diff --git a/.travis.yml b/.travis.yml index bdf2370..6acc210 100644 --- a/.travis.yml +++ b/.travis.yml @@ -37,7 +37,7 @@ addons: env: global: - - DOLONGDOUBLE="-DDCPROGS_LONG_DOUBLE=OFF" + - DOLONGDOUBLE="-DHJCFIT_LONG_DOUBLE=OFF" - UPLOADDOCS=false matrix: include: @@ -48,7 +48,7 @@ matrix: env: UPLOADDOCS=true - os: linux python: 3.5 - env: DOLONGDOUBLE="-DDCPROGS_LONG_DOUBLE=ON" + env: DOLONGDOUBLE="-DHJCFIT_LONG_DOUBLE=ON" - os: osx osx_image: xcode7.3 python: 2.7 diff --git a/CMakeLists.txt b/CMakeLists.txt index 698effd..5d0146e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,5 +1,5 @@ ######################## -# DCProgs computes missed-events likelihood as described in +# HJCFIT computes missed-events likelihood as described in # Hawkes, Jalali and Colquhoun (1990, 1992) # # Copyright (C) 2013 University College London @@ -35,7 +35,7 @@ option(pythonBindings "Enable python bindings." on) option(compileDocs "Compile c++11 documentation examples." on) option(openmp "Enable OpenMP parallelization." on) option(executenotebooks "Execute example Jupyter notebooks." on) -CMAKE_DEPENDENT_OPTION(DCPROGS_USE_MPFR "Enable fallback to MPFR Multi precision" ON "UNIX" OFF) +CMAKE_DEPENDENT_OPTION(HJCFIT_USE_MPFR "Enable fallback to MPFR Multi precision" ON "UNIX" OFF) set(JUPYTER_KERNEL "" CACHE STRING "Jupyter kernel to run notebooks with defaults to python2 or python3 depending on python version") # Provides backwards compatibility and prevents MACOS_RPATH warning @@ -132,28 +132,28 @@ if(NOT HAS_CXX11_NOEXCEPT) set(noexcept TRUE) endif() -if(NOT DCPROGS_LONG_DOUBLE) - set(DCPROGS_LONG_DOUBLE False CACHE BOOL +if(NOT HJCFIT_LONG_DOUBLE) + set(HJCFIT_LONG_DOUBLE False CACHE BOOL "If True, will use long doubles rather than simple doubles.") -endif(NOT DCPROGS_LONG_DOUBLE) +endif(NOT HJCFIT_LONG_DOUBLE) -if(NOT DCPROGS_USE_MPFR) - set(DCPROGS_USE_MPFR False CACHE BOOL +if(NOT HJCFIT_USE_MPFR) + set(HJCFIT_USE_MPFR False CACHE BOOL "If True, will use MPFR arbitrary precision floats as fall back if regular double calculations fail.") -endif(NOT DCPROGS_USE_MPFR) +endif(NOT HJCFIT_USE_MPFR) -if(DCPROGS_USE_MPFR) +if(HJCFIT_USE_MPFR) find_or_add_hunter_package(GMP) find_or_add_hunter_package(MPFR) include_directories(${PROJECT_SOURCE_DIR}/mpfr) -endif(DCPROGS_USE_MPFR) +endif(HJCFIT_USE_MPFR) configure_file ( - "${PROJECT_SOURCE_DIR}/DCProgsConfig.h.in" - "${PROJECT_BINARY_DIR}/DCProgsConfig.h" + "${PROJECT_SOURCE_DIR}/HJCFITConfig.h.in" + "${PROJECT_BINARY_DIR}/HJCFITConfig.h" ) -# Save all DCPROGS headers in the same place in Windows -install(FILES ${PROJECT_BINARY_DIR}/DCProgsConfig.h DESTINATION include/dcprogs) +# Save all HJCFIT headers in the same place in Windows +install(FILES ${PROJECT_BINARY_DIR}/HJCFITConfig.h DESTINATION include/HJCFIT) include(${CMAKE_SCRIPTS}/documentation.cmake) diff --git a/DCProgsConfig.h.in b/HJCFITConfig.h.in similarity index 71% rename from DCProgsConfig.h.in rename to HJCFITConfig.h.in index 5aab0f1..0b82f00 100644 --- a/DCProgsConfig.h.in +++ b/HJCFITConfig.h.in @@ -1,5 +1,5 @@ /*********************** - DCProgs computes missed-events likelihood as described in + HJCFIT computes missed-events likelihood as described in Hawkes, Jalali and Colquhoun (1990, 1992) Copyright (C) 2013 University College London @@ -18,17 +18,17 @@ along with this program. If not, see . ************************/ -#ifndef DCPROGS_CONFIG_H -#define DCPROGS_CONFIG_H +#ifndef HJCFIT_CONFIG_H +#define HJCFIT_CONFIG_H #include #include #include #include #include -#cmakedefine DCPROGS_USE_MPFR +#cmakedefine HJCFIT_USE_MPFR -#ifdef DCPROGS_USE_MPFR +#ifdef HJCFIT_USE_MPFR # include #endif @@ -47,18 +47,18 @@ #cmakedefine HAS_CXX11_CONSTRUCTOR_DELEGATE #ifdef HAS_CXX11_CONSTEXPR -# define DCPROGS_INIT_CONSTEXPR(TYPEANDNAME, VALUE) constexpr static TYPEANDNAME = VALUE -# define DCPROGS_DECL_CONSTEXPR(TYPEANDNAME, VALUE) constexpr TYPEANDNAME +# define HJCFIT_INIT_CONSTEXPR(TYPEANDNAME, VALUE) constexpr static TYPEANDNAME = VALUE +# define HJCFIT_DECL_CONSTEXPR(TYPEANDNAME, VALUE) constexpr TYPEANDNAME #else -# define DCPROGS_INIT_CONSTEXPR(TYPEANDNAME, VALUE) const static TYPEANDNAME -# define DCPROGS_DECL_CONSTEXPR(TYPEANDNAME, VALUE) const TYPEANDNAME = VALUE +# define HJCFIT_INIT_CONSTEXPR(TYPEANDNAME, VALUE) const static TYPEANDNAME +# define HJCFIT_DECL_CONSTEXPR(TYPEANDNAME, VALUE) const TYPEANDNAME = VALUE #endif // one should alway known when one is working on crapware #cmakedefine MSVC -#if defined(MSWINDOBE) && defined(DCPROGS_LIKELIHOOD_DLLEXPORT) +#if defined(MSWINDOBE) && defined(HJCFIT_LIKELIHOOD_DLLEXPORT) # undef MSWINDOBE # define MSWINDOBE __declspec(dllexport) #endif @@ -66,7 +66,7 @@ # define MSWINDOBE #endif -#cmakedefine DCPROGS_PYTHON_BINDINGS +#cmakedefine HJCFIT_PYTHON_BINDINGS #cmakedefine NUMPY_NPY_LONG_DOUBLE #cmakedefine NUMPY_NPY_BOOL #cmakedefine NUMPY_NPY_ARRAY @@ -81,48 +81,48 @@ #cmakedefine CXX_HAS_FLOAT_H_ISNAN #ifdef CXX_HAS_STD_ISNAN # include -# define DCPROGS_ISNAN(X) std::isnan(X) +# define HJCFIT_ISNAN(X) std::isnan(X) #elif defined(CXX_HAS_ISNAN) # include -# define DCPROGS_ISNAN(X) ::isnan(X) +# define HJCFIT_ISNAN(X) ::isnan(X) #elif defined CXX_HAS___ISNAN # include -# define DCPROGS_ISNAN(X) __isnan(X) +# define HJCFIT_ISNAN(X) __isnan(X) #elif defined(CXX_HAS_FLOAT_H_ISNAN) # include -# define DCPROGS_ISNAN(X) _isnan(X) +# define HJCFIT_ISNAN(X) _isnan(X) #else # error no macro defined for isnan #endif -#cmakedefine DCPROGS_LONG_DOUBLE -#cmakedefine DCPROGS_PYTHON3 +#cmakedefine HJCFIT_LONG_DOUBLE +#cmakedefine HJCFIT_PYTHON3 -#define DCPROGS_STACK_MATRIX_MAX 50 +#define HJCFIT_STACK_MATRIX_MAX 50 -namespace DCProgs { -# ifdef DCPROGS_LONG_DOUBLE - //! Types of reals across DCProgs. +namespace HJCFIT { +# ifdef HJCFIT_LONG_DOUBLE + //! Types of reals across HJCFIT. typedef long double t_real; # else - //! Types of reals across DCProgs. + //! Types of reals across HJCFIT. typedef double t_real; # endif //! Complex real type typedef std::complex t_complex; - //! Types of integers across DCProgs. + //! Types of integers across HJCFIT. typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE t_int; - //! Types of unsigned integers across DCProgs. + //! Types of unsigned integers across HJCFIT. typedef std::make_unsigned::type t_uint; - //! Types of real matrices across DCProgs. + //! Types of real matrices across HJCFIT. typedef Eigen::Matrix t_rmatrix; - //! Types of real matrices across DCProgs guaranteed to be allocated on the stack. Max size DCPROGS_STACK_MATRIX_MAX by DCPROGS_STACK_MATRIX_MAX - typedef Eigen::Matrix t_stack_rmatrix; - //! Types of boolean matrices across DCProgs. + //! Types of real matrices across HJCFIT guaranteed to be allocated on the stack. Max size HJCFIT_STACK_MATRIX_MAX by HJCFIT_STACK_MATRIX_MAX + typedef Eigen::Matrix t_stack_rmatrix; + //! Types of boolean matrices across HJCFIT. typedef Eigen::Matrix t_bmatrix; - //! Types of initial state vectors across DCProgs. + //! Types of initial state vectors across HJCFIT. typedef Eigen::Matrix t_initvec; - //! Types of final state vectors across DCProgs. + //! Types of final state vectors across HJCFIT. typedef Eigen::Matrix t_rvector; //! Type of complex matrices. typedef Eigen::Matrix t_cmatrix; @@ -134,14 +134,14 @@ namespace DCProgs { //! Type holding the bursts. typedef std::vector t_Bursts; -# ifdef DCPROGS_USE_MPFR - //! Types of multi-precision float across DCProgs. +# ifdef HJCFIT_USE_MPFR + //! Types of multi-precision float across HJCFIT. typedef mpfr::mpreal t_mpfr_real; - //! Types of multi-precision complex across DCProgs. + //! Types of multi-precision complex across HJCFIT. typedef std::complex t_mpfr_complex; - //! Types of multi-precision complex vector across DCProgs. + //! Types of multi-precision complex vector across HJCFIT. typedef Eigen::Matrix t_mpfr_cvector; - //! Types of multi-precision real matrix across DCProgs. + //! Types of multi-precision real matrix across HJCFIT. typedef Eigen::Matrix t_mpfr_rmatrix; # endif @@ -158,17 +158,17 @@ namespace DCProgs { bool eigen_nan(Eigen::DenseBase const &_matrix) { for(typename Eigen::DenseBase::Index i(0); i < _matrix.rows(); ++i) for(typename Eigen::ArrayBase::Index j(0); j < _matrix.cols(); ++j) - if(DCPROGS_ISNAN(_matrix(i, j))) return true; + if(HJCFIT_ISNAN(_matrix(i, j))) return true; return false; } // Check that quiet nan exists. Otherwise fails to compile here and now. static_assert(std::numeric_limits::has_quiet_NaN == true, - "Quiet NaN is not defined for the reals used by DCProgs."); + "Quiet NaN is not defined for the reals used by HJCFIT."); //! The quiet NaN value t_real static const quiet_nan = std::numeric_limits::quiet_NaN(); - t_uint static const dcprogs_stack_matrix = DCPROGS_STACK_MATRIX_MAX; + t_uint static const HJCFIT_stack_matrix = HJCFIT_STACK_MATRIX_MAX; } #endif diff --git a/LaunchNotebook.bat b/LaunchNotebook.bat new file mode 100644 index 0000000..4f7e123 --- /dev/null +++ b/LaunchNotebook.bat @@ -0,0 +1 @@ +jupyter notebook \ No newline at end of file diff --git a/cmake_modules/AllPythonBindings.cmake b/cmake_modules/AllPythonBindings.cmake index ea416b9..bb03bfa 100644 --- a/cmake_modules/AllPythonBindings.cmake +++ b/cmake_modules/AllPythonBindings.cmake @@ -72,7 +72,7 @@ if(MSYS) unset(NEED_CMATH_INCLUDE) endif(MSYS) -set(DCPROGS_PYTHON_BINDINGS True) +set(HJCFIT_PYTHON_BINDINGS True) if(tests) if(NOT DEFINED TEST_INSTALL_DIRECTORY) @@ -142,5 +142,5 @@ else() endif(WIN32) if(NOT PYTHON_VERSION VERSION_LESS "3.0.0") - set(DCPROGS_PYTHON3 TRUE) + set(HJCFIT_PYTHON3 TRUE) endif(NOT PYTHON_VERSION VERSION_LESS "3.0.0") diff --git a/cmake_modules/documentation.cmake b/cmake_modules/documentation.cmake index bdaca6f..c643a62 100644 --- a/cmake_modules/documentation.cmake +++ b/cmake_modules/documentation.cmake @@ -16,11 +16,11 @@ if(SPHINX_FOUND) else() set(SPHINX_EXTENSIONS "'sphinxcontrib.bibtex'") endif(DOXYGEN_FOUND) - if(DCPROGS_USE_MPFR) - set(SPHINX_DCPROGS_USE_MPFR "True") + if(HJCFIT_USE_MPFR) + set(SPHINX_HJCFIT_USE_MPFR "True") else() - set(SPHINX_DCPROGS_USE_MPFR "False") - endif(DCPROGS_USE_MPFR) + set(SPHINX_HJCFIT_USE_MPFR "False") + endif(HJCFIT_USE_MPFR) endif(SPHINX_FOUND) if (DOXYGEN_FOUND) diff --git a/data/CMakeLists.txt b/data/CMakeLists.txt index acb3d4d..af6974d 100644 --- a/data/CMakeLists.txt +++ b/data/CMakeLists.txt @@ -1,5 +1,5 @@ ######################## -# DCProgs computes missed-events likelihood as described in +# HJCFIT computes missed-events likelihood as described in # Hawkes, Jalali and Colquhoun (1990, 1992) # # Copyright (C) 2013 University College London @@ -17,7 +17,7 @@ if(pythonBindings) - install(FILES CH82.scn CCO.scn CO.scn DESTINATION ${PYINSTALL_DIRECTORY}/dcprogs/data) + install(FILES CH82.scn CCO.scn CO.scn DESTINATION ${PYINSTALL_DIRECTORY}/hjcfit/data) endif(pythonBindings) diff --git a/documentation/CMakeLists.txt b/documentation/CMakeLists.txt index 564d60f..bfb2592 100644 --- a/documentation/CMakeLists.txt +++ b/documentation/CMakeLists.txt @@ -1,5 +1,5 @@ ######################## -# DCProgs computes missed-events likelihood as described in +# HJCFIT computes missed-events likelihood as described in # Hawkes, Jalali and Colquhoun (1990, 1992) # # Copyright (C) 2013 University College London diff --git a/documentation/code/approx_survivor.cc b/documentation/code/approx_survivor.cc index 87ca448..af79abd 100644 --- a/documentation/code/approx_survivor.cc +++ b/documentation/code/approx_survivor.cc @@ -6,31 +6,31 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::ApproxSurvivor survivor(qmatrix, 1e-4); + HJCFIT::ApproxSurvivor survivor(qmatrix, 1e-4); std::cout << survivor << std::endl; std::cout << "AF values\n" "---------\n\n"; std::cout << " * at time t=" << 1e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(1e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(1e-4), " ") << "\n" << " * at time t=" << 1.5e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(1.5e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(1.5e-4), " ") << "\n" << " * at time t=" << 2.0e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(2.0e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(2.0e-4), " ") << "\n" << " * at time t=" << 2.5e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(2.5e-4), " ") << "\n\n"; + << HJCFIT::numpy_io(survivor.af(2.5e-4), " ") << "\n\n"; std::cout << " * Exponents: "; - for(DCProgs::t_uint i(0); i < survivor.nb_af_components(); ++i) + for(HJCFIT::t_uint i(0); i < survivor.nb_af_components(); ++i) std::cout << std::get<1>(survivor.get_af_components(i)) << " "; std::cout << std::endl; diff --git a/documentation/code/approx_survivor.py b/documentation/code/approx_survivor.py index 86bb14a..a65225e 100644 --- a/documentation/code/approx_survivor.py +++ b/documentation/code/approx_survivor.py @@ -1,4 +1,4 @@ -from dcprogs.likelihood import QMatrix, ApproxSurvivor +from HJCFIT.likelihood import QMatrix, ApproxSurvivor # Define parameters. qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], diff --git a/documentation/code/determinanteq.cc b/documentation/code/determinanteq.cc index 1de562b..f4991d4 100644 --- a/documentation/code/determinanteq.cc +++ b/documentation/code/determinanteq.cc @@ -6,40 +6,40 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); // Create determinant using a QMatrix and a matrix+nopen. - DCProgs::DeterminantEq det0(qmatrix, 1e-4); - DCProgs::DeterminantEq det1(matrix, 2, 1e-4); + HJCFIT::DeterminantEq det0(qmatrix, 1e-4); + HJCFIT::DeterminantEq det1(matrix, 2, 1e-4); std::cout << det0 << "\n\n" << det1 << "\n"; if( std::abs(det0(0) - det1(0)) > 1e-6 or std::abs(det0(-1) - det1(-1)) > 1e-6 or std::abs(det0(-1e2) - det1(-1e2)) > 1e-6) - throw DCProgs::errors::Runtime("instanciations differ."); + throw HJCFIT::errors::Runtime("instanciations differ."); if( std::abs( det0(-3045.285776037674) ) > 1e-6 * 3e3 or std::abs( det0(-162.92946543451328) ) > 1e-6 * 2e2 ) - throw DCProgs::errors::Runtime("Roots are not roots."); + throw HJCFIT::errors::Runtime("Roots are not roots."); - DCProgs::DeterminantEq transpose = det0.transpose(); + HJCFIT::DeterminantEq transpose = det0.transpose(); if( std::abs( transpose(-17090.192769236815) ) > 1e-6 * 2e5 or std::abs( transpose(-2058.0812921673496) ) > 1e-6 * 2e3 or std::abs( transpose(-0.24356535498785126) ) > 1e-6 ) - throw DCProgs::errors::Runtime("Roots are not roots."); + throw HJCFIT::errors::Runtime("Roots are not roots."); - std::cout << " * H(0):\n" << DCProgs::numpy_io(det0.H(0)) << "\n\n" - << " * H(-1e2):\n" << DCProgs::numpy_io(det0.H(-1e2)) << "\n\n"; + std::cout << " * H(0):\n" << HJCFIT::numpy_io(det0.H(0)) << "\n\n" + << " * H(-1e2):\n" << HJCFIT::numpy_io(det0.H(-1e2)) << "\n\n"; - std::cout << " * d[sI-H(s)]/ds for s=0:\n" << DCProgs::numpy_io(det0.s_derivative(0)) << "\n\n" - << " * d[sI-H(s)]/ds for s=-1e2:\n" << DCProgs::numpy_io(det0.s_derivative(-1e2)) << "\n\n"; + std::cout << " * d[sI-H(s)]/ds for s=0:\n" << HJCFIT::numpy_io(det0.s_derivative(0)) << "\n\n" + << " * d[sI-H(s)]/ds for s=-1e2:\n" << HJCFIT::numpy_io(det0.s_derivative(-1e2)) << "\n\n"; return 0; } diff --git a/documentation/code/determinanteq.py b/documentation/code/determinanteq.py index 2aef58b..07c6b46 100644 --- a/documentation/code/determinanteq.py +++ b/documentation/code/determinanteq.py @@ -1,6 +1,6 @@ from numpy import abs, all, array -from dcprogs import internal_dtype -from dcprogs.likelihood import DeterminantEq, QMatrix +from HJCFIT import internal_dtype +from HJCFIT.likelihood import DeterminantEq, QMatrix # Define parameters. matrix = [ [-3050, 50, 3000, 0, 0], diff --git a/documentation/code/exact_survivor.cc b/documentation/code/exact_survivor.cc index 9634ec4..370b12c 100644 --- a/documentation/code/exact_survivor.cc +++ b/documentation/code/exact_survivor.cc @@ -6,15 +6,15 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::ExactSurvivor survivor(qmatrix, 1e-4); + HJCFIT::ExactSurvivor survivor(qmatrix, 1e-4); std::cout << survivor << std::endl; @@ -22,21 +22,21 @@ int main() { std::cout << "AF values\n" "---------\n\n"; std::cout << " * at time t=" << 1e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(1e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(1e-4), " ") << "\n" << " * at time t=" << 1.5e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(1.5e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(1.5e-4), " ") << "\n" << " * at time t=" << 2.0e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(2.0e-4), " ") << "\n" + << HJCFIT::numpy_io(survivor.af(2.0e-4), " ") << "\n" << " * at time t=" << 2.5e-4 <<":\n " - << DCProgs::numpy_io(survivor.af(2.5e-4), " ") << "\n\n"; + << HJCFIT::numpy_io(survivor.af(2.5e-4), " ") << "\n\n"; std::cout << "AF recusion matrices\n" "--------------------\n\n"; - for(DCProgs::t_uint i(0); i < 5; ++i) - for(DCProgs::t_uint m(1); m < 3; ++m) - for(DCProgs::t_uint l(0); l <= m; ++l) + for(HJCFIT::t_uint i(0); i < 5; ++i) + for(HJCFIT::t_uint m(1); m < 3; ++m) + for(HJCFIT::t_uint l(0); l <= m; ++l) std::cout << " * C_{" << i << m << l << "}:\n " - << DCProgs::numpy_io(survivor.recursion_af(i, m, l), " ") << "\n\n"; + << HJCFIT::numpy_io(survivor.recursion_af(i, m, l), " ") << "\n\n"; return 0; } diff --git a/documentation/code/exact_survivor.py b/documentation/code/exact_survivor.py index 71c04c7..d401d07 100644 --- a/documentation/code/exact_survivor.py +++ b/documentation/code/exact_survivor.py @@ -1,4 +1,4 @@ -from dcprogs.likelihood import QMatrix, ExactSurvivor +from HJCFIT.likelihood import QMatrix, ExactSurvivor # Define parameters. qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], diff --git a/documentation/code/idealG.cc b/documentation/code/idealG.cc index 3bfad60..48dc50e 100644 --- a/documentation/code/idealG.cc +++ b/documentation/code/idealG.cc @@ -7,25 +7,25 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::IdealG idealG(qmatrix); + HJCFIT::IdealG idealG(qmatrix); std::cout << idealG << std::endl; - DCProgs::t_rmatrix const idealG_fa = (2e-4*qmatrix.ff()).exp()*qmatrix.fa(); + HJCFIT::t_rmatrix const idealG_fa = (2e-4*qmatrix.ff()).exp()*qmatrix.fa(); if( ((idealG.fa(2e-4) - idealG_fa).array().abs() > 1e-8).any() ) - throw DCProgs::errors::Runtime("Not so ideal idealG"); + throw HJCFIT::errors::Runtime("Not so ideal idealG"); - DCProgs::t_rmatrix const inversion = -0.5 * DCProgs::t_rmatrix::Identity(2, 2) - qmatrix.aa(); + HJCFIT::t_rmatrix const inversion = -0.5 * HJCFIT::t_rmatrix::Identity(2, 2) - qmatrix.aa(); if( ((inversion * idealG.laplace_af(-0.5) - qmatrix.af()).array().abs() > 1e-8).any() ) - throw DCProgs::errors::Runtime("Not so ideal idealG"); + throw HJCFIT::errors::Runtime("Not so ideal idealG"); return 0; } diff --git a/documentation/code/idealG.py b/documentation/code/idealG.py index d322ba3..ed6140c 100644 --- a/documentation/code/idealG.py +++ b/documentation/code/idealG.py @@ -1,5 +1,5 @@ from numpy import dot, identity, abs, all -from dcprogs.likelihood import QMatrix, IdealG, expm +from HJCFIT.likelihood import QMatrix, IdealG, expm qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], [2./3., -1502./3., 0, 500, 0], diff --git a/documentation/code/log10.cc b/documentation/code/log10.cc index f4d5577..9a5bcc8 100644 --- a/documentation/code/log10.cc +++ b/documentation/code/log10.cc @@ -3,25 +3,25 @@ int main() { - DCProgs::t_Bursts bursts{ + HJCFIT::t_Bursts bursts{ {0.1, 0.2, 0.1}, /* 1st burst */ {0.2}, /* 2nd burst */ {0.15, 0.16, 0.18, 0.05, 0.1} /* 3rd burst */ }; - DCProgs::Log10Likelihood likelihood( bursts, /*nopen=*/2, /*tau=*/1e-2, - /*tcrit=*/DCProgs::quiet_nan ); + HJCFIT::Log10Likelihood likelihood( bursts, /*nopen=*/2, /*tau=*/1e-2, + /*tcrit=*/HJCFIT::quiet_nan ); std::cout << likelihood << std::endl; - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::t_real const result = likelihood(matrix); + HJCFIT::t_real const result = likelihood(matrix); std::cout << "Computation: " << result << std::endl; diff --git a/documentation/code/log10.py b/documentation/code/log10.py index c733cf2..73adac7 100644 --- a/documentation/code/log10.py +++ b/documentation/code/log10.py @@ -1,5 +1,5 @@ from numpy import all, abs, NaN -from dcprogs.likelihood import Log10Likelihood +from HJCFIT.likelihood import Log10Likelihood bursts = [ [0.1, 0.2, 0.1], # 1st burst [0.2], # 2nd burst diff --git a/documentation/code/missedeventsG.cc b/documentation/code/missedeventsG.cc index 7bb3306..198ae0e 100644 --- a/documentation/code/missedeventsG.cc +++ b/documentation/code/missedeventsG.cc @@ -7,50 +7,50 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::t_real const tau(1e-4); // in seconds + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::t_real const tau(1e-4); // in seconds // Create eG from prior knowledge of roots - DCProgs::DeterminantEq determinant_eq(qmatrix, tau); - std::vector af_roots{ + HJCFIT::DeterminantEq determinant_eq(qmatrix, tau); + std::vector af_roots{ { /*root=*/ -3045.285776037674, /*multiplicity=*/ 1}, { /*root=*/ -162.92946543451328, /*multiplicity=*/ 1} }; - std::vector fa_roots{ + std::vector fa_roots{ { /*root=*/ -17090.192769236815, /*multiplicity=*/ 1}, { /*root=*/ -2058.0812921673496, /*multiplicity=*/ 1}, { /*root=*/ -0.24356535498785126, /*multiplicity=*/ 1} }; - DCProgs::MissedEventsG eG_from_roots( determinant_eq, af_roots, + HJCFIT::MissedEventsG eG_from_roots( determinant_eq, af_roots, determinant_eq.transpose(), fa_roots ); // Create eG by giving home-made root-finding function. - auto find_roots = [](DCProgs::DeterminantEq const &_det) { - return DCProgs::find_roots(_det, 1e-12, 1e-12, 100, DCProgs::quiet_nan, DCProgs::quiet_nan); + auto find_roots = [](HJCFIT::DeterminantEq const &_det) { + return HJCFIT::find_roots(_det, 1e-12, 1e-12, 100, HJCFIT::quiet_nan, HJCFIT::quiet_nan); }; - DCProgs::MissedEventsG eG_from_func(qmatrix, tau, find_roots); + HJCFIT::MissedEventsG eG_from_func(qmatrix, tau, find_roots); // Create eG automaticallye - DCProgs::MissedEventsG eG_automatic(qmatrix, tau); + HJCFIT::MissedEventsG eG_automatic(qmatrix, tau); // Checks the three initialization are equivalent - for(DCProgs::t_real t(tau); t < 10*tau; t += tau * 0.1) { + for(HJCFIT::t_real t(tau); t < 10*tau; t += tau * 0.1) { if( ((eG_from_roots.af(t) - eG_from_func.af(t)).array().abs() > 1e-8).any() or ((eG_from_roots.fa(t) - eG_from_func.fa(t)).array().abs() > 1e-8).any() ) - throw DCProgs::errors::Runtime("root != func"); + throw HJCFIT::errors::Runtime("root != func"); if( ((eG_from_roots.af(t) - eG_automatic.af(t)).array().abs() > 1e-8).any() or ((eG_from_roots.fa(t) - eG_automatic.fa(t)).array().abs() > 1e-8).any() ) - throw DCProgs::errors::Runtime("root != automatic"); + throw HJCFIT::errors::Runtime("root != automatic"); } return 0; diff --git a/documentation/code/missedeventsG.py b/documentation/code/missedeventsG.py index 9f983f1..7f880e2 100644 --- a/documentation/code/missedeventsG.py +++ b/documentation/code/missedeventsG.py @@ -1,5 +1,5 @@ from numpy import all, abs, arange -from dcprogs.likelihood import QMatrix, DeterminantEq, MissedEventsG +from HJCFIT.likelihood import QMatrix, DeterminantEq, MissedEventsG # Define parameters. qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], diff --git a/documentation/code/qmatrix.cc b/documentation/code/qmatrix.cc index 4b6aba9..fbfeef3 100644 --- a/documentation/code/qmatrix.cc +++ b/documentation/code/qmatrix.cc @@ -7,14 +7,14 @@ int main() { - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); std::cout << qmatrix << std::endl; @@ -23,7 +23,7 @@ int main() { if( ((qmatrix.matrix - matrix).array().abs() > 1e-12).any() ) return 1; // Compute sum over rows, row by row. - for(DCProgs::t_int i(0); i < qmatrix.matrix.rows(); ++i) + for(HJCFIT::t_int i(0); i < qmatrix.matrix.rows(); ++i) std::cout << "sum(row[" << i << "]): " << qmatrix.matrix.row(i).sum() << std::endl; // Compute sum over rows, but let eigen do it. diff --git a/documentation/code/qmatrix.py b/documentation/code/qmatrix.py index d20caba..d61d4fc 100644 --- a/documentation/code/qmatrix.py +++ b/documentation/code/qmatrix.py @@ -1,5 +1,5 @@ from numpy import sum, abs, all -from dcprogs.likelihood import QMatrix +from HJCFIT.likelihood import QMatrix matrix = [ [-3050, 50, 3000, 0, 0], [2./3., -1502./3., 0, 500, 0], diff --git a/documentation/code/roots.cc b/documentation/code/roots.cc index dbf605c..a69a6a2 100644 --- a/documentation/code/roots.cc +++ b/documentation/code/roots.cc @@ -7,30 +7,30 @@ int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::DeterminantEq det(qmatrix, 1e-4); + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::DeterminantEq det(qmatrix, 1e-4); // Find upper and lower bound - DCProgs::t_real upper_bound = DCProgs::find_upper_bound_for_roots(det); - DCProgs::t_real lower_bound = DCProgs::find_lower_bound_for_roots(det); + HJCFIT::t_real upper_bound = HJCFIT::find_upper_bound_for_roots(det); + HJCFIT::t_real lower_bound = HJCFIT::find_lower_bound_for_roots(det); // computes eigenvalues of H(s) for given s - auto get_eigenvalues = [&det](DCProgs::t_real _s) -> DCProgs::t_rvector { - return Eigen::EigenSolver(det.H(_s)).eigenvalues().real(); + auto get_eigenvalues = [&det](HJCFIT::t_real _s) -> HJCFIT::t_rvector { + return Eigen::EigenSolver(det.H(_s)).eigenvalues().real(); }; // Checks bounds are correct. if((get_eigenvalues(lower_bound).array() < lower_bound).any()) - throw DCProgs::errors::Runtime("Incorrect lower bound."); + throw HJCFIT::errors::Runtime("Incorrect lower bound."); if((get_eigenvalues(upper_bound).array() > upper_bound).any()) - throw DCProgs::errors::Runtime("Incorrect upper bound."); + throw HJCFIT::errors::Runtime("Incorrect upper bound."); std::cout << "Root Determination\n" "==================\n\n" @@ -41,20 +41,20 @@ int main() { << get_eigenvalues(upper_bound).transpose() << "\n\n"; // Figure out bracket for each root. - std::vector intervals - = DCProgs::find_root_intervals(det, lower_bound, upper_bound); + std::vector intervals + = HJCFIT::find_root_intervals(det, lower_bound, upper_bound); // Find root for each interval - for(DCProgs::RootInterval const& interval: intervals) { - auto brentq_result = DCProgs::brentq(det, interval.start, interval.end); + for(HJCFIT::RootInterval const& interval: intervals) { + auto brentq_result = HJCFIT::brentq(det, interval.start, interval.end); std::cout << " * Root interval: [" << interval.start << ", " << interval.end << "]\n" << " Corresponding root: " << std::get<0>(brentq_result) << "\n\n"; } // Look for roots in one go. - std::vector roots = DCProgs::find_roots(det); + std::vector roots = HJCFIT::find_roots(det); std::cout << " * All roots: "; - for(DCProgs::Root const &root: roots) std::cout << root.root << " "; + for(HJCFIT::Root const &root: roots) std::cout << root.root << " "; std::cout << "\n"; return 0; diff --git a/documentation/code/roots.py b/documentation/code/roots.py index 1ce8957..a4f1ccc 100644 --- a/documentation/code/roots.py +++ b/documentation/code/roots.py @@ -1,6 +1,6 @@ from numpy import all -from dcprogs.likelihood import eig -from dcprogs.likelihood import find_upper_bound_for_roots, find_lower_bound_for_roots, \ +from HJCFIT.likelihood import eig +from HJCFIT.likelihood import find_upper_bound_for_roots, find_lower_bound_for_roots, \ find_root_intervals, brentq, find_roots, QMatrix, DeterminantEq qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], diff --git a/documentation/code/occupancies.cc b/documentation/code/vectors.cc similarity index 50% rename from documentation/code/occupancies.cc rename to documentation/code/vectors.cc index 9a2f5cf..8814ec2 100644 --- a/documentation/code/occupancies.cc +++ b/documentation/code/vectors.cc @@ -2,44 +2,44 @@ #include #include -#include +#include int main() { // Define parameters. - DCProgs::t_rmatrix matrix(5 ,5); + HJCFIT::t_rmatrix matrix(5 ,5); matrix << -3050, 50, 3000, 0, 0, 2./3., -1502./3., 0, 500, 0, 15, 0, -2065, 50, 2000, 0, 15000, 4000, -19000, 0, 0, 0, 10, 0, -10; - DCProgs::QMatrix qmatrix(matrix, /*nopen=*/2); - DCProgs::t_real const tau(1e-4); // in seconds + HJCFIT::QMatrix qmatrix(matrix, /*nopen=*/2); + HJCFIT::t_real const tau(1e-4); // in seconds // Create missed-events G - DCProgs::MissedEventsG eG(qmatrix, tau); + HJCFIT::MissedEventsG eG(qmatrix, tau); // Create ideal G - DCProgs::IdealG idealG(qmatrix); + HJCFIT::IdealG idealG(qmatrix); - DCProgs::t_real const tcritical(5e-3); + HJCFIT::t_real const tcritical(5e-3); - std::cout << "Equilibrium Occupancies\n" + std::cout << "Equilibrium vectors\n" << "=======================\n\n" << "Ideal Likelihood\n" << "----------------\n\n" - << " * initial: " << DCProgs::occupancies(idealG) << "\n" - << " * final: " << DCProgs::occupancies(idealG, false) << "\n\n\n" + << " * initial: " << HJCFIT::vectors(idealG) << "\n" + << " * final: " << HJCFIT::vectors(idealG, false) << "\n\n\n" << "Missed-events Likelihood\n" << "------------------------\n\n" - << " * initial: " << DCProgs::occupancies(eG) << "\n" - << " * final: " << DCProgs::occupancies(eG, false) << "\n\n\n\n" - << "CHS Occupancies\n" + << " * initial: " << HJCFIT::vectors(eG) << "\n" + << " * final: " << HJCFIT::vectors(eG, false) << "\n\n\n\n" + << "CHS vectors\n" << "===============\n\n" << "Missed-events Likelihood\n" << "------------------------\n\n" << " * tcritical: " << tcritical << "\n" - << " * initial: " << DCProgs::CHS_occupancies(eG, tcritical) << "\n" - << " * final: " << DCProgs::CHS_occupancies(eG, tcritical, false) << "\n"; + << " * initial: " << HJCFIT::CHS_vectors(eG, tcritical) << "\n" + << " * final: " << HJCFIT::CHS_vectors(eG, tcritical, false) << "\n"; return 0; } diff --git a/documentation/code/occupancies.py b/documentation/code/vectors.py similarity index 71% rename from documentation/code/occupancies.py rename to documentation/code/vectors.py index 93ae149..034741d 100644 --- a/documentation/code/occupancies.py +++ b/documentation/code/vectors.py @@ -1,4 +1,4 @@ -from dcprogs.likelihood import QMatrix, IdealG, MissedEventsG +from HJCFIT.likelihood import QMatrix, IdealG, MissedEventsG # Define parameters. qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0], @@ -13,7 +13,7 @@ tcritical = 5e-3 -print("Equilibrium Occupancies\n" \ +print("Equilibrium Vectors\n" \ "=======================\n\n" \ "Ideal Likelihood\n" \ "----------------\n\n" \ @@ -23,7 +23,7 @@ "------------------------\n\n" \ " * initial: {equi_initial!r}\n" \ " * final: {equi_final!r}\n\n\n\n" \ - "CHS Occupancies\n" \ + "CHS Vectors\n" \ "===============\n\n" \ "Missed-events Likelihood\n" \ "------------------------\n\n" \ @@ -31,12 +31,12 @@ " * initial: {chs_initial!r}\n" \ " * final: {chs_final!r}" \ .format( - ideal_initial = idealG.initial_occupancies, - ideal_final = idealG.final_occupancies, - equi_initial = eG.initial_occupancies, - equi_final = eG.final_occupancies, - chs_initial = eG.initial_CHS_occupancies(tcritical), - chs_final = eG.final_CHS_occupancies(tcritical), + ideal_initial = idealG.initial_vectors, + ideal_final = idealG.final_vectors, + equi_initial = eG.initial_vectors, + equi_final = eG.final_vectors, + chs_initial = eG.initial_CHS_vectors(tcritical), + chs_final = eG.final_CHS_vectors(tcritical), tcritical = tcritical ) ) diff --git a/documentation/doxygen.in b/documentation/doxygen.in index 5148acd..b566603 100644 --- a/documentation/doxygen.in +++ b/documentation/doxygen.in @@ -667,7 +667,7 @@ WARN_LOGFILE = # directories like "/usr/src/myproject". Separate the files or directories # with spaces. -INPUT = @PROJECT_SOURCE_DIR@/likelihood @PROJECT_BINARY_DIR@/DCProgsConfig.h +INPUT = @PROJECT_SOURCE_DIR@/likelihood @PROJECT_BINARY_DIR@/HJCFITConfig.h # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is diff --git a/documentation/source/api/cpp/approx_survivor.rst b/documentation/source/api/cpp/approx_survivor.rst index 8300eb1..7964898 100644 --- a/documentation/source/api/cpp/approx_survivor.rst +++ b/documentation/source/api/cpp/approx_survivor.rst @@ -3,5 +3,5 @@ ApproxSurvivor -------------- -.. doxygenclass:: DCProgs::ApproxSurvivor +.. doxygenclass:: HJCFIT::ApproxSurvivor :members: diff --git a/documentation/source/api/cpp/determinanteq.rst b/documentation/source/api/cpp/determinanteq.rst index c7381d5..a7017b1 100644 --- a/documentation/source/api/cpp/determinanteq.rst +++ b/documentation/source/api/cpp/determinanteq.rst @@ -3,5 +3,5 @@ DeterminantEq ------------- -.. doxygenclass:: DCProgs::DeterminantEq +.. doxygenclass:: HJCFIT::DeterminantEq :members: diff --git a/documentation/source/api/cpp/exact_survivor.rst b/documentation/source/api/cpp/exact_survivor.rst index 617ad8d..e4cbd6b 100644 --- a/documentation/source/api/cpp/exact_survivor.rst +++ b/documentation/source/api/cpp/exact_survivor.rst @@ -3,5 +3,5 @@ ExactSurvivor ------------- -.. doxygenclass:: DCProgs::ExactSurvivor +.. doxygenclass:: HJCFIT::ExactSurvivor :members: diff --git a/documentation/source/api/cpp/exceptions.rst b/documentation/source/api/cpp/exceptions.rst index d3b47ac..67ff948 100644 --- a/documentation/source/api/cpp/exceptions.rst +++ b/documentation/source/api/cpp/exceptions.rst @@ -5,33 +5,33 @@ Exceptions Exceptions are located in the file ``likelihood/errors.h``. -.. doxygenclass:: DCProgs::errors::Root +.. doxygenclass:: HJCFIT::errors::Root General +++++++ -.. doxygenclass:: DCProgs::errors::Index -.. doxygenclass:: DCProgs::errors::Runtime -.. doxygenclass:: DCProgs::errors::NotImplemented +.. doxygenclass:: HJCFIT::errors::Index +.. doxygenclass:: HJCFIT::errors::Runtime +.. doxygenclass:: HJCFIT::errors::NotImplemented Math ++++ -.. doxygenclass:: DCProgs::errors::Math -.. doxygenclass:: DCProgs::errors::Mass -.. doxygenclass:: DCProgs::errors::ComplexEigenvalues -.. doxygenclass:: DCProgs::errors::NaN -.. doxygenclass:: DCProgs::errors::Domain -.. doxygenclass:: DCProgs::errors::MaxIterations -.. doxygenclass:: DCProgs::errors::NotInvertible +.. doxygenclass:: HJCFIT::errors::Math +.. doxygenclass:: HJCFIT::errors::Mass +.. doxygenclass:: HJCFIT::errors::ComplexEigenvalues +.. doxygenclass:: HJCFIT::errors::NaN +.. doxygenclass:: HJCFIT::errors::Domain +.. doxygenclass:: HJCFIT::errors::MaxIterations +.. doxygenclass:: HJCFIT::errors::NotInvertible Python ++++++ -.. doxygenclass:: DCProgs::errors::Python -.. doxygenclass:: DCProgs::errors::PythonErrorAlreadyThrown -.. doxygenclass:: DCProgs::errors::PythonTypeError -.. doxygenclass:: DCProgs::errors::PythonValueError +.. doxygenclass:: HJCFIT::errors::Python +.. doxygenclass:: HJCFIT::errors::PythonErrorAlreadyThrown +.. doxygenclass:: HJCFIT::errors::PythonTypeError +.. doxygenclass:: HJCFIT::errors::PythonValueError diff --git a/documentation/source/api/cpp/idealG.rst b/documentation/source/api/cpp/idealG.rst index 9b98546..cd9aa2a 100644 --- a/documentation/source/api/cpp/idealG.rst +++ b/documentation/source/api/cpp/idealG.rst @@ -3,4 +3,4 @@ IdealG ------ -.. doxygenclass:: DCProgs::IdealG +.. doxygenclass:: HJCFIT::IdealG diff --git a/documentation/source/api/cpp/laplace_survivor.rst b/documentation/source/api/cpp/laplace_survivor.rst index 6356303..6f5d930 100644 --- a/documentation/source/api/cpp/laplace_survivor.rst +++ b/documentation/source/api/cpp/laplace_survivor.rst @@ -3,6 +3,6 @@ LaplaceSurvivor --------------- -.. doxygenclass:: DCProgs::LaplaceSurvivor +.. doxygenclass:: HJCFIT::LaplaceSurvivor :members: diff --git a/documentation/source/api/cpp/log10likelihood.rst b/documentation/source/api/cpp/log10likelihood.rst index 8c3eda3..e968ba5 100644 --- a/documentation/source/api/cpp/log10likelihood.rst +++ b/documentation/source/api/cpp/log10likelihood.rst @@ -11,5 +11,5 @@ where :math:`L(Q)` is declared in :ref:`the likelihood equation `__ for more information. @@ -42,7 +42,7 @@ result, this package exposes some of Eigen_'s capabilities, as needed. Their int reminiscent of the numpy utility they mirror. -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autofunction:: eig .. autofunction:: inv .. autofunction:: svd diff --git a/documentation/source/api/python/approx_survivor.rst b/documentation/source/api/python/approx_survivor.rst index b4a9aaa..a83d452 100644 --- a/documentation/source/api/python/approx_survivor.rst +++ b/documentation/source/api/python/approx_survivor.rst @@ -3,7 +3,7 @@ ApproxSurvivor -------------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: ApproxSurvivor :members: :special-members: __call__ diff --git a/documentation/source/api/python/determinanteq.rst b/documentation/source/api/python/determinanteq.rst index a277336..3ad6da5 100644 --- a/documentation/source/api/python/determinanteq.rst +++ b/documentation/source/api/python/determinanteq.rst @@ -3,7 +3,7 @@ DeterminantEq ------------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: DeterminantEq :members: :special-members: __call__ diff --git a/documentation/source/api/python/exact_survivor.rst b/documentation/source/api/python/exact_survivor.rst index ac784c9..b12d3d8 100644 --- a/documentation/source/api/python/exact_survivor.rst +++ b/documentation/source/api/python/exact_survivor.rst @@ -3,7 +3,7 @@ ExactSurvivor ------------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: ExactSurvivor :members: :special-members: __call__ diff --git a/documentation/source/api/python/idealG.rst b/documentation/source/api/python/idealG.rst index 8d7e909..8d3cd2b 100644 --- a/documentation/source/api/python/idealG.rst +++ b/documentation/source/api/python/idealG.rst @@ -3,7 +3,7 @@ IdealG ------ -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: IdealG :members: diff --git a/documentation/source/api/python/log10likelihood.rst b/documentation/source/api/python/log10likelihood.rst index 5f354f1..1955cdd 100644 --- a/documentation/source/api/python/log10likelihood.rst +++ b/documentation/source/api/python/log10likelihood.rst @@ -7,7 +7,7 @@ Log10Likelihood :start-after: General Description Start :end-before: General Description End -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: Log10Likelihood :members: diff --git a/documentation/source/api/python/missed_eventsG.rst b/documentation/source/api/python/missed_eventsG.rst index 042ce2e..e84d981 100644 --- a/documentation/source/api/python/missed_eventsG.rst +++ b/documentation/source/api/python/missed_eventsG.rst @@ -3,7 +3,7 @@ MissedEventsG ------------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: MissedEventsG :members: :special-members: __call__ diff --git a/documentation/source/api/python/qmatrix.rst b/documentation/source/api/python/qmatrix.rst index da632a2..d647ba8 100644 --- a/documentation/source/api/python/qmatrix.rst +++ b/documentation/source/api/python/qmatrix.rst @@ -3,7 +3,7 @@ QMatrix ------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autoclass:: QMatrix :members: diff --git a/documentation/source/api/python/roots.rst b/documentation/source/api/python/roots.rst index ba38c0d..913874d 100644 --- a/documentation/source/api/python/roots.rst +++ b/documentation/source/api/python/roots.rst @@ -3,7 +3,7 @@ Searching for Roots ------------------- -.. currentmodule:: dcprogs.likelihood +.. currentmodule:: HJCFIT.likelihood .. autofunction:: find_roots Bracketing all Roots diff --git a/documentation/source/conf.py b/documentation/source/conf.py index d04813b..735d342 100644 --- a/documentation/source/conf.py +++ b/documentation/source/conf.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# DCProgs documentation build configuration file, created by +# HJCFIT documentation build configuration file, created by # sphinx-quickstart on Wed Jul 31 17:46:57 2013. # # This file is execfile()d with the current directory set to its containing dir. @@ -218,7 +218,7 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - ('index', 'dcprogs', u'@PROJECT_NAME@ Documentation', + ('index', 'HJCFIT', u'@PROJECT_NAME@ Documentation', [u'Mayeul d\'Avezac'], 1) ] @@ -305,8 +305,8 @@ def setup(app): app.add_config_value('python_bindings', "@pythonBindings@", True) - app.add_config_value('DCPROGS_USE_MPFR', - @SPHINX_DCPROGS_USE_MPFR@, 'env') + app.add_config_value('HJCFIT_USE_MPFR', + @SPHINX_HJCFIT_USE_MPFR@, 'env') python_bindings = "@pythonBindings@" diff --git a/documentation/source/index.rst b/documentation/source/index.rst index c184e27..087cc82 100644 --- a/documentation/source/index.rst +++ b/documentation/source/index.rst @@ -1,4 +1,4 @@ -.. DCProgs documentation master file, created by +.. HJCFIT documentation master file, created by sphinx-quickstart on Wed Jul 31 17:46:57 2013. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. @@ -7,11 +7,11 @@ Welcome to HJCFIT's documentation! ################################### -The goal of HJCFIT is to provide a collection of tools for scientific research on ion channels. The -package is derived from the DCPROGS_ suite and consists of a C++ implementation of the Likelihood -calculations along with Python wrappers. The code is a port of Fortran code with ~30 years usage at -University College London. The rationale is to preserve and cultivate these tools for future research -applications. +HJCFIT provides full maximum likelihood fitting of a kinetic mechanism directly to the entire sequence of open and shut times, with exact missed events correction. +The package is derived from the DCPROGS_ suite and consists of a C++ implementation of the Likelihood +calculations along with Python wrappers. + +The name of the program is an acronym for Hawkes, Jalali & Colquhoun, whose papers in 1990 and 1992 (HJC92) described the exact solution of the missed event problem, which is the basis of the program. The HJCFIT method was first described by Colquhoun, Hawkes & Srodzinski in 1996 (CHS96). For a description of the methods involved, see :cite:`colquhoun:1982`, :cite:`hawkes:1992`, :cite:`colquhoun:1995a`, :cite:`colquhoun:1995b`, :cite:`colquhoun:1996`. diff --git a/documentation/source/install/customisinginstall.rst b/documentation/source/install/customisinginstall.rst index 4956532..825c381 100644 --- a/documentation/source/install/customisinginstall.rst +++ b/documentation/source/install/customisinginstall.rst @@ -1,6 +1,6 @@ -****************************** -Customising build and install: -****************************** +***************************** +Customising build and install +***************************** Customising Installation location ================================= @@ -14,13 +14,13 @@ fairly easy: .. code-block:: bash - cd /path/to/dcprogs_source/build + cd /path/to/HJCFIT_source/build cmake .. -DCMAKE_INSTALL_PREFIX=/path/to/install/to make make install The above will put executable in ``/path/to/install/to/bin``, headers in -``/path/to/install/to/include``, and libraries in ```/path/to/install/to/lib``. +``/path/to/install/to/include``, and libraries in ``/path/to/install/to/lib``. Specific Eigen Installation =========================== @@ -31,7 +31,7 @@ done with: .. code-block:: bash - cd /path/to/dcprogs_source/build + cd /path/to/HJCFIT_source/build cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/include/eigen3 @@ -48,7 +48,7 @@ deleted (delete the build, not the source directory!) before attempting to set t .. code-block:: bash - cd /path/to/dcprogs_source + cd /path/to/HJCFIT_source mkdir build && build export CC=/path/to/ccompiler export CXX=/path/to/cppcompiler @@ -66,8 +66,8 @@ a step in the right direction. .. code-block:: bash - cd /path/to/dcprogs/build - cmake .. -DDCPROGS_LONG_DOUBLE=TRUE + cd /path/to/HJCFIT/build + cmake .. -DHJCFIT_LONG_DOUBLE=TRUE At this juncture, functions that return python scalars are still returning real numbers of 64bit. Functions that return numpy arrays have the correct size, however. @@ -81,7 +81,7 @@ You can explicitly disable it by doing: .. code-block:: bash - cd /path/to/dcprogs/build + cd /path/to/HJCFIT/build cmake .. -Dopenmp=off @@ -92,7 +92,7 @@ The Python bindings are automatically enabled but can be disabled by doing: .. code-block:: bash - cd /path/to/dcprogs/build + cd /path/to/HJCFIT/build cmake .. -DpythonBindings=off Enabling fallback to Multi precision arithmetic diff --git a/documentation/source/install/documentation.rst b/documentation/source/install/documentation.rst index c62757a..f42edc8 100644 --- a/documentation/source/install/documentation.rst +++ b/documentation/source/install/documentation.rst @@ -1,5 +1,5 @@ *********************** -Building documentation: +Building documentation *********************** The documentation is written using `doxygen `_ for c++, @@ -28,10 +28,7 @@ package. * Linux: apt-get, yum, pip or conda depending on your setup. -* Mac: ``pip install sphinx`` or ``conda install sphinx`` -* Windows: Depending on your setup - - ``conda.bat install sphinx`` - - or, ``pip install sphinx`` +* Mac and Windows: ``pip install sphinx`` or ``conda install sphinx`` .. warning:: If using a virtual environment, it is recommended to run @@ -68,7 +65,7 @@ This means: #. The library is in the ``PATH`` (windows), ``DYLD_LIBRARY_PATH`` (Mac), or the ``LD_LIBRARY_PATH`` (Linux) #. The python bindings are in the ``sys.path`` - (e.g. ``python -c "import dcprogs.likelihood"`` does not fail) + (e.g. ``python -c "import HJCFIT.likelihood"`` does not fail) The reason for this is that python documentation software will interrogate the package to find out what it contains. Hence the package needs to be @@ -127,57 +124,39 @@ Updating the web-page The data for the web page resides on the same git repository that the code does in a special branch called ``gh-pages``. And conversely, github knows to -render `here `__. anything that is in +render `here `__ anything that is in the branch ``gh-pages``. It is possible to update the data and the web-page with the following commands: #. Commit any changes to the code that should be kept safe. -#. Go to the build directory -#. Update the docs -.. code-block:: bash - - make documentation +#. Go to the build directory ``cd /path/to/build/``. +#. Update the docs with ``make documentation`` (or ``nmake documentation`` on Windows). #. Checkout the gh_pages using one the two lines below: -.. code-block:: bash - - git checkout -t origin/gh-pages # If first time, if the branch does not exist - git checkout gh-pages + .. code-block:: bash + git checkout -t origin/gh-pages # If first time, if the branch does not exist + git checkout gh-pages -At this point, the source directory does not contain code anymore. It contains data for the documentation webpage. + At this point, the source directory does not contain code anymore. It contains data for the documentation webpage. -1. Copy the new documentation from the build directory to the source directory: +5. Copy the new documentation from the build directory to the source directory: -.. code-block:: bash - - rsync -r documentation/sphinx/* .. + .. code-block:: bash -1. Commit the changes to the documentation. If nothing happens, - there were likely no changes: - -.. code-block:: bash - - git commit -a + rsync -r documentation/sphinx/* .. +6. Commit the changes to the documentation ``git commit -a``. If nothing happens, there were likely no changes. At this juncture, the data has been updated on the local computer. All that needs to be done is to push it to github, so github knows to render it. -1. Push the changes back to github so the web-site can be updated: - -.. code-block:: bash - - git push +7. Push the changes back to github so the web-site can be updated ``git push``. -1. Checkout the master branch again - -.. code-block:: bash - - git checkout master +8. Checkout the master branch again ``git checkout master``. Compiling the documentation without Python bindings =================================================== diff --git a/documentation/source/install/install.rst b/documentation/source/install/install.rst index de065c1..74b8ebf 100644 --- a/documentation/source/install/install.rst +++ b/documentation/source/install/install.rst @@ -1,29 +1,28 @@ -******************************* -Building and installing HJCFIT: -******************************* +****************************** +Building and installing HJCFIT +****************************** -Compiling DCProgs +Compiling HJCFIT ================= -A couple of design decisions affect the compilation of DCProgs. +A couple of design decisions affect the compilation of HJCFIT. -* `c++11 `_ is the new standard for - the C++ programming languages. It is almost fully implemented by modern +* `c++11 `_ is the new standard for the C++ programming languages. It is almost fully implemented by modern (2013) compilers. However, access to c++11 is now always default, and not always straight-forward. However, c++11 introduces a number of features that simplifies programming (e.g. `move semantics `_) greatly. This is a forward looking solution implying some temporary hassle. -* [GTest](https://code.google.com/p/googletest/) is the c++ unit-test - framework from google. It is required when running DCProgs' unit tests only. +* `GTest `_ is the c++ unit-test + framework from google. It is required when running HJCFIT' unit tests only. However, `GTest `_ must be compiled - by the code it is testing. This means it should be shipped with DCProgs, + by the code it is testing. This means it should be shipped with HJCFIT, or it should be downloaded automatically by the compilation tools. This is the option we have chosen. When compiling tests, `CMake `_ will automatically download and compile `GTest`_ * The math is done using `Eigen `_, - an efficient and widely used C++ numerical library. + an efficient and widely used C++ numerical library. Dependencies ------------ @@ -41,15 +40,15 @@ Dependencies or `mercurial `_ and let the build process download eigen. -To compile the python bindings for HJCFIT a few additional dependencies are + +To compile the Python bindings for HJCFIT a few additional dependencies are needed. #. A working Python installation. - -Multiple different ways of installing python exist. In general we recommend -`Anaconda `_ but alternatives should work -as well. In any case Python along with ``numpy`` and ``scipy`` should be -installed. HJCFIT supports both Python 2.7 and Python 3 + Multiple different ways of installing python exist. In general we recommend + `Anaconda `_ but alternatives should work + as well. In any case Python along with ``numpy`` and ``scipy`` should be + installed. HJCFIT supports both Python 2.7 and Python 3 #. `SWIG `_ used to generate the wrappings between C++ and Python. @@ -75,7 +74,7 @@ Then configure and build the code: .. code-block:: bash - cd /path/to/DCProgs + cd /path/to/HJCFIT mkdir build && cd build cmake .. make @@ -94,7 +93,7 @@ For any compiler, do: .. code-block:: bash - cd /path/to/DCProgs + cd /path/to/HJCFIT mkdir build && cd build diff --git a/documentation/source/install/runningarcher.rst b/documentation/source/install/runningarcher.rst index 77d298c..87d3a70 100644 --- a/documentation/source/install/runningarcher.rst +++ b/documentation/source/install/runningarcher.rst @@ -1,8 +1,8 @@ .. _runningonarcher: -************************* -Running HJCFIT on Archer: -************************* +************************ +Running HJCFIT on Archer +************************ There's good documentation about ARCHER on `their website `__, but here's an extract of what is useful for @@ -22,9 +22,6 @@ Once logged in ARCHER, there are two different filesystems we need to be aware o nodes and other files that the compute nodes need to have access to when running those executables. -Note we have a shared folder for RSDG and DCProgs team under -``/work/ecse0506/ecse0506/shared`` where we can place test data sets, etc. - .bashrc ======= @@ -35,8 +32,8 @@ to create variables to move around the filesystem: .. code-block:: bash - export WORK=/work/ecse0506/ecse0506/$USER - export SHARED=/work/ecse0506/ecse0506/shared + export WORK=/work/your/path/$USER + export SHARED=/work/your/path/shared You can also configure aliases, like: @@ -55,7 +52,7 @@ Python Virtual Environment To work with Python on ARCHER, we are using a virtual environment, which is the strategy recommended by ARCHER. To create it, you can run `this script `__ -that will install all the necessary packages to run HJCFIT in a virtual environment called ``dcprogs``. +that will install all the necessary packages to run HJCFIT in a virtual environment called ``HJCFIT``. You will also need to install any extra packages or projects you need, for example to work with DCPYPS, you'll need to clone it and then install it: @@ -69,8 +66,8 @@ Once the virtual environment is ready, you can activate or deactivate it with: .. code-block:: bash - source activate dcprogs - source deactivate dcprogs + source activate HJCFIT + source deactivate HJCFIT Loging in to ARCHER and getting HJCFIT diff --git a/documentation/source/manual.rst b/documentation/source/manual.rst index 55d4ffa..b1ba2f7 100644 --- a/documentation/source/manual.rst +++ b/documentation/source/manual.rst @@ -3,9 +3,9 @@ User Guide ********** The likelihood :math:`L` of a sequence of observed open and shut events :math:`\{t_{oi}, t_{si}\}` -can be computed as a series over the missed-events likelihoods for open events, -:math:`{}^e\mathcal{G}_{AF}(t_{oi})` and the missed-events likelihoods for shut events, -:math:`{}^e\mathcal{G}_{FA}(t_{oi})` :cite:`colquhoun:1996`: +can be computed as a product of open-shut, +:math:`{}^e\mathcal{G}_{AF}(t_{oi})` and shut-open transition densities, +:math:`{}^e\mathcal{G}_{FA}(t_{oi})`, both accounting for missed events :cite:`colquhoun:1996`: .. _log10likelihood_equation: .. math:: @@ -16,8 +16,8 @@ can be computed as a series over the missed-events likelihoods for open events, {}^e\mathcal{G}_{AF}(t_{on}) \phi_e where :math:`Q` is the transition rate matrix, and :math:`\phi_A` and :math:`\phi_e` are the initial -and final occupancies. All these objects -- as well as their components -- can be accessed both from -python and from c++. In the following, we try and show how they can be created and manipulated from +and final equilibrium vectors. All these objects -- as well as their components -- can be accessed both from +Python and from C++. In the following, we try and show how they can be created and manipulated from either language. @@ -29,7 +29,7 @@ either language. manual/likelihood_of_Q.rst manual/missedeventsG.rst manual/idealG.rst - manual/occupancies.rst + manual/vectors.rst manual/exact_survivor.rst manual/approx_survivor.rst manual/determinant_equation.rst @@ -38,10 +38,10 @@ either language. How to read this manual ----------------------- -Each topic below is illustrated by an example in c++, and another in python. These examples can be +Each topic below is illustrated by an example in C++, and another in Python. These examples can be found in the source code of the package within the directory ``documentation/code``. In any case, -one can copy paste. +one can copy and paste. The user guide should read using the left eye only, and keeping the right eye on the api, or -vice-versa. Most of the c++ functions and python bindings offer more functionality or parameters +vice-versa. Most of the C++ functions and Python bindings offer more functionality or parameters than mentioned in the manual. diff --git a/documentation/source/manual/approx_survivor.rst b/documentation/source/manual/approx_survivor.rst index f5a6f24..2254ca9 100644 --- a/documentation/source/manual/approx_survivor.rst +++ b/documentation/source/manual/approx_survivor.rst @@ -36,13 +36,13 @@ where :math:`c_i` and :math:`r_i` are the column and row eigenvectors of a The approximate survivor function can be initialized from a :math:`\mathcal{Q}`-matrix and the resolution :math:`\tau`: -:python: +:Python: .. literalinclude:: ../../code/approx_survivor.py :language: python :lines: 1-13 -:c++11: +:C++11: .. literalinclude:: ../../code/approx_survivor.cc :language: c++ @@ -56,16 +56,16 @@ The approximate survivor function can be initialized from a :math:`\mathcal{Q}`- The open and shut time survivor likelihood can be computed using a single call: -:python: +:Python: - The python bindings accept both scalars and array inputs. + The Python bindings accept both scalars and array inputs. .. literalinclude:: ../../code/approx_survivor.py :language: python :lines: 15-19 -:c++11: +:C++11: .. literalinclude:: ../../code/approx_survivor.cc :language: c++ @@ -75,13 +75,13 @@ The open and shut time survivor likelihood can be computed using a single call: The coefficient and the exponents of the exponentials that make up the asymptotic expression are exposed as shown below. -:python: +:Python: .. literalinclude:: ../../code/approx_survivor.py :language: python :lines: 23- -:c++11: +:C++11: .. literalinclude:: ../../code/approx_survivor.cc :language: c++ diff --git a/documentation/source/manual/determinant_equation.rst b/documentation/source/manual/determinant_equation.rst index e21de6f..fc1ce8a 100644 --- a/documentation/source/manual/determinant_equation.rst +++ b/documentation/source/manual/determinant_equation.rst @@ -10,18 +10,18 @@ The function :math:`H(s)` is an integral defined as: \int_0^\tau e^{-st}e^{\mathcal{Q}_{FF}t}\partial\,t\ \mathcal{Q}_{FA} It is possible the function :math:`H` as well as its determinant using the -:py:class:`~dcprogs.likelihood.DeterminantEq` objects. This is the object used when solving for the +:py:class:`~HJCFIT.likelihood.DeterminantEq` objects. This is the object used when solving for the approximate missed-events likelihood. The determinant equation is initialized in one of two ways, -either from a matrix or :py:class:`~dcprogs.likelihood.QMatrix`. +either from a matrix or :py:class:`~HJCFIT.likelihood.QMatrix`. -:python: +:Python: .. literalinclude:: ../../code/determinanteq.py :language: python :lines: 3-17 -:c++11: +:C++11: .. literalinclude:: ../../code/determinanteq.cc :language: c++ @@ -32,16 +32,16 @@ With an object in hand, it is possible to compute :math:`\mathop{det}W(s)` for a following we demonstrate that the two initialization methods are equivalent and that the determinant is zero at the roots of :math:`W(s)`, per definition. -:python: +:Python: - The python bindings accept both scalars and arrays as input. + The Python bindings accept both scalars and arrays as input. .. literalinclude:: ../../code/determinanteq.py :language: python :lines: 19-23 -:c++11: +:C++11: .. literalinclude:: ../../code/determinanteq.cc :language: c++ @@ -51,7 +51,7 @@ is zero at the roots of :math:`W(s)`, per definition. There exists a convenience function to transform a determinant equation into its "transpose", e.g. one where A states become F states and F states become A states: -:python: +:Python: .. literalinclude:: ../../code/determinanteq.py :language: python @@ -59,10 +59,10 @@ one where A states become F states and F states become A states: .. note:: - Here we choose to create an input which has same internal type as the dcprogs package. This may + Here we choose to create an input which has same internal type as the HJCFIT package. This may result in faster code since no conversion are required. -:c++11: +:C++11: .. literalinclude:: ../../code/determinanteq.cc :language: c++ @@ -72,13 +72,13 @@ one where A states become F states and F states become A states: Finally, it is possible to compute :math:`H(s)` directly, as well as :math:`\frac{\partial W(s)}{\partial s}`, as demonstrated below. -:python: +:Python: .. literalinclude:: ../../code/determinanteq.py :language: python :lines: 30- -:c++11: +:C++11: .. literalinclude:: ../../code/determinanteq.cc :language: c++ diff --git a/documentation/source/manual/exact_survivor.rst b/documentation/source/manual/exact_survivor.rst index 4f2d11c..9db98d2 100644 --- a/documentation/source/manual/exact_survivor.rst +++ b/documentation/source/manual/exact_survivor.rst @@ -47,14 +47,14 @@ Finally, the matrices :math:`D_i` are defined as: The survivor function can be initialized from a :math:`\mathcal{Q}`-matrix and the resolution :math:`\tau`: -:python: +:Python: .. literalinclude:: ../../code/exact_survivor.py :language: python :lines: 1-13 -:c++11: +:C++11: .. literalinclude:: ../../code/exact_survivor.cc :language: c++ @@ -62,16 +62,16 @@ The survivor function can be initialized from a :math:`\mathcal{Q}`-matrix and t The open and shut time survivor likelihood can be computed using a single call: -:python: +:Python: - The python bindings accept both scalars and array inputs. + The Python bindings accept both scalars and array inputs. .. literalinclude:: ../../code/exact_survivor.py :language: python :lines: 15-19 -:c++11: +:C++11: .. literalinclude:: ../../code/exact_survivor.cc :language: c++ @@ -81,13 +81,13 @@ The open and shut time survivor likelihood can be computed using a single call: The details of the recursions, i.e. the :math:`C_{iml}` matrices, can be accessed directly as shown below. -:python: +:Python: .. literalinclude:: ../../code/exact_survivor.py :language: python :lines: 23- -:c++11: +:C++11: .. literalinclude:: ../../code/exact_survivor.cc :language: c++ diff --git a/documentation/source/manual/idealG.rst b/documentation/source/manual/idealG.rst index 5de8232..8713530 100644 --- a/documentation/source/manual/idealG.rst +++ b/documentation/source/manual/idealG.rst @@ -1,8 +1,8 @@ -Ideal Likelihood :math:`\mathcal{G}(t)` +Ideal transition densities :math:`\mathcal{G}(t)` ======================================= -A wrapper around :py:class:`~dcprogs.likelihood.QMatrix` is provided which allows the calculation of -the ideal likelihood: +A wrapper around :py:class:`~HJCFIT.likelihood.QMatrix` is provided which allows the calculation of +the ideal transition densities: .. math:: @@ -16,22 +16,22 @@ the ideal likelihood: This object can be initialized directly from a :py:class:`QMatrix`. -:python: +:Python: .. literalinclude:: ../../code/idealG.py :language: python :lines: 2-11 -:c++11: +:C++11: .. literalinclude:: ../../code/idealG.cc :language: c++ :lines: 1-20, 30- -It provides the ideal likelihood as a function of time, as well as the laplace transforms: +It provides the ideal likelihood as a function of time, as well as the Laplace transforms: -:python: +:Python: .. literalinclude:: ../../code/idealG.py :language: python @@ -44,7 +44,7 @@ It provides the ideal likelihood as a function of time, as well as the laplace t few useful functions, such as ``expm`` in this example, are provided to remediate to this situation. -:c++11: +:C++11: .. literalinclude:: ../../code/idealG.cc :language: c++ diff --git a/documentation/source/manual/likelihood_of_Q.rst b/documentation/source/manual/likelihood_of_Q.rst index 079f6a2..0e05c66 100644 --- a/documentation/source/manual/likelihood_of_Q.rst +++ b/documentation/source/manual/likelihood_of_Q.rst @@ -19,23 +19,23 @@ The purpose of this class is to provide an interface for maximizing the likeliho for a given set of observed open and shut intervals, the likelihood :math:`\frac{\ln(L(Q))}{ln 10}`, where :math:`L(Q)` is declared in :ref:`the likelihood equation `. -A callable object :math:`L(Q)` exists in both :ref:`c++ ` and :ref:`python +A callable object :math:`L(Q)` exists in both :ref:`C++ ` and :ref:`Python `. It can be initialized as follows -:python: +:Python: .. literalinclude:: ../../code/log10.py :language: python :lines: 2-15 -:c++11: +:C++11: .. literalinclude:: ../../code/log10.cc :language: c++ :lines: 1-15, 28- - The initialization of `bursts` above is done in using two newer c++11 coding techniques: + The initialization of `bursts` above is done in using two newer C++11 coding techniques: `initializer lists `_, and `uniform initialization `_. It may not be available from all compilers just yet... @@ -45,13 +45,13 @@ Once the objects are initialized, the input attributes can be accessed (and modi .. note:: - :py:func:`~dcprogs.likelihood.Log10Likelihood` uses equilibrium occupancies depending on the - value of its attribute :py:attr:`~dcprogs.likelihood.Log10Likelihood.tcritical`: + :py:func:`~HJCFIT.likelihood.Log10Likelihood` uses equilibrium vectors depending on the + value of its attribute :py:attr:`~HJCFIT.likelihood.Log10Likelihood.tcritical`: - - if it is ``None``, ``numpy.NaN``, or negative, then the equilibrium occupancies are used - - if it a strictly positive real number, then the CHS vectors are computed + - if it is ``None``, ``numpy.NaN``, or negative, then the equilibrium vectors are used; + - if it is a strictly positive real number, then the CHS vectors are computed. - Similarly, in c++, ``tcritical`` can be set to :c:data:`DCProgs::quiet_nan` to trigger + Similarly, in C++, ``tcritical`` can be set to :c:data:`HJCFIT::quiet_nan` to trigger calculations with equilibrium occupancies. It is required that the bursts have been pre-filtered so that there are no intervals smaller than @@ -63,7 +63,7 @@ the resolution :math:`\tau`. This can be done using :cpp:func:`time_filter` The likelihood for any Q-matrix can then be computed by calling the `likelihood` objects as though they were function. The following snippets are inserted at the tail end of the previous code. -:python: +:Python: .. literalinclude:: ../../code/log10.py :language: python @@ -72,36 +72,36 @@ they were function. The following snippets are inserted at the tail end of the p The function can take any sort square matrix, whether using standard python lists or a numpy array. It can only take one matrix at a time. -:c++11: +:C++11: .. literalinclude:: ../../code/log10.cc :language: c++ :lines: 17-25 -The return is the log-likelihood associated with the bursts and the input Q-matrix. In both python -and c++, the functions accepts either a matrix or an actual :cpp:class:`DCProgs::QMatrix` -(:py:class:`python `) object. In the former case, the number of open +The return is the log-likelihood associated with the bursts and the input Q-matrix. In both Python +and C++, the functions accepts either a matrix or an actual :cpp:class:`HJCFIT::QMatrix` +(:py:class:`python `) object. In the former case, the number of open states is set to `nopen`. -It should be noted that the python the bursts are accessed in python directly from the likelihood +It should be noted that the bursts are accessed in Python directly from the likelihood using normal sequence operations. Only a small subset of sequence operations where implemented. -:python: +:Python: .. literalinclude:: ../../code/log10.py :language: python :lines: 1, 27-37 -:c++11: +:C++11: - :cpp:member:`DCProgs::Log10Likelihood::bursts` is a public member and can be accessed directly. + :cpp:member:`HJCFIT::Log10Likelihood::bursts` is a public member and can be accessed directly. Finally, some of the attributes, namely, :py:attr:`Log10Likelihood.tcritical`, :py:attr:`Log10Likelihood.upper_bound`, :py:attr:`Log10Likelihood.lower_bound`, act both as parameters and as switch when given special values. These special values are `None` and `numpy.NaN` -in python and :c:data:`DCProgs::quiet_nan` in c++. In python, the special values will always be transformed +in Python and :c:data:`HJCFIT::quiet_nan` in C++. In Python, the special values will always be transformed to `None`. :python: diff --git a/documentation/source/manual/missedeventsG.rst b/documentation/source/manual/missedeventsG.rst index 0957cc0..f6f786f 100644 --- a/documentation/source/manual/missedeventsG.rst +++ b/documentation/source/manual/missedeventsG.rst @@ -1,59 +1,59 @@ .. _manual_eG: -Missed-Events Likelihood :math:`{}^eG(t)` +Missed-Events transition densities :math:`{}^eG(t)` ========================================= -The callable object :cpp:class:`DCProgs::MissedEventsG` provides an interface to compute the +The callable object :cpp:class:`HJCFIT::MissedEventsG` provides an interface to compute the likelihood :math:`{}^eG(t)` of open and shut events as a function of their lengths, for a fixed :math:`Q`-matrix. It has the ability to compute both exact and approximate missed-events likelihood, -returning one or the other depending on a given time cutoff. +returning one or the other depending on a given time resolution. -The asymptotic expression of the likelihood can be computed from the knowledge of the roots of a +The asymptotic expression of the transition densities can be computed from the knowledge of the roots of a specific equations. On the one hand, root-finding can be a fairly difficult numerical operation. On -the other, it would be more convenient if we can initialize :cpp:class:`DCProgs::MissedEventsG` +the other, it would be more convenient if we can initialize :cpp:class:`HJCFIT::MissedEventsG` directly from a :math:`Q`-matrix object. As such, there are several means to initialize the functor: -- from the knowledge of the roots and the determinant equations -- directly from a :math:`Q`-matrix, using the default root-finding mechanism -- from a :math:`Q`-matrix, using a custom root-finding mechanism (c++ only) +- from the knowledge of the roots and the determinant equations; +- directly from a :math:`Q`-matrix, using the default root-finding mechanism; +- from a :math:`Q`-matrix, using a custom root-finding mechanism (C++ only). Initialization from a :math:`Q`-matrix """""""""""""""""""""""""""""""""""""" -:python: +:Python: .. literalinclude:: ../../code/missedeventsG.py :language: python :lines: 1-11, 19 -:c++11: +:C++11: .. literalinclude:: ../../code/missedeventsG.cc :language: c++ :lines: 1-18, 41, 55- A fair amount of work goes on behind the scene. First reasonable upper and lower bounds for the -roots obtained (:cpp:func:`DCProgs::find_lower_bound_for_roots`, and -:cpp:func:`DCProgs::find_upper_bound_for_roots`). Then intervals for each roots are computed -(:cpp:func:`DCProgs::find_root_intervals`). And finally, the roots themselves are obtained -(:cpp:func:`DCProgs::brentq`). All this work is done automatically in the case of this particular -instantiation. A few extra parameters to control the root-finding process can be passed to the c++ -and python constructors. +roots obtained (:cpp:func:`HJCFIT::find_lower_bound_for_roots`, and +:cpp:func:`HJCFIT::find_upper_bound_for_roots`). Then intervals for each roots are computed +(:cpp:func:`HJCFIT::find_root_intervals`). And finally, the roots themselves are obtained +(:cpp:func:`HJCFIT::brentq`). All this work is done automatically in the case of this particular +instantiation. A few extra parameters to control the root-finding process can be passed to the C++ +and Python constructors. Initialization from the roots and determinant equations """"""""""""""""""""""""""""""""""""""""""""""""""""""" -:python: +:Python: .. literalinclude:: ../../code/missedeventsG.py :language: python :lines: 13-16 -:c++11: +:C++11: .. literalinclude:: ../../code/missedeventsG.cc :language: c++ @@ -68,7 +68,7 @@ Initialization from the :math:`Q`-matrix and a root finding function Given a root-finding function, it is possible to instantiate :math:`{}^eG`. The root finding function should take a determinant equation as input, and return a vector of -:cpp:class:`DCProgs::Root` as output. In the code below, we show how the prior initialization could +:cpp:class:`HJCFIT::Root` as output. In the code below, we show how the prior initialization could be recreated. .. literalinclude:: ../../code/missedeventsG.cc @@ -76,7 +76,7 @@ be recreated. :lines: 35-38 This is mostly a convenience function, to make it slightly easier to interface with other -root-finding methods in c++. This interface is not explicitly available in python, although it can +root-finding methods in C++. This interface is not explicitly available in Python, although it can be created with ease. @@ -105,9 +105,9 @@ The python bindings accept any input that can be transformed to a numpy array of is a scalar, then the AF and FA blocks are returned. If the input is an array, then an array of similar shape is returned, where each component is a matrix. -The :cpp:class:`DCProgs::MissedEventsG` provides further functionality. For instance, the cutoff +The :cpp:class:`HJCFIT::MissedEventsG` provides further functionality. For instance, the cutoff point between exact and asymptotic calculations can be set explicitly (it defaults to :math:`t < 3\tau`). And the likelihood can be computed in Laplace space (see -:cpp:member:`DCProgs::MissedEventsG::laplace_af` and -:cpp:member:`DCProgs::MissedEventsG::laplace_fa`). We invite users to turn to the :ref:`python +:cpp:member:`HJCFIT::MissedEventsG::laplace_af` and +:cpp:member:`HJCFIT::MissedEventsG::laplace_fa`). We invite users to turn to the :ref:`python ` and the :ref:`c++ ` API for more details. diff --git a/documentation/source/manual/occupancies.rst b/documentation/source/manual/occupancies.rst deleted file mode 100644 index 0b1b6b9..0000000 --- a/documentation/source/manual/occupancies.rst +++ /dev/null @@ -1,58 +0,0 @@ -.. _manual_occupancies: - -Occupancies -=========== - -The start and end occupancies can be computed one of two way. They can be the equilibrium -occupancies determined from the equation: - -.. math:: - - \phi_A = \phi_A {}^e\mathcal{G}_{AF} {}^e\mathcal{G}_{FA},\\ - \phi_F = {}^e\mathcal{G}_{FA} {}^e\mathcal{G}_{AF} \phi_F,\\ - -subject to the constraints, - -.. math:: - - \sum_i [\phi_A]_i = 1,\\ - \sum_i [\phi_F]_i = 1. - -Where :math:`{}^e\mathcal{G}_{AF}` and :math:`{}^e\mathcal{G}_{FA}` are the laplacians of the -missed-events likelihoods (or equivalently, the ideal likelihoods) for :math:`s=0`, and -:math:`[a]_i` indicates the :math:`i^{th}` component of vector :math:`a`. - -Or they can be computed as CHS vectors, e.g. equation 5.11 and 5.8 from :cite:`colquhoun:1996`. - -The occupancies are accessed differently in :ref:`c++ ` and in python. - -:python: - - The equilibrium occupancies are accessed as properties of :py:class:`~dcprogs.likelihood.IdealG` - and :py:class:`~dcprogs.likelihood.MissedEventsG` instances. The CHS vectors are functions of - these same classes that take as arguments the critical time. - - - .. literalinclude:: ../../code/occupancies.py - :language: python - - -:c++11: - - Both equilibrium and CHS occupancies are accessed via function calls acting on the - :cpp:class:`IdealG` and :cpp:class:`MissedEventsG`. - - .. literalinclude:: ../../code/occupancies.cc - :language: c++ - -In c++, the occupancies are kept outside of the classes because computing these values is outside -the pure remit of the classes (which is to compute the likelihood). However, in python, practicality -beats purity, and it makes practical sense to keep likelihood and occupancies together. - -.. note:: - - :py:func:`~dcprogs.likelihood.Log10Likelihood` uses equilibrium occupancies depending on the - value of its attribute :py:attr:`~dcprogs.likelihood.Log10Likelihood.tcritical`: - - - if it is ``None``, ``numpy.NaN``, or negative, then the equilibrium occupancies are used - - if it a strictly positive real number, then the CHS vectors are computed diff --git a/documentation/source/manual/roots.rst b/documentation/source/manual/roots.rst index 96c349b..d2e97a9 100644 --- a/documentation/source/manual/roots.rst +++ b/documentation/source/manual/roots.rst @@ -3,9 +3,9 @@ Searching for Roots The default procedure for finding roots is a three step process: -1. Searching for sensible upper and lower bounds bracketing all roots -2. Bisecting the bracket above to obtain bracket with a single root each -3. Using a standard root-finding method to search for the root within that bracket +1. Searching for sensible upper and lower bounds bracketing all roots. +2. Bisecting the bracket above to obtain bracket with a single root each. +3. Using a standard root-finding method to search for the root within that bracket. The first step is carried out by iteratively computing the eigenvalues of :math:`H(s)` and setting the candidate lower(upper) boundary below(above) the smallest(largest) eigenvalue. Additionally, the @@ -13,7 +13,7 @@ upper boundary is set to a value such that :math:`\mathop{det}H(s)` is strictly two convenience functions to encapsulate this functionality, :py:func:`find_upper_bound_for_roots` and :py:func:`find_lower_bound_for_roots`. -:python: +:Python: .. literalinclude:: ../../code/roots.py :language: python @@ -25,7 +25,7 @@ and :py:func:`find_lower_bound_for_roots`. linear algebra utilities for such type. As consequence, this package exposes some of the functionality that it needs for its reals. -:c++11: +:C++11: .. literalinclude:: ../../code/roots.cc :language: c++ @@ -40,13 +40,13 @@ It is possible in most function and classes to pass actual values for the upper The second step of the process is to bisect the input bracket until intervals are found which contain a single root (e.g. a single eigenvalue of H(s)). -:python: +:Python: .. literalinclude:: ../../code/roots.py :language: python :lines: 31 -:c++11: +:C++11: .. literalinclude:: ../../code/roots.cc :language: c++ @@ -56,13 +56,13 @@ The third step is performed by calling (by default) the :py:func:`brentq` subrou copied straight from Scipy, with some modifications to allow it to cope with long doubles, if need be. -:python: +:Python: .. literalinclude:: ../../code/roots.py :language: python :lines: 32-35 -:c++11: +:C++11: .. literalinclude:: ../../code/roots.cc :language: c++ @@ -73,13 +73,13 @@ parameters in the snippets below, :py:func:`find_roots` can take variety of para the root-finding procedure. Most notably, it accepts ``lower_bound`` and ``upper_bound`` keywords, allowing users to by-pass the first step if need be. -:python: +:Python: .. literalinclude:: ../../code/roots.py :language: python :lines: 38-39 -:c++11: +:C++11: .. literalinclude:: ../../code/roots.cc :language: c++ diff --git a/documentation/source/manual/vectors.rst b/documentation/source/manual/vectors.rst new file mode 100644 index 0000000..7c18ed3 --- /dev/null +++ b/documentation/source/manual/vectors.rst @@ -0,0 +1,58 @@ +.. _manual_vectors: + +Equilibrium vectors +=========== + +The start and end equilibrium vectors can be computed in two ways depending on the burst/cluster nature. They can be the equilibrium +vectors determined from the equation: + +.. math:: + + \phi_A = \phi_A {}^e\mathcal{G}_{AF} {}^e\mathcal{G}_{FA},\\ + \phi_F = {}^e\mathcal{G}_{FA} {}^e\mathcal{G}_{AF} \phi_F,\\ + +subject to the constraints, + +.. math:: + + \sum_i [\phi_A]_i = 1,\\ + \sum_i [\phi_F]_i = 1. + +Where :math:`{}^e\mathcal{G}_{AF}` and :math:`{}^e\mathcal{G}_{FA}` are the laplacians of the +missed-events transition densities :math:`{}^e\mathcal{G}(t)` (or equivalently, the ideal transition densities) for :math:`s=0`, and +:math:`[a]_i` indicates the :math:`i^{th}` component of vector :math:`a`. + +On the other hand equilibrium vectors can be computed as CHS vectors, e.g. equation 5.11 and 5.8 from :cite:`colquhoun:1996`. + +The vectors are accessed differently in :ref:`C++ ` and in Python. + +:Python: + + The equilibrium vectors are accessed as properties of :py:class:`~HJCFIT.likelihood.IdealG` + and :py:class:`~HJCFIT.likelihood.MissedEventsG` instances. The CHS vectors are functions of + these same classes that take as arguments the critical time. + + + .. literalinclude:: ../../code/vectors.py + :language: python + + +:C++11: + + All vectors are accessed via function calls acting on the + :cpp:class:`IdealG` and :cpp:class:`MissedEventsG`. + + .. literalinclude:: ../../code/vectors.cc + :language: c++ + +In C++, the vectors are kept outside of the classes because computing these values is outside +the pure remit of the classes (which is to compute the likelihood). However, in Python, practicality +beats purity, and it makes practical sense to keep likelihood and equilibrium vectors together. + +.. note:: + + :py:func:`~HJCFIT.likelihood.Log10Likelihood` uses equilibrium vectors depending on the + value of its attribute :py:attr:`~HJCFIT.likelihood.Log10Likelihood.tcritical`: + + - if it is ``None``, ``numpy.NaN``, or negative, then the equilibrium vectors are used; + - if it is a strictly positive real number, then the CHS vectors are computed. diff --git a/documentation/source/mpfr.rst b/documentation/source/mpfr.rst index 595a429..bfa74fa 100644 --- a/documentation/source/mpfr.rst +++ b/documentation/source/mpfr.rst @@ -8,7 +8,7 @@ precision of the floating point numbers use to represent values within the code. To work around these issues the code is able to use GMP and MPFR to perform specific calculations at higher precision. As multi precision math is not implemented in hardware it comes with very significant run time overhead, -typically orders of magnitude slower we have added support for multi-precision +typically orders of magnitude slower. We have added support for multi-precision as a fall back mechanism. Currently there is only support for fallback in ``find_eigs_bound`` which is known to be problematic. The pattern that we follow is to do the calculations with regular precision floating point. If that @@ -24,4 +24,4 @@ linux if not found. However, this feature is not enabled on Windows by default. ``MPIR`` as a drop-in replacement for ``GMP`` on windows which ``MPFR`` can be linked against. However, there is currently no support for building this automatically on windows. To control if this option should be enabled the flag -``DCPROGS_USE_MPFR`` can be set to ``on`` or ``off``. +``HJCFIT_USE_MPFR`` can be set to ``on`` or ``off``. diff --git a/documentation/source/parallel.rst b/documentation/source/parallel.rst index d145889..0e755cb 100644 --- a/documentation/source/parallel.rst +++ b/documentation/source/parallel.rst @@ -8,20 +8,19 @@ Running likelihood calculations in parallel OpenMP ====== -HJCFIT will by default be compiled with openmp support. The parallelisation -is by default over either the number of bursts or over the individual open +HJCFIT will be compiled with openmp support by default. The parallelisation +is over either the number of bursts or over the individual open/ close transitions within the burst. Typically experiments either have many -short bursts or a few long bursts so it really only makes sense to parallelise +short bursts or a few long bursts so it makes sense to parallelise over one of these axes. The code takes care of detecting which axis to parallelise over automatically. The number of threads can be controlled by the usual environmental variable ``OMP_NUM_THREADS``. Running on a PC this -will probably be set to the number of cores in the computer which is probably -the optimal solution in most cases. +will probably be set to the number of cores in the computer which is the optimal solution in most cases. CMake takes care of identifying the correct compiler flags and enables OpenMP automatically on all supported platforms. Currently (2016) Clang on OSX does not support OpenMP (but the code can be compiled on OSX using gcc from homebrew -or similar) +or similar). OpenMP can be disabled explicitly by setting the CMake variable ``openmp`` to off. @@ -31,4 +30,4 @@ off. MPI === -Write something about MPI +TODO diff --git a/exploration/.ipynb_checkpoints/CB-checkpoint.ipynb b/exploration/.ipynb_checkpoints/CB-checkpoint.ipynb new file mode 100644 index 0000000..ebdaaa4 --- /dev/null +++ b/exploration/.ipynb_checkpoints/CB-checkpoint.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CB Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following tries to reproduce Fig 10 from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116). First we create the $Q$-matrix for this particular model from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116). First we create the $Q$-matrix for this particular model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "\n", + "tau = 0.2\n", + "qmatrix = QMatrix([ [-2, 1, 1, 0], \n", + " [ 1, -101, 0, 100], \n", + " [50, 0, -50, 0],\n", + " [ 0, 5.6, 0, -5.6]], 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create a function to plot each exponential component in the asymptotic expression. An explanation on how to get to these plots can be found in the **CH82** notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood._methods import exponential_pdfs\n", + "\n", + "def plot_exponentials(qmatrix, tau, x0=None, x=None, ax=None, nmax=2, shut=False):\n", + " from HJCFIT.likelihood import missed_events_pdf\n", + " from HJCFIT.likelihood._methods import exponential_pdfs\n", + " if ax is None: \n", + " fig, ax = plt.subplots(1,1)\n", + " if x is None: \n", + " x = np.arange(0, 5*tau, tau/10)\n", + " if x0 is None: \n", + " x0 = x\n", + " pdf = missed_events_pdf(qmatrix, tau, nmax=nmax, shut=shut)\n", + " graphb = [x0, pdf(x0+tau), '-k']\n", + " functions = exponential_pdfs(qmatrix, tau, shut=shut)\n", + " plots = ['.r', '.b', '.g']\n", + " together = None\n", + " for f, p in zip(functions[::-1], plots):\n", + " if together is None: \n", + " together = f(x+tau)\n", + " else: \n", + " together = together + f(x+tau)\n", + " graphb.extend([x, together, p])\n", + "\n", + " ax.plot(*graphb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For practical reasons, we plot the excess shut-time probability densities in the graph below. In all other particulars, it should reproduce Fig. 10 from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAKDCAYAAADsJhDzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import missed_events_pdf\n", + "\n", + "fig, ax = plt.subplots(2,2, figsize=(12,9))\n", + "x = np.arange(0, 10, tau/100)\n", + "pdf = missed_events_pdf(qmatrix, 0.2, nmax=2, shut=True)\n", + "ax[0,0].plot(x, pdf(x), '-k')\n", + "ax[0,0].set_xlabel('time $t$ (ms)')\n", + "ax[0,0].set_ylabel('Shut-time probability density $f_{\\\\bar{\\\\tau}=0.2}(t)$')\n", + "\n", + "ax[0,1].set_xlabel('time $t$ (ms)')\n", + "tau = 0.2\n", + "x, x0 = np.arange(0, 5*tau, tau/10.0), np.arange(0, 5*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[0,1], x=x, x0=x0)\n", + "ax[0,1].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[0,1].set_xlabel('time $t$ (ms)')\n", + "ax[0,1].yaxis.tick_right()\n", + "ax[0,1].yaxis.set_label_position(\"right\")\n", + "\n", + "tau = 0.05\n", + "x, x0 = np.arange(0, 5*tau, tau/10.0), np.arange(0, 5*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[1,0], x=x, x0=x0)\n", + "ax[1,0].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[1,0].set_xlabel('time $t$ (ms)')\n", + "\n", + "tau = 0.5\n", + "x, x0 = np.arange(0, 5*tau, tau/10.0), np.arange(0, 5*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[1,1], x=x, x0=x0)\n", + "ax[1,1].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[1,1].set_xlabel('time $t$ (ms)')\n", + "ax[1,1].yaxis.tick_right()\n", + "ax[1,1].yaxis.set_label_position(\"right\")\n", + "ax[0,1].legend(['a','b','c','d'], loc='best')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lower bound for all roots is -126.51309385718378\n", + "[ 0.00000000e+00 6.57926167e+33 -5.60000000e+00]\n", + "0.0\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import DeterminantEq, find_root_intervals, find_lower_bound_for_roots\n", + "from numpy.linalg import eig\n", + "tau = 0.5\n", + "determinant = DeterminantEq(qmatrix, tau).transpose()\n", + "x = np.arange(-100, -3, 0.1)\n", + "\n", + "matrix = qmatrix.transpose()\n", + "#qaffa = np.array(np.dot(matrix.af, matrix.fa), dtype=np.float128)\n", + "qaffa = np.array(np.dot(matrix.af, matrix.fa), dtype=np.longdouble)\n", + "#aa = np.array(matrix.aa, dtype=np.float128)\n", + "aa = np.array(matrix.aa, dtype=np.longdouble)\n", + "\n", + "def anaH(s):\n", + " from numpy.linalg import det \n", + " from numpy import identity, exp\n", + " #arg0 = 1e0/np.array(-2-s, dtype=np.float128)\n", + " #arg1 = np.array(-(2+s) * tau, dtype=np.float128)\n", + " #return qaffa * (exp(arg1) - np.array(1e0, dtype=np.float128)) * arg0 + aa\n", + " arg0 = 1e0/np.array(-2-s, dtype=np.longdouble)\n", + " arg1 = np.array(-(2+s) * tau, dtype=np.longdouble)\n", + " return qaffa * (exp(arg1) - np.array(1e0, dtype=np.longdouble)) * arg0 + aa\n", + "\n", + "def anadet(s):\n", + " from numpy.linalg import det \n", + " from numpy import identity, exp\n", + " #s = np.array(s, dtype=np.float128)\n", + " #matrix = s*identity(qaffa.shape[0], dtype=np.float128) - anaH(s)\n", + " s = np.array(s, dtype=np.longdouble)\n", + " matrix = s*identity(qaffa.shape[0], dtype=np.longdouble) - anaH(s)\n", + " return matrix[0,0] * matrix[1, 1] * matrix[2, 2] \\\n", + " + matrix[1,0] * matrix[2, 1] * matrix[0, 2] \\\n", + " + matrix[0,1] * matrix[1, 2] * matrix[2, 0] \\\n", + " - matrix[2,0] * matrix[1, 1] * matrix[0, 2] \\\n", + " - matrix[1,0] * matrix[0, 1] * matrix[2, 2] \\\n", + " - matrix[2,1] * matrix[1, 2] * matrix[0, 0] \n", + "\n", + "x = np.arange(-100, -3, 1e-2)\n", + "# For some reason gcc builds with regular doubles have trouble finding the\n", + "# roots with alpha=2.0 the default so override it here\n", + "\n", + "print(\"Lower bound for all roots is {}\".format(find_lower_bound_for_roots(determinant, alpha=1.9)))\n", + "print(eig(np.array(anaH(-160 ), dtype='float64'))[0])\n", + "print(anadet(-104))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/CH82 -- optimization-checkpoint.ipynb b/exploration/.ipynb_checkpoints/CH82 -- optimization-checkpoint.ipynb new file mode 100644 index 0000000..fba3a74 --- /dev/null +++ b/exploration/.ipynb_checkpoints/CH82 -- optimization-checkpoint.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CH82 -- optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Input: Defines the model and constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT import read_idealized_bursts\n", + "from HJCFIT.likelihood import QMatrix\n", + "\n", + "name = \"CH82.scn\"\n", + "tau = 1e-4\n", + "tcrit = 4e-3 \n", + "graph = [[\"V\", \"V\", \"V\", 0, 0],\n", + " [\"V\", \"V\", 0, \"V\", 0],\n", + " [\"V\", 0, \"V\", \"V\", \"V\"],\n", + " [ 0, \"V\", \"V\", \"V\", 0],\n", + " [ 0, 0, \"V\", 0, \"V\"]] \n", + "nopen = 2\n", + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)\n", + "\n", + "bursts = read_idealized_bursts(name, tau=tau, tcrit=tcrit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creates the constraints, the likelihood function, as well as a function to create random Q-matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.optimize import minimize\n", + "from numpy import NaN, zeros, arange\n", + "import numpy as np\n", + "from HJCFIT.likelihood.random import qmatrix as random_qmatrix\n", + "from HJCFIT.likelihood import QMatrix, Log10Likelihood\n", + "from HJCFIT.likelihood.optimization import reduce_likelihood\n", + "\n", + "likelihood = Log10Likelihood(bursts, nopen, tau, tcrit)\n", + "reduced = reduce_likelihood(likelihood, graph)\n", + "x = reduced.to_reduced_coords( random_qmatrix(5).matrix )\n", + "\n", + "constraints = []\n", + "def create_inequality_constraints(i, value=0e0, sign=1e0):\n", + " f = lambda x: sign * (x[i] - value)\n", + " def df(x):\n", + " a = zeros(x.shape)\n", + " a[i] = sign\n", + " return a\n", + " return f, df\n", + "\n", + "for i in range(len(x)):\n", + " f, df = create_inequality_constraints(i)\n", + " constraints.append({'type': 'ineq', 'fun': f, 'jac': df})\n", + " f, df = create_inequality_constraints(i, 1e4, -1)\n", + " constraints.append({'type': 'ineq', 'fun': f, 'jac': df})\n", + "\n", + " \n", + "def random_starting_point():\n", + " from numpy import infty, NaN\n", + " from HJCFIT.likelihood.random import rate_matrix as random_rate_matrix\n", + " \n", + " \n", + " for i in range(100):\n", + " matrix = random_rate_matrix(N=len(qmatrix.matrix), zeroprob=0)\n", + " x = reduced.to_reduced_coords( matrix )\n", + " try: \n", + " result = reduced(x)\n", + " print(result, reduced.to_full_coords(x))\n", + " except:\n", + " pass\n", + " else: \n", + " if result != NaN and result != infty and result != -infty: break\n", + " else: raise RuntimeError(\"Could not create random matrix\") \n", + " return x\n", + "\n", + "def does_not_throw(x):\n", + " try: return -reduced(x)\n", + " except: return NaN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performs the minimization" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-640.830069172272 [[ -6.21619693e-01 3.80010323e-01 2.41609369e-01 0.00000000e+00\n", + " 0.00000000e+00]\n", + " [ 3.10903942e+03 -3.10919359e+03 0.00000000e+00 1.54171557e-01\n", + " 0.00000000e+00]\n", + " [ 1.59277846e-01 0.00000000e+00 -9.08913654e+03 9.08867581e+03\n", + " 3.01453712e-01]\n", + " [ 0.00000000e+00 4.39644066e-01 2.78736992e-01 -7.18381058e-01\n", + " 0.00000000e+00]\n", + " [ 0.00000000e+00 0.00000000e+00 1.54808746e-01 0.00000000e+00\n", + " -1.54808746e-01]]\n", + "x= [ 3.80010323e-01 2.41609369e-01 3.10903942e+03 1.54171557e-01\n", + " 1.59277846e-01 9.08867581e+03 3.01453712e-01 4.39644066e-01\n", + " 2.78736992e-01 1.54808746e-01]\n", + " fun: -2062.8258070089187\n", + " maxcv: 8.7670065147940707e-16\n", + " message: 'Maximum number of function evaluations has been exceeded.'\n", + " nfev: 1000\n", + " status: 2\n", + " success: False\n", + " x: array([ -8.76700651e-16, 1.75097884e+02, 3.11563723e+03,\n", + " 2.68478978e+02, 6.06596893e+02, 9.06857871e+03,\n", + " 1.73472348e-18, 1.77190965e+01, -3.03804457e-16,\n", + " 7.92641819e-01])\n", + "-697.0699667052597 [[ -2.18554574e-01 8.96065006e-02 1.28948074e-01 0.00000000e+00\n", + " 0.00000000e+00]\n", + " [ 6.92249289e+02 -6.92781143e+02 0.00000000e+00 5.31853771e-01\n", + " 0.00000000e+00]\n", + " [ 8.75734586e-01 0.00000000e+00 -1.91477903e+00 6.89175466e-01\n", + " 3.49868979e-01]\n", + " [ 0.00000000e+00 5.54235637e-01 4.39234514e-02 -5.98159089e-01\n", + " 0.00000000e+00]\n", + " [ 0.00000000e+00 0.00000000e+00 9.79442657e+02 0.00000000e+00\n", + " -9.79442657e+02]]\n", + "Inequality constraints incompatible (Exit mode 4)\n", + " Current function value: -2284.629372492554\n", + " Iterations: 177\n", + " Function evaluations: 2189\n", + " Gradient evaluations: 177\n", + " fun: -2284.629372492554\n", + " jac: array([ -2.08709717e-01, -5.42224910e+08, -2.52990723e-02,\n", + " 2.75032878e+05, 1.75594303e+09, -1.57243136e+09,\n", + " -5.42756597e+08, -2.75028975e+05, 5.62684071e+08,\n", + " -6.88560304e+07, 0.00000000e+00])\n", + " message: 'Inequality constraints incompatible'\n", + " nfev: 2189\n", + " nit: 177\n", + " njev: 177\n", + " status: 4\n", + " success: False\n", + " x: array([ 4.64593912e-07, 3.41051917e+02, 2.65544934e+03,\n", + " 1.38876283e+03, 9.99999918e+03, 4.32621889e+03,\n", + " 2.19612726e+02, 2.99939102e+00, 2.05470191e+00,\n", + " -1.68337691e-14])\n", + "-447.89702673666727 [[ -2.96248722e+03 2.73428480e-01 2.96221379e+03 0.00000000e+00\n", + " 0.00000000e+00]\n", + " [ 9.99336618e-01 -1.00764635e+00 0.00000000e+00 8.30973004e-03\n", + " 0.00000000e+00]\n", + " [ 5.19874297e-03 0.00000000e+00 -5.01074858e+03 3.97100105e-01\n", + " 5.01034628e+03]\n", + " [ 0.00000000e+00 8.74501090e-01 2.32778358e+03 -2.32865808e+03\n", + " 0.00000000e+00]\n", + " [ 0.00000000e+00 0.00000000e+00 6.91322340e-03 0.00000000e+00\n", + " -6.91322340e-03]]\n", + "[ 3.80906388e-01 2.42898850e-01 3.11596192e+03 1.59490095e-01\n", + " 1.68035192e-01 9.08868270e+03 3.04952401e-01 4.45186422e-01\n", + " 2.79176226e-01 9.94923531e+00]\n", + "-2284.629372492554\n" + ] + } + ], + "source": [ + "import math\n", + "methods = ['COBYLA', 'SLSQP']\n", + "x = random_starting_point()\n", + "print ('x=', x)\n", + "maxx = (x.copy(), reduced(x))\n", + "for i in range(len(methods)):\n", + " result = minimize(does_not_throw,\n", + " x,\n", + " method=methods[i],\n", + " constraints=constraints,\n", + " options={'maxiter': 1000, 'disp':True}) \n", + "\n", + " print(result)\n", + " if not math.isnan(result.fun):\n", + " if result.fun < maxx[1]: maxx = (x.copy(), result.fun)\n", + " if result.success and i > 4: break\n", + " x += random_starting_point() * 1e-2\n", + " if np.all(np.isnan(x)): x = random_starting_point()\n", + "print(maxx[0])\n", + "print(maxx[1])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/CH82-checkpoint.ipynb b/exploration/.ipynb_checkpoints/CH82-checkpoint.ipynb new file mode 100644 index 0000000..fa6c791 --- /dev/null +++ b/exploration/.ipynb_checkpoints/CH82-checkpoint.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CH82 Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following tries to reproduce Fig 8 from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116).\n", + "First we create the $Q$-matrix for this particular model. Please note that the units are different from other publications.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "\n", + "tau = 1e-4\n", + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)\n", + "qmatrix.matrix /= 1000.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first reproduce the top tow panels showing $\\mathrm{det} W(s)$ for open and shut times.\n", + "These quantities can be accessed using `dcprogs.likelihood.DeterminantEq`. The plots are done using a standard plotting function from the `dcprogs.likelihood` package as well." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFjCAYAAAAtnDI1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJC4iIpJS/x8A+luC+6wRZwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import plot_roots, DeterminantEq\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(7,5))\n", + "\n", + "plot_roots(DeterminantEq(qmatrix, 0.2), ax=ax[0])\n", + "ax[0].set_xlabel('Laplace $s$')\n", + "ax[0].set_ylabel('$\\\\mathrm{det} ^{A}W(s)$')\n", + "\n", + "plot_roots(DeterminantEq(qmatrix, 0.2).transpose(), ax=ax[1])\n", + "ax[1].set_xlabel('Laplace $s$')\n", + "ax[1].set_ylabel('$\\\\mathrm{det} ^{F}W(s)$')\n", + "ax[1].yaxis.tick_right()\n", + "ax[1].yaxis.set_label_position(\"right\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we want to plot the panels c and d showing the excess shut and open-time probability densities$(\\tau = 0.2)$. To do this we need to access each exponential that makes up the approximate survivor function. We could use:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(array([[ 9.99994874e-01, -1.96070450e-02],\n", + " [ -2.61427266e-04, 5.12584244e-06]]), -3.050008571211625)]\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import ApproxSurvivor\n", + "approx = ApproxSurvivor(qmatrix, tau)\n", + "components = approx.af_components\n", + "print(components[:1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The list `components` above contain 2-tuples with the weight (as a matrix) and the exponant (or root) for each exponential component in $^{A}R_{\\mathrm{approx}}(t)$. We could then create python functions `pdf(t)` for each exponential component, as is done below for the first root:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import MissedEventsG\n", + "\n", + "weight, root = components[1]\n", + "eG = MissedEventsG(qmatrix, tau)\n", + "# Note: the sum below is equivalent to a scalar product with u_F\n", + "coefficient = sum(np.dot(eG.initial_vectors, np.dot(weight, eG.af_factor)))\n", + "pdf = lambda t: coefficient * exp((t)*root) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial occupancies, as well as the $Q_{AF}e^{-Q_{FF}\\tau}$ factor are obtained directly from the object implementing the weight, root = components[1]\n", + "missed event likelihood $^{e}G(t)$.\n", + "\n", + "However, there is a convenience function that does all the above in the package. Since it is generally of little use, it is not currently exported to the `dcprogs.likelihood` namespace. So we create below a plotting function that uses it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFjCAYAAAApaeIIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnHfmzjnnXI7zztw555zLcf8FHh1/CxxzJ84AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood._methods import exponential_pdfs\n", + "\n", + "def plot_exponentials(qmatrix, tau, x=None, ax=None, nmax=2, shut=False):\n", + " from HJCFIT.likelihood import missed_events_pdf\n", + " if ax is None:\n", + " fig, ax = plt.subplots(1,1)\n", + " if x is None: x = np.arange(0, 5*tau, tau/10)\n", + " pdf = missed_events_pdf(qmatrix, tau, nmax=nmax, shut=shut)\n", + " graphb = [x, pdf(x+tau), '-k']\n", + " functions = exponential_pdfs(qmatrix, tau, shut=shut)\n", + " plots = ['.r', '.b', '.g'] \n", + " together = None\n", + " for f, p in zip(functions[::-1], plots):\n", + " if together is None: together = f(x+tau)\n", + " else: together = together + f(x+tau)\n", + " graphb.extend([x, together, p])\n", + "\n", + " ax.plot(*graphb)\n", + "\n", + "fig, ax = plt.subplots(1,2, figsize=(7,5))\n", + "ax[0].set_xlabel('time $t$ (ms)')\n", + "ax[0].set_ylabel('Excess open-time probability density $f_{\\\\bar{\\\\tau}=0.2}(t)$')\n", + "plot_exponentials(qmatrix, 0.2, shut=False, ax=ax[0])\n", + "\n", + "plot_exponentials(qmatrix, 0.2, shut=True, ax=ax[1])\n", + "ax[1].set_xlabel('time $t$ (ms)')\n", + "ax[1].set_ylabel('Excess shut-time probability density $f_{\\\\bar{\\\\tau}=0.2}(t)$')\n", + "ax[1].yaxis.tick_right()\n", + "ax[1].yaxis.set_label_position(\"right\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we create the last plot (e), and throw in an (f) for good measure." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFjCAYAAAApaeIIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(7,5))\n", + "ax[0].set_xlabel('time $t$ (ms)')\n", + "ax[0].set_ylabel('Excess open-time probability density $f_{\\\\bar{\\\\tau}=0.5}(t)$')\n", + "plot_exponentials(qmatrix, 0.5, shut=False, ax=ax[0])\n", + "\n", + "plot_exponentials(qmatrix, 0.5, shut=True, ax=ax[1])\n", + "ax[1].set_xlabel('time $t$ (ms)')\n", + "ax[1].set_ylabel('Excess shut-time probability density $f_{\\\\bar{\\\\tau}=0.5}(t)$')\n", + "ax[1].yaxis.tick_right()\n", + "ax[1].yaxis.set_label_position(\"right\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 4.33875158e-12 -4.33864056e-12]]\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import QMatrix, MissedEventsG\n", + "\n", + "tau = 1e-4\n", + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)\n", + "eG = MissedEventsG(qmatrix, tau, 2, 1e-8, 1e-8)\n", + "meG = MissedEventsG(qmatrix, tau)\n", + "t = 3.5* tau\n", + "\n", + "print(eG.initial_CHS_vectors(t) - meG.initial_CHS_vectors(t))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/CHSvectors-checkpoint.ipynb b/exploration/.ipynb_checkpoints/CHSvectors-checkpoint.ipynb new file mode 100644 index 0000000..fc1dcfc --- /dev/null +++ b/exploration/.ipynb_checkpoints/CHSvectors-checkpoint.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CHS vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, create the $Q$-matrix from the CH82 model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "\n", + "tau = 1e-4\n", + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then create the missed-events likelihood function $^{e}G$ from which the CHS vectors can be found. \n", + "We compare the vectors to prior results." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import MissedEventsG\n", + "\n", + "eG = MissedEventsG(qmatrix, tau)\n", + "assert np.all(abs(eG.initial_CHS_vectors(4e-3) - [0.220418, 0.779582]) < 1e-5)\n", + "assert np.all(abs(eG.final_CHS_vectors(4e-3) - [0.974852, 0.21346, 0.999179]) < 1e-5)\n", + "np.set_printoptions(precision=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAEbCAYAAABk26sYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAzyt57GjpII2JMVAHuyAmAv9IUb1M3P1EkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2)\n", + "\n", + "x = np.arange(0, 5*tau, tau/10)\n", + "\n", + "ax[0].plot(x*1e3, [eG.initial_CHS_vectors(u)[0] for u in x])\n", + "ax[0].set_xlabel('$t_{\\mathrm{crit}}$ (ms)')\n", + "ax[0].set_ylabel('Components of the initial CHS vector $\\phi_A$')\n", + "\n", + "ax[1].plot(x*1e3, [eG.final_CHS_vectors(u)[0] for u in x])\n", + "ax[1].set_xlabel('$t_{\\mathrm{crit}}$ (ms)')\n", + "ax[1].set_ylabel('Components of the final CHS vector $e_F$')\n", + "ax[1].yaxis.tick_right()\n", + "ax[1].yaxis.set_label_position(\"right\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.17394315362718 0.82605684637282]]\n", + "[ 0.976491211386195 0.222305380522348 0.999257244552635]\n" + ] + } + ], + "source": [ + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)\n", + "qmatrix.matrix /= 1e3\n", + "eG = MissedEventsG(qmatrix, 0.2)\n", + "print(eG.initial_CHS_vectors(4))\n", + "print(eG.final_CHS_vectors(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.]\n", + "[ 0.369080824446409 0.942440306684312]\n" + ] + } + ], + "source": [ + "qmatrix = QMatrix([[-1, 1, 0], [19, -29, 10], [0, 0.026, -0.026]], 1)\n", + "eG = MissedEventsG(qmatrix, 0.2)\n", + "print(eG.initial_CHS_vectors(0.2))\n", + "print(eG.final_CHS_vectors(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.]\n", + "[ 0.846530054887703 0.168045183806245 0.852959014045745]\n" + ] + } + ], + "source": [ + "qmatrix = QMatrix([ [-2, 1, 1, 0], \n", + " [ 1, -101, 0, 100], \n", + " [50, 0, -50, 0],\n", + " [ 0, 5.6, 0, -5.6]], 1)\n", + "eG = MissedEventsG(qmatrix, 0.2)\n", + "print(eG.initial_CHS_vectors(4))\n", + "print(eG.final_CHS_vectors(4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/CKS-checkpoint.ipynb b/exploration/.ipynb_checkpoints/CKS-checkpoint.ipynb new file mode 100644 index 0000000..4d6bdd6 --- /dev/null +++ b/exploration/.ipynb_checkpoints/CKS-checkpoint.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CKF Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following tries to reproduce Fig 9 from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116). First we create the $Q$-matrix for this particular model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "\n", + "tau = 0.2\n", + "qmatrix = QMatrix([[-1, 1, 0], [19, -29, 10], [0, 0.026, -0.026]], 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create a function to plot each exponential component in the asymptotic expression. An explanation on how to get to these plots can be found in the **CH82** notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood._methods import exponential_pdfs\n", + "\n", + "def plot_exponentials(qmatrix, tau, x0=None, x=None, ax=None, nmax=2, shut=False):\n", + " from HJCFIT.likelihood import missed_events_pdf\n", + " from HJCFIT.likelihood._methods import exponential_pdfs\n", + " if ax is None: \n", + " fig,ax = plt.subplots(1, 1)\n", + " if x is None: x = np.arange(0, 5*tau, tau/10)\n", + " if x0 is None: x0 = x\n", + " pdf = missed_events_pdf(qmatrix, tau, nmax=nmax, shut=shut)\n", + " graphb = [x0, pdf(x0+tau), '-k']\n", + " functions = exponential_pdfs(qmatrix, tau, shut=shut)\n", + " plots = ['.r', '.b', '.g'] \n", + " together = None\n", + " for f, p in zip(functions[::-1], plots):\n", + " if together is None: together = f(x+tau)\n", + " else: together = together + f(x+tau)\n", + " graphb.extend([x, together, p])\n", + "\n", + " \n", + " ax.plot(*graphb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For practical reasons, we plot the excess shut-time probability densities in the graph below. In all other particulars, it should reproduce Fig. 9 from [Hawkes, Jalali, Colquhoun (1992)](http://dx.doi.org/10.1098/rstb.1992.0116)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAALLCAYAAAAPLZjyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import missed_events_pdf\n", + "\n", + "fig,ax = plt.subplots(2, 2, figsize=(12, 10 ))\n", + "#ax = fig.add_subplot(2, 2, 1)\n", + "x = np.arange(0, 10, tau/100)\n", + "pdf = missed_events_pdf(qmatrix, 0.2, nmax=2, shut=True)\n", + "ax[0, 0].plot(x, pdf(x), '-k')\n", + "ax[0, 0].set_xlabel('time $t$ (ms)')\n", + "ax[0, 0].set_ylabel('Shut-time probability density $f_{\\\\bar{\\\\tau}=0.2}(t)$')\n", + "\n", + "tau = 0.2\n", + "x, x0 = np.arange(0, 3*tau, tau/10.0), np.arange(0, 3*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[0, 1], x=x, x0=x0)\n", + "ax[0, 1].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[0, 1].set_xlabel('time $t$ (ms)')\n", + "ax[0, 1].yaxis.tick_right()\n", + "ax[0, 1].yaxis.set_label_position(\"right\")\n", + "\n", + "tau = 0.05\n", + "x, x0 = np.arange(0, 3*tau, tau/10.0), np.arange(0, 3*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[1, 0], x=x, x0=x0)\n", + "ax[1, 0].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[1, 0].set_xlabel('time $t$ (ms)')\n", + "\n", + "tau = 0.5\n", + "x, x0 = np.arange(0, 3*tau, tau/10.0), np.arange(0, 3*tau, tau/100) \n", + "plot_exponentials(qmatrix, tau, shut=True, ax=ax[1, 1], x=x, x0=x0)\n", + "ax[1, 1].set_ylabel('Excess shut-time probability density $f_{{\\\\bar{{\\\\tau}}={tau}}}(t)$'.format(tau=tau))\n", + "ax[1, 1].set_xlabel('time $t$ (ms)')\n", + "ax[1, 1].yaxis.tick_right()\n", + "ax[1, 1].yaxis.set_label_position(\"right\")\n", + "\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/Distribution_histograms_and_individual_benchmark-checkpoint.ipynb b/exploration/.ipynb_checkpoints/Distribution_histograms_and_individual_benchmark-checkpoint.ipynb new file mode 100644 index 0000000..bd75385 --- /dev/null +++ b/exploration/.ipynb_checkpoints/Distribution_histograms_and_individual_benchmark-checkpoint.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution histograms and individual benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook mainly serves to extract data used for the Archer eCSE report." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the number of OpenMP threads to use. Note that the number of threads is fixed at import time of DCPROGS so you will need to change restart the notebook and reexecute from the begining for any changes to take effect." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['OMP_NUM_THREADS'] = '1'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.text as mtext" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\n", + "import math\n", + "import sys\n", + "import numpy as np\n", + "from scipy.optimize import minimize\n", + "\n", + "from dcpyps import dcio\n", + "from dcpyps import dataset\n", + "from dcpyps import mechanism\n", + "from HJCFIT.likelihood import Log10Likelihood\n", + "\n", + "# LOAD DATA: Burzomato 2004 example set.\n", + "scnfiles = [[\"./samples/glydemo/A-10.scn\"], \n", + " [\"./samples/glydemo/B-30.scn\"],\n", + " [\"./samples/glydemo/C-100.scn\"], \n", + " [\"./samples/glydemo/D-1000.scn\"]]\n", + "tres = [0.000030, 0.000030, 0.000030, 0.000030]\n", + "tcrit = [0.004, -1, -0.06, -0.02]\n", + "conc = [10e-6, 30e-6, 100e-6, 1000e-6]\n", + "\n", + "recs = []\n", + "bursts = []\n", + "for i in range(len(scnfiles)):\n", + " rec = dataset.SCRecord(scnfiles[i], conc[i], tres[i], tcrit[i])\n", + " rec.record_type = 'recorded'\n", + " recs.append(rec)\n", + " bursts.append(rec.bursts.intervals())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_experiments = len(recs)\n", + "openings = []\n", + "opening_dists = []\n", + "n_bursts = np.empty(4)\n", + "for i,rec in enumerate(recs):\n", + " n_bursts[i] = rec.bursts.count()\n", + " openings = np.zeros(rec.bursts.count())\n", + " for i,burst in enumerate(rec.bursts.all()):\n", + " openings[i] = burst.get_openings_number()\n", + " opening_dists.append(openings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the distribution of bursts within the 4 different experiments showing how number of bursts corelate with the lenght of the bursts." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEbCAYAAABgLnslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztvXv8FVX1//98ISKigJc+ommCN9LMQkzEtHxnWl4Ky49m\npuatsvJC6te8lIG/6uOlvJaXLMNLKeYlxUJFP4mmJaCAIoJi3i+804+piIoC6/fH3geGw7nMOWfO\nec/7vNfz8TiPM7Nnz9prZvbMntl77bVkZjiO4zhOHunV1Qo4juM4Tjm8kXIcx3FyizdSjuM4Tm7x\nRspxHMfJLd5IOY7jOLnFGynHcRwnt3gj1SCSlkiaLmlG/P9hE8o4VdI8SXMkfaFMnsmSpiXWt5N0\nT4l8gyW9E3WdKel+SVtkpOc+krZsYP8rJHVKerTM9hMlLZW0TlzvLelKSY9Kmi3plETe4TH9SUkX\n1KuT0xjNvj8krSPpb5IWSLqoQr52uD8GSrohPgdmS9ohC73yjjdSjbPQzIab2bbx/5wshUvaCvga\nsBWwJ3CJJJXIasB/SfpiUVopnoq6DgOuBn5Uo07l6s1XgK1rkVXEOOCLpTZI2gjYHXgukbw/0MfM\nPgF8CjhK0sZx26XAkWY2FBhadF6c1tHU+wN4D/gxcGKVfO1wf1wITDSzrYBPAnMakNVt8EaqcVZq\nMCQNkDS38AYm6VpJR8blBZLOk/SYpLskrVtF/j7AeDNbbGbPAvOAEWXy/oJww9ai8wDg9ajboZJ+\nlTiO2yR9NqH3LyXNAEZKOjO+zc2UdI6kHYFRwDnxLXQTSccl8lxbTSkzux/4T5nN5wMnFe8CrCFp\nFaAfsAh4S9L6QH8zK7w5X014QDitp6n3h5m9Y2b/IFz7anTb+0PSAOAzZjYuHvdiM3srxbF0e7yR\napzVi7oz9o+V52jgKkkHAGuZ2RUx/xrAVDP7OHAfMAZA0lGSvlNC/obAC4n1l2JaMQb8E1gkaZcq\nOm8WdX0KOB44r0hOKdYA/mlm2wJzga+a2dbxbfNnZvZPYAJwUnwLfQY4GRgW83w3Hud2ki6vot8K\nSBoFvGBms4o23Qi8A7wCPAv80szeIJyfFxP5XqT0OXOaT7Pvj7R09/tjE+A1SeOibpdLWj3doXdv\nvJFqnHeKujNuADCz/wVmARcDRybyLwH+FJf/AOwc8//GzGp6eBdRePv7OXB6lbyF7ozNgR8Av00h\nfzFwc1x+E3hX0u8kfRV4t8w+jwDXSjqIcNyY2cNmlvphE2/E04gPqyJGRL3WBzYF/p+kIWllOy3B\n749s7o/ewHDgYjMbTng5O6VEvrbDG6kmEceNtgIWApW6LKo5T3wJ+EhifaOYVlqY2T1AX2BkOk25\nDfhMXF7MinWib2L5PYuOHs1sCaGBuBH4EnBHGdl7A78m3FzTKvTVV2IzYAjwiKRnCMc/XdJ6wDeA\nO8xsqZm9CjxAGJuq6Zw5rSfD+6MmuvH98SKhN+GhuH5j3K/t8UaqcUoZMQCcADxOeJCOi+MmAKsA\n+8Xlg4D7q8ifAHxdUh9JmwCbA1Or7PNzoJIVVVLnzwD/isvPAsMU+Agrjn0t20fSGoQumjsIx/mJ\nuGkBoQ+/8BDa2MzuJbzxDQDWrKJ3oZxlZZnZY2a2vpltamabEG7Wbc3s38DzwK4JnUYCc8xsPvCm\npBFRj28Ct6Yo28meZt8facoqptvdH2bWCbwgaWhM+jzh/LU9vbtagTagr6TphEpqhLemK4EjgO3N\n7B1J9xIGbM8gvDmOkHQ60AkcAKHPHbDiLg0ze1zSnwgV8gPg+4U3tiIssc/tkv5N+bfQTaPOvQgD\nzt+K+z0g6VlgNsFy6OFS8oH+wK2SCm+Sx8f/8cBvJR0LfB34vaSBcduFZvaWpO2Ao0p1acTB4w5g\nXUnPA2MKA8VFehQeCBcTHnCPxfUrzGx2XD6acB36Eiyiyr3NOs2lqfdH3PYMoU72kbQP8AUzm1uU\nrdvfH8BxwB8lrQo8DRxeRv+2Qs0M1SHpCsLnbmc0E0bSOcCXCRf/X8DhBSsVSacSKu9iYLSZTYrp\nw1nxgfODpindZCQtMLP+Xa2H0/UomNVfDQwClgK/NbOV5voozP/Zk/AAP8zMZrZU0Rbi94dTTLO7\n+0rNe5kEFKxe5gGnAkj6GOXnA7XTnBcP4OUUWAycYGZbAzsCR6tosqekPYHNzGwL4Cjgstar2VL8\n/nBWoKmNVKl5L2Z2t5ktjasPEga1IcwhWGk+kNpszouZDehqHZx8YGbzC19FZvY2oQup2FR+H0Kd\nx8ymAAMlDWqpoi3E7w+nmK42nDgCmBiXy80H8jkvTtsTTeeHAVOKNqWdJ+c4bUmXNVKSfgR8YGbX\ndZUOjpMHJK1JMCkeHb+oHMeJdIl1n6TDgL2I5sORcnNbaprzIsn7tJ3cYGYVzaIl9SY0UNeYWSkz\n+VT13+u9kzeq1f20tOJLaoV5L5L2IPhgG2VmSX9bJecD1TPn5bzzLuLoo48v+Tv22BPp7OzEzCr+\nxowZUzVPmp/L6X46ZSUnJb8HHjezC8tsnxDrPJJGAm9YmDOzElldj2aft6hthV/9x5JlveyO5edF\nhyxp6pdUqXkvBBc3fYC7ovHeg2b2fas8H6imOS8nnvgDzM6mVBvcp88fGTFiGAcffHAGR+g49SNp\nJ8KE1VkKjkmNcH8MJs4JMrOJkvaKfuQW0kPmxjhOgaY2Umb2jRLJxZMzk/nPBM4skf4wsE1tpR9P\nmLy+In36TK9NjOM0CTN7gFKVdOV8x7RAHcfJJV1t3ZdbOjo6XE4L5GQpK29yehp5OG9drUNXl58X\nHbKkqR4nugJJJvXC7H1KvaSuuebBXHrpHt7d5zQdSVhGg8cpyrLuci+Hbv5KuirzcQ2ntWRZ9/1L\nynEcx8kt3kg5juM4uaVqIyVpM0mrxeUOhZDHazVfNcdxHKenk+ZL6iZgiaTNgcsJEwuvbapWjuM4\njkO6RmqpmS0Gvgr8ysxOAjZorlqO4ziOk66R+kDSgcChwF9i2qrNU8lxHMdxAmkaqcMJsW5+bmbP\nRJdF16QRLukKSZ2SHk2krS1pkqQnJN2ZiEyJpFMlzZM0R9IXEunDJT0q6UlJF6Q/PMdxHKc7k6aR\n2t3MjrPordzMngHeSym/VNDDU4C7zeyjwN/oeUEPHcdxnJSkaaQOLZF2WBrhViLoISGI21Vx+SqW\nBzDsEUEPHcdxnPSU9d0Xx6G+AWwiaUJi0wDg9QbKXM+iF2czmy9pvZi+IfDPRL5CcLfFeNBDx3Gc\nHkklB7P/AF4BPgScm0hfADxaco/6yNz/SYhOfwbhQ7Ej/hynuUyePJnJkyd3tRqO01aUbaTM7Dng\nOUm7Ae+a2VJJQ4EtgVkNlNkpaZCZdcauvH/H9EyCHgIE331jSOFg2nEyo6OjYwXnnmeccUbXKeM4\nbUKaMan7gL6SNgQmAYcQYjulZYWgh4QgbofF5UNZHsAws6CHjuM4TnuQppGSmb0D7AtcYmb7A1un\nER6DHv6DYJH3vKTDgbOA3SU9AXw+rmNmjwOFoIcTWTno4RXAk8C8akEPHcdxnPYgTdBDSdqREEH0\nyJiWqh+tTNBDgN3K5M8w6KHj5B9JVwBfAjrN7BMltu9C6Dl4OibdbGY/a6GKjtOlpGmkRhPmMv3Z\nzGZL2hS4p7lqOU6PYRzwK8LUinLcZ2ajWqSP4+SKio2UpFWAUckbxMyeBo5rtmKO0xMws/slDa6S\nrSWBEx0nj1QckzKzJcDOLdLFcZzS7ChppqS/Rs8sjtNjSNPdNyNO5r0BWFhINLObm6aV4zgFHgY2\nNrN3JO0J3AIM7WKdHKdlpGmk+gL/B+yaSDPAGynHaTJm9nZi+XZJl0hax8xW8voyduzYZcvFc7Yc\np5k0cyK7llt5tweSLEzmfZ9SRohrrnkwl166BwcffHDrlXN6FJIws6rjSZKGALeZ2UoWrIWJ73F5\nBPAnMxtSIp91l3s5THespKvoLsfilCZt3U9D1S8pSeMoUaPM7IgsFHCcnkycS9gBrCvpeWAM0Acw\nM7sc2E/S94APgHeBA7pKV8fpCtJ09/0lsdyXEKH35eao4zg9iwpzCQvbLwYubpE6jpM7qjZSZnZT\ncl3SdcD9jRYs6XjC5OClBF+AhwNrANcDg4Fnga+Z2Zsx/6nAEQSv6KPNbFKjOjiO4zj5Jo1bpGK2\nANarmqsCkj4MHAsMj7PsewMHUl9ARMdxHKdNqdpISVog6a3CP3AbcHIGZa8CrCGpN7A6wbN5TQER\nM9DBcRzHyTFpuvv6Z12omb0s6VzgeeAdYJKZ3Z20ZEoZENFxHMdpY9IYTiBpX4LnCQP+bma3NFKo\npLUIX02DgTeBGyQdxMpWhHXZoXrQQ6cr8KCHjpM9aUzQLyHEdrouJn1X0u5mdnQD5e4GPF2YkCjp\nz8CnqT0gYhmdPeih03o86KHjZE+aL6ldga0KMwUlXQXMbrDc54GRkvoCiwhxpaYBbxMCIp7NygER\n/yjpfEI33+bA1AZ1cBzHcXJOmkbqKWBj4Lm4/pGYVjdmNlXSjcAMwiTFGcDlQH/gT5KOiOV9LeZ/\nXFIhIOIHrBgQ0XEcx2lTyjZSkm4jjAn1B+ZImhrXdyCDrxgzO4MwcJTkdWoMiOg4juO0L5W+pH7Z\nMi0cx3EcpwRlGykzu7eVijiO4zhOMfV4nHAcx3GcluCNlOM4jpNbyjZSkv43/p/dOnUcx3EcZzmV\nDCc2kPRpYJSk8cAKDl3NbHpTNXMcx3F6PJUaqZ8ApxO8O5xXtM1YMZy84ziO42RO2e4+M7vRzPYE\nzjGzzxX9Gm6gJA2UdIOkOZJmS9pB0tqSJkl6QtKdkgYm8p8qaV7M/4VGy3ecPCDpCkmdkh6tkOei\nWPdnShrWSv0cp6upajhhZj+VNErSL+PvSxmVfSEw0cy2Aj4JzMXjSTk9j3HAF8ttlLQnsJmZbQEc\nBVzWKsUcJw+kiSd1JjCa4JLocWC0pP9ppFBJA4DPmNk4gBgn6k08npTTwzCz+4H/VMiyD3B1zDsF\nGChpUCt0c5w8kMZ3397AMAvxLwoOZmcApzVQ7ibAa5LGEb6iHgJ+AHg8KcdZkQ2BFxLrhbrf2TXq\nOE5rSRVPCliL4FcPYGCljDWUOxw42sweit7NTyGjeFKO48ALL7zAccedxuLFS0pu33DD9bn44l+w\nyioe0sbJL2kaqTOBGZLuIZihf5bQoDTCi8ALZvZQXL8pyswknpQHPXS6giYFPUxd98eOHbtsuaOj\ng/nz53PHHU/x3nvHlhTcu/e3OOusn7DWWmtlp20mrEa5IedBgwYzf/6zrVWnCuuvP4TOzudKbsuj\nvtWo53iaGfBTaSJeSNoA2D6uTjWz+Q0XLN0LfNvMnpQ0BugXN71uZmdLOhlY28xOiYYTfyR4YN8Q\nuAvYolS4DkkWgh6+T6mgh2uueTCXXroHBx98cKOH4DgVkYSZVTXwkTQEuM3MtimxbS9Cj8PekkYC\nF5jZyBL5Vrodxo8fz3e+cwsLFowvWW6fPmvR2flsyxup0ABVeu5U2i7yFqWn8vHkT99qZHE8aet+\nGlJ195nZK4TAg1lyHCGQ4arA08DhhFbF40k5PQZJ1xI+9deV9DwwBugDmJldbmYTJe0l6SlgIeE+\ncZweQ9oxqcwxs0dY/nWWxONJOT0GM/tGijzHtEIXx8kj7mDWcRzHyS0VGylJq0ia2yplHMdxHCdJ\nxUbKzJYAT0jauEX6OI7jOM4y0oxJrQ3MljSVMHALgJmNappWjuM4jkO6Rur0pmvhOI7jOCWo2kiZ\n2b2SBhPmJd0tqR+lJiA5juM4TsakcTD7beBG4DcxaUPglmYq5TiO4ziQzgT9aGAn4C0AM5sHrFdx\nD8dxHMfJgDSN1CILPoYAkNSbjBy/SuolabqkCXHdgx46juM4y0jTSN0r6TRgdUm7AzcAt2VUfiFO\nVQEPeug4juMsI00jdQrwKjCLEBl0IvDjRguWtBGwF/C7RLIHPXQcx3GWkca6b2kMdDiF0M33REbO\nXc8HTmLF+FQe9NBxHMdZRhrrvr2BfwEXAb8GnpK0ZyOFRpmdZjaT4Je/HO7p3HEcpweTZjLvucDn\nzOwpAEmbAX8Fbm+g3J2AUTFWzupAf0nXAPM96KHTXWlm4DfH6amkaaQWFBqoyNPAgkYKNbPTgNMA\nJO0CnGhmh0g6BzgMOBs4FLg17jKBEHvqfEI33+bA1HLyQ9DDMficY6eVdHR00NHRsWz9jDPO6Dpl\nHKdNKNtISdo3Lj4kaSLwJ0L32/7AtCbpcxYe9NBxHMeJVPqS+nJiuRPYJS6/SuiiywQzuxe4Ny6/\njgc9dBzHcSJlGykz8zDVjtNkJO0BXEAYQL3CzM4u2r4Lodv76Zh0s5n9rLVaOk7XUXVMStImwLHA\nkGR+D9XhOI0hqRfBYvbzwMvANEm3mllxoNH7/H5zeippDCduAa4geJlY2lx1HKdHMQKYZ2bPAUga\nT5jQXtxIuXcVp8eSppF6z8wuaromjtPz2BB4IbH+IqU9qewoaSZh2sVJZvZ4iTyO05akaaQulDQG\nmAQsKiSa2fSmaeU4ToGHgY3N7J04if4WYGipjGPHjl22nDSFd5xm08w5gmkaqW2AQ4BdWd7dZ3Hd\ncZz6eQnYOLG+0iR1M3s7sXy7pEskrRMtYVcg2UgBjB8/PlNlHacczZwjmKaR2h/YNBmuw3GcTJgG\nbB4jX78CfB04MJmh4IElLo8AVKqBcpx2JU0j9RiwFstdFDmOkwFmtkTSMYSu9IIJ+hxJR4XNdjmw\nn6TvESaxvwsc0HUaO07rSdNIrQXMlTSNFcek6jaJjWE6rgYGEboQf2tmF0laG7geGAw8C3zNzN6M\n+5wKHAEsBkab2aR6y3ecvGBmdwAfLUr7TWL5YuDiVuvlOHkhTSM1pgnlLgZOMLOZktYEHpY0CTic\nEPTwHEknE4IenlIU9HAj4G5JW7hrJMdxnPYmTType7Mu1MzmA/Pj8tuS5hAan31Y7n7pKmAyIeji\nsqCHwLOSCkEPp2Stm+M4jpMf0sSTWiDprfh7T9ISSW9lpYCkIcAw4EGKgh4CyaCHyfkkHvTQcRyn\nB5DmS6p/YVmSCF87I7MoPHb13UgYY3pbUnH3XV3deR5PyukKPJ6U42RPmjGpZcQxoFvi5N5TGilY\nUm9CA3WNmRXiRnVmEfTQ40k5XYHHk3Kc7EnjYHbfxGov4FPAexmU/XvgcTO7MJE2gQyCHjqO4zjt\nQZovqWRcqcUE0/B9GilU0k7AQcAsSTMI3XqnERonD3roOI7jAOnGpDKPK2VmD1C+L86DHjqO4zhA\n5fDxP6mwn5nZT5ugT0s45pj/xyGHHFJy26BBg5k//9nWKuQ4juOUpNKX1MISaWsARwLrAt22kXrz\nzU7KGQ52dnroHsdxnLxQKXz8uYVlSf2B0QSPEOOBc8vt5ziO4zhZUXFMStI6wAkEI4ergOFm9p9W\nKOY4juM4lcakfgHsC1wObJOMa+M4juM4raCSW6QTgQ8DPwZeTrhGWpClWyTHcRzHKUelMamqfv3a\nk9UI3p9K49Z/juM4raMmt0g9g0VUchno1n+O4zito1t9LUnaQ9JcSU/GeFNNZHI2UjJyONqucrKU\nlTc5aUhTpyVdJGmepJmShrVMuRrJg3Pdrtehq8vPwznIlm7TSEnqBfwa+CKwNXCgpC2bV+LkMumh\nO7DUb/31h6wsJWcPzrzJyVJW3uRUI02dlrQnsJmZbQEcBVzWEuXqIA8Px67XoavLz8M5yJZu00gR\nghzOM7PnzOwDwnythnwI1kehO3DlX2fn/JUarjPOOKNsA+b0eNLU6X2AqwHMbAowUNKg1qrpOF1H\ndxqTKg58+CLhJi/DbynVBn/wwbyM1UpSajxrLDCWzs6+bpDhFJOmTpcL+NmZpoD333+KMItkZZYs\nWZRWT8fpMtRdnIlL+m/gi2b2nbh+MDDCzI4rytc9DsjpEZhZ2TeTNHVa0m3AmWb2j7h+N/BDM5te\nJMvrvZMrKtX9WuhOX1IvARsn1ksGPszqxDhOC0hTp1MF/PR677Qr3WlMahqwuaTBkvoAXycEQ3Sc\n7kqaOj0B+CaApJHAG2aWqqvPcdqBbvMlZWZLJB0DTCI0rleY2ZwuVstx6qZcnZZ0VNhsl5vZREl7\nSXqKEJkg8/hujpNnus2YlOM4jtPz6E7dfRVpZKKvpCskdUp6NJG2tqRJkp6QdKekgVVkbCTpb5Jm\nS5ol6bg65awmaYqkGVHOmHrkJOT1kjRd0oQG5Twr6ZGo19R6ZUkaKOkGSXPiudqhjnM0NOoxPf6/\nKem4OvU5XtJjkh6V9EdJfeqUMzper7qvfb00UvdrLKemOiDpVIVJyHMkfaHOMmu6N8uVKWl4vMZP\nSrogAx3GSHox1sHpkvZolg71PFtaoMOxLTsPZtbtf4TG9ilgMLAqMBPYsob9dwaGAY8m0s4mWFEB\nnAycVUXG+sCwuLwm8ASwZa1yYr5+8X8V4EGCWXLNcmLe44E/ABPqOa6EnKeBtYvS6jm2K4HD43Jv\nYGC9OiWu/csE44Jar9mH43H1ievXA4fWIWdr4FFgtXjNJgGbNXJcrar7NZaVug4AHwNmxGs8JOqo\nOspMfW9WKhOYAmwflycSrCob0WEMcEKJvFtlrQM1PluacR4q6ND085B5Re6KHzASuD2xfgpwco0y\nBhdVwrnAoMQFmlujvFuA3RqRA/QDHgK2r0cOwRLsLqCD5Y1UXfoAzwDrFqXVJAsYAPyrRHoj5+gL\nwN/r1OfDwHPA2vFmmlDPNQP2A36bWP8xcBIwp5E61Kq6X0NZqetAsR7A7cAOdZab6t4sV2bM83gi\n/evApQ3qMAY4sUS+pumQ2Lfis6WFOny+FeehXbr7Sk2K3LBBmetZtKIys/nAeml3lDSE8Ob1IKES\n1SRHoYtuBjAfuMvMptUjBzif8LBMDjzWI4co4y5J0yR9q05ZmwCvSRoXuwYul9SvAZ0ADgCurUcf\nM3uZEGX6eYJZ95tmdncd+jwGfCZ2v/QD9iJ82TVyXGlpRt0vRy11oNwk5Cwod2+WK3NDwnkpkNU5\nOkbBn+LvEl1tTdUh5bOlVTpMiUlNPQ/t0ki1glQWJpLWBG4ERlsIFFm8X1U5ZrbUzLYlfAmNkLR1\nrXIk7Q10mtlMoNIcmlTHBexkZsMJD+CjJX2mVp0IXyvDgYujrIWEN66azxGApFWBUcANZfardo7W\nIrgdGkz4qlpD0kG1yjGzuYSul7sI3RczgCWlslaS0w3Iog40g64o8xJgUzMbRniZPLfZBWbxbGmC\nDk0/D+3SSKWa6FsjnYo+0iStD/y72g6SehMu4DVmdmu9cgqY2VsEj5V71CFnJ2CUpKeB64BdJV0D\nzK9HHzN7Jf6/SvjUH1GHTi8CL5jZQ3H9JkKjVe852hN42Mxei+u1ytkNeNrMXjezJcCfgU/Xo4+Z\njTOzT5lZB/AGoc++7mtfA82o+yWpsQ6kmoRcJ7WWmbkuZvaqxf4qgg+2gjurpuhQ47OlZTq04jy0\nSyOVxURfseIXxwTgsLh8KHBr8Q4l+D2hv/XCeuVI+lDhk1nS6sDuhLGNmuSY2WlmtrGZbUo4H38z\ns0OA22o9Lkn94hsUktYgjAPNqkOnTuAFSUNj0ueB2bXKSXAgoQEuUKuc54GRkvpKUtTn8Xr0kfRf\n8X9j4KuELsh6j6sWWjLJvY46MAH4uoK15CbA5sDUeosn3b1ZsszYFfampBHxOn+T2q/FCjrERqHA\nvoQu32bqUMuzpWU6tOQ81DNolscf4WvjCWAecEqN+15LsBBbRHhwHU4YTL87ypwErFVFxk6ELp6Z\nhO6e6VGndWqUs03cdybBYuxHMb0mOUUyd2G54UTNcghjSYXjmlU4v3XK+iThwToTuJlg3VePnH7A\nq0D/RFo9csYQXgIeBa4iWMjVI+c+wg06A+ho9Jq1qu7XUEbNdQA4lWDVNQf4Qp3l1nRvlisT2C7q\nPQ+4MAMdro51Zibhq3JQs3SgjmdLC3Vo+nnwybyO4zhObmmX7r7cIWmJlk82nS7phxnL303SQwqT\nK6dJ+lyZfJMlTUusbyfpnhL5Bkt6J+o6U9L9krbISNd91ECASpWYTBnTz1GYKDhT0k2SBjSurdMo\nLaj720fZhd9XyuRr57r/CUn/iPf/rYWu2HbEG6nmsdDMhpvZtvH/nIzlvwp8ycw+SeiXvqZMPgP+\nS9IXi9JK8VTUdRjhM/5HtSikEGm2FF8hTHitl3GE6LXFTAK2jvrOI3QvOF1Ps+v+LGA7CxawewK/\nKVP32rnu/44wkfeTBIOfTF8E8oQ3Us1jJbNvSQMU3NdsEdevlXRkXF4g6TwFFz13SVq3knAze8TC\nICRmNhvoq2CSXYpfECaY1qLzAOD1qNuhkn6VOI7bJH02ofcvFeZ1jZR0poLrlJnxS2dHgpn4OfFN\ndRMFF0aFPNdSBTO7H/hPifS7zWxpXH2QYCnkdD3NrvvvJa776sDSCtnbsu4DW8RtEMal/jvFMXZL\nvJFqHqsXdXnsb8Gk/GjgKkkHEAY6r4j51yBYv3ycMAg/BkDSUZK+U6kgSfsB0y2EIC/GgH8CiyTt\nUkXnzaKuTxHcKZ1XJKcUawD/jG+1c4Gvmlnh6+ZnZvZPgqXPSfFN9RmCC5dhMc934zFsJ6l0CNl0\nHEGY1e50PU2v+wrWYY8BjwDfTTRaSdq57s+WNCouf412fkGrx+LGf6msYd6qsO03wGvABom0D4Be\ncXkTQqOTppytCV1dQ8psv4cwF+lzhDeu7Qjm6MX5it2+7E90t0Mwb70ose024LNx+X2W++RahWD5\n8zuCGfaqMX0csG9i/4mECbgHAWukPM4V9Cva9iPgpq6+5v5bdj1aUvdj/o8SPB/0KbGtbes+MBS4\nk2Apezrwaldf92b9/EuqxUgSwfniQqBSt0ZVs0tJGxHMuA8xs2cr5TWze4C+BF9vabgN+ExcXsyK\nX919E8vvWbxrLEyIHUGY8Pcl4I4ysvcGfk14gEyr0J9fFUmHETwgfKNeGU5ryLLuL8to9gTwNvDx\nCnnaru5WIu+GAAAdBklEQVSb2ZNm9kUz2x4YD/yrHjndAW+kmkc5V0QnECaMfgMYJ2mVmL4KwVEp\nhLes+0vsu1x4mPD7F4ITxwdT6vRzKg+wJnX+DMsr/rPAMAU+wvJZ5SvsozDJcy0zu4NwnJ+ImxYQ\n+vkLD6qNzexegkukAQSvytUontCJQliAk4BRZrYohQynNTS77g8p7CtpMOFr6tkqOrVb3S9MHu9F\nGHO7LIWcbkm3iczbDekraTqhchnhzepKwtjJ9mb2jqR7CRXsDMLb5QhJpwOdBMepKBGltUj+MYRw\nED9RiDllhAlzrxXlW/ZWama3S/o35d9UN4069yJMXPxW3O8BSc8SvEPMAR4uJR/oD9wqqfC2eXz8\nHw/8ViEGzdeB32u5I8oLzewtSdsBR5nZSmMQcYC5A1hX0vPAGDMbB/wK6ENwegrwoJl9v8yxOa2j\n2XV/Z+AUSe8TjCa+Z2avl9Cjnev+gZKOjjrcbGZXljmubk9uJvPGN4KHgBfNbFSJ7RcRzE0XAodZ\ncJzaNkhaYGb9u1oPJ5/EB+WbhIfyB2Y2ovIe3Qev+04l8vQlNZrQFbDShExJewKbmdkWknYgfNqm\n7V/uLuTjbcHJK0sJ7pZKmSN3d7zuO2XJxZhUNADYi2AZU4p9CBPsMLMpwEBF77/tgpm5twSnEiIn\n92vWeN13KpGXSl8qOF+SZgZQc5zugLE84OC3u1oZx2kVXd7dp0RwPkkdVA7Ql0aedx04ucHMGqrP\nCXYys1eiVdddkubYco8DXu+d3JFV3c/Dl1RxcL7PSbq6KE+NgbKsxO81+vVbJ/UEsjFjxmQyEc3l\ndD+dspKTJbZiwME/s6IpdCFPpr9azkP5+67wI9M604x62B3kdRcds6TLGykrHZzvm0XZJhCCYyFp\nJPCGhQB6jtP2qHTAwccq7+U47UGXd/eVIzlHwswmStor+tVaSAg65jg9hUHAn2OXXm/gj2Y2qYt1\ncpyWkKtGysJM7Hvj8m+Kth3TSl06OjpcTgvkZCkrb3KywoJj0mGtLjfr89CM85p3HXviMWdNbibz\nZkV42yx1TP9Hv35DWbjw/1quk9MzkYRlZzhRrSzryns5ePyoVL4yH6tw8kuWdb/Lx6Qcx3Ecpxze\nSDmO4zi5pcsbKUmrSZoSA6TNis5Si/PsIumNGJRsuqQ0kTYdx3Gcbk5mhhOSNiM4h10UJ+V+Arja\nzN6otF/M/zkLnpFXAR6QdLuZTS3Kep+VcDzrOI7jtC9ZfkndBCyRtDlwOWHy7bVpdjSzd+LiaoSG\ns9QIa0sGoB3HcZz8kGUjtdTMFhNCJ//KzE4CNkizo6RekmYA84G7zGxaiWw7Spop6a+SPpad2o7j\nOE5eybKR+kDSgcChhIixAKum2dHMlprZtgR3RzuUaIQeJkS0HEYIvXxLRjo7juM4OSbLybyHA98F\nfm5mz0jaBLimFgEWolTeA+xBiC1VSH87sXy7pEskrWOlo3ECYxPLHfHnOM1l8uTJTJ48uavVcJy2\nIrPJvJJGm9mF1dJK7PchQqTRNyWtDtwJnGVmExN5BhV89UkaAfzJzIaUkeeTeZ1c4JN5V8jhk3l7\nEHmdzHtoibTDUuy3AXCPpJnAFODO6KvvKEnfiXn2k/RYHLe6ADggE40dx3GcXNPwl1Qch/oGsDPw\n98SmAcASM/t8QwXUro9/STm5IMu3SUm9gIcI0zxWmorhX1JOnsiy7mcxJvUP4BXgQ8C5ifQFwKMZ\nyHccB0YTxmk91LrTo2i4u8/MnjOzycBuwN+jJ/NXCJZ6PrfJcRpE0kbAXsDvuloXx2k1WY5J3Qf0\nlbQhMAk4BLgyQ/mO01M5HziJyv1pjtOWZGmCruja6EjgEjM7JxpDOI5TJ5L2BjrNbGZ0N1a2d2Ls\n2LHLljs6OnIfJ8hpH5o5/SJLE/QZwPcJb31HmtlsSbPMbJsq+61G+ArrQ2g0bzSzM0rkuwjYkxCZ\n9zAzK9kAuuGEkxeyGDyW9D/AwcBiYHWgP3CzmX2zKJ8bTji5Ia8m6KOBU4E/xwZqU+CeajuZ2SLg\nc9HjxDBgzzgXahmS9gQ2M7MtgKOAyzLU23Fyi5mdZmYbm9mmwNeBvxU3UI7TzmTS3Re9l49Kmsaa\n2dPAcWn2T+Fgdh/g6ph3iqSByQm+juM4TnuSyZeUmS0hzJOqixQOZjcEXkisvxTTHKfHYGb3erga\np6eRpeHEDEkTgBsI40YAmNnN1XY0s6XAtpIGALdI+piZPV5tv/KMTSx34L77nFbgvvscJ3uyNJwY\nVyLZzOyIGuWcDiw0s/MSaZcB95jZ9XF9LrBLqe4+N5xw8oL77lshhxtO9CDy5nECADM7vJ79SjiY\n3R04qyjbBOBo4HpJI4E3fDzKcRyn/ckyfPw4SrxKpfiS2gC4Kvom6wVcX3AwG3a3y+P6XpKeInQl\n1tUgOo7jON2LLLv7/jux2pcQofdlM0tl4ZcV3t3n5AXv7lshh3f39SDy2t13U3Jd0nXA/VnJdxzH\ncXoeWU7mLWYLYL0mynccx3HanCzHpBYQvvcL3/3zgZOzku84juP0PLLs7uuflSzHcRzHgYy7+yTt\nK+k8SedK+krKfTaS9DdJsyXNkrSSoYWkXSS9IWl6/P04S70dx3GcfJJld98lwObAdTHpu5J2N7Oj\nq+y6GDghhiJYE3hY0iQzm1uU7z53CeM4jtOzyNIt0q7AVgU7WElXAbOr7WRm8wnjV5jZ25LmEPzy\nFTdSHuXX6ZGkDWfjOO1Ilt19TwEbJ9Y/EtNSI2kIIVzHlBKbd5Q0U9JfJX2sXiUdp7uRJpyN47Qr\nDX9JSbqNYM3XH5gjaWpc3wGYWoOcNYEbgdFm9nbR5oeBjWPk3z2BW4Ch5aWNTSx34A5mnVbQTAez\nKcLZOE5b0rDHCUm7VNpuZvemkNEb+Atwu5ldmCL/M8B2ZvZ6iW3uccLJBVnOuo9uwx4GNgMuNrNT\ni7a7xwknN+TK40SaRigFvwceL9dAJQMcxm4OlWqgHKddSRPOJjQUpVlnnfV5+eVnWW211ZqsqeNk\nS5aGE3UhaSfgIGBWDHxowGnAYKKDWWA/Sd8DPgDeBQ7oKn0dpysxs7ck3QPsARTFXPsBMDAud5Ds\n5l6woD8bbzyUf//7+ZJyBw0azPz5z2atrtNDaGZXd2YOZvOCd/c5eSGrLo8S4WzuBM4ys4mJPAbP\nsaLt0nJWXbU/H3zwNuW75BrrjvPuPidJlt19DVv3Sfrf+H924+o4jlOCDYB7JM0kWL7emWygHKed\nyaK7bwNJnwZGSRpP0XwmM5ueQRmO02Mxs1nA8K7Ww3G6giwaqZ8ApwMbAecVbTPCJF/HcRzHqZks\nrPtuBG6UdLqZ/bTW/SVtBFwNDAKWAr81s4tK5LsI2JMQmfcwM5vZmOaO4zhO3snSC/pPJY0CPhuT\nJpvZX1LsWtV3X5zAu5mZbSFpB+AyYGRWujuO4zj5JDO3SJLOBEYTzGIfB0ZL+p9q+5nZ/MJXUfQ0\nUfDdl2QfwtcWZjYFGChpUFa6O47jOPkky3lSewPD4qTDgoPZGYQ5T6mo4LtvQ+CFxPpLMa2zfnUd\nx3GcvJN1+Pi1EssDy+YqQRXffY7jOE4PJMsvqTOBGXE2vAhjU6ek2TH67rsRuMbMbi2R5SWCV/UC\nG8W0MoxNLHdQmHn/zjvvVnQd47PunUZo5qx7x+mpZOpxQtIGwPZxdWqMFZVmv6uB18zshDLb9wKO\nNrO9JY0ELjCzkoYTlTxOwIfwWfFOq8hy1n2KstzjhJMbcuVgNomZvQJMqGWfNL77zGyipL0kPUUw\nQT88S70dx3GcfNLlDmbN7AFglRT5jmmBOo7jOE6OyNpwwnEcx3EyI5NGStIqkuZWz+k4Tq1I2kjS\n3yTNljRL0nFdrZPjtIpMGikzWwI8Ian0qK3jOI1Q8MqyNbAjcLSkLbtYJ8dpCVmOSa0NzJY0lWDc\nAICZjcqwDMfpcUQr2flx+W1JBa8s3nvhtD1ZNlKn17ujpCuALwGdZvaJEtt3AW4Fno5JN5vZz+ot\nz3G6KxW8sjhOW5Klg9l7JQ0GtjCzuyX1I4XVXmQc8Cuif74y3OdfZU5Pxr2yOD2RzBopSd8GvgOs\nA2xG6I64DPh8tX3N7P7YwFUsomElHaebksIrC3A+y72RdVDwtJKO1draG8v66w+hs/O5stu7+/F1\nNc30tpKZx4kY2noEMMXMto1ps8xsm5T7DwZuq9DddxPwIsEd0klm9ngZOe5xwskFWc66T+GVpWGP\nE43cG3n3OJF3/dqNvHqcWGRm7xfexuKbX1ZX/WFgYzN7J8aWugUYWj772MRyB7W9UTpOfTTrbbKc\nVxYzuyPzwhwnZ2T5JXUO8AbwTeBY4PvA42b2o5T7l/2SKpH3GWA7M3u9xDb/knJyQXfz3edfUn7v\nZ0WWdT9LjxOnAK8Cs4CjgInAj2vYX5QZd0oGOJQ0gtC4rtRAOY7jOO1FltZ9S2OgwymEV5YnLOWr\niaRrCX1y60p6HhgD9CE6mAX2k/Q94APgXeCArPR2HMdx8kuW3X17E6z5/kX4ItoEOMrMbs+kgPR6\neHefkwu8uy/9/s0m7/q1G3k1nDgX+JyZPQUgaTPgr0BLGynHcRynfchyTGpBoYGKPA0syFC+4ziO\n08No+EtK0r5x8SFJE4E/Eb6r9wemNSrfcRzH6blk8SX15fjrC3QCuxCMIF4FVk8jQNIVkjolPVoh\nz0WS5kmaKWlY42o7juM4eafhLykzyyKUe0XffXEC72ZmtoWkHQgGGiMzKNdxHMfJMVn67tuEMIl3\nSFJuGqewKXz37UNswMxsiqSBkgaZWWdjWjuO4zh5JkvrvluAK4DbgKUZyoXgrPaFxPpLMc0bKcdx\nnDYmy0bqPTO7KEN5juM4Tg8ny0bqQkljgEnAokKimU3PQPZLwEcS6xvFtDKMTSx3kJWD2Uru/nv1\n6sfSpe+U3bfa9q4KFVAthEElvds1vEGlc1LpmJvoYLZiUFDHaWey9DhxJnAIweNEobvPzGzXlPsP\nITiYXSm0h6S9gKPNbG9JI4ELzKyk4UQzPU5UnrVefUZ7Hme8p5mJX+mY23GWfrXrnPaYs5p1L2ln\n4G3g6nKNlHucqEze9Ws38upxYn9gUzN7v9Ydq/nuM7OJkvaS9BSwEMjCotBxugUpg4I6TluSZSP1\nGLAW8O9adzSzb6TIc0w9SjmO4zjdlywbqbWAuZKmseKYVFUTdMdxHMcpRZaN1JgMZTmOUzPnAwPj\ncgf5iki9GoWo3aWoZoRTzcCnXY14sqJeY6Bq+xb2Hz/+yqYYDUGGhhN5wQ0nasMNJ1Ymb4YTUdYQ\nyhgWxe25N5zoSsOMnm440Uidrufc5TIyr6QFkt6Kv/ckLZH0VlbyHaenEg2L/gEMlfS8JDcccnoM\nWUbm7V9YVmh69yGlfz1JewAXEBrNK8zs7KLtuwC3EsJ/ANxsZj/LQm/HyTtpDIscp13JMp7UMixw\nC/DFankl9QJ+HfNuDRwoacsSWe8zs+Hx5w2U4zhODyBLB7P7JlZ7AZ8C3kux6whgnpk9F+WMJ3yF\nzS0uIgs9HcdxnO5DltZ9X04sLwaeJTQ21Sh2HvsioeEqZkdJMwnukE4ys8fr1NNxHMfpJmQ5JtXM\nwdyHgY3N7J0YW+oWYGj57GMTyx3kyxTXaVea5bvPcXoyDZugS/pJhc1mZj+tsv9IYKyZ7RHXT4n7\nnV1hn2eA7czs9RLb3AS9BtwEfWXyaIKeoiw3QXcT9LL0dBP0hSV+AEcCJ6fYfxqwuaTBkvoAXwcm\nJDNIGpRYHkFoXFdqoBzHcZz2Iovw8ecWliX1B0YTHMCOB84tt19i/yWSjiGE+CiYoM+RdBTRwSyw\nn6TvAR8A7wIHNKq34ziOk38y8TghaR3gBOAg4CrgQjP7T8OC69PFu/tqwLv7Vsa7+0pv9+6+7kt3\n7u5r+EtK0i+AfYHLgW3M7O2GtXIcx3EcsjGcWErwer6YFZtbEbrrBjRUQO36+JdUDfiX1Mr4l1Tp\n7f4l1X3p0V9SZtYUrxWO4ziOk4sGRtIekuZKelJSSYtASRdJmidppqRhrdbRcbqSNPeI47QjXd5I\npfHdFyfwbmZmWwBHAZc1W6/sJmVmIycrffJ2XJDHY8sXNfi3zJjJOZeXvcys61Az6mS71vNydHkj\nRcJ3n5l9QDBdL3antA9wNYCZTQEGJudONYO8Pczz9yDPSk4ejy13pLlHmsDknMvLXqY3UvkjD41U\nKd99G1bJ81KJPI7TrqS5RxynLcnSwWxuGDDgyyulmb3PggVdoIzjtIj+/Q9H6ldy28KFi1qsjeNk\nQ5eHj0/ju0/SZcA9ZnZ9XJ8L7GJmnSXkta8dqdPtyMIMN+U94vXeyRW5MUHPgGW++4BXCL77DizK\nMwE4Grg+3rBvlGqgILsT4zg5ouo94vXeaVe6vJFK47vPzCZK2kvSUwQHts0MC+I4uaLcPdLFajlO\nS+jy7j7HcRzHKYuZtcUP2IMQcv5J4OQqeTcC/gbMBmYBx8X0tQlvq08AdwIDE/ucCswD5gBfKJLX\nC5gOTKhXDjAQuCGmzwZ2qFPO8cBjwKPAH4E+Nci5A+gEHk1sr0eHWwge6xcBF8S0c2K+mcBNwIB6\n5CS2nQgsBdapVw5wbMw7CzirzuP6JPBPYAYwFfhUCjnD4/V5svi4ml33E/tckcW1btY9FbevBkyJ\n53YWMKZRmVndr0XyngUeKdSBDI47k2dB3DY06jU9/r8JHJfBMTfynCkps2xdbfQGycMvVrqngMHA\nqoQH4ZYV8q8PDIvLa8aTuiVwNvDDmH4y8cEFfCxe4N7AkFiWii7YHxKVvmY5wJXA4XG5d6yoNckB\nPgw8DfSJ+a4HDq1BzovAMFZ8cNVzLLMJHvEfBSYSJqHuBvSK288CzqxHTkzfiNCgPkNspICtatSn\ng3BD9Y55PlSnnDuJNx2wJ8HAp9pxTQG2j8vLjqsVdT+x385ZXOtm3VMJuf3i/yrAg4Q5Y43KbPh+\nLZL3NLB2UVoj5/JKGnwWVKgrLwMfaVC/Rp8zZXUsqXe9N0eefsBI4PbE+imkfKOM+W8hPETnAoMS\nN93cUvKA24Ed4vJGwF2Eh16h0tckBxgA/KuEXrXK+TDBy+jasUJMqOO49mHFB1etOqwPPE54aD5K\nGOS/tOi4vgJcU68cwlvmNqzYSNUkh3Bj7VrinNcq53Zg/5j3QOAPaeQk0lc6P62q+4VjqfdaN+ue\nKiOvH/AQsH0jMsngfi2h2zPAuo3cu4n1TJ4FZc7hF4C/NyqPbJ4zFetP8peHybxZUPdkR0lDCG+U\nDxJOcCeAmc0H1isjPzmZ+HzgJFZ0E1yrnE2A1ySNkzRd0uUKE15qkmNmLxMCTT4f0940s7trlLN+\n0Slar8Zj2ZBw/guUuhZHEL4gapYjaRTwgpnNKpJZqz5Dgc9KelDSPZK2q1PO8cAvJT1P6NI8tU45\n9ZLlRN9ar3VJMrinkrJ6SZoBzAfuMrNpDcrM4n4txoC7JE2T9K0GZWbyLCihI4Rgsdc2eswZPWdS\n19F2aaTqQtKawI3AaAtxsKwoS/F68f57A51mNpPQ3VaOinIIbyPDgYvNbDjBgvGUOvRZi/AlNJjw\ntrOGpINqlVOFRvZF0o+AD8zsujp27wWcBoxpRIdIb0IXzUjgh4Svs3r4HqH+bExosH6fgW55oeZr\n3eg9tZICZkvNbFvCF9AISVvXKzPD+7WYneJ9uxdwtKTP1KsjGT0LipG0KjCK5fW8bnktes4so10a\nqZdYMZDORjGtLJJ6E26ma8zs1pjcWfAJKGl94N8J+R8pIX8nYJSkp4HrgF0lXQPMr1HOi4Svg4di\n+k2EilqrPrsBT5vZ62a2BPgz8Oka5cwvOlW16lAuHUmHEW7kbyS21yJnIaFP+xFJz8S06ZLWo3wd\nKCf/BeBmgPh2vkTSunXIOdTMbolybiR0R9V1fuqk5rpfgVqv9QpkdE+VxMzeIjjq26MBmVndr8W6\nvRL/XyV0c45oQMesngXF7Ak8bGavxfVG5GXxnElfR9P2C+b5RxhULQwe9yEMHm9VZZ+rgfOK0s4m\n9p1SeuCvD+FzvNRg4i4s7+M+p1Y5wL3A0Lg8JupSkz6Em2MW0DeuX0mYBF2LnCHArEbOCaGbZ5+o\ny0TCg2UPguFBcd99TXKK9n2GOGBdhz7fAc6I24cCz9UpZzbB+wnA54FpKeWMiNdopeNqdt1P7Nvw\ntW7yPfUhooUYsDpwH+ElpyE9s7hfE3L6AWvG5TWABwhjP40cd8PPghLHex3hhSqL65LFc6ZnGU7E\nE7EHwaJoHnBKlbw7AUsIN3TBPHMPYB3g7ihnErBWYp9T48ktZzaarPQ1yyGYMk+LOt1MsOipR86Y\nmPYocBXB4iutnL8RrH8WEfqbDycMjtaqw+0EU+2lwFtRzjzCYOv0+LukHjlF5/xpVjZBT6tPb+Aa\nws32ELGhqUPOp+P+Mwim6NumkLNdLHcecGEr635in2uzuNZNvqe2iXJmEurzj+q9v7K+XxPbN0kc\n86zC+W9QZibPgsT2fsCrQP9EWkPnkMaeMzWZoPtkXsdxHCe3tMuYlOM4jtOGeCPlOI7j5BZvpBzH\ncZzc4o2U4ziOk1u8kXIcx3FyizdSjuM4Tm7xRqpFSNpQ0i2SnpQ0T9L5cYZ+1uUcJengrOXWK1/S\nLpJua4IegyUVR3B2cojX/cz16FF13xup1nEzcLOZDSV4OOgP/E/WhZjZb8zsD1nLbVB+3ZPxJK1S\nZtMmrOheyckvXvfrwOt+wBupFiBpV+BdM7sawMIM6uOBIyT1lXRofNO8R9ITkn6S2PcgSVOiR+RL\nJSmmL5D0M0kzJf1D0n/F9DGSTojL90g6K+4/V9JOMX11SddLekzSzdET+PDocXqcpEclPSJpdIlj\nqSq/BAMl/SXmuSQha0Fi+b8ljYvL4+KxPgicLemzkmbEc/CwpDWAM4GdY9pKejr5wOu+1/1GyfyT\n2ynJ1sDDyQQzWyDpOWDzmLR9zPceME3SX4B3CO71P21mSyRdTAi69weCn7B/mNmPJZ0NfJvSb6er\nmNkOkvYExgK7A98HXjezjyt4lZ4R8w4jhPz4BICkASmOrZT8YrYnBBN8HrhT0r5mdjOVvSZvaMFD\nOZImAN83s38qhC14j+AZ+kQzG5VCR6fr8Lrvdb8h/Euqa0mGC7jLzN4ws/cIno93Jjgs3Y5w484A\ndiV86gO8b2aFmEwPE5yFluLmRJ7BcXlnYDyAmc0m+N+C4AtvE0kXSvoisIDqlJJfzFQzey6+RV8X\ny4fK4RKSoTMeAM6XdCzBoezSFHo5+cbrfnm87ifwRqo1PA58KpkQ39Q+QnC6CCu+SSmxfqWZDTez\nbc1sKzP7aUx/P5F/CeW/ihelyCMAM3uD4NxyMnAU8LsKx1SL/HJvjcn0vkV5Fi7LbHY2cCTBE/YD\nkoam0MvJB173S6973U+JN1ItwMz+F1hd0TJIYUD0l8C4+PYIsLuktSStTgiv/gDBK/l+iT73tSUV\n4rJUehOrxgOErhQkfQz4eFxel9CF8WfgdGDbGuWW02kHBYukXrHcv8f0+ZI+GtO/WlaotKmZzTaz\ncwjeobckvOmm6ZJxuhCv+173G8UbqdbxVeBrkp4E5gLvAj9KbJ9K6D6YCdxgZtPNbA7wY2CSpEcI\n7u83iPnTWA2Vy3MJ8CFJjwH/HyEm0puEkM6TY/fKNYS+71rklytvKvDrWM6/LAYJJLjv/ytwPyFs\nRDk5P5A0S9JMwlv07YRumiVxULntB4+7OV73ve7XjYfqyAGSDgW2M7PjWlReL2BVM1skaVPgLuCj\nZra4FeU7TgGv+0413LqvZ9IPuEfSqnH9e36TOj0Er/vdDP+SchzHcXKLj0k5juM4ucUbKcdxHCe3\neCPlOI7j5BZvpBzHcZzc4o2U4ziOk1u8kXIcx3Fyy/8PHrJlFQBnhksAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,2)\n", + "ax = ax.flatten()\n", + "for i in range(n_experiments):\n", + " ax[i].hist(opening_dists[i], bins=20)\n", + " ax[i].set_title(\"Exp: {} N Bursts: {}\".format(i, int(n_bursts[i])), fontsize=10)\n", + "ylabel = ax[0].set_ylabel('Number of bursts')\n", + "ylabel = ax[2].set_ylabel('Number of bursts')\n", + "xlabel = ax[2].set_xlabel('Openings in burst')\n", + "xlabel = ax[3].set_xlabel('Openings in burst')\n", + "fig.tight_layout(w_pad=0.1, h_pad=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AF* \tto AF \talpha1 \t4500.0\n", + "1\tFrom AF \tto AF* \tbeta1 \t700.0\n", + "2\tFrom A2F* \tto A2F \talpha2 \t2500.0\n", + "3\tFrom A2F \tto A2F* \tbeta2 \t1800.0\n", + "4\tFrom A3F* \tto A3F \talpha3 \t900.0\n", + "5\tFrom A3F \tto A3F* \tbeta3 \t18000.0\n", + "6\tFrom A3F \tto A3R \tgama3 \t200.0\n", + "7\tFrom A3R \tto A3F \tdelta3 \t67459.4594595\n", + "8\tFrom A3F \tto A2F \t3kf(-3) \t7500.0\n", + "9\tFrom A2F \tto A3F \tkf(+3) \t400000000.0\n", + "10\tFrom A2F \tto A2R \tgama2 \t1850.0\n", + "11\tFrom A2R \tto A2F \tdelta2 \t10000.0\n", + "12\tFrom A2F \tto AF \t2kf(-2) \t5000.0\n", + "13\tFrom AF \tto A2F \t2kf(+2) \t800000000.0\n", + "14\tFrom AF \tto AR \tgama1 \t8500.0\n", + "15\tFrom AR \tto AF \tdelta1 \t736.313236313\n", + "16\tFrom A3R \tto A2R \t3k(-3) \t5850\n", + "17\tFrom A2R \tto A3R \tk(+3) \t5000000.0\n", + "18\tFrom A2R \tto AR \t2k(-2) \t3900\n", + "19\tFrom AR \tto A2R \t2k(+2) \t10000000.0\n", + "20\tFrom AR \tto R \tk(-1) \t1950\n", + "21\tFrom R \tto AR \t3k(+1) \t15000000.0\n", + "\n", + "Conductance of state AF* (pS) = 40\n", + "\n", + "Conductance of state A2F* (pS) = 40\n", + "\n", + "Conductance of state A3F* (pS) = 40\n", + "\n", + "Number of open states = 3\n", + "Number of short-lived shut states (within burst) = 6\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 2\n", + "Cycle 0 is formed of states: A3R A3F A2F A2R \n", + "\tforward product = 4.680000000e+18\n", + "\tbackward product = 4.680000000e+18\n", + "Cycle 1 is formed of states: AF A2F A2R AR \n", + "\tforward product = 4.250000000e+18\n", + "\tbackward product = 4.250000000e+18" + ] + } + ], + "source": [ + "# LOAD FLIP MECHANISM USED Burzomato et al 2004\n", + "mecfn = \"./samples/mec/demomec.mec\"\n", + "version, meclist, max_mecnum = dcio.mec_get_list(mecfn)\n", + "mec = dcio.mec_load(mecfn, meclist[2][0])\n", + "\n", + "# PREPARE RATE CONSTANTS.\n", + "rates = [4500.0, 700.0, 2500.0, 1800.0, 900.0, 18000.0, 200.0, 0.1100E+06, 4900.0, 0.4000E+09, 1850.0, 10000.0, 5000.0, 0.7500E+09, 8500.0, 1050.0, 3500.0, 0.5000E+07, 2300.0, 0.9500E+07, 1950, 0.130000E+08]\n", + "\n", + "mec.set_rateconstants(rates)\n", + "\n", + "# Fixed rates.\n", + "#fixed = np.array([False, False, False, False, False, False, False, True,\n", + "# False, False, False, False, False, False])\n", + "#if fixed.size == len(mec.Rates):\n", + "for i in range(len(mec.Rates)):\n", + " mec.Rates[i].fixed = False\n", + "\n", + "# Constrained rates.\n", + "mec.Rates[21].is_constrained = True\n", + "mec.Rates[21].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[21].constrain_args = [17, 3]\n", + "mec.Rates[19].is_constrained = True\n", + "mec.Rates[19].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[19].constrain_args = [17, 2]\n", + "mec.Rates[16].is_constrained = True\n", + "mec.Rates[16].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[16].constrain_args = [20, 3]\n", + "mec.Rates[18].is_constrained = True\n", + "mec.Rates[18].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[18].constrain_args = [20, 2]\n", + "mec.Rates[8].is_constrained = True\n", + "mec.Rates[8].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[8].constrain_args = [12, 1.5]\n", + "mec.Rates[13].is_constrained = True\n", + "mec.Rates[13].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[13].constrain_args = [9, 2]\n", + "mec.update_constrains()\n", + "\n", + "mec.set_mr(True, 7, 0)\n", + "mec.set_mr(True, 15, 1)\n", + "\n", + "mec.printout(sys.stdout)\n", + "theta = np.log(mec.theta())\n", + "\n", + "kwargs = {'nmax': 2, 'xtol': 1e-12, 'rtol': 1e-12, 'itermax': 100,\n", + " 'lower_bound': -1e6, 'upper_bound': 0}\n", + "likelihood = []\n", + "\n", + "for i in range(len(recs)):\n", + " likelihood.append(Log10Likelihood(bursts[i], mec.kA,\n", + " recs[i].tres, recs[i].tcrit, **kwargs))\n", + "\n", + "def dcprogslik(x, args=None):\n", + " mec.theta_unsqueeze(np.exp(x))\n", + " lik = 0\n", + " for i in range(len(conc)):\n", + " mec.set_eff('c', conc[i])\n", + " lik += -likelihood[i](mec.Q) * math.log(10)\n", + " return lik" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 55.1 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "dcprogslik(theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 13.9 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "i = 0\n", + "mec.set_eff('c', conc[i])\n", + "lik = -likelihood[i](mec.Q) * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 16.3 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "i = 1\n", + "mec.set_eff('c', conc[i])\n", + "lik = -likelihood[i](mec.Q) * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 13.6 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "i = 2\n", + "mec.set_eff('c', conc[i])\n", + "lik = -likelihood[i](mec.Q) * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 10.9 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "i = 3\n", + "mec.set_eff('c', conc[i])\n", + "lik = -likelihood[i](mec.Q) * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/exploration/.ipynb_checkpoints/Example_MLL_Fit_AChR_1patch-checkpoint.ipynb b/exploration/.ipynb_checkpoints/Example_MLL_Fit_AChR_1patch-checkpoint.ipynb new file mode 100644 index 0000000..f75d334 --- /dev/null +++ b/exploration/.ipynb_checkpoints/Example_MLL_Fit_AChR_1patch-checkpoint.ipynb @@ -0,0 +1,620 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HJCFIT- maximum likelihood fit of single-channel data: a simple example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some general settings:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys, time, math\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HJCFIT depends on DCPROGS/DCPYPS module for data input and setting kinetic mechanism:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from dcpyps.samples import samples\n", + "from dcpyps import dataset, mechanism, dcplots" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fname = \"CH82.scn\" # binary SCN file containing simulated idealised single-channel open/shut intervals\n", + "tr = 1e-4 # temporal resolution to be imposed to the record\n", + "tc = 4e-3 # critical time interval to cut the record into bursts\n", + "conc = 100e-9 # agonist concentration \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialise Single-Channel Record from dcpyps. Note that SCRecord takes a list of file names; several SCN files from the same patch can be loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " Data loaded from file: CH82.scn\n", + "Concentration of agonist = 0.100 microMolar\n", + "Resolution for HJC calculations = 100.0 microseconds\n", + "Critical gap length to define end of group (tcrit) = 4.000 milliseconds\n", + "\t(defined so that all openings in a group prob come from same channel)\n", + "Initial and final vectors for bursts calculated asin Colquhoun, Hawkes & Srodzinski, (1996, eqs 5.8, 5.11).\n", + "\n", + "Number of resolved intervals = 1672\n", + "Number of resolved periods = 1672\n", + "\n", + "Number of open periods = 836\n", + "Mean and SD of open periods = 5.703315580 +/- 6.217026586 ms\n", + "Range of open periods from 0.101663936 ms to 36.745440215 ms\n", + "\n", + "Number of shut intervals = 836\n", + "Mean and SD of shut periods = 2843.529462814 +/- 3982.407808304 ms\n", + "Range of shut periods from 0.100163714 ms to 30754.167556763 ms\n", + "Last shut period = 3821.345090866 ms\n", + "\n", + "Number of bursts = 572\n", + "Average length = 8.425049759 ms\n", + "Range: 0.102 to 62.906 millisec\n", + "Average number of openings= 1.461538462\n" + ] + } + ], + "source": [ + "# Initaialise SCRecord instance.\n", + "rec = dataset.SCRecord([fname], conc, tres=tr, tcrit=tc)\n", + "rec.printout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot dwell-time histograms for inspection. In single-channel analysis field it is common to plot these histograms with x-axis in log scale and y-axis in square-root scale. After such transformation exponential pdf has a bell-shaped form." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAFgCAYAAABJ67N/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n", + "\n", + "dcplots.xlog_hist_data(ax[0], rec.opint, rec.tres, shut=False)\n", + "\n", + "dcplots.xlog_hist_data(ax[1], rec.shint, rec.tres)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load demo mechanism (C&H82 numerical example)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AR \tto AR* \tbeta1 \t15.0\n", + "1\tFrom A2R \tto A2R* \tbeta2 \t15000.0\n", + "2\tFrom AR* \tto AR \talpha1 \t3000.0\n", + "3\tFrom A2R* \tto A2R \talpha2 \t500.0\n", + "4\tFrom AR \tto R \tk(-1) \t2000.0\n", + "5\tFrom A2R \tto AR \t2k(-2) \t4000.0\n", + "6\tFrom R \tto AR \t2k(+1) \t100000000.0\n", + "7\tFrom AR* \tto A2R* \tk*(+2) \t500000000.0\n", + "8\tFrom AR \tto A2R \tk(+2) \t500000000.0\n", + "9\tFrom A2R* \tto AR* \t2k*(-2) \t0.66667\n", + "\n", + "Conductance of state AR* (pS) = 60\n", + "\n", + "Conductance of state A2R* (pS) = 60\n", + "\n", + "Number of open states = 2\n", + "Number of short-lived shut states (within burst) = 2\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 1\n", + "Cycle 0 is formed of states: A2R* AR* AR A2R \n", + "\tforward product = 1.500007500e+16\n", + "\tbackward product = 1.500000000e+16" + ] + } + ], + "source": [ + "mec = samples.CH82()\n", + "mec.printout()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# PREPARE RATE CONSTANTS.\n", + "# Fixed rates\n", + "mec.Rates[7].fixed = True\n", + "# Constrained rates\n", + "mec.Rates[5].is_constrained = True\n", + "mec.Rates[5].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[5].constrain_args = [4, 2]\n", + "mec.Rates[6].is_constrained = True\n", + "mec.Rates[6].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[6].constrain_args = [8, 2]\n", + "# Rates constrained by microscopic reversibility\n", + "mec.set_mr(True, 9, 0)\n", + "# Update rates\n", + "mec.update_constrains()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AR \tto AR* \tbeta1 \t15.0\n", + "1\tFrom A2R \tto A2R* \tbeta2 \t15000.0\n", + "2\tFrom AR* \tto AR \talpha1 \t3000.0\n", + "3\tFrom A2R* \tto A2R \talpha2 \t500.0\n", + "4\tFrom AR \tto R \tk(-1) \t2000.0\n", + "5\tFrom A2R \tto AR \t2k(-2) \t4000.0\n", + "6\tFrom R \tto AR \t2k(+1) \t1000000000.0\n", + "7\tFrom AR* \tto A2R* \tk*(+2) \t500000000.0\n", + "8\tFrom AR \tto A2R \tk(+2) \t500000000.0\n", + "9\tFrom A2R* \tto AR* \t2k*(-2) \t0.666666666667\n", + "\n", + "Conductance of state AR* (pS) = 60\n", + "\n", + "Conductance of state A2R* (pS) = 60\n", + "\n", + "Number of open states = 2\n", + "Number of short-lived shut states (within burst) = 2\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 1\n", + "Cycle 0 is formed of states: A2R* AR* AR A2R \n", + "\tforward product = 1.500000000e+16\n", + "\tbackward product = 1.500000000e+16" + ] + } + ], + "source": [ + "#Propose initial guesses different from recorded ones \n", + "#initial_guesses = [100, 3000, 10000, 100, 1000, 1000, 1e+7, 5e+7, 6e+7, 10]\n", + "initial_guesses = mec.unit_rates()\n", + "mec.set_rateconstants(initial_guesses)\n", + "mec.update_constrains()\n", + "mec.printout()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "theta= [ 1.50000000e+01 1.50000000e+04 3.00000000e+03 5.00000000e+02\n", + " 2.00000000e+03 5.00000000e+08]\n" + ] + } + ], + "source": [ + "# Extract free parameters\n", + "theta = mec.theta()\n", + "print ('\\ntheta=', theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare likelihood function" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def dcprogslik(x, lik, m, c):\n", + " m.theta_unsqueeze(np.exp(x))\n", + " l = 0\n", + " for i in range(len(c)):\n", + " m.set_eff('c', c[i])\n", + " l += lik[i](m.Q)\n", + " return -l * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import HJCFIT likelihood function\n", + "from HJCFIT.likelihood import Log10Likelihood\n", + "\n", + "# Get bursts from the record\n", + "bursts = rec.bursts.intervals()\n", + "# Initiate likelihood function with bursts, number of open states,\n", + "# temporal resolution and critical time interval\n", + "likelihood = Log10Likelihood(bursts, mec.kA, tr, tc)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Initial likelihood = 5264.414344\n" + ] + } + ], + "source": [ + "lik = dcprogslik(np.log(theta), [likelihood], mec, [conc])\n", + "print (\"\\nInitial likelihood = {0:.6f}\".format(-lik))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "ScyPy.minimize (Nelder-Mead) Fitting started: 2017/01/20 15:39:52\n", + "\n", + "ScyPy.minimize (Nelder-Mead) Fitting finished: 2017/01/20 15:39:53\n", + "\n", + "CPU time in ScyPy.minimize (Nelder-Mead)= 0.683340251399483\n", + "Wall clock time in ScyPy.minimize (Nelder-Mead)= 0.6840391159057617\n", + "\n", + "Result ==========================================\n", + " final_simplex: (array([[ 2.33189798, 9.48999371, 8.20461943, 6.05142787,\n", + " 7.68244318, 19.98586637],\n", + " [ 2.33189349, 9.4899828 , 8.20463764, 6.05141417,\n", + " 7.68244086, 19.98586557],\n", + " [ 2.33189951, 9.48998465, 8.20463814, 6.05141301,\n", + " 7.68245471, 19.98587732],\n", + " [ 2.33197072, 9.48998516, 8.20466661, 6.05141671,\n", + " 7.68245672, 19.98595769],\n", + " [ 2.33192346, 9.48994174, 8.20463429, 6.05137283,\n", + " 7.68245989, 19.98590225],\n", + " [ 2.33185111, 9.48999371, 8.20457848, 6.05142289,\n", + " 7.68244199, 19.98585741],\n", + " [ 2.33199047, 9.48997486, 8.20460338, 6.05139678,\n", + " 7.68245295, 19.98593748]]), array([-5268.59140924, -5268.59140923, -5268.59140923, -5268.59140921,\n", + " -5268.5914092 , -5268.59140918, -5268.59140918]))\n", + " fun: -5268.5914092352823\n", + " message: 'Optimization terminated successfully.'\n", + " nfev: 415\n", + " nit: 256\n", + " status: 0\n", + " success: True\n", + " x: array([ 2.33189798, 9.48999371, 8.20461943, 6.05142787,\n", + " 7.68244318, 19.98586637])\n" + ] + } + ], + "source": [ + "from scipy.optimize import minimize\n", + "print (\"\\nScyPy.minimize (Nelder-Mead) Fitting started: \" +\n", + " \"%4d/%02d/%02d %02d:%02d:%02d\"%time.localtime()[0:6])\n", + "start = time.clock()\n", + "start_wall = time.time()\n", + "result = minimize(dcprogslik, np.log(theta), args=([likelihood], mec, [conc]),\n", + " method='Nelder-Mead')\n", + "t3 = time.clock() - start\n", + "t3_wall = time.time() - start_wall\n", + "print (\"\\nScyPy.minimize (Nelder-Mead) Fitting finished: \" +\n", + " \"%4d/%02d/%02d %02d:%02d:%02d\"%time.localtime()[0:6])\n", + "print ('\\nCPU time in ScyPy.minimize (Nelder-Mead)=', t3)\n", + "print ('Wall clock time in ScyPy.minimize (Nelder-Mead)=', t3_wall)\n", + "print ('\\nResult ==========================================\\n', result)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final likelihood = 5268.5914092352822991\n", + "\n", + "Final rate constants:\n", + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AR \tto AR* \tbeta1 \t10.2974673884\n", + "1\tFrom A2R \tto A2R* \tbeta2 \t13226.7121605\n", + "2\tFrom AR* \tto AR \talpha1 \t3657.80834791\n", + "3\tFrom A2R* \tto A2R \talpha2 \t424.719039018\n", + "4\tFrom AR \tto R \tk(-1) \t2169.9147908\n", + "5\tFrom A2R \tto AR \t2k(-2) \t4339.82958159\n", + "6\tFrom R \tto AR \t2k(+1) \t956712565.145\n", + "7\tFrom AR* \tto A2R* \tk*(+2) \t500000000.0\n", + "8\tFrom AR \tto A2R \tk(+2) \t478356282.573\n", + "9\tFrom A2R* \tto AR* \t2k*(-2) \t0.410062923425\n", + "\n", + "Conductance of state AR* (pS) = 60\n", + "\n", + "Conductance of state A2R* (pS) = 60\n", + "\n", + "Number of open states = 2\n", + "Number of short-lived shut states (within burst) = 2\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 1\n", + "Cycle 0 is formed of states: A2R* AR* AR A2R \n", + "\tforward product = 9.490188419e+15\n", + "\tbackward product = 9.490188419e+15" + ] + } + ], + "source": [ + "print (\"\\nFinal likelihood = {0:.16f}\".format(-result.fun))\n", + "mec.theta_unsqueeze(np.exp(result.x))\n", + "print (\"\\nFinal rate constants:\")\n", + "mec.printout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot experimental histograms and predicted pdfs" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "from HJCFIT.likelihood import missed_events_pdf, ideal_pdf, IdealG\n", + "from HJCFIT.likelihood import eig, inv\n", + "qmatrix = QMatrix(mec.Q, 2)\n", + "idealG = IdealG(qmatrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that to properly overlay ideal and missed-event corrected pdfs ideal pdf has to be scaled (need to renormailse to 1 the area under pdf from $\\tau_{res}$). " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Scale for ideal pdf\n", + "def scalefac(tres, matrix, phiA):\n", + " eigs, M = eig(-matrix)\n", + " N = inv(M)\n", + " k = N.shape[0]\n", + " A = np.zeros((k, k, k))\n", + " for i in range(k):\n", + " A[i] = np.dot(M[:, i].reshape(k, 1), N[i].reshape(1, k))\n", + " w = np.zeros(k)\n", + " for i in range(k):\n", + " w[i] = np.dot(np.dot(np.dot(phiA, A[i]), (-matrix)), np.ones((k, 1)))\n", + " return 1 / np.sum((w / eigs) * np.exp(-tres * eigs))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAFgCAYAAABJ67N/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n", + "\n", + "# Plot apparent open period histogram\n", + "ipdf = ideal_pdf(qmatrix, shut=False) \n", + "iscale = scalefac(tr, qmatrix.aa, idealG.initial_vectors)\n", + "epdf = missed_events_pdf(qmatrix, tr, nmax=2, shut=False)\n", + "dcplots.xlog_hist_HJC_fit(ax[0], rec.tres, rec.opint, epdf, ipdf, iscale, shut=False)\n", + "\n", + "# Plot apparent shut period histogram\n", + "ipdf = ideal_pdf(qmatrix, shut=True)\n", + "iscale = scalefac(tr, qmatrix.ff, idealG.final_vectors)\n", + "epdf = missed_events_pdf(qmatrix, tr, nmax=2, shut=True)\n", + "dcplots.xlog_hist_HJC_fit(ax[1], rec.tres, rec.shint, epdf, ipdf, iscale, tcrit=rec.tcrit)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that in this record only shut time intervals shorter than critical time ($t_{crit}$) were used to minimise likelihood. Thus, only a part of shut time histrogram (to the left from green line, indicating $t_{crit}$ value, in the above plot) is predicted well by rate constant estimates." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/Example_MLL_Fit_GlyR_4patches-checkpoint.ipynb b/exploration/.ipynb_checkpoints/Example_MLL_Fit_GlyR_4patches-checkpoint.ipynb new file mode 100644 index 0000000..c86fb71 --- /dev/null +++ b/exploration/.ipynb_checkpoints/Example_MLL_Fit_GlyR_4patches-checkpoint.ipynb @@ -0,0 +1,851 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HJCFIT- maximum likelihood fit of single-channel data: \n", + "### Records at four concentrations fitted simultaneously " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some general settings:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys, time, math\n", + "import numpy as np\n", + "from numpy import linalg as nplin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HJCFIT depends on DCPROGS/DCPYPS module for data input and setting kinetic mechanism:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from dcpyps.samples import samples\n", + "from dcpyps import dataset, mechanism, dcplots, dcio" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# LOAD DATA: Burzomato 2004 example set.\n", + "scnfiles = [[\"./samples/glydemo/A-10.scn\"], \n", + " [\"./samples/glydemo/B-30.scn\"],\n", + " [\"./samples/glydemo/C-100.scn\"], \n", + " [\"./samples/glydemo/D-1000.scn\"]]\n", + "tr = [0.000030, 0.000030, 0.000030, 0.000030]\n", + "tc = [0.004, -1, -0.06, -0.02]\n", + "conc = [10e-6, 30e-6, 100e-6, 1000e-6]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialise Single-Channel Record from dcpyps. Note that SCRecord takes a list of file names; several SCN files from the same patch can be loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " Data loaded from file: ./samples/glydemo/A-10.scn\n", + "Concentration of agonist = 10.000 microMolar\n", + "Resolution for HJC calculations = 30.0 microseconds\n", + "Critical gap length to define end of group (tcrit) = 4.000 milliseconds\n", + "\t(defined so that all openings in a group prob come from same channel)\n", + "Initial and final vectors for bursts calculated asin Colquhoun, Hawkes & Srodzinski, (1996, eqs 5.8, 5.11).\n", + "\n", + "Number of resolved intervals = 14553\n", + "Number of resolved periods = 12322\n", + "\n", + "Number of open periods = 6161\n", + "Mean and SD of open periods = 1.288416455 +/- 1.982357659 ms\n", + "Range of open periods from 0.030309819 ms to 29.300385504 ms\n", + "\n", + "Number of shut intervals = 6161\n", + "Mean and SD of shut periods = 69.358758628 +/- 259.539574385 ms\n", + "Range of shut periods from 0.030003177 ms to 6902.281761169 ms\n", + "Last shut period = 115.868724883 ms\n", + "\n", + "Number of bursts = 1480\n", + "Average length = 6.106142638 ms\n", + "Range: 0.039 to 261.102 millisec\n", + "Average number of openings= 4.162837838\n", + "\n", + "\n", + " Data loaded from file: ./samples/glydemo/B-30.scn\n", + "Concentration of agonist = 30.000 microMolar\n", + "Resolution for HJC calculations = 30.0 microseconds\n", + "Critical gap length to define end of group (tcrit) = -1000.000 milliseconds\n", + "\t(defined so that all openings in a group prob come from same channel)\n", + "Initial and final vectors for are calculated as for steady state openings and shuttings (this involves a slight approximation at start and end of bursts that are defined by shut times that have been set as bad).\n", + "\n", + "Number of resolved intervals = 15939\n", + "Number of resolved periods = 12580\n", + "\n", + "Number of open periods = 6290\n", + "Mean and SD of open periods = 1.702516108 +/- 2.243856294 ms\n", + "Range of open periods from 0.030157962 ms to 24.172481848 ms\n", + "\n", + "Number of shut intervals = 6290\n", + "Mean and SD of shut periods = 70.374920964 +/- 2950.499773026 ms\n", + "Range of shut periods from 0.030011479 ms to 194377.349853516 ms\n", + "Last shut period = 0.045992190 ms\n", + "\n", + "Number of bursts = 6\n", + "Average length = 18819.672168031 ms\n", + "Range: 1522.551 to 43719.292 millisec\n", + "Average number of openings= 1048.333333333\n", + "\n", + "\n", + " Data loaded from file: ./samples/glydemo/C-100.scn\n", + "Concentration of agonist = 100.000 microMolar\n", + "Resolution for HJC calculations = 30.0 microseconds\n", + "Critical gap length to define end of group (tcrit) = -60.000 milliseconds\n", + "\t(defined so that all openings in a group prob come from same channel)\n", + "Initial and final vectors for are calculated as for steady state openings and shuttings (this involves a slight approximation at start and end of bursts that are defined by shut times that have been set as bad).\n", + "\n", + "Number of resolved intervals = 15085\n", + "Number of resolved periods = 10306\n", + "\n", + "Number of open periods = 5153\n", + "Mean and SD of open periods = 3.107396297 +/- 3.542747918 ms\n", + "Range of open periods from 0.030413088 ms to 46.681848165 ms\n", + "\n", + "Number of shut intervals = 5153\n", + "Mean and SD of shut periods = 165.682286024 +/- 6593.143939972 ms\n", + "Range of shut periods from 0.030006107 ms to 386315.155029297 ms\n", + "Last shut period = 0.060205570 ms\n", + "\n", + "Number of bursts = 12\n", + "Average length = 1513.309260650 ms\n", + "Range: 0.853 to 5742.387 millisec\n", + "Average number of openings= 429.416666667\n", + "\n", + "\n", + " Data loaded from file: ./samples/glydemo/D-1000.scn\n", + "Concentration of agonist = 1000.000 microMolar\n", + "Resolution for HJC calculations = 30.0 microseconds\n", + "Critical gap length to define end of group (tcrit) = -20.000 milliseconds\n", + "\t(defined so that all openings in a group prob come from same channel)\n", + "Initial and final vectors for are calculated as for steady state openings and shuttings (this involves a slight approximation at start and end of bursts that are defined by shut times that have been set as bad).\n", + "\n", + "Number of resolved intervals = 11116\n", + "Number of resolved periods = 7948\n", + "\n", + "Number of open periods = 3974\n", + "Mean and SD of open periods = 5.501930670 +/- 5.808946772 ms\n", + "Range of open periods from 0.031116215 ms to 54.734716301 ms\n", + "\n", + "Number of shut intervals = 3974\n", + "Mean and SD of shut periods = 127.277284861 +/- 4903.965950012 ms\n", + "Range of shut periods from 0.030000978 ms to 293057.556152344 ms\n", + "Last shut period = 2.376423450 ms\n", + "\n", + "Number of bursts = 19\n", + "Average length = 1176.491361390 ms\n", + "Range: 21.215 to 4484.782 millisec\n", + "Average number of openings= 209.157894737\n" + ] + } + ], + "source": [ + "# Initaialise SCRecord instance.\n", + "recs = []\n", + "bursts = []\n", + "for i in range(len(scnfiles)):\n", + " rec = dataset.SCRecord(scnfiles[i], conc[i], tr[i], tc[i])\n", + " recs.append(rec)\n", + " bursts.append(rec.bursts.intervals())\n", + " rec.printout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load demo mechanism (C&H82 numerical example)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# LOAD FLIP MECHANISM USED in Burzomato et al 2004\n", + "mecfn = \"./samples/mec/demomec.mec\"\n", + "version, meclist, max_mecnum = dcio.mec_get_list(mecfn)\n", + "mec = dcio.mec_load(mecfn, meclist[2][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# PREPARE RATE CONSTANTS.\n", + "# Fixed rates.\n", + "#fixed = np.array([False, False, False, False, False, False, False, True,\n", + "# False, False, False, False, False, False])\n", + "for i in range(len(mec.Rates)):\n", + " mec.Rates[i].fixed = False\n", + "\n", + "# Constrained rates.\n", + "mec.Rates[21].is_constrained = True\n", + "mec.Rates[21].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[21].constrain_args = [17, 3]\n", + "mec.Rates[19].is_constrained = True\n", + "mec.Rates[19].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[19].constrain_args = [17, 2]\n", + "mec.Rates[16].is_constrained = True\n", + "mec.Rates[16].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[16].constrain_args = [20, 3]\n", + "mec.Rates[18].is_constrained = True\n", + "mec.Rates[18].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[18].constrain_args = [20, 2]\n", + "mec.Rates[8].is_constrained = True\n", + "mec.Rates[8].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[8].constrain_args = [12, 1.5]\n", + "mec.Rates[13].is_constrained = True\n", + "mec.Rates[13].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[13].constrain_args = [9, 2]\n", + "mec.update_constrains()\n", + "# Rates constrained by microscopic reversibility\n", + "mec.set_mr(True, 7, 0)\n", + "mec.set_mr(True, 14, 1)\n", + "\n", + "# Update constrains\n", + "mec.update_constrains()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AF* \tto AF \talpha1 \t5000.0\n", + "1\tFrom AF \tto AF* \tbeta1 \t500.0\n", + "2\tFrom A2F* \tto A2F \talpha2 \t2700.0\n", + "3\tFrom A2F \tto A2F* \tbeta2 \t2000.0\n", + "4\tFrom A3F* \tto A3F \talpha3 \t800.0\n", + "5\tFrom A3F \tto A3F* \tbeta3 \t15000.0\n", + "6\tFrom A3F \tto A3R \tgama3 \t300.0\n", + "7\tFrom A3R \tto A3F \tdelta3 \t120000.0\n", + "8\tFrom A3F \tto A2F \t3kf(-3) \t6000.0\n", + "9\tFrom A2F \tto A3F \tkf(+3) \t450000000.0\n", + "10\tFrom A2F \tto A2R \tgama2 \t1500.0\n", + "11\tFrom A2R \tto A2F \tdelta2 \t12000.0\n", + "12\tFrom A2F \tto AF \t2kf(-2) \t4000.0\n", + "13\tFrom AF \tto A2F \t2kf(+2) \t900000000.0\n", + "14\tFrom AF \tto AR \tgama1 \t7500.0\n", + "15\tFrom AR \tto AF \tdelta1 \t1200.0\n", + "16\tFrom A3R \tto A2R \t3k(-3) \t3000\n", + "17\tFrom A2R \tto A3R \tk(+3) \t4500000.0\n", + "18\tFrom A2R \tto AR \t2k(-2) \t2000\n", + "19\tFrom AR \tto A2R \t2k(+2) \t9000000.0\n", + "20\tFrom AR \tto R \tk(-1) \t1000\n", + "21\tFrom R \tto AR \t3k(+1) \t13500000.0\n", + "\n", + "Conductance of state AF* (pS) = 40\n", + "\n", + "Conductance of state A2F* (pS) = 40\n", + "\n", + "Conductance of state A3F* (pS) = 40\n", + "\n", + "Number of open states = 3\n", + "Number of short-lived shut states (within burst) = 6\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 2\n", + "Cycle 0 is formed of states: A3R A3F A2F A2R \n", + "\tforward product = 4.860000000e+18\n", + "\tbackward product = 4.860000000e+18\n", + "Cycle 1 is formed of states: AF A2F A2R AR \n", + "\tforward product = 3.240000000e+18\n", + "\tbackward product = 3.240000000e+18" + ] + } + ], + "source": [ + "#Propose initial guesses different from recorded ones \n", + "initial_guesses = [5000.0, 500.0, 2700.0, 2000.0, 800.0, 15000.0, 300.0, 120000, 6000.0,\n", + " 0.45E+09, 1500.0, 12000.0, 4000.0, 0.9E+09, 7500.0, 1200.0, 3000.0, \n", + " 0.45E+07, 2000.0, 0.9E+07, 1000, 0.135E+08]\n", + "\n", + "#initial_guesses = [3687.69, 6091.43, 2467.35, 32621.5, 7061.15, 129984., 1050.69, 20984., 3387.64,\n", + "# 0.166224E+09, 20783.8, 6308.02, 2258.42, 0.332447E+09, 31335.4, 144.530, 831.686, \n", + "# 0.620171E+06, 554.457, 0.124034E+07, 277.229, 0.186051E+07]\n", + "\n", + "#initial_guesses = mec.unit_rates()\n", + "mec.set_rateconstants(initial_guesses)\n", + "mec.update_constrains()\n", + "mec.printout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check data histograms and probability densities calculated from initial guesses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot dwell-time histograms for inspection. In single-channel analysis field it is common to plot these histograms with x-axis in log scale and y-axis in square-root scale. After such transformation exponential pdf has a bell-shaped form." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that to properly overlay ideal and missed-event corrected pdfs ideal pdf has to be scaled (need to renormailse to 1 the area under pdf from $\\tau_{res}$). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Scale for ideal pdf\n", + "def scalefac(tres, matrix, phiA):\n", + " eigs, M = eig(-matrix)\n", + " N = inv(M)\n", + " k = N.shape[0]\n", + " A, w = np.zeros((k, k, k)), np.zeros(k)\n", + " for i in range(k):\n", + " A[i] = np.dot(M[:, i].reshape(k, 1), N[i].reshape(1, k))\n", + " for i in range(k):\n", + " w[i] = np.dot(np.dot(np.dot(phiA, A[i]), (-matrix)), np.ones((k, 1)))\n", + " return 1 / np.sum((w / eigs) * np.exp(-tres * eigs))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import QMatrix\n", + "from HJCFIT.likelihood import missed_events_pdf, ideal_pdf, IdealG, eig, inv" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAQxCAYAAADcAUeKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKtsAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(len(recs), 2, figsize=(12,15))\n", + "for i in range(len(recs)):\n", + " mec.set_eff('c', recs[i].conc)\n", + " qmatrix = QMatrix(mec.Q, mec.kA)\n", + " idealG = IdealG(qmatrix)\n", + " \n", + " # Plot apparent open period histogram\n", + " ipdf = ideal_pdf(qmatrix, shut=False) \n", + " iscale = scalefac(recs[i].tres, qmatrix.aa, idealG.initial_vectors)\n", + " epdf = missed_events_pdf(qmatrix, recs[i].tres, nmax=2, shut=False)\n", + " dcplots.xlog_hist_HJC_fit(axes[i,0], recs[i].tres, recs[i].opint,\n", + " epdf, ipdf, iscale, shut=False)\n", + " axes[i,0].set_title('concentration = {0:3f} mM'.format(recs[i].conc*1000))\n", + "\n", + " # Plot apparent shut period histogram\n", + " ipdf = ideal_pdf(qmatrix, shut=True)\n", + " iscale = scalefac(recs[i].tres, qmatrix.ff, idealG.final_vectors)\n", + " epdf = missed_events_pdf(qmatrix, recs[i].tres, nmax=2, shut=True)\n", + " dcplots.xlog_hist_HJC_fit(axes[i,1], recs[i].tres, recs[i].shint,\n", + " epdf, ipdf, iscale, tcrit=math.fabs(recs[i].tcrit))\n", + " axes[i,1].set_title('concentration = {0:6f} mM'.format(recs[i].conc*1000))\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare likelihood function" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def dcprogslik(x, lik, m, c):\n", + " m.theta_unsqueeze(np.exp(x))\n", + " l = 0\n", + " for i in range(len(c)):\n", + " m.set_eff('c', c[i])\n", + " l += lik[i](m.Q)\n", + " return -l * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def printiter(theta):\n", + " global iternum, likelihood, mec, conc\n", + " iternum += 1\n", + " if iternum % 100 == 0:\n", + " lik = dcprogslik(theta, likelihood, mec, conc)\n", + " print(\"iteration # {0:d}; log-lik = {1:.6f}\".format(iternum, -lik))\n", + " print(np.exp(theta))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import HJCFIT likelihood function\n", + "from HJCFIT.likelihood import Log10Likelihood\n", + "\n", + "kwargs = {'nmax': 2, 'xtol': 1e-12, 'rtol': 1e-12, 'itermax': 100,\n", + " 'lower_bound': -1e6, 'upper_bound': 0}\n", + "likelihood = []\n", + "\n", + "for i in range(len(recs)):\n", + " likelihood.append(Log10Likelihood(bursts[i], mec.kA,\n", + " recs[i].tres, recs[i].tcrit, **kwargs))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "theta= [ 5.00000000e+03 5.00000000e+02 2.70000000e+03 2.00000000e+03\n", + " 8.00000000e+02 1.50000000e+04 3.00000000e+02 4.50000000e+08\n", + " 1.50000000e+03 1.20000000e+04 4.00000000e+03 1.20000000e+03\n", + " 4.50000000e+06 1.00000000e+03]\n", + "Number of free parameters = 14\n" + ] + } + ], + "source": [ + "# Extract free parameters\n", + "theta = mec.theta()\n", + "print ('\\ntheta=', theta)\n", + "print('Number of free parameters = ', len(theta))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Initial likelihood = 237961.490136\n" + ] + } + ], + "source": [ + "lik = dcprogslik(np.log(theta), likelihood, mec, conc)\n", + "print (\"\\nInitial likelihood = {0:.6f}\".format(-lik))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run optimisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To keep execution time of this notebook short we only run the optimization for 200 iterations. Change `maxiter` below for a more realistic optimisation." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "ScyPy.minimize (Nelder-Mead) Fitting started: 2017/01/20 15:41:23\n", + "iteration # 100; log-lik = 263117.756238\n", + "[ 5.29171999e+03 3.67684624e+02 1.41434237e+03 8.42349605e+03\n", + " 8.62838471e+02 5.14562348e+04 3.05197111e+02 5.19871117e+07\n", + " 2.31972504e+03 2.00532661e+04 4.15472395e+03 5.07080801e+02\n", + " 5.01742534e+05 8.94263677e+02]\n", + "Warning: Maximum number of iterations has been exceeded.\n", + "\n", + "ScyPy.minimize (Nelder-Mead) Fitting finished: 2017/01/20 15:41:29\n", + "\n", + "CPU time in ScyPy.minimize (Nelder-Mead)= 6.081180560386165\n", + "Wall clock time in ScyPy.minimize (Nelder-Mead)= 6.081347703933716\n", + "\n", + "Result ==========================================\n", + " final_simplex: (array([[ 8.17854356, 6.03708337, 7.10706279, 9.07985218,\n", + " 6.93061081, 10.98001309, 5.8136173 , 17.23832524,\n", + " 7.72769902, 9.70103791, 8.19758666, 6.45112468,\n", + " 13.39684638, 6.95508465],\n", + " [ 8.22403089, 6.05593405, 7.12875747, 9.02313412,\n", + " 6.90356027, 10.95712368, 5.77448477, 17.24438837,\n", + " 7.73601058, 9.73603764, 8.23203859, 6.49199196,\n", + " 13.35532125, 6.97085489],\n", + " [ 8.27202446, 6.03124292, 7.15576058, 9.10298967,\n", + " 6.91537324, 10.97906583, 5.87535892, 17.27877025,\n", + " 7.7104076 , 9.78828276, 8.22704485, 6.37777004,\n", + " 13.25294199, 6.93122741],\n", + " [ 8.32433001, 6.0289382 , 7.1404883 , 9.0383547 ,\n", + " 6.8205307 , 10.93158792, 5.8308612 , 17.34019634,\n", + " 7.75095584, 9.72800377, 8.25944946, 6.38964771,\n", + " 13.31653367, 6.93631765],\n", + " [ 8.33985033, 5.92301082, 7.09702954, 9.09805489,\n", + " 6.89673942, 10.97621154, 5.77517881, 17.32273873,\n", + " 7.74756632, 9.81249292, 8.25085744, 6.39354715,\n", + " 13.28852029, 6.94056203],\n", + " [ 8.41575544, 5.94125291, 7.19042536, 9.0435014 ,\n", + " 6.84337862, 10.89976979, 5.76975737, 17.40977212,\n", + " 7.775724 , 9.77032599, 8.30672733, 6.40129054,\n", + " 13.28746099, 6.87193334],\n", + " [ 8.23460356, 6.06544835, 7.15047868, 9.05344456,\n", + " 6.87980573, 10.9657658 , 5.76339175, 17.33221254,\n", + " 7.74893403, 9.78425826, 8.27631384, 6.45009588,\n", + " 13.17781919, 6.88956063],\n", + " [ 8.39823157, 5.92139609, 7.126189 , 9.0460798 ,\n", + " 6.85351439, 10.9340107 , 5.72892779, 17.42246383,\n", + " 7.78649285, 9.77082672, 8.31723055, 6.38863181,\n", + " 13.28356059, 6.92208135],\n", + " [ 8.41035142, 6.01081853, 7.19216558, 8.99209568,\n", + " 6.81683175, 10.90190722, 5.76531268, 17.4582171 ,\n", + " 7.77716363, 9.72993475, 8.33889227, 6.42272102,\n", + " 13.29925003, 6.88808181],\n", + " [ 8.40340552, 6.0273153 , 7.22178376, 9.03888376,\n", + " 6.84264901, 10.91086406, 5.79190033, 17.45077881,\n", + " 7.73933892, 9.80968022, 8.24822381, 6.33622711,\n", + " 13.28545754, 6.9169019 ],\n", + " [ 8.24388707, 6.05157538, 7.20532864, 8.941294 ,\n", + " 6.84121632, 10.89460159, 5.76758747, 17.41789114,\n", + " 7.74027848, 9.70505269, 8.28434299, 6.52015363,\n", + " 13.39744932, 6.94780185],\n", + " [ 8.27610963, 6.05970142, 7.18039864, 9.13973644,\n", + " 6.87300261, 10.92971067, 5.93330882, 17.26117058,\n", + " 7.73212301, 9.73937094, 8.24832269, 6.4324554 ,\n", + " 13.1763455 , 6.87493238],\n", + " [ 8.38279112, 5.8842607 , 7.13565288, 9.0174619 ,\n", + " 6.84330267, 10.88373368, 5.78974106, 17.45101242,\n", + " 7.79970867, 9.74499968, 8.29915767, 6.43571606,\n", + " 13.37719486, 6.93389061],\n", + " [ 8.42420488, 5.88127595, 7.11098537, 9.09632685,\n", + " 6.83661948, 10.95519365, 5.74824873, 17.27662587,\n", + " 7.8087689 , 9.797293 , 8.31344718, 6.37128333,\n", + " 13.36240644, 6.92608137],\n", + " [ 8.4266003 , 5.96016415, 7.18559361, 9.00606597,\n", + " 6.85428849, 10.93451427, 5.8372125 , 17.29392659,\n", + " 7.78143967, 9.70305234, 8.3055485 , 6.46089968,\n", + " 13.39443632, 6.98429024]]), array([-263352.60977117, -263336.3471002 , -263329.21681918,\n", + " -263318.1251304 , -263309.78955037, -263306.30534451,\n", + " -263302.2583715 , -263298.52765242, -263298.30026063,\n", + " -263297.42510587, -263292.18626348, -263291.93074637,\n", + " -263291.13476093, -263287.65737164, -263287.20390461]))\n", + " fun: -263352.60977116867\n", + " message: 'Maximum number of iterations has been exceeded.'\n", + " nfev: 281\n", + " nit: 200\n", + " status: 2\n", + " success: False\n", + " x: array([ 8.17854356, 6.03708337, 7.10706279, 9.07985218,\n", + " 6.93061081, 10.98001309, 5.8136173 , 17.23832524,\n", + " 7.72769902, 9.70103791, 8.19758666, 6.45112468,\n", + " 13.39684638, 6.95508465])\n" + ] + } + ], + "source": [ + "from scipy.optimize import minimize\n", + "print (\"\\nScyPy.minimize (Nelder-Mead) Fitting started: \" +\n", + " \"%4d/%02d/%02d %02d:%02d:%02d\"%time.localtime()[0:6])\n", + "iternum = 0\n", + "start = time.clock()\n", + "start_wall = time.time()\n", + "maxiter = 200\n", + "# maxiter = 30000\n", + "result = minimize(dcprogslik, np.log(theta), args=(likelihood, mec, conc), method='Nelder-Mead', callback=printiter, \n", + " options={'xtol':1e-5, 'ftol':1e-5, 'maxiter': maxiter, 'maxfev': 150000, 'disp': True})\n", + "t3 = time.clock() - start\n", + "t3_wall = time.time() - start_wall\n", + "print (\"\\nScyPy.minimize (Nelder-Mead) Fitting finished: \" +\n", + " \"%4d/%02d/%02d %02d:%02d:%02d\"%time.localtime()[0:6])\n", + "print ('\\nCPU time in ScyPy.minimize (Nelder-Mead)=', t3)\n", + "print ('Wall clock time in ScyPy.minimize (Nelder-Mead)=', t3_wall)\n", + "print ('\\nResult ==========================================\\n', result)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final likelihood = 263352.6097711686743423\n", + "\n", + "Final rate constants:\n", + "\n", + "class dcpyps.Mechanism\n", + "Values of unit rates [1/sec]:\n", + "0\tFrom AF* \tto AF \talpha1 \t3563.66062627\n", + "1\tFrom AF \tto AF* \tbeta1 \t418.670145871\n", + "2\tFrom A2F* \tto A2F \talpha2 \t1220.55723999\n", + "3\tFrom A2F \tto A2F* \tbeta2 \t8776.66858648\n", + "4\tFrom A3F* \tto A3F \talpha3 \t1023.11872432\n", + "5\tFrom A3F \tto A3F* \tbeta3 \t58689.3222512\n", + "6\tFrom A3F \tto A3R \tgama3 \t334.828110598\n", + "7\tFrom A3R \tto A3F \tdelta3 \t64801.2535522\n", + "8\tFrom A3F \tto A2F \t3kf(-3) \t5448.26105245\n", + "9\tFrom A2F \tto A3F \tkf(+3) \t30655579.3113\n", + "10\tFrom A2F \tto A2R \tgama2 \t2270.3721034\n", + "11\tFrom A2R \tto A2F \tdelta2 \t16334.5522045\n", + "12\tFrom A2F \tto AF \t2kf(-2) \t3632.17403497\n", + "13\tFrom AF \tto A2F \t2kf(+2) \t61311158.6227\n", + "14\tFrom AF \tto AR \tgama1 \t2368.25771823\n", + "15\tFrom AR \tto AF \tdelta1 \t633.414278499\n", + "16\tFrom A3R \tto A2R \t3k(-3) \t3145.40186848\n", + "17\tFrom A2R \tto A3R \tk(+3) \t657925.10102\n", + "18\tFrom A2R \tto AR \t2k(-2) \t2096.93457899\n", + "19\tFrom AR \tto A2R \t2k(+2) \t1315850.20204\n", + "20\tFrom AR \tto R \tk(-1) \t1048.46728949\n", + "21\tFrom R \tto AR \t3k(+1) \t1973775.30306\n", + "\n", + "Conductance of state AF* (pS) = 40\n", + "\n", + "Conductance of state A2F* (pS) = 40\n", + "\n", + "Conductance of state A3F* (pS) = 40\n", + "\n", + "Number of open states = 3\n", + "Number of short-lived shut states (within burst) = 6\n", + "Number of long-lived shut states (between bursts) = 1\n", + "Number of desensitised states = 0\n", + "\n", + "Number of cycles = 2\n", + "Cycle 0 is formed of states: A3R A3F A2F A2R \n", + "\tforward product = 5.273692624e+17\n", + "\tbackward product = 5.273692624e+17\n", + "Cycle 1 is formed of states: AF A2F A2R AR \n", + "\tforward product = 1.848882431e+17\n", + "\tbackward product = 1.848882431e+17" + ] + } + ], + "source": [ + "print (\"\\nFinal likelihood = {0:.16f}\".format(-result.fun))\n", + "mec.theta_unsqueeze(np.exp(result.x))\n", + "print (\"\\nFinal rate constants:\")\n", + "mec.printout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot experimental histograms and predicted pdfs" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAQxCAYAAADcAUeKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FNXXwPHvSQg9IQm9hiIgIkXBgg0QBVSK4k9ABHmx\nYMOGDVCaWMCCXRREpasoFjq2gIqIHURQBAk1CCQxgVACue8fdxM3YTfZJNtzPs+zT7IzszNnN7t7\ncufee0aMMSillFJKKaWU8o6IQAeglFJKKaWUUuFEG1lKKaWUUkop5UXayFJKKaWUUkopL9JGllJK\nKaWUUkp5kTaylFJKKaWUUsqLtJGllFJKKaWUUl6kjSylCiAiGSLSMNBxKKWUUgXRfKVUcNFGlgoL\nIpItIo1LuI8vReQG52XGmGhjzLYSBedFIpIgIl+IyCER+V1EuhSy/SQR2S8i+0RkYr51j4rIOhHJ\nEpExLh47QES2ORL3AhGJdVpXVkTeFJF/RWS3iNyb77FtReQHR5zfi0ibfOvvFZE9IpImIm+ISFTx\nXpGTYu7oeC98kG95a8fyL7xxHKWUKi7NV26313yF5qtwoo0sFS4KvKq2iET6KxAfmwf8CMQDjwDv\ni0hVVxuKyC1AL6AV0BroKSJDnTbZDDwALHLx2JbAa8B1QE3gMDDFaZPxQBOgPnAx8KCIdHU8Ngr4\nCJgJxDp+fiwiZRzruwEPAp2BBMd+xhfxdSjIPqCDiMQ5LRsM/OHFYyilVHFpvspH85Xmq7BkjNGb\n3lzegHrAB8A/2C+CFx3LBfuFuQ1IBt4GYhzrEoBs4HogyfHYUU77jABGAX8B/wLfA3Ud604FVgAH\ngI3ANU6Pewt4GfsFmw58CzRyrFvpOOZBx7prgI7ADuyX4x5gBvYLdKEjpgOO3+s49vEYcBzIdOwj\n57lmA40dv8dgv4D/Af4GHnaKbzDwFfA0kAJsAbp7+e/RFJs8KjktWwkMdbP9N8BNTveHAKtdbDcL\nGJNv2ePAbKf7jYGjOccGdgFdnNaPB+Y6fu8K7Mi3vySgq+P3OcBjTus6A3sKeN7ZwG3An473zKOO\neL4B0oB3gDKObXP+7q8Ctzu953Zi37NfBPpzpTe96c37NzRf5XxXar7SfKW3ILlpT5ZySUQisAni\nb6ABUBf75QD2y+967BdEYyAam1CcnY/9kr0EGCMizR3L7wP6Yb/QqwA3AJkiUhGbsGYD1YD+wKsi\ncqrTPvsBY7HJZwv2ixVjTEfH+lbGmBhjzHzH/VqObRsAQ7FfXm9iz2Y1wCaoVxz7eASbdIY59nGX\nYx/OZxxfdjzXhkAn4HoRGeK0/mxssq2KTV7TcUNEFopIqoikuPj5iZuHtQS2GmMOOS371bHc3fa/\nerhtgY81xmzFJq1mjmEYtYF1bvZ9Wr51+de7iqtGvjN5+XUFzgDOxf4j8jowAPu3bAVc67Stwf5z\ncb3jfjdgPfafF6VUmNF8pfkKzVcqCGkjS7lzNvaL6UFjzBFjzDFjzGrHugHAZGNMkjEmExgJ9Hck\nOrBfGuMcj1mH/VLKGeN8I/aM2l8Axpj1xphUoAfwtzFmprF+xZ6VvMYppg+NMT8aY7KxZ5fa5otZ\n8t0/AYw1xmQZY44aY1KMMR86fj8EPAlcVMjrIJCbxPsBI4wxmcaYJOBZYJDTtknGmDeNMQZ7JrKW\niNRwtVNjTE9jTJwxJt7Fz15uYqmMPTPmLB2bSD3ZPt2xzBMFHasy9m+cf985cRQWp6u4BPfPA2CS\nMeaQMWYj8BuwwvH+ywCWYhNaLmPMGiBORJphk9fMAvatlAptmq+c9qn5Ks+xNF+pgNFGlnKnPvZL\nONvFujrY7vQcSUAZ7FjoHHudfs/kvy/L+sBWF/tMAM51nBlLEZFUbHJ03meym326s88Yk5VzR0Qq\niMjrjsmxadihC7Eikj/ZuVIN+xy3Oy1Lwp4xPSk+Y8xh7Bexp0nCEwexQ0CcVQEyPNy+imNZSY+V\ns4/8+86Jo7A4XcVlcP88wA55yXGYvO+vw7h+nWcBw7BncT8sYN9KqdCm+SovzVear1QQ0EaWcmcH\n0MDpbJ+z3dgkkyMByCLvF0lB+23iZnmi48xYzlmyGGPMsKIG7iT/5OL7sENCzjLGxPLfWUFxs72z\n/djnmP957ypOYCKyxFEFKd3FbbGbh20AGotIJadlbRzL3W3vXCWpbQHbFvhYEWkCRAF/GmPSsEMZ\nnPftHMcG7MRlZ62xZ/TcxbXXcYbYm2YDtwOLjTFHvLxvpVTw0HyVl+YrzVcqCGgjS7mzFvvFNFFE\nKopIORE5z7FuHnCviDQUkcrYsebvOJ1FLOhM2xvABBE5BUBEWjnGNi/Cjp8eKCJlRCRKRNo7jY0v\nTDJ2vH1BorFnkdJFJB4Yl2/9Xnf7cDy394DHRaSyiCQA92LPPhWZMeZyY8vtxri4XeHmMZuBX4Cx\njr9HH+B07DAVV2YCw0WkjojUBYZjJ2QD4Hidy2O/B6Ic+8z5TpiDre50viNJPgp84DS+fhbwiIjE\nikgL4GanfScCJ0TkTrGlc+/CTgb+0imuG0WkheNv/4hzXN5ibCnjixz7V0qFL81XTjRfab5SwUEb\nWcolx5d0T+yZtO3YM3d9HavfxH5prcJO6M0E7nJ+eP7dOf0+Gfvlv0JE/sUmsQrGmIPYyaL9sWce\ndwMTgXIehjwOmOkYuvE/N9s8D1TEnuVbDSzJt/4F4BoROSAiz7uI/S7sc92Kfe6zjTEFfdkWWKa3\nmPoDZwGp2H8WrjbGHAAQkQtEJD334Ma8jq1ItR47z+ATY8w0p31Nwz6f/tgKWpnAQMdjfwduBeZi\n/yGoANzh9Nix2NchCfgCmGiM+dTx2CzgSmwFq1TsGPPexpjjjvXLgaewSexv7HtoXAHPuaD3U4GM\nMauNMcmFb6mUClWarzRfoflKBSGxcx59tHORetizADWxZwamGmNeEpGx2DMJOeNWRxljlvksEKWU\nUsoNzVVKKaW8zdeNrFpALWPML45u+h+B3tiqNxnGmMk+O7hSSinlAc1VSimlvK2ML3fu6PZMdvx+\nUEQ28l91G08q5CillFI+pblKKaWUt/ltTpaINMRWZfnOsWiYiPwiIm+ISBV/xaGUUkq5o7lKKaWU\nN/ilkeUYfvE+cLdjwuirQGNjTFvs2UMdiqGUUiqgNFcppZTyFp/OyQJbdhNb7nSpMeYFF+sTgIXG\nmPzXKUBEfBucUkqpgDDGBNUwPM1VSiml8itJrvJHT9abwO/OScsxyThHH/676NtJjDF+u40dO9av\n+/Bk28K2cbfe0+WutvPG6+DP172oj/f36+7JMn+/5qH4uhd1XTC+7v7+jvHl616Sz0CQCliu8tdn\n0RufoWB7XqH2nIwx0LHw90uoPa+Svgd99b+Ivgf987cKledV1L9VSfm08IWInA9cB6wXkZ+x1wsY\nBQwQkbbYUrnbgFt8GYenOnXq5Nd9eLJtYdu4W+/pcm8855IqaQxFfby/X3dPl/lbqL3uRV0XjK+7\nv79jPN2+OK97ST8DwSTQucpfn8Xifr6Kyx/PK9SeEwANS3acYHxeJX0P+uo7Qt+DxX+8/q28oCSt\nRl/fbHjK38aOHRvoEEodfc0DQ1/3wHB8twc8x3jrFq65Khw/H8HwnBjn/fdLMDwvbwvH52SMPq9Q\nUtJc5bfqgip0BPtZ53Ckr3lg6OuulHvh+PkIx+cE4fm8wvE5gT6v0sTnhS9KQkRMMMenlFKq6EQE\nE2SFL0pCc5UqChkvmLH6flEq2JU0V/l0TpZSSimllAp/DRs2JCkpKdBhKFVkCQkJbNu2zev71UaW\nUkoppZQqkaSkJK9UZFPK30R8M7BC52QppZRSSimllBdpI0sppZRSSimlvEgbWUoppZRSSinlRdrI\nUkoppZRSpVJSUhIRERFkZ2eXeF+NGjXiiy++8GjbGTNmcOGFF+bej46O9lrxhSeffJKhQ4cC3n1+\nADt27CAmJkbn33lAG1lKKaWUUipsFdb48VXhg8I4HzcjI4OGDRsWuP3KlSupX79+ofsdOXIkU6dO\ndXmcosr/2tWvX5/09PSAvWahRBtZSimllFJKBTljTKGNmxMnTvgpGlUYbWQppZRSSqlSITs7m/vv\nv5/q1atzyimnsHjx4jzr09PTuemmm6hTpw7169dn9OjRuUPjtm7dSpcuXahWrRo1atRg4MCBpKen\ne3TclJQUevXqRZUqVTj33HPZsmVLnvURERFs3boVgCVLltCyZUtiYmKoX78+kydPJjMzk8svv5zd\nu3cTHR1NTEwMycnJjB8/nmuuuYZBgwYRGxvLjBkzGD9+PIMGDcrdtzGG6dOnU7duXerWrcuzzz6b\nu27IkCGMGTMm975zb9n111/P9u3b6dmzJzExMTzzzDMnDT/cs2cPvXv3pmrVqjRr1ow33ngjd1/j\nx4+nX79+DB48mJiYGFq1asVPP/3k0esVDrSRpZRSSimlSoWpU6eyZMkSfv31V3744Qfef//9POsH\nDx5M2bJl2bp1Kz///DOffvppbsPBGMOoUaNITk5m48aN7Ny5k3Hjxnl03Ntvv52KFSuyd+9epk+f\nzptvvplnvXMP1U033cS0adNIT0/nt99+4+KLL6ZixYosXbqUOnXqkJGRQXp6OrVq1QLgk08+oW/f\nvqSlpTFgwICT9geQmJjIli1bWL58OZMmTfJo+OTMmTNp0KABixYtIj09nfvvv/+kfffr148GDRqQ\nnJzM/PnzGTVqFImJibnrFy5cyIABA/j333/p2bMnd9xxh0evVzjQRpZSSimllCoV5s+fzz333EOd\nOnWIjY1l5MiRuev27t3L0qVLee655yhfvjzVqlXjnnvuYd68eQA0adKELl26UKZMGapWrcq9997L\nypUrCz1mdnY2CxYsYMKECZQvX56WLVsyePDgPNs4F5IoW7YsGzZsICMjgypVqtC2bdsC99+hQwd6\n9uwJQPny5V1uM27cOMqXL8/pp5/OkCFDcp+TJ9wVudixYwfffvstkyZNIioqijZt2nDTTTcxc+bM\n3G0uuOACunXrhogwaNAg1q1b5/FxQ502spRSSimllG+NGwciJ9/c9QS52t7DXqOC7N69O0/xiISE\nhNzft2/fTlZWFrVr1yY+Pp64uDhuvfVW9u/fD8A///zDtddeS7169YiNjWXgwIG56wqyb98+Tpw4\nQb169VweN78PPviAxYsXk5CQQOfOnVmzZk2B+y+sGIaInHTs3bt3Fxp3Yfbs2UN8fDwVK1bMs+9d\nu3bl3s/pbQOoWLEiR44c8Vqlw2CnjSyllFJKKeVb48aBMSffCmpkebptEdSuXZsdO3bk3k9KSsr9\nvX79+pQvX54DBw6QkpJCamoqaWlpub0vo0aNIiIigg0bNpCWlsbs2bM9KmVevXp1ypQpk+e427dv\nd7t9u3bt+Oijj9i3bx+9e/emb9++gPsqgZ5U+st/7Dp16gBQqVIlMjMzc9ft2bPH433XqVOHlJQU\nDh06lGffdevWLTSe0kAbWUoppZRSqlTo27cvL774Irt27SI1NZVJkyblrqtVqxZdu3bl3nvvJSMj\nA2MMW7duZdWqVYAts165cmWio6PZtWsXTz/9tEfHjIiIoE+fPowbN47Dhw/z+++/M2PGDJfbZmVl\nMXfuXNLT04mMjCQ6OprIyEgAatasyYEDBzwutpHDGMOECRM4fPgwGzZs4K233qJ///4AtG3bliVL\nlpCamkpycjIvvPBCnsfWqlUrtyCH8/4A6tWrx3nnncfIkSM5evQo69atY/r06XmKbriKpbTQRpZS\nSimllApbzr0xN998M926daNNmza0b9+eq6++Os+2M2fO5NixY5x22mnEx8dzzTXXkJycDMDYsWP5\n8ccfiY2NpWfPnic9tqBen5deeomMjAxq167NDTfcwA033OD2sbNmzaJRo0bExsYydepU5syZA0Dz\n5s259tprady4MfHx8blxefL8O3bsyCmnnMKll17Kgw8+SJcuXQAYNGgQrVu3pmHDhnTv3j238ZVj\nxIgRTJgwgfj4eCZPnnxSrPPmzePvv/+mTp06XH311UyYMIHOnTsXGEtpIcHcohQRE8zxKaWUKjoR\nwRgTNplWc5UqChkvmLHh935xfK4DHYZSRebuvVvSXKU9WUoppVQIiI/PWwMgPj7QESmllHKn0EaW\niPQUEW2MKaWUClqlIVelpuatAZCaGuiIlFJKueNJQuoHbBaRp0TkVF8HpJRSShWD5iqllFJBo9BG\nljFmIHAGsAV4W0S+FZGhIhLt8+iUUkopD2iuUkopFUw8GlphjEkH3gfeAWoDVwE/icidPoxNKaWU\n8pjmKqWUUsHCkzlZvUXkQyARiALONsZcBrQB7vNteEoppVThNFcppZQKJmU82KYP8JwxZpXzQmNM\npojc6JuwlFJKqSLRXKWUUipoeDJcMDl/0hKRSQDGmM99EpVSSilVNJqrlFJKBQ1PGlmXulh2mbcD\nUUoppUpAc5VSqlSKiIhg69atHm07fvx4Bg0aBMCOHTuIiYnx2kWkb7vtNh5//HEAVq5cSf369b2y\nX4Cvv/6aFi1aeG1//uC2kSUit4nIeuBUEVnndPsbWOe/EJVSSinXNFcppTwxd+5czjrrLKKjo6lb\nty5XXHEF33zzTaDDYsaMGVx44YUl2oeIFGv7+vXrk56eXujjPY1xypQpPPzww8WOy1n+huMFF1zA\nxo0bi72/QChoTtZcYCnwJDDCaXmGMSbFp1EppZRSnim1uSouDvL/DxMXBylh/ayVKrrJkyfz1FNP\n8frrr9O1a1fKli3L8uXLWbhwIeeff36R9nXixAkiIyMLXeYpY0yJGiM5+/AlT2LMzs4mIsJ714Mv\n6WsSDAp6NYwxZhtwB5DhdENE4n0fmlJKKVWoUpurUlLAmLy31NRAR6VUcElPT2fs2LG8+uqr9O7d\nmwoVKhAZGcnll1/OxIkTATh27Bj33HMPdevWpV69etx7771kZWUB/w17e+qpp6hduzY33HCDy2UA\nixYt4owzziAuLo4LLriA9evX58axc+dOrr76amrUqEH16tW566672LRpE7fddhvffvst0dHRxMfH\n58Zz//33k5CQQO3atbn99ts5evRo7r6efvpp6tSpQ7169XjrrbcKbJBs27aNTp06UaVKFbp168b+\n/ftz1yUlJREREUF2djYAb7/9Nk2aNCEmJoYmTZowb948tzEOGTKE22+/nSuuuILo6GgSExMZMmQI\nY8aMyd2/MYYnn3yS6tWr07hxY+bOnZu7rnPnzrz55pu59517yzp27IgxhtatWxMTE8P8+fNPGn64\nadMmOnfuTFxcHK1atWLhwoW564YMGcKwYcPo0aMHMTExdOjQgb///rvgN4oPFNTIynklfgR+cPz8\n0em+UkopFWiaq5RSbn377bccPXqUK6+80u02jz32GGvXrmXdunX8+uuvrF27lsceeyx3fXJyMmlp\naWzfvp2pU6e6XPbzzz9z4403Mm3aNFJSUrjlllvo1asXWVlZZGdn06NHDxo1asT27dvZtWsX/fv3\n59RTT+W1116jQ4cOZGRkkOLohn7ooYf466+/WLduHX/99Re7du3i0UcfBWDZsmVMnjyZzz//nM2b\nN/PZZ58V+PwHDBjAWWedxf79+3nkkUeYMWNGnvU5DbTMzEzuvvtuli9fTnp6OqtXr6Zt27ZuYwSY\nN28eo0ePJiMjw2WPYHJyMikpKezevZu3336boUOHsnnzZrex5sSycuVKANavX096ejrXXHNNnvXH\njx+nZ8+edO/enX379vHiiy9y3XXX5dn3u+++y/jx40lLS6NJkyZ5hjH6i9tGljGmh+NnI2NMY8fP\nnFtj/4WolFJKuaa5SilVkAMHDlCtWrUCh7LNnTuXsWPHUrVqVapWrcrYsWOZNWtW7vrIyEjGjx9P\nVFQU5cqVc7ls2rRp3HrrrbRv3x4RYdCgQZQrV441a9awdu1a9uzZw1NPPUX58uUpW7Ys5513ntt4\npk2bxnPPPUeVKlWoVKkSI0aMYN68eQDMnz+fIUOG0KJFCypUqMC4cePc7mfHjh388MMPPProo0RF\nRXHhhRfSs2dPt9tHRkayfv16jhw5Qs2aNQstNNG7d2/OPfdcgNzXxZmIMGHCBKKiorjooou44oor\neO+99wrcpzN3wyC//fZbDh06xEMPPUSZMmXo3LkzPXr0yH2NAK666iratWtHREQE1113Hb/88ovH\nx/UWTy5GfL6IVHL8PlBEJotIA9+HppRSSnlGc5VSwU3EO7eiqlq1Kvv3788dEufK7t27adDgv6+L\nhIQEdu/enXu/evXqREVF5XlM/mVJSUk8++yzxMfHEx8fT1xcHDt37mT37t3s2LGDhIQEj+Ys7du3\nj8zMTNq1a5e7r8suu4wDBw7kxuo8bC4hIcFtY2T37t3ExcVRoUKFPNu7UrFiRd59912mTJlC7dq1\n6dmzJ3/88UeBsRZWPTAuLo7y5cvnObbz61pce/bsOenYCQkJ7Nq1K/d+rVq1cn+vWLEiBw8eLPFx\ni8qTGWpTgEwRaQPcB2wBZhX8EEtE6onIFyKyQUTWi8hdjuVxIrJCRP4QkeUiUqXYz0AppZTSXKVU\nUMs/f7C4t6Lq0KED5cqV46OPPnK7Td26dUlKSsq9n5SURJ06dXLvu5rzlH9Z/fr1efjhh0lJSSEl\nJYXU1FQOHjxIv379qF+/Ptu3b3fZ0Mu/n2rVqlGxYkU2bNiQu6+0tDT+/fdfAGrXrs2OHTvyxOpu\nTlbt2rVJTU3l8OHDucu2b9/u9nW49NJLWbFiBcnJyTRv3pyhQ4e6ff4FLc/h6tg5r2ulSpXIzMzM\nXZecnFzgvpzVqVMnz2uQs++6det6vA9/8KSRddzYJnJv4GVjzCtAtIf7Pw4MN8a0BDoAd4jIqdgK\nUJ8ZY5oDXwAjix66UkHk2WfhgQfg7rth5Eh4+mmYORMcX4pKKZ/TXKWUOklMTAzjx4/njjvu4OOP\nP+bw4cMcP36cpUuXMmKELUjav39/HnvsMfbv38/+/fuZMGFC7rWkPHXzzTfz2muvsXbtWgAOHTrE\nkiVLOHToEGeffTa1a9dmxIgRZGZmcvToUVavXg1AzZo12blzZ26hDRHh5ptv5p577mHfvn0A7Nq1\nixUrVgDQt29f3n77bTZu3EhmZmbuXC1XGjRoQPv27Rk7dixZWVl8/fXXeQpEwH9D8v755x8++eQT\nMjMziYqKonLlyrk9b/lj9JQxJvfYX331FYsXL6Zv374AtG3blgULFnD48GH++usvpk+fnuextWrV\ncnvtr3POOYeKFSvy1FNPcfz4cRITE1m0aBHXXnttkeLzNU8aWRkiMhIYCCwWkQggqpDHAGCMSTbG\n/OL4/SCwEaiHTYI5M+9mAO5nIyoVDI4fh1WrwPGFd5IqVaBGDWjUCKKjYe9eWLYMnM7gKKV8SnOV\nUsql4cOHM3nyZB577DFq1KhBgwYNePXVV3OLYTzyyCO0b9+e1q1b06ZNG9q3b1/kQgnt2rVj2rRp\nDBs2jPj4eJo1a5ZbZCIiIoKFCxeyefNmGjRoQP369XPnJl188cW0bNmSWrVqUaNGDQAmTpzIKaec\nwrnnnktsbCxdu3blzz//BKB79+7cc889XHzxxTRr1owuXboUGNfcuXNZs2YNVatWZcKECQwePDjP\n+pzeqOzsbCZPnkzdunWpVq0aq1atYsqUKW5j9ETt2rWJi4ujTp06DBo0iNdff52mTZsCcO+99xIV\nFUWtWrUYMmQIAwcOzPPYcePGcf311xMfH8/777+fZ11UVBQLFy5kyZIlVKtWjWHDhjFr1qzcfQdL\n+XcprLa+iNQCBgDfG2O+coxx72SMmVmkA4k0BBKB04Edxpg4p3UpxpiTSu2KiPF17X+l3DIGvv8e\nZs+Gd9+FevXg9dehffuS7ffIEdvTNXQo1KzpnViVCiEigjHGq1mwNOQqkcKHS3myjQosGS+YseH3\nR3J8rgMdhlJF5u69W9JcVWhPluMM32RjzFeO+9uLkbQqA+8DdzvOEuZ/JvqpVMHlq6+gbVu47jqo\nVg2++QZ+/LHkDSyAzEzYswdatIBhw8BpoqZSqng0VymllAomZQrbQET6AJOAGoA4bsYYE+PJAUSk\nDDZpzTLGfOxYvFdEahpj9jrOPv7j7vHOpSk7depEp06dPDmsUiVTrRpMmgRdu4IXr2AOQHw8vPoq\njBlj53K1bg333w/Dh4OLEqhKhbrExEQSExN9egzNVUoppUrC27nKk+GCfwE9jTEbi3UAkZnAfmPM\ncKdlk4AUY8wkEXkIiDPGjHDxWB0uqMLfli22gXXnnXDJJYGORimf89FwwbDPVTpcMDzocEGlgouv\nhgt60sj6xhhz8mWcPdm5yPnAKmA9dpiFAUYBa4H3gPpAEtDXGJPm4vHayFK+9c03ULcuNGwY6EiU\nKjV81MgK+1yljazwoI0spYKLrxpZhQ4XBH4QkXeBj4CjOQuNMQsKe6Ax5hsg0s1qPWWvAmf/fnjo\nIVsBcO5cnzWy4uMhNdX9+rg4SEnxyaGVKm00VymllAoanjSyYoBMoKvTMgMUmriUCkrz59uhedde\nCxs3QoxHUzaKJTW14LPKhVYZPXgQKlf2akxKhSnNVUoppYJGoY0sY8wQfwSilM8ZAzfdBF9/DZ98\nAmefHeiICpacbGP8+GM444xAR6NUUNNcpZRSKph4Ul2wGTAFqGmMOV1EWgO9jDGP+Tw6pbxJBPr3\nh5degooVPX5YYUP+YmNtdfcDB+zQv3//zdt79emnULUqVK8OdepApLtBSfnVqgXPPQfdu8NHH0GH\nDh7HrFRpo7lKqcBKSEgImovAKlUUCQkJPtmvJ4UvVgIPAK8bY85wLPvNGHO6TyLKe2wtfKF8rijz\npnbuhLVr7TWKf/oJ/voLtm51/9ioKLjoItsA++cfu5/GjaF5c9tJNXIkHDpUSJtv2TK4/nrbo6UN\nLRUGfFT4IuxzlSdFLfJ/n+m8z+ATroUvlAo3/ih8UdEYszbf2YnjxT2gUsGmoHlTR47AZ5/ZKVzL\nl9ttzzkWvQEHAAAgAElEQVTHNpDuvNM2lhISoGxZz46VmWkbZr//Dt99Z5fVqAHnnQe9etlbgwb5\nHtS9O8yYAVdeabvFWrcu9nNVKoxpruLkBpV2LCilVGB40sjaLyJNcFzpXkT+B+zxaVRKldTPP9s5\nTZddVuSHHj8OX3wB8+bZzqPWre1u3nvP/l6SaxNXrGj30bq1Hbn4/PM2zBUr7DSxcePsuptugj59\noHx5xwMvuwxefhkyMop/cKXCm+YqpZRSQcOT4YKNganAeUAq8Dcw0BizzefB6XBBVRwLFsAtt8Dr\nr9uWSiFyhuDs3Wsf8vrr9tJZ114L/frZeVS+kn/4z9GjtrH1xht2OOLtt8Pdd9shQEqFCx8NFwz7\nXFWca2DpdbOCjw4XVCo0+PxixE4HqgREGGP8dipdG1mqSIyBJ5+EKVNsoYh27Tx6mAgMHAiLFkHf\nvjBsGLRq5eNYnY7t7i2+eTNMmgQffmh7tkaMsPMrlAp1vmhkOe07bHOVNrLCgzaylAoNPpuTJSLD\n3R0QwBgzubgHVcrrjhyxLZFNm+xkJw+6n9atg/Hj7e+tWsELLwRXj1HTprZHa8wYePxxOPVUePRR\n+zQ9rlCoVJjTXKWUUioYFTS7JNpxaw/cBtR13G4FzvR9aEoVwcaN9pTtqlWFNrA2b4b//Q+6dYPz\nz7fLHnwwuBpYzho0sEMYly+HOXOgfXs7lBCA/fsDGptSQUBzlVJKqaDjyZysVcAVOUMvRCQaWGyM\nucjnwelwQeVF//4Ljz0Gb70F998Pd91lC1EEcjhNYccuqLx8ZTLYSAvqbVll68IrFSJ8NCcr7HOV\nDhcMDzpcUKnQUNJc5UmdtJrAMaf7xxzLlAoJxsD06bbcemoq/Pabnd9UhOsRB0xOefn8t5074fxu\n0ZzF9+y46i5bMUOp0k1zlVJKqaDhSU/Ww0Bf4EPHoiuBd40xT/o4Nu3JUiW2eTOcdpoty+5OIC/W\nWdhZ5oLWZ2dDZKShZrk0ZvX+gEvfvck3QSrlZT7qyQr7XKU9WeFBe7KUCg0+78kyxjwODMGWxE0F\nhvgjaSnl1mefwTvvFLhJVhZMnAgdOtgG1vHjrnuEjAlcA6uk7PW6hHfmwfXv9+SV+7cGOiSlAkZz\nlVJKqWDiycWIMcb8BPxU6IZK+drixTBkCHzwgdtN/voLrrsOqlSBH36ARo3Cuxpfp6vi+OalZVx+\n/2lsyYannw7v56uUO5qrThYXZ3uz8i8L1ZNLSikVKjyZk6VUcPjwQ7jhBli4EC688KTVOXOvOnSw\n171avhwaNvR/mEWR8w+Qu5un18VqfHt3Vv9RlR9/hOuvL3h4pFKq9EhJObn33l0xHaWUUt6jjSwV\nEm6q/A7JfW7jzH+WIueek6chEh8PBw7A1VfDiy9CYiLceefJZ2+Dkat/gIo7lDG+fiWWLbOP6dcP\njh0r/DFKKaWUUsr7PCl8cScw2xjj93NfWvhCAZCWxu9x53Ha+vfg9NNPWi1iryX1v//BE09AuXIn\nrw/Xt5Gr53b0qG1kZWfbUZVRUYGJTSl3fFT4IuxzVZ7P++HDdmx0uXLQrNnJG2/YYK8bWKGCvcXG\nQrVq0KABUqN62H4nhgItfKFUaChprvJkTlZN4HsR+Ql4E1iuLR/lV7GxtGYdx0/P+3Y1Bl57zf7+\nwgtw5ZUBiC0IlSsH8+dDnz7wf/8Hs2blFMlQKqyFd646cIDBLISBn8H33/NvUhpbal/AP536ktat\nGWlpcPDgf4V+jm+N5vgvCRzPMpzIOkHE0TQiM3cS0TQD6MTTT9vrttepA02bQt0KKUililC+fKCf\nqVJKhYVCe7IARESArtjKTe2B94DpxpgtPg1Oe7KUQ/4em8xMuPVW+OUXSEqC9HT3jw3nSd5ue+my\nsjh8IJPL+lehRQt49dXQGD6pSgdf9GQ59hu2uWrV6xt56db1/NviPH5OrsXhY5E0aSLUqmU7qWJj\noXJl23NdpkzeW0SE7dnOzoYTJ2DMGBg+HHbvhl27YNMmMJmZtD/yNZc13ULPm2rS6NZuUKmST59T\naaU9WUqFBn/0ZGGMMSKSDCQDx4E44H0R+dQY82BxD65UcWzZAlddBW3bwpo1oXFRYb978UUq/PQT\nn3wyh06dbDn7kSMDHZRSvhXOuWrRlha8Tws+mQTt20OtWsU/cTJmDDz77H/3jYHduyuy5ssLWfzG\nKTw2Kp7TRvzCHVfupM+UrkRW87ACj1JKqVyezMm6G7ge2A+8AXxkjMkSkQhgszGmic+C056s0ikp\nCRIS8izK6bFZudLONxo9Gm6/XXtn4uNdVwqryCH+pBn/F72Atzeew7nn2iGVffr4P0al8vPRnKyw\nz1Xeml9a2H6ysuDDV3bz/MTDpMc24KnJUVx+ecmPqyztyVIqNPj8YsRAPNDHGNPNGDPfGJMFYIzJ\nBnoU98BKuTR/Ppx3nsvxf9OnQ9++MHs23HGHNrDAfXXCQ6YSdadPYGzGfdStY/joI7jlFvjxx0BH\nrJTPaK7ykqgo6HtPHb7Z04Qnn47innugbNm8l5eIjw90lEopFdw8aWQtBXJntIhIjIicA2CM2eir\nwFQp9NFHtvb6kiUQE5O7+MQJ+3PiRFss65JLAhRfqBk8mGgyYMEC2rWD11+3xUH27Al0YEr5hOYq\nLxOBnj3t3NesLFvF9aef9FpbSinlCU8aWVOAg073DzqWKeU9ixbZrpbFi6FNm9zFBw/+VzXwu++g\nefMAxReKIiO5j2fhoYfg2DH69IEbb4QBA/5ruCoVRjRX+UjOvNdnnoGuXQ0fPevTOiJKKRUWPGlk\n5Rls7hh64VHBDKU8smIF+3rdwNn/LETat8szJCU62ra/YmN1eEpxfM4lMG1a7sWyRo+GyEgYNy6w\ncSnlA5qrfOyaa2DplCRufSCalvwW6HCUUiqoedLI2ioid4lIlON2N7DV14GpUqR6da40H7LWnJ07\np2jzZmjSxFbBys7WoSkl0rlz7gS2yEiYMwfefBNWrAhwXEp5l+YqP2j/v4Ysn7adfVRnwZN/BDoc\npZQKWp5UF6wBvAhcDBjgc+AeY8w/Pg9OqwuWGs7Vrn74AXr1grFj7QhCVXzuqoglJsK119r5FbVr\n+z0sVcr5qLpg2Ocqf1UX9OQx58vX/Ckt+Pj9LM7rU6vkQZUiWl1QqdBQ0lzl0cWIA0UbWaVHTgJf\ntgwGDYI33oDevQMdVegr6J+pMWNstcFFi7RSo/IvX12MOFBCrZGV/9IPnlywPf+x4+OhU+oHrKIj\nh6jIESqG9YXfvUkbWUqFBp9fjFhEqgM3Aw2dtzfG3FDcgyrlyowZtkbDxx/bKu6q5OLi3DegYmOh\nUSPboL35Zv/GpZS3aa7yXP6GUHFOsqSkAKYPk69cyTs7L+Cr1VC+vFfCU0qpsODJcMHVwFfAj0Bu\nTTJjzAe+DU17ssLS3r3w+ee2xJ0TEVseeNkyaNEiQLGVBs8/b7sKq1ZFBH77DTp1grVrbYNLKX/w\n0XDBsM9V3urJ8mS/nvZ2GQNXXWW/v2fPLnoPWWmkPVlKhQaf92QBFY0xDxX3AErlSkuDbt1OGgf4\nzDP256pVkJAQgLhKk40b7Qv+5JMAtGxpew8HD4Yvv7SFMZQKUZqrvCg11bMGnQi8/TaccQbMmgVX\nXJF3nVJKlVaeVBdcJCKX+zwSFd4OHbLZt2PHPPXDn3gCpk61v2sDyw8eftheldjpdPO999oKjlP0\nikIqtGmuCpDYWFux9JZbtBKsUkrl8KSRdTc2eR0RkXQRyRCRdE92LiLTRWSviKxzWjZWRHaKyE+O\nW/fiBq9CxNGj0KcPNG0Kzz0HIhhj21qzZsHKlYEOsBRp0MD2JL78cu6iyEh7Ka1x42DXrsCFplQJ\nFTtXQenOVzlzN51vcXFF20fnzvarZfhw38SolFKhxqfVBUXkAuAgMNMY09qxbCyQYYyZ7MHjdU5W\nOBg6FPbvh/fegzJlMMZ2qCxcCJ99BjVr+m6ugXJh0ya46CIq7fubQ6ZS7uIxY+wcrQULAhibKhWC\nsbpgSfJVqM/J8paDacc5vU4K02eXo0ufKkEfb6DonCylQkNJc1WhPVliDRSR0Y779UXkbE92boz5\nGnA1eCCokqvysYcegnnzchtYDzwAS5faOUA1awY6uFLo1FPhoovoycI8i0eNgg0bbHVHpUJNSXIV\naL7yhsqxZXi+y0LuvOEgx4657iGLjw90lEop5R+eDBd8FegA5JSDOwi8UsLjDhORX0TkDRGpUsJ9\nqWDXpAmUK4cxcPfd9kK4n38O1aoFOrBSbNYs3qV/nkXly9vpWnfeCRkZAYpLqeLzRa4CzVdF0ntO\nXxKO/MFLD+4gJcX2ZDnfdM6WUqq08KS64DnGmDNF5GcAY0yqiJQtwTFfBR41xhgReQyYDNzobuNx\nTkUSOnXqRKdOnUpwaBUo2dlwxx3w8892iGBsbKAjKuUqVCjwGloxMVp+WXlPYmIiiYmJvj6Mt3MV\nFCFfaa6yJCaaF0Yf4LxxZ3LtA4Y6dbUjUCkVGrydqzy5TtZ3wHnA944EVh1YYYw5w6MDiCQAC3PG\nuHu6zrFe52SFgRMn7LSsP/+ExYvtP/D56dj94LFnD7RqBVlZkF5A2QBthKni8tF1skqUqxz7KFa+\n0jlZ+Zw4wYO1Z5HWpiNTP817Ab6QeQ4+pHOylAoNPp+TBbwIfAjUEJHHga+BJ4pwDMFpTLuI1HJa\n1wf4rQj7UsFu5kw75szhxAkYMgS2brXzsFw1sFRwqV3bzpvr2PHkoT467EcFsZLmKtB85R2RkYx8\nsykfflebTZsCHYxSSgWGR9UFReRUoAs2+XxujNno0c5F5gKdgKrAXmAs0BloC2QD24BbjDF73Txe\ne7JCyccfw6232ooWp55KVhZcf70tLPjxx1CxovuH6tnN4HL0KJx+uq303q2b6230b6aKy1fVBYub\nqxyPLXa+0p4s155+Gr79Nm/F0vzPIT7+5BM24d5Lrj1ZSoWGkuYqT4YLNnC13BizvbgH9ZQ2skLI\nF19A//62u6pdO44dgwEDIDPTJtjy5Qt+eKj98xBWHn8cevWyYwSdLFxoC0P++itERZ38MP2bqeLy\n0XDBsM9VofaZO3wYmjWzV+/o0MEuy/8cXD2nUHueRaWNLKVCgz+GCy4GFjl+fg5sBZYW94AqDK1d\naxtY8+dDu3YcPQrXXAPHjsGHHxbewFJB4LnnTlrUo4e9dvEr3qjPppTvaa4KMhUqwPjxMGLEf42m\n/GXdi3rRY6WUChVFvhixiJwJ3G6Muck3IeU5lvZkBbvsbGjfHh59FHr04MgR6NPHJtd586Csh7W9\nwv3MZVDbvx+aNrUXKc534bKNG+Gii+CPP06+vo3+zVRx+eNixOGYq0LxM3f8OLRoAdOmgacFF0Px\neRaF9mQpFRr80ZOVhzHmJ+Cc4h5QhZmICFi9Gnr0IDPTjjqLiYF33vG8gaUCrFo16NsXpkw5aVWL\nFnD11fDkkwGIS6kS0FwVHMqUgYdHZvPogwcDHYpSSvmVJ3OyhjvdjQDOBKoaY9xMh/ce7ckKHQcP\nQs+eUK8evPWWTaxFEe5nLoPexo3QuTNs23bS+M49e2wRjJ9/tsMHc+jfTBWXj+ZkhX2uCtXPXNYf\nW2l+WgQzFlblwsujC90+fzGMcCuEoT1ZSoUGf/RkRTvdymHHu/cu7gFV+MnIgMsug0aN4O23XTew\n4uPzjsPPf9Nx+QHWooUd9vnVVyetql0bbr8dRo8OQFxKeU5zVZCKat6Yh8/9gkfv3OfR9ikperkI\npVToK/KcLH/SnqwgdPx4nlZUWpptYLVpA6++akcPuhKqZ2BLlRMnIDLS5ar0dFslbPly+7cG/Zuq\n4vPHnCx/0p6swmX99gfN2pRnzvLqnHdJAdfzcCGUn7cr2pOlVGjwRwn3hYDbjYwxvYp78MJoIyvI\nHD1qxwQOGwa9epGSYq+h1KEDvPCCTYTuhFuSLI1eegmWLLFV+kH/pqr4fDRcMOxzVah/5qa2f50F\nB7uybFOjIj0u1J93ftrIUio0+GO44FbgMDDNcTsIbAGeddxUaXD8OFx3na1qccUV7N8PXbrYynOF\nNbBUeLjlFvjzT3tJNKWCkOaqIPd/U85lw+Zy/LzmaKBDUUopn/OkJ+sHY0z7wpb5gvZkBQlj4Kab\nYMcOWLiQf/4tR5cu9jpKTzxhG1j5JyrnF24Tl0uruXNtj9bq1XZoqH48VXH4qCcr7HNVOPToPHvb\nX3yf0ph33vW8uHE4PG9n2pOlVGjwR09WJRFp7HTARkCl4h5QhRhj4P774fffYcEC9qSUo1Mney2s\nnAYW2AaW80Tl/DdtYIWHfv3s/KyleolXFXw0V4WAoU+dwmefR7BlS6AjUUop3/KkkXUvkCgiiSKy\nEvgSuMe3YamgkZoKmzfDkiXsTKtMx44wYACMH69DBMNWZibcfbe90HQ+kZH2utNaaVAFIc1VISA6\n2g49flYHcCqlwpxH1QVFpBxwquPuJmOMXwZU63DB4JGUBBdfbJPjgw+evD7chnOUasbYcu6PPw7d\nu5+0Ojsb2rWDX37Rv7kqHl9VFwz3XBUu37N799qrRmzaBDVqFL69q+HooTwEXYcLKhUafD5cUEQq\nAg8Aw4wxvwINRKRHcQ+oQs/WrdCpE9x5p+sGlgozInDbbbYmvwsREbY3C1x2dikVEJqrQkfNmnbo\n8YsverZ9/utm6bWzlFKhwJPCF+8CPwLXG2NOdySy1caYtj4PTnuy/Kqg4hUVKthRZO6EyxlW5XDo\nEDRoAD//bH/mY4xtbL3zjv1nSami8FHhi7DPVeH0PbtlUxbnnn2CrTvLER1T9LdCKL8W2pOlVGjw\nR+GLJsaYp4AsAGNMJqCzccJQaiqYdesx2QZjYONGqFsXpk6F8uVtUnN3i4sLdPTKqypVgv794a23\nXK4WgcqV7Sbu3hPx8X6OWZV2mqtCSJNmkXSJ+JKp920KdChKKeUTnjSyjolIBRwXeRSRJoBe5CIM\nXcmHcOmlsHcvv/1m52A9/jjcfLPr4RpaPTDM3XwzzJvn9nRxejpccAHMmOH6PaHDeZSfaa4KJRER\nPPSQ8NzMqhw7FuhglFLK+zxpZI0FlgH1RWQO8DmgM3PCzdKlvMatsGQJvyTX4tJL4ZlnYPDgQAem\nAqZtW/j+e7dlJEXgscdspcmsLD/HptTJNFeFmDMevJTTIv9kzpg/Ah2KUkp5XYGNLBERYBPQB/g/\nYB7Q3hiT6PPIlP98+SUMHkxvPub7E2fSrZudkDxgQKADUwEXHV3g6o4doVEjePtt/4SjlCuaq0JU\nZCQP3ZbOUy9X0CI6Sqmw40nhi/XGmFZ+iif/sbXwha+tWQO9esH8+UinjlSvDtOnQ8+egQ5MhYrV\nq22D/M8/oWzZ/5aH8sR05Vs+KnwR9rkqHD9T5ugxzo7eyMhJsfS5N8Hjx4Xya6GFL4JXRoYd6u6i\n3pMqhfxR+OInETmruAdQQa5uXXjnHb7M7gjArFnawFJFc955cOqpbmtkKOUvmqtCkJQry6jX6vPE\nnAYh22hSoev4cVg+cy8vX7yAl2IeZkHcjfzWfjCsWxfo0FQY8KQnaxNwCpAEHMJWazLGmNY+D057\nsryqoBLtYKvFZWT4Lx4VPr77Dq65BjZvhnLl7LJQPtOsfMtHPVlhn6vC9TOVnQ2tWsFzz0HXrp49\nJpRfC+3JCg7Z2fBazbEMTH2RlObnEX3xWcS1qkdE2TLQpQvUr59n+61boUwZ7eUqTUqaq9w2skSk\nkTHmbxFx2X9vjEkq7kE9pY0s73KVlD7+2BaR++gj2yOhlEuffw5Vq9piGG5ccYW93X67vR/K/wQp\n3/JmI6s05apw/kzNng3TpsHKlZ5tH8qvhTaygkfy9zuo1bIqVKxY6LbzZp9g3F0pjHulOtde64fg\nVMD5crjg+46fbxpjkvLfintAFTzmz4ehQ2HJEm1gqUKsX2/LTRZg3Dh44gk4csQ/ISnloLkqDPTv\nDzt2wNdfBzoSVZrUOqu+Rw0sgGvrfcVvZdrwwQNrGDkydBv5yn8KamRFiMgooJmIDM9/81eAynvO\nYQ08+ihgzxredResWAHt2wc4MBX8Bg2CRYsKHG961llw5pn24tVK+ZHmqjBQpgw89BA8+WSgI1Hh\nKHn6YszuPSXbSadORL01jfeO9OTw/EXcdZc2tFTBCmpk9QdOAGWAaBc3FUrWrOETekH79rzxBowY\nYUeAtWkT6MBUSKhaFS67zLbOCzB+PEycCIcP+ykupTRXhY3/+z/45at0fl62N9ChqHBx4gQbez5A\n1tDb2fadF95XV1xBxJLFTP73BrKXf0piYsl3qcKXJ4UvLjPGLPVTPPmPrXOyvMFRpv3yfW/T7fnL\nmTwZPvsMmjYNdGAqpHzxBdxzD/z6q9sLFANcdRVcdBEMH65n+ZRrPip8Efa5KpTnIXlq8sWLWLOt\nFu9tLXiIRSi/Fjonyz9MxkE2t7+WfdsPU2XZu5zesar3dv7115irrkK++MJWbVFhyWeFL4KBNrK8\n4OuvoU8fzJtvEdHzCpo1g08/1eo4qhiys23L/J137NhAN379Fbp3t3Oz0tLc7y4uDlJSfBCnCnq+\naGQFkjayvOfg9hQaNzzBqsUHOfWyRm63C+XXQhtZvpe9cze7zuzB9yfO5Pxfp1CzXpT3D/LZZ3DG\nGXakhwpL/rhOlgpV2dkwciTZs+Zw1/IrAPjqK21gqWKKiIDFi6F1wRWx27SB88+Hhx+2/wS5uxV0\nOQGlVOlUuUE8wy9Zz9hbkgMdigphy254l2WVrqbLlmm+aWABXHKJNrBUgQoq4X6NMWZ+TnlcP8eV\nE4P2ZBWB6+tgZZPTlo6N1X9slX/89pvNP3/9Za+/5koon4lWJePlEu6lJleVls/Mob0HaVrnIIvn\n/MsZ/Zu73CaUXwvtyfK9vXshJgYqVAh0JCqU+bIna6Tj5wfF3bnyr9TUvD0FmZnQo0cEl18Ohw5p\nA0v5z+mnQ8eO8MorgY5ElQKaq8JMpZqVGdXnDx4ZFxnoUFSIqlnT/w2sb76xw+WVylFQT9angAHO\nAr7Kv94Y08u3oWlPVlE5n9n791/o1Qvq1YO334YoH/WWK+XO779D5862NyvaRY23UD4TrUrGyz1Z\npSZXlabPzNEjhuanCrNnwwUXnLw+/8iNUJrjqT1Z4WnGW9m8+JLw3VqhTJlAR6O8wWeFL0SkLHAm\nMAu4Kf96Y4yH12UvPm1kFc0F8jVfZ5/Pvv1C9+5w7rnw0kt2Ko1SgXDdddCyJYwadfK60vQPo8rL\ny42sUpOrSttn5u234c03YeXKAguaAqH12mgjy7vMrt1w7BjSqGFg47juOh5Z148Gw3pxyy0BDUV5\nic+GCxpjjhlj1gDnOZLUj8CPxpiVniYtEZkuIntFZJ3TsjgRWSEif4jIchGpUtzglZPnn2cO17Ht\nxwNceCFcfjm8/LI2sJSPHDwIP/xQ6GZjxsBzz9meVaV8wRu5CjRfBaOBA+13xwc6EFS5c+AAB9p1\nZfmdCwMdCTJ4MKPThvPE2KNkZAQ6GhUMPPkXvKaI/AxsAH4XkR9F5HQP9/8W0C3fshHAZ8aY5sAX\n/DeeXhWHMbaM22uvcS7fcn7vatx2G0yYUPiZP6WKLSkJeveG48cL3Kx5c3sN4xdf9FNcqjQrSa4C\nzVdBp0wZeOEFuP9+vcC5ciE9nZRzuvNuZg9aTrkz0NFA166UP7Mlk+q+wNNPBzoYFQw8aWRNBYYb\nYxKMMQ2A+xzLCmWM+RrIX26hNzDD8fsM4EoPY1X5HT8OQ4fCihV8OmENydThhRfg7rsDHZgKey1b\nQv36sGxZoZuOHm3/USromllKeUGxcxVovgpWnTrZy/I9PVq7w5WTrCz+7dKHT3a1o8OXT1K/fqAD\ncpg0iWu2Pc3y9/4lKyvQwahA86SRVckY82XOHWNMIlCpBMesYYzZ69hXMlCjBPsq3UaPhm3bmHXz\nKgYOiwXgf/8LcEyq9LjxRjthohBNm0LPnvD8836ISZVm3s5VoPkqKDw98QQvPJfN9lnuR3/GxdnR\nG863+Hg/Bqn8xxgyBt7G2vUVqPbOK5zZLoiG7Zx6KpE9r2BNv+e04JjyqJG1VURGi0hDx+0RYKsX\nY9DZn8Vkht/HxI5LeeTxCnzxRaCjUaVOv37w5Zf2giSFGD3azhHUywgoH/J1rgLNVwHRsEkkdw9M\n4fahxzH/prvcJiVFL3hemszedgFbH59Hj95BWOZ/zBjklCaBjkIFAU+KTN4AjAcWYBPMV45lxbVX\nRGoaY/aKSC3gn4I2HjduXO7vnTp1olOnTiU4dPg4cQLuHl+NVatg9WqoWzfQEalSJyYGrrwSZs2y\nkyYK0Lix3XTyZDtfEP478+xOKJVkVgVLTEwkMTHR14fxdq6CIuQrzVW+NWJaE9ovLMecK+cz8Msb\nAx2OCiQRblj1f5QrF+hA3Gjc2N5UyPF2rnJbwt1rBxBpCCw0xrRy3J8EpBhjJonIQ0CcMWaEm8dq\nCXcXDh+2pbHT0uDDD6GKo95VKJWwVWFiwwb7Rjz//EI33bYN2rWDP/+EqlUL37W+n8OXN0u4e1Nx\n85WWcPePH1dmcHmXo/w6ZTW1bi788mfB+nppCXelQoPPSrh7g4jMBVYDzURku4gMASYCl4rIH0AX\nx31VmN9/hxMnSE62E4ErVIClS/9rYCkVEC1betTAAmjY0M4ZfPZZ34akVHFovgp+7TpGc+OgLG66\nJxpz9Figw1E+FB+vc+xU6PN5T1ZJaE+WwwcfwG23se611fS89xRuvNHOcck/1CpYz9oplWP7djjj\nDNi0CapXL3hbfT+Hr2DtySou7cnyn2PH4MLzT9C3fyT33VfwtsH6emlPVuHy/O1SU2HfPqR5s6D8\nexYkLQ369oUlS+wlCVRo8XlPloicdJra1TLlA8bYSSx3383iR76lyy2nMGmSvcCrXgNLhaIGDWy9\nDHmxgk0AACAASURBVL2GiPI2zVWlQ9my8O78SJ56CtasCXQ0yueOHSOtSx+WXTuj8G2DUGyFo8Sm\nbuXDDwMdiQoET4YLvuThMuVNx4/DnXdi3nyL529cz80Tm7BwIfTvH+jAlCqZUaNg+nTYtSvQkagw\no7mqlGjYEKZOtT0Ee/YEOhrlM8aQfu0trPk9hojHHg10NMXz+ee8ltZfL2FSSrntvBSRDsB5QHUR\nGe60KgYIwpqZYWb8eLI2beHOs3/gmwXl+PZbSEgIdFBKFeDAAY8qWtSrBzffbHtkp0/3Q1wqrGmu\nKp1694b16+01+FauhEolvSKaCjqZjzzB9sXr2PPMKoZcFqIf5W7diDt+B/Fbvuf778/irLMCHZDy\np4J6ssoClbENsWinWzqgl7z1sZQbH+ByFrM9uRzffKMNLBXkjIEzz4SNGz3afORIWLwYfv3Vx3Gp\n0kBzVSn18MNw+ulw3QXbODFzTqDDUV7Un3mkPzuVj25YyJBhnreg8xfMCHixjMhI5LbbeKLeq7zy\nSoBjUX5XaOELEUkwxiSJSGUAY8xBv0RG6S18sW4dXHWVvU2c+N9kyfj4gi+uqNcVUgE1YoQd5vrM\nMx5t/sor8NFHsGKF6zmGwTppXZWcLwpflIZcpZ+Jkx07Bj06H6T2T0t4a0YEEX3/a1cH6+ulhS8K\nN0TeIv6Sdjy9vDURju6Awv4HgpP/D8r/mID8n7R/P9mnNKVH879Y9G3V3Oejgl9Jc5UnjazTgVlA\nzvmA/cBgY8xvxT2op0pjI+vdd2HYMHjhBRgwIO+6YE0YSgH2AlgXXgg7d0JUVKGbZ2VBq1bw3HNw\n2WUnr9f3e/jyUSMr7HOVfiZcy8yEyy86SNPfP+b1l7OIuOH/gOB9vbSRVTgR+3etUMG7+y1OQ80r\nBg+2Ce/++728Y+VL/rhO1lRguDEmwRiTANznWKa84cQJmDKFE0eyeOgh2xmwYsXJDSylgl6zZtC8\nOSxa5NHmUVHw1FM25xw/fvL6uLiTr5MSNENAVDDSXFVKVawIixIrs7HFVQy9qzzHn30h0CEpL/B2\nAwts48mYgm+FNcKK5cEH7fVLVKniSSOrkjHmy5w7xphEQKeYekNaGvToQco7K7j8CuGHH+D77/Vz\nqELYjTcWqZpFz55Qo4brhxSUDH2SBFWo01xVilWuDEsTK7L9zN5c8+SZHN6lY+eDVam80HDLltCl\nS6CjUH7mSSNrq4iMFpGGjtsjwFZfBxb2NmyAc85hXexFnLVzAae3LcPy5VCtWqADU6oE/vc/aNrU\n4zE6Ina44NixtjihUiWguaqUi46GRZ9VoPylF9B9QLj/1x66UlPznTjLNlRJ/TvQYSnldZ40sm4A\nqgMLHLfqjmWquObNw3TsxJsXvkWXz0by6KPCs8/q1cBVGKhUybaainC17LZt7fVuRo3yYVyqNNBc\npShbFubMEc48097fvDmw8ajCHXpwPNO42eWwcaVCWaGFL3I3FIkGTDhWbPIrYzh0093csW8ca/+K\n5/334bTTPHtosE7iVaqk0tLs5+Cjj+DsswvfXj8Loc0XhS+c9h22uUrf90VTqZItnpAjWCrwlvbC\nF87v42PTZrDvzvGcefRb9pqaLrcJZHy+MGeOHd7au7fvjqG8w+eFL0SklYj8DPwGbBCRHx1VnFQx\nbNwknPPdi2THxvP99543sJQKZ7GxMGkS3HabrQWjVFFprlL5HToEiYlQsya8cOWXdEr9INAhlUr5\n52DFxdnlJz77ksw7H+T5SxbzDzUL3kkYqXDioF4zq5TwZLjg62jFJq+YPRsuugjuvRdmzNAr1Cvl\nbOBAe3bvtdf+n737Do+iWh84/n0TAiFAIKEmIYTiDwuCKKCgKE0FpdmoglwUsStwsWEhEbx29KpX\nFESlCCpioYpeFWxwEQUpgqJIEkpoARJ6yfn9MZO4CbvJJtnN7G7ez/PMk90pZ96Z7M67Z+bMGacj\nUUFKc5U6TYcOsGwZvLmuLRGc4NjjT+rlwDJW8B6szEzg11853Ls/KWe9x/g5ZzsdYtkxhmtSWpL9\nv1/Zts3pYJS/ae+C/nTqFGRmcuQI3HYbjBsHX35pdcBWjFtWlApexfgxIwITJ0JyMqSmFj5vYd27\nl4ueqpQ7mquUW40awQ+rKvMp19Dhxd5s731H/naEqswtm/o742s8z+Nfd6JSpdOP6blXu0KOCGF9\nbuDxpKnMnOl0MMrftHdBf9m+HS6/nF9GvE3r1pCdDStXQosWTgemVBkZOxbeeKNYi5xzDowaBbfe\nWnj9rKhnnWgX7+WS5irlUdWqcIxIeo4+kwu//BfLL7jDenC68il33bO7qzC1GncNI38anDet4DHd\nyXvn3J3E8+mJu0GD6Jwxk+lTc/Siaogrbu+Cc4BaaI9NhVu4kJzzWzEhcgyXLxrFww9bNzomJenZ\nd1WOtGtXrGdm5br/fquS9OabfohJhTLNVapQMTHwaHIE2w7H0Pm3idRIrKp518dO657dQ4WpYkWo\nV6/s4/OGu5N44MPfa+eeS8W4WM7Z8w2//+6TkFWAKrR3QREJB54xxowuu5DyrT+4ehc8fhzGjGH7\nrKX8I/5zDkbE8O67VlMFKLrHmtJOVyqgnDplffjnzy/2Jdx166BTJ/jpJ2jQoPir1u9KYPN174Ll\nJVfp59p3Nm6Ea66B336zUndERNmtO5R7Fywvn9FSb+dzz3F83e9UnDrZZzEp3/Nr74LGmFNA+5IW\nXu5Mn84n38Rywcn/cUmPGL755u8KljeKus8kZNsoq9AUHg7/+EeJrmade67VQcw//qG9Daqiaa5S\nxXXWWfC//1mvr7gCdu92Np6Ql57udAQ+VeomhQMGUDE60m/xqcBQ5HOyRGQikADMBg7ljjfGfOTf\n0ILrSpYxcNedhs8Ww4wZwsUXnz5PeTnDo1SezZvhoousex8qVSrWoqdOQZcu1g+gRx4p3mr1uxbY\n/PGcrPKQq/Rz7Xsi1vFlxgz4aPYpLjj7iHUDlz/XWc6uZB154TX2PvUGsVtWEVXVm7tUgpN+P0OP\n35+TBUQCe4HOQE976FHSFYYqEehyubB6tfsKllLlUuPG0K0bJWl4Hh5u3cv4yivw3Xd+iE2FGs1V\nqkTGj4fnn4eul59k5llPWCeHlE8cn/khB8c8yRvdPqFyldCtYCnlTpFXspwUTFeyvKFnOZQqvgUL\nrIcU//QT1K7t3TL6XQts/riS5SS9khW8XPfpml8M116exXWH3+Xpec0I79zBP+ssJ1eyTn3xFdk9\n+vOvjl/w1MLzCA93NjZ/0+9n6CmLK1lKKeWY7t2tBxXfcAOcOOF0NEqpUNXiPGHFxuqsOrMfPbqd\n5NAHC5wOKWiZFT9yqFd//nXeB4yfF/oVrNJYtw7WrnU6CuUPWslSSgW8ceOgWjUYMcLpSJRSoaxm\nTfhsRU3iurXgikF1yHz/C6dDCkpfvb+bcUlTGPt1RypWdDqawPbNN/DUU05HofxBmwuWIb2UrFTJ\nHTgAbdvCvfdazQcLo9+1wKbNBUu6Hv1c+5qnfZqTA/ffvIfPV9Tg8y8rEBfnw3WWg+aCp07BoUMQ\nHe10RGWnpN/Po9ffSMvPn+WnjASqVPF9XKrk/N5cUETqisgUEVlkvz9HRG4p6QqVUqokqle3Hrk1\nbhx8/LHT0ahAo7lK+VJYGDz/di0GDKrApZfCtm1ORxRcwsPLVwWrNCJrRDIy/n0WaOvUkONNc8F3\ngMVAvP3+d0Ab7SilimfNmuL3xV5AkyZWReu226wmFkq5eAfNVcqHRGDMGOt406ULZGQ4HZEKJrGx\nXj5La8AArj82k/feK/MQlZ95U8mqZYz5AMgBMMacBPTxoEqp4klMhP/8B/buLVUxF1wAM2daHWEs\nW+aj2FQo0Fyl/OL++2HgQLj8ctizx+loAlB6Oic/+6/TUTiu4AOKwWo+6Drs2+dmwU6diD26jdTP\nf+PAgTINWfmZN5WsQyJSEzAAItIW0I+BUqp4YmKsrgLffbfURV1+OUydCr176zO0VB7NVcpvHnsM\nevWCrs3SObh8ndPhOMLdlZmW1f/iwPkdmP7QeqfDc1xmZv4KVWamlwuGhxPWvx8zus/Kq5yp0OBN\nJWsUMBdoIiLfA9OAe/walVIqNA0bBpMm+eTu/auughkz4NprYenS/NMKnlH0qsmGCnaaq5TfiMCT\nT8L5zU7Qr9MuTqbvcDokvytYqYICV2Y2/cGnWR153vyTjh/f52ywwW7gQM5O/UzvYwsxhfYuKCJh\nQFtgBXAmIMBvxpgyeVqN9i6oVIgxBs49F159FTp18kmRX30F/frBG2/Addd5t4x+F53l694Fy0uu\n0s+t7xV3n544AT3O/pPGWat57a+rkCpRxV9nkPQuWOi++e03si68nNFZj/Fo6nAaNPBiGeV5/xhj\nfbi0v/uA4tfeBY0xOcB/jDEnjTHrjTHryippBSt3l9Nzh5gYp6NTymEiVh/sPuwesHNnWLzYKnbC\nBE3w5ZHmKlVWIiJg9k+N+eFEG164eE7IHHDc/Xbx9JvFHDvO3rbdebbqE3xQfThJSfo7p9REtIIV\ngop8TpaIPA8sAz4q68tKwXglS8/iKFWEkyet/n193Pg8Lc265euyy+Cll6wfQ57o99RZ/nhOVnnI\nVfq59b2S7tP0TUe5qFk279zzM1e+0LV46wzAK1nF2Q85OfDiw7sYcn8datXyb1yhRr/DwaW0ucqb\nSlY2UAU4CRzFaoZhjDF+bznqKXE1bNiQ1NRUf69eKZ9LSkpiy5YtTocRsg4cgBtvhP374YMPID7e\n/Xya6Jzlp0pWwOUq369HP7e+Fhubv8e3mBjvOyxYOmcP/e6sybLlQqNG3q8z2CtZquS82c85OVbL\nwUqVyiYm5Vlpc1WFomYwxlQraeGFEZEtWD0/5QAnjDEXertsamoqwXaFSymwvrDKf6pXh7lz4amn\noHVrq6v3jh2djkqVBX/lKihdvlKBrWCFqjiH6A7X1+KhdOte0O+/h6ji356l1Gn++U/riSejRjkd\niSotb3oXRERiRORCEbksd/DBunOAjsaY8zVhKaV8JSzMeubx1KnQvz/8619wSp+WVC74KVeB5ivl\nwX33wTnnwO23h/CVoN9/dzqC8sMYbg2bwuwZx5yORPlAkZUsERkGfAMsBlLsv8k+WLd4s36llCqJ\nK66AH3+Ezz+3OsfQFsahzY+5CjRfKQ9ErKdSrFwJ06Y5HY2PnTzJvpvu5a82fTh26KTT0YSEgo8X\nOe1xIiKc/eM0zvzrMzZtciRE5UPeJI37gDZAqjGmE3A+sN8H6zbAFyLyo4jc6oPylFLBZvLk0x9y\n5UOJifDll3D11dCmDcya5bdVKef5K1eB5itViCpV4P33YfQ/Db8t2ux0OF4p2Jvgab0CZmWxt30v\n1ry/gW/HLaVSlSLvLlFeKPjAYtf7AXPJjQO5t/ZMzVchwJtK1lFjzFEAEalkjNmI9RyS0rrEGHMB\ncDVwl4i090GZAS01NZWwsDBycnJKXVajRo346quvvJp36tSpXHrppXnvq1Wr5rPOF5566imGDx8O\n+Hb7ANLT04mOjtb770JZRIT1hE8/Cg+HBx+ERYsgJQUGD/br6pRz/JWroBzmK1U8zZvD+P7r6HfN\nUY7uPOB0OEXaty//j/1896alppJ59iXM/6UBJz5ZyE331nAsznLphhtosf0zPp2RHbpNUMsJb05N\nbBWRGsAnWGfy9gGlbnhjjNlh/90tIh8DFwLfFZwvOTk573XHjh3pGOB3sTdq1IgpU6bQuXNnt9Od\n6vjAdb3Z2dlFzr906VIGDRpEenp6ofM9/PDDHtdTXAX3XWJiIllZWSUuTwWBgQPh0Ufh55/hggv8\nuqpWreCnn2D0aOu+rcI+qsXpYUwVbcmSJSxZssTfq/FLrgLv8lWw5Srle8Nfac5/F/3E6Ev/x6u/\nXeHzx1SUhZzjJ9l1fjfeqnAbN6y+j6ZnBt82BL2aNQnvdBlDd3/KgQODqKF13DLj61zlTe+C19ov\nk0Xka6A68FlpVioiUUCYMeagiFQBrsRqQ38a18Slyo4xpsgK06lTpwgPDy+jiFRIqlgRRo6EZ5+F\n997z++qqVIGJE63mg8OHw9ChkJx8+jMgg/C3UUArWOlISXF7uC8Vf+Qq8D5faa5SIjD527M5v2Em\nXe5bwrUvd3I6pGKTiAosePh77rw1Vn/cO0gGDODuGTOgxiCnQylXfJ2rvOn4okHuAPwFrAbqlWqt\nUBf4TkRWAcuBecaYz0tZZsDJyclh9OjR1K5dmzPOOIMFCxbkm56VlcWwYcOIj48nMTGRxx57LK9p\n3ObNm+nSpQu1atWiTp06DBo0yOurOpmZmfTq1Yvq1avTtm1b/vzzz3zTw8LC2LzZaje+cOFCmjVr\nRnR0NImJiUyYMIHDhw9z9dVXs337dqpVq0Z0dDQZGRmkpKTQp08fBg8eTI0aNZg6dSopKSkMdml/\nZYxhypQpJCQkkJCQwAsvvJA3bejQoTz++ON575cuXUpiYiIAN910E2lpafTs2ZPo6Gief/7505of\n7tixg969e1OzZk2aNm3Km2++mVdWSkoK/fr1Y8iQIURHR9O8eXN+/vlnr/aXctjw4fDf/0KBz6k/\n9ewJq1fD2rXQrh1s2FBmq1Z+4qdcBeUkXylLwY4J3A2ndVbgokZ8FO+9dYTbX21G6pK/yiTmgvdX\nFRZfUUTglvu1guW43r3h/vudjkKVkjf3ZC0A5tt/vwQ2A4tKs1JjzF/GmJZ2d7jNjTFPl6a8QDVp\n0iQWLlzIL7/8wsqVK/nwww/zTR8yZAgVK1Zk8+bNrFq1ii+++CKv4mCMYcyYMWRkZLBhwwa2bt3q\n9ZnSO++8k6ioKHbu3MmUKVN466238k13vUI1bNgwJk+eTFZWFuvWraNz585ERUWxaNEi4uPjyc7O\nJisri3r1rN8qc+fOpW/fvuzfv5+BAweeVh5Yl1v//PNPFi9ezDPPPFPovWO5y06bNo0GDRowf/58\nsrKyGD169Gll9+vXjwYNGpCRkcHs2bMZM2ZMvsu68+bNY+DAgRw4cICePXty1113ebW/lMOqVbMq\nWvPnl+lq69a1nql1221w2WXwn/+EcBfM5YPPcxWUn3ylLAU7JnA3uOuswNVFg/6P0b02MWBIBCdO\n+D/mgvdXFRWfCgJVqkCn4LsSqvIrspJlJ5UW9t//w2qLvsz/oQW/2bNnM2LECOLj46lRo0a++5d2\n7tzJokWLePHFF4mMjKRWrVqMGDGCWXZ3Mk2aNKFLly5UqFCBmjVrMnLkSJZ60QtbTk4OH330EePG\njSMyMpJmzZoxZMiQfPO4diRRsWJF1q9fT3Z2NtWrV6dly5aFlt+uXTt69uwJQGRkpNt5kpOTiYyM\n5Nxzz2Xo0KF52+QNT51cpKens2zZMp555hkiIiI477zzGDZsGNNc+sxt3749Xbt2RUQYPHgwa9as\n8Xq9ymHjxlkPnCljIlb97vvvredqde8OGRllHobyAc1VKpD8c87F1GhWn8ceczoSz46+9wm7/zXZ\n6TCUClnFfu6HMeZn4CI/xOI7ycnur/F7uhLkbn4ftK/fvn17XnM4gKSkpLzXaWlpnDhxgri4OGJj\nY4mJieH2229nz549AOzatYsBAwZQv359atSowaBBg/KmFWb37t2cOnWK+vXru11vQXPmzGHBggUk\nJSXRqVMnli9fXmj5rtvjjoictu7t27cXGXdRduzYQWxsLFFRUfnK3rZtW9773KttAFFRURw9etRn\nPR0qP3P43r6mTa2KVuvWUMR5BhUkgiJXqZAVFi5MnQrvvguflfrOwNJzbVJYWY4wOeIOdt80io9+\nP9fp0JQKWd7ckzXKZRgtIjOB0v9q9qfkZPfX+AurZHk7bzHExcXl650v1eVpqImJiURGRrJ3714y\nMzPZt28f+/fvz7v6MmbMGMLCwli/fj379+9nxowZXnVlXrt2bSpUqJBvvWlpaR7nb9WqFZ988gm7\nd++md+/e9O3bF/DcS6A3vQcWXHd8fDwAVapU4fDhw3nTduzY4XXZ8fHxZGZmcujQoXxlJyQkFBmP\nUt6IiIAnnoCPPrLe33oruHzcVIALylylQlrt2jBjhtXBjg/ONZZKbpPCU6vXkl63DTFhB/j+lVUM\nf7uds4GpIr30knXbsgo+3lzJquYyVMJq797bn0GFir59+/Lyyy+zbds29u3bxzPPPJM3rV69elx5\n5ZWMHDmS7OxsjDFs3ryZb775BrC6Wa9atSrVqlVj27ZtPPfcc16tMywsjOuuu47k5GSOHDnCr7/+\nytSpU93Oe+LECWbOnElWVhbh4eFUq1Ytr7fAunXrsnfv3mJ3oW6MYdy4cRw5coT169fz9ttv079/\nfwBatmzJwoUL2bdvHxkZGfz73//Ot2y9evXyOuRwLQ+gfv36XHzxxTz88MMcO3aMNWvWMGXKlHyd\nbriLRaniuvhi6++xY3DRRdopRhDRXKUCTocOcOed1tMqTp1yNpa9k+aQ1aYzb0TfT6sN79L/tura\nk2oQiDm5m9dfdzoKVRLe3JOV4jI8aYx5N/eBj+p0rldjbr31Vrp27cp5551H69atuf766/PNO23a\nNI4fP84555xDbGwsffr0IcO+IWTs2LH89NNP1KhRg549e562bGFXfV555RWys7OJi4vj5ptv5uab\nb/a47PTp02nUqBE1atRg0qRJvPvuuwCceeaZDBgwgMaNGxMbG5sXlzfb36FDB8444wyuuOIKHnjg\nAbp06QLA4MGDadGiBQ0bNqRbt255la9cDz30EOPGjSM2NpYJEyacFuusWbP466+/iI+P5/rrr2fc\nuHF0KuTGUKeeSaZCw9SpVu/yl11mnY1WgU1zlQpUY8ZA+LFDpPT40dE4fjhyPjPv+oGHNgyhYSPN\nj0HBGAa/ciFbF6/HiztGVICRos72i8g8wONMxphevg7KZd3GXXwiolcpVFDSz64X5syxnhr8r385\nFoLI3z0NrlkDffpYZ6T//W+oXNmxsEKG/T3w6a+8QMxVvl+P9oAZCAr+H2JjT+/Rr+ADzTPW7qbN\n+Sd49f40rolshxnru39kwXjcfU70sxOYvPnscP/9fLKoIum3Pck995RpeOVeaXOVN80FNwNHgMn2\ncBD4E3jBHpRSynfatYPXX4cC9+w5pUUL+PFHyMqyQtu0yemIlAeaq5QjCnah7q4b9XrNazPnzf18\n/txqj+UUfN5VcZ/Rlct61pfJt1xMTCk3UvmFu0cGnNYF/+DBdNs1jalvOdzeVBWbN1eyVhpjWhc1\nzh/0SpYKNfrZ9dKoUXD0KLz2miOrd3d20VunnYVUp/HTlayAy1W+X49ejQgEpbly9OWdc7i87g2k\ndfyTxA6NvVqmqHnyjTt4kJ13pnAofS+Nv37rtOVV4CuYf2JiYO//XcSwtMd59IfuNGrkXGzlTVlc\nyaoiInlHAhFpBFQp6QqVUqpIjzwCs2fDxo2OrL6wB5L+739Qvz489ph1I3txH1Sq/EZzlQp4XV6z\n7q8+dUVXdq/14UP5jCFrymwy653N0tm7+PmGp3xXtipTBfPPvn0gw4Yxqc2bWsEKMt5UskYCS0Rk\niYgsBb4Gyv6poUqp8qNmTXjwQWsIMBdeCCtXwpIl0KsX7N/vdETKprlKBY3Nlw3lcKv2bPvur2It\nZzUFzD90rfo925u0J/228czo9i5X7pjKDXfV9VPkyhH9+xN+5hl6KTvIFNlcEEBEKgFn2W83GmOO\n+TWqv9erzQVVSNHPbjEcPWpVsp57DipWdDqa05w4Af/8p/Wg0U8+gXPOscZrk66i+aO5oF1uQOUq\n369HP1uBwF1zroJNhAubR1IEM9bw1Q2vUe+ztzk7awUSJiX+/77b5iV2HI+l69Qbad7S2Qe7K9/T\n771zSpurPFayRKQNkG6MybDf3wRcD6QCycYYv991oJUsFWr0sxt6pk6F0aPhjTfguus0IXrDl5Ws\nQM5Vvl+PfraClev/LreSFRsLOfv2c4AaQMnv5zxyRHs9DWX6vXeOP+/JegM4bq/kMuBpYBpwAJhU\n0hUqpVQoGTIEFi2ynqn1yCNOR1Muaa5SQWnfPthvauTde5OvguX6q/rgQY7M+y9b733W7a9trWAp\nFZgKq2SFu5wB7AdMMsbMMcY8Bpzh/9CUUio4tG5tdfP+/ffWe+38okxprlIBz/VeKii8W/XjR3PY\nUzGevZXiyKpYk6PRtVl1TTJL5x4g59iJsgtaBQR39+FV0S59gkKhlSwRqWC/7gJ85TKtgpv5VTkQ\nFhbG5s2bvZo3JSWFwYMHA5Cenk50dLTPmsrdcccdPPnkkwAsXbqUxMREn5QL8N1333H22Wf7rDxV\nPtSpA198AZUqFf68G2+ec6OKRXOVCniuPcaBmytXLsIjwti2ZBNr31rJyukb2bjyEK2PfMeNW54k\nLDLw7k9V/lWwt8GtW+HY4ZNkZTkdmSpKYZWsWcBSEfkU6wGP3wKIyBlYzTCUBzNnzqRNmzZUq1aN\nhIQEunfvzve5p7gdNHXqVC699NJSlSFSvKapufMnJiaSlZVV5PLexjhx4kQecWmbVdy4XBWsOLZv\n354NGzaUuDzlJ7/+CsuWOR1FoSIirP463n0XatWy/moX736nuUqFlPBwOO+SqnS8MYHO/WrT8oKw\nQOz7RzkkYcEkXmIEU6c6HYkqisdKljHmSeCfwDtAe5e7esOAe/wfWnCaMGECo0aN4tFHH2XXrl2k\npaVx1113MW/evGKXderU6U/3djfOW8aYUlVGcsvwJ29izMnJ8ek6S7tPVBlJS4MBAwiG03cDB8KX\nX1rP0hoxwuqJUPmH5iqlVLnSowc38i5vPrePkyedDkYVptDnZBljlhtjPjbGHHIZ97sx5mf/hxZ8\nsrKyGDt2LK+99hq9e/emcuXKhIeHc/XVV/P0008DcPz4cUaMGEFCQgL169dn5MiRnLB/geU2qxO1\nPQAAIABJREFUe3v22WeJi4vj5ptvdjsOYP78+Zx//vnExMTQvn171q5dmxfH1q1buf7666lTpw61\na9fm3nvvZePGjdxxxx0sW7aMatWqEWu3WTp+/DijR48mKSmJuLg47rzzTo4d+7vX4+eee474+Hjq\n16/P22+/XWiFZMuWLXTs2JHq1avTtWtX9uzZkzctNTWVsLCwvArSO++8Q5MmTYiOjqZJkybMmjXL\nY4xDhw7lzjvvpHv37lSrVo0lS5YwdOhQHn/88bzyjTE89dRT1K5dm8aNGzNz5sy8aZ06deKtt97K\ne+96taxDhw4YY2jRogXR0dHMnj37tOaHGzdupFOnTsTExNC8efN8FeahQ4dy991306NHD6Kjo2nX\nrh1//VW8554oL3XrBldcYfWbHgRatLCep/X773D55ZDhw+eOqvw0Vymlyo34eBbQnTsqTOb9950O\nRhXGm4cRKy8tW7aMY8eOcc0113icZ/z48axYsYI1a9bwyy+/sGLFCsaPH583PSMjg/3795OWlsak\nSZPcjlu1ahW33HILkydPJjMzk9tuu41evXpx4sQJcnJy6NGjB40aNSItLY1t27bRv39/zjrrLF5/\n/XXatWtHdnY2mXZj8AcffJA//viDNWvW8Mcff7Bt2zaeeOIJAD777DMmTJjAl19+yaZNm/jvf/9b\n6PYPHDiQNm3asGfPHh599FGmFriWnVtBO3z4MPfddx+LFy8mKyuLH374gZYtW3qMEWDWrFk89thj\nZGdnc8kll5y27oyMDDIzM9m+fTvvvPMOw4cPZ9OmTR5jzY1l6dKlAKxdu5asrCz69OmTb/rJkyfp\n2bMn3bp1Y/fu3bz88svceOON+cp+//33SUlJYf/+/TRp0iRfM0blYy+8YN34tHCh05F4JSYG5s+H\njh2hTZuAb+2olFIqCDzLA/xj/4u8+fJhp0NRhdBKlg/t3buXWrVqERbmebfOnDmTsWPHUrNmTWrW\nrMnYsWOZPn163vTw8HBSUlKIiIigUqVKbsdNnjyZ22+/ndatWyMiDB48mEqVKrF8+XJWrFjBjh07\nePbZZ4mMjKRixYpcfPHFHuOZPHkyL774ItWrV6dKlSo89NBDzJo1C4DZs2czdOhQzj77bCpXrkxy\ncrLHctLT01m5ciVPPPEEERERXHrppfTs2dPj/OHh4axdu5ajR49St27dIjua6N27N23btgXI2y+u\nRIRx48YRERHBZZddRvfu3fnggw8KLdOVp2aQy5Yt49ChQzz44INUqFCBTp060aNHj7x9BHDttdfS\nqlUrwsLCuPHGG1m9erXX61XFFB0N77wDt9wC27c7HY1XwsIgJQVeew1697bG6TNPlFJKldTWmBYs\n3NeOFisma4dKASwkK1meevUq7lBcNWvWZM+ePYXeM7R9+3YaNGiQ9z4pKYntLj8Wa9euTURERL5l\nCo5LTU3lhRdeIDY2ltjYWGJiYti6dSvbt28nPT2dpKSkQit6uXbv3s3hw4dp1apVXllXXXUVe/fu\nzYvVtdlcUlKSx8rI9u3biYmJobLLAzuSkpLczhsVFcX777/PxIkTiYuLo2fPnvz222+FxlpU74Ex\nMTFERkbmW/d2H/wI37Fjx2nrTkpKYtu2bXnv69Wrl/c6KiqKgwcPlnq9qhAdO1oPpVq1yulIiqVn\nz7+7eB88GLKznY1HKaVUcMrMhOtWPc6/J1bSDpUCWEhWsgr25lXSobjatWtHpUqV+OSTTzzOk5CQ\nQGpqat771NRU4uPj8967u+ep4LjExEQeeeQRMjMzyczMZN++fRw8eJB+/fqRmJhIWlqa24pewXJq\n1apFVFQU69evzytr//79HDhgdcgVFxdHenp6vlg93ZMVFxfHvn37OHLkSN64tLQ0j/vhiiuu4PPP\nPycjI4MzzzyT4cOHe9z+wsbncrfu3P1apUoVDh/++5J6RjFujomPj8+3D3LLTkhI8LoM5QcPPADd\nuzsdRbH93/9Zf6Oi4IIL4KefnI1HKaVUkGrZEm6/3ekoVCFCspLllOjoaFJSUrjrrrv49NNPOXLk\nCCdPnmTRokU89NBDAPTv35/x48ezZ88e9uzZw7hx4/KeJeWtW2+9lddff50VK1YAcOjQIRYuXMih\nQ4e48MILiYuL46GHHuLw4cMcO3aMH374AYC6deuydevWvI42RIRbb72VESNGsHv3bgC2bdvG559/\nDkDfvn1555132LBhA4cPH867V8udBg0a0Lp1a8aOHcuJEyf47rvvTutRMfcq2K5du5g7dy6HDx8m\nIiKCqlWr5l15Kxijt4wxeev+9ttvWbBgAX379gWgZcuWfPTRRxw5coQ//viDKVOm5Fu2Xr16Hp/9\nddFFFxEVFcWzzz7LyZMnWbJkCfPnz2fAgAHFik8pV5MmwZNPwlVXwYQJ4OMOM5VSSinlMK1k+dio\nUaOYMGEC48ePp06dOjRo0IDXXnstrzOMRx99lNatW9OiRQvOO+88WrduXeyOElq1asXkyZO5++67\niY2NpWnTpnmdTISFhTFv3jw2bdpEgwYNSExMzLs3qXPnzjRr1ox69epRp04dAJ5++mnOOOMM2rZt\nS40aNbjyyiv5/fffAejWrRsjRoygc+fONG3alC5duhQa18yZM1m+fDk1a9Zk3LhxDBkyJN/03KtR\nOTk5TJgwgYSEBGrVqsU333zDxIkTPcbojbi4OGJiYoiPj2fw4MG88cYb/J992WDkyJFERERQr149\nhg4dyqBBg/Itm5yczE033URsbCwffvhhvmkRERHMmzePhQsXUqtWLe6++26mT5+eV7Z2/66KKybG\nao7crx/s3m11lhgerg8rVkopVTK5eaWwQXNL2RN/P/eoNETEuItPRPz+vCal/EE/u350/DjB+MTO\nEycgORnefBNeesl6xlaof0Ts70HInKHwlKt8v57Q/2yUB5IimLH6j1S+c/w4XHMNTJ8ONWu6n0eP\nH8VX2lylV7KUUsFvxw5o1sx6KFWQiYiwmg7Onw+5T3PQZ2oppZTyVsUIQ8+Iz3joQa1FBRKtZCml\ngl9cHDz8MHTuDEX0VBmo2rSBn3+GyEhrc7TJh1JKKa/k5HDr1sepMWcK337rdDAql1aylFKh4eab\nYdw46NQJfvzR6WhKpFIlOHIEVq6ECy+Edu2sHghdez3VrnqVUkrlEx5OhXemMD7nYR4esIX9+50O\nSIFWspRSoWToUOupv1dfDYU8SiHQtWoFy5ZZz1y++mqrl15tQqiUUsqj5s2plDyGmSdu4I6hR/X+\nqwCglSylVGi55hpYvBiqVnU6klIJC7MqWRs2QOXKcM45VotIpZRSyq0RI0i4pBHjs+87rZcLb3og\n1ObpvqWVLKVU6LngArj8cqej8ImYGHjxRVi9GvbsscYlJ1vdvyullFJ5RAh/ZwpNqu9BsrPyTcrM\nzN/03JtBm6eXjlaylFIqCDRoAJMnW6+3bYOmTWH4cOtKl1JKKQVAdDTMmQPVqzsdSblXwekASiIp\nKUkfAquCUlJSktMhlG+vvAJRUTB4cFA+UyvX5MlWt+8TJ0LHjnDWWTBkCNxwg5VflVJKKeUsx65k\niUg3EdkoIr+LyIPFWXbLli0YYwJyAOdj0CFwhy1btvjnC6W8c/758MEHcMYZ8PzzsGuX0xGVWJ06\nMHYspKXBiBEwd651tWvAAHjvPW3m4SulyVVKKRUINm6EW2/VvFDWHKlkiUgY8CrQFWgGDBCRs5yI\nRZ1uyZIlTodQ7ug+LyPt21udYsyZA+vXs6RxY7j+ejh1yunISqxSJbj2WqszxT/+sK5svfsuJCVB\nhw7WA46//hoOHXI60uBT3nNVKB6XQnGbIDS3KxS3CZzZrsRE6PfjP3kjcTxvv3KQ48d9v45Q/X+V\nhlNXsi4ENhljUo0xJ4D3gN4OxaIK0C9K2dN9XsbatIG332bJPffAP/4B4eGnz3PyJJw4UeahlUat\nWnDbbTBvHuzcCQ88APv3w5gx1pWv1q1h2DCrI40vvoD09KCuX5aFcp2rQvG4FIrbBKG5XaG4TeDM\ndlWpApd/fDfDL1nH9aOSeCd2JJNvXsbuDN8lgFD9f5WGU5WsBCDd5f1We5yjfPMB8b4Mb9ZX1Dye\npns7PhC+FKWNobjLl/V+93ZcWQu2/V7caV6Nq1QJevZ0X+iyZVCtmtW0sFs3q/by6KPWVTB3jh2z\n2mIcPGg9Ufj4casGY/7uRrcsjzGVK0P37tCjxxKWLYO9e+Hll62K1ubN8K9/WQ88joqCevWW0KGD\ndavaqFHWtEmTrGc6l/YYU9i8QaBMclVZfRdL+v0qqbLYrmDbJgD+Kt16AnG7SvsZ9NcxQj+DLho1\nInbxe/w8/T9cc2NVus8dTuz5SZCTc9qsv2/MYfZbc6nAidNOxOn/ynvau6ALrWQ5I9h+7Bc2XStZ\npZu/zCtZhbn0UsjKggUL4J57oGVLq7MMT43av/4aGjWCunWtftejoqBCBej994WPfOtfuNDq/ang\n0L+/+/IXLYKYGB6gm1V+7jBwoOf5Y2NZctVVEBtLZHwsF/eI5fZvBvLKK1a4O3ZYm9i37xLGdvsf\nl398FwmvP0b2uJf48d5prP0hu7xXssqE0z9wfRGDP8oMxB9NPilzS+nWE4jbpZUs/6zfH2Uu2biR\nOm+MI37PWsLXrLIeyljAvf138dMtfTlCZaRCGCelAsekEjkNG7tdf5v6OziS8gyZYTXzDafObuZ9\nXCH4vxJjyv6R0CLSFkg2xnSz3z8EGGPMMwXm0+dVK6VUCDLGBHwXsZqrlFKqfCtNrnKqkhUO/AZ0\nAXYAK4ABxhh94otSSqmAoLlKKaVUSTnynCxjzCkRuRv4HKvJ4hRNWkoppQKJ5iqllFIl5ciVLKWU\nUkoppZQKVdrxhVJKKaWUUkr5UNBVskSkg4h8IyITReQyp+MpT0QkSkR+FJGrnY6lvBCRs+zP+gci\ncrvT8ZQHItJbRCaJyCwRucLpeMoLEWkkIm+KyAdOx+ILoZyrQi0XhOpxNlSPZSF4rIgSkXdE5A0R\n8dBdbPAJtf9TruJ8r4KukgUYIBuohPXMElV2HgTedzqI8sQYs9EYcwfQD7jY6XjKA2PMp8aY4cAd\nQF+n4ykvjDF/GWOGOR2HD4VyrgqpXBCqx9lQPZaF4LHiOmC2MeY2oJfTwfhKCP6fgOJ9rxyrZInI\nFBHZKSJrCozvJiIbReR3EXmw4HLGmG+MMd2Bh4AnyireUFHS/S4ilwO/AruBgO96OdCUdL/b8/QE\n5gMLyyLWUFGafW57FPiPf6MMPT7Y7wElVHNVKOaCUD3OhuqxLNSOFblKsF31+fuh5wUe/Rs49P91\nmqK/V8YYRwagPdASWOMyLgz4A0gCIoDVwFn2tMHABCDOfl8R+MCp+IN1KOF+fxGYYu//xcDHTm9H\nsA2l/bzb4+Y7vR3BNJRin8cDTwOdnd6GYBx8cGyf7fQ2+Hh7AjJXhWIuCNXjbKgey0LtWFGK7boR\nuNp+PdPp+H21XS7zBOT/qTTb5e33ypEu3AGMMd+JSFKB0RcCm4wxqQAi8h7QG9hojJkOTBeRa0Wk\nK1AdeLVMgw4BJd3vuTOKyE3AnrKKN1SU4vPeQawHoFYCFpRp0EGuFPv8HqznIkWLyBnGmEllGniQ\nK8V+jxWRiUBLEXnQFHjgr1NCNVeFYi4I1eNsqB7LQu1Ykau42wV8DLwqIt2BeWUabDEUd7tEJBZ4\nkgD9P+UqwXZ5/b1yrJLlQQJ/XzIFqx37ha4zGGM+xvpAKt8pcr/nMsZMK5OIygdvPu9LgaVlGVSI\n82afvwK8UpZBlQPe7PdMrDbuwSBUc1Uo5oJQPc6G6rEs1I4VuTxulzHmMHCzE0H5QGHbFYz/p1yF\nbZfX36tg7PhCKaWUUkoppQJWoFWytgENXN7Xt8cp/9L97gzd72VP97kzQm2/h9r25ArF7QrFbQLd\nrmCj2xVcfLJdTleyhPy9E/0InCEiSSJSEegPzHUkstCm+90Zut/Lnu5zZ4Tafg+17ckVitsVitsE\nul3BRrcruPhlu5zswn0m8APQVETSRGSoMeYUcA/wObAeeM8Ys8GpGEOR7ndn6H4ve7rPnRFq+z3U\ntidXKG5XKG4T6HbpdgUG3a7ib5fYXREqpZRSSimllPIBp5sLKqWUUkoppVRI0UqWUkoppZRSSvmQ\nVrKUUkoppZRSyoe0kqWUUkoppZRSPqSVLKWUUkoppZTyIa1kKaWUUkoppZQPaSVLKaWUUkoppXxI\nK1kqYIjINSKSIyJNnY7FExF52OkYfEVEbhORQcWYP0lE1hZzHV+KSNVCps8SkSbFKVMppQJBKOYs\nEflaRC7w5zqKWXZPEXmgmMtkF3P+2SLSsJDpz4lIp+KUqRRoJUsFlv7At8AAf69IRMJLuOgYnwbi\nEBEJN8a8YYyZUcxFvX56uYhcDaw2xhwsZLaJwIPFjEEppQKB5iw/rsPOU/OMMc8Wc9Hi5KlzgDBj\nzJZCZnsFeKiYMSillSwVGESkCnAJcAsuCUtEOojIUhGZLyIbReQ1l2nZIjJBRNaJyBciUtMeP0xE\nVojIKvsMVaQ9/m0RmSgiy4FnRCRKRKaIyHIR+UlEetrzDRGROSKySER+E5Gn7fFPAZVF5GcRme5m\nGwaIyBp7eNqLOBvb6/jR3samLnH+W0S+F5E/ROQ6N+tKEpENIjJDRH4VkQ9ctvMCEVlil7tIROra\n478WkRdFZAVwr4iMFZFR9rSWIrJMRFbb217dHt/KHrcKuMtl/eeIyP/sfbHaw9WoG4FP7fmj7P/h\nKnv/9LHn+Ra4XET0WKSUChrBnrNEJMwuf42I/CIi97lM7msf3zeKyCUu63jFZfl5InKZF3mxJPlv\noogss7c5b7123vvSzjlfiEh9e3xDEfnB3o5xLuuuZ5f9s72dl7j5V7rmKbf7xBiTBsSKSB2PHwil\n3DHG6KCD4wMwEJhsv/4OON9+3QE4DCQBAnwOXGdPywH6268fA16xX8e4lDsOuMt+/TYw12Xak8BA\n+3V14DegMjAE+AOoClQCtgAJ9nxZHuKPA1KBWKyTF18CvTzE+bL9+r9AE/v1hcCXLnG+b78+G9jk\nZn1Jdrlt7fdTgFFABeB7oKY9vi8wxX79NfCqSxljgVH261+A9vbrFGCCy/hL7NfPAmvs1y8DA+zX\nFYBKbmLcAlSxX18HvOEyrZrL68W5/28ddNBBh2AYQiBnXQB87vI+2v77NfCc/foq4Av79ZDc3GW/\nnwdcVtg6PGyzN/nPdZuHuCwzFxhkvx4KfGy//hS40X59Z248WDnxYfu15OajAvEtAZoVtk/s15OA\na53+3OkQXIOePVaBYgDwnv36fawElmuFMSbVGGOAWUB7e3wO8IH9egbWWUWAFiLyjYissctp5lLW\nbJfXVwIP2VdplgAVgQb2tC+NMQeNMceAX7ESZmHaAF8bYzKNMTnAu8BlHuJsb58FvRiYba//DaCu\nS3mfABhjNgCezp6lGWOWu5YLnAmcC3xhl/sIEO+yzPsFCxGRaKC6MeY7e9RU4DL7alZ1Y8z39njX\ns5TLgEdE5H6gob2fCooxxhyyX68FrhCRp0SkvTHGtc387gIxKqVUoAv2nLUZaCRWq4mugOsx+SP7\n709elFOUUxQ//83GvXZY+xOsfJS7/y7h7/+Fa576ERgqIo8DLVzykas4rBwEhe+TXWieUsVUwekA\nlBKRGKAzcK6IGCAcq031/fYsBdtXe2pvnTv+bayrSOtEZAjWmcVcBQ+y1xtjNhWIpy3gWmk4xd/f\nFSlsUwqZVjDOMGCfMcbTDcau6y9OuQKsM8a4axYBp29/UetwO94YM8tuwtIDWCgiw40xSwrMdtJl\n/k1i3Ux9NTBeRL40xuQ264gEjnhYv1JKBZRQyFnGmP0ich7QFbgd6AMMsyfnluVazkny32IS6RqC\nu3V44E3+85SnCrvXKndaXizGmG9F5DKgO/COiLxgTr8P+TD2thTYJ7dhtQS5xZ5P85QqNr2SpQJB\nH2CaMaaRMaaxMSYJ+EtEcs/+XWi3xQ4D+mHdxwPW5/cG+/WNLuOrAhkiEmGP92QxcG/uGxFp6UWs\nx8X9DcgrsK7+xNrTB2CdaXQX53f2lZy/RCR3PCLSwsM6PSWwBiJykf16INb2/wbUtpMuIlJBrBt7\nPTLGZAGZLu3VBwNLjTEHgH0icrE9Pq8nQhFpZIz5yxjzClZTDXex/yYije3544AjxpiZwHPA+S7z\nNQXWFRajUkoFkKDPWfa9UeHGmI+BR7GayrmTm3+2AC3FkojVxK/QddjCKV3+c/UDf9//Noi/9993\nLuPz9p+INAB2GWOmAG/ifhs3AGfY87vuk8fQPKVKSStZKhD0Az4uMG4Ofx80VwKvAuuBP40xn9jj\nD2Els7VAR6y27GAdHFdgHYA3uJRZ8CzYeCDCvsl1HfCEh/hcl5sErC14g68xJgOr96ElwCpgpTFm\nvoc4c9dzI3CLfRPvOqCXhzg9nb37DbhLRH4FagCvG2NOYCW0Z0RktR1LuyLKAfgH8Ly9zHkuMd4M\nvCYiPxdYvq99I/MqrKYt09yUuQDI7fa2ObDCnv9xrH2PfSPxYWPMrkJiU0qpQBL0OQtIAJbYx+Tp\n/N17ntv8Yzcb32Jv00tYTQmLWgeUPv+5uher+d9qe/nczjpGYOXCX7Ca/+XqCPxi56++wL/dlLmQ\nv/OU230iIhWAJlj/V6W8JlaTYaUCk4h0AP5pjOnlZlq2MaaaA2EViz/iFJEkYL4xprkvy/UlEakH\nTDXGdC1knhHAAWPM22UXmVJK+Uco5CxfCvRtFqsnx6+wOnhy+4NYRK7B6thkbJkGp4KeXslSwSxY\nzhD4K86A3n776t5kKeRhxMA+rI42lFIq1AX0MdtPAnqbjTFHsXraTShktnDghbKJSIUSvZKllFJK\nKaWUUj6kV7KUUkoppZRSyoe0kqWUUkoppZRSPqSVLKWUUkoppZTyIa1kKaWUUkoppZQPaSVLKaWU\nUkoppXxIK1lKKaWUUkop5UNayVJKKaWUUkopH9JKllJKKaWUUkr5kFaylFJKKaWUUsqHtJKlVCFE\nJFtEGjodh1JKKVUYzVdKBRatZKmQICI5ItK4lGV8LSI3u44zxlQzxmwpVXA+JCJJIvKViBwSkV9F\npEsR8z8jIntEZLeIPF1g2lcisktE9ovIKhHpVWD6QBHZYifuj0Skhsu0iiLylogcEJHtIjKywLIt\nRWSlHeePInJegekjRWSHve43RSSi5HslX7kd7M/CnALjW9jjv/LFepRSqqQ0X3mcX/MVmq9CiVay\nVKgwhU0UkfCyCsTPZgE/AbHAo8CHIlLT3YwichvQC2gOtAB6ishwl1nuAxKMMTWA24AZIlLXXrYZ\n8DpwI1AXOAJMdFk2BWgCJAKdgQdE5Ep72QjgE2AaUMP++6mIVLCndwUeADoBSXY5KSXfJafZDbQT\nkRiXcUOA33y4DqWUKinNVwVovtJ8FZKMMTro4HYA6gNzgF1YB4KX7fGCdcDcAmQA7wDR9rQkIAe4\nCUi1lx3jUmYYMAb4AzgA/Ih14AQ4C/gc2AtsAPq4LPc28CowH8gClgGN7GlL7XUetKf1AToA6VgH\nxx3AVKwD6Dw7pr3263i7jPHASeCwXUbutuYAje3X0VgH4F3AX8AjLvENAb4FngMygT+Bbj7+f/wf\nVvKo4jJuKTDcw/zfA8Nc3g8FfvAw74X2tre23z8JzHCZ3hg4lrtuYBvQxWV6CjDTfn0lkF6g/FTg\nSvv1u8B4l2mdgB2FbHcOcAfwu/2ZecKO53tgP/AeUMGeN/f//hpwp8tnbivWZ/Yrp79XOuigg+8H\nNF/lHis1X2m+0iFABr2SpdwSkTCsBPEX0ABIwDo4gHXwuwnrANEYqIaVUFxdgnWQvRx4XETOtMf/\nE+iHdUCvDtwMHBaRKKyENQOoBfQHXhORs1zK7AeMxUo+f2IdWDHGdLCnNzfGRBtjZtvv69nzNgCG\nYx283sI6m9UA6yD9H7uMR7GSzt12GffaZbiecXzV3taGQEfgJhEZ6jL9QqxkWxMreU3BAxGZJyL7\nRCTTzd+5HhZrBmw2xhxyGfeLPd7T/L8UNq8dxxFgObDEGLPS3bLGmM1YSaup3QwjDljjoexzCkwr\nON1dXHUKnMkr6ErgfKAt1g+RN4CBWP/L5sAAl3kN1o+Lm+z3XYG1WD9elFIhRvOV5is0X6kApJUs\n5cmFWAemB4wxR40xx40xP9jTBgITjDGpxpjDwMNAfzvRgXXQSLaXWYN1UMpt43wL1hm1PwCMMWuN\nMfuAHsBfxphpxvIL1lnJPi4xfWyM+ckYk4N1dqllgZilwPtTwFhjzAljzDFjTKYx5mP79SHgKeCy\nIvaDQF4S7wc8ZIw5bIxJBV4ABrvMm2qMecsYY7DORNYTkTruCjXG9DTGxBhjYt387eVuGaAq1pkx\nV1lYidSb+bPscfnisMddhfWjwZt1VcX6HxcsOzeOouJ0F5cUsh0AzxhjDhljNgDrgM/tz182sAgr\noblu13IgRkSaYiWvaYWUrZQKbpqvXMrUfJVvXZqvlGO0kqU8ScQ6COe4mRaPdTk9VypQAastdK6d\nLq8P8/fBMhHY7KbMJKCtfWYsU0T2YSVH1zIzPJTpyW5jzIncNyJSWUTesG+O3Y/VdKGGiBRMdu7U\nwtrGNJdxqVhnTE+LzxhzBOtAXFSMxXEQqwmIq+pAtpfzV7fH5WOMOWWMWQx0FZEeXqwrt4yCZefG\nUVSc7uIyhWwHWE1ech0h/+frCO7383TgbqyzuB8XUrZSKrhpvspP85XmKxUAtJKlPEkHGric7XO1\nHSvJ5EoCTpD/QFJYuU08jF9inxnLPUsWbYy5u7iBuyh4c/E/sZqEtDHWzbO5ZwXFw/yu9mBtY8Ht\n3laSwERkod0LUpabYYGHxdYDjUWkisu48+zxnuZ37SWpZSHzgpWUc/83+ZYVkSZABPCCig3dAAAg\nAElEQVS7MWY/VlMG17Jd41iPdeOyqxZYZ/Q8xbXTPkPsSzOAO4EFxpijPi5bKRU4NF/lp/lK85UK\nAFrJUp6swDowPS0iUSJSSUQutqfNAkaKSEMRqYrV1vw9l7OIhZ1pexMYJyJnAIhIc7tt83ys9tOD\nRKSCiESISGuXtvFFycBqb1+YalhnkbJEJBZILjB9p6cy7G37AHhSRKqKSBIwEuvsU7EZY642Vne7\n0W6G7h6W2QSsBsba/4/rgHOxmqm4Mw0YJSLxIpIAjMK6IRsROVNEuolIpL2/BwGXYp0tBat5S08R\nucROkk8Ac8zf7eunA4+KSA0RORu4NbdsYAlwSkTuEavr3Huxbgb+2iWuW0TkbPt//6jLsj5jrK6M\nL7PLV0qFLs1XLjRfab5SgUErWcot+yDdE+tMWhrWmbu+9uS3sA5a32Dd0HsYuNd18YLFubyegHXw\n/1xEDmAlscrGmINYN4v2xzrzuB14GqjkZcjJwDS76cYNHuZ5CYjCOsv3A7CwwPR/A31EZK+IvOQm\n9nuxtnUz1rbPMMYUdrAttJveEuoPtAH2Yf1YuN4YsxdARNqLSFbeyo15A6tHqrVY9xnMNcZMticL\n1j7bidW04R6grzFmtb3sr8DtwEysHwSVgbtc4hiLtR9Sga+Ap40xX9jLngCuwerBah9WG/PexpiT\n9vTFwLNYSewvrM9QciHbXNjnqVDGmB+MMRlFz6mUClaarzRfoflKBSCx7nn0U+Ei9bHOAtTFOjMw\nyRjzioiMxTqTkNtudYwx5jO/BaKUUkp5ICKVsH6IVsRqhvShMSbFPnP9PlZTqy1YP+wK3iSvlFJK\nncbflax6QD1jzGr7Mv1PQG+sXm+yjTET/LZypZRSyksiEmWMOSzWg2C/x7oScD2w1xjzrIg8CMQY\nYx5yNFCllFJBwa/NBY0xGS6Xcw9iPZMht3cbb3rIUUoppfzOWN17g9XkqwJWM5/eWN1bY/+9xoHQ\nlFJKBaEyuydLRBpi9cryP3vU3SKyWkTeFJHqZRWHUkopVZCIhInIKqx7Or4wxvwI1DXG7ATrpCHg\n9jlCSimlVEFlUsmymwp+CNxnX9F6DWhsjGmJldC02aBSSinHGGNyjDHnA/WBC0WkGaW4iV0ppVT5\nVsHfKxCRClgVrOnGmE8BjDG7XWaZjNWjjLtlNaEppVQIMsYEZJNxY0yWiCwBugE7RaSuMWanfY/x\nLnfLaK5SSqnQVJpcVRZXst4CfjXG/Dt3hJ2scl3H3w99O40xpsyGsWPHlmkZ3sxb1Dyepns73t18\nvtgPZbnfi7t8We93b8aV9T4Pxv1e3GmBuN/L+hjjz/1emu9AoBGRWrnN1kWkMnAF1j3Ec4F/2LMN\nAT71VEZZfh5K+j/15fFeY/ftd0Jj93/sdCj5b0qnYy/rfR7MsZcm5/k6V/n1SpaIXALcCKy127ob\nYAwwUERaYnXrvgW4zZ9xeKtjx45lWoY38xY1j6fp3o73xTaXVmljKO7yZb3fvR1X1oJtvxd3WiDu\n97I+xng7f0n2e2m/AwEmDpgqImFYJx/fN8YsFJHlwAcicjPWM3b6FlZIcZV0v5T0f+rL/4PG7v10\njb10ZWnsJVeacoI19tLkPJ/nqpLWcMtisMJTZW3s2LFOh1Du6D53hu53Z9jHdsdzjK+GYM5Vwfwd\n0NidEayx00G/p04I5thLm6vKrHdBFTyC4KxzyNF97gzd76q8C+bvgMbujKCNvaHTAZRc0O5zgjv2\n0vLrw4hLS0RMIMenlFKq+EQEE6AdX5SE5iqlAp+kCGasfk+V90qbq7SSpZRSqkxpJUup0NOwYUNS\nU1OdDkOpYktKSmLLli2njddKllJKqaCilSylQo/9vXY6DKWKzdNnt7S5Su/JUkoppZRSSikf0kqW\nUkopFaBiY0HEGmJjnY5GKaWUt/z6nCyllFJKldy+fZDbikVCpoGlUkqFPr2SpZRSSimlyqXU1FTC\nwsLIyckpdVmNGjXiq6++8mreqVOncumll+a9r1atmtvOF0riqaeeYvjw4YBvtw8gPT2d6Ohovf/O\nC1rJUkoppZRSIauoyo84dJnYdb3Z2dk0bNiw0PmXLl1KYmJikeU+/PDDTJo0ye16iqvgvktMTCQr\nK8uxfRZMtJKllFJKKaVUgDPGFFm5OXXqVBlFo4qilSyllFJKKVUu5OTkMHr0aGrXrs0ZZ5zBggUL\n8k3Pyspi2LBhxMfHk5iYyGOPPZbXNG7z5s106dKFWrVqUadOHQYNGkRWVpZX683MzKRXr15Ur16d\ntm3b8ueff+abHhYWxubNmwFYuHAhzZo1Izo6msTERCZMmMDhw4e5+uqr2b59O9WqVSM6OpqMjAxS\nUlLo06cPgwcPpkaNGkydOpWUlBQGDx6cV7YxhilTppCQkEBCQgIvvPBC3rShQ4fy+OOP5713vVp2\n0003kZaWRs+ePYmOjub5558/rfnhjh076N27NzVr1qRp06a8+eabeWWlpKTQr18/hgwZQnR0NM2b\nN+fnn3/2an+FAq1kKaWUUkqpcmHSpEksXLiQX375hZUrV/Lhhx/mmz5kyBAqVqzI5s2bWbVqFV98\n8UVexcEYw5gxY8jIyGDDhg1s3bqV5ORkr9Z75513EhUVxc6dO5kyZQpvvfVWvumuV6iGDRvG5MmT\nycrKYt26dXTu3JmoqCgWLVpEfHw82dnZZGVlUa9ePQDmzp1L37592b9/PwMHDjytPIAlS5bw559/\nsnjxYp555hmvmk9OmzaNBg0aMH/+fLKyshg9evRpZffr148GDRqQkZHB7NmzGTNmDEuWLMmbPm/e\nPAYOHMiBAwfo2bMnd911l1f7KxRoJUsppZRSSpULs2fPZsSIEcTHx1OjRg0efvjhvGk7d+5k0aJF\nvPjii0RGRlKrVi1GjBjBrFmzAGjSpAldunShQoUK1KxZk5EjR7J06dIi15mTk8NHH33EuHHjiIyM\npFmzZgwZMiTfPK4dSVSsWJH169eTnZ1N9erVadmyZaHlt2vXjp49ewIQGRnpdp7k5GQiIyM599xz\nGTp0aN42ecNTJxfp6eksW7aMZ555hoiICM477zyGDRvGtGnT8uZp3749Xbt2RUQYPHgwa9as8Xq9\nwU4rWUoppZRSyr+Sk/9+6Jvr4OlKkLv5vbxqVJjt27fn6zwiKSkp73VaWhonTpwgLi6O2NhYYmJi\nuP3229mzZw8Au3btYsCAAdSvX58aNWowaNCgvGmF2b17N6dOnaJ+/fpu11vQnDlzWLBgAUlJSXTq\n1Inly5cXWn5RnWGIyGnr3r59e5FxF2XHjh3ExsYSFRWVr+xt27blvc+92gYQFRXF0aNHfdbTYaDT\nSpZSSimllPKv5GTroW8Fh8IqWd7OWwxxcXGkp6fnvU9NTc17nZiYSGRkJHv37iUzM5N9+/axf//+\nvKsvY8aMISwsjPXr17N//35mzJjhVVfmtWvXpkKFCvnWm5aW5nH+Vq1a8cknn7B792569+5N3759\nAc+9BHrT01/BdcfHxwNQpUoVDh8+nDdtx44dXpcdHx9PZmYmhw4dyld2QkJCkfGUB1rJUkoppZRS\n5ULfvn15+eWX2bZtG/v+n707j7O5+h84/jozdmaYsQ1jb6ESsoXEIPuWpWRLo2ihknyLSkxUqFTf\nfl+JJCoKaSFZoqFQWsheWSLL2GaYsQwz5vz+OHfG7HPv3Pu527yfj8fnMfd+7md5z5hx7vuec94n\nLo6pU6emvRYWFkaHDh146qmnSEhIQGvNgQMH2LBhA2DKrJcqVYqgoCCOHj3Ka6+9Ztc9AwIC6N27\nNxMnTuTSpUvs3r2befPmZXtsUlISCxYsID4+nsDAQIKCgggMDASgYsWKnDlzxu5iG6m01kyaNIlL\nly6xa9cu5s6dy3333QdAgwYNWLFiBXFxccTExPD2229nODcsLCytIEf66wFUqVKFFi1aMG7cOC5f\nvsz27duZM2dOhqIb2cVSUEiSJYQQQggh/Fb63phhw4bRsWNH6tevT+PGjenTp0+GY+fPn8+VK1e4\n+eabCQ0N5Z577iEmJgaACRMm8Ntvv1GmTBm6d++e5dzcen3eeecdEhISqFSpEkOHDmXo0KE5nvvR\nRx9Rs2ZNypQpw6xZs/jkk08AqF27Nv3796dWrVqEhoamxWXP99+6dWuuv/562rdvzzPPPEO7du0A\nGDx4MPXq1aNGjRp06tQpLflKNXbsWCZNmkRoaCjTp0/PEuvChQs5ePAglStXpk+fPkyaNIk2bdrk\nGktBobw5o1RKaW+OTwghhOOUUmit/aaltbKtUsqMksr8WAhvY/u79nQYQjgsp99dZ9sq6ckSQggh\nhBBCCBfKM8lSSnVXSkkyJoQQwmtJWyWEEMKb2NMg9QP+VkpNU0rVsTogIYQQIh+krRJCCOE17JqT\npZQKBvoDkYAG5gILtdYJlgYnc7KEEMLvWDUnyx/bKpmTJXyFzMkSvsqjc7K01vHAEuBToBLQC/hd\nKfV4fm8shBBCuJK0VUIIIbyFPXOyeiqlvgCigcJAU611Z6A+8LS14QkhhBB5k7ZKCCGENylkxzG9\ngTe11hvS79RaX1RKPWhNWEIIIYRDpK0SQgjhNewZLhiTudFSSk0F0FqvtSQqIYQQwjHSVgkhhPAa\n9iRZ7bPZ19nVgQghhBBOyHdbpZSqopRap5TapZTakTqHSyk1QSl1RCn1u23r5NKIhRDCBQICAjhw\n4IBdx0ZFRTF48GAA/v33X4KDg11WsOTRRx/l5ZdfBmD9+vVUrVrVJdcF+PHHH7nppptcdj13yDHJ\nUko9qpTaAdRRSm1Ptx0EtrsvRCGEECJ7LmqrkoHRWutbgObAyHRl4KdrrRvatpUWfAtCCDdYsGAB\nTZo0ISgoiPDwcLp27crGjRs9HRbz5s3jzjvvdOoaSjlWAC/1+KpVqxIfH5/n+fbG+O677/L888/n\nO670MieOLVu2ZM+ePfm+nifkNidrAfAt8CowNt3+BK11rKVRCeEPfvsNPvoIduyA48fhzBm4fBlG\njYKJEz0dnRD+wum2SmsdA8TYHp9XSu0Bwm0vu7zUvBDCvaZPn860adN477336NChA0WKFGHVqlUs\nW7aMO+64w6FrXb16lcDAwDz32Utr7VQyknoNK9kTY0pKCgEBrlsP3tmfiTfI7aehtdb/ACOAhHQb\nSqlQ60MTwsclJ0OVKvDMM7B4MfzxBxw8CKNHZ3/855/DsmWQkuLeOIXwbS5tq5RSNYAGwM+2XSOV\nUtuUUu8rpUq7IuD8Cgkxa2WlbqHSEguRp/j4eCZMmMCMGTPo2bMnxYsXJzAwkC5dujBlyhQArly5\nwqhRowgPD6dKlSo89dRTJCUlAdeGvU2bNo1KlSoxdOjQbPcBLF++nNtuu42QkBBatmzJjh070uI4\ncuQIffr0oUKFCpQvX54nnniCvXv38uijj7J582aCgoIItf1RX7lyhTFjxlC9enUqVarEY489xuXL\nl9Ou9dprr1G5cmWqVKnC3Llzc01I/vnnHyIiIihdujQdO3bk9OnTaa8dOnSIgIAAUmzvOz788EOu\nu+46goODue6661i4cGGOMUZGRvLYY4/RtWtXgoKCiI6OJjIykhdffDHt+lprXn31VcqXL0+tWrVY\nsGBB2mtt2rThgw8+SHuevresdevWaK2pV68ewcHBLF68OMvww71799KmTRtCQkK49dZbWbZsWdpr\nkZGRjBw5km7duhEcHEzz5s05ePBg7r8oFsgtyUr9SfwG/Gr7+lu650KIlBT45ZfsX7v9dhgzBjp2\nhFtugbAw8y4pODj745WCqCi49Vb47DNZdVQI+7isrVJKlcKss/Wk1vo8MAOopbVugOnpmu6qoPMj\nNtb8t5C6xcV5MhohfMPmzZu5fPkyd999d47HTJ48mS1btrB9+3b++OMPtmzZwuTJk9Nej4mJ4ezZ\nsxw+fJhZs2Zlu2/r1q08+OCDzJ49m9jYWB5++GF69OhBUlISKSkpdOvWjZo1a3L48GGOHj3Kfffd\nR506dZg5cybNmzcnISGB2FjT+f7ss8+yb98+tm/fzr59+zh69CgvvfQSACtXrmT69OmsXbuWv//+\nm++++y7X73/AgAE0adKE06dP88ILLzBv3rwMr6cmaBcvXuTJJ59k1apVxMfHs2nTJho0aJBjjAAL\nFy5k/PjxJCQkZNsjGBMTQ2xsLMeOHePDDz9k+PDh/P333znGmhrL+vXrAdixYwfx8fHcc889GV5P\nTk6me/fudOrUiVOnTvHf//6XgQMHZrj2Z599RlRUFGfPnuW6667LMIzRXXJMsrTW3Wxfa2qta9m+\npm613BeiEF5Ia5MI1a8Pjz5qhgE6q3dvk7C9+SZMmQJt2sCuXc5fVwg/5qq2SilVCJNgfaS1/sp2\nzVP62jic2UCTnM6fOHFi2hYdHZ3v70cI4VpnzpyhXLlyuQ5lW7BgARMmTKBs2bKULVuWCRMm8NFH\nH6W9HhgYSFRUFIULF6Zo0aLZ7ps9ezaPPPIIjRs3RinF4MGDKVq0KD/99BNbtmzh+PHjTJs2jWLF\nilGkSBFatGiRYzyzZ8/mzTffpHTp0pQsWZKxY8eycOFCABYvXkxkZCQ33XQTxYsXZ2Iu0w/+/fdf\nfv31V1566SUKFy7MnXfeSffu3XM8PjAwkB07dpCYmEjFihXzLDTRs2dPmjVrBpD2c0lPKcWkSZMo\nXLgwrVq1omvXrixatCjXa6aX0zDIzZs3c+HCBZ599lkKFSpEmzZt6NatW9rPCKBXr140atSIgIAA\nBg4cyLZt2/K8X3R0dIb/y52V5zpZSqk7gG1a6wtKqUFAQ+AtrfVhp+8uhC/6+WczryopCV5/HTp0\nML1QrqCUuV67djBzJkyYYIYa+sHYZCGs5IK26gNgt9b67XTXDLPN1wKzDtfOnE52RYMshD9zVTPm\n6CCPsmXLcvr06VznDB07doxq1aqlPa9evTrHjh1Le16+fHkKFy6c4ZzM+w4dOsT8+fN55513bHFq\nkpKSOHbsGAEBAVSvXt2uOUunTp3i4sWLNGrUKG1fSkpKWsJx7NgxGjdunCHWnJKRY8eOERISQvHi\nxTMcf+TIkSzHlihRgs8++4zXXnuNoUOH0rJlS15//XVq166dY6x5VQ8MCQmhWLFiGe6d/ueaX8eP\nH89y7+rVq3P06NG052FhYWmPS5Qowfnz5/O8bkREBBEREWnPo6KinIrTnhlq7wIXlVL1gaeB/cBH\nuZ9iZFMW9wnb/hCl1Gql1J9KqVWeHucuhN3mzTM9To88Alu2mKGAViRAgYEwYgQsWSIJlhD2caat\nugMYCLRVSm1NV659mq1S4TagNfCURbEL4ffSD3V1ZnNU8+bNKVq0KF9++WWOx4SHh3Po0KG054cO\nHaJy5cppz7Ob85R5X9WqVXn++eeJjY0lNjaWuLg4zp8/T79+/ahatSqHDx9Om/uU23XKlStHiRIl\n2LVrV9q1zp49y7lz5wCoVKkS//77b4ZYc5qTValSJeLi4rh06VLavsOHc/7cqX379qxevZqYmBhq\n167N8OHDc/z+c9ufKrt7p/5cS5YsycWLF9Nei4mJyXJ+TipXrpzhZ5B67fDw8BzO8Ax7kqxk23CJ\nnsD/aa3/BwTZef3MZXFH2MrijgW+01rXBtYB4xwPXQgP6N0b9u6FIUPAhVV0hBBOy3dbpbXeqLUO\n1Fo30FrfllquXWt9v9a6nm3/3VrrE5Z+B0IIlwsODiYqKooRI0bw1VdfcenSJZKTk/n2228ZO9YU\nJL3vvvuYPHkyp0+f5vTp00yaNCltLSl7DRs2jJkzZ7JlyxYALly4wIoVK7hw4QJNmzalUqVKjB07\nlosXL3L58mU2bdoEQMWKFTly5EhaoQ2lFMOGDWPUqFGcOnUKgKNHj7J69WoA7r33Xj788EP27NnD\nxYsX0+ZqZadatWo0btyYCRMmkJSUxI8//pihQARcG5J38uRJvv76ay5evEjhwoUpVapUWs9b5hjt\npbVOu/cPP/zAN998w7333gtAgwYNWLp0KZcuXWLfvn3MmTMnw7lhYWE5rv11++23U6JECaZNm0Zy\ncjLR0dEsX76c/v37OxSf1ex5l5iglBoHDAK+UUoFAIXzOAcwZXG11ttsj88De4AqmEYwdebdPCDn\n2YhCeJOgILMJIbxNvtsqIYR/Gz16NNOnT2fy5MlUqFCBatWqMWPGjLRiGC+88AKNGzemXr161K9f\nn8aNGztcKKFRo0bMnj2bkSNHEhoayo033phWZCIgIIBly5bx999/U61aNapWrZo2N6lt27bccsst\nhIWFUaFCBQCmTJnC9ddfT7NmzShTpgwdOnTgr7/+AqBTp06MGjWKtm3bcuONN9KuXbtc41qwYAE/\n/fQTZcuWZdKkSQwZMiTD66m9USkpKUyfPp3w8HDKlSvHhg0bePfdd3OM0R6VKlUiJCSEypUrM3jw\nYN577z1uuOEGAJ566ikKFy5MWFgYkZGRDBo0KMO5EydO5P777yc0NJQlS5ZkeK1w4cIsW7aMFStW\nUK5cOUaOHMlHH32Udm1vKf+u8qqtr5QKAwYAv2itf1BKVQMitNbzHbqRKYsbDdQF/tVah6R7LVZr\nnaUYrVJK5xWfEJZJSfG+3iqtYfBgGDsW6tb1dDRC5ItSCq21S1tBV7VV+by3ZW2VUjkPkcrtNSHc\nzfZ37ekwhHBYTr+7zrZVeSZZrmArixsNTNJaf5U5qVJKndFal83mPEmyhGfMnQvz58O6dd43J+rT\nT+HppyE6Gmyf2gjhS6xIsjxJkiwhJMkSvsuqJMue6oK9galABUDZNq21zmGxnyznZymLC5xQSlXU\nWp+wffp4Mqfz01dsylz1QwiXS0oylQPXrfPeohP33QcXL8Jdd8HmzZBucq4Q3ig6OtrysubOtlVC\nCCGEK9kzXHAf0F1rvSdfN1BqPnBaaz063b6pQKzWeqpS6lkgRGs9NptzpSdLuE9cHNx7LxQqZHqL\nSnt50ctXXoEvv4T16yFdeVYhvJ1FwwWdaqucvLf0ZIkCT3qyhK+yqifLngknJ5xIsHIqizsVaK+U\n+hNoB0zJz/WFcJmzZ6FZMzPPadky70+wAMaNM8MFbWtyCFHA5butEkIIIVzNnp6st4Ew4Evgcup+\nrfVSa0OTnizhRlrDjz/CnXd6OhLHJCaanrdCeY78FcJrWNST5ZdtlfRkCV8hPVnCV3ms8IVSam42\nu7XWemh+b2ovSbKEv7tyBf78E3bvhmPH4MQJuHDBvHEKDISyZaF8edNhdcstEBbmndPEhHCERUmW\n/7VVFy8SULIYKTr7QSeSZAlvIkmW8FU+XV0wvyTJEv7m/HnYsAG++87U1vjzT6hRwyRQVapAhQpQ\nqpSpHJ+cDGfOwMmT5ridO6FYMWjXDjp2hB49zLFC+BqpLmifq8MfZdrsMozTr+ZwX0myhPeQJEv4\nKk9WF7wReBeoqLWuq5SqB/TQWk/O702F8LiEBLctKpyYCN98AwsXwpo10LChKQw4cyY0aGASJ3to\nDfv2wdq1sGABPPYYdO0KI0dC8+bWfg9CeDt/a6sSE6Hpxnd4lR4waxYMH+7pkITIVfXq1b1mEVgh\nHFG9enVLrmvPcMH1wH+A97TWt9n27dRaW74SqvRkCUtMnw7Ll5uuJAsdOADvvmuW3KpfH/r3h969\nITTLstv5c+qUSbbefhvCw2H8eOhw8xE4eND35paJAsWi4YJ+11Z9/DFEDk7iSNkGVFw1Hxo1ynRf\n6ckSwl4qSqEnyB+MsJ87qguW0FpvybQvOb83FMKjpk2DGTNg3jzLbrFtm0mmmjY1b4C2bDG9Tw89\n5LoEC8xcrSefhL/+ghEjTI9W53tKsrv3C2acoRAFi9+1VYMGQTKF6V12PUn3DoT4eE+HJIQQwk72\nJFmnlVLXARpAKdUXOG5pVEJY4dVX4f33zbpSVau6/PLbtkGvXtClC7RqBYcOweuvQ61aLr9VBoUK\nmfWJd+6Ejv1CaH3hG6LaRJOUZO19hfAyfttWBdUsx8TSb5quLSGEED7BnuGCtYBZQAsgDjgIDNJa\n/2N5cDJcULjKpEnwySdmiGDlyi699NGjZsmqNWvgmWfg4YehRAmX3sKxeP6+yLD6WzheuSELlgVz\n002ei0WI7Fg0XNAv2yqlTNXR227TzPsQ7mqvMrwmTaQQ9pHhgsJRlg8X1Fof0FrfBZQH6mitW7qj\n0RLCpcqVg+holyZYiYnwyitQr56pDPjXX/DUU55NsADCbyjBN5+d57GEabRqpVm0yLPxCOEO/txW\nVagA8+YpHohUxMV5OhohhBD2yLEnSyk1OrcTtdbTLYkoYwzSkyW8UnS0mWNVr557hgTmS+/ebO38\nHH1ebUzv3mY6WoA9A4SFsJgre7L8va1K31s1YgRcugQffJD1NSFE7qQnSzjKyp6sINvWGHgUCLdt\njwAN83tDIXzZuXNmOODgwfDWW7B0qbUJVmioeSOV05ZrIY3PP+e2YY359Vf45Rfo18/0vgnhZwpM\nWzVlihnxvHKlpyMRQgiRlxyTLK11lNY6CqgCNNRaP621fhpoBFRzV4BCeIsVK6BuXfPJ8c6d0K2b\n89fMK4kCc7+cNsjl/ABFaKi5x+rVEBgI7dubRFEIf1GQ2qqgIJg92yyZFf/b34SVSbT/QxchhBBu\nZc/goYrAlXTPr9j2CeGdVqyAv/922eUuXTLl0R97zFR+nzULSpd2zbXj4nJPomJjcz8/Njb381Pn\nbxQtatbUqlcPOnaUREv4pQLRVrVvb7Zx9x3g+OOvZPv3LoQQwvMK2XHMfGCLUuoL2/O7gQ8ti0gI\nZ6xZAw88AN9+65LL7dxpFhG+5RZTor1MGcfODw3N/Y1PSIhz8eUlJORaj1h6ZcqYRPHsWWvvL4Qb\nFZi26vXXoe5NdzHg7be5o/8epISoEEJ4H3uqC74MRGJK4sYBkVrrV60OTAiH/WJpRkgAACAASURB\nVPorDBhgJko1auTUpbSGd9+FNm1g9GhYuDDnBCu3IX+p18pvT5WzMvR0xSegNaSkwBNPmN6s8+et\nvb8Q7lKQ2qqQEPjv/wJ5qPjHJA5/QqpfCCGEF8pznSxPkuqCwm4HD8Idd8CMGXD33U5d6uJFGDYM\ndu2CRYvgxhtzP94nKnytWwfPPgtbtoBSaG0qDXbsCMuWQeHCng5QFCRWrJPlSe6qLpie1tC7l+bW\nLXN46eVAiIz0jf+LhPAQqS4oHGX5OllCeL1Ll6BzZ3j+eacTrP37oXlzUyRi0yaTYOVVnMLqIX8u\nERFhurA+/xy41stWpIgpRS9vzITwLUrB/2Yo3r00hB0vLoKkJE+HJIQQIh1JsoTvK14cPvzQLCLj\nhBUroEULU7lr3rxriwo7W5zCKwQEwEsvmS0lJW33p5+aRZSfey7rKU6VjxdCWK5yZXh5amEeqric\nqwHSHS2EEN4kz+GCSqnHgY+11m6vWyTDBYU7hITkXgAiJMRHEqm8aA2NG8MLL0CvXnYV5cjt+5ah\nSSK/rBgu6K9tVV5/ZykpZu5o794wapT8TQqRExkuKBzljuGCFYFflFKLlFKdlMquVpkQvikx0SRY\nTZvCsWM+3FNlD6Vg/HiYNAm0zlAUY+dOKFcOfv7ZD79vUVAUyLYqIMCsnTVpkqcjEUIIkZ491QVf\nAG4A5gAPAH8rpV5RSl1ncWxCuERuw96KFzdFH6KjoVIlT0fqBj16mKoeyckZdt9yC7z/PvTpA8eP\neyg2IZzgTFullKqilFqnlNqllNqhlHrCtj9EKbVaKfWnUmqVUspFK+S51o03wtNPm8fSkyWEEN7B\nrjlZtnEQMbYtGQgBliilplkYmxDZ++or+OMPuw/Pbk7Vrl1Qs6aplZGYaJKtAiEgAB59NNtygj17\nmvloffrIHHrhm5xoq5KB0VrrW4DmwAilVB1gLPCd1ro2sA4YZ1nwThozxnz9+CMtq40LIYQXyDPJ\nUko9qZT6DZgGbARu1Vo/CjQC+lgcnxAZ/fCD6Ylx4uPaNWtMsb2JE2HyZJN3COP5503PX3aFMITw\nZs60VVrrGK31Ntvj88AeoArQE5hnO2weZoFjr5T6ucmYJy5zuIOUDBVCCE+z5+1lKNBba91Ra71Y\na50EoLVOAbpZGp0Q6e3ZA337wiefQIMG+brEvHkwaBAsWQL33+/i+PxAQID5GX32GSxf7ulohHCI\nS9oqpVQNoAHwE1BRa33Cdp0YoIKrg3a1p8cWod+u8Vx5f76nQxFCiALNniTrWyBtCrxSKlgpdTuA\n1nqPVYEJkUFMDHTpAtOmQfv2Dp+uNUydChMmmPlXrVq5PkR/UbYsLFwIDz4Ihw97Ohoh7OZ0W6WU\nKgUsAZ609Whl7g7y+u6hMc8EUPa26ox98hKcOuXpcIQQosAqZMcx7wIN0z0/n80+IayTnAzdu8PQ\noTBkSL4u8dRTsHYtbNwI4eEujs9XpaSYuW233ZblpTvuMBPp77sP1q/PdgqXEN7GqbZKKVUIk2B9\npLX+yrb7hFKqotb6hFIqDDiZ0/kTJ05MexwREUFERIRDwbtCSIhZSB1Ks4l7KVzpA6YmPX1t9XEh\nhBA5io6OJjo62mXXs2edrG1a6waZ9m3XWtdzWRQ531vWyRLG5s3QrJnDbxYuX4ZixeDOO029jJAQ\ni+LzRWfPQq1asGNHtplnSgp06wYNG5q5a5nJOlkivyxaJ8uptkopNR84rbUenW7fVCBWaz1VKfUs\nEKK1HpvNuR5bJysnf/yaREST83w3cSONJsjIfiFknSzhKHesk3VAKfWEUqqwbXsSOJDfGwqRL82b\nO5xgJSSYJAFg1SpJsLIoUwYGD4Z33sn25YAA+OADU9p90yY3xyaE4/LdViml7gAGAm2VUluVUr8r\npToBU4H2Sqk/gXbAFMuid7H6jQuTQgC9P+jKiROejkYIIQoee3qyKgD/BdpixqOvBUZprXMcNuGy\n4KQnS9ghNNSUac9JmTK5v16gHTwITZqYr0FB2R7yxRfwn//Atm1QqtS1/dKTJfLLop4sv2yrnPk7\nS11/fO1a+O67ArRUhRDZkJ4s4Shn26o8kyxPkiRL2CPzm5BTp0xtjA4dTLELmY6Qh3vugdatYeTI\nHA8ZOtTMy3rvvWv7JMkS+WVFkuVJ3pxkXb1qKqpeuACffw6F7JmJLYQfkiRLOMryJEspVR4YBtQg\nXaEMrfXQ/N7UXpJkFVDffWe6TJo1s+vw9G9CTp6Edu2gRw8zj0gSLDusXw+PPAK7d+f4A4uPh/r1\nzcjC1CGYkmSJ/LKoJ8sv2ypnkyyt4coV839i5cowZ478vygKJkmyhKPcMSfrK6A08B3wTbpNCNfb\ntg0GDDAfvzro+HGzyHDfvpJgOaRVK5g0yVS6yEFwMMyfD8OHS1Vo4bWkrcpBkSKmF2v3zquMarQB\nfeGip0MSQgi/l6/qgu4iPVkFzKFDpnb4W2+ZTMlOSsGRI9C2rVlg+PnnLYyxgHvmGTN9a/Fi6ckS\n+eeu6oLu4u09WanOxqbQsfZBGhfbxTt72xNQUiZpiYJDerKEo9zRk7VcKdUlvzcQwi5xcdC5M4wZ\n41CClap1a7N4riRY1nrpJVPxfckST0ciRBbSVuWhTGgAq/+swdbEOjxyw1pSEi54OiQhhPBb9vRk\nJQAlgSu2TQFaax2c58WVmgN0A06krlWilJqAGTefWvHpOa31yhzOl56sgkBruOsuqFcP3nzToVP/\n+Qdq1oTp082Cw8J6mzZBnz5w6RKcO5fzcSEhEBvrvriE77CoJyvfbZUL7u0TPVmpEs5epWudfdTS\nB3h/zx0UCrX8RySEx0lPlnCUV1cXVEq1BM4D8zMlWQla6+l2nC9JVkHx00/QtKlZnMlO+/ebIheH\nDsmwNXcbPRpOnIBPPsn5GBlOKHIi1QUdubbrkyyACwkp9Lp5L2WCNR9vvYUiRfIfoxC+QJIs4SjL\nhwsqY5BSarzteVWlVFN7Lq61/hHIboUiv2lchYs0a5ZrghUaat4wpN+uv94kWLLIsAtdugQH8l6/\ndfJk+Pln+PprN8QkhB2caav8VUhIxv8zQ0OvvVYyKICv/7qJxFo30acPJCZ6Lk4hhPBH9nQbzACa\nAwNsz88D/3PyviOVUtuUUu8rpUo7eS1RAMTFmU9ktYY9eyA8HN5/3zyXIWkutHKlqR6ShxIlTCno\nxx6ThZ6F17CirfJpsbHX/t/UOuvfarHiis+XBlCypFma4YJM0RJCCJexJ8m6XWs9AkgE0FrHAc4M\nLJgB1LJVgYoB8hw2KESqXbtMFcGXXzaFLoSLde9uerL27Mnz0NatoWdPM3RQCC/g6raqQChc2Az7\nrVoVOnbMfZ6lEEII+9mz9nuSUioQ0JC24GPOC+rkQWudfpWd2cCy3I6fOHFi2uOIiAgiIiLye2vh\nLTZvNsPS2rZ16LTt282bgDfeMEtpCQsUKgRDhphuqtdfz/PwKVPg1ltNB1inThlfSx2qlBMpjFFw\nREdHEx0dbfVtXNpWFSSBgeZP/okn4K42yaycf4qydSt5OiwhhPBp9lQXHAj0AxoC84C+wAta68V2\n3UCpGsAyrfWttudhWusY2+OngCZa62zfMkvhCz/0119m8du5c03JdjspBRUrwjvvwD33WBifgL//\nNuuVHTmCPbPh16wxvYo7d5pFi+0lhTEKLouqCzrVVjl5b68sfOHotbSGZ7rtZv3aJNZtK0upOlVc\nc2MhvIAUvhCOckt1QaVUHaAdpmDFWq113mOJzHkLgAigLHACmAC0ARpgPmH8B3hYa30ih/MlyfIn\nJ09C8+Ywbhw89JDdp/3yiyk8+Pnn0Lu3hfGJa9q0gREj7F6zbNgwU7fkvffsv4UkWQWXVdUF89tW\nueC+fpFkgXn9wabbifkznq8O30bhMiVdc3MhPEySLOEoy5MspVS17PZrrQ/n96b2kiTLj1y4YN64\nd+4MUVF2n/bTT9CjB5w6JW/I3Wr9etOL1by5XYefO2eGDc6da8rq20OSrILLop4sv2yrXPl3Ehqa\nsfhFTkN2k65oetbaQVjgKeYcaIMKtH9pDSG8lSRZwlHuSLJ2YMa4K6AYUBP4U2t9S35vandwkmT5\nj969oXRp+OCDbCfqZG78M5P5O97v229N59f27VCqVN7HS5JVcFmUZPllW2Xl30mu62jFXqZltUM8\n0OogT67oaE0AQriRJFnCUZavk6W1vlVrXc/29QagKbA5vzcUBdS4cTBrVo6VENKXaNcaoqOhfHlY\ntUrKtPuKzp3NdLtx4zwdiSiIpK1yrZKhRVn6XWleWX8HP2yQN6ZCCOEoh8cAaK1/B263IBbhz5o0\nMbWC7bB2rSlu8emn0KGDxXEJl5o+HZYuhQ0bPB2JKOikrXJezWYV+XBJKe7rrzh50tPRCCGEb8mz\nhLtSKv0qOAGYyk3HLItIFGirVsGgQabIRatWno5GOCo0FGbMMNUG//jDLFoshDtIW2WNzp1h8GBT\n3ObLL3NflkEIIcQ19vRkBaXbigLfAD2tDEoUTCtWmMb8yy8lwfIqly45dHjPnqbj8oUXLIpHiOxJ\nW+Wg1LXsUrfQ0OyPi4qCQ4fMlFohhBD2sauEu6dI4Qsf9csvEBMD3bvbfYpSZg7W119Ds2YWxiYc\nc/Ei1Kxp1jcrXdru006fNtUGly7NuUChFL4ouKwq4e4pvlr4wpF77doFERHmv/caNdwTjxCuJIUv\nhKOcbavsGS64DFOxKVta6x75vbnwQ/v2ma4MBxZMWrrUfF2xAho3tigukT8lSkDLlrBokRkvZKdy\n5czC0UOHwtatUKyYhTEKgbRVVrvlFhg18BQjO8axbO+NMmxQCCHyYM9wwQPAJWC2bTsP7AfesG1C\nGCdPQqdOMHGi3b1Yn30Gjz1mHkuC5aWGDIH58x0+rW9fqFvX/DoI4QbSVlnsPy8U5cABWDptn6dD\nEUIIr2fPOlm/aq0b57XPCjJc0IecP28WG+7Sxe7Fhj/5BMaMMcUu6teXoWNeKykJwsNh82a47jqH\nTj15EurVg2XLzDyt9GS4YMFl0TpZftlWectwwVQbnlvJgNcbsvtkOYLLyCLFwnfIcEHhKMvXyQJK\nKqVqpbthTaBkfm8o/FRkpHk3bWe3xbx58J//wHffmdOEFytcGPr3z1dvVoUK8Oab5tcjMdGC2IS4\nRtoqN2g1uQMdymxhwr17PB2KEEJ4NXuSrKeAaKVUtFJqPfA9MMrasITPGT8eZs60q77vnDnw/POw\nbp0Z5y98QGQkXL2ar1Pvuw/q1JFqg8Jy0la5Q0AAUz6uwkffVeKv3897OhohhPBadlUXVEoVBerY\nnu7VWl+2NKpr95Xhgn5m5kx45RWz4PANN1zbL0PH/NuZM2ZI6Lx50K6d2Sf/5gWXVdUF/bGt8qbh\ngqGhEBdnHnfiWzbRnHjKAKYcfGysG4IUIp9kuKBwlOXDBZVSJYD/ACO11n8A1ZRS3fJ7Q1FwvfMO\nTJkC0dEZEyzh/8qWNWvsREbKGzFhDWfbKqXUHKXUCaXU9nT7JiiljiilfrdtnSwI3WvktW5WXJxJ\nwrSGLy51JrRGGb7/3jxPTb6EEEIY9gwXnAtcAVJXuzkKTLYsIuGXpk83c3Oio6FWrTwPF36oQwfo\n3RseecS8Kcv8hi7zltPCqELkwNm2ai7QMZv907XWDW3bSidj9GqxsdeSqLwSp2LFzIdmo0dDSor7\nYhRCCF9hT5J1ndZ6GpAEoLW+CMgKGQXZZ5/BkiV2Hz5tGsyYAevXyyKWBd2rr8Lu3fDxx1nf0GXe\n5JNx4SCn2iqt9Y9Adr910t7l4N57TV2cRYs8HYkQQngfe5KsK0qp4tgWeVRKXQe4ZZy78EIrV8IT\nT0Dt2nkeqjW89JIZJnb6NFSrlnOvRUiIG2IXHle8uCndP3o0HDjg6WiEn7GqrRqplNqmlHpfKVXa\nBdfzG0rByy/DhAmejkQIIbyPPUnWBGAlUFUp9QmwFnjG0qiEd9q8Ge6/H774Am69NddDtTbV5BYt\nMkMEz53LvddC5un4CK3NKsMnT+b7EvXrm2KU99wjZd2FS1nRVs0AammtGwAxwHQnr+d32rWDShVT\nKEW8p0MRQgivUii3F5VSCtgL9AaaYYZNPKm1Pu2G2IQ32bkTevUyayW1aJHroVqbRYbXrTMJVrly\n7glRuIFSULSoyZ5Hjsz3ZR5/HDZsgKefhv/9z4XxiQLJqrZKa30q3dPZwLKcjp2Ybo3AiIgIIiIi\nnLm1V0idN5n+eWZKweSGS+n1QysuXw6maFH3xSeEEK4UHR1NdHS0y66XZwl3pdQOrXXu3RYWkRLu\nXuLqVdNz9eKLZtGjbKQv7ZsdKe/rR775xowR2rTJqcucOwcNG5qS/v36ZX1dSrz7LytKuLuirVJK\n1QCWpV5HKRWmtY6xPX4KaKK1HpDNeX5Rwj3fLlygbamf6ft8HR6bXNnT0QiRLSnhLhxleQl34Hel\nVJP83kD4gcBA2LgxxwQLTIKVnAwPPWQ6us6eleGAfqtDB9i3z+lJVaVLw+LFpkPsr79cFJsoyJxq\nq5RSC4BNwI1KqcNKqUhgmlJqu1JqG9Aas+CxyKxkScI5wsvTi3HxoqeDEUII72BPT9Ze4HrgEHAB\nMwxDa63rWR6c9GT5DKVg8GA4fBiWLYOgIE9HJCw1YgRUqmQm3jnp3XfNkMHNmzP+3vjEJ/giXyzq\nyfLLtspX/g5Kqgt0LBpNi5ENGfN6JU+HI0QW0pMlHOVsW5VjkqWUqqm1PqiUqp7d61rrQ/m9qb0k\nyfIeeQ0HLFwYIiLgyy+hRAm3hSU8ZdMmePZZ+OEHpy+lNTz8sKmlsXQpBNj6133lzaVwnCuTLH9v\nq3zl7yA0FHrEzeVLenGOMjJEXHgdSbKEo6xMsn7TWjdSSq3VWrfLd4ROkCTLQ5KToVDGmig5NfSX\nL5v5NElJ8PnnZoFKUQBobX5PChd2yeWuXDFVyiIiYNIksy+3xF7ewPk2FydZft1W+UqSBcCFC/Tr\ndI5G3Svz7LM+FLcoECTJEo5ytq3KrbpggFLqOcz49NGZX9RaSylbf3T2LLRvb1YPbpL79IbEROjT\nxxSb++ILKFLETTEKz1PKZQkWmN+dzz+Hpk2hbl2TuOeWRCmXDjQTPk7aKm9RsiQT3iuJHxRWFEII\np+VW+OI+4ComEQvKZhP+JiEBOneGO+6Axo3tOjQ4GD77TBIs4bwKFcxw05Ej4ZdfPB2N8CHSVnmR\nm2+Gu+4yoxrSLzgfGurpyIQQwr3sKXzRWWv9rZviyXxvGS7oLgkJ0KUL3HKLqUSQqasg/ZCV06dN\ngtWwoenwCgz0QLzCby1bBsOHm+le11+f/TE+NYRKZGFR4Qu/bKt88Xd97164807Yv998EAe++X0I\n/yLDBYWjLC/h7qlGS7hRfDx06gQ33WSyplzGYh09Cq1bm/kzM2dKgiVcr3t3eOkl8yt54oSnoxG+\nQtoq71Gnjvn7/e9/PR2JEEJ4jj3rZAl/d/iwWdxq5sxrpd2ysX+/+XRy8GCYMkXmxQib2bPNBD0X\nGjYMBg2Crl3h/HmXXloI4QYvPn2Bt1+7zNmzno5ECCE8I8d31Eqpe2xfa7ovHOERdevCa6/lmmCB\n6cF65hkYO9ZNcQnfsGABrFjh8stOmGCGpPbq5fIcTvgRaau80w3XpdD18he89fwpT4cihBAekdu7\n6nG2r5+7IxDheaGhGScqZ95eew0eecTTUQqvM3CgSbRcTCkzerVsWejb15R5FyIb0lZ5o6Agxj9+\nlv97v1iuaywKIYS/ym2drDWABpoAWVYc1Vr3sDY0KXzhbtlNTF67Fvr3h7lzzdAtIbKIi4MaNcyw\n09KlXX75pCRT0l1rWLTIVI7Pa3FsWUfLu7l4nSy/bqt8umBEQgIPVfiKSg90YvLMcr77fQi/IIUv\nhKOsXIy4CNAQ+Ah4KPPrWuv1+b2pvSTJssCZM7BuHdxzT5aXMjfmixaZctpLlkCrVm6MUfieXr1M\nxYqhQy25/JUrZk224sVNp1mh3Fb4w8ffmBYALk6y/Lqt8vXf5X+emUGjtwcTeyXIp78P4fskyRKO\nsqy6oNb6itb6J6CFrZH6DfhNa73eHY2WsMDx42Zi1W+/5Xnof/8Lo0fDd99JgiXsMHAgfPKJZZcv\nUgQWL4Zz52DIEEhOtuxWwsdIW+XdaowfTN+ALwjinKdDEUIIt7KnumBFpdRWYBewWyn1m1Kqrj0X\nV0rNUUqdUEptT7cvRCm1Win1p1JqlVLK9eOLRFb//GNKAw4caEoD5iAlBZ591syF2bgR6tVzX4jC\nh3XrBi++aOktihUzixWfOQP33SdztEQW+W6rhIWCgnh+YxcSKM3p054ORggh3MeeJGsWMFprXV1r\nXQ142rbPHnOBjpn2jQW+01rXBtZxbdKysMrevaY7atQoGJf7j3vIELMI7MaNUL26m+ITvq9YMdNL\narHixeGrr+DqVbj7brh0yfJbCt/hTFslLFStYTnAFE8SQoiCwp4kq6TW+vvUJ1rraKCkPRfXWv8I\nZJ6e3hOYZ3s8D7jbnmuJfEpJMR/7T5pkJljlICHBfD13zgwRLFvWTfEJ4aCiRc18wZAQ6NLl2u+u\nKPDy3VYJ65UuDdOmXatWGxrq6YiEEMJa9iRZB5RS45VSNWzbC8ABJ+5ZQWt9AkBrHQNUcOJaIi8B\nAfDjj6aLKgcnTkBEhHm8dCmUKOGe0ITIr8KFYf58uP566NAh90qDosBwdVslXOjsWfM539NPm0Ie\n8jcrhPB3edToAmAoEAUsxZTJ/cG2z1VyLfUyceLEtMcRERFEpGYDwn6lSuX40t69pjT74MHw++95\nV20TwlsEBsKsWfDUU9C2LaxeDeXLezoqkZ3o6Giio6Otvo3VbZVw0rhxULcujBnj6UiEEMJ6OZZw\nd9kNlKoOLNNa17M93wNEaK1PKKXCgO+11jflcK6UcLfQ99+bkYSvvAIPPuj7pYKFlzh6FCpXNr9Q\nbqA1jB9vemHXrIHwcPld9nauLOHuDaSEu/1GNfsJdV0t3lpQwa++L+H9pIS7cJRlJdxdSNm2VF8D\nD9geDwG+ckMMBYPW8NNPdh364YcmwVq40CRYQrhMhw6webPbbqcUTJ4M999v6rv884/bbi2EcNDY\nnnuYt6i4p8MQQgjLWZpkKaUWAJuAG5VSh5VSkcAUoL1S6k+gne25cFZKiqkeOHw4JCbmetjzz5s6\nGOvXm2FWQrhU//6WrpmVk7FjzdBBWddNCO8V9vRAhpb8jCr86+lQhBDCUnkmWUqpO+zZlx2t9QCt\ndWWtdVGtdTWt9VytdZzW+i6tdW2tdQet9dn8BC7SSUw03VJ//AEbNphy2tm4dMm8/42ONh1edeq4\nN0xRQAwYYFYOTkpy+61HjoSoKPN4+/bcjxX+xZm2SrhRkSI881p5Egji8D8pno5GCCEsY09P1jt2\n7hOecPYsdOpkHq9cCWXKZHvYyZMQFGRKX2/aBBUqXCulm7qFhLgxbuG/atUyZf9Wr/bI7SMjzdcO\nHeCXXzwSgvAMaat8RIUHu9OXJYwbdNjToQghhGVyrCWnlGoOtADKK6VGp3spGAi0OjCRVWho1rK3\nK+jPX9TnKd6kzHcBxMZmPW/HDujZ0yzgmpLitnoEoiAbONAMGeza1WMhzJ5tbr9kiQwh9GfSVvmg\ngAD+5EYObg9h82Zo3tzTAQkhhOvlVrC7CFDKdkxQuv3xQF8rgxLZi4vLpsrUmY/pXLYsT2KSsNwS\nqJAQSbCEm9x7Lxw75tEQuneHBQugTx+T73Xo4NFwhHWkrfJBu0JaERcHLVqY5yEhZPshoRBC+Ko8\nS7grpaprrQ8ppUoBaK3PuyUypIR7Zo6U8k1JMXNT5s6FL76ARo2sjU0Ib5L+b2XjRujVCz74ALp1\n82xcwrCihLu/tlX+VsI9vZQUaNYMnnjCrNXor9+n8A5Swl04ytm2yp6lZ4OUUluBUNsNTwNDtNY7\n83tTYa2EBNNgnT5t5qRUrOjpiITwnDvugOXLTYL18cfSo+XHpK3yMQEB8NZb0K+fpyMRQgjXs6fw\nxSxgtNa6uta6OvC0bZ9wp8RE+rMgz8P27zfj2ytWhHXrJMESAqBpU9OjO3Cgqa4p/JJTbZVSao5S\n6oRSanu6fSFKqdVKqT+VUquUUqUtiLtAa9EC7rzT01EIIYTr2ZNkldRaf5/6RGsdDZS0LCKR1ZEj\n0KoVPfjaVK/IwerVpsEaORLeew+KFHFjjEJ4kdT5h+m3li1N726bNhAc7OkIhQWcbavmAh0z7RsL\nfKe1rg2sA8Y5G6TI6rXXIJBk9uzMuX0TQghfY0+SdUApNV4pVcO2vQAcsDowYfPDD+Zj+D596M9C\nCMxaLCt1/lVkpFme6JFHPBCnEF4kNtbM78huW7nSDKmV8u5+x6m2Smv9I5Cpfis9gXm2x/OAu10T\nqkgvPBxGBszgrltj0j4UCQ31dFRCCOEce5KsoUB5YKltK2/bJ6ykNcyYAX37muoVzz4LZJ17d/o0\ndOlihgb++quUqhZeqEcP2LfP01Gk6Wjrq+jRA/7+27OxCJeyoq2qoLU+AaC1jgEqOHk9kYM3/mhP\n5cCTfPB6LFpnXa5ECCF8TZ6FL7TWccATSqkg89R9FZsKtIsXzfi/jRvNwq7Z+PlnUym7Xz945RUo\nZE8ZEyHcrUYNU0v9xRc9HUkGL71k1vHetEnmLvoDN7VVOZYmmzhxYtrjiIgIIiIiLLi9/wqsexOz\nhs2i03M16DZYk92HikIIYaXo6GiiXThx254S7rcC87FVbALcVrFJSrhnlFrKN7WTKyoKZs2Cu2UA\ni/BmP/8M998Pe/d6zUJtqX9LEyfCsmWmGEZQUF5nCVexqIS7022VUqo6sExrXc/2fA8QobU+oZQK\nA77XWt+UzXlSwt0Vrlzh6coLOHVzBB/9UKPgfN/CLaSEu3CUs22VPcMFPgmM2gAAIABJREFU30Oq\nC3qN+HhTIW32bNi8WRIs4QOaNjUFW377zdORZDFhgllDrm9fuHLF09EIJ7mirVJk7EL5GnjA9ngI\n8JWzQYpcFClC1Gc38eNPgUCKp6MRQginSHVBb3D1Kly+bNehDRuaT9w3b4brrrM4LiFcQSkYMAA+\n+cTTkWShlOkVLlYMHn64APUY+Cen2iql1AJgE3CjUuqwUioSmAK0V0r9CbSzPRcWKtXudj74qhwQ\nwJkzno5GCCHyT6oLelpMDNx1F8ycmeMhKSkwxda0T51qyrMXL+6m+IRwhYEDzbBBL1SokJkytm0b\nTJ/u6WiEE5ytLjhAa11Za11Ua11Naz1Xax2ntb5La11ba91Ba33WwviFTURn08A9/riHAxFCCCc4\nWl3wc6AcUl3QNb7/3oxVat3aLG6VjWPHoEMH+OYb87xPHzfGJ4Sr1K5tirh4qZIl4auv4I03YMUK\nT0cj8knaKj/z22+wZImnoxBCiPzJtR6dUioQeF5r/YSb4ikYkpJMabP334f586F9e8CsC5Jb2dqQ\nEDfFJ4QVvKToRU6qVYPPP4eePU0hjJtv9nREwl7SVvmnDz+EXr3MQuJhYZ6OpuDQGk6ehKNHzduV\n4sWhZk0pDiSEo3JNsrTWV5VSLd0VTIExaZJZCXXr1gwtR1yc+c/twgV45hlYvhw+/hjuvNODsQpR\ngDRvDq+/Dt27w5YtULaspyMS9pC2yj81bw4PDU3h/l4XWLkxiAB7xt6IfDl1ChYvNivH/PgjcOUy\nVThCEZXMBUrwz6WKhJe/QqeexYgcVojbbvN0xEJ4P3tKuL8LhAOLgQup+7XWS60NzY9LuF+6BEWL\nkrnFUMr85zZkCLRoAW+/Lb1XQlghr7LYzzxjhiqtWiXrz1nBohLuftlWFagS7umkjuyoxT7KcYZj\nRWvxb2J5T4fld7ZsMXO9166Frl3NIu0tW0L4pX3XVms/e5aU3Xv5Y8VRlpV9gNl7WnL99WYOqy8l\nW1LCXTjK2bbKniRrbja7tdba8rHufptkZSMx0XTJh4XB//4HvXt7OiIh/Fdeb1yvXoWOHU31+Vde\ncV9cBYVFSZZftlUFNclK7/Crn3Dbc51YEV2S21sX83Q4fuGXjVcY9+AJ/j5clP9MLc+QB5R9wwG1\nJvmqYu5cGD/e1DR69VUoUsTykJ0mSZZwlOVJlif5RZKVmGjqQ+fi99/NWq27dsGJE1ChgptiE8IT\nZs0yxV5q1/ZYCPa8cT11ytSl+b//M5/uCtexIsnyJEmyLKY1owOm82XQ/fx+uDxlyng6IN91/Jjm\nuYH/sOqHErx086cMeacJhVu3yNe1zpyByEjzvuXLL6FSJRcH62KSZAlHuWMxYpEf58/DY49Bv345\nHnLlCkRFQadOMHas2ScJlvB7Bw6Yoi9ernx5WLQIHnoI9u/3dDRCFGBK8T7D6BKwisi7/iVF1il2\nmNYwb2oM9Wucpfzu9ez9Yi8PbX+Sir1aoBRpW2io/dcsW9ZUZe1WYyctG11k3z7r4hfCF0mSZYXv\nv4d69cwCw/Pnp+0ODSXDf2ZFi8LEieYT88GDZf6VKCAeeMBUdElO9nQkeWrWDF580SydcOmSp6MR\nouBKIJg31tTjxMELvPKy9EY44tgxU8znzXeLsvrJFUw7Npjg7q2BawW3UrfcKhxnRykY//hZxia8\nQMTtlzh40IJvQAgfJUmWK124wKxij3Ok7WC6HPw/1AdzUGVKpyVVAPHxZoHFsDD49FOz0HDqf26x\nsZ4NXwi3qFMHatSAlSs9HYldRoww5dxHjPB0JEIUbEWb1GPJjjrMfE+lrR0pcvftt9CwoRn6vOWv\nEBq8NhACA+0+P/OHw9n2erVsybANg3n+ygTa33GRmBhrvhchfE2eSZZSqqJSao5S6lvb85uVUg9a\nH5oPWrqUopfjqRK7gxW6S4ZPh7SGjz6CunXNSMJdu8xIQi9fOkgIa0RGmkVwfIBSZhrZpk2mA054\nJ2mrCobKlc0w3sjIa8XvRFbJyTBuHAwfbn5eUVH5K06Ruacrx16v227j0ZU9GXTuf/TucJ4rV1z2\nrQjhs+ypLvgtMBez0GN9pVQhYKvW+lbLg/O1whdaowJUlknKJ07AqFGmVOp778Fdd3kmPCG8xrlz\nUL26mZ/lyCQAF7Fn4e/MPcvbtpl1wzdtghtusDY+f2dRdUG/bKuk8IWR+efw3nvw1lvm71GG2md0\ndH8i/XpdJii8NPPnm/mlOcn8c83reW7nAqQsWUrvoaWpPLAtM971rk+RpfCFcJQ7Cl+U01ovAlIA\ntNbJwNX83tCvZeqWSk42a13VrQvVqsGOHZJgCQFA6dKwfbtHEiwwCVROn87mNC+hQQMzP6t/f+RT\nWu8kbZUfCwnJOFRt3Djo3Bl69dJcvuzp6LzHlm9OcftN8XQOWM033+SeYGUn88/Z0QQ2oG9v5v/R\ngLXrVPop6UIUSPYkWReUUmUBDaCUagacszQqP7BhgxkHvWyZeTx1KpQo4emohPAi1ap5OgKHjRwJ\n4eHmDZ7wOtJW+bHMH4zExcHrr0O5g78S2fGYVBwEFkzaT7ceihn91vP81r4E5GPWfeafsyNzxVPn\nb5WuVZa//jI1jg4dcjwGIfyFPX+Co4GvgeuUUhuB+cDjlkbl4wYNMtv48bBmDdx0k6cjEkK4glLw\nwQeweLGZUC68irRVBUxAAHw0L4V/Nh7hhaFHPR2Ox6SkwLjuO3lhYiHWvbWDHh/d45YJ35l7vSDr\nqIDISCQBFgVWodxeVEoFAMWA1kBtQAF/aq2T3BCbT9HajA8HqFoVdu+GUqU8G5MQwvXKljUFMPr1\nMwuJe/sCnAWBtFUFV/GI2/n6w1XcMSSUCpVPMuqVgrXYZEICDLrnMud+TGTL+kKUa9nGbffOq5er\nTBmzok1qMcPs5roK4c9y7cnSWqcA/9NaJ2utd2mtd0qjlT2lri378+qrkmAJ4c9atYKHHzY91ldl\n1o/HSVtVsJUb2JE1U37nrWlXeH9awXkXf/AgtGgBYdWLsvpMI8q1rJPlmMwl2N05DTYuDvbs1pQL\nSebYMcfX4BLC19kzXHCtUqqPUlJsPC//+Y+nIxDCBx0+DNHRno7CYS+8AElJZl6I8ArSVhVg1cbc\ny5r/rGbCSwF8+qmno7He+vUmwXr4YZg5E4oUzf7X3tnFhp1Vp9xphiX+H08Pk+mRouCxp4R7AlAS\nSAYSMcMwtNY62PLgciiLW6NGDQ7JbErhg6pXr84///zj6TC8y+bNcP/98Oef5GumtgXsLZt9+DA0\nbgzLl0PTptbH5S8sKuHudW2Va64tJdyzk9PPZecfV7mrYyCzZ0P37u6Pyx1mzTJzvj/5BO69N2Pi\nlHlIXuafU+blK9wxhO/itP/j5gl9OZRYkfR/9u6ORUq4C0c521blmWRZRSn1D6byUwqQpLXO8hYl\np4bL9k1bHqMQria/u9nQGurXh+nTvWaNA0fe2C5ZAmPHwtatEBRkbVz+wooky5MkyXK/3H4uv/wC\nXbvCggVe81+KSyQnaUa33cqafbX4ekMZbrjBO5KoPCUn8+V1TzPk8EucSSpNIVs1AEfW5HIFSbKE\no9yxThZKqRClVFOlVKvULb83TCcFiNBa35ZdgiWEKCCUgkceMauL+qC+fSEiAh6XOnYeZ1FbJXxM\nkybmw4/+/WHjRk9H4xqxxxLpXG0Xf/9xiZ/WJOS4ILozJdgtU6gQPef3oTZ7mfe+TJUUBUeeSZZS\n6iFgA7AKiLJ9neiCeyt77i+EKAAGDYK1a+Hffz0dSb68/bYZ9bhwoacjKbgsbKuED2rVylQB7XW3\n5vcfL3o6HKfsWX+S22udol7wQZYfaUDpulU9HZLDVOtW9GUJL469woULno5GCPewJ8l5EmgCHNJa\ntwFuA8664N4aWKOU+kUpNcwF1xNC+KrgYDMva8YMT0eSLyVLmgTriSdMxS/hEVa1VcILZV6jKbuq\neR07wntdv6ZL20R2/Zbo/iBdYMXbf9O6bQAvdN3KG3u7ERhc0tMh5ds0nuXODsWZPt3TkQjhHvYk\nWYla60QApVRRrfVezDokzrpDa90Q6AKMUEq1dME1vdqhQ4cICAggxQUr89WsWZN169bZdey8efO4\n8847054HBQW5rPjCq6++yvDhwwHXfn8A//77L8HBwTKHqaB49llTKstHNWxo5mYNHHhtOQfhVla1\nVcILZR4Wl1PVvF5zuvF640/p2PI8+/dccW+QTtAaXnsNhr1ak6+m7GXI5z3cssCwlc5QjlemBPDW\nW3DypKejEcJ6uS5GbHNEKVUG+BLT8xQHOF3aT2t93Pb1lFLqC6Ap8GPm4yZOnJj2OCIigoiICGdv\nbamaNWsyZ84c2rZtm+3rnqounP6+CQkJeR6/fv16Bg0axL95DN8aN25cjvdxVOafXdWqVYmPj8/3\n9YSP8YNVfZ96ClavhpdeMpswoqOjiba+TL8lbZXwcYGBDFo/jIuNZ3FXk3vYsLMsVWsEejqqXCUm\nwrBhsHs3/PRLIapWvfYZdHaFLXxJrVpmrtxrr3k6EiGsl2eSpbXuZXs4USn1PVAaWOnMTZVSJYAA\nrfV5pVRJoANmDH0W6ZMs4T5a6zwTpqtXrxIY6N2NlRDuFBAA8+bBbbeZqmatpOwCkPUDsqiobP+7\nd4oVbZXwE4ULM/znBzl/61zuatCLDXsrUjHMO3uFjh2DXr2gZk344QeoUiVrUuXrgzvGjYN69bLu\nTx0Cmv65VxTuECKf7Cl8US11Aw4C24AwJ+9bEfhRKbUV+AlYprVe7eQ1vU5KSgpjxoyhfPnyXH/9\n9XzzzTcZXo+Pj+ehhx6icuXKVK1alfHjx6cNjTtw4ADt2rWjXLlyVKhQgUGDBtndqxMbG0uPHj0o\nXbo0zZo1Y//+/RleDwgI4MCBAwCsWLGCW265heDgYKpWrcr06dO5ePEiXbp04dixYwQFBREcHExM\nTAxRUVHcc889DB48mDJlyjBv3jyioqIYPHhw2rW11syZM4fw8HDCw8N544030l6LjIzkxRdfTHu+\nfv16qlY1E3jvv/9+Dh8+TPfu3QkODub111/PMvzw+PHj9OzZk7Jly3LjjTfy/vvvp10rKiqKfv36\nMWTIEIKDg7n11lv5/fff7fp5CZFZ5vkembfs5n+kCguDOXNg8GD3L/xZkFnUVqVe+x+l1B9Kqa1K\nqS2uuKZws2LFGL11MAMb7KZ9e+988/7jkhiaNtV0727meJYokXUxYW+M21Hh4abWUWb2DgEVwlfY\nMyfrG2C57eta4ADwrTM31Vof1Fo3sJVvv1VrPcWZ63mrWbNmsWLFCv744w9+/fVXlixZkuH1IUOG\nUKRIEQ4cOMDWrVtZs2ZNWuKgtea5554jJiaGPXv2cOTIEbt79R577DFKlCjBiRMnmDNnDh988EGG\n19P3UD300EPMnj2b+Ph4du7cSdu2bSlRogTffvstlStXJiEhgfj4eMLCzHuVr7/+mnvvvZezZ88y\nYMCALNcDMzRo//79rFq1iqlTp+Y6dyz13Pnz51OtWjWWL19OfHw8Y8aMyXLtfv36Ua1aNWJiYli8\neDHPPfdchiFIy5YtY8CAAZw7d47u3bszYsQIu35eQmSWubHPvOXV+HfpAnffDcOH+/6nzj7E5W1V\nOrLkiD8oVYrx37elYydF585gx8h5t9Aa3hz4K33uDaBU7L+MH296xZXyveGAuUn/4dWG//5OcS5y\n/LinoxLCOnkmWbYkqJ7t6w2YuVObrQ/N9y1evJhRo0ZRuXJlypQpk2H+0okTJ/j222958803KVas\nGOXKlft/9u48PIoqe/j494QEwpJAwpqQEDa3QQEHdUQBQUZB2VyGVRBxRx0EdUZUNCD6E3DEZeaV\nEUQFFFTcQdw1KAiDO4iiCBICIbIbJKzJef+oSuiETtJJutPdyfk8Tz2pruXWqZvqun2rbt1i7Nix\nLHD7gG7Tpg09e/YkMjKShg0bMm7cOJYuXVrqNvPy8njttdeYPHky0dHRtGvXjpEjRxZaxrMjiZo1\na7J27Vr27dtH/fr16dixY4npd+7cmX79+gEQHR3tdZmJEycSHR3NqaeeyqhRowr2yRfFdXKRkZHB\nihUrmDp1KlFRUXTo0IFrr72WuXPnFizTpUsXevXqhYgwYsQIVq9e7fN2TYj56iv4+edgR1EhU6c6\nu1DkGocJkACXVfbKkSpCBKZNg44doX9/OHAguPHs23GQIW2/5PlXo1n59m5+OtCiyt25yud58eqb\n3a24IXouU8ZbB6Cm6ipzoaGqXwN/CUAs/jNxovc2PsXdCfK2vB+eBcvMzCxoDgeQkpJSML5582aO\nHDlCQkIC8fHxxMXFceONN7Jz504Atm/fztChQ0lKSqJBgwYMHz68YF5JduzYQW5uLklJSV63W9Sr\nr77K22+/TUpKCj169GDlypUlpu+5P96IyHHbzszMLDXu0mzbto34+Hjq1KlTKO2tW7cWfM6/2wZQ\np04dDh486LeeDk0lS0sDj6al4Sg62mnyM348rFsX7GiqHz+XVfbKkSpExHlbRLNmMGgQHAnS+3F/\nfD+Dv7TIJFZ/Z/nmFrS66OTgBBIMcXHcefMfzHsxEo9i3JgqxZdnsm7zGO4QkflAxX81B9LEid7b\n+JRUyfJ12TJISEgo1Dtfevqxjq6Sk5OJjo5m165d7N69mz179rB3796Cuy933303ERERrF27lr17\n9/L888/71JV548aNiYyMLLTdzZs3F7t8p06deOONN9ixYwcDBgxg0KBBQPG9BPrSe2DRbScmJgJQ\nt25dcnKOvRRyW5F2AiWlnZiYyO7du9nv8RbDzZs307x581LjMWHo+uudlxOvXx/sSCrkT3+CyZNh\n2DA4dCjY0VRtAS6rqt0rR6q6GjVg7lzg6BGuvHAbubmVt21VmDcPuvWN5Y5LNzJrw/lEN4mtvABC\nRLN7r+PqGnOYMt4evjJVky93smI8hlo47d0HBDKoqmLQoEE88cQTbN26lT179jB16tSCec2aNePC\nCy9k3Lhx7Nu3D1Vl48aNfPrpp4DTzXq9evWIiYlh69atPOxjf6cRERFcdtllTJw4kQMHDvDDDz8w\nZ84cr8seOXKE+fPnk52dTY0aNYiJiSnoLbBp06bs2rWrzF2oqyqTJ0/mwIEDrF27lmeffZYhQ4YA\n0LFjR5YsWcKePXvIysri8ccfL7Rus2bNCjrk8EwPICkpiXPOOYe77rqLQ4cOsXr1ambPnl2o0w1v\nsZgwFRMDN9/stOsJczfcACkpcM89wY6kygtYWeX5yhEg/5UjhUycOLFgqITu6o0fREXBy/+3gd+W\nb2D0xemV8vzk7787nT489BB8uLIeV8//a9i//6rc6tfnH2MO88LLUfihwYsxFZaWllboXF5hqhqy\ngxPe8YqbHgpatWqlH330kaqqHj16VG+77TZt2LChtm7dWp988kmNiIjQ3NxcVVXNzs7W0aNHa1JS\nkjZo0ED//Oc/60svvaSqqmvXrtVOnTppTEyMnn766Tp9+nRNTk72up2iduzYoX379tX69evrX/7y\nF73vvvu0a9euBfMjIiJ0w4YNevjwYe3du7fGx8dr/fr19ayzztLly5cXLHfNNddow4YNNS4uTrdt\n26YTJ07UESNGFNqW57RNmzZpRESEzpo1SxMTEzUhIUH/9a9/FSx78OBBHTx4sMbGxmqHDh30scce\nK7RPb775prZo0ULj4uL0kUceKUgvP7+2bt2qffv21fj4eG3btq3OnDnTaxyeseSvGypC+dgNOTt3\nqsbFqWZkBDuS45T137hzp2pSkuq77wYmnnDjfg+CXsb4MgB1gHrueF1gOXBhkWUCkEsOO2X4piL5\nlP3Jl3pW5Jd6+8BNmpfnv5iKWrFCtVUr1RtvVN2/3/sy1e7/vXevjmv5mo655ehxs/ydF0ysbplr\nKqqiZZVoKZduRGQRTnv04ipp/Stc0yt+2+otPhGxuxQmLNmxW0a33QaRkSF3R0uk7L0GfvyxcwX7\n22+hSZPAxBUu3O+BXy/fB6qsEpFWOHevFOfdki9okR5xiyur/KE8x1p1VNF82r1oOeddGsfAq+px\n39Mt/BcYEN8gj32/H+UoNYHj3//k+YLh6vhuqKwsp2n12rWF30nv72NfJgmaal8m47uKllWlvowY\npxvcZsDz7uehwG/AG+XdqDHG+OSee5y+jENM0ZdmFp3n7UfS+efDyJEwahQsXlx9WwgFUEDKKlX9\nFSi521UT9uL7ncsHz6fRY3gk+3O3M+WZJn75jn79ykYa/n6Us08QZn58AklJTqWq6Et3q3NFulkz\nuPJK51rao48GOxpj/MeXO1lfquoZpU0LBLuTZaoaO3arvpKuvh45Auee69zRGjOmcuMKJQG6kxVy\nZZV/0q7eP8B95a982vnZj/S942ROPkV46imoVat86ezfc5gHB6zi6WUncZqu5sOjPZAaoXfBKFRk\nZsKpp8IPPziVLrA7WSb4KlpW+fKNrysirT022AqnXboxxpgyiIpyunWfPBnsNW5+Z2VVNeb5olsR\n526Rp/y7R97meWrU9RQ++ljIzoauXWHTprLFkZcHz9/2NSc32cWvG3JZ/cVhPqanVbBKkZjoXHzy\nsY8vY8KCL3eyegMzcZpiCJACXK+q7wc8OLuTZaoYO3arPl+uvs6bB1OmwBdfgMer36qNAN3JCrmy\nyj9p252s8iiab56fPZ+BAu9NfFVh+nSnF8CcnMIvLfa2/NGjsHCh873esSaLJE3nC/c1bdXxOavy\n2LoVTjtNWbdOaNLE7mSZ4KtoWVVqJcvdSC0g/y1561S1Ut74YpUsU9XYsVv1+fLDQBVGjHD69Hj2\n2er3fFYgKlluuiFVVvknbatklUdJlazSlvX0889w6kmHaVjrD665aBsXXducLn0b8McfsHtnHt+u\njuCjj+Dll6FNG7jrLujTx/5n5XL0KLc0XECdwf2YNrOBVbJM0AWsuaCInCkizQDcgqoDcD/wsIiU\ncLPdGGMCYNMmuP32YEfhFyLw1FPw9dfw5JPBjia8WVllAunEE52/H01azsHvfuLW/hupz14a18uh\nY8s99O/vfJc//hg++wwuvji48Ya1yEjGX72d2XMj2bEj2MEYU3HF3skSka+Bv6rqbhHpBrwI/B2n\nl6VTVPVvAQ/O7mSZKsaO3Qo4fBjat4dHHnEuFYeoslx93bABzjnHaWbUrVtg4wol/ryTFcpllX/S\ntrsi5eGvO1nHzVd12hqKQN26ULOmT80PjY/27OGmhNeJveoyZr7cwK/5aneyTFkFsuOLGqqafzgP\nBmaq6quqei/QtrwbNMaYcqlZEx57DMaNg0OV0gos4Nq0gblzYcgQ2LIl2NGELSurTOXJ7zkjLs45\nJ+H88HdenesMVsGqgLg4xt+wh1nPRfLzz4Xz1bPCZUw4KLGSJSL579HqCXzsMc+X92uZKigiIoKN\nGzf6tOykSZMYMWIEABkZGcTGxvrtLs7o0aN58MEHAVi6dCnJycl+SRdg2bJlnHLKKX5Lz/hR795w\n0knw+OPBjsRvevWCW2+Fyy6DgweDHU1YsrLKmCqkReooBrKQ6fftDXYoxlRISZWsBcBSEXkTOAB8\nBiAibYHfKyG2sDV//nzOPPNMYmJiaN68OX369GH58uXBDos5c+bQtWvXCqUhZXxCP3/55ORksrOz\nS13f1xhnzJjBPffcU+64PBWtOHbp0oUff/yx3OmZAHv0UeetlZs3BzsSv/nnP6FVK7juOmsaVg5W\nVpkKKa37d1PJ4uO5a2ocTy2IYdeuYAdjTPkVW8lS1QeB24HngC4eDc4jcNq7Gy+mT5/ObbfdxoQJ\nE9i+fTubN2/m5ptvZtGiRWVOKzc316dpvlLVClVG8tMIJF9izMvL8+s2K5onppK1bQv33Qe//BLs\nSPxGxOll8OefITU12NGEFyurjDdFK05xccUvW7S5H/i+rgmMlFsv4fKBNXj00WBHYkz5lfh2PFVd\nqaqvq+p+j2k/q+rXgQ8t/GRnZ5OamsqTTz7JgAEDqF27NjVq1ODiiy9mypQpABw+fJixY8fSvHlz\nkpKSGDduHEeOHAGONXubNm0aCQkJXH311V6nASxevJjTTz+duLg4unTpwpo1awri2LJlC5dffjlN\nmjShcePGjBkzhnXr1jF69GhWrFhBTEwM8e6lusOHD3PHHXeQkpJCQkICN910E4c8nnd5+OGHSUxM\nJCkpiWeffbbECsmmTZvo3r079evXp1evXuzcubNgXnp6OhEREQUVpOeee442bdoQGxtLmzZtWLBg\nQbExjho1iptuuok+ffoQExNDWloao0aN4r777itIX1V56KGHaNy4Ma1bt2b+/PkF83r06MEzzzxT\n8Nnzbtl5552HqtK+fXtiY2NZuHDhcc0P161bR48ePYiLi+O0004rVGEeNWoUt9xyC3379iU2NpbO\nnTvz66+/lnygmIobMwbOPz/YUXhV9Mdd0aG4q+R16sCiRfDCC+BxuBofWFlliqrIc1L2jFVouPtu\nmDHD8t+EL3sFuR+tWLGCQ4cOcckllxS7zAMPPMCqVatYvXo13333HatWreKBBx4omJ+VlcXevXvZ\nvHkzM2fO9Drtm2++4ZprrmHWrFns3r2bG264gf79+3PkyBHy8vLo27cvrVq1YvPmzWzdupUhQ4Zw\n8skn89///pfOnTuzb98+drtnrTvvvJNffvmF1atX88svv7B161buv/9+AN59912mT5/ORx99xPr1\n6/nwww9L3P9hw4Zx5plnsnPnTiZMmMCcOXMKzc+voOXk5HDrrbfy3nvvkZ2dzeeff07Hjh2LjRFg\nwYIF3Hvvvezbt49zzz33uG1nZWWxe/duMjMzee6557j++utZv359sbHmx7J06VIA1qxZQ3Z2NgMH\nDiw0/+jRo/Tr14/evXuzY8cOnnjiCa644opCab/00ktMmjSJvXv8bZYQAAAgAElEQVT30qZNm0LN\nGE31U/QHWtGhpIe3mzSBJUucd+28H/BX6BpjTOhq2RIuvZSCu1nWrNOEG6tk+dGuXbto1KgRERHF\nZ+v8+fNJTU2lYcOGNGzYkNTUVObNm1cwv0aNGkyaNImoqChq1arlddqsWbO48cYbOeOMMxARRowY\nQa1atVi5ciWrVq1i27ZtTJs2jejoaGrWrMk555xTbDyzZs3i0UcfpX79+tStW5fx48ezYMECABYu\nXMioUaM45ZRTqF27NhMnTiw2nYyMDL788kvuv/9+oqKi6Nq1K/369St2+Ro1arBmzRoOHjxI06ZN\nS+1oYsCAAZx99tkABfniSUSYPHkyUVFRdOvWjT59+vDyyy+XmKan4ppBrlixgv3793PnnXcSGRlJ\njx496Nu3b0EeAVx66aV06tSJiIgIrrjiCr799luft2tMUSedBK++CsOHwxdfBDsaY4wJngkTnLtZ\nmZnHX8Cy3gZNqKuSlaySmuqUZSirhg0bsnPnzhKfGcrMzKRFixYFn1NSUsjMzCz43LhxY6Kiogqt\nU3Raeno6jzzyCPHx8cTHxxMXF8eWLVvIzMwkIyODlJSUEit6+Xbs2EFOTg6dOnUqSOuiiy5il/uk\naWZmZqFmcykpKcVWRjIzM4mLi6N27dqFlvemTp06vPTSS8yYMYOEhAT69evHTz/9VGKspfUeGBcX\nR3R0dKFte+ZreW3btu24baekpLB169aCz82aNSsYr1OnDn/88UeFt2uqty5d4OmnoW9fWL062NEY\nY0xwtGwJ1w7K5p6brM2gCT9VspJVUlOdsgxl1blzZ2rVqsUbb7xR7DLNmzcnPT294HN6ejqJiYkF\nn70981R0WnJyMvfccw+7d+9m9+7d7Nmzhz/++IPBgweTnJzM5s2bvVb0iqbTqFEj6tSpw9q1awvS\n2rt3L7//7nTIlZCQQEZGRqFYi3smKyEhgT179nDgwIGCaZtL6P3tggsu4P333ycrK4uTTjqJ66+/\nvtj9L2l6Pm/bzs/XunXrkpOTUzAvKyurxLQ8JSYmFsqD/LSbN2/ucxqmEsyYAV99FewofOLrM1v9\n+8MTTzi91q9bF9yYjTEmWO4+433efTuPr77wb6dXxgRalaxkBUtsbCyTJk3i5ptv5s033+TAgQMc\nPXqUd955h/HjxwMwZMgQHnjgAXbu3MnOnTuZPHlywbukfHXdddfx3//+l1WrVgGwf/9+lixZwv79\n+znrrLNISEhg/Pjx5OTkcOjQIT7//HMAmjZtypYtWwo62hARrrvuOsaOHcuOHTsA2Lp1K++7D4MM\nGjSI5557jh9//JGcnJyCZ7W8adGiBWeccQapqakcOXKEZcuWHdejYv5dsO3bt/PWW2+Rk5NDVFQU\n9erVK7jzVjRGX6lqwbY/++wz3n77bQYNGgRAx44dee211zhw4AC//PILs2fPLrRus2bNin3311/+\n8hfq1KnDtGnTOHr0KGlpaSxevJihQ4eWKT4TYA0bwsCB4B7Hoawsz2wNHgwPPQQXXAD2VgFjTHUU\nO+pyJiXPYtzw7faKCxNWrJLlZ7fddhvTp0/ngQceoEmTJrRo0YInn3yyoDOMCRMmcMYZZ9C+fXs6\ndOjAGWecUeaOEjp16sSsWbO45ZZbiI+P58QTTyzoZCIiIoJFixaxfv16WrRoQXJycsGzSeeffz7t\n2rWjWbNmNGnSBIApU6bQtm1bzj77bBo0aMCFF17Izz//DEDv3r0ZO3Ys559/PieeeCI9e/YsMa75\n8+ezcuVKGjZsyOTJkxk5cmSh+fl3o/Ly8pg+fTrNmzenUaNGfPrpp8yYMaPYGH2RkJBAXFwciYmJ\njBgxgqeeeooTTjgBgHHjxhEVFUWzZs0YNWoUw4cPL7TuxIkTufLKK4mPj+eVV14pNC8qKopFixax\nZMkSGjVqxC233MK8efMK0rbu30PEoEEwZAhcfjkcPhzsaPxq5Eh48EGnM8Wvra88Y0x1I8I1L17I\n7xt3sWDmvmBHY4zPJNDvPaoIEVFv8YlIwN/XZEwg2LEbQHl5TiUrLg5mzy7fg5UhQMR7c+U33oDr\nr4dXXoFu3So/Ln9yvwfh+Q/yoriyyj9p2wuqjQH435BHGfDm1XyfUZ9Gjcr+3ZBJgqbal8n4rqJl\nld3JMsZUDRERMG+e01OE+166quSSS2DBAvjb38DjNXDGGFMt/OXp6xha6zVuu946lzLhITLYARhj\njN/UqwfvvQfZ2cGOJCB69oQPP4QBA2DtWpg82albGmNMlVevHg9sHMapnWqxePGxToTyxcXZi4tN\naLHi2RhTtTRsCK1aBTuKgGnfHlatgk8/dV7UaT8qjDHVRd34WsybB9de6zRasPdmmVBmlSxjjAkz\njRvDRx85dcmOHSEtLdgRGWNM5ejSBW69FYYOhaNHy5dGfLz3V2cY409WyTLGVH2qsHdvsKPwq5o1\n4bHH4KmnYNgwGD8ePF4VZ4wxVdadd0LdunDbbeXrGGbPHrsLZgLPKlnGmKrv88/htNPgs8+CHYnf\nXXQRfPMNbNwI7drBkiXBjsgYYwIrIgJefBE+fjObR+/MCnY4xnhllSxjTNV37rnOLZ+BA51Ln/uq\n1rtWmjaFl1+GGTOcZjT9+zvPKxhjTFXVoAEsue0jHpuey5P3ZRV0hOFtqMrNAcOp6WNpsYbTvvgi\nLCtZKSkpiIgNNoTdkJKSEuyvT/V18cWwZo3TU0S7dvDCC867taqQXr2cXeze3RkfONAqW8aYqqvF\nrZeydNoq/vV/h/jn4HTy8go3A6wOzQHDqeljabGG0774ImgvIxaR3sBjOBW92ao61csyAXvBY6CI\nvTjSmND36acwdy7MmhVyLy321zlk/37nztajj0Lr1jB6tPOu5lq1Kp52RYmEz8uIg11WWZliTOm2\nzP2YS66Jp02HGJ7+uDUxsYVPLyLAxGMvIy76vQrn71k47UtpsYbavlS0rArKnSwRiQD+A/QC2gFD\nReTkYMRijpdmXZVVOsvzStatGzz9NGlLlwY7koCpWxfuuAM2bYKxY+GZZyAhAa68Et580zrJ8EV1\nKKvC+dxjsQdHKMaedOX5LPsimgZbv6fdKbm8/rqXH+e/BiU0vwjFPPdVOMdeUcFqLngWsF5V01X1\nCPAiMCBIsZgiqvMXIlgsz4PDa74//DDcdBO89BKkp4fuJUEfRUU5d7A+/NBpOnjmmc7drSZNnLtb\npkRVvqwK53OPxR4coRp7dMeTeWrbAOa+EMm998JZZzmn8Zwcd4FNwYyuYkI1z30RzrFXVLAqWc2B\nDI/PW9xpQeWfA8H3NHzZXmnLFDff1+mhcPBXNIayrl/Z+e7rtMoWbvle1nnlzvdLLoFWrWDBAqeU\nTkyEnj3hq69KX7cU/vi/VyTfk5Lg73933quVkeHUJUtLs6LnmNLSD3EBLavKmy/l/S758/9gsfs+\n32KvWFpljb17d+eCUv/+acye7ZzC43De2r7q6e/YlZFzfCIVjKG86wU63yuSTrjGXpHfGv4uq8Ky\n44tAsUpWcITbj/2S5lslq2LLh0Ql64QT4B//gDfegKwsWLnSeSlLUpL35S+80OkCKTkZTj4ZOnWC\nrl3hu++8bz81FYYMgSuugOHDjw3r1nlP/4EHYMSIgiFtzBhnvLjlH3wQRo4sGNJuvdUZ/+mnQos1\naOD0al9avlTzSlZAWUXF92XCIfaqVFGp6Pb8mVZ5Yo+IgNzcNN5/H37+GRLZCkCjMVdQp0VDdhHP\nr1EnsKbu2Xw+7mWv6S4b/QLLWo1gWasRzL1kDMtaj2BZ6xF8Ofkdr8svv2UBy9qMdIa2zjD3slv5\n6sF3i11+7mVjWdb2qkLDVw++63Wflt+y4Lhll7W9il6UPf1i4/dYLn/d4paf+89/e41n/v1PF5v+\nsxRe9llKTr/o8qXFn9b5rpCqZAWl4wsRORuYqKq93c/jAS36QLGIhHc7HWOMMV6FQ8cXVlYZY0z1\nVpGyKliVrBrAT0BPYBuwChiqqj9WejDGGGOMF1ZWGWOMKa/IYGxUVXNF5BbgfY51i2uFljHGmJBh\nZZUxxpjyCtp7sowxxhhjjDGmKrKOL4wxxhhjjDHGj8KukiUi54nIpyIyQ0S6BTue6kRE6ojIFyJy\ncbBjqS5E5GT3WH9ZRG4MdjzVgYgMEJGZIrJARC4IdjzVhYi0EpGnRcR7V19hJtzLqnA934fzOTOc\nzz3h+v11j/PnROQpERkW7HjKIlzzHML3WC/r+SXsKlmAAvuAWjjvLDGV507gpWAHUZ2o6jpVHQ0M\nBs4JdjzVgaq+qarXA6OBQcGOp7pQ1V9V9dpgx+FH4V5WheX5PpzPmeF87gnj7+9lwEJVvQHoH+xg\nyiKM8zxsj/Wynl+CVskSkdki8puIrC4yvbeIrBORn0XkzqLrqeqnqtoHGA/cX1nxVhXlzXcR+Svw\nA7ADCPmul0NNefPdXaYfsBhYUhmxVhUVyXPXBOD/BTbKqscP+R5SwrmsCufzfTifM8P53BPu399y\nxJ/EsReO51ZaoF6Ec95XIPaglrPlibtM5xdVDcoAdAE6Aqs9pkUAvwApQBTwLXCyO28EMB1IcD/X\nBF4OVvzhOpQz3x8FZrv5/x7werD3I9yGih7v7rTFwd6PcBoqkOeJwBTg/GDvQzgOfji3Lwz2Pvh5\nf4JWVoXz+T6cz5nhfO4J9+9vOeK/ArjYHZ8fTrF7LBP0c2Z5Yg/2sV6RPHeXK/X8EpQu3AFUdZmI\npBSZfBawXlXTAUTkRWAAsE5V5wHzRORSEekF1Af+U6lBVwHlzff8BUXkSmBnZcVbVVTgeD9PnBeg\n1gLertSgw1wF8vzvOO9FihWRtqo6s1IDD3MVyPd4EZkBdBSRO7XIC3+DJZzLqnA+34fzOTOczz3h\n/v0ta/zA68B/RKQPsKhSgy2irLGLSDzwICFwzixH7EE/1qFccZ+H08TUp/NL0CpZxWjOsdu24LRj\nP8tzAVV9HedLYfyn1HzPp6pzKyWi6sGX430psLQyg6rifMnzfwP/rsygqgFf8n03Tvv8cBDOZVU4\nn+/D+ZwZzueecP/+Fhu/quYAVwcjKB+VFHso5zmUHHuoHutQctxlOr+EY8cXxhhjjDHGGBOyQq2S\ntRVo4fE5yZ1mAsvyPTgs3yuf5XlwVLV8D+f9sdiDw2IPnnCO32KvfH6LO9iVLKFwz0VfAG1FJEVE\nagJDgLeCElnVZvkeHJbvlc/yPDiqWr6H8/5Y7MFhsQdPOMdvsVe+wMUdxB495gOZwCFgMzDKnX4R\n8BOwHhgfrPiq6mD5bvleXQbLc8v36r4/FrvFXp1iD/f4LfaqF7e4iRljjDHGGGOM8YNgNxc0xhhj\njDHGmCrFKlnGGGOMMcYY40dWyTLGGGOMMcYYP7JKljHGGGOMMcb4kVWyjDHGGGOMMcaPrJJljDHG\nGGOMMX5klSxjjDHGGGOM8SOrZJmQISKXiEieiJwY7FiKIyJ3BTsGfxGRG0RkeBmWTxGRNWXcxkci\nUq+E+QtEpE1Z0jTGmFBQFcssEflERP4cyG2UMe1+IvLPMq6zr4zLLxSRliXMf1hEepQlTWPAKlkm\ntAwBPgOGBnpDIlKjnKve7ddAgkREaqjqU6r6fBlX9fnt5SJyMfCtqv5RwmIzgDvLGIMxxoQCK7MC\nuA23nFqkqtPKuGpZyqk/ARGquqmExf4NjC9jDMZYJcuEBhGpC5wLXINHgSUi54nIUhFZLCLrRORJ\nj3n7RGS6iHwvIh+ISEN3+rUiskpEvnGvUEW7058VkRkishKYKiJ1RGS2iKwUka9EpJ+73EgReVVE\n3hGRn0Rkijv9IaC2iHwtIvO87MNQEVntDlN8iLO1u40v3H080SPOx0VkuYj8IiKXedlWioj8KCLP\ni8gPIvKyx37+WUTS3HTfEZGm7vRPRORREVkFjBGRVBG5zZ3XUURWiMi37r7Xd6d3cqd9A9zssf0/\nicj/3Lz4tpi7UVcAb7rL13H/h9+4+TPQXeYz4K8iYuciY0zYCPcyS0Qi3PRXi8h3InKrx+xB7vl9\nnYic67GNf3usv0hEuvlQLpan/JshIivcfS7YrlvufeSWOR+ISJI7vaWIfO7ux2SPbTdz0/7a3c9z\nvfwrPcspr3miqpuBeBFpUuwBYYw3qmqDDUEfgGHALHd8GXC6O34ekAOkAAK8D1zmzssDhrjj9wL/\ndsfjPNKdDNzsjj8LvOUx70FgmDteH/gJqA2MBH4B6gG1gE1Ac3e57GLiTwDSgXicixcfAf2LifMJ\nd/xDoI07fhbwkUecL7njpwDrvWwvxU33bPfzbOA2IBJYDjR0pw8CZrvjnwD/8UgjFbjNHf8O6OKO\nTwKme0w/1x2fBqx2x58AhrrjkUAtLzFuAuq645cBT3nMi/EYfy///22DDTbYEA5DFSiz/gy87/E5\n1v37CfCwO34R8IE7PjK/7HI/LwK6lbSNYvbZl/LPc59HeqzzFjDcHR8FvO6Ovwlc4Y7flB8PTpl4\nlzsu+eVRkfjSgHYl5Yk7PhO4NNjHnQ3hNdjVYxMqhgIvuuMv4RRg+VaparqqKrAA6OJOzwNedsef\nx7mqCNBeRD4VkdVuOu080lroMX4hMN69S5MG1ARauPM+UtU/VPUQ8ANOgVmSM4FPVHW3quYBLwDd\niomzi3sV9Bxgobv9p4CmHum9AaCqPwLFXT3brKorPdMFTgJOBT5w070HSPRY56WiiYhILFBfVZe5\nk+YA3dy7WfVVdbk73fMq5QrgHhH5B9DSzaei4lR1vzu+BrhARB4SkS6q6tlmfkeRGI0xJtSFe5m1\nEWglTquJXoDnOfk19+9XPqRTmlzKXv4txLvOOPkJTnmUn3/ncux/4VlOfQGMEpH7gPYe5ZGnBJwy\nCErOk+1YOWXKKDLYARgjInHA+cCpIqJADZw21f9wFynavrq49tb505/FuYv0vYiMxLmymK/oSfZy\nVV1fJJ6zAc9KQy7HvitS0q6UMK9onBHAHlUt7gFjz+2XJV0BvldVb80i4Pj9L20bXqer6gK3CUtf\nYImIXK+qaUUWO+qx/HpxHqa+GHhARD5S1fxmHdHAgWK2b4wxIaUqlFmquldEOgC9gBuBgcC17uz8\ntDzTOUrhR0yiPUPwto1i+FL+FVdOlfSsVf68glhU9TMR6Qb0AZ4TkUf0+OeQc3D3pUie3IDTEuQa\ndzkrp0yZ2Z0sEwoGAnNVtZWqtlbVFOBXEcm/+neW2xY7AhiM8xwPOMfv39zxKzym1wOyRCTKnV6c\n94Ax+R9EpKMPsR4W7w8gr8K5+xPvzh+Kc6XRW5zL3Ds5v4pI/nREpH0x2yyuAGshIn9xx4fh7P9P\nQGO30EVEIsV5sLdYqpoN7PZorz4CWKqqvwN7ROQcd3pBT4Qi0kpVf1XVf+M01fAW+08i0tpdPgE4\noKrzgYeB0z2WOxH4vqQYjTEmhIR9meU+G1VDVV8HJuA0lfMmv/zZBHQURzJOE78St+GqQcXKP0+f\nc+z5t+Ecy79lHtML8k9EWgDbVXU28DTe9/FHoK27vGee3IuVU6aCrJJlQsFg4PUi017l2EnzS+A/\nwFpgg6q+4U7fj1OYrQG647RlB+fkuArnBPyjR5pFr4I9AES5D7l+D9xfTHye680E1hR9wFdVs3B6\nH0oDvgG+VNXFxcSZv50rgGvch3i/B/oXE2dxV+9+Am4WkR+ABsB/VfUIToE2VUS+dWPpXEo6AFcB\n/3LX6eAR49XAkyLydZH1B7kPMn+D07Rlrpc03wbyu709DVjlLn8fTt7jPkico6rbS4jNGGNCSdiX\nWUBzIM09J8/jWO95Xssft9n4JnefHsNpSljaNqDi5Z+nMTjN/75118/vrGMsTln4HU7zv3zdge/c\n8msQ8LiXNJdwrJzymiciEgm0wfm/GuMzcZoMGxOaROQ84HZV7e9l3j5VjQlCWGUSiDhFJAVYrKqn\n+TNdfxKRZsAcVe1VwjJjgd9V9dnKi8wYYwKjKpRZ/hTq+yxOT44f43Tw5PUHsYhcgtOxSWqlBmfC\nnt3JMuEsXK4QBCrOkN5/9+7eLCnhZcTAHpyONowxpqoL6XN2gIT0PqvqQZyedpuXsFgN4JHKichU\nJXYnyxhjjDHGGGP8yO5kGWOMMcYYY4wfWSXLGGOMMcYYY/zIKlnGGGOMMcYY40dWyTLGGGOMMcYY\nP7JKljHGGGOMMcb4kVWyjDHGGGOMMcaPrJJljDHGGGOMMX5klSxjjDHGGGOM8SOrZBljjDHGGGOM\nH1klyxhjjDHGGGP8yCpZxpRARPaJSMtgx2GMMcaUxMorY0KLVbJMlSAieSLSuoJpfCIiV3tOU9UY\nVd1UoeD8SERSRORjEdkvIj+ISM8Slu3uLrtXRDaWNS0RGSYim9yC+zURaeAxr6aIPCMiv4tIpoiM\nK7JuRxH50k37CxHpUGT+OBHZ5sb2tIhElT9XCqV7nnssvFpkent3+sf+2I4xxpSXlVdel7Xy6th0\nK6+qCKtkmapCS5opIjUqK5AAWwB8BcQDE4BXRKRhMcvuB2YDd5Q1LRFpB/wXuAJoChwAZnisOwlo\nAyQD5wP/FJEL3XWjgDeAuUAD9++bIhLpzu8F/BPoAaS46UwqSyaUYgfQWUTiPKaNBH7y4zaMMaa8\nrLw6npVXx1h5VVWoqg02eB2AJOBVYDvOieAJd7rgnOQ2AVnAc0CsOy8FyAOuBNLdde/2SDMCuBv4\nBfgd+AJo7s47GXgf2AX8CAz0WO9Z4D/AYiAbWAG0cuctdbf5hztvIHAekIFzctwGzME5gS5yY9rl\njie6aTwAHAVy3DTy9zUPaO2Ox+KcgLcDvwL3eMQ3EvgMeBjYDWwAevv5/3ECTuFR12PaUuD6Utbr\nCWwsS1rAg8DzHvNaA4fylwe2Aj095k8C5rvjFwIZRbaXDlzojr8APOAxrwewrYT484DRwM/uMXO/\nG89yYC/wIhDpLpv/f38SuMnjmNuCc8x+HOzvlQ022OD/ASuv8s+VVl5ZeWVDiAx2J8t4JSIROAXE\nr0ALoDnOyQFgFE6hdB7OySMGp0DxdC7OifGvwH0icpI7/XZgMM4JvT5wNZAjInVwCqzngUbAEOBJ\nETnZI83BQCpO4bMB58SKqp7nzj9NVWNVdaH7uZm7bAvgepyT1zM4V7Na4BRQ/89NYwJOoXOLm8YY\nNw3PK47/cfe1JdAduFJERnnMPwunsG2IU3jNphgiskhE9ojIbi9/3ypmtXY4hc9+j2nfudPLqrS0\n2rmfAVDVjTiF1oluM4wEYHUx6/6pyLwS03bHmxS5klfUhcDpwNk4P0SeAobh/C9PA4Z6LKs4Py6u\ndD/3Atbg/HgxxlQxVl5ZeYWVVyYEWSXLFOcsnBPTP1X1oKoeVtXP3XnDgOmqmq6qOcBdwBC3oAPn\npDHRXWc1zkkpv43zNThX1H4BUNU1qroH6Av8qqpz1fEdzlXJgR4xva6qX6lqHs7VpY5FYpYin3OB\nVFU9oqqHVHW3qr7uju8HHgK6lZIPAgWF+GBgvKrmqGo68AgwwmPZdFV9RlUV50pkMxFp4i1RVe2n\nqnGqGu/lb/9iYqmHc2XMUzZOQVpWpaVV0vx6OP/j373MK0/a2Tj5XNJ+TFXV/ar6I/A98L57/O0D\n3sEp0Aqo6kogTkROxCm85paQtjEmvFl55ZGmlVeF5lt5ZYLGKlmmOMk4J+E8L/MScW6n50sHInHa\nQuf7zWM8B+dElZ/ucQ+14jTbONu9MrZbRPbgFI6eaWYVk2ZxdqjqkfwPIlJbRJ5yH47di9PcoIGI\nFC3svGmEs4+bPaal41wxPS4+VT2AcyIuLcay+AOnCYin+sC+AKRV0vw/3M+xXuaVJ+36OIVgSfux\n3WP8AIWPrwN4z+d5wC04V3FfLyFtY0x4s/KqMCuvrLwyIcAqWaY4GUALj6t9njJxCpl8KcARCp9I\nSkq3TTHT09wrY/lXyWJV9ZayBu6h6MPFt+M0CTlTVRtw7KqgFLO8p504+1h0v7eWJzARWeL2gpTt\nZXi7mNXWAq1FpK7HtA7u9LIqLa21HLuai4i0AaKAn1V1L05Thg4lrNu+yPba41zROy5tnCu8v7lX\niP3peeAm4G1VPejntI0xocPKq8KsvLLyyoQAq2SZ4qzCOTFNEZE6IlJLRM5x5y0AxolISxGph9PW\n/EWPq4glXWl7GpgsIm0BROQ0t23zYpz208NFJFJEokTkDI+28aXJwmlvX5IYnKtI2SISD0wsMv+3\n4tJw9+1l4EERqSciKcA4nKtPZaaqF6vT3W6sl6FPMeusB74FUt3/x2XAqTjNVI4jjlpATSDCXSfK\nx7ReAPqJyLluwXY/8KpHm/h5wAQRaSAipwDX4TzsDZAG5IrI38XpOncMzsPAn7jz5wLXiMgp7v9+\ngse6fqNOV8bd3PSNMVWXlVcerLyy8sqEBqtkGa/ck3Q/nCtpm3Gu3A1yZz+Dc9L6FOeB3hxgjOfq\nRZPzGJ+Oc/J/X0R+xynEaqvqHzgPiw7BufKYCUwBavkY8kRgrtt042/FLPMYUAfnKt/nwJIi8x8H\nBorILhF5zEvsY3D2dSPOvj+vqiWdbEvsprechgBnAntwfixcrqq7AESki4hkeyzbDaeQXozT7CUH\neM+XtFT1B+BGYD7OD4LawM0e66bi5EM68DEwRVU/cNc9AlyC04PVHpw25gNU9ag7/z1gGk4h9ivO\nMTSxhH0u6Xgqkap+rqpZpS9pjAlXVl5ZeYWVVyYEifPMY4ASF0nCuQrQFOfKwCxVfcK9GvASzu3r\nTcAgVS364KExxhgTcO4V9E9xrqJHAq+o6iQRScW56p3/jMXdqvpukMI0xhgTRgJdyWoGNFPVb93b\n9F8BA3C6VN2lqtNE5E4gTlXHBywQY4wxpgQiUkdVc8R5EexynDsBFwH7VHV6cKMzxhgTbgLaXFBV\ns1T1W3f8D5x3MiThVLTmuIvNwblVa4wxxgSFOt17g9PkK15DrRoAACAASURBVJJjzXx86c3NGGOM\nKaTSnskSkZY4vbKsBJqq6m/gVMQAr+9mMMYYYyqDiESIyDc4z3R8oKpfuLNuEZFvReRpEakfxBCN\nMcaEkUqpZLlNBV8BbnXvaJX7wUBjjDHG31Q1T1VPx2ltcZaI/Al4Emitqh1xKl/WbNAYY4xPIgO9\nARGJxKlgzVPVN93Jv4lIU1X9zX1ua3sx61rlyxhjqiBVDclmeKqaLSJpQO8iz2LNAhZ5W8fKKmOM\nqZoqUlZVxp2sZ4AfVPVxj2lvAVe54yOBN4uulE9VK21ITU2t1DR8Wba0ZYqb7+t0b8v5Ix8qM9/L\nun5l57sv0yo7z8Mx38s6LxTzvbLPMYHM94p8B0KNiDTKbwooIrWBC4B17kXAfJdx7AWlx6nM46G8\n/1N/nu8tdv9+J4IdO+d5P4bDIfaK/N6p6rEHcp9DNfaKlHn+LqsCeidLRM4FrgDWuG3dFbgbmAq8\nLCJX47y3YFDxqVSe7t27V2oavixb2jLFzfd1uj/2uaIqGkNZ16/sfPd1WmULt3wv67xQzPfKPsf4\nunx58r2i34EQkwDMEZEInIuPL6nqEhGZKyIdcV5Bsgm4wZ8bLW++lPd/6s//g8Xu+/xwiJ2W/t2e\nP9Oqyvke6Ngrkk64xl6RMs/vZVV5a7iVMTjhmcqWmpoa7BCqHcvz4LB8Dw733B70MsZfQziXVeH8\nHbDY/YeJvh/DoRa7r8I1blWLPVgqWlZVWu+CJnyEwVXnKsfyPDgs3011F87fAYs9OMI19nCNGyz2\ncBXQlxFXlIhoKMdnjDGm7EQEDdGOL8rDyioT7mSSoKl2DBvjqaJlVcB7FzTGADt3QlQU1Pfymp37\n73fmd+wIF14ISUmVH58xJiTFx8OePc54XBzs3h3ceIwpTsuWLUlPTw92GMaUWUpKCps2bfJ7ulbJ\nMiZQ/vgDXnwRnn0Wvv8e5s+HPn2OX65HD/jyS3j/ffjHP6BNG7jxRhg2DKKjKz9uY0zI2LMH8m+S\nSZW592eqovT0dL/0yGZMZZMAnVytkmWMv+3cCVOmwDPPQNeucNddcMEFUKuW9+W7dnUGgNxceO89\nmDcP/vY3q2QZY4wxxoQhq2QZ42+bN8PBg/Ddd5CcXLZ1a9SAiy92BmOMMcYYE5askmWMv/35z85g\njDHGGGOqJevC3ZhwkZMDjzwCR48GOxJjjDGmSkhPTyciIoK8vLwKp9WqVSs+/vhjn5adM2cOXfMf\nFQBiYmL81vnCQw89xPXXXw/4d/8AMjIyiI2NtefvfGCVLGPK66uvYMKEytvekSPwzjtOhxhHjlTe\ndo0xxpgwVlrlJ1AdH5TGc7v79u2jZcuWJS6/dOlSkn14DOGuu+5i5syZXrdTVkXzLjk5mezs7KDl\nWTixSpYx5fH003DRRdC+feVts359WLwYDhyAgQPh8OHK27YxxhhjgkpVS63c5ObmVlI0pjRWyTKm\nLHJzYdw4mDYNli2DQYMqd/vR0fDqq874qFHgp9v/xhhjTHWQl5fHHXfcQePGjWnbti1vv/12ofnZ\n2dlce+21JCYmkpyczL333lvQNG7jxo307NmTRo0a0aRJE4YPH052drZP2929ezf9+/enfv36nH32\n2WzYsKHQ/IiICDZu3AjAkiVLaNeuHbGxsSQnJzN9+nRycnK4+OKLyczMJCYmhtjYWLKyspg0aRID\nBw5kxIgRNGjQgDlz5jBp0iRGjBhRkLaqMnv2bJo3b07z5s155JFHCuaNGjWK++67r+Cz592yK6+8\nks2bN9OvXz9iY2P517/+dVzzw23btjFgwAAaNmzIiSeeyNNPP12Q1qRJkxg8eDAjR44kNjaW0047\nja+//tqn/KoKrJJljK+ys6FfP1izBv73PzjxxODEUbMmLFgA6ekwd25wYjDGGGPC0MyZM1myZAnf\nffcdX375Ja+88kqh+SNHjqRmzZps3LiRb775hg8++KCg4qCq3H333WRlZfHjjz+yZcsWJk6c6NN2\nb7rpJurUqcNvv/3G7NmzeeaZZwrN97xDde211zJr1iyys7P5/vvvOf/886lTpw7vvPMOiYmJ7Nu3\nj+zsbJo1awbAW2+9xaBBg9i7dy/Dhg07Lj2AtLQ0NmzYwHvvvcfUqVN9aj45d+5cWrRoweLFi8nO\nzuaOO+44Lu3BgwfTokULsrKyWLhwIXfffTdpaWkF8xctWsSwYcP4/fff6devHzfffLNP+VUVWCXL\nmLLo1s15LiouLrhx1K4NS5bA8OHBjcMYY4zxxcSJzhu1iw7FVVK8Le9jhaYkCxcuZOzYsSQmJtKg\nQQPuuuuugnm//fYb77zzDo8++ijR0dE0atSIsWPHsmDBAgDatGlDz549iYyMpGHDhowbN46lS5eW\nus28vDxee+01Jk+eTHR0NO3atWPkyJGFlvHsSKJmzZqsXbuWffv2Ub9+fTp27Fhi+p07d6Zfv34A\nRBfzfs2JEycSHR3NqaeeyqhRowr2yRfFdXKRkZHBihUrmDp1KlFRUXTo0IFrr72WuR4XgLt06UKv\nXr0QEUaMGMHq1at93m64s0qWMb6KjYXx4yEqKtiROGJjIdLewmCMMSYMTJwIqscPJVWyfF22DDIz\nMwt1HpGSklIwvnnzZo4cOUJCQgLx8fHExcVx4403snPnTgC2b9/O0KFDSUpKokGDBgwfPrxgXkl2\n7NhBbm4uSUlJXrdb1Kuvvsrbb79NSkoKPXr0YOXKlSWmX1pnGCJy3LYzMzNLjbs027ZtIz4+njp1\n6hRKe+vWrQWf8++2AdSpU4eDBw/6rafDUGeVLGOMMcYYUy0kJCSQkZFR8Dk9Pb1gPDk5mejoaHbt\n2sXu3bvZs2cPe/fuLbj7cvfddxMREcHatWvZu3cvzz//vE9dmTdu3JjIyMhC2928eXOxy3fq1Ik3\n3niDHTt2MGDAAAa5z38X1+mFLz39Fd12YmIiAHXr1iUnJ6dg3rZt23xOOzExkd27d7N///5CaTdv\n3rzUeKoDq2QZY4wxxphqYdCgQTzxxBNs3bqVPXv2MHXq1IJ5zZo148ILL2TcuHHs27cPVWXjxo18\n+umngNPNer169YiJiWHr1q08/PDDPm0zIiKCyy67jIkTJ3LgwAF++OEH5syZ43XZI0eOMH/+fLKz\ns6lRowYxMTHUqFEDgKZNm7Jr1y6fO9vIp6pMnjyZAwcOsHbtWp599lmGDBkCQMeOHVmyZAl79uwh\nKyuLxx9/vNC6zZo1K+iQwzM9gKSkJM455xzuuusuDh06xOrVq5k9e3ahTje8xVJdWCXLGG/S02HM\nGKd5QrjYsgU++CDYURhjjDEhxfNuzHXXXUevXr3o0KEDZ5xxBpdffnmhZefOncvhw4f505/+RHx8\nPAMHDiQrKwuA1NRUvvrqKxo0aEC/fv2OW7ekuz7//ve/2bdvHwkJCVx99dVcffXVxa47b948WrVq\nRYMGDZg5cyYvvPACACeddBJDhw6ldevWxMfHF8Tly/6fd955tG3blgsuuIB//vOf9OzZE4ARI0bQ\nvn17WrZsSe/evQsqX/nGjx/P5MmTiY+PZ/r06cfFumDBAn799VcSExO5/PLLmTx5Mj169CgxlupC\nQrlGKSIayvGZKmrLFjjvPBg7Fv7+92BH47tvv4ULLoDvvgO3GYAxoUhEUNUqU9IGsqwSOXatx3Pc\nGH+SSYKmVuzgcr/XforImMpT3LFb0bKq1DtZItJPROyOl6kesrKgZ08YPTq8KlgAHTvCDTc4lUMf\nxMd77+gpf4iPD3C8xviRlVXGGGNCiS8F0mBgvYhME5GTAx2QMUHz++/QuzdccQW474IIO/fcA19/\n7XTvXoo9e7x39JQ/7NlT8vpWSTMhxsoqY4wxIaPUSpaqDgdOBzYAz4nIChG5XkRiAh6dMZVp8mQ4\n91y4995gR1J+tWvDk0/CzTeDR28/gVBaJQ2sEmYqj5VVxhhjQolPTStUNRt4BXgRSAAuBb4WkTBr\nT2VMCSZPhieecGoA4ezCC6FHD/jsswolExdXciWptPcx795dsTtlxpRVecsqEaklIv8TkW9EZI2I\npLrT40TkfRH5SUTeE5H6Ad8JY4wxVUKpHV+IyADgKqAtMBeYo6rbRaQO8IOqtgxYcNbxhTEBE+yH\n6IO9fRM8gej4oqJllYjUUdUcEakBLAfGAJcDu1R1mojcCcSp6ngv61rHFyasWccXpjoLVMcXkT4s\ncxnwqKp+6jnRLYyuKe+GjTHGGD+qUFmlqvlv46yFUzYqMAA4z50+B0gDjqtkGWOMMUX50lwwq2ih\nJSJTAVT1o4BEZYwxxpRNhcoqEYkQkW+ALOADVf0CaKqqv7lpZAFN/B+2McaYqsiXO1kXAHcWmXaR\nl2nGhI8ff4QpU+C558L/GSxjDFSwrFLVPOB0EYkFXheRdjh3swotVtz6EydOLBjv3r073bt392Wz\nxhhjQkRaWhppaWl+S6/YZ7JEZDRwE9AG+MVjVgyw3O3JKaDsmSwTELt2wV/+4vQiOHJksKOpHPv2\nQUzhTtaC/XxHsLdvgsefz2QFoqwSkXuBHOBaoLuq/iYizYBPVPUUL8vbM1kmrNkzWeEtIiKCX375\nhdatW5e67KRJk/jll1+YN28eGRkZtGvXjt9//x3xwwXn0aNHk5SUxD333MPSpUsZPnw4GRkZFU4X\nYNmyZVx33XX8+OOPfknPUzBeRjwf6Ae86f7NHzpVRgXLmIA4fBguvxz+9rfqU8F67z24+OKQ+3VW\nWu+F1sW78VGFyyoRaZTfc6CI1Ma5K/Yj8BZOZxoAI91tGGPC0Pz58znzzDOJiYmhefPm9OnTh+XL\nlwc7LObMmUPXrl0rlEZZK0j5yycnJ5OdnV3q+r7GOGPGDO65555yx+UpIiKCjRs3Fnzu0qVLQCpY\ngVRSJUtVdRNwM7DPY0BE7OePCT+qcMst0KAB/N//BTsavyitohIdDZM+v4Bn1nclbfrXZGUFO+Jj\nrIt34yf+KKsSgE9E5Fvgf8B7qroEmApcICI/AT2BKX6O3RhTCaZPn85tt93GhAkT2L59O5s3b+bm\nm29m0aJFZU4rNzfXp2m+UtUK30UK9B1EX2LMy8vz6zb9cWct2Eq7kwXwFfCl+/crj8/GhJeXX4b/\n/Q+efx4ifHpFXMjZtw/eegv+/ndo3x727nX+DhwI48bBww/D//t/8PTT8NRTcOgQHM2LYOkJ13Df\n/TVo105JSoLLLnPS27QpqLtTopIqkHaXy3iocFmlqmtU9c+q2lFV26vqg+703ar6V1U9SVUvVNW9\ngdgBY0zgZGdnk5qaypNPPsmAAQOoXbs2NWrU4OKLL2bKFOe6yeHDhxk7dizNmzcnKSmJcePGceTI\nEQCWLl1KcnIy06ZNIyEhgauvvtrrNIDFixdz+umnExcXR5cuXVizZk1BHFu2bOHyyy+nSZMmNG7c\nmDFjxrBu3TpGjx7NihUriImJId4t3A4fPswdd9xBSkoKCQkJ3HTTTRw6dKggrYcffpjExESSkpJ4\n9tlnS6yQbNq0ie7du1O/fn169erFzp07C+alp6cTERFRUEF67rnnaNOmDbGxsbRp04YFCxYUG+Oo\nUaO46aab6NOnDzExMaSlpTFq1Cjuu+++gvRVlYceeojGjRvTunVr5s+fXzCvR48ePPPMMwWfPe+W\nnXfeeagq7du3JzY2loULFxbkeb5169bRo0cP4uLiOO200wpVmEeNGsUtt9xC3759iY2NpXPnzvz6\n668lHyiBoKohOzjhGeMnhw+rZmUFO4oyO3hQ9ZVXVC+/XDU2VrVnT9UpU1RXrXLu+ZSkYP6RI6on\nnKB5H36kGzaozp/vzGvSRPWUU1QnTFBdvz7gu+I3dmoIb+65PehljL+GQJZVnknbcW8ChYkVP7hC\n9Tfbu+++q1FRUZqbm1vsMvfee6927txZd+7cqTt37tRzzjlH77vvPlVVTUtL08jISL3rrrv08OHD\nevDgQa/Tvv76a23SpIl+8cUXmpeXp3PnztWWLVvq4cOHNTc3Vzt06KC33367HjhwQA8dOqTLly9X\nVdXnnntOu3btWiiesWPH6oABA3Tv3r36xx9/aP/+/fXuu+9WVdV33nlHmzVrpj/88IPm5OTosGHD\nNCIiQjds2OB13zp37qx33HGHHj58WD/99FONiYnRESNGqKrqpk2bNCIiQnNzc3X//v0aGxur690f\nA1lZWfrDDz8UG+NVV12lDRo00BUrVqiq6sGDB/Wqq67Se++9t1C+5W976dKlWrduXf35559VVbV7\n9+46e/bsgvSKbkNEdOPGjQWf09LSNDk5WVVVjxw5om3bttUpU6bokSNH9OOPP9aYmJiCtK+66ipt\n1KiRfvnll5qbm6tXXHGFDh06tNj/f3HHbkXLqlIv54vIuSJS1x0fLiLTRaRF4Kp9xgRIVBQ0bRrs\nKHyWlQWTJkHLlvCf/0Dv3s6dpw8/hDvvhDPPLD2NgrtBUZFcuX4Cn/z1Adq0gWHDnHnbtjkdLP7x\nB5xzDnTtCnPmOHfAjAknVlYZE9pKatpelqGsdu3aRaNGjYgooQXL/PnzSU1NpWHDhjRs2JDU1FTm\nzZtXML9GjRpMmjSJqKgoatWq5XXarFmzuPHGGznjjDMQEUaMGEGtWrVYuXIlq1atYtu2bUybNo3o\n6Ghq1qzJOeecU2w8s2bN4tFHH6V+/frUrVuX8ePHs2DBAgAWLlzIqFGjOOWUU6hdu3ahnk2LysjI\n4Msvv+T+++8nKiqKrl270q9fv2KXr1GjBmvWrOHgwYM0bdqUU045rp+fQgYMGMDZZ58NUJAvnkSE\nyZMnExUVRbdu3ejTpw8vv/xyiWl60mKaQa5YsYL9+/dz5513EhkZSY8ePejbt29BHgFceumldOrU\niYiICK644gq+/fZbn7frL760mZoB5IhIB+B2YAMwr+RVjDHllZEB118Pp5ziVII+/BA++QSuvdap\nGJWF53NPcw8P5fzHBqC5eag68yIi4Kyz4NFHYcsWuP12mD8fWrWCBx90OmI0JkxYWWVMCNMSnsEt\ny1BWDRs2ZOfOnSU+M5SZmUmLFseuyaSkpJCZmVnwuXHjxkRFRRVap+i09PR0HnnkEeLj44mPjycu\nLo4tW7aQmZlJRkYGKSkpJVb08u3YsYOcnBw6depUkNZFF13ELrdAzszMLNRs7v+zd+dxNpftA8c/\n19jJMGPfFWmREipaRaqnEiVrIT3pKVqQPFS2VFK/tKdNaNPqKUui0lBSKkoJyU4RhuzbzPX7456Z\nZp8zc873fM+cud6v1/c18z3L975mnHGf69z3fd316tXLMRn5448/iIuLo0yZMhken52yZcvyzjvv\nMGHCBGrUqEH79u1ZtWpVrrGmjyM7cXFxlC5dOkPb6X+vBfXnn39mabtevXps2bIl7bx69epp35ct\nW5Z9+/YF3W5+BZJkHUsZMusAPKuqz+FK4xpjQuivv9y6qqZNoVIl+P13eOEFaNw4RA2UKAF33ZXj\nerSSJaFjR1eMcM4cWLMGGjWC++6zIhSmULC+yhiTRatWrShVqhQffvhhjo+pVasWGzZsSDvfsGED\nNWvWTDvPbs1T5tvq1KnDfffdR2JiIomJiezatYt9+/bRtWtX6tSpw8aNG7NN9DJfp3LlypQtW5bl\ny5enXWv37t38/fffANSoUSNDWfQNGzbkuCarRo0a7Nq1i4MHD6bdtnHjxhx/D+3atWPu3Lls3bqV\nk046iVtuuSXHnz+321Nl13bq77VcuXIcOHAg7b6t+ajMVbNmzSyl4Tdu3EitWrUCvkY4BJJk7RWR\nYcANwCwRiQFK5PEcY/w3dy6kW+AZqY4dg6efdslUUhIsXw5jx7pEyy9NmsCrr8KSJS75O/FEGDPG\nFd4wJkJZX2WMySI2NpbRo0fTv39/PvroIw4ePMixY8eYPXs2Q4cOBaBbt248+OCD7Nixgx07djBm\nzBh69uyZr3b69u3LCy+8wOLFiwHYv38/H3/8Mfv37+fss8+mRo0aDB06lAMHDnD48GG+/vprAKpV\nq8bmzZvTCm2ICH379mXAgAFs374dgC1btjB37lwAunTpwuTJk1mxYgUHDhzggQceyDGmunXr0qJF\nC0aOHMnRo0f56quvslRUTB0F++uvv5g+fToHDhygRIkSHHfccWkjb5ljDJSqprX95ZdfMmvWLLp0\n6QJA06ZNmTZtGgcPHuT3339n4sSJGZ5bvXr1DCXc0zvnnHMoW7Ysjz76KMeOHSMhIYGZM2fSvXv3\nfMXntUCSrK7AYeDfqroVqA085mlUxgRr6VK4/nrYts3vSHL19dfQogV8+CEsWOCSrXQj3L6rVw9e\nfhkWLYKVK93I1sSJLhk0JsJYX2WMydagQYMYP348Dz74IFWrVqVu3bo8//zzdOzYEYD777+fFi1a\ncPrpp3PGGWfQokWLDPs9BaJ58+a8/PLL3H777cTHx9OoUSOmTJkCuD2fZsyYwerVq6lbty516tRJ\nW5vUpk0bGjduTPXq1alatSoAjzzyCA0bNqRly5ZUrFiRSy+9lN9++w2Ayy+/nAEDBtCmTRsaNWpE\n27Ztc43rrbfe4ptvvqFSpUqMGTOG3pn2CE0djUpOTmb8+PHUqlWLypUrs2DBAiZMmJBjjIGoUaMG\ncXFx1KxZk549e/Liiy9y4oknAjBw4EBKlChB9erV6dOnDzfckHFbw1GjRtGrVy/i4+N5//33M9xX\nokQJZsyYwccff0zlypW5/fbbef3119OuHSnl3yWneZyRQEQ0kuMzEWrnTpe5jBsHKZ+YRJpDh+D+\n+936p/HjoWvXgi3oFQnvHsPff+9mHB46BE89BeefH7620wv3z21CS0RQ1cjoBUPAs75q926KxZUn\nSYultGOve+MNGS3oyOBeXCl/1yGKyJjwyem1G2xfFUh1wWtFZLWI/C0ie0Rkr4jsKWiDxngqOdmN\nYHXqFLEJ1pIl0Lw5bNgAy5ZBt24FS7CCkpTkAsinFi3gq69g8GDo3t1VKQzBGlZjghaVfVXHjlzB\nx35HYYwxpgACmS74KHC1qlZQ1VhVLa+qsV4HZkyBPPII7N/vvkaY5GR4+GFXiv2++9zeyJUr+xTM\n4sXQrp0LKp9EXIK1ciWccAKccQZMmFCgSxkTSlHVVx09CveUeZZbmZB2W+YNum1TbmOMiVyBJFnb\nVHWF55EYEwrJyTB1KhQv7nckGezcCVdeCbNnu5GsHj18GL1Kr2VLKFPG1YcvoHLl4MEHISEB3njD\nTR385ZfQhWhMPkVVX1WiBHy9+1S2UxVWrwYybsmgalU/jTEmkgWSZH0vIu+ISPeU6RjXisi1nkdm\nTEHcfz/Uru13FBksXuymB552GsybFyHhiUD//vD880FfqnFj+PJL6N0bLr7YjdKlq9hqTLhEXV/V\n/44YHuR+t5eDMcaYQiXPwhciMimbm1VVb/ImpAxtW+ELU6i9+CIMH+6+XnNN6K8f1EL4/fuhbl1X\niTHdJozB+PNPVxhj6VKYNMm7whhWAKBw86LwRTT2VYcPQ7nSx/i5wgWcsi0BSpXK1K79HZjQsMIX\npijzqvCFVRc0xgNJSXD33fDJJzBjhttnygtBv8m66y437+/hh0MWE7iS9P36ubVbDz7oZiaGkr25\nLNysumB+rg23t/yeZz6qC5lKJ9vfgQkVS7JMUeZndcFGIvK5iPyScn66iNwfyMVFZKKIbBORZelu\nGykim0VkScpxeUGDNyYS32Hs3QsdOrj1SYsWeZdghcSdd8KZZ4b8sh07usqJf/zhLv/NNyFvwpgM\ngumrIt2bq1pwsHzge9MY44d69eohInbYUeiOevXqefI3EciarJeBYcBRAFVdBnQL8PqTgMuyuX28\nqjZLOT4J8FrGZPTXX9CqFeyJnCrNGzbAeedBrVquyEVcXHDXi4/PWE0s8xHs9WnQADp3DvIi2atc\n2dUgGTPGJV1Dh7r9tYzxSDB9VURr0QI++sjvKIzJ3fr161FVO+wodMf69es9+ZsIJMkqq6qLM912\nLJCLq+pXQHb1j6JmmojxSXIy9OwJbdpAbGRUaV6yBM49F266ya1TL1Ei+Gvu2pWxmljmIzEx+Da8\n1rmzG9VavdoVAPn+e78jMlGqwH1VpOvdG6ZM8TsKY4wx+RFIkrVDRBoACiAi1wF/Btnu7SLyo4i8\nIiIVgryWKYoeeQQOHIAHHvA7EgC++MLtf/XsszBggM/l2SNQ1arw/vuuCMiVV8JDD7l1a8aEkBd9\nVUS45ho35dY2/jbGmMIjkCSrP/AicLKIbAEGALcF0ebzwAmq2hTYCowP4lqmKJo/H55+OmL2w5o2\nDbp2dZsLe1FBMFqIQLdu8MMPbnuuNm1g0ya/ozJRJNR9VcQoWxauvRbefNPvSIwxxgQqz3eoqroW\nuEREygExqro3mAZVdXu605eBGbk9ftSoUWnft27dmtatWwfTvCnsDh6EG26AyZMjYsOpl1+GkSNh\nzhxP6keE16FDrkS0x8NwtWu7JOuxx9xak+eeg+uuy9814uJyDzMurnBMpSwqEhISSEhI8LSNUPdV\nkaZXL+jfbQeDS01F7rzD73CMMcbkIccS7iIyKLcnqmpAI1AiUh+YoapNUs6rq+rWlO8HAmepao8c\nnqs5xWeKsNWrI6Jk37hxbv+ruXOhYUNv2pBwlmg+7zw3DfOCC8LUoNuouUcPt4nxk0+6avKhENbf\nm8k3kdCVcA9VXxVkDJ71Vamv5eRkOL7GQWZVv5nTfnozw33GBCsUJdyNiTbB9lW5TRcsn3K0wE25\nqJVy3Ao0CzC4t4CvgUYislFE+gCPisgyEfkRuAgYWNDgTREVAQnWAw+4zXa/+iq4BMvz6oH5cc01\n8OqrYWwQzj7bbVx89Kgb1VqxIqzNm+gQir6qtojME5HlIvKziNyRcnvEbDkSEwPX9SjJu781tXm2\nxhhTCOS5GbGILACuTJ16ISLlgVmqeqHnwdlIlokwKZNAbAAAIABJREFUqjBiBPzvf/D551CtWnDX\ni6hPordtg5NOgs2b4bjjwt78pEkwZAg8/3zwVeUj6vdqsgjlSFa6axa4rxKR6kB1Vf1RRI4DfgA6\nAF2BvXmNhoVjJAvg22+h92V/smLkO8jAAfY6NyFjI1nGZOXlSFaqasCRdOdHUm4zpkhRdXs9TZ/u\nqgkGm2BFnGrV4Pzz4cMPfWm+Tx+3tm3IELjnHjgWFcW3TRgVuK9S1a2q+mPK9/uAFbjRMIigLUfO\nPhsOlqzIL1N+8DsUY4wxeQgkyXoNWCwio0RkFPAtMNnLoIxJk5wMP/7odxSowt13w6efwrx5UKWK\n3xF55PrrfS1h1qyZ20dr2TJo1w62b8/7OcakCElflbKOuGnK8yGCthwRgS43lOTdTZG1Cbsxxpis\n8kyyVPUhoA9uU+FdQB9VHet1YMYAMHYsDBrk65wYVbjzTrf+6vPPoVIl30LxXocOULGiS259UqkS\nfPwxtGwJrVrBqlW+hWIKkVD0VSlTBd8H7koZ0Yq4LUe6dC/Gu5VvQ8tHxibsxhhjshfQJkOqugRY\n4nEsxmQ0fz4884wb2vBpd9/UBOv7790oVoVo3zq7bFm3/5jPihVz+XXDhnDhhW4j4zAWPTSFVDB9\nlYgUxyVYr6vqRynXC3jLkXBtN9KiBRw5Iixb5snljTGmyAr1diN5Fr7wkxW+KML++svNHXvlFbjc\nn4Jeqm59UEKC29fJiwTLFq7n7dNP3SzGJ5905d4DYb/XyOZF4YtgichrwA5VHZTutoC2HAlX4YtU\nQ4ZAiRLw8MP2OjehYYUvjMkq2L4qoJEsY8IqOdltONy7t28JFrhNhufOdUUuon4EK4K1a+emaV55\nJezcCXfYPqwmxETkPOB64GcRWQoocC/QQ0SaAsnAeuA/vgWZTufO7oMHY4wxkSvPJCtlv5A3VHVX\nGOIxBtascSXER4/2LYSxY90UtYQEt5dVQcXHw65c/nLCug9WIdakCSxYAG3bwsGD7pN8Y9ILpq9S\n1YVAsWzu+iTowDzQogUcPux3FMYYY3ITaAn370TkXRG5XMSnxTGm6DjxRJg2DYr7M9D6xBNuT97P\nPoOqVYO71q5dbjpPTkdiYmhiLgrq13eJ1quvwqhRNk3KZFFk+ioR6HSt0owfbK8DY4yJUIFUF7wf\nOBGYCNwIrBaRh0WkgcexGRN2EybA00+76Wk1a/odjc+GDnUVPyJIrVquHsoHH8ADD/gdjYkkRa2v\nuq6zsJsKsHCh36EYY4zJRiAjWaSs6N2achwD4oD3ReRRD2MzJqwmT3bTBD//HOrW9TuaCFCyJLz9\ntt9RZFGtmhtlfPNNeOopv6MxkaQo9VUtW8J2qrJq0td+h2KMMSYbeSZZInKXiPwAPAosBJqo6m1A\nc6CTx/EZExbTpsG997pKdiec4Hc0EaJLF3jvvYicl1etmvu3evxxmDIl6/1xcW5KVU5HMOvsTGQq\nan1VTAyU5DAfzCgZkX+jxhhT1AUykhUPXKuql6nqe6p6FEBVk4GrPI3OFA3z58Ozz/rW/Lx5cOut\nMHMmnHRS/p8fH5/zm/lCXdiicWMoVw4WL/Y7kmzVq+eqPw4dCtOnZ7wvMTH3tXC5FSMxhVaR66t2\nUpn3914Gv/7qdyjGGGMyCSTJmg2kLc8XkVgROQdAVVd4FZgpIrZtc7WIGzXypfnvvoNu3dyATbNm\nBbtGbsUtCnVhCxFXK/rdd/2OJEcnn+wSrH//G5Yu9Tsa47Mi2FcJW4rXZe2k+X4HYowxJpNAkqwJ\nwL505/tSbjMmOElJbj+sG2+ESy8Ne/MrVkD79m6/44suCnvzhUOXLjBrlt9R5Oqss1zBkg4d4I8/\n/I7G+KhI9lUdr0rigyPt/Q7DGGNMJoEkWRm2sk+ZemGbGJvgjR0LR464etxhtnEjXHYZPPooXH11\n2JsvPBo3hh9+8DuKPF13Hdx2m/u3PHjQ72iMT4pkX3Vd3zg+WFzH7zCMMcZkEkiStVZE7hSREinH\nXcBarwMzUW7+fHjuOZg6Nez7YW3f7gbOBg2CXr3C2nThVK6c3xEEZOhQN+v09tv9jsT4pEj2Va1b\nw++/w6ZNfkdijDEmvUCSrFuBc4EtwGbgHOAWL4MyRUCDBm6zozBvRrVnD/zrX27kY8CAsDZtPCYC\nL70EixbBxIl+R2N8UCT7qhIl3AjutGl+R2KMMSY90Qgu/SqSYfaHMUE5dAiuuMJVEHz+efemPBRE\nrIJyJFmxAi68EObMybmYif2b+UtEUNUQ/QX6z8u+KrfXanx8xkqZcXGFvNiO8Y2MFnSk/adoTHrB\n9lV5ztMSkSpAX6B++ser6k0FbdSYcDt2zFURrFrVVYsPVYJlIs8pp8Azz7h/7yVL4Ljj/I7IhENR\n7KtSE6rDh6F6ddi9Kwko5mtMxhhjnECmC34EVAA+A2alO4wpFFShb183kvXaa1DM3oPkn6qb3pmc\n7HckAenWDVq1cuvuTJFRZPuqUqXgysbr6cFUv0MxxhiTIpCKA2VV9b+eR2Ki286dbi5LTCB5feio\nwj33wKpV8OmnULJkWJuPHiIwfDjUqgUtW/odTUCeeQaaNoWPPnLl3U3UK9J9VaeeZXliYV23NYZ9\nkmSMMb4L5B3vTBG5wvNITPQ6dMjVS//f/8Le9Lhxbm3OzJkFL5IXH+9yjJyOuLjQxhyxrrnGl3/D\ngoqNhddfh//8x+15baJeke6rLu9VlaWcyfaPv/M7FGOMMQSWZN2F67wOicgeEdkrInu8DsxEkbvu\nguOPh2uvDWuzL7/sqs3NmeMSpYLatcuNiOV0FJmF5qlJViGqGHHeedCnD9xxh9+RmDAo0n1VmTJQ\njw189KzVcjfGmEiQZ5KlquVVNUZVS6tqbMp5bDiCM1Fg8mS3J9bEiWGtNjFtGowcCXPnhr1KfPRq\n3tyNSv76q9+R5MuIEfDTT4VqEM4UgPVVcICyvP9VtUL1QYgxxkSrPJMscW4QkeEp53VE5GzvQzOF\n3k8/uQVRH3zg5m6FyRdfwK23wqxZ0LBh3o+36YABEil0UwbBfcL/yituk+L05a5NdLG+CtZxPF8f\nbs6u33f6HYoxxhR5gUwXfB5oBfRIOd8HPOdZRCZ6PP44PPUUNG4ctiaXLIGuXeHdd+HMMwN7jk0H\nzIe+feHcc/2OIt8uuAA6doQhQ/yOxHjI+iqEtu3LMf3ryn4HYowxRV4g1QXPUdVmIrIUQFV3iYjV\naDN5mzw5rNUEV6+Gq66CF1+E1q3D1mzRctppfkdQYGPHwsknw7ff+h2J8Yj1VUCnTu5Dpt69/Y7E\nGGOKtkDeAR8VkWKAQtqGj4VjsxzjrzAmWH/84QoYPvCAm9FmTGaxsfDII9C/v9+RGI9YXwW0b++W\nwe60GYPGGOOrQN4FPw38D6gqIg8BXwEPexqVMflQsaLbvmndOjebLfOaqmAqC5rocsMNbuNWE5UK\n3FeJSG0RmSciy0XkZxG5M+X2OBGZKyKrRGSOiFTwLvzQqFDBjei/+abfkRhjTNEmGkAVIhE5GWgL\nCPC5qq7wOrCUdjWQ+EzRdeCA2/9qwAAYPz77AoYiuRfbyut+E12WLoVmzdwn/ZaA+0NEUNWQlxst\naF8lItWB6qr6o4gcB/wAdAD6ADtV9VER+S8Qp6pDs3m+Z31Vfv5/Sn3sF1/AnXfCsmVhLepqCjEZ\nLehI6wiNSS/YviqQ6oJ1gQPADGA6sD/lNmP+sW+fm4d16FDYmjx61BW5AFdjw95MhFkhzUxTC6IM\nH+5vHCa0gumrVHWrqv6Y8v0+YAVQG5doTUl52BSgY6jj9sJFF8HBbX+zeIbtwm2MMX4JZLrgLGBm\nytfPgbXAbC+DMoWMKtx4o0uwwjQXS9VNDUxKcudhXP5lwH1U3qWL31EE5d13C92WXyZ3IemrRKQ+\n0BT4BqimqtvAJWJA1RDF6qmYGLi53me88pAlWcYY45dANiNuoqqnp3w9ETgbWOR9aKbQGDsWNm+G\n554L23DSkCHw22/w3nt5PzYuzvbBCrkzz4Q5c2DvXr8jKbD//hfuvdfvKEyohKKvSpkq+D5wV8qI\nVubh2kIzfNt7cBXe/6F+Yf4TNcaYQi2QEu4ZqOoSETnHi2BMITRrlkuuFi+G0qXD0uRjj8HHH8OX\nX7r1WHmxfa48ULGi2y/rk0+gc2e/oymQ22+HZ56BhQvhvPP8jsaEWn77KhEpjkuwXlfVj1Ju3iYi\n1VR1W8q6rb9yev6oUaPSvm/dujWtfd5Hokanc7mo16e8+2xz/j2sUAzAGWOMrxISEkhISAjZ9fIs\nfCEig9KdxgDNgEqqelnIosi5bSt8EcnWrYNzzoEPPwzbBrWTJsHo0fDVV1C7trvNClf45IUXXKZb\nCMuYpb5mpkxx+6otXGhr+sLJi8IXwfZVIvIasENVB6W7bRyQqKrjClPhi1Qzr5zAQ8vas2hTbU9i\nM9HDCl8Yk5XnhS+A8umOUrj57h0K2qCJInXqwEcfhS3Bmj7dTe+aM+efBMv4qH17N5J19KjfkRTY\nDTe4mi0ffuh3JCYECtxXich5wPVAGxFZKiJLRORyYBzQTkRW4aoWPuJJ5B65fPhZbPqzGL8sK3Lb\nhRljjO8CKuHuFxvJMqm+/BI6dXKzE886K+N9NpLlo3bt4Kmn4NRT/Y4kX+LjYdeunO+Pi7Nppl7y\nqoS7XyJ1JAtVht+yjb1lq/HkU1Hz6zYesJEsY7IKtq8KZLrgDHJZ7KuqVxe08bxYkmUAliyBFi1y\nfqNhb4hNMFTh4otdgcwbb3S3WeLuLY+mC0ZlXxVUkoWb1X3WWa42UZiWzZpCyJIsY7IKtq8KpPDF\nWqA68EbKeXdgG2ATbIznVq6EK690bxzsTa/xgohb5/fvf7vpg8XzXQ7IRAjrq7Jx/PGuGOiHH0K3\nbn5HY4wxRUcga7LOU9Wuqjoj5egBXKCq81V1vtcBmgjy3XewLXz7rqxfD5deCo8UqlUQpjC66CKo\nWxdef93vSEwQrK/Kwc03wyuv+B2FMcYULYEkWeVE5ITUExE5HgigcLaJKmvWuEIHy5eHpbk//4RL\nLoF77oHevcPSpCniRo2CBx8s1HU8ijrrq3LQsSMsWwarVvkdiTHGFB2BTIwZCCSIyFpAgHrAfzyN\nykSW3bvhqqtgxAho08bz5hIT3QjWjTfCHXd43pwxAFx4IdSvD2+8kedDTWSyvioHpUpB/+t38+gw\nZeI0233dGGPCIaDqgiJSCjg55XSlqh72NKp/2rXCF347dswtimrUyO3c6rG9e90I1oUXwqOP/rN3\nkRUiiHBvvw1t20KVKn5HEpQvv3Qjp+vW2evNS15VF4zGvio///dlrpqZvihQ4oR3aHjnv1i2Lta2\nwDBZWOELY7LyfJ8sESkL3APcrqo/AXVF5KoAg5soIttEZFm62+JEZK6IrBKROSJSoaDBmzAYMMD1\n8k884XlThw5Bhw5wxhkZEyxTCEybBjNm+B1F0C64AE44Ie/HmcgTTF8VLRIT/ykSpJox4Yr/9zXc\nVOotHh+63b8AjTGmCAlkTdYk4AjQKuV8C/BggNefBFyW6bahwGeqehIwDxgW4LVMuKlCw4bwzjue\nl1w7ehS6dIGqVWHCBEuwCp2rr3a7RUeBe+91X5Nt/9bCJpi+KvqVLMmgQTDl3dLs2OF3MMYYE/0C\nSbIaqOqjwFEAVT2Am++eJ1X9Csi85WcHYErK91OAjoGFasJOxI1kVfB2sDEpyU3RSk521d2KFfO0\nOeOFK66AefPg4EG/IwnaxRe7r1GSMxYlBe6rioqaw3rTvcQHPHxX+KrEGmNMURVIknVERMqQssmj\niDQAgpnnXlVVtwGo6lagahDXMoWcKvTv76oJvvcelCjhd0SmQOLjoVkz+PxzvyMJWuoo6iOP2Lqs\nQibUfVX0KVOGEcOVKe+VYd06v4MxxpjoFkiSNRL4BKgjIm8CnwNDQhiDvY0polThrrvgp5/go4+g\nTBm/IzJBiaIpg+DWtyxY4HcUJh+87quiQrWBPbir/zHuv9/vSIwxJrrlutBGRARYCVwLtMRNvbhL\nVYOZ0b1NRKqp6jYRqQ78lduDR40alfZ969atad26dRBNm1xt2wZHjkCdOp43pQqDB8OiRfDppxAb\n63mTxmtdu8Lq1X5HETL33APjxrmNik1wEhISSEhI8Oz6HvVV0alUKQaNKcXJJ8PChXDeeX4HZIwx\n0SnPEu4i8rOqNilwAyL1gRmp1xCRcUCiqo4Tkf8Ccao6NIfnWgn3cNm3D1q3hm7dXPbjIVVXXOCT\nT9zssvj4vJ9jJdxNOIm4apfHHw+zZ7uKlyZ0vCjhHmxfFWTbEVHCPbPcSrq/9x6MHg1LlkDJksHH\naQo3K+FuTFael3AHlojIWQW5uIi8BXwNNBKRjSLSB3gEaCciq4C2KefGT8eOuVGIM86Au+/2vLnR\no2HmTDeCFUiCZYwfSpVydV/GjfM7EhOgAvdV0Sq3ku7XXQf16sH//Z9/8RljTDQLZCRrJdAQ2ADs\nx03DUFU93fPgbCTLe6pwyy2webNbT+Nx5YmHHoI334SEBFeuPVXmT1wzS/8JrDFeSx092LPH7Zv1\n3XduVMuEhkcjWVHZV4VyFD/ztdavh7POUj7/VDm9aSCfuZpoZSNZxmQVbF+V45osETleVdeRdZ8r\nE02GDXOVJz7/3PME6//+D6ZMgfnzMyZY4BIsy6dNpImNhT594Nln4fHH/Y7GZMf6qoKrXx8ev3A6\n3S89l+83VLHiQ8YYE0K5fXT1fsrXV1V1Q+YjHMGZMEhddFK+vKfNPP00vPCC20qpRg1PmzKRICnJ\n7whC5vbbYfJk2LvX70hMDqyvCkLPp1pwxt6vuLvLJr9DMcaYqJJbdcEYEbkXt55qUOY7VXW8d2GZ\nsPnPfzxv4rnn4Ikn3BTB2rU9b874bcECePhhV9kkCtSrB23auETrjjv8jsZkw/qqIEjtWkx4vwrN\nOxzjtce30+vuKn6HZIwxUSG3kaxuQBIuESufzWFMnp580k0TnDfPvVk1RUDz5vD117B7t9+RhMyA\nAfDUU5Cc7HckJhvWVwWpwpXn89E9Cxn832J8O2+/3+EYY0xUCKTwxb9UdXaY4sncthW+KMQeewxe\nfNElWHXr5v5YK9EeZa66Cm64wW0JUAhlfj2qwtlnw4gR0L69f3FFC48KX0RlX+Vl4YsMVJn+rwn0\nW9iDb1dWpFat0LRpCgcrfGFMVp6XcPer0zIeWLwYfvstLE09/DC88oorcpFXgmWi0NVXu2qVUULE\njWY9+aTfkZicWF8VJBGunnkL/e8px9VXw34b0DLGmKBYzdaiYskS9xH8mjWeN/XAA/D6624Nln0a\nWkRddZVbk3X0qN+RhEznzrByJSxb5nckxgsiMlFEtonIsnS3jRSRzSKyJOW43M8YPVe8OEOHl6BJ\nEzcQbdNjjTGm4HJMskSkc8pX2x2msPvpJ7jiClfe71//8qwZVRg+HN591yVYVkWwCKtZE847LyxJ\nfbiULAn9+7u1WSZyhLCvmkT2ZeDHq2qzlKNQV3OJi3OjsqlHdpvBi7hp3omJMHRo+GM0xphokdtI\n1rCUrx+EIxDjkeXL4fLLXQ31a67xrBlVt+XW9OnwxRdQrZpnTZnCYsYMOPlkv6MIqVtugWnT4K+/\n/I7EpBOSvkpVvwKy2xI9pGvH/JSY6P6vTj1y2gC+VCn3Ov/f/9y0b2OMMfmXWwn3nSIyFzheRLIs\nrlDVq70Ly4TE7t1w6aWuvF+XLp41k5wMgwa5yt3z5kGlSlkfEx+fc4cO7hNWYyJd5cpu2uALL7gi\nGCYieN1X3S4iPYHvgbtV9e8gr1coVKoEM2coF565h+NjdtP2JisPa4wx+ZFjdUERKQk0A14Hbs58\nv6rO9zY0qy4YEr/+Cqee6tnljx2Dvn1dPY1Zs6BixewfZ9UDTWGS2+v155/d4PD69VCiRFjDihqh\nrC4Yyr5KROoBM1T19JTzKsAOVVUReRCooar/zuZ5haK6YEGu/cXQOXR9rDkLFggnn5fNJ2gmKlh1\nQWOyCravynEkS1WPAN+IyLmqul1Ejku5fV9BGzM+8DDBOnwYund3VajmzoVy5TxrypiI0aQJNGgA\nH37oRrWMv7zsq1R1e7rTl4EZOT121KhRad+3bt2a1q1bB9t8RLj4kcsYt3waV7Y9m29XH6RynTJ+\nh2SMMZ5ISEggISEhZNcLZJ+s03CfEMbj5qZvB3qr6i8hiyLntm0kK0IEMt0vMTHn+20kyxQmeb1e\n33nHTRn84ovwxRRNPNonK+i+SkTq40aymqScV1fVrSnfDwTOUtUe2TwvakeyAFBlWOPpfLXjZD7b\n2IhSpaNmmZpJYSNZxmTl+T5ZwEvAIFWtp6p1gbtTbjORxsN6u7t2/bNYOjERWraEm25yFbpzW0Bt\nDFOmwObNfkcRUtdcA6tWuboyJmIE1VeJyFvA10AjEdkoIn2AR0VkmYj8CFwEDPQi8IgnwkOL21Ht\nyEZuvnSjfWBmjDEBCCTJKqeqaZ/XqmoCYBPDIs0vv0CLFp7vILl1K7RuDa1auapTxXMrnWIMuHr+\n//uf31GEVMmSbi3i88/7HYlJJ6i+SlV7qGpNVS2lqnVVdZKq9lLV01W1qap2VNVtXgReGMQcV5bX\nljRh5YE6PPig39EYY0zkCyTJWisiw0WkfspxP7DW68BMPixdCpdcAkOGeLowas0auOAC6NQJHn/c\nTTUxJk8dOrgFTFHmlltg6lTYs8fvSEwK66tCLD4+475atVtUZ/qMGF5+2W3XYYwxJmeBJFk3AVWA\nabh9SCqn3GYiwXffuVJnzz0H3bp52tQFF8Dgwa50deYEK/Mml5kPK9FehLVr516nUTantFYtaNMG\n3njD70hMCuurQiz9NPHUaeE1argPF/r2hS1b/I7QGGMiV56FL/xkhS/ysGABXHcdTJwI7dt71szs\n2XDFFW4wokMHz5ox0axDB7dX2/XX+x1JQAItCPDFF3D77W62ro3sBs6Lwhd+itbCF5nvT3/+4IPw\n2Wfw+edQrJg38ZnwscIXxmQVjsIXJlKtXOk+UvQwwZo0Cfr0cd9bgmUKLEqnDKZW6Z7v+a6BxkSW\nYcNAjh3h4Xuia4TaGGNCxZKswuyWW6BtW08ureo+qXzgAXsDaUKgY0cYGH2F2USgXz8rgGGiQ+Y1\nWLlN8y5WDN646h2eeUZZsvhY+II0xphCIs8kS0TOC+Q2Ez2OHXNvHD/4AL7+Gk46ye+ITKEXHw/n\nnut3FJ7o2dNNm/rjD78jKdqsr8q/zGtpIeMarNz2PgSo9d8bePzEF7mx4y6OHPE+XmOMKUwCGcl6\nJsDbTCGX+ilmiRJuo9Uff4SaNa1whSl68irkEh//z2NjY13NmZds90C/WV+VT4mJ+UuqshDhho86\nU2/HEh4enN8nG2NMdMtxlyMRaQWcC1QRkUHp7ooFbJlrOB09Cvfc46YHnnqqZ83s2gWnnQbnnw9P\nP+2SLWOKorzebGYuctG/vyuieN999ncTbtZX+UtObMgLQz7lzEfPpmMfpemZUVPPxBhjgpLbSFZJ\n4DhcIlY+3bEHuM770AzgNuFp3x5Wr4a6dT1r5ptv3Nebb3brS+yNojGBa9wYGjWKuj2XCwvrq8Ik\n8whv6ohurVF9GVfrGfpev5+kJH9jNMaYSJFnCXcRqaeqG0TkOABV3ReWyLAS7mzeDFde6dayPPMM\nFM9x4DEob78Nd94J27d7VyrYmDQHD0KZMn5HEZTsSl+/+677gCIhwZeQChUvSrhHa1/lZQn3YKWP\nTQ8f4cJLSnL99XDrrf7GZfLPSrgbk1U4SriXF5GlwHJguYj8ICKnFbRBE6Aff4RWreCGG9w7Nw8S\nLFUYPRqGDnUL943x3M8/Q4sWfkfhiWuugd9+g+XL/Y6kyLK+ykdSqiTPP+82q9++3e9ojDHGf4Ek\nWS8Bg1S1nqrWA+5Ouc14aflyGD/ercXyYJfTAwegRw+30fA338Dpp4e8CWOyatzYLf5bvdrvSEKu\nRAno29fKufvI+qowSz99MD4emjRxnwv+979+R2aMMf4LJMkqp6pfpJ6oagJQzrOIjHP99dC5syeX\nXrfOzUAsWRK++AKqV/ekGWOyiolxaww/+sjvSDxxyy1uf/C9e/2OpEiyvirM0lcn3JWyJ/GoUTB3\nrtv+wxhjirJAkqy1IjJcROqnHPcDa70OzHjj00/dLMR//xsmTy70S2NMYXTNNTBtmt9ReKJWLbc/\n+Ouv+x1JkWR9VQSIjYWxY2HQXccidi2ZMcaEQyBJ1k1AFWBaylEl5TYTKmEox6QKjz0GvXrBO+/A\nHXd4MgvRmLy1aQOrVrnCLlGoXz83ZdDeYIad9VUR4voWqzi2bAXvvHHU71CMMcY3eVZTUNVdwJ0i\nUt6dhq9iU5GwbBl07+4qT9So4UkT+/fDTTfB2rWweDHUqeNJM8YEpmRJ+M9/3LzV2rX9jqZAUtei\nZKdiRfenvGABXHRReOMqyqyvihwxp5zE482HceOAoXTsXIHSpf2OyBhjwi/PkSwRaZJSsekXrGJT\naE2b5uYW3XdfSBKs+PiMe5ikHscd58pLf/+922oru8eIuDeOxoTFww/DBRf4HUWBpV+LkvnYvduN\nZj33nN9RFi3WV0WWi166njP2fc3T4w76HYoxxvgikOmCL2IVm0IrKQnuvx8GDHDl/Xr0CMlld+3K\n+GZv6lSoXBkmTIDk5JzfFKYeiYkhCcOYIq9XL7f+8Y8//I6kSLG+KpKcdhqPXrWAR8cls2OH38EY\nY0z4WXVBP1x3HSxaBN9958meQYcOwW23uTxu7ly3MaStvzImfGJjoVs3ePllvyMpUqyv8lH6cu6p\nJd0bPdWfbslTeWiYzdw0xhQ9Vl3QDyNHuuynWrWQX3r1amjZEnbuhCVL4MwzQ96EMSYA/frBSy/B\nUVv7Hy7WV/ko8xRaAKlTm5WH6/PUK2WpWNE7XKG6AAAgAElEQVTf+IwxJtzyW13wA6AyVrEpOE2b\nQrFinlz6vPNcTYF33nGfphtj/NGkCTRoELVbgkUi66siSGrS9Zlewj1DYvj7b78jMsaY8Mo1yRKR\nYsB9qnqnqjZT1eaqOiClipOJEPv3u8QK4JNP3FRBmx5oCoUZM6I6C+nf35VzN94KRV8lIhNFZJuI\nLEt3W5yIzBWRVSIyR0QqePIDRLkhQ9zXtTauaIwpQnJNslQ1CTg/TLFEn2++cdUnPPT999CsGRxM\nKeDUrJmnzRkTWocPR3UZvmuugRUr4Ndf/Y4kuoWor5oEXJbptqHAZ6p6EjAPGBZkG0VSpUru66hR\nvoZhjDFhFch0waUiMl1EeorItamH55EVZklJ8NBD0KEDlPNm3XVSEowdC1dcAQ88AK+95kkzxnjr\nX/+Cb791iwijUMmS0Levq/BpPBdUX6WqXwGZR746AFNSvp8CdAxRrEVOxYrw+usZC2MYY0w0E01d\noZrTA0QmZXOzqqrnc91FRPOKL+Js3gw9e7rvX389pJutxse7Mu05iYuzMuymELruOpds/fvffkcS\nEiL/LPwH91/C6afDhg1Qvrx/cUUSEUFVQzqpORR9lYjUA2ao6ukp54mqGp/u/gzn6W73rK/K/Hoq\nzB67bS3frq3M+3Nio+rnigYyWtCR9g9iTHrB9lV5jmSpap9sDltMnJ3PP4fmzaFdO/jss5AmWOAS\nrMmToUoVGDcOjh2zfa5MFOjaFd5+2+8oPFO7Nlx8Mbz5pt+RRLcw9VX2LjQI/etMZ9GCoyxd6nck\nxhjjvTxHsvxU6EayVq1ymU6rViG/9JYt7s3aGWe4RKtp05A3YYw/Dh6EmjVh5UpPtjUINxtxzpsX\nI1mhkM1I1gqgtapuE5HqwBeqeko2z9ORI0emnbdu3ZrWrVuHKKYoGvE5cIBnqj/Ep82GMGN+hej5\nuaKAjWQZAwkJCSQkJKSdjx49Oqi+ypKsCKfqkqr//he2b4cjR6BECb+jMibE1q+HevWitiymKpxy\nituc+MILo+hNcwFFcJJVH5dkNUk5Hwckquo4EfkvEKeqQ7N5nk0XDNChx57hxJHd2XywclT9XIWd\nJVnGZOX5dEGviMh6EflJRJaKyGK/4ohkmza5whbPPAOffupuswTLRKX69aM2wQL3o/XrF9WFFAs9\nEXkL+BpoJCIbRaQP8AjQTkRWAW1Tzk0QSt9+M/eV/D9isY2zjDHRLc8kS0SqpewfMjvl/FQRCcUK\n9WTcNIwzVfXsEFwvaiQnu0+8mzVzmwt/+62bJmiMKbx694Y5c/yOInoF21epag9VramqpVS1rqpO\nUtVdqnqJqp6kqpeq6m7vfoIiokwZbhpZh5IcYeFCv4MxxhjvBDKSNRmYA9RMOf8NGBCCtiXA9ouU\nn3+GCy6AV1+FefPg/vtt9MqYaFChgqvxYTwzGW/6KhNiJfvdzN/EMmKE35EYY4x3AklyKqvqu7iR\nJ1T1GJAUgrYV+FREvhORviG4XqG2f79bd9W2LfTqBQsXQpMmfkdljAmlfv3c16NH/Y0jSnnVV5lQ\nK1WKo5RiwwZIt8bcGGOiSiBJ1n4RqURK6VoRaQkhmUx9nqo2A64A+ovI+SG4ZqE0cyY0bgxPPumK\nW9x6KxQr9s+mjalHXJzfkRrjsTVrXCnNKHX66e7rtGn+xhGlvOqrjEdGjoThw6OrsIcxxqQqHsBj\nBgHTgQYishCoAlwXbMOq+mfK1+0i8j/gbOCrzI8bNWpU2vehLIsbCdavh0GD4Jdf4JVX3PZa1tmY\nIu3ll90fwbhxfkfiqfHjoUuXqK71kUHmsrge8aSvMt7p0QMefthtK9mund/RGGNMaOVawl1EYoCW\nwGLgJNw6qlWqGtRkFxEpC8So6j4RKQfMBUar6txMj4vKEu7798Mjj8Dzz8Ndd8GQIVC6dPSV6jUm\n3375BS6/HDZscMO5UUgEGjSA116Dc8/1Oxp/hLqEu1d9VT7atxLu+WT7yUUWK+FuTFaelnBX1WTg\nOVU9pqrLVfWXEHVa1YCvRGQp8A1uX5K5eTyn0EtOhjfegJNPhrVr4ccfYcQIl2AZY4DTToMaNf7Z\nsyBK3XUXPPGE31FEDw/7KuORxESXPCatXstp8VuYOdOdpx65JWDGGFMYBLIm63MR6SQSuoktqrpO\nVZumlG9voqpRv/fI4sWuHPuTT8Lbb8Obb0KdOn5HZUwEuvFGtwN3FOvTB774Atat8zuSqBLyvsp4\nL6ZeHUaXeJARg/ZG5YidMaboynW6IICI7AXKAceAQ7hpGKqqsZ4HFwXTBdetcwt7582Dv/+GAwdy\nfqxNjzAG90dwwgnujycKq72kTv+65x5ISnLrs4qaUE8XTLlmVPZV0TpdMD2dNJnmd57LiNcb0bGj\nu60o/NyRxKYLGpNVsH1VnkmWn3LquOrXr8+GDRt8iMiY4NSrV4/169f7HUbke+stuPRSqFzZ70hC\nLvXN48aNcOaZLpeM9TwNiCxeJFl+siQrSMeOMbNuP+4tM54fVx9HTEwR+bkjiCVZxmQVliRLROKA\nE4G01UOquqCgjQYqp44r5Yf2unljQs5euyb9m8du3eCcc2DgQH9jCjevkqxI66tCc+2ikWzo62/Q\nsl8z7n7lFLp0lSLzc0cKS7KMycrzJEtEbgbuAmoDP+IqOC1S1TYFbTTg4CzJMlHGXrsm/ZvHxYtd\nKffff4figWyoESU8mi4YcX1VaK5dRJKNpCTmnDuagbtH8POvxSlevIj83BHCkixjsvK0umCKu4Cz\ngA2qejFwJrC7oA0aY4xxzj4b6teHd97xO5KoYH1VYVasGJd+8wDxVYrz9ttuOabIP0d8vN8BGmNM\n/gSSZB1S1UMAIlJKVVfi9iExxhgTpKFD3b55ycl+R1LoWV9VyInAAw/AqFGwbZuVdDfGFG6BJFmb\nRaQi8CHwqYh8BFjViQLYsGEDMTExJIfg3dTxxx/PvHnzAnrslClTuOCCC9LOy5cvH7LiC2PHjuWW\nW24BQvvzAWzatInY2FibXleUqbqynFHsssugRAmYNcvvSAo966uiQJs2cPzx8PzzfkdijDHByTPJ\nUtVrVHW3qo4ChgMTgY5eB1ZY5ZX8+LWFS/p29+7dS/369XN9/Pz586kTwEZew4YN46WXXsq2nfzK\n/LurU6cOe/bs8e13ZiLA3LnQvr3fUXhKxI1mjR1ra1CCYX1V9HjqKXjwQfjrr5wfEx9v0wmNMZEt\nzyRLROqmHsA63ILi6p5HZnylqnkmN0lJSWGKxhRZbdrAmjXwyy9+R+KpTp1g+3b48ku/Iym8rK+K\nHqecAr26H2XYoEM5PmbXLptOaIyJbIFMF5wFzEz5+jmwFpjtZVDRIjk5mcGDB1OlShUaNmzIrEzz\ngfbs2cPNN99MzZo1qVOnDsOHD0+bGrd27Vratm1L5cqVqVq1KjfccAN79uwJqN3ExESuvvpqKlSo\nQMuWLVmzZk2G+2NiYli7di0AH3/8MY0bNyY2NpY6deowfvx4Dhw4wBVXXMEff/xB+fLliY2NZevW\nrYwePZrOnTvTs2dPKlasyJQpUxg9ejQ9e/ZMu7aqMnHiRGrVqkWtWrV4/PHH0+7r06cPI0aMSDtP\nP1rWq1cvNm7cSPv27YmNjeX//u//skw//PPPP+nQoQOVKlWiUaNGvPLKK2nXGj16NF27dqV3797E\nxsbSpEkTlixZEtDvy0SwEiWgb1+YMMHvSDxVrBgMGeJGs0yBWV8VRUbGP8PsDw6weLHfkRhjTMEE\nMl2wiaqenvL1ROBsYJH3oRV+L730Eh9//DE//fQT33//Pe+//36G+3v37k3JkiVZu3YtS5cu5dNP\nP01LHFSVe++9l61bt7JixQo2b97MqFGjAmq3X79+lC1blm3btjFx4kReffXVDPenH6G6+eabefnl\nl9mzZw+//PILbdq0oWzZssyePZuaNWuyd+9e9uzZQ/Xq7gPh6dOn06VLF3bv3k2PHj2yXA8gISGB\nNWvWMGfOHMaNGxfQ9MnXXnuNunXrMnPmTPbs2cPgwYOzXLtr167UrVuXrVu38t5773HvvfeSkJCQ\ndv+MGTPo0aMHf//9N+3bt6d///4B/b5MhOvbF6ZOhb17/Y7EU716wc8/w/ff+x1J4WR9VXSJHdqP\nxyo+zM1d/ubIEb+jMcaY/AtkJCsDVV0CnONBLKEzalTGydqpR05JSnaPDzChyc17773HgAEDqFmz\nJhUrVmTYsGFp923bto3Zs2fzxBNPULp0aSpXrsyAAQOYOnUqAA0aNKBt27YUL16cSpUqMXDgQObP\nn59nm8nJyUybNo0xY8ZQunRpGjduTO/evTM8Jn0hiZIlS7J8+XL27t1LhQoVaNq0aa7Xb9WqFe1T\n1siULl0628eMGjWK0qVLc9ppp9GnT5+0nykQORW52LRpE4sWLWLcuHGUKFGCM844g5tvvpnXXnst\n7THnn38+l112GSJCz549WbZsWcDtmghWqxZcfDG8/rrfkXiqVCkYNgxGjvQ7kuhQKPoqk7PSpenx\n9tUcv+1bxtx3KEtJ97g4vwM0xpjcBbIma1C6Y7CIvAX8EYbYCm7UqIyTtVOP3JKsQB+bD3/88UeG\n4hH16tVL+37jxo0cPXqUGjVqEB8fT1xcHLfeeis7duwA4K+//qJ79+7Url2bihUrcsMNN6Tdl5vt\n27eTlJRE7dq1s203sw8++IBZs2ZRr149Lr74Yr755ptcr59XMQwRydL2H38E/3L5888/iY+Pp2zZ\nshmuvWXLlrTz1NE2gLJly3Lo0KGQVTo0PhsyBMqX9zsKz918sxvNyuPP0GSjUPZVJldy0YW82Pkz\nXnr2MHPnZuyiExP9js4YY3IXyEhW+XRHKdx89w5eBhUtatSowaZNm9LON2z4p5pwnTp1KF26NDt3\n7iQxMZFdu3axe/futNGXe++9l5iYGJYvX87u3bt54403AiplXqVKFYoXL56h3Y0bN+b4+ObNm/Ph\nhx+yfft2OnToQJcuXYCcqwQGUukvc9s1a9YEoFy5chw4cCDtvj///DPga9esWZPExET279+f4dq1\natXKMx4TBc45B9Kt/YtWpUrB/fdDuqWLJnDWV0Wh6hNG8nT8aHpcezDad3MwxkSZQNZkjU53PKSq\nb6Zu+Ghy16VLF55++mm2bNnCrl27GDduXNp91atX59JLL2XgwIHs3bsXVWXt2rUsWLAAcGXWjzvu\nOMqXL8+WLVt47LHHAmozJiaGa6+9llGjRnHw4EF+/fVXpkyZku1jjx49yltvvcWePXsoVqwY5cuX\np1ixYgBUq1aNnTt3BlxsI5WqMmbMGA4ePMjy5cuZNGkS3bp1A6Bp06Z8/PHH7Nq1i61bt/LUU09l\neG716tXTCnKkvx5A7dq1Offccxk2bBiHDx9m2bJlTJw4MUPRjexiMaawufFGWL3aKg3ml5d9lYis\nF5GfRGSpiFgphnAqV46uv46kXfsy9OlT8G0O0pd8t3LvxphwCGS64AwRmZ7TEY4gC5P0ozF9+/bl\nsssu44wzzqBFixZ06tQpw2Nfe+01jhw5wqmnnkp8fDydO3dm69atAIwcOZIffviBihUr0r59+yzP\nzW3U55lnnmHv3r3UqFGDm266iZtuuinH577++uscf/zxVKxYkZdeeok333wTgJNOOonu3btzwgkn\nEB8fnxZXID//RRddRMOGDWnXrh1Dhgyhbdu2APTs2ZPTTz+d+vXrc/nll6clX6mGDh3KmDFjiI+P\nZ/z48VlinTp1KuvWraNmzZp06tSJMWPGcPHFF+caizGFTcmSMHy4rc3KL4/7qmSgtaqeqapnhyJe\nkw8VKvDEE/DnnzBmTMEukb7ku5V7N8aEg+T1ab+IPIXba+SNlJu6A9uADwFUNe9qDAUNTkSzi09E\nbJTCFEr22jUieX8af+yY2yvohRcg5TOKqJLydxDST0G87KtEZB3QQlV35nB/tn1VKATyeikqtm6F\n88+HwYPh1lsz3hcfnzF5iovLuG4r/e/RfqdZyWhBR9ovxZj0gu2rigfwmPNUtUW68xki8r2qDixo\no8YYU2DHjsG+fVCxot+ReKZ4cXjoIbjnHlfSPSbfdWCLJC/7KgU+FZEk4CVVfTkE1zT5VL06zJkD\nF14IpUu7qbWpMhfCsIkMxhi/BZJklRORE1R1LYCIHA+U8zYsY4zJwbPPwo8/wuTJfkfiqc6d4Ykn\n4M03i0TNj1Dwsq86T1X/FJEquGRrhap+lf4B6fcxbN26Na1btw5R0ya9Bg3g8ze3ctmVxdixsRx3\nDy+bbUKVWvI9/XlhcvQoHDjgPnApWdLty26M8VZCQkKG/VeDFch0wcuBl4C1gAD1gFtUdW7Iosi5\nbZsuaKKKvXZDYPduaNjQ1Tlv2NDvaPItP1OVvv4aunWDVaugTBlv4wonj6YLhqWvEpGRwF5VHZ/u\nNpsuGE5JSWy67WGumNKFZpdX4/m3KlIuH+l05t9pXlMNvXL4MCxdCosXw8qVruDN77/Djh1w8EAy\nZTnAMS3GES1B+eIHqR27h4anl+WsdnGcc46bOlmqVGhisemCxmQVbF+VZ5KV0kgp4OSU05Wqerig\nDeaHJVkm2thrN0RGj4Z16wrlaFZ+3zR37gzNmrmNiqOFF0lWynVD3leJSFkgRlX3iUg5YC4wOn3y\nZkmWP/a/9Cb97izO93GX8Nr0OJqfFdi82sy/07zOQyUpCb5L2M+sFzfz+Zcl+OmvGjSqsZdzrqpK\n48bQqJH73KhKFSifuAHZuwdE0D17Sdy4j80/7WRF3Ll8t60uCxe6xOyyy9y0yUsvhZTiwAViSZYx\nWXmWZInIWcAmVd2act4L6ARsAEapquef81iSZaKNvXZDpBCPZmX+1DyzzJ+ir1njtgn76SeIlm3h\nQplked1XpUw7/B9uXVZx4E1VfSTTYyzJ8omu+o03rpzK4E130v3Wiox5UPLctzycSdaBAzBrFnz0\n8jbmfFGSGslbuKLOL1x6KZzdvQHHtWriFpgVwLZt8NFH8NJLys41u7n71gP0HVWrQKNblmQZk5WX\nSdYS4BJVTRSRC4G3gTuApsApqnpdQRsNODhLskyUsdduCI0eDb/95hYtRZHs3uANH+5+1Hfe8Sem\nUAtxkhWxfVVorm1JVp6SktgxdwlD3juL2bPdqO9//pPzVLr8Jln5nU545AjMnQtvvw0zZ8LZZ8O1\nF+7ginrLqXvd2aGf+3vsGN8OmMoDr9TgZzmDUcOPcePQGvkqmGNJljFZeZlk/aSqZ6R8/xywXVVH\npZz/qKpNC9powMFZkmWijL12Q2jfPvj4Y+jSxe9IQiq7N9UHD0LjxvDSS3DJJf7EFUohTrIitq8K\nzbUtycqPH390H0osWwb33Qe9emUdKMpvkhXISFdSEsyfsYepz2xn2o8NOOUU6N4drrsOqlUL3c+X\nq0OH+GbINAZMaETxqpV4YVpVTjsnsMVqlmQZk1WwfVVun3MUE5HU6oNtgXnp7gukKqExxnjnuOOi\nLsHKSZky8PTT0L+/WyxvMrC+yqRp2hRmzICpU2H6dKhfZR+j+25i+1+hSyBSKxeKQDnZxynyK1WL\n72TANes5OO9r9iceYuFCuP12V3Y+Pj5kTeeudGlaPt2DrzfW4Ya6C7j4khjGjYPk5DC1b4zJILck\nayowX0Q+Ag4CXwKISEPg7zDEZiJQTEwMa9euDeixo0ePpmdK7elNmzYRGxsbslGc2267jYceegiA\n+fPnU6dOnZBcF+Crr77ilFNOCdn1jAmFq65yGxSPHet3JBHH+iqTxbnnwswZyhf93mfzO1/TqOZe\nep37O/NmHQzquqqu6ueYMXBy/Daqyk46nfgzX45bxLI9x/OG9uSQlkaVtCO3NZheiKlRjVsX9eaH\nX0ozYwZcfrnbyNkYE145Jlmq+hBwNzAZOD/dXIgY3Hx3k4O33nqLs846i/Lly1OrVi2uvPJKFi5c\n6HdYTJkyhQsuuCCoa0g+d3hMfXydOnXYs2dPns8PNMYJEyZw3333FTiu9DInjueffz4rVqwo8PWM\n8cpzz8Hzz7uyz8axvsrkSIRTxt3Iy7s789sb39F8zxfcffVq4khk4EC3sfGhQ4FdavZsuPtuVwHw\nkkvgzz9h4uN/s3ZHLA/+1pVTh1xFnhU3wqxuPSEhAVq2hObNYdEivyMypmjJdSqFqn6TzW2/eRdO\n4Td+/HgeffRRXnzxRS699FJKlizJnDlzmDFjBuedd16+rpWUlESxTDVZs7stUKoaVDKSeg0vBRJj\ncnIyMflZ0ZuHYH8nxoRS5k1Us9OsWe7PD8ceP5HE+iqTq5gYqnRry13d2nLXzp2cU3cLTz4Zz5NP\npt1N165QpfguKu7bzBnFhdMliaOUZC/lKUkVHnusFBdd5IrPnHlm6t9oIz9/qoAULw4PPOCKb3To\nAOPHHeGGPiX9DsuYIiF071QNe/bsYeTIkTz//PN06NCBMmXKUKxYMa644goeecRV/D1y5AgDBgyg\nVq1a1K5dm4EDB3L06FHgn2lvjz76KDVq1OCmm27K9jaAmTNncuaZZxIXF8f555/Pzz//nBbH5s2b\n6dSpE1WrVqVKlSrceeedrFy5kttuu41FixZRvnx54lMmiR85coTBgwdTr149atSoQb9+/TicbtHH\nY489Rs2aNalduzaTJk3KNSFZv349rVu3pkKFClx22WXs2LEj7b4NGzYQExNDcsrk8MmTJ9OgQQNi\nY2Np0KABU6dOzTHGPn360K9fP6688krKly9PQkICffr0YcSIEWnXV1XGjh1LlSpVOOGEE3jrrbfS\n7rv44ot59dVX087Tj5ZddNFFqCqnn346sbGxvPfee1mmH65cuZKLL76YuLg4mjRpwowZM9Lu69On\nD7fffjtXXXUVsbGxtGrVinXr1uX+QjHeeO45WL7c7yiClphIhqlGmY/kZLj6areoP7v7wz01yZhC\npVIlvt3fJO3v5eBB+P57l4A0ittOqXUr6d7kF/petJpHev7KJ8+uYf+GncybByNHug84gv1cLj7+\nnzVdmQ+v1m9ddRXMm32YEbds497Ov1kxFWPCQVUj9nDhZZXT7X775JNPtESJEpqUlJTjY4YPH66t\nWrXSHTt26I4dO/Tcc8/VESNGqKpqQkKCFi9eXIcNG6ZHjhzRQ4cOZXvbkiVLtGrVqvrdd99pcnKy\nvvbaa1q/fn09cuSIJiUl6RlnnKF33323Hjx4UA8fPqwLFy5UVdXJkyfrBRdckCGeAQMGaIcOHXT3\n7t26b98+vfrqq/Xee+9VVdXZs2dr9erV9ddff9UDBw5ojx49NCYmRtesWZPtz9aqVSsdPHiwHjly\nRBcsWKDly5fXnj17qqrq+vXrNSYmRpOSknT//v0aGxurq1evVlXVrVu36q+//ppjjDfeeKNWrFhR\nFy1apKqqhw4d0htvvFGHDx+e4feW2vb8+fO1XLly+ttvv6mqauv/Z+/Ow6uozgeOf99AWAIEEtYE\nQhAsLiigIIIii/uGuJRVEFHBBWrBtoqIBsT+RK3YqsUFAQELAq5sVqwa1ApFVAQREUTCEiKEsAeR\n5f39MZN4E+5NbpJ7M7nJ+3meeTJ3zsyZd05u5uTMnDnTrZtOmTIlN7/8+xAR3bRpU+7n1NRUTUpK\nUlXVo0eP6qmnnqoTJkzQo0eP6kcffaS1atXKzfvWW2/VevXq6cqVK/X48eN68803a79+/QL+/svq\nd7dcmDRJtVMn1QL+/sqLHTtUGzZUXbr05LRI+Iq5fwee1zGhmsL5dx0Jv09TsLi4vJdC4uICrxvu\n3/fOf3+pHaO/0Fs7fqdHj/rsd6x90YzJr6R1Vbm8kxXoClFRp6LavXs39erVK7Ar26xZs0hJSaFu\n3brUrVuXlJQUZs6cmZteqVIlxo0bR3R0NFXdl3zkXzZ58mTuuusu2rdvj4gwcOBAqlatyvLly1mx\nYgU7duzgySefpFq1alSpUoULLrggYDyTJ0/mmWeeoXbt2tSoUYNRo0Yxe/ZsAObNm8fgwYM544wz\nqF69OmPHjg2Yz9atW1m5ciWPPvoo0dHRXHTRRfTo0SPg+pUqVWLNmjX88ssvNGzYsNCBJnr27EnH\njh0BcsvFl4gwfvx4oqOj6dKlC9dccw1z584tME9fGuCy3rJlyzh06BAPPPAAlStXpnv37lx77bW5\nZQRwww030K5dO6Kiorj55ptZtWpV0Ps1IXTnnU6/n0mTvI4k7Bo1gldfhf79YedOr6MxxhQk/91p\nL7vz1r/iXP7zVV0yVu/ipjPWcjjbbmkZEy7lspHlrwtNcaaiqlu3LpmZmbld4vxJT0+nadOmuZ+T\nk5NJT0/P/Vy/fn2io6PzbJN/WVpaGk8//TTx8fHEx8cTFxfHtm3bSE9PZ+vWrSQnJwf1zNKuXbvI\nzs6mXbt2uXldddVV7N69OzdW325zycnJARsj6enpxMXFUd3nJYvJycl+142JiWHOnDm88MILJCQk\n0KNHD9avX19grIWNHhgXF0c1n5eh5C/X4tqxY8dJ+05OTmb79u25nxs1apQ7HxMTw8GDB0u8X1MM\nUVEwZYrzkuLvvvM6mrC78koYNAhuvtl5R48xxgSjxlmn8O7GVtTYs40rT93APhuD05iwKJeNLK90\n6tSJqlWr8s477wRcp3HjxqSlpeV+TktLIzExMfezv2ee8i9LSkrioYceIisri6ysLPbs2cPBgwfp\n06cPSUlJbNmyxW9DL38+9erVIyYmhrVr1+bmtXfvXva5Z9yEhAS2bt2aJ9ZAz2QlJCSwZ88eDh/+\nbXjcLVu2BCyHyy67jCVLlpCRkcFpp53G0KFDAx5/Qctz+Nt3TrnWqFGD7Ozs3LSMIoxlm5iYmKcM\ncvJu3Lhx0HmYUnTaaTBhAvTtG/ywYRFs3Dg4etR5VsQYE/l838EVzme0qiTU5bVNF9K6vTOghzEm\n9KyRFUKxsbGMGzeOYcOG8e6773L48FHIwAoAACAASURBVGGOHTvGe++9x6hRowDo27cvjz32GJmZ\nmWRmZjJ+/Pjcd0kFa8iQIbz44ousWLECgEOHDrF48WIOHTpEhw4dSEhIYNSoUWRnZ3PkyBE+//xz\nABo2bMi2bdtyB9oQEYYMGcKIESPYtWsXANu3b2fJkiUA9O7dm1dffZV169aRnZ3No48+GjCmpk2b\n0r59e1JSUjh69CifffZZngEi4LcueTt37mT+/PlkZ2cTHR1NzZo1c++85Y8xWKqau+9PP/2URYsW\n0dt9UW3btm156623OHz4MBs3bmTKlCl5tm3UqFHAd3+df/75xMTE8OSTT3Ls2DFSU1NZuHAh/fr1\nK1J8phTddhv06kVFuDxbuTLMnQuzZsH06V5HY4wpqfxdC8M5kE1UbE2efTeZHtc6dfPWddYLw5hQ\nskZWiN13331MnDiRxx57jAYNGtC0aVMmTZrE9ddfD8CYMWNo3749rVu3pk2bNrRv3z7P+56C0a5d\nOyZPnszw4cOJj4+nZcuWTHf/w4qKimLBggVs2LCBpk2bkpSUlPts0sUXX0yrVq1o1KgRDRo0AGDC\nhAmceuqpdOzYkTp16nD55Zfzww/OyMdXXnklI0aM4OKLL6Zly5ZccsklBcY1a9Ysli9fTt26dRk/\nfjyDBg3Kk55zN+rEiRNMnDiRxo0bU69ePT755BNeeOGFgDEGIyEhgbi4OBITExk4cCAvvfQSv/vd\n7wAYOXIk0dHRNGrUiMGDBzNgwIA8244dO5ZbbrmF+Ph43njjjTxp0dHRLFiwgMWLF1OvXj2GDx/O\nzJkzc/O24d/LIBF4+GFo2NDrSEpFgwawaBHcfz989JHX0RhjQincd7ZEYPw4p+fLrraX8sOy3aHd\ngTEVmAR6xqYsEBH1F5+IBHw2yJiyzL67JlxSU513/ezcWbxnSkuT+3dQbq5QBKqrQpN32f99mtIT\nru+DjBPW/O8vRC9ZxC/zl9DmausSb0xJ6yq7k2WMMeVAt27w4ovO/FdfeRqKMSYCnbX4SY7dPIg6\nPTqzfIa9y9uYkrJGljGm/Dt+HLZt8zqKsLvhBufnVVfBl196G4sxJvK0mn4/h+8bQ8yYkV6HYkzE\ns0aWMab8++wz6NABKsg7zF56yWloLVrkdSTGmHCKjw/9M1unP3U7rTe9W/KMjKngrJFljCn/unaF\n556Dyy+Hjz/2Opqwu/56mD8fhgyBv//dnukxprzasydMoxFWrhyijIypuKyRZYypGG66CV5/Hfr1\ncxpc5bzl0bEjLFsGU6c6rw3LyvI6ImNMSeUfbTAuruD0/FO43rsF4bmrZkwks0aWMabiuPhip+Ux\nZQpMnOh1NGGXnAz/+x8kJECbNvDBB15HZIwpifzv0cp/8SR/ev6puHe6jv6qvHP6KFbNWR9wnbDd\nVTMmQkVkIys5ORkRscmmiJuSk5O9/vMxp5wCy5c7Ly2uAKpXd7oMTpnidB/8/e/hp5+8jsoYUxbk\n3H2Cgu8+RUdDcpdkmvTrzIIeL3PsqJ5058rru2oV9U5aWTruwmIpaqxl6diKw7P3ZInIlcDfcRp6\nU1T1CT/rhO3dI+Ei9k4TY4yHCjoHHT4MTz8NzzwD/fvDffc5bc7SJhI578nyuq6yOsWEUv7vU85n\nGSdoihb6fdu5dB17r7uFg8eqcVv2c6zStiGLpaQCHVt5V5aOu7BYihqr18dW0rrKkztZIhIFPA9c\nAbQC+onI6V7EYk6WmprqdQgVjpW5N04q91WrYMYMOHjQk3jCrXp1GDMGvv0WataE9u2hd2947z04\ndszr6MqeilBXRfK5x2IvfQ26nsH2tybAwIG8zxV8+7d/ex1S0CK1zMFij1RedRfsAGxQ1TRVPQq8\nDvT0KBaTT0X+g/CKlbk3Tir3X3+FuXOhSRPnVs/rr8POnZ7EFk4JCfD44063wS5dYNw455DvvBPe\negv27vU6wjKj3NdVkXzusdi9sfTTTzn3xaGcxnrOuKur1+EELZLL3GKPTF41shoDW30+b3OXeSo0\nX4Tg8whmf4WtEyg92OVl4ctf0hiKun1pl3uwy0pbpJV7UdOKVe4dOsDChbBhA3TuDLNnQ8uWTssj\nBELxew9lucfGwvDhMGFCKp9+CqefDi+/7DS4zjgDBgyAYcNSWbQIvvsODh0qPN+y+n0vprDWVcUt\nl+L+LYXy92CxB58eCbET4DnNgrbbRx0q1ax+0vKfNx/mP79/gTVTv+DAz9nBxxBAccu9KP+PlTSG\n4m4X7u9MSfKJ1NhL8r9GqOsqexGCj9TUVLp161bSXIDg8ghmf4WtEyg92OWhOeaSKWkMRd2+tMs9\n2GWlLdLKvahpJSr3+vXhnnuc6dgxOHrU/3r9+8P69c4T3nXqQO3azjRsGLRocXKcr7xCt82b8z7J\nC877uxo29HdgsGtXnnVT58yhW6tWToz5LV3KjWTCmz5ZFLI+mZmkzpnD2D67GdkURg6Bo1O78N2u\n+nz5Jbz4Yio//tiNzZsh7afj1Kp2lPqxv3Lg8GLOOaUl8bV+pVaLBlSLi6FqVfjvf1NZubIb1apB\n1bT1VDp0gPkr/0Wt5zrT7nyrcnwV92+wuH9LoTzvWOzBp0dC7GwO3f5+3bWPmDUrqLL4JSrfvp60\nyonsiWnMzNhjdNv6+Unrb/8mk5+eXwRRUUiUQFQUREVRq1ldWv/pspNiyPgui81TP8r9/Nrnc6l2\nQSYXEwdc4hs90C13/W2ff8fyg2/kptZMiuOsP/qu7z//nH3U63M84Po/TTv5/Yu1kuJIzfrkpPLz\nXX/mf+dQ9cLdueufde/FTuQ+x5zxXRZbl3zHskNv5snHd31fcWSx7C8F5x9M/HO/ne/3d5+zvm/e\nOfnDxSf9vg7uOsyyvzix3wgs+0ve9QvK/0Z2B71+jpy4CjremaMn5Ym9av3anHv/pSH/38yTgS9E\npCMwVlWvdD+PAjT/A8UiUgEeWTTGmIonEga+sLrKGGMqtpLUVV41sioB63EuOewAVgD9VHVdqQdj\njDHG+GF1lTHGmOLypO+Gqh4XkeHAEn4bFtcqLWOMMWWG1VXGGGOKy7P3ZBljjDHGGGNMeeTV6ILG\nGGOMMcYYUy5ZI8sYY4wxxhhjQijiGlki0lVEPhGRF0Ski9fxVCQiEiMiX4jI1V7HUlGIyOnud32u\niNzldTwVgYj0FJGXRWS2iFzmdTwVhYicIiKviMhcr2MJhUivqyL1fB/J58xIPvdE6t+v+z1/VURe\nEpH+XsdTFJFa5hC53/Winl8irpEFKHAAqIrzYkhTeh4A5ngdREWiqt+r6t1AH+ACr+OpCFT1XVUd\nCtwN9PY6nopCVX9S1Tu8jiOEIr2uisjzfSSfMyP53BPBf783AvNU9U7gOq+DKYoILvOI/a4X9fzi\nWSNLRKaIyM8isjrf8itF5HsR+UFEHsi/nap+oqrXAKOAR0sr3vKiuOUuIpcC3wG7gDL/fpuyprjl\n7q7TA1gILC6NWMuLkpS5awzwz/BGWf6EoNzLlEiuqyL5fB/J58xIPvdE+t9vMeJvAmx154+XWqB+\nRHLZlyB2T+vZ4sRdpPOLqnoyAZ2BtsBqn2VRwEYgGYgGVgGnu2kDgYlAgvu5CjDXq/gjdSpmuT8D\nTHHL/33gba+PI9Kmkn7f3WULvT6OSJpKUOaJwATgYq+PIRKnEJzb53l9DCE+Hs/qqkg+30fyOTOS\nzz2R/vdbjPhvBq5252dFUuw+63h+zixO7F5/10tS5u56hZ5fPHlPFoCqfiYiyfkWdwA2qGoagIi8\nDvQEvlfVmcBMEblBRK4AagPPl2rQ5UBxyz1nRRG5BcgsrXjLixJ837uKyCicLkeLSjXoCFeCMv8D\nzstnY0XkVFV9uVQDj3AlKPd4EXkBaCsiD6jqE6UbuX+RXFdF8vk+ks+ZkXzuifS/36LGD7wNPC8i\n1wALSjXYfIoau4jEA3+lDJwzixG75991KFbcXXG6mAZ1fvGskRVAY367bQtOP/YOviuo6ts4fxQm\ndAot9xyqOqNUIqoYgvm+LwWWlmZQ5VwwZf4c8FxpBlUBBFPuWTj98yNBJNdVkXy+j+RzZiSfeyL9\n7zdg/KqaDdzmRVBBKij2slzmUHDsZfW7DgXHXaTzSyQOfGGMMcYYY4wxZVZZa2RtB5r6fG7iLjPh\nZeXuDSv30mdl7o3yVu6RfDwWuzcsdu9EcvwWe+kLWdxeN7KEvCMXfQGcKiLJIlIF6AvM9ySy8s3K\n3RtW7qXPytwb5a3cI/l4LHZvWOzeieT4LfbSF764PRzRYxaQDhwBtgCD3eVXAeuBDcAor+Irr5OV\nu5V7RZmszK3cK/rxWOwWe0WKPdLjt9jLX9ziZmaMMcYYY4wxJgS87i5ojDHGGGOMMeWKNbKMMcYY\nY4wxJoSskWWMMcYYY4wxIWSNLGOMMcYYY4wJIWtkGWOMMcYYY0wIWSPLGGOMMcYYY0LIGlnGGGOM\nMcYYE0LWyDJlhohcLyInRKSl17EEIiIPeh1DqIjInSIyoAjrJ4vImiLu40MRqVlA+mwRaVGUPI0x\npiwoj3WWiHwsIueGcx9FzLuHiNxfxG0OFHH9eSLSrID0p0Ske1HyNAaskWXKlr7Ap0C/cO9IRCoV\nc9PRIQ3EIyJSSVVfUtXXirhp0G8vF5GrgVWqerCA1V4AHihiDMYYUxZYnRXGfbj11AJVfbKImxal\nnjoTiFLVzQWs9hwwqogxGGONLFM2iEgN4ELgdnwqLBHpKiJLRWShiHwvIpN80g6IyEQR+VZEPhCR\nuu7yO0RkhYh87V6hquYunyYiL4jIcuAJEYkRkSkislxEvhSRHu56g0TkTRF5T0TWi8gEd/njQHUR\n+UpEZvo5hn4istqdJgQRZ3N3H1+4x9jSJ85/iMh/RWSjiNzoZ1/JIrJORF4Tke9EZK7PcZ4rIqlu\nvu+JSEN3+cci8oyIrADuFZEUEbnPTWsrIstEZJV77LXd5e3cZV8Dw3z2f6aI/M8ti1UB7kbdDLzr\nrh/j/g6/dsunl7vOp8ClImLnImNMxIj0OktEotz8V4vINyLyR5/k3u75/XsRudBnH8/5bL9ARLoE\nUS8Wp/57QUSWucecu1+33vvQrXM+EJEm7vJmIvK5exzjffbdyM37K/c4L/Tzq/Stp/yWiapuAeJF\npEHAL4Qx/qiqTTZ5PgH9gcnu/GfAOe58VyAbSAYEWALc6KadAPq68w8Dz7nzcT75jgeGufPTgPk+\naX8F+rvztYH1QHVgELARqAlUBTYDjd319geIPwFIA+JxLl58CFwXIM5n3fn/AC3c+Q7Ahz5xznHn\nzwA2+NlfsptvR/fzFOA+oDLwX6Cuu7w3MMWd/xh43iePFOA+d/4boLM7Pw6Y6LP8Qnf+SWC1O/8s\n0M+drwxU9RPjZqCGO38j8JJPWi2f+fdzft822WSTTZEwlYM661xgic/nWPfnx8BT7vxVwAfu/KCc\nusv9vADoUtA+AhxzMPWf7zEP8tlmPjDAnR8MvO3Ovwvc7M7fkxMPTp34oDsvOfVRvvhSgVYFlYk7\n/zJwg9ffO5sia7Krx6as6Ae87s7PwanAcqxQ1TRVVWA20NldfgKY686/hnNVEaC1iHwiIqvdfFr5\n5DXPZ/5yYJR7lyYVqAI0ddM+VNWDqnoE+A6nwizIecDHqpqlqieAfwFdAsTZ2b0KegEwz93/S0BD\nn/zeAVDVdUCgq2dbVHW5b77AacBZwAduvg8BiT7bzMmfiYjEArVV9TN30XSgi3s3q7aq/tdd7nuV\nchnwkIj8BWjmllN+cap6yJ1fA1wmIo+LSGdV9e0zvytfjMYYU9ZFep21CThFnF4TVwC+5+S33J9f\nBpFPYY5T9PpvHv51wilPcOqjnPK7kN9+F7711BfAYBF5BGjtUx/5SsCpg6DgMtmJ1VOmiCp7HYAx\nIhIHXAycJSIKVMLpU/0Xd5X8/asD9bfOWT4N5y7StyIyCOfKYo78J9mbVHVDvng6Ar6NhuP89rci\nBR1KAWn544wC9qhqoAeMffdflHwF+FZV/XWLgJOPv7B9+F2uqrPdLizXAotFZKiqpuZb7ZjP+hvE\neZj6auAxEflQVXO6dVQDDgfYvzHGlCnloc5S1b0i0ga4ArgL6AXc4Sbn5OWbzzHyPmJSzTcEf/sI\nIJj6L1A9VdCzVjlpubGo6qci0gW4BnhVRJ7Wk59DzsY9lnxlcidOT5Db3fWsnjJFZneyTFnQC5ih\nqqeoanNVTQZ+EpGcq38d3L7YUUAfnOd4wPn+/t6dv9lneU0gQ0Si3eWBvA/cm/NBRNoGEeuv4v8B\n5BU4d3/i3fR+OFca/cX5mXsn5ycRyVmOiLQOsM9AFVhTETnfne+Pc/zrgfpupYuIVBbnwd6AVHU/\nkOXTX30gsFRV9wF7ROQCd3nuSIQicoqq/qSqz+F01fAX+3oRae6unwAcVtVZwFPAOT7rtQS+LShG\nY4wpQyK+znKfjaqkqm8DY3C6yvmTU/9sBtqKIwmni1+B+3BVomT1n6/P+e35twH8Vn6f+SzPLT8R\naQrsVNUpwCv4P8Z1wKnu+r5l8jBWT5kSskaWKQv6AG/nW/Ymv500VwLPA2uBH1X1HXf5IZzKbA3Q\nDacvOzgnxxU4J+B1Pnnmvwr2GBDtPuT6LfBogPh8t3sZWJP/AV9VzcAZfSgV+BpYqaoLA8SZs5+b\ngdvdh3i/Ba4LEGegq3frgWEi8h1QB3hRVY/iVGhPiMgqN5ZOheQDcCvwN3ebNj4x3gZMEpGv8m3f\n232Q+Wucri0z/OS5CMgZ9vZsYIW7/iM4ZY/7IHG2qu4sIDZjjClLIr7OAhoDqe45eSa/jZ7nt/5x\nu41vdo/p7zhdCQvbB5S8/vN1L073v1Xu9jmDdYzAqQu/wen+l6Mb8I1bf/UG/uEnz8X8Vk/5LRMR\nqQy0wPm9GhM0cboMG1M2iUhX4E+qep2ftAOqWsuDsIokHHGKSDKwUFXPDmW+oSQijYDpqnpFAeuM\nAPap6rTSi8wYY8KjPNRZoVTWj1mckRw/whngye8/xCJyPc7AJimlGpyJeHYny0SySLlCEK44y/Tx\nu3f3JksBLyMG9uAMtGGMMeVdmT5nh0mZPmZV/QVnpN3GBaxWCXi6dCIy5YndyTLGGGOMMcaYELI7\nWcYYY4wxxhgTQtbIMsYYY4wxxpgQskaWMcYYY4wxxoSQNbKMMcYYY4wxJoSskWWMMcYYY4wxIWSN\nLGOMMcYYY4wJIWtkGWOMMcYYY0wIWSPLGGOMMcYYY0LIGlnGGGOMMcYYE0LWyDKmACJyQESaeR2H\nMcYYUxCrr4wpW6yRZcoFETkhIs1LmMfHInKb7zJVraWqm0sUXAiJyKMislpEjorII0Gs/4SIZIrI\nLhGZkC8tWUQ+EpFDIvKdiFySL72/iGx2K+63RKSOT1oVEZkqIvtEJF1ERubbtq2IrHTz/kJE2uRL\nHykiO0Rkr4i8IiLRxSuRk463q/tdeDPf8tbu8o9CsR9jjCkuq68Crm/1FVZflSfWyDLlhRaUKCKV\nSiuQMNsA/AVYWNiKInIncB1wNtAa6CEiQ31WmQ18CcQDY4A3RKSuu20r4EXgZqAhcBh4wWfbcUAL\nIAm4GLhfRC53t40G3gFmAHXcn++KSGU3/QrgfqA7kOzmM66I5VCQXUAnEYnzWTYIWB/CfRhjTHFZ\nfZWP1VdWX5VLqmqTTX4noAnwJrAT50TwrLtccE5ym4EM4FUg1k1LBk4AtwBp7rajffKMAkYDG4F9\nwBdAYzftdGAJsBtYB/Ty2W4a8DzOyXo/sAw4xU1b6u7zoJvWC+gKbMU5Oe4ApuOcQBe4Me125xPd\nPB4DjgHZbh45x3oCaO7Ox+KcgHcCPwEP+cQ3CPgUeArIAn4Ergzj72Ym8Egh6/wXuMPn82Dgc3e+\nJU5FVMMnfSkw1J3/K/CaT1pz4EjO+sB24BKf9HHALHf+cmBrvljSgMvd+X8Bj/mkdQd2FHAcJ4C7\ngR/c78yjbjz/BfYCrwOV3XVzfu+TgHt8vnPbcL6zH3n9d2WTTTaFfsLqq5xzpdVXVl/ZVEYmu5Nl\n/BKRKJwK4iegKdAY5+QAzsnvFpwTRHOgFk6F4utC4HfApcAjInKau/xPQB+cE3pt4DYgW0RicCqs\n14B6QF9gkoic7pNnHyAFp/L5EefEiqp2ddPPVtVYVZ3nfm7krtsUGIpz8pqKczWrKU4F9U83jzE4\nlc5wN4973Tx8rzg+7x5rM6AbcIuIDPZJ74BT2dbFqbymEICILBCRPSKS5efn/EDbFVEr4Bufz9+4\nywDOBDap6qEA6Xm2VdVNOJVWS7cbRgKwuoC8fdMKzNudb5DvSl5+lwPnAB1x/hF5CeiP87s8G+jn\ns67i/HNxi/v5CmANzj8vxphyxuorq6+w+sqUQdbIMoF0wDkx3a+qv6jqr6r6uZvWH5ioqmmqmg08\nCPR1KzpwThpj3W1W45yUcvo4345zRW0jgKquUdU9wLXAT6o6Qx3f4FyV7OUT09uq+qWqnsC5utQ2\nX8yS7/NxIEVVj6rqEVXNUtW33flDwONAl0LKQSC3Eu8DjFLVbFVNA54GBvqsm6aqU1VVca5ENhKR\nBv4yVdUeqhqnqvF+fl5XSEzBqolzJS3HfneZv7Sc9FpBpNfE+R3nzzuYbQPFJT7p/jyhqodUdR3w\nLbDE/f4dAN7DqdByqepyIE5EWuJUXjMKyNsYE9msvvLJ0+qrPOlWXxnPWCPLBJKEcxI+4SctEed2\neo40oDJOX+gcP/vMZ/PbyTIJ2OQnz2Sgo3tlLEtE9uBUjr55ZgTIM5Bdqno054OIVBeRl9yHY/fi\ndDeoIyL5Kzt/6uEc4xafZWk4V0xPik9VD+OciAuLMZwO4nQZyVHbXeYvLSf9QBDpOXnkzzuYbQPF\npT7p/uz0mT9M3u/XYfyX80xgOM5V3LcLyNsYE9msvsrL6iurr0wZYI0sE8hWoKnP1T5f6TiVTI5k\n4Ch5TyQF5dsiwPJU98pYzlWyWFUdXtTAfeR/uPhPOF1CzlPVOvx2VVACrO8rE+cY8x/39uIEJiKL\n3VGQ9vuZFhUnTz/W8tsVWXCupK71SWsuIjV80tvkS8/dVkRaANHAD6q6F6crQ5sCtm2dL5bWOFf0\nAsX1s3uFOJReA+4BFqnqLyHO2xhTdlh9lZfVV1ZfmTLAGlkmkBU4J6YJIhIjIlVF5AI3bTYwUkSa\niUhNnL7mr/tcRSzoStsrwHgRORVARM52+zYvxOk/PUBEKotItIi09+kbX5gMnP72BamFcxVpv4jE\nA2Pzpf8cKA/32OYCfxWRmiKSDIzEufpUZKp6tTrD7cb6ma4JtJ1bNtVw/naj3d9LoL/jGcB9IpIo\nIo2B+3AeyEZVNwCrgBQ3jxuBs3C6vIDTvaWHiFzoVmyPAm/69ImfCYwRkToicgYwJCdvIBU4LiJ/\nEGfo3HtxHgb+2Ceu20XkDPd3P8Zn25BRZyjjLm7+xpjyy+orH1ZfWX1lygZrZBm/3JN0D5wraVtw\nrtz1dpOn4py0PsF5oDcbuNd38/zZ+cxPxDn5LxGRfTiVWHVVPYjzsGhfnCuP6cAEoGqQIY8FZrhd\nN34fYJ2/AzE4V/k+BxbnS/8H0EtEdovI3/3Efi/OsW7COfbXVLWgk22Bw/QW02Q3hr44o15lAwMA\nRKSziOzP3bnqSzgjUq3Bec5gvqpO9smrL3AesAfnH4+bVHW3u+13wF3ALJx/CKoDw3y2TcEphzTg\nI2CCqn7gbnsUuB5nBKs9OH3Me6rqMTf9feBJnErsJ5zv0NgCjrmg71OBVPVzVc0ofE1jTKSy+srq\nK6y+MmWQOM88hilzkSk4D4j+rKqt3WVtcN5nUA3ndvY9qroybEEYY4wxBRCRqjj/iFbBeZblDVUd\nJyIpOFe9c56xGK2q//YoTGOMMREk3I2szjgPDc7waWS9DzytqktE5Cqc0YC6hy0IY4wxphAiEqOq\n2eK8CPa/OHcCrgIOqOpEb6MzxhgTacLaXVBVP8O5/errBM7oLOC8E6JYD2IaY4wxoeIO7w1Ol6/K\n/NbNJ5jR3Iwxxpg8vHgmayTwNxHZgtPP9UEPYjDGGGNyiUiUiHyN80zHB6r6hZs0XERWicgrIlK7\ngCyMMcaYXF40su4G/qiqTXEaXFM9iMEYY4zJpaonVPUcoAnQQUTOBCYBzVW1LU7jy7oNGmOMCUpY\nn8kCcIcOXeDzTNZe950POen7VNXv1UERCW9wxhhjPKGqZbYbnog8DBzyfRYrf12Wb32rq4wxphwq\nSV1VGneyhLx92reLSFcAEbkE+KGgjVU1LFNKSkpYtilonUBp/pYXtix/enGOJ1zlZGVlZWVlZWVV\nUFmVNSJSL6croIhUBy4DvheRRj6r3chvLyg9SST8boNdZrEXb7uS/s0EM9E1PN+10ojd63IvyTk6\nUmMP5zGX1dhLUveFuq6qXOIcCiAis4BuQF33Gayc4XCfdUdw+gUYGs4YAunWrVtYtilonUBp/pYX\ntqw48RdHcfdjZRXa7aysgt/Oyir47cpbWZVAAjDdfVFqFDBHVReLyAwRaYszYNNm4M5Q7rS0f7eh\n/D1Y7MGnh/T736x4m5WF2CO53CM19pLkE6mxl6TuC3ldVdwWbmlMTngmGCkpKV6HEDGsrIJnZRU8\nK6vgued2z+uYUE2RXFdF8ve2IsbOWO+/a5Fa7pEat6rF7pWS1lVeDHxhwiACrhSXGZFSVvHxIHLy\nFB9fejFESlmVBVZWJhJF8vfWYvdGpMYeqXGDxR6pwj7wRUmIiJbl+IwJJxHw9/UPtNyYSCEiaBke\n+KKorK4ypUXGCZpi3zVjSkNJ66qwPpNljDHGGGPKv2bNmpGWluZ1GMYUWXJyMps3bw55vtbIMsYY\nY4wxJZKWlhaSEdmMKW0i4elYYc9kGWOMMcYYY0wIWSPLGGOMMcYYY0LIuguaiuH4cdi9GypVgthY\niI72OiJjjDHGGFNO2Z0sU75taqHhEwAAIABJREFU3gxnnw3Vq8OZZ8Lvfuc0sm64wevIjDHGGOOx\ntLQ0oqKiOHHiRInzOuWUU/joo4+CWnf69OlcdNFFuZ9r1aoVssEXHn/8cYYOHQqE9vgAtm7dSmxs\nrD1/FwRrZJnyLTERpk6FAwcgMxOyspw7Wv/4h9eRGWNMwTIy4Ngxr6MwJuIV1vgJ18AHhfHd74ED\nB2jWrFmB6y9dupSkpKRC833wwQd5+eWX/e6nqPKXXVJSEvv37/eszCKJNbJM+ValCpx3HlSt+tuy\nmBho2tS7mMIk0MuLS/sFxsaYELn6ali2zOsojDFlhKoW2rg5fvx4KUVjCmONLBP5VOHpp+H//q9k\n+ezdC2PHwtGjIQmrtO3Z4xSFv2nPHq+jM8YUWY8eMH++11EYU66cOHGCP//5z9SvX59TTz2VRYsW\n5Unfv38/d9xxB4mJiSQlJfHwww/ndo3btGkTl1xyCfXq1aNBgwYMGDCA/fv3B7XfrKwsrrvuOmrX\nrk3Hjh358ccf86RHRUWxadMmABYvXkyrVq2IjY0lKSmJiRMnkp2dzdVXX016ejq1atUiNjaWjIwM\nxo0bR69evRg4cCB16tRh+vTpjBs3joEDB+bmrapMmTKFxo0b07hxY55++unctMGDB/PII4/kfva9\nW3bLLbewZcsWevToQWxsLH/7299O6n64Y8cOevbsSd26dWnZsiWvvPJKbl7jxo2jT58+DBo0iNjY\nWM4++2y++uqroMqrPLBGlolshw5Bnz4wZw7061eyvCpXhpUr4aab4NdfQxOfMcYUw7FjcPuqP3D0\n3cVeh2JMufLyyy+zePFivvnmG1auXMkbb7yRJ33QoEFUqVKFTZs28fXXX/PBBx/kNhxUldGjR5OR\nkcG6devYtm0bY8eODWq/99xzDzExMfz8889MmTKFqVOn5kn3vUN1xx13MHnyZPbv38+3337LxRdf\nTExMDO+99x6JiYkcOHCA/fv306hRIwDmz59P79692bt3L/379z8pP4DU1FR+/PFH3n//fZ544omg\nuk/OmDGDpk2bsnDhQvbv38+f//znk/Lu06cPTZs2JSMjg3nz5jF69GhSU1Nz0xcsWED//v3Zt28f\nPXr0YNiwYUGVV3lgjSwTuTIy4MILoUYN+OQTOOWUkuVXsya8/TZERcHNNzsjEpYhaWkwaZIz364d\ntGgBzZtD585w993O8sOH/W8bF2fdCI2JJJUrw+r0unyWdSasX+91OMaU3Nix/iuiQI0Uf+sH2aAp\nyLx58xgxYgSJiYnUqVOHBx98MDft559/5r333uOZZ56hWrVq1KtXjxEjRjB79mwAWrRowSWXXELl\nypWpW7cuI0eOZOnSpYXu88SJE7z11luMHz+eatWq0apVKwYNGpRnHd+BJKpUqcLatWs5cOAAtWvX\npm3btgXm36lTJ3r06AFAtWrV/K4zduxYqlWrxllnncXgwYNzjykYgQa52Lp1K8uWLeOJJ54gOjqa\nNm3acMcddzBjxozcdTp37swVV1yBiDBw4EBWr14d9H4jnTWyTGTasgUuusi56zR1KgQ4qRRZdLRz\nVywrC0aNCk2eJaAKCxfClVdC+/bwv/85yydNgiVLnOn//g9atnSWN20KY8ZA/t4LWVnWjdCYSNOz\npzA/8S7rMmjKh7Fj/VdEBTWygl23CNLT0/MMHpGcnJw7v2XLFo4ePUpCQgLx8fHExcVx1113kZmZ\nCcDOnTvp168fTZo0oU6dOgwYMCA3rSC7du3i+PHjNGnSxO9+83vzzTdZtGgRycnJdO/eneXLlxeY\nf2GDYYjISftOT08vNO7C7Nixg/j4eGJiYvLkvX379tzPOXfbAGJiYvjll19CNtJhWRfWRpaITBGR\nn0Vkdb7lfxCRdSKyRkQmhDMGU05VqwYPPQQPP+xc3QqlqlVh7lznrpbPLW8vXHghjB4N/fs77crp\n053l55/v3Mk69VTo0gVGjnSWL18O27bB6afb/2XGRLrrroN3My9Aq4boIpIxhoSEBLZu3Zr7OS0t\nLXc+KSmJatWqsXv3brKystizZw979+7NvfsyevRooqKiWLt2LXv37uW1114Laijz+vXrU7ly5Tz7\n3bJlS8D127VrxzvvvMOuXbvo2bMnvXv3BgKPEhjMSH/5952YmAhAjRo1yM7Ozk3bsWNH0HknJiaS\nlZXFoUOH8uTduHHjQuOpCMJ9J2sacIXvAhHpBvQAzlbVs4G/hTkGUx41aAC33hq+/OvWhf/+F7p2\nDd8+AsjOhnvvdeaHDoWvv4ZbbnFe9VWYFi3g1Vdh3jwnj5Ejy1yvR2NMkM4+G7Rqdb7t/gevQzGm\n3OjduzfPPvss27dvZ8+ePTzxxBO5aY0aNeLyyy9n5MiRHDhwAFVl06ZNfPLJJ4AzzHrNmjWpVasW\n27dv56mnngpqn1FRUdx4442MHTuWw4cP89133zE956ppPkePHmXWrFns37+fSpUqUatWLSpVqgRA\nw4YN2b17d9CDbeRQVcaPH8/hw4dZu3Yt06ZNo2/fvgC0bduWxYsXs2fPHjIyMvhHvlfcNGrUKHdA\nDt/8AJo0acIFF1zAgw8+yJEjR1i9ejVTpkzJM+iGv1gqirA2slT1MyB/h6S7gQmqesxdp/D7rMZ4\noWHD0N8lK8SaNdC2rdO9D5x2pHtuLZILL3QaZ6tXQ+/e8MsvIQ3TGFMKRGDHDmjd2p6hNKYkfO/G\nDBkyhCuuuII2bdrQvn17brrppjzrzpgxg19//ZUzzzyT+Ph4evXqRUZGBgApKSl8+eWX1KlThx49\nepy0bUF3fZ577jkOHDhAQkICt912G7fddlvAbWfOnMkpp5xCnTp1ePnll/nXv/4FwGmnnUa/fv1o\n3rw58fHxuXEFc/xdu3bl1FNP5bLLLuP+++/nkksuAWDgwIG0bt2aZs2aceWVV+Y2vnKMGjWK8ePH\nEx8fz8SJE0+Kdfbs2fz0008kJiZy0003MX78eLp3715gLBWFhLtFKSLJwAJVbe1+/hp4F7gSOAz8\nRVVXBthWK1KL11Rsb7/t3Ll65hkYMMD5p8rf178oy48ccfI6cgTefNN55CyYvIwJJxFBVctNTRvO\nukoEOnRwnse0v1cj4wRNKZtfAvfv2uswjCmyQN/dktZVXgx8URmIU9WOwP3AXA9iMJHkxAmYMsUZ\n07gcyhn578YbITMTBg50PsfFlTzvqlVh1iyny+Dtt9s/aMZEonXrYPdur6MwxhhTFJULW0FEegCL\nVDVUQ4FsBd4CUNUvROSEiNRVVb9ViO/7B7p160a3bt1CFIaJGGPGwGefOa0PL+3c6TwLFkInTjjv\nQG7bFt57D3wG4QmZ6GjnGa2uXeGpp+D++0O/D2MKkpqamue9KeEQhrqqzOjSBT780OsojDHGFEWh\n3QVF5DWgE/AmMFVVvy/SDkSa4XQXPNv9PBRorKopItIS+EBV/Y5jad0FDbNmOaMIrlgB9et7F4eq\nM4b6+PFw9dUhyfL4cRgyBKZNc4ZSr1MnuO3i4/0PvR4X99uzXP5s3ep0O5o5Ey691Flm3Y+MF8LR\nXbCkdVUJ9x3W7oJ//zt8u+AnNn64mY818LMOpvyz7oLGhJ5n3QVVdQBwDvAj8KqILBORoSJSq7Bt\nRWQW8DnQUkS2iMhgYCrQXETWALOAW4obvCnnvvoK/vhHePddbxtY4Pyn8/DD8OCDzu2nEjpxwum+\nlzOCa7ANLAj8zquCGlgASUlOA+vWW63rkSl/SlhXVRWR/4nI1+6rRVLc5XEiskRE1ovI+yJSO8yH\n4ddll8EHX8XTy3rXG2NMxAjqmSxV3Q+8AbwOJAA3AF+JSIHjyqpqf1VNVNWqqtpUVaep6jFVHaiq\nZ6tqe1Ut/FXZpuLZt88ZFu/5552htcqCnj2dcdRff71E2ajCiBGwcaPTfixNl14KvXrB3XeX7n6N\nKQ0lqKuOAN1V9RygLXCViHQARgH/UdXTgI+AB8MZfyBnnAFHK1cnmbTCVzbGGFMmFNrIEpGeIvI2\nkApEAx1U9SqgDfCn8IZnKqzjx+Evf4E+fbyO5Dci8Pjj8Mgj8Ouvxc7mkUecR8wWLYIaNUIYX5Ae\nf9wZ2t1eVmzKk5LWVaqa8zbOqjjPKyvQE8h5mc104PoQhx0UEbj8qsp8z+l2G9oYYyJEMHeybgSe\nce88PaWqOyG3Qro9rNGZiis+Hu680+soTta9OzRv7rzxtxj+8Q944w14/32o7UnHI6hWDf75T6cn\npjHlSInqKhGJcl8xkoHzrPAXQENV/dnNJwMI7cg3RXDZFVG8zY3OS9KNMcaUecE0sjJU9RPfBSLy\nBICq2nhHpuL5+9+d4b6KaMECePJJ+Pe/vX/E7JJL4PzzvY3BmBArUV2lqifc7oJNgA4i0grnblae\n1UIVbFF16QJfcS4nPvnMqxCMMcYUQTCNrMv8LLsq1IEYEzHOPBNOP71Im6xa5Qx08fbbkOx3LM3S\n9/TTzs9Nm7yNw5gQCUld5T7XlQpcCfwsIg0BRKQRsDPQdmPHjs2dwjFcfZMm8CvRrOs8JOR5G2Mi\nW1RUFJuCrMzHjRvHQPeVOFu3biU2NjZko0Lefffd/PWvfwVg6dKlJCUlhSRfgM8++4wzzjgjZPn5\nk5qamudcXlIB35MlIncD9wAtRGS1T1ItwPormNBTdR4+KGfS0+G662DSJGcI9bKicWPnZ0qKM+qg\nMZEoFHWViNQDjqrqPhGpjtNgmwDMB24FngAGAQGHqglFhVyY40TzyY7f0SrsezKm/Jk1axbPPPMM\n33//PbGxsbRt25bRo0dz4YUXehrX9OnTeeWVV/j000+LnYcU8X+nnPWTkpLYv39/oesHG+MLL7xQ\norh8RUVFsXHjRpo3bw5A586dWbduXbHzC0b+9/GOGzeuRPkVdCdrFtADp1Lp4TO1c4fKNSZ0liwp\nW4NchMiRI3DDDXDXXfD733sdjX9LlsCaNV5HYUyxhaKuSgA+FpFVwP+A91V1MU7j6jIRWQ9cgtPw\n8tRSG4/XmCKbOHEi9913H2PGjGHnzp1s2bKFYcOGsWDBgiLndfz48aCWBUtVS9QYyckjnIKJ8UQI\nXm/jq6RlUhYU1MhSVd0MDAMO+EyISHz4QzMVxp49Tl+6oUO9jiTk7r3XeT/VU085N+n8TXFx3sb4\nwAPOK8CMiVAlrqtUdY2qnquqbVW1tar+1V2epaqXquppqnq5qu4N0zEE7ZNP7AXixhTF/v37SUlJ\nYdKkSfTs2ZPq1atTqVIlrr76aiZMcK6b/Prrr4wYMYLGjRvTpEkTRo4cydGjR4Hfur09+eSTJCQk\ncNttt/ldBrBw4ULOOecc4uLi6Ny5M2t8rmBu27aNm266iQYNGlC/fn3uvfdevv/+e+6++26WLVtG\nrVq1iI+Pz43nz3/+M8nJySQkJHDPPfdw5MiR3LyeeuopEhMTadKkCdOmTSuwQbJ582a6detG7dq1\nueKKK8jMzMxNS0tLIyoqKreB9Oqrr9KiRQtiY2Np0aIFs2fPDhjj4MGDueeee7jmmmuoVasWqamp\nDB48mEceeSQ3f1Xl8ccfp379+jRv3pxZs2blpnXv3p2pU6fmfp4+fToXXXQRAF27dkVVad26NbGx\nscybN++k7offf/893bt3Jy4ujrPPPjtPg3nw4MEMHz6ca6+9ltjYWDp16sRPP/1U8BclDAq7kwXw\nJbDS/fmlz2djQmP4cOd2z6WXeh1J0a1dCzt2+E2aOtX5h2jaNNi71/8LhIN5iXC43XMPfPklfPGF\nt3EYU0wVqq6KioIff/Q6CmMix7Jlyzhy5AjXXx/4DQyPPfYYK1asYPXq1XzzzTesWLGCxx57LDc9\nIyODvXv3smXLFl5++WW/y77++mtuv/12Jk+eTFZWFnfeeSfXXXcdR48e5cSJE1x77bWccsopbNmy\nhe3bt9O3b19OP/10XnzxRTp16sSBAwfIcv8heOCBB9i4cSOrV69m48aNbN++nUcffRSAf//730yc\nOJEPP/yQDRs28J///KfA4+/fvz/nnXcemZmZjBkzhunTp+dJz2mgZWdn88c//pH333+f/fv38/nn\nn9O2bduAMQLMnj2bhx9+mAMHDvjtdpmRkUFWVhbp6em8+uqrDB06lA0bNgSMNSeWpe4t+zVr1rB/\n/3569eqVJ/3YsWP06NGDK6+8kl27dvHss89y880358l7zpw5jBs3jr1799KiRQseeuihAsspHAI2\nslT1WvfnKara3P2ZMzUvvRBNuTZ3LqxcCRM874VTPFOmwLPPnrR45UrnDtFbb0GtWh7EVQTVqsGf\n/gRPPOF1JMYUXUWrq7p0sS6DJjIF6s1R1Kmodu/eTb169YiKCnxfYdasWaSkpFC3bl3q1q1LSkoK\nM30eVq5UqRLjxo0jOjqaqlWr+l02efJk7rrrLtq3b4+IMHDgQKpWrcry5ctZsWIFO3bs4Mknn6Ra\ntWpUqVKFCy64IGA8kydP5plnnqF27drUqFGDUaNGMXv2bADmzZvH4MGDOeOMM6hevXqBz4Nu3bqV\nlStX8uijjxIdHc1FF11Ejx49Aq5fqVIl1qxZwy+//ELDhg0LHWiiZ8+edOzYESC3XHyJCOPHjyc6\nOpouXbpwzTXXMHfu3ALz9BWoG+SyZcs4dOgQDzzwAJUrV6Z79+5ce+21uWUEcMMNN9CuXTuioqK4\n+eabWbVqVdD7DZVgXkZ8oYjUcOcHiMhEEWka/tBMubdrF/zhDzBjBsTEeB1N8dx1l3PLyuc2/u7d\nzvNXL7wAYR4IJ2TuuMP5x+2HH7yOxJjiqSh1VZcusHT0+7Bxo9ehGFMkgXpzFHUqqrp165KZmVng\nM0Pp6ek0bfrb6SI5OZn09PTcz/Xr1yc6OjrPNvmXpaWl8fTTTxMfH098fDxxcXFs27aN9PR0tm7d\nSnJycoENvRy7du0iOzubdu3a5eZ11VVXsdt9EXl6enqebnPJyckBGyPp6enExcVRvXr1POv7ExMT\nw5w5c3jhhRdISEigR48erF+/vsBYCxs9MC4ujmrVquXZt2+5FteOHTtO2ndycjLbt2/P/dyoUaPc\n+ZiYGA4ePFji/RZVMEO4vwBki0gb4E/Aj4CNRWZKrm5dePfdyH5hU8uW0KYNzJsHOBXA4MFw441l\nd6ALf2rWhLvv/m1Yd2MiUIWoqzp3hs8PtYb//c/rUIyJCJ06daJq1aq88847Addp3LgxaWlpuZ/T\n0tJITEzM/ezvmaf8y5KSknjooYfIysoiKyuLPXv2cPDgQfr06UNSUhJbtmzx29DLn0+9evWIiYlh\n7dq1uXnt3buXffv2AZCQkMDWrVvzxBromayEhAT27NnD4cOHc5dt2bIlYDlcdtllLFmyhIyMDE47\n7TSGus/KB8q/sMEp/O07p1xr1KhBdnZ2blpGRkaBeflKTEzMUwY5eTfOGTa5jAimkXVMnSZyT+B5\nVf0nztC4xpRMVBS4t5kj2l13weTJADz/vDNkeyT2fvzDH5y2YhHOc8aUJRWirjrzTMg8HsfOj9d6\nHYoxESE2NpZx48YxbNgw3n33XQ4fPsyxY8d47733GDVqFAB9+/blscceIzMzk8zMTMaPH5/7Lqlg\nDRkyhBdffJEVK1YAcOjQIRYvXsyhQ4fo0KEDCQkJjBo1iuzsbI4cOcLnn38OQMOGDdm2bVvuQBsi\nwpAhQxgxYgS7du0CYPv27SxZsgSA3r178+qrr7Ju3Tqys7Nzn9Xyp2nTprRv356UlBSOHj3KZ599\ndtKIijl3wXbu3Mn8+fPJzs4mOjqamjVr5t55yx9jsFQ1d9+ffvopixYtonfv3gC0bduWt956i8OH\nD7Nx40amTJmSZ9tGjRoFfPfX+eefT0xMDE8++STHjh0jNTWVhQsX0q9fvyLFF27BNLIOiMiDwABg\nkYhEAdGFbGNMxXHttbBuHasWbOXRR+H116FKFa+DKrr69aFvX3jxRa8jMaZYyn1dFRcHlSqB/HKY\nGVOOEG/j/BoTlPvuu4+JEyfy2GOP0aBBA5o2bcqkSZNyB8MYM2YM7du3p3Xr1rRp04b27dsXeaCE\ndu3aMXnyZIYPH058fDwtW7bMHWQiKiqKBQsWsGHDBpo2bUpSUlLus0kXX3wxrVq1olGjRjRo0ACA\nCRMmcOqpp9KxY0fq1KnD5Zdfzg9uf/4rr7ySESNGcPHFF9OyZUsuueSSAuOaNWsWy5cvp27duowf\nP55BgwblSc+5G3XixAkmTpxI48aNqVevHp988knue6/8xRiMhIQE4uLiSExMZODAgbz00kv87ne/\nA2DkyJFER0fTqFEjBg8ezIABed+4MXbsWG655Rbi4+N544038qRFR0ezYMECFi9eTL169Rg+fDgz\nZ87MzbusDP8uhY2t777lvj/whap+6vZx76aqM8IenIiGe+x/Y0Lh0PwPafenroxJqcwAP2/mESmb\nwy7Hxzsj6OdXp47/5caEgoigqiGtBctrXeXv3JHy4K8c/ds/ePrYvRzRkx82N+WXjBM0pQxWJuT+\nXXsdhjFFFui7W9K6qtBGVkmIyBTgWuBnVW2dL+1PwFNAPVX1O4i1NbLKIdXiDQ9Uxt1+Oxw7BgsW\n+G+cxMV5P1R7sLp2dYZ0P3To5LRIOg5TdoWjkeWl0m5k/fvf8ESvL9h7sDJf6zlh2a8pm6yRZUzo\nhauRFczogjeKyAYR2Sci+0XkgIjsDzL/acAVfvJsAlwGpJ20hSm/Dh92nsHats3rSELq9dfh00/h\nn/90Glhl8V1YRTFsGLRv7/847O6WKatKWFdFlPPPh5W0ZxXWwDLGmLIqmGeyngSuU9XaqhqrqrVU\nNTaYzFX1M8Dfv2XPAH8pQpymPBg/Hpo2hSZNvI4kZLZuhXvvdRpaNWt6HU1oXH+9M5T7Wnuu3kSW\nYtdVkSYuDpo0KTc3Ao0xplwKppH1s6quC9UOReQ6YKuqrglVniYCfPMNvPIKPPec15GEjCoMGQJ/\n/COce67X0YROlSpO98dXXvE6EmOKJKR1VVnXqZPXERhjjClI5SDWWSkic4B3gNw3rqrqW0XdmYhU\nB0bjdBXMXVzUfEyEOXHCGeb8r38Fn5fDRbopU5z3Kd9/v+9ShX37oXZtr8IKiVtvdf6Je+KJyBwp\n0VRIIaurIkGnTjBtmtdRGGOMCSSYRlYskA1c7rNMgeJUXC2AZsA34oyv2AT4UkQ6qOpOfxuMHTs2\nd75bt25069atGLs1npo61Xl6+/bbvY4kZLZsgQcfhI8+At+XwN/CDBi6GObM8S64EGjRAk4/HRYt\nghtu8DoaE+lSU1NJTU0N925CWVeVeXYnyxhjyrawji4IICLNgAWqeraftJ+Ac1XV7+P0NrpgOfH1\n187tkFatvI4kJFThqqvgoosg/2s06kkmmbEtYPv2iH9Ia9o0ePttmD//t2VldSh6E1lsdMGi5O3/\nb+7ECahUSdn17U7qtWoYln2bsqcsjy7YrFkz0tJsPDMTeZKTk9m8efNJy0tjdMGWIvKhiHzrfm4t\nImOCyVxEZgGfAy1FZIuIDM63imLdBcu/c84pNw0scLoJZmbCAw+cnLabenDBBbBwYekHFmK9ejmj\nJmZkeB2JMYUrSV0ViaKiIJF0lg+b6XUoxgCwefNmVNUmmyJu8tfACoVgBr6YDDwIHAVQ1dVA32Ay\nV9X+qpqoqlVVtamqTsuX3lwDvCPLmLIop5vgq69C5UCdbfv0ifjuguDciLv+evjXv7yOxJigFLuu\nilTHqMwX31b3OgxjjDF+BNPIilHVFfmWHQtHMMaUZapwxx0wYgScdVYBK15/vfOw1r59pRZbuAwY\n4AxPb0wEqHB1VSZ1+WLPqXDwoNehGGOMySeYRlamiLTA6dqHiPwe2BHWqIwpg155xXmpsL9ugnnU\nqeO80Tc9vVTiCqdu3Zx3gW3Y4HUkxhSqwtVVJ6jMF1Ed0K9XeR2KMcaYfIJpZA0DXgJOF5HtwAjg\n7rBGZSLb1q3Oy6PKkS1bYPToQroJ+vq//4Mzzgh3WGFXqZLzbFY56P1oyr8KWVdFVxHS/mNXQYwx\npqwptJGlqptU9VKgPnC6qnZW1c1hj8xErhEjID7e6yhCJqeb4MiRhXQTLKf69rUug6bsq6h11Xln\nHOKLtAZeh2GMMSafgNfkReS+AMsBUNWJYYrJRLLFi2H16nI1WsIrr8CePflfOlxxdOoE+/fDt996\nHYkxJ6voddV51zfmi/2N6eV1IMYYY/Io6E5WLXdqj9PlorE73QWcG/7QTMTJzobhw+Gf/4Rq1byO\nJiRyuglOmxZkN8FyKCrKGTDR7maZMqrEdZWINBGRj0RkrYisEZE/uMtTRGSbiHzlTleG6RiK7bzz\n4IsvvI7CGGNMfoW+jFhEPgGuUdUD7udawCJV7RL24OxlxJHl4Yfhhx/KzQM8qnDFFc7gD6NHB7dN\neX1Z71dfQe/esHs37N3rf524OGdgEGMKE46XEZekrhKRRkAjVV0lIjWBL4GeQB/gQGF3w7x4GXFO\nWmYmNG/u3G2PCuYpaxPRyvLLiI0pb8L+MmKgIfCrz+df3WXG5PXrrzCx/PTMCUk3wb/9DVauDFlM\nXjnnHOfnhx86//D5m/bs8TZGU+EVu65S1QxVXeXOHwTW4dwNAwhpYzDU6taFevVg/XqvIzHGGOMr\nmEbWDGCFiIwVkbHA/4BXwxmUiVBPPAGNGxe+XgTI6Sb4ww8QHe1cMfadgh7XY98+mDs3rLGWBhG4\n4QZ45x2vIzEmoJDUVSLSDGjrbg8wXERWicgrIlI7NKGG1nnnlYtrOcYYU64U2l0Q4P/Zu/M4m+v9\ngeOv9zBomGHGvg4RpUhCCRlLJUsqt01U2rdfablpc3FpodKte28LCXWjUlqIiAwJSRRZSpF9n2HI\nMpj374/vmWlmzHJmzvI958z7+Xh8H5zz/Z7P9z1nvnM+5/P9fD7vj4i0BDp4Hi5Q1RUBjeqv89pw\nQRN0mcMEO3VyGlp5XYJrF2gUAAAgAElEQVT5DeE55fkffoC+fSPiNvOiRXDXXbBqVd77I3WopPG/\nQAwX9JTrU13lGSqYDAxX1c9EpCqwV1VVREYANVX1tjxep0OGDMl6nJSURFJSUjF/itxlFzxcUBVe\neu4YmxZu5dUvGvrlnCZ02XBBYwInOTmZ5OTkrMfDhg3zqa7yqpHlFmtkGTeMGQNjx8LixU4vlk+N\nLFWnd2/BAmjUKGAxB0NGhvOjfPNN3j+KNbKMtwLVyPKFiJQGpgMzVfWVPPYnAtNUtXke+1ybk6UK\nC2YdYVD3lSw+dn7JzdBTQlgjy5jgCcacLGNKjE2b4KmnirDocGFEoFs3mDnTD4W5KyoKrrjChgya\niPU2sCZ7A8uTECPT1UBILmTQst1prNRmHP85/HvMjTEmUlgjyxTfqlVOd0+EyFx0+OGH4eyz/Vhw\n9+7O+mER4Kqr4JNP3I7CGP8SkXbAjUBnEVmRLV37KBFZKSI/Ah2Bh1wNNB8VKkD92H38PGOz26EY\nY4zxKLSRJSL/JyLxwQjGhJGMDKdFsnat25H4zdixTp6Kv//dzwV37w5vv+3nQt3RuTOsWQM7d7od\niTE5+VJXqeq3qlpKVVuo6nmq2lJVv1TVm1S1uef5K1V1l7/j9pfWDVP4fv5ht8Mwxhjj4W0K9+9F\n5EMR6SYiITWO3rjkrbec8XS33OJ2JD5LSHBG9d11l7OoZ/ZsgvH+uL0QEwM1a/qhIPeVKeOMfvz8\nc7cjMeYUJbquan1BFN+vjnE7DGOMMR6FNrJU9WngDGAccAuwXkSeFZFC0xiJyDgR2SUiK7M9N0pE\n1npS4n4sInE+xG/csGcPPP00vP56RKx+mZoKXbvCc8+duvZTfovrxsefmtbdb42yEGep3E0o8qWu\nigStr6jF9xkt3Q7DGGOMh1ffkD1pk3Z6thNAPPCRiIwq5KXjgctyPTcbOFtVWwDrgSeKFLFx36BB\n0K8fND8lyVbYOnAAHn3U++NTUvJekDe/RlkkuewyWLgQDtvIJBNifKirwt65nSuz/kB1jhxxOxJj\njDHg3ZysB0XkB2AU8C3QTFXvAc4H+hT0WlVdCKTmem6OqmZ4Hi4B6hQncOOS1FT46ScYOtTtSPzi\njz+cf/2WTbAEqFgRWraEefPcjsSYv/hSV0WCsmXhrLOcj2djjDHu86YnKwG4WlUvU9UpqnocwNNQ\n6unj+W8Fwj+3dUkSHw/LlkFc+I/yzMwmCNC0aRBOePw47NgRhBMFXo8eEZMw0USOQNZVYaFVK2de\nqTHGGPd5c+9+JpA1CMozh+osVf1OVYudWk5EngKOq+qkgo4bmq3HJCkpiaSkpOKe0vhLhMwnHzMG\nDh4M4gmnTXNO+uWXQTxpYHTv7jS0/vOfiLkcTAAlJyeTnJwc6NMEpK4KJ61aOYuFG2OMcZ8Utkq9\niKwAWmYuZy8iUcAyVfVqhq2IJALTVLV5tuduAe4AOqvqsQJeq4XFZ0xx/PEHtG4N8+c7a2IF5TI7\ncADq1IFdu5yMg2FMFRo0cHqzMnsBRYL0PpqwJyKoql+b577WVT6eO2B1VUF/V7n3/fQT9O0Lq1cH\nJBQTAmSYoEPsg9aYYPC1rvJmuGCO2sMz9KIos1fEszkPnAUe/w5cUVADy5hAyRwm+OijQRommKli\nRWjRwskaEeZEnN6sL75wOxJjsvhaV4W9pk3hj/XpHNxlWWmMMcZt3jSyNojIAyIS7dkeBDZ4U7iI\nTAIWAY1FZLOIDAD+DVQAvhKR5SLyWrGjN8GxcSOcOOF2FH7z5pvOMMFHHnHh5F27wpw5LpzY/7p3\nt3lZJqQUu66KFNHR0Dx6LSs+3eR2KMYYU+J508i6G7gI2AZsBS4A7vSmcFXtq6q1VLWsqtZT1fGq\neoaqJqpqS892b/HDNwF39ChceiksWOB2JH7xxx8weDCMH+9SNsEIamR17uzkQDlwwO1IjAF8qKsi\nSet6u1g21/4ojTHGbYV+zVTV3cD1QYjFhKJRo6BZM+cbdZhzbZhgdm3aQP36Ts9gmOeMj4mB9u3h\nq6/gb39zOxpT0lld5Wh13klmLot2OwxjjCnxCv2WJyJVcZJU1M9+vKreGriwTEj4/Xd49VVYvtzt\nSPzC1WGCmaKjYepUFwPwr8xU7tbIMm4riXVVfHzO7J7x8bDwxXiGf1LNvaCMMcYA3mUXXAR8A/wA\nnMx8XlU/Dmxoll3QVapw+eXQqRMMGuR2ND7LzCa4YIGzYGd2lhWv+H7/3enN2r4doqLsfTTeCVB2\nwYisq4ry+SQCJ3buJb5GGTbtiyU+wdZXiDSWXdCY4PG1rvJmvFKMqob/t2xTNLNmOd+cH37Y7Uh8\nlpEBt90Gf//7qQ0s45uGDZ1hgz//7HYkxlhdBVCqehXOO2s3PyyNoWu38B6SbIwx4cybxBfTRaR7\nwCMxoeXSS53JNtHhP7b/zTfh0KGIaC+GpMxLxRiXWV3l0eryaiz70RpYxhjjJm8aWQ/iVF5HRSRN\nRA6KSFqgAzMui4qC6tXdjsJnGzY42QQnTAj7PBMh65JLYPZst6MwxuqqTK1bw/ffux2FMcaUbIU2\nslQ1VlWjVLWcqsZ5HscFIzhjfJGRAQMGwBNPhOAwwY0b4fXX3Y7CLzp3hkWL3I7ClHRWV/2lVStn\neQVjjDHuKbSRJY5+IjLY87iuiLQJfGjG+Obf/4aTJ2HgQLcjyUOZMvD0006AYa5SJTj7bLejMCWd\n1VV/adgQ0tJg9263IzHGmJLLm+GCrwFtgb6ex4eA/wYsIuOeI0fcjsBvfv0Vhg93hgmWKuU8l5Dg\nZN/KvcXHuxBg7drOcMwff3Th5P53ySVuR2CM1VWZRKw3yxhj3OZNI+sCVb0POAqgqqlAmYBGZYJv\n3To45xw4dsztSHx28iTccgsMGQKNGv31fGqqkwo595aS4lKgXbtGTMaISy91OwJjrK7KrtWBuSz7\ndKvbYRhjTInlTSPruIiUAhSyFnzMCGhUJrhU4e674cEHoWxZt6Px2UsvQblycN99bkdSiK5dYc4c\nt6PwiwsucP7ds8fdOEyJZnVVNq3if2fZ4nS3wzDGmBLLm0bWq8AnQDUReQZYCDwb0KhMcI0fD4cP\nh0GrpHCrV8MLL8DbbzsJEkPaxRfDd99Bevh/EcrM9B8hbUYTnopdV4lIHRH5WkRWi8gqEXnA83y8\niMwWkV9EZJaIVAxc+P7VOqk83/+eYAuEG2OMS7zJLvge8BjwHLADuFJVpwQ6MBMku3fD44/DmDF/\nTV4KU8ePw803wzPPQP36bkfjhUqV4OOPiaRvQREy+tGEIR/rqhPAw6p6Ns68rvtE5EzgcWCOqjYB\nvgae8H/kgVG3UyNOHs9g+3a3IzHGmJJJtJAveCJSL6/nVXVzQCLKeW4tLD7jo379oFYtGDXK7Uh8\nNmIELFwIM2c6E79zE4mo9kzIqVjRyWiWW3y8i/PeTEgSEVQ1j79Sn8r0W10lIp8C//FsHVV1l4jU\nAJJV9cw8jg9YXVWUz60cxx4+TPfYb7jrwy707mOLBEYKGSboEKvIjAkGX+sqbz55v8AZ4y5AOaAB\n8AtQaNJmERkH9AR2qWpzz3PxwAdAIvAHcK2qHihO8MYPBg1y8v2GuZ9+gldfheXL825gmcDbv9/p\nQfzyy5zrktnvwwRJseuq7ESkPtACWAJUV9VdAKq6U0Sq+THewIqJoXX8b3w/6zx69wmfsI0xJlIU\n2shS1WbZH4tIS+BeL8sfD/wbeCfbc5nDL0aJyCCc4RePe1me8bdmzQo/JsQdOwY33eR0xtWp43Y0\nJZeIk8p99uwQXPzZRDwf66rM11QAPgIeVNVDIpK7yyDfLoShQ4dm/T8pKYmkpKSinDogWj3fh/9+\nWMXtMIwxJiwkJyeTnJzst/IKHS6Y54tEVuWu0Ao4NhGYlq0nax1eDL/wHGvDBU2h/v53+O03mDq1\n4F4TGy4YeO+/D5Mnw2ef/fWcve8mt0AMF8znPEWpq0oD04GZqvqK57m1QFK2+mqeqp5yCyEkhwsC\nO3Y499H27LEe5UhhwwWNCZ6ADxcUkYezPYwCWgK+TKWtFrbDL0zISU6GSZOc4YJh/SVCNcx/AEen\nTnDPPc5aZWGeR8WEGT/UVW8DazIbWB6fA7cAI4Gbgc/yeF3IqlnTWc7ijz+gQQO3ozHGmJLFmyTX\nsdm2sjjj3nv7MQa7JRNMJ0+6HYHf7N/vZBN86y2oEs4jYpYsgZ493Y7CL6pXd/KorFjhdiSmBCp2\nXSUi7YAbgc4iskJElotIN5zG1SUi8gvQBXg+IJEHUKtWsGyZ21EYY0zJ482crGF+PucuEamebfjF\n7oIODsVx7mHtvvugdWu47Ta3I/HZ/fc7bZPLL3c7Eh81bQrz58PRo85t5zDXuTN8/bXz5c4Y8P84\n97z4Ulep6rdAfn2vXYtbbrDFx+fsEI+Ph4cfdhpZ11zjXlzGGFMSeZPCfRoF9Dap6hWFvL4+zpys\nZp7HI4EUVR3pSXwRr6p5Jr6wOVl+9vXXTtfPzz87+bbD2Pvvw9ChTjbBmBjvXhPSc4PatIEXX3QW\nKA5zn3wCb77pZBmEEH/fjSsClMLdp7rKx3OHxJysvF775ZcwcqTy9dfhPxzZ2JwsY4IpGCncNwA1\ngP95Ht8A7AI+9SK4SUASUFlENgNDcIZbTBGRW4FNwLVFD9sU2Z9/wh13wBtvhH0Da8sWeOABmDHD\n+wZWyOvUCebNi4hGVseOTrbH9HQoU8btaEwJUuy6KpKd//UL/LDo/8jIKEeUNxMEjDHG+IU3jax2\nqpp94M80EVmmqg8V9kJV7ZvPrrAZfhExHnsM2reHHj3cjsQnGRlwyy3w4IMRNhwtKcnJQT9kiNuR\n+CwhARo3hqVLnUvOmCApdl0Vyao0r0VC6TR++60cjRu7HY0xxpQc3tzXKi8ip2c+EJEGQPnAhWT8\nbu5cmD4dXnml8GND3CuvOFOXBg1yOxI/a9cO1q+PmMQknTs7HXPGBJHVVXlp0YLW8gPff+92IMYY\nU7J408h6CEgWkWQRmQ/MAwYGNizjVxdcAF98AZUquR2JT376CZ59Ft59F0oX0AebkODMRci9xccH\nL9Yii4uDzZsjJu95p07OFEBjgsjqqrw0aUKrY9+ybPFxtyMxxpgSxZvsgl+KyBlA5oLB61T1WGDD\nMn5VoQKcc47bUfjkzz/huuvg5ZfhdM+96oQESE099dj4+DBNtBBBEyY6dIBrr4UjR9yOxJQUVlfl\no3RpWtXfy5CFR4Bot6MxxpgSo9BvdSISA/wduF9VfwLqiUhkLOpjwsYDDzgdcv36/fVcaqrTmMq9\npaS4F6dxxMZC8+awaNFfaaVzbwkJbkdpIonVVflr2bYsP/5yGidOuB2JMcaUHN7cOh8PpANtPY+3\nASMCFpExubz/PnzzDfz3v25HYooic72slJS8G8N59UIa4wOrq/JRacK/qFUvmnXr3I7EGGNKDm8a\nWQ1VdRRwHEBVDwO24EYoU3WyQ0SADRucXqz333dGPZrwYckvTJBZXZUfEVq1chYlNsYYExzeNLLS\nReQ0PIs8ikhDwMa5h7IxY5w852EuPR1uuAGeegpatnQ7miDZs8fJ8BEB2raFlSvh4EG3IzElhNVV\nBWjVCsswaIwxQeRNI2sI8CVQV0TeA+YCjwU0KlN8a9Y4rZKhQ92OxGdPPw3Vqjk9WSXG8uXOImAR\n4LTToHVrZ6inMUFgdVUBWre2nixjjAkm0QLSsImIAHWAw8CFOEMvlqjq3qAEJ6IFxWdyOXrUyQ5x\n//1wxx1uR+OTWbPg9tthxQqoUiXvY0TCNItgQQ4dgho1YO9eKFfO7Wh8Nnw4HDgAL7546r6I/P0Z\nr4gIquq3oXyRXFf58neS/bWHDkH16s5cyDJl/BefCS4ZJugQ++A0Jhh8rasK7Mny1BozVHWfqn6h\nqtODVWmZYhg0CM44w2mdhLFt22DAAHjnnfwbWBGrQgVo1gyWLHE7Er+weVkmGKyuKlwF+ZMGddL5\n+We3IzHGmJLBm+GCy0WkdcAjMb5ZsQI+/RTGjnVuX4ap48ed9bDuu89Z0LZESkqC5GS3o/CL1q1h\n/XpLq2+CwuqqgsyZQ+s/59uQQWOMCRJvGlkXAItF5HcRWSkiq0RkZaADM0V03nnOgPv4eLcj8ckT\nT0DFis6/JVYENbLKlIGLLoL5892OxJQAVlcVpEULWh2cZ40sY4wJktL57RCRBqq6EbgsiPEYX1St\n6nYEPpk6FT76yMn9EOVN8z9StWsHbdq4HYXfZK6XddVVbkdiIpHVVfnLXAjcUY+vWMpbi48D0S5G\nZYwxJUNBX2U/8vz7tqpuyr0FIzhTcqxfD3ffDVOmQEKC29G4rEIFGDXK7Sj8xuZlmQCzuiofORcC\nFzKI4pf1URw54nZkxhgT+fLtyQKiRORJoLGIPJx7p6qO9uXEIvIQcBuQAawCBqhqui9lmvB0+DD8\n7W8wbJgzh8dElvPOc5KZ7NrlZDczxs8CWldFkjU05czKe1m5sjoXXOB2NMYYE9kK6sm6HjiJ0xCL\nzWMrNhGpBfwf0FJVm3vOcb0vZZY4e/YQKWmi7r8fzjnH6ckykadUKbj44oiZZmZCT8DqqkiTTBKt\n6u22RYmNMSYI8u3JUtVfgJEislJVZwbg3KWA8iKSAcQA2wNwjsh08iTceKMzb2fECLej8cm4cfDd\nd84WxkkRTSE6dXLmZV13nduRmEgThLoqYnzGlYy5Fb791u1IjDEm8hWaXiAQlZaqbgdeAjYD24D9\nqjrH3+eJWMOHQ3o6DB3qdiQ+WbLEySL48cfONKS8JCQ4ja+8tjBPpFiiZCa/MCZQfK2rRGSciOzK\nnpFQRIaIyFYRWe7ZuvkeqbtatcIyDBpjTBC4ksNNRCoBvYFEoBZQQUT6uhFL2Jk9G8aMgcmToXRB\nU+pC2/btzjyst9+GM8/M/7jU1OwTt3NuEb/20pw58O67bkfhF+ecA/v3w5YtbkdiTL7Gk3eGwtGq\n2tKzfRnsoPztnHNg40Y4dMjtSIwxJrIVlML9GlWdki09rj91BTaoaornXFOBi4BJuQ8cmq23Jikp\niaSkJD+HEka2boWbb3YaWDVruh1NsR09CldfDffcAz17uh1NCEtPh/HjoX9/tyPxWVSUs/zXvHlw\n001uR2OCLTk5meQATcrzV12lqgtFJDGvU/gQXsiJjoZmzZz16zt0cDsaY4yJXKKqee8QWa6qLTP/\n9etJRdoA44DWwDGcO4jfq+p/cx2n+cVXIs2YAWvXwiOPuB1JsanCbbfBwYPw4Yd/zcNKSHB6rXKL\njy8BPVb5SUuDWrVg714oV87taHz2+uuwdKnTbgTnd29/3iWTiKCqfmm8+LOu8jSypnkSMiEiQ4Bb\ngAPAMuARVT2Qx+sCVlf58+8ks6z774eGDeGhh/xTrgkeGSboEPvgNCYYfK2rChpvtk9EZgMNROTz\n3DtV9YrinlRVl4rIR8AK4Ljn3zHFLa/E6N7d2cLYf/7jzAdYtChnoovMYYEmm7g4aNrUaZlcfLHb\n0fisUyd4/nnn92xJTowfBayuAl4D/qmqKiIjgNE4S4+Er2PHaJW+jNnft3M7EmOMiWgFNbJ6AC2B\nd3GSVPiVqg4Dhvm7XBO6kpOdZIiLF+ef6MLkkpTkvHER0Mhq0gSOH4cNG5y76Mb4ScDqKlXdk+3h\nWGBafseGzdD20qVp/b+BPFtrCU6SX2OMMeD/oe35DhfMOkCkqqruEZEKAKoatOmyNlwwcvz2G7Rv\nD//7H3Tteup+GzqWjy++gJdeipjUfP36Oe3G22+333lJ5s/hgtnK9LmuEpH6OMMFm3ke11DVnZ7/\nPwS0VtVTkjSFy3DBzGHZ8+hIV+ZSoWJp9u/3T9kmOGy4oDHB42td5U12weoisgJYDawRkR9E5Jzi\nntAUQYR8A01NdRJcDBuWdwPLFCApCf71L7ej8JvM9bKMCQCf6ioRmQQsAhqLyGYRGQCMEpGVIvIj\n0BEI61lMKSlOtZI0sCVt6+/gwCmzy4wxxviLN42sMcDDqpqoqvWAR7D5U4F39KjzjfTnn92OxCfp\n6U4mwR494K678l/3yta8ykf58tC8udtR+E3nzk6GQVXnd57XtZCQ4HaUJkz5VFepal9VraWqZVW1\nnqqOV9WbVLW5qrZQ1StVdVfAog+m88+ndfRPbkdhjDERzZtGVnlVnZf5QFWTgfIBi8g430Dvuguq\nVYOzz3Y7mmLJbEyVLetMKRo9+q9kByVyzSsDQIMGzjWxbt1fd9Vzb3llmTTGC1ZXeatVK1odmON2\nFMYYE9G8aWRtEJHBIlLfsz0NbAh0YCXas8/CmjUwYULYpmFLTYXnnoPzznPStVtjymTq3NmGDJqA\nsLrKW40b0+rahkBkDEk3xphQ5E0j61agKjAV+Bio4nnOBMIHH8CYMfD55xAT43Y0PnntNZg2zTIJ\nmpw6dXKGDBrjZ1ZXeSsqikav/B8g7NlT6NHGGGOKodDsgm4qcdkFDx1y5t988gmce67b0RTbt986\nmQSXL3d6sowfqMKJExAd7XYkPtu6FVq0gN27ISqP2zyWdTDyBSK7oJvCJbtgXmXPnAndugWmfON/\nll3QmOAJRnZBEywVKsDq1WHdwFq92kl0AdbA8qt//hNGjnQ7Cr+oUwcqV4ZVq9yOxBjz/fduR2CM\nMZHJGlmh5rTT3I6g2LZsgcsvd5JcGD87/3wng0iEsFTuxoSGZcvcjsAYYyJToY0sEWnnzXOmZEtJ\ncYacPPgg3Hij29FEoPbt4bvv4NgxtyPxC0t+YfzN6qrisUaWMcYEhjc9Wf/28jlTQh05Aldc4fRi\nPfKI29FEqEqVoEmTiBnbk5QE33zjTDMzxk+sriqiW3mLY4fS2b7d7UiMMSbylM5vh4i0BS4CqorI\nw9l2xQGlAh1YxFOFgQPh0kudlXrD1IkTcP31UL8+jBrldjQRrmNHZ8hg+/ZuR+KzatWgbl0nOUqb\nNm5HY8KZ1VXFV4NdtE7YwLJlZ3LFFW5HY4wxkaWgnqwyQAWchlhsti0N+FvgQ4twzzwD8+eH9Rfm\njAy49VZnBNvbb+edKc74UefOTmq+CNG5s6VyN35hdVUxLeFCWp1cYkMGjTEmAApN4S4iiaq6SUQq\nAKjqoaBERgSncH/rLWfB4W+/hZo13Y6mWFThnntg7VonBXDuJb0sDbcpzKefwuuvw6xZOZ+3ayfy\nBSKFe6TWVYH8e4iTNN4tewdvJE1m5pd2lywcWAp3Y4InGCncY0VkBbAaWC0iP4jIOcU9YSYRqSgi\nU0RkrYisFpELfC0zLEyZAoMHw5dfhnUD6+GHnbbiggVQvrzzRSD7Fh/vdpQm1HXsCIsWQXq625GY\nCBGQuiqSlY6Po8KxPcyZdRIRSEhwOyJjjIkc3jSyxgAPq2qiqiYCj3ie89UrwAxVPQs4F1jrhzJD\n26FD8I9/OA2sxo3djqbYBg92pgadPOk0uPLaUlLcjtKEuvh4589g6VK3IzERIlB1VcRKSYEut51O\n1bh0Nm6E1FS3IzLGmMjhTSOrvKpmzZxQ1WSgvC8nFZE4oIOqjveUeUJV03wpMyxUqOCswBrGiw0/\n9xxMnQqzZ7sdiYkElsrd+JHf66oS4fHHaXVBlM3LMsYYP/OmkbVBRAaLSH3P9jSwwcfzNgD2ish4\nEVkuImNEJHxX4S2K0vkmdAx5o0c7CS7mzoWqVd2OxkSCTp0s+YXxm0DUVZGvUSMu6HgaS5a4HYgx\nxkQWbxpZtwJVgamerarnOV+UBloC/1XVlsBh4HEfyzQBNHKkk6Tg66/DdipZ5Fi6FDZudDsKv+jQ\nwVn668gRtyMxESAQdVWJ0L49LFzoDOHNPrfW5mgZY0zxFdqtoqqpwAMiEus89EvGpq3AFlXNHKDw\nETAorwOHDh2a9f+kpCSSkpL8cPog2bcPKld2OwqvJSTkPSa/XDlITHTmYdWuHfSwTG4ffgixsTBk\niNuR+Cw2Fpo3dxJgdOnidjQmUJKTk0lOTg7oOQJUV5UIrVs7I9n37MmZKVb8mv/RGGNKFm9SuDcD\n3gEy72ntBW5W1Z99OrHIfOAOVf1VRIYAMao6KNcx4ZvCffFiuOoq+OGHsGmZ5E4VrOrk6RgxAnbs\ngBo1Cj7eBMmsWc4v5Ztv3I7EL556yvn3mWecf+26inwBSuEekLrKy3OHZQr37Nq2debcZr+PaX+L\nocdSuBsTPMFI4f4mgcnY9ADwnoj8iJNd8Fk/lBkavvkGeveGCRPCpoGVmyo88QR89pnzOHcDy7io\nQwf48Uc4eNDtSPzikksskYrxi0DVVSVC+3YaKfdtjDEmJLiSXdBTzk+q2lpVW6jq1ap6wNcyQ0Jy\nMlx9Nbz3HnTr5nY0xZKR4ayDNWuWZX4LSTEx0KaNc61FgIsugl9+gb173Y7EhDnLLlhcJ07QfsLt\nLJx/0u1IjDEmYriVXTAyzZ0L11wDH3zg3J4PQ8ePwy23wHffOT9OlSpuR2Ty1LUrfPWV21H4RZky\nzsLEc+Y4j3NPvreJ+MZLVlcVV+nStGuwnSVLlBMn3A7GGGMiQ1GzC34MVMEyNuWtalVnEanOnd2O\npNiuusrJ1zFnjn2hDWlXX+10AUWIyy5zek7BWSA1v0WubbFUUwCf6ioRGSciu0RkZbbn4kVktoj8\nIiKzRKSi36MOEVU6N6f2aamsWuV2JMYYExkKbGSJSCngKVV9QFVbqur5qjrQk8XJ5Na8uTNfJgxl\nfnmNj4dPP82ZYcqEoCZN4Prr3Y7Cby67zJmXZZPsTXH4qa4aD1yW67nHgTmq2gT4GnjCTyGHng4d\naF9maY55WZbS3Rhjiq/ARpaqngTaBykWEyQJCXkPwypbFiZOhOhotyM0JU2jRs6wwdWr3Y7EhCN/\n1FWquhDI3SjrDUz0/H8icKUv5whp7drRPuVzFi7IyHoqd6+y9SQbY4z3vBkuuEJEPheR/iJydeYW\n8MhCXRjfck9N/cphd00AACAASURBVKvSXLMGGjRw0mcfOQJR3lwRxviZSM4hg8YUQyDqqmqqugtA\nVXcC1XwPM0TFx9O+yR4WLjgZztWbMcaEDG++UpcD9gGdgV6erWcggwp5R444Q7U+/tjtSHwyd66z\nJsrQofDkk7bwpHFX5pBBY4opGHVVRDc/Giz/mKgy0axf73YkxhgT/koXdoCqDghGIGFjzx5nDazE\nROjRw+1oim3cOKdh9eGHTmY3Y9zWuTPcdJNzD+O009yOxoSbANVVu0SkuqruEpEawO78Dhw6dGjW\n/5OSkkjKvqpvmJAooXNnmDcPGjd2OxpjjAmu5ORkkv24PI4EapV6fxARDan4fvnFaVhddx0MHx6W\nY+syMqBUKWcOzPTpTv6EwojkPToyv+dNEE2YACdPwm23uR2JX7RvD4MHO71aebFrLjKICKoacn3n\nIlIfmKaqzTyPRwIpqjpSRAYB8ar6eB6vC1hdFexrfsIEmDnTWYnE7VjMqWSYoEPsl2BMMPhaV4Vf\nK8EtixbBxRfDE084E5jCsIF1+PBfCekWL/augQX5r1sUHx+4WI2X4uJgyhS3o/AbGzJo3CIik4BF\nQGMR2SwiA4DngUtE5Begi+dxROvUyenJysgo/FhjjDH5s54sb/36K2zaFLaLDP/xh7MGVrNm8O67\ndjcyYuzfD3Xrwu7dETHGbulSuPVW+PnnvPfbnfTIEKo9WcUVST1Z4Ix0+OQTp75wOxaTk/VkGRM8\nAe/JEpHqnkUaZ3oeNxWRyBibVBSNG4dtA2vOHLjwQrjlFidFu4kglSpBixYwf77bkfjF+efDjh2w\ndavbkZhwY3WVnxw9SueztvP116fusnWzjDHGe96MeZsAzAJqeR7/CgwMVEDGf1ThpZegf3+YPBke\nfNAyCEak7t1hxgy3o/CLUqXg0kudOSHGFNEErK7y3fHjdPnqCb7+6sQpu2zdLGOM8Z43jawqqvoh\nkAGgqieAkwGNyvjs8GG48UaYNAm++84ZZ28iVI8eEbXAVK9eTlIWY4rI6ip/iI0lqXkKC+YrJ05t\nZxljjPGSN42sP0WkMp71QUTkQuBAQKMyPlm9Glq3huhoWLgQ6tVzOyITUM2aOS3pCNGtmzPx/sgR\ntyMxYcbqKj+p3v186pTby4oVbkdijDHhy5tG1sPA50BDEfkWeAf4P3+cXESiRGS5iHzuj/JKOlUY\nP95ZYPjRR51UvBGQC8EURsSZmxUhEhKcaWbz5rkdiQkzAaurSpyuXekSNa/QTJ/Z52jZ/CxjjMmp\nwEaWiEQB5YCOwEXAXcDZqrrST+d/EFjjp7IiWkJC3mnUMyu2Q4echVxffNHJgTBggM2/MuHLhgya\noghCXVWyXHABlx/6iJmfHy/wsOxztGx+ljHG5FRgI0tVM4D/quoJVV2tqj+rasGful4SkTpAd+At\nf5QX6VJTc044zl6x/fSTk5WtTBn4/nto2tTtaI3xTc+eTiMrd7ro/NZss7voJVsg66oSKTqajsM6\ns3JNKWs8GWNMMXkzXHCuiPQR8Xu/yMvA3/GMnzdFd9IzpbtrVxg8GMaNg5gYd2Myxh/OPNOZU7gy\nVz9E7uxmdhfdZBOouqpEKvfo/XS4OIqvvnI7EmOMCU/eNLLuAqYAx0QkTUQOikiaLycVkR7ALlX9\nERDP5rX69esjIiVqg1OfK13aeX7vXqF/f9/Ksi04W/369X350ynYsWNOt2YEEHGGDE6b5nYkJoz4\nva4q6S6/PGJWhzDGmKArXdgBqhobgPO2A64Qke7AaUCsiLyjqjflPnDo0KFZ/09KSiIpKYlNmzah\ntuy8CUNOIzdAduxwujV37IDShf5ph7yePeGpp+Dpp92OxPgqOTmZ5OTkgJ4jQHVVida9O4wYARkZ\nEOXNLVljjDFZxJvGiojEA2fgTCwGQFUX+CUAkY7AI6p6RR77NK/4RMQaWSYsBfzaPe88+Ne/oGPH\nwJ0jSNLToUYNZ8hgnToFHyty6vwtE7o8fwd+v+MQyLqqkPPmWVf5p2x3r+0mTZzF7Fu2LPg4t+Ms\nKWSYoEPsjTYmGHytqwq9NyUitwMLgFnAMM+/Q4t7QmNMAF11FXzyidtR+EWZMnDFFTB1qtuRmHBg\ndVVgXH45zJzpdhTGGBN+vBkA8CDQGtikqp2A84D9/gpAVefn1YtljCmGzEZWhNxS7tMHPv7Y7ShM\nmAhoXVVS9Vg9imkfHXM7DGOMCTveNLKOqupRABEpq6rrgCaBDSsybdq0iaioKDIyMnwuq0GDBnz9\n9ddeHTtx4kQ6dOiQ9Tg2NpY//vjD5xgAnnvuOe68807Avz8fwJYtW4iLi7OhoUVxzjlOWr4ff3Q7\nEr+45BInl8euXW5HYsKA1VUB0LH2b/z6i7Jtm9uRGGNMePGmkbVVRCoBnwJfichnwKbAhhW+Cmv8\nBDTxQQGyn/fgwYOFZrmbP38+devWLbTcJ554gjFjxuR5nqLK/d7VrVuXtLQ0196zsCQCQ4ZEzErU\n5co5k+8jZASkCSyrqwKgzHVX0bPCPPsbNMaYIiq0kaWqV6nqflUdCgwGxgFXBjow4y5VLbRxczJz\noS4TWvr3hxYt3I7Cb2zIoPGG1VUB0qULfQ7/j6mTbcigMcYUhTeJL+plbsBG4EegRsAjiwAZGRk8\n+uijVK1alUaNGvHFF1/k2J+Wlsbtt99OrVq1qFu3LoMHD84aGrdhwwa6dOlClSpVqFatGv369SMt\nzbslX1JSUrjiiiuoWLEiF154Ib///nuO/VFRUWzYsAGAGTNmcPbZZxMXF0fdunUZPXo0hw8fpnv3\n7mzfvp3Y2Fji4uLYuXMnw4YN45prrqF///5UqlSJiRMnMmzYMPr3759Vtqoybtw4ateuTe3atXnp\npZey9g0YMIB//OMfWY+z95bddNNNbN68mV69ehEXF8eLL754yvDDHTt20Lt3bypXrkzjxo156623\nssoaNmwY1113HTfffDNxcXE0a9aM5cuXe/V+mdDWrRssXQr79rkdiQllVlcFSJkyXHplDD8sh717\n3Q7GGGPChzfDBb8Apnv+nQtsACzXkBfGjBnDjBkz+Omnn1i2bBkfffRRjv0333wzZcqUYcOGDaxY\nsYKvvvoqq+Ggqjz55JPs3LmTtWvXsnXr1hxrhhXk3nvvJSYmhl27djFu3DjefvvtHPuz91Ddfvvt\njB07lrS0NH7++Wc6d+5MTEwMM2fOpFatWhw8eJC0tDRq1HC+q3z++edce+217N+/n759+55SHjhr\n4vz+++/MmjWLkSNHejV88p133qFevXpMnz6dtLQ0Hn300VPKvu6666hXrx47d+5kypQpPPnkkznW\n3pk2bRp9+/blwIED9OrVi/vuu8+r98uEtvLlnblZn33mdiQmxFldFSCnXd+bSyst5fPPvX9NQoIz\najlzS0gIXHzGGBOKvBku2ExVm3v+PQNoAywOfGg+GDo056d75pZfIyWv471s0BRkypQpDBw4kFq1\nalGpUiWeeOKJrH27du1i5syZvPzyy5QrV44qVaowcOBAJk+eDEDDhg3p0qULpUuXpnLlyjz00EPM\nnz+/0HNmZGQwdepUhg8fTrly5Tj77LO5+eabcxyTPZFEmTJlWL16NQcPHqRixYq0KGSYWdu2benV\nqxcA5cqVy/OYoUOHUq5cOc455xwGDBiQ9TN5I78kF1u2bGHx4sWMHDmS6Ohozj33XG6//Xbeeeed\nrGPat2/PZZddhojQv39/Vq5c6fV5TWi7/np47z23ozChLCzrqnDRrRt9XmhLrvuEBUpNdZKcZm6p\nqYELzxhjQlGR13BX1eXABQGIxX+GDs356Z65FdTI8vbYIti+fXuO5BGJiYlZ/9+8eTPHjx+nZs2a\nJCQkEB8fz913381ez3iM3bt3c8MNN1CnTh0qVapEv379svYVZM+ePZw8eZI62VZvzX7e3D7++GO+\n+OILEhMT6dSpE0uWLCmw/MKSYYjIKefevn17oXEXZseOHSQkJBATE5Oj7G3ZUl5l9rYBxMTEcPTo\nUb9lOjTu6tkTVqyArVvdjsSEi7Coq8JF6dL0vLI0ixbB7t15HxIfn/M+ZXx8cEM0xphQ482crIez\nbY+KyCTA92/NJUDNmjXZsmVL1uNNm/5KdFW3bl3KlSvHvn37SElJITU1lf3792f1vjz55JNERUWx\nevVq9u/fz//+9z+vUplXrVqV0qVL5zjv5s2b8z3+/PPP59NPP2XPnj307t2ba6+9Fsg/S6A3mf5y\nn7tWrVoAlC9fnsOHD2ft27Fjh9dl16pVi5SUFP78888cZdeuXbvQeEqsDz6AwYPdjsIvypVzEmBM\nmuR2JCZUWV0VWBUqQK9e8P77ee9PScl5nzIlJbjxGWNMqPGmJys221YWZ7x770AGFSmuvfZaXn31\nVbZt20ZqaiojR47M2lejRg0uvfRSHnroIQ4ePIiqsmHDBhYsWAA4adYrVKhAbGws27Zt44UXXvDq\nnFFRUVx99dUMHTqUI0eOsGbNGiZOnJjnscePH2fSpEmkpaVRqlQpYmNjKVWqFADVq1dn3759Xifb\nyKSqDB8+nCNHjrB69WrGjx/P9ddfD0CLFi2YMWMGqamp7Ny5k1deeSXHa2vUqJGVkCN7eQB16tTh\noosu4oknnuDYsWOsXLmScePG5Ui6kVcsJVrz5vD22xAhWSD794d3342YdZaN/wWsrhKRP0TkJxFZ\nISJL/VFmOMr8GzTGGFM4b+ZkDcu2PaOq72Uu+GhOlb035o477uCyyy7j3HPPpVWrVvTp0yfHse+8\n8w7p6ek0bdqUhIQErrnmGnbu3AnAkCFD+OGHH6hUqRK9evU65bUF9fr8+9//5uDBg9SsWZNbb72V\nW2+9Nd/XvvvuuzRo0IBKlSoxZswY3vNMfGnSpAk33HADp59+OgkJCVlxefPzd+zYkUaNGnHJJZfw\n2GOP0aVLFwD69+9P8+bNqV+/Pt26dctqfGV6/PHHGT58OAkJCYwePfqUWCdPnszGjRupVasWffr0\nYfjw4XTq1KnAWEq0s86CatXA03APd+3bQ1oa2FQ7k5cA11UZQJKqnqeqbfxUZtjp0gW2bYO1a92O\nxBhjQp8UdrdfRKYB+R6kqlf4O6hs59a84hMR66UwYSno1+4LL8Avv0C2dPfh7Kmn4NgxePHFnM+L\nWA9XOPH8Hfj1Lkgg6yoR2Qi0UtU8FxLIr67yh5C6tlV5tNvPlGnRlGdHlirSS0Pq5whjMkzQIfZG\nGhMMvtZV3gwX3AAcAcZ6tkPA78BLns0YE6r69oWpUyHbXLZw1q+fMy8rQkZAGv8KZF2lwFci8r2I\n3OFjWeFLhJt2vcC749I5ccLtYIwxJrR508hqp6rXqeo0z9YX6KCq81W18Jzixhj31K4NXbvCDz+4\nHYlfnHUW1KsHM231I3OqQNZV7VS1JdAduE9E2vsebnhq/sgl1D2xgenT3Y7EGGNCW2kvjikvIqer\n6gYAEWkAlA9sWMYYv/ngA2esToS4+2544w0nrbsx2QSsrlLVHZ5/94jIJzhrcC3Mfkz2xeKTkpJI\nSkryx6lDz7XXct/9D/HaCy9w5ZX2VcAYEzmSk5NJTk72W3nezMnqBozBGYohQCJwp6rO9lsU+Z/b\n5mSZiGLXru8OH3Z6s374ATKXgEtIyHux0/h4SyUdigI0JysgdZWIxABRqnpIRMoDs4Fh2cstMXOy\nPI49PoR6rz7CNz/G0bixd68JxZ8jHNmcLGOCx9e6qtBGluckZYEzPQ/Xqeqx4p7QU14d4B2gOk7W\nprGq+moex1kjy0QUu3b948EHITYWRowo+Dj7YheaAtHI8pTr17rKU2YD4BOceVmlgfdU9flcx5So\nRhZbt/LEGVM4fMu9vPJ6Wa9ekt+NkEx2Q8Q71sgyJngC1sgSkdbAFlXd6Xl8E9AH2AQMVdVifxyK\nSA2ghqr+KCIVgB+A3qq6Ltdx1sgyEcWuXf9Ys8ZJJ715M0RH539cSH5BNX5tZAWyripCDCWrkQVs\nnb2G5tefxfr1QuXKvpcXqj9nqLFGljHBE8jsgm8C6Z6TXAw8j9P7dABnSEaxqepOVf3R8/9DwFqg\nti9lGmNKjqZN4cwzYcoUtyMxISBgdZXJX51Lm9Knj/DqKWNQjDHGQMGNrFLZ7gBeB4xR1Y9VdTDQ\nyF8BiEh9oAXwnb/KNMbkIS0Nbr0VMjLcjsQvHn3UWQbM7n6XeEGpq8ypHnsMXnsNDh50OxJjjAk9\nBTayRCQz+2AX4Ots+7zJSlgoz1DBj4AHPT1apxg6dGjW5s+MH6Z4oqKi2LBhg1fHDhs2jP79+wOw\nZcsW4uLi/DZU7p577uGZZ54BYP78+dStW9cv5QIsXLiQs846y2/lhYzYWFi1Cr74wu1I/OLyyyE9\nHebOdTsSU5jk5OQcn+V+FvC6yuTtjDOcFSL+8x+3IzHGmNBT0Jysp3DWBNkL1ANaqqqKSCNgoqq2\n8+nETqU4HZipqq/kc0xYzsmaNGkSL7/8MuvWrSMuLo4WLVrw5JNP0q6dT2+ZzyZOnMhbb73FN998\nU+wySpUqxfr16zn99NMLPXbYsGH8/vvvvPPOOwGNcf78+fTv35/Nmzd7/ZrsoqKi+O2337z6mXzl\n+rU7aRKMHQvz5rkXgx+NHw/vvw+zZuW93+Z5hCY/z8kKaF3lZQwlbk5Wpl9+gXbtYN06qFKl+OWE\n+s8ZKmxOljHBE7A5War6DPAIMAFon60GiQL+r7gnzOZtYE1+DaxwNXr0aB5++GGefvppdu/ezebN\nm7nvvvuYNm1akcs6efKkV895S1URH9dLCnQDwZsYM/w83M3X9ySsXHMNbNwI337rdiR+0bcv/Pwz\nrFjhdiTGLUGoq0wBmjSB68/4geGD8hyMYowxJVZBwwVR1SWq+omq/pntuV9VdbkvJxWRdsCNQGcR\nWSEiyz1rnIS1tLQ0hgwZwmuvvUbv3r057bTTKFWqFN27d+f5552Mv+np6QwcOJDatWtTp04dHnro\nIY4fPw78Next1KhR1KxZk1tvvTXP5wCmT5/OeeedR3x8PO3bt2fVqlVZcWzdupU+ffpQrVo1qlat\nygMPPMC6deu45557WLx4MbGxsSQkJGTF8+ijj5KYmEjNmjW59957OXbsr6zHL7zwArVq1aJOnTqM\nHz++wAbJH3/8QVJSEhUrVuSyyy5j7969Wfs2bdpEVFRUVgNpwoQJNGzYkLi4OBo2bMjkyZPzjXHA\ngAHce++99OjRg9jYWJKTkxkwYAD/+Mc/sspXVZ577jmqVq3K6aefzqRJk7L2derUibfffjvr8cSJ\nE+nQoQMAHTt2RFVp3rw5cXFxTJky5ZThh+vWraNTp07Ex8fTrFmzHA3mAQMGcP/999OzZ0/i4uJo\n27YtGzduLPhCcVN0NDz9NAwZ4nYkflG2LAwaBNkuhRzi45075Lk3z6VlIkSg6irjnSFtZvLee8qv\nvxa/jNx/q/Y3aowJdwU2sgJFVb9V1VKq2kJVz1PVlqr6pRux+NPixYs5duwYV155Zb7HjBgxgqVL\nl7Jy5Up++uknli5dyohsi/3s3LmT/fv3s3nzZsaMGZPncytWrOC2225j7NixpKSkcNddd3HFFVdw\n/PhxMjIy6NmzJw0aNGDz5s1s27aN66+/njPPPJM33niDtm3bcvDgQVI8C5IMGjSI3377jZUrV/Lb\nb7+xbds2/vnPfwLw5ZdfMnr0aObOncv69euZM2dOgT9/3759ad26NXv37uXpp59m4sSJOfZnNtAO\nHz7Mgw8+yKxZs0hLS2PRokW0aNEi3xgBJk+ezODBgzl48GCewy537txJSkoK27dvZ8KECdx5552s\nX78+31gzY5k/fz4Aq1atIi0tjWuuuSbH/hMnTtCrVy+6devGnj17ePXVV7nxxhtzlP3BBx8wbNgw\n9u/fT8OGDXnqqacKfJ9cd/PNzkz17dvdjsQv7roLVq6ExYtP3ZeS4gxByr0VtF6PMaZoqo54kCdP\n+xd3XpNS7Lw6uf9W7W/UGBPuXGlkBVped66LsxXVvn37qFKlClFR+b+tkyZNYsiQIVSuXJnKlSsz\nZMgQ3n333az9pUqVYtiwYURHR1O2bNk8nxs7dix33303rVq1QkTo378/ZcuWZcmSJSxdupQdO3Yw\natQoypUrR5kyZbjooovyjWfs2LG8/PLLVKxYkfLly/P4448zefJkAKZMmcKAAQM466yzOO200wqc\nsL5lyxaWLVvGP//5T6Kjo+nQoQO9evXK9/hSpUqxatUqjh49SvXq1QtNNNG7d28uvPBCgKz3JTsR\nYfjw4URHR3PxxRfTo0cPPvzwwwLLzC6/YZCLFy/mzz//ZNCgQZQuXZpOnTrRs2fPrPcI4KqrruL8\n888nKiqKG2+8kR9//NHr87oiOhqWLIFatdyOxC/KlnV6skK9bWtMxIqN5cH32nBk3Wbe+s9Rt6Mx\nxpiQEJGNrLzuXBdnK6rKlSuzd+/eAucMbd++nXr16mU9TkxMZHu2HoWqVasSnWt11dzPbdq0iZde\neomEhAQSEhKIj49n69atbN++nS1btpCYmFhgQy/Tnj17OHz4MOeff35WWZdffjn79u3LijX7sLnE\nxMR8GyPbt28nPj6e0047LcfxeYmJieGDDz7g9ddfp2bNmvTq1YtffvmlwFgLyx4YHx9PuXLlcpx7\nux96anbs2HHKuRMTE9m2bVvW4xo1amT9PyYmhkOHwmBuQoTNQ7v5Zti6FWbPdjsSY0qmUt0vY1yP\nqTz12HFCecS0McYES0Q2stzStm1bypYty6effprvMbVr12bTpk1Zjzdt2kStbD0Kec15yv1c3bp1\neeqpp0hJSSElJYXU1FQOHTrEddddR926ddm8eXOeDb3c5VSpUoWYmBhWr16dVdb+/fs5cOAAADVr\n1mTLli05Ys1vTlbNmjVJTU3lyJEjWc8VlO3vkksuYfbs2ezcuZMmTZpw55135vvzF/R8przOnfm+\nli9fnsOHD2ft27lzZ4FlZVerVq0c70Fm2bVr29rZoaR0aXjxRXjgAcg2pdAYE0TnTPw7T7aaxTV/\ny+Conzu0EhIKHnlic7iMMaHGGll+FBcXx7Bhw7jvvvv47LPPOHLkCCdOnGDmzJk8/vjjAFx//fWM\nGDGCvXv3snfvXoYPH561lpS37rjjDt544w2WLl0KwJ9//smMGTP4888/adOmDTVr1uTxxx/n8OHD\nHDt2jEWLFgFQvXp1tm7dmpVoQ0S44447GDhwIHv27AFg27ZtzPZ0B1x77bVMmDCBtWvXcvjw4ay5\nWnmpV68erVq1YsiQIRw/fpyFCxeeklExsxds9+7dfP755xw+fJjo6GgqVKiQ1fOWO0ZvqWrWub/5\n5hu++OILrr32WgBatGjB1KlTOXLkCL/99hvjxo3L8doaNWrku/bXBRdcQExMDKNGjeLEiRMkJycz\nffp0brjhhiLFZwLviiucdXteftntSIwpoWJjGfjN36jfIIp77/VvSvbU1IJHntgcLmNMqLFGlp89\n/PDDjB49mhEjRlCtWjXq1avHa6+9lpUM4+mnn6ZVq1Y0b96cc889l1atWhU5UcL555/P2LFjuf/+\n+0lISKBx48ZZSSaioqKYNm0a69evp169etStWzdrblLnzp05++yzqVGjBtWqVQPg+eefp1GjRlx4\n4YVUqlSJSy+9lF89KaK6devGwIED6dy5M40bN6ZLly4FxjVp0iSWLFlC5cqVGT58ODfffHOO/Zm9\nURkZGYwePZratWtTpUoVFixYwOuvv55vjN6oWbMm8fHx1KpVi/79+/Pmm29yxhlnAPDQQw8RHR1N\njRo1GDBgAP369cvx2qFDh3LTTTeRkJDARx99lGNfdHQ006ZNY8aMGVSpUoX777+fd999N6vsiEj/\nfvKkM+s8ArzyitOjla2z2BgTRCLO+nWrVuWf9dMbubMNxscX7fWF9XxZL5gxJtDyXYw4FITrYsTG\n5Cckr92JE2HCBJg7F7yYyxfqnn0Wvv7amZ+V349jC5+6y5+LEYeCkrwYcX727IH27eGGG5wVIwJ9\nPyr3+1SU962wYxMScvaUxce7d1/KFiM2JngCthixMaaE6NcPTpyAf/3L7Uj84rHH4OhReOkltyMx\npuSqWhUWLIDPP83gjh7byDZlNuzkHqpoQxONMd6wRpYxJV2pUk5v1nPPQainn/dC6dLw3nvwwguw\nbFnex+S3SLENHTLGf6pXh+Qxv3Jo/nLa1NnG93PT3A7JL2zhZGOMN6yRZYyB00+H//4XrrrKGecT\n5hIT4bXXoE8fyCuZZH6LFNtdamP8K67NmUzedjF/bz6b3pce5vrma1g8fV9IDYEs6KZLXvPBirpw\ncu75YdYoM6ZksDlZxgRRyF+7Tz3lfAsYMcLtSPxi2DCYMcOZo1W+vHevCdc5MOHE5mQVpezIuR4P\nfb+WN+7/mTHLzkMS63FpjzK0bQtnneVkBq1Qofhl+zIny1eFncufsdmcLGOCx9e6yhpZxgRRyF+7\nGRlOtsFcC2KHK1W49VbYsgWmTYNsa2XnK5K+1IYqa2QVpezIux4zDhzkx/XlmfN1FMuWwdq1sH69\nk6im6skdVC2bRsJpR6gUc5xKFU5QqaJS6bI2VKpcmkqVyLHFl0qjUp0K1KoTxf79f50jmMkpCkuM\nkft36EsiDWtkGRM81sgyJozYtRt8J0/CzTfDjh0wdSpUrFjw8ZH4pTbUWCOrKGWXjOtRFQ4dgj0L\n1rJn4yH27zrG/t3p7N93kv2pyv5WXdmf5jSk/tqU/b/sYr9WJIMoKkWlUan0n1Qqe5hKF55Jpfio\nvxpj8VCnDjRI/4X6ratSs2k8UaUCcwkWteeqSJkQrZFlTND4WleV9mcwwZKYmBgZ6xOZEicxMdHt\nEEqczLweAwc6KaU/+QQaNXI7KmNMdiIQGwuxPc7idO9fBdSAkyc5uucgB7YcIXXrEfbvOMr+03M2\nyFJS4KcV5J05qwAAEWlJREFUJ9k4PZ2NR05wQI+RWGYnTSrvpdkZR2l2dzvOaSY0aRIxHfnGGJeF\nZU+WW0rKHUVjcjhwwJnUdNVVbkfiE1UnGcbQoTBqFNxyS95r99jfeeBZT1ZRyrbrMRAOb9/PxkU7\nWPftXlb9HMXPFduxahVs3gxnnw0XXABtzkvngno7adylbpF6vYo6HDD38dmdMvRwmMBQzXd/Yecu\n6FxFPb6o5w4nhb1PuRX1d1yU9ybQv1NfYinsXP4W7GssbNfJEpFuIrJORH4VkUFuxREpkpOT3Q4h\nbNh75b3k5GQnPd8TT0C3brB6tdshFZsI3Hef0158+WXo2hWWL/df+XZdRaZIr6vC+br1JfaYWpU4\n+29n0eflDgz9qh0ffQS//AL79sGrrzq93V9OPUL37kpCdBpdq6zgqQ4L+GzwMnb+vLfAsnNnH8zr\nS2D22Iua7bSg/YWt6ZV7f2Hny+v4efOSi3VutxXleinsfSpqVlpf3pvU1L/e80D8TosaS1HfB39+\nxoT6NZabK40sEYkC/gNcBpwN3CAiZ7oRS6QI54oy2Oy98l5ycjI0aQIrV8Lll0NSEvztb84qo2F6\ne71ZM/jhB7jmGujRA3r3htmznZwfvrDrKvKUhLoqnK/bQMQeEwMXXQQPPQSTZ1Rkw4lEfv3xCA/d\nc4xSeoLXX1Oanl+OevWcz5AXXnA+Dg8dcj/2YAnX2MM1brDYw5VbPVltgPWquklVjwPvA72DGUDx\nfumFv6agcvPbl9fzhT0XrIu2uOfx5nX2Xnn/OtffqzJl4MEHYcMGp6F1112wZk2+MRXG7fcqOhru\nvht++w169oR7702mXj24917nuJ077boKx7/BAAhoXRXs360/fw8lKfZqzWvQY/iFdB4RxZf7WrPv\naAXmzoUrr3Qylz72mLPwcsMa07nljIWM7D6fzwd/z/o5mzhx7KR/r/+NxXtZYTHkv7/g1/kzhuK+\nriifVcUVrrH7Uk64xu5L3efvusqtRlZtYEu2x1s9zwVN8d7Iwl8TaV9a7Aue90rEexUbC/ff7zSw\nmjY9db8qPPAAjBwJb7/tZJlIToYVK/I+pyqkpTnbwYPOduhQvreFk+fNg2PHnC09PeeW18+gmuOY\n5LlzcxxfvjzccQf065fMnDmQWE8RMmjaVOndex6XdMngjttO8uzwE7z1Frz2mnPcsmUwZUoy639V\n/vg1nW0b0/kz7SSpu9I5lHqcEycKfhuLokRcV6EroHVVSWqoFOV1oR67iLOu1403OkMLlyxxhvx1\nvngu7dueZPeuDN54Q7m0WxSx5dK5uvuXdOzoHP/YYzB6tDNU+aOXt/DV8z+wdPxqVk1dz7qZG9mQ\nvJktaw6ya9dfwwwPHYLDh+HP1HT4A47uP5q1leUox9KO5RlvxokMyuIcN+fLOQUen5ycjGYoZTjG\nsbS/tijm5Fs+aI5js782L/PmzSP9UHqeW56lZyjph9KZO3tugcdn/l4yj8/csr+uoPK9iSfz/Slq\n/JkxRJP38dmvxezlZ489moLjz112YeXnPj6/8vP6fWW+NnfZmeVHc+rv62T6yQLjz+/9zK/8/OLJ\n/TPnV37GiYygNbJcSXwhIn2Ay1T1Ts/jfkAbVX0g13HhOR7JGGNMgcIh8YXVVcYYU7KFYwr3bUC9\nbI/reJ7LIRwqYWOMMRHL6ipjjDHF4tZwwe+BRiKSKCJlgOuBz12KxRhjjMmL1VXGGGOKxZWeLFU9\nKSL3A7NxGnrjVHWtG7EYY4wxebG6yhhjTHGF9GLExhhjjDHGGBNuXFuM2BhjjDHGGGMiUdg1skSk\no4gsEJHXReRit+MJdSISIyLfi0h3t2MJZSJypuea+lBE7nY7nlAmIr1FZIyITBaRS9yOJ5SJSAMR\neUtEPnQ7llDm+ZyaICJvikhft+Pxh3Cvq8K17gjnz/Jw/mwN18+6cP7sCdf3HML3Wi/q50vYNbIA\nBQ4CZXHWLDEFGwR84HYQoU5V16nqPcB1wEVuxxPKVPUzT0rre4Br3Y4nlKnqRlW93e04wsDVwBRV\nvQu4wu1g/CTc66qwrDvC+bM8nD9bw/izLmw/e8L4PQ/ba72ony+uNbJEZJyI7BKRlbme7yYi60Tk\nVxEZlPt1qrpAVXsA/9/encfaUZZxHP/+2rIvlRKFAvayBZW1YKxAmxYRJYAgoBRKwcoSMRKRYJAi\nW2QJm4BQpAI2yCJbg2UpEMDaVspiWbra0hSlkGgIGJBdFPr4x7ynd7icc3rOvefeOXP7+yQT3vvO\nnJlnnnv6Psw578ydCJzfV/EWqbu5krQfsAR4HVgjHjHc3VylbQ4GpgMP9UWsRetJrpKzgV/3bpTt\noQW5WqN0I19b0flHf6v/5cqClLlWlbl2lHksL/PYWvaxrsxjT5lz34PYC/3/iO7E3dT4EhGFLMAo\nYDiwMNc3AHgR6ADWAuYDX0zrjgWuBIamn9cG7i4q/hLk6ipgSsrZI8C0os+jjXO16n2V+qYXfR5t\nnqstgEuAfYs+hxLkqjJeTS36HNo8X+OBA1P79qLjb/HvvrBaVebaUeaxvMxja9nHujKPPc3Gntum\n8PrSndiLfq/3JOdpu9WOL0X9MWIiYo6kji7dI4DlEfEygKQ7gW8DL0TErcCtkg6TtD8wGLi2T4Mu\nSHdzVdlQ0veAf/VVvEXqwftqjKSJZFN7HuzToAvSg1z9GPg6sLGk7SPihj4NvAA9yNUQSZOB4ZLO\niIhL+zbyYjSbL2AacK2kg4AH+jTY1ShzrSpz7SjzWF7msbXsY12Zx55mY5c0BLiINqgv3Yi98Pc6\ndCvuMWRTTBsaXwq7yKphSzq/toVsHvuI/AYRMY3sH8WabrW5qoiIW/okovbVyPtqNjC7L4NqU43k\nahIwqS+DalON5OoNsjnnVidfEfE+cHwRQXVTmWtVmWtHmcfyMo+tZR/ryjz21Iu9nXMO9WNv1/c6\n1I+7qfGljA++MDMzMzMza1vtdpH1D2BY7uetUp99mnPVOOeqcc5V45yr5vSnfJX5XBx7MRx7ccoc\nv2Pvey2Lu+iLLPHJJxc9A2wvqUPS2sBRwP2FRNZ+nKvGOVeNc64a51w1pz/lq8zn4tiL4diLU+b4\nHXvf6724C3yix+3AP4EPgVeA41L/AcAyYDkwsaj42mlxrpwr58q5KtPSn/JV5nNx7I59TYq97PE7\n9v4Xt9LOzMzMzMzMrAWKni5oZmZmZmbWr/giy8zMzMzMrIV8kWVmZmZmZtZCvsgyMzMzMzNrIV9k\nmZmZmZmZtZAvsszMzMzMzFrIF1lmZmZmZmYt5IssaxuSDpW0UtIORcdSi6Qzi46hVSSdJOmYJrbv\nkLSoyWPMkLRhnfV3SNqumX2ambWD/lizJM2UtEdvHqPJfR8s6WdNvuadJrefKmnrOusvl/S1ZvZp\nBr7IsvZyFPA4MK63DyRpYDdf+vOWBlIQSQMj4vqIuK3Jlzb818slHQjMj4h362w2GTijyRjMzNqB\na1YvHiPVqQci4rImX9pMndoRGBARK+psNgmY2GQMZr7IsvYgaQNgJHACuYIlaYyk2ZKmS3pB0nW5\nde9IulLSYkmPSdo09Z8oaa6keekTqnVT/02SJkt6GrhU0vqSpkh6WtJzkg5O202QdI+khyUtk3RJ\n6r8YWE/S85JurXIO4yQtTMslDcS5bTrGM+kcd8jFebWkJyS9KOnwKsfqkLRU0m2Slki6O3eee0ia\nlfb7sKTNUv9MSVdJmgucIuk8SaeldcMlPSVpfjr3wan/y6lvHnBy7vg7SvpLysX8Gt9GjQfuS9uv\nn36H81J+jkjbPA7sJ8ljkZmVRtlrlqQBaf8LJS2Q9JPc6rFpfH9B0sjcMSblXv+ApNEN1MXu1L/J\nkp5K57zquKnuzUg15zFJW6X+rSU9mc7jgtyxN0/7fj6d58gqv8p8naqak4h4BRgi6XM13xBm1USE\nFy+FL8DRwI2pPQfYPbXHAO8DHYCAR4HD07qVwFGpfQ4wKbU3ye33AuDk1L4JuD+37iLg6NQeDCwD\n1gMmAC8CGwLrACuALdN2b9eIfyjwMjCE7MOLGcAhNeK8JrX/CGyX2iOAGbk470rtLwHLqxyvI+13\nz/TzFOA0YBDwBLBp6h8LTEntmcC1uX2cB5yW2guAUan9C+DKXP/I1L4MWJja1wDjUnsQsE6VGFcA\nG6T24cD1uXUb5dqPVH7fXrx48VKGpR/UrD2AR3M/b5z+OxO4PLUPAB5L7QmV2pV+fgAYXe8YNc65\nkfqXP+cJudfcDxyT2scB01L7PmB8av+oEg9ZTTwztVWpR13imwXsVC8nqX0DcFjR7zsv5Vr86bG1\ni3HAnal9F1kBq5gbES9HRAB3AKNS/0rg7tS+jexTRYBdJf1Z0sK0n51y+5qaa38TmJi+pZkFrA0M\nS+tmRMS7EfEhsISsYNbzFWBmRLwRESuB3wOja8Q5Kn0KujcwNR3/emCz3P7uBYiIpUCtT89eiYin\n8/sFvgDsDDyW9nsWsEXuNXd13YmkjYHBETEndd0MjE7fZg2OiCdSf/5TyqeAsySdDmyd8tTVJhHx\nXmovAr4h6WJJoyIiP2f+9S4xmpm1u7LXrL8D2yibNbE/kB+T/5D++1wD+1mdj2m+/k2lur3I8glZ\nParkbySdv4t8nXoGOE7SucCuuXqUN5SsBkH9nLyG65Q1aVDRAZhJ2gTYF9hZUgADyeZUn5426Tq/\nutZ860r/TWTfIi2WNIHsk8WKroPsdyJieZd49gTyFw0f0/lvRfVOpc66rnEOAN6MiFo3GOeP38x+\nBSyOiGrTIuDT57+6Y1Ttj4g70hSWbwEPSfpBRMzqstlHue2XK7uZ+kDgQkkzIqIyrWNd4IMaxzcz\nayv9oWZFxL8l7QbsD/wQOAI4Ma2u7Cu/n4/45C0m6+ZDqHaMGhqpf7XqVL17rSrrVsUSEY9LGg0c\nBPxO0hXx6fuQ3yedS5ecnEQ2E+SEtJ3rlDXN32RZOzgCuCUitomIbSOiA3hJUuXTvxFpLvYA4Eiy\n+3gge/9+N7XH5/o3BF6VtFbqr+UR4JTKD5KGNxDrf1X9BuS5ZN/+DEnrx5F90lgtzjnpm5yXJFX6\nkbRrjWPWKmDDJH01tY8mO/9lwGdT0UXSIGU39tYUEW8Db+Tmqx8LzI6It4A3Je2d+lc9iVDSNhHx\nUkRMIpuqUS32ZZK2TdsPBT6IiNuBy4Hdc9vtACyuF6OZWRspfc1K90YNjIhpwNlkU+WqqdSfFcBw\nZT5PNsWv7jGSgfSs/uU9Sef9b8fQmb85uf5V+ZM0DHgtIqYAv6X6OS4Ftk/b53NyDq5T1kO+yLJ2\ncCQwrUvfPXQOms8C1wJ/Bf4WEfem/vfIitkiYB+yueyQDY5zyQbgpbl9dv0U7EJgrXST62Lg/Brx\n5V93A7Co6w2+EfEq2dOHZgHzgGcjYnqNOCvHGQ+ckG7iXQwcUiPOWp/eLQNOlrQE+Azwm4j4H1lB\nu1TS/BTLXqvZD8D3gV+m1+yWi/F44DpJz3d5/dh0I/M8sqktt1TZ54NA5bG3uwBz0/bnkuWedCPx\n+xHxWp3YzMzaSelrFrAlMCuNybfS+fS8qvUnTRtfkc7pV2RTCVd3DOh5/cs7hWz63/z0+srDOk4l\nq4ULyKb/VewDLEj1ayxwdZV9PkRnnaqaE0mDgO3Ifq9mDVM2ZdisPUkaA/w0Ig6psu6diNiogLCa\n0htxSuoApkfELq3cbytJ2hy4OSL2r7PNqcBbEXFT30VmZtY7+kPNaqV2P2dlT3L8E9kDnqr+D7Gk\nQ8kebHJenwZnpedvsqzMyvIJQW/F2dbnn77du1F1/hgx8CbZgzbMzPq7th6ze0lbn3NE/IfsSbtb\n1tlsIHBF30Rk/Ym/yTIzMzMzM2shf5NlZmZmZmbWQr7IMjMzMzMzayFfZJmZmZmZmbWQL7LMzMzM\nzMxayBdZZmZmZmZmLeSLLDMzMzMzsxb6P+CwD2/dcp7WAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(len(recs), 2, figsize=(12,15))\n", + "for i in range(len(recs)):\n", + " mec.set_eff('c', recs[i].conc)\n", + " qmatrix = QMatrix(mec.Q, mec.kA)\n", + " idealG = IdealG(qmatrix)\n", + " \n", + " # Plot apparent open period histogram\n", + " ipdf = ideal_pdf(qmatrix, shut=False) \n", + " iscale = scalefac(recs[i].tres, qmatrix.aa, idealG.initial_vectors)\n", + " epdf = missed_events_pdf(qmatrix, recs[i].tres, nmax=2, shut=False)\n", + " dcplots.xlog_hist_HJC_fit(axes[i,0], recs[i].tres, recs[i].opint,\n", + " epdf, ipdf, iscale, shut=False)\n", + " axes[i,0].set_title('concentration = {0:3f} mM'.format(conc[i]*1000))\n", + "\n", + " # Plot apparent shut period histogram\n", + " ipdf = ideal_pdf(qmatrix, shut=True)\n", + " iscale = scalefac(recs[i].tres, qmatrix.ff, idealG.final_vectors)\n", + " epdf = missed_events_pdf(qmatrix, recs[i].tres, nmax=2, shut=True)\n", + " dcplots.xlog_hist_HJC_fit(axes[i,1], recs[i].tres, recs[i].shint,\n", + " epdf, ipdf, iscale, tcrit=math.fabs(recs[i].tcrit))\n", + " axes[i,1].set_title('concentration = {0:6f} mM'.format(conc[i]*1000))\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that in this record only shut time intervals shorter than critical time ($t_{crit}$) were used to minimise likelihood. Thus, only a part of shut time histrogram (to the left from green line, indicating $t_{crit}$ value, in the above plot) is predicted well by rate constant estimates." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/MissedEvents-checkpoint.ipynb b/exploration/.ipynb_checkpoints/MissedEvents-checkpoint.ipynb new file mode 100644 index 0000000..3eccfd2 --- /dev/null +++ b/exploration/.ipynb_checkpoints/MissedEvents-checkpoint.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MissedEvents" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5.45096768e+02 3.58690111e-02 4.58704226e-17]\n", + " [ 7.25589315e+01 4.77459650e-03 -3.30159469e-18]\n", + " [ 1.26939914e-02 8.35302916e-07 5.28345648e-12]\n", + " [ 3.84501585e-05 2.53013637e-09 3.88147430e-01]\n", + " [ -7.28448003e-18 -4.79340753e-22 6.91575829e-01]\n", + " [ 1.03253949e-03 6.79442118e-08 5.22642795e-06]]\n", + "[[ 5.45096768e+02 3.58690111e-02 4.58704226e-17]\n", + " [ 7.25589315e+01 4.77459650e-03 -3.30159469e-18]\n", + " [ 1.26939914e-02 8.35302916e-07 5.28345648e-12]\n", + " [ 3.84501585e-05 2.53013637e-09 3.88147430e-01]\n", + " [ -7.28448003e-18 -4.79340753e-22 6.91575829e-01]\n", + " [ 1.03253949e-03 6.79442118e-08 5.22642795e-06]]\n" + ] + } + ], + "source": [ + "from numpy import array\n", + "from HJCFIT.likelihood import QMatrix, DeterminantEq, Asymptotes, find_roots, ExactSurvivor, \\\n", + " ApproxSurvivor, ApproxSurvivor, MissedEventsG, \\\n", + " expm\n", + "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ], 2)\n", + "qmatrix = QMatrix([[ -1.639102438935231, 0.9279328542626132, 0, 0.7111695846726181, 0, 0, 0, 0, 0],\n", + " [ 7319.818837397022, -7319.818837397022, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, -0.5849255773178983, 0, 0, 0, 0.05800330713458401, 0.5269222701833143, 0],\n", + " [ 554.9144283943098, 0, 0, -556.415038972956, 0.670095369096168, 0.8305152095500998, 0, 0, 0],\n", + " [ 0, 0, 0, 4445.029004693305, -4445.029004693305, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0.7249830360634507, 0, -0.7855125406770954, 0, 0, 0.06052950461364481],\n", + " [ 0, 0, 0.4346782743227515, 0, 0, 0, -3554.307968015994, 0, 3553.873289741671],\n", + " [ 0, 0, 0.6916315120151144, 0, 0, 0, 0, -0.6916315120151144, 0],\n", + " [ 0, 0, 0, 0, 0, 5390.604449280132, 0.435406457067279, 0, -5391.039855737199]], 3)\n", + "transitions = qmatrix\n", + "tau = 1e-4\n", + "a = DeterminantEq(transitions, tau)\n", + "G = MissedEventsG(transitions, tau, 4)\n", + "approx = ApproxSurvivor(transitions, tau)\n", + "exact = ExactSurvivor(transitions, tau)\n", + "factor = np.dot(qmatrix.fa, expm(tau*qmatrix.aa))\n", + "#print factor\n", + "print(G.fa(tau * 1.31838319649))\n", + "print(np.dot(exact.fa(tau * 1.31838319649-tau), factor))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFBCAYAAACrYazjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYgAADEQRAwBgIIoYAAAD/T+9XNrfksX6MgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import network\n", + "from networkx import draw as nx_draw, draw_spectral\n", + "\n", + "graph = network(qmatrix)\n", + "nx_draw(graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.00000000e+00 -3.72362979e-20 3.98565840e-17]\n", + " [ -1.90203993e-16 1.00000000e+00 8.58280665e-28]\n", + " [ -1.83880688e-16 -1.43995601e-20 1.00000000e+00]]\n", + "[[ 9.99998098e-01 7.10033034e-11 -1.14561128e-21]\n", + " [ 5.60101709e-07 1.00000000e+00 -1.14894920e-21]\n", + " [ -1.21873763e-13 -4.22346238e-17 9.99999998e-01]]\n" + ] + } + ], + "source": [ + "from numpy import outer\n", + "from HJCFIT.likelihood import Asymptotes, DeterminantEq, eig, inv\n", + "eigenvalues, eigenvectors = eig(-qmatrix.matrix)\n", + "def get_ci00(i): \n", + " return outer(eigenvectors[:, i], inv(eigenvectors)[i, :])[:qmatrix.nopen, :qmatrix.nopen]\n", + "s = get_ci00(0)\n", + "for i in range(1, len(eigenvalues)):\n", + " # if abs(eigenvalues[i]) > 1e-8:\n", + " s += get_ci00(i)\n", + "print(s)\n", + "print(approx.af_components[0][0] + approx.af_components[1][0] + approx.af_components[2][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00030000000000000003\n", + "[ 0.0001 0.00011 0.00012 0.00013 0.00014 0.00015 0.00016 0.00017\n", + " 0.00018 0.00019 0.0002 0.00021 0.00022 0.00023 0.00024 0.00025\n", + " 0.00026 0.00027 0.00028 0.00029 0.0003 ]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEbCAYAAACP7BAbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSzIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tau, i, j, n = 1e-4, 0, 0, 3\n", + "\n", + "x = np.arange(tau, n * tau, tau / 10.)\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(x, np.dot(exact.af(x-tau), G.af_factor)[:, i, j], '.', label=\"exact\")\n", + "ax.plot(x, np.dot(approx.af(x-tau), G.af_factor)[:, i, j], \"-.\", label=\"approx\")\n", + "ax.plot(x, G.af(x)[:, i, j], '--', label=\"G\")\n", + "ax.set_title(\"Component ${0}$ of the matrix $^{{e}}G_{{af}}$.\".format((i, j)))\n", + "fig.tight_layout()\n", + "#legend()\n", + "#display(gcf())\n", + "print(G.tmax)\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -6.59166060e+05 6.46519434e+05 -3.22595069e+01 2.85250486e+01\n", + " -6.72742082e-01 -2.90668063e-02]\n" + ] + }, + { + "data": { + "text/plain": [ + "-25304.6324023797" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from HJCFIT.likelihood import DeterminantEq, find_lower_bound_for_roots, eig\n", + "a = DeterminantEq([[ -0.9765569699831389, 0, 0, 0, 0, 0, 0.9765569699831388, 0, 0],\n", + " [ 0, -21087.12668613774, 0, 0, 0, 4972.429427806393, 9423.400001111493, 0.7672868902249316, 6690.529970329637],\n", + " [ 0, 0, -0.02903705186960781, 0, 0.02903705186960781, 0, 0, 0, 0],\n", + " [ 0, 0, 0, -8967.619224739678, 0, 0, 0, 8967.455151523045, 0.1640732166323416],\n", + " [ 0, 0, 0.2978885248190503, 0, -0.4287224564347299, 0, 0, 0.1308339316156797, 0],\n", + " [ 0, 0.7275421797587975, 0, 0, 0, -1.19022223735187, 0, 0.102814653125226, 0.3598654044678464],\n", + " [ 0.1209377584689361, 0.05943271459974253, 0, 0, 0, 0, -0.1803704730686787, 0, 0],\n", + " [ 0, 0.8265398081401302, 0, 0.6009896070678163, 3588.624442956896, 0.7814141825061616, 0, -3590.83338655461, 0],\n", + " [ 0, 0.8191655510502869, 0, 8172.231231533744, 0, 7421.575697898968, 0, 0, -15594.62609498376]], 6, 1e-4)\n", + "print(eig(a.H(-126523))[0])\n", + "find_lower_bound_for_roots(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/OpenMP_example-checkpoint.ipynb b/exploration/.ipynb_checkpoints/OpenMP_example-checkpoint.ipynb new file mode 100644 index 0000000..0ffe186 --- /dev/null +++ b/exploration/.ipynb_checkpoints/OpenMP_example-checkpoint.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# OpenMP example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we illustrate how OpenMP can be used to speedup the calculation of the likelihood." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we set the number of openmp threads. This is done via an environmental variable called `OMP_NUM_THREADS`. In this example we set the value of the variable from Python but typically this will be done directly in a shell script before running the example i.e. something like:\n", + "\n", + "```\n", + "export OMP_NUM_THREADS=4\n", + "python script.py\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['OMP_NUM_THREADS'] = '4'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that only the value of `OMP_NUM_THREADS` at import time infulences the execution. To experiment with OpenMP restart the notebook kernel, change the value in the cell above reexecute. You should see the time of execution change in the last cell." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some general settings:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys, time, math\n", + "import numpy as np\n", + "from numpy import linalg as nplin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HJCFIT depends on DCPROGS/DCPYPS module for data input and setting kinetic mechanism:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from dcpyps.samples import samples\n", + "from dcpyps import dataset, mechanism, dcplots, dcio" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# LOAD DATA: Burzomato 2004 example set.\n", + "scnfiles = [[\"./samples/glydemo/A-10.scn\"], \n", + " [\"./samples/glydemo/B-30.scn\"],\n", + " [\"./samples/glydemo/C-100.scn\"], \n", + " [\"./samples/glydemo/D-1000.scn\"]]\n", + "tr = [0.000030, 0.000030, 0.000030, 0.000030]\n", + "tc = [0.004, -1, -0.06, -0.02]\n", + "conc = [10e-6, 30e-6, 100e-6, 1000e-6]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialise Single-Channel Record from dcpyps. Note that SCRecord takes a list of file names; several SCN files from the same patch can be loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initaialise SCRecord instance.\n", + "recs = []\n", + "bursts = []\n", + "for i in range(len(scnfiles)):\n", + " rec = dataset.SCRecord(scnfiles[i], conc[i], tr[i], tc[i])\n", + " recs.append(rec)\n", + " bursts.append(rec.bursts.intervals())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load demo mechanism (C&H82 numerical example)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# LOAD FLIP MECHANISM USED in Burzomato et al 2004\n", + "mecfn = \"./samples/mec/demomec.mec\"\n", + "version, meclist, max_mecnum = dcio.mec_get_list(mecfn)\n", + "mec = dcio.mec_load(mecfn, meclist[2][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# PREPARE RATE CONSTANTS.\n", + "# Fixed rates.\n", + "#fixed = np.array([False, False, False, False, False, False, False, True,\n", + "# False, False, False, False, False, False])\n", + "for i in range(len(mec.Rates)):\n", + " mec.Rates[i].fixed = False\n", + "\n", + "# Constrained rates.\n", + "mec.Rates[21].is_constrained = True\n", + "mec.Rates[21].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[21].constrain_args = [17, 3]\n", + "mec.Rates[19].is_constrained = True\n", + "mec.Rates[19].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[19].constrain_args = [17, 2]\n", + "mec.Rates[16].is_constrained = True\n", + "mec.Rates[16].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[16].constrain_args = [20, 3]\n", + "mec.Rates[18].is_constrained = True\n", + "mec.Rates[18].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[18].constrain_args = [20, 2]\n", + "mec.Rates[8].is_constrained = True\n", + "mec.Rates[8].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[8].constrain_args = [12, 1.5]\n", + "mec.Rates[13].is_constrained = True\n", + "mec.Rates[13].constrain_func = mechanism.constrain_rate_multiple\n", + "mec.Rates[13].constrain_args = [9, 2]\n", + "mec.update_constrains()\n", + "# Rates constrained by microscopic reversibility\n", + "mec.set_mr(True, 7, 0)\n", + "mec.set_mr(True, 14, 1)\n", + "\n", + "# Update constrains\n", + "mec.update_constrains()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Propose initial guesses different from recorded ones \n", + "initial_guesses = [5000.0, 500.0, 2700.0, 2000.0, 800.0, 15000.0, 300.0, 120000, 6000.0,\n", + " 0.45E+09, 1500.0, 12000.0, 4000.0, 0.9E+09, 7500.0, 1200.0, 3000.0, \n", + " 0.45E+07, 2000.0, 0.9E+07, 1000, 0.135E+08]\n", + "mec.set_rateconstants(initial_guesses)\n", + "mec.update_constrains()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare likelihood function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def dcprogslik(x, lik, m, c):\n", + " m.theta_unsqueeze(np.exp(x))\n", + " l = 0\n", + " for i in range(len(c)):\n", + " m.set_eff('c', c[i])\n", + " l += lik[i](m.Q)\n", + " return -l * math.log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import HJCFIT likelihood function\n", + "from HJCFIT.likelihood import Log10Likelihood\n", + "\n", + "kwargs = {'nmax': 2, 'xtol': 1e-12, 'rtol': 1e-12, 'itermax': 100,\n", + " 'lower_bound': -1e6, 'upper_bound': 0}\n", + "likelihood = []\n", + "\n", + "for i in range(len(recs)):\n", + " likelihood.append(Log10Likelihood(bursts[i], mec.kA,\n", + " recs[i].tres, recs[i].tcrit, **kwargs))\n", + "theta = mec.theta()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time evaluation of likelihood function" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 37.6 ms per loop\n" + ] + } + ], + "source": [ + "%timeit dcprogslik(np.log(theta), likelihood, mec, conc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/TimeSeries-checkpoint.ipynb b/exploration/.ipynb_checkpoints/TimeSeries-checkpoint.ipynb new file mode 100644 index 0000000..21267e8 --- /dev/null +++ b/exploration/.ipynb_checkpoints/TimeSeries-checkpoint.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Time series" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just checking the logic." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 8.59256356 13.17355631 22.65902932 30.51518269\n", + " 39.80841094 43.44484256 52.40072443 59.61475713 67.98731603\n", + " 76.4723764 80.89963682 90.84416819 96.54831984 105.41180332\n", + " 115.19328038 123.54596142 132.36917277 142.20242392 148.21030341\n", + " 158.20874751 161.38849311 170.48725632 176.4956325 185.39531444\n", + " 191.89856023 198.83685826 204.49651737 209.7973213 216.95273892\n", + " 221.05919062 229.65418582 238.26067727 241.70173579 245.65145402\n", + " 249.52013766 256.54599658 262.18962559 266.15962003 273.93633196\n", + " 283.39856545 286.70842402 296.25077954 305.3712874 313.01989332\n", + " 320.42691522 328.14012926 337.8075964 341.70598939 350.20743812\n", + " 353.38204688 362.27510626 371.70551543 375.2230498 384.61762256\n", + " 389.15553529 392.73575791 401.43023923 408.29600194 413.83360224\n", + " 417.92996481 423.7774031 432.98776285 442.64181207 449.18417602\n", + " 452.27298482 456.38927169 462.51919708 466.31855265 470.69058382\n", + " 480.14110632 484.75552799 490.97614438 493.98123983 502.03687601\n", + " 507.35256576 513.18612587 522.63779955 525.99850407 534.83319294\n", + " 539.53052581 546.00109775 551.15733842 557.08909557 562.36622206\n", + " 569.95964467 574.04627895 579.80772582 583.72298285 593.37151631\n", + " 600.4412491 607.52752039 612.33312193 616.38161548 624.81590068\n", + " 634.03294875 640.49775222 646.01028819 651.79184332 656.47139117\n", + " 663.41252658]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEKCAYAAAAsDo9wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXIApiqIGpP8PKs6h4slHEPcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from HJCFIT.likelihood import plot_time_series\n", + "from HJCFIT.likelihood.random import time_series as random_time_series\n", + "\n", + "perfect, series = random_time_series(N=100, n=100, tau=1)\n", + "print(perfect)\n", + "fig, ax = plt.subplots(1,1)\n", + "plot_time_series(perfect, ax=ax)\n", + "plot_time_series(series, ax=ax, marker='*', color='k', linestyle=':')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEKCAYAAAAsDo9wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na4q4AUdERESsKeIGHBEREbGm+P/UHK87mpdplgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import time_filter as cpp_time_filter\n", + "filtered = cpp_time_filter(series, 1)\n", + "fig, ax = plt.subplots(1,1)\n", + "plot_time_series(perfect, ax=ax)\n", + "plot_time_series(filtered, ax=ax, marker='*', color='k', linestyle=':')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, computes the likelihood of this time series for a random QMatrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/approx_survivor-checkpoint.ipynb b/exploration/.ipynb_checkpoints/approx_survivor-checkpoint.ipynb new file mode 100644 index 0000000..14caa3a --- /dev/null +++ b/exploration/.ipynb_checkpoints/approx_survivor-checkpoint.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Approx Survivor" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import array\n", + "from HJCFIT.likelihood import QMatrix, DeterminantEq, Asymptotes, find_roots, ExactSurvivor, ApproxSurvivor\n", + "qmatrix = QMatrix( \n", + " array([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ]), 2)\n", + "\n", + "transitions = qmatrix\n", + "tau = 1e-4\n", + "a = DeterminantEq(transitions, tau)\n", + "approx = ApproxSurvivor(transitions, tau)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEbCAYAAACP7BAbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE5AsoIwxxgSk/wdFUDrdgI2WvgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tau, i, j, n = 1e-4, 2, 2, 4\n", + "\n", + "transitions = qmatrix.transpose()\n", + "exact = ExactSurvivor(transitions, tau)\n", + "approx = ApproxSurvivor(transitions, tau)\n", + "\n", + "x = np.arange(0, n * tau, tau / 10.)\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(x, exact.af(x)[:, i, j], label=\"exact\")\n", + "ax.plot(x, approx.af(x)[:, i, j], label=\"approx\")\n", + "ax.set_title(\"Component ${0}$ of the matrix $R_{{af}}$.\".format((i, j)))\n", + "ax.legend()\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEaCAYAAABEsMO+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHs1JREFUeJzt3X2wHXWd5/H3NwkhPBkikGQgPASQ8KASAwQfdmvvAiJg\nTWDGKQqkVlmnRgtYnVm2lGSnCsJUzQSmdETLwdISMSqK6K4lpREQ4Y6Ci6BREAIYwUAIJiAJMQIJ\n94bf/tF9kpOb+9gP54n3q6rr9PmdPt3fHG/z8df96+5IKSFJUqeZ1O4CJEkajgElSepIBpQkqSMZ\nUJKkjmRASZI6kgElSepIYwZURNwQERsi4qGmtn+NiEcj4tcR8X8i4g1Nny2JiNX552c2tS+IiIci\n4rcRcV31/xRJUi8ZTw/qRuA9Q9ruAE5IKc0HVgNLACLieOB84DjgbOD6iIj8O58H/jaldAxwTEQM\nXackSTuMGVAppXuATUPa7kwpvZa/vQ+Yk88vAm5OKQ2mlNaQhdfCiJgN7JdSeiBf7qvAeRXUL0nq\nUVWcg/oQsCKfPwRY2/TZurztEOCZpvZn8jZJkoZVKqAi4h+BgZTSNyuqR5IkAKYU/WJEXAycA5zW\n1LwOOLTp/Zy8baT2kdbtDQIlqQuklGLspYoZbw8q8il7E3EW8HFgUUppW9NytwIXRMTUiJgLHA3c\nn1JaD2yOiIX5oIkPAN8bbYMppa6arrrqqrbX8HqouVvr7saau7Vua27dVLcxe1AR8Q2gDzggIp4G\nrgL+NzAV+FE+SO++lNKlKaVVEXELsAoYAC5NO/8VlwFfAaYBK1JKt1X8b5Ek9ZAxAyql9P5hmm8c\nZfllwLJh2n8JvGVC1UmSXre8k0RF+vr62l3ChHVjzdCddXdjzdCddVtz74hWHEecqIhInViXJGmn\niCB1wCAJSZJayoCSJHUkA0qS1JEMKElSRzKgKrBlCzz3XLurkKTeYkBV4OtfhyuvbHcVktRbDKgK\nbN0KGze2uwpJ6i0GVAUGBuDFF9tdhST1FgOqAgaUJFXPgKqAASVJ1TOgKmBASVL1DKgKNALK2wdK\nUnUMqAoMDGTTK6+0uxJJ6h0GVAUGB7NXD/NJUnUMqAoMDGSvBpQkVceAqoABJUnVM6AqYEBJUvUM\nqAoMDECEASVJVTKgKjAwAG98I2ze3O5KJKl3GFAVGBiAgw6yByVJVTKgKmBASVL1DKgKDAzAgQca\nUJJUJQOqAoOD9qAkqWoGVAXsQUlS9QyoCngOSpKqZ0BVwICSpOoZUBUwoCSpegZUBZoDymdCSVI1\nDKgKDAzAfvtltzvaurXd1UhSbzCgKjAwAHvsAfvv72E+SaqKAVWBgQGYMsWAkqQqjRlQEXFDRGyI\niIea2mZExB0R8XhE3B4R05s+WxIRqyPi0Yg4s6l9QUQ8FBG/jYjrqv+ntM/goD0oSaraeHpQNwLv\nGdK2GLgzpTQPuAtYAhARxwPnA8cBZwPXR0Tk3/k88LcppWOAYyJi6Dq7VvMhPu9oLknVGDOgUkr3\nAJuGNJ8LLM/nlwPn5fOLgJtTSoMppTXAamBhRMwG9kspPZAv99Wm73Q9z0FJUvWKnoOamVLaAJBS\nWg/MzNsPAdY2LbcubzsEeKap/Zm8rSc0Amr6dANKkqoypaL1VH71z9KlS3fM9/X10dfXV/UmKpGS\nPShJrw/9/f309/e3bHtFA2pDRMxKKW3ID989l7evAw5tWm5O3jZS+4iaA6qTbd8OkyZlkwElqZcN\n7SxcffXVtW5vvIf4Ip8abgUuzuc/CHyvqf2CiJgaEXOBo4H788OAmyNiYT5o4gNN3+lqjd4TGFCS\nVKUxe1AR8Q2gDzggIp4GrgKuAb4dER8CniIbuUdKaVVE3AKsAgaAS1PacfOfy4CvANOAFSml26r9\np7SHASVJ9RgzoFJK7x/hozNGWH4ZsGyY9l8Cb5lQdV3AgJKkengniZIGB7O7SIABJUlVMqBKsgcl\nSfUwoEoyoCSpHgZUSQaUJNXDgCqpOaCmTcsu3PWZUJJUngFVUnNARXjDWEmqigFVUnNAgffjk6Sq\nGFAlDQ0oz0NJUjUMqJIMKEmqhwFVUvOFumBASVJVDKiS7EFJUj0MqJIMKEmqhwFVkgElSfUwoEoy\noCSpHgZUSQaUJNXDgCrJgJKkehhQJRlQklQPA6okb3UkSfUwoEryQl1JqocBVdJwh/i8m7kklWdA\nlTQ0oPbeO2vbtq19NUlSLzCgShoaUD4TSpKqYUCVNDSgwPNQklQFA6okA0qS6mFAlWRASVI9DKiS\nDChJqocBVZIBJUn1MKBKGnqhLhhQklQFA6oke1CSVA8DqqThAsr78UlSeQZUSfagJKkeBlRJBpQk\n1aNUQEXE/4yIhyPioYi4KSKmRsSMiLgjIh6PiNsjYnrT8ksiYnVEPBoRZ5Yvv/1GCihvdSRJ5RQO\nqIg4GPgosCCl9FZgCnAhsBi4M6U0D7gLWJIvfzxwPnAccDZwfUREufLbzx6UJNWj7CG+ycA+ETEF\n2AtYB5wLLM8/Xw6cl88vAm5OKQ2mlNYAq4GFJbffdgaUJNWjcECllJ4FPgU8TRZMm1NKdwKzUkob\n8mXWAzPzrxwCrG1axbq8rasZUJJUjzKH+PYn6y0dDhxM1pO6CEhDFh36vqcMDu4eUPvskz0P6tVX\n21OTJPWCKWMvMqIzgCdTShsBIuK7wDuBDRExK6W0ISJmA8/ly68DDm36/py8bVhLly7dMd/X10df\nX1+JUuszMLD7nSSanwl10EHtqUuSqtbf309/f3/LthcpFevgRMRC4AbgFGAbcCPwAHAYsDGldG1E\nXAHMSCktzgdJ3AScSnZo70fAm9IwBUTEcM0d6cQTYflymD9/1/ZjjoFbb4Vjj21PXZJUt4ggpVTb\nYLfCPaiU0v0R8R3gV8BA/vpFYD/gloj4EPAU2cg9UkqrIuIWYFW+/KVdk0KjGO4cFMCsWbBhgwEl\nSUWVOcRHSulq4OohzRvJDv8Nt/wyYFmZbXaa0QJq/frW1yNJvcI7SZQ0UkDNnp31oCRJxRhQJdmD\nkqR6GFAl2YOSpHoYUCWNNUhCklSMAVXScBfqQtaD8hCfJBVnQJU03IW6YA9KksoqfKFunbrpQt3J\nk7PbGg0Nqa1b4Q1vyD7r/nu2S9Lu6r5Q1x5UCa+9lk2TJ+/+2bRpsPfesGlT6+uSpF5gQJXQGCAx\nUg/JoeaSVJwBVcJII/gaHGouScUZUCWMFVAOlJCk4gyoEsbTg/IQnyQVY0CVYA9KkupjQJUw0kW6\nDfagJKk4A6qEkS7SbbAHJUnFGVAleA5KkupjQJXgOShJqo8BVcJYATVzJjz/fHa3CUnSxBhQJYwV\nUHvuCfvuCxs3tq4mSeoVBlQJYwUUeJhPkooyoEoYT0A5UEKSijGgSrAHJUn1MaBKGOtCXbAHJUlF\nGVAljHWhLtiDkqSiDKgSPAclSfUxoErwHJQk1ceAKmG8PSgDSpImzoAqYbw9KA/xSdLEGVAljCeg\nZs6EP/4Rtm9vTU2S1CsMqBLGE1B77AHTp8MLL7SmJknqFQZUCeMJKPA8lCQVYUCVMJ4LdcHzUJJU\nhAFVwngu1AWHmktSEaUCKiKmR8S3I+LRiHgkIk6NiBkRcUdEPB4Rt0fE9Kbll0TE6nz5M8uX314e\n4pOk+pTtQX0GWJFSOg44EXgMWAzcmVKaB9wFLAGIiOOB84HjgLOB6yMiSm6/rcYbUB7ik6SJKxxQ\nEfEG4D+nlG4ESCkNppQ2A+cCy/PFlgPn5fOLgJvz5dYAq4GFRbffCexBSVJ9yvSg5gJ/jIgbI2Jl\nRHwxIvYGZqWUNgCklNYDM/PlDwHWNn1/Xd7WtexBSVJ9xnGKf9TvLgAuSyn9IiI+TXZ4Lw1Zbuj7\ncVm6dOmO+b6+Pvr6+opVWSN7UJJeT/r7++nv72/Z9iKlQvlBRMwC/l9K6cj8/X8iC6ijgL6U0oaI\nmA3cnVI6LiIWAymldG2+/G3AVSmlnw+z7lS0rlb68IfhpJPgIx8Zfbk//AHmzzekJPWWiCClVNtY\ngsKH+PLDeGsj4pi86XTgEeBW4OK87YPA9/L5W4ELImJqRMwFjgbuL7r9TjDeHtRBB8HGjd7uSJIm\noswhPoCPATdFxB7Ak8B/ByYDt0TEh4CnyEbukVJaFRG3AKuAAeDSrugmjWK8F+pOmQIzZmT35Js1\nq/66JKkXlAqolNKDwCnDfHTGCMsvA5aV2WYnGW8PCnY+uNCAkqTx8U4SJYz3ThLg3SQkaaIMqBIm\n0oNyqLkkTYwBVcJED/HZg5Kk8TOgSrAHJUn1MaBKKDJIQpI0PgZUCRMJqMMPh6eeqrceSeolBlQJ\nEwmoI4+EJ56otx5J6iUGVAnjvVAX4OCD4cUX4eWX661JknqFAVXCRHpQkybBEUfAk0/WWpIk9QwD\nqoSJXKgLcNRRBpQkjZcBVcJEelDgeShJmggDqoSJBpQ9KEkaPwOqBHtQklQfA6oEe1CSVB8DqoSJ\nBtTcubBmjQ8ulKTxMKBKmGhA7bUXHHAAPPtsfTVJUq8woEqYyIW6DZ6HkqTxMaAKSikLqIlcBwWe\nh5Kk8TKgChochMmTIWJi37MHJUnjY0AVNNHzTw1HHmkPSpLGw4AqqGhAHXWUPShJGg8DqiB7UJJU\nLwOqoKIBNXMmbN0KmzdXX5Mk9RIDqqCiARVhL0qSxsOAKqhoQIHnoSRpPAyogopcpNtgD0qSxmZA\nFWQPSpLqZUAVNNGn6TazByVJYzOgCrIHJUn1MqAKKhNQhx8O69Zl65AkDc+AKqhMQE2dCn/xF/D0\n09XWJEm9xIAqqExAgeehJGkspQMqIiZFxMqIuDV/PyMi7oiIxyPi9oiY3rTskohYHRGPRsSZZbfd\nTmUDyvNQkjS6KnpQfw+sanq/GLgzpTQPuAtYAhARxwPnA8cBZwPXR0z0YRWdwx6UJNWrVEBFxBzg\nHOBLTc3nAsvz+eXAefn8IuDmlNJgSmkNsBpYWGb77VTmQl2wByVJYynbg/o08HEgNbXNSiltAEgp\nrQdm5u2HAGublluXt3Ule1CSVK/CARUR7wU2pJR+DYx2qC6N8lnXquocVOrJX0eSyit4LwQA3gUs\niohzgL2A/SLia8D6iJiVUtoQEbOB5/Ll1wGHNn1/Tt42rKVLl+6Y7+vro6+vr0Sp1StzJwmAGTOy\nR8a/8AIceGB1dUlSXfr7++nv72/Z9iJV8H/hI+K/AP8rpbQoIv4VeCGldG1EXAHMSCktzgdJ3ASc\nSnZo70fAm9IwBUTEcM0d5QtfgF/+Er74xeLrOOUU+Mxn4J3vrK4uSWqViCClVNtgtzqug7oGeHdE\nPA6cnr8npbQKuIVsxN8K4NKOT6FRlD3EB/C2t8HKldXUI0m9pswhvh1SSv8B/Ec+vxE4Y4TllgHL\nqthmu1URUAsWwP33V1OPJPUa7yRRUBUBddJJ2WFCSdLuDKiCqgiot7wFVq+GV16ppiZJ6iUGVEFl\nL9QFmDYN5s2Dhx6qpiZJ6iUGVEFV9KAgOw/lQAlJ2p0BVVBVAeV5KEkangFVUNkLdRsMKEkangFV\nUFU9qLe+FR5/HLZtK78uSeolBlRBVQXUXnvB0UfDb35Tfl2S1EsMqIKqCijwMJ8kDceAKsiAkqR6\nGVAFVRlQDjWXpN0ZUAVVcaFuw/z5sGoVvPpqNeuTpF5gQBVUZQ9q772zJ+w+/HA165OkXmBAFVRl\nQIGH+SRpKAOqoKou1G1woIQk7cqAKqjqHpQBJUm7MqAKqjqg5s/PzkENDFS3TknqZgZUQVUH1L77\nwuGHZ6P5JEkGVGFVBxR4mE+SmhlQBVV5HVSDASVJOxlQBdXRg1q4EH72s2rXKUndyoAqqI6AOvVU\neOopePbZatcrSd3IgCqojoCaMgXe/W647bZq1ytJ3ciAKqjqC3Ub3vte+MEPql+vJHUbA6qgOnpQ\nAGedBT/+sTeOlSQDqqC6AmrmTJg3D+65p/p1S1I3MaAKqiugAM45B1asqGfdktQtDKiC6gwoz0NJ\nkgFVWB0X6jYsWACbNsGTT9azfknqBgZUAdu3QwRMqunXmzQJzj7bw3ySXt8MqALqPLzX4HkoSa93\nkVJqdw27iYjUiXU1bNkCBx+cvdblxRfhsMNg/frskfCS1GkigpRS1LV+e1AFtKIHtf/+2bmou++u\ndzuS1KkKB1REzImIuyLikYj4TUR8LG+fERF3RMTjEXF7RExv+s6SiFgdEY9GxJlV/APaoa67SAx1\nzjmO5pP0+lWmBzUIXJ5SOgF4B3BZRBwLLAbuTCnNA+4ClgBExPHA+cBxwNnA9RFRW9ewTq3oQUE2\n3HzFCujgo52SVJvCAZVSWp9S+nU+/2fgUWAOcC6wPF9sOXBePr8IuDmlNJhSWgOsBhYW3X47tSqg\njj8+C6dHHql/W5LUaSo5BxURRwDzgfuAWSmlDZCFGDAzX+wQYG3T19blbV2nVQEVARdeCF/+cv3b\nkqROU/pMSkTsC3wH+PuU0p8jYugBqUIHqJYuXbpjvq+vj76+vqIlVq7Oi3SHuuSSbLDEP/0T7Ltv\na7YpScPp7++nv7+/ZdsrNcw8IqYA3wd+mFL6TN72KNCXUtoQEbOBu1NKx0XEYiCllK7Nl7sNuCql\n9PNh1tvRw8wffBA+8IHstRX++q+z50RdcklrtidJ49Hpw8y/DKxqhFPuVuDifP6DwPea2i+IiKkR\nMRc4Gri/5PbbolWH+Bo++lH43OccLCHp9aXMMPN3ARcBp0XEryJiZUScBVwLvDsiHgdOB64BSCmt\nAm4BVgErgEs7ups0ilYHVF9fdvuju+5q3TYlqd0Kn4NKKd0LTB7h4zNG+M4yYFnRbXaKVgdURNaL\n+uxn4fTTW7ddSWon7yRRQKsu1G120UVw773w+9+3druS1C4GVAGt7kEB7LMPXHwxXH99a7crSe1i\nQBXQjoACuPRSuPFGePnl1m9bklrNgCqgXQF15JHwrnfBTTe1ftuS1GoGVAGtvFB3qI99DD71KXj1\n1fZsX5JaxYAqoF09KIDTToOjjoJPfrI925ekVjGgCmhnQEXAv/87/Nu/wRNPtKcGSWoFA6qAdgYU\nwBFHwBVXZIMmuvNSZ0kamwFVQLsDCuAf/iF7HPzNN7e3DkmqiwFVQDsu1B1qjz3gC1+Ayy+HTZva\nW4sk1cGAKqATelAAb397dqfzxYvbXYkkVc+AKqBTAgrgX/4Fvv99byQrqfcYUAV0UkBNnw5f+xpc\ncAE89FC7q5Gk6hhQBbTzQt3hnHZa9ryoc87xZrKSekebT/V3p4EB2Guvdlexq/PPh+efh/e8B+65\nB2bObHdFklSOPagCOukQX7PLLssO9Z1zDmzZ0u5qJKkcA6qATg0ogKuvhpNOgkWL4IUX2l2NJBVn\nQBXQyQEVkT0zasGCLKjuu6/dFUlSMQZUAZ1woe5oJk/O7nh+3XVZT+q667wlkqTuY0AV0Mk9qGbn\nnZf1oL7+dXjf+2DjxnZXJEnjZ0AV0C0BBdlDDu+9Fw47DObNg2uugZdeandVkjQ2A6qAbgoogD33\nzA7z3XMP/OpX8KY3ZddNbdvW7sokaWQGVAGddqHueM2bB9/6FvzgB/DDH2ZB9c//DM8+2+7KJGl3\nBlQB3daDGuptb8tC6rvfhaefhhNOgL/6qyy0tm9vd3WSlDGgCuj2gGo46aTskR1PP51d3Hvlldnj\n5K+5Bp57rt3VSXq9M6AK6JWAathvP/i7v4MHHoDvfAdWr84OB150UXbeyiHqktrBgCqg1wKq2ckn\nww03wJNPwimnwIc/nD1i/vLL4Wc/g9dea3eFkl4vDKgCejmgGmbMyB4r/8gjsGJF9liPj3wEDj0U\nLrkk62l5KyVJdYrUgcdvIiJ1Yl0NJ58Mn/981sN4vXnssSywfvxj+OlPs5GAp58O73gHLFwIhxzS\n7goltUpEkFKK2tbfiUHQ6QF14omwfDnMn9/uStrr1Vfh/vuzp/n+/OfZ/NSpWVCddBK8+c3ZNHdu\ndvslSb3FgOpAxx8P3/52NjxbO6UEa9ZkQbVyZXZ48OGHs+dUHXssHHNMNkrw6KOz6cgjYfZsmOSB\nZqkrGVAd6Jhj4Pvfz141ti1bYNWqbHTgE0/A736XTb//PWzaBAcfnJ3bOvTQ7BDh7Nm7TgceCAcc\nYC9M6jQ9F1ARcRZwHdkAjRtSStcOs0xHB9Tcudlhrblz211J99u2DZ55BtauzaY//CGb1q/f+frC\nC1mQTZ++M6xmzNh1mj595/SGN2TTfvtl0777Zq9Tp7b7Xyv1lp4KqIiYBPwWOB14FngAuCCl9NiQ\n5To6oObMye4SPmfOzrb+/n76+vraVlMR3VTz9u1ZSD3/PNx5Zz9z5/axaRM7ps2b4U9/yl43b856\nbUMngH322XXae++d0157DT9Nm7brtOeeO1+HTlOn7nxtnv/pT7vnt27WTX8jDdbcOnUHVKufarQQ\nWJ1SegogIm4GzgUeG/VbHWa4Yebd+AfWTTVPnpz1ng48EL71rX4++tG+Ca/j1VezO7m/9BL8+c/w\n8svwyivZ68svZ+1bt2Ztjemll7LHlDTat27Nen2N1+bp1Vd3nW9M27ZBSv1Mm9bHHnvsDK899tg5\nDX0/2jRlys7X5vmhbZMn73w/0tRYZqTXb36znze+sW9HW/PnzdNwbZMnZw/QbLVu+rtu6MaaW6HV\nAXUIsLbp/TNkodVVXg/XQfWiRjDMmNH6bV95JSxZkgXWwEAWWgMDO6dG+3imwcGdU+N943Xr1t2X\naUzbt4/8fvv27Hvbt+/avm5ddoF2o63588b8cO+3b995UfdwwTV5cjY4Zuj8aG3NryO1TZqUnd9c\nuXLXtuGWG+8UMXZb8/vG/HBtze+bl1u5Em68cffvjzZfdLmRprGW2W+/bHBTK3Xsc2H/8i/bXcHI\ntmzxfIYmZtKknYcMu8nSpdlU1Guv7R5czQHW/Dpc22ivQ+cb09e+BhdcMPoy27dno05Hej+0LaXd\nl2m0DQ7uvsxw32u0Nb9vtK1dCz/5ya7bH7rMWPMT+WykabRlTj4ZvvSlqv6yxqfV56DeDixNKZ2V\nv18MpKEDJSKic09ASZJ26KVBEpOBx8kGSfwBuB+4MKX0aMuKkCR1hZYe4kspbY+I/wHcwc5h5oaT\nJGk3HXmhriRJtdxkJiLOiojHIuK3EXHFCMt8NiJWR8SvI2L+WN+NiBkRcUdEPB4Rt0fE9KbPluTr\nejQizuz0miPijIj4RUQ8GBEPRMR/LVJzq+tu+vywiNgSEZd3Q80R8daI+FlEPJz/5hMe4tLiv48p\nEfGViHgoIh7Jz9UWUlPdf5P/ltsjYsGQdXXqvjhszVXti63+nfPPS+2H7ah7wvtiSqnSiSz0fgcc\nDuwB/Bo4dsgyZwM/yOdPBe4b67vAtcAn8vkrgGvy+eOBX5Edrjwi/350eM0nArPz+ROAZ7rht25a\n57eBbwGXd3rNwGTgQeDN+fsZXfD3cSHwjXx+L+D3wGEd9FvPA94E3AUsaFrXcXTuvjhSzaX3xVbX\nXMV+2KbfesL7Yh09qB0X46aUBoDGxbjNzgW+CpBS+jkwPSJmjfHdc4Hl+fxy4Lx8fhFwc0ppMKW0\nBljNxK+tamnNKaUHU0rr8/lHgGkRUeTKqlb/1kTEucCTwCMF6m1HzWcCD6aUHs7Xtynle0cH15yA\nfSIbVLQ3sA340wRrrq3ulNLjKaXVwNDRW+fSofviSDVXtC+2+neuYj9sR90T3hfrCKjhLsYd+pSg\nkZYZ7buzUkobAPI/qJkjrGvdMNvrtJp3iIi/AVbm/yNPVKvqnpXXui/wCeBqhtlpOqzmxm99TF77\nbfmhnI93cM2z8vbvAC+TjXRdA3wypfRiB9U93u110r44phL7Yktrjoh9KL8fjlbTeJYp8ltPeF/s\nlAt1i/zI7R7dUbrmiDgBWAa8u5KKxqdI3Y0HvV8FfDql9HJk97Bp1Y1syvzWU4B3AScDW4EfR8Qv\nUkp3V1XcCMr8zqcCg8Bs4ADgpxFxZ94rqVsbbk5UWuma27Avlql5Ke3ZD8tua8L7Yh0BtQ44rOn9\nnLxt6DKHDrPM1FG+uz4iZqWUNkTEbOC5MdbVyTUTEXOA/wv8txL/4Wl13acC74uIfyU7frw9Il5J\nKV3fwTU/A/wkpbQJICJWAAuAiQRUq2u+ELgtpfQa8HxE3Eu2U6+ZQM111j3a9jp1XxxRBftiq2uu\nYj9sR90T3xcnclJtPBPZibDGybOpZCfPjhuyzDnsPPH2dnaeeBvxu2QnlK9Iu59QbgySmArMpdiJ\n2VbXvH++3Hnd9FsPWe9VFBsk0Y7f+hfANLL/Q/Yj4OwOrHlxU82fILtGEGAfsvMMb+6U37rpu3cD\nJzW979h9cZSap1NyX2x1zVXsh236rSe8Lxb+j+MY//CzyO4YsRpYnLd9BPhw0zKfy/+BD7LrSI/d\nvpu3vxG4M//sDmD/ps+W5Ot6FDiz02sG/hHYAqwk26FXAgd2et0V7hit/vt4P/Aw8BCwrNNrJgul\nW/KaHy76O9dY93lk5x9eITtP9sMu2BeHrZmK9sVW/85V7Idt+vuY0L7ohbqSpI5Uy4W6kiSVZUBJ\nkjqSASVJ6kgGlCSpIxlQkqSOZEBJkjqSASVJ6kgGlCSpI/1/qimkifxJArsAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import MissedEventsG, missed_events_pdf\n", + "\n", + "tau = 2e-4\n", + "x, i, j = np.arange(0, 8*tau, tau/10.0), 2, 0\n", + "missedG = MissedEventsG(qmatrix, tau)\n", + "pdf = missed_events_pdf(qmatrix, tau, shut=True)\n", + "print(missedG.fa(0))\n", + "#plot(x, [missedG.fa(u)[i, j] for u in x])\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(x, pdf(x))\n", + "# plot(x, missed_events_pdf(qmatrix, tau, shut=True)(x))\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.00078277e+00 1.95134590e-03]\n", + " [ 2.60179453e-05 1.02029429e+00]]\n", + "\n", + "[[ 1.00078277e+00 1.95134590e-03]\n", + " [ 2.60179453e-05 1.02029429e+00]]\n", + "[[ -1.47868762e-09 1.38459950e-10]\n", + " [ 8.32130331e-13 3.23290061e-10]]\n" + ] + } + ], + "source": [ + "def create_derivative(qmatrix, tau):\n", + " from HJCFIT.likelihood import inv, expm\n", + " \n", + " If = np.identity(qmatrix.nshut)\n", + " Ia = np.identity(qmatrix.nopen)\n", + " \n", + " def Xff(s): return s*If - qmatrix.ff\n", + " def Sff(s): return If - expm(-tau*Xff(s))\n", + " def Gaf(s): return np.dot(inv(Xff(s)), qmatrix.fa)\n", + " \n", + " def derivative(s):\n", + " result = np.dot(Sff(s), inv(Xff(s))) - tau * (If - Sff(s))\n", + " return Ia + np.dot(np.dot(qmatrix.af, result), Gaf(s)) \n", + " return derivative\n", + "\n", + "derivative = create_derivative(qmatrix, tau)\n", + "print(derivative(-1000))\n", + "print()\n", + "determinant = DeterminantEq(qmatrix, tau)\n", + "print(determinant.s_derivative(-1000))\n", + "print(-(determinant.H(-1000+1e-4) - determinant.H(-1000-1e-4)) / (2e-4) + np.identity(qmatrix.nopen) - determinant.s_derivative(-1000))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/asymptotes-checkpoint.ipynb b/exploration/.ipynb_checkpoints/asymptotes-checkpoint.ipynb new file mode 100644 index 0000000..e92139c --- /dev/null +++ b/exploration/.ipynb_checkpoints/asymptotes-checkpoint.ipynb @@ -0,0 +1,496 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from HJCFIT.likelihood import DeterminantEq, find_root_intervals, find_roots, QMatrix\n", + "from HJCFIT.likelihood.random import qmatrix as random_qmatrix\n", + "equation = DeterminantEq(random_qmatrix(), 1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAD7CAYAAACSXhiEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEUmakoeIiCTt/wE4uMoWx/pVTgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from HJCFIT.likelihood import plot_roots\n", + "plot_roots(equation, size=25000);" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnDDMzKxJ/j81Cs+VXYgklAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "x = np.arange(-10, 0e0, 1e-2)\n", + "ax.plot(x, equation(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[((-2072.648424828926, -1036.324212414463), 1), ((-1036.324212414463, 0.0), 1)]\n", + "[((-1382.6484248289262, -1372.6484248289262), 1)]\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import find_root_intervals_brute_force\n", + "print(find_root_intervals(equation))\n", + "print(find_root_intervals_brute_force(equation, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "def trial():\n", + " from numpy import all\n", + " from HJCFIT.likelihood import DeterminantEq, find_root_intervals, find_roots, QMatrix\n", + " from HJCFIT.likelihood.random import qmatrix as random_qmatrix\n", + " \n", + " while True:\n", + " #try: \n", + " matrix = random_qmatrix()\n", + " equation = DeterminantEq(matrix, 1e-4)\n", + " return all([r[1] == 1 for r in find_roots(equation)])\n", + " \n", + " #except: continue\n", + "\n", + "\n", + "print(all([trial() for i in range(500)]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Asymptotic vs Exact for classic Matrix\n", + "--------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import array\n", + "from HJCFIT.likelihood import QMatrix, DeterminantEq, Asymptotes, find_roots\n", + "qmatrix = QMatrix( \n", + " array([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ]), 2)\n", + "equation = DeterminantEq(qmatrix, 1e-4)\n", + "roots = find_roots(equation)\n", + "asymptotes = Asymptotes(equation, roots)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2.21137053e+03 2.09900610e+00 0.00000000e+00]\n", + " [ 1.67920488e-01 4.75583079e+02 0.00000000e+00]]\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import expm, eig, inv\n", + "transitions = qmatrix.transpose()\n", + "\n", + "right = eig(transitions.matrix)[1]\n", + "eigenvalues = eig(transitions.matrix)[0]\n", + "left = eig(transitions.matrix.T)[1].T\n", + "\n", + "tau = 1e-4\n", + "af_factor = np.dot(expm(tau * transitions.ff), transitions.fa)\n", + "print(af_factor)\n", + "print(np.all(abs(af_factor - np.dot(expm(tau * qmatrix.aa), qmatrix.af)) < 1e-8))\n", + "for i, j in zip(range(5), [0, 1, 2, 4, 3]):\n", + " col = right[:transitions.nopen, i]\n", + " row = inv(right)[i, transitions.nopen:]\n", + " one_way = np.dot(np.outer(col, row), af_factor)\n", + " col = eig(qmatrix.matrix)[1][qmatrix.nopen:, i]\n", + " row = inv(eig(qmatrix.matrix)[1])[i, :qmatrix.nopen]\n", + " other_way = np.dot(np.outer(col, row), np.dot(expm(tau * qmatrix.aa), qmatrix.af))\n", + " print(np.all(abs(one_way - other_way) < 1e-8))\n", + " # print i, \" is ok\", all(abs(np.dot(outer(col, row), af_factor) - ExactG(transitions, 1e-4).D_af(j)) < 1e-8)\n", + " # print\n", + " # print ExactG(transitions, tau).D_af(i)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -1.94082023e+04 -3.09352724e+03 -2.02211927e+03 -1.01817905e+02\n", + " -3.97221336e-14]\n", + "[ -1.94082023e+04 -3.09352724e+03 -2.02211927e+03 -1.01817905e+02\n", + " -3.41740525e-14]\n", + "Same order left and right: True\n", + "Is right eig: True -19408.2022554\n", + "Is right eig: True -3093.52723698\n", + "Is right eig: True -2022.1192695\n", + "Is right eig: True -101.8179048\n", + "Is right eig: True -3.41740524716e-14\n", + "Is left eig: True -3093.52723698\n", + "Is left eig: True -2022.1192695\n", + "Is left eig: True -3.97221336337e-14\n", + "Is left eig: True -19408.2022554\n", + "Is left eig: True -101.8179048\n", + "Is row of inv(right) a left eigenvector: True -19408.2022554\n", + "Is row of inv(right) a left eigenvector: True -3093.52723698\n", + "Is row of inv(right) a left eigenvector: True -2022.1192695\n", + "Is row of inv(right) a left eigenvector: True -101.8179048\n", + "Is row of inv(right) a left eigenvector: True -3.41740524716e-14\n", + "Is column of inv(left) a right eigenvector: False -19408.2022554\n", + "Is column of inv(left) a right eigenvector: False -3093.52723698\n", + "Is column of inv(left) a right eigenvector: False -2022.1192695\n", + "Is column of inv(left) a right eigenvector: False -101.8179048\n", + "Is column of inv(left) a right eigenvector: False -3.41740524716e-14\n" + ] + } + ], + "source": [ + "from HJCFIT.likelihood import eig, inv\n", + "from numpy import exp\n", + "from numpy import diag\n", + "\n", + "right = eig(qmatrix.matrix)[1]\n", + "eigenvalues = eig(qmatrix.matrix)[0]\n", + "# print eigenvalues, \"\\n\"\n", + "left = eig(qmatrix.matrix.T)[1].T\n", + "indices = [-2, 0, 1, -1, 2]\n", + "print(eig(qmatrix.matrix.T)[0][indices])\n", + "print(eigenvalues)\n", + "print(\"Same order left and right: \", all(abs(eigenvalues - eig(qmatrix.matrix.T)[0][indices]) < 1e-8))\n", + "\n", + "for eigenvalue, eigenvector in zip(eigenvalues, right.T): \n", + " null_mat = qmatrix.matrix - eigenvalue * np.identity(qmatrix.matrix.shape[0])\n", + " print(\"Is right eig: \", all(abs(np.dot(null_mat, eigenvector)) < 1e-8), eigenvalue)\n", + "for eigenvalue, eigenvector in zip(eig(qmatrix.matrix.T)[0], left): \n", + " null_mat = qmatrix.matrix - eigenvalue * np.identity(qmatrix.matrix.shape[0])\n", + " print(\"Is left eig: \", all(abs(np.dot(eigenvector, null_mat)) < 1e-8), eigenvalue)\n", + "\n", + "for eigenvalue, eigenvector in zip(eigenvalues, inv(right)): \n", + " null_mat = qmatrix.matrix - eigenvalue * np.identity(qmatrix.matrix.shape[0])\n", + " print(\"Is row of inv(right) a left eigenvector: \", all(abs(np.dot(eigenvector, null_mat)) < 1e-8), eigenvalue)\n", + "for eigenvalue, eigenvector in zip(eigenvalues, inv(left).T): \n", + " null_mat = qmatrix.matrix - eigenvalue * np.identity(qmatrix.matrix.shape[0])\n", + " print(\"Is column of inv(left) a right eigenvector: \", all(abs(np.dot(null_mat, eigenvector)) < 1e-8), eigenvalue)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Same order left and right: False\n", + "Is right eig: True -19408.2022554\n", + "Is right eig: True -3093.52723698\n", + "Is right eig: True -2022.1192695\n", + "Is right eig: True -101.8179048\n", + "Is right eig: True 6.74160411739e-14\n", + "Is left eig: True -19408.2022554\n", + "Is left eig: True -3093.52723698\n", + "Is left eig: True -2022.1192695\n", + "Is left eig: False -101.8179048\n", + "Is left eig: False 6.74160411739e-14\n", + "Is row of inv(right) a left eigenvector: True -19408.2022554\n", + "Is row of inv(right) a left eigenvector: True -3093.52723698\n", + "Is row of inv(right) a left eigenvector: True -2022.1192695\n", + "Is row of inv(right) a left eigenvector: True -101.8179048\n", + "Is row of inv(right) a left eigenvector: True 6.74160411739e-14\n", + "Is column of inv(left) a right eigenvector: True -19408.2022554\n", + "Is column of inv(left) a right eigenvector: True -3093.52723698\n", + "Is column of inv(left) a right eigenvector: True -2022.1192695\n", + "Is column of inv(left) a right eigenvector: False -101.8179048\n", + "Is column of inv(left) a right eigenvector: False 6.74160411739e-14\n" + ] + } + ], + "source": [ + "from numpy.linalg import eig, inv\n", + "from HJCFIT.likelihood import eig as dceig\n", + "from HJCFIT.likelihood import inv as dcinv\n", + "from numpy import exp\n", + "from numpy import diag\n", + "\n", + "Qmatrix2 = qmatrix.transpose()\n", + "try:\n", + " right = eig(Qmatrix2.matrix)[1]\n", + " eigenvalues = eig(Qmatrix2.matrix)[0]\n", + " eigenvaluesT = eig(Qmatrix2.matrix.T)[0]\n", + " left = eig(Qmatrix2.matrix.T)[1].T\n", + " invmat = inv(right)\n", + " invmatL = inv(left).T\n", + "except: #fallback for longdoubles to eigen\n", + " right = dceig(Qmatrix2.matrix)[1]\n", + " eigenvalues = dceig(Qmatrix2.matrix)[0]\n", + " left = dceig(Qmatrix2.matrix.T)[1].T\n", + " eigenvaluesT = dceig(Qmatrix2.matrix.T)[0]\n", + " invmat = dcinv(right)\n", + " invmatL = dcinv(left).T\n", + " \n", + "print(\"Same order left and right: \", all(abs(eigenvalues - eigenvaluesT) < 1e-8))\n", + "\n", + "for eigenvalue, eigenvector in zip(eigenvalues, right.T): \n", + " null_mat = Qmatrix2.matrix - eigenvalue * np.identity(Qmatrix2.matrix.shape[0])\n", + " print(\"Is right eig: \", all(abs(np.dot(null_mat, eigenvector)) < 1e-8), eigenvalue)\n", + "for eigenvalue, eigenvector in zip(eigenvalues, left): \n", + " null_mat = Qmatrix2.matrix - eigenvalue * np.identity(Qmatrix2.matrix.shape[0])\n", + " print(\"Is left eig: \", all(abs(np.dot(eigenvector, null_mat)) < 1e-8), eigenvalue)\n", + "\n", + "for eigenvalue, eigenvector in zip(eigenvalues, invmat): \n", + " null_mat = Qmatrix2.matrix - eigenvalue * np.identity(Qmatrix2.matrix.shape[0])\n", + " print(\"Is row of inv(right) a left eigenvector: \", all(abs(np.dot(eigenvector, null_mat)) < 1e-8), eigenvalue)\n", + "for eigenvalue, eigenvector in zip(eigenvalues, invmatL): \n", + " null_mat = Qmatrix2.matrix - eigenvalue * np.identity(Qmatrix2.matrix.shape[0])\n", + " print(\"Is column of inv(left) a right eigenvector: \", all(abs(np.dot(null_mat, eigenvector)) < 1e-8), eigenvalue)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "True\n", + "True\n", + "[[ 4.44037679e+01 -1.92479231e+02 -4.57813227e+00]\n", + " [ -1.53983385e+04 6.67479474e+04 1.58760470e+03]\n", + " [ -2.28906614e-02 9.92252936e-02 2.36008070e-03]]\n", + "[[ 4.44037679e+01 -1.92479231e+02 -4.57813227e+00]\n", + " [ -1.53983385e+04 6.67479474e+04 1.58760470e+03]\n", + " [ -2.28906614e-02 9.92252936e-02 2.36008070e-03]]\n" + ] + } + ], + "source": [ + "from numpy import array\n", + "from HJCFIT.likelihood import QMatrix, DeterminantEq, Asymptotes, find_roots, ExactSurvivor\n", + "qmatrix = QMatrix( \n", + " array([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ]), 2)\n", + "\n", + "transitions = qmatrix.transpose()\n", + "tau = 1e-4\n", + "exact = ExactSurvivor(transitions, tau)\n", + "equation = DeterminantEq(transitions, tau)\n", + "roots = find_roots(equation)\n", + "approx = Asymptotes(equation, roots)\n", + "try:\n", + " eigenvalues = eig(-transitions.matrix)[0]\n", + "except:\n", + " eigenvalues = dceig(-transitions.matrix)[0]\n", + "\n", + "def C_i10(i): \n", + " from numpy import zeros\n", + " result = zeros((transitions.nopen, transitions.nopen), dtype='float64')\n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result += np.dot(exact.D_af(i), exact.recursion_af(j, 0, 0)) / (eigenvalues[j] - eigenvalues[i])\n", + " result -= np.dot(exact.D_af(j), exact.recursion_af(i, 0, 0)) / (eigenvalues[i] - eigenvalues[j])\n", + " return result\n", + " \n", + "def C_i20(i): \n", + " from numpy import zeros\n", + " result = zeros((transitions.nopen, transitions.nopen), dtype='float64')\n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result += ( np.dot(exact.D_af(i), exact.recursion_af(j, 1, 0)) \n", + " + np.dot(exact.D_af(j), exact.recursion_af(i, 1, 0)) ) / (eigenvalues[j] - eigenvalues[i])\n", + " result += ( np.dot(exact.D_af(i), exact.recursion_af(j, 1, 1)) \n", + " - np.dot(exact.D_af(j), exact.recursion_af(i, 1, 1)) ) / (eigenvalues[j] - eigenvalues[i])**2\n", + " return result\n", + "\n", + "def C_i21(i): \n", + " result = np.dot(exact.D_af(i), exact.recursion_af(i, 1, 0)) \n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result -= np.dot(exact.D_af(j), exact.recursion_af(i, 1, 1)) / (eigenvalues[i] - eigenvalues[j])\n", + " return result\n", + "\n", + "def C_i22(i): return np.dot(exact.D_af(i), exact.recursion_af(i, 1, 1)) * 0.5 \n", + "\n", + "print(np.all([np.all(abs(C_i10(i) - exact.recursion_af(i, 1, 0)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i20(i) - exact.recursion_af(i, 2, 0)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i21(i) - exact.recursion_af(i, 2, 1)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i22(i) - exact.recursion_af(i, 2, 2)) < 1e-8) for i in range(5)]))\n", + " \n", + "print(C_i22(0))\n", + "print(exact.recursion_af(0, 2, 2))\n", + "#print np.dot(exact.D_af(1) * exact.recursion_af(1, 1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/.ipynb_checkpoints/exact_survivor-checkpoint.ipynb b/exploration/.ipynb_checkpoints/exact_survivor-checkpoint.ipynb new file mode 100644 index 0000000..db5185d --- /dev/null +++ b/exploration/.ipynb_checkpoints/exact_survivor-checkpoint.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exact Survivor Function\n", + "=======================\n", + "\n", + "This is equation 3.12 from [Hawkes, Jalali, Colqhoun (1990)](http://dx.doi.org/10.1098/rsta.1990.0129). A simpler form is also given in [Colquhoun, Hawkes and Srodzinski (1996)](http://dx.doi.org/10.1098/rsta.1996.0115).\n", + "\n", + "These equations were performed in two parts: \n", + "\n", + "- the recurrence on the one side (recursion_formula.h). It is a set of template functions. This means it can be tested more simply on scalars (rather than matrices, as in the paper), as is done in tests/recursion_formula.cc.\n", + "- the acrutal survivor functions $^{A}R(t)$ and $^{F}R(t)$ are implemented as instances of exact_survivor.cc:ExactSurvivor::RecursionInterface. \n", + "\n", + "\n", + "Checking the implementation\n", + "---------------------------\n", + "\n", + "The classic $Q$ matrix first:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from numpy import array\n", + "from HJCFIT.likelihood import QMatrix, DeterminantEq, Asymptotes, find_roots, ExactSurvivor, eig\n", + "qmatrix = QMatrix( \n", + " array([[ -3050, 50, 3000, 0, 0 ], \n", + " [ 2./3., -1502./3., 0, 500, 0 ], \n", + " [ 15, 0, -2065, 50, 2000 ], \n", + " [ 0, 15000, 4000, -19000, 0 ], \n", + " [ 0, 0, 10, 0, -10 ] ]), 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then compares a few recursion terms by hand and by c++" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "transitions = qmatrix.transpose()\n", + "tau = 1e-4\n", + "exact = ExactSurvivor(transitions, tau)\n", + "equation = DeterminantEq(transitions, tau)\n", + "roots = find_roots(equation)\n", + "approx = Asymptotes(equation, roots)\n", + "eigenvalues = eig(-transitions.matrix)[0]\n", + "\n", + "def C_i10(i): \n", + " from numpy import zeros\n", + " result = zeros((transitions.nopen, transitions.nopen), dtype='float64')\n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result += np.dot(exact.D_af(i), exact.recursion_af(j, 0, 0)) / (eigenvalues[j] - eigenvalues[i])\n", + " result -= np.dot(exact.D_af(j), exact.recursion_af(i, 0, 0)) / (eigenvalues[i] - eigenvalues[j])\n", + " return result\n", + " \n", + "def C_i20(i): \n", + " from numpy import zeros\n", + " result = zeros((transitions.nopen, transitions.nopen), dtype='float64')\n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result += ( np.dot(exact.D_af(i), exact.recursion_af(j, 1, 0)) \n", + " + np.dot(exact.D_af(j), exact.recursion_af(i, 1, 0)) ) / (eigenvalues[j] - eigenvalues[i])\n", + " result += ( np.dot(exact.D_af(i), exact.recursion_af(j, 1, 1)) \n", + " - np.dot(exact.D_af(j), exact.recursion_af(i, 1, 1)) ) / (eigenvalues[j] - eigenvalues[i])**2\n", + " return result\n", + "\n", + "def C_i21(i): \n", + " result = np.dot(exact.D_af(i), exact.recursion_af(i, 1, 0)) \n", + " for j in range(transitions.matrix.shape[0]):\n", + " if i == j: continue\n", + " result -= np.dot(exact.D_af(j), exact.recursion_af(i, 1, 1)) / (eigenvalues[i] - eigenvalues[j])\n", + " return result\n", + "\n", + "def C_i22(i): return np.dot(exact.D_af(i), exact.recursion_af(i, 1, 1)) * 0.5 \n", + "\n", + "print(np.all([np.all(abs(C_i10(i) - exact.recursion_af(i, 1, 0)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i20(i) - exact.recursion_af(i, 2, 0)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i21(i) - exact.recursion_af(i, 2, 1)) < 1e-8) for i in range(5)]))\n", + "print(np.all([np.all(abs(C_i22(i) - exact.recursion_af(i, 2, 2)) < 1e-8) for i in range(5)]))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try and compare exact and approx via plot. The following is for $^{A}R(t)$ and $^{F}R(t)$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEaCAYAAABEsMO+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tau, i, j, n = 1e-4, 0, 0, 4\n", + "\n", + "fig, ax = plt.subplots(2, 1)\n", + "\n", + "transitions = qmatrix\n", + "exact = ExactSurvivor(transitions, tau)\n", + "equation = DeterminantEq(transitions, tau)\n", + "roots = find_roots(equation)\n", + "approx = Asymptotes(equation, roots)\n", + "\n", + "x = np.arange(0, n * tau, tau / 10.)\n", + "ax[0].plot(x, exact.af(x)[:, i, j], label=\"exact\")\n", + "ax[0].plot(x, approx(x)[:, i, j], '.', label=\"approx\")\n", + "ax[0].set_title(\"Component ${0}$ of the matrix $^{{A}}R(t)$.\".format((i, j)))\n", + "ax[0].legend()\n", + "\n", + "roots = find_roots(equation.transpose())\n", + "approx = Asymptotes(equation.transpose(), roots)\n", + "\n", + "i, j = 1, 0\n", + "x = np.arange(0, n*tau, tau / 10.)\n", + "ax[1].plot(x, exact.fa(x)[:, i, j], label=\"exact\")\n", + "ax[1].plot(x, approx(x)[:, i, j], '.', label=\"approx\")\n", + "ax[1].set_title(\"Component ${0}$ of the matrix $^{{F}}R(t)$.\".format((i, j)))\n", + "ax[1].legend(loc=0)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/exploration/CB.ipynb b/exploration/CB.ipynb index 11b20bc..ebdaaa4 100644 --- a/exploration/CB.ipynb +++ b/exploration/CB.ipynb @@ -45,7 +45,7 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood import QMatrix\n", + "from HJCFIT.likelihood import QMatrix\n", "\n", "tau = 0.2\n", "qmatrix = QMatrix([ [-2, 1, 1, 0], \n", @@ -69,11 +69,11 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood._methods import exponential_pdfs\n", + "from HJCFIT.likelihood._methods import exponential_pdfs\n", "\n", "def plot_exponentials(qmatrix, tau, x0=None, x=None, ax=None, nmax=2, shut=False):\n", - " from dcprogs.likelihood import missed_events_pdf\n", - " from dcprogs.likelihood._methods import exponential_pdfs\n", + " from HJCFIT.likelihood import missed_events_pdf\n", + " from HJCFIT.likelihood._methods import exponential_pdfs\n", " if ax is None: \n", " fig, ax = plt.subplots(1,1)\n", " if x is None: \n", @@ -111,9 +111,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAKDCAYAAADsJhDzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nicD/A098/dPCR4eWAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAKDCAYAAADsJhDzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -121,7 +121,7 @@ } ], "source": [ - "from dcprogs.likelihood import missed_events_pdf\n", + "from HJCFIT.likelihood import missed_events_pdf\n", "\n", "fig, ax = plt.subplots(2,2, figsize=(12,9))\n", "x = np.arange(0, 10, tau/100)\n", @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -174,28 +174,35 @@ } ], "source": [ - "from dcprogs.likelihood import DeterminantEq, find_root_intervals, find_lower_bound_for_roots\n", + "from HJCFIT.likelihood import DeterminantEq, find_root_intervals, find_lower_bound_for_roots\n", "from numpy.linalg import eig\n", "tau = 0.5\n", "determinant = DeterminantEq(qmatrix, tau).transpose()\n", "x = np.arange(-100, -3, 0.1)\n", "\n", "matrix = qmatrix.transpose()\n", - "qaffa = np.array(np.dot(matrix.af, matrix.fa), dtype=np.float128)\n", - "aa = np.array(matrix.aa, dtype=np.float128)\n", + "#qaffa = np.array(np.dot(matrix.af, matrix.fa), dtype=np.float128)\n", + "qaffa = np.array(np.dot(matrix.af, matrix.fa), dtype=np.longdouble)\n", + "#aa = np.array(matrix.aa, dtype=np.float128)\n", + "aa = np.array(matrix.aa, dtype=np.longdouble)\n", "\n", "def anaH(s):\n", " from numpy.linalg import det \n", " from numpy import identity, exp\n", - " arg0 = 1e0/np.array(-2-s, dtype=np.float128)\n", - " arg1 = np.array(-(2+s) * tau, dtype=np.float128)\n", - " return qaffa * (exp(arg1) - np.array(1e0, dtype=np.float128)) * arg0 + aa\n", + " #arg0 = 1e0/np.array(-2-s, dtype=np.float128)\n", + " #arg1 = np.array(-(2+s) * tau, dtype=np.float128)\n", + " #return qaffa * (exp(arg1) - np.array(1e0, dtype=np.float128)) * arg0 + aa\n", + " arg0 = 1e0/np.array(-2-s, dtype=np.longdouble)\n", + " arg1 = np.array(-(2+s) * tau, dtype=np.longdouble)\n", + " return qaffa * (exp(arg1) - np.array(1e0, dtype=np.longdouble)) * arg0 + aa\n", "\n", "def anadet(s):\n", " from numpy.linalg import det \n", " from numpy import identity, exp\n", - " s = np.array(s, dtype=np.float128)\n", - " matrix = s*identity(qaffa.shape[0], dtype=np.float128) - anaH(s)\n", + " #s = np.array(s, dtype=np.float128)\n", + " #matrix = s*identity(qaffa.shape[0], dtype=np.float128) - anaH(s)\n", + " s = np.array(s, dtype=np.longdouble)\n", + " matrix = s*identity(qaffa.shape[0], dtype=np.longdouble) - anaH(s)\n", " return matrix[0,0] * matrix[1, 1] * matrix[2, 2] \\\n", " + matrix[1,0] * matrix[2, 1] * matrix[0, 2] \\\n", " + matrix[0,1] * matrix[1, 2] * matrix[2, 0] \\\n", @@ -214,10 +221,11 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [Root]", "language": "python", - "name": "python3" + "name": "Python [Root]" }, "language_info": { "codemirror_mode": { @@ -229,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/exploration/CH82 -- optimization.ipynb b/exploration/CH82 -- optimization.ipynb index eebea47..fba3a74 100644 --- a/exploration/CH82 -- optimization.ipynb +++ b/exploration/CH82 -- optimization.ipynb @@ -22,8 +22,8 @@ }, "outputs": [], "source": [ - "from dcprogs import read_idealized_bursts\n", - "from dcprogs.likelihood import QMatrix\n", + "from HJCFIT import read_idealized_bursts\n", + "from HJCFIT.likelihood import QMatrix\n", "\n", "name = \"CH82.scn\"\n", "tau = 1e-4\n", @@ -61,9 +61,9 @@ "from scipy.optimize import minimize\n", "from numpy import NaN, zeros, arange\n", "import numpy as np\n", - "from dcprogs.likelihood.random import qmatrix as random_qmatrix\n", - "from dcprogs.likelihood import QMatrix, Log10Likelihood\n", - "from dcprogs.likelihood.optimization import reduce_likelihood\n", + "from HJCFIT.likelihood.random import qmatrix as random_qmatrix\n", + "from HJCFIT.likelihood import QMatrix, Log10Likelihood\n", + "from HJCFIT.likelihood.optimization import reduce_likelihood\n", "\n", "likelihood = Log10Likelihood(bursts, nopen, tau, tcrit)\n", "reduced = reduce_likelihood(likelihood, graph)\n", @@ -87,7 +87,7 @@ " \n", "def random_starting_point():\n", " from numpy import infty, NaN\n", - " from dcprogs.likelihood.random import rate_matrix as random_rate_matrix\n", + " from HJCFIT.likelihood.random import rate_matrix as random_rate_matrix\n", " \n", " \n", " for i in range(100):\n", @@ -126,73 +126,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "-12.425436251427712 [[ -6.44069267e+03 5.74181654e-01 6.44011849e+03 0.00000000e+00\n", + "-640.830069172272 [[ -6.21619693e-01 3.80010323e-01 2.41609369e-01 0.00000000e+00\n", " 0.00000000e+00]\n", - " [ 1.16073652e+03 -1.16142765e+03 0.00000000e+00 6.91127353e-01\n", + " [ 3.10903942e+03 -3.10919359e+03 0.00000000e+00 1.54171557e-01\n", " 0.00000000e+00]\n", - " [ 3.22274403e-01 0.00000000e+00 -2.90372168e+03 2.90281124e+03\n", - " 5.88161958e-01]\n", - " [ 0.00000000e+00 3.41367880e-01 7.55204456e-01 -1.09657234e+00\n", + " [ 1.59277846e-01 0.00000000e+00 -9.08913654e+03 9.08867581e+03\n", + " 3.01453712e-01]\n", + " [ 0.00000000e+00 4.39644066e-01 2.78736992e-01 -7.18381058e-01\n", " 0.00000000e+00]\n", - " [ 0.00000000e+00 0.00000000e+00 5.08084841e-01 0.00000000e+00\n", - " -5.08084841e-01]]\n", - "x= [ 5.74181654e-01 6.44011849e+03 1.16073652e+03 6.91127353e-01\n", - " 3.22274403e-01 2.90281124e+03 5.88161958e-01 3.41367880e-01\n", - " 7.55204456e-01 5.08084841e-01]\n", - " fun: -1274.8592956677317\n", - " maxcv: 8.3266726846928772e-17\n", + " [ 0.00000000e+00 0.00000000e+00 1.54808746e-01 0.00000000e+00\n", + " -1.54808746e-01]]\n", + "x= [ 3.80010323e-01 2.41609369e-01 3.10903942e+03 1.54171557e-01\n", + " 1.59277846e-01 9.08867581e+03 3.01453712e-01 4.39644066e-01\n", + " 2.78736992e-01 1.54808746e-01]\n", + " fun: -2062.8258070089187\n", + " maxcv: 8.7670065147940707e-16\n", " message: 'Maximum number of function evaluations has been exceeded.'\n", " nfev: 1000\n", " status: 2\n", " success: False\n", - " x: array([ 7.94011045e+01, 6.43080806e+03, 7.21170754e+02,\n", - " -8.32667268e-17, 3.24655178e+01, 2.91303530e+03,\n", - " 3.95923541e+00, 1.26710451e+02, 7.33402048e+00,\n", - " 2.84881446e+00])\n", - "-82.59000956544635 [[ -9.51866450e-01 8.38697463e-02 8.67996704e-01 0.00000000e+00\n", + " x: array([ -8.76700651e-16, 1.75097884e+02, 3.11563723e+03,\n", + " 2.68478978e+02, 6.06596893e+02, 9.06857871e+03,\n", + " 1.73472348e-18, 1.77190965e+01, -3.03804457e-16,\n", + " 7.92641819e-01])\n", + "-697.0699667052597 [[ -2.18554574e-01 8.96065006e-02 1.28948074e-01 0.00000000e+00\n", " 0.00000000e+00]\n", - " [ 4.62211254e+03 -4.62275606e+03 0.00000000e+00 6.43518503e-01\n", + " [ 6.92249289e+02 -6.92781143e+02 0.00000000e+00 5.31853771e-01\n", " 0.00000000e+00]\n", - " [ 7.83410975e-01 0.00000000e+00 -1.32656283e+00 2.17272465e-01\n", - " 3.25879386e-01]\n", - " [ 0.00000000e+00 9.06343131e-01 3.58030477e-01 -1.26437361e+00\n", + " [ 8.75734586e-01 0.00000000e+00 -1.91477903e+00 6.89175466e-01\n", + " 3.49868979e-01]\n", + " [ 0.00000000e+00 5.54235637e-01 4.39234514e-02 -5.98159089e-01\n", " 0.00000000e+00]\n", - " [ 0.00000000e+00 0.00000000e+00 1.12428009e-01 0.00000000e+00\n", - " -1.12428009e-01]]\n", + " [ 0.00000000e+00 0.00000000e+00 9.79442657e+02 0.00000000e+00\n", + " -9.79442657e+02]]\n", "Inequality constraints incompatible (Exit mode 4)\n", - " Current function value: -1932.1916761250732\n", - " Iterations: 25\n", - " Function evaluations: 314\n", - " Gradient evaluations: 25\n", - " fun: -1932.1916761250732\n", - " jac: array([ -7.79186472e+07, -4.69091861e+07, -4.69091859e+07,\n", - " -4.69091861e+07, -9.37225744e+07, -1.72530690e+08,\n", - " -8.62568783e+07, -9.37225740e+07, -6.50024414e-02,\n", - " -9.06735762e+07, 0.00000000e+00])\n", + " Current function value: -2284.629372492554\n", + " Iterations: 177\n", + " Function evaluations: 2189\n", + " Gradient evaluations: 177\n", + " fun: -2284.629372492554\n", + " jac: array([ -2.08709717e-01, -5.42224910e+08, -2.52990723e-02,\n", + " 2.75032878e+05, 1.75594303e+09, -1.57243136e+09,\n", + " -5.42756597e+08, -2.75028975e+05, 5.62684071e+08,\n", + " -6.88560304e+07, 0.00000000e+00])\n", " message: 'Inequality constraints incompatible'\n", - " nfev: 314\n", - " nit: 25\n", - " njev: 25\n", + " nfev: 2189\n", + " nit: 177\n", + " njev: 177\n", " status: 4\n", " success: False\n", - " x: array([ 2.78696256e+02, 6.43421935e+03, -3.98123191e-14,\n", - " 1.79885994e+02, 3.33000335e+02, 2.92612560e+03,\n", - " 1.58470668e+01, 2.08011337e+02, 3.92637131e+01,\n", - " -1.11022302e-16])\n", - "20.291820543659554 [[ -1.00114729e+00 3.80618205e-02 9.63085473e-01 0.00000000e+00\n", + " x: array([ 4.64593912e-07, 3.41051917e+02, 2.65544934e+03,\n", + " 1.38876283e+03, 9.99999918e+03, 4.32621889e+03,\n", + " 2.19612726e+02, 2.99939102e+00, 2.05470191e+00,\n", + " -1.68337691e-14])\n", + "-447.89702673666727 [[ -2.96248722e+03 2.73428480e-01 2.96221379e+03 0.00000000e+00\n", " 0.00000000e+00]\n", - " [ 5.68001382e-02 -6.93721904e-01 0.00000000e+00 6.36921766e-01\n", + " [ 9.99336618e-01 -1.00764635e+00 0.00000000e+00 8.30973004e-03\n", " 0.00000000e+00]\n", - " [ 3.91225175e+03 0.00000000e+00 -3.91254095e+03 2.23487236e-01\n", - " 6.57170036e-02]\n", - " [ 0.00000000e+00 8.36963851e-01 4.68661911e-01 -1.30562576e+00\n", + " [ 5.19874297e-03 0.00000000e+00 -5.01074858e+03 3.97100105e-01\n", + " 5.01034628e+03]\n", + " [ 0.00000000e+00 8.74501090e-01 2.32778358e+03 -2.32865808e+03\n", " 0.00000000e+00]\n", - " [ 0.00000000e+00 0.00000000e+00 7.98806965e-01 0.00000000e+00\n", - " -7.98806965e-01]]\n", - "[ 5.75020352e-01 6.44012717e+03 1.20695765e+03 6.97562538e-01\n", - " 3.30108513e-01 2.90281341e+03 5.91420752e-01 3.50431311e-01\n", - " 7.58784761e-01 5.09209121e-01]\n", - "-1932.1916761250732\n" + " [ 0.00000000e+00 0.00000000e+00 6.91322340e-03 0.00000000e+00\n", + " -6.91322340e-03]]\n", + "[ 3.80906388e-01 2.42898850e-01 3.11596192e+03 1.59490095e-01\n", + " 1.68035192e-01 9.08868270e+03 3.04952401e-01 4.45186422e-01\n", + " 2.79176226e-01 9.94923531e+00]\n", + "-2284.629372492554\n" ] } ], @@ -221,10 +221,11 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [Root]", "language": "python", - "name": "python3" + "name": "Python [Root]" }, "language_info": { "codemirror_mode": { @@ -236,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/exploration/CH82.ipynb b/exploration/CH82.ipynb index 042b93d..fa6c791 100644 --- a/exploration/CH82.ipynb +++ b/exploration/CH82.ipynb @@ -46,7 +46,7 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood import QMatrix\n", + "from HJCFIT.likelihood import QMatrix\n", "\n", "tau = 1e-4\n", "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", @@ -74,9 +74,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFiCAYAAAAN91qTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmYJ5kRuZmaWYE7kZmZmCfZ/CfQlfjMe4ZcAAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFjCAYAAAAtnDI1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJC4iIpJS/x8A+luC+6wRZwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -84,7 +84,7 @@ } ], "source": [ - "from dcprogs.likelihood import plot_roots, DeterminantEq\n", + "from HJCFIT.likelihood import plot_roots, DeterminantEq\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(7,5))\n", "\n", @@ -125,7 +125,7 @@ } ], "source": [ - "from dcprogs.likelihood import ApproxSurvivor\n", + "from HJCFIT.likelihood import ApproxSurvivor\n", "approx = ApproxSurvivor(qmatrix, tau)\n", "components = approx.af_components\n", "print(components[:1])" @@ -140,18 +140,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from dcprogs.likelihood import MissedEventsG\n", + "from HJCFIT.likelihood import MissedEventsG\n", "\n", "weight, root = components[1]\n", "eG = MissedEventsG(qmatrix, tau)\n", "# Note: the sum below is equivalent to a scalar product with u_F\n", - "coefficient = sum(np.dot(eG.initial_occupancies, np.dot(weight, eG.af_factor)))\n", + "coefficient = sum(np.dot(eG.initial_vectors, np.dot(weight, eG.af_factor)))\n", "pdf = lambda t: coefficient * exp((t)*root) " ] }, @@ -167,16 +167,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFiCAYAAADiNTGtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFjCAYAAAApaeIIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnHfmzjnnXI7zztw555zLcf8FHh1/CxxzJ84AAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -184,10 +184,10 @@ } ], "source": [ - "from dcprogs.likelihood._methods import exponential_pdfs\n", + "from HJCFIT.likelihood._methods import exponential_pdfs\n", "\n", "def plot_exponentials(qmatrix, tau, x=None, ax=None, nmax=2, shut=False):\n", - " from dcprogs.likelihood import missed_events_pdf\n", + " from HJCFIT.likelihood import missed_events_pdf\n", " if ax is None:\n", " fig, ax = plt.subplots(1,1)\n", " if x is None: x = np.arange(0, 5*tau, tau/10)\n", @@ -225,16 +225,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFjCAYAAAApaeIIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLiIiUuMU5iIiIjXu/wHM8YUppizzCwAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFjCAYAAAApaeIIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -267,12 +267,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 4.33877934e-12 -4.33864056e-12]]\n" + "[[ 4.33875158e-12 -4.33864056e-12]]\n" ] } ], "source": [ - "from dcprogs.likelihood import QMatrix, MissedEventsG\n", + "from HJCFIT.likelihood import QMatrix, MissedEventsG\n", "\n", "tau = 1e-4\n", "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", @@ -284,15 +284,25 @@ "meG = MissedEventsG(qmatrix, tau)\n", "t = 3.5* tau\n", "\n", - "print(eG.initial_CHS_occupancies(t) - meG.initial_CHS_occupancies(t))" + "print(eG.initial_CHS_vectors(t) - meG.initial_CHS_vectors(t))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [Root]", "language": "python", - "name": "python3" + "name": "Python [Root]" }, "language_info": { "codemirror_mode": { @@ -304,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/exploration/CHSvectors.ipynb b/exploration/CHSvectors.ipynb index 0d745ae..fc1dcfc 100644 --- a/exploration/CHSvectors.ipynb +++ b/exploration/CHSvectors.ipynb @@ -45,7 +45,7 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood import QMatrix\n", + "from HJCFIT.likelihood import QMatrix\n", "\n", "tau = 1e-4\n", "qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ], \n", @@ -65,17 +65,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from dcprogs.likelihood import MissedEventsG\n", + "from HJCFIT.likelihood import MissedEventsG\n", "\n", "eG = MissedEventsG(qmatrix, tau)\n", - "assert np.all(abs(eG.initial_CHS_occupancies(4e-3) - [0.220418, 0.779582]) < 1e-5)\n", - "assert np.all(abs(eG.final_CHS_occupancies(4e-3) - [0.974852, 0.21346, 0.999179]) < 1e-5)\n", + "assert np.all(abs(eG.initial_CHS_vectors(4e-3) - [0.220418, 0.779582]) < 1e-5)\n", + "assert np.all(abs(eG.final_CHS_vectors(4e-3) - [0.974852, 0.21346, 0.999179]) < 1e-5)\n", "np.set_printoptions(precision=15)" ] }, @@ -88,9 +88,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAEZCAYAAADG0WEtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntZZCFRERtfYHTj9xf6XL16kAAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAEbCAYAAABk26sYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAzyt57GjpII2JMVAHuyAmAv9IUb1M3P1EkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -102,11 +102,11 @@ "\n", "x = np.arange(0, 5*tau, tau/10)\n", "\n", - "ax[0].plot(x*1e3, [eG.initial_CHS_occupancies(u)[0] for u in x])\n", + "ax[0].plot(x*1e3, [eG.initial_CHS_vectors(u)[0] for u in x])\n", "ax[0].set_xlabel('$t_{\\mathrm{crit}}$ (ms)')\n", "ax[0].set_ylabel('Components of the initial CHS vector $\\phi_A$')\n", "\n", - "ax[1].plot(x*1e3, [eG.final_CHS_occupancies(u)[0] for u in x])\n", + "ax[1].plot(x*1e3, [eG.final_CHS_vectors(u)[0] for u in x])\n", "ax[1].set_xlabel('$t_{\\mathrm{crit}}$ (ms)')\n", "ax[1].set_ylabel('Components of the final CHS vector $e_F$')\n", "ax[1].yaxis.tick_right()\n", @@ -127,7 +127,7 @@ "output_type": "stream", "text": [ "[[ 0.17394315362718 0.82605684637282]]\n", - "[ 0.976491211386196 0.222305380522348 0.999257244552635]\n" + "[ 0.976491211386195 0.222305380522348 0.999257244552635]\n" ] } ], @@ -139,8 +139,8 @@ " [ 0, 0, 10, 0, -10 ] ], 2)\n", "qmatrix.matrix /= 1e3\n", "eG = MissedEventsG(qmatrix, 0.2)\n", - "print(eG.initial_CHS_occupancies(4))\n", - "print(eG.final_CHS_occupancies(4))" + "print(eG.initial_CHS_vectors(4))\n", + "print(eG.final_CHS_vectors(4))" ] }, { @@ -162,8 +162,8 @@ "source": [ "qmatrix = QMatrix([[-1, 1, 0], [19, -29, 10], [0, 0.026, -0.026]], 1)\n", "eG = MissedEventsG(qmatrix, 0.2)\n", - "print(eG.initial_CHS_occupancies(0.2))\n", - "print(eG.final_CHS_occupancies(4))" + "print(eG.initial_CHS_vectors(0.2))\n", + "print(eG.final_CHS_vectors(4))" ] }, { @@ -188,16 +188,26 @@ " [50, 0, -50, 0],\n", " [ 0, 5.6, 0, -5.6]], 1)\n", "eG = MissedEventsG(qmatrix, 0.2)\n", - "print(eG.initial_CHS_occupancies(4))\n", - "print(eG.final_CHS_occupancies(4))" + "print(eG.initial_CHS_vectors(4))\n", + "print(eG.final_CHS_vectors(4))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [Root]", "language": "python", - "name": "python3" + "name": "Python [Root]" }, "language_info": { "codemirror_mode": { @@ -209,7 +219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/exploration/CKS.ipynb b/exploration/CKS.ipynb index 8568be5..4d6bdd6 100644 --- a/exploration/CKS.ipynb +++ b/exploration/CKS.ipynb @@ -45,7 +45,7 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood import QMatrix\n", + "from HJCFIT.likelihood import QMatrix\n", "\n", "tau = 0.2\n", "qmatrix = QMatrix([[-1, 1, 0], [19, -29, 10], [0, 0.026, -0.026]], 1)" @@ -66,11 +66,11 @@ }, "outputs": [], "source": [ - "from dcprogs.likelihood._methods import exponential_pdfs\n", + "from HJCFIT.likelihood._methods import exponential_pdfs\n", "\n", "def plot_exponentials(qmatrix, tau, x0=None, x=None, ax=None, nmax=2, shut=False):\n", - " from dcprogs.likelihood import missed_events_pdf\n", - " from dcprogs.likelihood._methods import exponential_pdfs\n", + " from HJCFIT.likelihood import missed_events_pdf\n", + " from HJCFIT.likelihood._methods import exponential_pdfs\n", " if ax is None: \n", " fig,ax = plt.subplots(1, 1)\n", " if x is None: x = np.arange(0, 5*tau, tau/10)\n", @@ -105,9 +105,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1oAAALLCAYAAADg7/PMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAALLCAYAAAAPLZjyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -115,7 +115,7 @@ } ], "source": [ - "from dcprogs.likelihood import missed_events_pdf\n", + "from HJCFIT.likelihood import missed_events_pdf\n", "\n", "fig,ax = plt.subplots(2, 2, figsize=(12, 10 ))\n", "#ax = fig.add_subplot(2, 2, 1)\n", @@ -152,10 +152,11 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [Root]", "language": "python", - "name": "python3" + "name": "Python [Root]" }, "language_info": { "codemirror_mode": { @@ -167,7 +168,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/exploration/Distribution_histograms_and_individual_benchmark.ipynb b/exploration/Distribution_histograms_and_individual_benchmark.ipynb index d75c505..bd75385 100644 --- a/exploration/Distribution_histograms_and_individual_benchmark.ipynb +++ b/exploration/Distribution_histograms_and_individual_benchmark.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -74,7 +74,7 @@ "from dcpyps import dcio\n", "from dcpyps import dataset\n", "from dcpyps import mechanism\n", - "from dcprogs.likelihood import Log10Likelihood\n", + "from HJCFIT.likelihood import Log10Likelihood\n", "\n", "# LOAD DATA: Burzomato 2004 example set.\n", "scnfiles = [[\"./samples/glydemo/A-10.scn\"], \n", @@ -123,795 +123,16 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " this.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('