From 78e5ec8dc7df71226193adc513fe3bdaf605931b Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Sun, 10 Aug 2025 22:46:23 -0400 Subject: [PATCH 01/14] Add visualization library to CMake build --- CMakeLists.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index bc3111bd..33420c7c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -73,10 +73,15 @@ target_include_directories(utils PUBLIC ${TESSERACT_SRC_DIR}) target_compile_options(utils PRIVATE ${OPT_COPTS}) target_link_libraries(utils PUBLIC common libstim Threads::Threads) +add_library(visualization ${TESSERACT_SRC_DIR}/visualization.cc ${TESSERACT_SRC_DIR}/visualization.h) +target_include_directories(visualization PUBLIC ${TESSERACT_SRC_DIR}) +target_compile_options(visualization PRIVATE ${OPT_COPTS}) +target_link_libraries(visualization PUBLIC common boost_headers) + add_library(tesseract_lib ${TESSERACT_SRC_DIR}/tesseract.cc ${TESSERACT_SRC_DIR}/tesseract.h) target_include_directories(tesseract_lib PUBLIC ${TESSERACT_SRC_DIR}) target_compile_options(tesseract_lib PRIVATE ${OPT_COPTS}) -target_link_libraries(tesseract_lib PUBLIC utils boost_headers) +target_link_libraries(tesseract_lib PUBLIC utils boost_headers visualization) add_library(simplex ${TESSERACT_SRC_DIR}/simplex.cc ${TESSERACT_SRC_DIR}/simplex.h) target_include_directories(simplex PUBLIC ${TESSERACT_SRC_DIR}) From 5c211bc872714ed03b2333c16d6c49a153c39d24 Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Wed, 13 Aug 2025 11:27:13 -0400 Subject: [PATCH 02/14] Fix CMake Python module placement and add agent instructions --- .gitignore | 3 +++ AGENTS.md | 5 +++++ CMakeLists.txt | 7 +++++++ 3 files changed, 15 insertions(+) create mode 100644 AGENTS.md diff --git a/.gitignore b/.gitignore index 5b5d20b4..66d8f700 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,6 @@ eclipse-*bin/ /.sass-cache # User-specific .bazelrc user.bazelrc + +# Ignore python extension module produced by CMake. +src/tesseract_decoder*.so diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 00000000..ea68e20e --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,5 @@ +# Agent Instructions + +- Use the **CMake** build system when interacting with this repository. Humans use Bazel. +- A bug in some LLM coding environments makes Bazel difficult to use, so agents should rely on CMake. +- Keep both the CMake and Bazel builds working at all times. diff --git a/CMakeLists.txt b/CMakeLists.txt index 33420c7c..10e5ea69 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -103,4 +103,11 @@ pybind11_add_module(tesseract_decoder MODULE ${TESSERACT_SRC_DIR}/tesseract.pybi target_compile_options(tesseract_decoder PRIVATE ${OPT_COPTS}) target_include_directories(tesseract_decoder PRIVATE ${TESSERACT_SRC_DIR}) target_link_libraries(tesseract_decoder PRIVATE common utils simplex tesseract_lib) +set_target_properties(tesseract_decoder PROPERTIES + LIBRARY_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/src + LIBRARY_OUTPUT_DIRECTORY_DEBUG ${PROJECT_SOURCE_DIR}/src + LIBRARY_OUTPUT_DIRECTORY_RELEASE ${PROJECT_SOURCE_DIR}/src + LIBRARY_OUTPUT_DIRECTORY_MINSIZEREL ${PROJECT_SOURCE_DIR}/src + LIBRARY_OUTPUT_DIRECTORY_RELWITHDEBINFO ${PROJECT_SOURCE_DIR}/src +) From 7c10268eef32c22ce9189b195b84e83dc87e69ef Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Wed, 20 Aug 2025 15:09:33 -0700 Subject: [PATCH 03/14] Allow decode_to_errors to accept bitstring --- README.md | 6 +-- src/py/README.md | 25 +++++------ src/py/shared_decoding_tests.py | 6 +-- src/py/simplex_test.py | 3 +- src/py/tesseract_test.py | 7 +++- src/simplex.pybind.h | 34 ++++++++++++--- src/tesseract.pybind.h | 73 ++++++++++++++++++++++++++------- 7 files changed, 113 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 30230f7f..2e19e436 100644 --- a/README.md +++ b/README.md @@ -190,15 +190,15 @@ config = tesseract.TesseractConfig(dem=dem, det_beam=50) # 3. Create a decoder instance decoder = config.compile_decoder() -# 4. Simulate detection events -syndrome = [0, 1, 1] +# 4. Simulate detector outcomes +syndrome = np.array([0, 1, 1], dtype=bool) # 5a. Decode to observables flipped_observables = decoder.decode(syndrome) print(f"Flipped observables: {flipped_observables}") # 5b. Alternatively, decode to errors -decoder.decode_to_errors(np.where(syndrome)[0]) +decoder.decode_to_errors(syndrome) predicted_errors = decoder.predicted_errors_buffer # Indices of predicted errors print(f"Predicted errors indices: {predicted_errors}") diff --git a/src/py/README.md b/src/py/README.md index 79c0c396..625e41bd 100644 --- a/src/py/README.md +++ b/src/py/README.md @@ -64,28 +64,28 @@ print(f"Custom configuration detection penalty: {config2.det_beam}") #### Class `tesseract.TesseractDecoder` This is the main class that implements the Tesseract decoding logic. * `TesseractDecoder(config: tesseract.TesseractConfig)` -* `decode_to_errors(detections: list[int])` -* `decode_to_errors(detections: list[int], det_order: int, det_beam: int)` +* `decode_to_errors(syndrome: np.ndarray)` +* `decode_to_errors(syndrome: np.ndarray, det_order: int, det_beam: int)` * `get_observables_from_errors(predicted_errors: list[int]) -> list[bool]` * `cost_from_errors(predicted_errors: list[int]) -> float` -* `decode(detections: list[int]) -> list[bool]` +* `decode(syndrome: np.ndarray) -> np.ndarray` Explanation of each method: -#### `decode_to_errors(detections: list[int])` +#### `decode_to_errors(syndrome: np.ndarray)` Decodes a single measurement shot to predict a list of errors. -* **Parameters:** `detections` is a list of integers that represent the indices of the detectors that have fired in a single shot. +* **Parameters:** `syndrome` is a 1D NumPy array of booleans representing the detector outcomes for a single shot. * **Returns:** A list of integers, where each integer is the index of a predicted error. -#### `decode_to_errors(detections: list[int], det_order: int, det_beam: int)` +#### `decode_to_errors(syndrome: np.ndarray, det_order: int, det_beam: int)` An overloaded version of the `decode_to_errors` method that allows for a different decoding strategy. * **Parameters:** - * `detections` is a list of integers representing the indices of the fired detectors. + * `syndrome` is a 1D NumPy array of booleans representing the detector outcomes for a single shot. * `det_order` is an integer that specifies a different ordering of detectors to use for the decoding. @@ -219,10 +219,10 @@ print(f"Configuration verbose enabled: {config.verbose}") This is the main class for performing decoding using the Simplex algorithm. * `SimplexDecoder(config: simplex.SimplexConfig)` * `init_ilp()` -* `decode_to_errors(detections: list[int])` +* `decode_to_errors(syndrome: np.ndarray)` * `get_observables_from_errors(predicted_errors: list[int]) -> list[bool]` * `cost_from_errors(predicted_errors: list[int]) -> float` -* `decode(detections: list[int]) -> list[bool]` +* `decode(syndrome: np.ndarray) -> np.ndarray` **Example Usage**: @@ -230,6 +230,7 @@ This is the main class for performing decoding using the Simplex algorithm. import tesseract_decoder.simplex as simplex import stim import tesseract_decoder.common as common +import numpy as np # Create a DEM and a configuration dem = stim.DetectorErrorModel(""" @@ -245,9 +246,9 @@ decoder = simplex.SimplexDecoder(config) decoder.init_ilp() # Decode a shot where detector D1 fired -detections = [1] -flipped_observables = decoder.decode(detections) -print(f"Flipped observables for detections {detections}: {flipped_observables}") +syndrome = np.array([0, 1], dtype=bool) +flipped_observables = decoder.decode(syndrome) +print(f"Flipped observables for syndrome {syndrome.tolist()}: {flipped_observables}") # Access predicted errors predicted_error_indices = decoder.predicted_errors_buffer diff --git a/src/py/shared_decoding_tests.py b/src/py/shared_decoding_tests.py index 4500b141..82259d4b 100644 --- a/src/py/shared_decoding_tests.py +++ b/src/py/shared_decoding_tests.py @@ -302,16 +302,16 @@ def shared_test_merge_errors_affects_cost(decoder_class, config_class): error(0.01) D0 """ ) - detections = [0] + syndrome = np.array([True], dtype=bool) config_no_merge = config_class(dem, merge_errors=False) decoder_no_merge = decoder_class(config_no_merge) - predicted_errors_no_merge = decoder_no_merge.decode_to_errors(detections) + predicted_errors_no_merge = decoder_no_merge.decode_to_errors(syndrome) cost_no_merge = decoder_no_merge.cost_from_errors(decoder_no_merge.predicted_errors_buffer) config_merge = config_class(dem, merge_errors=True) decoder_merge = decoder_class(config_merge) - predicted_errors_merge = decoder_merge.decode_to_errors(detections) + predicted_errors_merge = decoder_merge.decode_to_errors(syndrome) cost_merge = decoder_merge.cost_from_errors(decoder_merge.predicted_errors_buffer) p_merged = 0.1 * (1 - 0.01) + 0.01 * (1 - 0.1) diff --git a/src/py/simplex_test.py b/src/py/simplex_test.py index 3a228d9c..752f9e8f 100644 --- a/src/py/simplex_test.py +++ b/src/py/simplex_test.py @@ -13,6 +13,7 @@ # limitations under the License. import pytest +import numpy as np import stim from src import tesseract_decoder @@ -56,7 +57,7 @@ def test_create_simplex_decoder(): decoder = tesseract_decoder.simplex.SimplexDecoder( tesseract_decoder.simplex.SimplexConfig(_DETECTOR_ERROR_MODEL, window_length=5) ) - decoder.decode_to_errors([1]) + decoder.decode_to_errors(np.array([False, True], dtype=bool)) assert decoder.get_observables_from_errors([1]) == [] assert decoder.cost_from_errors([2]) == pytest.approx(1.0986123) diff --git a/src/py/tesseract_test.py b/src/py/tesseract_test.py index 5df3e329..b7b21835 100644 --- a/src/py/tesseract_test.py +++ b/src/py/tesseract_test.py @@ -13,6 +13,7 @@ # limitations under the License. import pytest +import numpy as np import stim from src import tesseract_decoder @@ -60,8 +61,10 @@ def test_create_tesseract_config(): def test_create_tesseract_decoder(): config = tesseract_decoder.tesseract.TesseractConfig(_DETECTOR_ERROR_MODEL) decoder = tesseract_decoder.tesseract.TesseractDecoder(config) - decoder.decode_to_errors([0]) - decoder.decode_to_errors(detections=[0], det_order=0, det_beam=0) + decoder.decode_to_errors(np.array([True, False], dtype=bool)) + decoder.decode_to_errors( + syndrome=np.array([True, False], dtype=bool), det_order=0, det_beam=0 + ) assert decoder.get_observables_from_errors([1]) == [] assert decoder.cost_from_errors([1]) == pytest.approx(0.5108256237659907) diff --git a/src/simplex.pybind.h b/src/simplex.pybind.h index 79c8d59d..9c0e6b96 100644 --- a/src/simplex.pybind.h +++ b/src/simplex.pybind.h @@ -20,6 +20,7 @@ #include #include #include +#include #include "common.h" #include "simplex.h" @@ -140,20 +141,43 @@ void add_simplex_module(py::module& root) { This method must be called before decoding. )pbdoc") - .def("decode_to_errors", &SimplexDecoder::decode_to_errors, py::arg("detections"), - py::call_guard(), R"pbdoc( + .def( + "decode_to_errors", + [](SimplexDecoder& self, const py::array_t& syndrome) { + if ((size_t)syndrome.size() != self.num_detectors) { + std::ostringstream msg; + msg << "Syndrome array size (" << syndrome.size() + << ") does not match the number of detectors in the decoder (" + << self.num_detectors << ")."; + throw std::invalid_argument(msg.str()); + } + + std::vector detections; + auto syndrome_unchecked = syndrome.unchecked<1>(); + for (size_t i = 0; i < (size_t)syndrome_unchecked.size(); ++i) { + if (syndrome_unchecked(i)) { + detections.push_back(i); + } + } + self.decode_to_errors(detections); + return self.predicted_errors_buffer; + }, + py::arg("syndrome"), + py::call_guard(), + R"pbdoc( Decodes a single shot to a list of error indices. Parameters ---------- - detections : list[int] - A list of indices of the detectors that have fired. + syndrome : np.ndarray + A 1D NumPy array of booleans representing the detector outcomes for a single shot. + The length of the array should match the number of detectors in the DEM. Returns ------- list[int] A list of predicted error indices. - )pbdoc") + )pbdoc") .def( "get_observables_from_errors", [](SimplexDecoder& self, const std::vector& predicted_errors) { diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 267aa115..9c09e108 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -20,6 +20,7 @@ #include #include #include +#include #include "stim_utils.pybind.h" #include "tesseract.h" @@ -170,33 +171,75 @@ void add_tesseract_module(py::module& root) { config : TesseractConfig The configuration object for the decoder. )pbdoc") - .def("decode_to_errors", - py::overload_cast&>(&TesseractDecoder::decode_to_errors), - py::arg("detections"), - py::call_guard(), R"pbdoc( + .def( + "decode_to_errors", + [](TesseractDecoder& self, const py::array_t& syndrome) { + if ((size_t)syndrome.size() != self.num_detectors) { + std::ostringstream msg; + msg << "Syndrome array size (" << syndrome.size() + << ") does not match the number of detectors in the decoder (" + << self.num_detectors << ")."; + throw std::invalid_argument(msg.str()); + } + + std::vector detections; + auto syndrome_unchecked = syndrome.unchecked<1>(); + for (size_t i = 0; i < (size_t)syndrome_unchecked.size(); ++i) { + if (syndrome_unchecked(i)) { + detections.push_back(i); + } + } + self.decode_to_errors(detections); + return self.predicted_errors_buffer; + }, + py::arg("syndrome"), + py::call_guard(), + R"pbdoc( Decodes a single shot to a list of error indices. Parameters ---------- - detections : list[int] - A list of indices of the detectors that have fired. + syndrome : np.ndarray + A 1D NumPy array of booleans representing the detector outcomes for a single shot. + The length of the array should match the number of detectors in the DEM. Returns ------- list[int] A list of predicted error indices. - )pbdoc") - .def("decode_to_errors", - py::overload_cast&, size_t, size_t>( - &TesseractDecoder::decode_to_errors), - py::arg("detections"), py::arg("det_order"), py::arg("det_beam"), - py::call_guard(), R"pbdoc( + )pbdoc") + .def( + "decode_to_errors", + [](TesseractDecoder& self, const py::array_t& syndrome, size_t det_order, + size_t det_beam) { + if ((size_t)syndrome.size() != self.num_detectors) { + std::ostringstream msg; + msg << "Syndrome array size (" << syndrome.size() + << ") does not match the number of detectors in the decoder (" + << self.num_detectors << ")."; + throw std::invalid_argument(msg.str()); + } + + std::vector detections; + auto syndrome_unchecked = syndrome.unchecked<1>(); + for (size_t i = 0; i < (size_t)syndrome_unchecked.size(); ++i) { + if (syndrome_unchecked(i)) { + detections.push_back(i); + } + } + self.decode_to_errors(detections, det_order, det_beam); + return self.predicted_errors_buffer; + }, + py::arg("syndrome"), py::arg("det_order"), py::arg("det_beam"), + py::call_guard(), + R"pbdoc( Decodes a single shot using a specific detector ordering and beam size. Parameters ---------- - detections : list[int] - A list of indices of the detectors that have fired. + syndrome : np.ndarray + A 1D NumPy array of booleans representing the detector outcomes for a single shot. + The length of the array should match the number of detectors in the DEM. det_order : int The index of the detector ordering to use. det_beam : int @@ -206,7 +249,7 @@ void add_tesseract_module(py::module& root) { ------- list[int] A list of predicted error indices. - )pbdoc") + )pbdoc") .def( "get_observables_from_errors", [](TesseractDecoder& self, const std::vector& predicted_errors) { From c34dd80aec9f031bec0dcc47e6486b481f62d7b2 Mon Sep 17 00:00:00 2001 From: noajshu Date: Wed, 20 Aug 2025 22:18:00 +0000 Subject: [PATCH 04/14] clang-format --- src/simplex.pybind.h | 1 + src/tesseract.pybind.h | 1 + 2 files changed, 2 insertions(+) diff --git a/src/simplex.pybind.h b/src/simplex.pybind.h index 9c0e6b96..78f9a9e5 100644 --- a/src/simplex.pybind.h +++ b/src/simplex.pybind.h @@ -20,6 +20,7 @@ #include #include #include + #include #include "common.h" diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 37c00ca8..32aa84d9 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -20,6 +20,7 @@ #include #include #include + #include #include "stim_utils.pybind.h" From 24c0d691dcd56e7e4aeea2341aac51e4fddbcfaa Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Wed, 20 Aug 2025 16:00:57 -0700 Subject: [PATCH 05/14] Update src/tesseract.pybind.h Co-authored-by: Noureldin --- src/tesseract.pybind.h | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 32aa84d9..efdd17d7 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -253,7 +253,10 @@ void add_tesseract_module(py::module& root) { msg << "Syndrome array size (" << syndrome.size() << ") does not match the number of detectors in the decoder (" << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "Syndrome array size (" + std:to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")." + throw std::invalid_argument(msg); } std::vector detections; From 96849b67693c5beceb913b8f7f521bafdce1dc01 Mon Sep 17 00:00:00 2001 From: noajshu Date: Wed, 20 Aug 2025 23:38:00 +0000 Subject: [PATCH 06/14] remove stringstream --- src/simplex.pybind.h | 9 ++++----- src/tesseract.pybind.h | 9 +++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/simplex.pybind.h b/src/simplex.pybind.h index 78f9a9e5..7297fab8 100644 --- a/src/simplex.pybind.h +++ b/src/simplex.pybind.h @@ -253,11 +253,10 @@ void add_simplex_module(py::module& root) { "decode", [](SimplexDecoder& self, const py::array_t& syndrome) { if ((size_t)syndrome.size() != self.num_detectors) { - std::ostringstream msg; - msg << "Syndrome array size (" << syndrome.size() - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "Syndrome array size (" + std::to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } std::vector detections; diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index efdd17d7..1d27adf2 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -253,10 +253,11 @@ void add_tesseract_module(py::module& root) { msg << "Syndrome array size (" << syndrome.size() << ") does not match the number of detectors in the decoder (" << self.num_detectors << ")."; - std::string msg = "Syndrome array size (" + std:to_string(syndrome.size()) - + ") does not match the number of detectors in the decoder (" - + std::to_string(self.num_detectors) + ")." - throw std::invalid_argument(msg); + std::string msg = "Syndrome array size (" + + std + : to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")." throw std::invalid_argument(msg); } std::vector detections; From 3d486b0975025c822e3db1309d1c181b36faf4cf Mon Sep 17 00:00:00 2001 From: noajshu Date: Wed, 20 Aug 2025 23:51:20 +0000 Subject: [PATCH 07/14] more fixes --- src/simplex.pybind.h | 21 ++++++-------- src/tesseract.pybind.h | 43 +++++++++++----------------- src/tesseract_sinter_compat.pybind.h | 4 +-- 3 files changed, 28 insertions(+), 40 deletions(-) diff --git a/src/simplex.pybind.h b/src/simplex.pybind.h index 7297fab8..9d00d3c6 100644 --- a/src/simplex.pybind.h +++ b/src/simplex.pybind.h @@ -21,8 +21,6 @@ #include #include -#include - #include "common.h" #include "simplex.h" #include "stim_utils.pybind.h" @@ -146,11 +144,10 @@ void add_simplex_module(py::module& root) { "decode_to_errors", [](SimplexDecoder& self, const py::array_t& syndrome) { if ((size_t)syndrome.size() != self.num_detectors) { - std::ostringstream msg; - msg << "Syndrome array size (" << syndrome.size() - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "Syndrome array size (" + std::to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } std::vector detections; @@ -311,11 +308,11 @@ void add_simplex_module(py::module& root) { size_t num_detectors = syndromes_unchecked.shape(1); if (num_detectors != self.num_detectors) { - std::ostringstream msg; - msg << "The number of detectors in the input array (" << num_detectors - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "The number of detectors in the input array (" + + std::to_string(num_detectors) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } // Allocate the result array. diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 1d27adf2..b94c27f5 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -21,8 +21,6 @@ #include #include -#include - #include "stim_utils.pybind.h" #include "tesseract.h" @@ -249,15 +247,10 @@ void add_tesseract_module(py::module& root) { "decode_to_errors", [](TesseractDecoder& self, const py::array_t& syndrome) { if ((size_t)syndrome.size() != self.num_detectors) { - std::ostringstream msg; - msg << "Syndrome array size (" << syndrome.size() - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - std::string msg = "Syndrome array size (" + - std - : to_string(syndrome.size()) + - ") does not match the number of detectors in the decoder (" + - std::to_string(self.num_detectors) + ")." throw std::invalid_argument(msg); + std::string msg = "Syndrome array size (" + std::to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } std::vector detections; @@ -291,11 +284,10 @@ void add_tesseract_module(py::module& root) { [](TesseractDecoder& self, const py::array_t& syndrome, size_t det_order, size_t det_beam) { if ((size_t)syndrome.size() != self.num_detectors) { - std::ostringstream msg; - msg << "Syndrome array size (" << syndrome.size() - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "Syndrome array size (" + std::to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } std::vector detections; @@ -403,11 +395,10 @@ void add_tesseract_module(py::module& root) { "decode", [](TesseractDecoder& self, const py::array_t& syndrome) { if ((size_t)syndrome.size() != self.num_detectors) { - std::ostringstream msg; - msg << "Syndrome array size (" << syndrome.size() - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "Syndrome array size (" + std::to_string(syndrome.size()) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } std::vector detections; @@ -461,11 +452,11 @@ void add_tesseract_module(py::module& root) { size_t num_detectors = syndromes_unchecked.shape(1); if (num_detectors != self.num_detectors) { - std::ostringstream msg; - msg << "The number of detectors in the input array (" << num_detectors - << ") does not match the number of detectors in the decoder (" - << self.num_detectors << ")."; - throw std::invalid_argument(msg.str()); + std::string msg = "The number of detectors in the input array (" + + std::to_string(num_detectors) + + ") does not match the number of detectors in the decoder (" + + std::to_string(self.num_detectors) + ")."; + throw std::invalid_argument(msg); } // Allocate the result array. diff --git a/src/tesseract_sinter_compat.pybind.h b/src/tesseract_sinter_compat.pybind.h index 3d15e6b2..623253d9 100644 --- a/src/tesseract_sinter_compat.pybind.h +++ b/src/tesseract_sinter_compat.pybind.h @@ -83,7 +83,7 @@ struct TesseractSinterCompiledDecoder { // Store predictions into the output buffer uint8_t* single_result_buffer = result_buffer + shot * num_observable_bytes; std::fill(single_result_buffer, single_result_buffer + num_observable_bytes, 0); - for (int obs_index : predictions) { + for (size_t obs_index : predictions) { if (obs_index >= 0 && obs_index < num_observables) { single_result_buffer[obs_index / 8] ^= (1 << (obs_index % 8)); } @@ -191,7 +191,7 @@ struct TesseractSinterDecoder { // Pack the predictions back into a bit-packed format. std::fill(single_result_data.begin(), single_result_data.end(), 0); - for (int obs_index : predictions) { + for (size_t obs_index : predictions) { if (obs_index >= 0 && obs_index < num_obs) { single_result_data[obs_index / 8] ^= (1 << (obs_index % 8)); } From 61867e29d82be2417f69635c26feda3d27800a2e Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Fri, 29 Aug 2025 13:47:19 -0700 Subject: [PATCH 08/14] Handle explicit DetIndex case --- src/py/utils_test.py | 17 ++++++++++++++-- src/tesseract.pybind.h | 4 ++-- src/tesseract_main.cc | 44 ++++++++++++++++++++++++++---------------- src/utils.cc | 19 +++++++++++++++--- src/utils.h | 5 ++++- src/utils.pybind.h | 24 +++++++++++++++-------- 6 files changed, 80 insertions(+), 33 deletions(-) diff --git a/src/py/utils_test.py b/src/py/utils_test.py index b5a07c90..304ddf3f 100644 --- a/src/py/utils_test.py +++ b/src/py/utils_test.py @@ -50,12 +50,25 @@ def test_build_det_orders(): ) == [[0, 1]] -def test_build_det_orders_no_bfs(): +def test_build_det_orders_coordinate(): assert tesseract_decoder.utils.build_det_orders( - _DETECTOR_ERROR_MODEL, num_det_orders=1, det_order_bfs=False, seed=0 + _DETECTOR_ERROR_MODEL, + num_det_orders=1, + method=tesseract_decoder.utils.DetOrder.DetCoordinate, + seed=0, ) == [[0, 1]] +def test_build_det_orders_index(): + res = tesseract_decoder.utils.build_det_orders( + _DETECTOR_ERROR_MODEL, + num_det_orders=1, + method=tesseract_decoder.utils.DetOrder.DetIndex, + seed=0, + ) + assert res == [[0, 1]] or res == [[1, 0]] + + def test_get_errors_from_dem(): expected = "Error{cost=1.945910, symptom=Symptom{detectors=[0], observables=[]}}, Error{cost=0.510826, symptom=Symptom{detectors=[0 1], observables=[]}}, Error{cost=1.098612, symptom=Symptom{detectors=[1], observables=[]}}" assert ( diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 6c145856..65111f0b 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -41,7 +41,7 @@ TesseractConfig tesseract_config_maker_no_dem( double det_penalty = 0.0, bool create_visualization = false) { stim::DetectorErrorModel empty_dem; if (det_orders.empty()) { - det_orders = build_det_orders(empty_dem, 20, /*det_order_bfs=*/true, 2384753); + det_orders = build_det_orders(empty_dem, 20, DetOrder::DetBFS, 2384753); } return TesseractConfig({empty_dem, det_beam, beam_climbing, no_revisit_dets, at_most_two_errors_per_detector, verbose, merge_errors, pqlimit, @@ -57,7 +57,7 @@ TesseractConfig tesseract_config_maker( double det_penalty = 0.0, bool create_visualization = false) { stim::DetectorErrorModel input_dem = parse_py_object(dem); if (det_orders.empty()) { - det_orders = build_det_orders(input_dem, 20, true, 2384753); + det_orders = build_det_orders(input_dem, 20, DetOrder::DetBFS, 2384753); } return TesseractConfig({input_dem, det_beam, beam_climbing, no_revisit_dets, at_most_two_errors_per_detector, verbose, merge_errors, pqlimit, diff --git a/src/tesseract_main.cc b/src/tesseract_main.cc index 8853f5ae..4785570e 100644 --- a/src/tesseract_main.cc +++ b/src/tesseract_main.cc @@ -34,7 +34,9 @@ struct Args { // Manifold orientation options uint64_t det_order_seed; size_t num_det_orders = 10; - bool det_order_bfs = true; + bool det_order_bfs = false; + bool det_order_index = false; + bool det_order_coordinate = false; // Sampling options size_t sample_num_shots = 0; @@ -88,6 +90,12 @@ struct Args { throw std::invalid_argument("Must provide at least one of --circuit or --dem"); } + int det_order_flags = int(det_order_bfs) + int(det_order_index) + int(det_order_coordinate); + if (det_order_flags > 1) { + throw std::invalid_argument( + "Only one of --det-order-bfs, --det-order-index, or --det-order-coordinate may be set."); + } + int num_data_sources = int(sample_num_shots > 0) + int(!in_fname.empty()); if (num_data_sources != 1) { throw std::invalid_argument("Requires exactly 1 source of shots."); @@ -180,8 +188,13 @@ struct Args { std::cout << ")" << std::endl; } } - config.det_orders = - build_det_orders(config.dem, num_det_orders, det_order_bfs, det_order_seed); + DetOrder order = DetOrder::DetBFS; + if (det_order_index) { + order = DetOrder::DetIndex; + } else if (det_order_coordinate) { + order = DetOrder::DetCoordinate; + } + config.det_orders = build_det_orders(config.dem, num_det_orders, order, det_order_seed); } if (sample_num_shots > 0) { @@ -296,21 +309,18 @@ int main(int argc, char* argv[]) { .metavar("N") .default_value(size_t(1)) .store_into(args.num_det_orders); - program.add_argument("--no-det-order-bfs") - .help("Disable BFS-based detector ordering and use geometric orientation") - .default_value(true) - .implicit_value(false) - .store_into(args.det_order_bfs); program.add_argument("--det-order-bfs") - .action([&](auto const&) { - std::cout << "BFS-based detector ordering is the default now; " - "--det-order-bfs is ignored." - << std::endl; - }) - .default_value(true) - .implicit_value(true) - .store_into(args.det_order_bfs) - .hidden(); + .help("Use BFS-based detector ordering (default if no method specified)") + .flag() + .store_into(args.det_order_bfs); + program.add_argument("--det-order-index") + .help("Randomly choose increasing or decreasing detector index order") + .flag() + .store_into(args.det_order_index); + program.add_argument("--det-order-coordinate") + .help("Random geometric detector orientation ordering") + .flag() + .store_into(args.det_order_coordinate); program.add_argument("--det-order-seed") .help( "Seed used when initializing the random detector traversal " diff --git a/src/utils.cc b/src/utils.cc index 1c678635..261a6aa1 100644 --- a/src/utils.cc +++ b/src/utils.cc @@ -83,7 +83,7 @@ std::vector> build_detector_graph(const stim::DetectorErrorM } std::vector> build_det_orders(const stim::DetectorErrorModel& dem, - size_t num_det_orders, bool det_order_bfs, + size_t num_det_orders, DetOrder method, uint64_t seed) { std::vector> det_orders(num_det_orders); std::mt19937_64 rng(seed); @@ -91,7 +91,7 @@ std::vector> build_det_orders(const stim::DetectorErrorModel auto detector_coords = get_detector_coords(dem); - if (det_order_bfs) { + if (method == DetOrder::DetBFS) { auto graph = build_detector_graph(dem); std::uniform_int_distribution dist_det(0, graph.size() - 1); for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { @@ -131,7 +131,7 @@ std::vector> build_det_orders(const stim::DetectorErrorModel } det_orders[det_order] = inv_perm; } - } else { + } else if (method == DetOrder::DetCoordinate) { std::vector inner_products(dem.count_detectors()); if (!detector_coords.size() || !detector_coords.at(0).size()) { for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { @@ -163,6 +163,19 @@ std::vector> build_det_orders(const stim::DetectorErrorModel det_orders[det_order] = inv_perm; } } + } else if (method == DetOrder::DetIndex) { + std::uniform_int_distribution dist_bool(0, 1); + size_t n = dem.count_detectors(); + for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { + det_orders[det_order].resize(n); + if (dist_bool(rng)) { + for (size_t i = 0; i < n; ++i) { + det_orders[det_order][i] = n - 1 - i; + } + } else { + std::iota(det_orders[det_order].begin(), det_orders[det_order].end(), 0); + } + } } return det_orders; } diff --git a/src/utils.h b/src/utils.h index b66537cd..73d7817e 100644 --- a/src/utils.h +++ b/src/utils.h @@ -34,8 +34,11 @@ std::vector> get_detector_coords(const stim::DetectorErrorMo // in the model activates them both. std::vector> build_detector_graph(const stim::DetectorErrorModel& dem); +enum class DetOrder { DetBFS, DetIndex, DetCoordinate }; + std::vector> build_det_orders(const stim::DetectorErrorModel& dem, - size_t num_det_orders, bool det_order_bfs = true, + size_t num_det_orders, + DetOrder method = DetOrder::DetBFS, uint64_t seed = 0); const double INF = std::numeric_limits::infinity(); diff --git a/src/utils.pybind.h b/src/utils.pybind.h index 2fa23d79..92ba6680 100644 --- a/src/utils.pybind.h +++ b/src/utils.pybind.h @@ -32,6 +32,12 @@ void add_utils_module(py::module& root) { m.attr("INF") = INF; m.doc() = "A representation of infinity for floating point numbers."; + py::enum_(m, "DetOrder", "Detector ordering methods") + .value("DetBFS", DetOrder::DetBFS) + .value("DetIndex", DetOrder::DetIndex) + .value("DetCoordinate", DetOrder::DetCoordinate) + .export_values(); + m.def( "get_detector_coords", [](py::object dem) { @@ -79,11 +85,11 @@ void add_utils_module(py::module& root) { )pbdoc"); m.def( "build_det_orders", - [](py::object dem, size_t num_det_orders, bool det_order_bfs, uint64_t seed) { + [](py::object dem, size_t num_det_orders, DetOrder method, uint64_t seed) { auto input_dem = parse_py_object(dem); - return build_det_orders(input_dem, num_det_orders, det_order_bfs, seed); + return build_det_orders(input_dem, num_det_orders, method, seed); }, - py::arg("dem"), py::arg("num_det_orders"), py::arg("det_order_bfs") = true, + py::arg("dem"), py::arg("num_det_orders"), py::arg("method") = DetOrder::DetBFS, py::arg("seed") = 0, R"pbdoc( Generates various detector orderings for decoding. @@ -93,17 +99,19 @@ void add_utils_module(py::module& root) { The detector error model to generate orders for. num_det_orders : int The number of detector orderings to generate. - det_order_bfs : bool, default=True - If True, uses a Breadth-First Search (BFS) to generate - the orders. If False, uses a randomized ordering. + method : tesseract_decoder.utils.DetOrder, default=tesseract_decoder.utils.DetOrder.DetBFS + Strategy for ordering detectors. ``DetBFS`` performs a breadth-first + traversal, ``DetCoordinate`` uses randomized geometric orientations, + and ``DetIndex`` chooses either increasing or decreasing detector + index order at random. seed : int, default=0 A seed for the random number generator. Returns ------- list[list[int]] - A list of detector orderings. Each inner list is a - permutation of the detector indices. + A list of detector orderings. Each inner list maps a detector index + to its position in the ordering. )pbdoc"); m.def( "get_errors_from_dem", From 454521fd3f52f7d815a38e427cfddfc5b1396e4b Mon Sep 17 00:00:00 2001 From: Noah Shutty Date: Fri, 29 Aug 2025 15:56:49 -0700 Subject: [PATCH 09/14] expand det index test --- src/py/utils_test.py | 12 ++- src/utils.cc | 186 ++++++++++++++++++++++++------------------- 2 files changed, 112 insertions(+), 86 deletions(-) diff --git a/src/py/utils_test.py b/src/py/utils_test.py index 304ddf3f..3cbec85d 100644 --- a/src/py/utils_test.py +++ b/src/py/utils_test.py @@ -27,6 +27,10 @@ """ ) +_DETECTOR_ERROR_MODEL_10 = stim.DetectorErrorModel( + "\n".join(f"error(0.1) D{i}" for i in range(10)) +) + def test_module_has_global_constants(): assert tesseract_decoder.utils.EPSILON <= 1e-7 @@ -44,7 +48,7 @@ def test_build_detector_graph(): ] -def test_build_det_orders(): +def test_build_det_orders_bfs(): assert tesseract_decoder.utils.build_det_orders( _DETECTOR_ERROR_MODEL, num_det_orders=1, seed=0 ) == [[0, 1]] @@ -61,12 +65,14 @@ def test_build_det_orders_coordinate(): def test_build_det_orders_index(): res = tesseract_decoder.utils.build_det_orders( - _DETECTOR_ERROR_MODEL, + _DETECTOR_ERROR_MODEL_10, num_det_orders=1, method=tesseract_decoder.utils.DetOrder.DetIndex, seed=0, ) - assert res == [[0, 1]] or res == [[1, 0]] + expected_asc = list(range(10)) + expected_desc = list(range(9, -1, -1)) + assert res == [expected_asc] or res == [expected_desc] def test_get_errors_from_dem(): diff --git a/src/utils.cc b/src/utils.cc index 261a6aa1..4100a945 100644 --- a/src/utils.cc +++ b/src/utils.cc @@ -82,104 +82,124 @@ std::vector> build_detector_graph(const stim::DetectorErrorM return neighbors; } -std::vector> build_det_orders(const stim::DetectorErrorModel& dem, - size_t num_det_orders, DetOrder method, - uint64_t seed) { +static std::vector> build_det_orders_bfs(const stim::DetectorErrorModel& dem, + size_t num_det_orders, + std::mt19937_64& rng) { std::vector> det_orders(num_det_orders); - std::mt19937_64 rng(seed); - std::normal_distribution dist(0, 1); - - auto detector_coords = get_detector_coords(dem); - - if (method == DetOrder::DetBFS) { - auto graph = build_detector_graph(dem); - std::uniform_int_distribution dist_det(0, graph.size() - 1); - for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { - std::vector perm; - perm.reserve(graph.size()); - std::vector visited(graph.size(), false); - std::queue q; - size_t start = dist_det(rng); - while (perm.size() < graph.size()) { - if (!visited[start]) { - visited[start] = true; - q.push(start); - perm.push_back(start); - } - while (!q.empty()) { - size_t cur = q.front(); - q.pop(); - auto neigh = graph[cur]; - std::shuffle(neigh.begin(), neigh.end(), rng); - for (size_t n : neigh) { - if (!visited[n]) { - visited[n] = true; - q.push(n); - perm.push_back(n); - } + auto graph = build_detector_graph(dem); + std::uniform_int_distribution dist_det(0, graph.size() - 1); + for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { + std::vector perm; + perm.reserve(graph.size()); + std::vector visited(graph.size(), false); + std::queue q; + size_t start = dist_det(rng); + while (perm.size() < graph.size()) { + if (!visited[start]) { + visited[start] = true; + q.push(start); + perm.push_back(start); + } + while (!q.empty()) { + size_t cur = q.front(); + q.pop(); + auto neigh = graph[cur]; + std::shuffle(neigh.begin(), neigh.end(), rng); + for (size_t n : neigh) { + if (!visited[n]) { + visited[n] = true; + q.push(n); + perm.push_back(n); } } - if (perm.size() < graph.size()) { - do { - start = dist_det(rng); - } while (visited[start]); - } } - std::vector inv_perm(graph.size()); - for (size_t i = 0; i < perm.size(); ++i) { - inv_perm[perm[i]] = i; + if (perm.size() < graph.size()) { + do { + start = dist_det(rng); + } while (visited[start]); } - det_orders[det_order] = inv_perm; } - } else if (method == DetOrder::DetCoordinate) { - std::vector inner_products(dem.count_detectors()); - if (!detector_coords.size() || !detector_coords.at(0).size()) { - for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { - det_orders[det_order].resize(dem.count_detectors()); - std::iota(det_orders[det_order].begin(), det_orders[det_order].end(), 0); - } - } else { - for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { - std::vector orientation_vector; - for (size_t i = 0; i < detector_coords.at(0).size(); ++i) { - orientation_vector.push_back(dist(rng)); - } + std::vector inv_perm(graph.size()); + for (size_t i = 0; i < perm.size(); ++i) { + inv_perm[perm[i]] = i; + } + det_orders[det_order] = inv_perm; + } + return det_orders; +} - for (size_t i = 0; i < detector_coords.size(); ++i) { - inner_products[i] = 0; - for (size_t j = 0; j < orientation_vector.size(); ++j) { - inner_products[i] += detector_coords[i][j] * orientation_vector[j]; - } - } - std::vector perm(dem.count_detectors()); - std::iota(perm.begin(), perm.end(), 0); - std::sort(perm.begin(), perm.end(), [&](const size_t& i, const size_t& j) { - return inner_products[i] > inner_products[j]; - }); - std::vector inv_perm(dem.count_detectors()); - for (size_t i = 0; i < perm.size(); ++i) { - inv_perm[perm[i]] = i; - } - det_orders[det_order] = inv_perm; +static std::vector> build_det_orders_coordinate( + const stim::DetectorErrorModel& dem, size_t num_det_orders, std::mt19937_64& rng) { + std::vector> det_orders(num_det_orders); + auto detector_coords = get_detector_coords(dem); + std::vector inner_products(dem.count_detectors()); + std::normal_distribution dist(0, 1); + if (detector_coords.empty() || detector_coords.at(0).empty()) { + for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { + det_orders[det_order].resize(dem.count_detectors()); + std::iota(det_orders[det_order].begin(), det_orders[det_order].end(), 0); + } + return det_orders; + } + for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { + std::vector orientation_vector; + for (size_t i = 0; i < detector_coords.at(0).size(); ++i) { + orientation_vector.push_back(dist(rng)); + } + for (size_t i = 0; i < detector_coords.size(); ++i) { + inner_products[i] = 0; + for (size_t j = 0; j < orientation_vector.size(); ++j) { + inner_products[i] += detector_coords[i][j] * orientation_vector[j]; } } - } else if (method == DetOrder::DetIndex) { - std::uniform_int_distribution dist_bool(0, 1); - size_t n = dem.count_detectors(); - for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { - det_orders[det_order].resize(n); - if (dist_bool(rng)) { - for (size_t i = 0; i < n; ++i) { - det_orders[det_order][i] = n - 1 - i; - } - } else { - std::iota(det_orders[det_order].begin(), det_orders[det_order].end(), 0); + std::vector perm(dem.count_detectors()); + std::iota(perm.begin(), perm.end(), 0); + std::sort(perm.begin(), perm.end(), [&](const size_t& i, const size_t& j) { + return inner_products[i] > inner_products[j]; + }); + std::vector inv_perm(dem.count_detectors()); + for (size_t i = 0; i < perm.size(); ++i) { + inv_perm[perm[i]] = i; + } + det_orders[det_order] = inv_perm; + } + return det_orders; +} + +static std::vector> build_det_orders_index(const stim::DetectorErrorModel& dem, + size_t num_det_orders, + std::mt19937_64& rng) { + std::vector> det_orders(num_det_orders); + std::uniform_int_distribution dist_bool(0, 1); + size_t n = dem.count_detectors(); + for (size_t det_order = 0; det_order < num_det_orders; ++det_order) { + det_orders[det_order].resize(n); + if (dist_bool(rng)) { + for (size_t i = 0; i < n; ++i) { + det_orders[det_order][i] = n - 1 - i; } + } else { + std::iota(det_orders[det_order].begin(), det_orders[det_order].end(), 0); } } return det_orders; } +std::vector> build_det_orders(const stim::DetectorErrorModel& dem, + size_t num_det_orders, DetOrder method, + uint64_t seed) { + std::mt19937_64 rng(seed); + switch (method) { + case DetOrder::DetBFS: + return build_det_orders_bfs(dem, num_det_orders, rng); + case DetOrder::DetCoordinate: + return build_det_orders_coordinate(dem, num_det_orders, rng); + case DetOrder::DetIndex: + return build_det_orders_index(dem, num_det_orders, rng); + } + throw std::invalid_argument("Unknown det order method"); +} + bool sampling_from_dem(uint64_t seed, size_t num_shots, stim::DetectorErrorModel dem, std::vector& shots) { stim::DemSampler sampler(dem, std::mt19937_64{seed}, num_shots); From 41f7c3e7189259383d8fed839dc90d67a02528e8 Mon Sep 17 00:00:00 2001 From: noajshu Date: Fri, 29 Aug 2025 23:33:56 +0000 Subject: [PATCH 10/14] update beam climbing for when det orders > beam+1 --- src/tesseract.cc | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/tesseract.cc b/src/tesseract.cc index 97c8f4d8..ca441331 100644 --- a/src/tesseract.cc +++ b/src/tesseract.cc @@ -201,8 +201,10 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections) } if (config.beam_climbing) { - for (int beam = config.det_beam; beam >= 0; --beam) { - size_t detector_order = beam % config.det_orders.size(); + int beam = 0; + int detector_order = 0; + for (int trial = 0; trial < std::max(config.det_beam + 1, int(config.det_orders.size())); + ++trial) { decode_to_errors(detections, detector_order, beam); double local_cost = cost_from_errors(predicted_errors_buffer); if (!low_confidence_flag && local_cost < best_cost) { @@ -215,6 +217,10 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections) << " and obs_mask " << get_flipped_observables(predicted_errors_buffer) << ". Best cost so far: " << best_cost << std::endl; } + beam += 1; + detector_order += 1; + beam %= (config.det_beam + 1); + detector_order %= config.det_orders.size(); } } else { for (size_t detector_order = 0; detector_order < config.det_orders.size(); ++detector_order) { From 8f269f78bdf27ed826d6f7254ee532d2bf19048f Mon Sep 17 00:00:00 2001 From: noajshu Date: Sun, 31 Aug 2025 00:17:33 +0000 Subject: [PATCH 11/14] Refactor: Remove --at-most-two-errors-per-detector feature --- docs/tutorial.ipynb | 3149 +++++++++++++------------- src/py/README.md | 4 +- src/py/tesseract_test.py | 10 +- src/tesseract.cc | 31 +- src/tesseract.h | 2 +- src/tesseract.pybind.h | 33 +- src/tesseract_main.cc | 46 +- src/tesseract_sinter_compat.pybind.h | 25 +- 8 files changed, 1636 insertions(+), 1664 deletions(-) diff --git a/docs/tutorial.ipynb b/docs/tutorial.ipynb index 056ee540..89e9754b 100644 --- a/docs/tutorial.ipynb +++ b/docs/tutorial.ipynb @@ -1,1616 +1,1631 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "KBmkwvhmupn-" - }, - "source": [ - "# Tesseract Tutorial\n", - "\n", - "- We will also, partly, explain how to use features of Stim and PyMatching\n", - "- Stim is a dependency of Tesseract but you can also use other sources of data\n", - "- This is not a comprehensive introduction." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jaZcr-NevBSB" - }, - "source": [ - "## Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "i6_88o7kKOVJ" - }, - "outputs": [], - "source": [ - "!pip install --quiet --upgrade stim galois tesseract-decoder pymatching python-sat -U" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RLXXX3eMT_LR" - }, - "source": [ - "## Getting a Surface Code Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8zcmVHFFUPq2" - }, - "outputs": [], - "source": [ - "import stim\n", - "\n", - "d = 11\n", - "p = 0.005\n", - "circuit = stim.Circuit.generated(\n", - " code_task=\"surface_code:rotated_memory_x\",\n", - " distance=d,\n", - " rounds=d,\n", - " after_clifford_depolarization=p,\n", - " before_round_data_depolarization=p,\n", - " before_measure_flip_probability=p,\n", - " after_reset_flip_probability=p\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UBMIlXY9U30Y" - }, - "source": [ - "## Sample" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GCkUlTJZU2T_" - }, - "outputs": [], - "source": [ - "sampler = circuit.compile_detector_sampler()\n", - "\n", - "num_shots = 10000\n", - "detector_outcomes, actual_observables = sampler.sample(shots=num_shots, separate_observables=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m9x8pivTVCir" - }, - "source": [ - "## Decode with uncorrelated matching" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-5W0AX8nVEyU", - "outputId": "562e78d3-4fef-449e-92ae-403e5ed7c862" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Logical error rate: 69/10000\n" - ] - } - ], - "source": [ - "import pymatching\n", - "import numpy as np\n", - "\n", - "dem = circuit.detector_error_model(decompose_errors=True)\n", - "matching = pymatching.Matching.from_detector_error_model(model=dem)\n", - "predicted_observables = matching.decode_batch(shots=detector_outcomes)\n", - "num_errors = np.sum(np.any(predicted_observables != actual_observables, axis=1))\n", - "\n", - "print(f\"Logical error rate: {num_errors}/{num_shots}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Xp7MyK0XVs_6" - }, - "source": [ - "## Decode with new correlated matching!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vufQ8G5iVx7b", - "outputId": "1e12759c-e1e4-4c51-8103-98ec2d6906f8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Logical error rate: 22/10000\n" - ] - } - ], - "source": [ - "dem = circuit.detector_error_model(decompose_errors=True)\n", - "matching_corr = pymatching.Matching.from_detector_error_model(\n", - " model=dem, enable_correlations=True\n", - " )\n", - "predicted_observables_corr = matching_corr.decode_batch(\n", - " shots=detector_outcomes,\n", - " enable_correlations=True\n", - " )\n", - "num_errors_corr = np.sum(np.any(predicted_observables_corr != actual_observables, axis=1))\n", - "\n", - "print(f\"Logical error rate: {num_errors_corr}/{num_shots}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a-AMqTUeuqOe" - }, - "source": [ - "## Getting a Color Code Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W7fU_MYJCRen", - "outputId": "6038fc3e-8707-4bac-fd69-b9d08a90f167" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 13295 100 13295 0 0 72032 0 --:--:-- --:--:-- --:--:-- 72255\n" - ] - } - ], - "source": [ - "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D5%2Cd%3D5%2Cp%3D0.001%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_Z%2Cq%3D37%2Cgates%3Dcz.stim' > d5r5colorcode_p001.stim" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E-vXEhbaTeQI" - }, - "source": [ - "# Visualizing with Stim" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 343 - }, - "id": "2jTOVijwKPXm", - "outputId": "5a0c63b5-384b-4729-8bf1-d205552de185" - }, - "outputs": [ - { - "output_type": "execute_result", 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AAA4wgAAOMIAAADCAAA4wgAAOMIAAAjCAAA4wgAAQMIAAADCAAA4wgAAQMIAAAjCAABEwgAACMIAAADCAABEwgAACMIAAAjCAABEwgAAEMIAAADCAABEwgAAEMIAAAjCAABEwgAAEMIAABDCAABEwgAAEMIAABjCAABEwgAAGMIAAAjCAABEwgAAGMIAABDCAABEwgAAGMIAABjCAABEwgAAGMIAACDCAABEwgAAIMIAAAjCAABEwgAAIMIAABDCAABEwgAAIMIAABjCAABEwgAAIMIAACDCAABEwgAAIMIAACjCAABEwgAAIMIAADDCAABEwgAAKMIAAADCAABEwgAAKMIAAAjCAABEwgAAKMIAABDCAABEwgAAKMIAABjCAABEwgAAKMIAACDCAABEwgAAKMIAACjCAABEwgAAMMIAAADCAABEwgAAMMIAAAjCAABEwgAAMMIAABDCAABEwgAAMMIAABjCAABEwgAAOMIAAAjCAABEwgAAOMIAABDCAABIwgAACMIAAADCAABIwgAACMIAAAjCAABIwgAAEMIAAADCAABIwgAAEMIAAAjCAABIwgAAEMIAABDCAABIwgAAEMIAABjCAABIwgAAGMIAAAjCAABIwgAAGMIAABDCAABIwgAAGMIAABjCAABIwgAAGMIAACDCAABIwgAAIMIAAAjCAABIwgAAIMIAABDCAABIwgAAIMIAABjCAABIwgAAIMIAACDCAABIwgAAIMIAACjCAABIwgAAIMIAADDCAABIwgAAKMIAAADCAABIwgAAKMIAAAjCAABIwgAAKMIAABDCAABIwgAAKMIAABjCAABIwgAAKMIAACDCAABIwgAAKMIAACjCAABIwgAAMMIAAADCAABIwgAAMMIAAAjCAABIwgAAMMIAABDCAABIwgAAMMIAABjCAABIwgAAOMIAAAjCAABIwgAAOMIAABDCAABMwgAAAMIAAADCAABMwgAACMIAAADCAABMwgAAEMIAAADCAABMwgAAGMIAAADCAABMwgAAEMIAAAjCAABMwgAAGMIAAAjCAABMwgAAEMIAABDCAABMwgAAGMIAABDCAABMwgAAEMIAABjCAABMwgAAGMIAABjCAABMwgAAIMIAAADCAABMwgAAKMIAAADCAABMwgAAIMIAAAjCAABMwgAAKMIAAAjCAABMwgAAIMIAABDCAABMwgAAKMIAABDCAABMwgAAIMIAABjCAABMwgAAKMIAABjCAABMwgAAIMIAACDCAABMwgAAKMIAACDCAABMwgAAIMIAACjCAABMwgAAKMIAACjCAABMwgAAMMIAAADCAABMwgAAOMIAAADCAABMwgAAMMIAAAjCAABMwgAAOMIAAAjCAABMwgAAMMIAABDCAABMwgAAOMIAABDCAABQwgAAAMIAAADCAABQwgAACMIAAADCAABQwgAAEMIAAADCAABQwgAAGMIAAADCAABQwgAAEMIAAAjCAABQwgAAGMIAAAjCAABQwgAAEMIAABDCAABQwgAAGMIAABDCAABQwgAAEMIAABjCAABQwgAAGMIAABjCAABQwgAAIMIAAADCAABQwgAAKMIAAADCAABQwgAAIMIAAAjCAABQwgAAKMIAAAjCAABQwgAAIMIAABDCAABQwgAAKMIAABDCAABQwgAAIMIAABjCAABQwgAAKMIAABjCAABQwgAAIMIAACDCAABQwgAAKMIAACDCAABQwgAAIMIAACjCAABQwgAAKMIAACjCAABQwgAAMMIAAADCAABQwgAAOMIAAADCAABQwgAAMMIAAAjCAABQwgAAOMIAAAjCAABQwgAAMMIAABDCAABQwgAAOMIAABDCAABUwgAACMIAAAjCAABUwgAACMIAABDCAABUwgAAEMIAAAjCAABUwgAAEMIAABDCAABUwgAAGMIAAADCAABUwgAAGMIAAAjCAABUwgAAGMIAABDCAABUwgAAGMIAABjCAABUwgAAGMIAACDCAABUwgAAGMIAACjCAABUwgAAIMIAAADCAABUwgAAIMIAAAjCAABUwgAAIMIAABDCAABUwgAAIMIAABjCAABUwgAAIMIAACDCAABUwgAAIMIAACjCAABUwgAAKMIAAAjCAABUwgAAKMIAABDCAABUwgAAKMIAABjCAABUwgAAKMIAACDCAABUwgAAMMIAAAjCAABUwgAAMMIAABDCAABUwgAAMMIAABjCAABUwgAAMMIAACDCAABUwgAAOMIAAADCAABUwgAAOMIAAAjCAABUwgAAQMIAAADCAABUwgAAQMIAAAjCAABYwgAACMIAAAjCAABYwgAACMIAABDCAABYwgAAEMIAAAjCAABYwgAAEMIAABDCAABYwgAAGMIAAADCAABYwgAAGMIAAAjCAABYwgAAGMIAABDCAABYwgAAGMIAABjCAABYwgAAGMIAACDCAABYwgAAGMIAACjCAABYwgAAIMIAAADCAABYwgAAIMIAAAjCAABYwgAAIMIAABDCAABYwgAAIMIAABjCAABYwgAAIMIAACDCAABYwgAAIMIAACjCAABYwgAAKMIAAAjCAABYwgAAKMIAABDCAABYwgAAKMIAABjCAABYwgAAKMIAACDCAABYwgAAMMIAAAjCAABYwgAAMMIAABDCAABYwgAAMMIAABjCAABYwgAAMMIAACDCAABYwgAAOMIAAADCAABYwgAAOMIAAAjCAABYwgAAQMIAAADCAABYwgAAQMIAAAjCAABkwgAACMIAAADCAABkwgAAEMIAAADCAABkwgAACMIAABDCAABkwgAAEMIAABDCAABkwgAAGMIAAAjCAABkwgAAIMIAAAjCAABkwgAAGMIAABjCAABkwgAAIMIAABjCAABkwgAAGMIAACjCAABkwgAAIMIAACjCAABkwgAAKMIAAADCAABkwgAAMMIAAADCAABkwgAAKMIAABDCAABkwgAAMMIAABDCAABkwgAAKMIAACDCAABkwgAAMMIAACDCAABkwgAAOMIAAAjCAABkwgAAQMIAAAjCAABowgAACMIAAADCAABowgAAEMIAAADCAABowgAACMIAABDCAABowgAAEMIAABDCAABowgAAGMIAAAjCAABowgAAIMIAAAjCAABowgAAGMIAABjCAABowgAAIMIAABjCAABowgAAGMIAACjCAABowgAAIMIAACjCAABowgAAKMIAAADCAABowgAAMMIAAADCAABowgAAKMIAABDCAABowgAAMMIAABDCAABowgAAKMIAACDCAABowgAAMMIAACDCAABowgAAOMIAAAjCAABowgAAQMIAAAjCAACMwgAACMIAAADCAACMwgAAEMIAAADCAACMwgAACMIAABDCAACMwgAAEMIAABDCAACMwgAAGMIAAAjCAACMwgAAIMIAAAjCAACMwgAAGMIAABjCAACMwgAAIMIAABjCAACMwgAAGMIAACjCAACMwgAAIMIAACjCAACMwgAAKMIAAADCAACMwgAAMMIAAADCAACMwgAAKMIAABDCAACMwgAAMMIAABDCAACMwgAAKMIAACDCAACMwgAAMMIAACDCAACMwgAAOMIAAAjCAACMwgAAQMIAAAjCAACOwgAACMIAAADCAACOwgAAEMIAAADCAACOwgAACMIAABDCAACOwgAAEMIAABDCAACOwgAAGMIAAAjCAACOwgAAIMIAAAjCAACOwgAAGMIAABjCAACOwgAAIMIAABjCAACOwgAAGMIAACjCAACOwgAAIMIAACjCAACOwgAAKMIAAADCAACOwgAAMMIAAADCAACOwgAAKMIAABDCAACOwgAAMMIAABDCAACOwgAAKMIAACDCAACOwgAAMMIAACDCAACOwgAAOMIAAAjCAACOwgAAQMIAAAjCAACUwgAACMIAAAjCAACUwgAACMIAABDCAACUwgAAEMIAAAjCAACUwgAAEMIAABDCAACUwgAAGMIAAADCAACUwgAAGMIAAAjCAACUwgAAGMIAABDCAACUwgAAGMIAABjCAACUwgAAGMIAACDCAACUwgAAGMIAACjCAACUwgAAIMIAAADCAACUwgAAIMIAAAjCAACUwgAAIMIAABDCAACUwgAAIMIAABjCAACUwgAAIMIAACDCAACUwgAAIMIAACjCAACUwgAAKMIAAAjCAACUwgAAKMIAABDCAACUwgAAKMIAABjCAACUwgAAKMIAACDCAACUwgAAMMIAAAjCAACUwgAAMMIAABDCAACUwgAAMMIAABjCAACUwgAAMMIAACDCAACUwgAAOMIAAADCAACUwgAAOMIAAAjCAACUwgAAQMIAAADCAACUwgAAQMIAAAjCAACWwgAACMIAAAjCAACWwgAACMIAABDCAACWwgAAEMIAAAjCAACWwgAAEMIAABDCAACWwgAAGMIAAADCAACWwgAAGMIAAAjCAACWwgAAGMIAABDCAACWwgAAGMIAABjCAACWwgAAGMIAACDCAACWwgAAGMIAACjCAACWwgAAIMIAAADCAACWwgAAIMIAAAjCAACWwgAAIMIAABDCAACWwgAAIMIAABjCAACWwgAAIMIAACDCAACWwgAAIMIAACjCAACWwgAAKMIAAAjCAACWwgAAKMIAABDCAACWwgAAKMIAABjCAACWwgAAKMIAACDCAACWwgAAMMIAAAjCAACWwgAAMMIAABDCAACWwgAAMMIAABjCAACWwgAAMMIAACDCAACWwgAAOMIAAADCAACWwgAAOMIAAAjCAACWwgAAQMIAAADCAACWwgAAQMIAAAjCAACYwgAAAMIAAADCAACYwgAACMIAAADCAACYwgAAEMIAAADCAACYwgAAGMIAAADCAACYwgAAEMIAAAjCAACYwgAAGMIAAAjCAACYwgAAEMIAABDCAACYwgAAGMIAABDCAACYwgAAEMIAABjCAACYwgAAGMIAABjCAACYwgAAIMIAAADCAACYwgAAKMIAAADCAACYwgAA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zBAADKwc3M/MEAADLCAADKwZqZ+cEAADLCAADKwZqZ+cEAAPzBAADKwZqZ+cEAADLCAADOwc3M/MEAAPzBAADOwZqZ+cEAAPzBAADOwc3M/MEAADLCAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADawZqZ+cEAAPzBAADOwZqZ+cEAADLCAADawZqZ+cEAADLCAADawc3M/MEAAPzBAADawZqZ+cEAAPzBAADawc3M/MEAADLCAADawZqZ+cEAADLCAADawZqZ+cEAAPzBAADawZqZ+cEAADLCAADewc3M/MEAAPzBAADewZqZ+cEAAPzBAADewc3M/MEAADLCAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADywZqZ+cEAAPzBAADewZqZ+cEAADLCAADywZqZ+cEAADLCAADywc3M/MEAAPzBAADywZqZ+cEAAPzBAADywc3M/MEAADLCAADywZqZ+cEAADLCAADywZqZ+cEAAPzBAADywZqZ+cEAADLCAAD+wc3M/MEAAPzBAAD+wZqZ+cEAAPzBAAD+wc3M/MEAADLCAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAAJwpqZ+cEAAPzBAAD+wZqZ+cEAADLCAAAJwpqZ+cEAADLCAAAJws3M/MEAAPzBAAAJwpqZ+cEAAPzBAAAJws3M/MEAADLCAAAJwpqZ+cEAADLCAAAJwpqZ+cEAAPzBAAAJwpqZ+cEAADLCAAALws3M/MEAAPzBAAALwpqZ+cEAAPzBAAALws3M/MEAADLCAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAARwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAARwpqZ+cEAADLCAAARws3M/MEAAPzBAAARwpqZ+cEAAPzBAAARws3M/MEAADLCAAARwpqZ+cEAADLCAAARwpqZ+cEAAPzBAAARwpqZ+cEAADLCAAATws3M/MEAAPzBAAATwpqZ+cEAAPzBAAATws3M/MEAADLCAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAAZwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAAZwpqZ+cEAADLCAAAZws3M/MEAAPzBAAAZwpqZ+cEAAPzBAAAZws3M/MEAADLCAAAZwpqZ+cEAADLCAAAZwpqZ+cEAAPzBAAAZwpqZ+cEAADLCAAAbws3M/MEAAPzBAAAbwpqZ+cEAAPzBAAAbws3M/MEAADLCAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAhwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAhwpqZ+cEAADLCAAAhws3M/MEAAPzBAAAhwpqZ+cEAAPzBAAAhws3M/MEAADLCAAAhwpqZ+cEAADLCAAAhwpqZ+cEAAPzBAAAhwpqZ+cEAADLCAAAjws3M/MEAAPzBAAAjwpqZ+cEAAPzBAAAjws3M/MEAADLCAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAApwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAApwpqZ+cEAADLCAAApws3M/MEAAPzBAAApwpqZ+cEAAPzBAAApws3M/MEAADLCAAApwpqZ+cEAADLCAAApwpqZ+cEAAPzBAAApwpqZ+cEAADLCAAArws3M/MEAAPzBAAArwpqZ+cEAAPzBAAArws3M/MEAADLCAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAAxwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAAxwpqZ+cEAADLCAAAxws3M/MEAAPzBAAAxwpqZ+cEAAPzBAAAxws3M/MEAADLCAAAxwpqZ+cEAADLCAAAxwpqZ+cEAAPzBAAAxwpqZ+cEAADLCAAAzws3M/MEAAPzBAAAzwpqZ+cEAAPzBAAAzws3M/MEAADLCAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAA5wpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAA5wpqZ+cEAADLCAAA5ws3M/MEAAPzBAAA5wpqZ+cEAAPzBAAA5ws3M/MEAADLCAAA5wpqZ+cEAADLCAAA5wpqZ+cEAAPzBAAA5wpqZ+cEAADLCAAA7ws3M/MEAAPzBAAA7wpqZ+cEAAPzBAAA7ws3M/MEAADLCAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAABBwpqZ+cEAAPzBAAA7wpqZ+cEAADLCAABBwpqZ+cEAADLCAABBws3M/MEAAPzBAABBwpqZ+cEAAPzBAABBws3M/MEAADLCAABBwpqZ+cEAADLCAABBwpqZ+cEAAPzBAABBwpqZ+cEAADLCAABDws3M/MEAAPzBAABDwpqZ+cEAAPzBAABDws3M/MEAADLCAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABJwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABJwpqZ+cEAADLCAABJws3M/MEAAPzBAABJwpqZ+cEAAPzBAABJws3M/MEAADLCAABJwpqZ+cEAADLCAABJwpqZ+cEAAPzBAABJwpqZ+cEAADLCAABLws3M/MEAAPzBAABLwpqZ+cEAAPzBAABLws3M/MEAADLCAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABRwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABRwpqZ+cEAADLCAABRws3M/MEAAPzBAABRwpqZ+cEAAPzBAABRws3M/MEAADLCAABRwpqZ+cEAADLCAABRwpqZ+cEAAPzBAABRwpqZ+cEAADLCAABTws3M/MEAAPzBAABTwpqZ+cEAAPzBAABTws3M/MEAADLCAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABZwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABZwpqZ+cEAADLCAABZws3M/MEAAPzBAABZwpqZ+cEAAPzBAABZws3M/MEAADLCAABZwpqZ+cEAADLCAABZwpqZ+cEAAPzBAABZwpqZ+cEAADLCAABbws3M/MEAAPzBAABbwpqZ+cEAAPzBAABbws3M/MEAADLCAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABhwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABhwpqZ+cEAADLCAABhws3M/MEAAPzBAABhwpqZ+cEAAPzBAABhws3M/MEAADLCAABhwpqZ+cEAADLCAABhwpqZ+cEAAPzBAABhwpqZ+cEAADLCAABjws3M/MEAAPzBAABjwpqZ+cEAAPzBAABjws3M/MEAADLCAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABpwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABpwpqZ+cEAADLCAABpws3M/MEAAPzBAABpwpqZ+cEAAPzBAABpws3M/MEAADLCAABpwpqZ+cEAADLCAABpwpqZ+cEAAPzBAABpwpqZ+cEAADLCAABrws3M/MEAAPzBAABrwpqZ+cEAAPzBAABrws3M/MEAADLCAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABxwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABxwpqZ+cEAADLCAABxws3M/MEAAPzBAABxwpqZ+cEAAPzBAABxws3M/MEAADLCAABxwpqZ+cEAADLCAABxwpqZ+cEAAPzBAABxwpqZ+cEAADLCAABzws3M/MEAAPzBAABzwpqZ+cEAAPzBAABzws3M/MEAADLCAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAAB9wpqZ+cEAAPzBAABzwpqZ+cEAADLCAAB9wpqZ+cEAADLCAAB9ws3M/MEAAPzBAAB9wpqZ+cEAAPzBAAB9ws3M/MEAADLCAAB9wpqZ+cEAADLCAAB9wpqZ+cEAAPzBAAB9wpqZ+cEAADLCAICBws3M/MEAAPzBAICBwpqZ+cEAAPzBAICBws3M/MEAADLCAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICGwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICGwpqZ+cEAADLCAICGws3M/MEAAPzBAICGwpqZ+cEAAPzBAICGws3M/MEAADLCAICGwpqZ+cEAADLCAICGwpqZ+cEAAPzBAICGwpqZ+cEAADLCAICHws3M/MEAAPzBAICHwpqZ+cEAAPzBAICHws3M/MEAADLCAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICKwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICKwpqZ+cEAADLCAICKws3M/MEAAPzBAICKwpqZ+cEAAPzBAICKws3M/MEAADLCAICKwpqZ+cEAADLCAICKwpqZ+cEAAPzBAICKwpqZ+cEAADLCAICLws3M/MEAAPzBAICLwpqZ+cEAAPzBAICLws3M/MEAADLCAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICOwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICOwpqZ+cEAADLCAICOws3M/MEAAPzBAICOwpqZ+cEAAPzBAICOws3M/MEAADLCAICOwpqZ+cEAADLCAICOwpqZ+cEAAPzBAICOwpqZ+cEAADLCAICPws3M/MEAAPzBAICPwpqZ+cEAAPzBAICPws3M/MEAADLCAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICSwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICSwpqZ+cEAADLCAICSws3M/MEAAPzBAICSwpqZ+cEAAPzBAICSws3M/MEAADLCAICSwpqZ+cEAADLCAICSwpqZ+cEAAPzBAICSwpqZ+cEAADLCAICTws3M/MEAAPzBAICTwpqZ+cEAAPzBAICTws3M/MEAADLCAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICWwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICWwpqZ+cEAADLCAICWws3M/MEAAPzBAICWwpqZ+cEAAPzBAICWws3M/MEAADLCAICWwpqZ+cEAADLCAICWwpqZ+cEAAPzBAICWwpqZ+cEAADLCAICXws3M/MEAAPzBAICXwpqZ+cEAAPzBAICXws3M/MEAADLCAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICawpqZ+cEAAPzBAICXwpqZ+cEAADLCAICawpqZ+cEAADLCAICaws3M/MEAAPzBAICawpqZ+cEAAPzBAICaws3M/MEAADLCAICawpqZ+cEAADLCAICawpqZ+cEAAPzBAICawpqZ+cEAADLCAICbws3M/MEAAPzBAICbwpqZ+cEAAPzBAICbws3M/MEAADLCAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICewpqZ+cEAAPzBAICbwpqZ+cEAADLCAICewpqZ+cEAADLCAICews3M/MEAAPzBAICewpqZ+cEAAPzBAICews3M/MEAADLCAICewpqZ+cEAADLCAICewpqZ+cEAAPzBAICewpqZ+cEAADLCAICfws3M/MEAAPzBAICfwpqZ+cEAAPzBAICfws3M/MEAADLCAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICiwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICiwpqZ+cEAADLCAICiws3M/MEAAPzBAICiwpqZ+cEAAPzBAICiws3M/MEAADLCAICiwpqZ+cEAADLCAICiwpqZ+cEAAPzBAICiwpqZ+cEAADLCAICjws3M/MEAAPzBAICjwpqZ+cEAAPzBAICjws3M/MEAADLCAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICmwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICmwpqZ+cEAADLCAICmws3M/MEAAPzBAICmwpqZ+cEAAPzBAICmws3M/MEAADLCAICmwpqZ+cEAADLCAICmwpqZ+cEAAPzBAICmwpqZ+cEAADLCAICnws3M/MEAAPzBAICnwpqZ+cEAAPzBAICnws3M/MEAADLCAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICqwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICqwpqZ+cEAADLCAICqws3M/MEAAPzBAICqwpqZ+cEAAPzBAICqws3M/MEAADLCAICqwpqZ+cEAADLCAICqwpqZ+cEAAPzBAICqwpqZ+cEAADLCAICrws3M/MEAAPzBAICrwpqZ+cEAAPzBAICrws3M/MEAADLCAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICuwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICuwpqZ+cEAADLCAICuws3M/MEAAPzBAICuwpqZ+cEAAPzBAICuws3M/MEAADLCAICuwpqZ+cEAADLCAICuwpqZ+cEAAPzBAICuwpqZ+cEAADLCAICvws3M/MEAAPzBAICvwpqZ+cEAAPzBAICvws3M/MEAADLCAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICywpqZ+cEAAPzBAICvwpqZ+cEAADLCAICywpqZ+cEAADLCAICyws3M/MEAAPzBAICywpqZ+cEAAPzBAICyws3M/MEAADLCAICywpqZ+cEAADLCAICywpqZ+cEAAPzBAICywpqZ+cEAADLCAICzws3M/MEAAPzBAICzwpqZ+cEAAPzBAICzws3M/MEAADLCAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAIC2wpqZ+cEAAPzBAICzwpqZ+cEAADLCAIC2wpqZ+cEAADLCAIC2ws3M/MEAAPzBAIC2wpqZ+cEAAPzBAIC2ws3M/MEAADLCAIC2wpqZ+cEAADLCAIC2wpqZ+cEAAPzBAIC2wpqZ+cEAADLCAIC3ws3M/MEAAPzBAIC3wpqZ+cEAAPzBAIC3ws3M/MEAADLCAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC6wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC6wpqZ+cEAADLCAIC6ws3M/MEAAPzBAIC6wpqZ+cEAAPzBAIC6ws3M/MEAADLCAIC6wpqZ+cEAADLCAIC6wpqZ+cEAAPzBAIC6wpqZ+cEAADLC\"}],\"images\":[{\"uri\":\"data:image/png;base64,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- ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 7 - } - ], - "source": [ - "import stim\n", - "\n", - "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", - "circuit.diagram('timeline-3d')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YDnwv2dacTbf" - }, - "source": [ - "# Estimating code distance with Stim" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "r2MFDDBMvkq3", - "outputId": "cc6b8bfe-9adb-4b55-9c47-ed2580177b44" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "estimated distance: 5\n" - ] - } - ], - "source": [ - "distance_estimate = len(circuit.search_for_undetectable_logical_errors(\n", - " dont_explore_detection_event_sets_with_size_above=6,\n", - " dont_explore_edges_with_degree_above=3,\n", - " dont_explore_edges_increasing_symptom_degree=False))\n", - "print(f'estimated distance: {distance_estimate}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UuYvEwq9cgYc" - }, - "source": [ - "# Create DEM, detection events and observables with Stim" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6oCW5jRsXy8-" - }, - "source": [ - "### Can't decode with pymatching..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "x6TQbGZ7b06k", - "outputId": "c4caa541-cef7-498d-a410-c00b64cf8a79" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Traceback (most recent call last):\n", - " File \"/tmp/ipython-input-2195602962.py\", line 5, in \n", - " circuit.detector_error_model(decompose_errors=True)\n", - "ValueError: Failed to decompose errors into graphlike components with at most two symptoms.\n", - "The error component that failed to decompose is 'D46, D49, D52, D63, D66, D73, D75'.\n", - "\n", - "In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.\n", - "From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.\n", - "\n", - "Circuit stack trace:\n", - " during TICK layer #46 of 75\n", - " at instruction #104 [which is a REPEAT 3 block]\n" - ] - } - ], - "source": [ - "import traceback\n", - "\n", - "try:\n", - " # decompose_errors=True needed for DEM to be matchable\n", - " circuit.detector_error_model(decompose_errors=True)\n", - "except:\n", - " traceback.print_exc()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ye5W7BJHX8DJ" - }, - "source": [ - "No need to decompose errors using tesseract:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "AVu7idoTYAdM" - }, - "outputs": [], - "source": [ - "dem = circuit.detector_error_model()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "vFDn06Xach0_" - }, - "outputs": [], - "source": [ - "num_shots = 1000\n", - "sampler = circuit.compile_detector_sampler()\n", - "dets, obs = sampler.sample(num_shots, separate_observables=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JrX13vNQcrm3" - }, - "source": [ - "# Decoding with Tesseract and ILP decoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Uds8S04a-z-G" - }, - "outputs": [], - "source": [ - "import tesseract_decoder\n", - "import tesseract_decoder.tesseract as tesseract\n", - "import numpy as np\n", - "import time\n", - "\n", - "# Helper functions for benchmarking\n", - "\n", - "def print_results(result):\n", - " print(\"Tesseract Decoder Stats:\")\n", - " print(f\" Number of Errors / num_shots: {results['num_errors']} / {results['num_shots']}\")\n", - " print(f\" Time: {results['time_seconds']:.4f} s\")\n", - " print()\n", - "\n", - "def run_tesseract_decoder(decoder, dets, obs):\n", - " # Run and time the Tesseract decoder\n", - " num_errors = 0\n", - " start_time = time.time()\n", - " obs_predicted = decoder.decode_batch(dets)\n", - " num_errors = np.sum(np.any(obs_predicted != obs, axis=1))\n", - " end_time = time.time()\n", - "\n", - " return {\n", - " 'num_errors': num_errors,\n", - " 'num_shots': len(dets),\n", - " 'time_seconds': end_time - start_time,\n", - " }\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D0Tx2eY3ctFw", - "outputId": "64f388af-f1db-4869-873f-3ab714ee8e9c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Tesseract decoder configurations --> TesseractConfig(dem=DetectorErrorModel_Object, det_beam=65535, no_revisit_dets=1, at_most_two_errors_per_detector=0, verbose=0, pqlimit=10000, det_orders=[[89, 86, 85, 83, 81, 88, 84, 82, 87, 74, 71, 78, 75, 69, 67, 63, 77, 66, 61, 72, 64, 65, 79, 68, 62, 73, 80, 70, 42, 76, 43, 46, 33, 32, 50, 44, 31, 57, 35, 36, 59, 51, 30, 58, 60, 48, 39, 45, 49, 34, 25, 7, 47, 5, 18, 26, 21, 2, 40, 24, 12, 29, 28, 55, 37, 56, 54, 53, 38, 15, 3, 16, 52, 20, 9, 19, 13, 8, 11, 10, 17, 41, 22, 6, 14, 23, 0, 1, 27, 4]], det_penalty=0, create_visualization=0)\n", - "\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 3 / 1000\n", - " Time: 3.6089 s\n", - "\n" - ] - } - ], - "source": [ - "# setup the tesseract decoder configuration\n", - "tesseract_config = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " no_revisit_dets=True,\n", - " # verbose=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=1, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print(f'Tesseract decoder configurations --> {tesseract_config}\\n')\n", - "\n", - "tesseract_dec = tesseract_config.compile_decoder()\n", - "\n", - "results = run_tesseract_decoder(tesseract_dec, dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "INvMKs7zc5T_" - }, - "source": [ - "#Decoding with ILP decoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9Npo7ibac4x5", - "outputId": "51af3bd2-5f53-43c8-a16d-f595a9596bde" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "ILP decoder configurations --> SimplexConfig(dem=DetectorErrorModel_Object, window_length=0, window_slide_length=0, verbose=0)\n", - "ILP stats:\n", - " Estimated time for full shots 1115.2181386947632 s\n", - " Number of Errors / num_shots: 0 / 10\n" - ] - } - ], - "source": [ - "simplex_config = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True\n", - ")\n", - "print(f'ILP decoder configurations --> {simplex_config}')\n", - "ilp_dec = simplex_config.compile_decoder()\n", - "\n", - "start_time = time.time()\n", - "\n", - "# Run and time ILP decoder -- so slow!\n", - "num_shots_to_decode = 10 # Only decoding 10 shots because it's soooo slow\n", - "obs_predicted = ilp_dec.decode_batch(dets[0:num_shots_to_decode])\n", - "num_errors = np.sum(np.any(obs_predicted != obs[0:num_shots_to_decode], axis=1))\n", - "\n", - "end_time = time.time()\n", - "print(f'ILP stats:\\n Estimated time for full shots {num_shots/num_shots_to_decode * (end_time - start_time)} s')\n", - "print(f\" Number of Errors / num_shots: {num_errors} / {num_shots_to_decode}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VQqlMqFRIZ2J" - }, - "source": [ - "# Tesseract Config and impact of heuristic\n", - "You can tune tesseract decoder through the Config that is passed to the decoder with this set of parameters:\n", - "Explanation of configuration arguments:\n", - "\n", - "* `pqlimit` - An integer that sets a limit on the number of nodes in the priority queue. This can be used to constrain the memory usage of the decoder. The default value is `sys.maxsize`, which means the size is effectively unbounded.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0pExdmuPQuGr", - "outputId": "45a20eca-fef6-425d-d6a2-9a619fe9e10c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Smaller pqlimit\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 183 / 1000\n", - " Time: 1.8420 s\n", - "\n", - "Larger pqlimit\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 3 / 1000\n", - " Time: 8.4409 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=100,\n", - " no_revisit_dets=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"Smaller pqlimit\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=20000,\n", - " no_revisit_dets=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Larger pqlimit\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ru-MRctAIq5-" - }, - "source": [ - "#More heurisitcs\n", - "* `det_beam` - This integer value represents the beam search cutoff. It specifies a threshold for the number of \"residual detection events\" a node can have before it is pruned from the search. A lower `det_beam` value makes the search more aggressive, potentially sacrificing accuracy for speed. The default value `INF_DET_BEAM` means no beam cutoff is applied.\n", - "* `beam_climbing` - A boolean flag that, when set to `True`, enables a heuristic called \"beam climbing.\" This optimization causes the decoder to try different `det_beam` values (up to a maximum) to find a good decoding path. This can improve the decoder's chance of finding the most likely error, even with an initial narrow beam search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cyctTUyzQ-cQ", - "outputId": "0f3c5a8a-0d09-49e4-e3b1-e193152bf2bf" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Smaller det_beam\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 2 / 1000\n", - " Time: 5.8029 s\n", - "\n", - "Larger det_beam\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 2 / 1000\n", - " Time: 6.1866 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=3,\n", - " beam_climbing=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"Smaller det_beam\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=5,\n", - " beam_climbing=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Larger det_beam\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VJiBCWpUQ9sf" - }, - "source": [ - "#Even More Heuristics\n", - "* `no_revisit_dets` - A boolean flag that, when `True`, activates a heuristic to prevent the decoder from revisiting nodes that have the same set of leftover detection events as a node it has already visited. This can help to reduce search redundancy and improve decoding speed.\n", - "* `at_most_two_errors_per_detector` - This boolean flag is a specific constraint that assumes at most two errors can affect a given detector. This can be a useful optimization for certain types of codes and noise models, as it prunes the search space by making a stronger assumption about the error distribution.\n", - "\n", - "* `det_orders` - A list of lists of integers, where each inner list represents an ordering of the detectors. This is used for \"ensemble reordering,\" an optimization that tries different detector orderings to improve the search's convergence. The default is an empty list, meaning a single, fixed ordering is used.\n", - "* `det_penalty` - A floating-point value that adds a cost for each residual detection event. This encourages the decoder to prioritize paths that resolve more detection events, steering the search towards more complete solutions. The default value is `0.0`, meaning no penalty is applied." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "KBmkwvhmupn-" + }, + "source": [ + "# Tesseract Tutorial\n", + "\n", + "- We will also, partly, explain how to use features of Stim and PyMatching\n", + "- Stim is a dependency of Tesseract but you can also use other sources of data\n", + "- This is not a comprehensive introduction." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jaZcr-NevBSB" + }, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "i6_88o7kKOVJ" + }, + "outputs": [], + "source": [ + "!pip install --quiet --upgrade stim galois tesseract-decoder pymatching python-sat -U" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RLXXX3eMT_LR" + }, + "source": [ + "## Getting a Surface Code Circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "8zcmVHFFUPq2" + }, + "outputs": [], + "source": [ + "import stim\n", + "\n", + "d = 11\n", + "p = 0.005\n", + "circuit = stim.Circuit.generated(\n", + " code_task=\"surface_code:rotated_memory_x\",\n", + " distance=d,\n", + " rounds=d,\n", + " after_clifford_depolarization=p,\n", + " before_round_data_depolarization=p,\n", + " before_measure_flip_probability=p,\n", + " after_reset_flip_probability=p\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UBMIlXY9U30Y" + }, + "source": [ + "## Sample" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "GCkUlTJZU2T_" + }, + "outputs": [], + "source": [ + "sampler = circuit.compile_detector_sampler()\n", + "\n", + "num_shots = 10000\n", + "detector_outcomes, actual_observables = sampler.sample(shots=num_shots, separate_observables=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m9x8pivTVCir" + }, + "source": [ + "## Decode with uncorrelated matching" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "-5W0AX8nVEyU", + "outputId": "562e78d3-4fef-449e-92ae-403e5ed7c862" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0VrW2z8sSXtN", - "outputId": "6f69c1ff-1c04-4dc6-d1a0-32aa0777d56b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "First version\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 9 / 1000\n", - " Time: 1.1290 s\n", - "\n", - "Second version\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 19 / 1000\n", - " Time: 0.9446 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " # no_revisit_dets=True,\n", - " # at_most_two_errors_per_detector = True,\n", - " det_penalty = 10,\n", - " # det_orders=tesseract_decoder.utils.build_det_orders(\n", - " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"First version\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " # no_revisit_dets=False,\n", - " # at_most_two_errors_per_detector = False,\n", - " det_penalty = 0,\n", - " # det_orders=tesseract_decoder.utils.build_det_orders(\n", - " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Second version\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Logical error rate: 61/10000\n" + ] + } + ], + "source": [ + "import pymatching\n", + "import numpy as np\n", + "\n", + "dem = circuit.detector_error_model(decompose_errors=True)\n", + "matching = pymatching.Matching.from_detector_error_model(model=dem)\n", + "predicted_observables = matching.decode_batch(shots=detector_outcomes)\n", + "num_errors = np.sum(np.any(predicted_observables != actual_observables, axis=1))\n", + "\n", + "print(f\"Logical error rate: {num_errors}/{num_shots}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xp7MyK0XVs_6" + }, + "source": [ + "## Decode with new correlated matching!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vufQ8G5iVx7b", + "outputId": "1e12759c-e1e4-4c51-8103-98ec2d6906f8" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "BoEALeo3OYGp" - }, - "source": [ - "# Decoding Wild Stabilizer Codes under Code Capacity Noise with Tesseract\n", - "\n", - "\n", - "\n", - "* checkout https://www.codetables.de/ for a qubit stabilizer code\n", - "* full table of qubit codes: [here](https://codetables.de/QECC/Tables_color.php?q=4&n0=1&n1=256&k0=0&k1=256)\n", - "* copy the stabilizer matrix for a code, such as: [this one used below](https://codetables.de/QECC/QECC.php?q=4&n=21&k=8)\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Logical error rate: 18/10000\n" + ] + } + ], + "source": [ + "dem = circuit.detector_error_model(decompose_errors=True)\n", + "matching_corr = pymatching.Matching.from_detector_error_model(\n", + " model=dem, enable_correlations=True\n", + " )\n", + "predicted_observables_corr = matching_corr.decode_batch(\n", + " shots=detector_outcomes,\n", + " enable_correlations=True\n", + " )\n", + "num_errors_corr = np.sum(np.any(predicted_observables_corr != actual_observables, axis=1))\n", + "\n", + "print(f\"Logical error rate: {num_errors_corr}/{num_shots}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a-AMqTUeuqOe" + }, + "source": [ + "## Getting a Color Code Circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "W7fU_MYJCRen", + "outputId": "6038fc3e-8707-4bac-fd69-b9d08a90f167" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pJ1gEKAgPbHO" - }, - "outputs": [], - "source": [ - "import time\n", - "import tesseract_decoder\n", - "import stim\n", - "import numpy as np\n", - "import numpy.typing as npt\n", - "from galois import GF2\n", - "from typing import List, Tuple\n", - "\n", - "\n", - "def paulis_from_symplectic_matrix(check_matrix: npt.NDArray[np.uint8]) -> List[stim.PauliString]:\n", - " n = check_matrix.shape[1] // 2\n", - " paulis = []\n", - " for i in range(check_matrix.shape[0]):\n", - " paulis.append(\n", - " stim.PauliString.from_numpy(\n", - " xs=check_matrix[i, :n].astype(bool), zs=check_matrix[i, n:].astype(bool)\n", - " )\n", - " )\n", - " return paulis\n", - "\n", - "def rank(H):\n", - " return np.linalg.matrix_rank(GF2(H))\n", - "\n", - "def stabilizer_code_logical_operators(\n", - " check_matrix: npt.NDArray[np.uint8]) -> Tuple[npt.NDArray[np.uint8], npt.NDArray[np.uint8]]:\n", - " check_matrix = np.array(check_matrix, dtype=np.uint8)\n", - "\n", - " r = rank(check_matrix)\n", - " n = check_matrix.shape[1] // 2\n", - "\n", - " stabilisers = paulis_from_symplectic_matrix(check_matrix=check_matrix)\n", - "\n", - " tableau = stim.Tableau.from_stabilizers(\n", - " stabilizers=stabilisers, allow_underconstrained=True, allow_redundant=True\n", - " )\n", - "\n", - " x2x, x2z, z2x, z2z, x_signs, z_signs = tableau.to_numpy()\n", - "\n", - " num_logicals = n - r\n", - "\n", - " Lx = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", - " Lz = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", - "\n", - " Lx[:, :n] = x2x[r:]\n", - " Lx[:, n:] = x2z[r:]\n", - " Lz[:, :n] = z2x[r:]\n", - " Lz[:, n:] = z2z[r:]\n", - " return Lx.astype(np.uint8), Lz.astype(np.uint8)\n", - "\n", - "\n", - "def pauli_to_observable_include_target(pauli: stim.PauliString) -> List[stim.GateTarget]:\n", - " obs_pauli_targets = []\n", - " for i in range(len(pauli)):\n", - " if pauli[i] != 0:\n", - " obs_pauli_targets.append(stim.target_pauli(i, pauli[i]))\n", - " return obs_pauli_targets\n", - "\n", - "\n", - "def append_observable_includes_for_paulis(circuit: stim.Circuit, paulis: List[stim.PauliString]) -> None:\n", - " for i, obs in enumerate(paulis):\n", - " circuit.append(\n", - " \"OBSERVABLE_INCLUDE\",\n", - " targets=pauli_to_observable_include_target(pauli=obs),\n", - " arg=i\n", - " )\n", - "\n", - "\n", - "def code_capacity_circuit(\n", - " stabilizers: npt.NDArray[np.uint8],\n", - " x_logicals: npt.NDArray[np.uint8],\n", - " z_logicals: npt.NDArray[np.uint8],\n", - " p: float\n", - ") -> stim.Circuit:\n", - " \"\"\"Generate a code capacity stim circuit for a stabilizer code\n", - "\n", - " Parameters\n", - " ----------\n", - " stabilizers : npt.NDArray[np.uint8]\n", - " The stabilizer generators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (r, 2 * n) where r is the number of stabilizer\n", - " generators and n is the number of physical qubits.\n", - " `stabilizers[i, j]` is 1 if stabilizer i is X or Y on qubit j and 0 otherwise.\n", - " `stabilizers[i, n + j]` is 1 if stabilizer i is Z or Y on qubit j and 0 otherwise.\n", - " x_logicals : npt.NDArray[np.uint8]\n", - " The X logical operators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", - " and n is the number of physical qubits.\n", - " z_logicals : npt.NDArray[np.uint8]\n", - " The Z logical operators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", - " and n is the number of physical qubits.\n", - " p : float\n", - " The strength of single-qubit depolarizing noise to use\n", - "\n", - " Returns\n", - " -------\n", - " stim.Circuit\n", - " The stim circuit of the code capacity circuit\n", - " \"\"\"\n", - " num_qubits = stabilizers.shape[1] // 2\n", - " num_stabilizers = stabilizers.shape[0]\n", - " stabilizer_paulis = paulis_from_symplectic_matrix(stabilizers)\n", - " x_logicals_paulis = paulis_from_symplectic_matrix(x_logicals)\n", - " z_logicals_paulis = paulis_from_symplectic_matrix(z_logicals)\n", - " all_logicals_paulis = x_logicals_paulis + z_logicals_paulis\n", - "\n", - " circuit = stim.Circuit()\n", - "\n", - " append_observable_includes_for_paulis(\n", - " circuit=circuit, paulis=all_logicals_paulis)\n", - " circuit.append(\"MPP\", stabilizer_paulis)\n", - " circuit.append(\"DEPOLARIZE1\", targets=list(range(num_qubits)), arg=p)\n", - " circuit.append(\"MPP\", stabilizer_paulis)\n", - "\n", - " for i in range(num_stabilizers):\n", - " circuit.append(\n", - " \"DETECTOR\",\n", - " targets=[\n", - " stim.target_rec(i - 2 * num_stabilizers),\n", - " stim.target_rec(i - num_stabilizers)\n", - " ]\n", - " )\n", - "\n", - " append_observable_includes_for_paulis(\n", - " circuit=circuit, paulis=all_logicals_paulis)\n", - " return circuit\n", - "\n", - "\n", - "def parse_symplectic_matrix(text: str) -> npt.NDArray[np.uint8]:\n", - " rows = []\n", - " for line in text.strip().splitlines():\n", - " line = line.strip()\n", - " if not line or line[0] != '[' or line[-1] != ']':\n", - " continue # skip malformed lines\n", - " body = line[1:-1]\n", - " if \"|\" in body:\n", - " left, right = body.split(\"|\")\n", - " bits = left.strip().split() + right.strip().split()\n", - " else:\n", - " bits = body.strip().split()\n", - " row = [int(b) for b in bits]\n", - " rows.append(row)\n", - " return np.array(rows, dtype=np.uint8)\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 13295 100 13295 0 0 124k 0 --:--:-- --:--:-- --:--:-- 124k\n" + ] + } + ], + "source": [ + "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D5%2Cd%3D5%2Cp%3D0.001%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_Z%2Cq%3D37%2Cgates%3Dcz.stim' > d5r5colorcode_p001.stim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E-vXEhbaTeQI" + }, + "source": [ + "# Visualizing with Stim" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 }, + "id": "2jTOVijwKPXm", + "outputId": "5a0c63b5-384b-4729-8bf1-d205552de185" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 343 - }, - "id": "pH_b3u1rBogl", - "outputId": "846dd807-82ed-4139-e46d-a3cfe65a7681" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - 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- ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 19 - } + "data": { + "text/html": [ + "" ], - "source": [ - "# Example QEC code:\n", - "text = '''[1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 0|0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0]\n", - " [0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0|1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0]\n", - " [0 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0|0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0]\n", - " [0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0|0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0]\n", - " [0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0|0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0]\n", - " [0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0|0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0]\n", - " [0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0|0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0]\n", - " [0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0|0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0]\n", - " [0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]'''\n", - "\n", - "H = parse_symplectic_matrix(text)\n", - "\n", - "LX, LZ = stabilizer_code_logical_operators(check_matrix=H)\n", - "\n", - "circuit = code_capacity_circuit(\n", - " stabilizers=H,\n", - " x_logicals=LX,\n", - " z_logicals=LZ,\n", - " p=0.025\n", - ")\n", - "\n", - "circuit.diagram('timeline-3d')" + "text/plain": [ + 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AAA4wgAAOMIAAADCAAA4wgAAOMIAAAjCAAA4wgAAQMIAAADCAAA4wgAAQMIAAAjCAABEwgAACMIAAADCAABEwgAACMIAAAjCAABEwgAAEMIAAADCAABEwgAAEMIAAAjCAABEwgAAEMIAABDCAABEwgAAEMIAABjCAABEwgAAGMIAAAjCAABEwgAAGMIAABDCAABEwgAAGMIAABjCAABEwgAAGMIAACDCAABEwgAAIMIAAAjCAABEwgAAIMIAABDCAABEwgAAIMIAABjCAABEwgAAIMIAACDCAABEwgAAIMIAACjCAABEwgAAIMIAADDCAABEwgAAKMIAAADCAABEwgAAKMIAAAjCAABEwgAAKMIAABDCAABEwgAAKMIAABjCAABEwgAAKMIAACDCAABEwgAAKMIAACjCAABEwgAAMMIAAADCAABEwgAAMMIAAAjCAABEwgAAMMIAABDCAABEwgAAMMIAABjCAABEwgAAOMIAAAjCAABEwgAAOMIAABDCAABIwgAACMIAAADCAABIwgAACMIAAAjCAABIwgAAEMIAAADCAABIwgAAEMIAAAjCAABIwgAAEMIAABDCAABIwgAAEMIAABjCAABIwgAAGMIAAAjCAABIwgAAGMIAABDCAABIwgAAGMIAABjCAABIwgAAGMIAACDCAABIwgAAIMIAAAjCAABIwgAAIMIAABDCAABIwgAAIMIAABjCAABIwgAAIMIAACDCAABIwgAAIMIAACjCAABIwgAAIMIAADDCAABIwgAAKMIAAADCAABIwgAAKMIAAAjCAABIwgAAKMIAABDCAABIwgAAKMIAABjCAABIwgAAKMIAACDCAABIwgAAKMIAACjCAABIwgAAMMIAAADCAABIwgAAMMIAAAjCAABIwgAAMMIAABDCAABIwgAAMMIAABjCAABIwgAAOMIAAAjCAABIwgAAOMIAABDCAABMwgAAAMIAAADCAABMwgAACMIAAADCAABMwgAAEMIAAADCAABMwgAAGMIAAADCAABMwgAAEMIAAAjCAABMwgAAGMIAAAjCAABMwgAAEMIAABDCAABMwgAAGMIAABDCAABMwgAAEMIAABjCAABMwgAAGMIAABjCAABMwgAAIMIAAADCAABMwgAAKMIAAADCAABMwgAAIMIAAAjCAABMwgAAKMIAAAjCAABMwgAAIMIAABDCAABMwgAAKMIAABDCAABMwgAAIMIAABjCAABMwgAAKMIAABjCAABMwgAAIMIAACDCAABMwgAAKMIAACDCAABMwgAAIMIAACjCAABMwgAAKMIAACjCAABMwgAAMMIAAADCAABMwgAAOMIAAADCAABMwgAAMMIAAAjCAABMwgAAOMIAAAjCAABMwgAAMMIAABDCAABMwgAAOMIAABDCAABQwgAAAMIAAADCAABQwgAACMIAAADCAABQwgAAEMIAAADCAABQwgAAGMIAAADCAABQwgAAEMIAAAjCAABQwgAAGMIAAAjCAABQwgAAEMIAABDCAABQwgAAGMIAABDCAABQwgAAEMIAABjCAABQwgAAGMIAABjCAABQwgAAIMIAAADCAABQwgAAKMIAAADCAABQwgAAIMIAAAjCAABQwgAAKMIAAAjCAABQwgAAIMIAABDCAABQwgAAKMIAABDCAABQwgAAIMIAABjCAABQwgAAKMIAABjCAABQwgAAIMIAACDCAABQwgAAKMIAACDCAABQwgAAIMIAACjCAABQwgAAKMIAACjCAABQwgAAMMIAAADCAABQwgAAOMIAAADCAABQwgAAMMIAAAjCAABQwgAAOMIAAAjCAABQwgAAMMIAABDCAABQwgAAOMIAABDCAABUwgAACMIAAAjCAABUwgAACMIAABDCAABUwgAAEMIAAAjCAABUwgAAEMIAABDCAABUwgAAGMIAAADCAABUwgAAGMIAAAjCAABUwgAAGMIAABDCAABUwgAAGMIAABjCAABUwgAAGMIAACDCAABUwgAAGMIAACjCAABUwgAAIMIAAADCAABUwgAAIMIAAAjCAABUwgAAIMIAABDCAABUwgAAIMIAABjCAABUwgAAIMIAACDCAABUwgAAIMIAACjCAABUwgAAKMIAAAjCAABUwgAAKMIAABDCAABUwgAAKMIAABjCAABUwgAAKMIAACDCAABUwgAAMMIAAAjCAABUwgAAMMIAABDCAABUwgAAMMIAABjCAABUwgAAMMIAACDCAABUwgAAOMIAAADCAABUwgAAOMIAAAjCAABUwgAAQMIAAADCAABUwgAAQMIAAAjCAABYwgAACMIAAAjCAABYwgAACMIAABDCAABYwgAAEMIAAAjCAABYwgAAEMIAABDCAABYwgAAGMIAAADCAABYwgAAGMIAAAjCAABYwgAAGMIAABDCAABYwgAAGMIAABjCAABYwgAAGMIAACDCAABYwgAAGMIAACjCAABYwgAAIMIAAADCAABYwgAAIMIAAAjCAABYwgAAIMIAABDCAABYwgAAIMIAABjCAABYwgAAIMIAACDCAABYwgAAIMIAACjCAABYwgAAKMIAAAjCAABYwgAAKMIAABDCAABYwgAAKMIAABjCAABYwgAAKMIAACDCAABYwgAAMMIAAAjCAABYwgAAMMIAABDCAABYwgAAMMIAABjCAABYwgAAMMIAACDCAABYwgAAOMIAAADCAABYwgAAOMIAAAjCAABYwgAAQMIAAADCAABYwgAAQMIAAAjCAABkwgAACMIAAADCAABkwgAAEMIAAADCAABkwgAACMIAABDCAABkwgAAEMIAABDCAABkwgAAGMIAAAjCAABkwgAAIMIAAAjCAABkwgAAGMIAABjCAABkwgAAIMIAABjCAABkwgAAGMIAACjCAABkwgAAIMIAACjCAABkwgAAKMIAAADCAABkwgAAMMIAAADCAABkwgAAKMIAABDCAABkwgAAMMIAABDCAABkwgAAKMIAACDCAABkwgAAMMIAACDCAABkwgAAOMIAAAjCAABkwgAAQMIAAAjCAABowgAACMIAAADCAABowgAAEMIAAADCAABowgAACMIAABDCAABowgAAEMIAABDCAABowgAAGMIAAAjCAABowgAAIMIAAAjCAABowgAAGMIAABjCAABowgAAIMIAABjCAABowgAAGMIAACjCAABowgAAIMIAACjCAABowgAAKMIAAADCAABowgAAMMIAAADCAABowgAAKMIAABDCAABowgAAMMIAABDCAABowgAAKMIAACDCAABowgAAMMIAACDCAABowgAAOMIAAAjCAABowgAAQMIAAAjCAACMwgAACMIAAADCAACMwgAAEMIAAADCAACMwgAACMIAABDCAACMwgAAEMIAABDCAACMwgAAGMIAAAjCAACMwgAAIMIAAAjCAACMwgAAGMIAABjCAACMwgAAIMIAABjCAACMwgAAGMIAACjCAACMwgAAIMIAACjCAACMwgAAKMIAAADCAACMwgAAMMIAAADCAACMwgAAKMIAABDCAACMwgAAMMIAABDCAACMwgAAKMIAACDCAACMwgAAMMIAACDCAACMwgAAOMIAAAjCAACMwgAAQMIAAAjCAACOwgAACMIAAADCAACOwgAAEMIAAADCAACOwgAACMIAABDCAACOwgAAEMIAABDCAACOwgAAGMIAAAjCAACOwgAAIMIAAAjCAACOwgAAGMIAABjCAACOwgAAIMIAABjCAACOwgAAGMIAACjCAACOwgAAIMIAACjCAACOwgAAKMIAAADCAACOwgAAMMIAAADCAACOwgAAKMIAABDCAACOwgAAMMIAABDCAACOwgAAKMIAACDCAACOwgAAMMIAACDCAACOwgAAOMIAAAjCAACOwgAAQMIAAAjCAACUwgAACMIAAAjCAACUwgAACMIAABDCAACUwgAAEMIAAAjCAACUwgAAEMIAABDCAACUwgAAGMIAAADCAACUwgAAGMIAAAjCAACUwgAAGMIAABDCAACUwgAAGMIAABjCAACUwgAAGMIAACDCAACUwgAAGMIAACjCAACUwgAAIMIAAADCAACUwgAAIMIAAAjCAACUwgAAIMIAABDCAACUwgAAIMIAABjCAACUwgAAIMIAACDCAACUwgAAIMIAACjCAACUwgAAKMIAAAjCAACUwgAAKMIAABDCAACUwgAAKMIAABjCAACUwgAAKMIAACDCAACUwgAAMMIAAAjCAACUwgAAMMIAABDCAACUwgAAMMIAABjCAACUwgAAMMIAACDCAACUwgAAOMIAAADCAACUwgAAOMIAAAjCAACUwgAAQMIAAADCAACUwgAAQMIAAAjCAACWwgAACMIAAAjCAACWwgAACMIAABDCAACWwgAAEMIAAAjCAACWwgAAEMIAABDCAACWwgAAGMIAAADCAACWwgAAGMIAAAjCAACWwgAAGMIAABDCAACWwgAAGMIAABjCAACWwgAAGMIAACDCAACWwgAAGMIAACjCAACWwgAAIMIAAADCAACWwgAAIMIAAAjCAACWwgAAIMIAABDCAACWwgAAIMIAABjCAACWwgAAIMIAACDCAACWwgAAIMIAACjCAACWwgAAKMIAAAjCAACWwgAAKMIAABDCAACWwgAAKMIAABjCAACWwgAAKMIAACDCAACWwgAAMMIAAAjCAACWwgAAMMIAABDCAACWwgAAMMIAABjCAACWwgAAMMIAACDCAACWwgAAOMIAAADCAACWwgAAOMIAAAjCAACWwgAAQMIAAADCAACWwgAAQMIAAAjCAACYwgAAAMIAAADCAACYwgAACMIAAADCAACYwgAAEMIAAADCAACYwgAAGMIAAADCAACYwgAAEMIAAAjCAACYwgAAGMIAAAjCAACYwgAAEMIAABDCAACYwgAAGMIAABDCAACYwgAAEMIAABjCAACYwgAAGMIAABjCAACYwgAAIMIAAADCAACYwgAAKMIAAADCAACYwgAA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zBAADKwc3M/MEAADLCAADKwZqZ+cEAADLCAADKwZqZ+cEAAPzBAADKwZqZ+cEAADLCAADOwc3M/MEAAPzBAADOwZqZ+cEAAPzBAADOwc3M/MEAADLCAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADawZqZ+cEAAPzBAADOwZqZ+cEAADLCAADawZqZ+cEAADLCAADawc3M/MEAAPzBAADawZqZ+cEAAPzBAADawc3M/MEAADLCAADawZqZ+cEAADLCAADawZqZ+cEAAPzBAADawZqZ+cEAADLCAADewc3M/MEAAPzBAADewZqZ+cEAAPzBAADewc3M/MEAADLCAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADywZqZ+cEAAPzBAADewZqZ+cEAADLCAADywZqZ+cEAADLCAADywc3M/MEAAPzBAADywZqZ+cEAAPzBAADywc3M/MEAADLCAADywZqZ+cEAADLCAADywZqZ+cEAAPzBAADywZqZ+cEAADLCAAD+wc3M/MEAAPzBAAD+wZqZ+cEAAPzBAAD+wc3M/MEAADLCAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAAJwpqZ+cEAAPzBAAD+wZqZ+cEAADLCAAAJwpqZ+cEAADLCAAAJws3M/MEAAPzBAAAJwpqZ+cEAAPzBAAAJws3M/MEAADLCAAAJwpqZ+cEAADLCAAAJwpqZ+cEAAPzBAAAJwpqZ+cEAADLCAAALws3M/MEAAPzBAAALwpqZ+cEAAPzBAAALws3M/MEAADLCAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAARwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAARwpqZ+cEAADLCAAARws3M/MEAAPzBAAARwpqZ+cEAAPzBAAARws3M/MEAADLCAAARwpqZ+cEAADLCAAARwpqZ+cEAAPzBAAARwpqZ+cEAADLCAAATws3M/MEAAPzBAAATwpqZ+cEAAPzBAAATws3M/MEAADLCAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAAZwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAAZwpqZ+cEAADLCAAAZws3M/MEAAPzBAAAZwpqZ+cEAAPzBAAAZws3M/MEAADLCAAAZwpqZ+cEAADLCAAAZwpqZ+cEAAPzBAAAZwpqZ+cEAADLCAAAbws3M/MEAAPzBAAAbwpqZ+cEAAPzBAAAbws3M/MEAADLCAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAhwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAhwpqZ+cEAADLCAAAhws3M/MEAAPzBAAAhwpqZ+cEAAPzBAAAhws3M/MEAADLCAAAhwpqZ+cEAADLCAAAhwpqZ+cEAAPzBAAAhwpqZ+cEAADLCAAAjws3M/MEAAPzBAAAjwpqZ+cEAAPzBAAAjws3M/MEAADLCAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAApwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAApwpqZ+cEAADLCAAApws3M/MEAAPzBAAApwpqZ+cEAAPzBAAApws3M/MEAADLCAAApwpqZ+cEAADLCAAApwpqZ+cEAAPzBAAApwpqZ+cEAADLCAAArws3M/MEAAPzBAAArwpqZ+cEAAPzBAAArws3M/MEAADLCAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAAxwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAAxwpqZ+cEAADLCAAAxws3M/MEAAPzBAAAxwpqZ+cEAAPzBAAAxws3M/MEAADLCAAAxwpqZ+cEAADLCAAAxwpqZ+cEAAPzBAAAxwpqZ+cEAADLCAAAzws3M/MEAAPzBAAAzwpqZ+cEAAPzBAAAzws3M/MEAADLCAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAA5wpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAA5wpqZ+cEAADLCAAA5ws3M/MEAAPzBAAA5wpqZ+cEAAPzBAAA5ws3M/MEAADLCAAA5wpqZ+cEAADLCAAA5wpqZ+cEAAPzBAAA5wpqZ+cEAADLCAAA7ws3M/MEAAPzBAAA7wpqZ+cEAAPzBAAA7ws3M/MEAADLCAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAABBwpqZ+cEAAPzBAAA7wpqZ+cEAADLCAABBwpqZ+cEAADLCAABBws3M/MEAAPzBAABBwpqZ+cEAAPzBAABBws3M/MEAADLCAABBwpqZ+cEAADLCAABBwpqZ+cEAAPzBAABBwpqZ+cEAADLCAABDws3M/MEAAPzBAABDwpqZ+cEAAPzBAABDws3M/MEAADLCAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABJwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABJwpqZ+cEAADLCAABJws3M/MEAAPzBAABJwpqZ+cEAAPzBAABJws3M/MEAADLCAABJwpqZ+cEAADLCAABJwpqZ+cEAAPzBAABJwpqZ+cEAADLCAABLws3M/MEAAPzBAABLwpqZ+cEAAPzBAABLws3M/MEAADLCAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABRwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABRwpqZ+cEAADLCAABRws3M/MEAAPzBAABRwpqZ+cEAAPzBAABRws3M/MEAADLCAABRwpqZ+cEAADLCAABRwpqZ+cEAAPzBAABRwpqZ+cEAADLCAABTws3M/MEAAPzBAABTwpqZ+cEAAPzBAABTws3M/MEAADLCAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABZwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABZwpqZ+cEAADLCAABZws3M/MEAAPzBAABZwpqZ+cEAAPzBAABZws3M/MEAADLCAABZwpqZ+cEAADLCAABZwpqZ+cEAAPzBAABZwpqZ+cEAADLCAABbws3M/MEAAPzBAABbwpqZ+cEAAPzBAABbws3M/MEAADLCAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABhwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABhwpqZ+cEAADLCAABhws3M/MEAAPzBAABhwpqZ+cEAAPzBAABhws3M/MEAADLCAABhwpqZ+cEAADLCAABhwpqZ+cEAAPzBAABhwpqZ+cEAADLCAABjws3M/MEAAPzBAABjwpqZ+cEAAPzBAABjws3M/MEAADLCAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABpwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABpwpqZ+cEAADLCAABpws3M/MEAAPzBAABpwpqZ+cEAAPzBAABpws3M/MEAADLCAABpwpqZ+cEAADLCAABpwpqZ+cEAAPzBAABpwpqZ+cEAADLCAABrws3M/MEAAPzBAABrwpqZ+cEAAPzBAABrws3M/MEAADLCAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABxwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABxwpqZ+cEAADLCAABxws3M/MEAAPzBAABxwpqZ+cEAAPzBAABxws3M/MEAADLCAABxwpqZ+cEAADLCAABxwpqZ+cEAAPzBAABxwpqZ+cEAADLCAABzws3M/MEAAPzBAABzwpqZ+cEAAPzBAABzws3M/MEAADLCAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAAB9wpqZ+cEAAPzBAABzwpqZ+cEAADLCAAB9wpqZ+cEAADLCAAB9ws3M/MEAAPzBAAB9wpqZ+cEAAPzBAAB9ws3M/MEAADLCAAB9wpqZ+cEAADLCAAB9wpqZ+cEAAPzBAAB9wpqZ+cEAADLCAICBws3M/MEAAPzBAICBwpqZ+cEAAPzBAICBws3M/MEAADLCAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICGwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICGwpqZ+cEAADLCAICGws3M/MEAAPzBAICGwpqZ+cEAAPzBAICGws3M/MEAADLCAICGwpqZ+cEAADLCAICGwpqZ+cEAAPzBAICGwpqZ+cEAADLCAICHws3M/MEAAPzBAICHwpqZ+cEAAPzBAICHws3M/MEAADLCAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICKwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICKwpqZ+cEAADLCAICKws3M/MEAAPzBAICKwpqZ+cEAAPzBAICKws3M/MEAADLCAICKwpqZ+cEAADLCAICKwpqZ+cEAAPzBAICKwpqZ+cEAADLCAICLws3M/MEAAPzBAICLwpqZ+cEAAPzBAICLws3M/MEAADLCAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICOwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICOwpqZ+cEAADLCAICOws3M/MEAAPzBAICOwpqZ+cEAAPzBAICOws3M/MEAADLCAICOwpqZ+cEAADLCAICOwpqZ+cEAAPzBAICOwpqZ+cEAADLCAICPws3M/MEAAPzBAICPwpqZ+cEAAPzBAICPws3M/MEAADLCAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICSwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICSwpqZ+cEAADLCAICSws3M/MEAAPzBAICSwpqZ+cEAAPzBAICSws3M/MEAADLCAICSwpqZ+cEAADLCAICSwpqZ+cEAAPzBAICSwpqZ+cEAADLCAICTws3M/MEAAPzBAICTwpqZ+cEAAPzBAICTws3M/MEAADLCAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICWwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICWwpqZ+cEAADLCAICWws3M/MEAAPzBAICWwpqZ+cEAAPzBAICWws3M/MEAADLCAICWwpqZ+cEAADLCAICWwpqZ+cEAAPzBAICWwpqZ+cEAADLCAICXws3M/MEAAPzBAICXwpqZ+cEAAPzBAICXws3M/MEAADLCAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICawpqZ+cEAAPzBAICXwpqZ+cEAADLCAICawpqZ+cEAADLCAICaws3M/MEAAPzBAICawpqZ+cEAAPzBAICaws3M/MEAADLCAICawpqZ+cEAADLCAICawpqZ+cEAAPzBAICawpqZ+cEAADLCAICbws3M/MEAAPzBAICbwpqZ+cEAAPzBAICbws3M/MEAADLCAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICewpqZ+cEAAPzBAICbwpqZ+cEAADLCAICewpqZ+cEAADLCAICews3M/MEAAPzBAICewpqZ+cEAAPzBAICews3M/MEAADLCAICewpqZ+cEAADLCAICewpqZ+cEAAPzBAICewpqZ+cEAADLCAICfws3M/MEAAPzBAICfwpqZ+cEAAPzBAICfws3M/MEAADLCAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICiwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICiwpqZ+cEAADLCAICiws3M/MEAAPzBAICiwpqZ+cEAAPzBAICiws3M/MEAADLCAICiwpqZ+cEAADLCAICiwpqZ+cEAAPzBAICiwpqZ+cEAADLCAICjws3M/MEAAPzBAICjwpqZ+cEAAPzBAICjws3M/MEAADLCAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICmwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICmwpqZ+cEAADLCAICmws3M/MEAAPzBAICmwpqZ+cEAAPzBAICmws3M/MEAADLCAICmwpqZ+cEAADLCAICmwpqZ+cEAAPzBAICmwpqZ+cEAADLCAICnws3M/MEAAPzBAICnwpqZ+cEAAPzBAICnws3M/MEAADLCAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICqwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICqwpqZ+cEAADLCAICqws3M/MEAAPzBAICqwpqZ+cEAAPzBAICqws3M/MEAADLCAICqwpqZ+cEAADLCAICqwpqZ+cEAAPzBAICqwpqZ+cEAADLCAICrws3M/MEAAPzBAICrwpqZ+cEAAPzBAICrws3M/MEAADLCAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICuwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICuwpqZ+cEAADLCAICuws3M/MEAAPzBAICuwpqZ+cEAAPzBAICuws3M/MEAADLCAICuwpqZ+cEAADLCAICuwpqZ+cEAAPzBAICuwpqZ+cEAADLCAICvws3M/MEAAPzBAICvwpqZ+cEAAPzBAICvws3M/MEAADLCAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICywpqZ+cEAAPzBAICvwpqZ+cEAADLCAICywpqZ+cEAADLCAICyws3M/MEAAPzBAICywpqZ+cEAAPzBAICyws3M/MEAADLCAICywpqZ+cEAADLCAICywpqZ+cEAAPzBAICywpqZ+cEAADLCAICzws3M/MEAAPzBAICzwpqZ+cEAAPzBAICzws3M/MEAADLCAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAIC2wpqZ+cEAAPzBAICzwpqZ+cEAADLCAIC2wpqZ+cEAADLCAIC2ws3M/MEAAPzBAIC2wpqZ+cEAAPzBAIC2ws3M/MEAADLCAIC2wpqZ+cEAADLCAIC2wpqZ+cEAAPzBAIC2wpqZ+cEAADLCAIC3ws3M/MEAAPzBAIC3wpqZ+cEAAPzBAIC3ws3M/MEAADLCAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC6wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC6wpqZ+cEAADLCAIC6ws3M/MEAAPzBAIC6wpqZ+cEAAPzBAIC6ws3M/MEAADLCAIC6wpqZ+cEAADLCAIC6wpqZ+cEAAPzBAIC6wpqZ+cEAADLC\"}],\"images\":[{\"uri\":\"data:image/png;base64,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] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import stim\n", + "\n", + "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", + "circuit.diagram('timeline-3d')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YDnwv2dacTbf" + }, + "source": [ + "# Estimating code distance with Stim" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "r2MFDDBMvkq3", + "outputId": "cc6b8bfe-9adb-4b55-9c47-ed2580177b44" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "cK2Mf2fTCAWO" - }, - "source": [ - "## Computing minimum distance with Stim + SAT Solver" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "estimated distance: 5\n" + ] + } + ], + "source": [ + "distance_estimate = len(circuit.search_for_undetectable_logical_errors(\n", + " dont_explore_detection_event_sets_with_size_above=6,\n", + " dont_explore_edges_with_degree_above=3,\n", + " dont_explore_edges_increasing_symptom_degree=False))\n", + "print(f'estimated distance: {distance_estimate}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UuYvEwq9cgYc" + }, + "source": [ + "# Create DEM, detection events and observables with Stim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6oCW5jRsXy8-" + }, + "source": [ + "### Can't decode with pymatching..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "x6TQbGZ7b06k", + "outputId": "c4caa541-cef7-498d-a410-c00b64cf8a79" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZdVK4Dq1Bp1B", - "outputId": "61d8eb3e-7274-41c0-bd6b-af3e3ac75d54" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Distance of code: 4\n" - ] - } - ], - "source": [ - "# Note: this maxSAT solver only works for very small codes.\n", - "# For larger codes, use the solvers at https://maxsat-evaluations.github.io/2024/\n", - "from pysat.examples.rc2 import RC2\n", - "from pysat.formula import WCNF\n", - "\n", - "wcnf = WCNF(from_string=circuit.shortest_error_sat_problem())\n", - "\n", - "with RC2(wcnf) as rc2:\n", - " rc2.compute()\n", - " print(f'Distance of code: {rc2.cost}')" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/tmp/ipykernel_3802485/2195602962.py\", line 5, in \n", + " circuit.detector_error_model(decompose_errors=True)\n", + " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", + "ValueError: Failed to decompose errors into graphlike components with at most two symptoms.\n", + "The error component that failed to decompose is 'D46, D49, D52, D63, D66, D73, D75'.\n", + "\n", + "In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.\n", + "From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.\n", + "\n", + "Circuit stack trace:\n", + " during TICK layer #46 of 75\n", + " at instruction #104 [which is a REPEAT 3 block]\n" + ] + } + ], + "source": [ + "import traceback\n", + "\n", + "try:\n", + " # decompose_errors=True needed for DEM to be matchable\n", + " circuit.detector_error_model(decompose_errors=True)\n", + "except:\n", + " traceback.print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ye5W7BJHX8DJ" + }, + "source": [ + "No need to decompose errors using tesseract:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "AVu7idoTYAdM" + }, + "outputs": [], + "source": [ + "dem = circuit.detector_error_model()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "vFDn06Xach0_" + }, + "outputs": [], + "source": [ + "num_shots = 1000\n", + "sampler = circuit.compile_detector_sampler()\n", + "dets, obs = sampler.sample(num_shots, separate_observables=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JrX13vNQcrm3" + }, + "source": [ + "# Decoding with Tesseract and ILP decoder" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "Uds8S04a-z-G" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "GQjQkhD4C4rK" - }, - "source": [ - "## Sample new data for this stabilizer code" - ] + "ename": "ImportError", + "evalue": "/usr/local/google/home/shutty/pyenv/lib/python3.13/site-packages/tesseract_decoder.so: undefined symbol: _PyThreadState_UncheckedGet", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract_decoder\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract_decoder\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtesseract\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n", + "\u001b[31mImportError\u001b[39m: /usr/local/google/home/shutty/pyenv/lib/python3.13/site-packages/tesseract_decoder.so: undefined symbol: _PyThreadState_UncheckedGet" + ] + } + ], + "source": [ + "import tesseract_decoder\n", + "import tesseract_decoder.tesseract as tesseract\n", + "import numpy as np\n", + "import time\n", + "\n", + "# Helper functions for benchmarking\n", + "\n", + "def print_results(result):\n", + " print(\"Tesseract Decoder Stats:\")\n", + " print(f\" Number of Errors / num_shots: {results['num_errors']} / {results['num_shots']}\")\n", + " print(f\" Time: {results['time_seconds']:.4f} s\")\n", + " print()\n", + "\n", + "def run_tesseract_decoder(decoder, dets, obs):\n", + " # Run and time the Tesseract decoder\n", + " num_errors = 0\n", + " start_time = time.time()\n", + " obs_predicted = decoder.decode_batch(dets)\n", + " num_errors = np.sum(np.any(obs_predicted != obs, axis=1))\n", + " end_time = time.time()\n", + "\n", + " return {\n", + " 'num_errors': num_errors,\n", + " 'num_shots': len(dets),\n", + " 'time_seconds': end_time - start_time,\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "D0Tx2eY3ctFw", + "outputId": "64f388af-f1db-4869-873f-3ab714ee8e9c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7iOIl7vjC3uG" - }, - "outputs": [], - "source": [ - "num_shots = 1000\n", - "dem = circuit.detector_error_model()\n", - "sampler = circuit.compile_detector_sampler(seed=23845386)\n", - "dets, obs = sampler.sample(num_shots, separate_observables=True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Tesseract decoder configurations --> TesseractConfig(dem=DetectorErrorModel_Object, det_beam=65535, no_revisit_dets=1, at_most_two_errors_per_detector=0, verbose=0, pqlimit=10000, det_orders=[[89, 86, 85, 83, 81, 88, 84, 82, 87, 74, 71, 78, 75, 69, 67, 63, 77, 66, 61, 72, 64, 65, 79, 68, 62, 73, 80, 70, 42, 76, 43, 46, 33, 32, 50, 44, 31, 57, 35, 36, 59, 51, 30, 58, 60, 48, 39, 45, 49, 34, 25, 7, 47, 5, 18, 26, 21, 2, 40, 24, 12, 29, 28, 55, 37, 56, 54, 53, 38, 15, 3, 16, 52, 20, 9, 19, 13, 8, 11, 10, 17, 41, 22, 6, 14, 23, 0, 1, 27, 4]], det_penalty=0, create_visualization=0)\n", + "\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 3 / 1000\n", + " Time: 3.6089 s\n", + "\n" + ] + } + ], + "source": [ + "# setup the tesseract decoder configuration\n", + "tesseract_config = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " no_revisit_dets=True,\n", + " # verbose=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=1, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print(f'Tesseract decoder configurations --> {tesseract_config}\\n')\n", + "\n", + "tesseract_dec = tesseract_config.compile_decoder()\n", + "\n", + "results = run_tesseract_decoder(tesseract_dec, dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "INvMKs7zc5T_" + }, + "source": [ + "#Decoding with ILP decoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "9Npo7ibac4x5", + "outputId": "51af3bd2-5f53-43c8-a16d-f595a9596bde" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "63xjagbBCj8x" - }, - "source": [ - "## Decode code capacity noise data with ILP and Tesseract" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "ILP decoder configurations --> SimplexConfig(dem=DetectorErrorModel_Object, window_length=0, window_slide_length=0, verbose=0)\n", + "ILP stats:\n", + " Estimated time for full shots 1115.2181386947632 s\n", + " Number of Errors / num_shots: 0 / 10\n" + ] + } + ], + "source": [ + "simplex_config = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True\n", + ")\n", + "print(f'ILP decoder configurations --> {simplex_config}')\n", + "ilp_dec = simplex_config.compile_decoder()\n", + "\n", + "start_time = time.time()\n", + "\n", + "# Run and time ILP decoder -- so slow!\n", + "num_shots_to_decode = 10 # Only decoding 10 shots because it's soooo slow\n", + "obs_predicted = ilp_dec.decode_batch(dets[0:num_shots_to_decode])\n", + "num_errors = np.sum(np.any(obs_predicted != obs[0:num_shots_to_decode], axis=1))\n", + "\n", + "end_time = time.time()\n", + "print(f'ILP stats:\\n Estimated time for full shots {num_shots/num_shots_to_decode * (end_time - start_time)} s')\n", + "print(f\" Number of Errors / num_shots: {num_errors} / {num_shots_to_decode}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VQqlMqFRIZ2J" + }, + "source": [ + "# Tesseract Config and impact of heuristic\n", + "You can tune tesseract decoder through the Config that is passed to the decoder with this set of parameters:\n", + "Explanation of configuration arguments:\n", + "\n", + "* `pqlimit` - An integer that sets a limit on the number of nodes in the priority queue. This can be used to constrain the memory usage of the decoder. The default value is `sys.maxsize`, which means the size is effectively unbounded.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "0pExdmuPQuGr", + "outputId": "45a20eca-fef6-425d-d6a2-9a619fe9e10c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IM7W37cHaKfT", - "outputId": "3f2f7666-9586-4cb6-b422-1d295bf8747c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 60 / 1000\n", - " Time: 0.2323 s\n", - "\n", - "ILP: num_errors / num_shots = 61 / 1000 time 11.911995649337769 s\n" - ] - } - ], - "source": [ - "tesseract_config = tesseract_decoder.tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=10,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", - " # no_revisit_dets=True,\n", - ")\n", - "\n", - "results = run_tesseract_decoder(tesseract_config.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "# Run and time ILP decoder\n", - "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True).compile_decoder()\n", - "start_time = time.time()\n", - "obs_predicted = ilp_dec.decode_batch(dets)\n", - "num_errors_ilp = np.sum(np.any(obs_predicted != obs, axis=1))\n", - "end_time = time.time()\n", - "print(\n", - " f'ILP: num_errors / num_shots = {num_errors_ilp} / {len(dets)} time {end_time - start_time} s')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Smaller pqlimit\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 183 / 1000\n", + " Time: 1.8420 s\n", + "\n", + "Larger pqlimit\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 3 / 1000\n", + " Time: 8.4409 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=100,\n", + " no_revisit_dets=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"Smaller pqlimit\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=20000,\n", + " no_revisit_dets=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Larger pqlimit\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ru-MRctAIq5-" + }, + "source": [ + "#More heurisitcs\n", + "* `det_beam` - This integer value represents the beam search cutoff. It specifies a threshold for the number of \"residual detection events\" a node can have before it is pruned from the search. A lower `det_beam` value makes the search more aggressive, potentially sacrificing accuracy for speed. The default value `INF_DET_BEAM` means no beam cutoff is applied.\n", + "* `beam_climbing` - A boolean flag that, when set to `True`, enables a heuristic called \"beam climbing.\" This optimization causes the decoder to try different `det_beam` values (up to a maximum) to find a good decoding path. This can improve the decoder's chance of finding the most likely error, even with an initial narrow beam search.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "cyctTUyzQ-cQ", + "outputId": "0f3c5a8a-0d09-49e4-e3b1-e193152bf2bf" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "K0QvSpXQIwgf" - }, - "source": [ - "# Visualize the Tesseract's decoding\n", - "For visualizing tesseract we use the `verbose` flag to get the decoding information.\n", - "## [Link to visualizer](https://quantumlib.github.io/tesseract-decoder/viz/)\n", - "* `verbose` - A boolean flag that, when `True`, enables verbose logging. This is useful for debugging and understanding the decoder's internal behavior, as it will print information about the search process." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Smaller det_beam\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 2 / 1000\n", + " Time: 5.8029 s\n", + "\n", + "Larger det_beam\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 2 / 1000\n", + " Time: 6.1866 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=3,\n", + " beam_climbing=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"Smaller det_beam\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=5,\n", + " beam_climbing=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Larger det_beam\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "0VrW2z8sSXtN", + "outputId": "6f69c1ff-1c04-4dc6-d1a0-32aa0777d56b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DzWRL1cNjyix", - "outputId": "4a3df084-499f-43b2-97ba-1874b697f06a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'tesseract-decoder'...\n", - "remote: Enumerating objects: 2086, done.\u001b[K\n", - "remote: Counting objects: 100% (606/606), done.\u001b[K\n", - "remote: Compressing objects: 100% (304/304), done.\u001b[K\n", - "remote: Total 2086 (delta 493), reused 317 (delta 302), pack-reused 1480 (from 3)\u001b[K\n", - "Receiving objects: 100% (2086/2086), 3.17 MiB | 8.58 MiB/s, done.\n", - "Resolving deltas: 100% (1667/1667), done.\n" - ] - } - ], - "source": [ - "# Remove the existing directory and its contents\n", - "!rm -rf tesseract-decoder\n", - "# Clone the repository\n", - "!git clone https://github.com/quantumlib/tesseract-decoder.git" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "First version\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 9 / 1000\n", + " Time: 1.1290 s\n", + "\n", + "Second version\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 19 / 1000\n", + " Time: 0.9446 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " # no_revisit_dets=True,\n", + " # at_most_two_errors_per_detector = True,\n", + " det_penalty = 10,\n", + " # det_orders=tesseract_decoder.utils.build_det_orders(\n", + " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"First version\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " # no_revisit_dets=False,\n", + " # at_most_two_errors_per_detector = False,\n", + " det_penalty = 0,\n", + " # det_orders=tesseract_decoder.utils.build_det_orders(\n", + " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Second version\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BoEALeo3OYGp" + }, + "source": [ + "# Decoding Wild Stabilizer Codes under Code Capacity Noise with Tesseract\n", + "\n", + "\n", + "\n", + "* checkout https://www.codetables.de/ for a qubit stabilizer code\n", + "* full table of qubit codes: [here](https://codetables.de/QECC/Tables_color.php?q=4&n0=1&n1=256&k0=0&k1=256)\n", + "* copy the stabilizer matrix for a code, such as: [this one used below](https://codetables.de/QECC/QECC.php?q=4&n=21&k=8)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pJ1gEKAgPbHO" + }, + "outputs": [], + "source": [ + "import time\n", + "import tesseract_decoder\n", + "import stim\n", + "import numpy as np\n", + "import numpy.typing as npt\n", + "from galois import GF2\n", + "from typing import List, Tuple\n", + "\n", + "\n", + "def paulis_from_symplectic_matrix(check_matrix: npt.NDArray[np.uint8]) -> List[stim.PauliString]:\n", + " n = check_matrix.shape[1] // 2\n", + " paulis = []\n", + " for i in range(check_matrix.shape[0]):\n", + " paulis.append(\n", + " stim.PauliString.from_numpy(\n", + " xs=check_matrix[i, :n].astype(bool), zs=check_matrix[i, n:].astype(bool)\n", + " )\n", + " )\n", + " return paulis\n", + "\n", + "def rank(H):\n", + " return np.linalg.matrix_rank(GF2(H))\n", + "\n", + "def stabilizer_code_logical_operators(\n", + " check_matrix: npt.NDArray[np.uint8]) -> Tuple[npt.NDArray[np.uint8], npt.NDArray[np.uint8]]:\n", + " check_matrix = np.array(check_matrix, dtype=np.uint8)\n", + "\n", + " r = rank(check_matrix)\n", + " n = check_matrix.shape[1] // 2\n", + "\n", + " stabilisers = paulis_from_symplectic_matrix(check_matrix=check_matrix)\n", + "\n", + " tableau = stim.Tableau.from_stabilizers(\n", + " stabilizers=stabilisers, allow_underconstrained=True, allow_redundant=True\n", + " )\n", + "\n", + " x2x, x2z, z2x, z2z, x_signs, z_signs = tableau.to_numpy()\n", + "\n", + " num_logicals = n - r\n", + "\n", + " Lx = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", + " Lz = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", + "\n", + " Lx[:, :n] = x2x[r:]\n", + " Lx[:, n:] = x2z[r:]\n", + " Lz[:, :n] = z2x[r:]\n", + " Lz[:, n:] = z2z[r:]\n", + " return Lx.astype(np.uint8), Lz.astype(np.uint8)\n", + "\n", + "\n", + "def pauli_to_observable_include_target(pauli: stim.PauliString) -> List[stim.GateTarget]:\n", + " obs_pauli_targets = []\n", + " for i in range(len(pauli)):\n", + " if pauli[i] != 0:\n", + " obs_pauli_targets.append(stim.target_pauli(i, pauli[i]))\n", + " return obs_pauli_targets\n", + "\n", + "\n", + "def append_observable_includes_for_paulis(circuit: stim.Circuit, paulis: List[stim.PauliString]) -> None:\n", + " for i, obs in enumerate(paulis):\n", + " circuit.append(\n", + " \"OBSERVABLE_INCLUDE\",\n", + " targets=pauli_to_observable_include_target(pauli=obs),\n", + " arg=i\n", + " )\n", + "\n", + "\n", + "def code_capacity_circuit(\n", + " stabilizers: npt.NDArray[np.uint8],\n", + " x_logicals: npt.NDArray[np.uint8],\n", + " z_logicals: npt.NDArray[np.uint8],\n", + " p: float\n", + ") -> stim.Circuit:\n", + " \"\"\"Generate a code capacity stim circuit for a stabilizer code\n", + "\n", + " Parameters\n", + " ----------\n", + " stabilizers : npt.NDArray[np.uint8]\n", + " The stabilizer generators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (r, 2 * n) where r is the number of stabilizer\n", + " generators and n is the number of physical qubits.\n", + " `stabilizers[i, j]` is 1 if stabilizer i is X or Y on qubit j and 0 otherwise.\n", + " `stabilizers[i, n + j]` is 1 if stabilizer i is Z or Y on qubit j and 0 otherwise.\n", + " x_logicals : npt.NDArray[np.uint8]\n", + " The X logical operators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", + " and n is the number of physical qubits.\n", + " z_logicals : npt.NDArray[np.uint8]\n", + " The Z logical operators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", + " and n is the number of physical qubits.\n", + " p : float\n", + " The strength of single-qubit depolarizing noise to use\n", + "\n", + " Returns\n", + " -------\n", + " stim.Circuit\n", + " The stim circuit of the code capacity circuit\n", + " \"\"\"\n", + " num_qubits = stabilizers.shape[1] // 2\n", + " num_stabilizers = stabilizers.shape[0]\n", + " stabilizer_paulis = paulis_from_symplectic_matrix(stabilizers)\n", + " x_logicals_paulis = paulis_from_symplectic_matrix(x_logicals)\n", + " z_logicals_paulis = paulis_from_symplectic_matrix(z_logicals)\n", + " all_logicals_paulis = x_logicals_paulis + z_logicals_paulis\n", + "\n", + " circuit = stim.Circuit()\n", + "\n", + " append_observable_includes_for_paulis(\n", + " circuit=circuit, paulis=all_logicals_paulis)\n", + " circuit.append(\"MPP\", stabilizer_paulis)\n", + " circuit.append(\"DEPOLARIZE1\", targets=list(range(num_qubits)), arg=p)\n", + " circuit.append(\"MPP\", stabilizer_paulis)\n", + "\n", + " for i in range(num_stabilizers):\n", + " circuit.append(\n", + " \"DETECTOR\",\n", + " targets=[\n", + " stim.target_rec(i - 2 * num_stabilizers),\n", + " stim.target_rec(i - num_stabilizers)\n", + " ]\n", + " )\n", + "\n", + " append_observable_includes_for_paulis(\n", + " circuit=circuit, paulis=all_logicals_paulis)\n", + " return circuit\n", + "\n", + "\n", + "def parse_symplectic_matrix(text: str) -> npt.NDArray[np.uint8]:\n", + " rows = []\n", + " for line in text.strip().splitlines():\n", + " line = line.strip()\n", + " if not line or line[0] != '[' or line[-1] != ']':\n", + " continue # skip malformed lines\n", + " body = line[1:-1]\n", + " if \"|\" in body:\n", + " left, right = body.split(\"|\")\n", + " bits = left.strip().split() + right.strip().split()\n", + " else:\n", + " bits = body.strip().split()\n", + " row = [int(b) for b in bits]\n", + " rows.append(row)\n", + " return np.array(rows, dtype=np.uint8)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 }, + "id": "pH_b3u1rBogl", + "outputId": "846dd807-82ed-4139-e46d-a3cfe65a7681" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZNKaqvN8dE-X", - "outputId": "8d80e5bc-c30b-469d-d9cd-452d89604c30" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 44154 100 44154 0 0 230k 0 --:--:-- --:--:-- --:--:-- 230k\n" - ] - } + "data": { + "text/html": [ + "" ], - "source": [ - "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D9%2Cd%3D9%2Cp%3D0.002%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_X%2Cq%3D121%2Cgates%3Dcz.stim' > d9r9colorcode_p002.stim\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Cdo-oenEdF1-" - }, - "outputs": [], - "source": [ - "import stim\n", - "\n", - "circuit = stim.Circuit.from_file('d9r9colorcode_p002.stim')" + "text/plain": [ + 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] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example QEC code:\n", + "text = '''[1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 0|0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0]\n", + " [0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0|1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0|0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0|0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0]\n", + " [0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0|0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0]\n", + " [0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0|0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0]\n", + " [0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0|0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0]\n", + " [0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0|0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0]\n", + " [0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]'''\n", + "\n", + "H = parse_symplectic_matrix(text)\n", + "\n", + "LX, LZ = stabilizer_code_logical_operators(check_matrix=H)\n", + "\n", + "circuit = code_capacity_circuit(\n", + " stabilizers=H,\n", + " x_logicals=LX,\n", + " z_logicals=LZ,\n", + " p=0.025\n", + ")\n", + "\n", + "circuit.diagram('timeline-3d')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cK2Mf2fTCAWO" + }, + "source": [ + "## Computing minimum distance with Stim + SAT Solver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ZdVK4Dq1Bp1B", + "outputId": "61d8eb3e-7274-41c0-bd6b-af3e3ac75d54" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "awJYxAOMTc3t", - "outputId": "2da93975-0ede-41cb-897b-23b7da9dca93" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Tesseract: num_errors / num_shots = 51 / 100 \n", - " time 16.068939685821533 s\n" - ] - } - ], - "source": [ - "import tesseract_decoder\n", - "import tesseract_decoder.tesseract as tesseract\n", - "import numpy as np\n", - "import time\n", - "import contextlib\n", - "import io\n", - "\n", - "num_shots = 100\n", - "dem = circuit.detector_error_model()\n", - "dets, obs = circuit.compile_detector_sampler().sample(num_shots, separate_observables=True)\n", - "\n", - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " verbose=False,\n", - " create_visualization=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "tesseract_dec = tesseract.TesseractDecoder(tesseract_config1)\n", - "\n", - "# Run and time the Tesseract decoder\n", - "num_errors = 0\n", - "start_time = time.time()\n", - "for shot in range(len(dets)):\n", - " obs_predicted = tesseract_dec.decode(dets[shot])\n", - " obs_actual = obs[shot]\n", - " if np.any(obs_predicted != obs_actual):\n", - " num_errors += 1\n", - "end_time = time.time()\n", - "print(f'Tesseract: num_errors / num_shots = {num_errors} / {len(dets)} \\n time {end_time - start_time} s')\n", - "\n", - "# Print with the visualizer\n", - "tesseract_dec.visualizer.write('/content/tmp.txt')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance of code: 4\n" + ] + } + ], + "source": [ + "# Note: this maxSAT solver only works for very small codes.\n", + "# For larger codes, use the solvers at https://maxsat-evaluations.github.io/2024/\n", + "from pysat.examples.rc2 import RC2\n", + "from pysat.formula import WCNF\n", + "\n", + "wcnf = WCNF(from_string=circuit.shortest_error_sat_problem())\n", + "\n", + "with RC2(wcnf) as rc2:\n", + " rc2.compute()\n", + " print(f'Distance of code: {rc2.cost}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GQjQkhD4C4rK" + }, + "source": [ + "## Sample new data for this stabilizer code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7iOIl7vjC3uG" + }, + "outputs": [], + "source": [ + "num_shots = 1000\n", + "dem = circuit.detector_error_model()\n", + "sampler = circuit.compile_detector_sampler(seed=23845386)\n", + "dets, obs = sampler.sample(num_shots, separate_observables=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "63xjagbBCj8x" + }, + "source": [ + "## Decode code capacity noise data with ILP and Tesseract" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "IM7W37cHaKfT", + "outputId": "3f2f7666-9586-4cb6-b422-1d295bf8747c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MuQb8XQlpvU6" - }, - "outputs": [], - "source": [ - "!cat tmp.txt | grep -E 'Error|Detector|activated_errors|activated_detectors' > logfile.txt" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 60 / 1000\n", + " Time: 0.2323 s\n", + "\n", + "ILP: num_errors / num_shots = 61 / 1000 time 11.911995649337769 s\n" + ] + } + ], + "source": [ + "tesseract_config = tesseract_decoder.tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=10,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", + " # no_revisit_dets=True,\n", + ")\n", + "\n", + "results = run_tesseract_decoder(tesseract_config.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "# Run and time ILP decoder\n", + "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True).compile_decoder()\n", + "start_time = time.time()\n", + "obs_predicted = ilp_dec.decode_batch(dets)\n", + "num_errors_ilp = np.sum(np.any(obs_predicted != obs, axis=1))\n", + "end_time = time.time()\n", + "print(\n", + " f'ILP: num_errors / num_shots = {num_errors_ilp} / {len(dets)} time {end_time - start_time} s')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K0QvSpXQIwgf" + }, + "source": [ + "# Visualize the Tesseract's decoding\n", + "For visualizing tesseract we use the `verbose` flag to get the decoding information.\n", + "## [Link to visualizer](https://quantumlib.github.io/tesseract-decoder/viz/)\n", + "* `verbose` - A boolean flag that, when `True`, enables verbose logging. This is useful for debugging and understanding the decoder's internal behavior, as it will print information about the search process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "DzWRL1cNjyix", + "outputId": "4a3df084-499f-43b2-97ba-1874b697f06a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WExtQ3x4j_Md", - "outputId": "b8ad37c9-4d69-4abd-f176-71dc76042687" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✅ JSON written to logfile.json with 10215 frames and 23994 error coords.\n" - ] - } - ], - "source": [ - "!python tesseract-decoder/viz/to_json.py logfile.txt -o logfile.json" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'tesseract-decoder'...\n", + "remote: Enumerating objects: 2086, done.\u001b[K\n", + "remote: Counting objects: 100% (606/606), done.\u001b[K\n", + "remote: Compressing objects: 100% (304/304), done.\u001b[K\n", + "remote: Total 2086 (delta 493), reused 317 (delta 302), pack-reused 1480 (from 3)\u001b[K\n", + "Receiving objects: 100% (2086/2086), 3.17 MiB | 8.58 MiB/s, done.\n", + "Resolving deltas: 100% (1667/1667), done.\n" + ] + } + ], + "source": [ + "# Remove the existing directory and its contents\n", + "!rm -rf tesseract-decoder\n", + "# Clone the repository\n", + "!git clone https://github.com/quantumlib/tesseract-decoder.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ZNKaqvN8dE-X", + "outputId": "8d80e5bc-c30b-469d-d9cd-452d89604c30" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "HSdTwXBINjkH" - }, - "source": [ - "copy the json file and upload it [here to see the visualizaion](https://quantumlib.github.io/tesseract-decoder/viz/)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "\r", + " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r", + "100 44154 100 44154 0 0 230k 0 --:--:-- --:--:-- --:--:-- 230k\n" + ] + } + ], + "source": [ + "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D9%2Cd%3D9%2Cp%3D0.002%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_X%2Cq%3D121%2Cgates%3Dcz.stim' > d9r9colorcode_p002.stim\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cdo-oenEdF1-" + }, + "outputs": [], + "source": [ + "import stim\n", + "\n", + "circuit = stim.Circuit.from_file('d9r9colorcode_p002.stim')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "metadata": { - "id": "QehTGJcB7-Ca" - }, - "source": [ - "# Accuracy Comparison between Tesseract and ILP" - ] + "collapsed": true, + "id": "awJYxAOMTc3t", + "jupyter": { + "outputs_hidden": true }, + "outputId": "2da93975-0ede-41cb-897b-23b7da9dca93" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GOY0hHYx79HC", - "outputId": "c73ea4f6-ea5b-42c4-8271-fe6320c790ab" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Tesseract: num_errors / num_shots = 2 / 1000\n", - "num_errors_tesseract_no_error_ilp = 0\n", - "time 25.137925148010254 s\n" - ] - } - ], - "source": [ - "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", - "dem = circuit.detector_error_model()\n", - "\n", - "tesseract_dec = tesseract_decoder.tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " det_beam=5,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", - " no_revisit_dets=True,\n", - ").compile_decoder()\n", - "\n", - "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True).compile_decoder()\n", - "\n", - "num_shots = 1000\n", - "dets, obs = circuit.compile_detector_sampler(seed=237435).sample(num_shots, separate_observables=True)\n", - "\n", - "num_errors_tesseract = 0\n", - "num_errors_tesseract_no_error_ilp = 0\n", - "start_time = time.time()\n", - "for shot in range(len(dets)):\n", - " obs_predicted = tesseract_dec.decode(dets[shot])\n", - " obs_actual = obs[shot]\n", - " if np.any(obs_predicted != obs_actual):\n", - " num_errors_tesseract += 1\n", - " obs_predicted_ilp = ilp_dec.decode(dets[shot])\n", - " if not np.any(obs_predicted_ilp != obs_actual):\n", - " num_errors_tesseract_no_error_ilp += 1\n", - "\n", - "end_time = time.time()\n", - "print(f'Tesseract: num_errors / num_shots = {num_errors_tesseract} / {len(dets)}')\n", - "print(f'num_errors_tesseract_no_error_ilp = {num_errors_tesseract_no_error_ilp}')\n", - "print(f'time {end_time - start_time} s')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Tesseract: num_errors / num_shots = 51 / 100 \n", + " time 16.068939685821533 s\n" + ] + } + ], + "source": [ + "import tesseract_decoder\n", + "import tesseract_decoder.tesseract as tesseract\n", + "import numpy as np\n", + "import time\n", + "import contextlib\n", + "import io\n", + "\n", + "num_shots = 100\n", + "dem = circuit.detector_error_model()\n", + "dets, obs = circuit.compile_detector_sampler().sample(num_shots, separate_observables=True)\n", + "\n", + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " verbose=False,\n", + " create_visualization=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "tesseract_dec = tesseract.TesseractDecoder(tesseract_config1)\n", + "\n", + "# Run and time the Tesseract decoder\n", + "num_errors = 0\n", + "start_time = time.time()\n", + "for shot in range(len(dets)):\n", + " obs_predicted = tesseract_dec.decode(dets[shot])\n", + " obs_actual = obs[shot]\n", + " if np.any(obs_predicted != obs_actual):\n", + " num_errors += 1\n", + "end_time = time.time()\n", + "print(f'Tesseract: num_errors / num_shots = {num_errors} / {len(dets)} \\n time {end_time - start_time} s')\n", + "\n", + "# Print with the visualizer\n", + "tesseract_dec.visualizer.write('/content/tmp.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MuQb8XQlpvU6" + }, + "outputs": [], + "source": [ + "!cat tmp.txt | grep -E 'Error|Detector|activated_errors|activated_detectors' > logfile.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "WExtQ3x4j_Md", + "outputId": "b8ad37c9-4d69-4abd-f176-71dc76042687" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "44xKnbKb2y_b" - }, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ JSON written to logfile.json with 10215 frames and 23994 error coords.\n" + ] } - ], - "metadata": { + ], + "source": [ + "!python tesseract-decoder/viz/to_json.py logfile.txt -o logfile.json" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HSdTwXBINjkH" + }, + "source": [ + "copy the json file and upload it [here to see the visualizaion](https://quantumlib.github.io/tesseract-decoder/viz/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QehTGJcB7-Ca" + }, + "source": [ + "# Accuracy Comparison between Tesseract and ILP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "GOY0hHYx79HC", + "outputId": "c73ea4f6-ea5b-42c4-8271-fe6320c790ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tesseract: num_errors / num_shots = 2 / 1000\n", + "num_errors_tesseract_no_error_ilp = 0\n", + "time 25.137925148010254 s\n" + ] } + ], + "source": [ + "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", + "dem = circuit.detector_error_model()\n", + "\n", + "tesseract_dec = tesseract_decoder.tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " det_beam=5,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", + " no_revisit_dets=True,\n", + ").compile_decoder()\n", + "\n", + "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True).compile_decoder()\n", + "\n", + "num_shots = 1000\n", + "dets, obs = circuit.compile_detector_sampler(seed=237435).sample(num_shots, separate_observables=True)\n", + "\n", + "num_errors_tesseract = 0\n", + "num_errors_tesseract_no_error_ilp = 0\n", + "start_time = time.time()\n", + "for shot in range(len(dets)):\n", + " obs_predicted = tesseract_dec.decode(dets[shot])\n", + " obs_actual = obs[shot]\n", + " if np.any(obs_predicted != obs_actual):\n", + " num_errors_tesseract += 1\n", + " obs_predicted_ilp = ilp_dec.decode(dets[shot])\n", + " if not np.any(obs_predicted_ilp != obs_actual):\n", + " num_errors_tesseract_no_error_ilp += 1\n", + "\n", + "end_time = time.time()\n", + "print(f'Tesseract: num_errors / num_shots = {num_errors_tesseract} / {len(dets)}')\n", + "print(f'num_errors_tesseract_no_error_ilp = {num_errors_tesseract_no_error_ilp}')\n", + "print(f'time {end_time - start_time} s')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "44xKnbKb2y_b" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/src/py/README.md b/src/py/README.md index a7bc6452..bc42efd8 100644 --- a/src/py/README.md +++ b/src/py/README.md @@ -5,7 +5,7 @@ The `tesseract_decoder.tesseract` module provides the Tesseract decoder, which e #### Class `tesseract.TesseractConfig` This class holds the configuration parameters that control the behavior of the Tesseract decoder. -* `TesseractConfig(dem: stim.DetectorErrorModel, det_beam: int = INF_DET_BEAM, beam_climbing: bool = False, no_revisit_dets: bool = False, at_most_two_errors_per_detector: bool = False, verbose: bool = False, pqlimit: int = sys.maxsize, det_orders: list[list[int]] = [], det_penalty: float = 0.0)` +* `TesseractConfig(dem: stim.DetectorErrorModel, det_beam: int = INF_DET_BEAM, beam_climbing: bool = False, no_revisit_dets: bool = False, verbose: bool = False, pqlimit: int = sys.maxsize, det_orders: list[list[int]] = [], det_penalty: float = 0.0)` * `__str__()` Explanation of configuration arguments: @@ -13,7 +13,7 @@ Explanation of configuration arguments: * `det_beam` - This integer value represents the beam search cutoff. It specifies a threshold for the number of "residual detection events" a node can have before it is pruned from the search. A lower `det_beam` value makes the search more aggressive, potentially sacrificing accuracy for speed. The default value `INF_DET_BEAM` means no beam cutoff is applied. * `beam_climbing` - A boolean flag that, when set to `True`, enables a heuristic called "beam climbing." This optimization causes the decoder to try different `det_beam` values (up to a maximum) to find a good decoding path. This can improve the decoder's chance of finding the most likely error, even with an initial narrow beam search. * `no_revisit_dets` - A boolean flag that, when `True`, activates a heuristic to prevent the decoder from revisiting nodes that have the same set of leftover detection events as a node it has already visited. This can help to reduce search redundancy and improve decoding speed. -* `at_most_two_errors_per_detector` - This boolean flag is a specific constraint that assumes at most two errors can affect a given detector. This can be a useful optimization for certain types of codes and noise models, as it prunes the search space by making a stronger assumption about the error distribution. + * `verbose` - A boolean flag that, when `True`, enables verbose logging. This is useful for debugging and understanding the decoder's internal behavior, as it will print information about the search process. * `pqlimit` - An integer that sets a limit on the number of nodes in the priority queue. This can be used to constrain the memory usage of the decoder. The default value is `sys.maxsize`, which means the size is effectively unbounded. * `det_orders` - A list of lists of integers, where each inner list represents an ordering of the detectors. This is used for "ensemble reordering," an optimization that tries different detector orderings to improve the search's convergence. The default is an empty list, meaning a single, fixed ordering is used. diff --git a/src/py/tesseract_test.py b/src/py/tesseract_test.py index 476d0775..69b72e26 100644 --- a/src/py/tesseract_test.py +++ b/src/py/tesseract_test.py @@ -52,7 +52,7 @@ def test_create_tesseract_config(): assert config.dem == _DETECTOR_ERROR_MODEL assert config.det_beam == 5 assert config.no_revisit_dets is True - assert config.at_most_two_errors_per_detector is False + assert config.verbose is False assert config.merge_errors is True assert config.pqlimit == 200000 @@ -71,7 +71,7 @@ def test_create_tesseract_config_with_dem(): assert config.dem == _DETECTOR_ERROR_MODEL assert config.det_beam == 5 assert config.no_revisit_dets is True - assert config.at_most_two_errors_per_detector is False + assert config.verbose is False assert config.merge_errors is True assert config.pqlimit == 200000 @@ -94,7 +94,7 @@ def test_create_tesseract_config_with_dem_and_custom_args(): assert config.dem == _DETECTOR_ERROR_MODEL assert config.det_beam == 100 assert config.no_revisit_dets is True - assert config.at_most_two_errors_per_detector is False + assert config.verbose is False assert config.merge_errors is False assert config.pqlimit == 200000 @@ -167,7 +167,7 @@ def test_create_tesseract_config_no_dem(): assert config.dem == stim.DetectorErrorModel() assert config.det_beam == 5 assert config.no_revisit_dets is True - assert config.at_most_two_errors_per_detector is False + assert config.verbose is False assert config.merge_errors is True assert config.pqlimit == 200000 @@ -184,7 +184,7 @@ def test_create_tesseract_config_no_dem_with_custom_args(): assert config.dem == stim.DetectorErrorModel() assert config.det_beam == 15 assert config.no_revisit_dets is True - assert config.at_most_two_errors_per_detector is False + assert config.verbose is True assert config.merge_errors is True assert config.pqlimit == 200000 diff --git a/src/tesseract.cc b/src/tesseract.cc index ca441331..f3d24601 100644 --- a/src/tesseract.cc +++ b/src/tesseract.cc @@ -57,7 +57,7 @@ std::string TesseractConfig::str() { ss << "dem=DetectorErrorModel_Object" << ", "; ss << "det_beam=" << config.det_beam << ", "; ss << "no_revisit_dets=" << config.no_revisit_dets << ", "; - ss << "at_most_two_errors_per_detector=" << config.at_most_two_errors_per_detector << ", "; + ss << "verbose=" << config.verbose << ", "; ss << "merge_errors=" << config.merge_errors << ", "; ss << "pqlimit=" << config.pqlimit << ", "; @@ -256,16 +256,11 @@ void TesseractDecoder::flip_detectors_and_block_errors( for (int oei : d2e[min_detector]) { detector_cost_tuples[oei].error_blocked = 1; - if (!config.at_most_two_errors_per_detector && oei == ei) break; + if (oei == ei) break; } for (int d : edets[ei]) { detectors[d] = !detectors[d]; - if (!detectors[d] && config.at_most_two_errors_per_detector) { - for (int oei : d2e[d]) { - detector_cost_tuples[oei].error_blocked = 1; - } - } } } } @@ -398,12 +393,6 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, } } - if (config.at_most_two_errors_per_detector) { - for (int ei : d2e[min_detector]) { - next_detector_cost_tuples[ei].error_blocked = 1; - } - } - size_t prev_ei = std::numeric_limits::max(); std::vector detector_cost_cache(num_detectors, -1); @@ -415,11 +404,6 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, int fired = detectors[d] ? 1 : -1; for (int oei : d2e[d]) { next_detector_cost_tuples[oei].detectors_count += fired; - - if (config.at_most_two_errors_per_detector && - next_detector_cost_tuples[oei].error_blocked == 2) { - next_detector_cost_tuples[oei].error_blocked = 0; - } } } } @@ -440,17 +424,6 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, for (int oei : d2e[d]) { next_detector_cost_tuples[oei].detectors_count += fired; } - - if (!next_detectors[d] && config.at_most_two_errors_per_detector) { - for (int oei : d2e[d]) { - next_detector_cost_tuples[oei].error_blocked = - next_detector_cost_tuples[oei].error_blocked == 1 - ? 1 - : 2; // we store '2' value to indicate an error that was blocked due to the - // '--at-most-two-error-per-detector' heuristic, in order to revert it in - // the next decoding iteration - } - } } if (next_num_detectors > max_num_detectors) continue; diff --git a/src/tesseract.h b/src/tesseract.h index ead2b2ac..62d6ed62 100644 --- a/src/tesseract.h +++ b/src/tesseract.h @@ -36,7 +36,7 @@ struct TesseractConfig { int det_beam = DEFAULT_DET_BEAM; bool beam_climbing = false; bool no_revisit_dets = true; - bool at_most_two_errors_per_detector = false; + bool verbose = false; bool merge_errors = true; size_t pqlimit = DEFAULT_PQLIMIT; diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 65111f0b..85603be9 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -35,7 +35,7 @@ std::unique_ptr _compile_tesseract_decoder_helper(const Tesser TesseractConfig tesseract_config_maker_no_dem( int det_beam = INF_DET_BEAM, bool beam_climbing = false, bool no_revisit_dets = false, - bool at_most_two_errors_per_detector = false, bool verbose = false, bool merge_errors = true, + bool verbose = false, bool merge_errors = true, size_t pqlimit = std::numeric_limits::max(), std::vector> det_orders = std::vector>(), double det_penalty = 0.0, bool create_visualization = false) { @@ -43,15 +43,13 @@ TesseractConfig tesseract_config_maker_no_dem( if (det_orders.empty()) { det_orders = build_det_orders(empty_dem, 20, DetOrder::DetBFS, 2384753); } - return TesseractConfig({empty_dem, det_beam, beam_climbing, no_revisit_dets, - at_most_two_errors_per_detector, verbose, merge_errors, pqlimit, - det_orders, det_penalty, create_visualization}); + return TesseractConfig({empty_dem, det_beam, beam_climbing, no_revisit_dets, verbose, + merge_errors, pqlimit, det_orders, det_penalty, create_visualization}); } TesseractConfig tesseract_config_maker( py::object dem, int det_beam = INF_DET_BEAM, bool beam_climbing = false, - bool no_revisit_dets = false, bool at_most_two_errors_per_detector = false, - bool verbose = false, bool merge_errors = true, + bool no_revisit_dets = false, bool verbose = false, bool merge_errors = true, size_t pqlimit = std::numeric_limits::max(), std::vector> det_orders = std::vector>(), double det_penalty = 0.0, bool create_visualization = false) { @@ -59,9 +57,8 @@ TesseractConfig tesseract_config_maker( if (det_orders.empty()) { det_orders = build_det_orders(input_dem, 20, DetOrder::DetBFS, 2384753); } - return TesseractConfig({input_dem, det_beam, beam_climbing, no_revisit_dets, - at_most_two_errors_per_detector, verbose, merge_errors, pqlimit, - det_orders, det_penalty, create_visualization}); + return TesseractConfig({input_dem, det_beam, beam_climbing, no_revisit_dets, verbose, + merge_errors, pqlimit, det_orders, det_penalty, create_visualization}); } }; // namespace @@ -83,8 +80,7 @@ void add_tesseract_module(py::module& root) { )pbdoc") .def(py::init(&tesseract_config_maker_no_dem), py::arg("det_beam") = 5, py::arg("beam_climbing") = false, py::arg("no_revisit_dets") = true, - py::arg("at_most_two_errors_per_detector") = false, py::arg("verbose") = false, - py::arg("merge_errors") = true, py::arg("pqlimit") = 200000, + py::arg("verbose") = false, py::arg("merge_errors") = true, py::arg("pqlimit") = 200000, py::arg("det_orders") = std::vector>(), py::arg("det_penalty") = 0.0, py::arg("create_visualization") = false, R"pbdoc( @@ -99,9 +95,7 @@ void add_tesseract_module(py::module& root) { If True, enables a beam climbing heuristic. no_revisit_dets : bool, default=False If True, prevents the decoder from revisiting a syndrome pattern more than once. - at_most_two_errors_per_detector : bool, default=False - If True, an optimization is enabled that assumes at most two errors - are correlated with each detector. + verbose : bool, default=False If True, enables verbose logging from the decoder. merge_errors : bool, default=True @@ -118,8 +112,7 @@ void add_tesseract_module(py::module& root) { )pbdoc") .def(py::init(&tesseract_config_maker), py::arg("dem"), py::arg("det_beam") = 5, py::arg("beam_climbing") = false, py::arg("no_revisit_dets") = true, - py::arg("at_most_two_errors_per_detector") = false, py::arg("verbose") = false, - py::arg("merge_errors") = true, py::arg("pqlimit") = 200000, + py::arg("verbose") = false, py::arg("merge_errors") = true, py::arg("pqlimit") = 200000, py::arg("det_orders") = std::vector>(), py::arg("det_penalty") = 0.0, py::arg("create_visualization") = false, R"pbdoc( @@ -135,9 +128,7 @@ void add_tesseract_module(py::module& root) { If True, enables a beam climbing heuristic. no_revisit_dets : bool, default=False If True, prevents the decoder from revisiting a syndrome pattern more than once. - at_most_two_errors_per_detector : bool, default=False - If True, an optimization is enabled that assumes at most two errors - are correlated with each detector. + verbose : bool, default=False If True, enables verbose logging from the decoder. merge_errors : bool, default=True @@ -160,9 +151,7 @@ void add_tesseract_module(py::module& root) { "Whether to use a beam climbing heuristic.") .def_readwrite("no_revisit_dets", &TesseractConfig::no_revisit_dets, "Whether to prevent revisiting same syndrome patterns during decoding.") - .def_readwrite("at_most_two_errors_per_detector", - &TesseractConfig::at_most_two_errors_per_detector, - "Whether to assume at most two errors per detector for optimization.") + .def_readwrite("verbose", &TesseractConfig::verbose, "If True, the decoder will print verbose output.") .def_readwrite("merge_errors", &TesseractConfig::merge_errors, diff --git a/src/tesseract_main.cc b/src/tesseract_main.cc index 4785570e..5b483b7b 100644 --- a/src/tesseract_main.cc +++ b/src/tesseract_main.cc @@ -75,7 +75,7 @@ struct Args { double det_penalty = 0; bool beam_climbing = false; bool no_revisit_dets = false; - bool at_most_two_errors_per_detector = false; + size_t pqlimit; bool verbose = false; @@ -289,7 +289,7 @@ struct Args { config.det_penalty = det_penalty; config.beam_climbing = beam_climbing; config.no_revisit_dets = no_revisit_dets; - config.at_most_two_errors_per_detector = at_most_two_errors_per_detector; + config.pqlimit = pqlimit; config.verbose = verbose; } @@ -445,10 +445,7 @@ int main(int argc, char* argv[]) { .help("Use no-revisit-dets heuristic") .flag() .store_into(args.no_revisit_dets); - program.add_argument("--at-most-two-errors-per-detector") - .help("Use heuristic limitation of at most 2 errors per detector") - .flag() - .store_into(args.at_most_two_errors_per_detector); + program.add_argument("--pqlimit") .help("Maximum size of the priority queue (default = infinity)") .metavar("N") @@ -594,25 +591,24 @@ int main(int argc, char* argv[]) { bool print_final_stats = true; if (!args.stats_out_fname.empty()) { - nlohmann::json stats_json = { - {"circuit_path", args.circuit_path}, - {"dem_path", args.dem_path}, - {"max_errors", args.max_errors}, - {"sample_seed", args.sample_seed}, - {"at_most_two_errors_per_detector", args.at_most_two_errors_per_detector}, - {"det_beam", args.det_beam}, - {"det_penalty", args.det_penalty}, - {"beam_climbing", args.beam_climbing}, - {"no_revisit_dets", args.no_revisit_dets}, - {"pqlimit", args.pqlimit}, - {"num_det_orders", args.num_det_orders}, - {"det_order_seed", args.det_order_seed}, - {"total_time_seconds", total_time_seconds}, - {"num_errors", num_errors}, - {"num_low_confidence", num_low_confidence}, - {"num_shots", shot}, - {"num_threads", args.num_threads}, - {"sample_num_shots", args.sample_num_shots}}; + nlohmann::json stats_json = {{"circuit_path", args.circuit_path}, + {"dem_path", args.dem_path}, + {"max_errors", args.max_errors}, + {"sample_seed", args.sample_seed}, + + {"det_beam", args.det_beam}, + {"det_penalty", args.det_penalty}, + {"beam_climbing", args.beam_climbing}, + {"no_revisit_dets", args.no_revisit_dets}, + {"pqlimit", args.pqlimit}, + {"num_det_orders", args.num_det_orders}, + {"det_order_seed", args.det_order_seed}, + {"total_time_seconds", total_time_seconds}, + {"num_errors", num_errors}, + {"num_low_confidence", num_low_confidence}, + {"num_shots", shot}, + {"num_threads", args.num_threads}, + {"sample_num_shots", args.sample_num_shots}}; if (args.stats_out_fname == "-") { std::cout << stats_json << std::endl; diff --git a/src/tesseract_sinter_compat.pybind.h b/src/tesseract_sinter_compat.pybind.h index 623253d9..6dcbcc72 100644 --- a/src/tesseract_sinter_compat.pybind.h +++ b/src/tesseract_sinter_compat.pybind.h @@ -292,14 +292,14 @@ void pybind_sinter_compat(py::module& root) { R"pbdoc(Checks if two TesseractSinterDecoder instances are not equal.)pbdoc") .def(py::pickle( [](const TesseractSinterDecoder& self) -> py::tuple { // __getstate__ - return py::make_tuple( - std::string(self.config.dem.str()), self.config.det_beam, self.config.beam_climbing, - self.config.no_revisit_dets, self.config.at_most_two_errors_per_detector, - self.config.verbose, self.config.merge_errors, self.config.pqlimit, - self.config.det_orders, self.config.det_penalty, self.config.create_visualization); + return py::make_tuple(std::string(self.config.dem.str()), self.config.det_beam, + self.config.beam_climbing, self.config.no_revisit_dets, + self.config.verbose, self.config.merge_errors, + self.config.pqlimit, self.config.det_orders, + self.config.det_penalty, self.config.create_visualization); }, [](py::tuple t) { // __setstate__ - if (t.size() != 11) { + if (t.size() != 10) { throw std::runtime_error("Invalid state for TesseractSinterDecoder!"); } TesseractConfig config; @@ -307,13 +307,12 @@ void pybind_sinter_compat(py::module& root) { config.det_beam = t[1].cast(); config.beam_climbing = t[2].cast(); config.no_revisit_dets = t[3].cast(); - config.at_most_two_errors_per_detector = t[4].cast(); - config.verbose = t[5].cast(); - config.merge_errors = t[6].cast(); - config.pqlimit = t[7].cast(); - config.det_orders = t[8].cast>>(); - config.det_penalty = t[9].cast(); - config.create_visualization = t[10].cast(); + config.verbose = t[4].cast(); + config.merge_errors = t[5].cast(); + config.pqlimit = t[6].cast(); + config.det_orders = t[7].cast>>(); + config.det_penalty = t[8].cast(); + config.create_visualization = t[9].cast(); return TesseractSinterDecoder(config); })); From a1e86a762ee4809a2d340551c11c45a18ee4f68f Mon Sep 17 00:00:00 2001 From: noajshu Date: Sun, 31 Aug 2025 00:43:35 +0000 Subject: [PATCH 12/14] Refactor: Rename num_detectors to num_dets --- src/tesseract.cc | 40 ++++++++++++++++++++-------------------- src/tesseract.h | 5 +++-- src/tesseract.pybind.h | 6 +++--- 3 files changed, 26 insertions(+), 25 deletions(-) diff --git a/src/tesseract.cc b/src/tesseract.cc index f3d24601..ba3e06d1 100644 --- a/src/tesseract.cc +++ b/src/tesseract.cc @@ -74,12 +74,12 @@ std::string Node::str() { ss << "Node("; ss << "errors=" << self.errors << ", "; ss << "cost=" << self.cost << ", "; - ss << "num_detectors=" << self.num_detectors << ", "; + ss << "num_dets=" << self.num_dets << ", "; return ss.str(); } bool Node::operator>(const Node& other) const { - return cost > other.cost || (cost == other.cost && num_detectors < other.num_detectors); + return cost > other.cost || (cost == other.cost && num_dets < other.num_dets); } double TesseractDecoder::get_detcost( @@ -293,27 +293,27 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, return; } - size_t min_num_detectors = detections.size(); - size_t max_num_detectors = min_num_detectors + detector_beam; + size_t min_num_dets = detections.size(); + size_t max_num_dets = min_num_dets + detector_beam; std::vector next_errors; boost::dynamic_bitset<> next_detectors; std::vector next_detector_cost_tuples; - pq.push({initial_cost, min_num_detectors, std::vector()}); + pq.push({initial_cost, min_num_dets, std::vector()}); size_t num_pq_pushed = 1; while (!pq.empty()) { const Node node = pq.top(); pq.pop(); - if (node.num_detectors > max_num_detectors) continue; + if (node.num_dets > max_num_dets) continue; boost::dynamic_bitset<> detectors = initial_detectors; std::vector detector_cost_tuples(num_errors); flip_detectors_and_block_errors(detector_order, node.errors, detectors, detector_cost_tuples); - if (node.num_detectors == 0) { + if (node.num_dets == 0) { if (config.create_visualization) { visualizer.add_activated_errors(node.errors); visualizer.add_activated_detectors(detectors, num_detectors); @@ -339,7 +339,7 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, return; } - if (config.no_revisit_dets && !visited_detectors[node.num_detectors].insert(detectors).second) + if (config.no_revisit_dets && !visited_detectors[node.num_dets].insert(detectors).second) continue; if (config.create_visualization) { @@ -349,8 +349,8 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, if (config.verbose) { std::cout.precision(13); std::cout << "len(pq) = " << pq.size() << " num_pq_pushed = " << num_pq_pushed << std::endl; - std::cout << "num_detectors = " << node.num_detectors - << " max_num_detectors = " << max_num_detectors << " cost = " << node.cost + std::cout << "num_dets = " << node.num_dets + << " max_num_dets = " << max_num_dets << " cost = " << node.cost << std::endl; std::cout << "activated_errors = "; for (size_t oei : node.errors) { @@ -366,14 +366,14 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, std::cout << std::endl; } - if (node.num_detectors < min_num_detectors) { - min_num_detectors = node.num_detectors; + if (node.num_dets < min_num_dets) { + min_num_dets = node.num_dets; if (config.no_revisit_dets) { - for (size_t i = min_num_detectors + detector_beam + 1; i <= max_num_detectors; ++i) { + for (size_t i = min_num_dets + detector_beam + 1; i <= max_num_dets; ++i) { visited_detectors[i].clear(); } } - max_num_detectors = std::min(max_num_detectors, min_num_detectors + detector_beam); + max_num_dets = std::min(max_num_dets, min_num_dets + detector_beam); } for (size_t d = 0; d < num_detectors; ++d) { @@ -415,21 +415,21 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, next_detector_cost_tuples[ei].error_blocked = 1; double next_cost = node.cost + errors[ei].likelihood_cost; - size_t next_num_detectors = node.num_detectors; + size_t next_num_dets = node.num_dets; for (int d : edets[ei]) { next_detectors[d] = !next_detectors[d]; int fired = next_detectors[d] ? 1 : -1; - next_num_detectors += fired; + next_num_dets += fired; for (int oei : d2e[d]) { next_detector_cost_tuples[oei].detectors_count += fired; } } - if (next_num_detectors > max_num_detectors) continue; + if (next_num_dets > max_num_dets) continue; - if (config.no_revisit_dets && visited_detectors[next_num_detectors].find(next_detectors) != - visited_detectors[next_num_detectors].end()) + if (config.no_revisit_dets && visited_detectors[next_num_dets].find(next_detectors) != + visited_detectors[next_num_dets].end()) continue; for (int d : edets[ei]) { @@ -454,7 +454,7 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, if (next_cost == INF) continue; - pq.push({next_cost, next_num_detectors, next_errors}); + pq.push({next_cost, next_num_dets, next_errors}); ++num_pq_pushed; if (num_pq_pushed > config.pqlimit) { diff --git a/src/tesseract.h b/src/tesseract.h index 62d6ed62..0eeb3ea3 100644 --- a/src/tesseract.h +++ b/src/tesseract.h @@ -50,7 +50,8 @@ struct TesseractConfig { class Node { public: double cost; - size_t num_detectors; + // The number of activated detectors (dets for short) at this node + size_t num_dets; std::vector errors; bool operator>(const Node& other) const; @@ -118,4 +119,4 @@ struct TesseractDecoder { std::vector& detector_cost_tuples) const; }; -#endif // TESSERACT_DECODER_H \ No newline at end of file +#endif // TESSERACT_DECODER_H diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index 85603be9..d40173d6 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -205,20 +205,20 @@ void add_tesseract_module(py::module& root) { This is used internally by the decoder to track decoding progress. )pbdoc") .def(py::init>(), py::arg("cost") = 0.0, - py::arg("num_detectors") = 0, py::arg("errors") = std::vector(), R"pbdoc( + py::arg("num_dets") = 0, py::arg("errors") = std::vector(), R"pbdoc( The constructor for the `Node` class. Parameters ---------- cost : float, default=0.0 The cost of the path to this node. - num_detectors : int, default=0 + num_dets : int, default=0 The number of detectors this search node has. errors : list[int], default=empty The list of error indices this search node has. )pbdoc") .def_readwrite("cost", &Node::cost, "The cost of the node.") - .def_readwrite("num_detectors", &Node::num_detectors, + .def_readwrite("num_dets", &Node::num_dets, "The number of detectors this search node has.") .def_readwrite("errors", &Node::errors, "The list of error indices this search node has.") .def(py::self > py::self, From 1902a2ce0708078864d8780fd8f7c4d79e433168 Mon Sep 17 00:00:00 2001 From: noajshu Date: Sun, 31 Aug 2025 01:12:02 +0000 Subject: [PATCH 13/14] Style: Apply clang-format --- src/tesseract.cc | 5 ++--- src/tesseract.pybind.h | 3 +-- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/src/tesseract.cc b/src/tesseract.cc index ba3e06d1..a2c16915 100644 --- a/src/tesseract.cc +++ b/src/tesseract.cc @@ -349,9 +349,8 @@ void TesseractDecoder::decode_to_errors(const std::vector& detections, if (config.verbose) { std::cout.precision(13); std::cout << "len(pq) = " << pq.size() << " num_pq_pushed = " << num_pq_pushed << std::endl; - std::cout << "num_dets = " << node.num_dets - << " max_num_dets = " << max_num_dets << " cost = " << node.cost - << std::endl; + std::cout << "num_dets = " << node.num_dets << " max_num_dets = " << max_num_dets + << " cost = " << node.cost << std::endl; std::cout << "activated_errors = "; for (size_t oei : node.errors) { std::cout << oei << ", "; diff --git a/src/tesseract.pybind.h b/src/tesseract.pybind.h index d40173d6..72f92e5a 100644 --- a/src/tesseract.pybind.h +++ b/src/tesseract.pybind.h @@ -218,8 +218,7 @@ void add_tesseract_module(py::module& root) { The list of error indices this search node has. )pbdoc") .def_readwrite("cost", &Node::cost, "The cost of the node.") - .def_readwrite("num_dets", &Node::num_dets, - "The number of detectors this search node has.") + .def_readwrite("num_dets", &Node::num_dets, "The number of detectors this search node has.") .def_readwrite("errors", &Node::errors, "The list of error indices this search node has.") .def(py::self > py::self, "Comparison operator for nodes based on cost. This is necessary to prioritize " From a4ed74a961c2ecf97d535a87c3f71b6f31c0ea58 Mon Sep 17 00:00:00 2001 From: noajshu Date: Sun, 31 Aug 2025 01:26:21 +0000 Subject: [PATCH 14/14] undo changes to tutorial --- docs/tutorial.ipynb | 3149 +++++++++++++++++++++---------------------- 1 file changed, 1567 insertions(+), 1582 deletions(-) diff --git a/docs/tutorial.ipynb b/docs/tutorial.ipynb index 89e9754b..056ee540 100644 --- a/docs/tutorial.ipynb +++ b/docs/tutorial.ipynb @@ -1,1631 +1,1616 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "KBmkwvhmupn-" - }, - "source": [ - "# Tesseract Tutorial\n", - "\n", - "- We will also, partly, explain how to use features of Stim and PyMatching\n", - "- Stim is a dependency of Tesseract but you can also use other sources of data\n", - "- This is not a comprehensive introduction." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jaZcr-NevBSB" - }, - "source": [ - "## Installation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "i6_88o7kKOVJ" - }, - "outputs": [], - "source": [ - "!pip install --quiet --upgrade stim galois tesseract-decoder pymatching python-sat -U" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RLXXX3eMT_LR" - }, - "source": [ - "## Getting a Surface Code Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "8zcmVHFFUPq2" - }, - "outputs": [], - "source": [ - "import stim\n", - "\n", - "d = 11\n", - "p = 0.005\n", - "circuit = stim.Circuit.generated(\n", - " code_task=\"surface_code:rotated_memory_x\",\n", - " distance=d,\n", - " rounds=d,\n", - " after_clifford_depolarization=p,\n", - " before_round_data_depolarization=p,\n", - " before_measure_flip_probability=p,\n", - " after_reset_flip_probability=p\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UBMIlXY9U30Y" - }, - "source": [ - "## Sample" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "GCkUlTJZU2T_" - }, - "outputs": [], - "source": [ - "sampler = circuit.compile_detector_sampler()\n", - "\n", - "num_shots = 10000\n", - "detector_outcomes, actual_observables = sampler.sample(shots=num_shots, separate_observables=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m9x8pivTVCir" - }, - "source": [ - "## Decode with uncorrelated matching" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "KBmkwvhmupn-" + }, + "source": [ + "# Tesseract Tutorial\n", + "\n", + "- We will also, partly, explain how to use features of Stim and PyMatching\n", + "- Stim is a dependency of Tesseract but you can also use other sources of data\n", + "- This is not a comprehensive introduction." + ] }, - "id": "-5W0AX8nVEyU", - "outputId": "562e78d3-4fef-449e-92ae-403e5ed7c862" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Logical error rate: 61/10000\n" - ] - } - ], - "source": [ - "import pymatching\n", - "import numpy as np\n", - "\n", - "dem = circuit.detector_error_model(decompose_errors=True)\n", - "matching = pymatching.Matching.from_detector_error_model(model=dem)\n", - "predicted_observables = matching.decode_batch(shots=detector_outcomes)\n", - "num_errors = np.sum(np.any(predicted_observables != actual_observables, axis=1))\n", - "\n", - "print(f\"Logical error rate: {num_errors}/{num_shots}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Xp7MyK0XVs_6" - }, - "source": [ - "## Decode with new correlated matching!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "jaZcr-NevBSB" + }, + "source": [ + "## Installation" + ] }, - "id": "vufQ8G5iVx7b", - "outputId": "1e12759c-e1e4-4c51-8103-98ec2d6906f8" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Logical error rate: 18/10000\n" - ] - } - ], - "source": [ - "dem = circuit.detector_error_model(decompose_errors=True)\n", - "matching_corr = pymatching.Matching.from_detector_error_model(\n", - " model=dem, enable_correlations=True\n", - " )\n", - "predicted_observables_corr = matching_corr.decode_batch(\n", - " shots=detector_outcomes,\n", - " enable_correlations=True\n", - " )\n", - "num_errors_corr = np.sum(np.any(predicted_observables_corr != actual_observables, axis=1))\n", - "\n", - "print(f\"Logical error rate: {num_errors_corr}/{num_shots}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a-AMqTUeuqOe" - }, - "source": [ - "## Getting a Color Code Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i6_88o7kKOVJ" + }, + "outputs": [], + "source": [ + "!pip install --quiet --upgrade stim galois tesseract-decoder pymatching python-sat -U" + ] }, - "id": "W7fU_MYJCRen", - "outputId": "6038fc3e-8707-4bac-fd69-b9d08a90f167" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 13295 100 13295 0 0 124k 0 --:--:-- --:--:-- --:--:-- 124k\n" - ] - } - ], - "source": [ - "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D5%2Cd%3D5%2Cp%3D0.001%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_Z%2Cq%3D37%2Cgates%3Dcz.stim' > d5r5colorcode_p001.stim" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E-vXEhbaTeQI" - }, - "source": [ - "# Visualizing with Stim" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 343 + "cell_type": "markdown", + "metadata": { + "id": "RLXXX3eMT_LR" + }, + "source": [ + "## Getting a Surface Code Circuit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8zcmVHFFUPq2" + }, + "outputs": [], + "source": [ + "import stim\n", + "\n", + "d = 11\n", + "p = 0.005\n", + "circuit = stim.Circuit.generated(\n", + " code_task=\"surface_code:rotated_memory_x\",\n", + " distance=d,\n", + " rounds=d,\n", + " after_clifford_depolarization=p,\n", + " before_round_data_depolarization=p,\n", + " before_measure_flip_probability=p,\n", + " after_reset_flip_probability=p\n", + ")" + ] }, - "id": "2jTOVijwKPXm", - "outputId": "5a0c63b5-384b-4729-8bf1-d205552de185" - }, - "outputs": [ { - "data": { - "text/html": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "UBMIlXY9U30Y" + }, + "source": [ + "## Sample" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GCkUlTJZU2T_" + }, + "outputs": [], + "source": [ + "sampler = circuit.compile_detector_sampler()\n", + "\n", + "num_shots = 10000\n", + "detector_outcomes, actual_observables = sampler.sample(shots=num_shots, separate_observables=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m9x8pivTVCir" + }, + "source": [ + "## Decode with uncorrelated matching" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-5W0AX8nVEyU", + "outputId": "562e78d3-4fef-449e-92ae-403e5ed7c862" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Logical error rate: 69/10000\n" + ] + } ], - "text/plain": [ - 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AAA4wgAAOMIAAADCAAA4wgAAOMIAAAjCAAA4wgAAQMIAAADCAAA4wgAAQMIAAAjCAABEwgAACMIAAADCAABEwgAACMIAAAjCAABEwgAAEMIAAADCAABEwgAAEMIAAAjCAABEwgAAEMIAABDCAABEwgAAEMIAABjCAABEwgAAGMIAAAjCAABEwgAAGMIAABDCAABEwgAAGMIAABjCAABEwgAAGMIAACDCAABEwgAAIMIAAAjCAABEwgAAIMIAABDCAABEwgAAIMIAABjCAABEwgAAIMIAACDCAABEwgAAIMIAACjCAABEwgAAIMIAADDCAABEwgAAKMIAAADCAABEwgAAKMIAAAjCAABEwgAAKMIAABDCAABEwgAAKMIAABjCAABEwgAAKMIAACDCAABEwgAAKMIAACjCAABEwgAAMMIAAADCAABEwgAAMMIAAAjCAABEwgAAMMIAABDCAABEwgAAMMIAABjCAABEwgAAOMIAAAjCAABEwgAAOMIAABDCAABIwgAACMIAAADCAABIwgAACMIAAAjCAABIwgAAEMIAAADCAABIwgAAEMIAAAjCAABIwgAAEMIAABDCAABIwgAAEMIAABjCAABIwgAAGMIAAAjCAABIwgAAGMIAABDCAABIwgAAGMIAABjCAABIwgAAGMIAACDCAABIwgAAIMIAAAjCAABIwgAAIMIAABDCAABIwgAAIMIAABjCAABIwgAAIMIAACDCAABIwgAAIMIAACjCAABIwgAAIMIAADDCAABIwgAAKMIAAADCAABIwgAAKMIAAAjCAABIwgAAKMIAABDCAABIwgAAKMIAABjCAABIwgAAKMIAACDCAABIwgAAKMIAACjCAABIwgAAMMIAAADCAABIwgAAMMIAAAjCAABIwgAAMMIAABDCAABIwgAAMMIAABjCAABIwgAAOMIAAAjCAABIwgAAOMIAABDCAABMwgAAAMIAAADCAABMwgAACMIAAADCAABMwgAAEMIAAADCAABMwgAAGMIAAADCAABMwgAAEMIAAAjCAABMwgAAGMIAAAjCAABMwgAAEMIAABDCAABMwgAAGMIAABDCAABMwgAAEMIAABjCAABMwgAAGMIAABjCAABMwgAAIMIAAADCAABMwgAAKMIAAADCAABMwgAAIMIAAAjCAABMwgAAKMIAAAjCAABMwgAAIMIAABDCAABMwgAAKMIAABDCAABMwgAAIMIAABjCAABMwgAAKMIAABjCAABMwgAAIMIAACDCAABMwgAAKMIAACDCAABMwgAAIMIAACjCAABMwgAAKMIAACjCAABMwgAAMMIAAADCAABMwgAAOMIAAADCAABMwgAAMMIAAAjCAABMwgAAOMIAAAjCAABMwgAAMMIAABDCAABMwgAAOMIAABDCAABQwgAAAMIAAADCAABQwgAACMIAAADCAABQwgAAEMIAAADCAABQwgAAGMIAAADCAABQwgAAEMIAAAjCAABQwgAAGMIAAAjCAABQwgAAEMIAABDCAABQwgAAGMIAABDCAABQwgAAEMIAABjCAABQwgAAGMIAABjCAABQwgAAIMIAAADCAABQwgAAKMIAAADCAABQwgAAIMIAAAjCAABQwgAAKMIAAAjCAABQwgAAIMIAABDCAABQwgAAKMIAABDCAABQwgAAIMIAABjCAABQwgAAKMIAABjCAABQwgAAIMIAACDCAABQwgAAKMIAACDCAABQwgAAIMIAACjCAABQwgAAKMIAACjCAABQwgAAMMIAAADCAABQwgAAOMIAAADCAABQwgAAMMIAAAjCAABQwgAAOMIAAAjCAABQwgAAMMIAABDCAABQwgAAOMIAABDCAABUwgAACMIAAAjCAABUwgAACMIAABDCAABUwgAAEMIAAAjCAABUwgAAEMIAABDCAABUwgAAGMIAAADCAABUwgAAGMIAAAjCAABUwgAAGMIAABDCAABUwgAAGMIAABjCAABUwgAAGMIAACDCAABUwgAAGMIAACjCAABUwgAAIMIAAADCAABUwgAAIMIAAAjCAABUwgAAIMIAABDCAABUwgAAIMIAABjCAABUwgAAIMIAACDCAABUwgAAIMIAACjCAABUwgAAKMIAAAjCAABUwgAAKMIAABDCAABUwgAAKMIAABjCAABUwgAAKMIAACDCAABUwgAAMMIAAAjCAABUwgAAMMIAABDCAABUwgAAMMIAABjCAABUwgAAMMIAACDCAABUwgAAOMIAAADCAABUwgAAOMIAAAjCAABUwgAAQMIAAADCAABUwgAAQMIAAAjCAABYwgAACMIAAAjCAABYwgAACMIAABDCAABYwgAAEMIAAAjCAABYwgAAEMIAABDCAABYwgAAGMIAAADCAABYwgAAGMIAAAjCAABYwgAAGMIAABDCAABYwgAAGMIAABjCAABYwgAAGMIAACDCAABYwgAAGMIAACjCAABYwgAAIMIAAADCAABYwgAAIMIAAAjCAABYwgAAIMIAABDCAABYwgAAIMIAABjCAABYwgAAIMIAACDCAABYwgAAIMIAACjCAABYwgAAKMIAAAjCAABYwgAAKMIAABDCAABYwgAAKMIAABjCAABYwgAAKMIAACDCAABYwgAAMMIAAAjCAABYwgAAMMIAABDCAABYwgAAMMIAABjCAABYwgAAMMIAACDCAABYwgAAOMIAAADCAABYwgAAOMIAAAjCAABYwgAAQMIAAADCAABYwgAAQMIAAAjCAABkwgAACMIAAADCAABkwgAAEMIAAADCAABkwgAACMIAABDCAABkwgAAEMIAABDCAABkwgAAGMIAAAjCAABkwgAAIMIAAAjCAABkwgAAGMIAABjCAABkwgAAIMIAABjCAABkwgAAGMIAACjCAABkwgAAIMIAACjCAABkwgAAKMIAAADCAABkwgAAMMIAAADCAABkwgAAKMIAABDCAABkwgAAMMIAABDCAABkwgAAKMIAACDCAABkwgAAMMIAACDCAABkwgAAOMIAAAjCAABkwgAAQMIAAAjCAABowgAACMIAAADCAABowgAAEMIAAADCAABowgAACMIAABDCAABowgAAEMIAABDCAABowgAAGMIAAAjCAABowgAAIMIAAAjCAABowgAAGMIAABjCAABowgAAIMIAABjCAABowgAAGMIAACjCAABowgAAIMIAACjCAABowgAAKMIAAADCAABowgAAMMIAAADCAABowgAAKMIAABDCAABowgAAMMIAABDCAABowgAAKMIAACDCAABowgAAMMIAACDCAABowgAAOMIAAAjCAABowgAAQMIAAAjCAACMwgAACMIAAADCAACMwgAAEMIAAADCAACMwgAACMIAABDCAACMwgAAEMIAABDCAACMwgAAGMIAAAjCAACMwgAAIMIAAAjCAACMwgAAGMIAABjCAACMwgAAIMIAABjCAACMwgAAGMIAACjCAACMwgAAIMIAACjCAACMwgAAKMIAAADCAACMwgAAMMIAAADCAACMwgAAKMIAABDCAACMwgAAMMIAABDCAACMwgAAKMIAACDCAACMwgAAMMIAACDCAACMwgAAOMIAAAjCAACMwgAAQMIAAAjCAACOwgAACMIAAADCAACOwgAAEMIAAADCAACOwgAACMIAABDCAACOwgAAEMIAABDCAACOwgAAGMIAAAjCAACOwgAAIMIAAAjCAACOwgAAGMIAABjCAACOwgAAIMIAABjCAACOwgAAGMIAACjCAACOwgAAIMIAACjCAACOwgAAKMIAAADCAACOwgAAMMIAAADCAACOwgAAKMIAABDCAACOwgAAMMIAABDCAACOwgAAKMIAACDCAACOwgAAMMIAACDCAACOwgAAOMIAAAjCAACOwgAAQMIAAAjCAACUwgAACMIAAAjCAACUwgAACMIAABDCAACUwgAAEMIAAAjCAACUwgAAEMIAABDCAACUwgAAGMIAAADCAACUwgAAGMIAAAjCAACUwgAAGMIAABDCAACUwgAAGMIAABjCAACUwgAAGMIAACDCAACUwgAAGMIAACjCAACUwgAAIMIAAADCAACUwgAAIMIAAAjCAACUwgAAIMIAABDCAACUwgAAIMIAABjCAACUwgAAIMIAACDCAACUwgAAIMIAACjCAACUwgAAKMIAAAjCAACUwgAAKMIAABDCAACUwgAAKMIAABjCAACUwgAAKMIAACDCAACUwgAAMMIAAAjCAACUwgAAMMIAABDCAACUwgAAMMIAABjCAACUwgAAMMIAACDCAACUwgAAOMIAAADCAACUwgAAOMIAAAjCAACUwgAAQMIAAADCAACUwgAAQMIAAAjCAACWwgAACMIAAAjCAACWwgAACMIAABDCAACWwgAAEMIAAAjCAACWwgAAEMIAABDCAACWwgAAGMIAAADCAACWwgAAGMIAAAjCAACWwgAAGMIAABDCAACWwgAAGMIAABjCAACWwgAAGMIAACDCAACWwgAAGMIAACjCAACWwgAAIMIAAADCAACWwgAAIMIAAAjCAACWwgAAIMIAABDCAACWwgAAIMIAABjCAACWwgAAIMIAACDCAACWwgAAIMIAACjCAACWwgAAKMIAAAjCAACWwgAAKMIAABDCAACWwgAAKMIAABjCAACWwgAAKMIAACDCAACWwgAAMMIAAAjCAACWwgAAMMIAABDCAACWwgAAMMIAABjCAACWwgAAMMIAACDCAACWwgAAOMIAAADCAACWwgAAOMIAAAjCAACWwgAAQMIAAADCAACWwgAAQMIAAAjCAACYwgAAAMIAAADCAACYwgAACMIAAADCAACYwgAAEMIAAADCAACYwgAAGMIAAADCAACYwgAAEMIAAAjCAACYwgAAGMIAAAjCAACYwgAAEMIAABDCAACYwgAAGMIAABDCAACYwgAAEMIAABjCAACYwgAAGMIAABjCAACYwgAAIMIAAADCAACYwgAAKMIAAADCAACYwgAA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zBAADKwc3M/MEAADLCAADKwZqZ+cEAADLCAADKwZqZ+cEAAPzBAADKwZqZ+cEAADLCAADOwc3M/MEAAPzBAADOwZqZ+cEAAPzBAADOwc3M/MEAADLCAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADawZqZ+cEAAPzBAADOwZqZ+cEAADLCAADawZqZ+cEAADLCAADawc3M/MEAAPzBAADawZqZ+cEAAPzBAADawc3M/MEAADLCAADawZqZ+cEAADLCAADawZqZ+cEAAPzBAADawZqZ+cEAADLCAADewc3M/MEAAPzBAADewZqZ+cEAAPzBAADewc3M/MEAADLCAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADywZqZ+cEAAPzBAADewZqZ+cEAADLCAADywZqZ+cEAADLCAADywc3M/MEAAPzBAADywZqZ+cEAAPzBAADywc3M/MEAADLCAADywZqZ+cEAADLCAADywZqZ+cEAAPzBAADywZqZ+cEAADLCAAD+wc3M/MEAAPzBAAD+wZqZ+cEAAPzBAAD+wc3M/MEAADLCAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAAJwpqZ+cEAAPzBAAD+wZqZ+cEAADLCAAAJwpqZ+cEAADLCAAAJws3M/MEAAPzBAAAJwpqZ+cEAAPzBAAAJws3M/MEAADLCAAAJwpqZ+cEAADLCAAAJwpqZ+cEAAPzBAAAJwpqZ+cEAADLCAAALws3M/MEAAPzBAAALwpqZ+cEAAPzBAAALws3M/MEAADLCAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAARwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAARwpqZ+cEAADLCAAARws3M/MEAAPzBAAARwpqZ+cEAAPzBAAARws3M/MEAADLCAAARwpqZ+cEAADLCAAARwpqZ+cEAAPzBAAARwpqZ+cEAADLCAAATws3M/MEAAPzBAAATwpqZ+cEAAPzBAAATws3M/MEAADLCAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAAZwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAAZwpqZ+cEAADLCAAAZws3M/MEAAPzBAAAZwpqZ+cEAAPzBAAAZws3M/MEAADLCAAAZwpqZ+cEAADLCAAAZwpqZ+cEAAPzBAAAZwpqZ+cEAADLCAAAbws3M/MEAAPzBAAAbwpqZ+cEAAPzBAAAbws3M/MEAADLCAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAhwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAhwpqZ+cEAADLCAAAhws3M/MEAAPzBAAAhwpqZ+cEAAPzBAAAhws3M/MEAADLCAAAhwpqZ+cEAADLCAAAhwpqZ+cEAAPzBAAAhwpqZ+cEAADLCAAAjws3M/MEAAPzBAAAjwpqZ+cEAAPzBAAAjws3M/MEAADLCAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAApwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAApwpqZ+cEAADLCAAApws3M/MEAAPzBAAApwpqZ+cEAAPzBAAApws3M/MEAADLCAAApwpqZ+cEAADLCAAApwpqZ+cEAAPzBAAApwpqZ+cEAADLCAAArws3M/MEAAPzBAAArwpqZ+cEAAPzBAAArws3M/MEAADLCAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAAxwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAAxwpqZ+cEAADLCAAAxws3M/MEAAPzBAAAxwpqZ+cEAAPzBAAAxws3M/MEAADLCAAAxwpqZ+cEAADLCAAAxwpqZ+cEAAPzBAAAxwpqZ+cEAADLCAAAzws3M/MEAAPzBAAAzwpqZ+cEAAPzBAAAzws3M/MEAADLCAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAA5wpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAA5wpqZ+cEAADLCAAA5ws3M/MEAAPzBAAA5wpqZ+cEAAPzBAAA5ws3M/MEAADLCAAA5wpqZ+cEAADLCAAA5wpqZ+cEAAPzBAAA5wpqZ+cEAADLCAAA7ws3M/MEAAPzBAAA7wpqZ+cEAAPzBAAA7ws3M/MEAADLCAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAABBwpqZ+cEAAPzBAAA7wpqZ+cEAADLCAABBwpqZ+cEAADLCAABBws3M/MEAAPzBAABBwpqZ+cEAAPzBAABBws3M/MEAADLCAABBwpqZ+cEAADLCAABBwpqZ+cEAAPzBAABBwpqZ+cEAADLCAABDws3M/MEAAPzBAABDwpqZ+cEAAPzBAABDws3M/MEAADLCAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABJwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABJwpqZ+cEAADLCAABJws3M/MEAAPzBAABJwpqZ+cEAAPzBAABJws3M/MEAADLCAABJwpqZ+cEAADLCAABJwpqZ+cEAAPzBAABJwpqZ+cEAADLCAABLws3M/MEAAPzBAABLwpqZ+cEAAPzBAABLws3M/MEAADLCAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABRwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABRwpqZ+cEAADLCAABRws3M/MEAAPzBAABRwpqZ+cEAAPzBAABRws3M/MEAADLCAABRwpqZ+cEAADLCAABRwpqZ+cEAAPzBAABRwpqZ+cEAADLCAABTws3M/MEAAPzBAABTwpqZ+cEAAPzBAABTws3M/MEAADLCAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABZwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABZwpqZ+cEAADLCAABZws3M/MEAAPzBAABZwpqZ+cEAAPzBAABZws3M/MEAADLCAABZwpqZ+cEAADLCAABZwpqZ+cEAAPzBAABZwpqZ+cEAADLCAABbws3M/MEAAPzBAABbwpqZ+cEAAPzBAABbws3M/MEAADLCAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABhwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABhwpqZ+cEAADLCAABhws3M/MEAAPzBAABhwpqZ+cEAAPzBAABhws3M/MEAADLCAABhwpqZ+cEAADLCAABhwpqZ+cEAAPzBAABhwpqZ+cEAADLCAABjws3M/MEAAPzBAABjwpqZ+cEAAPzBAABjws3M/MEAADLCAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABpwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABpwpqZ+cEAADLCAABpws3M/MEAAPzBAABpwpqZ+cEAAPzBAABpws3M/MEAADLCAABpwpqZ+cEAADLCAABpwpqZ+cEAAPzBAABpwpqZ+cEAADLCAABrws3M/MEAAPzBAABrwpqZ+cEAAPzBAABrws3M/MEAADLCAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABxwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABxwpqZ+cEAADLCAABxws3M/MEAAPzBAABxwpqZ+cEAAPzBAABxws3M/MEAADLCAABxwpqZ+cEAADLCAABxwpqZ+cEAAPzBAABxwpqZ+cEAADLCAABzws3M/MEAAPzBAABzwpqZ+cEAAPzBAABzws3M/MEAADLCAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAAB9wpqZ+cEAAPzBAABzwpqZ+cEAADLCAAB9wpqZ+cEAADLCAAB9ws3M/MEAAPzBAAB9wpqZ+cEAAPzBAAB9ws3M/MEAADLCAAB9wpqZ+cEAADLCAAB9wpqZ+cEAAPzBAAB9wpqZ+cEAADLCAICBws3M/MEAAPzBAICBwpqZ+cEAAPzBAICBws3M/MEAADLCAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICGwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICGwpqZ+cEAADLCAICGws3M/MEAAPzBAICGwpqZ+cEAAPzBAICGws3M/MEAADLCAICGwpqZ+cEAADLCAICGwpqZ+cEAAPzBAICGwpqZ+cEAADLCAICHws3M/MEAAPzBAICHwpqZ+cEAAPzBAICHws3M/MEAADLCAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICKwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICKwpqZ+cEAADLCAICKws3M/MEAAPzBAICKwpqZ+cEAAPzBAICKws3M/MEAADLCAICKwpqZ+cEAADLCAICKwpqZ+cEAAPzBAICKwpqZ+cEAADLCAICLws3M/MEAAPzBAICLwpqZ+cEAAPzBAICLws3M/MEAADLCAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICOwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICOwpqZ+cEAADLCAICOws3M/MEAAPzBAICOwpqZ+cEAAPzBAICOws3M/MEAADLCAICOwpqZ+cEAADLCAICOwpqZ+cEAAPzBAICOwpqZ+cEAADLCAICPws3M/MEAAPzBAICPwpqZ+cEAAPzBAICPws3M/MEAADLCAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICSwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICSwpqZ+cEAADLCAICSws3M/MEAAPzBAICSwpqZ+cEAAPzBAICSws3M/MEAADLCAICSwpqZ+cEAADLCAICSwpqZ+cEAAPzBAICSwpqZ+cEAADLCAICTws3M/MEAAPzBAICTwpqZ+cEAAPzBAICTws3M/MEAADLCAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICWwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICWwpqZ+cEAADLCAICWws3M/MEAAPzBAICWwpqZ+cEAAPzBAICWws3M/MEAADLCAICWwpqZ+cEAADLCAICWwpqZ+cEAAPzBAICWwpqZ+cEAADLCAICXws3M/MEAAPzBAICXwpqZ+cEAAPzBAICXws3M/MEAADLCAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICawpqZ+cEAAPzBAICXwpqZ+cEAADLCAICawpqZ+cEAADLCAICaws3M/MEAAPzBAICawpqZ+cEAAPzBAICaws3M/MEAADLCAICawpqZ+cEAADLCAICawpqZ+cEAAPzBAICawpqZ+cEAADLCAICbws3M/MEAAPzBAICbwpqZ+cEAAPzBAICbws3M/MEAADLCAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICewpqZ+cEAAPzBAICbwpqZ+cEAADLCAICewpqZ+cEAADLCAICews3M/MEAAPzBAICewpqZ+cEAAPzBAICews3M/MEAADLCAICewpqZ+cEAADLCAICewpqZ+cEAAPzBAICewpqZ+cEAADLCAICfws3M/MEAAPzBAICfwpqZ+cEAAPzBAICfws3M/MEAADLCAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICiwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICiwpqZ+cEAADLCAICiws3M/MEAAPzBAICiwpqZ+cEAAPzBAICiws3M/MEAADLCAICiwpqZ+cEAADLCAICiwpqZ+cEAAPzBAICiwpqZ+cEAADLCAICjws3M/MEAAPzBAICjwpqZ+cEAAPzBAICjws3M/MEAADLCAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICmwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICmwpqZ+cEAADLCAICmws3M/MEAAPzBAICmwpqZ+cEAAPzBAICmws3M/MEAADLCAICmwpqZ+cEAADLCAICmwpqZ+cEAAPzBAICmwpqZ+cEAADLCAICnws3M/MEAAPzBAICnwpqZ+cEAAPzBAICnws3M/MEAADLCAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICqwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICqwpqZ+cEAADLCAICqws3M/MEAAPzBAICqwpqZ+cEAAPzBAICqws3M/MEAADLCAICqwpqZ+cEAADLCAICqwpqZ+cEAAPzBAICqwpqZ+cEAADLCAICrws3M/MEAAPzBAICrwpqZ+cEAAPzBAICrws3M/MEAADLCAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICuwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICuwpqZ+cEAADLCAICuws3M/MEAAPzBAICuwpqZ+cEAAPzBAICuws3M/MEAADLCAICuwpqZ+cEAADLCAICuwpqZ+cEAAPzBAICuwpqZ+cEAADLCAICvws3M/MEAAPzBAICvwpqZ+cEAAPzBAICvws3M/MEAADLCAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICywpqZ+cEAAPzBAICvwpqZ+cEAADLCAICywpqZ+cEAADLCAICyws3M/MEAAPzBAICywpqZ+cEAAPzBAICyws3M/MEAADLCAICywpqZ+cEAADLCAICywpqZ+cEAAPzBAICywpqZ+cEAADLCAICzws3M/MEAAPzBAICzwpqZ+cEAAPzBAICzws3M/MEAADLCAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAIC2wpqZ+cEAAPzBAICzwpqZ+cEAADLCAIC2wpqZ+cEAADLCAIC2ws3M/MEAAPzBAIC2wpqZ+cEAAPzBAIC2ws3M/MEAADLCAIC2wpqZ+cEAADLCAIC2wpqZ+cEAAPzBAIC2wpqZ+cEAADLCAIC3ws3M/MEAAPzBAIC3wpqZ+cEAAPzBAIC3ws3M/MEAADLCAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC6wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC6wpqZ+cEAADLCAIC6ws3M/MEAAPzBAIC6wpqZ+cEAAPzBAIC6ws3M/MEAADLCAIC6wpqZ+cEAADLCAIC6wpqZ+cEAAPzBAIC6wpqZ+cEAADLC\"}],\"images\":[{\"uri\":\"data:image/png;base64,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+ "source": [ + "import pymatching\n", + "import numpy as np\n", + "\n", + "dem = circuit.detector_error_model(decompose_errors=True)\n", + "matching = pymatching.Matching.from_detector_error_model(model=dem)\n", + "predicted_observables = matching.decode_batch(shots=detector_outcomes)\n", + "num_errors = np.sum(np.any(predicted_observables != actual_observables, axis=1))\n", + "\n", + "print(f\"Logical error rate: {num_errors}/{num_shots}\")" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import stim\n", - "\n", - "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", - "circuit.diagram('timeline-3d')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YDnwv2dacTbf" - }, - "source": [ - "# Estimating code distance with Stim" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "r2MFDDBMvkq3", - "outputId": "cc6b8bfe-9adb-4b55-9c47-ed2580177b44" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "estimated distance: 5\n" - ] - } - ], - "source": [ - "distance_estimate = len(circuit.search_for_undetectable_logical_errors(\n", - " dont_explore_detection_event_sets_with_size_above=6,\n", - " dont_explore_edges_with_degree_above=3,\n", - " dont_explore_edges_increasing_symptom_degree=False))\n", - "print(f'estimated distance: {distance_estimate}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UuYvEwq9cgYc" - }, - "source": [ - "# Create DEM, detection events and observables with Stim" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6oCW5jRsXy8-" - }, - "source": [ - "### Can't decode with pymatching..." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "Xp7MyK0XVs_6" + }, + "source": [ + "## Decode with new correlated matching!" + ] }, - "id": "x6TQbGZ7b06k", - "outputId": "c4caa541-cef7-498d-a410-c00b64cf8a79" - }, - "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/tmp/ipykernel_3802485/2195602962.py\", line 5, in \n", - " circuit.detector_error_model(decompose_errors=True)\n", - " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n", - "ValueError: Failed to decompose errors into graphlike components with at most two symptoms.\n", - "The error component that failed to decompose is 'D46, D49, D52, D63, D66, D73, D75'.\n", - "\n", - "In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.\n", - "From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.\n", - "\n", - "Circuit stack trace:\n", - " during TICK layer #46 of 75\n", - " at instruction #104 [which is a REPEAT 3 block]\n" - ] - } - ], - "source": [ - "import traceback\n", - "\n", - "try:\n", - " # decompose_errors=True needed for DEM to be matchable\n", - " circuit.detector_error_model(decompose_errors=True)\n", - "except:\n", - " traceback.print_exc()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ye5W7BJHX8DJ" - }, - "source": [ - "No need to decompose errors using tesseract:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "AVu7idoTYAdM" - }, - "outputs": [], - "source": [ - "dem = circuit.detector_error_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "vFDn06Xach0_" - }, - "outputs": [], - "source": [ - "num_shots = 1000\n", - "sampler = circuit.compile_detector_sampler()\n", - "dets, obs = sampler.sample(num_shots, separate_observables=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JrX13vNQcrm3" - }, - "source": [ - "# Decoding with Tesseract and ILP decoder" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "Uds8S04a-z-G" - }, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vufQ8G5iVx7b", + "outputId": "1e12759c-e1e4-4c51-8103-98ec2d6906f8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Logical error rate: 22/10000\n" + ] + } + ], + "source": [ + "dem = circuit.detector_error_model(decompose_errors=True)\n", + "matching_corr = pymatching.Matching.from_detector_error_model(\n", + " model=dem, enable_correlations=True\n", + " )\n", + "predicted_observables_corr = matching_corr.decode_batch(\n", + " shots=detector_outcomes,\n", + " enable_correlations=True\n", + " )\n", + "num_errors_corr = np.sum(np.any(predicted_observables_corr != actual_observables, axis=1))\n", + "\n", + "print(f\"Logical error rate: {num_errors_corr}/{num_shots}\")" + ] + }, { - "ename": "ImportError", - "evalue": "/usr/local/google/home/shutty/pyenv/lib/python3.13/site-packages/tesseract_decoder.so: undefined symbol: _PyThreadState_UncheckedGet", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract_decoder\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract_decoder\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtesseract\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtesseract\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n", - "\u001b[31mImportError\u001b[39m: /usr/local/google/home/shutty/pyenv/lib/python3.13/site-packages/tesseract_decoder.so: undefined symbol: _PyThreadState_UncheckedGet" - ] - } - ], - "source": [ - "import tesseract_decoder\n", - "import tesseract_decoder.tesseract as tesseract\n", - "import numpy as np\n", - "import time\n", - "\n", - "# Helper functions for benchmarking\n", - "\n", - "def print_results(result):\n", - " print(\"Tesseract Decoder Stats:\")\n", - " print(f\" Number of Errors / num_shots: {results['num_errors']} / {results['num_shots']}\")\n", - " print(f\" Time: {results['time_seconds']:.4f} s\")\n", - " print()\n", - "\n", - "def run_tesseract_decoder(decoder, dets, obs):\n", - " # Run and time the Tesseract decoder\n", - " num_errors = 0\n", - " start_time = time.time()\n", - " obs_predicted = decoder.decode_batch(dets)\n", - " num_errors = np.sum(np.any(obs_predicted != obs, axis=1))\n", - " end_time = time.time()\n", - "\n", - " return {\n", - " 'num_errors': num_errors,\n", - " 'num_shots': len(dets),\n", - " 'time_seconds': end_time - start_time,\n", - " }\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "a-AMqTUeuqOe" + }, + "source": [ + "## Getting a Color Code Circuit" + ] }, - "id": "D0Tx2eY3ctFw", - "outputId": "64f388af-f1db-4869-873f-3ab714ee8e9c" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tesseract decoder configurations --> TesseractConfig(dem=DetectorErrorModel_Object, det_beam=65535, no_revisit_dets=1, at_most_two_errors_per_detector=0, verbose=0, pqlimit=10000, det_orders=[[89, 86, 85, 83, 81, 88, 84, 82, 87, 74, 71, 78, 75, 69, 67, 63, 77, 66, 61, 72, 64, 65, 79, 68, 62, 73, 80, 70, 42, 76, 43, 46, 33, 32, 50, 44, 31, 57, 35, 36, 59, 51, 30, 58, 60, 48, 39, 45, 49, 34, 25, 7, 47, 5, 18, 26, 21, 2, 40, 24, 12, 29, 28, 55, 37, 56, 54, 53, 38, 15, 3, 16, 52, 20, 9, 19, 13, 8, 11, 10, 17, 41, 22, 6, 14, 23, 0, 1, 27, 4]], det_penalty=0, create_visualization=0)\n", - "\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 3 / 1000\n", - " Time: 3.6089 s\n", - "\n" - ] - } - ], - "source": [ - "# setup the tesseract decoder configuration\n", - "tesseract_config = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " no_revisit_dets=True,\n", - " # verbose=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=1, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print(f'Tesseract decoder configurations --> {tesseract_config}\\n')\n", - "\n", - "tesseract_dec = tesseract_config.compile_decoder()\n", - "\n", - "results = run_tesseract_decoder(tesseract_dec, dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "INvMKs7zc5T_" - }, - "source": [ - "#Decoding with ILP decoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W7fU_MYJCRen", + "outputId": "6038fc3e-8707-4bac-fd69-b9d08a90f167" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 13295 100 13295 0 0 72032 0 --:--:-- --:--:-- --:--:-- 72255\n" + ] + } + ], + "source": [ + "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D5%2Cd%3D5%2Cp%3D0.001%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_Z%2Cq%3D37%2Cgates%3Dcz.stim' > d5r5colorcode_p001.stim" + ] }, - "id": "9Npo7ibac4x5", - "outputId": "51af3bd2-5f53-43c8-a16d-f595a9596bde" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "ILP decoder configurations --> SimplexConfig(dem=DetectorErrorModel_Object, window_length=0, window_slide_length=0, verbose=0)\n", - "ILP stats:\n", - " Estimated time for full shots 1115.2181386947632 s\n", - " Number of Errors / num_shots: 0 / 10\n" - ] - } - ], - "source": [ - "simplex_config = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True\n", - ")\n", - "print(f'ILP decoder configurations --> {simplex_config}')\n", - "ilp_dec = simplex_config.compile_decoder()\n", - "\n", - "start_time = time.time()\n", - "\n", - "# Run and time ILP decoder -- so slow!\n", - "num_shots_to_decode = 10 # Only decoding 10 shots because it's soooo slow\n", - "obs_predicted = ilp_dec.decode_batch(dets[0:num_shots_to_decode])\n", - "num_errors = np.sum(np.any(obs_predicted != obs[0:num_shots_to_decode], axis=1))\n", - "\n", - "end_time = time.time()\n", - "print(f'ILP stats:\\n Estimated time for full shots {num_shots/num_shots_to_decode * (end_time - start_time)} s')\n", - "print(f\" Number of Errors / num_shots: {num_errors} / {num_shots_to_decode}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VQqlMqFRIZ2J" - }, - "source": [ - "# Tesseract Config and impact of heuristic\n", - "You can tune tesseract decoder through the Config that is passed to the decoder with this set of parameters:\n", - "Explanation of configuration arguments:\n", - "\n", - "* `pqlimit` - An integer that sets a limit on the number of nodes in the priority queue. This can be used to constrain the memory usage of the decoder. The default value is `sys.maxsize`, which means the size is effectively unbounded.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "E-vXEhbaTeQI" + }, + "source": [ + "# Visualizing with Stim" + ] }, - "id": "0pExdmuPQuGr", - "outputId": "45a20eca-fef6-425d-d6a2-9a619fe9e10c" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smaller pqlimit\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 183 / 1000\n", - " Time: 1.8420 s\n", - "\n", - "Larger pqlimit\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 3 / 1000\n", - " Time: 8.4409 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=100,\n", - " no_revisit_dets=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"Smaller pqlimit\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=20000,\n", - " no_revisit_dets=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Larger pqlimit\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ru-MRctAIq5-" - }, - "source": [ - "#More heurisitcs\n", - "* `det_beam` - This integer value represents the beam search cutoff. It specifies a threshold for the number of \"residual detection events\" a node can have before it is pruned from the search. A lower `det_beam` value makes the search more aggressive, potentially sacrificing accuracy for speed. The default value `INF_DET_BEAM` means no beam cutoff is applied.\n", - "* `beam_climbing` - A boolean flag that, when set to `True`, enables a heuristic called \"beam climbing.\" This optimization causes the decoder to try different `det_beam` values (up to a maximum) to find a good decoding path. This can improve the decoder's chance of finding the most likely error, even with an initial narrow beam search.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 + }, + "id": "2jTOVijwKPXm", + "outputId": "5a0c63b5-384b-4729-8bf1-d205552de185" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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AAA4wgAAOMIAAADCAAA4wgAAOMIAAAjCAAA4wgAAQMIAAADCAAA4wgAAQMIAAAjCAABEwgAACMIAAADCAABEwgAACMIAAAjCAABEwgAAEMIAAADCAABEwgAAEMIAAAjCAABEwgAAEMIAABDCAABEwgAAEMIAABjCAABEwgAAGMIAAAjCAABEwgAAGMIAABDCAABEwgAAGMIAABjCAABEwgAAGMIAACDCAABEwgAAIMIAAAjCAABEwgAAIMIAABDCAABEwgAAIMIAABjCAABEwgAAIMIAACDCAABEwgAAIMIAACjCAABEwgAAIMIAADDCAABEwgAAKMIAAADCAABEwgAAKMIAAAjCAABEwgAAKMIAABDCAABEwgAAKMIAABjCAABEwgAAKMIAACDCAABEwgAAKMIAACjCAABEwgAAMMIAAADCAABEwgAAMMIAAAjCAABEwgAAMMIAABDCAABEwgAAMMIAABjCAABEwgAAOMIAAAjCAABEwgAAOMIAABDCAABIwgAACMIAAADCAABIwgAACMIAAAjCAABIwgAAEMIAAADCAABIwgAAEMIAAAjCAABIwgAAEMIAABDCAABIwgAAEMIAABjCAABIwgAAGMIAAAjCAABIwgAAGMIAABDCAABIwgAAGMIAABjCAABIwgAAGMIAACDCAABIwgAAIMIAAAjCAABIwgAAIMIAABDCAABIwgAAIMIAABjCAABIwgAAIMIAACDCAABIwgAAIMIAACjCAABIwgAAIMIAADDCAABIwgAAKMIAAADCAABIwgAAKMIAAAjCAABIwgAAKMIAABDCAABIwgAAKMIAABjCAABIwgAAKMIAACDCAABIwgAAKMIAACjCAABIwgAAMMIAAADCAABIwgAAMMIAAAjCAABIwgAAMMIAABDCAABIwgAAMMIAABjCAABIwgAAOMIAAAjCAABIwgAAOMIAABDCAABMwgAAAMIAAADCAABMwgAACMIAAADCAABMwgAAEMIAAADCAABMwgAAGMIAAADCAABMwgAAEMIAAAjCAABMwgAAGMIAAAjCAABMwgAAEMIAABDCAABMwgAAGMIAABDCAABMwgAAEMIAABjCAABMwgAAGMIAABjCAABMwgAAIMIAAADCAABMwgAAKMIAAADCAABMwgAAIMIAAAjCAABMwgAAKMIAAAjCAABMwgAAIMIAABDCAABMwgAAKMIAABDCAABMwgAAIMIAABjCAABMwgAAKMIAABjCAABMwgAAIMIAACDCAABMwgAAKMIAACDCAABMwgAAIMIAACjCAABMwgAAKMIAACjCAABMwgAAMMIAAADCAABMwgAAOMIAAADCAABMwgAAMMIAAAjCAABMwgAAOMIAAAjCAABMwgAAMMIAABDCAABMwgAAOMIAABDCAABQwgAAAMIAAADCAABQwgAACMIAAADCAABQwgAAEMIAAADCAABQwgAAGMIAAADCAABQwgAAEMIAAAjCAABQwgAAGMIAAAjCAABQwgAAEMIAABDCAABQwgAAGMIAABDCAABQwgAAEMIAABjCAABQwgAAGMIAABjCAABQwgAAIMIAAADCAABQwgAAKMIAAADCAABQwgAAIMIAAAjCAABQwgAAKMIAAAjCAABQwgAAIMIAABDCAABQwgAAKMIAABDCAABQwgAAIMIAABjCAABQwgAAKMIAABjCAABQwgAAIMIAACDCAABQwgAAKMIAACDCAABQwgAAIMIAACjCAABQwgAAKMIAACjCAABQwgAAMMIAAADCAABQwgAAOMIAAADCAABQwgAAMMIAAAjCAABQwgAAOMIAAAjCAABQwgAAMMIAABDCAABQwgAAOMIAABDCAABUwgAACMIAAAjCAABUwgAACMIAABDCAABUwgAAEMIAAAjCAABUwgAAEMIAABDCAABUwgAAGMIAAADCAABUwgAAGMIAAAjCAABUwgAAGMIAABDCAABUwgAAGMIAABjCAABUwgAAGMIAACDCAABUwgAAGMIAACjCAABUwgAAIMIAAADCAABUwgAAIMIAAAjCAABUwgAAIMIAABDCAABUwgAAIMIAABjCAABUwgAAIMIAACDCAABUwgAAIMIAACjCAABUwgAAKMIAAAjCAABUwgAAKMIAABDCAABUwgAAKMIAABjCAABUwgAAKMIAACDCAABUwgAAMMIAAAjCAABUwgAAMMIAABDCAABUwgAAMMIAABjCAABUwgAAMMIAACDCAABUwgAAOMIAAADCAABUwgAAOMIAAAjCAABUwgAAQMIAAADCAABUwgAAQMIAAAjCAABYwgAACMIAAAjCAABYwgAACMIAABDCAABYwgAAEMIAAAjCAABYwgAAEMIAABDCAABYwgAAGMIAAADCAABYwgAAGMIAAAjCAABYwgAAGMIAABDCAABYwgAAGMIAABjCAABYwgAAGMIAACDCAABYwgAAGMIAACjCAABYwgAAIMIAAADCAABYwgAAIMIAAAjCAABYwgAAIMIAABDCAABYwgAAIMIAABjCAABYwgAAIMIAACDCAABYwgAAIMIAACjCAABYwgAAKMIAAAjCAABYwgAAKMIAABDCAABYwgAAKMIAABjCAABYwgAAKMIAACDCAABYwgAAMMIAAAjCAABYwgAAMMIAABDCAABYwgAAMMIAABjCAABYwgAAMMIAACDCAABYwgAAOMIAAADCAABYwgAAOMIAAAjCAABYwgAAQMIAAADCAABYwgAAQMIAAAjCAABkwgAACMIAAADCAABkwgAAEMIAAADCAABkwgAACMIAABDCAABkwgAAEMIAABDCAABkwgAAGMIAAAjCAABkwgAAIMIAAAjCAABkwgAAGMIAABjCAABkwgAAIMIAABjCAABkwgAAGMIAACjCAABkwgAAIMIAACjCAABkwgAAKMIAAADCAABkwgAAMMIAAADCAABkwgAAKMIAABDCAABkwgAAMMIAABDCAABkwgAAKMIAACDCAABkwgAAMMIAACDCAABkwgAAOMIAAAjCAABkwgAAQMIAAAjCAABowgAACMIAAADCAABowgAAEMIAAADCAABowgAACMIAABDCAABowgAAEMIAABDCAABowgAAGMIAAAjCAABowgAAIMIAAAjCAABowgAAGMIAABjCAABowgAAIMIAABjCAABowgAAGMIAACjCAABowgAAIMIAACjCAABowgAAKMIAAADCAABowgAAMMIAAADCAABowgAAKMIAABDCAABowgAAMMIAABDCAABowgAAKMIAACDCAABowgAAMMIAACDCAABowgAAOMIAAAjCAABowgAAQMIAAAjCAACMwgAACMIAAADCAACMwgAAEMIAAADCAACMwgAACMIAABDCAACMwgAAEMIAABDCAACMwgAAGMIAAAjCAACMwgAAIMIAAAjCAACMwgAAGMIAABjCAACMwgAAIMIAABjCAACMwgAAGMIAACjCAACMwgAAIMIAACjCAACMwgAAKMIAAADCAACMwgAAMMIAAADCAACMwgAAKMIAABDCAACMwgAAMMIAABDCAACMwgAAKMIAACDCAACMwgAAMMIAACDCAACMwgAAOMIAAAjCAACMwgAAQMIAAAjCAACOwgAACMIAAADCAACOwgAAEMIAAADCAACOwgAACMIAABDCAACOwgAAEMIAABDCAACOwgAAGMIAAAjCAACOwgAAIMIAAAjCAACOwgAAGMIAABjCAACOwgAAIMIAABjCAACOwgAAGMIAACjCAACOwgAAIMIAACjCAACOwgAAKMIAAADCAACOwgAAMMIAAADCAACOwgAAKMIAABDCAACOwgAAMMIAABDCAACOwgAAKMIAACDCAACOwgAAMMIAACDCAACOwgAAOMIAAAjCAACOwgAAQMIAAAjCAACUwgAACMIAAAjCAACUwgAACMIAABDCAACUwgAAEMIAAAjCAACUwgAAEMIAABDCAACUwgAAGMIAAADCAACUwgAAGMIAAAjCAACUwgAAGMIAABDCAACUwgAAGMIAABjCAACUwgAAGMIAACDCAACUwgAAGMIAACjCAACUwgAAIMIAAADCAACUwgAAIMIAAAjCAACUwgAAIMIAABDCAACUwgAAIMIAABjCAACUwgAAIMIAACDCAACUwgAAIMIAACjCAACUwgAAKMIAAAjCAACUwgAAKMIAABDCAACUwgAAKMIAABjCAACUwgAAKMIAACDCAACUwgAAMMIAAAjCAACUwgAAMMIAABDCAACUwgAAMMIAABjCAACUwgAAMMIAACDCAACUwgAAOMIAAADCAACUwgAAOMIAAAjCAACUwgAAQMIAAADCAACUwgAAQMIAAAjCAACWwgAACMIAAAjCAACWwgAACMIAABDCAACWwgAAEMIAAAjCAACWwgAAEMIAABDCAACWwgAAGMIAAADCAACWwgAAGMIAAAjCAACWwgAAGMIAABDCAACWwgAAGMIAABjCAACWwgAAGMIAACDCAACWwgAAGMIAACjCAACWwgAAIMIAAADCAACWwgAAIMIAAAjCAACWwgAAIMIAABDCAACWwgAAIMIAABjCAACWwgAAIMIAACDCAACWwgAAIMIAACjCAACWwgAAKMIAAAjCAACWwgAAKMIAABDCAACWwgAAKMIAABjCAACWwgAAKMIAACDCAACWwgAAMMIAAAjCAACWwgAAMMIAABDCAACWwgAAMMIAABjCAACWwgAAMMIAACDCAACWwgAAOMIAAADCAACWwgAAOMIAAAjCAACWwgAAQMIAAADCAACWwgAAQMIAAAjCAACYwgAAAMIAAADCAACYwgAACMIAAADCAACYwgAAEMIAAADCAACYwgAAGMIAAADCAACYwgAAEMIAAAjCAACYwgAAGMIAAAjCAACYwgAAEMIAABDCAACYwgAAGMIAABDCAACYwgAAEMIAABjCAACYwgAAGMIAABjCAACYwgAAIMIAAADCAACYwgAAKMIAAADCAACYwgAA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zBAADKwc3M/MEAADLCAADKwZqZ+cEAADLCAADKwZqZ+cEAAPzBAADKwZqZ+cEAADLCAADOwc3M/MEAAPzBAADOwZqZ+cEAAPzBAADOwc3M/MEAADLCAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADOwZqZ+cEAADLCAADOwZqZ+cEAAPzBAADawZqZ+cEAAPzBAADOwZqZ+cEAADLCAADawZqZ+cEAADLCAADawc3M/MEAAPzBAADawZqZ+cEAAPzBAADawc3M/MEAADLCAADawZqZ+cEAADLCAADawZqZ+cEAAPzBAADawZqZ+cEAADLCAADewc3M/MEAAPzBAADewZqZ+cEAAPzBAADewc3M/MEAADLCAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADewZqZ+cEAADLCAADewZqZ+cEAAPzBAADywZqZ+cEAAPzBAADewZqZ+cEAADLCAADywZqZ+cEAADLCAADywc3M/MEAAPzBAADywZqZ+cEAAPzBAADywc3M/MEAADLCAADywZqZ+cEAADLCAADywZqZ+cEAAPzBAADywZqZ+cEAADLCAAD+wc3M/MEAAPzBAAD+wZqZ+cEAAPzBAAD+wc3M/MEAADLCAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAD+wZqZ+cEAADLCAAD+wZqZ+cEAAPzBAAAJwpqZ+cEAAPzBAAD+wZqZ+cEAADLCAAAJwpqZ+cEAADLCAAAJws3M/MEAAPzBAAAJwpqZ+cEAAPzBAAAJws3M/MEAADLCAAAJwpqZ+cEAADLCAAAJwpqZ+cEAAPzBAAAJwpqZ+cEAADLCAAALws3M/MEAAPzBAAALwpqZ+cEAAPzBAAALws3M/MEAADLCAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAALwpqZ+cEAAPzBAAARwpqZ+cEAAPzBAAALwpqZ+cEAADLCAAARwpqZ+cEAADLCAAARws3M/MEAAPzBAAARwpqZ+cEAAPzBAAARws3M/MEAADLCAAARwpqZ+cEAADLCAAARwpqZ+cEAAPzBAAARwpqZ+cEAADLCAAATws3M/MEAAPzBAAATwpqZ+cEAAPzBAAATws3M/MEAADLCAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAATwpqZ+cEAAPzBAAAZwpqZ+cEAAPzBAAATwpqZ+cEAADLCAAAZwpqZ+cEAADLCAAAZws3M/MEAAPzBAAAZwpqZ+cEAAPzBAAAZws3M/MEAADLCAAAZwpqZ+cEAADLCAAAZwpqZ+cEAAPzBAAAZwpqZ+cEAADLCAAAbws3M/MEAAPzBAAAbwpqZ+cEAAPzBAAAbws3M/MEAADLCAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAbwpqZ+cEAAPzBAAAhwpqZ+cEAAPzBAAAbwpqZ+cEAADLCAAAhwpqZ+cEAADLCAAAhws3M/MEAAPzBAAAhwpqZ+cEAAPzBAAAhws3M/MEAADLCAAAhwpqZ+cEAADLCAAAhwpqZ+cEAAPzBAAAhwpqZ+cEAADLCAAAjws3M/MEAAPzBAAAjwpqZ+cEAAPzBAAAjws3M/MEAADLCAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAAjwpqZ+cEAAPzBAAApwpqZ+cEAAPzBAAAjwpqZ+cEAADLCAAApwpqZ+cEAADLCAAApws3M/MEAAPzBAAApwpqZ+cEAAPzBAAApws3M/MEAADLCAAApwpqZ+cEAADLCAAApwpqZ+cEAAPzBAAApwpqZ+cEAADLCAAArws3M/MEAAPzBAAArwpqZ+cEAAPzBAAArws3M/MEAADLCAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAArwpqZ+cEAAPzBAAAxwpqZ+cEAAPzBAAArwpqZ+cEAADLCAAAxwpqZ+cEAADLCAAAxws3M/MEAAPzBAAAxwpqZ+cEAAPzBAAAxws3M/MEAADLCAAAxwpqZ+cEAADLCAAAxwpqZ+cEAAPzBAAAxwpqZ+cEAADLCAAAzws3M/MEAAPzBAAAzwpqZ+cEAAPzBAAAzws3M/MEAADLCAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAAzwpqZ+cEAAPzBAAA5wpqZ+cEAAPzBAAAzwpqZ+cEAADLCAAA5wpqZ+cEAADLCAAA5ws3M/MEAAPzBAAA5wpqZ+cEAAPzBAAA5ws3M/MEAADLCAAA5wpqZ+cEAADLCAAA5wpqZ+cEAAPzBAAA5wpqZ+cEAADLCAAA7ws3M/MEAAPzBAAA7wpqZ+cEAAPzBAAA7ws3M/MEAADLCAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAAA7wpqZ+cEAADLCAAA7wpqZ+cEAAPzBAABBwpqZ+cEAAPzBAAA7wpqZ+cEAADLCAABBwpqZ+cEAADLCAABBws3M/MEAAPzBAABBwpqZ+cEAAPzBAABBws3M/MEAADLCAABBwpqZ+cEAADLCAABBwpqZ+cEAAPzBAABBwpqZ+cEAADLCAABDws3M/MEAAPzBAABDwpqZ+cEAAPzBAABDws3M/MEAADLCAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABDwpqZ+cEAAPzBAABJwpqZ+cEAAPzBAABDwpqZ+cEAADLCAABJwpqZ+cEAADLCAABJws3M/MEAAPzBAABJwpqZ+cEAAPzBAABJws3M/MEAADLCAABJwpqZ+cEAADLCAABJwpqZ+cEAAPzBAABJwpqZ+cEAADLCAABLws3M/MEAAPzBAABLwpqZ+cEAAPzBAABLws3M/MEAADLCAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABLwpqZ+cEAAPzBAABRwpqZ+cEAAPzBAABLwpqZ+cEAADLCAABRwpqZ+cEAADLCAABRws3M/MEAAPzBAABRwpqZ+cEAAPzBAABRws3M/MEAADLCAABRwpqZ+cEAADLCAABRwpqZ+cEAAPzBAABRwpqZ+cEAADLCAABTws3M/MEAAPzBAABTwpqZ+cEAAPzBAABTws3M/MEAADLCAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABTwpqZ+cEAAPzBAABZwpqZ+cEAAPzBAABTwpqZ+cEAADLCAABZwpqZ+cEAADLCAABZws3M/MEAAPzBAABZwpqZ+cEAAPzBAABZws3M/MEAADLCAABZwpqZ+cEAADLCAABZwpqZ+cEAAPzBAABZwpqZ+cEAADLCAABbws3M/MEAAPzBAABbwpqZ+cEAAPzBAABbws3M/MEAADLCAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABbwpqZ+cEAAPzBAABhwpqZ+cEAAPzBAABbwpqZ+cEAADLCAABhwpqZ+cEAADLCAABhws3M/MEAAPzBAABhwpqZ+cEAAPzBAABhws3M/MEAADLCAABhwpqZ+cEAADLCAABhwpqZ+cEAAPzBAABhwpqZ+cEAADLCAABjws3M/MEAAPzBAABjwpqZ+cEAAPzBAABjws3M/MEAADLCAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABjwpqZ+cEAAPzBAABpwpqZ+cEAAPzBAABjwpqZ+cEAADLCAABpwpqZ+cEAADLCAABpws3M/MEAAPzBAABpwpqZ+cEAAPzBAABpws3M/MEAADLCAABpwpqZ+cEAADLCAABpwpqZ+cEAAPzBAABpwpqZ+cEAADLCAABrws3M/MEAAPzBAABrwpqZ+cEAAPzBAABrws3M/MEAADLCAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABrwpqZ+cEAAPzBAABxwpqZ+cEAAPzBAABrwpqZ+cEAADLCAABxwpqZ+cEAADLCAABxws3M/MEAAPzBAABxwpqZ+cEAAPzBAABxws3M/MEAADLCAABxwpqZ+cEAADLCAABxwpqZ+cEAAPzBAABxwpqZ+cEAADLCAABzws3M/MEAAPzBAABzwpqZ+cEAAPzBAABzws3M/MEAADLCAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAABzwpqZ+cEAADLCAABzwpqZ+cEAAPzBAAB9wpqZ+cEAAPzBAABzwpqZ+cEAADLCAAB9wpqZ+cEAADLCAAB9ws3M/MEAAPzBAAB9wpqZ+cEAAPzBAAB9ws3M/MEAADLCAAB9wpqZ+cEAADLCAAB9wpqZ+cEAAPzBAAB9wpqZ+cEAADLCAICBws3M/MEAAPzBAICBwpqZ+cEAAPzBAICBws3M/MEAADLCAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICBwpqZ+cEAAPzBAICGwpqZ+cEAAPzBAICBwpqZ+cEAADLCAICGwpqZ+cEAADLCAICGws3M/MEAAPzBAICGwpqZ+cEAAPzBAICGws3M/MEAADLCAICGwpqZ+cEAADLCAICGwpqZ+cEAAPzBAICGwpqZ+cEAADLCAICHws3M/MEAAPzBAICHwpqZ+cEAAPzBAICHws3M/MEAADLCAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICHwpqZ+cEAAPzBAICKwpqZ+cEAAPzBAICHwpqZ+cEAADLCAICKwpqZ+cEAADLCAICKws3M/MEAAPzBAICKwpqZ+cEAAPzBAICKws3M/MEAADLCAICKwpqZ+cEAADLCAICKwpqZ+cEAAPzBAICKwpqZ+cEAADLCAICLws3M/MEAAPzBAICLwpqZ+cEAAPzBAICLws3M/MEAADLCAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICLwpqZ+cEAAPzBAICOwpqZ+cEAAPzBAICLwpqZ+cEAADLCAICOwpqZ+cEAADLCAICOws3M/MEAAPzBAICOwpqZ+cEAAPzBAICOws3M/MEAADLCAICOwpqZ+cEAADLCAICOwpqZ+cEAAPzBAICOwpqZ+cEAADLCAICPws3M/MEAAPzBAICPwpqZ+cEAAPzBAICPws3M/MEAADLCAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICPwpqZ+cEAAPzBAICSwpqZ+cEAAPzBAICPwpqZ+cEAADLCAICSwpqZ+cEAADLCAICSws3M/MEAAPzBAICSwpqZ+cEAAPzBAICSws3M/MEAADLCAICSwpqZ+cEAADLCAICSwpqZ+cEAAPzBAICSwpqZ+cEAADLCAICTws3M/MEAAPzBAICTwpqZ+cEAAPzBAICTws3M/MEAADLCAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICTwpqZ+cEAAPzBAICWwpqZ+cEAAPzBAICTwpqZ+cEAADLCAICWwpqZ+cEAADLCAICWws3M/MEAAPzBAICWwpqZ+cEAAPzBAICWws3M/MEAADLCAICWwpqZ+cEAADLCAICWwpqZ+cEAAPzBAICWwpqZ+cEAADLCAICXws3M/MEAAPzBAICXwpqZ+cEAAPzBAICXws3M/MEAADLCAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICXwpqZ+cEAADLCAICXwpqZ+cEAAPzBAICawpqZ+cEAAPzBAICXwpqZ+cEAADLCAICawpqZ+cEAADLCAICaws3M/MEAAPzBAICawpqZ+cEAAPzBAICaws3M/MEAADLCAICawpqZ+cEAADLCAICawpqZ+cEAAPzBAICawpqZ+cEAADLCAICbws3M/MEAAPzBAICbwpqZ+cEAAPzBAICbws3M/MEAADLCAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICbwpqZ+cEAADLCAICbwpqZ+cEAAPzBAICewpqZ+cEAAPzBAICbwpqZ+cEAADLCAICewpqZ+cEAADLCAICews3M/MEAAPzBAICewpqZ+cEAAPzBAICews3M/MEAADLCAICewpqZ+cEAADLCAICewpqZ+cEAAPzBAICewpqZ+cEAADLCAICfws3M/MEAAPzBAICfwpqZ+cEAAPzBAICfws3M/MEAADLCAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICfwpqZ+cEAAPzBAICiwpqZ+cEAAPzBAICfwpqZ+cEAADLCAICiwpqZ+cEAADLCAICiws3M/MEAAPzBAICiwpqZ+cEAAPzBAICiws3M/MEAADLCAICiwpqZ+cEAADLCAICiwpqZ+cEAAPzBAICiwpqZ+cEAADLCAICjws3M/MEAAPzBAICjwpqZ+cEAAPzBAICjws3M/MEAADLCAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICjwpqZ+cEAAPzBAICmwpqZ+cEAAPzBAICjwpqZ+cEAADLCAICmwpqZ+cEAADLCAICmws3M/MEAAPzBAICmwpqZ+cEAAPzBAICmws3M/MEAADLCAICmwpqZ+cEAADLCAICmwpqZ+cEAAPzBAICmwpqZ+cEAADLCAICnws3M/MEAAPzBAICnwpqZ+cEAAPzBAICnws3M/MEAADLCAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICnwpqZ+cEAAPzBAICqwpqZ+cEAAPzBAICnwpqZ+cEAADLCAICqwpqZ+cEAADLCAICqws3M/MEAAPzBAICqwpqZ+cEAAPzBAICqws3M/MEAADLCAICqwpqZ+cEAADLCAICqwpqZ+cEAAPzBAICqwpqZ+cEAADLCAICrws3M/MEAAPzBAICrwpqZ+cEAAPzBAICrws3M/MEAADLCAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICrwpqZ+cEAAPzBAICuwpqZ+cEAAPzBAICrwpqZ+cEAADLCAICuwpqZ+cEAADLCAICuws3M/MEAAPzBAICuwpqZ+cEAAPzBAICuws3M/MEAADLCAICuwpqZ+cEAADLCAICuwpqZ+cEAAPzBAICuwpqZ+cEAADLCAICvws3M/MEAAPzBAICvwpqZ+cEAAPzBAICvws3M/MEAADLCAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICvwpqZ+cEAADLCAICvwpqZ+cEAAPzBAICywpqZ+cEAAPzBAICvwpqZ+cEAADLCAICywpqZ+cEAADLCAICyws3M/MEAAPzBAICywpqZ+cEAAPzBAICyws3M/MEAADLCAICywpqZ+cEAADLCAICywpqZ+cEAAPzBAICywpqZ+cEAADLCAICzws3M/MEAAPzBAICzwpqZ+cEAAPzBAICzws3M/MEAADLCAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAICzwpqZ+cEAADLCAICzwpqZ+cEAAPzBAIC2wpqZ+cEAAPzBAICzwpqZ+cEAADLCAIC2wpqZ+cEAADLCAIC2ws3M/MEAAPzBAIC2wpqZ+cEAAPzBAIC2ws3M/MEAADLCAIC2wpqZ+cEAADLCAIC2wpqZ+cEAAPzBAIC2wpqZ+cEAADLCAIC3ws3M/MEAAPzBAIC3wpqZ+cEAAPzBAIC3ws3M/MEAADLCAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC3wpqZ+cEAAPzBAIC6wpqZ+cEAAPzBAIC3wpqZ+cEAADLCAIC6wpqZ+cEAADLCAIC6ws3M/MEAAPzBAIC6wpqZ+cEAAPzBAIC6ws3M/MEAADLCAIC6wpqZ+cEAADLCAIC6wpqZ+cEAAPzBAIC6wpqZ+cEAADLC\"}],\"images\":[{\"uri\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAYAAAD0eNT6AAAABGdBTUEAALGPC/xhBQAAAAFzUkdCAdnJLH8AAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAIABJREFUeNrsnXdUVLnbx79DlSaCIlWKAiIIorgWsPxsrGLDgg2xYFkL9l5AwYqKvWJbC1bWrruKiuDq6iq6KDZQUSyAgiIIAjPwvH/sO/c4SxF17h2EfM6ZI95k5klyk5vvTZ4kIiIiMBgMBoPBqFQosSJgMBgMBoMJAAaDwWAwGEwAMBgMBoPBYAKAwWAwGAwGEwAMBoPBYDCYAGBUMs6cOYM3b96wgmAwGAwmABiVidOnTzMBwAAAvHr1Cjdv3uTdTkxMDFJTU8tFnj09PeHu7s5uPkPhDBkyBMeOHas8AuDDhw9wcnKCo6MjMjMzBbN7+/Zt6OvrY8yYMQCA/Px8NGzYEPb29vj48aNg6di2bRtGjx4tcy00NBTjxo3j1W5ubi4CAwPRrFkzhIeHw8vLC1ZWVhg5ciSuXLkid3vr1q2Dq6sr3N3d0a5dOyQkJJQaPyEhAQ4ODnj//j0v+c/Pz0dISAjq16+P2rVrw87ODmvXrhW8/icnJ8PU1LRcdEDbt2/HmjVrUL9+fd5t1atXDwEBAdizZ4/C83379m3cunWL9T4MhZKfn4/IyEh06tTp679MPygnT54kAASATp8+LZjdjRs3EgCysLAgIqKEhAQuHXFxcYKlY8SIEbRz506Za4MHD6awsDDebGZmZpKTkxN5eHhQZmYmjR07lu7evUupqanUrVs3AkCfPn2Sm71Vq1aRmpoaJSYmEhGRp6cn2djYkFgsLjZ+fn4+NWnShKKjo3nJf2FhIXXu3JmqVKlCV69eJSKiuLg4qlq1KoWGhgpa/8+dO8fVO3mW+dcyd+5cmjhxoqA2CwoKqHfv3rRw4UJB7T548IBq1KhBPj4+9OnTJxoyZAh16dKF8vLyyMfHh/T19QV9BkgkEpJIJMSo3Jw4cYJ8fHy+6bs/7AiAjo4O97eamppgdg0NDQEAVlZW3P9FIhEAwMjISLB0/P3332jatKnMtatXr6J58+a82Vy7di3u3LmDhQsXypR/zZo1sW/fPpiamsrNVmFhIYKDg+Hk5ARLS0tuyDUhIQFHjhwp9juzZ89Gp06d0LJlS17yHxkZidOnT6Nv375cOTs4OKBbt24lpokvjI2NuX+rVKmikDa4bds2hIeHY8WKFYLaVVJSwo4dO7B161YcPnxYMLv6+vrQ19fHnj174OnpicaNG8PFxQV9+vTBnj17ULVqVejp6QmWniVLlmDVqlXsFbiSc/DgQfTt2/fb2tKPmulatWpxf5uZmQlm197eHgDg5OTECZHatWtDW1sb1atXFyQNOTk5ePHiBezs7Lhrb9++RWZmJidM+ODGjRsAgIKCgiJhWlpa3zYEVQKvX79GSkoKatSowV0zMTHhhl7/y7lz53D9+nX4+/vzlv87d+5wHdDnpKenw8DAQND6b2trC1VVVTg6Oiqk/T19+hQTJ06Ev78/lJWVFfICMHfuXAwdOhQvX74UxGbNmjXx8OFD3Lp1C61bt8b69etx8OBBNGnSBH/99ReePHnC1VEGQwhyc3Nx+fJldOjQoXIJADMzM+7N29zcXNAHr6amJicAAKBBgwZo0KCBYGmIiYlBo0aNuPxL3/6bNWvGq11pJ7d8+fJiw8eNGwdVVVW52FJRUQEASCQS7pr02Ir/jvi8efMGEyZMwN69e3ntjKRi5Pr16zL3IioqClOnThW0/qupqcHOzk6mHgrJ3LlzIZFI0L17d4U9A/r27QuJRIKFCxcKZjMqKgpr165FaGgo7O3tYWFhgbCwMOzatQuXL19mPRJDUM6cOYN27dp98yi4yo+acTU1NdSsWRMSiQSampqC2VVSUoKjo6NMh9+gQQOkp6fzanfWrFnYtGkTAODTp09QVlZGtWrVuPDPr40ePRpLliyRexoGDRqEbdu24dChQ9DQ0CjyJizPzsjIyAh16tTBq1evuGvPnj0DADRs2FBGFAwdOhRBQUG8C0HplMv9+/dx+/ZtvHnzBtOnT8fp06fh5ORUxAteVVWVV2EotPCU8uLFCxw8eBBt27aFlpaWwp4BOjo6cHFxwY4dOzBv3jxuWoQv4uLi0LZtWygpKeHAgQOIj49HVlYWhgwZAm9vb2zZsgWxsbEKG5VhVD4OHTqEoUOHfvsP/MjOD40bNyZnZ2fB7QYGBlJOTg73//Pnz9ORI0cEs9+zZ0/67bffZK517dpVEGfI4OBgzvkMAK/5Dg8PJ2VlZYqJiaH8/HxydXWlFi1aUGFhoYyj4PDhwwUrezc3NwJAxsbGNHfuXMrKyuLCli5dypVLv3796Ny5c7ymZceOHXT//n3B6//y5csJAE2ePFnhz4AxY8YQAEGcMDMzM2nKlCl04cIF7vnTuHFjIiK6cOECzZw5kzIzMwXL+4IFC2j58uXMC66Skp2dTRYWFiU6RZeFH1oA9OjRg7p161bpbrylpSU9f/5c5pqxsTGlpqYKYv/w4cNUrVo1AkDKyso0d+5c3ryR//jjD+rfvz8NGjSI5s+fL+Pxfvv2bWrQoAFlZ2dzXtErVqyg3r17k7e3N928eVOuKwB27dpFlpaWXCdf3IoLHR0d6tq1a4Wufx4eHgSAtmzZovC0hISEEADq3r274LYNDAyoRo0aCss7EwCVm4MHD9LIkSO/6zd+aAEwfvx48vPzq1Q3PT09nQwMDGSuvXz5kszMzARNx+vXr6lJkyZcZ9i6dWt6+/atoOq3QYMGdPv2be6ar68vOTk5UW5uLkVERJCGhgZdv379u22lpKRQu3btqHnz5vTkyRNydXUlAGRoaEjv3r3j4iUlJZGOjg69fPmyQtdBMzMzAlBkFEpRD0EAZGtrK7jtiIgI+v3333m3c/LkSdLS0iryUVNTIzU1tWLDTp48yXrICk7Pnj250ahv5YfeCbBWrVqCOgCWB2JiYuDi4iJz7caNG2jcuLGg6TA2NsZPP/2EgIAA6OrqIioqCi1bthRsM6Tx48dj6NChcHZ2BgDcvXsXO3bswIABA6Curo727dvDwMAAs2bN+i47r169QtOmTZGeno6IiAjUrl0bK1euhEgkQmpqKiZOnMjFDQkJwcqVK+W6HLI88vbtW24OXtFI/X9SUlIEt92+fXt07NiRdztdunTBx48fi3z8/f2xaNGiYsO6dOnCJsgrMB8/fuRWo3wPKj9yIXy+FLCiI3UCzMvLAxHJOADm5uZCJBJx1/hyAiwOLy8veHt7o3379nj48CFWrFiB+fPn82rz8OHDSE5OxtatW7lrf/zxBwCgTp063DVra2tERUUhOzv7m53VhgwZgufPn2Pjxo3cbzRt2hSjR4/Gxo0bsXv3bnTq1Am1atVCUlISVq9eXeHr4ucrMxSNhoYG90BkVHzy8/PRtm3bYsMuXrwo6J4wiuT48ePw8PD47lVPP7QA+O+bcEVmyZIlWLJkCXr27AkfHx/06NGDC+vcuTOmTJlSYsOQp+rU1tYuct3W1hYbN25E165dedkO+HOeP3+OefPmISoqSmYZpPQN8PMVIVpaWigoKMDbt2+/SQDcvXsX58+fBwBupEHKihUrEBUVhXv37mHUqFGwsLBAREREpaiLBgYGSElJQU5OjsLT8unTJwBA1apVWe9YCSgsLCzxGVNYWFhpyuHQoUOYMmXKd//ODz0FYG1tDWtr60rVAK5fv15kvX9MTIwgUwC//PLLF8WYdP0+HxQUFMDHxwdr1qwpsvGOrq4uAEAsFnPX8vLyAPy7gcu3EBcXx/39+PHjIm+ehw4dgpaWFj58+ICsrCwZ21Lu3btX4eqgdIojIyND4WnJysoCANSuXZv1jpWAKlWq4P9914p8FLUjptB8+PABd+/eRYsWLSqvACAirFu3Dhs3bqw0lf/FixdQVVWVWe+cmJiIGjVqCPIGlJqaisjIyGLDrl27BuDfKQG+CAoKQrNmzYrd9apVq1YAgKSkJJmykW7c9C1ItyAGgICAAOTm5sqEP336FAYGBlBVVUViYiLc3Nxkyufo0aO4dOlShauH0q2WExMTFZ6W58+fA4DgPjCVnZcvX2Ls2LFYu3ZtpXrz/pw1a9Yo5CCwY8eOoVu3bkX2YfnWjvSH5NKlS5wHujw8vX8EDh06RH379pW5duDAAfL19RXEfrt27cjMzIzOnj1LRMQdBnT37l0yMzOjQYMGUUFBAS+2o6OjqWnTppSfn1/iMr3//e9/1KZNGyosLKSYmJgSl+p9DZ6enlw9s7e3p4CAAFq+fDm1b9+e6tevTw8fPqQ//viDdHR0uHhmZmZUp04dsrW1FXRduFBIDyLq37+/wtMyePBgAkBnzpypdF7gS5YsoVWrVinE9qBBg7j6HhISUunK/s8//+Tyf+XKFUFtd+rUiTuM7Hv5YQXAmzdvyNbWlurVqyezFKsiM2XKlCINfvLkybR582ZB7N++fZsmTZpEjRo1IkdHRzI1NaVmzZqRh4cHr0vC3r17R/b29hQfH19qvIyMDBo4cCC5u7uTs7MzrVmz5rtti8ViWrduHTk7O5O2tjbp6upSixYtaNOmTTIbcCQmJtLAgQOpevXqpKenR15eXkX2aqgoiMVisra2JisrK4WnxdbWlszNzb9rMxTG17NhwwbS1tYmZ2dn6tChQ6XLf1paGtnZ2VHdunUpLS1NMLvp6elUp04dmc3QvgcR0f9vsM5gfCV+fn4YNWqUIOfAM8oXYWFhGDhwIO7du8cdkCU0CQkJsLW1xdatWzF8+HB2UxRAdHQ0Nm/ejH379rHC+AFRYkXA+FY8PDy+2cGO8WPTv39/tG/fHuvWrVNYGtatW4dWrVrB19eX3RAFsWfPnlKdgxnlGzYCwGAwvol3797B1dUVu3bt4g5KEooHDx6gW7duiIyMFPQ4cMa/5OTkIDg4GCYmJkwAMAHAYDAqI0lJSRg5ciTWrl0LW1tbQWy+fv0avr6+gtpkyBIeHg4XFxdYWVmxwmACgMFgVFays7Oxdu1adOjQgffleDdv3sSJEycwZcoUbu8HBoPBBACDwVAghYWF8lmbrGAbDAYTAAwGg8FgMCosTEozGAwGg8EEAIPBYDAYDCYAGAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMBoPxowmA9+/fY9y4cWjYsCEaNWqEAQMG4NWrV4ImPDc3F+vXr4e5uTkyMjIEs5uTk4MZM2bA0tISampqMDc3x/jx4/Hu3TtB7EskEoSEhKBevXrQ0NCAhYUF5syZA7FYrLBKNGzYMIhEIuTm5gpmc+bMmRCJREU+//zzj6B5T05Oxty5c9GpUyeMHTsWu3bt4tVe06ZNi8239GNhYcF7nnfv3o2WLVuiQ4cOcHd3R8uWLbF7927ByvzkyZNo06YNmjVrBktLS3h5eeHRo0fsac6oFJw4cQK+vr7w9fWFvb09HB0dERwcDIlE8vU/Rl9JamoqOTo6kre3N4nFYiIimjNnDpmYmNDTp0+Jb3Jzc2n16tVUp04dAkAA6P379yQEBQUF1KpVK1JWViZra2uqUaMGl4Y6depQcnIyr/YLCwvJ29ub6tWrRz4+PuTq6srZ9/X1JUVw8OBBLg2fPn0SxGZ6ejpVrVqVlJWVZT4dO3YUNO/Lli0jHR0dWrx4MeXl5fFu79atWwSAlJWVqXr16mRoaCjzEYlENG7cOF7TMG3aNDIxMaFHjx5x1+7fv096eno0c+ZM3stg+fLlZGJiQnfv3iUiog8fPlCHDh2oatWqdO3aNWIwKjKBgYHk7e1NBQUFREQkFotp5MiRBIAGDBjw1b/31QKgW7dupKOjQxkZGdy1vLw8MjQ0JDc3NyosLOS1AMRiMb17945SU1NJVVVVUAGwZs0aat++PSUmJnLXDhw4QJqamgSAvL29ebW/f/9+Cg4OlrkWGhrKdcB8C5D/kpiYSHXq1KGqVasKKgDmzJlDS5cuVVgjlEgk1KdPH1JTU6M//vhDMLujRo2iJUuWUHZ2drH3AgD9+eefvNmPj48nkUhEq1evLhI2Y8YMEolElJSUxJv9v/76i5SUlGj79u0y19PS0khTU5MsLCyKLRuGsG0jPDycYmNjK0yebt++TUeOHOE6XUWRkZFBqqqqtGzZMpnrOTk5pKenRwDo77//5k8AREVFEQDy8vIqEubr60sA6Pjx44IViJmZmaACYNCgQZSTk1Pk+qpVqwgAaWlp8Wq/pJtra2tLAOjBgweClb1YLCZXV1c6efIkmZqaCiYA3r17R1ZWVpSVlaWwhiit6//tiPgu740bN5YYHhwcTGZmZrwK8P379xOAYtOxbt06AsDrW7inpycBoMePHxcJ8/HxIQC0du1a1gsrgPT0dFq6dClZWlpS9+7d6dmzZxUmbwkJCfS///2PrKysKCQkROblV0iePn1KAKh27dpF2rl0NDg0NPSrfvOrfAAOHjwIAHBxcSl2bhIA73Ogn6Ompibo3Iufnx80NDSKXO/Xrx8AQCwWg3g8XPGnn34q9nrNmjXh4OCAunXrClYW8+bNQ5MmTdClSxdB78Hq1auRkpKCHj16YNmyZUhOThbU/u7du7Fjxw60bdsWvr6+gtlVUVHB6NGjSww/dOgQ+vTpA5FIxFsaTE1NAQCbN29Gfn6+TFhsbCyMjY3RoEED3uxfvHgRAGBoaFgkrHXr1gCA48ePs0liAYmLi8PIkSNRv359vHnzBhcvXsSxY8cE8UURCmtra0RGRuLo0aOIi4uDtbU1xo0bh/j4eEHTYWlpic6dO0NFRQWFhYVF/PI+b6O8+ADUrl2bANC+ffuKhEVERBAAMjQ0FEwRSf0AhBoBKG3YSyQSUcOGDRUyLFSzZk2KiYkRzGZkZCQ1btyYm/cWagQgIyODqlWrxk15AKAqVarQ/PnzBRme+/jxIxkbGxMAOn/+fLl5Q3ny5AkBoOvXr/Nqp7CwkBwdHQkAdevWjRtuv337NlWrVo1+//133mzn5uZy9/zFixdFws+ePUsAyNjYWLARmVatWpGZmZmMP4RQfPjwgZycnKh27dr08uVLQW0XFBTQsWPHqG3btmRnZ0cbNmygjx8/8mbv5MmTpKWl9VWfo0eP8paeN2/eUFBQEJmYmJCHhwedO3dOoe0/OTmZVFRUyMLCgnJzc/mZAigsLCRlZWUCQJcuXSp2eFraQDMzMyuVAIiLiyMAtGrVKkHtZmVlUZcuXWjbtm2C2UxLS6O6detSfHw8d00oAZCZmUlXrlyh48eP04wZM8jS0pKrc7169eJdBOzZs4frZKKjo8nb25tsbGzI2tqaRo0aRampqQqpf4sXLyZLS0tBbMXHx3MiyNnZmc6cOUMtW7akGzdu8G5bS0uLABT7cD99+jQBIG1tbcEeuiKRiADQrl27BL/nsbGxXN0/ffq0YC8bISEhVKdOHerUqRP98ccfvPt8lWfy8vJo9+7d5OLiQvb29rR582aF+KBMmzaNlJWVv+mlpMwCIC0tjatwxTX2e/fuceF8OgKVRwEwZ84csrCwKNY/gA9evHhBCxcu5HwglJWVafbs2YLY7tatG+3evVvmmpA+AP99KwwKCuIexCtXruTVXo8ePTgBEBQURLGxsXT79m1ubtrS0lIhIsDZ2ZlmzJghmL3ExERycHDg2rtQwrd79+4EgH7++eciYVJn2Fq1aglWDnv27KGgoCBBVoAUR2hoKIWEhPAufJOTk2nMmDFkbGxMfn5+MuKf8S+XL1+m3r17U82aNWnGjBmUlpYmiN2UlBTS1NQs1T9ILgLg5cuXXIO/c+dOqYpUqIdgeRAAqamppK+vTxEREYJ2fImJibR3715ycXHhyn3Lli282l27di0NGjSoyHVFCQApK1euJABkZmbGq526desSANqwYYPM9fz8fLK3tycANHjwYEHzHh8fTwDo1q1bgtm8fv069e7dmwIDA0lJSYkA0OjRo3nviO7cucOtuJkyZQp9+PCBMjMz6cCBA9yzoHPnzqw3kjPPnj0jT09PMjIyooCAAEpJSWGF8h+ysrJo3bp1ZG5uTj///LMgS+KJiCZNmkTTpk375u+XWQB8/Pix1BGAq1evEgASiUQkkUgqjQDo1asXLVy4UGH2JRIJDRo0iACQvb09b3ZiY2PJycmpWO97RQuAwsJCatiwIQGg169f82ZHV1e3xCHo9evXEwCqWrWqoMOiCxYsIFtbW8HsRUREkImJCbfk9LfffqMqVapwIoBvrl27xoleJSUlqlevHq1bt46srKwIAG3atIn1Rjzx9OlTmjx5MtWsWZN8fHwE8zs6deoU6erqftVHqNVojx8/pokTJ5KpqSmNGTOGHj58KOiLoIeHx3f1t1/lBCh90Bfn7HPq1CnBh+AULQBWrFhBI0aMUHjDTEtLIzU1NVJVVeXNhnTpW1k+0k1ahCQwMJAA8OoQJZ37Lq7+379/n8v/hw8fBMu3o6Mj+fv7C2Lr3bt3pKenR9OnT5e5/vvvv3N7cgi1GU96ejo3zHrjxg0CQHp6eoKWfWV/27W1taWWLVtSeHi4YC995YXz589T165dydraWmFLA69evUqHDh36rt9Q+ZoVA61atcL+/fvx5MmTImHPnj0DALi7u1eK5S+HDx/GzZs3ERYWpvC0VK9eHS4uLrxuh9q4cWNkZ2eXuDXlp0+f4OXlBSUlJVSrVk3wMjAxMUH16tVhZGTEmw07OzskJyfjzZs3RcLMzMwAAOrq6tDW1hYkz48ePcLdu3exf/9+Qezt378f79+/h6urq8z1jh07IjAwELNnz8aJEye4JcF8oq+vz/3t7+8PAFi4cCGqVq3K1ubxjLa2Nvz8/DB27FicOXMGa9aswdSpU+Hn54dhw4YppP0LQU5ODvbu3Yu1a9fC0NAQ48ePR9euXaGkpJgjdZ4+ffr9be1r1ILU07a4eeDhw4cTADp16lSFHwE4deoU9ezZk/Lz84sdklcE9evXp379+inEtqKnAIiIfvnlF5oyZQqvNtauXUsAaNSoUUXCXrx4UaKDGp+jHg4ODoLZ8/f3L3EKJDk5mQCQn5+foPdduhV17969K7VHuqK5d+8ejRw5kgwMDGjs2LEKWxHDB2/fvqXp06eTqakpjRgxguLi4spFuuRR3796K2BXV1eqXr26zMM+Ly+PDAwMqGnTpoI2Qum+BEIKgJMnT1LXrl2LXW/56tUr8vHx4c12fn5+sQLj2rVrpK2tTffu3avQAuDp06d04cKFYvPv4ODAez3Iyckhc3Nz0tPTK+ILcejQIRKJRMUukeULe3t7CgoKEsxeZGQkAaCpU6cWCXv48CEBoBMnTgiWnuvXr5OmpiZ5enoqRHxu376d5s+fr5BVAO/fv6fJkydTUFDQV6/95ntqZunSpfTXX39VGAHw559/0tKlSyk9Pb3cpCk/P58WL1783UvAv1oAPHnyhAwNDbmDPwoKCsjPz49MTEzoyZMnghaC9DCe58+fC2IvLCyMVFVVycXFhdzc3LiPq6srOTk5kYqKCq9LoszNzUlXV5dmz55NCQkJ9O7dOzp06BDZ2NjQ2bNnFVYZhRIA0u0uW7ZsSYcPH6bo6GiaP38+NWvWTOZ8Bj6JiYkhHR0dGjBgACfGXr58STY2NrRo0SJB37gACL4JzYgRI6hKlSoUHR3NXcvOzqauXbt+02Ek38qvv/5KNWrUoEWLFilkj/Znz55xPh979+4V3H5AQABnn+8DoBjlj/DwcO7+f88zAN/ypcTEROrduze5urqSm5ub4EM+GzZsoN69e3MF4OrqSkuXLuW1Azpy5Ai33rykj4qKCq/lEBgYSKampqSqqkpVq1YlZ2dnmjlzpsKX5QglAKKjo8nFxYU0NDRIT0+PWrduTaGhocVOxfDJ3bt3qXPnzuTs7ExeXl7UpUsXQc/AkHYAzs7Ogt/rwsJC2rJlCzVp0oQ6duxIAwYMoC5dutDmzZt5H/3bt28fTZ06lbp160bTpk1T6H7zubm51LRpUzIzM6OEhATB7R8/fpx0dHTIxcWFbGxsWI9YyXj69CmZm5tT48aNv2vzIRERj5vXMxgMBoM3kpKS0K9fP1y9epUVBuOrUWJFwGAwGD8me/bswahRo1hBML4JFVYEDAaD8WMhkUiwceNGiMVi+Pj4sAJhfBNsCoDBYDB+MP744w+YmprC0dGRFQaDCQAGg8FgMBhlh/kAMBgMBoPBBACDwWAwGAwmABgMBoPBYDABwGAwGAwGgwkABoPBYDAYTAAwGAwGg8FgAoDBYDAYDAYTAAwGg8FgMJgAYDAYDAaDwQQAg8FgMBgMIfnqw4DEYjFOnjyJM2fOQCwWQ11dHUSEnJwcqKio4KeffsLgwYOho6PDSpfBYDAYjPIKfQW7d+8mNzc32rBhA2VkZBQJLygooN9//508PT3pjz/+IL4ZO3YsXbx4kVcbFy5coJkzZ5Kuri4BIACkoaFBtra25OLiQpaWlmRra0sDBgygs2fPkhBERUXR4MGDqU6dOmRsbEwNGzak5s2b04IFC+j58+d05coVWrhw4XfbefToES1cuJDq1avH5R0AqampkbGxMenr65OlpSW1adOGZs2aRf/88w8v+T18+DBNmDCBNDQ0CACpq6uThYWFzMfU1JTU1dUJAPn7+8vV/oMHD2jBggVkZWXFlYGJiYmMfX19fS5swYIFvN37t2/fUkhICLVo0YIcHBzIycmJmjRpQm3atKFVq1ZRUlISjRkzhvLy8uR2/+vWrcvlzdjYmGbMmEE3btzg4h07dowmTJhAampqXLz//e9/tHz5csrOzpZr/u/cuUOBgYFkaWnJ2TI3N6f58+fTnTt3BGl/0vpQu3Ztmfowb948iomJkZsdafnXqVNHpvznzZvHtbWMjAxauHAhaWtrEwASiUTUpUsXOnDggNzScfDgQRozZgypqqpy6XBxcaGAgAC6ffu23Mv34cOHNHPmTDIyMuLsbd++vczfHzhrtvZMAAAgAElEQVRwoEw9DA4O/up6mJubS4sWLSI3Nzfut/r3719i/GfPntGiRYvIycmJAJCTkxMtWrSIXrx4IdeyiYmJoV9++YXs7e3J1taWLCwsyN7eniZOnEhPnjz56t8rkwDIzs6mHj160LBhw8pUkBKJhGbMmEGhoaG8NcKMjAzS1tamHj16CNLoV69eTQBIS0urSNjly5fJ0dGRAJCPjw+JxWJe0pCZmUl9+vQhZWVlmj59OiUlJXFhOTk5tGvXLjI1NSVlZWWaNGmSXB+60kYQHh5OEomEC4uNjaXx48dzDwcfHx/6+PEjL/kfPXo0ASA3N7cSG+3gwYNp4sSJvNj/+++/uXJ48OBBsQ/shg0b0syZM3mxf+jQIdLR0aEWLVpQdHQ0FRYWcmGpqak0Z84cUlFRIQByffDExsZy+T5x4kSJ8aZNm0YAqEaNGpSfn8+7CJamKTIykhTBP//8w6Xh9OnTvNmJiYnh7Jw8ebLYOI6OjmRoaEhRUVG8pcPX15cAkKGhoVwE5pc4e/Ysl+969erJ1PeSePnyJfcs0tbWlks9nD9/PpeO4ODgL4oXAJSYmCjXssjOziZfX19SU1OjJUuW0Lt377iwhIQE8vb25sLkKgByc3OpRYsW3/RWNWTIELp37x4vlSMkJIQAkLKyMj1//pz3ynjs2LESBQARUXp6OqdYQ0JCeBE89erVIyUlJTp27FiJ8ZKSksjMzIwGDx4sV9vSBvD5m9/n/Pnnn6Snp8epbj46gMDAwFIFgPQNecSIEbzUgZcvX5YqAKRiacKECXK3vWDBAu4tpKCgoFSRAIBu3bolN9tpaWlcvkt7w5W2SUdHR97b4+PHj7k0fcubjzxISUnh0hAbG6swO8uWLSNLS0t6+vQpr/mVdoRNmzYVpHyTkpJITU2NG1k6fvz4F78zdepUbjSkTp06ckuLdARYSUmJfv/991I7agAyL0nfS25uLrVu3ZoA0KFDh0qM5+fnRwBo/PjxZf7tLzoBjhkzBqampggKCvrq6YXVq1fD399f7tMWhYWFWL9+PbS1tVFQUIBNmzbxPlWirKxcari+vj769u0LANi9e7fc7Q8fPhwPHjzA8OHD0b179xLj1apVC1u2bMH79+8FyzsAuLm5ISwsDABw6dIlLF26VO5loKT0ZZ/VGjVqoGvXrgqpAwDg6OhY6v35FiIiIhAQEAArKyvs3Lmz1HLw8vLCoEGD8ObNG17yXZptaVhZ7pNQaaoIaSjNzq+//ooNGzYgMjISVlZWguRXRUVFkPJVVVWFhoYGBgwYAABYtmxZqfGzsrKwbds2DBs2jJd01qtXD4WFhejfvz8SEhJKbQNleVaUlQkTJiAqKgo9evSAl5dXifGCg4NhaGiItWvXYu/evWV7ppYWGBkZiWPHjmHjxo3flHBdXV3UqFEDz549k+uNOH78ODQ0NBAYGAgA2LZtG3JzcxXuTyG96ampqXL93d9//x3h4eEAgGnTpn0xvoeHB8zNzQXPf6dOndCxY0cAwIoVK5CVlaWQ+8CXACgrbdq0kdtv5eTkYPDgwSAiTJ8+Herq6l/8zvTp0yGRSJiDUwVn586d8Pf3x/nz52FpaVlh8zl16lSIRCJcuXIFV69eLTFeaGgoWrVqBTs7O17SsX//fpibmyMjIwPdu3cX5PkWFxeHrVu3AgBGjx5dalxNTU0MHDgQADBr1izk5eV9nwAICAjA9OnToa+vX+xb+NatW9G3b19MmjQJgYGBOHXqFFq0aIFjx47JdEbnz5+Xa6GsWbMG48aNg6+vLzQ1NZGWloYDBw4otJLm5OTgyJEjAIAmTZrI9bdDQ0MBADY2NrC2ti7Td+bNm6eQchg0aBAAIDMzE6dPnxbU9v79+/HPP/8oJN9isRizZs2S+++GhYUhOTkZANCrV68yfcfBwQGdO3dmPWQFZt26dfD398eFCxfK/Ez4UXFwcOBeLEoaBZBIJFi7di2mTp3KWzoMDQ1x/PhxaGlp4cGDBxg4cCCIiNe8h4WFobCwECoqKmjRosUX47du3RoA8PLlS0RGRn67ALh//z5u3LiBkSNHFgnLy8tDr169EB0djb1792LVqlVwcHDAhAkTcPXqVTRv3pyLa2FhUeJwybcQGxuL2NhY+Pj4oFq1avD29uYahFAUFBTI/H39+nV06tQJz549g76+PhYvXixXe9Ib6eDgUObv1KhRQyGNtVmzZtzfN27cEMzu27dvsWHDBoWJv8WLF+PTp09y/+2TJ08CAExNTWFgYMB6PgYWLFiApUuX4uLFi7C1ta0UeZaOfJ44cQKPHj0qEn7w4EEYGxujZcuWvKbD2dkZe/bsgUgkwokTJxAQEMCrvcuXLwMAzM3NoaGh8cX49vb2Rb5bGiVOkhw/fhxt2rSBnp5ekc6vc+fOeP/+Pa5evQpVVVUAgK2tLZ4+fYqffvoJhoaGXHwtLS25Ds+vWbMGw4YNg5aWFgDAz88PW7duxa1bt/DXX3/JiA8+yM7OhpGREQwNDZGdnY2XL19yw61dunTBqlWr5KrI09LS8OHDB4V26l+rkqWkpKTwYuPWrVsyw3zZ2dl49eoV72r8cxo0aACRSAQAyM/PBxFhwoQJcrfz4MEDAED16tVLjbdhwwZcuXIF7969k0njuHHjYGZmJrf09OjRo8RpCHn6nTCKZ8aMGThz5gzc3Nzkel/LO23atIGLiwtiYmKwfPlybNu2TSY8JCQEc+bMESQtPXr0wIIFCzB37lwsWrQIzs7OZR6d+1qko3/VqlUrU/zP40m/+00jADdv3ixWTQUHB+PixYvYsWOHzINAOs//36HH169fw9jYWG5veQcPHoSfnx93zcnJiUunEKMAWlpaePv2LeLi4pCYmIh3794hPDwc9evXx7lz5zBz5kwkJSXJzV5+fj73d1kUoKL53ElJTU2NFxuNGjXCw4cPuc+LFy/w5MkT1K9fX7B8xsbGIjc3F7m5ucjJycHcuXN5sSO9/5mZmaXGGzt2LJYvX46oqCicPXsWGhoaCA4OlnsncfToUZmy//zDxxQIo6jwVFZWxpUrV9ClSxfk5ORUmrxLh/f37t0r07lduHABmZmZ6NGjh2BpmTNnDvr37w8iwuDBg3H37l1e7X0+6lwanz9zy+KIWKIAeP78OerVqydz7cmTJwgMDISnpycaNGggE3bx4sViBUB8fLzcHFQ2b94Md3f3Ir83duxYAEB4eDhvb50loaOjg169euHmzZtwdnbGb7/9hmbNmsnNC1tfX5/rVN++fVvuG+nn+TYxMRHMrpWVFUaNGqWQPFepUgWBgYG87H4pHVF5/fo1CgsLS41ramrKOX82bNiQ9ZYVkAEDBmDPnj1QVlZGZGQkunbtysvUU3nEy8sLlpaWyMvLw5o1a7jrK1aswOTJkwVfDbJjxw40adIE2dnZ6N69O9LT0+Vuw8jICEDZR9c+d0w0NTX9dgHw8ePHIsMOK1euRH5+PiZNmlREnRw7dgwGBgZwcXGRCTtz5gw6dOjw3QUhFouxadMmxMTEwM7OTubj7+8PFRUViMVibNmyRSGVU11dnZv7T05Oxvr16+XWuUjfbO/fv1/uG+nff//N/e3m5iaobXd3d67BKGLkQ7pcSZ5IR7fy8/Px559/fjG+dEqOr9EXRvHIc9nXl+jfvz/27dsHFRUVXLx4Ed26dasUIkBZWZnrezZv3oysrCzcu3cPMTExGDp0qEKE/7Fjx2BqaorExET07du3zG/qZUX6DH3x4sUXRwGlL+lSpA6B3yQANDU1ZZYSEREOHToEY2PjIt6IYWFheP78OX7++WduXhT4d/5aIpF8cf6yLBw6dAg1a9ZEUlJSkaHH+Ph4zJgxAwCwZcsWiMVihVTQz73/5dlZ9+vXDwBw584dJCYmluk72dnZgs6Jf14XpG//7du3F9S2jY2NwgQAgCIjZvJg2LBhXJuSli2j/KGrqyuovT59+nAi4Pz58+jevXu5WAot5eHDh7z87rBhw6Cvr48PHz5gy5YtWLFiBcaMGaOw6VFjY2OcOHECmpqauHDhAqZMmSL3ER9p/1sWr37pMsk6deqUySGyRAFgYWEhM5yemJiItLQ0Gecn4N/lBtL5T3d3d5nfWLx4MTc8/72sWbOm1MIdO3YsVFVVkZycjN9++00hleHzIXoLCwu5/a50MyYAmD17dplGS2bMmCHjPyAEly9fxokTJwAACxcuVNhbaF5eHqZPn14hOhZ7e3uMGTMGwL9DjrGxsay3LWfo6OjIOL8KhZeXFw4cOAAVFRVERETA09OzXIiA/Pz8Mm9E87VoaWlxU30hISE4evSo3PqYb6VRo0b49ddfIRKJ5D4C7ezszL0AfmnDO4lEgp07dwIAli9fXqaNkEoUAD/99BNu3bol81AFgIyMDO5aSkoKgoKC0LZtWwCAq6srF3bu3Dm8fv2aW7/5PURHRyMpKYkriJKUmKenJwBg1apVcr/JZRlVCAkJ+bdQlZS43ajk9XZx4MABaGpq4sCBAwgKCirx7T4vLw8jR47EiBEjyrRpjLzyHhcXh759+6KwsBAjRozgZUhOOrz2pZGN+fPn8zIH/vkcvLyH+kpjxYoV8PDwgEQiQf/+/fHixQtBH3Blzbc0TIiyUcS9SE9PR926ddG1a1cUFhZydjt37szrFMB/lx1/Tq9evXDw4EGoqKjg7Nmz6Ny5M28b1EifA196HgQEBKBx48bfbU8ikRTr9zJ+/Hioq6sjJSUF/fv3L7I8Vjpy/SWfGXmk5XMxxteSwNDQUDg6OuLs2bOljgLOnTsXjx49wuzZs8vuEFnSHsFxcXFkbW0tsx9xjRo1CAD98ssvNHfuXOrWrRu9f/+eO33p3r17lJeXRytWrCAPDw/uUJjr16/T5cuXv2kfZLFYTE2bNiUvL68vxt2yZQu3Z7Y8T8MiIlqxYgUBIE1NTfrw4UORPeKlB9UoKyvzdghSVFQUWVhYcPvh7927l+Lj4ykjI4MeP35MW7dupQ4dOtBff/0lV7u3bt3iyvW/py+mpKTQ4sWLSUdHh7S1tb94WMb3MGzYMAJAlpaWxZ418OnTJ1q0aBFpaWnxciDRtWvXBDn8pTjy8/Np6tSppKamRkZGRhQaGko5OTkyed+1axfp6emRurq6XOv/54feHD16tMR448aN4w4D4utALCmXLl3i0hQdHS3IPbh79y5nc+/evXT+/HmqWbMm73vw37hxg7N76tSpYuOMHDmSi+Po6MjL2QRDhgwhAKSrq0sJCQkyZ1Lk5OTQ/fv3afTo0aSpqSmXUyCvXLlCIpGoyPOWiGj48OGkpKRE8fHxJR5KpaOjI5c9+d+/f1/qOShSCgsLycvLi/B1h+yWOQ1dunQhVVVVCg4OpszMTC7s6dOn5OPjQxoaGrRmzRr5HQbUtm1bmQfdmTNnyNTUlMzMzGj+/PmUm5tLRESRkZFkampKJiYm5O7uTmFhYVzlKCgoIHd392Jv4pf4/fffqVmzZtwRvKNHjy7xJixZskTm2FJ1dXUaMWJEsRXka7hw4QJNnz6dO2ACnx3LaWdnR+bm5qSrq0sODg40bNgwunv3Lq8Pg+zsbFq/fj21bduWjI2NuaN5W7RoQRs2bJCpGN/Lo0ePaMGCBWRjYyOTdwMDA3JwcCB7e3uysrKizp07U0hICKWlpfGS58OHD9PYsWNJSUmJS0P16tWpTp063MfMzIw7Baxv375ytf/gwQMKCgoia2trzr6hoSEFBATI9fjXspCYmEiLFi2iVq1aUe3atcnJyYnq1atHlpaW5O7uTitXrqTk5GS53v/P25WRkRH5+/sXOQ7Yz89P5rhYIY8DtrKyosDAQEGOA16wYAEZGBiQoaEheXt7f/fzpTTu379Pc+bMkTmG2sjIiGbMmCFT77Zs2UK1atWSaaPKysrk4eFBmzdv/u50HDx4kEaNGkXKysoyNkr6dO/e/bvrXVBQEHcMspubGy1dupTevHkj0yZ79eol870zZ87QpEmTqEqVKlxaOnToQMuWLfumevjmzRtasmQJNWnShDuRcOHChfTo0aMSv5OTk0MuLi681YmIiAjy9vYmGxsbcnBwoHr16lGDBg1o5syZ33QCqIhKGU+9desWBg8ejJs3b37zcPKCBQtgbGyM4cOHs8lCBoPBYDDKCUpfcm7w9fXFkCFDvmk+JTQ0FMnJyazzZzAYDAbjRxIAADBp0iQ4OzvD09OzzJvbfPz4EX5+fkhMTJTbengGg8FgMBjyo9QpgM+Jjo6Gv78/WrZsiUGDBhV7CMXjx4+xf/9+REdHY/bs2XI9FpXBYDAYDIYCBADw7/Krc+fOITw8HM+fP4eqqiqUlJQgEolQUFAAKysreHl5oVWrVjJ7BTAYDAaDwfiBBQCDwWAwGIyKgRIrAgaDwWAwmABgMBgMBoPBBACDwWAwGAwmABgMBoPBYDABwGAwGAwGgwkABoPBYDAYTAAwGAwGg8FgAoDBYDAYDAYTAAwGg8FgMJgAYDAYDEY55927d7C2tgbbQBa4ceMGkpKSKr4AiI+Px8KFC2FnZweRSMR9NDQ0oKenBzs7O/j6+iImJob3BN+/fx/jx4+Hvb09zMzMUL16dTg5OWHmzJl49eqV3O1dvHgRs2bNgp6eHpdvZWVlGBoaomrVqrCwsICHhwcOHz4sSKO4f/8++vfvD0NDQ+jq6sLGxgbjxo3DlStXsHLlSly6dEnuNsPCwmTue1k+ffv2/W67586dw6xZs1CtWjXudxs2bIglS5bg5cuXMnETExOxePFiODk5QSQSoVatWpg/fz4eP378zfajo6PRs2dPmXzVq1cPixcvLjb+6dOn0aJFCy5ut27d8OTJk6+2u2zZMpibm3O/U7t2baxcuRIAEBcXB19fX+4MDpFIhI4dOyIsLEzmNy5fvoyRI0dCSUkJqqqqGDFiBJKTk78qHUlJSZg0aRJq1aolUwaGhoaYM2cOsrOzubi//fYbevfuzcWpX78+goKCvuv+R0ZGol27djK2DQwM4O/vjxcvXsjEffLkCUaNGsWVS9WqVTFt2jS8fv1aLm0gKipKpv1ra2sX+SgrK0MkEkFFRQVXr17l7RmQm5sLkUgENTU11KhRg/v8t0z4QF9fH7a2trh27ZpCOqwXL17I5FlNTQ0ikQi5ubmCpkMsFmPQoEGYNGnSj61i6Cu4efMmASAAdOnSJSIiSk9PpwULFpCysjKJRCJatWoV8UFBQQFNnz6dVFVVacqUKZSUlERERBKJhKKiosjNzY20tLRo69atvNhfu3YtAaCqVatSdnY2ERF9+vSJtm/fTtra2gSABg8eTIWFhcQXly5dIg0NDRowYAC9fPmSiIiSkpJo1qxZpKKiQgAoMjJS7nY3b95M9vb2dPPmTcrLy+PyLq0Lf/31F3cvHj16RJ6enuTh4SE3+8HBwZytlJSUUuMmJCQQALp+/brc7M+cOZOzf/HixVLjisViUldXp7Fjx36XzUePHpGSkhIBKLZNTZkyhUvTo0ePSvyd+vXr08KFC78rLbm5udSvXz/O3tSpU4uNl5aWRgBo7NixlJ+fL7fynzZtGmc7ICCg1LjNmzcnCwsLio+Pl2sbOHz4MNWtW5f+/vvvYtv4vXv3qEqVKgSAZs+eTXwibXvt2rUjRbBz506aMGEClQd+/vlnAkCfPn0S1O6yZcvIx8eH6tevT+fPn6cfla8SAKmpqVxDvHv3rkzYnDlzCACJRCK5PnyJiAoLC6lPnz4EgDZs2FBsnLy8POrYsSMBoODgYLkX1LFjxwgA6erqFgnbtm0bVy779+/n5UZJJBIyNzen+vXrk0QiKRJ+5MgREolEvAiA5cuXc538fx9CnwuAz8O8vLzkZj88PJwAkKam5hfj5uXlEQB69+6d3Oy/e/eOtLS0CACtWLGi1LgPHz4kXV1dysjI+G677u7uBID69OlTJOzt27ekqqpKAOjIkSPFfj8nJ4eqVasml7IQi8XUqlUrAkA1a9ak9+/fF4nj5+dHffv2lXv9E4vF1LBhQwJAdnZ2JBaLi42Xnp5Oenp6dO3aNbmnYePGjXTq1Kliw/Lz87n0NWrUSK7ipzwKgPfv35OVlRWvLzvlWQC8fv2azMzMKCUlhS5evEgODg4l1skKJQDevn1bogB49eoVFzZy5Ei5JnLRokUEgP73v/+VGi8pKYk0NDRISUmJzp07J9c0nDx5skQBIJFISF1dnQCQp6cnLzfq9u3bBIB69+5dYpxOnTrxIgD27t1bpLGXJgCIiHbt2iU3+0ePHi2x7IvrLABQVlaWXMtgzJgxBIDs7e1LjTdnzhyaOHGi3ModAGlpadHHjx+LhHft2pUA0MCBA4v9/pEjR6hr165yK4OXL1+Snp4eAaAhQ4bIhO3fv58cHR250TE+6r90lGvp0qXFxhk7dixNnjyZF/tLliwpMW/SEaIqVarQvXv3eH9oK1oAEBF16dKFoqOjK6UAGDBggMyonJeXF61Zs6ZyCwAi4t6Sfv75Z7klMCUlhTQ1NQkAhYeHfzF+z549CQA5OTnJVaGWJgCIiOrUqUMAqEWLFrzcqFu3bnGdQUkPmR07dvAiAEp7CJUkAORJeRAA8fHxJBKJCACdPXu2xDdBY2PjUofkv4bs7GxueiksLKxI+Pr16wkAaWtrF9s59e7dm/bt2yd3MSi972fOnCEiort375KxsbHch92LE1cASENDgxISEmTCrl27RlZWVsUKJT65fPkyN1WzevVqQdueIgXAnj17yM/Pr9IJgOjoaKpfv77MG//z58/JxMSE3r59W3kFwIcPH7iwoUOHyi2By5Yt4363LEOZ27dv5+JfvXpVEAHw6dMnTqSMGjWKlxslFovJ1NSUS8Ovv/5aJM6rV6/o+fPnTADwIACkbz0AqGPHjsWG79+/n9q3by9Xm4MGDSIAxfpU9O7dm7sH/+3oMzMzqUaNGry8kffo0YMAkJmZGT1//pxsbGxKnIaQJ7m5uVSvXj1uNFAq8PPz88nR0bHEIXq+yMzMJCsrK64zFmpIvDwIgMzMTLK0tKSCgoJKIwAkEgk1aNCALly4UCQsKChI7iPfP5QA2LRpExdW0hvS99xgU1PTr3pTBvDdzk9lFQALFiwgAKSmpsbrW9D58+c5RyMA1Lx5c4qKilJIxamMAuDChQucn8v9+/eLhLds2VLuHWFERAQBIBUVFXrz5o2M4K5WrRonAv4rEH799Vfy9vbm5X6kpqZSjRo1OKfYadOmCVbvrl69yr1xSx1+Fy1aVKyfBN8MHTqUAFC1atXoxYsXgrc9RQoAIiJPT88vOsVWJAGwdu3aEn2bPn36RNbW1nTr1q3KIQBu3LhBRP965x8+fJh0dHQIgNzmP6XY2dkRAHJ0dCxT/BcvXvDii1CcAEhKSqIpU6aQSCSi6tWr04kTJ3i/YdevX6e6detyeQRAnTt3LrZDYgJA/jRo0KDYuhUXF0e1atUq1kHzeygoKOBGftatW8dd37lzJ/Xs2ZOioqKKFQju7u50+vRp3u7J4cOHuft/8uRJQevexIkTuY43OjqajIyMKDk5WdA0SOtkSdMzlUEA7N+/n7cRz/ImAN6+fUumpqaljrAeO3aM3NzcKocA6NGjB3l6elKjRo3IwcGBvLy8eBmCMzc3JwDk4uJSpvg5OTlcGvv37y93AaCiokLu7u7k4OBAAEhJSYk2b97MW4dTHHl5eRQcHEy6urpcXlVVVWnRokVMAPAsAHbu3MnNQ6elpXHXR48eTQsWLODFpnQZXLNmzbhrHTp0oKNHj1JhYSFZWloSAFq7di0R/es3Y2hoyKtn8tWrV0lZWZmbCvjw4YNgdS87O5sbeldRUaHNmzcL+tBMSUnhRkD4WPXwowiAjx8/koWFhdxFb3kdAaiIyNUJkA9cXFwIANna2pYp/rt377g0jhkzhrcRgIyMDKpduzYBoBEjRijk5qWlpdGkSZO45WBlWSf9IwqAEydOlFkA5ObmEgDKycnhTXwZGhoSAE5wZWVlUfXq1b+4R8G3cufOHa6sHz9+TCkpKWRgYMDtyTB79mwCQE2aNCEiojVr1tDo0aN57QAtLS0pPDycE6HDhg0TtO7v2bOHE2JCL0fz8PDgpiXludz0RxMARP/6oURERDAB8INS7rcCtrKyAgC8fv0ahYWFX4z/9u1b7u8GDRrwli5dXV0cPnwY6urq2Lp1K/bv389rOeTk5OD+/fsy16pXr46VK1ciNjYWdevWBQAsWbIEaWlpFWrLTQ0NDa4M6Au7LWZnZ0NZWRlVqlThJS1qamoYM2YMAGDDhg0Qi8XYvXs32rdvD0NDQ15sOjo6cnV53759OHDgAHr16gU1NTUAgI+PDwDg77//xuPHj7Fv3z4MGDCAl7RIJBL07dsXkydPRq9evRASEgIA2L59O86dOydYnahWrdq/W5n+/85/QrFp0yacOXMGIpEIO3fuhJ6eXqXeDrdv3744ePAg2yO5Im8FrEi6dOkCAPj48SMePXr0xfixsbEAAGVlZXh4ePCatkaNGnFbtP7yyy9ISEjgzVZmZia2bt1abFi9evVw8uRJqKioQCwW4/bt2xWqktasWZPbfjM1NfWLW4WamJjw2imMHj0aVapUwevXr3Hw4EFs2rQJY8eO5bUMpJ18WFgYwsLCMHDgQC7Mzs4OLi4uAICgoCCkpqbCzc2Nl3RMnz4dxsbGGDduHABg2LBh6NChAwBgxIgRyMrKqrAPy4SEBEydOhUA4Ofnx+W7pHpYGejcuTPOnTsHiUTCelMmAORPjx49uA4gPDz8i/GPHTsGAOjXrx/MzMx4T9+YMWPQt29fZGVloU+fPrzuSX3s2LESR0FsbGxgZ2fHjU5UJGxtbaGlpQUAX9yDPCIiAs2aNeM1PQYGBvD29gYATJkyBQDQsmVLXm0OGDAAysrKePToEdLS0op08FJBsPe5hhUAACAASURBVGfPHvTr148XAXTo0CGcPXu2iBDdunUrtLW1kZSUxJVHRUMikWDgwIHIycmBnZ0dgoODS4wrFouxadOmStGBaGhowNXVFefPn2e96Y/I18wXJCcnc3ORsbGxgnqbAiAjI6NSt1hNSEggdXV1MjQ0lLtXsNQRTUdHp0hYZmYm2djYEADy9fXlpQykZV+So9+bN29IQ0ODbGxsBFmbm5WVxdWFK1eu8G4vICCA22ipJOe227dvk66uLsXExPCenri4OC7/GzduFKQdSLcG9vf3L3ZeXuqUd+fOHbnbvnHjBhkYGJS42kS6KREAQVbDSNujhoaGIGU/b948zulQugKqJLZs2UIrV66sFD4ARP/uOPnfnSGZD0AFdAL8+++/uUYu9KYb0g2BevbsWWwH8P79e3JxcaHq1at/sYF+C6tXr+bWgBfn8fzPP/9wa/QnT54sdw/sz8XXwIEDOQFWWFhIt27doiZNmpC+vr4gnR8R0YMHD7j0HDx4kHd7ubm51KtXLwJArVu3psjISMrMzKSsrCy6desWTZs2jbS1tWnHjh2C1ckOHTqQjo6OYCtApI5vJe002LFjR6pfv77c7Z48eZL09PRKdfTLy8vj1ufr6enxviXuunXruPqXnp7Oq63r169z2xAHBQWVGC8jI4NCQ0NJS0uL1wNiypsA+PTpE5mbm3NOqUwAVDAB8OjRIwoKCiJra2uu0dWqVYsCAwMF63CI/l1nWatWLWrUqBHt3buX7ty5Qzdv3qQ1a9aQmZkZtWvXjp48eSJXmxcuXKAZM2Zw+xwAIFdXVwoODi4yyhAaGsrFMTc3Jz8/P3r9+rXcBMD48ePpxo0btGDBAnJ1dSVjY2OqWrUqWVhY0KhRowTZjOTZs2e0cOFCql+/PpdXExMTmjdvHi/C63MKCgpo586d1KFDBzI0NCQVFRXS1dUle3t7GjNmjOB7IZw5c0auK02+xMePH6lt27YlhoeFhdHixYvlZu/06dPUrl077j7XrFmT5s6dS7m5uTLxoqOjZXYlxP9vWT1q1KgiW/Z+L9HR0TRr1izuTAIA1LRpU1qyZAmlpqbyUu6f13VNTU3S0tIq8tHQ0JDJP19pKY8CgIho4MCBgu8HwQTA9yMiEuAQezkiFouxZ88eTJw4kXM4UldXx4ULF3hzfGIwGIzyQm5uLjQ0NNCuXbtKP/fesWNHnD17Fp8+feJt5Q9zAixHqKqqwtfXF5cuXYKpqSkAIC8vDxcvXmR3k8FgMBiMiioApDRq1Ah37txBnz59AACBgYE4deoUu6MMBoPBYFRkAQAA+vr6OHjwIKKjo+Hm5oZevXph1apVyM/PZ3eWwWAwGIxS+OF8AErj/v37OHToEJ4+fQpbW1u0b9+e9zXhDAaDISRSHwA1NTWZnQhv3LiBWrVqVei8v3jxAg0bNuT+n5mZCbFYXKF9AGJiYtC0adOv+s6VK1fK9J0KJQAYDAaDwWCUDSVWBAwGg8FgMAHAYDAYDAaDCQAGg8FgMBhMADAYDAaDwWACgMFgMBgMBhMADAaDwWAwmABgMBgMBoPBBACDwWAwGAwmABgMBoPBYDAB8EMQERGBLl26oGfPnqwwPiMjIwMrVqyAhYVFpTueVCwWY+nSpejQoQO0tbXRqFEj/PbbbxUyr7GxsRg6dCgsLS3LRXr69+8PQ0NDxMXFKcT+wIEDYWRkhHv37gli78aNGxgyZEi52O735cuX8Pf3h7GxMf755x/2EPxRoW/g2rVrpKGhQQDo4cOHVNEpLCykCRMmkLm5OQGg7t27E+Nfbt68SV5eXqSkpEQAKCIiotLkXSKRUPv27Sk0NJSIiK5evUpVqlQhAHT+/PkKldf169eTs7MzASBDQ8NykSZLS0sCQEeOHFGIfenzIDw8nHdbO3fupPbt2xMAql69ukLL/cqVK+Tj40MikYgA0O3bt9mD8AcF3/rFw4cPk0gkomXLllWawgoPD2cCoATc3NwqnQDYsGEDAaCsrCzu2u7du8nIyIj++uuvCpffx48flysB8OTJEzp9+rTC7MfHx9OJEyeosLBQEHvJycnlQgBIcXBwYALgB+ebpwB69+6NJUuW4Pjx45VmtKR69epsyKgEatSoUenyvH//fqiqqkJbW5u75uPjg+Tk5Ap5CmV5q/+1a9eGh4eHwuzb2Niga9euEIlEgtj7/OS/8kB5Sw9DYB+AGTNmwNHREW/evKkUhaWiosJqDCsbjocPH0JVVZXdY4YgKCsrs/Qw5Numv/cHNm3axEqRUSnJyMiAuro6KwgGg1H5RgAUQWFhIbZu3YrWrVujR48eqFu3Ln766SeEhYUJmo68vDzMnDkTRkZG0NHRgZeXF5KSkgSxnZubi8WLF8PV1RVNmjRB7dq18csvvyAlJUUQ++fPn4e7uzvatGkDNzc3+Pr64v3794KV/YcPHzBz5ky0aNECZmZmMDY2xvDhw5GamipIp29nZwc7OztIJBLk5ORw/x8xYoQg+d+5cyfc3d0xatQoODs7QyQSyXwCAgIEScfZs2fx008/QVNTEy1atMDjx48FqwOJiYmYM2cOjI2NcfPmTcGfQ0lJSQgICICpqSn+/PNPhT0P8/LyMGDAAIhEIhgbG2PmzJmIioqqEJ2TRCLBkSNH8PPPP3Mrr44ePQoXFxdoa2ujTZs2XJ17+vQpPD09oaOjA1NTU2zcuFHu6bl8+TK8vb1hZ2cHALh48SKaN28OTU1NNG/eHAkJCYKUy6lTp9C5c2d07NgRNjY2cHNzw969e7/tx340p4Xx48cTALp37x4REWVnZ5O9vT0BoD///JNX25cvXyb8H3vnHRbV0TXw39JZmiAiRUFEVBCwK/auscbEGqxR7L2baDQx9tiiSey9RI1GjRpfe+81iqIIilIVkN5h5/vDZT9Q7OwicH/P4/PmnTvsmTt37p0zZ845A6JNmzaiZcuWon79+qJ169Yqz29bW1sREhKi1jbExsaKmjVrCm9vb5GWliaEEGL37t0CEI6OjiIuLk6t8lesWCFMTEzEqVOnVGVTpkwRgEacACMjI0W1atXEgQMHhBBCZGZmimXLlqnu/8WLFxobi9ra2sLIyEij43/ChAlCV1dX+Pn5CSGESEtLEw0aNBCA6NWrl0hISBAZGRlqkR0fH69yAly5cqXo0KGDWLRokWjTpo0AhKenp0b64MyZM6Jv376qMXf16lWNPoNLly6JgQMHqrzgz549qxG56enpuToBjhkzRjRt2lSjY18IIRo1aqRWJ8Dvv/9euLi4qByvp0+fLvr16ycWL14sPD09BSAqV64srl27JurVqyfmzp0rpk6dqopQO3PmTJ61ZdOmTaJbt24CEA4ODmLlypWiVatWYubMmaJFixYCENWqVVN7n0+bNk04OTmJwMBA1ZgYNmyYAIS3t7fmogDyCxMTE6Grq5ujbOrUqQIQc+bM0YgCULJkSXH+/Pkc3sC2trYCEF5eXmptQ8+ePUWpUqVEUlKSqiwlJUUj4Wc3btwQOjo6YsGCBTnKMzIyROnSpTWiAHh5eYmpU6e+Vu7m5iYA8fPPPxdaBeDevXtCJpOJpk2b5ig/ePCgAIS5ubla5WcpAHp6emL9+vU5nr+dnZ0ARHBwsMb6w93dPV8UgCxq1aqV7wrAhAkTRIcOHURKSorG71/dCoAQ/x955eDgIG7cuKEqT0xMFKampgIQI0eOVC2GhBBi/vz5AhDDhw/P07YEBQUJQBgaGuYY/+np6cLKykoA4vHjx2rri6NHjwpA/Pnnn6+Ni6zv39q1azUTBZBfjBkzhokTJ+YoMzExASApKUkjbfD09KRu3bqq/+/s7MzcuXMB2Lt3L+np6WqRGxISwrZt22jZsiWGhoaqcn19fU6ePMm6deto3Lix2u572rRpZGRk0L179xzl2traVK9eXe39/uLFC3bs2MHhw4fp2LFjjn96enpUqFBBI9sA+cWFCxcQQmBlZZWjvEqVKgBER0eTkpKi9naYm5vTt2/fHM/f1dVVNUY1RX5HJVhYWOTrVuigQYMIDg5m9+7dhdYXpVixYqoxXrVqVVW5XC5XjbnevXvncMZ1d3cHIDQ0NE/bkjXPWFlZ5Rj/Ojo6VK5cGYCwsDC19cXs2bMBaNasWY5yHR0dhg4dCsCcOXM+6DcLnFvvTz/9BEBaWho7d+5kz549BAUFqV6K/KJLly706dOHpKQkgoODcXR0VMsEoFAocs0E5unpqdbQs8TERA4fPoy+vj52dnavXdeER/D169fJzMxkzJgxfPPNNxQ1sia8V309siYiMzMzDAwM8qVtWQqpJhQQTY65z1G+EIIePXqwZ88e/P39C3V0xtv62MjISNUf2cl6B1JTUzXWFlNTU9W8pA4SEhI4derUGxXfhg0bAuDv7094eDjW1tbv9bsFMhXwunXrqF27NsnJyWzbto3evXvne5sMDAzeu9M/ZQWsbi3zTQQGBpKeno6WVv4Nmaz7f/ToEUWRVq1aYWdnx61bt4iPj1eVP3nyBICuXbvmW9uyYuHzUwkvKshkMkxMTFQOgBkZGVKnfCa8qozkFcHBwarfzvoOZqdUqVKq//4QS3iBUwD69+/P5MmT+eeffxgwYMBnZfpSKBTo6elhY2Ojlt83MzNTWQLehLrykmdpv8nJyURERORL/2Yl3Nm/f/8b61y9erXQflwMDQ05dOgQJUuWZOrUqaoxN2vWLFxdXZk3b570BS4iLF26lCpVqnD27FkmT54sdUghJ+vbn13hz07WPKijo5OrhbZQKABnzpxh3bp1tG3b9rM4ECM7UVFRPHv2jFatWqnNDFujRg0AfHx8OHjw4GvXT58+rbaDURwdHVVm3rdNwOpcAWbtAV6+fJnt27e/dt3Hx4cTJ04U6g+BXC7H2NiYpKQkvv32W7y9vXF1deXSpUtSZrYihIGBAbt27cLMzIyFCxeyZ88eqVMKMTY2Njg7OwPk+qyzFmWtW7f+oEVxgVIAspw67ty5ozKHZGZmcvv2beD/93wiIyM13ra1a9eir6/PzJkz1SajXLlyNGnSRGUJOX/+vOra0aNHGTVqFO3bt1eLbH19fby8vICXzoCvbkMkJCQAL2P01YWtrS3t2rUDoG/fvvz222+qPefLly/To0cPjfkGCCFQKBRkZmZqbIwlJyfTokULvLy8WL16NevXr2fdunVMnjxZ5aCk7nvOizqabE9h4tX7dXJyYt26dar34cGDB/nansL+jN9ncaPO9k6aNAl4mQckMTHxtcWflpbWh1uDClIIYEBAgNDV1RWAaN68uZg4caKoVauW6Nq1qwCEk5OT6NmzpypHQF7j4+MjDAwMhFwuF6tWrVKFnuzatUtYWFiIffv2aaQPbGxsVDHQpUuXFpaWlkJXV1ecPn1arbKjoqJE+fLlBSDs7OzE0qVLxe7du0WvXr2Eg4ODAISbm5uYPn262g5ICQ4OVskChK6urjA2NhaAWL16tUbHYlYbnj9/rhGZd+/eFYDQ1tYWjo6OokKFCsLFxUW4ubkJT09PMWjQIFV+AHXw5MkTAQgjI6PXnm/Tpk01djJeFh4eHgIQR44cyZfvUdu2bTUaBph1GJC+vn6OXA8DBw4UgHB2dhbh4eEau/+sw4DUGXqcFQbYqFGjN4ZhnjhxIkf5P//8IwBRr169PG3LgwcPBCCKFSv22vjPOqlRneNfoVCIXr16qfIixMTECCGEuH37trC3txfz5s0r/HkAtm7dKhwcHIRcLhcdOnQQgYGB4tGjR8LGxkZUqlRJXLhwQa3yAwMDxahRo4STk5OwsLAQlStXFr179xYPHjzQWB88ffpU9OzZU5ibmwtDQ0PRvHlzcenSJY3IjoyMFIMGDRKWlpbC0NBQNGvWTFy5ckV07txZNGnSROzcuVOkp6ertQ3Pnj0TgwcPFtbW1kJPT09UqVJF7Ny5U2P9P3/+fFUMOiDq1KkjFi9eLHx8fNQue+rUqcLW1lbY2NgIuVyuOoY565+5ubkICwvLc7l79+4VDRs2VMnp3LmzOHTokLh9+7YYP368KimOi4uLWLdundrzIYwYMULVFnd3d7Fhw4Z8UwCy5wRRF5s3bxZNmjRR3XO3bt3Ev//+K5KTk0WnTp1yLAhmz56tmhzUwcOHD8Xw4cNVMitVqiR+++23PJezcuVK4ezsLAChpaUlxo4dK27evCmuXr0qhgwZkuP5L1myRAghxIIFC1SLFC0tLTFq1CgREBDwyW3Zt2+faNy4sUpm9+7dxeHDh8WdO3fEhAkTVOO/YsWKYsWKFWpVAlatWiWqVasmihcvLqpVqya++OKLj1aCZaKo2dEkJAooERERfPPNN+zatUsVH51FSkoKgYGBDBo0CG9vb3r16iV1mJpp164dBw8e5L///sPDw0PqEIkCh5bUBRISBQMvLy+aNWv22uQPL53CKlasSJMmTYrk0cz5tSespaWllpwfEhKSAiAhIQHAtWvXOHbsGGfPnn1jsp3//vuPS5cu0bJlS6nD1EBMTEyO5DIJCQk0aNBAIw6YEhLqQDrgW0KiAODi4oKHhweHDh3C0dGRdu3aUaFCBeRyObGxsVy7do3IyEh27NghndOuBlJSUihbtiy6urr8+eefVKpUCT8/v7eGxEpIfO5IPgASEgVoElq5ciV//fUXPj4+JCYmYm5uTrVq1VQhkIU5LWx+M2DAAHbs2IFCoaBRo0b89NNPqtwcEhKSAiAhISEhISFRIJB8ACQkJCQkJCQFQEJCQkJCQqIoIG0YSkhISEhI5AOyrGM0JQuAhISEhISEhKQASEhISEhISEgKgISEhISEhISkAEhISEhISEhICoCEhISEhISEpABISEhISEhISAqAhISERGEmICCAX3/9lcTERI3J9Pb2ZsuWLRq9z/3797N//34yMzOlhy4pABISEhJFFyEE06ZNY9myZfTp0wcjI6NCfb/t2rVDW1ub1q1bExgYKA2AT0RKBCTxSZw9e5YGDRpIHSEhkQ/MnTuX8+fPc/z48SJxvzKZjDZt2pCamkqLFi24efMmxsbG0kCQLAASmubmzZusW7dO6ggJiXwgLi6On3/+mSZNmhS5e2/cuDH+/v6sWLFCGgiSAiChaVJSUhg0aBDSYZISEvnD9evXSU5OJioqqsjde9Y9nz17VhoIkgIgoUlSU1Pp1asXV69elTpDQiKfyEojf+vWrSJ371n3rKUlTWEaUQAUCgUHDx6kY8eOtG7dGiEEc+fOpXTp0sjlcr744gvu3bunkUbfuHGDLl26UKtWLcqXL0+dOnVYs2YNtWvX5tSpU2qXf+HCBfr06YOzszNCCCZMmICZmRnt27dHoVCoXf7Zs2dp06YNHTt2pHz58shkMooVK6aRvhdC0LdvX65duwbAP//8Q5UqVahSpQqhoaFqk7tgwQLc3NyQyWR4enqqys+fP0///v2RyWTIZDLu37+vFvl//PEHVlZWKjn9+/cnODhYdX337t24u7tjbm7OqlWr8kTmvn37cHBwUMmcOXMmAIcOHaJRo0aq8g4dOqhWQpmZmUycOBEtLS08PDy4c+dOnrRl165d1KhRQyXTw8ODu3fvkpqaSufOnZHJZFSrVo0jR46opf9nzJiBoaEhMpkMHR0dJk+eTGxsrOr6oUOHcHFxQV9fX9VPavlgamlhbm6Ou7u7atxXqVIFU1NTZDIZ9vb2GrOKVahQAQAfH58iN3HdvXsXAFdXV2kW/8QP+nsxa9YsUblyZQGIZs2aiZEjR4oOHTqIAQMGCCsrKwEICwsL8eTJE6FO1qxZI6ytrcWpU6dUZVu2bBFaWloCECdPnlSr/GXLlok6deoIQNjZ2Ykff/xRfPnll0JbW1toa2uLyMhItcp/8OCBsLa2FiEhIUIIIRQKhZg1a5YwMzMTmmTPnj0CEH369NGYzAsXLghA1K5d+7Vrrq6uAhC+vr5qk3/z5k0hk8kEIF68ePHadW9vb7F+/fo8lXn37l2hpaUlDA0NRXp6uqo8ISFBWFpaCkD4+fnl+JukpCRRvHhx8fz58zxtS3JysqhVq5YAxNdff60q//XXX4Wnp6dITExU6/P/448/BCCsra1zvd6jRw8xZcoUtclPT08XlSpVEsnJyTnK79y5IwwMDIS2trY4c+aMRt9DGxsbYWFhIfKD/v37i82bN+eL7B9++EEAYvfu3aIgU2AUACGEOHr0qABEiRIlxNatW1XlISEhwt7eXgCie/fuauuss2fPCm1t7Vwfer169TSiAAghxJMnTwQgDAwMxO+//676UJ87d07tsmfOnCmsra1FRkaGqkyhUIi6desWegXA19f3jQpA1vNXpwIghBCtW7cWgNiyZctrk66rq6tIS0tTm8yjR4/mKB8zZowAxIIFC3KU7969WwwePFgt9x8QECCMjY0FII4cOSKCg4OFs7OzSiFVJwqFQnh4eAhAnD17Nse1lJQUYWVlJZ4+fao2+UlJSWL69Om5PndAzJgxQ+MTSLVq1XIoY0VFAThx4oQAxMWLFyUFQFM+AFnhFu7u7nh5eanKbW1t+emnn1Rmy7S0NLU0dtq0aRgbG9OxY8fXrllbW2us07LM7cbGxgwePFhliqpXr57aZaelpREeHk7//v2JiYlR7QVOmDBBMmdpgBEjRgDw+++/5yjfsWMHX3/9Nbq6unkus1+/fgBs2LAh1zG/evXqHOXr1q3D29tbLfdftmxZfvnlFwCGDRtG3759WbRoEba2thrZ8548eTLwMvzt1S2K2rVrU7p0abXJNzQ0ZMqUKTnKRo0axb1792jSpMlr19RNeHg4ERERr/VFUaBJkyZ069aNkydPakTe06dP6dixI3Z2dtSpU4cZM2bw4MGDXOuuW7eOR48eFa4tACGEuHjxomoL4FUiIyMFIADh7++f55pSXFyc0NbWFtWrV8/1eqdOnTRmAYiPj1dtAWgaf39/YWJiIgBhbm4upk6dmuemXskC8PZVqLOzswDEtWvXVOV169YVQUFBapGZmpoqLC0thVwuF7GxsUIIIdLS0kTlypVFjRo1BCBOnz4thBAiLCzsje9IXtKiRQsBiJYtW2p03GVkZAgnJycBiFu3bqnKGzRoIA4ePKjRtuzcuVMAwtLSUiMWkOx9cPbsWTFkyBBx7969fFu95qcFIGtLZuLEiWLZsmUiKipKre98o0aNxObNm4Wvr6/4+++/Ra9evYSxsbGoVauWWLp0qWrr+9atW6Jp06YiMzOz8FkA3kbx4sUxMTEBICMjI88bGhQURGZmplp+uyDh5OTElStXaNKkCdHR0cycOZNy5cqxZs0aaXmuAWQyGcOGDQNg2bJlwEunVGtra0qVKqUWmXp6evTo0YOkpCR27twJwNatW/nyyy8ZPnw4gMrxcOPGjfTt21ft/TB69GgATpw4oXII1QTa2toqa9fs2bMB8PX1JSgoiC+++EJj7Xjy5AkDBw5EJpOxYcMGjVhAsjh37hyLFy+mU6dOuLi4FNl3McsZNCgoSK1WEH9/f1q1akXPnj2pWLEiX331FZs2bSIsLIyhQ4eyb98+nJycMDQ0pHv37ixcuLDgRCfklQVACCGMjIyElpaWapWSl/j4+AhAmJqaFmkLQHaOHz+ucsrStENMflgA7t+/n+8WACGEiI2NFcbGxsLAwEBEREQIb29vcezYMbXKvH37tgBE/fr1hUKhEDVq1BDPnz8XiYmJwszMTBgYGIioqChRpUqVXB0U83r8V61aVUyePFkAolKlSiIlJUVj4yAlJUXY2NgILS0t8eDBAzFy5Egxa9Ysja48sxyBx4wZk2/v/7x588TYsWOFQqEokhaAa9euiVatWomIiAi1yklOTn7N8fNV0tLSPqodBdICkFuoW0REBImJidSsWRNTU9M8b6ijoyM6OjrExcWxf//+Iqv1rly5ktTUVACaNm3KxYsXGTVqFACbNm0q1Peup6cH8NYDTzQRhmlqakrv3r1JSUlhwYIF3Lx5k2bNmqlVpru7O9WrV+fcuXMsWbKEmjVrUqJECeRyOT169CAlJYWhQ4dSqVIlzM3N1dqWYcOGMXLkSObMmcMXX3zB3bt3mT59usbGgb6+PqNHj0ahUDB9+nS2b99O//79NSZ/+vTpXLx4kerVq7+28nz48KHG2jFx4kT+/vtv1q9fX+S+g3FxcbRp04bhw4djaWmpVlkGBgYYGBi8tY6urq7a2/HZWABcXV1fu7Zq1SoBiF27dqlNE+vYsaMAhJOTk3j8+LGq3M/PT5QuXVrjFgAbGxuNa72TJk16TevOao86IzBe5eDBgwIQX375pRDipTe0ukNAExMThZaWlpDL5TnCLbdu3SrMzc1z9Q5XF/fu3ROAkMlk4tdff9WIzN9//10AQldXVzx8+FBVfvPmTZUV6MSJE2ptw+bNm4WXl5fq/wcFBQkTExOhra0tLly4oLHxFxcXJ4oVKyYA0aVLF41a3bS0tISJiUmOZ5DFTz/9pNHvgYeHh3BzcytyFoDly5dr9H1XFwVSAQDEmjVrVOUPHz4UdnZ2YsCAAWrtrEePHqlinw0NDUWbNm1E27ZthZeXl/jyyy81pgCEhoYKQOjp6Yn4+HiNKwBmZmY54o2PHDkidHV1NfoyZJnj5XK5+PXXX0Xnzp1FeHi42uVmOZ9VqFBBjBw5UtSvX1/MnDlTtQVQs2ZNMXfuXI30QfPmzYWRkZGIiYnRiLzo6GhhYGAgOnfu/Nq1GjVqCCcnJ7Wag2/duiVsbGxEdHR0jvKZM2cKQJQtW/a1a+pkypQpAhDHjx/XiLyIiAhha2srgBxh0Fn4+/uLNm3aaHQrQl9fXxgZGRU5BWDixIkCEMuXL5cUAE0rAJ6enmLo0KGibdu2olmzZqJWrVrijz/+0MhelJ+fn2jXrp2Qy+WiVKlSYtasWSIjI0NjPgA7d+4UDRo0UClCnp6eYtu2bRpVobhQ7AAAIABJREFUALJkV6lSRXTs2FG0bdtWXL58WeODd9q0acLY2Fi4u7trJAeCEC9zTrRo0UIYGBgIFxcXVd83atRIdOjQQfzvf//T2J7ovn37xMCBAzXa515eXrk+65UrV4rZs2erTe6uXbuEpaWl0NbWzhHvfufOnRx+KG5ubmL79u0a6YurV6+K8uXLa7TvsywwtWvXzvHP3d1d6OnpiVatWmmsPVl+Ic7OzkVOAViyZIkAhLe3t6QAfC5OgPmJJp0AJSQk8p9x48aJhQsXFtn7P3z4sMa3QD4XBeD06dMC0KjFpTAqADpSYJeEhERBIyEhge3bt3P79u0i2wclS5YEimY+/KzwR00mgCuMfJACkKWwiM/wCFghHUsrIVGoOXjwIHp6ejRs2JBJkybRrVs3LCwsimx/eHh44OHhQVJSUpG79+TkZAB69eolvRiaUgCyTt/KfgrX50J0dPRn2zYJCYlP4+zZs7Rr1w54eSJfxYoVOXfuXJHuE5lMxubNm+nSpQujRo3Czs6uyNz7rFmzmDRpEo0bN5Zejk/gvfIApKSkMH36dFW8+fXr1xkwYACnT5/O9xu4c+cO48ePV7Vl0qRJRTI3toREYcbNzY2aNWtiZmaGl5cXJ0+eVHu+g4JiBTh48CA///wzv/zyiypHSGHl1KlTDB06lDp16kjf+bxQIoVkO5eQkJAo8CQmJqKvr4+OTuF17YqLi1NLorl8m4BlMpmkAEhISEhISBS1FXg+KwBa0iOQkJCQkJAoeuigdJ7LN44dk55CfqI8RU4in1YA0vjPV0Tz5vnbgIEDP+v+2XbuHF7166tPQH73f5FXACQKHQkpKbiPG8f+yZNxK11a6pACir2BAWPs7elcsiSl9PVV5c/T0lgTEsLswEASMzMB6GRlxTfW1nSysgLgbmIiO589Y8ajR5J8iY9CIQSXHz5UrwIgka9IWwCFUavT1iYyPh4dLenxFmSepqQwxs+PcufPs/3ZM1X5prAwpgQEqCY/gN3PnzPI1xeA34OCqHrp0idPfkVdflEn8PlzyigVKglJAZAoAAgh0NfRYXLHjlS0s0Mh+XgWeFIVCnr5+HBGuV3X28aGYrl4ev9Ytiw7nj1j+IMHpOfhcy/q8osqviEhuHzmuQWCQ0P5dvhwho4fT8uvv6bX4MFkZGQwf+lSflm2jAZt2nD91i0A/vPxYdbChXw3YwZf9epFkjKZkKQASBQKMhUKrLy9qTJxIn5hYXSYNw8DLy+uSyuhAk+GEHj5+BCdno6Vnh6Ly5fPcb17yZI0Mjen3717knyJPFUANp0+Tc3vvkPWtSu633zD8iNHVHX+vnwZK29vKo4ezZazZ4lNSmLB/v3YDhqErGtXHIcN46gyXXNSaiqLDhxA1rUrrWfP5qzSYvMplLK1pUzp0jzw92fPli38MGECKzdsoLyTExNGjKB39+4MGD2a5JQUhk+cyKRRo5gzbRrp6en4PnggKQDSMC88aGtpcX3ePG7On09iSgo7x47l4dKlVHV0lDqnEBCSmsoI5Uerr60trYsXB8DN2JhF5cvT6fZtkrKZxSX574dfUhLf3ruH7NgxfnnyJNc6cRkZmJ48ieP58+yLiMA/KYnRfn7Ijh2j4bVr9Lt3jxpXrjAnMBABvEhPZ01ICNrHj1PhwgW8792j7tWrDPT1JTo9vUCMt6eRkdhbWtK7USPOzphBHaXS1bpqVVWdxpUq4VqqFJdnz6ZngwaYyeWMb9+e8z//jIWxMfq6ujR1cwNArq9PDScnejZowKHvv6eBMp//p2JkZIS7qytGcjnlnZz43/HjPHz0iA3bthETG4ujgwPXb93CSC5X5Ug4sH071atUkRQA6bNauLC3tORJRAS7L1/m1N27OJQogVb+hppK5CFbw8PZ8/w5AKtcXbE3MOBvDw+GPXjAQw3khC+M8svL5XxfpgyGWlosffo01+2DNaGhZAhBcwsLvixRgnJyOcNLlQJghpMT61xdWV6xIlP8/Zn9+DEWurp429lho6fHN9bWrHF15VDVqvwbGUnXO3c+u3EVFBVF6iuKiUKhICtM3UBXlz9HjUKup8ewNWuAl9uNw9auZcWAAZjJ5Tn+1tHKirVDhvAgNJRFBw4AEBUfz/x9+1g1aJB6rWUZGVTz8KCvlxcTRoxg26pVKBQK/AICcpwZ8ywiQlIApE9q4aNMiRIYGxjgbm8vdUYhZPD9+0Smp1NKX587np7sjYhQTYqS/I9DVybjG2trwtPS2B4enuNaphCciY7Gw8QE7WzlOq8o1jVNTXEzNubPbH+fvY6Zjg5fW1lx7MULIt/TCrBNzecdRMXHM3bjRtrPncvaEydyXHs1R41DiRL80qsX/968ybZz55i7dy/tq1en4hv8BDrWrMk39eoxfedOHoaFMXj1ahb27o2hnl6e34dCoVD9d7NGjRj13Xdcv3WLp8HBLFmxgqoeHsTFxzN70SKSkpPZsWcPzyUF4O0KwNPgYMZMmUJpNzdkFhaqfyUrVGDKzJkkZtO4d+/fT+c+fVR13OrWZcb8+QW6c6ITExm7cSNlhw/HpHdv7AYNovuSJey9epVz9+/z8+7dn6V8mUxGQxcX7N7jpLSkzExmPn5MtcuXkR07huzYMXrfvfvebVwbGqr6O+cLF5jx6BF+H7kSO/HiBd/5+2N+6pTqN7WPH6fkmTOYnjyJw7lztLl5k7+ePaMou3g9T0tjiHL/1FRHR+UcJ8n/NEobGNDZyoqFT5/mKN8TEcFXH+ANb/KOVLxaMhlG2trvntSUYXjqRFdHh/EdOrB34kQWHjhAWkYGAOExMdjkctbCwObNaebuzrC1awmKinpniOCyfv0wMTSkztSpfFWrFhVsbXO+82fO0GPgQPRKlswxx/QbMYKb2Y56PnHmDL0GD1Zd92zRgrVbthD+/DlnLlzg1Llz+Pr5ATBy4EDq1a5Ns44d+bJHD9q0aIGJsTHb165l/bZtlKlcmZjYWNyVxyhHx8Tw3YwZ/LJsGTWbNSMhMZEvOnemWceOxMTG0mvwYKo0bEhwaChBISE0bNuWsGfPOHXuHIv++IPWXbqw8c8/AUhNTWXukiX8NG8eX3TuTHRMDMvXraN+69YsXbkSBw8PegwcmENh+WwVAPtSpVg8axb+16/T/euvVeW9u3Vj1tSpGGUz+3Rq356VixcDMMzbm5unTzNt4sQC+5ENj4mhxuTJnPH1Zde4ccRt3IjvkiU0d3fHe8UKGkybRqYaH+Knyq9Wtux7yZFrazPV0ZHTNWqgrwwb3B4eTlBKyjv/VgCLsu2Zbq5UiWlly1L+FXPg+9LUwoI55coxw8lJ9XGPb9yYZw0b8rxRI6aXLcvZmBi63rnDt3fvFmklICQ1lUylOXO5iwumGs7/Xljlj3Nw4L/4eI69eKEq2x4ezjclS77zb4+/eIFPQgKD3rAiDktNZeezZ/SytsbwPUJ0NRGGZ2poiK25OWVKlKChiwsbTp0C3h4BML9nT2ISE4lOTHzn7xc3MWHSl18SFR9PfC5e900bNmTrqlVcOXaM4soFi4W5OeuWLaOqh0eOeisWLQLguzFjuHD4MP179sTayop/tm3j9rlzuCh9FPT09Fi5eDExgYHcPH1aNdE3b9QI/+vXee7nx6C+fVW/fejYMUqWKMGEESMYM2QIxkZGzJk2jeiYGIqZmfHjpEm8iI7G1toaPT09Bvbpg5mpKWu3bGHs0KGsXLyYYRMmEBcfz9JVq2hUrx7TJ03C1saGxcuX06ppU/wCAmjbsiV3zp/n7MWL7Prnn89fAchCX1+fzStW0LBuXQA27dhBTC7H7v44bx7dvvqK3+bPR1dX970acD8khGk7dmDl7Y2sa1e0u3VjxLp1HPnvP1WdP8+fp/uSJci6dkXWtSsVRo1i7t69PFfj0b/jN2/mSUQE+ydNopqjIzKZDFNDQ7ybNePK7NlYGBur9cF8qvzSSgep9161aGtjp6+PsbY26UKw+JVVUG78GxnJ02yKQikDgzy5d3vl78iUCgqAgZYW/WxtWVKhAgAbw8LYkS02vChRUk+PbW5udLtzh9iMDErp67PoFa94Sf7HUcPUlAbFirFAqdheiYujmokJem+ZsPdGRDDz8WO2hoezt3Jl+r6yyr0aF8eip0+ZGhDAd2XKsEY5Ib0LTYfhff/VV8zft4+MzEx8g4NzlS2EYNGBA4xs3Zrt58+z//r1t/5mVHw85x88oHXVqkzcsoXgqKhc61Vxd2fH2rXIZDJeREezc+/e1+rMXbKEbl99xewffkArD3OceNaowc8LFtB/xAgaKy0aVT08SE1N5YG/P9f/+w8Lc3NOnz/PP4cO8WWbNty+e5eIyEg2bNvGiTNnaNGkCVEvXnD89Gn+8/Fhw7ZtlCxRAkMDA/T09DA1McHJ0RFTExM6d+jA1Rs3Co4CAKCjo8O21asxL1aM5xERjJkyJcf17X//zenz51n3228f1ICKdnbM6NaNKUoLQ5tq1VjWrx8tK1dW1fmmXj22jx6No1IbXj5gAJM7dsTKzExtHXPg+nUsjI1zNYOVLVmSSV9+qdYH86nyLU1MPtwcKJPhrXzpV4eEEKM0B76JBU+eMCDbR0Inj5wNtd/yO31tbFSWih2v7NV+DPcTExl2/z6yY8do/paXMjQ1Fb3jx7E8fZr1oaGEpKayJiQEk5MnMT55ko7//cdX//2Hy8WLjPbzU5s3vI5Mxg53dxY9fcru588ZpzR79re1peUHKn2S/NwZ6+DA4agofBISWBEczCCls9+b6FiiBFMdHVnn6kqHEiVeu17T1JSx9vasdXVllL39e78nmg7Dc7axoWa5cmw6cwb/8HCcrK1fn4T37qVT7dos6tOHao6ODFm9mtg3bPkJIRi+bh0LevVi5cCBKIRgiNKBMDeaNWrESKWD4NgpU4hPSFBdO3n2LDv37GH1r7/m+ZhyKF2aO+fPk5ScTLVGjVSLW6/Ondm+ezchYWGMGjSIzTt3Ep+QgImxMRkZGRgZGdHXy4u+Xl7s2bwZW2trMjIzqVe7Nn29vJgzbRpjhw59TZ6FuTmmH/F9zlcFAMDOxoZl8+YBsGHbNg4p85j7+PoydsoUdm/ciNzQ8KMakrWiNXuL+dhU+dvmRkZq7xghBBFxcWw8fTrX613q1Pms5Zt85HPwsrbGTl+fhMxM/ggKemO9G/Hx3EtMpLeNjUYHrLZMpkoLG5kH4VQVjYxYUqECOjIZx1+84L/4+Fzr/R4cjIKX2xTf2tpip6+Pt50dnmZmVDQyYm/lyuypXJnVLi78FhREXzXFo893diYsLY1lymezNjSUo0pz9WoXF0zeY29Zkv92OlhaUk4uZ/zDhxhpa1P8Pa2ZeU1+hOFN+fpr5uzZQ3JaGrqv9OXJu3eJjI/nq1q10NbSYs3gwTyLjWXC5s25tn/m33/TtU4dHK2sKF28OHO8vDhw/fpbHRtnTZ1KGXt7QsLCmDJzJgDPIyL4dvhwtq5ahYkaLK+7/vkHYyMj/lyzhspubjxWWn+8Onfm97VrqV65Mp06dGDfv/9SXrk96VGpEqfPn2f91q08i4jgj7VrSUpOplHdugwdP56HAQH4+Pry1759ACQkJKgiEHz9/GjbsmXBUwAAenTpwlft2gEwcPRongYH83Xv3vz+yy84KzvnY/iQUxE1cYJi1kvW748/mLx1K8lpaTmuO1pZ8YUa40g/VX6LbPtnH2oFGKmMHlgaFETqG/wMfgkMZHjp0hhoON1wikJBmLIv3PLoY6Ark9HE3BwLXd3XHMAAkhUKrsTGUsbAAL1Xxp7+K/dfv1gxqpmYsOf5c9UedV7RtWRJWhUvzoBXlIsB9+6RkJmJvYEBC9Voii/M8jOEIEP5vLRkMkaVLs2RqCiGZztLIzNbnay/yf6/7/rdt/G5hOG5lS6Nu709EXFxOcofhIYyY9cu5nh5qcqqOjoyuEULVh8/zr83b+acVC9dIuTFC76qVUtVNrRVKzwcHBixbt0btwKM5HLVXv/va9Zw5cYNeg0ezPABA6iRTfHJS+ITEmjbrRu/r1lDtcqVqeLu/rIPHRzo1L49DevWxdTEhG5ffUWrpk1fLkZNTNi0fDk/zZ9PlQYNKGllhXmxYowbPhw7GxuqN2nCdzNm0LFtWwBS09JY8Ntv/L5mDXVr1aJaNgt3gVIAAFYsXIhl8eIEh4biXq8eHdu0USkFhYVFffrgUKIECiGYt28fFUePZsOpUzlS63o6OxdK+YPs7DDV0eFZWhobw8JeX5mkpPBvVBRD32EaVQcLnjwhKTMTPS0txuZhmKNcW5tBdnZsDw8nNDU1x7XNYWH0+gBLR0xGBiX09N66lfGh1DA15bcKFeh8+zYJr2wvPElJYbK//8vJ0M6O9paWed7vhVm+f1ISvwYFcTAyUuX8962tLT1tbKggl5OUmcmWsDDuJSZyPDpalQhoqdIKsS40lFuvWI5epKezMiSEsLQ0DkZGcuQNE97nGIY3tVMnXJTvdlJqKjN27aLmd9/xLCaGG48fq+r5hYXxWBl++c2SJczft497wcEMXLmSbosX8zw2lsBsoXZn7t0jOS2NFwkJNP/55zdaAlo1bUrPrl1RKBQ079gRgHHDhqntm+Ldqxdn//2XYd7ezJk2LUe/L1+4UPXffyxYkMO3rU2LFgT+9x9h9+/TqX37l98RQ0O2r11L3NOn7P/zT4yV1uriFhZMGDGCYd7eDPP2/mzmuY9SAKxKlFB1TFx8vMo5sDBha27OpVmzVCvxp5GRfPvHH3iMH8+hV7TdwibfTEdHtbe/8MmT184TWPz0Kb1tbDRqGg1KSWH8w4dMCwiguK4uu9zdcf7IaIM3kbXaW5Zt60MAO549o/t7eIGnC8H0R494kpLyWqraT6GdpSVHqlbln8hIfN/geb06JET1nDZWqoRrHm6TFXb55eRyllWowM3atWmu9EQ30tZmU6VKKuWwp40NiU2a8LhePVUioKUVKiCaN2ebmxtVXtnTtdDVZZCdHZnNmnGzdu03+ifkdxheblRzdGRAs2Yv711fn2mdOxO3cSP3Fi/Osegob2PDgcmTETt3ErtxIxO//BLXUqVYNWgQmTt28Pf48ZTJ5hPRuFIl/H79FbFzJ/eXLHlr25fMnk0JS0viExJoWLeuRqy+RZGPtt/a2digrdwjGjJuHHFv2Dv9UA7fuoXnlCm5/nuYB05fH4J1sWL8+913/D1+PM7KFeDdoCDazJlDt8WLSXiPULmCKn+0vT26Mhl+SUnszabFx2ZksCE0lDEaSDKUmJlJq5s3cbt4Eftz51j89CnLXVwIrF+f9rk4W32y0qWvTzdra1aGhKhOmjscFUUTc/O3eoGHpKQw8sEDSp45w6noaO56etLtPRSGd9HG0pJj1aqxv0oVzHV1aW9pyc9OTq9tOzQoVoytbm6qjI/murpcqVWL5RUrUu4TlKSiLl8T5HcY3puwV4MV50MwNDSkuFIBmr1okWpfvqChUCjY/c8/hD97xvnLlz+79n1U8OyziAi8Bgxgx7p19B8xguDQUMZOmcKapUs/uUGtqlRhy4gRuV6rMmEC/+XDQPiqVi3aVa/OqmPH+HHnTiLj49l58SLP4+I4Pm2a2lPt5of8UsrJcEtYGPOfPOFrZQTGiuBgWhQvTtmPdDL8EIy0tTlctSqxGRlUu3yZR8nJXI+Le2OcdV4wxt6eLWFhrA8NZXjp0qwMDmb1O8K27AwMWFqhAulCsDksLM9WK/9GRvJvZOQ7652NieFsTEye90VRl69pvv/qK1rPnk2/Jk3wDQ5WOe9lJ3sY3tJDh/CqX5/21au/8TdfDcNrW60apfIwWiM+ORn38eOZ8vXXKqtBnljjJk6kS8eOHDt1iotXrzJk3Dj+t2tXwVtha2kxavBgRg0eXDgsABkZGXTr14+xQ4fSqX17Fio9Nddu2cKRkycLjWnkWkBATlOdtjbDWrXCb+lSvqxZE4BTd++y/9q1QikfYIKDAwCXY2M5Ex1NuhAsCwpivLJcU5jp6PCXhwf6WlqsDgnJkWo1r6lmYkJDc3N+DQrCJyEBKz09LN9zq2OBszPWenr0vXtXOoZZ4oPJ7zC8j0FXR4fYpKQ8XYRs27WLhwEBTJ84kZWLF6Ojo8PhEyf4U82ZV4siH6wATJw+HZuSJRkxcCAA/Xv2pEXjxgAMGDUqR+xmQWah0nP2VcyNjNgxejTllSb5K0rno8ImH8DD2Fi1dzn/yRO2hYdTTi6nlqmpxp9HNRMTVbKXQb6+aj14Zqy9Pf5JSXS5c4dRH7DVYaStzYZKlbgQG8ui90ikJFEwOR8TQ6fbt5EdO4bF6dMcVzoOvkhPZ7K/PyYnTzIvMJC4d+TRyI38DsP7UAx0dRncogXd69XLk997GBDAdzNmsG31arS1tXF3dVXF0o/5/nuiC4Glp8AqADv37uXwiROvJWNY/euvGBsZ8TQ4mHFTp2qs8clpaUzYvBlZ1670/f13YpR7Yg9CQ3EYOpSJW7YQl5zMF7NmMWr9eqZu387U7dtxGzcOt3HjSH9Lspb/AgPxf8NKU19XV5WoKCv8ZtelS7iNG4esa1cmbtlCUmoqCiFYc/w4Zn368Pvhw6+F8r2ND5WvLrJW+/9GRvJDQMB7r/53PX+O28WLyI4dY+LDhyRlZr7sj5AQzE6d4vegIJI/MJXy0FKl6FayJPGZmXS9c4eUV/7+UXIykx4+ROvYMUqfPavyzA5ITqbhtWu0vXWLm7n4qmQKkSNxT3tLS5wMDXEwMMjhTJYmxGthkakKBSnZ/rZ+sWKMs7fne39/LqgxU6VE/lGvWDF2e3jQw9qalMxMVeprC11dZMDeypWZVKbMR6Unzu8wvPflf7duoe/lRbG+fbn+6BHt585Fq1s3Gk6f/tG/mZqaSrf+/Vkyezals23zTZ80iTL29jyLiGDiJ/x+XhEdE8PcJUvQtbKiVrNmBIWEAPDH2rU4eHhw4PDhwqcAXLt5k+ETJ7Jr40ZVaEMWDqVLM1f5YFZv2sT+//3vgxuSlSRBvMV0+qpZ1VBPj1969aK5uzs62toUU7arTIkStK9enfk9e6rS5/767bfM7N6db5s04dGzZ6wYMOA1DftVWSPXr881375QHtKhp6NDJ09PADp7enL6xx+xt7QkJjERub4+WjIZkfHxHJg8mWGtWn3QKVgfKj8vyBCCV6W1sLCgiokJAjDW1qbtK85B2WOcsz+fzlZWnK5RA3sDA2IyMpBra7/sj/R0DlSpwrDSpd+YDz3rN3Mzo692dcVZLudWfDzD7t/Pca2soSHznJ2Z7+xMZHq66gNsoq1NebmcfypXpuor3toPkpL43t+fS7GxrA0NJSYj42UcuL09o5Wr/+DUVBY8eUJQSgono6PZoMwEuCokhEuxsdxPSuK3oCDClOGDPzs5UV4up9WNG3zv70/IK2GFEoWDFS4ulNTXZ4hyHB6MjMRCV5dm73EI19vI7zC896GctTWj2rTh3uLF1K1QgWPTprF+6FA6f8L3aMj48dSsWvW1kHK5oaEqAd2azZs5qnSUzC/MixVj8ujRzP7hB8KfP6eE8puYkJjIvzt30q5VqwIzhmXixYt3blYeOHyY3kOG8HW7dm909EtLS8PQ1haFQoF5sWKcO3QIV2Xe9reizCa46MABxm3aROuqVfn3u+9yrWo9YADPYmM5Pm1aDgeZe8HBVJs0iatz5uBub8+Sgwf5smZNVerg+ORkVWa8VrNmYWdhwbohQ97arIqjR/MgNJRa5coxs3t3mlSqhI62NqHR0Xy/bRtbzp5l5cCB9FcmhlC9ZL6+NPnxR47+8AOZCgUPw8IY+hED4kPl77p0iR//+ou7QUFM6NCBH7t0wUBPj3UnTjBu0yZme3nRr0mT15WQVauAlyFs5qdOsd3dnXavTPJbw8Pp6ePDWldX+r0SRvRvZCRtb90C4HKtWq9tD5yJjqbJjRscrVqVTOBhUtI78wf8+vQpo/38kAExjRu/tpL6Lz4ez6tXSVEoGGtvzzxn5xzpVQXQ8sYNMoTgSLVqDPH1ZUH58hTT8IE17/UCKsc/QDMLC9a5uhKSmso/yg+3oZYWPW1scDp/nmomJiytUAFnuZw1ISEU19OjkpERPwQEcEp5It771HkXnmZm9Le1JSg1lVSFArmWFpZ6eiwLCkJPJmOeszNVTUxYotzm0JbJ6GJlxbf37uVqYXkXJtratCtRgm1ubmwND8dHuY1op6+Pnb4+X9++zRfFizPP2ZlRDx5wPS7unfXfe+HRvPknPb/jL17Q/MYN5pQrh29iIhsqVeKDdsOVW6mvkpUF8HMnPjmZiqNHs6B3b775mG2A5s1JTklh7JQprFi/niAfH0q9IVSxhLMzkVFRWFtZceX48RxWgvwgMzOTGk2b0rFNG9p/8QUXrlxh+IABH/b+W1jka3yj9o+TJv34pov/Hj3KkPHjmbVwISkpKYSEhREXH0+9WrXQyfYxPXvxIpN+/JG7Sk04JSWFjX/+SVBICBWdnbHIJZ5VtQI7d471J0+y6MABElJSePTsGRFxcejr6lJWGUr118WLzN27l4vKvN9X/P1JSU/H2cYGI319Spia8jwujq1nz9K6alUu+PmpHOWyTOYA28+fZ+2JE+ybOBG5Mp3sm7j5+DGbRozAwtiYzWfOMHnbNmb9/Tcrjx7Fulgx1g4ZQocaNV77O4cSJXiRkMD8f/5BIQQ/dunyUQ/mQ+W7lipFt7p12X7hAqUsLPi6dm1kMhlHbt9mcseOdPb0zNXikXTlCouDgpgeEMDDpCQuxMQQl5GBqY4ONso+cjUy4nBUFIvLl1dNtHeUedJ/fvxYtdd5LiaG2IwMrPX1VTkCHAwNeZGeznxlPoEf33JK4YkXL1gZEsL8wEDSlKv/M9HRRGVk4CSXY6xsv7W+PiX19NgfGcnF2Fg2hoXxKDmZqiZD1imsAAAgAElEQVQmmOjoIAMam5vzQ0AAh6OimF62LI4aiFr4GH569Ej134+Tk2lqbs79xES+DwjgXEwMp6KjScjM5GZ8PGFpaZQ2MMBSVxcvHx8OREbiLJezuHx5fg8OJlWZJfFddd5GJysrFleoQC8fHw5FRXE+JobT0dF4WVvjk5DAtfh4SurpUcHICC8fH84pPfBPREdjrqub43Co9yVNCHwSEhhnb8+8wEBWhYRwLiaGQ1FRGGlrczM+Hv/kZL4vU4Y9ERH4JSW9s/778uN7npr5JsoaGhKamsr8J0/Y7u5OiQ896/4NHvxmn3n4YnZFZfaePTjb2NBcmUHvQ1h8+DB9hg7lmHJV/yQoiNJ2djkm9/sPHzL5p59UYXQJiYls3rmTZ8+fU9/TE718StWspaWFR6VKDB47lpSUFGZ8//0HRwD9NG/eT5+9BUCtZFsBfdK+TGIi5UeOpKqjIzvHjFFtB2QRp9RUf+7W7bVVe14Tm5SE9YAB9GvalN/799dod36wBUJpAVBrf2RkYH3mDP1sbfm9YkWN9cX3/v4sDQrCx9OTMvmoADxKTmbiw4dEpqfzv6pVCUhO5oeAAJZVqECps2dz1N1buTLBKSkMf/BAVWaopaXyl5jq6Eg7S0s8r14FXqbH3eHuToULF/BTOka+T53cMNXR4Wn9+gz29WX7KyctltTTw9XIiJPR0Yy2t8fbzg63ixdz1Mnezo8hpnFjvO/dY5fSrP3qb96vW5fBvr4qS8a76mvCAgAwJSCANSEhNDY3Z8eHToJvsAAUFBRCYNyrF6sHDaJHgwYfZQEo6DTr2BEzU1P+3rTpwyfgfLYAaFFIMDcyom/jxpQqXvy1yR/g+23bKGtlRb8mTdTell/++YfFffuy4sgRzrxy4pa6aejiwvAvvqDf8uXsvXr1o7Yf8rw/njxhcfnyrAgJ4cx7mqHzYtLNEIKapqZ4a/gZ5LZK3OLmRlxGBgHJyRx/8YJtbm7YvcMKBS8jMSq8IaudXFsbbzs7TkZHvzEq4n3qZNHe0hIzHR2O5/KMnqWlcfINz05HJuMba2uSFQqqm5pypFo1Zjk5cb5GDba5ufF9mTL41a1Lf1tbIhs1wuM9z3DobWPzQZN5Vn0DLa3XZI53cOBenToMLlWKwPr1c5xi+Snsi4iglL4+K11c2PnsGfuy7bkXBbRkMlzs7HDXQGKwz5Hzly/Tunlzjp48WSDD4HUK08PQ19XNNR71WkAAa06c4OqcOSoTTaZCwYuEBErkcUjbn+fPU9nBgS516nDz8WP6L1/O7QULPsgB8FOZ0a0bq/LIsvLJ/REeTmVjY7qULMnN+Hj6+/py29PzjQ6AeUGyQsHMx4/5o2JFQlJT8bh0idUhIXn20f8YDLS0WO3iwhc3bnCyevW3HqJUzdSUyWXKqCbWHj4+Oa6X0NPjuzJlGOvgwKzHj/kjOJhXzXjvU+dVsqwkUe8RrWKpq8vkMmVeKp3FinFEGQp3PS6OVIWC0gYGtLh5k1L6+pjo6DCtbFkuxMbS4No1Hr0lI11HKyvKyeUYaWvT09qaTbmcRfGu+ikKBYdfvMgh83FyMj84OpKmUFD36tX3OqDnfZTM/0VFsVxp1epkZcXQ+/dpZG7+WfqbqIty1taUUfpbFSXiExLYe/Agv8yYQUZGBiMnTeLO+fM5zgv47BW4wvRAFEK85jmeqVAwePVqRrZunUNLPXTz5munb30q1x894vaTJ6qjen/p1YuU9HTGbtyo2RV3PlogcvRHXBy3ExLoovTl+MXZmZTMTMYqfTnUgQDGPHjAD46OGGhp4WRoyEwnJ8b5+eGvxtwB77taqmFqyu5sJuvcuBEXx9zAQGY+fkzPVyZ/gIi0NOYEBnI1NpZ6xYqRlssq+X3qvEq4MlrB7D0mr8j0dOYGBjI3MJCOt2/zPJvSkJiZyY34eJIyM/FLSiIxM5MUhQLfxER8ExPf6oew9/lz5gYG8kNAADOyebx/aP1XZaYoFCQrFNyIjyc0NTVHez+GmIwMxvj58Uu23Pi/VaxIXEbGa9EphR0rMzNMDAyKnAIwfc4cJo4cCcDYoUPJyMzkl2XLCpYFp7A8DN+QEE7dvcsVf39uBQaqylcdO8b1R49Iz8xU5QEYtX49w9ety7OUmEmpqSzcv5+mP/2EhbGxKpTxeWwsNsWKseLoUSZv3Uq4BpJYZFkgBrdogXezZvRfvvyD8g/kSX9kZrLwyROa3riBha6uauX5PC0NG319VgQHM9nfn/A8btfVuDha3bjBhdjYHEfxagHxmZm0u3WLw58Y//yxRKWnczM+nu3u7mx/9uyNh9q8ys34eG7Fx2OUiwNnv3v3aGxu/taTCt+nThZHX7wgVaGgxRveC+s3WLHSFAq2hYfn2sZPYX1oKMB7/+6H1v9YtoSFqVJTP85mzbibkIC+lhbbwsMZ5Oub41phxsbcvEgd1vM0OBivAQM4feECqcpvWERUFFaWlvw4bx4r1q9H8Qm+MJqk0NipXOzsuKBMS5ydIS1bMqRly9fKf/322zyTLdfXZ1z79oxTHgmZ3TR2Zc4cza24lRaIrGQhv/TqRaWxYxm7cSPLPzA85ZP6Q1ubcQ4OjHslaVA5uZwr2RKT5DU1lfvPrzLK3v6DMvrlNbcTEhj14AFb3NzQ19KivaUl3e7cYVsuud5z+4yW1NOjZfHibA4LQ0smU21zhaelMdDXlw2urlyOjVU5+L1PnVw/bCkpzHz8mPnOzlyNi8sxgX1jbc0JpZk/tzZqy2QMtLNjsTI0UOsjVhq5/W5TCwti0tO5kYtn/9vqJykUucrMixVPTxsbeuaiUDWzsCCyUaMitxIu/p4+HYUF+1Kl2LZ6dY4yOxsbLhSgBECFTgEoyiSlprL8yBFm7NrF1E6dEEIgk8lyWCDM5HJGt22LdbFiUodpGA9jY05mC/ea4eTEDCen1+q1Kl6c6qamlJPL+a5MGYRSmeppbU2Da9eoZmJCKwsLysvltLW05H9RUex5/pz2lpacqF6daQEB3EtMfGedreHhbzTDz3z8mKCUFDZXqsTTlBQeJScTnZHBX8+e8SwtjSomJrSxtMTBwIAfHB1JFwJdmYxWxYvze3AwbsbGuBsbU0JXl30RETxNSaGzlRUmOjr0tbVlg3KVnh0TbW2+trLCVFkn6wS/knp6NDQ3p/rly9QxM8NOX5/WxYvzIDGRlsWLv7G+55UrTCpTJofM1sWLY66jQ28bGx4lJxPzEWl6JXKnoIQsSuSidBeWMECJj0QDYYASb3kBpfGfr4j8DkMr4GGAAPuvX3/riYRvpRCEAX7S+5/PYYA60eb52wHHuiCRn/O/1P/5/AWQuiA/aXE0n+f/z7x/zm07R32v+m+v1KU6f33k7zeXhmC+Im0BFEJSElIY5z6OyfsnU9qttNQhBRTzJuY4TnXEoun/55ZPj0wneEUwIatCSAn6/6x7hk6GlJlQBruBdiCDjPgMQlaG8HTxU1JDUyX5Eh+MUAgeXn74bgVAQlIAJD4ftHW0iY+MR0tHS+qMAkz0yWiiT0bjPN8ZhwkvHSqD/gji0fRHr9VNDkjGd7Avxh7G6Nvqc6PFDZIeJknyJT6a54HPsSpjJXVEIUaaIQqb1i4EOvo6dJzcEbuKdgiFkDqlgOP/vT/xN196wZfsWhKZTu77BroWusgryrnT7U6eTn5FXX5RJcQ3BDsXu8+6jaHBoQz/djjjh47n65ZfM7jXYDIyMlg6fynLfllGmwZtuHX95WFlPv/5sHDWQmZ8N4NeX/UiOSm5yD9jSQEoRCgyFXhbeTOxykTC/MKY12EeXgZePLr+SOqcgqzUZQju9buHyBAYVTTCYZxDrvXKzihL2PowYi/HSvIl8kQBKOlUkr9++gsvfS+6yrqyvN9ywv3DVXX8LvoxxnUMfcz6sH/hfsIDwvmt9290lXWlq6wr+xfsJyn2/5Wxc9vO0cuoF6Mrjuby35c/uY22pWwpXaY0/g/82bJnCxN+mMCGlRtwKu/EiAkj6N67O6MHjCYlOYWJwycyatIops2ZRnp6Og98H0gKgDTMC9HD1NZi3vV5zL85n5TEFMbuHMvSh0txrOoodU4BJ/5WPIHzAl9OdNPLIi+XM/TKrLYZlm0sCZgWIMn/QJL8krj37T2OyY7x5JcnudbJiMvgpOlJzjueJ2JfBEn+SfiN9uOY7BjXGl7jXr97XKlxhcA5gSAg/UU6IWtCOK59nAsVLnDP+x5X617Fd6Av6dHpBWLMRT6NxLqcNV2md6HHvB4AlK9THuty1qo65euUp3Sl0nx/6Hvaj2uPtZM1wzcNp+aXL09jrdGhBnKz/39WdbrUwbaiLTMvzKT217XzpJ1GRka4ursiN5LjVN6J4/87zqOHj9i2YRuxMbE4ODpw6/ot5EZy1Sm22w9sp0r1KtKcIX1aCxeW9pZEPIng8u7L3D11lxIOJZBpSa7mhYHHPz8m0TcRLUMtXFa7qCIIZLoyXFa78GDEAzITMyX5H4i8/P+xd97xNV9vHH/fmXmz9yaSEIkQO2oXra01SqlRFS1FjRq1tUUptUfNKqrjZ7SlI7ZQe2YIkb1k73nv/f1x4xIZkkiCyOf1yovX/T7f85zv+Z7veZ7znGdo4zDHAaGWkPC14Sjzix+bRW+LRlmgxOhNI0z7maLdQBubiTYAOC52xHWHKw03NeT+F/cJ+ToEiZEE67HWSC2lWAy1wHWbK82ONSPhaAK3B99+6eZWYkQi+blFFROFQqHO8NdzUk8atGrAzwt/Jjvtsen8wdUHGNsY4+LlUuTesRvHoqWnxQ/TilbI+2vDX7w79110jaoveVBBQQFNPJswbNQwPp3xKVv3bUWhUBAcFKzO0goQHxf/2q8pdQpALYSpgymauprYudvVDUYtgiJXgf+H/igVSgw7GWL9oep81n6GPZkBmST8mVDHv5IQSARYDLUgLzaP2J9ii1xTypUkn0lG1kQGT2QZftoXQa+lHrpuusTujy2RRqwvxuwdM5J8kshPKJ8V4Ny+c9VrWUpMZ/fU3Szrs4wT208UHZMn0vsKhAK8v/cm7WEa++bsU42LQslvS35j8KLBxdo1tDJk2NJhXP3jKv/9+h8AydHJ3L94n1YDqj4b6JOpdzt27cjsybO5cfUGkeGRbP5uM02aNSE9LZ1VX68iOyubgwcOEv+wTgEoUwE4e/Is/bv2x0hgpP5zMnXi63lfExURVVQ7Dw5h6vipGAuNMRIYYadnx/wZ84mNjq1052KCYvjfV/9jSsMp6jMlbytv9s7aS/CVx6a+y4cvs2vKLvU51WDBYBZ1XsTvK38nN6vyIUCZyZnsnrqbifUn8oHsA7ytvfnuve+4fOgygecC+W3Jb9X6cirLXyAQ0KhDI4ysjZ7JQ54lJ+TLEC56XsRH4IOPwAe/D/zK3cfo7dHq+847nefB4gdkBVXOASvpRBL3Z9/nlOEpdZvHRcc5Y36Gk3onOWd/jus9rxP3Sxy8pr6NqRdSiVgbAYDTCicM2htg96kddyffreP/nNC01cRsoBnh34YX+T3+YDxmA8rvDS+WlR1cJRAKEOk8u17BozC86oRYIqbv9L58fuhz/vj2DwryVBkSU2JTMLQsmiTGvok9vaf25p9N/3Dv4j3+2fwP7Ya2Q0tPq8S2u4/vjnNbZ3ZO2kl2Wjb75uxj6NdDi9Ds2rKLJvZNisiYUYNGceLvospIfFw8c6bMwURkgpHACBcLF5YtWEZMVAznz5zn3KlzBAWoioyNmzSO1u1a079rf97v9z7denZDV6bL9p+2s2/nPjwcPEhNScXV3VX1rMkpLJ69mHUr1tG1ZVcyMzIZ+NZA+nftT2pKKuNHjKdD0w5ER0YTFRFFrw69iIuJ49ypc2xctZFBbw9i/+79AOTm5vLdsu9Yvmg5A98aSEpyCjs27eDtN95my9otNLFvwrj3x700tQIEScpnZwJc8PkC1q1QVTn6fP7nzFo0q1TaHl49iI2O5X///g9HJ8dndsCHZ2dCC7sVxgyPGQDMPDKT5n1Kzjr14+c/cmTFEWQmMrZGb0UkqXxRkJTYFOa1m4eOoQ7eW71xaOZATnoO538+z75Z+0hPTGfQgkEMWlg9mXSel/+BeQcYsmTIM/lsRZUJUJ4u57TpaRS5CgQSAe2C26Fp+4wKX0q44HaBTH9VYZuWF1qi30b/uZ89Yl0EdyfdRawnpn1Me0TaIhQ5CmL3xXJ38l3kGXIsR1rSeGfjVz6Rjo+g4pkARdoi2txpg1Y9LZQFSgInBhK1JarG+lyb+L+pVKWiyQ7NJmZXDCa9TbjU8hKe/3pi9KZKgb418BZu+9y42uEquk11abS5kfoe33q+ND/ZHMNOhiQdT+Jat2u47nDFapSVagfvcA6rUVbUX1if3JhcLja7iPFbxjTe1VglrMpIBRT3II4rh6/Q67NeNTKuG0dvxLmtM2+OexO/k35kpmQW263nZecxzW0aEk0Jtm62fHbgs7K/5TsRfO75OQ1aNcCzlycDZg8oOv68SWpKKl1bduXB/QdoaGoQnRVdanGh3h17kxCfwCGfQ1hYWVTJc/+671fiH8bz8ZSP+XXfrwwcNpBb128x6cNJnLp2ipDgEPp27svN0Jskxidy8t+T9HmnD595f8bmPZuJDI+kjWsbAqID2LVlF23eaEPLti35dMynWNlYMXTUULq36c7fF/7GxNQELzcvlqxcQv/B/TESvNhMgOU6Apj39TyaNGsCwMGfD1JQSh7t5KRk7gXeY8eBHeUS/uXFkzvZsna1BhaqPPeGlobPJfwB9kzfQ3xYPDN/n0k9z3oIBAK09LToOrYrX1/6ulrPsKqCv7FtxSodimQiNKw1EOmKUOYrCV8d/sx7Eo4mkBP+OBmLpk3VlATVtCtsR6Ba7AGEmkKsxljh8p3qrDFmdwxxB+JeSyuAPEvOg/mqyA5lvpKorVF1/KsIei30MGhvQNhKlTNg2qU0ZJ4yhNLSl8r4Q/GEfBlC7N5YPA55qIX/I6RdTiN8VTjBc4NxmO2A6zbXcvWlpsPwBswZwOFvDiMvkBMZEFkib6mWlMGLBxPpH0n3j7s/s01bN1s6jezE/Uv36Tu9b4k0+gb6rN+5HoFAQG5OLseOHCvZIpqRSVBAEN/v+77KhD9AizYtWLlkJZ9++ClvdFIlPWrSrAm5ubncv3ufm1dvYmhkiO9pX44dOUbPfj3xu+VHQnwC+3bt48yJM3Tu1pmkxCROHz/NnZt32LdrH6bmpmhqaSKVSpHpyajnWA+Znoy+A/ty7fK1l2ItKZcCIBaLWbdjHWKxmHuB99jw7YYS6ZbOX8qw0cNo3rp51XZSJCxiPivLtPYsmvLi6h9X0TXSLWYGAzCvb06/mf2q9cU8L3+Ziazi5iCJAOuxqo8+6vsoClLKLpgStjIM648eLxKlxWdXuB+i0tuxHGWJUEM1H2IPxD43r8zATAInBOIj8OHam6V/lLnRuRyXHue0yWmid0aTG5VL1LYoTspOclL3JDf73+TmgJtcaHSBoClByLPk1To/8lPy1WbiF3EcUpv520+1J/HvRDLuZBC5ORIbb5sy6U37m1Jvbj1cd7hi2te0uFLRUg+7qXa4bnfFbrJdub+Tmg7Ds3SypEHLBpz54Qyx92OxcCxZyMqMVWuLVFNarufQNdZFKBSWuSlr80Yb3h/zvtrinFdCqfB1K9YxcNhA3Ju6V+n7trW3xfe2L9lZ2XT07EhqiiqMdOCwgfz202/ERMXgPdmbn/f8TEZ6BroyXQoKCtDR0WHYqGEMGzWMPQf3YGFlgbxATut2rRk2ahjzl87nk6mfFONnaGSITE/Gy4ByOwG6N3Vn8szJACxftJwH94vGll+9eJV/j/7LnMVzasUuS6lUkhafxundp0u83nZQ25eav5ZMq1J8LYZZoGGtgTxDTsTGiFLp0q+lk+mfieUHljX6XgQiARo2GiohkPD84VQ6DXVw+c4FgVhA0vEk0m+ml0gXuSESFGDUxQir0VZoWGtgPdYa/Tb66DTUweOQBx4HPWj0fSMi1kfgP8qfOryaMOlrgnYDbe5Nv4dIR4TEWPJC+vEiwvDe+eIdDi49SF523nNbUSuKhcsXYmRsRHBQsPrI+RFCH4Syf/f+Mo+fK4sjvx5BR1eHbfu34ebhRlhImFoB2L5hOx7NPej7bl+OHj6Ko7PKst24SWN8T/uyd+de4uPi2b5xO9lZ2Xh19GL6J9MJvhdMwJ0ADv9yGICMjAx1BEJQQBDde3V/KeZ6haIAps+bjnMjZ3Kyc5jy0RT1A+Xn5zP5o8ksX7ccbZ3aURqy2dvNANg4ZiN7Z+0lL7uoRmpWz4ymbzV9afk36dakcgJWIsBukip6IGJtBIrckp1VQleEYjvRFqFmzQaSKHIU5MWoxkLXrWqOYQQSAYadDZEYSYo5gAEoshWkXkpF00ETgbTo7u2RNeIRDN4wQOYp4+HBhyjldVkYXxmFv0CJskCptiDaTrYl8Z9EbCc+rqWhlD+meXTPk/8+q92y8LKE4dm62WLnbkdafFqpfX3kKPh0f8uiL8gvKBKCVxKMjI1Y9M0iAL796lvCQx9/i7Mnz2bWolno6etV+bvPSM9gSK8hbNuwDQ9PD7WFwb6ePX3e7YNXBy9kejIGDBlAlx5dVFYQPRmbftjEN4u+oX3T9piZm2FgaMDEaROxtLakc/POLJ69mF79Vf4bebl5rF+5nm0bttHKqxUenh6vngKgoaHBuu3rEAqFnDt1jh+3/6g2zTg3cn5ptJqqwMhVIzG1N0WpUHJ4+WGmNJzCqV2niqTWdWrjVCv5W3tbI9YTkxeXR8zumGLXc8JzSDyaiM0nNjX+XsJWhiHPkiOUCrGbWnVhjiJtEdbe1sT+FFuseEzMnhgsR5Tf0lGQUoDUVFrmUcZzKy3iqjvuet35Z93PImJNBAl/JpDkkwSA1WgrLIdbou2ijTxLTsyPMWT6Z5J8PFmdCOhRNEL0jmjSbxS1HOUn5RO1JYq8mDwS/kwg8Z/Eki1pL2EY3rtz38WmUcnf9p0Td/hr/V8A/PndnwSeCyxd+VEoObfvHJcOXkKpUPLT3J+IuRdTJu9ho4fRul1rcrJzmD15NgB///E3yUnJvPfBe9Uyl0aMHcHRs0cZO2Es85fOLzLu3276Vv3/lRtXIpE8tgZ169mNm6E3CYwJpM+7fVSWV20ttv+0nfC0cPb/vh8dXR21cvPpjE8ZO2EsYyeMfWnkXIWLAbVs2xLvSd5s+m4T82fMp4FLA7au28qZ62dqpMMrBqxAolGySS4zObPK+BhaGfLVf1+xacwmrh+7TkJ4AhtHb+T3lb8zfMVw9Q69uvAi+Yv1xVh/ZE3Yt2GEfRuG1VirIgtt+OpwLD+wRGIsIS8+r0bee05EDhFrIghbFYbEWILrTle0narW2mQ70ZawlWFErIugwdIGhasYxB2Io+mxpjxYXHZKZWW+kpAvQ8gJy6HxD42rdTweOVwKtYRIjCTkJ9VsdrnaxF+7gTYu64ruoEU6IvU7FGmLsBxuieXwokqgy1oXXNa6lNimxEiCtbc11t5lO/E9CsPrOakni7supuvYroil4jLD8I6sPEKHER14cPXBM8Pwzv54lp2TduLR3aPEMLySUM+zXqk+RG5d3HDr4lY+JU0o4I1hb1SomqBAIODbTd/SybMTx44c4/fffmfx7MVsP7C91MiAOtSQBeAR5n41F/t69qSmpNKvSz9mLZyFmUXNVI2acXAG3wV+V+Jf/9n9q5SXgYUBs4/OZvr/pmPppPr4I/wiWNpzKauHrCYnI6dan/VF8rebYodAIiArKIv4Q48TZhSkFhC9Kxq7z6o/yZA8U871Hte54HaBc3bnCF8dTqNNjXgj9A1M+5hWOT8NKw0shlgQtSVKnVEu8e9EDDsblukFnhOVw91JdzljfobkU8m08WuD+RDz6pkT7Q1o8HUD6i+ur/6t6dGmOMxyQGomrfZ38rrzr2po6WlhaGWIqYMpjTo04tSuU0DpEQCDFg7CzMGMTWM24X/aH68hXmUKYO+t3qQlpPF1z6+xcrHCrF751mkTO5MXNiau7q6MnzIegA/f+5BO3Tqpo9BeNSgUCo78doS42Dgu+l586fpXKQVAS1uL2YtnqzXYkeNG1motqdWAVqzyW8WH6z9Ua8YXfr7A8j7La6Ta3ovgr2GjEoYAYd88zo8euTkS427GaNXXqvbnFumIaPZ3M1r6tkSrvhZKhZK0q2mIdKvPOcnuMzvyk/OJ3hmtet4tkdiML/uoQ9NaE5e1LpgPMSftalq17lRSzqZwf859ThudVidLutzmMqHLQsl7WP3WmNedf3XiRYXhPQ+y07OZ4DCB498fr9J2Zy2chVgspqCggA8/+fDV3WELhYyfPJ7IjEhat2v98vWvsjfqG+irH7A2mmaezDQIIJKI6DGhB2uD1qo9bP1O+XHl9yu1kj+grsGeejGV5DPJKPOVRKyLwH66fY2+C7G+mCa/NEGoISTq+6giqVarGjJPGYYdDIlYE0HGnQykZlIkJuXzAnda6YTUQorfKL+6Msx1qDBeZBhepb9NiZis1Kwq9wXR1tFGJFL199G/dXiJFIDajj++/aPE33UMdZhyYAqWziqT/P1L92slfwDdJroYdzdWWwFi98Wi3UAbvVZ6Nf4+ZJ4ynFc5AxDgHVCt9d7tptqRdT+L24NuYze5/EcdIh0RjXc1JvV8KuGrwus+olqKuANxnLE4g4/Ah3sz7j0OR1WqnFR9BD4EfhJIbmTF05C/yDC8ykCiKaHb+G60e69d3cSoUwBqD0JvhhZJuFFk0mtI8OiuCuPQ1tcmPzefIyuOMEQ0hElOkwi5FqKmve1zm/c13+fAvANkJGVUC//qxKPdfsLRBILnBZdr96/IVRC2IkxVCtXpPOnXHntIJ/kkcULzBMHzgivsuEiXZ9kAACAASURBVGXziQ3mQ8yRp8u5Pfg2ipyiIYrpN9K51PoSPgIfgucHI89QnePnJ+Rze/Bt/nP/j+RTycXaVcqVRRL3mPQxQctRC017TXRcdR7T5SmLhUUqchXIcx7fa/CGAXbT7Lg/5z6p5+vq0tdGmA8xx/0ndxCAlqPWYwuRAAw7GGI7yZaGGxuq81VUBC8yDK8iuPHXDYZpDGOUwSgeXH3Asj7LGCIcwoIOC2r9+4+OjGbgWwMxEhixec1m9e8+x3yw0bVRR8fVagXgUTrgmihq8KQ5VSEvnd+ja2XRVITnzkk7S2xLqVQV6RBLxbR5tw0SDQl9Z/Sl92e9yUrNwtzxsQOYkY0Rvaf2ZsiSIRVKH1wR/lU2zgVKeIqdUTcjZE1loASRrgiTXibF73nqPQk1hNjPsMfuMzsKUgvQcnzsL6Bho4HdVDsclzgiMZKU3o+n3vsjuH6v8v5Pv5FO4ISiIUiypjI8/ueBWF+MSEuk9hWQmEiQmklp9k8zDDsV9azOupulEtb/pRK9PZqClAIEQgF2k+2wm6La/edG5hK2MoyciBySTyYTvaswE+DWKFL/SyUrMIuI9RHkxqh2fI5LHNF21uZaj2vcn3Of3Khc6lC78KgaYfC8YPITH1sAwteE0+DrBs/V9osMwysvLBpY0HNyT1b7r8bFy4X5PvP5ZOcntBlYdeuRXC6vMRlTEVjZWLFt/zZMTE0wMn6cmt7c0pwFyxYw/MPhr8w8Flf2xuhIlZNUTnYOyUnJGBoZVlsnEyMSi/y/fvP6JdIlhKnKgabEpiAvkCMSP58J7fqx68z1mst7X75H486NEYlFJEcns2/OPkKuhTBuy7giwn7IkiFcPnyZvTP38tHmj1Qf6eo/Gb1mdLXyz8/N59jaY+ydtRfz+uZ8duAz6nnWU1sglvVeRt8Zfen1Wa9SlRBlvpK82Dxyo3ORecqKWQHuDL+j2v0/ddT3ZC2A3MhcNKwe73oclzgSfzieezPvqQuohK8Ox2WNS5nPnR2mSnQiz5BTkFaAWO/xNBXJRLj/4s7lNpeJ3hGN2ECM03IndVy4hrUGTiucuDv5LuZDzNGqr0Xy6WRknjI0LIvvyLRdtHFa7oTT8qI5FWw/tS2itNhPty9m/bAeZ431uOKOWkINIW3vVDxTpFFXI1x3uJIblUv8EVXkhVBLiOVwS3wdfZF5ynBZ64K2kzZR26KQGkvRaaxD8LxgtWWjPDTPgn4bfaw+tCI3IhdFrgKhthCpiZSIdREIpAKcljshayYj/DvVMYdAJMBskBn+o/1Jv55e4ecWyUSY9jbFbZ8bsXtjybiToX6XGtYa3HrnFsZvGeO0XPVe066mPZO+JuC0won4P+K5N/0erjtdid4Rjfkg83JV+isLLzIMryIKwPBvhpOdno3P9z5YuVjRcWTHKms//mG8epMZGx2Li6vLSyU4DQwN+OLLL/jyiy/pN7AfGpoaHNhzgIXLF75Siqxo5sKZFerxhbMX2LVlF+tWrCMnR7X4+572JSkhCUdnR3R0dCrUgQeUHlsdExTDP5v+4cD8A6QnqhYW/9P+ZKVloSXTwshKpX1dPnyZvzf8zb9b/kWpUJKXlUfg2UDSE9JxaOqAWFJxPSfkegif/vApuka6nNlzhn2z9vG/r/7Hv1v+xcDCgI+3f0yLvi2KDqZEhL2HPTsn78Stixv+p/xxbuusPq+vLv4isQiXdi7kpOdw/9J9Bi0YhERTojb/aWhr8N5X7yHVKu40dCnrEhGrIwheEEzWvSxSzqeohe4jganjqkPi34k4r3ZWC9qM26o86SFLQihIKywhei6FgtQCNCw0kBhLEEgE6HroEjQ5CKMuRiSfSka/rT7aziUfWySdSCJqSxSh34SizFPt/pPPJFOQWIC2o7Z6R69hoYHUXErC7wmkXkglZncM2Q+ykTWTIZaJ0WuuR/KJZBL+TMB8iDlhy8OoP6/+S1k58MGix/M/OyQbwy6GZAZmEjwnmJRzKSSfSkaeISf9ejp5MXlo2moiMZFwZ9gdEv5IQNtJG+fVzkRuiESRqygXTVkwe9cMl9Uu3Blxh8RjiaT4ppB8OhmLYRZk3Mkg/Uo6UnMpOi463Bl2h5RzKaScTSH5RDISQ0kRhbDclqc8JRl3MrCbZkfo8lCitkaRci6FxGOJiHREpF9PJ/t+Ng5zHIg/GE9WUNYz6cuL+gvrV958qilE006T4LnB6DbRJcknCYdZDhVqozkl102p7qO9qkJCeAIHvz6IpZMl7m9WPEd/fYqOf3ZWNhtXbWTpgqXERKmsFZfOXyI9LR1be1u18/nLgCbNmnBgzwFSUlLIzcnF1t4Wp4YVS862fNHyRS/yGcpVDrg6UZ5ywK8avh//PXdO3MGztycjV9VciGRedh7Tm0zHvau72gKx1Xsro9eMVisET+NROeDqRMD4AJJPJGPS20TtyFfdyH6QzX/u/6HXUo+Gmxui01Dnhc2HjNsZXO95nXpf1MNmvA15D/O4/d5tGm1pxHnn80VoPQ55kBOZw92Jj+vbC7WEKLJVgrve3HqY9DbhcpvLKrPjYHPcD7hz3uU8WUFZ5aYp0RyoJ+aN8DcIGB9A3E9FKy1KzaXouOqQfDIZuyl2WI+15oLbhaIC8Yl+VgadUjrhP9afh78+LLFNr0Av1VwqtGQ8i748eFQO+Hlwo/cNknySaBvYFi2HioXHllUO+FWAUqFkhO4IvL/3pv377St8/5u8+Uo//4WzFxjcczBDRgxh5caVFbf6vQrlgOtQMQxaOIiYezE17hkr1ZIyftt4fL73IeBsAKd3n6bt4LalCv+aguNCR7LuZWHxnkWN8dSqr4XFCAuE2sIXKvwBdN11abSlEQl/qI6o0q+l02hTo3JlMtRtoouOS8n9F2mLsB5rTfLJ5FKjIspD8wgmfUwQ64tJPl78qCAvLo/kkyUfIQjEAiyGWqDIVqDXXA/Pfzxx/MqRFr4tcNvnhsMcB7yCvLD60IqOCR3RbVI+XxjLDywrJMwf0Qs1hcV42k+3p61/W2zG2/BG6BtFqlg+Lww7GSLSFVVY+NcGCIQCrBtZY+dux+uItu3b4tzQmRZtWryS/RdThyrHI4H7IvKku3Z05c2P3mTzh5vx7O1ZpedyldYyHxUMqmF1U6QpemG56osJ154mxO2PI2pbFAKRAOO3jEul1fPUw2GWg1qw3nn/TlFFz1SKw2wH7KfaE/JVCJEbI4uVxC0PTTGlqVCA5SU+O6GOxESiNncbdDAg6R9VDv20q2kochVo2mpyvdt1NGw0EMvE1J9fn9TzqVxpf4XsB9mltmvW3wztBtqIdERYDLcg5oeyndZKolfkKEj6O6koz5Bs6s2rhyJPwWWvy+Uq0FOH8sGigQVmDmav7fNLNaQIha/mXrpOAagGPPJef1HJYAYtHMS/W/59aWJz1eOgqHm+L1NCHufvnLngdoE3wsp2ykq7lkboslAAEv5MKL4bj88jdGkoBu0NMGhnoHbGqyjN08iNVUUriPXFFCQXlEmbn5Cv7qNwlRCzgY8FgDxTTvq1dORZcrKCstBpqIMiR0FmwLNrdTw89FBt0i9LUXgWvTxTXoynIltB+rX0YsWennuelbPiX22Fvpk+mjLN1/b5FQrFSxepUO7NWZ24rlqkJ6ZzcudJAHz3+5IYmfhaWSCKCYrEx2l1Y/fHVio5SmWQejGVlDMpZNzIKLUSW42/F2MJYn1xmXUFis2n6+mk30gv0bPcf4w/hp0My6xUWB6aR0j6NwlFrgLjbiVbJ6QWJWeeU+QpiN0X+9ze70/j0bwpb7sVpa8KJJ9K5uHBhxSkFhCxLqLGimO9TDC0NHxtC/WcOXGGe4H38DnmQ2R4ZJ0F4HWHzFhGn2l96DOtz2trgXha6NlPs8d+Ws2mD9ZvrU/rGy9X7u20K2nkxeaR6Z9ZJMFQEZSwjkrNpRh3NyZmTwwCoUCt2OXF5hEwLgDXXa6kXkxVO/iVh6Yk5ITnEPJlCE7fOJF2OY3skMc7aouhFiSdSCq1jwKRAOtx1oSvDi95a1EenaeEdo26GJGfkl8kmVR56BVZipJ5VvGWx7CTIa0utnqt1zxdY93X9tk7dOnAg6QHr2z/6xSAWmiBeFRRzHe/L4ZWhhjbGNcNzEsAvRZ6dErpVOp14x7G6DXXQ7uBNg6zHVTJl7RVZ9tX2l9B5inDqIcR2s7amPQyIfGvRB4efIhJHxOan2hO8PxgMv0zn0kTuze21HDAkC9DyInIofGexuSE55D9IJuC5ALifokjLy4PWVMZJj1N0LTXpN68eijzlQgkAox7GBO5IRJdN1103XWRmEqIPxxPTngOZgPNEMvEWI2yInpXdDGeIpkIs3fMEOupaLQbaKsVH8MOhlxsfhH9tvpoWGtg/LYxmXczMe5uXCr9pTaXcJjpUISn8dvGiA3FWH5gqXqmlIK6CVlFeFVCFutQgg5dFwb4eqMmwgDrUMb8F9TN/xeJqggDfB686mGAAFd/v0rzPs0rN/6veBjg8+JFhwGKSTZ8sSPgM6huFXqhGkDd+L9gHbxuCF4kuv37Yvm/AvL/7Nm9NGjQEkvLknN4NGcQ/FJZDez1nn7KqizQUCkFoA61Djk5GUyb5s6sWb9ja+tWNyC1AIaGHWne/FThoqFAoSjuIS8UaiEQCFEq5Vy50oHU1PN1/Ovw3AgKOk+rVv3rBuIlwO3btzl48CA7d+4kNDQUADs7O8aMGcM777yDu3vFsjHWKQC1ECKRmPT0BITCutdbWyCRmJKVdZc7d0aQlnaFp4P6dXRcad36KgKBJqGhy6tc+L3u/F/vDUUmGho6dQPxEsDd3R13d3c6depEx46qHC+7d++mU6dOlWqvLgywlkGpVCIWa9C//yysrRuiVCrqBqUWQCo1JShoGmlpl4sJP4FAgpvbjwiFmqSnX+PBg4V1/OtQh1oMa+vHmSzt7CqfhbFui1iLoFDI+egjC4yMrHBwaMry5X25ceMvvvrqAvXrN68boFcYYrE+ycknS7zm6LgYmawZCkUOd+6MQKnMr+NfhypBZmYyurqFRdcuH2b16sG4uHihrf24KE9q6kOCgi5gaenMihU3kEq1+PzzZmRnp2Fj44pQ+Dgvg7//GTIzk/n44+107jzmufp26NDPLFu2EG/vSXz33TKmTfuCfv0GsXXrOsRiMX/99Ttff72a5s1bk5mZwebNazA0NOLIkV/58MNP6NPn3Vf2vYhEj8f0ebIQ1ikAtQhCoYjly69ibGzDqlWDmTr1Z1JS4jAxsa0bnFccoaHLSvzdwOAN7O0/B+D+/VlkZvrX8a8gsrKCCA1dSnT0LpycvsHefkYxmoKCNM6etUEqNcbZ+Tt0dBoTGbme8PA1GBi0R1u7ARkZtzAzexcHh1nk5yfz8OH/CAz0RkurAQYG7cnM9EdX140GDZYjkRi+EvMuMjIAGxtVKe/09ASmTfuN5s17F6FZurQnAoGQTz7ZiVSqSidtZeXCxIk/IBY/Th4VFHSBK1d+p2nTt55b+AP06zeIyZM/QiqV8vff5xGJxMydO41PP52Os3Mj9PT08fYezpUr95g1azJDh47Ey6sDVlY2/Pzzj6+0AlBlMqNuaa1dMDGxIz4+jIsXf8PP7xSmpvYIBHWvuTZCJJLRuPEPCARCkpKOEx6+to5/JaCt7YyDwxyEQi3Cw9eWaEGIjt6GUlmAkdGbmJr2Q1u7ATY2E9UWCFfXHTRsuIn7978gJORrJBIjrK3HIpVaYmExFFfXbTRrdoyEhKPcvj34lZljUVEBWFs3KhxvcTHhf+LEdq5fP0bv3p/h4uKl/r1Zs7eLCP+8vGw2bBiFlpYMb+/vq6RvAoEATU0tmjTxxMLCClNTM06c+JsrVy6yb98uMjMzaNiwMTk52Rw58iuNGzcB4K23+rBjx4G6BaROAaidMDV1QFNTFzs797rBqMVwcVmDllY9CgpS8PMbxTOr/dTxL0OYSLCwGEpeXiyxsT8VuaZUyklOPoNM1gQQPXFPUQOqnl5LdHXdiI3dXyKNWKyPmdk7JCX5kJ+fUO6+nT27l5iYoGody5s3/+GLL9py586JUhWAjh2LljZPTIxk9+6pWFo6M2TIkiLXnqbdv/8LYmKCGDlyNcbGNtX2HNnZWXTq9CbDho1i0qTP2bnzF6RSDeRyOYGBfmq6hw9j6xaQiigAvr6nMTISYGQkwMREhI2NbrE/ExMRRkYCTE3FXLpUO71wIyP9WbNmKB99ZM7IkfpMmuTEjh2fcveuL3/8sQo/v1NVzvP69aMsWtSFkSP1GT3aiJkzPfnttyVERNxh9eohJWrGjRp1wMiociVPs7Lucv/+bM6cscTHR4CPj4Do6B3lvt/Pb4T6vqtXOxMW9g1yeVal+pKUdIL792dz6pShus3jx0WcOWPOyZN6nDtnz/XrPYmL+6XGBdCLVfL6Y2U1GoDAwAnk5kbW8X9OaGraYmY2kPDwb4v8Hh9/EDOzAeVuRyyWPUPZECISld+rPijofKW/5fIJ/7+JiLhDly4f8uuvi4tcS0tLQCYrOZPo5s1jycnJYMKEXWrTf0m4e9eXo0fXFJr+R5dIs3z5IrV8MTeXlihfHl13dbUmJeVxaeonC/F07tydcePeJzDQj4iIMNau/QaALl2688UXU4mKiiAuLoYDB/ao70lJSWbx4tmsW7eCrl1bkpmZwcCBb9G/f1dSU1MYP34EHTo0JTo6kqioCHr16kBcXAznzp1i48ZVDBr0Nvv37wYgNzeX775bxvLlixg48C1SUpLZsWMTb7/9Blu2rKVJE3vGjXv/pSkeVG4FIDExngYNXDh+/BLx8QVERmYU+Tt+/BISicrkM3nyTFq18qp1i66//2lmzWqBQCBk+fJr7N6dyoIFJ9DSkrFwYSd++GFalfM8fPgbli3rQ7Nmb7NlSxQ7diTw8cc7CA29ybRp7ly48HOJ99Wv71lpntraLjRosJTGjXerfwsLW1kuAZubG0Vs7IFCk6Eunp7/YG//OSJR5dKFGhl1oUGDpTg6Li5cXPXo1CmdDh3i6NjxIfXrLyAl5Sy3bw/Gz2/0a6EESKXmuLqqzKhxcQeIjd1Xx7+KYG8/jfT0myQlPc7QGBv7E+bmQ8uhrB4nI+MO1tbepXwbMcTF/YyFxQiEQq1y96m6w/A8PHrQu/dUunQZQ3JyDAEBZ555z/Hj27h582969/4MZ+e2pdLl5WWzcePoZ5r+ExPj6dWrP7duhREXl1dMvixfvla9udmwYScGBob4+BwjNTWZAwd+IDU1pVCRWIehoRHdurXhgw/eoUeP3giFQlas2ICRkTFt2rjy8ccjGTx4uJq3j88xTE3N+fTTGXz88Wfo6Ogyf/5SUlKS0dc3YObMhSQnJ2FhYYVUKmXkyHHo6enz44/b+eSTqaxevYUZMyaQnp7G1q1radeuIzNnLsDS0opNm1bTpUsPgoOD6N69F76+t7lw4SxHjvz6Uqwl5XYCTEiI58svv6VZs5bFruXn5+PtPZzc3Bw8PDyZOXNhhTrh67ufc+f2c/Xq72rzkZfXEJo1exuACxd+4dy5fVy+fAiADh1G4OU1BE/PXjU2UAqFnPXrP8Dc3JGJE39Qe7YaG9sydOjXODq25Ntvq9apJDr6Lvv3z6FbN2/69n3smOTg0JRp035l164pHD26psR7jY2f3/FPR6cRQqFKqcvMDCA+/ndMTfuWeU94+HcIhRrI5flIpeYIBJIqGQtNzUehLgK1MiEUamJlNQZQ4u8/lpiY3ZiYvIW5+XvlbregIIWYmB8IC1tJTk4EMllTWre+/gwz4wPOn3dGqZRjbj4Ic/P30NV1JyHhD0JCviQ/Pwmp1BxtbWfk8gzy8xORyTxxcJiJvn6b5x4LV9cdSCQm5OZGERDwcY0vGrWZv55eCwwM2hMWthIjozdJS7uETOap/g5KQnz8IVJSzpGd/QAPj0PFvpG0tMuEh68iI8MPB4fZ2NpOeCkVS4FAyIABs/n11yXMm/cveXnZaGholyALwvnhh2lYWbnw3ntfltnmvn2ziYm5x8cf7yjT9J+ensa6dTswMCjuHBkWFsLs2VMAGDt2Ap07dwfgzTffJja2aHVRExNT9uw5WKwNc3NLfv75aIm8W7RoQ9euLfH3v80XX6iOMpo0aUZubi7379/lzp2bGBoa4et7mpCQ+7zzznv4+d0iISGefft2FVoeupGUlMjp08fR1ZVx795dTE3N0dTUQiqVIpPpUa+eIwB9+w7k2rXL9O//4n1Byq0ApKWl0r595xKvLV06n1u3rqOhocnmzXuQSCq26LdrN5SmTd9i9Ggj9PXNmTBhV5HrbdsOonnz3gwfro2OjgETJ/5Q4wMVHn6bhIRw2rQZWCSs5RFatRpA06ZvVSnP69ePoVDIsbVtXOL1YcOWcubMnhKvyWQmz28eEkoQCrUwMxtAdPQuwsK+KVMBkMvTiYrahrX1h4SHryl2Rvp8i1PpJV4tLUcRGDgBhSKX2NgDFVIAxGIDbG0nIRbr4+c3ivT0GyQmHsPY+O1S7wkLW4lSKVcLI5FIVQ3Nzu4zsrNDiIhYh5PTSiwthxd+O5e4fv0trlz5k2bNjmJkVPn8pzY2H2Ni0hNQ4uc3moKC5Br9Dl4H/vb2U7l5cwAZGXeIjNyMk9OKMulNTftjaNipDKWiJXZ2UyvVl5oOw2vffji//LKIoKALSKVaWFm5lGr6/+STnUgkmqX2PTDwHMeOraNZs7dLNf0/gp2dQ4nCX6FQ8PHHH5CRkY6TU0MWLfqmyt+3ra09vr63mTt3Gh07enLpUiD6+gYMHDiM3377CT09Pby9J/Pzz3to1MgNXV0ZBQUF6OjoMGzYqMK1eBS5ubnI5QW0bt0OV1f3QqtPLomJ8UX4GRoa8YIzAD9e48tLOGXKLLS0imuD//13Tn3OsmDBMlxcXCu5w5MV/qtbitlPC6FQ9MIyUj16YdevHyMysuRQozZtqjqvvornv/9uITs7vcQxedor9xG0tGRVuCBOBwSkpPiWmWEtMnIrhoYd0NZuWMM7FxEaGjaF1qiESrUhkRijp9cCgJCQpWWYNB+SlHQcqdQMgUCkFv6P2zEqQQC0wsFhNkplPsHBCyr9nNraTjg5rQQgImI9SUn/lvE9VX3o5+vC38SkL9raDbh3bzoikQ4SyYurpllSGN6CBSeZMeOQ+k9Hx6DEMLzVqwOYOfN3NV2/fjPJykotMwxPJBLTv/9Mfv11cREHwMfm8q3cuvUvvXtPLdH0n52dVij4sso0/T+ie4TZsxeX2J81a5bz33/nEIvFbN68B01NrSof4yNHfkVHR5dt2/bj5uZBWFgIAAMHDmP79g14eDSnb993OXr0MI6OqnoIjRs3wdf3NHv37iQ+Po7t2zeSnZ2Fl1dHpk//hODgewQE3OHwYVWRhIyMDLUMCQoKoHv3XrwMeK4ogIyMdD7++AMUCgUdO3bF23sStRV2du4YGVmTm5vJ3LlenD69uxhN06Y9MDevX2U8PTx6IBAICQ+/zZw5rbh3779iNN27l2wCbdKkW5X1Q0enMcbGKutGaOg3pShIBURErC1UFmoWCkUOeXkxAOjqVr72gYFBOwwM2pGScpaUFN8SaSIi1mJj80mFjza0tBoUKhCV8z4WCMS4uf2ISKRNZmYg9+7NLINWgo1N1ZrGazt/pbIApbKg8H4htraTSUz8B1vbiU/QyNU0j+558t9ntVsZvIgwvE6dRhMefpvTp39QKx+gMv3v2TO90PS/pIRv4w6hoTcAlek/NvY+I0euLtGB8cyZH5/57LduXWfZMpXCPGPGfJo1a1Et60dGRjpDhvRi27YNeHh44u7etHDjU48+fd7Fy6sDMpkeAwYMoUuXHoUWVj02bfqBb75ZRPv2TTEzM8fAwJCJE6dhaWlN587NWbx4Nr169S8c/1zWr1/Jtm0baNXKCw8PT14GPJcCMHv2ZMLCQtDXN2DDhl0IBLW3splIJGbChN1IJJpkZaWyYcMo5s71KuIwY2hohYmJXZXxtLFxVX9oUVGBzJ3rxbp1w4mLe6CmcXJqUyPP7+Cg8kGIjz9CVtbdYtfj4g6goWGJgUH7Gn83YWErkcuzEAqllTa1Pn7OWYWKTnErgFyeQVzcAaytx1a43UdZ7AwNO1eqX/XqzUVPrxVKZQF+fiNKLIbzCFZWo8nLi6/SMa7N/LOy7hMRsYaEhD/Vzn9WVqOxtByOtrYLcnkWMTE/kpnpT3LyceLjDxfeo3JMi47eQXr6jSJt5ucnERW1hby8GBIS/iQx8Z8y+/AyheFJJBr06TOdwMBzGBnZqC2gmzZ9SE5OZomm/4KCPPbtm42trRsBAWf466/STf8BAWeJjb1fZh9yc3Pw9h5Ofn4+zZu3ZurUOdW2fowYMZajR88yduwE5s9fWkSOffvtJvX/V67cWOR4u1u3nty8GUpgYIw6qZCWljbbt/9EeHga+/f/jo6OykJoZGTMp5/OYOzYCYwd+/w+IE9GEcjl8kq3U+lD2j//PMTevTsBWLFiA1ZWNtR2uLt3ZdGi06xf/wHR0XcJCrrAggUd8fTsxYgRK4qZy6oCAwbMwcjIml27ppCZmcLZs3u5cOEXunUbz6BBC9Tng9UNQ8PO6Ok1Jy3tKqGhK3B13faUEP6WevW+qNH3kZMTQUTEGsLCViGRGOPquhNtbafnatPEpFehQ9+fZGTcQle3yROL8fdYWLxfoRAuuTyLiIh1RESsx9CwA05OyyvcJz29VuqxffBgcWExnBI+ZrE+5uaDcXZexc2b/apsnGs7f23tBri4rHtK4dehceMfCv+vjaXlcLVPxyO4uKzFxWVtKULUCGtr71IjAooK/7+JiPBTh+G5uXVRX6upMLyn8eab47h920ctDM+c+YHbt33Q0THk8OHlxYR/aOhNQImOjgEbN45BqVSSzNBwjAAAIABJREFUmZnCihVFqwimpcUTFPQfH320qUz+CxfO5O5df7S1ddi8eU+R1Ld1gIiICPX/o6OjcXR0rDkFID4+jilTPioUUEMYOHBYlT1YSkpssUnz2Jz24mMnGzRoxcqVt/jzz+84ePBrsrJSuXbtT27e/IfBgxcyYEDVa6odO46kadO3+emnuZw8uYOCgjyOHVuLr+9+PvlkZ41FQ9jbT+f27aHExv6Io+MSNDQsAVX4U0FBGqamA6q9D3J5Jtev9yAnJ4rMTD8EAiGNGm0qFMy6VWFsxt7+c/z8RhAaugw3t32Fcy+fyMgttGzpW65WYmJ28fDhzyQm/o1EYoKnpw+Ghp0qlZXR1XW72qHSwWEWDg7Fzd8CgbBIaFlGxu0qG/PXnX91w8OjBx4ePVAqFRw5soKAgDM0atShzHseheH16TOtSsLwnoaGhjajR68psgY9bVVQ7dQzmTbNne7dP+bdd+cCsG7d/ecaj9Onfdi6VaWQLVmyEkdHJ15VKBQKjhz5jbi4WC5e9KV163bP1d6T5YAfYeTIkYwaNYoBAwbUTDngiRPHkJiYgKWldRETSVXAwMCCGTMOlXjtvfdejtIFYrGUfv0+p0uXD/nf/77ir7/WI5fns3//F+Tn5zJ48KIq56mvb4a391Z69/6MH3+cydWrv5OWFs833/RjzpxjVXrmXxrMzAahpTWb7OxQIiLW0KDBssLd/0rs7afWSMphkUiHZs3+pqAglYsXPcnOfkBa2tVy7bTKCwuL93jwYB5xcT/j6LgELS1HYmP3YWzco9wOYZaWo7C0fJ9r17qRlHSc/Pz4So/Pf/+92IyOrzv/msKLDMMrCebmz95V5uZm8fBhCJGRflW0AUzmk09GoVQq6datJ6NHj3+l36lQKGT8+MmMHz+5Stp7VA54/vz5VdO/it6wY8cm/v33aJGEDK8DcnOzinn/y2TGjBy5ipUrb6rDZQ4eXEp6ekKV8IyJuUdWVmqR36ytGzFz5hE+//wwmpq6KBRyfvzx8xpaoETY2X0GQGTkZuTydDIz/UhPv6rOylZzSpg+TZr8glCoQVTU90XSrz7/c4qxs5uGUikvdHpUEh7+Hfb2FU30JKBx4z1IJCYEBHiTkxNGHepQFtq3H05s7H2Cgi4QHX23xsLwKgs9PVOcnNrQokXVHPlMm/YxMTFRGBubsG7d9roJUd0KSkWIg4PvMW+eysv7o48m0qlT6bvOqKiIWjVQ2dlpHD9esglNJZR/RyQSI5fnExJyvUp4PnhwtdTUwi1a9GXMmHWFO/Cb5Ofn1sg4WFl9iERiREFBKpGRWwgLW4mNzScVymxWVZDJPHF2XgVAQIA3WVn3qqxta+sPkUpNiYnZTVTU9+jquj+RjKj80NCwpHHjnRQUpHLnzvvq/AF1eLWRkPAnZ8/a4O8/hoCA8QQEjOfmzf74+AgICKj8rvVFheE9D9q2HUSLFn2eu51fftnLwYOqLKKrVm3BzMzitZEvL70CUFBQgLf3cLKzs3ByasjChaU7M+Xn57Njx6ZaN1iXLh0q1Q/B0tIJKytV/PuTSTqeFxcv/lbqtebNVR+dVKpVJOSnOiES6WBjM75Q8fiWhw8PYmPz4jKb2dh8grn5EOTydG7fHoxCkVM1H4ZQC1vbSSgUuQQGTijx3LlsPE70YWLSG1vbiaSk+PLgwYK6VacWID8/mZYtz+PquoNGjTbTqNFmlMp8tLTq4ey88rnaflnC8MrCsWNrGTJExMiR+ly+fIi1a4czZIiI0aMNiYoKrHB7UVERzJihWkeGDh1Jnz7vlErr73+bs2dPvtD3L5fL8fR0ZOHCmXz11Vy++moutrYypk59tY4syq0AfPvtl1y7dqlcCRn27duJiYlphTqSmZlcqLlmlrIDT0ehkJOTk/HCBis+PpRDh0quS56WFk9cXDCWlk44OlZdvKqv70/4+58u8VpQ0AUAvLzeq5YQTFUMc3GFx9Z2EkKhBnl5sVhYDEUqNS12nwqKKu2L6t/ibbq6fo+2thPp6TcIDKycMlJQkEpBQepTysUERCIZxsZvoaPTuIhwl8vTUSrlyOUZT7WTUiggkor87uS0Al1dd0JCviYmZk+dBH3Foa/fsohFKCrqexIT/8LVdddzO6O+DGF4z4KDQzN6957K2rVBNG7cmU8/3cOcOcfo0GEEBgYWFfy2lUyYMIq0tFRsbe1Ztqzsss6rVy9VZ9p7UUhKSmT9+p0sXLicL774EienhmhpaTF//tJXah6Xy6vu2rVLfPvtV0DZCRnS0lI5dOhnvvhiKnv3Hi53J86fP8D58yrTT0pKLJs3j6VNm4Hq1LoXL/6Gr6+qROejGHwvr8Ho6Zmxf/8c/PxOsXz5Vezs3Ll924c9e2YwevQadHWNWb9+BHl52cyZ8xempvakpj7km2/64eU1hG7dvMsMnykJ+/d/QWRkAH37zsDevglKpZLQ0Bt8//14pFItpkz5qUqd4eTyfL76qgf9+8/mzTc/wtDQCrk8n+vXj7FlyzgcHJrywQcrq2Vy5OSEI5dnUFCQhlisp/5dKjXH0nIE0dE7Soy7z8kJL1TmYlEq5WWm8S0vsrPDCsejeH9EIhnu7r9w+XIboqN3IBYb4OS0vFypiAsKUomLO0BExHpyciLQ1XXH2LgnOjoNkUgMsbEZVyS6ITHxLx4+PERBQVrhYjoOM7OBhaGDv6uFe0SEqiaCmVl/pFILhEJN3N1/4uLFFvj5jeThw/9hafl+kb4YGXXF1XUHublRxMcfUVsiLC2H4+vriEzmiYvLWrS1nYiK2oZUaoyOTmOCg+eRnHwKoFw0zxZubbCy+pDc3AgUilyEQm2kUhMiItYhEEhxclqOTNaM8PDvAJVviJnZIPz9R5OeXvHjL5FIhqlpb9zc9hEbu5eMjDsAaGhYo6Fhza1b72Bs/BZOTsu5e3cyaWlXn0lf3dDWdnliboYSFDQNW9vJGBp2qJL2X3QY3rPQqFF7GjVqT25uFr6++9HQ0KZfv5l4eHSvcFsbN67izJkTCIVCNm7cjUymVyJdTEwUGzeu5vDhX1i/fucLFZx6evq0bKk6gklNTWHevOksXrzyuXzijh8/zpgxY7C2tqZv376FcyubH3/8keDgYK5du8akSZO4d+8eY8eOJTExET8/P5YsWUKnTp0KZfWzaZ6EICnp2UmJ27VzJyBA9ZFpaWmXuNtUKBTk5DxOznH3bhympmbPfGgfn+c1xeUwd247unQZQ48eE/jtty/p3v1jdezsw4chzJ7dkoULT2Fr60Zycgy+vvvp3btiCWNSUmI5eHApHTqM4MaNv7hx4xgPH4aQk5OJrq4hTZu+zTvvfFGlta5VSo8SPT0zrl37g1u3/iUjI4ns7HRMTR3w8hpCnz7TKqzEPImtW4v/lpUVRFzcAaKjd5OdHYyBQTtMTPpgZTVGvdvPzAwkOHguTZr8+oRwPEZi4r9ERm5Sm+KNjLphbNytcDdd8YqASUknSEr6h4iIjcjl6YUCygszs35YWn6AVGpRZBcWEDAOUBUPMjXti4PDHHW44ssIH5+i35KHxxFycsK5e/dxBjorqzHqcsz16y/A2PhtLl9WJYBq0OBrbGwmcu6cjVopKQ9NaTAzexdHx0VcudLpibTKAtzcfiQiYgOpqeexs5uKtfUYLlxwe0IgOiOVmpGScu45TN9p+PuP4eHDX0t89jfeCMfP7wO1IvMs+vIJ2ufNya7k6tUu5OXF0rr1dYRCzQrdPW5c6dfi4oLL5Yn/IhEXF8ynnzagVasBTJ/+vwrf37hxFJ6ejuTm5iIQCEpMN69ScvLJy8sDwNm5Ef/95//SjMHUqeO5dy+Q338/VeF7DZ/SF/r27YudnR3r169X/7Zjxw7GjFGlbl60aBHHjh3jv/9UWWHnzJnD+vXriYyMRE9Pr9w0FbIA+Pq+vDG1EokmU6b8xLx57YiPD6Njx5FFEmeYmdVj+PBvWLt2OEuXXuKffzYycGDFQygMDCzUcbGOji3UMa/ViXbtHhe1cXfvWmNjqq3tTL1686hXb16pNDo6DYsIfwBj47cxNn5b7ZhXFTAy6lJYEnjZM2mtrT/C2vqjV9y4XPyI48kIh6edCNPTbyAWy5BKLdTCvTw0JZoDxXq4um4nIGD8UzUVlAQFTUVHx7XUPmZlBZGTE1Gtz65QZFWIviYQHr6GlJRztGx5vsLC/1l42YW/an2tj6amLvXqNavU/ZaW1sTE5LyyX+vVqxfZv383p09fq5L2hMLi1uOhQ4c+YS0rak1t2rQp6enpxMbGqoV7eWjU/KgFsLR04s03x3Ht2p9YWDQodr1z5zGYmdVjyZJueHkNQSSSUIc6vArQ1W2Cjo5LiddEIm2srceSnHyy1AiI8tA8golJH8RifZKTjxe7lpcXp05nXMyMKBBjYTEUhSIbPb3meHr+g6PjV7Ro4Yub2z4cHObg5RWEldWHdOyYUCS7Ytnf9QdlpvwtjV4o1CzG095+Om3b+mNjM5433gitEkUxK+su9+/PwcFhFnp6LV/L+SkQCLCzc8fBoelr9+xyuZypU8czYcJUnJ0bVQuPW7ducffu3VLmXxbbtm2jc+fOODk5VYqmVigAERF+mJs7Ym3diH37ZpVI07PnJHJzM7G1daMOdXiZoafniYPDLOrVm4u7e/EdrVRqioPDbN54I4yEhKNcv/4WT0YdlJfmaWhpORQK+8Rn9lEiMSnMyjcLD48jSKXmAKSlXUWhyEVT05br17vx4MFCkpL+RlPTjtTU81y50r7EWhKPd5T9cXCYhaPjEurXf3ZCrZLoFYqcYjwjItajoWGNQpHH5ctexMcffq53pFTKuXPnA3R0XKhfv6hFMS3t8jPHujbBysrllbBWVDW2bFlDWloq06c/tgY/fBj73O1eu3aNZcuW8eWXXxbZ/T9CfHw8S5cuxd7enp49e/LXX38VO5YvDw08Ry2AlwWpqXFcuXKYAQPm0LJlP6ZPb0KTJt1o1qzn07pqnWSpwyuBtLRrhIaqjjwSEv4sYTceT2joUgwM2mNg0E7tjFdRmqeRm6tavMRifQoKksukzc9PUPdRKFyFmdnAJ3ZGmaSnX0MuzyIrKwgdnYYoFDlkZgY8sw8PHx5Sn+lnZz+oNL1cnlmMp0KRTXr6NXJzo5/7HYWGLiUj4watWl0pVhkyJmbPa2UR0NMzrbGaJC8LoqMjWbp0ATt2HCgSEffnn4eeO3uhp6cns2apNrK9ehVP825qasrs2bM5e/Ysvr6+TJkypVI0r7wF4OrVP5g/v4M6IYZEokHDhm+wdu1wzp3bp6bLzEzh1q1/SUgIJzDwHHWow6uC9PTrpKffKLEAkb//GAwNO2FpOaLU+8tD8whJSf+iUORibFxygq8nHS6fhEKRR2zsvgoVSSrfIqvy9C5vuxWlrywyMwN58GAxGhpWhIevwd9/bOHfaC5ebEZubtRrNUd1dAzQ1NR9rZ557txpaGpqcunSeXUegAkTRnHixN9VyqdZs2Y0bdqUzMzi4fE7duzg1KlT7NlTeljxs2heaQtA8+a9i9TH1tDQYcqUn0qcoEOHfsXQoV/VSZQ6vOQQlCB4zTE27k5MzB4EAqE6zDQvL5aAgHG4uu4iNfUiWVlBqhbKQVMScnLCCQn5Eienb0hLu0x2doj6moXFUJKSTpTaR4FAhLX1OMLDV5eytxBW6tmNjLqQn59Cevq1CtGrHAaF1bLn0dFpSNeueXVT9Yl1t6SaBbUZO3YcqJZ2lSUE5cXFxfHPP/8wYsQIFAqFuhSwhYUFW7duZdSoUbRu3RpnZ+dChfzZNLVCAahDHWoTjI17oKfXHG3tBjg4zAaUiETaWFgM58qV9shknhgZ9UBb2xkTk16FOQkOYmLSh+bNTxAcPJ/MTP9n0sTG7kWhKDl1dEjIl+TkRNC48R5ycsLJzn5AQUEycXG/kJcXh0zWFBOTnmhq2lOv3jyUynwEAgnGxj2IjNyAru7/2TvvuCrL94+/z4HDlD0EGYIg4iAHguLelpor98AytCxLzVXOMtNSy4a/0kzN1CL3yPymGTkKQVFQUZbsLRvZHM7vjwcOHDYKgnk+r5cvD89zP8/nnPsZ93Vf93V9ri60auWERGLCw4enyM+PxtR0EqqqOrRp8yrx8T9W4VRR0cHUdCKqqrq0afMqWlr2csPHwGAAPj7O6Om5oa5ugZHRS+TkBGNkNKLG9r6+vbGxWanAaWT0EqqqBpibu5f+pgzlDddI0NLSeyqFwP7r+OOPP/Dz8yMsLIzNmzcjEonIzc3l4MGDXLlyhZs3b/LHH38QEhLC2bNnefHFF5kwYQJnzpxhyJAhbNiwgU6dOtXZZubMmairqwsmdH10AJoST6oDoMSToTodACWe5v2vjE1pTjy5DsCToTYdgGcF3t5HcHOb/Jj9/3zffwbNXEtPJJPJmjlc9Ugzs09uVv4poud7AGj22+85h+h5v/+aeQQafqGZO6CZv8CwYZ81K39ZsN3zimdyCeDy5fvs3fsXV68GkZtbiJmZPhoaEkaN6o67+0BiY1Px8gpk9eqmkQS9f/kyf+3dS9DVqxTm5qJvZoZEQ4Puo0Yx0N2d1NhYAr28mLh6tXKEUUIJJZRoAB4+fIifnx9+fn5kZwvqn5MmTaJnz/rVWPn111+5dUuQpG7Xrh0dOnSgT58+SCRK/ZfKeKYWbrKz85g6dTtDhnxE69b6eHl9SHz8Lm7e/IyLF9dhbW1Mnz5rGDBgPamp2Y3On5edzfapU/loyBD0W7fmQy8vdsXH89nNm6y7eBFja2vW9OnD+gEDyE5NVd5dDcCePXvQ19fH19f3ueRXQgklBJiYmPDiiy8yZcqUCpO+y/XyFmZlZXH79m0A1NTUeP311xk4cKBy8H/WDYDMzFx69VrF0aPXOHZsKZ99NhMrq3LJX01NNdzdB+Lt/Qnm5gakpTVu1cDczExW9erFtaNHWXrsGDM/+wwjKyv5fjVNTQa6u/OJtzcG5uY8SktT3l0NgKamJvr6+vLglDKEhoYyefJk+vTpQ7du3VBTU0MkEiESibh79+5/hl8JJZRQhKmpKSoqKqioqJCcnMz9+3XrSFy9elUuhaujo1NFFrclQVUVPv0Uvv0W1NRgxgz46y+wtIT//Q/efRcmToS1a0FPT9i3bBns3l01dmLjRihbzdPVhQsXYMECKFMWLjt+9Woh7uSHH8DY+BkyADw8dnL/fhweHkMZN65mkQ0rKyN27ZpPenpOo/Lv9PAg7v59hnp44DJuXI3tjKysmL9rFznp6fU+d9++fTl27FhpZcFITp8+zeHDh7l+/TqHDx+mf//+8rY6Ojq4u7uTnJxMZmYmP/74o/zfiRMnKCoqQk1NDQcHBzZu3IhMJiMuLo5Tp07h5+fH+fPn6du3b4viB5gxYwaRkZF07dpVvi0wMBBnZ2eGDx/Ov//+i7+/PzExMUyYMKHR76/m5v8vws7Ojl27dnHixAn5tiVLlnD48OHngl+JJ5ydisVIJBK6dRNkhi9dulRr+4KCAq5fv46Li4v8+JaM4mK4exdu3oTCQvD1hfBwiI2FsDBhwD5+HLZvh8xMCAmBkyeFv5cuLT+Pvj688AIMHFjmBYHgYLhyBUqzAeXHHzsmBH4fPSqc55kwAM6du8XRo0Jlo+XLx9bZftSo7lhbGzca/61z57h2VFAbG7t8eZ3tu48ahbG1db3P/88//7B9u5A/vXDhQsaOHcuUKVPo378/0dHRXLp0iSVLlgCQnZ3NTz/9xNWrV0lISODVV1+V/5swYQJLliyhVatWhISEsHbtWkpKSjhw4ADjxo2jd+/eFBUVcfHiRbp06dJi+GvCtm3bMDU1ZX6FUOnWrVvz66+/KgzUTYXm5n9acHNz48KFC8hkMjw9PfH09MTb25txtRi69UFCQgJSqRRNTc0Kz/I5du/e3aL4lWjZGDBgACKRiKioKKKiomps5+vri62tLaampv+J3923L7z2WvnAXoa2bSE6usJ40x08PKAa1eAaceECDBnyjBgA338v5Aq2b2+Ovb1ZvY5Zv77xovv/LM2VM2/fHjN7+3odM3n9+gZx5OfnV7tt2bJl/PLLL2zdupUePXrI95WVxqyMvXv3kpVVVhVORlFRkXxfUVERX375Jerq6sycObNF8VeHpKQk4uLiCAlRFK+RSCS8+eabTX7fNTf/04K3tze//ioIm0ybNo1p06Zx7Ngxjh8/ruD9aShyc3OJjY1V2BYUFMSFCxdaFL8SLRutW7eWC9jU5AUoKSnhn3/+eaL7pbnQrRuMH1/Vrf/PP7BvHyQllW8bOxbeeUfRA9ChA/TpIxgGreopyCiTQX7+M2IAeHkFAtC5s2W9jzE21mk0/kAvoQqaZefO9T5Gx7jxPBAbNmxARUWFd955p9Z2r7zyCqamphQXF9dy4WXymXxL4Y+NjeXjjz/GxsZGXsMa4MUXXyQ/P5++ffvi6amo8Dh69GhatxYK0Hz++eeoq6sjEon48ktB837//v2YmZkhEomYNWsWoaGh8sHG0dERNzc3+eDQ3Pwtwx1ZXMWQE4vFTJ069YnOW6ZI1tL5lWj5XgCA+/fv8/Dhwyr7AwIC0NHRwdbW9pn7bf7+gmu/Jk2cu3eFdX2A06chMVFw+QM4OQnHnjwprOtXiJuUQ0sLjIwUtw0ZIngBGmwA3Llzhw0bNmBraysPhmrbti0fffQRd+7cafTOSUnJJjMzt3RQ133qFyc7JYXczEwAdBtxUG8IgoODSUlJoXfv3grbzc3N5evvp06dqjJIVYaGhgbLly8nMzOTgwcPthj+wMBArly5UsW9t3DhQmbMmEFKSgrTp0+nd+/eXLwolKq1srLCxMQEgKVLl8qLXQwfLujYz5kzh48//hiAqVOnykthurm5YWZmxunTp7G0tGwR/C0ZZYbaqlWr2Lp1K99//z0nT55EU1OTdu3acerUKfz9/QFwdHTk77//5rfffqv2XB07dmTv3r0Ka/ItnV+JlgE7OzssLCyQyWRcvny5yv4rV64wsLKvvIVDVVWY/XfqJAQBdu8OtrZgYQF2djBqlBAEuHkzqKiAoyP06CF4AD76CMaMEYICy4L/MjNh8WKhXYcOwpKAuzt8+aUQC+DoCC+/DJMnw4gRsGLFYxgATk5OrFu3jv3798u37d+/n/Xr1+Pk5NTonVRYWD4z0NRUe/ozowqubrUKa4lPG6mpqVXWtiquwY8bN45PP/202mNdXV1ZvXo1P/zwAyEhIfTo0YPoiotIzcw/cuRIRo0aVeU4sVjMoUOHOHToEJaWlvj4+DBs2DBGjhxZxS3/5ptvIhKJOHTokHzbpEmTEIlE7Nu3T74tKCiI9u3bywfvlsDfErF48WLy8/PZv38/jo6OrF27luXLl/PGG2/g5ubGsGHDCA8PVxhMg4KC+OOPmouhhIWFkZWVpbAm31L5lWi5XoBbt24peBDDwsIoKCigcwM8tC3D6yYM4O+9JwQBHjkCQ4dCXBy89BJs2SIEAS5ZAunpMGgQHD4MOTkwfDj89hvMmQMJCcL5LlwQPANBQcL+1avhp5+EqP+y47duFXhWrBCCBR97CcDCwkL+2boBAW8NhaFhK8RiwcR5+DDrqV+kVoaGiEqjSbOqcT09LRgYGJCSklJrm7Nnz1a73dfXl08++YRZs2bxzjvvEB4e3uL4K6ffVcSMGTMICQnh008/RV9fn/Pnz9OzZ08Fd72trS1Dhgzhp59+QiqVAnDy5Ens7e05c+YMCaVPyZ49exSC+loKf0vBwoUL2bx5M6ampri6uhIUFERYWBgjR45ELBbz0ksvIZPJ0NWt3htXW652UVERSRUXNFsA/8W0NCYEBCD680+0vbxIqxCzUh3WPHiA6M8/sbpyhe9iY+tsXxfSLqYRMCGAP0V/4qXtRVFa7ed7sOYBf4r+5IrVFWK/i62zfV0QKht+yF9/afDnnyJCQhbXeUx8/I/8+aeIixdVCAv7gMzMa0/l3nRycsLAwIDi4mKuXi2v6nr58mX69ev33KtaPg4e2wComF/ZlOkWGhoSunQRDIx7957+mqlEQwPr0oj12Hv3muUi2dvbY2pqWq3rqyKuXbtGZGTkM8lf3cNbJugheH80WblyJWFhYYwbN47s7GwWLFig0H7evHnExcVx/vx5ZDIZR44c4ddff6W4uJg9e/ZQVFTEnTt35GlCLYm/pWDHjh188MEHvPnmm/IlveLiYgwMDPj000+JjIwkJSXlsV+2dYm5PG3+oYaGeDo5oSISkSuV8n1czaV8C0pK5PvnWliwwNISwycUmDEcaoiTpxMiFRHSXClx39fMX1JQIt9vMdcCywWWSAyfjF9b25F27T7EzEwIIY+L201RUW2GvoyoqG0A6Oj0wN5+M3p6vZ/OYCUW069fPwB8fHwoKCiQB+rWVyVQ8XwlzZ6Hr/h9BPe8VCrM0vfuFb5HPePOFTBlipBKWD45g+peO89EEOC0aX1KX8hRREQk19OyLWg0nfk+06YBEHX7NskREfU6piAnp9H4V61aRWFhoTxVry7Mnj27Ufu/ufh3796tkEUAYGRkxOHDh7GxscHf318heGzChAkYGRnJ13lHjx5N9+7d6d27N7t37+bUqVMNSi1rbv6Wgr59+/LZZ5+xcuVK7lUygqVSaZOLrTQ1v7pYjKOWFsYSCTtiYiiq4bn9OTERy1JPkU4j/maxuhgtRy0kxhJidsQgK6qeP/HnRNQtBX4Vncbtc4nECF3dnkiluURHf11ju4cPf0NFRVhCUVXVe+r3oouLC1paWuTn5+Pj48Ply5dxc3N7LKW/khJxs+fhK34fYeBPTYXPP4e5cyEmBn7+ueH9dOaMYMiU4d13hWDDZ9IAeOutkVhYGJYORr/U2b6oSMrKlQcV4geeBCPfegvD0iWPX1atqrO9tKiIgytXKsQP1PlJX/OeAAAgAElEQVQSqsYFraqqyvr165k1axYeHh4KLz+JRFLtTT98+HDMzMzks1qJRFKvNc/m5q8O2dnZCmvqZVBTU8PKygobGxtUVVUVts+ePZvTp0/zf//3f8ydOxeA+fPnEx0dzQcffFCv9MOWwv80UfY7Kv6eMvTs2RMtLS10dHTo0aMHxsbGaGlpYWtrS2JiIra2tlhYWNCxY0cGDhyIiYmJ/N4oCxSu6Gmpbvbe3PwaKiq8YWlJXEEBR2pYptgZG8uCJgrcVNFQwfINSwriCkg6Uj1/7M5YLBc0XeCopeVbqKjoEBu7A6m0+iyhqKgt2NisbLb7VE1NjV69egFC4F9gYCBubm5NZHg2fR5+9YZJ+eerV4UgwYYiL0/x7wcPoLrVqmfCANDT08LTczFaWup4ev7Dhg1Ha5xdFxQUMX/+LubNG4a6euPoP2vp6bHY0xN1LS3+8fTk6IYNNfIXFRSwa/58hs2bh6SWdeWK6NevHytWrABg06ZN7N+/nx9++IELFy5gZWVFjx49OHDgQKnbTYd58+YxZMgQbG1tOXLkiDwS/8yZM/z222+cOXMGBwcHNm3ahFgsZvz48cyYMaNGK7m5+aFch6CyvsDChQsV1tUBfvnlF65du8YXX3xR5TweHh4UFhYyZMgQueExdepU9PT0GDBgQI1rx0+b/+7duxhX8AE+fPiQRYsWsXXrVlxdXZk1axZFRUWsWbMGMzMzYmJiuHbtGnp6enz++efyY0aPHo23t3fpzCNLno1QhsjISPr378+YMWPYsGEDQ4cOJTAwUKGNm5sb00vfXsuXL8eqgsQ1wLFjx3j06BF3796lZ8+e/PXXX8ydO5ecnBwuXrzIxYsXuX37Nq+++iqXLl0iMzOTgQMH0rZtW0aOHEnXrl3p378/tra2DB8+HCcnJ4WMkubmL8PblpZIRCK2VxMgeyk9HQdtbczr+Uw/1gD8tiUiiYjo7VX50y+lo+2gjbp50/FLJAZYWs6nqCid2NhdVfZnZv6Lioo2rVp1eyrv/ZKSkmrfs3379kVVVZXs7Gy6du2KtrZ2Fa8Q1L/SaHPm4deFIUPK0wM3bxai/M+cgX79hKWGXbugLKFq6VIhZbAynJ3Bx0c4porh/ay4Ifv1c+TcuVW4u+9g/frDnD8fwIIFI3B1tcfUVI+UlGy8vO5y+LA3GzZMpWvXto3K79ivH6vOnWOHuzuH168n4Px5RixYgL2rK3qmpmSnpHDXywvvw4eZumEDbRugFHf16lWFoJa6ZqW7d++ul5rZBx98wAcffNDi+X///Xd++OEHALZu3Yq2tjbOzs4A5OTkMGfOHN577z3s7e3Jzc3F1NSU8+fPM2jQoCrn6ty5MyNGjODtt98uN+C0tJg9ezazZs1qMfxRUVGkVigYtWPHDvr168fkyZNZuHAh27ZtQyKRsHr1ar777jvU1NTo3bs3s2fPlr/gTExMGDRokHwGdOjQIX7++WfWrFkjNy5sbGxwdnbGxsaGxYsXs379epYtW8a5c+fk3N7e3gwdOrTG6xMbG0unCtOQ70uFscpQeVmjYjZI5T4aUs20p7n5y2Curs6U1q05lJjI1YwM+unry/d9FRPDKhsbEhvg1WvwUoS5Oq2ntCbxUCIZVzPQ71fOH/NVDDarbChMLGzS96y19XvExHxDdPQXWFm9g1isXsGY/Awbm6dXPjcjI4PCwkIKCgoUPJStWrWie/fu3Lhxo1rhn4yMDPm7qqSkpM4YtbI8fHt7qC6UoHIefpcugsv/33/L8/ATE4W0vilThLV7hQmkFlR2gpbl4deEF1+EwYMFT8O2baCtLWQIuLpCWprgmXj9deEc48cLx5w6JWyvDD+/Wjx/PEMYMKAj9+59wb59f3P8uA/Llx8kNTUbIyMd7OxaM316X44dW4qOTtOk+XQcMIAv7t3j73378Dl+nIPLl5OdmoqOkRGt7ezoO306S48dQ1NHByXqj1GjRlWbhlfmWWgoqksF++abb1oU/9ChQzE0NKwwC+nGokWL0NLSYvTo0bi7uwNC8OGECRPw9PSU7z948CArVqzA39+f7t27y2dL6enpzJgxg127drG6hlLUjx49ok2bNsqbrgYssbbmUGIi26Oj5QZAZF4eqUVF9NTV5bc6MmGeeABeYk3ioUSit0fLDYC8yDyKUovQ7alLym9Ny6+u3gYzs9nEx+8hIWE/FhbzSw3h+xQWJmNgMIjc3LAm/Q4pKSkEBARw8+ZNZDIZe/fupVOnTvTs2VM+2x8wYAB5eXkKXrTg4GBCQ0Pl2TkFBQXs27eP9u3bVxsnIBaX0K2bEHxXUx6+gwP07w8bNijm4Z88CV99JQTtvf9+mYcE1q0TDIOyPPzgYGHmvXJleR6+k5MQkFfqdK0W//sfXKuUXNG/P0ydKhgBDXVEVV4SeCYNAMGaUuftt0fy9tsjm4VfXUuLkW+/zcgKM7zmRIcOHVi3bh3+/v5s3br1qXL379+fr776ivbt23P79m0WL17M9evXlaNIDUhOTmb8+PHY29uzaNEi+SAPQgChVCrlrbfeol27duzaVe6CdXd3Z+nSpSxYsAAzMzOkUin+/v54eXmxaNGi0pnJacaOHYuWlhaDBw9m5cqVCuvpgYGB7N27F11dXdY3UKb6eYKzri599fU5+fAhEXl52GpqsiM2loVPSbRJ11kX/b76PDz5kLyIPDRtNYndEYvlwqcnGmVjs4KEhH1ERm6hTZvXEYlUiIraQtu2K54Kv7GxMUOHDq3VK2RiYlLFo9ehQwc6dOjAmDFj6sVTUiJWGISPHBH+gZCHX4bjx8u8SeXbSvW+qKg5VZaHX3E/CLn4lY8v46kvtLXhl1+EWb9UWj7rf9I4c7HykX92YW5ujouLCxMnTnzqZS8tLCzYunUr3377LcuWLaNt27b88ccf8gDApkJAQAATJ05k8eLFvPjii7i6uvLXX38BwtrfqVOnGD16NG+88QaBgYH07duXVq1a0a9fP7liXEORmprKvHnzeOONN5g5cyaOjo4KM/rr168zb948unfvTmZmJtOnT0dHR4eOHTsqqCNmZ2cTERHB9evXOXjwoFwbACAmJoZJkyYRHBxMv379GD58uFzGtn///qSmpvLll18yevRoZs+eLRfiKnNvXrhwgRs3bnD58mUMDQ05duyYwm/o3Lkzc+fOZf369TXGQSghYLG1NSUyGd/ExJAjlXIhNZWJT7HAjPVia2QlMmK+iUGaIyX1QiqmE58ev5aWAyYmE8jLe0BS0mEKCuLIyvLD1HT8f/J6W1gIbvKlS4WBdelScHMTZumZmTBrFuzZA6++WvXYb78VVPrKYGgoqPTNmQPe3oIrv1s3yMgQzj1+POzcWcegLFY8JwgBiSYm8PAhtGkjtGnVCrKzy6P9u3atutRQF1SVj/uzi4SEBA4ePMjatWufOveQIUMYPXq0fB3bx8eHW7duMWLECH4qM3kbGbm5uQwdOpR33nlHPovt1asXM2fOJCEhgdDQUKKjo/n9998ZNWoUn332GYsWLSIgIIDPPvuMgQMHcufOnQYLV82ePZu8vDy8SmtCrFy5knfffZdhw4bRunVroqKiOHbsGPr6+ixfvpwRI0YwcOBA1qxZw/Tp09HQ0GD8+PHY2dkpDPojRoyQfz569CivvfYa+vr6fPzxxxw4cID8/Hy0tLTk9QTOnDnDihUrmDVrFu3bt+fff/8FBGW00aNHy5cxzMzM2LBhwxPr6Jdh9OjRbNu2DSMjI/m1FYlEdOvWDX9/f5ZWjIiqzyxXV5eFCxcyePBguXTy0+JXU1Pj66+/ZsqUKTx69EjItaqECSYmtNXQYE98PKZqasw0N0flKYrMmEwwQaOtBvF74lEzVcN8pjkilacrcmNj8z7JyceIjPyUrKwbWFsvBv6bQjtxcRARAZcuwY0bwjYdHWFwTU8Xguz+/hsCAqDiimDHjmBuDhMmCGl9ICwbFBbC/v1CsF63bkKMQUaGsGwAUKomXu3A/8orgm7/pElC2mCZ9tzNm8L2P/4QvA5du4KVFVy+LAQHXrsGO3YIrv6+fYXURHV1GDlS+G329sLnf/9VzDJQGgD/ARQ9oRrZ4+DXX39ViJj39/cnPT2dgoKCJuPMy8tDKpXK170BevToga+vL7m5uTg6OtKuXTveffddcnJyOH36NCoqKkyZMoW8vDy2b9/Ojh072LJlS4N4MzIy6Nu3rwInQEREBB07dmTSpEl88cUXBAcHs3HjRrlkcps2bRg3bhybNm1i/HjF2dOJEycUqtKlpqbSu3dvZsyYQUFBARs2bEBLS0u+393dXe5dsbS0ZP78+XTv3p2EhAQWL17Mxo0bFVyo3t7ebN26lYkTJ3Lr1i1iYmKYNm3aY3lozp49y0svvUS/fv1YtmyZfLtIJKqzQFR1sLOzw9LSst5yyI3Jv2TJEm7dusWOHTuYNm0aZZESFSPGVUQiFlpZsTw0lM2RkURWuPZNhYr8IhURVgutCF0eSuTmSPpG9n3qz7eubk8MDYeSlnaR4uIM7O03/+ffo25ugjfAza18Xb8M9vZQWs9LYdvrrwt5+mUGwLlzgn5Ax45CPECpcxIVFcEbYGwszNyr8wKU6QBUtzyQkiLEI5ShYkhRabwyUJ4RIDwf5Z9rWsF6bAOgYpWtiilSSjwfqJwu16pVK6RSaa1a7E8KIyMj0tLSEIlEhIWFceDAAfksuLCwEC0tLblL3N7eXmFZ5J133mH79u34+Pg0mPeff/5BJBKRnp7OgQMH+N///lelD8RiMQYGBgr1EsaOHYu1tTV+fn5VBGtaVcoX2rhxo8IgXhn29vbYV5AE++qrrwBhGahyidSePXsqDCg1lVBtCKqr8CiTydizZ0+Dz3Xr1i2uXbtGnz59njr/rVu3OH/+PABr1qxh9bBhSGWyKtH9HhYWfBQezgwzMwwqBI9lln6PzOLiRruvZVJZleh+Cw8Lwj8Kx2yGGRKDcv7izGKF/xsLxcWZFBdnVvECpKVdxMrqXcRiNYW2wv8Z/6l3mre34AFITy/fJpEIyn1TpgjFd8pgaiqk/amoCDN+V1dBSCg1VcgkWLBAEALy8BCMAqlUCOwDKC1pUCPatoVPPxVSC8uSrUxMBDXB6pYhqjeyhXP88IPgNagJjx0DEBMTI/8cHx//xJ0fHp7EypWHEIunYmW1AH//SAAePEhiwID1jB69mVu3Ijh58jqtW89DVXUax4+Xv8yDg+Pp2HEJr7zyOSEhCTx4kISDwyJWrDjImjWerFnjSf/+61BTmy4/d2XscHdnh7s7nmvW4LlmDQdXrGC6RMIXkycT6e/Pql69mCIS8eu6deQ/egQI1QK3T5nCUicnAv/+m1AfH7aMG8cUkYhPXnyR1NKSr6HXrvG6sTEHli+Xb/svYcaMGXzyySfyFJymQkxMDNOnT+fAgQNyN3J90LZtWyQSyWN5KPLz81mxYgVLly5l2LBhDdLyt7e3p6SkpIqXxtXV9Zm/5m+++SY5OTmYm5uzbds2bt68yZw5c0hJScHd3b3KtorBWY1Rpvdx+MsG/zL8nZ7O/Pv3iS8oYGlICNdKK3/qq6ryWps2LCrVJCiWyfghLo4tpVLX+xMSGqUWQPrf6dyff5+C+AJCloaQeU3gV9VXpc1rbbBaJPDLimXE/RBH5BaBP2F/QqPUAsjNDSEq6nOSko4SFbWtVApYGAENDYdhaDgcS8s3ykwV4uP3EhYmRM5lZ9/iwYN1ZGZeo6gohVu3RhIT8zUxMTsIClqAl5cuubmhte6riLi4OL777js2btzIb7/9xo8//sjBgwfJzFQ0TAoKCqrEuACkp6dz8uRJ3n//fY4ePcpff/3F6dOn+fHHH7lbXYJ8Dbh8WfAECN5VYRB++FAxiK9vX8HlfvKkIBW8ZImwfWRpbPrXXwtZANWV5614/uoQFSUYDTExgsTwxo2waBH8/ntDDDqwtlb0AjSKB+DOnTucOHFCocLZnDlzePXVV5kwYcJjVwRs1641n302ExMTXdau9URXV4hm0NHRwMHBnF275qOiIqZ7d1tUVcW8/PJnmJmV58na2pri7NyO/fvfRkVFzLVrofz22/s4OJiXGinpfPfdeT76aArdutlU+x2chg5l4Jw58r8Pvf8+uiYmzNu5Ex0jI5YdP857nTujpqmJRukMTsfYGF1TU9Z89RUG5gLX8pMn2TJ2LMkREeiX1owvzM9n3MqVjF2+/D83+JuYmNCzZ0/eeOONJuUJDw+nV69efP755woR9PWFSCRqcL3woqIiBg8eTKdOndhbmuBbuRJgXZxmZmZoaGgobNfT06tSXbGlw9zcXJ5j3759e3R1ddm5cycpKSk8ePCAefPmER8fz9tvv82NGzfQ1tZW2Pak5cIbm18kEjHIwIBBBgbsqUZu7esOHcpflCIRHhYWeFR6c8uAgwkJLAsNpb++Pvs7dyZbKmXy7du8bmFBb11dNkdGsj8hAR9XV1x1dfk+Lo6zKSl84eCAwSADDAYZ0GlPVf4OX5fzi1RFWHhYYOGhyF+SX8KDtQ+I2BhB99+7Y/SSEcVZxdyZdgf9fvoYDjEkcHYgmvaadDnUBYmhhLQLaYQsC6Hz/s7oaDnQtu1S2ratPo6iR4+KBpOINm3m0qbN3GqM5Bi6dDmERGKMVJqDr29POnbchZZW+1r3KXg9LCzkXq4xY8ZQUlLC999/Lzf2y+Dn54efnx/Dhw9XCGg1MDDghRde4Nq1a4wfP16eBRMXF8f3339PWlqavKKg4n0FNjaCi97SUhg4k5OFtXNjYyFt79VXhTV9U1O4c0dYoz9/XqjMl5MjBPe9/LIwS//9dzh0SFij//prITPAyEhIGSwuFtICv/22Lg971W2nT9f/WYmKErQJ6kKDDQAnJyd5SeCmwNKlY/jjD39ef30n58+vYdWqX9i2bTYqKuXOijFjnJkxox9vvPE9N29uQSJR4fPPz7B69UR5u44dLdDTK19DnTv3WxwdLVixomYtdpcK67RBV69yZts2Vp45g46RkWARW1gwe+tW9i1aRJ+pU2ndrh33Ll2iXY8e8sG/7MXy1r59vNe5M8c2bmT4G29w7cgRXv+///vPDf4SiYSVK1eydOnSRqt9UBN+/vlnUlJSqi38UXlGWXmJIjQ0lMLCQiZNmtQgTh8fH3x8fKo1OOriLCkpISgoqEZOGxubZ+paJyQk8H7p4qhIJJK/A4qKikhMTCQjI0MhrqG6bS2Jf8aMGYoyb48BETDL3BxbTU1m3r1LsUxGcE4O79vYMKo0R31f584kFRZyNSODnjo6JBQUcMTJCbVGKKIm1hBj97EdjwIekROUg9FLRoglYgz6G2DzgXB/dTnUhdtTbiOWCHwFCQW8cOwFtOy1Gu3e0NAoV28MCnoTff3+8gJDte2rzmCW/zaxmHbt2nHp0iVkMhkikQiZTEZ6ejpWVlb4+PhUCSKtTvTHwsKCESNGcO7cOZydnasoByYkVC8ABIrKfhVidrlypfxzWJhi9H3FdfgyVJSGqVDBut545RWhjoCmplBQqEMHIQVQJhNiE8r+Li4WihpB/VIEW1waoEgkYs+eBfj5hTNkyEe8/fZI9PW1q7T76qvXSErK5LPPThIcHE9JiYyOHS0qzLDKb+4dO/7H1atB/PTTQgVDojK09ITiFnnZ2exwd2fovHl0r5gQCgydNw8HNze+f+MNigoKuHLoEIOqkV/SMTZm3s6dnNi8mb3vvsuMzf+9IBoVFRXWrl3Lli1b5PW5NTU1m6w6pHmpkbVixQrOnz/P6tWr5S/348ePc7QsEgdBJ7xizfCPP/6YUaNGMXHixAZxlgXNbd++nbNnz/LNN9/IiyJdvXqV/6tg1MXFxSmkGu7ZswexWFxj3n3rUu/QswiZTMYvv/yi8HdlA7C6bS2Fv127dtjZ2TXa9+mrr880MzNeu3ePgEeP5IN/mZGwp1MnPo+K4oOwMDwsLBpl8K8Ix+8cidoWRV5kHrG7FGsG6Lrq0npya0LfD6UwsZCSvJJGHfwV3fg/kJ0dQIcOXzVoX3WQSqWEhYXh6OgoNwyCgoLo3LkzAwYMwMfHp97xZ506daK4uJjg4OBn5hlr00aQ/l2+XIj0ByHK39dXUCP08BCWHyr+/eGHDeNokVkA1tbGLFz4Il9//TsGBtWLKxsb6/D116/x6qvfcvduDPv3Vy/MExKSwMqVh/jyy1exs6vfC/fHxYtRUVXFfdu2ave/sXs3y5yc+GTkSObt3FljaVLXCROw69mTxLAw1LS0mqy/VFRUmrQkc02c33//PT4+PvKoeF1dXcaNG9dkBW9mz57NH3/8wblz50hJSeHTTz+lV69ezJgxA29vb7777jt5WysrK9566y0MDAyIjo7Gzs6OH374ocFlZO3t7dm0aRNbt25l8eLFLFmyhIMHD9KzZ0+uX7/Oe++9pzCgf/vtt2hqapKamkpRURFXr15VUCurCP0KUrPPIkJCQtDS0mpy7YfG5jcxMcHd3Z2PPvqIj2oRm2ko1traYn75MnOrUVpso67Om5aWnE1JYfPj1HetA+oW6rT7qB23X7mN7RpbVPUVX+12G+y41u0aJQUldNzZsUmux6NHdwkL+4CePa8gFmvWe19l5OTk4OfnR3JyMp07d1Yo9hMbG8vw4cORyWScO3eO27dvK2QF1YSyoNtHpbFbzwLi4+HLL4XP4eHl23NzheI+WVnCP2trxb+feQMgPDyJ4mIpLi72eHjs5M8/q89znzatLx9+eIQePWyrLfxTXCxl1qyvGTy4M/Pm1e9Bv37qFJf27+fjq1dR19aufubWrh0DZs8mJToaC0fHGs/le+IEfadN4+jHH3Ny82ZeaeR8fT09PaZPn46trS1jx47Fz8+vSaPwK+LQoUNMnTpVXvGuDJ988kmTcaqpqXH48OFqXjyPKlxzITrawcFBru//pKiupkFSNa5jLS2tKjr1tcHAwOCZeRmJxeJqjad169bJr3l1YlQ1CVTVVJWvqflNTEzYtGkTW7ZsoW3bxq0Xsic+noNduvBWUBC3e/dGr4ISY1R+Phbq6hioqvJFdDTLGpkbhMyBoDeDMJ1QNbZErCnGfJY5MqkMkWrj5/NLpTncuTMZB4dtaGuXvxPT0i6ip9e7xn3VoWItjopITk4mLS2Nv//+W34tvb2962UA5OTkAEIxs2cRZTGPjT2PbHEGQF5eIRs3Hufbbz2Ii0vjhReWsXv3xRoHcA0NSY2z348/PkZERDKnT6+s5IpKk5cXrojM5GR2zZvHhA8+oH2FamGP0tJQ19JCUiGQS6KhgaiWWXd8cDBhvr7M2LwZXRMT/u/VV3GdOBGrzp0bra8yMzPZuXMnO+uSlmoCTJs2jWnTpqHE48OoNLakpWPcuHGMHDmStm3bsmPHDvLz8xGLxXTt2pWcnBy0tLSYNGkSFhYWLFiwgJ07d2JqalplW5k73snJibFjx9Y7ILOx+PX19bly5QodOnTAw8NDOHk9hIjqg+PJyfTW08NVV5e/0tN5JziYn0qf9VyplJ8SElhra8swQ0N6+voyytiYTjVMMJoMTajjExy8EKk0l6KidKKjvwRkZGZ6Y27uXuu+huD27dtMnjxZ/r7Py8vjk08+ITY2Fss6pJrv37+PRCLBoWIyfQtGdbbxmDFw61aZQVzZQK7fOVq0ASCTyViyZD9r176ChoYEO7vWbNw4jaVLf2Lw4M7Y21d19ZWUyKpNKfL1DWPTphP8+utihWyB9PQczp27hYdHVYNip4cHRlZWTKoU4Oi1dy9jKrh6AWQlJchqSGXKSU/n2MaNLCjNUe47fTr/eHryzaxZfHLtWr3LBCvRcJSl+RUWFj513oZyPiuSvKdOneLUqVO1tpk1a5aCNntSUlKVbWW4c+cOkydPfur86enpOFby2Mkq14Bt6ISlpITvYmM5kZzM6dIKoIMNDBgfEIC1hgajjI1ZHhLCW6XphHqqqnTR1uaV27fZ26kToNdo1ynltxRkUhnJR5MxnaToBShIKCDLN4uSohIK4gpQt2jcd1CnTvuq2Srkxhkbj6lxHwRWGQOqi9t49OgRMplMYbKnqamJo6Mj//zzj1z1srpjk5OT+fPPPxk3blyVAMCWiLZthbLDjo6wapWQEWBkJBQrGjVKkCru1k34OzBQ2Fb2d5mB8MILwvHDhwulgCtqG7RIA+D69QesXv0LiYkZSKUlFSwbEdnZeYwZ8ylfffUaI0cKD1lxsZTff79FeHgSf/wRwIsvduOFFwS3WlGRlNmzv8HIqBU3b0Zw82ZE6Uu6iLNnb7JtW1XL89L+/fidOYPb5Mkc+egj+faHkZEkhYfzcgUFslAfH+5dvkxWcjIB58/TtUJ46LWjR/ll1SrsXV0pyMlBVU2NnPR0dIyMuHH6NF9MmsSMzZux6tJFOVo3Mnx9feVBeWfPnmXHjh289tprTfrQx8bGsnv3bm7fvk1RURFr167l1VdfrVeA2bPwMlKidmiKxbxnbc17FeSlx5mYKBgW/7i4yD/rqaryVzXu7caA8RhjhsmqN2jUzdXperpri+7LuLg4QkNDSUpK4v79+/Lgv6ysLE6cOIGqqiqZmZnolQZrZ2Zmkp+fT2BgIFZWVnTo0IGAgABAqMipq6tLTk4OqampzJgxo1GDPpsSUVFCymBNeO894V9NfwveEiEzoE5Pg6ypc7fqxJFmZp/crPxTRP9Nfe2GeH2UaD6Invf77wk9AE+K4ReauQOa+QsMG/ZZs/K/X1nz93l7/mXDhsmUD0Dz4QLDUfZ/M/b/hSMo8fwa4M87Jh9p5glYc1/+Zv4Ck5v59yuLASmhRANx+fJ99u79i6tXg8jNLcTMTB8NDQmjRnXH3X0gsbGpeHkFsnr1RCV/E+D+5cv8tXcvQVevUpibi76ZGRINDbqPGsVAd3dSY2MJ9PJi4urVSv4nQMe0I3YAACAASURBVEhCAke8vTlw+TLBpXLv5gYGuA8YwKTevelZ6lI/df06XoGBfHf+PIWlWTiDOndmdI8evDViBFqPGfOUEJKA9xFvLh+4THywwG9gbsAA9wH0ntQbu54C//VT1wn0CuT8d+cpLhT4Ow/qTI/RPRjx1gjUtR6TPyEEb+8jXL58gPh4QT/AwMCcAQPc6d17EnZ2gnrQ9eunCAz04vz57yguFuKAOnceRI8eoxkx4i3U1bVa7LtMaQAooUQ9kZ2dh4fHTo4d82Hp0pfx8voQKyshkj8vr5AjR7zp02cNiYkZvPvuS0r+RkZedjY7PTzwOXaMl5cu5UMvL4xKg+sK8/LwPnKENX36kJGYyEvvvqvkf0I4mJuzeuJEXnZ2pmuphPmu+fN5uVIMwzgXF8a5uKCmqsrW06cx1tHh/Jo1SGpIAa0vzB3Mmbh6Is4vO7O8q8A/f9d8nF9W5HcZ54LLOBdU1VQ5vfU0OsY6rDm/BhXJE/KbOzBx4mqcnV9m+XIhfmL+/F04O7+syO8yDheXcaiqqnH69FZ0dIxZs+Y8KiqSFv9OkxsAuVIpH4WHcykjg6KSEu7l5JBfGuWePXgwrVRUOJSYyO64OC6lp6MpFuOorU1eSQnqYjETTUxYbmODplhMUE4Ox5OT2RARQUFJCW01NDBVUyOpsJDuOjq8b2NDbz3F6FdprpTwj8LJuJRBSVEJOfdyKMkX+AdnD0allQqJhxKJ2x1H+qV0xJpitB21KckrQawuxmSiCTbLbRBriskJyiH5eDIRGyIoKShBo60GaqZqFCYVotNdB5v3bdDrXYlfmkt4+EdkZFyipKSInJx7lJTkC/yDs1FRaUVi4iHi4naTnn4JsVgTbW1HSkryEIvVMTGZiI3NcsRiTXJygkhOPk5ExAZKSgrQ0GiLmpophYVJ6Oh0x8bmffT0eivwK/u/efu/LmRm5uLmtprg4HiOH1/GuHEuCvs1NdVwdx/I4MFd6NNnDWlpjSs48rzz52ZmstrNjfjgYJYdP47LOEVJbzVNTQa6u9Nl8GDW9OnDo7Q0JX8jwcLQsNrPlWFWKmxlbmDwxIN/RRhWSNk2tKiZX78028vA3OCJB38FfkOLaj9X4dc3k3sJnoXBHypIAY8NCCCmoIBLzs749epFbP/+vFKpWMlMMzM2lrp95lpYcLNXL+65uTHH3Jz14eGMvnULGeCorc0qW1v6ld4QN3r1wtfVlX9cXAjJzWXAjRtcqHSDBowNoCCmAOdLzvTy60X/2P6YvqLIbzbTDLuNAr/FXAt63eyF2z03zOeYE74+nFujb4EMtB21sV1li34/gb/XjV64+rri8o8LuSG53Bhwg7QLlfgDxlJQEIOz8yV69fKjf/9YTE1fUeQ3m4mdnVCy1cJiLr163cTN7R7m5nMID1/PrVujARna2o7Y2q5CX7+fwN/rBq6uvri4/ENubgg3bgwgLU1x7VvZ/83b/3XBw2Mn9+/H4eExtMrgVxFWVkbs2jWf9PScRn1Qn3f+nR4exN2/z1APjyqDX0UYWVkxf9cucmrKe1LyNxgqFVLvxLUEjZbtEzdyYKm4gny7SFzzucv21dbmsfjF5caESFSz9kvZvtratEgD4HpWFhfT0lhta4t66cU2kkj4uUsXHCpJD+mrKq4aiIAl1tZ01dHBKz2d31NSamxrqa7OJnt7imQyVoWFybdnXc8i7WIatqttEasL/BIjCV1+7oKWgyJ/ZYlLRGC9xBqdrjqke6WT8ntKjW3VLdWx32SPrEhG2KoK/FnXSUu7iK3tasRiYb1IIjGiS5ef0dJSFI5QVa0s3yrC2noJOjpdSU/3IiXl9xrbqqtbYm+/CZmsiLCwVfLtyv5v3v6vC+fO3eLo0WsALF8+ts72o0Z1x9rauNEe0ued/9a5c1wrrfNQn2qa3UeNwrhCWp6SXwklajEAkksFTK5UshrVxGJmVahyVxu6lOY0R+TlNbhdYbLAn35FkV+sJshX1gfaXYTz5kXkNbhdYWGywJ9+pZLlp4a5+az68WsLef15eRENbqfs/+bt/7rw/fd/AtC+vXm1YlTVYf36xgvvfd75/yyVVzZv3x6zeuroT66hAJOSXwklKhkAvfX00FJR4Z3gYDZFRFBcITd7sqmpfFZaG0JzcwHo3KpV7e1KB56K7fR666GipULwO8FEbIpAVlzObzrZVD4rrQ25oQJ/q8618+eF5lVpp6fXGxUVLYKD3yEiYhMyWXE5v+lk+ay0Vv7cUOG8rWqX+s3Lq9pO2f/N2/91wctLUCvr3Nmy3scYGzee5vjzzh/o5SV4sBogo61jbKzkV0KJOqAKgrt5X6dOzLp7l9UPHnAwMZEt7dszxtgYx3qole2MjcU3K4vRxsYMrqXASWJhIR+EhSERiRQqYkmMJHTa14m7s+7yYPUDEg8m0n5Le4zHGKPtWDd/7M5YsnyzMB5tjMHgmvkLEwsJ+yAMkUSE/eYK/BIjOnXax927s3jwYDWJiQdp334LxsZjFIpX1Mgfu5OsLF+MjUdjYDC4Zv7CRMLCPkAkkmBvX14eWNn/zdv/tSElJZvMzNzSQe3pS/c+7/zZKSnkZmYCoNsMg9rzzl8ZE7ZuRV1SfYBbek5Ok/NvnbAViXr1/DnpT4F/6wQkkuonJDk56Y3Od+fOHU6cOMG+ffuIjIwEwNramrlz58pLm9e238nJqW4DAGBK69Y4aGkx7/59bmRl8bK/P8MNDdnVsSO2mlXLN3pnZLA8NJTo/HyKZTK+dXRkvkX1EZIbwsMpAcJzc3HT0+NXJyc6VFrbbj2lNVoOWtyfd5+sG1n4v+yP4XBDOu7qiKZtVf4M7wxCl4eSH52PrFiG47eOWMyvnj98QziUQG54Lnpuejj96oRWh0r8raegpeXA/fvzyMq6gb//yxgaDqdjx11oalYtWpKR4U1o6HLy86ORyYpxdPwWC4v51fOHbwBKyM0NR0/PDSenX9HSUtRpVPZ/8/Z/zUZDuTdCU1Ptqb9wn3f+4gr1FdQ0NZX8zYwTy5fTzcam2n1fnj3Lkv37m5R/+Ynl2HSrnv/sl2fZv6SJ+ZefwMamW/X8Z79k//4ljcrn5OSEk5MTgwYNYuDAgQDs37+fQYMGKbSpbX+9DACAbjo6+Li4sDc+njUPHnAhLQ1XX1+8XVywrzRguOnrs7V9+3qRrGvXDmNJ3WkROt10cPFxIX5vPA/WPCDtQhq+rr64eLugZV8pGM5Nn/Zb68ffbl07JMb14NfphouLD/Hxe3nwYA1paRfw9XXFxcUbLS3FtTd9fTfat99aP/5265BI6rbelf3fvP1fHQwNWyEWiygpkfHwYdZTf+E+7/ytDA0RicXISkrIevhQya/EcwmLCpM762oCPOvaXxPEILiGU4uKhA0iER4WFgT16cM4ExNSiopY8+BB084yEgspShX4RWIRFh4W9Anqg8k4E4pSiniwpon5CxMpKkoV+EViLCw86NMnCBOTcRQVpfDgwZom5Vf2f/P2f23Q0JDQpYvwQN27F6vkf8qQaGhgXVo4K/bePSW/Es8lVCroKoiriQmra3+tBkBkXh4XK+WF66uq4unkhL6qKv7Z2U364/Ii80i7qMivqq+Kk6cTqvqqZPs3MX9eJGlpFxX5VfVxcvJEVVWf7Gz/JuVX9n/z9n9dmDatDwC3b0cREZFcr2NycgoardDR887fZ9o0AKJu3yY5on7ZGwU5OUp+ZaEtJepjAADsT0ioav2LxVhpaNCumrWnhtxc9WmbsL8qv1hDjIaVBprtngJ/QtW1I7FYAw0NKzQ12zU5v7L/m7f/a8Nbb43EolSBbNWqX+psX1QkZeXKgwrr50r+x8fIt97CsNTF+cuquvUbpEVFHFy5UmH9XMmvhBK1GAC/p6SwOCSER1KpfOfPiYmE5ubyYYU6ypmlxR7Si+t+uBvSNuX3FEIWhyB9VM6f+HMiuaG52H1Yzl+cKZyrOL3uczakbUrK74SELEYqLZcwTUz8mdzcUOzsPiw/Z3Fm6f91R3w2pK2y/5u3/2uDnp4Wnp6L0dJSx9PzHzZsOFqjUVFQUMT8+buYN28Y6uqNIwf6vPNr6emx2NMTdS0t/vH05OiGDTXyFxUUsGv+fIbNm4fkMYvQKPkVUVKBS1oqT16t4VG6r7Y2jwNZSTl/ibTmc5ftq63NY/HLys9XUiKtmb90X21tWhoUggC/io7mh7g4OmlrUyiTYSKR8LezM666QvrPocREvoqOBsAzMZFfEhMBmG9hwUobG9ppapJZXMzGiAi2R0cjLb1xFgYF8aalJRMrSdtWRvRX0cT9EId2J21khTIkJhKc/3ZG11XgTzyUSNTWKOHzL4kk/pKIXm892n3YDqORRvLzRG6OJGJjBNJc4ULcn38fq3etMJ1YB3/0V8TF/YC2didkskIkEhOcnf9GV9e1dEA6RFycIMqRlHSEpKQjaGk5oKpanvMslebx6NFtRCIVuSRkSMhS2rR5DVPT2quj1dT/nbS12RQRwZcxMTwsteoPJyVhKJGw1NoaW01NfkpIYN2DB0Tl5zPG2BhrDQ0uZ2QAsDQkhEEGBnwSGcl2Bwfm1CAuVFP/S0wlBLoHknCg3EuQfCKZ6C+iMZlggqatJul/pxO6LJQsvyx0uunQ6oVWZFwW+EOWhmAwyIDITyJx2O6A+Rzzx+r/iIhP5PEASUmHycj4B4nEELFYndzcUIqK0jAzm46t7TqSk4+RkXEZAD+/IYhEYvr0CUMsfrxI9n79HDl3bhXu7jtYv/4w588HsGDBCFxd7TE11SMlJRsvr7scPuzNhg1T6dq1baM+qM87v2O/fqw6d44d7u4cXr+egPPnGbFgAfauruiZmpKdksJdLy+8Dx9m6oYNtO3aVcnfSIhJTVX47NyuXbXtokpVSBMzMiiWSlFtpHoAqTGpCp/bOVfPnxIl8GckZiAtlqKi2kj8qTEKn9u1c65hEiOMTRkZiUilxaiotPxaeyLZsGGP5R+9++gR/W7cILO4mGsuLvSqUFzmkVRKJ29vTnftSjed2gVBHqccfElhCdd7XSfbP5uOuzti4VE1/SxgfAB6vfSw+cCGRv8CQEDAOBwcvkBT005he3DwO8TE7MDO7mNsbesOXrvA8HpzZhUXM8jPj1vZ2Xxqb8/KSuk4Q2/eZKaZGXPbtKly7MmHD5kQEMCytm0Vsgca8vOD3g4i9ttYzGaa0eVgl6oD+JfRpF1Io+uprohUFfW4H558SMCEANoua6uYPdCAL5Caep7U1P/h4PCFwvb8/Ci8vbugoqKNm1sgEomRwn4fn248enSXgQPTUFVVzGW/cKFh9dBzcwvYt+9vjh/34f79OFJTszEy0sHOrjXTp/dl9uwB6Og0XbrWf43/CA1TDCzIzeXvffvwOX6cuPv3yU5NRcfIiNZ2dvSdPp0Bs2ejqaPTZL//v8Y/+UjN939IQgKH//2Xg1euyMsBm+nrM2/oUMb27KlQDvjPO3fYdeECRaUezPqWAz5Sy+VPCEng38P/cuXgFXk5YH0zfYbOG0rPsT0VygHf+fMOF3ZdQFok8Ne7HHAtXyAhIYR//z3MlSsH5eWA9fXNGDp0Hj17jlUoB3znzp9cuLALqVQIpq5vOeDJ9bz9IyMjsbW1LZ0IRWBT6d1f1/5GNwAAfk1KYtqdOwwyMMCrQonIVwMDGWtiUueM/wnGX7JvZePr4otmO0163+2NWK088jE/Kp+7s+/i/Ldz3YUhHvMLREd/gbX1ewrb0tMv4ec3GB2d7ri6+iAS1W0BNsQAAHiQl0fXa9fQEIsJ6tNHnt73Y3w8t7Kz+apD9fntj6RSzC9fxsvZmZ66uo/186WPpHh39qYgoQC3u24KdQJkUhk3+t3ghWMvoN5GvdpjL5tfxtnLGd2euo/V/4mJv2BoOBg1tYpytDJu3hxGWtpfvPDCsWq9LOHhH5GVdZ1u3X6r2v8NNACUaFw01ABQonFRmwHwVK5/c1/+Zv4CzW0APFHZoqmtWzPR1JS/09PZW2oh7ouPR1dVtV6D/5NAp7sOVu9akRuaS9SWKEXLdWkIDtsdGr0qVEWYmc1UHOCkOdy7NxeRSJXOnffVa/B/HNhparLJ3p7UoiLeCwkBIDAnh30JCVV0AUpkMsYGBDDtzh2Cc3J4y9ISiUjE0pAQXHx9ufuoYSVbVVqp0OGbDsiKZAS9FaToJtwRQ+sprRUGf1mJjICxAdyZdoec4Bws37JEJBERsjQEXxdfHt1tGL+h4ZBKg7+gApiW9hetW09VGPxTUn7D19eVmJgdGBu/hJHRCJKSPLl5cyghIUtQQgkllHje8cR1C//P0REDiYRloaFcTEtjb3w82+opUPPEg+EGOzSsNIj4JIK8B4LGfOq5VNRM1dB1blrZUjW11gp/h4WtJC8vnHbt1tKq1QtNyr3Q0pI+enocSEjg5MOHvH7vHns7dUKtUv6nDHiQm4tfdja/JCXxsKiIC2lpeGdmEpSTQ1qp9kBDYDLWBJPxJqRdTCPxZyEGpDCxkOQjyVi9Y0XlL5D7IJdsv2ySfkmi6GERaRfSyPTOJCcoh6K0oifq87y8SEJDV6CmZoqj4w6FfUVFqeTmhpCWdp6UlP+RlxdBWtpFHj26S05OsPLJV0IJJZ57PPE01UxNjc/bt2fuvXu87O/P7d69qwxETYWyGWnA+ACCFgbR9URXwjeE0+33bk+1E9PT/yYm5lt0dLpjY/NB01ttIhF7OnWim48Pr9y+zY+dOmFXTaqgikhEoJub/O+hN2/ydYcOLGv7ZAFajt84knYxjZD3QjAebUzo8lDsNtlVWfcXqYhwCyznvzn0Jh2+7kDbZY0RICbj/v3XkUof0bnzj1WU/szN52BuPqfUG3CWrCw/HBy207HjbuVTr4QSSijRGB4AgNfatKGTtjZ5JSUEl1ale1owGSfMSFP/l8qtF29h4WGBxEDy1Pgruv47dWo613+VQVhbm9fbtKFEJuN2PVz5R5KS+CstjfWNoCqobqmO3cd2FCYVEjAuAERgMMCg1mOSjiSR9lcaD9Y3jqpgbOx3pa7/KZiavlJr29DQFURGbpaXHVZCCSWUeJZQUiG1UiqVNnh/kxoAR5OTcdTWRkMsZkFQkEIu+1MZDL9xRKQqIj82nzZz2zxV7tDQFeTlRWBruxodna5PjfdBXh6BOTl01NZme3Q0N+tQC1QRCbNzQ0njGEdWC63Q7qRN+qV07D6xq7O9SEXglxg+OX9eXgShoStRUzPB0fH/6uYWqQAiJBJD5ZtECSUqISAqCvcdO9CePZtbFZQGS2QyfvPzw3rBAs74+RGamMjKQ4cQT52K1YIF+JdWn3uQlMSA9esZvXkzPqGhbD19GpWpU2n/7rvcrHC+P+/cQWPmTNb++itplSYt/v/zZ3Xv1Sx2XExuZvkkMic9h3Nfn2Ol80rCfMMI9Qlly7gtTBFN4ZMXPyE1VkgRDL0WyuvGr3Ng+QFSY1NJepDEIodFHFxxEM81nniu8WRd/3VMV5tOpH9klT7w9/8fq1f3ZvFiR3JzM8v5c9I5d+5rVq50JizMl+vXTzJvXmumTVPFx+e4vF18fDBLlnTk889fISEhhKSkByxa5MDBgyvw9FyDp+ca1q3rz/TpakRGNlzZNCYmpgJXfIP314Qnnq6G5ebyTUwM57t3Z0tUFOsePGB1WFiN0ehNAXVLdcTqYiT6EhA9vQcnPd2L2Njv0NHphq3tqqfGW1BSwtx79/ihY0cSCwsZeOMG8+7dw9fVVT7QV0aXVq0A6N5IKUoiFRGatprk3Mupl8elVReBX6f7k/LLuHdPcP136rS3XkV+WrXqglis8dS8M0oo8Syha9u2/LRwIWdv3mT81q3c+PRTTHR1EYtEjHF25rebN3m5NMvrs5kzMdHVZa2nJ7qly446Gho4mJuza/58VMRierVvT1JmJj9duoRd6/K4HUtDQ94bM4aPp06t8h26vdgNA3MDVnRfwf+3d+dxUVf748dfwzDMMCqbwgjIpoC7ueUSeutqy7eyxBQt03LLn6V1Na9paZq5Ua6ZaZnltTIrr2ZZ1yVNb6mZmIoLBsq+oyIg2zDb7w8QQYYBXJq6vp+Ph49H8Vne8znnfM7n8znn8zln5dMrmf7tdBQOChq5N6LfuH5cSL5AcI/yCcGmbZvG24+/TU5iDm46NwDKSssYOH0gj097vPKGYMZ3M/AOLR9z5HLGZXav2c3QuUOtzibYufP/4e7uzSuvdGHlyqeZPv1bFAoHGjVyp1+/cVy4kExwcPl4JA4Ojrz11mO4uV17IdnLK4iWLbsxceIGHByUnDt3mBkzvsPbO7TiWpHB7t1rGDp0bq2zCVpTdTrgq5599llGjRrFoEGDAGwur2s64JtqASitciFSOzgwPSCAto0asSotjV/z8/+nTxqTqZCYmLEVTf//QqGoeREsKro9k3f8IzaWCb6+hGi19HVzY7SPD8euXGF5xSBN1gQ7O6NxcKCVVmuX9HIOdsZB44C21c3FT01dzeXL+9DpItDpan5DU1qajMlUvRuqUaMONcZruNaCk8nIke8SFvY6paXlLyWWlRlZt24vzz77HufOZbFmzW602hGMGbOGkpIy8vKKCA9fzPz5W8jMvMzSpdtRKIayZMl2zBWjlq1fv49evWZy8mQyW7b8ygsvrGPVqp2sWrWT+++fR+fO0ygtNZCfX2xz/1FR523+vtTUS7c1flxcBiNGvItSOYzDh8+V34DqDfy//7eWsWPXcOZMKosXf4tO9xzx8dmV6XrwYCz33juH2NgMm/Ezz53j3ZEjeT0sDENpKVA+Be7edet479lnyc/O5vNXX2XLvHnsXLWKnatW8UJAAB9OmEDqmTNM79qVKW3bciG5/Eug/JwcZvbuzfcrVpCXnc3uNWsYodWyZswYykpKKMrLY3F4OFvmz+dKxQA3te3/9wMHePXuu9nw8rXPfQtzc/nw+efZsXIlCb/9dlvj13V8p/butfn7Lmdl1Sv+VeF3342niwtDli6t/J4fwPG6d7qmDhhAnzZtGPv++xhMJl7btIklI0eirLLevGHDcNVqmb5xY+Xfln//PbOHDKn9YqR04JF/PELswVi+mPXFtb87OKCo8mCjUCh4Yf0LFFwoYMv8LVzOuMzhzYcrL/4Avm19Ky/+AKvHrMa3jS8DXxlYe3wHJY888g9iYw/yxRezao3frdsA+vQZztq1/6/yu//t25fyxBMzcXAoH3zI17dt5cUfYPXqMfj6tmHgwFcaVN917NiR2bNnk5iYiMViwWKxkJCQwOzZsyunCra1/La2ALwYG8uEFi0IqbioODk48EHbttx79Cjjzp7ltx49/rAXAv9oV5v+W7acY7Xp32DIJTd3D40atbulcTdmZWEGnmp+7e5zcUgI3128yOz4eMI9PWtMHQzlLw62dHamxS0aHrTBLQYOCpxbOqNucePxS0oSOX++vOm/dWvrTf8ZGRtqDMCk1YbUOhxwSIg3U6c+xqBBixk58l2++moKTk6OhIf3wGg0ExLSnJCQ5jRr1oQZMz7HaDSRmnqJxx/vzpgxfy+vEKc+RmJiDseOJeBQ8enpxYtX+OabV9DpXDGZzAwePA6A06dTmTdvCz///CYajQqNRsXzzz9Y5/5r+31+fk1ve/z161/gzJlUTp5MplevEFQqRzw8GrNgwVM4OCho396PjRt/5pFHFnLo0HyaNm1CWFhrHnigE61b+1BcrK81vndICI9NncriQYN4d+RIpnz1FY5OTvQID8dsNOKq09F76FCCunQBYO+6dTRyc2PUihWoNBqmbtnCq3ffTWlFF5jZZKJ3RASPTp4MwIPPP0+TZs34fMYMTEYjl1JT6f744/x9zJjKMmBr/32efpr/vPMOXkFBPPziizT28KBj//74deiAb5s2tz1+Xfu39fvcmzevV/zKm3QnJ7555RXufvVVJv/rX7w3dmwtXWoKPnr+eTpMnUq/uXNZOXo0bo0a1djXugkT6Dd3Lk/36UNCTg5De/dGU0cXpE9rHyZ/OZnIRyMJ7BxI76G9ra7XpFkTnnv/OZYPW07qmVRe+PiF6ue867U6cOeqnfx+4HeWRC/BQWn7euTj05rJk78kMvJRAgM707v3UKvrjR79DlOmtGPbtrfo3TsCi8WMr2/bKnXOtYHxdu5cxe+/H2DJkujKG4Q/ixu+Oi9MTCSltJThzat/l93XzY3HPT05XVjItHPn/pCDsBgsmEvMlWPP3265uT+SlvY+TZrcRVDQzBrLzeYSYmNfsjqJzc3Yk5vL9HPnWB4aWu3vHioVrwYGUmI28/Tp0+hrGYu7hUZDI+WtK4CVY/3XM901LTQoG91ofAsxMWMwmYpo3XoVTk6eNdbIz/+Fy5f3Vg7BfJWTk2ed/f9LljxDTk4+r7zymdXlERG9uf/+jowatZqtW3+tvDhW3oQtHsmxY4l89dUv/PrrOUJCvNHpyiuBLl2CKlqE9ERELGPZsmcIDfVu0P7r+n23M75KpWTjxpeYOXMTCQnZfPjhHsaPv7/yZgNgyJBehIf3IDx8MXp99c876xP/mSVLyM/J4bNXaj4hXb04pp4+zabXXmPKV1+h0mjKm16Dghjx9tusHDECY1kZu1ev5uEXX6y2fe+ICDrefz+rR43i161ba1z8bO0fYPq337ItMpKj335b47fd7vj12b+t31ef+FX5eniwbdo0Pv7xRz7cu7fW9fybNWPS//0fxxMTca/oXrzeve3a8dz99zP2/feJTkqifz2eSAHuevAunln2DKtHryY5OrnW9XoM6kGr7q3IOp+Fk9b6EN+ZcZlsnL6RUctHoWulq1/8ux7kmWeWsXr1aJKTo63fgDRpxujRK9m6dT5ffTWHxx77p/X4mXFs3DidUaOWo9O14s+mwTcAe3Nz+ftvvzEzPp7YoiK+zqn+ZvW/c3KIJVFxMgAAIABJREFUrnjBY2VqKsNPn+ZoQcHtuxj/mMvZ8WexmC0Uny8mflY8V47dzulryz8/AwsGQx5Hj/YlKqpX5b9ff+3GTz95k5W1kUaN2t+SiOeLixkdE8ODx46RXVbG6rQ0qg7feKSggO0V43AfKSjg3t9+Y2tOzTfeb9XTf9HZIhLnJZL/a3k3T9yUOC5+d7HO7W7m6T8j419cvrwfhUJJSsqyamkeFdWLQ4dCiYoKQ6Op2b/n6OiKUmn73QO12pFvvnmFHTtOsGbNbqvrvP32CHbuPEHLljUrEmdnJz777EVeeuljdu48QXj43TXWmTBhLX36tOHpp/s2eP91/b7bHb9duxbMnPkETzyxhEaNNAQF1RzoKzJyOAEBnjz77HtWJ6uxFd9RreaVb77hxI4d7F6zpsZyfVERyyIieHbZMnyue7/o72PG4BUUxLwHHuCeYcNQWnnKHPH225zYuRNdLePY29q/V1AQM7Zv54Px44k/erTGtrc7fl37r+v31Sd+tQtrcDAfv/ACkz76iEOx1sfMSMjOxmgycXdwMOPef7/Wfb0REcG5zEyeDAtr0Pn+8IsP03dEX94e+DYFF61fP458fYSwJ8PITc9l26JtNbtpjSZWjlhJ+7+3p/9z/RsW/+EX6dt3BG+/PZCCAut1W1jYk3h6BhIU1BWVysropyYjK1eOoH37v9O//3N/ypbsBncB9PfwoL9H7U9TQ7y8GHKbRwGs9vTbzwOPfh60W9/uD4qoICws8Q/NpGCtlvXt2rG+nfVj7OHiwt6uXevcT/NbdAPQqG0jgl4PIuj1oAZtp25+4/F9fEbj4zP6hrZVKpugVDaqcz03t0bs2PEaffq8jtbK+OGrV+9i27ZpPP30Sv72t7YEBFRvhejevRWtW/vQzcpkJevW7eXEiSSOHFlUa/y69l/X77vd8f/xj0eYMmWD1ZuLq03D69e/wCOPLOTVVz+ncWNNg+I3cnPjtR07eL1PH9TXdWOtnTCB0Hvuoe+IEVa3feSll/h02jT8OnSwunzX6tVM27aNlU8/Tdu//Q3P68bCqGv/QV27MmnDBpYMGsQj//hHjTi3O35d+6/r99UV/3pPhYVxJjWVwUuX8re2bat3xZWVMX/rVlaPG0d6bi6d/vlPPty7l+f617zIXm3yd1A0/O3ssavGMv/B+awYtoLQ3tVbPTNiMzh/5DzDFw3HxdOF90a9R48neuDX/tpgZFvmbSEnMYfp306v/tCYnouHb91fBI0du4r58x9kxYphhIZa74pQqTQ41NLNvWXLPHJyEpk+/dvrWpDT8fDw/Wt3AYi/Hg9H+74F7+hhn/hKpQalsn4vH/r5NeW772Ywbdqn1f7+wQc/EB7egwce6MTUqY/x9NMrMRpNVi+C1zt9OpVXX/2cr756GWfn8qbKixevEF2lebO++6/t9/0R8RX1qMRVKiVbtvyTXbuiOXcuq97xr2rq58eM777j02nTrrU6rltH0vHjjHn33cq/ndm3D0vVri4bv+2HDz6gR3g4nR54gMemTmXl009jqjJFdr32D9z10EM8OX8+X8yaZS3hb2/8eqR9bb+vrvhXGa77fHvesGHc07o18dnXXu60WCxM2bCB1wcPRqNS0UqnY/6TTzL1k084XzE7bFVXpxI2W+qecsZsNlf7nl2pUjJ1y1TysvOqt0BeLmLL/C0MnVvePx/2VBid/68z7454F0NF99P5I+f5euHXjP9gPG7N3apte3zH8frFV6qYOnULeXnZtbcHW6pvU9lqe/4IX3+9kPHjP6j2tUBR0WWOH9/x1+0CEH9dLva+AXCxT3yFwgkHB43VZYWFpezeHc2uXdFculTeddSxoz9ffjkFtdqRzMzLTJ36Cd99dww/v/JZBvv378DBg7E888yqam++HzuWSHx8Njt3nqjcl15vICJiGZ06BbBr1wlWrPiexYu/5dFHFxEU5FXn/k+dSrH5+6q6HfGvHp/JZGbr1l8B2Lz5FwyGaxeL3buj+fHH05w/X34BcHFx5j//eRVvb7c645cWFhK9ezfRu3ZVvpXu37EjU778Eke1mvTff2f9Sy/ROiyMPWvX8v2KFWyZN4/vli1DUfHkVZSXx8kffuBiSgq/HzhQ+bsuZ2byydSpHPvuO5r6lT8Zdujfn9iDB1n1zDNkx8fb3H9eVhbnDh/ml82bMVUMm33vs88yZPbs6hek2xS/zuPLyLD5++oTH6BIr+fzAwfYc+oU+8+cqXbD98mkSXSpmGQmKj6ehxYs4FBsLKYqFz0HhYIrJSUMiIxkV/S1PvNLV66wft8+ADYdPEjadV8dVHU54zIHNh7g+H+OkxaTVvn3xh6NmbF9RuVLfYf/fZjXer4GFtAX6Ssv6k2aNiHpRBLLhiwj6UQS7458l8ZNG5N4LLFyHIBPp33KrLBZePjUfPq/fDmDAwc2cvz4f0hLu/b1VuPGHsyYsb3aS31Xm/ePHv2W7OwEoqN3kZx8ssoyA+++O5LGjZuSmHischyATz+dxqxZYXh4+Pxprgk3NRvgrXCjswH+r/yAhs4GeDM2ZWVV+3rgjz78rE1ZNH+q+R+e/gZDLgUFUTRt+lDN9JfZAO1KZgO0L5kN8K8xG6DcAMgNwP+mOzz/H/jhgTs7+e/0AviAnH53dPrb+filC0AIIYT4Ezp16hRvvvkmQUFBKBQKFAoFAQEBzJ07l1OnTtW5vC4yNqoQQgjxJ3R1tL/77ruPe++9F4ANGzZw3333VVvH1nJpARBCCCH+onx9r3026O/v3+DlcgMghBBC/AUpq4zgam3cgbqW16ZGF0ChycSKlBQO5eXRXK1GqVDgolTSzcWFAqORoTodW3NymBIXh4XyoX9LzGYKTSYmtmjBaB8f4oqL+SQzkwWJifio1XR3cSGttJSmKhVzWrYkzM2t1h9kKjSRsiKFvEN5qJurUSgVKF2UuHRzwVhgRDdUR87WHOKmxIEF3Pq6YS4xYyo00WJiC3xG+1AcV0zmJ5kkLkhE7aPGpbsLpWmlqJqqaDmnJW5hNuKbCklJWUFe3iHU6uYoFEqUShdcXLphNBag0w0lJ2crcXFTAAtubn0xm0swmQpp0WIiPj6jKS6OIzPzExITF6BW++Di0p3S0jRUqqa0bDkHN7faR8Wyd/rbPX6hiRUrUjh0KI/mzdUolQpcXJR06+ZCQYGRoUN1bN2aw5QpcVgs0LevGyUlZgoLTUyc2ILRo32Iiyvmk08yWbAgER8fNd27u5CWVkrTpirmzGlJWJit4y9kRcoKDuUdorm6OUqFEhelC91culFgLGCobihbc7YyJW4KFiz0detLibmEQlMhE1tMZLTPaOKK4/gk8xMWJC7AR+1Dd5fupJWm0VTVlDkt5xBmI//tXf7tnf52P/8LC0lZsYK8Q4dQN2+OQqlE6eKCS7duGAsK0A0dSs7WrcRNmQIWC259+2IuKcFUWEiLiRPxGT2a4rg4Mj/5hMQFC1D7+ODSvTulaWmomjal5Zw5uNkYFc/+9Y+9y/+dnf5/tGo3AKmlpTx4/DiPNWvG9s6dK6eWzS4rY8CJEwz09MRDpWKcry8bs7IoMZvZUTGO9cLERMbExGC0WHjO15d5rVqxKCmJkd7eRAYHY7BYCI+Opv+xYxzt0aNyetqqSlNLOf7gcZo91ozO2ztXziFfll3GiQEn8BzoicpDhe84X7I2ZmEuMdNlR3n8xIWJxIyJwWK04PucL63mtSJpURLeI70JjgzGYrAQHR7Nsf7H6HG0R+X0tNXil6Zy/PiDNGv2GJ07b6+YRx7KyrI5cWIAnp4DUak88PUdR1bWRszmErp0KR/UITFxITExY7BYjPj6PkerVvNISlqEt/dIgoMjsVgMREeHc+xYf3r0OErjxjVH9LJ3+ts9fmopDz54nMcea8b27Z1RVuR/dnYZAwacYOBATzw8VIwb58vGjVmUlJjZUZH/CxcmMmZMDEajheee82XevFYsWpTEyJHeREYGYzBYCA+Ppn//Yxw92oMOHawdfyoPHn+Qx5o9xvbO21FW5H92WTYDTgxgoOdAPFQejPMdx8asjZSYS9hRkf8LExcyJmYMRouR53yfY16reSxKWsRI75FEBkdisBgIjw6n/7H+HO1xlA5W8t/e5d/e6W/38z81leMPPkizxx6j8/btKCqeqsqyszkxYACeAwei8vDAd9w4sjZuxFxSQpcdFef/woXEjBmDxWjE97nnaDVvHkmLFuE9ciTBkZFYDAaiw8M51r8/PY4epbGVEf3sX//Yu/zf2elvD5VtBRZg+OnTuDk68lZISLV55XVOTmzt1InCKiNFqa9rZng5IAClQsHHGRkAKABVlX2oFAom+/ujN5vZaGXEKCxwevhpHN0cCXkrpPLkB3DSOdFpaydMhdfiO6irxw94OQCFUkHGx+XxUYBCVWUKSZUC/8n+mPVmsjZaiY+F06eH4+joRkjIW5WZD+DkpKNTp62YTIVVmlmqD8UaEPAyCoWSjIyPr0asNkWwQqHC338yZrOerKyN1g7frulv9/gWGD78NG5ujrz1VkjlxQdAp3Ni69ZOFFbJf/V1+f/yywEolQo+rsh/hQJUVfJfpVIwebI/er2ZjRutHb+F4aeH4+boxlshb1VWfuXHr2Nrp60UVsl/9XX5/3LAyygVSj6uyH8FClRV8l+lUDHZfzJ6s56NVvLf3uXf3ulv9/PfYuH08OE4urkR8tZblRef8vg6Om3diqmwyvl/3bDaAS+/jEKpJOPjj6+e8CiqjNmvUKnwnzwZs15P1saNf8L6x97l/85Of7vfAPx0+TIH8vIY7eODtUEn/TQam2P8X92miY3Z5mytc/mny+QdyMNntA/WfoDGT4PXEBtzDFRso2yivKF1Ll/+iby8AxXjzdf8ARqNH15eQ6hr57Ynnal9HXunv93j/3SZAwfyGD3ax+qop35+GobYyP+r2zSxkf+21vnp8k8cyDvAaJ/RKKykgJ/GjyE28v/qNk1s5L+tdexd/u2d/nY//3/6ibwDB/AZPdrqsLsaPz+8bMxlf3UbZZMmN7SO/esfe5f/Ozv97X4DsKNimMZuNhKwu4tLrcsWJiVhsliY6OdndXmp2czi5GRcHR0Z4e1dY/mlHeXxm3SrPb5L99rjJy1MwmKy4DfRenxzqZnkxck4ujriPcJK/Es7KiqnbrXHd+lee/ykhVgsJvz8JlqPby4lOXkxjo6ueHvXnPDD3ulv9/gV+d/NRv53t5H/CxcmYTJZmFhL/peWmlm8OBlXV0dGjLB2/Dsqjr+bjePvbuP4F2KymJhYS/6XmktZnLwYV0dXRljJf3uXf3unv93P/4qm5CbdbJz/3W2c/wsXYjGZ8JtYy/lfWkry4sU4urribWXCH/vXP/Yu/3d2+ttL5TsAaaWlQPnc8vWVqdcTmZREQkkJerOZfd26cZ+7e7V1juTnsyAxkbNFRYRqtaxp0wZ/Tc1x2UvTyuOrPOofX5+pJykyiZKEEsx6M932dcP9vurx84/kk7ggkaKzRWhDtbRZ0waNv5X4pWkVTZUe9Y+vzyQpKZKSkgTMZj3duu3D3f2+6vHzj5CYuICiorNotaG0abMGjabmZxr2Tn+7x6/If48G5H9mpp7IyCQSEkrQ683s29eN+67L/yNH8lmwIJGzZ4sIDdWyZk0b/P2tHX9axfF7NOD4M4lMiiShJAG9Wc++bvu477r8P5J/hAWJCzhbdJZQbShr2qzB30r+27v82zv97X7+p1Wc/x4NOP8zM0mKjKQkIQGzXk+3fftwv+776/wjR0hcsICis2fRhobSZs0aNFY+07J//WPv8n9np7/dbwC0Fc2yV0ymem/srVYzIzDQ5jo9XF2ZGVT3tLFKbXl805X6x1d7qwmcYTu+aw9XgmbWI37FbHEm05X6x1d7Exg4w3Z81x4EBc2sc1/2Tn+7x6/I/ysNyH9vbzUz6sj/Hj1cmTmzPsevrTj+Kw04fm9m1JH/PVx7MLMe+W/v8m/v9Lf7+V8x/bDpSgPOf29vAmfUcf736EHQzJl/gfrH3uX/zk5/e6nsArjHtXy2o2MFBXb5Ia73lMcvOGan+K73lMcvOGaX+PZOf7vHr8j/Y8fsdfz3VBz/sTuy/Ns7/e1+/t9Tcf4fs1P+273+sXf5v7PT3+43AEN1Olqo1axKS8NkZe5ms8XCJmtv798iuqE61C3UpK1Kw2KqGd9itpC16TbG1w1FrW5BWtoqLJaaTyEWi5msrE23Lb6909/u8YfqaNFCzapVaZis5L/ZbGHTptt5/ENpoW7BqrRVmKzkv9liZtNtzH97l397p7/dz/+hQ1G3aEHaqlVYrLSCWcxmsjZt+h+uf+xd/u/s9Lf7DYBWqWRzp07EFhUx7NQpssvKKlfKMxp5IyGB/lX6Z/RmMyU2mostgMFisblO9SYgJZ02d6IotohTw05Rln0tvjHPSMIbCXj0vxbfrDdjKrGxbwtYDBbb61zXBNSp02aKimI5dWoYZWXX5nk3GvNISHgDD4/+VSpEPSZTCbZ+gMViqGOda+yd/naPr1WyeXMnYmOLGDbsFNlV8j8vz8gbbyTQv0r+6/VmSmzkrcUCBoPF5jrVj1/L5k6biS2KZdipYWRXyf88Yx5vJLxB/yr5rzfrKbGRtxYsGCwGm+v8mcq/vdPf7ue/VkunzZspio3l1LBhlGVXOf/z8kh44w08+lc5//V6TCU28tZiwWIw2F7nT1X/2Lv839npXxez2Vz53yYrdWpdy2tTbSCgXq6uRPfqxZsJCfQ8cgQvJycCNBqCtVqmBQTgoVKRazCwOSeHqIIC9GYzq1JTGezlhXeV7zLjiotZn5GB2WJh24UL9HR1JUKnq/ZduNVmmF6u9IruRcKbCRzpeQQnLyc0ARq0wVoCpgWg8lBhyDWQszmHgqgCzHozqatS8Rrshdr7WvziuGIy1mdgMVu4sO0Crj1d0UXoqn0XbL0ZqBe9ekWTkPAmR470xMnJC40mAK02mICAaahUHhgMueTkbKagIAqzWU9q6iq8vAajVl97s7i4OI6MjPVYLGYuXNiGq2tPdLqIat+FWmPv9Ld7/F6uREf34s03E+jZ8wheXk4EBGgIDtYybVoAHh4qcnMNbN6cQ1RUAXq9mVWrUhk82AvvKvkfF1fM+vUZmM0Wtm27QM+erkRE6Kp9l279+HsR3SuaNxPepOeRnng5eRGgCSBYG8y0gGl4qDzINeSyOWczUQVR6M16VqWuYrDXYLyr5H9ccRzrM9ZjtpjZdmEbPV17EqGLqPZd9J+x/Ns7/e1+/vfqRa/oaBLefJMjPXvi5OWFJiAAbXAwAdOmofLwwJCbS87mzRRERWHW60ldtQqvwYNRV/mypTgujoz167GYzVzYtg3Xnj3RRURU+y79z1n/2Lv839npb0tqamrlf2dkZNCqVasGLa+NwnL//RZ7NkE8cIdPSP3Dn2BGdDsnwB2d/w/88MCdnfx3egF8QE6/Ozr96zj+U6dO8fXXX7N+/XqSkpIACAoKYtSoUQwaNAjA5vKOHTvWvwVACCGEEH8OV6cDnj17ts11bC23xeq0Qel6PW8nJ+O4dy/u+/fzUUYG+UZjjfX2X77Mk6dOodizB8WePUyOi+OiwQDAr/n5hEVF4b5/P4uSkihuQL+EPl1P8tvJ7HXcy373/WR8lIExv2b8rM+z+Nn3Z/Yo9hAVFkXez3mVy0qSSogeFM1e5V5iX4pFn6FvUMLo9ekkJ7/N3r2O7N/vTkbGRxiN+VbXPXkygoMHg4mOHsTJk0M4eXIIJ048yp49Cn75pT1mc8NixxUXM/3cOdQ//ohizx4eOn6cM0VFACSVlDAuJgbHvXuZFBtLgpU+rvrm362MedPbxxUzffo51OofUSj28NBDxzlzpmL7pBLGjYvB0XEvkybFkpBQc/vDh/Pp1SsKhWIPzZv/xOefX3thrLjYxMyZ8SgUe3jkkeMcPWr9TfP9l/fz5KknUexRoNijYHLcZC4aLlaU518JiwrDfb87i5IWUWwqtrqPiJMRBB8MZlD0IIacHMKQk0N49MSjKPYoaP9Le/T1LAs3WrbNejMZ6zMqt014M4GS+Pr1Q37+eRa+vj+jUOwhLCyKn6vETEoqYdCgaJTKvbz0UiwZVWLm5xtZsiQZH5/ybYOCDvLDD7mVab9sWQoKxR4efvh4tX3eqmMuTSnlzDNn2KPYwx7FHpKXJFerL7I+z2Jfo30canOInK05tcY36/UkzJ3Lj2o1exQKYsaMofj8+WvH+csv/NKuHftdXUleuhRzlfdkAIrOnOFHtZpjDzzAySFDKv8dDApij0JB5qefNqgeSE19j//+tyknTjxWWa+cPDmEffsa8+OPWoqL42oeg1lPRsZ6fv7Zlz17FCQkvElJSXy9Y95I+Y0rjmP6uemof1Sj2KPgoeMPcaboTMW5n8S4mHE47nVkUuwkEkoSbMY/GRHBweBgogcNqky/E48+yh6Fgl/at8esrxk///Bhonr1Yo9CwU/Nm5P1+eeVy0zFxcTPnMkehYLjjzxCwdGjdabBjdbnN5Jf9ma1BcBXreaVgADWpafTWqtlrI+P1Y3vc3fnPnd3fNRqlqek0L1JE5pV9LP0dHWlmZMT+9u04a4mDRv6UO2rJuCVANLXpaNtrcVnrPX4zYc3RxuiJap3FM6Bzrj1vTbLl3OgMx79PHDt5Urg9MAGJ4xa7UtAwCukp69Dq22Nj8/YWtfVaPzo0OFTHBw0VS5ok1EoHGnffkONcaPrEqrV8lZICL3d3HgiOho/tZr2jRoBEOjsjL9Gw/tt2jCuyhzQN5J/tzLmTW8fquWtt0Lo3duNJ56Ixs9PTfv2FdsHOuPvr+H999swbpz17Xv1cmX37i506HAYB4fyt9qv0mqVPPmkjuPHC/j++y7U9irCfe73cZ/7ffiofViespzuTbrTTNWsojz3pJlTM/a32c9dTe6qNR39NH582uFTNFXKwuS4yTgqHNnQfkONMdRrc6Nl20HtgM9oH/J/ySdrUxYtZ7esd7kbPrw5ISFaeveOIjDQmb5VYgYGOtOvnwe9erky/bqYrq6O/POfAQwe7EX37kdQqxX06+demfbduzdhxAhvPv20/W05Zo2/hvaftMdYYOTCNxfwfNwTR9drVZsuQkfy0mS6/tDV5kBDDmo1LefMwdHVlbgpU3Dt3RttcPC14+zdm0bt29Nu3brKz9aqKrt4kfaffIJu2LAqNycpHO7YEc+BA/EeObJB9YDZXESPHr/h7HzteC9c+IacnC2Ehi5Hqw2teQwOanx8RpOf/wtZWZto2bJhT4Y3Un5DtaG8FfIWvd1680T0E/ip/WjfqH3FuR+Iv8af99u8zzjfcXXG1/j50eHTT3GoMlhY3OTJKBwdab9hQ405AKD83YEuu3dzuEMHcHBAN3Ro5TKlVovuyScpOH6cLt9/D3W8h3Qz9fmN5Je92Zw42EmhqDHpizVvhYTQ3cWF6efPVz5pLk5OZqhO1+CLf1UKJ0WNST+u53K3C/5T/Mn+IpuCI9ee7EyFJnJ/yCXgnwE3lUAKhVOdF/BmzQZUKyyXL/+XlJSVBAZOtzl8ZF3CPT15yd+f9ZmZHM4vb304lJ9PVllZrRfSG8m/WxnzprcP9+Sll/xZvz6Tw4crtj+UT1ZWWa0X/8qy4OLImjVtSE4u5Z13Uqoti4xM4oMP2tbn/OetkLfo7tKd6eenk1/R6rM4eTFDdUNtXvwBBjQbUK3y/O/l/7IyZSXTA6fbHEr1VpdtByeHOs8da+6+24UpU/z54otsjlSJWVho4ocfcvmnjZhBQc589FE7YmOLWbasPP0vXTLw9tvJrF3b9rYfc5vVbXB0cSRuavUnrdT3UgmaFVTvUQb9XnoJlx49SHjjDYxVxsUo+O03NC1aWL34AygcHfEMD7/2B4uFmDFjUKhUtP3ggwbnRZMm3atdTAyGi5w9Ox43t774+b1ku2J3cGrwg8fNlt9wz3Be8n+J9ZnrOZx/uOLcP0RWWVa9Lv4AzQYMqHbxv/zf/5KyciWB06fbHArY0cWFNmvWUJqcTMo771RblhQZWZ7+9Tn5b6I+v5n8+lPeAFgz4vRphp06Ve1vKoWC9e3acdFgYNq5c/ycl0dSSQlPN29+y3/w6RGnOTWsevyWc1ui8ddwdvxZLMbydxoT5iYQNCuo2qxit4LFYuDgwVakpa2u/JuHR79rFZWpkJiY0TRu3JGgoNk3HW9hq1YEaTSMi4khXa9nQWIiS0Nr3kmuTU8n8MAB9FU+B7ndMa2VhYZsX2v8ha0ICtIwblwM6el6FixIZOlSK/FHnGbYdWXh0UebERGhY86cBJKTy4eX3b79Al26NMHPT1Ov+CqFivXt1nPRcJFp56bxc97PJJUk8XTzp62kwQiGnbr2xNevSlkoNBUyOmY0HRt3ZPYNloX6lO3Ck4X81+O/XDl+5ZaU8blzW+Lvr2H8+LMYK2LOnZvArFlBlbMErl2bTmDgAfR6c40buKeeas6cOfGcO1fMhAlnWbo0FGdnh1t6zOlr0zkQeABzlfhqHzXBi4K5+N1Fcv5d3tSvz9CT/2s+XoO86n/T7+BAuw8/pCwnh/jXXis/781mEufNo+Xcude62tau5UBgYGWztFtYWLUn1NT33iN3717avPceTjpdg/Ohar0CcPbs85hMRbRvvx6F4lp6pqev5cCBwAZ3NVpT3/K7Nn0tgQcCa3QJLGy1kCBNEONixpGuT2dB4gKWhi6t/zH3q1KXFhYSM3o0jTt2JOi6Pu7r0x6g2aOPoouIIGHOHEqTk8ufwLdvp0mXLmhqmaOkrnS3VZ+fPj2CU1XO/frm11/6BsBbrcbHSjNMh8aNmRUUxLr0dGbHxzeowm9Q07y3GrVP9fhKrZI2q9twJfoKKctTKDxZiFlvxqWHy234BUo0Gv9ax4yOi5vZXodwAAAP5UlEQVRKaWlaRVOR001H0yqVfNSuHTFFRYRFRbE8NBRnK0/17o6OBDg746hQ/GExaysL9d2+1vhaJR991I6YmCLCwqJYvtz6BcTbW42PT834K1e2RqVS8MILv1NcbOLDDzOYPLlh4293aNyBWUGzWJe+jtnxs2utxLzV3viorXexTI2bSlppGhvab8DpBstCfcq2g9YBTYAGZSPlLSnhWq2S1avbEB19heXLUzh5shC93kyPKjHd3R0JCHDG0bFmeXv33dY0aeJI795RDBrkRevW2lt+zI7ujjgHOKO4Lr7vBF9ce7sS+1IsxgIj5187T/DC4AanQeNOnfB/+WXS1qwh/9dfSX//fZo/9RSOLlV/gzvOAQEoHGv2pJbEx3N++nS8hgyp1iVwo7KyNpGT829CQt7G2bn6J16Oju44OwegUNzad7ptlV93R3cCnANwvC6mVqnlo3YfEVMUQ1hUGMtDl+Ps4HxD8eOmTqU0La286d+pevza0r71ypUoVCp+f+EFTMXFZHz4If6TJ99wGtiqz9Vqb9S1nPu28qs2e/fuJSAggHvuuYfIyEgiIyOZM2dO5Sd9x44do0+fPuh0OmbOnMmECRPo27cv+/fvr9xHfdaplo4NTZDFISG1LpsRGMg7KSmklJZittyerwtDFluP3/Thpuie1JHwRgK5e3Pp+EXH2xJfoXCgW7d9VpddurSL9PS1tGw5lyZNOt+ymPe6uzPA05OdFy/W+oQfodMRcQNPGTcT01ZZqM/2NuPf686AAZ7s3HmxxlNmZfxaykLz5k5ERoYwYcJZHnroOJGRwVYvVHWZETiDd1LeIaU0BbOltjRYbPXvuy7tYm36Wua2nEvnmywLdZVtbbCWnsd73tJy/vDDTXnySR1vvJHA3r25fHFdzIgIHRER1stb06Yqpk8PZOrUuAbNLdCQY9ZF6NBZia9wUNB2bVt+7forJx45QbNHm+EcdGMXoFZvvEHOv/9NzJgxNO7QgY5ffnndb4hAFxFRs5XQbObMs8+ibNyYtmvW3HRe6PWZxMZOwsOjHy1aPF9juU4XgU4XcUvzv67yG6GLIKKWmPe638sAzwHsvLiz3i+91qhLd+0ife1aWs6dS5PONePXlvZOzZsTEhnJ2QkTOP7QQwRHRlq9QavXb6ijPg+p5dyvK79q079/f+666y78/f2ZUWWOg4CA8m6vrl278sADD2A0GlmwYAEAr732Go8//jhpaWm4uLjUa52bagGwZUVKChNatCCptJRZ8fH80UKXhGIqNuHayxVHtz/2C0ejMY+YmLE0adKVoKDXbum+D+Xn46tW4+nkxNiYGKtD9d5qNxvzprc/lI+vrxpPTyfGjo2xOjytLePH+xIaqkWpVBAW5naD5XkFE1pMIKk0iVnxs+q9XZ4xj7ExY+napCuv3aKyYI+yvWRJKMXFJnr1csWtATEvXTJw8GAeDz/clFdeOUdamv4PPebGHRrj86wP+Ufyb+odIAdnZ1q9+SZFMTG0eL7+FXnK0qXkHTxImzVrUDVrdtP5cPbsc5jNBtq1+xhrc9Xfajdbfg/lH8JX7YunkydjY8ZaHVrYZl2al0fM2LE06dqVoNcaHt93/Hi0oaEolErcwsL+8Pr8ZvLLwUpL6VNPPXWtdUxZvZWvc+fOXLlyhawqw7TXZ51bfgPwc14e2WVlzG/VikktWvBOaipH/uCJZVRNy1/ycdD88f0tsbEvYjBcoH37Dbe0Ke5CWRmLk5J4JzSU99q0IaqggOUpKbf1WG425k1vf6GMxYuTeOedUN57rw1RUQUsX96wY1YowN1dheYGy8LPeT+TXZbN/FbzmdRiEu+kvsORgiP12vbF2Be5YLjAhvYbajSR/pXKdtOKmA1JQ4sFJk36nSVLQvjgg7aYzRaef/7sH37MqqYqFA6KOkf/q3s/TSt+Q/3eHymKiSH+9ddpPnw4Xk88cdN5kJHxERcvfk9o6FI0moA/JN9vpvxeKLvA4qTFvBP6Du+1eY+ogiiWpyxvWF364osYLlyg/YYNN/b0rlCgcnevd57dyvr8VufXyZMniY2NtbqsuLiYdevW8fe//52QWlpj61rnltQm2WVlLElOZmFFX8WC4GD81GrGnDlD2S14Ke3P7sKFb8jM/IyWLefSuHGHastKS1PIzz90Q/s1WyxMjI1lWWgoTg4OhHt6MtjLi9nx8ZwvLr4tx3KzMW96e7OFiRNjWbYsFCcnB8LDPRk82IvZs+M5f774D8nP7LJsliQvYWGrhRXleQF+aj/GnBlDmbnM5rbfXPiGzzI/Y27LuXS4riyklKZw6AbLwl/F/PmJDB2qIyjIGT8/DYsWBfPddxerjcvwv8piNHLm2WdReXjQ+t13ayxv6GQ2paUpxMW9TNOmD+Hr+1zNcpr95S0/hpspv2aL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+ ], + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "import stim\n", + "\n", + "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", + "circuit.diagram('timeline-3d')" + ] }, - "id": "cyctTUyzQ-cQ", - "outputId": "0f3c5a8a-0d09-49e4-e3b1-e193152bf2bf" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smaller det_beam\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 2 / 1000\n", - " Time: 5.8029 s\n", - "\n", - "Larger det_beam\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 2 / 1000\n", - " Time: 6.1866 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=3,\n", - " beam_climbing=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"Smaller det_beam\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=5,\n", - " beam_climbing=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Larger det_beam\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] - }, - - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "YDnwv2dacTbf" + }, + "source": [ + "# Estimating code distance with Stim" + ] }, - "id": "0VrW2z8sSXtN", - "outputId": "6f69c1ff-1c04-4dc6-d1a0-32aa0777d56b" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "First version\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 9 / 1000\n", - " Time: 1.1290 s\n", - "\n", - "Second version\n", - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 19 / 1000\n", - " Time: 0.9446 s\n", - "\n" - ] - } - ], - "source": [ - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " # no_revisit_dets=True,\n", - " # at_most_two_errors_per_detector = True,\n", - " det_penalty = 10,\n", - " # det_orders=tesseract_decoder.utils.build_det_orders(\n", - " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "print (\"First version\")\n", - "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "\n", - "tesseract_config2 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " # no_revisit_dets=False,\n", - " # at_most_two_errors_per_detector = False,\n", - " det_penalty = 0,\n", - " # det_orders=tesseract_decoder.utils.build_det_orders(\n", - " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "print (\"Second version\")\n", - "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", - "print_results(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BoEALeo3OYGp" - }, - "source": [ - "# Decoding Wild Stabilizer Codes under Code Capacity Noise with Tesseract\n", - "\n", - "\n", - "\n", - "* checkout https://www.codetables.de/ for a qubit stabilizer code\n", - "* full table of qubit codes: [here](https://codetables.de/QECC/Tables_color.php?q=4&n0=1&n1=256&k0=0&k1=256)\n", - "* copy the stabilizer matrix for a code, such as: [this one used below](https://codetables.de/QECC/QECC.php?q=4&n=21&k=8)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pJ1gEKAgPbHO" - }, - "outputs": [], - "source": [ - "import time\n", - "import tesseract_decoder\n", - "import stim\n", - "import numpy as np\n", - "import numpy.typing as npt\n", - "from galois import GF2\n", - "from typing import List, Tuple\n", - "\n", - "\n", - "def paulis_from_symplectic_matrix(check_matrix: npt.NDArray[np.uint8]) -> List[stim.PauliString]:\n", - " n = check_matrix.shape[1] // 2\n", - " paulis = []\n", - " for i in range(check_matrix.shape[0]):\n", - " paulis.append(\n", - " stim.PauliString.from_numpy(\n", - " xs=check_matrix[i, :n].astype(bool), zs=check_matrix[i, n:].astype(bool)\n", - " )\n", - " )\n", - " return paulis\n", - "\n", - "def rank(H):\n", - " return np.linalg.matrix_rank(GF2(H))\n", - "\n", - "def stabilizer_code_logical_operators(\n", - " check_matrix: npt.NDArray[np.uint8]) -> Tuple[npt.NDArray[np.uint8], npt.NDArray[np.uint8]]:\n", - " check_matrix = np.array(check_matrix, dtype=np.uint8)\n", - "\n", - " r = rank(check_matrix)\n", - " n = check_matrix.shape[1] // 2\n", - "\n", - " stabilisers = paulis_from_symplectic_matrix(check_matrix=check_matrix)\n", - "\n", - " tableau = stim.Tableau.from_stabilizers(\n", - " stabilizers=stabilisers, allow_underconstrained=True, allow_redundant=True\n", - " )\n", - "\n", - " x2x, x2z, z2x, z2z, x_signs, z_signs = tableau.to_numpy()\n", - "\n", - " num_logicals = n - r\n", - "\n", - " Lx = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", - " Lz = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", - "\n", - " Lx[:, :n] = x2x[r:]\n", - " Lx[:, n:] = x2z[r:]\n", - " Lz[:, :n] = z2x[r:]\n", - " Lz[:, n:] = z2z[r:]\n", - " return Lx.astype(np.uint8), Lz.astype(np.uint8)\n", - "\n", - "\n", - "def pauli_to_observable_include_target(pauli: stim.PauliString) -> List[stim.GateTarget]:\n", - " obs_pauli_targets = []\n", - " for i in range(len(pauli)):\n", - " if pauli[i] != 0:\n", - " obs_pauli_targets.append(stim.target_pauli(i, pauli[i]))\n", - " return obs_pauli_targets\n", - "\n", - "\n", - "def append_observable_includes_for_paulis(circuit: stim.Circuit, paulis: List[stim.PauliString]) -> None:\n", - " for i, obs in enumerate(paulis):\n", - " circuit.append(\n", - " \"OBSERVABLE_INCLUDE\",\n", - " targets=pauli_to_observable_include_target(pauli=obs),\n", - " arg=i\n", - " )\n", - "\n", - "\n", - "def code_capacity_circuit(\n", - " stabilizers: npt.NDArray[np.uint8],\n", - " x_logicals: npt.NDArray[np.uint8],\n", - " z_logicals: npt.NDArray[np.uint8],\n", - " p: float\n", - ") -> stim.Circuit:\n", - " \"\"\"Generate a code capacity stim circuit for a stabilizer code\n", - "\n", - " Parameters\n", - " ----------\n", - " stabilizers : npt.NDArray[np.uint8]\n", - " The stabilizer generators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (r, 2 * n) where r is the number of stabilizer\n", - " generators and n is the number of physical qubits.\n", - " `stabilizers[i, j]` is 1 if stabilizer i is X or Y on qubit j and 0 otherwise.\n", - " `stabilizers[i, n + j]` is 1 if stabilizer i is Z or Y on qubit j and 0 otherwise.\n", - " x_logicals : npt.NDArray[np.uint8]\n", - " The X logical operators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", - " and n is the number of physical qubits.\n", - " z_logicals : npt.NDArray[np.uint8]\n", - " The Z logical operators of the code, as a binary symplectic matrix.\n", - " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", - " and n is the number of physical qubits.\n", - " p : float\n", - " The strength of single-qubit depolarizing noise to use\n", - "\n", - " Returns\n", - " -------\n", - " stim.Circuit\n", - " The stim circuit of the code capacity circuit\n", - " \"\"\"\n", - " num_qubits = stabilizers.shape[1] // 2\n", - " num_stabilizers = stabilizers.shape[0]\n", - " stabilizer_paulis = paulis_from_symplectic_matrix(stabilizers)\n", - " x_logicals_paulis = paulis_from_symplectic_matrix(x_logicals)\n", - " z_logicals_paulis = paulis_from_symplectic_matrix(z_logicals)\n", - " all_logicals_paulis = x_logicals_paulis + z_logicals_paulis\n", - "\n", - " circuit = stim.Circuit()\n", - "\n", - " append_observable_includes_for_paulis(\n", - " circuit=circuit, paulis=all_logicals_paulis)\n", - " circuit.append(\"MPP\", stabilizer_paulis)\n", - " circuit.append(\"DEPOLARIZE1\", targets=list(range(num_qubits)), arg=p)\n", - " circuit.append(\"MPP\", stabilizer_paulis)\n", - "\n", - " for i in range(num_stabilizers):\n", - " circuit.append(\n", - " \"DETECTOR\",\n", - " targets=[\n", - " stim.target_rec(i - 2 * num_stabilizers),\n", - " stim.target_rec(i - num_stabilizers)\n", - " ]\n", - " )\n", - "\n", - " append_observable_includes_for_paulis(\n", - " circuit=circuit, paulis=all_logicals_paulis)\n", - " return circuit\n", - "\n", - "\n", - "def parse_symplectic_matrix(text: str) -> npt.NDArray[np.uint8]:\n", - " rows = []\n", - " for line in text.strip().splitlines():\n", - " line = line.strip()\n", - " if not line or line[0] != '[' or line[-1] != ']':\n", - " continue # skip malformed lines\n", - " body = line[1:-1]\n", - " if \"|\" in body:\n", - " left, right = body.split(\"|\")\n", - " bits = left.strip().split() + right.strip().split()\n", - " else:\n", - " bits = body.strip().split()\n", - " row = [int(b) for b in bits]\n", - " rows.append(row)\n", - " return np.array(rows, dtype=np.uint8)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 343 + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r2MFDDBMvkq3", + "outputId": "cc6b8bfe-9adb-4b55-9c47-ed2580177b44" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "estimated distance: 5\n" + ] + } + ], + "source": [ + "distance_estimate = len(circuit.search_for_undetectable_logical_errors(\n", + " dont_explore_detection_event_sets_with_size_above=6,\n", + " dont_explore_edges_with_degree_above=3,\n", + " dont_explore_edges_increasing_symptom_degree=False))\n", + "print(f'estimated distance: {distance_estimate}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UuYvEwq9cgYc" + }, + "source": [ + "# Create DEM, detection events and observables with Stim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6oCW5jRsXy8-" + }, + "source": [ + "### Can't decode with pymatching..." + ] }, - "id": "pH_b3u1rBogl", - "outputId": "846dd807-82ed-4139-e46d-a3cfe65a7681" - }, - "outputs": [ { - "data": { - "text/html": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "x6TQbGZ7b06k", + "outputId": "c4caa541-cef7-498d-a410-c00b64cf8a79" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Traceback (most recent call last):\n", + " File \"/tmp/ipython-input-2195602962.py\", line 5, in \n", + " circuit.detector_error_model(decompose_errors=True)\n", + "ValueError: Failed to decompose errors into graphlike components with at most two symptoms.\n", + "The error component that failed to decompose is 'D46, D49, D52, D63, D66, D73, D75'.\n", + "\n", + "In Python, you can ignore this error by passing `ignore_decomposition_failures=True` to `stim.Circuit.detector_error_model(...)`.\n", + "From the command line, you can ignore this error by passing the flag `--ignore_decomposition_failures` to `stim analyze_errors`.\n", + "\n", + "Circuit stack trace:\n", + " during TICK layer #46 of 75\n", + " at instruction #104 [which is a REPEAT 3 block]\n" + ] + } ], - "text/plain": [ - 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+ "source": [ + "import traceback\n", + "\n", + "try:\n", + " # decompose_errors=True needed for DEM to be matchable\n", + " circuit.detector_error_model(decompose_errors=True)\n", + "except:\n", + " traceback.print_exc()" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Example QEC code:\n", - "text = '''[1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 0|0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0]\n", - " [0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0|1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0]\n", - " [0 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0|0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0]\n", - " [0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0|0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0]\n", - " [0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0|0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0]\n", - " [0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0|0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0]\n", - " [0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0|0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0]\n", - " [0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0|0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0]\n", - " [0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", - " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]'''\n", - "\n", - "H = parse_symplectic_matrix(text)\n", - "\n", - "LX, LZ = stabilizer_code_logical_operators(check_matrix=H)\n", - "\n", - "circuit = code_capacity_circuit(\n", - " stabilizers=H,\n", - " x_logicals=LX,\n", - " z_logicals=LZ,\n", - " p=0.025\n", - ")\n", - "\n", - "circuit.diagram('timeline-3d')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cK2Mf2fTCAWO" - }, - "source": [ - "## Computing minimum distance with Stim + SAT Solver" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "ZdVK4Dq1Bp1B", - "outputId": "61d8eb3e-7274-41c0-bd6b-af3e3ac75d54" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Distance of code: 4\n" - ] - } - ], - "source": [ - "# Note: this maxSAT solver only works for very small codes.\n", - "# For larger codes, use the solvers at https://maxsat-evaluations.github.io/2024/\n", - "from pysat.examples.rc2 import RC2\n", - "from pysat.formula import WCNF\n", - "\n", - "wcnf = WCNF(from_string=circuit.shortest_error_sat_problem())\n", - "\n", - "with RC2(wcnf) as rc2:\n", - " rc2.compute()\n", - " print(f'Distance of code: {rc2.cost}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GQjQkhD4C4rK" - }, - "source": [ - "## Sample new data for this stabilizer code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7iOIl7vjC3uG" - }, - "outputs": [], - "source": [ - "num_shots = 1000\n", - "dem = circuit.detector_error_model()\n", - "sampler = circuit.compile_detector_sampler(seed=23845386)\n", - "dets, obs = sampler.sample(num_shots, separate_observables=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "63xjagbBCj8x" - }, - "source": [ - "## Decode code capacity noise data with ILP and Tesseract" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "ye5W7BJHX8DJ" + }, + "source": [ + "No need to decompose errors using tesseract:" + ] }, - "id": "IM7W37cHaKfT", - "outputId": "3f2f7666-9586-4cb6-b422-1d295bf8747c" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tesseract Decoder Stats:\n", - " Number of Errors / num_shots: 60 / 1000\n", - " Time: 0.2323 s\n", - "\n", - "ILP: num_errors / num_shots = 61 / 1000 time 11.911995649337769 s\n" - ] - } - ], - "source": [ - "tesseract_config = tesseract_decoder.tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=1000,\n", - " det_beam=10,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", - " # no_revisit_dets=True,\n", - ")\n", - "\n", - "results = run_tesseract_decoder(tesseract_config.compile_decoder(), dets, obs)\n", - "print_results(results)\n", - "\n", - "# Run and time ILP decoder\n", - "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True).compile_decoder()\n", - "start_time = time.time()\n", - "obs_predicted = ilp_dec.decode_batch(dets)\n", - "num_errors_ilp = np.sum(np.any(obs_predicted != obs, axis=1))\n", - "end_time = time.time()\n", - "print(\n", - " f'ILP: num_errors / num_shots = {num_errors_ilp} / {len(dets)} time {end_time - start_time} s')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K0QvSpXQIwgf" - }, - "source": [ - "# Visualize the Tesseract's decoding\n", - "For visualizing tesseract we use the `verbose` flag to get the decoding information.\n", - "## [Link to visualizer](https://quantumlib.github.io/tesseract-decoder/viz/)\n", - "* `verbose` - A boolean flag that, when `True`, enables verbose logging. This is useful for debugging and understanding the decoder's internal behavior, as it will print information about the search process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AVu7idoTYAdM" + }, + "outputs": [], + "source": [ + "dem = circuit.detector_error_model()" + ] }, - "id": "DzWRL1cNjyix", - "outputId": "4a3df084-499f-43b2-97ba-1874b697f06a" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloning into 'tesseract-decoder'...\n", - "remote: Enumerating objects: 2086, done.\u001b[K\n", - "remote: Counting objects: 100% (606/606), done.\u001b[K\n", - "remote: Compressing objects: 100% (304/304), done.\u001b[K\n", - "remote: Total 2086 (delta 493), reused 317 (delta 302), pack-reused 1480 (from 3)\u001b[K\n", - "Receiving objects: 100% (2086/2086), 3.17 MiB | 8.58 MiB/s, done.\n", - "Resolving deltas: 100% (1667/1667), done.\n" - ] - } - ], - "source": [ - "# Remove the existing directory and its contents\n", - "!rm -rf tesseract-decoder\n", - "# Clone the repository\n", - "!git clone https://github.com/quantumlib/tesseract-decoder.git" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vFDn06Xach0_" + }, + "outputs": [], + "source": [ + "num_shots = 1000\n", + "sampler = circuit.compile_detector_sampler()\n", + "dets, obs = sampler.sample(num_shots, separate_observables=True)" + ] }, - "id": "ZNKaqvN8dE-X", - "outputId": "8d80e5bc-c30b-469d-d9cd-452d89604c30" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "\r", - " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r", - "100 44154 100 44154 0 0 230k 0 --:--:-- --:--:-- --:--:-- 230k\n" - ] - } - ], - "source": [ - "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D9%2Cd%3D9%2Cp%3D0.002%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_X%2Cq%3D121%2Cgates%3Dcz.stim' > d9r9colorcode_p002.stim\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Cdo-oenEdF1-" - }, - "outputs": [], - "source": [ - "import stim\n", - "\n", - "circuit = stim.Circuit.from_file('d9r9colorcode_p002.stim')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "JrX13vNQcrm3" + }, + "source": [ + "# Decoding with Tesseract and ILP decoder" + ] }, - "collapsed": true, - "id": "awJYxAOMTc3t", - "jupyter": { - "outputs_hidden": true + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Uds8S04a-z-G" + }, + "outputs": [], + "source": [ + "import tesseract_decoder\n", + "import tesseract_decoder.tesseract as tesseract\n", + "import numpy as np\n", + "import time\n", + "\n", + "# Helper functions for benchmarking\n", + "\n", + "def print_results(result):\n", + " print(\"Tesseract Decoder Stats:\")\n", + " print(f\" Number of Errors / num_shots: {results['num_errors']} / {results['num_shots']}\")\n", + " print(f\" Time: {results['time_seconds']:.4f} s\")\n", + " print()\n", + "\n", + "def run_tesseract_decoder(decoder, dets, obs):\n", + " # Run and time the Tesseract decoder\n", + " num_errors = 0\n", + " start_time = time.time()\n", + " obs_predicted = decoder.decode_batch(dets)\n", + " num_errors = np.sum(np.any(obs_predicted != obs, axis=1))\n", + " end_time = time.time()\n", + "\n", + " return {\n", + " 'num_errors': num_errors,\n", + " 'num_shots': len(dets),\n", + " 'time_seconds': end_time - start_time,\n", + " }\n" + ] }, - "outputId": "2da93975-0ede-41cb-897b-23b7da9dca93" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tesseract: num_errors / num_shots = 51 / 100 \n", - " time 16.068939685821533 s\n" - ] - } - ], - "source": [ - "import tesseract_decoder\n", - "import tesseract_decoder.tesseract as tesseract\n", - "import numpy as np\n", - "import time\n", - "import contextlib\n", - "import io\n", - "\n", - "num_shots = 100\n", - "dem = circuit.detector_error_model()\n", - "dets, obs = circuit.compile_detector_sampler().sample(num_shots, separate_observables=True)\n", - "\n", - "tesseract_config1 = tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " verbose=False,\n", - " create_visualization=True,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", - ")\n", - "\n", - "tesseract_dec = tesseract.TesseractDecoder(tesseract_config1)\n", - "\n", - "# Run and time the Tesseract decoder\n", - "num_errors = 0\n", - "start_time = time.time()\n", - "for shot in range(len(dets)):\n", - " obs_predicted = tesseract_dec.decode(dets[shot])\n", - " obs_actual = obs[shot]\n", - " if np.any(obs_predicted != obs_actual):\n", - " num_errors += 1\n", - "end_time = time.time()\n", - "print(f'Tesseract: num_errors / num_shots = {num_errors} / {len(dets)} \\n time {end_time - start_time} s')\n", - "\n", - "# Print with the visualizer\n", - "tesseract_dec.visualizer.write('/content/tmp.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MuQb8XQlpvU6" - }, - "outputs": [], - "source": [ - "!cat tmp.txt | grep -E 'Error|Detector|activated_errors|activated_detectors' > logfile.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D0Tx2eY3ctFw", + "outputId": "64f388af-f1db-4869-873f-3ab714ee8e9c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tesseract decoder configurations --> TesseractConfig(dem=DetectorErrorModel_Object, det_beam=65535, no_revisit_dets=1, at_most_two_errors_per_detector=0, verbose=0, pqlimit=10000, det_orders=[[89, 86, 85, 83, 81, 88, 84, 82, 87, 74, 71, 78, 75, 69, 67, 63, 77, 66, 61, 72, 64, 65, 79, 68, 62, 73, 80, 70, 42, 76, 43, 46, 33, 32, 50, 44, 31, 57, 35, 36, 59, 51, 30, 58, 60, 48, 39, 45, 49, 34, 25, 7, 47, 5, 18, 26, 21, 2, 40, 24, 12, 29, 28, 55, 37, 56, 54, 53, 38, 15, 3, 16, 52, 20, 9, 19, 13, 8, 11, 10, 17, 41, 22, 6, 14, 23, 0, 1, 27, 4]], det_penalty=0, create_visualization=0)\n", + "\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 3 / 1000\n", + " Time: 3.6089 s\n", + "\n" + ] + } + ], + "source": [ + "# setup the tesseract decoder configuration\n", + "tesseract_config = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " no_revisit_dets=True,\n", + " # verbose=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=1, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print(f'Tesseract decoder configurations --> {tesseract_config}\\n')\n", + "\n", + "tesseract_dec = tesseract_config.compile_decoder()\n", + "\n", + "results = run_tesseract_decoder(tesseract_dec, dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "INvMKs7zc5T_" + }, + "source": [ + "#Decoding with ILP decoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9Npo7ibac4x5", + "outputId": "51af3bd2-5f53-43c8-a16d-f595a9596bde" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ILP decoder configurations --> SimplexConfig(dem=DetectorErrorModel_Object, window_length=0, window_slide_length=0, verbose=0)\n", + "ILP stats:\n", + " Estimated time for full shots 1115.2181386947632 s\n", + " Number of Errors / num_shots: 0 / 10\n" + ] + } + ], + "source": [ + "simplex_config = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True\n", + ")\n", + "print(f'ILP decoder configurations --> {simplex_config}')\n", + "ilp_dec = simplex_config.compile_decoder()\n", + "\n", + "start_time = time.time()\n", + "\n", + "# Run and time ILP decoder -- so slow!\n", + "num_shots_to_decode = 10 # Only decoding 10 shots because it's soooo slow\n", + "obs_predicted = ilp_dec.decode_batch(dets[0:num_shots_to_decode])\n", + "num_errors = np.sum(np.any(obs_predicted != obs[0:num_shots_to_decode], axis=1))\n", + "\n", + "end_time = time.time()\n", + "print(f'ILP stats:\\n Estimated time for full shots {num_shots/num_shots_to_decode * (end_time - start_time)} s')\n", + "print(f\" Number of Errors / num_shots: {num_errors} / {num_shots_to_decode}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VQqlMqFRIZ2J" + }, + "source": [ + "# Tesseract Config and impact of heuristic\n", + "You can tune tesseract decoder through the Config that is passed to the decoder with this set of parameters:\n", + "Explanation of configuration arguments:\n", + "\n", + "* `pqlimit` - An integer that sets a limit on the number of nodes in the priority queue. This can be used to constrain the memory usage of the decoder. The default value is `sys.maxsize`, which means the size is effectively unbounded.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0pExdmuPQuGr", + "outputId": "45a20eca-fef6-425d-d6a2-9a619fe9e10c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Smaller pqlimit\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 183 / 1000\n", + " Time: 1.8420 s\n", + "\n", + "Larger pqlimit\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 3 / 1000\n", + " Time: 8.4409 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=100,\n", + " no_revisit_dets=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"Smaller pqlimit\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=20000,\n", + " no_revisit_dets=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Larger pqlimit\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ru-MRctAIq5-" + }, + "source": [ + "#More heurisitcs\n", + "* `det_beam` - This integer value represents the beam search cutoff. It specifies a threshold for the number of \"residual detection events\" a node can have before it is pruned from the search. A lower `det_beam` value makes the search more aggressive, potentially sacrificing accuracy for speed. The default value `INF_DET_BEAM` means no beam cutoff is applied.\n", + "* `beam_climbing` - A boolean flag that, when set to `True`, enables a heuristic called \"beam climbing.\" This optimization causes the decoder to try different `det_beam` values (up to a maximum) to find a good decoding path. This can improve the decoder's chance of finding the most likely error, even with an initial narrow beam search.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cyctTUyzQ-cQ", + "outputId": "0f3c5a8a-0d09-49e4-e3b1-e193152bf2bf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Smaller det_beam\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 2 / 1000\n", + " Time: 5.8029 s\n", + "\n", + "Larger det_beam\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 2 / 1000\n", + " Time: 6.1866 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=3,\n", + " beam_climbing=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"Smaller det_beam\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=5,\n", + " beam_climbing=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=5, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Larger det_beam\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VJiBCWpUQ9sf" + }, + "source": [ + "#Even More Heuristics\n", + "* `no_revisit_dets` - A boolean flag that, when `True`, activates a heuristic to prevent the decoder from revisiting nodes that have the same set of leftover detection events as a node it has already visited. This can help to reduce search redundancy and improve decoding speed.\n", + "* `at_most_two_errors_per_detector` - This boolean flag is a specific constraint that assumes at most two errors can affect a given detector. This can be a useful optimization for certain types of codes and noise models, as it prunes the search space by making a stronger assumption about the error distribution.\n", + "\n", + "* `det_orders` - A list of lists of integers, where each inner list represents an ordering of the detectors. This is used for \"ensemble reordering,\" an optimization that tries different detector orderings to improve the search's convergence. The default is an empty list, meaning a single, fixed ordering is used.\n", + "* `det_penalty` - A floating-point value that adds a cost for each residual detection event. This encourages the decoder to prioritize paths that resolve more detection events, steering the search towards more complete solutions. The default value is `0.0`, meaning no penalty is applied." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0VrW2z8sSXtN", + "outputId": "6f69c1ff-1c04-4dc6-d1a0-32aa0777d56b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "First version\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 9 / 1000\n", + " Time: 1.1290 s\n", + "\n", + "Second version\n", + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 19 / 1000\n", + " Time: 0.9446 s\n", + "\n" + ] + } + ], + "source": [ + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " # no_revisit_dets=True,\n", + " # at_most_two_errors_per_detector = True,\n", + " det_penalty = 10,\n", + " # det_orders=tesseract_decoder.utils.build_det_orders(\n", + " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "print (\"First version\")\n", + "results = run_tesseract_decoder(tesseract_config1.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "\n", + "tesseract_config2 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " # no_revisit_dets=False,\n", + " # at_most_two_errors_per_detector = False,\n", + " det_penalty = 0,\n", + " # det_orders=tesseract_decoder.utils.build_det_orders(\n", + " # dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "print (\"Second version\")\n", + "results = run_tesseract_decoder(tesseract_config2.compile_decoder(), dets, obs)\n", + "print_results(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BoEALeo3OYGp" + }, + "source": [ + "# Decoding Wild Stabilizer Codes under Code Capacity Noise with Tesseract\n", + "\n", + "\n", + "\n", + "* checkout https://www.codetables.de/ for a qubit stabilizer code\n", + "* full table of qubit codes: [here](https://codetables.de/QECC/Tables_color.php?q=4&n0=1&n1=256&k0=0&k1=256)\n", + "* copy the stabilizer matrix for a code, such as: [this one used below](https://codetables.de/QECC/QECC.php?q=4&n=21&k=8)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pJ1gEKAgPbHO" + }, + "outputs": [], + "source": [ + "import time\n", + "import tesseract_decoder\n", + "import stim\n", + "import numpy as np\n", + "import numpy.typing as npt\n", + "from galois import GF2\n", + "from typing import List, Tuple\n", + "\n", + "\n", + "def paulis_from_symplectic_matrix(check_matrix: npt.NDArray[np.uint8]) -> List[stim.PauliString]:\n", + " n = check_matrix.shape[1] // 2\n", + " paulis = []\n", + " for i in range(check_matrix.shape[0]):\n", + " paulis.append(\n", + " stim.PauliString.from_numpy(\n", + " xs=check_matrix[i, :n].astype(bool), zs=check_matrix[i, n:].astype(bool)\n", + " )\n", + " )\n", + " return paulis\n", + "\n", + "def rank(H):\n", + " return np.linalg.matrix_rank(GF2(H))\n", + "\n", + "def stabilizer_code_logical_operators(\n", + " check_matrix: npt.NDArray[np.uint8]) -> Tuple[npt.NDArray[np.uint8], npt.NDArray[np.uint8]]:\n", + " check_matrix = np.array(check_matrix, dtype=np.uint8)\n", + "\n", + " r = rank(check_matrix)\n", + " n = check_matrix.shape[1] // 2\n", + "\n", + " stabilisers = paulis_from_symplectic_matrix(check_matrix=check_matrix)\n", + "\n", + " tableau = stim.Tableau.from_stabilizers(\n", + " stabilizers=stabilisers, allow_underconstrained=True, allow_redundant=True\n", + " )\n", + "\n", + " x2x, x2z, z2x, z2z, x_signs, z_signs = tableau.to_numpy()\n", + "\n", + " num_logicals = n - r\n", + "\n", + " Lx = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", + " Lz = np.zeros((num_logicals, check_matrix.shape[1]), dtype=np.uint8)\n", + "\n", + " Lx[:, :n] = x2x[r:]\n", + " Lx[:, n:] = x2z[r:]\n", + " Lz[:, :n] = z2x[r:]\n", + " Lz[:, n:] = z2z[r:]\n", + " return Lx.astype(np.uint8), Lz.astype(np.uint8)\n", + "\n", + "\n", + "def pauli_to_observable_include_target(pauli: stim.PauliString) -> List[stim.GateTarget]:\n", + " obs_pauli_targets = []\n", + " for i in range(len(pauli)):\n", + " if pauli[i] != 0:\n", + " obs_pauli_targets.append(stim.target_pauli(i, pauli[i]))\n", + " return obs_pauli_targets\n", + "\n", + "\n", + "def append_observable_includes_for_paulis(circuit: stim.Circuit, paulis: List[stim.PauliString]) -> None:\n", + " for i, obs in enumerate(paulis):\n", + " circuit.append(\n", + " \"OBSERVABLE_INCLUDE\",\n", + " targets=pauli_to_observable_include_target(pauli=obs),\n", + " arg=i\n", + " )\n", + "\n", + "\n", + "def code_capacity_circuit(\n", + " stabilizers: npt.NDArray[np.uint8],\n", + " x_logicals: npt.NDArray[np.uint8],\n", + " z_logicals: npt.NDArray[np.uint8],\n", + " p: float\n", + ") -> stim.Circuit:\n", + " \"\"\"Generate a code capacity stim circuit for a stabilizer code\n", + "\n", + " Parameters\n", + " ----------\n", + " stabilizers : npt.NDArray[np.uint8]\n", + " The stabilizer generators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (r, 2 * n) where r is the number of stabilizer\n", + " generators and n is the number of physical qubits.\n", + " `stabilizers[i, j]` is 1 if stabilizer i is X or Y on qubit j and 0 otherwise.\n", + " `stabilizers[i, n + j]` is 1 if stabilizer i is Z or Y on qubit j and 0 otherwise.\n", + " x_logicals : npt.NDArray[np.uint8]\n", + " The X logical operators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", + " and n is the number of physical qubits.\n", + " z_logicals : npt.NDArray[np.uint8]\n", + " The Z logical operators of the code, as a binary symplectic matrix.\n", + " The matrix has dimensions (k, 2 * n) where k is the number of logical qubits\n", + " and n is the number of physical qubits.\n", + " p : float\n", + " The strength of single-qubit depolarizing noise to use\n", + "\n", + " Returns\n", + " -------\n", + " stim.Circuit\n", + " The stim circuit of the code capacity circuit\n", + " \"\"\"\n", + " num_qubits = stabilizers.shape[1] // 2\n", + " num_stabilizers = stabilizers.shape[0]\n", + " stabilizer_paulis = paulis_from_symplectic_matrix(stabilizers)\n", + " x_logicals_paulis = paulis_from_symplectic_matrix(x_logicals)\n", + " z_logicals_paulis = paulis_from_symplectic_matrix(z_logicals)\n", + " all_logicals_paulis = x_logicals_paulis + z_logicals_paulis\n", + "\n", + " circuit = stim.Circuit()\n", + "\n", + " append_observable_includes_for_paulis(\n", + " circuit=circuit, paulis=all_logicals_paulis)\n", + " circuit.append(\"MPP\", stabilizer_paulis)\n", + " circuit.append(\"DEPOLARIZE1\", targets=list(range(num_qubits)), arg=p)\n", + " circuit.append(\"MPP\", stabilizer_paulis)\n", + "\n", + " for i in range(num_stabilizers):\n", + " circuit.append(\n", + " \"DETECTOR\",\n", + " targets=[\n", + " stim.target_rec(i - 2 * num_stabilizers),\n", + " stim.target_rec(i - num_stabilizers)\n", + " ]\n", + " )\n", + "\n", + " append_observable_includes_for_paulis(\n", + " circuit=circuit, paulis=all_logicals_paulis)\n", + " return circuit\n", + "\n", + "\n", + "def parse_symplectic_matrix(text: str) -> npt.NDArray[np.uint8]:\n", + " rows = []\n", + " for line in text.strip().splitlines():\n", + " line = line.strip()\n", + " if not line or line[0] != '[' or line[-1] != ']':\n", + " continue # skip malformed lines\n", + " body = line[1:-1]\n", + " if \"|\" in body:\n", + " left, right = body.split(\"|\")\n", + " bits = left.strip().split() + right.strip().split()\n", + " else:\n", + " bits = body.strip().split()\n", + " row = [int(b) for b in bits]\n", + " rows.append(row)\n", + " return np.array(rows, dtype=np.uint8)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 + }, + "id": "pH_b3u1rBogl", + "outputId": "846dd807-82ed-4139-e46d-a3cfe65a7681" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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+ ], + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "# Example QEC code:\n", + "text = '''[1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 0|0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0]\n", + " [0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0|1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 0 0 0 0|0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0|0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0]\n", + " [0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0|0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0]\n", + " [0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0|0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0]\n", + " [0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0|0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0]\n", + " [0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0|0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0]\n", + " [0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1|0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]'''\n", + "\n", + "H = parse_symplectic_matrix(text)\n", + "\n", + "LX, LZ = stabilizer_code_logical_operators(check_matrix=H)\n", + "\n", + "circuit = code_capacity_circuit(\n", + " stabilizers=H,\n", + " x_logicals=LX,\n", + " z_logicals=LZ,\n", + " p=0.025\n", + ")\n", + "\n", + "circuit.diagram('timeline-3d')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cK2Mf2fTCAWO" + }, + "source": [ + "## Computing minimum distance with Stim + SAT Solver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZdVK4Dq1Bp1B", + "outputId": "61d8eb3e-7274-41c0-bd6b-af3e3ac75d54" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Distance of code: 4\n" + ] + } + ], + "source": [ + "# Note: this maxSAT solver only works for very small codes.\n", + "# For larger codes, use the solvers at https://maxsat-evaluations.github.io/2024/\n", + "from pysat.examples.rc2 import RC2\n", + "from pysat.formula import WCNF\n", + "\n", + "wcnf = WCNF(from_string=circuit.shortest_error_sat_problem())\n", + "\n", + "with RC2(wcnf) as rc2:\n", + " rc2.compute()\n", + " print(f'Distance of code: {rc2.cost}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GQjQkhD4C4rK" + }, + "source": [ + "## Sample new data for this stabilizer code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7iOIl7vjC3uG" + }, + "outputs": [], + "source": [ + "num_shots = 1000\n", + "dem = circuit.detector_error_model()\n", + "sampler = circuit.compile_detector_sampler(seed=23845386)\n", + "dets, obs = sampler.sample(num_shots, separate_observables=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "63xjagbBCj8x" + }, + "source": [ + "## Decode code capacity noise data with ILP and Tesseract" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IM7W37cHaKfT", + "outputId": "3f2f7666-9586-4cb6-b422-1d295bf8747c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tesseract Decoder Stats:\n", + " Number of Errors / num_shots: 60 / 1000\n", + " Time: 0.2323 s\n", + "\n", + "ILP: num_errors / num_shots = 61 / 1000 time 11.911995649337769 s\n" + ] + } + ], + "source": [ + "tesseract_config = tesseract_decoder.tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=1000,\n", + " det_beam=10,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", + " # no_revisit_dets=True,\n", + ")\n", + "\n", + "results = run_tesseract_decoder(tesseract_config.compile_decoder(), dets, obs)\n", + "print_results(results)\n", + "\n", + "# Run and time ILP decoder\n", + "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True).compile_decoder()\n", + "start_time = time.time()\n", + "obs_predicted = ilp_dec.decode_batch(dets)\n", + "num_errors_ilp = np.sum(np.any(obs_predicted != obs, axis=1))\n", + "end_time = time.time()\n", + "print(\n", + " f'ILP: num_errors / num_shots = {num_errors_ilp} / {len(dets)} time {end_time - start_time} s')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K0QvSpXQIwgf" + }, + "source": [ + "# Visualize the Tesseract's decoding\n", + "For visualizing tesseract we use the `verbose` flag to get the decoding information.\n", + "## [Link to visualizer](https://quantumlib.github.io/tesseract-decoder/viz/)\n", + "* `verbose` - A boolean flag that, when `True`, enables verbose logging. This is useful for debugging and understanding the decoder's internal behavior, as it will print information about the search process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DzWRL1cNjyix", + "outputId": "4a3df084-499f-43b2-97ba-1874b697f06a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'tesseract-decoder'...\n", + "remote: Enumerating objects: 2086, done.\u001b[K\n", + "remote: Counting objects: 100% (606/606), done.\u001b[K\n", + "remote: Compressing objects: 100% (304/304), done.\u001b[K\n", + "remote: Total 2086 (delta 493), reused 317 (delta 302), pack-reused 1480 (from 3)\u001b[K\n", + "Receiving objects: 100% (2086/2086), 3.17 MiB | 8.58 MiB/s, done.\n", + "Resolving deltas: 100% (1667/1667), done.\n" + ] + } + ], + "source": [ + "# Remove the existing directory and its contents\n", + "!rm -rf tesseract-decoder\n", + "# Clone the repository\n", + "!git clone https://github.com/quantumlib/tesseract-decoder.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZNKaqvN8dE-X", + "outputId": "8d80e5bc-c30b-469d-d9cd-452d89604c30" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 44154 100 44154 0 0 230k 0 --:--:-- --:--:-- --:--:-- 230k\n" + ] + } + ], + "source": [ + "!curl 'https://raw.githubusercontent.com/quantumlib/tesseract-decoder/refs/heads/main/testdata/colorcodes/r%3D9%2Cd%3D9%2Cp%3D0.002%2Cnoise%3Dsi1000%2Cc%3Dsuperdense_color_code_X%2Cq%3D121%2Cgates%3Dcz.stim' > d9r9colorcode_p002.stim\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cdo-oenEdF1-" + }, + "outputs": [], + "source": [ + "import stim\n", + "\n", + "circuit = stim.Circuit.from_file('d9r9colorcode_p002.stim')" + ] }, - "id": "WExtQ3x4j_Md", - "outputId": "b8ad37c9-4d69-4abd-f176-71dc76042687" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ JSON written to logfile.json with 10215 frames and 23994 error coords.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "awJYxAOMTc3t", + "outputId": "2da93975-0ede-41cb-897b-23b7da9dca93" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tesseract: num_errors / num_shots = 51 / 100 \n", + " time 16.068939685821533 s\n" + ] + } + ], + "source": [ + "import tesseract_decoder\n", + "import tesseract_decoder.tesseract as tesseract\n", + "import numpy as np\n", + "import time\n", + "import contextlib\n", + "import io\n", + "\n", + "num_shots = 100\n", + "dem = circuit.detector_error_model()\n", + "dets, obs = circuit.compile_detector_sampler().sample(num_shots, separate_observables=True)\n", + "\n", + "tesseract_config1 = tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " verbose=False,\n", + " create_visualization=True,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=2, det_order_bfs=True, seed=2384753),\n", + ")\n", + "\n", + "tesseract_dec = tesseract.TesseractDecoder(tesseract_config1)\n", + "\n", + "# Run and time the Tesseract decoder\n", + "num_errors = 0\n", + "start_time = time.time()\n", + "for shot in range(len(dets)):\n", + " obs_predicted = tesseract_dec.decode(dets[shot])\n", + " obs_actual = obs[shot]\n", + " if np.any(obs_predicted != obs_actual):\n", + " num_errors += 1\n", + "end_time = time.time()\n", + "print(f'Tesseract: num_errors / num_shots = {num_errors} / {len(dets)} \\n time {end_time - start_time} s')\n", + "\n", + "# Print with the visualizer\n", + "tesseract_dec.visualizer.write('/content/tmp.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MuQb8XQlpvU6" + }, + "outputs": [], + "source": [ + "!cat tmp.txt | grep -E 'Error|Detector|activated_errors|activated_detectors' > logfile.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WExtQ3x4j_Md", + "outputId": "b8ad37c9-4d69-4abd-f176-71dc76042687" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✅ JSON written to logfile.json with 10215 frames and 23994 error coords.\n" + ] + } + ], + "source": [ + "!python tesseract-decoder/viz/to_json.py logfile.txt -o logfile.json" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HSdTwXBINjkH" + }, + "source": [ + "copy the json file and upload it [here to see the visualizaion](https://quantumlib.github.io/tesseract-decoder/viz/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QehTGJcB7-Ca" + }, + "source": [ + "# Accuracy Comparison between Tesseract and ILP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GOY0hHYx79HC", + "outputId": "c73ea4f6-ea5b-42c4-8271-fe6320c790ab" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tesseract: num_errors / num_shots = 2 / 1000\n", + "num_errors_tesseract_no_error_ilp = 0\n", + "time 25.137925148010254 s\n" + ] + } + ], + "source": [ + "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", + "dem = circuit.detector_error_model()\n", + "\n", + "tesseract_dec = tesseract_decoder.tesseract.TesseractConfig(\n", + " dem=dem,\n", + " pqlimit=10000,\n", + " det_beam=5,\n", + " det_orders=tesseract_decoder.utils.build_det_orders(\n", + " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", + " no_revisit_dets=True,\n", + ").compile_decoder()\n", + "\n", + "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", + " dem=dem, parallelize=True).compile_decoder()\n", + "\n", + "num_shots = 1000\n", + "dets, obs = circuit.compile_detector_sampler(seed=237435).sample(num_shots, separate_observables=True)\n", + "\n", + "num_errors_tesseract = 0\n", + "num_errors_tesseract_no_error_ilp = 0\n", + "start_time = time.time()\n", + "for shot in range(len(dets)):\n", + " obs_predicted = tesseract_dec.decode(dets[shot])\n", + " obs_actual = obs[shot]\n", + " if np.any(obs_predicted != obs_actual):\n", + " num_errors_tesseract += 1\n", + " obs_predicted_ilp = ilp_dec.decode(dets[shot])\n", + " if not np.any(obs_predicted_ilp != obs_actual):\n", + " num_errors_tesseract_no_error_ilp += 1\n", + "\n", + "end_time = time.time()\n", + "print(f'Tesseract: num_errors / num_shots = {num_errors_tesseract} / {len(dets)}')\n", + "print(f'num_errors_tesseract_no_error_ilp = {num_errors_tesseract_no_error_ilp}')\n", + "print(f'time {end_time - start_time} s')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "44xKnbKb2y_b" + }, + "outputs": [], + "source": [] } - ], - "source": [ - "!python tesseract-decoder/viz/to_json.py logfile.txt -o logfile.json" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HSdTwXBINjkH" - }, - "source": [ - "copy the json file and upload it [here to see the visualizaion](https://quantumlib.github.io/tesseract-decoder/viz/)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QehTGJcB7-Ca" - }, - "source": [ - "# Accuracy Comparison between Tesseract and ILP" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { + ], + "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "provenance": [] }, - "id": "GOY0hHYx79HC", - "outputId": "c73ea4f6-ea5b-42c4-8271-fe6320c790ab" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tesseract: num_errors / num_shots = 2 / 1000\n", - "num_errors_tesseract_no_error_ilp = 0\n", - "time 25.137925148010254 s\n" - ] + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ], - "source": [ - "circuit = stim.Circuit.from_file('d5r5colorcode_p001.stim')\n", - "dem = circuit.detector_error_model()\n", - "\n", - "tesseract_dec = tesseract_decoder.tesseract.TesseractConfig(\n", - " dem=dem,\n", - " pqlimit=10000,\n", - " det_beam=5,\n", - " det_orders=tesseract_decoder.utils.build_det_orders(\n", - " dem, num_det_orders=10, det_order_bfs=True, seed=2384753),\n", - " no_revisit_dets=True,\n", - ").compile_decoder()\n", - "\n", - "ilp_dec = tesseract_decoder.simplex.SimplexConfig(\n", - " dem=dem, parallelize=True).compile_decoder()\n", - "\n", - "num_shots = 1000\n", - "dets, obs = circuit.compile_detector_sampler(seed=237435).sample(num_shots, separate_observables=True)\n", - "\n", - "num_errors_tesseract = 0\n", - "num_errors_tesseract_no_error_ilp = 0\n", - "start_time = time.time()\n", - "for shot in range(len(dets)):\n", - " obs_predicted = tesseract_dec.decode(dets[shot])\n", - " obs_actual = obs[shot]\n", - " if np.any(obs_predicted != obs_actual):\n", - " num_errors_tesseract += 1\n", - " obs_predicted_ilp = ilp_dec.decode(dets[shot])\n", - " if not np.any(obs_predicted_ilp != obs_actual):\n", - " num_errors_tesseract_no_error_ilp += 1\n", - "\n", - "end_time = time.time()\n", - "print(f'Tesseract: num_errors / num_shots = {num_errors_tesseract} / {len(dets)}')\n", - "print(f'num_errors_tesseract_no_error_ilp = {num_errors_tesseract_no_error_ilp}')\n", - "print(f'time {end_time - start_time} s')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "44xKnbKb2y_b" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 0 }