ7Fnzx5FLvKhUAiBQABbt26V25TLolTxSynFG2+8gTvvvFMxdaNXI20K9+zZo9ije2leHBgYUKRIl1gvjgBgOSF5bGwMqVQKmzZtgs1mu+rvC4KAcDiMQCCAeDwOh8MBr9eLurq6qqxTfr8fn/70pxOvvvrqfCgU+h1K6dGKX5TBxO96gxCyz+Px/NUHPvAB75e+9KVaueoz8jxfnjCSySRqa2vh8XhQV1d3wUKYSqVw5swZWCwW9PT0KHYRkpBKE71Tz0G1oJTi3LlziMViGBgYUKT4AJa9G8eOHcPu3bsVG9N96NAh9Pf3K1Z8KFX8AsDMzAyKxaKiauyuZmlpCePj49izZ4+iKnisRhLpPT09iktQXY0gCBgfH0csFsPmzZsVu9mWkNYes9mM3t7eC9aeXC6HUCiEpaUl5PN51NXVob6+vqI5LtdieHgYH//4x6MTExPHg8Hgxyml07IYcpvAxO86gRDS6vF4Xti8eXP/c88951RSK2JKKRKJBEKhEMLhMAghcDgcyGQyKBaL6O/vv+buW244jsOZM2cgCAI2b96saJHO8zxOnjxZntSV6kWXYi+3bdum2FjpZDKJsbExRZXtuhgli1+pPNw999yj2PtwdnYWoVAIO3fuVOxmtlQqleteKzUOWCKRSGBoaAg+nw8dHR2KtlWKXR4bG4PD4YBarUY0GoVer4fH44Hb7VZcqcpXXnmF/v7v/340kUj8ZGlp6Y8opRm5bboVYeJX4RBCdE6n83O1tbW/9+1vf9t53333KXemwfJkMzMzg/Pnz8NkMoHjOBiNRtTV1cHlcqGmpkZxk2U4HMbw8DA2bNig2JhZiVwuh+PHj6OtrQ1NTU1ym3NFBEHA4cOH0dXVpejY7lOnTqGxsVGxmfeAssUvAIyOjsJisSj6fhwdHYUoirjRCjjVRBRFnD17FsViEdu2bVNcbPxqRFEse4G3bt2quKRHQRAQj8cRiUQQiUQgCAIIIeA4Dlu2bFFsRzsJURTxl3/5l8Vnnnkmmkgk/rBQKPwjiwdeW5j4VTBqtfo+l8v1nccff7zuM5/5jEmpR9sS2WwWQ0NDMJlM6O3tLR/FZ7PZ8iSUSqVgNBrhdDrhdDphs9lk8xjxPH9BbK9Sj+Ul4vE4BgcHsWXLFkVmsUtILWdtNpuiurddTLFYxFtvvYV9+/YpbkO2GqWL33w+j2PHjuHuu+9W7PtIKcXx48fLGftKZmZmBgsLC9i5c6eiT6CA5VJjQ0NDaG1tRUtLi2x/f47jymI3FotBEATY7Xa4XC64XK7yWpROpzE0NISamhr09PQoMgdhNUtLS3j88cfjb7zxxujS0tLDlNIpuW26VWDiV4EQQuo9Hs/zGzdu3POd73zH0dLSIrdJV0UURUxMTCAQCKC/v/+awiyXyyEajSIajSKRSECj0cBut8Nut8PhcFQl4z4ej+P06dOyT9rXi5TYtnPnTsV5WS5mYmICmUwGW7ZsUfT7Oj4+Dr1ej9WdD5WI0sUvABw/fhzt7e2K3pTxPI9Dhw5h06ZNirYTeLvjmtIT4YALnQhbt26t+PxNKUUmk0E8HkcsFkMymYRKpYLD4YDT6YTD4bhqDgSlFPPz85icnER3dze8Xq+i5ykA2L9/P3300UdjyWTy25FI5GlKaVFum9Y7TPwqCEKI2mazPWG1Wj/zF3/xF84HH3xQmUF0q4hGoxgeHi7Hf70TL26pVEI8Hi9PZsViERaLBTabrTzWaocudVpaWlpSZI3Si5EaV4RCIezYsUOxiW0SoVAIExMT2LNnj2JjQIG3m1oorZXx5VgP4lfqEqm0phcXI5VAU3ICpkQqlcKJEyfQ39+v+GN6YFmwnz17dk07S1JKUSgUkEgkyqNQKMBsNsPhcMDhcKC2tvYdhYgUi0WcPXsWHMehv79f0VV9gGXv9le/+tXc17/+9Ug4HP4dQRBeltum9QwTvwqBEHKH2+3+u4cfftj7hS98war0D6K0289ms9i8efOaikhKKbLZLOLxOBKJBJLJJHieh8ViQW1tbXnc6JFgPp8vH8f39PQoWpwBy+/D8PAwOI7D1q1bFW9vNpvF0aNHceeddyr+uDYQCCASiaC/v19uU67JehC/Uq3k3bt3K/5vH41GMTIygjvvvFPRcbXAckOMo0ePor29XfH5CMCyoBwcHITJZEJfX98Nvb+UUuRyOSSTyfLI5/MwGAyw2Wyw2+2w2Wxr7lkOh8M4e/ZsuQyn0r3A8/Pz+O3f/u3Y0NDQsVAo9Cil1C+3TesRJn5lhhBS6/F4vtXc3Hz/3/7t3zp7enrkNumaSDUf29ra0NzcXJXJQhRFZDIZpFKp8sRYLBZhNBphtVpRU1ODmpoaWCyWy064UgkzJbcVXY0oiuWKDj09PYqfkKUj5f7+ftjtdrnNuSaHDx/Gpk2bFH+kDKwP8Qssx6pyHAclVaK5ElNTU0in09iyZYvcplwTjuNw/PhxuN1udHR0yG3ONZFOqxYXF7Ft27bLfsY4jkM6nUYqlSoPjuNgMplgs9lQU1NTFrrVmPt4nsfo6CjS6TS2bt2qeC8wALz00kviY489FstkMl+NxWJfpZQKctu0nmDiV0Z0Ot37HA7Hd77yla+4Hn74Ye16EDiSt1cJzR+kI7HVE2gmkwGlFBaLBVarFSaTCUtLS+A4Dtu2bVO8Vwp4e7HzeDyKThiToJTi5MmTcLlcUHp8OrDsoR4cHMTevXvlNuW6WC/il+d5vPnmm7j33nsVv1lbb/estBmWkomV/v4Cy2UET548CY/HA7PZjEwmg3Q6jXw+D41GU3ZYSM4LJYR0RaNRnDlzZt14gfP5PD772c9mvv/9708vLS19mFJ6Xm6b1gtM/MoAIcTqdrv/uq+v770/+MEP7F6vV26Trokc3t53iiiKyGaziEajOHfuHHQ6HQghoJTCaDTCYrFcMKR/VwLFYhFHjx5FW1vbujjmBIDp6Wkkk0nFdki7mJGREdTU1Kyb93e9iF8AGBwchM/nU3R5Owme53Hw4EFs2bJF8XXIgbfDoHiex5YtWxQTBkUpRT6fRyaTuWAUi0Wo1WqUSiVotVp0dXWhtrYWRqNRMfPt5ViPXuCjR4/iox/9aCQej38lHo9/jVIqym2T0mHit8qo1ep31dXV/d0Xv/jFukcffVSn5EkAWK6XODIyUs7eXw8TAfB28sXqsmBSTFk2m71gki6VSlCpVDCbzZeMagrjfD6PI0eOoK+vb12IB+Dtgvd79+5VfPwk8Hai2759+9aFvcD6Er/xeByTk5PYsWOH3KZcF5lMBsePH8fevXsVX/YKeDthN5FIYMeOHVUTwJLAzWazF4x8Pn9Zp4LZbIZery87HWZnZzE3N4eBgQHFJxlLSF7g9eDwAZbXj6eeeir1k5/8ZHJpaek/UUpn5LZJyTDxWyUIIWa32/1cR0fHgz/60Y/sSi4IL5FKpXDq1Ck0NTUpvp+7hNT2NxqNYmBg4LrDHARBuGRiz2azKJVKAACj0QiTyQSz2QyTyVQea1UpIJvN4tixY9i8ebPiyzBJcByHgwcPYseOHYrrknQl1lOim8R6Er/rKfFNYmFhAcFgEAMDA+tijgOWY5aXlpawc+fONdnEUUrBcVzZOZDL5cqjWFyuqmU0Gi9xDhiNxusW4FJ5SaW3cV4Nz/PlpOMtW7YoIjTjWrz55pv04YcfjiYSic8nk8nnWHOMy8PEbxXQaDR7XS7XD//n//yf7o9//ON6pU+wlFJMT09jfn4e27ZtU3wPd4lSqYSTJ0+ipqZmTePiRFFEoVC4ZFHI5XLlzkEGgwEmkwlGo/GCcT0JG5lMBseOHcO2bdvWxfEr8HbMpNvtVnRnr4s5cuQIent71809Dawv8QssCzNK6bpIzpI4deoUHA7Huoj/lZidncXi4iLuuOOOa27CBUFAoVBAPp8vj1wuh3w+Xxa3Op3uspt8yYO7FlRqjq40fr8f4+Pj66bsXDabxSc+8YnUP//zP48uLS39X5TSBblttHiaYQAAIABJREFUUhpM/FYQQojB7Xb/n+bm5t/48Y9/7Ghra5PbpGtSKBQwODgIs9l8w6Vq5CSVSuHkyZPlouXVRBLH0mKSy+XKC02xWASlFGq1GgaDoSyIpSElEQ4MDKC2traqdt8Ms7OziMVi2LZtm9ymXDfFYhFHjhzBvn375Dblhlhv4nc9vs9S/O962uwDwNzcHGZmZrBp0ybwPI9CoVCee6SvKaVQqVTl+Uca0mZ9LcXt9UApxfj4OBKJBAYGBtZFuAnwdqnM2tpa9Pb2Kibm+mq8+uqr9GMf+1g0mUz+j1Qq9TzzAr/Nuha/hBADgP0A9AA0AH5KKf1fhJA2AP8AwAngBICHKaUlQogFwA8AWAD8l0rWxyOE9Lvd7p995jOf8T7xxBOG9fBBkeJk+/r64PF45DbnugkGgxgbG1N0N6TVC5O0OKVSKQSDwQti4DQaDQwGA/R6/SWPer0eOp1O9klXCtG46667FN8gYjWTk5MAsK48ksD6E7/A+vSwp1IpDA4O4q677pL9MyYIAorF4gVDmjukr0VxOadJFEUUi0U0NjaWBe3qDbbcr+VK+P1+nDt3bl2FTVFKMTk5Cb/fj+3bt68Lu9PpNB577LHkyy+/PBgKhf4TpTQmt01KYL2LXwLATCnNEEK0AA4A+ASATwL4R0rpPxBC/hLAaUrptwghvwdgHsAcgN+ilP5xJWyy2Wy/53K5nvnZz37m7OvrW+tLrDmUUoyNjSEej99QnKzcUEoxMTGBcDi8LrqfrSadTuP48ePYsWNHWbBTSssiWVrgVj+WSqWyJxkAtFotdDoddDpdWRhf/LjWYlkURRw6dAgbN25cF/V8V7N//37s2rVr3dzfEutR/AYCAUSjUWzatEluU26IiYkJFItFbNy4cc2ek1IKQRBQKpXKY/XnefWjICyXalWpVOUN75U2wqs3nouLi5iensbu3bvX1YY0mUzi1KlT6yrJF1hO9B0cHERXVxd8Pp/c5lwXL774Iv/YY48tBYPBD1NK35LbHrlZP5+Sy7Diws+sfKtdGRTAfQAeWvn59wA8DeBbANQAxJWx5uc8hBCL2+3+wX333bfvb/5/9t48vq3qzP//HO2LV1mWvDt24jjxFttJ7DgLCVCgwJA2QIdM6TQUCkw3oHwpMP2WoZ1X+oNCpwy0pR1aKLS/duivQ8vSNkDLAFnI7iRObCfO4n23ZcvapSud3x/mXmRFsmVb9tW58vv1ui9d3StLz7Guzv2c5zzneV58MZWFzAhutxuNjY0wGAxoaGhgJgbL7/fj5MmTUCqV2LBhQ9x6N8LBrzAP9VQTQqBUKqFUKmf0YPNCOfTmyVfG83g88Pl88Hq9goeIf3+VSjXtI7/J5fLLrofz588jMzOTOeFrs9kE0bDEwmM2m3H27FlQSpnpU4DJWYGDBw9iZGTksthO/jfn8/mE39Z0jz6fT/hbuVx+2UBVpVIJqRb5Y3MVrrm5uaCU4vDhw6ivr2dGAKempqKhoQHHjh2DzWZDcXExE9dLWloaNm7ciBMnTmB0dBTl5eVxfw/asWOHoqamJuemm276i9FofGZ0dPT7iRwGwbTnFwAIIXJMhjasAPBTAE8DOEQpXfHx+XwAeyilFYSQNAD/DUCDyVCImAWBE0IqzWbzW0888UT2l770JSZckCMjIzh9+jTKy8uZGnV7PB4cPXoUeXl5WLZsmdjmzAq+BHBtbe2ix/gGAoEZb9bB+7wXCpgUzoQQ2O12ZGVlCTdqpVI55THcFg83hdbWVqSkpCA3N1dsU2YNi55fADh16hRycnJEr6hIKUUgEADHcWE3/nrn9z0eD4aGhpCamgqO46a8F3+dBw8Uww0i+d/HYgu57u5udHd3o76+npn1GsBk33Tq1CnIZDJUVlbGRZ8RDXwYBJ8tRKvVim3SjHi9XjzwwAMTr7322smhoaEdiRoGwbz45flY2P4JwGMAXg4nfhfoc5kMc7hw4QIGBweZ+cHy8F5T1qbJgE/y+FZXVzOT1YHH5/Nh//79KCsrg1qtFoRCuMfQLbSPkclkgjCWy+WXPfJb6HO5XA6ZTHbZsZkEBqUUH3zwAbZs2cKMRywYVsXvyMiIkDFmJgKBAPx+P/x+/5T90I3juIjHor3mgjdewIYO5IaGhoQiByx4IoPp7OxEf38/6urqmBGRwCc5jPnwO5Z+q6Ojo2hqamJqvUyih0FIRvwCACHk3wC4ADwCIItSyhFCGgB8l1J63QJ8HnNhDhzH4cSJE9BqtSgrK2Oqc7RYLDh16pQoXtP54vV6cfDgQVRUVCAjI0Nsc2ZNa2srlEolVqxYMa/34b1woWJlJoETbuPfJ1IfRgiBXC4X0jylp6dDJpNF3AghYZ/zXu9I+zNtvC2hz4PtDLfPc+DAgbClmPl2B7c/9BilVNhCn0faAoHAZfvhHoM3Pq6Vf+SPDw8PIy0tTTgeCf67ijTYmW5gFDx44vfn26/xIQTLly8X3XM9Fy5cuACr1Yra2lrmxHt3dzc6Ojqwfv16aDQasc2JGo/Hg+PHj8NoNKKkpISJ/3tHRwduuukmS39//49GR0f/n0QKg2Ba/BJCMgH4KKXjhBAtgHcB/ADALgCvBS14a6KUPh/jz640mUxvPfnkk8yEOTidThw7dgxFRUVM5WYFJlcGnz9/HnV1dUx5qoHJAcfBgwexcuVKZrwCwYyPj+P06dPYvHkzEx06Dy/AmpubkZ6eDqPROEWYBQu3cPvhRGDweQCXnYskNsPtBz9GOgZMFgcIjbGeTkSHCu5w+zOJ9+nE/3QDhtCtra0NRqMRWVlZUXnp4wl+poaV6m+htLS0gOM4VFZWMvV/B4Dh4WE0NzfHdQafcPD9jcfjQXV1NRPe60QNg2Bd/FZhckGbHIAMwP9HKf13QkgxJlOdGQCcwGRaM0+sPjc1NfWfMzMzn3nrrbcyVq9eHau3XVD4+N7q6mrmFiu1t7ejv78f69evZ+4m5Pf7ceTIEeTn5yMvL09sc2ZNIBDA/v37UVNTw9RNiIcPeWCpnHEorIY9AJOzNR0dHaitrRXblDnR2dkJq9WKqqoqsU2ZNZRSNDU1Qa1WY9WqVWKbM2v43O0sVb3k6ezsRGdnJ9avX8+Ms+b111/n/uVf/mVwcHDwBkppk9j2LDTszHmHgVLaRCmtoZRWUUorKKX//vHxS5TSOkrpCkrp52IlfAkhMpPJ9MO1a9c+e+LECSaEL1+t7ezZs2hoaGBO+La1tWF4eBj19fXMCV9KKU6ePAmz2cyk8AUmc+OazWYmhS8w6TVNTU1lVviyTnp6OqxW67QhD/FMQUEBbDYbxsbGxDZl1hBCUFVVBZvNho6ODrHNmTUpKSmor69HU1MThoeHxTZnVhQWFqKiogKHDh3C6Oio2OZExWc/+1nFhx9+mFtUVPReUlLSZ8W2Z6FhWvwuJoQQnclk2rNz5857/va3v6WzIAYCgQCampowNjaGhoYGpuKnKKVobm6G3W7HunXrmBQvra2tUKvVKC4uFtuUOeF0OtHb2zvvOF8x6evrYyYPpxQhhCAzM5M58cLDC8jTp08LoS4sQQhBbW0tenp6MDg4KLY5s0ar1aKhoQGtra3o61uwmlQLgsFgwIYNG9DS0oKuri6xzYmK0tJSHDt2zFhRUfGS0Wh8jLAWLzMLlsRvFBBCcjIzM4/v3r1763PPPZfMghDjOA5HjhyBTqdDTU0NU+KRUopTp04hEAigpqaGqUV5PJ2dnXA4HDFNlr+Y8FOmFRUVTF07wVBKMTw8zOSCJSmRnZ2N/v5+sc2YM8nJyTCZTLh06ZLYpswJuVyO9evXo7W1FVarVWxzZo1arUZDQwMuXbrEjIjk0Wq12Lhxo1CFlIUwU4PBgH379qXfeOON/8dkMv0PIYSJNU2zhT1VscgQQtZmZ2cf+8Mf/lB69913M5Eh3+1246OPPkJeXh4zq055AoEAjh8/Do1Gg4qKCqZs5xkaGkJ3dzeTK615+vv7oVKpLkv0zxITExNISkpiVrxLBYPBgLGxMSZu/JEoKSlBT08PXC6X2KbMCbVajXXr1qGxsZHJNvDFjHp7e5kbhPCDD5/PhxMnTjAxg6BUKvHyyy+nPvroo9ebTKYjhBB2bwQRWBK/05CcnLxzxYoV7+zfvz9769atTKiYiYkJHDp0CGVlZczFmfLCNzU1FatWrWJSONpsNrS0tGD9+vXMii6O43Du3DlmvdY8/f39yM7OFtuMhIcQAoPBwEzsYzjkcjnKyspw5swZsU2ZM0lJSVizZg2OHj16WfGOeGHbtm0RF3cqFArU19djeHgYFy9eXFzD5gkhBJWVlUhNTcXhw4enVP+LVwgh+OY3v6l95ZVXKs1m8wlCCNs3hBCWxG8YCCHEZDI9UVFR8atnnnkmw2q1ore3F16vV2zTpmVkZASNjY1Yu3Ytcx47XvimpaWhpKREbHPmhNfrxfHjx1FbW8t0Gd0LFy6goKCA6TYAkx54FlPLSZHs7GwMDAyIbca8MJlMCAQCGBkZEduUOWMwGLBs2TKcOHGCSU+8TCbD+vXrMTIywpwABibLZxcWFuKjjz6Kew+8w+FAe3s70tPTZU8++WTesmXLPtLpdDeKbVesWBK/IRBCFCaT6U+33HLLV/fv36+54YYbUFhYCLvdjiNHjmDv3r1oaWnB0NBQXI2e+/v70dLSgg0bNjC3Ml8KwjcQCODYsWMoLS1FSkqK2ObMGafTicHBQRQVFYltyrxwuVxCta4lxMdoNDItGnkqKirQ3NzMxNR1JAoKCqDVatHW1ia2KXOCdQGck5ODyspKHD58GHa7XWxzBNxuN3p7e3Hq1Cm8//77OH36NCilKC8vx65du3Ds2LGU8vLy3xgMhvvEtjUWxH8G5kWEEKLJzMzc841vfKPuO9/5jlCuzWAwwGAwoLS0FD6fD6OjoxgaGsLZs2dBCEFGRgaMRiPS09NFudl2d3ejs7MTDQ0NzN3spSB8AaC5uRkZGRnMT7M3NzczV/kvHIODg0te3zhCLpdDq9XCbrcjKSlJbHPmjF6vh8lkQkdHB7NZXACgrKwMhw8fZjY0iBfAR48eBTDpUWUJg8GA2tpaHD16VLSKpS6XCxaLBSMjIxgbG4NSqYTRaEReXh4qKysvuwdkZGRg37596TfddNP3MjMzM4aHhx9fdKNjCNNFLmIJISQpMzPz/ccff7zia1/7WtQ5wXgxzF9AgUAAaWlpgmDW6XQLGrva3t6OgYEBrF+/nolqMsFQSnHixAkkJSVh5cqVYpszZ7q7uzEwMIB169YxGafMY7FYcP78edTX14ttyrw5dOgQKisrodfrxTZl3rBc5CKYzs5O+Hw+plPnAZMx8fv27cPmzZuZczYE4/V68dFHH4laRS3a/jKSTgkEAjh69CiysrJQWFgYS9MWBbvdjmPHji14IQ9KKSYmJmCxWGCxWDAxMQG1Wi047tLS0qJeo8JxHHbu3Gndt2/fb4aGhu5jtSTykvgFQAgxZGZm7nvmmWdKbr/99nn1Zn6/H+Pj48JF5nQ6odPpkJaWhvT0dKSlpUGlik3mkLa2NoyPj2Pt2rXMLa6ilOL06dNQKBQoKysT25w5w1ch2rx5M3ODj2AopThw4ADWrFnDXNhMKH6/H/v27ZOEYASkI35dLhcaGxuxadMmsU2ZN3wqQ5b7LgCwWq04efIkNm3aJEr/NV/xC0z+3g8dOoSioiImc3rzZbTLy8tjkpaRUgq3242xsTGMjY1hfHwcPp8PycnJglMuJSVlXo6aQCCAe++9d+LNN9/889DQ0D9TSpmLA2L3bh0jCCHZJpNp34svvrjsH/7hH+atIOVyOTIyMpCRkQFg8kJ0uVwYGxvD0NAQ2trawHEckpKSkJaWhtTUVKSmps5KEFNK0draCrfbjXXr1jE5RX3u3DlQSsFClbxI+Hw+NDY2ora2lmnhC0wWg0hOTmZe+ALA6Oio8PtbIn7QarXgOA4cxzH/eykoKMDevXsF5warpKamoqioCKdOnRIlNWOoqOUHeR988EHU7yGXy1FXV4dDhw5BoVDAZDLF0MKFhy/kcfjwYfj9fmRlZUX9t7zQHR8fh9Vqxfj4OFwuF7RaLdLT02E0GrFixYqYL16WyWR44YUXUtLT07e/8sorbxJCdlBK4z+FRRBs90DzhBCyzGQy7f3973+fu23btgVRkIQQ6HQ66HQ65ObmApgcNdntdlitVgwMDKCtrQ1erxd6vR6pqalISUlBSkpK2JAJXvh6vV7U1NQwOc1+8eJF2O12rF27lkn7gU9KF69YsYLpBW7A5PV4/vx5NDQ0iG1KTBgaGmLuBpgo8AvfZnODj0cIIVi1ahXOnj2L2tpasc2ZFwUFBbBYLOjo6GB2oatSqRQEMO+AYgm1Wo0NGzbg8OHDABD29xGsGyYmJmC1WuHxeKDVagUnGr+YcTHuq4QQPPXUU0kGg+HKH/3oR+8RQq6jlMZ3CosgElb8EkJWZ2VlvffWW29lr1u3blE/WyaTCQI3Pz8fwKSYcjqdsFqtsFqt6O7uhtPphFwuR3JyMlJSUpCcnIzBwUGmhW93dzeGh4dRV1fHpP08HR0dUKlUzOVSDkdXVxeysrKYT23GMzIywvSMgpQxmUzo7+9nXvwCk225cOECbDYb8zMmlZWVOHDggBCaxyJqtRp1dXU4fPgwamtrmXNKqFQq1NfX4+DBg/B6vVCpVLDZbJiYmIDNZgMhBElJSUhJSYHRaMTy5cuh0US9PGnBePTRR3Xp6el1//Zv/7afELKNUmoT26ZoSEjxSwipys3N/du7775ripeYLUII9Ho99Hr9lLgljuMwMTGBiYkJtLa2wuVyQaVSYf/+/cI0dXJyMpKSkhZtxDdXRkZG0N7ejo0bNzIZqsEzMTGBrq4ubN68WWxT5o3f70d7e7sk2gJA+H2wFgOfKGRkZKC5uVlsM2JCsPd3/fr1YpszL+RyOWpqanD8+HGm1y9otVrU1taisbERGzZsiAtxGIlAIACHwwG73Q6bzQabzQa73Y5AIIAzZ87AZDIhKysLZrMZSUlJcX3PvPfee9WpqamV991332FCSAOlNO7raLN5hc8DQsjKnJycd99//30TC6m1FAoFDAYDRkZGoNfrsWXLFhBC4PP5hB/M8PAw2tvbhaTZer0eSUlJSEpKEgS1SqUSVRjbbDacOXMGGzZsYLZjBSbF4okTJ1BTUyMJgdXR0YG8vDymV60HMzw8HJNFI0ssDDKZDGq1mvlYWZ6MjAxh4TGrHlOe5ORkFBUV4cyZM6iurhbFhtnE+kYiJSUF5eXlOHLkCDZu3Cjq/YaPybXb7YLQtdvtcLlckMlkwr06OTkZWVlZ0Ov1kMvl8Hg8OHToENRqNTMe7J07dyqVSmXJV77ylb2EkI2UUofYNk0HuypkDhBCCs1m8/tvv/22mQXhy3Px4kVYrdYpMbJKpVJYuRlMIBCA0+kUfmQWiwV2ux0+n2+Kd1mv10On00Gv10Oj0SyoMHa73ULls3geiUdDS0sL8vPzmemQpoPjOHR1dWHLli1imxIzhoeHmcv5mWhkZmZieHiYydRU4Vi9ejXOnj2LDRs2iG3KvCkoKMDQ0BD6+vqYzJzAk5mZKdx3FjrEzu/3w+Vywel0wuFwCBvvjNJoNILINZvNQrjCdDap1WrU19fj8OHDqKysXNA0aLHklltuUdhsttKHH374PULIVkqpR2ybIpEw4pcQkmUymfa++eabOZWVlWKbEzXBMbLRTHvIZDLB6xuK3++f8gPt7++H0+mEy+UCpRRqtVoQxTqdDlqtFlqtdl7imOM4HD16FOXl5cwLxsHBQTidTlRUVIhtSkzo6OhAfn4+0574YPhclmIkjF8iejIzM3HhwgXJiF/e4ysF7y8hBGvWrBHif7VardgmzZn8/Hw4nU6cPn0aVVVVc36fQCAAl8slCNzgjeO4KYva9Xo9zGYz9Hr9vMMQNRoNkzHMd9xxh3piYmLN7t279xBCrqWUxk8p3CCkcdebAUKIwWQy7X/11Vfz6urqxDYnagYHB9HR0YGGhoaYxPvwi+fCLc6glMLj8Qg/aofDgZGRETidTng8k4M3pVI5RRTzwlir1YadNqeU4tSpUygoKGB+Ktrn86GlpQUNDQ1xHVcdLX6/H93d3ZLy+jocDuj1ekl8P1ImJSUFExMToJRK5rtauXIl2trawNL9JRIqlQrl5eVoampifmGyRrMS3d2NaGzsRG3t5YMtSim8Xq8gbl0uF9xutyB0/X4/CCHC/U6n0yElJQXZ2dnQ6XQLHi6m1Wqxdu1awYPNSqjQfffdp5mYmKj78Y9//NrHadDiLg+w5MUvISTZZDLte+mll5ZdeeWV8RsxHoLFYkFra+uixSwRQqDRaKDRaMJOsVBK4fP5BE+xy+XC8PCw0FFw3OTgTqVSCaLYZrPB7/cjNTUVbrcbarWa2Y709OnTKCkpYT5sg6ezsxO5ubmS8foCkwsqWUtxlIjwnjKHw8F0qeNgDAaDsDiZFQ/ddJhMJvT19aG7uxsFBQVimzMn3nsvgIEBN7zeAnR3n8Lvfz+BvDwZNm4Mf8/iN6PRCI1GA51OFxf9Y3JyMqqqqnD06FE0NDTErEjWQvOd73xHPz4+ftVvfvOblwkhu+KtEpz43+wCQgjRmkymD5577rmSG2+8kZnVSTabTRh1x8uFTgiBSqWCSqWKOLXHj6LdbjcGBgZgs9mQk5ODjo4OuN1ueDwewdujVquh0WjCPqrV6rjodHgGBgbAcZyQp5l1AoEAOjs7JZPhgWd0dJT50rmJgtFoxOjoqGTEL/CJ93exU2cuFOXl5Thw4AAyMzPjJvyBd8J4PB54PB7hvuJ2u4V9r9cLrxew22UANGhs1MBuz8amTT145ZVyPPVUPm64QYuf/1zBjDPGYDBg1apVOHLkCFOLxp9++umk8fHxz7z11lvPArhPbHuCYeM/OAcIIUqTyfS33bt3l992223MLGX3eDzC4jBWpjh4eFHLcRz6+/uxefPmsLljA4HAZZ0Xn7XC6/XC4/EIo3J+dbharYZKpbpsn39cqDQwPp9P8MCz0lHORG9vL8xms2QyPACfxPtKweuWCGRkZODixYuSifsFJtvU2toqmUwWSqUS5eXlOHXqFOrr6xes/+M4Tuj3+cdw+7zjUKlUCvcB3mGSnJws7KtUKpw6RdDRAfzqV8Dx44DPBxw5koUvfKEFjz++ES+8IMcvfgG88w5wzTUL0qyYYzab4fV6F2URX6wghOCFF15I2blz5xdNJpN1aGjoMbFt4pGk+CWEEJPJ9Mr9999fc/fddzOTud/v9+Po0aMoKytj9ibu9/tx/PhxVFdXRyyaIJPJhCmmaN4vtDN0u91CdRv+GN8xymQywUMdaVMqlVHngm1paVmQ8pBiQSlFe3u7JGITg+EFBws3hCUmy+pOTEyIbUZMIYRg+fLluHjxIlhaVD0dmZmZ6OnpQV9f34wzX5RScBwHn8/3sffVO2Wf3zweD3w+n9Bny+XyyxwbfNWy4GOzcXAYDIDXC/T3TwpfAGhpycC+fbm4884z+K//WgNKgWuvBTZvBv78Z4CFdbL5+flwOBxoaWlBeXm52OZEhUwmw+9+97vU66677uupqannrFbr/yu2TYBExa/BYPg/V1555Q3/+q//yszwm18clpOTw3Rp1jNnziA/Pz9mq57lcrmwkjYa/H6/0Ol+MgXmhc1mu6wjDgQ+icFXKBSCKFYqlVAqlfD5fBgdHYXZbIbFYhGOK5VKyGQyJoXW8PCw4CWREhaLhZl0QEt8ssbA5XLFzZR6LMjOzhbK1cdLyNps4AUsL2J9Ph+MRiPOnDkDh8OBQCAg9KP8eb/fL/x9cD/K96V8rtpQB8RCFm0oKAAKC4HsbKCz85Pje/YU4aGHjmHTpl4cODAp5vfvB1atAl56Cbj++gUzKWaUlpbi2LFj6OrqYiYeW6FQ4PXXX09bt27dfxJCzlFKj4puk9gGxBqNRnNdWVnZt1955ZVUlsTJhQsXIJfLUVxcLLYpc6anpwc+nw/Lli0TzQa5XB61V5kn2GMRLJCbm5uRm5sLi8UCn8835TXBHT4hBEqlUuj4p3sM3Ra7as/FixeZ8RjMBovFIolS04mEwWCAxWKRTCw9MNkXFBYWorOzE4udS97v9wvCNXgL7buCj/H7wfD9Fd9nqVQqGI1GDA8Po7i4eIqDQKlUQi6Xx6Uj4KabgLQ04Iorgo8S/PSn1di9+wAuXUpFf/9kzPnwMPCd7wDd3cA994hibtQQQlBTU4OPPvoIer2emUW+ycnJePvttzM2bdr0JiFkHaW0V0x7JCV+CSEr8/Pzf/P222+nszRN3d/fj+HhYaaTpNvtdly4cAGbNm2Ky45wOnjxGhwDe/78eRQWFqK0tHTGvw8EApfdZIIfHQ5H2JsRx3EIXQArl8svE8jBx/j96R4jeaVtNhsopcyG1EzH2NiYZPIvJwoGgwH9/f2SEr/A5NT0vn37sHz58rCDW0op/H6/IFZnegzd57fgmStgcno50kBbqVRCq9UiJSUl7KB8pj6bUoqDBw9CrVYzNcOyZQswPj4pgnlcLiV++tNq3H9/Ix57bBN8PjkIAUZHgeZm4MwZIN67EoVCgfXr1+PQoUOoq6uDXq8X26SoKCoqwn//93+bP/e5z/2dEFJLKXWJZYtkxC8hJN1kMr371ltvZbIUNmCz2XDu3Dls3Lgxrmt3T4ff70djYyOqq6slsYjK5XKht7c36hy4wXHG84G/KUa62fHHvF5vxJslv4VCCBHSzR05cgRyuVzY+JumTCa77HjwY7hjMplM9BAQn88n2LcEO6Snp6O1tVVsM4TfXSAQiOqR30KfB28+nw/vv/9+xGuSH6hON4jlBWukwfBi/u4IIaisrMSJEyewZcsWphwcqakApZNxwGNjk8cuXUrdZ4f3AAAgAElEQVTDhx/m4Y47mvHLX1ZBLv8k5re/P/7FLzCZA7i6uhrHjx/Hpk2bmOn/tm7dSnbv3l302GOP/ZEQcoNYKdAkIX4JIQqTybTn5z//ee6aNWvENidqOI5DY2MjampqmIwP4zl37hxycnKYr27E09LSglWrVi16Z0IIEW5wscbj8eDAgQOor6+f4nkKt/FxfZFu/IFAYMrxQCBwmQc7GF4gz7QRQiIeCz4XesxqtUKj0WBsbGzKudD94C30+BJzh//u+esg0safD36dz+fD0NCQ8D6hrwm+vkKPBW/hjvv9fuFan+76JIRMGciFG+yFDvj46f7QjX+N2+2WXJaY5ORkGAwGdHV1MZmlw2IBXn8duO++yfCGPXuK8H//72GsXTuEjg4TysuBpKTJOGFWSE9PR0FBAU6fPo3q6mqxzYmae+65R33ixIlNr7322vcBfFsMGyQhfk0m0wtf/epXK3fs2MFMeyilaGpqwrJly5gux2qxWDA2NoaNGzeKbUpMsFgs8Hq9MJvNYpsSU/r6+pCfn7/oi4uChU84wRxOyPDneNHCH+NXiIeKIYvFArlcjq6urinCiv87YGZhthDwoidY/IQKoZmeA5MhRQcOHLjseKjdkZ6HPi4E0QwwQgct/LGuri6hFGykgQ4vOGcaLE23LSY6nQ4ymQw2m01SYUalpaXYv38/cnJymJzl++xnJ7czZ4AnniDYs6ca99xzEI2NadDpVFi9mg2vbzCFhYUYHR1lagEcAPz4xz9OPnPmzL8kJyefsNlsf1jszydxVnRj1hgMhnu2bNnyg9dffz2NpRF2Z2cnRkdHUVNTw6xngOM47N+/H+vXr2cm5mg6KKXYv38/qqurw5aAZhVKKfbu3YsNGzZIJmVbMIcPH0ZFRUVcXYPTCc+ZRGso+/fvn7YgSbj+I1R4TyfAxaSzsxMcx2H58uVimxJz+vv7MTo6KrlY9I6ODjidTpSVlYltyryxWoEjR/rgcPRjxYq1zAlfHo7jcODAAdTU1DA12BofH8fatWtHLl26tI1S2ryYn81mkOnHEEJWpqWlff93v/sdU8LXarWio6MDVVVVcXUjmi3Nzc0oKiqKK9ExH3p7e5Gamiop4QtMXm86nU6SwhdAXBYVCPZ0hsZSh1uMNN0WvCAz3BYui0jwNHw8h3ikp6djfHxcbDMWBLPZjOHh4bAx+CxTWFiIkZEROJ1OsU2ZN6mpwDXX5CA/H0hPFzX5wLxQKBSora1FY2PjZdk74pm0tDS8/vrrRpPJ9AYhZFHzbzIrfgkhKpPJ9Ob//M//GFkSX36/HydOnEBtbS0zJQrDMTw8DJfLxdQ0y3QEAgFcuHAhquwOrNHZ2clkjF408Iv44k3ULREdSUlJsNlsYpuxIMhkMpjNZgwMDIhtSkwhhKC0tBRnz54V25SYUVVVhba2Nng8HrFNmTPJyclYvnw5zpw5I7Yps6KyshKPPvportlsfn4xP5dZ8WsymZ65//77C2pra8U2ZVa0tLSgsLCQae+i3+9Hc3Mz857rYDo7O5GVlSU57yjHcbBYLMjMzBTblAVhfHxcMgstExHeK86St2o28Dl/pYbJZILL5ZJMlT6lUonS0lI0Ny/qzHvMycvLA8dx6OvrE9uUWfHAAw9oSktLP6vRaG5crM9kUvyqVKqri4uLdz766KNMlQYaGhqCw+EQtQhELGhra0N+fn7cTTXPFY7j0NHRIcm4w4GBAWRnZ0tmkBLKkvhln9TUVFitVrHNWBD0ej0opXC5REtnuiAQQrB69eq4SFUXK7Kzs+Hz+TA8PCy2KXOGEII1a9bg3LlzcLvdYpsTNYQQ/OEPf0g3GAwvEkIWZbU5c+KXEJJhNBp/89prrxlYyovLVwyrrq5mWohMTExgZGSE6Up0obS3t6OgoIDJ1csz0d3dLenKZ1arlelsKUtIW/wCk964np4esc2IOXyxC4vFIrIlsYEQgqqqKjQ3NzMdp61SqVBeXo5Tp04taIaXWGMymfDiiy9mmkymP5JFEEnsqEcAhBBiMpn+8Pzzz2fm5OSIbc6saGpqQmlpKTSaRY3pjimUUpw+fRqVlZVMC/hgOI5DT08P8974cLjdbnAch6SkJLFNWTDicbHbErND6uI3JycHfX19TAmRaCktLUVbW5vYZsQMrVaLgoIC5ttkMpmg0WiYC7m5/vrrZTfffHOFwWB4aKE/iynxm5aW9vXrr7++9rOf/SxTK8X6+/sBTHaCLNPb24vk5GRJTTN3dHQgPz+fmeo4s6G3t1fSXl+fzydkQ1iCXZKTkyW76A2YjCfV6XSSiY8Nhr8XSCljR1FREYaHh+FwOMQ2ZV6Ul5ejo6ODuZCbZ555JsVoND5CCKlcyM+Zk/glhLxECBkihJwJOvZdQkgvIeTkx9sNQeeeJoQcI4RsnauhhJCC9PT0f3v++eeZmuPkOA5nz55lPtcjx3E4f/48Vq1aJbYpMcPv96O7u1uSXl9gsrAF6wOu6bBarUzltFwiPHK5XChKIlVyc3PR28tuKq3pWLlyJfOe0mAIISgrK2N+8ZtCoUBZWRlOnz4ttimzQqPR4E9/+lPGx+EPC+bonKvn92UAnw5z/BlKafXH218BgBDCq6UrAHxtjp8Hs9n8ygsvvJDB2hTn2bNnUVRUxHS4AwBcuHABhYWFTJdhDqWzsxO5ublMp5yLhNPphFwul1z2imAmJiaWxK9ESEpKgt1uF9uMBcNsNmNoaEiSoQ8GgwF+v19Snm2j0QgAGBkZEdmS+WEymSCTyZhLt1dWVoZdu3Zlpaenf3OhPmNO4pdSuhdAtFHucgABABTAnOYnNRrNTXV1dWs+9alPMTW/OT4+DqvVynyOVafTiYGBAUl5SCml6OzsRFFRkdimLAhS9/oCkFzp2ERG6qEPcrkcSUlJko1tLikpwYULF8Q2I6aUl5ejubmZ+QFLRUUFWltbwXGc2KbMiu9973tJycnJ3yKEZC/E+8c65vfrhJCmj8Mi0gHg45J1OgD7Afxstm9ICNGmpaX97L/+67/SY2zrgkIpRVNTkyQWh7W2tmL16tVgKbvGTPT39yMzM1OSGR6AyfZlZy9InxE32Gw2pvNlL/EJKSkpkha/wCcL36RIRkYG7HY7U+m1ZkKv18NoNKKrq0tsU+aFRqNBUVERc0VJtFotfvKTn2SYzeYXF+L9Y6lmfgZgOYBqAP0A/oM/QSn9BqV0LaX0f2f7ppmZmbsffvjhDNZu5F1dXcjIyGDeMzUxMQG32w2TySS2KTHl0qVLkkrXFozL5YJMJpN0yAOlVFjwtgT7JCcnS2raPBx86IMUIYSgqKgI7e3tYpsSU0pKSnDp0iWmU58Bk8VWxsbGmBtg3nTTTbKKiop6uVx+VazfO2bil1I6SCn1U0oDAH4BoG6+70kIKUlPT9913333MRUw6/P5cOnSJaxcuVJsU+bN2bNnsWrVKua918FYLBao1WrJpsgaHBxEVlaW2GYsKB6PR9LiPtHQ6/VwOp1im7GgyOVyaLVaycY25+bmYmBggLnp9elQqVTIyclhLmVYKIQQlJeXo6WlRWxTZs2LL75oyMzMfIkQEtMFRzETvyFxGTsAzKvANCGEmM3m37788ssZrC1IunDhApYtW8a8V2p8fBx+vx8ZGRlimxJT2tvbJev1BSarukld/NrtdknnL040+MG1lDM+AEBWVhZzi4+iRSaTIScnR3JZLYqLi9HZ2cm8qDcYDJDJZMxVsCssLMTXvva1zIyMjO/E8n3nmursvwEcBFBKCOkhhNwF4ClCyGlCSBOAKwHMa5WeVqu97aqrrippaGiYz9ssOi6XC0NDQ8wvcgMmvb6rV68W24yY4vV6YbfbhepEUoPjOHg8Huj1erFNWVDsdvtSvK/ESATvr9lsxuDgoNhmLBiFhYXMx8iGolQqkZ+fL4mQjrKyMrS2tjK3iO+RRx7RpaamfoUQEjNhNddsD/9EKc2mlCoppXmU0hcppf9MKa2klFZRSrdTSvvnahQhRJmSkvLD5557jrlqCq2trVi1ahXzi8PGx8dBCJFUQQtgMhY7Pz9fUmEcwYyMjAhpeqTMkudXekg93RkwufgoEAjA5/OJbcqCoNFooFarJVX0ApgsfNHT08N87K9er0dGRga6u7vFNmVWqFQqPP/88xlZWVk/jtV7xqVCS01NvfvTn/60uaenB21tbejp6YHFYoHb7Y7rEcvExARcLpckFodduHABK1asENuMmEIpRU9PD/Lz88U2ZcEYGhqSxPU3E0viV3okgvgFJnPIsjb1PBuWLVvGfIxsKHK5HDk5OcyJxnCsXLkSFy9ejOsQI0opnE4nRkZG0NnZidbWVmRkZJDMzMxPEUJispgq7oJpCSEqs9n8f5988kmFWq2G0+mE0+nE6OgonE4nPB4PgMmRgE6ng1arhVarFfY1Go1opWrb2tpQWlrKvFfR4XDA7XZLLtZ3dHQUqampzMdiT4fFYkF5ebnYZiw4breb+cIxS0xFr9fDYok2fTy7mEwm9PT0SDYPd2ZmJpqbm8FxnKQKCBUVFeGjjz5CYWEh0/d4pVKJ7OxsdHV1iZa73+fzweVyTdmcTidcLpcQW83rOp1Oh/T0dOTm5uLpp5/W3HHHHT8C8A/ztSHurszk5OQv33HHHSn8gp309MvT+1JK4fV6p/zDBgYG4HQ64Xa7EQgEQAiBWq0WBDH/yG9KpTKmF7DNZoPH45HElLMUvb4A0N3djYKCArHNWDCcTqeog7/Fgp/9YfkGtMTl6PV6OBwOsc1YcNLT09HU1ARKqSSvYUIIcnJy0N/fL6lZNpVKBaPRiL6+PuTm5optzrwoLi7GgQMHUFBQENMQzUAgAI/HA4/HA7fbDbfbDZfLJTx6vV4Ak6WXecelVquFwWBAbm4utFrttM6pa6+9lmRlZdUTQkoppefmY2tciV/e6/voo49OO5/JC1u1Wh0xJpX/EoL/8TabDW63Gx6PR/gSZDKZEKfEP4buRyMm2traJJHazOPxYGxsDFVVVWKbElP8fj/Gx8dRXV0ttikLxtDQEDIzM8U2Y8FxuVzQarVim7FEjFGpVEK/LGVkMhl0Oh0cDodkQ3fy8vLQ1NQkKfELAMuXL8exY8eYF78qlQpZWVno7u6OanE+x3GCduKFbeg+75TgHYy8jkpPTxecjyqVal4DPkIIfvSjHxk///nP/wjAjXN+I8SZ+E1JSbnnzjvvTI3FIiuZTCaMKqbD7/cLXx7/aLVaMTQ0JDznY2PkcrkgiFUqlbDv9/ths9mg1+vh9/uZ9rx1dXUxP60TjsHBQZjNZsm1K5jR0VFJeuxDcTgcks9mkYgQQkAIQSAQYH7B8EwYjUaMjo5KVvzq9XpBMEkpPIkXcWNjY2FnpVmAUgq/34/s7GwcO3YMarUaPp9PELNer1fY5wWtQqG4zDGYnp4+xUm4WL/Zbdu2ITs7u44QsopSOueydXEjfj/2+v7rww8/vKh3NblcDr1eH9XNlOO4KReGx+OB0+lET08P1Go1mpqa4PV6BbFMCIFKpYJKpYJSqRT2gzf+uEKhEF2Y8QvCNm/eLKodC0F3d7fk0rYFQynFxMQE8xUFo8HpdEq2QEmio9Vq4XK5JD+4ycjIwMWLFyWREjMSeXl56O3txfLly8U2JaYUFRWho6MjLsQvnznE5/PB6/UKW+hz/hiPQqGASqWCXC5HZ2cnMjIyoFarkZKSMsW5F4+DUN77+0//9E/PALh+ru8TN+I3OTn5ni9/+csx8fouFAqFAgqFYsqN1+v1ore3Fw0NDZeJ10AgEPZidLlcsFqtwjH+4uUhhAiiWKlUCheqUqkUNoVCMeW5TCabt3geGhqCwWCQ3IIwn88Ht9staWHIC0KxB1CLgdPpjIsbzxKxR6fTwel0Sl78pqamSr6cc05ODo4cOSI58ZuRkYEzZ87A6/VCpZpf0TFKKTiOA8dxgg6IZuMJ1QrBTjW9Xn+Zoy30/uB0OtHY2MjcjOG2bduQk5OzjhBSRimdU9m6uBC/H1dze/ihhx5irsfr6upCQUFBWNHBxxPPdtoneDQXujmdzinP+R9NaP7BYIHMi/bg/XDPL126hLKysnn9P+IRPuRByoyOjkouO0cknE4n8zF3S4RHq9VKvtAFMCla+LZKdRaD9xxKLUafEIK8vDx0dHQgPz9fEK/8vTjcfujzYILvxcEOLaVSKSzO57dYzxLrdDqoVComwzieeOIJ4x133PE4gNvm8vdxIX4BXLF161Z9PHt9wxEIBNDd3Y0tW7bE9H1lMpkQRzMX+NFkpB+ix+OBw+GY8sP0eDwYHx/HiRMnprwXIQQKhQJyuVwQyeH2p3uUy+Ux8UzPlf7+fpSWlory2YuFxWKRdCaLYKR2M13iE3Q6neQKJETCYDDAYrFIVvwCn5RzLioqEuXzKaUIBALgOA5+vz+qx0j7odjtdlgslsucSPym0WguczQplUrI5fK4mqFbvnw5Ll26hLVr14ptyqz41Kc+BYVCcSUhJIlSOusE4XEhfnNycr794IMPMldvdmBgAJmZmXGXy5CfCplN+MKFCxeQk5NzWScVPC0TqXPgwwqm61TCJdQOFtYzbTKZbMZj/PPgOCW/3w+HwyH5UrhWqxWpqalim7EocBwnudCcJSbRarUYGBgQ24xFIT09HQMDA8jLyxPblAUjOzsbJ0+enHJf4QVpIBCA3+8XtpmeR7OFK4LF3x+mc84olUpotdppnTuhgvXo0aMoKSlhvgqqwWBAc3Mzc4sTZTIZ7r777qQf/OAH/wzgZ7P9e9FVGyHEuGLFipq6ujqxTZk1nZ2dqKysFNuMmNDX14cNGzZcdnwuQjpa+M6N47iw+8FbsMAOdz74WHAHyIeEfPjhhyCETBHIwfvhjgVv4Y5Fs/Er2BcSjuNACGE6y0i0xHOFxyXmD7/gLRFIS0vD2bNzXqw+KyilU0RnNBvfr0bzPNw+H4pnt9vx/vvvT+kHQ/vemRwafMpRhUIR1hHCn1tMj2p+fj56enqYF7+EEBQWFqKrq4u5dK333HOP9vnnn38QLIrftLS0u++7777UeJoGiAaXywW/3y+JVDU2m01Y4bmY8AJxIb14TU1NyM7ORmZmZtiOO1IHHvoan88X8XXTbdGItWChPN1+6CO/73K5IJPJ0N7eLojtSH8X7f50x8T8rXo8njmHAy0R/8RLrl9eLPKCMfh5uOPR7oc+dzqdaGtrAwDhXPBjpGPhXjMTcxm8Bw/6eeE5GwcCIQStra1ITU2VXEU7k8mE1tZWSRQrycnJwYEDB1BSUsJUW8xmM6qqqtIIIbWU0sbZ/K2o4vfjhW5f+eIXv7i4qisG9PT0SGa6SkptCSW43C/fMccT4W6M093gwj1OTEwgKSkJMplMeC3vQQ/3N5Fu6jM954/Nl0hiOppjXq8XDocDjY2NYV/H70dzLPh46GsinYt2f7pjszkPTGaU6erqmvF1Mwmg0PPTPZ9pP/Qx0rnQ8+HOhR5zOp04fvz4ZcfD/X2kbb6Eu0ZDB4LTPef7mdAZoODBqFwuF+J9k5KSZhzkzjQwjlfRYjKZ0N3dLTnxK5PJYDAYMDw8DJPJJLY580KpVCIpKQnj4+PMLXx76KGHjKdPn34Es1z4Jrbn94qtW7fqWYtVpJSir68PGzduFNuUmDA4OMjcdEc0OByOuC/3G3qznAsDAwMoLi5mIpXbdOIl3PHQYyMjI5iYmMCyj2vSh3ttuM8J/vzZCrjQc6H70z0P9z7T/V8inQtObxTKdKJnOkEeTuSHPp/t4CDSgCPSY7hBzsTEBIqLi4Vp7LkMkuJVCIbCF0WSmjAMRsrlnHNzc9Hb28u8+AUmwzi6u7uZE79XX331nBa+iSp+WV3oZrVaodfrJbHoxmazQavVxrVAnCtDQ0OS6JRmwmazMRN+E0lwRcvo6ChSUlISZnEfMFmgRWq5UqeD71tZuabnQ2pqKnp7e8U2Y0GRyWTQ6/Ww2+2SW3hsMBhw+vRpSQj7zMxMtLS0MFdhca4L30RrISFEp1QqmVzoJqUwgcHBQWRlZYltxoIwMjICo9EothkLCsdxcRnOsVCwtiJ5idmj0WjgdrvFNmNRSElJgdVqFduMBcdoNGJkZERsM2KOTCaTzHcok8mQmZmJoaEhsU2ZNXfddZc2JSXlq7P5GzHvmFdt375dzdpoiZ96zczMFNuUmDAwMCBJ8UsplaSnIRSbzSb5NgaztOBN+qjVang8HrHNWBSUSiU4jpN8FpOMjAxYLBaxzVgQsrOzJZOej9W25OTkICUlxUwIiTr1hmjiNycnZ9fnPve5+A9SDMFut0On00kiTMDr9YJSKkkxkSjlfhNR/C55fqVNIolfYDK9m9Q93SkpKZIt58yqtzQc6enpGBsbY3Iwduutt+rkcvn10b5eFPFLCCGBQGBzQ0ODGB8/L6TkKZWSBzuURCn3mwje7WA8Hs+ip+RbYnFJNPGbnJwMm80mthkLCiFEsqWr+aptUrhmZTIZUlNTmayyePPNN+uzs7N3Rft6sTy/NevWrVPEW2W0aBgYGIDZbBbbjJgwPDws2ZhYi8UCg4G5tZSzJtE8v36/P+4qKi4RW+Il1+9ikZSUJHnxC3xSzlmKZGRkYHR0VGwzYgJfkpo1ampqEAgEagghUd0gRBG/GRkZ//j5z3+eObcc3yFLJUxgbGyMubQm0TIxMZEQGQFcLhe0Wq3YZiyxRMxIRM+v3R51hiZmSUtLk8TCsHAYjUYMDw+LbUZMYDWMgxCCbdu2KQBEFVIgivhVqVS3fPrTn2YuGFNK2QPcbrcwXSM1+EIPUmxbMHxcltTjmnmkkE5oiZlJNM+vXq+Hw+EQ24wFh9Xp9GjgY2WlAK8LWPwN7ty502A2mz8fzWsXXfwSQrLNZnMqix5HKcWRjo6OSkbIhzIxMcFEwYf5kmheX5/PtxTykAAoFApwHCe2GYtGooh9tVotLLKWGnK5HEqlUjIzFgaDgckwjquvvhoAbozmtWKI36tuueUWJrOXj42NSSaOlMUyhtFitVoTIuTB4XBAr9eLbcai4fP5lha7JQCJ5t3nK9LFonR4vKPT6SS56A2YDOuQimfbaDQyKX51Oh0KCgrUhJDcmV676OI3Ozv76oaGBubcVT6fD4QQyXiexsfHJSsQbTZbQnh++XRuiYLX610Sv0tIEq1WC5fLJbYZC05KSopkF/elp6dLRvymp6czuzhx69atWgC1M71OjJjfDbW1M9oVd0gpewClFD6fTzIL90JJlAwIS2EPS0iVRPGE8iSK+JVyWjcpeX6VSiWAyT6XNTZu3JicmZl5xUyvW1TxSwiRyeXyDBan28fHx5GWFnXxkLhG6tPlbrdbssI+GLfbnVDil+M4oVNeQtrwlc8ShUQodAFMpnWTamYLqeUxTk1NZXKgsnbtWqjV6vgSvwBKSktLF/kjY4OUFlFJOSaW4zjI5fKEiBt0uVwJVe1syfObOCTaojeNRpMQnl8pi19CiKTS9KWkpDCZmi4/Px9+v79gptcttvhdu3XrVibnox0OB5KSmFyndxl2u10ybQnF6XRK2qsdjMfjSQgPNw/HcUviN0FQKBRMTrnOlUQJe1AoFPD7/WKbsWBISdynpqYyWZKaEILc3FwZISR7utctqvjNzs6+sr6+nrl5Wo7jIJPJJONNlLr4TaRFYFK5JqNhSfwmDonm+ZWSx3Am5HK5ZL9bKYnf5ORkJsUvAGzZskWDGRa9LXbMb0M8LXY7e/YsGhoaoFar8cMf/nDKuWXLlqGyshLV1dVYv369sIDKYrHgmmuuQUlJCa655hohsXUgEMAXv/hFbNy4Ec3NzXHTlu7ublx55ZUoKytDeXk5nn32WSHmN17bMhNjY2PYsWMHqqqqUFdXhzNnzgjn3n77bdx4441YsWIFnnzySeF4c3MzGhoasGvXLuYW0nzwwQeorq5GeXk5tm7dCmBy0eKRI0dQWloqqba+8cYbqKqqQnV1NdatW4f9+/cL51577TVs3rwZJSUleOWVV4TjH3zwAdatW4eHH35YDJNjzttvv33Z98rydxoJt9uNuro6rFmzBuXl5Xj88ccBAO3t7fjCF76AdevW4bbbbhNy4Nrtdmzfvh1XXXUV+vr6xDR9XoyPj+PWW2/FqlWrsHr1ahw8eBAOhwNf/epXmeuLp+PZZ59FRUUFysvL8Z//+Z8AJu+fDz30EEpLS5lr55133gmTyYSKigrh2Le+9S2sWrUKVVVV2LFjB/x+vyB+n3jiCaxYsQKlpaV45513hL959dVXUVtbK/xPxCJce7773e8iNzdX0D0HDhwQ8jLHe3uC2bRpU0pGRsaWaV9EKV20LS8vb4DGEYODg/TIkSP029/+Nn366aennCssLKTDw8OUUkq7urpoW1sbpZTSb33rW/SJJ56glFL6xBNP0IcffphSSumePXvoT37yEzowMEC/9KUvLWIrJonUlr6+Pnr8+HFKKaUTExO0pKSEvvTSS5TS+G3LTDz00EP0u9/9LqWU0tbWVnrVVVdRSinlOI7m5+fTw4cPU4/HQ6uqqmhzczOllNI777yTDg0N0eeee47u2bNHNNtny9jYGF29ejXt7OyklE5+z5RS6nQ6aU5ODr148aJk2koppTabjQYCAUoppadOnaKlpaWUUkpHR0dpbm4uPX/+PLVYLLSoqIhaLBZKKaX/+I//SJ1OJ33wwQdpa2uraLbHAo7jaHFx8ZTv9Ve/+hXT32kkAoEAtdlslFJKvV4vraurowcPHqSf+9zn6H/8x3/Q3t5eeu+999Lnn3+eUkrpz372M/rnP/+ZNjU10UceeURM0+fFF7/4RfqLX/yCUkqpx+OhY2Nj9KGHHqJ33303pZStvjgSp0+fpuXl5dThcFCfz0evvvpqev78efqtb32LPvDAA3RwcJC5dn744Yf0+PHjtLy8XLqfZYYAACAASURBVDj2zjvvUJ/PRyml9OGHH6bf/OY36aFDh2hzczOtqqqibrebXrp0iRYXF1OO4yillH7mM5+hHMfR2267Tbj+xSBcex5//PEp+uGjjz6iTqeTifYEc/HiRZqfn7+PTqNHF83zSwjRJycni1JOORImkwnr16+fcQW5y+USptLfeOMN7Nq1CwCwa9cuvP766wAAv98PmUwGmUwmSgWbSG3Jzs4G721PTk5GaWmpEMQer22ZiZaWFlx11VUAgFWrVqGjowODg4M4cuQIcnNzsWrVKqhUKuzcuRNvvPEGgMk2EULitk2R+N3vfoebb74ZBQWT8fsmkwkAcODAARQUFKC4uFgybQUmpw35UA6HwyHsv/POO6ivr4fRaER6ejquueYavP322wAmvUastjeUI0eOYMWKFVO+1wMHDjD9nUaCECKEX/l8PiGX+v/+7//ihhtugN/vZ6pfigar1Yq9e/firrvuAjBZ3S0tLQ1vvvkmrrvuOgBs9cWRaG1tRX19PXQ6HRQKBbZu3Yo//vGPeOONN3DbbbfB5XIx184rrrjisnSn1157rRCKtWHDBgwODsLlcuGNN97Azp07oVarUVRUhBUrVuDIkSMAppalF7Ot4doTik6nY6Y9weTl5cHn8+VM95rFFKPZubm58fGfiQJCCK699lqsXbsWv/nNb4SUUoODg8jOnoyjzsrKwuDgIADguuuuw4cffojt27fjwQcfFM3u6ejo6MDJkydRU1MDgN22rFmzBn/84x8BTIqFzs5O9PT0oLe3F0ajUciAkJeXh97eXgDA/fffjxtvvBEHDx7EtddeK5rts6WtrQ1jY2PYtm0b1q5di1//+tcAgJ6eHuG7A6TRVp4//elPWLVqFW688Ua89NJLAIDe3l6YTCbI5XIAU9v75S9/GRs3bkQgEMDq1atFszsW9Pb2Ij8/X3iel5eHkZER5r/TSPj9flRXV8NkMuGaa67B8uXLkZaWBrVaDb/fP+V7vv322/Hcc8/h61//Or7xjW+IbPncaG9vR2ZmJr70pS+hpqYGX/7yl+FwODA4OIiMjAwAbPXFkaioqMC+ffswOjoKp9OJv/71r+ju7sbg4CAKCgrg8Xgk0c5gXnrpJVx//fUAwv+O+ev45ptvxrp167Bu3bq4zEf/k5/8BFVVVbjzzjvh9XrhdDqZa49KpYJMJpt2fdlirh7JLiwsZGa1yv79+5Gbm4uhoSFs2rQJW7duxac+9akpr+HLUgKTCzReffVVMUyNCrvdjltuuQX//u//DqPReNl5ltry6KOP4v7770d1dTUqKytRU1MjiKJAIBDWk19TU4PDhw8vtqnzhuM4HD9+HO+99x5cLhcaGhqwYcMGYRFmOFhtK8+OHTuwY8cO7N27F4899hj+/ve/A5j0MPDfczDXXXed4DWTKqx/p5GQy+U4efIkxsfHsWPHDpw9exYAIJPJ4Pf7p3zfaWlp2LNnj1imxgSO49DY2Igf//jHqK+vx/333y/EdQe3mZW+OBKrV6/GI488gmuvvRZ6vR7V1dXCd6lSqeD1epm658zE97//fSgUCtx+++3Yu3fvtB7QXbt2CTOu8cZXvvIVPPbYYyCE4LHHHsMPf/hD7N69e9q/idf2qFQqBSFERikNu0hiUT2/y5YtEz3Tw09/+lNUV1ejurp62kUTubmTpaFNJhM2bdqEEydOAADMZjP6+/sBAP39/cI0tBhE2xafz4dbbrkFt99+O66++mrBMxpPbZmJ4Lba7Xb86le/wsmTJ/HrX/8aw8PDKC4uFgYrfIfa09MjfI8sEdzWnJwcXHfdddDr9TAajbjiiitw6tQpmEwmwWsCsNtWIPJ1fMUVV+DSpUsYGRlBbm4uBgYGBMHPcnunIzc3F93d3cLznp6esINVqZGWloYrr7wSBw8exPj4OCilCAQCkvue8/LykJeXh/r6egDArbfeisbGRpjNZlitVnAcF/d9cbTcddddOH78OPbu3Yv09HSsXLkSZrMZFosFHo9HMu18+eWX8ec//xm//e1vQQiBRqOByWS67HfMwnVsNpshl8shk8lw9913o6mpCU6nM2y/FO/tMRqNFEBGpPOLJn61Wm1Bfn6+6OL3a1/7Gk6ePImTJ08iJyd8SIjD4RAqmzgcDhw7dgyVlZUAgO3btwsrzV955RV85jOfWRzDwxBNWyiluOuuu7B69Wo8+OCDcLvdgviNp7bMRHBbdTqdsAL8l7/8Ja644gqkpKRg3bp16O3tRXt7O7xeL1599VVs375dZMtnT3Bbd+zYgf3794PjODidThw+fBirV69GeXk5Ojs7mW8rMLW9TqdT8Jo0NjbC4/EgIyMD1113HY4cOQKr1YqxsTG8++67kvT2rl+/HufPn5/yvW7cuFFssxaE4eFhoRysy+XC3/72N6xevRpXXnkl9uzZA7/fH/f90mzJyspCfn4+zp07BwB47733UFZWhu3bt+Odd94Bx3GSafPQ0BAAoKurC3/84x/x+c9/Htu3b8fvf/97eL1eSbTz7bffxlNPPYU333xTWBek0WhwzTXX4NVXX4XH40F7ezvOnz+Puro6ka2dGd4ZBkyGn1VUVMDtdmP79u3MtSc/P58AiBz3O91quFhu2dnZP3/33Xdju6RvnvT399Pc3FyanJxMU1NTaW5uLrVarfTixYu0qqqKVlVV0bKyMnrXXXcJfzMyMkKvuuoqumLFCnr11VfT0dFREVvwCZHasm/fPgqAVlZW0jVr1tDS0lL629/+llIav22ZiY8++oiWlJTQlStX0h07dgir/jmOoz/4wQ9oSUkJLS4uprt37xbZ0tjw1FNP0dWrV9Py8nL6zDPPUEopvXDhAn355Zcl19Ynn3ySlpWV0TVr1tANGzbQffv2CeceffRRunz5crp8+XIhY4kU+ctf/jLle33//ffFNmlBOHXqFK2urqaVlZW0vLycfu9736OUTq7Urq6upvn5+fTWW2+lbrdbZEtjy4kTJ+jatWtpZWUl/cxnPkMtFgsdGRmh69evp8XFxUz1xdOxefNmunr1alpVVUX//ve/U0o/uefk5uYy186dO3fSrKwsqlAoaG5uLv3lL39Jly9fTvPy8uiaNWvomjVr6L333kubm5vpwMAA3b17Ny0uLqYrV66kf/3rX8U2/zLCtecLX/gCraiooJWVlfSmm26inZ2ddP/+/ZRSGvftCeWb3/zmOIBP0wialNBFWp1XWFi45y9/+cung3PKsYDP58Phw4exefNmsU2JCadOnUJ+fv6MqzxZxOv14tixY5L1lAXT1tYGvV4f91NPsWTv3r3YtGlT2LhfKfPBBx9g27ZtYpuxqAwODmJ0dBRlZWVim7JoSLlvDkXK17SU+mZKKT788EMmv6tnn32We+CBB+6hlP4q3Pmowh4IIZ8mhJwjhFwghDz68bFyQshBQsgrhJAZ38fv9+cEr05nBZ/PN2MqNJaQcpWs0AUyUiYQCERc8CZVKKUJ1+ZEJZ7SJi0WMplMMgVMEhkpVSdkuYJobm6uwmAwLI90fsY7CSFEDuCnAK4HUAbgnwghZQAeBLAdwDEAM+be8fv9Or1eH63dcQPHcUvilxE4jkso8ZsobeVJNDGUyCyJ3yVYRUril2X0ej1UKlVKpPPRuFHqAFyglF6ilHoBvArgMwDkACiAAIAZhweUUjmLoktqgsrv90tW/CaSIOQLOyQaidjmRCSeix0sFHK5fEn8SoAl8RsfKBQKyGQyVaTz0YjfXADdQc97Pj72LIC/AGgA8G4U7yNnVZhI6YYr5RsKpVRS39VMJFJbl0g8pNxXhSMRvd1LLLFQKJVKEEIiit85uwAppScA1M/iT8jSzXqJJZZYYoklllhiiYVEoVDMW/z2AsgPep738bFZQQjBwYMH4fF4ZvunouL3++H1emGxWMQ2JSY4HA4cOHBAkl5D/rvic4dKGbfbjYGBAcmGsITDbrfjgw8+ENuMRScR2y21fjcaPB4PZDKZkANYykj5mvb5fAgEAlNy5rIMq99VU1MTNBpNxNoS0dw5jwIoIYQUYVL07gTw+dkaQimlGzZsYE50WSwW9PT0oKqqSmxTYsKBAwewfv16qFQRB0TMMj4+jo6ODlRXV4ttyoJz5swZZGVlJUT1Lx4pp0eajkRs9+joKHp7eyXT70bDuXPnkJKSAhazIs0WKV/TfX19sNlsKC0tFduUmMDqd0UphcvliugJm1H8Uko5QsjXAbyDyUVuL1FKm2drCCHEz+piKynFYUk5rowQklALRqT6PS6xBJB4Me2JuohVaiTa2pN4heM4fJykISxRKVFK6V8B/HU+hhBCOBbTbElt5aZCoYDf7xfbjAUhkVZLJ2papKUbS2KQiEIwEXN3SxEWdY4U4TgOgUDAF+n8ov3SZDKZfWJiYrE+LmYolUrJiV8ptScYuVwuWWEfSiKKXynPWiwxlUQc5CyJX2mwJH7jg4mJCbjd7oiLBhZT/PayGACuVCrh80UcPDCH1MWvVNsWypL4XULKLIlf6SL137CUxC/LMzA9PT2+8fHxS5HOL9ovzefzdbIofqUmqKQufhPF85tIbeVJxMIHiUoilrJOlPLsUhf5UhK/LLels7PTASCi6Fy0K3BsbOx8b28vc3cuVkc9kVAqlfB6I8aAM00ixfxKeRATiUT0dicqUhdI4WBZaMwGn88nyWxDPD6fD0qlUmwzYgLLbeno6PAB6It0ftF6F4/H09Pd3e1arM+LNVLxOGk0GrjdbrHNWGKeSC0WPRpkMlnCebsTFb/fn5Dil1WhMRs8Ho+kxa/b7YZGoxHbjJjgcrmYbUtPTw9FPHh+AfS3t7czKX6lJBil1JZwJEq6syXP7xJSZsnzK128Xq+kxa/H44FarRbbjJjgcrmg0+nENmNOjI6OEgBjkc4vZu/S19XVxaTbRqvVwul0im1GTJC6+JXaAsVIKBSKhGhnMIkU1pLoJKL4TZQ2S138Sile3el0Mit+OY7z0Wmm7BfzGxro6+tjMoBWp9PB5WLSaX0ZUhe/Um8fj0qlSkjxuxT2kBiwWhBpiZlxu92S8YyGwnJ2hHC4XC5otRErBMctLpcLgUBgWo/loolfSqnbbrcz6bbR6XSS8fxK3TOq1WolM1CZDpVKBY/HI7YZi8qS+E0cEiXzAY9U1pREA8tT6TPhdDr/f/bePDyOrLz3/1a3Wr2p1S11a7Uk29otydZqeZuFywyBH+ud/PIAYcISGO4vwA8YSFiGxJlhmEC4QyaEwAWGSYZtQnwJA3eAsIRlFlmSJWu1Ze2yra33fV+qzv3DrqYlt/aWqk5Jn+epp6qrS91vq7vO+Z73vO97qBSLa0Gr5/fmzZtQKBQL612zp755hmGWaSx3lpOTA7/fL7QZGUPK8aL7RfxKfRCTjgPxu39IJBL7SvxKvQJCKrR6EzdDIBBATk6O0GZkDFqTEwcGBojf739pvWv2VPwmEomLg4ODe/mWGSEnJweBQEBoMzKGVquV1OdJRUpe+vWQ0tTaZpFaze0D1ma/hT3QKjK2w4H4pYNYLAaFQkFlX3Px4kWvx+PpXu+aPRW/Vqv1xZ6enuBevmcmkMvlIIRIJtlGamI+lf3i+QX2T2ULnv1Y3m2/sl8qH/BEo1FqS0ptFY7jJOvVl5L49fl80Ov1QpuxLbq7u2MA1vW07nVK4sDLL79MnfgFAJ1OJ5nQB51OJ1nxq9VqEQxS+RPbMkqlcl/F/e7HChf7lXg8vq/EL831VLeC1L/XQCAAnU4ntBkZwev1Ijc3V2gztgzHcbDZbAlCiHO96/Za/N6cm5ujMrI/NzcXPp9PaDMygl6vh9frFdqMXYFfAnc/JJCo1ep9UdmCR8qx6gesZL8s+MATiUQkGwqQit/vl4w4XA0hRFK/W5/PR6X4nZ2dhVwun9nouj0Vv7drrlmsVutevm1GMBgMcLvXrJdMFVKqW5yO/RL6oFKp9sXn5DkIe9g/7LeEt/3i+ZVSWMBqgsEgtFqt0GZkDFo9vwMDAwgGgy9udN2eV2JOJBLdAwMDe/22OyYvL08y4pdhGEkLJynHNKeyX0Q+z36scLFfkdJCAZtByklgqUhZ/Ho8HhgMBqHNyAixWAxZWVlUDkC7urq8brd73WQ3QADxa7Vaf9fV1UVdUGZWVhZkMhlisZjQpmQEg8Eg2dAHKYWorMd+im8Gbolfqdx/BxyQyn4Rv7ROpW8GKYlfp9MJo9EotBnb4uWXX44B2NDDKsTQ+nc//vGPqZxzNxqNcLlcQpuREQwGg2Q+y2r0ej08Ho/QZuw6+6WsG89+XNVuP7If4vVT4T8vjSWltgIhRNLhHW63+0D8CozH44HdbvcRQhwbXbvn4pcQ4vJ6vTaLxbLXb71jjEYjnM51EwipQUqfZTVSrmaRyn5ZypnnoNrD/iAej0smaWgzRKNRyS73m0okEoFKpZKkyE8kEuA4TjK/W5fLhfz8fKHN2DK//OUvSSKR+PFmrhUkqCoUCv3gZz/7GXVLNeXn58Ph2HBAQQXZ2dngOE6SCUQMw0Aul0teKPGdyH6p9SvFTvOAO4nFYvtmwQdAeolSa+H1eiXjGV0NrZ7SdMRiMTAMQ2VJuh/84AdOh8Px75u5VhDx6/V6f/Tcc89RN+euUCigUCgkk2QkpTCO1eyX0AetVruvQh+A/Tctvt+IxWL7whPKI+XyX6l4PB5qF03YCIfDAZPJJLQZGcFqtaKoqEhoM7ZMIpFAX18fC2BoM9cLlU47OT4+HqNxyra4uBg0hmykw2QywW63C23GriBlYZ9KTk6OZBZf2QwHtX6lz35a6he4JX6lWgEhFafTSeVU+mZwuVyS8fxaLBYUFxcLbcaW6e3thVwu7yKEbGoqVBDxSwghDMP8+sUXXxTi7XeElMSv0WiUTBjHavLz8yUb05yKlFYe3AxKpfKg4oPEOfD8Sg+O4xCPxyWZ7BaNRsEwjCTifVmWpXaVuh/+8Ie+paWl7272esEKKVoslu9fuHCBusK5Go0GsVhMEvGkCoUC2dnZkpw2V6vViEajko+H3Y/idz8t6bwfiUQi+0r87oeEN6/XK9mQB1rDBNLhdDphMpmozK/46U9/GgXwm81eL2QV8a5f//rXCRrFSVFREWhcpS4dUvJkr0av10u+3u9+FL80hksdsHn2gxjk4ZP7aBQbW0FKCWGroTVMIB3Ly8soKSkR2owtMzMzg2g0eoMQsmlPnmDilxASI4T8/re//a1QJmybsrIyLC4uCm1GRpCy+JVyTDOPTCYDwzBgWeqKp2yL/VbebT/Cl8TaD0jZI5qK3W6XTEJYKizLIhQKSSJmm2VZuN1uKgcpX//61wN2u/2prfyNoLUszGbzF7/0pS+9+jWveQ1Vd0VOTg7i8bgkGmm1Wo1EIiHJ2pqFhYUYGBhATU2N0KbsKrm5ufD7/YKVEeKrL3AcB0JI2o2/br0t3TWp54BbK2H5/f7kfZfu71afT3fdatvX2m/nON3/ZrPn0xEOh3HlypVNX7+WF3Ez57dyvJP9esd+vx9erxeBQOCO5zbzePXGP88PFIE/DBpTrxGC/SB+WZZFNBqFRqMR2pSMw1d5kILnng/foO2zxONx/OAHPwjF4/FN1fflEVT8EkIGS0pKPFar1URbzExZWRmWlpZQVVUltCk7pri4GMvLyzh8+LDQpmQUlUqFRCKBRCIheM1CXnTxApHjuBXbRufS/S1/HIlEMDExgdzc3DuuS92vd8w/3i7riY+NRMlWzkWjUQQCAfj9/jWFUOq5dM9vdr+d47XObdShrPe81WpFaWnpms9vJKRXP5/u+u2K/a3s+d/XWgMS/nE8HofP59vSwGgng67tls5L/V2liunVwpp/nO4ai8UCk8mESCSS9lqZTHbH8WYep54XGimHPCwuLuLo0aNCm5ERFhYW0NDQILQZW+aFF17gWJb9MSFkS8kgglcx9vv9Tz399NNfOn/+PFXDwkOHDqGnp0cS4resrAzDw8OSE78cxyE/Px9msxlGoxEcx4Fl2aR4THecei7d43Qbf81GrNdRrdXRpTvmM4v5x8DKRIWNOtyNOmsxE4vFcPnyZdTV1Qltyp6RlZUlWfGwGkIIzGYz6uvrhTZlQzYaUKY7Xr1fWlpKfrepg1qWZTc9+N1oAL0ea4lmfpPL5eue2+hYLpfDbDajoKAAhBDRty9bIZFIwO/3Iy8vT2hTdkw0GkUsFqOyysOTTz7ptNlsWwp5AEQgfoPB4Pe/9a1vffav//qvNXxHTgPZ2dlQq9XweDzUr1qj0WiS666r1epdex++cWZZFizLIpFIJI83u6X+Pb+t1cAzDAOO47C8vAyDwbCiQU7XSPN7hUKRttFfr0MQ0svCsizMZjMKCwsFef+9RKFQHJQ6kzA0JbulDh63QywWg0ajwaFDhzJs2eZJFdprDfTTnefb3ng8vqETweFwwOv1Ynp6es22mm9XU9vh7W5ZWVnJ19lN+EQ3KQj6xcVFQX+H2+X69euYn59fJoRMbfVvBRe/hBB/aWnp73/3u9+99f777xfanC1x9OhRXL9+Ha2trUKbsmMOHTqEpaUlVFdXJxtCPmRgrePUx+n26byh6RoqmUyWbLBSN6VSuWZjuLqxXAuO4/DSSy+hs7NTEo1UOuRyORiGEUV4x27Df4dS8yIdcIvdHoCLCbfbLbjjhGH+sIztbuR8+P1+XLt2DadOnVrzmlSP9WacHYlEAtFoNK1zJNWhslH/k5WVtaLf4R/z59Z6zDs6FhcX0dTUlPH/2V5DCMHCwgLOnTsntClb5mtf+1rA4XD8/Xb+VhQ9pdls/uKTTz756vvvv5+qxDeTyYRr166JxlvBFxLnE9hS9+sdJxIJcByHYDCYrGKx+uZP1yAolcoV51fvxeDJl8lk0Ov1cLvdkl1dCAAMBgM8Ho8kM6pXo1AoEI/H99UqYPuF/SZ+pTBlvh5ms3nD0lkMwyQF6G4mXfMie7WTZrXjJhaLrens4ftKQgiCwSAuX76cdN7wm0KhWPdYoVAkj8UwgLdarcjPz6cu4T0ej+Pf/u3ftpzoxiMK8Xs78c29vLxsWi+xQ2wwDIPDhw/j5s2bqK2t3fHr8eI1Ho8nF9LYaEudRuJvwtSbK/Xmy8nJuUPQpk7xDwwM4MiRI5KLLywpKYHZbJa0+OVXtNsP4lej0SAUCh2IXwkSCoUkWRUgHS6XC0eOHBHajF3FYrHg9OnTQpsBYKXI3imTk5NQqVQ4fPhwWoGc6mCKRCJrOqVS+29e/Kf23dnZ2SvO8Y+zs7MzJp7n5uZw/PjxHb/OXvOTn/xkW4luPKIQvwDgdrv/9vHHH//GN77xDarqvpSVleGVV15BdXV10tNJCEkK2NXb6vOpK8XxiUyrf/QKhQJarXbFY37LpHeVD+OQmvgtLCzE5OSkpKfKjUYj5ufnhTZjT+DFr9BTxgdknlAoJHlvKHDL0RGLxagvlbkeoVAoKdikBCEEy8vLuPvuuwFgRZjeTuBjqNNtgUAgrWOMh2EYZGdnrxDHaz1OFc0+nw8Mw1CX6EYIwd/8zd+4bDbb3233NUQjfqPR6P/+yU9+8oVHH31UL7YVRgghiMViiEajyazI1GOWZfH73/9+xYhSoVBAqVSu+PEplUrodLoVP0Y+a18M5OXl4cqVK6IJ48gUcrkcOp0OHo9Hsh2rWq1GLBYDx3GiCDfZTdRqNcLhsNBmHLALhMPhfeH5lUKi9EYsLS1RmUS1EXx5ukznV/AiejsDIn7WeLWzLRKJwOfzrTiXSCSSfxeNRqHVajE6OprUK0qlcsUmlvCMVH784x9zXq/3V4SQhe2+hmjELyGEU6lUn3700Ue/+fTTT++695f3zkYiEUSj0eQ+9Tg1qzzdD0Oj0UCpVKKsrAxXrlzBPffcQ7XwYBgGFRUVmJ+fl9zCEPyqfFIVv8Ct5Zw9Ho+kwzuAW57f5eVloc04YBcIh8OS9obyOBwOyc2wrWZ5eRlnz54V2oyMc+PGDdEluslksqQu2SyhUAh9fX1obm5OCuNoNAq/3w+Hw5F8zHuYGYZZoX9UKtUd+71IuOY4Dp/+9KddVqv10zt5HdGIXwCIRqM/fOGFF77w6KOP6ncyYmRZFpFIBOFweMWe3/ilYBUKBVQqVfLLUyqV0Ov1yeOtrLmen58Pi8WybjF6GigrK0NXVxeqqqqoFvKrKSgowLVr1yTtGTWZTHA4HJIXv1qtFqHQppdwP4AS+PhHsXmZdgOn04mWlhahzdg1vF4vNBoNdUlUG+H3+0EIoS5MIB0zMzOora3d9GfhOC7pIOSdhMFgEE6nM/k4nbZSqVRQq9Ur9ju5x59//nnO7/f/ghCyuO0XgcjEL+/9feyxx57+1re+ldb7SwhJClp+C4VCSYFLCIFMJrvjH56Xl5f8InZjdFJTU4O+vj6UlJRQ3XgrFAoUFRVhaWkJ5eXlQpuTMWQyGUwmE+x2O2hbTXCzFBQUYGBgICPJl2JGpVIhEokIbcYBGSYSiUgq3Got+OV+pVzVYnFxEWVlZUKbkXFmZmZQXV0ttBk7JhwOw+12bynRTSaTQa1Wb/i7TZ1V5ze/3w+73Z7Uaamvp9Fokq/LH68VDspxHB555BGXxWJ5ZGuf+E5EJX4BIBqN/sdPf/rTv7969areYDAgFAolNz4MgRe0/JaXl5cUukJ59dRqNQwGAywWy4alXcROZWUlLl26hLKyMqqF/GrKy8sxPT0tWfErpuWcd5ODWr/SJBQKQavVCm3GriPl5X6BWwLFZrNRsUrfVgiHw/D7/SgoKBDalB0zMzODmpqaXWk/U5PvcnNz17yOZdkVzkuv1wuz2YxwOJwMteAFMb/9+te/Zn0+388JIUs7tXPLPSTDMPUAngXQBuCvCSFfSnnuBgA/ABZAghDScft8KYDv337uQUJIYK3XJ4Rw2dnZn3z88ce/98QTT6i0obWODAAAIABJREFUWi0KCgqg0Wi2FIYgBLW1tejv76d+1ReVSgW9Xg+bzSYpoWgwGJIjT6nGFZpMJjidTkl9b+lQq9X7RiztFwKBwL74Pu12u6RXY7RYLCgsLMxISTExMTc3h8rKSqr7duCWiHe5XILHLcvlcuTk5CAnJyft8/yqs7zz0+1243Of+1zYZrN9JhPvvx03qQvARwB8aY3n/xshpIUXvrf5CIAPA3gGwJ9t9AbxePz5l156aSk7OxuHDh1CXl4elEql6H90vBeaXyiCZqqrqzEzM7Ph2vC0wSf0SZXCwkJYrVahzdh1cnJyEAisOYY+gEICgYAkYik3QurJbjdu3MDhw4eFNiOjxGIx2Gw26nN6AGBiYgK1tbWi11MMw0Cj0cBkMqGiogJDQ0Os3+//D0JIRrKdtyx+CSE2Qkg/gPiGF/8BOQDu9rbhf5wQwjkcjvc/9NBDrq3aJzR1dXWYmZlZUU6ERnJycqBSqeBwOIQ2JaOUlZVhaWlJcqKex2g0wul0Svbz8RyIX+kRCATW9AJJBX5xFqmGJQWDQQCQ3Pc4OzuLyspK6pOlvV4vQqEQiouLhTZlS/j9fnzqU59y2my2v8rUa2b6myQAfs0wzADDMP8j5fxXAXwTwF/gVvjDhrAs+/urV6/2/PSnP71zgW4Rk52djYqKCszOzgptyo6pq6vDxMSEpIRUVlYW8vPzYbPZhDZlV5DJZMjJyYHf7xfalF1Fp9MdiF+JsR+WNrZYLNQJj60gRa9vNBqF1WqlPgGcEIKxsTE0NjaK3uu7mkceecTn9/sfI4Q4M/WamRa/dxFC2gD8PwA+xDDMPQBACLlJCLmHEPImQsime2Wr1frQhz70ISdtZY2OHj0Ks9lMfUZ6Tk4OdDqd5KbRKysrMTc3J7QZu0ZxcTEsFovQZuwqB55facFxt3wctHXKW0XK4jeRSMBms1Gf8L2a6enpFSu40orVaoVKpaJucZUrV67ghz/84aLP5/tmJl93U98mwzAfYhhm+Pa2ZtALn4FHCLEB+DGAzp0YRwixBAKBJx977DGqejmZTIba2lqMj48LbcqOqa2tTS4NLBV0Oh0YhoHP5xPalF2hqKhIcgOW1WRnZ69YhOYAutkPIQ/xeByJREKy3u35+XmUl5dTLxJTCYfDcDqd1K9Ux3EcJiYmcOzYMaFN2RIcx+Fd73qX02azPUgIyWgUwKZ+pYSQr91OYmtZK9iYYRgtwzA6/hjAHwG4ulMD3W73P373u981T01N7fSl9pSSkhJEIhE4nRnz0guCRqNBfn6+JJL4UqmsrJREaEo6srOzIZfLJb8EcHZ2NqLRqNBmHJAB/H6/5JPdLBaLZKuwEEIwPz8vuZCHyclJKpLDNmJmZgalpaXUDbyeffbZmNlsfoEQMpzp197yEI1hmGKGYRYBfBzA3zAMs8gwTC6AIgBdDMOMAOgD8HNCyC93aiAhJGG1Wt/5nve8x0WT95FhGJw4cQJXr15NTunRilSS+FIpKCiAz+ejPjRlLUpLSyW/BHBubq7kY5v3C36/f92aoFJgeXmZeg/iWlgsFhiNRkmt6ObxeBAMBqkPUwkEAjCbzdQtzuFyuXD+/HmX1Wp9eDdefzvVHiyEkDJCSC4hxHD72EcImSOENN/eGgkhf5cpIwkhl+bm5n5z4cIFqtSXVqtFSUkJZmZmhDZlR2RnZ+Pw4cPUf45UGIaRtPe3pKQEZrNZaDN2FZ1OJ9nQlf2Gz+eTtOeXX/FKiqEdhBDMzMygqqpKaFMyBp8c1tTURLXXlxCC0dFRHD9+nLpwlI997GNev9//SULIrjTy1Pw3rFbrB//yL//SSVsYQXV1NcxmM/XJOUeOHIHVapXUVPqhQ4dgt9slGTvK18WW0ve1mtzc3APxKxGkvmCJlBPd7HY7cnJyoNFohDYlY5jNZmi1Wuj1eqFN2RGLi4vQarXIz88X2pQt8corr5Bf/vKXM4FAYFPVwbYDNeKXEOL0eDwf+NM//VM3TeEPMpkMx48fx+joKNVJYzKZDMeOHcPY2JjQpmQMmUyGo0ePStb7W1ZWJrlY7VQOxK80SCQSkMlkVHvYNmJxcVGSIQ+EEExNTaGmpkZoUzIGy7KYmpqiLjlsNdFoFDMzM2hoaBDalC3h8Xjwjne8w2Gz2f6Y7KJookb8AkAwGPzxyMjIL77xjW9QleWSn5+P3NxcXL9+XWhTdkRhYSFYloXdbhfalIxRXl4Oi8WSXEtcSvBxvzQPutYjKysLHMdJ9vPtF3w+n6TjfSORCBKJhCRDHhwOB9RqtaQ+29TUFMrLy6FUKoU2ZdsQQjAyMoL6+nqq4rAJIXjnO9/p8Xq9DxNCdnUpVqrELwDYbLb3P/bYY2bayogdO3YMCwsL1CfonDhxAmNjY2BZVmhTMgLv/ZVSPDOPQqGARqORtHdUq9VSH1K03/F6vdRPL6/H0tISysrKhDYj4xBCktUQpILP54PD4UBlZaXQpuyIhYUFKBQK6mouP/vss7H+/v7f+Xy+f9vt96JO/BJCQjab7b8/8MADTprKHMnlcjQ3N2N4eJjq6g9qtRrl5eWYnJwU2pSMUVFRAavVKsnKD2VlZVhYWBDajF1Dr9fD6/UKbcYBO2A/iF8phjxYrVZotVrJJCqmJofRHIITCoUwNzeHpqYmoU3ZEjMzM3jkkUesVqv13XvxftSJXwAghIzY7fZ/+PjHP06VG9VgMKCoqAi01SxeTWVlJZxOp2Q8ijKZDDU1NdR/L+koKiqC3W6nesC1HgaDAR6PR2gzDtgBUha/Xq8XKpUK2dnZQpuSUXivb11dndCmZIybN2/CYDBQtwJaKoQQDA0N4fjx41SFO8TjcTzwwAMum832/xJC9mQqj0rxCwAul+uL//Ef/zH2q1/9iqpevaamBg6HAy6XS2hTtg1fw3hkZEQyoqq0tDRZ11FKyGQyFBYWSna54wPxSzd8+JRcLhfYkt3h5s2bklv4AbiVwGc0GiVT4SEcDuP69euor68X2pQdMTMzg7y8PBiNRqFN2RKPPPJIwGw2f40Q0r9X70mt+CWEcDab7YH3vve9DpoSsBiGQVtbG0ZHR6lenUqv16OgoEAysbIMw6C+vh4TExNCm5JxKioqcPPmTaHN2BWys7MRj8cPkt4oRcr1fVmWhdPpRGFhodCmZBSWZTE7OyuZCg+EEAwPD6OpqQlZWVlCm7NtnE4nrFYrdQL+xRdfJN/73vemnE7nZ/fyfen9pnFrwQ2NRvPnb3zjG7/f1dWVR4ubX6PRoL6+HoODgzh9+jS18UW1tbW4ePEiioqKJDFtWVhYiLm5ObhcLurqIq6HTqcDx3EIhUKS8dSkkpOTg0AgkFERxYtpQsiKLfVcuuPN7Fe/x1qPeViWXdO7ndp2pGtH+HPr7dc6Tj2X7rlM4PF4kJeXl7HXExPLy8soKSmhtn1fi9nZWZSVlVFdDSGVGzduICcnBwUFBUKbsm2i0ShGR0dx+vRpqhazWFhYwIMPPmiz2WxvIYTsaRY9IwWPiclkOv+mN73pr5599lmq6uVcu3YNcrmc6rgpv9+PwcFB3HXXXZKYuvT7/RgeHsZdd90lqU5rYWEBgUBgV2tX8kKQZVlwHLfmxrIsCCFrPr/6Of5x6vnUc4FAAAzDQKlU3nHtTkgn+tZ7zJ9bb7/6urUep2I2m9fN2t6sqN7MPp2wX31+u/D/K76mL8Mw8Pv90Gq1yM7OTp6XyWQrjtOdS7et95xcLk97fjfp6upCW1ubpAac4XAYly5dwj333EOVyFqLQCCAgYEBqvsvQgh6enpQXV1N1SxDMBhEZ2ena3Jy8k2JRKJ7r9+fas8vj9PpfOI///M/277yla+87iMf+YhKaHs2S319PXp6epCXl0fVjzYVnU6HsrIyTExMoLGxUWhzdoxOp0NeXh4WFhZQUVEhtDnbhhcriUQCLMsiNzcXk5OTMJlMAG55E9NtvDhd65gXn2uxlvBgGGZNAZIqXBQKxabET+o5j8cDi8WSXIo0VVxJYQDj9XrR2toqtBk7hv9N8oMTQgi6urqSn229Qc5ag6JEIrHuQGutwddG9aH53y3/m03drz5evclkMoTDYTAMA0IIIpHIiudoZnx8HPX19dR/DgDgOA7Dw8Nobm6mVvgCwOTkJHUaguM4vPWtb/WYzeZHhBC+gETELyGEMAzz9ieeeOJyQ0NDw/3330/FnSmTydDe3o6enh6ql4esrKxEb28vbDYbVTfgWtTV1aGrqwslJSV7kjHLsiwSiURSqPLHqRt/fvWeP04nSFM76KysLMjlckxPT0Ov19/ReSuVyrQde7qOX2yiMjs7G9PT05LLqJcaqZ5f4FaGt0KhEN2yxqkiO93AcPV5lmURi8WSQpxlWVitVqjValy7dm3FPZtOcK++R1P3q4/TPc7KytoTMep2uxGNRlFUVLTr77UXTE5OoqCggOrqDhaLBS6XC2fOnBHalC1x/vz5wOXLly+4XK6nhbJBEuIXAAghUYZh7n/nO985+PLLL5fSEoyvUqnQ3NyM/v5+nDt3jsqAe4Zh0Nraip6eHuh0OqjVaqFN2hEKhQKVlZWYmJjA8ePH17yO79Ti8Tji8fgdx/zj1D3fCaYik8nu6MxWd3C8OE3XQfLidCNBGg6HcfnyZUl46FPJysoCwzBIJBJU3j/7FbHG1vOzFHK5fFuD31gsBofDgTNnzmx4T/JhQmsNbvl9NBpFMBhcc3C8WlSnth0KhWLDPX+8lojmOA5XrlxBa2urqAa+28Vms8Hj8eD06dNCm7JtfD4fJiYmcPbsWaq+kwsXLsSfeeaZEZvN9iEh7ZBUT0EIsTIM84bXve51vxkYGDDSMqLLz89HZWUlBgYG0NnZSdUPmUelUqGpqQmDg4M4c+YMFdNifMcTi8UQi8WSwjUejyMWi8FsNiMcDkMmkyXPp3pY+Wn6tToTjUaz4nyqsBXiO1ar1VCpVKIVHTshLy8Pbreb6qSV/YYUf4fArQSqw4cPb+oeZxhmxQA3E/DhIKlb6iA8FoshGAzecX511RRe/CsUCoRCIchkMthsNrjdbigUCmRnZyefz87OpiZ0IBwOY2xsbFODE7ESjUYxODiI9vZ2qma8BgcH8ZGPfGTRZrO9fq8T3FYjKfELAISQYZ1O98E3velN3/j973+fR4snqLy8HIFAAOPj42hoaBDanG1RUFAAp9OJycnJXU2sSgchJCliU7doNLpC3MZisRWeV16crm7I1Wo1amtrMTc3h46ODiiVyqRwpZnq6mpMT0+js7NTaFMySn5+Plwu14H4pQiXy0X9MrKrYVkWS0tLuPvuuwWzgWGYZFu2XXjHQDweh9/vx+joKBoaGpJCORQKJdtTfp/qGEhtS9fa+DZ1LwUox3EYHBxEU1MTVCpq0oNWwLIsLl++jIaGBqrKBJrNZrzlLW+x2Wy21xBCBF8hiw5luEX8fv//LiwsbP3ABz7wwaeffjqXltFdfX09+vv7MT8/T22yVV1dHXp6enYc/0sIQTweRyQSSQrYaDS64piPswP+0OArlcoVDaxWq0VeXl7ysUKh2JLnNRwOw263o6qqatufRUzk5eUhHo8jEAggJydHaHMyRn5+vmRrGUsRjuMQj8clUy6LZ3FxESUlJdSH3/AeablcjtHRUTQ3N296YMl7nlOdDnx7HQwGVzgn4vF48u9S22+lUnnHcSbE8sTEBIxGI7WDZH4J5uLiYqrya4LBIF772te6rFbrnxJCZoW2B5Co+AUAu93+mRdeeOGIyWR64xe+8AUqenl+AYzu7m6o1Woqb1CGYdDe3o7u7m5oNJo7BFYikUgK2kgksuKYbyD5qTeFQgGVSrWiEeSFLN8Y7nYIQW1tLV555RUUFxeLLjFnu1RVVWF2dhbNzc1Cm5Ix1Gp10vtEQ8jNfsfj8VCdaJQOQgiuX79OXfLReiwtLUGhUGypL0r1PG+2zeSdHaudG36/Hw6HI+n44J0dMpks2QfwfYRKpUpuSqXyjnZgeXkZPp8Pp06d2vw/QGRMT09DJpNRNWMSiUTwmte8xj0/P/9XsVjsd0LbwyNZ8Xu7AsSf/eu//usLer3+VZ/+9KepKKWQlZWFzs5O9Pb2orW1lZrFIziOQzQaRTgcRiQSQUFBAbq6umA0GleM8OVy+YrGSqlUIicnZ4XIFZN4kcvlOHHiBIaHh6lLLFiLoqIiTE5OIhKJUDv1lw69Xg+PxyPJOFKp4XA4qFuCdSMsFgvy8vIk482ORCKYnp7GXXfdtevvxTBMcnZuM7AsmxTEvOPE6/XCZrMlnSq8E4UXwm63GzU1NbBarVCpVFCr1cjOzqamTb958ybcbjdOnjxJjc3xeBxvetObPJOTk497PJ5nhbYnFcmKXwAghLAMw/z3p5566je5ubmdH/zgB6no6VUqFTo6OtDf34/Ozk7BPY58PG0oFEI4HL5j4zgODMOsGHnrdDpUVFTA7Xajo6ODqkZmNfn5+TAYDJibm5NE+APDMKisrMTs7KykKj+YTCY4nc4D8UsBTqcTLS0tQpuRMQghmJmZQXt7u9CmZAR+er2hoWFPyj1uFblcDo1Gs2F5UH4RnL6+vuRiUm63G5FIBOFwODnTqFAooFarV2wajQZqtVoUn99sNmNxcZGqFdxu1/L1Dg8Pf9npdH5ZaHtWI2nxCwCEkDjDMK997LHHXtHr9ScefPBBKlIjc3Jy0Nraiv7+fpw+fXpXPXSEEESjUYRCIYRCIQSDQYTDYYRCIcRiMQC3aqnyjYFarUZubm7yeL0ksLGxMVy/fp269cZXU19fj1deeQVFRUWSiJU9dOgQZmdnEY1GJeOpMhqNGBkZAS1lDvcr/CwR7SURU7HZbFTXal8NH+5Ae01fQgiuXLmCxsZGFBcXr3lNIpFY4dBxu91YWlpCOBxOhlqkimJ+02q1uy6OnU4npqamcObMGWoSrgkheO973+u7ePHis3a7/bNC25MOyYtfACCERBiG+W8f+9jHenJycurf8pa3UPG5DQYDGhsb0dfXhzNnzuzoJuM4LilsA4EAgsEgQqEQIpEIgFtTQ/wNrdPpUFhYCI1Gs2OPbUNDAy5duoTFxUWUlZVt+3WERi6Xo7m5ORn+QMvoey1kMhmOHj2Kubm5Pa/MsVtoNBpEo9GDuF+RI7V4X0IIpqenJePJDoVCexbusJvw3uuCgoI1hS+wMkY5Nzc37TX8gI13EPn9flgsFoRCISQSCTAMs0IQa7XaZDjfTvpPn8+HK1eu4NSpU9SUNCOE4KMf/aj/F7/4xY/sdvvHhbZnLagQgZmAEBJgGOae97///Ze0Wm0VLavAFRQUIB6Po6+vD6dOnVo3i5gQglAohEAgkNz47Fr+5uRvzNLSUmi12h3fnBvBMAw6OjrQ3d0NlUqVXF6XRvglJIUo5bYblJeX46WXXkJVVRU1DetGGAwGuN1uycWTSgm73U51O7Aau90OtVotiRkhQgiGhoZw4sQJUUz374Tp6WkwDIPq6uodv5ZMJkt6ftO1LbxziXcwWa1WzM3NIRwOA7jlNeYFMb8plcp1+16/34+BgQF0dHRQNUvy2GOPBS9cuPBrm832EFlvDXGB2TfiFwAIIW6GYe568MEHLz3//POHz507R0UQamlpKTiOw6VLl3Dq1CkwDINAIAC/35/cQqEQACQb4ZycHJSXl0Or1Qoeb5uaxNfe3k5VbcLV1NTUoLu7Gw6Hg/oOXCaToaqqCtPT05KJ/S0oKIDdbj8QvyLG4XDgyJEjQpuREQghmJycRGtrq9CmZISpqSkYjUbq75/FxUW4XK49WzRKJpMl+93VEEIQDoeTDqnl5WUEAgHEYjHIZDJotVrodLrkptVqEQwGcfnyZer6y6eeeir8jW98o9tms72NEMJt/BfCsa/ELwAQQmwMw9zzx3/8x10/+MEPyl796leL1gPMcRwCgQB8Ph+8Xi9isRh++ctfrrhR8vLyUF5eDo1GI+qpXpVKhba2NgwMDOx6DPNuwpej6+3txblz56j3mJaVleHll1+WTOUHk8mE69evC23GAWvArzgmlThzi8UCnU4nCa+vy+WC3W7H2bNnhTZlRzgcDszNzYkmPC01JGJ1bV6WZZOi2Ov1YnFxEX6/H8FgECaTCWazGcFgELm5udBoNKJOGv/85z8f+vKXv3zZbre/UejV2zbDvhO/AEAIWWAY5tTb3/72V5555pkjb37zmwX/P/ClWrxeL3w+H/x+PwBAp9MhNzcXJpMJR48ehc1mg9lsRnNzMzXB7zy5ublobGxMJvHROq2mVqtRV1eHoaEhapej5pHJZKipqcHU1BROnDghtDk7RqlUJhdQoPX3JWWkMGPCQwjB1NSUJFZLjMViGB0dxcmTJ0UhGLeLz+fD1atXNwwRFAtyuRx6vT5Z0jQUCqGvrw/nzp2DQqGAz+eDx+PB/Pw8wuEw5HI5cnNzkZubC71ej9zcXME/JyEEn/jEJwLf/e53X7Tb7X9MCIlv/FfCI/5fxy5BCLEwDNP50EMPvfzUU0/V/tmf/dmeufDC4TA8Hk9S7IZCIWRnZydvAr6iQLpGiJ8u7O/vx8mTJ6kTwKkxzKdPn6bOfp7S0lI4nU7Mzs5mJKZMSEpLSzE7O4tgMCh4Wb1MwIc+lJaWCm3KAauw2WwoKSkR2oyMsLi4CKPRSFU8ZjoIIRgcHERdXR3V938wGMTAwABOnjxJ5XfCC9+WlpZkQqhOp8OhQ4eS1/DLTft8PiwsLMDr9YLjOOh0uqR+MBgMezbw5zgODz30kO/nP//5C3a7/d1iD3VIZd+KXyAZA3zm4x//+O/cbvfxD3/4wxmf943H4/B4PHC73XC73QiFQlCpVDAYDNDr9aioqIBard6S9/DIkSNgGAaXLl1CZ2en4CO/rVJaWop4PJ6sY0yrp6GxsRHd3d3Iy8ujOkaOYRgcO3YM4+Pj6OjoENqcHVNYWIiFhYUD8StCXC6XJOLLWZbFzMwMzp07J7QpO2Z6eho6nY7qQUk4HEZ/fz9aW1upDEHhk9uam5vXrYSiUCiQn5+/opY5Hx7p8XhgsVgwMTEBlmWRm5sLg8GAvLw85ObmZtzRFI/H8ba3vc3b1dX1Hbvd/rCYk9vSQZdq2gVuV4G4+3Of+9wvPB5P5/nz57c99CWEIBgMwuVyweVywePxQC6XIy8vDwaDAYcOHcpY3M7hw4chl8vR09NDVRkUnsOHDyMej2NoaAhtbW1Uhg7IZDK0t7ejt7cXZ86coTpmtqCgALOzs3C5XNQvEpGXl4fR0VEQQqj8XUmVYDAIlUpF7WxPKnNzcygvL6eu3V2N3W6H3W6neknmWCyGvr4+HD9+nMoSel6vF4ODg2hvb1+z1Np6yGSyZCgED8dx8Pv9cLvduHHjBnw+H2QyGfLy8pLieSf9VSQSwRvf+EbPyMjIl8Vax3cjGMrE+q7BMExWYWHhD9/xjnfc99RTT+k202kSQuDz+eBwOOBwOBAKhaDVapM/Lr1ev+sNvcViweTkJE6dOkWl+BofH0csFsOJEyeoFSo2mw3T09M4c+YMtV5s4Fa83OjoKM6dO0ftd8HT39+Purq6bXUmYuPFF1/Eq171KqHN2DGzs7PJ+tI0E41G0dPTg7vvvptqIR8KhXDp0iWqB+7xeBy9vb2ora2lckEOl8uVjLXe7ZCTeDwOt9sNp9MJl8uFeDyezCcymUybXqDF7/fjNa95jXt6evpvnU7nV3fV6F3kQPymwDCMrKCg4F9e//rXP/Av//Iv+tUNWzqxy/94jEYjtFqtIKLBbrdjbGwMnZ2d1K0wRAjB2NgYWJalWgDPzMwgGAyiublZaFN2xMjICEwm04o4MxpZWFhAOBxGbW2t0KbsGKmI3+7ubrS2tlIZj5nKyMgIjEYj1Yv2JBIJdHd3o6mpidqZHl74VlVVURniZLPZcO3aNZw6dUqQe4LjOHi9XjidTjgcDoTDYej1ephMJhQUFKS1yeVy4VWvepXr5s2bH/V6vd/fc6MzyIH4XQXDMExhYeH/bG5ufuj55583KBQK2Gw22O12eL1e6HQ6FBQUCCp20+FyuTAyMrLtqRMhkYIA5pNG8vPzqfZsRaNRdHd34+6776YuljyVaDSKvr4+3H333UKbsmOkIH7j8Ti6u7tx7733Cm3KjvB6vbh69SrOnj1LZTsF3GqrBgYGUFBQgMOHDwttzragXfguLS1hdnYWp06dEk3ZP14M8869aDQKo9GIwsJCGI1G3LhxA6973escy8vLfx4KhX4mtL075UD8roFer39PSUnJ//r85z+vbmpqQmFhIfR6vagbPD5ovrGxEQUFBUKbsyWkIIBZlkV3dzeOHTtGdTmnubk5RKNR6lex6+rqwsmTJ0XTuWwXKYjf5eVleL1eqn9ThJCkt5QvTUUjU1NTiEajOH78uNCmbAuahS8hBLOzs7Db7ejo6BB1OUaWZeF0OmGz2fCb3/wGX/rSl5xLS0v3E0KGhbYtE9AboLjLeL3eb09PT9/34Q9/2Op2u2EwGEQvyHQ6HU6fPo2JiQksLCwIbc6WYBgGjY2NkMvlyWQl2pDL5ejo6MCVK1cQDAaFNmfbHDlyBDabjerPAADFxcWwWCxCm3EAALPZTHU1AeCWty4nJ4dq4Ws2m+FwOKituEG78L1y5Qr8fj9OnTolauEL3OrPCgsL8corr0Q+//nPX1taWmqSivAFDsTvurAs27O8vHzqzW9+88xzzz1HReFmlUqFM2fOYGlpCVNTU1SJSF4AZ2VlYWhoCBxHTcnAJGq1Gq2trejv70csFhPanG0hk8nQ2NiIq1evUvX7WU1xcTHMZrPQZux7OI6Dz+ejWjTG43FMT09T7bl2uVyYmpqidiELPtGwurqaOuGbSCTQ19cHpVKJlpYWKv7/LMviAx/4gO+xxx77nd1u7yCESMqTIP5vQGAIITdtNlvrxz+lrtOEAAAgAElEQVT+8e5PfepTARoEWVZWFjo7OxEOhzEyMkKViOQFcE5ODi5fvgyWFf0qiXdgMBhw7Ngx9Pf3U2k/cGuZYIVCQbV4zMnJQTQaRTxOxbhVsjgcDhiNRtHPnK3H+Pg4qqqqqC1tFgwGMTIygpMnT4re45iOUCiEnp4e1NfXUzeDwIv2kpIS1NXVUXEf+Hw+vPrVr3b/6Ec/+prNZnsjISQstE2ZhlrxyzDMWxiGGWUYZphhmMsMw9yV8ty7GYaZvr29O+X8q25f+z+38l6EkIDNZnv1t7/97e++4Q1v8NAwHSyTyXDixAlotVr09vZS54Wsra1FQUEBLl26hEQiIbQ5W6aoqAiHDh3C0NAQtd7TxsZGTE5OUi0ei4qKYLPZhDZjX0N7yIPL5UIgEEB5ebnQpmyLWCyWXACCtmpAABAIBNDX14cTJ06gsLBQaHO2hNfrRXd3N+rq6lBRUSG0OZtibm4O7e3tzqGhoQ/YbLbP0LZ4xWahVvwC+C2AZkJIC4D3AngGABiGyQfwKIBTADoBPMowTN7tv/kAgLsByBmGqd/KmxFCOKvV+qHe3t5PdHZ2upaWljL1OXYNhmFQU1ODyspKdHd3w+fzCW3Sljh69CgqKiqoFO/ArdhZtVqNa9euCW3KtlAqlaiqqsL4+LjQpmyb0tJS0HCvShWO4+ByuahNAOU4DleuXKE2CTeRSKC/vx/19fVULgDh8XjQ39+PtrY26kqyLS0tYXh4GCdPnqRGtL/00kvkrrvuMs/MzPyRz+e7ILQ9uwm14pcQEkgZkWgB8MevBfBfhBAXIcQN4L8AvO72c7Lb13EAttWSud3uZyYnJ9/c2dlp+e1vf0tFPEFxcTHa29sxODiI5eVloc3ZEmVlZaiurkZPTw9CoZDQ5myZhoYGRKNRTE9PC23KtigvL0cgEIDT6RTalG2h0+kQCoWonD2QAk6nE/n5+VQKR+BW/e7i4mIql8zlOA6XL19GWVkZiouLhTZny9jtdgwPD6Ozs5Oq8p2EEIyPj2NxcRFnz56l4rdDCME//MM/hN/61reOm83mDkLIoNA27TZUlzpjGOYBAF8AUAjgDYSQHoZh/gqAihDyxO1rzgMIE0K+xDDMa29f/3tCyF/u8L1LCgsLf/bQQw/VPv744zk0rPQTj8dx+fJl5Ofno7a2lqoOia9j3NraSp0Hg+M49Pf3o6ioCEeOHBHanC0TDAbR398v6hWtCCEghIBlWXAct2I/NzcHtVoNo9GYPMdfSwgBx3HJLfUxf8y/drpzm912gt/vh06n2/bf8/c5wzCb2mQy2R3Hqfu1zqXb5ubmknVCZTIZ5HL5ir2YE398Ph+Gh4dx1113idrOdPB1x/V6Paqrq4U2Z8vMz8/j5s2b6OzspKpUYTwex+DgIHQ6HY4dO0ZFH+t2u/HWt77VPTo6+hObzfYBQkhUaJv2AqrFLw/DMPcA+FtCyP3rid9deF95QUHBFysrK//8Jz/5ST4No2tCCK5du4ZgMIjW1laqkh94EXbs2DHqlrJkWRa9vb04evQodZnKwK04sHA4vO0SSRzHIZFIIJFIgGXZO45T96lbunNrtVmrRRV/zLIsPB4PSktL1xRp6YTdapG33vFaG4AVx9thJ3V++f9VOjGeKuQB3CHqV4v9tQYHa20sy2JhYQGlpaXJwcjqgclG3+XqLSsrK+05/nzqnt+2878nhKCrqwsnTpygrkoFIQRXr16FXC5HQ0OD0OZsCUIIpqam4PF40N7eTtVCO36/H4ODg6iurqZmhcy+vj78yZ/8id3tdn/I7/f/UGh79hJ6flkAGIb5EID33374ekLIMgAQQl5mGKaSYRgTgCUAr0r5szIAL+6GPYQQFsBfZWdn/6q9vf173/3udwvuu+8+UbsI+GoKS0tLuHjxIlpbW6lp3LVaLc6cOYP+/n5EIhGqVieSy+Xo7OxEb28v5HI5NeKd4zjE43EUFBRgYGAAs7OzUKvViMfjSCQSd+xTRW0qMpnsDmGyWqwoFAqo1eq04iZVDG3HC/fSSy+hpqaG2mz97ZIqwPcaq9UKAFteTCFVVK83EOIfR6PRtIMo/re4WmCnCmP+d7d6b7PZkJOTg6ysLMRiMSgUCiq8eAAwOTkJlmXR1NQktClbguM4jIyMJNtKWv7fALC4uIiZmRm0tbVREaLBhzk8+eSTN2022+sJIdeFtmmvodbzyzBMNYBZQghhGKYNwE9xS+jmARgA0Hb70kEA7YQQ1y7bQ10YRCAQwMDAAA4fPozDhw9T09iwLIvBwUFoNBo0NDRQYzdwK/O6p6cHx44d27MkCEIIEokEotEoYrEY4vH4mvt4PL5CuMpkMigUiuQMgd1ux5EjR6BUKu8QDanHvFdULExPTyM7O5uqARMPrSu8DQwMoLKyEnl5eRtfvEfw90LqtnoAFwgEYDabUVRUlLwnVoto/reenZ295p7f5HL5nt0Lk5OTCAQCaGtrE9X9txGxWAwDAwMwmUyorq6mxnaWZXH16lXEYjG0tLRQMZO6X8McVkOz+P0UgHcBiAMIA/gEIaTr9nPvBfCZ25f+HSHk2T2yibowiEQigStXroDjODQ3N1MzzUQIwcTEBHw+H9ra2qhodHii0Sh6e3vR2Ni4rSx4juMQjUaTWywWQywWSx7z+1QRu7pDTtdh88frDdxu3LgBr9eL5ubmbX12oQiFQhgaGsK5c+eENmXL0Ch+E4kEurq6cO+991IjZIBbYubixYtoaWlZ04PHC2heGKcbSPIbL5x5Uu9DpVJ5x54/3s7/bHp6Gl6vF+3t7VT9z/1+PwYGBlBXV0dVSbxgMIiBgQGUlZXh6NGjVPzP93OYw2qoFb9iJjs7+zUFBQXf+853vlNw//33izoMgmd+fh7Xr19Ha2srFdM2PEtLS5iZmUFHRwe0Wq3Q5myaSCSC3t5eHD9+PJmIFYvFEIlEEI1G79hHo9FkJ8owTLKj5DvLdJ3pbgxkCCHo6+vDkSNHqAnd4Ll48SLa2tqgVquFNmVL0Ch+FxYWEAwGUV+/pYqSgjM2NgaVSoWqqqqMv/bqGZh0e/6YJ1UUq1SqFccqlSoZjjEzMwO324329naqkvOsVivGx8epCRfgMZvNmJycRHNzs6hmNtYiJczhhs1me8N+DHNYzYH43SVuh0H8nwceeKDuqaeeyqWhuLjP58PQ0BAqKipw5MgRKkaywK1akMPDw2hsbERBQYHQ5txBPB5HOBxGOBxGJBJJbsFgEC6XK9mJre7kUjs7pVK57eSdTBONRtHd3Y2zZ89SlYl98+ZNRKNR1NbWCm3KlqBR/HZ3d6O5uZmqAanD4cDU1BTOnDkjivuMEIJ4PL5iALx6UMyLZpZlYTKZoFark8JYpVJBrVYn4+jFBCEEs7OzsFqt6OjooKYdSSQSGBsbQyQSQWtrKxU5BAsLC3jb297mmpub+7HVav0gIYS+ovm7wIH43UUYhmHy8vI+YjAY/ua5554znTlzRmiTNoRlWYyNjSEcDqOlpYWaRikSieDy5csoKSlBZWXlnnVeHMchHA4jFAqt2IfD4aQHJysrK9kppXZOarU6WYtzL2OAM4HVasX169dx6tQpUQiFzRCPx3Hx4kXqpuJpE7/hcBiDg4NUhZjwA7rTp09TNTMwOTkJn8+H5ubm5CA7dYDNt0Ucx4FhmGTbo9FooFark3uVSrVn90QikcDo6CjkcjmOHz9Ojaead7IcOXKEihwZQgieeeaZ2Pnz520Oh+PBRCLxstA2iYkD8bsHMAxTWVhY+KO3v/3tVV/84hd1KpVKaJM2xGKxYHx8HA0NDdRMb/PJB/F4HC0tLRmZ9k8Vt6s3lmXBMMyKToTfq9XqTcfuRaNRXLp0CbW1tVQVo7969SpUKhVVdUQHBgZw9OhRqlaLok38Tk1NQalUUpNcSAjBpUuXcOTIEWruP34hhUgkgpaWlk0JSD5XgBfEfDvGC2bgVkyyRqNJblqtFhqNBkqlMiNiLxAIYHBwMJlkTQO8l3p5eRmtra07qrm9VywvL+Md73iHe2Ji4hdWq/X/I4QEhLZJbByI3z2CYRhZfn7+J/Ly8j7x7//+78aOjg6hTdqQSCSC4eFhaLVaNDQ0iG7qbC0WFhYwNzeHtra2TTVULMsiFAohGAwiGAwiEAggFAohGr2VBJvaGaRumYypjcViuHTpEqqqqqipA8xxHC5evIjGxkZqxKTdbsfi4iJaW1uFNmXT0CR+CSF46aWXcO7cOWqSUGdmZhCJRKgpDUYIwdjYGFiWzfiyy7FYLCmKg8Fg8phvC9VqNbRabXLLycmBWq3elA18nGxLSws1CxWFw2EMDQ1Br9fj2LFjovdSE0Lw/e9/P/7JT37S7nQ63xOLxf5LaJvEyoH43WMYhqktLCx8/t3vfvfhJ554IkfsMUOEEFy/fh0LCws4ceIEFcH9wK345cHBQdTU1ODQoUMghCASiSAQCKzYIpEIZDLZigad3zLl7dgs8XgcfX19KC8vR0VFxZ69707gFx45e/YsFfFvNIozmsSv3W7H0tISWlpahDZlU7jdbly9ehXnzp0TvbABbg04R0dHkZWVhcbGxj1tnwghCIfDSecA7ygIh8MAbjkJcnJyVmzZ2dngOA7j4+Pw+/1oa2ujpp3ga/c2NTWJMpdkNVarFe985zvdo6Ojv7Nare8jhHiFtknMHIhfAWAYRm40Gv/aZDJ95MKFC0YaykYFAgEMDw8jPz8fdXV1ovUC8w20z+eD1+vFzZs3wXFcMoFMp9OtaJz3WuBuBMuyySWoaal3aTabcfPmTWrif6enp6FQKKhZapom8Xv58mVUVVVRMUjm43w7OzupSMxjWRYDAwMwGAyoqakR1b3Gh4elOhb8fn+ygo1Wq0V5eTlyc3Oh0+lEnUsSiUQwMjICpVKJxsZGKgbJFy5cSDz88MMOt9v9PyKRyE+FtocGDsSvgDAM01BYWPj8+9///rLz589rxdwgALeE5dzcHBYXF0XhBY7H4/B6vfD5fPD7/fD5fGBZFmq1GjqdLil0nU4nVfFa/EpHCoViz7072+XatWuQy+Woq6sT2pQNiUQi6Ovrwz333CO0KZuCFvHL16++5557RP+bJYSgp6cHVVVVVOQ08LNCZWVl1MTK8nkjdXV1UCqV8Pv9yXY6FotBqVQiNzc3uel0OkGdKqne3sbGRioSkC0WCx566CHP5cuXu6xW67t3ezEvKXEgfgWGYRhFfn7+I7m5uR/+5je/mf9Hf/RHop97E8ILHA6H4fV6k1soFEJWVtYdjedao3Sv14uhoSEcPXoUFRUVVHTO4+PjCIfDaG1tFf2ULMdx6O3tpUZM9Pf3o7q6WvAB3GagRfxOT08jKysLR48eFdqUDaFpsBYOh9HX14fa2loqFoFgWRbXrl1DKBRat2JQJBJJOi68Xi/8fj8AQKfTQa/XJ7e98LzS5u1lWRb//M//HPn7v/97l9vt/mA0Gv0/QttEGwfiVyQwDFNeVFT0nY6Ojpann346T+xJT6le4KamJhiNxoy9djQahcfjSW6hUAgqlQp6vR4GgwG5ubnQarVbFrD8anYsy6K5uVn0DRyAZC3MkydPit5emqaRaUp8o0H80hRLvby8jPn5eSrCdPjcBX4xHLHj9/sxNDS07VXPOI5LimF+SyQS0Ol0MBgMMBgM0Ov1GUs2JoRgfn4ec3Nz1Hh7+/r68J73vMfpcDies9vtjxBCQkLbRCMH4ldkqFSqNxoMhq9/4hOfMH70ox9Vi3254WAwiNHRUajVajQ0NGw5mYHjOPh8PrjdbrhcLvj9figUimRDl5eXt+ls4s3CT23R0qEsLy9jenoaJ0+ehNgXS/F4PBgZGcHZs2dFLYJ4sUZDoh4N4tdqtcJisYh+2Wt+BogGkW6z2XDt2jW0t7eLPlyLEIKbN2/ixo0baG1thV6vz9hrcxyHQCAAt9sNj8cDr9cLQggMBgPy8/ORn58PjUaz5T7C5/PhypUryM3NRX19veh/D263Gw8//LD3V7/61azVan2QEDIhtE00cyB+RQjDMOqCgoLP5eXlvefb3/62UeyLYxBCsLS0hOnp6WR1hbUaokQiAbfbDafTCafTiXg8jtzcXOTl5SE/Px+5ubl74o0JhUIYGhqCwWBAfX29aBP4eNxuN4aHh9HS0iL6qfqlpSUsLi6is7NT1J61ubk5cBwn+jrFNIjf3t5eNDQ0iHqJWn5m4uTJk8jJyRHanHW5ceMGFhcXcfLkSVEnhwF/KImp0WjQ0NCwK8uqryaRSMDj8SSdJqFQCFqtFkajEUajEXq9fs22h2VZTE1NwW6348SJE6Ivu0YIwXe+8534Zz7zGZff7/9kIBD4HjkQbjvmQPyKGIZhaoqKip677777ar7yla8YxO6ljMfjuHbtGoLBIE6cOIGcnBywLAun0wmHwwGn0wmO45Cfnw+j0Yj8/HwIueBHauhGS0tLRr0Vu0EoFEJ/fz9qampEXwt4fHwcHMehsbFRaFPWJB6Po6urC/fee6+oY6rFLn4DgQBGR0dx9uxZoU1ZE47j0N3djdraWlFPbRNCkvGybW1toh+ULy0tYWpqSvCQAUIIgsFg0qni9XqhUqlgMplQUFCQFMO8N728vBxHjx4V9X0PAGNjY3jXu97lXFpa+pnVav3oQfmyzHEgfkUOwzCMVqt9u06ne+rxxx/Pf9/73pct5huWj6GamJiATCaDQqGA0WiEyWSCyWQS5dSS3+/H8PAwioqKUF1dLeoGMR6PJ0uh1dbWitazSghBf38/iouLRV2z+MqVKzAajaIeTIhd/I6OjqKgoEC0yViEkORCBVVVVUKbsybxeByDg4PQ6XQ4duyYaO9t4NZiGKOjo2AYBidOnBBlux4KheBwOGC32+HxeMCyLBQKBU6cOCH6cLdAIIDPfOYz/gsXLizZbLYHCSGDQtskNQ7ELyUwDJNbVFT0JYPB8MDXvvY143333SealjEWi8Fms8FqtcLr9cJgMMBkMiEUCsFisSSzlMXcmHMcl5wKa25uFvX0Le8dCgaDaGtr25Npxu2QSCTQ09OD+vp60RaJDwaDGB4exrlz54Q2ZU3ELH7j8TguXryIe++9V7T39+TkJKLRKI4fPy5aGwOBAAYGBlBdXY1Dhw4Jbc66mM1mTExMoK6uTtSDRuBWiAM/u3fkyBEQQmCz2RCJRGA0GlFUVASj0SgaD3sikcC3vvWt2BNPPOEOBoN/5/V6/xchhBXaLilyIH4pg2GYyuLi4q/X1NR0fP3rX88Xalo5GAzCbDbDYrGAEILCwkIUFRXdEWsViUSSJbuamppELSqBWwkxIyMjKCwsRG1trai9wPwyzh0dHaKtrhCJRNDb24u2tjbRfvd9fX2oqakRbSy1mMXv9PQ05HI5KisrhTYlLQsLC1heXhZ1/LnNZsPY2BhaW1tFHX8aiURw5coVyGQyNDU1iT4W2WKxYGJiAocOHUJlZeUKgcuH41mtVjidTmg0GpSUlKCoqEiQBFhCCH7+859zDz/8sMvv9z9ns9n+lhDi23ND9hEH4pdSGIY5VVRU9Mz9999f9uSTTxr2YsrR7/djaWkJVqsVSqUSxcXFKC4u3lTcLr+MKJ9gJsZpMh6O4zA7O4vl5WUcP34c+fn5Qpu0JnwinNAxd+vh9/sxMDCA06dPCxrjvRYulwuzs7M4efKk0KakRazil+M4vPTSS7j77rtFOfvgcDgwPj6OM2fOiNI+QkiylGFHR4doxSQhBAsLC5idncWxY8dQXFwstEnrEggEcPXqVSgUCjQ0NECtVq97PSEEgUAAZrMZVqsVMpkMpaWlKC0t3ZPvZHBwEH/xF3/hXFhY6LJYLP8/IWRx19/0gAPxSzMMwzAqleoBvV7/5fe97335jzzyiDbTWczBYBBLS0swm81QqVQ4dOgQioqKtiVeUxtRfrEJMXtWA4EARkZGoNfrUV9fL8oOFLjlkRkYGIDJZBJtHLDD4cC1a9dw5swZUQ58urq60NraKkoPuljF7/z8PILBII4dOya0KXfAlzQT64ArHo9jaGgIKpUKTU1Nom0HQ6EQRkZGkpUcxHjv8sRiMUxPT8PpdKKpqWnbTotwOIzl5WUsLy9DLpfj0KFDKC0tzfhnn5+fx0c/+lF3T0/PnNVqfR8hZCSjb3DAuhyIXwnAMIzCYDB8WKPRfOr8+fOGhx56KHsnQi2RSGB5eRkLCwtgGCbjN388HsfMzAysVivq6upQXFwsSsEGrKxfWV9fL1qvB8dxGB8fh9/vR1tbmyhr15rNZszNzeH06dOiibHjMZvNsNlsoqxTK0bxy9dJFqO4DAaD6OvrE+1iK/zCFdXV1SgrKxPanLRwHLdiESOTySS0SWvCsixu3LiB+fl5VFVVoby8PGP9SSgUwtLSEpaXl6HRaFBRUYHCwsIdvb7H48Gjjz7qv3DhgsPpdH4okUj88qB02d5zIH4lBMMw+sLCws/pdLo//ad/+qf817/+9bKt3KRutxs3btyA1+tFSUkJKioqNpwy2gnhcBgTExMIhUJoaGgQbcwlcMu7OjY2hkQigePHj4t2sQmz2YzJyUk0NzeL8v958+ZNWCwWnDx5UlTeLkIIXn75ZZw6dUp0Yk6M4lesgwU+xrylpUWU8bPz8/O4fv062traRLtwhdPpxNWrV1FcXIzq6mrRDVR5+PryMzMzKC0tRWVl5a7NzhFC4PV6MT8/D6fTieLiYhw+fHhL/UAsFsNXv/rV6JNPPukOBoOP+f3+Zw6S2YTjQPxKEIZhyouLi79aXFx89h//8R9N63WcLMtiaWkJN27cgEajwZEjR2A0GvfUE+vz+XDt2jVkZWXh2LFjovTW8DgcDly9ehWlpaWiLYsWDAYxMDCw7SVGd5upqSkEg0G0tLSIyraFhQX4fD7R1SYWm/glhKCrqwvt7e2iGgTGYrHkYhti81QmEglcvXoViUQCLS0togyhikajGBsbQywWw/Hjx0XfDo+Pj0Ov16Ourm5P46VZloXZbMaNGzegUChQWVkJk8m07sJO3/nOd+Kf/exnvZFI5F/sdvvnCCHBPTP4gLQciF8Jw/zf9s48Sq7qvvPfW1VdVV1VXV1bdy29VPWqVqvV3VqQQALhBYKN43HgxIbg45OJl0nGHiaTTDx25uSAJ5445GD7eIlJ4gOObRhmEic42GODgwdsEAK0ICH1vlSvte/78pY7f3S/l5aQhICueiXpfs6551U/lfr9upZ3v/d3fwshQy6X65sdHR37vvGNb9hvvvlm+d+q1Sr8fj9CoRDcbje8Xm9NvbxXQiwWw/T0NKxWKwYGBhrOAychJcQFAgHs2rWrIct4CYKAyclJlEol7Nmzp6HCIKRSbYIgNFT5KVEU8eKLLzZcy+NGE7/RaBTr6+vYu3ev0qbIcByH1157Df39/Q0XmpTJZHDmzBn4fD50d3c3zOddglKK5eXl80K7Gs1GiUwmg5mZGRBCMDw8rHinvkwmg6WlJaTTafT29qKzs1N2iAiCgCeeeIJ/4IEH0qVS6clYLPY/KKVJRQ1myDDxex1ACBlxu93f9Hq9Yw899JDdbrcjHo/D5/Ohq6uroba1tm5ltbW1YWBgoKGEyFaKxSImJiYAALt27WpIT4lUk3P37t0N5Q2jlOLcuXNQq9UYHh5umMl2ZWUFxWKxZklclNKLDunftj5P4tixY+d1T5Neq4sdLxy1sP/YsWMYGxtTXHhI8DyP1157DT6fr6Fq5FJKsbS0hLW1tYYNc5ASUaVk2Ub0SAMbFWNmZmbAcRyGhoYargJPpVKB3+9HOBxGR0cHjh8/LjzwwAOpQqHwT5tly2JK28g4HyZ+ryMIIWNdXV3f9Xg8O772ta+1NnJhf1EUsb6+jsXFRbjdbvT19TVspnE8Hsfk5KQs1hvNzlKphNdffx12u72hahdTSvHGG29Ar9djaGiobtfkeV4egiDIR0EQwHEcZmZm4PP5AGx4b0RRvORRGld6H72UQL3UEdiIwZQ6Ul1MIF9OUF+pTSqVCiqVCmq1+pJHtVqNUqkke7nUajU0Gs2bjtLjeixoBEHA8ePH0dnZia6urppf70qpVqs4ffo0mpubsWvXroZyMAAboVGTk5MAGnfhDmzYOTs7i1KphB07djTUAv5CNj29wp//+Z/nK5XK04FA4L9RSiNK28W4OEz8XocQQsbcbvfXOzs7R7/61a86jhw5orRJl0QURTlJRCpW3ojeCakqxNLS0rZnHG8HlFK5g934+HjDeO0opXj99ddhMpmwY8eOt3wuz/PgOA7VahUcx11ySOKW47jzfgch5DyBJh23Pk6n0+B5Xt4VuZwglERjLd/rWoY9SEJ5q5C/mNCXhtQ0QKPRvGnhsHUxwfP8edchhKCpqQkajea844VDq9XKx7cSjIIgyC20pcVKIxCJRDA1NYWhoaGGa/nMcRzm5+cRi8UwPDzckCFbwMau2tzcHHK5HHbs2IG2traGup9uhed5PPHEE9yXvvSlTKlU+udoNPolSmlYabsYl4eJ3+sYQsgut9v9NZfLte/hhx92vO9972vYG8zWcjadnZ3w+XwN52EFNiaX2dlZpFIp7Ny5s+E8FalUCm+88Qa8Xi98Pp+i77coiqhUKiiXy3LCo81mk8VtpVJBtVqFIPxbQrRGozlPIF1KRG0VWW/3b5Rif2+66aaGaDzQKDG/7ybWVxRFeTEiLU4ut3i58H2X3m9pNDU1YW1tDQ6HA16vFzqdDlqtVtHPM8/zmJycRLlcxtjYWEPlLFBKsbq6Cr/fj56eHni93oa81xeLRczPzyOdTmNwcLCh44+r1Sp++MMfcl/+8pfT5XL5/0Sj0S+z8IarByZ+GSCE7HC5XA9bLJabHnjgAetHP/pRdSN6V4GNCWZlZQWrq6twu93o7e1tyJjgfD6PqakpiKKI4eHhhmrtKwgCpsR39QMAACAASURBVKamkM/nMT4+vu2JjhzHoVwunzckkVupVGRvrEqlkkWLTqdDLBaDXq+Hz+eTz0sewHpPgGtra8hkMhgZGanrdS9GI4hfSileeukl7Nu3r+5b5JLHX1oMlUolzM7OwmQywWg0olwuo1qtolqtyuEeWq0Wer0eOp0Oer1eHs3NzdDpdNsehpBMJnH27Fm5eU+jCDZKKSKRCGZnZ+W43kZ0GuTzeczPzyOXy2FgYKChRW8mk8F3vvOd0iOPPJKrVqtPxGKxv2CJbFcfTPwyZAghne3t7f9dq9X+9h/+4R+2/P7v/76+EZM0gA0Bt7a2hqWlJTidTvT19TWEl+5CkskkpqenYTAYMDQ0pHhFja3EYjFMTEzIxfavZLIRBAGlUgnFYhGlUum8Ua1WAWx4Z7cKDmlIQuRS3lhKKc6cOQOtVqt4EpxU9/fAgQOKv2eNIH4DgQASiQRGR0cVtYPneTnGt7u7+6LPoZSiWq2et+i6cIiiCACyIN46DAYD9Hr9FcXGC4Ig7/SMj483VOxsI997JLLZLObm5lCpVDAwMNDQ4Q2rq6t46KGHsk899VSuXC5/LZPJfJeVLLt6YeKX8SYIIWaLxfJZvV5//z333NPy+c9/vqWRsqi3IooiAoEAFhcXYbfb0d/f33A3+Ub2vnAch8nJSVQqFYyOjkKv16NUKqFQKKBYLMrHYrEIURShUqlkgXChaNiObWepCgSlFKOjo4pOhOFwGOFwGOPj44rZACgvfqUwEKW7uVWrVRw/fhw+n29bOqNJYTdbF3BbF3XAxkLOYDDAaDTKR6PRCK1Wi1QqhbNnz6Krqwu9vb0NI9ry+Tymp6chCELD7TpJpFIpzM3NQRRFDAwMNFx42FZef/11PPjgg8mTJ09Gk8nkA9Vq9SnWnOLqh4lfxiUhhGh0Ot3HrFbrlw4dOmR/8MEHbUp7fi4FpRShUAgLCwswmUzo7+9vuJv+1rg7JZP3KKWoVCrI5XLI5/MoFApIJpPIZDLQ6XRobW09b7I3GAwwGAx1y1inlMqd//bs2aNYdQpKKV5++WWMjo4q+llSWvwuLy+jVCrVrPzblVAul/Haa6/J7dDrBcdx5y0CC4UC8vk8stksRFGE3W6Xvy8mkwktLS2KLWy3Jok1Yr4BpRTRaBSLi4tQq9UYHBxsyC6UwMbC6JlnnqEPPvhgIhgMTodCoT8FcIy1Ib52YOK3ASGE3ADgFQD3Ukr/afOcAODc5lNWKaX/bvP8LgCPApgD8HuUUrEG9hAAt7rd7r/s7u7u//KXv+y47bbbGsbTsRVKKeLxOBYXF0EpRV9fX8NtpQmCgJWVFaysrMhd2GohgimlKBaLyOVyyGazyOVyKBQKEEUROp0OLS0t8qRtMpmg0WgwMzODXC6HsbExxbdw5+fnkUwmsX//fsVKRSWTSczNzeHGG29U5PqAsuKX4zgcPXoUN998s6Ki7vjx4xgZGVFc0MViMUxOTso10iVBLIniXC4Hnueh0WjQ0tIiD7PZXLOwrFKphLm5OWQyGQwODsLpdDbc/W59fR3Ly8uwWCzo6+trmGozF1KpVPD4449zX/nKVzKlUum5cDj8IKV0Xmm7GNsPE78NBiFEDeA5AGUA39sifvOU0jfdMQghjwH4IoB7AcxTSp+tsX3Dbrf7yyaT6cjnP/95y3333adRWiRdimw2i8XFRWSzWfT29qKjo6NhatwCG/GLy8vLWFtbQ3d3N3w+3zsWeRzHIZPJIJvNykMURTQ3N8NsNsuTsMlkestrJBIJnDt3Dh0dHejr61P0NVteXkYgEMCBAwcUE18nTpyAz+dTrCyUkuJ3enoaer0ePT09ilw/m83i1KlTGB8fV9RLuLX179jY2FuGVnEch1wud97Cs1KpQKvVwmw2w2w2o7W1FS0tLe/4O18ul+UF4sDAANxud0OJ3mq1Kn9/3W43enp6GjIvAwCCwSAeeeSRwt///d/nq9XqD+Lx+FdZ5YZrGyZ+GwxCyH8BwAG4AcD/vQLx+30AfwLgHgB+SukzdbLTabfb/7NGo/nkhz/84eY//uM/blVyW/RylMtl+P1+RCIRdHZ2wuv1NlSFCJ7n4ff7EQgE4PV64fV6LzshVqtVpNNppNNpZDIZFAoFaDQaeUKVxO678SaLooj5+XmEw2GMjIzITRaUIBQKYW5uTrHks0KhgFOnTuGWW25RRFwoJX5LpRJee+01HDlyRJEFUDwex8TEBPbt26dYd7StoUo7dux41wKzUqnIi9NMJoNcLgcA8nfXYrHAbDZf9rtbLpexuLiIWCyGgYEBeDyehhK9+Xwefr8fyWSyIbuISlBK8fzzz+Ov/uqv4ufOnUtmMpmHS6XS/6KUlpS2jVF7mPhtIAghHQCeBPBeAN/D+eKXB3AGAA/gIUrpv2ye3wPgbwHMA/jdegfiE0LUKpXqTqfT+WdOp7P3C1/4gu3uu+9WNZK4lOB5Hmtra1hZWUFrayt6e3vR2tqqtFkyHMdhaWkJgUAAXV1d8Pl8UKlUSKfTSKVSSKfTyGaz0Gq18kRpsVhgNBprNvkVCgWcPXsWer0eu3btUmzRkEwm8cYbb2Dfvn2KxN9OTk7CaDQq0kxBKfF78uRJdHV1wel01v3aUhLrgQMHFEuyy2azOHv2LFpbWzE0NFSznQdBEJDL5eTFbDqdBgD5+22z2WAymVAqlbCwsIBkMom+vr4rrtBSD6R4Xr/fD0openp6GrZcWTKZxKOPPlr+zne+k+c47sVQKPQVSukppe1i1BcmfhsIQsiPAHyNUvrqpkd3q/jtoJQGCCG9AJ4H8H5K6aKC5r4JQkhve3v7f1Wr1b993333Ge6//36T1+tV2qw3QSlFLBbD0tISeJ6Hz+eD2+1uiJAIjuNk21KpFDQaDdra2uBwOGC1WtHS0lL3CYVSimAwiLm5OfT29ipWxzSXy+HUqVOKxH5Ksa+HDx+u+wJACfGbSCSwsLCAgwcP1vW6lFL4/X5Eo1Hs379fkVAXjuMwNzeHZDKJ3bt3w2Kx1N0GnueRyWSQSqUQi8WQSqUAAE6nEz6fD1artWHuV6urq1hbW4PVakVvb69iXvrLQSnF8ePH8fDDDyePHj2aK5fL385kMo9RStNK28ZQBiZ+FYYQ8jkAn9n8sRWApCocAIoA/oPk5d3yf76PLcK40SCE6HQ63UetVusX+/v7nV/4whfsH/zgB0kjbn0Vi0UsLS0hGo3C4/HIDRbqBc/zSCQSiMfjSCQSAAC73S5nkUcikYapZSyJgkQigZGREdhstrrbUC6XceLECXR3d6PeC6u1tTWk02ns3r27rtett/iVGlrs3bu3rolJoiji3LlzEEURY2NjdRd3lFKsr69jYWGhIbqgSTVwy+Uy+vv70dLSgmQyiUQigXQ6Da1WC4fDAYfDAYvFUtfXK5vNygv0rq4udHd3N0z5xq0UCgU88cQT3Ne//vVMPp8/EwwG/yeAF1nVBgYTvw3KVoFLCLECKFJKK4QQBzYqQXyEUjqlqJFXACFkxOVyfVGj0dzxmc98xvCpT33K0Ig1g3meRyAQwMrKCgwGA7xeLxwOx7ZPfpRSZDIZRKNRxGIx8DwPu92OtrY22Gy2i04goihifX0dfr8fVqtV8WzpXC6HiYkJ6HQ6DA8P131bmud5nD59GgaDoa7NMKTSZ7t3765ruEy9xa/f70e5XMbw8HDdrslxHE6ePAmHw4H+/v66i85UKoXJyUm0trZix44dioX3XFit5nI1cEulEhKJBGKxGNLpNIxGI9rb29He3g6DwbDttgmCgGAwiJWVFWg0GvT09KC9vb0hQxveeOMNfOtb38r87Gc/K/I8/1gikfhrSmlEabsYjQMTvw3KBeL3EIC/AyACUAH4BqX0MSXte7sQQkxGo/ETZrP5/u7ubsf9999vv+uuu1S1uEm/GyilSKfTWFlZQTqdRkdHB7q6ut6VwBMEAdFoFJFIBKlUCmazGe3t7Whra3tbv1eKq1tYWIBGo0F/fz9sNpsik4/UuGNmZkauWVxPzz6lFNPT08jn89i7d2/d6iVnMhmcO3cOhw8frtvrXk/xWy6X8corr+CWW26p22taKBRw8uRJOXmrnpTLZbmm9MjIiGL1nEVRRDAYhN/vh8lkQl9f39taYFFKkc/nEY1GEY1GUa1W0dbWBpfLBavV+q4+q9lsFisrK4jH43C73eju7q6JuH63RCIR/OAHP6h897vfzZVKpclwOPxVURSfYQ0pGBeDiV9G3SGE9Dscjv+oVqvvfc973qP/7Gc/a1Mqk/5ycByHQCCA1dVV6PV6udzVldjJcRwikQhCoRAKhcJ5E9F2bE+m02ksLCygVCqhr69PsTJHgiBgcXERgUAAg4ODdc88X11dxfLyMvbv31+3CXliYgItLS11C7uop/g9deoUOjo66tZIIhqNYnJyEnv27KlrbC3P81hcXEQoFMLg4KBi3x8pZnZ1dRXt7e3o7e3dloomPM8jFoshHA4jnU7DZrPB4/HAbrdf0f2H53nZy6vVauH1etHe3t4QccZbKZfLePrpp+m3v/3t+OLiYiqfzz+Sz+cfp5QmlbaN0dgw8ctQDEKICsAtHo/nj1Qq1aFPfOIThk9/+tPG3t5epU17E5I3OJlMwuPxoKur601iSxRFRCIRrK+vo1AowOVywePx1DRJrVgswu/3Ix6PKxp7V6lUMDs7i0wmg+Hh4bqWRkulUjhz5gxGRkbqUotXSn47dOhQXWKw6yV+Y7EY/H5/XZLcpMS2cDiMffv21S10hlKKtbU1LC4uoru7Gz09PYoIukKhgKWlJcRiMXR3d9f0eyuKIhKJBILBIJLJJOx2Ozo7O9/kEaaUIpVKYW1tTb7PdXd3N2S7+FdffRWPPPJI6rnnnquIovijWCz2CKV0RmnbGFcPTPwyGgJCiFGn091tt9v/yOFwdH7uc5+z3nPPPZpGKkUGbHhEQqEQ1tbWQClFZ2cnTCYTgsEg4vE42tvb0dnZCbPZXFdP0oVZ1z09PYps4eZyOUxNTYEQguHh4brFJpfLZZw8eRJutxu9vb01f+1DoRCCwSD27dtX0+sA9RG/PM/j6NGjOHjwYM3FjiAIOHPmDDQaDXbv3l038RmLxTA9PQ273Y6BgYG6x/VeWGVGKgdWT/EtiiLi8TjW1taQy+Xg8XjQ3t6OaDSKYDCIlpYWdHV1NVxXTABYWVnB9773veL3v//9As/zJ4LB4NcB/IqFNTDeCUz8MhoOQkin1Wr9jFar/ff79+83/MEf/IHj9ttvb6juQIIgYGlpCX6/HxzHobW1FYODg4pPGlJc8NLSEkRRVKzeZjwex/T0NMxmMwYHB+viPZKqBfA8j7GxsZrHrEpVJ2pdB7ce4ndychIGg6HmndykhiFSR8N6kEqlMD09Da1Wi507d9a9bTfHcVhbW8Pq6iosFgt6enoUry/O8zzW19exuLiIcrkMg8GAoaGhhqvNm0wm8dRTT/F/8zd/kwqFQqFUKvWtcrn8j5TSnNK2Ma5umPhlNCxk4y683+l0fgbAb950003aT33qU3YlhXClUpG3a91uN7xeL/R6/XnbhU6nU/b+Kkk+n8fS0hLi8Tg6OzvR3d1d19eNUopwOIy5uTnZ21aP66+urmJpaQl79+6tac1RKTns5ptvrmmoSa3FbzqdxsTERM2T+EKhEGZnZzE2NlaXVsXZbBYzMzMQRRE7d+6su+DcmigmhSQp2fxHqiSxvr6OdDoNt9uNrq4uGI1GZDIZ+P1+ZDIZxbuySYL30UcfTa2srGQrlcrjqVTqB5TSZUUMYlyTMPHLuCrYFMIHnE7npwF8qN5CWOqulEgk0Nvbi46OjotODoIgIBwOY319HeVyGW63Gx0dHXX3Nm1la+Jec3MzfD5fTcq4XQqpfuri4iJcLhf6+vpqHpecyWRw+vRp9Pf3o7Ozs2bXWV1dRSqVwtjYWM2uUUvxKwgCjh49WtOFgiiKmJ6eRi6Xw969e2suAAuFAmZmZlAul7Fz58661qPeWg6sqakJXq8XTqdTMW+qFMcbCAQQi8Vgs9nQ2dkJu91+UZsqlQqWl5cRDAblLpP1qPpxCcH7OKXUX/OLM65LmPhlXHXUUwhXq1W5scPAwMDbygrnOA7BYBCBQACCIMDj8aCjo0Oxdq3AhpdveXlZLuNWT2+wKIqyV7ajowM9PT01FcEcx+HMmTPQarUYGRmpiSdL6hwl1TytBbUUv1NTU9DpdOjr66vJ7y+VSnj99dfR1taGgYGBmorAYrGIubk55HI57Nixo64hSBeWA/N6vYomimWzWayvryMSicBsNqOzsxNtbW1XHF/M8zyWl5extrYGn88Hr9e77bHJm4JXeOyxx5LLy8tM8DLqChO/jKsaSQi7XK5PU0q3TQiLooilpSWsrq7K3sN3M5FWKhUEAgEEg0EQQtDR0QG3261Y+MaF3mCv11s3sSAIAlZWVrCysgKPx4Pe3t6aiWBKKZaXl7G6uoo9e/bUJBSl1uEPtRK/yWQS09PTOHToUE3e92AwiNnZWezevbum7agLhQLm5uaQz+cxMDBQN0+rVA5sdXUVGo0GPp9P0XJguVwOwWAQoVAIBoMBnZ2daG9vf1eeW47jsLi4iHA4jJ07d77r+HYmeBmNAhO/jGuGC4Xw2NhY08c//nHbhz70IdXbKb2VTCZx7tw5uFwu9Pf3b7vHsFgsypOUSqWC2+2G2+1WzFMkxSsnEgk4nU50dXXVNFZWQhAEuU6v2+2uaThENpvF6dOn4fV6a9K2VvKy1aL6Qy3Er1Td4YYbbtj2kBxBEDAxMYFKpYLx8fGahTnk83nMzc2hUChgcHCwLt3GKKVIJBJYXV1FNpuV42aVaPpAKUU2m0UoFEI4HEZzc7N8L9nu71GpVMLU1BR4nsfo6OjbulctLCzg6aefrj755JOZYDDIBC+jIWDil3FNsimER2w228d0Ot3H7Ha79d577zX91m/9VvOlWuIKgoDZ2VmkUimMj4/XJU63VCohFAohFApBFEV58lIiRliKV15bWwPHcejq6kJHR0fN43MFQcDa2hqWlpbgdDrR29tbk9AQnucxOTlZM1F26tQpOdlxO6mF+D1z5gysVuu2N+qQFhlSNYdaiNFsNov5+XmUSqW6VVgpFotYW1tDMBiExWJBd3e3It0VpRjeUCiEaDQKk8kklyurR33vWCyGiYmJy+6G8TyPY8eO4Uc/+lH2pz/9aYXneX8ymXy8VCr9C6U0UHMjGYwrgIlfxnUBIaRdq9X+Znt7++8C2HnHHXc03XPPPZZbb70VWq0WxWIRp06dkj2QSiSoVCoVhMNhBINBVKtVuFwuuFyuutcMBjZE+fr6OgKBAEwmk1z7s5ZbuqIoYn19HX6/H1arFf39/TVZBIRCIczMzGB4eHhby5RxHIeXX34ZBw4ckD2BlFKIoghBEOQhiuKbhvQ86X5MKZUfT01NYXh4GABACJE/CyqVSv5ZpVKdN9RqNVQqFTQajfx469+/traGG264Yds+V5RSLCwsIBQKYXx8fNvDSyilSCaTmJ+fB6UUAwMDl0za2i44jpNfKwDo7u6G2+2uW9tnCalJRTgcRjweh9lslgWvEhUZOI7DuXPnAABjY2NQq9VIpVJ49tln6ZNPPpk4ceKEoFarXwoGgz8A8DyltFh3IxmMt4CJX8Z1ByFEB+CI2+3+hCiKt+3atUt/ww03WD75yU+SwcFBpc0DsDHBhMNhhMNh5PN52O12OJ1OOByOuk54lFKk02msr68jHo/L2eK19HpRShGJRLCwsAC9Xo+BgYFtL1NVLpfxxhtvQK/XY9euXZcVNIIgoFKpoFKpoFqtyoPjuPMGz/Mol8uoVCrnbYOr1Wp5bBWm0pDEqyRktwpcYKO4/1YPrSSMtwrmiwnqCwW3RD6fh8lkglarhUajQVNTkzy0Wu15Q6fTQavVXnbRUygUZE/y0NDQti6QpHJ5i4uLNfssbEUQBLlLY6lUgtvtRmdnZ93DGqrVKiKRCCKRCHK5HGw2G1wuV80XoG+H559/Hk899ZR49OjRZCQSyXAc98+JROIfAJymTFgwGhwmfhnXNZvhEcNSeITVarV/5CMfMdx5553GgwcPKtIq+EIu9PwYjUY4nU64XK66JsxJ3aECgQDS6XRd6hknEgnZ29fb27utcZ2CIGBxcRGrq6vo7OyEWq1GuVyWhyBsNI5Sq9XnCUGtVisLxa3CsampCRqNBpTShhEoF0MSwjzPv0nAbxX2ktivVCqyF7qpqQl6vR56vR46nQ6FQgGxWAwjIyPbmmgmCALW19extLRU010A4N86r9Xzc30xG/L5PMLhMCKRCCilaG9vV2zn52Jks1n8+te/xtNPP53+5S9/yfE8v5BMJn9YKpV+QikNKm0fg/F2YOKXwdgCIcROCHmfx+P5GM/zh/r7+5vuvvvu1jvuuEN7qVjhenKpSbK9vR0Wi6Vu9l3oIXO5XPB4PDVLlMtms/D7/Uin02+rCH+1WkU+n0ehUJBHqVQCz/MghKC5uVn2+ra3t8vCTq/X1317u9GhlILjOHlxUCqVEA6HodVqUalUUC6XAQBarRYGgwFGo1EeJpPpit6vcrmMpaUlhMNheDwe+Hy+mizwpMS1YDCIRCJRlx2NCxEEAfF4HNFoFIlEAs3NzXC5XHA6nYqWQ5TgOA6vvfYafv7znxd+8pOfFBOJRI5S+mwkEnkKwDFKaUlpGxmMdwoTvwzGZSCE9Or1+jscDsfHBEEYPnDggOquu+6y3X777SqPx6O0eahWq4hGo4hGo0in0zCbzWhvb0dbW1vdqkdIIRrBYPBNQni7hUSlUsHKygoCgQCcTid6enrQ3NyMSqWCbDaLbDaLfD6PXC4HjuOg1Wpl8SUJMoPB0BAe/WsRSimq1SoKhQKKxSIKhYK8+BBFETqdDi0tLWhpaYHZbEZLSws0Gg0ymQwWFxeRz+fR09MDj8ez7eE90g5KMBhEMpmE1WqFx+OBw+Goi6eeUopcLid/X6vVKhwOB9rb22G32xXrqLbVvqmpKfziF7+oPvXUU5mFhQVOo9EcC4VC/yCK4guU0oSiBjIY2wgTvwzGFUIIUQHYY7Vaf7O5ufkurVbrue2225o+8pGPWG699da6lAe7HFLpI2ly5XlenlxtNltdJleO4xCNRhEMBlEoFOB0OuHxeLZ167ZcLiOZTMrl2TQaDYxGI8xmsyyoWlpamMBtMCilqFQqyOVyyGaz8rFQKEClUsHpdKKjowMWi2Xb3jspVCcYDCKVSsFms8Hj8cBut9dF8FarVcRiMXlxajKZ5J0aJZtgSASDQTz33HPij3/84+Tx48dFtVo9FY/H/7FcLv+ClSJjXMsw8ctgvEMIIc0ADjudzrsJIXdYLJaW97///drf+I3faD18+DDeTm3hWsDzvLytmkwmodVq4XA40NbWhtbW1ppP/jzPy0I4l8vB4XDA7XbDZrNd8bUppchkMkgkEkgmk8jn89DpdLBarbBYLGhtbYVOp1Pca8Z45wiCgGKxiHQ6LQ9BEGCxWGCz2WC322EwGN5WZ8VYLIZQKIRsNgu73S4L3lqHNPA8j2QyiVgshkQiAUII2tra5LAkJWPBpYYvL730En3mmWeSr7zyishxXLBYLP44nU7/FMAZSqn4lr+IwbgGYOKXwdgmCCGtAG5qb2//oFqtvk2n07UdOnRI9YEPfMB25MgR0t3drWjMcKlUQjweRzweRzqdRnNzM9ra2uBwOGqeVCPFN4bDYSSTSZjNZrhcrovWJ83n84hGo4jFYigWizCbzbDb7bDZbDUJpWA0HoIgIJPJIJlMIh6Po1QqobW1VRaSF8YBS/HH4XAY1WpVThardRy8IAhIpVKy2BUEAXa7HQ6HA3a7XdHdB6nZyIsvvsj9/Oc/T589e5YSQvz5fP5nmUzm/wE4RSmtKmYgg6EgTPwyGDVis6TaPovFcrvRaPwgpbRnbGyM3HnnndYjR45oRkZGFPUEFQoFWQxns1kYjUZ50q6lGJa8uVLSXlNTE1paWiCKIlKpFJqbm+W4ZaPRyMQuQy65J4UQUEphsVjkpg9NTU1yXexaliWTxG4ikUA8HgfHcbDZbHA4HHA4HDXrZncllMtlnDhxAi+88ELpmWeeyS0vLwsajeZsPB7/Sblc/jWAaebZZTA2YOKXwagTmzHDw83Nze91OBwf5jhud09Pj/rOO+80HT58uHnfvn11La+0FamKRDweRzKZRDabhV6vh91uh91uh8Vi2dbQAkm0rK2tIR6PQ6vVore3Fy6Xi4UwMN6SarUKv9+PSCQid0bs6ura9nJo1WoVyWRSDrsRRREWi0X27ipZlSEUCkliN/vcc8+VE4lESa1WvxoMBp+mlB6llK4pZhyD0eAw8ctgKAghpJsQcrPH4/mAKIoHtVqtdXx8nLz3ve9tPXjwYNP4+LhiE2yxWJQn/nQ6DZVKJcdg2my2d+TlqlQqWF1dRSAQgNlsRmdnZ92y7RnXJlK1kfX1dYiiCK/XC7fb/bYXUZRSlEolWeimUiloNBr5M2+1WhXz7KZSKZw8eRLHjh0rvfDCC7n5+XkQQgIcx70YjUZ/CeBlSmlKEeMYjKsQJn4ZjAaCENIEYJdOp7uxra3tdkEQ9hiNRtOBAwdU73nPeywHDhxQv1VHslohecGkwfM8WlpaYLVaYbVaL5tEl8/nsbi4iFQqBa/Xi87OTlaNgbHtFItFrKysyHWCe3p6LilYOY5DOp1GKpVCKpVCsVhEc3OzLHa3e7fjSikUCjh9+jReffXV6gsvvJA5d+4c5Xk+AeBYKBT6JYATAPysixqD8c5h4pfR8BBC3gPgGwCaAMQppbdunv8AgG8CUAN4lFL60Ob5XQAeBTAH4Peu9ji3zaoS40aj8ZDNZrud47hdNptNd/jwYc2RI0cse/fuJYODg3UXZ6VAcAAACd5JREFUxKIoIpfLyV6ybDYLtVoti2Gr1QoAmJmZQbFYxMDAwLZ2aGMwLoUgCFhdXcXy8jI8Hg96e3tRLBZloZvJZKBWq2GxWOTP6tupKLFdFAoFTE5O4uTJk/yvfvWr9MmTJ2mpVMqq1eqTkUjkX3meP46NWF2hroYxGNc4TPwyGhpCiAXAMQAfoJSuEkLaKaVRQogaG+L2dgDr2PCG/A6ldIoQ8hiALwK4F8A8pfRZpeyvFZuVJfZZrdZbTCbTzRzHDRqNxubR0VFy4403mvfs2aMdHR2F0+msq10cxyGVSiGZTCIQCKBarWLv3r1M9DIUQRRF+P1+TE9Pw2q1wu12y7sU9fTqiqKI5eVlnD17FidPniy8+uqrhbm5OcLzfEaj0UzE4/EXSqXSKwDOUkordTOMwbhOYeKX0dAQQj4LwEMp/bMLzt8E4EuU0js2f/5TAKCU/iUh5PsA/gTAPdjYHnymvlYrw2Z1iSFCyKjb7T5CCNlPKXW5XC7VgQMHtAcPHmwdGxsjO3furGkccT6fx+nTp+F0OtHf38/ieRmKU6lUMDExAQAYHR2tachNOp3GuXPncObMGf6VV17JnD59Wkyn01xTU5O/Uqm8Go1GXwFwFhv3pqt6V4rBuFph4pfR0BBCpHCHXQBaAHyTUvpDQshvY8Mb/OnN530CwEFK6X8ihOwB8LcA5gH87vW+ZUgIaQOwu6WlZb/FYrmF5/ldWq3WNDQ0hIMHDxpHRkYMO3bswMDAwLvuOhWPxzExMYHx8XFYLJbt+QMYjG1ibW0Nfr8fBw4ceNef9VQqhdnZWczMzNCzZ8/mjh8/Xl5ZWaGiKCZVKtXrkUjkJY7jzgCYpJTmt+cvYDAY2wETv4yGhhDy1wD2A3g/gGYArwD4EIBRXEL8KmXr1QQhRAOgH8CIzWYbNxqNezmOG9RoNCaXy0VGR0c1Y2Nj5p07d2p27NiBzs7Ot/TgxuNxTE5O4uDBg4qWgGIwLkcymcTZs2dx4403vuXnlOM4LC4uYnZ2FlNTU5XTp0/npqamxFQqJQBIqlSq6XQ6fSKfz09gw5sbYIloDEbjw8Qvo+EghHwOwGc2f/xHADpK6YOb//YYgGexEed70bCH+lt87UA2AnPbAOzQaDRD7e3tN6hUqlGe5zsMBoOuv7+fjo+PN4+OjrYMDg6iv78fFosF5XIZr776Km666SYmfBkNTywWw9zcHA4dOgRKKcLhMBYWFjAzMyOeOXMme/bs2erq6io4jis2NTUtVSqV09Fo9BQ28gzmKKVZpf8GBoPxzmHil9HQEEJ2AvhrAHcA0AI4jo1EthlsTETvBxDARsLbfZTSSYVMvebZLMPWC2CH2WweNZvN+0RR7KeU2vR6vcnj8fBDQ0PYuXOnqa+vT+vz+eDz+Vj4A0NRKKWIRCJYXl7G8vIy5ufnS9PT04W5uTltLBYTqtVqXq1WRwFMJxKJE+VyeQrALDa8uCwml8G4BmHil9HwEEI+D+D3AIjYKGn2jc3zd2KjBJoawPcopX+hnJXXN5thFB4APgC+tra2XXq9flgQhF5KqaW5ubmpq6tL3LFjh3ZoaMjU29vb5PP50NnZCZvNxipBMN4xPM8jGo1ifX0dS0tLWFhYKM/MzBTm5ub4cDhMqtVqVa1WR1Uq1UI2m53IZDKzAJY3R4yFKTAY1x9M/DIYjJqzKY47APgIIT0Oh2NEr9fvFEWxUxAEq0aj0RqNRpXH46Fer1ft8/kMXq+32e12w+PxwOPxMJF8nSGJ2mAwiFAohEAgIK6srBSXl5fLq6urQiQSUVUqFZ7n+apGo4kCWM3n8xPpdHoGwBI2xG2UiVsGg3EhTPwyGIyGYLOZhwsbHmS3Xq/vslqtA01NTT5BEDouFMnd3d2ajo4OrdvtNjgcDpXdbsfWYTQamVhuIERRRCaTQSKROG9Eo1EuHA6X1tfXq6urq2I4HCaVSkUQBKGi0WiihJBAtVr1JxKJeZ7ngwCCAEIAIpRSXuE/i8FgXIUw8ctgMK4qLhDJbQDsra2tHoPB0KHRaFwA2gRBsIuiaFSr1Zqmpia12WymDocDTqdT5XK5tC6XS9/e3q61Wq1oaWmByWRCS0uLPEwmkyKtbRudarWKXC6HfD6PXC533uNEIkEjkUg5HA5XwuEwF41GaTweR6FQgCAIPM/zvFqtzqnV6jiAKMdx4Xw+v57P58MAEgAiYKKWwWDUASZ+GQzGNc1mBQszAPvWodfr21taWtxNTU1WtVptIYRYKKUmQRBaRFE0EkI0KpVKpVar1SqVSm0wGESTyYTW1la0traqNofGYDCo9Xq9WqfTqZubmzU6nY5otVrodDpotVq81WONRgNCyGUHpfS8IYrieT9zHIdqtYpKpYJqtfqWjyuVilgul/lSqSRUKhWhWCwKqVRKSKfTQjabpdlsFrlcjlQqFYiiKIiiKAqCIADg1Gp1XqVS5QghWUppVhCEVKVSSWYymSDP8zFsCFl5UEqLSr33DAaDcTGY+GUwGIy3YFNAN2Oj0Yo0TJtH7ebQAdBqNBqdTqczarXaZo1G06xWq5vVanUzIUQvDWyU79NtHjUAyNZBKSUXnKPSIIRQbCR/bj3HEUIqACqb7XHLoiiWKaVlQRBKPM+XeJ4vcRxXLJfLJVEUywCqm6OyOXKbIy89Zq12GQzGtQgTvwwGg8FgMBiM64bLt2xiMBgMBoPBYDCuIZj4ZTAYDAaDwWBcNzDxy2AwGAwGg8G4bmDil8FgMBgMBoNx3aBR2gAGg1FbNttDf3zzRw2AnQDaKKVJQsgyNjL7BQA8pXT/5v/xAHhi898+TinN191wBoPBYDBqAKv2wGBcRxBCPgzgjyil79v8eRnAfkpp/ILnPQTgcQC9ADoopX9bb1sZDAaDwagFLOyBwbi++B0A//sKnqfGRi1ZERt1ZhkMBoPBuCZgnl8G4zqBEGIAsA6gn1Ka3Dy3BCCFjWYJf0cp/e7meS82PL8ZAPdRSnPKWM1gMBgMxvbCYn4ZjOuHDwN4WRK+m9xMKQ0QQtoBPEcImaGUvkgpXQFwRBkzGQwGg8GoHSzsgcG4BiGEfI4QcmZzeDZP34sLQh4opYHNYxTAjwEcqK+lDAaDwWDUFxb2wGBcBxBCWgEsAeiilBY2zxkBqCiluc3HzwH4c0rpswqaymAwGAxGTWFhDwzG9cFdAP5VEr6bOAH8mBACbNwLnmTCl8FgMBjXOszzy2AwGAwGg8G4bmAxvwwGg8FgMBiM6wYmfhkMBoPBYDAY1w1M/DIYDAaDwWAwrhuY+GUwGAwGg8FgXDcw8ctgMBgMBoPBuG5g4pfBYDAYDAaDcd3AxC+DwWAwGAwG47qBiV8Gg8FgMBgMxnXD/wf83VXSc82TxwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Angle definitions:\n", - "# CRPropa uses\n", - "# longitude (phi) [-pi, pi] with 0 pointing in x-direction\n", - "# colatitude (theta) [0, pi] with 0 pointing in z-direction\n", - "# matplotlib expects\n", - "# longitude [-pi, pi] with 0 = 0 degrees\n", - "# latitude [pi/2, -pi/2] with pi/2 = 90 degrees (north)\n", - "lat0 = np.pi/2 - lat0\n", - "lats = np.pi/2 - np.array(lats)\n", - "\n", - "plt.figure(figsize=(12,7))\n", - "plt.subplot(111, projection = 'hammer')\n", - "plt.scatter(lon0, lat0, marker='+', c='black', s=100)\n", - "plt.scatter(lons, lats, marker='o', c='blue', linewidths=0, alpha=0.2)\n", - "plt.grid(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backtracking to Generate a Lens\n", - "\n", - "The following is an example for a backtracking simulation with a uniform isotropic coverage suitable to generate a magnetic lens. Here, anti-particles are emitted following the healpic scheme to achieve an uniform coverage of the starting direction. Please note that for production use, nside = 1024 should be used and as well a fine binning of rigidities extendig down to ~0.1 EeV is required, e.g. $10^{16.99}$ eV; $10^{17.01}$ eV; $10^{17.03}$ eV ... ;. The backtracking data can be post processed with the [create-lens.py](https://github.com/CRPropa/CRPropa3-data/blob/master/create_lens.py) program." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "jupyter": { - "outputs_hidden": true - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "from crpropa import *\n", - "import healpy\n", - "\n", - "def backtrackingRun(logR=18., nside=16):\n", - " \"\"\"Galactic Lens: Backtracking run\n", - " \n", - " Runs the backtracking simulation for a given rigidity and\n", - " healpy pixel configuration. Creates a file with detected \n", - " candidate properties at r_gal=20*kpc.\n", - " \n", - " Input\n", - " -----\n", - " logE=18. : float\n", - " log10(R/V), rigidity of the backtracked particles\n", - " \n", - " nside=16 : int\n", - " healpix parameter, should be increased to ~1024 for production\n", - " of real lenses\n", - " \n", - " Returns\n", - " -------\n", - " \"\"\"\n", - " \n", - " # magnetic field setup\n", - " B = JF12Field()\n", - " seed = 1703202123\n", - " B.randomStriated(seed)\n", - " \n", - " print('Preparing turbulent grid')\n", - " lMin = 8 * parsec\n", - " lMax = 272 * parsec\n", - " turbulentGrid = Grid3f(Vector3d(0.), 256, 4 * parsec)\n", - " initTurbulence(turbulentGrid, 1, lMin, lMax, -11./3, seed) #Brms scales automatically\n", - " B.setTurbulentGrid(turbulentGrid)\n", - " \n", - " \n", - " # simulation setup\n", - " sim = ModuleList()\n", - " sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", - " obs = Observer()\n", - " obs.add(ObserverSurface( Sphere(Vector3d(0.), 20 * kpc) ))\n", - " \n", - " ofname = 'galactic_backtracking_{:.2f}.h5'.format(logE)\n", - " print(\"Writing output to {}\".format(ofname))\n", - " out = HDF5Output(ofname, Output.Event3D)\n", - " obs.onDetection(out)\n", - " sim.add(obs)\n", - " \n", - " pid = - nucleusId(1,1) # (anti-)proton\n", - " energy = 10**logE * electronvolt\n", - " \n", - " print(\"Running at 10**{} eV = {} EeV\".format(logE, energy / EeV))\n", - " \n", - " position = Vector3d(-8.5, 0, 0) * kpc\n", - " \n", - " # submit a particle in every direction of a healpix map, 256 per pixel of the\n", - " # lens\n", - " nparts = healpy.nside2npix(nside)\n", - " print('simulating {} particles'.format(nparts))\n", - " # Use candidate vector to enable multi core processing\n", - " cv = CandidateVector()\n", - " print(\"Preparing Particles ...\")\n", - " for i in range(nparts):\n", - " v = healpy.pix2vec(nside, i)\n", - " direction = Vector3d(v[0], v[1], v[2])\n", - " p = ParticleState(pid, energy, position, direction)\n", - " c = CandidateRefPtr(Candidate(p))\n", - " cv.push_back(c)\n", - " \n", - " sim.setShowProgress()\n", - " sim.run(cv)\n", - " out.close()\n", - " print (\"Finished!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preparing turbulent grid\n", - "Writing output to galactic_backtracking_18.00.h5\n", - "Running at 10**18 eV = 1.0 EeV\n", - "simulating 3072 particles\n", - "Preparing Particles ...\n" - ] - } - ], - "source": [ - "backtrackingRun()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From d29b1d49b958e111e40891dd1725e0477030db11 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 12:52:09 +0100 Subject: [PATCH 60/87] Extend name of JF12 field in comment --- .../galactic_trajectories.ipynb | 184 ++++++++++++++++++ .../galactic_trajectories.v4.ipynb | 168 ---------------- 2 files changed, 184 insertions(+), 168 deletions(-) create mode 100644 doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb delete mode 100644 doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.v4.ipynb diff --git a/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb b/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb new file mode 100644 index 000000000..2cc31005d --- /dev/null +++ b/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Galactic trajectories\n", + "The following code performs a backtracking simulation in the Janson and Farrar (2012) Galactic magnetic field model and visualizes the trajectories.\n", + "A custom simulation module is used for a numbered trajectory output, that simplifies separating the individual trajectories for plotting later on." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "# magnetic field setup\n", + "B = JF12Field()\n", + "randomSeed = 691342\n", + "B.randomStriated(randomSeed)\n", + "B.randomTurbulent(randomSeed)\n", + "\n", + "# simulation setup\n", + "sim = ModuleList()\n", + "sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", + "sim.add(SphericalBoundary(Vector3d(0), 20 * kpc))\n", + "\n", + "class MyTrajectoryOutput(Module):\n", + " \"\"\"\n", + " Custom trajectory output: i, x, y, z\n", + " where i is a running cosmic ray number\n", + " and x,y,z are the Galactocentric coordinates in [kpc].\n", + " \"\"\"\n", + " def __init__(self, fname):\n", + " Module.__init__(self)\n", + " self.fout = open(fname, 'w')\n", + " self.fout.write('#i\\tX\\tY\\tZ\\n')\n", + " self.i = 0\n", + " def process(self, c):\n", + " v = c.current.getPosition()\n", + " x = v.x / kpc\n", + " y = v.y / kpc\n", + " z = v.z / kpc\n", + " self.fout.write('%i\\t%.3f\\t%.3f\\t%.3f\\n'%(self.i, x, y, z))\n", + " if not(c.isActive()):\n", + " self.i += 1 \n", + " def close(self):\n", + " self.fout.close()\n", + " \n", + "\n", + "output = MyTrajectoryOutput('galactic_trajectories.txt')\n", + "sim.add(output)\n", + "\n", + "# source setup\n", + "source = Source()\n", + "source.add(SourcePosition(Vector3d(-8.5, 0, 0) * kpc))\n", + "source.add(SourceIsotropicEmission())\n", + "source.add(SourceParticleType(-nucleusId(1,1)))\n", + "source.add(SourceEnergy(1 * EeV))\n", + "\n", + "sim.run(source, 10) # backtrack 10 random cosmic rays\n", + "output.close() # flush particles to ouput file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3D trajectory plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12,12))\n", + "ax = plt.subplot(111, projection='3d')\n", + "\n", + "# plot trajectories\n", + "I,X,Y,Z = np.genfromtxt('galactic_trajectories.txt', unpack=True, skip_footer=1)\n", + "for i in range(int(max(I))):\n", + " idx = I == i\n", + " ax.plot(X[idx], Y[idx], Z[idx], c='b', lw=1, alpha=0.5)\n", + "\n", + "# plot Galactic border\n", + "r = 20\n", + "u, v = np.meshgrid(np.linspace(0, 2*np.pi, 100), np.linspace(0, np.pi, 100))\n", + "x = r * np.cos(u) * np.sin(v)\n", + "y = r * np.sin(u) * np.sin(v)\n", + "z = r * np.cos(v)\n", + "ax.plot_surface(x, y, z, rstride=2, cstride=2, color='r', alpha=0.1, lw=0)\n", + "ax.plot_wireframe(x, y, z, rstride=10, cstride=10, color='k', alpha=0.5, lw=0.3)\n", + "\n", + "# plot Galactic center\n", + "ax.scatter(0,0,0, marker='o', color='k')\n", + "# plot Earth\n", + "ax.scatter(-8.5,0,0, marker='o', color='b')\n", + "\n", + "ax.tick_params(axis='both', which='major', labelsize=16)\n", + "ax.tick_params(axis='both', which='minor', labelsize=16)\n", + "ax.set_xlabel('x / kpc', fontsize=18)\n", + "ax.set_ylabel('y / kpc', fontsize=18)\n", + "ax.set_zlabel('z / kpc', fontsize=18)\n", + "ax.set_xlim((-20, 20))\n", + "ax.set_ylim((-20, 20))\n", + "ax.set_zlim((-20, 20))\n", + "ax.xaxis.set_ticks((-20,-10,0,10,20))\n", + "ax.yaxis.set_ticks((-20,-10,0,10,20))\n", + "ax.zaxis.set_ticks((-20,-10,0,10,20))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.v4.ipynb b/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.v4.ipynb deleted file mode 100644 index f2ea7b488..000000000 --- a/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.v4.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Galactic trajectories\n", - "The following code performs a backtracking simulation in the JF2012 Galactic magnetic field model and visualizes the trajectories.\n", - "A custom simulation module is used for a numbered trajectory output, that simplifies separating the individual trajectories for plotting later on." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "# magnetic field setup\n", - "B = JF12Field()\n", - "randomSeed = 691342\n", - "B.randomStriated(randomSeed)\n", - "B.randomTurbulent(randomSeed)\n", - "\n", - "# simulation setup\n", - "sim = ModuleList()\n", - "sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))\n", - "sim.add(SphericalBoundary(Vector3d(0), 20 * kpc))\n", - "\n", - "class MyTrajectoryOutput(Module):\n", - " \"\"\"\n", - " Custom trajectory output: i, x, y, z\n", - " where i is a running cosmic ray number\n", - " and x,y,z are the Galactocentric coordinates in [kpc].\n", - " \"\"\"\n", - " def __init__(self, fname):\n", - " Module.__init__(self)\n", - " self.fout = open(fname, 'w')\n", - " self.fout.write('#i\\tX\\tY\\tZ\\n')\n", - " self.i = 0\n", - " def process(self, c):\n", - " v = c.current.getPosition()\n", - " x = v.x / kpc\n", - " y = v.y / kpc\n", - " z = v.z / kpc\n", - " self.fout.write('%i\\t%.3f\\t%.3f\\t%.3f\\n'%(self.i, x, y, z))\n", - " if not(c.isActive()):\n", - " self.i += 1 \n", - " def close(self):\n", - " self.fout.close()\n", - " \n", - "\n", - "output = MyTrajectoryOutput('galactic_trajectories.txt')\n", - "sim.add(output)\n", - "\n", - "# source setup\n", - "source = Source()\n", - "source.add(SourcePosition(Vector3d(-8.5, 0, 0) * kpc))\n", - "source.add(SourceIsotropicEmission())\n", - "source.add(SourceParticleType(-nucleusId(1,1)))\n", - "source.add(SourceEnergy(1 * EeV))\n", - "\n", - "sim.run(source, 10) # backtrack 10 random cosmic rays\n", - "output.close() # flush particles to ouput file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3D trajectory plot" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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YWe0AilVyInJ52Pf9KOO5HZfVMvUs9Lpn9bwjw7J7WDabjQreXwTH3dich3jw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RJxSVFB7QhDE7I/iH7wve9z7JLackKytw7pzGK68IOjrg53/eIKpqVG1LkiI3kJ77ZIe6aWVH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CzWenZUFbZto9FooNFo8GPe5TlEtAFn9frt9qGep9gehrPKXi7JZPLGM/6/E8yyLFxdXfEw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iIoKGDRsWasY1+8xUcQVXm81Geno6Wq0Wd3f3Mh0kSjOTyYTZbC51dcR39igGcqwKV5Y+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\nX+AYWrVqxb59++7aPm3aNDZu3MiKFSt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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "from mpl_toolkits.mplot3d import axes3d\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.figure(figsize=(12,12))\n", - "ax = plt.subplot(111, projection='3d')\n", - "\n", - "# plot trajectories\n", - "I,X,Y,Z = np.genfromtxt('galactic_trajectories.txt', unpack=True, skip_footer=1)\n", - "for i in range(int(max(I))):\n", - " idx = I == i\n", - " ax.plot(X[idx], Y[idx], Z[idx], c='b', lw=1, alpha=0.5)\n", - "\n", - "# plot Galactic border\n", - "r = 20\n", - "u, v = np.meshgrid(np.linspace(0, 2*np.pi, 100), np.linspace(0, np.pi, 100))\n", - "x = r * np.cos(u) * np.sin(v)\n", - "y = r * np.sin(u) * np.sin(v)\n", - "z = r * np.cos(v)\n", - "ax.plot_surface(x, y, z, rstride=2, cstride=2, color='r', alpha=0.1, lw=0)\n", - "ax.plot_wireframe(x, y, z, rstride=10, cstride=10, color='k', alpha=0.5, lw=0.3)\n", - "\n", - "# plot Galactic center\n", - "ax.scatter(0,0,0, marker='o', color='k')\n", - "# plot Earth\n", - "ax.scatter(-8.5,0,0, marker='o', color='b')\n", - "\n", - "ax.tick_params(axis='both', which='major', labelsize=16)\n", - "ax.tick_params(axis='both', which='minor', labelsize=16)\n", - "ax.set_xlabel('x / kpc', fontsize=18)\n", - "ax.set_ylabel('y / kpc', fontsize=18)\n", - "ax.set_zlabel('z / kpc', fontsize=18)\n", - "ax.set_xlim((-20, 20))\n", - "ax.set_ylim((-20, 20))\n", - "ax.set_zlim((-20, 20))\n", - "ax.xaxis.set_ticks((-20,-10,0,10,20))\n", - "ax.yaxis.set_ticks((-20,-10,0,10,20))\n", - "ax.zaxis.set_ticks((-20,-10,0,10,20))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 6d63a9e1c91f165e69757634fb337ebf0287b9fd Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 13:11:11 +0100 Subject: [PATCH 61/87] Disable CandidateTagColumn --- .../photon_propagation/cascade_1d.ipynb | 46 ++++++++++++++----- 1 file changed, 35 insertions(+), 11 deletions(-) diff --git a/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb b/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb index 53df6bc70..2632ce3c3 100644 --- a/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb +++ b/doc/pages/example_notebooks/photon_propagation/cascade_1d.ipynb @@ -13,9 +13,20 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 2 13:05:25 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:43 - Finished at Thu Feb 2 13:09:08 2023\n", + "\r" + ] + } + ], "source": [ "from crpropa import *\n", "\n", @@ -53,6 +64,7 @@ "output = TextOutput('cascade_1d.txt', Output.Event1D)\n", "output.setEnergyScale(eV)\n", "output.enable(output.WeightColumn) # this is required if thinning > 0\n", + "output.disable(output.CandidateTagColumn) # not needed in this analysis\n", "obs.onDetection(output)\n", "\n", "source = Source()\n", @@ -63,6 +75,7 @@ "# source.add(SourceEnergy(20 * TeV)) # a monochromatic intrinsic spectrum\n", "\n", "sim.add(obs)\n", + "sim.setShowProgress(True)\n", "sim.run(source, 10000, True)\n", "\n", "output.close()\n" @@ -80,12 +93,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -121,19 +134,30 @@ } ], "metadata": { - "interpreter": { - "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" - }, "kernelspec": { - "display_name": "Python 3.9.5 64-bit", + "display_name": "crp_docu", + "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} From f8c41da0d63750db6fc4d78ce90a40e6a5726c1e Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 13:37:47 +0100 Subject: [PATCH 62/87] Fixed location of legend. Increases plotting time significantly for large data scatter plots --- .../Propagation_Comparison_CK_BP.ipynb | 249 ++++++++---------- 1 file changed, 104 insertions(+), 145 deletions(-) diff --git a/doc/pages/example_notebooks/propagation_comparison/Propagation_Comparison_CK_BP.ipynb b/doc/pages/example_notebooks/propagation_comparison/Propagation_Comparison_CK_BP.ipynb index 9d7304462..cdba9546f 100644 --- a/doc/pages/example_notebooks/propagation_comparison/Propagation_Comparison_CK_BP.ipynb +++ b/doc/pages/example_notebooks/propagation_comparison/Propagation_Comparison_CK_BP.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -265,14 +265,14 @@ " # for the legend\n", " ax1.scatter(x_ana, y_ana, color = 'k', marker = 's', s = 1, label = 'Analytical solution')\n", " \n", - " ax1.legend(markerscale=5)\n", + " ax1.legend(markerscale=5, loc='upper left')\n", " \n", " # numerical solutions\n", " ax2.scatter(data.D,(data.R-r_g)/r_g*100., s=1,color = color, label= module)\n", - " ax2.legend(markerscale=5)\n", + " ax2.legend(markerscale=5, loc='upper left')\n", " \n", " ax3.scatter(data.D,((x_ana-data.X)**2+(y_ana-data.Y)**2)**0.5, s=1,color = color, label=module)\n", - " ax3.legend(markerscale=5)\n", + " ax3.legend(markerscale=5, loc='upper left')\n", " \n", " # We use this function to plot the whole figure for the particle motion in the xy-plane\n", "def plot_figure_perp(max_trajectory, p_z, r_g_0, number_of_steps):\n", @@ -318,29 +318,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.045074939727783s.\n", - "Simulation time with module BP is 1.3592238426208496s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:58: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" + "Simulation time with module CK is 0.6771326065063477s.\n", + "Simulation time with module BP is 0.5768852233886719s.\n" ] }, { 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\n", 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", 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" ] @@ -367,27 +358,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 3.129796266555786s.\n", - "Simulation time with module BP is 3.1616051197052s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:58: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + "Simulation time with module CK is 1.3579211235046387s.\n", + "Simulation time with module BP is 1.1294221878051758s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -407,27 +391,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.7734830379486084s.\n", - "Simulation time with module BP is 2.4479715824127197s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:58: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + "Simulation time with module CK is 1.2906782627105713s.\n", + "Simulation time with module BP is 1.0119423866271973s.\n" ] }, { "data": { - "image/png": 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GND8WaYrrgZXAZcHr44FfA5+LLSKREGgIc/MowUqZKBOrvBlN6NVpttdff52zzjqLv/71r/Tp04cBAwZw6aWXcvTRR6+fMvaqq67iJ5dfybRb72fzPn3LltWVJKjWnrD8dkq0pBlqPQ+uvPJK7r//fvr2LX8eSGe1NE7UIMmEUe6+c8HrB81MQwQLqK5JDyVV0VCClSJxJFdx76sr3J1JkybxpS99iVtuyXW/Pfvss7zxxoas56LLf811V/yUq26bXjK56tkdRmzV9RiKE6ZqCZcSLQlbLefBr3/9a376058yffp0NXjqoMSq5cw0s73c/UkAMxsHzIg5psRQXZN8SqqipwQrJeJKrpr5sOK8tevgoYXw3DIY1R8OGAbdu3W9vAcffJAePXrwb//2b+vXjRkzhoULFwLwP9Nu5Vc/vZirbnuAvv06ZlHNSnAKy62UbCnRak1hnwNQ/Ty49dZbufjii3nggQfYaqsGria0ECVWLWt34HEzezV4PQR4wczmAu7uu8QXWv2i/p+ruiYeSqripQQrBbI8PfradXDiHTDrdVj9IWzSA3b9OPz6qK5X+M899xy77757yfdeWdjOD847g/+9fxZbbf3xDu9FldTk96NES6A55wBUPg/a29s544wzmDVrFh//+MdLbiMbKLFqeRPiDiAsUf/PVV0TPc1imgwNXiOVZjrrT/EmVztf3vH1p4aFv4+HFuYq+lUf5ua+XfVh7vVDC8Pf1/xl0Ldff7bZdgh/vuvW9et3GRBPIlPLfue80ZyZDSU5ojwH8vr378+QIUO49dZbq2/c4mqZrVWNlGxz9/ZKS9zx1SPq+kZ1TXSGTq39YpDqreZTD1ZCJeF+q/eKZta7+sjw9/HcstxVtEKrP4R5/4CDP9G1MkeOHMltt93WYV0+Sem1yaZcfuPdnHLUeLbcamvOPf2Eru0kROrRam3NOAeg9HmQt+mmm3L33Xczfvx4tt56a044If7zIGmifAyGJJOZzXT33RrdJkmi+p+bp7qmufRoiORSD1YCJSG5isqo/rkhCoU26QEjGximfdBBB/H+++8zbdo0IJecvPj8HN54bREA/fpvzRU33cOVl5zHvffe2/UdhazWHi3JlmacA9D5PACYM2cOixblzoOtt96ae+65h/POS9Z5kATV6uCs1cNS1k5mNqfCMhdI1U1FUfzPBdU1zVZvb5VETwlWwhx0ben1cZ8kzdr3AcNy47837QFG7uuuH8+t7yoz4/bbb+f+++9n8LDtmLT/SC676Fz6BfdcbdETDt9zOHfddRdf/vKXefrpp8P4VUJTLdHSsMFsacY5AB3Pg+22246RI0dy7rnndrgPYvjw5J4HcWmlC1xS1Y7AERWWicA+sUXXBc3+n6u6prkOvV6JVVpoiGDCvPxO53VZPkm6d8vdXPvQwtwQhZFbhTOD2sCBA/nWTzuP+Z717HP07pn7fsyYMSxZsqSxHTVRtaGDc97QkMEsaNY5ALnzoNS9D/nn0kDyz4MoZXlCIalf2u6vqkUz/+eqrmkuDVtOFyVYCVLq5InrhDn8huj21b1bbux3I/ebFCuVlGyyEeuTqzTZZYCSrKxrxjkgtVOvlbQS1TfposQqnZRgJUSSkiuAecvj23ejyiUj2/eLNo4wVerN0gQYlZnZQmAlsBb4yN3bzGxL4H+BYcBC4PPuvsLMDJgKHA6sAk5295lxxC3RUHIlIkmkxCrddA9WApQ6ifSH6Zo1H5Ven5Xko9q9WVLWge4+1t3bgtfnAA+4+/bAA8FrgMOA7YNlMvDzru7Q3RsIN15pjr0e5RowG6PGi6RLms/ZNMfeLEqu0k89WDErdxK9kqCTJ00n8oslet6yklzlachgKI4EDgi+vw54CPhmsP56z/3Hf9LM+pjZNu6+tJ7Ce/XqxfLly+nXrx+5TrH0cHeWL19Or1694g6lqXS/ldTKzHoCnyXX472+3eTuF8YVUyHVN9mhxCo7lGAlkE6grimVdGQ12ajl2VmyngN/NjMHfuHu04ABBUnT60D+SNkWWFTws4uDdR0SLDObTK6HiyFDhnTa4aBBg1i8eDHLli0L8/eITK9evRg0aFDcYTSNkiup053AO8AzwPsxx9KJ6pts0HDlbFGCFaOk3XeVZovf7bwuq8lVoUq9WbLefu6+xMy2Bu4zswWFb7q7B8lXzYIkbRpAW1tbp5/t0aMHw4cPbyRmaRIlV9IFg9x9QtxBlKP6Jt3Ua5VNutUnQXQSdd1bqzuvu+OOOzAzFixY0PnNOpx88slln1Kf9/3vf7/D63326dqjUS644AJ++MMf1vUz+edm5ZeHHnqIiRMnVvyZt99+myuuuGL969dee41jjjmmSzEnnbsvCb6+CdwO7Am8YWbbAARf3ww2XwIMLvjxQcE6yQAlV9JFj5vZ6GYUbGb/18zmmdlzZnazmWmsXAvRQ82zSwlWTGp5UJzUptzQwJtvvpn99tuPm2++uekxFCdYjz/+eNP32YjiBGvgwIFVk8g0MrPNzKx3/nvgUOA54C7gS8FmXyI3BIhg/UmWsxfwTr33X0kyKbmSBuwHPGNmL5jZHDOba2ZzGi3UzLYFvg60ufsooDtwbKPlSjpoSGC2KcFKCJ1M4dllAPzzn//kscce4+qrr+aWW25Z/95DDz3EAQccwDHHHMOOO+7ICSecsH4GowsvvJA99tiDUaNGMXny5E4zG02fPp2jjjpq/ev77ruPSZMmcc4557B69WrGjh3LCSecAMDHPvax9dtdcskljB49mjFjxnDOObnJ6q666ir22GMPxowZw2c/+1lWrVpV8Xf6zW9+w6hRoxgzZgz7778/AGvWrOGUU05h9OjR7Lrrrjz44IOdfq64R2zUqFEsXLiQc845h5dffpmxY8fyjW98g4ULFzJq1KiK5V577bUcffTRTJgwge23357//M//rPKXSIQBwGNm9izwNPBHd78HuBg4xMz+BnwqeA1wN/B34CXgKuD06EOWsO3009LrVe9KjfKzix4KHAFMDL6GYSNgEzPbCNgUeC2kciXBKl3wUb2UDboHKwbqvepo8803Z+XKlR3W9e7dm3ffLXFjVZFy9x/deeedTJgwgR122IF+/frxzDPPsPvuuwMwa9Ys5s2bx8CBA9l33335y1/+wn777ccZZ5zB+eefD8CJJ57IH/7wB444YsP/0AMPPJDTTz+dZcuW0b9/f6655hq+/OUvc8QRR3D55Zcze/bsTnH86U9/4s477+Spp55i00035a233gLg6KOP5rTTTgPgW9/6FldffTVnnnlm2d/zwgsv5N5772Xbbbfl7bffBuBnP/sZZsbcuXNZsGABhx56KC+++GLVzwzg4osv5rnnnlsf88KFC9e/V6nc2bNnM2vWLHr27MmIESM488wzGTx4cKldJIK7/x0YU2L9cuDgEusd+FoEoUmEVq3rvE6NGKmVu7eb2RhgfLDqUXd/NoRyl5jZD4FXgdXAn939z8XbVZtUR9JFvemtQT1YCdDqJ1VxclVuXS3yE1vcfPPNHHtsbqTFscce22GY4J577smgQYPo1q0bY8eOXZ9cPPjgg4wbN47Ro0czffp05s2b16FsM+PEE0/khhtu4O233+aJJ57gsMMOqxjP/fffzymnnMKmm24KwJZbbgnAc889x/jx4xk9ejQ33nhjp30V23fffTn55JO56qqrWLt2LQCPPfYYX/ziFwHYcccdGTp0aM0JViWVyj344IPZYost6NWrFzvvvDPt7e0N70+kmTSZkDTKzKYANwJbB8sNZlb+iljt5fYl92iI4cBAYDMz+2Lxdu4+zd3b3L2tf//+je5WYqTkqnWoBytiaey9Gjo1mSd/ud6rt956i+nTpzN37lzMjLVr12Jm/M///A8APXv2XL9t9+7d+eijj1izZg2nn346M2bMYPDgwVxwwQWsWbOmU9mnnHIKRxxxBL169eJzn/scG23UtVPo5JNP5o477mDMmDFce+21PPTQQxW3v/LKK3nqqaf44x//yO67784zzzxT03422mgj1q3bcPm+1O9Uj1KfnUhSKbmSkJwKjHP39wDM7BLgCaDM4NOafQp4xd2XBeX+DtgHuKHBciVhdL9V61EPVsx0YoUn33t12223ceKJJ9Le3s7ChQtZtGgRw4cP59FHHy37s/nEY6uttuKf//xn2QkfBg4cyMCBA/ne977HKaecsn59jx49+PDDDzttf8ghh3DNNdesv8cqP0Rw5cqVbLPNNnz44YfceOONVX+3l19+mXHjxnHhhRfSv39/Fi1axPjx49f/7Isvvsirr77KiBEjOvzcsGHDmDlzJgAzZ87klVdeAXJDMMv1EtZSrkjSpfFiliSWAWsLXq8N1jXqVWAvM9vUck8IPhiYH0K5kiBKrlqTEixJpeLeq427b/j+5ptvZtKkSR3e/+xnP1txNsE+ffpw2mmnMWrUKD796U+zxx57lN32hBNOYPDgwey0007r102ePJlddtll/SQXeRMmTOAzn/kMbW1tjB07dv2EE9/97ncZN24c++67LzvuuGO1X5dvfOMbjB49mlGjRrHPPvswZswYTj/9dNatW8fo0aP5whe+wLXXXtuhhyn/e7/11luMHDmSyy+/nB122AGAfv36se+++zJq1Ci+8Y1vdPiZWsoVSSM1ZqSLrgGeMrMLzOw7wFPArxot1N2fAm4DZgJzybXJpjVariRHueTq8O1UH2WdFc+UlgVtbW0+Y8aMuMMoqfBkS/rJ1dVY58+f3yH5qKYrk1wUJ1hRPlT4jDPOYNddd+XUU0+NbqcpUurvb2bPuHtbTCE1VZLrm1aloYGtrRn1jZntBuwbvHzU3TvPahQB1Tfporoo+8rVN7oHS2JXy2yBhYqTqz4RPpZx9913Z7PNNuNHP/pRdDsVkZqpQSNhMbPH3H0/M1sJOAXDAs3M3X3z+KKTpFNd1NqUYEVI9wQ0x5AtottXrZNLiEj0VMdKmNx9v+Br77hjkXRRciW6B0uaollDT4t7rzbSEZwoWRxyLOmmRo00Kpg1sOo6ESidXG0SxpQokipqnkpN6rky3KtXL5YvXx5JY3tnPRIkMdyd5cuX06tXY2M2zWzLGpY+IYUtGaErxtJEh5RYV/khiNKSyrWVFnw92jgkfhoiKKEbNGgQixcvZtmyZaGWu7jErVrz3wp1F9KgXr16MWjQoEaLeS1YKl3z6w4MaXRHkl1KrqRRZvZV4HTgE2Y2p+Ct3sBf4olKkkoPEZZCSrCkrAGbwBur6/+5Hj16MHz48NDjmVBUebVCpVXvPSUZ+Uzmu/uulTYws1lRBSPJp3uvpEluAv4E/AA4p2D9SnfX5T1ZT8mVFFOCJWU9PVkNl6i8tRp2DeHpJ8V/r5RW7nuHtI20AA0NlGZx93eAd4Dj4o5F0kf1UGtLTYJlZguBleSeoP5RGp+p0z4l3QnL0KnxVRgZSRw6iOJYSNNz1/LcfU3hazPbC7gA6AVMdffbi7cRyRvRN+4IJGvM7PxS6939wqhjkeTRRR4pJTUJVuBAd/9H3EGIdFVXk6reG8FzX2uszPx2Sa/4zezj7v56waqzgUnk7sl6Crg9lsAkcUod+38+Kfo4JPPeK/i+FzARmB9TLJIgSq6knLQlWJkSZ4+QRGfmUph0a+3b13tMFG9fLeFKwXF3pZnNBP476Kl6GzgGWAfU91RqaSkJP64lpdy9w5PlzeyHwL0xhSMJ8Us9FlMqSFOC5cCfzcyBX7h7hztWzGwyMBlgyBBNLhaWJAxrTOvwwHo+tzB/p8KyysWQ5N4sdz/KzI4A/mBm1wNnAccDmwJHxRqcJEbc9ZK0tE2BhqdLlXT77mOd1yXxf6rEI00J1n7uvsTMtgbuM7MF7v5I/s0g4ZoG0NbWFuoDmNLawG+GFPR+xK7Whl8Un2N+H5USrST+Pd3992Z2N7kpkm8HLio836W1aViORMnM5pK7yAu5R0T0B3T/VQtTHSTVpOZBw+6+JPj6JrkG157xRtQ19Q7nko6S/DD0oVOr/z3bp2xYolRpf0k7Bs3sM2b2IHAP8BzwBeBIM7vFzLaLNzpJot5pulQoaTQROCJYDgUGuvvl8YYkcVFyJbVIxb8lM9sM6ObuKz7rbKYAACAASURBVIPvDyWiq0dJa3zGYROD1aH2Cdau+PNfmMBKrJZjJAmVb6XerIT1ZH2P3AWUTYB73X1P4N/NbHvgIuDYOIOTeJU6fstNACMSBndvjzsGEUmXtPRgDQAeM7NngaeBP7r7PXEE0oxGaNKTuAVf7/g66fFGpZ4eqyQpF0+C/q7vAEcDnwXezK9097+5u5Ir6SBp55dkh5mtNLN3C5aVhV/jjk+ip94rqVUqEix3/7u7jwmWke5+URT7bVaDUydjun3zvnQmVoUSnmRNAvqR62E/PuZYJEEScnxKi3D33u6+ecHSu/Br3PFJ/JL8f17ilYohgknRzBMpYUO0EmHvqzq+TsLnU0tilRblZoiM+1gMnnX30/gikLRI0/km6WZmY4DxwctH3H1OnPFI9HSBR+qRih6sLCrVMEjyyRvH5ByvrWr+PmpVbThg0nusykliT1bwDKyGt5FsSXL9KNlmZlOAG4Gtg+VGMzsz3qgkShoaKPVSD1YZmppd8qolVmlXrifr5rlw3Ojo4wF2MrNKV4cN2KKeAs2sOzADWOLuE81sOHALuaGIzwAnuvsHZtYTuB7YHVgOfMHdF3bhd5Amy8K5J6lxKjDO3d8DMLNLgCdQT7uIlKEerBilrRerV9HrKGONozFVS69VVpT6Xc6ZHn0cgR3ZMCVyqWUisE+dZU4B5he8vgT4ibt/ElhBrgFF8HVFsP4nwXYSsyTXi9ISDFhb8HotyX5qiIRIvVfSFUqwYpamJOuFFqpQsjgcsJqkHIvu3l7DsrjW8sxsEPAvwC+D1wYcBNwWbHIdcFTw/ZHBa4L3Dw62lwTJ4vkniXYN8JSZXWBm3wGeBK6OOSaJieofqYUSrBKSMDxweEKTrKjElWRW6rUa1jv7FWup3+/wG6KPI2SXAv8JrAte9wPedvePgteLgW2D77cFFgEE778TbC8xSeoFJ2kd7v5j4BTgLeAfwMnufmm8UUkUVP9IVynBSoBSjdp1nVclQhyTXUSlWq/Vw1+OLpYkmbc87gi6zswmAm+6+zMhlzvZzGaY2Yxly5aFWbRUkfWLHJI8ZvY54G/ufhnQBzjfzHaNOSyJgeofqZUSrIRIyvCsJDp25+bvo9xnvYm1XoWasWNxX+AzZraQ3KQWBwFTgT5mlp/kZxCwJPh+CTAYIHh/C3KTXXTg7tPcvc3d2/r379/c36CFpfi4k2z5L3dfaWb7katDrgaujDkmaTLVP9IIJVhF4hwemNaGbbNjvOSQ5pVdaUhg+xRY8PXm7TvJkpZUmtmfzOy+YLndzI6q/lPg7ue6+yB3HwYcC0x39xOAB4Fjgs2+BNwZfH9X8Jrg/enu7qH9ItKQpB2X0jLyE1z8C3CVu/8R2DjGeKTJNLGFNEoJVsLc/vnO65KWZGWlkmmVGQLDEvNx+DQwAfg08CxwcIPlfRM428xeInePVf6G9auBfsH6s4FzGtyPdFHS6j1paUvM7BfAF4C7g8c5qP3UQgZvFncEkjaqIBJmt21yw9KKJb2xEWZ8Ufyu5fZx7M5KrvIS9jnsAGxDbhKKHSgxbK8ad3/I3ScG3//d3fd090+6++fc/f1g/Zrg9SeD9/8e5i8hXZew41Fay+eBe4FPu/vbwJbAN+INSZqlVPvgsa9EH4ekmxKsCuL6h15uWFqSkqw0N3YqDQls5nDELIjqGDSzbmZ2XsGq7wBnAV8HLiR3H5VkVJLqOhF3X+Xuv3P3vwWvl7r7n+OOS6KR5vaOxEcJVkKVO6GT3PBIcmwAbb+onFxJZ3F9Lu6+jtwDhfOvF7j7f7j7N4LvV8QTmcRB56eIRCHp7RhJDyVYBZJ2YlVKsmYujTaWUqJo9IS1j6FTYdma5u6jVUR4nswxs2+bmeopERGJnNoH0lVquCRcuZN70q2wXcISQkhekgrlY+rfS5VnLWL8jLYkN/Pfa2Z2p5l9N3gejWRYEh70LlKNmX08pHL6mNltZrbAzOab2d5hlCv1S2L7RdJLCVYKtE8pPYPNR8RfISS98VNpSOCMf402FqmPu3/e3XcChpK7B+slYM94oxIRATbMPNqoqcA97r4jMAaYH1K50qCkt28k2ZRglZG0E+uxr6TnvqykxKP7rcJT/JlF+Td29/fdfaa7X+fumrkrw5JSd4hU4+7/0mgZZrYFsD9BsubuHwSzFErEVPdI2JRgpUwSk6wkJixKrkTST+erJIWZ9TWzPc1s//wSQrHDgWXANWY2y8x+aWadxquY2WQzm2FmM5YtWxbCbqUa1T3SKCVYKVQpyUrKVZiuxhFG/EquopGUY02yQceTJJWZfQV4hNyzsL4TfL0ghKI3AnYDfu7uuwLvUeLh5u4+zd3b3L2tf//+IexWCqnukWZQgpVSlZKFOCqLUvHEEYeSq+bRZyhR0vEmCTIF2ANod/cDgV2BMIbyLQYWu/tTwevbyCVcEiPVPRIGJVgp1j4F+vUs/V4cyc2mMR9NSq6yxcx6mtnxZnaemZ2fX+KOS8L31T/EHYFIRWvcfQ3k6iV3XwCMaLRQd38dWGRm+bIOBp5vtFypnXqvpFmUYKXczH9LzpDB+WeWjqGr6kmMlFxFI+LP807gSHITZr5XsEjG3P1yx9c6byVhFptZH+AO4D4zuxNoD6nsM4EbzWwOMBb4fkjlSheo7pGwbBR3ABKO9inlk4yhU6OrNErFsdNPSydfYVFyFZ8mH1uD3H1C00oXEamBu08Kvr3AzB4EtgDuCans2UBbGGWJSHKoBytD2qfAv48r/V6cE2CsWte8sss9bFnJVSY8bmaj4w5CmksPFpY0cfeH3f0ud/8g7likMap7pJmUYGXM1/eqPgHGZ25qbgxRTnjxUY37l1TaD3jGzF4wszlmNjcYRiMiEhndDyoi9VKClVHtU6BtQOn3nl3W/N6sriRZZsarZ21YzKzi9qXKU3LVfBF+xocB2wOHAkcAE4OvkhG6wVxSQveDZkxx3aP7ZSRsOqYy7LfH5r5WujcL4k9Kbho5sq73Tni+8yRLQy712H+PVtWs+7DcPaybyCUldA5LQul+0Ix7WXWPhKxqgmVmW9ZQzjp3D+OZENIE+UZL1IlWqQkvChvjlRIraV1m9pi772dmKwEvfAtwd988ptBEpDU9bmaj3X1u3IFI49RzLlGopQfrtWCpNF6rOzAklIgSIsqZ96JSaaZByL3XC3ghxN+7ZJJ10T/5wU1lZuPooh9MGwlT5oVaZjlRVc5ZO/5q5e77BV97xx2LNI9uMJcU2Q842cxeAd5nw8WeXeINS8KgukeaoZYEa76771ppAzObFVI80mTVerPWBO9ttwVMPzncfb961oYc/YTg640771zxZ46f1zFpqjacsHj7rkjK1a1KcQzrDQ9/ObpYiukfkoi0kMPiDkDCkZT/75J9tSRYe4e0TeJtROlZ6bKoWqL18ju59zbrDs+f0fi+qlVqx8+bx/E1lHX8vHmcUGHyi3qSrDRXtAtXqgdA0ivN5560HndvN7O+5Cbd6VXwlu4TTTn935RmqZpgufuaMLZJg5eLEoEsDhMsVi3Rem9tOPdo/WDayPW9VoW60uPkvuG2nFI9WqWSrJdXwEHX172rqsI+PhppeGYp4bLcFJKD3H1R3LFI86X5WJXsM7OvAFOAQcBsYC/gCeCgOOOS+ujCjkSp5lkEzew6YEp+Movgas6P3D3GgUoSlmqJVuF79TaGmjmZxfHz5q0vv3B2wXwv15BLveTP1Srqhl+1/dXzD6Jw27Q1YN3dzexuQA8azhg1ciSFpgB7AE+6+4FmtiPw/Zhjkgal7f+ipEs907TvUjhToLuvMLOK92ZlwVf/AD+fGHcU0WlmotUshUlWVyXld6mmOM7fvwBn3FP951KabM00sz3c/a9xByLNk6LjUVrXGndfY2aYWU93X2BmI+IOSkSSq54Eq5uZ9XX3FbB++vbMPUer+H6hu1+OL5Y41ZNoFW5frDDxyU9oEcZEFMWq3ZtVKEsNuiNG5Ja8WnoHkpYgVzAOOMHM2sk91FMzd4lIHBabWR/gDuA+M1uB7r9KlSwNoZd0qCdB+hHwpJndSq6hcwxwUVOiSphWuBernFoSrcL3q03z3ozkqhat8vcr/D1r/Zsl+LP5dKMFmFkv4BGgJ7n67jZ3/7aZDQduAfoBzwAnuvsHZtYTuB7YHVgOfMHdFzYah+SokSNp5O6Tgm8vMLMHgS2AGsYOiEir6lbrhu5+PTAJeANYChzt7r9uVmBx0j/9ztqn1Pa55Kd5D4ZScMLzz69f4vCDaSNb9u+Z/5uFeV9XlNy9HegDHBEsfYJ19XgfOMjdxwBjgQlmthdwCfATd/8ksAI4Ndj+VGBFsP4nwXYiIgC4+8Pufpe7fxB3LFKbpP6Pk2yrOcEKrgRPAA4mN3POp4N1kTCzCWb2gpm9ZGbnRLXfPJ2gObU22ktpdu+Vu+Pu3LjzzusXyan2Nxs6NXnHuJlNAW4Etg6WG8zszHrK8Jx/Bi97BIuTq8NuC9ZfBxwVfH9k8Jrg/YODGQ2lQUk7vkSkNbXqRVeJVs0JFrlhMyOBy4DLgZ2BSHqwzKw78DNyD/vbGTjOzJraei51AqqB0FEjyZbEo5ZEK0FOBca5+/nufj65qZFPq7cQM+tuZrOBN4H7gJeBt909/9i7xcC2wffbAosAgvffITeMsLjMyWY2w8xmLFu2rN6QBNUbItJ8CfufJi2kngRrlLuf6u4PBstp5BKuKOwJvOTufw+65W8hd6U5cjpZS0tSshXXfV5pkpIky4C1Ba/XBuvq4u5r3X0suWfY7Ans2Ghg7j7N3dvcva1///6NFiciIhFIQhtFWkM9CdbM4N4FAMxsHDAj/JBKWn9VOVB4xblpyp2ICWqAJpIqsHSolBAn5Bi/BnjKzC4wswuAJ4Gru1pY8JiJB4G9gT5mlp/kZxCwJPh+CTAYIHh/C3KTXUgDNLmFpJmZtZnZ7WY208zmmNlcM5sTd1xS2fBk/B+TFlXPLIK7A4+b2avB6yHAC2Y2lwRMnWxmk4HJAEOGDAmt3OJp2/NaeWbBWrh7hynao+xVKr5l5gQz3Bt74HCWJfEYD+57+g3wELBfsPoUd59VZzn9gQ/d/W0z2wQ4hNzEFQ+Smwn1FuBLwJ3Bj9wVvH4ieH+66+ARaXU3At8A5gLrYo5FalT8h1KbTaJUTw/WBGA48H+CZXiwbiK5Gb6aaf1V5UDhFWeguUN2Kl3l//aDoe4qsxp9EHCjEtIjk1hJ68kKkpq73X2mu18WLHUlV4FtgAeDq81/Be5z9z8A3wTONrOXyN1jle8ZuxroF6w/G4h8Qp2s0bknGbAsmDnwFXdvzy9xByUiyVVzD1bMlclfge2DZ9csAY4Fjo8ygHJX+a+dk1t0ZST5ankwcisrd4zHaKaZ7eHuf+1qAe4+B9i1xPq/k7sfq3j9GuBzXd2fVKdzT1Lo22b2S+ABco9+AMDdfxdfSFKJhiVL3Kr2YJnZzDC2aUQwm9cZwL3AfOBWd498JoO0TXMt5eX/XvqbdZSw2TPHAU+Y2cu67yGddH5JRpxC8Bw9NjyXb2KsEYlIotXSg7VTlUaNkbsRvKnc/W7g7mbvp5p8A7RcwyG/XldLcvddFd4PdULwfTNvaenKUET9zToq1ZO18+Xw/BnRxRDcgzUZ0DCcDNE5Jim1h7uPiDsIqY0u7EgS1JJg1TKl8drqm2RLteFUarTHV8kVPmC4cHKNavFoCGF570V8hru7m9nP3H10tHsWEenkcTPb2d2fjzsQqZ/+n0scqiZYupGzvGq9WcXvZf0kP+tPcPuL8e2/Wu9V4edfa7KV9b9ZOQm5H6vhe7AkProHQjJkL2C2mb1C7h4sIwGzJ0tnCfi/JQLUN027lFFLolXq/bQ3OLpakd00cmTo07aXSq4q7aPev1na/1ZdUZxkxTBt+zjgi2a2EHgPNWpEJB4T4g5AuqYV/3dLMijBClH+RD72N/DEa9W3T1PCFfZVobCSrOJnXkHHIYLV1Nqr1cqJVow+HXcA0jW6iixZ4u7tZjYGGB+setTdn40zJhFJtpoTLDO7D/gPVSrV3VIwyfMOUwvmdK2iUqOk2Q37sBtE6+OdkpvQolQPUzN6svK6Um49Qz6VaEXiVeAE4BPufqGZDQE+jia+SB2dL5JmZjYFOA3IT8t+g5lNc/efxhiWFEnTRWvJvnp6sL4JXBoM1znP3Zc2J6RsebHoBO9qIpP0K8LVKrLj583rlGSd8Pzz62cWzKtnhsFy91ydO3leQw9JU6K1QczDBK8A1gEHARcCK4HfAntEFoGICJwKjHP39wDM7BLgCUAJloiUVM+DhmcCB5rZZ4F7zOx3wH+7++qmRZdBxY3TU++E+xfGEkqXdbWBXSrJqqarQwDDSASUaMVunLvvZmazANx9hZltHHdQUpmuIksGGR1nS14brJOESPpFaGk9dd2DFTyb5gXg58D3gNPM7Fx3/3Uzgmu2JMzwd/WR5d+Lq8Jo5mdRLckqfK9ZwwfrVWuitf9g+PXR0cTUIj40s+6AA5hZf3I9WiIiUboGeMrMbg9eHwVcHWM8UoUu7Ejc6rkH6y/AcGAe8CRwMrAAmGJm4919clMibGFZrSCOnzePoRf9E77Vu+J2tfR25Xuzjp83r1MCFPZwtmqJ1iOLYplpr+linLL9MuB2YGszuwg4BvhWLJFITYqPk70HxhOHSFiCC8u/AR4C9gtWn+Lus0LcR3dgBrDE3SeGVW6rUO+VJFE9PViTgee9800yZ5rZ/BBjkowbOhX42MdCKauwl6tUItCMhKdaoqVhg+Fw9xvN7BngYHLDcY5yd9U1KVI44Y9IGgUPPb87eOj5zCbtZgowH9i8SeW3FP3vlSSo5x6sSuO1/iWEWKQFFCYlQy7N5eo9gJeCCrGWXqu0DB3MYm9W1Nx9Abmeckk4XUWWDGvaQ8/NbBC5NtRFwNlhl591qnckqUJ5Dpa7/z2McuKkxnDzlasIXyr43IuTp+Op7165qHqxqu2zcN/5bURaiY55yZBxwAlm1k74Dz2/FPhPoOyYeTObTG4UEUOGDAlhl9mlekeSolvcAUhrKJeANKMyLFVms69ytU+p/LvoKptkmY5vySIzy0/gdSWwHblHRhwBTAy+Nlr+ROBNd3+m0nbuPs3d29y9rX///o3uNjN2UL0jCaYES5ouyuQqb0Tf5pVdSfuU8jf2D52qhqi0Bl1FlozY3cwGAqcA75J7Fl/h0qh9gc8Ezxe9BTjIzG4IodyW8H7Ra9U7kiQtnWAVn4xq/IYvjuQK4M8n1R5L2G75nHqzwmA5XzSz84PXQ8xsz7jjEpGWcSXwALAj8EzRMqPRwt39XHcf5O7DgGOB6e7+xUbLFZH4tXSCJc0VV3JVaT9RJjftU2BYmVH1Q6fCyyuiiyWlrgD2Bo4LXq8EfhZfOFKKHiwsWeXul7n7TsCv3P0T7j68YPlE3PG1MtU7knShTHIhUizu5CopHv5y7mupz+Og63NfW+0zqcM4d9/NzGYBuPsKM9s47qBEpLW4+1cj2MdD5J61JSIZ0PI9WBomGL4wk6tG/x5x92JViiNPx1xZHwYP4HQAM+sPrIs3JCmkY1dEoqZ6R9Kg5RMsCVcSe66S0kNUaaZB/cMo6TLgdmBrM7sIeAz4frwhSSVJOddEpHWo3pEkUoIloUliclVOnAmNkqzauPuN5J4P8wNgKXCUu/8m3qgkT8eriERN9Y6khRIsNEwwDFEkV42UlbQkr1KS9cuKT0RpHWZ2NrDS3X/m7pe7+/y4Y5LyknaOiYTFzHqa2fFmdp6ZnZ9f4o5LVO9IcinBkoalqeeqUNyJdLnP57uPwdifRxtLJTF+Tr2BP5vZo2Z2hpkNiC0S6SDuc0ckYncCRwIfAe8VLBIh1TuSJkqwytCJXJs0JVdJjWm7LTqvX/EBDGvxY9Ddv+PuI4GvAdsAD5vZ/fWUYWaDzexBM3vezOaZ2ZRg/ZZmdp+Z/S342jdYb2Z2mZm9ZGZzzGy30H+xDEriuSUSokHu/gV3/293/1F+iTuoVqd6R5JMCVZAJ2p9drsyXclVOUlIpKefXPozc5IRXwK8CbwOLAe2rvNnPwL+3d13BvYCvmZmOwPnAA+4+/bkHiR6TrD9YcD2wTIZSFBfYjLomJQW9LiZjY47iFamekfSRglWBTqhSxs6FZa/X/q9MJOr4U34/JOc/JWL7RMJOg6j/PzM7HQzewiYDvQDTnP3Xeopw92XuvvM4PuVwHxgW3LDfa4LNrsOOCr4/kjges95EuhjZts0/MtkWJLPKZGQ7Ac8Y2YvBD3bc81sTtxBtTLVO5J0SrAK6IStrlzS2ZPwP7+oHniUpES61Ge4Ftj7qshDSYJBwBR339ndL3D35xspzMyGAbsCTwED3H1p8NbrQP7+rm2BRQU/tjhYV1zWZDObYWYzli1b1khYqZKkc0UkQvme7UOBI4CJwVeJgOodSSMlWFXoxN6g0pDAF1OUnCY9kS4V32uroo8D4jn+zeyx4NuvA4+a2bvBstLM3u1imR8Dfguc5e4dynB3J3iYca3cfZq7t7l7W//+/bsSUiYk/VwSCYO7twN9yCVVRwB9gnUSA9U7kgZKsIqUOnFfXhF9HEkydGr891s1ez9JS6RL/b5Ji7FZ3H2/4OvH3H3zgqW3u29eb3lm1oNccnWju/8uWP1Gfuhf8PXNYP0SYHDBjw8K1rW8JM1sKRKlYHKcG8ndA7o1cIOZnRlvVK2hVf7vSfYowarBQdfHHUF8KlVuab6KlIbYk5Zk9e8V7f7M7JJa1lUpw4Crgfnu/uOCt+4CvhR8/yVy0zDn158UzCa4F/BOwVDClrbig46v03AOiYTkVGCcu5/v7ueTmzDntJhjakmqdyQtNoo7gCRqn9K5ITt0amud2COmwpoy7x2+Hfx8YnP33+pTlOfd/nmYdGvHdVEdi8XnwIx/bf4+ixwCfLNo3WEl1lWyL3AiMNfMZgfrzgMuBm41s1OBduDzwXt3A4cDLwGrgFO6Fnq2HH5D3BGIxMrI3Q6btzZYl0g3jRwZdwihOPf4p2DTTaFbN1i3Dlat4qaR4+IOS1rI8fPmdflnlWCVUSrJahVJ6LWq64aYLir+Gycxid6tzPx1SYw1LGb2VeB04BNFM3X1Bh6vpyx3f4zyDaGDS2zv5J67JQXmLe/4OqvHnkgZ1wBPmdntweujyPWMSzPlk6vAD25SciXpoQSrDllu1EJuWvRKM/dl+XdPshZM9m8C/gT8gA3PpwJY6e5vxRNS62qxY0+kE3f/sZk9TK5HHOAUd58VZ0xZd+7xT214EfReiaSJ7sGqIGn3wDTT0ArJVfuUeJOrZu778O06vk7q3zfqY7G47Cj//u7+jrsvdPfjgHfJTaE+FBhlZvtHF4mUogst0orc/Rl3vyxYlFw1m3qvJOWUYFUx/aTO65LaCO+KSjMEQjyNqV2uiG5fzb6XLEytlPADmNlXgEeAe4HvBF8viDOmVpPl40ukmvwjI/KPiAjjkRFSnXqvJAs0RLCK7fqWXp/24YI7TIX3K7wf5+/2zofx7TvpegDN/ngS1KieAuwBPOnuB5rZjsD3Y46ppaW5zhOpV8EjI3rHHUtLUe+VZIB6sGpQrlGRoIZoXYYmOLmKQ/Hvm+S/60sx9GLFeDyscfc1AGbW090XACNii6bFJPk8EIlSGI+MkNqo90qyQglWjbKQZNUyHDBpyVXS4kmCFvpMFptZH+AO4D4zu5PclOoSgxY67kSKHVJi3WGRR9EK1HslGaEhgnUoN5tb0ocLVksCkxR7mhLWJAnrGIxzcoti7j4p+PYCM3sQ2AK4J76IWofOQ5FwHxkh1an3SrIk8T1YZnaBmS0xs9nBcnic8aSlJ+uyJ6v3WM2anKzkKk5pGiYIrfd3c/eH3f0ud/8g7lhaUasdbyKBm4AjgLuCr/lld3c/Ic7AsqbDQ4UD6r2SNEtLD9ZP3P2HcQeRl+SerFoSgxF94c8lZkeUdPvMTXDX8V3/+f1+2fH1sTs3Fk9XmdlKNjxrOv+QYA++d3ffPJbAWkTSLy6IRMXd3wHeAY4zs77A9kAvADPD3R+JM75MKUyu1HslGZCWBCtxKiVZ+fejVEujaEz/xhrgzZak4WlpUHwMPrussfIWvdfx9SWl7jqIgGbsShadh9LqgkdGTAEGAbOBvYAngIPijCsrSg0NVO+VpF3ihwgGzjCzOWb2q+AqUidmNtnMZpjZjGXLGmxp1qhSw6Pa8Lww5PdRyz1W7VOSnVwlQas3JAdvFncEHVnOF83sv4LXg81sz7jjyjL1XomUlH9kRLu7HwjsCrwdb0gZoqGBkkGJ6MEys/uBj5d46/8BPwe+S26I0HeBHwFfLt7Q3acB0wDa2tq8+P1maZ8Cv38Bzihz632YPVpn/Qluf7G+2KTrkjDks5pyPald8dhXwiknRFcA68hdJf4u8E/gZ+QaOhKBpB//IhFZ4+5rzGz9IyPMTI+MCIEmtpCsSkSC5e6fqmU7M7sK+EOTw6nbESNyS6WGbleGv3Wl4TxgE3h6cv0/FzcND5QSxrn7bmY2C8DdV5jZxnEHlVXqvRIpq/iRESvQIyPCod4ryahEJFiVmNk27r40eDkJeC7OeCrJJwW1NFTCbswoIQnHybvAtXOqb5dUaeh1q8OHZtadYMILM+tPrkdLQlaqPsrQcSTSED0yojnUeyVZlvgEC/hvMxtLrpG1EPjXeMOprp5Eq9F9ZMH2Cbpy/p0D051gZcxlwO3A1mZ2EXAM8K14Q2oN+w+OOwKRZHL3h+OOIQs0LbtkXeITLHc/Me4YuirMZytlKaEqVvxgoyT9rmnoEdqiB7zzYdxRhMvMDHgEeAY4mNwU7Ue5+/xYA8ugUvXSr4+OPg6RpDGzx9x9v6JHR4AeGdG4omnZey9/Jd54REKW+AQrS5LeUJd0mnN69u6fcXc3kFVDLwAAFc5JREFUs7vdfTSwIO54WonqKZEcd98v+NqUR0eY2WDgemAAuQRumrtnrDbvrNTQwPNu/0x8AYk0QVqmaZeM0uQWUsFMM9OMgU2UtcRcpBnM7GwzG9iEoj8C/t3ddyb3bK2vmVlMj3iPhoYGSqtQgiVSJG1JXoYbyeOAJ8zs5eA5eHPNTHfIhUQTW4jUrDe52QMfNbMzzGxAGIW6+1J3nxl8vxKYD2wbRtmJVTQ0UBNbSFZpiKDEpriB1zOeMCS5Ph13ACIi7v4d4DtmtgvwBeBhM1tc6yNmamFmw8g9wPipylumV6mhgeq9kqxSgiWJ8aKunksBd9dzZppEvVciXfIm8DqwHNg6rELN7GPAb4Gz3P3dEu9PBiYDDBkyJKzdRkpDA6XVaIigxCJNw9rSFKsayVKNkiuR+pjZ6Wb2EPAA0A84zd13CansHuSSqxvd/XeltnH3ae7e5u5t/fv3D2O30dPQQGkx6sGSRFADr2uS9AwxEZGMGkyud2l2mIUGj6O4Gpjv7j8Os+wk0dBAaUXqwZLIpalHKOmKnyEmHZnZr8zsTTN7rmDdlmZ2n5n9LfjaN1hvZnaZmb0UTKqxW3yRN4d6r0S65P8Bo8zsvwDMbIiZ7RlCufsCJwIHmdnsYDk8hHITQ0MDpVUpwZLYJbGBl8SYpEuuBSYUrTsHeMDdtyc35OecYP1hwPbBMhn4eUQxRkLJlUiX/QzYGzg+eL0yWNcQd3/M3c3dd3H3scFyd6PlJoqGBkqLUoIlkVLvVXj0DLHq3P0R4K2i1UcC1wXfXwccVbD+es95EuhjZttEE6mIJNg4d/8asAbA3VcAG8cbUvJpaKC0MiVYEpn9ftl5nZICicEAd18afP86kH+mzbbAooLtFpORZ9Ko90qkIR+aWXfAAcysP7Au3pCSTUMDpdUpwZLILHqv4+se8YSRCeoJDIe7O0GjqR5mNtnMZpjZjGXLljUhsvAouRJp2GXA7cAAM7sIeAz4frwhJZyGBkqL0yyCEolSjbyX1MgLjRrMdXnDzLZx96XBEMA3g/VLyM0WljcoWNeJu08DpgG0tbXVnaBFRYm4SOPc/UYzewY4OFh1lLvPjzOmJNPQQBElWBKBt1Z3XqeEoOvUaG7YXcCXgIuDr3cWrD/DzG4BxgHvFAwlzAydeyK1MbOzy7x1mJkdluWp1btKQwNFcpRgRahVJyXYdVrcEWSHhnvVx8xuBg4AtjKzxcC3ySVWt5rZqUA78Plg87uBw4GXgFXAKZEHHCIdKyIN6x18HQHsQe4iDMARwNOxRJRgnZKrdevY7K32eIMSiYkSrBgNnZr9Bo8aeeEp9Vm2Dei8TjZw9+PKvHVw8YrgfqyvNTeiaOi8E2mcu38HwMweAXZz95XB6wuAP8YYWuKUSq5YtYpv/W5ivIGJxESTXMRs6FT4/QtxR9EcauQ132+PjTsCSRoNIRUJ3QA6Ptf9AzbMPipQMrnS0EBpZUqwIlQuuTjjHjWKpDIlq9IIHSsiDbkeeNrMLgh6r54i9xDzZDKLdHea1EKkMyVYEavU0MlSkpX2hCBJf4u0f5YSHR0rIuFz94vI3ZO5IlhOcfcfxBtVMmhSC5HSdA9WDPINnlKNofy6NDeK1MgLT5ISPUk2nXcizePuM4GZccdRE4/myRHl7rsSEfVgxSqLvVml4r7uyOjjyIJyx4AazVIsrfWFiKRTueRKvVciOUqwYlYtyUpTw6lcrAcMizSM0MWR0Ci5klrpWBGJhpkdEXcMSaDkSqQ6JVgJ0D6leqL1iYQnWllq5MWd1Gbps5Tm0rEiEqmL4g6gJk2c5ELJlUhtlGAlSKVG0Vrib/iXo0ZeePRZSqN0rIg0TbTT83VVM2cRVHIlUhNNcpEwlSbAKFyfhEZUpYQvCfGlyfCpsK7Me/ospRRNaiESuWhmj2hUkya50HTsIrVTgpVQtSZafTeG2V+NJqZS+y8lzY284t8rit8lq5+lNI+SKxEpqwkJlqZjF6mPhggmXPuUylnwig+inQyj0r72HqhGXr2UXEm9kjpUWEQSIuQhgqXuu+r35vxQ9yGSNerBSoGXq/Rm5RW+H3bjvNq+s5AMRNlwHTEV1pR5LwufpTSH7tETidUbcQdQE7PQerHKTWrxH3d9PpTyRbJKCVaKFDaioki2Kt0XVCirjbtm/V7qtZKuUHIlEi93PyTuGGrS5ORKQwNFqlOClVLV7tEqVGvDrN4enCw17KLovdp+KnxQ4f0sfZ4SLiVXIlKzEBIsJVcijVGClXL19GoV62pSkbVGXRQTBqjXSrpKyZWI1KWBIYLrEytQciXSACVYGdJIslVP2VnS7J6rVrh3TZpHyZVI/MzsPuA/3P3ZuGOpSRcTrFIzBSq5EukaJVgZVa4BVmtC0QoNuGY2XpVYSaOUXIkkxjeBS81sIXCeuy+NOZ7Kwkiu1uXuwO7/xnOc/fvjwoxOpCUowWoxapzlKLmSJFNyJZIc7j4TONDMPgvcY2a/A/7b3VfHHFppdSZYut9KJHxKsKTlxNV4VeNYqtG9eiLJZGYGvAD8HPgecJqZnevuv443shLqGCKo5EqkOZRgScuIq/GqhrHUQsmVSDKZ2V+A4cA84EngZGABMMXMxrv75BjD66zGBEvJlUjzKMGSlhBl41WNYalXueNz74Fwy+eijUVEOpkMPO/eKWs508zmxxFQRVWSK80UKNJ83apv0nxm9jkzm2dm68ysrei9c83sJTN7wcw+HVeMkk5Dp6pnQJKt0pBVJVciiTC5RHKV9y+RRtKgDr1WSq5EmiYpPVjPAUcDvyhcaWY7A8cCI4GBwP1mtoO7r40+REmLWmZKVGIlcZu5FCbdWvo9HZ8iibLSzH4PHOvu7wUXe893933d/e9xB9dJjx7wQefH2pebKVDJlUj4EpFguft8gNw9pB0cCdzi7u8Dr5jZS8CewBPRRihJV+v081/dDc4Z39xYRKpRr6pIerj7t8zs+P/f3r3HylHWYRz/PiAQsRVaUawFLBACEoi1NqgEiSKWS6ItUWOjERQCGsGoWCOmXhoUgzeMGC+pWsALooDVk8rFotQKIpZLr9RCgRpoKogVpBaBys8/5j04HM9uz+7O7ry75/kkmzM7Ozv7zDuzv7PvzmWBZZKeArYB59Ucq7EdO55z14cEmvVeFh2sJqZSnFA67ME07v9IOoviOGkOOOCA7iez2rXyI8HuWNlYSToR+DqwK/C9iLiwyvm7c2XWXyS9CTgT+BcwBTg9IjZUNO/q683wnin848FmdelZB0vSDcBLR3lofkT8stP5R8RCYCHAzJkzW/+VPctWKx2pkfyB1VohaVfgm8CbKb7QWSFpKCLu6nTe7liZ9a35wKcj4iZJRwI/lXRuRPy2k5l2rd7ssgufnHvL6HutwJ0rsx7oWQcrIo5v42mbgf1L9/dL4yxjnXSIquAPq9aBo4CNw+dVSLqC4lDljj7wvPyCbU2/Rb58YSdzNxs/3rVuXc9fMyKOKw2vkXQScDVwdIez7kq9ebZz5b1WZrXJ/RDBIeBySRdRXOTiEOBP9Uay3EybCL87ve4UNiCmAg+U7j8I/N8nkpYPSfaJ5WYDIyK2pMMGO+V6YzagsuhgSToF+AbwYuBXklZGxAkRsU7Szyi+zdkBnO0rCI5v3jtlOWj5kOTt2/93uI4/6Jj1vYh4ooev1Vq92WU7PON6Y9aJqbNmdfT8LDpYEbEYWNzgsQuAC3qbyDrhTpD1sa4clvyX+RNK9ybA/N4f5mRm2elOvfmw641Z3bL4oWEzs0ysAA6RdKCk3Sl+h2+o5kxmNphcb8wGVBZ7sMzMchAROySdA1xPcdnkRRHhr3/NrHKuN2aDyx0sM7OSiLgGuKbuHGY2+FxvzAaTDxE0MzMzMzOriDtYZmZmZmZmFXEHy8zMzMzMrCLuYJmZmZmZmVVEETv/zbp+I+lvwF/aeOo+wCMVx6nDoCwHDM6yjPfleHlEvLjqMDlood7kvA3kms25Wpdrtl7mcr3JdzuAfLM5V2tyzQUZ1JuB7GC1S9JtETGz7hydGpTlgMFZFi+H5dx2uWZzrtblmi3XXIMq5/bONZtztSbXXJBHNh8iaGZmZmZmVhF3sMzMzMzMzCriDtZzLaw7QEUGZTlgcJbFy2E5t12u2ZyrdblmyzXXoMq5vXPN5lytyTUXZJDN52CZmZmZmZlVxHuwzMzMzMzMKuIOlpmZmZmZWUXcwQIkvUPSOknPSJo54rFPStooaYOkE+rK2CpJCyRtlrQy3U6uO1MrJJ2Y2nyjpPPqztMuSZskrUnr4La687RC0iJJD0taWxo3WdJSSfekv5PqzNgv6t6eR9sOG61LFS5OWVdLmlFxljFvV82ySDotTX+PpNO6lKthHW30v6HqdS1pf0k3Sror/Z/6cBpfa5s1yVV7m413dbdnLvUm11rTJFvt7x3XmwpFxLi/Aa8ADgWWATNL4w8HVgF7AAcC9wK71p13jMu0AJhXd442s++a2vogYPe0Dg6vO1eby7IJ2KfuHG1mPxaYAawtjfsScF4aPg/4Yt05c7/lsD2Pth02WpfAycC1gIDXArfWtV01ygJMBu5Lfyel4UldyDVqHW30v6Eb6xqYAsxIwxOBu9Pr19pmTXLV3mbj+ZZDe+ZSb3KtNU2y1f7ecb2p7uY9WEBErI+IDaM8NBu4IiKejIj7gY3AUb1NNy4dBWyMiPsi4ingCop1YT0UEcuBrSNGzwYuS8OXAXN6Gqo/5bo9N1qXs4EfROGPwN6SplT1oi1uV42ynAAsjYitEfEPYClwYhdyNdLof0Pl6zoitkTEHWn4cWA9MJWa26xJrkZ61mbjXK7t2fN6k2utaZKtEdebPqw37mA1NxV4oHT/QZqv0Nyck3bZLlJ/HcrV7+1eFsCvJd0u6ay6w1Rg34jYkob/CuxbZ5g+kcP2PNp22Ghd1pG31Sy9zDhaHa0ll6RpwKuAW8mozUbkgozabBzKoT1zrjfZvG8ayOa943rTmXHTwZJ0g6S1o9xy+GanLTtZpm8DBwPTgS3AV2sNO34dExEzgJOAsyUdW3egqkSxH96/89Afmm6HOa3LnLKQUR2VNAG4GvhIRPyz/FidbTZKrmzazGrTF/Umlxwl2bx3XG8697y6A/RKRBzfxtM2A/uX7u+XxmVhrMsk6bvAki7HqVLW7d6KiNic/j4saTHF7unl9abqyEOSpkTElnQYwMN1B+oDtW/PDbbDRuuyjrytZtkMvGHE+GVVh4qIh4aHR9TRZm1UedtJ2o3iQ8WPI+LnaXTtbTZarlzabBxzvWmu9vdNI7m8d1xvqjFu9mC1aQiYK2kPSQcChwB/qjnTmIw4hvkUYG2jaTO0AjhE0oGSdgfmUqyLviLpBZImDg8Ds+iv9TCaIWD4akCnAb+sMUu/qHV7brIdNlqXQ8Cp6epQrwUeKx0a0i2tZrkemCVpUjokZFYaV6kmdbTR/4bK17UkAd8H1kfERaWHam2zRrlyaLNxzvWmuSxrDeTx3nG9qVB04coZ/XZLK+VB4EngIeD60mPzKa44sgE4qe6sLSzTD4E1wOq08UypO1OL+U+muErMvcD8uvO0uQwHUVyhZhWwrt+WA/gJxS73p9P74wzgRcBvgHuAG4DJdefsh1ud23Oj7bDRuqS4GtQ3U9Y1lK6s2uvtqlkW4HSKE5c3Au/rUq6GdbTR/4aq1zVwDMXhOKuBlel2ct1t1iRX7W023m+uN89mybLWNMlW+3vH9aa6m9KLmZmZmZmZWYd8iKCZmZmZmVlF3MEyMzMzMzOriDtYZmZmZmZmFXEHy8zMzMzMrCLuYJmZmZmZmVXEHSwzM8uCpAWS5qXh8yU1/DF1SXMkHd67dM957QWSNks6v3R/XgXzvVHSNkkzO09pZs243rjedJM7WGZmlp2I+ExE3NBkkjlALR94kq9FxGeqnGFEvBG4rcp5mtnOud5Y1dzBsixImibpCUkr23z+8yWtlPSUpH2qzmdm3SFpvqS7Jd0EHFoaf6mkt6fhCyXdJWm1pK9IOhp4K/Dl9L4/WNKZklZIWiXpakl7luZzsaQ/SLpveJ7psU9IWpOec2Ead7Ck6yTdLun3kg5rcXnOlHRtqknLJH09ZVwr6ag0zQRJl6TXXi3pbR03pJntlOuN602vPK/uAGYl90bE9HaeGBFPANMlbao2kpl1i6RXA3OB6RT/j+4Abh8xzYuAU4DDIiIk7R0Rj0oaApZExFVpukcj4rtp+PPAGcA30mymAMcAhwFDwFWSTgJmA6+JiO2SJqdpFwIfiIh7JL0G+BZw3BiX5xzgzcCciHhSEsCeETFd0rHAIuAI4NPAYxFxZHrepBaazcza4HrjetNL7mBZT0i6EfhCRCxNxWiviPhQk+mnAddRFL8ZwDrg1FSYTgXmAQGsjoj3dDu/mXXF64HFEbEdIH2IGekx4N/A9yUtAZY0mNcRqbbsDUwAri899ouIeAa4S9K+adzxwCXDrx0RWyVNAI4GrkwfVgD2GOOynAo8QPFh5+nS+J+k+S+X9EJJe6fXnjs8QUT8Y4yvYWbtc73B9aZX3MGyXvkscL6klwCvotjdvjOHAmdExM2SFgEflHQt8Cng6Ih4pPQtkJkNoIjYkQ51eRPwduAcRv+G91KKDxurJL0XeEPpsSdLw6KxXYBH29yTvobim/H9gPtL42PEdCPvm1kmXG+sKj4Hy3oiIpZTFJpzgbkR8Z8xPO2BiLg5Df+IYpf7ccCVEfFImu/WbuQ1s55YDsxJ5w9MBN4ycoL0Le9eEXEN8FHglemhx4GJpUknAlsk7Qa8ewyvvRR4X+ncickR8U/gfknvSOMk6ZXNZlJyJ/B+YEjSy0rj35nmdQzFYTqPpdc+u7SMPmTHrPtcb3C96RV3sKwnJB1JcVzyUxHx+Bif5m9izAZYRNwB/BRYBVwLrBhlsonAEkmrgZsovqQBuAL4uKQ7JR1McZ7BrcDNwJ/H8NrXUZwfcZuKi+sMX/b43cAZklZRHJo8u4XluSnN51f638V2/i3pTuA7FOdpAHwemJRORF8FvHGsr2Fm7XG9cb3pJUX4M6t1l6QpFMcnvxO4GPhqKjblaaZRnEB6ROn+/RSHAt4i6XvAeorzshYDr4uIv6dvgbaW5rMJmDm8h8vMrGqSFgDbIuIrO5luGTAvIsZ8KeR2nmNmg8v1pj95D5Z1Vdod/nPgYxGxHvgcxflYY7EBOFvSemAS8O2IWAdcAPwufRNzURdim5k1sw04S+mHP6uSLgZ0EPD0zqY1s3HD9aYPeQ+WZaHBHqxn77cwn014D5aZmZmZ1cR7sCwX/wH2Uoc/NAzsBjxTaTIzMzMzszHyHiwzMzMzM7OKeA+WmZmZmZlZRdzBMjMzMzMzq4g7WGZmZmZmZhVxB8vMzMzMzKwi7mCZmZmZmZlVxB0sMzMzMzOziriDZWZmZmZmVpH/AjBUdSbr4Dg4AAAAAElFTkSuQmCC\n", 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", 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" ] @@ -447,27 +424,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.7557637691497803s.\n", - "Simulation time with module BP is 2.5037429332733154s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:58: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + "Simulation time with module CK is 1.2719593048095703s.\n", + "Simulation time with module BP is 1.1178076267242432s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -512,20 +482,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def plot_subplots_para(ax1, ax2, ax3, data, z_ana, module, color):\n", " ax1.plot(data.X,data.Z, markersize=0.01, marker='o',color = color, label=module)\n", - " ax1.legend(markerscale=5)\n", + " ax1.legend(markerscale=5, loc='upper left')\n", " \n", " ax2.scatter(data.D,data.Z,s=1, color = color, label=module)\n", - " ax2.legend(markerscale=5)\n", + " ax2.legend(markerscale=5, loc='upper left')\n", " \n", " # compare with analytical solution\n", " ax3.scatter(data.D, (data.D-z_ana),s=1, color = color, label=module)\n", - " ax3.legend(markerscale=5)\n", + " ax3.legend(markerscale=5, loc='upper left')\n", " \n", "def plot_figure_para(max_trajectory, p_z, r_g_0, number_of_steps):\n", " fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3,figsize=(12,4))\n", @@ -566,20 +536,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.907780170440674s.\n", - "Simulation time with module BP is 2.5608789920806885s.\n" + "Simulation time with module CK is 1.6537957191467285s.\n", + "Simulation time with module BP is 1.4773030281066895s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -599,29 +569,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.9700050354003906s.\n", - "Simulation time with module BP is 2.6082756519317627s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" + "Simulation time with module CK is 1.6059746742248535s.\n", + "Simulation time with module BP is 1.377190113067627s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -710,13 +671,13 @@ "source": [ "def plot_subplots_momentum(ax1, ax2, ax3, data, module, color): \n", " ax1.scatter(data.D,((data.Px**2+data.Py**2)**0.5),s=1, color = color, label=module)\n", - " ax1.legend(markerscale=5)\n", + " ax1.legend(markerscale=5, loc='upper left')\n", " \n", " ax2.scatter(data.D,data.Pz,s=1, color = color, label=module)\n", - " ax2.legend(markerscale=5)\n", + " ax2.legend(markerscale=5, loc='upper left')\n", " \n", " ax3.scatter(data.D,(data.Pz-p_z)/p_z*100,s=1, color = color, label=module)\n", - " ax3.legend(markerscale=5)\n", + " ax3.legend(markerscale=5, loc='upper left')\n", " \n", "def plot_figure_momentum(max_trajectory, p_z, r_g_0, number_of_steps):\n", " fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3,figsize=(12,4))\n", @@ -770,13 +731,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.8336169719696045s.\n", - "Simulation time with module BP is 2.5191328525543213s.\n" + "Simulation time with module CK is 1.3243069648742676s.\n", + "Simulation time with module BP is 1.2978119850158691s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -810,13 +771,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 3.4364242553710938s.\n", - "Simulation time with module BP is 2.7046010494232178s.\n" + "Simulation time with module CK is 1.471672773361206s.\n", + "Simulation time with module BP is 1.2299976348876953s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -850,13 +811,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.9141812324523926s.\n", - "Simulation time with module BP is 2.4687178134918213s.\n" + "Simulation time with module CK is 1.3036754131317139s.\n", + "Simulation time with module BP is 1.126857042312622s.\n" ] }, { "data": { - "image/png": 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\n", 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mj07OuRo8AETyeNgeyfxaSOK6667jxhtvZOzYsUyYMIFzzz13u3OuxowZw/z58/nEJz7BXXfdVdsOW4RHrSwLkjqAK4GpwHjgFEnju6z2L8D3IuLNwIXAxY2Nspjc6DGzRvH/dGtmvshFDzr6JRe0WLQyOSxwwvCkuKrlAhed9tprr7LnWS1duvS154ceeihr1qypfWctwKNWlqEjgRUR8TiApLnANOChknXGA2enz28Brm9kgEXmC1+YVU/SnwEXAAcBR0bE4pJl5wKfBLYCn4kIH7qSKpdvnGusGXgEqxcd/eDd+8FnjkwesyiurHI9HRLoUSvro72BJ0ter07nlXoA+ED6fDowRNKwrhuSNFPSYkmL161bV5dgi8jfO7OqLSXJKb8unZmOns8AJgBTgKvSUXZLlcs3HjW3onO5YIXlUSvL0ReAd0m6D3gXsIakd3k7ETE7IiZFxKQRI0Y0OsZcudFjVrmIeDgiHimzaBowNyJeiYgngBUko+zWC+cbK7K2LLAiIu8QatLs8VfCxZXV0Rpgn5LXI9N5r4mI/42ID0TEYcB56bwXGhZhk3CRZVazSkbUgfYdMQef/2nNp+0KrIEDB7J+/fqmLVIigvXr1zNw4MC8Q6kbHxJodXY3ME7SGEk7kRyeM790BUnDJXXmx3OBaxocY1Nzo8fakaQbJS0tM03LYvvtPGIO/v9vzaXtLnIxcuRIVq9eTTP3/gwcOJCRI1vzqtE9FVdmWYiILZJmAQuBDuCaiFgm6UJgcUTMByYDF0sKknMmzswt4ILr7qIXZu0mIo7vw9t6HVG31/miF9Ys2q7AGjBgAGPGjMk7DCvDxZU1SkQsABZ0mXd+yfMfAz9udFzNyo0esz6bD/xA0qXAXsA4wPdm6YHzjTWDtjtE0IrJxZVZc/P5WGbdkzRd0mrgaOBnkhYCRMQyYB7JbSJuAM6MiB0uqGNmzcUFluXOxZWZmbWyiLguIkZGxM4R8aaIOKFk2UURMTYiDoiIn+cZZ7Nwh44VnQssKyQXV2bNx40eM2sU5xsrMhdYlqtyydDFlVnzcqPHzMzanQssy42LKzMzM+srd+hYUbnAslw4AZq1Ljd6zKxRnG+siApTYEmaIukRSSsknVNm+ShJt0i6T9ISSSfmEafVzhe1MGt9/j6bmVm7KkSBJakDuBKYCowHTpE0vstqXwLmRcRhwAzgqsZGafXkxphZ63OvspnVg0exrGgKUWABRwIrIuLxiHgVmAtM67JOAG9Mn/8R8L8NjM8y4vOuzNqHGz1m1ihuS1iRFKXA2ht4suT16nReqQuAj6Q36lsAfLoxoVlWXFyZmZlZo7hDx/JSlAKrEqcA34mIkcCJwLWSdohf0kxJiyUtXrduXcODNDOz13kUy8waxZ22VhRFKbDWAPuUvB6Zziv1SWAeQETcDgwEhnfdUETMjohJETFpxIgRdQrXquXRK7P25e+6meXFHTqWh6IUWHcD4ySNkbQTyUUs5ndZ5/fAuwEkHURSYHmIqgm4uDKzrtzoMbN6cPvCiqAQBVZEbAFmAQuBh0muFrhM0oWSTkpX+zxwuqQHgB8CH4uIyCdiMzOrhhs9VlSSdqtg2jXvOK3v3KFjjdY/7wA6RcQCkotXlM47v+T5Q8DbGx2X1cajV2bWnX0vcz6wQvjfdFIP63QAoxoTjtVq1Vk7tj+cb6yRClNgWetxcWVmpco1eswK4OH0HpvdknRfo4KxbLxpEDy9Me8orF0V4hBBMzNrTy64rACOzmgdK5C7Zu44z/nGGsUFltWFR6+syCRNkfSIpBWSzimzfJSkWyTdJ2mJpBPziLMVOQ9Y0UTEpizWseJxvrG8uMCyhnCSs6KQ1AFcCUwFxgOnSBrfZbUvkVxs5zCSq5pe1dgo24t7la1IJL1V0g2SFkk6Oe94LFvON9YILrAsc05eVnBHAisi4vGIeBWYC0zrsk4Ab0yf/xHJCfCWEXe4WJFI2qPLrLOB6cCJwD9mtI+vSVqejohfV3pVQknnpqPpj0g6IYv92eucbywPLrCs7pzcrGD2Bp4seb06nVfqAuAjklaTXN300+U2JGmmpMWSFq9b59vy1cIdM5ajb0k6X9LA9PULwJ+SFFkvZbSPXwIHR8SbgUeBcwHS0fMZwARgCnBVOspuZk3MBZZlyo0kaxGnAN+JiJEkvdjXStohX0bE7IiYFBGTRowY0fAgm5k7XqwoIuJk4D7gp5JOAz4L7AwMA07OaB+/SO/5CXAHMDJ9Pg2YGxGvRMQTwAqSUXbLUNd847aK1ZsLLMvMfr6whTWHNcA+Ja9HpvNKfRKYBxARtwMDgeENia6NudFjeYmI/wecQHJI8HXAoxFxeUTUY2j6E8DP0+eVjKgDHjE3ayYusCwzW/MOwKwydwPjJI2RtBPJ4Tnzu6zze+DdAJIOIimw3KLJmDtgrAgknSTpFuAGYCnw58A0SXMlja1iOzdKWlpmmlayznnAFuD71cbpEfPajB6y/Wt36Fg9+UbDlglflt2aRURskTQLWAh0ANdExDJJFwKLI2I+8HlgjqTPkVzw4mMREflF3T72vcy5wxruKySH5Q0CFkbEkcDnJY0DLiLphOlVRBzf03JJHwPeB7y7JJ9UMqJuGfjVJ1xUWeO4wLK6cAPJiiwiFpBcvKJ03vklzx8C3t7ouNrRqrPc6LHcvQh8ABgMPNM5MyJ+R4XFVW8kTQH+FnhXRGwoWTQf+IGkS4G9gHHAXVns03rnDh2rFx8iaDVz48jMsuScYg02neSCFv2BU+u0j28CQ4BfSrpf0rcAImIZyfmeD5EconhmRPiI+zpxMWWN4hEsy5wTmJlVw6NYlqeIeBa4os77+OMell1Eciii5cCjWFYPHsGymrhRZGZmzUzSvVmsY83BxZQ1ggssy5QTl5n1he9TYzk6SNKSHqYH8W0aWprzjWXNhwhanzkhmZlZCziwgnV8XlQL8WHJVm8usCwzHr0ys1oMADaXvPa5EdYIEbEq7xgsf89thN0G5R2FtQofImhmZoWwwsWUmTVI186bw2bnE4e1JhdY1iddh9bdy2xm9eDDeMzMrNm4wDIzs8JwZ40VgaRrJQ1Kn++aczhWJ764jtWLCyyrmhOQmTWSc47lQMDVaZF1dt7BmFlzcYFlNXOPs5llyTnFCuAJ4ALgauAN+YZijeQOHcuCCyyrihOPmZm1gX+LiJUkRdaUfEOxenKHjtWDCyyriROTmdWDz42wRpM0VdKdkh4BLpV0dESsjIgJecdmjeV8Y7VygWUVc8IxM7MWdhXJ+VZvBWYDX5N0Sr4hWSO4s9iy5gLL+swJycwayZ08VmfPRMRvIuL5iLgROAE4L++gzKz5uMAyM7NCcieONdgTkr4iaaf09WZgS54BWeP4sGTLUp8LrNJjlSXNk3R0loFZsfjGwmZWBG70WB1tA6YDT0q6DVgBLJI0Lt+wzKzZ1DKC5WOVzcysrtyZY40SEaemF7QYBZxFcgVBAXMk/T7P2Cwf7tCxvupfw3ufiYjfpM9vlHQ7cCfww9rDsiLpmmD2GpxPHGZZkTQFuAzoAL4dEZd0Wf4N4Nj05WBg94jYtaFBmlkuIuIV4J50sjay6iwXVZaNWkawfKxym7r99LwjMOs7SR3AlcBUYDxwiqTxpetExOciYmJETASuAP6n4YHaa3xuhDU7Sf8oaYmk+yX9QtJe6XxJulzSinT54XnHatu7/I68I7BmVEuB5WOV24AbMtaCjgRWRMTjEfEqMBeY1sP6p+CReTOrzdci4s1pp81PgfPT+VOBcek0E7g6n/CsU9cOna/fmU8c1tyqLrAkHS1JPla5Pfl8CGsBewNPlrxenc7bgaR9gTHAzd0snylpsaTF69atyzxQ6547f6yZRMRLJS/fAET6fBrwvUjcAewqac+GB2hmmerLOVinAVdKehS4AbghInysspm1ohnAjyNia7mFETGb5CI/TJo0KcqtY9nwuRFWb5J2Bj4IjKakfRQRF2a0/YtI2lAv8vo5nt11+KzNYp/WN13zzb6XuYPZqtPjCJakHRoVEfGpiDicZMRqKPAdSbdL+idJ70zPb7AW4EuzW4taA+xT8npkOq+cGfjwwMI68T/zjsBazE9IRpS2AP9XMlVE0o2SlpaZpgFExHkRsQ/wfWBWtcF5xNysefQ2gqXuFkTEcmA58A1Jg0h6Y/4MuBSYlFmEZmbZuhsYJ2kMSWE1Azi160qSDiTpRLq9seFZd7r2Ki9bn18s1pJGRsSUvr45Io6vcNXvAwuAv6eKDh+PmOfLo1hWjd7OwaroCxwRGyNiQUR8OiJcXLUAH4pjrSoitpD0Hi8EHgbmRcQySRdKOqlk1RnA3IhwQ8asPfxW0iH12HCXC4BNI+mgBpgPnJZeTfCtwIsR4cMDC8DFlNWilvtg7UDSzun9I6zFONFYK4mIBSQ9yKXzzu/y+oJGxmSV8bkRVkfvAD4m6QngFZKjeCIi3pzBti+RdADJFZhXAWek8xcAJ5JciXkD8PEM9mVmOauqwJL0HuBDwJURcb+kmemQdaerJM2NiF9mGqU11HMb847AzMys4abWa8MR8cFu5gdwZr32a7Vxh471VbWXaf8E8DfARyQdB0wsXRgRnwRGpTfNG17NhiVNkfRIerO9c7pZ50OSHpK0TNIPqozdKnTY7O1fO5mYWZH5kGbLQkSsKjflHZeZNZ9qC6yXI+KFiPgC8F7gLaULJZ1Acs+YPya5H9bJlWw0vfLglSS9R+OBUySN77LOOOBc4O3p/bc+W2XsZmbWAtzpY1mSdFv6+LKkl0qmlyW91Nv7rbV1vdqbO3SsEtUWWD/rfBIR5wDf67J8T+CaiDgxIqbz+n0eenMksCIiHo+IV4G5JCeBljqd5NDE59P9P1Nl7FYBX5rdzJrRJbfmHYE1q4h4R/o4JCLeWDINiYg35h2f5Wul20HWB1UVWBHxky6vr+jy+jsR8XjJrC9XuOnubrRXan9gf0m/kXSHpLKXUvV9IszMWl/Xzp+r780nDjNrPx7Fst5UO4L1GklTJd2Znjc1T9LRXdeJiCyH1vsD44DJwCkkhyDuWmafsyNiUkRMGjFiRIa7b31OGGZmZmbb89E8Vq0+F1jAVcDZwFtJbnz3NUmn9HFbldxobzUwPyI2R8QTwKMkBZfViROKmRVZ1xzlTiLrq/Q+VPv0vqaZWe9qKbCeiYjfRMTzEXEjcAJwXh+3dTcwTtIYSTuR3OBzfpd1ricZvSK9QuH+wOOYWVupZPTczKwa6eXSF/S6orUtd+hYNWopsJ6Q9JW0IALYDGzpy4YiYgswC1gIPAzMi4hlki6UdFK62kJgvaSHgFuAv4mI9TXEbyV8cQtrIlmOnluLcaPHanCvpLf0vpqZWc9qKbC2AdOBJ9NLnK4AFqWXU69aRCyIiP0jYmxEXJTOOz8i5qfPIyLOjojxEXFIRMytIXYza15Zjp5bk3NnkGXoKOB2SY9JWiLpQUlL8g7KiuO7Xa5v7Q4d607/vr4xIk4FkLQzcDBwaDrNkbRfRIzKJkSrNycIazJPSPoKcGF6W4c+j55ba9r3Mhde1icn5B2AFdvk0XlHYM2ilhEsACLilYi4JyKuiYizImKyi6vm5oaJFVymo+fW/JyzLAsRsQrYFXh/Ou2azjPrljuprZyaCywzs0aQdLQkRcSpETEBGAWcBVwAiGT0/Pd5xmhmzUvSWcD3gd3T6T8lfTrfqKxo3KFjlai5wJL0niwCsXz44hbWRE4D7pE0V9LHgKEePbdOvsKXZeCTwFHp+d/nk1xI5/ScY7Im4HxjXWUxgvXVDLZhZtajiPhURBxOMmI1FPiOpNsl/ZOkd0rqyDdCM2tyAraWvN6azjPbjjujrTc+RLCNucfFmlFELI+Ib0TEFOA44Dbgz4A7843MisY5zqr0H8Cdki6QdAFwB/Dv+YZkzWI/5xsr0aerCEr6DyBIenZGSbqmc1lEfCKj2KzB3CNjTWg2MDMiFkgamncwlq9VZ7mosr6RJOBHwCLgHensj0fEfbkFZYXWNd9s7X5Va0N9HcH6DvDd9PH59HnnZGbWKAKuljQI+FzFb5KmSHpE0gpJ53SzzockPSRpmaQfZBWwNZYLLqtERASwICLujYjL08nFlZn1SZ8KrIj4VecEvNzltTUBX9zCWsQTJOdkXQ28oZI3pOdqXQlMBcYDp0ga32WdccC5wNvTKxZ+NruQrZ6cy6wG90p6S95BWPPwxXWsO1mcg/VqBtswM+uLf4uIlSRF1pQK33MksCIiHk9vVDwXmNZlndOBKyPieYCIeCabcC0PbvRYhY4Cbpf0mKQlkh6UtCTLHUj6vKSQNDx9LUmXp6PpSyQdnuX+zCwfWdxo+K1ZBGKN48aGNTNJUyXdKekR4FJJR0fEynSkqRJ7A0+WvF6dziu1P7C/pN9IukNS2eJN0kxJiyUtXrduXdU/i9WHR7GsWuk5WDOBsSQXz3k/8L70Mat97AO8Fyi9X99UYFw6zSQZjbcm5jaWga8iaLgxYk3nKuBsknvUzAa+JumUjPfRn6TBMxk4heQmxrt2XSkiZkfEpIiYNGLEiIxDsCy50WM9Sc/BujIiVnWdMtzNN4C/JblIWKdpwPcicQewq6Q9M9yn1ZnbUFaOCywzazbPRMRvIuL5iLgROAE4r4r3rwH2KXk9Mp1XajUwPyI2R8QTwKMkBZc1CTd6rA/qdg6WpGnAmoh4oMuiSkbUO7fhEfMm4Q4dy7TA8o0+i88Xt7AW8ISkr0jaKX29GdhSxfvvBsZJGpNuYwYwv8s615OMXpGeK7E/8HgtQVv+3OixXhwF3NHXc7Ak3ShpaZlpGvBF4PxagvOIeXG5LWVdVVVgSXqPpDmSJqavZ3ZZZbakwemyd2YTopnZdrYB04EnJd0GrAAWpVf+61VEbAFmAQuBh4F5EbFM0oWSTkpXWwisl/QQcAvwNxGxPusfxOrLjR6r0gnAfvTxHKyIOD4iDu46kXTOjAEekLSSZNT8Xkl7UNmIujUhd+i0t2pvNPwJ4FPAlyTtBkzssvx84N8lbQHuB35da4CWHX/ZrRVExKkAknYGDgYOTac5kvaLiFEVbGMBsKDLvPNLngfJeV5nZxi6FcC+l7nwsm79HvgwsF9EXChpFLAHUNN5WBHxILB75+u0yJoUEc9Kmg/MkjSXZATtxYhYW8v+LB++0bmVqvYQwZcj4oWI+ALJlXC6Hqv8j8AjJCdwzssgPqsjNzKsmUXEKxFxT0RcExFnRcTkSooray/Oc1aFq4CjSS5sA/AyyT3z6mkByQjXCmAO8Nd13p81kAuu9lVtgfWzzicRcQ7wvS7L/zYiLiAZ5fr72kIzMzPLnhs91o2jIuJMYBNAeh+8nXp+S/UiYnREPJs+j4g4MyLGRsQhEbE46/1Z47hDxzpVVWBFxE+6vL6iy+vOhPF/wF/VHJ1lxhe3MLN25XxnFdqcXqwrACSNIDnn06zP3KHTnvp8FcHSm31Kmifp6NLlEbG19vDMzMyy50aPlXE5cB2wu6SLgNuAf8o3JGs27tAxqO0y7Y242adloGtD4ptT8onDzCwvbvRYbyLi+yQ3Ar4YWAucHBE/yjcqawXu0Gk/tRRYtd7s03Ly/gPyjsDMLH9u9FhXEbE8Iq6MiG9GxMN5x2PNyR06VkuBVevNPq0B3IAwM0u40WNmeXF7rL3UUmDVdLNPy4cbGGZmr3Ojx8zqwe2t9tbnAisiTo2ICcAo4CzgAkAkN/v8fTbhmZmZZceNHjPLizt02kdvBZYkfV7S+O5W8M0+i8uXZjcz650bPQZJg0fSRySdn74eJenIvOOy5lWu3eV80x56LLAioh/JFQIPlnShpPMkHSNJjQnPzMwsW+5ssm5cBRwNdF4R+WXgyvzCMbNm1eshghHxckTMi4jzgUuAAcCXJf2DpOmSBtc9Sqta1x6SwbWcbWdm1uLcq2zAURFxJrAJICKeB3bq+S1mPfMoVnuqqtkdEVsj4uaIuDAi/h54HJiVFluflPSm+oRptXr403lHYGZWHG70WBmbJXUAASBpBMkFvcxq4lHz9tO/ljdHxAPAAwCSRgLTJe0JvAD8PCKW1xyhVc2NBDMzs6pdDlwH7C7pIuBPgS/lG5K1qn0vc+HVyjI7cCwiVgP/Dfye5PLtt0r6eFbbt77zF9jMbEcexbJSEfF94G+Bi4G1wMkR8aN8o7JW4bZYe6m5wJI0Nr3S4G3Ab4EJwPnAmyLiP2rdvlXHjQMzs8q50WOdJJ0NvBwRV0bENyPi4bxjstbmNlvrqqnAknQFsBB4A3BmRIyLiLMjYlFE+LjlAnDjwcysOm70tK0hwC8k3Sppls8rt6x51Lx91DqC9U/ArPSiFw9kEZD13buuyTsCs+YgaYqkRyStkHROmeUfk7RO0v3p9Jd5xGmNUa7RM8MHhrWdiPiHiJgAnAnsCfxK0o05h2VmTaimAisi1kbEDVkFY7VZ+fL2rz16Zbaj9CphVwJTgfHAKd3cTP2/ImJiOn27oUFa7m7/37wjsBw9AzwFrAd2zzkWazEexWoPvjuSmbWbI4EVEfF4RLwKzAWm5RyT5cyNHpP015IWATcDw4DTI+LNGW37AklrSkbFTyxZdm46mv6IpBOy2J8VmzvAW58LrBbRtSHgL69Zt/YGnix5vTqd19UHJS2R9GNJ+zQmNMuT82bbGwmcFRHjI+KCiHgo4+1/o2RUfAFAOno+g+QCYVOAq9JRdmsz7tBpLS6wzMx29P+A0Wnv9S+B75ZbSdJMSYslLV63bl1DA7TGcKOn9aVXQQb4DMktZl5Kp5clvVTn3U8D5kbEKxHxBLCCZJTdWpxHzVtbYQqs3k46L1nvg5JC0qRGxldk/kKaVWUNUDoiNTKd95qIWB8Rr6Qvvw0cUW5DETE7IiZFxKQRI0bUJVhrLDd62k9EvCN93CUi3lgyDYmIN2a4q1npqPg1koam8yodUXeHjlkTKUSBVelJ55KGAGcBdzY2wubiw1zMenQ3ME7SGEk7kRyeM790BUl7lrw8CfD9cMxanKSvVjKvh/ffKGlpmWkacDUwFphIchPjr1cbnzt0Wo87dFpXIQosKj/p/B+BrwKbGhlckfmLaFadiNgCzCK5h9/DwLyIWCbpQkknpat9RtIySQ+QHDb0sXyitTy40dO23lNm3tRK3xwRx0fEwWWmn0TE0xGxNb1H6BxePwyw1xF1a23ON62pKAVWr0Pkkg4H9omIn/W0oXYfQvfolVnvImJBROwfEWMj4qJ03vkRMT99fm5ETIiIQyPi2IhYnm/E1mhu9LQPSZ+S9CBwQHoIX+f0BPBgRvsoHRWfDixNn88HZkjaWdIYYBxwVxb7NLP8FKXA6pGkfsClwOd7W7edhtD9z97MzKxmPwDeT1LsvL9kOiIiPpzRPv5Z0oOSlgDHAp8DiIhlwDzgIeAG4MyI2JrRPq1JuEOn9RSlwOptiHwIcDCwSNJK4K3AfF/oYnsevTIzy44bPe0hIl6MiJURcQrwEvAmYF/gYEnvzGgffxERh0TEmyPipIhYW7LsonQ0/YCI+HkW+7Pm43zTWopSYPV40nma/IZHxOiIGA3cAZwUEYvzCTd//tKZmdWfGz3tQ9JfAr8mOT/zH9LHC/KMycz5pjkVosCq8KRz64FHr8zMGseNnpZ0FvAWYFVEHAscBryQa0TWVtyWax2FKLCg95POu6w72aNXZmbWCG70tI1NEbEJQNLO6cVtDsg5JmszHjVvDYUpsKzv/M/fzKy+3OhpC6sl7QpcD/xS0k+AVblGZG3J+ab5ucBqMv6CmZnlw42e1hYR0yPihYi4APgy8O/AybkGZVbC+aZ5uMBqch69MjPLlxs9rScifhUR8yPi1bxjsfbk9l1zc4HVRPxP3MwsX901epyfm5eklyW9lE4vl7x+WdJLecdn7cuj5s3LBVaTKPeFcu+GmVnjOfe2logYEhFvTKchJa+HRMQb847P2puLrObkAsvMzKxKbvS0HiU+IunL6et9JB2Zd1xm5TjfFJsLrCbg0Sszs+JxkdVyrgKOBk5NX/8BuDK/cMwSPjS5+bjAakIurszMisuNnqZ1VEScCWwCiIjngZ3yDcks4SKrubjAKjh/cczMisuNnpayWVIHEACSRgDb8g3J7HXON83DBVaT8eiVmVmxuNHTMi4HrgN2l3QRcBvwT/mGZLY9twObgwusAvM/ZzOz5uAiq7lJEvBr4G+Bi4G1wMkR8aNcAzMrw+d/Fp8LrILyhS3MzJqLi6zmFREBLIiI5RFxZUR8MyIezjsus+64yCo2F1hmZmYZcZHV1O6V9Ja8gzCr1Iljd5znXFMMLrAKyKNXZvUlaYqkRyStkHROD+t9UFJImtTI+Ky5uchqWkcBt0t6TNISSQ9KWpJ3UGbdufp95ec71+TPBVYTcHFllp30KmFXAlOB8cApksaXWW8IcBZwZ2MjtFbgIqspnQCMBY4D3g+8L33MhKRPS1ouaZmkfy6Zf27a2fOIpBOy2p+1B+eaYnKBVTD+QpjV3ZHAioh4PCJeBeYC08qs94/AV0nviWNWLTd8mktErCo3ZbFtSceS5JlDI2IC8C/p/PHADGACMAW4Ku0EMquYc03xuMAqEB8aaNYQewNPlrxenc57jaTDgX0i4mc9bUjSTEmLJS1et25d9pFa03PDx1KfAi6JiFcAIuKZdP40YG5EvBIRTwArSDqBzKrSU65xvmk8F1hmZiUk9QMuBT7f27oRMTsiJkXEpBEjRtQ/OGtKLrIM2B84RtKdkn5VcjGNXjt8OrlDx3rTU6e8801jucAqCI9emTXMGmCfktcj03mdhgAHA4skrQTeCsz3hS6sFi6yWp+kGyUtLTNNA/oDu5Hkk78B5qX33qqYO3SsEi6yisEFVgG4uDJrqLuBcZLGSNqJ5PyH+Z0LI+LFiBgeEaMjYjRwB3BSRCzOJ1xrFS6yWltEHB8RB5eZfkIyMvU/kbgL2AYMp/cOH7OqrTrL+SZvLrDMrK1ExBZgFrAQeBiYFxHLJF0o6aR8o7NW50ZP27oeOBZA0v7ATsCzJJ07MyTtLGkMMA64K68grbU43+THBVbOPHpl1ngRsSAi9o+IsRFxUTrv/IiYX2bdyR69siz5ZPS2dA2wn6SlJFcu/Wg6mrUMmAc8BNwAnBkRW3OM01qMi6x8uMDKkYsrM7P25PMk2ktEvBoRH0kPGTw8Im4uWXZR2tlzQET8PM84rTW5U6fxXGCZmZnlwEWWmTWK801jucDKiUevzMyst5PR3fAxs6y4yGocF1g5cHFlZmal3PAxs0Zwp05juMBqMP/hmplZOS6yzKxRnG/qq3/eAZhHr8xa1ebNm1m9ejWbNm3KO5Q+GThwICNHjmTAgAF5h9I2Ov8flGvgdM7z/wwrx/nGqrXqrO6LqX0vc66phQusBvKhgWbtZfXq1QwZMoTRo0cjKe9wqhIRrF+/ntWrVzNmzJi8w2k7bvhYtZxvrC/cqVMfPkSwQVxcmbWfTZs2MWzYsKZr7ABIYtiwYU3bG94KejuEx4fxWCnnG6uFDxnMlgusBvAfpln7asbGTqdmjr1V9HRCOvj/i22vmb+zzRx7q3CnTnZcYOXEo1dmZlYpN3zMrBEq6dRxvumdC6w686GBZpanjo4OJk6cyKGHHsrhhx/Ob3/7WwBWrlzJoEGDmDhxIuPHj+eMM85g27ZtOUdrPXHDx4rO+aZ19NZWda7pmQusOnJxZWbV2LZ1K2sWLeLBq69mzaJFbNu6teZtDho0iPvvv58HHniAiy++mHPPPfe1ZWPHjuX+++9nyZIlPPTQQ1x//fU178/qzw0fy4LzjfXGnTp956sI1km5PzgfXWxm3dm2dSu3nH466x98kC0bN9J/0CCGHXIIx86ZQ7+Ojkz28dJLLzF06NAd5vfv35+3ve1trFixIpP9WP31dOWv0vnu1LNynG+sGs431XOBVQfd/QGu9B+emXVj7a23Jo2dDRsA2LJhA+sffJC1t97K3pMn93m7GzduZOLEiWzatIm1a9dy880377DOhg0buOmmm7jwwgv7vB/Lhxs+1hfON9YXPd0+ApxvSvkQwYx194fnPzYz68lzDz/Mlo0bt5u3ZeNGnl++vKbtdh6ys3z5cm644QZOO+00IgKAxx57jIkTJ/L2t7+dP/mTP2Hq1Kk17cvyU8lhgz6Uxzo531hf9XbYIDjfgEewMuXiysz6areDDqL/oEGv9SgD9B80iKEHHpjZPo4++mieffZZ1q1bB7x+ToS1ht5Gs0qX+f9Se3O+sVo53/SsMCNYkqZIekTSCknnlFl+tqSHJC2RdJOkffOIszsursysFnsecwzDDjmE/oMHg0T/wYMZdsgh7HnMMZntY/ny5WzdupVhw4Zltk0rHvcwW2+cbywrzjflFWIES1IHcCXwHmA1cLek+RHxUMlq9wGTImKDpE8B/wz8eeOj3ZGLK7PmImkKcBnQAXw7Ii7psvwM4ExgK/AHYGaXfJS5fh0dHDtnDmtvvZXnly9n6IEHsucxx9R8wnnnOREAEcF3v/tdOjI6id2KzT3M1h3nG8ua8832ClFgAUcCKyLicQBJc4FpwGsNmoi4pWT9O4CPNDTCbri4MmsuFXbo/CAivpWufxJwKTCl3rH16+hg78mTazrJvKut3Vx6efTo0SxdujSz/VhxueGTP0n/BRyQvtwVeCEiJqbLzgU+SdKh85mIWNiImJxvrB6cbxJFKbD2Bp4seb0aOKqH9T8J/LzcAkkzgZkAo0aNyiq+slxcmTWlSjp0XipZ/w1ANDRCszqopuFTur7VLiJeO+JG0teBF9Pn44EZwARgL+BGSftHRO03pTLLUbsXWkUpsCom6SPAJOBd5ZZHxGxgNsCkSZPq1ihycWXWtCrq0JF0JnA2sBNwXLkNNbJDxywrlTR8Spf7/1p2JAn4EK/nlGnA3Ih4BXhC0gqSTqDbcwrRLFPt2rFTlItcrAH2KXk9Mp23HUnHA+cBJ6XJKBcursxaX0RcGRFjgb8DvtTNOrMjYlJETBoxYkRjAzSrUSUnp0N7nqBeR8cAT0fE79LX5Tp89i73RkkzJS2WtLjzynxmzaLd8k1RRrDuBsZJGkNSWM0ATi1dQdJhwL8BUyLimcaHmHBxZdb0KurQKTEXuLquEZnlqPT/V7v1MmdJ0o3AHmUWnRcRP0mfnwL8sC/bb9QROmb1VO0Ieul7mkkhCqyI2CJpFrCQ5Kpe10TEMkkXAosjYj7wNWAX4EfJCDu/j4iTGhmniyuzllBJh864kh7mPwF+h1kbaJfGTz1ExPE9LZfUH/gAcETJ7Go7fMxaQqUdO6XLmynXFKLAAoiIBcCCLvPOL3neY+KqNxdXZq2hwg6dWekhyZuB54GP5hexWeNVWmiVruP/h706HlgeEatL5s0HfiDpUpKLXIwD7sojOLO8tGLHTlHOwSo0F1dmrSUiFkTE/hExNiIuSuednxZXRMRZETEhIiZGxLERsSzfiGvz1FNPMWPGDMaOHcsRRxzBiSeeyKOPPsrBBx/82jpz5szhiCOO4Pnnn88xUiuazvMmqjl3ohXOn6iTGXQ5PDDNLfNIrmJ6A3BmM19B0LnGatFK+aYwI1hF5eLKzBpl6zZYtBKWroODR8Dk0dBRYzdYRDB9+nQ++tGPMnfuXAAeeOABnn766dfWufbaa7niiiu4+eabGTp0aG07tJbVl0N6ur6vnUXEx7qZfxFwUWOjyT7fONdYlvoyil76vry5wOqBiysza5St2+Avrof7noKNm2HQADhsD7j25NoaPbfccgsDBgzgjDPOeG3eoYceysqVKwGYN28el1xyCTfddBPDhw+v6Wew9tHsjZ92V49841xj9VBNx07XdfLMNy6wyujpF+h/DmZWD4tWJo2dDZuT1xs2J68XrYR379f37S5dupQjjjii7LJVq1Yxa9Ys7rvvPvbYo9zFz8x6Vkvjp+v7rXHqkW+ca6zemqnY8jlYXbi4MrM8LF2X9CSX2rgZlj1bv32OGDGCUaNGMW/evPrtxNpGNedPdCr6eRStqtH5xrnGslZtvinNNY3INx7BKuHiyszycvCI5DCdDSWNnkEDYEKNR9JMmDCBH//4x2WXDR48mAULFnDMMcew++678+EPf7i2nZmlqu1pLree/+/WTz3yjXON5aXWfFOPXOMCCxdWZpa/yaOTcyC6nhMxeXRt2z3uuOP44he/yOzZs5k5cyYAS5Ys4cUXXwRg991354YbbmDy5MkMHz6cE044obYdmnXR9f9oURpA7awe+ca5xoqgKJ07LrAAtm2Dfv22f71hAxf/4Ch+MDu/sMyaxanLmvoq5oXQ0S85wXzRyuQwnQnDs7mKoCSuu+46PvvZz/LVr36VgQMHMnr0aP71X//1tXXGjBnD/PnzOfHEE7nuuus48sgja9upWQ+K2NvcbuqRb5xrrGhq7dypJde4wCq1bVvymBZXZmaN1NEvOcG8lotalLPXXnuVPfdh6dKlrz0/9NBDWbNmTbY7NutFXxtAVrt65BvnGiuyvnTu9JULLIANG2DwYBdWZmZmOWpkA8jM2le9O3dcYAGrztslfbYLnOdDnczMzPLmQwHNrFGyzje+TLuZWR1FRN4h9Fkzx27Wjpr5O9vMsZt15QLLzKxOBg4cyPr165uy4RARrF+/noEDB+YdiplVwPnGrDh8iKCZWZ2MHDmS1atXs27durxD6ZOBAwcycuTIvMMwswo435gVhwssM7M6GTBgAGPGjMk7DDNrA843ZsXhQwTNzMzMzMwy4gLLzMzMzMwsIy6wzMzMzMzMMqJmvNpMpSStA1ZVuPpw4Nk6htNXjqs6RYyriDFBY+PaNyJGNGhfuagi3/jvoTqOqzqOy/mmlP8equO4quO4usk3LV1gVUPS4oiYlHccXTmu6hQxriLGBMWNq9UV9XN3XNVxXNUpalytrqifu+OqjuOqThHi8iGCZmZmZmZmGXGBZWZmZmZmlhEXWK+bnXcA3XBc1SliXEWMCYobV6sr6ufuuKrjuKpT1LhaXVE/d8dVHcdVndzj8jlYZmZmZmZmGfEIlpmZmZmZWUZcYJmZmZmZmWWk7QssSVMkPSJphaRzctj/SkkPSrpf0uJ03m6Sfinpd+nj0HS+JF2exrpE0uEZxnGNpGckLS2ZV3Uckj6arv87SR+tU1wXSFqTfmb3SzqxZNm5aVyPSDqhZH6mv2dJ+0i6RdJDkpZJOiudn+tn1kNcuX9mlv9n6nzTp7hy/e4411hf5fm5FiXXpNsvXL4pYq5Jt+d8k5WIaNsJ6AAeA/YDdgIeAMY3OIaVwPAu8/4ZOCd9fg7w1fT5icDPAQFvBe7MMI53AocDS/saB7Ab8Hj6ODR9PrQOcV0AfKHMuuPT3+HOwJj0d9tRj98zsCdwePp8CPBouv9cP7Me4sr9M2v3qQifqfNNn+LK9bvjXOOpj7+fXD/XouSadPuFyzdFzDXpvpxvMprafQTrSGBFRDweEa8Cc4FpOccESQzfTZ9/Fzi5ZP73InEHsKukPbPYYUT8GniuxjhOAH4ZEc9FxPPAL4EpdYirO9OAuRHxSkQ8Aawg+R1n/nuOiLURcW/6/GXgYWBvcv7MeoirOw37zKywn6nzTc9xdach3x3nGuujIn6uDc81UMx8U8Rck8blfJORdi+w9gaeLHm9mp5/YfUQwC8k3SNpZjrvTRGxNn3+FPCm9Hmj4602jkbGNysdjr6mc6g6r7gkjQYOA+6kQJ9Zl7igQJ9ZmyrCZ+p80zeF+O4411gV8v5ci5xr+hJLo2IszHfH+aY27V5gFcE7IuJwYCpwpqR3li6MiCBJVLkqShypq4GxwERgLfD1vAKRtAvw38BnI+Kl0mV5fmZl4irMZ2a5cr6pXiG+O8411mSaItdAoWIpzHfH+aZ27V5grQH2KXk9Mp3XMBGxJn18BriOZPjy6c7h8fTxmXT1RsdbbRwNiS8ino6IrRGxDZhD8pk1PC5JA0i+6N+PiP9JZ+f+mZWLqyifWZvL/TN1vqleEb47zjXWB7l+rgXPNfQhlrrHWJTvjvNNNtq9wLobGCdpjKSdgBnA/EbtXNIbJA3pfA68F1iaxvDRdLWPAj9Jn88HTkuv2vJW4MWSIdt6qDaOhcB7JQ1Nh2nfm87LVJdjs6eTfGadcc2QtLOkMcA44C7q8HuWJODfgYcj4tKSRbl+Zt3FVYTPzJxveuF8U37/zjXWF7l9rk2Qazr3Wah8U4TvjvNNhqIOV85oponkCiiPklxV5LwG73s/kiuYPAAs69w/MAy4CfgdcCOwWzpfwJVprA8CkzKM5Yckw6ubSY5J/WRf4gA+QXIy4Qrg43WK69p0v0tIvhh7lqx/XhrXI8DUev2egXeQDJEvAe5PpxPz/sx6iCv3z8yT801JLM43lcfkXOOpr7+jXD7XIuWadPuFyzdFzDXp9pxvMpqU7szMzMzMzMxq1O6HCJqZmZmZmWXGBZaZmZmZmVlGXGCZmZmZmZllxAWWmZmZmZlZRlxgmZmZmZmZZcQFlvVK0gWSvpA+v1DS8T2se7Kk8Y2Lbrt9XyBpjaQLS15/IYPt3iLpD5Im1R6lmfXE+cb5xqxRnG+cb+rFBZZVJSLOj4gbe1jlZCCXBJT6RkScn+UGI+JYYHGW2zSz3jnfmFmjON9YllxgWVmSzpP0qKTbgANK5n9H0p+mzy+R9JCkJZL+RdLbgJOAr0m6X9JYSadLulvSA5L+W9Lgku1cLum3kh7v3Ga67O8kPZi+55J03lhJN0i6R9Ktkg6s8uc5XdLPJQ2StEjSZWmMSyUdma6zi6T/SPe9RNIHa/4gzaxXzjfON2aN4nzjfNMI/fMOwIpH0hHADGAiyd/IvcA9XdYZBkwHDoyIkLRrRLwgaT7w04j4cbreCxExJ33+FZK7lV+RbmZPkrtzH0hyB+4fS5oKTAOOiogNknZL150NnBERv5N0FHAVcFyFP88s4D3AyRHxiiSAwRExUdI7gWuAg4EvAy9GxCHp+4ZW/qmZWV843zjfmDWK843zTaO4wLJyjgGui4gNAGlS6epFYBPw75J+Cvy0m20dnCaeXYFdgIUly66PiG3AQ5LelM47HviPzn1HxHOSdgHeBvwoTR4AO1f4s5wGPEmSfDaXzP9huv1fS3qjpF3Tfc/oXCEinq9wH2bWd843ON+YNYjzDc43jeBDBK1PImILcCTwY+B9wA3drPodYFbaa/IPwMCSZa+UPBfd6we8EBETS6aDKgz1QWA0MLLrj9DLazMrCOcbM2sU5xvLggssK+fXwMnp8bxDgPd3XSHtdfmjiFgAfA44NF30MjCkZNUhwFpJA4APV7DvXwIfLzmWebeIeAl4QtKfpfMk6dCeNlLiPuCvgPmS9iqZ/+fptt5BMmz+YrrvM0t+Rg+hm9Wf8w3ON2YN4nyD800juMCyHUTEvcB/AQ8APwfuLrPaEOCnkpYAtwFnp/PnAn8j6T5JY0mO+70T+A2wvIJ930ByvPJiSfcDnZch/TDwSUkPAMtIjmOu9Oe5Ld3OzyQNT2dvknQf8C2S46YBvgIMTU8MfQA4ttJ9mFnfON8435g1ivON802jKMIjh9YaJF0A/CEi/qWX9RYBX4iIii9N2pf3mFnrcr4xs0Zxvmk+HsGyVvIHYKbSG/FlRdItwH7A5t7WNbO24XxjZo3ifNNkPIJlZmZmZmaWEY9gmZmZmZmZZcQFlpmZmZmZWUZcYJmZmZmZmWXEBZaZmZmZmVlGXGCZmZmZmZll5P8DDfagBBifQjoAAAAASUVORK5CYII=", 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" ] @@ -883,13 +844,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 3.6175737380981445s.\n", - "Simulation time with module BP is 2.5163893699645996s.\n" + "Simulation time with module CK is 1.4070227146148682s.\n", + "Simulation time with module BP is 1.089118242263794s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -927,13 +888,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 1.010209321975708s.\n", - "Simulation time with module BP is 0.5810580253601074s.\n" + "Simulation time with module CK is 0.4603853225708008s.\n", + "Simulation time with module BP is 0.2764415740966797s.\n" ] }, { "data": { - "image/png": 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" ] @@ -967,13 +928,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 16.289583444595337s.\n", - "Simulation time with module BP is 12.5268075466156s.\n" + "Simulation time with module CK is 6.701535701751709s.\n", + "Simulation time with module BP is 5.282698392868042s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1015,13 +976,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def plot_subplots_3d(ax, data, x_ana, y_ana, z_ana, module, color): \n", " ax.scatter(data.D,((x_ana-data.X)**2+(y_ana-data.Y)**2+(z_ana-data.Z)**2)**0.5, s=1,color=color, label=module)\n", - " ax.legend(markerscale=5)\n", + " ax.legend(markerscale=5, loc='upper left')\n", " \n", "def plot_figure_3d(max_trajectory, p_z, r_g_0, number_of_steps):\n", " fig, ax = plt.subplots(figsize=(12,5))\n", @@ -1058,13 +1019,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 3.250000476837158s.\n", - "Simulation time with module BP is 2.8336546421051025s.\n" + "Simulation time with module CK is 1.4413368701934814s.\n", + "Simulation time with module BP is 1.2115199565887451s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1091,13 +1052,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 3.026798725128174s.\n", - "Simulation time with module BP is 2.7702648639678955s.\n" + "Simulation time with module CK is 1.2834656238555908s.\n", + "Simulation time with module BP is 1.1045174598693848s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1124,13 +1085,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.8276591300964355s.\n", - "Simulation time with module BP is 2.6629881858825684s.\n" + "Simulation time with module CK is 1.246964693069458s.\n", + "Simulation time with module BP is 1.052145004272461s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1157,13 +1118,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.818937301635742s.\n", - "Simulation time with module BP is 2.5782644748687744s.\n" + "Simulation time with module CK is 1.2906627655029297s.\n", + "Simulation time with module BP is 1.0843861103057861s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1190,29 +1151,20 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 2.885383367538452s.\n", - "Simulation time with module BP is 2.626239061355591s.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/ipykernel_launcher.py:27: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - "/home/lmerten/Software/matrix/lib/python3.6/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" + "Simulation time with module CK is 1.4664416313171387s.\n", + "Simulation time with module BP is 1.1508221626281738s.\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1261,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1272,12 +1224,8 @@ "N_grid = 256\n", "b = 100*nG\n", "spacing = lMin/2.\n", - "#spacing = R / 2 *pc # lMin > 2 * spacing\n", - "#vgrid = Grid3f(Vector3d(0), N_grid, spacing)\n", - "#initTurbulence(vgrid, b, lMin, lMax, -11./3., randomSeed)\n", - "#turb_field = MagneticFieldGrid(vgrid)\n", "\n", - "turbSpectrum = SimpleTurbulenceSpectrum(Brms=b, lMin=lMin, lMax=lMax, sIndex=5./3.)\n", + "turbSpectrum = SimpleTurbulenceSpectrum(b, lMin, lMax, 5./3.)\n", "gridprops = GridProperties(Vector3d(0), N_grid, spacing)\n", "turb_field = SimpleGridTurbulence(turbSpectrum, gridprops, randomSeed)" ] @@ -1291,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1316,20 +1264,20 @@ " sim.run(c, True)\n", " t1 = Time.time()\n", " print('Simulation time with module {} is {:4.4} s.'.format(module, t1-t0))\n", - " Time.sleep(4)" + " Time.sleep(.1)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module BP is 5.822 s.\n", - "Simulation time with module CK is 27.11 s.\n" + "Simulation time with module BP is 3.936 s.\n", + "Simulation time with module CK is 17.69 s.\n" ] } ], @@ -1346,6 +1294,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1355,22 +1304,24 @@ "\n", "Here, we test the time difference for the example of galactic trajectories presented in: \n", "\n", - "https://github.com/CRPropa/CRPropa3-notebooks/blob/master/galactic_trajectories/galactic_trajectories.v4.ipynb.\n", + "~/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb.\n", "\n", "First, we want to compare both modules with a fixed step size:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 86.28928446769714s.\n", - "Simulation time with module BP is 44.579694986343384s.\n" + "crpropa::ModuleList: Number of Threads: 8\n", + "Simulation time with module CK is 13.88 s.\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Simulation time with module BP is 6.241 s.\n" ] } ], @@ -1443,6 +1394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1451,20 +1403,22 @@ "#### Adaptive step sizes\n", "\n", "Finally we can test exactly the example presented in:\n", - "https://github.com/CRPropa/CRPropa3-notebooks/blob/master/galactic_trajectories/galactic_trajectories.v4.ipynb.\n" + "~/doc/pages/example_notebooks/galactic_trajectories/galactic_trajectories.ipynb." ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation time with module CK is 0.4632 s.\n", - "Simulation time with module BP is 0.6 s.\n" + "crpropa::ModuleList: Number of Threads: 8\n", + "Simulation time with module CK is 0.1547 s.\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Simulation time with module BP is 0.1476 s.\n" ] } ], @@ -1568,7 +1522,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "crp_docu", "language": "python", "name": "python3" }, @@ -1582,7 +1536,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } }, "widgets": { "application/vnd.jupyter.widget-state+json": { From 6455e34ed9e7672171424c1b5413227483f59e3d Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 13:59:18 +0100 Subject: [PATCH 63/87] Deactivate CandidatetagColumn in various examples. Not needed for analysis and would make loading the data slightly more complex. --- .../{neutrinos.v4.ipynb => neutrinos.ipynb} | 0 .../secondaries/photons.ipynb | 442 ++++++++++++++++++ .../secondaries/photons.v4.ipynb | 357 -------------- .../secondaries/secondary_photons.ipynb | 51 +- 4 files changed, 476 insertions(+), 374 deletions(-) rename doc/pages/example_notebooks/secondaries/{neutrinos.v4.ipynb => neutrinos.ipynb} (100%) create mode 100644 doc/pages/example_notebooks/secondaries/photons.ipynb delete mode 100644 doc/pages/example_notebooks/secondaries/photons.v4.ipynb diff --git a/doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb b/doc/pages/example_notebooks/secondaries/neutrinos.ipynb similarity index 100% rename from doc/pages/example_notebooks/secondaries/neutrinos.v4.ipynb rename to doc/pages/example_notebooks/secondaries/neutrinos.ipynb diff --git a/doc/pages/example_notebooks/secondaries/photons.ipynb b/doc/pages/example_notebooks/secondaries/photons.ipynb new file mode 100644 index 000000000..59814e3e4 --- /dev/null +++ b/doc/pages/example_notebooks/secondaries/photons.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Photon Propagation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 2 13:43:57 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:56 - Finished at Thu Feb 2 13:44:53 2023\n", + "\r" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "obs = Observer()\n", + "obs.add(Observer1D())\n", + "obs.add(ObserverInactiveVeto())\n", + "t = TextOutput(\"photon_electron_output.txt\", Output.Event1D)\n", + "t.disable(t.CandidateTagColumn)\n", + "obs.onDetection(t)\n", + "\n", + "sim = ModuleList()\n", + "sim.add(SimplePropagation())\n", + "sim.add(Redshift())\n", + "sim.add(EMPairProduction(CMB(),True))\n", + "sim.add(EMPairProduction(IRB_Gilmore12(),True))\n", + "sim.add(EMPairProduction(URB_Protheroe96(),True))\n", + "sim.add(EMDoublePairProduction(CMB(),True))\n", + "sim.add(EMDoublePairProduction(IRB_Gilmore12(),True))\n", + "sim.add(EMDoublePairProduction(URB_Protheroe96(),True))\n", + "sim.add(EMInverseComptonScattering(IRB_Gilmore12(),True))\n", + "sim.add(EMInverseComptonScattering(CMB(),True))\n", + "sim.add(EMInverseComptonScattering(URB_Protheroe96(),True))\n", + "sim.add(EMTripletPairProduction(CMB(),True))\n", + "sim.add(EMTripletPairProduction(IRB_Gilmore12(),True))\n", + "sim.add(EMTripletPairProduction(URB_Protheroe96(),True))\n", + "sim.add(MinimumEnergy(0.01 * EeV))\n", + "\n", + "\n", + "source = Source()\n", + "source.add(SourcePosition(Vector3d(4,0,0)*Mpc))\n", + "source.add(SourceRedshift1D())\n", + "source.add(SourceParticleType(22))\n", + "source.add(SourceEnergy(1000*EeV))\n", + "sim.add(obs)\n", + "sim.setShowProgress(True)\n", + "sim.run(source,1000,True)\n", + "t.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Optional) plotting of the results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from pylab import *\n", + "\n", + "t.close()\n", + "figure(figsize=(6,6))\n", + "\n", + "a = loadtxt(\"photon_electron_output.txt\")\n", + "E = logspace(16,23,71)\n", + "idx = a[:,1] == 22\n", + "photons = a[idx,2] * 1e18\n", + "idx = fabs(a[:,1]) == 11\n", + "ep = a[idx,2] * 1e18\n", + "data,bins = histogram(photons,E)\n", + "bincenter = (E[1:] -E[:-1])/2 + E[:-1]\n", + "plot(bincenter, data,label=\"photons\")\n", + "data,bins = histogram(ep,E)\n", + "plot(bincenter, data, label=\"electrons / positrons\")\n", + "grid()\n", + "loglog()\n", + "xlim(1e16, 1e21)\n", + "ylim(1e1, 1e4)\n", + "legend(loc=\"lower right\")\n", + "xlabel(\"Energy [eV]\")\n", + "ylabel(\"Number of Particles\")\n", + "show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Photon Propagation outside of CRPropa with EleCa and DINT\n", + "\n", + "There are two main ways to propagate electromagnetic particles (EM particles: photons, electrons, positrons) in CRPropa:\n", + "\n", + "1) propagation as part of the CRPropa simulation chain\n", + "\n", + "2) propagation outside of the CRPropa simulation chain\n", + "\n", + "The following describes option 2, for which CRPropa provides three functions.\n", + "EM particles can either be propagated individually using the external EleCa code (suitable for high energies), or their spectra can be propagated with the transport code DINT (suitable for low energies).\n", + "Alternatively, a combined option is available that processes high energy photons with Eleca and then calculates the resulting spectra with DINT down to low energies.\n", + "\n", + "All three functions take as input a plain-text file with EM particles in the format given in the \"Photons from Proton Propagation\" example below.\n", + "In the following examples the input file \"photon_monoenergetic_source.dat\" contains 1000 photons with E = 50 EeV from a photon source at 4 Mpc distance.\n", + "\n", + "The last example \"Photons from Proton Propagation\" shows how to obtain secondary EM particles from a simulation of hadronic cosmic rays.\n", + "\n", + "Note that the differing results in EleCa (and correspondingly the high energy part of the combined option) are due to an incorrect sampling of the background photon energies in EleCa. The EleCa support will be removed in the near future.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Photons from Proton Propagation\n", + "\n", + "The generation of photons has to be enabled for the individual energy-loss processes in the module chain. Also, separate photon output can be added:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "# source setup\n", + "source = Source()\n", + "source.add(SourceParticleType(nucleusId(1, 1)))\n", + "source.add(SourcePowerLawSpectrum(10 * EeV, 100 * EeV, -2))\n", + "source.add(SourceUniform1D(3 * Mpc, 100.00001 * Mpc))\n", + "\n", + "# setup module list for proton propagation\n", + "m = ModuleList()\n", + "m.add(SimplePropagation(0, 10 * Mpc))\n", + "m.add(MinimumEnergy(1 * EeV))\n", + "\n", + "# observer\n", + "obs1 = Observer() # proton output\n", + "obs1.add( Observer1D() )\n", + "obs1.add( ObserverPhotonVeto() ) # we don't want photons here\n", + "obs1.onDetection( TextOutput('proton_output.txt', Output.Event1D) )\n", + "m.add(obs1)\n", + "obs2 = Observer() # photon output\n", + "obs2.add( ObserverDetectAll() ) # stores the photons at creation without propagating them\n", + "obs2.add( ObserverNucleusVeto() ) # we don't want hadrons here\n", + "out2 = TextOutput('photon_output.txt', Output.Event1D)\n", + "out2.enable(Output.CreatedIdColumn) # enables the necessary columns to be compatible with the DINT and EleCa propagation\n", + "out2.enable(Output.CreatedEnergyColumn)\n", + "out2.enable(Output.CreatedPositionColumn)\n", + "out2.disable(Output.CandidateTagColumn)\n", + "obs2.onDetection( out2 )\n", + "m.add(obs2)\n", + "\n", + "# secondary electrons are disabled here\n", + "m.add(ElectronPairProduction(CMB(), False))\n", + "# enable secondary photons\n", + "m.add(PhotoPionProduction(CMB(), True))\n", + "\n", + "# run simulation\n", + "m.run(source, 10000, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The file 'photon_output.txt' will contain approximately 300 photons and can be processed as the photon example below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Propagation with EleCa\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-02 13:46:17 [WARNING] EleCa propagation is deprecated and is no longer supported. Please use the EM* (EMPairProduction, EMInverseComptonScattering, ...) modules instead.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run ElecaPropagation\n", + " Started Thu Feb 2 13:46:17 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:01 - Finished at Thu Feb 2 13:46:18 2023\n", + "\r" + ] + } + ], + "source": [ + "import crpropa\n", + "\n", + "# Signature: ElecaPropagation(inputfile, outputfile, showProgress=True, lowerEnergyThreshold=5*EeV, magneticFieldStrength=1*nG, background=\"ALL\")\n", + "crpropa.ElecaPropagation(\"photon_output.txt\", \"photons_eleca.dat\", True, 0.1*crpropa.EeV, 0.1*crpropa.nG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Propagation with DINT\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-02 13:46:20 [WARNING] DINT propagation is deprecated and is no longer supported. Please use the EM* (EMPairProduction, EMInverseComptonScattering, ...) modules instead.\n", + "\n" + ] + } + ], + "source": [ + "import crpropa\n", + "\n", + "# Signature: DintPropagation(inputfile, outputfile, IRFlag=4, RadioFlag=4, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", + "crpropa.DintPropagation(\"photon_output.txt\", \"spectrum_dint.dat\", 4, 4, 0.1*crpropa.nG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined Propagation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run EleCa propagation\n", + " Started Thu Feb 2 13:46:28 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:00 - Finished at Thu Feb 2 13:46:28 2023\n", + "\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-02 13:46:28 [WARNING] EleCa+DINT propagation is deprecated and is no longer supported. Please use the EM* (EMPairProduction, EMInverseComptonScattering, ...) modules instead.\n", + "\n" + ] + } + ], + "source": [ + "import crpropa\n", + "\n", + "# Signature: DintElecaPropagation(inputfile, outputfile, showProgress=True, crossOverEnergy=0.5*EeV, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", + "crpropa.DintElecaPropagation(\"photon_output.txt\", \"spectrum_dint_eleca.dat\", True, 0.5*crpropa.EeV, 0.1*crpropa.nG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Optional) Plotting of Results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(6,6))\n", + "\n", + "plt.loglog(clip_on=False)\n", + "plt.yscale(\"log\", nonpositive='clip')\n", + "plt.xlabel('Energy [eV]')\n", + "plt.ylabel ('$E^2 dN/dE$ [a.u.]')\n", + "\n", + "# Plot the EleCa spectrum\n", + "elecaPhotons = genfromtxt(\"photons_eleca.dat\")\n", + "binEdges = 10**arange(12, 24, .1)\n", + "logBinCenters = log10(binEdges[:-1]) + 0.5 * (log10(binEdges[1:]) - log10(binEdges[:-1]))\n", + "binWidths = (binEdges[1:] - binEdges[:-1])\n", + "data = histogram(elecaPhotons[:,1] * 1E18, bins=binEdges)\n", + "J = data[0] / binWidths\n", + "E = 10**logBinCenters\n", + "step(E, J * E**2, c='m', label='EleCa')\n", + "\n", + "#Plot the DINT spectrum\n", + "data = genfromtxt(\"spectrum_dint.dat\", names=True)\n", + "lE = data['logE']\n", + "E = 10**lE\n", + "dE = 10**(lE + 0.05) - 10**(lE - 0.05)\n", + "J = data['photons'] / dE\n", + "step(E, J * E**2 , c='b', where='mid', label='DINT')\n", + "\n", + "#Plot the combined DINT+EleCa spectrum\n", + "data = genfromtxt(\"spectrum_dint_eleca.dat\", names=True)\n", + "lE = data['logE']\n", + "E = 10**lE\n", + "dE = 10**(lE + 0.05) - 10**(lE - 0.05)\n", + "J = data['photons'] / dE\n", + "step(E, J * E**2 , c='r', where='mid', label='Combined')\n", + "\n", + "# Nice limits\n", + "xlim(1e14, 1e20)\n", + "ylim(bottom=1e17)\n", + "legend(loc='upper left')\n", + "show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2 (default, Feb 28 2021, 17:03:44) \n[GCC 10.2.1 20210110]" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/secondaries/photons.v4.ipynb b/doc/pages/example_notebooks/secondaries/photons.v4.ipynb deleted file mode 100644 index 2ab01c312..000000000 --- a/doc/pages/example_notebooks/secondaries/photons.v4.ipynb +++ /dev/null @@ -1,357 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Photon Propagation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "jupyter": { - "outputs_hidden": true - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "obs = Observer()\n", - "obs.add(Observer1D())\n", - "obs.add(ObserverInactiveVeto())\n", - "t = TextOutput(\"photon_electron_output.txt\", Output.Event1D)\n", - "obs.onDetection(t)\n", - "\n", - "sim = ModuleList()\n", - "sim.add(SimplePropagation())\n", - "sim.add(Redshift())\n", - "sim.add(EMPairProduction(CMB(),True))\n", - "sim.add(EMPairProduction(IRB_Gilmore12(),True))\n", - "sim.add(EMPairProduction(URB_Protheroe96(),True))\n", - "sim.add(EMDoublePairProduction(CMB(),True))\n", - "sim.add(EMDoublePairProduction(IRB_Gilmore12(),True))\n", - "sim.add(EMDoublePairProduction(URB_Protheroe96(),True))\n", - "sim.add(EMInverseComptonScattering(IRB_Gilmore12(),True))\n", - "sim.add(EMInverseComptonScattering(CMB(),True))\n", - "sim.add(EMInverseComptonScattering(URB_Protheroe96(),True))\n", - "sim.add(EMTripletPairProduction(CMB(),True))\n", - "sim.add(EMTripletPairProduction(IRB_Gilmore12(),True))\n", - "sim.add(EMTripletPairProduction(URB_Protheroe96(),True))\n", - "sim.add(MinimumEnergy(0.01 * EeV))\n", - "\n", - "\n", - "source = Source()\n", - "source.add(SourcePosition(Vector3d(4,0,0)*Mpc))\n", - "source.add(SourceRedshift1D())\n", - "source.add(SourceParticleType(22))\n", - "source.add(SourceEnergy(1000*EeV))\n", - "sim.add(obs)\n", - "sim.run(source,1000,True)\n", - "t.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (Optional) plotting of the results" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "from pylab import *\n", - "\n", - "t.close()\n", - "figure(figsize=(6,6))\n", - "\n", - "a = loadtxt(\"photon_electron_output.txt\")\n", - "E = logspace(16,23,71)\n", - "idx = a[:,1] == 22\n", - "photons = a[idx,2] * 1e18\n", - "idx = fabs(a[:,1]) == 11\n", - "ep = a[idx,2] * 1e18\n", - "data,bins = histogram(photons,E)\n", - "bincenter = (E[1:] -E[:-1])/2 + E[:-1]\n", - "plot(bincenter, data,label=\"photons\")\n", - "data,bins = histogram(ep,E)\n", - "plot(bincenter, data, label=\"electrons / positrons\")\n", - "grid()\n", - "loglog()\n", - "xlim(1e16, 1e21)\n", - "ylim(1e1, 1e4)\n", - "legend(loc=\"lower right\")\n", - "xlabel(\"Energy [eV]\")\n", - "ylabel(\"Number of Particles\")\n", - "show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Photon Propagation outside of CRPropa with EleCa and DINT\n", - "\n", - "There are two main ways to propagate electromagnetic particles (EM particles: photons, electrons, positrons) in CRPropa:\n", - "\n", - "1) propagation as part of the CRPropa simulation chain\n", - "\n", - "2) propagation outside of the CRPropa simulation chain\n", - "\n", - "The following describes option 2, for which CRPropa provides three functions.\n", - "EM particles can either be propagated individually using the external EleCa code (suitable for high energies), or their spectra can be propagated with the transport code DINT (suitable for low energies).\n", - "Alternatively, a combined option is available that processes high energy photons with Eleca and then calculates the resulting spectra with DINT down to low energies.\n", - "\n", - "All three functions take as input a plain-text file with EM particles in the format given in the \"Photons from Proton Propagation\" example below.\n", - "In the following examples the input file \"photon_monoenergetic_source.dat\" contains 1000 photons with E = 50 EeV from a photon source at 4 Mpc distance.\n", - "\n", - "The last example \"Photons from Proton Propagation\" shows how to obtain secondary EM particles from a simulation of hadronic cosmic rays.\n", - "\n", - "Note that the differing results in EleCa (and correspondingly the high energy part of the combined option) are due to an incorrect sampling of the background photon energies in EleCa. The EleCa support will be removed in the near future.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Photons from Proton Propagation\n", - "\n", - "The generation of photons has to be enabled for the individual energy-loss processes in the module chain. Also, separate photon output can be added:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "# source setup\n", - "source = Source()\n", - "source.add(SourceParticleType(nucleusId(1, 1)))\n", - "source.add(SourcePowerLawSpectrum(10 * EeV, 100 * EeV, -2))\n", - "source.add(SourceUniform1D(3 * Mpc, 100.00001 * Mpc))\n", - "\n", - "# setup module list for proton propagation\n", - "m = ModuleList()\n", - "m.add(SimplePropagation(0, 10 * Mpc))\n", - "m.add(MinimumEnergy(1 * EeV))\n", - "\n", - "# observer\n", - "obs1 = Observer() # proton output\n", - "obs1.add( Observer1D() )\n", - "obs1.add( ObserverPhotonVeto() ) # we don't want photons here\n", - "obs1.onDetection( TextOutput('proton_output.txt', Output.Event1D) )\n", - "m.add(obs1)\n", - "obs2 = Observer() # photon output\n", - "obs2.add( ObserverDetectAll() ) # stores the photons at creation without propagating them\n", - "obs2.add( ObserverNucleusVeto() ) # we don't want hadrons here\n", - "out2 = TextOutput('photon_output.txt', Output.Event1D)\n", - "out2.enable(Output.CreatedIdColumn) # enables the necessary columns to be compatible with the DINT and EleCa propagation\n", - "out2.enable(Output.CreatedEnergyColumn)\n", - "out2.enable(Output.CreatedPositionColumn)\n", - "obs2.onDetection( out2 )\n", - "m.add(obs2)\n", - "\n", - "# secondary electrons are disabled here\n", - "m.add(ElectronPairProduction(CMB(), False))\n", - "# enable secondary photons\n", - "m.add(PhotoPionProduction(CMB(), True))\n", - "\n", - "# run simulation\n", - "m.run(source, 10000, True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The file 'photon_output.txt' will contain approximately 300 photons and can be processed as the photon example below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Propagation with EleCa\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import crpropa\n", - "\n", - "# Signature: ElecaPropagation(inputfile, outputfile, showProgress=True, lowerEnergyThreshold=5*EeV, magneticFieldStrength=1*nG, background=\"ALL\")\n", - "crpropa.ElecaPropagation(\"photon_output.txt\", \"photons_eleca.dat\", True, 0.1*crpropa.EeV, 0.1*crpropa.nG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Propagation with DINT\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import crpropa\n", - "\n", - "# Signature: DintPropagation(inputfile, outputfile, IRFlag=4, RadioFlag=4, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", - "crpropa.DintPropagation(\"photon_output.txt\", \"spectrum_dint.dat\", 4, 4, 0.1*crpropa.nG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combined Propagation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import crpropa\n", - "\n", - "# Signature: DintElecaPropagation(inputfile, outputfile, showProgress=True, crossOverEnergy=0.5*EeV, magneticFieldStrength=1*nG, aCutcascade_Magfield=0)\n", - "crpropa.DintElecaPropagation(\"photon_output.txt\", \"spectrum_dint_eleca.dat\", True, 0.5*crpropa.EeV, 0.1*crpropa.nG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (Optional) Plotting of Results" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "from pylab import *\n", - "\n", - "figure(figsize=(6,6))\n", - "\n", - "loglog(clip_on=False)\n", - "yscale(\"log\", nonposy='clip')\n", - "xlabel('Energy [eV]')\n", - "ylabel ('$E^2 dN/dE$ [a.u.]')\n", - "\n", - "# Plot the EleCa spectrum\n", - "elecaPhotons = genfromtxt(\"photons_eleca.dat\")\n", - "binEdges = 10**arange(12, 24, .1)\n", - "logBinCenters = log10(binEdges[:-1]) + 0.5 * (log10(binEdges[1:]) - log10(binEdges[:-1]))\n", - "binWidths = (binEdges[1:] - binEdges[:-1])\n", - "data = histogram(elecaPhotons[:,1] * 1E18, bins=binEdges)\n", - "J = data[0] / binWidths\n", - "E = 10**logBinCenters\n", - "step(E, J * E**2, c='m', label='EleCa')\n", - "\n", - "#Plot the DINT spectrum\n", - "data = genfromtxt(\"spectrum_dint.dat\", names=True)\n", - "lE = data['logE']\n", - "E = 10**lE\n", - "dE = 10**(lE + 0.05) - 10**(lE - 0.05)\n", - "J = data['photons'] / dE\n", - "step(E, J * E**2 , c='b', where='mid', label='DINT')\n", - "\n", - "#Plot the combined DINT+EleCa spectrum\n", - "data = genfromtxt(\"spectrum_dint_eleca.dat\", names=True)\n", - "lE = data['logE']\n", - "E = 10**lE\n", - "dE = 10**(lE + 0.05) - 10**(lE - 0.05)\n", - "J = data['photons'] / dE\n", - "step(E, J * E**2 , c='r', where='mid', label='Combined')\n", - "\n", - "# Nice limits\n", - "xlim(1e14, 1e20)\n", - "ylim(bottom=1e17)\n", - "legend(loc='upper left')\n", - "show()" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" - }, - "kernelspec": { - "display_name": "Python 3.9.5 64-bit", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb b/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb index 034e84ae2..c64fb9172 100644 --- a/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb +++ b/doc/pages/example_notebooks/secondaries/secondary_photons.ipynb @@ -31,14 +31,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n" + ] + } + ], "source": [ "from crpropa import *\n", "\n", @@ -76,6 +84,7 @@ "out1 = TextOutput(filename1, Output.Event1D)\n", "out1.setEnergyScale(eV)\n", "out1.enable(Output.WeightColumn)\n", + "out1.disable(Output.CandidateTagColumn)\n", "obs1.onDetection(out1)\n", "\n", "obs2 = Observer() # photon output\n", @@ -85,12 +94,14 @@ "obs2.add(ObserverNucleusVeto()) # we don't want nuclei here\n", "out2 = TextOutput(filename2, Output.Event1D)\n", "out2.setEnergyScale(eV)\n", + "\n", "# enables the necessary columns to be compatible with the DINT and EleCa propagation\n", "# out2.enable(Output.CreatedIdColumn) \n", "# out2.enable(Output.CreatedEnergyColumn)\n", "# out2.enable(Output.CreatedPositionColumn)\n", "out2.enable(Output.WeightColumn)\n", "obs2.onDetection(out2)\n", + "out2.disable(Output.CandidateTagColumn)\n", "\n", "obs3 = Observer() # electron output\n", "obs3.add(Observer1D())\n", @@ -100,6 +111,8 @@ "out3 = TextOutput(filename3, Output.Event1D)\n", "out3.setEnergyScale(eV)\n", "out3.enable(Output.WeightColumn)\n", + "out3.disable(Output.CandidateTagColumn)\n", + "\n", "# enables the necessary columns to be compatible with the DINT and EleCa propagation\n", "# out2.enable(Output.CreatedIdColumn) \n", "# out2.enable(Output.CreatedEnergyColumn)\n", @@ -144,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, @@ -154,7 +167,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -200,28 +213,32 @@ "plt.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { - "interpreter": { - "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" - }, "kernelspec": { - "display_name": "Python 3.9.5 64-bit", + "display_name": "crp_docu", + "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} From e28311284472334d8262baf3092dd4f691670717 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 2 Feb 2023 14:58:35 +0100 Subject: [PATCH 64/87] Make CandidateTagColumn available. Replace FutureRedshift with Redshift until PR #416 is merged. --- doc/pages/example_notebooks/sim1D/sim1D.ipynb | 204 ++++++++++++++++++ .../example_notebooks/sim1D/sim1D.v4.ipynb | 175 --------------- doc/pages/example_notebooks/sim4D/sim4D.ipynb | 185 ++++++++++++++++ .../example_notebooks/sim4D/sim4D.v4.ipynb | 159 -------------- 4 files changed, 389 insertions(+), 334 deletions(-) create mode 100644 doc/pages/example_notebooks/sim1D/sim1D.ipynb delete mode 100644 doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb create mode 100644 doc/pages/example_notebooks/sim4D/sim4D.ipynb delete mode 100644 doc/pages/example_notebooks/sim4D/sim4D.v4.ipynb diff --git a/doc/pages/example_notebooks/sim1D/sim1D.ipynb b/doc/pages/example_notebooks/sim1D/sim1D.ipynb new file mode 100644 index 000000000..770465632 --- /dev/null +++ b/doc/pages/example_notebooks/sim1D/sim1D.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1D simulation\n", + "\n", + "### Simulation Setup\n", + "\n", + "The following is a 1D simulation including cosmic evolution. \n", + "The sources are modeled to be uniformly distributed and emit a mixed composition of H, He, N and Fe with a power-law spectrum and a charge dependent maximum energy.\n", + "\n", + "To include cosmological effects in this 1D simulation we need two things:\n", + "\n", + "First, the ```Redshift``` module updates the current redshift of the particle in each propagation step. It is best added directly after the propagation module, although the position shouldn't matter much for the typically small propagation steps.\n", + "\n", + "Second, we need to set the initial redshift of the particles.\n", + "In 1D simulations the intial redshift is determined by the source distance. To have the source automatically set the redshift we add ```SourceRedshift1D```. **Please note** that it has to be added after the source property that defines the source position.\n", + "\n", + "**Note: The simulation might take a few minutes**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 2 14:15:45 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:20 - Finished at Thu Feb 2 14:19:05 2023\n", + "\r" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "# simulation setup\n", + "sim = ModuleList()\n", + "sim.add( SimplePropagation(1*kpc, 10*Mpc) )\n", + "sim.add( Redshift() )\n", + "sim.add( PhotoPionProduction(CMB()) )\n", + "sim.add( PhotoPionProduction(IRB_Kneiske04()) )\n", + "sim.add( PhotoDisintegration(CMB()) )\n", + "sim.add( PhotoDisintegration(IRB_Kneiske04()) )\n", + "sim.add( NuclearDecay() )\n", + "sim.add( ElectronPairProduction(CMB()) )\n", + "sim.add( ElectronPairProduction(IRB_Kneiske04()) )\n", + "sim.add( MinimumEnergy( 1 * EeV) )\n", + "\n", + "# observer and output\n", + "obs = Observer()\n", + "obs.add( Observer1D() )\n", + "output = TextOutput('events.txt', Output.Event1D)\n", + "obs.onDetection( output )\n", + "sim.add( obs )\n", + "\n", + "# source\n", + "source = Source()\n", + "source.add( SourceUniform1D(1 * Mpc, 1000 * Mpc) )\n", + "source.add( SourceRedshift1D() )\n", + "\n", + "# power law spectrum with charge dependent maximum energy Z*100 EeV\n", + "# elements: H, He, N, Fe with equal abundances at constant energy per nucleon\n", + "composition = SourceComposition(1 * EeV, 100 * EeV, -1)\n", + "composition.add(1, 1, 1) # H\n", + "composition.add(4, 2, 1) # He-4\n", + "composition.add(14, 7, 1) # N-14\n", + "composition.add(56, 26, 1) # Fe-56\n", + "source.add( composition )\n", + "\n", + "# run simulation\n", + "sim.setShowProgress(True)\n", + "sim.run(source, 20000, True)\n", + "output.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Optional) Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import pylab as pl\n", + "# load events\n", + "output.close()\n", + "d = pl.genfromtxt('events.txt', names=True)\n", + "\n", + "# observed quantities\n", + "Z = pl.array([chargeNumber(int(id)) for id in d['ID'].astype(int)]) # element\n", + "A = pl.array([massNumber(int(id)) for id in d['ID'].astype(int)]) # atomic mass number\n", + "lE = pl.log10(d['E']) + 18 # energy in log10(E/eV))\n", + "\n", + "lEbins = pl.arange(18, 20.51, 0.1) # logarithmic bins\n", + "lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers\n", + "dE = 10**lEbins[1:] - 10**lEbins[:-1] # bin widths\n", + "\n", + "# identify mass groups\n", + "idx1 = A == 1\n", + "idx2 = (A > 1) * (A <= 7)\n", + "idx3 = (A > 7) * (A <= 28)\n", + "idx4 = (A > 28)\n", + "\n", + "# calculate spectrum: J(E) = dN/dE \n", + "J = pl.histogram(lE, bins=lEbins)[0] / dE\n", + "J1 = pl.histogram(lE[idx1], bins=lEbins)[0] / dE\n", + "J2 = pl.histogram(lE[idx2], bins=lEbins)[0] / dE\n", + "J3 = pl.histogram(lE[idx3], bins=lEbins)[0] / dE\n", + "J4 = pl.histogram(lE[idx4], bins=lEbins)[0] / dE\n", + "\n", + "# normalize\n", + "J1 /= J[0]\n", + "J2 /= J[0]\n", + "J3 /= J[0] \n", + "J4 /= J[0]\n", + "J /= J[0]\n", + "\n", + "pl.figure(figsize=(10,7))\n", + "pl.plot(lEcens, J, color='SaddleBrown')\n", + "pl.plot(lEcens, J1, label='A = 1')\n", + "pl.plot(lEcens, J2, label='A = 2-7')\n", + "pl.plot(lEcens, J3, label='A = 8-28')\n", + "pl.plot(lEcens, J4, label='A $>$ 28')\n", + "pl.legend(fontsize=20, frameon=True)\n", + "pl.semilogy()\n", + "pl.ylim(1e-5)\n", + "pl.grid()\n", + "pl.ylabel('$J(E)$ [a.u.]')\n", + "pl.xlabel('$\\log_{10}$(E/eV)')\n", + "pl.savefig('sim1D_spectrum.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb b/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb deleted file mode 100644 index 412741de0..000000000 --- a/doc/pages/example_notebooks/sim1D/sim1D.v4.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1D simulation\n", - "\n", - "### Simulation Setup\n", - "\n", - "The following is a 1D simulation including cosmic evolution. \n", - "The sources are modeled to be uniformly distributed and emit a mixed composition of H, He, N and Fe with a power-law spectrum and a charge dependent maximum energy.\n", - "\n", - "To include cosmological effects in this 1D simulation we need two things:\n", - "\n", - "First, the ```Redshift``` module updates the current redshift of the particle in each propagation step. It is best added directly after the propagation module, although the position shouldn't matter much for the typically small propagation steps.\n", - "\n", - "Second, we need to set the initial redshift of the particles.\n", - "In 1D simulations the intial redshift is determined by the source distance. To have the source automatically set the redshift we add ```SourceRedshift1D```. **Please note** that it has to be added after the source property that defines the source position.\n", - "\n", - "**Note: The simulation might take a few minutes**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "# simulation setup\n", - "sim = ModuleList()\n", - "sim.add( SimplePropagation(1*kpc, 10*Mpc) )\n", - "sim.add( Redshift() )\n", - "sim.add( PhotoPionProduction(CMB()) )\n", - "sim.add( PhotoPionProduction(IRB_Kneiske04()) )\n", - "sim.add( PhotoDisintegration(CMB()) )\n", - "sim.add( PhotoDisintegration(IRB_Kneiske04()) )\n", - "sim.add( NuclearDecay() )\n", - "sim.add( ElectronPairProduction(CMB()) )\n", - "sim.add( ElectronPairProduction(IRB_Kneiske04()) )\n", - "sim.add( MinimumEnergy( 1 * EeV) )\n", - "\n", - "# observer and output\n", - "obs = Observer()\n", - "obs.add( Observer1D() )\n", - "output = TextOutput('events.txt', Output.Event1D)\n", - "obs.onDetection( output )\n", - "sim.add( obs )\n", - "\n", - "# source\n", - "source = Source()\n", - "source.add( SourceUniform1D(1 * Mpc, 1000 * Mpc) )\n", - "source.add( SourceRedshift1D() )\n", - "\n", - "# power law spectrum with charge dependent maximum energy Z*100 EeV\n", - "# elements: H, He, N, Fe with equal abundances at constant energy per nucleon\n", - "composition = SourceComposition(1 * EeV, 100 * EeV, -1)\n", - "composition.add(1, 1, 1) # H\n", - "composition.add(4, 2, 1) # He-4\n", - "composition.add(14, 7, 1) # N-14\n", - "composition.add(56, 26, 1) # Fe-56\n", - "source.add( composition )\n", - "\n", - "# run simulation\n", - "sim.setShowProgress(True)\n", - "sim.run(source, 20000, True)\n", - "output.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (Optional) Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import pylab as pl\n", - "# load events\n", - "output.close()\n", - "d = pl.genfromtxt('events.txt', names=True)\n", - "\n", - "# observed quantities\n", - "Z = pl.array([chargeNumber(int(id)) for id in d['ID'].astype(int)]) # element\n", - "A = pl.array([massNumber(int(id)) for id in d['ID'].astype(int)]) # atomic mass number\n", - "lE = pl.log10(d['E']) + 18 # energy in log10(E/eV))\n", - "\n", - "lEbins = pl.arange(18, 20.51, 0.1) # logarithmic bins\n", - "lEcens = (lEbins[1:] + lEbins[:-1]) / 2 # logarithmic bin centers\n", - "dE = 10**lEbins[1:] - 10**lEbins[:-1] # bin widths\n", - "\n", - "# identify mass groups\n", - "idx1 = A == 1\n", - "idx2 = (A > 1) * (A <= 7)\n", - "idx3 = (A > 7) * (A <= 28)\n", - "idx4 = (A > 28)\n", - "\n", - "# calculate spectrum: J(E) = dN/dE \n", - "J = pl.histogram(lE, bins=lEbins)[0] / dE\n", - "J1 = pl.histogram(lE[idx1], bins=lEbins)[0] / dE\n", - "J2 = pl.histogram(lE[idx2], bins=lEbins)[0] / dE\n", - "J3 = pl.histogram(lE[idx3], bins=lEbins)[0] / dE\n", - "J4 = pl.histogram(lE[idx4], bins=lEbins)[0] / dE\n", - "\n", - "# normalize\n", - "J1 /= J[0]\n", - "J2 /= J[0]\n", - "J3 /= J[0] \n", - "J4 /= J[0]\n", - "J /= J[0]\n", - "\n", - "pl.figure(figsize=(10,7))\n", - "pl.plot(lEcens, J, color='SaddleBrown')\n", - "pl.plot(lEcens, J1, color='blue', label='A = 1')\n", - "pl.plot(lEcens, J2, color='grey', label='A = 2-7')\n", - "pl.plot(lEcens, J3, color='green', label='A = 8-28')\n", - "pl.plot(lEcens, J4, color='red', label='A $>$ 28')\n", - "pl.legend(fontsize=20, frameon=True)\n", - "pl.semilogy()\n", - "pl.ylim(1e-5)\n", - "pl.grid()\n", - "pl.ylabel('$J(E)$ [a.u.]')\n", - "pl.xlabel('$\\log_{10}$(E/eV)')\n", - "pl.savefig('sim1D_spectrum.png')" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" - }, - "kernelspec": { - "display_name": "Python 3.9.5 64-bit", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/doc/pages/example_notebooks/sim4D/sim4D.ipynb b/doc/pages/example_notebooks/sim4D/sim4D.ipynb new file mode 100644 index 000000000..8fd639191 --- /dev/null +++ b/doc/pages/example_notebooks/sim4D/sim4D.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4D Simulation\n", + "\n", + "The following is a simple 4D simulation where cosmic rays are emitted by a source at a specified spatial position at a specified time-point. A cosmic ray is detected if it arrives at the observer position within a specified time window.\n", + "\n", + "**Note:** In CRPropa, time is always expressed in terms of redshift $z$, whereas positions are always expressed in terms of comoving coordinates as Cartesian 3-vectors.\n", + "\n", + "### Simulation setup\n", + "The simulation setup is that of a 3D simulation with a few additions:\n", + "1. We add a source property for the redshift at emission. This can be either ```SourceRedshift```, ```SourceUniformRedshift``` or ```SourceRedshiftEvolution```.\n", + "2. The simulation module ```FutureRedshift``` implements adiabatic energy loss and updates the redshift. In contrast to ```Redshift``` it allows particles to be propagated into the future $z < 0$ which enables faster convergence for finite observation windows.\n", + "3. The observer feature ```ObserverRedshiftWindow``` specifies a time window $z_\\mathrm{min} < z < z_\\mathrm{max}$ in which particles are detected if they hit the observer. Note that this can also be done after the simulation by cutting on the redshifts at observation. For this we also output the current redshift at observation.\n", + "4. A minimum redshift is defined via MinimumRedshift which we set to the lower bound of the observer time window.\n", + "\n", + "### Periodic boundaries\n", + "Due to the additional time dimension, particles are detected much less often. In order to increase the otherwhise horrible simulation efficiency, a ```PeriodicBox``` is defined: Particles that leave this simulation volume, enter again from the opposite side and their source position is moved accordingly.\n", + "As a result the periodic boundaries keep the particles close to the observer and therefore increase the chance of detection. A careful setup is required however:\n", + "1. Sources should only be defined inside the volume as sources outside are filled up by the periodic conditions.\n", + "2. The magnetic field at the boundaries should be periodic as well. This is the case for ```initTurbulence``` as long as the simulation volume coincides with (multiples of) the magnetic field grid.\n", + "\n", + "### Source positions\n", + "In the example below, a single source is defined. For specifying multiple identical discrete sources ```SourceMultiplePositions``` can be used. Multiple non-identical sources can be added to a ```SourceList```. For continous source distributions ```SourceUniformSphere```, ```SourceUniformBox``` and ```SourceUniformCylinder``` can be used. ```SourceDensityGrid``` allows to specify a source distribution via a 3D grid." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: \n", + "This simulation may take **several** minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 2 14:33:15 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:04:17 - Finished at Thu Feb 2 14:37:32 2023\n", + "\r" + ] + } + ], + "source": [ + "from crpropa import *\n", + "\n", + "\n", + "# set up random turbulent field\n", + "Brms = 1 * nG\n", + "lMin = 60 * kpc\n", + "lMax = 800 * kpc\n", + "sIndex = 5./3.\n", + "turbSpectrum = SimpleTurbulenceSpectrum(Brms, lMin, lMax, sIndex)\n", + "gridprops = GridProperties(Vector3d(0), 256, 30 * kpc)\n", + "Bfield = SimpleGridTurbulence(turbSpectrum, gridprops, 42)\n", + "\n", + "# simulation setup\n", + "sim = ModuleList()\n", + "sim.add(PropagationCK(Bfield))\n", + "sim.add(FutureRedshift())\n", + "#sim.add(FutureRedshift()) # Switch back to FutureRedshift when PR #416 is merged\n", + "sim.add(Redshift())\n", + "sim.add(PhotoPionProduction(CMB()))\n", + "sim.add(PhotoPionProduction(IRB_Kneiske04()))\n", + "sim.add(PhotoDisintegration(CMB()))\n", + "sim.add(PhotoDisintegration(IRB_Kneiske04()))\n", + "sim.add(ElectronPairProduction(CMB()))\n", + "sim.add(ElectronPairProduction(IRB_Kneiske04()))\n", + "sim.add(NuclearDecay())\n", + "sim.add(MinimumEnergy(1 * EeV))\n", + "#sim.add(MinimumRedshift(-0.1)) # Switch back to z_min=-0.1 when PR #416 is merged\n", + "sim.add(MinimumRedshift(0.))\n", + "\n", + "\n", + "# periodic boundaries\n", + "extent = 256 * 30 * kpc # size of the magnetic field grid\n", + "sim.add(PeriodicBox(Vector3d(-extent), Vector3d(2 * extent)))\n", + "\n", + "# define the observer\n", + "obs = Observer()\n", + "obs.add(ObserverSurface( Sphere(Vector3d(0.), 0.5 * Mpc)))\n", + "obs.add(ObserverRedshiftWindow(-0.1, 0.1))\n", + "output = TextOutput('output.txt', Output.Event3D)\n", + "output.enable(output.RedshiftColumn)\n", + "obs.onDetection(output)\n", + "sim.add(obs)\n", + "\n", + "# define the source(s)\n", + "source = Source()\n", + "source.add(SourcePosition(Vector3d(10, 0, 0) * Mpc))\n", + "source.add(SourceIsotropicEmission())\n", + "source.add(SourceParticleType(nucleusId(1, 1)))\n", + "source.add(SourcePowerLawSpectrum(1 * EeV, 200 * EeV, -1))\n", + "source.add(SourceRedshiftEvolution(1.5, 0.001, 3))\n", + "\n", + "# run simulation\n", + "sim.setShowProgress(True)\n", + "sim.run(source, 10000)\n", + "output.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "columnnames=['D', 'z', 'ID', 'E', 'X', 'Y', 'Z', 'Px', 'Py', 'Pz','ID0', 'E0', 'X0', 'Y0', 'Z0', 'P0x', 'P0y', 'P0z', 'tag']\n", + "types = [float]*18 + [str]\n", + "import numpy as np\n", + "data = np.loadtxt('./output.txt', dtype={'names': columnnames, 'formats': types})" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "bins = np.linspace(-0.1,0.1, 10)\n", + "plt.hist(data['z'], bins=bins, histtype='step')\n", + "plt.xlabel(r'observed redshift $z$')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/sim4D/sim4D.v4.ipynb b/doc/pages/example_notebooks/sim4D/sim4D.v4.ipynb deleted file mode 100644 index 911a327dc..000000000 --- a/doc/pages/example_notebooks/sim4D/sim4D.v4.ipynb +++ /dev/null @@ -1,159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4D Simulation\n", - "\n", - "The following is a simple 4D simulation where cosmic rays are emitted by a source at a specified spatial position at a specified time-point. A cosmic ray is detected if it arrives at the observer position within a specified time window.\n", - "\n", - "**Note:** In CRPropa, time is always expressed in terms of redshift $z$, whereas positions are always expressed in terms of comoving coordinates as Cartesian 3-vectors.\n", - "\n", - "### Simulation setup\n", - "The simulation setup is that of a 3D simulation with a few additions:\n", - "1. We add a source property for the redshift at emission. This can be either ```SourceRedshift```, ```SourceUniformRedshift``` or ```SourceRedshiftEvolution```.\n", - "2. The simulation module ```FutureRedshift``` implements adiabatic energy loss and updates the redshift. In contrast to ```Redshift``` it allows particles to be propagated into the future $z < 0$ which enables faster convergence for finite observation windows.\n", - "3. The observer feature ```ObserverRedshiftWindow``` specifies a time window $z_\\rm{min} < z < z_\\rm{max}$ in which particles are detected if they hit the observer. Note that this can also be done after the simulation by cutting on the redshifts at observation. For this we also output the current redshift at observation.\n", - "4. A minimum redshift is defined via MinimumRedshift which we set to the lower bound of the observer time window.\n", - "\n", - "### Periodic boundaries\n", - "Due to the additional time dimension, particles are detected much less often. In order to increase the otherwhise horrible simulation efficiency, a ```PeriodicBox``` is defined: Particles that leave this simulation volume, enter again from the opposite side and their source position is moved accordingly.\n", - "As a result the periodic boundaries keep the particles close to the observer and therefore increase the chance of detection. A careful setup is required however:\n", - "1. Sources should only be defined inside the volume as sources outside are filled up by the periodic conditions.\n", - "2. The magnetic field at the boundaries should be periodic as well. This is the case for ```initTurbulence``` as long as the simulation volume coincides with (multiples of) the magnetic field grid.\n", - "\n", - "### Source positions\n", - "In the example below, a single source is defined. For specifying multiple identical discrete sources ```SourceMultiplePositions``` can be used. Multiple non-identical sources can be added to a ```SourceList```. For continous source distributions ```SourceUniformSphere```, ```SourceUniformBox``` and ```SourceUniformCylinder``` can be used. ```SourceDensityGrid``` allows to specify a source distribution via a 3D grid." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Note: \n", - "This simulation may take **several** minutes." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "\n", - "# set up random turbulent field\n", - "turbSpectrum = SimpleTurbulenceSpectrum(Brms=1 * nG, lMin = 60 * kpc, lMax=800 * kpc, sIndex=5./3.)\n", - "gridprops = GridProperties(Vector3d(0), 256, 30 * kpc)\n", - "Bfield = SimpleGridTurbulence(turbSpectrum, gridprops, 42)\n", - "\n", - "# simulation setup\n", - "sim = ModuleList()\n", - "sim.add(PropagationCK(Bfield))\n", - "sim.add(FutureRedshift())\n", - "sim.add(PhotoPionProduction(CMB()))\n", - "sim.add(PhotoPionProduction(IRB_Kneiske04()))\n", - "sim.add(PhotoDisintegration(CMB()))\n", - "sim.add(PhotoDisintegration(IRB_Kneiske04()))\n", - "sim.add(ElectronPairProduction(CMB()))\n", - "sim.add(ElectronPairProduction(IRB_Kneiske04()))\n", - "sim.add(NuclearDecay())\n", - "sim.add(MinimumEnergy(1 * EeV))\n", - "sim.add(MinimumRedshift(-0.1))\n", - "\n", - "# periodic boundaries\n", - "extent = 256 * 30 * kpc # size of the magnetic field grid\n", - "sim.add(PeriodicBox(Vector3d(-extent), Vector3d(2 * extent)))\n", - "\n", - "# define the observer\n", - "obs = Observer()\n", - "obs.add(ObserverSurface( Sphere(Vector3d(0.), 0.5 * Mpc)))\n", - "obs.add(ObserverRedshiftWindow(-0.1, 0.1))\n", - "output = TextOutput('output.txt', Output.Event3D)\n", - "output.enable(output.RedshiftColumn)\n", - "obs.onDetection(output)\n", - "sim.add(obs)\n", - "\n", - "# define the source(s)\n", - "source = Source()\n", - "source.add(SourcePosition(Vector3d(10, 0, 0) * Mpc))\n", - "source.add(SourceIsotropicEmission())\n", - "source.add(SourceParticleType(nucleusId(1, 1)))\n", - "source.add(SourcePowerLawSpectrum(1 * EeV, 200 * EeV, -1))\n", - "source.add(SourceRedshiftEvolution(1.5, 0.001, 3))\n", - "\n", - "# run simulation\n", - "sim.setShowProgress(True)\n", - "sim.run(source, 10000)\n", - "output.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "columnnames=['D', 'z', 'ID', 'E', 'X', 'Y', 'Z', 'Px', 'Py', 'Pz','ID0', 'E0', 'X0', 'Y0', 'Z0', 'P0x', 'P0y', 'P0z']\n", - "import numpy as np\n", - "data = np.loadtxt('./output.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "bins = np.linspace(-0.1,0.1, 10)\n", - "plt.hist(data[:,1], bins=bins, histtype='step')\n", - "plt.xlabel(r'observed redshift $z$')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 7d307b614e2f1dd599bb6c7afe98214ddd312e7a Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 3 Feb 2023 16:47:39 +0100 Subject: [PATCH 65/87] Comment out use of ParticleMapsContainer.get*() functions as they all segfault --- .../targeting/Targeting.ipynb | 108 ++++++++++-------- 1 file changed, 62 insertions(+), 46 deletions(-) diff --git a/doc/pages/example_notebooks/targeting/Targeting.ipynb b/doc/pages/example_notebooks/targeting/Targeting.ipynb index a4db8af15..0ce0a8813 100644 --- a/doc/pages/example_notebooks/targeting/Targeting.ipynb +++ b/doc/pages/example_notebooks/targeting/Targeting.ipynb @@ -1,12 +1,13 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## The targeting algorithm of CRPropa\n", "\n", - "Here we will introduce you to the targeting algorithm in CRPropa, which emits particles from their sources using a von-Mises-Fisher distribution instead of an isotropic distribution. After emission from their sources, the particles get a weight assigned to them so that the resulting distribution at the observer can be reweighted to resemble an isotropically emitted distribution from the sources. This can lead to significantly larger number of hits compared with starting with an isotropic emission." + "Here, we will introduce you to the targeting algorithm in CRPropa, which emits particles from their sources using a von-Mises-Fisher distribution instead of an isotropic distribution. After emission from their sources, the particles get a weight assigned to them so that the resulting distribution at the observer can be reweighted to resemble an isotropically emitted distribution from the sources. This can lead to significantly larger number of hits compared with starting with an isotropic emission." ] }, { @@ -18,9 +19,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Fri Feb 3 16:44:36 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:01 - Finished at Fri Feb 3 16:44:37 2023\n", + "\r" + ] + } + ], "source": [ "import numpy as np\n", "from crpropa import *\n", @@ -46,6 +58,7 @@ "obs.setDeactivateOnDetection(True)\n", "FilenameObserver = 'TargetedEmission.txt'\n", "output = TextOutput(FilenameObserver)\n", + "output.disable(output.CandidateTagColumn)\n", "obs.onDetection(output)\n", "sim.add(obs)\n", " \n", @@ -78,22 +91,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import healpy as hp\n", "import matplotlib.pylab as plt\n", @@ -112,16 +112,22 @@ "for i in range(len(E)):\n", " M.addParticle(int(Id[i]), E[i], lons[i], lats[i], w[i])\n", "\n", - "#stack all maps\n", - "crMap = np.zeros(49152)\n", - "for pid in M.getParticleIds():\n", - " energies = M.getEnergies(int(pid))\n", - " for i, energy in enumerate(energies):\n", - " crMap += M.getMap(int(pid), energy * eV )\n", + "###################################################################\n", + "# WARNING\n", + "# The calls M.getEnergies()/getParticleIds()/getMap() all segfault.\n", + "################################################################### \n", "\n", - "#plot maps using healpy\n", - "hp.mollview(map=crMap, title='Targeted emission')\n", - "plt.show()" + "#stack all maps\n", + "#crMap = np.zeros(49152)\n", + "#for pid in M.getParticleIds():\n", + " #energies = M.getEnergies(int(pid))\n", + " #for i, energy in enumerate(energies):\n", + " #continue\n", + " #crMap += M.getMap(int(pid), energy * eV )\n", + "#\n", + "##plot maps using healpy\n", + "#hp.mollview(map=crMap, title='Targeted emission')\n", + "#plt.show()" ] }, { @@ -133,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -285,21 +291,33 @@ "text": [ "Processing epoch Nr.: 0\n", "starting simulation...\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Fri Feb 3 16:44:45 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:04 - Finished at Fri Feb 3 16:44:49 2023\n", "simulation done...\n", "Processing epoch Nr.: 1\n", "starting simulation...\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Fri Feb 3 16:44:49 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:01 - Finished at Fri Feb 3 16:44:50 2023\n", "simulation done...\n", "Processing epoch Nr.: 2\n", "starting simulation...\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Fri Feb 3 16:45:01 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:01 - Finished at Fri Feb 3 16:45:02 2023\n", "simulation done...\n", "Processing epoch Nr.: 3\n", "starting simulation...\n", + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Fri Feb 3 16:45:13 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:00:01 - Finished at Fri Feb 3 16:45:14 2023\n", "simulation done...\n", - "[[-9.99999337e-01 -1.22891790e-04 -1.14518693e-03]\n", - " [-9.99999968e-01 8.32059924e-05 2.38223738e-04]\n", - " [-9.99999983e-01 -1.49458293e-04 1.11972390e-04]\n", - " [-9.99999961e-01 1.77345407e-05 2.77207671e-04]]\n", - "[448.35812021 460.20604565 460.15188938 460.26029095]\n" + "[[-9.99995457e-01 -2.96500797e-03 5.42450363e-04]\n", + " [-9.99999914e-01 3.76654617e-04 1.74703161e-04]\n", + " [-9.99999990e-01 -4.79027931e-05 1.29321485e-04]\n", + " [-9.99999903e-01 4.15652682e-04 -1.45140435e-04]]\n", + "[436.79931971 458.02808237 461.4045643 459.33300034]\n" ] } ], @@ -326,12 +344,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -348,23 +366,16 @@ "plt.title('Acceptance rate as a function of epoch')\n", "plt.plot(acceptance, label='sample acceptance',color='black')\n", "plt.plot(acceptance*0+Phit, label='target acceptance',color='red')\n", - "plt.xlabel(r'$n_{\\rm{epoch}}$')\n", + "plt.xlabel(r'$n_{\\mathrm{epoch}}$')\n", "plt.ylabel('Acceptance rate')\n", "plt.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "crp_docu", "language": "python", "name": "python3" }, @@ -378,7 +389,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } } }, "nbformat": 4, From afe97770d6f37d09c6976b5a32e2eb2c72b85ea0 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 3 Feb 2023 16:54:09 +0100 Subject: [PATCH 66/87] Include dumping and loading the magnetic fields --- .../trajectories/trajectories.v4.ipynb | 219 ------------------ 1 file changed, 219 deletions(-) delete mode 100644 doc/pages/example_notebooks/trajectories/trajectories.v4.ipynb diff --git a/doc/pages/example_notebooks/trajectories/trajectories.v4.ipynb b/doc/pages/example_notebooks/trajectories/trajectories.v4.ipynb deleted file mode 100644 index efe99b96d..000000000 --- a/doc/pages/example_notebooks/trajectories/trajectories.v4.ipynb +++ /dev/null @@ -1,219 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3D trajectories in a turbulent field\n", - "\n", - "The following simulation tracks a single UHE nucleus and its secondary nucleons/nuclei through a turbulent magnetic field.\n", - "\n", - "First we create a random realization of a turbulent field with a Kolmogorov power spectrum on 60-800 kpc lengthscales and an RMS field strength of 8 nG.\n", - "The field is stored on a $256^3$ grid with 30 kpc grid spacing, and thus has an extent of $(256 \\cdot 30 \\rm{kpc})^3$.\n", - "The field is by default periodically repeated in space to cover an arbitrary volume.\n", - "\n", - "The chosen grid size consumes only very little memory. For practical purposes a larger grid is advised in order to represent more variations of turbulent modes, provide a larger turbulent range, or a higher resolution." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "randomSeed = 42\n", - "turbSpectrum = SimpleTurbulenceSpectrum(Brms=8*nG, lMin = 60*kpc, lMax=800*kpc, sIndex=5./3.)\n", - "gridprops = GridProperties(Vector3d(0), 256, 30*kpc)\n", - "BField = SimpleGridTurbulence(turbSpectrum, gridprops, randomSeed)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lc = 192.0 kpc\n", - "sqrt() = 8.0 nG\n", - "<|B|> = 7.4 nG\n", - "B(10 Mpc, 0, 0) = Vector(0.210589, -1.94174, 1.69713) nG\n" - ] - } - ], - "source": [ - "# print some properties of our field\n", - "print('Lc = {:.1f} kpc'.format(BField.getCorrelationLength() / kpc)) # correlation length\n", - "print('sqrt() = {:.1f} nG'.format(BField.getBrms() / nG)) # RMS\n", - "print('<|B|> = {:.1f} nG'.format(BField.getMeanFieldStrength() / nG)) # mean\n", - "print('B(10 Mpc, 0, 0) =', BField.getField(Vector3d(10,0,0) * Mpc) / nG, 'nG')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving and loading fields\n", - "\n", - "In addition to creating random turbulent fields, we can also load and save custom magnetic field grids.\n", - "As input and output we currently support binary files in single precision and ASCII files." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "# save the field\n", - "# format: (Bx, By, Bz)(x, y, z) with z changing the quickest.\n", - "#dumpGrid(BField.getGrid(), 'myfield.dat') # binary, single precision\n", - "#dumpGridToTxt(Bfield.getGrid(), 'myfield.txt') # ASCII\n", - "# load your own field\n", - "#vgrid=Grid3f(gridprops)\n", - "#loadGrid(vgrid, 'myfield.dat')\n", - "#loadGridFromTxt(vgrid, 'myfield.txt')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running the simulation\n", - "Now that we have our magnetic field ready we can fire up our simulation and hope that something visually interesting is going to happen." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "sim = ModuleList()\n", - "sim.add(PropagationCK(BField))\n", - "sim.add(PhotoPionProduction(CMB()))\n", - "sim.add(PhotoPionProduction(IRB_Kneiske04()))\n", - "sim.add(PhotoDisintegration(CMB()))\n", - "sim.add(PhotoDisintegration(IRB_Kneiske04()))\n", - "sim.add(ElectronPairProduction(CMB()))\n", - "sim.add(ElectronPairProduction(IRB_Kneiske04()))\n", - "sim.add(NuclearDecay())\n", - "sim.add(MaximumTrajectoryLength(25 * Mpc))\n", - "output = TextOutput('trajectory.txt', Output.Trajectory3D) \n", - "sim.add(output)\n", - "\n", - "x = Vector3d(0,0,0) # position\n", - "p = Vector3d(1,1,0) # direction\n", - "c = Candidate(nucleusId(16, 8), 100 * EeV, x, p)\n", - "\n", - "sim.run(c, True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (Optional) Plotting\n", - "\n", - "We plot the trajectory of our oxygen-16 nucleus. To distinguish between secondary nuclei the following colors are used: protons are blue, alpha particles are green, everthing heavier is red." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "from pylab import *\n", - "from mpl_toolkits.mplot3d import axes3d\n", - "\n", - "output.close()\n", - "data = genfromtxt('trajectory.txt', names=True)\n", - "\n", - "# trajectory points\n", - "x, y, z = data['X'], data['Y'], data['Z']\n", - "\n", - "# translate particle ID to charge number\n", - "Z = [chargeNumber(int(Id)) for Id in data['ID'].astype(int)]\n", - "\n", - "# translate the charge number to color and size\n", - "# --> protons are blue, Helium is green, everthing else is red\n", - "colorDict = {0:'k', 1:'b', 2:'g', 3:'r', 4:'r', 5:'r', 6:'r', 7:'r', 8:'r'}\n", - "sizeDict = {0:4, 1:4, 2:8, 3:10, 4:10, 5:10, 6:10, 7:10, 8:10}\n", - "colors = [colorDict[z] for z in Z]\n", - "sizes = [sizeDict[z] for z in Z]\n", - "\n", - "fig = plt.figure(figsize=(12, 5))#plt.figaspect(0.5))\n", - "ax = fig.gca(projection='3d')# , aspect='equal'\n", - "\n", - "ax.scatter(x,y,z+6, 'o', s=sizes, color=colors)\n", - "\n", - "ax.set_xlabel('x / Mpc', fontsize=18)\n", - "ax.set_ylabel('y / Mpc', fontsize=18)\n", - "ax.set_zlabel('z / Mpc', fontsize=18)\n", - "ax.set_xlim((-1, 16))\n", - "ax.set_ylim((-1, 16))\n", - "ax.set_zlim((-1, 16))\n", - "ax.xaxis.set_ticks((0, 5, 10, 15))\n", - "ax.yaxis.set_ticks((0, 5, 10, 15))\n", - "ax.zaxis.set_ticks((0, 5, 10, 15))\n", - "\n", - "show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f317bb2622303ae74e087fefd694334a4632102d Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 3 Feb 2023 17:34:58 +0100 Subject: [PATCH 67/87] Update Filenames --- .../{lensing_liouville.v4.ipynb => lensing_liouville.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/pages/example_notebooks/galactic_lensing/{lensing_liouville.v4.ipynb => lensing_liouville.ipynb} (100%) diff --git a/doc/pages/example_notebooks/galactic_lensing/lensing_liouville.v4.ipynb b/doc/pages/example_notebooks/galactic_lensing/lensing_liouville.ipynb similarity index 100% rename from doc/pages/example_notebooks/galactic_lensing/lensing_liouville.v4.ipynb rename to doc/pages/example_notebooks/galactic_lensing/lensing_liouville.ipynb From ca286b4d42bc5fe6c2f621e595c32d6b7739bed6 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 9 Feb 2023 14:34:37 +0100 Subject: [PATCH 68/87] Update extra-galactic magnetic field example. Include figure --- .../extragalactic_fields/MHD_models.ipynb | 363 ++++++++++++++++++ .../extragalactic_fields/MHD_models.v4.ipynb | 345 ----------------- 2 files changed, 363 insertions(+), 345 deletions(-) create mode 100644 doc/pages/example_notebooks/extragalactic_fields/MHD_models.ipynb delete mode 100644 doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb diff --git a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.ipynb b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.ipynb new file mode 100644 index 000000000..2b982efa6 --- /dev/null +++ b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3D MHD models\n", + "This notebook explains how to use cubic results of 3D MHD models on a uniform grid in CRPropa.\n", + "\n", + "## Supplied data\n", + "\n", + "The fields need to be supplied in a raw binary file that contains only single floats, arranged as follows: Starting with the cell values (Bx,By,Bz for magnetic field or rho for density) at the origin of the box, the code continues to read along z, then y and finally x.\n", + "\n", + "On https://crpropa.github.io/CRPropa3/ under \"Additional resources\" you can find a number of MHD models used with CRPropa in the literature. \n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: \n", + "The parameters used for the following example refer to the MHD model by Hackstein et al. (2018), as provided under \"Additional resources\". However, CRPropa does in general not take any warranty on the accuracy of any of those external data files.\n", + "\n", + "In some previous version of this notebook the used MHD model has not been representing the results from Hackstein et al. (2018). This has been due to two issues: (1.) the size of the grid has not taken the dimensionless Hubble parameter into account and (2.) the X- and Z-coordinates of the available data files have been transposed. But since 20.05.2022 both of these issues have been fixed and the following example can be used to include the MHD model data from Hackstein et al. (2018)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "from crpropa import *\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "## settings for MHD model (must be set according to model)\n", + "filepath = \"/rest/MagneticFields/clues\" ## directory of clues data\n", + "filename_bfield = \"clues_primordial.dat\" ## filename of the magnetic field\n", + "filename_halos = \"clues_halos.dat\" ## filename of the halo data\n", + "filename_mass = \"mass-density_clues.dat\" ## filename of the mass distribution\n", + "\n", + "gridOrigin = Vector3d(0,0,0) ## origin of the 3D data, preferably at boxOrigin\n", + "gridSize = 1024 ## size of uniform grid in data points\n", + "h = 0.677 ## dimensionless Hubble parameter\n", + "size = 249.827/h *Mpc ## physical edgelength of volume in Mpc\n", + "b_factor = 1. ## global renormalization factor for the field\n", + "\n", + "## settings of simulation\n", + "boxOrigin = Vector3d( 0, 0, 0,) ## origin of the full box of the simulation\n", + "boxSize = Vector3d( size, size, size ) ## end of the full box of the simulation\n", + "\n", + "## settings for computation\n", + "minStep = 10.*kpc ## minimum length of single step of calculation\n", + "maxStep = 4.*Mpc ## maximum length of single step of calculation\n", + "tolerance = 1e-2 ## tolerance for error in iterative calculation of propagation step\n", + "\n", + "spacing = size/(gridSize) ## resolution, physical size of single cell\n", + "\n", + "## initiate grid to hold field values\n", + "vgrid = Grid3f( gridOrigin, gridSize, spacing )\n", + "## load values to the grid\n", + "loadGrid( vgrid, os.path.join(filepath, filename_bfield), b_factor )\n", + "## use grid as magnetic field\n", + "bField = MagneticFieldGrid( vgrid )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source settings\n", + "\n", + "The default in this example is a uniform source distribution (SourceUniformBox). To try the other examples uncomment the corresponding lines in the following cells." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "source = Source()\n", + "\n", + "#####################################################\n", + "# Uniform source distribution\n", + "#####################################################\n", + "\n", + "# The most simple scenario of UHECR sources is a uniform distribution of their sources. \n", + "# This can be realized via use of:\n", + "source.add( SourceUniformBox( boxOrigin, boxSize )) \n", + "\n", + "\n", + "#####################################################\n", + "# Sampling from the density field\n", + "#####################################################\n", + "\n", + "# The distribution of gas density can be used as a probability density function for \n", + "# the injection of particles from random positions.\n", + "\n", + "\n", + "# initialize grid to hold field values\n", + "#mgrid = Grid1f( gridOrigin, gridSize, spacing )\n", + "# load values to grid\n", + "#loadGrid( mgrid, os.path.join(filepath, filename_mass) )\n", + "# add source module to simulation\n", + "#source.add( SourceDensityGrid( mgrid ) )\n", + "\n", + "\n", + "#####################################################\n", + "# Sampling from discrete halo positions\n", + "#####################################################\n", + "\n", + "# Alternatively, for the CLUES models, a list of mass halo positions is provided. \n", + "# These positions can be used as sources with the same properties by use of the following\n", + "\n", + "# read data from file\n", + "#data = np.loadtxt(os.path.join(filepath, filename_halos), unpack=True, skiprows=39)\n", + "#sX = data[0] \n", + "#sY = data[1] \n", + "#sZ = data[2] \n", + "#mass_halo = data[5] \n", + "#\n", + "### find only those mass halos inside the provided volume (see Hackstein et al. 2018 for more details)\n", + "#Xdown= sX >= 0.25 \n", + "#Xup= sX <= 0.75 \n", + "#Ydown= sY >= 0.25 \n", + "#Yup= sY <= 0.75 \n", + "#Zdown= sZ >= 0.25 \n", + "#Zup= sZ <= 0.75 \n", + "#insider= Xdown*Xup*Ydown*Yup*Zdown*Zup \n", + "#\n", + "### transform relative positions to physical positions within given grid\n", + "#sX = (sX[insider]-0.25)*2*size\n", + "#sY = (sY[insider]-0.25)*2*size\n", + "#sZ = (sZ[insider]-0.25)*2*size\n", + "#\n", + "### collect all sources in the multiple sources container\n", + "#smp = SourceMultiplePositions()\n", + "#for i in range(0,len(sX)):\n", + "# pos = Vector3d( sX[i], sY[i], sZ[i] )\n", + "# smp.add( pos, 1. )\n", + "# \n", + "### add collected sources\n", + "#source.add( smp )\n", + "\n", + "\n", + "#####################################################\n", + "# Additional source settings\n", + "#####################################################\n", + "\n", + "\n", + "## use isotropic emission from all sources\n", + "source.add( SourceIsotropicEmission() )\n", + "\n", + "## set particle type to be injected\n", + "A, Z = 1, 1 # proton\n", + "source.add( SourceParticleType( nucleusId(A,Z) ) )\n", + "\n", + "## set injected energy spectrum\n", + "Emin, Emax = 1*EeV, 1000*EeV\n", + "specIndex = -1\n", + "source.add( SourcePowerLawSpectrum( Emin, Emax, specIndex ) ) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Observer\n", + "\n", + "To register particles, an observer has to be defined. In the provided constrained simulations the position of the Milky Way is, by definition, in the center of the volume." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "filename_output = 'output_MW.txt'\n", + "output = TextOutput(filename_output)\n", + "output.disable(output.CandidateTagColumn)\n", + "\n", + "obsPosition = Vector3d(0.5*size,0.5*size,0.5*size) # position of observer, MW is in center of constrained simulations\n", + "obsSize = 10*Mpc ## physical size of observer sphere. Note: This is very large observer. In a real simulation the size should be reduces significantly.\n", + "\n", + "## initialize observer that registers particles that enter into sphere of given size around its position\n", + "obs = Observer()\n", + "obs.add( ObserverSurface( Sphere( obsPosition, obsSize ) ) )\n", + "## write registered particles to output file\n", + "obs.onDetection( output )\n", + "## choose to not further follow particles paths once detected\n", + "obs.setDeactivateOnDetection(True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ModuleList\n", + "\n", + "Add all created modules to the ModuleList and run the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crpropa::ModuleList: Number of Threads: 8\n", + "Run ModuleList\n", + " Started Thu Feb 9 12:12:17 2023 : [\u001b[1;32m Finished \u001b[0m] 100% Needed: 00:03:47 - Finished at Thu Feb 9 12:16:04 2023\n", + "\r" + ] + } + ], + "source": [ + "N = 1000000\n", + "\n", + "m = ModuleList()\n", + "m.add(PropagationCK( bField, tolerance, minStep, maxStep))\n", + "\n", + "#make use of periodicity of the magnetic field model\n", + "m.add( PeriodicBox( boxOrigin, boxSize ) )\n", + "\n", + "m.add(obs)\n", + "\n", + "#end trajectories to speed up simulation\n", + "maxTra = MaximumTrajectoryLength(400 * Mpc)\n", + "m.add(maxTra)\n", + "\n", + "#m.showModules() ## optional, see summary of loaded modules\n", + "m.setShowProgress(True) ## optional, see progress during runtime\n", + "m.run(source, N, True) ## perform simulation with N particles injected from source\n", + "\n", + "output.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2296 candidates have been recorded that is ~0.23% of all injected candidates\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with open('output_MW.txt', 'r') as f:\n", + " names = f.readline()[:-1].split('\\t')[1:]\n", + "\n", + "df = pd.read_csv('output_MW.txt', delimiter = '\\t', comment='#', names=names)\n", + "import numpy as np\n", + "\n", + "print(\"{} candidates have been recorded that is ~{:.2}% of all injected candidates\".format(df.D.count(), df.D.count()/N*100))\n", + "\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot()\n", + "ax.scatter(df.X0, df.Y0, alpha=0.3, label=\"Sources\")\n", + "ax.scatter(obsPosition.x/Mpc, obsPosition.y/Mpc, marker='x', color='red', label=('Observer'))\n", + "ax.plot([0,size/Mpc], [0, 0], color='r', linestyle='--')\n", + "ax.plot([0,size/Mpc], [size/Mpc, size/Mpc], color='r', linestyle='--')\n", + "ax.plot([0,0], [0, size/Mpc], color='r', linestyle='--')\n", + "ax.plot([size/Mpc,size/Mpc], [0, size/Mpc], color='r', linestyle='--', label=\"Original Volume\")\n", + "\n", + "ax.legend(loc=\"upper left\")\n", + "ax.set_aspect(True)\n", + "\n", + "ax.set_xlabel('X [Mpc]')\n", + "ax.set_ylabel('Y [Mpc]')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb b/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb deleted file mode 100644 index e33a4544b..000000000 --- a/doc/pages/example_notebooks/extragalactic_fields/MHD_models.v4.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3D MHD models\n", - "This notebook explains how to use cubic results of 3D MHD models on a uniform grid in CRPropa.\n", - "\n", - "## Supplied data\n", - "\n", - "The fields need to be supplied in a raw binary file that contains only single floats, arranged as follows: Starting with the cell values (Bx,By,Bz for magnetic field or rho for density) at the origin of the box, the code continues to read along z, then y and finally x.\n", - "\n", - "On https://crpropa.github.io/CRPropa3/ under \"Additional resources\" you can find a number of MHD models used with CRPropa in the literature. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Note: \n", - "The parameters used for the following example refer to the MHD model by Hackstein et al. (2018), as provided under \"Additional resources\". However, CRPropa does in general not take any warranty on the accuracy of any of those external data files.\n", - "\n", - "Note that in some previous version of this notebook the used MHD model has not been representing the results from Hackstein et al. (2018). This has been due to two issues: (1.) the size of the grid has not taken the dimensionless Hubble parameter into account and (2.) the X- and Z-coordinates of the available data files have been transposed. But since 20.05.2022 both of these issues have been fixed and the following example can be used to include the MHD model data from Hackstein et al. (2018)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "from crpropa import *\n", - "\n", - "## settings for MHD model (must be set according to model)\n", - "filename_bfield = \"clues_primordial.dat\" ## filename of the magnetic field\n", - "gridOrigin = Vector3d(0,0,0) ## origin of the 3D data, preferably at boxOrigin\n", - "gridSize = 1024 ## size of uniform grid in data points\n", - "h = 0.677 ## dimensionless Hubble parameter\n", - "size = 249.827/h *Mpc ## physical edgelength of volume in Mpc\n", - "b_factor = 1. ## global renormalization factor for the field\n", - "\n", - "## settings of simulation\n", - "boxOrigin = Vector3d( 0, 0, 0,) ## origin of the full box of the simulation\n", - "boxSize = Vector3d( size, size, size ) ## end of the full box of the simulation\n", - "\n", - "## settings for computation\n", - "minStep = 10.*kpc ## minimum length of single step of calculation\n", - "maxStep = 4.*Mpc ## maximum length of single step of calculation\n", - "tolerance = 1e-2 ## tolerance for error in iterative calculation of propagation step\n", - "\n", - "spacing = size/(gridSize) ## resolution, physical size of single cell\n", - "\n", - "m = ModuleList()\n", - "\n", - "\n", - "## instead of computing propagation without Lorentz deflection via\n", - "# m.add(SimplePropagation(minStep,maxStep))\n", - "\n", - "## initiate grid to hold field values\n", - "vgrid = Grid3f( gridOrigin, gridSize, spacing )\n", - "## load values to the grid\n", - "loadGrid( vgrid, filename_bfield, b_factor )\n", - "## use grid as magnetic field\n", - "bField = MagneticFieldGrid( vgrid )\n", - "## add propagation module to the simulation to activate deflection in supplied field\n", - "m.add(PropagationCK( bField, tolerance, minStep, maxStep))\n", - "#m.add(DeflectionCK( bField, tolerance, minStep, maxStep)) ## this was used in older versions of CRPropa\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "to make use of periodicity of the provided data grid, use" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "m.add( PeriodicBox( boxOrigin, boxSize ) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "to not follow particles forever, use" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "m.add( MaximumTrajectoryLength( 400*Mpc ) ) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Uniform injection\n", - "\n", - "The most simple scenario of UHECR sources is a uniform distribution of their sources. This can be realized via use of" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "source = Source()\n", - "source.add( SourceUniformBox( boxOrigin, boxSize )) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Injection following density field\n", - "\n", - "The distribution of gas density can be used as a probability density function for the injection of particles from random positions." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "filename_density = \"mass-density_clues.dat\" ## filename of the density field\n", - "\n", - "source = Source()\n", - "## initialize grid to hold field values\n", - "mgrid = Grid( gridOrigin, gridSize, spacing )\n", - "## load values to grid\n", - "loadGrid( mgrid, filename_density )\n", - "## add source module to simulation\n", - "source.add( SourceDensityGrid( mgrid ) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mass Halo injection\n", - "\n", - "Alternatively, for the CLUES models, we also provide a list of mass halo positions. These positions can be used as sources with the same properties by use of the following" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "filename_halos = 'clues_halos.dat'\n", - "\n", - "# read data from file\n", - "data = np.loadtxt(filename_halos, unpack=True, skiprows=39)\n", - "sX = data[0] \n", - "sY = data[1] \n", - "sZ = data[2] \n", - "mass_halo = data[5] \n", - "\n", - "## find only those mass halos inside the provided volume (see Hackstein et al. 2018 for more details)\n", - "Xdown= sX >= 0.25 \n", - "Xup= sX <= 0.75 \n", - "Ydown= sY >= 0.25 \n", - "Yup= sY <= 0.75 \n", - "Zdown= sZ >= 0.25 \n", - "Zup= sZ <= 0.75 \n", - "insider= Xdown*Xup*Ydown*Yup*Zdown*Zup \n", - "\n", - "## transform relative positions to physical positions within given grid\n", - "sX = (sX[insider]-0.25)*2*size\n", - "sY = (sY[insider]-0.25)*2*size\n", - "sZ = (sZ[insider]-0.25)*2*size\n", - "\n", - "## collect all sources in the multiple sources container\n", - "smp = SourceMultiplePositions()\n", - "for i in range(0,len(sX)):\n", - " pos = Vector3d( sX[i], sY[i], sZ[i] )\n", - " smp.add( pos, 1. )\n", - " \n", - "## add collected sources\n", - "source = Source()\n", - "source.add( smp )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "additional source properties" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "## use isotropic emission from all sources\n", - "source.add( SourceIsotropicEmission() )\n", - "\n", - "## set particle type to be injected\n", - "A, Z = 1, 1 # proton\n", - "source.add( SourceParticleType( nucleusId(A,Z) ) )\n", - "\n", - "## set injected energy spectrum\n", - "Emin, Emax = 1*EeV, 1000*EeV\n", - "specIndex = -1\n", - "source.add( SourcePowerLawSpectrum( Emin, Emax, specIndex ) ) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Observer\n", - "\n", - "To register particles, an observer has to be defined. In the provided constrained simulations the position of the Milky Way is, by definition, in the center of the volume." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "filename_output = 'data/output_MW.txt'\n", - "\n", - "obsPosition = Vector3d(0.5*size,0.5*size,0.5*size) # position of observer, MW is in center of constrained simulations\n", - "obsSize = 800*kpc ## physical size of observer sphere\n", - "\n", - "\n", - "## initialize observer that registers particles that enter into sphere of given size around its position\n", - "obs = Observer()\n", - "obs.add( ObserverSurface( Sphere( obsPosition, obsSize ) ) )\n", - "## write registered particles to output file\n", - "obs.onDetection( TextOutput( filename_output ) )\n", - "## choose to not further follow particles paths once detected\n", - "obs.setDeactivateOnDetection(True)\n", - "## add observer to module list\n", - "m.add(obs)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "finally run the simulation by" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "N = 1000\n", - "\n", - "m.showModules() ## optional, see summary of loaded modules\n", - "m.setShowProgress(True) ## optional, see progress during runtime\n", - "m.run(source, N, True) ## perform simulation with N particles injected from source" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" - }, - "kernelspec": { - "display_name": "Python 3.9.5 64-bit", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file From 05b43dac6439ce507452a91f1b7c76eccbfe0113 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 9 Feb 2023 14:45:38 +0100 Subject: [PATCH 69/87] Update trajectories example --- .../trajectories/trajectories.ipynb | 236 ++++++++++++++++++ 1 file changed, 236 insertions(+) create mode 100644 doc/pages/example_notebooks/trajectories/trajectories.ipynb diff --git a/doc/pages/example_notebooks/trajectories/trajectories.ipynb b/doc/pages/example_notebooks/trajectories/trajectories.ipynb new file mode 100644 index 000000000..cf2c0aa80 --- /dev/null +++ b/doc/pages/example_notebooks/trajectories/trajectories.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3D trajectories in a turbulent field\n", + "\n", + "The following simulation tracks a single UHE nucleus and its secondary nucleons/nuclei through a turbulent magnetic field.\n", + "\n", + "First we create a random realization of a turbulent field with a Kolmogorov power spectrum on 60-800 kpc lengthscales and an RMS field strength of 8 nG.\n", + "The field is stored on a $256^3$ grid with 30 kpc grid spacing, and thus has an extent of $(256 \\cdot 30 \\rm{kpc})^3$.\n", + "The field is by default periodically repeated in space to cover an arbitrary volume.\n", + "\n", + "The chosen grid size consumes only very little memory. For practical purposes a larger grid is advised in order to represent more variations of turbulent modes, provide a larger turbulent range, or a higher resolution." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from crpropa import *\n", + "\n", + "randomSeed = 42\n", + "Brms=8*nG\n", + "lMin = 60*kpc\n", + "lMax=800*kpc\n", + "sIndex=5./3.\n", + "turbSpectrum = SimpleTurbulenceSpectrum(Brms, lMin, lMax, sIndex)\n", + "gridprops = GridProperties(Vector3d(0), 256, 30*kpc)\n", + "BField = SimpleGridTurbulence(turbSpectrum, gridprops, randomSeed)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lc = 192.0 kpc\n", + "sqrt() = 8.0 nG\n", + "<|B|> = 7.4 nG\n", + "B(10 Mpc, 0, 0) = Vector(0.21059, -1.94174, 1.69713) nG\n" + ] + } + ], + "source": [ + "# print some properties of our field\n", + "print('Lc = {:.1f} kpc'.format(BField.getCorrelationLength() / kpc)) # correlation length\n", + "print('sqrt() = {:.1f} nG'.format(BField.getBrms() / nG)) # RMS\n", + "print('<|B|> = {:.1f} nG'.format(BField.getMeanFieldStrength() / nG)) # mean\n", + "print('B(10 Mpc, 0, 0) =', BField.getField(Vector3d(10,0,0) * Mpc) / nG, 'nG')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving and loading fields\n", + "\n", + "In addition to creating random turbulent fields, we can also load and save custom magnetic field grids.\n", + "As input and output we currently support binary files in single precision and ASCII files." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "# save the field\n", + "# format: (Bx, By, Bz)(x, y, z) with z changing the quickest.\n", + "dumpGrid(BField.getGrid(), 'myfield.dat') # binary, single precision\n", + "dumpGridToTxt(BField.getGrid(), 'myfield.txt') # ASCII\n", + "# load your own field\n", + "vgrid=Grid3f(gridprops)\n", + "loadGrid(vgrid, 'myfield.dat')\n", + "loadGridFromTxt(vgrid, 'myfield.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running the simulation\n", + "Now that we have our magnetic field ready we can fire up our simulation and hope that something visually interesting is going to happen." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "sim = ModuleList()\n", + "sim.add(PropagationCK(BField))\n", + "sim.add(PhotoPionProduction(CMB()))\n", + "sim.add(PhotoPionProduction(IRB_Kneiske04()))\n", + "sim.add(PhotoDisintegration(CMB()))\n", + "sim.add(PhotoDisintegration(IRB_Kneiske04()))\n", + "sim.add(ElectronPairProduction(CMB()))\n", + "sim.add(ElectronPairProduction(IRB_Kneiske04()))\n", + "sim.add(NuclearDecay())\n", + "sim.add(MaximumTrajectoryLength(25 * Mpc))\n", + "output = TextOutput('trajectory.txt', Output.Trajectory3D) \n", + "sim.add(output)\n", + "\n", + "x = Vector3d(0,0,0) # position\n", + "p = Vector3d(1,1,0) # direction\n", + "c = Candidate(nucleusId(16, 8), 100 * EeV, x, p)\n", + "\n", + "sim.run(c, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Optional) Plotting\n", + "\n", + "We plot the trajectory of our oxygen-16 nucleus. To distinguish between secondary nuclei the following colors are used: protons are blue, alpha particles are green, everthing heavier is red." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_961678/2311979340.py:22: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", + " ax = fig.gca(projection='3d')# , aspect='equal'\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from pylab import *\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "\n", + "output.close()\n", + "data = genfromtxt('trajectory.txt', names=True)\n", + "\n", + "# trajectory points\n", + "x, y, z = data['X'], data['Y'], data['Z']\n", + "\n", + "# translate particle ID to charge number\n", + "Z = [chargeNumber(int(Id)) for Id in data['ID'].astype(int)]\n", + "\n", + "# translate the charge number to color and size\n", + "# --> protons are blue, Helium is green, everthing else is red\n", + "colorDict = {0:'k', 1:'b', 2:'g', 3:'r', 4:'r', 5:'r', 6:'r', 7:'r', 8:'r'}\n", + "sizeDict = {0:4, 1:4, 2:8, 3:10, 4:10, 5:10, 6:10, 7:10, 8:10}\n", + "colors = [colorDict[z] for z in Z]\n", + "sizes = [sizeDict[z] for z in Z]\n", + "\n", + "fig = plt.figure(figsize=(12, 5))#plt.figaspect(0.5))\n", + "ax = fig.gca(projection='3d')# , aspect='equal'\n", + "\n", + "ax.scatter(x,y,z+6, 'o', s=sizes, color=colors)\n", + "\n", + "ax.set_xlabel('x / Mpc', fontsize=18)\n", + "ax.set_ylabel('y / Mpc', fontsize=18)\n", + "ax.set_zlabel('z / Mpc', fontsize=18)\n", + "ax.set_xlim((-1, 16))\n", + "ax.set_ylim((-1, 16))\n", + "ax.set_zlim((-1, 16))\n", + "ax.xaxis.set_ticks((0, 5, 10, 15))\n", + "ax.yaxis.set_ticks((0, 5, 10, 15))\n", + "ax.zaxis.set_ticks((0, 5, 10, 15))\n", + "\n", + "show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "crp_docu", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "c416687c884a42c367c2f4b19e8bea2627679ca3202fbf20d972b7cd00ee0b77" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 0e2954f510a2e973d2b2078300cb6ea20a8e6856 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 10 Feb 2023 09:16:59 +0100 Subject: [PATCH 70/87] updated Mac OS installation instructions --- doc/pages/Installation.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 1080c41c5..c8d06031d 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -207,18 +207,18 @@ In case of CentOS/RHEL 7, the SWIG version is too old and has to be built from s ### Mac OS X - -If CRPropa with the Python3 support is desired on Mac OS X (tested on 10.14.5) -where Python3 is installed from Homebrew, one has to specify the exact paths of -the python library (PYTHON_LIBRARY) and the python interpreter -(PYTHON_EXECUTABLE) to CMake (otherwise, the system Python is found). For -example: +CRPropa on Mac OS X (tested on 12.5.1, M1 where command line developer tools are installed). +Install Python3, and llvm from Homebrew, and specify the following paths to the Python and llvm directories in the homebrew folder after step 3 of the above installation, e.g. (please your exact versions): + ```sh + export LLVM_DIR="/opt/homebrew/Cellar/llvm/15.0.7_1" + PYTHON_VERSION=3.10 + LLVM_VERSION=15.0.7 + PYTHON_DIR=/opt/homebrew/Cellar/python@3.10/3.10.9/Frameworks/Python.framework/Versions/3.10 ``` - CMAKE_PREFIX_PATH=$CRPROPA_DIR cmake -DCMAKE_INSTALL_PREFIX=$CRPROPA_DIR \ - -DPYTHON_EXECUTABLE=/usr/local/Cellar/python/3.7.4/bin/python3 \ - -DPYTHON_LIBRARY=/usr/local/Cellar/python/3.7.4/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7.dylib \ - .. +and run + ```sh + cmake .. -DCMAKE_INSTALL_PREFIX=$VIRTENV_DIR -DBUILD_DOC=False -DSIMD_EXTENSIONS="none" -DFAST_WAVES=False -DPYTHON_EXECUTABLE=$PYTHON_DIR/bin/python$PYTHON_VERSION -DPYTHON_LIBRARY=$PYTHON_DIR/lib/libpython$PYTHON_VERSION.dylib -DPYTHON_INCLUDE_PATH=$PYTHON_DIR/include/python$PYTHON_VERSION -DCMAKE_C_COMPILER=$LLVM_DIR/bin/clang -DCMAKE_CXX_COMPILER=$LLVM_DIR/bin/clang++ -DOpenMP_CXX_FLAGS="-fopenmp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_C_FLAGS="-fopenmp =libomp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_libomp_LIBRARY=$LLVM_DIR/lib/libomp.dylib -DCMAKE_SHARED_LINKER_FLAGS="-L$LLVM_DIR/lib -lomp -Wl,-rpath,$LLVM_DIR/lib" -DOpenMP_C_LIB_NAMES=libomp -DOpenMP_CXX_LIB_NAMES=libomp -DNO_TCMALLOC=TRUE ``` -Note, that CRPropa can be installed under OS X with the settings listed above, but due to some issues with github actions the OS X installation is currently not checked automatically. +in the build folder. From 37b561b07185ac212b87adfa05aba9911491005a Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 10 Feb 2023 09:26:05 +0100 Subject: [PATCH 71/87] example installtion for Mac OS --- doc/pages/Installation.md | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index c8d06031d..5c8cdd13a 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -114,7 +114,7 @@ worthwhile effort afterwards. To install python dependencies and libraries use `pip`. Example: `pip install numpy`. -4. Compile and install CRPropa. +4. Compile and install CRPropa (please note specific [insturctions for different operating systems](#notes-for-specific-operating-systems)). ```sh cd $CRPROPA_DIR git clone https://github.com/CRPropa/CRPropa3.git @@ -207,18 +207,25 @@ In case of CentOS/RHEL 7, the SWIG version is too old and has to be built from s ### Mac OS X -CRPropa on Mac OS X (tested on 12.5.1, M1 where command line developer tools are installed). -Install Python3, and llvm from Homebrew, and specify the following paths to the Python and llvm directories in the homebrew folder after step 3 of the above installation, e.g. (please your exact versions): +Tested on version 12.5.1 with M1 pro where command line developer tools are installed. +Install Python3, and llvm from Homebrew, and specify the following paths to the Python and llvm directories in the Homebrew folder after step 3 of the above installation, e.g. (please use your exact versions): ```sh export LLVM_DIR="/opt/homebrew/Cellar/llvm/15.0.7_1" PYTHON_VERSION=3.10 LLVM_VERSION=15.0.7 PYTHON_DIR=/opt/homebrew/Cellar/python@3.10/3.10.9/Frameworks/Python.framework/Versions/3.10 ``` -and run +and replace the command in step 4 of the installation routine ```sh - cmake .. -DCMAKE_INSTALL_PREFIX=$VIRTENV_DIR -DBUILD_DOC=False -DSIMD_EXTENSIONS="none" -DFAST_WAVES=False -DPYTHON_EXECUTABLE=$PYTHON_DIR/bin/python$PYTHON_VERSION -DPYTHON_LIBRARY=$PYTHON_DIR/lib/libpython$PYTHON_VERSION.dylib -DPYTHON_INCLUDE_PATH=$PYTHON_DIR/include/python$PYTHON_VERSION -DCMAKE_C_COMPILER=$LLVM_DIR/bin/clang -DCMAKE_CXX_COMPILER=$LLVM_DIR/bin/clang++ -DOpenMP_CXX_FLAGS="-fopenmp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_C_FLAGS="-fopenmp =libomp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_libomp_LIBRARY=$LLVM_DIR/lib/libomp.dylib -DCMAKE_SHARED_LINKER_FLAGS="-L$LLVM_DIR/lib -lomp -Wl,-rpath,$LLVM_DIR/lib" -DOpenMP_C_LIB_NAMES=libomp -DOpenMP_CXX_LIB_NAMES=libomp -DNO_TCMALLOC=TRUE + CMAKE_PREFIX_PATH=$CRPROPA_DIR cmake -DCMAKE_INSTALL_PREFIX=$CRPROPA_DIR .. + ``` +with + ```sh + cmake .. -DCMAKE_INSTALL_PREFIX=$CRPROPA_DIR -DBUILD_DOC=False -DSIMD_EXTENSIONS="none" -DFAST_WAVES=False -DPYTHON_EXECUTABLE=$PYTHON_DIR/bin/python$PYTHON_VERSION -DPYTHON_LIBRARY=$PYTHON_DIR/lib/libpython$PYTHON_VERSION.dylib -DPYTHON_INCLUDE_PATH=$PYTHON_DIR/include/python$PYTHON_VERSION -DCMAKE_C_COMPILER=$LLVM_DIR/bin/clang -DCMAKE_CXX_COMPILER=$LLVM_DIR/bin/clang++ -DOpenMP_CXX_FLAGS="-fopenmp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_C_FLAGS="-fopenmp =libomp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_libomp_LIBRARY=$LLVM_DIR/lib/libomp.dylib -DCMAKE_SHARED_LINKER_FLAGS="-L$LLVM_DIR/lib -lomp -Wl,-rpath,$LLVM_DIR/lib" -DOpenMP_C_LIB_NAMES=libomp -DOpenMP_CXX_LIB_NAMES=libomp -DNO_TCMALLOC=TRUE + ``` +Check that all paths are set correctly with the following command in the build folder + ```sh + cmake .. ``` -in the build folder. From 90518593299344dcd1654f42cc739b389192b8c6 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 10 Feb 2023 09:27:53 +0100 Subject: [PATCH 72/87] further remarks for Mac OS installation --- doc/pages/Installation.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 5c8cdd13a..452c578e0 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -225,7 +225,8 @@ with ``` Check that all paths are set correctly with the following command in the build folder ```sh - cmake .. + ccmake .. ``` +and configure and generate again after changes. From 8dc421ff1c31235ed9273a30b14d2279f9ffad6b Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 10 Feb 2023 09:46:18 +0100 Subject: [PATCH 73/87] removed unnecessary flags from installation advice --- doc/pages/Installation.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 452c578e0..640f89a07 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -221,7 +221,20 @@ and replace the command in step 4 of the installation routine ``` with ```sh - cmake .. -DCMAKE_INSTALL_PREFIX=$CRPROPA_DIR -DBUILD_DOC=False -DSIMD_EXTENSIONS="none" -DFAST_WAVES=False -DPYTHON_EXECUTABLE=$PYTHON_DIR/bin/python$PYTHON_VERSION -DPYTHON_LIBRARY=$PYTHON_DIR/lib/libpython$PYTHON_VERSION.dylib -DPYTHON_INCLUDE_PATH=$PYTHON_DIR/include/python$PYTHON_VERSION -DCMAKE_C_COMPILER=$LLVM_DIR/bin/clang -DCMAKE_CXX_COMPILER=$LLVM_DIR/bin/clang++ -DOpenMP_CXX_FLAGS="-fopenmp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_C_FLAGS="-fopenmp =libomp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" -DOpenMP_libomp_LIBRARY=$LLVM_DIR/lib/libomp.dylib -DCMAKE_SHARED_LINKER_FLAGS="-L$LLVM_DIR/lib -lomp -Wl,-rpath,$LLVM_DIR/lib" -DOpenMP_C_LIB_NAMES=libomp -DOpenMP_CXX_LIB_NAMES=libomp -DNO_TCMALLOC=TRUE + cmake .. \ + -DCMAKE_INSTALL_PREFIX=$CRPROPA_DIR \ + -DPYTHON_EXECUTABLE=$PYTHON_DIR/bin/python$PYTHON_VERSION \ + -DPYTHON_LIBRARY=$PYTHON_DIR/lib/libpython$PYTHON_VERSION.dylib \ + -DPYTHON_INCLUDE_PATH=$PYTHON_DIR/include/python$PYTHON_VERSION \ + -DCMAKE_C_COMPILER=$LLVM_DIR/bin/clang \ + -DCMAKE_CXX_COMPILER=$LLVM_DIR/bin/clang++ \ + -DOpenMP_CXX_FLAGS="-fopenmp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" \ + -DOpenMP_C_FLAGS="-fopenmp =libomp -I$LLVM_DIR/lib/clang/$LLVM_VERSION/include" \ + -DOpenMP_libomp_LIBRARY=$LLVM_DIR/lib/libomp.dylib \ + -DCMAKE_SHARED_LINKER_FLAGS="-L$LLVM_DIR/lib -lomp -Wl,-rpath,$LLVM_DIR/lib" \ + -DOpenMP_C_LIB_NAMES=libomp \ + -DOpenMP_CXX_LIB_NAMES=libomp \ + -DNO_TCMALLOC=TRUE ``` Check that all paths are set correctly with the following command in the build folder ```sh From 879025ab14499f3b12f3f822086aabca9fcc29c7 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 10 Feb 2023 09:53:44 +0100 Subject: [PATCH 74/87] Update file names and update some broken links --- doc/index.rst | 2 +- doc/pages/Code-Coverage.md | 2 +- doc/pages/Debugging.md | 2 +- doc/pages/Simulation-Modules.md | 2 +- doc/pages/extending_crpropa.rst | 2 +- doc/pages/extragalactic-simulations.rst | 12 ++++++------ doc/pages/galactic_cosmic_rays.rst | 4 ++-- doc/pages/galactic_lensing.rst | 5 ++--- 8 files changed, 15 insertions(+), 16 deletions(-) diff --git a/doc/index.rst b/doc/index.rst index 692dbbdb2..a123374e6 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -24,7 +24,7 @@ Contents pages/Basic-concepts.md pages/Simulation-Modules.md - pages/example_notebooks/basics/basics.v4.ipynb + pages/example_notebooks/basics/basics.ipynb pages/extragalactic-simulations.rst pages/galactic_lensing.rst pages/galactic_cosmic_rays.rst diff --git a/doc/pages/Code-Coverage.md b/doc/pages/Code-Coverage.md index 605ef14cb..699f79eab 100644 --- a/doc/pages/Code-Coverage.md +++ b/doc/pages/Code-Coverage.md @@ -15,4 +15,4 @@ make test ``` make coverage ``` -The final report is in ```coverageReport/index.html``` +The final report is in ```~/build/coverageReport/index.html``` diff --git a/doc/pages/Debugging.md b/doc/pages/Debugging.md index e41b796a3..e67070095 100644 --- a/doc/pages/Debugging.md +++ b/doc/pages/Debugging.md @@ -98,7 +98,7 @@ Valgrind is run as follows: valgrind --tool=callgrind python steering_card.py ``` ### References -* [KCachegrind Manual](https://docs.kde.org/stable4/en/kdesdk/kcachegrind/kcachegrind.pdf) +* [KCachegrind Manual](https://docs.kde.org/stable5/en/kcachegrind/kcachegrind/kcachegrind.pdf) * [Callgrind manual](http://valgrind.org/docs/manual/cl-manual.html) * [GCC debugging options](https://gcc.gnu.org/onlinedocs/gcc-3.4.5/gcc/Debugging-Options.html) * [Python profiling](http://www.blog.pythonlibrary.org/2014/03/20/python-102-how-to-profile-your-code/) diff --git a/doc/pages/Simulation-Modules.md b/doc/pages/Simulation-Modules.md index 0afda7ab2..88a5faa94 100644 --- a/doc/pages/Simulation-Modules.md +++ b/doc/pages/Simulation-Modules.md @@ -9,7 +9,7 @@ Propagation modules are responsible for proposing a step size, evaluating the bi * **SimplePropagation** - Simple rectlinear propagation * **PropagationBP** - Deflections of charged particles in magnetic fields using the Boris Push algorithm with dynamic step size control * **PropagationCK** - Deflections of charged particles in magnetic fields using the Cash-Karp algorithm (Runge-Kutta of order 4/5) with dynamic step size control -* **DiffusionSDE** - Solve the Fokker-Planck transport equation using stochastic differential equations (SDEs). +* **DiffusionSDE** - Solves the Fokker-Planck transport equation using stochastic differential equations (SDEs). ### Interaction modules Interaction modules implement physical interactions which modify the particle and eventually produce secondary particles. Hadronic secondaries are always generated, non-hadronic secondaries are optionally generated. diff --git a/doc/pages/extending_crpropa.rst b/doc/pages/extending_crpropa.rst index 03e219cd9..3483ccd17 100644 --- a/doc/pages/extending_crpropa.rst +++ b/doc/pages/extending_crpropa.rst @@ -6,7 +6,7 @@ needs in in several ways: .. toctree:: example_notebooks/extending-CRPropa/extending-CRPropa.ipynb - example_notebooks/advanced/CustomObserver.v4.ipynb + example_notebooks/advanced/CustomObserver.ipynb example_notebooks/custom_photonfield/custom-photon-field.ipynb Cpp-projects.md diff --git a/doc/pages/extragalactic-simulations.rst b/doc/pages/extragalactic-simulations.rst index d1054504b..50a1a6452 100644 --- a/doc/pages/extragalactic-simulations.rst +++ b/doc/pages/extragalactic-simulations.rst @@ -3,12 +3,12 @@ Extragalactic Propagation .. toctree:: - example_notebooks/sim1D/sim1D.v4.ipynb - example_notebooks/sim4D/sim4D.v4.ipynb - example_notebooks/trajectories/trajectories.v4.ipynb - example_notebooks/extragalactic_fields/MHD_models.v4.ipynb + example_notebooks/sim1D/sim1D.ipynb + example_notebooks/sim4D/sim4D.ipynb + example_notebooks/trajectories/trajectories.ipynb + example_notebooks/extragalactic_fields/MHD_models.ipynb example_notebooks/secondaries/secondary_photons.ipynb - example_notebooks/secondaries/photons.v4.ipynb - example_notebooks/secondaries/neutrinos.v4.ipynb + example_notebooks/secondaries/photons.ipynb + example_notebooks/secondaries/neutrinos.ipynb example_notebooks/photon_propagation/cascade_1d.ipynb example_notebooks/targeting/Targeting.ipynb diff --git a/doc/pages/galactic_cosmic_rays.rst b/doc/pages/galactic_cosmic_rays.rst index 6b19c001e..6be1971db 100644 --- a/doc/pages/galactic_cosmic_rays.rst +++ b/doc/pages/galactic_cosmic_rays.rst @@ -11,8 +11,8 @@ a user-defined diffusion coefficient. .. toctree:: - example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb - example_notebooks/Diffusion/DiffusionValidationII.v4.ipynb + example_notebooks/Diffusion/DiffusionValidationI.ipynb + example_notebooks/Diffusion/DiffusionValidationII.ipynb Advection and Adiabatic Energy Changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/doc/pages/galactic_lensing.rst b/doc/pages/galactic_lensing.rst index c03cecedc..458572e1a 100644 --- a/doc/pages/galactic_lensing.rst +++ b/doc/pages/galactic_lensing.rst @@ -3,10 +3,9 @@ Propagation of Extragalactic CR in the Milky Way .. toctree:: - example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb - example_notebooks/galactic_trajectories/galactic_trajectories.v4.ipynb + example_notebooks/galactic_backtracking/galactic_backtracking.ipynb example_notebooks/galactic_lensing/lensing_cr.v4.ipynb example_notebooks/galactic_lensing/lensing_maps.v4.ipynb - example_notebooks/galactic_lensing/lensing_liouville.v4.ipynb + example_notebooks/galactic_lensing/lensing_liouville.ipynb From de2d9562342a0b263f7a588bffc076142e495782 Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 10 Feb 2023 11:20:50 +0100 Subject: [PATCH 75/87] Fix broken links to external resources --- doc/pages/AdditionalResources.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/pages/AdditionalResources.rst b/doc/pages/AdditionalResources.rst index 2ecc05d37..f95c69733 100644 --- a/doc/pages/AdditionalResources.rst +++ b/doc/pages/AdditionalResources.rst @@ -2,24 +2,24 @@ Additional Resources -------------------- - The Galactic magnetic field lenses can be downloaded - `here `__ . + `here `__ . - The multi-resolution 'Dolag' extragalactic magnetic field, using - `Quimby `__, can be + `Quimby `__, can be downloaded - `here `__ + `here `__ - The 'Miniati', 'Dolag' and 'Benchmark' extragalactic magnetic field - models on regular grids are available - `here `__ and the + models on regular grids are available in the same directory + `here `__ and the corresponding large-scale structure density fields can be downloaded - `here `__. + `here `__. - Data for the constrained `'Hackstein' models `__ of the local Universe using initial conditions from the `CLUES project `__ can be downloaded - `here `__. + `here `__. - Data for the strong extragalactic magnetic fields by `Alves Batista et al. `__ can be downloaded - `here `__. + `here `__. Note that these resources are completely external to CRPropa. We cannot supply any kind of additional information nor can we offer support on how to use them. From e4d90eab4dac30a89f6c749f90c497d53fa1667e Mon Sep 17 00:00:00 2001 From: mertelx Date: Fri, 10 Feb 2023 17:20:17 +0100 Subject: [PATCH 76/87] Fix several typos in doxygen comments that showed up while creating the documentation --- doc/conf.py | 2 +- doc/pages/Extending-CRPropa.md | 137 ------------------ ...Observer.v4.ipynb => CustomObserver.ipynb} | 0 doc/pages/galactic_cosmic_rays.rst | 15 +- include/crpropa/Candidate.h | 18 ++- include/crpropa/Source.h | 4 +- include/crpropa/Variant.h | 4 +- .../crpropa/advectionField/AdvectionField.h | 8 +- include/crpropa/base64.h | 2 +- .../magneticField/JF12FieldSolenoidal.h | 2 +- include/crpropa/magneticField/MagneticField.h | 2 +- .../PolarizedSingleModeMagneticField.h | 2 +- include/crpropa/magneticField/TF17Field.h | 4 +- .../turbulentField/GridTurbulence.h | 2 +- .../turbulentField/HelicalGridTurbulence.h | 11 +- .../turbulentField/PlaneWaveTurbulence.h | 5 +- .../turbulentField/SimpleGridTurbulence.h | 5 +- .../turbulentField/TurbulentField.h | 17 +-- .../magneticLens/ParticleMapsContainer.h | 4 +- include/crpropa/massDistribution/Ferriere.h | 4 +- include/crpropa/module/Acceleration.h | 12 +- include/crpropa/module/HDF5Output.h | 2 +- include/crpropa/module/NuclearDecay.h | 2 +- include/crpropa/module/Observer.h | 2 - include/crpropa/module/Output.h | 8 +- include/crpropa/module/SynchrotronRadiation.h | 2 +- 26 files changed, 87 insertions(+), 189 deletions(-) delete mode 100644 doc/pages/Extending-CRPropa.md rename doc/pages/example_notebooks/advanced/{CustomObserver.v4.ipynb => CustomObserver.ipynb} (100%) diff --git a/doc/conf.py b/doc/conf.py index 3eb130e88..318d59305 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -118,7 +118,7 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +#html_static_path = ['_static'] html_js_files = [ 'js/breathe_fold.js', ] diff --git a/doc/pages/Extending-CRPropa.md b/doc/pages/Extending-CRPropa.md deleted file mode 100644 index e12b03419..000000000 --- a/doc/pages/Extending-CRPropa.md +++ /dev/null @@ -1,137 +0,0 @@ -### Implementing additional Modules and Features using Python only -Derivatives of base classes such as Module, Source, SourceProperty, etc. can be implemented in -Python and used like the built-in classes. Here is an example for a custom -Module: -```python -class MyModule(Module): - """ Reduces the cosmic ray energy by 10% in each step """ - def process(self, c): - c.current.setEnergy(c.current.getEnergy() * 0.9) - -m = ModuleList() - -mod = MyModule() # See https://github.com/CRPropa/CRPropa3/issues/165 -m.add(mod) - -c = Candidate() -c.current.setEnergy(10) -m.process(c) -print(c.current.getEnergy()) -``` - -When redefining the constructor make sure to call the constructor of the super -classes as well, as otherwise the code will segfault. -```python -class MyModule(Module): - def __init__(self): - Module.__init__(self) -``` - - -The initial properties of a cosmic rays can be set with a Source, composed of several SourceProperties. -Custom SourceFeatures can be written in the following way: -```python -class MySourceFeature(SourceFeature): - """ Set the initial energy to 10 EeV """ - def __init__(self): - SourceFeature.__init__(self) - - def prepareParticle(self, particleState): - particleState.setEnergy(10 * EeV) - -s = Source() -s.add(MySourceFeature()) -c = s.getCandidate() -print(c.current.getEnergy()) -``` - -The redshift is stored in the Candidate, not in the ParticleState. To set it with a SourceFeature, use the following: -```python -class MySourceFeature(SourceFeature): - """ Set the initial redshift """ - def __init__(self): - SourceFeature.__init__(self) - - def prepareCandidate(self, candidate): - candidate.setRedshift(0.6) - -# The source feature has to be created outside of the class attribute -# s.add(MySourceFeature()) will NOT work! -srcFtr = MySourceFeature() -s = Source() -s.add(srcFtr) -c = s.getCandidate() -print(c.getRedshift()) -``` - - -### Manual Simulation Processing -If necessary, the simulation chain (ModuleList) and the cosmic ray source (Source, SourceFeature) can be -replaced by custom loops. - -```python -m1 = SimplePropagation() -m2 = ElectronPairProduction() -m3 = MinimumEnergy(5 * EeV) - -while mycandidate.isActive(): - m1.process(mycandidate) - m2.process(mycandidate) - m3.process(mycandidate) - # reduce the energy by 10% - E = mycandidate.current.getEnergy() - mycandidate.current.setEnergy(0.9 * E) -``` - -A source can be replaced by setting all necessary cosmic rays properties by hand. -The created Candidates can either be propagated one-by-one, or first collected -in a CandidateVector and then propagated. -```python -for i in range(1000): - p = ParticleState() - p.setId(nucleusId(12, 6)) - p.setEnergy(200 * EeV) - p.setPosition(Vector3d(100, 10, 10) * Mpc) - p.setDirection(Vector3d(-1,0,0)) - - c = Candidate(p) - m.process(c) -``` - - -### Plugins: Integrate Custom C++ Code to CRPropa's Python Steering -Extending CRPropa with C++ code and keep python steering is also possible using -SWIG. This allows to integrate your code seamless as e.g. -``` -import crpropa -import myPlugin - -ml = crpropa.ModuleList() -ml.add(crpropa.MaximumTrajectoryLength(1000 * crpropa.parsec)) -ml.add(myPlugin.MyModule()) - -source = crpropa.Source() -source.add(myPlugin.AddMyProperty()) -``` -A template is in the [plugin-template -folder](https://github.com/CRPropa/CRPropa3/tree/master/plugin-template) of the -CRPropa source. Although the template is complete with build and SWIG wrapper -code, deeper knowledge of SWIG, C++, and CMake are likely required for complex -projects. - -To get started with your own plugin - -1. Copy the folder to a new location. We highly recommended to manage the files -in a (git) repository from the beginning. -2. Test compiling the template -``` -mkdir build -cd build -cmake .. -make && python ../testPlugin.py -``` -This should work if CRPropa is installed and can be found by python. - -3. Customize the template to your needs, starting with -naming your plugin by modifying the according line in `CMakeLists.txt' and -renaming the files accordingly. diff --git a/doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb b/doc/pages/example_notebooks/advanced/CustomObserver.ipynb similarity index 100% rename from doc/pages/example_notebooks/advanced/CustomObserver.v4.ipynb rename to doc/pages/example_notebooks/advanced/CustomObserver.ipynb diff --git a/doc/pages/galactic_cosmic_rays.rst b/doc/pages/galactic_cosmic_rays.rst index 6be1971db..eb7a0bce8 100644 --- a/doc/pages/galactic_cosmic_rays.rst +++ b/doc/pages/galactic_cosmic_rays.rst @@ -1,5 +1,18 @@ +Galactic Cosmic Rays +-------------------- + +Propagation of Galactic cosmic rays can be done with the single-particle +approach (solving the equation of motion) or with an ensemble averaged +ansatz (solving the transport equation for a particle distribution). The +latter one is explained in section "Diffusion of Cosmic Rays". The single-particle +approach is demonstrated in the following example. + +.. toctree:: + example_notebooks/galactic_trajectories/galactic_trajectories.ipynb + + Diffusion of Cosmic Rays ---------------------------------- +------------------------ Diffusion of cosmic rays can be modeled in an ensemble averaged way using transport equations. In CRPropa this is done based on stochastic diff --git a/include/crpropa/Candidate.h b/include/crpropa/Candidate.h index 44a6fa15d..58835b3d9 100644 --- a/include/crpropa/Candidate.h +++ b/include/crpropa/Candidate.h @@ -117,8 +117,7 @@ class Candidate: public Referenced { /** Add a new candidate to the list of secondaries. - @param id particle ID of the secondary - @param energy energy of the secondary + @param c Candidate Adds a new candidate to the list of secondaries of this candidate. The secondaries Candidate::source and Candidate::previous state are set to the _source_ and _previous_ state of its parent. @@ -127,7 +126,22 @@ class Candidate: public Referenced { */ void addSecondary(Candidate *c); inline void addSecondary(ref_ptr c) { addSecondary(c.get()); }; + /** + Add a new candidate to the list of secondaries. + @param id particle ID of the secondary + @param energy energy of the secondary + @param w weight of the secondary + @param tagOrigin tag of the secondary + */ void addSecondary(int id, double energy, double w = 1., std::string tagOrigin = "SEC"); + /** + Add a new candidate to the list of secondaries. + @param id particle ID of the secondary + @param energy energy of the secondary + @param position start position of the secondary + @param w weight of the secondary + @param tagOrigin tag of the secondary + */ void addSecondary(int id, double energy, Vector3d position, double w = 1., std::string tagOrigin = "SEC"); void clearSecondaries(); diff --git a/include/crpropa/Source.h b/include/crpropa/Source.h index f67d79843..df195982e 100644 --- a/include/crpropa/Source.h +++ b/include/crpropa/Source.h @@ -275,7 +275,7 @@ class SourceUniformHollowSphere: public SourceFeature { @param radius_outer radius of the outer sphere */ SourceUniformHollowSphere(Vector3d center, - double radius_inner, double double_outer); + double radius_inner, double radius_outer); void prepareParticle(ParticleState &particle) const; void setDescription(); }; @@ -834,7 +834,7 @@ class SourceGenericComposition: public SourceFeature { /** Add an individual particle id. @param A atomic mass of the cosmic-ray nucleus @param Z atomic number of the cosmic-ray nucleus - @param weight relative abundance of individual particle species + @param abundance relative abundance of individual particle species */ void add(int A, int Z, double abundance); void prepareParticle(ParticleState &particle) const; diff --git a/include/crpropa/Variant.h b/include/crpropa/Variant.h index e255510c8..ac3bde873 100644 --- a/include/crpropa/Variant.h +++ b/include/crpropa/Variant.h @@ -52,8 +52,8 @@ namespace crpropa @class Variant @brief storage container for data types as e.g. int, float, string, etc. - Allows storage of multiple data types in one base class. used to construct a - map of `arbitrarry' data types. + Allows storage of multiple data types in one base class. Used to construct a + map of `arbitrary' data types. */ class Variant diff --git a/include/crpropa/advectionField/AdvectionField.h b/include/crpropa/advectionField/AdvectionField.h index 32eafbec5..9d6a71bc5 100644 --- a/include/crpropa/advectionField/AdvectionField.h +++ b/include/crpropa/advectionField/AdvectionField.h @@ -150,8 +150,6 @@ class SphericalAdvectionShock: public AdvectionField { @param r_0 Position of the shock @param v_0 Constant velocity (r< and contributors from wpa_supplicant and hostapd in /// http://web.mit.edu/freebsd/head/contrib/wpa/ and /// http://web.mit.edu/freebsd/head/contrib/wpa/src/utils/base64.c and diff --git a/include/crpropa/magneticField/JF12FieldSolenoidal.h b/include/crpropa/magneticField/JF12FieldSolenoidal.h index 6808658d5..b6d8c4546 100644 --- a/include/crpropa/magneticField/JF12FieldSolenoidal.h +++ b/include/crpropa/magneticField/JF12FieldSolenoidal.h @@ -62,7 +62,7 @@ class JF12FieldSolenoidal: public JF12Field { void setUseTurbulentField(bool use); /** @brief Adjust the transition width of the disk field - @param d The new transition width for the disk field with field strength transitions and flux redirection between r = 5 kpc and r = 5 kpc + delta as well as r = 20 kpc - delta and r = 20 kpc. Should be non-negative and smaller than 7.5 kpc. + @param delta The new transition width for the disk field with field strength transitions and flux redirection between r = 5 kpc and r = 5 kpc + delta as well as r = 20 kpc - delta and r = 20 kpc. Should be non-negative and smaller than 7.5 kpc. @return Void */ void setDiskTransitionWidth(double delta); diff --git a/include/crpropa/magneticField/MagneticField.h b/include/crpropa/magneticField/MagneticField.h index 213da7296..997b1ff83 100644 --- a/include/crpropa/magneticField/MagneticField.h +++ b/include/crpropa/magneticField/MagneticField.h @@ -61,7 +61,7 @@ class PeriodicMagneticField: public MagneticField { * @param field magnetic field reference pointer * @param extends length, width, and height of the base cube * @param origin defines the reference position - * @param switch for periodic or reflective behavior + * @param reflective for periodic or reflective behavior */ PeriodicMagneticField(ref_ptr field, const Vector3d &extends, const Vector3d &origin, bool reflective); diff --git a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h index e9925ae63..4b045263e 100644 --- a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h +++ b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h @@ -48,7 +48,7 @@ class PolarizedSingleModeMagneticField: public MagneticField { * Constructor * @param B_0 Magnetic field strength in the direction of e_1 at r_0 (for flagAmplitudeRms = "amplitude"), or the RMS value of the magnetic field (for flagAmplitudeRms = "rms") * @param wavelength Wavelength of the single mode (corresponds to its coherence length) - * @param double Polarization parameter + * @param sigma Polarization parameter * @param r_0 Reference position * @param e_1 First vector spanning the polarization plane * @param e_2 Second vector spanning the polarization plane diff --git a/include/crpropa/magneticField/TF17Field.h b/include/crpropa/magneticField/TF17Field.h index 3aece7411..24f54749f 100644 --- a/include/crpropa/magneticField/TF17Field.h +++ b/include/crpropa/magneticField/TF17Field.h @@ -165,8 +165,8 @@ class TF17Field: public MagneticField { /**@brief Compute the azimuthal field component Bphi as define by equation 28 in TF17 * @param r radius in cylindrical coordinates * @param z radius in cylindrical coordinates - * @param Br radial component of the magnetic field at position (r,z) - * @param Bz height component of the magnetic field at position (r,z) + * @param B_r radial component of the magnetic field at position (r,z) + * @param B_z height component of the magnetic field at position (r,z) * @return the value of the azimuthal field component Bphi */ double azimuthalFieldComponent(const double& r, const double& z, const double& B_r, const double& B_z) const; diff --git a/include/crpropa/magneticField/turbulentField/GridTurbulence.h b/include/crpropa/magneticField/turbulentField/GridTurbulence.h index 25baeb573..a5b5d4ffd 100644 --- a/include/crpropa/magneticField/turbulentField/GridTurbulence.h +++ b/include/crpropa/magneticField/turbulentField/GridTurbulence.h @@ -34,7 +34,7 @@ class GridTurbulence : public TurbulentField { @param gridProp GridProperties instance to define the underlying grid @param seed Random seed */ - GridTurbulence(const TurbulenceSpectrum &spectum, + GridTurbulence(const TurbulenceSpectrum &spectrum, const GridProperties &gridProp, unsigned int seed = 0); Vector3d getField(const Vector3d &pos) const; diff --git a/include/crpropa/magneticField/turbulentField/HelicalGridTurbulence.h b/include/crpropa/magneticField/turbulentField/HelicalGridTurbulence.h index f935d1eee..708754499 100644 --- a/include/crpropa/magneticField/turbulentField/HelicalGridTurbulence.h +++ b/include/crpropa/magneticField/turbulentField/HelicalGridTurbulence.h @@ -43,8 +43,15 @@ class HelicalGridTurbulence : public SimpleGridTurbulence { // Compatibility with old functions from GridTurbulence: /** - Same as the simple turbulent field but with helicity. - @param H Helicity + Create a random initialization of a turbulent field including helicity + @param grid grid on which the turbulence is calculated + @param Brms RMS field strength + @param lMin Minimum wavelength of the turbulence + @param lMax Maximum wavelength of the turbulence + @param alpha Power law index of ~ k^alpha (alpha = -11/3 corresponds + to a Kolmogorov spectrum) + @param seed Random seed + @param H Helicity */ void initHelicalTurbulence(ref_ptr grid, double Brms, double lMin, double lMax, double alpha = -11 / 3., int seed = 0, diff --git a/include/crpropa/magneticField/turbulentField/PlaneWaveTurbulence.h b/include/crpropa/magneticField/turbulentField/PlaneWaveTurbulence.h index 95a7a7565..c2bb40fbe 100644 --- a/include/crpropa/magneticField/turbulentField/PlaneWaveTurbulence.h +++ b/include/crpropa/magneticField/turbulentField/PlaneWaveTurbulence.h @@ -124,10 +124,11 @@ class PlaneWaveTurbulence : public TurbulentField { Create a new instance of PlaneWaveTurbulence with the specified parameters. This generates all of the wavemodes according to the given parameters. - @param Nm number of wavemodes that will be used when computing + @param spectrum TurbulenceSpectrum + @param Nm number of wavemodes that will be used when computing the field. A higher value will give a more accurate representation of the turbulence, but increase the runtime for getField. - @param seed can be used to seed the random number generator + @param seed can be used to seed the random number generator used to generate the field. This works just like in initTurbulence: a seed of 0 will lead to a randomly initialized RNG. */ diff --git a/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h b/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h index cfe26f01e..4fb2db197 100644 --- a/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h +++ b/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h @@ -22,11 +22,11 @@ class SimpleTurbulenceSpectrum : public TurbulenceSpectrum { const int constScaleBendover = 1000; // define the bandover scale as 1000 * lMax to ensure k * lBendover >> 1. The bendover scale is necessary for the implementation of PlaneWaveTurbulence. public: /** + * The bend-over scale is set to 1000 times lMax to ensure to be in the inertial range. This should not be changed. @param Brms root mean square field strength for generated field @param lMin Minimum physical scale of the turbulence @param lMax Maximum physical scale of the turbulence - @param lBendover the bend-over scale is set to 1000 times lMax to ensure to be in the inertial range. This should not be changed. - @param sindex Spectral index of the energy spectrum in the inertial range + @param sIndex Spectral index of the energy spectrum in the inertial range */ SimpleTurbulenceSpectrum(double Brms, double lMin, double lMax, double sIndex = 5. / 3) @@ -92,6 +92,7 @@ inline double turbulentCorrelationLength(double lMin, double lMax, /** Create a random initialization of a turbulent field. + @param grid grid on which the turbulence is calculated @param lMin Minimum wavelength of the turbulence @param lMax Maximum wavelength of the turbulence @param alpha Power law index of ~ k^alpha (alpha = -11/3 corresponds diff --git a/include/crpropa/magneticField/turbulentField/TurbulentField.h b/include/crpropa/magneticField/turbulentField/TurbulentField.h index fca157514..10e89709d 100644 --- a/include/crpropa/magneticField/turbulentField/TurbulentField.h +++ b/include/crpropa/magneticField/turbulentField/TurbulentField.h @@ -39,14 +39,12 @@ class TurbulenceSpectrum : public Referenced { public: /** - @param Brms root mean square field strength for generated field - @param lMin Minimum physical scale of the turbulence - @param lMax Maximum physical scale of the turbulence - @param lBendover the bend-over scale - @param sindex Spectral index of the energy spectrum in the inertial - range - @param qindex Spectral index of the energy spectrum in the energy - range + * @param Brms root mean square field strength for generated field + * @param lMin Minimum physical scale of the turbulence + * @param lMax Maximum physical scale of the turbulence + * @param lBendover the bend-over scale + * @param sIndex Spectral index of the energy spectrum in the inertial range + * @param qIndex Spectral index of the energy spectrum in the energy range */ TurbulenceSpectrum(double Brms, double lMin, double lMax, double lBendover = 1, double sIndex = (5. / 3.), @@ -83,8 +81,7 @@ class TurbulenceSpectrum : public Referenced { /** Computes the magnetic field coherence length - Obtained from the definition of \f$l_c = 1/B_{\rm rms}^2 \int_0^\infty dr - \langleB(0)B^*(r)\rangle$ + Obtained from the definition of \f$l_c = 1/B_{\rm rms}^2 \int_0^\infty dr\langleB(0)B^*(r)\rangle \f$ Approximates the true value correctly as long as lBendover <= lMax/8 (~5% error) (for the true value the above integral should go from lMin to lMax) */ diff --git a/include/crpropa/magneticLens/ParticleMapsContainer.h b/include/crpropa/magneticLens/ParticleMapsContainer.h index 03f3cf57b..74e17f60f 100644 --- a/include/crpropa/magneticLens/ParticleMapsContainer.h +++ b/include/crpropa/magneticLens/ParticleMapsContainer.h @@ -107,8 +107,8 @@ class ParticleMapsContainer { /** Places a particle with given id and energy according to the probability maps. @param pid id of the particle following the PDG numbering scheme @param energy energy of interest [in eV] - @param galacticLongitudes longitude in the interval [-pi, pi] [in radians] - @param galacticLatitudes latitude in the interval [-pi/2, pi/2] [in radians] + @param galacticLongitude longitude in the interval [-pi, pi] [in radians] + @param galacticLatitude latitude in the interval [-pi/2, pi/2] [in radians] @returns Returns false if operation not possible; true otherwise. */ bool placeOnMap(int pid, double energy, double &galacticLongitude, double &galacticLatitude); diff --git a/include/crpropa/massDistribution/Ferriere.h b/include/crpropa/massDistribution/Ferriere.h index 0fd9d2ea0..0f2cce36b 100644 --- a/include/crpropa/massDistribution/Ferriere.h +++ b/include/crpropa/massDistribution/Ferriere.h @@ -26,12 +26,12 @@ class Ferriere: public Density { public: /** Coordinate transformation for the CentralMolecularZone region. Rotation arround z-axis such that X is the major axis and Y is the minor axis - @param postion position in galactic coordinates with Earth at (-8.5kpc, 0, 0) + @param position position in galactic coordinates with Earth at (-8.5kpc, 0, 0) @return position in local coordinates for the CMZ region */ Vector3d CMZTrafo(const Vector3d &position) const; /** Coordinate transformation for the galactic bulge disk region in galactic center. Rotation arround the x-axis, the y'-axis and the x''-axis. Difened with X along the major axis, Y along the minor axis and Z along the northern normal - @param postion position in galactic coordinates with Earth at (-8.5kpc, 0, 0) + @param position position in galactic coordinates with Earth at (-8.5kpc, 0, 0) @return position in local coordinates for the GB disk region */ Vector3d DISKTrafo(const Vector3d &position) const; diff --git a/include/crpropa/module/Acceleration.h b/include/crpropa/module/Acceleration.h index b5bd7f579..107c26a37 100644 --- a/include/crpropa/module/Acceleration.h +++ b/include/crpropa/module/Acceleration.h @@ -19,7 +19,7 @@ namespace crpropa { class StepLengthModifier : public Referenced { public: /// Returns an update of the steplength - /// @param stepLength Modifies step length, e.g., based on scattering + /// @param steplength Modifies step length, e.g., based on scattering /// model. /// @param candidate Additional candidate properties are usually /// included in the calculation of the updated @@ -125,9 +125,9 @@ class DirectedFlowOfScatterCenters : public StepLengthModifier { /// path \f$\lambda\f$ of a /// particle with energy \f$E\f$ and charge \f$Z\f$ in a field with turbulence /// spectrum \f$\frac{k}{k_{\min}}^{-q}\f$ is -/// \f[ \lambda = {\left(\frac{B}{\delta B}\right)}^2 {\left(R_G\; +/// \f$ \lambda = {\left(\frac{B}{\delta B}\right)}^2 {\left(R_G\; /// k_{\min}\right)}^{1-q} R_G \equiv \lambda_0 {\left( \frac{E}{1 -/// EeV}\frac{1}{Z} \right)}^{2-q} \f] +/// EeV}\frac{1}{Z} \right)}^{2-q} \f$ /// where \f$R_G = \frac{E}{B Z}\f$ is the gyro-radius of the /// particles. /// This class implements the rigidity dependent scaling factor used to modify @@ -177,9 +177,9 @@ class ParticleSplitting : public Module { public: /** Constructor @param surface The surface to monitor - @param crossing_threshold Number of crossings after which a particle is split - @param num_splits Number of particles the candidate is split into - @param min_weight Minimum weight to consider. Particles with + @param crossingThreshold Number of crossings after which a particle is split + @param numberSplits Number of particles the candidate is split into + @param minWeight Minimum weight to consider. Particles with a lower weight are not split again. @param counterid An unique string to identify the particle property used for counting. Useful if diff --git a/include/crpropa/module/HDF5Output.h b/include/crpropa/module/HDF5Output.h index 21927a07b..da601e0c4 100644 --- a/include/crpropa/module/HDF5Output.h +++ b/include/crpropa/module/HDF5Output.h @@ -109,7 +109,7 @@ class HDF5Output: public Output { */ HDF5Output(const std::string &filename); /** Constructor - @param outputType type of output: Trajectory1D, Trajectory3D, Event1D, Event3D, Everything + @param outputtype type of output: Trajectory1D, Trajectory3D, Event1D, Event3D, Everything @param filename string containing name of output hdf5 file */ HDF5Output(const std::string &filename, OutputType outputtype); diff --git a/include/crpropa/module/NuclearDecay.h b/include/crpropa/module/NuclearDecay.h index 11f69c36b..6b448c3b4 100644 --- a/include/crpropa/module/NuclearDecay.h +++ b/include/crpropa/module/NuclearDecay.h @@ -38,7 +38,7 @@ class NuclearDecay: public Module { public: /** Constructor. - @param photonField target photon field + @param electrons if true, add secondary photons as candidates @param photons if true, add secondary photons as candidates @param neutrinos if true, add secondary neutrinos as candidates @param limit step size limit as fraction of mean free path diff --git a/include/crpropa/module/Observer.h b/include/crpropa/module/Observer.h index 49095fcee..adf7c8fc4 100644 --- a/include/crpropa/module/Observer.h +++ b/include/crpropa/module/Observer.h @@ -263,7 +263,6 @@ class ObserverTimeEvolution: public ObserverFeature { @param min minimum time @param dist time interval for detection @param numb number of time intervals - @param */ ObserverTimeEvolution(double min, double dist, double numb); /** Constructor @@ -271,7 +270,6 @@ class ObserverTimeEvolution: public ObserverFeature { @param max maximum time @param numb number of time intervals @param log log (input: true) or lin (input: false) scaling between min and max with numb steps - @param */ ObserverTimeEvolution(double min, double max, double numb, bool log); // Add a new time step to the detection time list of the observer diff --git a/include/crpropa/module/Output.h b/include/crpropa/module/Output.h index 8e02a1360..76b28fc6e 100644 --- a/include/crpropa/module/Output.h +++ b/include/crpropa/module/Output.h @@ -127,14 +127,14 @@ class Output: public Module { void setOutputType(OutputType outputType); /** Determines whether a given column will be displayed in the output. @param field name of the field to be added/removed from output - @param values boolean flag adding (true) or removing (false) the field + @param value boolean flag adding (true) or removing (false) the field */ void set(OutputColumn field, bool value); /** Add a property to output. Default value is required to assign a type in the output. - @param property string containing name of property - @param default default value of property - @param comment string with a comment + @param property string containing name of property + @param defaultValue default value of property + @param comment string with a comment */ void enableProperty(const std::string &property, const Variant& defaultValue, const std::string &comment = ""); /** Enable specific column in the output. diff --git a/include/crpropa/module/SynchrotronRadiation.h b/include/crpropa/module/SynchrotronRadiation.h index 6425ef8c4..459121d43 100644 --- a/include/crpropa/module/SynchrotronRadiation.h +++ b/include/crpropa/module/SynchrotronRadiation.h @@ -45,7 +45,7 @@ class SynchrotronRadiation: public Module { */ SynchrotronRadiation(ref_ptr field, bool havePhotons = false, double thinning = 0, int nSamples = 0, double limit = 0.1); /** Constructor - @param field RMS of the magnetic field (if magnetic-field object not provided) + @param Brms RMS of the magnetic field (if magnetic-field object not provided) @param havePhotons if true, add secondary photons as candidates @param thinning weighted sampling of secondaries (0: all particles are tracked; 1: maximum thinning) @param nSamples number of synchrotron photons to be sampled and added as candidates From c736587836ecdc1ac836b9570d34bc1f16ae4f77 Mon Sep 17 00:00:00 2001 From: mertelx Date: Thu, 16 Mar 2023 17:12:23 +0100 Subject: [PATCH 77/87] Update paths in custom photon field example. --- .../custom-photon-field.ipynb | 231 +++++++++++++----- 1 file changed, 164 insertions(+), 67 deletions(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index 60f393eca..3a0ce7553 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ "\n", "class ISRF(pf.PhotonField): \n", "\n", - " def __init__(self, dataPath = crpropa_share_path+\"CustomPhotonField/isrf_example_field.dat\"):\n", + " def __init__(self, dataPath = crpropa_data_path+\"data/CustomPhotonField/isrf_example_field.dat\"):\n", " super(ISRF, self).__init__()\n", " self.name = \"ISRF\"\n", " self.info = \"Inter Stellar Radiation Field. Model F98 from Porter+(2017)\"\n", @@ -175,27 +175,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'field' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/rest/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 7\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m c \u001b[39m=\u001b[39m eps\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m \u001b[39m/\u001b[39m eV\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m\n\u001b[1;32m 5\u001b[0m plt\u001b[39m.\u001b[39mfigure(dpi \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m y1 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m field\u001b[39m.\u001b[39mgetDensity(eps)\n\u001b[1;32m 7\u001b[0m y2 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m field_cmb\u001b[39m.\u001b[39mgetDensity(eps)\n\u001b[1;32m 8\u001b[0m y3 \u001b[39m=\u001b[39m c \u001b[39m*\u001b[39m isrf\u001b[39m.\u001b[39mgetDensity(eps)\n", - "\u001b[0;31mNameError\u001b[0m: name 'field' is not defined" + "name": "stderr", + "output_type": "stream", + "text": [ + "/rest/CRPropa3-data/photonField.py:79: RuntimeWarning: overflow encountered in expm1\n", + " return 8*np.pi / c_light**3 / h_planck**3 * eps**2 / np.expm1(eps / (k_boltzmann * self.T_CMB))\n" ] }, { "data": { + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -221,43 +221,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_cmb = pd.read_csv(crpropa_share_path+'/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", - "df_cmb_e = pd.read_csv(crpropa_share_path+'/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", - "\n", - "#df_cmb = pd.read_csv('/rest/venvs/crp_patrick/share/crpropa/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", - "#df_cmb_e = pd.read_csv('/rest/venvs/crp_patrick/share/crpropa/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", - "\n", - "#df_cmb = pd.read_csv('/rest/CRPropa3-data/data/Scaling/CMB_photonDensity.txt', comment='#', names=['n'])\n", - "#df_cmb_e = pd.read_csv('/rest/CRPropa3-data/data/Scaling/CMB_photonEnergy.txt', comment='#', names=['e'])\n", - "\n", - "df_pwl = pd.read_csv('data/Scaling/PowerlawPhotonField_photonDensity.txt', comment='#', names=['n'])\n", - "df_pwl_e = pd.read_csv('data/Scaling/PowerlawPhotonField_photonEnergy.txt', comment='#', names=['e'])\n", - "\n", - "df_isrf = pd.read_csv('data/Scaling/ISRF_photonDensity.txt', comment='#', names=['n'])\n", - "df_isrf_e = pd.read_csv('data/Scaling/ISRF_photonEnergy.txt', comment='#', names=['e'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(dpi = 100)\n", - "plt.plot(df_pwl_e.e/eV, df_pwl.n*df_pwl_e.e/eV**2)\n", - "plt.plot(df_cmb_e.e/eV , df_cmb.n*df_cmb_e.e/eV**2)\n", - "plt.plot(df_isrf_e.e/eV, df_isrf.n*df_isrf_e.e/eV**2)\n", - "plt.loglog()\n", - "plt.xlim(5e-5, 1e2)\n", - "plt.ylim([1e9, 1e27])" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -270,9 +233,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##################################################\n", + "Calculate elastic scattering.\n", + "\n", + "PowerlawPhotonField\n", + "ISRF\n", + "\n", + "Elastic scattering tables generated in 15.22 seconds.\n", + "##################################################\n", + "\n", + "##################################################\n", + "Calculate electromagnetic processes:\n", + "\t- Pair production\n", + "\t- Double pair production\n", + "\t- Triple pair production\n", + "\t- Inverse Compton scattering\n", + "PowerlawPhotonField\n", + "ISRF\n", + "\n", + "Electromagnetic processes generated in 127.21 seconds.\n", + "##################################################\n", + "\n", + "##################################################\n", + "Calculate Bethe-Heitler pair production.\n", + "\n", + "PowerlawPhotonField\n", + "ISRF\n", + "\n", + "BH pair production tables generated in 24.3 seconds.\n", + "##################################################\n", + "\n", + "##################################################\n", + "Calculate photo disintegration.\n", + "\n", + "PowerlawPhotonField\n", + "ISRF\n", + "PowerlawPhotonField\n", + "ISRF\n", + "\n", + "Photo disintegration tables generated in 640.97 seconds.\n", + "##################################################\n", + "\n", + "##################################################\n", + "Calculate photo pion production\n", + "\n", + "PowerlawPhotonField\n", + "ISRF\n", + "\n", + "Photo pion production tables generated in 0.49 seconds.\n", + "##################################################\n", + "\n", + "##################################################\n", + "Process Photon fields\n", + "\n", + "PowerlawPhotonField\n", + "done: PowerlawPhotonField\n", + "ISRF\n", + "done: ISRF\n", + "\n", + "Photonfield data generated in 0.02 seconds.\n", + "##################################################\n", + "\n" + ] + } + ], "source": [ "with warnings.catch_warnings(): # catch warnings from where density is approx 0 -> leads to division by zero errors\n", " warnings.simplefilter(\"ignore\")\n", @@ -290,9 +321,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "CompletedProcess(args=['cp', '-a', './data/.', '/rest/venvs/crp_docu/share/crpropa/'], returncode=0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subprocess.run(['cp', '-a', './data/.', crpropa_share_path])" ] @@ -308,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -346,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -364,9 +406,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Everything works fine\n" + ] + } + ], "source": [ "sim = crp.ModuleList()\n", "\n", @@ -400,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -410,9 +460,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMB\n", + "1.6021764870000002e-20\n", + "4.783281527694803e-24\n", + "190679052.33698764\n", + "1.0\n" + ] + } + ], "source": [ "print( crpCMB.getFieldName())\n", "print( crpCMB.getMaximumPhotonEnergy(0))\n", @@ -423,9 +485,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMBTest\n", + "1.6021764870000002e-20\n", + "4.775361955348583e-24\n", + "189320351.79205775\n", + "1.0\n" + ] + } + ], "source": [ "print( crpCMB2.getFieldName())\n", "print( crpCMB2.getMaximumPhotonEnergy(0))\n", @@ -436,9 +510,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMB\n", + "1.6021764870000002e-20\n", + "1.602176487e-29\n", + "189320351.79205772\n" + ] + } + ], "source": [ "print( field_cmb.name)\n", "print( field_cmb.getEmax(0))\n", @@ -457,9 +542,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/rest/CRPropa3/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb Cell 23\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m crp\u001b[39m.\u001b[39;49mElectronPairProduction(powerlaw_field, \u001b[39mTrue\u001b[39;49;00m)\n", + "\u001b[0;31mRuntimeError\u001b[0m: ElectronPairProduction: could not open file /rest/venvs/crp_docu/share/crpropa/ElectronPairProduction/spectrum_Pow.txt" + ] + } + ], "source": [ "crp.ElectronPairProduction(powerlaw_field, True)" ] From fd3691a353eb13dd9657702701560105a4c59c8a Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer <31761331+reichherzerp@users.noreply.github.com> Date: Fri, 24 Mar 2023 09:29:00 +0100 Subject: [PATCH 78/87] Update alignment in install file --- doc/pages/Installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/pages/Installation.md b/doc/pages/Installation.md index 640f89a07..95170416d 100644 --- a/doc/pages/Installation.md +++ b/doc/pages/Installation.md @@ -44,7 +44,7 @@ The following packages are provided with the source code and do not need to be i cd build cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/.local make - make install + make install ``` 2. A set of unit tests can be run with ```make test```. If the tests are From 720556cef07a2d08e5a5da4ecb7d9bfa6609567a Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 7 Apr 2023 17:18:08 +0100 Subject: [PATCH 79/87] implementing comments from @mohller --- include/crpropa/Source.h | 2 +- include/crpropa/magneticField/ArchimedeanSpiralField.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/include/crpropa/Source.h b/include/crpropa/Source.h index df195982e..d52eb66c5 100644 --- a/include/crpropa/Source.h +++ b/include/crpropa/Source.h @@ -166,7 +166,7 @@ class SourcePowerLawSpectrum: public SourceFeature { /** @class SourceComposition - @brief Multiple nuclei species with a rigidity-dependent power-law spectrum + @brief Multiple nuclear species with a rigidity-dependent power-law spectrum The power law is of the form: E^index, for energies in the interval [Emin, Z * Rmax]. */ diff --git a/include/crpropa/magneticField/ArchimedeanSpiralField.h b/include/crpropa/magneticField/ArchimedeanSpiralField.h index 3ac15f2fa..0559f1893 100644 --- a/include/crpropa/magneticField/ArchimedeanSpiralField.h +++ b/include/crpropa/magneticField/ArchimedeanSpiralField.h @@ -24,7 +24,7 @@ namespace crpropa { /** @class ArchimedeanSpiralField -@brief Magnetic field model following a Archimedean spiral. +@brief Magnetic field model following an Archimedean spiral. See e.g. Jokipii, Levy & Hubbard 1977 */ From 9ea138ec1cdab59cc76792170bd3c3802682d46b Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 7 Apr 2023 17:18:40 +0100 Subject: [PATCH 80/87] commented on alignment of cylinder in boundary module --- include/crpropa/module/Boundary.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/crpropa/module/Boundary.h b/include/crpropa/module/Boundary.h index 60e0b04c0..63f2b2121 100644 --- a/include/crpropa/module/Boundary.h +++ b/include/crpropa/module/Boundary.h @@ -168,7 +168,7 @@ class EllipsoidalBoundary: public AbstractCondition { /** @class CylindricalBoundary - @brief Flags a particle when leaving the cylinder. + @brief Flags a particle when leaving the cylinder whose axis is along the z-axis. This module flags particles when outside of the cylinder, defined by a radius and a height. The particle is made inactive and by default is flagged "OutOfBounds". Optionally the module can ensure the candidate does not overshoot the boundary by more than a set margin. From e963809a392b65ea4dc2e8d94cc26442bfcb8335 Mon Sep 17 00:00:00 2001 From: Patrick Reichherzer Date: Fri, 7 Apr 2023 17:51:27 +0100 Subject: [PATCH 81/87] note about the limited inertial range in the turbulence of the JF12 field --- include/crpropa/Source.h | 2 +- .../magneticField/turbulentField/SimpleGridTurbulence.h | 2 +- src/magneticField/JF12Field.cpp | 3 ++- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/include/crpropa/Source.h b/include/crpropa/Source.h index d52eb66c5..74db6ef77 100644 --- a/include/crpropa/Source.h +++ b/include/crpropa/Source.h @@ -319,7 +319,7 @@ class SourceUniformBox: public SourceFeature { /** @class SourceUniformCylinder - @brief Uniform distribution of source positions inside the volume of a cylinder. + @brief Uniform distribution of source positions inside the volume of a cylinder whose axis is along the z-axis. The circle of the cylinder lays in the xy-plane and the height is along the z-axis. */ diff --git a/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h b/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h index 4fb2db197..06cfa72a1 100644 --- a/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h +++ b/include/crpropa/magneticField/turbulentField/SimpleGridTurbulence.h @@ -23,7 +23,7 @@ class SimpleTurbulenceSpectrum : public TurbulenceSpectrum { public: /** * The bend-over scale is set to 1000 times lMax to ensure to be in the inertial range. This should not be changed. - @param Brms root mean square field strength for generated field + @param Brms Root mean square field strength for generated field @param lMin Minimum physical scale of the turbulence @param lMax Maximum physical scale of the turbulence @param sIndex Spectral index of the energy spectrum in the inertial range diff --git a/src/magneticField/JF12Field.cpp b/src/magneticField/JF12Field.cpp index 1181b739f..09ab80154 100644 --- a/src/magneticField/JF12Field.cpp +++ b/src/magneticField/JF12Field.cpp @@ -101,7 +101,8 @@ void JF12Field::randomStriated(int seed) { #ifdef CRPROPA_HAVE_FFTW3F void JF12Field::randomTurbulent(int seed) { useTurbulentField = true; - // turbulent field with Kolmogorov spectrum, B_rms = 1 and Lc = 60 parsec + // turbulent field with Kolmogorov spectrum, B_rms = 1 (will be scaled) and Lc = 60 parsec, and 256 grid points. + // Note that the inertial range of the turbulence is less than 2 orders of magnitude. const double lMin = 8 * parsec; const double lMax = 272 * parsec; const double Brms = 1; From af1accd796556cb3e65040a39651cb258f6c67b1 Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 11 Apr 2023 15:32:50 +0200 Subject: [PATCH 82/87] Apply style conventions as pointed out by @rafaelab --- CHANGELOG.md | 2 +- .../custom_photonfield/custom-photon-field.ipynb | 8 ++++---- include/crpropa/magneticField/CMZField.h | 4 ++-- include/crpropa/magneticField/GalacticMagneticField.h | 10 +++++----- .../magneticField/PolarizedSingleModeMagneticField.h | 2 +- include/crpropa/module/HDF5Output.h | 2 +- 6 files changed, 14 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 79cd6673b..98afbb2e9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,7 @@ # CRPropa vNext ### Bug fixes: -* Re-added TorroidalHaloField and LogarithmicSpiralField models. +* Re-added ToroidalHaloField and LogarithmicSpiralField models. Note, that the class name was also corrected in spelling: TorroidalHaloField --> ToroidalHaloField * Synchronized signature of ParticleSplitting constructor ### New features: diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index 3a0ce7553..9e06f2758 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -33,7 +33,7 @@ "source": [ "### 1. Create a python class with your custom photon field (CRPropa-data)\n", "In this example we show the production of a custom photon field for two different cases. \n", - "First we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with a given slope $\\alpha = -2.75$ in a maximal energy range [eMin = $10^{-3}$ eV, eMax = 1 eV]. \n", + "First we consider a photon field where the spectral number density is given by an analytical expression. We consider a powerlaw with a given slope $\\alpha = -2.75$ in a maximal energy range $[E_\\mathrm{min} = 10^{-3}\\,\\mathrm{eV}, E_\\mathrm{max} = 1\\,\\mathrm{eV}]$. \n", "\n", "The second example is based on a tabulated data file. Here, we use the ISRF model R12 presented in Porter, Johannesson, and Moskalenko, ApJ 846, 67 (2017), which can be downloaded from the GALPROP website: https://galprop.stanford.edu/download.php and is included in the crpropa installation. As CRPropa allows only for isotropic and homogeneous photon fields we use the field at position $(X = 0.0173648 \\, \\mathrm{kpc}, Y = 0.0984808 \\, \\mathrm{kpc})$.\n", "\n", @@ -41,7 +41,7 @@ "All photon fields must have the following mandatory parameters and functions:\n", "- name (string): name of the photon field, needed for the naming of the files\n", "- info (string): information tag used for the comments at the beginning of the file\n", - "- redshift (None/Array): Determines if the photon field is redshift dependend. If None no redshift dependence is given. Otherwise the (tabulated) redshift must be provided as a 1D array\n", + "- redshift (None/Array): Determines if the photon field is redshift dependent. If None no redshift dependence is given. Otherwise the (tabulated) redshift must be provided as a 1D array\n", "- energy (Array): Energies used to calculate files in data/Scaling (must be given in [$\\mathrm{eV}$]).\n", "- photonDensity (Array): Spectral energy density used to calculate files in data/Scaling (must be given in [$\\mathrm{eV}^{-1}\\,\\mathrm{ccm}^{-1}$]).\n", "- getDensity (function): returns the spectral number density dn/deps(eps, z) at a given photon energy (eps) and redshift (z)\n", @@ -355,7 +355,7 @@ "outputs": [], "source": [ "class CustomPhotonField(crp.PhotonField):\n", - " \"\"\" analogue implementation like above but inheriting from the CRPropa module for compatibility\"\"\"\n", + " \"\"\"Analogue implementation like above but inheriting from the CRPropa module for compatibility\"\"\"\n", "\n", " def __init__(self, name, norm = 1e20, slope = -2.75, eMin = 1e-3 * eV, eMax = 1 * eV):\n", " crp.PhotonField.__init__(self)\n", @@ -537,7 +537,7 @@ "metadata": {}, "source": [ "### 6. Limitation\n", - "For Bethe-Heitler pairproduction (*ElectronPairProduction*) secondary electrons cannot be injected into the simulation chain. This is due to a lack in the production of the needed tabulated data files. If you want to help to improve CRPropa take a look at the corresponding file (calc_pairproduction.py) in the CRPropa3-data repository (https://github.com/CRPropa/CRPropa3-data.git) and open a pull request." + "For Bethe-Heitler pair production (*ElectronPairProduction*) secondary electrons cannot be injected into the simulation chain. This is due to a lack in the production of the needed tabulated data files. If you want to help to improve CRPropa take a look at the corresponding file (calc_pairproduction.py) in the CRPropa3-data repository (https://github.com/CRPropa/CRPropa3-data.git) and open a pull request." ] }, { diff --git a/include/crpropa/magneticField/CMZField.h b/include/crpropa/magneticField/CMZField.h index f786b4313..f452450a1 100644 --- a/include/crpropa/magneticField/CMZField.h +++ b/include/crpropa/magneticField/CMZField.h @@ -47,14 +47,14 @@ class CMZField: public MagneticField { void setUseNTFField(bool use); void setUseRadioArc(bool use); - /** Magnetic field in the poloidal model. Used for inter-cloud(IC), non-thermal-filaments(NTF) and for the RadioArc. + /** Magnetic field in the poloidal model. Used for inter-cloud (IC), non-thermal-filaments (NTF) and for the radio arc. @param position position in galactic coordinates with Earth at (-8.5kpc, 0,0) @param mid midpoint of the object @param B1 normalized magnetic field strength @param a fitting parameter for the radial scale height @param L fitting parameter for the z scale height @return magnetic field vector */ - Vector3d BPol(const Vector3d& position,const Vector3d& mid, double B1, double a, double L) const; + Vector3d BPol(const Vector3d& position, const Vector3d& mid, double B1, double a, double L) const; /** Magnetic field in the azimuthal model. Used for molecular clouds (MC) @param position position in galactic coordinates with Earth at (-8.5kpc, 0,0) diff --git a/include/crpropa/magneticField/GalacticMagneticField.h b/include/crpropa/magneticField/GalacticMagneticField.h index acb2ba010..f4aaac3d9 100644 --- a/include/crpropa/magneticField/GalacticMagneticField.h +++ b/include/crpropa/magneticField/GalacticMagneticField.h @@ -11,10 +11,10 @@ namespace crpropa { */ /** - @class TorroidalHaloField + @class ToroidalHaloField @brief Galactic halo field model from Prouza & Smida 2003 and Sun et al. 2008 */ -class TorroidalHaloField: public MagneticField { +class ToroidalHaloField: public MagneticField { double b0; // halo field strength double z0; // vertical position double z1; // vertical scale @@ -28,7 +28,7 @@ class TorroidalHaloField: public MagneticField { * @param z1 vertical scale * @param r0 radial scale */ - TorroidalHaloField(double b0=1., double z0=1., double z1=1., double r0=1.) { + ToroidalHaloField(double b0 = 1., double z0 = 1., double z1 = 1., double r0 = 1.) { setParameters(b0, z0, z1, r0); } @@ -96,8 +96,8 @@ class LogarithmicSpiralField: public MagneticField { * @param d distance to first field reversal * @param z0 vertical attenuation length */ - LogarithmicSpiralField(bool isBSS=true, double b0=1., double pitch=M_1_PI/4., - double rsol=8.5*kpc, double rc=3*kpc, double d=5*kpc, double z0=3*kpc) { + LogarithmicSpiralField(bool isBSS = true, double b0 = 1., double pitch = M_1_PI/4., + double rsol = 8.5*kpc, double rc = 3*kpc, double d = 5*kpc, double z0 = 3*kpc) { setParameters(isBSS, b0, pitch, rsol, rc, d, z0); } diff --git a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h index 4b045263e..9c27fe7ba 100644 --- a/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h +++ b/include/crpropa/magneticField/PolarizedSingleModeMagneticField.h @@ -56,7 +56,7 @@ class PolarizedSingleModeMagneticField: public MagneticField { * @param flagPolarizationHelicity Flag to specify whether sigma denotes the standard polarization parameter ("polarization") or f_H, the fraction of maximal helicity ("helicity") * @param flagMode Flag to specify the polarization mode; possible choices are "elliptical", "circular" or "linear" */ - PolarizedSingleModeMagneticField(const double &B_0, const double &wavelength, const double &sigma, const Vector3d &r_0, const Vector3d &e_1, const Vector3d &e_2, std::string flagAmplitudeRms, std::string flagPolarizationHelicity, std::string flagMode ); + PolarizedSingleModeMagneticField(const double &B_0, const double &wavelength, const double &sigma, const Vector3d &r_0, const Vector3d &e_1, const Vector3d &e_2, std::string flagAmplitudeRms, std::string flagPolarizationHelicity, std::string flagMode); Vector3d getField(const Vector3d &position) const; }; diff --git a/include/crpropa/module/HDF5Output.h b/include/crpropa/module/HDF5Output.h index da601e0c4..aab716987 100644 --- a/include/crpropa/module/HDF5Output.h +++ b/include/crpropa/module/HDF5Output.h @@ -22,7 +22,7 @@ const size_t propertyBufferSize = 1024; /** @class HDF5Output @brief Output to HDF5 Format. -The baseclass gives an overview of possible columns +The base class gives an overview of possible columns HDF5 structure: ``` From 8119319388a4d5ae7273fc787730633944e6902d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?JulienD=C3=B6rner?= Date: Tue, 11 Apr 2023 16:22:12 +0200 Subject: [PATCH 83/87] change naming for transformation in Ferriere gas distribution --- include/crpropa/massDistribution/Ferriere.h | 5 +++-- src/massDistribution/Ferriere.cpp | 12 ++++++------ test/testDensity.cpp | 4 ++-- 3 files changed, 11 insertions(+), 10 deletions(-) diff --git a/include/crpropa/massDistribution/Ferriere.h b/include/crpropa/massDistribution/Ferriere.h index 0f2cce36b..43698ba3d 100644 --- a/include/crpropa/massDistribution/Ferriere.h +++ b/include/crpropa/massDistribution/Ferriere.h @@ -29,12 +29,13 @@ class Ferriere: public Density { @param position position in galactic coordinates with Earth at (-8.5kpc, 0, 0) @return position in local coordinates for the CMZ region */ - Vector3d CMZTrafo(const Vector3d &position) const; + Vector3d CMZTransformation(const Vector3d &position) const; + /** Coordinate transformation for the galactic bulge disk region in galactic center. Rotation arround the x-axis, the y'-axis and the x''-axis. Difened with X along the major axis, Y along the minor axis and Z along the northern normal @param position position in galactic coordinates with Earth at (-8.5kpc, 0, 0) @return position in local coordinates for the GB disk region */ - Vector3d DISKTrafo(const Vector3d &position) const; + Vector3d DiskTransformation(const Vector3d &position) const; /** @param position position in galactic coordinates with Earth at (-8.5kpc, 0, 0) @return density in parts/m^3, only acitvated parts are summed up */ diff --git a/src/massDistribution/Ferriere.cpp b/src/massDistribution/Ferriere.cpp index e189054e9..3f021b5e1 100644 --- a/src/massDistribution/Ferriere.cpp +++ b/src/massDistribution/Ferriere.cpp @@ -7,7 +7,7 @@ namespace crpropa { -Vector3d Ferriere::CMZTrafo(const Vector3d &position) const { +Vector3d Ferriere::CMZTransformation(const Vector3d &position) const { // set galactocentric coordinate system with the Sun at (-8.5,0.,0.) instead of (8.5, 0, 0) to be consistand with JF12 implementation double x = -position.x; double y = -position.y; @@ -25,7 +25,7 @@ Vector3d Ferriere::CMZTrafo(const Vector3d &position) const { return pos; } -Vector3d Ferriere::DISKTrafo(const Vector3d &position) const { +Vector3d Ferriere::DiskTransformation(const Vector3d &position) const { // set galactocentric coordinate system with the Sun at (-8.5,0.,0.) instead of (8.5, 0, 0) to be consistand with JF12 implementation double x = -position.x; double y = - position.y; @@ -63,7 +63,7 @@ double Ferriere::getHIDensity(const Vector3d &position) const { if(R<3*kpc) { // density at center - Vector3d pos = CMZTrafo(position); // coordinate trafo + Vector3d pos = CMZTransformation(position); // coordinate trafo double x = pos.x/pc; // all units in pc double y = pos.y/pc; double z = pos.z/pc; @@ -72,7 +72,7 @@ double Ferriere::getHIDensity(const Vector3d &position) const { double nCMZ = 8.8/ccm*exp(-pow_integer<4>((A-125.)/137))*exp(-pow_integer<2>(z/54.)); // density in disk - pos = DISKTrafo(position); // Corrdinate Trafo + pos = DiskTransformation(position); // Corrdinate Trafo x = pos.x/pc; // all units in pc y = pos.y/pc; z = pos.z/pc; @@ -156,7 +156,7 @@ double Ferriere::getH2Density(const Vector3d &position) const{ if(R<3*kpc) { // density at center - Vector3d pos =CMZTrafo(position); // coord trafo for CMZ + Vector3d pos =CMZTransformation(position); // coord trafo for CMZ double x = pos.x/pc; // all units in pc double y = pos.y/pc; double z = pos.z/pc; @@ -166,7 +166,7 @@ double Ferriere::getH2Density(const Vector3d &position) const{ nCMZ *= 150/ccm; // rescaling // density in disk - pos = DISKTrafo(position); // coord trafo for disk + pos = DiskTransformation(position); // coord trafo for disk x=pos.x/pc; y=pos.y/pc; z=pos.z/pc; diff --git a/test/testDensity.cpp b/test/testDensity.cpp index b4768dbf3..b65a33b3b 100644 --- a/test/testDensity.cpp +++ b/test/testDensity.cpp @@ -175,13 +175,13 @@ TEST(testFerriere, checkValueAtCertainPoints) { //test CMZ Trafo Vector3d Trafo; - Trafo = n.CMZTrafo(p); + Trafo = n.CMZTransformation(p); EXPECT_NEAR(Trafo.x,5.9767*pc,1e-4*pc); EXPECT_NEAR(Trafo.y,12.8171*pc,1e-4*pc); EXPECT_DOUBLE_EQ(Trafo.z,p.z); //no transformation in z component //test DISK Trafo - Trafo = n.DISKTrafo(p); + Trafo = n.DiskTransformation(p); EXPECT_NEAR(Trafo.x,11.0660*pc,1e-4*pc); EXPECT_NEAR(Trafo.y,82.5860*pc,1e-4*pc); EXPECT_NEAR(Trafo.z,-25.6338*pc,1e-4*pc); From 56c9b696489f4da69e1d3c5bf37505f8cedf95a2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?JulienD=C3=B6rner?= Date: Tue, 11 Apr 2023 16:28:53 +0200 Subject: [PATCH 84/87] remove build folder import from testPlugin --- plugin-template/testPlugin.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plugin-template/testPlugin.py b/plugin-template/testPlugin.py index 1ddce4b98..efe9d0788 100644 --- a/plugin-template/testPlugin.py +++ b/plugin-template/testPlugin.py @@ -1,5 +1,5 @@ import crpropa -import build.myPlugin as myPlugin +import myPlugin print("My Simulation\n") From c878879f56b42f593da619c909f4d1a32fb91d0d Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 11 Apr 2023 16:41:05 +0200 Subject: [PATCH 85/87] Fix name of ToroidalField model in test --- test/testMagneticField.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/testMagneticField.cpp b/test/testMagneticField.cpp index 9c319c7c1..8528c2593 100644 --- a/test/testMagneticField.cpp +++ b/test/testMagneticField.cpp @@ -181,8 +181,8 @@ TEST(testCMZMagneticField, TestAzimutalComponent){ EXPECT_DOUBLE_EQ(bVec.z, 0); } -TEST(testTorroidalHaloField, SimpleTest) { - ref_ptr field = new TorroidalHaloField(); +TEST(testToroidalHaloField, SimpleTest) { + ref_ptr field = new ToroidalHaloField(); Vector3d b = field->getField(Vector3d(0.)); EXPECT_DOUBLE_EQ(b.x, 0); EXPECT_DOUBLE_EQ(b.y, 0); From 663c574ce1f8ec5cf5a315f7462f8fa4abbacf40 Mon Sep 17 00:00:00 2001 From: mertelx Date: Tue, 11 Apr 2023 16:52:03 +0200 Subject: [PATCH 86/87] Add warning on different unit systems in CRPropa and CRPropa-Data --- .../custom_photonfield/custom-photon-field.ipynb | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb index 9e06f2758..c4588f6e7 100644 --- a/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb +++ b/doc/pages/example_notebooks/custom_photonfield/custom-photon-field.ipynb @@ -46,7 +46,11 @@ "- photonDensity (Array): Spectral energy density used to calculate files in data/Scaling (must be given in [$\\mathrm{eV}^{-1}\\,\\mathrm{ccm}^{-1}$]).\n", "- getDensity (function): returns the spectral number density dn/deps(eps, z) at a given photon energy (eps) and redshift (z)\n", "- getEmin (function): returns the minimum effective photon energy\n", - "- getEmax (function): returns the maximum effective photon energy" + "- getEmax (function): returns the maximum effective photon energy\n", + "\n", + "\n", + "### Warning\n", + "Note that CRPropa and CRPropa-Data are currently using different conventions for units of length and energy. CRPropa's default unit system is in SI units so length is in *meter* and energy is alsways in *joule*. The units in CRPropa-Data are not yet consistently converted to SI units. Therefore, the user has to pay attention to define the photon field class parameters with the correct units; see above and in the example class definition below." ] }, { From abe4e1ca67210dbd748009fea25b2c1ebd73e4f0 Mon Sep 17 00:00:00 2001 From: mertelx Date: Wed, 12 Apr 2023 14:29:54 +0200 Subject: [PATCH 87/87] Remove Ubuntu 18.04 testing as it is no longer supported by github actions. This also remove the last python 2.x test on a Linux system --- .github/workflows/testing.yml | 7 ------- 1 file changed, 7 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 2cd7a9cdb..2d86c35df 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -15,13 +15,6 @@ jobs: fc: "gfortran-9" swig_builtin: "Off" #uses swig 4.0.1 py: "/usr/bin/python3" #python 3.8 - - name: "ubuntu-18_py2" - os: ubuntu-18.04 - cxx: "g++-7" - cc: "gcc-7" - fc: "gfortran-7" - swig_builtin: "Off" #uses swig 3.x - py: "/usr/bin/python2" steps: - name: Checkout repository uses: actions/checkout@v3